PEP8

theano/tensor/tests/test_basic.py

@@ -5110,7 +5110,7 @@ class test_arithmetic_cast(unittest.TestCase):
             warnings.filterwarnings('ignore', message='Division of two integer',
                                     category=DeprecationWarning)
         try:
-            for cfg in ('numpy+floatX', ): # Used to test 'numpy' as well.
+            for cfg in ('numpy+floatX', ):  # Used to test 'numpy' as well.
                 config.cast_policy = cfg
                 for op in (operator.add, operator.sub, operator.mul,
                            operator.div, operator.floordiv):
@@ -5237,7 +5237,7 @@ class test_broadcast(unittest.TestCase):
     def test_broadcast_bigdim(self):
         def f():
             x = matrix()
-            addbroadcast(x,2)
+            addbroadcast(x, 2)
         self.assertRaises(ValueError, f)
 
     def test_unbroadcast_addbroadcast(self):
@@ -5246,41 +5246,41 @@ class test_broadcast(unittest.TestCase):
         and fuse consecutive Rebroadcast op
         """
 
-        x=matrix()
-        assert unbroadcast(x,0) is x
-        assert unbroadcast(x,1) is x
-        assert unbroadcast(x,1,0) is x
-        assert unbroadcast(x,0,1) is x
+        x = matrix()
+        assert unbroadcast(x, 0) is x
+        assert unbroadcast(x, 1) is x
+        assert unbroadcast(x, 1, 0) is x
+        assert unbroadcast(x, 0, 1) is x
 
-        assert addbroadcast(x,0) is not x
-        assert addbroadcast(x,1) is not x
-        assert addbroadcast(x,1,0).owner.inputs[0] is x
+        assert addbroadcast(x, 0) is not x
+        assert addbroadcast(x, 1) is not x
+        assert addbroadcast(x, 1, 0).owner.inputs[0] is x
 
-        assert unbroadcast(addbroadcast(x,0),0) is x
-        assert addbroadcast(unbroadcast(x,0),0) is not x
-        x=row()
-        assert unbroadcast(x,0) is not x
-        assert unbroadcast(x,1) is x
-        assert unbroadcast(x,1,0) is not x
-        assert unbroadcast(x,0,1) is not x
+        assert unbroadcast(addbroadcast(x, 0), 0) is x
+        assert addbroadcast(unbroadcast(x, 0), 0) is not x
+        x = row()
+        assert unbroadcast(x, 0) is not x
+        assert unbroadcast(x, 1) is x
+        assert unbroadcast(x, 1, 0) is not x
+        assert unbroadcast(x, 0, 1) is not x
 
-        assert addbroadcast(x,0) is x
-        assert addbroadcast(x,1).owner.inputs[0] is x
-        assert addbroadcast(x,1,0).owner.inputs[0] is x
-        assert addbroadcast(x,0,1).owner.inputs[0] is x
+        assert addbroadcast(x, 0) is x
+        assert addbroadcast(x, 1).owner.inputs[0] is x
+        assert addbroadcast(x, 1, 0).owner.inputs[0] is x
+        assert addbroadcast(x, 0, 1).owner.inputs[0] is x
 
-        assert unbroadcast(addbroadcast(x,1),1) is x
-        assert addbroadcast(unbroadcast(x,1),1) is not x
+        assert unbroadcast(addbroadcast(x, 1), 1) is x
+        assert addbroadcast(unbroadcast(x, 1), 1) is not x
 
         # The first broadcast is remove the broadcast, so the second
         # should not make one
-        assert unbroadcast(unbroadcast(x,0),0).owner.inputs[0] is x
+        assert unbroadcast(unbroadcast(x, 0), 0).owner.inputs[0] is x
 
         # Test that consecutive Rebroadcast op are fused
-        x=TensorType(dtype = 'float64', broadcastable = (True,True))()
-        assert unbroadcast(unbroadcast(x,1),0).owner.inputs[0] is x
-        assert addbroadcast(unbroadcast(x,1),0).owner.inputs[0] is x
-        assert addbroadcast(unbroadcast(x,0),0) is x
+        x = TensorType(dtype='float64', broadcastable=(True, True))()
+        assert unbroadcast(unbroadcast(x, 1), 0).owner.inputs[0] is x
+        assert addbroadcast(unbroadcast(x, 1), 0).owner.inputs[0] is x
+        assert addbroadcast(unbroadcast(x, 0), 0) is x
 
     def test_patternbroadcast(self):
         # Test that patternbroadcast with an empty broadcasting pattern works
@@ -5295,7 +5295,7 @@ class test_broadcast(unittest.TestCase):
         x = matrix()
         y = addbroadcast(x, 0)
         f = theano.function([x], y.shape)
-        assert (f(numpy.zeros((1,5), dtype=config.floatX)) == [1,5]).all()
+        assert (f(numpy.zeros((1, 5), dtype=config.floatX)) == [1, 5]).all()
         topo = f.maker.env.toposort()
         if theano.config.mode != 'FAST_COMPILE':
             assert len(topo) == 2
@@ -5305,7 +5305,7 @@ class test_broadcast(unittest.TestCase):
         x = matrix()
         y = unbroadcast(x, 0)
         f = theano.function([x], y.shape)
-        assert (f(numpy.zeros((2,5), dtype=config.floatX)) == [2,5]).all()
+        assert (f(numpy.zeros((2, 5), dtype=config.floatX)) == [2, 5]).all()
         topo = f.maker.env.toposort()
         if theano.config.mode != 'FAST_COMPILE':
             assert len(topo) == 3
@@ -5316,7 +5316,7 @@ class test_broadcast(unittest.TestCase):
         x = row()
         y = unbroadcast(x, 0)
         f = theano.function([x], y.shape)
-        assert (f(numpy.zeros((1,5), dtype=config.floatX)) == [1,5]).all()
+        assert (f(numpy.zeros((1, 5), dtype=config.floatX)) == [1, 5]).all()
         topo = f.maker.env.toposort()
         if theano.config.mode != 'FAST_COMPILE':
             assert len(topo) == 2
@@ -5588,7 +5588,7 @@ class test_sort(unittest.TestCase):
 
     def setUp(self):
         self.rng = numpy.random.RandomState(seed=utt.fetch_seed())
-        self.m_val = self.rng.rand(3,2)
+        self.m_val = self.rng.rand(3, 2)
         self.v_val = self.rng.rand(4)
 
     def test1(self):