import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
routes_url = "https://raw.githubusercontent.com/jpatokal/openflights/master/data/routes.dat"
routes_df = pd.read_csv(routes_url, header=None)
routes_df.columns = ["airline", "id", "source", "source_id", "target", "target_id", "codeshare", "stops",
"equipments"]
routes_df = routes_df[["airline", "id", "source", "source_id", "target", "target_id"]]
routes_df.head()
| airline | id | source | source_id | target | target_id |
|---|
| 0 | 2B | 410 | AER | 2965 | KZN | 2990 |
|---|
| 1 | 2B | 410 | ASF | 2966 | KZN | 2990 |
|---|
| 2 | 2B | 410 | ASF | 2966 | MRV | 2962 |
|---|
| 3 | 2B | 410 | CEK | 2968 | KZN | 2990 |
|---|
| 4 | 2B | 410 | CEK | 2968 | OVB | 4078 |
|---|
airport_url = "https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat"
airport_df = pd.read_csv(airport_url, header=None)
airport_df.columns = ["airport_id", "name", "city", "country", "iata", "icao", "lat", "long", "altitude", "tmz",
"dst", "tzt", "type", "source"]
airport_df = airport_df[["airport_id", "iata", "icao", "country", "city", "name"]]
airport_df.head()
| airport_id | iata | icao | country | city | name |
|---|
| 0 | 1 | GKA | AYGA | Papua New Guinea | Goroka | Goroka Airport |
|---|
| 1 | 2 | MAG | AYMD | Papua New Guinea | Madang | Madang Airport |
|---|
| 2 | 3 | HGU | AYMH | Papua New Guinea | Mount Hagen | Mount Hagen Kagamuga Airport |
|---|
| 3 | 4 | LAE | AYNZ | Papua New Guinea | Nadzab | Nadzab Airport |
|---|
| 4 | 5 | POM | AYPY | Papua New Guinea | Port Moresby | Port Moresby Jacksons International Airport |
|---|
us_airport_df = airport_df[airport_df.country == "United States"]
us_airport_df.head()
| airport_id | iata | icao | country | city | name |
|---|
| 3212 | 3411 | BTI | PABA | United States | Barter Island | Barter Island LRRS Airport |
|---|
| 3213 | 3412 | \N | PAWT | United States | Fort Wainwright | Wainwright Air Station |
|---|
| 3214 | 3413 | LUR | PALU | United States | Cape Lisburne | Cape Lisburne LRRS Airport |
|---|
| 3215 | 3414 | PIZ | PPIZ | United States | Point Lay | Point Lay LRRS Airport |
|---|
| 3216 | 3415 | ITO | PHTO | United States | Hilo | Hilo International Airport |
|---|
kept_iata = set(us_airport_df.iata.tolist())
kept_icao = set(us_airport_df.icao.tolist())
kept_codes = kept_iata.union(kept_icao)
us_routes = routes_df[routes_df.source.isin(kept_codes)]
us_routes = us_routes[us_routes.target.isin(kept_codes)]
len(us_routes)
# keep only topK most popular
from collections import Counter
topK = 100
cnt = Counter(us_routes.source.tolist())
cnt.update(us_routes.target.tolist())
kept_codes = sorted([(k, v) for k, v in cnt.items()], key=lambda x: x[1], reverse=True)
kept_codes = [k for k, _ in kept_codes[:topK]]
kept_routes = us_routes[us_routes.source.isin(kept_codes)]
kept_routes = kept_routes[kept_routes.target.isin(kept_codes)]
Convert into Matrices
networks = {}
airlines = list(set(kept_routes.airline))
airports = list(set(kept_routes.source).union(set(kept_routes.target)))
airport2ind = {k: i for i, k in enumerate(airports)}
for airl in airlines:
curr = kept_routes[kept_routes.airline == airl].copy()
net = np.zeros((len(airports), len(airports)))
for ind, row in curr.iterrows():
net[airport2ind[row.source], airport2ind[row.target]] = 1
net[airport2ind[row.target], airport2ind[row.source]] = 1
networks[airl] = net.astype(np.int32)
fig, axes = plt.subplots(7, 8, figsize=(20, 20))
axes = axes.flat
for i, air in enumerate(airlines):
axes[i].set_title(air)
axes[i].imshow(networks[air])
axes[i].set_xticks([])
axes[i].set_yticks([])
for i in range(52, 56):
axes[i].set_visible(False)
plt.show()
basefile = "data/airline_network/{0}.csv"
for airline, net in networks.items():
file = basefile.format(airline)
np.savetxt(file, net, fmt='%i', delimiter=",")
