# -*- coding: utf-8 -*- """Functions to make 3D plots with M/EEG data.""" # Authors: Alexandre Gramfort # Denis Engemann # Martin Luessi # Eric Larson # Mainak Jas # Mark Wronkiewicz # # License: Simplified BSD import base64 from distutils.version import LooseVersion from io import BytesIO from itertools import cycle import os.path as op import warnings from functools import partial import numpy as np from scipy import linalg, sparse from ..defaults import DEFAULTS from ..fixes import einsum, _crop_colorbar, _get_img_fdata from ..io import _loc_to_coil_trans from ..io.pick import pick_types, _picks_to_idx from ..io.constants import FIFF from ..io.meas_info import read_fiducials, create_info from ..source_space import _ensure_src, _create_surf_spacing, _check_spacing from ..surface import (get_meg_helmet_surf, read_surface, _DistanceQuery, transform_surface_to, _project_onto_surface, mesh_edges, _reorder_ccw, _complete_sphere_surf) from ..transforms import (_find_trans, apply_trans, rot_to_quat, combine_transforms, _get_trans, _ensure_trans, invert_transform, Transform, _read_ras_mni_t, _print_coord_trans) from ..utils import (get_subjects_dir, logger, _check_subject, verbose, warn, has_nibabel, check_version, fill_doc, _pl, _ensure_int, _validate_type, _check_option) from .utils import (mne_analyze_colormap, _prepare_trellis, _get_color_list, plt_show, tight_layout, figure_nobar, _check_time_unit) from ..bem import (ConductorModel, _bem_find_surface, _surf_dict, _surf_name, read_bem_surfaces) verbose_dec = verbose FIDUCIAL_ORDER = (FIFF.FIFFV_POINT_LPA, FIFF.FIFFV_POINT_NASION, FIFF.FIFFV_POINT_RPA) # XXX: to unify with digitization def _fiducial_coords(points, coord_frame=None): """Generate 3x3 array of fiducial coordinates.""" points = points or [] # None -> list if coord_frame is not None: points = [p for p in points if p['coord_frame'] == coord_frame] points_ = {p['ident']: p for p in points if p['kind'] == FIFF.FIFFV_POINT_CARDINAL} if points_: return np.array([points_[i]['r'] for i in FIDUCIAL_ORDER]) else: # XXX eventually this should probably live in montage.py if coord_frame is None or coord_frame == FIFF.FIFFV_COORD_HEAD: # Try converting CTF HPI coils to fiducials out = np.empty((3, 3)) out.fill(np.nan) for p in points: if p['kind'] == FIFF.FIFFV_POINT_HPI: if np.isclose(p['r'][1:], 0, atol=1e-6).all(): out[0 if p['r'][0] < 0 else 2] = p['r'] elif np.isclose(p['r'][::2], 0, atol=1e-6).all(): out[1] = p['r'] if np.isfinite(out).all(): return out return np.array([]) def plot_head_positions(pos, mode='traces', cmap='viridis', direction='z', show=True, destination=None, info=None, color='k', axes=None): """Plot head positions. Parameters ---------- pos : ndarray, shape (n_pos, 10) | list of ndarray The head position data. Can also be a list to treat as a concatenation of runs. mode : str Can be 'traces' (default) to show position and quaternion traces, or 'field' to show the position as a vector field over time. The 'field' mode requires matplotlib 1.4+. cmap : colormap Colormap to use for the trace plot, default is "viridis". direction : str Can be any combination of "x", "y", or "z" (default: "z") to show directional axes in "field" mode. show : bool Show figure if True. Defaults to True. destination : str | array-like, shape (3,) | None The destination location for the head, assumed to be in head coordinates. See :func:`mne.preprocessing.maxwell_filter` for details. .. versionadded:: 0.16 info : instance of mne.Info | None Measurement information. If provided, will be used to show the destination position when ``destination is None``, and for showing the MEG sensors. .. versionadded:: 0.16 color : color object The color to use for lines in ``mode == 'traces'`` and quiver arrows in ``mode == 'field'``. .. versionadded:: 0.16 axes : array-like, shape (3, 2) The matplotlib axes to use. Only used for ``mode == 'traces'``. .. versionadded:: 0.16 Returns ------- fig : instance of matplotlib.figure.Figure The figure. """ from ..chpi import head_pos_to_trans_rot_t from ..preprocessing.maxwell import _check_destination import matplotlib.pyplot as plt _check_option('mode', mode, ['traces', 'field']) dest_info = dict(dev_head_t=None) if info is None else info destination = _check_destination(destination, dest_info, head_frame=True) if destination is not None: destination = _ensure_trans(destination, 'head', 'meg') # probably inv destination = destination['trans'][:3].copy() destination[:, 3] *= 1000 if not isinstance(pos, (list, tuple)): pos = [pos] for ii, p in enumerate(pos): p = np.array(p, float) if p.ndim != 2 or p.shape[1] != 10: raise ValueError('pos (or each entry in pos if a list) must be ' 'dimension (N, 10), got %s' % (p.shape,)) if ii > 0: # concatenation p[:, 0] += pos[ii - 1][-1, 0] - p[0, 0] pos[ii] = p borders = np.cumsum([len(pp) for pp in pos]) pos = np.concatenate(pos, axis=0) trans, rot, t = head_pos_to_trans_rot_t(pos) # also ensures pos is okay # trans, rot, and t are for dev_head_t, but what we really want # is head_dev_t (i.e., where the head origin is in device coords) use_trans = einsum('ijk,ik->ij', rot[:, :3, :3].transpose([0, 2, 1]), -trans) * 1000 use_rot = rot.transpose([0, 2, 1]) use_quats = -pos[:, 1:4] # inverse (like doing rot.T) surf = rrs = lims = None if info is not None: meg_picks = pick_types(info, meg=True, ref_meg=False, exclude=()) if len(meg_picks) > 0: rrs = 1000 * np.array([info['chs'][pick]['loc'][:3] for pick in meg_picks], float) if mode == 'traces': lims = np.array((rrs.min(0), rrs.max(0))).T else: # mode == 'field' surf = get_meg_helmet_surf(info) transform_surface_to(surf, 'meg', info['dev_head_t'], copy=False) surf['rr'] *= 1000. helmet_color = (0.0, 0.0, 0.6) if mode == 'traces': if axes is None: axes = plt.subplots(3, 2, sharex=True)[1] else: axes = np.array(axes) if axes.shape != (3, 2): raise ValueError('axes must have shape (3, 2), got %s' % (axes.shape,)) fig = axes[0, 0].figure labels = ['xyz', ('$q_1$', '$q_2$', '$q_3$')] for ii, (quat, coord) in enumerate(zip(use_quats.T, use_trans.T)): axes[ii, 0].plot(t, coord, color, lw=1., zorder=3) axes[ii, 0].set(ylabel=labels[0][ii], xlim=t[[0, -1]]) axes[ii, 1].plot(t, quat, color, lw=1., zorder=3) axes[ii, 1].set(ylabel=labels[1][ii], xlim=t[[0, -1]]) for b in borders[:-1]: for jj in range(2): axes[ii, jj].axvline(t[b], color='r') for ii, title in enumerate(('Position (mm)', 'Rotation (quat)')): axes[0, ii].set(title=title) axes[-1, ii].set(xlabel='Time (s)') if rrs is not None: pos_bads = np.any([(use_trans[:, ii] <= lims[ii, 0]) | (use_trans[:, ii] >= lims[ii, 1]) for ii in range(3)], axis=0) for ii in range(3): oidx = list(range(ii)) + list(range(ii + 1, 3)) # knowing it will generally be spherical, we can approximate # how far away we are along the axis line by taking the # point to the left and right with the smallest distance from scipy.spatial.distance import cdist dists = cdist(rrs[:, oidx], use_trans[:, oidx]) left = rrs[:, [ii]] < use_trans[:, ii] left_dists_all = dists.copy() left_dists_all[~left] = np.inf # Don't show negative Z direction if ii != 2 and np.isfinite(left_dists_all).any(): idx = np.argmin(left_dists_all, axis=0) left_dists = rrs[idx, ii] bads = ~np.isfinite( left_dists_all[idx, np.arange(len(idx))]) | pos_bads left_dists[bads] = np.nan axes[ii, 0].plot(t, left_dists, color=helmet_color, ls='-', lw=0.5, zorder=2) else: axes[ii, 0].axhline(lims[ii][0], color=helmet_color, ls='-', lw=0.5, zorder=2) right_dists_all = dists right_dists_all[left] = np.inf if np.isfinite(right_dists_all).any(): idx = np.argmin(right_dists_all, axis=0) right_dists = rrs[idx, ii] bads = ~np.isfinite( right_dists_all[idx, np.arange(len(idx))]) | pos_bads right_dists[bads] = np.nan axes[ii, 0].plot(t, right_dists, color=helmet_color, ls='-', lw=0.5, zorder=2) else: axes[ii, 0].axhline(lims[ii][1], color=helmet_color, ls='-', lw=0.5, zorder=2) for ii in range(3): axes[ii, 1].set(ylim=[-1, 1]) if destination is not None: vals = np.array([destination[:, 3], rot_to_quat(destination[:, :3])]).T.ravel() for ax, val in zip(fig.axes, vals): ax.axhline(val, color='r', ls=':', zorder=2, lw=1.) else: # mode == 'field': from matplotlib.colors import Normalize from mpl_toolkits.mplot3d.art3d import Line3DCollection from mpl_toolkits.mplot3d import axes3d # noqa: F401, analysis:ignore fig, ax = plt.subplots(1, subplot_kw=dict(projection='3d')) # First plot the trajectory as a colormap: # http://matplotlib.org/examples/pylab_examples/multicolored_line.html pts = use_trans[:, np.newaxis] segments = np.concatenate([pts[:-1], pts[1:]], axis=1) norm = Normalize(t[0], t[-2]) lc = Line3DCollection(segments, cmap=cmap, norm=norm) lc.set_array(t[:-1]) ax.add_collection(lc) # now plot the head directions as a quiver dir_idx = dict(x=0, y=1, z=2) kwargs = dict(pivot='tail') for d, length in zip(direction, [5., 2.5, 1.]): use_dir = use_rot[:, :, dir_idx[d]] # draws stems, then heads array = np.concatenate((t, np.repeat(t, 2))) ax.quiver(use_trans[:, 0], use_trans[:, 1], use_trans[:, 2], use_dir[:, 0], use_dir[:, 1], use_dir[:, 2], norm=norm, cmap=cmap, array=array, length=length, **kwargs) if destination is not None: ax.quiver(destination[0, 3], destination[1, 3], destination[2, 3], destination[dir_idx[d], 0], destination[dir_idx[d], 1], destination[dir_idx[d], 2], color=color, length=length, **kwargs) mins = use_trans.min(0) maxs = use_trans.max(0) if surf is not None: ax.plot_trisurf(*surf['rr'].T, triangles=surf['tris'], color=helmet_color, alpha=0.1, shade=False) ax.scatter(*rrs.T, s=1, color=helmet_color) mins = np.minimum(mins, rrs.min(0)) maxs = np.maximum(maxs, rrs.max(0)) scale = (maxs - mins).max() / 2. xlim, ylim, zlim = (maxs + mins)[:, np.newaxis] / 2. + [-scale, scale] ax.set(xlabel='x', ylabel='y', zlabel='z', xlim=xlim, ylim=ylim, zlim=zlim) _set_aspect_equal(ax) ax.view_init(30, 45) tight_layout(fig=fig) plt_show(show) return fig def _set_aspect_equal(ax): # XXX recent MPL throws an error for 3D axis aspect setting, not much # we can do about it at this point try: ax.set_aspect('equal') except NotImplementedError: pass @fill_doc def plot_evoked_field(evoked, surf_maps, time=None, time_label='t = %0.0f ms', n_jobs=1): """Plot MEG/EEG fields on head surface and helmet in 3D. Parameters ---------- evoked : instance of mne.Evoked The evoked object. surf_maps : list The surface mapping information obtained with make_field_map. time : float | None The time point at which the field map shall be displayed. If None, the average peak latency (across sensor types) is used. time_label : str How to print info about the time instant visualized. %(n_jobs)s Returns ------- fig : instance of mayavi.mlab.Figure The mayavi figure. """ # Update the backend from .backends.renderer import _Renderer types = [t for t in ['eeg', 'grad', 'mag'] if t in evoked] time_idx = None if time is None: time = np.mean([evoked.get_peak(ch_type=t)[1] for t in types]) del types if not evoked.times[0] <= time <= evoked.times[-1]: raise ValueError('`time` (%0.3f) must be inside `evoked.times`' % time) time_idx = np.argmin(np.abs(evoked.times - time)) # Plot them alphas = [1.0, 0.5] colors = [(0.6, 0.6, 0.6), (1.0, 1.0, 1.0)] colormap = mne_analyze_colormap(format='mayavi') colormap_lines = np.concatenate([np.tile([0., 0., 255., 255.], (127, 1)), np.tile([0., 0., 0., 255.], (2, 1)), np.tile([255., 0., 0., 255.], (127, 1))]) renderer = _Renderer(bgcolor=(0.0, 0.0, 0.0), size=(600, 600)) for ii, this_map in enumerate(surf_maps): surf = this_map['surf'] map_data = this_map['data'] map_type = this_map['kind'] map_ch_names = this_map['ch_names'] if map_type == 'eeg': pick = pick_types(evoked.info, meg=False, eeg=True) else: pick = pick_types(evoked.info, meg=True, eeg=False, ref_meg=False) ch_names = [evoked.ch_names[k] for k in pick] set_ch_names = set(ch_names) set_map_ch_names = set(map_ch_names) if set_ch_names != set_map_ch_names: message = ['Channels in map and data do not match.'] diff = set_map_ch_names - set_ch_names if len(diff): message += ['%s not in data file. ' % list(diff)] diff = set_ch_names - set_map_ch_names if len(diff): message += ['%s not in map file.' % list(diff)] raise RuntimeError(' '.join(message)) data = np.dot(map_data, evoked.data[pick, time_idx]) # Make a solid surface vlim = np.max(np.abs(data)) alpha = alphas[ii] renderer.surface(surface=surf, color=colors[ii], opacity=alpha) # Now show our field pattern renderer.surface(surface=surf, vmin=-vlim, vmax=vlim, scalars=data, colormap=colormap) # And the field lines on top renderer.contour(surface=surf, scalars=data, contours=21, vmin=-vlim, vmax=vlim, opacity=alpha, colormap=colormap_lines) if '%' in time_label: time_label %= (1e3 * evoked.times[time_idx]) renderer.text2d(x=0.01, y=0.01, text=time_label, width=0.4) renderer.set_camera(azimuth=10, elevation=60) renderer.show() return renderer.scene() def _plot_mri_contours(mri_fname, surf_fnames, orientation='coronal', slices=None, show=True, img_output=False): """Plot BEM contours on anatomical slices. Parameters ---------- mri_fname : str The name of the file containing anatomical data. surf_fnames : list of str The filenames for the BEM surfaces in the format ['inner_skull.surf', 'outer_skull.surf', 'outer_skin.surf']. orientation : str 'coronal' or 'transverse' or 'sagittal' slices : list of int Slice indices. show : bool Call pyplot.show() at the end. img_output : None | tuple If tuple (width and height), images will be produced instead of a single figure with many axes. This mode is designed to reduce the (substantial) overhead associated with making tens to hundreds of matplotlib axes, instead opting to re-use a single Axes instance. Returns ------- fig : instance of matplotlib.figure.Figure | list The figure. Will instead be a list of png images if img_output is a tuple. """ import matplotlib.pyplot as plt import nibabel as nib _check_option('orientation', orientation, ['coronal', 'axial', 'sagittal']) # Load the T1 data nim = nib.load(mri_fname) data = _get_img_fdata(nim) try: affine = nim.affine except AttributeError: # old nibabel affine = nim.get_affine() n_sag, n_axi, n_cor = data.shape orientation_name2axis = dict(sagittal=0, axial=1, coronal=2) orientation_axis = orientation_name2axis[orientation] if slices is None: n_slices = data.shape[orientation_axis] slices = np.linspace(0, n_slices, 12, endpoint=False).astype(np.int) # create of list of surfaces surfs = list() trans = linalg.inv(affine) # XXX : next line is a hack don't ask why trans[:3, -1] = [n_sag // 2, n_axi // 2, n_cor // 2] for surf_fname in surf_fnames: surf = read_surface(surf_fname, return_dict=True)[-1] # move back surface to MRI coordinate system surf['rr'] = nib.affines.apply_affine(trans, surf['rr']) surfs.append(surf) if img_output is None: fig, axs = _prepare_trellis(len(slices), 4) else: fig, ax = plt.subplots(1, 1, figsize=(7.0, 7.0)) axs = [ax] * len(slices) fig_size = fig.get_size_inches() w, h = img_output[0], img_output[1] w2 = fig_size[0] fig.set_size_inches([(w2 / float(w)) * w, (w2 / float(w)) * h]) plt.close(fig) inds = dict(coronal=[0, 1, 2], axial=[2, 0, 1], sagittal=[2, 1, 0])[orientation] outs = [] for ax, sl in zip(axs, slices): # adjust the orientations for good view if orientation == 'coronal': dat = data[:, :, sl].transpose() elif orientation == 'axial': dat = data[:, sl, :] elif orientation == 'sagittal': dat = data[sl, :, :] # First plot the anatomical data if img_output is not None: ax.clear() ax.imshow(dat, cmap=plt.cm.gray) ax.axis('off') # and then plot the contours on top for surf in surfs: with warnings.catch_warnings(record=True): # no contours warnings.simplefilter('ignore') ax.tricontour(surf['rr'][:, inds[0]], surf['rr'][:, inds[1]], surf['tris'], surf['rr'][:, inds[2]], levels=[sl], colors='yellow', linewidths=2.0) if img_output is not None: ax.set_xticks([]) ax.set_yticks([]) ax.set_xlim(0, img_output[1]) ax.set_ylim(img_output[0], 0) output = BytesIO() fig.savefig(output, bbox_inches='tight', pad_inches=0, format='png') outs.append(base64.b64encode(output.getvalue()).decode('ascii')) if show: plt.subplots_adjust(left=0., bottom=0., right=1., top=1., wspace=0., hspace=0.) plt_show(show) return fig if img_output is None else outs @verbose def plot_alignment(info=None, trans=None, subject=None, subjects_dir=None, surfaces='head', coord_frame='head', meg=None, eeg='original', fwd=None, dig=False, ecog=True, src=None, mri_fiducials=False, bem=None, seeg=True, show_axes=False, fig=None, interaction='trackball', verbose=None): """Plot head, sensor, and source space alignment in 3D. Parameters ---------- info : dict | None The measurement info. If None (default), no sensor information will be shown. %(trans)s subject : str | None The subject name corresponding to FreeSurfer environment variable SUBJECT. Can be omitted if ``src`` is provided. subjects_dir : str | None The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR. surfaces : str | list Surfaces to plot. Supported values: * scalp: one of 'head', 'outer_skin' (alias for 'head'), 'head-dense', or 'seghead' (alias for 'head-dense') * skull: 'outer_skull', 'inner_skull', 'brain' (alias for 'inner_skull') * brain: one of 'pial', 'white', 'inflated', or 'brain' (alias for 'pial'). Defaults to 'head'. .. note:: For single layer BEMs it is recommended to use 'brain'. coord_frame : str Coordinate frame to use, 'head', 'meg', or 'mri'. meg : str | list | bool | None Can be "helmet", "sensors" or "ref" to show the MEG helmet, sensors or reference sensors respectively, or a combination like ``('helmet', 'sensors')`` (same as None, default). True translates to ``('helmet', 'sensors', 'ref')``. eeg : bool | str | list Can be "original" (default; equivalent to True) or "projected" to show EEG sensors in their digitized locations or projected onto the scalp, or a list of these options including ``[]`` (equivalent of False). fwd : instance of Forward The forward solution. If present, the orientations of the dipoles present in the forward solution are displayed. dig : bool | 'fiducials' If True, plot the digitization points; 'fiducials' to plot fiducial points only. ecog : bool If True (default), show ECoG sensors. src : instance of SourceSpaces | None If not None, also plot the source space points. mri_fiducials : bool | str Plot MRI fiducials (default False). If ``True``, look for a file with the canonical name (``bem/{subject}-fiducials.fif``). If ``str`` it should provide the full path to the fiducials file. bem : list of dict | instance of ConductorModel | None Can be either the BEM surfaces (list of dict), a BEM solution or a sphere model. If None, we first try loading `'$SUBJECTS_DIR/$SUBJECT/bem/$SUBJECT-$SOURCE.fif'`, and then look for `'$SUBJECT*$SOURCE.fif'` in the same directory. For `'outer_skin'`, the subjects bem and bem/flash folders are searched. Defaults to None. seeg : bool If True (default), show sEEG electrodes. show_axes : bool If True (default False), coordinate frame axis indicators will be shown: * head in pink * MRI in gray (if ``trans is not None``) * MEG in blue (if MEG sensors are present) .. versionadded:: 0.16 fig : mayavi.mlab.Figure | None Mayavi Scene in which to plot the alignment. If ``None``, creates a new 600x600 pixel figure with black background. .. versionadded:: 0.16 interaction : str Can be "trackball" (default) or "terrain", i.e. a turntable-style camera. .. versionadded:: 0.16 %(verbose)s Returns ------- fig : instance of mayavi.mlab.Figure The mayavi figure. See Also -------- mne.viz.plot_bem Notes ----- This function serves the purpose of checking the validity of the many different steps of source reconstruction: - Transform matrix (keywords ``trans``, ``meg`` and ``mri_fiducials``), - BEM surfaces (keywords ``bem`` and ``surfaces``), - sphere conductor model (keywords ``bem`` and ``surfaces``) and - source space (keywords ``surfaces`` and ``src``). .. versionadded:: 0.15 """ from ..forward import _create_meg_coils, Forward # Update the backend from .backends.renderer import _Renderer if eeg is False: eeg = list() elif eeg is True: eeg = 'original' if meg is None: meg = ('helmet', 'sensors') # only consider warning if the value is explicit warn_meg = False else: warn_meg = True if meg is True: meg = ('helmet', 'sensors', 'ref') elif meg is False: meg = list() elif isinstance(meg, str): meg = [meg] if isinstance(eeg, str): eeg = [eeg] _check_option('interaction', interaction, ['trackball', 'terrain']) for kind, var in zip(('eeg', 'meg'), (eeg, meg)): if not isinstance(var, (list, tuple)) or \ not all(isinstance(x, str) for x in var): raise TypeError('%s must be list or tuple of str, got %s' % (kind, type(var))) if not all(x in ('helmet', 'sensors', 'ref') for x in meg): raise ValueError('meg must only contain "helmet", "sensors" or "ref", ' 'got %s' % (meg,)) if not all(x in ('original', 'projected') for x in eeg): raise ValueError('eeg must only contain "original" and ' '"projected", got %s' % (eeg,)) info = create_info(1, 1000., 'misc') if info is None else info _validate_type(info, "info") if isinstance(surfaces, str): surfaces = [surfaces] surfaces = list(surfaces) for s in surfaces: _validate_type(s, "str", "all entries in surfaces") is_sphere = False if isinstance(bem, ConductorModel) and bem['is_sphere']: if len(bem['layers']) != 4 and len(surfaces) > 1: raise ValueError('The sphere conductor model must have three ' 'layers for plotting skull and head.') is_sphere = True _check_option('coord_frame', coord_frame, ['head', 'meg', 'mri']) if src is not None: src = _ensure_src(src) src_subject = src[0].get('subject_his_id', None) subject = src_subject if subject is None else subject if src_subject is not None and subject != src_subject: raise ValueError('subject ("%s") did not match the subject name ' ' in src ("%s")' % (subject, src_subject)) src_rr = np.concatenate([s['rr'][s['inuse'].astype(bool)] for s in src]) src_nn = np.concatenate([s['nn'][s['inuse'].astype(bool)] for s in src]) else: src_rr = src_nn = np.empty((0, 3)) if fwd is not None: _validate_type(fwd, [Forward]) fwd_rr = fwd['source_rr'] if fwd['source_ori'] == FIFF.FIFFV_MNE_FIXED_ORI: fwd_nn = fwd['source_nn'].reshape(-1, 1, 3) else: fwd_nn = fwd['source_nn'].reshape(-1, 3, 3) ref_meg = 'ref' in meg meg_picks = pick_types(info, meg=True, ref_meg=ref_meg) eeg_picks = pick_types(info, meg=False, eeg=True, ref_meg=False) ecog_picks = pick_types(info, meg=False, ecog=True, ref_meg=False) seeg_picks = pick_types(info, meg=False, seeg=True, ref_meg=False) if trans == 'auto': # let's try to do this in MRI coordinates so they're easy to plot subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) trans = _find_trans(subject, subjects_dir) head_mri_t, _ = _get_trans(trans, 'head', 'mri') dev_head_t, _ = _get_trans(info['dev_head_t'], 'meg', 'head') del trans # Figure out our transformations if coord_frame == 'meg': head_trans = invert_transform(dev_head_t) meg_trans = Transform('meg', 'meg') mri_trans = invert_transform(combine_transforms( dev_head_t, head_mri_t, 'meg', 'mri')) elif coord_frame == 'mri': head_trans = head_mri_t meg_trans = combine_transforms(dev_head_t, head_mri_t, 'meg', 'mri') mri_trans = Transform('mri', 'mri') else: # coord_frame == 'head' head_trans = Transform('head', 'head') meg_trans = dev_head_t mri_trans = invert_transform(head_mri_t) # both the head and helmet will be in MRI coordinates after this surfs = dict() # Head: sphere_level = 4 head = False for s in surfaces: if s in ('head', 'outer_skin', 'head-dense', 'seghead'): if head: raise ValueError('Can only supply one head-like surface name') surfaces.pop(surfaces.index(s)) head = True head_surf = None # Try the BEM if applicable if s in ('head', 'outer_skin'): if bem is not None: head_missing = ( 'Could not find the surface for ' 'head in the provided BEM model, ' 'looking in the subject directory.') if isinstance(bem, ConductorModel): if is_sphere: head_surf = _complete_sphere_surf( bem, 3, sphere_level, complete=False) else: # BEM solution try: head_surf = _bem_find_surface( bem, FIFF.FIFFV_BEM_SURF_ID_HEAD) except RuntimeError: logger.info(head_missing) elif bem is not None: # list of dict for this_surf in bem: if this_surf['id'] == FIFF.FIFFV_BEM_SURF_ID_HEAD: head_surf = this_surf break else: logger.info(head_missing) if head_surf is None: if subject is None: raise ValueError('To plot the head surface, the BEM/sphere' ' model must contain a head surface ' 'or "subject" must be provided (got ' 'None)') subject_dir = op.join( get_subjects_dir(subjects_dir, raise_error=True), subject) if s in ('head-dense', 'seghead'): try_fnames = [ op.join(subject_dir, 'bem', '%s-head-dense.fif' % subject), op.join(subject_dir, 'surf', 'lh.seghead'), ] else: try_fnames = [ op.join(subject_dir, 'bem', 'outer_skin.surf'), op.join(subject_dir, 'bem', 'flash', 'outer_skin.surf'), op.join(subject_dir, 'bem', '%s-head.fif' % subject), ] for fname in try_fnames: if op.exists(fname): logger.info('Using %s for head surface.' % (op.basename(fname),)) if op.splitext(fname)[-1] == '.fif': head_surf = read_bem_surfaces(fname)[0] else: head_surf = read_surface( fname, return_dict=True)[2] head_surf['rr'] /= 1000. head_surf.update(coord_frame=FIFF.FIFFV_COORD_MRI) break else: raise IOError('No head surface found for subject ' '%s after trying:\n%s' % (subject, '\n'.join(try_fnames))) surfs['head'] = head_surf # Skull: skull = list() for name, id_ in (('outer_skull', FIFF.FIFFV_BEM_SURF_ID_SKULL), ('inner_skull', FIFF.FIFFV_BEM_SURF_ID_BRAIN)): if name in surfaces: surfaces.pop(surfaces.index(name)) if bem is None: fname = op.join( get_subjects_dir(subjects_dir, raise_error=True), subject, 'bem', name + '.surf') if not op.isfile(fname): raise ValueError('bem is None and the the %s file cannot ' 'be found:\n%s' % (name, fname)) surf = read_surface(fname, return_dict=True)[2] surf.update(coord_frame=FIFF.FIFFV_COORD_MRI, id=_surf_dict[name]) surf['rr'] /= 1000. skull.append(surf) elif isinstance(bem, ConductorModel): if is_sphere: if len(bem['layers']) != 4: raise ValueError('The sphere model must have three ' 'layers for plotting %s' % (name,)) this_idx = 1 if name == 'inner_skull' else 2 skull.append(_complete_sphere_surf( bem, this_idx, sphere_level)) skull[-1]['id'] = _surf_dict[name] else: skull.append(_bem_find_surface(bem, id_)) else: # BEM model for this_surf in bem: if this_surf['id'] == _surf_dict[name]: skull.append(this_surf) break else: raise ValueError('Could not find the surface for %s.' % name) if mri_fiducials: if mri_fiducials is True: subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) if subject is None: raise ValueError("Subject needs to be specified to " "automatically find the fiducials file.") mri_fiducials = op.join(subjects_dir, subject, 'bem', subject + '-fiducials.fif') if isinstance(mri_fiducials, str): mri_fiducials, cf = read_fiducials(mri_fiducials) if cf != FIFF.FIFFV_COORD_MRI: raise ValueError("Fiducials are not in MRI space") fid_loc = _fiducial_coords(mri_fiducials, FIFF.FIFFV_COORD_MRI) fid_loc = apply_trans(mri_trans, fid_loc) else: fid_loc = [] if 'helmet' in meg and len(meg_picks) > 0: surfs['helmet'] = get_meg_helmet_surf(info, head_mri_t) assert surfs['helmet']['coord_frame'] == FIFF.FIFFV_COORD_MRI # Brain: brain = np.intersect1d(surfaces, ['brain', 'pial', 'white', 'inflated']) if len(brain) > 1: raise ValueError('Only one brain surface can be plotted. ' 'Got %s.' % brain) elif len(brain) == 0: brain = False else: # exactly 1 brain = brain[0] surfaces.pop(surfaces.index(brain)) brain = 'pial' if brain == 'brain' else brain if is_sphere: if len(bem['layers']) > 0: surfs['lh'] = _complete_sphere_surf( bem, 0, sphere_level) # only plot 1 else: subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) for hemi in ['lh', 'rh']: fname = op.join(subjects_dir, subject, 'surf', '%s.%s' % (hemi, brain)) surfs[hemi] = read_surface(fname, return_dict=True)[2] surfs[hemi]['rr'] /= 1000. surfs[hemi].update(coord_frame=FIFF.FIFFV_COORD_MRI) brain = True # we've looked through all of them, raise if some remain if len(surfaces) > 0: raise ValueError('Unknown surface type%s: %s' % (_pl(surfaces), surfaces,)) skull_alpha = dict() skull_colors = dict() hemi_val = 0.5 if src is None or (brain and any(s['type'] == 'surf' for s in src)): hemi_val = 1. alphas = (4 - np.arange(len(skull) + 1)) * (0.5 / 4.) for idx, this_skull in enumerate(skull): if isinstance(this_skull, dict): skull_surf = this_skull this_skull = _surf_name[skull_surf['id']] elif is_sphere: # this_skull == str this_idx = 1 if this_skull == 'inner_skull' else 2 skull_surf = _complete_sphere_surf(bem, this_idx, sphere_level) else: # str skull_fname = op.join(subjects_dir, subject, 'bem', 'flash', '%s.surf' % this_skull) if not op.exists(skull_fname): skull_fname = op.join(subjects_dir, subject, 'bem', '%s.surf' % this_skull) if not op.exists(skull_fname): raise IOError('No skull surface %s found for subject %s.' % (this_skull, subject)) logger.info('Using %s for head surface.' % skull_fname) skull_surf = read_surface(skull_fname, return_dict=True)[2] skull_surf['rr'] /= 1000. skull_surf['coord_frame'] = FIFF.FIFFV_COORD_MRI skull_alpha[this_skull] = alphas[idx + 1] skull_colors[this_skull] = (0.95 - idx * 0.2, 0.85, 0.95 - idx * 0.2) surfs[this_skull] = skull_surf if src is None and brain is False and len(skull) == 0 and not show_axes: head_alpha = 1.0 else: head_alpha = alphas[0] for key in surfs.keys(): # Surfs can sometimes be in head coords (e.g., if coming from sphere) surfs[key] = transform_surface_to(surfs[key], coord_frame, [mri_trans, head_trans], copy=True) if src is not None: src_rr, src_nn = _update_coord_frame(src[0], src_rr, src_nn, mri_trans, head_trans) if fwd is not None: fwd_rr, fwd_nn = _update_coord_frame(fwd, fwd_rr, fwd_nn, mri_trans, head_trans) # determine points meg_rrs, meg_tris = list(), list() ecog_loc = list() seeg_loc = list() hpi_loc = list() ext_loc = list() car_loc = list() eeg_loc = list() eegp_loc = list() if len(eeg) > 0: eeg_loc = np.array([info['chs'][k]['loc'][:3] for k in eeg_picks]) if len(eeg_loc) > 0: eeg_loc = apply_trans(head_trans, eeg_loc) # XXX do projections here if necessary if 'projected' in eeg: eegp_loc, eegp_nn = _project_onto_surface( eeg_loc, surfs['head'], project_rrs=True, return_nn=True)[2:4] if 'original' not in eeg: eeg_loc = list() del eeg if 'sensors' in meg: coil_transs = [_loc_to_coil_trans(info['chs'][pick]['loc']) for pick in meg_picks] coils = _create_meg_coils([info['chs'][pick] for pick in meg_picks], acc='normal') offset = 0 for coil, coil_trans in zip(coils, coil_transs): rrs, tris = _sensor_shape(coil) rrs = apply_trans(coil_trans, rrs) meg_rrs.append(rrs) meg_tris.append(tris + offset) offset += len(meg_rrs[-1]) if len(meg_rrs) == 0: if warn_meg: warn('MEG sensors not found. Cannot plot MEG locations.') else: meg_rrs = apply_trans(meg_trans, np.concatenate(meg_rrs, axis=0)) meg_tris = np.concatenate(meg_tris, axis=0) del meg if dig: if dig == 'fiducials': hpi_loc = ext_loc = [] elif dig is not True: raise ValueError("dig needs to be True, False or 'fiducials', " "not %s" % repr(dig)) else: hpi_loc = np.array([ d['r'] for d in (info['dig'] or []) if (d['kind'] == FIFF.FIFFV_POINT_HPI and d['coord_frame'] == FIFF.FIFFV_COORD_HEAD)]) ext_loc = np.array([ d['r'] for d in (info['dig'] or []) if (d['kind'] == FIFF.FIFFV_POINT_EXTRA and d['coord_frame'] == FIFF.FIFFV_COORD_HEAD)]) car_loc = _fiducial_coords(info['dig'], FIFF.FIFFV_COORD_HEAD) # Transform from head coords if necessary if coord_frame == 'meg': for loc in (hpi_loc, ext_loc, car_loc): loc[:] = apply_trans(invert_transform(info['dev_head_t']), loc) elif coord_frame == 'mri': for loc in (hpi_loc, ext_loc, car_loc): loc[:] = apply_trans(head_mri_t, loc) if len(car_loc) == len(ext_loc) == len(hpi_loc) == 0: warn('Digitization points not found. Cannot plot digitization.') del dig if len(ecog_picks) > 0 and ecog: ecog_loc = np.array([info['chs'][pick]['loc'][:3] for pick in ecog_picks]) if len(seeg_picks) > 0 and seeg: seeg_loc = np.array([info['chs'][pick]['loc'][:3] for pick in seeg_picks]) # initialize figure renderer = _Renderer(fig, bgcolor=(0.5, 0.5, 0.5), size=(800, 800)) if interaction == 'terrain': renderer.set_interactive() # plot surfaces alphas = dict(head=head_alpha, helmet=0.25, lh=hemi_val, rh=hemi_val) alphas.update(skull_alpha) colors = dict(head=(0.6,) * 3, helmet=(0.0, 0.0, 0.6), lh=(0.5,) * 3, rh=(0.5,) * 3) colors.update(skull_colors) for key, surf in surfs.items(): renderer.surface(surface=surf, color=colors[key], opacity=alphas[key], backface_culling=(key != 'helmet')) if brain and 'lh' not in surfs: # one layer sphere assert bem['coord_frame'] == FIFF.FIFFV_COORD_HEAD center = bem['r0'].copy() center = apply_trans(head_trans, center) renderer.sphere(center, scale=0.01, color=colors['lh'], opacity=alphas['lh']) if show_axes: axes = [(head_trans, (0.9, 0.3, 0.3))] # always show head if not np.allclose(head_mri_t['trans'], np.eye(4)): # Show MRI axes.append((mri_trans, (0.6, 0.6, 0.6))) if len(meg_picks) > 0: # Show MEG axes.append((meg_trans, (0., 0.6, 0.6))) for ax in axes: x, y, z = np.tile(ax[0]['trans'][:3, 3], 3).reshape((3, 3)).T u, v, w = ax[0]['trans'][:3, :3] renderer.sphere(center=np.column_stack((x[0], y[0], z[0])), color=ax[1], scale=3e-3) renderer.quiver3d(x=x, y=y, z=z, u=u, v=v, w=w, mode='arrow', scale=2e-2, color=ax[1], scale_mode='scalar', resolution=20, scalars=[0.33, 0.66, 1.0]) # plot points defaults = DEFAULTS['coreg'] datas = [eeg_loc, hpi_loc, ext_loc, ecog_loc, seeg_loc] colors = [defaults['eeg_color'], defaults['hpi_color'], defaults['extra_color'], defaults['ecog_color'], defaults['seeg_color']] alphas = [0.8, 0.5, 0.25, 0.8, 0.8] scales = [defaults['eeg_scale'], defaults['hpi_scale'], defaults['extra_scale'], defaults['ecog_scale'], defaults['seeg_scale']] for kind, loc in (('dig', car_loc), ('mri', fid_loc)): if len(loc) > 0: datas.extend(loc[:, np.newaxis]) colors.extend((defaults['lpa_color'], defaults['nasion_color'], defaults['rpa_color'])) alphas.extend(3 * (defaults[kind + '_fid_opacity'],)) scales.extend(3 * (defaults[kind + '_fid_scale'],)) for data, color, alpha, scale in zip(datas, colors, alphas, scales): if len(data) > 0: renderer.sphere(center=data, color=color, scale=scale, opacity=alpha, backface_culling=True) if len(eegp_loc) > 0: renderer.quiver3d( x=eegp_loc[:, 0], y=eegp_loc[:, 1], z=eegp_loc[:, 2], u=eegp_nn[:, 0], v=eegp_nn[:, 1], w=eegp_nn[:, 2], color=defaults['eegp_color'], mode='cylinder', scale=defaults['eegp_scale'], opacity=0.6, glyph_height=defaults['eegp_height'], glyph_center=(0., -defaults['eegp_height'], 0), glyph_resolution=20, backface_culling=True) if len(meg_rrs) > 0: color, alpha = (0., 0.25, 0.5), 0.25 surf = dict(rr=meg_rrs, tris=meg_tris) renderer.surface(surface=surf, color=color, opacity=alpha, backface_culling=True) if len(src_rr) > 0: renderer.quiver3d( x=src_rr[:, 0], y=src_rr[:, 1], z=src_rr[:, 2], u=src_nn[:, 0], v=src_nn[:, 1], w=src_nn[:, 2], color=(1., 1., 0.), mode='cylinder', scale=3e-3, opacity=0.75, glyph_height=0.25, glyph_center=(0., 0., 0.), glyph_resolution=20, backface_culling=True) if fwd is not None: red = (1.0, 0.0, 0.0) green = (0.0, 1.0, 0.0) blue = (0.0, 0.0, 1.0) for ori, color in zip(range(fwd_nn.shape[1]), (red, green, blue)): renderer.quiver3d(fwd_rr[:, 0], fwd_rr[:, 1], fwd_rr[:, 2], fwd_nn[:, ori, 0], fwd_nn[:, ori, 1], fwd_nn[:, ori, 2], color=color, mode='arrow', scale=1.5e-3) renderer.set_camera(azimuth=90, elevation=90, distance=0.6, focalpoint=(0., 0., 0.)) renderer.show() return renderer.scene() def _make_tris_fan(n_vert): """Make tris given a number of vertices of a circle-like obj.""" tris = np.zeros((n_vert - 2, 3), int) tris[:, 2] = np.arange(2, n_vert) tris[:, 1] = tris[:, 2] - 1 return tris def _sensor_shape(coil): """Get the sensor shape vertices.""" from scipy.spatial import ConvexHull id_ = coil['type'] & 0xFFFF pad = True # Square figure eight if id_ in (FIFF.FIFFV_COIL_NM_122, FIFF.FIFFV_COIL_VV_PLANAR_W, FIFF.FIFFV_COIL_VV_PLANAR_T1, FIFF.FIFFV_COIL_VV_PLANAR_T2, ): # wound by right hand rule such that +x side is "up" (+z) long_side = coil['size'] # length of long side (meters) offset = 0.0025 # offset of the center portion of planar grad coil rrs = np.array([ [offset, -long_side / 2.], [long_side / 2., -long_side / 2.], [long_side / 2., long_side / 2.], [offset, long_side / 2.], [-offset, -long_side / 2.], [-long_side / 2., -long_side / 2.], [-long_side / 2., long_side / 2.], [-offset, long_side / 2.]]) tris = np.concatenate((_make_tris_fan(4), _make_tris_fan(4)[:, ::-1] + 4), axis=0) # Square elif id_ in (FIFF.FIFFV_COIL_POINT_MAGNETOMETER, FIFF.FIFFV_COIL_VV_MAG_T1, FIFF.FIFFV_COIL_VV_MAG_T2, FIFF.FIFFV_COIL_VV_MAG_T3, FIFF.FIFFV_COIL_KIT_REF_MAG, ): # square magnetometer (potentially point-type) size = 0.001 if id_ == 2000 else (coil['size'] / 2.) rrs = np.array([[-1., 1.], [1., 1.], [1., -1.], [-1., -1.]]) * size tris = _make_tris_fan(4) # Circle elif id_ in (FIFF.FIFFV_COIL_MAGNES_MAG, FIFF.FIFFV_COIL_MAGNES_REF_MAG, FIFF.FIFFV_COIL_CTF_REF_MAG, FIFF.FIFFV_COIL_BABY_MAG, FIFF.FIFFV_COIL_BABY_REF_MAG, FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG, ): n_pts = 15 # number of points for circle circle = np.exp(2j * np.pi * np.arange(n_pts) / float(n_pts)) circle = np.concatenate(([0.], circle)) circle *= coil['size'] / 2. # radius of coil rrs = np.array([circle.real, circle.imag]).T tris = _make_tris_fan(n_pts + 1) # Circle elif id_ in (FIFF.FIFFV_COIL_MAGNES_GRAD, FIFF.FIFFV_COIL_CTF_GRAD, FIFF.FIFFV_COIL_CTF_REF_GRAD, FIFF.FIFFV_COIL_CTF_OFFDIAG_REF_GRAD, FIFF.FIFFV_COIL_MAGNES_REF_GRAD, FIFF.FIFFV_COIL_MAGNES_OFFDIAG_REF_GRAD, FIFF.FIFFV_COIL_KIT_GRAD, FIFF.FIFFV_COIL_BABY_GRAD, FIFF.FIFFV_COIL_ARTEMIS123_GRAD, FIFF.FIFFV_COIL_ARTEMIS123_REF_GRAD, ): # round coil 1st order (off-diagonal) gradiometer baseline = coil['base'] if id_ in (5004, 4005) else 0. n_pts = 16 # number of points for circle # This time, go all the way around circle to close it fully circle = np.exp(2j * np.pi * np.arange(-1, n_pts) / float(n_pts - 1)) circle[0] = 0 # center pt for triangulation circle *= coil['size'] / 2. rrs = np.array([ # first, second coil np.concatenate([circle.real + baseline / 2., circle.real - baseline / 2.]), np.concatenate([circle.imag, -circle.imag])]).T tris = np.concatenate([_make_tris_fan(n_pts + 1), _make_tris_fan(n_pts + 1) + n_pts + 1]) # 3D convex hull (will fail for 2D geometry, can extend later if needed) else: rrs = coil['rmag_orig'].copy() pad = False tris = _reorder_ccw(rrs, ConvexHull(rrs).simplices) # Go from (x,y) -> (x,y,z) if pad: rrs = np.pad(rrs, ((0, 0), (0, 1)), mode='constant') assert rrs.ndim == 2 and rrs.shape[1] == 3 return rrs, tris def _limits_to_control_points(clim, stc_data, colormap, transparent, allow_pos_lims=True, linearize=False): """Convert limits (values or percentiles) to control points. This function also does the nonlinear scaling of the colormap in the case of a diverging colormap, and it forces transparency in the alpha channel. """ # Based on type of limits specified, get cmap control points import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap if colormap == 'auto': if clim == 'auto': if allow_pos_lims and (stc_data < 0).any(): colormap = 'mne' else: colormap = 'hot' else: if 'lims' in clim: colormap = 'hot' else: # 'pos_lims' in clim colormap = 'mne' diverging_maps = ['PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'] diverging_maps += [d + '_r' for d in diverging_maps] diverging_maps += ['mne', 'mne_analyze', ] if clim == 'auto': # this is merely a heuristic! if allow_pos_lims and colormap in diverging_maps: key = 'pos_lims' else: key = 'lims' clim = {'kind': 'percent', key: [96, 97.5, 99.95]} if not isinstance(clim, dict): raise ValueError('"clim" must be "auto" or dict, got %s' % (clim,)) if ('lims' in clim) + ('pos_lims' in clim) != 1: raise ValueError('Exactly one of lims and pos_lims must be specified ' 'in clim, got %s' % (clim,)) if 'pos_lims' in clim and not allow_pos_lims: raise ValueError('Cannot use "pos_lims" for clim, use "lims" ' 'instead') diverging_lims = 'pos_lims' in clim ctrl_pts = np.array(clim['pos_lims' if diverging_lims else 'lims']) ctrl_pts = np.array(ctrl_pts, float) if ctrl_pts.shape != (3,): raise ValueError('clim has shape %s, it must be (3,)' % (ctrl_pts.shape,)) if (np.diff(ctrl_pts) < 0).any(): raise ValueError('colormap limits must be monotonically ' 'increasing, got %s' % (ctrl_pts,)) clim_kind = clim.get('kind', 'percent') _check_option("clim['kind']", clim_kind, ['value', 'values', 'percent']) if clim_kind == 'percent': perc_data = np.abs(stc_data) if diverging_lims else stc_data ctrl_pts = np.percentile(perc_data, ctrl_pts) logger.info('Using control points %s' % (ctrl_pts,)) if len(set(ctrl_pts)) == 1: # three points match if ctrl_pts[0] == 0: # all are zero warn('All data were zero') ctrl_pts = np.arange(3, dtype=float) ticks = [0] else: ctrl_pts *= [0., 0.5, 1] # all nonzero pts == max ticks = ctrl_pts[-1:] elif len(set(ctrl_pts)) == 2: # two points match # if points one and two are identical, add a tiny bit to the # control point two; if points two and three are identical, # subtract a tiny bit from point two. bump = 1e-5 if ctrl_pts[0] == ctrl_pts[1] else -1e-5 ctrl_pts[1] = ctrl_pts[0] + bump * (ctrl_pts[2] - ctrl_pts[0]) ticks = ctrl_pts[::2] else: ticks = ctrl_pts if colormap in ('mne', 'mne_analyze'): colormap = mne_analyze_colormap([0, 1, 2], format='matplotlib') # scale colormap so that the bounds given by scale_pts actually work colormap = plt.get_cmap(colormap) if diverging_lims: # remap -ctrl_norm[2]->ctrl_norm[2] to 0->1 ctrl_norm = np.concatenate([-ctrl_pts[::-1] / ctrl_pts[2], [0], ctrl_pts / ctrl_pts[2]]) / 2 + 0.5 linear_norm = [0, 0.25, 0.5, 0.5, 0.5, 0.75, 1] trans_norm = [1, 1, 0, 0, 0, 1, 1] scale_pts = [-ctrl_pts[2], ctrl_pts[2]] idx = int(ticks[0] == 0) ticks = list(-np.array(ticks[idx:])[::-1]) + [0] + list(ticks[idx:]) else: # remap ctrl_norm[0]->ctrl_norm[2] to 0->1 ctrl_norm = [ 0, (ctrl_pts[1] - ctrl_pts[0]) / (ctrl_pts[2] - ctrl_pts[0]), 1] linear_norm = [0, 0.5, 1] trans_norm = [0, 1, 1] scale_pts = [ctrl_pts[0], ctrl_pts[2]] if linearize: # matplotlib # do the piecewise linear transformation interp_to = np.linspace(0, 1, 256) colormap = np.array(colormap( np.interp(interp_to, ctrl_norm, linear_norm))) if transparent: colormap[:, 3] = np.interp(interp_to, ctrl_norm, trans_norm) assert len(scale_pts) == 2 scale_pts = np.array([scale_pts[0], np.mean(scale_pts), scale_pts[1]]) colormap = ListedColormap(colormap) else: # mayavi / PySurfer will do the transformation for us scale_pts = ctrl_pts return colormap, scale_pts, diverging_lims, transparent, ticks def _handle_time(time_label, time_unit, times): """Handle time label string and units.""" if time_label == 'auto': if time_unit == 's': time_label = 'time=%0.3fs' elif time_unit == 'ms': time_label = 'time=%0.1fms' _, times = _check_time_unit(time_unit, times) return time_label, times def _key_pressed_slider(event, params): """Handle key presses for time_viewer slider.""" step = 1 if event.key.startswith('ctrl'): step = 5 event.key = event.key.split('+')[-1] if event.key not in ['left', 'right']: return time_viewer = event.canvas.figure value = time_viewer.slider.val times = params['stc'].times if params['time_unit'] == 'ms': times = times * 1000. time_idx = np.argmin(np.abs(times - value)) if event.key == 'left': time_idx = np.max((0, time_idx - step)) elif event.key == 'right': time_idx = np.min((len(times) - 1, time_idx + step)) this_time = times[time_idx] time_viewer.slider.set_val(this_time) def _smooth_plot(this_time, params): """Smooth source estimate data and plot with mpl.""" from ..morph import _morph_buffer ax = params['ax'] stc = params['stc'] ax.clear() times = stc.times scaler = 1000. if params['time_unit'] == 'ms' else 1. if this_time is None: time_idx = 0 else: time_idx = np.argmin(np.abs(times - this_time / scaler)) if params['hemi_idx'] == 0: data = stc.data[:len(stc.vertices[0]), time_idx:time_idx + 1] else: data = stc.data[len(stc.vertices[0]):, time_idx:time_idx + 1] array_plot = _morph_buffer(data, params['vertices'], params['e'], params['smoothing_steps'], params['n_verts'], params['inuse'], params['maps']) range_ = params['scale_pts'][2] - params['scale_pts'][0] colors = (array_plot - params['scale_pts'][0]) / range_ faces = params['faces'] greymap = params['greymap'] cmap = params['cmap'] polyc = ax.plot_trisurf(*params['coords'].T, triangles=faces, antialiased=False, vmin=0, vmax=1) color_ave = np.mean(colors[faces], axis=1).flatten() curv_ave = np.mean(params['curv'][faces], axis=1).flatten() # matplotlib/matplotlib#11877 facecolors = polyc._facecolors3d colors = cmap(color_ave) # alpha blend colors[:, :3] *= colors[:, [3]] colors[:, :3] += greymap(curv_ave)[:, :3] * (1. - colors[:, [3]]) colors[:, 3] = 1. facecolors[:] = colors ax.set_title(params['time_label'] % (times[time_idx] * scaler), color='w') _set_aspect_equal(ax) ax.axis('off') ax.set(xlim=[-80, 80], ylim=(-80, 80), zlim=[-80, 80]) ax.figure.canvas.draw() def _plot_mpl_stc(stc, subject=None, surface='inflated', hemi='lh', colormap='auto', time_label='auto', smoothing_steps=10, subjects_dir=None, views='lat', clim='auto', figure=None, initial_time=None, time_unit='s', background='black', spacing='oct6', time_viewer=False, colorbar=True, transparent=True): """Plot source estimate using mpl.""" import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.widgets import Slider import nibabel as nib from scipy import stats from ..morph import _get_subject_sphere_tris if hemi not in ['lh', 'rh']: raise ValueError("hemi must be 'lh' or 'rh' when using matplotlib. " "Got %s." % hemi) lh_kwargs = {'lat': {'elev': 0, 'azim': 180}, 'med': {'elev': 0, 'azim': 0}, 'ros': {'elev': 0, 'azim': 90}, 'cau': {'elev': 0, 'azim': -90}, 'dor': {'elev': 90, 'azim': -90}, 'ven': {'elev': -90, 'azim': -90}, 'fro': {'elev': 0, 'azim': 106.739}, 'par': {'elev': 30, 'azim': -120}} rh_kwargs = {'lat': {'elev': 0, 'azim': 0}, 'med': {'elev': 0, 'azim': 180}, 'ros': {'elev': 0, 'azim': 90}, 'cau': {'elev': 0, 'azim': -90}, 'dor': {'elev': 90, 'azim': -90}, 'ven': {'elev': -90, 'azim': -90}, 'fro': {'elev': 16.739, 'azim': 60}, 'par': {'elev': 30, 'azim': -60}} kwargs = dict(lh=lh_kwargs, rh=rh_kwargs) _check_option('views', views, sorted(lh_kwargs.keys())) colormap, scale_pts, _, _, _ = _limits_to_control_points( clim, stc.data, colormap, transparent, linearize=True) del transparent time_label, times = _handle_time(time_label, time_unit, stc.times) fig = plt.figure(figsize=(6, 6)) if figure is None else figure ax = Axes3D(fig) hemi_idx = 0 if hemi == 'lh' else 1 surf = op.join(subjects_dir, subject, 'surf', '%s.%s' % (hemi, surface)) if spacing == 'all': coords, faces = nib.freesurfer.read_geometry(surf) inuse = slice(None) else: stype, sval, ico_surf, src_type_str = _check_spacing(spacing) surf = _create_surf_spacing(surf, hemi, subject, stype, ico_surf, subjects_dir) inuse = surf['vertno'] faces = surf['use_tris'] coords = surf['rr'][inuse] shape = faces.shape faces = stats.rankdata(faces, 'dense').reshape(shape) - 1 faces = np.round(faces).astype(int) # should really be int-like anyway del surf vertices = stc.vertices[hemi_idx] n_verts = len(vertices) tris = _get_subject_sphere_tris(subject, subjects_dir)[hemi_idx] e = mesh_edges(tris) e.data[e.data == 2] = 1 n_vertices = e.shape[0] maps = sparse.identity(n_vertices).tocsr() e = e + sparse.eye(n_vertices, n_vertices) cmap = cm.get_cmap(colormap) greymap = cm.get_cmap('Greys') curv = nib.freesurfer.read_morph_data( op.join(subjects_dir, subject, 'surf', '%s.curv' % hemi))[inuse] curv = np.clip(np.array(curv > 0, np.int), 0.33, 0.66) params = dict(ax=ax, stc=stc, coords=coords, faces=faces, hemi_idx=hemi_idx, vertices=vertices, e=e, smoothing_steps=smoothing_steps, n_verts=n_verts, inuse=inuse, maps=maps, cmap=cmap, curv=curv, scale_pts=scale_pts, greymap=greymap, time_label=time_label, time_unit=time_unit) _smooth_plot(initial_time, params) ax.view_init(**kwargs[hemi][views]) try: ax.set_facecolor(background) except AttributeError: ax.set_axis_bgcolor(background) if time_viewer: time_viewer = figure_nobar(figsize=(4.5, .25)) fig.time_viewer = time_viewer ax_time = plt.axes() if initial_time is None: initial_time = 0 slider = Slider(ax=ax_time, label='Time', valmin=times[0], valmax=times[-1], valinit=initial_time, valfmt=time_label) time_viewer.slider = slider callback_slider = partial(_smooth_plot, params=params) slider.on_changed(callback_slider) callback_key = partial(_key_pressed_slider, params=params) time_viewer.canvas.mpl_connect('key_press_event', callback_key) time_viewer.subplots_adjust(left=0.12, bottom=0.05, right=0.75, top=0.95) fig.subplots_adjust(left=0., bottom=0., right=1., top=1.) # add colorbar from mpl_toolkits.axes_grid1.inset_locator import inset_axes sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(scale_pts[0], scale_pts[2])) cax = inset_axes(ax, width="80%", height="5%", loc=8, borderpad=3.) plt.setp(plt.getp(cax, 'xticklabels'), color='w') sm.set_array(np.linspace(scale_pts[0], scale_pts[2], 256)) if colorbar: cb = plt.colorbar(sm, cax=cax, orientation='horizontal') cb_yticks = plt.getp(cax, 'yticklabels') plt.setp(cb_yticks, color='w') cax.tick_params(labelsize=16) cb.patch.set_facecolor('0.5') cax.set(xlim=(scale_pts[0], scale_pts[2])) plt.show() return fig @verbose def plot_source_estimates(stc, subject=None, surface='inflated', hemi='lh', colormap='auto', time_label='auto', smoothing_steps=10, transparent=True, alpha=1.0, time_viewer=False, subjects_dir=None, figure=None, views='lat', colorbar=True, clim='auto', cortex="classic", size=800, background="black", foreground="white", initial_time=None, time_unit='s', backend='auto', spacing='oct6', title=None, verbose=None): """Plot SourceEstimate with PySurfer. By default this function uses :mod:`mayavi.mlab` to plot the source estimates. If Mayavi is not installed, the plotting is done with :mod:`matplotlib.pyplot` (much slower, decimated source space by default). Parameters ---------- stc : SourceEstimate The source estimates to plot. subject : str | None The subject name corresponding to FreeSurfer environment variable SUBJECT. If None stc.subject will be used. If that is None, the environment will be used. surface : str The type of surface (inflated, white etc.). hemi : str, 'lh' | 'rh' | 'split' | 'both' The hemisphere to display. %(colormap)s The default ('auto') uses 'hot' for one-sided data and 'mne' for two-sided data. time_label : str | callable | None Format of the time label (a format string, a function that maps floating point time values to strings, or None for no label). The default is ``time=%%0.2f ms``. smoothing_steps : int The amount of smoothing %(transparent)s alpha : float Alpha value to apply globally to the overlay. Has no effect with mpl backend. time_viewer : bool Display time viewer GUI. subjects_dir : str The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR. figure : instance of mayavi.core.api.Scene | instance of matplotlib.figure.Figure | list | int | None If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate length. If int is provided it will be used to identify the Mayavi figure by it's id or create a new figure with the given id. If an instance of matplotlib figure, mpl backend is used for plotting. views : str | list View to use. See `surfer.Brain`. Supported views: ['lat', 'med', 'ros', 'cau', 'dor' 'ven', 'fro', 'par']. Using multiple views is not supported for mpl backend. colorbar : bool If True, display colorbar on scene. %(clim)s cortex : str or tuple Specifies how binarized curvature values are rendered. Either the name of a preset PySurfer cortex colorscheme (one of 'classic', 'bone', 'low_contrast', or 'high_contrast'), or the name of mayavi colormap, or a tuple with values (colormap, min, max, reverse) to fully specify the curvature colors. Has no effect with mpl backend. size : float or tuple of float The size of the window, in pixels. can be one number to specify a square window, or the (width, height) of a rectangular window. Has no effect with mpl backend. background : matplotlib color Color of the background of the display window. foreground : matplotlib color Color of the foreground of the display window. Has no effect with mpl backend. initial_time : float | None The time to display on the plot initially. ``None`` to display the first time sample (default). time_unit : 's' | 'ms' Whether time is represented in seconds ("s", default) or milliseconds ("ms"). backend : 'auto' | 'mayavi' | 'matplotlib' Which backend to use. If ``'auto'`` (default), tries to plot with mayavi, but resorts to matplotlib if mayavi is not available. .. versionadded:: 0.15.0 spacing : str The spacing to use for the source space. Can be ``'ico#'`` for a recursively subdivided icosahedron, ``'oct#'`` for a recursively subdivided octahedron, or ``'all'`` for all points. In general, you can speed up the plotting by selecting a sparser source space. Has no effect with mayavi backend. Defaults to 'oct6'. .. versionadded:: 0.15.0 title : str | None Title for the figure. If None, the subject name will be used. .. versionadded:: 0.17.0 %(verbose)s Returns ------- figure : instance of surfer.Brain | matplotlib.figure.Figure An instance of :class:`surfer.Brain` from PySurfer or matplotlib figure. """ # noqa: E501 # import here to avoid circular import problem from ..source_estimate import SourceEstimate _validate_type(stc, SourceEstimate, "stc", "Surface Source Estimate") subjects_dir = get_subjects_dir(subjects_dir=subjects_dir, raise_error=True) subject = _check_subject(stc.subject, subject, True) _check_option('backend', backend, ['auto', 'matplotlib', 'mayavi']) plot_mpl = backend == 'matplotlib' if not plot_mpl: try: from mayavi import mlab # noqa: F401 except ImportError: if backend == 'auto': warn('Mayavi not found. Resorting to matplotlib 3d.') plot_mpl = True else: # 'mayavi' raise if plot_mpl: return _plot_mpl_stc(stc, subject=subject, surface=surface, hemi=hemi, colormap=colormap, time_label=time_label, smoothing_steps=smoothing_steps, subjects_dir=subjects_dir, views=views, clim=clim, figure=figure, initial_time=initial_time, time_unit=time_unit, background=background, spacing=spacing, time_viewer=time_viewer, colorbar=colorbar, transparent=transparent) from surfer import Brain, TimeViewer _check_option('hemi', hemi, ['lh', 'rh', 'split', 'both']) time_label, times = _handle_time(time_label, time_unit, stc.times) # convert control points to locations in colormap colormap, scale_pts, diverging, transparent, _ = _limits_to_control_points( clim, stc.data, colormap, transparent) if hemi in ['both', 'split']: hemis = ['lh', 'rh'] else: hemis = [hemi] if title is None: title = subject if len(hemis) > 1 else '%s - %s' % (subject, hemis[0]) with warnings.catch_warnings(record=True): # traits warnings brain = Brain(subject, hemi=hemi, surf=surface, title=title, cortex=cortex, size=size, background=background, foreground=foreground, figure=figure, subjects_dir=subjects_dir, views=views) ad_kwargs, sd_kwargs = _get_ps_kwargs( initial_time, diverging, scale_pts[1], transparent) del initial_time, transparent for hemi in hemis: hemi_idx = 0 if hemi == 'lh' else 1 data = getattr(stc, hemi + '_data') vertices = stc.vertices[hemi_idx] if len(data) > 0: with warnings.catch_warnings(record=True): # traits warnings brain.add_data(data, colormap=colormap, vertices=vertices, smoothing_steps=smoothing_steps, time=times, time_label=time_label, alpha=alpha, hemi=hemi, colorbar=colorbar, min=scale_pts[0], max=scale_pts[2], **ad_kwargs) if 'mid' not in ad_kwargs: # PySurfer < 0.9 brain.scale_data_colormap(fmin=scale_pts[0], fmid=scale_pts[1], fmax=scale_pts[2], **sd_kwargs) if time_viewer: TimeViewer(brain) return brain def _get_ps_kwargs(initial_time, diverging, mid, transparent): """Triage arguments based on PySurfer version.""" import surfer surfer_version = LooseVersion(surfer.__version__) require = '0.8' if surfer_version < LooseVersion(require): raise ImportError("This function requires PySurfer %s (you are " "running version %s). You can update PySurfer " "using:\n\n $ pip install -U pysurfer" % (require, surfer.__version__)) ad_kwargs = dict(verbose=False) sd_kwargs = dict(transparent=transparent, verbose=False) if initial_time is not None: ad_kwargs['initial_time'] = initial_time if surfer_version >= LooseVersion('0.9'): ad_kwargs.update(mid=mid, transparent=transparent) ad_kwargs['center'] = 0. if diverging else None sd_kwargs['center'] = 0. if diverging else None return ad_kwargs, sd_kwargs def _glass_brain_crosshairs(params, x, y, z): for ax, a, b in ((params['ax_y'], x, z), (params['ax_x'], y, z), (params['ax_z'], x, y)): ax.axvline(a, color='0.75') ax.axhline(b, color='0.75') def _cut_coords_to_ijk(cut_coords, img): ijk = apply_trans(linalg.inv(img.affine), cut_coords) ijk = np.clip(np.round(ijk).astype(int), 0, np.array(img.shape[:3]) - 1) return ijk def _ijk_to_cut_coords(ijk, img): return apply_trans(img.affine, ijk) @verbose def plot_volume_source_estimates(stc, src, subject=None, subjects_dir=None, mode='stat_map', bg_img=None, colorbar=True, colormap='auto', clim='auto', transparent=None, show=True, initial_time=None, initial_pos=None, verbose=None): """Plot Nutmeg style volumetric source estimates using nilearn. Parameters ---------- stc : VectorSourceEstimate The vector source estimate to plot. src : instance of SourceSpaces | instance of SourceMorph The source space. Can also be a SourceMorph to morph the STC to a new subject (see Examples). .. versionchanged:: 0.18 Support for :class:`~nibabel.spatialimages.SpatialImage`. subject : str | None The subject name corresponding to FreeSurfer environment variable SUBJECT. If None stc.subject will be used. If that is None, the environment will be used. subjects_dir : str The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR. mode : str The plotting mode to use. Either 'stat_map' (default) or 'glass_brain'. For "glass_brain", activation absolute values are displayed after being transformed to a standard MNI brain. bg_img : instance of SpatialImage | None The background image used in the nilearn plotting function. If None, it is the T1.mgz file that is found in the subjects_dir. Not used in "glass brain" plotting. colorbar : boolean, optional If True, display a colorbar on the right of the plots. %(colormap)s %(clim)s %(transparent)s show : bool Show figures if True. Defaults to True. initial_time : float | None The initial time to plot. Can be None (default) to use the time point with the maximal absolute value activation across all voxels or the ``initial_pos`` voxel (if ``initial_pos is None`` or not, respectively). .. versionadded:: 0.19 initial_pos : ndarray, shape (3,) | None The initial position to use (in m). Can be None (default) to use the voxel with the maximum absolute value activation across all time points or at ``initial_time`` (if ``initial_time is None`` or not, respectively). .. versionadded:: 0.19 %(verbose)s Notes ----- Click on any of the anatomical slices to explore the time series. Clicking on any time point will bring up the corresponding anatomical map. The left and right arrow keys can be used to navigate in time. To move in time by larger steps, use shift+left and shift+right. In ``'glass_brain'`` mode, values are transformed to the standard MNI brain using the FreeSurfer Talairach transformation ``$SUBJECTS_DIR/$SUBJECT/mri/transforms/talairach.xfm``. .. versionadded:: 0.17 .. versionchanged:: 0.19 MRI volumes are automatically transformed to MNI space in ``'glass_brain'`` mode. Examples -------- Passing a :class:`mne.SourceMorph` as the ``src`` parameter can be useful for plotting in a different subject's space (here, a ``'sample'`` STC in ``'fsaverage'``'s space):: >>> morph = mne.compute_source_morph(src_sample, subject_to='fsaverage') # doctest: +SKIP >>> fig = stc_vol_sample.plot(morph) # doctest: +SKIP """ # noqa: E501 from matplotlib import pyplot as plt, colors from matplotlib.cbook import mplDeprecation import nibabel as nib from ..source_estimate import VolSourceEstimate from ..morph import SourceMorph if not check_version('nilearn', '0.4'): raise RuntimeError('This function requires nilearn >= 0.4') from nilearn.plotting import plot_stat_map, plot_glass_brain from nilearn.image import index_img _check_option('mode', mode, ('stat_map', 'glass_brain')) plot_func = dict(stat_map=plot_stat_map, glass_brain=plot_glass_brain)[mode] _validate_type(stc, VolSourceEstimate, 'stc') if isinstance(src, SourceMorph): img = src.apply(stc, 'nifti1', mri_resolution=False, mri_space=False) stc = src.apply(stc, mri_resolution=False, mri_space=False) kind, src_subject = 'morph.subject_to', src.subject_to else: src = _ensure_src(src, kind='volume', extra=' or SourceMorph') img = stc.as_volume(src, mri_resolution=False) kind, src_subject = 'src subject', src[0].get('subject_his_id', None) _print_coord_trans(Transform('mri_voxel', 'ras', img.affine), prefix='Image affine ', units='mm', level='debug') subject = _check_subject(src_subject, subject, True, kind=kind) stc_ijk = np.array( np.unravel_index(stc.vertices, img.shape[:3], order='F')).T assert stc_ijk.shape == (len(stc.vertices), 3) del src, kind # XXX this assumes zooms are uniform, should probably mult by zooms... dist_to_verts = _DistanceQuery(stc_ijk, allow_kdtree=True) def _cut_coords_to_idx(cut_coords, img): """Convert voxel coordinates to index in stc.data.""" ijk = _cut_coords_to_ijk(cut_coords, img) del cut_coords logger.debug(' Affine remapped cut coords to [%d, %d, %d] idx' % tuple(ijk)) dist, loc_idx = dist_to_verts.query(ijk[np.newaxis]) dist, loc_idx = dist[0], loc_idx[0] logger.debug(' Using vertex %d at a distance of %d voxels' % (stc.vertices[loc_idx], dist)) return loc_idx ax_name = dict(x='X (saggital)', y='Y (coronal)', z='Z (axial)') def _click_to_cut_coords(event, params): """Get voxel coordinates from mouse click.""" if event.inaxes is params['ax_x']: ax = 'x' x = params['ax_z'].lines[0].get_xdata()[0] y, z = event.xdata, event.ydata elif event.inaxes is params['ax_y']: ax = 'y' y = params['ax_x'].lines[0].get_xdata()[0] x, z = event.xdata, event.ydata elif event.inaxes is params['ax_z']: ax = 'z' x, y = event.xdata, event.ydata z = params['ax_x'].lines[1].get_ydata()[0] else: logger.debug(' Click outside axes') return None cut_coords = np.array((x, y, z)) logger.debug('') if params['mode'] == 'glass_brain': # find idx for MIP # Figure out what XYZ in world coordinates is in our voxel data codes = ''.join(nib.aff2axcodes(params['img_idx'].affine)) assert len(codes) == 3 # We don't care about directionality, just which is which dim codes = codes.replace('L', 'R').replace('P', 'A').replace('I', 'S') idx = codes.index(dict(x='R', y='A', z='S')[ax]) img_data = np.abs(_get_img_fdata(params['img_idx'])) ijk = _cut_coords_to_ijk(cut_coords, params['img_idx']) if idx == 0: ijk[0] = np.argmax(img_data[:, ijk[1], ijk[2]]) logger.debug(' MIP: i = %d idx' % (ijk[0],)) elif idx == 1: ijk[1] = np.argmax(img_data[ijk[0], :, ijk[2]]) logger.debug(' MIP: j = %d idx' % (ijk[1],)) else: ijk[2] = np.argmax(img_data[ijk[0], ijk[1], :]) logger.debug(' MIP: k = %d idx' % (ijk[2],)) cut_coords = _ijk_to_cut_coords(ijk, params['img_idx']) logger.debug(' Cut coords for %s: (%0.1f, %0.1f, %0.1f) mm' % ((ax_name[ax],) + tuple(cut_coords))) return cut_coords def _press(event, params): """Manage keypress on the plot.""" pos = params['lx'].get_xdata() idx = params['stc'].time_as_index(pos)[0] if event.key == 'left': idx = max(0, idx - 2) elif event.key == 'shift+left': idx = max(0, idx - 10) elif event.key == 'right': idx = min(params['stc'].shape[1] - 1, idx + 2) elif event.key == 'shift+right': idx = min(params['stc'].shape[1] - 1, idx + 10) _update_timeslice(idx, params) params['fig'].canvas.draw() def _update_timeslice(idx, params): params['lx'].set_xdata(idx / params['stc'].sfreq + params['stc'].tmin) ax_x, ax_y, ax_z = params['ax_x'], params['ax_y'], params['ax_z'] plot_map_callback = params['plot_func'] # Crosshairs are the first thing plotted in stat_map, and the last # in glass_brain idxs = [0, 0, 1] if mode == 'stat_map' else [-2, -2, -1] cut_coords = ( ax_y.lines[idxs[0]].get_xdata()[0], ax_x.lines[idxs[1]].get_xdata()[0], ax_x.lines[idxs[2]].get_ydata()[0]) ax_x.clear() ax_y.clear() ax_z.clear() params.update({'img_idx': index_img(img, idx)}) params.update({'title': 'Activation (t=%.3f s.)' % params['stc'].times[idx]}) plot_map_callback( params['img_idx'], title='', cut_coords=cut_coords) @verbose_dec def _onclick(event, params, verbose=None): """Manage clicks on the plot.""" ax_x, ax_y, ax_z = params['ax_x'], params['ax_y'], params['ax_z'] plot_map_callback = params['plot_func'] if event.inaxes is params['ax_time']: idx = params['stc'].time_as_index( event.xdata, use_rounding=True)[0] _update_timeslice(idx, params) cut_coords = _click_to_cut_coords(event, params) if cut_coords is None: return # not in any axes ax_x.clear() ax_y.clear() ax_z.clear() plot_map_callback(params['img_idx'], title='', cut_coords=cut_coords) loc_idx = _cut_coords_to_idx(cut_coords, params['img_idx']) ydata = stc.data[loc_idx] if loc_idx is not None: ax_time.lines[0].set_ydata(ydata) else: ax_time.lines[0].set_ydata(0.) params['fig'].canvas.draw() if mode == 'glass_brain': subject = _check_subject(stc.subject, subject, True) ras_mni_t = _read_ras_mni_t(subject, subjects_dir) if not np.allclose(ras_mni_t['trans'], np.eye(4)): _print_coord_trans( ras_mni_t, prefix='Transforming subject ', units='mm') logger.info('') # To get from voxel coords to world coords (i.e., define affine) # we would apply img.affine, then also apply ras_mni_t, which # transforms from the subject's RAS to MNI RAS. So we left-multiply # these. img = nib.Nifti1Image( img.dataobj, np.dot(ras_mni_t['trans'], img.affine)) bg_img = None # not used else: # stat_map if bg_img is None: subject = _check_subject(stc.subject, subject, True) subjects_dir = get_subjects_dir(subjects_dir=subjects_dir, raise_error=True) t1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz') bg_img = nib.load(t1_fname) if initial_time is None: time_sl = slice(0, None) else: initial_time = float(initial_time) logger.info('Fixing initial time: %s sec' % (initial_time,)) initial_time = np.argmin(np.abs(stc.times - initial_time)) time_sl = slice(initial_time, initial_time + 1) if initial_pos is None: # find max pos and (maybe) time loc_idx, time_idx = np.unravel_index( np.abs(stc.data[:, time_sl]).argmax(), stc.data[:, time_sl].shape) time_idx += time_sl.start else: # position specified initial_pos = np.array(initial_pos, float) if initial_pos.shape != (3,): raise ValueError('initial_pos must be float ndarray with shape ' '(3,), got shape %s' % (initial_pos.shape,)) initial_pos *= 1000 logger.info('Fixing initial position: %s mm' % (initial_pos.tolist(),)) loc_idx = _cut_coords_to_idx(initial_pos, img) if initial_time is not None: # time also specified time_idx = time_sl.start else: # find the max time_idx = np.argmax(np.abs(stc.data[loc_idx])) img_idx = index_img(img, time_idx) assert img_idx.shape == img.shape[:3] del initial_time, initial_pos ijk = stc_ijk[loc_idx] cut_coords = _ijk_to_cut_coords(ijk, img_idx) np.testing.assert_allclose(_cut_coords_to_ijk(cut_coords, img_idx), ijk) logger.info('Showing: t = %0.3f s, (%0.1f, %0.1f, %0.1f) mm, ' '[%d, %d, %d] vox, %d vertex' % ((stc.times[time_idx],) + tuple(cut_coords) + tuple(ijk) + (stc.vertices[loc_idx],))) del ijk # Plot initial figure fig, (axes, ax_time) = plt.subplots(2) axes.set(xticks=[], yticks=[]) marker = 'o' if len(stc.times) == 1 else None ydata = stc.data[loc_idx] ax_time.plot(stc.times, ydata, color='k', marker=marker) if len(stc.times) > 1: ax_time.set(xlim=stc.times[[0, -1]]) ax_time.set(xlabel='Time (s)', ylabel='Activation') lx = ax_time.axvline(stc.times[time_idx], color='g') fig.tight_layout() allow_pos_lims = (mode != 'glass_brain') colormap, scale_pts, diverging, _, ticks = _limits_to_control_points( clim, stc.data, colormap, transparent, allow_pos_lims, linearize=True) ylim = [min((scale_pts[0], ydata.min())), max((scale_pts[-1], ydata.max()))] ylim = np.array(ylim) + np.array([-1, 1]) * 0.05 * np.diff(ylim)[0] dup_neg = False if stc.data.min() < 0: ax_time.axhline(0., color='0.5', ls='-', lw=0.5, zorder=2) dup_neg = not diverging # glass brain with signed data yticks = list(ticks) if dup_neg: yticks += [0] + list(-np.array(ticks)) yticks = np.unique(yticks) ax_time.set(yticks=yticks) ax_time.set(ylim=ylim) del yticks if not diverging: # set eq above iff one-sided # there is a bug in nilearn where this messes w/transparency # Need to double the colormap if (scale_pts < 0).any(): # XXX We should fix this, but it's hard to get nilearn to # use arbitrary bounds :( # Should get them to support non-mirrored colorbars, or # at least a proper `vmin` for one-sided things. # Hopefully this is a sufficiently rare use case! raise ValueError('Negative colormap limits for sequential ' 'control points clim["lims"] not supported ' 'currently, consider shifting or flipping the ' 'sign of your data for visualization purposes') # due to nilearn plotting weirdness, extend this to go # -scale_pts[2]->scale_pts[2] instead of scale_pts[0]->scale_pts[2] colormap = plt.get_cmap(colormap) colormap = colormap( np.interp(np.linspace(-1, 1, 256), scale_pts / scale_pts[2], [0, 0.5, 1])) colormap = colors.ListedColormap(colormap) vmax = scale_pts[-1] # black_bg = True is needed because of some matplotlib # peculiarity. See: https://stackoverflow.com/a/34730204 # Otherwise, event.inaxes does not work for ax_x and ax_z plot_kwargs = dict( threshold=None, axes=axes, resampling_interpolation='nearest', vmax=vmax, figure=fig, colorbar=colorbar, bg_img=bg_img, cmap=colormap, black_bg=True, symmetric_cbar=True) def plot_and_correct(*args, **kwargs): axes.clear() if params.get('fig_anat') is not None and plot_kwargs['colorbar']: params['fig_anat']._cbar.ax.clear() with warnings.catch_warnings(record=True): # nilearn bug; ax recreated warnings.simplefilter('ignore', mplDeprecation) params['fig_anat'] = partial( plot_func, **plot_kwargs)(*args, **kwargs) params['fig_anat']._cbar.outline.set_visible(False) for key in 'xyz': params.update({'ax_' + key: params['fig_anat'].axes[key].ax}) # Fix nilearn bug w/cbar background being white if plot_kwargs['colorbar']: params['fig_anat']._cbar.patch.set_facecolor('0.5') # adjust one-sided colorbars if not diverging: _crop_colorbar(params['fig_anat']._cbar, *scale_pts[[0, -1]]) params['fig_anat']._cbar.set_ticks(params['cbar_ticks']) if mode == 'glass_brain': _glass_brain_crosshairs(params, *kwargs['cut_coords']) params = dict(stc=stc, ax_time=ax_time, plot_func=plot_and_correct, img_idx=img_idx, fig=fig, lx=lx, mode=mode, cbar_ticks=ticks) plot_and_correct(stat_map_img=params['img_idx'], title='', cut_coords=cut_coords) if show: plt.show() fig.canvas.mpl_connect('button_press_event', partial(_onclick, params=params, verbose=verbose)) fig.canvas.mpl_connect('key_press_event', partial(_press, params=params)) return fig @fill_doc def plot_vector_source_estimates(stc, subject=None, hemi='lh', colormap='hot', time_label='auto', smoothing_steps=10, transparent=None, brain_alpha=0.4, overlay_alpha=None, vector_alpha=1.0, scale_factor=None, time_viewer=False, subjects_dir=None, figure=None, views='lat', colorbar=True, clim='auto', cortex='classic', size=800, background='black', foreground='white', initial_time=None, time_unit='s'): """Plot VectorSourceEstimate with PySurfer. A "glass brain" is drawn and all dipoles defined in the source estimate are shown using arrows, depicting the direction and magnitude of the current moment at the dipole. Additionally, an overlay is plotted on top of the cortex with the magnitude of the current. Parameters ---------- stc : VectorSourceEstimate The vector source estimate to plot. subject : str | None The subject name corresponding to FreeSurfer environment variable SUBJECT. If None stc.subject will be used. If that is None, the environment will be used. hemi : str, 'lh' | 'rh' | 'split' | 'both' The hemisphere to display. %(colormap)s This should be a sequential colormap. time_label : str | callable | None Format of the time label (a format string, a function that maps floating point time values to strings, or None for no label). The default is ``time=%%0.2f ms``. smoothing_steps : int The amount of smoothing %(transparent)s brain_alpha : float Alpha value to apply globally to the surface meshes. Defaults to 0.4. overlay_alpha : float Alpha value to apply globally to the overlay. Defaults to ``brain_alpha``. vector_alpha : float Alpha value to apply globally to the vector glyphs. Defaults to 1. scale_factor : float | None Scaling factor for the vector glyphs. By default, an attempt is made to automatically determine a sane value. time_viewer : bool Display time viewer GUI. subjects_dir : str The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR. figure : instance of mayavi.core.api.Scene | list | int | None If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate length. If int is provided it will be used to identify the Mayavi figure by it's id or create a new figure with the given id. views : str | list View to use. See `surfer.Brain`. colorbar : bool If True, display colorbar on scene. %(clim_onesided)s cortex : str or tuple specifies how binarized curvature values are rendered. either the name of a preset PySurfer cortex colorscheme (one of 'classic', 'bone', 'low_contrast', or 'high_contrast'), or the name of mayavi colormap, or a tuple with values (colormap, min, max, reverse) to fully specify the curvature colors. size : float or tuple of float The size of the window, in pixels. can be one number to specify a square window, or the (width, height) of a rectangular window. background : matplotlib color Color of the background of the display window. foreground : matplotlib color Color of the foreground of the display window. initial_time : float | None The time to display on the plot initially. ``None`` to display the first time sample (default). time_unit : 's' | 'ms' Whether time is represented in seconds ("s", default) or milliseconds ("ms"). Returns ------- brain : surfer.Brain A instance of :class:`surfer.Brain` from PySurfer. Notes ----- .. versionadded:: 0.15 If the current magnitude overlay is not desired, set ``overlay_alpha=0`` and ``smoothing_steps=1``. """ # Import here to avoid circular imports from surfer import Brain, TimeViewer from ..source_estimate import VectorSourceEstimate _validate_type(stc, VectorSourceEstimate, "stc", "Vector Source Estimate") subjects_dir = get_subjects_dir(subjects_dir=subjects_dir, raise_error=True) subject = _check_subject(stc.subject, subject, True) _check_option('hemi', hemi, ['lh', 'rh', 'split', 'both']) time_label, times = _handle_time(time_label, time_unit, stc.times) # convert control points to locations in colormap colormap, scale_pts, _, transparent, _ = _limits_to_control_points( clim, stc.data, colormap, transparent, allow_pos_lims=False) if hemi in ['both', 'split']: hemis = ['lh', 'rh'] else: hemis = [hemi] if overlay_alpha is None: overlay_alpha = brain_alpha if overlay_alpha == 0: smoothing_steps = 1 # Disable smoothing to save time. title = subject if len(hemis) > 1 else '%s - %s' % (subject, hemis[0]) with warnings.catch_warnings(record=True): # traits warnings brain = Brain(subject, hemi=hemi, surf='white', title=title, cortex=cortex, size=size, background=background, foreground=foreground, figure=figure, subjects_dir=subjects_dir, views=views, alpha=brain_alpha) ad_kwargs, sd_kwargs = _get_ps_kwargs( initial_time, False, scale_pts[1], transparent) del initial_time, transparent for hemi in hemis: hemi_idx = 0 if hemi == 'lh' else 1 data = getattr(stc, hemi + '_data') vertices = stc.vertices[hemi_idx] if len(data) > 0: with warnings.catch_warnings(record=True): # traits warnings brain.add_data(data, colormap=colormap, vertices=vertices, smoothing_steps=smoothing_steps, time=times, time_label=time_label, alpha=overlay_alpha, hemi=hemi, colorbar=colorbar, vector_alpha=vector_alpha, scale_factor=scale_factor, min=scale_pts[0], max=scale_pts[2], **ad_kwargs) brain.scale_data_colormap(fmin=scale_pts[0], fmid=scale_pts[1], fmax=scale_pts[2], **sd_kwargs) if time_viewer: TimeViewer(brain) return brain @verbose def plot_sparse_source_estimates(src, stcs, colors=None, linewidth=2, fontsize=18, bgcolor=(.05, 0, .1), opacity=0.2, brain_color=(0.7,) * 3, show=True, high_resolution=False, fig_name=None, fig_number=None, labels=None, modes=('cone', 'sphere'), scale_factors=(1, 0.6), verbose=None, **kwargs): """Plot source estimates obtained with sparse solver. Active dipoles are represented in a "Glass" brain. If the same source is active in multiple source estimates it is displayed with a sphere otherwise with a cone in 3D. Parameters ---------- src : dict The source space. stcs : instance of SourceEstimate or list of instances of SourceEstimate The source estimates (up to 3). colors : list List of colors linewidth : int Line width in 2D plot. fontsize : int Font size. bgcolor : tuple of length 3 Background color in 3D. opacity : float in [0, 1] Opacity of brain mesh. brain_color : tuple of length 3 Brain color. show : bool Show figures if True. high_resolution : bool If True, plot on the original (non-downsampled) cortical mesh. fig_name : str Mayavi figure name. fig_number : int Matplotlib figure number. labels : ndarray or list of ndarray Labels to show sources in clusters. Sources with the same label and the waveforms within each cluster are presented in the same color. labels should be a list of ndarrays when stcs is a list ie. one label for each stc. modes : list Should be a list, with each entry being ``'cone'`` or ``'sphere'`` to specify how the dipoles should be shown. scale_factors : list List of floating point scale factors for the markers. %(verbose)s **kwargs : kwargs Keyword arguments to pass to mlab.triangular_mesh. Returns ------- surface : instance of mayavi.mlab.pipeline.surface The triangular mesh surface. """ import matplotlib.pyplot as plt from matplotlib.colors import ColorConverter # Update the backend from .backends.renderer import _Renderer known_modes = ['cone', 'sphere'] if not isinstance(modes, (list, tuple)) or \ not all(mode in known_modes for mode in modes): raise ValueError('mode must be a list containing only ' '"cone" or "sphere"') if not isinstance(stcs, list): stcs = [stcs] if labels is not None and not isinstance(labels, list): labels = [labels] if colors is None: colors = _get_color_list() linestyles = ['-', '--', ':'] # Show 3D lh_points = src[0]['rr'] rh_points = src[1]['rr'] points = np.r_[lh_points, rh_points] lh_normals = src[0]['nn'] rh_normals = src[1]['nn'] normals = np.r_[lh_normals, rh_normals] if high_resolution: use_lh_faces = src[0]['tris'] use_rh_faces = src[1]['tris'] else: use_lh_faces = src[0]['use_tris'] use_rh_faces = src[1]['use_tris'] use_faces = np.r_[use_lh_faces, lh_points.shape[0] + use_rh_faces] points *= 170 vertnos = [np.r_[stc.lh_vertno, lh_points.shape[0] + stc.rh_vertno] for stc in stcs] unique_vertnos = np.unique(np.concatenate(vertnos).ravel()) color_converter = ColorConverter() renderer = _Renderer(bgcolor=bgcolor, size=(600, 600), name=fig_name) surface = renderer.mesh(x=points[:, 0], y=points[:, 1], z=points[:, 2], triangles=use_faces, color=brain_color, opacity=opacity, backface_culling=True, shading=True, **kwargs) # Show time courses fig = plt.figure(fig_number) fig.clf() ax = fig.add_subplot(111) colors = cycle(colors) logger.info("Total number of active sources: %d" % len(unique_vertnos)) if labels is not None: colors = [next(colors) for _ in range(np.unique(np.concatenate(labels).ravel()).size)] for idx, v in enumerate(unique_vertnos): # get indices of stcs it belongs to ind = [k for k, vertno in enumerate(vertnos) if v in vertno] is_common = len(ind) > 1 if labels is None: c = next(colors) else: # if vertex is in different stcs than take label from first one c = colors[labels[ind[0]][vertnos[ind[0]] == v]] mode = modes[1] if is_common else modes[0] scale_factor = scale_factors[1] if is_common else scale_factors[0] if (isinstance(scale_factor, (np.ndarray, list, tuple)) and len(unique_vertnos) == len(scale_factor)): scale_factor = scale_factor[idx] x, y, z = points[v] nx, ny, nz = normals[v] renderer.quiver3d(x=x, y=y, z=z, u=nx, v=ny, w=nz, color=color_converter.to_rgb(c), mode=mode, scale=scale_factor) for k in ind: vertno = vertnos[k] mask = (vertno == v) assert np.sum(mask) == 1 linestyle = linestyles[k] ax.plot(1e3 * stcs[k].times, 1e9 * stcs[k].data[mask].ravel(), c=c, linewidth=linewidth, linestyle=linestyle) ax.set_xlabel('Time (ms)', fontsize=18) ax.set_ylabel('Source amplitude (nAm)', fontsize=18) if fig_name is not None: ax.set_title(fig_name) plt_show(show) renderer.show() return surface @verbose def plot_dipole_locations(dipoles, trans=None, subject=None, subjects_dir=None, mode='orthoview', coord_frame='mri', idx='gof', show_all=True, ax=None, block=False, show=True, scale=5e-3, color=None, highlight_color='r', fig=None, verbose=None): """Plot dipole locations. If mode is set to 'arrow' or 'sphere', only the location of the first time point of each dipole is shown else use the show_all parameter. The option mode='orthoview' was added in version 0.14. Parameters ---------- dipoles : list of instances of Dipole | Dipole The dipoles to plot. trans : dict | None The mri to head trans. Can be None with mode set to '3d'. subject : str | None The subject name corresponding to FreeSurfer environment variable SUBJECT. Can be None with mode set to '3d'. subjects_dir : None | str The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR. The default is None. mode : str Can be ``'arrow'``, ``'sphere'`` or ``'orthoview'``. .. versionadded:: 0.19.0 coord_frame : str Coordinate frame to use, 'head' or 'mri'. Defaults to 'mri'. .. versionadded:: 0.14.0 idx : int | 'gof' | 'amplitude' Index of the initially plotted dipole. Can also be 'gof' to plot the dipole with highest goodness of fit value or 'amplitude' to plot the dipole with the highest amplitude. The dipoles can also be browsed through using up/down arrow keys or mouse scroll. Defaults to 'gof'. Only used if mode equals 'orthoview'. .. versionadded:: 0.14.0 show_all : bool Whether to always plot all the dipoles. If True (default), the active dipole is plotted as a red dot and it's location determines the shown MRI slices. The the non-active dipoles are plotted as small blue dots. If False, only the active dipole is plotted. Only used if mode equals 'orthoview'. .. versionadded:: 0.14.0 ax : instance of matplotlib Axes3D | None Axes to plot into. If None (default), axes will be created. Only used if mode equals 'orthoview'. .. versionadded:: 0.14.0 block : bool Whether to halt program execution until the figure is closed. Defaults to False. Only used if mode equals 'orthoview'. .. versionadded:: 0.14.0 show : bool Show figure if True. Defaults to True. Only used if mode equals 'orthoview'. scale: float The scale of the dipoles if ``mode`` is 'arrow' or 'sphere'. color : tuple The color of the dipoles. The default (None) will use ``'y'`` if mode is ``'orthoview'`` and ``show_all`` is True, else 'r'. .. versionchanged:: 0.19.0 Color is now passed in orthoview mode. highlight_color : color The highlight color. Only used in orthoview mode with ``show_all=True``. .. versionadded:: 0.19.0 fig : mayavi.mlab.Figure | None 3D Scene in which to plot the alignment. If ``None``, creates a new 600x600 pixel figure with black background. .. versionadded:: 0.19.0 %(verbose)s Returns ------- fig : instance of mayavi.mlab.Figure or matplotlib.figure.Figure The mayavi figure or matplotlib Figure. Notes ----- .. versionadded:: 0.9.0 """ if mode == 'orthoview': fig = _plot_dipole_mri_orthoview( dipoles, trans=trans, subject=subject, subjects_dir=subjects_dir, coord_frame=coord_frame, idx=idx, show_all=show_all, ax=ax, block=block, show=show, color=color, highlight_color=highlight_color) elif mode in ['arrow', 'sphere']: from .backends.renderer import _Renderer color = (1., 0., 0.) if color is None else color renderer = _Renderer(fig=fig, size=(600, 600)) pos = dipoles.pos ori = dipoles.ori if coord_frame != 'head': trans = _get_trans(trans, fro='head', to=coord_frame)[0] pos = apply_trans(trans, pos) ori = apply_trans(trans, ori) renderer.sphere(center=pos, color=color, scale=scale) if mode == 'arrow': x, y, z = pos.T u, v, w = ori.T renderer.quiver3d(x, y, z, u, v, w, scale=3 * scale, color=color, mode='arrow') fig = renderer.scene() else: raise ValueError('Mode must be "cone", "arrow" or orthoview", ' 'got %s.' % (mode,)) return fig def snapshot_brain_montage(fig, montage, hide_sensors=True): """Take a snapshot of a Mayavi Scene and project channels onto 2d coords. Note that this will take the raw values for 3d coordinates of each channel, without applying any transforms. If brain images are flipped up/dn upon using `imshow`, check your matplotlib backend as this behavior changes. Parameters ---------- fig : instance of ~mayavi.core.api.Scene The figure on which you've plotted electrodes using :func:`mne.viz.plot_alignment`. montage : instance of DigMontage or Info | dict The digital montage for the electrodes plotted in the scene. If `Info`, channel positions will be pulled from the `loc` field of `chs`. dict should have ch:xyz mappings. hide_sensors : bool Whether to remove the spheres in the scene before taking a snapshot. Returns ------- xy : array, shape (n_channels, 2) The 2d location of each channel on the image of the current scene view. im : array, shape (m, n, 3) The screenshot of the current scene view """ from ..channels import DigMontage from .. import Info # Update the backend from .backends.renderer import _Renderer if fig is None: raise ValueError('The figure must have a scene') if isinstance(montage, DigMontage): chs = montage._get_ch_pos() ch_names, xyz = zip(*[(ich, ixyz) for ich, ixyz in chs.items()]) elif isinstance(montage, Info): xyz = [ich['loc'][:3] for ich in montage['chs']] ch_names = [ich['ch_name'] for ich in montage['chs']] elif isinstance(montage, dict): if not all(len(ii) == 3 for ii in montage.values()): raise ValueError('All electrode positions must be length 3') ch_names, xyz = zip(*[(ich, ixyz) for ich, ixyz in montage.items()]) else: raise TypeError('montage must be an instance of `DigMontage`, `Info`,' ' or `dict`') # initialize figure renderer = _Renderer(fig, show=True) xyz = np.vstack(xyz) proj = renderer.project(xyz=xyz, ch_names=ch_names) if hide_sensors is True: proj.visible(False) im = renderer.screenshot() proj.visible(True) return proj.xy, im @fill_doc def plot_sensors_connectivity(info, con, picks=None): """Visualize the sensor connectivity in 3D. Parameters ---------- info : dict | None The measurement info. con: array, shape (n_channels, n_channels) The computed connectivity measure(s). %(picks_good_data)s Indices of selected channels. Returns ------- fig : instance of mayavi.mlab.Figure The mayavi figure. """ _validate_type(info, "info") from .backends.renderer import _Renderer renderer = _Renderer(size=(600, 600), bgcolor=(0.5, 0.5, 0.5)) picks = _picks_to_idx(info, picks) if len(picks) != len(con): raise ValueError('The number of channels picked (%s) does not ' 'correspond the size of the connectivity data ' '(%s)' % (len(picks), len(con))) # Plot the sensor locations sens_loc = [info['chs'][k]['loc'][:3] for k in picks] sens_loc = np.array(sens_loc) renderer.sphere(np.c_[sens_loc[:, 0], sens_loc[:, 1], sens_loc[:, 2]], color=(1, 1, 1), opacity=1, scale=0.005) # Get the strongest connections n_con = 20 # show up to 20 connections min_dist = 0.05 # exclude sensors that are less than 5cm apart threshold = np.sort(con, axis=None)[-n_con] ii, jj = np.where(con >= threshold) # Remove close connections con_nodes = list() con_val = list() for i, j in zip(ii, jj): if linalg.norm(sens_loc[i] - sens_loc[j]) > min_dist: con_nodes.append((i, j)) con_val.append(con[i, j]) con_val = np.array(con_val) # Show the connections as tubes between sensors vmax = np.max(con_val) vmin = np.min(con_val) for val, nodes in zip(con_val, con_nodes): x1, y1, z1 = sens_loc[nodes[0]] x2, y2, z2 = sens_loc[nodes[1]] tube = renderer.tube(origin=np.c_[x1, y1, z1], destination=np.c_[x2, y2, z2], scalars=np.c_[val, val], vmin=vmin, vmax=vmax, radius=0.001, reverse_lut=True) renderer.scalarbar(source=tube, title='Phase Lag Index (PLI)') # Add the sensor names for the connections shown nodes_shown = list(set([n[0] for n in con_nodes] + [n[1] for n in con_nodes])) for node in nodes_shown: x, y, z = sens_loc[node] renderer.text3d(x, y, z, text=info['ch_names'][picks[node]], scale=0.005, color=(0, 0, 0)) renderer.set_camera(azimuth=-88.7, elevation=40.8, distance=0.76, focalpoint=np.array([-3.9e-4, -8.5e-3, -1e-2])) renderer.show() return renderer.scene() def _plot_dipole_mri_orthoview(dipole, trans, subject, subjects_dir=None, coord_frame='head', idx='gof', show_all=True, ax=None, block=False, show=True, color=None, highlight_color='r'): """Plot dipoles on top of MRI slices in 3-D.""" import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from .. import Dipole if not has_nibabel(): raise ImportError('This function requires nibabel.') _check_option('coord_frame', coord_frame, ['head', 'mri']) if not isinstance(dipole, Dipole): from ..dipole import _concatenate_dipoles dipole = _concatenate_dipoles(dipole) if idx == 'gof': idx = np.argmax(dipole.gof) elif idx == 'amplitude': idx = np.argmax(np.abs(dipole.amplitude)) else: idx = _ensure_int(idx, 'idx', 'an int or one of ["gof", "amplitude"]') dipole_locs, ori, scatter_points, t1 = \ _get_dipole_loc(dipole, trans, subject, subjects_dir=subjects_dir, coord_frame=coord_frame) zooms = t1.header.get_zooms() data = _get_img_fdata(t1) dims = len(data) # Symmetric size assumed. dd = dims / 2. dd *= t1.header.get_zooms()[0] if ax is None: fig = plt.figure() ax = Axes3D(fig) else: _validate_type(ax, Axes3D, "ax", "Axes3D") fig = ax.get_figure() gridx, gridy = np.meshgrid(np.linspace(-dd, dd, dims), np.linspace(-dd, dd, dims)) _plot_dipole(ax, data, dipole_locs, idx, dipole, gridx, gridy, ori, coord_frame, zooms, show_all, scatter_points, color, highlight_color) params = {'ax': ax, 'data': data, 'idx': idx, 'dipole': dipole, 'dipole_locs': dipole_locs, 'gridx': gridx, 'gridy': gridy, 'ori': ori, 'coord_frame': coord_frame, 'zooms': zooms, 'show_all': show_all, 'scatter_points': scatter_points, 'color': color, 'highlight_color': highlight_color} ax.view_init(elev=30, azim=-140) callback_func = partial(_dipole_changed, params=params) fig.canvas.mpl_connect('scroll_event', callback_func) fig.canvas.mpl_connect('key_press_event', callback_func) plt_show(show, block=block) return fig def _get_dipole_loc(dipole, trans, subject, subjects_dir=None, coord_frame='head'): """Get the dipole locations and orientations.""" import nibabel as nib from nibabel.processing import resample_from_to _check_option('coord_frame', coord_frame, ['head', 'mri']) subjects_dir = get_subjects_dir(subjects_dir=subjects_dir, raise_error=True) t1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz') t1 = nib.load(t1_fname) vox2ras = t1.header.get_vox2ras_tkr() ras2vox = linalg.inv(vox2ras) trans = _get_trans(trans, fro='head', to='mri')[0] zooms = t1.header.get_zooms() if coord_frame == 'head': affine_to = trans['trans'].copy() affine_to[:3, 3] *= 1000 # to mm aff = t1.affine.copy() aff[:3, :3] /= zooms affine_to = np.dot(affine_to, aff) t1 = resample_from_to(t1, ([int(t1.shape[i] * zooms[i]) for i in range(3)], affine_to)) dipole_locs = apply_trans(ras2vox, dipole.pos * 1e3) * zooms dipole_oris = dipole.ori scatter_points = dipole.pos * 1e3 else: scatter_points = apply_trans(trans['trans'], dipole.pos) * 1e3 dipole_oris = apply_trans(trans['trans'], dipole.ori, move=False) dipole_locs = apply_trans(ras2vox, scatter_points) return dipole_locs, dipole_oris, scatter_points, t1 def _plot_dipole(ax, data, points, idx, dipole, gridx, gridy, ori, coord_frame, zooms, show_all, scatter_points, color, highlight_color): """Plot dipoles.""" import matplotlib.pyplot as plt from matplotlib.colors import ColorConverter color_converter = ColorConverter() point = points[idx] xidx, yidx, zidx = np.round(point).astype(int) xslice = data[xidx][::-1] yslice = data[:, yidx][::-1].T zslice = data[:, :, zidx][::-1].T[::-1] if coord_frame == 'head': zooms = (1., 1., 1.) else: point = points[idx] * zooms xidx, yidx, zidx = np.round(point).astype(int) xyz = scatter_points ori = ori[idx] if color is None: color = 'y' if show_all else 'r' color = np.array(color_converter.to_rgba(color)) highlight_color = np.array(color_converter.to_rgba(highlight_color)) if show_all: colors = np.repeat(color[np.newaxis], len(points), axis=0) colors[idx] = highlight_color size = np.repeat(5, len(points)) size[idx] = 20 visible = np.arange(len(points)) else: colors = color size = 20 visible = idx offset = np.min(gridx) ax.scatter(xs=xyz[visible, 0], ys=xyz[visible, 1], zs=xyz[visible, 2], zorder=2, s=size, facecolor=colors) xx = np.linspace(offset, xyz[idx, 0], xidx) yy = np.linspace(offset, xyz[idx, 1], yidx) zz = np.linspace(offset, xyz[idx, 2], zidx) ax.plot(xx, np.repeat(xyz[idx, 1], len(xx)), zs=xyz[idx, 2], zorder=1, linestyle='-', color=highlight_color) ax.plot(np.repeat(xyz[idx, 0], len(yy)), yy, zs=xyz[idx, 2], zorder=1, linestyle='-', color=highlight_color) ax.plot(np.repeat(xyz[idx, 0], len(zz)), np.repeat(xyz[idx, 1], len(zz)), zs=zz, zorder=1, linestyle='-', color=highlight_color) ax.quiver(xyz[idx, 0], xyz[idx, 1], xyz[idx, 2], ori[0], ori[1], ori[2], length=50, color=highlight_color, pivot='tail') dims = np.array([(len(data) / -2.), (len(data) / 2.)]) ax.set_xlim(-1 * dims * zooms[:2]) # Set axis lims to RAS coordinates. ax.set_ylim(-1 * dims * zooms[:2]) ax.set_zlim(dims * zooms[:2]) # Plot slices. ax.contourf(xslice, gridx, gridy, offset=offset, zdir='x', cmap='gray', zorder=0, alpha=.5) ax.contourf(gridx, gridy, yslice, offset=offset, zdir='z', cmap='gray', zorder=0, alpha=.5) ax.contourf(gridx, zslice, gridy, offset=offset, zdir='y', cmap='gray', zorder=0, alpha=.5) plt.suptitle('Dipole #%s / %s @ %.3fs, GOF: %.1f%%, %.1fnAm\n' % ( idx + 1, len(dipole.times), dipole.times[idx], dipole.gof[idx], dipole.amplitude[idx] * 1e9) + '(%0.1f, %0.1f, %0.1f) mm' % tuple(xyz[idx])) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') plt.draw() def _dipole_changed(event, params): """Handle dipole plotter scroll/key event.""" if event.key is not None: if event.key == 'up': params['idx'] += 1 elif event.key == 'down': params['idx'] -= 1 else: # some other key return elif event.step > 0: # scroll event params['idx'] += 1 else: params['idx'] -= 1 params['idx'] = min(max(0, params['idx']), len(params['dipole'].pos) - 1) params['ax'].clear() _plot_dipole(params['ax'], params['data'], params['dipole_locs'], params['idx'], params['dipole'], params['gridx'], params['gridy'], params['ori'], params['coord_frame'], params['zooms'], params['show_all'], params['scatter_points'], params['color'], params['highlight_color']) def _update_coord_frame(obj, rr, nn, mri_trans, head_trans): if obj['coord_frame'] == FIFF.FIFFV_COORD_MRI: rr = apply_trans(mri_trans, rr) nn = apply_trans(mri_trans, nn, move=False) elif obj['coord_frame'] == FIFF.FIFFV_COORD_HEAD: rr = apply_trans(head_trans, rr) nn = apply_trans(head_trans, nn, move=False) return rr, nn @fill_doc def plot_brain_colorbar(ax, clim, colormap='auto', transparent=True, orientation='vertical', label='Activation', bgcolor='0.5'): """Plot a colorbar that corresponds to a brain activation map. Parameters ---------- ax : instance of Axes The Axes to plot into. %(clim)s %(colormap)s %(transparent)s orientation : str Orientation of the colorbar, can be "vertical" or "horizontal". label : str The colorbar label. bgcolor : color The color behind the colorbar (for alpha blending). Returns ------- cbar : instance of ColorbarBase The colorbar. Notes ----- .. versionadded:: 0.19 """ from matplotlib.colorbar import ColorbarBase from matplotlib.colors import Normalize cmap, scale_pts, diverging, _, ticks = _limits_to_control_points( clim, 0., colormap, transparent=True, linearize=True) norm = Normalize(vmin=scale_pts[0], vmax=scale_pts[-1]) cbar = ColorbarBase(ax, cmap, norm=norm, ticks=ticks, label=label, orientation=orientation) # make the colorbar background match the brain color cbar.patch.set(facecolor=bgcolor) # remove the colorbar frame except for the line containing the ticks cbar.outline.set_visible(False) cbar.ax.set_frame_on(True) for key in ('left', 'top', 'bottom' if orientation == 'vertical' else 'right'): ax.spines[key].set_visible(False) return cbar