pep8

doc/tutorial/using_gpu.txt

@@ -685,15 +685,19 @@ Modify and execute to work for a matrix of shape (20, 10).
     class PyCUDADoubleOp(theano.Op):
         def __eq__(self, other):
             return type(self) == type(other)
+
         def __hash__(self):
             return hash(type(self))
+
         def __str__(self):
             return self.__class__.__name__
+
         def make_node(self, inp):
             inp = cuda.basic_ops.gpu_contiguous(
                cuda.basic_ops.as_cuda_ndarray_variable(inp))
             assert inp.dtype == "float32"
             return theano.Apply(self, [inp], [inp.type()])
+
         def make_thunk(self, node, storage_map, _, _2):
             mod = SourceModule("""
         __global__ void my_fct(float * i0, float * o0, int size) {
@@ -703,15 +707,16 @@ Modify and execute to work for a matrix of shape (20, 10).
         }
       }""")
             pycuda_fct = mod.get_function("my_fct")
-            inputs = [ storage_map[v] for v in node.inputs]
-            outputs = [ storage_map[v] for v in node.outputs]
+            inputs = [storage_map[v] for v in node.inputs]
+            outputs = [storage_map[v] for v in node.outputs]
+
             def thunk():
                 z = outputs[0]
-                if z[0] is None or z[0].shape!=inputs[0][0].shape:
+                if z[0] is None or z[0].shape != inputs[0][0].shape:
                     z[0] = cuda.CudaNdarray.zeros(inputs[0][0].shape)
-                grid = (int(numpy.ceil(inputs[0][0].size / 512.)),1)
+                grid = (int(numpy.ceil(inputs[0][0].size / 512.)), 1)
                 pycuda_fct(inputs[0][0], z[0], numpy.intc(inputs[0][0].size),
-                           block=(512,1,1), grid=grid)
+                           block=(512, 1, 1), grid=grid)
             return thunk
 
 
@@ -719,7 +724,7 @@ Use this code to test it:
 
 >>> x = theano.tensor.fmatrix()
 >>> f = theano.function([x], PyCUDADoubleOp()(x))
->>> xv=numpy.ones((4,5), dtype="float32")
+>>> xv = numpy.ones((4, 5), dtype="float32")
 >>> assert numpy.allclose(f(xv), xv*2)
 >>> print numpy.asarray(f(xv))
 

theano/misc/tests/test_pycuda_example.py

@@ -9,7 +9,7 @@ if not theano.misc.pycuda_init.pycuda_available:
                    " with pycuda code.")
 
 import theano.sandbox.cuda as cuda_ndarray
-if cuda_ndarray.cuda_available == False:
+if not cuda_ndarray.cuda_available:
     from nose.plugins.skip import SkipTest
     raise SkipTest('Optional package cuda disabled')