def run_model_2():
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='households',
table_data=household_data)
households = HouseholdDataset(in_storage=storage,
in_table_name='households')
storage.write_table(table_name='gridcells',
table_data=gridcell_data)
gridcells = GridcellDataset(in_storage=storage,
in_table_name='gridcells')
hlcm = HouseholdLocationChoiceModelCreator().get_model(
location_set=gridcells,
compute_capacity_flag=False,
choices="opus_core.random_choices_from_index",
sample_size_locations=8)
hlcm.run(specification,
coefficients,
agent_set=households,
debuglevel=1)
# get results
gridcells.compute_variables(
["urbansim.gridcell.number_of_households"],
resources=Resources({"household": households}))
result_more_attractive = gridcells.get_attribute_by_id(
"number_of_households",
arange(ngcs_attr) + 1)
result_less_attractive = gridcells.get_attribute_by_id(
"number_of_households", arange(ngcs_attr + 1, ngcs + 1))
return array(
[result_more_attractive.sum(),
result_less_attractive.sum()])
def test_unplaced_agents_decrease_available_space(self):
"""Using the household location choice model, create a set of available spaces and
2000 unplaced agents (along with 5000 placed agents). Run the model, and check that
the unplaced agents were placed, and the number of available spaces has decreased"""
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='households',
table_data={
'grid_id': array(2000 * [0] + 5000 * [1]),
'household_id': arange(7000) + 1
})
storage.write_table(table_name='gridcells',
table_data={
'residential_units': array(50 * [10000]),
'grid_id': arange(50) + 1
})
households = HouseholdDataset(in_storage=storage,
in_table_name='households')
gridcells = GridcellDataset(in_storage=storage,
in_table_name='gridcells')
coefficients = Coefficients(names=("dummy", ), values=(0.1, ))
specification = EquationSpecification(
variables=("gridcell.residential_units", ),
coefficients=("dummy", ))
"""need to specify to the household location choice model exactly which households are moving,
because by default it assumes all current households want to move, but in this test,
the 5000 households already in gridcell #1 shouldn't move.
here, we specify that only the unplaced households should be moved."""
agents_index = where(households.get_attribute("grid_id") == 0)[0]
hlcm = HouseholdLocationChoiceModelCreator().get_model(
location_set=gridcells,
choices="opus_core.random_choices_from_index",
sample_size_locations=30)
hlcm.run(specification,
coefficients,
agent_set=households,
agents_index=agents_index,
debuglevel=1)
gridcells.compute_variables(
["urbansim.gridcell.vacant_residential_units"],
resources=Resources({"household": households}))
vacancies = gridcells.get_attribute("vacant_residential_units")
"""since there were 5000 households already in gridcell #1, and gridcell #1 has
10000 residential units, there should be no more than 5000 vacant residential units
in gridcell #1 after running this model"""
self.assertEqual(vacancies[0] <= 5000, True,
"Error: %d" % (vacancies[0], ))
"""there should be exactly 430000 vacant residential units after the model run,
because there were originally 50 gridcells with 10000 residential units each,
and a total of 7000 units are occupied after the run"""
self.assertEqual(
sum(vacancies) == 50 * 10000 - 7000, True,
"Error: %d" % (sum(vacancies)))
def run_ALCM(niter):
nhhs = 100
ngcs = 10
ngcs_attr = ngcs/2
ngcs_noattr = ngcs - ngcs_attr
hh_grid_ids = array(nhhs*[-1])
storage = StorageFactory().get_storage('dict_storage')
households_table_name = 'households'
storage.write_table(
table_name = households_table_name,
table_data = {
'household_id': arange(nhhs)+1,
'grid_id': hh_grid_ids
}
)
gridcells_table_name = 'gridcells'
storage.write_table(
table_name = gridcells_table_name,
table_data = {
'grid_id': arange(ngcs)+1,
'cost':array(ngcs_attr*[100]+ngcs_noattr*[1000])
}
)
households = HouseholdDataset(in_storage=storage, in_table_name=households_table_name)
gridcells = GridcellDataset(in_storage=storage, in_table_name=gridcells_table_name)
# create coefficients and specification
coefficients = Coefficients(names=('costcoef', ), values=(-0.001,))
specification = EquationSpecification(variables=('gridcell.cost', ), coefficients=('costcoef', ))
logger.be_quiet()
result = zeros((niter,ngcs))
for iter in range(niter):
hlcm = HouseholdLocationChoiceModelCreator().get_model(location_set=gridcells, compute_capacity_flag=False,
choices = 'opus_core.random_choices_from_index',
sampler=None,
#sample_size_locations = 30
)
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1,
chunk_specification={'nchunks':1})
# get results
gridcells.compute_variables(['urbansim.gridcell.number_of_households'],
resources=Resources({'household':households}))
result_more_attractive = gridcells.get_attribute_by_id('number_of_households', arange(ngcs_attr)+1)
result_less_attractive = gridcells.get_attribute_by_id('number_of_households', arange(ngcs_attr+1, ngcs+1))
households.set_values_of_one_attribute(attribute='grid_id', values=hh_grid_ids)
gridcells.delete_one_attribute('number_of_households')
result[iter,:] = concatenate((result_more_attractive, result_less_attractive))
#print result #, result_more_attractive.sum(), result_less_attractive.sum()
return result
def test_scaling_jobs_model(self):
# Places 1750 jobs of sector 15
# gridcell has expected about
# 1 4000 sector 15 jobs 5000 sector 15 jobs
# 1000 sector 1 jobs 1000 sector 1 jobs
# 2 2000 sector 15 jobs 2500 sector 15 jobs
# 1000 sector 1 jobs 1000 sector 1 jobs
# 3 1000 sector 15 jobs 1250 sector 15 jobs
# 1000 sector 1 jobs 1000 sector 1 jobs
# unplaced 1750 sector 15 jobs 0
storage = StorageFactory().get_storage('dict_storage')
jobs_table_name = 'building_types'
storage.write_table(
table_name=jobs_table_name,
table_data={
"job_id":
arange(11750) + 1,
"sector_id":
array(7000 * [15] + 3000 * [1] + 1750 * [15]),
"grid_id":
array(4000 * [1] + 2000 * [2] + 1000 * [3] + 1000 * [1] +
1000 * [2] + 1000 * [3] + 1750 * [-1])
})
jobs = JobDataset(in_storage=storage, in_table_name=jobs_table_name)
gridcells_table_name = 'gridcells'
storage.write_table(table_name=gridcells_table_name,
table_data={"grid_id": arange(3) + 1})
gridcells = GridcellDataset(in_storage=storage,
in_table_name=gridcells_table_name)
# run model
model = ScalingJobsModel(debuglevel=4)
model.run(gridcells, jobs, agents_index=arange(10001, 11750))
# get results
gridcells.compute_variables([
"urbansim.gridcell.number_of_jobs_of_sector_15",
"urbansim.gridcell.number_of_jobs_of_sector_1"
],
resources=Resources({"job": jobs}))
# sector 1 jobs should be exactly the same
result1 = gridcells.get_attribute("number_of_jobs_of_sector_1")
self.assertEqual(
ma.allclose(result1, array([1000, 1000, 1000]), rtol=0), True)
# the distribution of sector 15 jobs should be the same with higher means
result2 = gridcells.get_attribute("number_of_jobs_of_sector_15")
# logger.log_status(result2)
self.assertEqual(
ma.allclose(result2, array([5000, 2500, 1250]), rtol=0.05), True)
def test_agents_do_not_go_to_inferior_locations(self):
"""100 gridcells - 99 with attractiveness 2000, 1 with attractiveness 1, no capacity restrictions
10,000 households
We set the coefficient value for attracitiveness to 1.
"""
storage = StorageFactory().get_storage('dict_storage')
#create households
storage.write_table(table_name='households',
table_data={
'household_id': arange(10000) + 1,
'grid_id': array(10000 * [-1])
})
households = HouseholdDataset(in_storage=storage,
in_table_name='households')
# create gridcells
storage.write_table(table_name='gridcells',
table_data={
'grid_id': arange(100) + 1,
'attractiveness': array(99 * [2000] + [1])
})
gridcells = GridcellDataset(in_storage=storage,
in_table_name='gridcells')
# create coefficients and specification
coefficients = Coefficients(names=("attractcoef", ), values=(1, ))
specification = EquationSpecification(
variables=("gridcell.attractiveness", ),
coefficients=("attractcoef", ))
# run the model
hlcm = HouseholdLocationChoiceModelCreator().get_model(
location_set=gridcells,
compute_capacity_flag=False,
choices="opus_core.random_choices_from_index",
sample_size_locations=30)
hlcm.run(specification,
coefficients,
agent_set=households,
debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources(
{"household": households}))
result = gridcells.get_attribute_by_id("number_of_households", 100)
# nobody should choose gridcell 100
self.assertEqual(result, 0, "Error: %s is not equal to 0" % (result, ))
def test_unplaced_agents_decrease_available_space(self):
"""Using the household location choice model, create a set of available spaces and
2000 unplaced agents (along with 5000 placed agents). Run the model, and check that
the unplaced agents were placed, and the number of available spaces has decreased"""
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='households',
table_data = {
'grid_id': array(2000*[0] + 5000*[1]),
'household_id': arange(7000)+1
}
)
storage.write_table(table_name='gridcells',
table_data= {
'residential_units':array(50*[10000]),
'grid_id': arange(50)+1
}
)
households = HouseholdDataset(in_storage=storage, in_table_name='households')
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
coefficients = Coefficients(names=("dummy",), values=(0.1,))
specification = EquationSpecification(variables=("gridcell.residential_units",), coefficients=("dummy",))
"""need to specify to the household location choice model exactly which households are moving,
because by default it assumes all current households want to move, but in this test,
the 5000 households already in gridcell #1 shouldn't move.
here, we specify that only the unplaced households should be moved."""
agents_index = where(households.get_attribute("grid_id") == 0)[0]
hlcm = HouseholdLocationChoiceModelCreator().get_model(location_set=gridcells,
choices = "opus_core.random_choices_from_index", sample_size_locations = 30)
hlcm.run(specification, coefficients, agent_set=households, agents_index=agents_index, debuglevel=1)
gridcells.compute_variables(["urbansim.gridcell.vacant_residential_units"],
resources=Resources({"household":households}))
vacancies = gridcells.get_attribute("vacant_residential_units")
"""since there were 5000 households already in gridcell #1, and gridcell #1 has
10000 residential units, there should be no more than 5000 vacant residential units
in gridcell #1 after running this model"""
self.assertEqual(vacancies[0] <= 5000,
True, "Error: %d" % (vacancies[0],))
"""there should be exactly 430000 vacant residential units after the model run,
because there were originally 50 gridcells with 10000 residential units each,
and a total of 7000 units are occupied after the run"""
self.assertEqual(sum(vacancies) == 50 * 10000 - 7000,
True, "Error: %d" % (sum(vacancies)))
def run_model1():
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name = 'households', table_data = household_data)
households = HouseholdDataset(in_storage=storage, in_table_name='households')
storage.write_table(table_name = 'gridcells', table_data = gridcell_data)
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
hlcm = HouseholdLocationChoiceModelCreator().get_model(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 30)
hlcm.run(specification, coefficients, agent_set = households)
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
return gridcells.get_attribute("number_of_households")
def xtest_gracefully_handle_empty_choice_sets(self):
storage = StorageFactory().get_storage('dict_storage')
#create households
storage.write_table(table_name='households',
table_data={
'household_id': arange(10000) + 1,
'grid_id': array(100 * range(100)) + 1
})
households = HouseholdDataset(in_storage=storage,
in_table_name='households')
# create gridcells
storage.write_table(table_name='gridcells',
table_data={
'grid_id': arange(100) + 1,
'residential_units': array(100 * [100])
})
gridcells = GridcellDataset(in_storage=storage,
in_table_name='gridcells')
# create coefficients and specification
coefficients = Coefficients(names=("dummy", ), values=(0, ))
specification = EquationSpecification(
variables=("gridcell.residential_units", ),
coefficients=("dummy", ))
# run the model
hlcm = HouseholdLocationChoiceModelCreator().get_model(
location_set=gridcells,
choices="opus_core.random_choices_from_index",
sample_size_locations=30)
hlcm.run(specification,
coefficients,
agent_set=households,
debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources(
{"household": households}))
result = gridcells.get_attribute_by_id("number_of_households", 100)
# nobody should choose gridcell 100
self.assertEqual(ma.allclose(result.sum(), 0, rtol=0), True,
"Error: %s is not equal to 0" % (result.sum(), ))
def test_do_nothing_if_no_agents(self):
storage = StorageFactory().get_storage('dict_storage')
households_table_name = 'households'
storage.write_table(table_name=households_table_name,
table_data={
"household_id": arange(10000) + 1,
"grid_id": array(10000 * [-1])
})
households = HouseholdDataset(in_storage=storage,
in_table_name=households_table_name)
gridcells_table_name = 'gridcells'
storage.write_table(table_name=gridcells_table_name,
table_data={
"grid_id": arange(100) + 1,
"cost": array(50 * [100] + 50 * [1000])
})
gridcells = GridcellDataset(in_storage=storage,
in_table_name=gridcells_table_name)
# create coefficients and specification
coefficients = Coefficients(names=("costcoef", ), values=(-0.001, ))
specification = EquationSpecification(variables=("gridcell.cost", ),
coefficients=("costcoef", ))
# run the model
hlcm = HouseholdLocationChoiceModelCreator().get_model(
location_set=gridcells,
compute_capacity_flag=False,
choices="opus_core.random_choices_from_index",
sample_size_locations=30)
hlcm.run(specification,
coefficients,
agent_set=households,
agents_index=array([], dtype='int32'),
debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources(
{"household": households}))
result = gridcells.get_attribute("number_of_households")
# check the individual gridcells
self.assertEqual(ma.allclose(result, zeros((100, )), rtol=0), True)
def test_scaling_jobs_model(self):
# Places 1750 jobs of sector 15
# gridcell has expected about
# 1 4000 sector 15 jobs 5000 sector 15 jobs
# 1000 sector 1 jobs 1000 sector 1 jobs
# 2 2000 sector 15 jobs 2500 sector 15 jobs
# 1000 sector 1 jobs 1000 sector 1 jobs
# 3 1000 sector 15 jobs 1250 sector 15 jobs
# 1000 sector 1 jobs 1000 sector 1 jobs
# unplaced 1750 sector 15 jobs 0
storage = StorageFactory().get_storage('dict_storage')
jobs_table_name = 'jobs'
storage.write_table(
table_name=jobs_table_name,
table_data={
"job_id": arange(11750)+1,
"sector_id": array(7000*[15]+3000*[1]+1750*[15]),
"grid_id":array(4000*[1]+2000*[2]+1000*[3]+1000*[1]+1000*[2]+1000*[3]+1750*[-1])
}
)
jobs = JobDataset(in_storage=storage, in_table_name=jobs_table_name)
gridcells_table_name = 'gridcells'
storage.write_table(
table_name=gridcells_table_name,
table_data={"grid_id":arange(3)+1}
)
gridcells = GridcellDataset(in_storage=storage, in_table_name=gridcells_table_name)
# run model
model = ScalingAgentsModel(submodel_string='sector_id', debuglevel=4)
model.run(gridcells, jobs, agents_index = arange(10000, 11750))
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_jobs_of_sector_15", "urbansim.gridcell.number_of_jobs_of_sector_1"],
resources = Resources({"job":jobs}))
self.assertEqual((jobs['grid_id']>0).all(), True)
# sector 1 jobs should be exactly the same
result1 = gridcells.get_attribute("number_of_jobs_of_sector_1")
self.assertEqual(ma.allclose(result1, array([1000, 1000, 1000]), rtol=0), True)
# the distribution of sector 15 jobs should be the same with higher means
result2 = gridcells.get_attribute("number_of_jobs_of_sector_15")
# logger.log_status(result2)
self.assertEqual(ma.allclose(result2, array([5000, 2500, 1250]), rtol=0.05), True)
def run_model_2():
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name = 'households', table_data = household_data)
households = HouseholdDataset(in_storage=storage, in_table_name='households')
storage.write_table(table_name = 'gridcells', table_data = gridcell_data)
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
hlcm = HouseholdLocationChoiceModelCreator().get_model(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 8)
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result_more_attractive = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr)+1)
result_less_attractive = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr+1, ngcs+1))
return array([result_more_attractive.sum(), result_less_attractive.sum()])
def test_agents_do_not_go_to_inferior_locations(self):
"""100 gridcells - 99 with attractiveness 2000, 1 with attractiveness 1, no capacity restrictions
10,000 households
We set the coefficient value for attracitiveness to 1.
"""
storage = StorageFactory().get_storage('dict_storage')
#create households
storage.write_table(table_name='households',
table_data = {
'household_id': arange(10000)+1,
'grid_id': array(10000*[-1])
}
)
households = HouseholdDataset(in_storage=storage, in_table_name='households')
# create gridcells
storage.write_table(table_name='gridcells',
table_data = {
'grid_id': arange(100)+1,
'attractiveness':array(99*[2000]+[1])
}
)
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
# create coefficients and specification
coefficients = Coefficients(names=("attractcoef", ), values=(1,))
specification = EquationSpecification(variables=("gridcell.attractiveness", ), coefficients=("attractcoef", ))
# run the model
hlcm = HouseholdLocationChoiceModelCreator().get_model(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 30)
hlcm.run(specification, coefficients, agent_set = households, debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result = gridcells.get_attribute_by_id("number_of_households", 100)
# nobody should choose gridcell 100
self.assertEqual(result, 0, "Error: %s is not equal to 0" % (result,))
def test_do_nothing_if_no_agents(self):
storage = StorageFactory().get_storage('dict_storage')
households_table_name = 'households'
storage.write_table(
table_name = households_table_name,
table_data = {
"household_id": arange(10000)+1,
"grid_id": array(10000*[-1])
}
)
households = HouseholdDataset(in_storage=storage, in_table_name=households_table_name)
gridcells_table_name = 'gridcells'
storage.write_table(
table_name = gridcells_table_name,
table_data = {
"grid_id": arange(100)+1,
"cost":array(50*[100]+50*[1000])
}
)
gridcells = GridcellDataset(in_storage=storage, in_table_name=gridcells_table_name)
# create coefficients and specification
coefficients = Coefficients(names=("costcoef", ), values=(-0.001,))
specification = EquationSpecification(variables=("gridcell.cost", ), coefficients=("costcoef", ))
# run the model
hlcm = HouseholdLocationChoiceModelCreator().get_model(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 30)
hlcm.run(specification, coefficients, agent_set = households, agents_index=array([], dtype='int32'), debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result = gridcells.get_attribute("number_of_households")
# check the individual gridcells
self.assertEqual(ma.allclose(result, zeros((100,)) , rtol=0), True)
def xtest_gracefully_handle_empty_choice_sets(self):
storage = StorageFactory().get_storage('dict_storage')
#create households
storage.write_table(table_name='households',
table_data = {
'household_id': arange(10000)+1,
'grid_id': array(100*range(100))+1
}
)
households = HouseholdDataset(in_storage=storage, in_table_name='households')
# create gridcells
storage.write_table(table_name='gridcells',
table_data = {
'grid_id': arange(100)+1,
'residential_units':array(100*[100])
}
)
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
# create coefficients and specification
coefficients = Coefficients(names=("dummy",), values=(0,))
specification = EquationSpecification(variables=("gridcell.residential_units",), coefficients=("dummy",))
# run the model
hlcm = HouseholdLocationChoiceModelCreator().get_model( location_set=gridcells,
choices = "opus_core.random_choices_from_index", sample_size_locations = 30)
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result = gridcells.get_attribute_by_id("number_of_households", 100)
# nobody should choose gridcell 100
self.assertEqual(ma.allclose(result.sum(), 0 , rtol=0),
True, "Error: %s is not equal to 0" % (result.sum(),))
def test_capacity_jobs_model(self):
storage = StorageFactory().get_storage('dict_storage')
jobs_table_name = 'building_types'
storage.write_table(
table_name=jobs_table_name,
table_data={
"job_id": arange(200)+1,
"grid_id":array(10*[1]+20*[2]+10*[3]+160*[-1])
}
)
jobs = JobDataset(in_storage=storage, in_table_name=jobs_table_name)
capacity = array([60, 25, 200])
gridcells_table_name = 'gridcells'
storage.write_table(
table_name=gridcells_table_name,
table_data={"grid_id":arange(3)+1,
"capacity": capacity}
)
gridcells = GridcellDataset(in_storage=storage, in_table_name=gridcells_table_name)
dataset_pool = DatasetPool(datasets_dict={'job': jobs, 'gridcell':gridcells},
package_order=["urbansim", "opus_core"])
current = gridcells.compute_variables(["urbansim.gridcell.number_of_jobs"], dataset_pool=dataset_pool)
# run model
model = CapacityLocationModel(capacity_attribute='gridcell.capacity',
number_of_agents_attribute="urbansim.gridcell.number_of_jobs",
agents_filter="job.grid_id<0",
dataset_pool=dataset_pool
)
model.run(gridcells, jobs)
# get results
result = gridcells.compute_variables(["urbansim.gridcell.number_of_jobs"], dataset_pool=dataset_pool)
#logger.log_status((result - current)/float(result.sum()))
#logger.log_status((capacity - current)/float(capacity.sum()))
#logger.log_status(result)
self.assertEqual(result[0] > result[1], True)
self.assertEqual(result[1] < result[2], True)
self.assertEqual(result[0] < result[2], True)
def run_model1():
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='households',
table_data=household_data)
households = HouseholdDataset(in_storage=storage,
in_table_name='households')
storage.write_table(table_name='gridcells',
table_data=gridcell_data)
gridcells = GridcellDataset(in_storage=storage,
in_table_name='gridcells')
hlcm = HouseholdLocationChoiceModelCreator().get_model(
location_set=gridcells,
compute_capacity_flag=False,
choices="opus_core.random_choices_from_index",
sample_size_locations=30)
hlcm.run(specification, coefficients, agent_set=households)
gridcells.compute_variables(
["urbansim.gridcell.number_of_households"],
resources=Resources({"household": households}))
return gridcells.get_attribute("number_of_households")
agents.summary()
agents.get_attribute("income")
agents.plot_histogram("income", bins = 10)
agents.r_histogram("income")
agents.r_scatter("income", "persons")
# gridcells from PSRC
locations_psrc = GridcellDataset(in_storage = StorageFactory().get_storage('flt_storage',
storage_location = "/home/hana/bandera/urbansim/data/GPSRC"),
in_table_name = "gc")
locations_psrc.summary()
locations_psrc.plot_histogram("distance_to_highway", bins = 15)
locations_psrc.r_image("distance_to_highway")
locations_psrc.plot_map("distance_to_highway")
locations_psrc.compute_variables("urbansim.gridcell.ln_total_land_value")
locations_psrc.plot_map("ln_total_land_value")
# Models
########
#HLCM
# locations from gridcellset.tab
locations= GridcellDataset(in_storage = StorageFactory().get_storage('tab_storage', storage_location = "."),
in_table_name = "gridcellset", id_name="location")
locations.summary()
seed(1)
coefficients = Coefficients(names=("costcoef", ), values=(-0.01,))
def test_erm_correct_distribution_of_jobs_relocate(self):
# In addition to unplaced jobs choose 50% of jobs of sector 2 to relocate and
# no job of sector 1.
# gridcell has expected
# 1 100 sector 1 jobs 100 sector 1 jobs
# 400 sector 2 jobs about 200 sector 2 jobs
# 2 100 sector 1 jobs 100 sector 1 jobs
# 200 sector 2 jobs about 100 sector 2 jobs
# 3 100 sector 1 jobs 100 sector 1 jobs
# 100 sector 2 jobs about 50 sector 2 jobs
# unplaced 10 sector 1 jobs
# 10 sector 2 jobs
storage = StorageFactory().get_storage("dict_storage")
# create jobs
job_grid_ids = array(100 * [1] + 100 * [2] + 100 * [3] + 400 * [1] + 200 * [2] + 100 * [3] + 20 * [-1])
storage.write_table(
table_name="jobs",
table_data={
"job_id": arange(1020) + 1,
"sector_id": array(300 * [1] + 700 * [2] + 10 * [1] + 10 * [2]),
"grid_id": job_grid_ids,
},
)
jobs = JobDataset(in_storage=storage, in_table_name="jobs")
# create gridcells
storage.write_table(table_name="gridcells", table_data={"grid_id": arange(3) + 1})
gridcells = GridcellDataset(in_storage=storage, in_table_name="gridcells")
# create rate set with rate 0 for jobs of sector 1 and 0.5 for jobs of sector 2
storage.write_table(
table_name="rates", table_data={"sector_id": array([1, 2]), "job_relocation_probability": array([0, 0.5])}
)
rates = JobRelocationRateDataset(in_storage=storage, in_table_name="rates")
# run model
model = EmploymentRelocationModelCreator().get_model(debuglevel=0)
hrm_resources = Resources({"annual_job_relocation_rate": rates})
# get results from one run
movers_indices = model.run(jobs, resources=hrm_resources)
jobs.compute_variables(["urbansim.job.is_in_employment_sector_1"])
# unplace chosen jobs
compute_resources = Resources({"job": jobs, "urbansim_constant": {"industrial_code": 1, "commercial_code": 2}})
jobs.set_values_of_one_attribute(attribute="grid_id", values=-1, index=movers_indices)
gridcells.compute_variables(
["urbansim.gridcell.number_of_jobs_of_sector_1", "urbansim.gridcell.number_of_jobs_of_sector_2"],
resources=compute_resources,
)
# only 100 jobs of sector 1 (unplaced jobs) should be selected
result1 = jobs.get_attribute_by_index("is_in_employment_sector_1", movers_indices).astype(int8).sum()
self.assertEqual(result1 == 10, True)
# number of sector 1 jobs should not change
result2 = gridcells.get_attribute("number_of_jobs_of_sector_1")
self.assertEqual(ma.allclose(result2, array([100, 100, 100]), rtol=0), True)
def run_model():
jobs.modify_attribute(name="grid_id", data=job_grid_ids)
indices = model.run(jobs, resources=hrm_resources)
jobs.modify_attribute(name="grid_id", data=-1, index=indices)
gridcells.compute_variables(["urbansim.gridcell.number_of_jobs_of_sector_2"], resources=compute_resources)
return gridcells.get_attribute("number_of_jobs_of_sector_2")
# distribution of sector 2 jobs should be the same, the mean are halfs of the original values
should_be = array([200, 100, 50])
self.run_stochastic_test(__file__, run_model, should_be, 10)
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 run_ALCM(niter):
nhhs = 100
ngcs = 10
ngcs_attr = ngcs / 2
ngcs_noattr = ngcs - ngcs_attr
hh_grid_ids = array(nhhs * [-1])
storage = StorageFactory().get_storage('dict_storage')
households_table_name = 'households'
storage.write_table(table_name=households_table_name,
table_data={
'household_id': arange(nhhs) + 1,
'grid_id': hh_grid_ids
})
gridcells_table_name = 'gridcells'
storage.write_table(table_name=gridcells_table_name,
table_data={
'grid_id': arange(ngcs) + 1,
'cost':
array(ngcs_attr * [100] + ngcs_noattr * [1000])
})
households = HouseholdDataset(in_storage=storage,
in_table_name=households_table_name)
gridcells = GridcellDataset(in_storage=storage,
in_table_name=gridcells_table_name)
# create coefficients and specification
coefficients = Coefficients(names=('costcoef', ), values=(-0.001, ))
specification = EquationSpecification(variables=('gridcell.cost', ),
coefficients=('costcoef', ))
logger.be_quiet()
result = zeros((niter, ngcs))
for iter in range(niter):
hlcm = HouseholdLocationChoiceModelCreator().get_model(
location_set=gridcells,
compute_capacity_flag=False,
choices='opus_core.random_choices_from_index',
sampler=None,
#sample_size_locations = 30
)
hlcm.run(specification,
coefficients,
agent_set=households,
debuglevel=1,
chunk_specification={'nchunks': 1})
# get results
gridcells.compute_variables(['urbansim.gridcell.number_of_households'],
resources=Resources(
{'household': households}))
result_more_attractive = gridcells.get_attribute_by_id(
'number_of_households',
arange(ngcs_attr) + 1)
result_less_attractive = gridcells.get_attribute_by_id(
'number_of_households', arange(ngcs_attr + 1, ngcs + 1))
households.set_values_of_one_attribute(attribute='grid_id',
values=hh_grid_ids)
gridcells.delete_one_attribute('number_of_households')
result[iter, :] = concatenate(
(result_more_attractive, result_less_attractive))
#print result #, result_more_attractive.sum(), result_less_attractive.sum()
return result
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"
)
agents.summary()
agents.get_attribute("income")
agents.plot_histogram("income", bins=10)
agents.r_histogram("income")
agents.r_scatter("income", "persons")
# gridcells from PSRC
locations_psrc = GridcellDataset(in_storage=StorageFactory().get_storage(
'flt_storage', storage_location="/home/hana/bandera/urbansim/data/GPSRC"),
in_table_name="gc")
locations_psrc.summary()
locations_psrc.plot_histogram("distance_to_highway", bins=15)
locations_psrc.r_image("distance_to_highway")
locations_psrc.plot_map("distance_to_highway")
locations_psrc.compute_variables("urbansim.gridcell.ln_total_land_value")
locations_psrc.plot_map("ln_total_land_value")
# Models
########
#HLCM
# locations from gridcellset.tab
locations = GridcellDataset(in_storage=StorageFactory().get_storage(
'tab_storage', storage_location="."),
in_table_name="gridcellset",
id_name="location")
locations.summary()
seed(1)
def test_place_agents_to_correct_areas(self):
"""10 gridcells - 5 in area 1, 5 in area 2, with equal cost, no capacity restrictions
100 households - 70 live in area 1, 30 live in area 2.
We set the coefficient value for cost -0.001.
"""
storage = StorageFactory().get_storage('dict_storage')
nhhs = 100
ngcs = 10
ngcs_attr = ngcs/2
hh_grid_ids = array(nhhs*[-1])
lareas = array(ngcs_attr*[1] + ngcs_attr*[2])
hh_lareas = array(70*[1] + 30*[2])
household_data = {
'household_id': arange(nhhs)+1,
'grid_id': hh_grid_ids,
'faz_id': hh_lareas
}
gridcell_data = {
'grid_id': arange(ngcs)+1,
'cost':array(ngcs*[100]),
'faz_id': lareas
}
storage.write_table(table_name = 'households', table_data = household_data)
storage.write_table(table_name = 'gridcells', table_data = gridcell_data)
households = HouseholdDataset(in_storage=storage, in_table_name='households')
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
# create coefficients and specification
coefficients = Coefficients(names=("costcoef", ), values=(-0.001,))
specification = EquationSpecification(variables=("gridcell.cost", ), coefficients=("costcoef", ))
# check the individual gridcells
def run_model():
households = HouseholdDataset(in_storage=storage, in_table_name='households')
hlcm = SubareaHouseholdLocationChoiceModel(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 4, subarea_id_name="faz_id")
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result_area1 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr)+1)
result_area2 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr+1, ngcs+1))
gridcells.delete_one_attribute("number_of_households")
result = concatenate((result_area1, result_area2))
return result
expected_results = array(ngcs_attr*[nhhs*0.7/float(ngcs_attr)] + ngcs_attr*[nhhs*0.3/float(ngcs_attr)])
self.run_stochastic_test(__file__, run_model, expected_results, 10)
# check the exact sum
hlcm = SubareaHouseholdLocationChoiceModel(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 4, subarea_id_name="faz_id")
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1)
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result_area1 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr)+1).sum()
result_area2 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr+1, ngcs+1)).sum()
results = array([result_area1, result_area2])
expected_results = array([70, 30])
self.assertEqual(ma.allequal(expected_results, results), True, "Error, should_be: %s, but result: %s" % (
expected_results, results))
def test_place_agents_to_correct_areas(self):
"""10 gridcells - 5 in area 1, 5 in area 2, with equal cost, no capacity restrictions
100 households - 70 live in area 1, 30 live in area 2.
We set the coefficient value for cost -0.001.
"""
storage = StorageFactory().get_storage('dict_storage')
nhhs = 100
ngcs = 10
ngcs_attr = ngcs/2
hh_grid_ids = array(nhhs*[-1])
lareas = array(ngcs_attr*[1] + ngcs_attr*[2])
hh_lareas = array(70*[1] + 30*[2])
household_data = {
'household_id': arange(nhhs)+1,
'grid_id': hh_grid_ids,
'faz_id': hh_lareas
}
gridcell_data = {
'grid_id': arange(ngcs)+1,
'cost':array(ngcs*[100]),
'faz_id': lareas
}
storage.write_table(table_name = 'households', table_data = household_data)
storage.write_table(table_name = 'gridcells', table_data = gridcell_data)
households = HouseholdDataset(in_storage=storage, in_table_name='households')
gridcells = GridcellDataset(in_storage=storage, in_table_name='gridcells')
# create coefficients and specification
coefficients = Coefficients(names=("costcoef", ), values=(-0.001,))
specification = EquationSpecification(variables=("gridcell.cost", ), coefficients=("costcoef", ))
# check the individual gridcells
def run_model():
households = HouseholdDataset(in_storage=storage, in_table_name='households')
hlcm = SubareaHouseholdLocationChoiceModel(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 4, subarea_id_name="faz_id")
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1)
# get results
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result_area1 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr)+1)
result_area2 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr+1, ngcs+1))
gridcells.delete_one_attribute("number_of_households")
result = concatenate((result_area1, result_area2))
return result
expected_results = array(ngcs_attr*[nhhs*0.7/float(ngcs_attr)] + ngcs_attr*[nhhs*0.3/float(ngcs_attr)])
self.run_stochastic_test(__file__, run_model, expected_results, 10)
# check the exact sum
hlcm = SubareaHouseholdLocationChoiceModel(location_set=gridcells, compute_capacity_flag=False,
choices = "opus_core.random_choices_from_index", sample_size_locations = 4, subarea_id_name="faz_id")
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1)
gridcells.compute_variables(["urbansim.gridcell.number_of_households"],
resources=Resources({"household":households}))
result_area1 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr)+1).sum()
result_area2 = gridcells.get_attribute_by_id("number_of_households", arange(ngcs_attr+1, ngcs+1)).sum()
results = array([result_area1, result_area2])
expected_results = array([70, 30])
self.assertEqual(ma.allequal(expected_results, results), True, "Error, should_be: %s, but result: %s" % (
expected_results, results))
