pep8

theano/sandbox/cuda/tests/test_tensor_op.py

@@ -16,7 +16,7 @@ if cuda.cuda_available == False:
     raise SkipTest('Optional package cuda disabled')
 
 
-if theano.config.mode=='FAST_COMPILE':
+if theano.config.mode == 'FAST_COMPILE':
     mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
     mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
 else:
@@ -26,26 +26,28 @@ else:
 
 def test_shape_i():
     x = cuda.ftensor3()
-    v = cuda.CudaNdarray(numpy.zeros((3,4,5),dtype='float32'))
-    f = theano.function([x],x.shape[1])
+    v = cuda.CudaNdarray(numpy.zeros((3, 4, 5), dtype='float32'))
+    f = theano.function([x], x.shape[1])
     topo = f.maker.fgraph.toposort()
-    assert f(v)==4
-    if theano.config.mode!='FAST_COMPILE':
-        assert len(topo)==1
-        assert isinstance(topo[0].op,T.opt.Shape_i)
+    assert f(v) == 4
+    if theano.config.mode != 'FAST_COMPILE':
+        assert len(topo) == 1
+        assert isinstance(topo[0].op, T.opt.Shape_i)
+
 
 def test_shape():
     x = cuda.ftensor3()
-    v = cuda.CudaNdarray(numpy.zeros((3,4,5),dtype='float32'))
-    f = theano.function([x],x.shape)
+    v = cuda.CudaNdarray(numpy.zeros((3, 4, 5), dtype='float32'))
+    f = theano.function([x], x.shape)
     topo = f.maker.fgraph.toposort()
-    assert numpy.all(f(v)==(3,4,5))
-    if theano.config.mode!='FAST_COMPILE':
-        assert len(topo)==4
-        assert isinstance(topo[0].op,T.opt.Shape_i)
-        assert isinstance(topo[1].op,T.opt.Shape_i)
-        assert isinstance(topo[2].op,T.opt.Shape_i)
-        assert isinstance(topo[3].op,T.opt.MakeVector)
+    assert numpy.all(f(v) == (3, 4, 5))
+    if theano.config.mode != 'FAST_COMPILE':
+        assert len(topo) == 4
+        assert isinstance(topo[0].op, T.opt.Shape_i)
+        assert isinstance(topo[1].op, T.opt.Shape_i)
+        assert isinstance(topo[2].op, T.opt.Shape_i)
+        assert isinstance(topo[3].op, T.opt.MakeVector)
+
 
 def test_softmax_optimizations():
     from theano.tensor.nnet.nnet import softmax, crossentropy_categorical_1hot
@@ -66,16 +68,17 @@ def test_softmax_optimizations():
     assert fgraph.outputs[0].owner.inputs[0].owner.op == cuda.host_from_gpu
     assert fgraph.outputs[0].owner.inputs[0].owner.inputs[0].owner.op == cuda.nnet.gpu_crossentropy_softmax_argmax_1hot_with_bias
 
+
 def test_may_share_memory_cuda():
     from theano.misc.may_share_memory import may_share_memory
-    a = cuda.CudaNdarray(numpy.zeros((3,4),dtype='float32'))
-    b = cuda.CudaNdarray(numpy.zeros((3,4),dtype='float32'))
-    na = numpy.zeros((3,4))
-    nb = numpy.zeros((3,4))
+    a = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))
+    b = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))
+    na = numpy.zeros((3, 4))
+    nb = numpy.zeros((3, 4))
     va = a.view()
     vb = b.view()
-    ra = a.reshape((4,3))
-    rb = b.reshape((4,3))
+    ra = a.reshape((4, 3))
+    rb = b.reshape((4, 3))
 
     #can't test the transpose as ta._strides = is not implemented
     #manual transpose of a
@@ -84,25 +87,28 @@ def test_may_share_memory_cuda():
     #elem_size=elem_size = numpy.zeros(0,dtype=a.dtype).dtype.itemsize
     #ta.gpudata += ta.size*elem_size
 
-    for a_,b_,rep in [(a,a,True),(b,b,True),(a,b,False),
-                      (a,na,False),(b,nb,False),(na,b,False),(nb,a,False),
-                      (a,va,True),(b,vb,True),(va,b,False),(a,vb,False),
-                      (a,ra,True),(b,rb,True),(ra,b,False),(a,rb,False),
+    for a_, b_, rep in [(a, a, True), (b, b, True), (a, b, False),
+                        (a, na, False), (b, nb, False),
+                        (na, b, False), (nb, a, False),
+                        (a, va, True), (b, vb, True),
+                        (va, b, False), (a, vb, False),
+                        (a, ra, True), (b, rb, True),
+                        (ra, b, False), (a, rb, False),
                       ]:
-        assert may_share_memory(a_,b_)==rep
-        assert may_share_memory(b_,a_)==rep
+        assert may_share_memory(a_, b_) == rep
+        assert may_share_memory(b_, a_) == rep
 
     #test that it raise error when needed.
-    for a_,b_,rep in [(a,(0,),False),(a,1,False),(a,None,False)]:
-        assert may_share_memory(a_,b_,False)==rep
-        assert may_share_memory(b_,a_,False)==rep
+    for a_, b_, rep in [(a, (0,), False), (a, 1, False), (a, None, False)]:
+        assert may_share_memory(a_, b_, False) == rep
+        assert may_share_memory(b_, a_, False) == rep
         try:
-            may_share_memory(a_,b_)
+            may_share_memory(a_, b_)
             raise Exception("An error was expected")
         except TypeError:
             pass
         try:
-            may_share_memory(b_,a_)
+            may_share_memory(b_, a_)
             raise Exception("An error was expected")
         except TypeError:
             pass