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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:
 Working with ECoG data
MNE supports working with more than just MEG and EEG data. Here we show some
of the functions that can be used to facilitate working with
electrocorticography (ECoG) data.
This example shows how to use
Step1:  Load in data and perform basic preprocessing
Let's load some ECoG electrode data with mne-bids
&lt;https
Step2:  Explore the electrodes on a template brain
Our electrodes are shown after being morphed to fsaverage brain so we'll use
this fsaverage brain to plot the locations of our electrodes. We'll use
Step3:  Compute frequency features of the data
Next, we'll compute the signal power in the gamma (30-90 Hz) band,
downsampling the result to 10 Hz (to save time).
Step4:  Visualize the time-evolution of the gamma power on the brain
Say we want to visualize the evolution of the power in the gamma band,
instead of just plotting the average. We can use
matplotlib.animation.FuncAnimation to create an animation and apply this
to the brain figure.
Step5:  We can project gamma power from the sensor data to the nearest locations on
the pial surface and visualize that
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Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
#          Chris Holdgraf <choldgraf@gmail.com>
#          Adam Li <adam2392@gmail.com>
#          Alex Rockhill <aprockhill@mailbox.org>
#          Liberty Hamilton <libertyhamilton@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from mne_bids import BIDSPath, read_raw_bids
import mne
from mne.viz import plot_alignment, snapshot_brain_montage
print(__doc__)
# paths to mne datasets - sample ECoG and FreeSurfer subject
bids_root = mne.datasets.epilepsy_ecog.data_path()
sample_path = mne.datasets.sample.data_path()
subjects_dir = op.join(sample_path, 'subjects')
Explanation: Working with ECoG data
MNE supports working with more than just MEG and EEG data. Here we show some
of the functions that can be used to facilitate working with
electrocorticography (ECoG) data.
This example shows how to use:
ECoG data (available here &lt;https://openneuro.org/datasets/ds003029&gt;__) from
  an epilepsy patient during a seizure
channel locations in FreeSurfer's fsaverage MRI space
projection onto a pial surface
For a complementary example that involves sEEG data, channel locations in
MNI space, or projection into a volume, see tut-working-with-seeg.
End of explanation
# first define the bids path
bids_path = BIDSPath(root=bids_root, subject='pt1', session='presurgery',
                     task='ictal', datatype='ieeg', extension='vhdr')
# then we'll use it to load in the sample dataset
# Here we use a format (iEEG) that is only available in MNE-BIDS 0.7+, so it
# will emit a warning on versions <= 0.6
raw = read_raw_bids(bids_path=bids_path, verbose=False)
# Pick only the ECoG channels, removing the EKG channels
raw.pick_types(ecog=True)
# Load the data
raw.load_data()
# Then we remove line frequency interference
raw.notch_filter([60], trans_bandwidth=3)
# drop bad channels
raw.drop_channels(raw.info['bads'])
# the coordinate frame of the montage
montage = raw.get_montage()
print(montage.get_positions()['coord_frame'])
# add fiducials to montage
montage.add_mni_fiducials(subjects_dir)
# now with fiducials assigned, the montage will be properly converted
# to "head" which is what MNE requires internally (this is the coordinate
# system with the origin between LPA and RPA whereas MNI has the origin
# at the posterior commissure)
raw.set_montage(montage)
# Find the annotated events
events, event_id = mne.events_from_annotations(raw)
# Make a 25 second epoch that spans before and after the seizure onset
epoch_length = 25  # seconds
epochs = mne.Epochs(raw, events, event_id=event_id['onset'],
                    tmin=13, tmax=13 + epoch_length, baseline=None)
# Make evoked from the one epoch and resample
evoked = epochs.average().resample(200)
del epochs
Explanation: Load in data and perform basic preprocessing
Let's load some ECoG electrode data with mne-bids
&lt;https://mne.tools/mne-bids/&gt;_.
<div class="alert alert-info"><h4>Note</h4><p>Downsampling is just to save execution time in this example, you should
    not need to do this in general!</p></div>
End of explanation
fig = plot_alignment(raw.info, trans='fsaverage',
                     subject='fsaverage', subjects_dir=subjects_dir,
                     surfaces=['pial'], coord_frame='head')
mne.viz.set_3d_view(fig, azimuth=0, elevation=70)
xy, im = snapshot_brain_montage(fig, raw.info)
Explanation: Explore the electrodes on a template brain
Our electrodes are shown after being morphed to fsaverage brain so we'll use
this fsaverage brain to plot the locations of our electrodes. We'll use
:func:~mne.viz.snapshot_brain_montage to save the plot as image data
(along with xy positions of each electrode in the image), so that later
we can plot frequency band power on top of it.
End of explanation
sfreq = 10
gamma_power_t = evoked.copy().filter(30, 90).apply_hilbert(
    envelope=True).resample(sfreq)
gamma_info = gamma_power_t.info
Explanation: Compute frequency features of the data
Next, we'll compute the signal power in the gamma (30-90 Hz) band,
downsampling the result to 10 Hz (to save time).
End of explanation
# convert from a dictionary to array to plot
xy_pts = np.vstack([xy[ch] for ch in raw.info['ch_names']])
# get a colormap to color nearby points similar colors
cmap = get_cmap('viridis')
# create the figure of the brain with the electrode positions
fig, ax = plt.subplots(figsize=(5, 5))
ax.set_title('Gamma power over time', size='large')
ax.imshow(im)
ax.set_axis_off()
# normalize gamma power for plotting
gamma_power = -100 * gamma_power_t.data / gamma_power_t.data.max()
# add the time course overlaid on the positions
x_line = np.linspace(-0.025 * im.shape[0], 0.025 * im.shape[0],
                     gamma_power_t.data.shape[1])
for i, pos in enumerate(xy_pts):
    x, y = pos
    color = cmap(i / xy_pts.shape[0])
    ax.plot(x_line + x, gamma_power[i] + y, linewidth=0.5, color=color)
Explanation: Visualize the time-evolution of the gamma power on the brain
Say we want to visualize the evolution of the power in the gamma band,
instead of just plotting the average. We can use
matplotlib.animation.FuncAnimation to create an animation and apply this
to the brain figure.
End of explanation
xyz_pts = np.array([dig['r'] for dig in evoked.info['dig']])
src = mne.read_source_spaces(
    op.join(subjects_dir, 'fsaverage', 'bem', 'fsaverage-ico-5-src.fif'))
stc = mne.stc_near_sensors(gamma_power_t, trans='fsaverage',
                           subject='fsaverage', src=src,
                           mode='nearest', subjects_dir=subjects_dir,
                           distance=0.02)
vmin, vmid, vmax = np.percentile(gamma_power_t.data, [10, 25, 90])
clim = dict(kind='value', lims=[vmin, vmid, vmax])
brain = stc.plot(surface='pial', hemi='rh', colormap='inferno', colorbar=False,
                 clim=clim, views=['lat', 'med'], subjects_dir=subjects_dir,
                 size=(250, 250), smoothing_steps='nearest',
                 time_viewer=False)
brain.add_sensors(raw.info, trans='fsaverage')
# You can save a movie like the one on our documentation website with:
# brain.save_movie(time_dilation=1, interpolation='linear', framerate=3,
#                  time_viewer=True)
Explanation: We can project gamma power from the sensor data to the nearest locations on
the pial surface and visualize that:
As shown in the plot, the epileptiform activity starts in the temporal lobe,
progressing posteriorly. The seizure becomes generalized eventually, after
this example short time section. This dataset is available using
:func:mne.datasets.epilepsy_ecog.data_path for you to examine.
End of explanation
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