Question about performance

まだ誰も着手していません。

## Describe the issue

I realise there has already been several issues opened about the performance of `ipympl` (e.g. #55), compared to other libraries that render in jupyter. But I still don't fully understand why the performance is so much worse than using e.g. the `%matplotlib qt` backend.

Take for example the 'Looking glass' example from the [matplotlib docs](https://matplotlib.org/stable/gallery/event_handling/looking_glass.html#sphx-glr-gallery-event-handling-looking-glass-py).
The ineractivity is very nice in the Qt backend (more than enough for our purposes, even if not quite as snappy as javascript based libs such as `bokeh` or `plotly`), but terrible in jupyter lab (laggy and leaving phantom parts of the ellipse around the plot).

Is there really no way to improve the performance of interactivity in `ipympl`? (I have tried to use `fig.canvas.draw_idle()` instead of `.draw()`, and it helps a little but we are still so far behind the Qt backend.

Many thanks.

## Versions

```
3.9.5 (default, Jun 4 2021, 12:28:51)
[GCC 7.5.0]
ipympl version: 0.8.2
Selected Jupyter core packages...
IPython : 7.29.0
ipykernel : 6.5.0
ipywidgets : 7.6.5
jupyter_client : 7.0.6
jupyter_core : 4.9.1
jupyter_server : 1.11.2
jupyterlab : 3.2.3
nbclient : 0.5.8
nbconvert : 6.3.0
nbformat : 5.1.3
notebook : 6.4.5
qtconsole : not installed
traitlets : 5.1.1
Known nbextensions:
config dir: /home/nvaytet/software/miniconda3/etc/jupyter/nbconfig
notebook section
ipycanvas/extension enabled
- Validating: OK
ipyevents/extension enabled
- Validating: OK
jupyter-datawidgets/extension enabled
- Validating: OK
jupyter-matplotlib/extension enabled
- Validating: OK
jupyter-threejs/extension enabled
- Validating: OK
jupyter_bokeh/extension enabled
- Validating: OK
jupyter_dash/main enabled
- Validating: OK
jupyterlab-plotly/extension enabled
- Validating: OK
jupyter-js-widgets/extension enabled
- Validating: OK
JupyterLab v3.2.3
/home/nvaytet/software/miniconda3/share/jupyter/labextensions
ipycanvas v0.12.0 enabled OK
ipyevents v2.0.1 enabled OK
jupyter-matplotlib v0.10.2 enabled OK
jupyterlab-datawidgets v7.0.0 enabled OK
jupyterlab-plotly v5.4.0 enabled OK
jupyter-threejs v2.3.0 enabled OK (python, pythreejs)
@jupyter-widgets/jupyterlab-manager v3.0.1 enabled OK (python, jupyterlab_widgets)
@bokeh/jupyter_bokeh v3.0.2 enabled OK (python, jupyter_bokeh)

Other labextensions (built into JupyterLab)
app dir: /home/nvaytet/software/miniconda3/share/jupyter/lab
jupyterlab-dash v0.4.0 enabled OK

Build recommended, please run `jupyter lab build`:
jupyterlab-dash needs to be included in build

```

まず、ipympl を使って JupyterLab で Looking Glass の例を再現し、そのインタラクションを Qt バックエンドと比較します。報告されている draw_idle の動作と、記載されている JupyterLab、ipympl、widget のバージョンを調査します。具体的なパフォーマンス原因を特定し、インタラクティブなレンダリングにおける測定可能な改善を定義できれば完了です。

索引モデルが issue の本文から書いたものです。