def run_model():
storage.write_table(table_name = 'households', table_data = household_data)
households = HouseholdCharacteristicDataset(in_storage=storage, in_table_name='households')
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]
results = array([age[idx1].mean(), age[idx2].mean(), age[idx3].mean(), age[idx4].mean()])
print results
return results
def _update_household_set(self, household_set):
"""Updates also person set."""
hh_ids_to_copy = household_set.get_id_attribute()[self.mapping_existing_hhs_to_new_hhs]
npersons_in_hhs = household_set.get_attribute_by_index('persons', self.mapping_existing_hhs_to_new_hhs)
result = USHouseholdTransitionModel._update_household_set(self, household_set)
new_hh_ids = household_set.get_id_attribute()[(household_set.size()-self.mapping_existing_hhs_to_new_hhs.size):household_set.size()]
# remove person that have non-existing households
eliminate_index = where(logical_not(ismember(self.person_set.get_attribute(household_set.get_id_name()[0]), household_set.get_id_attribute())))[0]
self.person_set.remove_elements(eliminate_index)
# duplicate persons
unique_persons_to_duplicate = ismember(self.person_set.get_attribute(household_set.get_id_name()[0]), hh_ids_to_copy)
person_considered_idx = where(unique_persons_to_duplicate)[0]
npersons_in_hhs_sum = npersons_in_hhs.sum()
persons_to_duplicate = -1*ones(npersons_in_hhs_sum, dtype=int32)
new_person_hh_ids = zeros(npersons_in_hhs_sum, dtype=int32)
considered_person_hh_ids = self.person_set.get_attribute(household_set.get_id_name()[0])[person_considered_idx]
j = 0
for i in arange(hh_ids_to_copy.size):
idx = where(considered_person_hh_ids == hh_ids_to_copy[i])[0]
if idx.size == npersons_in_hhs[i]:
persons_to_duplicate[j:(j+npersons_in_hhs[i])] = person_considered_idx[idx]
new_person_hh_ids[j:(j+npersons_in_hhs[i])] = new_hh_ids[i]
j+=npersons_in_hhs[i]
if hh_ids_to_copy.size > 0:
if j < npersons_in_hhs_sum:
persons_to_duplicate = persons_to_duplicate[0:j]
new_person_hh_ids = new_person_hh_ids[0:j]
if persons_to_duplicate.size <= 0:
return result
new_persons_idx = self.person_set.duplicate_rows(persons_to_duplicate)
# assign job_id to 'no job', but only if the job_id is present in the person dataset
dtype=self.person_set.get_data_type('job_id')
if dtype!=None:
self.person_set.modify_attribute(name='job_id', data=zeros(new_persons_idx.size, dtype=dtype), index=new_persons_idx)
# assign the right household_id
self.person_set.modify_attribute(name=household_set.get_id_name()[0], data=new_person_hh_ids, index=new_persons_idx)
self.debug.print_debug("Created %s persons." % new_persons_idx.size, 3)
# check if number of persons in the household_set correspond to those in person set
if household_set.get_attribute('persons').sum() <> self.person_set.size():
logger.log_warning('Number of persons in household set (%s) does not correspond to those in person set (%s).' % (household_set.get_attribute('persons').sum(),
self.person_set.size()))
return result
def run_model():
storage.write_table(table_name='households',
table_data=household_data)
households = HouseholdCharacteristicDataset(
in_storage=storage, in_table_name='households')
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]
results = array([
age[idx1].mean(), age[idx2].mean(), age[idx3].mean(),
age[idx4].mean()
])
print results
return results
def _update_household_set(self, household_set):
"""Updates also person set."""
hh_ids_to_copy = household_set.get_id_attribute()[self.mapping_existing_hhs_to_new_hhs]
npersons_in_hhs = household_set.get_attribute_by_index('persons', self.mapping_existing_hhs_to_new_hhs)
result = USHouseholdTransitionModel._update_household_set(self, household_set)
new_hh_ids = household_set.get_id_attribute()[(household_set.size()-self.mapping_existing_hhs_to_new_hhs.size):household_set.size()]
# remove person that have non-existing households
eliminate_index = where(logical_not(ismember(self.person_set.get_attribute(household_set.get_id_name()[0]), household_set.get_id_attribute())))[0]
self.person_set.remove_elements(eliminate_index)
# duplicate persons
unique_persons_to_duplicate = ismember(self.person_set.get_attribute(household_set.get_id_name()[0]), hh_ids_to_copy)
person_considered_idx = where(unique_persons_to_duplicate)[0]
npersons_in_hhs_sum = npersons_in_hhs.sum()
persons_to_duplicate = -1*ones(npersons_in_hhs_sum, dtype=int32)
new_person_hh_ids = zeros(npersons_in_hhs_sum, dtype=int32)
considered_person_hh_ids = self.person_set.get_attribute(household_set.get_id_name()[0])[person_considered_idx]
j = 0
for i in arange(hh_ids_to_copy.size):
idx = where(considered_person_hh_ids == hh_ids_to_copy[i])[0]
if idx.size == npersons_in_hhs[i]:
persons_to_duplicate[j:(j+npersons_in_hhs[i])] = person_considered_idx[idx]
new_person_hh_ids[j:(j+npersons_in_hhs[i])] = new_hh_ids[i]
j+=npersons_in_hhs[i]
if hh_ids_to_copy.size > 0:
if j < npersons_in_hhs_sum:
persons_to_duplicate = persons_to_duplicate[0:j]
new_person_hh_ids = new_person_hh_ids[0:j]
if persons_to_duplicate.size <= 0:
return result
new_persons_idx = self.person_set.duplicate_rows(persons_to_duplicate)
# assign job_id to 'no job'
self.person_set.modify_attribute(name='job_id', data=zeros(new_persons_idx.size, dtype=self.person_set.get_data_type('job_id')), index=new_persons_idx)
# assign the right household_id
self.person_set.modify_attribute(name=household_set.get_id_name()[0], data=new_person_hh_ids, index=new_persons_idx)
self.debug.print_debug("Created %s persons." % new_persons_idx.size, 3)
# check if number of persons in the household_set correspond to those in person set
if household_set.get_attribute('persons').sum() <> self.person_set.size():
logger.log_warning('Number of persons in household set (%s) does not correspond to those in person set (%s).' % (household_set.get_attribute('persons').sum(),
self.person_set.size()))
return result
def _do_initialize_for_run(self, household_set):
HouseholdTransitionModel._do_initialize_for_run(self, household_set)
self.new_households[self.subarea_id_name] = array([], dtype="int32")
def run(self, year, household_set, person_set, control_totals, characteristics, resources=None):
self.person_set = person_set
return USHouseholdTransitionModel.run(self, year, household_set, control_totals, characteristics, resources=resources)
def __init__(self, location_id_name="building_id", **kwargs):
USHouseholdTransitionModel.__init__(self, location_id_name=location_id_name, **kwargs)
def _do_initialize_for_run(self, household_set):
HouseholdTransitionModel._do_initialize_for_run(self, household_set)
self.new_households["large_area_id"] = array([], dtype="int32")
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
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
def _do_initialize_for_run(self, household_set):
HouseholdTransitionModel._do_initialize_for_run(self, household_set)
self.new_households[self.subarea_id_name] = array([], dtype="int32")
def _do_initialize_for_run(self, household_set):
HouseholdTransitionModel._do_initialize_for_run(self, household_set)
self.new_households["large_area_id"] = array([], dtype="int32")
