def run(self, specification, coefficients, agent_set, agents_index=None, **kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
large_areas = agent_set.get_attribute(self.large_area_id_name)
self.choice_set.compute_variables(["washtenaw.%s.%s" % (self.choice_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" % (self.choice_set.get_dataset_name(), self.large_area_id_name, area)
logger.log_status("ELCM for area %s" % area)
EmploymentLocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index=new_index, **kwargs)
agent_index_no_large_area = agents_index[ large_areas[agents_index] <= 0 ]
if agent_index_no_large_area.size > 0: # run the ELCM for jobs that don't have assigned large_area
self.filter = None
logger.log_status("ELCM for jobs with no area assigned")
choices = EmploymentLocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index=agent_index_no_large_area, **kwargs)
where_valid_choice = where(choices > 0)[0]
choices_index = self.choice_set.get_id_index(choices[where_valid_choice])
chosen_large_areas = self.choice_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=agent_index_no_large_area[where_valid_choice])
def run(self,
specification,
coefficients,
agent_set,
agents_index=None,
**kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
large_areas = agent_set.get_attribute(self.large_area_id_name)
self.choice_set.compute_variables([
"washtenaw.%s.%s" %
(self.choice_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" % (
self.choice_set.get_dataset_name(),
self.large_area_id_name, area)
logger.log_status("ELCM for area %s" % area)
EmploymentLocationChoiceModel.run(self,
specification,
coefficients,
agent_set,
agents_index=new_index,
**kwargs)
agent_index_no_large_area = agents_index[
large_areas[agents_index] <= 0]
if agent_index_no_large_area.size > 0: # run the ELCM for jobs that don't have assigned large_area
self.filter = None
logger.log_status("ELCM for jobs with no area assigned")
choices = EmploymentLocationChoiceModel.run(
self,
specification,
coefficients,
agent_set,
agents_index=agent_index_no_large_area,
**kwargs)
where_valid_choice = where(choices > 0)[0]
choices_index = self.choice_set.get_id_index(
choices[where_valid_choice])
chosen_large_areas = self.choice_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=agent_index_no_large_area[where_valid_choice])
def run(self, specification, coefficients, agent_set, agents_index=None, agents_filter=None,
flush_after_each_subarea=False, run_no_area=True, **kwargs):
if agents_index is None:
if agents_filter is None:
agents_index = arange(agent_set.size())
else:
agents_index = where(agent_set.compute_variables(agents_filter))[0]
if self.location_id_string is not None:
agent_set.compute_variables(self.location_id_string, dataset_pool=self.dataset_pool)
if not self.subarea_id_name in agent_set.get_attribute_names():
agent_set.compute_one_variable_with_unknown_package(variable_name="%s" % (self.subarea_id_name), dataset_pool=self.dataset_pool)
regions = agent_set.get_attribute(self.subarea_id_name)
self.choice_set.compute_one_variable_with_unknown_package(variable_name="%s" % (self.subarea_id_name), dataset_pool=self.dataset_pool)
valid_region = where(regions[agents_index] > 0)[0]
filter0 = self.filter #keep a copy of the original self.filter
if valid_region.size > 0:
unique_regions = unique(regions[agents_index][valid_region])
cond_array = zeros(agent_set.size(), dtype="bool8")
cond_array[agents_index[valid_region]] = True
for area in unique_regions:
new_index = where(logical_and(cond_array, regions == area))[0]
#append subarea_id filter to the original filter string if it is set
subarea_filter = "(%s.%s==%s)" % (self.choice_set.get_dataset_name(), self.subarea_id_name, area)
if filter0:
self.filter = "(" + filter0 + ")" + "*" + subarea_filter
else:
self.filter = subarea_filter
logger.log_status("ELCM for area %s" % area)
EmploymentLocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index=new_index, **kwargs)
if flush_after_each_subarea:
agent_set.flush_dataset()
self.choice_set.flush_dataset()
if run_no_area:
no_region = where(regions[agents_index] <= 0)[0]
self.filter = filter0
if no_region.size > 0: # run the ELCM for jobs that don't have assigned region
#self.filter = None
logger.log_status("ELCM for jobs with no area assigned")
choices = EmploymentLocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index=agents_index[no_region], **kwargs)
where_valid_choice = where(choices > 0)[0]
choices_index = self.choice_set.get_id_index(choices[where_valid_choice])
chosen_regions = self.choice_set.get_attribute_by_index(self.subarea_id_name, choices_index)
agent_set.modify_attribute(name=self.subarea_id_name, data=chosen_regions,
index=no_region[where_valid_choice])
def test_agents_placed_in_appropriate_types(self):
"""Create 1000 unplaced industrial jobs and 1 commercial job. Allocate 50 commercial
gridcells with enough space for 10 commercial jobs per gridcell. After running the
EmploymentLocationChoiceModel, the 1 commercial job should be placed,
but the 100 industrial jobs should remain unplaced
"""
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='job_building_types',
table_data = {
'id':array([2,1]),
'name': array(['commercial', 'industrial'])
}
)
job_building_types = JobBuildingTypeDataset(in_storage=storage, in_table_name='job_building_types')
storage.write_table(table_name='jobs',
table_data = {
'job_id': arange(1001)+1,
'grid_id': array([0]*1001),
'building_type': array([1]*1000 + [2])
}
)
jobs = JobDataset(in_storage=storage, in_table_name='jobs')
storage.write_table(table_name='gridcells',
table_data = {
'grid_id': arange(50)+1,
'commercial_sqft': array([1000]*50),
'commercial_sqft_per_job': array([100]*50)
}
)
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
coefficients = Coefficients(names=("dummy",), values=(0.1,))
specification = EquationSpecification(variables=("gridcell.commercial_sqft",), coefficients=("dummy",))
compute_resources = Resources({"job":jobs, "job_building_type": job_building_types})
agents_index = where(jobs.get_attribute("grid_id") == 0)
unplace_jobs = DatasetSubset(jobs, agents_index)
agents_index = where(unplace_jobs.get_attribute("building_type") == 2)[0]
gridcells.compute_variables(["urbansim.gridcell.number_of_commercial_jobs"],
resources=compute_resources)
commercial_jobs = gridcells.get_attribute("number_of_commercial_jobs")
gridcells.compute_variables(["urbansim.gridcell.number_of_industrial_jobs"],
resources=compute_resources)
industrial_jobs = gridcells.get_attribute("number_of_industrial_jobs")
model_group = ModelGroup(job_building_types, "name")
elcm = EmploymentLocationChoiceModel(ModelGroupMember(model_group,"commercial"), location_set=gridcells,
agents_grouping_attribute = "job.building_type",
choices = "opus_core.random_choices_from_index", sample_size_locations = 30)
elcm.run(specification, coefficients, agent_set = jobs, agents_index=agents_index, debuglevel=1)
gridcells.compute_variables(["urbansim.gridcell.number_of_commercial_jobs"],
resources=compute_resources)
commercial_jobs = gridcells.get_attribute("number_of_commercial_jobs")
gridcells.compute_variables(["urbansim.gridcell.number_of_industrial_jobs"],
resources=compute_resources)
industrial_jobs = gridcells.get_attribute("number_of_industrial_jobs")
self.assertEqual(commercial_jobs.sum() == 1,
True, "Error, there should only be a total of 1 commercial job")
self.assertEqual(industrial_jobs.sum() == 0,
True, "Error, there should be no industrial jobs because there's no space for them")
def test_agents_placed_in_appropriate_types(self):
"""Create 1000 unplaced industrial jobs and 1 commercial job. Allocate 50 commercial
gridcells with enough space for 10 commercial jobs per gridcell. After running the
EmploymentLocationChoiceModel, the 1 commercial job should be placed,
but the 100 industrial jobs should remain unplaced
"""
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='job_building_types',
table_data={
'id': array([2, 1]),
'name': array(['commercial', 'industrial'])
})
job_building_types = JobBuildingTypeDataset(
in_storage=storage, in_table_name='job_building_types')
storage.write_table(table_name='jobs',
table_data={
'job_id': arange(1001) + 1,
'grid_id': array([0] * 1001),
'building_type': array([1] * 1000 + [2])
})
jobs = JobDataset(in_storage=storage, in_table_name='jobs')
storage.write_table(table_name='gridcells',
table_data={
'grid_id': arange(50) + 1,
'commercial_sqft': array([1000] * 50),
'commercial_sqft_per_job': array([100] * 50)
})
gridcells = GridcellDataset(in_storage=storage,
in_table_name='gridcells')
coefficients = Coefficients(names=("dummy", ), values=(0.1, ))
specification = EquationSpecification(
variables=("gridcell.commercial_sqft", ), coefficients=("dummy", ))
compute_resources = Resources({
"job": jobs,
"job_building_type": job_building_types
})
agents_index = where(jobs.get_attribute("grid_id") == 0)
unplace_jobs = DatasetSubset(jobs, agents_index)
agents_index = where(
unplace_jobs.get_attribute("building_type") == 2)[0]
gridcells.compute_variables(
["urbansim.gridcell.number_of_commercial_jobs"],
resources=compute_resources)
commercial_jobs = gridcells.get_attribute("number_of_commercial_jobs")
gridcells.compute_variables(
["urbansim.gridcell.number_of_industrial_jobs"],
resources=compute_resources)
industrial_jobs = gridcells.get_attribute("number_of_industrial_jobs")
model_group = ModelGroup(job_building_types, "name")
elcm = EmploymentLocationChoiceModel(
ModelGroupMember(model_group, "commercial"),
location_set=gridcells,
agents_grouping_attribute="job.building_type",
choices="opus_core.random_choices_from_index",
sample_size_locations=30)
elcm.run(specification,
coefficients,
agent_set=jobs,
agents_index=agents_index,
debuglevel=1)
gridcells.compute_variables(
["urbansim.gridcell.number_of_commercial_jobs"],
resources=compute_resources)
commercial_jobs = gridcells.get_attribute("number_of_commercial_jobs")
gridcells.compute_variables(
["urbansim.gridcell.number_of_industrial_jobs"],
resources=compute_resources)
industrial_jobs = gridcells.get_attribute("number_of_industrial_jobs")
self.assertEqual(
commercial_jobs.sum() == 1, True,
"Error, there should only be a total of 1 commercial job")
self.assertEqual(
industrial_jobs.sum() == 0, True,
"Error, there should be no industrial jobs because there's no space for them"
)
def __init__(self, group_member, location_set, **kwargs):
UrbansimEmploymentLocationChoiceModel.__init__(self, group_member, location_set, **kwargs)
location_set.compute_variables(
["urbansim_parcel.building.%s" % self.geography_id_name], dataset_pool=self.dataset_pool
)
def __init__(self, group_member, location_set, **kwargs):
UrbansimEmploymentLocationChoiceModel.__init__(self, group_member, location_set, **kwargs)
location_set.compute_variables(["urbansim_parcel.building.%s" % self.geography_id_name], dataset_pool = self.dataset_pool)
def __init__(self, group_member, location_set, **kwargs):
UrbansimEmploymentLocationChoiceModel.__init__(self, group_member, location_set, **kwargs)
location_set.compute_one_variable_with_unknown_package("%s" % self.geography_id_name, dataset_pool = self.dataset_pool)
def run(self,
specification,
coefficients,
agent_set,
agents_index=None,
agents_filter=None,
**kwargs):
if agents_index is None:
if agents_filter is None:
agents_index = arange(agent_set.size())
else:
agents_index = where(
agent_set.compute_variables(agents_filter))[0]
if self.location_id_string is not None:
agent_set.compute_variables(self.location_id_string,
dataset_pool=self.dataset_pool)
if not self.subarea_id_name in agent_set.get_attribute_names():
agent_set.compute_one_variable_with_unknown_package(
variable_name="%s" % (self.subarea_id_name),
dataset_pool=self.dataset_pool)
regions = agent_set.get_attribute(self.subarea_id_name)
self.choice_set.compute_one_variable_with_unknown_package(
variable_name="%s" % (self.subarea_id_name),
dataset_pool=self.dataset_pool)
valid_region = where(regions[agents_index] > 0)[0]
if valid_region.size > 0:
unique_regions = unique(regions[agents_index][valid_region])
cond_array = zeros(agent_set.size(), dtype="bool8")
cond_array[agents_index[valid_region]] = True
for area in unique_regions:
new_index = where(logical_and(cond_array, regions == area))[0]
self.filter = "%s.%s == %s" % (
self.choice_set.get_dataset_name(), self.subarea_id_name,
area)
logger.log_status("ELCM for area %s" % area)
EmploymentLocationChoiceModel.run(self,
specification,
coefficients,
agent_set,
agents_index=new_index,
**kwargs)
no_region = where(regions[agents_index] <= 0)[0]
if no_region.size > 0: # run the ELCM for jobs that don't have assigned region
self.filter = None
logger.log_status("ELCM for jobs with no area assigned")
choices = EmploymentLocationChoiceModel.run(
self,
specification,
coefficients,
agent_set,
agents_index=agents_index[no_region],
**kwargs)
where_valid_choice = where(choices > 0)[0]
choices_index = self.choice_set.get_id_index(
choices[where_valid_choice])
chosen_regions = self.choice_set.get_attribute_by_index(
self.subarea_id_name, choices_index)
agent_set.modify_attribute(name=self.subarea_id_name,
data=chosen_regions,
index=no_region[where_valid_choice])