diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 65881573..94bb4955 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -206,6 +206,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
 
     if plot_sim:
         import pylab
+        import matplotlib.cm as cm
         import itertools
         fig = pylab.figure("MRD Simulation Data", figsize=(8, 6))
         fig.clf()
@@ -216,7 +217,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
         ax.legend()
         for i, Y in enumerate(Ylist):
             ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
-            ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
+            ax.imshow(Y, aspect='auto', cmap=cm.gray)
             ax.set_title("Y{}".format(i + 1))
         pylab.draw()
         pylab.tight_layout()
@@ -450,9 +451,12 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
     if in_place:
         # Make figure move in place.
         data['Y'][:, 0:3] = 0.0
+
     m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
 
-    if optimize: m.optimize(messages=verbose, max_f_eval=10000)
+    if optimize:
+        m.optimize(messages=verbose, max_f_eval=10000)
+
     if plot:
         ax = m.plot_latent()
         y = m.likelihood.Y[0, :]
diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py
index a525b1c9..9998590a 100644
--- a/GPy/testing/examples_tests.py
+++ b/GPy/testing/examples_tests.py
@@ -10,6 +10,7 @@ import os
 import random
 from nose.tools import nottest
 import sys
+import itertools
 
 class ExamplesTests(unittest.TestCase):
     def _checkgrad(self, Model):
@@ -39,8 +40,19 @@ def model_instance(model):
     #assert isinstance(model, GPy.core.model)
     return isinstance(model, GPy.core.model.Model)
 
-@nottest
+def flatten_nested(lst):
+    result = []
+    for element in lst:
+        if hasattr(element, '__iter__'):
+            result.extend(flatten_nested(element))
+        else:
+            result.append(element)
+    return result
+
+#@nottest
 def test_models():
+    optimize=False
+    plot=True
     examples_path = os.path.dirname(GPy.examples.__file__)
     # Load modules
     failing_models = {}
@@ -54,29 +66,34 @@ def test_models():
         print "After"
         print functions
         for example in functions:
-            if example[0] in ['oil', 'silhouette', 'GPLVM_oil_100', 'brendan_faces']:
-                print "SKIPPING"
-                continue
+            #if example[0] in ['oil', 'silhouette', 'GPLVM_oil_100', 'brendan_faces']:
+                #print "SKIPPING"
+                #continue
 
             print "Testing example: ", example[0]
             # Generate model
+
             try:
-                model = example[1]()
+                models = [ example[1](optimize=optimize, plot=plot) ]
+                #If more than one model returned, flatten them
+                models = flatten_nested(models)
             except Exception as e:
                 failing_models[example[0]] = "Cannot make model: \n{e}".format(e=e)
             else:
-                print model
+                print models
                 model_checkgrads.description = 'test_checkgrads_%s' % example[0]
                 try:
-                    if not model_checkgrads(model):
-                        failing_models[model_checkgrads.description] = False
+                    for model in models:
+                        if not model_checkgrads(model):
+                            failing_models[model_checkgrads.description] = False
                 except Exception as e:
                     failing_models[model_checkgrads.description] = e
 
                 model_instance.description = 'test_instance_%s' % example[0]
                 try:
-                    if not model_instance(model):
-                        failing_models[model_instance.description] = False
+                    for model in models:
+                        if not model_instance(model):
+                            failing_models[model_instance.description] = False
                 except Exception as e:
                     failing_models[model_instance.description] = e
 
diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index 732e2a1b..c95998a7 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -435,7 +435,7 @@ def simulation_BGPLVM():
     Y = np.array(mat_data['Y'], dtype=float)
     S = np.array(mat_data['initS'], dtype=float)
     mu = np.array(mat_data['initMu'], dtype=float)
-    return data_details_return({'S': S, 'Y': Y, 'mu': mu}, data_set)
+    #return data_details_return({'S': S, 'Y': Y, 'mu': mu}, data_set)
     return {'Y': Y, 'S': S,
             'mu' : mu,
             'info': "Simulated test dataset generated in MATLAB to compare BGPLVM between python and MATLAB"}
@@ -594,11 +594,11 @@ def olympic_sprints(data_set='rogers_girolami_data'):
         'Y': Y,
         'info': "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning.",
         'output_info': {
-          0:'100m Men', 
-          1:'100m Women', 
-          2:'200m Men', 
-          3:'200m Women', 
-          4:'400m Men', 
+          0:'100m Men',
+          1:'100m Women',
+          2:'200m Men',
+          3:'200m Women',
+          4:'400m Men',
           5:'400m Women'}
         }, data_set)