erp5_wendelin_drone: create dataframe in a separate function

bt5/erp5_wendelin_drone/PathTemplateItem/portal_callables/DataAnalysisLine_convertDroneDataArrayToScoreArray.py

@@ -36,8 +36,11 @@ def rated_value(next_value, previous_value, rate):
   else:
     return previous_value + (next_value - previous_value) * rate
 
-
-
+def nparray_to_dataframe(nparray):
+  return pd.DataFrame(
+    data=nparray.getArray(),
+    columns=["timestamp (ms)","latitude ()","longitude ()","AMSL (m)","rel altitude (m)","yaw ()","ground speed (m/s)","climb rate (m/s)"],
+  )
 
 
 
@@ -51,10 +54,7 @@ sim_flight_names = list(sim_array)
 if len(real_flight_names) == 0:
   context.log("The real flight data is still missing")
   return
-nparray = real_array.get(real_flight_names[0])
-
-nparray_real = nparray.getArray()
-real_flight = pd.DataFrame(data=nparray_real, columns=["timestamp (ms)","latitude ()","longitude ()","AMSL (m)","rel altitude (m)","yaw ()","ground speed (m/s)","climb rate (m/s)"])
+real_flight = nparray_to_dataframe(real_array.get(real_flight_names[0]))
 
 
 
@@ -106,9 +106,7 @@ for name in not_seen_list[:]:
   if name[:14] == 'simulation_log':
     splitted_filename = name[:-4].split('_')
     distance_list_tuple = ([],[], [], [], [])
-    nparray = sim_array.get(name)
-    nparray_sim = nparray.getArray()
-    simulated_flight = pd.DataFrame(data=nparray_sim, columns=["timestamp (ms)","latitude ()","longitude ()","AMSL (m)","rel altitude (m)","yaw ()","ground speed (m/s)","climb rate (m/s)"])
+    simulated_flight = nparray_to_dataframe(sim_array.get(name))
     simulated_flight = simulated_flight.applymap(lambda value: np.format_float_scientific(float(value)) if isinstance(value, str) and 'e' in value else value)
     simulated_flight_list.append(simulated_flight)
     max_simulator_timestamp = simulated_flight["timestamp (ms)"].max()