Matplotlib Fails to Update Axis Limits with ipywidgets in Jupyter Lab

I'm using ipywidgets along with matplotlib in Jupyter Lab and stumbled across some strange behavior. Here's an example of the code:

import matplotlib.pyplot as plt
import ipywidgets as widgets
%matplotlib widget
plt.ioff()

d = {
    "a": [1,2,3],
    "b": [2,3,4]
}

fig, ax = plt.subplots()

def update(change):
    data = d[change["new"]]
    ax.clear()  # Clear the previous plot
    ax.plot(data, data)
    ax.set_xlim(min(data), max(data))
    ax.set_ylim(min(data), max(data))
    ax.figure.canvas.draw()
    
dropdown = widgets.Dropdown(options = list(d.keys()))
dropdown.observe(update, "value")

display(dropdown, fig.canvas)
update({"new": "a"})

The steps I carry out are as follows:

• Select b from dropdown
• Pan the plot
• Select a from dropdown
• Click on the "Reset original view" button on the plot

Instead of snapping the plot extents back to what they are set for choice a, I instead get the following:

Where it looks like matplotlib has indeed plotted the data [1,2,3], but somehow failed to update the axis limits based on that data.

I really hope this isn't a bug, and that I'm doing something wrong. Has anyone seen this behavior before?

首先运行 issue 中使用 ipywidgets 下拉菜单和 Matplotlib widget backend 的 Jupyter Lab reproducer，然后按照列出的选择、平移和重置顺序操作。将恢复的坐标轴范围与为每个数据集设置的范围进行比较；从 b 切换到 a 后，重置视图与当前选中的数据匹配即表示完成。