From 101700710dbe5bdfa891d43f1fc14fb6439082ea Mon Sep 17 00:00:00 2001 From: Peter Andreas Entschev Date: Wed, 19 Aug 2020 18:27:23 +0200 Subject: Update doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst Co-authored-by: Matti Picus --- doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) (limited to 'doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst') diff --git a/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst b/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst index 4b6147ca8..d783df5b0 100644 --- a/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst +++ b/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst @@ -24,9 +24,8 @@ Motivation and Scope -------------------- Many libraries implement the NumPy API, such as Dask for graph -computing, CuPy for GPGPU computing, xarray for N-D labeled arrays, etc. All -the libraries mentioned have yet another thing in common: they have also adopted -the ``__array_function__`` protocol; a protocol that allows NumPy to understand +computing, CuPy for GPGPU computing, xarray for N-D labeled arrays, etc. Underneath, +they have adopted the ``__array_function__`` protocol which allows NumPy to understand and treat downstream objects as if they are the native ``numpy.ndarray`` object. Hence the community while using various libraries still benefits from a unified NumPy API. This not only brings great convenience for standardization but also -- cgit v1.2.1