Ryan May wrote:
> Pierre,
>
> I know you did some preliminary work on helping to make sure that doing
> operations on masked arrays doesn't change the underlying data. I ran into
> the
> following today.
>
> import numpy as np
> a = np.ma.array([1,2,3], mask=[False, True, False])
> b = a * 10
> c = 10 * a
> print b.data # Prints [10 2 30] Good!
> print c.data # Prints [10 10 30] Oops.
>
> I tracked it down to __call__ on the _MaskedBinaryOperation class. If
> there's a
> mask on the data, you use:
>
> result = np.where(m, da, self.f(da, db, *args, **kwargs))
>
> You can see that if a (and hence da) is a scalar, your masked values end up
> with
> the value of the scalar. If this is getting too hairy to handle not touching
> data, I understand. I just thought I should point out the inconsistency here.
Well, I guess I hit send too soon. Here's one easy solution (consistent with
what you did for __radd__), change the code for __rmul__ to do:
return multiply(self, other)
instead of:
return multiply(other, self)
That fixes it for me, and I don't see how it would break anything.
Ryan
--
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma
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