diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index f1fdaaf1..61a4abd8 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -133,32 +133,3 @@ def stick():
     plt.close('all')
 
     return m
-
-
-def BGPLVM_oil():
-    data = GPy.util.datasets.oil()
-    Y, X = data['Y'], data['X']
-    X -= X.mean(axis=0)
-    X /= X.std(axis=0)
-
-    Q = 10
-    M = 30
-
-    kernel = GPy.kern.rbf(Q, ARD = True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
-    m = GPy.models.Bayesian_GPLVM(X, Q, kernel=kernel, M=M)
-    # m.scale_factor = 100.0
-    m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)')
-    from sklearn import cluster
-    km = cluster.KMeans(M, verbose=10)
-    Z = km.fit(m.X).cluster_centers_
-    # Z = GPy.util.misc.kmm_init(m.X, M)
-    m.set('iip', Z)
-    m.set('bias', 1e-4)
-    # optimize
-    # m.ensure_default_constraints()
-
-    import pdb; pdb.set_trace()
-    m.optimize('tnc', messages=1)
-    print m
-    m.plot_latent(labels=data['Y'].argmax(axis=1))
-    return m
diff --git a/GPy/models/GPLVM.py b/GPy/models/GPLVM.py
index 470aff96..2ce55dda 100644
--- a/GPy/models/GPLVM.py
+++ b/GPy/models/GPLVM.py
@@ -89,7 +89,7 @@ class GPLVM(GP):
         Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
         Xtest_full[:, :2] = Xtest
         mu, var, low, up = self.predict(Xtest_full)
-        var = var[:, :1] # FIXME: this was a :2
+        var = var[:, :1] 
         pb.imshow(var.reshape(resolution,resolution).T[::-1,:],
                   extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear')
 
@@ -119,5 +119,6 @@ class GPLVM(GP):
         pb.xlim(xmin[0],xmax[0])
         pb.ylim(xmin[1],xmax[1])
         pb.grid(b=False) # remove the grid if present, it doesn't look good
+        ax = pb.gca()
         ax.set_aspect('auto') # set a nice aspect ratio
-        return pb.gca() #input_1, input_2 temporary removal, to return axes.
+        return ax