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#!/usr/bin/env python3 #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) @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