def run(self, specification, coefficients, agent_set, agents_index=None, **kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
cond_array = zeros(agent_set.size(), dtype="bool8")
cond_array[agents_index] = True
geography_ids = self.geography_dataset.get_id_attribute()
geographies_of_agents = agent_set.get_attribute(self.geography_id_name)
orig_filter = self.filter
for geography_id in geography_ids:
new_index = where(logical_and(cond_array, geographies_of_agents == geography_id))[0]
if orig_filter is not None:
self.filter = "(building.%s == %s) * %s" % (self.geography_id_name, geography_id, orig_filter)
else:
self.filter = "building.%s == %s" % (self.geography_id_name, geography_id)
logger.log_status("ELCM for %s %s" % (self.geography_dataset.get_dataset_name(), geography_id))
EmploymentLocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index=new_index, **kwargs)
agent_set.flush_dataset()
# self.choice_set.flush_dataset()
# set the right parcels
parcels = agent_set.compute_variables(["job.disaggregate(building.parcel_id)"],
dataset_pool = self.dataset_pool)
agent_set.modify_attribute(name="parcel_id", data = parcels)
def run(self, specification, coefficients, agent_set, agents_index=None, increase_job_capacity_if_needed=True, **kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
cond_array = zeros(agent_set.size(), dtype="bool8")
cond_array[agents_index] = True
geography_ids = self.geography_dataset.get_id_attribute()
geographies_of_agents = agent_set.get_attribute(self.geography_id_name)
orig_filter = self.filter
for geography_id in geography_ids:
#for geography_id in [197]:
new_index = where(logical_and(cond_array, geographies_of_agents == geography_id))[0]
if orig_filter is not None:
self.filter = "(building.%s == %s) * %s" % (self.geography_id_name, geography_id, orig_filter)
else:
self.filter = "building.%s == %s" % (self.geography_id_name, geography_id)
logger.log_status("ELCM for %s %s" % (self.geography_dataset.get_dataset_name(), geography_id))
for irun in [1,2]:
EmploymentLocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index=new_index, **kwargs)
if not increase_job_capacity_if_needed or irun > 1:
break
# increase buildings job_capacity to fit all jobs
unplaced_size = (agent_set['building_id'][agent_set[self.geography_id_name]==geography_id] <= 0).sum()
if unplaced_size <= 0 or not 'job_capacity' in self.choice_set.get_known_attribute_names():
break
filt = where(self.choice_set.compute_variables(self.filter, dataset_pool=self.dataset_pool)>0)[0]
if filt.size <= 0:
break
noa = self.choice_set.compute_variables('noj = building.number_of_agents(job)',
dataset_pool=self.dataset_pool)
cap = maximum(self.choice_set['job_capacity'][filt], self.choice_set['noj'][filt])
if unplaced_size <= cap.sum():
break
# TODO: make these two lines optional
# self.choice_set.modify_attribute('job_capacity', data=ceil(cap + cap*(unplaced_size/float(cap.sum()))).astype(cap.dtype), index=filt)
# logger.log_warning('Capacity increased by %s jobs.' % round(cap*unplaced_size/float(cap.sum())))
agent_set.flush_dataset()
def __init__(self, group_member, location_set, geography_dataset, *args, **kwargs):
self.geography_dataset = geography_dataset
self.geography_id_name = geography_dataset.get_id_name()[0]
EmploymentLocationChoiceModel.__init__(self, group_member, location_set, *args, **kwargs)
