STY: PEP8 and other clean up.

skimage/morphology/greyreconstruct.py

@@ -87,62 +87,57 @@ def reconstruction(image, mask, selem=None, offset=None):
         raise ImportError("_greyreconstruct extension not available.")
 
     if selem is None:
-        selem = np.ones([3]*image.ndim, bool)
+        selem = np.ones([3]*image.ndim, dtype=bool)
     else:
         selem = selem.copy()
 
     if offset == None:
-        assert all([d % 2 == 1 for d in selem.shape]),\
-               "Footprint dimensions must all be odd"
-        offset = np.array([d/2 for d in selem.shape])
+        if not all([d % 2 == 1 for d in selem.shape]):
+            ValueError("Footprint dimensions must all be odd")
+        offset = np.array([d / 2 for d in selem.shape])
     # Cross out the center of the selem
-    selem[[slice(d,d+1) for d in offset]] = False
-    #
+    selem[[slice(d, d + 1) for d in offset]] = False
+
     # Construct an array that's padded on the edges so we can ignore boundaries
     # The array is a dstack of the image and the mask; this lets us interleave
     # image and mask pixels when sorting which makes list manipulations easier
-    #
-    padding = (np.array(selem.shape)/2).astype(int)
-    dims = np.zeros(image.ndim+1,int)
+    padding = (np.array(selem.shape) / 2).astype(int)
+    dims = np.zeros(image.ndim + 1, dtype=int)
     dims[1:] = np.array(image.shape)+2*padding
     dims[0] = 2
     inside_slices = [slice(p,-p) for p in padding]
-    values = np.ones(dims)*np.min(image)
-    values[[0]+inside_slices] = image
-    values[[1]+inside_slices] = mask
-    #
+    values = np.ones(dims) * np.min(image)
+    values[[0] + inside_slices] = image
+    values[[1] + inside_slices] = mask
+
     # Create a list of strides across the array to get the neighbors
     # within a flattened array
-    #
     value_stride = np.array(values.strides[1:]) / values.dtype.itemsize
     image_stride = values.strides[0] / values.dtype.itemsize
-    selem_mgrid = np.mgrid[[slice(-o,d - o)
-                            for d,o in zip(selem.shape,offset)]]
-    selem_offsets = selem_mgrid[:,selem].transpose()
+    selem_mgrid = np.mgrid[[slice(-o, d - o)
+                            for d, o in zip(selem.shape, offset)]]
+    selem_offsets = selem_mgrid[:, selem].transpose()
     strides = np.array([np.sum(value_stride * selem_offset)
                         for selem_offset in selem_offsets], np.int32)
     values = values.flatten()
     value_sort = np.lexsort([-values]).astype(np.int32)
-    #
+
     # Make a linked list of pixels sorted by value. -1 is the list terminator.
-    #
     prev = -np.ones(len(values), np.int32)
     next = -np.ones(len(values), np.int32)
     prev[value_sort[1:]] = value_sort[:-1]
     next[value_sort[:-1]] = value_sort[1:]
-    #
+
     # Create a rank-order value array so that the Cython inner-loop
     # can operate on a uniform data type
-    #
     values, value_map = rank_order(values)
     current = value_sort[0]
 
     reconstruction_loop(values, prev, next, strides, current, image_stride)
-    #
+
     # Reshape the values array to the shape of the padded image
     # and return the unpadded portion of that result
-    #
     values = value_map[values[:image_stride]]
-    values.shape = np.array(image.shape)+2*padding
+    values.shape = np.array(image.shape) + 2 * padding
     return values[inside_slices]
 

skimage/morphology/tests/test_reconstruction.py

@@ -26,7 +26,7 @@ def test_image_less_than_mask():
     """Test reconstruction where the image is uniform and less than mask"""
     image = np.ones((5, 5))
     mask = np.ones((5, 5)) * 2
-    assert np.all(reconstruction(image,mask) == 1)
+    assert np.all(reconstruction(image, mask) == 1)
 
 
 def test_one_image_peak():
@@ -34,7 +34,7 @@ def test_one_image_peak():
     image = np.ones((5, 5))
     image[2, 2] = 2
     mask = np.ones((5, 5)) * 3
-    assert np.all(reconstruction(image,mask) == 2)
+    assert np.all(reconstruction(image, mask) == 2)
 
 
 def test_two_image_peaks():
@@ -59,7 +59,7 @@ def test_two_image_peaks():
                          [1, 1, 1, 1, 1, 3, 3, 3],
                          [1, 1, 1, 1, 1, 3, 3, 3],
                          [1, 1, 1, 1, 1, 3, 3, 3]])
-    assert np.all(reconstruction(image,mask) == expected)
+    assert np.all(reconstruction(image, mask) == expected)
 
 
 def test_zero_image_one_mask():