terrassumptions/server/lib.py

import json
import math

import requests
import haversine

# Google API key, with access to Street View Static API
google_api_key = "AIzaSyAqjCYR6Szph0X0H_iD6O1HenFhL9jySOo"
metadata_url = "https://maps.googleapis.com/maps/api/streetview/metadata"
mapcrunch_url = "http://www.mapcrunch.com/_r/"


def generate_coord(max_retries=100000):
    """
    Returns (latitude, longitude) of usable coord (where google has data).
    This function will attempt at most max_retries calls to map crunch to fetch
    candidate points, and will exit as soon as a suitable candidate is found.

    This function calls the streetview metadata endpoint - there is no quota consumed
    """
    for _ in range(max_retries):
        points_res = requests.get(mapcrunch_url).text
        points_js = json.loads(points_res.strip("while(1); "))
        for lat, lng in points_js["points"]:
            params = {
                "key": google_api_key,
                "location": f"{lat},{lng}",
            }
            js = requests.get(metadata_url, params=params).json()
            if js["status"] == "OK":
                return (lat, lng)


mean_earth_radius_km = (6378 + 6357) / 2
# the farthest you can be from another point on Earth
antipode_dist_km = math.pi * mean_earth_radius_km
min_dist_km = 0.15  # if you're within 150m, you get a perfect score
# if you're more than 1/4 of the Earth away, you get 0
max_dist_km = antipode_dist_km / 2
# this has been tuned by hand based on the max dist to get a nice Gaussian
exp_denom = max_dist_km * max_dist_km / 4
perfect_score = 5000


def score(target, guess):
    """
    Takes in two (latitude, longitude) pairs and produces an int score.
    Score is in the (inclusive) range [0, 5000]
    Higher scores are closer.

    Returns (score, distance in km)
    """
    dist_km = haversine.haversine(target, guess)
    if dist_km <= min_dist_km:
        return perfect_score, dist_km

    if dist_km >= max_dist_km:
        return 0, dist_km

    # Gaussian, with some manual tuning to get good fall off
    exponent = -((dist_km - min_dist_km) ^ 2) / exp_denom
    return int(perfect_score * math.exp(exponent)), dist_km