erp5_wendelin_drone: work with floats

bt5/erp5_wendelin_drone/PathTemplateItem/portal_callables/DataAnalysisLine_convertDroneDataArrayToScoreArray.py

@@ -122,7 +122,7 @@ for name in not_seen_list:
     splitted_filename = name[:-4].split('_')
     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 = simulated_flight.applymap(float)
     simulated_flight_list.append(simulated_flight)
     timestamp_diff = simulated_flight['timestamp (ms)'].min() - real_flight['timestamp (ms)'].min()
     max_simulator_timestamp = simulated_flight['timestamp (ms)'].max() - timestamp_diff
@@ -139,24 +139,23 @@ for name in not_seen_list:
       except IndexError:
         continue
       over_timestamp = simulated_flight[simulated_flight['timestamp (ms)'] - timestamp_diff >= row['timestamp (ms)']].head(1)
-      rate = (float(over_timestamp["timestamp (ms)"]) - timestamp_diff - row["timestamp (ms)"])/(float(over_timestamp["timestamp (ms)"]) - float(under_timestamp["timestamp (ms)"]))
+      rate = (over_timestamp["timestamp (ms)"] - timestamp_diff - row["timestamp (ms)"])/(over_timestamp["timestamp (ms)"] - under_timestamp["timestamp (ms)"])
 
       for criterion in distance_criterion_tuple:
-        tmp_sim[criterion].append(rated_value(float(over_timestamp[criterion]), float(under_timestamp[criterion]), rate))
+        tmp_sim[criterion].append(rated_value(over_timestamp[criterion], under_timestamp[criterion], rate))
         tmp_real[criterion].append(row[criterion])
 
       for index, value in enumerate((
-            row['timestamp (ms)'] / 1000,
-            distance(row['latitude ()'],
-                row['longitude ()'],
-                tmp_sim['latitude ()'][-1],
-                tmp_sim['longitude ()'][-1],
-            ),
-            tmp_sim['AMSL (m)'][-1],
-            tmp_sim['ground speed (m/s)'][-1],
-            tmp_real['climb rate (m/s)'][-1]
-        )):
-
+        row['timestamp (ms)'] / 1000,
+        distance(row['latitude ()'],
+            row['longitude ()'],
+            tmp_sim['latitude ()'][-1],
+            tmp_sim['longitude ()'][-1],
+        ),
+        tmp_sim['AMSL (m)'][-1],
+        tmp_sim['ground speed (m/s)'][-1],
+        tmp_real['climb rate (m/s)'][-1],
+      )):
         distance_list_tuple[index].append(value)
         # If we have at least 100 entries (timestamps) analysed, add the filename to the list