File: plot_evoked_topomap.py

package info (click to toggle)
python-mne 0.17%2Bdfsg-1
  • links: PTS, VCS
  • area: main
  • in suites: buster
  • size: 95,104 kB
  • sloc: python: 110,639; makefile: 222; sh: 15
file content (50 lines) | stat: -rw-r--r-- 1,723 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
"""
========================================
Plotting topographic maps of evoked data
========================================

Load evoked data and plot topomaps for selected time points.
"""
# Authors: Christian Brodbeck <christianbrodbeck@nyu.edu>
#          Tal Linzen <linzen@nyu.edu>
#          Denis A. Engeman <denis.engemann@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 and subtract baseline
condition = 'Left Auditory'
evoked = read_evokeds(fname, condition=condition, baseline=(None, 0))

# set time instants in seconds (from 50 to 150ms in a step of 10ms)
times = np.arange(0.05, 0.15, 0.01)
# If times is set to None only 10 regularly spaced topographies will be shown

# plot magnetometer data as topomaps
evoked.plot_topomap(times, ch_type='mag', time_unit='s')

# compute a 50 ms bin to stabilize topographies
evoked.plot_topomap(times, ch_type='mag', average=0.05, time_unit='s')

# plot gradiometer data (plots the RMS for each pair of gradiometers)
evoked.plot_topomap(times, ch_type='grad', time_unit='s')

# plot magnetometer data as an animation
evoked.animate_topomap(ch_type='mag', times=times, frame_rate=10,
                       time_unit='s')

# plot magnetometer data as topomap at 1 time point : 100 ms
# and add channel labels and title
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)