def define_submarket(choice_set, submarket_id_expression, compute_variables=[], filter=None):
submarket_ids = choice_set.compute_variables("submarket_id=" + submarket_id_expression)
unique_submarket_ids = unique(submarket_ids)
storage = StorageFactory().get_storage("dict_storage")
storage.write_table(table_name="submarkets", table_data={"submarket_id": unique_submarket_ids})
submarkets = Dataset(
in_storage=storage, in_table_name="submarkets", id_name="submarket_id", dataset_name="submarket"
)
if len(compute_variables):
submarkets.compute_variables(compute_variables)
if filter is not None:
from numpy import logical_not
submarkets.remove_elements(index=where(logical_not(submarkets.compute_variables(filter)))[0])
# submarkets = DatasetSubset(submarkets, index=where(submarkets.compute_variables(filter))[0])
return submarkets
def prepare_for_estimate(self,
add_member_prefix=True,
specification_dict=None,
specification_storage=None,
specification_table=None,
building_set=None,
buildings_for_estimation_storage=None,
buildings_for_estimation_table=None,
constants=None,
base_year=0,
building_categories=None,
location_id_variable=None,
join_datasets=False,
data_objects=None,
**kwargs):
# buildings = None
if (building_set is not None):
if location_id_variable is not None:
building_set.compute_variables(
location_id_variable, resources=Resources(data_objects))
# create agents for estimation
if buildings_for_estimation_storage is not None:
estimation_set = Dataset(
in_storage=buildings_for_estimation_storage,
in_table_name=buildings_for_estimation_table,
id_name=building_set.get_id_name(),
dataset_name=building_set.get_dataset_name())
if location_id_variable:
estimation_set.compute_variables(
location_id_variable, resources=Resources(data_objects))
# needs to be a primary attribute because of the join method below
estimation_set.add_primary_attribute(
estimation_set.get_attribute(location_id_variable),
VariableName(location_id_variable).alias())
years = estimation_set.get_attribute("scheduled_year")
recent_years = constants['recent_years']
indicator = zeros(estimation_set.size())
for year in range(base_year - recent_years, base_year + 1):
indicator = logical_or(indicator, years == year)
idx = where(logical_not(indicator))[0]
estimation_set.remove_elements(idx)
#if filter:
#estimation_set.compute_variables(filter, resources=Resources(data_objects))
#index = where(estimation_set.get_attribute(filter) > 0)[0]
#estimation_set.subset_by_index(index, flush_attributes_if_not_loaded=False)
if join_datasets:
building_set.join_by_rows(estimation_set,
require_all_attributes=False,
change_ids_if_not_unique=True)
index = arange(building_set.size() - estimation_set.size(),
agent_set.size())
else:
index = building_set.get_id_index(
estimation_set.get_id_attribute())
else:
if building_set is not None:
index = arange(building_set.size())
else:
index = None
if add_member_prefix:
specification_table = self.group_member.add_member_prefix_to_table_names(
[specification_table])
from opus_core.model import get_specification_for_estimation
#from urbansim.functions import compute_supply_and_add_to_location_set
specification = get_specification_for_estimation(
specification_dict, specification_storage, specification_table)
#specification, dummy = AgentLocationChoiceModelMember.prepare_for_estimate(self, add_member_prefix,
#specification_dict, specification_storage,
#specification_table,
#location_id_variable=location_id_variable,
#data_objects=data_objects, **kwargs)
return (specification, index)
def prepare_for_estimate(
self,
add_member_prefix=True,
specification_dict=None,
specification_storage=None,
specification_table=None,
building_set=None,
buildings_for_estimation_storage=None,
buildings_for_estimation_table=None,
constants=None,
base_year=0,
building_categories=None,
location_id_variable=None,
join_datasets=False,
data_objects=None,
**kwargs
):
# buildings = None
if building_set is not None:
if location_id_variable is not None:
building_set.compute_variables(location_id_variable, resources=Resources(data_objects))
# create agents for estimation
if buildings_for_estimation_storage is not None:
estimation_set = Dataset(
in_storage=buildings_for_estimation_storage,
in_table_name=buildings_for_estimation_table,
id_name=building_set.get_id_name(),
dataset_name=building_set.get_dataset_name(),
)
if location_id_variable:
estimation_set.compute_variables(location_id_variable, resources=Resources(data_objects))
# needs to be a primary attribute because of the join method below
estimation_set.add_primary_attribute(
estimation_set.get_attribute(location_id_variable), VariableName(location_id_variable).alias()
)
years = estimation_set.get_attribute("scheduled_year")
recent_years = constants["recent_years"]
indicator = zeros(estimation_set.size(), dtype="int32")
for year in range(base_year - recent_years, base_year + 1):
indicator = logical_or(indicator, years == year)
idx = where(logical_not(indicator))[0]
estimation_set.remove_elements(idx)
# if filter:
# estimation_set.compute_variables(filter, resources=Resources(data_objects))
# index = where(estimation_set.get_attribute(filter) > 0)[0]
# estimation_set.subset_by_index(index, flush_attributes_if_not_loaded=False)
if join_datasets:
building_set.join_by_rows(estimation_set, require_all_attributes=False, change_ids_if_not_unique=True)
index = arange(building_set.size() - estimation_set.size(), building_set.size())
else:
index = building_set.get_id_index(estimation_set.get_id_attribute())
else:
if building_set is not None:
index = arange(building_set.size())
else:
index = None
if add_member_prefix:
specification_table = self.group_member.add_member_prefix_to_table_names([specification_table])
from opus_core.model import get_specification_for_estimation
# from urbansim.functions import compute_supply_and_add_to_location_set
specification = get_specification_for_estimation(specification_dict, specification_storage, specification_table)
# specification, dummy = AgentLocationChoiceModelMember.prepare_for_estimate(self, add_member_prefix,
# specification_dict, specification_storage,
# specification_table,
# location_id_variable=location_id_variable,
# data_objects=data_objects, **kwargs)
return (specification, index)
