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					86 lines
				
				2.6 KiB
			
		
		
			
		
	
	
					86 lines
				
				2.6 KiB
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											3 years ago
										 
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								from typing import Any
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								from numpy.lib.index_tricks import AxisConcatenator
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								from numpy.ma.core import (
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								    dot as dot,
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								    mask_rowcols as mask_rowcols,
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								)
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								__all__: list[str]
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								def count_masked(arr, axis=...): ...
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								def masked_all(shape, dtype = ...): ...
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								def masked_all_like(arr): ...
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								class _fromnxfunction:
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								    __name__: Any
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								    __doc__: Any
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								    def __init__(self, funcname): ...
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								    def getdoc(self): ...
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								    def __call__(self, *args, **params): ...
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								class _fromnxfunction_single(_fromnxfunction):
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								    def __call__(self, x, *args, **params): ...
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								class _fromnxfunction_seq(_fromnxfunction):
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								    def __call__(self, x, *args, **params): ...
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								class _fromnxfunction_allargs(_fromnxfunction):
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								    def __call__(self, *args, **params): ...
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								atleast_1d: _fromnxfunction_allargs
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								atleast_2d: _fromnxfunction_allargs
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								atleast_3d: _fromnxfunction_allargs
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								vstack: _fromnxfunction_seq
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								row_stack: _fromnxfunction_seq
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								hstack: _fromnxfunction_seq
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								column_stack: _fromnxfunction_seq
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								dstack: _fromnxfunction_seq
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								stack: _fromnxfunction_seq
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								hsplit: _fromnxfunction_single
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								diagflat: _fromnxfunction_single
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								def apply_along_axis(func1d, axis, arr, *args, **kwargs): ...
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								def apply_over_axes(func, a, axes): ...
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								def average(a, axis=..., weights=..., returned=..., keepdims=...): ...
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								def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ...
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								def compress_nd(x, axis=...): ...
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								def compress_rowcols(x, axis=...): ...
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								def compress_rows(a): ...
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								def compress_cols(a): ...
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								def mask_rows(a, axis = ...): ...
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								def mask_cols(a, axis = ...): ...
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								def ediff1d(arr, to_end=..., to_begin=...): ...
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								def unique(ar1, return_index=..., return_inverse=...): ...
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								def intersect1d(ar1, ar2, assume_unique=...): ...
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								def setxor1d(ar1, ar2, assume_unique=...): ...
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								def in1d(ar1, ar2, assume_unique=..., invert=...): ...
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								def isin(element, test_elements, assume_unique=..., invert=...): ...
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								def union1d(ar1, ar2): ...
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								def setdiff1d(ar1, ar2, assume_unique=...): ...
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								def cov(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ...
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								def corrcoef(x, y=..., rowvar=..., bias = ..., allow_masked=..., ddof = ...): ...
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								class MAxisConcatenator(AxisConcatenator):
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								    concatenate: Any
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								    @classmethod
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								    def makemat(cls, arr): ...
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								    def __getitem__(self, key): ...
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								class mr_class(MAxisConcatenator):
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								    def __init__(self): ...
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								mr_: mr_class
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								def ndenumerate(a, compressed=...): ...
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								def flatnotmasked_edges(a): ...
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								def notmasked_edges(a, axis=...): ...
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								def flatnotmasked_contiguous(a): ...
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								def notmasked_contiguous(a, axis=...): ...
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								def clump_unmasked(a): ...
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								def clump_masked(a): ...
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								def vander(x, n=...): ...
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								def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ...
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