pep8.

theano/tensor/tests/test_opt_uncanonicalize.py

@@ -13,93 +13,95 @@ from theano.tests import unittest_tools as utt
 
 class T_max_and_argmax(unittest.TestCase):
     def test_optimization(self):
-        #If we use only the max output, we should replace this op with a faster one.
-        mode = theano.compile.mode.get_default_mode().including('canonicalize','fast_run')
+        #If we use only the max output, we should replace this op with
+        #a faster one.
+        mode = theano.compile.mode.get_default_mode().including(
+            'canonicalize', 'fast_run')
 
         for axis in [0, 1, -1]:
-            data = numpy.asarray(numpy.random.rand(2,3),dtype=config.floatX)
+            data = numpy.asarray(numpy.random.rand(2, 3), dtype=config.floatX)
             n = tensor.matrix()
 
             f = function([n], tensor.max_and_argmax(n, axis)[0], mode=mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==1
+            assert len(topo) == 1
             assert isinstance(topo[0].op, CAReduce)
 
             f = function([n], tensor.max_and_argmax(n, axis), mode=mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==1
+            assert len(topo) == 1
             assert isinstance(topo[0].op, tensor.MaxAndArgmax)
 
 
 class T_min_max(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()
-        self.mode = theano.compile.mode.get_default_mode().including('canonicalize','fast_run')
+        self.mode = theano.compile.mode.get_default_mode().including(
+            'canonicalize', 'fast_run')
 
     def test_optimization_max(self):
-        data = numpy.asarray(numpy.random.rand(2,3),dtype=config.floatX)
+        data = numpy.asarray(numpy.random.rand(2, 3), dtype=config.floatX)
         n = tensor.matrix()
 
         for axis in [0, 1, -1]:
-            f = function([n],tensor.max(n, axis), mode=self.mode)
+            f = function([n], tensor.max(n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==1
-            assert isinstance(topo[0].op,CAReduce)
+            assert len(topo) == 1
+            assert isinstance(topo[0].op, CAReduce)
             f(data)
 
-
-            f = function([n],tensor.max(-n, axis), mode=self.mode)
+            f = function([n], tensor.max(-n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==2
+            assert len(topo) == 2
             assert isinstance(topo[0].op, Elemwise)
             assert isinstance(topo[0].op.scalar_op, scalar.Neg)
-            assert isinstance(topo[1].op,CAReduce)
+            assert isinstance(topo[1].op, CAReduce)
             f(data)
 
-            f = function([n],-tensor.max(n, axis), mode=self.mode)
+            f = function([n], -tensor.max(n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==2
-            assert isinstance(topo[0].op,CAReduce)
+            assert len(topo) == 2
+            assert isinstance(topo[0].op, CAReduce)
             assert isinstance(topo[1].op, Elemwise)
             assert isinstance(topo[1].op.scalar_op, scalar.Neg)
             f(data)
 
-            f = function([n],-tensor.max(-n, axis), mode=self.mode)
+            f = function([n], -tensor.max(-n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==1
-            assert isinstance(topo[0].op,CAReduce)#min
+            assert len(topo) == 1
+            assert isinstance(topo[0].op, CAReduce)  # min
             f(data)
 
     def test_optimization_min(self):
-        data = numpy.asarray(numpy.random.rand(2,3),dtype=config.floatX)
+        data = numpy.asarray(numpy.random.rand(2, 3), dtype=config.floatX)
         n = tensor.matrix()
 
         for axis in [0, 1, -1]:
-            f = function([n],tensor.min(n, axis), mode=self.mode)
+            f = function([n], tensor.min(n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==1
-            assert isinstance(topo[0].op,CAReduce)
+            assert len(topo) == 1
+            assert isinstance(topo[0].op, CAReduce)
             f(data)
 
             #test variant with neg to make sure we optimize correctly
-            f = function([n],tensor.min(-n, axis), mode=self.mode)
+            f = function([n], tensor.min(-n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==2
-            assert isinstance(topo[0].op,CAReduce)#max
+            assert len(topo) == 2
+            assert isinstance(topo[0].op, CAReduce)  # max
             assert isinstance(topo[1].op, Elemwise)
             assert isinstance(topo[1].op.scalar_op, scalar.Neg)
             f(data)
 
-            f = function([n],-tensor.min(n, axis), mode=self.mode)
+            f = function([n], -tensor.min(n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==2
+            assert len(topo) == 2
             assert isinstance(topo[0].op, Elemwise)
             assert isinstance(topo[0].op.scalar_op, scalar.Neg)
-            assert isinstance(topo[1].op,CAReduce)#max
+            assert isinstance(topo[1].op, CAReduce)  # max
             f(data)
 
-            f = function([n],-tensor.min(-n, axis), mode=self.mode)
+            f = function([n], -tensor.min(-n, axis), mode=self.mode)
             topo = f.maker.env.toposort()
-            assert len(topo)==1
-            assert isinstance(topo[0].op,CAReduce)#max
+            assert len(topo) == 1
+            assert isinstance(topo[0].op, CAReduce)  # max
             f(data)