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souvik0306
commited on
Commit
·
8ac0324
1
Parent(s):
30a7a75
weather and optimizer operational
Browse files- flight_distance.py +4 -0
- main.py +15 -5
- optimizer.py +48 -40
- weather.py +20 -19
flight_distance.py
CHANGED
@@ -101,6 +101,10 @@ def calculate_distances(airport_identifiers):
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# aircraft_specs = get_aircraft_details(aircraft_type)
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# print(aircraft_specs)
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# fuel_burn_rate = aircraft_specs['Fuel_Consumption_kg/hr']
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# cruising_speed = aircraft_specs['Max_Fuel_Capacity_kg']
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# max_fuel_capacity = aircraft_specs['Speed_kmh']
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# aircraft_specs = get_aircraft_details(aircraft_type)
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# print(aircraft_specs)
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# airport_list = ['SIN','CDG']
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# print(get_airport_lat_long(airport_list))
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# print(calculate_distances(airport_list))
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# fuel_burn_rate = aircraft_specs['Fuel_Consumption_kg/hr']
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# cruising_speed = aircraft_specs['Max_Fuel_Capacity_kg']
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# max_fuel_capacity = aircraft_specs['Speed_kmh']
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main.py
CHANGED
@@ -1,9 +1,9 @@
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import pandas as pd
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from flight_distance import *
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from weather import *
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airport_identifiers = ['
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#Get Airport Coordinates
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lat_long_dict = get_airport_lat_long(airport_identifiers)
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@@ -14,15 +14,25 @@ trip_distance = calculate_distances(airport_identifiers)
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print("Distance b/w Airports: \n",trip_distance)
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#Get onroute weather
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-
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# # Ensure the graph is bidirectional (undirected)
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# for (a, b), dist in list(trip_distance.items()):
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# trip_distance[(b, a)] = dist
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# # Find the optimal route with the new cost metric
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-
#
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# print("Optimal Route:", " -> ".join(optimal_route) + f" -> {optimal_route[0]}")
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# print("Total Adjusted Distance/Cost:", optimal_distance)
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import pandas as pd
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from flight_distance import *
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from optimizer import *
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from weather import *
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airport_identifiers = ['BOM', 'CCU', 'DEL'] # Replace with actual identifiers
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#Get Airport Coordinates
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lat_long_dict = get_airport_lat_long(airport_identifiers)
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print("Distance b/w Airports: \n",trip_distance)
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#Get onroute weather
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raw_weather = fetch_weather_for_all_routes(airport_identifiers, lat_long_dict)
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route_factors = extract_route_factors(raw_weather)
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print("On Route weather: \n", raw_weather)
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# # Ensure the graph is bidirectional (undirected)
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# for (a, b), dist in list(trip_distance.items()):
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# trip_distance[(b, a)] = dist
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# # Find the optimal route with the new cost metric
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# Ensure the graph is bidirectional (undirected)
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for (a, b), dist in list(trip_distance.items()):
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trip_distance[(b, a)] = dist
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# Find the optimal route with the new cost metric
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optimal_route, optimal_distance = find_optimal_route(airport_identifiers, trip_distance, route_factors)
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# Display the optimal route and the total adjusted distance/cost
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print("Optimal Route:", " -> ".join(optimal_route) + f" -> {optimal_route[0]}")
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print("Total Adjusted Distance/Cost:", optimal_distance)
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# print("Optimal Route:", " -> ".join(optimal_route) + f" -> {optimal_route[0]}")
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# print("Total Adjusted Distance/Cost:", optimal_distance)
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optimizer.py
CHANGED
@@ -1,24 +1,27 @@
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import itertools
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('LAX', 'JFK'): 3974.20,
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('JFK', 'CDG'): 5833.66,
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}
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'CDG'): {'weather': 'clear sky', 'temperature': 21.18}}
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for (a, b), factors in list(route_factors.items()):
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route_factors[(b, a)] = factors
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# Function to assign a risk factor based on weather
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def weather_risk(weather):
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risk_factors = {
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"clear sky": 0.1,
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@@ -40,20 +43,30 @@ def temperature_impact(temperature):
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return 0.1 # Low impact in the ideal range
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# Calculate the adjusted cost for each route segment
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def calculate_adjusted_cost(segment, base_distance):
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weather_cost = weather_risk(
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temperature_cost = temperature_impact(
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total_cost = base_distance + weather_cost + temperature_cost
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return total_cost
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# Update the distance function to include additional factors
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def calculate_route_distance(route, distances):
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"""Calculate the total cost for a given route, including additional factors."""
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total_distance = 0
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for i in range(len(route) - 1):
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@@ -61,35 +74,30 @@ def calculate_route_distance(route, distances):
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if segment not in distances:
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segment = (route[i + 1], route[i])
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base_distance = distances[segment]
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total_distance += calculate_adjusted_cost(segment, base_distance)
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# Add distance to return to the starting point
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last_segment = (route[-1], route[0])
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if last_segment not in distances:
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last_segment = (route[0], route[-1])
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base_distance = distances[last_segment]
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total_distance += calculate_adjusted_cost(last_segment, base_distance)
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return total_distance
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def find_optimal_route(airports, distances):
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"""Find the optimal route that covers all airports."""
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best_route = None
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min_distance = float('inf')
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# Generate all possible permutations of the route
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for route in itertools.permutations(airports):
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return best_route, min_distance
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# List of all airports
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airports = ['SIN', 'LAX', 'JFK', 'CDG']
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# Find the optimal route with the new cost metric
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optimal_route, optimal_distance = find_optimal_route(airports, distances)
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print("Optimal Route:", " -> ".join(optimal_route) + f" -> {optimal_route[0]}")
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print("Total Adjusted Distance/Cost:", optimal_distance)
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import itertools
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def extract_route_factors(raw_weather):
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"""
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Extracts route factors from raw weather data by breaking down routes into individual segments.
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Parameters:
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- raw_weather (dict): The raw weather data with routes and corresponding weather details.
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Returns:
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- dict: A dictionary with segments as keys (in tuple format) and a list of weather and temperature data.
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"""
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route_factors = {}
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for route, segments in raw_weather.items():
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for segment in segments:
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segment_key = tuple(segment['segment'].split(' -> '))
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if segment_key not in route_factors:
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route_factors[segment_key] = []
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route_factors[segment_key].append({
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'weather': segment['weather'],
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'temperature': segment['temperature']
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})
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return route_factors
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def weather_risk(weather):
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risk_factors = {
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"clear sky": 0.1,
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return 0.1 # Low impact in the ideal range
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# Calculate the adjusted cost for each route segment
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def calculate_adjusted_cost(segment, base_distance, route_factors):
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# Handle both directions of the segment
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if segment in route_factors:
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factors = route_factors[segment]
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elif (segment[1], segment[0]) in route_factors:
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factors = route_factors[(segment[1], segment[0])]
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else:
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raise ValueError(f"Segment {segment} not found in route factors.")
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# Aggregate weather and temperature data if there are multiple entries for the segment
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weather_descriptions = [factor["weather"] for factor in factors]
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temperatures = [factor["temperature"] for factor in factors]
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most_common_weather = max(set(weather_descriptions), key=weather_descriptions.count)
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avg_temperature = sum(temperatures) / len(temperatures)
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weather_cost = weather_risk(most_common_weather) * 100 # Weight for weather impact
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temperature_cost = temperature_impact(avg_temperature) * 50 # Weight for temperature impact
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total_cost = base_distance + weather_cost + temperature_cost
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return total_cost
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# Update the distance function to include additional factors
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def calculate_route_distance(route, distances, route_factors):
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"""Calculate the total cost for a given route, including additional factors."""
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total_distance = 0
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for i in range(len(route) - 1):
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if segment not in distances:
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segment = (route[i + 1], route[i])
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base_distance = distances[segment]
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total_distance += calculate_adjusted_cost(segment, base_distance, route_factors)
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# Add distance to return to the starting point
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last_segment = (route[-1], route[0])
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if last_segment not in distances:
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last_segment = (route[0], route[-1])
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base_distance = distances[last_segment]
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total_distance += calculate_adjusted_cost(last_segment, base_distance, route_factors)
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return total_distance
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def find_optimal_route(airports, distances, route_factors):
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"""Find the optimal route that covers all airports."""
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best_route = None
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min_distance = float('inf')
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# Generate all possible permutations of the route
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for route in itertools.permutations(airports):
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try:
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current_distance = calculate_route_distance(route, distances, route_factors)
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if current_distance < min_distance:
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min_distance = current_distance
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best_route = route
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except ValueError as e:
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print(e) # Log the error to debug missing segments
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return best_route, min_distance
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weather.py
CHANGED
@@ -1,12 +1,13 @@
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import requests
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import itertools
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from geopy.distance import geodesic
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# Replace with your OpenWeather API key
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API_KEY = '9811dd1481209c64fba6cb2c90f27140'
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# Interpolation function to get intermediate points between airports
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def get_intermediate_points(start, end, num_points=
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points = []
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lat_step = (end[0] - start[0]) / (num_points + 1)
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lon_step = (end[1] - start[1]) / (num_points + 1)
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return points
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# Fetch weather data for a given coordinate
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def fetch_weather(lat, lon):
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url = f'http://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={API_KEY}&units=metric'
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response = requests.get(url)
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"weather": most_common_weather,
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"temperature": round(avg_temperature, 2)
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})
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return route_factors
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# Example airport coordinates
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airports = {
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}
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airport_identifiers = ['SIN', 'LAX', 'JFK', 'CDG', 'LHR'] # Replace with actual identifiers
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# Fetch the weather along all possible routes
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route_weather = fetch_weather_for_all_routes(airport_identifiers, airports)
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# Display the weather data for each route
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for route, factors in route_weather.items():
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import requests
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import itertools
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from geopy.distance import geodesic
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from functools import lru_cache
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# Replace with your OpenWeather API key
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API_KEY = '9811dd1481209c64fba6cb2c90f27140'
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# Interpolation function to get intermediate points between airports
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def get_intermediate_points(start, end, num_points=2):
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points = []
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lat_step = (end[0] - start[0]) / (num_points + 1)
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lon_step = (end[1] - start[1]) / (num_points + 1)
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return points
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# Fetch weather data for a given coordinate
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@lru_cache(maxsize=128)
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def fetch_weather(lat, lon):
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url = f'http://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={API_KEY}&units=metric'
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response = requests.get(url)
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"weather": most_common_weather,
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"temperature": round(avg_temperature, 2)
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})
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return route_factors
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# # Example airport coordinates
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# airports = {
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# 'SIN': (1.3644, 103.9915), # Singapore Changi Airport
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# 'LAX': (33.9416, -118.4085), # Los Angeles International Airport
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# 'JFK': (40.6413, -73.7781), # John F. Kennedy International Airport
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# 'CDG': (49.0097, 2.5479), # Charles de Gaulle Airport
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# 'LHR': (51.4700, -0.4543) # London Heathrow Airport
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# }
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# airport_identifiers = ['SIN', 'LAX', 'JFK', 'CDG', 'LHR'] # Replace with actual identifiers
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# # Fetch the weather along all possible routes
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# route_weather = fetch_weather_for_all_routes(airport_identifiers, airports)
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# # Display the weather data for each route
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# for route, factors in route_weather.items():
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# print(f"Route: {route}")
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# for factor in factors:
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# print(f" Segment: {factor['segment']}, Weather: {factor['weather']}, Temperature: {factor['temperature']} °C")
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# print()
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