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    <p>Here's a tiny but complete example of passing numpy arrays to an external C function, logically</p> <pre><code>fc( int N, double* a, double* b, double* z ) # z = a + b </code></pre> <p>using Cython. (This is surely well-known to those who know it well. Comments are welcome. Last change: 23 Feb 2011, for Cython 0.14.)</p> <p>First read or skim <a href="http://docs.cython.org/src/quickstart/build.html" rel="nofollow noreferrer">Cython build</a> and <a href="http://docs.cython.org/src/tutorial/numpy.html" rel="nofollow noreferrer">Cython with NumPy</a> .</p> <p>2 steps:</p> <ul> <li><code>python f-setup.py build_ext --inplace</code><br> turns <code>f.pyx</code> and <code>fc.cpp</code> -> <code>f.so</code>, a dynamic library</li> <li><code>python test-f.py</code><br> <code>import f</code> loads <code>f.so</code>; <code>f.fpy( ... )</code> calls the C <code>fc( ... )</code>.</li> </ul> <p><code>python f-setup.py</code> uses <code>distutils</code> to run cython, compile and link:<br> <code>cython f.pyx -&gt; f.cpp</code><br> compile <code>f.cpp</code> and <code>fc.cpp</code><br> link <code>f.o fc.o</code> -> <code>f.so</code>, a dynamic lib that python <code>import f</code> will load.</p> <p>For students, I'd suggest: make a diagram of these steps, look through the files below, then download and run them.</p> <p>(<code>distutils</code> is a huge, convoluted package used to make Python packages for distribution, and install them. Here we're using just a small part of it to compile and link to <code>f.so</code>. This step has nothing to do with Cython, but it can be confusing; simple mistakes in a .pyx can cause pages of obscure error messages from g++ compile and link. See also <a href="http://docs.python.org/distutils" rel="nofollow noreferrer">distutils doc</a> and/or <a href="https://stackoverflow.com/questions/tagged/distutils+python">SO questions on distutils</a> .)</p> <p>Like <code>make</code>, <code>setup.py</code> will rerun <code>cython f.pyx</code> and <code>g++ -c ... f.cpp</code> if <code>f.pyx</code> is newer than <code>f.cpp</code>.<br> To cleanup, <code>rm -r build/</code> .</p> <p>An alternative to <code>setup.py</code> would be to run the steps separately, in a script or Makefile:<br> <code>cython --cplus f.pyx -&gt; f.cpp # see cython -h</code><br> <code>g++ -c ... f.cpp -&gt; f.o</code><br> <code>g++ -c ... fc.cpp -&gt; fc.o</code><br> <code>cc-lib f.o fc.o -&gt; dynamic library f.so</code>.<br> Modify the <code>cc-lib-mac</code> wrapper below for your platform and installation: it's not pretty, but small.</p> <p>For real examples of Cython wrapping C, look at .pyx files in just about any <a href="http://scikits.appspot.com/scikits" rel="nofollow noreferrer">SciKit</a> .</p> <p>See also: <a href="http://docs.cython.org/src/userguide/numpy_tutorial.html" rel="nofollow noreferrer">Cython for NumPy users</a> and <a href="https://stackoverflow.com/questions/tagged/cython">SO questions/tagged/cython</a> .</p> <hr> <p>To unpack the following files, cut-paste the lot to one big file, say <code>cython-numpy-c-demo</code>, then in Unix (in a clean new directory) run <code>sh cython-numpy-c-demo</code>.</p> <pre><code>#-------------------------------------------------------------------------------- cat &gt;f.pyx &lt;&lt;\! # f.pyx: numpy arrays -&gt; extern from "fc.h" # 3 steps: # cython f.pyx -&gt; f.c # link: python f-setup.py build_ext --inplace -&gt; f.so, a dynamic library # py test-f.py: import f gets f.so, f.fpy below calls fc() import numpy as np cimport numpy as np cdef extern from "fc.h": int fc( int N, double* a, double* b, double* z ) # z = a + b def fpy( N, np.ndarray[np.double_t,ndim=1] A, np.ndarray[np.double_t,ndim=1] B, np.ndarray[np.double_t,ndim=1] Z ): """ wrap np arrays to fc( a.data ... ) """ assert N &lt;= len(A) == len(B) == len(Z) fcret = fc( N, &lt;double*&gt; A.data, &lt;double*&gt; B.data, &lt;double*&gt; Z.data ) # fcret = fc( N, A.data, B.data, Z.data ) grr char* return fcret ! #-------------------------------------------------------------------------------- cat &gt;fc.h &lt;&lt;\! // fc.h: numpy arrays from cython , double* int fc( int N, const double a[], const double b[], double z[] ); ! #-------------------------------------------------------------------------------- cat &gt;fc.cpp &lt;&lt;\! // fc.cpp: z = a + b, numpy arrays from cython #include "fc.h" #include &lt;stdio.h&gt; int fc( int N, const double a[], const double b[], double z[] ) { printf( "fc: N=%d a[0]=%f b[0]=%f \n", N, a[0], b[0] ); for( int j = 0; j &lt; N; j ++ ){ z[j] = a[j] + b[j]; } return N; } ! #-------------------------------------------------------------------------------- cat &gt;f-setup.py &lt;&lt;\! # python f-setup.py build_ext --inplace # cython f.pyx -&gt; f.cpp # g++ -c f.cpp -&gt; f.o # g++ -c fc.cpp -&gt; fc.o # link f.o fc.o -&gt; f.so # distutils uses the Makefile distutils.sysconfig.get_makefile_filename() # for compiling and linking: a sea of options. # http://docs.python.org/distutils/introduction.html # http://docs.python.org/distutils/apiref.html 20 pages ... # https://stackoverflow.com/questions/tagged/distutils+python import numpy from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext # from Cython.Build import cythonize ext_modules = [Extension( name="f", sources=["f.pyx", "fc.cpp"], # extra_objects=["fc.o"], # if you compile fc.cpp separately include_dirs = [numpy.get_include()], # .../site-packages/numpy/core/include language="c++", # libraries= # extra_compile_args = "...".split(), # extra_link_args = "...".split() )] setup( name = 'f', cmdclass = {'build_ext': build_ext}, ext_modules = ext_modules, # ext_modules = cythonize(ext_modules) ? not in 0.14.1 # version= # description= # author= # author_email= ) # test: import f ! #-------------------------------------------------------------------------------- cat &gt;test-f.py &lt;&lt;\! #!/usr/bin/env python # test-f.py import numpy as np import f # loads f.so from cc-lib: f.pyx -&gt; f.c + fc.o -&gt; f.so N = 3 a = np.arange( N, dtype=np.float64 ) b = np.arange( N, dtype=np.float64 ) z = np.ones( N, dtype=np.float64 ) * np.NaN fret = f.fpy( N, a, b, z ) print "fpy -&gt; fc z:", z ! #-------------------------------------------------------------------------------- cat &gt;cc-lib-mac &lt;&lt;\! #!/bin/sh me=${0##*/} case $1 in "" ) set -- f.cpp fc.cpp ;; # default: g++ these -h* | --h* ) echo " $me [g++ flags] xx.c yy.cpp zz.o ... compiles .c .cpp .o files to a dynamic lib xx.so " exit 1 esac # Logically this is simple, compile and link, # but platform-dependent, layers upon layers, gloom, doom base=${1%.c*} base=${base%.o} set -x g++ -dynamic -arch ppc \ -bundle -undefined dynamic_lookup \ -fno-strict-aliasing -fPIC -fno-common -DNDEBUG `# -g` -fwrapv \ -isysroot /Developer/SDKs/MacOSX10.4u.sdk \ -I/Library/Frameworks/Python.framework/Versions/2.6/include/python2.6 \ -I${Pysite?}/numpy/core/include \ -O2 -Wall \ "$@" \ -o $base.so # undefs: nm -gpv $base.so | egrep '^ *U _+[^P]' ! # 23 Feb 2011 13:38 </code></pre>
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    1. COIt's really not necessary to use a wrapper function that takes a `char*` pointer. You can wrap `fcreal()` directly in Cython and call it as `fcret = fcreal( N, A.data, B.data, <double*> Z.data )`. Also, it's error prone and not portable to compile `fc.o` separately. Just include `fc.cpp` in `sources=`.
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    2. COThis will probably produce unexpected results if the passed numpy array is not continous in memory or has Fortran byte order. Also, the required cast is a bit nasty. See below for better cython code.
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