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%matplotlib inline



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# Author: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt
import mne
from mne.viz import tight_layout
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()

fname = data_path + '/MEG/sample/sample_audvis-ave.fif'

# Reading evoked data
condition = 'Left Auditory'
evoked = mne.read_evokeds(fname, condition=condition, baseline=(None, 0),
                          proj=True)

ch_names = evoked.info['ch_names']
picks = mne.pick_channels(ch_names=ch_names, include=["MEG 2332"])

# Create subplots
f, (ax1, ax2, ax3) = plt.subplots(3)
evoked.plot(exclude=[], picks=picks, axes=ax1,
            titles=dict(grad='Before time shifting'))

# Apply relative time-shift of 500 ms
evoked.shift_time(0.5, relative=True)

evoked.plot(exclude=[], picks=picks, axes=ax2,
            titles=dict(grad='Relative shift: 500 ms'))

# Apply absolute time-shift of 500 ms
evoked.shift_time(0.5, relative=False)

evoked.plot(exclude=[], picks=picks, axes=ax3,
            titles=dict(grad='Absolute shift: 500 ms'))

tight_layout()

Shifting time-scale in evoked data