diff --git a/GPy/core/parameter.py b/GPy/core/parameter.py
index ff13c961..a8685a64 100644
--- a/GPy/core/parameter.py
+++ b/GPy/core/parameter.py
@@ -102,9 +102,6 @@ class Param(numpy.ndarray):
         self._realshape = getattr(obj, '_realshape', None)
         self.constraints = getattr(obj, 'constraints', None)
         self._current_slice = getattr(obj, '_current_slice', None)
-
-    def __array_wrap__(self, out_arr, context=None):
-        return numpy.ndarray.__array_wrap__(self, out_arr, context)
     
     @property
     def value(self):
@@ -121,20 +118,16 @@ class Param(numpy.ndarray):
         return ' '.join([str(c) if c else '' for c in self.constraints.keys()])
 
     def _set_params(self, param):
-        #with self.slicing():
-            self.value.flat = param
+        self.value.flat = param
 
     def _get_params(self):
-        #with self.slicing():
-            return self.value.flat
+        return self.value.flat
     
     def _get_params_transformed(self):
-        #with self.slicing():
-            params = self.value.copy()
+        params = self.value.copy()
         
     def constrain(self, constraint):
-        #with self.slicing():
-            self.constraints.add(constraint, self._current_slice)
+        self.constraints.add(constraint, self._current_slice)
     
     def constrain_positive(self):
         self.constrain(Logexp())
@@ -143,13 +136,12 @@ class Param(numpy.ndarray):
         self.constrain(NegativeLogexp())
 
     def unconstrain(self, constraints=None):
-        #with self.slicing():
-            if constraints is None:
-                constraints = self.constraints.keys()
-            elif not isinstance(constraints, (tuple, list, numpy.ndarray)):
-                constraints = [constraints]
-            for constr in constraints:
-                self.constraints.remove(constr, self._current_slice)
+        if constraints is None:
+            constraints = self.constraints.keys()
+        elif not isinstance(constraints, (tuple, list, numpy.ndarray)):
+            constraints = [constraints]
+        for constr in constraints:
+            self.constraints.remove(constr, self._current_slice)
 
     def unconstrain_positive(self):
         self.unconstrain(Logexp())
@@ -169,23 +161,22 @@ class Param(numpy.ndarray):
         return view
         
     def __str__(self, format_spec=None):
-        #with self.slicing():
-            if format_spec is None:
-                constr_matrix = numpy.empty(self._realshape, dtype=object)
-                constr_matrix[:] = ''
-                for constr, indices in self.constraints.iteritems():
-                    constr_matrix[indices] = numpy.vectorize(lambda x: " ".join([x,str(constr)]) if x else str(constr))(constr_matrix[indices])
-                constr_matrix = constr_matrix.astype(numpy.string_)[self._current_slice]
-                p = numpy.get_printoptions()['precision']
-                constr = constr_matrix.flat
-                ind = numpy.array(list(itertools.product(*itertools.imap(range, self._realshape))))[self.constraints.create_raveled_indices(self._current_slice),...]
-                c_name, x_name, i_name = "Constraint", "Value", "Index"
-                lc = max(reduce(lambda a,b: max(a, len(b)), constr_matrix.flat, 0), len(c_name))
-                lx = max(reduce(lambda a,b: max(a, len("{x:=.{0}G}".format(p,x=b))), self.value.flat, 0), len(x_name))
-                li = max(reduce(lambda a,b: max(a, len(str(b))), ind, 0), len(i_name))
-                header = "  {i:^{3}s}  |  {x:^{1}s}  |  {c:^{0}s}".format(lc,lx,p,li, x=x_name, c=c_name, i=i_name)
-                return "\n".join([header]+["  {i:^{3}s}  |  {x: >{1}.{2}G}  |  {c:^{0}s}".format(lc,lx,p,li, x=x, c=constr.next(), i=i) for i,x in itertools.izip(ind,self.value.flat)])
-            return format_spec.format(self=self)
+        if format_spec is None:
+            constr_matrix = numpy.empty(self._realshape, dtype=object)
+            constr_matrix[:] = ''
+            for constr, indices in self.constraints.iteritems():
+                constr_matrix[indices] = numpy.vectorize(lambda x: " ".join([x,str(constr)]) if x else str(constr))(constr_matrix[indices])
+            constr_matrix = constr_matrix.astype(numpy.string_)[self._current_slice]
+            p = numpy.get_printoptions()['precision']
+            constr = constr_matrix.flat
+            ind = numpy.array(list(itertools.product(*itertools.imap(range, self._realshape))))[self.constraints.create_raveled_indices(self._current_slice),...]
+            c_name, x_name, i_name = "Constraint", "Value", "Index"
+            lc = max(reduce(lambda a,b: max(a, len(b)), constr_matrix.flat, 0), len(c_name))
+            lx = max(reduce(lambda a,b: max(a, len("{x:=.{0}G}".format(p,x=b))), self.value.flat, 0), len(x_name))
+            li = max(reduce(lambda a,b: max(a, len(str(b))), ind, 0), len(i_name))
+            header = "  {i:^{3}s}  |  {x:^{1}s}  |  {c:^{0}s}".format(lc,lx,p,li, x=x_name, c=c_name, i=i_name)
+            return "\n".join([header]+["  {i:^{3}s}  |  {x: >{1}.{2}G}  |  {c:^{0}s}".format(lc,lx,p,li, x=x, c=constr.next(), i=i) for i,x in itertools.izip(ind,self.value.flat)])
+        return format_spec.format(self=self)
         
 if __name__ == '__main__':
     X = numpy.random.randn(2,4)