reviewed code structure, cleaned comments, now pep8 conform

backend/src/amenities/nature.am

@@ -0,0 +1,11 @@
+'leisure'='park'
+geological
+'natural'='geyser'
+'natural'='hot_spring'
+'natural'='arch'
+'natural'='volcano'
+'natural'='stone'
+'tourism'='alpine_hut'
+'tourism'='viewpoint'
+'tourism'='zoo'
+'waterway'='waterfall'

backend/src/amenities/shopping.am

@@ -0,0 +1,2 @@
+'shop'='department_store'
+'shop'='mall'

backend/src/amenities/sightseeing.am

@@ -0,0 +1,9 @@
+'tourism'='museum'
+'tourism'='attraction'
+'tourism'='gallery'
+historic
+'amenity'='arts_centre'
+'amenity'='planetarium'
+'amenity'='place_of_worship'
+'amenity'='fountain'
+'water'='reflecting_pool'

backend/src/landmarks_manager.py

@@ -1,21 +1,13 @@
 import math as m
+import json, os
 
-from OSMPythonTools.api import Api
+from typing import List, Tuple
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder, Nominatim
+
 from structs.landmarks import Landmark, LandmarkType
 from structs.preferences import Preferences, Preference
-from typing import List
-from typing import Tuple
 
 
-BBOX_SIDE = 10              # size of bbox in *km* for general area, 10km
-RADIUS_CLOSE_TO = 27.5        # size of area in *m* for close features, 30m radius
-MIN_SCORE = 30              # DEPRECIATED. discard elements with score < 30
-MIN_TAGS = 5                # DEPRECIATED. discard elements withs less than 5 tags
-CHURCH_PENALTY = 0.6        # penalty to reduce score of curches
-PARK_COEFF = 1.4             # multiplier for parks
-N_IMPORTANT = 40            # take the 30 most important landmarks
-
 SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
 NATURE = LandmarkType(landmark_type='nature')
 SHOPPING = LandmarkType(landmark_type='shopping')
@@ -23,71 +15,64 @@ SHOPPING = LandmarkType(landmark_type='shopping')
 
 # Include the json here
 # Create a list of all things to visit given some preferences and a city. Ready for the optimizer
-def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None)->Tuple[List[Landmark], List[Landmark]] :
+def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
-
-    l_sights = ["'tourism'='museum'", "'tourism'='attraction'", "'tourism'='gallery'", 'historic', "'amenity'='arts_centre'", "'amenity'='planetarium'", "'amenity'='place_of_worship'", "'amenity'='fountain'", '"water"="reflecting_pool"'] 
-    l_nature = ["'leisure'='park'", 'geological', "'natural'='geyser'", "'natural'='hot_spring'", '"natural"="arch"', '"natural"="cave_entrance"', '"natural"="volcano"', '"natural"="stone"', '"tourism"="alpine_hut"', '"tourism"="picnic_site"', '"tourism"="viewpoint"', '"tourism"="zoo"', '"waterway"="waterfall"'] 
-    l_shop = ["'shop'='department_store'", "'shop'='mall'"] #, '"shop"="collector"', '"shop"="antiques"'] 
 
+    l_sights, l_nature, l_shop = get_amenities()
     L = []
 
-    # Use 'City, Country'
-    if city_country is not None :
-
     # List for sightseeing
     if preferences.sightseeing.score != 0 :
-            L1 = get_landmarks_nominatim(city_country, l_sights, SIGHTSEEING)
+        L1 = get_landmarks(l_sights, SIGHTSEEING, city_country=city_country, coordinates=coordinates)
         correct_score(L1, preferences.sightseeing)
         L += L1
     
     # List for nature
     if preferences.nature.score != 0 :
-            L2 = get_landmarks_nominatim(city_country, l_nature, NATURE)
+        L2 = get_landmarks(l_nature, NATURE, city_country=city_country, coordinates=coordinates)
         correct_score(L2, preferences.nature)
         L += L2
     
     # List for shopping
     if preferences.shopping.score != 0 :
-            L3 = get_landmarks_nominatim(city_country, l_shop, SHOPPING)
+        L3 = get_landmarks(l_shop, SHOPPING, city_country=city_country, coordinates=coordinates)
         correct_score(L3, preferences.shopping)
         L += L3
 
-    # Use coordinates
-    elif coordinates is not None :
-
-        # List for sightseeing
-        if preferences.sightseeing.score != 0 :
-            L1 = get_landmarks_coords(coordinates, l_sights, SIGHTSEEING)
-            correct_score(L1, preferences.sightseeing)
-            L += L1
-        
-        # List for nature
-        if preferences.nature.score != 0 :
-            L2 = get_landmarks_coords(coordinates, l_nature, NATURE)
-            correct_score(L2, preferences.nature)
-            L += L2
-        
-        # List for shopping
-        if preferences.shopping.score != 0 :
-            L3 = get_landmarks_coords(coordinates, l_shop, SHOPPING)
-            correct_score(L3, preferences.shopping)
-            L += L3
-
-
     return remove_duplicates(L), take_most_important(L)
-    #return L, cleanup_list(L)
 
-# Determines if two locations are close to each other
-def is_close_to(loc1: Tuple[float, float], loc2: Tuple[float, float])->bool :
 
-    alpha = (180*RADIUS_CLOSE_TO)/(6371000*m.pi)
+# Helper function to gather the amenities list
-    if abs(loc1[0] - loc2[0]) + abs(loc1[1] - loc2[1]) < alpha*2 :
+def get_amenities() -> List[List[str]] :
-        return True
+    
-    else : 
+    # Get the list of amenities from the files
-        return False
+    sightseeing = get_list('/amenities/sightseeing.am')
+    nature = get_list('/amenities/nature.am')
+    shopping = get_list('/amenities/shopping.am')
+
+    return sightseeing, nature, shopping
+
+
+# Helper function to read a .am file and generate the corresponding list
+def get_list(path: str) -> List[str] :
+
+    with open(os.path.dirname(os.path.abspath(__file__)) + path) as f :
+        content = f.readlines()
+
+        amenities = []
+        for line in content :
+            amenities.append(line.strip('\n'))
+
+    return amenities
+
 
 # Take the most important landmarks from the list
-def take_most_important(L: List[Landmark])->List[Landmark] :
+def take_most_important(L: List[Landmark]) -> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        N_important = parameters['N important']
+    
     L_copy = []
     L_clean = []
     scores = [0]*len(L)
@@ -110,11 +95,12 @@ def take_most_important(L: List[Landmark])->List[Landmark] :
                 for old in L_copy :
                     if old.name == elem.name :
                         old.attractiveness = L[t].attractiveness
+    
     scores = [0]*len(L_copy)
     for i, elem in enumerate(L_copy) :
         scores[i] = elem.attractiveness
 
-    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-N_IMPORTANT:]
+    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-N_important:]
 
     for i, elem in enumerate(L_copy) :
         if i in res :
@@ -122,13 +108,14 @@ def take_most_important(L: List[Landmark])->List[Landmark] :
 
     return L_clean
 
+
 # Remove duplicate elements and elements with low score
-def remove_duplicates(L: List[Landmark])->List[Landmark] :
+def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
     L_clean = []
     names = []
 
     for landmark in L :
-        if landmark.name in names :                 # Remove duplicates
+        if landmark.name in names : 
             continue     
         else :
             names.append(landmark.name)
@@ -136,7 +123,8 @@ def remove_duplicates(L: List[Landmark])->List[Landmark] :
     
     return L_clean
     
-# Correct the score of a list of landmarks by taking into account preferences and the number of tags
+
+# Correct the score of a list of landmarks by taking into account preference settings
 def correct_score(L: List[Landmark], preference: Preference) :
 
     if len(L) == 0 :
@@ -148,53 +136,32 @@ def correct_score(L: List[Landmark], preference: Preference) :
     for elem in L :
         elem.attractiveness = int(elem.attractiveness*preference.score/500)     # arbitrary computation
 
-# Correct the score of a list of landmarks by taking into account preferences and the number of tags
-def correct_score_test(L: List[Landmark], preference: Preference) :
 
-    if len(L) == 0 :
+# Function to count elements within a certain radius of a location
-        return
+def count_elements_within_radius(coordinates: Tuple[float, float], radius: int) -> int:
-    
-    if L[0].type != preference.type :
-        raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
-
-    for elem in L :
-        elem.attractiveness = int(elem.attractiveness/100) + elem.n_tags      # arbitrary correction of the balance score vs number of tags
-        elem.attractiveness = elem.attractiveness*preference.score        # arbitrary computation
-
-# Function to count elements within a 25m radius of a location
-def count_elements_within_radius(coordinates: Tuple[float, float]) -> int:
     
     lat = coordinates[0]
     lon = coordinates[1]
 
-    alpha = (180*RADIUS_CLOSE_TO)/(6371000*m.pi)
+    alpha = (180*radius)/(6371000*m.pi)
-
     bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
-    overpass = Overpass()
         
     # Build the query to find elements within the radius
     radius_query = overpassQueryBuilder(bbox=[bbox['latLower'],bbox['lonLower'],bbox['latHigher'],bbox['lonHigher']],
                              elementType=['node', 'way', 'relation'])
 
     try : 
+        overpass = Overpass()
         radius_result = overpass.query(radius_query)
-    
-        # The count is the number of elements found
         return radius_result.countElements()
     
     except :
         return None
 
-# Creates a bounding box around precise coordinates
+
+# Creates a bounding box around given coordinates
 def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]:
-    """
+
-    Create a simple bounding box around given coordinates.
-    :param coordinates: tuple (lat, lon)
-    -> lat: Latitude of the center point.
-    -> lon: Longitude of the center point.
-    :param side_length: int     - side length of the bbox in km
-    :return: Bounding box as (min_lat, min_lon, max_lat, max_lon).
-    """
     lat = coordinates[0]
     lon = coordinates[1]
 
@@ -214,18 +181,26 @@ def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[flo
     return min_lat, min_lon, max_lat, max_lon
 
 # Generates the list of landmarks for a given Landmarktype. Needs coordinates, a list of amenities and the corresponding LandmarkType
-def get_landmarks_coords(coordinates: Tuple[float, float], l: List[Landmark], landmarktype: LandmarkType)->List[Landmark]:
+def get_landmarks_coords(coordinates: Tuple[float, float], list_amenity: list, landmarktype: LandmarkType) -> List[Landmark]:
 
-    overpass = Overpass()
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        tag_coeff = parameters['tag coeff']
+        park_coeff = parameters['park coeff']
+        church_coeff = parameters['church coeff']
+        radius = parameters['radius close to']
+        bbox_side = parameters['city bbox side']
     
     # Generate a bbox around current coordinates
-    bbox = create_bbox(coordinates, BBOX_SIDE)
+    bbox = create_bbox(coordinates, bbox_side)
 
     # Initialize some variables
+    overpass = Overpass()
     N = 0
     L = []
 
-    for amenity in l :
+    for amenity in list_amenity :
         query = overpassQueryBuilder(bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
         result = overpass.query(query)
         N += result.countElements()
@@ -235,16 +210,15 @@ def get_landmarks_coords(coordinates: Tuple[float, float], l: List[Landmark], la
             name = elem.tag('name')                             # Add name, decode to ASCII
             location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
 
-            # skip if unprecise location
+            # Skip if unprecise location
             if name is None or location[0] is None:
                 continue
 
-            # skip if unused
+            # Skip if unused
             if 'disused:leisure' in elem.tags().keys():
                     continue
             
             else :
-                
                 osm_type = elem.type()              # Add type : 'way' or 'relation'
                 osm_id = elem.id()                  # Add OSM id 
                 elem_type = landmarktype            # Add the landmark type as 'sightseeing
@@ -252,11 +226,11 @@ def get_landmarks_coords(coordinates: Tuple[float, float], l: List[Landmark], la
                 
                 # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
                 if amenity == "'amenity'='place_of_worship'" :
-                    score = int((count_elements_within_radius(location) + n_tags*100 )*CHURCH_PENALTY)  
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
                 elif amenity == "'leisure'='park'" :
-                    score = int((count_elements_within_radius(location) + n_tags*100 )*PARK_COEFF)  
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
                 else :
-                    score = count_elements_within_radius(location) + n_tags*100
+                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
                 
                 if score is not None :
                     # Generate the landmark and append it to the list
@@ -265,7 +239,15 @@ def get_landmarks_coords(coordinates: Tuple[float, float], l: List[Landmark], la
 
     return L
 
-def get_landmarks_nominatim(city_country: str, l: List[Landmark], landmarktype: LandmarkType)->List[Landmark] :
+def get_landmarks_nominatim(city_country: str, list_amenity: list, landmarktype: LandmarkType) -> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        tag_coeff = parameters['tag coeff']
+        park_coeff = parameters['park coeff']
+        church_coeff = parameters['church coeff']
+        radius = parameters['radius close to']
     
     overpass = Overpass()
     nominatim = Nominatim()
@@ -275,7 +257,7 @@ def get_landmarks_nominatim(city_country: str, l: List[Landmark], landmarktype:
     N = 0
     L = []
 
-    for amenity in l :
+    for amenity in list_amenity :
         query = overpassQueryBuilder(area=areaId, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
         result = overpass.query(query)
         N += result.countElements()
@@ -294,18 +276,96 @@ def get_landmarks_nominatim(city_country: str, l: List[Landmark], landmarktype:
                     continue
             
             else :
-                
                 osm_type = elem.type()              # Add type : 'way' or 'relation'
                 osm_id = elem.id()                  # Add OSM id 
                 elem_type = landmarktype            # Add the landmark type as 'sightseeing
                 n_tags = len(elem.tags().keys())    # Add number of tags
                 
-                # Add score of given landmark based on the number of surrounding elements
+                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
                 if amenity == "'amenity'='place_of_worship'" :
-                    score = int(count_elements_within_radius(location)*CHURCH_PENALTY)
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
+                elif amenity == "'leisure'='park'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
                 else :
-                    score = count_elements_within_radius(location)
+                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
-
+
+                if score is not None :
+                    # Generate the landmark and append it to the list
+                    landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
+                    L.append(landmark)
+
+    return L
+
+
+
+
+def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
+    
+    if city_country is None and coordinates is None :
+        raise ValueError("Either one of 'city_country' and 'coordinates' arguments must be specified")
+
+    if city_country is not None and coordinates is not None :
+        raise ValueError("Cannot specify both 'city_country' and 'coordinates' at the same time, please choose either one")
+
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        tag_coeff = parameters['tag coeff']
+        park_coeff = parameters['park coeff']
+        church_coeff = parameters['church coeff']
+        radius = parameters['radius close to']
+        bbox_side = parameters['city bbox side']
+    
+    # If city_country is specified :
+    if city_country is not None :
+        nominatim = Nominatim()
+        areaId = nominatim.query(city_country).areaId()
+        bbox = None
+
+    # If coordinates are specified :
+    elif coordinates is not None :
+        bbox = create_bbox(coordinates, bbox_side)
+        areaId = None
+
+    else :
+        raise ValueError("Argument number is not corresponding.")
+    
+    # Initialize some variables
+    N = 0
+    L = []
+    overpass = Overpass()
+
+    for amenity in list_amenity :
+        query = overpassQueryBuilder(area=areaId, bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
+        result = overpass.query(query)
+        N += result.countElements()
+
+        for elem in result.elements():
+
+            name = elem.tag('name')                             # Add name
+            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+
+            # skip if unprecise location
+            if name is None or location[0] is None:
+                continue
+
+            # skip if unused
+            if 'disused:leisure' in elem.tags().keys():
+                    continue
+            
+            else :
+                osm_type = elem.type()              # Add type : 'way' or 'relation'
+                osm_id = elem.id()                  # Add OSM id 
+                elem_type = landmarktype            # Add the landmark type as 'sightseeing
+                n_tags = len(elem.tags().keys())    # Add number of tags
+                
+                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
+                if amenity == "'amenity'='place_of_worship'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
+                elif amenity == "'leisure'='park'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
+                else :
+                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
 
                 if score is not None :
                     # Generate the landmark and append it to the list

backend/src/main.py

@@ -12,50 +12,37 @@ app = FastAPI()
 
 # Assuming frontend is calling like this : 
 #"http://127.0.0.1:8000/process?param1={param1}&param2={param2}"
-@app.post("/optimizer_coords/{longitude}/{latitude}/{city_country}")
+@app.post("/optimizer_coords/{latitude}/{longitude}/{city_country}")
-def main1(preferences: Preferences = Body(...), longitude: float = None, latitude: float = None, city_country: str = None) -> List[Landmark]:
+def main1(preferences: Preferences = Body(...), latitude: float = None, longitude: float = None, city_country: str = None) -> List[Landmark]:
+    
+    if preferences is None :
+        raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
+    elif latitude is None and longitude is None and city_country is None :
+        raise ValueError("Please provide GPS coordinates or a 'city_country' string.")
+    elif latitude is not None and longitude is not None and city_country is not None :
+        raise ValueError("Please provide EITHER GPS coordinates or a 'city_country' string.")
+
 
     # From frontend get longitude, latitude and prefence list
-    
+    if city_country is None :
-    # Generate the landmark list
+        coordinates = tuple((latitude, longitude))
-    landmarks = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=tuple((longitude, latitude)))
-
-    # Set the max distance
-    max_steps = 90
-
-    # Compute the visiting order
-    visiting_order = solve_optimization(landmarks, max_steps, True)
-
-    return visiting_order
     
     
+    [], landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
 
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
 
-@app.get("test")
+    landmarks_short.insert(0, start)
-def test():
+    landmarks_short.append(finish)
 
-    # CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
+    max_walking_time = 4 # hours
-    max_steps = 16
 
+    visiting_list = solve_optimization(landmarks_short, max_walking_time*60, True)
 
-    # Initialize all landmarks (+ start and goal). Order matters here
+    return visiting_list
-    landmarks = []
-    landmarks.append(LandmarkTest("départ", -1, (0, 0)))
-    landmarks.append(LandmarkTest("tour eiffel", 99, (0,2)))                           # PUT IN JSON
-    landmarks.append(LandmarkTest("arc de triomphe", 99, (0,4)))
-    landmarks.append(LandmarkTest("louvre", 99, (0,6)))
-    landmarks.append(LandmarkTest("montmartre", 99, (0,10)))
-    landmarks.append(LandmarkTest("concorde", 99, (0,8)))
-    landmarks.append(LandmarkTest("arrivée", -1, (0, 0)))
 
 
-    visiting_order = solve_optimization(landmarks, max_steps, True)
-
-    return visiting_order
-
-    # should return landmarks = the list of Landmark (ordered list)
-    #return("max steps :", max_steps, "\n", visiting_order)
-
 
 # input city, country in the form of 'Paris, France'
 @app.post("/test2/{city_country}")

backend/src/optimizer.py

@@ -1,85 +1,17 @@
 import numpy as np
+import json, os
 
-from typing import List
+from typing import List, Tuple
-from typing import Tuple
 from scipy.optimize import linprog
-from scipy.linalg import block_diag
-from structs.landmarks import Landmark
 from math import radians, sin, cos, acos
 
+from structs.landmarks import Landmark
 
     
-DETOUR_FACTOR = 1.3         # detour factor for straightline distance
+# Function to print the result
-AVG_WALKING_SPEED = 4.8     # average walking speed in km/h
+def print_res(L: List[Landmark], L_tot) -> list:
 
-
+    if len(L) == L_tot: 
-# Function that returns the distance in meters from one location to another
-def get_distance(p1: Tuple[float, float], p2: Tuple[float, float]) :
-    
-    # Compute the straight-line distance in km
-    if p1 == p2 :
-        return 0, 0
-    else: 
-        dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
-
-    # Consider the detour factor for average city
-    wdist = dist*DETOUR_FACTOR
-
-    # Time to walk this distance (in minutes)
-    wtime = wdist/AVG_WALKING_SPEED*60
-
-    if wtime > 15 :
-        wtime = 5*round(wtime/5)
-    else :
-        wtime = round(wtime)
-  
-
-    return round(wdist, 1), wtime
-
-
-# landmarks = [Landmark_1, Landmark_2, ...]
-
-# Convert the solution of the optimization into the list of edges to follow. Order is taken into account
-def untangle(resx: list) -> list:
-    N = len(resx)                   # length of res
-    L = int(np.sqrt(N))             # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
-    n_edges = resx.sum()      # number of edges
-
-    order = []
-    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
-
-    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
-
-    indx = nonzero_tup[0].tolist()
-    indy = nonzero_tup[1].tolist()
-
-    vert = (indx[0], indy[0])
-
-    order.append(vert[0])
-    order.append(vert[1])
-    
-    while len(order) < n_edges + 1 :
-        ind = indx.index(vert[1])
-
-        vert = (indx[ind], indy[ind])
-
-        order.append(vert[1])
-
-    return order
-
-    
-# Just to print the result
-def print_res(L: List[Landmark], landmarks: List[Landmark], P) -> list:
-
-    """N = int(np.sqrt(len(X)))
-    for i in range(N):
-        print(X[i*N:i*N+N])
-    print("Optimal value:", -res.fun)  # Minimization, so we negate to get the maximum
-    print("Optimal point:", res.x)
-    for i,x in enumerate(X) : X[i] = round(x,0)
-    print(order)"""
-
-    if len(L) == len(landmarks): 
         print('\nAll landmarks can be visited within max_steps, the following order is suggested : ')
     else :
         print('Could not visit all the landmarks, the following order is suggested : ')
@@ -92,26 +24,20 @@ def print_res(L: List[Landmark], landmarks: List[Landmark], P) -> list:
         else : 
             print('- ' + elem.name)
 
-    #steps = path_length(P, abs(res.x))
     print("\nMinutes walked : " + str(dist))
-    print(f"\nVisited {len(L)} out of {len(landmarks)} landmarks")
+    print(f"Visited {len(L)} out of {L_tot} landmarks")
-
-    return 
 
 
+# Prevent the use of a particular set of nodes
+def prevent_config(resx, A_ub, b_ub) -> bool:
     
-# prevent the creation of similar circles
-def prevent_circle(resx, landmarks: List[Landmark], A_ub, b_ub) -> bool:
     for i, elem in enumerate(resx):
         resx[i] = round(elem)
     
-    N = len(resx)               # length of res
+    N = len(resx)               # Number of edges
-    L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
+    L = int(np.sqrt(N))         # Number of landmarks
-    n_edges = resx.sum()        # number of edges
-
-    
-    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
 
+    nonzeroind = np.nonzero(resx)[0]                    # the return is a little funky so I use the [0]
     nonzero_tup = np.unravel_index(nonzeroind, (L,L))
 
     ind_a = nonzero_tup[0].tolist()
@@ -130,19 +56,16 @@ def prevent_circle(resx, landmarks: List[Landmark], A_ub, b_ub) -> bool:
     return A_ub, b_ub
 
 
-def break_circle2(circle_vertices, landmarks: List[Landmark], A_ub, b_ub) -> bool:
+# Prevent the possibility of a given set of vertices
-    
+def break_cricle(circle_vertices: list, L: int, A_ub: list, b_ub: list) -> bool:
-    L = len(landmarks)         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
-
 
     if L-1 in circle_vertices :
         circle_vertices.remove(L-1)
 
-    ones = [1]*L
     h = [0]*L*L
     for i in range(L) :
         if i in circle_vertices :
-            h[i*L:i*L+L] = ones
+            h[i*L:i*L+L] = [1]*L
 
     A_ub = np.vstack((A_ub, h))
     b_ub.append(len(circle_vertices)-1)
@@ -150,8 +73,10 @@ def break_circle2(circle_vertices, landmarks: List[Landmark], A_ub, b_ub) -> boo
     return A_ub, b_ub
 
 
-# Checks if the path is connected
+# Checks if the path is connected, returns a circle if it finds one
-def is_connected(resx, landmarks: List[Landmark]) -> bool:
+def is_connected(resx) -> bool:
+    
+    # first round the results to have only 0-1 values
     for i, elem in enumerate(resx):
         resx[i] = round(elem)
     
@@ -159,7 +84,6 @@ def is_connected(resx, landmarks: List[Landmark]) -> bool:
     L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
     n_edges = resx.sum()        # number of edges
 
-    
     nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
 
     nonzero_tup = np.unravel_index(nonzeroind, (L,L))
@@ -178,12 +102,9 @@ def is_connected(resx, landmarks: List[Landmark]) -> bool:
     for i, a in enumerate(ind_a) :
         edges.append((a, ind_b[i]))      # Create the list of edges
 
-    flag = False
-
     remaining = edges
     remaining.remove(edge1)
-    # This can be further optimized
+
-    #while len(vertices_visited) < n_edges + 1 :
     break_flag = False
     while len(remaining) > 0 and not break_flag:
         for edge2 in remaining :
@@ -192,7 +113,6 @@ def is_connected(resx, landmarks: List[Landmark]) -> bool:
                     edges_visited.append(edge2)
                     break_flag = True
                     break
-                    #continue # continue vs break vs needed at all ?
                 else :    
                     vertices_visited.append(edge1[1])
                     edges_visited.append(edge2)
@@ -202,171 +122,131 @@ def is_connected(resx, landmarks: List[Landmark]) -> bool:
             elif edge1[1] == L-1 or edge1[1] in vertices_visited:
                         break_flag = True
                         break
-                #break
+
-        #if flag is True :
-        #    break
     vertices_visited.append(edge1[1])
 
 
     if len(vertices_visited) == n_edges +1 :
-        flag = True
+        return vertices_visited, []
-        circle = []
     else: 
-        flag = False
+        return vertices_visited, edges_visited
-        circle = edges_visited
-
-    """j = 0
-    for i in vertices_visited :
-        if landmarks[i].name == 'start' :
-            ordered_visit = vertices_visited[j:] + vertices_visited[:j]
-            break
-        j+=1"""
 
 
-    return flag, vertices_visited, circle
+# Function that returns the distance in meters from one location to another
+def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
     
+    # Compute the straight-line distance in km
+    if p1 == p2 :
+        return 0, 0
+    else: 
+        dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
 
+    # Consider the detour factor for average city
+    wdist = dist*detour
 
+    # Time to walk this distance (in minutes)
+    wtime = wdist/speed*60
 
-# Checks for cases of circular symmetry in the result
+    if wtime > 15 :
-def has_circle(resx: list) :
+        wtime = 5*round(wtime/5)
-    N = len(resx)               # length of res
-    L = int(np.sqrt(N))         # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
-    n_edges = resx.sum()        # number of edges
-
-    
-    nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
-
-    nonzero_tup = np.unravel_index(nonzeroind, (L,L))
-
-    indx = nonzero_tup[0].tolist()
-    indy = nonzero_tup[1].tolist()
-    
-
-    verts = []
-
-    for i, x in enumerate(indx) :
-        verts.append((x, indy[i]))
-
-    
-    for vert in verts :
-        visited = []
-        visited.append(vert)
-
-        while len(visited) < n_edges + 1 :
-
-            try : 
-                ind = indx.index(vert[1])
-
-                vert = (indx[ind], indy[ind])
-
-                if vert in visited :
-                    return visited
     else :
-                    visited.append(vert)
+        wtime = round(wtime)
-            except :
-                break
   
-    return []
 
-# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
+    return round(wdist, 1), wtime
-def break_sym(N, A_ub, b_ub):
-    upper_ind = np.triu_indices(N,0,N)
 
-    up_ind_x = upper_ind[0]
-    up_ind_y = upper_ind[1]
 
-    for i, _ in enumerate(up_ind_x) :
+# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
-        l = [0]*N*N
+# We want to maximize the sightseeing :  max(c) st. A*x < b   and   A_eq*x = b_eq
-        if up_ind_x[i] != up_ind_y[i] :
+def init_ub_dist(landmarks: List[Landmark], max_steps: int):
-            l[up_ind_x[i]*N + up_ind_y[i]] = 1
-            l[up_ind_y[i]*N + up_ind_x[i]] = 1
     
-            A_ub = np.vstack((A_ub,l))
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
-            b_ub.append(1)
+        parameters = json.loads(f.read())
+        detour = parameters['detour factor']
+        speed = parameters['average walking speed']
     
-            """for i in range(7):
+    # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
-                print(l[i*7:i*7+7])
+    c = []
-            print("\n")"""
+    # Coefficients of inequality constraints (left-hand side)
+    A_ub = []
 
-    return A_ub, b_ub
+    for spot1 in landmarks :
+        dist_table = [0]*len(landmarks)
+        c.append(-spot1.attractiveness)
+        for j, spot2 in enumerate(landmarks) :
+            t = get_distance(spot1.location, spot2.location, detour, speed)[1]
+            dist_table[j] = t
+        A_ub += dist_table
+    c = c*len(landmarks)
 
-# Constraint to not have circular paths. Want to go from start -> finish without unconnected loops
+    return c, A_ub, [max_steps]
-def break_circle(L, A_ub, b_ub, circle) :
-    l = [0]*L*L
 
-    for index in circle :
-        x = index[0]
-        y = index[1]
-        l[x*L+y] = 1
-
-    A_ub = np.vstack((A_ub,l))
-    b_ub.append(len(circle)-1)
-
-    """print("\n\nPREVENT CIRCLE")
-    for i in range(7):
-        print(l[i*7:i*7+7])
-    print("\n")"""
-
-    return A_ub, b_ub
 
 # Constraint to respect max number of travels
-def respect_number(N, A_ub, b_ub):
+def respect_number(L, A_ub, b_ub):
-    """h = []
-    for i in range(N) : h.append([1]*N)
-    T = block_diag(*h)
-    for l in T :
-        for i in range(7):
-            print(l[i*7:i*7+7])
-        print("\n")"""
-    #return np.vstack((A_ub, T)), b_ub + [1]*N
-    ones = [1]*N
-    zeros = [0]*N
-    for i in range(N) :
-        h = zeros*i + ones + zeros*(N-1-i)
 
+    ones = [1]*L
+    zeros = [0]*L
+    for i in range(L) :
+        h = zeros*i + ones + zeros*(L-1-i)
         A_ub = np.vstack((A_ub, h))
         b_ub.append(1)
 
     return A_ub, b_ub
 
 
-# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
+# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
-def respect_order(N: int, A_eq, b_eq): 
+def break_sym(L, A_ub, b_ub):
-    for i in range(N-1) :     # Prevent stacked ones
+    upper_ind = np.triu_indices(L,0,L)
-        if i == 0 or i == N-1:  # Don't touch start or finish
-            continue
-        else : 
-            l = [0]*N
-            l[i] = -1
-            l = l*N
-            for j in range(N) :
-                l[i*N + j] = 1
 
-            A_eq = np.vstack((A_eq,l))
+    up_ind_x = upper_ind[0]
-            b_eq.append(0)
+    up_ind_y = upper_ind[1]
 
-    return A_eq, b_eq
+    for i, _ in enumerate(up_ind_x) :
+        l = [0]*L*L
+        if up_ind_x[i] != up_ind_y[i] :
+            l[up_ind_x[i]*L + up_ind_y[i]] = 1
+            l[up_ind_y[i]*L + up_ind_x[i]] = 1
+
+            A_ub = np.vstack((A_ub,l))
+            b_ub.append(1)
+
+    return A_ub, b_ub
 
-# Compute manhattan distance between 2 locations
-def manhattan_distance(loc1: tuple, loc2: tuple):
-    x1, y1 = loc1
-    x2, y2 = loc2
-    return abs(x1 - x2) + abs(y1 - y2)
 
 # Constraint to not stay in position. Removes d11, d22, d33, etc.
-def init_eq_not_stay(N: int): 
+def init_eq_not_stay(L: int): 
-    l = [0]*N*N
+    l = [0]*L*L
 
-    for i in range(N) :
+    for i in range(L) :
-        for j in range(N) :
+        for j in range(L) :
             if j == i :
-                l[j + i*N] = 1
+                l[j + i*L] = 1
     
     l = np.array(np.array(l))
 
     return [l], [0]
 
+
+# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
+def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
+    L = len(landmarks)
+
+    for i, elem in enumerate(landmarks) :
+        if elem.must_do is True and elem.name not in ['finish', 'start']:
+            l = [0]*L*L
+            for j in range(L) :     # sets the horizontal ones (go from)
+                l[j +i*L] = 1       # sets the vertical ones (go to)        double check if good
+                
+            for k in range(L-1) :
+                l[k*L+L-1] = 1  
+
+            A_eq = np.vstack((A_eq,l))
+            b_eq.append(2)
+
+    return A_eq, b_eq
+
+
 # Constraint to ensure start at start and finish at goal
 def respect_start_finish(L: int, A_eq: list, b_eq: list):
     ls = [1]*L + [0]*L*(L-1)    # sets only horizontal ones for start (go from)
@@ -391,144 +271,101 @@ def respect_start_finish(L: int, A_eq: list, b_eq: list):
 
     return A_eq, b_eq
 
-# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
-# We want to maximize the sightseeing :  max(c) st. A*x < b   and   A_eq*x = b_eq
-def init_ub_dist(landmarks: List[Landmark], max_steps: int):
-    # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
-    c = []
-    # Coefficients of inequality constraints (left-hand side)
-    A_ub = []
-    for i, spot1 in enumerate(landmarks) :
-        dist_table = [0]*len(landmarks)
-        c.append(-spot1.attractiveness)
-        for j, spot2 in enumerate(landmarks) :
-            d, t = get_distance(spot1.location, spot2.location)
-            dist_table[j] = t
-        A_ub += dist_table
-    c = c*len(landmarks)
-    """A_ub = []
-    for line in A :
-        #print(line)
-        A_ub += line"""
-    return c, A_ub, [max_steps]
-
-# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
-def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
-    L = len(landmarks)
-    H = 0       # sort of heuristic to get an idea of the number of steps needed
-    
-    elem_prev = landmarks[0]
-    
-    for i, elem in enumerate(landmarks) :
-        if elem.must_do is True and elem.name not in ['finish', 'start']:
-            l = [0]*L*L
-            for j in range(L) :     # sets the horizontal ones (go from)
-                l[j +i*L] = 1       # sets the vertical ones (go to)        double check if good
-                
-            for k in range(L-1) :
-                l[k*L+L-1] = 1  
 
+# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
+def respect_order(N: int, A_eq, b_eq): 
+    for i in range(N-1) :           # Prevent stacked ones
+        if i == 0 or i == N-1:      # Don't touch start or finish
+            continue
+        else : 
+            l = [0]*N
+            l[i] = -1
+            l = l*N
+            for j in range(N) :
+                l[i*N + j] = 1
 
             A_eq = np.vstack((A_eq,l))
-            b_eq.append(2)
+            b_eq.append(0)
 
-        d, t = get_distance(elem.location, elem_prev.location)
+    return A_eq, b_eq
-        H += t
-        elem_prev = elem
 
 
-
-    return A_eq, b_eq, H
-
 # Computes the path length given path matrix (dist_table) and a result
-def path_length(P: list, resx: list) :
+def add_time_to_reach(order: List[Landmark], landmarks: List[Landmark])->List[Landmark] :
-    return np.dot(P, resx)
+    
+    j = 0
+    L =  []
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    prev = landmarks[0]
+    while(len(L) != len(order)) :
+        
+        elem = landmarks[order[j]]
+        if elem != prev :
+            elem.time_to_reach = get_distance(elem.location, prev.location, detour, speed)[1]
+        L.append(elem)
+        prev = elem   
+        j += 1
+
+    return L
+
 
 # Main optimization pipeline
 def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool) :
 
-    N = len(landmarks)
+    L = len(landmarks)
 
     # SET CONSTRAINTS FOR INEQUALITY
     c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
-    P = A_ub                                                # store the paths for later. Needed to compute path length
+    A_ub, b_ub = respect_number(L, A_ub, b_ub)                      # Respect max number of visits (no more possible stops than landmarks). 
-    A_ub, b_ub = respect_number(N, A_ub, b_ub)              # Respect max number of visits (no more possible stops than landmarks). 
+    A_ub, b_ub = break_sym(L, A_ub, b_ub)                           # break the 'zig-zag' symmetry
-
-    # TODO : Problems with circular symmetry
-    A_ub, b_ub = break_sym(N, A_ub, b_ub)                  # break the symmetry. Only use the upper diagonal values
 
     # SET CONSTRAINTS FOR EQUALITY
-    A_eq, b_eq = init_eq_not_stay(N)                                # Force solution not to stay in same place
+    A_eq, b_eq = init_eq_not_stay(L)                                # Force solution not to stay in same place
-    A_eq, b_eq, H = respect_user_mustsee(landmarks, A_eq, b_eq)     # Check if there are user_defined must_see. Also takes care of start/goal
+    A_eq, b_eq = respect_user_mustsee(landmarks, A_eq, b_eq)     # Check if there are user_defined must_see. Also takes care of start/goal
-    A_eq, b_eq = respect_start_finish(N, A_eq, b_eq)                # Force start and finish positions
+    A_eq, b_eq = respect_start_finish(L, A_eq, b_eq)                # Force start and finish positions
-    A_eq, b_eq = respect_order(N, A_eq, b_eq)                       # Respect order of visit (only works when max_steps is limiting factor)
+    A_eq, b_eq = respect_order(L, A_eq, b_eq)                       # Respect order of visit (only works when max_steps is limiting factor)
 
-    # Bounds for variables (x can only be 0 or 1)
+    # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
-    x_bounds = [(0, 1)] * len(c)
+    x_bounds = [(0, 1)]*L*L
 
     # Solve linear programming problem
-
     res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
 
-
-
     # 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")
 
-        # Override the max_steps using the heuristic
+    # If there is a solution, we're good to go, just check for connectiveness
-        for i, val in enumerate(b_ub) :
-            if val == max_steps : b_ub[i] = H
-
-        # Solve problem again :
-        res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
-
-        if not res.success :
-            s = "No solution could be found, even when increasing max_steps using the heuristic"
-            return s
-            #raise ValueError("No solution could be found, even when increasing max_steps using the heuristic")
-    
-    # If there is a solution, we're good to go, just check for
     else :
-        t, order, circle = is_connected(res.x, landmarks)
+        order, circle = is_connected(res.x)
         i = 0
-
+        timeout = 300
-        # Break the circular symmetry if needed
+        while len(circle) != 0 and i < timeout:
-        while len(circle) != 0 :
+            A_ub, b_ub = prevent_config(res.x, A_ub, b_ub)
-            A_ub, b_ub = prevent_circle(res.x, landmarks, A_ub, b_ub)
+            A_ub, b_ub = break_cricle(order, len(landmarks), A_ub, b_ub)
-            A_ub, b_ub = break_circle(len(landmarks), A_ub, b_ub, circle)
-            A_ub, b_ub = break_circle2(order, landmarks, A_ub, b_ub)
             res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
-            t, order, circle = is_connected(res.x, landmarks)
+            order, circle = is_connected(res.x)
-            if t :
+            if len(circle) == 0 :
+                # Add the times to reach and stop optimizing
+                L = add_time_to_reach(order, landmarks)
                 break
-            #circle = has_circle(res.x)
             print(i)
             i += 1
         
-        t, order, [] = is_connected(res.x, landmarks)
+        if i == timeout :
-
+            raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
-        
-
-        prev = landmarks[order[0]]
-        i = 0
-        L =  []
-        #prev = landmarks[order[i]]
-        while(len(L) != len(order)) :
-            elem = landmarks[order[i]]
-            if elem != prev :
-                d, t = get_distance(elem.location, prev.location)
-                elem.time_to_reach = t
-            L.append(elem)
-            prev = elem   
-            i += 1
 
         if printing_details is True :
             if i != 0 :
                 print(f"Neded to recompute paths {i} times because of unconnected loops...")
-            
+            print_res(L, len(landmarks))
-            print_res(L, landmarks, P)
+            print("\nTotal score : " + str(int(-res.fun)))
-
-            print(np.dot(P, res.x))
 
         return L
 

backend/src/parameters/landmarks_manager.params

@@ -0,0 +1,8 @@
+{
+  "city bbox side" : 10,
+  "radius close to" : 27.5,
+  "church coeff" : 0.6,
+  "park coeff" : 1.4,
+  "tag coeff" : 100,
+  "N important" : 30
+}

backend/src/parameters/optimizer.params

@@ -0,0 +1,4 @@
+{
+  "detour factor" : 10,
+  "average walking speed" : 27.5
+}

backend/src/structs/landmarks.py

@@ -1,13 +1,9 @@
 from typing import Optional
 from pydantic import BaseModel
-from OSMPythonTools.api import Api
-from .landmarktype import LandmarkType
-from .preferences import Preferences
 
-class LandmarkTest(BaseModel) :
+from .landmarktype import LandmarkType
-    name : str
+
-    attractiveness : int
+
-    loc : tuple
 
 # Output to frontend
 class Landmark(BaseModel) :

backend/src/tester.py

@@ -1,11 +1,13 @@
 import pandas as pd
-from optimizer import solve_optimization
+
+from typing import List
 from landmarks_manager import generate_landmarks
+from fastapi.encoders import jsonable_encoder
+
+from optimizer import solve_optimization
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
-from fastapi.encoders import jsonable_encoder
-from typing import List
 
 
 # Helper function to create a .txt file with results
@@ -37,17 +39,24 @@ def test3(city_country: str) -> List[Landmark]:
                                   type=LandmarkType(landmark_type='shopping'),
                                   score = 5))
 
-    coords = None
+    coordinates = None
 
-    landmarks = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coords)
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
 
-    max_steps = 9
+    #write_data(landmarks)
 
-    visiting_order = solve_optimization(landmarks, max_steps, True)
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
-    print(len(visiting_order))
 
-    return len(visiting_order)
+    test = landmarks_short
+    
+    test.insert(0, start)
+    test.append(finish)
+
+    max_walking_time = 2 # hours
+
+    visiting_list = solve_optimization(test, max_walking_time*60, True)
 
 
 def test4(coordinates: tuple[float, float]) -> List[Landmark]:
@@ -77,7 +86,6 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
-
     test = landmarks_short
     
     test.insert(0, start)
@@ -92,3 +100,4 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
 
 
 test4(tuple((48.8795156, 2.3660204)))
+#test3('Vienna, Austria')