Added alternative ESRM20 file naming

GDE_TOOLS_read_SERA.py

@@ -66,7 +66,15 @@ def get_admin_level_definition(country_name, path, output_dfs=False, full_files=
     actual_levels_list= [0,0,0]
     out_dataframes= {}
     for k,case in enumerate(exposure_cases):
-        aux_df= pd.read_csv(os.path.join(path, 'Exposure_'+case+'_'+country_name+'.csv'), sep=',')
+        try:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_%s_%s.csv' % (case, country_name)),
+                                sep=',')
+        except:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_Model_%s_%s.csv'
+                                             % (country_name, case)),
+                                sep=',')
         if full_files:
             for col in aux_df.columns:
                 aux_df= aux_df.rename(columns={col: col.lower()})            
@@ -142,7 +150,15 @@ def check_admin_levels_unequivocally_defined(country_name, path, full_files=Fals
     is_unequivocally_defined= [True, True, True]
     other_levels_checked= []
     for k,case in enumerate(exposure_cases):
-        aux_df= pd.read_csv(os.path.join(path, 'Exposure_'+case+'_'+country_name+'.csv'), sep=',')
+        try:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_%s_%s.csv' % (case, country_name)),
+                                sep=',')
+        except:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_Model_%s_%s.csv'
+                                             % (country_name, case)),
+                                sep=',')
         if full_files:
             for col in aux_df.columns:
                 aux_df= aux_df.rename(columns={col: col.lower()})  
@@ -155,7 +171,15 @@ def check_admin_levels_unequivocally_defined(country_name, path, full_files=Fals
         available_levels_list.append(aux_list_levels)
     for k,case in enumerate(exposure_cases):
         print('   Checking '+case)
-        aux_df= pd.read_csv(os.path.join(path, 'Exposure_'+case+'_'+country_name+'.csv'), sep=',')
+        try:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_%s_%s.csv' % (case, country_name)),
+                                sep=',')
+        except:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_Model_%s_%s.csv'
+                                             % (country_name, case)),
+                                sep=',')
         if full_files:
             for col in aux_df.columns:
                 aux_df= aux_df.rename(columns={col: col.lower()}) 
@@ -199,14 +223,30 @@ def check_number_points_per_admin_level(country_name, path):
     actual_levels_list= [0,0,0]
     have_one_point_per_ID= [True, True, True]
     for k,case in enumerate(exposure_cases):
-        aux_df= pd.read_csv(os.path.join(path, 'Exposure_'+case+'_'+country_name+'.csv'), sep=',')
+        try:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_%s_%s.csv' % (case, country_name)),
+                                sep=',')
+        except:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_Model_%s_%s.csv'
+                                             % (country_name, case)),
+                                sep=',')
         for i in range(0,max_possible_level+1):
             if 'id_'+str(i) in aux_df.columns:
                 if np.all(aux_df['id_'+str(i)].values!='No_tag'): # if not('No_tag' in aux_df['id_'+str(i)].values):
                     actual_levels_list[k]= i
     for k,case in enumerate(exposure_cases):
         print('   Checking '+case)
-        aux_df= pd.read_csv(os.path.join(path, 'Exposure_'+case+'_'+country_name+'.csv'), sep=',')
+        try:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_%s_%s.csv' % (case, country_name)),
+                                sep=',')
+        except:
+            aux_df= pd.read_csv(os.path.join(path,
+                                             'Exposure_Model_%s_%s.csv'
+                                             % (country_name, case)),
+                                sep=',')
         try:
             mixcase= False
             unique_ids_actual= np.unique(aux_df['id_'+str(actual_levels_list[k])].values) # unique admin unit IDs of the level that defines the exposure