def output_pre_road_data(trail,
direction,
n,
type='cell',
interpolate_interval=1,
smoothing=0,
penalty=0.9,
direction_window=15):
'''
Output predicted road data with CTrack. For test wuhu data.
:param trail: <Int> The i_th trail
:param direction: <-1/1> -1 means reverse side; 1 means front.
:param n: The n_th test of this trail. If None, return all the tests of the trail.
:param type: 'cell' or 'GPS'. See details in function get_obs_data.
:param interpolate_interval: Interval seconds of processed observables. Default = 1.
:param smoothing: Window length of smoothing. If smoothing == 0, do not smooth.
:param penalty:
:param direction_window:
:return: None.
'''
df = get_road_tests(trail, direction, n)
graph = get_graph('../../data/road_test/link_baidu_wuhu.txt')
obs = get_obs_data(df, type=type)
track = CTrack(obs,
graph,
smoothing=smoothing,
interpolate_interval=interpolate_interval,
penalty=penalty,
direction_window=direction_window)
states = track.viterbi()
direction = u'正向' if direction == 1 else u'反向'
output_data_js(
states,
'../../res/MapMatching_test/pre_%s/wuhu_pre_线路%d_%s%d_smoothing=%d_window=%d.js'
% (type, trail, direction, n, smoothing, interpolate_interval))
def test_multi_mm(user_number,
path_need,
smoothing=2,
interpolate_interval=15,
interpolate=True):
'''
Test Multi_mm class.
:param user_number: Random User number use
:param path_need: Path number used
:param smoothing:
:param interpolate_interval:
:param interpolate:
:return:
'''
# Get observations
with open('../../res/work/0311_random_trajectories/random_user%d.json' %
user_number) as data_file:
data = json.load(data_file)
obses = []
for i in path_need:
tmp = data['trajectories'][i]
dates = [get_datetime(t) for t in tmp['dates']]
obs = pd.DataFrame({
'lon': tmp['lon'],
'lat': tmp['lat'],
'dates': dates
})
obses.append(obs)
# Get graph
graph = get_graph('../../data/hefei_road/link_baidu.txt')
matched_obses = []
mean_obs = Observations(obses).get_mean_obs(30)
obses.append(mean_obs)
for obs in obses:
track = CTrack(obs,
g=graph,
smoothing=smoothing,
interpolate_interval=interpolate_interval,
interpolate=interpolate)
matched_obs = track.viterbi()
matched_obses.append(matched_obs)
trajectories_tojson(
matched_obses,
data['user_id'],
'../../res/work/0315_similar_trajectories/multiHMM_random_user%d.js' %
user_number,
is_json=False)
trajectories_tojson(
matched_obses,
data['user_id'],
'../../res/work/0315_similar_trajectories/multiHMM_random_user%d.json'
% user_number,
is_json=True)
def __init__(self, signal_dir=SIGNAL_DIR,
cells_path=CELLS_PATH,
user_path=USER_PATH,
work_dir=WORK_DIR,
graph_path=GRAPH_PATH,
user_num=None,
max_seconds=60.0 * 30,
min_set_len=5, max_distance=3000,
max_angle=5, max_speed=200 * 1000 / 3600):
'''
A BigEye system environment
:param signal_dir: The directory saves signal data. Must end with '/'
Signal data is a csv, with columns ['cell_id', 'user_id', 'dates']
E.x: '../../res/work/0307_oneuser_alldata/'
:param cells_path: The path of cells csv.
Cells data is a csv, with columns ['cell_id', 'lon', 'lat']
E.x: '../../res/[0926]cells_process.csv'
:param user_path: The path of random users csv.
Random users data is a csv, with columns ['user_id']
E.x: '../../res/work/0307_oneuser_alldata/rand_users.csv'
:param user_num: If user == None, signals is from the same user.
:param max_seconds:
:param min_set_len:
:param max_distance:
:param max_angle:
:param max_speed:
'''
self.signal_dir = signal_dir
self.work_dir = work_dir
self.graph_path = graph_path
self.random_user_signal_dir = work_dir + 'random_user_signals/'
self.check_dir(self.random_user_signal_dir)
self.user_num = user_num
self.max_seconds = max_seconds
self.min_set_len = min_set_len
self.max_distance = max_distance
self.max_angle = max_angle
self.max_speed = max_speed
# Get cells
self.cells = self.get_cells(cells_path)
# Get graph
self.graph = get_graph(self.graph_path)
# Random users
self.users = self.get_users(user_path)
def output_obs_data(trail,
direction,
n,
type='cell',
interpolate_interval=5,
smoothing=20,
interpolate=True):
'''
Output observables data after pre-processing. For test wuhu data
:param trail: <Int> The i_th trail
:param direction: <-1/1> -1 means reverse side; 1 means front.
:param n: The n_th test of this trail. If None, return all the tests of the trail.
:param type: 'cell' or 'GPS'. See details in function get_obs_data.
:param interpolate_interval: Interval seconds of processed observables. Default = 1.
:param smoothing: Window length of smoothing. If smoothing == 0, do not smooth.
:param interpolate:
:return: None.
'''
df = get_road_tests(trail, direction, n)
graph = get_graph('../../data/road_test/link_baidu_wuhu.txt')
obs = get_obs_data(df, type=type)
track = CTrack(obs,
graph,
smoothing=smoothing,
interpolate_interval=interpolate_interval,
interpolate=interpolate)
out = track.obs
out['dates'] = out.time
direction = u'正向' if direction == 1 else u'反向'
if interpolate:
output_data_js(
out,
'../../res/MapMatching_test/processed_%s/wuhu_线路%d_%s%d_smoothing=%d_window=%d.js'
% (type, trail, direction, n, smoothing, interpolate_interval))
else:
output_data_js(
out, '../../res/MapMatching_test/raw_%s/wuhu_线路%d_%s%d.js' %
(type, trail, direction, n))
def read_js(file):
f = open(file)
content = f.read()
content = re.search(r'\[\[(.*?)\]\]', content).groups(0)[0]
pos = content.split('], [')
lon = []
lat = []
for p in pos:
lon.append(float(p.split(', ')[0]))
lat.append(float(p.split(', ')[1]))
f.close()
res1 = ['%.5f|%.5f' % (lon[i], lat[i]) for i in range(len(lon))]
# res2 = [[lon[i], [lat[i]]] for i in range(len(lon))]
return res1
if __name__ == '__main__':
graph = get_graph('../../data/road_test/link_baidu_wuhu.txt')
for n in range(1, 11):
for direction in [-1, 1]:
for trail in range(1, 9):
evaluate_test(trail,
direction,
n,
graph,
smoothing=20,
interpolate_interval=5)
# df = get_road_tests(9, -1, 1)
# print(df.head())
file_date = '2017%.2d%.2d' % (mon, dd)
'''Get Data'''
# get signals data
filename = (
'../../data/signals_pro/%.2d%.2d/%.2dt%.2d/signals_%.2dt%.2d_within_users.csv'
% (mon, dd, hh_start, hh_end, hh_start, hh_end))
signals = get_signals_from_csv(filename)
signals = signals[['cell_id', 'user_id', 'dates']]
# get cells data
cells = pd.read_csv('../../res/[0926]cells_process.csv')
cells_loc = cells[['cell_id', 'lat', 'lon']]
users = signals['user_id'].unique()
graph = get_graph('../../data/hefei_road/link_baidu.txt')
user_id = users[10]
signal, ind_sets, signal_sets = get_trajectory(signals, cells_loc, user_id)
print(cal_rough_speed_per_cell2(signal))
for i in range(len(signal_sets)):
if i != 1: continue
obs = pd.DataFrame({
'time': signal_sets[i]['dates'],
'lon': signal_sets[i]['lon'],
'lat': signal_sets[i]['lat']
})
# print(obs)
obs = deal_same_obs(obs)[0:8]
check_directory('../../res/MapMatching/%s' % user_id)
output_data_js(
obs, '../../res/MapMatching/%s/raw_trajectory%d.js' % (user_id, i))