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]))
 
 
 
@@ -65,10 +65,10 @@ seen_sims = progress_indicator_sim.getStringOffsetIndex()
 new_seen = ""
 if seen_sims is None:
   seen_sims = ""
-  score_dtypes = {'name': 'S256', 'Param1': 'f16', 'Param2': 'f16', 
-          'distance_reciprocal': 'f8', 'ASML_reciprocal': 'f8', 
-          'ground_speed_reciprocal': 'f8', 'climb_rate_reciprocal': 'f8', 
-          'score_reciprocal': 'f16', 'score_cosine_row': 'f16', 
+  score_dtypes = {'name': 'S256', 'Param1': 'f16', 'Param2': 'f16',
+          'distance_reciprocal': 'f8', 'ASML_reciprocal': 'f8',
+          'ground_speed_reciprocal': 'f8', 'climb_rate_reciprocal': 'f8',
+          'score_reciprocal': 'f16', 'score_cosine_row': 'f16',
           'score_cosine_column': 'f16'}
 
   _ = out_array_scores["Data Array"].initArray(shape=(0,), dtype=list(score_dtypes.items()))
@@ -105,16 +105,14 @@ not_seen_list = [x for x in sim_flight_names if x not in seen_sims]
 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)"])
+    distance_list_tuple = ([],[], [], [], [])
+    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()
     min_simulator_timestamp = simulated_flight["timestamp (ms)"].min()
-    
-    
+
+
     tmp_sim = {
     'longitude': [],
     'latitude': [],
@@ -129,7 +127,7 @@ for name in not_seen_list[:]:
     'ground_speed': [],
     'climb_rate': []
     }
-    
+
     for idx, row in real_flight.iterrows():
       if max_simulator_timestamp < row["timestamp (ms)"]:
         break
@@ -137,17 +135,17 @@ for name in not_seen_list[:]:
         continue
       over_timestamp = simulated_flight[simulated_flight["timestamp (ms)"] >= row["timestamp (ms)"]].head(1)
       under_timestamp = simulated_flight[simulated_flight["timestamp (ms)"] <= row["timestamp (ms)"]].tail(1)
-      
+
       if (float(over_timestamp["timestamp (ms)"]) - float(under_timestamp["timestamp (ms)"])) == 0:
         rate = 0
       else:
         rate = (float(over_timestamp["timestamp (ms)"]) - row["timestamp (ms)"])/(float(over_timestamp["timestamp (ms)"]) - float(under_timestamp["timestamp (ms)"]))
 
-      tmp_sim["latitude"].append(rated_value(float(over_timestamp["latitude ()"]), float(under_timestamp["latitude ()"]), rate)) 
+      tmp_sim["latitude"].append(rated_value(float(over_timestamp["latitude ()"]), float(under_timestamp["latitude ()"]), rate))
       tmp_sim["longitude"].append(rated_value(float(over_timestamp["longitude ()"]), float(under_timestamp["longitude ()"]), rate))
       tmp_sim["asml"].append(rated_value(float(over_timestamp["AMSL (m)"]), float(under_timestamp["AMSL (m)"]), rate))
       tmp_sim["ground_speed"].append(rated_value(float(over_timestamp["ground speed (m/s)"]), float(under_timestamp["ground speed (m/s)"]), rate))
-      tmp_sim["climb_rate"].append(rated_value(float(over_timestamp["climb rate (m/s)"]), float(under_timestamp["climb rate (m/s)"]), rate))  
+      tmp_sim["climb_rate"].append(rated_value(float(over_timestamp["climb rate (m/s)"]), float(under_timestamp["climb rate (m/s)"]), rate))
       tmp_real["latitude"].append(row["latitude ()"])
       tmp_real["longitude"].append(row["longitude ()"])
       tmp_real["asml"].append(row["AMSL (m)"])
@@ -179,7 +177,7 @@ for name in not_seen_list[:]:
       if match:
         values_str = match.group(1)
         parameters = [float(val.strip()) for val in values_str.split(',')]
-        
+
       reciprocal_of_difference = [1/(1+mean_operation(list(map(abs, x)))) for x in distance_list_tuple[1:]]
       score_reciprocal = sum(reciprocal_of_difference)
 
@@ -243,9 +241,9 @@ plot_dtypes = {
     'ASML_reciprocal': 'float64',
     'ground_speed_reciprocal': 'float64',
     'climb_rate_reciprocal': 'float64',
-    'score_reciprocal': 'float64',  
-    'score_cosine_row': 'float64',  
-    'score_cosine_column': 'float64', 
+    'score_reciprocal': 'float64',
+    'score_cosine_row': 'float64',
+    'score_cosine_column': 'float64',
 }
 
 
@@ -266,16 +264,16 @@ for data_dict in selected_simulation_data_dict_list:
     # Initialize a list for the key if it doesn't exist
     if key not in combined_data:
       combined_data[key] = []
-        
+
     # Append the value to the list
     combined_data[key].append(value)
 
 
 
-score_dtypes = {'name': 'S256', 'Param1': 'float64', 'Param2': 'float64', 
-          'distance_reciprocal': 'float64', 'ASML_reciprocal': 'float64', 
-          'ground_speed_reciprocal': 'float64', 'climb_rate_reciprocal': 'float64', 
-          'score_reciprocal': 'float64', 'score_cosine_row': 'float64', 
+score_dtypes = {'name': 'S256', 'Param1': 'float64', 'Param2': 'float64',
+          'distance_reciprocal': 'float64', 'ASML_reciprocal': 'float64',
+          'ground_speed_reciprocal': 'float64', 'climb_rate_reciprocal': 'float64',
+          'score_reciprocal': 'float64', 'score_cosine_row': 'float64',
           'score_cosine_column': 'float64'}
 scores_df = pd.DataFrame(combined_data)
 scores_df = scores_df.astype(score_dtypes)