Added convergence check

Strucutral codes/Kerr resonator/kerrdpt_order1.py

@@ -130,6 +130,7 @@ def get_cutoff(p, c0=5, cmax=100, step=5, precision=0.005):
             c += step
     return np.inf
 
+
 def map_cutoff(Utilderange, Δrange, Grange):
     """Create map with optimal truncations for a user-defined parameter range.
 
@@ -150,4 +151,56 @@ def map_cutoff(Utilderange, Δrange, Grange):
                 Grat=Grange[k]
                 p = Parameters(Δ=Δ, N=10, Grat=Grat, ηtilde=1, Utilde=Utilde)
                 C[i,j,k] = get_cutoff(p, c0=5, cmax=100, step=5, precision=0.005)
-    return C
\ No newline at end of file
+    return C
+
+
+
+
+
+#Previous iterations 
+"""     def get_cutoff_MK1(cuts, p, precision=0.01):
+    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)
+    q = deque()
+    
+    k = Kerr_2γ(Nfock=cuts[0], p=p)
+    q.append(0)
+    q.append(k.bd()[0])
+    
+    for i in range(1,cuts.size):
+        k = Kerr_2γ(Nfock=cuts[i], p=p)
+        ni = k.bd()[0]
+        μi = metric(ni,q.pop())
+        q.append(μi)
+        print(q[0],q[1],i)
+        if (q.popleft())*(q[0])==True : 
+            return i-1
+        q.append(ni)
+    
+    if q[0]==True:
+        print("Carefull: Convergence on border")
+        return i
+    else: return np.inf
+    
+    
+    
+    
+def get_cutoff_MK2(p, c0=5, cmax=100, step=5, precision=0.005):
+    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)
+    k = Kerr_2γ(Nfock=c0,p=p)
+    nprev = k.bd()[0]
+    c = c0+step
+    while c<cmax:
+        k = Kerr_2γ(Nfock=c,p=p)
+        n = k.bd()[0]
+        if metric(n,nprev):
+            c -= step
+            while 1:
+                k = Kerr_2γ(Nfock=c-1,p=p)
+                if metric(k.bd()[0],n): c -= 1
+                else: return c
+        else:
+            nprev = n
+            c += step
+    return np.inf """
+        
+    
\ No newline at end of file