pep8

theano/tensor/basic.py

@@ -109,7 +109,8 @@ if 0:
     # - JB 20100226
     def as_cuda_or_tensor_variable(x, name=None, ndim=None):
         """
-        Do the same as_tensor_variable, but do not transfer the value on the gpu.
+        Do the same as_tensor_variable,
+        but do not transfer the value on the gpu.
         """
         if hasattr(x, '_as_CudaNdarrayVariable'):
             # TODO: pass name and ndim arguments
@@ -516,7 +517,8 @@ def _allclose(a, b, rtol=None, atol=None):
     if atol is not None:
         atol_ = atol
 
-    # Work around bug in Numpy, see http://projects.scipy.org/numpy/ticket/1684
+    # Work around bug in Numpy, see
+    # http://projects.scipy.org/numpy/ticket/1684
     if str(b.dtype) in int_dtypes and (numpy.absolute(b) < 0).any():
         b = theano._asarray(b, dtype='float64')
 
@@ -1289,18 +1291,12 @@ class MaxAndArgmax(Op):
         if isinstance(axis, (tuple, list, numpy.ndarray)):
             # List of axes: make them non-negative, and sort them
             axis = [int(a) for a in axis]
-            
-            #if axis == list(range(-x.type.ndim, 0, 1)):
-                #axis = list(range(x.type.ndim))
-            #assert axis == list(range(x.type.ndim)), (
-                #"MaxAndArgmax does not support multiple"
-                #" axes. the max fct supports it. Got %s" % axis)
             if axis == list(range(x.type.ndim)):
                 axis = None
         elif isinstance(axis, (int, numpy.integer)):
             axis = [int(axis)]
         elif isinstance(axis, numpy.ndarray) and axis.ndim == 0:
-            axis = [int(axis)]    
+            axis = [int(axis)]
         elif isinstance(axis, Variable):
             if NoneConst.equals(axis):
                 axis = None
@@ -1311,18 +1307,19 @@ class MaxAndArgmax(Op):
                 assert (axis.dtype.startswith("int") or
                         axis.dtype.startswith("uint"))
                 if isinstance(axis.data, (int, numpy.integer)) or \
-                   (isinstance(axis.data, numpy.ndarray) and axis.data.ndim == 0):
+                   (isinstance(axis.data, numpy.ndarray) and
+                        axis.data.ndim == 0):
                     axis = [int(axis.data)]
                 elif isinstance(axis.data, (list, numpy.ndarray)):
                     axis = [int(i) for i in axis.data]
-                
+
         # Make axis entries non-negative, and sort them
         if isinstance(axis, list):
             for idx in xrange(len(axis)):
                 if axis[idx] < 0:
                     axis[idx] += x.type.ndim
             axis.sort()
-   
+
         # Verify that axes are valid
         all_axes = []
         if isinstance(axis, list):
@@ -1335,7 +1332,7 @@ class MaxAndArgmax(Op):
                     all_axes.append(ax)
         else:
             all_axes = list(range(x.ndim))
-  
+
         if axis is None or axis == list(range(x.type.ndim)):
             axis = NoneConst.clone()
         else:
@@ -1355,8 +1352,8 @@ class MaxAndArgmax(Op):
         x, axes = inp
         max, max_idx = outs
         if axes is None:
-            axes = tuple(range(x.ndim)) 
-        else:    
+            axes = tuple(range(x.ndim))
+        else:
             axes = tuple(axes)
         max[0] = theano._asarray(numpy.max(x, axes),
                                  dtype=node.outputs[0].dtype)
@@ -1365,10 +1362,12 @@ class MaxAndArgmax(Op):
         keep_axes = numpy.array([i for i in range(x.ndim) if i not in axes])
         # Not-reduced axes in front
         transposed_x = numpy.transpose(x, numpy.concatenate((keep_axes, axes)))
-        reshaped_x = transposed_x.reshape(transposed_x.shape[:len(keep_axes)] + (-1,))
-        
-        max_idx[0] = theano._asarray(numpy.argmax(reshaped_x, axis=-1), dtype='int64')
-        
+        reshaped_x = transposed_x.reshape(
+                        transposed_x.shape[:len(keep_axes)] + (-1,))
+
+        max_idx[0] = theano._asarray(numpy.argmax(reshaped_x, axis=-1),
+                                     dtype='int64')
+
     def c_code(self, node, name, inp, out, sub):
         x, axis = inp
         max, argmax = out
@@ -1378,11 +1377,13 @@ class MaxAndArgmax(Op):
         else:
             assert node.inputs[1].ndim == 1
             # Fall back to perform() if there are multiple axes
-            if len(node.inputs[1].data) > 1: raise NotImplementedError()
+            if len(node.inputs[1].data) > 1:
+                raise NotImplementedError()
             axis_code = """
             axis = ((dtype_%(axis)s*)PyArray_DATA(%(axis)s))[0];
             if(axis > PyArray_NDIM(%(x)s)-1 || axis < -PyArray_NDIM(%(x)s)){
-                PyErr_SetString(PyExc_ValueError, "MaxAndArgmax, bad axis argument");
+                PyErr_SetString(PyExc_ValueError,
+                "MaxAndArgmax, bad axis argument");
                 %(fail)s
             }
             """ % locals()
@@ -1439,7 +1440,7 @@ class MaxAndArgmax(Op):
         rval = tuple([ishape[i] for (i, b) in enumerate(
             node.inputs[0].type.broadcastable) if i not in axis.data])
         return [rval, rval]
-        
+
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
             return [None, None]

theano/tensor/tests/test_basic.py

@@ -2641,7 +2641,7 @@ def _approx_eq(a, b, eps=1.0e-4):
         if _approx_eq.debug:
             print(a, b)
         return False
-    return  True
+    return True
 _approx_eq.debug = 0
 
 
@@ -2799,10 +2799,10 @@ class T_max_and_argmax(unittest.TestCase):
     def test2(self):
         data = rand(2, 3)
         n = as_tensor_variable(data)
-        for (axis, np_axis)  in [(-1, -1), (0, 0), (1, 1), (None, None),
-                                 ([0, 1], None), ([1, 0], None),
-                                 (NoneConst.clone(), None),
-                                 (constant(0), 0)]:
+        for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
+                                ([0, 1], None), ([1, 0], None),
+                                (NoneConst.clone(), None),
+                                (constant(0), 0)]:
             v, i = eval_outputs(max_and_argmax(n, axis))
             assert i.dtype == 'int64'
             self.assertTrue(numpy.all(v == numpy.max(data, np_axis)))
@@ -2860,8 +2860,8 @@ class T_max_and_argmax(unittest.TestCase):
     def test3(self):
         data = rand(2, 3, 4)
         n = as_tensor_variable(data)
-        for (axis, np_axis)  in [(-1, -1), (0, 0), (1, 1), (None, None),
-                                 ([0, 1, 2], None), ([1, 2, 0], None)]:
+        for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
+                                ([0, 1, 2], None), ([1, 2, 0], None)]:
             v, i = eval_outputs(max_and_argmax(n, axis))
             assert i.dtype == 'int64'
             self.assertTrue(numpy.all(v == numpy.max(data, np_axis)))
@@ -2922,8 +2922,8 @@ class T_max_and_argmax(unittest.TestCase):
                 z[argmax] += 1
             else:
                 for id, v in enumerate(argmax):
-                    z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
-                      + id] += 1
+                    z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1]) +
+                      id] += 1
 
             z = z.reshape(data.shape)
             assert numpy.all(max_grad_data == z)
@@ -2931,11 +2931,11 @@ class T_max_and_argmax(unittest.TestCase):
         for axis in (-1, 0, 1, None):
             for j in xrange(2):
                 safe_verify_grad(lambda v: max_and_argmax(v, axis=axis)[j],
-                                [data])
+                                 [data])
                 if axis != 1:
                     safe_verify_grad(lambda v: max_and_argmax(v.flatten(),
-                                                             axis=axis)[j],
-                                    [data])
+                                                              axis=axis)[j],
+                                     [data])
             if axis in (0, None):
                 check_grad_max(data, eval_outputs(grad(
                     max_and_argmax(n, axis=axis)[0].sum(), n)), axis=axis)
@@ -2951,11 +2951,11 @@ class T_max_and_argmax(unittest.TestCase):
 
         # Test 4d inner dimensions
         data = rand(2, 3, 4, 5)
-        
+
         for i in [0, 1, 2, 3]:
             safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
             safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
-        
+
         # Test grad with multiple axes
         for i in [[0, 1], [0, 0]]:
             safe_verify_grad(lambda v: max_and_argmax(v, axis=i)[0], [data])
@@ -2968,17 +2968,17 @@ class T_max_and_argmax(unittest.TestCase):
         x = tensor.matrix().dimshuffle('x', 0, 'x', 1, 'x')
         y = x.max(axis=1)
         assert y.type.broadcastable == (True, True, False, True)
-        
+
     def test_multiple_axes(self):
         data = numpy.arange(24).reshape(3, 2, 4)
         x = as_tensor_variable(data)
         v, i = eval_outputs(max_and_argmax(x, [1, -1]))
         assert numpy.all(v == numpy.array([7, 15, 23]))
         assert numpy.all(i == numpy.array([7, 7, 7]))
-        
+
         v = eval_outputs(max_and_argmax(x, [1, -1])[0].shape)
         assert tuple(v) == numpy.max(data, (1, -1)).shape
-        
+
 
 class T_argmin_argmax(unittest.TestCase):
     def setUp(self):