pep8

theano/tensor/opt_uncanonicalize.py

@@ -22,9 +22,6 @@ Also, we should make the fgraph refuse optimization that break the canonization
 
 # TODO: intelligent merge for mul/add
 # TODO: 0*x -> 0
-
-
-
 import logging
 _logger = logging.getLogger('theano.tensor.opt')
 
@@ -35,10 +32,12 @@ from theano.tensor import basic as T
 from theano.gof.opt import Optimizer
 from theano.gof import InconsistencyError, toolbox
 
-from theano.tensor.basic import get_scalar_constant_value, NotScalarConstantError
+from theano.tensor.basic import (get_scalar_constant_value,
+                                 NotScalarConstantError)
 from theano.tensor.opt import register_uncanonicalize
 from theano import scalar as scal
 
+
 class MaxAndArgmaxOptimizer(Optimizer):
     """Replace MaxAndArgmax by CAReduce when the argmax is not used
 
@@ -56,23 +55,25 @@ class MaxAndArgmaxOptimizer(Optimizer):
             did_something = False
             for node in nodelist:
                 if node.op == T._max_and_argmax:
-                    if len(node.outputs[1].clients)==0:
+                    if len(node.outputs[1].clients) == 0:
                         try:
-                            axis=get_scalar_constant_value(node.inputs[1])
+                            axis = get_scalar_constant_value(node.inputs[1])
                         except NotScalarConstantError:
                             return False
 
-                        new = CAReduce(scal.maximum,axis)(node.inputs[0])
+                        new = CAReduce(scal.maximum, axis)(node.inputs[0])
                         try:
                             fgraph.replace_all_validate(
-                                ((node.outputs[0],new),),
-                                reason = self.__class__.__name__)
+                                ((node.outputs[0], new),),
+                                reason=self.__class__.__name__)
                             did_something = True
                             break
                         except InconsistencyError, e:
                             pass
 
-register_uncanonicalize(MaxAndArgmaxOptimizer(),name='MaxAndArgmaxOptimizer')
+register_uncanonicalize(MaxAndArgmaxOptimizer(),
+                        name='MaxAndArgmaxOptimizer')
+
 
 @register_uncanonicalize
 @gof.local_optimizer([T._shape])
@@ -87,9 +88,12 @@ def local_max_to_min(node):
     """
     if node.op == T.neg and node.inputs[0].owner:
         max = node.inputs[0]
-        if max.owner and isinstance(max.owner.op, CAReduce) and max.owner.op.scalar_op==scal.maximum:
+        if (max.owner and
+            isinstance(max.owner.op, CAReduce)
+            and max.owner.op.scalar_op == scal.maximum):
             neg = max.owner.inputs[0]
             if neg.owner and neg.owner.op == T.neg:
-                return [CAReduce(scal.minimum,max.owner.op.axis)(neg.owner.inputs[0])]
+                return [CAReduce(scal.minimum,
+                                 max.owner.op.axis)(neg.owner.inputs[0])]
 
     return False