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#!/usr/bin/env python
#cython: language_level=3
from libc.stdint cimport uint32_t
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
import numpy as np
cimport numpy as np
cimport cython
from numpy.random cimport bitgen_t
from numpy.random import PCG64
np.import_array()
@cython.boundscheck(False)
@cython.wraparound(False)
def uniform_mean(Py_ssize_t n):
cdef Py_ssize_t i
cdef bitgen_t *rng
cdef const char *capsule_name = "BitGenerator"
cdef double[::1] random_values
cdef np.ndarray randoms
x = PCG64()
capsule = x.capsule
if not PyCapsule_IsValid(capsule, capsule_name):
raise ValueError("Invalid pointer to anon_func_state")
rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
random_values = np.empty(n)
# Best practice is to acquire the lock whenever generating random values.
# This prevents other threads from modifying the state. Acquiring the lock
# is only necessary if if the GIL is also released, as in this example.
with x.lock, nogil:
for i in range(n):
random_values[i] = rng.next_double(rng.state)
randoms = np.asarray(random_values)
return randoms.mean()
# This function is declared nogil so it can be used without the GIL below
cdef uint32_t bounded_uint(uint32_t lb, uint32_t ub, bitgen_t *rng) nogil:
cdef uint32_t mask, delta, val
mask = delta = ub - lb
mask |= mask >> 1
mask |= mask >> 2
mask |= mask >> 4
mask |= mask >> 8
mask |= mask >> 16
val = rng.next_uint32(rng.state) & mask
while val > delta:
val = rng.next_uint32(rng.state) & mask
return lb + val
@cython.boundscheck(False)
@cython.wraparound(False)
def bounded_uints(uint32_t lb, uint32_t ub, Py_ssize_t n):
cdef Py_ssize_t i
cdef bitgen_t *rng
cdef uint32_t[::1] out
cdef const char *capsule_name = "BitGenerator"
x = PCG64()
out = np.empty(n, dtype=np.uint32)
capsule = x.capsule
if not PyCapsule_IsValid(capsule, capsule_name):
raise ValueError("Invalid pointer to anon_func_state")
rng = <bitgen_t *>PyCapsule_GetPointer(capsule, capsule_name)
with x.lock, nogil:
for i in range(n):
out[i] = bounded_uint(lb, ub, rng)
return np.asarray(out)
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