fix (mostly pre-existing) pep8 violations in tensor.basic

theano/tensor/basic.py

@@ -48,6 +48,7 @@ continuous_dtypes = map(str, scal.continuous_types)
 discrete_dtypes = map(str, scal.discrete_types)
 all_dtypes = map(str, scal.all_types)
 
+
 class ShapeError(Exception):
     """Raised when the shape cannot be computed."""
     pass
@@ -395,6 +396,7 @@ def constant(x, name=None, ndim=None, dtype=None):
     return constant_or_value(x, rtype=TensorConstant, name=name, ndim=ndim,
                              dtype=dtype)
 
+
 def _obj_is_wrappable_as_tensor(x):
     try:
         constant(x)
@@ -406,7 +408,7 @@ def _obj_is_wrappable_as_tensor(x):
 def _wrap_tensor_into_member(x):
     return compile.module.Member(constant(x))
 compile.module.register_wrapper(_obj_is_wrappable_as_tensor,
-                                _wrap_tensor_into_member, no_warn = True)
+                                _wrap_tensor_into_member, no_warn=True)
 
 
 if int(config.tensor.cmp_sloppy) > 1:
@@ -1503,10 +1505,9 @@ class _tensor_py_operators:
         """
 
         if ndim is not None:
-            if not isinstance(ndim,int):
+            if not isinstance(ndim, int):
                 raise ValueError("Expected ndim to be an integer, is "\
-                        +str(type(ndim)))
-
+                        + str(type(ndim)))
 
         return reshape(self, shape, ndim=ndim)
 
@@ -1803,7 +1804,6 @@ class TensorConstant(_tensor_py_operators, Constant):
 TensorType.Constant = TensorConstant
 
 
-
 Tensor = TensorType
 
 
@@ -1817,6 +1817,7 @@ elemwise.TensorConstant = TensorConstant
 # Utilities
 #########################
 
+
 def _redefine(real_symbol_value, module='tensor'):
     """Replace the value associated with a function symbol.
 
@@ -2062,6 +2063,7 @@ def cast(x, dtype):
 # Unary Operations
 ##########################
 
+
 class Shape(Op):
     """
     L{Op} to return the shape of a matrix.
@@ -2333,16 +2335,16 @@ class MaxAndArgmax(Op):
 
         #if the op is totally disconnected, so are its inputs
         if g_max_disconnected and g_max_idx_disconnected:
-            return [ DisconnectedType()(), DisconnectedType()() ]
+            return [DisconnectedType()(), DisconnectedType()()]
 
         axis_grad = grad_undefined(self, 1, axis,
                 "argmax is not defined for non-integer axes so"
-                " argmax(x, axis+eps) is undefined" )
+                " argmax(x, axis+eps) is undefined")
 
         #if the max is disconnected but the argmax is not,
         #the gradient on its inputs is zero
         if g_max_disconnected:
-            return [ x.zeros_like(), axis_grad ]
+            return [x.zeros_like(), axis_grad]
         xmax = max(x, axis)
 
         # Raise the g_max and xmax to the same number of dim as the input.
@@ -2875,6 +2877,7 @@ def complex_from_polar(abs, angle):
 # Misc
 ##########################
 
+
 #fill, _fill_inplace = _elemwise(scal.second, 'fill',
     #"""fill WRITEME (elemwise)""")
 @_scal_elemwise
@@ -2943,7 +2946,7 @@ class Eye(gof.Op):
         return [out_shape]
 
     def grad(self, inp, grads):
-        return [ grad_undefined(self,i,inp[i]) for i in xrange(3) ]
+        return [grad_undefined(self, i, inp[i]) for i in xrange(3)]
 
     def __eq__(self, other):
         return type(self) == type(other) and self.dtype == other.dtype
@@ -3418,15 +3421,20 @@ def var(input, axis=None, keepdims=False):
 @constructor
 def std(input, axis=None, keepdims=False):
     """
-    Computes the standard deviation along the given axis(es) of a tensor `input`.
+    Computes the standard deviation along the given axis(es)
+    of a tensor `input`.
 
-    :param axis: Compute the standard deviation along this axis of the tensor.
+    :param axis: Compute the standard deviation along this
+                axis of the tensor.
                  None means all axes (like numpy).
     :type axis: None or int or (list of int) (see `Sum`)
 
-    :param keepdims: If this is set to True, the axes which are reduced are
-        left in the result as dimensions with size one. With this option,
-        the result will broadcast correctly against the original tensor.
+    :param keepdims: If this is set to True, the axes
+        which are reduced are
+        left in the result as dimensions with size one.
+        With this option,
+        the result will broadcast correctly against the
+        original tensor.
     """
 
     return sqrt(var(input=input, axis=axis, keepdims=keepdims))
@@ -5402,7 +5410,6 @@ class Reshape(Op):
             raise ValueError('Cannot reshape input of shape %s to shape %s' %
                              (x.shape, shp))
 
-
     def grad(self, inp, grads):
         x, shp = inp
         g_out, = grads
@@ -5488,7 +5495,8 @@ class Reshape(Op):
                         %(shp)s->data + ii * %(shp)s->strides[0]))[0];
             }
             Py_XDECREF(%(z)s);
-            %(z)s = (PyArrayObject *) PyArray_Newshape(%(x)s, &newshape, PyArray_CORDER);
+            %(z)s = (PyArrayObject *) PyArray_Newshape(%(x)s, &newshape,
+                PyArray_CORDER);
             if (!%(z)s)
             {
                 PyErr_Format(PyExc_ValueError,
@@ -6351,6 +6359,7 @@ advanced_inc_subtensor = AdvancedIncSubtensor()
 #
 # TODO: Dotinv should go here, Eigs, Svd, etc.
 
+
 class Dot(Op):
     """Compute matrix-matrix, matrix-vector products and vector inner-products.