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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 .. _tut_inverse_basics
Step1:  Process MEG data
Step2:  Compute the evoked response
Step3:  Inverse modeling
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Python Code:
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import (make_inverse_operator, apply_inverse,
                              write_inverse_operator)
mne.set_log_level('WARNING')
Explanation: .. _tut_inverse_basics:
Basics of source localization
Authors: Alexandre Gramfort &#97;&#108;&#101;&#120;&#97;&#110;&#100;&#114;&#101;&#46;&#103;&#114;&#97;&#109;&#102;&#111;&#114;&#116;&#64;&#116;&#101;&#108;&#101;&#99;&#111;&#109;&#45;&#112;&#97;&#114;&#105;&#115;&#116;&#101;&#99;&#104;&#46;&#102;&#114;
         Denis Engemann &#100;&#101;&#110;&#105;&#115;&#46;&#101;&#110;&#103;&#101;&#109;&#97;&#110;&#110;&#64;&#103;&#109;&#97;&#105;&#108;&#46;&#99;&#111;&#109;
End of explanation
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.Raw(raw_fname)
events = mne.find_events(raw, stim_channel='STI 014')
event_id = dict(aud_r=1)  # event trigger and conditions
tmin = -0.2  # start of each epoch (200ms before the trigger)
tmax = 0.5  # end of each epoch (500ms after the trigger)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
                       exclude='bads')
baseline = (None, 0)  # means from the first instant to t = 0
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                    picks=picks, baseline=baseline, reject=reject)
# compute regularized noise covariance
noise_cov = mne.compute_covariance(
    epochs, tmax=0., method=['shrunk', 'empirical'])
fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info)
Explanation: Process MEG data
End of explanation
evoked = epochs.average()
evoked.plot()
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag')
Explanation: Compute the evoked response
End of explanation
# Read the forward solution and compute the inverse operator
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'
fwd = mne.read_forward_solution(fname_fwd, surf_ori=True)
# Restrict forward solution as necessary for MEG
fwd = mne.pick_types_forward(fwd, meg=True, eeg=False)
# make an M/EEG, MEG-only, and EEG-only inverse operators
info = evoked.info
inverse_operator = make_inverse_operator(info, fwd, noise_cov,
                                         loose=0.2, depth=0.8)
write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',
                       inverse_operator)
# Compute inverse solution
method = "dSPM"
snr = 3.
lambda2 = 1. / snr ** 2
stc = apply_inverse(evoked, inverse_operator, lambda2,
                    method=method, pick_ori=None)
# visualize
subjects_dir = data_path + '/subjects'
brain = stc.plot(surface='inflated', hemi='rh', subjects_dir=subjects_dir)
brain.set_data_time_index(45)
brain.scale_data_colormap(fmin=8, fmid=12, fmax=15, transparent=True)
brain.show_view('lateral')
# morph data to average brain
stc_fsaverage = stc.morph(subject_to='fsaverage', subjects_dir=subjects_dir)
brain_fsaverage = stc_fsaverage.plot(surface='inflated', hemi='rh',
                                     subjects_dir=subjects_dir)
brain_fsaverage.set_data_time_index(45)
brain_fsaverage.scale_data_colormap(fmin=8, fmid=12, fmax=15, transparent=True)
brain_fsaverage.show_view('lateral')
Explanation: Inverse modeling: MNE and dSPM on evoked and raw data
End of explanation
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