diff --git a/circle.yml b/circle.yml
index 5e0328d116a..6f6451163f1 100644
--- a/circle.yml
+++ b/circle.yml
@@ -37,8 +37,8 @@ dependencies:
       else
         echo "Conda already set up.";
       fi
-    - conda install -n circleenv --yes numpy scipy scikit-learn matplotlib sphinx=1.4.1 pillow six IPython pandas apptools=4.4.0 envisage=4.5.1 pyface=5.1.0 traits=4.6.0 traitsui=5.1.0 vtk=5.10.1;
-    - pip install mayavi==4.4.4;
+    # Let's use old Mayavi because 4.5.0 (as of 2016/12/20) breaks plot_mne_inverse_coherence_epochs.py
+    - conda install -n circleenv --yes numpy scipy scikit-learn matplotlib sphinx pillow six IPython pandas mayavi vtk;
     - ls -al /home/ubuntu/miniconda;
     - ls -al /home/ubuntu/miniconda/bin;
     - echo $PATH;
diff --git a/mne/viz/misc.py b/mne/viz/misc.py
index 084e310a8e9..cb80ae4953d 100644
--- a/mne/viz/misc.py
+++ b/mne/viz/misc.py
@@ -108,28 +108,25 @@ def plot_cov(cov, info, exclude=[], colorbar=True, proj=False, show_svd=True,
 
     import matplotlib.pyplot as plt
 
-    fig_cov, axes = plt.subplots(1, len(idx_names),
+    fig_cov, axes = plt.subplots(1, len(idx_names), squeeze=False,
                                  figsize=(2.5 * len(idx_names), 2.7))
-    if isinstance(axes, plt.Axes):
-        axes = [axes]
     for k, (idx, name, _, _) in enumerate(idx_names):
-        axes[k].imshow(C[idx][:, idx], interpolation="nearest", cmap='RdBu_r')
-        axes[k].set(title=name)
+        axes[0, k].imshow(C[idx][:, idx], interpolation="nearest",
+                          cmap='RdBu_r')
+        axes[0, k].set(title=name)
     fig_cov.subplots_adjust(0.04, 0.0, 0.98, 0.94, 0.2, 0.26)
     tight_layout(fig=fig_cov)
 
     fig_svd = None
     if show_svd:
-        fig_svd, axes = plt.subplots(1, len(idx_names))
-        if isinstance(axes, plt.Axes):
-            axes = [axes]
+        fig_svd, axes = plt.subplots(1, len(idx_names), squeeze=False)
         for k, (idx, name, unit, scaling) in enumerate(idx_names):
             s = linalg.svd(C[idx][:, idx], compute_uv=False)
             # Protect against true zero singular values
             s[s <= 0] = 1e-10 * s[s > 0].min()
-            axes[k].semilogy(np.sqrt(s) * scaling)
-            axes[k].set(ylabel='Noise std (%s)' % unit,
-                        xlabel='Eigenvalue index', title=name)
+            axes[0, k].semilogy(np.sqrt(s) * scaling)
+            axes[0, k].set(ylabel='Noise std (%s)' % unit,
+                           xlabel='Eigenvalue index', title=name)
         tight_layout(fig=fig_svd)
 
     plt_show(show)
diff --git a/tutorials/plot_brainstorm_auditory.py b/tutorials/plot_brainstorm_auditory.py
index 7e1cfb365ab..07ef7f19bf4 100644
--- a/tutorials/plot_brainstorm_auditory.py
+++ b/tutorials/plot_brainstorm_auditory.py
@@ -10,6 +10,7 @@
     http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory
 
 Experiment:
+
     - One subject, 2 acquisition runs 6 minutes each.
     - Each run contains 200 regular beeps and 40 easy deviant beeps.
     - Random ISI: between 0.7s and 1.7s seconds, uniformly distributed.