From 49ce8527a36abded3d564f6abf60eecd149c2405 Mon Sep 17 00:00:00 2001
From: Remy Moll <me@moll.re>
Date: Sun, 30 Jun 2024 18:42:59 +0200
Subject: [PATCH 1/6] cleanup path handling for easier dockerization

---
 ...ld-image.yaml => backend_build-image.yaml} |  2 +
 backend/Dockerfile                            |  9 +-
 backend/src/constants.py                      |  9 ++
 backend/src/landmarks_manager.py              | 84 ++++++++-----------
 backend/src/main_example.py                   | 23 -----
 backend/src/optimizer.py                      | 39 +++++----
 backend/src/parameters/amenity_selectors.yaml | 26 ++++++
 .../src/parameters/landmark_parameters.yaml   |  6 ++
 .../src/parameters/landmarks_manager.params   |  8 --
 backend/src/parameters/optimizer.params       |  5 --
 .../src/parameters/optimizer_parameters.yaml  |  3 +
 backend/src/refiner.py                        | 30 ++++---
 12 files changed, 126 insertions(+), 118 deletions(-)
 rename .gitea/workflows/{backed_build-image.yaml => backend_build-image.yaml} (95%)
 create mode 100644 backend/src/constants.py
 delete mode 100644 backend/src/main_example.py
 create mode 100644 backend/src/parameters/amenity_selectors.yaml
 create mode 100644 backend/src/parameters/landmark_parameters.yaml
 delete mode 100644 backend/src/parameters/landmarks_manager.params
 delete mode 100644 backend/src/parameters/optimizer.params
 create mode 100644 backend/src/parameters/optimizer_parameters.yaml

diff --git a/.gitea/workflows/backed_build-image.yaml b/.gitea/workflows/backend_build-image.yaml
similarity index 95%
rename from .gitea/workflows/backed_build-image.yaml
rename to .gitea/workflows/backend_build-image.yaml
index 5542a1f..1552c03 100644
--- a/.gitea/workflows/backed_build-image.yaml
+++ b/.gitea/workflows/backend_build-image.yaml
@@ -2,6 +2,8 @@ on:
   pull_request:
     branches:
       - main
+    paths:
+      - backend/**
 
 name: Build and push docker image
 
diff --git a/backend/Dockerfile b/backend/Dockerfile
index 79dbe76..01d58a2 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -6,6 +6,11 @@ COPY Pipfile Pipfile.lock .
 RUN pip install pipenv
 RUN pipenv install --deploy --system
 
-COPY . /src
+COPY src src
 
-CMD ["pipenv", "run", "python", "/app/src/main.py"]
+EXPOSE 8000
+
+# Set environment variables used by the deployment. These can be overridden by the user using this image.
+ENV NUM_WORKERS=1
+
+CMD ["pipenv", "run", "fastapi", "run", "src/main.py", '--port 8000', '--workers $NUM_WORKERS']
diff --git a/backend/src/constants.py b/backend/src/constants.py
new file mode 100644
index 0000000..515a040
--- /dev/null
+++ b/backend/src/constants.py
@@ -0,0 +1,9 @@
+from pathlib import Path
+
+
+PARAMETERS_DIR = Path('src/parameters')
+AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
+LANDMARK_PARAMETERS_PATH = PARAMETERS_DIR / 'landmark_parameters.yaml'
+OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
+
+OSM_CACHE_DIR = Path('cache')
diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
index 618e169..39fa404 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/landmarks_manager.py
@@ -1,9 +1,11 @@
 import math as m
 import json, os
+import yaml
 
 from typing import List, Tuple, Optional
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 
+import constants
 from structs.landmarks import Landmark, LandmarkType
 from structs.preferences import Preferences, Preference
 
@@ -13,34 +15,40 @@ NATURE = LandmarkType(landmark_type='nature')
 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, coordinates: Tuple[float, float]) :
+    with constants.AMENITY_SELECTORS_PATH.open('r') as f:
+        amenity_selectors = yaml.safe_load(f)
+    
+    with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+        # even though we don't use the parameters here, we already load them to avoid unnecessary io operations
+        parameters = yaml.safe_load(f)
 
-    l_sights, l_nature, l_shop = get_amenities()
     L = []
 
     # List for sightseeing
     if preferences.sightseeing.score != 0 :
-        L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates)
+        L1 = get_landmarks(amenity_selectors['sightseeing'], SIGHTSEEING, coordinates, parameters)
         correct_score(L1, preferences.sightseeing)
         L += L1
     
     # List for nature
     if preferences.nature.score != 0 :
-        L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates)
+        L2 = get_landmarks(amenity_selectors['nature'], NATURE, coordinates, parameters)
         correct_score(L2, preferences.nature)
         L += L2
     
     # List for shopping
     if preferences.shopping.score != 0 :
-        L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates)
+        L3 = get_landmarks(amenity_selectors['shopping'], SHOPPING, coordinates, parameters)
         correct_score(L3, preferences.shopping)
         L += L3
 
     L = remove_duplicates(L)
 
-    return L, take_most_important(L)
+    return L, take_most_important(L, parameters)
 
 
 """def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
@@ -69,37 +77,8 @@ def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float
     return remove_duplicates(L), take_most_important(L)
 """
 # Helper function to gather the amenities list
-def get_amenities() -> List[List[str]] :
-    
-    # Get the list of amenities from the files
-    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], N = 0) -> 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']
-    
+def take_most_important(L: List[Landmark], parameters: dict, N: int = 0) -> List[Landmark]:
     L_copy = []
     L_clean = []
     scores = [0]*len(L)
@@ -127,7 +106,8 @@ def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
     for i, elem in enumerate(L_copy) :
         scores[i] = elem.attractiveness
 
-    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-N):]
+    
+    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(parameters['N_important']-N):]
 
     for i, elem in enumerate(L_copy) :
         if i in res :
@@ -220,19 +200,25 @@ def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[flo
     return min_lat, min_lon, max_lat, max_lon
 
 
-def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] :
+def get_landmarks(
+    list_amenity: list,
+    landmarktype: LandmarkType,
+    coordinates: Tuple[float, float],
+    parameters: dict
+) -> 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']
-        bbox_side = parameters['city bbox side']
+    
+    # # 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']
     
     # Create bbox around start location
-    bbox = create_bbox(coordinates, bbox_side)
+    bbox = create_bbox(coordinates, parameters['city_bbox_side'])
 
     # Initialize some variables
     N = 0
@@ -269,11 +255,11 @@ def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: T
                 
                 # 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)  
+                    score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['church_coeff'])  
                 elif amenity == "'leisure'='park'" :
-                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
+                    score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['park_coeff'])  
                 else :
-                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+                    score = count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff']
 
                 if score is not None :
                     # Generate the landmark and append it to the list
diff --git a/backend/src/main_example.py b/backend/src/main_example.py
deleted file mode 100644
index 1a49305..0000000
--- a/backend/src/main_example.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import fastapi
-from dataclasses import dataclass
-
-
-@dataclass
-class Destination:
-    name: str
-    location: tuple
-    attractiveness: int
-
-
-
-d = Destination()
-
-
-
-def get_route() -> list[Destination]:
-    return {"route": "Hello World"}
-
-endpoint = ("/get_route", get_route)
-end
-if __name__ == "__main__":
-    fastapi.run()
diff --git a/backend/src/optimizer.py b/backend/src/optimizer.py
index b346a8a..68362c6 100644
--- a/backend/src/optimizer.py
+++ b/backend/src/optimizer.py
@@ -1,13 +1,13 @@
 import numpy as np
 import json, os
+import yaml
 
 from typing import List, Tuple
 from scipy.optimize import linprog
 from math import radians, sin, cos, acos
-from shapely import Polygon
 
 from structs.landmarks import Landmark
-
+import constants
     
 # Function to print the result
 def print_res(L: List[Landmark], L_tot):
@@ -161,10 +161,11 @@ def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float
 # 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):
     
-    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']
+    # Read the parameters from the file
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        detour = parameters['detour_factor']
+        speed = parameters['average_walking_speed']
     
     # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
     c = []
@@ -194,9 +195,9 @@ def respect_number(L:int, A_ub, b_ub):
         b_ub.append(1)
 
     # 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())
-        max_landmarks = parameters['max landmarks']
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        max_landmarks = parameters['max_landmarks']
 
     A_ub = np.vstack((A_ub, ones*L))
     b_ub.append(max_landmarks+1)
@@ -300,13 +301,14 @@ def respect_order(N: int, A_eq, b_eq):
 
 
 # Computes the time to reach from each landmark to the next
-def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+def link_list(order: List[int], landmarks: List[Landmark]) -> List[Landmark]:
     
     # 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_factor = parameters['detour factor']
-        speed = parameters['average walking speed']
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        
+    detour_factor = parameters['detour_factor']
+    speed = parameters['average_walking_speed']
     
     L =  []
     j = 0
@@ -329,10 +331,11 @@ def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
 def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
     
     # 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_factor = parameters['detour factor']
-        speed = parameters['average walking speed']
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        
+    detour_factor = parameters['detour_factor']
+    speed = parameters['average_walking_speed']
 
     L =  []
     j = 0
diff --git a/backend/src/parameters/amenity_selectors.yaml b/backend/src/parameters/amenity_selectors.yaml
new file mode 100644
index 0000000..43e8220
--- /dev/null
+++ b/backend/src/parameters/amenity_selectors.yaml
@@ -0,0 +1,26 @@
+nature:
+  - "'leisure'='park'"
+  - "geological"
+  - "'natural'='geyser'"
+  - "'natural'='hot_spring'"
+  - "'natural'='arch'"
+  - "'natural'='volcano'"
+  - "'natural'='stone'"
+  - "'tourism'='alpine_hut'"
+  - "'tourism'='viewpoint'"
+  - "'tourism'='zoo'"
+  - "'waterway'='waterfall'"
+
+shopping:
+  - "'shop'='department_store'"
+  - "'shop'='mall'"
+
+sightseeing:
+  - "'tourism'='museum'"
+  - "'tourism'='attraction'"
+  - "'tourism'='gallery'"
+  - "historic"
+  - "'amenity'='planetarium'"
+  - "'amenity'='place_of_worship'"
+  - "'amenity'='fountain'"
+  - "'water'='reflecting_pool'"
diff --git a/backend/src/parameters/landmark_parameters.yaml b/backend/src/parameters/landmark_parameters.yaml
new file mode 100644
index 0000000..3cf1c76
--- /dev/null
+++ b/backend/src/parameters/landmark_parameters.yaml
@@ -0,0 +1,6 @@
+city_bbox_side: 10
+radius_close_to: 27.5
+church_coeff: 0.6
+park_coeff: 1.5
+tag_coeff: 100
+N_important: 40
diff --git a/backend/src/parameters/landmarks_manager.params b/backend/src/parameters/landmarks_manager.params
deleted file mode 100644
index 942c214..0000000
--- a/backend/src/parameters/landmarks_manager.params
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "city bbox side" : 10,
-  "radius close to" : 27.5,
-  "church coeff" : 0.6,
-  "park coeff" : 1.5,
-  "tag coeff" : 100,
-  "N important" : 40
-}
\ No newline at end of file
diff --git a/backend/src/parameters/optimizer.params b/backend/src/parameters/optimizer.params
deleted file mode 100644
index 18ca240..0000000
--- a/backend/src/parameters/optimizer.params
+++ /dev/null
@@ -1,5 +0,0 @@
-{
-  "detour factor" : 1.4,
-  "average walking speed" : 4.8,
-  "max landmarks" : 10
-}
\ No newline at end of file
diff --git a/backend/src/parameters/optimizer_parameters.yaml b/backend/src/parameters/optimizer_parameters.yaml
new file mode 100644
index 0000000..b6dca0e
--- /dev/null
+++ b/backend/src/parameters/optimizer_parameters.yaml
@@ -0,0 +1,3 @@
+detour_factor: 1.4
+average_walking_speed: 4.8
+max_landmarks: 10
diff --git a/backend/src/refiner.py b/backend/src/refiner.py
index 282f2f9..a292b73 100644
--- a/backend/src/refiner.py
+++ b/backend/src/refiner.py
@@ -1,7 +1,8 @@
 from collections import defaultdict
 from heapq import heappop, heappush
 from itertools import permutations
-import os, json
+import os
+import yaml
 
 from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
 from typing import List, Tuple
@@ -10,6 +11,7 @@ from math import pi
 from structs.landmarks import Landmark
 from landmarks_manager import take_most_important
 from optimizer import solve_optimization, link_list_simple, print_res, get_distance
+import constants
 
 
 def create_corridor(landmarks: List[Landmark], width: float) :
@@ -122,12 +124,12 @@ def total_path_distance(path: List[Landmark], detour, speed) -> float:
 
 
 def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
-    
-  # Read from data
-  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']
+  # Read the parameters from the file
+  with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+    parameters = yaml.safe_load(f)
+
+    detour = parameters['detour_factor']
+    speed = parameters['average_walking_speed']
     
   # Step 1: Find 'start' and 'finish' landmarks
   start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
@@ -174,8 +176,10 @@ def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[L
   for landmark in all_landmarks :
     if is_in_area(area, landmark.location) and landmark.name not in visited_names:
       second_order_landmarks.append(landmark)
-
-  return take_most_important(second_order_landmarks, len(visited_landmarks))
+  
+  with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+    parameters = yaml.safe_load(f)
+  return take_most_important(second_order_landmarks, parameters, len(visited_landmarks))
 
 
 
@@ -195,10 +199,10 @@ def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[L
 
 def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
 
-  # Read from the file
-  with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
-    parameters = json.loads(f.read())
-    max_landmarks = parameters['max landmarks']
+  # Read the parameters from the file
+  with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+    parameters = yaml.safe_load(f)
+    max_landmarks = parameters['max_landmarks']
   
   if len(base_tour)-2 >= max_landmarks :
     return base_tour

From f9c86261cbf8935868a01751378a837aa896691a Mon Sep 17 00:00:00 2001
From: Remy Moll <me@moll.re>
Date: Sun, 7 Jul 2024 14:49:10 +0200
Subject: [PATCH 2/6] switch to osmnx

---
 backend/Dockerfile                            |   1 +
 backend/Pipfile                               |   3 +-
 backend/Pipfile.lock                          | 943 ++++++------------
 backend/src/amenities/nature.am               |  11 -
 backend/src/amenities/shopping.am             |   2 -
 backend/src/amenities/sightseeing.am          |   8 -
 backend/src/constants.py                      |   7 +-
 backend/src/landmarks_manager.py              | 393 +++-----
 backend/src/optimizer.py                      |   1 -
 backend/src/parameters/amenity_selectors.yaml |  48 +-
 .../src/parameters/landmark_parameters.yaml   |   4 +-
 backend/src/structs/landmarks.py              |   4 +-
 backend/src/tester.py                         |   4 +-
 13 files changed, 470 insertions(+), 959 deletions(-)
 delete mode 100644 backend/src/amenities/nature.am
 delete mode 100644 backend/src/amenities/shopping.am
 delete mode 100644 backend/src/amenities/sightseeing.am

diff --git a/backend/Dockerfile b/backend/Dockerfile
index 01d58a2..056c3a0 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -12,5 +12,6 @@ EXPOSE 8000
 
 # Set environment variables used by the deployment. These can be overridden by the user using this image.
 ENV NUM_WORKERS=1
+ENV OSM_CACHE_DIR=/cache
 
 CMD ["pipenv", "run", "fastapi", "run", "src/main.py", '--port 8000', '--workers $NUM_WORKERS']
diff --git a/backend/Pipfile b/backend/Pipfile
index 62e84e8..16c3768 100644
--- a/backend/Pipfile
+++ b/backend/Pipfile
@@ -7,8 +7,9 @@ name = "pypi"
 numpy = "*"
 scipy = "*"
 fastapi = "*"
-osmpythontools = "*"
 pydantic = "*"
 shapely = "*"
+osmnx = "*"
+networkx = "*"
 
 [dev-packages]
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
index 2e5aac3..0532049 100644
--- a/backend/Pipfile.lock
+++ b/backend/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "0f88c01cde3be9a6332acec33fa0ccf13b6e122a6df8ee5cfefa52ba1e98034f"
+            "sha256": "937d6d3cf34dbe638ce1b4a8d8ed85b2365920264471798d81d5a49d4f6e5d2e"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -30,14 +30,6 @@
             "markers": "python_version >= '3.8'",
             "version": "==4.4.0"
         },
-        "beautifulsoup4": {
-            "hashes": [
-                "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051",
-                "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"
-            ],
-            "markers": "python_full_version >= '3.6.0'",
-            "version": "==4.12.3"
-        },
         "certifi": {
             "hashes": [
                 "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516",
@@ -46,6 +38,102 @@
             "markers": "python_version >= '3.6'",
             "version": "==2024.6.2"
         },
+        "charset-normalizer": {
+            "hashes": [
+                "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027",
+                "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087",
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@@ -54,64 +142,6 @@
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@@ -128,14 +158,6 @@
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@@ -153,61 +175,13 @@
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@@ -622,41 +338,6 @@
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@@ -665,6 +346,15 @@
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@@ -769,12 +459,14 @@
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@@ -819,174 +511,108 @@
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+                "sha256:c40003d880f39c11c1edbae8144e3813904b10514cd3d3d00c277ae996488cdb",
+                "sha256:d91db2c41dd6c20646af280355d41dfa1ec7eead235642178bd57635a3f82209",
+                "sha256:f0a50da861a7ec4573b7c716b2ebdcdf142b66b756a0d392c236ae568b3a93fb"
             ],
             "index": "pypi",
-            "markers": "python_version >= '3.9'",
-            "version": "==1.13.1"
+            "markers": "python_version >= '3.10'",
+            "version": "==1.14.0"
         },
         "shapely": {
             "hashes": [
@@ -1202,14 +893,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.3.1"
         },
-        "soupsieve": {
-            "hashes": [
-                "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690",
-                "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==2.5"
-        },
         "starlette": {
             "hashes": [
                 "sha256:6fe59f29268538e5d0d182f2791a479a0c64638e6935d1c6989e63fb2699c6ee",
@@ -1326,6 +1009,14 @@
             "markers": "python_version >= '3.8'",
             "version": "==5.10.0"
         },
+        "urllib3": {
+            "hashes": [
+                "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472",
+                "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==2.2.2"
+        },
         "uvicorn": {
             "extras": [
                 "standard"
@@ -1529,14 +1220,6 @@
                 "sha256:ffefa1374cd508d633646d51a8e9277763a9b78ae71324183693959cf94635a7"
             ],
             "version": "==12.0"
-        },
-        "xarray": {
-            "hashes": [
-                "sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7",
-                "sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==2024.6.0"
         }
     },
     "develop": {}
diff --git a/backend/src/amenities/nature.am b/backend/src/amenities/nature.am
deleted file mode 100644
index dcc4061..0000000
--- a/backend/src/amenities/nature.am
+++ /dev/null
@@ -1,11 +0,0 @@
-'leisure'='park'
-geological
-'natural'='geyser'
-'natural'='hot_spring'
-'natural'='arch'
-'natural'='volcano'
-'natural'='stone'
-'tourism'='alpine_hut'
-'tourism'='viewpoint'
-'tourism'='zoo'
-'waterway'='waterfall'
\ No newline at end of file
diff --git a/backend/src/amenities/shopping.am b/backend/src/amenities/shopping.am
deleted file mode 100644
index 14504a0..0000000
--- a/backend/src/amenities/shopping.am
+++ /dev/null
@@ -1,2 +0,0 @@
-'shop'='department_store'
-'shop'='mall'
\ No newline at end of file
diff --git a/backend/src/amenities/sightseeing.am b/backend/src/amenities/sightseeing.am
deleted file mode 100644
index 33bcb1a..0000000
--- a/backend/src/amenities/sightseeing.am
+++ /dev/null
@@ -1,8 +0,0 @@
-'tourism'='museum'
-'tourism'='attraction'
-'tourism'='gallery'
-historic
-'amenity'='planetarium'
-'amenity'='place_of_worship'
-'amenity'='fountain'
-'water'='reflecting_pool'
\ No newline at end of file
diff --git a/backend/src/constants.py b/backend/src/constants.py
index 515a040..c60bf84 100644
--- a/backend/src/constants.py
+++ b/backend/src/constants.py
@@ -1,9 +1,12 @@
 from pathlib import Path
-
+import os
 
 PARAMETERS_DIR = Path('src/parameters')
 AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
 LANDMARK_PARAMETERS_PATH = PARAMETERS_DIR / 'landmark_parameters.yaml'
 OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
 
-OSM_CACHE_DIR = Path('cache')
+
+
+cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
+OSM_CACHE_DIR = Path(cache_dir_string)
diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
index 39fa404..8dcaa8c 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/landmarks_manager.py
@@ -1,150 +1,71 @@
-import math as m
-import json, os
 import yaml
+import os
+import osmnx as ox
+from shapely.geometry import Point, Polygon, LineString, MultiPolygon
 
-from typing import List, Tuple, Optional
-from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
-
-import constants
 from structs.landmarks import Landmark, LandmarkType
 from structs.preferences import Preferences, Preference
+import constants
 
 
 SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
 NATURE = LandmarkType(landmark_type='nature')
 SHOPPING = LandmarkType(landmark_type='shopping')
 
-
+ox.config(cache_folder=constants.OSM_CACHE_DIR)
 
 # 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, coordinates: Tuple[float, float]) :
+def generate_landmarks(preferences: Preferences, center_coordinates: tuple[float, float]) :
     with constants.AMENITY_SELECTORS_PATH.open('r') as f:
         amenity_selectors = yaml.safe_load(f)
     
     with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
         # even though we don't use the parameters here, we already load them to avoid unnecessary io operations
         parameters = yaml.safe_load(f)
-
+        max_distance = parameters['city_bbox_side']
     L = []
 
     # List for sightseeing
-    if preferences.sightseeing.score != 0 :
-        L1 = get_landmarks(amenity_selectors['sightseeing'], SIGHTSEEING, coordinates, parameters)
+    if preferences.sightseeing.score != 0:
+        score_func = lambda loc, n_tags: int((count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']) * parameters['church_coeff'])  
+        L1 = get_landmarks(amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, max_distance, score_func)
         correct_score(L1, preferences.sightseeing)
         L += L1
     
     # List for nature
-    if preferences.nature.score != 0 :
-        L2 = get_landmarks(amenity_selectors['nature'], NATURE, coordinates, parameters)
+    if preferences.nature.score != 0:
+        score_func = lambda loc, n_tags: int((count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']) * parameters['park_coeff'])  
+        L2 = get_landmarks(amenity_selectors['nature'], NATURE, center_coordinates, max_distance, score_func)
         correct_score(L2, preferences.nature)
         L += L2
     
     # List for shopping
-    if preferences.shopping.score != 0 :
-        L3 = get_landmarks(amenity_selectors['shopping'], SHOPPING, coordinates, parameters)
+    if preferences.shopping.score != 0:
+        score_func = lambda loc, n_tags: count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']
+        L3 = get_landmarks(amenity_selectors['shopping'], SHOPPING, center_coordinates, max_distance, score_func)
         correct_score(L3, preferences.shopping)
         L += L3
 
-    L = remove_duplicates(L)
-
-    return L, take_most_important(L, parameters)
+    # remove duplicates
+    L = list(set(L))
+    print(len(L))
+    L_constrained = take_most_important(L, parameters['N_important'])
+    print(len(L_constrained))
+    return L, L_constrained
 
 
-"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
 
-    l_sights, l_nature, l_shop = get_amenities()
-    L = []
-
-    # List for sightseeing
-    if preferences.sightseeing.score != 0 :
-        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(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(l_shop, SHOPPING, city_country=city_country, coordinates=coordinates)
-        correct_score(L3, preferences.shopping)
-        L += L3
-
-    return remove_duplicates(L), take_most_important(L)
-"""
-# Helper function to gather the amenities list
 # Take the most important landmarks from the list
-def take_most_important(L: List[Landmark], parameters: dict, N: int = 0) -> List[Landmark]:
-    L_copy = []
-    L_clean = []
-    scores = [0]*len(L)
-    names = []
-    name_id = {}
+def take_most_important(landmarks: list[Landmark], n_max: int) -> list[Landmark]:
 
-    for i, elem in enumerate(L) :
-        if elem.name not in names :
-            names.append(elem.name)
-            name_id[elem.name] = [i]
-            L_copy.append(elem)
-        else :
-            name_id[elem.name] += [i]
-            scores = []
-            for j in name_id[elem.name] :
-                scores.append(L[j].attractiveness)
-            best_id = max(range(len(scores)), key=scores.__getitem__)
-            t = name_id[elem.name][best_id]
-            if t == i :
-                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])[-(parameters['N_important']-N):]
-
-    for i, elem in enumerate(L_copy) :
-        if i in res :
-            L_clean.append(elem)
-
-    return L_clean
+    landmarks_sorted = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
+    return landmarks_sorted[:n_max]
 
 
-# Remove duplicate elements and elements with low score
-def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
-    """
-    Removes duplicate landmarks based on their names from the given list.
-
-    Parameters:
-    L (List[Landmark]): A list of Landmark objects.
-
-    Returns:
-    List[Landmark]: A list of unique Landmark objects based on their names.
-    """
-
-    L_clean = []
-    names = []
-
-    for landmark in L :
-        if landmark.name in names: 
-            continue  
-
-
-        else :
-            names.append(landmark.name)
-            L_clean.append(landmark)
-    
-    return L_clean
-    
 
 # Correct the score of a list of landmarks by taking into account preference settings
-def correct_score(L: List[Landmark], preference: Preference) :
+def correct_score(L: list[Landmark], preference: Preference) :
 
     if len(L) == 0 :
         return
@@ -157,195 +78,111 @@ def correct_score(L: List[Landmark], preference: Preference) :
 
 
 # Function to count elements within a certain radius of a location
-def count_elements_within_radius(coordinates: Tuple[float, float], radius: int) -> int:
+def count_elements_within_radius(point: Point, radius: int) -> int:
     
-    lat = coordinates[0]
-    lon = coordinates[1]
-
-    alpha = (180*radius)/(6371000*m.pi)
-    bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
-        
-    # 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)
-        return radius_result.countElements()
-    
-    except :
-        return None
+    center_coordinates = (point.x, point.y)
+    try:
+        landmarks = ox.features_from_point(
+            center_point = center_coordinates,
+            dist = radius,
+            tags = {'building': True} # this is a common tag to give an estimation of the number of elements in the area
+        )
+        return len(landmarks)
+    except ox._errors.InsufficientResponseError:
+        return 0    
 
 
-# Creates a bounding box around given coordinates
-def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]:
-
-    lat = coordinates[0]
-    lon = coordinates[1]
-
-    # Half the side length in km (since it's a square bbox)
-    half_side_length_km = side_length / 2.0
-
-    # Convert distance to degrees
-    lat_diff = half_side_length_km / 111  # 1 degree latitude is approximately 111 km
-    lon_diff = half_side_length_km / (111 * m.cos(m.radians(lat)))  # Adjust for longitude based on latitude
-
-    # Calculate bbox
-    min_lat = lat - lat_diff
-    max_lat = lat + lat_diff
-    min_lon = lon - lon_diff
-    max_lon = lon + lon_diff
-
-    return min_lat, min_lon, max_lat, max_lon
 
 
 def get_landmarks(
-    list_amenity: list,
+    amenity_selectors: list[dict],
     landmarktype: LandmarkType,
-    coordinates: Tuple[float, float],
-    parameters: dict
-) -> List[Landmark]:
+    center_coordinates: tuple[float, float],
+    distance: int,
+    score_function: callable
+) -> 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']
-    #     bbox_side = parameters['city bbox side']
-    
-    # Create bbox around start location
-    bbox = create_bbox(coordinates, parameters['city_bbox_side'])
+    landmarks = ox.features_from_point(
+        center_point = center_coordinates,
+        dist = distance,
+        tags = amenity_selectors
+    )
 
-    # Initialize some variables
-    N = 0
-    L = []
-    overpass = Overpass()
+    # cleanup the list
+    # remove rows where name is None
+    landmarks = landmarks[landmarks['name'].notna()]
+    # TODO: remove rows that are part of another building
 
-    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()
+    ret_landmarks = []
+    for element, description in landmarks.iterrows():
+        osm_type = element[0]
+        osm_id = element[1]
+        location = description['geometry']
+        n_tags = len(description['nodes']) if type(description['nodes']) == list else 1
 
-        for elem in result.elements():
+        # print(description['nodes'])
+        print(description['name'])
+        # print(location, type(location))
+        if type(location) == Point:
+            location = location
+        elif type(location) == Polygon or type(location) == MultiPolygon:
+            location = location.centroid
+        elif type(location) == LineString:
+            location = location.interpolate(location.length/2)
 
-            name = elem.tag('name')                             # Add name
-            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+        score = score_function(location, n_tags)
+        print(score)
+        landmark = Landmark(
+            name = description['name'],
+            type = landmarktype,
+            location = (location.x, location.y),
+            osm_type = osm_type,
+            osm_id = osm_id,
+            attractiveness = score,
+            must_do = False,
+            n_tags = n_tags
+        )
+        ret_landmarks.append(landmark)
 
-            # skip if unprecise location
-            if name is None or location[0] is None:
-                continue
+    return ret_landmarks
+    # for elem in G.iterrows():
+    #     print(elem)
+    #     print(elem.name)
+    #     print(elem.address)
+    #     name = elem.tag('name')                             # Add name
+    #     location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
 
-            # skip if unused
-            if 'disused:leisure' in elem.tags().keys():
-                continue
+    #     # 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
+        
+    #     # skip if part of another building
+    #     if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
+    #         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
             
-            # skip if part of another building
-            if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
-                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, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['church_coeff'])  
-                elif amenity == "'leisure'='park'" :
-                    score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['park_coeff'])  
-                else :
-                    score = count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff']
+    #         # 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, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['church_coeff'])  
+    #         elif amenity == "'leisure'='park'" :
+    #             score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['park_coeff'])  
+    #         else :
+    #             score = count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['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)
+    #         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
+    # 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
-            
-            # skip if part of another building
-            if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
-                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
-                    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
-"""
\ No newline at end of file
diff --git a/backend/src/optimizer.py b/backend/src/optimizer.py
index 68362c6..91251bc 100644
--- a/backend/src/optimizer.py
+++ b/backend/src/optimizer.py
@@ -1,5 +1,4 @@
 import numpy as np
-import json, os
 import yaml
 
 from typing import List, Tuple
diff --git a/backend/src/parameters/amenity_selectors.yaml b/backend/src/parameters/amenity_selectors.yaml
index 43e8220..4f6b223 100644
--- a/backend/src/parameters/amenity_selectors.yaml
+++ b/backend/src/parameters/amenity_selectors.yaml
@@ -1,26 +1,32 @@
 nature:
-  - "'leisure'='park'"
-  - "geological"
-  - "'natural'='geyser'"
-  - "'natural'='hot_spring'"
-  - "'natural'='arch'"
-  - "'natural'='volcano'"
-  - "'natural'='stone'"
-  - "'tourism'='alpine_hut'"
-  - "'tourism'='viewpoint'"
-  - "'tourism'='zoo'"
-  - "'waterway'='waterfall'"
+  leisure: park
+  geological: ''
+  natural:
+    - geyser
+    - hot_spring
+    - arch
+    - volcano
+    - stone
+  tourism:
+    - alpine_hut
+    - viewpoint
+    - zoo
+  waterway: waterfall
 
 shopping:
-  - "'shop'='department_store'"
-  - "'shop'='mall'"
+  shop:
+    - department_store
+    - mall
 
 sightseeing:
-  - "'tourism'='museum'"
-  - "'tourism'='attraction'"
-  - "'tourism'='gallery'"
-  - "historic"
-  - "'amenity'='planetarium'"
-  - "'amenity'='place_of_worship'"
-  - "'amenity'='fountain'"
-  - "'water'='reflecting_pool'"
+  tourism:
+    - museum
+    - attraction
+    - gallery
+  historic: ''
+  amenity:
+    - planetarium
+    - place_of_worship
+    - fountain
+  water:
+    - reflecting_pool
diff --git a/backend/src/parameters/landmark_parameters.yaml b/backend/src/parameters/landmark_parameters.yaml
index 3cf1c76..524b7b6 100644
--- a/backend/src/parameters/landmark_parameters.yaml
+++ b/backend/src/parameters/landmark_parameters.yaml
@@ -1,5 +1,5 @@
-city_bbox_side: 10
-radius_close_to: 27.5
+city_bbox_side: 1500 #m
+radius_close_to: 30
 church_coeff: 0.6
 park_coeff: 1.5
 tag_coeff: 100
diff --git a/backend/src/structs/landmarks.py b/backend/src/structs/landmarks.py
index d02dc5f..ae17850 100644
--- a/backend/src/structs/landmarks.py
+++ b/backend/src/structs/landmarks.py
@@ -1,6 +1,5 @@
 from typing import Optional
 from pydantic import BaseModel, Field
-
 from .landmarktype import LandmarkType
 
 from uuid import uuid4
@@ -28,3 +27,6 @@ class Landmark(BaseModel) :
     
     time_to_reach_next : Optional[int] = 0                      # TODO fix this in existing code
     next_uuid : Optional[str] = None                            # TODO implement this ASAP
+
+    def __hash__(self) -> int:
+        return self.uuid.int
\ No newline at end of file
diff --git a/backend/src/tester.py b/backend/src/tester.py
index 91a12b2..fe7220d 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -90,7 +90,7 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
     # Generate the landmarks from the start location
-    landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, center_coordinates=start.location)
     #write_data(landmarks, "landmarks.txt")
 
     # Insert start and finish to the landmarks list
@@ -98,7 +98,7 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     landmarks_short.append(finish)
 
     # TODO use these parameters in another way
-    max_walking_time = 2    # hours
+    max_walking_time = 3    # hours
     detour = 30             # minutes
 
     # First stage optimization

From 8f23a4747dc2bb9ac9d8775e9d9ed025638fe08a Mon Sep 17 00:00:00 2001
From: Remy Moll <me@moll.re>
Date: Mon, 8 Jul 2024 11:55:00 +0200
Subject: [PATCH 3/6] further cleanup

---
 backend/src/constants.py         |   7 +
 backend/src/landmarks_manager.py | 271 +++++++++++++------------------
 backend/src/main.py              |  85 ++++------
 backend/src/tester.py            |  96 ++++-------
 4 files changed, 182 insertions(+), 277 deletions(-)

diff --git a/backend/src/constants.py b/backend/src/constants.py
index c60bf84..d5b7b5a 100644
--- a/backend/src/constants.py
+++ b/backend/src/constants.py
@@ -1,5 +1,6 @@
 from pathlib import Path
 import os
+import logging
 
 PARAMETERS_DIR = Path('src/parameters')
 AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
@@ -10,3 +11,9 @@ OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
 
 cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
 OSM_CACHE_DIR = Path(cache_dir_string)
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(
+    level = logging.INFO,
+    format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
index 8dcaa8c..690fae8 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/landmarks_manager.py
@@ -1,5 +1,5 @@
 import yaml
-import os
+import logging
 import osmnx as ox
 from shapely.geometry import Point, Polygon, LineString, MultiPolygon
 
@@ -7,182 +7,145 @@ from structs.landmarks import Landmark, LandmarkType
 from structs.preferences import Preferences, Preference
 import constants
 
-
 SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
 NATURE = LandmarkType(landmark_type='nature')
 SHOPPING = LandmarkType(landmark_type='shopping')
 
-ox.config(cache_folder=constants.OSM_CACHE_DIR)
 
-# 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, center_coordinates: tuple[float, float]) :
-    with constants.AMENITY_SELECTORS_PATH.open('r') as f:
-        amenity_selectors = yaml.safe_load(f)
-    
-    with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
-        # even though we don't use the parameters here, we already load them to avoid unnecessary io operations
-        parameters = yaml.safe_load(f)
-        max_distance = parameters['city_bbox_side']
-    L = []
+class LandmarkManager:
+    logger = logging.getLogger(__name__)
 
-    # List for sightseeing
-    if preferences.sightseeing.score != 0:
-        score_func = lambda loc, n_tags: int((count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']) * parameters['church_coeff'])  
-        L1 = get_landmarks(amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, max_distance, score_func)
-        correct_score(L1, preferences.sightseeing)
-        L += L1
-    
-    # List for nature
-    if preferences.nature.score != 0:
-        score_func = lambda loc, n_tags: int((count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']) * parameters['park_coeff'])  
-        L2 = get_landmarks(amenity_selectors['nature'], NATURE, center_coordinates, max_distance, score_func)
-        correct_score(L2, preferences.nature)
-        L += L2
-    
-    # List for shopping
-    if preferences.shopping.score != 0:
-        score_func = lambda loc, n_tags: count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']
-        L3 = get_landmarks(amenity_selectors['shopping'], SHOPPING, center_coordinates, max_distance, score_func)
-        correct_score(L3, preferences.shopping)
-        L += L3
+    def __init__(self) -> None:
+        ox.config(cache_folder=constants.OSM_CACHE_DIR)
+        with constants.AMENITY_SELECTORS_PATH.open('r') as f:
+            self.amenity_selectors = yaml.safe_load(f)
+        with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+            self.parameters = yaml.safe_load(f)
+            # max_distance = parameters['city_bbox_side']
 
-    # remove duplicates
-    L = list(set(L))
-    print(len(L))
-    L_constrained = take_most_important(L, parameters['N_important'])
-    print(len(L_constrained))
-    return L, L_constrained
+
+    def get_landmark_lists(self, preferences: Preferences, center_coordinates: tuple[float, float]) -> tuple[list[Landmark], list[Landmark]]:
+        '''
+        Generate a list of landmarks based on the preferences of the user and the center (ie. start) coordinates.
+        The list is then used by the pathfinding algorithm to generate a path that goes through the most interesting landmarks.
+        :param preferences: the preferences specified by the user
+        :param center_coordinates: the coordinates of the starting point
+        '''
+
+        L = []
+
+        # List for sightseeing
+        if preferences.sightseeing.score != 0:
+            score_func = lambda loc, n_tags: int((self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']) * self.parameters['church_coeff'])  
+            L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, self.parameters['city_bbox_side'], score_func)
+            self.correct_score(L1, preferences.sightseeing)
+            L += L1
+
+        # List for nature
+        if preferences.nature.score != 0:
+            score_func = lambda loc, n_tags: int((self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']) * self.parameters['park_coeff'])  
+            L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, center_coordinates, self.parameters['city_bbox_side'], score_func)
+            self.correct_score(L2, preferences.nature)
+            L += L2
+
+        # List for shopping
+        if preferences.shopping.score != 0:
+            score_func = lambda loc, n_tags: self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']
+            L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, center_coordinates, self.parameters['city_bbox_side'], score_func)
+            self.correct_score(L3, preferences.shopping)
+            L += L3
+
+        # remove duplicates
+        L = list(set(L))
+        L_constrained = self.take_most_important(L, self.parameters['N_important'])
+        self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
+        return L, L_constrained
 
 
 
-# Take the most important landmarks from the list
-def take_most_important(landmarks: list[Landmark], n_max: int) -> list[Landmark]:
+    # Take the most important landmarks from the list
+    def take_most_important(self, landmarks: list[Landmark], n_max: int) -> list[Landmark]:
 
-    landmarks_sorted = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
-    return landmarks_sorted[:n_max]
+        landmarks_sorted = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
+        return landmarks_sorted[:n_max]
 
 
 
-# Correct the score of a list of landmarks by taking into account preference settings
-def correct_score(L: list[Landmark], preference: Preference) :
+    # Correct the score of a list of landmarks by taking into account preference settings
+    def correct_score(self, L: list[Landmark], preference: Preference):
 
-    if len(L) == 0 :
-        return
-    
-    if L[0].type != preference.type :
-        raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
+        if len(L) == 0 :
+            return
 
-    for elem in L :
-        elem.attractiveness = int(elem.attractiveness*preference.score/500)     # arbitrary computation
+        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*preference.score/500)     # arbitrary computation
 
 
-# Function to count elements within a certain radius of a location
-def count_elements_within_radius(point: Point, radius: int) -> int:
-    
-    center_coordinates = (point.x, point.y)
-    try:
+    # Function to count elements within a certain radius of a location
+    def count_elements_within_radius(self, point: Point, radius: int) -> int:
+        
+        center_coordinates = (point.x, point.y)
+        try:
+            landmarks = ox.features_from_point(
+                center_point = center_coordinates,
+                dist = radius,
+                tags = {'building': True} # this is a common tag to give an estimation of the number of elements in the area
+            )
+            return len(landmarks)
+        except ox._errors.InsufficientResponseError:
+            return 0    
+
+
+
+
+    def fetch_landmarks(
+        self,
+        amenity_selectors: list[dict],
+        landmarktype: LandmarkType,
+        center_coordinates: tuple[float, float],
+        distance: int,
+        score_function: callable
+    ) -> list[Landmark]:
+        
         landmarks = ox.features_from_point(
             center_point = center_coordinates,
-            dist = radius,
-            tags = {'building': True} # this is a common tag to give an estimation of the number of elements in the area
+            dist = distance,
+            tags = amenity_selectors
         )
-        return len(landmarks)
-    except ox._errors.InsufficientResponseError:
-        return 0    
 
+        # cleanup the list
+        # remove rows where name is None
+        landmarks = landmarks[landmarks['name'].notna()]
+        # TODO: remove rows that are part of another building
 
+        ret_landmarks = []
+        for element, description in landmarks.iterrows():
+            osm_type = element[0]
+            osm_id = element[1]
+            location = description['geometry']
+            n_tags = len(description['nodes']) if type(description['nodes']) == list else 1
 
+            if type(location) == Point:
+                location = location
+            elif type(location) == Polygon or type(location) == MultiPolygon:
+                location = location.centroid
+            elif type(location) == LineString:
+                location = location.interpolate(location.length/2)
 
-def get_landmarks(
-    amenity_selectors: list[dict],
-    landmarktype: LandmarkType,
-    center_coordinates: tuple[float, float],
-    distance: int,
-    score_function: callable
-) -> list[Landmark]:
-    
-    landmarks = ox.features_from_point(
-        center_point = center_coordinates,
-        dist = distance,
-        tags = amenity_selectors
-    )
-
-    # cleanup the list
-    # remove rows where name is None
-    landmarks = landmarks[landmarks['name'].notna()]
-    # TODO: remove rows that are part of another building
-
-    ret_landmarks = []
-    for element, description in landmarks.iterrows():
-        osm_type = element[0]
-        osm_id = element[1]
-        location = description['geometry']
-        n_tags = len(description['nodes']) if type(description['nodes']) == list else 1
-
-        # print(description['nodes'])
-        print(description['name'])
-        # print(location, type(location))
-        if type(location) == Point:
-            location = location
-        elif type(location) == Polygon or type(location) == MultiPolygon:
-            location = location.centroid
-        elif type(location) == LineString:
-            location = location.interpolate(location.length/2)
-
-        score = score_function(location, n_tags)
-        print(score)
-        landmark = Landmark(
-            name = description['name'],
-            type = landmarktype,
-            location = (location.x, location.y),
-            osm_type = osm_type,
-            osm_id = osm_id,
-            attractiveness = score,
-            must_do = False,
-            n_tags = n_tags
-        )
-        ret_landmarks.append(landmark)
-
-    return ret_landmarks
-    # for elem in G.iterrows():
-    #     print(elem)
-    #     print(elem.name)
-    #     print(elem.address)
-    #     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
-        
-    #     # skip if part of another building
-    #     if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
-    #         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, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['church_coeff'])  
-    #         elif amenity == "'leisure'='park'" :
-    #             score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['park_coeff'])  
-    #         else :
-    #             score = count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['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
-
+            score = score_function(location, n_tags)
+            landmark = Landmark(
+                name = description['name'],
+                type = landmarktype,
+                location = (location.x, location.y),
+                osm_type = osm_type,
+                osm_id = osm_id,
+                attractiveness = score,
+                must_do = False,
+                n_tags = n_tags
+            )
+            ret_landmarks.append(landmark)
 
+        return ret_landmarks
diff --git a/backend/src/main.py b/backend/src/main.py
index 89b49fa..a1ff712 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -1,51 +1,45 @@
 from optimizer import solve_optimization
-from refiner import refine_optimization
-from landmarks_manager import generate_landmarks
+# from refiner import refine_optimization
+from landmarks_manager import LandmarkManager
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
-from structs.preferences import Preferences, Preference
+from structs.preferences import Preferences
 from fastapi import FastAPI, Query, Body
-from typing import List
 
 app = FastAPI()
-
+manager = LandmarkManager()
 
 # TODO: needs a global variable to store the landmarks accross function calls
 # linked_tour = []
 
 
-# Assuming frontend is calling like this : 
-#"http://127.0.0.1:8000/process?param1={param1}&param2={param2}"
-@app.post("/optimizer_coords/{start_lat}/{start_lon}/{finish_lat}/{finish_lon}")
-def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(...), finish_lat: float = None, finish_lon: float = None) -> List[Landmark]:
-    
+@app.post("/route/new")
+def main1(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] = None) -> str:
+    '''
+    Main function to call the optimizer.
+    :param preferences: the preferences specified by the user as the post body
+    :param start: the coordinates of the starting point as a tuple of floats (as url query parameters)
+    :param end: the coordinates of the finishing point as a tuple of floats (as url query parameters)
+    :return: the uuid of the first landmark in the optimized route
+    '''
     if preferences is None :
         raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
-    if bool(start_lat) ^ bool(start_lon) :
-        raise ValueError("Please provide both latitude and longitude for the starting point")
-    if bool(finish_lat) ^ bool(finish_lon) :
-        raise ValueError("Please provide both latitude and longitude for the finish point")
+    if start is None:
+        raise ValueError("Please provide the starting coordinates as a tuple of floats.")
+    if end is None:
+        end = start
 
-    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start_lat, start_lon), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    start_landmark = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start[0], start[1]), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    end_landmark = Landmark(name='end', type=LandmarkType(landmark_type='end'), location=(end[0], end[1]), osm_type='end', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
-    if bool(finish_lat) and bool(finish_lon) :
-        finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(finish_lat, finish_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    else :
-        finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(start_lat, start_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    
-    
-    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)
-
     # Generate the landmarks from the start location
-    landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
-
+    landmarks, landmarks_short = LandmarkManager.get_landmark_lists(preferences=preferences, coordinates=start.location)
+    print([l.name for l in landmarks_short])
     # insert start and finish to the landmarks list
-    landmarks_short.insert(0, start)
-    landmarks_short.append(finish)
+    landmarks_short.insert(0, start_landmark)
+    landmarks_short.append(end_landmark)
     
-
-    # TODO use these parameters in another way
+    # TODO infer these parameters from the preferences
     max_walking_time = 4    # hours
     detour = 30             # minutes
 
@@ -53,32 +47,11 @@ def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(..
     base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
     
     # Second stage optimization
-    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
+    # refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
 
-
-    # TODO: should look something like this 
-    # # set time to reach and transform into fully functional linked list
-    # linked_tour += link(refined_tour)
-    # return {
-    #     'city_name': 'Paris',
-    #     'n_stops': len(linked_tour),
-    #     'first_landmark_uuid': linked_tour[0].uuid,
-    # }
-    
-    return refined_tour
-
-
-
-
-# input city, country in the form of 'Paris, France'
-@app.post("/test2/{city_country}")
-def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landmark]:
-
-    landmarks = generate_landmarks(city_country, preferences)
-
-    max_steps = 9000000
-
-    visiting_order = solve_optimization(landmarks, max_steps, True)
+    # linked_tour = ...
+    # return linked_tour[0].uuid
+    return base_tour[0].uuid
 
 
 
@@ -86,5 +59,3 @@ def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landm
 def get_landmark(landmark_uuid: str) -> Landmark:
     #cherche dans linked_tour et retourne le landmark correspondant
     pass
-
-
diff --git a/backend/src/tester.py b/backend/src/tester.py
index fe7220d..57692d2 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -1,11 +1,11 @@
 import pandas as pd
 
 from typing import List
-from landmarks_manager import generate_landmarks
+from landmarks_manager import LandmarkManager
 from fastapi.encoders import jsonable_encoder
 
 from optimizer import solve_optimization
-from refiner import refine_optimization
+# from refiner import refine_optimization
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
@@ -23,74 +23,37 @@ def write_data(L: List[Landmark], file_name: str):
 
     data.to_json(file_name, indent = 2, force_ascii=False)
 
-def test3(city_country: str) -> List[Landmark]:
+def main(coordinates: tuple[float, float]) -> List[Landmark]:
 
+    manager = LandmarkManager()
 
     preferences = Preferences(
-                    sightseeing=Preference(
-                                  name='sightseeing', 
-                                  type=LandmarkType(landmark_type='sightseeing'),
-                                  score = 5),
-                    nature=Preference(
-                                  name='nature', 
-                                  type=LandmarkType(landmark_type='nature'),
-                                  score = 0),
-                    shopping=Preference(
-                                  name='shopping', 
-                                  type=LandmarkType(landmark_type='shopping'),
-                                  score = 5))
-
-    coordinates = None
-
-    landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
-
-    #write_data(landmarks)
-
-    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)
-    
-
-    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]:
-
-
-    preferences = Preferences(
-                    sightseeing=Preference(
-                                  name='sightseeing', 
-                                  type=LandmarkType(landmark_type='sightseeing'),
-                                  score = 5),
-                    nature=Preference(
-                                  name='nature', 
-                                  type=LandmarkType(landmark_type='nature'),
-                                  score = 5),
-                    shopping=Preference(
-                                  name='shopping', 
-                                  type=LandmarkType(landmark_type='shopping'),
-                                  score = 5))
+        sightseeing=Preference(
+            name='sightseeing', 
+            type=LandmarkType(landmark_type='sightseeing'),
+            score = 5
+            ),
+        nature=Preference(
+            name='nature', 
+            type=LandmarkType(landmark_type='nature'),
+            score = 5
+            ),
+        shopping=Preference(
+            name='shopping', 
+            type=LandmarkType(landmark_type='shopping'),
+            score = 5
+            )
+        )
 
 
     # Create start and finish 
     start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    #start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
     # Generate the landmarks from the start location
-    landmarks, landmarks_short = generate_landmarks(preferences=preferences, center_coordinates=start.location)
+    landmarks, landmarks_short = manager.get_landmark_lists(preferences=preferences, center_coordinates=start.location)
+    print([l.name for l in landmarks_short])
+
     #write_data(landmarks, "landmarks.txt")
 
     # Insert start and finish to the landmarks list
@@ -105,12 +68,13 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
     
     # Second stage optimization
-    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
+    # refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
     
-    return refined_tour
+    return base_tour
 
 
-test4(tuple((48.8344400, 2.3220540)))       # Café Chez César 
-#test4(tuple((48.8375946, 2.2949904)))       # Point random
-#test4(tuple((47.377859, 8.540585)))         # Zurich HB
-#test3('Vienna, Austria')
\ No newline at end of file
+
+if __name__ == '__main__':
+    start = (48.847132, 2.312359) # Café Chez César
+    # start = (47.377859, 8.540585) # Zurich HB
+    main(start)

From 25cc0fa300a8e0a4e39f19f6ecd22617c36612fa Mon Sep 17 00:00:00 2001
From: Remy Moll <me@moll.re>
Date: Mon, 8 Jul 2024 11:55:27 +0200
Subject: [PATCH 4/6] (theoretically) functional deployment

---
 deployment/deployment.yaml    | 37 +++++++++++++++++++++++++++++++++++
 deployment/ingress.yaml       | 15 ++++++++++++++
 deployment/kustomization.yaml | 15 ++++++++++++++
 deployment/namespace.yaml     |  4 ++++
 deployment/pvc.yaml           | 11 +++++++++++
 deployment/service.yaml       | 11 +++++++++++
 6 files changed, 93 insertions(+)
 create mode 100644 deployment/deployment.yaml
 create mode 100644 deployment/ingress.yaml
 create mode 100644 deployment/namespace.yaml
 create mode 100644 deployment/pvc.yaml
 create mode 100644 deployment/service.yaml

diff --git a/deployment/deployment.yaml b/deployment/deployment.yaml
new file mode 100644
index 0000000..616b587
--- /dev/null
+++ b/deployment/deployment.yaml
@@ -0,0 +1,37 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: nav-backend
+spec:
+  replicas: 3
+  selector:
+    matchLabels:
+      app: nav-backend
+  template:
+    metadata:
+      labels:
+        app: nav-backend
+    spec:
+      containers:
+      - name: worker
+        image: backend-image
+        ports:
+        - containerPort: 8000
+        env:
+        - name: NUM_WORKERS
+          value: "3"
+        - name: OSM_CACHE_DIR
+          value: "/osm-cache"
+        volumeMounts:
+        - name: osm-cache
+          mountPath: /osm-cache
+        resources:
+          requests:
+            cpu: 100m
+            memory: 100Mi
+          limits:
+            cpu: 4
+            memory: 8Gi
+      volumes:
+      - name: osm-cache
+        emptyDir: {}
diff --git a/deployment/ingress.yaml b/deployment/ingress.yaml
new file mode 100644
index 0000000..8fa3b41
--- /dev/null
+++ b/deployment/ingress.yaml
@@ -0,0 +1,15 @@
+kind: IngressRoute
+apiVersion: traefik.io/v1alpha1
+metadata:
+  name: nav-ingress
+spec:
+  entryPoints:
+    - websecure
+  routes:
+    - match: Host(`nav.kluster.moll.re`)
+      kind: Rule
+      services:
+        - name: nav-service
+          port: 8000
+  tls:
+    certResolver: default-tls
diff --git a/deployment/kustomization.yaml b/deployment/kustomization.yaml
index e69de29..28040ec 100644
--- a/deployment/kustomization.yaml
+++ b/deployment/kustomization.yaml
@@ -0,0 +1,15 @@
+apiVersion: kustomize.config.k8s.io/v1beta1
+kind: Kustomization
+
+
+namespace: nav
+resources:
+  - namespace.yaml
+  - deployment.yaml
+  - service.yaml
+  - ingress.yaml
+
+images:
+  - name: backend-image
+    newName: git.kluster.moll.re/remoll/fast_network_navigation/backend
+    newTag: latest
\ No newline at end of file
diff --git a/deployment/namespace.yaml b/deployment/namespace.yaml
new file mode 100644
index 0000000..0a074bd
--- /dev/null
+++ b/deployment/namespace.yaml
@@ -0,0 +1,4 @@
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: placeholder
diff --git a/deployment/pvc.yaml b/deployment/pvc.yaml
new file mode 100644
index 0000000..15897ec
--- /dev/null
+++ b/deployment/pvc.yaml
@@ -0,0 +1,11 @@
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: cache
+spec:
+  storageClassName: "nfs-client"
+  accessModes:
+    - ReadWriteOnce
+  resources:
+    requests:
+      storage: "5Gi"
diff --git a/deployment/service.yaml b/deployment/service.yaml
new file mode 100644
index 0000000..c4e5de3
--- /dev/null
+++ b/deployment/service.yaml
@@ -0,0 +1,11 @@
+apiVersion: v1
+kind: Service
+metadata:
+  name: nav-service
+spec:
+  selector:
+    app: nav-backend
+  ports:
+    - protocol: TCP
+      port: 8000
+      targetPort: 8000

From 8d71cab8d5941cd70681524e8ddbcc4f1791fcf3 Mon Sep 17 00:00:00 2001
From: Remy Moll <me@moll.re>
Date: Wed, 17 Jul 2024 12:00:40 +0200
Subject: [PATCH 5/6] osmnx does not behave

---
 backend/src/landmarks_manager.py                 | 11 +++++++----
 backend/src/parameters/optimizer_parameters.yaml |  2 +-
 deployment/deployment.yaml                       |  9 ++++++---
 deployment/kustomization.yaml                    |  1 +
 deployment/pvc.yaml                              |  2 +-
 5 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
index 690fae8..0392245 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/landmarks_manager.py
@@ -16,7 +16,9 @@ class LandmarkManager:
     logger = logging.getLogger(__name__)
 
     def __init__(self) -> None:
-        ox.config(cache_folder=constants.OSM_CACHE_DIR)
+        ox.settings.cache_folder = constants.OSM_CACHE_DIR
+        ox.settings.use_cache = True
+        ox.config(use_cache=True, cache_folder=constants.OSM_CACHE_DIR)
         with constants.AMENITY_SELECTORS_PATH.open('r') as f:
             self.amenity_selectors = yaml.safe_load(f)
         with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
@@ -36,21 +38,21 @@ class LandmarkManager:
 
         # List for sightseeing
         if preferences.sightseeing.score != 0:
-            score_func = lambda loc, n_tags: int((self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']) * self.parameters['church_coeff'])  
+            score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']) * self.parameters['church_coeff'])  
             L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, self.parameters['city_bbox_side'], score_func)
             self.correct_score(L1, preferences.sightseeing)
             L += L1
 
         # List for nature
         if preferences.nature.score != 0:
-            score_func = lambda loc, n_tags: int((self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']) * self.parameters['park_coeff'])  
+            score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']) * self.parameters['park_coeff'])  
             L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, center_coordinates, self.parameters['city_bbox_side'], score_func)
             self.correct_score(L2, preferences.nature)
             L += L2
 
         # List for shopping
         if preferences.shopping.score != 0:
-            score_func = lambda loc, n_tags: self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']
+            score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']))
             L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, center_coordinates, self.parameters['city_bbox_side'], score_func)
             self.correct_score(L3, preferences.shopping)
             L += L3
@@ -115,6 +117,7 @@ class LandmarkManager:
             dist = distance,
             tags = amenity_selectors
         )
+        self.logger.info(f'Fetched {len(landmarks)} landmarks around {center_coordinates}.')
 
         # cleanup the list
         # remove rows where name is None
diff --git a/backend/src/parameters/optimizer_parameters.yaml b/backend/src/parameters/optimizer_parameters.yaml
index b6dca0e..515d028 100644
--- a/backend/src/parameters/optimizer_parameters.yaml
+++ b/backend/src/parameters/optimizer_parameters.yaml
@@ -1,3 +1,3 @@
 detour_factor: 1.4
 average_walking_speed: 4.8
-max_landmarks: 10
+max_landmarks: 40
diff --git a/deployment/deployment.yaml b/deployment/deployment.yaml
index 616b587..aabc6fa 100644
--- a/deployment/deployment.yaml
+++ b/deployment/deployment.yaml
@@ -3,7 +3,7 @@ kind: Deployment
 metadata:
   name: nav-backend
 spec:
-  replicas: 3
+  replicas: 1
   selector:
     matchLabels:
       app: nav-backend
@@ -31,7 +31,10 @@ spec:
             memory: 100Mi
           limits:
             cpu: 4
-            memory: 8Gi
+            memory: 10Gi
       volumes:
       - name: osm-cache
-        emptyDir: {}
+        persistentVolumeClaim:
+          claimName: osm-cache
+
+    
\ No newline at end of file
diff --git a/deployment/kustomization.yaml b/deployment/kustomization.yaml
index 28040ec..bbb5d0c 100644
--- a/deployment/kustomization.yaml
+++ b/deployment/kustomization.yaml
@@ -5,6 +5,7 @@ kind: Kustomization
 namespace: nav
 resources:
   - namespace.yaml
+  - pvc.yaml
   - deployment.yaml
   - service.yaml
   - ingress.yaml
diff --git a/deployment/pvc.yaml b/deployment/pvc.yaml
index 15897ec..a7e747f 100644
--- a/deployment/pvc.yaml
+++ b/deployment/pvc.yaml
@@ -1,7 +1,7 @@
 apiVersion: v1
 kind: PersistentVolumeClaim
 metadata:
-  name: cache
+  name: osm-cache
 spec:
   storageClassName: "nfs-client"
   accessModes:

From 7d7a25e2f3bb4a9ba854cd830f33f8e9f2948be7 Mon Sep 17 00:00:00 2001
From: Remy Moll <me@moll.re>
Date: Wed, 17 Jul 2024 12:33:53 +0200
Subject: [PATCH 6/6] stage changes as reference implementation

---
 ...landmarks_manager.py => example_landmarks_manager.py} | 9 +++------
 backend/src/{optimizer.py => example_optimizer.py}       | 0
 backend/src/{refiner.py => example_refiner.py}           | 2 +-
 backend/src/main.py                                      | 2 +-
 backend/src/parameters/landmark_parameters.yaml          | 2 +-
 backend/src/tester.py                                    | 2 +-
 6 files changed, 7 insertions(+), 10 deletions(-)
 rename backend/src/{landmarks_manager.py => example_landmarks_manager.py} (93%)
 rename backend/src/{optimizer.py => example_optimizer.py} (100%)
 rename backend/src/{refiner.py => example_refiner.py} (98%)

diff --git a/backend/src/landmarks_manager.py b/backend/src/example_landmarks_manager.py
similarity index 93%
rename from backend/src/landmarks_manager.py
rename to backend/src/example_landmarks_manager.py
index 0392245..30df6dc 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/example_landmarks_manager.py
@@ -1,8 +1,6 @@
 import yaml
 import logging
-import osmnx as ox
-from shapely.geometry import Point, Polygon, LineString, MultiPolygon
-
+from OSMPythonTools import cachingStrategy, overpass
 from structs.landmarks import Landmark, LandmarkType
 from structs.preferences import Preferences, Preference
 import constants
@@ -16,9 +14,8 @@ class LandmarkManager:
     logger = logging.getLogger(__name__)
 
     def __init__(self) -> None:
-        ox.settings.cache_folder = constants.OSM_CACHE_DIR
-        ox.settings.use_cache = True
-        ox.config(use_cache=True, cache_folder=constants.OSM_CACHE_DIR)
+        strategy = cachingStrategy.JSON(cacheDir=constants.OSM_CACHE_DIR)
+        self.query_builder = overpass.Overpass()
         with constants.AMENITY_SELECTORS_PATH.open('r') as f:
             self.amenity_selectors = yaml.safe_load(f)
         with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
diff --git a/backend/src/optimizer.py b/backend/src/example_optimizer.py
similarity index 100%
rename from backend/src/optimizer.py
rename to backend/src/example_optimizer.py
diff --git a/backend/src/refiner.py b/backend/src/example_refiner.py
similarity index 98%
rename from backend/src/refiner.py
rename to backend/src/example_refiner.py
index a292b73..5eb4368 100644
--- a/backend/src/refiner.py
+++ b/backend/src/example_refiner.py
@@ -10,7 +10,7 @@ from math import pi
 
 from structs.landmarks import Landmark
 from landmarks_manager import take_most_important
-from optimizer import solve_optimization, link_list_simple, print_res, get_distance
+from backend.src.example_optimizer import solve_optimization, link_list_simple, print_res, get_distance
 import constants
 
 
diff --git a/backend/src/main.py b/backend/src/main.py
index a1ff712..8a76288 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -1,4 +1,4 @@
-from optimizer import solve_optimization
+from backend.src.example_optimizer import solve_optimization
 # from refiner import refine_optimization
 from landmarks_manager import LandmarkManager
 from structs.landmarks import Landmark
diff --git a/backend/src/parameters/landmark_parameters.yaml b/backend/src/parameters/landmark_parameters.yaml
index 524b7b6..b85c89a 100644
--- a/backend/src/parameters/landmark_parameters.yaml
+++ b/backend/src/parameters/landmark_parameters.yaml
@@ -1,4 +1,4 @@
-city_bbox_side: 1500 #m
+city_bbox_side: 5000 #m
 radius_close_to: 30
 church_coeff: 0.6
 park_coeff: 1.5
diff --git a/backend/src/tester.py b/backend/src/tester.py
index 57692d2..71d82e7 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -4,7 +4,7 @@ from typing import List
 from landmarks_manager import LandmarkManager
 from fastapi.encoders import jsonable_encoder
 
-from optimizer import solve_optimization
+from backend.src.example_optimizer import solve_optimization
 # from refiner import refine_optimization
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType