GeezerVison

• The filtering weight at one extreme is not being calculated correctly. In the case of linear interpolation, this makes the one weight that should be zero be instead nonzero. This then results in a pixels of constant values manifesting a small blip after linear interpolation if the center of an input pixel exactly coincides with the center of a output pixel (within the agg backend, its pixels may not be exactly aligned with the true pixels).

You can see such a blip in the example above, but it's easy to overlook. Here's a starker example: an array of two pixels with the same value (0.1) is resampled to an array of ten pixels. Before this PR, two of the pixels in the output array have values slightly greater than 0.1.

>>> import numpy as np
>>> from matplotlib.transforms import Affine2D
>>> from matplotlib.image import resample, BILINEAR
>>>
>>> in_data = np.array([[0.1, 0.1]])
>>> in_shape = in_data.shape
>>>
>>> out_shape = (1, 10)
>>>
>>> # Create a simple affine transform for scaling the input array
>>> affine = Affine2D().scale(sx=out_shape[1] / in_shape[1], sy=1)
>>>
>>> affine_data = np.empty(out_shape)
>>> resample(in_data, affine_data, affine, interpolation=BILINEAR)
>>>
>>> print(affine_data)
[[0.1        0.10001221 0.1        0.1        0.1        0.1
  0.10001221 0.1        0.1        0.1       ]]

After this PR, the output is the expected:

>>> print(affine_data)
[[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]]

This killed the log-scale images?

Also this changed a bunch of image tests, but I can't see any difference by eye. Is it worth just relaxing the tolerance on those tests a bit and at the same time adding new tests for these fixes?

This killed the log-scale images?

Indeed, I need to track down why.

Also this changed a bunch of image tests, but I can't see any difference by eye. Is it worth just relaxing the tolerance on those tests a bit and at the same time adding new tests for these fixes?

Yes, the other 17 image tests are mostly subtle differences, but merely relaxing the tolerance doesn't make sense to me. The updated baseline images do represent what the output should be.

Fair enough - however, it would be good if there were some tests devised where it showed a visual effect of these inaccuracies.

I fixed the issue with log_scale_image, so this PR is now ready for review. The final count is 65 baseline images that need to be updated. Maybe a third of those images have (subtle) changes in the rendering of content, e.g.:

Before this PR:

After this PR:

Difference:

On the other hand, most of the updated images have solely very slight changes in the appearance of text. As @jklymak suggested, it might make more sense in those cases to simply increase the tolerance instead for the comparison instead of updating the baseline image, so let me know if I should do that instead.

Are you sure you have the right set of images? lib/matplotlib/tests/baseline_images/test_axes/formatter_ticker_004.png for example doesn't have any images to be resampled.

OK, I see that RendererAgg::draw_text_image uses image_filter_spline36 when rotating text, so it only affects tests with rotated text. So for any tests that are purely rotated text, we should just increase the tolerance and make a note of them, then revert the tolerance changes in #30161 when regenerating the images. Or re-target this PR to the text-overhaul branch without test image changes (but then they'd all be regenerated much later.)

I've updated this PR now that #30824 has been merged

Whew, thanks!