JezK
Edit File: _array_object.py
""" Wrapper class around the ndarray object for the array API standard. The array API standard defines some behaviors differently than ndarray, in particular, type promotion rules are different (the standard has no value-based casting). The standard also specifies a more limited subset of array methods and functionalities than are implemented on ndarray. Since the goal of the array_api namespace is to be a minimal implementation of the array API standard, we need to define a separate wrapper class for the array_api namespace. The standard compliant class is only a wrapper class. It is *not* a subclass of ndarray. """ from __future__ import annotations import operator from enum import IntEnum from ._creation_functions import asarray from ._dtypes import ( _all_dtypes, _boolean_dtypes, _integer_dtypes, _integer_or_boolean_dtypes, _floating_dtypes, _numeric_dtypes, _result_type, _dtype_categories, ) from typing import TYPE_CHECKING, Optional, Tuple, Union, Any, SupportsIndex import types if TYPE_CHECKING: from ._typing import Any, PyCapsule, Device, Dtype import numpy.typing as npt import numpy as np from numpy import array_api class Array: """ n-d array object for the array API namespace. See the docstring of :py:obj:`np.ndarray <numpy.ndarray>` for more information. This is a wrapper around numpy.ndarray that restricts the usage to only those things that are required by the array API namespace. Note, attributes on this object that start with a single underscore are not part of the API specification and should only be used internally. This object should not be constructed directly. Rather, use one of the creation functions, such as asarray(). """ _array: np.ndarray # Use a custom constructor instead of __init__, as manually initializing # this class is not supported API. @classmethod def _new(cls, x, /): """ This is a private method for initializing the array API Array object. Functions outside of the array_api submodule should not use this method. Use one of the creation functions instead, such as ``asarray``. """ obj = super().__new__(cls) # Note: The spec does not have array scalars, only 0-D arrays. if isinstance(x, np.generic): # Convert the array scalar to a 0-D array x = np.asarray(x) if x.dtype not in _all_dtypes: raise TypeError( f"The array_api namespace does not support the dtype '{x.dtype}'" ) obj._array = x return obj # Prevent Array() from working def __new__(cls, *args, **kwargs): raise TypeError( "The array_api Array object should not be instantiated directly. Use an array creation function, such as asarray(), instead." ) # These functions are not required by the spec, but are implemented for # the sake of usability. def __str__(self: Array, /) -> str: """ Performs the operation __str__. """ return self._array.__str__().replace("array", "Array") def __repr__(self: Array, /) -> str: """ Performs the operation __repr__. """ suffix = f", dtype={self.dtype.name})" if 0 in self.shape: prefix = "empty(" mid = str(self.shape) else: prefix = "Array(" mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix) return prefix + mid + suffix # This function is not required by the spec, but we implement it here for # convenience so that np.asarray(np.array_api.Array) will work. def __array__(self, dtype: None | np.dtype[Any] = None) -> npt.NDArray[Any]: """ Warning: this method is NOT part of the array API spec. Implementers of other libraries need not include it, and users should not assume it will be present in other implementations. """ return np.asarray(self._array, dtype=dtype) # These are various helper functions to make the array behavior match the # spec in places where it either deviates from or is more strict than # NumPy behavior def _check_allowed_dtypes(self, other: bool | int | float | Array, dtype_category: str, op: str) -> Array: """ Helper function for operators to only allow specific input dtypes Use like other = self._check_allowed_dtypes(other, 'numeric', '__add__') if other is NotImplemented: return other """ if self.dtype not in _dtype_categories[dtype_category]: raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}") if isinstance(other, (int, float, bool)): other = self._promote_scalar(other) elif isinstance(other, Array): if other.dtype not in _dtype_categories[dtype_category]: raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}") else: return NotImplemented # This will raise TypeError for type combinations that are not allowed # to promote in the spec (even if the NumPy array operator would # promote them). res_dtype = _result_type(self.dtype, other.dtype) if op.startswith("__i"): # Note: NumPy will allow in-place operators in some cases where # the type promoted operator does not match the left-hand side # operand. For example, # >>> a = np.array(1, dtype=np.int8) # >>> a += np.array(1, dtype=np.int16) # The spec explicitly disallows this. if res_dtype != self.dtype: raise TypeError( f"Cannot perform {op} with dtypes {self.dtype} and {other.dtype}" ) return other # Helper function to match the type promotion rules in the spec def _promote_scalar(self, scalar): """ Returns a promoted version of a Python scalar appropriate for use with operations on self. This may raise an OverflowError in cases where the scalar is an integer that is too large to fit in a NumPy integer dtype, or TypeError when the scalar type is incompatible with the dtype of self. """ # Note: Only Python scalar types that match the array dtype are # allowed. if isinstance(scalar, bool): if self.dtype not in _boolean_dtypes: raise TypeError( "Python bool scalars can only be promoted with bool arrays" ) elif isinstance(scalar, int): if self.dtype in _boolean_dtypes: raise TypeError( "Python int scalars cannot be promoted with bool arrays" ) elif isinstance(scalar, float): if self.dtype not in _floating_dtypes: raise TypeError( "Python float scalars can only be promoted with floating-point arrays." ) else: raise TypeError("'scalar' must be a Python scalar") # Note: scalars are unconditionally cast to the same dtype as the # array. # Note: the spec only specifies integer-dtype/int promotion # behavior for integers within the bounds of the integer dtype. # Outside of those bounds we use the default NumPy behavior (either # cast or raise OverflowError). return Array._new(np.array(scalar, self.dtype)) @staticmethod def _normalize_two_args(x1, x2) -> Tuple[Array, Array]: """ Normalize inputs to two arg functions to fix type promotion rules NumPy deviates from the spec type promotion rules in cases where one argument is 0-dimensional and the other is not. For example: >>> import numpy as np >>> a = np.array([1.0], dtype=np.float32) >>> b = np.array(1.0, dtype=np.float64) >>> np.add(a, b) # The spec says this should be float64 array([2.], dtype=float32) To fix this, we add a dimension to the 0-dimension array before passing it through. This works because a dimension would be added anyway from broadcasting, so the resulting shape is the same, but this prevents NumPy from not promoting the dtype. """ # Another option would be to use signature=(x1.dtype, x2.dtype, None), # but that only works for ufuncs, so we would have to call the ufuncs # directly in the operator methods. One should also note that this # sort of trick wouldn't work for functions like searchsorted, which # don't do normal broadcasting, but there aren't any functions like # that in the array API namespace. if x1.ndim == 0 and x2.ndim != 0: # The _array[None] workaround was chosen because it is relatively # performant. broadcast_to(x1._array, x2.shape) is much slower. We # could also manually type promote x2, but that is more complicated # and about the same performance as this. x1 = Array._new(x1._array[None]) elif x2.ndim == 0 and x1.ndim != 0: x2 = Array._new(x2._array[None]) return (x1, x2) # Note: A large fraction of allowed indices are disallowed here (see the # docstring below) def _validate_index(self, key): """ Validate an index according to the array API. The array API specification only requires a subset of indices that are supported by NumPy. This function will reject any index that is allowed by NumPy but not required by the array API specification. We always raise ``IndexError`` on such indices (the spec does not require any specific behavior on them, but this makes the NumPy array API namespace a minimal implementation of the spec). See https://data-apis.org/array-api/latest/API_specification/indexing.html for the full list of required indexing behavior This function raises IndexError if the index ``key`` is invalid. It only raises ``IndexError`` on indices that are not already rejected by NumPy, as NumPy will already raise the appropriate error on such indices. ``shape`` may be None, in which case, only cases that are independent of the array shape are checked. The following cases are allowed by NumPy, but not specified by the array API specification: - Indices to not include an implicit ellipsis at the end. That is, every axis of an array must be explicitly indexed or an ellipsis included. This behaviour is sometimes referred to as flat indexing. - The start and stop of a slice may not be out of bounds. In particular, for a slice ``i:j:k`` on an axis of size ``n``, only the following are allowed: - ``i`` or ``j`` omitted (``None``). - ``-n <= i <= max(0, n - 1)``. - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``. - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``. - Boolean array indices are not allowed as part of a larger tuple index. - Integer array indices are not allowed (with the exception of 0-D arrays, which are treated the same as scalars). Additionally, it should be noted that indices that would return a scalar in NumPy will return a 0-D array. Array scalars are not allowed in the specification, only 0-D arrays. This is done in the ``Array._new`` constructor, not this function. """ _key = key if isinstance(key, tuple) else (key,) for i in _key: if isinstance(i, bool) or not ( isinstance(i, SupportsIndex) # i.e. ints or isinstance(i, slice) or i == Ellipsis or i is None or isinstance(i, Array) or isinstance(i, np.ndarray) ): raise IndexError( f"Single-axes index {i} has {type(i)=}, but only " "integers, slices (:), ellipsis (...), newaxis (None), " "zero-dimensional integer arrays and boolean arrays " "are specified in the Array API." ) nonexpanding_key = [] single_axes = [] n_ellipsis = 0 key_has_mask = False for i in _key: if i is not None: nonexpanding_key.append(i) if isinstance(i, Array) or isinstance(i, np.ndarray): if i.dtype in _boolean_dtypes: key_has_mask = True single_axes.append(i) else: # i must not be an array here, to avoid elementwise equals if i == Ellipsis: n_ellipsis += 1 else: single_axes.append(i) n_single_axes = len(single_axes) if n_ellipsis > 1: return # handled by ndarray elif n_ellipsis == 0: # Note boolean masks must be the sole index, which we check for # later on. if not key_has_mask and n_single_axes < self.ndim: raise IndexError( f"{self.ndim=}, but the multi-axes index only specifies " f"{n_single_axes} dimensions. If this was intentional, " "add a trailing ellipsis (...) which expands into as many " "slices (:) as necessary - this is what np.ndarray arrays " "implicitly do, but such flat indexing behaviour is not " "specified in the Array API." ) if n_ellipsis == 0: indexed_shape = self.shape else: ellipsis_start = None for pos, i in enumerate(nonexpanding_key): if not (isinstance(i, Array) or isinstance(i, np.ndarray)): if i == Ellipsis: ellipsis_start = pos break assert ellipsis_start is not None # sanity check ellipsis_end = self.ndim - (n_single_axes - ellipsis_start) indexed_shape = ( self.shape[:ellipsis_start] + self.shape[ellipsis_end:] ) for i, side in zip(single_axes, indexed_shape): if isinstance(i, slice): if side == 0: f_range = "0 (or None)" else: f_range = f"between -{side} and {side - 1} (or None)" if i.start is not None: try: start = operator.index(i.start) except TypeError: pass # handled by ndarray else: if not (-side <= start <= side): raise IndexError( f"Slice {i} contains {start=}, but should be " f"{f_range} for an axis of size {side} " "(out-of-bounds starts are not specified in " "the Array API)" ) if i.stop is not None: try: stop = operator.index(i.stop) except TypeError: pass # handled by ndarray else: if not (-side <= stop <= side): raise IndexError( f"Slice {i} contains {stop=}, but should be " f"{f_range} for an axis of size {side} " "(out-of-bounds stops are not specified in " "the Array API)" ) elif isinstance(i, Array): if i.dtype in _boolean_dtypes and len(_key) != 1: assert isinstance(key, tuple) # sanity check raise IndexError( f"Single-axes index {i} is a boolean array and " f"{len(key)=}, but masking is only specified in the " "Array API when the array is the sole index." ) elif i.dtype in _integer_dtypes and i.ndim != 0: raise IndexError( f"Single-axes index {i} is a non-zero-dimensional " "integer array, but advanced integer indexing is not " "specified in the Array API." ) elif isinstance(i, tuple): raise IndexError( f"Single-axes index {i} is a tuple, but nested tuple " "indices are not specified in the Array API." ) # Everything below this line is required by the spec. def __abs__(self: Array, /) -> Array: """ Performs the operation __abs__. """ if self.dtype not in _numeric_dtypes: raise TypeError("Only numeric dtypes are allowed in __abs__") res = self._array.__abs__() return self.__class__._new(res) def __add__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __add__. """ other = self._check_allowed_dtypes(other, "numeric", "__add__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__add__(other._array) return self.__class__._new(res) def __and__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __and__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__and__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__and__(other._array) return self.__class__._new(res) def __array_namespace__( self: Array, /, *, api_version: Optional[str] = None ) -> types.ModuleType: if api_version is not None and not api_version.startswith("2021."): raise ValueError(f"Unrecognized array API version: {api_version!r}") return array_api def __bool__(self: Array, /) -> bool: """ Performs the operation __bool__. """ # Note: This is an error here. if self._array.ndim != 0: raise TypeError("bool is only allowed on arrays with 0 dimensions") if self.dtype not in _boolean_dtypes: raise ValueError("bool is only allowed on boolean arrays") res = self._array.__bool__() return res def __dlpack__(self: Array, /, *, stream: None = None) -> PyCapsule: """ Performs the operation __dlpack__. """ return self._array.__dlpack__(stream=stream) def __dlpack_device__(self: Array, /) -> Tuple[IntEnum, int]: """ Performs the operation __dlpack_device__. """ # Note: device support is required for this return self._array.__dlpack_device__() def __eq__(self: Array, other: Union[int, float, bool, Array], /) -> Array: """ Performs the operation __eq__. """ # Even though "all" dtypes are allowed, we still require them to be # promotable with each other. other = self._check_allowed_dtypes(other, "all", "__eq__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__eq__(other._array) return self.__class__._new(res) def __float__(self: Array, /) -> float: """ Performs the operation __float__. """ # Note: This is an error here. if self._array.ndim != 0: raise TypeError("float is only allowed on arrays with 0 dimensions") if self.dtype not in _floating_dtypes: raise ValueError("float is only allowed on floating-point arrays") res = self._array.__float__() return res def __floordiv__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __floordiv__. """ other = self._check_allowed_dtypes(other, "numeric", "__floordiv__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__floordiv__(other._array) return self.__class__._new(res) def __ge__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __ge__. """ other = self._check_allowed_dtypes(other, "numeric", "__ge__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__ge__(other._array) return self.__class__._new(res) def __getitem__( self: Array, key: Union[ int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array ], /, ) -> Array: """ Performs the operation __getitem__. """ # Note: Only indices required by the spec are allowed. See the # docstring of _validate_index self._validate_index(key) if isinstance(key, Array): # Indexing self._array with array_api arrays can be erroneous key = key._array res = self._array.__getitem__(key) return self._new(res) def __gt__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __gt__. """ other = self._check_allowed_dtypes(other, "numeric", "__gt__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__gt__(other._array) return self.__class__._new(res) def __int__(self: Array, /) -> int: """ Performs the operation __int__. """ # Note: This is an error here. if self._array.ndim != 0: raise TypeError("int is only allowed on arrays with 0 dimensions") if self.dtype not in _integer_dtypes: raise ValueError("int is only allowed on integer arrays") res = self._array.__int__() return res def __index__(self: Array, /) -> int: """ Performs the operation __index__. """ res = self._array.__index__() return res def __invert__(self: Array, /) -> Array: """ Performs the operation __invert__. """ if self.dtype not in _integer_or_boolean_dtypes: raise TypeError("Only integer or boolean dtypes are allowed in __invert__") res = self._array.__invert__() return self.__class__._new(res) def __le__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __le__. """ other = self._check_allowed_dtypes(other, "numeric", "__le__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__le__(other._array) return self.__class__._new(res) def __lshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __lshift__. """ other = self._check_allowed_dtypes(other, "integer", "__lshift__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__lshift__(other._array) return self.__class__._new(res) def __lt__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __lt__. """ other = self._check_allowed_dtypes(other, "numeric", "__lt__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__lt__(other._array) return self.__class__._new(res) def __matmul__(self: Array, other: Array, /) -> Array: """ Performs the operation __matmul__. """ # matmul is not defined for scalars, but without this, we may get # the wrong error message from asarray. other = self._check_allowed_dtypes(other, "numeric", "__matmul__") if other is NotImplemented: return other res = self._array.__matmul__(other._array) return self.__class__._new(res) def __mod__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __mod__. """ other = self._check_allowed_dtypes(other, "numeric", "__mod__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__mod__(other._array) return self.__class__._new(res) def __mul__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __mul__. """ other = self._check_allowed_dtypes(other, "numeric", "__mul__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__mul__(other._array) return self.__class__._new(res) def __ne__(self: Array, other: Union[int, float, bool, Array], /) -> Array: """ Performs the operation __ne__. """ other = self._check_allowed_dtypes(other, "all", "__ne__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__ne__(other._array) return self.__class__._new(res) def __neg__(self: Array, /) -> Array: """ Performs the operation __neg__. """ if self.dtype not in _numeric_dtypes: raise TypeError("Only numeric dtypes are allowed in __neg__") res = self._array.__neg__() return self.__class__._new(res) def __or__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __or__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__or__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__or__(other._array) return self.__class__._new(res) def __pos__(self: Array, /) -> Array: """ Performs the operation __pos__. """ if self.dtype not in _numeric_dtypes: raise TypeError("Only numeric dtypes are allowed in __pos__") res = self._array.__pos__() return self.__class__._new(res) def __pow__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __pow__. """ from ._elementwise_functions import pow other = self._check_allowed_dtypes(other, "numeric", "__pow__") if other is NotImplemented: return other # Note: NumPy's __pow__ does not follow type promotion rules for 0-d # arrays, so we use pow() here instead. return pow(self, other) def __rshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __rshift__. """ other = self._check_allowed_dtypes(other, "integer", "__rshift__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rshift__(other._array) return self.__class__._new(res) def __setitem__( self, key: Union[ int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array ], value: Union[int, float, bool, Array], /, ) -> None: """ Performs the operation __setitem__. """ # Note: Only indices required by the spec are allowed. See the # docstring of _validate_index self._validate_index(key) if isinstance(key, Array): # Indexing self._array with array_api arrays can be erroneous key = key._array self._array.__setitem__(key, asarray(value)._array) def __sub__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __sub__. """ other = self._check_allowed_dtypes(other, "numeric", "__sub__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__sub__(other._array) return self.__class__._new(res) # PEP 484 requires int to be a subtype of float, but __truediv__ should # not accept int. def __truediv__(self: Array, other: Union[float, Array], /) -> Array: """ Performs the operation __truediv__. """ other = self._check_allowed_dtypes(other, "floating-point", "__truediv__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__truediv__(other._array) return self.__class__._new(res) def __xor__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __xor__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__xor__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__xor__(other._array) return self.__class__._new(res) def __iadd__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __iadd__. """ other = self._check_allowed_dtypes(other, "numeric", "__iadd__") if other is NotImplemented: return other self._array.__iadd__(other._array) return self def __radd__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __radd__. """ other = self._check_allowed_dtypes(other, "numeric", "__radd__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__radd__(other._array) return self.__class__._new(res) def __iand__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __iand__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__iand__") if other is NotImplemented: return other self._array.__iand__(other._array) return self def __rand__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __rand__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__rand__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rand__(other._array) return self.__class__._new(res) def __ifloordiv__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __ifloordiv__. """ other = self._check_allowed_dtypes(other, "numeric", "__ifloordiv__") if other is NotImplemented: return other self._array.__ifloordiv__(other._array) return self def __rfloordiv__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __rfloordiv__. """ other = self._check_allowed_dtypes(other, "numeric", "__rfloordiv__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rfloordiv__(other._array) return self.__class__._new(res) def __ilshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __ilshift__. """ other = self._check_allowed_dtypes(other, "integer", "__ilshift__") if other is NotImplemented: return other self._array.__ilshift__(other._array) return self def __rlshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __rlshift__. """ other = self._check_allowed_dtypes(other, "integer", "__rlshift__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rlshift__(other._array) return self.__class__._new(res) def __imatmul__(self: Array, other: Array, /) -> Array: """ Performs the operation __imatmul__. """ # Note: NumPy does not implement __imatmul__. # matmul is not defined for scalars, but without this, we may get # the wrong error message from asarray. other = self._check_allowed_dtypes(other, "numeric", "__imatmul__") if other is NotImplemented: return other # __imatmul__ can only be allowed when it would not change the shape # of self. other_shape = other.shape if self.shape == () or other_shape == (): raise ValueError("@= requires at least one dimension") if len(other_shape) == 1 or other_shape[-1] != other_shape[-2]: raise ValueError("@= cannot change the shape of the input array") self._array[:] = self._array.__matmul__(other._array) return self def __rmatmul__(self: Array, other: Array, /) -> Array: """ Performs the operation __rmatmul__. """ # matmul is not defined for scalars, but without this, we may get # the wrong error message from asarray. other = self._check_allowed_dtypes(other, "numeric", "__rmatmul__") if other is NotImplemented: return other res = self._array.__rmatmul__(other._array) return self.__class__._new(res) def __imod__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __imod__. """ other = self._check_allowed_dtypes(other, "numeric", "__imod__") if other is NotImplemented: return other self._array.__imod__(other._array) return self def __rmod__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __rmod__. """ other = self._check_allowed_dtypes(other, "numeric", "__rmod__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rmod__(other._array) return self.__class__._new(res) def __imul__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __imul__. """ other = self._check_allowed_dtypes(other, "numeric", "__imul__") if other is NotImplemented: return other self._array.__imul__(other._array) return self def __rmul__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __rmul__. """ other = self._check_allowed_dtypes(other, "numeric", "__rmul__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rmul__(other._array) return self.__class__._new(res) def __ior__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __ior__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__ior__") if other is NotImplemented: return other self._array.__ior__(other._array) return self def __ror__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __ror__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__ror__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__ror__(other._array) return self.__class__._new(res) def __ipow__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __ipow__. """ other = self._check_allowed_dtypes(other, "numeric", "__ipow__") if other is NotImplemented: return other self._array.__ipow__(other._array) return self def __rpow__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __rpow__. """ from ._elementwise_functions import pow other = self._check_allowed_dtypes(other, "numeric", "__rpow__") if other is NotImplemented: return other # Note: NumPy's __pow__ does not follow the spec type promotion rules # for 0-d arrays, so we use pow() here instead. return pow(other, self) def __irshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __irshift__. """ other = self._check_allowed_dtypes(other, "integer", "__irshift__") if other is NotImplemented: return other self._array.__irshift__(other._array) return self def __rrshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __rrshift__. """ other = self._check_allowed_dtypes(other, "integer", "__rrshift__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rrshift__(other._array) return self.__class__._new(res) def __isub__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __isub__. """ other = self._check_allowed_dtypes(other, "numeric", "__isub__") if other is NotImplemented: return other self._array.__isub__(other._array) return self def __rsub__(self: Array, other: Union[int, float, Array], /) -> Array: """ Performs the operation __rsub__. """ other = self._check_allowed_dtypes(other, "numeric", "__rsub__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rsub__(other._array) return self.__class__._new(res) def __itruediv__(self: Array, other: Union[float, Array], /) -> Array: """ Performs the operation __itruediv__. """ other = self._check_allowed_dtypes(other, "floating-point", "__itruediv__") if other is NotImplemented: return other self._array.__itruediv__(other._array) return self def __rtruediv__(self: Array, other: Union[float, Array], /) -> Array: """ Performs the operation __rtruediv__. """ other = self._check_allowed_dtypes(other, "floating-point", "__rtruediv__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rtruediv__(other._array) return self.__class__._new(res) def __ixor__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __ixor__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__ixor__") if other is NotImplemented: return other self._array.__ixor__(other._array) return self def __rxor__(self: Array, other: Union[int, bool, Array], /) -> Array: """ Performs the operation __rxor__. """ other = self._check_allowed_dtypes(other, "integer or boolean", "__rxor__") if other is NotImplemented: return other self, other = self._normalize_two_args(self, other) res = self._array.__rxor__(other._array) return self.__class__._new(res) def to_device(self: Array, device: Device, /, stream: None = None) -> Array: if stream is not None: raise ValueError("The stream argument to to_device() is not supported") if device == 'cpu': return self raise ValueError(f"Unsupported device {device!r}") @property def dtype(self) -> Dtype: """ Array API compatible wrapper for :py:meth:`np.ndarray.dtype <numpy.ndarray.dtype>`. See its docstring for more information. """ return self._array.dtype @property def device(self) -> Device: return "cpu" # Note: mT is new in array API spec (see matrix_transpose) @property def mT(self) -> Array: from .linalg import matrix_transpose return matrix_transpose(self) @property def ndim(self) -> int: """ Array API compatible wrapper for :py:meth:`np.ndarray.ndim <numpy.ndarray.ndim>`. See its docstring for more information. """ return self._array.ndim @property def shape(self) -> Tuple[int, ...]: """ Array API compatible wrapper for :py:meth:`np.ndarray.shape <numpy.ndarray.shape>`. See its docstring for more information. """ return self._array.shape @property def size(self) -> int: """ Array API compatible wrapper for :py:meth:`np.ndarray.size <numpy.ndarray.size>`. See its docstring for more information. """ return self._array.size @property def T(self) -> Array: """ Array API compatible wrapper for :py:meth:`np.ndarray.T <numpy.ndarray.T>`. See its docstring for more information. """ # Note: T only works on 2-dimensional arrays. See the corresponding # note in the specification: # https://data-apis.org/array-api/latest/API_specification/array_object.html#t if self.ndim != 2: raise ValueError("x.T requires x to have 2 dimensions. Use x.mT to transpose stacks of matrices and permute_dims() to permute dimensions.") return self.__class__._new(self._array.T)