Now all the files in the example directory are tested.

1 files changed, 39 insertions, 0 deletions

diff --git a/docs/examples/userguide/numpy_tutorial/convolve_memview.pyx b/docs/examples/userguide/numpy_tutorial/convolve_memview.pyx
new file mode 100644
index 000000000..b23ebc8ba
--- /dev/null
+++ b/docs/examples/userguide/numpy_tutorial/convolve_memview.pyx
@@ -0,0 +1,39 @@
+import numpy as np

+

+DTYPE = np.intc

+

+# It is possible to declare types in the function declaration.

+def naive_convolve(int [:,:] f, int [:,:] g):

+    if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:

+        raise ValueError("Only odd dimensions on filter supported")

+

+    # We don't need to check for the type of NumPy array here because

+    # a check is already performed when calling the function.

+    cdef Py_ssize_t x, y, s, t, v, w, s_from, s_to, t_from, t_to

+    cdef Py_ssize_t vmax = f.shape[0]

+    cdef Py_ssize_t wmax = f.shape[1]

+    cdef Py_ssize_t smax = g.shape[0]

+    cdef Py_ssize_t tmax = g.shape[1]

+    cdef Py_ssize_t smid = smax // 2

+    cdef Py_ssize_t tmid = tmax // 2

+    cdef Py_ssize_t xmax = vmax + 2*smid

+    cdef Py_ssize_t ymax = wmax + 2*tmid

+

+    h_np =  np.zeros([xmax, ymax], dtype=DTYPE)

+    cdef int [:,:] h = h_np

+

+    cdef int value

+    for x in range(xmax):

+        for y in range(ymax):

+            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_np
\ No newline at end of file