diff --git a/GPy/kern/_src/coregionalize.py b/GPy/kern/_src/coregionalize.py
index dcafeb88..3fcf1c98 100644
--- a/GPy/kern/_src/coregionalize.py
+++ b/GPy/kern/_src/coregionalize.py
@@ -62,7 +62,7 @@ class Coregionalize(Kern):
                 return self._K_weave(X, X2)
             except:
                 print "\n Weave compilation failed. Falling back to (slower) numpy implementation\n"
-                config.set('weave', 'working', False)
+                config.set('weave', 'working', 'False')
                 return self._K_numpy(X, X2)
         else:
             return self._K_numpy(X, X2)
@@ -121,13 +121,13 @@ class Coregionalize(Kern):
         #attempt to use weave for a nasty double indexing loop: fall back to numpy
         if config.getboolean('weave', 'working'):
             try:
-                dL_dK_small = self._gradient_reduce_weave(dL_dK, index1, index2)
+                dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2)
             except:
                 print "\n Weave compilation failed. Falling back to (slower) numpy implementation\n"
-                config.set('weave', 'working', False)
-                dL_dK_small = self._gradient_reduce_weave(dL_dK, index1, index2)
+                config.set('weave', 'working', 'False')
+                dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2)
         else:
-            dL_dK_small = self._gradient_reduce_weave(dL_dK, index1, index2)
+            dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2)
 
 
 
@@ -150,19 +150,16 @@ class Coregionalize(Kern):
         N, num_inducing, output_dim = index.size, index2.size, self.output_dim
         weave.inline(code, ['N', 'num_inducing', 'output_dim', 'dL_dK', 'dL_dK_small', 'index', 'index2'])
         return dL_dK_small
-1
+
     def _gradient_reduce_numpy(self, dL_dK, index, index2):
         index, index2 = index[:,0], index2[:,0]
-        for i in range(k.output_dim):
         dL_dK_small = np.zeros_like(self.B)
+        for i in range(k.output_dim):
             tmp1 = dL_dK[index==i]
             for j in range(k.output_dim):
                 dL_dK_small[j,i] = tmp1[:,index2==j].sum()
         return dL_dK_small
 
-
-
-
     def update_gradients_diag(self, dL_dKdiag, X):
         index = np.asarray(X, dtype=np.int).flatten()
         dL_dKdiag_small = np.array([dL_dKdiag[index==i].sum() for i in xrange(self.output_dim)])
diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py
index cde024f6..dcfb9de4 100644
--- a/GPy/testing/kernel_tests.py
+++ b/GPy/testing/kernel_tests.py
@@ -360,43 +360,44 @@ class Coregionalize_weave_test(unittest.TestCase):
     """
     Make sure that the coregionalize kernel work with and without weave enabled
     """
-    k = GPy.kern.coregionalize(1, output_dim=12)
-    N1, N2 = 100, 200
-    X = np.random.randint(0,12,(N1,1))
-    X2 = np.random.randint(0,12,(N2,1))
+    def setUp(self):
+        self.k = GPy.kern.Coregionalize(1, output_dim=12)
+        self.N1, self.N2 = 100, 200
+        self.X = np.random.randint(0,12,(self.N1,1))
+        self.X2 = np.random.randint(0,12,(self.N2,1))
 
-    #symmetric case
-    dL_dK = np.random.randn(N1, N1)
-    GPy.util.config.config.set('weave', 'working', True)
-    K_weave = k.K(X)
-    k.update_gradients_full(dL_dK, X)
-    grads_weave = k.gradient.copy()
+    def test_sym(self):
+        dL_dK = np.random.randn(self.N1, self.N1)
+        GPy.util.config.config.set('weave', 'working', 'True')
+        K_weave = self.k.K(self.X)
+        self.k.update_gradients_full(dL_dK, self.X)
+        grads_weave = self.k.gradient.copy()
 
-    GPy.util.config.config.set('weave', 'working', False)
-    K_numpy = k.K(X)
-    k.update_gradients_full(dL_dK, X)
-    grads_numpy = k.gradient.copy()
+        GPy.util.config.config.set('weave', 'working', 'False')
+        K_numpy = self.k.K(self.X)
+        self.k.update_gradients_full(dL_dK, self.X)
+        grads_numpy = self.k.gradient.copy()
 
-    self.assertTrue(np.allclose(K_numpy, K_weave))
-    self.assertTrue(np.allclose(grads_numpy, grads_weave))
+        self.assertTrue(np.allclose(K_numpy, K_weave))
+        self.assertTrue(np.allclose(grads_numpy, grads_weave))
 
-    #non-symmetric case
-    dL_dK = np.random.randn(N1, N2)
-    GPy.util.config.config.set('weave', 'working', True)
-    K_weave = k.K(X, X2)
-    k.update_gradients_full(dL_dK, X, X2)
-    grads_weave = k.gradient.copy()
+    def test_nonsym(self):
+        dL_dK = np.random.randn(self.N1, self.N2)
+        GPy.util.config.config.set('weave', 'working', 'True')
+        K_weave = self.k.K(self.X, self.X2)
+        self.k.update_gradients_full(dL_dK, self.X, self.X2)
+        grads_weave = self.k.gradient.copy()
 
-    GPy.util.config.config.set('weave', 'working', False)
-    K_numpy = k.K(X, X2)
-    k.update_gradients_full(dL_dK, X, X2)
-    grads_numpy = k.gradient.copy()
+        GPy.util.config.config.set('weave', 'working', 'False')
+        K_numpy = self.k.K(self.X, self.X2)
+        self.k.update_gradients_full(dL_dK, self.X, self.X2)
+        grads_numpy = self.k.gradient.copy()
 
-    self.assertTrue(np.allclose(K_numpy, K_weave))
-    self.assertTrue(np.allclose(grads_numpy, grads_weave))
+        self.assertTrue(np.allclose(K_numpy, K_weave))
+        self.assertTrue(np.allclose(grads_numpy, grads_weave))
 
     #reset the weave state for any other tests
-    GPy.util.config.config.set('weave', 'working', False)
+    GPy.util.config.config.set('weave', 'working', 'False')