def run(route, prd, **kwargs):
"""
:type route: pyticas.ttypes.Route
:type prd: pyticas.ttypes.Period
:return:
"""
# load_data speed data
us = moe_helper.get_speed(route.get_stations(), prd, **kwargs)
us_results = moe_helper.add_virtual_rnodes(us, route)
us_data = [res.data for res in us_results]
us_data = spatial_avg.imputation(us_data)
# make empty whole_data for STT by copying speed whole_data and reseting data
stt_results = [res.clone() for res in us_results]
for ridx, res in enumerate(stt_results):
stt_results[ridx].data = [0] * len(prd.get_timeline())
stt_results[ridx].prd = prd
# calculate STT
cm_data = _calculate_stt(us_data, prd.interval, **kwargs)
for ridx, res in enumerate(stt_results):
res.data = cm_data[ridx]
return stt_results
def run(route, prd, **kwargs):
"""
:type route: pyticas.ttypes.Route
:type prd: pyticas.ttypes.Period
:return:
"""
# load_data speed data
moe_congestion_threshold_speed = kwargs.pop("moe_congestion_threshold_speed",
get_system_config_info().moe_congestion_threshold_speed)
us = moe_helper.get_speed(route.get_stations(), prd, **kwargs)
us_results = moe_helper.add_virtual_rnodes(us, route)
us_data = [res.data for res in us_results]
us_data = spatial_avg.imputation(us_data)
# make empty whole_data for congested miles by copying speed whole_data and reseting data
cmh_results = [res.clone() for res in us_results]
for ridx, res in enumerate(cmh_results):
cmh_results[ridx].data = [0] * len(prd.get_timeline())
cmh_results[ridx].prd = prd
# calculate CMH
cm_data = _calculate_cmh(us_data, prd.interval, moe_congestion_threshold_speed, **kwargs)
for ridx, res in enumerate(cmh_results):
res.data = cm_data[ridx]
return cmh_results
def run(route, prd, **kwargs):
"""
:type route: pyticas.ttypes.Route
:type prd: pyticas.ttypes.Period
:return:
"""
return moe_helper.get_speed(route.get_stations(), prd, **kwargs)
def run(route, prd, **kwargs):
"""
:type route: pyticas.ttypes.Route
:type prd: pyticas.ttypes.Period
:rtype: list[RNodeData]
"""
# load_data speed data
return moe_helper.get_speed(route.get_stations(), prd, **kwargs)
def run(route, prd, **kwargs):
"""
:type route: pyticas.ttypes.Route
:type prd: pyticas.ttypes.Period
:return:
"""
rnode_list = route.get_stations()
# load_data total flow data
us = moe_helper.get_speed(rnode_list, prd, **kwargs)
us_results = moe_helper.add_virtual_rnodes(us, route)
us_data = [res.data for res in us_results]
us_data = spatial_avg.imputation(us_data)
return us, us_results, us_data
def run(route, prd, **kwargs):
"""
:type route: pyticas.ttypes.Route
:type prd: pyticas.ttypes.Period
:return:
"""
rnode_list = route.get_stations()
# load_data speed data (load_data more data than given period)
us_results = moe_helper.get_speed(rnode_list, prd, **kwargs)
# make empty whole_data for acceleration by copying speed whole_data and reseting data
accel_results = [res.clone() for res in us_results]
# calculate travel time by increasing time index
for ridx, rnode_data in enumerate(
_calculate_accel(us_results, prd.interval, **kwargs)):
accel_results[ridx].data = rnode_data
return accel_results
def run(route, prd, **kwargs):
"""
:type route: pyticas.ttypes.Route
:type prd: pyticas.ttypes.Period
:return:
"""
ext_hour = kwargs.get('ext_hour', DEFAULT_END_HOUR_EXTENSION)
now = datetime.datetime.now()
ext_prd = prd.clone().extend_end_hour(ext_hour)
if ext_prd.end_date > datetime.datetime.now():
ext_prd.end_date = now
n_origin_data = len(prd.get_timeline())
# load_data speed data (load_data more data than given period)
us = moe_helper.get_speed(route.get_stations(), ext_prd, **kwargs)
us_results = moe_helper.add_virtual_rnodes(us, route)
us_data = [res.data for res in us_results]
us_data = spatial_avg.imputation(us_data)
# make empty whole_data for travel time by copying speed whole_data and reseting data
tt_results = [res.clone() for res in us_results]
for idx, res in enumerate(tt_results):
tt_results[idx].traffic_type = TrafficType.travel_time
for ridx, res in enumerate(tt_results):
tt_results[ridx].data = [-1] * n_origin_data
tt_results[ridx].prd = prd
# calculate travel time by increasing time index
for tidx in range(n_origin_data):
partial_data = [pd[tidx:] for pd in us_data]
tts = _calculate_tt(partial_data, prd.interval, **kwargs)
for ridx, tt_data in enumerate(tt_results):
tt_results[ridx].data[tidx] = tts[ridx]
return tt_results
