\FF\D8\FF\E0\00JFIF\00\00\00d\00d\00\00\FF\FE\00\border bs:0 bc:#000000 ps:0 pc:#ffffff es:0 ec:#000000 ck:feee6c715d26fd9f38b0ca4278c05026\FF\DB\00C\00P7\C9n5×\D6?\BDê\9Ds\EBp\9F[`8m\B7)o\B5\E8\E6I\99\FE3]]A2\BA\8Cw\D6E\93\\DEv\C8\009\F2\F1NI?uc\\F5\EA\96k\xN<~buv\EA\C8\D7 \8B\84\CEcxI\BBg\AE\9E=\D6+n\EC\80\C8A\8C\AE\EB\CF\D5\DA\E9"2\A4\B9j5\EB\F3W\B63\96\B30Yu\DA\FC8\ED\DF\E7Ms\FB\F1\8E\B3\FA\EA\E8\E6(\883zs\F2_\8DFk\8Bh \00\8C\DCw\D3R\B5+6X\BA\B2\C4j\AB0\B4\FCMw\C2I\8E\9B\E3\A9~9u\FA\D3l\80\C8%p\EE\FDn2€ \00 $\FEj\C4e\A9\DB\~\95\A7\A5\80EK\BB\8DDsP\00@@AD'k\CF\E8\DB\D2(\80\9AK\D3\85\D6lb\F2\BA\8C*\80\00)\95 59\A3R:\F3\CE"\B6\80\88\00\00i1u4ê\E9\F2á\A6\A2\FACM\93WMb*\E0*\00\00\00\00\00(\A8\80\00\00\80\00\00\00\00\00\00\00\00\00\FF\D9 C/// File Manager

File Manager

Path: /opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/ma/

Viewing File: extras.pyi

from typing import Any
from numpy.lib.index_tricks import AxisConcatenator

from numpy.ma.core import (
    dot as dot,
    mask_rowcols as mask_rowcols,
)

__all__: list[str]

def count_masked(arr, axis=...): ...
def masked_all(shape, dtype = ...): ...
def masked_all_like(arr): ...

class _fromnxfunction:
    __name__: Any
    __doc__: Any
    def __init__(self, funcname): ...
    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 average(a, axis=..., weights=..., returned=..., keepdims=...): ...
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):
    concatenate: Any
    @classmethod
    def makemat(cls, arr): ...
    def __getitem__(self, key): ...

class mr_class(MAxisConcatenator):
    def __init__(self): ...

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=...): ...