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Handle ImageNormalize cases when vmin >= vmax #15622

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merged 3 commits into from Jan 18, 2024

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larrybradley
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@larrybradley larrybradley commented Nov 15, 2023

Description

With this PR, if vmin == vmax, the ImageNormalize class now maps the input
data to 0. That is what the matplotlib Normalize class does. Currently a divide-by-zero RuntimeWarning is issued and the output data are (+/-) np.inf.

Also if vmin > vmax, the ImageNormalize class now raises a ValueError instead of returning invalid results. This also matches the matplotlib Normalize behavior.

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@larrybradley larrybradley added visualization API change PRs and issues that change an existing API, possibly requiring a deprecation period labels Nov 15, 2023
@larrybradley larrybradley added this to the v6.1 milestone Nov 15, 2023
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@larrybradley
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ping @astrofrog

@pllim
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pllim commented Jan 18, 2024

Can you please rebase so I can see if #15837 really works? Thanks!

@larrybradley
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Rebased

def test_vmin_vmax_equal(self):
norm = ImageNormalize(vmin=2.0, vmax=2.0)
data = np.arange(10) - 5.0
assert_equal(norm(data), np.zeros(data.shape))
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@pllim pllim Jan 18, 2024

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This would do smart broadcasting (I think).

Suggested change
assert_equal(norm(data), np.zeros(data.shape))
assert_array_equal(norm(data), 0)

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Updated, thanks.

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LGTM. Just a small nitpick on test comparison. I'll leave it to you if you want to pursue or not. Feel free to merge when you are ready. Thanks!

@larrybradley larrybradley merged commit 0b50fdd into astropy:main Jan 18, 2024
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@larrybradley larrybradley deleted the norm-vmin-vmax branch January 18, 2024 18:51
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2 participants