ENH: pep8 in gradient.py

theano/gradient.py

@@ -26,10 +26,11 @@ tensor = None
 
 _msg_retType = 'op.grad(...) returned a non-list'
 
+
 def format_as(use_list, use_tuple, outputs):
     """
     Formats the outputs according to the flags `use_list` and `use_tuple`.
-    If `use_list` is True, `outputs` is returned as a list (if `outputs`
+"    If `use_list` is True, `outputs` is returned as a list (if `outputs`
     is not a list or a tuple then it is converted in a one element list).
     If `use_tuple` is True, `outputs` is returned as a tuple (if `outputs`
     is not a list or a tuple then it is converted into a one element tuple).
@@ -54,7 +55,8 @@ def format_as(use_list, use_tuple, outputs):
     else:
         return outputs
 
-def grad_not_implemented(op, x_pos, x, comment = ""):
+
+def grad_not_implemented(op, x_pos, x, comment=""):
     """
     Return an un-computable symbolic variable of type `x.type`.
 
@@ -68,11 +70,14 @@ def grad_not_implemented(op, x_pos, x, comment = ""):
     gradient is not implemented.
     """
 
-    return NullType("This variable is NaN because the grad method for " + \
-            "input "+str(x_pos)+" ("+str(x)+") of the "+str(op)+" op is" + \
-            " not implemented."+comment)()
+    return NullType(
+        (
+            "This variable is Null because the grad method for "
+            "input %s (%s) of the %s op is not implemented. %s"
+        ) % (x_pos, x, op, comment))
+
 
-def grad_undefined(op, x_pos, x, comment = ""):
+def grad_undefined(op, x_pos, x, comment=""):
     """
     Return an un-computable symbolic variable of type `x.type`.
 
@@ -86,9 +91,12 @@ def grad_undefined(op, x_pos, x, comment = ""):
     gradient is not defined.
     """
 
-    return NullType("This variable is NaN because the gradient for " + \
-            "input "+str(x_pos)+" ("+str(x)+") of the "+str(op)+" op is" + \
-             " mathematically undefined."+comment)()
+    return NullType(
+        (
+            "This variable is Null because the grad method for "
+            "input %s (%s) of the %s op is mathematically undefined. %s"
+        ) % (x_pos, x, op, comment))
+
 
 class DisconnectedType(theano.gof.type.Type):
 
@@ -105,9 +113,12 @@ class DisconnectedType(theano.gof.type.Type):
         whose type doesn't support zeros_like has 0 gradient. """
 
     def filter(self, data, strict=False, allow_downcast=None):
-        raise AssertionError("If you're assigning to a DisconnectedType you're"
-                " doing something wrong. It should only be used as "
-                "symbolic placeholder.")
+        raise AssertionError(
+            (
+                "If you're assigning to a DisconnectedType you're"
+                " doing something wrong. It should only be used as"
+                " a symbolic placeholder."
+            ))
 
     def fiter_variable(self, other):
         raise
@@ -253,7 +264,7 @@ def Rop(f, wrt, eval_points):
 
 
 def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
-         disconnected_inputs='raise'):
+        disconnected_inputs='raise'):
     """
     Computes the L operation on `f` wrt to `wrt` evaluated at points given
     in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
@@ -288,8 +299,8 @@ def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
     if not isinstance(f, (list, tuple)):
         f = [f]
 
-    f = [ elem for elem in f ]
-    grads = [ elem for elem in eval_points ]
+    f = [elem for elem in f]
+    grads = [elem for elem in eval_points]
 
     for elem in consider_constant:
         assert elem not in f
@@ -319,10 +330,11 @@ def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
         if p in gmap:
             ret.append(gmap[p])
         else:
-            message = ("Lop method was asked to compute the gradient "
-                    "with respect to a variable that is not part of "
-                    "the computational graph of the cost, or is used "
-                    "only by a non-differentiable operator: %s" % p)
+            message = (
+                "Lop method was asked to compute the gradient "
+                "with respect to a variable that is not part of "
+                "the computational graph of the cost, or is used "
+                "only by a non-differentiable operator: %s" % p)
             if disconnected_inputs == 'ignore':
                 pass
             elif disconnected_inputs == 'warn':
@@ -330,9 +342,10 @@ def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
             elif disconnected_inputs == 'raise':
                 raise ValueError(message)
             else:
-                raise ValueError("Invalid value for keyword "
-                        "'disconnected_inputs', valid values are "
-                        "'ignore', 'warn' and 'raise'.")
+                raise ValueError(
+                    "Invalid value for keyword "
+                    "'disconnected_inputs', valid values are "
+                    "'ignore', 'warn' and 'raise'.")
             ret.append(p.zeros_like())
 
     return format_as(using_list, using_tuple, ret)