Hi Matthew,
> Just to be clear, you mean you might have something like this?
>
> def my_func('array_name', some_offset):
> arr = load_somehow('array_name') # dtype hitherto unknown
> return arr + some_offset
>
> ? And the problem is that it fails late? Is it really better that
> something bad happens for the addition than that it raises an error?
>
> You'll also often get an error when trying to add structured dtypes,
> but maybe you cant return these from a 'load'?
In this specific case I would like to just use "+" and say "We add
your offset using the NumPy rules," which is a problem if there are no
NumPy rules for addition in the specific case where some_offset
happens to be a scalar and not an array, and also slightly larger than
arr.dtype can hold. I personally prefer upcasting to some reasonable
type big enough to hold some_offset, as I described earlier, although
that's not crucial.
But I think we're getting a little caught up in the details of this
example. My basic point is: yes, people should be careful to check
dtypes, etc. where it's important to their application; but people who
want to rely on some reasonable NumPy-supplied default behavior should
be excused from doing so.
Andrew
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