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📁 ..	-	2026-03-21 15:45
📄 __init__.py """ ============================ 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(x2 for x in range(10)) array(<generator object <genexpr> 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(x2 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(<generator object <genexpr> 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 <arrays.dtypes>` 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 numpy._typing import ( ArrayLike, DTypeLike, NBitBase, NDArray, ) __all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"] if __doc__ is not None: from numpy._typing._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	5.11 KB	2026-01-20 12:58
📁 __pycache__	-	2026-03-21 15:45
📄 mypy_plugin.py """A mypy_ plugin for managing a number of platform-specific annotations. Its functionality can be split into three distinct parts: * Assigning the (platform-dependent) precisions of certain `~numpy.number` subclasses, including the likes of `~numpy.int_`, `~numpy.intp` and `~numpy.longlong`. See the documentation on :ref:`scalar types <arrays.scalars.built-in>` for a comprehensive overview of the affected classes. Without the plugin the precision of all relevant classes will be inferred as `~typing.Any`. * Removing all extended-precision `~numpy.number` subclasses that are unavailable for the platform in question. Most notably this includes the likes of `~numpy.float128` and `~numpy.complex256`. Without the plugin all extended-precision types will, as far as mypy is concerned, be available to all platforms. * Assigning the (platform-dependent) precision of `~numpy.ctypeslib.c_intp`. Without the plugin the type will default to `ctypes.c_int64`. .. versionadded:: 1.22 Examples -------- To enable the plugin, one must add it to their mypy `configuration file`_: .. code-block:: ini [mypy] plugins = numpy.typing.mypy_plugin .. _mypy: http://mypy-lang.org/ .. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html """ from __future__ import annotations from collections.abc import Iterable from typing import Final, TYPE_CHECKING, Callable import numpy as np try: import mypy.types from mypy.types import Type from mypy.plugin import Plugin, AnalyzeTypeContext from mypy.nodes import MypyFile, ImportFrom, Statement from mypy.build import PRI_MED _HookFunc = Callable[[AnalyzeTypeContext], Type] MYPY_EX: None \| ModuleNotFoundError = None except ModuleNotFoundError as ex: MYPY_EX = ex __all__: list[str] = [] def _get_precision_dict() -> dict[str, str]: names = [ ("_NBitByte", np.byte), ("_NBitShort", np.short), ("_NBitIntC", np.intc), ("_NBitIntP", np.intp), ("_NBitInt", np.int_), ("_NBitLongLong", np.longlong), ("_NBitHalf", np.half), ("_NBitSingle", np.single), ("_NBitDouble", np.double), ("_NBitLongDouble", np.longdouble), ] ret = {} for name, typ in names: n: int = 8 * typ().dtype.itemsize ret[f'numpy._typing._nbit.{name}'] = f"numpy._{n}Bit" return ret def _get_extended_precision_list() -> list[str]: extended_types = [np.ulonglong, np.longlong, np.longdouble, np.clongdouble] extended_names = { "uint128", "uint256", "int128", "int256", "float80", "float96", "float128", "float256", "complex160", "complex192", "complex256", "complex512", } return [i.__name__ for i in extended_types if i.__name__ in extended_names] def _get_c_intp_name() -> str: # Adapted from `np.core._internal._getintp_ctype` char = np.dtype('p').char if char == 'i': return "c_int" elif char == 'l': return "c_long" elif char == 'q': return "c_longlong" else: return "c_long" #: A dictionary mapping type-aliases in `numpy._typing._nbit` to #: concrete `numpy.typing.NBitBase` subclasses. _PRECISION_DICT: Final = _get_precision_dict() #: A list with the names of all extended precision `np.number` subclasses. _EXTENDED_PRECISION_LIST: Final = _get_extended_precision_list() #: The name of the ctypes quivalent of `np.intp` _C_INTP: Final = _get_c_intp_name() def _hook(ctx: AnalyzeTypeContext) -> Type: """Replace a type-alias with a concrete ``NBitBase`` subclass.""" typ, _, api = ctx name = typ.name.split(".")[-1] name_new = _PRECISION_DICT[f"numpy._typing._nbit.{name}"] return api.named_type(name_new) if TYPE_CHECKING or MYPY_EX is None: def _index(iterable: Iterable[Statement], id: str) -> int: """Identify the first ``ImportFrom`` instance the specified `id`.""" for i, value in enumerate(iterable): if getattr(value, "id", None) == id: return i raise ValueError("Failed to identify a `ImportFrom` instance " f"with the following id: {id!r}") def _override_imports( file: MypyFile, module: str, imports: list[tuple[str, None \| str]], ) -> None: """Override the first `module`-based import with new `imports`.""" # Construct a new `from module import y` statement import_obj = ImportFrom(module, 0, names=imports) import_obj.is_top_level = True # Replace the first `module`-based import statement with `import_obj` for lst in [file.defs, file.imports]: # type: list[Statement] i = _index(lst, module) lst[i] = import_obj class _NumpyPlugin(Plugin): """A mypy plugin for handling versus numpy-specific typing tasks.""" def get_type_analyze_hook(self, fullname: str) -> None \| _HookFunc: """Set the precision of platform-specific `numpy.number` subclasses. For example: `numpy.int_`, `numpy.longlong` and `numpy.longdouble`. """ if fullname in _PRECISION_DICT: return _hook return None def get_additional_deps( self, file: MypyFile ) -> list[tuple[int, str, int]]: """Handle all import-based overrides. * Import platform-specific extended-precision `numpy.number` subclasses (e.g. `numpy.float96`, `numpy.float128` and `numpy.complex256`). * Import the appropriate `ctypes` equivalent to `numpy.intp`. """ ret = [(PRI_MED, file.fullname, -1)] if file.fullname == "numpy": _override_imports( file, "numpy._typing._extended_precision", imports=[(v, v) for v in _EXTENDED_PRECISION_LIST], ) elif file.fullname == "numpy.ctypeslib": _override_imports( file, "ctypes", imports=[(_C_INTP, "_c_intp")], ) return ret def plugin(version: str) -> type[_NumpyPlugin]: """An entry-point for mypy.""" return _NumpyPlugin else: def plugin(version: str) -> type[_NumpyPlugin]: """An entry-point for mypy.""" raise MYPY_EX	6.33 KB	2026-01-20 12:58
📄 setup.py def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('typing', parent_package, top_path) config.add_subpackage('tests') config.add_data_dir('tests/data') return config if __name__ == '__main__': from numpy.distutils.core import setup setup(configuration=configuration)	374 B	2026-01-20 12:58
📁 tests	-	2026-03-21 15:45

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