pep8 tensor/basic.py

theano/tensor/basic.py

@@ -474,7 +474,6 @@ def get_constant_value(v):
         code, but I'm not sure where it is.
     """
 
-
     if isinstance(v, Constant):
         if getattr(v.tag, 'unique_value', None) is not None:
             data = v.tag.unique_value
@@ -483,7 +482,7 @@ def get_constant_value(v):
         # handle case where data is numpy.array([])
         if hasattr(data, 'shape') and len(data.shape) == 0 or \
             __builtins__['max'](data.shape) == 0:
-            assert numpy.all(numpy.array([])==data)
+            assert numpy.all(numpy.array([]) == data)
             return data
         try:
             numpy.complex(data)  # works for all numeric scalars
@@ -2126,7 +2125,7 @@ class Shape(Op):
         # the elements of the tensor variable do not participate
         # in the computation of the shape, so they are not really
         # part of the graph
-        return [ DisconnectedType()() ]
+        return [DisconnectedType()()]
 
     def R_op(self, inputs, eval_points):
         return [None]
@@ -2209,7 +2208,7 @@ class SpecifyShape(Op):
         return [new_shape]
 
     def connection_pattern(self, node):
-        return [[True],[False]]
+        return [[True], [False]]
 
     def grad(self, inp, grads):
         x, s = inp
@@ -3121,7 +3120,6 @@ class Alloc(gof.Op):
     def infer_shape(self, node, input_shapes):
         return [node.inputs[1:]]
 
-
     def connection_pattern(self, node):
 
         rval = [[True]]
@@ -3810,7 +3808,6 @@ class Subtensor(Op):
         else:
             return scal.as_scalar(a)
 
-
     def make_node(self, x, *inputs):
         x = as_tensor_variable(x)
         inputs = tuple(self.my_as_scalar(a) for a in inputs)
@@ -3908,7 +3905,7 @@ class Subtensor(Op):
 
     def connection_pattern(self, node):
 
-        rval = [ [True] ]
+        rval = [[True]]
 
         for ipt in node.inputs[1:]:
             rval.append([False])
@@ -4569,7 +4566,7 @@ class IncSubtensor(Op):
 
     def connection_pattern(self, node):
 
-        rval = [ [True], [True] ]
+        rval = [[True], [True]]
 
         for ipt in node.inputs[2:]:
             rval.append([False])
@@ -4979,7 +4976,7 @@ class Join(Op):
         gz, = grads
         axis, tensors = axis_and_tensors[0], axis_and_tensors[1:]
 
-        rval = [ grad_undefined(self, 0, axis) ]
+        rval = [grad_undefined(self, 0, axis)]
 
         if 'float' in tensors[0].dtype or 'complex' in tensors[0].dtype:
             # assume that this is differentiable
@@ -4994,7 +4991,7 @@ class Join(Op):
         else:
             # the output has integer type, so the gradient through it
             # is 0
-            rval = rval + [ tensor.zeros_like() for tensor in tensors ]
+            rval = rval + [tensor.zeros_like() for tensor in tensors]
 
         return rval
 
@@ -5239,6 +5236,7 @@ def vertical_stack(*args):
         assert arg.type.ndim == 2
     return concatenate(args, axis=0)
 
+
 class Reshape(Op):
     """Perform a reshape operation of the input x to the new shape shp.
 
@@ -5657,7 +5655,7 @@ class ARange(Op):
 
     def connection_pattern(self, node):
 
-        return [ [True], [False], [True] ]
+        return [[True], [False], [True]]
 
     def grad(self, inputs, grads):
         start, stop, step = inputs
@@ -5667,9 +5665,9 @@ class ARange(Op):
         # no gradient through them
         # stop does not affect the output values,
         # just the output shape, so it is disconnected
-        return [ start.zeros_like(),
+        return [start.zeros_like(),
                 DisconnectedType()(),
-                step.zeros_like() ]
+                step.zeros_like()]
 
     def R_op(self, inputs, eval_points):
         return [None]
@@ -5903,8 +5901,8 @@ class PermuteRowElements(Op):
         # are non-integer, so the gradient with respect to them is
         # undefined
 
-        return [gx, grad_undefined(self,1,y),
-                grad_undefined(self,1,inverse)]
+        return [gx, grad_undefined(self, 1, y),
+                grad_undefined(self, 1, inverse)]
 
 _permute_row_elements = PermuteRowElements()
 
@@ -5970,10 +5968,10 @@ class AdvancedSubtensor1(Op):
 
     def connection_pattern(self, node):
 
-        rval = [ [True ] ]
+        rval = [[True]]
 
         for ipt in node.inputs[1:]:
-            rval.append([ False ])
+            rval.append([False])
 
         return rval
 
@@ -6081,10 +6079,9 @@ class AdvancedIncSubtensor1(Op):
         return self.make_node(eval_points[0], eval_points[1],
                               *inputs[2:]).outputs
 
-
     def connection_pattern(self, node):
 
-        rval = [[True], [True] ]
+        rval = [[True], [True]]
 
         for ipt in node.inputs[2:]:
             rval.append([False])
@@ -6099,7 +6096,7 @@ class AdvancedIncSubtensor1(Op):
         gx = g_output
         gy = advanced_subtensor1(g_output, *idx_list)
 
-        return [gx, gy] + [ DisconnectedType()()] * len(idx_list)
+        return [gx, gy] + [DisconnectedType()()] * len(idx_list)
 
 advanced_inc_subtensor1 = AdvancedIncSubtensor1()
 
@@ -6190,7 +6187,7 @@ class AdvancedSubtensor(Op):
 
     def connection_pattern(self, node):
 
-        rval = [ [True] ]
+        rval = [[True]]
 
         for ipt in node.inputs[1:]:
             rval.append([False])
@@ -6290,7 +6287,7 @@ class AdvancedIncSubtensor(Op):
 
     def connection_pattern(self, node):
 
-        rval = [ [True], [True] ]
+        rval = [[True], [True]]
 
         for ipt in node.inputs[2:]:
             rval.append([False])