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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:
 Compute LCMV beamformer on evoked data
Compute LCMV beamformer solutions on evoked dataset for three different choices
of source orientation and stores the solutions in stc files for visualisation.
Step1:  Get epochs
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Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.datasets import sample
from mne.beamformer import lcmv
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
subjects_dir = data_path + '/subjects'
Explanation: Compute LCMV beamformer on evoked data
Compute LCMV beamformer solutions on evoked dataset for three different choices
of source orientation and stores the solutions in stc files for visualisation.
End of explanation
event_id, tmin, tmax = 1, -0.2, 0.5
# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True, proj=True)
raw.info['bads'] = ['MEG 2443', 'EEG 053']  # 2 bads channels
events = mne.read_events(event_fname)
# Set up pick list: EEG + MEG - bad channels (modify to your needs)
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,
                       exclude='bads', selection=left_temporal_channels)
# Pick the channels of interest
raw.pick_channels([raw.ch_names[pick] for pick in picks])
# Re-normalize our empty-room projectors, so they are fine after subselection
raw.info.normalize_proj()
# Read epochs
proj = False  # already applied
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
                    baseline=(None, 0), preload=True, proj=proj,
                    reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
evoked = epochs.average()
forward = mne.read_forward_solution(fname_fwd, surf_ori=True)
# Compute regularized noise and data covariances
noise_cov = mne.compute_covariance(epochs, tmin=tmin, tmax=0, method='shrunk')
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15,
                                  method='shrunk')
plt.close('all')
pick_oris = [None, 'normal', 'max-power']
names = ['free', 'normal', 'max-power']
descriptions = ['Free orientation', 'Normal orientation', 'Max-power '
                'orientation']
colors = ['b', 'k', 'r']
for pick_ori, name, desc, color in zip(pick_oris, names, descriptions, colors):
    stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01,
               pick_ori=pick_ori)
    # View activation time-series
    label = mne.read_label(fname_label)
    stc_label = stc.in_label(label)
    plt.plot(1e3 * stc_label.times, np.mean(stc_label.data, axis=0), color,
             hold=True, label=desc)
plt.xlabel('Time (ms)')
plt.ylabel('LCMV value')
plt.ylim(-0.8, 2.2)
plt.title('LCMV in %s' % label_name)
plt.legend()
plt.show()
# Plot last stc in the brain in 3D with PySurfer if available
brain = stc.plot(hemi='lh', subjects_dir=subjects_dir,
                 initial_time=0.1, time_unit='s')
brain.show_view('lateral')
Explanation: Get epochs
End of explanation
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