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),