Fixed the PEP8 Error. Modified some tests to meet the standards without changing the output

theano/compile/tests/test_builders.py

@@ -4,7 +4,6 @@ from theano import config, shared
 
 from theano.compile import function
 
-from theano import tensor
 from theano import tensor as T
 from theano.tensor.shared_randomstreams import RandomStreams
 
@@ -24,8 +23,8 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
 
         fn = function([x, y, z], f)
         xv = numpy.ones((2, 2), dtype=config.floatX)
-        yv = numpy.ones((2, 2), dtype=config.floatX)*3
-        zv = numpy.ones((2, 2), dtype=config.floatX)*5
+        yv = numpy.ones((2, 2), dtype=config.floatX) * 3
+        zv = numpy.ones((2, 2), dtype=config.floatX) * 5
         # print function, function.__module__
         # print fn.maker.fgraph.toposort()
         fn(xv, yv, zv)
@@ -39,8 +38,8 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
         f = op(x, op(y, z))
         fn = function([x, y, z], f)
         xv = numpy.ones((2, 3), dtype=config.floatX)
-        yv = numpy.ones((3, 4), dtype=config.floatX)*3
-        zv = numpy.ones((4, 5), dtype=config.floatX)*5
+        yv = numpy.ones((3, 4), dtype=config.floatX) * 3
+        zv = numpy.ones((4, 5), dtype=config.floatX) * 5
         res = fn(xv, yv, zv)
         assert res.shape == (2, 5)
         assert numpy.all(180.0 == res)
@@ -56,8 +55,8 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
         f = f - T.grad(T.sum(f), y)
         fn = function([x, y, z], f)
         xv = numpy.ones((2, 2), dtype=config.floatX)
-        yv = numpy.ones((2, 2), dtype=config.floatX)*3
-        zv = numpy.ones((2, 2), dtype=config.floatX)*5
+        yv = numpy.ones((2, 2), dtype=config.floatX) * 3
+        zv = numpy.ones((2, 2), dtype=config.floatX) * 5
         assert numpy.all(11.0 == fn(xv, yv, zv))
 
     def test_grad_grad(self):
@@ -69,8 +68,8 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
         f = f - T.grad(T.sum(f), y)
         fn = function([x, y, z], f)
         xv = numpy.ones((2, 2), dtype=config.floatX)
-        yv = numpy.ones((2, 2), dtype=config.floatX)*3
-        zv = numpy.ones((2, 2), dtype=config.floatX)*5
+        yv = numpy.ones((2, 2), dtype=config.floatX) * 3
+        zv = numpy.ones((2, 2), dtype=config.floatX) * 5
         assert numpy.allclose(6.0, fn(xv, yv, zv))
 
     def test_shared(self):
@@ -83,8 +82,8 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
 
         fn = function([x, y, z], f)
         xv = numpy.ones((2, 2), dtype=config.floatX)
-        yv = numpy.ones((2, 2), dtype=config.floatX)*3
-        zv = numpy.ones((2, 2), dtype=config.floatX)*5
+        yv = numpy.ones((2, 2), dtype=config.floatX) * 3
+        zv = numpy.ones((2, 2), dtype=config.floatX) * 5
         # print function, function.__module__
         # print fn.maker.fgraph.toposort()
         assert numpy.allclose(8.0, fn(xv, yv, zv))
@@ -116,7 +115,7 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
         out1 = x * y
         out2 = y * z
 
-        op1 = OpFromGraph([x ,y, z], [out1, out2])
+        op1 = OpFromGraph([x, y, z], [out1, out2])
         results = op1.connection_pattern(None)
         expect_result = [[True, False],
                          [True, True],
@@ -139,7 +138,7 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
 
         # Inner graph where some computation doesn't rely on explicit inputs
         srng = RandomStreams(seed=234)
-        rv_u = srng.uniform((2,2))
+        rv_u = srng.uniform((2, 2))
         x, y = T.matrices('xy')
         out1 = x + rv_u
         out2 = y + 3
@@ -155,14 +154,14 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
     def test_infer_shape(self):
         x = T.matrix('x')
         y = T.matrix('y')
-        o1 = x+y
-        o2 = x*y
-        op_graph = OpFromGraph([x,y], [o1,o2])
+        o1 = x + y
+        o2 = x * y
+        op_graph = OpFromGraph([x, y], [o1, o2])
 
         q = T.matrix('q')
         p = T.matrix('p')
-        self._compile_and_check([q,p],
-                                op_graph(q,p),
-                                [numpy.ones([3,4], dtype=config.floatX),
-                                 numpy.ones([3,4], dtype=config.floatX)],
+        self._compile_and_check([q, p],
+                                op_graph(q, p),
+                                [numpy.ones([3, 4], dtype=config.floatX),
+                                 numpy.ones([3, 4], dtype=config.floatX)],
                                 OpFromGraph)

theano/compile/tests/test_debugmode.py

@@ -160,16 +160,16 @@ class WeirdBrokenOp(gof.Op):
 
         if self.behaviour == 'times2':
             behaviour = "     Dz[m * Sz] = 2 * Da[m * Sa]; "
-            #out[0] = a * 2
+            # out[0] = a * 2
         elif self.behaviour == 'times2_inplace':
-            #out[0] = a
-            #out[0] *= 2
+            # out[0] = a
+            # out[0] *= 2
             behaviour = "     Dz[m * Sz] = 2 * Da[m * Sa]; "
         elif self.behaviour == 'times1':
-            #out[0] = a * 1
+            # out[0] = a * 1
             behaviour = "     Dz[m * Sz] = Da[m * Sa]; "
         elif self.behaviour == 'times1_inplace':
-            #out[0] = a
+            # out[0] = a
             behaviour = ""
         else:
             raise ValueError(self.behaviour)
@@ -189,7 +189,7 @@ wb1 = WeirdBrokenOp('times1')
 
 
 def test_badthunkoutput():
-# Check if the c and python code is consistent.
+    # Check if the c and python code is consistent.
     a = theano.tensor.dvector()
     b = theano.tensor.dvector()
 
@@ -533,7 +533,7 @@ class Test_ViewMap(unittest.TestCase):
         # pretending that it is aliased to both the outputs.
         # This unfairly disables any destructive operations on the
         # input, but guarantees correctness.
-        #custom_op.view_map = {0:[0], 1:[1]}
+        # custom_op.view_map = {0:[0], 1:[1]}
         # f([1,2,3,4],[5,6,7,8])
 
 
@@ -737,7 +737,7 @@ class Test_preallocated_output(unittest.TestCase):
             check_preallocated_output=['c_contiguous'])
 
         f = theano.function([a, b], out, mode=mode)
-        out_val = f(a_val, b_val)
+        # out_val = f(a_val, b_val)
         # print 'out_val =', out_val
         # print out_val.strides
 
@@ -769,7 +769,7 @@ class Test_preallocated_output(unittest.TestCase):
             check_preallocated_output=['c_contiguous'])
 
         f = theano.function([a, b], out, mode=mode)
-        out_val = f(a_val, b_val)
+        # out_val = f(a_val, b_val)
         # print 'out_val =', out_val
         # print out_val.strides
 

theano/compile/tests/test_misc.py

-import numpy, theano, unittest
+import numpy
+import unittest
 
 from theano.compile.pfunc import pfunc
 from theano.compile.sharedvalue import shared
@@ -56,5 +57,7 @@ class TestNnet(unittest.TestCase):
             # print 'Mean cost at epoch %s: %s' % (epoch, mean_cost)
         self.assertTrue(abs(mean_cost - 0.20588975452) < 1e-6)
         # Just call functions to make sure they do not crash.
-        out = nnet.compute_output(input)
-        out = nnet.output_from_hidden(numpy.ones(10))
+        # out = nnet.compute_output(input)
+        # out = nnet.output_from_hidden(numpy.ones(10))
+        nnet.compute_output(input)
+        nnet.output_from_hidden(numpy.ones(10))

theano/compile/tests/test_monitormode.py

@@ -75,7 +75,7 @@ def test_not_inplace():
 
     x = theano.tensor.vector('x')
     mode = theano.compile.MonitorMode(post_func=detect_nan)
-    #mode = mode.excluding('fusion', 'inplace')
+    # mode = mode.excluding('fusion', 'inplace')
     mode = mode.excluding('local_elemwise_fusion',
                           'inplace_elemwise_optimizer')
     o = theano.tensor.outer(x, x)

theano/compile/tests/test_pfunc.py

@@ -712,7 +712,7 @@ class Test_aliasing_rules(unittest.TestCase):
             pass
 
         from theano.sparse import enable_sparse
-        if enable_sparse == False:
+        if not enable_sparse:
             raise SkipTest('Optional package sparse disabled')
 
         from theano import sparse
@@ -816,9 +816,9 @@ class Test_aliasing_rules(unittest.TestCase):
                 theano.In(m1, mutable=True),
                 theano.In(m2, mutable=True),
                 theano.In(m3, mutable=True)],
-                (theano.dot((x * 2), m1)
-                    + theano.dot((y * 3), m2)
-                    + theano.dot((z * 4), m3)))
+            (theano.dot((x * 2), m1) +
+                theano.dot((y * 3), m2) +
+                theano.dot((z * 4), m3)))
 
         # Compute bogus values
         v = numpy.asarray([1, 2, 3, 4, 5], dtype='float64')

theano/compile/tests/test_profiling.py

@@ -44,7 +44,7 @@ class Test_profiling(unittest.TestCase):
                                 mode=m)
 
             inp = [numpy.arange(1024, dtype='float32') + 1 for i in range(len(x))]
-            output = f(*inp)
+            f(*inp)
 
             buf = StringIO()
             f.profile.summary(buf)
@@ -72,7 +72,6 @@ class Test_profiling(unittest.TestCase):
             theano.config.profile_memory = config2
             theano.config.profiling.min_peak_memory = config3
 
-
     def test_ifelse(self):
         config1 = theano.config.profile
         config2 = theano.config.profile_memory
@@ -101,7 +100,8 @@ class Test_profiling(unittest.TestCase):
             big_mat1 = 10
             big_mat2 = 11
 
-            out = f_ifelse(val1, val2, big_mat1, big_mat2)
+            # out = f_ifelse(val1, val2, big_mat1, big_mat2)
+            f_ifelse(val1, val2, big_mat1, big_mat2)
 
         finally:
             theano.config.profile = config1

theano/compile/tests/test_shared.py

@@ -31,8 +31,7 @@ class Test_SharedVariable(unittest.TestCase):
         b = shared(numpy.random.rand(4, 5))
         assert b.type == TensorType('float64', broadcastable=[False, False])
         b = shared(numpy.random.rand(5, 1, 2))
-        assert b.type == TensorType('float64',
-                broadcastable=[False, False, False])
+        assert b.type == TensorType('float64', broadcastable=[False, False, False])
 
         assert shared([]).type == generic