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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 sLORETA inverse solution on raw data
Compute sLORETA inverse solution on raw dataset restricted
to a brain label and stores the solution in stc files for
visualisation.
Step1:  View activation time-series
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Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = (
    data_path / 'MEG' / 'sample' / 'sample_audvis-meg-oct-6-meg-inv.fif')
fname_raw = data_path / 'MEG' / 'sample' / 'sample_audvis_raw.fif'
label_name = 'Aud-lh'
fname_label = data_path / 'MEG' / 'sample' / 'labels' / f'{label_name}.label'
snr = 1.0  # use smaller SNR for raw data
lambda2 = 1.0 / snr ** 2
method = "sLORETA"  # use sLORETA method (could also be MNE or dSPM)
# Load data
raw = mne.io.read_raw_fif(fname_raw)
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
raw.set_eeg_reference('average', projection=True)  # set average reference.
start, stop = raw.time_as_index([0, 15])  # read the first 15s of data
# Compute inverse solution
stc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label,
                        start, stop, pick_ori=None)
# Save result in stc files
stc.save('mne_%s_raw_inverse_%s' % (method, label_name), overwrite=True)
Explanation: Compute sLORETA inverse solution on raw data
Compute sLORETA inverse solution on raw dataset restricted
to a brain label and stores the solution in stc files for
visualisation.
End of explanation
plt.plot(1e3 * stc.times, stc.data[::100, :].T)
plt.xlabel('time (ms)')
plt.ylabel('%s value' % method)
plt.show()
Explanation: View activation time-series
End of explanation
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