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# -*- coding: utf-8 -*-
"""
.. _ex-evoked-topomap:
========================================
Plotting topographic maps of evoked data
========================================
Load evoked data and plot topomaps for selected time points using multiple
additional options.
"""
# Authors: Christian Brodbeck <christianbrodbeck@nyu.edu>
# Tal Linzen <linzen@nyu.edu>
# Denis A. Engeman <denis.engemann@gmail.com>
# MikoĊaj Magnuski <mmagnuski@swps.edu.pl>
# Eric Larson <larson.eric.d@gmail.com>
# Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause
# %%
# sphinx_gallery_thumbnail_number = 5
import numpy as np
import matplotlib.pyplot as plt
from mne.datasets import sample
from mne import read_evokeds
print(__doc__)
path = sample.data_path()
fname = path / 'MEG' / 'sample' / 'sample_audvis-ave.fif'
# load evoked corresponding to a specific condition
# from the fif file and subtract baseline
condition = 'Left Auditory'
evoked = read_evokeds(fname, condition=condition, baseline=(None, 0))
# %%
# Basic :func:`~mne.viz.plot_topomap` options
# -------------------------------------------
#
# We plot evoked topographies using :func:`mne.Evoked.plot_topomap`. The first
# argument, ``times`` allows to specify time instants (in seconds!) for which
# topographies will be shown. We select timepoints from 50 to 150 ms with a
# step of 20ms and plot magnetometer data:
times = np.arange(0.05, 0.151, 0.02)
evoked.plot_topomap(times, ch_type='mag')
# %%
# If times is set to None at most 10 regularly spaced topographies will be
# shown:
evoked.plot_topomap(ch_type='mag')
# %%
# We can use ``nrows`` and ``ncols`` parameter to create multiline plots
# with more timepoints.
all_times = np.arange(-0.2, 0.5, 0.03)
evoked.plot_topomap(all_times, ch_type='mag', ncols=8, nrows='auto')
# %%
# Instead of showing topographies at specific time points we can compute
# averages of 50 ms bins centered on these time points to reduce the noise in
# the topographies:
evoked.plot_topomap(times, ch_type='mag', average=0.05)
# %%
# We can plot gradiometer data (plots the RMS for each pair of gradiometers)
evoked.plot_topomap(times, ch_type='grad')
# %%
# Additional :func:`~mne.viz.plot_topomap` options
# ------------------------------------------------
#
# We can also use a range of various :func:`mne.viz.plot_topomap` arguments
# that control how the topography is drawn. For example:
#
# * ``cmap`` - to specify the color map
# * ``res`` - to control the resolution of the topographies (lower resolution
# means faster plotting)
# * ``contours`` to define how many contour lines should be plotted
evoked.plot_topomap(times, ch_type='mag', cmap='Spectral_r', res=32,
contours=4)
# %%
# If you look at the edges of the head circle of a single topomap you'll see
# the effect of extrapolation. There are three extrapolation modes:
#
# - ``extrapolate='local'`` extrapolates only to points close to the sensors.
# - ``extrapolate='head'`` extrapolates out to the head circle.
# - ``extrapolate='box'`` extrapolates to a large box stretching beyond the
# head circle.
#
# The default value ``extrapolate='auto'`` will use ``'local'`` for MEG sensors
# and ``'head'`` otherwise. Here we show each option:
extrapolations = ['local', 'head', 'box']
fig, axes = plt.subplots(figsize=(7.5, 4.5), nrows=2, ncols=3)
# Here we look at EEG channels, and use a custom head sphere to get all the
# sensors to be well within the drawn head surface
for axes_row, ch_type in zip(axes, ('mag', 'eeg')):
for ax, extr in zip(axes_row, extrapolations):
evoked.plot_topomap(0.1, ch_type=ch_type, size=2, extrapolate=extr,
axes=ax, show=False, colorbar=False,
sphere=(0., 0., 0., 0.09))
ax.set_title('%s %s' % (ch_type.upper(), extr), fontsize=14)
fig.tight_layout()
# %%
# More advanced usage
# -------------------
#
# Now we plot magnetometer data as topomap at a single time point: 100 ms
# post-stimulus, add channel labels, title and adjust plot margins:
fig = evoked.plot_topomap(0.1, ch_type='mag', show_names=True, colorbar=False,
size=6, res=128)
fig.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88)
fig.suptitle('Auditory response')
# %%
# We can also highlight specific channels by adding a mask, to e.g. mark
# channels exceeding a threshold at a given time:
# Define a threshold and create the mask
mask = evoked.data > 1e-13
# Select times and plot
times = (0.09, 0.1, 0.11)
mask_params = dict(markersize=10, markerfacecolor='y')
evoked.plot_topomap(times, ch_type='mag', mask=mask, mask_params=mask_params)
# %%
# Or by manually picking the channels to highlight at different times:
times = (0.09, 0.1, 0.11)
_times = ((np.abs(evoked.times - t)).argmin() for t in times)
significant_channels = [
('MEG 0231', 'MEG 1611', 'MEG 1621', 'MEG 1631', 'MEG 1811'),
('MEG 2411', 'MEG 2421'),
('MEG 1621')]
_channels = [np.in1d(evoked.ch_names, ch) for ch in significant_channels]
mask = np.zeros(evoked.data.shape, dtype='bool')
for _chs, _time in zip(_channels, _times):
mask[_chs, _time] = True
evoked.plot_topomap(times, ch_type='mag', mask=mask, mask_params=mask_params)
# %%
# Interpolating topomaps
# ----------------------
# We can also look at the effects of interpolating our data. Perhaps, we
# might have data per channel such as the impedance of each electrode that
# we don't want interpolated, or we just want to visualize the data without
# interpolation as a sanity check. We can use ``image_interp='nearest'`` to
# prevent any interpolation or ``image_interp='linear'`` to do a linear
# interpolation instead of the default cubic interpolation.
# %%
# The default cubic interpolation is the smoothest and is great for
# publications.
evoked.plot_topomap(times, ch_type='eeg', image_interp='cubic')
# %%
# The linear interpolation might be helpful in some cases.
evoked.plot_topomap(times, ch_type='eeg', image_interp='linear')
# %%
# The nearest (Voronoi, no interpolation) interpolation is especially helpful
# for debugging and seeing the values assigned to the topomap unaltered.
evoked.plot_topomap(times, ch_type='eeg', image_interp='nearest', contours=0)
# %%
# Animating the topomap
# ---------------------
#
# Instead of using a still image we can plot magnetometer data as an animation,
# which animates properly only in matplotlib interactive mode.
# sphinx_gallery_thumbnail_number = 9
times = np.arange(0.05, 0.151, 0.01)
fig, anim = evoked.animate_topomap(
times=times, ch_type='mag', frame_rate=2, blit=False)
|
