F[t] = (dfe / (ve * df)) * T2
F = F.reshape((n_channels, n_times))
F_evoked = EvokedArray(F, epochs_info, tmin)
font = 'Mukta' # noqa
fig, ax = plt.subplots(figsize=(10, 10))
axes = [
plt.subplot2grid((10, 6), (0, 0), rowspan=10, colspan=4),
plt.subplot2grid((10, 6), (2, 5), rowspan=6, colspan=1),
]
F_evoked.plot_image(cmap='magma',
mask=F > 10.0,
axes=axes[0],
clim=dict(eeg=[0, F.max()]),
unit=False,
scalings=dict(eeg=1),
colorbar=False)
axes[0].spines['top'].set_visible(False)
axes[0].spines['right'].set_visible(False)
axes[0].spines['left'].set_bounds(0, 33)
axes[0].spines['left'].set_linewidth(1.5)
axes[0].spines['bottom'].set_bounds(-0.250, 2.0)
axes[0].spines['bottom'].set_linewidth(1.5)
axes[0].set_ylabel('EEG sensors', labelpad=10.0, fontsize=10.5)
axes[0].set_yticks(np.arange(0, 34, 1))
axes[0].set_yticklabels(F_evoked.ch_names, fontname=font)
# add line marking stimulus presentation
