583 lines
9.4 KiB
Python
583 lines
9.4 KiB
Python
![]() |
from typing import Any, Literal, TypeAlias, TypeVar, overload
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import numpy as np
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import numpy.typing as npt
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from numpy import ( # noqa: F401
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False_,
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ScalarType,
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True_,
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__array_namespace_info__,
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__version__,
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abs,
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absolute,
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acos,
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acosh,
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add,
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all,
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allclose,
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amax,
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amin,
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angle,
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any,
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append,
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apply_along_axis,
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apply_over_axes,
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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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argpartition,
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argsort,
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argwhere,
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around,
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array,
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array2string,
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array_equal,
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array_equiv,
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array_repr,
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array_split,
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array_str,
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asanyarray,
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asarray,
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asarray_chkfinite,
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ascontiguousarray,
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asfortranarray,
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asin,
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asinh,
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asmatrix,
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astype,
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atan,
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atan2,
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atanh,
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atleast_1d,
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atleast_2d,
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atleast_3d,
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average,
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bartlett,
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base_repr,
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binary_repr,
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bincount,
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bitwise_and,
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bitwise_count,
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bitwise_invert,
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bitwise_left_shift,
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bitwise_not,
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bitwise_or,
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bitwise_right_shift,
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bitwise_xor,
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blackman,
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block,
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bmat,
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bool,
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bool_,
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broadcast,
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broadcast_arrays,
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broadcast_shapes,
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broadcast_to,
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busday_count,
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busday_offset,
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busdaycalendar,
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byte,
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bytes_,
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c_,
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can_cast,
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cbrt,
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cdouble,
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ceil,
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char,
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character,
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choose,
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clip,
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clongdouble,
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column_stack,
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common_type,
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complex64,
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complex128,
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complex256,
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complexfloating,
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compress,
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concat,
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concatenate,
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conj,
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conjugate,
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convolve,
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copy,
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copysign,
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copyto,
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core,
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corrcoef,
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correlate,
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cos,
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cosh,
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count_nonzero,
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cov,
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cross,
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csingle,
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ctypeslib,
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cumprod,
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cumsum,
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cumulative_prod,
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cumulative_sum,
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datetime64,
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datetime_as_string,
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datetime_data,
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deg2rad,
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degrees,
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delete,
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diag,
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diag_indices,
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diag_indices_from,
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diagflat,
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diagonal,
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diff,
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digitize,
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divide,
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divmod,
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dot,
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double,
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dsplit,
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dstack,
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dtype,
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dtypes,
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e,
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ediff1d,
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einsum,
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einsum_path,
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emath,
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empty_like,
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equal,
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errstate,
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euler_gamma,
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exceptions,
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exp,
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exp2,
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expand_dims,
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expm1,
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extract,
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f2py,
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fabs,
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fft,
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fill_diagonal,
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finfo,
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fix,
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flatiter,
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flatnonzero,
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flexible,
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flip,
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fliplr,
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flipud,
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float16,
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float32,
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float64,
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float128,
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float_power,
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floating,
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floor,
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floor_divide,
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fmax,
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fmin,
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fmod,
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format_float_positional,
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format_float_scientific,
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frexp,
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from_dlpack,
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frombuffer,
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fromfile,
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fromfunction,
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fromiter,
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frompyfunc,
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fromregex,
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fromstring,
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full,
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full_like,
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gcd,
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generic,
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genfromtxt,
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geomspace,
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get_include,
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get_printoptions,
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getbufsize,
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geterr,
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geterrcall,
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gradient,
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greater,
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greater_equal,
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half,
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hamming,
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hanning,
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heaviside,
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histogram,
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histogram2d,
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histogram_bin_edges,
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histogramdd,
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hsplit,
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hstack,
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hypot,
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i0,
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iinfo,
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imag,
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in1d,
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index_exp,
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indices,
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inexact,
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inf,
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info,
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inner,
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insert,
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int8,
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int16,
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int32,
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int64,
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int_,
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intc,
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integer,
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interp,
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intersect1d,
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intp,
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invert,
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is_busday,
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isclose,
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iscomplex,
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iscomplexobj,
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isdtype,
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isfinite,
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isfortran,
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isin,
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isinf,
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isnan,
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isnat,
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isneginf,
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isposinf,
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isreal,
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isrealobj,
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isscalar,
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issubdtype,
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iterable,
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ix_,
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kaiser,
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kron,
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lcm,
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ldexp,
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left_shift,
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less,
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less_equal,
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lexsort,
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lib,
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linalg,
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linspace,
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little_endian,
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load,
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loadtxt,
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log,
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log1p,
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log2,
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log10,
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logaddexp,
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logaddexp2,
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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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logspace,
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long,
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longdouble,
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longlong,
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ma,
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mask_indices,
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matmul,
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matrix,
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matrix_transpose,
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matvec,
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max,
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maximum,
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may_share_memory,
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mean,
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median,
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memmap,
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meshgrid,
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mgrid,
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min,
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min_scalar_type,
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minimum,
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mintypecode,
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mod,
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modf,
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moveaxis,
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multiply,
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nan,
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nan_to_num,
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nanargmax,
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nanargmin,
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nancumprod,
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nancumsum,
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nanmax,
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nanmean,
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nanmedian,
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nanmin,
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nanpercentile,
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nanprod,
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nanquantile,
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nanstd,
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nansum,
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nanvar,
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ndarray,
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ndenumerate,
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ndim,
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ndindex,
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nditer,
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negative,
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nested_iters,
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newaxis,
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nextafter,
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nonzero,
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not_equal,
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number,
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object_,
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ogrid,
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ones_like,
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outer,
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packbits,
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pad,
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partition,
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percentile,
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permute_dims,
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pi,
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piecewise,
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place,
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poly,
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poly1d,
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polyadd,
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polyder,
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polydiv,
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polyfit,
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polyint,
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polymul,
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polynomial,
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polysub,
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polyval,
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positive,
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pow,
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power,
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printoptions,
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prod,
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promote_types,
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ptp,
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put,
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put_along_axis,
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putmask,
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quantile,
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r_,
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rad2deg,
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radians,
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random,
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ravel,
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ravel_multi_index,
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real,
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real_if_close,
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rec,
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recarray,
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reciprocal,
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record,
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remainder,
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repeat,
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require,
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reshape,
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resize,
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result_type,
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right_shift,
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rint,
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roll,
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rollaxis,
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roots,
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rot90,
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round,
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row_stack,
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s_,
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save,
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savetxt,
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savez,
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savez_compressed,
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sctypeDict,
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searchsorted,
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select,
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set_printoptions,
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setbufsize,
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setdiff1d,
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seterr,
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seterrcall,
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setxor1d,
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shape,
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shares_memory,
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short,
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show_config,
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show_runtime,
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sign,
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signbit,
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signedinteger,
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sin,
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sinc,
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single,
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sinh,
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size,
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sort,
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sort_complex,
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spacing,
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split,
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sqrt,
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square,
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squeeze,
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stack,
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std,
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str_,
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strings,
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subtract,
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sum,
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swapaxes,
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take,
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take_along_axis,
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tan,
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tanh,
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tensordot,
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test,
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testing,
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tile,
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timedelta64,
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trace,
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transpose,
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|
trapezoid,
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|
trapz,
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|
tri,
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tril,
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tril_indices,
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tril_indices_from,
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trim_zeros,
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|
triu,
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|
triu_indices,
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triu_indices_from,
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|
true_divide,
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|
trunc,
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|
typecodes,
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|
typename,
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|
typing,
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||
|
ubyte,
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||
|
ufunc,
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||
|
uint,
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||
|
uint8,
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|
uint16,
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|
uint32,
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|
uint64,
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|
uintc,
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uintp,
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ulong,
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ulonglong,
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union1d,
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|
unique,
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unique_all,
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unique_counts,
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unique_inverse,
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unique_values,
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|
unpackbits,
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|
unravel_index,
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|
unsignedinteger,
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|
unstack,
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|
unwrap,
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||
|
ushort,
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|
vander,
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||
|
var,
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|
vdot,
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||
|
vecdot,
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||
|
vecmat,
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|
vectorize,
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|
void,
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|
vsplit,
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vstack,
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|
where,
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zeros_like,
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)
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from numpy._typing import _ArrayLike, _DTypeLike
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__all__ = ["rand", "randn", "repmat"]
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__all__ += np.__all__
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###
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_T = TypeVar("_T", bound=np.generic)
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_Matrix: TypeAlias = np.matrix[tuple[int, int], np.dtype[_T]]
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_Order: TypeAlias = Literal["C", "F"]
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|
###
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#
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|
@overload
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def empty(shape: int | tuple[int, int], dtype: None = None, order: _Order = "C") -> _Matrix[np.float64]: ...
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@overload
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def empty(shape: int | tuple[int, int], dtype: _DTypeLike[_T], order: _Order = "C") -> _Matrix[_T]: ...
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@overload
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def empty(shape: int | tuple[int, int], dtype: npt.DTypeLike, order: _Order = "C") -> _Matrix[Any]: ...
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#
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||
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@overload
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def ones(shape: int | tuple[int, int], dtype: None = None, order: _Order = "C") -> _Matrix[np.float64]: ...
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|
@overload
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def ones(shape: int | tuple[int, int], dtype: _DTypeLike[_T], order: _Order = "C") -> _Matrix[_T]: ...
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|
@overload
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||
|
def ones(shape: int | tuple[int, int], dtype: npt.DTypeLike, order: _Order = "C") -> _Matrix[Any]: ...
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|
#
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||
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@overload
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||
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def zeros(shape: int | tuple[int, int], dtype: None = None, order: _Order = "C") -> _Matrix[np.float64]: ...
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@overload
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||
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def zeros(shape: int | tuple[int, int], dtype: _DTypeLike[_T], order: _Order = "C") -> _Matrix[_T]: ...
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||
|
@overload
|
||
|
def zeros(shape: int | tuple[int, int], dtype: npt.DTypeLike, order: _Order = "C") -> _Matrix[Any]: ...
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||
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#
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||
|
@overload
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||
|
def identity(n: int, dtype: None = None) -> _Matrix[np.float64]: ...
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||
|
@overload
|
||
|
def identity(n: int, dtype: _DTypeLike[_T]) -> _Matrix[_T]: ...
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||
|
@overload
|
||
|
def identity(n: int, dtype: npt.DTypeLike | None = None) -> _Matrix[Any]: ...
|
||
|
|
||
|
#
|
||
|
@overload
|
||
|
def eye(
|
||
|
n: int,
|
||
|
M: int | None = None,
|
||
|
k: int = 0,
|
||
|
dtype: type[np.float64] | None = ...,
|
||
|
order: _Order = "C",
|
||
|
) -> _Matrix[np.float64]: ...
|
||
|
@overload
|
||
|
def eye(n: int, M: int | None, k: int, dtype: _DTypeLike[_T], order: _Order = "C") -> _Matrix[_T]: ...
|
||
|
@overload
|
||
|
def eye(n: int, M: int | None = None, k: int = 0, *, dtype: _DTypeLike[_T], order: _Order = "C") -> _Matrix[_T]: ...
|
||
|
@overload
|
||
|
def eye(n: int, M: int | None = None, k: int = 0, dtype: npt.DTypeLike = ..., order: _Order = "C") -> _Matrix[Any]: ...
|
||
|
|
||
|
#
|
||
|
@overload
|
||
|
def rand(arg: int | tuple[()] | tuple[int] | tuple[int, int], /) -> _Matrix[np.float64]: ...
|
||
|
@overload
|
||
|
def rand(arg: int, /, *args: int) -> _Matrix[np.float64]: ...
|
||
|
|
||
|
#
|
||
|
@overload
|
||
|
def randn(arg: int | tuple[()] | tuple[int] | tuple[int, int], /) -> _Matrix[np.float64]: ...
|
||
|
@overload
|
||
|
def randn(arg: int, /, *args: int) -> _Matrix[np.float64]: ...
|
||
|
|
||
|
#
|
||
|
@overload
|
||
|
def repmat(a: _Matrix[_T], m: int, n: int) -> _Matrix[_T]: ...
|
||
|
@overload
|
||
|
def repmat(a: _ArrayLike[_T], m: int, n: int) -> npt.NDArray[_T]: ...
|
||
|
@overload
|
||
|
def repmat(a: npt.ArrayLike, m: int, n: int) -> npt.NDArray[Any]: ...
|