import mne from mne.time_frequency import csd_morlet from mne.beamformer import make_dics, apply_dics_csd import numpy as np from itertools import product import pandas as pd import config from config import fname from utils import make_dipole, evaluate_stc # Read in the simulated data stc_signal = mne.read_source_estimate( fname.stc_signal(noise=config.noise, vertex=config.vertex)) epochs = mne.read_epochs( fname.simulated_epochs(noise=config.noise, vertex=config.vertex)) fwd = mne.read_forward_solution(fname.fwd) # For pick_ori='normal', the fwd needs to be in surface orientation fwd = mne.convert_forward_solution(fwd, surf_ori=True) # The DICS beamformer currently only uses one sensor type epochs_grad = epochs.copy().pick_types(meg='grad') epochs_mag = epochs.copy().pick_types(meg='mag') # Make CSD matrix csd = csd_morlet(epochs, [config.signal_freq]) # Compute the settings grid regs = [0.05, 0.1, 0.5] sensor_types = ['grad', 'mag']
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)