def __init__(
self,
cache_directory,
year,
travel_model_data_directory='baseline_travel_model_psrc_baseline'):
config = AbstractUrbansimConfiguration()
config_changes = {
'description':
'baseline with travel model',
'cache_directory':
cache_directory,
'creating_baseyear_cache_configuration':
CreatingBaseyearCacheConfiguration(
cache_scenario_database=
'urbansim.model_coordinators.cache_scenario_database',
unroll_gridcells=False,
tables_to_cache=[],
tables_to_copy_to_previous_years={},
),
'models': [], # run no urbansim models
'scenario_database_configuration':
ScenarioDatabaseConfiguration(
database_name='PSRC_2000_baseyear', ),
'base_year':
year,
'years': (year, year),
}
config.merge(config_changes)
years_to_run = {
2005: '2000_06',
2010: '2010_06',
2015: '2010_06',
2020: '2020_06',
2025: '2020_06',
2030: '2030_06',
}
year_to_run = {year: years_to_run[year]}
travel_model_config = create_travel_model_configuration(
travel_model_data_directory,
mode='get_emme2_data_after_run',
years_to_run=year_to_run,
emme2_batch_file='QUICKRUN.BAT')
config['travel_model_configuration'] = travel_model_config
self.merge(config)
def __init__(self, cache_directory, year,
travel_model_data_directory='baseline_travel_model_psrc_baseline'):
config = AbstractUrbansimConfiguration()
config_changes = {
'description':'baseline with travel model',
'cache_directory':cache_directory,
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_mysql_data = 'urbansim.model_coordinators.cache_mysql_data',
unroll_gridcells = False,
tables_to_cache = [],
tables_to_copy_to_previous_years = {},
),
'models':[], # run no urbansim models
'scenario_database_configuration': ScenarioDatabaseConfiguration(
database_name = 'PSRC_2000_baseyear',
),
'base_year':year,
'years':(year, year),
}
config.merge(config_changes)
years_to_run = {
2005:'2000_06',
2010:'2010_06',
2015:'2010_06',
2020:'2020_06',
2025:'2020_06',
2030:'2030_06',
}
year_to_run = {year:years_to_run[year]}
travel_model_config = create_travel_model_configuration(travel_model_data_directory,
mode='get_emme2_data',
years_to_run = year_to_run,
emme2_batch_file='QUICKRUN.BAT')
config['travel_model_configuration'] = travel_model_config
self.merge(config)
def setUp(self):
self.storage = dict_storage()
self.storage.write_table(table_name='development_type_groups',
table_data={
"group_id":
array([1, 2, 3, 4]),
"name":
array([
'residential', 'commercial',
'industrial', 'governmental'
]),
})
run_configuration = AbstractUrbansimConfiguration()
self.model_configuration = run_configuration['models_configuration']
SessionConfiguration(
new_instance=True,
package_order=run_configuration['dataset_pool_configuration'].
package_order,
in_storage=self.storage)
# See opus_core/LICENSE
import os
from opus_core.database_management.configurations.scenario_database_configuration import ScenarioDatabaseConfiguration
from opus_core.configurations.dataset_pool_configuration import DatasetPoolConfiguration
from opus_core.configurations.baseyear_cache_configuration import BaseyearCacheConfiguration
#from urbansim.estimation.config import config
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
from urbansim.configurations.creating_baseyear_cache_configuration import CreatingBaseyearCacheConfiguration
from randstad.run_config.controller_config import models_configuration
run_configuration = AbstractUrbansimConfiguration()
my_run_configuration = {
'models_configuration':models_configuration,
'scenario_database_configuration':ScenarioDatabaseConfiguration(
database_name = 'randstad_021105_estimation',
),
#'cache_directory':'C:/urbansim_cache/randstad_source',
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = 'C:/urbansim_cache/randstad',
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
unroll_gridcells = False,
cache_from_database = False,
# location of existing baseyear cache to use
baseyear_cache = BaseyearCacheConfiguration(
existing_cache_to_copy = r'C:\urbansim_cache\randstad\run_618.2006_07_16_18_06',
def __init__(self):
config = AbstractUrbansimConfiguration()
db_server = DatabaseServer(ScenarioDatabaseConfiguration())
db = db_server.get_database('PSRC_2000_baseyear')
config_changes = {
'description':'baseline with travel model',
'in_storage':StorageFactory().get_storage('sql_storage',
storage_location = db
),
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = 'd:/urbansim_cache',
cache_from_database = True,
baseyear_cache = BaseyearCacheConfiguration(
r'D:\urbansim_cache\run_1417.2006_12_08_01_50'),
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache = [
'annual_employment_control_totals',
'annual_household_control_totals',
'buildings',
'building_types',
'development_event_history',
'gridcells',
'households',
'job_building_types',
'jobs',
'travel_data',
'zones',
'counties',
'commercial_development_location_choice_model_coefficients',
'commercial_development_location_choice_model_specification',
'commercial_employment_location_choice_model_coefficients',
'commercial_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_specification',
'industrial_development_location_choice_model_coefficients',
'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
'fazes',
'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
'land_price_model_coefficients',
'land_price_model_specification',
'residential_land_share_model_coefficients',
'residential_land_share_model_specification',
'plan_type_group_definitions',
'plan_type_groups',
'large_areas',
'household_characteristics_for_ht',
'development_types',
'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
'annual_relocation_rates_for_jobs',
'base_year',
'cities',
'development_events',
'development_type_groups',
'employment_adhoc_sector_group_definitions',
'employment_adhoc_sector_groups',
'employment_events',
'employment_sectors',
'land_use_events',
'plan_types',
'race_names',
'target_vacancies',
'jobs_for_estimation',
'households_for_estimation',
'development_events_exogenous',
'job_building_types'
],
tables_to_cache_nchunks = {'gridcells': 4},
tables_to_copy_to_previous_years = {
'development_type_groups': 1995,
'development_types': 1995,
'development_type_group_definitions': 1995,
},
),
'models':[], # run no urbansim models
'scenario_database_configuration': ScenarioDatabaseConfiguration(
database_name = 'PSRC_2000_baseyear',
),
'base_year':2000,
'years':(2001, 2002),
'unroll_gridcells':False
}
config.merge(config_changes)
travel_model_config = create_travel_model_configuration('baseline_travel_model_psrc_fresh',
mode='get_emme2_data_after_run',
years_to_run = {2001:'2000_06'},
emme2_batch_file='MODEL05.BAT')
config['travel_model_configuration'] = travel_model_config
self.merge(config)
def __init__(self):
from copy import deepcopy
config = deepcopy(AbstractUrbansimConfiguration())
cache_directory = os.path.join(package().get_opus_core_path(), 'data',
'test_cache')
config_changes = {
'description':
'Test cache',
'base_year':
1980,
'years': (1981, 1982),
'models': [
'prescheduled_events',
'events_coordinator',
'residential_land_share_model',
'land_price_model',
'development_project_transition_model',
'residential_development_project_location_choice_model',
'commercial_development_project_location_choice_model',
'industrial_development_project_location_choice_model',
'development_event_transition_model',
'events_coordinator',
'residential_land_share_model',
'household_transition_model',
'employment_transition_model',
'household_relocation_model',
'household_location_choice_model',
'employment_relocation_model',
{
'employment_location_choice_model': {
'group_members': '_all_'
}
},
'distribute_unplaced_jobs_model',
],
'scenario_database_configuration':
ScenarioDatabaseConfiguration(
database_name='eugene_1980_baseyear', ),
'cache_directory':
cache_directory,
'creating_baseyear_cache_configuration':
CreatingBaseyearCacheConfiguration(
cache_directory_root='c:/urbansim_cache',
cache_from_database=False,
baseyear_cache=BaseyearCacheConfiguration(
existing_cache_to_copy=cache_directory, ),
cache_scenario_database=
'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache=[
'annual_employment_control_totals',
'annual_household_control_totals',
'buildings',
'building_types',
'development_event_history',
'gridcells',
'households',
'job_building_types',
'jobs',
'travel_data',
'zones',
'counties',
'commercial_development_location_choice_model_coefficients',
'commercial_development_location_choice_model_specification',
'commercial_employment_location_choice_model_coefficients',
'commercial_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_specification',
'industrial_development_location_choice_model_coefficients',
'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
#'fazes',
'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
'land_price_model_coefficients',
'land_price_model_specification',
'residential_land_share_model_coefficients',
'residential_land_share_model_specification',
#'plan_type_group_definitions',
#'plan_type_groups',
#'large_areas',
'household_characteristics_for_ht',
'development_types',
'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
'annual_relocation_rates_for_jobs',
'base_year',
'cities',
#'development_events',
'development_type_groups',
'employment_adhoc_sector_group_definitions',
'employment_adhoc_sector_groups',
'employment_sectors',
'plan_types',
'race_names',
'target_vacancies'
#'jobs_for_estimation',
#'households_for_estimation',
#'development_events_exogenous',
],
tables_to_cache_nchunks={'gridcells': 1},
tables_to_copy_to_previous_years={
'development_type_group_definitions': 1975,
'development_type_groups': 1975,
'development_types': 1975,
'urbansim_constants': 1975,
},
),
}
config.merge(config_changes)
self.merge(config)
def __init__(self):
config = AbstractUrbansimConfiguration()
config_changes = {
"project_name": "washtenaw",
"description": "Region Pilot Baseline",
"scenario_database_configuration": ScenarioDatabaseConfiguration(database_name="washtenaw_class"),
"models": [
"prescheduled_events",
"events_coordinator",
"residential_land_share_model",
"land_price_model",
"regional_development_project_transition_model",
"residential_regional_development_project_location_choice_model",
"commercial_regional_development_project_location_choice_model",
"industrial_regional_development_project_location_choice_model",
"development_event_transition_model",
"events_coordinator",
"residential_land_share_model",
"jobs_event_model",
#'households_event_model',
"regional_household_transition_model",
"regional_household_relocation_model",
"regional_household_location_choice_model",
"regional_employment_transition_model",
"regional_employment_relocation_model",
{"regional_employment_location_choice_model": {"group_members": ["_all_"]}},
"regional_distribute_unplaced_jobs_model",
],
"cache_directory": None, ### TODO: Set this cache_directory to something useful.
"creating_baseyear_cache_configuration": CreatingBaseyearCacheConfiguration(
cache_directory_root="/urbansim_cache/washtenaw",
cache_from_database=True,
baseyear_cache=BaseyearCacheConfiguration(
existing_cache_to_copy="/urbansim_cache/washtenaw/cache_source"
),
cache_scenario_database="urbansim.model_coordinators.cache_scenario_database",
tables_to_cache=self.tables_to_cache,
tables_to_cache_nchunks={"gridcells": 1},
tables_to_copy_to_previous_years=self.tables_to_copy_to_previous_years,
),
"datasets_to_preload": {
"development_constraint": {},
"development_event_history": {},
"development_type": {},
"gridcell": {"nchunks": 2},
"household": {},
"job": {},
"job_building_type": {},
"target_vacancy": {},
"zone": {},
"jobs_event": {},
#'households_event': {},
},
"dataset_pool_configuration": DatasetPoolConfiguration(
package_order=["washtenaw", "urbansim", "opus_core"]
),
"base_year": 2005,
"years": (2006, 2010),
}
config.merge(config_changes)
self.merge(config)
self.merge_with_controller()
try:
exec("from %s_local_config import my_configuration" % getuser())
local_config = True
except:
logger.log_note("No user's settings found or error occured when loading.")
local_config = False
if local_config:
self.merge(my_configuration)
# Opus/UrbanSim urban simulation software.
# Copyright (C) 2005-2009 University of Washington
# See opus_core/LICENSE
#from urbansim.estimation.config import config
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
from numpy import array
import os
from urbansim.datasets.development_event_dataset import DevelopmentEventTypeOfChange
run_configuration = AbstractUrbansimConfiguration()
models_configuration = run_configuration['models_configuration']
config_changes = {
'landuse_development_types': {
'residential': {
'units': 'residential_units',
'residential': True,
},
'commercial': {
'units': 'commercial_sqft',
'residential': False,
},
'industrial': {
'units': 'industrial_sqft',
'residential': False,
},
'governmental': {
'units': 'governmental_sqft',
'residential': False,
},
# Opus/UrbanSim urban simulation software.
# Copyright (C) 2010-2011 University of California, Berkeley, 2005-2009 University of Washington
# See opus_core/LICENSE
from opus_core.configuration import Configuration
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
class EstimationBaseConfig(Configuration):
def __init__(self, base_configuration=AbstractUrbansimConfiguration):
config = base_configuration()
config.merge(get_changes_for_estimation(config))
self.merge(config)
def get_changes_for_estimation(config):
estimation_configuration = {}
if "base_year" in config:
estimation_configuration["years"] = range(config["base_year"], config["base_year"])
estimation_configuration["sample_size_locations"] = 30
estimation_configuration["seed"] = 1,#(1,1)
return estimation_configuration
run_configuration = AbstractUrbansimConfiguration()
estimation_configuration = get_changes_for_estimation(run_configuration)
run_configuration.merge(estimation_configuration)
def setUp(self):
"""specify the development event history for the gridcells. here, we simulate 50 residential units
and 5000 commercial, industrial, and governmental sqft added to each of the gridcells in previous years.
Also, we specify $1000 in value added to each gridcell for residential, commercial, industrial, governmental.
These values are so boring because they really don't matter for this test case, where there will be absolutely
no development, and thus, nothing will be sampled from the development_history"""
self.storage = StorageFactory().get_storage('dict_storage')
self.storage.write_table(table_name='development_event_history',
table_data={
"grid_id":
arange(1, 100 + 1),
"scheduled_year":
array(100 * [1999]),
"residential_units":
array(100 * [50]),
"commercial_sqft":
array(100 * [5000]),
"industrial_sqft":
array(100 * [5000]),
"governmental_sqft":
array(100 * [5000]),
"residential_improvement_value":
array(100 * [1000]),
"commercial_improvement_value":
array(100 * [1000]),
"industrial_improvement_value":
array(100 * [1000]),
"governmental_improvement_value":
array(100 * [1000])
})
# create 100 gridcells, each with 200 residential units and space for 100 commercial jobs,
# 100 industrial jobs, and residential, industrial, and commercial value at $500,000 each
self.storage.write_table(table_name='gridcells',
table_data={
"grid_id":
arange(1, 100 + 1),
"residential_units":
array(100 * [200]),
"commercial_sqft":
array(100 * [10000]),
"commercial_sqft_per_job":
array(100 * [100]),
"industrial_sqft":
array(100 * [10000]),
"industrial_sqft_per_job":
array(100 * [100]),
"residential_improvement_value":
array(100 * [500000]),
"commercial_improvement_value":
array(100 * [500000]),
"industrial_improvement_value":
array(100 * [500000])
})
# create 10000 households, 100 in each of the 100 gridcells.
# there will initially be 100 vacant residential units in each gridcell then.
self.storage.write_table(table_name='households',
table_data={
"household_id": arange(1, 10000 + 1),
"grid_id": array(100 * range(1, 100 + 1))
})
self.storage.write_table(table_name='job_building_types',
table_data={
"id":
array([
Constants._governmental_code,
Constants._commercial_code,
Constants._industrial_code,
Constants._residential_code
]),
"name":
array([
"governmental", "commercial",
"industrial", "home_based"
]),
"home_based":
array([0, 0, 0, 1])
})
# create 2500 commercial jobs and distribute them equally across the 100 gridcells,
# 25 commercial jobs/gridcell
self.storage.write_table(table_name='jobs',
table_data={
"job_id":
arange(1, 2500 + 1),
"grid_id":
array(25 * range(1, 100 + 1)),
"sector_id":
array(2500 * [1]),
"home_based":
array(2500 * [0]),
"building_type":
array(2500 * [Constants._commercial_code])
})
self.storage.write_table(table_name='urbansim_constants',
table_data={
"acres": array([105.0]),
})
self.dataset_pool = DatasetPool(package_order=['urbansim'],
storage=self.storage)
self.compute_resources = Resources({
"household":
self.dataset_pool.get_dataset('household'),
"job":
self.dataset_pool.get_dataset('job'),
"job_building_type":
self.dataset_pool.get_dataset('job_building_type'),
'urbansim_constant':
self.dataset_pool.get_dataset('urbansim_constant'),
})
#
# ? dynamically get these datasets?
# Replace uses of 'constants' with 'urbansim_constant'?
#
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
# fake development project models
#dev_models = {
#'residential_model':{
#'controller':{
#'init':{
#'arguments':{
#'project_type':'residential',
#'residential':True,
#'units':'residential_units'
#}
#},
#'prepare_for_estimate':{
#'arguments':{
#'categories':[1, 2, 3, 5, 10, 20]
#}
#}
#}
#},
#'commercial_model':{
#'controller':{
#'init':{
#'arguments':{
#'project_type':'commercial',
#'residential':False,
#'units': 'commercial_sqft'
#}
#},
#'prepare_for_estimate':{
#'arguments': {
#'categories':[1000, 2000, 5000, 10000]
#}
#}
#}
#},
#'industrial_model':{
#'controller':{
#'init':{
#'arguments':{
#'project_type':'industrial',
#'residential':False,
#'units':'industrial_sqft'
#}
#},
#'prepare_for_estimate':{
#'arguments':{
#'categories':[1000, 2000, 5000, 10000]
#}
#}
#}
#}
#}
self.models_configuration = AbstractUrbansimConfiguration(
)['models_configuration']
def __init__(self):
config = AbstractUrbansimConfiguration()
config_changes = {
'project_name':'washtenaw',
'description':'Region Pilot Baseline',
'scenario_database_configuration': ScenarioDatabaseConfiguration(
database_name = 'washtenaw_class',
),
'models': [
'prescheduled_events',
'events_coordinator',
'residential_land_share_model',
'land_price_model',
'regional_development_project_transition_model',
'residential_regional_development_project_location_choice_model',
'commercial_regional_development_project_location_choice_model',
'industrial_regional_development_project_location_choice_model',
'development_event_transition_model',
'events_coordinator',
'residential_land_share_model',
'jobs_event_model',
#'households_event_model',
'regional_household_transition_model',
'regional_household_relocation_model',
'regional_household_location_choice_model',
'regional_employment_transition_model',
'regional_employment_relocation_model',
{'regional_employment_location_choice_model': {'group_members': ['_all_']}},
'regional_distribute_unplaced_jobs_model'
],
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = "/urbansim_cache/washtenaw",
cache_from_database = True,
baseyear_cache = BaseyearCacheConfiguration(
existing_cache_to_copy = "/urbansim_cache/washtenaw/cache_source",
),
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache = self.tables_to_cache,
tables_to_cache_nchunks = {'gridcells': 1},
tables_to_copy_to_previous_years = self.tables_to_copy_to_previous_years,
),
'datasets_to_preload': {
'development_constraint': {},
'development_event_history': {},
'development_type': {},
'gridcell': {'nchunks': 2},
'household': {},
'job': {},
'job_building_type': {},
'target_vacancy': {},
'zone': {},
'jobs_event': {},
#'households_event': {},
},
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['washtenaw', 'urbansim', 'opus_core'],
),
'base_year':2005,
'years':(2006, 2010),
}
config.merge(config_changes)
self.merge(config)
self.merge_with_controller()
try:
exec('from %s_local_config import my_configuration' % getuser())
local_config = True
except:
logger.log_note("No user's settings found or error occured when loading.")
local_config = False
if local_config:
self.merge(my_configuration)
def __init__(self):
config = AbstractUrbansimConfiguration()
db_server = DatabaseServer(ScenarioDatabaseConfiguration())
db = db_server.get_database('PSRC_2000_baseyear')
config_changes = {
'description':'baseline with travel model',
'in_storage':StorageFactory().get_storage('sql_storage',
storage_location = db
),
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = 'd:/urbansim_cache',
cache_from_database = True,
baseyear_cache = BaseyearCacheConfiguration(
r'D:\urbansim_cache\run_1417.2006_12_08_01_50'),
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache = [
'annual_employment_control_totals',
'annual_household_control_totals',
'buildings',
'building_types',
'development_event_history',
'gridcells',
'households',
'job_building_types',
'jobs',
'travel_data',
'zones',
'counties',
'commercial_development_location_choice_model_coefficients',
'commercial_development_location_choice_model_specification',
'commercial_employment_location_choice_model_coefficients',
'commercial_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_specification',
'industrial_development_location_choice_model_coefficients',
'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
'fazes',
'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
'land_price_model_coefficients',
'land_price_model_specification',
'residential_land_share_model_coefficients',
'residential_land_share_model_specification',
'plan_type_group_definitions',
'plan_type_groups',
'large_areas',
'household_characteristics_for_ht',
'development_types',
'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
'annual_relocation_rates_for_jobs',
'base_year',
'cities',
'development_events',
'development_type_groups',
'employment_adhoc_sector_group_definitions',
'employment_adhoc_sector_groups',
'employment_events',
'employment_sectors',
'land_use_events',
'plan_types',
'race_names',
'target_vacancies',
'jobs_for_estimation',
'households_for_estimation',
'development_events_exogenous',
'job_building_types'
],
tables_to_cache_nchunks = {'gridcells': 4},
tables_to_copy_to_previous_years = {
'development_type_groups': 1995,
'development_types': 1995,
'development_type_group_definitions': 1995,
},
),
'models':[], # run no urbansim models
'scenario_database_configuration': ScenarioDatabaseConfiguration(
database_name = 'PSRC_2000_baseyear',
),
'base_year':2000,
'years':(2001, 2002),
'unroll_gridcells':False
}
config.merge(config_changes)
travel_model_config = create_travel_model_configuration('baseline_travel_model_psrc_fresh',
mode='get_emme2_data_after_run',
years_to_run = {2001:'2000_06'},
emme2_batch_file='MODEL05.BAT')
config['travel_model_configuration'] = travel_model_config
self.merge(config)
# Opus/UrbanSim urban simulation software.
# Copyright (C) 2005-2009 University of Washington
# See opus_core/LICENSE
import os
from opus_core.storage_factory import StorageFactory
from opus_core.database_management.configurations.scenario_database_configuration import ScenarioDatabaseConfiguration
from opus_core.database_management.configurations.estimation_database_configuration import EstimationDatabaseConfiguration
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
from urbansim.configurations.creating_baseyear_cache_configuration import CreatingBaseyearCacheConfiguration
from opus_core.database_management.database_server import DatabaseServer
config = AbstractUrbansimConfiguration()
db_server = DatabaseServer(ScenarioDatabaseConfiguration())
db = db_server.get_database('PSRC_2000_scenario_E_constrained_king_county')
config_changes = {
'description':
'constrained king county capacity baseline with full travel model',
'in_storage':
StorageFactory().get_storage('sql_storage', storage_location=db),
'cache_directory':
None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':
CreatingBaseyearCacheConfiguration(
cache_directory_root='d:/urbansim_cache',
cache_from_database=True,
cache_scenario_database=
'urbansim.model_coordinators.cache_scenario_database',
# Copyright (C) 2005-2009 University of Washington
# See opus_core/LICENSE
import os
from opus_core.database_management.configurations.scenario_database_configuration import ScenarioDatabaseConfiguration
from opus_core.configurations.dataset_pool_configuration import DatasetPoolConfiguration
from opus_core.configurations.baseyear_cache_configuration import BaseyearCacheConfiguration
#from urbansim.estimation.config import config
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
from urbansim.configurations.creating_baseyear_cache_configuration import CreatingBaseyearCacheConfiguration
from randstad.run_config.controller_config import models_configuration
run_configuration = AbstractUrbansimConfiguration()
my_run_configuration = {
'models_configuration':models_configuration,
'scenario_database_configuration':ScenarioDatabaseConfiguration(
database_name = 'randstad_021105_estimation',
),
#'cache_directory':'C:/urbansim_cache/randstad_source',
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = 'C:/urbansim_cache/randstad',
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
unroll_gridcells = False,
cache_from_database = False,
# location of existing baseyear cache to use
baseyear_cache = BaseyearCacheConfiguration(
existing_cache_to_copy = r'C:\urbansim_cache\randstad\run_618.2006_07_16_18_06',
def __init__(self):
AbstractUrbansimConfiguration.__init__(self)
config_changes = {
'project_name':'vibe_gridcell',
'description':'Eugene baseline',
'base_year':1980,
'years':(1981,1982,1983),
'models': [
'prescheduled_events',
'events_coordinator',
#'residential_land_share_model',
#'fertility_model'
#'land_price_model',
#'development_project_transition_model',
#'residential_development_project_location_choice_model',
#'commercial_development_project_location_choice_model',
#'industrial_development_project_location_choice_model',
#'development_event_transition_model',
#'events_coordinator',
#'residential_land_share_model',
'household_transition_model',
#'employment_transition_model',
'household_relocation_model',
'household_location_choice_model',
#'employment_relocation_model',
#{ 'employment_location_choice_model': { 'group_members': '_all_'}},
#'distribute_unplaced_jobs_model',
],
#'scenario_database_configuration': ScenarioDatabaseConfiguration(database_name = 'eugen_base_year_data_1980'),
#'scenario_database_configuration': ScenarioDatabaseConfiguration(database_name = 'vibe_base_year_data_1980'),
'scenario_database_configuration': ScenarioDatabaseConfiguration(database_name = 'urbansim_export_test'),
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
#cache_directory_root = os.path.join(os.environ['OPUS_HOME'], 'data/eugene_gridcell/runs'),
cache_directory_root = os.path.join(os.environ['OPUS_HOME'], 'data/vibe_gridcell/runs'),
#cache_scenario_database = storage,
cache_from_database = True,
baseyear_cache = BaseyearCacheConfiguration(
existing_cache_to_copy = os.path.join(os.environ['OPUS_HOME'], 'data/vibe_gridcell/base_year_data'),
),
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache = [
#'annual_employment_control_totals',
'annual_household_control_totals',
#'buildings',
'building_types',
'development_event_history',
'gridcells',
'households',
'job_building_types',
'jobs',
#'travel_data',
'zones',
#'counties',
#'commercial_development_location_choice_model_coefficients',
#'commercial_development_location_choice_model_specification',
#'commercial_employment_location_choice_model_coefficients',
#'commercial_employment_location_choice_model_specification',
#'home_based_employment_location_choice_model_specification',
#'home_based_employment_location_choice_model_coefficients',
#'industrial_employment_location_choice_model_coefficients',
#'industrial_employment_location_choice_model_specification',
#'industrial_development_location_choice_model_coefficients',
#'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
#'land_price_model_coefficients',
#'land_price_model_specification',
#'residential_land_share_model_coefficients',
#'residential_land_share_model_specification',
'household_characteristics_for_ht',
'development_types',
'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
#'annual_relocation_rates_for_jobs',
'base_year',
#'cities',
'development_type_groups',
#'employment_adhoc_sector_group_definitions',
#'employment_adhoc_sector_groups',
#'employment_sectors',
'plan_types',
'plan_type_groups',
'plan_type_group_definitions',
'race_names',
'target_vacancies',
#'jobs_for_estimation',
'households_for_estimation',
],
tables_to_cache_nchunks = {'gridcells': 1},
tables_to_copy_to_previous_years = {
'development_type_group_definitions': 1975,
'development_type_groups': 1975,
'development_types': 1975,
'development_constraints':1975,
'urbansim_constants': 1975,
},
),
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['vibe_min', 'urbansim', 'opus_core'],
),
}
self.merge(config_changes)
def __init__(self):
config = AbstractUrbansimConfiguration()
db_server = DatabaseServer(ScenarioDatabaseConfiguration())
db = db_server.get_database("PSRC_2000_baseyear")
config_changes = {
"description": "baseline with travel model",
"in_storage": StorageFactory().get_storage("sql_storage", storage_location=db),
"cache_directory": None, ### TODO: Set this cache_directory to something useful.
"creating_baseyear_cache_configuration": CreatingBaseyearCacheConfiguration(
cache_directory_root="d:/urbansim_cache",
cache_from_database=True,
baseyear_cache=BaseyearCacheConfiguration(r"D:\urbansim_cache\run_1417.2006_12_08_01_50"),
cache_scenario_database="urbansim.model_coordinators.cache_scenario_database",
tables_to_cache=[
"annual_employment_control_totals",
"annual_household_control_totals",
"buildings",
"building_types",
"development_event_history",
"gridcells",
"households",
"job_building_types",
"jobs",
"travel_data",
"zones",
"counties",
"commercial_development_location_choice_model_coefficients",
"commercial_development_location_choice_model_specification",
"commercial_employment_location_choice_model_coefficients",
"commercial_employment_location_choice_model_specification",
"home_based_employment_location_choice_model_specification",
"home_based_employment_location_choice_model_coefficients",
"industrial_employment_location_choice_model_coefficients",
"industrial_employment_location_choice_model_specification",
"industrial_development_location_choice_model_coefficients",
"industrial_development_location_choice_model_specification",
"residential_development_location_choice_model_coefficients",
"residential_development_location_choice_model_specification",
"fazes",
"urbansim_constants",
"household_location_choice_model_coefficients",
"household_location_choice_model_specification",
"land_price_model_coefficients",
"land_price_model_specification",
"residential_land_share_model_coefficients",
"residential_land_share_model_specification",
"plan_type_group_definitions",
"plan_type_groups",
"large_areas",
"household_characteristics_for_ht",
"development_types",
"development_type_group_definitions",
"development_constraints",
"annual_relocation_rates_for_households",
"annual_relocation_rates_for_jobs",
"base_year",
"cities",
"development_events",
"development_type_groups",
"employment_adhoc_sector_group_definitions",
"employment_adhoc_sector_groups",
"employment_events",
"employment_sectors",
"land_use_events",
"plan_types",
"race_names",
"target_vacancies",
"jobs_for_estimation",
"households_for_estimation",
"development_events_exogenous",
"job_building_types",
],
tables_to_cache_nchunks={"gridcells": 4},
tables_to_copy_to_previous_years={
"development_type_groups": 1995,
"development_types": 1995,
"development_type_group_definitions": 1995,
},
),
"models": [], # run no urbansim models
"scenario_database_configuration": ScenarioDatabaseConfiguration(database_name="PSRC_2000_baseyear"),
"base_year": 2000,
"years": (2001, 2001),
"unroll_gridcells": False,
}
config.merge(config_changes)
travel_model_config = create_travel_model_configuration(
"baseline_travel_model_psrc_fresh",
mode="get_emme2_data_after_run",
years_to_run={2001: "2000_06"},
emme2_batch_file="QUICKRUN.BAT",
)
config["travel_model_configuration"] = travel_model_config
self.merge(config)
# Opus/UrbanSim urban simulation software.
# Copyright (C) 2005-2009 University of Washington
# See opus_core/LICENSE
from opus_core.configuration import Configuration
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
class EstimationBaseConfig(Configuration):
def __init__(self, base_configuration=AbstractUrbansimConfiguration):
config = base_configuration()
config.merge(get_changes_for_estimation(config))
self.merge(config)
def get_changes_for_estimation(config):
estimation_configuration = {}
if "base_year" in config:
estimation_configuration["years"] = range(config["base_year"],
config["base_year"])
estimation_configuration["sample_size_locations"] = 30
estimation_configuration["seed"] = 1, #(1,1)
return estimation_configuration
run_configuration = AbstractUrbansimConfiguration()
estimation_configuration = get_changes_for_estimation(run_configuration)
run_configuration.merge(estimation_configuration)
def __init__(self):
AbstractUrbansimConfiguration.__init__(self)
config_changes = {
'project_name':
'vibe_gridcell',
'description':
'Eugene baseline',
'base_year':
1980,
'years': (1981, 1982, 1983),
'models': [
'prescheduled_events',
'events_coordinator',
#'residential_land_share_model',
#'fertility_model'
#'land_price_model',
#'development_project_transition_model',
#'residential_development_project_location_choice_model',
#'commercial_development_project_location_choice_model',
#'industrial_development_project_location_choice_model',
#'development_event_transition_model',
#'events_coordinator',
#'residential_land_share_model',
'household_transition_model',
#'employment_transition_model',
'household_relocation_model',
'household_location_choice_model',
#'employment_relocation_model',
#{ 'employment_location_choice_model': { 'group_members': '_all_'}},
#'distribute_unplaced_jobs_model',
],
#'scenario_database_configuration': ScenarioDatabaseConfiguration(database_name = 'eugen_base_year_data_1980'),
#'scenario_database_configuration': ScenarioDatabaseConfiguration(database_name = 'vibe_base_year_data_1980'),
'scenario_database_configuration':
ScenarioDatabaseConfiguration(
database_name='urbansim_export_test'),
'creating_baseyear_cache_configuration':
CreatingBaseyearCacheConfiguration(
#cache_directory_root = os.path.join(os.environ['OPUS_HOME'], 'data/eugene_gridcell/runs'),
cache_directory_root=os.path.join(os.environ['OPUS_HOME'],
'data/vibe_gridcell/runs'),
#cache_scenario_database = storage,
cache_from_database=True,
baseyear_cache=BaseyearCacheConfiguration(
existing_cache_to_copy=os.path.join(
os.environ['OPUS_HOME'],
'data/vibe_gridcell/base_year_data'), ),
cache_scenario_database=
'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache=[
#'annual_employment_control_totals',
'annual_household_control_totals',
#'buildings',
'building_types',
'development_event_history',
'gridcells',
'households',
'job_building_types',
'jobs',
#'travel_data',
'zones',
#'counties',
#'commercial_development_location_choice_model_coefficients',
#'commercial_development_location_choice_model_specification',
#'commercial_employment_location_choice_model_coefficients',
#'commercial_employment_location_choice_model_specification',
#'home_based_employment_location_choice_model_specification',
#'home_based_employment_location_choice_model_coefficients',
#'industrial_employment_location_choice_model_coefficients',
#'industrial_employment_location_choice_model_specification',
#'industrial_development_location_choice_model_coefficients',
#'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
#'land_price_model_coefficients',
#'land_price_model_specification',
#'residential_land_share_model_coefficients',
#'residential_land_share_model_specification',
'household_characteristics_for_ht',
'development_types',
'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
#'annual_relocation_rates_for_jobs',
'base_year',
#'cities',
'development_type_groups',
#'employment_adhoc_sector_group_definitions',
#'employment_adhoc_sector_groups',
#'employment_sectors',
'plan_types',
'plan_type_groups',
'plan_type_group_definitions',
'race_names',
'target_vacancies',
#'jobs_for_estimation',
'households_for_estimation',
],
tables_to_cache_nchunks={'gridcells': 1},
tables_to_copy_to_previous_years={
'development_type_group_definitions': 1975,
'development_type_groups': 1975,
'development_types': 1975,
'development_constraints': 1975,
'urbansim_constants': 1975,
},
),
'dataset_pool_configuration':
DatasetPoolConfiguration(
package_order=['vibe_min', 'urbansim', 'opus_core'], ),
}
self.merge(config_changes)
def __init__(self):
AbstractUrbansimConfiguration.__init__(self)
models_configuration = self['models_configuration']
self['project_name'] = 'urbansim_parcel'
## employment_location_choice_model is defined for gridcells at urbansim package
if "home_based_employment_location_choice_model" in models_configuration:
del models_configuration["home_based_employment_location_choice_model"]
if "non_home_based_employment_location_choice_model" in models_configuration:
del models_configuration["non_home_based_employment_location_choice_model"]
models_configuration['workplace_choice_model_for_resident'] = {
"estimation":"opus_core.bhhh_mnl_estimation",
"sampler":"opus_core.samplers.weighted_sampler",
"sample_size_locations":30,
"weights_for_estimation_string":"is_placed_job=(urbansim_parcel.job.zone_id > 0).astype(int32)",
"compute_capacity_flag":True,
"capacity_string":"urbansim_parcel.job.is_untaken_non_home_based_job",
#"capacity_string":"(job.building_type==2).astype(int32)",
### each non home-based job can only be chosen once by one person
"number_of_units_string":"(job.building_type==2).astype(int32)",
'lottery_max_iterations': 10,
'number_of_agents_string': 'job.number_of_agents(person)',
# "sampler":"opus_core.samplers.stratified_sampler",
# "stratum":"district_id = job.disaggregate(psrc_parcel.building.district_id)",
# "sample_size_from_each_stratum": 5,
# "sample_size_from_chosen_stratum":4,
# "include_chosen_choice":True,
# "sample_size_locations":95,
# "weights_for_estimation_string":"is_placed_job=(urbansim_parcel.job.zone_id > 0).astype(int32)",
# "compute_capacity_flag":True,
# "capacity_string":"(job.building_type==2).astype(int32)",
# ### each non home-based job can only be chosen once by one person
# "number_of_units_string":"(job.building_type==2).astype(int32)",
}
my_controller_configuration = {
'real_estate_price_model': {
"import": {"urbansim.models.real_estate_price_model":
"RealEstatePriceModel"},
"init": {
"name": "RealEstatePriceModel",
"arguments": {"submodel_string": "'land_use_type_id'",
"outcome_attribute": "'ln_unit_price=ln(urbansim_parcel.parcel.unit_price)'",
"filter_attribute": "'numpy.logical_or(urbansim_parcel.parcel.building_sqft, urbansim_parcel.parcel.is_land_use_type_vacant)'",
"dataset_pool": "dataset_pool",
"estimate_config": "{'save_predicted_values_and_errors':True}"
},
},
"prepare_for_run": {
"name": "prepare_for_run",
"arguments": {"specification_storage": "base_cache_storage",
"specification_table": "'real_estate_price_model_specification'",
"coefficients_storage": "base_cache_storage",
"coefficients_table": "'real_estate_price_model_coefficients'"},
"output": "(specification, coefficients)"
},
"run": {
"arguments": {
"specification": "specification",
"coefficients":"coefficients",
"dataset": "parcel",
"data_objects": "datasets",
"run_config": "Resources({'exclude_outliers_from_initial_error': True, 'outlier_is_less_than': %s, 'outlier_is_greater_than': %s})" % (log(UNIT_PRICE_RANGE[0]), log(UNIT_PRICE_RANGE[1]))
}
},
"prepare_for_estimate": {
"name": "prepare_for_estimate",
"arguments": {"specification_storage": "base_cache_storage",
"specification_table": "'real_estate_price_model_specification'",
"filter_variable":"'numpy.logical_and(urbansim_parcel.parcel.unit_price>%s,urbansim_parcel.parcel.unit_price<%s, urbansim_parcel.parcel.existing_units>100)'" % (UNIT_PRICE_RANGE[0], UNIT_PRICE_RANGE[1]),
"dataset": "parcel",
"threshold": 0},
"output": "(specification, index)"
},
"estimate": {
"arguments": {
"specification": "specification",
"outcome_attribute": "'ln_unit_price=ln(urbansim_parcel.parcel.unit_price)'",
"dataset": "parcel",
"index": "index",
"data_objects": "datasets",
"debuglevel": 'debuglevel',
#'procedure': "'opus_core.bma_for_linear_regression_r'",
},
"output": "(coefficients, dummy)"
}
},
'employment_transition_model':
EmploymentTransitionModelConfigurationCreator(
location_id_name="building_id"
).execute(),
'employment_relocation_model':
EmploymentRelocationModelConfigurationCreator(
location_id_name = 'building_id',
output_index = 'erm_index').execute(),
'employment_location_choice_model':
EmploymentLocationChoiceModelConfigurationCreator(
sample_size_locations = 60,
location_set = "building",
input_index = 'erm_index',
estimation_weight_string = None,
#agents_for_estimation_table = None, # will take standard jobs table
agents_for_estimation_table = "jobs_for_estimation",
join_datasets = True,
#estimation_size_agents = 0.3,
number_of_units_string = "total_SSS_job_space",
filter = "building.non_residential_sqft>0",
filter_for_estimation = "numpy.logical_and(job.building_id>0, job.disaggregate(building.non_residential_sqft) > 0)",
#filter_for_estimation = "numpy.logical_and(job.building_id>0,job.join_flag<3)",
portion_to_unplace = 0,
capacity_string = "vacant_SSS_job_space",
variable_package = "urbansim_parcel",
lottery_max_iterations = 10
).execute(),
'home_based_employment_location_choice_model':
EmploymentLocationChoiceModelConfigurationCreator(
location_set = "building",
input_index = 'erm_index',
estimation_weight_string = "vacant_home_based_job_space", #"pre_2001=building.year_built<=2000",
agents_for_estimation_table = None, # will take standard jobs table
estimation_size_agents = 0.4,
number_of_units_string = None,
filter = "numpy.logical_and(building.residential_units, building.sqft_per_unit)",
filter_for_estimation = "numpy.logical_and(job.building_id>0, job.disaggregate(building.sqft_per_unit>0))",
portion_to_unplace = 0,
capacity_string = "vacant_home_based_job_space",
variable_package = "urbansim_parcel",
lottery_max_iterations = 7
).execute(),
'distribute_unplaced_jobs_model': DistributeUnplacedJobsModelConfigurationCreator(
location_set = 'building',
filter = 'urbansim_parcel.building.is_governmental',
agents_filter = 'urbansim.job.is_in_employment_sector_group_scalable_sectors'
).execute(),
'distribute_unplaced_mining_utilities_jobs_model': DistributeUnplacedJobsModelConfigurationCreator(
location_set = 'building',
filter = None,
agents_filter = 'numpy.logical_or(job.sector_id==1, job.sector_id==9)'
).execute(),
'governmental_employment_location_choice_model':
GovernmentalEmploymentLocationChoiceModelConfigurationCreator(
input_index = 'erm_index',
location_set = 'building',
filter = 'urbansim_parcel.building.is_governmental'
).execute(),
"household_relocation_model" : {
"import": {"urbansim.models.household_relocation_model_creator":
"HouseholdRelocationModelCreator"
},
"init": {
"name": "HouseholdRelocationModelCreator().get_model",
"arguments": {
"debuglevel": 'debuglevel',
"location_id_name": "'building_id'",
},
},
"prepare_for_run": {
"name": "prepare_for_run",
"arguments": {"what": "'households'", "rate_storage": "base_cache_storage",
"rate_table": "'annual_relocation_rates_for_households'"},
"output": "hrm_resources"
},
"run": {
"arguments": {"agent_set": "household", "resources": "hrm_resources"},
"output": "hrm_index"
}
},
"process_pipeline_events":{
"import": {"urbansim_parcel.models.process_pipeline_events":
"ProcessPipelineEvents"},
"init": {
"name": "ProcessPipelineEvents"},
"run": {
"arguments": {'building_dataset': 'building',
'year':'year',
"storage": "base_cache_storage"},
"output": "scheduled_development_events"
}
},
"tour_schedule_model":{
"import": {"urbansim_parcel.models.tour_schedule_model":
"TourScheduleModel"},
"init": {
"name": "TourScheduleModel"},
"run": {
"arguments": {'person_set': 'person',
'tour_set': 'tour',
'tour_filter': "'tour.w2h_act == 1'"
},
"output": "sampled_tour"
}
},
'household_transition_model': {
'import': {
'urbansim_parcel.models.household_transition_model': 'HouseholdTransitionModel'
},
'init': {
'arguments': {'debuglevel': 'debuglevel'},
'name': 'HouseholdTransitionModel'
},
'prepare_for_run': {
'arguments': {'storage': 'base_cache_storage'},
'name': 'prepare_for_run',
'output': '(control_totals, characteristics)'
},
'run': {
'arguments': {
'characteristics': 'characteristics',
'control_totals': 'control_totals',
'person_set': 'person',
'household_set': 'household',
'year': 'year'
}
}
},
# configuration for parcel-based developer model
'expected_sale_price_model': {
"import": {"urbansim_parcel.models.development_project_proposal_regression_model":
"DevelopmentProjectProposalRegressionModel"},
"init": {
"name": "DevelopmentProjectProposalRegressionModel",
"arguments": {"submodel_string": "'land_use_type_id=development_project_proposal.disaggregate(development_template.land_use_type_id)'",
#"submodel_string": "'building_type_id=development_project_proposal.disaggregate(development_template.building_type_id)'",
#"filter_attribute": "'urbansim_parcel.development_project_proposal.is_viable'",
"filter_attribute": "'urbansim_parcel.development_project_proposal.is_size_fit'",
"outcome_attribute_name":"'ln_unit_price_expected'",
"dataset_pool": "dataset_pool"
},
},
"prepare_for_run": {
"name": "prepare_for_run",
"arguments": {"parcel_filter_for_new_development":"'has_vacant_land=urbansim_parcel.parcel.vacant_land_area > 0'",
"parcel_filter_for_redevelopment":"'low_improvement_ratio_of_parcels_with_bldg=(parcel.number_of_agents(building)>0)*(urbansim_parcel.parcel.improvement_value / ( urbansim_parcel.parcel.unit_price * urbansim_parcel.parcel.existing_units ) < 0.1)*(parcel.aggregate(urbansim_parcel.building.age_masked, function=mean)>30)'",
"template_filter":"'development_template.is_active > 0'",
"specification_storage": "base_cache_storage",
"specification_table": "'real_estate_price_model_specification'",
"coefficients_storage": "base_cache_storage",
"coefficients_table": "'real_estate_price_model_coefficients'",
"spec_replace_module_variable_pair": "('psrc_parcel.estimation.repm_specification', 'variables_for_development_project_proposal')",
"dataset_pool": "dataset_pool",},
"output": "(development_project_proposal, specification, coefficients)"
},
"run": {
"arguments": {
"specification": "specification",
"coefficients":"coefficients",
"dataset": 'development_project_proposal',
"data_objects": "datasets" },
"output":"development_project_proposal" #get the development project proposal back
},
},
'development_proposal_choice_model': {
"import": {"urbansim_parcel.models.development_project_proposal_sampling_model":
"DevelopmentProjectProposalSamplingModel"},
"init": {
"name": "DevelopmentProjectProposalSamplingModel",
"arguments": {"proposal_set": "development_project_proposal",
#weight_string omitted to use defalut value "exp_ROI = exp(urbansim_parcel.development_project_proposal.expected_rate_of_return_on_investment)",
"filter_attribute": None, # the filter has been handled in the process of creating proposal set
# (prepare_for_run in expected_sale_price_model)
},
},
"run": {
"arguments": {'n':500, # sample 500 proposal at a time, evaluate them one by one
},
"output":"(development_project_proposal, demolished_buildings)"
},
},
'development_project_proposal_choice_model': {
"import": {"urbansim_parcel.models.development_project_proposal_choice_model":
"DevelopmentProjectProposalChoiceModel"},
"init": {
"name": "DevelopmentProjectProposalChoiceModel",
"arguments": {"proposal_set": "development_project_proposal",
#weight_string omitted to use defalut value "exp_ROI = exp(urbansim_parcel.development_project_proposal.expected_rate_of_return_on_investment)",
"filter": "'development_project_proposal.status_id==4'" # id_tentative
},
},
"prepare_for_run":{
"name": "prepare_for_run",
'arguments': {
'coefficients_storage': 'base_cache_storage',
'coefficients_table': "'development_project_proposal_choice_model_coefficients'",
'specification_storage': 'base_cache_storage',
'specification_table':"'development_project_proposal_choice_model_specification'",
},
'output': '(specification, coefficients)'
},
"run": {
"arguments": {
'n':500, # sample 500 proposal at a time, evaluate them one by one
"specification": "specification",
"coefficients":"coefficients",
#"agent_set": 'development_project_proposal',
},
"output":"(development_project_proposal, demolished_buildings)"
},
"prepare_for_estimate": {
"name": "prepare_for_estimate",
"arguments": {
"agent_set":"development_project_proposal",
"agents_for_estimation_storage": "base_cache_storage",
"agents_for_estimation_table": "'development_project_proposals_for_estimation'",
"filter_for_estimation_set":None,
"data_objects": "datasets"
},
"output": "(specification, index)"
},
'estimate': {
'arguments': {
'agent_set': 'development_project_proposal',
'agents_index':'index',
'data_objects': 'datasets',
'debuglevel': 0,
'procedure': "'opus_core.bhhh_mnl_estimation'",
'specification': 'specification'
},
'output': '(coefficients, _)'
},
},
'building_construction_model': {
"import": {"urbansim_parcel.models.building_construction_model":
"BuildingConstructionModel"},
"init": {
"name": "BuildingConstructionModel"
},
"run": {
"arguments": {
"development_proposal_set": "development_project_proposal",
"building_dataset": "building",
"dataset_pool": "dataset_pool",
"buildings_to_be_demolished": "demolished_buildings"
}
}
},
# models related to workplace_choice_model
# 'household_person_consistency_keeper':{
# "import": {"urbansim_parcel.models.persons_consistency_keeper_model":"PersonDatasetConsistencyKeeperModel"},
# "init": {
# "name": "PersonDatasetConsistencyKeeperModel",
# "arguments": {},
# },
# "run": {
# "arguments": {"household_set": "household",
# "person_set":"person",
# "expand_person_set":True,
# }
# },
# },
'job_person_consistency_keeper':{
"import": {"urbansim_parcel.models.persons_consistency_keeper_model":"PersonDatasetConsistencyKeeperModel"},
"init": {
"name": "PersonDatasetConsistencyKeeperModel",
"arguments": {},
},
"run": {
"arguments": {"job_set": "job",
"person_set":"person",
"expand_person_set":False,
}
},
},
'workplace_choice_model_for_resident': {
"import": {"urbansim_parcel.models.workplace_choice_model":"WorkplaceChoiceModel"},
"init": {
"name": "WorkplaceChoiceModel",
"arguments": {
"location_set":"job",
"model_name":"'Non-home-based Workplace Choice Model for residents'",
"short_name":"'NHBWCM'",
"choices":"'urbansim.lottery_choices'",
"submodel_string":None, #"'psrc.person.household_income'",
"filter": "'urbansim_parcel.job.is_untaken_non_home_based_job'",
"location_id_string":"'job_id'",
"run_config":models_configuration['workplace_choice_model_for_resident'],
"estimate_config":models_configuration['workplace_choice_model_for_resident']
}},
"prepare_for_run": {
"name": "prepare_for_run",
"arguments": {"specification_storage": "base_cache_storage", #"models_configuration['specification_storage']",
"specification_table": "'workplace_choice_model_for_resident_specification'",
"coefficients_storage": "base_cache_storage", #"models_configuration['coefficients_storage']",
"coefficients_table": "'workplace_choice_model_for_resident_coefficients'",
},
"output": "(specification, coefficients)"
},
"run": {
"arguments": {"specification": "specification",
"coefficients":"coefficients",
"agent_set": "person",
"agents_index": None,
"agents_filter":"'urbansim_parcel.person.is_non_home_based_worker_without_job'",
"data_objects": "datasets",
"chunk_specification":"{'records_per_chunk':50000}",
"debuglevel": 'debuglevel',
},
},
# the estimate method is not available before the estimation data is ready
"prepare_for_estimate": {
"name": "prepare_for_estimate",
"arguments": {
"agent_set":"person",
"household_set": "household",
"join_datasets": "True",
"agents_for_estimation_storage": "base_cache_storage",
"agents_for_estimation_table": "'persons_for_estimation'",
"households_for_estimation_table":"'households_for_estimation'",
"filter":"'urbansim_parcel.person.is_non_home_based_worker_with_job'",
"data_objects": "datasets"
},
"output": "(specification, workers_index)"
},
"estimate": {
"arguments": {
"specification": "specification",
"agent_set": "person",
"agents_index": "workers_index",
"data_objects": "datasets",
"debuglevel": 'debuglevel',
},
"output": "(coefficients, dummy)"
},
},
'work_at_home_choice_model': {
'import': {
'urbansim_parcel.models.work_at_home_choice_model': 'WorkAtHomeChoiceModel'
},
'init': {
'arguments': {
'choice_set': 'job',
'filter': "'urbansim_parcel.job.is_untaken_home_based_job'",
},
'name': 'WorkAtHomeChoiceModel'
},
'prepare_for_run': {
"name": "prepare_for_run",
'arguments': {
'coefficients_storage': 'base_cache_storage',
'coefficients_table': "'work_at_home_choice_model_coefficients'",
'specification_storage': 'base_cache_storage',
'specification_table': "'work_at_home_choice_model_specification'",
'agent_set': 'person',
'agents_filter': "'urbansim_parcel.person.is_worker_without_job'",
'data_objects': 'datasets'
},
'output': '(specification, coefficients, _index)'
},
'run': {
'arguments': {
'agent_set': 'person',
'agents_index' : '_index',
'choose_job_only_in_residence_zone': False,
'chunk_specification': "{'nchunks':1}",
'coefficients': 'coefficients',
'data_objects': 'datasets',
'specification': 'specification'
}
},
"prepare_for_estimate": {
"name": "prepare_for_estimate",
"arguments": {
"agent_set":"person",
"household_set": "household",
"join_datasets": "True",
"agents_for_estimation_storage": "base_cache_storage",
"agents_for_estimation_table": "'persons_for_estimation'",
"households_for_estimation_table":"'households_for_estimation'",
"filter":"'urbansim_parcel.person.is_worker'",
"data_objects": "datasets"
},
"output": "(specification, agents_index)"
},
'estimate': {
'arguments': {
'agent_set': 'person',
'agents_index': 'agents_index',
'data_objects': 'datasets',
'debuglevel': 0,
'procedure': "'opus_core.bhhh_mnl_estimation'",
'specification': 'specification'
},
'output': '(coefficients, _)'
},
},
# "job_change_model":{
# "import": {"urbansim.models.agent_relocation_model":
# "AgentRelocationModel"
# },
# "init": {
# "name": "AgentRelocationModel",
# "arguments": {"choices":"opus_core.random_choices",
# "probabilities":"psrc.job_change_probabilities",
# "location_id_name":"job_id",
# "model_name":"job change model",
# "debuglevel": 'debuglevel',
# },
# },
# "prepare_for_run": {
# "name": "prepare_for_run",
# "arguments": {"what": "'person'", "rate_storage": "base_cache_storage",
# "rate_table": "'annual_job_change_rates_for_workers'"},
# "output": "jcm_resources"
# },
# "run": {
# "arguments": {"agent_set": "person", "resources": "jcm_resources"},
# "output": "jcm_index"
# }
# },
'modify_workers_jobs_after_moving_households': {
"import": {"opus_core.join_attribute_modification_model": "JoinAttributeModificationModel"},
"init": {"name": "JoinAttributeModificationModel"},
"run": {"arguments": {
"dataset": "person",
"secondary_dataset": "household",
#"index": "hrm_index", # this must be the same that goes into the HLCM argument 'agents_index'
"attribute_to_be_modified": "'job_id'",
"value": -1,
"filter": "'household.building_id <> urbansim.household.building_id_lag1'"
}
},
},
'modify_workers_jobs_after_moving_jobs': {
"import": {"opus_core.join_attribute_modification_model": "JoinAttributeModificationModel"},
"init": {"name": "JoinAttributeModificationModel"},
"run": {"arguments": {
"dataset": "person",
"secondary_dataset": "job",
#"index": "erm_index", # this must be the same that goes into the ELCM argument 'agents_index'
"attribute_to_be_modified": "'job_id'",
"value": -1,
"filter": "'job.building_id <> urbansim.job.building_id_lag1'"
}
},
},
'water_demand_model': {
"import": {"psrc_parcel.models.water_demand_model":"WaterDemandModel"},
"init": {
"name": "WaterDemandModel",
"arguments": {"submodel_string": "'land_use_type_id'",
"outcome_attribute": "'ln_month_combination_2'",
"filter_attribute": None,
"dataset_pool": "dataset_pool",
},
},
"prepare_for_run": {
"name": "prepare_for_run",
"arguments": {"specification_storage": "base_cache_storage",
"specification_table": "'water_demand_model_specification_for_month_combination_2'",
"coefficients_storage": "base_cache_storage",
"coefficients_table": "'water_demand_model_coefficients_for_month_combination_2'"},
"output": "(specification, coefficients, _)"
},
"run": {
"arguments": {
"specification": "specification",
"coefficients":"coefficients",
"dataset": "parcel",
"data_objects": "datasets",
}
},
},
}
for month in range(2, 13, 2):
month_string = "month_combination_" + str(month)
my_controller_configuration['water_demand_model_for_' + month_string] = copy.deepcopy(my_controller_configuration['water_demand_model'])
my_controller_configuration['water_demand_model_for_' + month_string]['init']['arguments']['outcome_attribute'] = "'%s'" % month_string
my_controller_configuration['water_demand_model_for_' + month_string]['prepare_for_run']['arguments']['specification_table'] = "'water_demand_model_specification_for_%s'" % month_string
my_controller_configuration['water_demand_model_for_' + month_string]['prepare_for_run']['arguments']['coefficients_table'] = "'water_demand_model_coefficients_for_%s'" % month_string
my_controller_configuration["workplace_choice_model_for_immigrant"] = copy.deepcopy(my_controller_configuration["workplace_choice_model_for_resident"])
my_controller_configuration["workplace_choice_model_for_immigrant"]["init"]["arguments"]["model_name"] = "'Non-home-based Workplace Choice Model for immigrants'"
my_controller_configuration["workplace_choice_model_for_immigrant"]["prepare_for_run"]["arguments"]["specification_table"] = "'workplace_choice_model_for_immigrant_specification'"
my_controller_configuration["workplace_choice_model_for_immigrant"]["prepare_for_run"]["arguments"]["coefficients_table"] = "'workplace_choice_model_for_immigrant_coefficients'"
my_controller_configuration["workplace_choice_model_for_immigrant"]["run"]["arguments"]["agents_filter"] = "'psrc.person.is_immigrant_worker_without_job'"
for model in my_controller_configuration.keys():
if model not in self["models_configuration"].keys():
self["models_configuration"][model] = {}
self['models_configuration'][model]['controller'] = my_controller_configuration[model]
#settings for the HLCM
hlcm_controller = self["models_configuration"]["household_location_choice_model"]["controller"]
hlcm_controller["init"]["arguments"]["location_set"] = "building"
hlcm_controller["init"]["arguments"]["location_id_string"] = "'building_id'"
hlcm_controller["init"]["arguments"]["estimation_weight_string"] = "'urbansim_parcel.building.vacant_residential_units'"
hlcm_controller["init"]["arguments"]["simulation_weight_string"] = "'has_vacant_units=urbansim_parcel.building.vacant_residential_units>0'"
hlcm_controller["init"]["arguments"]["capacity_string"] = "'urbansim_parcel.building.vacant_residential_units'"
#hlcm_controller["init"]["arguments"]["estimation_weight_string"] = "'has_eg_1_units=building.residential_units>=1'"
#hlcm_controller["init"]["arguments"]["capacity_string"] = "'has_eg_1_units=building.residential_units>=1'"
hlcm_controller["init"]["arguments"]['sample_size_locations']=30
hlcm_controller["init"]["arguments"]['sampler']="'opus_core.samplers.weighted_sampler'"
#hlcm_controller["init"]["arguments"]["submodel_string"] = "'urbansim.household.income_category'"
hlcm_controller["init"]["arguments"]["estimation_size_agents"] = None
#hlcm_controller["init"]["arguments"]["number_of_units_string"] = None
hlcm_controller["init"]["arguments"]["variable_package"] = "'urbansim_parcel'"
hlcm_controller["init"]["arguments"]["run_config"] = "{'lottery_max_iterations': 7}"
hlcm_controller["init"]["arguments"]["filter"] = "'numpy.logical_and(urbansim_parcel.building.is_residential, numpy.logical_and(numpy.logical_and(building.residential_units, building.sqft_per_unit), urbansim_parcel.building.unit_price > 0))'"
#hlcm_controller["init"]["arguments"]["filter"] = "'urbansim_parcel.building.is_residential'"
# hlcm_controller["init"]["arguments"]["filter"] = "'numpy.logical_and(numpy.logical_and(building.residential_units, building.sqft_per_unit), numpy.logical_and(urbansim_parcel.building.unit_price > %s, urbansim_parcel.building.unit_price< %s))'" % (UNIT_PRICE_RANGE[0], UNIT_PRICE_RANGE[1])
#hlcm_controller["init"]["arguments"]["filter"] = "'numpy.logical_and(building.residential_units, building.sqft_per_unit)'"
hlcm_controller["init"]["arguments"]["estimate_config"] = "{'wesml_sampling_correction_variable':'psrc_parcel.building.wesml_sampling_correction_variable'}"
hlcm_controller["prepare_for_estimate"]["arguments"]["agents_for_estimation_table"] = "'households_for_estimation'"
hlcm_controller["prepare_for_estimate"]["arguments"]["filter"] = "'numpy.logical_and(household.building_id>1, household.disaggregate(building.sqft_per_unit>0)) * household.move'" # filtering out agents for estimation with valid location
#hlcm_controller["prepare_for_estimate"]["arguments"]["filter"] = "'household.move==1'" # filtering out agents for estimation with valid location
hlcm_controller["run"]["arguments"]["chunk_specification"] ="{'records_per_chunk':50000}"
hlcm_controller["prepare_for_estimate"]["arguments"]["join_datasets"] = True
hlcm_controller["prepare_for_estimate"]["arguments"]["index_to_unplace"] = 'hrm_index' #None
# settng estimation procedure
#hlcm_controller["estimate"]["arguments"]["procedure"] = "'bhhh_wesml_mnl_estimation'"
hlcm_controller["estimate"]["arguments"]["procedure"] = "'bhhh_mnl_estimation'"
models_configuration['household_location_choice_model']["controller"].replace(hlcm_controller)
self["datasets_to_preload"] = {
'zone':{},
'household':{},
'building':{},
'parcel':{'package_name':'urbansim_parcel'},
"building_type":{'package_name':'urbansim_parcel'},
'travel_data':{}
}
models_configuration['governmental_employment_location_choice_model']['controller']['import'] = {
'urbansim_parcel.models.scaling_jobs_model': 'ScalingJobsModel'}
models_configuration['distribute_unplaced_jobs_model']['controller']['import'] = {
'urbansim_parcel.models.distribute_unplaced_jobs_model': 'DistributeUnplacedJobsModel'}
models_configuration['distribute_unplaced_mining_utilities_jobs_model']['controller']['import'] = {
'urbansim_parcel.models.distribute_unplaced_jobs_model': 'DistributeUnplacedJobsModel'}
models_configuration['real_estate_price_model_for_all_parcels'] = Configuration(models_configuration['real_estate_price_model'])
models_configuration['real_estate_price_model_for_all_parcels']['controller']['init']['arguments']['filter_attribute'] = None
def __init__(self):
config = AbstractUrbansimConfiguration()
end_year = 2030
config_changes = {
'description':'baseline with travel model',
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = '/urbansim_cache/psrc/gridcell_run_set', #'/projects/urbansim5/urbansim_cache/psrc_gridcell',
cache_from_database = True,
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache = [
'annual_employment_control_totals',
'annual_household_control_totals',
'buildings',
'building_types',
'development_event_history',
'gridcells',
'households',
'job_building_types',
'jobs',
'travel_data',
'zones',
'counties',
'commercial_development_location_choice_model_coefficients',
'commercial_development_location_choice_model_specification',
'commercial_employment_location_choice_model_coefficients',
'commercial_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_specification',
'industrial_development_location_choice_model_coefficients',
'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
'fazes',
'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
'land_price_model_coefficients',
'land_price_model_specification',
'residential_land_share_model_coefficients',
'residential_land_share_model_specification',
'plan_type_group_definitions',
'plan_type_groups',
'large_areas',
'household_characteristics_for_ht',
'development_types',
'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
'annual_relocation_rates_for_jobs',
'base_year',
'cities',
'development_events',
'development_type_groups',
'employment_adhoc_sector_group_definitions',
'employment_adhoc_sector_groups',
'employment_events',
'employment_sectors',
'land_use_events',
'plan_types',
'race_names',
'target_vacancies',
'jobs_for_estimation',
'households_for_estimation',
'development_events_exogenous',
'job_building_types',
'persons',
'persons_for_estimation',
],
tables_to_cache_nchunks = {'gridcells': 4},
tables_to_copy_to_previous_years = {
'development_type_groups': 1995,
'development_types': 1995,
'development_type_group_definitions': 1995,
'development_constraints': 1995,
'urbansim_constants': 1995,
},
),
'scenario_database_configuration': ScenarioDatabaseConfiguration(
database_name = 'PSRC_2000_baseyear',
),
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['psrc', 'urbansim', 'opus_core'],
),
'base_year':2000,
'years':(2001, end_year),
}
config.merge(config_changes)
self.merge(config)
self['post_year_configuration'] = create_post_year_configuration(end_year)
def __init__(self):
config = AbstractUrbansimConfiguration()
config_changes = {
'project_name':'psrc',
'description':'baseline with travel model',
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = 'd:/urbansim_cache',
cache_from_database = True,
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache = [
'annual_employment_control_totals',
'annual_household_control_totals',
'buildings',
'building_types',
'development_event_history',
'gridcells',
'households',
'job_building_types',
'jobs',
'travel_data',
'zones',
'counties',
'commercial_development_location_choice_model_coefficients',
'commercial_development_location_choice_model_specification',
'commercial_employment_location_choice_model_coefficients',
'commercial_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_specification',
'industrial_development_location_choice_model_coefficients',
'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
'fazes',
'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
'land_price_model_coefficients',
'land_price_model_specification',
'residential_land_share_model_coefficients',
'residential_land_share_model_specification',
'plan_type_group_definitions',
'plan_type_groups',
'large_areas',
'household_characteristics_for_ht',
'development_types',
'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
'annual_relocation_rates_for_jobs',
'base_year',
'cities',
'development_events',
'development_type_groups',
'employment_adhoc_sector_group_definitions',
'employment_adhoc_sector_groups',
'employment_events',
'employment_sectors',
'land_use_events',
'plan_types',
'race_names',
'target_vacancies',
'jobs_for_estimation',
'households_for_estimation',
'development_events_exogenous',
'job_building_types',
'persons',
'persons_for_estimation',
'constant_taz_columns'
],
tables_to_cache_nchunks = {'gridcells': 4},
tables_to_copy_to_previous_years = {
'development_type_groups': 1995,
'development_types': 1995,
'development_type_group_definitions': 1995,
'development_constraints': 1995,
'urbansim_constants': 1995,
},
),
'scenario_database_configuration': ScenarioDatabaseConfiguration(
database_name = 'PSRC_2000_baseyear',
),
'dataset_pool_configuration': DatasetPoolConfiguration(
package_order=['psrc', 'urbansim', 'opus_core'],
),
'base_year':2000,
'years':(2001, 2030),
}
config.merge(config_changes)
travel_model_config = create_travel_model_configuration('baseline_travel_model_psrc')
config['travel_model_configuration'] = travel_model_config
self.merge(config)
def __init__(self):
config = AbstractUrbansimConfiguration()
config_changes = {
'project_name':
'washtenaw',
'description':
'Region Pilot Baseline',
'scenario_database_configuration':
ScenarioDatabaseConfiguration(database_name='washtenaw_class', ),
'models': [
'prescheduled_events',
'events_coordinator',
'residential_land_share_model',
'land_price_model',
'regional_development_project_transition_model',
'residential_regional_development_project_location_choice_model',
'commercial_regional_development_project_location_choice_model',
'industrial_regional_development_project_location_choice_model',
'development_event_transition_model',
'events_coordinator',
'residential_land_share_model',
'jobs_event_model',
#'households_event_model',
'regional_household_transition_model',
'regional_household_relocation_model',
'regional_household_location_choice_model',
'regional_employment_transition_model',
'regional_employment_relocation_model',
{
'regional_employment_location_choice_model': {
'group_members': ['_all_']
}
},
'regional_distribute_unplaced_jobs_model'
],
'cache_directory':
None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':
CreatingBaseyearCacheConfiguration(
cache_directory_root="/urbansim_cache/washtenaw",
cache_from_database=True,
baseyear_cache=BaseyearCacheConfiguration(
existing_cache_to_copy=
"/urbansim_cache/washtenaw/cache_source", ),
cache_scenario_database=
'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache=self.tables_to_cache,
tables_to_cache_nchunks={'gridcells': 1},
tables_to_copy_to_previous_years=self.
tables_to_copy_to_previous_years,
),
'datasets_to_preload': {
'development_constraint': {},
'development_event_history': {},
'development_type': {},
'gridcell': {
'nchunks': 2
},
'household': {},
'job': {},
'job_building_type': {},
'target_vacancy': {},
'zone': {},
'jobs_event': {},
#'households_event': {},
},
'dataset_pool_configuration':
DatasetPoolConfiguration(
package_order=['washtenaw', 'urbansim', 'opus_core'], ),
'base_year':
2005,
'years': (2006, 2010),
}
config.merge(config_changes)
self.merge(config)
self.merge_with_controller()
try:
exec('from %s_local_config import my_configuration' % getuser())
local_config = True
except:
logger.log_note(
"No user's settings found or error occured when loading.")
local_config = False
if local_config:
self.merge(my_configuration)
# Opus/UrbanSim urban simulation software.
# Copyright (C) 2010-2011 University of California, Berkeley, 2005-2009 University of Washington
# See opus_core/LICENSE
import os
from opus_core.storage_factory import StorageFactory
from opus_core.database_management.configurations.scenario_database_configuration import ScenarioDatabaseConfiguration
from opus_core.database_management.configurations.estimation_database_configuration import EstimationDatabaseConfiguration
from urbansim.configs.base_configuration import AbstractUrbansimConfiguration
from urbansim.configurations.creating_baseyear_cache_configuration import CreatingBaseyearCacheConfiguration
from opus_core.database_management.database_server import DatabaseServer
config = AbstractUrbansimConfiguration()
db_server = DatabaseServer(ScenarioDatabaseConfiguration())
db = db_server.get_database('PSRC_2000_baseyear_old_elcm_coeff')
config_changes = {
'description':'baseline with skims using old coefficients',
'in_storage':StorageFactory().get_storage('sql_storage',
storage_location = db
),
'cache_directory':None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':CreatingBaseyearCacheConfiguration(
cache_directory_root = 'd:/urbansim_cache',
cache_from_database = True,
cache_scenario_database = 'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache = [
'annual_employment_control_totals',
'annual_household_control_totals',
def __init__(self):
config = AbstractUrbansimConfiguration()
db_server = DatabaseServer(ScenarioDatabaseConfiguration())
db = db_server.get_database('PSRC_2000_baseyear')
config_changes = {
'description':
'baseline with no travel model',
'in_storage':
StorageFactory().get_storage('sql_storage', storage_location=db),
'cache_directory':
None, ### TODO: Set this cache_directory to something useful.
'creating_baseyear_cache_configuration':
CreatingBaseyearCacheConfiguration(
cache_directory_root='c:/urbansim_cache',
cache_from_database=True,
cache_scenario_database=
'urbansim.model_coordinators.cache_scenario_database',
tables_to_cache=[
'annual_employment_control_totals',
'annual_household_control_totals', 'buildings',
'building_types', 'development_event_history', 'gridcells',
'households', 'job_building_types', 'jobs', 'travel_data',
'zones', 'counties',
'commercial_development_location_choice_model_coefficients',
'commercial_development_location_choice_model_specification',
'commercial_employment_location_choice_model_coefficients',
'commercial_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_specification',
'home_based_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_coefficients',
'industrial_employment_location_choice_model_specification',
'industrial_development_location_choice_model_coefficients',
'industrial_development_location_choice_model_specification',
'residential_development_location_choice_model_coefficients',
'residential_development_location_choice_model_specification',
'fazes', 'urbansim_constants',
'household_location_choice_model_coefficients',
'household_location_choice_model_specification',
'land_price_model_coefficients',
'land_price_model_specification',
'residential_land_share_model_coefficients',
'residential_land_share_model_specification',
'plan_type_group_definitions', 'plan_type_groups',
'large_areas', 'household_characteristics_for_ht',
'development_types', 'development_type_group_definitions',
'development_constraints',
'annual_relocation_rates_for_households',
'annual_relocation_rates_for_jobs', 'base_year', 'cities',
'development_events', 'development_type_groups',
'employment_adhoc_sector_group_definitions',
'employment_adhoc_sector_groups', 'employment_events',
'employment_sectors', 'land_use_events', 'plan_types',
'race_names', 'target_vacancies', 'jobs_for_estimation',
'households_for_estimation',
'development_events_exogenous', 'job_building_types'
],
tables_to_cache_nchunks={'gridcells': 1},
tables_to_copy_to_previous_years={
'development_type_groups': 1995,
'development_types': 1995,
'development_type_group_definitions': 1995,
'urbansim_constants': 1995,
},
),
'scenario_database_configuration':
ScenarioDatabaseConfiguration(
database_name='PSRC_2000_baseyear', ),
'base_year':
2000,
'years': (2001, 2030),
}
config.merge(config_changes)
self.merge(config)
