Issues with "Mixing arrays and Python scalars" section

A few issues with the mixing arrays and Python scalars section that came up from testing:

• The matmul operator (@) does not (and should not) support scalar types, so it should be excluded.
• Should boolean arrays be required to work with arithmetic operators. NumPy gives errors in some cases, for example:

>>> import numpy as np
>>> np.array(False) - False
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead. 

• How should casting work? For example, int8 array + large Python int. Should it cast the int, or give an error. Or should this be unspecified.
• The above question also applies for Python float -> float32 casting, which is implicitly supported by the text that is currently there, but is unsafe in general (is there a “safe” variant of float64 -> float32, or is it always unsafe? Either way, this would be value based casting, which I thought we were trying to avoid).

[shoyer]

Unsigned integer overflow is technically undefined behavior in C/C++, and that seems to work out OK. It’s not great, but I think these inconsistencies are mostly an indication that nobody expects Python’s big ints to do something sensible when combined with fixed size arrays.

[shoyer]

Right, we intentionally deviated from NumPy here. There is no notion of separate “scalar” types, and 0d arrays are not treated differently from other arrays.

On Wed, Dec 16, 2020 at 5:29 PM Aaron Meurer [e-mail] wrote:

Yeah, the behavior is the same whether it’s an “array scalar” or “scalar constant” (or whatever NumPy calls them). But we don’t make any such distinction in the spec, and don’t special-case 0-D arrays as far as type promotion is concerned:

Type promotion rules must apply when determining the common result type for two array operands during an arithmetic operation, regardless of array dimension. Accordingly, zero-dimensional arrays must be subject to the same type promotion rules as dimensional arrays.

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