# -*- coding: utf-8 -*-

"""

Created on Mon Aug 31 10:00:32 2015

@author: mje

"""

import mne

from mne.minimum_norm import (make_inverse_operator, apply_inverse,

write_inverse_operator)

import socket

import numpy as np

import matplotlib.pyplot as plt

# Setup paths and prepare raw data

hostname = socket.gethostname()

if hostname == "Wintermute":

data_path = "/home/mje/mnt/caa/scratch/"

n_jobs = 1

else:

data_path = "/projects/MINDLAB2015_MEG-CorticalAlphaAttention/scratch/"

n_jobs = 1

subjects_dir = data_path + "fs_subjects_dir/"

fname_fwd = data_path + '0001-fwd.fif'

fname_cov = data_path + '0001-cov.fif'

fname_evoked = data_path + "0001_p_03_filter_ds_ica-mc_raw_tsss-ave.fif"

snr = 1.0

lambda2 = 1.0 / snr ** 2

# Load data

evoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))

forward_meeg = mne.read_forward_solution(fname_fwd, surf_ori=True)

noise_cov = mne.read_cov(fname_cov)

# Restrict forward solution as necessary for MEG

forward_meg = mne.pick_types_forward(forward_meeg, meg=True, eeg=False)

# Alternatively, you can just load a forward solution that is restricted

# make an M/EEG, MEG-only, and EEG-only inverse operators

inverse_operator_meg = make_inverse_operator(evoked.info, forward_meg,

noise_cov,

loose=0.2, depth=0.8)

write_inverse_operator('0001-meg-oct-6-inv.fif',

inverse_operator_meg)