""" Auto-generated Python stub file from numpy.__init__.py Module: numpy.__init__ """ from stdint import * import stdlib import string import vipermath import stdio import t, c import memhub MAX_NDIM: t.CDefine = 4 float64: t.CTypedef = t.CDouble float32: t.CTypedef = t.CFloat int64: t.CTypedef = t.CLong int32: t.CTypedef = t.CInt uint8: t.CTypedef = t.CUnsignedChar pi: t.CDefine = 3.14159265358979323846 e: t.CDefine = 2.71828182845904523536 @t.Object class ndarray[T]: data: T | t.CPtr shape: t.CArray[t.CSizeT, MAX_NDIM] strides: t.CArray[t.CSizeT, MAX_NDIM] ndim: t.CInt size: t.CSizeT owns_data: t.CInt pool: memhub.MemManager | t.CPtr def __new__(self: ndarray, pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> t.CInt: pass def __add__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass def __sub__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass def __mul__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass def __truediv__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass def __floordiv__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass def __mod__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass def __neg__(self: ndarray) -> ndarray[T] | t.CPtr: pass def __len__(self: ndarray) -> t.CInt: pass def at2d(self: ndarray, row: t.CSizeT, col: t.CSizeT) -> T: pass def set2d(self: ndarray, row: t.CSizeT, col: t.CSizeT, val: T) -> t.CInt: pass def delete(self: ndarray) -> t.CInt: pass def fill(self: ndarray, val: T) -> t.CInt: pass def copy(self: ndarray) -> ndarray[T] | t.CPtr: pass def reshape(self: ndarray, new_shape: INTPTR, new_ndim: t.CInt) -> ndarray[T] | t.CPtr: pass def sum(self: ndarray) -> T: pass def mean(self: ndarray) -> T: pass def min(self: ndarray) -> T: pass def max(self: ndarray) -> T: pass def argmax(self: ndarray) -> t.CInt: pass def argmin(self: ndarray) -> t.CInt: pass def dot(self: ndarray, other: ndarray[T] | t.CPtr) -> T: pass def T(self: ndarray) -> ndarray[T] | t.CPtr: pass def print_arr(self: ndarray) -> t.CInt: pass Float64Array: t.CTypedef = ndarray[t.CDouble] Float32Array: t.CTypedef = ndarray[t.CFloat] Int64Array: t.CTypedef = ndarray[t.CLong] Int32Array: t.CTypedef = ndarray[t.CInt] Uint8Array: t.CTypedef = ndarray[t.CUnsignedChar] def _alloc_ndarray(pool: memhub.MemManager | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def _compute_strides(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass def _empty_like(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def array(pool: memhub.MemManager | t.CPtr, data: t.CDouble | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def zeros(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def ones(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def full(pool: memhub.MemManager | t.CPtr, n: t.CSizeT, val: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def arange(pool: memhub.MemManager | t.CPtr, start: t.CDouble, stop: t.CDouble, step: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def linspace(pool: memhub.MemManager | t.CPtr, start: t.CDouble, stop: t.CDouble, num: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def empty2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def zeros2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def ones2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def eye(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def diag(pool: memhub.MemManager | t.CPtr, vals: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_abs(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_sqrt(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_exp(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_log(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_sin(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_cos(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_tan(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_pow(a: ndarray[t.CDouble] | t.CPtr, p: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def add_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def mul_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def sub_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def div_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def matmul(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def dot_product(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def np_sum(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def np_mean(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def np_min(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def np_max(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def np_argmax(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass def np_argmin(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass def var(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def std(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def norm(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def clip(a: ndarray[t.CDouble] | t.CPtr, lo: t.CDouble, hi: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def concatenate(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def sort_arr(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def reverse(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_log10(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_log2(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_floor(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_ceil(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_round(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_sign(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_tanh(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_sinh(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_cosh(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_arcsin(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_arccos(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_arctan(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_arctan2(y: ndarray[t.CDouble] | t.CPtr, x: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_degrees(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_radians(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_isnan(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_isinf(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_maximum(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_minimum(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def cumsum(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def diff(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def flatten(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def trace(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def outer(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_where(condition: ndarray[t.CDouble] | t.CPtr, x: ndarray[t.CDouble] | t.CPtr, y: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_count_nonzero(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass def np_all(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass def np_any(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass def np_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_not_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_less(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_greater(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_less_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_greater_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def empty(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass def full2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT, val: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def zeros_like(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def ones_like(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def arange1(pool: memhub.MemManager | t.CPtr, stop: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def linspace2(pool: memhub.MemManager | t.CPtr, start: t.CDouble, stop: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass def meshgrid(x: ndarray[t.CDouble] | t.CPtr, y: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def det2x2(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def inv2x2(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def cross3(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass def np_interp(x: t.CDouble, xp: ndarray[t.CDouble] | t.CPtr, fp: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def prod(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def median(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass def percentile(a: ndarray[t.CDouble] | t.CPtr, q: t.CDouble) -> t.CDouble: pass