本文整理匯總了Python中urbansim.datasets.household_dataset.HouseholdDataset.get_attribute方法的典型用法代碼示例。如果您正苦於以下問題：Python HouseholdDataset.get_attribute方法的具體用法？Python HouseholdDataset.get_attribute怎麽用？Python HouseholdDataset.get_attribute使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類urbansim.datasets.household_dataset.HouseholdDataset的用法示例。
在下文中一共展示了HouseholdDataset.get_attribute方法的12個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_same_distribution_after_household_addition
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_same_distribution_after_household_addition(self):
"""Using the control_totals and no marginal characteristics,
add households and ensure that the distribution within each group stays the same
"""
annual_household_control_totals_data = {
"year": array([2000, 2000]),
"total_number_of_households": array([20000, 30000]),
"large_area_id": array([1,2])
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name = 'hh_set', table_data = self.households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name = 'hct_set', table_data = annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what="household")
storage.write_table(table_name = 'hc_set', table_data = self.household_characteristics_for_ht_data)
hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
model = RegionalHouseholdTransitionModel()
model.run(year=2000, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
#check that there are 20000 (area 1) and 30000 (area 2) total households after running the model
areas = hh_set.get_attribute("large_area_id")
results = array([0,0])
for iarea in [0,1]:
results[iarea] = where(areas == [1,2][iarea])[0].size
should_be = [20000, 30000]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
#check that the number of unplaced households is exactly the number of new households created
results = where(hh_set.get_attribute("grid_id")<=0)[0].size
should_be = [17000]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
#check that the distribution of households in each group and each area is the same as before running the model
results = self.get_count_all_groups(hh_set)
should_be = array([
# area 1
3000.0/16500.0*20000.0, 1000.0/16500.0*20000.0, 1500.0/16500.0*20000.0, 2000.0/16500.0*20000.0,
1000.0/16500.0*20000.0, 2500.0/16500.0*20000.0, 1500.0/16500.0*20000.0, 4000.0/16500.0*20000.0,
# area 2
3000.0/16500.0*30000.0, 1000.0/16500.0*30000.0, 1500.0/16500.0*30000.0, 2000.0/16500.0*30000.0,
1000.0/16500.0*30000.0, 2500.0/16500.0*30000.0, 1500.0/16500.0*30000.0, 4000.0/16500.0*30000.0])
self.assertEqual(ma.allclose(results, should_be, rtol=0.1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# check the types of the attributes
self.assertEqual(hh_set.get_attribute("age_of_head").dtype, int32,
"Error in data type of the new household set. Should be: int32, is: %s" % str(hh_set.get_attribute("age_of_head").dtype))
self.assertEqual(hh_set.get_attribute("income").dtype, int32,
"Error in data type of the new household set. Should be: int32, is: %s" % str(hh_set.get_attribute("income").dtype))
self.assertEqual(hh_set.get_attribute("persons").dtype, int8,
"Error in data type of the new household set. Should be: int8, is: %s" % str(hh_set.get_attribute("persons").dtype))示例2: test_same_distribution_after_household_addition
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_same_distribution_after_household_addition(self):
"""Using the control_totals and no marginal characteristics,
add households and ensure that the distribution within each group stays the same
"""
annual_household_control_totals_data = {
"year": array([2000]),
"total_number_of_households": array([50000])
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='hh_set', table_data=self.households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name='prs_set', table_data=self.person_data)
prs_set = PersonDataset(in_storage=storage, in_table_name='prs_set')
storage.write_table(table_name='hct_set', table_data=annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what="household", id_name="year")
storage.write_table(table_name='hc_set', table_data=self.household_characteristics_for_ht_data)
hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
model = HouseholdTransitionModel()
model.run(year=2000, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
#check that there are indeed 50000 total households after running the model
results = hh_set.size()
should_be = [50000]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
#check that the number of unplaced households is exactly the number of new households created
results = where(hh_set.get_attribute("building_id")<=0)[0].size
should_be = [17000]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
#check that the distribution of households in each group is the same as before running the model
results = self.get_count_all_groups(hh_set)
should_be = array([6000.0/33000.0*50000.0, 2000.0/33000.0*50000.0, 3000.0/33000.0*50000.0, 4000.0/33000.0*50000.0,
2000.0/33000.0*50000.0, 5000.0/33000.0*50000.0, 3000.0/33000.0*50000.0, 8000.0/33000.0*50000.0])
self.assertEqual(ma.allclose(results, should_be, rtol=0.05),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# check the types of the attributes
self.assertEqual(hh_set.get_attribute("age_of_head").dtype, int32,
"Error in data type of the new household set. Should be: int32, is: %s" % str(hh_set.get_attribute("age_of_head").dtype))
self.assertEqual(hh_set.get_attribute("income").dtype, int32,
"Error in data type of the new household set. Should be: int32, is: %s" % str(hh_set.get_attribute("income").dtype))
self.assertEqual(hh_set.get_attribute("persons").dtype, int8,
"Error in data type of the new household set. Should be: int8, is: %s" % str(hh_set.get_attribute("persons").dtype))示例3: test_controlling_with_three_marginal_characteristics
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_controlling_with_three_marginal_characteristics(self):
"""Controlling with all three possible marginal characteristics in this example, age_of_head, income, and persons,
this would partition the 8 groups into the same 8 groups, and with a control total specified for each group, we must
ensure that the control totals for each group exactly meet the specifications.
"""
#IMPORTANT: marginal characteristics grouping indices have to start at 0!
annual_household_control_totals_data = {
"year": array(8*[2000]),
#"age_of_head": array(4*[0] + 4*[1]),
"age_of_head_min": array([ 0, 0, 0, 0, 50, 50, 50, 50]),
"age_of_head_max": array([49,49,49,49,100,100,100,100]),
#"income": array(2*[0] + 2*[1] + 2*[0] + 2*[1]),
"income_min": array([ 0, 0,40000,40000, 0, 0,40000,40000]),
"income_max": array([39999,39999, -1, -1,39999,39999, -1, -1]),
#"persons": array([0,1,0,1,0,1,0,1]),
"persons_min": array([0, 3,0, 3,0, 3,0, 3]),
"persons_max": array([2,-1,2,-1,2,-1,2,-1]),
"total_number_of_households": array([4000, 5000, 1000, 3000, 0, 6000, 3000, 8000])
}
##size of columns was not even, removed last element of min and max
#household_characteristics_for_ht_data = {
#"characteristic": array(2*['age_of_head'] + 2*['income'] + 2*['persons']),
#"min": array([0, 50, 0, 40000, 0, 3]),
#"max": array([49, 100, 39999, -1, 2, -1])
#}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='hh_set', table_data=self.households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name='hct_set', table_data=annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what='household', id_name=[])
#storage.write_table(table_name='hc_set', table_data=household_characteristics_for_ht_data)
#hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
# unplace some households
where10 = where(hh_set.get_attribute("grid_id")<>10)[0]
hh_set.modify_attribute(name="grid_id", data=zeros(where10.size), index=where10)
model = TransitionModel(hh_set, control_total_dataset=hct_set)
model.run(year=2000, target_attribute_name="total_number_of_households", reset_dataset_attribute_value={'grid_id':-1})
#check that there are indeed 33000 total households after running the model
results = hh_set.size()
should_be = [30000]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
#check that the number of households in each group exactly match the control totals specified
results = self.get_count_all_groups(hh_set)
should_be = [4000, 5000, 1000, 3000, 0, 6000, 3000, 8000]
self.assertEqual(ma.allclose(results, should_be),
True, "Error, should_be: %s, but result: %s" % (should_be, results))示例4: test_unplaced_agents_decrease_available_space
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
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)))示例5: test_same_distribution_after_household_subtraction
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_same_distribution_after_household_subtraction(self):
"""Using the control_totals and no marginal characteristics,
subtract households and ensure that the distribution within each group stays the same
"""
annual_household_control_totals_data = {
"year": array([2000, 2000]),
"total_number_of_households": array([8000, 12000]),
"faz_id": array([1,2])
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name = 'hh_set', table_data = self.households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name = 'hct_set', table_data = annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what="household")
storage.write_table(table_name = 'hc_set', table_data = self.household_characteristics_for_ht_data)
hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
# storage.write_table(table_name='prs_set', table_data=self.person_data)
# prs_set = PersonDataset(in_storage=storage, in_table_name='prs_set')
model = SubareaHouseholdTransitionModel(subarea_id_name="faz_id")
# model.run(year=2000, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
model.run(year=2000, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
#check that there are indeed 8000 (area 1) and 12000 (area 2) total households after running the model
areas = hh_set.get_attribute("faz_id")
results = array([0,0])
for iarea in [0,1]:
results[iarea] = where(areas == [1,2][iarea])[0].size
should_be = [8000, 12000]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
#check that the distribution of households in each group is the same as before running the model
results = self.get_count_all_groups(hh_set)
should_be = array([# area 1
3000.0/16500.0*8000.0, 1000.0/16500.0*8000.0, 1500.0/16500.0*8000.0, 2000.0/16500.0*8000.0,
1000.0/16500.0*8000.0, 2500.0/16500.0*8000.0, 1500.0/16500.0*8000.0, 4000.0/16500.0*8000.0,
# area 2
3000.0/16500.0*12000.0, 1000.0/16500.0*12000.0, 1500.0/16500.0*12000.0, 2000.0/16500.0*12000.0,
1000.0/16500.0*12000.0, 2500.0/16500.0*12000.0, 1500.0/16500.0*12000.0, 4000.0/16500.0*12000.0])
self.assertEqual(ma.allclose(results, should_be, rtol=0.1),
True, "Error, should_be: %s,\n but result: %s" % (should_be, results))示例6: test_controlling_with_one_marginal_characteristic
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_controlling_with_one_marginal_characteristic(self):
"""Using the age_of_head as a marginal characteristic, which would partition the 8 groups into two larger groups
(those with age_of_head < 40 and >= 40), ensure that the control totals are met and that the distribution within
each large group is the same before and after running the model
"""
#IMPORTANT: marginal characteristics grouping indices have to start at 0!
#i.e. below, there is one marg. char. "age_of_head". here we indicate that the first "large group" (groups 1-4),
#consisting of those groups with age_of_head < 40 should total 25000 households after running this model for one year,
#and the second large group, those groups with age_of_head > 40, should total 15000 households
annual_household_control_totals_data = {
"year": array([2000, 2000, 2000, 2000]),
"age_of_head": array([0, 1, 0, 1]),
"total_number_of_households": array([20000, 10000, 5000, 5000]),
"faz_id": array([1, 1, 2, 2] )
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name = 'hh_set', table_data = self.households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name = 'hct_set', table_data = annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what='household')
# storage.write_table(table_name='prs_set', table_data=self.person_data)
# prs_set = PersonDataset(in_storage=storage, in_table_name='prs_set')
storage.write_table(table_name = 'hc_set', table_data = self.household_characteristics_for_ht_data)
hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
model = SubareaHouseholdTransitionModel(subarea_id_name="faz_id")
# model.run(year=2000, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
model.run(year=2000, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
#check that there are indeed 40000 total households after running the model
areas = hh_set.get_attribute("faz_id")
results = array([0,0])
for iarea in [0,1]:
results[iarea] = where(areas == [1,2][iarea])[0].size
should_be = [30000, 10000]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
#check that the number of households within the groups correspond to the control totals
results = self.get_count_all_groups(hh_set)
should_be = [20000, 10000, 5000, 5000]
idx1 = arange(0,4)
idx2 = arange(4,8)
idx3 = arange(8,12)
idx4 = arange(12,16)
self.assertEqual(ma.allclose([results[idx1].sum(), results[idx2].sum(), results[idx3].sum(), results[idx4].sum()], should_be, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be,
array([results[idx1].sum(), results[idx2].sum(), results[idx3].sum(), results[idx4].sum()])))
#check that the distribution of households within the groups are the same before and after
#running the model, respectively
should_be = [# area 1
3000.0/7500.0*20000.0, 1000.0/7500.0*20000.0, 1500.0/7500.0*20000.0, 2000.0/7500.0*20000.0,
1000.0/9000.0*10000.0, 2500.0/9000.0*10000.0, 1500.0/9000.0*10000.0, 4000.0/9000.0*10000.0,
# area 2
3000.0/7500.0*5000.0, 1000.0/7500.0*5000.0, 1500.0/7500.0*5000.0, 2000.0/7500.0*5000.0,
1000.0/9000.0*5000.0, 2500.0/9000.0*5000.0, 1500.0/9000.0*5000.0, 4000.0/9000.0*5000.0]
self.assertEqual(ma.allclose(results, should_be, rtol=0.1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))示例7: test_controlling_age_of_head
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_controlling_age_of_head(self):
""" Controls for one marginal characteristics, namely age_of_head.
"""
annual_household_control_totals_data = {
"year": array([2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002]),
"age_of_head": array([0,1,2,0,1,2, 0,1,2]),
"total_number_of_households": array([25013, 21513, 18227, # 2000
10055, 15003, 17999, # 2001
15678, 14001, 20432]) # 2002
}
household_characteristics_for_ht_data = {
"characteristic": array(3*['age_of_head']),
"min": array([0, 35, 65]),
"max": array([34, 64, -1])
}
households_data = {
"household_id":arange(15000)+1,
"building_id": array(15000*[1]),
"age_of_head": array(1000*[25] + 1000*[28] + 2000*[32] + 1000*[34] +
2000*[35] + 1000*[40] + 1000*[54]+ 1000*[62] +
1000*[65] + 1000*[68] + 2000*[71] + 1000*[98]),
"persons": array(1000*[2] + 2000*[3] + 1000*[1] + 1000*[6] + 1000*[1] + 1000*[4] +
3000*[1]+ 5000*[5], dtype=int8)
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='hh_set', table_data=households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name='hct_set', table_data=annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what='household',
id_name=['year' ,'age_of_head'])
storage.write_table(table_name='hc_set', table_data=household_characteristics_for_ht_data)
hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
storage.write_table(table_name='prs_set', table_data=self.person_data)
prs_set = PersonDataset(in_storage=storage, in_table_name='prs_set')
model = HouseholdTransitionModel(debuglevel=3)
# this run should add households in all four categories
model.run(year=2000, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[0:3]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(hc_set.size(), dtype=int32)
results[0] = where(hh_set.get_attribute('age_of_head') <= hc_set.get_attribute("max")[0], 1,0).sum()
for i in range(1, hc_set.size()-1):
results[i] = logical_and(where(hh_set.get_attribute('age_of_head') >= hc_set.get_attribute("min")[i], 1,0),
where(hh_set.get_attribute('age_of_head') <= hc_set.get_attribute("max")[i], 1,0)).sum()
results[hc_set.size()-1] = where(hh_set.get_attribute('age_of_head') >= hc_set.get_attribute("min")[hc_set.size()-1], 1,0).sum()
should_be = hct_set.get_attribute("total_number_of_households")[0:3]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should remove households in all four categories
model.run(year=2001, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[3:6]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(hc_set.size(), dtype=int32)
results[0] = where(hh_set.get_attribute('age_of_head') <= hc_set.get_attribute("max")[0], 1,0).sum()
for i in range(1, hc_set.size()-1):
results[i] = logical_and(where(hh_set.get_attribute('age_of_head') >= hc_set.get_attribute("min")[i], 1,0),
where(hh_set.get_attribute('age_of_head') <= hc_set.get_attribute("max")[i], 1,0)).sum()
results[hc_set.size()-1] = where(hh_set.get_attribute('age_of_head') >= hc_set.get_attribute("min")[hc_set.size()-1], 1,0).sum()
should_be = hct_set.get_attribute("total_number_of_households")[3:6]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should add and remove households
model.run(year=2002, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[6:9]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(hc_set.size(), dtype=int32)
results[0] = where(hh_set.get_attribute('age_of_head') <= hc_set.get_attribute("max")[0], 1,0).sum()
for i in range(1, hc_set.size()-1):
results[i] = logical_and(where(hh_set.get_attribute('age_of_head') >= hc_set.get_attribute("min")[i], 1,0),
where(hh_set.get_attribute('age_of_head') <= hc_set.get_attribute("max")[i], 1,0)).sum()
results[hc_set.size()-1] = where(hh_set.get_attribute('age_of_head') >= hc_set.get_attribute("min")[hc_set.size()-1], 1,0).sum()
should_be = hct_set.get_attribute("total_number_of_households")[6:9]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))示例8: test_controlling_income
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_controlling_income(self):
""" Controls for one marginal characteristics, namely income.
"""
annual_household_control_totals_data = {
"year": array([2000, 2000, 2000, 2000, 2001, 2001, 2001, 2001, 2002, 2002, 2002, 2002]),
"income": array([0,1,2,3,0,1,2,3, 0,1,2,3]),
"total_number_of_households": array([25013, 21513, 18227, 18493, # 2000
10055, 15003, 17999, 17654, # 2001
15678, 14001, 20432, 14500]) # 2002
}
household_characteristics_for_ht_data = {
"characteristic": array(4*['income']),
"min": array([0, 40000, 120000, 70000]), # category 120000 has index 3 and category 70000 has index 2
"max": array([39999, 69999, -1, 119999]) # (testing row invariance)
}
hc_sorted_index = array([0,1,3,2])
households_data = {
"household_id":arange(20000)+1,
"building_id": array(19950*[1] + 50*[0]),
"income": array(1000*[1000] + 1000*[10000] + 2000*[20000] + 1000*[35000] + 2000*[45000] +
1000*[50000] + 2000*[67000]+ 2000*[90000] + 1000*[100005] + 2000*[110003] +
1000*[120000] + 1000*[200000] + 2000*[500000] + 1000*[630000]),
"persons": array(3000*[2] + 2000*[3] + 1000*[1] + 1000*[6] + 1000*[1] + 1000*[4] +
3000*[1]+ 8000*[5], dtype=int8)
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='hh_set', table_data=households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name='hct_set', table_data=annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what='household', id_name=['year' ,'income'])
storage.write_table(table_name='hc_set', table_data=household_characteristics_for_ht_data)
hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
storage.write_table(table_name='prs_set', table_data=self.person_data)
prs_set = PersonDataset(in_storage=storage, in_table_name='prs_set')
model = HouseholdTransitionModel(debuglevel=3)
# this run should add households in all four categories
model.run(year=2000, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
results = hh_set.size()
should_be = [83246]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(hc_set.size(), dtype=int32)
results[0] = where(hh_set.get_attribute('income') <=
hc_set.get_attribute("max")[hc_sorted_index[0]], 1,0).sum()
for i in range(1, hc_set.size()-1):
results[i] = logical_and(where(hh_set.get_attribute('income') >=
hc_set.get_attribute("min")[hc_sorted_index[i]], 1,0),
where(hh_set.get_attribute('income') <=
hc_set.get_attribute("max")[hc_sorted_index[i]], 1,0)).sum()
results[-1] = where(hh_set.get_attribute('income') >= hc_set.get_attribute("min")[hc_sorted_index[-1]], 1,0).sum()
should_be = hct_set.get_attribute("total_number_of_households")[0:4]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should remove households in all four categories
model.run(year=2001, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[4:8]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(hc_set.size(), dtype=int32)
results[0] = where(hh_set.get_attribute('income') <=
hc_set.get_attribute("max")[hc_sorted_index[0]], 1,0).sum()
for i in range(1, hc_set.size()-1):
results[i] = logical_and(where(hh_set.get_attribute('income') >=
hc_set.get_attribute("min")[hc_sorted_index[i]], 1,0),
where(hh_set.get_attribute('income') <=
hc_set.get_attribute("max")[hc_sorted_index[i]], 1,0)).sum()
results[-1] = where(hh_set.get_attribute('income') >= hc_set.get_attribute("min")[hc_sorted_index[-1]], 1,0).sum()
should_be = hct_set.get_attribute("total_number_of_households")[4:8]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should add and remove households
model.run(year=2002, person_set=prs_set, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[8:13]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(hc_set.size(), dtype=int32)
results[0] = where(hh_set.get_attribute('income') <= hc_set.get_attribute("max")[hc_sorted_index[0]], 1,0).sum()
for i in range(1, hc_set.size()-1):
results[i] = logical_and(where(hh_set.get_attribute('income') >=
hc_set.get_attribute("min")[hc_sorted_index[i]], 1,0),
where(hh_set.get_attribute('income') <=
hc_set.get_attribute("max")[hc_sorted_index[i]], 1,0)).sum()
results[-1] = where(hh_set.get_attribute('income') >= hc_set.get_attribute("min")[hc_sorted_index[-1]], 1,0).sum()
should_be = hct_set.get_attribute("total_number_of_households")[8:13]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))示例9: run_HTM
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def run_HTM(niter):
nhhs = 5000
ngroups = 4
nhhsg = int(nhhs/ngroups)
nhhslg = nhhs-(ngroups-1)*nhhsg
should_nhhs = nhhs-2000
storage = StorageFactory().get_storage('dict_storage')
hc_set_table_name = 'hc_set'
storage.write_table(
table_name = hc_set_table_name,
table_data = {
'characteristic': array(4*['income']+4*['age_of_head']),
'min':array([0,1001,5001, 10001, 0, 31, 41, 61]),
'max':array([1000, 5000, 10000,-1, 30, 40, 60, -1])
},
)
hct_set_table_name = 'hct_set'
storage.write_table(
table_name = hct_set_table_name,
table_data = {
'year':array([2000]),
'total_number_of_households':array([should_nhhs])
},
)
households_table_name = 'households'
storage.write_table(
table_name = households_table_name,
table_data = {
'age_of_head': array(nhhsg/2*[18]+(nhhsg-nhhsg/2)*[35] +
nhhsg/2*[30] + (nhhsg-nhhsg/2)*[40] +
nhhsg/2*[38] + (nhhsg-nhhsg/2)*[65] +
nhhslg/2*[50] + (nhhslg-nhhslg/2)*[80]
),
'income': array(nhhsg*[500] + nhhsg*[2000] +
nhhsg*[7000] + nhhslg*[15000]
),
'household_id':arange(nhhs)+1
},
)
hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name=hc_set_table_name)
hct_set = ControlTotalDataset(
in_storage = storage,
in_table_name = hct_set_table_name,
what = 'household',
id_name = ['year']
)
logger.be_quiet()
result = zeros((niter,4))
for iter in range(niter):
households = HouseholdDataset(in_storage=storage, in_table_name=households_table_name)
model = HouseholdTransitionModel()
model.run(year=2000, household_set=households, control_totals=hct_set, characteristics=hc_set)
income = households.get_attribute('income')
age = households.get_attribute('age_of_head')
idx1 = where(income <= 1000)[0]
idx2 = where(logical_and(income <= 5000, income > 1000))[0]
idx3 = where(logical_and(income <= 10000, income > 5000))[0]
idx4 = where(income > 10000)[0]
result[iter,:] = array([age[idx1].mean(), age[idx2].mean(), age[idx3].mean(), age[idx4].mean()])
return result示例10: test_controlling_age_of_head
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_controlling_age_of_head(self):
""" Controls for one marginal characteristics, namely age_of_head.
"""
annual_household_control_totals_data = {
"year": array([2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002]),
#"age_of_head": array([0,1,2,0,1,2, 0,1,2]),
"age_of_head_min": array([ 0,35,65, 0,35,65, 0,35,65]),
"age_of_head_max": array([34,64,-1, 34,64,-1, 34,64,-1]),
"total_number_of_households": array([25013, 21513, 18227, # 2000
10055, 15003, 17999, # 2001
15678, 14001, 20432]) # 2002
}
#household_characteristics_for_ht_data = {
#"characteristic": array(3*['age_of_head']),
#"min": array([0, 35, 65]),
#"max": array([34, 64, -1])
#}
households_data = {
"household_id":arange(15000)+1,
"grid_id": array(15000*[1]),
"age_of_head": array(1000*[25] + 1000*[28] + 2000*[32] + 1000*[34] +
2000*[35] + 1000*[40] + 1000*[54]+ 1000*[62] +
1000*[65] + 1000*[68] + 2000*[71] + 1000*[98])
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='hh_set', table_data=households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name='hct_set', table_data=annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what='household',
id_name=[])
#storage.write_table(table_name='hc_set', table_data=household_characteristics_for_ht_data)
#hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
model = TransitionModel(hh_set, control_total_dataset=hct_set)
model.run(year=2000, target_attribute_name="total_number_of_households", reset_dataset_attribute_value={'grid_id':-1})
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[0:3]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
cats = 3
results = zeros(cats, dtype=int32)
results[0] = (hh_set.get_attribute('age_of_head') <= hct_set.get_attribute("age_of_head_max")[0]).sum()
for i in range(1, cats-1):
results[i] = logical_and(hh_set.get_attribute('age_of_head') >= hct_set.get_attribute("age_of_head_min")[i],
hh_set.get_attribute('age_of_head') <= hct_set.get_attribute("age_of_head_max")[i]).sum()
results[-1] = (hh_set.get_attribute('age_of_head') >= hct_set.get_attribute("age_of_head_min")[i+1]).sum()
should_be = hct_set.get_attribute("total_number_of_households")[0:3]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should remove households in all four categories
#model.run(year=2001, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
model.run(year=2001, target_attribute_name="total_number_of_households", reset_dataset_attribute_value={'grid_id':-1})
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[3:6]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(cats, dtype=int32)
results[0] = (hh_set.get_attribute('age_of_head') <= hct_set.get_attribute("age_of_head_max")[0]).sum()
for i in range(1, cats-1):
results[i] = logical_and(hh_set.get_attribute('age_of_head') >= hct_set.get_attribute("age_of_head_min")[i+3],
hh_set.get_attribute('age_of_head') <= hct_set.get_attribute("age_of_head_max")[i+3]).sum()
results[-1] = (hh_set.get_attribute('age_of_head') >= hct_set.get_attribute("age_of_head_min")[i+4]).sum()
should_be = hct_set.get_attribute("total_number_of_households")[3:6]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should add and remove households
#model.run(year=2002, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
model.run(year=2002, target_attribute_name="total_number_of_households", reset_dataset_attribute_value={'grid_id':-1})
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[6:9]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(cats, dtype=int32)
results[0] = where(hh_set.get_attribute('age_of_head') <= hct_set.get_attribute("age_of_head_max")[0], 1,0).sum()
for i in range(1, cats-1):
results[i] = logical_and(hh_set.get_attribute('age_of_head') >= hct_set.get_attribute("age_of_head_min")[i+6],
hh_set.get_attribute('age_of_head') <= hct_set.get_attribute("age_of_head_max")[i+6]).sum()
results[-1] = (hh_set.get_attribute('age_of_head') >= hct_set.get_attribute("age_of_head_min")[i+7]).sum()
should_be = hct_set.get_attribute("total_number_of_households")[6:9]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))示例11: test_controlling_income
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
def test_controlling_income(self):
""" Controls for one marginal characteristics, namely income.
"""
annual_household_control_totals_data = {
"year": array([2000, 2000, 2000, 2000, 2001, 2001, 2001, 2001, 2002, 2002, 2002, 2002]),
#"income": array([0,1,2,3,0,1,2,3, 0,1,2,3]),
"income_min": array([ 0,40000, 70000,120000, 0,40000, 70000,120000, 0,40000, 70000,120000]),
"income_max": array([39999,69999,119999, -1, 39999,69999,119999, -1, 39999,69999,119999, -1]),
"total_number_of_households": array([25013, 21513, 18227, 18493, # 2000
10055, 15003, 17999, 17654, # 2001
15678, 14001, 20432, 14500]) # 2002
}
#household_characteristics_for_ht_data = {
#"characteristic": array(4*['income']),
#"min": array([0, 40000, 120000, 70000]), # category 120000 has index 3 and category 70000 has index 2
#"max": array([39999, 69999, -1, 119999]) # (testing row invariance)
#}
#hc_sorted_index = array([0,1,3,2])
households_data = {
"household_id":arange(20000)+1,
"grid_id": array(19950*[1] + 50*[0]),
"income": array(1000*[1000] + 1000*[10000] + 2000*[20000] + 1000*[35000] + 2000*[45000] +
1000*[50000] + 2000*[67000]+ 2000*[90000] + 1000*[100005] + 2000*[110003] +
1000*[120000] + 1000*[200000] + 2000*[500000] + 1000*[630000])
}
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='hh_set', table_data=households_data)
hh_set = HouseholdDataset(in_storage=storage, in_table_name='hh_set')
storage.write_table(table_name='hct_set', table_data=annual_household_control_totals_data)
hct_set = ControlTotalDataset(in_storage=storage, in_table_name='hct_set', what='household', id_name=[])
#storage.write_table(table_name='hc_set', table_data=household_characteristics_for_ht_data)
#hc_set = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='hc_set')
model = TransitionModel(hh_set, control_total_dataset=hct_set)
model.run(year=2000, target_attribute_name="total_number_of_households", reset_dataset_attribute_value={'grid_id':-1})
results = hh_set.size()
should_be = [83246]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
cats = 4
results = zeros(cats, dtype=int32)
results[0] = (hh_set.get_attribute('income') <= hct_set.get_attribute("income_max")[0]).sum()
for i in range(1, cats-1):
results[i] = logical_and(hh_set.get_attribute('income') >= hct_set.get_attribute("income_min")[i],
hh_set.get_attribute('income') <= hct_set.get_attribute("income_max")[i]).sum()
results[-1] = (hh_set.get_attribute('income') >= hct_set.get_attribute("income_min")[i+1]).sum()
should_be = hct_set.get_attribute("total_number_of_households")[0:4]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should remove households in all four categories
#model.run(year=2001, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
model.run(year=2001, target_attribute_name="total_number_of_households", reset_dataset_attribute_value={'grid_id':-1})
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[4:8]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(cats, dtype=int32)
results[0] = (hh_set.get_attribute('income') <= hct_set.get_attribute("income_max")[4]).sum()
for i in range(1, cats-1):
results[i] = logical_and(hh_set.get_attribute('income') >= hct_set.get_attribute("income_min")[i+4],
hh_set.get_attribute('income') <= hct_set.get_attribute("income_max")[i+4]).sum()
results[-1] = (hh_set.get_attribute('income') >= hct_set.get_attribute("income_min")[i+5]).sum()
should_be = hct_set.get_attribute("total_number_of_households")[4:8]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
# this run should add and remove households
#model.run(year=2002, household_set=hh_set, control_totals=hct_set, characteristics=hc_set)
model.run(year=2002, target_attribute_name="total_number_of_households", reset_dataset_attribute_value={'grid_id':-1})
results = hh_set.size()
should_be = [(hct_set.get_attribute("total_number_of_households")[8:12]).sum()]
self.assertEqual(ma.allclose(should_be, results, rtol=1e-1),
True, "Error, should_be: %s, but result: %s" % (should_be, results))
results = zeros(cats, dtype=int32)
results[0] = (hh_set.get_attribute('income') <= hct_set.get_attribute("income_max")[8]).sum()
for i in range(1, cats-1):
results[i] = logical_and(hh_set.get_attribute('income') >= hct_set.get_attribute("income_min")[i+8],
hh_set.get_attribute('income') <= hct_set.get_attribute("income_max")[i+8]).sum()
results[-1] = (hh_set.get_attribute('income') >= hct_set.get_attribute("income_min")[i+9]).sum()
should_be = hct_set.get_attribute("total_number_of_households")[8:12]
self.assertEqual(ma.allclose(results, should_be, rtol=1e-6),
True, "Error, should_be: %s, but result: %s" % (should_be, results))示例12: HouseholdDataset
# 需要導入模塊: from urbansim.datasets.household_dataset import HouseholdDataset [as 別名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import get_attribute [as 別名]
from urbansim.datasets.zone_dataset import ZoneDataset
from urbansim.datasets.job_dataset import JobDataset
from urbansim.models.household_location_choice_model_creator import HouseholdLocationChoiceModelCreator
from opus_core.database_management.database_server import DatabaseServer
from opus_core.database_management.configurations.scenario_database_configuration import ScenarioDatabaseConfiguration
# Datasets
##########
# agents from householdset.tab
agents = HouseholdDataset(in_storage = StorageFactory().get_storage('tab_storage', storage_location='.'),
in_table_name = "householdset", id_name="agent_id")
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")