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Why can't this code be simplified to: `np.arange(vmin, vmax + base, base)? Floating point errors?
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Sorry… np.arange(self._base.ge(vmin), vmax + base, base) would be the correct solution.
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Your proposal can include a value that's above vmax (eg. vmin=.5, vmax=2.5, base=1 will return [1, 2, 3]). I guess another solution that's safe against floating point errors is
np.arange(self._base.ge(vmin), self._base.le(vmax) + base / 2, base)
(the + base / 2 should take care of any fpe).
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Yep: I am just concern about the tests that don't make sense to me.
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I had a quick look and am pretty sure that the test values are wrong (i.e. they were picked to match the previous algorithm, which returns out of bounds values; they should be changed to match the new algorithm).
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I have done a couple of tests locally, and I have to admit that the current tests seems obscure… |
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I think that this ties back to the other issue with assuming all of the ticks are in view limits (#6647) and what layer of the stack is responsible for the final filtering. |
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When this is cleared up please clean up the figure added in #7084 |
QuLogic
modified the milestones:
2.0.1 (next bug fix release),
2.0.2 (next bug fix release)
tacaswell
modified the milestones:
2.1.1 (next bug fix release),
2.2 (next feature release)
8 participants
This is the fix proposed by @anntzer in #7122 related to a bug in MultipleLocator ticker.