On Tue, Apr 12, 2011 at 11:17 AM, Charles R Harris wrote:

On Tue, Apr 12, 2011 at 11:56 AM, Robert Kern wrote:
On Tue, Apr 12, 2011 at 12:27, Charles R Harris
<charlesr.harris@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