PR summary

For large wireframe plots (in my testing, 8000x8000), a large chunk of time is spent doing autoscaling of the axes. There is some opportunity to vectorize these calculations and get speedups.

Changes here:

1. Vectorize bounding box limits updates
2. Faster array creation fortransforms.update_from_data_x andtransforms.update_from_data_y
3. Switch from list comprehensions to vectorized approach foraxes3d.plot_wireframe. This also stores the data as a numpy array, which gives speedups when drawing.

The compiled functionupdate_path_extents is actually just about equal with a vectorized numpy approach speedwise when updating both x and y limits. But when doing single axis limits, it needs to check for NaNs for each pair of x or y, so the numpy approach lets you skip the extra x or y calculations and comes out ahead. This bit is a benefit for all 3D functions, not justplot_wireframe. I spent some time trying to refactor the compiled function to operate on just a single axis, but it's tricky to disentangle x and y. The numpy approach was easier and just as fast.

For my test case I'm seeing an average over 5 runs on main of 6.82 seconds, and on this PR 4.00 seconds - a 1.7x speedup.

importtimeimportnumpyasnpimportmatplotlib.pyplotaspltfig=plt.figure()ax=fig.add_subplot(111,projection='3d')x=y=np.linspace(-1,1,8000)X,Y=np.meshgrid(x,y)Z=X**2+Y**2print("Timing...")start_time=time.perf_counter()ax.plot_wireframe(X,Y,Z,axlim_clip=False)ax.set_xlim(0,1)fig.canvas.draw()end_time=time.perf_counter()plt.close()print(f"Time taken:{end_time-start_time:.4f} seconds")

Plotting and drawing on main (avg 6.82 sec):

Plotting and drawing on this PR (avg 4.00 sec):

PR checklist

• [n/a] "closes #0000" is in the body of the PR description tolink the related issue
• new and changed code istested
• [n/a]Plotting related features are demonstrated in anexample
• [n/a]New Features andAPI Changes are noted with adirective and release note
• Documentation complies withgeneral anddocstring guidelines