/
_3d.py
3327 lines (2977 loc) · 132 KB
/
_3d.py
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# -*- coding: utf-8 -*-
"""Functions to make 3D plots with M/EEG data."""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
# Mark Wronkiewicz <wronk.mark@gmail.com>
#
# License: Simplified BSD
from itertools import cycle
import os.path as op
import sys
import warnings
from collections.abc import Iterable
from functools import partial
import numpy as np
from ..defaults import DEFAULTS
from ..fixes import einsum, _crop_colorbar, _get_img_fdata, _get_args
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,
_read_mri_info, SourceSpaces, read_freesurfer_lut)
from ..surface import (get_meg_helmet_surf, _read_mri_surface, _DistanceQuery,
transform_surface_to, _project_onto_surface,
_reorder_ccw)
from ..transforms import (_find_trans, apply_trans, rot_to_quat,
combine_transforms, _get_trans, _ensure_trans,
invert_transform, Transform, rotation,
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, get_config,
_ensure_int, _validate_type, _check_option)
from .utils import (mne_analyze_colormap, _get_color_list,
plt_show, tight_layout, figure_nobar, _check_time_unit)
from .misc import _check_mri
from ..bem import (ConductorModel, _bem_find_surface,
read_bem_surfaces, _ensure_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.
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
@verbose
def plot_evoked_field(evoked, surf_maps, time=None, time_label='t = %0.0f ms',
n_jobs=1, fig=None, vmax=None, n_contours=21,
verbose=None):
"""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 | None
How to print info about the time instant visualized.
%(n_jobs)s
fig : instance of mayavi.core.api.Scene | None
If None (default), a new figure will be created, otherwise it will
plot into the given figure.
.. versionadded:: 0.20
vmax : float | None
Maximum intensity. Can be None to use the max(abs(data)).
.. versionadded:: 0.21
n_contours : int
The number of contours.
.. versionadded:: 0.21
%(verbose)s
Returns
-------
fig : instance of mayavi.mlab.Figure
The mayavi figure.
"""
# Update the backend
from .backends.renderer import _get_renderer
types = [t for t in ['eeg', 'grad', 'mag'] if t in evoked]
_validate_type(vmax, (None, 'numeric'), 'vmax')
n_contours = _ensure_int(n_contours, 'n_contours')
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 = _get_renderer(fig, 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
if vmax is None:
vmax = np.max(np.abs(data))
vmax = float(vmax)
alpha = alphas[ii]
renderer.surface(surface=surf, color=colors[ii],
opacity=alpha)
# Now show our field pattern
renderer.surface(surface=surf, vmin=-vmax, vmax=vmax,
scalars=data, colormap=colormap,
polygon_offset=-1)
# And the field lines on top
renderer.contour(surface=surf, scalars=data, contours=n_contours,
vmin=-vmax, vmax=vmax, opacity=alpha,
colormap=colormap_lines)
if time_label is not None:
if '%' in time_label:
time_label %= (1e3 * evoked.times[time_idx])
renderer.text2d(x_window=0.01, y_window=0.01, text=time_label)
renderer.set_camera(azimuth=10, elevation=60)
renderer.show()
return renderer.scene()
@verbose
def plot_alignment(info=None, trans=None, subject=None, subjects_dir=None,
surfaces='auto', coord_frame='head',
meg=None, eeg='original', fwd=None,
dig=False, ecog=True, src=None, mri_fiducials=False,
bem=None, seeg=True, fnirs=True, show_axes=False, dbs=True,
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)s
surfaces : str | list | dict
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').
Can be dict to specify alpha values for each surface. Use None
to specify default value. Specified values must be between 0 and 1.
for example::
surfaces=dict(brain=0.4, outer_skull=0.6, head=None)
Defaults to 'auto', which will look for a head surface and plot
it if found.
.. 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
String options are:
- "original" (default; equivalent to ``True``)
Shows EEG sensors using their digitized locations (after
transformation to the chosen ``coord_frame``)
- "projected"
The EEG locations projected onto the scalp, as is done in forward
modeling
Can also be a list of these options, or an empty list (``[]``,
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 can be ``'estimated'`` to use :func:`mne.coreg.get_mni_fiducials`,
otherwise it should provide the full path to the fiducials file.
.. versionadded:: 0.22
Support for ``'estimated'``.
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.
fnirs : str | list | bool | None
Can be "channels", "pairs", "detectors", and/or "sources" to show the
fNIRS channel locations, optode locations, or line between
source-detector pairs, or a combination like ``('pairs', 'channels')``.
True translates to ``('pairs',)``.
.. versionadded:: 0.20
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
dbs : bool
If True (default), show DBS (deep brain stimulation) electrodes.
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
from ..coreg import get_mni_fiducials
# Update the backend
from .backends.renderer import _get_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]
if fnirs is True:
fnirs = ['pairs']
elif fnirs is False:
fnirs = list()
elif isinstance(fnirs, str):
fnirs = [fnirs]
_check_option('interaction', interaction, ['trackball', 'terrain'])
for kind, var in zip(('eeg', 'meg', 'fnirs'), (eeg, meg, fnirs)):
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)))
for xi, x in enumerate(meg):
_check_option('meg[%d]' % xi, x, ('helmet', 'sensors', 'ref'))
for xi, x in enumerate(eeg):
_check_option('eeg[%d]' % xi, x, ('original', 'projected'))
for xi, x in enumerate(fnirs):
_check_option('fnirs[%d]' % xi, x, ('channels', 'pairs',
'sources', 'detectors'))
info = create_info(1, 1000., 'misc') if info is None else info
_validate_type(info, "info")
if isinstance(surfaces, str):
surfaces = [surfaces]
if isinstance(surfaces, dict):
user_alpha = surfaces.copy()
for key, val in user_alpha.items():
_validate_type(key, "str", f"surfaces key {repr(key)}")
_validate_type(val, (None, "numeric"), f"surfaces[{repr(key)}]")
if val is not None:
user_alpha[key] = float(val)
if not 0 <= user_alpha[key] <= 1:
raise ValueError(
f'surfaces[{repr(key)}] ({val}) must be'
' between 0 and 1'
)
else:
user_alpha = {}
surfaces = list(surfaces)
for si, s in enumerate(surfaces):
_validate_type(s, "str", f"surfaces[{si}]")
brain = sorted(
set(surfaces) & set(['brain', 'pial', 'white', 'inflated']))
bem = _ensure_bem_surfaces(bem, extra_allow=(ConductorModel, None))
assert isinstance(bem, ConductorModel) or bem is None
_check_option('coord_frame', coord_frame, ['head', 'meg', 'mri'])
if src is not None:
src = _ensure_src(src)
src_subject = src._subject
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))
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)
fnirs_picks = pick_types(info, meg=False, eeg=False, ref_meg=False,
fnirs=True)
other_bools = dict(ecog=ecog, seeg=seeg, dbs=dbs,
fnirs=(('channels' in fnirs) |
('sources' in fnirs) |
('detectors' in fnirs)))
del ecog, seeg, dbs
other_keys = sorted(other_bools.keys())
other_picks = {key: pick_types(info, meg=False, ref_meg=False,
**{key: True}) for key in other_keys}
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:
head = False
for s in surfaces:
if s in ('auto', '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 ('auto', '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.')
try:
head_surf = _bem_find_surface(bem, 'head')
except RuntimeError:
logger.info(head_missing)
if head_surf is None:
if subject is None:
if s == 'auto':
# ignore
continue
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_mri_surface(fname)
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 in ('outer_skull', 'inner_skull'):
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_mri_surface(fname)
else:
surf = _bem_find_surface(bem, name).copy()
surf['name'] = name
skull.append(surf)
assert all(isinstance(s, dict) for s in skull)
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):
if mri_fiducials == 'estimated':
mri_fiducials = get_mni_fiducials(subject, subjects_dir)
else:
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:
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))
if brain in user_alpha:
user_alpha['lh'] = user_alpha['rh'] = user_alpha.pop(brain)
if bem is not None and bem['is_sphere'] and brain == 'brain':
surfs['lh'] = _bem_find_surface(bem, 'brain')
else:
brain = 'pial' if brain == 'brain' else brain
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_mri_surface(fname)
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
no_deep = all(len(other_picks[key]) == 0 for key in ('dbs', 'seeg'))
max_alpha = 1.0 if no_deep else 0.75
if src is None or (brain and any(s['type'] == 'surf' for s in src)):
hemi_val = max_alpha
alphas = np.linspace(max_alpha / 2., 0, 5)[:len(skull) + 1]
for idx, this_skull in enumerate(skull):
name = this_skull['name']
skull_alpha[name] = alphas[idx + 1]
skull_colors[name] = (0.95 - idx * 0.2, 0.85, 0.95 - idx * 0.2)
surfs[name] = this_skull
if src is None and brain is False and len(skull) == 0 and not show_axes:
head_alpha = max_alpha
else:
head_alpha = alphas[0]
for key in surfs:
# Surfs can sometimes be in head coords (e.g., if coming from sphere)
surf = surfs[key]
assert isinstance(surf, dict), f'{key}: {type(surf)}'
surfs[key] = transform_surface_to(surfs[key], coord_frame,
[mri_trans, head_trans], copy=True)
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()
hpi_loc = list()
ext_loc = list()
car_loc = list()
eeg_loc = list()
eegp_loc = list()
other_loc = dict()
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
for key, picks in other_picks.items():
if other_bools[key] and len(picks):
title = DEFAULTS["titles"][key] if key != 'fnirs' else 'fNIRS'
if key != 'fnirs' or 'channels' in fnirs:
other_loc[key] = [
info['chs'][pick]['loc'][:3] for pick in picks
]
# deal with NaN
other_loc[key] = np.array([loc for loc in other_loc[key]
if np.isfinite(loc).all()], float)
logger.info(
f'Plotting {len(other_loc[key])} {title}'
f' location{_pl(other_loc[key])}')
if key == 'fnirs':
if 'sources' in fnirs:
other_loc['source'] = np.array(
[info['chs'][pick]['loc'][3:6]
for pick in picks])
logger.info('Plotting %d %s source%s'
% (len(other_loc['source']),
title, _pl(other_loc['source'])))
if 'detectors' in fnirs:
other_loc['detector'] = np.array(
[info['chs'][pick]['loc'][6:9]
for pick in picks])
logger.info('Plotting %d %s detector%s'
% (len(other_loc['detector']),
title, _pl(other_loc['detector'])))
for v in other_loc.values():
v[:] = apply_trans(head_trans, v)
other_keys = sorted(other_loc) # re-sort and only keep non-empty
del other_bools
# initialize figure
renderer = _get_renderer(fig, bgcolor=(0.5, 0.5, 0.5), size=(800, 800))
if interaction == 'terrain':
renderer.set_interaction('terrain')
# plot surfaces
alphas = dict(head=head_alpha, helmet=0.25, lh=hemi_val, rh=hemi_val)
alphas.update(skull_alpha)
# replace default alphas with specified user_alpha
for k, v in user_alpha.items():
if v is not None:
alphas[k] = v
colors = dict(head=DEFAULTS['coreg']['head_color'],
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] + list(other_loc[key] for key in other_keys)
colors = [defaults['eeg_color'],
defaults['hpi_color'],
defaults['extra_color']
] + [defaults[key + '_color'] for key in other_keys]
alphas = [0.8,
0.5,
0.25] + [0.8] * len(other_keys)
scales = [defaults['eeg_scale'],
defaults['hpi_scale'],