def disaggregate_odflow(
self,
time_start,
time_end,
id_mode,
id_zone_orig,
id_zone_dest,
tripnumber,
probs_fac,
ids_fac,
id_landusetype_orig,
id_landusetype_dest,
):
print 'disaggregate_odflow', time_start, time_end, id_mode, id_zone_orig, id_zone_dest, tripnumber
"""
Disaggregation of demand dem from taz id_zone_orig to id_zone_dest with id_mode
during time interval time_start,time_end, and creation of persons
which are parameterized accordingly.
The facility type at origin will be landusetype_orig
and at destination landusetype_dest.
The probability vector to start/end the trip in the different facilities
of facility type id_landusetype_orig/id_landusetype_dest is given by the
dictionary probs_fac[id_zone][id_landusetype].
The ids_fac is an array that contains the facility ids in correspondence
to the probability vector.
These information are obtained from
probs_fac, ids_fac = landuse.get_departure_probabilities()
"""
print 'disaggregate_odm', tripnumber, id_mode, id_zone_orig, id_zone_dest
probs_orig = probs_fac[id_zone_orig][id_landusetype_orig]
probs_dest = probs_fac[id_zone_dest][id_landusetype_dest]
print ' probs_orig', sum(probs_orig) # ,'\n',probs_orig
print ' probs_dest', sum(probs_dest) # ,'\n',probs_dest
# is there a chance to find facilities to locate persons in
# origin and destination zone
# print ' ',self.times_start.value.dtype,np.random.randint(time_start,time_end,tripnumber)
# print ' ',self.ids_mode_preferred.value.dtype,id_mode *
# np.ones(tripnumber, np.int32),
if (np.sum(probs_orig) > 0) & (np.sum(probs_dest) > 0):
return self.add_rows(
n=tripnumber,
ids_fac_home=ids_fac[random_choice(tripnumber, probs_orig)],
ids_fac_activity=ids_fac[random_choice(tripnumber,
probs_dest)],
ids_mode_preferred=id_mode *
np.ones(tripnumber, dtype=np.int32),
times_start=np.random.randint(time_start, time_end,
tripnumber))
else:
return []
def disaggregate_odflow(self, time_start, time_end, id_mode,
id_zone_orig, id_zone_dest, tripnumber,
probs_fac, ids_fac,
id_landusetype_orig,
id_landusetype_dest,):
print 'disaggregate_odflow', time_start, time_end, id_mode, id_zone_orig, id_zone_dest, tripnumber
"""
Disaggregation of demand dem from taz id_zone_orig to id_zone_dest with id_mode
during time interval time_start,time_end, and creation of persons
which are parameterized accordingly.
The facility type at origin will be landusetype_orig
and at destination landusetype_dest.
The probability vector to start/end the trip in the different facilities
of facility type id_landusetype_orig/id_landusetype_dest is given by the
dictionary probs_fac[id_zone][id_landusetype].
The ids_fac is an array that contains the facility ids in correspondence
to the probability vector.
These information are obtained from
probs_fac, ids_fac = landuse.get_departure_probabilities()
"""
print 'disaggregate_odm', tripnumber, id_mode, id_zone_orig, id_zone_dest
probs_orig = probs_fac[id_zone_orig][id_landusetype_orig]
probs_dest = probs_fac[id_zone_dest][id_landusetype_dest]
print ' probs_orig', sum(probs_orig) # ,'\n',probs_orig
print ' probs_dest', sum(probs_dest) # ,'\n',probs_dest
# is there a chance to find facilities to locate persons in
# origin and destination zone
# print ' ',self.times_start.value.dtype,np.random.randint(time_start,time_end,tripnumber)
# print ' ',self.ids_mode_preferred.value.dtype,id_mode *
# np.ones(tripnumber, np.int32),
if (np.sum(probs_orig) > 0) & (np.sum(probs_dest) > 0):
return self.add_rows(n=tripnumber,
ids_fac_home=ids_fac[
random_choice(tripnumber, probs_orig)],
ids_fac_activity=ids_fac[
random_choice(tripnumber, probs_dest)],
ids_mode_preferred=id_mode *
np.ones(tripnumber, dtype=np.int32),
times_start=np.random.randint(
time_start, time_end, tripnumber)
)
else:
return []
def get_modes_random(self, n):
"""
Return a vector with mode IDs of length n.
"""
ids = self.get_ids()
ids_modes_all = self.ids_mode[ids]
return ids_modes_all[random_choice(n, self.shares[ids])]
