from __future__ import division
import os
import sys
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
%precision 4
plt.style.use('ggplot')

! pip install git+https://github.com/dabeaz/bitey.git
%install_ext https://raw.github.com/mgaitan/fortran_magic/master/fortranmagic.py
%install_ext https://gist.githubusercontent.com/bfroehle/3458310/raw/biteymagic.py

Downloading/unpacking git+https://github.com/dabeaz/bitey.git
  Cloning https://github.com/dabeaz/bitey.git to /var/folders/bh/x038t1s943qftp7jzrnkg1vm0000gn/T/pip-_UHN_B-build
  Running setup.py (path:/var/folders/bh/x038t1s943qftp7jzrnkg1vm0000gn/T/pip-_UHN_B-build/setup.py) egg_info for package from git+https://github.com/dabeaz/bitey.git

    warning: no files found matching '*' under directory 'doc'
  Requirement already satisfied (use --upgrade to upgrade): bitey==0.0 from git+https://github.com/dabeaz/bitey.git in /Users/cliburn/anaconda/lib/python2.7/site-packages
Cleaning up...
Installed fortranmagic.py. To use it, type:
  %load_ext fortranmagic
Installed biteymagic.py. To use it, type:
  %load_ext biteymagic

Using functions from various compiled languages in Python¶

There are 2 main reasons why interpreted Python code is slower than code in a compiled lanauge such as C (or other compiled langauge):

Python executes byte code in a virtual machine (minor effect) while C compiles down to machine instructions specific for the processor
Python has dynamic typing (major effect) while C is statically typed. In a dynamically typed language, the simple expression a + b can mean many, many different things, and the interrpeter has to figure out which interpretation is intended. In contrast, a and b must have a type in C such as double and there is no ambiguity about what + means to resolve.

If speed is critical, it is often necessary to exploit the efficiency of compiled languges - this can be done while retaining the nice features of Python in 2 directions

From C to Python
From Python to C

Here we will look at how to go from C (C++, Fortran, Julia) to Python,

def python_fib(n):
    a, b = 0,  1
    for i in range(n):
        a, b = a+b, a
    return a

%timeit python_fib(100)

100000 loops, best of 3: 8.47 µs per loop

C¶

%%file fib.h

double fib(int n);

Writing fib.h

%%file fib.c

double fib(int n) {
    double a = 0, b = 1;
    for (int i=0; i<n; i++) {
        double tmp = b;
        b = a;
        a += tmp;
     }
    return a;
}

Writing fib.c

Using bitey and clang¶

This is perhaps the simplest method, but it only works with the clang compiler and does not geenrate highly optimized code.

import bitey

!clang -O3 -emit-llvm -c fib.c -o fib1.o

import fib1

fib1.fib(100)

354224848179261997056.0000

%timeit fib1.fib(100)

1000000 loops, best of 3: 941 ns per loop

Using Cython¶

I recomment using Cython for all your C/C++ interface needs as it is highly optimized and can do boht C \(\rightarrow\) Python and Python \(\rightarrow\) C. It is a littel more involved, but the steps always follow the same template.

Define functions to be imported from C¶

%%file fib.pxd

cdef extern from "fib.h":
    double fib(int n)

Writing fib.pxd

Define wrapper for calling function from Python¶

%%file fib2.pyx

cimport fib

def fib(n):
    return fib.fib(n)

Writing fib2.pyx

Use distutils to compile shared library for Python¶

This is the standard way all Python modules are compiled for distribution, and results in a build that is portable over different platforms.

%%file setup.py
from distutils.core import setup, Extension
from Cython.Build import cythonize

ext = Extension("fib2",
              sources=["fib2.pyx", "fib.c"])

setup(name = "cython_fib",
      ext_modules = cythonize(ext))

Overwriting setup.py

! python setup.py build_ext -i &> /dev/null

import fib2

fib2.fib(100)

354224848179261997056.0000

%timeit fib2.fib(100)

1000000 loops, best of 3: 224 ns per loop

C++¶

C++ is a superset of C - the syntax for the fib program is exactly the same except for change in the filname extensions.

%%file fib.hpp

double fib(int n);

Writing fib.hpp

%%file fib.cpp

double fib(int n) {
    double a = 0, b = 1;
    for (int i=0; i<n; i++) {
        double tmp = b;
        b = a;
        a += tmp;
     }
    return a;
}

Writing fib.cpp

%%file setup.py
from distutils.core import setup, Extension
from Cython.Build import cythonize

ext = Extension("fib2cpp",
              sources=["fib2cpp.pyx", "fib.cpp"],
              language="c++",)

setup(name = "cython_fibcpp",
      ext_modules = cythonize(ext))

Overwriting setup.py

%%file fib2cpp.pyx

cimport fib

def fib(n):
    return fib.fib(n)

Writing fib2cpp.pyx

! python setup.py build_ext -i &> /dev/null

import fib2cpp

fib2cpp.fib(100)

354224848179261997056.0000

Fortran¶

This is almost trivial with the Fortran Magic extnesion.

! pip install fortran-magic &> /dev/null

%load_ext fortranmagic

%%fortran

subroutine fib3(n, a)
    integer, intent(in) :: n
    real, intent(out) :: a

    integer :: i
    real :: b, tmp

    a = 0
    b = 1
    do i = 1, n
        tmp = b
        b = a
        a = a + tmp
    end do
end subroutine

fib3(100)

354224717716315439104.0000

Antoher example from the documentation

%%fortran --link lapack

subroutine solve(A, b, x, n)
    ! solve the matrix equation A*x=b using LAPACK
    implicit none

    real*8, dimension(n,n), intent(in) :: A
    real*8, dimension(n), intent(in) :: b
    real*8, dimension(n), intent(out) :: x

    integer :: pivot(n), ok

    integer, intent(in) :: n
    x = b

    ! find the solution using the LAPACK routine SGESV
    call DGESV(n, 1, A, n, pivot, x, n, ok)

end subroutine

A = np.array([[1, 2.5], [-3, 4]])
b = np.array([1, 2.5])

solve(A, b)

array([-0.1957,  0.4783])

Benchmarking¶

%timeit python_fib(100) # Python
%timeit fib1.fib(100)   # bitey
%timeit fib2.fib(100)   # Cython
%timeit fib3(100)       # Fortran

100000 loops, best of 3: 11 µs per loop
1000000 loops, best of 3: 957 ns per loop
1000000 loops, best of 3: 253 ns per loop
1000000 loops, best of 3: 255 ns per loop

Wrapping a function from a C library for use in Python¶

Cython ships with a set of standard .pxd files that provide these declarations in a readily usable way that is adapted to their use in Cython. The main packages are cpython, libc and libcpp. The NumPy library also has a standard .pxd file numpy, as it is often used in Cython code. See Cython’s Cython/Includes/ source package for a complete list of provided .pxd files. (From http://docs.cython.org/src/tutorial/clibraries.html).

Additional .pxd are also avaialbel for:

However, here is an example of how to write functions from an external C library if you have to do it yourself. The example is taken from https://github.com/cythonbook/examples and wraps the Mersenne Twister from http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html for use in Python.

if not os.path.exists('mt19937ar.h'):
    ! wget http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.sep.tgz
    ! tar -xzvf mt19937ar.sep.tgz

--2015-03-26 16:02:41--  http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.sep.tgz
Resolving www.math.sci.hiroshima-u.ac.jp... 133.41.16.48
Connecting to www.math.sci.hiroshima-u.ac.jp|133.41.16.48|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 15433 (15K) [application/x-gzip]
Saving to: ‘mt19937ar.sep.tgz’

100%[======================================>] 15,433      37.3KB/s   in 0.4s

2015-03-26 16:02:42 (37.3 KB/s) - ‘mt19937ar.sep.tgz’ saved [15433/15433]

x mt19937ar.c
x mt19937ar.h
x mt19937ar.out
x mtTest.c
x readme-mt.txt

%%file mt.pxd

cdef extern from "mt19937ar.h":
    void init_genrand(unsigned long s)
    double genrand_real1()

Writing mt.pxd

%%file mt_random.pyx

cimport mt

def init_state(unsigned long s):
    mt.init_genrand(s)

def rand():
    return mt.genrand_real1()

Writing mt_random.pyx

%%file setup.py

from distutils.core import setup, Extension
from Cython.Build import cythonize

ext = Extension("mt_random",
                sources=["mt_random.pyx", "mt19937ar.c"])

setup(name="mersenne_random",
      ext_modules = cythonize([ext]))

Overwriting setup.py

! python setup.py build_ext -i &> /dev/null

import mt_random

mt_random.init_state(123)
for i in range(10):
    print mt_random.rand(),
print

0.696469187433 0.712955321584 0.28613933881 0.428470925062 0.226851454989 0.690884851546 0.55131476525 0.71915030892 0.719468970718 0.491118932723

Wrapping functions from C++ library for use in Pyton¶

Example - Andrew Cron (DSS PhD graduate) has a GitHub repository wrapping the C++ Armadillo linear algebra package with Cython at https://github.com/andrewcron/cy_armadillo