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796222a3002d04a41570b66e8b523cf79a28c90e
TransPyC/Test/TestProject/temp/c3a6aed1f1fb8b1e.pyi
t 796222a300 snapshot before regression test
2026-07-18 19:25:40 +08:00

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"""
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
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