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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Functions to plot EEG sensor montages or digitizer montages."""
from copy import deepcopy
import numpy as np
from scipy.spatial.distance import cdist
from .._fiff._digitization import _get_fid_coords
from .._fiff.meas_info import create_info
from ..utils import _check_option, _validate_type, logger, verbose
from .utils import plot_sensors
@verbose
def plot_montage(
montage,
*,
scale=None,
scale_factor=None,
show_names=True,
kind="topomap",
show=True,
sphere=None,
axes=None,
verbose=None,
):
"""Plot a montage.
Parameters
----------
montage : instance of DigMontage
The montage to visualize.
scale : float
Determines the scale of the channel points and labels; values < 1 will scale
down, whereas values > 1 will scale up. Default to None, which implies 1.
scale_factor : float
Determines the size of the points. Deprecated, use scale instead.
show_names : bool | list
Whether to display all channel names. If a list, only the channel
names in the list are shown. Defaults to True.
kind : str
Whether to plot the montage as '3d' or 'topomap' (default).
show : bool
Show figure if True.
%(sphere_topomap_auto)s
%(axes_montage)s
.. versionadded:: 1.4
%(verbose)s
Returns
-------
fig : instance of matplotlib.figure.Figure
The figure object.
"""
import matplotlib.pyplot as plt
from ..channels import DigMontage, make_dig_montage
if scale_factor is not None:
msg = "scale_factor has been deprecated and will be removed. Use scale instead."
if scale is not None:
raise ValueError(
" ".join(["scale and scale_factor cannot be used together.", msg])
)
logger.info(msg)
if scale is None:
scale = 1
_check_option("kind", kind, ["topomap", "3d"])
_validate_type(montage, DigMontage, item_name="montage")
ch_names = montage.ch_names
title = None
if len(ch_names) == 0:
raise RuntimeError("No valid channel positions found.")
pos = np.array(list(montage._get_ch_pos().values()))
dists = cdist(pos, 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 = pos.shape[0]
n_dupes = dupes.shape[0]
idx = np.setdiff1d(np.arange(len(pos)), dupes[:, 1]).tolist()
logger.info(f"{n_dupes} duplicate electrode labels found:")
logger.info(", ".join([ch_names[d[0]] + "/" + ch_names[d[1]] for d in dupes]))
logger.info(f"Plotting {n_chans - n_dupes} unique labels.")
ch_names = [ch_names[i] for i in idx]
ch_pos = dict(zip(ch_names, pos[idx, :]))
# XXX: this might cause trouble if montage was originally in head
fid, _ = _get_fid_coords(montage.dig)
montage = make_dig_montage(ch_pos=ch_pos, **fid)
info = create_info(ch_names, sfreq=256, ch_types="eeg")
info.set_montage(montage, on_missing="ignore")
fig = plot_sensors(
info,
kind=kind,
show_names=show_names,
show=show,
title=title,
sphere=sphere,
axes=axes,
)
if scale_factor is not None:
# scale points
collection = fig.axes[0].collections[0]
collection.set_sizes([scale_factor])
elif scale is not None:
# scale points
collection = fig.axes[0].collections[0]
collection.set_sizes([scale * 10])
# scale labels
labels = fig.findobj(match=plt.Text)
x_label, y_label = fig.axes[0].xaxis.label, fig.axes[0].yaxis.label
z_label = fig.axes[0].zaxis.label if kind == "3d" else None
tick_labels = fig.axes[0].get_xticklabels() + fig.axes[0].get_yticklabels()
if kind == "3d":
tick_labels += fig.axes[0].get_zticklabels()
for label in labels:
if label not in [x_label, y_label, z_label] + tick_labels:
label.set_fontsize(label.get_fontsize() * scale)
return fig
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