Fix/norm inputs - #693
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@gepcel @munechika-koyo can this be merged? |
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| @pytest.mark.parametrize( | ||
| "norm", | ||
| ["linear", ["linear"], ("linear",)], |
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| ["linear", ["linear"], ("linear",)], | |
| ["linear", "log", ["linear"], ("linear",)], |
What about trying a different norm?
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We could; I am currently just testing against the explicit inputs which are str, list or tuple
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I understand that we conducted the type testing there.
It is probably confirmed somewhere that parsing a string and converting it into the corresponding normalizer is possible, so it might not be necessary here.
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I will check
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I added this test
@pytest.mark.parametrize("norm", [uplt.DiscreteNorm, uplt.colors.mcolors.Normalize])
def test_normalize_types(norm):
data = np.random.rand(10, 10)
target = norm
print(norm)
if norm is uplt.DiscreteNorm:
norm = uplt.DiscreteNorm(levels=[0, 1])
discrete = True
elif norm is uplt.colors.mcolors.Normalize:
norm = uplt.colors.mcolors.Normalize(vmin=0, vmax=1)
discrete = False
else:
raise ValueError("Norm not understood.")
fig, ax = uplt.subplots()
cm = ax.pcolormesh(data, norm=norm, discrete=discrete)
assert isinstance(cm.norm, target)
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Closes #689
When vmin, vmax is given, norms should not be allowed as inputs (as they are part of the norm object). When strings are given, however, we must allow the logic to run.