Your IP : 216.73.216.74
# pyright: reportIncompatibleMethodOverride=false
# ruff: noqa: ANN001, ANN002, ANN003, ANN201, ANN202 ANN204, ANN401
from collections.abc import Sequence
from typing import Any, Literal, Self, SupportsIndex, TypeAlias, overload
from _typeshed import Incomplete
from typing_extensions import TypeIs, TypeVar
import numpy as np
from numpy import (
_HasDTypeWithRealAndImag,
_ModeKind,
_OrderKACF,
_PartitionKind,
_SortKind,
amax,
amin,
bool_,
bytes_,
character,
complexfloating,
datetime64,
dtype,
dtypes,
expand_dims,
float64,
floating,
generic,
int_,
integer,
intp,
ndarray,
object_,
str_,
timedelta64,
)
from numpy._globals import _NoValueType
from numpy._typing import (
ArrayLike,
NDArray,
_AnyShape,
_ArrayLike,
_ArrayLikeBool_co,
_ArrayLikeBytes_co,
_ArrayLikeComplex_co,
_ArrayLikeFloat_co,
_ArrayLikeInt,
_ArrayLikeInt_co,
_ArrayLikeStr_co,
_ArrayLikeString_co,
_ArrayLikeTD64_co,
_DTypeLikeBool,
_IntLike_co,
_ScalarLike_co,
_Shape,
_ShapeLike,
)
__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 = TypeVar("_ShapeT", bound=_Shape)
_ShapeT_co = TypeVar("_ShapeT_co", bound=_Shape, default=_AnyShape, covariant=True)
_DTypeT = TypeVar("_DTypeT", bound=dtype)
_DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, default=dtype, covariant=True)
_ArrayT = TypeVar("_ArrayT", bound=ndarray[Any, Any])
_ScalarT = TypeVar("_ScalarT", bound=generic)
_ScalarT_co = TypeVar("_ScalarT_co", bound=generic, covariant=True)
# A subset of `MaskedArray` that can be parametrized w.r.t. `np.generic`
_MaskedArray: TypeAlias = MaskedArray[_AnyShape, dtype[_ScalarT]]
_Array1D: TypeAlias = np.ndarray[tuple[int], np.dtype[_ScalarT]]
MaskType = bool_
nomask: bool_[Literal[False]]
class MaskedArrayFutureWarning(FutureWarning): ...
class MAError(Exception): ...
class MaskError(MAError): ...
def default_fill_value(obj): ...
def minimum_fill_value(obj): ...
def maximum_fill_value(obj): ...
def set_fill_value(a, fill_value): ...
def common_fill_value(a, b): ...
@overload
def filled(a: ndarray[_ShapeT_co, _DTypeT_co], fill_value: _ScalarLike_co | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
@overload
def filled(a: _ArrayLike[_ScalarT_co], fill_value: _ScalarLike_co | None = None) -> NDArray[_ScalarT_co]: ...
@overload
def filled(a: ArrayLike, fill_value: _ScalarLike_co | None = None) -> NDArray[Any]: ...
def getdata(a, subok=...): ...
get_data = getdata
def fix_invalid(a, mask=..., copy=..., fill_value=...): ...
class _MaskedUFunc:
f: Any
__doc__: Any
__name__: Any
def __init__(self, ufunc): ...
class _MaskedUnaryOperation(_MaskedUFunc):
fill: Any
domain: Any
def __init__(self, mufunc, fill=..., domain=...): ...
def __call__(self, a, *args, **kwargs): ...
class _MaskedBinaryOperation(_MaskedUFunc):
fillx: Any
filly: Any
def __init__(self, mbfunc, fillx=..., filly=...): ...
def __call__(self, a, b, *args, **kwargs): ...
def reduce(self, target, axis=..., dtype=...): ...
def outer(self, a, b): ...
def accumulate(self, target, axis=...): ...
class _DomainedBinaryOperation(_MaskedUFunc):
domain: Any
fillx: Any
filly: Any
def __init__(self, dbfunc, domain, fillx=..., filly=...): ...
def __call__(self, a, b, *args, **kwargs): ...
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
def make_mask_descr(ndtype): ...
@overload
def getmask(a: _ScalarLike_co) -> bool_: ...
@overload
def getmask(a: MaskedArray[_ShapeT_co, Any]) -> np.ndarray[_ShapeT_co, dtype[bool_]] | bool_: ...
@overload
def getmask(a: ArrayLike) -> NDArray[bool_] | bool_: ...
get_mask = getmask
def getmaskarray(arr): ...
# It's sufficient for `m` to have dtype with type: `type[np.bool_]`,
# which isn't necessarily a ndarray. Please open an issue if this causes issues.
def is_mask(m: object) -> TypeIs[NDArray[bool_]]: ...
def make_mask(m, copy=..., shrink=..., dtype=...): ...
def make_mask_none(newshape, dtype=...): ...
def mask_or(m1, m2, copy=..., shrink=...): ...
def flatten_mask(mask): ...
def masked_where(condition, a, copy=...): ...
def masked_greater(x, value, copy=...): ...
def masked_greater_equal(x, value, copy=...): ...
def masked_less(x, value, copy=...): ...
def masked_less_equal(x, value, copy=...): ...
def masked_not_equal(x, value, copy=...): ...
def masked_equal(x, value, copy=...): ...
def masked_inside(x, v1, v2, copy=...): ...
def masked_outside(x, v1, v2, copy=...): ...
def masked_object(x, value, copy=..., shrink=...): ...
def masked_values(x, value, rtol=..., atol=..., copy=..., shrink=...): ...
def masked_invalid(a, copy=...): ...
class _MaskedPrintOption:
def __init__(self, display): ...
def display(self): ...
def set_display(self, s): ...
def enabled(self): ...
def enable(self, shrink=...): ...
masked_print_option: _MaskedPrintOption
def flatten_structured_array(a): ...
class MaskedIterator:
ma: Any
dataiter: Any
maskiter: Any
def __init__(self, ma): ...
def __iter__(self): ...
def __getitem__(self, indx): ...
def __setitem__(self, index, value): ...
def __next__(self): ...
class MaskedArray(ndarray[_ShapeT_co, _DTypeT_co]):
__array_priority__: Any
def __new__(cls, data=..., mask=..., dtype=..., copy=..., subok=..., ndmin=..., fill_value=..., keep_mask=..., hard_mask=..., shrink=..., order=...): ...
def __array_finalize__(self, obj): ...
def __array_wrap__(self, obj, context=..., return_scalar=...): ...
def view(self, dtype=..., type=..., fill_value=...): ...
def __getitem__(self, indx): ...
def __setitem__(self, indx, value): ...
@property
def shape(self) -> _ShapeT_co: ...
@shape.setter
def shape(self: MaskedArray[_ShapeT, Any], shape: _ShapeT, /) -> None: ...
def __setmask__(self, mask: _ArrayLikeBool_co, copy: bool = False) -> None: ...
@property
def mask(self) -> NDArray[MaskType] | MaskType: ...
@mask.setter
def mask(self, value: _ArrayLikeBool_co, /) -> None: ...
@property
def recordmask(self): ...
@recordmask.setter
def recordmask(self, mask): ...
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[Any]]: ...
data: Any
@property
def flat(self): ...
@flat.setter
def flat(self, value): ...
@property
def fill_value(self): ...
@fill_value.setter
def fill_value(self, value=...): ...
get_fill_value: Any
set_fill_value: Any
def filled(self, /, fill_value: _ScalarLike_co | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
def compressed(self) -> ndarray[tuple[int], _DTypeT_co]: ...
def compress(self, condition, axis=..., out=...): ...
def __eq__(self, other): ...
def __ne__(self, other): ...
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]
def __add__(self, other): ...
def __radd__(self, other): ...
def __sub__(self, other): ...
def __rsub__(self, other): ...
def __mul__(self, other): ...
def __rmul__(self, other): ...
def __truediv__(self, other): ...
def __rtruediv__(self, other): ...
def __floordiv__(self, other): ...
def __rfloordiv__(self, other): ...
def __pow__(self, other, mod: None = None, /): ...
def __rpow__(self, other, mod: None = None, /): ...
# Keep in sync with `ndarray.__iadd__`
@overload
def __iadd__(
self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __iadd__(self: _MaskedArray[integer], other: _ArrayLikeInt_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __iadd__(
self: _MaskedArray[floating], other: _ArrayLikeFloat_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __iadd__(
self: _MaskedArray[complexfloating], other: _ArrayLikeComplex_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __iadd__(
self: _MaskedArray[timedelta64 | datetime64], other: _ArrayLikeTD64_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __iadd__(self: _MaskedArray[bytes_], other: _ArrayLikeBytes_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __iadd__(
self: MaskedArray[Any, dtype[str_] | dtypes.StringDType],
other: _ArrayLikeStr_co | _ArrayLikeString_co,
/,
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __iadd__(
self: _MaskedArray[object_], other: Any, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
# Keep in sync with `ndarray.__isub__`
@overload
def __isub__(self: _MaskedArray[integer], other: _ArrayLikeInt_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __isub__(
self: _MaskedArray[floating], other: _ArrayLikeFloat_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __isub__(
self: _MaskedArray[complexfloating], other: _ArrayLikeComplex_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __isub__(
self: _MaskedArray[timedelta64 | datetime64], other: _ArrayLikeTD64_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __isub__(
self: _MaskedArray[object_], other: Any, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
# Keep in sync with `ndarray.__imul__`
@overload
def __imul__(
self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __imul__(
self: MaskedArray[Any, dtype[integer] | dtype[character] | dtypes.StringDType], other: _ArrayLikeInt_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __imul__(
self: _MaskedArray[floating | timedelta64], other: _ArrayLikeFloat_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __imul__(
self: _MaskedArray[complexfloating], other: _ArrayLikeComplex_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __imul__(
self: _MaskedArray[object_], other: Any, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
# Keep in sync with `ndarray.__ifloordiv__`
@overload
def __ifloordiv__(self: _MaskedArray[integer], other: _ArrayLikeInt_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __ifloordiv__(
self: _MaskedArray[floating | timedelta64], other: _ArrayLikeFloat_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __ifloordiv__(
self: _MaskedArray[object_], other: Any, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
# Keep in sync with `ndarray.__itruediv__`
@overload
def __itruediv__(
self: _MaskedArray[floating | timedelta64], other: _ArrayLikeFloat_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __itruediv__(
self: _MaskedArray[complexfloating],
other: _ArrayLikeComplex_co,
/,
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __itruediv__(
self: _MaskedArray[object_], other: Any, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
# Keep in sync with `ndarray.__ipow__`
@overload
def __ipow__(self: _MaskedArray[integer], other: _ArrayLikeInt_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __ipow__(
self: _MaskedArray[floating], other: _ArrayLikeFloat_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __ipow__(
self: _MaskedArray[complexfloating], other: _ArrayLikeComplex_co, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
@overload
def __ipow__(
self: _MaskedArray[object_], other: Any, /
) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
#
@property # type: ignore[misc]
def imag(self: _HasDTypeWithRealAndImag[object, _ScalarT], /) -> MaskedArray[_ShapeT_co, dtype[_ScalarT]]: ...
get_imag: Any
@property # type: ignore[misc]
def real(self: _HasDTypeWithRealAndImag[_ScalarT, object], /) -> MaskedArray[_ShapeT_co, dtype[_ScalarT]]: ...
get_real: Any
# 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 = ..., *, keepdims: Literal[True]) -> NDArray[int_]: ...
@overload
def count(self, axis: _ShapeLike | None, keepdims: Literal[True]) -> NDArray[int_]: ...
def ravel(self, order: _OrderKACF = "C") -> MaskedArray[tuple[int], _DTypeT_co]: ...
def reshape(self, *s, **kwargs): ...
def resize(self, newshape, refcheck=..., order=...): ...
def put(self, indices: _ArrayLikeInt_co, values: ArrayLike, mode: _ModeKind = "raise") -> None: ...
def ids(self) -> tuple[int, int]: ...
def iscontiguous(self) -> bool: ...
@overload
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(
self,
axis: _ShapeLike | None = None,
*,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def all(
self,
axis: _ShapeLike | None,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
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(
self,
axis: _ShapeLike | None = None,
*,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def any(
self,
axis: _ShapeLike | None,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
def nonzero(self) -> tuple[_Array1D[intp], *tuple[_Array1D[intp], ...]]: ...
def trace(self, offset=..., axis1=..., axis2=..., dtype=..., out=...): ...
def dot(self, b, out=..., strict=...): ...
def sum(self, axis=..., dtype=..., out=..., keepdims=...): ...
def cumsum(self, axis=..., dtype=..., out=...): ...
def prod(self, axis=..., dtype=..., out=..., keepdims=...): ...
product: Any
def cumprod(self, axis=..., dtype=..., out=...): ...
def mean(self, axis=..., dtype=..., out=..., keepdims=...): ...
def anom(self, axis=..., dtype=...): ...
def var(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ...
def std(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ...
def round(self, decimals=..., out=...): ...
def argsort(self, axis=..., kind=..., order=..., endwith=..., fill_value=..., *, stable=...): ...
# Keep in-sync with np.ma.argmin
@overload # type: ignore[override]
def argmin(
self,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload
def argmin(
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def argmin(
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def argmin(
self,
axis: SupportsIndex | None,
fill_value: _ScalarLike_co | None,
out: _ArrayT,
*,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
# Keep in-sync with np.ma.argmax
@overload # type: ignore[override]
def argmax(
self,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload
def argmax(
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def argmax(
self,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def argmax(
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,
) -> None: ...
#
@overload # type: ignore[override]
def min(
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(
self,
axis: _ShapeLike | None,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def min(
self,
axis: _ShapeLike | None = None,
*,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
#
@overload # type: ignore[override]
def max(
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(
self,
axis: _ShapeLike | None,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def max(
self,
axis: _ShapeLike | None = None,
*,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
#
@overload
def ptp(
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(
self,
axis: _ShapeLike | None,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool = False,
) -> _ArrayT: ...
@overload
def ptp(
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: ...
#
@overload
def argpartition(
self,
/,
kth: _ArrayLikeInt,
axis: SupportsIndex | None = -1,
kind: _PartitionKind = "introselect",
order: None = None,
) -> _MaskedArray[intp]: ...
@overload
def argpartition(
self: _MaskedArray[np.void],
/,
kth: _ArrayLikeInt,
axis: SupportsIndex | None = -1,
kind: _PartitionKind = "introselect",
order: str | Sequence[str] | None = None,
) -> _MaskedArray[intp]: ...
# Keep in-sync with np.ma.take
@overload
def take( # type: ignore[overload-overlap]
self: _MaskedArray[_ScalarT],
indices: _IntLike_co,
axis: None = None,
out: None = None,
mode: _ModeKind = 'raise'
) -> _ScalarT: ...
@overload
def take(
self: _MaskedArray[_ScalarT],
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
out: None = None,
mode: _ModeKind = 'raise',
) -> _MaskedArray[_ScalarT]: ...
@overload
def take(
self,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None,
out: _ArrayT,
mode: _ModeKind = 'raise',
) -> _ArrayT: ...
@overload
def take(
self,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
*,
out: _ArrayT,
mode: _ModeKind = 'raise',
) -> _ArrayT: ...
copy: Any
diagonal: Any
flatten: Any
@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]: ...
squeeze: Any
def swapaxes(
self,
axis1: SupportsIndex,
axis2: SupportsIndex,
/
) -> MaskedArray[_AnyShape, _DTypeT_co]: ...
#
def toflex(self) -> Incomplete: ...
def torecords(self) -> Incomplete: ...
def tolist(self, fill_value: Incomplete | None = None) -> Incomplete: ...
def tobytes(self, /, fill_value: Incomplete | None = None, order: _OrderKACF = "C") -> bytes: ... # type: ignore[override]
def tofile(self, /, fid: Incomplete, sep: str = "", format: str = "%s") -> Incomplete: ...
#
def __reduce__(self): ...
def __deepcopy__(self, memo=...): ...
# Keep `dtype` at the bottom to avoid name conflicts with `np.dtype`
@property
def dtype(self) -> _DTypeT_co: ...
@dtype.setter
def dtype(self: MaskedArray[_AnyShape, _DTypeT], dtype: _DTypeT, /) -> None: ...
class mvoid(MaskedArray[_ShapeT_co, _DTypeT_co]):
def __new__(
self, # pyright: ignore[reportSelfClsParameterName]
data,
mask=...,
dtype=...,
fill_value=...,
hardmask=...,
copy=...,
subok=...,
): ...
def __getitem__(self, indx): ...
def __setitem__(self, indx, value): ...
def __iter__(self): ...
def __len__(self): ...
def filled(self, fill_value=...): ...
def tolist(self): ...
def isMaskedArray(x): ...
isarray = isMaskedArray
isMA = isMaskedArray
# 0D float64 array
class MaskedConstant(MaskedArray[_AnyShape, dtype[float64]]):
def __new__(cls): ...
__class__: Any
def __array_finalize__(self, obj): ...
def __array_wrap__(self, obj, context=..., return_scalar=...): ...
def __format__(self, format_spec): ...
def __reduce__(self): ...
def __iop__(self, other): ...
__iadd__: Any
__isub__: Any
__imul__: Any
__ifloordiv__: Any
__itruediv__: Any
__ipow__: Any
def copy(self, *args, **kwargs): ...
def __copy__(self): ...
def __deepcopy__(self, memo): ...
def __setattr__(self, attr, value): ...
masked: MaskedConstant
masked_singleton: MaskedConstant
masked_array = MaskedArray
def array(
data,
dtype=...,
copy=...,
order=...,
mask=...,
fill_value=...,
keep_mask=...,
hard_mask=...,
shrink=...,
subok=...,
ndmin=...,
): ...
def is_masked(x: object) -> bool: ...
class _extrema_operation(_MaskedUFunc):
compare: Any
fill_value_func: Any
def __init__(self, ufunc, compare, fill_value): ...
# NOTE: in practice `b` has a default value, but users should
# explicitly provide a value here as the default is deprecated
def __call__(self, a, b): ...
def reduce(self, target, axis=...): ...
def outer(self, a, b): ...
@overload
def min(
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(
obj: ArrayLike,
axis: _ShapeLike | None,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def min(
obj: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def max(
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(
obj: ArrayLike,
axis: _ShapeLike | None,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def max(
obj: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def ptp(
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(
obj: ArrayLike,
axis: _ShapeLike | None,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def ptp(
obj: ArrayLike,
axis: _ShapeLike | None = None,
*,
out: _ArrayT,
fill_value: _ScalarLike_co | None = None,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
class _frommethod:
__name__: Any
__doc__: Any
reversed: Any
def __init__(self, methodname, reversed=...): ...
def getdoc(self): ...
def __call__(self, a, *args, **params): ...
all: _frommethod
anomalies: _frommethod
anom: _frommethod
any: _frommethod
compress: _frommethod
cumprod: _frommethod
cumsum: _frommethod
copy: _frommethod
diagonal: _frommethod
harden_mask: _frommethod
ids: _frommethod
mean: _frommethod
nonzero: _frommethod
prod: _frommethod
product: _frommethod
ravel: _frommethod
repeat: _frommethod
soften_mask: _frommethod
std: _frommethod
sum: _frommethod
swapaxes: _frommethod
trace: _frommethod
var: _frommethod
@overload
def count(self: ArrayLike, axis: None = None, keepdims: Literal[False] | _NoValueType = ...) -> int: ...
@overload
def count(self: ArrayLike, axis: _ShapeLike, keepdims: bool | _NoValueType = ...) -> NDArray[int_]: ...
@overload
def count(self: ArrayLike, axis: _ShapeLike | None = ..., *, keepdims: Literal[True]) -> NDArray[int_]: ...
@overload
def count(self: ArrayLike, axis: _ShapeLike | None, keepdims: Literal[True]) -> NDArray[int_]: ...
@overload
def argmin(
self: ArrayLike,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload
def argmin(
self: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def argmin(
self: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def argmin(
self: ArrayLike,
axis: SupportsIndex | None,
fill_value: _ScalarLike_co | None,
out: _ArrayT,
*,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
#
@overload
def argmax(
self: ArrayLike,
axis: None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: Literal[False] | _NoValueType = ...,
) -> intp: ...
@overload
def argmax(
self: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
out: None = None,
*,
keepdims: bool | _NoValueType = ...,
) -> Any: ...
@overload
def argmax(
self: ArrayLike,
axis: SupportsIndex | None = None,
fill_value: _ScalarLike_co | None = None,
*,
out: _ArrayT,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
@overload
def argmax(
self: ArrayLike,
axis: SupportsIndex | None,
fill_value: _ScalarLike_co | None,
out: _ArrayT,
*,
keepdims: bool | _NoValueType = ...,
) -> _ArrayT: ...
minimum: _extrema_operation
maximum: _extrema_operation
@overload
def take(
a: _ArrayLike[_ScalarT],
indices: _IntLike_co,
axis: None = None,
out: None = None,
mode: _ModeKind = 'raise'
) -> _ScalarT: ...
@overload
def take(
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(
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None,
out: _ArrayT,
mode: _ModeKind = 'raise',
) -> _ArrayT: ...
@overload
def take(
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: SupportsIndex | None = None,
*,
out: _ArrayT,
mode: _ModeKind = 'raise',
) -> _ArrayT: ...
def power(a, b, third=...): ...
def argsort(a, axis=..., kind=..., order=..., endwith=..., fill_value=..., *, stable=...): ...
@overload
def sort(
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 = False,
) -> _ArrayT: ...
@overload
def sort(
a: ArrayLike,
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,
) -> NDArray[Any]: ...
@overload
def compressed(x: _ArrayLike[_ScalarT_co]) -> _Array1D[_ScalarT_co]: ...
@overload
def compressed(x: ArrayLike) -> _Array1D[Any]: ...
def concatenate(arrays, axis=...): ...
def diag(v, k=...): ...
def left_shift(a, n): ...
def right_shift(a, n): ...
def put(a: NDArray[Any], indices: _ArrayLikeInt_co, values: ArrayLike, mode: _ModeKind = 'raise') -> None: ...
def putmask(a: NDArray[Any], mask: _ArrayLikeBool_co, values: ArrayLike) -> None: ...
def transpose(a, axes=...): ...
def reshape(a, new_shape, order=...): ...
def resize(x, new_shape): ...
def ndim(obj: ArrayLike) -> int: ...
def shape(obj): ...
def size(obj: ArrayLike, axis: SupportsIndex | None = None) -> int: ...
def diff(a, /, n=..., axis=..., prepend=..., append=...): ...
def where(condition, x=..., y=...): ...
def choose(indices, choices, out=..., mode=...): ...
def round_(a, decimals=..., out=...): ...
round = round_
def inner(a, b): ...
innerproduct = inner
def outer(a, b): ...
outerproduct = outer
def correlate(a, v, mode=..., propagate_mask=...): ...
def convolve(a, v, mode=..., propagate_mask=...): ...
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 asarray(a, dtype=..., order=...): ...
def asanyarray(a, dtype=...): ...
def fromflex(fxarray): ...
class _convert2ma:
def __init__(self, /, funcname: str, np_ret: str, np_ma_ret: str, params: dict[str, Any] | None = None) -> None: ...
def __call__(self, /, *args: object, **params: object) -> Any: ...
def getdoc(self, /, np_ret: str, np_ma_ret: str) -> str | None: ...
arange: _convert2ma
clip: _convert2ma
empty: _convert2ma
empty_like: _convert2ma
frombuffer: _convert2ma
fromfunction: _convert2ma
identity: _convert2ma
indices: _convert2ma
ones: _convert2ma
ones_like: _convert2ma
squeeze: _convert2ma
zeros: _convert2ma
zeros_like: _convert2ma
def append(a, b, axis=...): ...
def dot(a, b, strict=..., out=...): ...
def mask_rowcols(a, axis=...): ...