def run(self, specification, coefficients, agent_set, agents_index=None, sync_persons=False, **kwargs):
"""Set sync_persons to True if the model is run on households level and the persons table
should be synchronized.
"""
results = ChoiceModel.run(self, specification, coefficients, agent_set, agents_index=agents_index, **kwargs)
if sync_persons:
persons = self.dataset_pool.get_dataset('person')
choice_id_name = self.choice_set.get_id_name()[0]
values = persons.compute_variables(
['_tmp_ = person.disaggregate(%s.%s)' % (agent_set.get_dataset_name(),
choice_id_name)],
dataset_pool=self.dataset_pool)
if agents_index==None:
agents_index=arange(agent_set.size())
pers_idx = where(ismember(persons['%s' % agent_set.get_id_name()[0]], agent_set.get_id_attribute()[agents_index]))
if choice_id_name not in persons.get_known_attribute_names():
persons.add_primary_attribute(data=zeros(persons.size(), dtype=values.dtype), name=choice_id_name)
persons.modify_attribute(data=values, name=choice_id_name, index=pers_idx)
persons.delete_one_attribute('_tmp_')
agent_set.modify_attribute(data=results, name=self.choice_attribute_name.get_alias(), index=agents_index)
return results
def run(
self,
specification,
coefficients,
agent_set,
agents_index=None,
chunk_specification=None,
data_objects=None,
run_config=None,
debuglevel=0,
):
""" Run a simulation and return a numpy array of length agents_index, giving agent choices (ids of locations).
'specification' is of type EquationSpecification,
'coefficients' is of type Coefficients,
'agent_set' is of type Dataset,
'agent_index' are indices of individuals in the agent_set for which
the model runs. If it is None, the whole agent_set is considered.
'chunk_specification' determines number of chunks in which the simulation is processed.
Default is to use 300 rows per chunk.
'data_objects' is a dictionary where each key is the name of an data object
('zone', ...) and its value is an object of class Dataset.
'run_config' is of type Resources, it gives additional arguments for the run.
'debuglevel' overwrites the constructor 'debuglevel'.
"""
if run_config == None:
run_config = Resources()
self.run_config = run_config.merge_with_defaults(self.run_config)
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
## what is the use of compute location_id string in run? it gets new values anyway
# if self.location_id_string is not None:
# location_id = agent_set.compute_variables(self.location_id_string, dataset_pool=self.dataset_pool)
## done in choice_model
# location_id_name = self.choice_set.get_id_name()[0]
# if (location_id_name not in agent_set.get_known_attribute_names()):
# agent_set.add_attribute(name=location_id_name, data=resize(array([-1]), agent_set.size()))
if self.run_config.get(
"agent_units_string", None
): # used when agents take different amount of capacity from the total capacity
agent_set.compute_variables([self.run_config["agent_units_string"]], dataset_pool=self.dataset_pool)
self.compute_capacity_flag = self.run_config.get("compute_capacity_flag", False)
capacity_string = None
self.capacity = None
if self.compute_capacity_flag:
capacity_string = self.run_config.get("capacity_string", None)
if capacity_string is None:
raise KeyError, "Entry 'capacity_string' has to be specified in 'run_config' if 'compute_capacity_flag' is True"
## if weights is None, use capacity for weights
if self.run_config.get("weights_for_simulation_string", None) is None and capacity_string is not None:
self.run_config.merge({"weights_for_simulation_string": capacity_string})
return ChoiceModel.run(
self,
specification,
coefficients,
agent_set,
agents_index=agents_index,
chunk_specification=chunk_specification,
run_config=self.run_config,
debuglevel=debuglevel,
)
