# -*- coding: utf-8 -*-
"""Functions to make 3D plots with M/EEG data."""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
# Mark Wronkiewicz <wronk.mark@gmail.com>
#
# License: Simplified BSD
import numpy as np
from mne.io.constants import FIFF
from mne.io.pick import _picks_to_idx
from mne.utils import _validate_type, fill_doc, verbose
verbose_dec = verbose
FIDUCIAL_ORDER = (FIFF.FIFFV_POINT_LPA, FIFF.FIFFV_POINT_NASION,
FIFF.FIFFV_POINT_RPA)
[docs]@fill_doc
def plot_sensors_connectivity(info, con, picks=None,
cbar_label='Connectivity'):
"""Visualize the sensor connectivity in 3D.
Parameters
----------
info : dict | None
The measurement info.
con : array, shape (n_channels, n_channels) | Connectivity
The computed connectivity measure(s).
%(picks_good_data)s
Indices of selected channels.
cbar_label : str
Label for the colorbar.
Returns
-------
fig : instance of Renderer
The 3D figure.
"""
_validate_type(info, "info")
from mne.viz.backends.renderer import _get_renderer
from mne_connectivity.base import BaseConnectivity
if isinstance(con, BaseConnectivity):
con = con.get_data()
renderer = _get_renderer(size=(600, 600), bgcolor=(0.5, 0.5, 0.5))
picks = _picks_to_idx(info, picks)
if len(picks) != len(con):
raise ValueError('The number of channels picked (%s) does not '
'correspond to the size of the connectivity data '
'(%s)' % (len(picks), len(con)))
# Plot the sensor locations
sens_loc = [info['chs'][k]['loc'][:3] for k in picks]
sens_loc = np.array(sens_loc)
renderer.sphere(np.c_[sens_loc[:, 0], sens_loc[:, 1], sens_loc[:, 2]],
color=(1, 1, 1), opacity=1, scale=0.005)
# Get the strongest connections
n_con = 20 # show up to 20 connections
min_dist = 0.05 # exclude sensors that are less than 5cm apart
threshold = np.sort(con, axis=None)[-n_con]
ii, jj = np.where(con >= threshold)
# Remove close connections
con_nodes = list()
con_val = list()
for i, j in zip(ii, jj):
if np.linalg.norm(sens_loc[i] - sens_loc[j]) > min_dist:
con_nodes.append((i, j))
con_val.append(con[i, j])
con_val = np.array(con_val)
# Show the connections as tubes between sensors
vmax = np.max(con_val)
vmin = np.min(con_val)
for val, nodes in zip(con_val, con_nodes):
x1, y1, z1 = sens_loc[nodes[0]]
x2, y2, z2 = sens_loc[nodes[1]]
tube = renderer.tube(origin=np.c_[x1, y1, z1],
destination=np.c_[x2, y2, z2],
scalars=np.c_[val, val],
vmin=vmin, vmax=vmax,
reverse_lut=True)
renderer.scalarbar(source=tube, title=cbar_label)
# Add the sensor names for the connections shown
nodes_shown = list(set([n[0] for n in con_nodes] +
[n[1] for n in con_nodes]))
for node in nodes_shown:
x, y, z = sens_loc[node]
renderer.text3d(x, y, z, text=info['ch_names'][picks[node]],
scale=0.005,
color=(0, 0, 0))
renderer.set_camera(azimuth=-88.7, elevation=40.8,
distance=0.76,
focalpoint=np.array([-3.9e-4, -8.5e-3, -1e-2]))
renderer.show()
return renderer.scene()