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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:
 Display sensitivity maps for EEG and MEG sensors
Sensitivity maps can be produced from forward operators that
indicate how well different sensor types will be able to detect
neural currents from different regions of the brain.
To get started with forward modeling see tut-forward.
Step1:  Compute sensitivity maps
Step2:  Show gain matrix a.k.a. leadfield matrix with sensitivity map
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Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
subjects_dir = data_path + '/subjects'
# Read the forward solutions with surface orientation
fwd = mne.read_forward_solution(fwd_fname)
mne.convert_forward_solution(fwd, surf_ori=True, copy=False)
leadfield = fwd['sol']['data']
print("Leadfield size : %d x %d" % leadfield.shape)
Explanation: Display sensitivity maps for EEG and MEG sensors
Sensitivity maps can be produced from forward operators that
indicate how well different sensor types will be able to detect
neural currents from different regions of the brain.
To get started with forward modeling see tut-forward.
End of explanation
grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')
mag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='fixed')
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')
Explanation: Compute sensitivity maps
End of explanation
picks_meg = mne.pick_types(fwd['info'], meg=True, eeg=False)
picks_eeg = mne.pick_types(fwd['info'], meg=False, eeg=True)
fig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
fig.suptitle('Lead field matrix (500 dipoles only)', fontsize=14)
for ax, picks, ch_type in zip(axes, [picks_meg, picks_eeg], ['meg', 'eeg']):
    im = ax.imshow(leadfield[picks, :500], origin='lower', aspect='auto',
                   cmap='RdBu_r')
    ax.set_title(ch_type.upper())
    ax.set_xlabel('sources')
    ax.set_ylabel('sensors')
    fig.colorbar(im, ax=ax, cmap='RdBu_r')
fig_2, ax = plt.subplots()
ax.hist([grad_map.data.ravel(), mag_map.data.ravel(), eeg_map.data.ravel()],
        bins=20, label=['Gradiometers', 'Magnetometers', 'EEG'],
        color=['c', 'b', 'k'])
fig_2.legend()
ax.set(title='Normal orientation sensitivity',
       xlabel='sensitivity', ylabel='count')
grad_map.plot(time_label='Gradiometer sensitivity', subjects_dir=subjects_dir,
              clim=dict(lims=[0, 50, 100]))
Explanation: Show gain matrix a.k.a. leadfield matrix with sensitivity map
End of explanation
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