On 1/6/19 7:31 pm, Charles R Harris wrote:
I generally agree with this. The most useful aspect of this exercise
is likely to be clarifying NumPy for its own developers, and maybe
offering a guide to future simplification. Trying to put something
together that everyone agrees to as an official standard would be a
big project and, as Nathaniel points out, would involve an enormous
amount of work, much time, and doubtless many arguments. What might
be a less ambitious exercise would be identifying commonalities in the
current numpy-like languages. That would have the advantage of
feedback from actual user experience, and would be more like a lessons
learned document that would be helpful to others.
More concretely, to address Nathaniel's (very reasonable) worry
about ending up wasting a lot of time, I think it may be good to
identify smaller parts, each of which are useful on their own.
In this respect, I think an excellent place to start might be
something you are planning already anyway: update the user
documentation
I would include tests as well. Rather than hammer out a full standard
based on extensive discussions and negotiations, I would suggest NumPy
might be able set a de-facto "standard" based on pieces of the the
current numpy user documentation and test suite. Then other projects
could use "passing the tests" as an indication that they implement the
NumPy API, and could refer to the documentation where appropriate. Once
we have a base repo under numpy with tests and documentations for the
generally accepted baseline interfaces. we can discuss on a case-by-case
basis via pull requests and issues whether other interfaces should be
included. If we find general classes of similarity that can be concisely
described but not all duckarray packages support (structured arrays, for
instance), these could become test-specifiers `@pytest.skipif(not
HAVE_STRUCTURED_ARRAYS)`, the tests and documentation would only apply
if that specifier exists.
Matti
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