from _typeshed import Incomplete
import numpy as np
from numpy.lib._function_base_impl import average
from numpy.lib._index_tricks_impl import AxisConcatenator
from .core import MaskedArray, dot
__all__ = [
"apply_along_axis",
"apply_over_axes",
"atleast_1d",
"atleast_2d",
"atleast_3d",
"average",
"clump_masked",
"clump_unmasked",
"column_stack",
"compress_cols",
"compress_nd",
"compress_rowcols",
"compress_rows",
"corrcoef",
"count_masked",
"cov",
"diagflat",
"dot",
"dstack",
"ediff1d",
"flatnotmasked_contiguous",
"flatnotmasked_edges",
"hsplit",
"hstack",
"in1d",
"intersect1d",
"isin",
"mask_cols",
"mask_rowcols",
"mask_rows",
"masked_all",
"masked_all_like",
"median",
"mr_",
"ndenumerate",
"notmasked_contiguous",
"notmasked_edges",
"polyfit",
"row_stack",
"setdiff1d",
"setxor1d",
"stack",
"union1d",
"unique",
"vander",
"vstack",
]
def count_masked(arr, axis=...): ...
def masked_all(shape, dtype = ...): ...
def masked_all_like(arr): ...
class _fromnxfunction:
__name__: Incomplete
__doc__: Incomplete
def __init__(self, funcname) -> None: ...
def getdoc(self): ...
def __call__(self, *args, **params): ...
class _fromnxfunction_single(_fromnxfunction):
def __call__(self, x, *args, **params): ...
class _fromnxfunction_seq(_fromnxfunction):
def __call__(self, x, *args, **params): ...
class _fromnxfunction_allargs(_fromnxfunction):
def __call__(self, *args, **params): ...
atleast_1d: _fromnxfunction_allargs
atleast_2d: _fromnxfunction_allargs
atleast_3d: _fromnxfunction_allargs
vstack: _fromnxfunction_seq
row_stack: _fromnxfunction_seq
hstack: _fromnxfunction_seq
column_stack: _fromnxfunction_seq
dstack: _fromnxfunction_seq
stack: _fromnxfunction_seq
hsplit: _fromnxfunction_single
diagflat: _fromnxfunction_single
def apply_along_axis(func1d, axis, arr, *args, **kwargs): ...
def apply_over_axes(func, a, axes): ...
def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ...
def compress_nd(x, axis=...): ...
def compress_rowcols(x, axis=...): ...
def compress_rows(a): ...
def compress_cols(a): ...
def mask_rows(a, axis = ...): ...
def mask_cols(a, axis = ...): ...
def ediff1d(arr, to_end=..., to_begin=...): ...
def unique(ar1, return_index=..., return_inverse=...): ...
def intersect1d(ar1, ar2, assume_unique=...): ...
def setxor1d(ar1, ar2, assume_unique=...): ...
def in1d(ar1, ar2, assume_unique=..., invert=...): ...
def isin(element, test_elements, assume_unique=..., invert=...): ...
def union1d(ar1, ar2): ...
def setdiff1d(ar1, ar2, assume_unique=...): ...
def cov(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ...
def corrcoef(x, y=..., rowvar=..., bias = ..., allow_masked=..., ddof = ...): ...
class MAxisConcatenator(AxisConcatenator):
@staticmethod
def concatenate(arrays: Incomplete, axis: int = 0) -> Incomplete: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
@classmethod
def makemat(cls, arr: Incomplete) -> Incomplete: ... # type: ignore[override] # pyright: ignore[reportIncompatibleVariableOverride]
class mr_class(MAxisConcatenator):
def __init__(self) -> None: ...
mr_: mr_class
def ndenumerate(a, compressed=...): ...
def flatnotmasked_edges(a): ...
def notmasked_edges(a, axis=...): ...
def flatnotmasked_contiguous(a): ...
def notmasked_contiguous(a, axis=...): ...
def clump_unmasked(a): ...
def clump_masked(a): ...
def vander(x, n=...): ...
def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ...
#
def mask_rowcols(a: Incomplete, axis: Incomplete | None = None) -> MaskedArray[Incomplete, np.dtype[Incomplete]]: ...