pep8

theano/sandbox/test_rng_mrg.py

@@ -166,7 +166,8 @@ def test_consistency_cpu_parallel():
         rstate = theano.shared(rstate)
 
         new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
-                dtype=config.floatX, size=(n_substreams,))
+                                                     dtype=config.floatX,
+                                                     size=(n_substreams,))
         # Not really necessary, just mimicking
         # rng_mrg.MRG_RandomStreams' behavior
         sample.rstate = rstate
@@ -219,7 +220,8 @@ def test_consistency_GPU_serial():
             rstate = float32_shared_constructor(tmp_float_buf)
 
             new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
-                    dtype='float32', size=(1,))
+                                                             dtype='float32',
+                                                             size=(1,))
             rstate.default_update = new_rstate
 
             # Not really necessary, just mimicking
@@ -278,7 +280,8 @@ def test_consistency_GPU_parallel():
         rstate = float32_shared_constructor(tmp_float_buf)
 
         new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
-                dtype='float32', size=(n_substreams,))
+                                                         dtype='float32',
+                                                         size=(n_substreams,))
         rstate.default_update = new_rstate
 
         # Not really necessary, just mimicking
@@ -381,7 +384,8 @@ def test_consistency_GPUA_parallel():
         rstate = gpuarray_shared_constructor(rstate)
 
         new_rstate, sample = rng_mrg.GPUA_mrg_uniform.new(rstate, ndim=None,
-                dtype='float32', size=(n_substreams,))
+                                                          dtype='float32',
+                                                          size=(n_substreams,))
         rstate.default_update = new_rstate
 
         # Not really necessary, just mimicking
@@ -887,12 +891,14 @@ def test_multMatVect():
     g0 = rng_mrg.DotModulo()(A1, s1, m1, A2, s2, m2)
     f0 = theano.function([A1, s1, m1, A2, s2, m2], g0)
 
-    A1 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, (3, 3)).astype('int64')
-    s1 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, 3).astype('int32')
-    m1 = numpy.asarray(numpy.random.randint(numpy.iinfo(numpy.int32).max), dtype="int32")
-    A2 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, (3, 3)).astype('int64')
-    s2 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, 3).astype('int32')
-    m2 = numpy.asarray(numpy.random.randint(numpy.iinfo(numpy.int32).max), dtype="int32")
+    i32max = numpy.iinfo(numpy.int32).max
+
+    A1 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
+    s1 = numpy.random.randint(0, i32max, 3).astype('int32')
+    m1 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
+    A2 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
+    s2 = numpy.random.randint(0, i32max, 3).astype('int32')
+    m2 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
 
     f0.input_storage[0].storage[0] = A1
     f0.input_storage[1].storage[0] = s1