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])
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)
kml.save("KMLs/air_travel_network.kml")
def __calculateLinearRegressionInput(self):
inputRows = []
outputRows = []
flightNetworkInfoByDate = {}
# isolationDate = '2009-03-04'
self.cursor_.execute(
'select OriginAirportID, DestAirportID, Carrier, FlightDate, AirTime, Distance, \
DepTime, ArrTime, ArrDelayMin from Flight where Cancelled = 0 and Diverted = 0 and FlightDate < ?',
(self.ISOLATION_DATE, ))
for OriginAirportID, DestAirportID, Carrier, FlightDate, AirTime, Distance, DepTime, ArrTime, ArrDelayMin in self.cursor_.fetchall(
):
if FlightDate not in flightNetworkInfoByDate:
flightNetworkInfoByDate[
FlightDate] = centralities.gen_node_info(FlightDate)
flightNetworkInfo = flightNetworkInfoByDate[FlightDate]
CarrierAvgDepDelay = self.avgDepDelaysByCarrier_[Carrier]
CarrierAvgArrDelay = self.avgArrDelaysByCarrier_[Carrier]
AirportAvgDepDelay = self.avgDepDelaysByAirport_[OriginAirportID]
AirportAvgArrDelay = self.avgArrDelaysByAirport_[DestAirportID]
if not CarrierAvgDepDelay:
CarrierAvgDepDelay = 0.0
if not CarrierAvgArrDelay:
CarrierAvgArrDelay = 0.0
if not AirportAvgDepDelay:
AirportAvgDepDelay = 0.0
if not AirportAvgArrDelay:
AirportAvgArrDelay = 0.0
OriginAirportInfo = flightNetworkInfo[OriginAirportID]
DestAirportInfo = flightNetworkInfo[DestAirportID]
OriginOutDeg = OriginAirportInfo[0]
OriginInDeg = OriginAirportInfo[1]
OriginBtwCentrality = OriginAirportInfo[2]
OriginCloseCentrality = OriginAirportInfo[3]
DestOutDeg = DestAirportInfo[0]
DestInDeg = DestAirportInfo[1]
DestBtwCentrality = DestAirportInfo[2]
DestCloseCentrality = DestAirportInfo[3]
# missing: AirTime, DepTime, ArrTime
inputRow = (CarrierAvgDepDelay, CarrierAvgArrDelay, AirportAvgDepDelay, AirportAvgArrDelay, Distance, \
OriginOutDeg, OriginInDeg, OriginBtwCentrality, OriginCloseCentrality, DestOutDeg, DestInDeg, DestBtwCentrality, \
DestCloseCentrality)
inputRows.append(inputRow)
if ArrDelayMin is None:
ArrDelayMin = 0
outputRows.append(ArrDelayMin)
self.linRegInputRows_ = inputRows
self.linRegOutputRows_ = outputRows
inputRowsFile = open('extra_data/inputRows.pickle', 'w')
outputRowsFile = open('extra_data/outputRows.pickle', 'w')
pickle.dump(inputRows, inputRowsFile)
pickle.dump(outputRows, outputRowsFile)
return inputRows, outputRows
def __calculateLinearRegressionInput(self):
inputRows = []
outputRows = []
flightNetworkInfoByDate = {}
# isolationDate = '2009-03-04'
self.cursor_.execute('select OriginAirportID, DestAirportID, Carrier, FlightDate, AirTime, Distance, \
DepTime, ArrTime, ArrDelayMin from Flight where Cancelled = 0 and Diverted = 0 and FlightDate < ?', (self.ISOLATION_DATE, ))
for OriginAirportID, DestAirportID, Carrier, FlightDate, AirTime, Distance, DepTime, ArrTime, ArrDelayMin in self.cursor_.fetchall():
if FlightDate not in flightNetworkInfoByDate:
flightNetworkInfoByDate[FlightDate] = centralities.gen_node_info(FlightDate)
flightNetworkInfo = flightNetworkInfoByDate[FlightDate]
CarrierAvgDepDelay = self.avgDepDelaysByCarrier_[Carrier]
CarrierAvgArrDelay = self.avgArrDelaysByCarrier_[Carrier]
AirportAvgDepDelay = self.avgDepDelaysByAirport_[OriginAirportID]
AirportAvgArrDelay = self.avgArrDelaysByAirport_[DestAirportID]
if not CarrierAvgDepDelay:
CarrierAvgDepDelay = 0.0
if not CarrierAvgArrDelay:
CarrierAvgArrDelay = 0.0
if not AirportAvgDepDelay:
AirportAvgDepDelay = 0.0
if not AirportAvgArrDelay:
AirportAvgArrDelay = 0.0
OriginAirportInfo = flightNetworkInfo[OriginAirportID]
DestAirportInfo = flightNetworkInfo[DestAirportID]
OriginOutDeg = OriginAirportInfo[0]
OriginInDeg = OriginAirportInfo[1]
OriginBtwCentrality = OriginAirportInfo[2]
OriginCloseCentrality = OriginAirportInfo[3]
DestOutDeg = DestAirportInfo[0]
DestInDeg = DestAirportInfo[1]
DestBtwCentrality = DestAirportInfo[2]
DestCloseCentrality = DestAirportInfo[3]
# missing: AirTime, DepTime, ArrTime
inputRow = (CarrierAvgDepDelay, CarrierAvgArrDelay, AirportAvgDepDelay, AirportAvgArrDelay, Distance, \
OriginOutDeg, OriginInDeg, OriginBtwCentrality, OriginCloseCentrality, DestOutDeg, DestInDeg, DestBtwCentrality, \
DestCloseCentrality)
inputRows.append(inputRow)
if ArrDelayMin is None:
ArrDelayMin = 0
outputRows.append(ArrDelayMin)
self.linRegInputRows_ = inputRows
self.linRegOutputRows_ = outputRows
inputRowsFile = open('extra_data/inputRows.pickle', 'w')
outputRowsFile = open('extra_data/outputRows.pickle', 'w')
pickle.dump(inputRows, inputRowsFile)
pickle.dump(outputRows, outputRowsFile)
return inputRows, outputRows
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)
kml.save("KMLs/air_travel_network.kml")
