def run(self, location_set, agent_set, agents_index=None, data_objects=None, **kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
large_areas = agent_set.get_attribute(self.large_area_id_name)
location_large_area = location_set.compute_variables(["washtenaw.%s.%s" % (location_set.get_dataset_name(), self.large_area_id_name)],
dataset_pool=self.dataset_pool)
valid_large_area = where(large_areas[agents_index] > 0)[0]
if valid_large_area.size > 0:
unique_large_areas = unique(large_areas[agents_index][valid_large_area])
cond_array = zeros(agent_set.size(), dtype="bool8")
cond_array[agents_index[valid_large_area]] = True
for area in unique_large_areas:
new_index = where(logical_and(cond_array, large_areas == area))[0]
self.filter = "%s.%s == %s" % (location_set.get_dataset_name(), self.large_area_id_name, area)
logger.log_status("%s for area %s" % (self.model_short_name, area))
ScalingJobsModel.run(self, location_set, agent_set, agents_index=new_index, **kwargs)
no_large_area = where(large_areas[agents_index] <= 0)[0]
if no_large_area.size > 0: # run the model for jobs that don't have assigned large_area
self.filter = None
logger.log_status("%s for jobs with no area assigned" % self.model_short_name)
choices = ScalingJobsModel.run(self, location_set, agent_set, agents_index=agents_index[no_large_area], **kwargs)
where_valid_choice = where(choices > 0)[0]
choices_index = location_set.get_id_index(choices[where_valid_choice])
chosen_large_areas = location_set.get_attribute_by_index(self.large_area_id_name, choices_index)
agent_set.modify_attribute(name=self.large_area_id_name, data=chosen_large_areas,
index=no_large_area[where_valid_choice])
def run(self,
location_set,
agent_set,
agents_index=None,
data_objects=None,
**kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
large_areas = agent_set.get_attribute(self.large_area_id_name)
location_large_area = location_set.compute_variables(
[
"washtenaw.%s.%s" %
(location_set.get_dataset_name(), self.large_area_id_name)
],
dataset_pool=self.dataset_pool)
valid_large_area = where(large_areas[agents_index] > 0)[0]
if valid_large_area.size > 0:
unique_large_areas = unique(
large_areas[agents_index][valid_large_area])
cond_array = zeros(agent_set.size(), dtype="bool8")
cond_array[agents_index[valid_large_area]] = True
for area in unique_large_areas:
new_index = where(logical_and(cond_array,
large_areas == area))[0]
self.filter = "%s.%s == %s" % (location_set.get_dataset_name(),
self.large_area_id_name, area)
logger.log_status("%s for area %s" %
(self.model_short_name, area))
ScalingJobsModel.run(self,
location_set,
agent_set,
agents_index=new_index,
**kwargs)
no_large_area = where(large_areas[agents_index] <= 0)[0]
if no_large_area.size > 0: # run the model for jobs that don't have assigned large_area
self.filter = None
logger.log_status("%s for jobs with no area assigned" %
self.model_short_name)
choices = ScalingJobsModel.run(
self,
location_set,
agent_set,
agents_index=agents_index[no_large_area],
**kwargs)
where_valid_choice = where(choices > 0)[0]
choices_index = location_set.get_id_index(
choices[where_valid_choice])
chosen_large_areas = location_set.get_attribute_by_index(
self.large_area_id_name, choices_index)
agent_set.modify_attribute(name=self.large_area_id_name,
data=chosen_large_areas,
index=no_large_area[where_valid_choice])
def run(self, location_set, agent_set, agents_filter=None, **kwargs):
"""
'location_set', 'agent_set' are of type Dataset. The model selects all unplaced jobs which pass the given agents_filter (if any),
and passes them to ScalingJobsModel.
"""
agents_index = agent_set.get_attribute(location_set.get_id_name()[0]) <= 0
if agents_filter:
dataset_pool = kwargs.get('dataset_pool', None)
filter_values = agent_set.compute_variables([agents_filter], dataset_pool=dataset_pool)
agents_index = logical_and(agents_index, filter_values>0)
return ScalingJobsModel.run(self, location_set, agent_set, where(agents_index)[0], **kwargs)
def run(self, location_set, agent_set, agents_filter=None, **kwargs):
"""
'location_set', 'agent_set' are of type Dataset. The model selects all unplaced jobs which pass the given agents_filter (if any),
and passes them to ScalingJobsModel.
"""
agents_index = agent_set.get_attribute(location_set.get_id_name()[0]) <= 0
if agents_filter:
dataset_pool = kwargs.get("dataset_pool", None)
filter_values = agent_set.compute_variables([agents_filter], dataset_pool=dataset_pool)
agents_index = logical_and(agents_index, filter_values > 0)
return ScalingJobsModel.run(self, location_set, agent_set, where(agents_index)[0], **kwargs)
