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 get_normalized_datastore_trajectories():
raw_trajectories = model_datastore.get_all_location_updates()
print "HERE ARE THE RAW TRAJECTORIES: \n" + str(raw_trajectories)
def dict_list_to_point_list(dict_list):
return map(lambda x: Point(x['lat'], x['lng']), dict_list)
normal_traj_lists = model_datastore.get_raw_trajectories()
print "\n\nHERE ARE THE NORMALIZED FORMAT TRAJECTORIES: \n" + str(normal_traj_lists)
print "\n LENGTH OF NORMAL TRAJ LIST IS " + str(len(normal_traj_lists))
all_raw_point_lists = []
for dict_list in normal_traj_lists:
print "here is what we're trying to go from dict list to point list on: " \
+ str(dict_list)
point_list = dict_list_to_point_list(dict_list)
if len(point_list) < 2:
continue
traj_list = remove_successive_points_at_same_spots(point_list)
if len(traj_list) >= 2:
all_raw_point_lists.append(traj_list)
if len(all_raw_point_lists) <= 1:
raise ValueError("length of all raw point lists is " + \
str(len(all_raw_point_lists)))
def get_min_dist():
min_dist = 1000
for traj in all_raw_point_lists:
p_iter = iter(traj)
prev = p_iter.next()
for p in p_iter:
min_dist = min(min_dist, prev.distance_to(p))
return min_dist
print "min dist is " + str(get_min_dist())
def get_scaled_trajs(traj_list, scale):
def scale_coordinates(point_list):
return map(lambda p: Point(p.x * scale, p.y * scale), point_list)
return map(scale_coordinates, traj_list)
all_raw_point_lists = get_scaled_trajs(all_raw_point_lists, COORDINATE_SCALER)
all_raw_point_lists = map(remove_points_too_close, all_raw_point_lists)
all_raw_point_lists = filter(lambda x: len(x) >= 2, all_raw_point_lists)
return all_raw_point_lists