def create_scaled_array(characteristics, agent_set):
scaled_array={}
if "characteristic_opus" in characteristics.get_known_attribute_names():
all_categories = characteristics.get_attribute("characteristic_opus") # to be changed to 'characteristic'
else:
all_categories = characteristics.get_attribute("characteristic")
all_categories = array(all_categories.amap(lambda x: x.lower()))
categories = get_distinct_names(all_categories)
mins = characteristics.get_attribute("min")
maxs = characteristics.get_attribute("max")
for cat in categories:
idx = where(all_categories == cat)[0]
scaled_array[cat+"_index_mapping"] = {}
scaled_array[cat+"_index"] = {}
j=0
for i in idx:
this_min = mins[i]
this_max = maxs[i]
is_in_cat = reshape(array(agent_set.get_attribute(cat) >= this_min), shape=(1,agent_set.size()))
if not scaled_array.has_key(cat):
scaled_array[cat] = is_in_cat
else:
scaled_array[cat] = concatenate((scaled_array[cat], is_in_cat))
if this_max >= 0: # there is a maximum
scaled_array[cat][j,:] = logical_and(scaled_array[cat][j,:], array(agent_set.get_attribute(cat) <= this_max))
scaled_array[cat+"_index_mapping"][(this_min, this_max)] = j
scaled_array[cat+"_index"][j] = (this_min, this_max)
j+=1
return (scaled_array, categories)
def create_scaled_array(characteristics, agent_set):
scaled_array = {}
if "characteristic_opus" in characteristics.get_known_attribute_names():
all_categories = characteristics.get_attribute(
"characteristic_opus") # to be changed to 'characteristic'
else:
all_categories = characteristics.get_attribute("characteristic")
all_categories = array(all_categories.amap(lambda x: x.lower()))
categories = get_distinct_names(all_categories)
mins = characteristics.get_attribute("min")
maxs = characteristics.get_attribute("max")
for cat in categories:
idx = where(all_categories == cat)[0]
scaled_array[cat + "_index_mapping"] = {}
scaled_array[cat + "_index"] = {}
j = 0
for i in idx:
this_min = mins[i]
this_max = maxs[i]
is_in_cat = reshape(
array(agent_set.get_attribute(cat) >= this_min),
shape=(1, agent_set.size()))
if not scaled_array.has_key(cat):
scaled_array[cat] = is_in_cat
else:
scaled_array[cat] = concatenate((scaled_array[cat], is_in_cat))
if this_max >= 0: # there is a maximum
scaled_array[cat][j, :] = logical_and(
scaled_array[cat][j, :],
array(agent_set.get_attribute(cat) <= this_max))
scaled_array[cat + "_index_mapping"][(this_min, this_max)] = j
scaled_array[cat + "_index"][j] = (this_min, this_max)
j += 1
return (scaled_array, categories)
def run(self, year, household_set, control_totals, characteristics, resources=None):
# self.person_set = person_set
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")
self.marginal_characteristic_names.remove(self.subarea_id_name)
region_ids = control_totals.get_attribute(self.subarea_id_name)
households_region_ids = household_set.compute_one_variable_with_unknown_package(variable_name="%s" % (self.subarea_id_name), dataset_pool=self.dataset_pool)
unique_regions = unique(region_ids)
is_year = control_totals.get_attribute("year")==year
all_households_index = arange(household_set.size())
for area in unique_regions:
idx = where(logical_and(is_year, region_ids == area))[0]
self.control_totals_for_this_year = DatasetSubset(control_totals, idx)
households_index = where(households_region_ids == area)[0]
if households_index.size == 0:
continue
households_for_this_area = DatasetSubset(household_set, households_index)
logger.log_status("HTM for area %s (currently %s households)" % (area, households_for_this_area.size()))
last_remove_idx = self.remove_households.size
last_new_hhs_idx = self.mapping_existing_hhs_to_new_hhs.size
self._do_run_for_this_year(households_for_this_area)
add_hhs_size = self.new_households[self.location_id_name].size-self.new_households[self.subarea_id_name].size+self.mapping_existing_hhs_to_new_hhs.size-last_new_hhs_idx
remove_hhs_size = self.remove_households.size-last_remove_idx
logger.log_status("add %s, remove %s, total %s" % (add_hhs_size, remove_hhs_size,
households_for_this_area.size()+add_hhs_size-remove_hhs_size
))
self.new_households[self.subarea_id_name] = concatenate((self.new_households[self.subarea_id_name],
array((self.new_households[self.location_id_name].size-self.new_households[self.subarea_id_name].size)*[area], dtype="int32")))
# transform indices of removing households into indices of the whole dataset
self.remove_households[last_remove_idx:self.remove_households.size] = all_households_index[households_index[self.remove_households[last_remove_idx:self.remove_households.size]]]
# do the same for households to be duplicated
self.mapping_existing_hhs_to_new_hhs[last_new_hhs_idx:self.mapping_existing_hhs_to_new_hhs.size] = all_households_index[households_index[self.mapping_existing_hhs_to_new_hhs[last_new_hhs_idx:self.mapping_existing_hhs_to_new_hhs.size]]]
self._update_household_set(household_set)
idx_new_households = arange(household_set.size()-self.new_households[self.subarea_id_name].size, household_set.size())
#household_region_ids = household_set.compute_variables("urbansim_parcel.household.%s" % self.subarea_id_name)
#household_region_ids[idx_new_households] = self.new_households[self.subarea_id_name]
region_ids = household_set.get_attribute(self.subarea_id_name).copy()
household_set.delete_one_attribute(self.subarea_id_name)
household_set.add_attribute(region_ids, self.subarea_id_name, metadata=AttributeType.PRIMARY)
# return an index of new households
return idx_new_households
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 run(self, year, household_set, control_totals, characteristics, resources=None):
# self.person_set = person_set
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")
self.marginal_characteristic_names.remove(self.subarea_id_name)
region_ids = control_totals.get_attribute(self.subarea_id_name)
households_region_ids = household_set.compute_one_variable_with_unknown_package(variable_name="%s" % (self.subarea_id_name), dataset_pool=self.dataset_pool)
unique_regions = unique(region_ids)
is_year = control_totals.get_attribute("year")==year
all_households_index = arange(household_set.size())
for area in unique_regions:
idx = where(logical_and(is_year, region_ids == area))[0]
self.control_totals_for_this_year = DatasetSubset(control_totals, idx)
households_index = where(households_region_ids == area)[0]
if households_index.size == 0:
continue
households_for_this_area = DatasetSubset(household_set, households_index)
logger.log_status("HTM for area %s (currently %s households)" % (area, households_for_this_area.size()))
last_remove_idx = self.remove_households.size
last_new_hhs_idx = self.mapping_existing_hhs_to_new_hhs.size
self._do_run_for_this_year(households_for_this_area)
add_hhs_size = self.new_households[self.location_id_name].size-self.new_households[self.subarea_id_name].size+self.mapping_existing_hhs_to_new_hhs.size-last_new_hhs_idx
remove_hhs_size = self.remove_households.size-last_remove_idx
logger.log_status("add %s, remove %s, total %s" % (add_hhs_size, remove_hhs_size,
households_for_this_area.size()+add_hhs_size-remove_hhs_size
))
self.new_households[self.subarea_id_name] = concatenate((self.new_households[self.subarea_id_name],
array((self.new_households[self.location_id_name].size-self.new_households[self.subarea_id_name].size)*[area], dtype="int32")))
# transform indices of removing households into indices of the whole dataset
self.remove_households[last_remove_idx:self.remove_households.size] = all_households_index[households_index[self.remove_households[last_remove_idx:self.remove_households.size]]]
# do the same for households to be duplicated
self.mapping_existing_hhs_to_new_hhs[last_new_hhs_idx:self.mapping_existing_hhs_to_new_hhs.size] = all_households_index[households_index[self.mapping_existing_hhs_to_new_hhs[last_new_hhs_idx:self.mapping_existing_hhs_to_new_hhs.size]]]
self._update_household_set(household_set)
idx_new_households = arange(household_set.size()-self.new_households[self.subarea_id_name].size, household_set.size())
#household_region_ids = household_set.compute_variables("urbansim_parcel.household.%s" % self.subarea_id_name)
#household_region_ids[idx_new_households] = self.new_households[self.subarea_id_name]
region_ids = household_set.get_attribute(self.subarea_id_name).copy()
household_set.delete_one_attribute(self.subarea_id_name)
household_set.add_attribute(region_ids, self.subarea_id_name, metadata=AttributeType.PRIMARY)
# return an index of new households
return idx_new_households
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 get_distinct_names(self):
return get_distinct_names(self.get_names())
def get_distinct_names(self):
return get_distinct_names(self.get_names())
