68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
|
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])
|