cfiber memory, power

pypho_cfiber.py

@@ -67,7 +67,7 @@ class pypho_cfiber(object):
 
         self.set(fibertype, D, S, gamma, alpha, l, phi_max)
 
-        self.E = E
+        #self.E = E
 
         self.gamma_intern = self.gamma * 1e-3
         self.max_step = 200
@@ -75,10 +75,32 @@ class pypho_cfiber(object):
         z = 0
 
         #for Ei in E:
-        self.fibtrans(z)
+        #self.fibtrans(z)
         #z += 1
+        
+        print('Start!')  
+
+        tic0 = time.time()   
+        n = self.glova.sps*self.glova.nos
+        #Ef_out = np.zeros((1,n)) + 1j*np.ones((1,n))
+        Ex_out = np.zeros(n) + 1j*np.ones(n)
+        Ey_out = np.zeros(n) + 1j*np.ones(n)
+        birefarraydoubles = np.zeros((len(self.birefarray), 3))
+        for i in range (0, len(self.birefarray)):
+            birefarraydoubles[i,0] = self.birefarray[i].angle
+            birefarraydoubles[i,1] = self.birefarray[i].z_point
+            birefarraydoubles[i,2] = self.birefarray[i].delta_beta
+            
+        cyfiber(self.glova.sps*self.glova.nos, self.l, np.asarray(E[z]['E'][0]), np.asarray(E[z]['E'][1]),
+                self.alpha, self.gamma_intern, self.phi_max, birefarraydoubles, len(self.birefarray), self.max_step, self.beta_fac,
+                np.asarray(1j * scipy.fftpack.fftshift( self.Domega )), Ex_out, Ey_out)
+           
+        E[z]['E'][0] = Ex_out
+        E[z]['E'][1] = Ey_out
+ 
+        print ('Fertig: ', time.time() - tic0 )        
 
-        return self.E
+        return E
 
 
  ########################################################################

speedfiber.pyx

@@ -170,11 +170,15 @@ def cyfiber(int noofsamples,float l, complex[:] E_x, complex[:] E_y, float alpha
             #print (lc, zleff[zleff_i], zleff_i, sqrt(exp(-alpha * next_l)))
             if lc >= zleff[zleff_i] :
                 zleff_i +=1
-                        
-            powmean_x = np.mean( Elx[i][0]*Elx[i][0] + Elx[i][1]*Elx[i][1] )
-            powmean_y = np.mean( Ely[i][0]*Ely[i][0] + Ely[i][1]*Ely[i][1] )
+            
+            powmean_x = 0
+            powmean_y = 0
+            for i in range(noofsamples):
+                powmean_x += Elx[i][0]*Elx[i][0] + Elx[i][1]*Elx[i][1]
+                powmean_y += Ely[i][0]*Ely[i][0] + Ely[i][1]*Ely[i][1]            
+            powmean_x /= noofsamples
+            powmean_y /= noofsamples
             powmax_x = np.mean([powmean_x, powmean_y])
-            #print (powmax_x) 
             next_leff = phi_max / powmax_x
             
             if next_leff*alpha < 1.0 and alpha > 0.0: