Example code for in-memory proximity search with Python (1).

gistfile1.py

from scipy.spatial import cKDTree
from scipy import inf

# ...  Some code removed for clarity ...

class PortlandCrimeTracker(object):
    DEFAULT_DATABASE_NAME = 'db'

    def __init__(self, db_filename=DEFAULT_DATABASE_NAME):
        crime_db = self.load_crimes_db(db_filename)
        self.crimes = crime_db['crimes']
        self.points = self.crimes.keys()
        self.crime_kdtree = cKDTree(self.points)

    def get_points_nearby(self, point, max_points=250):
        """
        Find the nearest points within 1/2 a mile of the tuple ``point``, to a
        maximum of ``max_points``.
        """
        # Find crimes within approximately 1/2 a mile. 1/4 mile is .005,
        # 1/2 mile is .01, full mile is .02.
        distances, indices = self.crime_kdtree.query(point, k=max_points,
                                                     distance_upper_bound=0.01)
        point_neighbors = []
        for index, max_points in zip(indices, distances):
            if max_points == inf:
                break
            point_neighbors.append(self.points[index])

        return point_neighbors