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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, sparse
from ..defaults import DEFAULTS
from ..externals.six import BytesIO, string_types, advance_iterator
from ..fixes import einsum, _crop_colorbar
from ..io import _loc_to_coil_trans
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, _reorder_ccw,
_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, _validate_type)
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)
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."""
points = points or [] # None -> list
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
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, 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,))
_validate_type(info, "info")
if isinstance(surfaces, string_types):
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
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)
for ti, trans in enumerate(trans): # we got at least 1
try:
trans = _ensure_trans(trans, 'head', 'mri')
except Exception:
if ti == len(trans) - 1:
raise
else:
break
elif trans is None:
trans = Transform('head', 'mri')
else:
_validate_type(trans, (Transform,), "str, Transform, 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], copy=True)
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()
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])
ext_loc = np.array([d['r'] for d in (info['dig'] or [])
if d['kind'] == FIFF.FIFFV_POINT_EXTRA])
car_loc = _fiducial_coords(info['dig'])
# 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
if fig is None:
fig = _mlab_figure(bgcolor=(0.5, 0.5, 0.5), size=(800, 800))
if interaction == 'terrain' and fig.scene is not None:
fig.scene.interactor.interactor_style = \
tvtk.InteractorStyleTerrain()
_toggle_mlab_render(fig, False)
# 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():
# Make a solid surface
mesh = _create_mesh_surf(surf, fig)
with warnings.catch_warnings(record=True): # traits
surface = mlab.pipeline.surface(
mesh, color=colors[key], opacity=alphas[key], figure=fig)
if key != 'helmet':
surface.actor.property.backface_culling = True
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)
mlab.points3d(*center, scale_factor=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(mri_trans['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]
mlab.points3d(x[0], y[0], z[0], color=ax[1], scale_factor=3e-3)
mlab.quiver3d(x, y, z, u, v, w, mode='arrow', scale_factor=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:
with warnings.catch_warnings(record=True): # traits
points = mlab.points3d(data[:, 0], data[:, 1], data[:, 2],
color=color, scale_factor=scale,
opacity=alpha, figure=fig)
points.actor.property.backface_culling = True
if len(eegp_loc) > 0:
with warnings.catch_warnings(record=True): # traits
quiv = mlab.quiver3d(
eegp_loc[:, 0], eegp_loc[:, 1], eegp_loc[:, 2],
eegp_nn[:, 0], eegp_nn[:, 1], eegp_nn[:, 2],
color=defaults['eegp_color'], mode='cylinder',
scale_factor=defaults['eegp_scale'], opacity=0.6, figure=fig)
quiv.glyph.glyph_source.glyph_source.height = defaults['eegp_height']
quiv.glyph.glyph_source.glyph_source.center = \
(0., -defaults['eegp_height'], 0)
quiv.glyph.glyph_source.glyph_source.resolution = 20
quiv.actor.property.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)
mesh = _create_mesh_surf(surf, fig)
with warnings.catch_warnings(record=True): # traits
surface = mlab.pipeline.surface(mesh, color=color,
opacity=alpha, figure=fig)
surface.actor.property.backface_culling = True
if len(src_rr) > 0:
with warnings.catch_warnings(record=True): # traits
quiv = mlab.quiver3d(
src_rr[:, 0], src_rr[:, 1], src_rr[:, 2],
src_nn[:, 0], src_nn[:, 1], src_nn[:, 2], color=(1., 1., 0.),
mode='cylinder', scale_factor=3e-3, opacity=0.75, figure=fig)
quiv.glyph.glyph_source.glyph_source.height = 0.25
quiv.glyph.glyph_source.glyph_source.center = (0., 0., 0.)
quiv.glyph.glyph_source.glyph_source.resolution = 20
quiv.actor.property.backface_culling = True
with SilenceStdout():
mlab.view(90, 90, focalpoint=(0., 0., 0.), distance=0.6, figure=fig)
_toggle_mlab_render(fig, True)
return fig
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')
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,))
elif clim_kind not in ('value', 'values'):
raise ValueError('clim["kind"] must be "value" or "percent", got %s'
% (clim['kind'],))
if len(set(ctrl_pts)) != 3:
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)
else:
ctrl_pts *= [0., 0.5, 1] # all nonzero pts == max
else: # 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])
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]]
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
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')
ax.set_aspect('equal')
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)
if views not in lh_kwargs:
raise ValueError("views must be one of ['lat', 'med', 'ros', 'cau', "
"'dor' 'ven', 'fro', 'par']. Got %s." % views)
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 SourceEstimates 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 : SourceEstimates
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 : str | np.ndarray of float, shape(n_colors, 3 | 4)
Name of colormap to use or a custom look up table. If array, must
be (n x 3) or (n x 4) array for with RGB or RGBA values between
0 and 255. 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 : bool
If True, use a linear transparency between fmin and fmid.
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.scene.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 : str | dict
Colorbar properties specification. If 'auto', set clim automatically
based on data percentiles. If dict, should contain:
``kind`` : 'value' | 'percent'
Flag to specify type of limits.
``lims`` : list | np.ndarray | tuple of float, 3 elements
Left, middle, and right bound for colormap.
``pos_lims`` : list | np.ndarray | tuple of float, 3 elements
Left, middle, and right bound for colormap. Positive values
will be mirrored directly across zero during colormap
construction to obtain negative control points.
.. note:: Only sequential colormaps should be used with ``lims``, and
only divergent colormaps should be used with ``pos_lims``.
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 pair of floats
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 : 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
-------
figure : surfer.viz.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)
if backend not in ['auto', 'matplotlib', 'mayavi']:
raise ValueError("backend must be 'auto', 'mayavi' or 'matplotlib'. "
"Got %s." % backend)
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
if hemi not in ['lh', 'rh', 'split', 'both']:
raise ValueError('hemi has to be either "lh", "rh", "split", '
'or "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')
@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, verbose=None):
"""Plot Nutmeg style volumetric source estimates using nilearn.
Parameters
----------
stc : VectorSourceEstimate
The vector source estimate to plot.
src : instance of SourceSpaces
The source space.
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 : Niimg-like object | 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 : str | np.ndarray of float, shape(n_colors, 3 | 4)
Name of colormap to use or a custom look up table. If array, must
be (n x 3) or (n x 4) array for with RGB or RGBA values between
0 and 255. Default ('auto') uses 'hot' for one-sided data and 'mne'
for two-sided data.
clim : str | dict
Colorbar properties specification. If 'auto', set clim automatically
based on data percentiles. If dict, should contain:
``kind`` : 'value' | 'percent'
Flag to specify type of limits.
``lims`` : list | np.ndarray | tuple of float, 3 elements
Left, middle, and right bound for colormap.
``pos_lims`` : list | np.ndarray | tuple of float, 3 elements
Left, middle, and right bound for colormap. Positive values
will be mirrored directly across zero during colormap
construction to obtain negative control points.
.. note:: Only sequential colormaps should be used with ``lims``, and
only divergent colormaps should be used with ``pos_lims``.
transparent : bool | None
If True, use a linear transparency between fmin and fmid.
None will choose automatically based on colormap type.
show : bool
Show figures if True. Defaults to True.
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).
Notes
-----
.. versionadded:: 0.17
"""
from matplotlib import pyplot as plt, colors
from matplotlib.cbook import mplDeprecation
import nibabel as nib
from ..source_estimate import VolSourceEstimate
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, resample_to_img
if mode == 'stat_map':
plot_func = plot_stat_map
elif mode == 'glass_brain':
plot_func = plot_glass_brain
else:
raise ValueError('Plotting function must be one of'
' stat_map | glas_brain. Got %s' % mode)
if not isinstance(stc, VolSourceEstimate):
raise ValueError('Only VolSourceEstimate objects are supported.'
'Got %s' % type(stc))
def _cut_coords_to_idx(cut_coords, img_idx):
"""Convert voxel coordinates to index in stc.data."""
# XXX: check lines below
cut_coords = apply_trans(linalg.inv(img.affine), cut_coords)
cut_coords = np.array([int(round(c)) for c in cut_coords])
# the affine transformation can sometimes lead to corner
# cases near the edges?
shape = img_idx.shape
cut_coords = np.clip(cut_coords, 0, np.array(shape) - 1)
loc_idx = np.ravel_multi_index(
cut_coords, shape, order='F')
dist_vertices = [abs(v - loc_idx) for v in stc.vertices]
nearest_idx = int(round(np.argmin(dist_vertices)))
if dist_vertices[nearest_idx] == 0:
return nearest_idx
else:
return None
def _get_cut_coords_stat_map(event, params):
"""Get voxel coordinates from mouse click."""
if event.inaxes is params['ax_x']:
cut_coords = (params['ax_z'].lines[0].get_xdata()[0],
event.xdata, event.ydata)
elif event.inaxes is params['ax_y']:
cut_coords = (event.xdata,
params['ax_x'].lines[0].get_xdata()[0],
event.ydata)
else:
cut_coords = (event.xdata, event.ydata,
params['ax_x'].lines[1].get_ydata()[0])
return cut_coords
def _get_cut_coords_glass_brain(event, params):
"""Get voxel coordinates with max intensity projection."""
img_data = np.abs(params['img_idx_resampled'].get_data())
shape = img_data.shape
if event.inaxes is params['ax_x']:
y, z = int(round(event.xdata)), int(round(event.ydata))
x = np.argmax(img_data[:, y + shape[1] // 2, z + shape[2] // 2])
x -= shape[0] // 2
elif event.inaxes is params['ax_y']:
x, z = int(round(event.xdata)), int(round(event.ydata))
y = np.argmax(img_data[x + shape[0] // 2, :, z + shape[2] // 2])
y -= shape[1] // 2
else:
x, y = int(round(event.xdata)), int(round(event.ydata))
z = np.argmax(img_data[x + shape[0] // 2, y + shape[1] // 2, :])
z -= shape[2] // 2
return (x, y, z)
def _resample(event, params):
"""Precompute the resampling as the mouse leaves the time axis."""
if event.inaxes is params['ax_time'] and mode == 'glass_brain':
img_resampled = resample_to_img(params['img_idx'],
params['bg_img'])
params.update({'img_idx_resampled': img_resampled})
def _onclick(event, params):
"""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)[0]
params['lx'].set_xdata(event.xdata)
cut_coords = (0, 0, 0)
if mode == 'stat_map':
cut_coords = (ax_y.lines[0].get_xdata()[0],
ax_x.lines[0].get_xdata()[0],
ax_x.lines[1].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)
if event.inaxes in [ax_x, ax_y, ax_z]:
if mode == 'stat_map':
cut_coords = _get_cut_coords_stat_map(event, params)
elif mode == 'glass_brain':
cut_coords = _get_cut_coords_glass_brain(event, params)
x, y, z = cut_coords
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'])
if loc_idx is not None:
ax_time.lines[0].set_ydata(stc.data[loc_idx].T)
else:
ax_time.lines[0].set_ydata(0.)
params['fig'].canvas.draw()
if bg_img is None:
subjects_dir = get_subjects_dir(subjects_dir=subjects_dir,
raise_error=True)
subject = _check_subject(stc.subject, subject, True)
t1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz')
bg_img = nib.load(t1_fname)
bg_img_param = bg_img
if mode == 'glass_brain':
bg_img_param = None
img = stc.as_volume(src, mri_resolution=False)
vmax = np.abs(stc.data).max()
loc_idx, idx = np.unravel_index(np.abs(stc.data).argmax(),
stc.data.shape)
img_idx = index_img(img, idx)
x, y, z = np.unravel_index(stc.vertices[loc_idx], img_idx.shape,
order='F')
cut_coords = apply_trans(img.affine, (x, y, z))
# Plot initial figure
fig, (axes, ax_time) = plt.subplots(2)
ax_time.plot(stc.times, stc.data[loc_idx].T, color='k')
ax_time.set(xlim=stc.times[[0, -1]],
xlabel='Time (s)', ylabel='Activation')
lx = ax_time.axvline(stc.times[idx], color='g')
axes.set(xticks=[], yticks=[])
fig.tight_layout()
allow_pos_lims = (mode != 'glass_brain')
colormap, scale_pts, diverging, _ = _limits_to_control_points(
clim, stc.data, colormap, transparent, allow_pos_lims, linearize=True)
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_param, 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)
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]])
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, bg_img=bg_img, lx=lx)
plot_and_correct(stat_map_img=params['img_idx'], title='',
cut_coords=cut_coords)
if mode == 'glass_brain':
params.update(img_idx_resampled=resample_to_img(
params['img_idx'], params['bg_img']))
if show:
plt.show()
fig.canvas.mpl_connect('button_press_event',
partial(_onclick, params=params))
fig.canvas.mpl_connect('axes_leave_event',
partial(_resample, params=params))
return fig
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 VectorSourceEstimates 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 : str | np.ndarray of float, shape(n_colors, 3 | 4)
Name of colormap to use or a custom look up table. If array, must
be (n x 3) or (n x 4) array for with RGB or RGBA values between
0 and 255. 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 : bool
If True, use a linear transparency between fmin and fmid.
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.scene.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 : str | dict
Colorbar properties specification. If 'auto', set clim automatically
based on data percentiles. If dict, should contain:
``kind`` : 'value' | 'percent'
Flag to specify type of limits.
``lims`` : list | np.ndarray | tuple of float, 3 elements
Left, middle, and right bound for colormap.
Unlike :meth:`stc.plot <mne.SourceEstimate.plot>`, it cannot use
``pos_lims``, as the surface plot must show the magnitude.
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 pair of floats
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 : 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)
if hemi not in ['lh', 'rh', 'split', 'both']:
raise ValueError('hemi has to be either "lh", "rh", "split", '
'or "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)
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 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 :
Mayavi figure name.
fig_number :
Matplotlib figure number.
labels : ndarray or list of ndarrays
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 : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
**kwargs : kwargs
Keyword arguments to pass to mlab.triangular_mesh.
Returns
-------
surface : instance of mlab Surface
The triangular mesh surface.
"""
mlab = _import_mlab()
import matplotlib.pyplot as plt
from matplotlib.colors import ColorConverter
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()
f = _mlab_figure(figure=fig_name, bgcolor=bgcolor, size=(600, 600))
_toggle_mlab_render(f, False)
with warnings.catch_warnings(record=True): # traits warnings
surface = mlab.triangular_mesh(points[:, 0], points[:, 1],
points[:, 2], use_faces,
color=brain_color,
opacity=opacity, **kwargs)
surface.actor.property.backface_culling = True
# 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 = [advance_iterator(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 = advance_iterator(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]
with warnings.catch_warnings(record=True): # traits
mlab.quiver3d(x, y, z, nx, ny, nz, color=color_converter.to_rgb(c),
mode=mode, scale_factor=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)
surface.actor.property.backface_culling = True
surface.actor.property.shading = True
_toggle_mlab_render(f, True)
return surface
def _mlab_figure(**kwargs):
"""Create a Mayavi figure using our defaults."""
from mayavi import mlab
fig = mlab.figure(**kwargs)
# If using modern VTK/Mayavi, improve rendering with FXAA
if hasattr(getattr(fig.scene, 'renderer', None), 'use_fxaa'):
fig.scene.renderer.use_fxaa = True
return fig
def _toggle_mlab_render(fig, render):
mlab = _import_mlab()
if mlab.options.backend != 'test':
fig.scene.disable_render = not render
def plot_dipole_locations(dipoles, trans, subject, subjects_dir=None,
mode='orthoview', coord_frame='mri', idx='gof',
show_all=True, ax=None, block=False,
show=True, verbose=None):
"""Plot dipole locations.
If mode is set to 'cone' 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
The mri to head trans.
subject : str
The subject name corresponding to FreeSurfer environment
variable SUBJECT.
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
Currently only ``'orthoview'`` is supported.
.. versionadded:: 0.14.0
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'.
.. versionadded:: 0.14.0
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 or matplotlib 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)
else:
raise ValueError('Mode must be "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 Scene
The figure on which you've plotted electrodes using
:func:`mne.viz.plot_alignment`.
montage : instance of `DigMontage` or `Info` | dict of ch: xyz mappings.
The digital montage for the electrodes plotted in the scene. If `Info`,
channel positions will be pulled from the `loc` field of `chs`.
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
"""
mlab = _import_mlab()
from ..channels import Montage, DigMontage
from .. import Info
if isinstance(montage, (Montage, DigMontage)):
chs = montage.dig_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`')
xyz = np.vstack(xyz)
xy = _3d_to_2d(fig, xyz)
xy = dict(zip(ch_names, xy))
pts = fig.children[-1]
if hide_sensors is True:
pts.visible = False
with warnings.catch_warnings(record=True):
im = mlab.screenshot(fig)
pts.visible = True
return xy, im
def _3d_to_2d(fig, xyz):
"""Convert 3d points to a 2d perspective using a Mayavi Scene."""
from mayavi.core.scene import Scene
_validate_type(fig, Scene, "fig", "Scene")
xyz = np.column_stack([xyz, np.ones(xyz.shape[0])])
# Transform points into 'unnormalized' view coordinates
comb_trans_mat = _get_world_to_view_matrix(fig.scene)
view_coords = np.dot(comb_trans_mat, xyz.T).T
# Divide through by the fourth element for normalized view coords
norm_view_coords = view_coords / (view_coords[:, 3].reshape(-1, 1))
# Transform from normalized view coordinates to display coordinates.
view_to_disp_mat = _get_view_to_display_matrix(fig.scene)
xy = np.dot(view_to_disp_mat, norm_view_coords.T).T
# Pull the first two columns since they're meaningful for 2d plotting
xy = xy[:, :2]
return xy
def _get_world_to_view_matrix(scene):
"""Return the 4x4 matrix to transform xyz space to the current view.
This is a concatenation of the model view and perspective transforms.
"""
from mayavi.core.ui.mayavi_scene import MayaviScene
from tvtk.pyface.tvtk_scene import TVTKScene
_validate_type(scene, (MayaviScene, TVTKScene), "scene",
"TVTKScene/MayaviScene")
cam = scene.camera
# The VTK method needs the aspect ratio and near and far
# clipping planes in order to return the proper transform.
scene_size = tuple(scene.get_size())
clip_range = cam.clipping_range
aspect_ratio = float(scene_size[0]) / scene_size[1]
# Get the vtk matrix object using the aspect ratio we defined
vtk_comb_trans_mat = cam.get_composite_projection_transform_matrix(
aspect_ratio, clip_range[0], clip_range[1])
vtk_comb_trans_mat = vtk_comb_trans_mat.to_array()
return vtk_comb_trans_mat
def _get_view_to_display_matrix(scene):
"""Return the 4x4 matrix to convert view coordinates to display coordinates.
It's assumed that the view should take up the entire window and that the
origin of the window is in the upper left corner.
""" # noqa: E501
from mayavi.core.ui.mayavi_scene import MayaviScene
from tvtk.pyface.tvtk_scene import TVTKScene
_validate_type(scene, (MayaviScene, TVTKScene), "scene",
"TVTKScene/MayaviScene")
# normalized view coordinates have the origin in the middle of the space
# so we need to scale by width and height of the display window and shift
# by half width and half height. The matrix accomplishes that.
x, y = tuple(scene.get_size())
view_to_disp_mat = np.array([[x / 2.0, 0., 0., x / 2.0],
[0., -y / 2.0, 0., y / 2.0],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
return view_to_disp_mat
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):
"""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.')
import nibabel as nib
from nibabel.processing import resample_from_to
if coord_frame not in ['head', 'mri']:
raise ValueError("coord_frame must be 'head' or 'mri'. "
"Got %s." % coord_frame)
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"]')
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
ori = dipole.ori
scatter_points = dipole.pos * 1e3
else:
scatter_points = apply_trans(trans['trans'], dipole.pos) * 1e3
ori = apply_trans(trans['trans'], dipole.ori, move=False)
dipole_locs = apply_trans(ras2vox, scatter_points)
data = t1.get_data()
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)
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}
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 _plot_dipole(ax, data, points, idx, dipole, gridx, gridy, ori, coord_frame,
zooms, show_all, scatter_points):
"""Plot dipoles."""
import matplotlib.pyplot as plt
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 show_all:
colors = np.repeat('y', len(points))
colors[idx] = 'r'
size = np.repeat(5, len(points))
size[idx] = 20
visible = np.arange(len(points))
else:
colors = 'r'
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='r')
ax.plot(np.repeat(xyz[idx, 0], len(yy)), yy, zs=xyz[idx, 2], zorder=1,
linestyle='-', color='r')
ax.plot(np.repeat(xyz[idx, 0], len(zz)),
np.repeat(xyz[idx, 1], len(zz)), zs=zz, zorder=1,
linestyle='-', color='r')
kwargs = _pivot_kwargs()
ax.quiver(xyz[idx, 0], xyz[idx, 1], xyz[idx, 2], ori[0], ori[1],
ori[2], length=50, color='r', **kwargs)
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'])
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