WIP: Prototype of MultiPlotter

[GuillaumeFavelier]

I have been experimenting with the new PyVistaQt class MultiPlotter to bring proper SSAO and/or EDL to MNE on #8771 (comment) (among other features like multiple toolbars, menu bars and multi-renderers). And I don't think it's reasonable to do that in one go.

This PR is far from being mergeable but I believe it could drive smaller PRs that can help with 3d backend refactoring in general. For instance:

• Fix show
• Create proper Renderer bindings for add_mesh and add_actor
• Generic way to pick a 3d viewer
• Get rid of self.interactor in Brain
• Get rid of self.plotter in Brain and use self._renderer instead
• Get rid of self.plotter in _Renderer and use self.figure instead
• Generic way to subplot
• Generic way to iterate through the 3d viewers
• Trace dock widget
• Separate the concepts of "plotter" and 3d "viewer"

[larsoner]

I would add to this list, if we haven't done it already:

• Avoid using PyVista's toolbar and menu bar and create/use our own as needed

[GuillaumeFavelier]

Done in here on this PR:

mne-python/mne/viz/backends/_pyvista.py

Lines 82 to 83 in f20f816

	self.store["menu_bar"] = False
	self.store["toolbar"] = False

[GuillaumeFavelier]

Update after dd0d67d (with experimental margin=False):

I see the main challenges being 1) ensuring correct size and 2) managing picking nicely for all the viewers

[larsoner]

Can you check to make sure volumetric rendering looks okay? Just want to make sure it plays nicely with the ambient occlusion etc.

[GuillaumeFavelier]

There are some issues with the colorbar:

BackgroundPlotter has no attribute named scalar_bar

SSAO=False	SSAO=True

import os.path as op
import mne
data_path = mne.datasets.sample.data_path()
sample_dir = op.join(data_path, 'MEG', 'sample')
subjects_dir = op.join(data_path, 'subjects')
inv = mne.minimum_norm.read_inverse_operator(op.join(
    sample_dir, 'sample_audvis-meg-vol-7-meg-inv.fif'))
    # sample_dir, 'sample_audvis-meg-oct-6-meg-inv.fif'))
evoked = mne.read_evokeds(op.join(sample_dir, 'sample_audvis-ave.fif'))[0]
evoked.apply_baseline((None, 0)).pick_types(meg=True)
stc, res = mne.minimum_norm.apply_inverse(
    evoked, inv, return_residual=True, pick_ori='vector')

initial_time = 0.1
# stc, kwargs = stc, dict(overlay_alpha=0.5, brain_alpha=0.5, vector_alpha=0.)
stc, kwargs = stc.magnitude(), dict(alpha=0.5, surface='white')
kwargs['src'] = inv['src']
meth = stc.plot_3d if isinstance(stc, mne.VolSourceEstimate) else stc.plot
brain = meth(
    subjects_dir=subjects_dir, initial_time=initial_time,
    clim='auto', hemi='both', # views=['sag', 'axi', 'cor'],
    view_layout='horizontal', smoothing_steps='nearest', verbose=True,
    time_viewer=True, size=(800, 600), background='k',
    show_traces=False,
    **kwargs)

and plot_beamformer_lcmv:

[GuillaumeFavelier]

It's better after #9121

[GuillaumeFavelier]

This PR now requires pyvista/pyvistaqt#90.

I plan to move the mplcanvas to a bottom dock and use the new resize_content() function to replace ensure_minimum_size.

[GuillaumeFavelier]

In my experiments, the mplcanvas as a dock has several advantages:

• it can be made separate dynamically with the default dock features (meaning that the "separate" particular case can be removed)
• it does not mess with the central widget layout meaning that the resize_content() function is enough to ensure that the viewer has correct dimensions (important for screenshot). I have to demonstrate that by updating the tests.
• the size of the dock can be handled separately

This is how it looks locally:

Probably the margins can be nullified.

[larsoner]

Agreed docking is useful, can that be a first PR, separate from the MultiPlotter stuff? It seems orthogonal, easier to review, and will get in sooner than if we tie it into this PR

[GuillaumeFavelier]

Circle does not seem ready yet. I'll investigate locally:

RuntimeWarning: No 3D backend found. Resorting to matplotlib 3d

[GuillaumeFavelier]

Closing for now. It already served the purpose of refactor _Renderer and the pyvista 3d backend. I can reopen when I get back to SSAO.