File size: 1,427 Bytes
45efbb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | from math import radians, cos, sin, asin, sqrt
from typing import Sequence
from uuid import UUID
def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
R = 6371000
lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
dlat = lat2 - lat1
dlon = lon2 - lon1
a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
c = 2 * asin(sqrt(a))
return R * c
def is_within_radius(
lat1: float, lon1: float,
lat2: float, lon2: float,
radius_meters: float
) -> bool:
return haversine_distance(lat1, lon1, lat2, lon2) <= radius_meters
def find_nearby_issues(
target_lat: float,
target_lon: float,
issues: Sequence[tuple[UUID, float, float]],
radius_meters: float
) -> list[tuple[UUID, float]]:
nearby = []
for issue_id, lat, lon in issues:
distance = haversine_distance(target_lat, target_lon, lat, lon)
if distance <= radius_meters:
nearby.append((issue_id, distance))
return sorted(nearby, key=lambda x: x[1])
def get_bounding_box(lat: float, lon: float, radius_meters: float) -> tuple[float, float, float, float]:
R = 6371000
lat_delta = (radius_meters / R) * (180 / 3.14159265359)
lon_delta = lat_delta / cos(radians(lat))
return (
lat - lat_delta,
lat + lat_delta,
lon - lon_delta,
lon + lon_delta
)
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