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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:
 Permutation F-test on sensor data with 1D cluster level
One tests if the evoked response is significantly different
between conditions. Multiple comparison problem is addressed
with cluster level permutation test.
Step1:  Set parameters
Step2:  Read epochs for the channel of interest
Step3:  Compute statistic
Step4:  Plot
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Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
Explanation: Permutation F-test on sensor data with 1D cluster level
One tests if the evoked response is significantly different
between conditions. Multiple comparison problem is addressed
with cluster level permutation test.
End of explanation
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin = -0.2
tmax = 0.5
#   Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)
channel = 'MEG 1332'  # include only this channel in analysis
include = [channel]
Explanation: Set parameters
End of explanation
picks = mne.pick_types(raw.info, meg=False, eog=True, include=include,
                       exclude='bads')
event_id = 1
reject = dict(grad=4000e-13, eog=150e-6)
epochs1 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                     baseline=(None, 0), reject=reject)
condition1 = epochs1.get_data()  # as 3D matrix
event_id = 2
epochs2 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                     baseline=(None, 0), reject=reject)
condition2 = epochs2.get_data()  # as 3D matrix
condition1 = condition1[:, 0, :]  # take only one channel to get a 2D array
condition2 = condition2[:, 0, :]  # take only one channel to get a 2D array
Explanation: Read epochs for the channel of interest
End of explanation
threshold = 6.0
T_obs, clusters, cluster_p_values, H0 = \
    permutation_cluster_test([condition1, condition2], n_permutations=1000,
                             threshold=threshold, tail=1, n_jobs=1,
                             out_type='mask')
Explanation: Compute statistic
End of explanation
times = epochs1.times
plt.close('all')
plt.subplot(211)
plt.title('Channel : ' + channel)
plt.plot(times, condition1.mean(axis=0) - condition2.mean(axis=0),
         label="ERF Contrast (Event 1 - Event 2)")
plt.ylabel("MEG (T / m)")
plt.legend()
plt.subplot(212)
for i_c, c in enumerate(clusters):
    c = c[0]
    if cluster_p_values[i_c] <= 0.05:
        h = plt.axvspan(times[c.start], times[c.stop - 1],
                        color='r', alpha=0.3)
    else:
        plt.axvspan(times[c.start], times[c.stop - 1], color=(0.3, 0.3, 0.3),
                    alpha=0.3)
hf = plt.plot(times, T_obs, 'g')
plt.legend((h, ), ('cluster p-value < 0.05', ))
plt.xlabel("time (ms)")
plt.ylabel("f-values")
plt.show()
Explanation: Plot
End of explanation
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