def __init__(self):
in_storage = StorageFactory().get_storage(
'sql_storage',
hostname=settings.get_db_host_name(),
username=settings.get_db_user_name(),
password=settings.get_db_password(),
database_name=settings.db)
zones = ZoneDataset(in_storage=in_storage)
print "Read and Write ZoneDataset."
out_storage = StorageFactory().build_storage_for_dataset(
type='flt_storage', storage_location=settings.dir)
ReadWriteADataset(zones,
out_storage=out_storage,
out_table_name=settings.zonesubdir)
def test_getting_several_emme2_data_into_travel_data_set(self):
if self._has_travel_model:
num_zones = 30
zone_storage = StorageFactory().get_storage('dict_storage')
zone_table_name = 'zone'
zone_storage.write_table(
table_name=zone_table_name,
table_data={'zone_id': array(range(num_zones)) + 1},
)
zone_set = ZoneDataset(in_storage=zone_storage,
in_table_name=zone_table_name)
matrix_attribute_map = {
'au1tim': 'single_vehicle_to_work_travel_time',
'biketm': 'bike_to_work_travel_time'
}
tm_output = TravelModelOutput()
travel_data_set = tm_output.get_travel_data_set(
zone_set, matrix_attribute_map, self.real_bank_path)
self.assertEqual(
travel_data_set.get_attribute(
'single_vehicle_to_work_travel_time').size,
num_zones * num_zones)
self.assertEqual(
travel_data_set.get_attribute('bike_to_work_travel_time').size,
num_zones * num_zones)
from numpy import ma
self.assertEqual(
False,
ma.allclose(
travel_data_set.get_attribute(
'single_vehicle_to_work_travel_time'),
travel_data_set.get_attribute('bike_to_work_travel_time')))
else:
logger.log_warning('Test skipped. TRAVELMODELROOT environment '
'variable not found.')
coef, result = hlcm_psrc.estimate(specification,
agents_psrc,
agents_index=idx,
estimate_config=Resources(
{"estimation_size_agents": 0.01}),
debuglevel=4)
result = hlcm_psrc.run(specification,
coef,
agents_psrc,
agents_index=sample(range(agents_psrc.size()), 500),
debuglevel=4)
storage = StorageFactory().get_storage('sql_storage', storage_location=db)
zones_psrc = ZoneDataset(in_storage=storage)
hlcm_psrc_zones = HouseholdLocationChoiceModelCreator().get_model(
location_set=zones_psrc,
sampler="opus_core.samplers.weighted_sampler",
sample_size_locations=10,
choices="opus_core.random_choices_from_index",
compute_capacity_flag=False,
estimate_config=Resources({"weights_for_estimation_string": None}))
specification = EquationSpecification(
variables=("urbansim.zone.number_of_jobs", "urbansim.household.income"),
coefficients=("NOJ", "INC"))
agents_psrc.compute_variables(["urbansim.household.zone_id"],
resources=Resources({"gridcell":
locations_psrc}))
def get_resources(self, data_dictionary, dataset):
"""Create resources for computing a variable. """
resources = Resources()
for key in data_dictionary.keys():
if key in self.datasets:
data = data_dictionary[key]
storage = StorageFactory().get_storage('dict_storage')
if self.id_names[key] not in data_dictionary[key].keys(
) and not isinstance(self.id_names[key], list):
data[self.id_names[key]] = arange(
1,
len(data_dictionary[key][data_dictionary[key].keys()
[0]]) + 1) # add id array
id_name = self.id_names[key]
storage.write_table(table_name='data', table_data=data)
if key == "gridcell":
gc = GridcellDataset(in_storage=storage,
in_table_name='data')
# add relative_x and relative_y
gc.get_id_attribute()
n = int(ceil(sqrt(gc.size())))
if "relative_x" not in data.keys():
x = (indices((n, n)) + 1)[1].ravel()
gc.add_attribute(x[0:gc.size()],
"relative_x",
metadata=1)
if "relative_y" not in data.keys():
y = (indices((n, n)) + 1)[0].ravel()
gc.add_attribute(y[0:gc.size()],
"relative_y",
metadata=1)
resources.merge({key: gc})
elif key == "household":
resources.merge({
key:
HouseholdDataset(in_storage=storage,
in_table_name='data')
})
elif key == "development_project":
resources.merge({
key:
DevelopmentProjectDataset(in_storage=storage,
in_table_name='data')
})
elif key == "development_event":
resources.merge({
key:
DevelopmentEventDataset(in_storage=storage,
in_table_name='data')
})
elif key == "neighborhood":
resources.merge({
key:
NeighborhoodDataset(in_storage=storage,
in_table_name='data')
})
elif key == "job":
resources.merge({
key:
JobDataset(in_storage=storage, in_table_name='data')
})
elif key == "zone":
resources.merge({
key:
ZoneDataset(in_storage=storage, in_table_name='data')
})
elif key == "travel_data":
resources.merge({
key:
TravelDataDataset(in_storage=storage,
in_table_name='data')
})
elif key == "faz":
resources.merge({
key:
FazDataset(in_storage=storage, in_table_name='data')
})
elif key == "fazdistrict":
resources.merge({
key:
FazdistrictDataset(in_storage=storage,
in_table_name='data')
})
elif key == "race":
resources.merge({
key:
RaceDataset(in_storage=storage, in_table_name='data')
})
elif key == "county":
resources.merge({
key:
CountyDataset(in_storage=storage, in_table_name='data')
})
elif key == "large_area":
resources.merge({
key:
LargeAreaDataset(in_storage=storage,
in_table_name='data')
})
elif key == "development_group":
resources.merge({
key:
DevelopmentGroupDataset(in_storage=storage,
in_table_name='data')
})
elif key == "employment_sector_group":
resources.merge({
key:
EmploymentSectorGroupDataset(in_storage=storage,
in_table_name='data')
})
elif key == "plan_type_group":
resources.merge({
key:
PlanTypeGroupDataset(in_storage=storage,
in_table_name='data')
})
elif key == "building":
resources.merge({
key:
BuildingDataset(in_storage=storage,
in_table_name='data')
})
else:
resources.merge({key: data_dictionary[key]})
if dataset in self.interactions:
if dataset == "household_x_gridcell":
resources.merge({
"dataset":
HouseholdXGridcellDataset(dataset1=resources["household"],
dataset2=resources["gridcell"])
})
if dataset == "job_x_gridcell":
resources.merge({
"dataset":
JobXGridcellDataset(dataset1=resources["job"],
dataset2=resources["gridcell"])
})
if dataset == "household_x_zone":
resources.merge({
"dataset":
HouseholdXZoneDataset(dataset1=resources["household"],
dataset2=resources["zone"])
})
if dataset == "household_x_neighborhood":
resources.merge({
"dataset":
HouseholdXNeighborhoodDataset(
dataset1=resources["household"],
dataset2=resources["neighborhood"])
})
if dataset == "development_project_x_gridcell":
resources.merge({
"dataset":
DevelopmentProjectXGridcellDataset(
dataset1=resources["development_project"],
dataset2=resources["gridcell"])
})
else:
resources.merge({"dataset": resources[dataset]})
resources.merge({"check_variables": '*', "debug": 4})
return resources
