pep8

theano/sparse/tests/test_basic.py

@@ -17,7 +17,7 @@ from theano.sparse import enable_sparse
 from theano.gof.python25 import all, any, product
 
 
-if enable_sparse == False:
+if not enable_sparse:
     raise SkipTest('Optional package sparse disabled')
 
 from theano.sparse.basic import _is_dense, _is_sparse, _mtypes
@@ -399,7 +399,6 @@ class SparseInferShapeTester(utt.InferShapeTester):
                                 [csc_from_dense(x)],
                                 [numpy.random.randn(10, 40).astype(
                                     config.floatX)],
-
                                 csc_from_dense.__class__)
 
     def test_sparse_from_list(self):
@@ -789,7 +788,8 @@ class test_csm(unittest.TestCase):
                         numpy.asarray(spmat.shape, 'int32'))
 
                 col1 = sp_types[format]((res, spmat.indices, spmat.indptr),
-                    shape=numpy.asarray(spmat.shape, 'int32'))[:, 1].data
+                                        shape=numpy.asarray(spmat.shape,
+                                                            'int32'))[:, 1].data
 
                 assert numpy.all(col1 == 2)
 
@@ -909,8 +909,7 @@ class test_structureddot(unittest.TestCase):
         spmat = sp.csc_matrix(spmat)
 
         images = tensor.Tensor(dtype='float32',
-                               broadcastable=[False, False])(
-            'images')
+                               broadcastable=[False, False])('images')
 
         cscmat = CSC(kerns, spmat.indices[:spmat.size],
                      spmat.indptr, spmat.shape)
@@ -931,7 +930,8 @@ class test_structureddot(unittest.TestCase):
         #print 'type of kernvals = ', kernvals.dtype
         bsize = 3
         imvals = 1.0 * numpy.array(numpy.arange(bsize * spmat.shape[1]).\
-                reshape(bsize, spmat.shape[1]), dtype='float32')
+                                   reshape(bsize, spmat.shape[1]),
+                                   dtype='float32')
         outvals = f(kernvals, imvals)
         #print outvals
 
@@ -1281,7 +1281,7 @@ class UsmmTests(unittest.TestCase):
                              for node in topo]) == len(topo) - 5)
                 new_topo = []
                 for node in topo:
-                    if not (isinstance(node.op, tensor.Elemwise) and \
+                    if not (isinstance(node.op, tensor.Elemwise) and
                        isinstance(node.op.scalar_op,
                                   theano.scalar.basic.Cast)):
                         new_topo.append(node)
@@ -1571,7 +1571,7 @@ class SpSumTester(utt.InferShapeTester):
                                                       shape=(10, 10))
 
                 z = theano.sparse.sp_sum(variable[0], axis=axis)
-                if axis == None:
+                if axis is None:
                     assert z.type.broadcastable == ()
                 else:
                     assert z.type.broadcastable == (False, )
@@ -1962,13 +1962,15 @@ class Test_getitem(unittest.TestCase):
 
             # Advanced indexing is not supported
             self.assertRaises(ValueError,
-                    x.__getitem__, (tensor.ivector('l'), slice(a, b)))
+                              x.__getitem__,
+                              (tensor.ivector('l'), slice(a, b)))
 
             # Indexing with random things is not supported either
             self.assertRaises(ValueError,
                               x.__getitem__, slice(tensor.fscalar('f'), None))
             self.assertRaises(ValueError,
-                    x.__getitem__, (slice(None), slice([1, 3, 4], None)))
+                              x.__getitem__,
+                              (slice(None), slice([1, 3, 4], None)))
 
     def test_GetItemScalar(self):
         sparse_formats = ('csc', 'csr')
@@ -2558,7 +2560,8 @@ class MulSVTester(unittest.TestCase):
                 mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
 
                 theano.sparse.verify_grad_sparse(mul_s_v,
-                    [spmat, mat], structured=True)
+                                                 [spmat, mat],
+                                                 structured=True)
 
     def test_mul_s_v(self):
         sp_types = {'csc': sp.csc_matrix,
@@ -2592,7 +2595,8 @@ class StructuredAddSVTester(unittest.TestCase):
                 mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
 
                 theano.sparse.verify_grad_sparse(structured_add_s_v,
-                    [spmat, mat], structured=True)
+                                                 [spmat, mat],
+                                                 structured=True)
 
     def test_structured_add_s_v(self):
         sp_types = {'csc': sp.csc_matrix,
@@ -2674,7 +2678,7 @@ test_shared_options = theano.tensor.tests.test_sharedvar.makeSharedTester(
     ref_fct_=lambda a: numpy.asarray((a * 2).todense()),
     cast_value_=scipy.sparse.csr_matrix,
     name='test_shared_options',
-    )
+)
 
 
 if __name__ == '__main__':