raw = mne.concatenate_raws(raw_list)
###############################################################################
# Use empty room noise as sensor noise
###############################################################################
er_raw = mne.io.read_raw_fif(fname.ernoise, preload=True)
raw_picks = mne.pick_types(raw.info, meg=True, eeg=False)
er_raw_picks = mne.pick_types(er_raw.info, meg=True, eeg=False)
raw._data[raw_picks] += er_raw._data[er_raw_picks, :len(raw.times)]
###############################################################################
# Save everything
###############################################################################
raw.save(fname.simulated_raw(noise=config.noise, vertex=config.vertex),
overwrite=True)
###############################################################################
# Plot it!
###############################################################################
with mne.open_report(fname.report(noise=config.noise,
vertex=config.vertex)) as report:
fig = plt.figure()
plt.plot(times, generate_signal(times, freq=10))
plt.xlabel('Time (s)')
report.add_figs_to_section(fig,
'Signal time course',
section='Sensor-level',
replace=True)
import mne
import numpy as np
import pandas as pd
from mne.beamformer import make_lcmv, apply_lcmv
from scipy.stats import pearsonr
import config
from config import fname, lcmv_settings
from time_series import simulate_raw, create_epochs
# Don't be verbose
mne.set_log_level(False)
fn_stc_signal = fname.stc_signal(vertex=config.vertex)
fn_simulated_raw = fname.simulated_raw(vertex=config.vertex)
fn_simulated_epochs = fname.simulated_epochs(vertex=config.vertex)
# fn_report_h5 = fname.report(vertex=config.vertex)
fn_report_h5 = None # Don't produce a report
###############################################################################
# Simulate raw data and create epochs
###############################################################################
print('simulate data')
info = mne.io.read_info(fname.sample_raw)
info = mne.pick_info(info, mne.pick_types(info, meg=True, eeg=False))
fwd_disc_true = mne.read_forward_solution(fname.fwd_discrete_true)
fwd_disc_true = mne.pick_types_forward(fwd_disc_true, meg=True, eeg=False)
er_raw = mne.io.read_raw_fif(fname.ernoise, preload=True)
import os.path as op
import mne
import numpy as np
import config
from config import fname
# Read simulated raw
raw = mne.io.Raw(fname.simulated_raw(noise=config.noise, vertex=config.vertex),
preload=True)
###############################################################################
# Create epochs
###############################################################################
events = np.hstack((
(np.arange(config.n_trials) * config.trial_length *
raw.info['sfreq'])[:, np.newaxis],
np.zeros((config.n_trials, 1)),
np.ones((config.n_trials, 1)),
)).astype(np.int)
epochs = mne.Epochs(raw=raw,
events=events,
event_id=1,
tmin=0.1,
tmax=config.trial_length - 0.1,
baseline=(None, 0.3),
preload=True)
epochs.save(fname.simulated_epochs(noise=config.noise, vertex=config.vertex),