# Using Cython with Pypy and Numpy

Posted on 13 November 2014

Pypy is making tremendous progress with its fork of Numpy. Unfortunately, Scipy is currently not supported. I wanted to use a particular bit of Scipy from Pypy, and I noticed that there was some Cython wizardry involved. So I had to piece together how these things would work together.

I used Pypy 2.4.0 and the latest git of the Numpy fork, which I installed manually:

```git clone https://bitbucket.org/pypy/numpy.git
cd numpy; pypy setup.py install --user
```

I used the Pypy version of Cython, which I installed from AUR. It was version 0.21.1.

I adapted this example to test whether it works. We need to rely on Pypy's `cpyext` module to load the C extension, which has been around for four years, but documentation is extremely sparse.

The Cython file, named convolve.pyx, is an unmodified version from the Cython tutorial:

```from __future__ import division
import numpy as np
def naive_convolve(f, g):
# f is an image and is indexed by (v, w)
# g is a filter kernel and is indexed by (s, t),
#   it needs odd dimensions
# h is the output image and is indexed by (x, y),
#   it is not cropped
if g.shape % 2 != 1 or g.shape % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
# smid and tmid are number of pixels between the center pixel
# and the edge, ie for a 5x5 filter they will be 2.
#
# The output size is calculated by adding smid, tmid to each
# side of the dimensions of the input image.
vmax = f.shape
wmax = f.shape
smax = g.shape
tmax = g.shape
smid = smax // 2
tmid = tmax // 2
xmax = vmax + 2*smid
ymax = wmax + 2*tmid
# Allocate result image.
h = np.zeros([xmax, ymax], dtype=f.dtype)
# Do convolution
for x in range(xmax):
for y in range(ymax):
# Calculate pixel value for h at (x,y). Sum one component
# for each pixel (s, t) of the filter g.
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h
```

I generated the C file and compiled it with GCC:

```cython-pypy convolve.pyx
gcc -shared -pthread -fPIC -fwrapv -O2 -Wall -fno-strict-aliasing
-I/opt/pypy/include -I\$HOME/.local/lib/pypy2.7/include
-o convolve.so convolve.c
```

If you run into weird missing Numpy header files during compilation, check whether the include directory under Pypy contains a numpy with just three files or many. If there are only three, you did not install numpy as above. For instance, the AUR variant pypy-numpy-git does this, which is why I install the library manually to my home folder.

The following test works:

```import numpy as np
import cpyext
cpyext.load_module("convolve.so","convolve")
import convolve

print convolve.naive_convolve(np.array([[1, 1, 1]], dtype=np.int),
np.array([,,], dtype=np.int))
```

Tags: C++, Python