parseData.py

from collections import defaultdict
import requests
import sys
import json
import csv
import geopy.distance
import statistics
import os

OPEN_WEAHTER_MAP_ENDPOINT = "https://api.openweathermap.org/data/2.5/weather"
OPEN_WEAHTER_MAP_API_KEY = "3ca11b63c2ad4dc9fa4e0fc32d8708ae"


# if len(sys.argv) != 3:
#   print("usage: python genData.py <lat> <long>")
#   sys.exit(0)

with open("data.csv", "r") as f:
  lines = list(csv.reader(f, delimiter=","))[1:]

# lat, lon = map(float, sys.argv[1:])
lat, lon = [os.environ["LAT"], os.environ["LONG"]]
weather_params = {"lat": lat, "lon": lon, "appid": OPEN_WEAHTER_MAP_API_KEY}

def is_near(coord_1, coord_2):
  return geopy.distance.geodesic(coord_1, coord_2).km <= 10


stations_by_id = defaultdict(lambda: [])
for line in lines:
  stations_by_id[line[0]].append(line)

near_stations = {station_id: station for station_id, station in stations_by_id.items() if is_near((float(station[0][16]), float(station[0][15])), (lat, lon))}

weather = requests.get(OPEN_WEAHTER_MAP_ENDPOINT, params=weather_params).json()

print(weather["weather"][0]["main"])

data = {
  "ville": os.environ["CITY"],
  "center": {"lat": lat, "lon": lon}, 
  "weather": weather["weather"][0]["main"], 
  "stations": [
      {
        "center": {"lat": station[0][16], "lon": station[0][15]},
        "atmo": statistics.mean(map(lambda x: int(x[2]), station)),
        "no2": statistics.mean(map(lambda x: int(x[10]), station)),
        "so2": statistics.mean(map(lambda x: int(x[11]), station)),
        "o3": statistics.mean(map(lambda x: int(x[12]), station)),
        "pm10": statistics.mean(map(lambda x: int(x[13]), station)),
        "pm25": statistics.mean(map(lambda x: int(x[14]), station)),
        "radius": 40
      } for station in near_stations.values()
    ]
  }

with open("data.json", "w+") as f:
  json.dump(data, f)