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Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.20) but the running NumPy has C-API version 0x%x. 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Parameters ---------- seed : {None, int, array_like[ints], SeedSequence}, optional A seed to initialize the `BitGenerator`. If None, then fresh, unpredictable entropy will be pulled from the OS. If an ``int`` or ``array_like[ints]`` is passed, then it will be passed to `SeedSequence` to derive the initial `BitGenerator` state. One may also pass in a `SeedSequence` instance. Notes ----- `SFC64` is a 256-bit implementation of Chris Doty-Humphrey's Small Fast Chaotic PRNG ([1]_). `SFC64` has a few different cycles that one might be on, depending on the seed; the expected period will be about :math:`2^{255}` ([2]_). `SFC64` incorporates a 64-bit counter which means that the absolute minimum cycle length is :math:`2^{64}` and that distinct seeds will not run into each other for at least :math:`2^{64}` iterations. `SFC64` provides a capsule containing function pointers that produce doubles, and unsigned 32 and 64- bit integers. These are not directly consumable in Python and must be consumed by a `Generator` or similar object that supports low-level access. **State and Seeding** The `SFC64` state vector consists of 4 unsigned 64-bit values. The last is a 64-bit counter that increments by 1 each iteration. The input seed is processed by `SeedSequence` to generate the first 3 values, then the `SFC64` algorithm is iterated a small number of times to mix. **Compatibility Guarantee** `SFC64` makes a guarantee that a fixed seed will always produce the same random integer stream. References ---------- .. [1] `"PractRand" `_ .. 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