numpydoc for theano/sandbox/neighbourhoods.py

theano/sandbox/neighbourhoods.py

-"""WARNING: This code is not recommanded. It is not finished, it is
-slower then the version in sandbox/neighbours.py, and it do not work
+"""
+.. warning:: This code is not recommanded. It is not finished, it is
+slower than the version in sandbox/neighbours.py, and it does not work
 on the GPU.
 
 We only keep this version here as it is a little bit more generic, so
@@ -16,15 +17,9 @@ from theano import gof, Op
 
 
 class NeighbourhoodsFromImages(Op):
-
-    __props__ = ("n_dims_before", "dims_neighbourhoods", "strides",
-                 "ignore_border", "inverse")
-
-    def __init__(self, n_dims_before, dims_neighbourhoods,
-                 strides=None, ignore_border=False, inverse=False):
     """
-        This extracts neighbourhoods from "images", but in a
-        dimension-generic manner.
+    This extracts neighbourhoods from "images", but in a dimension-generic
+    manner.
 
     In the 2D case, this is similar to downsampling, but instead of reducing
     a group of 2x2 pixels (for example) to a single new pixel in the output,
@@ -40,21 +35,20 @@ class NeighbourhoodsFromImages(Op):
             [ [ [ 0.5, 0.6, 0.1, 0.2 ] ], # the first 2x2 group of pixels
               [ [ 0.7, 0.8, 0.3, 0.4 ] ] ] # the second one
 
-        so think of a 2D downsampling where each pixel of the resulting array
+    So think of a 2D downsampling where each pixel of the resulting array
     is replaced by an array containing the (flattened) pixels of the
     corresponding neighbourhood.
 
-        If you provide a stack of 2D image, or multiple stacks, each image
+    If you provide a stack of 2D images, or multiple stacks, each image
     will be treated independently, and the first dimensions of the array
     will be preserved as such.
 
     This also makes sense in the 1D or 3D case. Below I'll still be calling
     those "images", by analogy.
 
-        In the 1D case, you're
-        extracting subsequences from the original sequence. In the 3D case,
-        you're extracting cuboids. If you ever find a 4D use, tell me! It
-        should be possible, anyhow.
+    In the 1D case, you're extracting subsequences from the original sequence.
+    In the 3D case, you're extracting cuboids.
+    If you ever find a 4D use, tell me! It should be possible, anyhow.
 
     Parameters
     ----------
@@ -75,7 +69,15 @@ class NeighbourhoodsFromImages(Op):
     inverse : bool
         You shouldn't have to use this. Only used by child class
         ImagesFromNeighbourhoods which simply reverses the assignment.
+
     """
+
+    __props__ = ("n_dims_before", "dims_neighbourhoods", "strides",
+                 "ignore_border", "inverse")
+
+    def __init__(self, n_dims_before, dims_neighbourhoods,
+                 strides=None, ignore_border=False, inverse=False):
+
         self.n_dims_before = n_dims_before
         self.dims_neighbourhoods = dims_neighbourhoods
         if strides is not None: