1 files changed, 43 insertions, 43 deletions
diff --git a/docs/examples/tutorial/numpy/convolve_py.py b/docs/examples/tutorial/numpy/convolve_py.py
index c3cbc5f86..39b276a04 100644
--- a/docs/examples/tutorial/numpy/convolve_py.py
+++ b/docs/examples/tutorial/numpy/convolve_py.py
@@ -1,43 +1,43 @@
-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[0] % 2 != 1 or g.shape[1] % 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[0]
-    wmax = f.shape[1]
-    smax = g.shape[0]
-    tmax = g.shape[1]
-    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
+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[0] % 2 != 1 or g.shape[1] % 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[0]
+    wmax = f.shape[1]
+    smax = g.shape[0]
+    tmax = g.shape[1]
+    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