diff --git a/GPy/kern/Matern52.py b/GPy/kern/Matern52.py
index 377526d5..9338db15 100644
--- a/GPy/kern/Matern52.py
+++ b/GPy/kern/Matern52.py
@@ -90,7 +90,7 @@ class Matern52(kernpart):
         else:
             dl = (self.variance * 5./3 * dist * (1 + np.sqrt(5.)*dist ) * np.exp(-np.sqrt(5.)*dist)) * dist2M.sum(-1)*invdist
             #dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist)) * dist2M.sum(-1)*invdist
-            target[1] += np.sum(dl*dL_dKdiag)
+            target[1] += np.sum(dl*dL_dK)
 
     def dKdiag_dtheta(self,dL_dKdiag,X,target):
         """derivative of the diagonal of the covariance matrix with respect to the parameters."""
diff --git a/GPy/likelihoods/EP.py b/GPy/likelihoods/EP.py
index efd887ae..30b21d9b 100644
--- a/GPy/likelihoods/EP.py
+++ b/GPy/likelihoods/EP.py
@@ -114,7 +114,7 @@ class EP(likelihood):
             Sroot_tilde_K = np.sqrt(self.tau_tilde)[:,None]*K
             B = np.eye(self.N) + np.sqrt(self.tau_tilde)[None,:]*Sroot_tilde_K
             L = jitchol(B)
-            V,info = linalg.flapack.dtrtrs(L,Sroot_tilde_K,lower=1)
+            V,info = linalg.lapack.flapack.dtrtrs(L,Sroot_tilde_K,lower=1)
             Sigma = K - np.dot(V.T,V)
             mu = np.dot(Sigma,self.v_tilde)
             epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N
@@ -190,7 +190,7 @@ class EP(likelihood):
                 #Posterior distribution parameters update
                 LLT = LLT + np.outer(Kmn[:,i],Kmn[:,i])*Delta_tau
                 L = jitchol(LLT)
-                V,info = linalg.flapack.dtrtrs(L,Kmn,lower=1)
+                V,info = linalg.lapack.flapack.dtrtrs(L,Kmn,lower=1)
                 Sigma_diag = np.sum(V*V,-2)
                 si = np.sum(V.T*V[:,i],-1)
                 mu = mu + (Delta_v-Delta_tau*mu[i])*si
@@ -198,8 +198,8 @@ class EP(likelihood):
             #Sigma recomputation with Cholesky decompositon
             LLT0 = LLT0 + np.dot(Kmn*tau_tilde[None,:],Kmn.T)
             L = jitchol(LLT)
-            V,info = linalg.flapack.dtrtrs(L,Kmn,lower=1)
-            V2,info = linalg.flapack.dtrtrs(L.T,V,lower=0)
+            V,info = linalg.lapack.flapack.dtrtrs(L,Kmn,lower=1)
+            V2,info = linalg.lapack.flapack.dtrtrs(L.T,V,lower=0)
             Sigma_diag = np.sum(V*V,-2)
             Knmv_tilde = np.dot(Kmn,v_tilde)
             mu = np.dot(V2.T,Knmv_tilde)
@@ -297,7 +297,7 @@ class EP(likelihood):
             P = (Diag / Diag0)[:,None] * P0
             RPT0 = np.dot(R0,P0.T)
             L = jitchol(np.eye(self.M) + np.dot(RPT0,(1./Diag0 - Diag/(Diag0**2))[:,None]*RPT0.T))
-            R,info = linalg.flapack.dtrtrs(L,R0,lower=1)
+            R,info = linalg.lapack.flapack.dtrtrs(L,R0,lower=1)
             RPT = np.dot(R,P.T)
             Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)
             self.w = Diag * self.v_tilde
diff --git a/GPy/util/linalg.py b/GPy/util/linalg.py
index 7414eb29..26105789 100644
--- a/GPy/util/linalg.py
+++ b/GPy/util/linalg.py
@@ -11,7 +11,7 @@ import re
 import pdb
 import cPickle
 import types
-import scipy.lib.lapack.flapack
+#import scipy.lib.lapack.flapack
 import scipy as sp
 
 def mdot(*args):
@@ -101,7 +101,7 @@ def chol_inv(L):
 
     """
 
-    return linalg.flapack.dtrtri(L, lower = True)[0]
+    return linalg.lapack.flapack.dtrtri(L, lower = True)[0]
 
 
 def multiple_pdinv(A):
@@ -118,7 +118,7 @@ def multiple_pdinv(A):
     N = A.shape[-1]
     chols = [jitchol(A[:,:,i]) for i in range(N)]
     halflogdets = [np.sum(np.log(np.diag(L[0]))) for L in chols]
-    invs = [linalg.flapack.dpotri(L[0],True)[0] for L in chols]
+    invs = [linalg.lapack.flapack.dpotri(L[0],True)[0] for L in chols]
     invs = [np.triu(I)+np.triu(I,1).T for I in invs]
     return np.dstack(invs),np.array(halflogdets)