extending.pyx — NumPy v2.3.dev0 Manual (original) (raw)

#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 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)