Gracefully handle 0s when plotting in log scale #563
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Previously the behaviour was to add 1 before taking the log to avoid 0s causing inf's in the output, but in some cases with very small values that gives wildly incorrect results. Now we explicitly only take the log of non-zero values.
zhujun98
reviewed
Jul 29, 2022
| @@ -129,7 +129,7 @@ def toLogScale(arr, policy=None): | |||
| """Convert array result to logarithmic scale.""" | |||
| ret = np.nan_to_num(arr) | |||
| ret[ret < 0] = 0 | |||
| return np.log10(ret + 1) | |||
| return np.log10(ret, where=(ret > 0)) | |||
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I agree that the original solution does not work in some situations, as you have shown. However, there are also corner cases for the new one. It makes 1 and 0 the same. 0 will not be affected while 1 will become 0 after the log operation. Imaging you have a step function f(x) = 1 if x > 0 else 0, with the new solution the curve will become completely flat (all 0). Therefore, a better solution might be letting users choose among different methods.
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Previously the behaviour was to add 1 before taking the log to avoid 0s causing
inf's in the output, but in some cases with very small values that gives wildly
incorrect results. Now we explicitly only take the log of non-zero values.
Before:
After:
