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import numpy as np
import matplotlib.pyplot as pl
from mne.datasets import sample
import mne

from jumeg.jumeg_surrogates import Surrogates, check_power_spectrum

data_path = sample.data_path()
subjects_dir = data_path + '/subjects'

fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'

epochs_testing = False

raw = mne.io.read_raw_fif(fname_raw)
events = mne.read_events(fname_event)

# add a bad channel
raw.info['bads'] += ['MEG 2443']

# pick MEG channels
picks = mne.pick_types(raw.info, meg='mag', eeg=False, stim=False, eog=False,
                       exclude='bads')

# Define epochs for left-auditory condition
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=dict(mag=4e-12))

# initialize the Surrogates object
surr_epochs = Surrogates(epochs)

n_surr = 10  # number of surrogates
mode = 'randomize_phase'
mysurr = surr_epochs.compute_surrogates(n_surr=n_surr,
                                        mode=mode, return_generator=False)

# get one epochs for plotting
# for epochs, a generator is always returned
first_surr_epoch = next(mysurr)

# visualize surrogates
fig, (ax1, ax2) = pl.subplots(2, 1)
epochs.average().plot(axes=ax1, show=False, titles='Evoked')
first_surr_epoch.average().plot(axes=ax2, show=False, titles='Surrogate Evoked')
pl.show()
Example to show surrogate generation on Epochs using the jumeg Surrogates module.
