backend/Pipfile

@@ -9,6 +9,7 @@ name = "pypi"
 numpy = "*"
 fastapi = "*"
 pydantic = "*"
+geopy = "*"
 shapely = "*"
 scipy = "*"
 osmpythontools = "*"

backend/src/persistence.py

@@ -21,8 +21,8 @@ if constants.MEMCACHED_HOST_PATH is None:
 else:
     client = Client(
         constants.MEMCACHED_HOST_PATH,
-        timeout = 1,
+        timeout=1,
-        allow_unicode_keys = True,
+        allow_unicode_keys=True,
-        encoding = 'utf-8',
+        encoding='utf-8',
-        serde = serde.pickle_serde
+        serde=serde.pickle_serde
     )

backend/src/structs/trip.py

@@ -22,8 +22,7 @@ class Trip(BaseModel):
 
         # Store the trip in the cache
         cache_client.set(f"trip_{trip.uuid}", trip)
-        # make sure to await the result (noreply=False). Otherwise the cache might not be inplace when the trip is actually requested
+        cache_client.set_many({f"landmark_{landmark.uuid}": landmark for landmark in landmarks}, expire=3600)
-        cache_client.set_many({f"landmark_{landmark.uuid}": landmark for landmark in landmarks}, expire=3600, noreply=False)
         # is equivalent to:
         # for landmark in landmarks:
         #     cache_client.set(f"landmark_{landmark.uuid}", landmark, expire=3600)

backend/src/utils/get_time_separation.py

@@ -1,5 +1,5 @@
 import yaml
-from math import sin, cos, sqrt, atan2, radians
+from geopy.distance import geodesic
 
 import constants
 
@@ -8,7 +8,6 @@ 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:
     """
@@ -23,28 +22,16 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     """
 
 
-    if p1 == p2:
+    # Compute the straight-line distance in km
+    if p1 == p2 :
         return 0
     else: 
-        # Compute the distance in km along the surface of the Earth
+        dist = geodesic(p1, p2).kilometers
-        # (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])
 
-        dlon = lon2 - lon1
+    # Consider the detour factor for average cityto deterline walking distance (in km)
-        dlat = lat2 - lat1
+    walk_dist = dist*DETOUR_FACTOR
-
-        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_distance / AVERAGE_WALKING_SPEED * 60
+    walk_time = walk_dist/AVERAGE_WALKING_SPEED*60
 
     return round(walk_time)

backend/src/utils/optimizer.py

@@ -3,6 +3,7 @@ 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