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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:
 Sensitivity map of SSP projections
This example shows the sources that have a forward field
similar to the first SSP vector correcting for ECG.
Step1:  Show sensitivity map
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Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from mne import read_forward_solution, read_proj, sensitivity_map
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ecg_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif'
fwd = read_forward_solution(fname)
projs = read_proj(ecg_fname)
# take only one projection per channel type
projs = projs[::2]
# Compute sensitivity map
ssp_ecg_map = sensitivity_map(fwd, ch_type='grad', projs=projs, mode='angle')
Explanation: Sensitivity map of SSP projections
This example shows the sources that have a forward field
similar to the first SSP vector correcting for ECG.
End of explanation
plt.hist(ssp_ecg_map.data.ravel())
plt.show()
args = dict(clim=dict(kind='value', lims=(0.2, 0.6, 1.)), smoothing_steps=7,
            hemi='rh', subjects_dir=subjects_dir)
ssp_ecg_map.plot(subject='sample', time_label='ECG SSP sensitivity', **args)
Explanation: Show sensitivity map
End of explanation
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