On Tue, Apr 12, 2011 at 11:17 AM, Charles R Harris < charlesr.har...@gmail.com> wrote:
> > > On Tue, Apr 12, 2011 at 11:56 AM, Robert Kern <robert.k...@gmail.com>wrote: > >> On Tue, Apr 12, 2011 at 12:27, Charles R Harris >> <charlesr.har...@gmail.com> wrote: >> >> > IIRC, the behavior with respect to scalars sort of happened in the code >> on >> > the fly, so this is a good discussion to have. We should end up with >> > documented rules and tests to enforce them. I agree with Mark that the >> tests >> > have been deficient up to this point. >> >> It's been documented for a long time now. >> >> http://docs.scipy.org/doc/numpy/reference/ufuncs.html#casting-rules >> >> > Nope, the kind stuff is missing. Note the cast to float32 that Mark pointed > out. Also that the casting of python integers depends on their sign and > magnitude. > > In [1]: ones(3, '?') + 0 > Out[1]: array([1, 1, 1], dtype=int8) > > In [2]: ones(3, '?') + 1000 > Out[2]: array([1001, 1001, 1001], dtype=int16) > This is the behaviour with master - it's a good idea to cross-check with an older NumPy. I think we're discussing 3 things here, what NumPy 1.5 and earlier did, what NumPy 1.6 beta currently does, and what people think NumPy did. The old implementation had a bit of a spaghetti-factor to it, and had problems like asymmetry and silent overflows. The new implementation is in my opinion cleaner and follows well-defined semantics while trying to stay true to the old implementation. I admit the documentation I wrote doesn't fully explain them, but here's the rule for a set of arbitrary arrays (not necessarily just 2): - if all the arrays are scalars, do type promotion on the types as is - otherwise, do type promotion on min_scalar_type(a) of each array a The function min_scalar_type returns the array type if a has >= 1 dimensions, or the smallest type of the same kind (allowing int->uint in the case of positive-valued signed integers) to which the value can be cast without overflow if a has 0 dimensions. The promote_types function used for the type promotion is symmetric and associative, so the result won't change when shuffling the inputs. There's a bit of a wrinkle in the implementation to handle the fact that the uint type values aren't a strict subset of the same-sized int type values, but otherwise this is what happens. https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/convert_datatype.c#L1075 The change I'm proposing is to modify this as follows: - if all the arrays are scalars, do type promotion on the types as is - if the maximum kind of all the scalars is > the maximum kind of all the arrays, do type promotion on the types as is - otherwise, do type promotion on min_scalar_type(a) of each array a One case where this may not capture a possible desired semantics is [complex128 scalar] * [float32 array] -> [complex128]. In this case [complex64] may be desired. This is directly analogous to the original [float64 scalar] * [int8 array], however, and in the latter case it's clear a float64 should result. -Mark
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