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# 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')

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

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

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

# 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')
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88)
Plotting topographic maps of evoked data
