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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>
#
# 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))
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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')
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. By default extrapolate='box' is used which
extrapolates to a large box stretching beyond the head circle.
Compare this with extrapolate='head' (second topography below) where
extrapolation goes to 0 at the head circle and extrapolate='local' where
extrapolation is performed only within some distance from channels:
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extrapolations = ['box', 'head', 'local']
fig, axes = plt.subplots(figsize=(7.5, 2.5), 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 ax, extr in zip(axes, extrapolations):
evoked.plot_topomap(0.1, ch_type='eeg', size=2, extrapolate=extr, axes=ax,
show=False, colorbar=False, sphere=(0., 0., 0., 0.09))
ax.set_title(extr, fontsize=14)
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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', extrapolate='local', border='mean')
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88)
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evoked.animate_topomap(ch_type='mag', times=times, frame_rate=10,
time_unit='s')