diff --git a/SimPEG/Tests/TestUtils.py b/SimPEG/Tests/TestUtils.py
index da18f537..c4d11443 100644
--- a/SimPEG/Tests/TestUtils.py
+++ b/SimPEG/Tests/TestUtils.py
@@ -220,35 +220,44 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None, expectedOrder=2, tole
     """
 
     print "%s checkDerivative %s" % ('='*20, '='*20)
-    print "iter\th\t\t|J0-Jt|\t\t|J0+h*dJ'*dx-Jt|\tOrder\n%s" % ('-'*57)
+    print "iter    h         |f0-ft|   |f0-ft-h*J0*dx|  Order\n%s" % ('-'*57)
 
-    Jc = fctn(x0)
+    f0, J0 = fctn(x0)
 
     x0 = mkvc(x0)
 
     if dx is None:
         dx = np.random.randn(len(x0))
 
-    t  = np.logspace(-1, -num, num)
-    E0 = np.ones(t.shape)
-    E1 = np.ones(t.shape)
+    h  = np.logspace(-1, -num, num)
+    E0 = np.ones(h.shape)
+    E1 = np.ones(h.shape)
+
+    def l2norm(x):
+        # because np.norm breaks if they are scalars?
+        return np.sqrt(np.real(np.vdot(x, x)))
 
-    l2norm = lambda x: np.sqrt(np.inner(x, x))  # because np.norm breaks if they are scalars?
     for i in range(num):
-        Jt = fctn(x0+t[i]*dx)
-        E0[i] = l2norm(Jt[0]-Jc[0])               # 0th order Taylor
-        if inspect.isfunction(Jc[1]):
-            E1[i] = l2norm(Jt[0]-Jc[0]-t[i]*Jc[1](dx))  # 1st order Taylor
+        # Evaluate at test point
+        ft, Jt = fctn( x0 + h[i]*dx )
+        # 0th order Taylor
+        E0[i] = l2norm( ft - f0 )
+        # 1st order Taylor
+        if inspect.isfunction(J0):
+            E1[i] = l2norm( ft - f0 - h[i]*J0(dx) )
         else:
             # We assume it is a numpy.ndarray
-            E1[i] = l2norm(Jt[0]-Jc[0]-t[i]*Jc[1].dot(dx))  # 1st order Taylor
+            E1[i] = l2norm( ft - f0 - h[i]*J0.dot(dx) )
+
         order0 = np.log10(E0[:-1]/E0[1:])
         order1 = np.log10(E1[:-1]/E1[1:])
-        print "%d\t%1.2e\t%1.3e\t\t%1.3e\t\t%1.3f" % (i, t[i], E0[i], E1[i], np.nan if i == 0 else order1[i-1])
+        print " %d   %1.2e    %1.3e     %1.3e      %1.3f" % (i, h[i], E0[i], E1[i], np.nan if i == 0 else order1[i-1])
 
+    # Ensure we are about precision
     order0 = order0[E0[1:] > eps]
     order1 = order1[E1[1:] > eps]
     belowTol = order1.size == 0 and order0.size > 0
+    # Make sure we get the correct order
     correctOrder = order1.size > 0 and np.mean(order1) > tolerance * expectedOrder
 
     passTest = belowTol or correctOrder
@@ -264,8 +273,8 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None, expectedOrder=2, tole
     if plotIt:
         plt.figure()
         plt.clf()
-        plt.loglog(t, E0, 'b')
-        plt.loglog(t, E1, 'g--')
+        plt.loglog(h, E0, 'b')
+        plt.loglog(h, E1, 'g--')
         plt.title('checkDerivative')
         plt.xlabel('h')
         plt.ylabel('error of Taylor approximation')