1462 lines
40 KiB
Python
1462 lines
40 KiB
Python
# pyright: reportIncompatibleMethodOverride=false
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# ruff: noqa: ANN001, ANN002, ANN003, ANN201, ANN202 ANN204, ANN401
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from collections.abc import Sequence
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from typing import Any, Literal, Self, SupportsIndex, TypeAlias, overload
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from _typeshed import Incomplete
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from typing_extensions import TypeIs, TypeVar
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import numpy as np
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from numpy import (
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_HasDTypeWithRealAndImag,
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_ModeKind,
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_OrderKACF,
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_PartitionKind,
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_SortKind,
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amax,
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amin,
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bool_,
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bytes_,
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character,
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complexfloating,
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datetime64,
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dtype,
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dtypes,
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expand_dims,
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float64,
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floating,
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generic,
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int_,
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integer,
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intp,
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ndarray,
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object_,
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str_,
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timedelta64,
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)
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from numpy._globals import _NoValueType
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from numpy._typing import (
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ArrayLike,
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NDArray,
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_AnyShape,
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_ArrayLike,
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_ArrayLikeBool_co,
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_ArrayLikeBytes_co,
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_ArrayLikeComplex_co,
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_ArrayLikeFloat_co,
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_ArrayLikeInt,
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_ArrayLikeInt_co,
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_ArrayLikeStr_co,
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_ArrayLikeString_co,
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_ArrayLikeTD64_co,
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_DTypeLikeBool,
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_IntLike_co,
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_ScalarLike_co,
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_Shape,
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_ShapeLike,
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)
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__all__ = [
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"MAError",
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"MaskError",
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"MaskType",
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"MaskedArray",
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"abs",
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"absolute",
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"add",
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"all",
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"allclose",
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"allequal",
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"alltrue",
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"amax",
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"amin",
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"angle",
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"anom",
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"anomalies",
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"any",
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"append",
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"arange",
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"arccos",
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"arccosh",
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"arcsin",
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"arcsinh",
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"arctan",
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"arctan2",
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"arctanh",
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"argmax",
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"argmin",
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"argsort",
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"around",
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"array",
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"asanyarray",
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"asarray",
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"bitwise_and",
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"bitwise_or",
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"bitwise_xor",
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"bool_",
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"ceil",
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"choose",
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"clip",
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"common_fill_value",
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"compress",
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"compressed",
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"concatenate",
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"conjugate",
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"convolve",
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"copy",
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"correlate",
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"cos",
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"cosh",
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"count",
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"cumprod",
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"cumsum",
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"default_fill_value",
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"diag",
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"diagonal",
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"diff",
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"divide",
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"empty",
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"empty_like",
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"equal",
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"exp",
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"expand_dims",
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"fabs",
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"filled",
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"fix_invalid",
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"flatten_mask",
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"flatten_structured_array",
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"floor",
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"floor_divide",
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"fmod",
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"frombuffer",
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"fromflex",
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"fromfunction",
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"getdata",
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"getmask",
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"getmaskarray",
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"greater",
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"greater_equal",
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"harden_mask",
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"hypot",
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"identity",
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"ids",
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"indices",
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"inner",
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"innerproduct",
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"isMA",
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"isMaskedArray",
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"is_mask",
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"is_masked",
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"isarray",
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"left_shift",
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"less",
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"less_equal",
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"log",
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"log2",
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"log10",
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"logical_and",
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"logical_not",
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"logical_or",
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"logical_xor",
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"make_mask",
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"make_mask_descr",
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"make_mask_none",
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"mask_or",
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"masked",
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"masked_array",
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"masked_equal",
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"masked_greater",
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"masked_greater_equal",
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"masked_inside",
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"masked_invalid",
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"masked_less",
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"masked_less_equal",
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"masked_not_equal",
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"masked_object",
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"masked_outside",
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"masked_print_option",
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"masked_singleton",
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"masked_values",
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"masked_where",
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"max",
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"maximum",
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"maximum_fill_value",
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"mean",
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"min",
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"minimum",
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"minimum_fill_value",
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"mod",
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"multiply",
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"mvoid",
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"ndim",
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"negative",
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"nomask",
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"nonzero",
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"not_equal",
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"ones",
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"ones_like",
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"outer",
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"outerproduct",
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"power",
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"prod",
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"product",
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"ptp",
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"put",
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"putmask",
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"ravel",
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"remainder",
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"repeat",
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"reshape",
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"resize",
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"right_shift",
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"round",
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"round_",
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"set_fill_value",
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"shape",
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"sin",
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"sinh",
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"size",
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"soften_mask",
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"sometrue",
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"sort",
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"sqrt",
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"squeeze",
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"std",
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"subtract",
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"sum",
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"swapaxes",
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"take",
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"tan",
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"tanh",
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"trace",
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"transpose",
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"true_divide",
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"var",
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"where",
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"zeros",
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"zeros_like",
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]
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_ShapeT = TypeVar("_ShapeT", bound=_Shape)
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_ShapeT_co = TypeVar("_ShapeT_co", bound=_Shape, default=_AnyShape, covariant=True)
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_DTypeT = TypeVar("_DTypeT", bound=dtype)
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_DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, default=dtype, covariant=True)
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_ArrayT = TypeVar("_ArrayT", bound=ndarray[Any, Any])
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_ScalarT = TypeVar("_ScalarT", bound=generic)
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_ScalarT_co = TypeVar("_ScalarT_co", bound=generic, covariant=True)
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# A subset of `MaskedArray` that can be parametrized w.r.t. `np.generic`
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_MaskedArray: TypeAlias = MaskedArray[_AnyShape, dtype[_ScalarT]]
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_Array1D: TypeAlias = np.ndarray[tuple[int], np.dtype[_ScalarT]]
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MaskType = bool_
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nomask: bool_[Literal[False]]
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class MaskedArrayFutureWarning(FutureWarning): ...
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class MAError(Exception): ...
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class MaskError(MAError): ...
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def default_fill_value(obj): ...
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def minimum_fill_value(obj): ...
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def maximum_fill_value(obj): ...
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def set_fill_value(a, fill_value): ...
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def common_fill_value(a, b): ...
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@overload
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def filled(a: ndarray[_ShapeT_co, _DTypeT_co], fill_value: _ScalarLike_co | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def filled(a: _ArrayLike[_ScalarT_co], fill_value: _ScalarLike_co | None = None) -> NDArray[_ScalarT_co]: ...
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@overload
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def filled(a: ArrayLike, fill_value: _ScalarLike_co | None = None) -> NDArray[Any]: ...
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def getdata(a, subok=...): ...
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get_data = getdata
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def fix_invalid(a, mask=..., copy=..., fill_value=...): ...
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class _MaskedUFunc:
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f: Any
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__doc__: Any
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__name__: Any
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def __init__(self, ufunc): ...
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class _MaskedUnaryOperation(_MaskedUFunc):
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fill: Any
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domain: Any
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def __init__(self, mufunc, fill=..., domain=...): ...
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def __call__(self, a, *args, **kwargs): ...
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class _MaskedBinaryOperation(_MaskedUFunc):
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fillx: Any
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filly: Any
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def __init__(self, mbfunc, fillx=..., filly=...): ...
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def __call__(self, a, b, *args, **kwargs): ...
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def reduce(self, target, axis=..., dtype=...): ...
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def outer(self, a, b): ...
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def accumulate(self, target, axis=...): ...
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class _DomainedBinaryOperation(_MaskedUFunc):
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domain: Any
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fillx: Any
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filly: Any
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def __init__(self, dbfunc, domain, fillx=..., filly=...): ...
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def __call__(self, a, b, *args, **kwargs): ...
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exp: _MaskedUnaryOperation
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conjugate: _MaskedUnaryOperation
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sin: _MaskedUnaryOperation
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cos: _MaskedUnaryOperation
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arctan: _MaskedUnaryOperation
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arcsinh: _MaskedUnaryOperation
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sinh: _MaskedUnaryOperation
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cosh: _MaskedUnaryOperation
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tanh: _MaskedUnaryOperation
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abs: _MaskedUnaryOperation
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absolute: _MaskedUnaryOperation
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angle: _MaskedUnaryOperation
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fabs: _MaskedUnaryOperation
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negative: _MaskedUnaryOperation
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floor: _MaskedUnaryOperation
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ceil: _MaskedUnaryOperation
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around: _MaskedUnaryOperation
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logical_not: _MaskedUnaryOperation
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sqrt: _MaskedUnaryOperation
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log: _MaskedUnaryOperation
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log2: _MaskedUnaryOperation
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log10: _MaskedUnaryOperation
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tan: _MaskedUnaryOperation
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arcsin: _MaskedUnaryOperation
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arccos: _MaskedUnaryOperation
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arccosh: _MaskedUnaryOperation
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arctanh: _MaskedUnaryOperation
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add: _MaskedBinaryOperation
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subtract: _MaskedBinaryOperation
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multiply: _MaskedBinaryOperation
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arctan2: _MaskedBinaryOperation
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equal: _MaskedBinaryOperation
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not_equal: _MaskedBinaryOperation
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less_equal: _MaskedBinaryOperation
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greater_equal: _MaskedBinaryOperation
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less: _MaskedBinaryOperation
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greater: _MaskedBinaryOperation
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logical_and: _MaskedBinaryOperation
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def alltrue(target: ArrayLike, axis: SupportsIndex | None = 0, dtype: _DTypeLikeBool | None = None) -> Incomplete: ...
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logical_or: _MaskedBinaryOperation
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def sometrue(target: ArrayLike, axis: SupportsIndex | None = 0, dtype: _DTypeLikeBool | None = None) -> Incomplete: ...
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logical_xor: _MaskedBinaryOperation
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bitwise_and: _MaskedBinaryOperation
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bitwise_or: _MaskedBinaryOperation
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bitwise_xor: _MaskedBinaryOperation
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hypot: _MaskedBinaryOperation
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divide: _DomainedBinaryOperation
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true_divide: _DomainedBinaryOperation
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floor_divide: _DomainedBinaryOperation
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remainder: _DomainedBinaryOperation
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fmod: _DomainedBinaryOperation
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mod: _DomainedBinaryOperation
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def make_mask_descr(ndtype): ...
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@overload
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def getmask(a: _ScalarLike_co) -> bool_: ...
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@overload
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def getmask(a: MaskedArray[_ShapeT_co, Any]) -> np.ndarray[_ShapeT_co, dtype[bool_]] | bool_: ...
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@overload
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def getmask(a: ArrayLike) -> NDArray[bool_] | bool_: ...
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get_mask = getmask
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def getmaskarray(arr): ...
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# It's sufficient for `m` to have dtype with type: `type[np.bool_]`,
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# which isn't necessarily a ndarray. Please open an issue if this causes issues.
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def is_mask(m: object) -> TypeIs[NDArray[bool_]]: ...
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def make_mask(m, copy=..., shrink=..., dtype=...): ...
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def make_mask_none(newshape, dtype=...): ...
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def mask_or(m1, m2, copy=..., shrink=...): ...
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def flatten_mask(mask): ...
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def masked_where(condition, a, copy=...): ...
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def masked_greater(x, value, copy=...): ...
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def masked_greater_equal(x, value, copy=...): ...
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def masked_less(x, value, copy=...): ...
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def masked_less_equal(x, value, copy=...): ...
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def masked_not_equal(x, value, copy=...): ...
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def masked_equal(x, value, copy=...): ...
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def masked_inside(x, v1, v2, copy=...): ...
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def masked_outside(x, v1, v2, copy=...): ...
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def masked_object(x, value, copy=..., shrink=...): ...
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def masked_values(x, value, rtol=..., atol=..., copy=..., shrink=...): ...
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def masked_invalid(a, copy=...): ...
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class _MaskedPrintOption:
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def __init__(self, display): ...
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def display(self): ...
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def set_display(self, s): ...
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def enabled(self): ...
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def enable(self, shrink=...): ...
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masked_print_option: _MaskedPrintOption
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def flatten_structured_array(a): ...
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class MaskedIterator:
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ma: Any
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dataiter: Any
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maskiter: Any
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def __init__(self, ma): ...
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def __iter__(self): ...
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def __getitem__(self, indx): ...
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def __setitem__(self, index, value): ...
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def __next__(self): ...
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class MaskedArray(ndarray[_ShapeT_co, _DTypeT_co]):
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__array_priority__: Any
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def __new__(cls, data=..., mask=..., dtype=..., copy=..., subok=..., ndmin=..., fill_value=..., keep_mask=..., hard_mask=..., shrink=..., order=...): ...
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def __array_finalize__(self, obj): ...
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def __array_wrap__(self, obj, context=..., return_scalar=...): ...
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def view(self, dtype=..., type=..., fill_value=...): ...
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def __getitem__(self, indx): ...
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def __setitem__(self, indx, value): ...
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@property
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def shape(self) -> _ShapeT_co: ...
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@shape.setter
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def shape(self: MaskedArray[_ShapeT, Any], shape: _ShapeT, /) -> None: ...
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def __setmask__(self, mask: _ArrayLikeBool_co, copy: bool = False) -> None: ...
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@property
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def mask(self) -> NDArray[MaskType] | MaskType: ...
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@mask.setter
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def mask(self, value: _ArrayLikeBool_co, /) -> None: ...
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@property
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def recordmask(self): ...
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@recordmask.setter
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def recordmask(self, mask): ...
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def harden_mask(self) -> Self: ...
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def soften_mask(self) -> Self: ...
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@property
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def hardmask(self) -> bool: ...
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def unshare_mask(self) -> Self: ...
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@property
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def sharedmask(self) -> bool: ...
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def shrink_mask(self) -> Self: ...
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@property
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def baseclass(self) -> type[NDArray[Any]]: ...
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data: Any
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@property
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def flat(self): ...
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@flat.setter
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def flat(self, value): ...
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@property
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def fill_value(self): ...
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@fill_value.setter
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def fill_value(self, value=...): ...
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get_fill_value: Any
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set_fill_value: Any
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def filled(self, /, fill_value: _ScalarLike_co | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
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def compressed(self) -> ndarray[tuple[int], _DTypeT_co]: ...
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def compress(self, condition, axis=..., out=...): ...
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def __eq__(self, other): ...
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def __ne__(self, other): ...
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def __ge__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
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def __gt__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
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def __le__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
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def __lt__(self, other: ArrayLike, /) -> _MaskedArray[bool_]: ... # type: ignore[override]
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def __add__(self, other): ...
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def __radd__(self, other): ...
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def __sub__(self, other): ...
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def __rsub__(self, other): ...
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def __mul__(self, other): ...
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def __rmul__(self, other): ...
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def __truediv__(self, other): ...
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def __rtruediv__(self, other): ...
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def __floordiv__(self, other): ...
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def __rfloordiv__(self, other): ...
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def __pow__(self, other, mod: None = None, /): ...
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def __rpow__(self, other, mod: None = None, /): ...
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# Keep in sync with `ndarray.__iadd__`
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@overload
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def __iadd__(
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self: _MaskedArray[np.bool], other: _ArrayLikeBool_co, /
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) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def __iadd__(self: _MaskedArray[integer], other: _ArrayLikeInt_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def __iadd__(
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self: _MaskedArray[floating], other: _ArrayLikeFloat_co, /
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) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def __iadd__(
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self: _MaskedArray[complexfloating], other: _ArrayLikeComplex_co, /
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) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def __iadd__(
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self: _MaskedArray[timedelta64 | datetime64], other: _ArrayLikeTD64_co, /
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) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def __iadd__(self: _MaskedArray[bytes_], other: _ArrayLikeBytes_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def __iadd__(
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self: MaskedArray[Any, dtype[str_] | dtypes.StringDType],
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other: _ArrayLikeStr_co | _ArrayLikeString_co,
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/,
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) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@overload
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def __iadd__(
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self: _MaskedArray[object_], other: Any, /
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) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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# Keep in sync with `ndarray.__isub__`
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@overload
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def __isub__(self: _MaskedArray[integer], other: _ArrayLikeInt_co, /) -> MaskedArray[_ShapeT_co, _DTypeT_co]: ...
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@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=...): ...
|