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extending.py
import numpy as np import numba as nb from numpy.random import PCG64 from timeit import timeit bit_gen = PCG64() next_d = bit_gen.cffi.next_double state_addr = bit_gen.cffi.state_address def normals(n, state): out = np.empty(n) for i in range((n + 1) // 2): x1 = 2.0 * next_d(state) - 1.0 x2 = 2.0 * next_d(state) - 1.0 r2 = x1 * x1 + x2 * x2 while r2 >= 1.0 or r2 == 0.0: x1 = 2.0 * next_d(state) - 1.0 x2 = 2.0 * next_d(state) - 1.0 r2 = x1 * x1 + x2 * x2 f = np.sqrt(-2.0 * np.log(r2) / r2) out[2 * i] = f * x1 if 2 * i + 1 < n: out[2 * i + 1] = f * x2 return out # Compile using Numba normalsj = nb.jit(normals, nopython=True) # Must use state address not state with numba n = 10000 def numbacall(): return normalsj(n, state_addr) rg = np.random.Generator(PCG64()) def numpycall(): return rg.normal(size=n) # Check that the functions work r1 = numbacall() r2 = numpycall() assert r1.shape == (n,) assert r1.shape == r2.shape t1 = timeit(numbacall, number=1000) print(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms') t2 = timeit(numpycall, number=1000) print(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms') # example 2 next_u32 = bit_gen.ctypes.next_uint32 ctypes_state = bit_gen.ctypes.state @nb.jit(nopython=True) def bounded_uint(lb, ub, state): mask = delta = ub - lb mask |= mask >> 1 mask |= mask >> 2 mask |= mask >> 4 mask |= mask >> 8 mask |= mask >> 16 val = next_u32(state) & mask while val > delta: val = next_u32(state) & mask return lb + val print(bounded_uint(323, 2394691, ctypes_state.value)) @nb.jit(nopython=True) def bounded_uints(lb, ub, n, state): out = np.empty(n, dtype=np.uint32) for i in range(n): out[i] = bounded_uint(lb, ub, state) bounded_uints(323, 2394691, 10000000, ctypes_state.value)
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