def __init__(self):
config = Baseline()
config_changes = {
'description':
'Run REPM and HLCM with bidding choice only',
'models': [
"real_estate_price_model",
"household_relocation_model",
"household_location_choice_model_with_price_adj",
],
# 'models_in_year': {2000:
# [ "household_relocation_model_for_2000",
# "household_location_choice_model_for_2000",]
# },
'years': (2001, 2001),
}
config.replace(config_changes)
config["models_configuration"][
"household_location_choice_model_with_price_adj"] = {}
config["models_configuration"]["household_location_choice_model_with_price_adj"]["controller"] = \
HouseholdLocationChoiceModelWithPriceAdjConfigurationCreator(
location_set = "building",
sampler = "opus_core.samplers.weighted_sampler",
sample_size_locations = 50,
input_index = 'hrm_index',
# filter = 'building.unit_price',
demand_string="demand",
capacity_string = "urbansim_parcel.building.vacant_residential_units",
nchunks=1,
lottery_max_iterations=20
).execute()
# config['models_configuration']['household_location_choice_model_with_price_adj']['controller']['run']['arguments']['chunk_specification'] = "{'nchunks':1}"
self.merge(config)
def __init__(self):
config = Baseline()
config_changes = {
"description": "Run REPM and HLCM with bidding choice only",
"models": [
"real_estate_price_model",
"household_relocation_model",
"household_location_choice_model_with_price_adj",
],
# 'models_in_year': {2000:
# [ "household_relocation_model_for_2000",
# "household_location_choice_model_for_2000",]
# },
"years": (2001, 2001),
}
config.replace(config_changes)
config["models_configuration"]["household_location_choice_model_with_price_adj"] = {}
config["models_configuration"]["household_location_choice_model_with_price_adj"][
"controller"
] = HouseholdLocationChoiceModelWithPriceAdjConfigurationCreator(
location_set="building",
sampler="opus_core.samplers.weighted_sampler",
sample_size_locations=50,
input_index="hrm_index",
# filter = 'building.unit_price',
demand_string="demand",
capacity_string="urbansim_parcel.building.vacant_residential_units",
nchunks=1,
lottery_max_iterations=20,
).execute()
# config['models_configuration']['household_location_choice_model_with_price_adj']['controller']['run']['arguments']['chunk_specification'] = "{'nchunks':1}"
self.merge(config)
def __init__(self):
config = Baseline()
config_changes = {
'description':'assigning jobs to workers',
'models_in_year':{2001:[ "work_at_home_choice_model",],
2002:[ "workplace_choice_model_for_resident" ] },
'years': (2001,2002),
'datasets_to_cache_after_each_model':['person'],
}
config.replace(config_changes)
self.merge(config)
self['models_configuration']['work_at_home_choice_model']['controller']['run']['arguments']['run_choice_model'] = False
self['models_configuration']['work_at_home_choice_model']['controller']['run']['arguments']['choose_job_only_in_residence_zone'] = False
def __init__(self):
config = Baseline()
config_changes = {
'description':
'assigning jobs to workers',
'models': [
#"process_pipeline_events",
"real_estate_price_model",
"expected_sale_price_model",
"development_proposal_choice_model",
"building_construction_model",
"household_transition_model",
"employment_transition_model",
"household_relocation_model",
"household_location_choice_model",
"employment_relocation_model",
{
"employment_location_choice_model": {
'group_members': '_all_'
}
},
"work_at_home_choice_model",
"workplace_choice_model_for_resident",
'distribute_unplaced_jobs_model'
],
# {2001:[ "work_at_home_choice_model",],
# 2002:[ "workplace_choice_model_for_resident" ] },
'years': (2001, 2006),
# 'datasets_to_cache_after_each_model':['person'],
}
from psrc.configs.create_travel_model_configuration import create_travel_model_configuration
travel_model_configuration = create_travel_model_configuration(
'baseline_travel_model_psrc_2008_lmwang',
emme2_batch_file='MODEL1-0.BAT',
mode='full',
years_to_run={
2005: '2006_v1.0aTG',
2010: '2010_v1.0aTG',
2015: '2010_v1.0aTG_2015',
2020: '2020_v1.0aTG'
})
config['travel_model_configuration'] = travel_model_configuration
config.replace(config_changes)
self.merge(config)
def __init__(self):
config = Baseline()
config_changes = {
'description':'assigning jobs to workers',
'models_in_year':{2001:[ "work_at_home_choice_model",],
2002:[ "workplace_choice_model_for_resident" ] },
'years': (2001,2002),
'datasets_to_cache_after_each_model':['person'],
}
config.replace(config_changes)
self.merge(config)
self['models_configuration']['work_at_home_choice_model']['controller']['run']['arguments']['run_choice_model'] = True
self['models_configuration']['work_at_home_choice_model']['controller']['run']['arguments']['choose_job_only_in_residence_zone'] = True
self['models_configuration']['work_at_home_choice_model']['controller']['init']['arguments']['filter'] = "'job.building_type==2'"
self['models_configuration']['work_at_home_choice_model']['controller']['prepare_for_run']['arguments']['agents_filter'] = "'urbansim_parcel.person.is_worker'"
self['models_configuration']['workplace_choice_model_for_resident']['controller']['init']['arguments']["run_config"].merge({"capacity_string":"job.building_type==1"})
self['models_configuration']['workplace_choice_model_for_resident']['controller']['init']['arguments']["filter"] = "'job.building_type==1'"
self['models_configuration']['workplace_choice_model_for_resident']['controller']['run']['arguments']["agents_filter"] = "'urbansim_parcel.person.is_non_home_based_worker'"
self['models_configuration']['workplace_choice_model_for_resident']['controller']['run']['arguments']["chunk_specification"] = "{'records_per_chunk':20000}"
def __init__(self):
config = Baseline()
config_changes = {
'description': 'assigning jobs to workers',
'models_in_year': {
2001: [
"work_at_home_choice_model",
],
2002: ["workplace_choice_model_for_resident"]
},
'years': (2001, 2002),
'datasets_to_cache_after_each_model': ['person'],
}
config.replace(config_changes)
self.merge(config)
self['models_configuration']['work_at_home_choice_model'][
'controller']['run']['arguments']['run_choice_model'] = False
self['models_configuration']['work_at_home_choice_model'][
'controller']['run']['arguments'][
'choose_job_only_in_residence_zone'] = False
def __init__(self):
config = Baseline()
config_changes = {
'description':'assigning jobs to workers',
'models':[
#"process_pipeline_events",
"real_estate_price_model",
"expected_sale_price_model",
"development_proposal_choice_model",
"building_construction_model",
"household_transition_model",
"employment_transition_model",
"household_relocation_model",
"household_location_choice_model",
"employment_relocation_model",
{"employment_location_choice_model":{'group_members': '_all_'}},
"work_at_home_choice_model",
"workplace_choice_model_for_resident",
'distribute_unplaced_jobs_model'
],
# {2001:[ "work_at_home_choice_model",],
# 2002:[ "workplace_choice_model_for_resident" ] },
'years': (2001,2006),
# 'datasets_to_cache_after_each_model':['person'],
}
from psrc.configs.create_travel_model_configuration import create_travel_model_configuration
travel_model_configuration = create_travel_model_configuration('baseline_travel_model_psrc_2008_lmwang',
emme2_batch_file='MODEL1-0.BAT',
mode='full', years_to_run={2005: '2006_v1.0aTG',
2010: '2010_v1.0aTG',
2015: '2010_v1.0aTG_2015',
2020: '2020_v1.0aTG'})
config['travel_model_configuration'] = travel_model_configuration
config.replace(config_changes)
self.merge(config)
'person_id': arange(len(hh_ids)) + 1,
'household_id': array(hh_ids),
'member_id': array(member_ids),
'is_worker': array(is_worker),
'job_id': array(job_ids),
},
)
persons = PersonDataset(in_storage=in_storage,
in_table_name=persons_table_name)
persons.write_dataset(out_storage=out_storage,
out_table_name=persons_table_name)
if __name__ == '__main__':
config = Baseline()
config.replace(
{
'scenario_database_configuration':
ScenarioDatabaseConfiguration(
database_name='psrc_2005_parcel_baseyear', ),
'estimation_database_configuration':
EstimationDatabaseConfiguration(
database_name='psrc_2005_parcel_baseyear_change_20080219', ),
'dataset_pool_configuration':
DatasetPoolConfiguration(
package_order=['urbansim_parcel', 'urbansim', 'opus_core'], ),
'low_memory_mode':
False,
'cache_directory':
'/urbansim_cache/psrc_parcel/persons',
in_storage.write_table(
table_name=persons_table_name,
table_data={
'person_id':arange(len(hh_ids))+1,
'household_id':array(hh_ids),
'member_id':array(member_ids),
'is_worker':array(is_worker),
'job_id':array(job_ids),
},
)
persons = PersonDataset(in_storage=in_storage, in_table_name=persons_table_name)
persons.write_dataset(out_storage=out_storage, out_table_name=persons_table_name)
if __name__ == '__main__':
config = Baseline()
config.replace({
'scenario_database_configuration': ScenarioDatabaseConfiguration(
database_name = 'psrc_2005_parcel_baseyear',
),
'estimation_database_configuration': EstimationDatabaseConfiguration(
database_name = 'psrc_2005_parcel_baseyear_change_20080219',
),
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['urbansim_parcel','urbansim', 'opus_core'],
),
'low_memory_mode': False,
'cache_directory': '/urbansim_cache/psrc_parcel/persons',
'debuglevel':7,
'base_year': 2000,
'years': (2000,2000),
def __init__(self):
config = PsrcParcelConfiguration()
config[
'creating_baseyear_cache_configuration'].cache_directory_root = r'/Users/hana/urbansim_cache/psrc/data_preparation/run_zipcodes'
config[
'creating_baseyear_cache_configuration'].baseyear_cache.existing_cache_to_copy = r'/Users/hana/urbansim_cache/psrc/data_preparation/cache_for_zipcodes'
#config['scenario_database_configuration'].database_name = 'psrc_2005_parcel_baseyear_data_prep_start'
config[
'scenario_database_configuration'].database_name = 'psrc_2005_parcel_baseyear_data_prep_business_zip'
config_changes = {
'description':
'data preparation for PSRC parcel (buildings)',
'flush_variables':
False,
'cache_directory':
'/Users/hana/urbansim_cache/psrc/data_preparation/cache_for_zipcodes',
'dataset_pool_configuration':
DatasetPoolConfiguration(package_order=[
'psrc_parcel', 'urbansim_parcel', 'urbansim', 'opus_core'
], ),
'base_year':
2000,
'years': (2001, 2003),
'models_in_year': {
2001: [
"employment_relocation_model", {
"employment_location_choice_model": {
"group_members": "_all_"
}
}
],
2002: [
"employment_relocation_model",
"governmental_employment_location_choice_model",
],
2003: [
"employment_relocation_model",
"governmental_employment_location_choice_model_without_filter"
]
},
"datasets_to_preload": {
'zone': {},
'job': {},
'building': {},
"job_building_type": {},
"zipcode": {}
},
"datasets_to_cache_after_each_model":
['parcel', 'building', 'job']
}
#use configuration in config as defaults and merge with config_changes
config.replace(config_changes)
self.merge(config)
self['models_configuration'][
'non_home_based_employment_location_choice_model'] = {}
self['models_configuration']['non_home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
records_per_chunk = 3000,
capacity_string = "urbansim_parcel.building.vacant_non_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration'][
'governmental_employment_location_choice_model'] = {}
self['models_configuration']['governmental_employment_location_choice_model']['controller'] = \
ScalingJobsModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
filter = "urbansim_parcel.building.is_governmental"
).execute()
self['models_configuration']['governmental_employment_location_choice_model_without_filter'] = {}
self['models_configuration']['governmental_employment_location_choice_model_without_filter']['controller'] = \
ScalingJobsModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
).execute()
self['models_configuration'][
'home_based_employment_location_choice_model'] = {}
self['models_configuration']['home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
capacity_string = "urbansim_parcel.building.vacant_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration']['employment_relocation_model']['controller'] = \
EmploymentRelocationModelConfigurationCreator(
location_id_name = 'building_id',
probabilities = None,
rate_table=None,
output_index = 'erm_index').execute()
def __init__(self):
config = PsrcParcelConfiguration()
config['creating_baseyear_cache_configuration'].cache_directory_root = r'/Users/hana/urbansim_cache/psrc/data_preparation/run'
config['creating_baseyear_cache_configuration'].baseyear_cache.existing_cache_to_copy = r'/Users/hana/urbansim_cache/psrc/data_preparation/cache_tmp'
config['scenario_database_configuration'].database_name = 'psrc_2005_parcel_baseyear_data_prep_start'
config_changes = {
'description':'data preparation for PSRC parcel (buildings)',
'flush_variables': False,
'cache_directory': '/Users/hana/urbansim_cache/psrc/data_preparation/cache',
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['psrc_parcel', 'urbansim_parcel', 'urbansim', 'opus_core'],
),
'base_year':2000,
'years':(2001, 2005),
'models_in_year': {
2001: # Step 2 (Create new residential buildings)
[
"real_estate_price_model",
"expected_sale_price_model",
"development_proposal_choice_model",
"building_construction_model",
],
2002: # Step 3 (Assign buildings to households)
[
"household_relocation_model",
"household_location_choice_model",
],
2003: # Step 4 (Assign buildings to jobs with known parcel_id)
[
'assign_buildings_to_jobs'
],
2004: # Step 5 (Create new non-residential buildings)
[
"expected_sale_price_model",
"development_proposal_choice_model_nr",
"building_construction_model",
],
2005: # Step 6 (Assign buildings to remaining jobs)
[
"employment_relocation_model",
{"employment_location_choice_model": {"group_members": "_all_" }},
"employment_relocation_model",
"governmental_employment_location_choice_model_without_filter"
]
},
"datasets_to_preload":{
'zone':{},
'household':{},
'job': {},
'building': {},
'parcel': {},
"job_building_type":{},
#'development_project_proposal': {}
},
#"low_memory_mode": True,
"datasets_to_cache_after_each_model": [
'parcel', 'building', 'development_project_proposal', 'household', 'job',
'development_project_proposal_component'
]
}
#use configuration in config as defaults and merge with config_changes
config.replace(config_changes)
self.merge(config)
self['models_configuration']['development_proposal_choice_model']['controller']["import"] = {
"psrc_parcel.models.development_proposal_sampling_model_by_zones":
"DevelopmentProposalSamplingModelByZones"
}
self['models_configuration']['development_proposal_choice_model']['controller']["init"]["name"] =\
"DevelopmentProposalSamplingModelByZones"
self['models_configuration']['development_proposal_choice_model']['controller']['run']['arguments']["zones"] = 'zone'
self['models_configuration']['development_proposal_choice_model']['controller']['run']['arguments']["type"] = "'residential'"
self['models_configuration']['development_proposal_choice_model']['controller']['run']['arguments']["n"] = 50
self['models_configuration']['development_proposal_choice_model_nr'] = Configuration(self['models_configuration']['development_proposal_choice_model'])
self['models_configuration']['development_proposal_choice_model_nr']['controller']['run']['arguments']["type"] = "'non_residential'"
self['models_configuration']['expected_sale_price_model']['controller']["prepare_for_run"]['arguments']["parcel_filter_for_redevelopment"] = None
#self['models_configuration']['expected_sale_price_model']['controller']["prepare_for_run"]['arguments']["create_proposal_set"] = False
self['models_configuration']['expected_sale_price_model']['controller']["run"]['arguments']["chunk_specification"] = "{'nchunks': 5}"
#self['models_configuration']['building_construction_model']['controller']["run"]['arguments']["consider_amount_built_in_parcels"] = False
self['models_configuration']['building_construction_model']['controller']['run']['arguments']["current_year"] = 2000
self['models_configuration']['household_location_choice_model'] = {}
self['models_configuration']['household_location_choice_model']['controller'] = \
HouseholdLocationChoiceModelByZonesConfigurationCreator(
location_set = "building",
sampler = None,
input_index = 'hrm_index',
capacity_string = "urbansim_parcel.building.vacant_residential_units",
number_of_units_string = None,
nchunks=1,
lottery_max_iterations=20
).execute()
self['models_configuration']['household_relocation_model']['controller'] = \
HouseholdRelocationModelConfigurationCreator(
location_id_name = 'building_id',
probabilities = None,
rate_table=None,
output_index = 'hrm_index').execute()
self['models_configuration']['non_home_based_employment_location_choice_model'] = {}
self['models_configuration']['non_home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
capacity_string = "urbansim_parcel.building.vacant_non_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration']['governmental_employment_location_choice_model'] = {}
self['models_configuration']['governmental_employment_location_choice_model']['controller'] = \
ScalingJobsModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
filter = "urbansim_parcel.building.is_governmental"
).execute()
self['models_configuration']['governmental_employment_location_choice_model_without_filter'] = {}
self['models_configuration']['governmental_employment_location_choice_model_without_filter']['controller'] = \
ScalingJobsModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
).execute()
self['models_configuration']['home_based_employment_location_choice_model'] = {}
self['models_configuration']['home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
capacity_string = "urbansim_parcel.building.vacant_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration']['employment_relocation_model']['controller'] = \
EmploymentRelocationModelConfigurationCreator(
location_id_name = 'building_id',
probabilities = None,
rate_table=None,
output_index = 'erm_index').execute()
self['models_configuration']['assign_buildings_to_jobs'] = {}
self['models_configuration']['assign_buildings_to_jobs']['controller'] = {
"import": {
"psrc_parcel.data_preparation.assign_bldgs_to_jobs_when_multiple_bldgs_in_parcel": "RunAssignBldgsToJobs"
},
"init": {
"name": "RunAssignBldgsToJobs"
},
"run": {
"arguments": {
"job_dataset": "job",
"dataset_pool": "dataset_pool"}
}
}
'repm': ['real_estate_price_model', 'psrc_parcel.estimation.repm_specification', None],
'wcm' : ['workplace_choice_model_for_resident', 'wcm_specification', None],
'wahcm': ['work_at_home_choice_model', 'wahcm_specification', None],
'dppcm': ['development_project_proposal_choice_model', 'dppcm_specification', None]
}
if __name__ == '__main__':
try: import wingdbstub
except: pass
model = 'hlcm'
#model = 'elcm-non-home-based'
#model = 'elcm-home_based'
#model = 'repm'
#model = 'wcm'
#model = 'wahcm'
#model = 'dppcm'
config = Baseline()
if 'models_in_year' in config.keys():
del config['models_in_year']
config.merge(my_configuration)
config['config_changes_for_estimation'] = ConfigChangesForEstimation()
## set base_year and years to 2006 for HLCM for the psrc_parcel project
config['config_changes_for_estimation']['household_location_choice_model'].merge({'base_year': 2006, 'years':(2006, 2006)})
er = EstimationRunner(models[model][0], specification_module=models[model][1], model_group=models[model][2],
configuration=config, save_estimation_results=False)
er.estimate()
#er.predict()
#er.create_prediction_success_table(geography_id_expression='choice.is_chosen')
def __init__(self):
config = PsrcParcelConfiguration()
config['creating_baseyear_cache_configuration'].cache_directory_root = r'/Users/hana/urbansim_cache/psrc/data_preparation/run_zipcodes'
config['creating_baseyear_cache_configuration'].baseyear_cache.existing_cache_to_copy = r'/Users/hana/urbansim_cache/psrc/data_preparation/cache_for_zipcodes'
#config['scenario_database_configuration'].database_name = 'psrc_2005_parcel_baseyear_data_prep_start'
config['scenario_database_configuration'].database_name = 'psrc_2005_parcel_baseyear_data_prep_business_zip'
config_changes = {
'description':'data preparation for PSRC parcel (buildings)',
'flush_variables': False,
'cache_directory': '/Users/hana/urbansim_cache/psrc/data_preparation/cache_for_zipcodes',
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['psrc_parcel', 'urbansim_parcel', 'urbansim', 'opus_core'],
),
'base_year':2000,
'years':(2001, 2003),
'models_in_year': {
2001:
[
"employment_relocation_model",
{"employment_location_choice_model": {"group_members": "_all_" }}
],
2002: [
"employment_relocation_model",
"governmental_employment_location_choice_model",
],
2003: [
"employment_relocation_model",
"governmental_employment_location_choice_model_without_filter"
]
},
"datasets_to_preload":{
'zone':{},
'job': {},
'building': {},
"job_building_type":{},
"zipcode": {}
},
"datasets_to_cache_after_each_model": [
'parcel', 'building', 'job'
]
}
#use configuration in config as defaults and merge with config_changes
config.replace(config_changes)
self.merge(config)
self['models_configuration']['non_home_based_employment_location_choice_model'] = {}
self['models_configuration']['non_home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
records_per_chunk = 3000,
capacity_string = "urbansim_parcel.building.vacant_non_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration']['governmental_employment_location_choice_model'] = {}
self['models_configuration']['governmental_employment_location_choice_model']['controller'] = \
ScalingJobsModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
filter = "urbansim_parcel.building.is_governmental"
).execute()
self['models_configuration']['governmental_employment_location_choice_model_without_filter'] = {}
self['models_configuration']['governmental_employment_location_choice_model_without_filter']['controller'] = \
ScalingJobsModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
).execute()
self['models_configuration']['home_based_employment_location_choice_model'] = {}
self['models_configuration']['home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZipcodesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
capacity_string = "urbansim_parcel.building.vacant_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration']['employment_relocation_model']['controller'] = \
EmploymentRelocationModelConfigurationCreator(
location_id_name = 'building_id',
probabilities = None,
rate_table=None,
output_index = 'erm_index').execute()
def __init__(self):
config = PsrcParcelConfiguration()
config['creating_baseyear_cache_configuration'].cache_directory_root = r'/Users/hana/urbansim_cache/psrc/data_preparation/run'
config['creating_baseyear_cache_configuration'].baseyear_cache.existing_cache_to_copy = r'/Users/hana/urbansim_cache/psrc/data_preparation/cache_tmp'
config['scenario_database_configuration'].database_name = 'psrc_2005_parcel_baseyear_data_prep_start'
config_changes = {
'description':'data preparation for PSRC parcel (buildings)',
'flush_variables': False,
'cache_directory': '/Users/hana/urbansim_cache/psrc/data_preparation/cache',
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['psrc_parcel', 'urbansim_parcel', 'urbansim', 'opus_core'],
),
'base_year':2000,
'years':(2001, 2005),
'models_in_year': {
2001: # Step 2 (Create new residential buildings)
[
"real_estate_price_model",
"expected_sale_price_model",
"development_proposal_choice_model",
"building_construction_model",
],
2002: # Step 3 (Assign buildings to households)
[
"household_relocation_model",
"household_location_choice_model",
],
2003: # Step 4 (Assign buildings to jobs with known parcel_id)
[
'assign_buildings_to_jobs'
],
2004: # Step 5 (Create new non-residential buildings)
[
"expected_sale_price_model",
"development_proposal_choice_model_nr",
"building_construction_model",
],
2005: # Step 6 (Assign buildings to remaining jobs)
[
"employment_relocation_model",
{"employment_location_choice_model": {"group_members": "_all_" }},
"employment_relocation_model",
"governmental_employment_location_choice_model_without_filter"
]
},
"datasets_to_preload":{
'zone':{},
'household':{},
'job': {},
'building': {},
'parcel': {},
"job_building_type":{},
#'development_project_proposal': {}
},
#"low_memory_mode": True,
"datasets_to_cache_after_each_model": [
'parcel', 'building', 'development_project_proposal', 'household', 'job',
'development_project_proposal_component'
]
}
#use configuration in config as defaults and merge with config_changes
config.replace(config_changes)
self.merge(config)
self['models_configuration']['development_proposal_choice_model']['controller']["import"] = {
"psrc_parcel.models.development_proposal_sampling_model_by_zones":
"DevelopmentProposalSamplingModelByZones"
}
self['models_configuration']['development_proposal_choice_model']['controller']["init"]["name"] =\
"DevelopmentProposalSamplingModelByZones"
self['models_configuration']['development_proposal_choice_model']['controller']['run']['arguments']["zones"] = 'zone'
self['models_configuration']['development_proposal_choice_model']['controller']['run']['arguments']["type"] = "'residential'"
self['models_configuration']['development_proposal_choice_model']['controller']['run']['arguments']["n"] = 50
self['models_configuration']['development_proposal_choice_model_nr'] = Configuration(self['models_configuration']['development_proposal_choice_model'])
self['models_configuration']['development_proposal_choice_model_nr']['controller']['run']['arguments']["type"] = "'non_residential'"
self['models_configuration']['expected_sale_price_model']['controller']["prepare_for_run"]['arguments']["parcel_filter_for_redevelopment"] = None
#self['models_configuration']['expected_sale_price_model']['controller']["prepare_for_run"]['arguments']["create_proposal_set"] = False
self['models_configuration']['expected_sale_price_model']['controller']["run"]['arguments']["chunk_specification"] = "{'nchunks': 5}"
#self['models_configuration']['building_construction_model']['controller']["run"]['arguments']["consider_amount_built_in_parcels"] = False
self['models_configuration']['building_construction_model']['controller']['run']['arguments']["current_year"] = 2000
self['models_configuration']['household_location_choice_model'] = {}
self['models_configuration']['household_location_choice_model']['controller'] = \
HouseholdLocationChoiceModelByZonesConfigurationCreator(
location_set = "building",
sampler = None,
input_index = 'hrm_index',
capacity_string = "urbansim_parcel.building.vacant_residential_units",
number_of_units_string = None,
nchunks=1,
lottery_max_iterations=20
).execute()
self['models_configuration']['household_relocation_model']['controller'] = \
HouseholdRelocationModelConfigurationCreator(
location_id_name = 'building_id',
probabilities = None,
rate_table=None,
output_index = 'hrm_index').execute()
self['models_configuration']['non_home_based_employment_location_choice_model'] = {}
self['models_configuration']['non_home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
capacity_string = "urbansim_parcel.building.vacant_non_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration']['governmental_employment_location_choice_model'] = {}
self['models_configuration']['governmental_employment_location_choice_model']['controller'] = \
ScalingJobsModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
filter = "urbansim_parcel.building.is_governmental"
).execute()
self['models_configuration']['governmental_employment_location_choice_model_without_filter'] = {}
self['models_configuration']['governmental_employment_location_choice_model_without_filter']['controller'] = \
ScalingJobsModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
).execute()
self['models_configuration']['home_based_employment_location_choice_model'] = {}
self['models_configuration']['home_based_employment_location_choice_model']['controller'] = \
EmploymentLocationChoiceModelByZonesConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
sampler = None,
capacity_string = "urbansim_parcel.building.vacant_home_based_job_space",
number_of_units_string = None,
lottery_max_iterations=30
).execute()
self['models_configuration']['employment_relocation_model']['controller'] = \
EmploymentRelocationModelConfigurationCreator(
location_id_name = 'building_id',
probabilities = None,
rate_table=None,
output_index = 'erm_index').execute()
self['models_configuration']['assign_buildings_to_jobs'] = {}
self['models_configuration']['assign_buildings_to_jobs']['controller'] = {
"import": {
"psrc_parcel.data_preparation.assign_bldgs_to_jobs_when_multiple_bldgs_in_parcel": "RunAssignBldgsToJobs"
},
"init": {
"name": "RunAssignBldgsToJobs"
},
"run": {
"arguments": {
"job_dataset": "job",
"dataset_pool": "dataset_pool"}
}
}