PEP8 fixes

theano/tensor/basic.py

@@ -3985,7 +3985,6 @@ class IncSubtensor(Op):
         else:
             return ()
 
-
     def infer_shape(self, node, shapes):
         return [shapes[0]]
 
@@ -4004,13 +4003,13 @@ class IncSubtensor(Op):
 
         if self.set_instead_of_inc:
             gx = set_subtensor(
-                Subtensor(idx_list=self.idx_list)(g_output,*idx_list),
+                Subtensor(idx_list=self.idx_list)(g_output, *idx_list),
                 zeros_like(y))
         else:
             gx = g_output
-        gy = Subtensor(idx_list = self.idx_list)(g_output, *idx_list)
+        gy = Subtensor(idx_list=self.idx_list)(g_output, *idx_list)
 
-        return [gx, gy] + [None]*len(idx_list)
+        return [gx, gy] + [None] * len(idx_list)
 
 
 def split(x, splits_size, n_splits, axis=0):
@@ -4149,7 +4148,6 @@ class Rebroadcast(Op):
         items.sort()  # no ambiguity because each item key is unique
         return hash(type(self)) ^ hash(tuple(items))
 
-
     def __str__(self):
         if len(self.axis) == 0:
             broadcast_pattern = []
@@ -4215,6 +4213,7 @@ def addbroadcast(x, *axes):
     rval = Rebroadcast(*[(axis, True) for axis in axes])(x)
     return theano.tensor.opt.apply_rebroadcast_opt(rval)
 
+
 def unbroadcast(x, *axes):
     """
     Make the input impossible to broadcast in the specified axes.
@@ -4230,9 +4229,11 @@ def patternbroadcast(x, broadcastable):
     """
     Make the input adopt a specific broadcasting pattern.
 
-    We apply the opt here not to pollute the graph especially during the gpu optimization
+    We apply the opt here not to pollute the graph especially during the gpu
+    optimization.
     """
-    rval = Rebroadcast(*[(i,broadcastable[i]) for i in xrange(len(broadcastable))])(x)
+    rval = Rebroadcast(*[(i, broadcastable[i])
+                         for i in xrange(len(broadcastable))])(x)
     return theano.tensor.opt.apply_rebroadcast_opt(rval)
 
 
@@ -4331,8 +4332,8 @@ class Join(Op):
                 # be broadcastable for the output.
                 for x in as_tensor_variable_args:
                     for current_axis, bflag in enumerate(x.type.broadcastable):
-                    # Not sure if this Op supports/supported/will support
-                    # negative indices, but just to be sure...
+                        # Not sure if this Op supports/supported/will support
+                        # negative indices, but just to be sure...
                         if current_axis == axis % ndim:
                             continue
                         if bflag:
@@ -4554,14 +4555,15 @@ def stack(*tensors):
     # This should be an optimization!
     # Doing it here make the graph less canonicalized
     # (more type need to be understood by all optimization)
-    # And DebugMode can't detect error in this code as it is not in an optimization.
+    # And DebugMode can't detect error in this code as it is not in an
+    # optimization.
     # See ticket #660
     if numpy.all([
                   # in case there is direct int in tensors.
                   isinstance(t, (numpy.number, float, int, python_complex)) or
                   (isinstance(t, Variable) and
                    isinstance(t.type, TensorType) and
-                   t.ndim==0)
+                   t.ndim == 0)
                   for t in tensors]):
         # in case there is direct int
         tensors = map(as_tensor_variable, tensors)
@@ -4650,7 +4652,9 @@ def vertical_stack(*args):
     return concatenate(args, axis=0)
 
 
-if 0: #vertical and horizontal stacking are deprecated.  Better to use stack() and join().
+# Vertical and horizontal stacking are deprecated. Better to use stack() and
+# join().
+if 0:
     class VerticalStack(Op):
         """
         Vertically stack two L{TensorType}s.
@@ -4788,8 +4792,8 @@ class Reshape(Op):
         # because it tries to replace the Shape_i node by the switch
         # statement, which depends on Shape_i.
         #return [tuple([switch(eq(node.inputs[1][i], -1),
-        #                         theano.tensor.opt.Shape_i(i)(node.outputs[0]),
-        #                         node.inputs[1][i])
+        #                      theano.tensor.opt.Shape_i(i)(node.outputs[0]),
+        #                      node.inputs[1][i])
         #                    for i in xrange(self.ndim)]
         #    )]
 
@@ -5168,17 +5172,17 @@ class PermuteRowElements(Op):
             if xs0 == ys0:
                 for i in xrange(xs0):
                     self._rec_perform(node, x[i], y[i], inverse, out[i],
-                                      curdim+1)
+                                      curdim + 1)
             elif ys0 == 1 and node.inputs[1].type.broadcastable[curdim]:
                 # Broadcast y
                 for i in xrange(xs0):
                     self._rec_perform(node, x[i], y[0], inverse, out[i],
-                                      curdim+1)
+                                      curdim + 1)
             elif xs0 == 1 and node.inputs[0].type.broadcastable[curdim]:
                 # Broadcast x
                 for i in xrange(ys0):
                     self._rec_perform(node, x[0], y[i], inverse, out[i],
-                                      curdim+1)
+                                      curdim + 1)
             else:
                 raise ValueError('Dimension mismatch: %s, %s' % (xs0, ys0))