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Diffstat (limited to 'doc/source/user/basics.dispatch.rst')
| -rw-r--r-- | doc/source/user/basics.dispatch.rst | 27 |
1 files changed, 17 insertions, 10 deletions
diff --git a/doc/source/user/basics.dispatch.rst b/doc/source/user/basics.dispatch.rst index 089a7df17..35c73dde4 100644 --- a/doc/source/user/basics.dispatch.rst +++ b/doc/source/user/basics.dispatch.rst @@ -57,7 +57,7 @@ array([[2., 0., 0., 0., 0.], Notice that the return type is a standard ``numpy.ndarray``. >>> type(np.multiply(arr, 2)) -numpy.ndarray +<class 'numpy.ndarray'> How can we pass our custom array type through this function? Numpy allows a class to indicate that it would like to handle computations in a custom-defined @@ -119,7 +119,9 @@ DiagonalArray(N=5, value=0.8414709848078965) At this point ``arr + 3`` does not work. >>> arr + 3 -TypeError: unsupported operand type(s) for *: 'DiagonalArray' and 'int' +Traceback (most recent call last): +... +TypeError: unsupported operand type(s) for +: 'DiagonalArray' and 'int' To support it, we need to define the Python interfaces ``__add__``, ``__lt__``, and so on to dispatch to the corresponding ufunc. We can achieve this @@ -193,14 +195,14 @@ functions to our custom variants. ... return self.__class__(N, ufunc(*scalars, **kwargs)) ... else: ... return NotImplemented -... def __array_function__(self, func, types, args, kwargs): -... if func not in HANDLED_FUNCTIONS: -... return NotImplemented -... # Note: this allows subclasses that don't override -... # __array_function__ to handle DiagonalArray objects. -... if not all(issubclass(t, self.__class__) for t in types): -... return NotImplemented -... return HANDLED_FUNCTIONS[func](*args, **kwargs) +... def __array_function__(self, func, types, args, kwargs): +... if func not in HANDLED_FUNCTIONS: +... return NotImplemented +... # Note: this allows subclasses that don't override +... # __array_function__ to handle DiagonalArray objects. +... if not all(issubclass(t, self.__class__) for t in types): +... return NotImplemented +... return HANDLED_FUNCTIONS[func](*args, **kwargs) ... A convenient pattern is to define a decorator ``implements`` that can be used @@ -241,14 +243,19 @@ this operation is not supported. For example, concatenating two supported. >>> np.concatenate([arr, arr]) +Traceback (most recent call last): +... TypeError: no implementation found for 'numpy.concatenate' on types that implement __array_function__: [<class '__main__.DiagonalArray'>] Additionally, our implementations of ``sum`` and ``mean`` do not accept the optional arguments that numpy's implementation does. >>> np.sum(arr, axis=0) +Traceback (most recent call last): +... TypeError: sum() got an unexpected keyword argument 'axis' + The user always has the option of converting to a normal ``numpy.ndarray`` with :func:`numpy.asarray` and using standard numpy from there. |