Filter out invalid value warnings in log scaling #10480
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Perhaps you can just protect the whole function in a single errstate block? |
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efiring
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Feb 20, 2018
| @@ -98,25 +98,26 @@ def __init__(self, nonpos='clip'): | ||
| self._clip = {"clip": True, "mask": False}[nonpos] | ||
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| def transform_non_affine(self, a): | ||
| # Ignore invalid values due to nans being passed to the transform | ||
| with np.errstate(divide="ignore", invalid="ignore"): |
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It's not hurting anything, but I don't think the "divide" kwarg is needed, since the only division is by np.log(self.base).
Wrap all logscaling in ignore invalid values
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Backport PR #10480 on branch v2.2.x
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Currently in
LogTransformBasethe lineout[a <= 0] = -1000will raise a numpy warning if a contains nans. This PR filters out this warning, which seems reasonable since it's also done a few lines earlier, and log-scaling with data that includes nans should probably work without errors or warnings.