/
_3d.py
2468 lines (2204 loc) · 102 KB
/
_3d.py
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# -*- coding: utf-8 -*-
"""Functions to make 3D plots with M/EEG data."""
from __future__ import print_function
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.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
import base64
from distutils.version import LooseVersion
from itertools import cycle
import os.path as op
import warnings
from functools import partial
import numpy as np
from scipy import linalg
from ..defaults import DEFAULTS
from ..externals.six import BytesIO, string_types, advance_iterator
from ..fixes import einsum
from ..io import _loc_to_coil_trans, Info
from ..io.pick import pick_types
from ..io.constants import FIFF
from ..io.meas_info import read_fiducials
from ..source_space import SourceSpaces, _create_surf_spacing, _check_spacing
from ..surface import (get_meg_helmet_surf, read_surface,
transform_surface_to, _project_onto_surface,
mesh_edges,
_complete_sphere_surf, _normalize_vectors)
from ..transforms import (read_trans, _find_trans, apply_trans, rot_to_quat,
combine_transforms, _get_trans, _ensure_trans,
invert_transform, Transform)
from ..utils import (get_subjects_dir, logger, _check_subject, verbose, warn,
_import_mlab, SilenceStdout, has_nibabel, check_version,
_ensure_int)
from .utils import (mne_analyze_colormap, _prepare_trellis, COLORS, plt_show,
tight_layout, figure_nobar, _check_time_unit)
from ..bem import (ConductorModel, _bem_find_surface, _surf_dict, _surf_name,
read_bem_surfaces)
FIDUCIAL_ORDER = (FIFF.FIFFV_POINT_LPA, FIFF.FIFFV_POINT_NASION,
FIFF.FIFFV_POINT_RPA)
def _fiducial_coords(points, coord_frame=None):
"""Generate 3x3 array of fiducial coordinates."""
if coord_frame is not None:
points = [p for p in points if p['coord_frame'] == coord_frame]
points_ = dict((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 : matplotlib 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
if not isinstance(mode, string_types) or mode not in ('traces', 'field'):
raise ValueError('mode must be "traces" or "field", got %s' % (mode,))
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)
if cmap == 'viridis' and not check_version('matplotlib', '1.5'):
warn('viridis is unavailable on matplotlib < 1.4, using "YlGnBu_r"')
cmap = 'YlGnBu_r'
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':
if not check_version('matplotlib', '1.4'):
raise RuntimeError('The "field" mode requires matplotlib version '
'1.4+')
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 = _pivot_kwargs()
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, aspect='equal')
ax.view_init(30, 45)
tight_layout(fig=fig)
plt_show(show)
return fig
def _pivot_kwargs():
"""Get kwargs for quiver."""
kwargs = dict()
if check_version('matplotlib', '1.5'):
kwargs['pivot'] = 'tail'
else:
import matplotlib
warn('pivot cannot be set in matplotlib %s (need version 1.5+), '
'locations are approximate' % (matplotlib.__version__,))
return kwargs
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 : int
Number of jobs to run in parallel.
Returns
-------
fig : instance of mlab.Figure
The mayavi figure.
"""
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])
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))
types = [sm['kind'] for sm in surf_maps]
# Plot them
mlab = _import_mlab()
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))])
fig = mlab.figure(bgcolor=(0.0, 0.0, 0.0), size=(600, 600))
_toggle_mlab_render(fig, False)
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]
mesh = _create_mesh_surf(surf, fig)
with warnings.catch_warnings(record=True): # traits
surface = mlab.pipeline.surface(mesh, color=colors[ii],
opacity=alpha, figure=fig)
surface.actor.property.backface_culling = True
# Now show our field pattern
mesh = _create_mesh_surf(surf, fig, scalars=data)
with warnings.catch_warnings(record=True): # traits
fsurf = mlab.pipeline.surface(mesh, vmin=-vlim, vmax=vlim,
figure=fig)
fsurf.module_manager.scalar_lut_manager.lut.table = colormap
fsurf.actor.property.backface_culling = True
# And the field lines on top
mesh = _create_mesh_surf(surf, fig, scalars=data)
with warnings.catch_warnings(record=True): # traits
cont = mlab.pipeline.contour_surface(
mesh, contours=21, line_width=1.0, vmin=-vlim, vmax=vlim,
opacity=alpha, figure=fig)
cont.module_manager.scalar_lut_manager.lut.table = colormap_lines
if '%' in time_label:
time_label %= (1e3 * evoked.times[time_idx])
with warnings.catch_warnings(record=True): # traits
mlab.text(0.01, 0.01, time_label, width=0.4, figure=fig)
with SilenceStdout(): # setting roll
mlab.view(10, 60, figure=fig)
_toggle_mlab_render(fig, True)
return fig
def _create_mesh_surf(surf, fig=None, scalars=None, vtk_normals=True):
"""Create Mayavi mesh from MNE surf."""
mlab = _import_mlab()
x, y, z = surf['rr'].T
with warnings.catch_warnings(record=True): # traits
mesh = mlab.pipeline.triangular_mesh_source(
x, y, z, surf['tris'], scalars=scalars, figure=fig)
if vtk_normals:
mesh = mlab.pipeline.poly_data_normals(mesh)
mesh.filter.compute_cell_normals = False
mesh.filter.consistency = False
mesh.filter.non_manifold_traversal = False
mesh.filter.splitting = False
else:
# make absolutely sure these are normalized for Mayavi
nn = surf['nn'].copy()
_normalize_vectors(nn)
mesh.data.point_data.normals = nn
mesh.data.cell_data.normals = None
return mesh
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
if orientation not in ['coronal', 'axial', 'sagittal']:
raise ValueError("Orientation must be 'coronal', 'axial' or "
"'sagittal'. Got %s." % orientation)
# Load the T1 data
nim = nib.load(mri_fname)
data = nim.get_data()
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
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, trans=None, subject=None, subjects_dir=None,
surfaces='head', coord_frame='head',
meg=None, eeg='original',
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
The measurement info.
trans : str | 'auto' | dict | None
The full path to the head<->MRI transform ``*-trans.fif`` file
produced during coregistration. If trans is None, an identity matrix
is assumed.
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).
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 figure object | None
Mayavi Scene (instance of mlab.Figure) 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 : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
fig : instance of 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
mlab = _import_mlab()
from tvtk.api import tvtk
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, string_types):
meg = [meg]
if isinstance(eeg, string_types):
eeg = [eeg]
if not isinstance(interaction, string_types) or \
interaction not in ('trackball', 'terrain'):
raise ValueError('interaction must be "trackball" or "terrain", '
'got "%s"' % (interaction,))
for kind, var in zip(('eeg', 'meg'), (eeg, meg)):
if not isinstance(var, (list, tuple)) or \
not all(isinstance(x, string_types) 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,))
if not isinstance(info, Info):
raise TypeError('info must be an instance of Info, got %s'
% type(info))
if isinstance(surfaces, string_types):
surfaces = [surfaces]
surfaces = list(surfaces)
if not all(isinstance(s, string_types) for s in surfaces):
raise TypeError('all entries in surfaces must be strings')
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
valid_coords = ['head', 'meg', 'mri']
if coord_frame not in valid_coords:
raise ValueError('coord_frame must be one of %s' % (valid_coords,))
if src is not None:
if not isinstance(src, SourceSpaces):
raise TypeError('src must be None or SourceSpaces, got %s'
% (type(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))
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 isinstance(trans, string_types):
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)
trans = read_trans(trans, return_all=True)
exp = None
for trans in trans: # we got at least 1
try:
trans = _ensure_trans(trans, 'head', 'mri')
except Exception as exp:
pass
else:
break
else:
raise exp
elif trans is None:
trans = Transform('head', 'mri')
elif not isinstance(trans, dict):
raise TypeError('trans must be str, dict, or None')
head_mri_t = _ensure_trans(trans, 'head', 'mri')
dev_head_t = info['dev_head_t']
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 = info['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:
if isinstance(bem, ConductorModel):
if is_sphere:
head_surf = _complete_sphere_surf(
bem, 3, sphere_level, complete=False)
else: # BEM solution
head_surf = _bem_find_surface(
bem, FIFF.FIFFV_BEM_SURF_ID_HEAD)
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:
raise ValueError('Could not find the surface for '
'head in the provided BEM model.')
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, string_types):
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 surfaces types: %s' % (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])
if src is not None:
if src[0]['coord_frame'] == FIFF.FIFFV_COORD_MRI:
src_rr = apply_trans(mri_trans, src_rr)
src_nn = apply_trans(mri_trans, src_nn, move=False)
elif src[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD:
src_rr = apply_trans(head_trans, src_rr)
src_nn = apply_trans(head_trans, src_nn, move=False)
# determine points
meg_rrs, meg_tris = list(), list()
ecog_loc = list()
seeg_loc = list()