# pyright: reportIncompatibleMethodOverride=false
import datetime as dt
import types
from _typeshed import Incomplete, SupportsLenAndGetItem
from collections.abc import Buffer, Callable, Iterator, Sequence
from typing import (
Any,
Concatenate,
Final,
Generic,
Literal,
Never,
NoReturn,
Protocol,
Self,
SupportsComplex,
SupportsFloat,
SupportsIndex,
SupportsInt,
Unpack,
final,
overload,
override,
type_check_only,
)
from typing_extensions import TypeIs, TypeVar, deprecated
import numpy as np
from numpy import (
_HasDType,
_HasDTypeWithRealAndImag,
_ModeKind,
_OrderACF,
_OrderCF,
_OrderKACF,
_PartitionKind,
_SortKind,
_ToIndices,
amax,
amin,
bool_,
bytes_,
complex128,
complexfloating,
datetime64,
dtype,
expand_dims,
float64,
floating,
generic,
inexact,
int8,
int64,
int_,
integer,
intp,
ndarray,
number,
object_,
signedinteger,
str_,
timedelta64,
unsignedinteger,
)
from numpy._core.fromnumeric import _UFuncKwargs # type-check only
from numpy._globals import _NoValueType
from numpy._typing import (
ArrayLike,
DTypeLike,
NDArray,
_32Bit,
_64Bit,
_AnyShape,
_ArrayLike,
_ArrayLikeBool_co,
_ArrayLikeBytes_co,
_ArrayLikeComplex128_co,
_ArrayLikeComplex_co,
_ArrayLikeDT64_co,
_ArrayLikeFloat64_co,
_ArrayLikeFloat_co,
_ArrayLikeInt,
_ArrayLikeInt_co,
_ArrayLikeNumber_co,
_ArrayLikeObject_co,
_ArrayLikeStr_co,
_ArrayLikeString_co,
_ArrayLikeTD64_co,
_ArrayLikeUInt_co,
_CharLike_co,
_DT64Codes,
_DTypeLike,
_DTypeLikeBool,
_DTypeLikeVoid,
_FloatLike_co,
_IntLike_co,
_NestedSequence,
_ScalarLike_co,
_Shape,
_ShapeLike,
_SupportsArrayFunc,
_SupportsDType,
_TD64Like_co,
)
from numpy._typing._dtype_like import _VoidDTypeLike
__all__ = [
"MAError",
"MaskError",
"MaskType",
"MaskedArray",
"abs",
"absolute",
"add",
"all",
"allclose",
"allequal",
"alltrue",
"amax",
"amin",
"angle",
"anom",
"anomalies",
"any",
"append",
"arange",
"arccos",
"arccosh",
"arcsin",
"arcsinh",
"arctan",
"arctan2",
"arctanh",
"argmax",
"argmin",
"argsort",
"around",
"array",
"asanyarray",
"asarray",
"bitwise_and",
"bitwise_or",
"bitwise_xor",
"bool_",
"ceil",
"choose",
"clip",
"common_fill_value",
"compress",
"compressed",
"concatenate",
"conjugate",
"convolve",
"copy",
"correlate",
"cos",
"cosh",
"count",
"cumprod",
"cumsum",
"default_fill_value",
"diag",
"diagonal",
"diff",
"divide",
"empty",
"empty_like",
"equal",
"exp",
"expand_dims",
"fabs",
"filled",
"fix_invalid",
"flatten_mask",
"flatten_structured_array",
"floor",
"floor_divide",
"fmod",
"frombuffer",
"fromflex",
"fromfunction",
"getdata",
"getmask",
"getmaskarray",
"greater",
"greater_equal",
"harden_mask",
"hypot",
"identity",
"ids",
"indices",
"inner",
"innerproduct",
"isMA",
"isMaskedArray",
"is_mask",
"is_masked",
"isarray",
"left_shift",
"less",
"less_equal",
"log",
"log2",
"log10",
"logical_and",
"logical_not",
"logical_or",
"logical_xor",
"make_mask",
"make_mask_descr",
"make_mask_none",
"mask_or",
"masked",
"masked_array",
"masked_equal",
"masked_greater",
"masked_greater_equal",
"masked_inside",
"masked_invalid",
"masked_less",
"masked_less_equal",
"masked_not_equal",
"masked_object",
"masked_outside",
"masked_print_option",
"masked_singleton",
"masked_values",
"masked_where",
"max",
"maximum",
"maximum_fill_value",
"mean",
"min",
"minimum",
"minimum_fill_value",
"mod",
"multiply",
"mvoid",
"ndim",
"negative",
"nomask",
"nonzero",
"not_equal",
"ones",
"ones_like",
"outer",
"outerproduct",
"power",
"prod",
"product",
"ptp",
"put",
"putmask",
"ravel",
"remainder",
"repeat",
"reshape",
"resize",
"right_shift",
"round",
"round_",
"set_fill_value",
"shape",
"sin",
"sinh",
"size",
"soften_mask",
"sometrue",
"sort",
"sqrt",
"squeeze",
"std",
"subtract",
"sum",
"swapaxes",
"take",
"tan",
"tanh",
"trace",
"transpose",
"true_divide",
"var",
"where",
"zeros",
"zeros_like",
]
_ShapeT_co = TypeVar("_ShapeT_co", bound=_Shape, default=_AnyShape, covariant=True)
_DTypeT_co = TypeVar("_DTypeT_co", bound=np.dtype, default=np.dtype, covariant=True)
# the additional `Callable[...]` bound simplifies self-binding to the ufunc's callable signature
_UFuncT_co = TypeVar("_UFuncT_co", bound=np.ufunc | Callable[..., object], default=np.ufunc, covariant=True)
_AnyNumericScalarT = TypeVar(
"_AnyNumericScalarT",
np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64,
np.float16, np.float32, np.float64, np.longdouble,
np.complex64, np.complex128, np.clongdouble,
np.timedelta64,
np.object_,
) # fmt: skip
type _RealNumber = np.floating | np.integer
type _InnerScalar = np.number | np.bool | np.timedelta64
type _Ignored = object
# A subset of `MaskedArray` that can be parametrized w.r.t. `np.generic`
type _MaskedArray[ScalarT: np.generic] = MaskedArray[_AnyShape, np.dtype[ScalarT]]
type _Masked1D[ScalarT: np.generic] = MaskedArray[tuple[int], np.dtype[ScalarT]]
type _Masked2D[ScalarT: np.generic] = MaskedArray[tuple[int, int], np.dtype[ScalarT]]
type _Masked3D[ScalarT: np.generic] = MaskedArray[tuple[int, int, int], np.dtype[ScalarT]]
type _MaskedArrayUInt_co = _MaskedArray[np.unsignedinteger | np.bool]
type _MaskedArrayInt_co = _MaskedArray[np.integer | np.bool]
type _MaskedArrayFloat64_co = _MaskedArray[np.floating[_64Bit] | np.float32 | np.float16 | np.integer | np.bool]
type _MaskedArrayFloat_co = _MaskedArray[np.floating | np.integer | np.bool]
type _MaskedArrayComplex128_co = _MaskedArray[np.number[_64Bit] | np.number[_32Bit] | np.float16 | np.integer | np.bool]
type _MaskedArrayComplex_co = _MaskedArray[np.inexact | np.integer | np.bool]
type _MaskedArrayNumber_co = _MaskedArray[np.number | np.bool]
type _MaskedArrayTD64_co = _MaskedArray[np.timedelta64 | np.integer | np.bool]
type _ArrayInt_co = NDArray[np.integer | np.bool]
type _Array1D[ScalarT: np.generic] = np.ndarray[tuple[int], np.dtype[ScalarT]]
type _Array2D[ScalarT: np.generic] = np.ndarray[tuple[int, int], np.dtype[ScalarT]]
# Workaround for https://github.com/microsoft/pyright/issues/10232
type _ArrayNoD[ScalarT: np.generic] = np.ndarray[tuple[Never] | tuple[Never, Never], np.dtype[ScalarT]]
type _ToArray1D[ScalarT: np.generic] = _Array1D[ScalarT] | Sequence[ScalarT]
type _ToArray2D[ScalarT: np.generic] = _Array2D[ScalarT] | Sequence[Sequence[ScalarT]]
type _ConvertibleToInt = SupportsInt | SupportsIndex | _CharLike_co
type _ConvertibleToFloat = SupportsFloat | SupportsIndex | _CharLike_co
type _ConvertibleToComplex = SupportsComplex | SupportsFloat | SupportsIndex | _CharLike_co
type _ConvertibleToTD64 = dt.timedelta | int | _CharLike_co | np.character | np.number | np.timedelta64 | np.bool | None
type _ConvertibleToDT64 = dt.date | int | _CharLike_co | np.character | np.number | np.datetime64 | np.bool | None
type _ArangeScalar = _RealNumber | np.datetime64 | np.timedelta64
type _NoMaskType = np.bool_[Literal[False]] # type of `np.False_`
type _MaskArray[ShapeT: _Shape] = np.ndarray[ShapeT, np.dtype[np.bool]]
type _FillValue = complex | None # int | float | complex | None
type _FillValueCallable = Callable[[np.dtype | ArrayLike], _FillValue]
type _DomainCallable = Callable[..., NDArray[np.bool]]
type _PyArray[T] = list[T] | tuple[T, ...]
type _PyScalar = complex | bytes | str
type _Seq2D[T] = Sequence[Sequence[T]]
type _Seq3D[T] = Sequence[_Seq2D[T]]
type _CorrelateMode = Literal["valid", "same", "full"]
@type_check_only
class _HasShape[ShapeT_co: _Shape](Protocol):
@property
def shape(self, /) -> ShapeT_co: ...
###
MaskType = np.bool_
nomask: Final[_NoMaskType] = ...
class MaskedArrayFutureWarning(FutureWarning): ...
class MAError(Exception): ...
class MaskError(MAError): ...
# not generic at runtime
class _MaskedUFunc(Generic[_UFuncT_co]):
f: _UFuncT_co # readonly
def __init__(self, /, ufunc: _UFuncT_co) -> None: ...
# not generic at runtime
class _MaskedUnaryOperation(_MaskedUFunc[_UFuncT_co], Generic[_UFuncT_co]):
fill: Final[_FillValue]
domain: Final[_DomainCallable | None]
def __init__(self, /, mufunc: _UFuncT_co, fill: _FillValue = 0, domain: _DomainCallable | None = None) -> None: ...
# NOTE: This might not work with overloaded callable signatures might not work on
# pyright, which is a long-standing issue, and is unique to pyright:
# https://github.com/microsoft/pyright/issues/9663
# https://github.com/microsoft/pyright/issues/10849
# https://github.com/microsoft/pyright/issues/10899
# https://github.com/microsoft/pyright/issues/11049
def __call__[**Tss, T](
self: _MaskedUnaryOperation[Callable[Concatenate[Any, Tss], T]],
/,
a: ArrayLike,
*args: Tss.args,
**kwargs: Tss.kwargs,
) -> T: ...
# not generic at runtime
class _MaskedBinaryOperation(_MaskedUFunc[_UFuncT_co], Generic[_UFuncT_co]):
fillx: Final[_FillValue]
filly: Final[_FillValue]
def __init__(self, /, mbfunc: _UFuncT_co, fillx: _FillValue = 0, filly: _FillValue = 0) -> None: ...
# NOTE: See the comment in `_MaskedUnaryOperation.__call__`
def __call__[**Tss, T](
self: _MaskedBinaryOperation[Callable[Concatenate[Any, Any, Tss], T]],
/,
a: ArrayLike,
b: ArrayLike,
*args: Tss.args,
**kwargs: Tss.kwargs,
) -> T: ...
# NOTE: We cannot meaningfully annotate the return (d)types of these methods until
# the signatures of the corresponding `numpy.ufunc` methods are specified.
def reduce(self, /, target: ArrayLike, axis: SupportsIndex = 0, dtype: DTypeLike | None = None) -> Incomplete: ...
def outer(self, /, a: ArrayLike, b: ArrayLike) -> _MaskedArray[Incomplete]: ...
def accumulate(self, /, target: ArrayLike, axis: SupportsIndex = 0) -> _MaskedArray[Incomplete]: ...
# not generic at runtime
class _DomainedBinaryOperation(_MaskedUFunc[_UFuncT_co], Generic[_UFuncT_co]):
domain: Final[_DomainCallable]
fillx: Final[_FillValue]
filly: Final[_FillValue]
def __init__(
self,
/,
dbfunc: _UFuncT_co,
domain: _DomainCallable,
fillx: _FillValue = 0,
filly: _FillValue = 0,
) -> None: ...
# NOTE: See the comment in `_MaskedUnaryOperation.__call__`
def __call__[**Tss, T](
self: _DomainedBinaryOperation[Callable[Concatenate[Any, Any, Tss], T]],
/,
a: ArrayLike,
b: ArrayLike,
*args: Tss.args,
**kwargs: Tss.kwargs,
) -> T: ...
# not generic at runtime
class _extrema_operation(_MaskedUFunc[_UFuncT_co], Generic[_UFuncT_co]):
compare: Final[_MaskedBinaryOperation]
fill_value_func: Final[_FillValueCallable]
def __init__(
self,
/,
ufunc: _UFuncT_co,
compare: _MaskedBinaryOperation,
fill_value: _FillValueCallable,
) -> None: ...
# NOTE: This class is only used internally for `maximum` and `minimum`, so we are
# able to annotate the `__call__` method specifically for those two functions.
@overload
def __call__[ScalarT: np.generic](self, /, a: _ArrayLike[ScalarT], b: _ArrayLike[ScalarT]) -> _MaskedArray[ScalarT]: ...
@overload
def __call__(self, /, a: ArrayLike, b: ArrayLike) -> _MaskedArray[Incomplete]: ...
# NOTE: We cannot meaningfully annotate the return (d)types of these methods until
# the signatures of the corresponding `numpy.ufunc` methods are specified.
def reduce(self, /, target: ArrayLike, axis: SupportsIndex | _NoValueType = ...) -> Incomplete: ...
def outer(self, /, a: ArrayLike, b: ArrayLike) -> _MaskedArray[Incomplete]: ...
@final
class _MaskedPrintOption:
_display: str
_enabled: bool | Literal[0, 1]
def __init__(self, /, display: str) -> None: ...
def display(self, /) -> str: ...
def set_display(self, /, s: str) -> None: ...
def enabled(self, /) -> bool: ...
def enable(self, /, shrink: bool | Literal[0, 1] = 1) -> None: ...
masked_print_option: Final[_MaskedPrintOption] = ...
exp: _MaskedUnaryOperation = ...
conjugate: _MaskedUnaryOperation = ...
sin: _MaskedUnaryOperation = ...
cos: _MaskedUnaryOperation = ...
arctan: _MaskedUnaryOperation = ...
arcsinh: _MaskedUnaryOperation = ...
sinh: _MaskedUnaryOperation = ...
cosh: _MaskedUnaryOperation = ...
tanh: _MaskedUnaryOperation = ...
abs: _MaskedUnaryOperation = ...
absolute: _MaskedUnaryOperation = ...
angle: _MaskedUnaryOperation = ...
fabs: _MaskedUnaryOperation = ...
negative: _MaskedUnaryOperation = ...
floor: _MaskedUnaryOperation = ...
ceil: _MaskedUnaryOperation = ...
around: _MaskedUnaryOperation = ...
logical_not: _MaskedUnaryOperation = ...
sqrt: _MaskedUnaryOperation = ...
log: _MaskedUnaryOperation = ...
log2: _MaskedUnaryOperation = ...
log10: _MaskedUnaryOperation = ...
tan: _MaskedUnaryOperation = ...
arcsin: _MaskedUnaryOperation = ...
arccos: _MaskedUnaryOperation = ...
arccosh: _MaskedUnaryOperation = ...
arctanh: _MaskedUnaryOperation = ...
add: _MaskedBinaryOperation = ...
subtract: _MaskedBinaryOperation = ...
multiply: _MaskedBinaryOperation = ...
arctan2: _MaskedBinaryOperation = ...
equal: _MaskedBinaryOperation = ...
not_equal: _MaskedBinaryOperation = ...
less_equal: _MaskedBinaryOperation = ...
greater_equal: _MaskedBinaryOperation = ...
less: _MaskedBinaryOperation = ...
greater: _MaskedBinaryOperation = ...
logical_and: _MaskedBinaryOperation = ...
def alltrue(target: ArrayLike, axis: SupportsIndex | None = 0, dtype: _DTypeLikeBool | None = None) -> Incomplete: ...
logical_or: _MaskedBinaryOperation = ...
def sometrue(target: ArrayLike, axis: SupportsIndex | None = 0, dtype: _DTypeLikeBool | None = None) -> Incomplete: ...
logical_xor: _MaskedBinaryOperation = ...
bitwise_and: _MaskedBinaryOperation = ...
bitwise_or: _MaskedBinaryOperation = ...
bitwise_xor: _MaskedBinaryOperation = ...
hypot: _MaskedBinaryOperation = ...
divide: _DomainedBinaryOperation = ...
true_divide: _DomainedBinaryOperation = ...
floor_divide: _DomainedBinaryOperation = ...
remainder: _DomainedBinaryOperation = ...
fmod: _DomainedBinaryOperation = ...
mod: _DomainedBinaryOperation = ...
# `obj` can be anything (even `object()`), and is too "flexible", so we can't
# meaningfully annotate it, or its return type.
def default_fill_value(obj: object) -> Any: ...
def minimum_fill_value(obj: object) -> Any: ...
def maximum_fill_value(obj: object) -> Any: ...
#
@overload # returns `a.fill_value` if `a` is a `MaskedArray`
def get_fill_value[ScalarT: np.generic](a: _MaskedArray[ScalarT]) -> ScalarT: ...
@overload # otherwise returns `default_fill_value(a)`
def get_fill_value(a: object) -> Any: ...
# this is a noop if `a` isn't a `MaskedArray`, so we only accept `MaskedArray` input
def set_fill_value(a: MaskedArray, fill_value: _ScalarLike_co) -> None: ...
# the return type depends on the *values* of `a` and `b` (which cannot be known
# statically), which is why we need to return an awkward `_ | None`
@overload
def common_fill_value[ScalarT: np.generic](a: _MaskedArray[ScalarT], b: MaskedArray) -> ScalarT | None: ...
@overload
def common_fill_value(a: object, b: object) -> Any: ...
# keep in sync with `fix_invalid`, but return `ndarray` instead of `MaskedArray`
@overload
def filled[ShapeT: _Shape, DTypeT: np.dtype](
a: ndarray[ShapeT, DTypeT],
fill_value: _ScalarLike_co | None = None,
) -> ndarray[ShapeT, DTypeT]: ...
@overload
def filled[ScalarT: np.generic](a: _ArrayLike[ScalarT], fill_value: _ScalarLike_co | None = None) -> NDArray[ScalarT]: ...
@overload
def filled(a: ArrayLike, fill_value: _ScalarLike_co | None = None) -> NDArray[Incomplete]: ...
# keep in sync with `filled`, but return `MaskedArray` instead of `ndarray`
@overload
def fix_invalid[ShapeT: _Shape, DTypeT: np.dtype](
a: np.ndarray[ShapeT, DTypeT],
mask: _ArrayLikeBool_co = nomask,
copy: bool = True,
fill_value: _ScalarLike_co | None = None,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload
def fix_invalid[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
mask: _ArrayLikeBool_co = nomask,
copy: bool = True,
fill_value: _ScalarLike_co | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def fix_invalid(
a: ArrayLike,
mask: _ArrayLikeBool_co = nomask,
copy: bool = True,
fill_value: _ScalarLike_co | None = None,
) -> _MaskedArray[Incomplete]: ...
#
def get_masked_subclass(*arrays: object) -> type[MaskedArray]: ...
#
@overload
def getdata[ShapeT: _Shape, DTypeT: np.dtype](
a: np.ndarray[ShapeT, DTypeT],
subok: bool = True,
) -> np.ndarray[ShapeT, DTypeT]: ...
@overload
def getdata[ScalarT: np.generic](a: _ArrayLike[ScalarT], subok: bool = True) -> NDArray[ScalarT]: ...
@overload
def getdata(a: ArrayLike, subok: bool = True) -> NDArray[Incomplete]: ...
get_data = getdata
#
@overload
def getmask(a: _ScalarLike_co) -> _NoMaskType: ...
@overload
def getmask[ShapeT: _Shape](a: MaskedArray[ShapeT, Any]) -> _MaskArray[ShapeT] | _NoMaskType: ...
@overload
def getmask(a: ArrayLike) -> _MaskArray[_AnyShape] | _NoMaskType: ...
get_mask = getmask
# like `getmask`, but instead of `nomask` returns `make_mask_none(arr, arr.dtype?)`
@overload
def getmaskarray(arr: _ScalarLike_co) -> _MaskArray[tuple[()]]: ...
@overload
def getmaskarray[ShapeT: _Shape](arr: np.ndarray[ShapeT, Any]) -> _MaskArray[ShapeT]: ...
# It's sufficient for `m` to have dtype with type: `type[np.bool_]`,
# which isn't necessarily an ndarray. Please open an issue if this causes issues.
def is_mask(m: object) -> TypeIs[NDArray[bool_]]: ...
#
@overload
def make_mask_descr(ndtype: _VoidDTypeLike) -> np.dtype[np.void]: ...
@overload
def make_mask_descr(ndtype: _DTypeLike[np.generic] | str | type) -> np.dtype[np.bool_]: ...
#
@overload # m is nomask
def make_mask(
m: _NoMaskType,
copy: bool = False,
shrink: bool = True,
dtype: _DTypeLikeBool = ...,
) -> _NoMaskType: ...
@overload # m: ndarray, shrink=True (default), dtype: bool-like (default)
def make_mask[ShapeT: _Shape](
m: np.ndarray[ShapeT],
copy: bool = False,
shrink: Literal[True] = True,
dtype: _DTypeLikeBool = ...,
) -> _MaskArray[ShapeT] | _NoMaskType: ...
@overload # m: ndarray, shrink=False (kwarg), dtype: bool-like (default)
def make_mask[ShapeT: _Shape](
m: np.ndarray[ShapeT],
copy: bool = False,
*,
shrink: Literal[False],
dtype: _DTypeLikeBool = ...,
) -> _MaskArray[ShapeT]: ...
@overload # m: ndarray, dtype: void-like
def make_mask[ShapeT: _Shape](
m: np.ndarray[ShapeT],
copy: bool = False,
shrink: bool = True,
*,
dtype: _DTypeLikeVoid,
) -> np.ndarray[ShapeT, np.dtype[np.void]]: ...
@overload # m: array-like, shrink=True (default), dtype: bool-like (default)
def make_mask(
m: ArrayLike,
copy: bool = False,
shrink: Literal[True] = True,
dtype: _DTypeLikeBool = ...,
) -> _MaskArray[_AnyShape] | _NoMaskType: ...
@overload # m: array-like, shrink=False (kwarg), dtype: bool-like (default)
def make_mask(
m: ArrayLike,
copy: bool = False,
*,
shrink: Literal[False],
dtype: _DTypeLikeBool = ...,
) -> _MaskArray[_AnyShape]: ...
@overload # m: array-like, dtype: void-like
def make_mask(
m: ArrayLike,
copy: bool = False,
shrink: bool = True,
*,
dtype: _DTypeLikeVoid,
) -> NDArray[np.void]: ...
@overload # fallback
def make_mask(
m: ArrayLike,
copy: bool = False,
shrink: bool = True,
*,
dtype: DTypeLike = ...,
) -> NDArray[Incomplete] | _NoMaskType: ...
#
@overload # known shape, dtype: unstructured (default)
def make_mask_none[ShapeT: _Shape](newshape: ShapeT, dtype: np.dtype | type | str | None = None) -> _MaskArray[ShapeT]: ...
@overload # known shape, dtype: structured
def make_mask_none[ShapeT: _Shape](newshape: ShapeT, dtype: _VoidDTypeLike) -> np.ndarray[ShapeT, dtype[np.void]]: ...
@overload # unknown shape, dtype: unstructured (default)
def make_mask_none(newshape: _ShapeLike, dtype: np.dtype | type | str | None = None) -> _MaskArray[_AnyShape]: ...
@overload # unknown shape, dtype: structured
def make_mask_none(newshape: _ShapeLike, dtype: _VoidDTypeLike) -> NDArray[np.void]: ...
#
@overload # nomask, scalar-like, shrink=True (default)
def mask_or(
m1: _NoMaskType | Literal[False],
m2: _ScalarLike_co,
copy: bool = False,
shrink: Literal[True] = True,
) -> _NoMaskType: ...
@overload # nomask, scalar-like, shrink=False (kwarg)
def mask_or(
m1: _NoMaskType | Literal[False],
m2: _ScalarLike_co,
copy: bool = False,
*,
shrink: Literal[False],
) -> _MaskArray[tuple[()]]: ...
@overload # scalar-like, nomask, shrink=True (default)
def mask_or(
m1: _ScalarLike_co,
m2: _NoMaskType | Literal[False],
copy: bool = False,
shrink: Literal[True] = True,
) -> _NoMaskType: ...
@overload # scalar-like, nomask, shrink=False (kwarg)
def mask_or(
m1: _ScalarLike_co,
m2: _NoMaskType | Literal[False],
copy: bool = False,
*,
shrink: Literal[False],
) -> _MaskArray[tuple[()]]: ...
@overload # ndarray, ndarray | nomask, shrink=True (default)
def mask_or[ShapeT: _Shape, ScalarT: np.generic](
m1: np.ndarray[ShapeT, np.dtype[ScalarT]],
m2: np.ndarray[ShapeT, np.dtype[ScalarT]] | _NoMaskType | Literal[False],
copy: bool = False,
shrink: Literal[True] = True,
) -> _MaskArray[ShapeT] | _NoMaskType: ...
@overload # ndarray, ndarray | nomask, shrink=False (kwarg)
def mask_or[ShapeT: _Shape, ScalarT: np.generic](
m1: np.ndarray[ShapeT, np.dtype[ScalarT]],
m2: np.ndarray[ShapeT, np.dtype[ScalarT]] | _NoMaskType | Literal[False],
copy: bool = False,
*,
shrink: Literal[False],
) -> _MaskArray[ShapeT]: ...
@overload # ndarray | nomask, ndarray, shrink=True (default)
def mask_or[ShapeT: _Shape, ScalarT: np.generic](
m1: np.ndarray[ShapeT, np.dtype[ScalarT]] | _NoMaskType | Literal[False],
m2: np.ndarray[ShapeT, np.dtype[ScalarT]],
copy: bool = False,
shrink: Literal[True] = True,
) -> _MaskArray[ShapeT] | _NoMaskType: ...
@overload # ndarray | nomask, ndarray, shrink=False (kwarg)
def mask_or[ShapeT: _Shape, ScalarT: np.generic](
m1: np.ndarray[ShapeT, np.dtype[ScalarT]] | _NoMaskType | Literal[False],
m2: np.ndarray[ShapeT, np.dtype[ScalarT]],
copy: bool = False,
*,
shrink: Literal[False],
) -> _MaskArray[ShapeT]: ...
#
@overload
def flatten_mask[ShapeT: _Shape](mask: np.ndarray[ShapeT]) -> _MaskArray[ShapeT]: ...
@overload
def flatten_mask(mask: ArrayLike) -> _MaskArray[_AnyShape]: ...
# NOTE: we currently don't know the field types of `void` dtypes, so it's not possible
# to know the output dtype of the returned array.
@overload
def flatten_structured_array[ShapeT: _Shape](a: MaskedArray[ShapeT, np.dtype[np.void]]) -> MaskedArray[ShapeT]: ...
@overload
def flatten_structured_array[ShapeT: _Shape](a: np.ndarray[ShapeT, np.dtype[np.void]]) -> np.ndarray[ShapeT]: ...
@overload # for some reason this accepts unstructured array-likes, hence this fallback overload
def flatten_structured_array(a: ArrayLike) -> np.ndarray: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_invalid[ShapeT: _Shape, DTypeT: np.dtype](
a: ndarray[ShapeT, DTypeT],
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_invalid[ScalarT: np.generic](a: _ArrayLike[ScalarT], copy: bool = True) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_invalid(a: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # array-like of known scalar-type
def masked_where[ShapeT: _Shape, DTypeT: np.dtype](
condition: _ArrayLikeBool_co,
a: ndarray[ShapeT, DTypeT],
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_where[ScalarT: np.generic](
condition: _ArrayLikeBool_co,
a: _ArrayLike[ScalarT],
copy: bool = True,
) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_where(condition: _ArrayLikeBool_co, a: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_greater[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
value: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_greater[ScalarT: np.generic](x: _ArrayLike[ScalarT], value: ArrayLike, copy: bool = True) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_greater(x: ArrayLike, value: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_greater_equal[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
value: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_greater_equal[ScalarT: np.generic](
x: _ArrayLike[ScalarT],
value: ArrayLike,
copy: bool = True,
) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_greater_equal(x: ArrayLike, value: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_less[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
value: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_less[ScalarT: np.generic](x: _ArrayLike[ScalarT], value: ArrayLike, copy: bool = True) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_less(x: ArrayLike, value: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_less_equal[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
value: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_less_equal[ScalarT: np.generic](
x: _ArrayLike[ScalarT],
value: ArrayLike,
copy: bool = True,
) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_less_equal(x: ArrayLike, value: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_not_equal[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
value: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_not_equal[ScalarT: np.generic](
x: _ArrayLike[ScalarT],
value: ArrayLike,
copy: bool = True,
) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_not_equal(x: ArrayLike, value: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_equal[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
value: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_equal[ScalarT: np.generic](x: _ArrayLike[ScalarT], value: ArrayLike, copy: bool = True) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_equal(x: ArrayLike, value: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_inside[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
v1: ArrayLike,
v2: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_inside[ScalarT: np.generic](
x: _ArrayLike[ScalarT],
v1: ArrayLike,
v2: ArrayLike,
copy: bool = True,
) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_inside(x: ArrayLike, v1: ArrayLike, v2: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# keep in sync with other the `masked_*` functions
@overload # known array with known shape and dtype
def masked_outside[ShapeT: _Shape, DTypeT: np.dtype](
x: ndarray[ShapeT, DTypeT],
v1: ArrayLike,
v2: ArrayLike,
copy: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # array-like of known scalar-type
def masked_outside[ScalarT: np.generic](
x: _ArrayLike[ScalarT],
v1: ArrayLike,
v2: ArrayLike,
copy: bool = True,
) -> _MaskedArray[ScalarT]: ...
@overload # unknown array-like
def masked_outside(x: ArrayLike, v1: ArrayLike, v2: ArrayLike, copy: bool = True) -> _MaskedArray[Incomplete]: ...
# only intended for object arrays, so we assume that's how it's always used in practice
@overload
def masked_object[ShapeT: _Shape](
x: np.ndarray[ShapeT, np.dtype[np.object_]],
value: object,
copy: bool = True,
shrink: bool = True,
) -> MaskedArray[ShapeT, np.dtype[np.object_]]: ...
@overload
def masked_object(
x: _ArrayLikeObject_co,
value: object,
copy: bool = True,
shrink: bool = True,
) -> _MaskedArray[np.object_]: ...
# keep roughly in sync with `filled`
@overload
def masked_values[ShapeT: _Shape, DTypeT: np.dtype](
x: np.ndarray[ShapeT, DTypeT],
value: _ScalarLike_co,
rtol: float = 1e-5,
atol: float = 1e-8,
copy: bool = True,
shrink: bool = True,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload
def masked_values[ScalarT: np.generic](
x: _ArrayLike[ScalarT],
value: _ScalarLike_co,
rtol: float = 1e-5,
atol: float = 1e-8,
copy: bool = True,
shrink: bool = True,
) -> _MaskedArray[ScalarT]: ...
@overload
def masked_values(
x: ArrayLike,
value: _ScalarLike_co,
rtol: float = 1e-5,
atol: float = 1e-8,
copy: bool = True,
shrink: bool = True,
) -> _MaskedArray[Incomplete]: ...
# TODO: Support non-boolean mask dtypes, such as `np.void`. This will require adding an
# additional generic type parameter to (at least) `MaskedArray` and `MaskedIterator` to
# hold the dtype of the mask.
class MaskedIterator(Generic[_ShapeT_co, _DTypeT_co]):
ma: MaskedArray[_ShapeT_co, _DTypeT_co] # readonly
dataiter: np.flatiter[ndarray[_ShapeT_co, _DTypeT_co]] # readonly
maskiter: Final[np.flatiter[NDArray[np.bool]]]
def __init__(self, ma: MaskedArray[_ShapeT_co, _DTypeT_co]) -> None: ...
def __iter__(self) -> Self: ...
# Similar to `MaskedArray.__getitem__` but without the `void` case.
@overload
def __getitem__(self, indx: _ArrayInt_co | tuple[_ArrayInt_co, ...], /) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
@overload
def __getitem__(self, indx: SupportsIndex | tuple[SupportsIndex, ...], /) -> Incomplete: ...
@overload
def __getitem__(self, indx: _ToIndices, /) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
# Similar to `ndarray.__setitem__` but without the `void` case.
@overload # flexible | object_ | bool
def __setitem__(
self: MaskedIterator[Any, dtype[np.flexible | object_ | np.bool] | np.dtypes.StringDType],
index: _ToIndices,
value: object,
/,
) -> None: ...
@overload # integer
def __setitem__(
self: MaskedIterator[Any, dtype[integer]],
index: _ToIndices,
value: _ConvertibleToInt | _NestedSequence[_ConvertibleToInt] | _ArrayLikeInt_co,
/,
) -> None: ...
@overload # floating
def __setitem__(
self: MaskedIterator[Any, dtype[floating]],
index: _ToIndices,
value: _ConvertibleToFloat | _NestedSequence[_ConvertibleToFloat | None] | _ArrayLikeFloat_co | None,
/,
) -> None: ...
@overload # complexfloating
def __setitem__(
self: MaskedIterator[Any, dtype[complexfloating]],
index: _ToIndices,
value: _ConvertibleToComplex | _NestedSequence[_ConvertibleToComplex | None] | _ArrayLikeNumber_co | None,
/,
) -> None: ...
@overload # timedelta64
def __setitem__(
self: MaskedIterator[Any, dtype[timedelta64]],
index: _ToIndices,
value: _ConvertibleToTD64 | _NestedSequence[_ConvertibleToTD64],
/,
) -> None: ...
@overload # datetime64
def __setitem__(
self: MaskedIterator[Any, dtype[datetime64]],
index: _ToIndices,
value: _ConvertibleToDT64 | _NestedSequence[_ConvertibleToDT64],
/,
) -> None: ...
@overload # catch-all
def __setitem__(self, index: _ToIndices, value: ArrayLike, /) -> None: ...
# TODO: Returns `mvoid[(), _DTypeT_co]` for masks with `np.void` dtype.
def __next__[ScalarT: np.generic](self: MaskedIterator[Any, np.dtype[ScalarT]]) -> ScalarT: ...
class MaskedArray(ndarray[_ShapeT_co, _DTypeT_co]):
__array_priority__: Final[Literal[15]] = 15
@overload
def __new__[ScalarT: np.generic](
cls,
data: _ArrayLike[ScalarT],
mask: _ArrayLikeBool_co = nomask,
dtype: None = None,
copy: bool = False,
subok: bool = True,
ndmin: int = 0,
fill_value: _ScalarLike_co | None = None,
keep_mask: bool = True,
hard_mask: bool | None = None,
shrink: bool = True,
order: _OrderKACF | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def __new__[ScalarT: np.generic](
cls,
data: object,
mask: _ArrayLikeBool_co,
dtype: _DTypeLike[ScalarT],
copy: bool = False,
subok: bool = True,
ndmin: int = 0,
fill_value: _ScalarLike_co | None = None,
keep_mask: bool = True,
hard_mask: bool | None = None,
shrink: bool = True,
order: _OrderKACF | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def __new__[ScalarT: np.generic](
cls,
data: object,
mask: _ArrayLikeBool_co = nomask,
*,
dtype: _DTypeLike[ScalarT],
copy: bool = False,
subok: bool = True,
ndmin: int = 0,
fill_value: _ScalarLike_co | None = None,
keep_mask: bool = True,
hard_mask: bool | None = None,
shrink: bool = True,
order: _OrderKACF | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def __new__(
cls,
data: object = None,
mask: _ArrayLikeBool_co = nomask,
dtype: DTypeLike | None = None,
copy: bool = False,
subok: bool = True,
ndmin: int = 0,
fill_value: _ScalarLike_co | None = None,
keep_mask: bool = True,
hard_mask: bool | None = None,
shrink: bool = True,
order: _OrderKACF | None = None,
) -> _MaskedArray[Any]: ...
def __array_wrap__[ShapeT: _Shape, DTypeT: np.dtype](
self,
obj: ndarray[ShapeT, DTypeT],
context: tuple[np.ufunc, tuple[Any, ...], int] | None = None,
return_scalar: bool = False,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload # type: ignore[override] # ()
def view(self, /, dtype: None = None, type: None = None, fill_value: _ScalarLike_co | None = None) -> Self: ...
@overload # (dtype: DTypeT)
def view[DTypeT: np.dtype](
self,
/,
dtype: DTypeT | _HasDType[DTypeT],
type: None = None,
fill_value: _ScalarLike_co | None = None,
) -> MaskedArray[_ShapeT_co, DTypeT]: ...
@overload # (dtype: dtype[ScalarT])
def view[ScalarT: np.generic](
self,
/,
dtype: _DTypeLike[ScalarT],
type: None = None,
fill_value: _ScalarLike_co | None = None,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload # ([dtype: _, ]*, type: ArrayT)
def view[ArrayT: np.ndarray](
self,
/,
dtype: DTypeLike | None = None,
*,
type: type[ArrayT],
fill_value: _ScalarLike_co | None = None,
) -> ArrayT: ...
@overload # (dtype: _, type: ArrayT)
def view[ArrayT: np.ndarray](
self,
/,
dtype: DTypeLike | None,
type: type[ArrayT],
fill_value: _ScalarLike_co | None = None,
) -> ArrayT: ...
@overload # (dtype: ArrayT, /)
def view[ArrayT: np.ndarray](
self,
/,
dtype: type[ArrayT],
type: None = None,
fill_value: _ScalarLike_co | None = None,
) -> ArrayT: ...
@overload # (dtype: ?)
def view(
self,
/,
# `_VoidDTypeLike | str | None` is like `DTypeLike` but without `_DTypeLike[Any]` to avoid
# overlaps with previous overloads.
dtype: _VoidDTypeLike | str | None,
type: None = None,
fill_value: _ScalarLike_co | None = None,
) -> MaskedArray[_ShapeT_co, np.dtype]: ...
# Keep in sync with `ndarray.__getitem__`
@overload
def __getitem__(self, key: _ArrayInt_co | tuple[_ArrayInt_co, ...], /) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
@overload
def __getitem__(self, key: SupportsIndex | tuple[SupportsIndex, ...], /) -> Any: ...
@overload
def __getitem__(self, key: _ToIndices, /) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
@overload
def __getitem__(self: _MaskedArray[np.void], indx: str, /) -> MaskedArray[_ShapeT_co]: ...
@overload
def __getitem__(self: _MaskedArray[np.void], indx: list[str], /) -> MaskedArray[_ShapeT_co, np.dtype[np.void]]: ...
@property
def shape(self) -> _ShapeT_co: ...
@shape.setter # type: ignore[override]
def shape[ShapeT: _Shape](self: MaskedArray[ShapeT, Any], shape: ShapeT, /) -> None: ...
def __setmask__(self, mask: _ArrayLikeBool_co, copy: bool = False) -> None: ...
@property
def mask(self) -> np.ndarray[_ShapeT_co, np.dtype[MaskType]] | MaskType: ...
@mask.setter
def mask(self, value: _ArrayLikeBool_co, /) -> None: ...
@property
def recordmask(self) -> np.ndarray[_ShapeT_co, np.dtype[MaskType]] | MaskType: ...
@recordmask.setter
def recordmask(self, mask: Never, /) -> NoReturn: ...
def harden_mask(self) -> Self: ...
def soften_mask(self) -> Self: ...
@property
def hardmask(self) -> bool: ...
def unshare_mask(self) -> Self: ...
@property
def sharedmask(self) -> bool: ...
def shrink_mask(self) -> Self: ...
@property
def baseclass(self) -> type[ndarray]: ...
@property
def _data(self) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
@property
def data(self) -> ndarray[_ShapeT_co, _DTypeT_co]: ... # type: ignore[override]
@property # type: ignore[override]
def flat(self) -> MaskedIterator[_ShapeT_co, _DTypeT_co]: ...
@flat.setter
def flat(self, value: ArrayLike, /) -> None: ...
@property
def fill_value[ScalarT: np.generic](self: _MaskedArray[ScalarT]) -> ScalarT: ...
@fill_value.setter
def fill_value(self, value: _ScalarLike_co | None = None, /) -> None: ...
def get_fill_value[ScalarT: np.generic](self: _MaskedArray[ScalarT]) -> ScalarT: ...
def set_fill_value(self, /, value: _ScalarLike_co | None = None) -> None: ...
def filled(self, /, fill_value: _ScalarLike_co | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
def compressed(self) -> ndarray[tuple[int], _DTypeT_co]: ...
# keep roughly in sync with `ma.core.compress`, but swap the first two arguments
@overload # type: ignore[override]
def compress[ArrayT: np.ndarray](
self,
condition: _ArrayLikeBool_co,
axis: _ShapeLike | None,
out: ArrayT,
) -> ArrayT: ...
@overload
def compress[ArrayT: np.ndarray](
self,
condition: _ArrayLikeBool_co,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
@overload
def compress(
self,
condition: _ArrayLikeBool_co,
axis: None = None,
out: None = None,
) -> MaskedArray[tuple[int], _DTypeT_co]: ...
@overload
def compress(
self,
condition: _ArrayLikeBool_co,
axis: _ShapeLike | None = None,
out: None = None,
) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
# TODO: How to deal with the non-commutative nature of `==` and `!=`?
# xref numpy/numpy#17368
def __eq__(self, other: Incomplete, /) -> Incomplete: ...
def __ne__(self, other: Incomplete, /) -> Incomplete: ...
def __ge__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
def __gt__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
def __le__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
def __lt__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
# Keep in sync with `ndarray.__add__`
@overload # type: ignore[override]
def __add__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __add__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __add__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[np.bool]: ...
@overload
def __add__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __add__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __add__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __add__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __add__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __add__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __add__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __add__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __add__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __add__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __add__(self: _MaskedArrayTD64_co, other: _ArrayLikeTD64_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __add__(self: _MaskedArrayTD64_co, other: _ArrayLikeDT64_co, /) -> _MaskedArray[datetime64]: ...
@overload
def __add__(self: _MaskedArray[datetime64], other: _ArrayLikeTD64_co, /) -> _MaskedArray[datetime64]: ...
@overload
def __add__(self: _MaskedArray[bytes_], other: _ArrayLikeBytes_co, /) -> _MaskedArray[bytes_]: ...
@overload
def __add__(self: _MaskedArray[str_], other: _ArrayLikeStr_co, /) -> _MaskedArray[str_]: ...
@overload
def __add__(
self: MaskedArray[Any, np.dtypes.StringDType],
other: _ArrayLikeStr_co | _ArrayLikeString_co,
/,
) -> MaskedArray[_AnyShape, np.dtypes.StringDType]: ...
@overload
def __add__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __add__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__radd__`
@overload # type: ignore[override] # signature equivalent to __add__
def __radd__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __radd__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __radd__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[np.bool]: ...
@overload
def __radd__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __radd__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __radd__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __radd__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __radd__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __radd__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __radd__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __radd__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __radd__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __radd__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __radd__(self: _MaskedArrayTD64_co, other: _ArrayLikeTD64_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __radd__(self: _MaskedArrayTD64_co, other: _ArrayLikeDT64_co, /) -> _MaskedArray[datetime64]: ...
@overload
def __radd__(self: _MaskedArray[datetime64], other: _ArrayLikeTD64_co, /) -> _MaskedArray[datetime64]: ...
@overload
def __radd__(self: _MaskedArray[bytes_], other: _ArrayLikeBytes_co, /) -> _MaskedArray[bytes_]: ...
@overload
def __radd__(self: _MaskedArray[str_], other: _ArrayLikeStr_co, /) -> _MaskedArray[str_]: ...
@overload
def __radd__(
self: MaskedArray[Any, np.dtypes.StringDType],
other: _ArrayLikeStr_co | _ArrayLikeString_co,
/,
) -> MaskedArray[_AnyShape, np.dtypes.StringDType]: ...
@overload
def __radd__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __radd__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__sub__`
@overload # type: ignore[override]
def __sub__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __sub__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __sub__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> NoReturn: ...
@overload
def __sub__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __sub__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __sub__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __sub__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __sub__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __sub__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __sub__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __sub__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __sub__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __sub__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __sub__(self: _MaskedArrayTD64_co, other: _ArrayLikeTD64_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __sub__(self: _MaskedArray[datetime64], other: _ArrayLikeTD64_co, /) -> _MaskedArray[datetime64]: ...
@overload
def __sub__(self: _MaskedArray[datetime64], other: _ArrayLikeDT64_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __sub__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __sub__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__rsub__`
@overload # type: ignore[override]
def __rsub__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __rsub__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __rsub__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> NoReturn: ...
@overload
def __rsub__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __rsub__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __rsub__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __rsub__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __rsub__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __rsub__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __rsub__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __rsub__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __rsub__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __rsub__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __rsub__(self: _MaskedArrayTD64_co, other: _ArrayLikeTD64_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __rsub__(self: _MaskedArrayTD64_co, other: _ArrayLikeDT64_co, /) -> _MaskedArray[datetime64]: ...
@overload
def __rsub__(self: _MaskedArray[datetime64], other: _ArrayLikeDT64_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __rsub__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __rsub__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__mul__`
@overload # type: ignore[override]
def __mul__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __mul__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __mul__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[np.bool]: ...
@overload
def __mul__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __mul__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __mul__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __mul__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __mul__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __mul__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __mul__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __mul__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __mul__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __mul__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __mul__(self: _MaskedArray[timedelta64], other: _ArrayLikeFloat_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __mul__(self: _MaskedArrayFloat_co, other: _ArrayLike[timedelta64], /) -> _MaskedArray[timedelta64]: ...
@overload
def __mul__(
self: MaskedArray[Any, np.dtype[np.character] | np.dtypes.StringDType],
other: _ArrayLikeInt,
/,
) -> MaskedArray[tuple[Any, ...], _DTypeT_co]: ...
@overload
def __mul__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __mul__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__rmul__`
@overload # type: ignore[override] # signature equivalent to __mul__
def __rmul__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __rmul__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __rmul__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[np.bool]: ...
@overload
def __rmul__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __rmul__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __rmul__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __rmul__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __rmul__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __rmul__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __rmul__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __rmul__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __rmul__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __rmul__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __rmul__(self: _MaskedArray[timedelta64], other: _ArrayLikeFloat_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __rmul__(self: _MaskedArrayFloat_co, other: _ArrayLike[timedelta64], /) -> _MaskedArray[timedelta64]: ...
@overload
def __rmul__(
self: MaskedArray[Any, np.dtype[np.character] | np.dtypes.StringDType],
other: _ArrayLikeInt,
/,
) -> MaskedArray[tuple[Any, ...], _DTypeT_co]: ...
@overload
def __rmul__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __rmul__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__truediv__`
@overload # type: ignore[override]
def __truediv__(self: _MaskedArrayInt_co | _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __truediv__(self: _MaskedArrayFloat64_co, other: _ArrayLikeInt_co | _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __truediv__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __truediv__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __truediv__(self: _MaskedArray[floating], other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __truediv__(self: _MaskedArrayFloat_co, other: _ArrayLike[floating], /) -> _MaskedArray[floating]: ...
@overload
def __truediv__(self: _MaskedArray[complexfloating], other: _ArrayLikeNumber_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __truediv__(self: _MaskedArrayNumber_co, other: _ArrayLike[complexfloating], /) -> _MaskedArray[complexfloating]: ...
@overload
def __truediv__(self: _MaskedArray[inexact], other: _ArrayLikeNumber_co, /) -> _MaskedArray[inexact]: ...
@overload
def __truediv__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __truediv__(self: _MaskedArray[timedelta64], other: _ArrayLike[timedelta64], /) -> _MaskedArray[float64]: ...
@overload
def __truediv__(self: _MaskedArray[timedelta64], other: _ArrayLikeBool_co, /) -> NoReturn: ...
@overload
def __truediv__(self: _MaskedArray[timedelta64], other: _ArrayLikeFloat_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __truediv__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __truediv__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__rtruediv__`
@overload # type: ignore[override]
def __rtruediv__(self: _MaskedArrayInt_co | _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __rtruediv__(self: _MaskedArrayFloat64_co, other: _ArrayLikeInt_co | _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __rtruediv__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __rtruediv__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __rtruediv__(self: _MaskedArray[floating], other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __rtruediv__(self: _MaskedArrayFloat_co, other: _ArrayLike[floating], /) -> _MaskedArray[floating]: ...
@overload
def __rtruediv__(self: _MaskedArray[complexfloating], other: _ArrayLikeNumber_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __rtruediv__(self: _MaskedArrayNumber_co, other: _ArrayLike[complexfloating], /) -> _MaskedArray[complexfloating]: ...
@overload
def __rtruediv__(self: _MaskedArray[inexact], other: _ArrayLikeNumber_co, /) -> _MaskedArray[inexact]: ...
@overload
def __rtruediv__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __rtruediv__(self: _MaskedArray[timedelta64], other: _ArrayLike[timedelta64], /) -> _MaskedArray[float64]: ...
@overload
def __rtruediv__(self: _MaskedArray[integer | floating], other: _ArrayLike[timedelta64], /) -> _MaskedArray[timedelta64]: ...
@overload
def __rtruediv__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __rtruediv__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__floordiv__`
@overload # type: ignore[override]
def __floordiv__[ScalarT: _RealNumber](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __floordiv__[ScalarT: _RealNumber](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __floordiv__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[int8]: ...
@overload
def __floordiv__[ScalarT: _RealNumber](
self: _MaskedArray[np.bool],
other: _ArrayLike[ScalarT],
/,
) -> _MaskedArray[ScalarT]: ...
@overload
def __floordiv__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __floordiv__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __floordiv__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __floordiv__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __floordiv__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __floordiv__(self: _MaskedArray[timedelta64], other: _ArrayLike[timedelta64], /) -> _MaskedArray[int64]: ...
@overload
def __floordiv__(self: _MaskedArray[timedelta64], other: _ArrayLikeBool_co, /) -> NoReturn: ...
@overload
def __floordiv__(self: _MaskedArray[timedelta64], other: _ArrayLikeFloat_co, /) -> _MaskedArray[timedelta64]: ...
@overload
def __floordiv__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __floordiv__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__rfloordiv__`
@overload # type: ignore[override]
def __rfloordiv__[ScalarT: _RealNumber](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __rfloordiv__[ScalarT: _RealNumber](
self: _MaskedArray[ScalarT],
other: _ArrayLikeBool_co,
/,
) -> _MaskedArray[ScalarT]: ...
@overload
def __rfloordiv__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[int8]: ...
@overload
def __rfloordiv__[ScalarT: _RealNumber](
self: _MaskedArray[np.bool],
other: _ArrayLike[ScalarT],
/,
) -> _MaskedArray[ScalarT]: ...
@overload
def __rfloordiv__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __rfloordiv__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __rfloordiv__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __rfloordiv__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __rfloordiv__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __rfloordiv__(self: _MaskedArray[timedelta64], other: _ArrayLike[timedelta64], /) -> _MaskedArray[int64]: ...
@overload
def __rfloordiv__(self: _MaskedArray[floating | integer], other: _ArrayLike[timedelta64], /) -> _MaskedArray[timedelta64]: ...
@overload
def __rfloordiv__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __rfloordiv__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__pow__` (minus the `mod` parameter)
@overload # type: ignore[override]
def __pow__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __pow__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __pow__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[int8]: ...
@overload
def __pow__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __pow__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __pow__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __pow__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __pow__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __pow__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __pow__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __pow__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __pow__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __pow__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __pow__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __pow__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
# Keep in sync with `ndarray.__rpow__` (minus the `mod` parameter)
@overload # type: ignore[override]
def __rpow__[ScalarT: np.number](
self: _MaskedArray[ScalarT],
other: int | np.bool,
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
@overload
def __rpow__[ScalarT: np.number](self: _MaskedArray[ScalarT], other: _ArrayLikeBool_co, /) -> _MaskedArray[ScalarT]: ...
@overload
def __rpow__(self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /) -> _MaskedArray[int8]: ...
@overload
def __rpow__[ScalarT: np.number](self: _MaskedArray[np.bool], other: _ArrayLike[ScalarT], /) -> _MaskedArray[ScalarT]: ...
@overload
def __rpow__(self: _MaskedArray[float64], other: _ArrayLikeFloat64_co, /) -> _MaskedArray[float64]: ...
@overload
def __rpow__(self: _MaskedArrayFloat64_co, other: _ArrayLike[floating[_64Bit]], /) -> _MaskedArray[float64]: ...
@overload
def __rpow__(self: _MaskedArray[complex128], other: _ArrayLikeComplex128_co, /) -> _MaskedArray[complex128]: ...
@overload
def __rpow__(self: _MaskedArrayComplex128_co, other: _ArrayLike[complexfloating[_64Bit]], /) -> _MaskedArray[complex128]: ...
@overload
def __rpow__(self: _MaskedArrayUInt_co, other: _ArrayLikeUInt_co, /) -> _MaskedArray[unsignedinteger]: ...
@overload
def __rpow__(self: _MaskedArrayInt_co, other: _ArrayLikeInt_co, /) -> _MaskedArray[signedinteger]: ...
@overload
def __rpow__(self: _MaskedArrayFloat_co, other: _ArrayLikeFloat_co, /) -> _MaskedArray[floating]: ...
@overload
def __rpow__(self: _MaskedArrayComplex_co, other: _ArrayLikeComplex_co, /) -> _MaskedArray[complexfloating]: ...
@overload
def __rpow__(self: _MaskedArray[number], other: _ArrayLikeNumber_co, /) -> _MaskedArray[number]: ...
@overload
def __rpow__(self: _MaskedArray[object_], other: Any, /) -> Any: ...
@overload
def __rpow__(self: _MaskedArray[Any], other: _ArrayLikeObject_co, /) -> Any: ...
#
@property # type: ignore[misc]
def imag[ScalarT: np.generic]( # type: ignore[override]
self: _HasDTypeWithRealAndImag[object, ScalarT],
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
def get_imag[ScalarT: np.generic](
self: _HasDTypeWithRealAndImag[object, ScalarT],
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
#
@property # type: ignore[misc]
def real[ScalarT: np.generic]( # type: ignore[override]
self: _HasDTypeWithRealAndImag[ScalarT, object],
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
def get_real[ScalarT: np.generic](
self: _HasDTypeWithRealAndImag[ScalarT, object],
/,
) -> MaskedArray[_ShapeT_co, np.dtype[ScalarT]]: ...
# keep in sync with `np.ma.count`
@overload
def count(self, axis: None = None, keepdims: Literal[False] | _NoValueType = ...) -> int: ...
@overload
def count(self, axis: _ShapeLike, keepdims: bool | _NoValueType = ...) -> NDArray[int_]: ...
@overload
def count(self, axis: _ShapeLike | None = None, *, keepdims: Literal[True]) -> NDArray[int_]: ...
@overload
def count(self, axis: _ShapeLike | None, keepdims: Literal[True]) -> NDArray[int_]: ...
# Keep in sync with `ndarray.reshape`
# NOTE: reshape also accepts negative integers, so we can't use integer literals
@overload # (None)
def reshape(self, shape: None, /, *, order: _OrderACF = "C", copy: bool | None = None) -> Self: ...
@overload # (empty_sequence)
def reshape(
self,
shape: Sequence[Never],
/,
*,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[tuple[()], _DTypeT_co]: ...
@overload # (() | (int) | (int, int) | ....) # up to 8-d
def reshape[ShapeT: _Shape](
self,
shape: ShapeT,
/,
*,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[ShapeT, _DTypeT_co]: ...
@overload # (index)
def reshape(
self,
size1: SupportsIndex,
/,
*,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[tuple[int], _DTypeT_co]: ...
@overload # (index, index)
def reshape(
self,
size1: SupportsIndex,
size2: SupportsIndex,
/,
*,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[tuple[int, int], _DTypeT_co]: ...
@overload # (index, index, index)
def reshape(
self,
size1: SupportsIndex,
size2: SupportsIndex,
size3: SupportsIndex,
/,
*,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[tuple[int, int, int], _DTypeT_co]: ...
@overload # (index, index, index, index)
def reshape(
self,
size1: SupportsIndex,
size2: SupportsIndex,
size3: SupportsIndex,
size4: SupportsIndex,
/,
*,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[tuple[int, int, int, int], _DTypeT_co]: ...
@overload # (int, *(index, ...))
def reshape(
self,
size0: SupportsIndex,
/,
*shape: SupportsIndex,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
@overload # (sequence[index])
def reshape(
self,
shape: Sequence[SupportsIndex],
/,
*,
order: _OrderACF = "C",
copy: bool | None = None,
) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
def resize(self, newshape: Never, refcheck: bool = True, order: bool = False) -> NoReturn: ... # type: ignore[override]
def put(self, indices: _ArrayLikeInt_co, values: ArrayLike, mode: _ModeKind = "raise") -> None: ...
def ids(self) -> tuple[int, int]: ...
def iscontiguous(self) -> bool: ...
# Keep in sync with `ma.core.all`
@overload # type: ignore[override]
def all(
self,
axis: None = None,
out: None = None,
keepdims: Literal[False] | _NoValueType = ...,
) -> bool_: ...
@overload
def all(
self,
axis: _ShapeLike | None = None,
out: None = None,
*,
keepdims: Literal[True],
) -> _MaskedArray[bool_]: ...
@overload
def all(
self,
axis: _ShapeLike | None,
out: None,
keepdims: Literal[True],
) -> _MaskedArray[bool_]: ...
@overload
def all(
self,
axis: _ShapeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> bool_ | _MaskedArray[bool_]: ...
@overload
def all[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def all[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `ma.core.any`
@overload # type: ignore[override]
def any(
self,
axis: None = None,
out: None = None,
keepdims: Literal[False] | _NoValueType = ...,
) -> bool_: ...
@overload
def any(
self,
axis: _ShapeLike | None = None,
out: None = None,
*,
keepdims: Literal[True],
) -> _MaskedArray[bool_]: ...
@overload
def any(
self,
axis: _ShapeLike | None,
out: None,
keepdims: Literal[True],
) -> _MaskedArray[bool_]: ...
@overload
def any(
self,
axis: _ShapeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> bool_ | _MaskedArray[bool_]: ...
@overload
def any[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def any[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `ndarray.trace` and `ma.core.trace`
@overload
def trace(
self, # >= 2D MaskedArray
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
dtype: DTypeLike | None = None,
out: None = None,
) -> Any: ...
@overload
def trace[ArrayT: np.ndarray](
self, # >= 2D MaskedArray
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
@overload
def trace[ArrayT: np.ndarray](
self, # >= 2D MaskedArray
offset: SupportsIndex,
axis1: SupportsIndex,
axis2: SupportsIndex,
dtype: DTypeLike | None,
out: ArrayT,
) -> ArrayT: ...
# This differs from `ndarray.dot`, in that 1D dot 1D returns a 0D array.
@overload
def dot(self, b: ArrayLike, out: None = None, strict: bool = False) -> _MaskedArray[Any]: ...
@overload
def dot[ArrayT: np.ndarray](self, b: ArrayLike, out: ArrayT, strict: bool = False) -> ArrayT: ...
# Keep in sync with `ma.core.sum`
@overload # type: ignore[override]
def sum(
self,
/,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def sum[ArrayT: np.ndarray](
self,
/,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def sum[ArrayT: np.ndarray](
self,
/,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `ma.core.prod`
@overload # type: ignore[override]
def prod(
self,
/,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def prod[ArrayT: np.ndarray](
self,
/,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def prod[ArrayT: np.ndarray](
self,
/,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
product = prod
# Keep in sync with `ndarray.cumprod`
@override # type: ignore[override]
@overload
def cumprod[DTypeT: dtype[number | object_]](
self: MaskedArray[Any, DTypeT],
axis: None = None,
dtype: None = None,
out: None = None,
) -> MaskedArray[tuple[int], DTypeT]: ...
@overload # bool_
def cumprod(
self: _MaskedArray[np.bool],
axis: None = None,
dtype: None = None,
out: None = None,
) -> _Masked1D[np.int_]: ...
@overload # dtype: <known> (keyword)
def cumprod[ScalarT: np.generic](
self: _MaskedArray[number | bool_ | object_],
axis: None = None,
*,
dtype: _DTypeLike[ScalarT],
out: None = None,
) -> _Masked1D[ScalarT]: ...
@overload # dtype: <unknown> (keyword)
def cumprod(
self: _MaskedArray[number | bool_ | object_],
axis: None = None,
*,
dtype: DTypeLike,
out: None = None,
) -> _Masked1D[Any]: ...
@overload # dtype: <known> (positional)
def cumprod[ScalarT: np.generic](
self: _MaskedArray[number | bool_ | object_],
axis: None,
dtype: _DTypeLike[ScalarT],
out: None = None,
) -> _Masked1D[ScalarT]: ...
@overload # dtype: <unknown> (positional)
def cumprod(
self: _MaskedArray[number | bool_ | object_],
axis: None,
dtype: DTypeLike,
out: None = None,
) -> _Masked1D[Any]: ...
@overload # axis: <given>
def cumprod[ArrayT: _MaskedArray[number | object_]](
self: ArrayT,
axis: SupportsIndex,
dtype: None = None,
out: None = None,
) -> ArrayT: ...
@overload # bool_, axis: <given>
def cumprod[ShapeT: _Shape](
self: MaskedArray[ShapeT, np.dtype[np.bool]],
axis: SupportsIndex,
dtype: None = None,
out: None = None,
) -> MaskedArray[ShapeT, np.dtype[np.int_]]: ...
@overload # axis: <given>, dtype: <known>
def cumprod[ShapeT: _Shape, ScalarT: np.generic](
self: MaskedArray[ShapeT, dtype[number | bool_ | object_]],
axis: SupportsIndex,
dtype: _DTypeLike[ScalarT],
out: None = None,
) -> MaskedArray[ShapeT, dtype[ScalarT]]: ...
@overload # axis: <given>, dtype: <unknown>
def cumprod[ShapeT: _Shape](
self: MaskedArray[ShapeT, dtype[number | bool_ | object_]],
axis: SupportsIndex,
dtype: DTypeLike,
out: None = None,
) -> MaskedArray[ShapeT]: ...
@overload # out: ndarray
def cumprod[ArrayT: ndarray](
self: _MaskedArray[number | bool_ | object_],
axis: SupportsIndex | None,
dtype: DTypeLike | None,
out: ArrayT,
) -> ArrayT: ...
@overload
def cumprod[ArrayT: ndarray]( # pyright: ignore[reportIncompatibleMethodOverride]
self: _MaskedArray[number | bool_ | object_],
axis: SupportsIndex | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
# Keep in sync with `ndarray.cumsum`
@override # type: ignore[override]
@overload
def cumsum[DTypeT: dtype[number | timedelta64 | object_]](
self: MaskedArray[Any, DTypeT],
axis: None = None,
dtype: None = None,
out: None = None,
) -> MaskedArray[tuple[int], DTypeT]: ...
@overload # bool_
def cumsum(
self: _MaskedArray[np.bool],
axis: None = None,
dtype: None = None,
out: None = None,
) -> _Masked1D[np.int_]: ...
@overload # dtype: <known> (keyword)
def cumsum[ScalarT: np.generic](
self: _MaskedArray[number | bool_ | timedelta64 | object_],
axis: None = None,
*,
dtype: _DTypeLike[ScalarT],
out: None = None,
) -> _Masked1D[ScalarT]: ...
@overload # dtype: <unknown> (keyword)
def cumsum(
self: _MaskedArray[number | bool_ | timedelta64 | object_],
axis: None = None,
*,
dtype: DTypeLike,
out: None = None,
) -> _Masked1D[Any]: ...
@overload # dtype: <known> (positional)
def cumsum[ScalarT: np.generic](
self: _MaskedArray[number | bool_ | timedelta64 | object_],
axis: None,
dtype: _DTypeLike[ScalarT],
out: None = None,
) -> _Masked1D[ScalarT]: ...
@overload # dtype: <unknown> (positional)
def cumsum(
self: _MaskedArray[number | bool_ | timedelta64 | object_],
axis: None,
dtype: DTypeLike,
out: None = None,
) -> _Masked1D[Any]: ...
@overload # axis: <given>
def cumsum[ArrayT: _MaskedArray[number | timedelta64 | object_]](
self: ArrayT,
axis: SupportsIndex,
dtype: None = None,
out: None = None,
) -> ArrayT: ...
@overload # bool_, axis: <given>
def cumsum[ShapeT: _Shape](
self: MaskedArray[ShapeT, np.dtype[np.bool]],
axis: SupportsIndex,
dtype: None = None,
out: None = None,
) -> MaskedArray[ShapeT, np.dtype[np.int_]]: ...
@overload # axis: <given>, dtype: <known>
def cumsum[ShapeT: _Shape, ScalarT: np.generic](
self: MaskedArray[ShapeT, dtype[number | bool_ | timedelta64 | object_]],
axis: SupportsIndex,
dtype: _DTypeLike[ScalarT],
out: None = None,
) -> MaskedArray[ShapeT, dtype[ScalarT]]: ...
@overload # axis: <given>, dtype: <unknown>
def cumsum[ShapeT: _Shape](
self: MaskedArray[ShapeT, dtype[number | bool_ | timedelta64 | object_]],
axis: SupportsIndex,
dtype: DTypeLike,
out: None = None,
) -> MaskedArray[ShapeT]: ...
@overload # out: ndarray
def cumsum[ArrayT: ndarray](
self: _MaskedArray[number | bool_ | timedelta64 | object_],
axis: SupportsIndex | None,
dtype: DTypeLike | None,
out: ArrayT,
) -> ArrayT: ...
@overload
def cumsum[ArrayT: ndarray]( # pyright: ignore[reportIncompatibleMethodOverride]
self: _MaskedArray[number | bool_ | timedelta64 | object_],
axis: SupportsIndex | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
# Keep in sync with `ma.core.mean`
@overload # type: ignore[override]
def mean(
self,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def mean[ArrayT: np.ndarray](
self,
/,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def mean[ArrayT: np.ndarray](
self,
/,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# keep roughly in sync with `ma.core.anom`
@overload
def anom(self, axis: SupportsIndex | None = None, dtype: None = None) -> Self: ...
@overload
def anom(self, axis: SupportsIndex | None = None, *, dtype: DTypeLike) -> MaskedArray[_ShapeT_co, np.dtype]: ...
@overload
def anom(self, axis: SupportsIndex | None, dtype: DTypeLike) -> MaskedArray[_ShapeT_co, np.dtype]: ...
# keep in sync with `std` and `ma.core.var`
@overload # type: ignore[override]
def var(
self,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> Any: ...
@overload
def var[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
@overload
def var[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
# keep in sync with `var` and `ma.core.std`
@overload # type: ignore[override]
def std(
self,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> Any: ...
@overload
def std[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
@overload
def std[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `ndarray.round`
@overload # out=None (default)
def round(self, /, decimals: SupportsIndex = 0, out: None = None) -> Self: ...
@overload # out=ndarray
def round[ArrayT: np.ndarray](self, /, decimals: SupportsIndex, out: ArrayT) -> ArrayT: ...
@overload
def round[ArrayT: np.ndarray](self, /, decimals: SupportsIndex = 0, *, out: ArrayT) -> ArrayT: ...
def argsort( # type: ignore[override]
self,
axis: SupportsIndex | _NoValueType = ...,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: bool = False,
descending: bool = False,
) -> _MaskedArray[intp]: ...
# keep in sync with `MaskedArray.argmin` (below) and `ndarray.argmax`
@override # type: ignore[override]
@overload
def argmax(
self,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload # axis: <given>
def argmax(
self,
axis: SupportsIndex,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> _MaskedArray[intp]: ...
@overload # keepdims: True
def argmax(
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[True],
) -> MaskedArray[_ShapeT_co, dtype[intp]]: ...
@overload # out: <given> (keyword)
def argmax[ArrayT: NDArray[intp]](
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload # out: <given> (positional)
def argmax[ArrayT: NDArray[intp]]( # pyright: ignore[reportIncompatibleMethodOverride]
self,
axis: SupportsIndex | None,
fill_value: _ScalarLike_co | None,
out: ArrayT,
*,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# keep in sync with `MaskedArray.argmax` (above) and `ndarray.argmin`
@override # type: ignore[override]
@overload
def argmin(
self,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload # axis: <given>
def argmin(
self,
axis: SupportsIndex,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> _MaskedArray[intp]: ...
@overload # keepdims: True
def argmin(
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[True],
) -> MaskedArray[_ShapeT_co, dtype[intp]]: ...
@overload # out: <given> (keyword)
def argmin[ArrayT: NDArray[intp]](
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload # out: <given> (positional)
def argmin[ArrayT: NDArray[intp]]( # pyright: ignore[reportIncompatibleMethodOverride]
self,
axis: SupportsIndex | None,
fill_value: _ScalarLike_co | None,
out: ArrayT,
*,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
#
def sort( # type: ignore[override]
self,
axis: SupportsIndex = -1,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: Literal[False] | None = False,
descending: Literal[False] | None = False,
) -> None: ...
#
@overload # type: ignore[override]
def min[ScalarT: np.generic](
self: _MaskedArray[ScalarT],
axis: None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: Literal[False] | _NoValueType = ...,
) -> ScalarT: ...
@overload
def min(
self,
axis: _ShapeLike | None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...
) -> Any: ...
@overload
def min[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def min[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
#
@overload # type: ignore[override]
def max[ScalarT: np.generic](
self: _MaskedArray[ScalarT],
axis: None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: Literal[False] | _NoValueType = ...,
) -> ScalarT: ...
@overload
def max(
self,
axis: _ShapeLike | None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...
) -> Any: ...
@overload
def max[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def max[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
#
@overload
def ptp[ScalarT: np.generic](
self: _MaskedArray[ScalarT],
axis: None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: Literal[False] = False,
) -> ScalarT: ...
@overload
def ptp(
self,
axis: _ShapeLike | None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: bool = False,
) -> Any: ...
@overload
def ptp[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool = False,
) -> ArrayT: ...
@overload
def ptp[ArrayT: np.ndarray](
self,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool = False,
) -> ArrayT: ...
#
@overload
def partition(
self,
/,
kth: _ArrayLikeInt,
axis: SupportsIndex = -1,
kind: _PartitionKind = "introselect",
order: None = None
) -> None: ...
@overload
def partition(
self: _MaskedArray[np.void],
/,
kth: _ArrayLikeInt,
axis: SupportsIndex = -1,
kind: _PartitionKind = "introselect",
order: str | Sequence[str] | None = None,
) -> None: ...
# keep in sync with ndarray.argpartition
@override
@overload # axis: None
def argpartition(
self,
kth: _ArrayLikeInt,
/,
axis: None,
kind: _PartitionKind = "introselect",
order: None = None,
) -> MaskedArray[tuple[int], np.dtype[intp]]: ...
@overload # axis: index (default)
def argpartition(
self,
kth: _ArrayLikeInt,
/,
axis: SupportsIndex = -1,
kind: _PartitionKind = "introselect",
order: None = None,
) -> MaskedArray[_ShapeT_co, np.dtype[intp]]: ...
@overload # void, axis: None
def argpartition(
self: _MaskedArray[np.void],
kth: _ArrayLikeInt,
/,
axis: None,
kind: _PartitionKind = "introselect",
order: str | Sequence[str] | None = None,
) -> MaskedArray[tuple[int], np.dtype[intp]]: ...
@overload # void, axis: index (default)
def argpartition(
self: _MaskedArray[np.void],
kth: _ArrayLikeInt,
/,
axis: SupportsIndex = -1,
kind: _PartitionKind = "introselect",
order: str | Sequence[str] | None = None,
) -> MaskedArray[_ShapeT_co, np.dtype[intp]]: ...
# Keep in-sync with np.ma.take
@overload # type: ignore[override]
def take[ScalarT: np.generic](
self: _MaskedArray[ScalarT],
indices: _IntLike_co,
axis: None = None,
out: None = None,
mode: _ModeKind = "raise"
) -> ScalarT: ...
@overload
def take[ScalarT: np.generic](
self: _MaskedArray[ScalarT],
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
out: None = None,
mode: _ModeKind = "raise",
) -> _MaskedArray[ScalarT]: ...
@overload
def take[ArrayT: np.ndarray](
self,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None,
out: ArrayT,
mode: _ModeKind = "raise",
) -> ArrayT: ...
@overload
def take[ArrayT: np.ndarray](
self,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
*,
out: ArrayT,
mode: _ModeKind = "raise",
) -> ArrayT: ...
# keep in sync with `ndarray.diagonal`
@override
@overload # ?d (workaround)
def diagonal[DTypeT: dtype](
self: MaskedArray[tuple[Never, Never, Never, Never], DTypeT],
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
) -> MaskedArray[_AnyShape, DTypeT]: ...
@overload # 2d
def diagonal[DTypeT: dtype](
self: MaskedArray[tuple[int, int], DTypeT],
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
) -> MaskedArray[tuple[int], DTypeT]: ...
@overload # 3d
def diagonal[DTypeT: dtype](
self: MaskedArray[tuple[int, int, int], DTypeT],
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
) -> MaskedArray[tuple[int, int], DTypeT]: ...
@overload # Nd (fallback)
def diagonal(
self,
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
# keep in sync with `ndarray.repeat`
@override
@overload
def repeat(
self,
/,
repeats: _ArrayLikeInt_co,
axis: None = None,
) -> MaskedArray[tuple[int], _DTypeT_co]: ...
@overload
def repeat(
self,
/,
repeats: _ArrayLikeInt_co,
axis: SupportsIndex,
) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
# keep in sync with `ndarray.flatten` and `ndarray.ravel`
@override
def flatten(self, /, order: _OrderKACF = "C") -> MaskedArray[tuple[int], _DTypeT_co]: ...
@override
def ravel(self, order: _OrderKACF = "C") -> MaskedArray[tuple[int], _DTypeT_co]: ...
# keep in sync with `ndarray.squeeze`
@override
def squeeze(
self,
/,
axis: SupportsIndex | tuple[SupportsIndex, ...] | None = None,
) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
#
def toflex(self) -> MaskedArray[_ShapeT_co, np.dtype[np.void]]: ...
def torecords(self) -> MaskedArray[_ShapeT_co, np.dtype[np.void]]: ...
#
@override
def tobytes(self, /, fill_value: Incomplete | None = None, order: _OrderKACF = "C") -> bytes: ... # type: ignore[override]
# keep in sync with `ndarray.tolist`
@override
@overload
def tolist[T](self: MaskedArray[tuple[Never], np.dtype[generic[T]]], /, fill_value: _ScalarLike_co | None = None) -> Any: ...
@overload
def tolist[T](self: MaskedArray[tuple[()], np.dtype[generic[T]]], /, fill_value: _ScalarLike_co | None = None) -> T: ...
@overload
def tolist[T](self: _Masked1D[np.generic[T]], /, fill_value: _ScalarLike_co | None = None) -> list[T]: ...
@overload
def tolist[T](
self: MaskedArray[tuple[int, int], np.dtype[generic[T]]],
/,
fill_value: _ScalarLike_co | None = None,
) -> list[list[T]]: ...
@overload
def tolist[T](
self: MaskedArray[tuple[int, int, int], np.dtype[generic[T]]],
/,
fill_value: _ScalarLike_co | None = None,
) -> list[list[list[T]]]: ...
@overload
def tolist(self, /, fill_value: _ScalarLike_co | None = None) -> Any: ...
# NOTE: will raise `NotImplementedError`
@override
def tofile(self, /, fid: Never, sep: str = "", format: str = "%s") -> NoReturn: ... # type: ignore[override]
#
@override
def __getstate__(self) -> tuple[Any, ...]: ...
@override
def __setstate__(self, state: tuple[Any, ...]) -> None: ...
@override
def __reduce__(self) -> tuple[Any, ...]: ...
@override
def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self: ...
# Keep `dtype` at the bottom to avoid name conflicts with `np.dtype`
@property
def dtype(self) -> _DTypeT_co: ...
@dtype.setter
def dtype[DTypeT: np.dtype](self: MaskedArray[_AnyShape, DTypeT], dtype: DTypeT, /) -> None: ...
class mvoid(MaskedArray[_ShapeT_co, _DTypeT_co]):
def __new__(
cls,
/,
data: ArrayLike,
mask: _ArrayLikeBool_co = nomask,
dtype: DTypeLike | None = None,
fill_value: _FillValue = None,
hardmask: bool = False,
copy: bool = False,
subok: bool = True,
) -> Self: ...
@override
def __getitem__(self, indx: _ToIndices, /) -> Incomplete: ... # type: ignore[override]
@override
def __setitem__(self, indx: _ToIndices, value: ArrayLike, /) -> None: ... # type: ignore[override]
@override
def __iter__[ScalarT: np.generic](self: mvoid[Any, np.dtype[ScalarT]], /) -> Iterator[MaskedConstant | ScalarT]: ...
@override
def __len__(self, /) -> int: ...
@override
def filled(self, /, fill_value: _ScalarLike_co | None = None) -> Self | np.void: ... # type: ignore[override]
@override # list or tuple
def tolist(self) -> Sequence[Incomplete]: ... # type: ignore[override]
def isMaskedArray(x: object) -> TypeIs[MaskedArray]: ...
def isarray(x: object) -> TypeIs[MaskedArray]: ... # alias to isMaskedArray
def isMA(x: object) -> TypeIs[MaskedArray]: ... # alias to isMaskedArray
# 0D float64 array
class MaskedConstant(MaskedArray[tuple[()], dtype[float64]]):
def __new__(cls) -> Self: ...
# these overrides are no-ops
@override
def __iadd__(self, other: _Ignored, /) -> Self: ... # type: ignore[override]
@override
def __isub__(self, other: _Ignored, /) -> Self: ... # type: ignore[override]
@override
def __imul__(self, other: _Ignored, /) -> Self: ... # type: ignore[override]
@override
def __ifloordiv__(self, other: _Ignored, /) -> Self: ...
@override
def __itruediv__(self, other: _Ignored, /) -> Self: ... # type: ignore[override]
@override
def __ipow__(self, other: _Ignored, /) -> Self: ... # type: ignore[override]
@override
def __deepcopy__(self, /, memo: _Ignored) -> Self: ... # type: ignore[override]
@override
def copy(self, /, *args: _Ignored, **kwargs: _Ignored) -> Self: ...
masked: Final[MaskedConstant] = ...
masked_singleton: Final[MaskedConstant] = ...
# this should NOT be a (PEP 695) type alias, see https://github.com/numpy/numpy/issues/31737
masked_array = MaskedArray
# keep in sync with `MaskedArray.__new__`
@overload
def array[ScalarT: np.generic](
data: _ArrayLike[ScalarT],
dtype: None = None,
copy: bool = False,
order: _OrderKACF | None = None,
mask: _ArrayLikeBool_co = nomask,
fill_value: _ScalarLike_co | None = None,
keep_mask: bool = True,
hard_mask: bool = False,
shrink: bool = True,
subok: bool = True,
ndmin: int = 0,
) -> _MaskedArray[ScalarT]: ...
@overload
def array[ScalarT: np.generic](
data: object,
dtype: _DTypeLike[ScalarT],
copy: bool = False,
order: _OrderKACF | None = None,
mask: _ArrayLikeBool_co = nomask,
fill_value: _ScalarLike_co | None = None,
keep_mask: bool = True,
hard_mask: bool = False,
shrink: bool = True,
subok: bool = True,
ndmin: int = 0,
) -> _MaskedArray[ScalarT]: ...
@overload
def array(
data: object,
dtype: DTypeLike | None = None,
copy: bool = False,
order: _OrderKACF | None = None,
mask: _ArrayLikeBool_co = nomask,
fill_value: _ScalarLike_co | None = None,
keep_mask: bool = True,
hard_mask: bool = False,
shrink: bool = True,
subok: bool = True,
ndmin: int = 0,
) -> _MaskedArray[Any]: ...
# keep in sync with `array`
@overload
def asarray[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
dtype: None = None,
order: _OrderKACF | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def asarray[ScalarT: np.generic](
a: object,
dtype: _DTypeLike[ScalarT],
order: _OrderKACF | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def asarray(
a: object,
dtype: DTypeLike | None = None,
order: _OrderKACF | None = None,
) -> _MaskedArray[Any]: ...
# keep in sync with `asarray` (but note the additional first overload)
@overload
def asanyarray[MArrayT: MaskedArray](a: MArrayT, dtype: None = None, order: _OrderKACF | None = None) -> MArrayT: ...
@overload
def asanyarray[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
dtype: None = None,
order: _OrderKACF | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def asanyarray[ScalarT: np.generic](
a: object,
dtype: _DTypeLike[ScalarT],
order: _OrderKACF | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def asanyarray(
a: object,
dtype: DTypeLike | None = None,
order: _OrderKACF | None = None,
) -> _MaskedArray[Any]: ...
#
def is_masked(x: object) -> bool: ...
@overload
def min[ScalarT: np.generic](
obj: _ArrayLike[ScalarT],
axis: None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: Literal[False] | _NoValueType = ...,
) -> ScalarT: ...
@overload
def min(
obj: ArrayLike,
axis: _ShapeLike | None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...
) -> Any: ...
@overload
def min[ArrayT: np.ndarray](
obj: ArrayLike,
axis: _ShapeLike | None,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def min[ArrayT: np.ndarray](
obj: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def max[ScalarT: np.generic](
obj: _ArrayLike[ScalarT],
axis: None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: Literal[False] | _NoValueType = ...,
) -> ScalarT: ...
@overload
def max(
obj: ArrayLike,
axis: _ShapeLike | None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...
) -> Any: ...
@overload
def max[ArrayT: np.ndarray](
obj: ArrayLike,
axis: _ShapeLike | None,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def max[ArrayT: np.ndarray](
obj: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def ptp[ScalarT: np.generic](
obj: _ArrayLike[ScalarT],
axis: None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: Literal[False] | _NoValueType = ...,
) -> ScalarT: ...
@overload
def ptp(
obj: ArrayLike,
axis: _ShapeLike | None = None,
out: None = None,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...
) -> Any: ...
@overload
def ptp[ArrayT: np.ndarray](
obj: ArrayLike,
axis: _ShapeLike | None,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def ptp[ArrayT: np.ndarray](
obj: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# we cannot meaningfully annotate `frommethod` further, because the callable signature
# of the return type fully depends on the *value* of `methodname` and `reversed` in
# a way that cannot be expressed in the Python type system.
def _frommethod(methodname: str, reversed: bool = False) -> types.FunctionType: ...
# NOTE: The following `*_mask` functions will accept any array-like input runtime, but
# since their use-cases are specific to masks, they only accept `MaskedArray` inputs.
# keep in sync with `MaskedArray.harden_mask`
def harden_mask[MArrayT: MaskedArray](a: MArrayT) -> MArrayT: ...
# keep in sync with `MaskedArray.soften_mask`
def soften_mask[MArrayT: MaskedArray](a: MArrayT) -> MArrayT: ...
# keep in sync with `MaskedArray.shrink_mask`
def shrink_mask[MArrayT: MaskedArray](a: MArrayT) -> MArrayT: ...
# keep in sync with `MaskedArray.ids`
def ids(a: ArrayLike) -> tuple[int, int]: ...
# keep in sync with `ndarray.nonzero`
def nonzero(a: ArrayLike) -> tuple[_Array1D[np.intp], ...]: ...
# keep first overload in sync with `MaskedArray.ravel`
@overload
def ravel[DTypeT: np.dtype](a: np.ndarray[Any, DTypeT], order: _OrderKACF = "C") -> MaskedArray[tuple[int], DTypeT]: ...
@overload
def ravel[ScalarT: np.generic](a: _ArrayLike[ScalarT], order: _OrderKACF = "C") -> _Masked1D[ScalarT]: ...
@overload
def ravel(a: ArrayLike, order: _OrderKACF = "C") -> MaskedArray[tuple[int], _DTypeT_co]: ...
# keep roughly in sync with `lib._function_base_impl.copy`
@overload
def copy[MArrayT: MaskedArray](a: MArrayT, order: _OrderKACF = "C") -> MArrayT: ...
@overload
def copy[ShapeT: _Shape, DTypeT: np.dtype](
a: np.ndarray[ShapeT, DTypeT],
order: _OrderKACF = "C",
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload
def copy[ScalarT: np.generic](a: _ArrayLike[ScalarT], order: _OrderKACF = "C") -> _MaskedArray[ScalarT]: ...
@overload
def copy(a: ArrayLike, order: _OrderKACF = "C") -> _MaskedArray[Incomplete]: ...
# keep in sync with `_core.fromnumeric.diagonal`
@overload
def diagonal[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
) -> NDArray[ScalarT]: ...
@overload
def diagonal(
a: ArrayLike,
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
) -> NDArray[Incomplete]: ...
# keep in sync with `_core.fromnumeric.repeat`
@overload
def repeat[ScalarT: np.generic](a: _ArrayLike[ScalarT], repeats: _ArrayLikeInt_co, axis: None = None) -> _Masked1D[ScalarT]: ...
@overload
def repeat[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
repeats: _ArrayLikeInt_co,
axis: SupportsIndex,
) -> _MaskedArray[ScalarT]: ...
@overload
def repeat(a: ArrayLike, repeats: _ArrayLikeInt_co, axis: None = None) -> _Masked1D[Incomplete]: ...
@overload
def repeat(a: ArrayLike, repeats: _ArrayLikeInt_co, axis: SupportsIndex) -> _MaskedArray[Incomplete]: ...
# keep in sync with `_core.fromnumeric.swapaxes`
@overload
def swapaxes[MArrayT: MaskedArray](a: MArrayT, axis1: SupportsIndex, axis2: SupportsIndex) -> MArrayT: ...
@overload
def swapaxes[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
axis1: SupportsIndex,
axis2: SupportsIndex,
) -> _MaskedArray[ScalarT]: ...
@overload
def swapaxes(a: ArrayLike, axis1: SupportsIndex, axis2: SupportsIndex) -> _MaskedArray[Incomplete]: ...
# NOTE: The `MaskedArray.anom` definition is specific to `MaskedArray`, so we need
# additional overloads to cover the array-like input here.
@overload # a: MaskedArray, dtype=None
def anom[MArrayT: MaskedArray](a: MArrayT, axis: SupportsIndex | None = None, dtype: None = None) -> MArrayT: ...
@overload # a: array-like, dtype=None
def anom[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
axis: SupportsIndex | None = None,
dtype: None = None,
) -> _MaskedArray[ScalarT]: ...
@overload # a: unknown array-like, dtype: dtype-like (positional)
def anom[ScalarT: np.generic](a: ArrayLike, axis: SupportsIndex | None, dtype: _DTypeLike[ScalarT]) -> _MaskedArray[ScalarT]: ...
@overload # a: unknown array-like, dtype: dtype-like (keyword)
def anom[ScalarT: np.generic](
a: ArrayLike,
axis: SupportsIndex | None = None,
*,
dtype: _DTypeLike[ScalarT],
) -> _MaskedArray[ScalarT]: ...
@overload # a: unknown array-like, dtype: unknown dtype-like (positional)
def anom(a: ArrayLike, axis: SupportsIndex | None, dtype: DTypeLike) -> _MaskedArray[Incomplete]: ...
@overload # a: unknown array-like, dtype: unknown dtype-like (keyword)
def anom(a: ArrayLike, axis: SupportsIndex | None = None, *, dtype: DTypeLike) -> _MaskedArray[Incomplete]: ...
anomalies = anom
# Keep in sync with `any` and `MaskedArray.all`
@overload
def all(a: ArrayLike, axis: None = None, out: None = None, keepdims: Literal[False] | _NoValueType = ...) -> np.bool: ...
@overload
def all(a: ArrayLike, axis: _ShapeLike | None, out: None, keepdims: Literal[True]) -> _MaskedArray[np.bool]: ...
@overload
def all(a: ArrayLike, axis: _ShapeLike | None = None, out: None = None, *, keepdims: Literal[True]) -> _MaskedArray[np.bool]: ...
@overload
def all(
a: ArrayLike,
axis: _ShapeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> np.bool | _MaskedArray[np.bool]: ...
@overload
def all[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def all[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `all` and `MaskedArray.any`
@overload
def any(a: ArrayLike, axis: None = None, out: None = None, keepdims: Literal[False] | _NoValueType = ...) -> np.bool: ...
@overload
def any(a: ArrayLike, axis: _ShapeLike | None, out: None, keepdims: Literal[True]) -> _MaskedArray[np.bool]: ...
@overload
def any(a: ArrayLike, axis: _ShapeLike | None = None, out: None = None, *, keepdims: Literal[True]) -> _MaskedArray[np.bool]: ...
@overload
def any(
a: ArrayLike,
axis: _ShapeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> np.bool | _MaskedArray[np.bool]: ...
@overload
def any[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def any[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: ArrayT, keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# NOTE: The `MaskedArray.compress` definition uses its `DTypeT_co` type parameter,
# which wouldn't work here for array-like inputs, so we need additional overloads.
@overload
def compress[ScalarT: np.generic](
condition: _ArrayLikeBool_co,
a: _ArrayLike[ScalarT],
axis: None = None,
out: None = None,
) -> _Masked1D[ScalarT]: ...
@overload
def compress[ScalarT: np.generic](
condition: _ArrayLikeBool_co,
a: _ArrayLike[ScalarT],
axis: _ShapeLike | None = None,
out: None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def compress(condition: _ArrayLikeBool_co, a: ArrayLike, axis: None = None, out: None = None) -> _Masked1D[Incomplete]: ...
@overload
def compress(
condition: _ArrayLikeBool_co,
a: ArrayLike,
axis: _ShapeLike | None = None,
out: None = None,
) -> _MaskedArray[Incomplete]: ...
@overload
def compress[ArrayT: np.ndarray](condition: _ArrayLikeBool_co, a: ArrayLike, axis: _ShapeLike | None, out: ArrayT) -> ArrayT: ...
@overload
def compress[ArrayT: np.ndarray](
condition: _ArrayLikeBool_co,
a: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
# Keep in sync with `cumprod` and `MaskedArray.cumsum`
@overload # out: None (default)
def cumsum(
a: ArrayLike,
axis: SupportsIndex | None = None,
dtype: DTypeLike | None = None,
out: None = None,
) -> _MaskedArray[Incomplete]: ...
@overload # out: ndarray (positional)
def cumsum[ArrayT: np.ndarray](a: ArrayLike, axis: SupportsIndex | None, dtype: DTypeLike | None, out: ArrayT) -> ArrayT: ...
@overload # out: ndarray (kwarg)
def cumsum[ArrayT: np.ndarray](
a: ArrayLike,
axis: SupportsIndex | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
# Keep in sync with `cumsum` and `MaskedArray.cumsum`
@overload # out: None (default)
def cumprod(
a: ArrayLike,
axis: SupportsIndex | None = None,
dtype: DTypeLike | None = None,
out: None = None,
) -> _MaskedArray[Incomplete]: ...
@overload # out: ndarray (positional)
def cumprod[ArrayT: np.ndarray](a: ArrayLike, axis: SupportsIndex | None, dtype: DTypeLike | None, out: ArrayT) -> ArrayT: ...
@overload # out: ndarray (kwarg)
def cumprod[ArrayT: np.ndarray](
a: ArrayLike,
axis: SupportsIndex | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
# Keep in sync with `sum`, `prod`, `product`, and `MaskedArray.mean`
@overload
def mean(
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> Incomplete: ...
@overload
def mean[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def mean[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `mean`, `prod`, `product`, and `MaskedArray.sum`
@overload
def sum(
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> Incomplete: ...
@overload
def sum[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def sum[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `product` and `MaskedArray.prod`
@overload
def prod(
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> Incomplete: ...
@overload
def prod[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def prod[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `prod` and `MaskedArray.prod`
@overload
def product(
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
keepdims: bool | _NoValueType = ...,
) -> Incomplete: ...
@overload
def product[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def product[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# Keep in sync with `MaskedArray.trace` and `_core.fromnumeric.trace`
@overload
def trace(
a: ArrayLike,
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
dtype: DTypeLike | None = None,
out: None = None,
) -> Incomplete: ...
@overload
def trace[ArrayT: np.ndarray](
a: ArrayLike,
offset: SupportsIndex,
axis1: SupportsIndex,
axis2: SupportsIndex,
dtype: DTypeLike | None,
out: ArrayT,
) -> ArrayT: ...
@overload
def trace[ArrayT: np.ndarray](
a: ArrayLike,
offset: SupportsIndex = 0,
axis1: SupportsIndex = 0,
axis2: SupportsIndex = 1,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
) -> ArrayT: ...
# keep in sync with `std` and `MaskedArray.var`
@overload
def std(
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> Incomplete: ...
@overload
def std[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
@overload
def std[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
# keep in sync with `std` and `MaskedArray.var`
@overload
def var(
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
out: None = None,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> Incomplete: ...
@overload
def var[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None,
dtype: DTypeLike | None,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
@overload
def var[ArrayT: np.ndarray](
a: ArrayLike,
axis: _ShapeLike | None = None,
dtype: DTypeLike | None = None,
*,
out: ArrayT,
ddof: float = 0,
keepdims: bool | _NoValueType = ...,
mean: _ArrayLikeNumber_co | _NoValueType = ...,
) -> ArrayT: ...
# (a, b)
minimum: _extrema_operation = ...
maximum: _extrema_operation = ...
# NOTE: this is a `_frommethod` instance at runtime
@overload
def count(a: ArrayLike, axis: None = None, keepdims: Literal[False] | _NoValueType = ...) -> int: ...
@overload
def count(a: ArrayLike, axis: _ShapeLike, keepdims: bool | _NoValueType = ...) -> NDArray[int_]: ...
@overload
def count(a: ArrayLike, axis: _ShapeLike | None = None, *, keepdims: Literal[True]) -> NDArray[int_]: ...
@overload
def count(a: ArrayLike, axis: _ShapeLike | None, keepdims: Literal[True]) -> NDArray[int_]: ...
# NOTE: this is a `_frommethod` instance at runtime
@overload
def argmin(
a: ArrayLike,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload
def argmin(
a: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def argmin[ArrayT: np.ndarray](
a: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def argmin[ArrayT: np.ndarray](
a: ArrayLike,
axis: SupportsIndex | None,
fill_value: _ScalarLike_co | None,
out: ArrayT,
*,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
# keep in sync with `argmin`
@overload
def argmax(
a: ArrayLike,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload
def argmax(
a: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def argmax[ArrayT: np.ndarray](
a: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: ArrayT,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def argmax[ArrayT: np.ndarray](
a: ArrayLike,
axis: SupportsIndex | None,
fill_value: _ScalarLike_co | None,
out: ArrayT,
*,
keepdims: bool | _NoValueType = ...,
) -> ArrayT: ...
@overload
def take[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
indices: _IntLike_co,
axis: None = None,
out: None = None,
mode: _ModeKind = "raise",
) -> ScalarT: ...
@overload
def take[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
out: None = None,
mode: _ModeKind = "raise",
) -> _MaskedArray[ScalarT]: ...
@overload
def take(
a: ArrayLike,
indices: _IntLike_co,
axis: SupportsIndex | None = None,
out: None = None,
mode: _ModeKind = "raise",
) -> Any: ...
@overload
def take(
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
out: None = None,
mode: _ModeKind = "raise",
) -> _MaskedArray[Any]: ...
@overload
def take[ArrayT: np.ndarray](
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None,
out: ArrayT,
mode: _ModeKind = "raise",
) -> ArrayT: ...
@overload
def take[ArrayT: np.ndarray](
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
*,
out: ArrayT,
mode: _ModeKind = "raise",
) -> ArrayT: ...
#
def power(a: ArrayLike, b: ArrayLike, third: None = None) -> _MaskedArray[Incomplete]: ...
#
@overload # axis: <default> (deprecated)
@deprecated(
"In the future the default for argsort will be axis=-1, not the current None, to match its documentation and np.argsort. "
"Explicitly pass -1 or None to silence this warning.",
category=MaskedArrayFutureWarning,
stacklevel=2,
)
def argsort(
a: ArrayLike,
axis: _NoValueType = ...,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: bool | None = None,
descending: bool | None = None,
) -> _Array1D[np.intp]: ...
@overload # MaskedArray, axis: None
def argsort(
a: MaskedArray,
axis: None,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: bool | None = None,
descending: bool | None = None,
) -> _Masked1D[np.intp]: ...
@overload # MaskedArray, axis: int-like
def argsort(
a: MaskedArray,
axis: SupportsIndex,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: bool | None = None,
descending: bool | None = None,
) -> _MaskedArray[np.intp]: ...
@overload # array-like, axis: None
def argsort(
a: ArrayLike,
axis: None,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: bool | None = None,
descending: bool | None = None,
) -> _Array1D[np.intp]: ...
@overload # array-like, axis: int-like
def argsort(
a: ArrayLike,
axis: SupportsIndex,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: bool | None = None,
descending: bool | None = None,
) -> NDArray[np.intp]: ...
#
@overload
def sort[ArrayT: np.ndarray](
a: ArrayT,
axis: SupportsIndex = -1,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: Literal[False] | None = None,
descending: Literal[False] | None = False,
) -> ArrayT: ...
@overload
def sort(
a: ArrayLike,
axis: SupportsIndex | None = -1,
kind: _SortKind | None = None,
order: str | Sequence[str] | None = None,
endwith: bool | None = True,
fill_value: _ScalarLike_co | None = None,
*,
stable: Literal[False] | None = None,
descending: Literal[False] | None = False,
) -> NDArray[Any]: ...
#
@overload
def compressed[ScalarT: np.generic](x: _ArrayLike[ScalarT]) -> _Array1D[ScalarT]: ...
@overload
def compressed(x: ArrayLike) -> _Array1D[Any]: ...
#
@overload
def concatenate[ScalarT: np.generic](arrays: _ArrayLike[ScalarT], axis: SupportsIndex | None = 0) -> _MaskedArray[ScalarT]: ...
@overload
def concatenate(arrays: SupportsLenAndGetItem[ArrayLike], axis: SupportsIndex | None = 0) -> _MaskedArray[Incomplete]: ...
# keep in sync with `diag` and `lib._twodim_base_impl.diag`
@overload
def diag[ScalarT: np.generic](v: _ArrayNoD[ScalarT] | Sequence[Sequence[ScalarT]], k: int = 0) -> _MaskedArray[ScalarT]: ...
@overload
def diag[ScalarT: np.generic](v: _Array2D[ScalarT] | Sequence[Sequence[ScalarT]], k: int = 0) -> _Masked1D[ScalarT]: ...
@overload
def diag[ScalarT: np.generic](v: _Array1D[ScalarT] | Sequence[ScalarT], k: int = 0) -> _Masked2D[ScalarT]: ...
@overload
def diag(v: Sequence[Sequence[_ScalarLike_co]], k: int = 0) -> _Masked1D[Incomplete]: ...
@overload
def diag(v: Sequence[_ScalarLike_co], k: int = 0) -> _Masked2D[Incomplete]: ...
@overload
def diag[ScalarT: np.generic](v: _ArrayLike[ScalarT], k: int = 0) -> _MaskedArray[ScalarT]: ...
@overload
def diag(v: ArrayLike, k: int = 0) -> _MaskedArray[Incomplete]: ...
# keep in sync with `right_shift`
@overload
def left_shift[ShapeT: _Shape, ScalarT: np.bool | np.integer | np.object_](
a: ndarray[ShapeT, np.dtype[ScalarT]], n: int
) -> MaskedArray[ShapeT, np.dtype[ScalarT]]: ...
@overload
def left_shift[ScalarT: np.bool | np.integer | np.object_](a: _ArrayLike[ScalarT], n: int) -> _MaskedArray[ScalarT]: ...
@overload
def left_shift(a: _ArrayLikeInt_co, n: int) -> _MaskedArray[Incomplete]: ...
# keep in sync with `left_shift`
@overload
def right_shift[ShapeT: _Shape, ScalarT: np.bool | np.integer | np.object_](
a: ndarray[ShapeT, np.dtype[ScalarT]], n: int
) -> MaskedArray[ShapeT, np.dtype[ScalarT]]: ...
@overload
def right_shift[ScalarT: np.bool | np.integer | np.object_](a: _ArrayLike[ScalarT], n: int) -> _MaskedArray[ScalarT]: ...
@overload
def right_shift(a: _ArrayLikeInt_co, n: int) -> _MaskedArray[Incomplete]: ...
# keep in sync with `_core.fromnumeric.put`
def put(a: np.ndarray, indices: _ArrayLikeInt_co, values: ArrayLike, mode: _ModeKind = "raise") -> None: ...
#
def putmask(a: np.ndarray, mask: _ArrayLikeBool_co, values: ArrayLike) -> None: ...
# keep in sync with `_core.fromnumeric.transpose`
@overload
def transpose[ArrayT: np.ndarray](a: ArrayT, axes: _ShapeLike | None = None) -> ArrayT: ...
@overload
def transpose[ScalarT: np.generic](a: _ArrayLike[ScalarT], axes: _ShapeLike | None = None) -> _MaskedArray[ScalarT]: ...
@overload # `_MaskedArray | np.ndarray` is equivalent to `np.ndarray`
def transpose(a: ArrayLike, axes: _ShapeLike | None = None) -> np.ndarray: ...
# keep in sync with `_core.fromnumeric.reshape`
@overload # shape: index
def reshape[ScalarT: np.generic](
a: _ArrayLike[ScalarT], new_shape: SupportsIndex, order: _OrderACF = "C"
) -> _Masked1D[ScalarT]: ...
@overload # shape: ~ShapeT
def reshape[ScalarT: np.generic, ShapeT: _Shape](
a: _ArrayLike[ScalarT], new_shape: ShapeT, order: _OrderACF = "C"
) -> MaskedArray[ShapeT, np.dtype[ScalarT]]: ...
@overload # shape: Sequence[index]
def reshape[ScalarT: np.generic](
a: _ArrayLike[ScalarT], new_shape: Sequence[SupportsIndex], order: _OrderACF = "C"
) -> _MaskedArray[ScalarT]: ...
@overload # shape: index
def reshape(a: ArrayLike, new_shape: SupportsIndex, order: _OrderACF = "C") -> _Masked1D[Incomplete]: ...
@overload # shape: ~ShapeT
def reshape[ShapeT: _Shape](a: ArrayLike, new_shape: ShapeT, order: _OrderACF = "C") -> MaskedArray[ShapeT]: ...
@overload # shape: Sequence[index]
def reshape(a: ArrayLike, new_shape: Sequence[SupportsIndex], order: _OrderACF = "C") -> _MaskedArray[Incomplete]: ...
# keep in sync with `_core.fromnumeric.resize`
@overload
def resize[ScalarT: np.generic](
x: _ArrayLike[ScalarT], new_shape: SupportsIndex | tuple[SupportsIndex]
) -> _Masked1D[ScalarT]: ...
@overload
def resize[ScalarT: np.generic, ShapeT: _Shape](
x: _ArrayLike[ScalarT], new_shape: ShapeT
) -> MaskedArray[ShapeT, np.dtype[ScalarT]]: ...
@overload
def resize[ScalarT: np.generic](x: _ArrayLike[ScalarT], new_shape: _ShapeLike) -> _MaskedArray[ScalarT]: ...
@overload
def resize(x: ArrayLike, new_shape: SupportsIndex | tuple[SupportsIndex]) -> _Masked1D[Incomplete]: ...
@overload
def resize[ShapeT: _Shape](x: ArrayLike, new_shape: ShapeT) -> MaskedArray[ShapeT]: ...
@overload
def resize(x: ArrayLike, new_shape: _ShapeLike) -> _MaskedArray[Incomplete]: ...
#
def ndim(obj: ArrayLike) -> int: ...
# keep in sync with `_core.fromnumeric.shape`
@overload # this prevents `Any` from being returned with Pyright
def shape(obj: _HasShape[Never]) -> _AnyShape: ...
@overload
def shape[ShapeT: _Shape](obj: _HasShape[ShapeT]) -> ShapeT: ...
@overload
def shape(obj: _PyScalar) -> tuple[()]: ...
@overload # `collections.abc.Sequence` can't be used because `bytes` and `str` are assignable to it
def shape(obj: _PyArray[_PyScalar]) -> tuple[int]: ...
@overload
def shape(obj: _PyArray[_PyArray[_PyScalar]]) -> tuple[int, int]: ...
@overload # requires PEP 688 support
def shape(obj: memoryview | bytearray) -> tuple[int]: ...
@overload
def shape(obj: ArrayLike) -> _AnyShape: ...
#
def size(obj: ArrayLike, axis: SupportsIndex | None = None) -> int: ...
# keep in sync with `lib._function_base_impl.diff`
@overload # known array-type
def diff[MArrayT: _MaskedArray[np.inexact | np.timedelta64 | np.object_]](
a: MArrayT,
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> MArrayT: ...
@overload # known shape, datetime64
def diff[ShapeT: _Shape](
a: MaskedArray[ShapeT, np.dtype[np.datetime64]],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> MaskedArray[ShapeT, np.dtype[np.timedelta64]]: ...
@overload # unknown shape, known scalar-type
def diff[ScalarT: np.inexact | np.timedelta64 | np.object_](
a: _ArrayLike[ScalarT],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _MaskedArray[ScalarT]: ...
@overload # unknown shape, datetime64
def diff(
a: _ArrayLike[np.datetime64],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _MaskedArray[np.timedelta64]: ...
@overload # 1d int
def diff(
a: Sequence[int],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _Masked1D[np.int_]: ...
@overload # 2d int
def diff(
a: Sequence[Sequence[int]],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _Masked2D[np.int_]: ...
@overload # 1d float (the `list` avoids overlap with the `int` overloads)
def diff(
a: list[float],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _Masked1D[np.float64]: ...
@overload # 2d float
def diff(
a: Sequence[list[float]],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _Masked2D[np.float64]: ...
@overload # 1d complex (the `list` avoids overlap with the `int` overloads)
def diff(
a: list[complex],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _Masked1D[np.complex128]: ...
@overload # 2d complex
def diff(
a: Sequence[list[complex]],
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _Masked2D[np.complex128]: ...
@overload # unknown shape, unknown scalar-type
def diff(
a: ArrayLike,
/,
n: int = 1,
axis: SupportsIndex = -1,
prepend: ArrayLike | _NoValueType = ...,
append: ArrayLike | _NoValueType = ...,
) -> _MaskedArray[Incomplete]: ...
# keep in sync with `_core.multiarray.where`
@overload
def where(condition: ArrayLike, x: _NoValueType = ..., y: _NoValueType = ...) -> tuple[_MaskedArray[np.intp], ...]: ...
@overload
def where(condition: ArrayLike, x: ArrayLike, y: ArrayLike) -> _MaskedArray[Incomplete]: ...
# keep in sync with `_core.fromnumeric.choose`
@overload
def choose(
indices: _IntLike_co,
choices: ArrayLike,
out: None = None,
mode: _ModeKind = "raise",
) -> Any: ...
@overload
def choose[ScalarT: np.generic](
indices: _ArrayLikeInt_co,
choices: _ArrayLike[ScalarT],
out: None = None,
mode: _ModeKind = "raise",
) -> _MaskedArray[ScalarT]: ...
@overload
def choose(
indices: _ArrayLikeInt_co,
choices: ArrayLike,
out: None = None,
mode: _ModeKind = "raise",
) -> _MaskedArray[Incomplete]: ...
@overload
def choose[ArrayT: np.ndarray](
indices: _ArrayLikeInt_co,
choices: ArrayLike,
out: ArrayT,
mode: _ModeKind = "raise",
) -> ArrayT: ...
#
@overload # a: masked_array, out: None (default)
def round[MArray: MaskedArray](a: MArray, decimals: int = 0, out: None = None) -> MArray: ...
@overload # a: known array-like, out: None (default)
def round[ScalarT: np.number](a: _ArrayLike[ScalarT], decimals: int = 0, out: None = None) -> _MaskedArray[ScalarT]: ...
@overload # a: unknown array-like, out: None (default)
def round(a: _ArrayLikeNumber_co, decimals: int = 0, out: None = None) -> _MaskedArray[Incomplete]: ...
@overload # out: ndarray (positional)
def round[ArrayT: np.ndarray](a: ArrayLike, decimals: int, out: ArrayT) -> ArrayT: ...
@overload # out: ndarray (keyword)
def round[ArrayT: np.ndarray](a: ArrayLike, decimals: int = 0, *, out: ArrayT) -> ArrayT: ...
#
@overload # a: masked_array, out: None (default)
@deprecated("numpy.ma.round_ is deprecated. Use numpy.ma.round instead.")
def round_[MArray: MaskedArray](a: MArray, decimals: int = 0, out: None = None) -> MArray: ...
@overload # a: known array-like, out: None (default)
@deprecated("numpy.ma.round_ is deprecated. Use numpy.ma.round instead.")
def round_[ScalarT: np.number](a: _ArrayLike[ScalarT], decimals: int = 0, out: None = None) -> _MaskedArray[ScalarT]: ...
@overload # a: unknown array-like, out: None (default)
@deprecated("numpy.ma.round_ is deprecated. Use numpy.ma.round instead.")
def round_(a: _ArrayLikeNumber_co, decimals: int = 0, out: None = None) -> _MaskedArray[Incomplete]: ...
@overload # out: ndarray (positional)
@deprecated("numpy.ma.round_ is deprecated. Use numpy.ma.round instead.")
def round_[ArrayT: np.ndarray](a: ArrayLike, decimals: int, out: ArrayT) -> ArrayT: ...
@overload # out: ndarray (keyword)
@deprecated("numpy.ma.round_ is deprecated. Use numpy.ma.round instead.")
def round_[ArrayT: np.ndarray](a: ArrayLike, decimals: int = 0, *, out: ArrayT) -> ArrayT: ...
# keep in sync with `_core.multiarray.inner`
@overload # (?d T, Nd T) -> 0d|Nd T (workaround)
def inner[ScalarT: _InnerScalar | np.object_](a: _ArrayNoD[ScalarT], b: _ArrayLike[ScalarT]) -> _MaskedArray[ScalarT] | Any: ...
@overload # (Nd T, ?d T) -> 0d|Nd T (workaround)
def inner[ScalarT: _InnerScalar | np.object_](a: _ArrayLike[ScalarT], b: _ArrayNoD[ScalarT]) -> _MaskedArray[ScalarT] | Any: ...
@overload # (1d T, 1d T) -> 0d T
def inner[ScalarT: _InnerScalar](a: _ToArray1D[ScalarT], b: _ToArray1D[ScalarT]) -> ScalarT: ...
@overload # (1d object_, 1d _) -> 0d object
def inner(a: _Array1D[np.object_], b: _Array1D[np.object_] | _ToArray1D[_InnerScalar]) -> Any: ...
@overload # (1d _, 1d object_) -> 0d object
def inner(a: _ToArray1D[_InnerScalar], b: _Array1D[np.object_]) -> Any: ...
@overload # (1d bool, 1d bool) -> bool_
def inner(a: Sequence[bool], b: Sequence[bool]) -> np.bool: ...
@overload # (1d ~int, 1d +int) -> int_
def inner(a: list[int], b: Sequence[int]) -> np.int_: ...
@overload # (1d +int, 1d ~int) -> int_
def inner(a: Sequence[int], b: list[int]) -> np.int_: ...
@overload # (1d ~float, 1d +float) -> float64
def inner(a: list[float], b: Sequence[float]) -> np.float64: ...
@overload # (1d +float, 1d ~float) -> float64
def inner(a: Sequence[float], b: list[float]) -> np.float64: ...
@overload # (1d ~complex, 1d +complex) -> complex128
def inner(a: list[complex], b: Sequence[complex]) -> np.complex128: ...
@overload # (1d +complex, 1d ~complex) -> complex128
def inner(a: Sequence[complex], b: list[complex]) -> np.complex128: ...
@overload # (1d T, 2d T) -> 1d T
def inner[ScalarT: _InnerScalar | np.object_](a: _ToArray1D[ScalarT], b: _Array2D[ScalarT]) -> _Masked1D[ScalarT]: ...
@overload # (2d T, 1d T) -> 1d T
def inner[ScalarT: _InnerScalar | np.object_](a: _ToArray2D[ScalarT], b: _Array1D[ScalarT]) -> _Masked1D[ScalarT]: ...
@overload # (2d T, 2d T) -> 2d _Masked1D
def inner[ScalarT: _InnerScalar | np.object_](a: _ToArray2D[ScalarT], b: _Array2D[ScalarT]) -> _Masked2D[ScalarT]: ...
@overload # fallback
def inner(a: ArrayLike, b: ArrayLike) -> Any: ...
innerproduct = inner
# NOTE: we ignore UP047 because inlining `_AnyScalarT` would result in a lot of code duplication
# keep in sync with `_core.numeric.outer`
@overload
def outer(a: _ArrayLike[_AnyNumericScalarT], b: _ArrayLike[_AnyNumericScalarT]) -> _Masked2D[_AnyNumericScalarT]: ... # noqa: UP047
@overload
def outer(a: _ArrayLikeBool_co, b: _ArrayLikeBool_co) -> _Masked2D[np.bool]: ...
@overload
def outer(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co) -> _Masked2D[np.int_ | Any]: ...
@overload
def outer(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co) -> _Masked2D[np.float64 | Any]: ...
@overload
def outer(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co) -> _Masked2D[np.complex128 | Any]: ...
@overload
def outer(a: _ArrayLikeTD64_co, b: _ArrayLikeTD64_co) -> _Masked2D[np.timedelta64 | Any]: ...
outerproduct = outer
# keep in sync with `convolve` and `_core.numeric.correlate`
@overload
def correlate( # noqa: UP047
a: _ArrayLike[_AnyNumericScalarT],
v: _ArrayLike[_AnyNumericScalarT],
mode: _CorrelateMode = "valid",
propagate_mask: bool = True,
) -> _Masked1D[_AnyNumericScalarT]: ...
@overload
def correlate(
a: _ArrayLikeBool_co,
v: _ArrayLikeBool_co,
mode: _CorrelateMode = "valid",
propagate_mask: bool = True,
) -> _Masked1D[np.bool]: ...
@overload
def correlate(
a: _ArrayLikeInt_co,
v: _ArrayLikeInt_co,
mode: _CorrelateMode = "valid",
propagate_mask: bool = True,
) -> _Masked1D[np.int_ | Any]: ...
@overload
def correlate(
a: _ArrayLikeFloat_co,
v: _ArrayLikeFloat_co,
mode: _CorrelateMode = "valid",
propagate_mask: bool = True,
) -> _Masked1D[np.float64 | Any]: ...
@overload
def correlate(
a: _ArrayLikeNumber_co,
v: _ArrayLikeNumber_co,
mode: _CorrelateMode = "valid",
propagate_mask: bool = True,
) -> _Masked1D[np.complex128 | Any]: ...
@overload
def correlate(
a: _ArrayLikeTD64_co,
v: _ArrayLikeTD64_co,
mode: _CorrelateMode = "valid",
propagate_mask: bool = True,
) -> _Masked1D[np.timedelta64 | Any]: ...
# keep in sync with `correlate` and `_core.numeric.convolve`
@overload
def convolve( # noqa: UP047
a: _ArrayLike[_AnyNumericScalarT],
v: _ArrayLike[_AnyNumericScalarT],
mode: _CorrelateMode = "full",
propagate_mask: bool = True,
) -> _Masked1D[_AnyNumericScalarT]: ...
@overload
def convolve(
a: _ArrayLikeBool_co,
v: _ArrayLikeBool_co,
mode: _CorrelateMode = "full",
propagate_mask: bool = True,
) -> _Masked1D[np.bool]: ...
@overload
def convolve(
a: _ArrayLikeInt_co,
v: _ArrayLikeInt_co,
mode: _CorrelateMode = "full",
propagate_mask: bool = True,
) -> _Masked1D[np.int_ | Any]: ...
@overload
def convolve(
a: _ArrayLikeFloat_co,
v: _ArrayLikeFloat_co,
mode: _CorrelateMode = "full",
propagate_mask: bool = True,
) -> _Masked1D[np.float64 | Any]: ...
@overload
def convolve(
a: _ArrayLikeNumber_co,
v: _ArrayLikeNumber_co,
mode: _CorrelateMode = "full",
propagate_mask: bool = True,
) -> _Masked1D[np.complex128 | Any]: ...
@overload
def convolve(
a: _ArrayLikeTD64_co,
v: _ArrayLikeTD64_co,
mode: _CorrelateMode = "full",
propagate_mask: bool = True,
) -> _Masked1D[np.timedelta64 | Any]: ...
#
def allequal(a: ArrayLike, b: ArrayLike, fill_value: bool = True) -> bool: ...
def allclose(a: ArrayLike, b: ArrayLike, masked_equal: bool = True, rtol: float = 1e-5, atol: float = 1e-8) -> bool: ...
#
def fromflex[ShapeT: _Shape](fxarray: np.ndarray[ShapeT, np.dtype[np.void]]) -> MaskedArray[ShapeT, np.dtype[Incomplete]]: ...
# keep in sync with `lib._function_base_impl.append`
@overload # known array type, axis specified
def append[MArrayT: MaskedArray](
a: MArrayT,
b: MArrayT,
axis: SupportsIndex,
) -> MArrayT: ...
@overload # 1d, known scalar type, axis specified
def append[ScalarT: np.generic](
a: Sequence[ScalarT],
b: Sequence[ScalarT],
axis: SupportsIndex,
) -> _Masked1D[ScalarT]: ...
@overload # 2d, known scalar type, axis specified
def append[ScalarT: np.generic](
a: _Seq2D[ScalarT],
b: _Seq2D[ScalarT],
axis: SupportsIndex,
) -> _Masked2D[ScalarT]: ...
@overload # 3d, known scalar type, axis specified
def append[ScalarT: np.generic](
a: _Seq3D[ScalarT],
b: _Seq3D[ScalarT],
axis: SupportsIndex,
) -> _Masked3D[ScalarT]: ...
@overload # ?d, known scalar type, axis specified
def append[ScalarT: np.generic](
a: _NestedSequence[ScalarT],
b: _NestedSequence[ScalarT],
axis: SupportsIndex,
) -> _MaskedArray[ScalarT]: ...
@overload # ?d, unknown scalar type, axis specified
def append(
a: np.ndarray | _NestedSequence[_ScalarLike_co],
b: _NestedSequence[_ScalarLike_co],
axis: SupportsIndex,
) -> _MaskedArray[Incomplete]: ...
@overload # known scalar type, axis=None
def append[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
b: _ArrayLike[ScalarT],
axis: None = None,
) -> _Masked1D[ScalarT]: ...
@overload # unknown scalar type, axis=None
def append(
a: ArrayLike,
b: ArrayLike,
axis: None = None,
) -> _Masked1D[Incomplete]: ...
# keep in sync with `_core.multiarray.dot`
@overload
def dot(a: ArrayLike, b: ArrayLike, strict: bool = False, out: None = None) -> Incomplete: ...
@overload
def dot[OutT: np.ndarray](a: ArrayLike, b: ArrayLike, strict: bool = False, *, out: OutT) -> OutT: ...
# internal wrapper functions for the functions below
def _convert2ma(
funcname: str,
np_ret: str,
np_ma_ret: str,
params: dict[str, Any] | None = None,
) -> Callable[..., Any]: ...
# keep in sync with `_core.multiarray.arange`
@overload # dtype=<known>
def arange[ScalarT: _ArangeScalar](
start_or_stop: _ArangeScalar | float,
/,
stop: _ArangeScalar | float | None = None,
step: _ArangeScalar | float | None = 1,
*,
dtype: _DTypeLike[ScalarT],
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[ScalarT]: ...
@overload # (int-like, int-like?, int-like?)
def arange(
start_or_stop: _IntLike_co,
/,
stop: _IntLike_co | None = None,
step: _IntLike_co | None = 1,
*,
dtype: type[int] | _DTypeLike[np.int_] | None = None,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.int_]: ...
@overload # (float, float-like?, float-like?)
def arange(
start_or_stop: float | floating,
/,
stop: _FloatLike_co | None = None,
step: _FloatLike_co | None = 1,
*,
dtype: type[float] | _DTypeLike[np.float64] | None = None,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.float64 | Any]: ...
@overload # (float-like, float, float-like?)
def arange(
start_or_stop: _FloatLike_co,
/,
stop: float | floating,
step: _FloatLike_co | None = 1,
*,
dtype: type[float] | _DTypeLike[np.float64] | None = None,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.float64 | Any]: ...
@overload # (timedelta, timedelta-like?, timedelta-like?)
def arange(
start_or_stop: np.timedelta64,
/,
stop: _TD64Like_co | None = None,
step: _TD64Like_co | None = 1,
*,
dtype: _DTypeLike[np.timedelta64] | None = None,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.timedelta64[Incomplete]]: ...
@overload # (timedelta-like, timedelta, timedelta-like?)
def arange(
start_or_stop: _TD64Like_co,
/,
stop: np.timedelta64,
step: _TD64Like_co | None = 1,
*,
dtype: _DTypeLike[np.timedelta64] | None = None,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.timedelta64[Incomplete]]: ...
@overload # (datetime, datetime, timedelta-like) (requires both start and stop)
def arange(
start_or_stop: np.datetime64,
/,
stop: np.datetime64,
step: _TD64Like_co | None = 1,
*,
dtype: _DTypeLike[np.datetime64] | None = None,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.datetime64[Incomplete]]: ...
@overload # (str, str, timedelta-like, dtype=dt64-like) (requires both start and stop)
def arange(
start_or_stop: str,
/,
stop: str,
step: _TD64Like_co | None = 1,
*,
dtype: _DTypeLike[np.datetime64] | _DT64Codes,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.datetime64[Incomplete]]: ...
@overload # dtype=<unknown>
def arange(
start_or_stop: _ArangeScalar | float | str,
/,
stop: _ArangeScalar | float | str | None = None,
step: _ArangeScalar | float | None = 1,
*,
dtype: DTypeLike | None = None,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[Incomplete]: ...
# based on `_core.fromnumeric.clip`
@overload
def clip[ScalarT: np.generic](
a: ScalarT,
a_min: ArrayLike | _NoValueType | None = ...,
a_max: ArrayLike | _NoValueType | None = ...,
out: None = None,
*,
min: ArrayLike | _NoValueType | None = ...,
max: ArrayLike | _NoValueType | None = ...,
fill_value: _FillValue | None = None,
hardmask: bool = False,
dtype: None = None,
**kwargs: Unpack[_UFuncKwargs],
) -> ScalarT: ...
@overload
def clip[ScalarT: np.generic](
a: NDArray[ScalarT],
a_min: ArrayLike | _NoValueType | None = ...,
a_max: ArrayLike | _NoValueType | None = ...,
out: None = None,
*,
min: ArrayLike | _NoValueType | None = ...,
max: ArrayLike | _NoValueType | None = ...,
fill_value: _FillValue | None = None,
hardmask: bool = False,
dtype: None = None,
**kwargs: Unpack[_UFuncKwargs],
) -> _MaskedArray[ScalarT]: ...
@overload
def clip[MArrayT: MaskedArray](
a: ArrayLike,
a_min: ArrayLike | None,
a_max: ArrayLike | None,
out: MArrayT,
*,
min: ArrayLike | _NoValueType | None = ...,
max: ArrayLike | _NoValueType | None = ...,
fill_value: _FillValue | None = None,
hardmask: bool = False,
dtype: DTypeLike | None = None,
**kwargs: Unpack[_UFuncKwargs],
) -> MArrayT: ...
@overload
def clip[MArrayT: MaskedArray](
a: ArrayLike,
a_min: ArrayLike | _NoValueType | None = ...,
a_max: ArrayLike | _NoValueType | None = ...,
*,
out: MArrayT,
min: ArrayLike | _NoValueType | None = ...,
max: ArrayLike | _NoValueType | None = ...,
fill_value: _FillValue | None = None,
hardmask: bool = False,
dtype: DTypeLike | None = None,
**kwargs: Unpack[_UFuncKwargs],
) -> MArrayT: ...
@overload
def clip(
a: ArrayLike,
a_min: ArrayLike | _NoValueType | None = ...,
a_max: ArrayLike | _NoValueType | None = ...,
out: None = None,
*,
min: ArrayLike | _NoValueType | None = ...,
max: ArrayLike | _NoValueType | None = ...,
fill_value: _FillValue | None = None,
hardmask: bool = False,
dtype: DTypeLike | None = None,
**kwargs: Unpack[_UFuncKwargs],
) -> Incomplete: ...
# keep in sync with `_core.multiarray.ones`
@overload
def empty(
shape: SupportsIndex,
dtype: None = None,
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[np.float64]: ...
@overload
def empty[DTypeT: np.dtype](
shape: SupportsIndex,
dtype: DTypeT | _SupportsDType[DTypeT],
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[tuple[int], DTypeT]: ...
@overload
def empty[ScalarT: np.generic](
shape: SupportsIndex,
dtype: type[ScalarT],
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[ScalarT]: ...
@overload
def empty(
shape: SupportsIndex,
dtype: DTypeLike | None = None,
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _Masked1D[Any]: ...
@overload # known shape
def empty[ShapeT: _Shape](
shape: ShapeT,
dtype: None = None,
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[ShapeT, np.dtype[np.float64]]: ...
@overload
def empty[ShapeT: _Shape, DTypeT: np.dtype](
shape: ShapeT,
dtype: DTypeT | _SupportsDType[DTypeT],
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[ShapeT, DTypeT]: ...
@overload
def empty[ShapeT: _Shape, ScalarT: np.generic](
shape: ShapeT,
dtype: type[ScalarT],
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[ShapeT, np.dtype[ScalarT]]: ...
@overload
def empty[ShapeT: _Shape](
shape: ShapeT,
dtype: DTypeLike | None = None,
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[ShapeT]: ...
@overload # unknown shape
def empty[ShapeT: _Shape](
shape: _ShapeLike,
dtype: None = None,
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _MaskedArray[np.float64]: ...
@overload
def empty[DTypeT: np.dtype](
shape: _ShapeLike,
dtype: DTypeT | _SupportsDType[DTypeT],
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[_AnyShape, DTypeT]: ...
@overload
def empty[ScalarT: np.generic](
shape: _ShapeLike,
dtype: type[ScalarT],
order: _OrderCF = "C",
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _MaskedArray[ScalarT]: ...
@overload
def empty(
shape: _ShapeLike,
dtype: DTypeLike | None = None,
*,
device: Literal["cpu"] | None = None,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray: ...
# keep in sync with `_core.multiarray.empty_like`
@overload
def empty_like[MArrayT: MaskedArray](
a: MArrayT,
/,
dtype: None = None,
order: _OrderKACF = "K",
subok: bool = True,
shape: _ShapeLike | None = None,
*,
device: Literal["cpu"] | None = None,
) -> MArrayT: ...
@overload
def empty_like[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
/,
dtype: None = None,
order: _OrderKACF = "K",
subok: bool = True,
shape: _ShapeLike | None = None,
*,
device: Literal["cpu"] | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def empty_like[ScalarT: np.generic](
a: Incomplete,
/,
dtype: _DTypeLike[ScalarT],
order: _OrderKACF = "K",
subok: bool = True,
shape: _ShapeLike | None = None,
*,
device: Literal["cpu"] | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def empty_like(
a: Incomplete,
/,
dtype: DTypeLike | None = None,
order: _OrderKACF = "K",
subok: bool = True,
shape: _ShapeLike | None = None,
*,
device: Literal["cpu"] | None = None,
) -> _MaskedArray[Incomplete]: ...
# This is a bit of a hack to avoid having to duplicate all those `empty` overloads for
# `ones` and `zeros`, that relies on the fact that empty/zeros/ones have identical
# type signatures, but may cause some type-checkers to report incorrect names in case
# of user errors. Mypy and Pyright seem to handle this just fine.
ones = empty
ones_like = empty_like
zeros = empty
zeros_like = empty_like
# keep in sync with `_core.multiarray.frombuffer`
@overload
def frombuffer(
buffer: Buffer,
*,
count: SupportsIndex = -1,
offset: SupportsIndex = 0,
like: _SupportsArrayFunc | None = None,
) -> _MaskedArray[np.float64]: ...
@overload
def frombuffer[ScalarT: np.generic](
buffer: Buffer,
dtype: _DTypeLike[ScalarT],
count: SupportsIndex = -1,
offset: SupportsIndex = 0,
*,
like: _SupportsArrayFunc | None = None,
) -> _MaskedArray[ScalarT]: ...
@overload
def frombuffer(
buffer: Buffer,
dtype: DTypeLike | None = float,
count: SupportsIndex = -1,
offset: SupportsIndex = 0,
*,
like: _SupportsArrayFunc | None = None,
) -> _MaskedArray[Incomplete]: ...
# keep roughly in sync with `_core.numeric.fromfunction`
def fromfunction[ShapeT: _Shape, DTypeT: np.dtype](
function: Callable[..., np.ndarray[ShapeT, DTypeT]],
shape: Sequence[int],
*,
dtype: DTypeLike | None = float,
like: _SupportsArrayFunc | None = None,
**kwargs: object,
) -> MaskedArray[ShapeT, DTypeT]: ...
# keep roughly in sync with `_core.numeric.identity`
@overload
def identity(
n: int,
dtype: None = None,
*,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[tuple[int, int], np.dtype[np.float64]]: ...
@overload
def identity[ScalarT: np.generic](
n: int,
dtype: _DTypeLike[ScalarT],
*,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[tuple[int, int], np.dtype[ScalarT]]: ...
@overload
def identity(
n: int,
dtype: DTypeLike | None = None,
*,
like: _SupportsArrayFunc | None = None,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> MaskedArray[tuple[int, int], np.dtype[Incomplete]]: ...
# keep roughly in sync with `_core.numeric.indices`
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int] = int,
sparse: Literal[False] = False,
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _MaskedArray[np.intp]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int],
sparse: Literal[True],
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> tuple[_MaskedArray[np.intp], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: type[int] = int,
*,
sparse: Literal[True],
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> tuple[_MaskedArray[np.intp], ...]: ...
@overload
def indices[ScalarT: np.generic](
dimensions: Sequence[int],
dtype: _DTypeLike[ScalarT],
sparse: Literal[False] = False,
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _MaskedArray[ScalarT]: ...
@overload
def indices[ScalarT: np.generic](
dimensions: Sequence[int],
dtype: _DTypeLike[ScalarT],
sparse: Literal[True],
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> tuple[_MaskedArray[ScalarT], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike | None = int,
sparse: Literal[False] = False,
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _MaskedArray[Incomplete]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike | None,
sparse: Literal[True],
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> tuple[_MaskedArray[Incomplete], ...]: ...
@overload
def indices(
dimensions: Sequence[int],
dtype: DTypeLike | None = int,
*,
sparse: Literal[True],
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> tuple[_MaskedArray[Incomplete], ...]: ...
# keep roughly in sync with `_core.fromnumeric.squeeze`
@overload
def squeeze[ScalarT: np.generic](
a: _ArrayLike[ScalarT],
axis: _ShapeLike | None = None,
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _MaskedArray[ScalarT]: ...
@overload
def squeeze(
a: ArrayLike,
axis: _ShapeLike | None = None,
*,
fill_value: _FillValue | None = None,
hardmask: bool = False,
) -> _MaskedArray[Incomplete]: ...