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)
