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| import numpy as np
| import numpy.typing as npt
|
| AR_f8: npt.NDArray[np.float64] = np.array([1.0])
| AR_i4 = np.array([1], dtype=np.int32)
| AR_u1 = np.array([1], dtype=np.uint8)
|
| AR_LIKE_f = [1.5]
| AR_LIKE_i = [1]
|
| b_f8 = np.broadcast(AR_f8)
| b_i4_f8_f8 = np.broadcast(AR_i4, AR_f8, AR_f8)
|
| next(b_f8)
| b_f8.reset()
| b_f8.index
| b_f8.iters
| b_f8.nd
| b_f8.ndim
| b_f8.numiter
| b_f8.shape
| b_f8.size
|
| next(b_i4_f8_f8)
| b_i4_f8_f8.reset()
| b_i4_f8_f8.ndim
| b_i4_f8_f8.index
| b_i4_f8_f8.iters
| b_i4_f8_f8.nd
| b_i4_f8_f8.numiter
| b_i4_f8_f8.shape
| b_i4_f8_f8.size
|
| np.inner(AR_f8, AR_i4)
|
| np.where([True, True, False])
| np.where([True, True, False], 1, 0)
|
| np.lexsort([0, 1, 2])
|
| np.can_cast(np.dtype("i8"), int)
| np.can_cast(AR_f8, "f8")
| np.can_cast(AR_f8, np.complex128, casting="unsafe")
|
| np.min_scalar_type([1])
| np.min_scalar_type(AR_f8)
|
| np.result_type(int, AR_i4)
| np.result_type(AR_f8, AR_u1)
| np.result_type(AR_f8, np.complex128)
|
| np.dot(AR_LIKE_f, AR_i4)
| np.dot(AR_u1, 1)
| np.dot(1.5j, 1)
| np.dot(AR_u1, 1, out=AR_f8)
|
| np.vdot(AR_LIKE_f, AR_i4)
| np.vdot(AR_u1, 1)
| np.vdot(1.5j, 1)
|
| np.bincount(AR_i4)
|
| np.copyto(AR_f8, [1.6])
|
| np.putmask(AR_f8, [True], 1.5)
|
| np.packbits(AR_i4)
| np.packbits(AR_u1)
|
| np.unpackbits(AR_u1)
|
| np.shares_memory(1, 2)
| np.shares_memory(AR_f8, AR_f8, max_work=1)
|
| np.may_share_memory(1, 2)
| np.may_share_memory(AR_f8, AR_f8, max_work=1)
|
|
