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Less stringent normalization test for float128. #7167

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tacaswell merged 1 commit into from
Sep 24, 2016
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Less stringent normalization test for float128. #7167

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4 changes: 3 additions & 1 deletion lib/matplotlib/tests/test_colors.py
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Expand Up @@ -208,7 +208,9 @@ def test_Normalize():
# and for scalar ones.
eps = np.finfo(np.longdouble).resolution
norm = plt.Normalize(1, 1 + 100 * eps)
assert_equal(norm(1 + 50 * eps), .5)
# 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).
assert 0 < norm(1 + 50 * eps) < 1
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@NelleV NelleV Sep 23, 2016

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You could also use one of the "almost equal" asserts from numpy (or nose, or pytest)

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@anntzer anntzer Sep 23, 2016

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It's not almost equal (it's around 0.2% off).

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@NelleV NelleV Sep 23, 2016

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I don't understand your comment. The tests "assert_almost_equal" has a epsilon value. It is used exactly for that case where there are floating points error. You need to set the epsilon value yourself.

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@anntzer anntzer Sep 23, 2016

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Ah, I was thinking about the default of assert_allclose. Anyways, see the discussion starting at #7159 (comment): the point is that I'd rather let a FP expert confirm what is the tolerance that makes sense; without that the safest is just to check that the normalization is not trivial (does not map everything to 0 or 1).



def test_SymLogNorm():
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