Bug summary

When an Axes is initialized using a Bbox object, changing the Axes' position (for example, viaax.set_position) will also alter the original Bbox object. This is because the Axes retains a reference to the Bbox object rather than creating a separate copy during initialization.

Code for reproduction

importmatplotlib.pyplotaspltfrommatplotlib.transformsimportBboxbbox=Bbox([[0.1,0.1], [0.9,0.9]])fig=plt.figure()ax=fig.add_axes(bbox)ax.set_position([0.25,0.25,0.5,0.5])print(bbox)print(id(bbox),id(ax._position))

Actual outcome

Bbox(x0=0.25, y0=0.25, x1=0.75, y1=0.75)
140507022809120 140507022809120

Expected outcome

Bbox(x0=0.1, y0=0.1, x1=0.9, y1=0.9)
... ... # Two different IDs

Additional information

This issue originates from_AxesBase.__init__,

whereargs[0] is directly assigned toself._position without making a copy.

I don't know if this is a bug or a feature, but it does break encapsulation.

Additionally, some other initialization methods (for example,Bbox.__init__) that usenp.asarray instead ofnp.array can cause similar issues.

Operating system

No response

Matplotlib Version

3.10.0

Matplotlib Backend

No response

Python version

No response

Jupyter version

No response

Installation

None