def _do_run(self, location_set, agent_set, agents_index, data_objects=None, resources=None):
location_id_name = location_set.get_id_name()[0]
jobsubset = DatasetSubset(agent_set, agents_index)
if jobsubset.size() <= 0:
return array([], dtype='int32')
#unplace jobs
agent_set.set_values_of_one_attribute(location_id_name,
resize(array([-1.0]), jobsubset.size()), agents_index)
sector_ids = jobsubset.get_attribute("sector_id")
sectors = unique(sector_ids)
counts = ndimage_sum(ones((jobsubset.size(),)), labels=sector_ids.astype('int32'), index=sectors.astype('int32'))
if sectors.size <=1 :
counts = array([counts])
variables = map(lambda x: "number_of_jobs_of_sector_"+str(int(x)), sectors)
compute_variables = map(lambda var: self.variable_package + "." +
location_set.get_dataset_name()+ "." + var, variables)
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
self.dataset_pool.add_datasets_if_not_included({agent_set.get_dataset_name():agent_set})
location_set.compute_variables(compute_variables, dataset_pool=self.dataset_pool)
if self.filter is None:
location_index = arange(location_set.size())
else:
filter_values = location_set.compute_variables([self.filter], dataset_pool=self.dataset_pool)
location_index = where(filter_values > 0)[0]
if location_index.size <= 0:
logger.log_status("No locations available. Nothing to be done.")
return array([])
location_subset = DatasetSubset(location_set, location_index)
i=0
for sector in sectors:
distr = location_subset.get_attribute(variables[i])
if ma.allclose(distr.sum(), 0):
uniform_prob = 1.0/distr.size
distr = resize(array([uniform_prob], dtype='float64'), distr.size)
logger.log_warning("Probabilities in scaling model for sector " + str(sector) + " sum to 0.0. Substituting uniform distribution!")
# random_sample = sample(location_set.get_attribute("grid_id"), k=int(counts[i]), \
# probabilities = distr)
distr = distr/float(distr.sum())
random_sample = probsample_replace(location_subset.get_id_attribute(), size=int(counts[i]),
prob_array=distr)
idx = where(sector_ids == sector)[0]
#modify job locations
agent_set.set_values_of_one_attribute(location_id_name, random_sample, agents_index[idx])
i+=1
return agent_set.get_attribute_by_index(location_id_name, agents_index)
def _do_run(self, location_set, agent_set, agents_index, resources=None):
location_id_name = location_set.get_id_name()[0]
asubset = DatasetSubset(agent_set, agents_index)
if asubset.size() <= 0:
return array([], dtype='int32')
#unplace agents
agent_set.modify_attribute(location_id_name,
resize(array([-1]), asubset.size()), agents_index)
if self.filter is None:
location_index = arange(location_set.size())
else:
filter_values = location_set.compute_variables([self.filter], dataset_pool=self.dataset_pool)
location_index = where(filter_values > 0)[0]
if location_index.size <= 0:
logger.log_status("No locations available. Nothing to be done.")
return array([])
location_subset = DatasetSubset(location_set, location_index)
if self.consider_capacity:
location_set.compute_variables([self.capacity_attribute],
dataset_pool=self.dataset_pool)
weights = location_subset[self.capacity_attribute]
if self.number_of_agents_attribute is not None:
location_set.compute_variables([self.number_of_agents_attribute],
dataset_pool=self.dataset_pool)
weights = clip(weights - location_subset[self.number_of_agents_attribute],
0, location_subset[self.capacity_attribute])
else:
weights = ones(location_subset.size())
if weights.sum() <=0:
logger.log_status("Locations' capacity sums to zero. Nothing to be done.")
return array([])
distr = weights/float(weights.sum())
random_sample = probsample_replace(location_subset.get_id_attribute(), size=asubset.size(),
prob_array=distr)
agent_set.modify_attribute(location_id_name, random_sample, agents_index)
return agent_set.get_attribute_by_index(location_id_name, agents_index)
class HouseholdTransitionModel(Model):
"""Creates and removes households from household_set. New households are duplicated from the existing households, keeping
the joint distribution of all characteristics.
"""
model_name = "Household Transition Model"
def __init__(self, location_id_name="grid_id", dataset_pool=None, debuglevel=0):
self.debug = DebugPrinter(debuglevel)
self.location_id_name = location_id_name
self.dataset_pool = self.create_dataset_pool(dataset_pool, ["urbansim", "opus_core"])
def run(self, year, household_set, control_totals, characteristics, resources=None):
self._do_initialize_for_run(household_set)
control_totals.get_attribute("total_number_of_households") # to make sure they are loaded
self.characteristics = characteristics
self.all_categories = self.characteristics.get_attribute("characteristic")
self.all_categories = array(map(lambda x: x.lower(), self.all_categories))
self.scaled_characteristic_names = get_distinct_names(self.all_categories).tolist()
self.marginal_characteristic_names = copy(control_totals.get_id_name())
index_year = self.marginal_characteristic_names.index("year")
self.marginal_characteristic_names.remove("year")
idx = where(control_totals.get_attribute("year")==year)[0]
self.control_totals_for_this_year = DatasetSubset(control_totals, idx)
self._do_run_for_this_year(household_set)
return self._update_household_set(household_set)
def _update_household_set(self, household_set):
index_of_duplicated_hhs = household_set.duplicate_rows(self.mapping_existing_hhs_to_new_hhs)
household_set.modify_attribute(name=self.location_id_name, data=-1 * ones((index_of_duplicated_hhs.size,),
dtype=household_set.get_data_type(self.location_id_name)),
index=index_of_duplicated_hhs)
household_set.remove_elements(self.remove_households)
if self.new_households[self.location_id_name].size > 0:
max_id = household_set.get_id_attribute().max()
self.new_households[self.household_id_name]=concatenate((self.new_households[self.household_id_name],
arange(max_id+1, max_id+self.new_households[self.location_id_name].size+1)))
household_set.add_elements(self.new_households, require_all_attributes=False)
difference = household_set.size()-self.household_size
self.debug.print_debug("Difference in number of households: %s"
" (original %s, new %s, created %s, deleted %s)"
% (difference,
self.household_size,
household_set.size(),
self.new_households[self.household_id_name].size + self.mapping_existing_hhs_to_new_hhs.size,
self.remove_households.size),
3)
if self.location_id_name in household_set.get_attribute_names():
self.debug.print_debug("Number of unplaced households: %s"
% where(household_set.get_attribute(self.location_id_name) <=0)[0].size,
3)
return difference
def _do_initialize_for_run(self, household_set):
self.household_id_name = household_set.get_id_name()[0]
self.new_households = {
self.location_id_name:array([], dtype=household_set.get_data_type(self.location_id_name, int32)),
self.household_id_name:array([], dtype=household_set.get_data_type(self.household_id_name, int32))
}
self.remove_households = array([], dtype='int32')
self.household_size = household_set.size()
self.max_id = household_set.get_id_attribute().max()
self.arrays_from_categories = {}
self.arrays_from_categories_mapping = {}
self.mapping_existing_hhs_to_new_hhs = array([], dtype=household_set.get_data_type(self.household_id_name, int32))
def _do_run_for_this_year(self, household_set):
self.household_set = household_set
groups = self.control_totals_for_this_year.get_id_attribute()
self.create_arrays_from_categories(self.household_set)
all_characteristics = self.arrays_from_categories.keys()
self.household_set.load_dataset_if_not_loaded(attributes = all_characteristics) # prevents from lazy loading to save runtime
idx_shape = []
number_of_combinations=1
num_attributes=len(all_characteristics)
for iattr in range(num_attributes):
attr = all_characteristics[iattr]
max_bins = self.arrays_from_categories[attr].max()+1
idx_shape.append(max_bins)
number_of_combinations=number_of_combinations*max_bins
if attr not in self.new_households.keys():
self.new_households[attr] = array([], dtype=self.household_set.get_data_type(attr, float32))
self.number_of_combinations = int(number_of_combinations)
idx_tmp = indices(tuple(idx_shape))
categories_index = zeros((self.number_of_combinations,num_attributes))
for i in range(num_attributes): #create indices of all combinations
categories_index[:,i] = idx_tmp[i].ravel()
categories_index_mapping = {}
for i in range(self.number_of_combinations):
categories_index_mapping[tuple(categories_index[i,].tolist())] = i
def get_category(values):
bins = map(lambda x, y: self.arrays_from_categories[x][int(y)], all_characteristics, values)
try:
return categories_index_mapping[tuple(bins)]
except KeyError, msg:
where_error = where(array(bins) == -1)[0]
if where_error.size > 0:
raise KeyError, \
"Invalid value of %s for attribute %s. It is not included in the characteristics groups." % (
array(values)[where_error],
array(all_characteristics)[where_error])
raise KeyError, msg
if num_attributes > 0:
# the next array must be a copy of the household values, otherwise, it changes the original values
values_array = reshape(array(self.household_set.get_attribute(all_characteristics[0])), (self.household_set.size(),1))
if num_attributes > 1:
for attr in all_characteristics[1:]:
values_array = concatenate((values_array, reshape(array(self.household_set.get_attribute(attr)),
(self.household_set.size(),1))), axis=1)
for i in range(values_array.shape[1]):
if values_array[:,i].max() > 10000:
values_array[:,i] = values_array[:,i]/10
values_array[:,i] = clip(values_array[:,i], 0, self.arrays_from_categories[all_characteristics[i]].size-1)
# determine for each household to what category it belongs to
self.household_categories = array(map(lambda x: get_category(x), values_array)) # performance bottleneck
number_of_households_in_categories = array(ndimage_sum(ones((self.household_categories.size,)),
labels=self.household_categories+1,
index = arange(self.number_of_combinations)+1))
else:
# no marginal characteristics; consider just one group
self.household_categories = zeros(self.household_set.size(), dtype='int32')
number_of_households_in_categories = array([self.household_set.size()])
g=arange(num_attributes)
#iterate over marginal characteristics
for group in groups:
if groups.ndim <= 1: # there is only one group (no marginal char.)
id = group
else:
id = tuple(group.tolist())
group_element = self.control_totals_for_this_year.get_data_element_by_id(id)
total = group_element.total_number_of_households
for i in range(g.size):
g[i] = eval("group_element."+self.arrays_from_categories.keys()[i])
if g.size <= 0:
l = ones((number_of_households_in_categories.size,))
else:
l = categories_index[:,0] == g[0]
for i in range(1,num_attributes):
l = logical_and(l, categories_index[:,i] == g[i])
# l has 1's for combinations of this group
number_in_group = array(ndimage_sum(number_of_households_in_categories, labels=l, index = 1))
diff = int(total - number_in_group)
if diff < 0: # households to be removed
is_in_group = l[self.household_categories]
w = where(is_in_group)[0]
sample_array, non_placed, size_non_placed = \
get_array_without_non_placed_agents(self.household_set, w, -1*diff,
self.location_id_name)
self.remove_households = concatenate((self.remove_households, non_placed, sample_noreplace(sample_array,
max(0,abs(diff)-size_non_placed))))
if diff > 0: # households to be created
self._create_households(diff, l)
def run(self, in_storage, out_storage=None, business_dsname="business", zone_dsname=None):
dataset_pool = DatasetPool(storage=in_storage, package_order=['psrc_parcel', 'urbansim_parcel', 'urbansim', 'opus_core'] )
seed(1)
allbusinesses = dataset_pool.get_dataset(business_dsname)
parcels = dataset_pool.get_dataset('parcel')
buildings = dataset_pool.get_dataset('building')
parcels.compute_variables(["urbansim_parcel.parcel.residential_units", "number_of_buildings = parcel.number_of_agents(building)",
"non_residential_sqft = (parcel.aggregate(building.non_residential_sqft)).astype(int32)",
"number_of_res_buildings = parcel.aggregate(urbansim_parcel.building.is_residential)",
"number_of_nonres_buildings = parcel.aggregate(urbansim_parcel.building.is_non_residential)",
"number_of_mixed_use_buildings = parcel.aggregate(urbansim_parcel.building.is_generic_building_type_6)"
],
dataset_pool=dataset_pool)
restypes = [12, 4, 19, 11, 34, 10, 33]
reslutypes = [13,14,15,24]
is_valid_business = ones(allbusinesses.size(), dtype='bool8')
parcels_not_matched = logical_and(in1d(allbusinesses["parcel_id"], parcels.get_id_attribute(), invert=True), allbusinesses["parcel_id"] > 0)
if(parcels_not_matched.sum() > 0):
is_valid_business[where(parcels_not_matched)] = False
logger.log_warning(message="No parcel exists for %s businesses (%s jobs)" % (parcels_not_matched.sum(),
allbusinesses[self.number_of_jobs_attr][where(parcels_not_matched)].sum()))
zero_parcel = allbusinesses["parcel_id"]<=0
if zero_parcel.sum() > 0:
is_valid_business[where(zero_parcel)] = False
logger.log_warning(message="%s businesses (%s jobs) located on zero parcel_id" % (zero_parcel.sum(),
allbusinesses[self.number_of_jobs_attr][where(zero_parcel)].sum()))
zero_size = logical_and(is_valid_business, allbusinesses[self.number_of_jobs_attr].round() == 0)
if(sum(zero_size) > 0):
is_valid_business[where(zero_size)] = False
logger.log_warning(message="%s businesses are of size 0." % sum(zero_size))
businesses = DatasetSubset(allbusinesses, index=where(is_valid_business)[0])
parcels.add_attribute(name="number_of_workplaces", data=parcels.sum_dataset_over_ids(businesses, constant=1))
has_single_res_buildings = logical_and(parcels["number_of_buildings"] == 1, parcels["number_of_res_buildings"] == 1) # 1 (1 residential)
parcels.add_attribute(data=has_single_res_buildings.astype("int32"), name="buildings_code")
has_mult_res_buildings = logical_and(parcels["number_of_buildings"] > 1, parcels["number_of_nonres_buildings"] == 0) # 2 (mult residential)
parcels.modify_attribute("buildings_code", data=2*ones(has_mult_res_buildings.sum()), index=where(has_mult_res_buildings))
has_single_nonres_buildings = logical_and(logical_and(parcels["number_of_buildings"] == 1, parcels["number_of_nonres_buildings"] == 1), parcels["number_of_mixed_use_buildings"] == 0) # 3 (1 non-res)
parcels.modify_attribute("buildings_code", data=3*ones(has_single_nonres_buildings.sum()), index=where(has_single_nonres_buildings))
has_mult_nonres_buildings = logical_and(logical_and(parcels["number_of_buildings"] > 1, parcels["number_of_res_buildings"] == 0), parcels["number_of_mixed_use_buildings"] == 0) # 4 (mult non-res)
parcels.modify_attribute("buildings_code", data=4*ones(has_mult_nonres_buildings.sum()), index=where(has_mult_nonres_buildings))
has_single_mixed_buildings = logical_and(parcels["number_of_buildings"] == 1, parcels["number_of_mixed_use_buildings"] == 1) # 5 (1 mixed-use)
parcels.modify_attribute("buildings_code", data=5*ones(has_single_mixed_buildings.sum()), index=where(has_single_mixed_buildings))
has_mult_mixed_buildings = logical_and(parcels["number_of_buildings"] > 1,
logical_or(logical_and(parcels["number_of_res_buildings"] > 0, parcels["number_of_nonres_buildings"] > 0),
logical_or(parcels["number_of_mixed_use_buildings"] > 1,
logical_and(parcels["number_of_res_buildings"] == 0,
parcels["number_of_mixed_use_buildings"] > 0)))) # 6
parcels.modify_attribute("buildings_code", data=6*ones(has_mult_mixed_buildings.sum()), index=where(has_mult_mixed_buildings))
has_no_building_res_lutype = logical_and(parcels["number_of_buildings"] == 0, in1d(parcels["land_use_type_id"], reslutypes)) # 7 (vacant with res LU type)
parcels.modify_attribute("buildings_code", data=7*ones(has_no_building_res_lutype.sum()), index=where(has_no_building_res_lutype))
has_no_building_nonres_lutype = logical_and(parcels["number_of_buildings"] == 0, in1d(parcels["land_use_type_id"], reslutypes)==0) # 8 (vacant with non-res LU type)
parcels.modify_attribute("buildings_code", data=8*ones(has_no_building_nonres_lutype.sum()), index=where(has_no_building_nonres_lutype))
business_sizes = businesses[self.number_of_jobs_attr].round().astype("int32")
business_location = {}
business_location1wrkpl = zeros(businesses.size(), dtype="int32")
business_location1wrkplres = zeros(businesses.size(), dtype="int32")
business_ids = businesses.get_id_attribute()
# sample one building for cases when sampling is required.
for ibusid in range(businesses.size()):
idx = where(buildings['parcel_id'] == businesses['parcel_id'][ibusid])[0]
bldgids = buildings['building_id'][idx]
business_location[business_ids[ibusid]] = bldgids
if bldgids.size == 1:
business_location1wrkpl[ibusid] = bldgids[0]
elif bldgids.size > 1:
business_location1wrkpl[ibusid] = bldgids[sample_noreplace(arange(bldgids.size), 1)]
if buildings['residential_units'][idx].sum() > 0:
# Residential buildings are sampled with probabilities proportional to residential units
business_location1wrkplres[ibusid] = bldgids[probsample_noreplace(arange(bldgids.size), 1, prob_array=buildings['residential_units'][idx])]
else:
business_location1wrkplres[ibusid] = business_location1wrkpl[ibusid]
home_based = zeros(business_sizes.sum(), dtype="bool8")
job_building_id = zeros(business_sizes.sum(), dtype="int32")
job_array_labels = business_ids.repeat(business_sizes)
job_assignment_case = zeros(business_sizes.sum(), dtype="int32")
processed_bindicator = zeros(businesses.size(), dtype="bool8")
business_codes = parcels.get_attribute_by_id("buildings_code", businesses["parcel_id"])
business_nworkplaces = parcels.get_attribute_by_id("number_of_workplaces", businesses["parcel_id"])
logger.log_status("Total number of jobs: %s" % home_based.size)
# 1. 1-2 worker business in 1 residential building
idx_sngl_wrk_1bld_fit = where(logical_and(business_sizes < 3, business_codes == 1))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_1bld_fit])
home_based[jidx] = True
job_building_id[jidx] = business_location1wrkpl[idx_sngl_wrk_1bld_fit].repeat(business_sizes[idx_sngl_wrk_1bld_fit])
job_assignment_case[jidx] = 1
processed_bindicator[idx_sngl_wrk_1bld_fit] = True
logger.log_status("1. %s jobs (%s businesses) set as home-based due to 1-2 worker x 1 residential building fit." % (
business_sizes[idx_sngl_wrk_1bld_fit].sum(), idx_sngl_wrk_1bld_fit.size))
# 2. 1-2 worker business in multiple residential buildings
idx_sngl_wrk_multbld_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes < 3), business_codes == 2))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_multbld_fit])
home_based[jidx] = True
job_building_id[jidx] = business_location1wrkplres[idx_sngl_wrk_multbld_fit].repeat(business_sizes[idx_sngl_wrk_multbld_fit])
job_assignment_case[jidx] = 2
processed_bindicator[idx_sngl_wrk_multbld_fit] = True
logger.log_status("2. %s jobs (%s businesses) set as home-based due to 1-2 worker x multiple residential buildings fit." % (
business_sizes[idx_sngl_wrk_multbld_fit].sum(), idx_sngl_wrk_multbld_fit.size))
# 3. 1-2 worker in single non-res building (not mixed-use)
idx_sngl_wrk_single_nonres_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes < 3), business_codes == 3))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_single_nonres_fit])
job_building_id[jidx] = business_location1wrkpl[idx_sngl_wrk_single_nonres_fit].repeat(business_sizes[idx_sngl_wrk_single_nonres_fit])
job_assignment_case[jidx] = 3
processed_bindicator[idx_sngl_wrk_single_nonres_fit] = True
logger.log_status("3. %s jobs (%s businesses) placed due to 1-2 worker x single non-res building fit." % (
business_sizes[idx_sngl_wrk_single_nonres_fit].sum(), idx_sngl_wrk_single_nonres_fit.size))
# 4. 1-2 worker in multiple non-res building (not mixed-use)
idx_sngl_wrk_mult_nonres_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes < 3), business_codes == 4))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_mult_nonres_fit])
job_building_id[jidx] = business_location1wrkpl[idx_sngl_wrk_mult_nonres_fit].repeat(business_sizes[idx_sngl_wrk_mult_nonres_fit])
job_assignment_case[jidx] = 4
processed_bindicator[idx_sngl_wrk_mult_nonres_fit] = True
logger.log_status("4. %s jobs (%s businesses) placed due to 1-2 worker x multiple non-res building fit." % (
business_sizes[idx_sngl_wrk_mult_nonres_fit].sum(), idx_sngl_wrk_mult_nonres_fit.size))
# 5. 1-2 worker in single mixed-use building
idx_sngl_wrk_smu_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes < 3), business_codes == 5))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_smu_fit])
job_building_id[jidx] = business_location1wrkpl[idx_sngl_wrk_smu_fit].repeat(business_sizes[idx_sngl_wrk_smu_fit])
job_assignment_case[jidx] = 5
processed_bindicator[idx_sngl_wrk_smu_fit] = True
logger.log_status("5. %s jobs (%s businesses) in 1-2 worker x single mixed-use building." % (
business_sizes[idx_sngl_wrk_smu_fit].sum(), idx_sngl_wrk_smu_fit.size))
# 6. 1-2 worker in multiple mixed-type buildings
idx_sngl_wrk_mmu_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes < 3), business_codes == 6))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_mmu_fit])
job_building_id[jidx] = business_location1wrkpl[idx_sngl_wrk_mmu_fit].repeat(business_sizes[idx_sngl_wrk_mmu_fit])
bldtype = buildings.get_attribute_by_id("building_type_id", business_location1wrkpl[idx_sngl_wrk_mmu_fit])
is_bldtype_res = in1d(bldtype, restypes)
home_based[in1d(job_array_labels, business_ids[idx_sngl_wrk_mmu_fit][where(is_bldtype_res)])] = True
job_assignment_case[jidx] = 6
processed_bindicator[idx_sngl_wrk_mmu_fit] = True
logger.log_status("6. %s jobs (%s businesses) in 1-2 worker x multiple mixed-type buildings. %s jobs classified as home-based." % (
business_sizes[idx_sngl_wrk_mmu_fit].sum(), idx_sngl_wrk_mmu_fit.size, business_sizes[idx_sngl_wrk_mmu_fit][where(is_bldtype_res)].sum()))
# 7. 1-2 worker business in residential parcel with no building
idx_sngl_wrk_vacant_res = where(logical_and(logical_and(processed_bindicator==0, business_sizes < 3), business_codes == 7))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_vacant_res])
job_assignment_case[jidx] = 7
home_based[jidx] = True
processed_bindicator[idx_sngl_wrk_vacant_res] = True
logger.log_status("7. %s jobs (%s businesses of size 1-2) could not be placed due to non-existing buildings in parcels with residential LU type." % (
business_sizes[idx_sngl_wrk_vacant_res].sum(), idx_sngl_wrk_vacant_res.size))
# 8. 3+ workers of governmental workplaces in 1+ residential building
ind_bussiness_case8 = logical_and(logical_and(processed_bindicator==0, logical_and(business_sizes > 2, in1d(businesses['sector_id'], [18,19]))), in1d(business_codes, [1,2]))
idx_wrk_fit = where(ind_bussiness_case8)[0]
jidx = in1d(job_array_labels, business_ids[idx_wrk_fit])
job_assignment_case[jidx] = 8
processed_bindicator[idx_wrk_fit] = True
logger.log_status("8. %s governmental jobs (%s businesses of size 3+) could not be placed due to residing in residential buildings only." % (
business_sizes[idx_wrk_fit].sum(), idx_wrk_fit.size))
# 9. 3-30 workers in single residential building. Make two of them home based.
idx_sngl_wrk_fit = where(logical_and(logical_and(processed_bindicator==0, logical_and(business_sizes > 2, business_sizes <= 30)), business_codes == 1))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_fit])
job_building_id[jidx] = business_location1wrkpl[idx_sngl_wrk_fit].repeat(business_sizes[idx_sngl_wrk_fit])
bsizeminus2 = vstack((2*ones(idx_sngl_wrk_fit.size), business_sizes[idx_sngl_wrk_fit]-2)).ravel("F").astype("int32") # interweaving 2 and remaining business size
hbidx = tile(array([True, False]), bsizeminus2.size/2).repeat(bsizeminus2) # set the first two jobs of every business to True, others to False
home_based[(where(jidx)[0])[hbidx]] = True
job_assignment_case[jidx] = 9
processed_bindicator[idx_sngl_wrk_fit] = True
logger.log_status("9. %s jobs (%s businesses) in 3-30 worker x single residential building. %s jobs assigned as home-based." % (
business_sizes[idx_sngl_wrk_fit].sum(), idx_sngl_wrk_fit.size, hbidx.sum()))
# 10. 3-30 workers in multiple residential buildings. Make two of them home based.
idx_sngl_wrk_fit = where(logical_and(logical_and(processed_bindicator==0, logical_and(business_sizes > 2, business_sizes <= 30)), business_codes == 2))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrk_fit])
job_assignment_case[jidx] = 10
processed_bindicator[idx_sngl_wrk_fit] = True
# sample buildings to businesses by parcels
bpcls = unique(businesses["parcel_id"][idx_sngl_wrk_fit])
for ipcl in range(bpcls.size):
bidx = where(buildings['parcel_id'] == bpcls[ipcl])[0]
bldgids = buildings['building_id'][bidx]
bussids = intersect1d(business_ids[businesses["parcel_id"] == bpcls[ipcl]], business_ids[idx_sngl_wrk_fit])
# multiply by units for sampling prop. to units rather than buildings
bldgids = bldgids.repeat(maximum(1, buildings['residential_units'][bidx].astype('int32')))
if bldgids.size < bussids.size:
bldarray = bldgids.repeat(1+ceil((bussids.size - bldgids.size)/float(bldgids.size)) )
else:
bldarray = bldgids
shuffle(bldarray) # randomly reorder in-place
for ib in range(bussids.size):
jidx = where(job_array_labels == bussids[ib])[0]
job_building_id[jidx] = bldarray[ib]
home_based[jidx[0:2]] = True
logger.log_status("10. %s jobs (%s businesses) in 3-30 worker x multiple residential building. %s jobs assigned as home-based." % (
business_sizes[idx_sngl_wrk_fit].sum(), idx_sngl_wrk_fit.size, idx_sngl_wrk_fit.size*2))
# 11. single workplace, 3+ workers in single non-res or mixed-use building (11.)
idx_sngl_wrkplace_2plus_workers = where(logical_and(logical_and(logical_and(processed_bindicator==0, business_sizes > 2),
logical_or(business_codes==3, business_codes==5)),
business_nworkplaces==1))[0]
which_labels = where(in1d(job_array_labels, business_ids[idx_sngl_wrkplace_2plus_workers]))[0]
job_building_id[which_labels] = business_location1wrkpl[idx_sngl_wrkplace_2plus_workers].repeat(business_sizes[idx_sngl_wrkplace_2plus_workers])
job_assignment_case[which_labels] = 11
processed_bindicator[idx_sngl_wrkplace_2plus_workers] = True
logger.log_status("11. %s jobs (%s businesses) could be placed due to single workplace x 3+ workers x single non-res/mixed-use building fit." % (
business_sizes[idx_sngl_wrkplace_2plus_workers].sum(), idx_sngl_wrkplace_2plus_workers.size))
# 12. single workplace, 3+ workers in multiple mixed-type building
idx_sngl_wrkplace_2plus_workers = where(logical_and(logical_and(logical_and(processed_bindicator==0, business_sizes > 2),
logical_or(business_codes==4, business_codes==6)),
business_nworkplaces==1))[0]
jidx = in1d(job_array_labels, business_ids[idx_sngl_wrkplace_2plus_workers])
job_building_id[jidx] = business_location1wrkpl[idx_sngl_wrkplace_2plus_workers].repeat(business_sizes[idx_sngl_wrkplace_2plus_workers])
job_assignment_case[jidx] = 12
processed_bindicator[idx_sngl_wrkplace_2plus_workers] = True
logger.log_status("12. %s jobs (%s businesses) could be placed due to single workplace x 3+ workers x multiple non-res/mixed building fit." % (
business_sizes[idx_sngl_wrkplace_2plus_workers].sum(), idx_sngl_wrkplace_2plus_workers.size))
# 13. multiple workplaces, 3+ workers in single non-res or mixed building
idx_mult_wrkplace_2plus_workers = where(logical_and(logical_and(logical_and(processed_bindicator==0, business_sizes > 2),
logical_or(business_codes==3, business_codes==5)),
business_nworkplaces > 1))[0]
jidx = in1d(job_array_labels, business_ids[idx_mult_wrkplace_2plus_workers])
job_building_id[jidx] = business_location1wrkpl[idx_mult_wrkplace_2plus_workers].repeat(business_sizes[idx_mult_wrkplace_2plus_workers])
job_assignment_case[jidx] = 13
processed_bindicator[idx_mult_wrkplace_2plus_workers] = True
logger.log_status("13. %s jobs (%s businesses) could be placed due to multiple workplaces x 3+ workers x single non-res/mixed building fit." % (
business_sizes[idx_mult_wrkplace_2plus_workers].sum(), idx_mult_wrkplace_2plus_workers.size))
# 14. multiple workplaces, 3+ workers in multiple non-res or mixed building
idx_mult_wrkplace_2plus_workers = where(logical_and(logical_and(logical_and(processed_bindicator==0, business_sizes > 2),
logical_or(business_codes==4, business_codes==6)),
business_nworkplaces > 1))[0]
processed_bindicator[idx_mult_wrkplace_2plus_workers] = True
# sample buildings to businesses by parcels
bpcls = unique(businesses["parcel_id"][idx_mult_wrkplace_2plus_workers])
#hbasedsum = home_based.sum()
for ipcl in range(bpcls.size):
bldgids = buildings['building_id'][buildings['parcel_id'] == bpcls[ipcl]]
bussids = intersect1d(business_ids[businesses["parcel_id"] == bpcls[ipcl]], business_ids[idx_mult_wrkplace_2plus_workers])
if bldgids.size < bussids.size:
bldarray = bldgids.repeat(1+ceil((bussids.size - bldgids.size)/float(bldgids.size)))
else:
bldarray = bldgids
shuffle(bldarray) # randomly reorder in-place
is_res = in1d(bldarray, restypes)
for ib in range(bussids.size):
jidx = where(job_array_labels == bussids[ib])
job_building_id[jidx] = bldarray[ib]
#home_based[jidx] = is_res
job_assignment_case[jidx] = 14
logger.log_status("14. %s jobs (%s businesses) could be placed due to multiple workplaces x 3+ workers x multiple non-res/mixed building fit." % (
business_sizes[idx_mult_wrkplace_2plus_workers].sum(), idx_mult_wrkplace_2plus_workers.size))
# 15. 3+ workers in residential parcel with no building
idx_wrk_vacant_res = where(logical_and(logical_and(processed_bindicator==0, business_sizes > 2), business_codes == 7))[0]
jidx = in1d(job_array_labels, business_ids[idx_wrk_vacant_res])
job_assignment_case[jidx] = 15
processed_bindicator[idx_wrk_vacant_res] = True
logger.log_status("15. %s jobs (%s businesses of 3+ workers) could not be placed due to non-existing buildings in parcels with residential LU type." % (
business_sizes[idx_wrk_vacant_res].sum(), idx_wrk_vacant_res.size))
# 16. nonresidential parcel with no building
idx_wrk_vacant_nonres = where(logical_and(processed_bindicator==0, business_codes == 8))[0]
jidx = in1d(job_array_labels, business_ids[idx_wrk_vacant_nonres])
job_assignment_case[jidx] = 16
processed_bindicator[idx_wrk_vacant_nonres] = True
logger.log_status("16. %s jobs (%s businesses) could not be placed due to non-existing buildings in parcels with non-esidential LU type." % (
business_sizes[idx_wrk_vacant_nonres].sum(), idx_wrk_vacant_nonres.size))
# 17. 31+ workers in single residential building. Do not place - will go into ELCM.
idx_wrk_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes > 30), business_codes == 1))[0]
jidx = in1d(job_array_labels, business_ids[idx_wrk_fit])
job_assignment_case[jidx] = 17
processed_bindicator[idx_wrk_fit] = True
logger.log_status("17. %s jobs (%s businesses) in 31+ workers x single residential building." % (
business_sizes[idx_wrk_fit].sum(), idx_wrk_fit.size))
# 18. 31+ workers in multiple residential buildings.
idx_wrk_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes > 30), business_codes == 2))[0]
jidx = in1d(job_array_labels, business_ids[idx_wrk_fit])
job_assignment_case[jidx] = 18
processed_bindicator[idx_wrk_fit] = True
logger.log_status("18. %s jobs (%s businesses) in 31+ workers x multiple residential building." % (
business_sizes[idx_wrk_fit].sum(), idx_wrk_fit.size))
# jobs in messy buildings
idx_messy_fit = where(logical_and(logical_and(processed_bindicator==0, business_sizes > 0), business_codes == 0))[0]
processed_bindicator[idx_messy_fit] = True
logger.log_status("%s jobs (%s businesses) could not be placed due to messy buildings." % (
business_sizes[idx_messy_fit].sum(), idx_messy_fit.size))
# build new buildings for jobs in cases 7, 8, 15 and 16
jidx_no_bld = where(in1d(job_assignment_case, [7,8,15,16]))[0]
bus = unique(job_array_labels[jidx_no_bld])
bsidx = businesses.get_id_index(bus)
# first create buildings for single workplaces per parcel
single_workplace_idx = where(business_nworkplaces[bsidx] == 1)[0]
newbld_parcel_id = businesses['parcel_id'][bsidx][single_workplace_idx]
newbld_bt = sector2building_type(businesses['sector_id'][bsidx][single_workplace_idx])
newbids = arange(buildings.get_id_attribute().max()+1, buildings.get_id_attribute().max()+single_workplace_idx.size+1)
bbldid = zeros(bsidx.size, dtype='int32')
bbldid[single_workplace_idx] = newbids
# for parcels with multiple workplaces select the largest business to determine its building type
mult_bsidx = bsidx[where(business_nworkplaces[bsidx] > 1)[0]]
empty_parcels = businesses['parcel_id'][mult_bsidx]
uempty_parcels = unique(empty_parcels)
bsize_on_empty_pcl = ndmax(business_sizes[mult_bsidx], labels=empty_parcels, index=uempty_parcels)
newbld2_sec = zeros(uempty_parcels.size, dtype='int32')
newbids2 = arange(newbids.max()+1, newbids.max()+uempty_parcels.size+1)
for ipcl in range(uempty_parcels.size):
newbld2_sec[ipcl] = businesses['sector_id'][mult_bsidx][logical_and(businesses['parcel_id'][mult_bsidx] == uempty_parcels[ipcl],
business_sizes[mult_bsidx]==bsize_on_empty_pcl[ipcl])][0]
this_bidx = where(businesses['parcel_id'][bsidx] == uempty_parcels[ipcl])
bbldid[this_bidx] = newbids2[ipcl]
newbld_parcel_id = concatenate((newbld_parcel_id, uempty_parcels))
newbld_bt = concatenate((newbld_bt, sector2building_type(newbld2_sec)))
newbldgs = {'building_id': concatenate((newbids, newbids2)),
'parcel_id': newbld_parcel_id,
'building_type_id': newbld_bt,
}
buildings.add_elements(newbldgs, require_all_attributes=False)
jidx = where(in1d(job_array_labels, business_ids[bsidx]))[0]
job_building_id[jidx] = bbldid.repeat(business_sizes[bsidx])
logger.log_status("Build %s new buildings to accommodate %s jobs (out of which %s are governmental) from cases 7, 15, 16." % (
newbld_parcel_id.size, jidx.size, business_sizes[bsidx][where(in1d(businesses['sector_id'][bsidx], [18,19]))].sum()))
logger.log_status("Assigned %s (%s percent) home-based jobs." % (home_based.sum(), round(home_based.sum()/(home_based.size/100.),2)))
logger.log_status("Finished %s percent (%s) jobs (%s businesses) processed. %s jobs (%s businesses) remain to be processed." % \
(round(business_sizes[processed_bindicator].sum()/(home_based.size/100.),2),
business_sizes[processed_bindicator].sum(), processed_bindicator.sum(),
business_sizes[logical_not(processed_bindicator)].sum(), business_sizes[logical_not(processed_bindicator)].size))
logger.start_block("Storing jobs data.")
# create job dataset
job_data = {"job_id": (arange(job_building_id.size)+1).astype("int32"),
"home_based_status" : home_based,
"building_id": job_building_id,
"business_id": job_array_labels.astype("int32"),
"sector_id": businesses['sector_id'].repeat(business_sizes).astype("int32"),
"parcel_id": businesses['parcel_id'].repeat(business_sizes).astype("int32"),
"assignment_case": job_assignment_case}
# join with zones
if zone_dsname is not None:
zones = dataset_pool.get_dataset(zone_dsname)
idname = zones.get_id_name()[0]
#jpcls = buildings.get_attribute_by_id('parcel_id', job_building_id)
job_data[idname] = parcels.get_attribute_by_id(idname, job_data["parcel_id"])
dictstorage = StorageFactory().get_storage('dict_storage')
dictstorage.write_table(table_name="jobs", table_data=job_data)
jobs = Dataset(in_storage=dictstorage, in_table_name="jobs", dataset_name="job", id_name="job_id")
if out_storage is not None:
jobs.write_dataset(out_storage=out_storage, out_table_name="jobs")
buildings.write_dataset(out_storage=out_storage, attributes=AttributeType.PRIMARY)
logger.end_block()
return jobs
class HouseholdTransitionModel(Model):
"""Creates and removes households from household_set. New households are duplicated from the existing households, keeping
the joint distribution of all characteristics.
"""
model_name = "Household Transition Model"
def __init__(self,
location_id_name="grid_id",
dataset_pool=None,
debuglevel=0):
self.debug = DebugPrinter(debuglevel)
self.location_id_name = location_id_name
self.dataset_pool = self.create_dataset_pool(dataset_pool,
["urbansim", "opus_core"])
def run(self,
year,
household_set,
control_totals,
characteristics,
resources=None):
self._do_initialize_for_run(household_set)
control_totals.get_attribute(
"total_number_of_households") # to make sure they are loaded
self.characteristics = characteristics
self.all_categories = self.characteristics.get_attribute(
"characteristic")
self.all_categories = array(
map(lambda x: x.lower(), self.all_categories))
self.scaled_characteristic_names = get_distinct_names(
self.all_categories).tolist()
self.marginal_characteristic_names = copy(control_totals.get_id_name())
index_year = self.marginal_characteristic_names.index("year")
self.marginal_characteristic_names.remove("year")
idx = where(control_totals.get_attribute("year") == year)[0]
self.control_totals_for_this_year = DatasetSubset(control_totals, idx)
self._do_run_for_this_year(household_set)
return self._update_household_set(household_set)
def _update_household_set(self, household_set):
index_of_duplicated_hhs = household_set.duplicate_rows(
self.mapping_existing_hhs_to_new_hhs)
household_set.modify_attribute(
name=self.location_id_name,
data=-1 * ones(
(index_of_duplicated_hhs.size, ),
dtype=household_set.get_data_type(self.location_id_name)),
index=index_of_duplicated_hhs)
household_set.remove_elements(self.remove_households)
if self.new_households[self.location_id_name].size > 0:
max_id = household_set.get_id_attribute().max()
self.new_households[self.household_id_name] = concatenate(
(self.new_households[self.household_id_name],
arange(
max_id + 1, max_id +
self.new_households[self.location_id_name].size + 1)))
household_set.add_elements(self.new_households,
require_all_attributes=False)
difference = household_set.size() - self.household_size
self.debug.print_debug(
"Difference in number of households: %s"
" (original %s, new %s, created %s, deleted %s)" %
(difference, self.household_size, household_set.size(),
self.new_households[self.household_id_name].size +
self.mapping_existing_hhs_to_new_hhs.size,
self.remove_households.size), 3)
if self.location_id_name in household_set.get_attribute_names():
self.debug.print_debug(
"Number of unplaced households: %s" %
where(household_set.get_attribute(self.location_id_name) <= 0)
[0].size, 3)
return difference
def _do_initialize_for_run(self, household_set):
self.household_id_name = household_set.get_id_name()[0]
self.new_households = {
self.location_id_name:
array([],
dtype=household_set.get_data_type(self.location_id_name,
int32)),
self.household_id_name:
array([],
dtype=household_set.get_data_type(self.household_id_name,
int32))
}
self.remove_households = array([], dtype='int32')
self.household_size = household_set.size()
self.max_id = household_set.get_id_attribute().max()
self.arrays_from_categories = {}
self.arrays_from_categories_mapping = {}
self.mapping_existing_hhs_to_new_hhs = array(
[],
dtype=household_set.get_data_type(self.household_id_name, int32))
def _do_run_for_this_year(self, household_set):
self.household_set = household_set
groups = self.control_totals_for_this_year.get_id_attribute()
self.create_arrays_from_categories(self.household_set)
all_characteristics = self.arrays_from_categories.keys()
self.household_set.load_dataset_if_not_loaded(
attributes=all_characteristics
) # prevents from lazy loading to save runtime
idx_shape = []
number_of_combinations = 1
num_attributes = len(all_characteristics)
for iattr in range(num_attributes):
attr = all_characteristics[iattr]
max_bins = self.arrays_from_categories[attr].max() + 1
idx_shape.append(max_bins)
number_of_combinations = number_of_combinations * max_bins
if attr not in self.new_households.keys():
self.new_households[attr] = array(
[], dtype=self.household_set.get_data_type(attr, float32))
self.number_of_combinations = int(number_of_combinations)
idx_tmp = indices(tuple(idx_shape))
categories_index = zeros((self.number_of_combinations, num_attributes))
for i in range(num_attributes): #create indices of all combinations
categories_index[:, i] = idx_tmp[i].ravel()
categories_index_mapping = {}
for i in range(self.number_of_combinations):
categories_index_mapping[tuple(categories_index[i, ].tolist())] = i
def get_category(values):
bins = map(lambda x, y: self.arrays_from_categories[x][int(y)],
all_characteristics, values)
try:
return categories_index_mapping[tuple(bins)]
except KeyError, msg:
where_error = where(array(bins) == -1)[0]
if where_error.size > 0:
raise KeyError, \
"Invalid value of %s for attribute %s. It is not included in the characteristics groups." % (
array(values)[where_error],
array(all_characteristics)[where_error])
raise KeyError, msg
if num_attributes > 0:
# the next array must be a copy of the household values, otherwise, it changes the original values
values_array = reshape(
array(self.household_set.get_attribute(
all_characteristics[0])), (self.household_set.size(), 1))
if num_attributes > 1:
for attr in all_characteristics[1:]:
values_array = concatenate(
(values_array,
reshape(array(self.household_set.get_attribute(attr)),
(self.household_set.size(), 1))),
axis=1)
for i in range(values_array.shape[1]):
if values_array[:, i].max() > 10000:
values_array[:, i] = values_array[:, i] / 10
values_array[:, i] = clip(
values_array[:, i], 0,
self.arrays_from_categories[all_characteristics[i]].size -
1)
# determine for each household to what category it belongs to
self.household_categories = array(
map(lambda x: get_category(x),
values_array)) # performance bottleneck
number_of_households_in_categories = array(
ndimage_sum(ones((self.household_categories.size, )),
labels=self.household_categories + 1,
index=arange(self.number_of_combinations) + 1))
else:
# no marginal characteristics; consider just one group
self.household_categories = zeros(self.household_set.size(),
dtype='int32')
number_of_households_in_categories = array(
[self.household_set.size()])
g = arange(num_attributes)
#iterate over marginal characteristics
for group in groups:
if groups.ndim <= 1: # there is only one group (no marginal char.)
id = group
else:
id = tuple(group.tolist())
group_element = self.control_totals_for_this_year.get_data_element_by_id(
id)
total = group_element.total_number_of_households
for i in range(g.size):
g[i] = eval("group_element." +
self.arrays_from_categories.keys()[i])
if g.size <= 0:
l = ones((number_of_households_in_categories.size, ))
else:
l = categories_index[:, 0] == g[0]
for i in range(1, num_attributes):
l = logical_and(l, categories_index[:, i] == g[i])
# l has 1's for combinations of this group
number_in_group = array(
ndimage_sum(number_of_households_in_categories,
labels=l,
index=1))
diff = int(total - number_in_group)
if diff < 0: # households to be removed
is_in_group = l[self.household_categories]
w = where(is_in_group)[0]
sample_array, non_placed, size_non_placed = \
get_array_without_non_placed_agents(self.household_set, w, -1*diff,
self.location_id_name)
self.remove_households = concatenate(
(self.remove_households, non_placed,
sample_noreplace(sample_array,
max(0,
abs(diff) - size_non_placed))))
if diff > 0: # households to be created
self._create_households(diff, l)
class EstablishmentReappearanceModel(TransitionModel):
"""
"""
model_name = "Establishment Reappearance Model"
model_short_name = "ERM"
def run(self,
year=None,
target_attribute_name='number_of_jobs',
sampling_filter="establishment.disappeared == 1",
reset_dataset_attribute_value={'disappeared':0, 'building_id':-1},
dataset_pool=None,
**kwargs
):
"""
"""
id_name = 'control_total_id'
ct_known_attributes = self.control_totals_all.get_primary_attribute_names()
if target_attribute_name not in ct_known_attributes:
raise AttributeError, "Target attribute %s must be an attribute of control_total dataset" % target_attribute_name
if id_name not in ct_known_attributes:
self.control_totals_all.add_attribute(name=id_name,
data = np.arange(1, self.control_totals_all.size()+1)
)
if self.control_totals_all.get_id_name() != [id_name]:
self.control_totals_all._id_names = [id_name]
if year is None:
year = SimulationState().get_current_time()
this_year_index = np.where(self.control_totals_all['year']==year)[0]
self.control_totals = DatasetSubset(self.control_totals_all, this_year_index)
if dataset_pool is None:
try:
dataset_pool = SessionConfiguration().get_dataset_pool()
except AttributeError:
dataset_pool = DatasetPool(datasets_dict={
self.dataset.dataset_name:self.dataset,
#sync_dataset.dataset_name:sync_dataset,
'control_total': self.control_totals
})
column_names = list( set( ct_known_attributes ) \
- set( [ target_attribute_name,
'year',
'_hidden_id_',
id_name,
'_actual_',
] )
)
column_names.sort(reverse=True)
#column_values = dict([ (name, self.control_totals.get_attribute(name))
# for name in column_names + [target_attribute_name]])
self._code_control_total_id(column_names,
dataset_pool=dataset_pool)
target = self.control_totals[target_attribute_name]
if self.dataset_accounting_attribute is None:
self.dataset_accounting_attribute = '_one_'
self.dataset.add_attribute(name = self.dataset_accounting_attribute,
data = ones(self.dataset.size(),
dtype=target.dtype))
exp_actual = '_actual_ = control_total.aggregate(%s.%s)' % \
(self.dataset.dataset_name,
self.dataset_accounting_attribute)
actual = self.control_totals_all.compute_variables(exp_actual,
dataset_pool=dataset_pool)[this_year_index]
actual = actual.astype(target.dtype)
dataset_known_attributes = self.dataset.get_known_attribute_names() #update after compute
#update control_total_id after removing disappeared
column_names_new = list(set(column_names) - set(["disappeared"]))
#self.control_totals_all.touch_attribute(target_attribute_name)
self._code_control_total_id(column_names_new,
dataset_pool=dataset_pool)
if sampling_filter:
short_name = VariableName(sampling_filter).get_alias()
if short_name not in dataset_known_attributes:
filter_indicator = self.dataset.compute_variables(sampling_filter, dataset_pool=dataset_pool)
else:
filter_indicator = self.dataset[short_name]
else:
filter_indicator = 1
to_reappear = np.array([], dtype=np.int32)
#log header
if PrettyTable is not None:
status_log = PrettyTable()
status_log.set_field_names(column_names + ["actual", "target", "difference", "action", "N", "note"])
else:
logger.log_status("\t".join(column_names + ["actual", "target", "difference", "action", "N", "note"]))
error_log = ''
error_num = 1
def log_status():
##log status
action = "0"
N = "0"
if lucky_index is not None:
if actual_num < target_num:
action = "+" + str(action_num)
N = "+" + str(lucky_index.size)
if actual_num > target_num:
action = "-" + str(action_num)
N = "-" + str(lucky_index.size)
cat = [ str(self.control_totals[col][index]) for col in column_names]
cat += [str(actual_num), str(target_num), str(diff), action, N, error_str]
if PrettyTable is not None:
status_log.add_row(cat)
else:
logger.log_status("\t".join(cat))
for index, control_total_id in enumerate(self.control_totals.get_id_attribute()):
target_num = target[index]
actual_num = actual[index]
action_num = 0
n_num = 0
diff = target_num - actual_num
accounting = self.dataset[self.dataset_accounting_attribute]
lucky_index = None
error_str = ''
if actual_num < target_num:
indicator = self.dataset[id_name]==control_total_id
n_indicator = indicator.sum()
# do sampling from legitimate records
legit_index = np.where(np.logical_and(indicator, filter_indicator))[0]
legit_size = sum(accounting[legit_index])
if legit_size > diff:
##there are more establishments that are marked as 'disappeared' than the gap between target and actual
##sample required
mean_size = float(legit_size) / n_indicator if n_indicator != 0 else 1
n = int(np.ceil(diff / mean_size))
i = 0
while diff > 0 and action_num < diff:
if n > 1: # adjust number of records to sample in each iteration
n = int( np.ceil((diff - action_num) / (mean_size * STEP_SIZE**i)) )
sampleable_index = legit_index[np.logical_not(np.in1d(legit_index, to_reappear))]
if n < sampleable_index.size:
lucky_index = sample_noreplace(sampleable_index, n)
else:
lucky_index = sampleable_index
temp_num = accounting[lucky_index].sum()
if action_num + temp_num <= diff:
## accept the last batch of samples only when it does not overshoot
to_reappear = np.concatenate((to_reappear, lucky_index))
action_num += temp_num
else:
## already overshoot, reject the last batch and reduce the number of samples
i += 1
if i > MAX_ITERATIONS:
## we're in trouble
error_str = str(error_num)
error_log += "%s. We exhausted %s iterations and could not find samples to match target %s exactly.\n" % \
(error_num, MAX_ITERATIONS, target_num)
error_num += 1
break
elif 0 < legit_size <= diff:
# let all re-appear
lucky_index = legit_index
to_reappear = np.concatenate((to_reappear, lucky_index))
action_num += legit_size
else:
error_str = str(error_num)
error_log += "%s. There is no suitable %s to sample from.\n" % (error_num, self.dataset.get_dataset_name())
#+ \ ','.join([col+"="+str(self.control_totals[col][index]) for col in column_names]) + '\n'
error_num += 1
log_status()
if PrettyTable is not None:
logger.log_status("\n" + status_log.get_string() + '\n')
if error_log:
logger.log_error( '\n' + error_log)
## TODO: this sequence of add_elements first and then remove_elements works only when
## add_elements method appends data to the end of dataset and doesn't change the
## indices of existing elements.
if to_reappear.size > 0:
self._reset_attribute(self.dataset,
reset_attribute_dict = reset_dataset_attribute_value,
index=to_reappear)
return self.dataset
def _do_run(self,
location_set,
agent_set,
agents_index,
data_objects=None,
resources=None):
location_id_name = location_set.get_id_name()[0]
jobsubset = DatasetSubset(agent_set, agents_index)
if jobsubset.size() <= 0:
return array([], dtype='int32')
#unplace jobs
agent_set.set_values_of_one_attribute(
location_id_name, resize(array([-1.0]), jobsubset.size()),
agents_index)
sector_ids = jobsubset.get_attribute("sector_id")
sectors = unique(sector_ids)
counts = ndimage_sum(ones((jobsubset.size(), )),
labels=sector_ids.astype('int32'),
index=sectors.astype('int32'))
if sectors.size <= 1:
counts = array([counts])
variables = map(lambda x: "number_of_jobs_of_sector_" + str(int(x)),
sectors)
compute_variables = map(
lambda var: self.variable_package + "." + location_set.
get_dataset_name() + "." + var, variables)
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
self.dataset_pool.add_datasets_if_not_included(
{agent_set.get_dataset_name(): agent_set})
location_set.compute_variables(compute_variables,
dataset_pool=self.dataset_pool)
if self.filter is None:
location_index = arange(location_set.size())
else:
filter_values = location_set.compute_variables(
[self.filter], dataset_pool=self.dataset_pool)
location_index = where(filter_values > 0)[0]
if location_index.size <= 0:
logger.log_status("No locations available. Nothing to be done.")
return array([])
location_subset = DatasetSubset(location_set, location_index)
i = 0
for sector in sectors:
distr = location_subset.get_attribute(variables[i])
if ma.allclose(distr.sum(), 0):
uniform_prob = 1.0 / distr.size
distr = resize(array([uniform_prob], dtype='float64'),
distr.size)
logger.log_warning(
"Probabilities in scaling model for sector " +
str(sector) +
" sum to 0.0. Substituting uniform distribution!")
# random_sample = sample(location_set.get_attribute("grid_id"), k=int(counts[i]), \
# probabilities = distr)
distr = distr / float(distr.sum())
random_sample = probsample_replace(
location_subset.get_id_attribute(),
size=int(counts[i]),
prob_array=distr)
idx = where(sector_ids == sector)[0]
#modify job locations
agent_set.set_values_of_one_attribute(location_id_name,
random_sample,
agents_index[idx])
i += 1
return agent_set.get_attribute_by_index(location_id_name, agents_index)
