""" ============================ Typing (:mod:`numpy.typing`) ============================ .. versionadded:: 1.20 Large parts of the NumPy API have PEP-484-style type annotations. In addition a number of type aliases are available to users, most prominently the two below: - `ArrayLike`: objects that can be converted to arrays - `DTypeLike`: objects that can be converted to dtypes .. _typing-extensions: https://pypi.org/project/typing-extensions/ Mypy plugin ----------- .. versionadded:: 1.21 .. automodule:: numpy.typing.mypy_plugin .. currentmodule:: numpy.typing Differences from the runtime NumPy API -------------------------------------- NumPy is very flexible. Trying to describe the full range of possibilities statically would result in types that are not very helpful. For that reason, the typed NumPy API is often stricter than the runtime NumPy API. This section describes some notable differences. ArrayLike ~~~~~~~~~ The `ArrayLike` type tries to avoid creating object arrays. For example, .. code-block:: python >>> np.array(x**2 for x in range(10)) array( at ...>, dtype=object) is valid NumPy code which will create a 0-dimensional object array. Type checkers will complain about the above example when using the NumPy types however. If you really intended to do the above, then you can either use a ``# type: ignore`` comment: .. code-block:: python >>> np.array(x**2 for x in range(10)) # type: ignore or explicitly type the array like object as `~typing.Any`: .. code-block:: python >>> from typing import Any >>> array_like: Any = (x**2 for x in range(10)) >>> np.array(array_like) array( at ...>, dtype=object) ndarray ~~~~~~~ It's possible to mutate the dtype of an array at runtime. For example, the following code is valid: .. code-block:: python >>> x = np.array([1, 2]) >>> x.dtype = np.bool_ This sort of mutation is not allowed by the types. Users who want to write statically typed code should instead use the `numpy.ndarray.view` method to create a view of the array with a different dtype. DTypeLike ~~~~~~~~~ The `DTypeLike` type tries to avoid creation of dtype objects using dictionary of fields like below: .. code-block:: python >>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)}) Although this is valid NumPy code, the type checker will complain about it, since its usage is discouraged. Please see : :ref:`Data type objects ` Number precision ~~~~~~~~~~~~~~~~ The precision of `numpy.number` subclasses is treated as a covariant generic parameter (see :class:`~NBitBase`), simplifying the annotating of processes involving precision-based casting. .. code-block:: python >>> from typing import TypeVar >>> import numpy as np >>> import numpy.typing as npt >>> T = TypeVar("T", bound=npt.NBitBase) >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]": ... ... Consequently, the likes of `~numpy.float16`, `~numpy.float32` and `~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to runtime, they're not necessarily considered as sub-classes. Timedelta64 ~~~~~~~~~~~ The `~numpy.timedelta64` class is not considered a subclass of `~numpy.signedinteger`, the former only inheriting from `~numpy.generic` while static type checking. 0D arrays ~~~~~~~~~ During runtime numpy aggressively casts any passed 0D arrays into their corresponding `~numpy.generic` instance. Until the introduction of shape typing (see :pep:`646`) it is unfortunately not possible to make the necessary distinction between 0D and >0D arrays. While thus not strictly correct, all operations are that can potentially perform a 0D-array -> scalar cast are currently annotated as exclusively returning an `ndarray`. If it is known in advance that an operation _will_ perform a 0D-array -> scalar cast, then one can consider manually remedying the situation with either `typing.cast` or a ``# type: ignore`` comment. Record array dtypes ~~~~~~~~~~~~~~~~~~~ The dtype of `numpy.recarray`, and the `numpy.rec` functions in general, can be specified in one of two ways: * Directly via the ``dtype`` argument. * With up to five helper arguments that operate via `numpy.format_parser`: ``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``. These two approaches are currently typed as being mutually exclusive, *i.e.* if ``dtype`` is specified than one may not specify ``formats``. While this mutual exclusivity is not (strictly) enforced during runtime, combining both dtype specifiers can lead to unexpected or even downright buggy behavior. API --- """ # NOTE: The API section will be appended with additional entries # further down in this file from __future__ import annotations from numpy import ufunc from typing import TYPE_CHECKING, final if not TYPE_CHECKING: __all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"] else: # Ensure that all objects within this module are accessible while # static type checking. This includes private ones, as we need them # for internal use. # # Declare to mypy that `__all__` is a list of strings without assigning # an explicit value __all__: list[str] __path__: list[str] @final # Disallow the creation of arbitrary `NBitBase` subclasses class NBitBase: """ A type representing `numpy.number` precision during static type checking. Used exclusively for the purpose static type checking, `NBitBase` represents the base of a hierarchical set of subclasses. Each subsequent subclass is herein used for representing a lower level of precision, *e.g.* ``64Bit > 32Bit > 16Bit``. .. versionadded:: 1.20 Examples -------- Below is a typical usage example: `NBitBase` is herein used for annotating a function that takes a float and integer of arbitrary precision as arguments and returns a new float of whichever precision is largest (*e.g.* ``np.float16 + np.int64 -> np.float64``). .. code-block:: python >>> from __future__ import annotations >>> from typing import TypeVar, TYPE_CHECKING >>> import numpy as np >>> import numpy.typing as npt >>> T1 = TypeVar("T1", bound=npt.NBitBase) >>> T2 = TypeVar("T2", bound=npt.NBitBase) >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]: ... return a + b >>> a = np.float16() >>> b = np.int64() >>> out = add(a, b) >>> if TYPE_CHECKING: ... reveal_locals() ... # note: Revealed local types are: ... # note: a: numpy.floating[numpy.typing._16Bit*] ... # note: b: numpy.signedinteger[numpy.typing._64Bit*] ... # note: out: numpy.floating[numpy.typing._64Bit*] """ def __init_subclass__(cls) -> None: allowed_names = { "NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit", "_64Bit", "_32Bit", "_16Bit", "_8Bit", } if cls.__name__ not in allowed_names: raise TypeError('cannot inherit from final class "NBitBase"') super().__init_subclass__() # Silence errors about subclassing a `@final`-decorated class class _256Bit(NBitBase): # type: ignore[misc] pass class _128Bit(_256Bit): # type: ignore[misc] pass class _96Bit(_128Bit): # type: ignore[misc] pass class _80Bit(_96Bit): # type: ignore[misc] pass class _64Bit(_80Bit): # type: ignore[misc] pass class _32Bit(_64Bit): # type: ignore[misc] pass class _16Bit(_32Bit): # type: ignore[misc] pass class _8Bit(_16Bit): # type: ignore[misc] pass from ._nested_sequence import _NestedSequence from ._nbit import ( _NBitByte, _NBitShort, _NBitIntC, _NBitIntP, _NBitInt, _NBitLongLong, _NBitHalf, _NBitSingle, _NBitDouble, _NBitLongDouble, ) from ._char_codes import ( _BoolCodes, _UInt8Codes, _UInt16Codes, _UInt32Codes, _UInt64Codes, _Int8Codes, _Int16Codes, _Int32Codes, _Int64Codes, _Float16Codes, _Float32Codes, _Float64Codes, _Complex64Codes, _Complex128Codes, _ByteCodes, _ShortCodes, _IntCCodes, _IntPCodes, _IntCodes, _LongLongCodes, _UByteCodes, _UShortCodes, _UIntCCodes, _UIntPCodes, _UIntCodes, _ULongLongCodes, _HalfCodes, _SingleCodes, _DoubleCodes, _LongDoubleCodes, _CSingleCodes, _CDoubleCodes, _CLongDoubleCodes, _DT64Codes, _TD64Codes, _StrCodes, _BytesCodes, _VoidCodes, _ObjectCodes, ) from ._scalars import ( _CharLike_co, _BoolLike_co, _UIntLike_co, _IntLike_co, _FloatLike_co, _ComplexLike_co, _TD64Like_co, _NumberLike_co, _ScalarLike_co, _VoidLike_co, ) from ._shape import _Shape, _ShapeLike from ._dtype_like import ( DTypeLike as DTypeLike, _SupportsDType, _VoidDTypeLike, _DTypeLikeBool, _DTypeLikeUInt, _DTypeLikeInt, _DTypeLikeFloat, _DTypeLikeComplex, _DTypeLikeTD64, _DTypeLikeDT64, _DTypeLikeObject, _DTypeLikeVoid, _DTypeLikeStr, _DTypeLikeBytes, _DTypeLikeComplex_co, ) from ._array_like import ( ArrayLike as ArrayLike, _ArrayLike, _FiniteNestedSequence, _SupportsArray, _ArrayLikeInt, _ArrayLikeBool_co, _ArrayLikeUInt_co, _ArrayLikeInt_co, _ArrayLikeFloat_co, _ArrayLikeComplex_co, _ArrayLikeNumber_co, _ArrayLikeTD64_co, _ArrayLikeDT64_co, _ArrayLikeObject_co, _ArrayLikeVoid_co, _ArrayLikeStr_co, _ArrayLikeBytes_co, ) from ._generic_alias import ( NDArray as NDArray, _DType, _GenericAlias, ) if TYPE_CHECKING: from ._ufunc import ( _UFunc_Nin1_Nout1, _UFunc_Nin2_Nout1, _UFunc_Nin1_Nout2, _UFunc_Nin2_Nout2, _GUFunc_Nin2_Nout1, ) else: # Declare the (type-check-only) ufunc subclasses as ufunc aliases during # runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834) _UFunc_Nin1_Nout1 = ufunc _UFunc_Nin2_Nout1 = ufunc _UFunc_Nin1_Nout2 = ufunc _UFunc_Nin2_Nout2 = ufunc _GUFunc_Nin2_Nout1 = ufunc # Clean up the namespace del TYPE_CHECKING, final, ufunc if __doc__ is not None: from ._add_docstring import _docstrings __doc__ += _docstrings __doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n' del _docstrings from numpy._pytesttester import PytestTester test = PytestTester(__name__) del PytestTester