PR summary

This PR fixes a difference between plotting an image with a nonaffine transform versus with an affine transform. When plotting with an affine transform, the transform for rendering gets an additional transform step to scale the image to the axes edges in the situation where the axes bbox is not a whole number of pixels. However, when plotting with a nonaffine transform, this additional scaling step is skipped, and instead the fractional pixel at the end is clipped off. I can't think of any reason why this difference is desirable, so this PR removes the different handling of a nonaffine transform versus an affine transform.

Before this PR

Here's an example image before this PR and generating script. Note that the image plotted using the nonaffine transform has a obvious gap to the axis on the right edge due to the fractional pixel. The discrepancy on the top edge is not quite as obvious.

importnumpyasnpimportmatplotlib.pyplotaspltfrommatplotlib.transformsimportTransform# Create a nonaffine identity transformclassNonAffineIdentityTransform(Transform):input_dims=2output_dims=2definverted(self):returnselfnonaffine_transform=NonAffineIdentityTransform()data=np.arange(16).reshape((4,4))%3fig,axs=plt.subplots(2,figsize=(6,3),dpi=100)# Choose axes bboxs with with fractional pixelsaxs[0].set_position([0.1,0.15,200.75/fig.bbox.width,200.75/fig.bbox.height])axs[1].set_position([0.6,0.15,200.75/fig.bbox.width,200.75/fig.bbox.height])im0=axs[0].imshow(data)im1=axs[1].imshow(data,transform=nonaffine_transform+axs[1].transData)axs[0].spines[:].set_ls((0, (5,5)))axs[1].spines[:].set_ls((0, (5,5)))axs[0].set_title("Affine")axs[1].set_title("Nonaffine")plt.show()

After this PR

PR checklist

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