""" Author: M. Graham Macy Date Created: 5/28/2024 Date Edited: 8/5/2024 Description: Contains two classes: Airport and Airports """ import math import random import pandas as pd import matplotlib.pyplot as plt from constants import * class Airport(object): """ Airport() functions as an object that contains location data such as lat/long, runway length, and also parameters for computing OAT (Outside Air Temperature). For temperature calculations a = amplitude of sine wave through the year b = amplitude of daily sine wave c = average yearly temp d = coldest day Contains a function that can compute OAT given a time indicator. (this must be updated depending on functionality) """ code: str lat: float lon: float alt_ft: float a: float b: float c: float d: float runway_length_ft: int def __init__(self, code: str, lat: float, lon: float, alt_ft: float, a: float, b: float, c: float, d: float, runway_length_ft: int, fuel_price: float, depart_time: int=0, depart_station: str="N/A", arrival_station: str="N/A"): self.code = code self.lat = lat self.lon = lon self.alt_ft = alt_ft self.a = a self.b = b self.c = c self.d = d self.runway_length_ft = runway_length_ft self.fuel_price = fuel_price self.depart_time = depart_time self.depart_station = depart_station self.arrival_station = arrival_station return def OAT(self, hrs_since_jan1_midnight: float): """ Computes outside air temperature according to time !!!time is hours since 1/1/20!!! """ # source for d: # https://www.climate.gov/news-features/understanding-climate/interactive-map-coldest-day-year-across-united-states # Temperature estimated as a function of time of year for each airport day = 24 year = 365.25*day t = hrs_since_jan1_midnight - self.d temp = -self.a*math.cos(t*2*math.pi/(year)) +\ self.b*math.sin((t+15)*2*math.pi/(day))+self.c OAT = temp+random.uniform(-0.5, 0.5) return OAT class Airports(object): """ Airports() is an object that can calculate things about airports. create_AirportList() Creates a list of initialized airport objects from a CSV file with headers: code, lat, lon, alt_ft, a, b, c, d, runway_length_ft, fuel price create_ScheduleList() Creates a list of airport objects from a CSV file with headers: depart_time, depart_station, arrival_station code, lat, lon, alt_ft, a, b, c, d, runway_length_ft, fuel price This list is a "Schedule" where airports appear in order. distance_nmi() calculates the distance between two airports """ def __init__(): return def create_AirportList(filename: str) -> list: """ Takes in a csv file that necessarily includes information formatted into the following columns: "code" - airport code "lat" - airport latitude "lon" - longitude "alt_ft" - altitude in ft "a,b,c,d" - 4 columns that factor into calculating the weather "runway_length_ft" - airport runway length in ft "fuel_price" - price of fuel at airport !FUEL PRICE DOES NOT HAVE REFERENCE, AND HAS BEEN ESTIMATED AND SIMPLIFIED! """ df = pd.read_csv(filename) airports = [] for i,row in df.iterrows(): code = row.code lat = row.lat lon = row.lon alt_ft = row.alt_ft a = row.a b = row.b c = row.c d = row.d runway_length_ft = row.runway_length_ft fuel_price = row.fuel_price airport = Airport(code, lat, lon, alt_ft, a, b, c, d, runway_length_ft, fuel_price) airports.append(airport) return airports def create_ScheduleList(filename: str) -> list: """ This schedule data "flightSchedule.csv" comes from real flight data. If flightSchedule.csv is missing from files, it will be necessary to recreate this from scratch. Takes in a csv file that is formatted to include the following columns: "depart_time" - departure time (hours after midnight Jan 1) "depart_station" - departure station airport code "arrival_station" - arrival station airport code "code" - airport code "lat" - airport latitude "lon" - longitude "alt_ft" - altitude in ft "a,b,c,d" - 4 columns that factor into calculating the weather "runway_length_ft" - airport runway length in ft "fuel_price" - price of fuel at airport !FUEL PRICE DOES NOT HAVE REFERENCE, AND HAS BEEN ESTIMATED AND SIMPLIFIED! """ df = pd.read_csv(filename) schedule = [] for i, row in df.iterrows(): depart_time = row.depart_time depart_station = row.depart_station arrival_station = row.arrival_station code = row.code lat = row.lat lon = row.lon alt_ft = row.alt_ft a = row.a b = row.b c = row.c d = row.d runway_length_ft = row.runway_length_ft fuel_price = row.fuel_price leg = Airport(code, lat, lon, alt_ft, a, b, c, d, runway_length_ft, fuel_price, depart_time, depart_station, arrival_station, ) schedule.append(leg) return schedule def distance_nmi(origin: Airport, destination: Airport) -> float: lat1, lon1 = origin.lat, origin.lon lat2, lon2 = destination.lat, destination.lon Great_Circle_Inner_Step = (math.sin(math.radians(lat1)) * math.sin(math.radians(lat2)) + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.cos(math.radians(lon2-lon1))) if Great_Circle_Inner_Step >= 1: distance_nmi = math.acos(1) else: distance_nmi = math.acos(math.sin(math.radians(lat1)) * math.sin(math.radians(lat2)) + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.cos(math.radians(lon2-lon1)))*3440 return distance_nmi if __name__ == "__main__": filename = schedule_filename airports = Airports.create_AirportList(filename) for i in range(len(airports)): print(airports[i].code) print("altitude") print(airports[i].alt_ft) print("runway length") print(airports[i].runway_length_ft) distance_printout = Airports.distance_nmi(airports[1], airports[5]) print("The distance between " + airports[1].code + " and " + airports[5].code + " is " + str(distance_printout) + " nmi.") time = 1 while time < 24*365: temp = airports[0].OAT(time) plt.figure(1) plt.plot(time/24, temp, '.b') plt.xlabel("Time - days since 1am Jan 1, 2020") plt.ylabel("Predicted EGTM (deg C)") plt.title("OAT") time += 24 plt.grid() plt.show()