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"""Functions to plot EEG sensor montages or digitizer montages."""
from copy import deepcopy
import numpy as np
from ..utils import check_version, logger
from . import plot_sensors
def plot_montage(montage, scale_factor=20, show_names=True, kind='topomap',
show=True):
"""Plot a montage.
Parameters
----------
montage : instance of Montage or DigMontage
The montage to visualize.
scale_factor : float
Determines the size of the points.
show_names : bool
Whether to show the channel names.
kind : str
Whether to plot the montage as '3d' or 'topomap' (default).
show : bool
Show figure if True.
Returns
-------
fig : Instance of matplotlib.figure.Figure
The figure object.
"""
from scipy.spatial.distance import cdist
from ..channels import Montage, DigMontage
from .. import create_info
if isinstance(montage, Montage):
ch_names = montage.ch_names
title = montage.kind
elif isinstance(montage, DigMontage):
ch_names = montage.point_names
title = None
else:
raise TypeError("montage must be an instance of "
"mne.channels.montage.Montage or"
"mne.channels.montage.DigMontage")
if kind not in ['topomap', '3d']:
raise ValueError("kind must be 'topomap' or '3d'")
if isinstance(montage, Montage): # check for duplicate labels
dists = cdist(montage.pos, montage.pos)
# only consider upper triangular part by setting the rest to np.nan
dists[np.tril_indices(dists.shape[0])] = np.nan
dupes = np.argwhere(np.isclose(dists, 0))
if dupes.any():
montage = deepcopy(montage)
n_chans = montage.pos.shape[0]
n_dupes = dupes.shape[0]
idx = np.setdiff1d(montage.selection, dupes[:, 1]).tolist()
logger.info("{} duplicate electrode labels found:".format(n_dupes))
logger.info(", ".join([ch_names[d[0]] + "/" + ch_names[d[1]]
for d in dupes]))
logger.info("Plotting {} unique labels.".format(n_chans - n_dupes))
montage.ch_names = [montage.ch_names[i] for i in idx]
ch_names = montage.ch_names
montage.pos = montage.pos[idx, :]
montage.selection = np.arange(n_chans - n_dupes)
info = create_info(ch_names, sfreq=256, ch_types="eeg", montage=montage)
fig = plot_sensors(info, kind=kind, show_names=show_names, show=show,
title=title)
collection = fig.axes[0].collections[0]
if check_version("matplotlib", "1.4"):
collection.set_sizes([scale_factor])
else:
collection._sizes = [scale_factor]
return fig
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