Fixed Flake8 errors.

theano/tensor/tests/test_opt.py

@@ -3495,43 +3495,43 @@ def test_local_elemwise_sub_zeros():
     mat_val = rng.rand(3, 2)
 
     mode = theano.compile.get_default_mode()\
-            .excluding('canonicalize', 'uncanonicalize',\
-                       'ShapeOpt', 'local_fill_to_alloc',\
+        .excluding('canonicalize', 'uncanonicalize',
+                       'ShapeOpt', 'local_fill_to_alloc',
                        'local_elemwise_alloc')\
-            .including('local_elemwise_sub_zeros')
+        .including('local_elemwise_sub_zeros')
 
     # Test scalar minus scalar
-    f = function([scalar], scalar-scalar, mode=mode)
+    f = function([scalar], scalar - scalar, mode=mode)
     # Check optimized graph is correct
     assert isinstance(f.maker.fgraph.toposort()[0].op, T.Elemwise)
-    assert isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
+    assert isinstance(f.maker.fgraph.toposort()[0].inputs[1],
                       T.TensorConstant) or\
-                isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
-                           T.TensorConstant)
+        isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
+                   T.TensorConstant)
     utt.assert_allclose(f(scalar_val), 0.0)
     # Check stack trace is copied over
     assert check_stack_trace(f, ops_to_check='all')
 
     # Test vector minus vector
-    f = function([vect], vect-vect, mode=mode)
+    f = function([vect], vect - vect, mode=mode)
     # Check optimized graph is correct
     assert isinstance(f.maker.fgraph.toposort()[0].op, T.Elemwise)
-    assert isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
+    assert isinstance(f.maker.fgraph.toposort()[0].inputs[1],
                       T.TensorConstant) or\
-                isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
-                           T.TensorConstant)
+        isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
+                   T.TensorConstant)
     utt.assert_allclose(f(vect_val), numpy.zeros(vect_val.shape))
     # Check stack trace is copied over
     assert check_stack_trace(f, ops_to_check='all')
 
     # Test vector minus vector
-    f = function([mat], mat-mat, mode=mode)
+    f = function([mat], mat - mat, mode=mode)
     # Check optimized graph is correct
     assert isinstance(f.maker.fgraph.toposort()[0].op, T.Elemwise)
-    assert isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
+    assert isinstance(f.maker.fgraph.toposort()[0].inputs[1],
                       T.TensorConstant) or\
-                isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
-                           T.TensorConstant)
+        isinstance(f.maker.fgraph.toposort()[0].inputs[1],\
+                   T.TensorConstant)
     utt.assert_allclose(f(mat_val), numpy.zeros(mat_val.shape))
     # Check stack trace is copied over
     assert check_stack_trace(f, ops_to_check='all')
@@ -5659,27 +5659,27 @@ class T_local_sum_prod(unittest.TestCase):
         Test that stack trace is copied over correctly.
         """
         m0 = theano.compile.get_default_mode()\
-                .excluding('inplace_elemwise_opt')\
-                .including('canonicalize', 'specialize')
+          .excluding('inplace_elemwise_opt')\
+          .including('canonicalize', 'specialize')
 
         vect = T.dvector()
         mat = T.dmatrix()
         scalar = T.dscalar()
 
-        f = theano.function([vect, scalar], T.sum(vect*scalar), mode=m0)
+        f = theano.function([vect, scalar], T.sum(vect * scalar), mode=m0)
         assert check_stack_trace(f, ops_to_check='all')
 
         f = theano.function([vect], T.sum(-vect), mode=m0)
         assert check_stack_trace(f, ops_to_check=[T.Sum])
 
-        f = theano.function([vect, scalar], 
-                            T.elemwise.Prod()(vect*scalar), mode=m0)
+        f = theano.function([vect, scalar],
+                            T.elemwise.Prod()(vect * scalar), mode=m0)
         assert check_stack_trace(f, ops_to_check=[T.elemwise.Prod])
 
         f = theano.function([vect], T.elemwise.Prod()(-vect), mode=m0)
         assert check_stack_trace(f, ops_to_check=[T.elemwise.Prod])
 
-        f = theano.function([mat, scalar], T.sum(mat*scalar), mode=m0)
+        f = theano.function([mat, scalar], T.sum(mat * scalar), mode=m0)
         assert check_stack_trace(f, ops_to_check='all')
 
         f = theano.function([mat], T.sum(-mat), mode=m0)