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%matplotlib inline
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# Authors: Christian Brodbeck <christianbrodbeck@nyu.edu>
# Tal Linzen <linzen@nyu.edu>
# Denis A. Engeman <denis.engemann@gmail.com>
# Mikołaj Magnuski <mmagnuski@swps.edu.pl>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
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))
~mne.viz.plot_topomap optionsWe 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:
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times = np.arange(0.05, 0.151, 0.02)
evoked.plot_topomap(times, ch_type='mag', time_unit='s')
If times is set to None at most 10 regularly spaced topographies will be shown:
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evoked.plot_topomap(ch_type='mag', time_unit='s')
We can use nrows and ncols parameter to create multiline plots
with more timepoints.
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all_times = np.arange(-0.2, 0.5, 0.03)
evoked.plot_topomap(all_times, ch_type='mag', time_unit='s',
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:
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evoked.plot_topomap(times, ch_type='mag', average=0.05, time_unit='s')
We can plot gradiometer data (plots the RMS for each pair of gradiometers)
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evoked.plot_topomap(times, ch_type='grad', time_unit='s')
~mne.viz.plot_topomap optionsWe 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 mapres - to control the resolution of the topographies (lower resolution
means faster plotting)outlines='skirt' to see the topography stretched beyond the head circlecontours to define how many contour lines should be plotted
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evoked.plot_topomap(times, ch_type='mag', cmap='Spectral_r', res=32,
outlines='skirt', contours=4, time_unit='s')
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 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:
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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()
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evoked.plot_topomap(0.1, ch_type='mag', show_names=True, colorbar=False,
size=6, res=128, title='Auditory response',
time_unit='s')
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88)
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times = np.arange(0.05, 0.151, 0.01)
fig, anim = evoked.animate_topomap(
times=times, ch_type='mag', frame_rate=2, time_unit='s', blit=False)