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test_Normalize fails on aarch64/ppc64le/s390x #23707

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QuLogic opened this issue Aug 22, 2022 · 2 comments · Fixed by #23708
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test_Normalize fails on aarch64/ppc64le/s390x #23707

QuLogic opened this issue Aug 22, 2022 · 2 comments · Fixed by #23708
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v3.6.0

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@QuLogic
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QuLogic commented Aug 22, 2022 •
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matplotlib/lib/matplotlib/tests/test_colors.py

Lines 577 to 579 in 4cf54f4

# This returns exactly 0.5 when longdouble is extended precision (80-bit),
# but only a value close to it when it is quadruple precision (128-bit).
np.testing.assert_array_almost_equal_nulp(norm(1 + 50 * eps), 0.5)

This seems to be a bit too strict now, as it is failing on aarch64, ppc64le, and s390x, where the result is 0.50096339.

Also, on my 64-bit AMD system, which is apparently using np.float128 for np.longdouble (though I don't know if that means 80-bit internally), it seems to return 0.5 exactly, which seems the opposite of the comment.

Originally posted by @QuLogic in #21634 (comment)
@QuLogic QuLogic added this to the v3.6.0 milestone Aug 22, 2022
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QuLogic commented Aug 22, 2022

On my (64-bit AMD) system, we have this precision info:

>>> np.finfo(np.longdouble)
finfo(resolution=1e-18, min=-1.189731495357231765e+4932, max=1.189731495357231765e+4932, dtype=float128)

On aarch64, ppc64le, and s390x, it is:

>>> np.finfo(np.longdouble)
finfo(resolution=1e-33, min=-1.189731495357231765085759326628007e+4932, max=1.189731495357231765085759326628007e+4932, dtype=float128)

So I guess that resolution difference explains the comment at least.

But that means the test needs some loosening. However, I'm not sure why the nightly tests precipitated this change, @greglucas.

@QuLogic QuLogic changed the title test_Normalize fails on ppc64le/s390x test_Normalize fails on arch64/ppc64le/s390x Aug 22, 2022
@QuLogic QuLogic changed the title test_Normalize fails on arch64/ppc64le/s390x test_Normalize fails on aarch64/ppc64le/s390x Aug 22, 2022
@greglucas
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I think that the necessary change was above that update and I was updating the area and saw that we were only testing that was between 0 < x < 1 which seemed way too loose to me. So, it seems fine to loosen this back up again if it is causing issues on other platforms.

QuLogic added a commit to QuLogic/matplotlib that referenced this issue Aug 22, 2022
@QuLogic
Loosen up test_Normalize test
e3424d0 
Fixes matplotlib#23707
@QuLogic QuLogic mentioned this issue Aug 22, 2022
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Loosen up test_Normalize test QuLogic/matplotlib
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