backend/src/tester.py

@@ -23,7 +23,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
         sightseeing=Preference(type='sightseeing', score = 5),
         nature=Preference(type='nature', score = 5),
         shopping=Preference(type='shopping', score = 5),
-        max_time_minute=100,
+        max_time_minute=15,
         detour_tolerance_minute=0
     )
 
@@ -74,6 +74,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
 # test(tuple((48.8344400, 2.3220540)))       # Café Chez César 
 # test(tuple((48.8375946, 2.2949904)))       # Point random
 # test(tuple((47.377859, 8.540585)))         # Zurich HB
-# test(tuple((45.758217, 4.831814)))      # Lyon Bellecour
+# test(tuple((45.758217, 4.831814)))         # Lyon Bellecour
-test(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
+# test(tuple((48.5848435, 7.7332974)))       # Strasbourg Gare
 # test(tuple((48.2067858, 16.3692340)))      # Vienne
+test(tuple((48.084588, 7.280405)))         # Turckheim 

backend/src/utils/optimizer.py

@@ -487,7 +487,7 @@ class Optimizer:
 
         # Raise error if no solution is found
         if not res.success :
-            raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
+            raise ArithmeticError("No solution could be found, the problem is overconstrained. Try with a longer trip (>30 minutes).")
 
         # If there is a solution, we're good to go, just check for connectiveness
         order, circles = self.is_connected(res.x)

backend/src/utils/refiner.py

@@ -2,6 +2,7 @@ import yaml, logging
 
 from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
 from math import pi
+from typing import List
 
 from structs.landmark import Landmark
 from . import take_most_important, get_time_separation
@@ -133,6 +134,21 @@ class Refiner :
             i += 1
     
         return tour
+    
+    def integrate_landmarks(self, sub_list: List[Landmark], main_list: List[Landmark]) :
+        """
+        Inserts 'sub_list' of Landmarks inside the 'main_list' by leaving the ends untouched.
+        
+        Args: 
+            sub_list    : the list of Landmarks to be inserted inside of the 'main_list'.
+            main_list   : the original list with start and finish.
+
+        Returns:
+            the full list.
+        """
+        sub_list.append(main_list[-1])          # add finish back
+        return main_list[:-1] + sub_list        # create full set of possible landmarks
+
 
 
     def find_shortest_path_through_all_landmarks(self, landmarks: list[Landmark]) -> tuple[list[Landmark], Polygon]:
@@ -253,6 +269,11 @@ class Refiner :
         except :
             better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
             xs, ys = better_tour_poly.exterior.xy
+            """ 
+            ERROR HERE : 
+                Exception has occurred: AttributeError
+                'LineString' object has no attribute 'exterior'
+            """
 
 
         # reverse the xs and ys
@@ -315,26 +336,30 @@ class Refiner :
 
         self.logger.info(f"Using {len(minor_landmarks)} minor landmarks around the predicted path")
 
-        # full set of visitable landmarks
+        # Full set of visitable landmarks.
-        full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
+        full_set = self.integrate_landmarks(minor_landmarks, base_tour)     # could probably be optimized with less overhead
-        full_set.append(base_tour[-1])                # add finish back
 
-        # get a new tour
+        # Generate a new tour with the optimizer.
         new_tour = self.optimizer.solve_optimization(
             max_time = max_time + detour,
             landmarks = full_set, 
             max_landmarks = self.max_landmarks_refiner
         )
 
+        # If unsuccessful optimization, use the base_tour.
         if new_tour is None:
             self.logger.warning("No solution found for the refined tour. Returning the initial tour.")
             new_tour = base_tour
 
+        # If only one landmark, return it.
+        if len(new_tour) < 4 :
+            return new_tour
 
-        # Find shortest path using the nearest neighbor heuristic
+        # Find shortest path using the nearest neighbor heuristic.
         better_tour, better_poly = self.find_shortest_path_through_all_landmarks(new_tour)
 
-        # Fix the tour using Polygons if the path looks weird
+        # Fix the tour using Polygons if the path looks weird. 
+        # Conditions : circular trip and invalid polygon.
         if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid :
             better_tour = self.fix_using_polygon(better_tour)