Partial update to use the code in python 3

.gitignore

@@ -0,0 +1,6 @@
+.idea/**
+__pycache__
+venv/
+*.csv
+*.pdf
+*.jpg

AAC_KAGM_SingleLoop_file.py

@@ -73,7 +73,7 @@ def AAC_KAGM_SingleLoop(R_data, Tau_K, N, Parameters, Dt, dTau, KappaP_Constrain
     F = numpy.matrix(scipy.linalg.expm(-KappaP*Dt))
     D, V = numpy.linalg.eig(F)
     if (numpy.any(numpy.abs(D)>1.0001)):
-        print 'SingleLoop:argChk   abs(eig(F))>1'
+        print('SingleLoop:argChk   abs(eig(F))>1')
         exit()
 
     Q = numpy.matrix([[G(2*d1,Dt), G(d1+d2,Dt)], [0, G(2*d2,Dt)]])
@@ -116,8 +116,8 @@ def AAC_KAGM_SingleLoop(R_data, Tau_K, N, Parameters, Dt, dTau, KappaP_Constrain
         # Observations for time t.
         y_Obs = numpy.matrix(0.01 * R_data[t,:]).getH()
         y_Missing = numpy.squeeze(numpy.array(numpy.isnan(y_Obs))) #numpy.array(numpy.isnan(y_Obs))#[:,0]
-        y_Obs = y_Obs[-y_Missing]
-        R = numpy.diag(numpy.power(Sigma_Nu[-y_Missing], 2))
+        y_Obs = y_Obs[~y_Missing]
+        R = numpy.diag(numpy.power(Sigma_Nu[~y_Missing], 2))
 
         x_Plus_i_Minus_1 = numpy.copy(x_Minus)
         x_Plus_i0 = numpy.copy(x_Minus)
@@ -131,18 +131,18 @@ def AAC_KAGM_SingleLoop(R_data, Tau_K, N, Parameters, Dt, dTau, KappaP_Constrain
 
             (y_Hat, H_i) = AAD_KAGM_R_and_dR_dx(x_Plus_i0, rL, KappaQ2, Sigma1, Sigma2, Rho12, Tau_K, dTau, ZLB_Imposed)
 
-            y_Hat = y_Hat[-y_Missing]
-            H_i = H_i[-y_Missing,:]
+            y_Hat = y_Hat[~y_Missing]
+            H_i = H_i[~y_Missing,:]
             HPHR_i = (H_i * P_Minus * H_i.getH() + R)
             K_i = numpy.linalg.solve(HPHR_i.getH(), (P_Minus*H_i.getH()).getH()).getH()
             w_i = y_Obs - y_Hat - H_i * (x_Minus - x_Plus_i0)
             x_Plus_i1 = x_Minus + K_i * w_i
 
             if (IEKF_Count == 20):
-                # Using tolerance, so check for convergence. 
+                # Using tolerance, so check for convergence.
                 if (i > 15):
                     # Large number of iterations, so print output to screen.
-                    print t, i-1, numpy.matrix(x_Plus_i1).getH(), numpy.matrix(x_Plus_i0).getH(), numpy.matrix(x_Plus_i1).getH() - numpy.matrix(x_Plus_i0).getH()
+                    print(t, i-1, numpy.matrix(x_Plus_i1).getH(), numpy.matrix(x_Plus_i0).getH(), numpy.matrix(x_Plus_i1).getH() - numpy.matrix(x_Plus_i0).getH())
                 if (numpy.all(numpy.abs(x_Plus_i1-x_Plus_i0)<x_Tolerance)):
                     # Difference from last update within tolerance, so exit.
                     break
@@ -161,9 +161,11 @@ def AAC_KAGM_SingleLoop(R_data, Tau_K, N, Parameters, Dt, dTau, KappaP_Constrain
         P_Plus = (numpy.matrix(numpy.eye(N)) - K_i * H_i) * P_Minus
         x_T[:,t] = x_Plus[:,0]
         P_T[:,:,t] = numpy.copy(P_Plus)
-        logL = logL + numpy.log(numpy.linalg.det(HPHR_i)) + numpy.linalg.solve(HPHR_i.getH(),w_i).getH() * w_i
+        logL = logL + numpy.log(numpy.linalg.det(HPHR_i), where=numpy.linalg.det(HPHR_i)>0) + numpy.linalg.solve(HPHR_i.getH(),w_i).getH() * w_i
 
         # Hold IEKF count.
+        if Max_IEKF_Count is None:
+            Max_IEKF_Count = IEKF_Count
         if (i-1 > Max_IEKF_Count):
             Max_IEKF_Count = i - 1
             Max_IEKF_Point = t
@@ -182,7 +184,7 @@ def AAC_KAGM_SingleLoop(R_data, Tau_K, N, Parameters, Dt, dTau, KappaP_Constrain
     # Negate the log likelihood value because fminunc minimizes.
     EKF_logL = -EKF_logL
 
-    print EKF_logL*1e-3, Parameters[0:10], Rho12
+    print(EKF_logL*1e-3, Parameters[0:10], Rho12)
 
     tmp1 = numpy.sum(x_T, axis=0)
     figure = pyplot.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w')
@@ -193,7 +195,4 @@ def AAC_KAGM_SingleLoop(R_data, Tau_K, N, Parameters, Dt, dTau, KappaP_Constrain
     pyplot.savefig("plot.pdf")
     pyplot.show()
 
-    return (EKF_logL,x_T)
-
-
-
+    return (EKF_logL,x_T)

AAE_KAGM_f_and_df_dx_file.py

@@ -33,7 +33,7 @@ def AAE_KAGM_f_and_df_dx(x_t, rL, KappaQ2, Sigma1, Sigma2, Rho12, TauGrid, ZLB_I
         Omega = numpy.sqrt(Sigma1**2 * G_11 + Sigma2**2 * G_22 + 2 * Rho12 * Sigma1 * Sigma2 * G_12)
 
         # Calculate cumulative normal probabilities for N(0,1) distribution.
-        d = numpy.divide(GATSM_f-rL, Omega)
+        d = numpy.divide(GATSM_f-rL, Omega,where=Omega!=0)
         normsdist_erf_d = normsdist_erf(d)
 
         # Calculate gradiant and CAB_GATSM_f

data_read.py

@@ -8,8 +8,8 @@
 import os
 
 def write_to_csv(idx,idx1,val,val1,sub_datenum,name):
-    daily_values_data=map(list,zip(*val))
-    daily_values_data1=map(list,zip(*val1))
+    daily_values_data=list(map(list,list(zip(*val))))
+    daily_values_data1=list(map(list,list(zip(*val1))))
 
     daily_date_index=list(idx)
     daily_date_index[:] = [x - sub_datenum for x in idx]
@@ -22,19 +22,19 @@ def write_to_csv(idx,idx1,val,val1,sub_datenum,name):
     for i in range(len(daily_values_data1)):
         daily_values_data1[i].insert(0,daily_date_index1[i])
 
-    with open(name+'.csv', 'wb') as csvfile:
+    with open(name+'.csv', 'w') as csvfile:
         spamwriter = csv.writer(csvfile, delimiter=',',quotechar='|', quoting=csv.QUOTE_NONNUMERIC)
         spamwriter.writerows(daily_values_data1)
         spamwriter.writerows(daily_values_data)
 
 def filter_data_index(a,thr,method):
-    if method is 'ge':
+    if method =='ge':
         index=list([idx for idx,i in enumerate(a) if i<thr])
-    elif method is 'le':
+    elif method =='le':
         index=list([idx for idx,i in enumerate(a) if i>thr])
-    elif method is 'l':
+    elif method =='l':
         index=list([idx for idx,i in enumerate(a) if i>=thr])
-    elif method is 'g':
+    elif method =='g':
         index=list([idx for idx,i in enumerate(a) if i<=thr])
     return index
 
@@ -62,16 +62,16 @@ def filter_data(a,index,dim):
 CurveType='OIS'
 PathName=os.getcwd()
 ExcelName=os.path.join(PathName, 'A_'+Country+'_All_Data_Bloomberg.xlsx')
-if CurveType is 'OIS':
+if CurveType =='OIS':
     ExcelSheetName='D. Live OIS data'
-if CurveType is 'Govt':
+if CurveType =='Govt':
     ExcelSheetName='D. Live Govt data';
 
 Maturities=[0.25,0.5,1,2,3,4,5,7,10,15,20,30]
 
 wb=openpyxl.load_workbook(ExcelName)
-sheet_names=wb.get_sheet_names()
-sheet=wb.get_sheet_by_name('D. Live OIS data')
+sheet_names=wb.get_sheet_names
+sheet=wb['D. Live OIS data']
 datenum = [[] for i in range(len(datelist))]
 values_data=[[] for i in range(len(datelist))]
 for col_num in range(len(datelist)):
@@ -97,15 +97,15 @@ def filter_data(a,index,dim):
     diff_dates=list(set(common_datenum) ^ set(datenum[i]))
     for j in diff_dates:
         index=datenum[i].index(j)
-        print(str(j)+' date num at '+str(index)+'removed') 
+        print((str(j)+' date num at '+str(index)+'removed')) 
         value_to_remove = values_data[i][index]
         values_data[i].remove(value_to_remove)
 
-ref_friday=datetime.date(2013,05,24).toordinal()
+ref_friday=datetime.date(2013,0o5,24).toordinal()
 weeks_to_step_back = (ref_friday - common_datenum[0])/7
 first_friday = ref_friday - (weeks_to_step_back*7)
 
-week_date_index = range(first_friday,common_datenum[-1],7)
+week_date_index = list(range(int(first_friday),common_datenum[-1],7))
 
 week_values_data=[[] for i in range(len(datelist))]
 for i in range(len(datelist)):
@@ -139,7 +139,7 @@ def filter_data(a,index,dim):
 
 
 #GOVT
-if Country is 'EA':
+if Country =='EA':
     wbG=openpyxl.load_workbook(os.path.join(PathName, 'A_GE_All_Data_Bloomberg.xlsx'))
     sheet=wbG.get_sheet_by_name('D. Live Govt data')
     datenumG = [[] for i in range(len(datelist))]
@@ -168,7 +168,7 @@ def filter_data(a,index,dim):
         diff_dates=list(set(common_datenumG) ^ set(datenumG[i]))
         for j in diff_dates:
             index=datenumG[i].index(j)
-            print(str(j)+' date num at '+str(index)+'removed') 
+            print((str(j)+' date num at '+str(index)+'removed')) 
             value_to_remove = values_dataG[i][index]
             values_dataG[i].remove(value_to_remove)
 
@@ -210,7 +210,7 @@ def filter_data(a,index,dim):
         diff_dates=list(set(common_datenumF) ^ set(datenumF[i]))
         for j in diff_dates:
             index=datenumG[i].index(j)
-            print(str(j)+' date num at '+str(index)+'removed') 
+            print((str(j)+' date num at '+str(index)+'removed')) 
             value_to_remove = values_dataF[i][index]
             values_dataF[i].remove(value_to_remove)
 
@@ -227,14 +227,14 @@ def filter_data(a,index,dim):
         diff_dates=list(set(post_inter_index) ^ set(post_euro_fr_index))
         for j in diff_dates:
             index=post_euro_fr_index.index(j)
-            print(str(j)+' date num at '+str(index)+'removed') 
+            print((str(j)+' date num at '+str(index)+'removed')) 
             value_to_remove = post_euro_fr_vlues[i][index]
             post_euro_fr_vlues[i].remove(value_to_remove)
     for i in range(len(post_euro_ge_vlues)):
         diff_dates=list(set(post_inter_index) ^ set(post_euro_ge_index))
         for j in diff_dates:
             index=post_euro_ge_index.index(j)
-            print(str(j)+' date num at '+str(index)+'removed') 
+            print((str(j)+' date num at '+str(index)+'removed')) 
             value_to_remove = post_euro_ge_vlues[i][index]
             post_euro_ge_vlues[i].remove(value_to_remove)
 
@@ -246,7 +246,7 @@ def filter_data(a,index,dim):
         values_data1[i] = pre_euro_ge_vlues[i] + post_euro_avg_values
 
 else:
-    sheet=wb.get_sheet_by_name('D. Live Govt data')
+    sheet=wb['D. Live Govt data']
     datenum1 = [[] for i in range(len(datelist))]
     values_data1=[[] for i in range(len(datelist))]
     for col_num in range(len(datelist)):
@@ -273,15 +273,15 @@ def filter_data(a,index,dim):
         diff_dates=list(set(common_datenum1) ^ set(datenum1[i]))
         for j in diff_dates:
             index=datenum1[i].index(j)
-            print(str(j)+' date num at '+str(index)+'removed') 
+            print((str(j)+' date num at '+str(index)+'removed')) 
             value_to_remove = values_data1[i][index]
             values_data1[i].remove(value_to_remove)
 
-ref_friday=datetime.date(2013,05,24).toordinal()
+ref_friday=datetime.date(2013,0o5,24).toordinal()
 weeks_to_step_back = (ref_friday - common_datenum1[0])/7
 first_friday = ref_friday - (weeks_to_step_back*7)
 
-week_date_index1 = range(first_friday,common_datenum1[-1],7)
+week_date_index1 = list(range(int(first_friday),common_datenum1[-1],7))
 week_values_data1=[[] for i in range(len(datelist))]
 for i in range(len(datelist)):
     f=interpolate.interp1d(common_datenum1,values_data1[i],'zero',bounds_error=False)

functions.py

@@ -1,15 +1,15 @@
 import datetime
 import numpy
 
+
 #######################################################################################################################
 def datetime_to_matlab_datenum(dt):
     return 366 + 1 + (dt - datetime.datetime(1, 1, 1, 0, 0)).total_seconds() / datetime.timedelta(1).total_seconds()
 
+
 #######################################################################################################################
 def matlab_datenum_to_datetime(dn):
     # Convert a datetime object to its MATLAB datenum() equivalent
-    return datetime.datetime(1,1,1,0,0) + datetime.timedelta(dn-366-1)
+    return datetime.datetime(1, 1, 1, 0, 0) + datetime.timedelta(int(dn - 366 - 1))
 
 #######################################################################################################################
-
-