AverageTFR.plot_topo color scale bug

MWE adapted from #10920

import numpy as np
import mne

data_path = mne.datasets.sample.data_path()
subjects_dir = data_path / 'subjects'
raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_raw.fif'

# Read the raw data
raw = mne.io.read_raw_fif(raw_fname)
raw.info['bads'] = ['MEG 2443']  # bad MEG channel

# Set up the epoching
events = mne.find_events(raw)

# pick data channel types
raw.pick(['meg', 'eeg', 'eog'])

# create epochs, leave room for cutting out edge artifact
epochs = mne.Epochs(raw, events, event_id=dict(left_aud=1),
                    tmin=-0.5, tmax=0.5, baseline=(None, 0),
                    preload=True, proj=False,
                    reject=dict(grad=4000e-13, mag=4e-12,
                                eeg=1e-4, eog=150e-6))

# done with eog now
del raw
epochs.pick(['meg', 'eeg'])

# set average reference
epochs.set_eeg_reference('average', projection=True)

# focus on alpha and beta oscillations
freqs = np.logspace(*np.log10([8, 35]), num=6)

# select subset of trials for speed
epochs = epochs[:10]

# time-frequency decomposition
average_tfr = mne.time_frequency.tfr_morlet(
    epochs, freqs=freqs, n_cycles=freqs / 2, return_itc=False,
    average=True)

average_tfr.plot_topo()

plotting average_tfr.plot_topo(picks=slice(0, 2)) or slice(0, 5) or even slice(0, 40) yields (partial) topo plots where at least some of the sensors have visible activity. I'm assuming some sort of bad color normalization (across sensors) is the cause, but that's a wild guess.

Start by running the provided MWE and calling AverageTFR.plot_topo() with the listed picks slices. Inspect the plot_topo color normalization and related plotting path; done means the full plot and sliced plots consistently show the expected sensor activity without the reported partial topo behavior.