def filter_trajectories(): print "Entered function to filter trajectories: BEEF" unfiltered = model_datastore.get_all_location_updates() filtered_trajectories = \ model_datastore.filter_trajectories(trajectories=unfiltered) model_datastore.store_filtered_trajectories(filtered_trajectories=filtered_trajectories) return
def run_the_whole_enchilada(epsilon, min_neighbors, min_num_trajectories_in_cluster, \ min_vertical_lines, min_prev_dist): all_raw_point_lists = get_normalized_datastore_trajectories() print "HERE ARE THE POINT LISTS WERE PASSING IN TO TRACLUS: " + str(all_raw_point_lists) print "ABOUT to run the whole enchilada with a min neighbors of " + str(min_neighbors) result_trajectories = the_whole_enchilada(point_iterable_list=all_raw_point_lists, \ epsilon=epsilon, \ min_neighbors=min_neighbors, \ min_num_trajectories_in_cluster=min_num_trajectories_in_cluster, \ min_vertical_lines=min_vertical_lines, \ min_prev_dist=min_prev_dist, \ partitioned_points_hook=model_datastore.store_partitioned_trajectories, \ clusters_hook=model_datastore.store_clusters) if len(result_trajectories) == 0: raise ValueError("length of resulting trajectories is " + str(len(result_trajectories))) model_datastore.store_filtered_trajectories(filtered_trajectories=result_trajectories)