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

#cython: language_level=3 """ This file shows how the to use a BitGenerator to create a distribution. """ import numpy as np cimport numpy as np cimport cython from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer from libc.stdint cimport uint16_t, uint64_t from numpy.random cimport bitgen_t from numpy.random import PCG64 from numpy.random.c_distributions cimport ( random_standard_uniform_fill, random_standard_uniform_fill_f)

np.import_array()

@cython.boundscheck(False) @cython.wraparound(False) def uniforms(Py_ssize_t n): """ Create an array of n uniformly distributed doubles. A 'real' distribution would want to process the values into some non-uniform distribution """ cdef Py_ssize_t i cdef bitgen_t *rng cdef const char *capsule_name = "BitGenerator" cdef double[::1] random_values

x = PCG64()
capsule = x.capsule
# Optional check that the capsule if from a BitGenerator
if not PyCapsule_IsValid(capsule, capsule_name):
    raise ValueError("Invalid pointer to anon_func_state")
# Cast the pointer
rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
random_values = np.empty(n, dtype='float64')
with x.lock, nogil:
    for i in range(n):
        # Call the function
        random_values[i] = rng.next_double(rng.state)
randoms = np.asarray(random_values)

return randoms

cython example 2

@cython.boundscheck(False) @cython.wraparound(False) def uint10_uniforms(Py_ssize_t n): """Uniform 10 bit integers stored as 16-bit unsigned integers""" cdef Py_ssize_t i cdef bitgen_t *rng cdef const char *capsule_name = "BitGenerator" cdef uint16_t[::1] random_values cdef int bits_remaining cdef int width = 10 cdef uint64_t buff, mask = 0x3FF

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, dtype='uint16')
# Best practice is to release GIL and acquire the lock
bits_remaining = 0
with x.lock, nogil:
    for i in range(n):
        if bits_remaining < width:
            buff = rng.next_uint64(rng.state)
        random_values[i] = buff & mask
        buff >>= width

randoms = np.asarray(random_values)
return randoms

cython example 3

def uniforms_ex(bit_generator, Py_ssize_t n, dtype=np.float64): """ Create an array of n uniformly distributed doubles via a "fill" function.

A 'real' distribution would want to process the values into
some non-uniform distribution

Parameters
----------
bit_generator: BitGenerator instance
n: int
    Output vector length
dtype: {str, dtype}, optional
    Desired dtype, either 'd' (or 'float64') or 'f' (or 'float32'). The
    default dtype value is 'd'
"""
cdef Py_ssize_t i
cdef bitgen_t *rng
cdef const char *capsule_name = "BitGenerator"
cdef np.ndarray randoms

capsule = bit_generator.capsule
# Optional check that the capsule if from a BitGenerator
if not PyCapsule_IsValid(capsule, capsule_name):
    raise ValueError("Invalid pointer to anon_func_state")
# Cast the pointer
rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)

_dtype = np.dtype(dtype)
randoms = np.empty(n, dtype=_dtype)
if _dtype == np.float32:
    with bit_generator.lock:
        random_standard_uniform_fill_f(rng, n, <float*>np.PyArray_DATA(randoms))
elif _dtype == np.float64:
    with bit_generator.lock:
        random_standard_uniform_fill(rng, n, <double*>np.PyArray_DATA(randoms))
else:
    raise TypeError('Unsupported dtype %r for random' % _dtype)
return randoms