def aggregate_zone_benefits(aggregate_zone_summary):
trace_label = 'aggregate_zone_benefits'
zone_summary = aggregate_zone_summary.to_frame()
model_settings = config.read_model_settings('aggregate_zone.yaml')
spec_file_name = model_settings.get('spec_file_name', 'aggregate_zone.csv')
aggregate_zone_spec = bca.read_assignment_spec(spec_file_name)
add_aggregate_results(zone_summary,
aggregate_zone_spec,
source=trace_label)
def aggregate_zone_processor(zones, trace_od):
"""
zones: orca table
zone data for base and build scenario dat files combined into a single dataframe
with columns names prefixed with base_ or build_ indexed by ZONE
"""
trace_label = 'aggregate_zone'
model_settings = config.read_model_settings('aggregate_zone.yaml')
spec_file_name = model_settings.get('spec_file_name', 'aggregate_zone.csv')
aggregate_zone_spec = bca.read_assignment_spec(spec_file_name)
zones_df = zones.to_frame()
logger.info("Running aggregate_zone_processor with %d zones" %
(len(zones_df.index), ))
if trace_od:
trace_orig, trace_dest = trace_od
trace_od_rows = (zones_df.index == trace_orig) | (zones_df.index
== trace_dest)
else:
trace_od_rows = None
# locals whose values will be accessible to the execution context
# when the expressions in spec are applied to choosers
locals_dict = config.get_model_constants(model_settings)
locals_dict.update(config.setting('globals'))
# eval_variables evaluates each of the expressions in spec
# in the context of each row in of the choosers dataframe
results, trace_results, trace_assigned_locals = \
assign.assign_variables(aggregate_zone_spec,
zones_df,
locals_dict,
df_alias='zones',
trace_rows=trace_od_rows)
pipeline.replace_table('aggregate_zone_summary', results)
if trace_results is not None:
tracing.write_csv(trace_results,
file_name="aggregate_zone",
index_label='zone',
column_labels=['label', 'zone'])
if trace_assigned_locals:
tracing.write_csv(trace_assigned_locals,
file_name="aggregate_zone_locals")
def aggregate_demographics_spec():
return bca.read_assignment_spec('aggregate_demographics.csv')
def link_daily_spec():
return bca.read_assignment_spec('link_daily.csv')
def link_spec():
return bca.read_assignment_spec('link.csv')
def demographics_spec():
return bca.read_assignment_spec('demographics.csv')
def auto_ownership_spec():
return bca.read_assignment_spec('auto_ownership.csv')
def physical_activity_person_spec():
return bca.read_assignment_spec('physical_activity_person.csv')
def physical_activity_trip_spec():
return bca.read_assignment_spec('physical_activity_trip.csv')
def person_trips_spec():
return bca.read_assignment_spec('person_trips.csv')
def aggregate_od_processor(zone_districts, zones, data_dir, trace_od):
trace_label = 'aggregate_od'
logger.info("Running %s" % (trace_label, ))
model_settings = config.read_model_settings('aggregate_od.yaml')
spec_file_name = model_settings.get('spec_file_name', 'aggregate_od.csv')
aggregate_od_spec = bca.read_assignment_spec(spec_file_name)
zones = zones.to_frame()
zone_districts = zone_districts.to_frame()
zone_count = zone_districts.shape[0]
assert zones.index.equals(zone_districts.index)
# create OD dataframe in order compatible with ODSkims
od_df = pd.DataFrame(
data={
'orig': np.repeat(np.asanyarray(zones.index), zone_count),
'dest': np.tile(np.asanyarray(zones.index), zone_count),
})
# locals whose values will be accessible to the execution context
# when the expressions in spec are applied to choosers
locals_dict = config.get_model_constants(model_settings)
locals_dict.update(config.setting('globals'))
locals_dict['logger'] = logger
logger.debug('%s mem before create_skim_locals_dict, %s' % (
trace_label,
memory_info(),
))
# - add ODSkims to locals (note: we use local_skims list later to close omx files)
cache_skims = model_settings.get('cache_skims', False)
local_skims = create_skim_locals_dict(model_settings, data_dir, zones,
cache_skims)
locals_dict.update(local_skims)
# - create_zone_matrices dicts
locals_dict.update(create_zone_matrices(model_settings, zones))
if trace_od:
trace_orig, trace_dest = trace_od
trace_od_rows = (od_df.orig == trace_orig) & (od_df.dest == trace_dest)
else:
trace_od_rows = None
logger.debug("%s assigning variables" % (trace_label, ))
results, trace_results, trace_assigned_locals = \
assign.assign_variables(aggregate_od_spec,
od_df,
locals_dict=locals_dict,
df_alias='od',
trace_rows=trace_od_rows)
logger.debug('%s mem after assign_variables, %s' % (
trace_label,
memory_info(),
))
for local_name, od_skims in local_skims.items():
logger.debug("closing %s" % local_name)
od_skims.log_skim_usage()
od_skims.close()
# summarize aggregate_od_benefits by orig and dest districts
logger.debug("%s district summary" % (trace_label, ))
results['orig'] = np.repeat(np.asanyarray(zone_districts.district),
zone_count)
results['dest'] = np.tile(np.asanyarray(zone_districts.district),
zone_count)
district_summary = results.groupby(['orig', 'dest']).sum()
pipeline.replace_table('aggregate_od_district_summary', district_summary)
# attribute aggregate_results benefits to origin zone
logger.debug("%s zone summary" % (trace_label, ))
results['orig'] = od_df['orig']
del results['dest']
zone_summary = results.groupby(['orig']).sum()
pipeline.replace_table('aggregate_od_zone_summary', zone_summary)
add_aggregate_results(zone_summary, aggregate_od_spec, source=trace_label)
if trace_results is not None:
tracing.write_csv(trace_results,
file_name=trace_label,
index_label='index',
column_labels=['label', 'od'])
if trace_assigned_locals:
tracing.write_csv(trace_assigned_locals,
file_name="%s_locals" % trace_label,
index_label='variable',
columns='value')
def aggregate_trips_spec():
return bca.read_assignment_spec('aggregate_trips.csv')