PEP8

theano/sandbox/cuda/tests/test_opt.py

@@ -166,35 +166,38 @@ def test_print_op():
     assert topo[3].op == cuda.host_from_gpu
     f(numpy.random.random((5,5)).astype('float32'))
 
+
 def test_huge_elemwise_fusion():
     """ Test the the GpuElemwise fusion work correctly
         We check that we fuse one node with part of its input
         in case their is too many inputs and that would make it bust the 256
         bytes limits.
     """
-    shape = (2,3,4,5,6)
-    ttype = tensor.tensor(dtype='float32',broadcastable=(False,)*len(shape))
+    shape = (2, 3, 4, 5, 6)
+    ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))
     vars = [tensor.tanh(ttype) for x in range(7)]
-    f = pfunc(vars, [vars[0]-vars[1]-vars[2]-vars[3]-vars[4]-vars[5]-vars[6]], mode=mode_with_gpu)
+    f = pfunc(vars, [vars[0] - vars[1] - vars[2] - vars[3] - vars[4] -
+                     vars[5] - vars[6]], mode=mode_with_gpu)
     topo = f.maker.env.toposort()
     #theano.printing.debugprint(f)
     #for i, node in enumerate(topo):
     #    print >> sys.stdout, i, node
-    assert len(topo)==10
-    assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo])==2
-    assert isinstance(topo[7].op.scalar_op,theano.scalar.basic.Sub)
-    assert isinstance(topo[8].op.scalar_op,theano.scalar.basic.Composite)
+    assert len(topo) == 10
+    assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 2
+    assert isinstance(topo[7].op.scalar_op, theano.scalar.basic.Sub)
+    assert isinstance(topo[8].op.scalar_op, theano.scalar.basic.Composite)
     #let debugmode catch errors
-    gen = lambda : theano._asarray(numpy.random.rand(*shape), dtype='float32')
-    f(gen(),gen(),gen(),gen(),gen(),gen(),gen())
+    gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')
+    f(gen(), gen(), gen(), gen(), gen(), gen(), gen())
 
-    # Test the case where we can't put the computation on the gpu! their is too many
-    # dimensions to the input to have 2 inputs to the op!
+    # Test the case where we can't put the computation on the gpu! their is too
+    # many dimensions to the input to have 2 inputs to the op!
 
-    shape = (1,2,3,4,5,6,7,2,2,3,2,1,2,2,2,)
-    ttype = tensor.tensor(dtype='float32',broadcastable=(False,)*len(shape))
+    shape = (1, 2, 3, 4, 5, 6, 7, 2, 2, 3, 2, 1, 2, 2, 2,)
+    ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))
     vars = [tensor.tanh(ttype) for x in range(7)]
-    f = pfunc(vars, [vars[0]-vars[1]-vars[2]-vars[3]-vars[4]-vars[5]-vars[6]], mode=mode_with_gpu)
+    f = pfunc(vars, [vars[0] - vars[1] - vars[2] - vars[3] - vars[4] -
+                     vars[5] - vars[6]], mode=mode_with_gpu)
     topo = f.maker.env.toposort()
     #theano.printing.debugprint(f)
     assert len(topo) == 1
@@ -279,18 +282,20 @@ def test_local_gpu_elemwise_0():
 
 def test_elemwise_fusion():
     """ Test the the GpuElemwise fusion work correctly"""
-    shape = (3,4)
-    a = cuda.shared_constructor(theano._asarray(numpy.random.rand(*shape), dtype='float32'), 'a')
+    shape = (3, 4)
+    a = cuda.shared_constructor(theano._asarray(numpy.random.rand(*shape),
+                                                dtype='float32'), 'a')
     b = tensor.fmatrix()
     c = tensor.fmatrix()
-    f = pfunc([b,c], [a+b+c], mode=mode_with_gpu)
+    f = pfunc([b, c], [a + b + c], mode=mode_with_gpu)
     topo = f.maker.env.toposort()
     for i, node in enumerate(topo):
         print >> sys.stdout, i, node
-    assert len(topo)==4
-    assert isinstance(topo[2].op.scalar_op,theano.scalar.basic.Composite)
+    assert len(topo) == 4
+    assert isinstance(topo[2].op.scalar_op, theano.scalar.basic.Composite)
     #let debugmode catch errors
-    f(theano._asarray(numpy.random.rand(*shape), dtype='float32'), theano._asarray(numpy.random.rand(*shape), dtype='float32'))
+    f(theano._asarray(numpy.random.rand(*shape), dtype='float32'),
+      theano._asarray(numpy.random.rand(*shape), dtype='float32'))
 
 
 class test_local_gpu_tensordot(unittest.TestCase):