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