DOC: move grouped OPM demo to kernel_phantom example

Author: PragnyaKhandelwal

examples/datasets/kernel_phantom.py

@@ -51,6 +51,14 @@
 t_peak = 0.016  # based on visual inspection of evoked
 fig.axes[0].axvline(t_peak, color="k", ls=":", lw=3, zorder=2)
 
+# %%
+# Demonstrating grouped OPM topomaps
+# The Kernel OPM phantom data is triaxial, so we can demonstrate the grouped
+# radial and tangential topomap rendering:
+
+fig = evoked.plot_topomap(times=[t_peak], ch_type="mag", merge_channels=True)
+fig = evoked.plot_joint(times=[t_peak], picks="mag")
+
 # %%
 # The data covariance has an interesting structure because of densely packed sensors:
 

tutorials/preprocessing/80_opm_processing.py

@@ -243,14 +243,6 @@
 )
 evoked = epochs.average()
 t_peak = evoked.times[np.argmax(np.std(evoked.copy().pick("meg").data, axis=0))]
-fig = evoked.plot_joint(picks="mag")
-
-# %%
-# Demonstrating grouped OPM topomaps
-# ----------------------------------
-# The UCL OPM auditory dataset has already been loaded above, so this extra
-# figure reuses the same real evoked object without any additional downloads.
-fig = evoked.plot_topomap(times=[t_peak], ch_type="mag")
 
 # %%
 # Visualizing coregistration