def make_zone_dbfs(cache_directory):
xmlconfig = XMLConfiguration(filename="sanfrancisco.xml",
default_directory=r'C:\opus\project_configs',
is_parent=False)
runconfig = xmlconfig.get_run_configuration("sanfrancisco_baseline2009",
merge_controllers=True)
tm_config = runconfig['travel_model_configuration']
print tm_config['urbansim_to_tm_variable_mapping']
travel_model_years = []
for key in tm_config.iterkeys():
if isinstance(key, int) and tm_config[key].has_key('year_dir'):
travel_model_years.append(key)
travel_model_years.sort()
zonedbfs_source_data = SourceData(
cache_directory=cache_directory,
run_description="Run description is used for what?",
years=travel_model_years,
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['sanfrancisco', 'urbansim', 'opus_core'], ),
)
attrs = []
for key, val in tm_config['urbansim_to_tm_variable_mapping'].iteritems():
key = key.replace(".", "_")
attrs.append("%s=%s" % (key, val))
attrs.extend([\
"pwac_bus=sanfrancisco.zone.bus_travel_time_weighted_access_by_population",
"pwac_exp=sanfrancisco.zone.exp_travel_time_weighted_access_by_population",
"pwac_lrt=sanfrancisco.zone.lrt_travel_time_weighted_access_by_population",
"pwac_bart=sanfrancisco.zone.bart_travel_time_weighted_access_by_population",
"pwac_hwy=sanfrancisco.zone.hwy_travel_time_weighted_access_by_population",
"ewac_bus=sanfrancisco.zone.bus_travel_time_weighted_access_to_employment",
"ewac_exp=sanfrancisco.zone.exp_travel_time_weighted_access_to_employment",
"ewac_lrt=sanfrancisco.zone.lrt_travel_time_weighted_access_to_employment",
"ewac_bart=sanfrancisco.zone.bart_travel_time_weighted_access_to_employment",
"ewac_hwy=sanfrancisco.zone.hwy_travel_time_weighted_access_to_employment",
"ttpw_bus=sanfrancisco.zone.bus_travel_time_to_751",
"ttpw_exp=sanfrancisco.zone.exp_travel_time_to_751",
"ttpw_lrt=sanfrancisco.zone.lrt_travel_time_to_751",
"ttpw_bart=sanfrancisco.zone.bart_travel_time_to_751",
"ttpw_hwy=sanfrancisco.zone.hwy_travel_time_to_751",
"d2powell=sanfrancisco.zone.dist_travel_time_to_751"
])
zonedbf_indicators = [
DatasetTable(source_data=zonedbfs_source_data,
dataset_name='zone',
name='zone Indicators',
output_type='dbf',
attributes=attrs)
]
IndicatorFactory().create_indicators(indicators=zonedbf_indicators,
display_error_box=False,
show_results=False)
def test_one_indicator(self):
source_data = SourceData(
cache_directory = self.temp_dir,
run_description = 'test',
years = [self.year],
dataset_pool_configuration = DatasetPoolConfiguration(
package_order=['psrc','urbansim','opus_core'],
),
)
indicator_defs = [
Table(
attribute = 'psrc.large_area.average_land_value_for_plan_type_group_residential',
dataset_name = 'large_area',
source_data = source_data,
),
]
IndicatorFactory().create_indicators(
indicators = indicator_defs,
display_error_box = False,
show_results = False)
Table(
source_data = source_data,
dataset_name = 'alldata',
name = 'Business Counts 5',
output_type='csv',
attribute = 'bus_5 = alldata.aggregate_all(business.sector_id == 5)',
years = [2001, 2002],
),
Table(
source_data = source_data,
dataset_name = 'alldata',
name = 'Business Counts 6',
output_type='csv',
attribute = 'bus_6 = alldata.aggregate_all(business.sector_id == 6)',
years = [2001, 2002],
),
]
if __name__ == '__main__':
from opus_core.indicator_framework.core.indicator_factory import IndicatorFactory
IndicatorFactory().create_indicators(
indicators = single_year_requests,
display_error_box = False,
show_results = True)
#IndicatorFactory().create_indicators(
#indicators = multi_year_requests,
#display_error_box = False,
#show_results = True)
#example of using an operation ("change since baseyear"). Other available operations
#are "percent_change" and "size" (of the dataset)
# Table(
# source_data = source_data,
# attribute = 'urbansim.faz.population',
# dataset_name = 'faz',
# name = 'population_change(DDDD-00)',
# operation = 'change',
# years = [1980]
# ),
#example using regional-level aggregators
# Table(
# attribute = 'alldata.aggregate_all(urbansim.zone.number_of_home_based_jobs)',
# dataset_name = 'alldata',
# source_data = source_data,
# name = 'number_of_home_based_jobs',
# years = [1980, 1981]
# ),
]
if __name__ == '__main__':
from opus_core.indicator_framework.core.indicator_factory import IndicatorFactory
IndicatorFactory().create_indicators(
indicators = indicators,
display_error_box = False,
show_results = True)
def opusRun(progressCB,logCB,params):
params_dict = {}
for key, val in params.iteritems():
params_dict[str(key)] = str(val)
cache_directory = params_dict['cache_directory']
output_type = params_dict['output_type']
storage_location = params_dict['storage_location']
year = params_dict['year']
if output_type == '':
output_type = 'tab'
""" Prerequisite:
1. run buildingout_query.sql to create building_sqft_per_job_by_zone_generic_land_use_type_id table, or similar table for other geography
2. cache the table to the cache that needs to create buildout indicators for
3. get/create building_sqft_per_job_by_zone_generic_land_use_type_id_dataset.py (in urbansim_parcel/datasets)
4. active/planned/proposed projects are cached in development_project_proposals and development_project_proposal_components
5. add is_redevelopable attribute to parcel
"""
run_description = 'Create indicators for buildout analysis'
source_data = SourceData(
cache_directory = cache_directory,
run_description = run_description,
years = [int(year)],
dataset_pool_configuration = DatasetPoolConfiguration(
package_order=['psrc_parcel','urbansim_parcel','psrc', 'urbansim','opus_core'],
),
)
attrs_by_glu = []
# this loop create indicators for each generic_land_type from 1 to 8
for glu_id in range(3, 9):
attrs_by_glu += [
## existing
"existing_job_spaces_glu%s = zone.aggregate(parcel.aggregate(building.non_residential_sqft)) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id),
## existing units/job spaces excluding redevelopable parcels
"existing_job_spaces_unredev_glu%s=zone.aggregate(parcel.aggregate(building.non_residential_sqft) * numpy.logical_not(urbansim_parcel.parcel.is_redevelopable).astype(int32) ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id),
## redevelopment
"redev_buildout_units_glu%s=zone.aggregate(urbansim_parcel.parcel.max_units_per_acre_capacity_for_generic_land_use_type_%s * parcel.parcel_sqft * (urbansim_parcel.parcel.is_redevelopable).astype(int32) )" % (glu_id, glu_id),
"redev_buildout_job_spaces_glu%s=zone.aggregate(urbansim_parcel.parcel.max_far_capacity_for_generic_land_use_type_%s * parcel.parcel_sqft * (urbansim_parcel.parcel.is_redevelopable).astype(int32) ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id, glu_id),
## active(status_id==1)/planned(status_id==3)/proposed(status_id==2)
"proposed_job_spaces_glu%s = zone.aggregate(development_project_proposal.aggregate(urbansim_parcel.development_project_proposal_component.units_proposed * (numpy.logical_not(urbansim_parcel.development_project_proposal_component.is_residential)).astype(int32)) * (urbansim_parcel.development_project_proposal.status_id==2).astype(int32), intermediates=[parcel] ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id),
## vacant/agriculture buildout
"va_buildout_glu%s = zone.aggregate( urbansim_parcel.parcel.max_units_per_acre_capacity_for_generic_land_use_type_%s * ( parcel.parcel_sqft / 43560.0 ) * ( parcel.number_of_agents(development_project_proposal) == 0) * numpy.logical_or( parcel.disaggregate(land_use_type.land_use_name)=='vacant', parcel.disaggregate(land_use_type.land_use_name)=='agriculture') )" % (glu_id, glu_id),
"va_buildout_job_spaces_glu%s = zone.aggregate( urbansim_parcel.parcel.max_far_capacity_for_generic_land_use_type_%s * parcel.parcel_sqft * ( parcel.number_of_agents(development_project_proposal) == 0) * numpy.logical_or( parcel.disaggregate(land_use_type.land_use_name)=='vacant', parcel.disaggregate(land_use_type.land_use_name)=='agriculture') ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id, glu_id),
]
for glu_id in range(1, 3):
attrs_by_glu += [
## existing
"existing_job_spaces_glu%s = zone.aggregate(parcel.aggregate(building.non_residential_sqft)) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id),
## existing units/job spaces excluding redevelopable parcels
"existing_job_spaces_unredev_glu%s=zone.aggregate(parcel.aggregate(building.non_residential_sqft) * numpy.logical_not(urbansim_parcel.parcel.is_redevelopable).astype(int32) ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id),
## redevelopment
"redev_buildout_units_glu%s=zone.aggregate(urbansim_parcel.parcel.max_units_per_acre_capacity_for_generic_land_use_type_%s * parcel.parcel_sqft * (urbansim_parcel.parcel.is_redevelopable).astype(int32) )" % (glu_id, glu_id),
# "redev_buildout_job_spaces_glu%s=zone.aggregate(urbansim_parcel.parcel.max_far_capacity_for_generic_land_use_type_%s * parcel.parcel_sqft * (urbansim_parcel.parcel.is_redevelopable).astype(int32) ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id, glu_id),
## active(status_id==1)/planned(status_id==3)/proposed(status_id==2)
"proposed_job_spaces_glu%s = zone.aggregate(development_project_proposal.aggregate(urbansim_parcel.development_project_proposal_component.units_proposed * (numpy.logical_not(urbansim_parcel.development_project_proposal_component.is_residential)).astype(int32)) * (urbansim_parcel.development_project_proposal.status_id==2).astype(int32), intermediates=[parcel] ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id),
## vacant/agriculture buildout
"va_buildout_glu%s = zone.aggregate( urbansim_parcel.parcel.max_units_per_acre_capacity_for_generic_land_use_type_%s * ( parcel.parcel_sqft / 43560.0 ) * ( parcel.number_of_agents(development_project_proposal) == 0) * numpy.logical_or( parcel.disaggregate(land_use_type.land_use_name)=='vacant', parcel.disaggregate(land_use_type.land_use_name)=='agriculture') )" % (glu_id, glu_id),
# "va_buildout_job_spaces_glu%s = zone.aggregate( urbansim_parcel.parcel.max_far_capacity_for_generic_land_use_type_%s * parcel.parcel_sqft * ( parcel.number_of_agents(development_project_proposal) == 0) * numpy.logical_or( parcel.disaggregate(land_use_type.land_use_name)=='vacant', parcel.disaggregate(land_use_type.land_use_name)=='agriculture') ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu%s)" % (glu_id, glu_id, glu_id),
]
indicators=[
DatasetTable(
attributes = [
### indicators don't need to iterate by generic_land_use_type
## existing
"existing_units = zone.aggregate(urbansim_parcel.parcel.residential_units)",
#"existing_job_spaces_glu1 = zone.aggregate(parcel.aggregate(building.non_residential_sqft)) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu1)",
## existing units/job spaces excluding redevelopable parcels
"existing_units_unredev=zone.aggregate(urbansim_parcel.parcel.residential_units * numpy.logical_not(urbansim_parcel.parcel.is_redevelopable).astype(int32) )",
#"existing_job_spaces_unredev_glu1=zone.aggregate(parcel.aggregate(building.non_residential_sqft) * numpy.logical_not(urbansim_parcel.parcel.is_redevelopable).astype(int32) ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu1)",
## redevelopment
#"redev_buildout_units_glu1=zone.aggregate(urbansim_parcel.parcel.max_units_per_acre_capacity_for_generic_land_use_type_1 * parcel.parcel_sqft * (urbansim_parcel.parcel.is_redevelopable).astype(int32) )",
#"redev_buildout_job_spaces_glu1=zone.aggregate(urbansim_parcel.parcel.max_far_capacity_for_generic_land_use_type_1 * parcel.parcel_sqft * (urbansim_parcel.parcel.is_redevelopable).astype(int32) ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu1)",
## active(status_id==1)/planned(status_id==3)/proposed(status_id==2)
"proposed_units = zone.aggregate(development_project_proposal.aggregate(urbansim_parcel.development_project_proposal_component.units_proposed * (urbansim_parcel.development_project_proposal_component.is_residential).astype(int32)) * (urbansim_parcel.development_project_proposal.status_id==2).astype(int32), intermediates=[parcel] )",
#"proposed_job_spaces_glu1 = zone.aggregate(development_project_proposal.aggregate(urbansim_parcel.development_project_proposal_component.units_proposed * (numpy.logical_not(urbansim_parcel.development_project_proposal_component.is_residential)).astype(int32)) * (urbansim_parcel.development_project_proposal.status_id==2).astype(int32), intermediates=[parcel] ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu1)",
## vacant/agriculture buildout
#"va_buildout_glu1 = zone.aggregate( urbansim_parcel.parcel.max_units_per_acre_capacity_for_generic_land_use_type_1 * parcel.parcel_sqft * ( parcel.number_of_agents(development_project_proposal) == 0) * numpy.logical_or( parcel.disaggregate(land_use_type.land_use_name)=='vacant', parcel.disaggregate(land_use_type.land_use_name)=='agriculture') )",
#"va_buildout_job_spaces_glu1 = zone.aggregate( urbansim_parcel.parcel.max_far_capacity_for_generic_land_use_type_1 * parcel.parcel_sqft * ( parcel.number_of_agents(development_project_proposal) == 0) * numpy.logical_or( parcel.disaggregate(land_use_type.land_use_name)=='vacant', parcel.disaggregate(land_use_type.land_use_name)=='agriculture') ) / zone.disaggregate(building_sqft_per_job_by_zone_generic_land_use_type_id.building_sqft_per_job_glu1)",
] + attrs_by_glu,
dataset_name = 'zone',
source_data = source_data,
name = 'zone_buildout',
output_type = output_type,
storage_location = storage_location
),
]
IndicatorFactory().create_indicators(
indicators = indicators,
display_error_box = False,
show_results = False)
def create_travel_model_input_file(self, config, year, *args, **kwargs):
""""""
logger.start_block('Starting GetCacheDataIntoMatsim.run(...)')
# # When this is called for the first time, the 'matsim_flag' is not there. Will be constructed here:
# if not 'matsim_flag' in persons.get_known_attribute_names():
# persons = SessionConfiguration().get_dataset_from_pool('person')
# persons_size = persons.size()
# sampling_rate = config['travel_model_configuration']['sampling_rate']
# matsim_flag = zeros(persons_size, dtype='int32')
# sampled_person_index = sample_noreplace( arange(persons_size),
# int(sampling_rate * persons_size),
# return_indices=True )
# matsim_flag[sampled_person_index] = 1
# persons.add_attribute(matsim_flag, 'matsim_flag', metadata=AttributeType.PRIMARY)
# persons.flush_attribute('matsim_flag')
# I guess this is access to the full urbansim cache data.
source_data = SourceData(
cache_directory=config['cache_directory'],
years=[year],
dataset_pool_configuration=DatasetPoolConfiguration(package_order=[
'psrc_parcel', 'urbansim_parcel', 'psrc', 'urbansim',
'opus_core'
], ),
)
output_root = os.path.join(os.environ['OPUS_HOME'], "opus_matsim")
if not os.path.exists(output_root):
try:
os.mkdir(output_root)
except:
pass
output_directory = os.path.join(os.environ['OPUS_HOME'], "opus_matsim",
"tmp")
if not os.path.exists(output_directory):
try:
os.mkdir(output_directory)
except:
pass
### PERSONS
export_indicators = [
DatasetTable(
attributes=[
'parcel_id_home = person.disaggregate(parcel.parcel_id, intermediates=[building,household])',
'parcel_id_work = person.disaggregate(parcel.parcel_id, intermediates=[building,job])',
],
dataset_name='person',
# exclude_condition = 'person.matsim_flag==0',
storage_location=output_directory,
source_data=source_data,
output_type='tab',
name='exported_indicators',
)
]
# This is (I assume) executing the export
IndicatorFactory().create_indicators(indicators=export_indicators,
display_error_box=False,
show_results=False)
### "FACILITIES"
export_indicators = [
DatasetTable(
attributes=[
'parcel.x_coord_sp',
'parcel.y_coord_sp',
'parcel.zone_id',
],
dataset_name='parcel',
storage_location=output_directory,
source_data=source_data,
output_type='tab',
name='exported_indicators',
)
]
IndicatorFactory().create_indicators(indicators=export_indicators,
display_error_box=False,
show_results=False)
logger.end_block()
def main():
project_name = 'eugene_gridcell'
run_name1 = 'run_6473.2008_05_11_22_27'
run_name2 = 'run_6478.2008_05_12_19_04'
source_data = SourceData(
cache_directory=paths.get_opus_data_path_path(
project_name, 'runs',
run_name1), # r'D:\urbansim_cache\run_1090.2006_11_14_12_12',
# comparison_cache_directory = os.path.join(os.environ['OPUS_DATA_PATH'],project_name,'runs',run_name2),
years=[1980, 1981],
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['eugene', 'urbansim', 'opus_core'], ),
)
indicators = [
Map(
source_data=source_data,
dataset_name='zone',
attribute='urbansim.zone.population',
years=[1980],
),
# Chart(
# source_data = source_data,
# dataset_name = 'gridcell',
# attribute = 'urbansim.gridcell.population',
# ),
#
# Table(
# source_data = source_data,
# dataset_name = 'zone',
# attribute = 'urbansim.zone.industrial_sqft',
# output_type = 'tab'
# ),
# Table(
# source_data = source_data,
# dataset_name = 'zone',
# attribute = 'urbansim.zone.industrial_sqft',
# output_type = 'dbf',
# years = [1980, 1981]
# ),
# GeotiffMap(
# source_data = source_data,
# dataset_name = 'gridcell',
# package = 'psrc',
# attribute = 'urbansim.gridcell.number_of_jobs',
# name = 'jobs',
# ),
# DatasetTable(
# source_data = source_data,
# dataset_name = 'zone',
# name = 'pop_and_ind_sqft',
# attributes = [
# 'urbansim.zone.population',
# 'urbansim.zone.industrial_sqft',
# ],
# exclude_condition = 'urbansim.zone.population<100' #this accepts any opus expression
# ),
#Expression example
# Table(
# source_data = source_data,
# dataset_name = 'large_area',
# name = 'de_population_change',
# attribute = 'psrc.large_area.de_population_DDDD - psrc.large_area.de_population_2000',
# ),
#example of using an operation ("change since baseyear"). Other available operations
#are "percent_change" and "size" (of the dataset)
# Table(
# source_data = source_data,
# attribute = 'urbansim.faz.population',
# dataset_name = 'faz',
# name = 'population_change(DDDD-00)',
# operation = 'change',
# years = [1980]
# ),
#example using regional-level aggregators
# Table(
# attribute = 'alldata.aggregate_all(urbansim.zone.number_of_home_based_jobs)',
# dataset_name = 'alldata',
# source_data = source_data,
# name = 'number_of_home_based_jobs',
# years = [1980, 1981]
# ),
]
from opus_core.indicator_framework.core.indicator_factory import IndicatorFactory
IndicatorFactory().create_indicators(indicators=indicators,
display_error_box=False,
show_results=True)
def create_travel_model_input_file(self, config, year, *args, **kwargs):
""""""
logger.start_block('Starting GetCacheDataIntoMatsimTest.run(...)')
source_data = SourceData(
cache_directory=config['cache_directory'],
years=[year],
dataset_pool_configuration=DatasetPoolConfiguration(package_order=[
'psrc_parcel', 'urbansim_parcel', 'psrc', 'urbansim',
'opus_core'
], ),
)
output_root = config['root']
if not os.path.exists(output_root):
try:
os.mkdir(output_root)
except:
pass
output_root_extended = os.path.join(config['root'], 'opus_matsim')
if not os.path.exists(output_root_extended):
try:
os.mkdir(output_root_extended)
except:
pass
output_directory = os.path.join(output_root_extended, 'tmp')
if not os.path.exists(output_directory):
try:
os.mkdir(output_directory)
except:
pass
### PERSONS
export_indicators = [
DatasetTable(
attributes=[
'parcel_id_home = person.disaggregate(parcel.parcel_id, intermediates=[building,household])',
'parcel_id_work = person.disaggregate(parcel.parcel_id, intermediates=[building,job])',
],
dataset_name='person',
storage_location=output_directory,
source_data=source_data,
output_type='tab',
name='exported_indicators',
)
]
# This is (I assume) executing the export
IndicatorFactory().create_indicators(indicators=export_indicators,
display_error_box=False,
show_results=False)
### "FACILITIES"
export_indicators = [
DatasetTable(
attributes=[
'parcel.x_coord_sp',
'parcel.y_coord_sp',
'parcel.zone_id',
],
dataset_name='parcel',
storage_location=output_directory,
source_data=source_data,
output_type='tab',
name='exported_indicators',
)
]
IndicatorFactory().create_indicators(indicators=export_indicators,
display_error_box=False,
show_results=False)
logger.end_block()
def make_multiyear_workbook(cache_directory, yearstart=2010, yearend=2035):
""" This spits out the indicators for a multiyear workbook and then combines them
into a single file, cache_directory/alldata.csv
You can then copy this a copy of the MultiyearLU_template.xls and see some basic
performance/troubleshooting graphs for the various submodels.
"""
multiyear_workbook_source_data = SourceData(
cache_directory=cache_directory,
run_description="Run description is used for what?",
years=range(yearstart, yearend + 1),
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['sanfrancisco', 'urbansim', 'opus_core'], ),
)
multiyear_workbook_alldata_attributes = \
[
# commercial
'bldg_count_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,1,0))',
'bldg_occsqft_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_comm=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.residential_units,0))',
# institutional
'bldg_count_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,1,0))',
'bldg_occsqft_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_inst=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.residential_units,0))',
# office
'bldg_count_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,1,0))',
'bldg_occsqft_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_offc=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.residential_units,0))',
# residential
'bldg_count_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,1,0))',
'bldg_occunit_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.number_of_households,0))',
'bldg_count_totunit_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.residential_units,0))',
'bldg_vacrate_res=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.residential_units,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.number_of_households,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.residential_units,0))',
'bldg_count_totsqft_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.non_residential_sqft,0))',
# res in non-res group buildings
'bldg_count_reso=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,1,0))',
'bldg_occunit_reso=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.number_of_households,0))',
'bldg_count_totunit_reso=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.residential_units,0))',
'bldg_vacrate_reso=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.residential_units,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.number_of_households,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.residential_units,0))',
'bldg_count_totsqft_ores=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.non_residential_sqft,0))',
# visitor
'bldg_count_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,1,0))',
'bldg_occsqft_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_vis=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.residential_units,0))',
# mixed
'bldg_count_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,1,0))',
'bldg_occsqft_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_mix=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.residential_units,0))',
# unplaced buildings
'bldg_count_unplaced=alldata.aggregate_all(where(sanfrancisco.building.parcel_id<0,1,0))',
'bldg_count_unplaced_BLCMspec=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.parcel_id<0,sanfrancisco.building.is_placed_type>0),1,0))',
'bldg_count_unplaced_noBLCMspec=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.parcel_id<0,sanfrancisco.building.is_placed_type==0),1,0))',
# household counts
'hhld_count_sz1=alldata.aggregate_all(where(household.household_size==1,1,0))',
'hhld_count_sz2=alldata.aggregate_all(where(household.household_size==2,1,0))',
'hhld_count_sz34=alldata.aggregate_all(where(numpy.logical_or(household.household_size==3,household.household_size==4),1,0))',
'hhld_count_sz56=alldata.aggregate_all(where(numpy.logical_or(household.household_size==5,household.household_size==6),1,0))',
'hhld_count_sz7=alldata.aggregate_all(where(household.household_size>=7,1,0))',
# business counts
'jobs_count_sect1=alldata.aggregate_all(where(business.sector_id==1,business.employment,0))',
'jobs_count_sect2=alldata.aggregate_all(where(business.sector_id==2,business.employment,0))',
'jobs_count_sect3=alldata.aggregate_all(where(business.sector_id==3,business.employment,0))',
'jobs_count_sect4=alldata.aggregate_all(where(business.sector_id==4,business.employment,0))',
'jobs_count_sect5=alldata.aggregate_all(where(business.sector_id==5,business.employment,0))',
'jobs_count_sect6=alldata.aggregate_all(where(business.sector_id==6,business.employment,0))',
'jobs_count_sect7=alldata.aggregate_all(where(business.sector_id==7,business.employment,0))',
'jobs_count_sect8=alldata.aggregate_all(where(business.sector_id==8,business.employment,0))',
'jobs_count_sect9=alldata.aggregate_all(where(business.sector_id==9,business.employment,0))',
'jobs_count_sect10=alldata.aggregate_all(where(business.sector_id==10,business.employment,0))',
'jobs_count_sect11=alldata.aggregate_all(where(business.sector_id==11,business.employment,0))',
# unplaced businesses, how full are buildings? overall nonres sqft totals
'business_count_unplaced=alldata.aggregate_all(where(business.building_id<1,1,0))',
'bldg_count_overfullbiz=alldata.aggregate_all(where(sanfrancisco.building.occupied_sqft>building.non_residential_sqft,1,0))',
'bldg_count_partialfullbiz=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.occupied_sqft<building.non_residential_sqft,'+
'sanfrancisco.building.occupied_sqft>0),1,0))',
'bldg_count_vacantbiz=alldata.aggregate_all(where(numpy.logical_and(building.non_residential_sqft>0,'+
'sanfrancisco.building.occupied_sqft==0),1,0))',
# these are covered above for building_groups
'bldg_nonres_sqft_total=alldata.aggregate_all(building.non_residential_sqft)',
'bldg_nonres_sqft_occ=alldata.aggregate_all(sanfrancisco.building.occupied_sqft)',
'bldg_nonres_sqft_vacant=alldata.aggregate_all(building.non_residential_sqft)-alldata.aggregate_all(sanfrancisco.building.occupied_sqft)',
# unplaced households, how full are buildings? overall hhunit totals
'hhld_count_unplaced=alldata.aggregate_all(where(household.building_id<1,1,0))',
'hhld_count_overfullhh=alldata.aggregate_all(where(sanfrancisco.building.number_of_households>building.residential_units,1,0))',
'hhld_count_partialfullhh=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.number_of_households<building.residential_units,'+
'sanfrancisco.building.number_of_households>0),1,0))',
'hhld_count_vacanthh=alldata.aggregate_all(where(numpy.logical_and(building.residential_units>0,'+
'sanfrancisco.building.number_of_households==0),1,0))',
# these are covered above for building_groups
'hhld_res_unit_total=alldata.aggregate_all(building.residential_units)',
'hhld_res_unit_occ=alldata.aggregate_all(sanfrancisco.building.number_of_households)',
'hhld_res_unit_vacant=alldata.aggregate_all(building.residential_units)-alldata.aggregate_all(sanfrancisco.building.number_of_households)',
]
class SFIndicatorDialect(csv.excel):
lineterminator = '\n'
csv.register_dialect("SFIndicatorDialect", SFIndicatorDialect)
onetableWriter = csv.writer(open(
os.path.join(cache_directory, "alldata.csv"), 'w'),
dialect='SFIndicatorDialect')
headeryears = []
for year in multiyear_workbook_source_data.years:
headeryears.append("y" + str(year))
onetableWriter.writerow(['variable'] + headeryears)
for attr in multiyear_workbook_alldata_attributes:
attr_name = attr.partition('=')[0]
request = [
Table(source_data=multiyear_workbook_source_data,
dataset_name='alldata',
name=attr_name,
attribute=attr)
]
IndicatorFactory().create_indicators(indicators=request,
display_error_box=False,
show_results=False)
# open this file
filename = request[0].get_file_path()
indicatorReader = csv.reader(open(filename, 'r'))
# title row
fields = indicatorReader.next()
assert (len(fields) == len(multiyear_workbook_source_data.years) + 1
) # quick check
# data row
fields = indicatorReader.next()
fields[0] = attr_name
onetableWriter.writerow(fields)
# for *_count_* rows, add *_new_* row
if attr_name.find("_count_") >= 0:
attr_new_name = attr_name.replace("_count_", "_new_")
new_fields = [attr_new_name, 0]
for ind in range(1, len(multiyear_workbook_source_data.years)):
new_fields.append(float(fields[ind + 1]) - float(fields[ind]))
onetableWriter.writerow(new_fields)
def make_topsheet(cache_directory):
""" Run the indicators for the LU topsheet
"""
run_description = '(baseline 11/02/2008)'
yearstart = 2010
yearend = 2035
source_data = SourceData(
cache_directory=cache_directory,
run_description=run_description,
years=[yearstart, yearend],
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['sanfrancisco', 'urbansim', 'opus_core'], ),
)
single_year_requests = [
DatasetTable(
source_data=source_data,
dataset_name='tract',
name='Tract Indicators',
output_type='csv',
attributes=[
'hholds_0_wrk=tract.aggregate(sanfrancisco.building.number_of_households_with_0_workers, intermediates=[parcel])',
'hholds_1_wrk =tract.aggregate(sanfrancisco.building.number_of_households_with_1_workers, intermediates=[parcel])',
'hholds_2_wrk=tract.aggregate(sanfrancisco.building.number_of_households_with_2_workers, intermediates=[parcel])',
'hholds_3_wrk=tract.aggregate(sanfrancisco.building.number_of_households_with_3_workers, intermediates=[parcel])',
'hholds_4_wrk=tract.aggregate(sanfrancisco.building.number_of_households_with_4_workers, intermediates=[parcel])',
'hoolds_5_wrk=tract.aggregate(sanfrancisco.building.number_of_households_with_5_workers, intermediates=[parcel])',
'hholds_6_wrk=tract.aggregate(sanfrancisco.building.number_of_households_with_6_workers, intermediates=[parcel])',
'hholds_7_wrk=tract.aggregate(sanfrancisco.building.number_of_households_with_7_workers, intermediates=[parcel])',
'res_units=tract.aggregate(sanfrancisco.building.residential_units, intermediates=[parcel])',
'res_sqft=tract.aggregate(sanfrancisco.building.residential_sqft, intermediates=[parcel])',
'avg_total_price=tract.aggregate(sanfrancisco.building.total_price, function=mean, intermediates=[parcel])',
'households=tract.aggregate(sanfrancisco.building.number_of_households, intermediates=[parcel])',
'population=tract.aggregate(sanfrancisco.building.population, intermediates=[parcel])',
'avg_hhsize=safe_array_divide(tract.aggregate(sanfrancisco.building.population, intermediates=[parcel]),tract.aggregate(sanfrancisco.building.number_of_households, intermediates=[parcel]))',
'sector_1_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_1, intermediates=[parcel])',
'sector_2_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_2, intermediates=[parcel])',
'sector_3_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_3, intermediates=[parcel])',
'sector_4_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_4, intermediates=[parcel])',
'sector_5_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_5, intermediates=[parcel])',
'sector_6_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_6, intermediates=[parcel])',
'sector_7_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_7, intermediates=[parcel])',
'sector_8_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_8, intermediates=[parcel])',
'sector_9_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_9, intermediates=[parcel])',
'sector_10_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_10, intermediates=[parcel])',
'sector_11_emp=tract.aggregate(sanfrancisco.building.employment_of_sector_11, intermediates=[parcel])',
r"sector_CIE_emp=tract.aggregate(where(sanfrancisco.business.activity_id==1,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_RET_emp=tract.aggregate(where(sanfrancisco.business.activity_id==5,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_PDR_emp=tract.aggregate(where(sanfrancisco.business.activity_id==4,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_MIPS_emp=tract.aggregate(where(sanfrancisco.business.activity_id==3,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_MED_emp=tract.aggregate(where(sanfrancisco.business.activity_id==2,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_VIS_emp=tract.aggregate(where(sanfrancisco.business.activity_id==6,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
'employment=tract.aggregate(sanfrancisco.building.employment, intermediates=[parcel])',
'sector_1_bus=tract.aggregate(business.sector_id == 1, intermediates=[building, parcel])',
'sector_2_bus=tract.aggregate(business.sector_id == 2, intermediates=[building, parcel])',
'sector_3_bus=tract.aggregate(business.sector_id == 3, intermediates=[building, parcel])',
'sector_4_bus=tract.aggregate(business.sector_id == 4, intermediates=[building, parcel])',
'sector_5_bus=tract.aggregate(business.sector_id == 5, intermediates=[building, parcel])',
'sector_6_bus=tract.aggregate(business.sector_id == 6, intermediates=[building, parcel])',
'sector_7_bus=tract.aggregate(business.sector_id == 7, intermediates=[building, parcel])',
'sector_8_bus=tract.aggregate(business.sector_id == 8, intermediates=[building, parcel])',
'sector_9_bus=tract.aggregate(business.sector_id == 9, intermediates=[building, parcel])',
'sector_10_bus=tract.aggregate(business.sector_id == 10, intermediates=[building, parcel])',
'sector_11_bus=tract.aggregate(business.sector_id == 11, intermediates=[building, parcel])',
'businesses=tract.aggregate(sanfrancisco.building.number_of_businesses, intermediates=[parcel])',
'sqft_cie=tract.aggregate(where(sanfrancisco.building.building_use_id==7,sanfrancisco.building.non_residential_sqft,0), intermediates=[parcel])',
'sqft_retail=tract.aggregate(where(sanfrancisco.building.building_use_id==14,sanfrancisco.building.non_residential_sqft,0), intermediates=[parcel])',
'sqft_pdr=tract.aggregate(where(sanfrancisco.building.building_use_id==13,sanfrancisco.building.non_residential_sqft,0), intermediates=[parcel])',
'sqft_mips=tract.aggregate(where(sanfrancisco.building.building_use_id==8,sanfrancisco.building.non_residential_sqft,0), intermediates=[parcel])',
'sqft_visitor=tract.aggregate(where(sanfrancisco.building.building_use_id==17,sanfrancisco.building.non_residential_sqft,0), intermediates=[parcel])',
'sqft_nonres=tract.aggregate(sanfrancisco.building.non_residential_sqft, intermediates=[parcel])',
],
#exclude_condition = '==0' #exclude_condition now accepts opus expressions
),
DatasetTable(
source_data=source_data,
dataset_name='pdist',
name='pdist_indicators',
output_type='csv',
attributes=[
'pdist.district',
'hholds_0_wrk=pdist.aggregate(sanfrancisco.building.number_of_households_with_0_workers, intermediates=[parcel])',
'hholds_1_wrk =pdist.aggregate(sanfrancisco.building.number_of_households_with_1_workers, intermediates=[parcel])',
'hholds_2_wrk=pdist.aggregate(sanfrancisco.building.number_of_households_with_2_workers, intermediates=[parcel])',
'hholds_3_wrk=pdist.aggregate(sanfrancisco.building.number_of_households_with_3_workers, intermediates=[parcel])',
'hholds_4_wrk=pdist.aggregate(sanfrancisco.building.number_of_households_with_4_workers, intermediates=[parcel])',
'hholds_5_wrk=pdist.aggregate(sanfrancisco.building.number_of_households_with_5_workers, intermediates=[parcel])',
'hholds_6_wrk=pdist.aggregate(sanfrancisco.building.number_of_households_with_6_workers, intermediates=[parcel])',
'hholds_7_wrk=pdist.aggregate(sanfrancisco.building.number_of_households_with_7_workers, intermediates=[parcel])',
'res_units=pdist.aggregate(sanfrancisco.building.residential_units, intermediates=[parcel])',
'res_sqft=pdist.aggregate(sanfrancisco.building.residential_sqft, intermediates=[parcel])',
'avg_total_price=pdist.aggregate(where(sanfrancisco.building.residential_units>0,sanfrancisco.building.total_price,0), function=mean, intermediates=[parcel])',
'stdev_total_price=pdist.aggregate(sanfrancisco.building.total_price, function=standard_deviation, intermediates=[parcel])',
'stdev_total_price2=pdist.aggregate(where(sanfrancisco.building.residential_units>0,sanfrancisco.building.total_price,0), function=standard_deviation, intermediates=[parcel])',
'households=pdist.aggregate(sanfrancisco.building.number_of_households, intermediates=[parcel])',
'population=pdist.aggregate(sanfrancisco.building.population, intermediates=[parcel])',
'avg_hhsize=safe_array_divide(pdist.aggregate(sanfrancisco.building.population, intermediates=[parcel]),pdist.aggregate(sanfrancisco.building.number_of_households, intermediates=[parcel]))',
'price_per_person=pdist.aggregate(safe_array_divide(sanfrancisco.building.total_price, sanfrancisco.building.population), function=mean, intermediates=[parcel])',
'ag_natural_resources_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_1, intermediates=[parcel])',
'construction_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_2, intermediates=[parcel])',
'manufacturing_wholesale_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_3, intermediates=[parcel])',
'retail_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_4, intermediates=[parcel])',
'transportation_utilities_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_5, intermediates=[parcel])',
'information_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_6, intermediates=[parcel])',
'financial_leasing_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_7, intermediates=[parcel])',
'prof_managerial_services_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_8, intermediates=[parcel])',
'health_educ_services_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_9, intermediates=[parcel])',
'arts_rec_other_services_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_10, intermediates=[parcel])',
'government_emp=pdist.aggregate(sanfrancisco.building.employment_of_sector_11, intermediates=[parcel])',
r"sector_CIE_emp=pdist.aggregate(where(sanfrancisco.business.activity_id==1,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_RET_emp=pdist.aggregate(where(sanfrancisco.business.activity_id==5,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_PDR_emp=pdist.aggregate(where(sanfrancisco.business.activity_id==4,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_MIPS_emp=pdist.aggregate(where(sanfrancisco.business.activity_id==3,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_MED_emp=pdist.aggregate(where(sanfrancisco.business.activity_id==2,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_VIS_emp=pdist.aggregate(where(sanfrancisco.business.activity_id==6,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r'employment=pdist.aggregate(sanfrancisco.building.employment, intermediates=[parcel])',
'ag_natural_resources_bus=pdist.aggregate(business.sector_id == 1, intermediates=[building, parcel])',
'construction_bus=pdist.aggregate(business.sector_id == 2, intermediates=[building, parcel])',
'manufacturing_wholesale_bus=pdist.aggregate(business.sector_id == 3, intermediates=[building, parcel])',
'retail_bus=pdist.aggregate(business.sector_id == 4, intermediates=[building, parcel])',
'transportation_utilities_bus=pdist.aggregate(business.sector_id == 5, intermediates=[building, parcel])',
'information_bus=pdist.aggregate(business.sector_id == 6, intermediates=[building, parcel])',
'financial_leasing_bus=pdist.aggregate(business.sector_id == 7, intermediates=[building, parcel])',
'prof_managerial_services_bus=pdist.aggregate(business.sector_id == 8, intermediates=[building, parcel])',
'health_educ_services_bus=pdist.aggregate(business.sector_id == 9, intermediates=[building, parcel])',
'arts_rec_other_services_bus=pdist.aggregate(business.sector_id == 10, intermediates=[building, parcel])',
'government_bus=pdist.aggregate(business.sector_id == 11, intermediates=[building, parcel])',
'businesses=pdist.aggregate(sanfrancisco.building.number_of_businesses, intermediates=[parcel])',
'office_sqft=pdist.aggregate(where(sanfrancisco.building.building_type_id==10,sanfrancisco.building.building_sqft,0), intermediates=[parcel])',
'office_occ=safe_array_divide(pdist.aggregate(where(sanfrancisco.business.activity_id==3,sanfrancisco.business.sqft,0), intermediates=[building, parcel]),pdist.aggregate(where(sanfrancisco.building.building_type_id==10,sanfrancisco.building.non_residential_sqft,0), intermediates=[parcel]))'
#'xxx=123',
#'planningdistrict=pdist.get_data_element(pdists.district)'
],
#exclude_condition = '==0' #exclude_condition now accepts opus expressions
),
DatasetTable(
source_data=source_data,
dataset_name='zone',
name='zone Indicators',
output_type='csv',
attributes=[
'hholds_0_wrk=zone.aggregate(sanfrancisco.building.number_of_households_with_0_workers, intermediates=[parcel])',
'hholds_1_wrk =zone.aggregate(sanfrancisco.building.number_of_households_with_1_workers, intermediates=[parcel])',
'hholds_2_wrk=zone.aggregate(sanfrancisco.building.number_of_households_with_2_workers, intermediates=[parcel])',
'hholds_3_wrk=zone.aggregate(sanfrancisco.building.number_of_households_with_3_workers, intermediates=[parcel])',
'hholds_4_wrk=zone.aggregate(sanfrancisco.building.number_of_households_with_4_workers, intermediates=[parcel])',
'hoolds_5_wrk=zone.aggregate(sanfrancisco.building.number_of_households_with_5_workers, intermediates=[parcel])',
'hholds_6_wrk=zone.aggregate(sanfrancisco.building.number_of_households_with_6_workers, intermediates=[parcel])',
'hholds_7_wrk=zone.aggregate(sanfrancisco.building.number_of_households_with_7_workers, intermediates=[parcel])',
'res_units=zone.aggregate(sanfrancisco.building.residential_units, intermediates=[parcel])',
'res_sqft=zone.aggregate(sanfrancisco.building.residential_sqft, intermediates=[parcel])',
'avg_total_price=zone.aggregate(sanfrancisco.building.total_price, function=mean, intermediates=[parcel])',
'households=zone.aggregate(sanfrancisco.building.number_of_households, intermediates=[parcel])',
'population=zone.aggregate(sanfrancisco.building.population, intermediates=[parcel])',
'avg_hhsize=safe_array_divide(zone.aggregate(sanfrancisco.building.population, intermediates=[parcel]),zone.aggregate(sanfrancisco.building.number_of_households, intermediates=[parcel]))',
'sector_1_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_1, intermediates=[parcel])',
'sector_2_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_2, intermediates=[parcel])',
'sector_3_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_3, intermediates=[parcel])',
'sector_4_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_4, intermediates=[parcel])',
'sector_5_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_5, intermediates=[parcel])',
'sector_6_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_6, intermediates=[parcel])',
'sector_7_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_7, intermediates=[parcel])',
'sector_8_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_8, intermediates=[parcel])',
'sector_9_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_9, intermediates=[parcel])',
'sector_10_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_10, intermediates=[parcel])',
'sector_11_emp=zone.aggregate(sanfrancisco.building.employment_of_sector_11, intermediates=[parcel])',
r"sector_CIE_emp=zone.aggregate(where(sanfrancisco.business.activity_id==1,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_RET_emp=zone.aggregate(where(sanfrancisco.business.activity_id==5,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_PDR_emp=zone.aggregate(where(sanfrancisco.business.activity_id==4,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_MIPS_emp=zone.aggregate(where(sanfrancisco.business.activity_id==3,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_MED_emp=zone.aggregate(where(sanfrancisco.business.activity_id==2,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
r"sector_VIS_emp=zone.aggregate(where(sanfrancisco.business.activity_id==6,sanfrancisco.business.employment,0), intermediates=[building, parcel])",
'employment=zone.aggregate(sanfrancisco.building.employment, intermediates=[parcel])',
'sector_1_bus=zone.aggregate(business.sector_id == 1, intermediates=[building, parcel])',
'sector_2_bus=zone.aggregate(business.sector_id == 2, intermediates=[building, parcel])',
'sector_3_bus=zone.aggregate(business.sector_id == 3, intermediates=[building, parcel])',
'sector_4_bus=zone.aggregate(business.sector_id == 4, intermediates=[building, parcel])',
'sector_5_bus=zone.aggregate(business.sector_id == 5, intermediates=[building, parcel])',
'sector_6_bus=zone.aggregate(business.sector_id == 6, intermediates=[building, parcel])',
'sector_7_bus=zone.aggregate(business.sector_id == 7, intermediates=[building, parcel])',
'sector_8_bus=zone.aggregate(business.sector_id == 8, intermediates=[building, parcel])',
'sector_9_bus=zone.aggregate(business.sector_id == 9, intermediates=[building, parcel])',
'sector_10_bus=zone.aggregate(business.sector_id == 10, intermediates=[building, parcel])',
'sector_11_bus=zone.aggregate(business.sector_id == 11, intermediates=[building, parcel])',
'businesses=zone.aggregate(sanfrancisco.building.number_of_businesses, intermediates=[parcel])'
]
#exclude_condition = '==0' #exclude_condition now accepts opus expressions
),
]
IndicatorFactory().create_indicators(indicators=single_year_requests,
display_error_box=False,
show_results=False)
def write_info(directory, description, restrictions):
# Store Description and Restrictions info
path = os.path.join(directory, "indicators")
descrfile = open(os.path.join(path, "Description.txt"), "w")
descrfile.write(description)
descrfile.close()
restrfile = open(os.path.join(path, "Restrictions.txt"), "w")
restrfile.write(restrictions)
restrfile.close()
if __name__ == '__main__':
ind_cache = os.path.join(os.environ['QC_BASE_DIRECTORY'],
os.environ['QC_RUN1'])
indicators = get_indicators(
ind_cache,
os.getenv('QC_RUN1_DESCR', '') #, years = [2014,2015,2020]
)
IndicatorFactory().create_indicators(indicators=indicators,
display_error_box=False,
show_results=False)
# runs buildings indicator only for the simulation end year
IndicatorFactory().create_indicators(indicators=get_end_year_indicators(
ind_cache, os.getenv('QC_RUN1_DESCR', ''), years=[2050]),
display_error_box=False,
show_results=False)
write_info(ind_cache, os.getenv('QC_RUN1_DESCR', ''),
os.getenv('QC_RUN1_RESTR', ''))