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)