Minor refactoring

skimage/transform/rescale.py

@@ -4,43 +4,36 @@ import numpy as np
 
 def downsample(image, factors, method='sum'):
 
-    is0 = image.shape[0]
-    is1 = image.shape[1]
-    f0 = factors[0]
-    f1 = factors[1]
+    is = image.shape
+    f = factors
 
-    if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
-        print "Use resample() for non-integer downsampling"
-        return
-    cropped = image[:is0 - (is0 % f0), :is1 - (is1 % f1)]
-    out = np.zeros((cropped.shape[0] / f0, cropped.shape[1] / f1))
+    if (f[0] - int(f[0]) != 0) or (f[1] - int(f[1]) != 0):
+        raise ValueError('Use resample() for non-integer downsampling')
+    cropped = image[:is[0] - (is[0] % f[0]), :is[1] - (is[1] % f[1])]
+    out = np.zeros((cropped.shape[0] / f[0], cropped.shape[1] / f[1]))
 
     for i in range(cropped.shape[0]):
         for j in range(cropped.shape[1]):
-            out[int(i / f0)][int(j / f1)] += cropped[i][j]
+            out[int(i / f[0])][int(j / f[1])] += cropped[i][j]
     if method == 'sum':
         return out
     else:
-        return out / float(f0 * f1)
+        return out / float(f[0] * f[1])
 
 
 def upsample(image, factors, method='divide'):
 
-    is0 = image.shape[0]
-    is1 = image.shape[1]
-    f0 = factors[0]
-    f1 = factors[1]
-
-    if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
-        print "Use resample() for non-integer upsampling"
-        return
-    out = np.zeros((f0 * image.shape[0], f1 * image.shape[1]))
+    is = image.shape
+    f = factors
 
+    if (f[0] - int(f[0]) != 0) or (f[1] - int(f[1]) != 0):
+        raise ValueError('Use resample() for non-integer upsampling')
+    out = np.zeros((f[0] * image.shape[0], f[1] * image.shape[1]))
 
     for i in range(out.shape[0]):
         for j in range(out.shape[1]):
-            out[i][j] = (image[i / f0][j / f1])
+            out[i][j] = (image[i / f[0]][j / f[1]])
     if method == 'divide':
-        return out / float(f0 * f1)
+        return out / float(f[0] * f[1])
     else:
         return out