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Using np.arange for our coordinates means we don't necessarily get any coordinate at 1:
>>> np.arange(0, 1, .2)
array([ 0. , 0.2, 0.4, 0.6, 0.8])
Given the definition by Ashlock (which defines this as an analytical function on the unit cube) I think it would be better to have coordinates at 0 and 1 (inclusive) with a setting for the granulatiry. Numpy has this:
>>> np.linspace(0, 1, 5)
array([ 0. , 0.25, 0.5 , 0.75, 1. ])
I should have noticed this on the PR.
Just pushing a PR that implements this change, I'm suggesting a change of parameter name there which is a pity given that I've also just pushed a release but perhaps that's ok...
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