remove geopy dependency

backend/src/utils/get_time_separation.py

@@ -1,5 +1,5 @@
 import yaml
-from geopy.distance import geodesic
+from math import sin, cos, sqrt, atan2, radians
 
 import constants
 
@@ -8,6 +8,7 @@ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
     DETOUR_FACTOR = parameters['detour_factor']
     AVERAGE_WALKING_SPEED = parameters['average_walking_speed']
 
+EARTH_RADIUS_KM = 6373
 
 def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     """
@@ -22,16 +23,28 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     """
 
 
-    # Compute the straight-line distance in km
-    if p1 == p2 :
+    if p1 == p2:
         return 0
-    else: 
-        dist = geodesic(p1, p2).kilometers
+    else:
+        # Compute the distance in km along the surface of the Earth
+        # (assume spherical Earth)
+        # this is the haversine formula, stolen from stackoverflow
+        # in order to not use any external libraries
+        lat1, lon1 = radians(p1[0]), radians(p1[1])
+        lat2, lon2 = radians(p2[0]), radians(p2[1])
 
-    # Consider the detour factor for average cityto deterline walking distance (in km)
-    walk_dist = dist*DETOUR_FACTOR
+        dlon = lon2 - lon1
+        dlat = lat2 - lat1
+
+        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
+        c = 2 * atan2(sqrt(a), sqrt(1 - a))
+
+        distance = EARTH_RADIUS_KM * c
+
+    # Consider the detour factor for average an average city
+    walk_distance = distance * DETOUR_FACTOR
 
     # Time to walk this distance (in minutes)
-    walk_time = walk_dist/AVERAGE_WALKING_SPEED*60
+    walk_time = walk_distance / AVERAGE_WALKING_SPEED * 60
 
     return round(walk_time)

backend/src/utils/optimizer.py

@@ -3,7 +3,6 @@ import numpy as np
 
 from scipy.optimize import linprog
 from collections import defaultdict, deque
-from geopy.distance import geodesic
 
 from structs.landmark import Landmark
 from .get_time_separation import get_time