modular weather script

flight_distance.py

@@ -101,9 +101,10 @@ def calculate_distances(airport_identifiers):
 # aircraft_specs = get_aircraft_details(aircraft_type)
 # print(aircraft_specs)
 
-# airport_list = ['SIN','CDG']
+# airport_list = ['SIN','CDG', 'BOM']
 # print(get_airport_lat_long(airport_list))
-# print(calculate_distances(airport_list))
+# trip_distance = calculate_distances(airport_list)
+# print(trip_distance)
 
 # fuel_burn_rate = aircraft_specs['Fuel_Consumption_kg/hr']
 # cruising_speed = aircraft_specs['Max_Fuel_Capacity_kg']

main.py

@@ -3,7 +3,7 @@ from flight_distance import *
 from optimizer import *
 from weather import *
 
-airport_identifiers = ['BOM', 'CCU', 'DEL']  # Replace with actual identifiers
+airport_identifiers = ['BLR', 'CCU', 'DEL']  # Replace with actual identifiers
 
 #Get Airport Coordinates
 lat_long_dict = get_airport_lat_long(airport_identifiers)
@@ -19,11 +19,6 @@ route_factors = extract_route_factors(raw_weather)
 print("On Route weather: \n", raw_weather)
 
 # # Ensure the graph is bidirectional (undirected)
-# for (a, b), dist in list(trip_distance.items()):
-#     trip_distance[(b, a)] = dist
-
-# # Find the optimal route with the new cost metric
-# Ensure the graph is bidirectional (undirected)
 for (a, b), dist in list(trip_distance.items()):
     trip_distance[(b, a)] = dist
 
@@ -34,5 +29,3 @@ optimal_route, optimal_distance = find_optimal_route(airport_identifiers, trip_d
 print("Optimal Route:", " -> ".join(optimal_route) + f" -> {optimal_route[0]}")
 print("Total Adjusted Distance/Cost:", optimal_distance)
 
-# print("Optimal Route:", " -> ".join(optimal_route) + f" -> {optimal_route[0]}")
-# print("Total Adjusted Distance/Cost:", optimal_distance)

weather.py

@@ -25,51 +25,69 @@ def fetch_weather(lat, lon):
     response = requests.get(url)
     return response.json()
 
-# Fetch weather along all possible routes
+# Fetch weather data for a specific segment (using caching to avoid redundant requests)
+def get_segment_weather(start_coords, end_coords):
+    points = get_intermediate_points(start_coords, end_coords)
+    points.insert(0, start_coords)
+    points.append(end_coords)
+
+    weather_descriptions = []
+    temperatures = []
+
+    for point in points:
+        weather = fetch_weather(point[0], point[1])
+        weather_descriptions.append(weather['weather'][0]['description'])
+        temperatures.append(weather['main']['temp'])
+
+    avg_temperature = sum(temperatures) / len(temperatures)
+    most_common_weather = max(set(weather_descriptions), key=weather_descriptions.count)
+
+    return {
+        "weather": most_common_weather,
+        "temperature": round(avg_temperature, 2)
+    }
+
+# Fetch and cache weather data for each segment in the routes
+def fetch_segment_weather_data(airport_identifiers, airports):
+    segment_weather_cache = {}
+
+    for route in itertools.permutations(airport_identifiers, len(airport_identifiers)):
+        for i in range(len(route) - 1):
+            start_airport = route[i]
+            end_airport = route[i + 1]
+            segment_key = tuple(sorted([start_airport, end_airport]))
+
+            if segment_key not in segment_weather_cache:
+                start_coords = (airports[start_airport][0], airports[start_airport][1])
+                end_coords = (airports[end_airport][0], airports[end_airport][1])
+                segment_weather_cache[segment_key] = get_segment_weather(start_coords, end_coords)
+
+    return segment_weather_cache
+
+# Aggregate weather data for all routes using the cached segment data
 def fetch_weather_for_all_routes(airport_identifiers, airports):
     route_factors = {}
+    segment_weather_cache = fetch_segment_weather_data(airport_identifiers, airports)
 
-    # Generate all possible routes (permutations) 
     for route in itertools.permutations(airport_identifiers, len(airport_identifiers)):
-        route_key = " -> ".join(route)  # Key for route factors
+        route_key = " -> ".join(route)
         route_factors[route_key] = []
 
         for i in range(len(route) - 1):
             start_airport = route[i]
             end_airport = route[i + 1]
-            start_coords = (airports[start_airport][0], airports[start_airport][1])
-            end_coords = (airports[end_airport][0], airports[end_airport][1])
-            
-            # Get 4 intermediate points along the route
-            points = get_intermediate_points(start_coords, end_coords)
-            
-            # Include start and end airport coordinates
-            points.insert(0, start_coords)
-            points.append(end_coords)
-            
-            # Fetch weather for each point
-            weather_descriptions = []
-            temperatures = []
-            
-            for point in points:
-                weather = fetch_weather(point[0], point[1])
-                weather_descriptions.append(weather['weather'][0]['description'])
-                temperatures.append(weather['main']['temp'])
-            
-            # Aggregate weather for the route segment
-            avg_temperature = sum(temperatures) / len(temperatures)
-            most_common_weather = max(set(weather_descriptions), key=weather_descriptions.count)
-            
-            # Store the result in the route_factors dictionary for each route segment
-            segment_key = f"{start_airport} -> {end_airport}"
+            segment_key = tuple(sorted([start_airport, end_airport]))
+            segment_weather = segment_weather_cache[segment_key]
+
             route_factors[route_key].append({
-                "segment": segment_key,
-                "weather": most_common_weather,
-                "temperature": round(avg_temperature, 2)
+                "segment": f"{start_airport} -> {end_airport}",
+                "weather": segment_weather["weather"],
+                "temperature": segment_weather["temperature"]
             })
+
     return route_factors
 
-# # Example airport coordinates
+# # Example usage
 # airports = {
 #     'SIN': (1.3644, 103.9915),  # Singapore Changi Airport
 #     'LAX': (33.9416, -118.4085),  # Los Angeles International Airport