""" ============================ 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 as _NestedSequence, ) from ._nbit import ( _NBitByte as _NBitByte, _NBitShort as _NBitShort, _NBitIntC as _NBitIntC, _NBitIntP as _NBitIntP, _NBitInt as _NBitInt, _NBitLongLong as _NBitLongLong, _NBitHalf as _NBitHalf, _NBitSingle as _NBitSingle, _NBitDouble as _NBitDouble, _NBitLongDouble as _NBitLongDouble, ) from ._char_codes import ( _BoolCodes as _BoolCodes, _UInt8Codes as _UInt8Codes, _UInt16Codes as _UInt16Codes, _UInt32Codes as _UInt32Codes, _UInt64Codes as _UInt64Codes, _Int8Codes as _Int8Codes, _Int16Codes as _Int16Codes, _Int32Codes as _Int32Codes, _Int64Codes as _Int64Codes, _Float16Codes as _Float16Codes, _Float32Codes as _Float32Codes, _Float64Codes as _Float64Codes, _Complex64Codes as _Complex64Codes, _Complex128Codes as _Complex128Codes, _ByteCodes as _ByteCodes, _ShortCodes as _ShortCodes, _IntCCodes as _IntCCodes, _IntPCodes as _IntPCodes, _IntCodes as _IntCodes, _LongLongCodes as _LongLongCodes, _UByteCodes as _UByteCodes, _UShortCodes as _UShortCodes, _UIntCCodes as _UIntCCodes, _UIntPCodes as _UIntPCodes, _UIntCodes as _UIntCodes, _ULongLongCodes as _ULongLongCodes, _HalfCodes as _HalfCodes, _SingleCodes as _SingleCodes, _DoubleCodes as _DoubleCodes, _LongDoubleCodes as _LongDoubleCodes, _CSingleCodes as _CSingleCodes, _CDoubleCodes as _CDoubleCodes, _CLongDoubleCodes as _CLongDoubleCodes, _DT64Codes as _DT64Codes, _TD64Codes as _TD64Codes, _StrCodes as _StrCodes, _BytesCodes as _BytesCodes, _VoidCodes as _VoidCodes, _ObjectCodes as _ObjectCodes, ) from ._scalars import ( _CharLike_co as _CharLike_co, _BoolLike_co as _BoolLike_co, _UIntLike_co as _UIntLike_co, _IntLike_co as _IntLike_co, _FloatLike_co as _FloatLike_co, _ComplexLike_co as _ComplexLike_co, _TD64Like_co as _TD64Like_co, _NumberLike_co as _NumberLike_co, _ScalarLike_co as _ScalarLike_co, _VoidLike_co as _VoidLike_co, ) from ._shape import ( _Shape as _Shape, _ShapeLike as _ShapeLike, ) from ._dtype_like import ( DTypeLike as DTypeLike, _SupportsDType as _SupportsDType, _VoidDTypeLike as _VoidDTypeLike, _DTypeLikeBool as _DTypeLikeBool, _DTypeLikeUInt as _DTypeLikeUInt, _DTypeLikeInt as _DTypeLikeInt, _DTypeLikeFloat as _DTypeLikeFloat, _DTypeLikeComplex as _DTypeLikeComplex, _DTypeLikeTD64 as _DTypeLikeTD64, _DTypeLikeDT64 as _DTypeLikeDT64, _DTypeLikeObject as _DTypeLikeObject, _DTypeLikeVoid as _DTypeLikeVoid, _DTypeLikeStr as _DTypeLikeStr, _DTypeLikeBytes as _DTypeLikeBytes, _DTypeLikeComplex_co as _DTypeLikeComplex_co, ) from ._array_like import ( ArrayLike as ArrayLike, _ArrayLike as _ArrayLike, _FiniteNestedSequence as _FiniteNestedSequence, _SupportsArray as _SupportsArray, _ArrayLikeInt as _ArrayLikeInt, _ArrayLikeBool_co as _ArrayLikeBool_co, _ArrayLikeUInt_co as _ArrayLikeUInt_co, _ArrayLikeInt_co as _ArrayLikeInt_co, _ArrayLikeFloat_co as _ArrayLikeFloat_co, _ArrayLikeComplex_co as _ArrayLikeComplex_co, _ArrayLikeNumber_co as _ArrayLikeNumber_co, _ArrayLikeTD64_co as _ArrayLikeTD64_co, _ArrayLikeDT64_co as _ArrayLikeDT64_co, _ArrayLikeObject_co as _ArrayLikeObject_co, _ArrayLikeVoid_co as _ArrayLikeVoid_co, _ArrayLikeStr_co as _ArrayLikeStr_co, _ArrayLikeBytes_co as _ArrayLikeBytes_co, ) from ._generic_alias import ( NDArray as NDArray, _DType as _DType, _GenericAlias as _GenericAlias, ) if TYPE_CHECKING: from ._ufunc import ( _UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1, _UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1, _UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2, _UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2, _GUFunc_Nin2_Nout1 as _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