ak.typetracer.TypeTracerArray

Defined in awkward._nplikes.typetracer on line 248.

class ak.typetracer.TypeTracerArray

Mixin defining all operator special methods using __array_ufunc__.

This class implements the special methods for almost all of Python’s builtin operators defined in the operator module, including comparisons (``==``, ``>``, etc.) and arithmetic (``+``, ``*``, ``-``, etc.), by deferring to the ``__array_ufunc__`` method, which subclasses must implement.

It is useful for writing classes that do not inherit from numpy.ndarray, but that should support arithmetic and numpy universal functions like arrays as described in ``A Mechanism for Overriding Ufuncs <https://numpy.org/neps/nep-0013-ufunc-overrides.html>``_.

As an trivial example, consider this implementation of an ``ArrayLike`` class that simply wraps a NumPy array and ensures that the result of any arithmetic operation is also an ``ArrayLike`` object:

class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
    def __init__(self, value):
        self.value = np.asarray(value)

    # One might also consider adding the built-in list type to this
    # list, to support operations like np.add(array_like, list)
    _HANDLED_TYPES = (np.ndarray, numbers.Number)

    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
        out = kwargs.get('out', ())
        for x in inputs + out:
            # Only support operations with instances of _HANDLED_TYPES.
            # Use ArrayLike instead of type(self) for isinstance to
            # allow subclasses that don't override __array_ufunc__ to
            # handle ArrayLike objects.
            if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
                return NotImplemented

        # Defer to the implementation of the ufunc on unwrapped values.
        inputs = tuple(x.value if isinstance(x, ArrayLike) else x
                       for x in inputs)
        if out:
            kwargs['out'] = tuple(
                x.value if isinstance(x, ArrayLike) else x
                for x in out)
        result = getattr(ufunc, method)(*inputs, **kwargs)

        if type(result) is tuple:
            # multiple return values
            return tuple(type(self)(x) for x in result)
        elif method == 'at':
            # no return value
            return None
        else:
            # one return value
            return type(self)(result)

    def __repr__(self):
        return '%s(%r)' % (type(self).__name__, self.value)

In interactions between ``ArrayLike`` objects and numbers or numpy arrays, the result is always another ``ArrayLike``:

>>> x = ArrayLike([1, 2, 3])
>>> x - 1
ArrayLike(array([0, 1, 2]))
>>> 1 - x
ArrayLike(array([ 0, -1, -2]))
>>> np.arange(3) - x
ArrayLike(array([-1, -1, -1]))
>>> x - np.arange(3)
ArrayLike(array([1, 1, 1]))

Note that unlike ``numpy.ndarray``, ``ArrayLike`` does not allow operations with arbitrary, unrecognized types. This ensures that interactions with ArrayLike preserve a well-defined casting hierarchy.

Added in version 1.13.

_dtype: numpy.dtype

_shape: tuple[awkward._nplikes.shape.ShapeItem, Ellipsis]

runtime_typechecks = True

classmethod _new(dtype: awkward._typing.DType, shape: tuple[awkward._nplikes.shape.ShapeItem, Ellipsis], form_key: str | None = None, report: TypeTracerReport | None = None)

property T: awkward._typing.Self

property dtype: awkward._typing.DType

property size: awkward._nplikes.shape.ShapeItem

property shape: tuple[awkward._nplikes.shape.ShapeItem, Ellipsis]

property strides

property inner_shape: tuple[awkward._nplikes.shape.ShapeItem, Ellipsis]

property form_key: str | None

property report: TypeTracerReport | None

touch_shape()

touch_data()

property nplike: TypeTracer

property ndim: int

property nbytes: awkward._nplikes.shape.ShapeItem

view(dtype: numpy.typing.DTypeLike) → awkward._typing.Self

forget_length() → awkward._typing.Self

property ctypes

copy()

tolist() → list

Classes

_CTypes