pep8

theano/typed_list/basic.py

@@ -36,7 +36,7 @@ class _typed_list_py_operators:
     def count(self, elem):
         return count(self, elem)
 
-    #name "index" is already used by an attribute
+    # name "index" is already used by an attribute
     def ind(self, elem):
         return index_(self, elem)
 

theano/typed_list/tests/test_basic.py

@@ -21,13 +21,13 @@ except ImportError:
     scipy_imported = False
 
 
-#took from tensors/tests/test_basic.py
+# took from tensors/tests/test_basic.py
 def rand_ranged_matrix(minimum, maximum, shape):
     return numpy.asarray(numpy.random.rand(*shape) * (maximum - minimum)
                          + minimum, dtype=theano.config.floatX)
 
 
-#took from sparse/tests/test_basic.py
+# took from sparse/tests/test_basic.py
 def random_lil(shape, dtype, nnz):
     rval = sp.lil_matrix(shape, dtype=dtype)
     huge = 2 ** 30
@@ -35,7 +35,7 @@ def random_lil(shape, dtype, nnz):
         # set non-zeros in random locations (row x, col y)
         idx = numpy.random.random_integers(huge, size=2) % shape
         value = numpy.random.rand()
-        #if dtype *int*, value will always be zeros!
+        # if dtype *int*, value will always be zeros!
         if "int" in dtype:
             value = int(value * 100)
         # The call to tuple is needed as scipy 0.13.1 do not support
@@ -84,8 +84,9 @@ class test_get_item(unittest.TestCase):
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
-        self.assertTrue(numpy.array_equal(f([x], numpy.asarray(0,
-                                    dtype='int64')), x))
+        self.assertTrue(numpy.array_equal(f([x],
+                                            numpy.asarray(0, dtype='int64')),
+                                          x))
 
     def test_interface(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
@@ -99,8 +100,9 @@ class test_get_item(unittest.TestCase):
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
 
-        self.assertTrue(numpy.array_equal(f([x], numpy.asarray(0,
-                                dtype='int64')), x))
+        self.assertTrue(numpy.array_equal(f([x],
+                                            numpy.asarray(0, dtype='int64')),
+                                          x))
 
         z = mySymbolicMatricesList[0]
 
@@ -258,8 +260,10 @@ class test_insert(unittest.TestCase):
 
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
-        self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1,
-                                dtype='int64'), y), [x, y]))
+        self.assertTrue(numpy.array_equal(f([x],
+                                            numpy.asarray(1, dtype='int64'),
+                                            y),
+                                          [x, y]))
 
     def test_sanity_check(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
@@ -292,8 +296,10 @@ class test_insert(unittest.TestCase):
 
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
-        self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1,
-                                    dtype='int64'), y), [x, y]))
+        self.assertTrue(numpy.array_equal(f([x],
+                                            numpy.asarray(1, dtype='int64'),
+                                            y),
+                                          [x, y]))
 
 
 class test_remove(unittest.TestCase):
@@ -443,8 +449,8 @@ class test_index(unittest.TestCase):
     def test_sparse(self):
         if not scipy_imported:
             raise SkipTest('Optional package SciPy not installed')
-        mySymbolicSparseList = TypedListType(sparse.SparseType('csr',
-                                                theano.config.floatX))()
+        mySymbolicSparseList = TypedListType(
+            sparse.SparseType('csr', theano.config.floatX))()
         mySymbolicSparse = sparse.csr_matrix()
 
         z = Index()(mySymbolicSparseList, mySymbolicSparse)
@@ -509,8 +515,8 @@ class test_count(unittest.TestCase):
     def test_sparse(self):
         if not scipy_imported:
             raise SkipTest('Optional package SciPy not installed')
-        mySymbolicSparseList = TypedListType(sparse.SparseType('csr',
-                                                theano.config.floatX))()
+        mySymbolicSparseList = TypedListType(
+            sparse.SparseType('csr', theano.config.floatX))()
         mySymbolicSparse = sparse.csr_matrix()
 
         z = Count()(mySymbolicSparseList, mySymbolicSparse)