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=100, only_america=False):
    """
    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
    """
    mc_url = mapcrunch_url + ("?c=21" if only_america else "")
    for _ in range(max_retries):
        points_res = requests.get(mc_url).text
        points_js = json.loads(points_res.strip("while(1); "))
        if "c=" not in mc_url:
            mc_url += f"?c={points_js['country']}" # lock to the first country randomed
        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

# if you're more than 1/4 of the Earth's circumfrence away, you get 0
max_dist_km = (math.pi * mean_earth_radius_km) / 2 # this is about 10,000 km

# if you're within 1/16 of the Earth's circumfrence away, you get at least 1000 points
quarter_of_max_km = max_dist_km / 4 # this is about 2,500 km

# https://www.wolframalpha.com/input/?i=sqrt%28%28%28land+mass+of+earth%29+%2F+7%29%29+%2F+pi%29+in+kilometers
# this is the average "radius" of a continent
# within this radius, you get at least 2000 points
avg_continental_rad_km = 1468.0

# somewhat arbitrarily, if you're within 1000 km, you get at least 3000 points
one_thousand = 1000.0

# https://www.wolframalpha.com/input/?i=sqrt%28%28%28land+mass+of+earth%29+%2F+%28number+of+countries+on+earth%29%29+%2F+pi%29+in+kilometers
# this is the average "radius" of a country
# within this radius, you get at least 4000 points
avg_country_rad_km = 479.7

# if you're within 150m, you get a perfect score of 5000
min_dist_km = 0.15  


def score_within(raw_dist, min_dist, max_dist):
    """
    Gives a score between 0 and 1000, with 1000 for the min_dist and 0 for the max_dist
    """
    # scale the distance down to [0.0, 1.0], then multiply it by 2 for easing
    pd2 = 2 * (raw_dist - min_dist) / (max_dist - min_dist)
    # perform a quadratic ease-in-out on pd2
    r = (pd2 ** 2) / 2 if pd2 < 1 else 1 - (((2 - pd2) ** 2) / 2)
    # use this to ease between 1000 and 0
    return int(1000 * (1 - r))


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:
        point_score = 5000
    elif dist_km <= avg_country_rad_km:
        point_score = 4000 + score_within(dist_km, min_dist_km, avg_country_rad_km)
    elif dist_km <= one_thousand:
        point_score = 3000 + score_within(dist_km, avg_country_rad_km, one_thousand)
    elif dist_km <= avg_continental_rad_km:
        point_score = 2000 + score_within(dist_km, one_thousand, avg_continental_rad_km)
    elif dist_km <= quarter_of_max_km:
        point_score = 1000 + score_within(dist_km, avg_continental_rad_km, quarter_of_max_km)
    elif dist_km <= max_dist_km:
        point_score = score_within(dist_km, quarter_of_max_km, max_dist_km)
    else: # dist_km > max_dist_km
        point_score = 0

    return point_score, dist_km