diff --git a/GPy/kern/_src/hierarchical.py b/GPy/kern/_src/hierarchical.py
deleted file mode 100644
index ac360ec7..00000000
--- a/GPy/kern/_src/hierarchical.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright (c) 2012, James Hesnsman
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-from kernpart import Kernpart
-import numpy as np
-from independent_outputs import index_to_slices
-
-class Hierarchical(Kernpart):
-    """
-    A kernel part which can reopresent a hierarchy of indepencnce: a generalisation of independent_outputs
-
-    """
-    def __init__(self,parts,name='hierarchy'):
-        self.levels = len(parts)
-        self.input_dim = parts[0].input_dim + 1
-        self.num_params = np.sum([k.num_params for k in parts])
-        self.name = name
-        self.parts = parts
-
-        self.param_starts = np.hstack((0,np.cumsum([k.num_params for k in self.parts[:-1]])))
-        self.param_stops = np.cumsum([k.num_params for k in self.parts])
-
-    def _get_params(self):
-        return np.hstack([k._get_params() for k in self.parts])
-
-    def _set_params(self,x):
-        [k._set_params(x[start:stop]) for k, start, stop in zip(self.parts, self.param_starts, self.param_stops)]
-
-    def _get_param_names(self):
-        return sum([[str(i)+'_'+k.name+'_'+n for n in k._get_param_names()] for i,k in enumerate(self.parts)],[])
-
-    def _sort_slices(self,X,X2):
-        slices = [index_to_slices(x) for x in X[:,-self.levels:].T]
-        X = X[:,:-self.levels]
-        if X2 is None:
-            slices2 = slices
-            X2 = X
-        else:
-            slices2 = [index_to_slices(x) for x in X2[:,-self.levels:].T]
-            X2 = X2[:,:-self.levels]
-        return X, X2, slices, slices2
-
-    def K(self,X,X2,target):
-        X, X2, slices, slices2 = self._sort_slices(X,X2)
-
-        [[[[k.K(X[s],X2[s2],target[s,s2]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_,slices2_)] for k, slices_, slices2_ in zip(self.parts,slices,slices2)]
-
-    def Kdiag(self,X,target):
-        raise NotImplementedError
-        #X,slices = X[:,:-1],index_to_slices(X[:,-1])
-        #[[self.k.Kdiag(X[s],target[s]) for s in slices_i] for slices_i in slices]
-
-    def _param_grad_helper(self,dL_dK,X,X2,target):
-        X, X2, slices, slices2 = self._sort_slices(X,X2)
-        [[[[k._param_grad_helper(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)]
-
-
-    def gradients_X(self,dL_dK,X,X2,target):
-        raise NotImplementedError
-        #X,slices = X[:,:-1],index_to_slices(X[:,-1])
-        #if X2 is None:
-            #X2,slices2 = X,slices
-        #else:
-            #X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
-        #[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
-#
-    def dKdiag_dX(self,dL_dKdiag,X,target):
-        raise NotImplementedError
-        #X,slices = X[:,:-1],index_to_slices(X[:,-1])
-        #[[self.k.dKdiag_dX(dL_dKdiag[s],X[s],target[s,:-1]) for s in slices_i] for slices_i in slices]
-
-
-    def dKdiag_dtheta(self,dL_dKdiag,X,target):
-        raise NotImplementedError
-        #X,slices = X[:,:-1],index_to_slices(X[:,-1])
-        #[[self.k.dKdiag_dX(dL_dKdiag[s],X[s],target) for s in slices_i] for slices_i in slices]
diff --git a/GPy/plotting/matplot_dep/kernel_plots.py b/GPy/plotting/matplot_dep/kernel_plots.py
index cbf0fa54..dd0f1cf5 100644
--- a/GPy/plotting/matplot_dep/kernel_plots.py
+++ b/GPy/plotting/matplot_dep/kernel_plots.py
@@ -161,7 +161,6 @@ def plot(kernel,x=None, fignum=None, ax=None, title=None, plot_limits=None, reso
         Xnew = np.vstack((xx.flatten(), yy.flatten())).T
         Kx = kernel.K(Xnew, x)
         Kx = Kx.reshape(resolution, resolution).T
-        stop
         ax.contour(xx, yy, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, **mpl_kwargs) # @UndefinedVariable
         ax.set_xlim(xmin[0], xmax[0])
         ax.set_ylim(xmin[1], xmax[1])