def __init__(self, date, lin_reg_coef_filename): lin_reg_coef_file = open(lin_reg_coef_filename) self.coef_ = pickle.load(lin_reg_coef_file) self.date_ = date self.flight_graph_ = centralities.gen_daily(date) self.flight_network_info_ = FlightNetworkInfo() self.conn_ = sqlite3.connect("Flights.sqlite") self.cursor_ = self.conn_.cursor() depDelayMap, arrDelayMap = self.flight_network_info_.AvgDelaysByAirport() self.airportDepDelays_ = depDelayMap self.airportArrDelays_ = arrDelayMap depDelayMap, arrDelayMap = self.flight_network_info_.AvgDelaysByCarrier() self.carrierDepDelays_ = depDelayMap self.carrierArrDelays_ = arrDelayMap self.centralities_ = centralities.gen_node_info(date) ymd = self.date_.split("-") self.year_ = int(ymd[0]) self.month_ = int(ymd[1]) self.day_ = int(ymd[2])
def __init__(self, date, lin_reg_coef_filename): lin_reg_coef_file = open(lin_reg_coef_filename) self.coef_ = pickle.load(lin_reg_coef_file) self.date_ = date self.flight_graph_ = centralities.gen_daily(date) self.flight_network_info_ = FlightNetworkInfo() self.conn_ = sqlite3.connect('Flights.sqlite') self.cursor_ = self.conn_.cursor() depDelayMap, arrDelayMap = self.flight_network_info_.AvgDelaysByAirport( ) self.airportDepDelays_ = depDelayMap self.airportArrDelays_ = arrDelayMap depDelayMap, arrDelayMap = self.flight_network_info_.AvgDelaysByCarrier( ) self.carrierDepDelays_ = depDelayMap self.carrierArrDelays_ = arrDelayMap self.centralities_ = centralities.gen_node_info(date) ymd = self.date_.split('-') self.year_ = int(ymd[0]) self.month_ = int(ymd[1]) self.day_ = int(ymd[2])
def generate_degree_heatmap_points(airports, airport_info): pts = [] log_degs = [log(int(v[0]) + int(v[1])) for k, v in airport_info.items()] max_log_deg = max(x for x in log_degs) for key, val in airports.iteritems(): if key in airport_info.keys(): log_deg = log(int(airport_info[key][0])+int(airport_info[key][1])) normalized_log_deg = int(heatmap_scale*float(log_deg)/max_log_deg) + 1 for i in range(normalized_log_deg): pts.append(val) else: pts.append(val) return pts airports = read_in_airports() daily_flights = c.gen_daily('2010-12-23') airport_info = c.gen_node_info('2010-12-23', daily_flights) arr_delays, dep_delays = read_in_delays() avg_delays = calculate_normalised_delays(arr_delays, dep_delays) del_pts = generate_delay_heatmap_points(airports, avg_delays) deg_pts = generate_degree_heatmap_points(airports, airport_info) del_deg_pts = generate_delay_over_degree(airports, avg_delays, airport_info) del_bc_pts = generate_delay_times_betw_centrality(airports, avg_delays, airport_info) kml = simplekml.Kml() for airport in daily_flights.nodes(): for neighbor in nx.all_neighbors(daily_flights, airport): flightpath = (airports[airport], airports[neighbor]) path = kml.newlinestring(name='flightpath', coords=flightpath)
max_log_deg = max(x for x in log_degs) for key, val in airports.iteritems(): if key in airport_info.keys(): log_deg = log( int(airport_info[key][0]) + int(airport_info[key][1])) normalized_log_deg = int( heatmap_scale * float(log_deg) / max_log_deg) + 1 for i in range(normalized_log_deg): pts.append(val) else: pts.append(val) return pts airports = read_in_airports() daily_flights = c.gen_daily('2010-12-23') airport_info = c.gen_node_info('2010-12-23', daily_flights) arr_delays, dep_delays = read_in_delays() avg_delays = calculate_normalised_delays(arr_delays, dep_delays) del_pts = generate_delay_heatmap_points(airports, avg_delays) deg_pts = generate_degree_heatmap_points(airports, airport_info) del_deg_pts = generate_delay_over_degree(airports, avg_delays, airport_info) del_bc_pts = generate_delay_times_betw_centrality(airports, avg_delays, airport_info) kml = simplekml.Kml() for airport in daily_flights.nodes(): for neighbor in nx.all_neighbors(daily_flights, airport): flightpath = (airports[airport], airports[neighbor]) path = kml.newlinestring(name='flightpath', coords=flightpath)