import requests
import json
from sklearn.cluster import DBSCAN
from geopy.distance import great_circle
from geopy.point import Point
import numpy as np

# Set up the Overpass API query
# TODO: remove the bbox
query = """
[out:json];
node["man_made"="surveillance"]["surveillance:type"="ALPR"];
out body;
"""

# Request data from Overpass API
print("Requesting data from Overpass API...")
url = "http://overpass-api.de/api/interpreter"
response = requests.get(url, params={'data': query}, headers={'User-Agent': 'DeFlock/1.0'})
data = response.json()
print("Data received. Parsing nodes...")

# Parse nodes and extract lat/lon for clustering
coordinates = []
node_ids = []
for element in data['elements']:
    if element['type'] == 'node':
        coordinates.append([element['lat'], element['lon']])
        node_ids.append(element['id'])

# Convert coordinates to NumPy array for DBSCAN
coordinates = np.array(coordinates)

# Define the clustering radius (10 miles in meters)
radius_miles = 50
radius_km = radius_miles * 1.60934  # 1 mile = 1.60934 km
radius_in_radians = radius_km / 6371.0  # Earth's radius in km

# Perform DBSCAN clustering
db = DBSCAN(eps=radius_in_radians, min_samples=1, algorithm='ball_tree', metric='haversine').fit(np.radians(coordinates))
labels = db.labels_

# Prepare clusters and calculate centroids
clusters = {}
for label in set(labels):
    cluster_points = coordinates[labels == label]
    centroid = np.mean(cluster_points, axis=0)
    first_node_id = node_ids[labels.tolist().index(label)]
    
    # Store in clusters dict with centroid and first node ID
    clusters[label] = {
        "lat": centroid[0],
        "lon": centroid[1],
        "id": first_node_id
    }

# Save clusters to JSON
output = {"clusters": list(clusters.values())}
with open("alpr_clusters.json", "w") as outfile:
    json.dump(output, outfile, indent=2)

print("Clustering complete. Results saved to alpr_clusters.json.")