terrassumptions/server/lib.py

import json
import math

import requests
import haversine

metadata_url = "https://maps.googleapis.com/maps/api/streetview/metadata"
mapcrunch_url = "http://www.mapcrunch.com/_r/"


def generate_coord(key):
    """
    Takes in a Google API key.
    Returns (latitude, longitude) of usable coord (where google has data).
    Returns None if, after trying several random points, no usable coords could be found.

    This function calls the streetview metadata endpoint - there is no quota consumed
    """
    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": key,
            "location": f"{lat},{lng}",
        }
        js = requests.get(metadata_url, params=params).json()
        if js["status"] != "ZERO_RESULTS":
            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
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.
    """
    dist_km = haversine.haversine(target, guess)
    if dist_km <= min_dist_km:
        return perfect_score

    if dist_km >= max_dist_km:
        return 0

    # TODO probably still needs tweaking
    return int(perfect_score * (1 - (dist_km / max_dist_km) ** 2))