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Updated script that can be controled by Nodejs web app

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mac OS
2024-11-25 12:24:18 +07:00
parent c440eda1f4
commit 8b0ab2bd3a
8662 changed files with 1803808 additions and 34 deletions
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78
lib/python3.13/site-packages/numpy/random/_examples/cython/extending.pyx Normal file
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#!/usr/bin/env python3
#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)

117
lib/python3.13/site-packages/numpy/random/_examples/cython/extending_distributions.pyx Normal file
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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

53
lib/python3.13/site-packages/numpy/random/_examples/cython/meson.build Normal file
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project('random-build-examples', 'c', 'cpp', 'cython')
py_mod = import('python')
py3 = py_mod.find_installation(pure: false)
cc = meson.get_compiler('c')
cy = meson.get_compiler('cython')
# Keep synced with pyproject.toml
if not cy.version().version_compare('>=3.0.6')
error('tests requires Cython >= 3.0.6')
endif
base_cython_args = []
if cy.version().version_compare('>=3.1.0')
base_cython_args += ['-Xfreethreading_compatible=True']
endif
_numpy_abs = run_command(py3, ['-c',
'import os; os.chdir(".."); import numpy; print(os.path.abspath(numpy.get_include() + "../../.."))'],
check: true).stdout().strip()
npymath_path = _numpy_abs / '_core' / 'lib'
npy_include_path = _numpy_abs / '_core' / 'include'
npyrandom_path = _numpy_abs / 'random' / 'lib'
npymath_lib = cc.find_library('npymath', dirs: npymath_path)
npyrandom_lib = cc.find_library('npyrandom', dirs: npyrandom_path)
py3.extension_module(
'extending_distributions',
'extending_distributions.pyx',
install: false,
include_directories: [npy_include_path],
dependencies: [npyrandom_lib, npymath_lib],
cython_args: base_cython_args,
)
py3.extension_module(
'extending',
'extending.pyx',
install: false,
include_directories: [npy_include_path],
dependencies: [npyrandom_lib, npymath_lib],
cython_args: base_cython_args,
)
py3.extension_module(
'extending_cpp',
'extending_distributions.pyx',
install: false,
override_options : ['cython_language=cpp'],
cython_args: base_cython_args + ['--module-name', 'extending_cpp'],
include_directories: [npy_include_path],
dependencies: [npyrandom_lib, npymath_lib],
)