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authorDan Allan <dallan@bnl.gov>2019-07-14 10:03:40 -0500
committerDan Allan <dallan@bnl.gov>2019-07-14 10:04:06 -0500
commit54911b6365bd4931055cc134dd10c9b0639d07dc (patch)
tree4259f191bd718e2df73fc2bb26e7866d9870ab8f
parentd96618f25db01d814a2159f740cc36e1f355d9d8 (diff)
downloadnumpy-54911b6365bd4931055cc134dd10c9b0639d07dc.tar.gz
Copy editing
-rw-r--r--doc/source/reference/arrays.classes.rst6
-rw-r--r--numpy/doc/dispatch.py5
2 files changed, 5 insertions, 6 deletions
diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst
index addc403b8..a91215476 100644
--- a/doc/source/reference/arrays.classes.rst
+++ b/doc/source/reference/arrays.classes.rst
@@ -169,7 +169,7 @@ NumPy provides several hooks that classes can customize:
- ``func`` is an arbitrary callable exposed by NumPy's public API,
which was called in the form ``func(*args, **kwargs)``.
- - ``types`` is a `collection <https://docs.python.org/3/library/collections.abc.html#collections.abc.Collection>`_
+ - ``types`` is a `collection <collections.abc.Collection>`_
of unique argument types from the original NumPy function call that
implement ``__array_function__``.
- The tuple ``args`` and dict ``kwargs`` are directly passed on from the
@@ -179,7 +179,7 @@ NumPy provides several hooks that classes can customize:
provides all argument types with an ``'__array_function__'`` attribute.
This allows implementors to quickly identify cases where they should defer
to ``__array_function__`` implementations on other arguments.
- Implementaitons should not rely on the iteration order of ``types``.
+ Implementations should not rely on the iteration order of ``types``.
Most implementations of ``__array_function__`` will start with two
checks:
@@ -255,7 +255,7 @@ NumPy provides several hooks that classes can customize:
- If all ``__array_function__`` methods return ``NotImplemented``,
NumPy will raise ``TypeError``.
- If no ``__array_function__`` methods exist, NumPy will default to calling
+ If no ``__array_function__`` methods exists, NumPy will default to calling
its own implementation, intended for use on NumPy arrays. This case arises,
for example, when all array-like arguments are Python numbers or lists.
(NumPy arrays do have a ``__array_function__`` method, given below, but it
diff --git a/numpy/doc/dispatch.py b/numpy/doc/dispatch.py
index f592d4ffd..313167005 100644
--- a/numpy/doc/dispatch.py
+++ b/numpy/doc/dispatch.py
@@ -214,6 +214,8 @@ to add functions to ``HANDLED_FUNCTIONS``.
...
Now we write implementations of numpy functions for ``DiagonalArray``.
+For completeness, to support the usage ``arr.sum()`` add a method ``sum`` that
+calls ``numpy.sum(self)``, and the same for ``mean``.
>>> @implements(np.sum)
... def sum(a, axis=None, out=None):
@@ -233,9 +235,6 @@ Now we write implementations of numpy functions for ``DiagonalArray``.
>>> np.mean(arr)
0.2
-For completeness, to support the usage ``arr.sum()`` add a method ``sum`` that
-calls ``numpy.sum(self)``, and the same for ``mean``.
-
If the user tries to use any numpy functions not included in
``HANDLED_FUNCTIONS``, a ``TypeError`` will be raised by numpy, indicating that
this operation is not supported. For example, concatenating two