# -*- 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", n_con=20, cmap="RdBu"
):
"""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.
n_con : int
Number of strongest connections shown. By default 20.
cmap : str | instance of matplotlib.colors.Colormap
Colormap for coloring connections by strength. If :class:`str`, must
be a valid Matplotlib colormap (i.e. a valid key of
``matplotlib.colormaps``). Default is ``"RdBu"``.
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 n_con connections
threshold = np.sort(con, axis=None)[-n_con]
ii, jj = np.where(con >= threshold)
# Remove close connections
min_dist = 0.05 # exclude sensors that are less than 5cm apart
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,
colormap=cmap,
)
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()