using python setuptools to put cython-compiled pyd files in their original folders?

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So, if you modify your to:


extensions = [
      Extension(name = '', # using dots!
         sources = ['mpf/mc/core/audio/audio_interface.pyx'],
         include_dirs = include_dirs,
         library_dirs = library_dirs,
         libraries = libraries,
         extra_objects = extra_objects,
         extra_compile_args = extra_compile_args,
         extra_link_args = extra_link_args),


Suggestion : 2

The cython command takes a .py or .pyx file and compiles it into a C/C++ file.,The cythonize command takes a .py or .pyx file and compiles it into a C/C++ file. It then compiles the C/C++ file into an extension module which is directly importable from Python.,Run the cythonize compiler command with your options and list of .pyx files to generate an extension module. For example:,pyximport does not use cythonize(). Thus it is not possible to do things like using compiler directives at the top of Cython files or compiling Cython code to C++.

$ cython primes.pyx
$ cythonize - a - i yourmod.pyx
$ gcc - shared - pthread - fPIC - fwrapv - O2 - Wall - fno - strict - aliasing\ -
   I / usr / include / python3 .5 - o yourmod.c
from setuptools
import setup
from Cython.Build
import cythonize

   ext_modules = cythonize("example.pyx")
[build - system]
requires = ["setuptools", "wheel", "Cython"]
$ python build_ext--inplace

Suggestion : 3

A C extension for CPython is a shared library (e.g. a .so file on Linux, .pyd on Windows), which exports an initialization function.,Extension modules can be built using distutils, which is included in Python. Since distutils also supports creation of binary packages, users don’t necessarily need a compiler and distutils to install the extension.,In many cases, building an extension is more complex, since additional preprocessor defines and libraries may be needed. This is demonstrated in the example below.,To be importable, the shared library must be available on PYTHONPATH, and must be named after the module name, with an appropriate extension. When using distutils, the correct filename is generated automatically.

def initfunc_name(name):
   suffix = b '_' + name.encode('ascii')
except UnicodeEncodeError:
   suffix = b 'U_' + name.encode('punycode').replace(b '-', b '_')
return b 'PyInit' + suffix
from distutils.core
import setup, Extension

module1 = Extension('demo',
   sources = ['demo.c'])

setup(name = 'PackageName',
   version = '1.0',
   description = 'This is a demo package',
   ext_modules = [module1])
python build
from distutils.core
import setup, Extension

module1 = Extension('demo',
   define_macros = [('MAJOR_VERSION', '1'),
      ('MINOR_VERSION', '0')
   include_dirs = ['/usr/local/include'],
   libraries = ['tcl83'],
   library_dirs = ['/usr/local/lib'],
   sources = ['demo.c'])

setup(name = 'PackageName',
   version = '1.0',
   description = 'This is a demo package',
   author = 'Martin v. Loewis',
   author_email = '',
   url = '',
   long_description = ''
   This is really just a demo package.
   ext_modules = [module1])
gcc - DNDEBUG - g - O3 - Wall - Wstrict - prototypes - fPIC - DMAJOR_VERSION = 1 - DMINOR_VERSION = 0 - I / usr / local / include - I / usr / local / include / python2 .2 - c demo.c - o build / temp.linux - i686 - 2.2 / demo.o

gcc - shared build / temp.linux - i686 - 2.2 / demo.o - L / usr / local / lib - ltcl83 - o build / lib.linux - i686 - 2.2 /
python install

Suggestion : 4


from random
import random
from time
import perf_counter

COUNT = 500000 # Change this value depending on the speed of your computer
DATA = [(random() - 0.5) * 3
   for _ in range(COUNT)

e = 2.7182818284590452353602874713527

def sinh(x):
   return (1 - (e ** (-2 * x))) / (2 * (e ** -x))

def cosh(x):
   return (1 + (e ** (-2 * x))) / (2 * (e ** -x))

def tanh(x):
   tanh_x = sinh(x) / cosh(x)
return tanh_x

def test(fn, name):
   start = perf_counter()
result = fn(DATA)
duration = perf_counter() - start
print('{} took {:.3f} seconds\n\n'.format(name, duration))

for d in result:
   assert - 1 <= d <= 1, " incorrect values"

if __name__ == "__main__":
   print('Running benchmarks with COUNT = {}'.format(COUNT))

test(lambda d: [tanh(x) for x in d], '[tanh(x) for x in d] (Python implementation)')

In the C++ project's module.cpp file, add the following code:

#include <Windows.h>
#include <cmath>

const double e = 2.7182818284590452353602874713527;

double sinh_impl(double x) {
    return (1 - pow(e, (-2 * x))) / (2 * pow(e, -x));

double cosh_impl(double x) {
    return (1 + pow(e, (-2 * x))) / (2 * pow(e, -x));

double tanh_impl(double x) {
    return sinh_impl(x) / cosh_impl(x);

At the top of the module.cpp file, include Python.h:

#include <Python.h>

Modify the tanh_impl method to accept and return Python types (that is, a PyObject*):

PyObject * tanh_impl(PyObject * /* unused module reference */ , PyObject * o) {
   double x = PyFloat_AsDouble(o);
   double tanh_x = sinh_impl(x) / cosh_impl(x);
   return PyFloat_FromDouble(tanh_x);

Add a structure that defines how the C++ tanh_impl function is presented to Python:

static PyMethodDef superfastcode_methods[] = {
   // The first property is the name exposed to Python, fast_tanh
   // The second is the C++ function with the implementation
   // METH_O means it takes a single PyObject argument
      (PyCFunction) tanh_impl,

   // Terminate the array with an object containing nulls.

In the following example, the "superfastcode" module name means that you can use from superfastcode import fast_tanh in Python, because fast_tanh is defined within superfastcode_methods. File names that are internal to the C++ project, such as module.cpp, are inconsequential.

static PyModuleDef superfastcode_module = {
   "superfastcode", // Module name to use with Python import statements
   "Provides some functions, but faster", // Module description
   superfastcode_methods // Structure that defines the methods of the module

Suggestion : 5

In order to build the Cython file, issue the command below in the command prompt. The current directory of the command prompt is expected to be the same as the directory of the file.,Just double-click the file to open it, paste the code, and save it. After that we can create the file, which is exactly the same as we discussed previously. Next we must issue the following command for building the Cython script.,After this command completes, two files will be placed beside the .pyx file. The first one has the .c extension and the other file will have the extension .pyd (or similar, based on the operating system used). In order to use the generated file, just import the test_cython module and the "Hello Cython" message will appear directly, as you see below.,After this command completes successfully, you can find the output files listed in the left pane according to the next figure. Note that the extension of the module to be imported is now .so, as we are no longer using Windows.

Python is much slower than C, but many programmers still prefer it since it's so much easier to use. Python hides many details from the programmer, which can help prevent frustrating debugging. For instance, since Python is a dynamically-typed language you do not have to explicitly specify the type of each variable in your code – Python will deduce it automatically. In contrast, with statically-typed languages (like C, C++ or Java) you must specify the types of the variables, as seen below.

int x = 10
string s = "Hello"

Compare this to the implementation below in Python. Dynamic typing makes it easier to code, but adds much more burden on the machine to find the suitable datatype. This makes the process slower.

x = 10
s = "Hello"

Before going forward, make sure Cython is installed. You can do so with the following command.

pip install cython

In order to build the Cython file, issue the command below in the command prompt. The current directory of the command prompt is expected to be the same as the directory of the file.

python build_ext--inplace

Now let's optimize our aforementioned task: a for loop that iterates through 1 million numbers and sums them. Let's start by looking at the efficiency of just the iterations of the loop. The time module is imported for estimating how long it takes to execute.

import time
t1 = time.time()

for k in range(1000000):

t2 = time.time()
t = t2 - t1
print("%.20f" % t)

Suggestion : 6

Developing with Cython,Extending Python with Complied Code,First, install cython with:,Ships with Python, since 2.5

#include <stdio.h>

int add(int x, int y) {
    return x+y;
int main(void) {
    int w = 3;
    int q = 2;
    printf("%d + %d = %d\n\n", w, q, add(w,q));
all: add;
gcc - o add add.c
$ make
gcc - o add add.c
$. / add
3 + 2 = 5
#include <Python.h>
   Note: Since Python may define some pre-processor definitions which
   affect the standard headers on some systems, you must include
   Python.h before any standard headers are included.

   stdio.h, string.h, errno.h, and stdlib.h are included for you.
if (!PyArg_ParseTuple(args, "s", & var1, ...))
   return NULL;