errors rectified in R7 and R17

CHANGELOG.md

@@ -19,11 +19,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.6.23] - 2022-03-31
+
+### Changed
+
++ errors rectified in R17_Run_Routines1.py and R7_Offset_Correction.py
+
 ## [0.6.22] - 2022-03-30
 
 ### Changed
 
-+ updated R17_Run_Routines1.py and R7_Offset_Correction.py for differential doppler
++ updated R17_Plot_profiles.py, R17_Run_Routines1.py and
+  R7_Offset_Correction.py for differential doppler
 
 ## [0.6.21] - 2022-03-29
 

pyproject.toml

 [tool.poetry]
 name = "radiocc"
-version = "0.6.22"
+version = "0.6.23"
 description = "Radio occultations"
 license = "Apache-2.0"
 authors = ["Ananya Krishnan <ananyakrishnaniiserk@gmail.com>", "Greg Henry <gregoire.henry@oma.be>"]

radiocc/old/R17_Run_Routines1.py

@@ -202,30 +202,30 @@ def run(
     if Bande == "S":
         if radiocc.cfg.use_diff_doppler:
             Doppler_debias_dn_iono_s = Diff_Doppler_debias_dn_iono_s
-            if item == "IONO":
-                # R8: bending angle & impact parameter up
-                (
-                    imp_param_up,
-                    bend_ang_up,
-                    delta_r_up,
-                    beta_r_up,
-                ) = R9_BendAng_ImpParam_up.Bend_up(
-                    N_data,
-                    r_MEX_up,
-                    z_MEX_up,
-                    z_GS_up,
-                    vr_MEX_up,
-                    vz_MEX_up,
-                    vr_GS_up,
-                    vz_GS_up,
-                    gamma_up,
-                    beta_e_up,
-                    delta_s_up,
-                    Doppler_debias_up_iono_s,
-                    fsup,
-                    c,
-                    DATA_TYPE,
-                )
+        if item == "IONO":
+            # R8: bending angle & impact parameter up
+            (
+                imp_param_up,
+                bend_ang_up,
+                delta_r_up,
+                beta_r_up,
+            ) = R9_BendAng_ImpParam_up.Bend_up(
+                N_data,
+                r_MEX_up,
+                z_MEX_up,
+                z_GS_up,
+                vr_MEX_up,
+                vz_MEX_up,
+                vr_GS_up,
+                vz_GS_up,
+                gamma_up,
+                beta_e_up,
+                delta_s_up,
+                Doppler_debias_up_iono_s,
+                fsup,
+                c,
+                DATA_TYPE,
+            )
 
             # R8: bending angle & impact parameter down
             (

radiocc/old/R7_Offset_Correction.py

@@ -12,77 +12,77 @@ import radiocc
 def Off_Cor(Type, threshold, Doppler, Diff_doppler, ET, N_data, distance, CODE_DIR,i_integral):
     if radiocc.cfg.use_diff_doppler:
         Doppler = Diff_doppler
-        Doppler_debias = np.full(N_data, np.nan)
-        Doppler_biasfit = np.full(N_data, np.nan)
-        delET = np.full(N_data, np.nan)
+    Doppler_debias = np.full(N_data, np.nan)
+    Doppler_biasfit = np.full(N_data, np.nan)
+    delET = np.full(N_data, np.nan)
 
-        # altitude threshold for fit
-        Corr_ind = 0
-        for i in range(len(distance)):
-            if distance[i] > threshold:  
-                Corr_ind += 1
-        if Corr_ind == len(distance):
-            Corr_ind = Corr_ind-1
-        # Search for maximum
+    # altitude threshold for fit
+    Corr_ind = 0
+    for i in range(len(distance)):
+        if distance[i] > threshold:  
+            Corr_ind += 1
+    if Corr_ind == len(distance):
+        Corr_ind = Corr_ind-1
+    # Search for maximum
+    
+    if len(Doppler)%2==1 :
+        taille = len(Doppler)
+    else:
+        taille = len(Doppler)-1
         
-        if len(Doppler)%2==1 :
-            taille = len(Doppler)
-        else:
-            taille = len(Doppler)-1
-            
-        Smoothed_Doppler = sg.savgol_filter(Doppler,taille,40)
-        deriv = np.gradient(Smoothed_Doppler)
+    Smoothed_Doppler = sg.savgol_filter(Doppler,taille,40)
+    deriv = np.gradient(Smoothed_Doppler)
 
-        good=np.logical_and(deriv[np.array(np.where(deriv[:-1]<1E-5))-1]<0,\
-                                        deriv[np.array(np.where(deriv[:-1]<1E-5))+1]>0)
-        
-        
-        targ=np.where(deriv[:-1]<1E-5)
-        index = targ[0][good[0]]
-        good_one = index[-1]
+    good=np.logical_and(deriv[np.array(np.where(deriv[:-1]<1E-5))-1]<0,\
+                                    deriv[np.array(np.where(deriv[:-1]<1E-5))+1]>0)
+    
+    
+    targ=np.where(deriv[:-1]<1E-5)
+    index = targ[0][good[0]]
+    good_one = index[-1]
 
-        # FIT
-        if Type == 'Fit':
-            p = np.polyfit(np.append(ET[:Corr_ind],ET[good_one])\
-                            ,np.append(Doppler[:Corr_ind],Doppler[good_one]),2)
+    # FIT
+    if Type == 'Fit':
+        p = np.polyfit(np.append(ET[:Corr_ind],ET[good_one])\
+                        ,np.append(Doppler[:Corr_ind],Doppler[good_one]),2)
+        
+        for i in range(i_integral,N_data):
+            Doppler_biasfit[i] = p[0] * ET[i]**2 + p[1]*ET[i] + p[2]
             
-            for i in range(i_integral,N_data):
-                Doppler_biasfit[i] = p[0] * ET[i]**2 + p[1]*ET[i] + p[2]
-                
-    ###########################################################################################
+###########################################################################################
+    for i in range(N_data):
+        delET[i]= ET[i]-ET[0]
+    
+    # bias fit
+    if Type == 'Linear':
+        p = np.polyfit(delET[i_integral:Corr_ind],Doppler[i_integral:Corr_ind],1)
+        p0 = p[0]
+        p1 = p[1]
         for i in range(N_data):
-            delET[i]= ET[i]-ET[0]
-        
-        # bias fit
-        if Type == 'Linear':
-            p = np.polyfit(delET[i_integral:Corr_ind],Doppler[i_integral:Corr_ind],1)
-            p0 = p[0]
-            p1 = p[1]
-            for i in range(N_data):
-                Doppler_biasfit[i] = p[0] * (delET[i]) + p[1]
-                
-        elif Type == 'Quadratic': 
-            p = np.polyfit(delET[i_integral:Corr_ind],Doppler[i_integral:Corr_ind],2)
-            p0 = p[0]
-            p1 = p[1]
-            p2 = p[2]
-            for i in range(N_data):
-                Doppler_biasfit[i] = p[0] * (delET[i])**2 + p[1]*(delET[i]) + p[2]
-                
-        else: 
-            print('Type is not recognized')
-        
-        # subtract bias and scale residual
+            Doppler_biasfit[i] = p[0] * (delET[i]) + p[1]
+            
+    elif Type == 'Quadratic': 
+        p = np.polyfit(delET[i_integral:Corr_ind],Doppler[i_integral:Corr_ind],2)
+        p0 = p[0]
+        p1 = p[1]
+        p2 = p[2]
         for i in range(N_data):
-            Doppler_debias[i]  = (Doppler[i]- Doppler_biasfit[i])
-        #print(ET[13542],Doppler[13542],Doppler_biasfit[13542], Doppler_debias[13542])
-        # back to code directory (why in this subroutine?)   
-        # os.chdir(CODE_DIR)
-        if Type == 'Linear':
-            return ET, delET, Doppler_debias, Doppler_biasfit, Corr_ind, p0, p1
-        
-        if Type == 'Quadraticuadratic':
-            return ET, delET, Doppler_debias, Doppler_biasfit, Corr_ind, p0, p1,p2
+            Doppler_biasfit[i] = p[0] * (delET[i])**2 + p[1]*(delET[i]) + p[2]
+            
+    else: 
+        print('Type is not recognized')
+    
+    # subtract bias and scale residual
+    for i in range(N_data):
+        Doppler_debias[i]  = (Doppler[i]- Doppler_biasfit[i])
+    #print(ET[13542],Doppler[13542],Doppler_biasfit[13542], Doppler_debias[13542])
+    # back to code directory (why in this subroutine?)   
+    # os.chdir(CODE_DIR)
+    if Type == 'Linear':
+        return ET, delET, Doppler_debias, Doppler_biasfit, Corr_ind, p0, p1
+    
+    if Type == 'Quadratic':
+        return ET, delET, Doppler_debias, Doppler_biasfit, Corr_ind, p0, p1,p2