def execute(self, goal):
temp_start_time = rospy.Time.now()
curr_date = datetime.datetime.fromtimestamp(rospy.Time.now().secs)
# curr_date = datetime.datetime(2016, 3, 10, 0, 50)
if curr_date.hour >= 0 and curr_date.hour < 8:
curr_date = curr_date - datetime.timedelta(hours=24)
curr_date = datetime.datetime(curr_date.year, curr_date.month, curr_date.day, 0, 0)
prev_date = curr_date - datetime.timedelta(hours=24)
self.rotm.calculate_region_observation_duration([prev_date, curr_date], self.minute_interval)
self.rotm.store_to_mongo()
if self._as.is_preempt_requested():
self._as.set_preempted()
return
curr_traj_est = self.tre.estimate_trajectories_daily([curr_date.day], curr_date.month, curr_date.year)
curr_traj_est = trajectory_estimate_for_date(curr_traj_est, curr_date)
prev_traj_est = self.tre.estimate_trajectories_daily([prev_date.day], prev_date.month, prev_date.year)
prev_traj_est = trajectory_estimate_for_date(prev_traj_est, prev_date)
if self._as.is_preempt_requested():
self._as.set_preempted()
return
self.tof.update_tof_daily(curr_traj_est, prev_traj_est, curr_date)
self.tof.store_tof()
self.rotm.reinit()
self.tof.reinit()
temp_end_time = rospy.Time.now()
rospy.loginfo("Time needed to complete this is %d" % (temp_end_time - temp_start_time).secs)
self._as.set_succeeded(OccurrenceRateLearningResult())
def execute(self, goal):
temp_start_time = rospy.Time.now()
curr_date = datetime.datetime.fromtimestamp(rospy.Time.now().secs)
# curr_date = datetime.datetime(2016, 3, 10, 0, 50)
if curr_date.hour >= 0 and curr_date.hour < 8:
curr_date = curr_date - datetime.timedelta(hours=24)
curr_date = datetime.datetime(curr_date.year, curr_date.month,
curr_date.day, 0, 0)
prev_date = curr_date - datetime.timedelta(hours=24)
self.rotm.calculate_region_observation_duration([prev_date, curr_date],
self.minute_interval)
self.rotm.store_to_mongo()
if self._as.is_preempt_requested():
self._as.set_preempted()
return
curr_traj_est = self.tre.estimate_trajectories_daily([curr_date.day],
curr_date.month,
curr_date.year)
curr_traj_est = trajectory_estimate_for_date(curr_traj_est, curr_date)
prev_traj_est = self.tre.estimate_trajectories_daily([prev_date.day],
prev_date.month,
prev_date.year)
prev_traj_est = trajectory_estimate_for_date(prev_traj_est, prev_date)
if self._as.is_preempt_requested():
self._as.set_preempted()
return
self.tof.update_tof_daily(curr_traj_est, prev_traj_est, curr_date)
self.tof.store_tof()
self.rotm.reinit()
self.tof.reinit()
temp_end_time = rospy.Time.now()
rospy.loginfo("Time needed to complete this is %d" %
(temp_end_time - temp_start_time).secs)
self._as.set_succeeded(OccurrenceRateLearningResult())
if len(sys.argv) < 4:
rospy.logerr("usage: synthetic minute_interval window_interval store(0)/test(1)")
sys.exit(2)
interval = int(sys.argv[1])
window = int(sys.argv[2])
sw = SyntheticWave(interval)
if not int(sys.argv[3]):
waves = sw.get_one_month_synthetic_wave(True)
tof = TOF("synthetic", "synthetic_config", interval, window)
tof.load_tof()
for i in range(4, 31+1):
prev_traj_est = trajectory_estimate_for_date(
waves, datetime.date(2015, 5, i-1)
)
curr_traj_est = trajectory_estimate_for_date(
waves, datetime.date(2015, 5, i)
)
tof.update_tof_daily(
curr_traj_est, prev_traj_est, datetime.date(2015, 5, i)
)
tof.store_tof()
else:
waves = sw.get_one_week_synthetic_wave(True)
tp = TrajectoryPeriodicity("synthetic", "synthetic_config", interval, window)
inp = raw_input("MSE(0) or Prediction MSE(1): ")
if int(inp) == 0:
rospy.loginfo("Start model selection...")
tp.model_selection(waves, 5, 2015, True)
rospy.logerr(
"usage: synthetic minute_interval window_interval store(0)/test(1)"
)
sys.exit(2)
interval = int(sys.argv[1])
window = int(sys.argv[2])
sw = SyntheticWave(interval)
if not int(sys.argv[3]):
waves = sw.get_one_month_synthetic_wave(True)
# waves = sw.get_one_month_synthetic_wave(False) # no noise
tof = TOF("synthetic", "synthetic_config", interval, window)
tof.load_tof()
for i in range(4, 31 + 1):
prev_traj_est = trajectory_estimate_for_date(
waves, datetime.date(2015, 5, i - 1))
curr_traj_est = trajectory_estimate_for_date(
waves, datetime.date(2015, 5, i))
tof.update_tof_daily(curr_traj_est, prev_traj_est,
datetime.date(2015, 5, i))
tof.store_tof()
else:
waves = sw.get_one_week_synthetic_wave(True)
tp = TrajectoryPeriodicity("synthetic", "synthetic_config", interval,
window)
# comment from here if just want the plot
inp = raw_input("MSE(0) or Prediction MSE(1): ")
if int(inp) == 0:
rospy.loginfo("Start model selection...")
tp.model_selection(waves, 5, 2015, True)
rospy.loginfo("End model selection...")
tof = TrajectoryOccurrenceFrequencies(sys.argv[1],
sys.argv[2],
minute_interval=interval,
window_interval=window)
tof.load_tof()
for i in range(start_date, end_date + 1):
if i == 1:
prev_month = month - 1
prev_year = year
if prev_month == 0:
prev_month = 12
prev_year -= 1
prev_traj_est = tre.estimate_trajectories_daily(
[calendar.monthrange(prev_year, prev_month)[1]], prev_month,
prev_year)
prev_traj_est = trajectory_estimate_for_date(
prev_traj_est,
datetime.date(prev_year, prev_month,
calendar.monthrange(prev_year, prev_month)[1]))
else:
prev_traj_est = trajectory_estimate_for_date(
trajectory_estimate, datetime.date(year, month, i - 1))
curr_traj_est = trajectory_estimate_for_date(
trajectory_estimate, datetime.date(year, month, i))
tof.update_tof_daily(curr_traj_est, prev_traj_est,
datetime.date(year, month, i))
tof.store_tof()
temp_end_time = rospy.Time.now()
rospy.loginfo("Time needed to complete this %d" %
(temp_end_time - temp_start_time).secs)
)
tof.load_tof()
for i in range(start_date, end_date+1):
if i == 1:
prev_month = month - 1
prev_year = year
if prev_month == 0:
prev_month = 12
prev_year -= 1
prev_traj_est = tre.estimate_trajectories_daily(
[calendar.monthrange(prev_year, prev_month)[1]],
prev_month, prev_year
)
prev_traj_est = trajectory_estimate_for_date(
prev_traj_est, datetime.date(
prev_year, prev_month,
calendar.monthrange(prev_year, prev_month)[1]
)
)
else:
prev_traj_est = trajectory_estimate_for_date(
trajectory_estimate, datetime.date(year, month, i-1)
)
curr_traj_est = trajectory_estimate_for_date(
trajectory_estimate, datetime.date(year, month, i)
)
tof.update_tof_daily(
curr_traj_est, prev_traj_est, datetime.date(year, month, i)
)
tof.store_tof()
temp_end_time = rospy.Time.now()
rospy.loginfo("Time needed to complete this %d" % (temp_end_time - temp_start_time).secs)
