diff --git a/pyKriging/coKriging.py b/pyKriging/coKriging.py
index 3f98506..f4120bd 100644
--- a/pyKriging/coKriging.py
+++ b/pyKriging/coKriging.py
@@ -111,9 +111,9 @@ def distanceXcXe(self):
 
 
     def updatePsi(self):
-        self.PsicXc = np.zeros((self.nc,self.nc), dtype=np.float)
-        self.PsicXe = np.zeros((self.ne,self.ne), dtype=np.float)
-        self.PsicXcXe = np.zeros((self.nc,self.ne), dtype=np.float)
+        self.PsicXc = np.zeros((self.nc,self.nc), dtype=float)
+        self.PsicXe = np.zeros((self.ne,self.ne), dtype=float)
+        self.PsicXcXe = np.zeros((self.nc,self.ne), dtype=float)
         #
         # print self.thetac
         # print self.pc
diff --git a/pyKriging/krige.py b/pyKriging/krige.py
index ead9883..079da05 100644
--- a/pyKriging/krige.py
+++ b/pyKriging/krige.py
@@ -208,7 +208,7 @@ def expimp(self, x):
         y_min = np.min(self.y)
         if S <= 0.:
             EI = 0.
-        elif S > 0.:
+        else:
             EI_one = ((y_min - self.predict_normalized(x)) * (0.5 + 0.5*m.erf((
                       1./np.sqrt(2.))*((y_min - self.predict_normalized(x)) /
                                        S))))
@@ -223,7 +223,7 @@ def weightedexpimp(self, x, w):
         y_min = np.min(self.y)
         if S <= 0.:
             EI = 0.
-        elif S > 0.:
+        else:
             EI_one = w*((y_min - self.predict_normalized(x)) * (0.5 +
                         0.5*m.erf((1./np.sqrt(2.))*((y_min -
                                   self.predict_normalized(x)) / S))))
diff --git a/pyKriging/matrixops.py b/pyKriging/matrixops.py
index 2cdb5c4..df75258 100644
--- a/pyKriging/matrixops.py
+++ b/pyKriging/matrixops.py
@@ -6,7 +6,7 @@ class matrixops():
 
     def __init__(self):
         self.LnDetPsi = None
-        self.Psi = np.zeros((self.n,self.n), dtype=np.float)
+        self.Psi = np.zeros((self.n,self.n), dtype=float)
         self.psi = np.zeros((self.n,1))
         self.one = np.ones(self.n)
         self.mu = None
@@ -22,7 +22,7 @@ def updateData(self):
                 self.distance[i,j]= np.abs((self.X[i]-self.X[j]))
 
     def updatePsi(self):
-        self.Psi = np.zeros((self.n,self.n), dtype=np.float)
+        self.Psi = np.zeros((self.n,self.n), dtype=float)
         self.one = np.ones(self.n)
         self.psi = np.zeros((self.n,1))
         newPsi = np.exp(-np.sum(self.theta*np.power(self.distance,self.pl), axis=2))
@@ -32,7 +32,7 @@ def updatePsi(self):
         self.U = self.U.T
 
     def regupdatePsi(self):
-        self.Psi = np.zeros((self.n,self.n), dtype=np.float)
+        self.Psi = np.zeros((self.n,self.n), dtype=float)
         self.one = np.ones(self.n)
         self.psi = np.zeros((self.n,1))
         newPsi = np.exp(-np.sum(self.theta*np.power(self.distance,self.pl), axis=2))
@@ -107,4 +107,4 @@ def regression_predicterr_normalized(self,x):
             pass
 
         SSqr = np.abs(SSqr[0])
-        return np.power(SSqr,0.5)[0]
\ No newline at end of file
+        return np.power(SSqr,0.5)[0]