From a4c435c398e62ac623b8fdcff61a053206b2206a Mon Sep 17 00:00:00 2001
From: Helldragon67 <kilian.scheidecker@orange.fr>
Date: Wed, 4 Dec 2024 19:23:26 +0100
Subject: [PATCH] better pep8
---
backend/src/main.py | 2 +-
backend/src/structs/shopping_location.py | 23 -----
backend/src/utils/cluster_processing.py | 103 +++++++++++++++++++----
backend/src/utils/landmarks_manager.py | 2 +-
4 files changed, 89 insertions(+), 41 deletions(-)
delete mode 100644 backend/src/structs/shopping_location.py
diff --git a/backend/src/main.py b/backend/src/main.py
index 11e3039..2cc558f 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -61,7 +61,7 @@ def new_trip(preferences: Preferences,
attractiveness=0,
must_do=True,
n_tags = 0)
-
+
end_landmark = Landmark(name='finish',
type='finish',
location=(end[0], end[1]),
diff --git a/backend/src/structs/shopping_location.py b/backend/src/structs/shopping_location.py
deleted file mode 100644
index d784ca4..0000000
--- a/backend/src/structs/shopping_location.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from typing import Literal, Optional
-from pydantic import BaseModel
-
-
-class ShoppingLocation(BaseModel):
- """"
- A classe representing an interesting area for shopping.
-
- It can represent either a general area or a specifc route with start and end point.
- The importance represents the number of shops found in this cluster.
-
- Attributes:
- type : either a 'street' or 'area' (representing a denser field of shops).
- importance : size of the cluster (number of points).
- centroid : center of the cluster.
- start : if the type is a street it goes from here...
- end : ...to here
- """
- type: Literal['street', 'area']
- importance: int
- centroid: tuple
- start: Optional[list] = None
- end: Optional[list] = None
\ No newline at end of file
diff --git a/backend/src/utils/cluster_processing.py b/backend/src/utils/cluster_processing.py
index a6ef15b..2114bcb 100644
--- a/backend/src/utils/cluster_processing.py
+++ b/backend/src/utils/cluster_processing.py
@@ -11,13 +11,28 @@ from ..structs.landmark import Landmark
from ..utils.get_time_separation import get_distance
from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
+
class ShoppingLocation(BaseModel):
+ """"
+ A classe representing an interesting area for shopping.
+
+ It can represent either a general area or a specifc route with start and end point.
+ The importance represents the number of shops found in this cluster.
+
+ Attributes:
+ type : either a 'street' or 'area' (representing a denser field of shops).
+ importance : size of the cluster (number of points).
+ centroid : center of the cluster.
+ start : if the type is a street it goes from here...
+ end : ...to here
+ """
type: Literal['street', 'area']
importance: int
centroid: tuple
# start: Optional[list] = None # for later use if we want to have streets as well
# end: Optional[list] = None
+
class ShoppingManager:
logger = logging.getLogger(__name__)
@@ -31,7 +46,11 @@ class ShoppingManager:
def __init__(self, bbox: tuple) -> None:
"""
- Upon intialization, generate the list of shops used for cluster points.
+ Upon intialization, generate the point cloud used for cluster detection.
+ The points represent bag/clothes shops and general boutiques.
+
+ Args:
+ bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
"""
# Initialize overpass and cache
@@ -52,27 +71,34 @@ class ShoppingManager:
except Exception as e:
self.logger.error(f"Error fetching landmarks: {e}")
- if len(result.elements()) > 0 :
-
+ if len(result.elements()) == 0 :
+ self.valid = False
+
+ else :
points = []
for elem in result.elements() :
points.append(tuple((elem.lat(), elem.lon())))
self.all_points = np.array(points)
- self.valid = True
-
- else :
- self.valid = False
+ self.valid = True
def generate_shopping_landmarks(self) -> list[Landmark]:
+ """
+ Generate shopping landmarks based on clustered locations.
+
+ This method first generates clusters of locations and then extracts shopping-related
+ locations from these clusters. It transforms each shopping location into a `Landmark` object.
+
+ Returns:
+ list[Landmark]: A list of `Landmark` objects representing shopping locations.
+ Returns an empty list if no clusters are found.
+ """
- # First generate the clusters
self.generate_clusters()
- # Return empty list if no clusters were found
if len(set(self.cluster_labels)) == 0 :
- return []
+ return [] # Return empty list if no clusters were found
# Then generate the shopping locations
self.generate_shopping_locations()
@@ -87,6 +113,19 @@ class ShoppingManager:
def generate_clusters(self) :
+ """
+ Generate clusters of points using DBSCAN.
+
+ This method applies the DBSCAN clustering algorithm with different
+ parameters depending on the size of the city (number of points).
+ It filters out noise points and keeps only the largest clusters.
+
+ The method updates:
+ - `self.cluster_points`: The points belonging to clusters.
+ - `self.cluster_labels`: The labels for the points in clusters.
+
+ The method also calls `filter_clusters()` to retain only the largest clusters.
+ """
# Apply DBSCAN to find clusters. Choose different settings for different cities.
if len(self.all_points) > 200 :
@@ -105,6 +144,19 @@ class ShoppingManager:
def generate_shopping_locations(self) :
+ """
+ Generate shopping locations based on clustered points.
+
+ This method iterates over the different clusters, calculates the centroid
+ (as the mean of the points within each cluster), and assigns an importance
+ based on the size of the cluster.
+
+ The generated shopping locations are stored in `self.shopping_locations`
+ as a list of `ShoppingLocation` objects, each with:
+ - `type`: Set to 'area'.
+ - `centroid`: The calculated centroid of the cluster.
+ - `importance`: The number of points in the cluster.
+ """
locations = []
@@ -127,6 +179,21 @@ class ShoppingManager:
def create_landmark(self, shopping_location: ShoppingLocation) -> Landmark:
+ """
+ Create a Landmark object based on the given shopping location.
+
+ This method queries the Overpass API for nearby neighborhoods and shopping malls
+ within a 1000m radius around the shopping location centroid. It selects the closest
+ result and creates a landmark with the associated details such as name, type, and OSM ID.
+
+ Parameters:
+ shopping_location (ShoppingLocation): A ShoppingLocation object containing
+ the centroid and importance of the area.
+
+ Returns:
+ Landmark: A Landmark object containing details such as the name, type,
+ location, attractiveness, and OSM details.
+ """
# Define the bounding box for a given radius around the coordinates
lat, lon = shopping_location.centroid
@@ -153,10 +220,10 @@ class ShoppingManager:
try:
result = self.overpass.query(query)
except Exception as e:
- raise Exception("query unsuccessful")
+ self.logger.error(f"Error fetching landmarks: {e}")
+ continue
for elem in result.elements():
-
location = (elem.centerLat(), elem.centerLon())
if location[0] is None :
@@ -168,10 +235,10 @@ class ShoppingManager:
if d < min_dist :
min_dist = d
new_name = elem.tag('name')
- osm_type = elem.type() # Add type: 'way' or 'relation'
- osm_id = elem.id() # Add OSM id
+ osm_type = elem.type() # Add type: 'way' or 'relation'
+ osm_id = elem.id() # Add OSM id
- # add english name if it exists
+ # Add english name if it exists
try :
new_name_en = elem.tag('name:en')
except:
@@ -191,7 +258,11 @@ class ShoppingManager:
def filter_clusters(self):
"""
- Remove clusters of lesser importance.
+ Filter clusters to retain only the 5 largest clusters by point count.
+
+ This method calculates the size of each cluster and filters out all but the
+ 5 largest clusters. It then updates the cluster points and labels to reflect
+ only those from the top 5 clusters.
"""
label_counts = np.bincount(self.cluster_labels)
diff --git a/backend/src/utils/landmarks_manager.py b/backend/src/utils/landmarks_manager.py
index 310692f..883fca9 100644
--- a/backend/src/utils/landmarks_manager.py
+++ b/backend/src/utils/landmarks_manager.py
@@ -184,7 +184,7 @@ class LandmarkManager:
Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
Args:
- bbox (tuple[float, float, float, float]): The bounding box coordinates (min_lat, min_lon, max_lat, max_lon).
+ bbox (tuple[float, float, float, float]): The bounding box coordinates (around:radius, center_lat, center_lon).
amenity_selector (dict): The Overpass API query selector for the desired landmark type.
landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
score_function (callable): The function to compute the score of the landmark based on its attributes.