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Extending via Numba

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This is documentation for an old release of NumPy (version 1.19). Read this page in the documentation of the latest stable release (version 2.2).

Extending via Numba and CFFI

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])