I'm strongly in support of this proposal. Type annotations have really helped me write more correct code.
I started working on numpy type stubs a few months ago. I needed a mypy plugin to support shape-aware functions. Those whole thing is pretty tricky. Still very WIP, but I'll clean them up a little bit and opensource it shortly. -Robert On Sun, Nov 26, 2017 at 1:58 PM, Stephan Hoyer <sho...@gmail.com> wrote: > On Sat, Nov 25, 2017 at 3:34 PM Matthew Rocklin <mrock...@gmail.com> > wrote: > >> Thoughts on basing this on a more generic Array type rather than the >> np.ndarray? I can imagine other nd-array libraries (XArray, Tensorflow, >> Dask.array) wanting to reuse this work. For dask.array in particular we >> would want to copy this entirely, but we probably can't specify that >> dask.arrays are np.ndarrays. It would be nice to ensure that the container >> type was swappable. >> > > Yes, absolutely. I do briefly mention this in my longer doc (see the > "Syntax" section). This is also one of my personal goals for this project. > > This will be most relevant when we start working on typing support for > array shapes and broadcasting: details like data types can be more library > specific, and can probably be expressed with the existing generics system > in the typing module. > > After we do some experimentation to figure out appropriate syntax and > semantics for array shape typing, I would like to standardize the rules for > typing multi-dimensional arrays in Python. This will probably entail > writing a PEP, so we can add appropriate base classes in the typing module. > I view this as the natural complement to existing standard library features > that make it easier to interchange between multiple multi-dimensional array > libraries, such as memory views and the buffer protocol. > >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > > -- -Robert
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