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from geopandas import GeoDataFrame
from networkx import MultiDiGraph
import pandas as pd
import numpy as np
import osmnx as ox
from shapely.geometry import LineString, MultiLineString
from sklearn.neighbors import BallTree
import requests
from sklearn.cluster import KMeans
from datetime import datetime
def filter_by_direction(selected_road: GeoDataFrame, road_direction: str) -> GeoDataFrame:
if road_direction == 'North':
return selected_road[
(selected_road['bearing'] >= 270) | (selected_road['bearing'] <= 90)
]
elif road_direction == 'South':
return selected_road[
(selected_road['bearing'] > 90) & (selected_road['bearing'] < 270)
]
elif road_direction == 'East':
return selected_road[
(selected_road['bearing'] >= 0) & (selected_road['bearing'] <= 180)
]
elif road_direction == 'West':
return selected_road[
(selected_road['bearing'] > 180) & (selected_road['bearing'] < 360)
]
else:
raise ValueError(f"Invalid road_direction: {road_direction}. Must be one of: North, South, East, West.")
def add_weather_to_df(df: pd.DataFrame, num_clusters: int = 4 , api_key = 'FLMEW5QEEB8WT8YGUJXF6KAPK', time: datetime | None = None) -> pd.DataFrame:
if df.empty:
df['weather'] = None
return df
if time is None:
time = datetime.now()
coords = df[['Latitude', 'Longitude']].dropna().values
kmeans = KMeans(n_clusters=min(num_clusters, len(coords)), random_state=42)
df['weather_cluster'] = kmeans.fit_predict(coords)
weather_data = {}
date_str = time.strftime("%Y-%m-%d")
target_hour = time.strftime("%H:%M:%S")
for cluster_id in range(kmeans.n_clusters): # type: ignore
lat, lon = kmeans.cluster_centers_[cluster_id]
url = f"https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{lat},{lon}/{date_str}"
params = {
"key": api_key,
"unitGroup": "metric",
"contentType": "json"
}
try:
response = requests.get(url=url, params=params)
response.raise_for_status()
data = response.json()
hours = data.get("days", [{}])[0].get("hours", [])
def hour_diff(hour_entry):
try:
return abs(datetime.strptime(hour_entry["datetime"], "%H:%M:%S") - datetime.strptime(target_hour, "%H:%M:%S"))
except:
return datetime.max
if hours:
best_match = min(hours, key=hour_diff)
weather = best_match.get("conditions", "Unknown")
weather_time = best_match.get("datetime", None)
else:
weather = "Unknown"
weather_time = None
except Exception as e:
print(f"Weather api error for cluster {cluster_id}: {e}")
weather = "Unknown"
weather_time = None
weather_data[cluster_id] = {
"conditions": weather,
"datetime": weather_time
}
df['time'] = time
df['weather'] = df['weather_cluster'].map(lambda x: weather_data[x]["conditions"])
df['weather_time'] = df['weather_cluster'].map(lambda x: weather_data[x]["datetime"])
df.drop(columns=['weather_cluster'], inplace=True)
return df
def get_coordinates_from_network(G : MultiDiGraph, road_name: str, road_direction: str):
edges = ox.graph_to_gdfs(G, nodes=False, edges=True)
edges_motorway = edges[edges['highway'].isin(['motorway', 'motorway_link'])]
selected_road = edges_motorway[
edges_motorway['ref'].str.contains(road_name, na=False, case=False)
]
selected_road = filter_by_direction(selected_road, road_direction)
rows = []
for _, row in selected_road.iterrows():
lanes = row.get("lanes", None)
maxspeed = row.get("maxspeed", None)
road_name = row.get("name", None) # type: ignore
ref = row.get("ref", None)
geometry = row.geometry
if isinstance(geometry, LineString):
coords = geometry.coords
elif isinstance(geometry, MultiLineString):
coords = [pt for line in geometry.geoms for pt in line.coords]
else:
continue
for lon, lat in coords:
rows.append({
"Longitude": lon,
"Latitude": lat,
"lanes": lanes,
"maxspeed": maxspeed,
"road_name": road_name,
"ref": ref,
"direction" : road_direction
})
# Step 6: Build DataFrame
road_df = pd.DataFrame(rows)
print(f"Total points in {road_name} - {road_direction}: {len(road_df)}")
return road_df
def sort_gps_by_greedy_path(df: pd.DataFrame) -> pd.DataFrame:
"""
Greedy nearest-neighbor sorting of GPS coordinates.
Args:
df (pd.DataFrame): DataFrame with 'Latitude' and 'Longitude' columns.
Returns:
pd.DataFrame: Reordered DataFrame.
"""
coords_rad = np.radians(df[['Latitude', 'Longitude']].values)
tree = BallTree(coords_rad, metric='haversine')
visited = np.zeros(len(df), dtype=bool)
path = []
current_idx = 0 # or use farthest-point-start logic
for _ in range(len(df)):
visited[current_idx] = True
path.append(current_idx)
dist, ind = tree.query([coords_rad[current_idx]], k=len(df))
for next_idx in ind[0]:
if not visited[next_idx]:
current_idx = next_idx
break
return df.iloc[path].reset_index(drop=True)
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