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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)	1.91 KB	2026-01-20 12:58
📄 extending_distributions.py r""" Building the required library in this example requires a source distribution of NumPy or clone of the NumPy git repository since distributions.c is not included in binary distributions. On nix, execute in numpy/random/src/distributions export ${PYTHON_VERSION}=3.8 # Python version export PYTHON_INCLUDE=#path to Python's include folder, usually \ ${PYTHON_HOME}/include/python${PYTHON_VERSION}m export NUMPY_INCLUDE=#path to numpy's include folder, usually \ ${PYTHON_HOME}/lib/python${PYTHON_VERSION}/site-packages/numpy/core/include gcc -shared -o libdistributions.so -fPIC distributions.c \ -I${NUMPY_INCLUDE} -I${PYTHON_INCLUDE} mv libdistributions.so ../../_examples/numba/ On Windows rem PYTHON_HOME and PYTHON_VERSION are setup dependent, this is an example set PYTHON_HOME=c:\Anaconda set PYTHON_VERSION=38 cl.exe /LD .\distributions.c -DDLL_EXPORT \ -I%PYTHON_HOME%\lib\site-packages\numpy\core\include \ -I%PYTHON_HOME%\include %PYTHON_HOME%\libs\python%PYTHON_VERSION%.lib move distributions.dll ../../_examples/numba/ """ import os import numba as nb import numpy as np from cffi import FFI from numpy.random import PCG64 ffi = FFI() if os.path.exists('./distributions.dll'): lib = ffi.dlopen('./distributions.dll') elif os.path.exists('./libdistributions.so'): lib = ffi.dlopen('./libdistributions.so') else: raise RuntimeError('Required DLL/so file was not found.') ffi.cdef(""" double random_standard_normal(void bitgen_state); """) x = PCG64() xffi = x.cffi bit_generator = xffi.bit_generator random_standard_normal = lib.random_standard_normal def normals(n, bit_generator): out = np.empty(n) for i in range(n): out[i] = random_standard_normal(bit_generator) return out normalsj = nb.jit(normals, nopython=True) # Numba requires a memory address for void * # Can also get address from x.ctypes.bit_generator.value bit_generator_address = int(ffi.cast('uintptr_t', bit_generator)) norm = normalsj(1000, bit_generator_address) print(norm[:12])	1.99 KB	2026-01-20 12:58

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