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160 lines
4.7 KiB
160 lines
4.7 KiB
import argparse
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import concurrent.futures
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import csv
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import itertools
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from concurrent.futures import as_completed
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from functools import lru_cache
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import geonamescache
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import numpy as np
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from geopy.distance import geodesic
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from tqdm import tqdm
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MAX_DISTANCE = 20_037.5
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# Add argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-c", "--country", help="Specify the country code", type=str, default="US"
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)
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parser.add_argument(
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"-w", "--workers", help="Specify the number of workers", type=int, default=1
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)
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parser.add_argument(
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"-s",
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"--chunk-size",
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help="Specify chunk size for batching calculations",
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type=int,
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default=1000,
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)
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parser.add_argument(
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"-o",
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"--output-file",
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help="Specify the name of the output file (file.csv)",
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type=str,
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default="city_distances_full.csv",
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)
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args = parser.parse_args()
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gc = geonamescache.GeonamesCache()
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cities = gc.get_cities()
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us_cities = {
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k: c
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for k, c in cities.items()
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if (c.get("countrycode") == args.country) # & (c.get("population", 0) > 5e4)
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}
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@lru_cache(maxsize=None)
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def get_coordinates(city_name, country_code="US"):
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"""
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Get the coordinates of a city.
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Parameters
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----------
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city_name : str
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The name of the city.
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country_code : str, optional
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The country code of the city, by default 'US'.
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Returns
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-------
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tuple
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A tuple containing the latitude and longitude of the city,
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or None if the city is not found.
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"""
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search_results = gc.search_cities(city_name, case_sensitive=True)
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search_results = [
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d for d in search_results if (d.get("countrycode") == country_code)
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]
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if not search_results:
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return None
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populations = [city.get("population") for city in search_results]
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city = search_results[np.argmax(populations)]
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return city.get("latitude"), city.get("longitude")
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def get_distance(city1, city2, country1="US", country2="US"):
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"""
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Get the distance between two cities in kilometers.
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Parameters
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----------
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city1 : str
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The name of the first city.
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city2 : str
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The name of the second city.
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country1 : str, optional
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The country code of the first city, by default 'US'.
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country2 : str, optional
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The country code of the second city, by default 'US'.
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Returns
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-------
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float
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The distance between the two cities in kilometers,
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or None if one or both city names were not found.
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"""
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city1_coords = get_coordinates(city1, country1)
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city2_coords = get_coordinates(city2, country2)
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if (city1_coords is None) or (city2_coords is None):
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return None
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return geodesic(city1_coords, city2_coords).km
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def calculate_distance(pair):
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city1, city2 = pair
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distance = get_distance(city1["name"], city2["name"])
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return city1["name"], city2["name"], distance
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def main():
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cities = list(us_cities.values())
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print(f"Num cities: {len(cities)}")
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city_combinations = list(itertools.combinations(cities, 2))
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# np.random.shuffle(city_combinations) # will this help or hurt caching? 1.03it/s
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chunk_size = args.chunk_size
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num_chunks = len(city_combinations) // chunk_size + 1
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output_file = args.output_file
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with open(output_file, "w", newline="") as csvfile:
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fieldnames = ["city_from", "city_to", "distance"]
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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try:
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executor = concurrent.futures.ProcessPoolExecutor(max_workers=args.workers)
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for i in tqdm(
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range(num_chunks),
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total=num_chunks,
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desc="Processing chunks",
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ncols=100,
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bar_format="{l_bar}{bar}{r_bar}",
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):
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chunk = city_combinations[(i * chunk_size) : (i + 1) * chunk_size]
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futures = {
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executor.submit(calculate_distance, pair): pair for pair in chunk
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}
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for future in as_completed(futures):
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city_from, city_to, distance = future.result()
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if distance is not None:
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writer.writerow(
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{
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"city_from": city_from,
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"city_to": city_to,
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"distance": distance,
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}
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)
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csvfile.flush() # write to disk immediately
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except KeyboardInterrupt:
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print("Interrupted. Terminating processes...")
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executor.shutdown(wait=False)
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raise SystemExit("Execution terminated by user.")
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if __name__ == "__main__":
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main()
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