diff --git a/skimage/transform/rescale.py b/skimage/transform/rescale.py
index 69e004a9..4ec4ab99 100644
--- a/skimage/transform/rescale.py
+++ b/skimage/transform/rescale.py
@@ -2,50 +2,52 @@
 
 import numpy as np
 
-def downsample(image, factors, how = 'sum'):
+def downsample(image, factors, method='sum'):
 
-	is0 = image.shape[0]
-	is1 = image.shape[1]
-	f0 = factors[0]
-	f1 = factors[1]
+    is0 = image.shape[0]
+    is1 = image.shape[1]
+    f0 = factors[0]
+    f1 = factors[1]
 
-	if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
-		return "Use resample for non-integer downsampling"
-	cropped = image[: is0 - (is0 % f0), : is1 - (is1 % f1)]
-	out = np.zeros((cropped.shape[0] / f0, cropped.shape[1] / f1))
+    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 how == 'sum':
-		for i in range(cropped.shape[0]):
-			for j in range(cropped.shape[1]):
-				out[int(i / f0)][int(j / f1)] += cropped[i][j]
-		return out
+    if method == 'sum':
+        for i in range(cropped.shape[0]):
+            for j in range(cropped.shape[1]):
+                out[int(i / f0)][int(j / f1)] += cropped[i][j]
+        return out
 
-	if how == 'mean':
-		for i in range(cropped.shape[0]):
-			for j in range(cropped.shape[1]):
-				out[int(i / f0)][int(j / f1)] += cropped[i][j] / float(f0 * f1)
-		return out
+    if method == 'mean':
+        for i in range(cropped.shape[0]):
+            for j in range(cropped.shape[1]):
+                out[int(i / f0)][int(j / f1)] += cropped[i][j]
+        return out / float(f0 * f1)
 
 
-def upsample(image, factors, how = 'divide'):
+def upsample(image, factors, method='divide'):
 
-	is0 = image.shape[0]
-	is1 = image.shape[1]
-	f0 = factors[0]
-	f1 = factors[1]
+    is0 = image.shape[0]
+    is1 = image.shape[1]
+    f0 = factors[0]
+    f1 = factors[1]
 
-	if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
-		return "Use resample for non-integer upsampling"
-	out = np.zeros((f0 * image.shape[0], f1 * image.shape[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]))
 
-	if how == 'divide':
-		for i in range(out.shape[0]):
-			for j in range(out.shape[1]):
-				out[i][j] = (image[i / f0][j / f1]) / float(f0 * f1)
-		return out
+    if method == 'divide':
+        for i in range(out.shape[0]):
+            for j in range(out.shape[1]):
+                out[i][j] = (image[i / f0][j / f1])
+        return out / float(f0 * f1)
 
-	if how == 'uniform':
-		for i in range(out.shape[0]):
-			for j in range(out.shape[1]):
-				out[i][j] = (image[i / f0][j / f1])
-		return out
+    if method == 'uniform':
+        for i in range(out.shape[0]):
+            for j in range(out.shape[1]):
+                out[i][j] = (image[i / f0][j / f1])
+        return out