polaris.analyze.result_kpis.ResultKPIs#

class polaris.analyze.result_kpis.ResultKPIs(inputs: PolarisInputs, cache_dir: Path, population_scale_factor: float)#

Bases: object

This class provides an easy way to extract relevant metrics for a single simulation run of POLARIS. The easiest way to generate an instance is via the factory method from_iteration which takes the path to the outputs of a simulation run (or a ConvergenceIteration())

from polaris.analyze.result_kpi import ResultKPIs

kpis = ResultKPIs.from_iteration(ref_project_dir / f"{city}_iteration_2")

Metric comparison plots can then be generated in a notebook using:

results = KpiComparator()
results.add_run(kpis, 'an-arbitrary-label')
results.plot_mode_share()
results.plot_vmt()
results.plot_vmt_by_link_type()

Any number of runs can be added using add_run up to the limit of readability on the generated plots.

__init__(inputs: PolarisInputs, cache_dir: Path, population_scale_factor: float)#

Methods

__init__(inputs, cache_dir, ...)
available_metrics()
cache_all_available_metrics([verbose, ...])
cached_metrics()
close()
from_args(files, iteration_dir[, ...])
from_dir(iteration_dir, **kwargs)	Create a KPI object from a given directory.
from_iteration(iteration, **kwargs)	Create a KPI object from a ConvergenceIteration object.
get_cached_kpi_value(kpi_name[, skip_cache, ...])
get_kpi_value(kpi_name)
has_cached_kpi(kpi_name)
m_etric_average_vehicle_ownership()	This metric retrieves the average household vehicle ownership from the synthetic population in the output
m_etric_traffic_cumulative_gap()	This metric is slow, memory heavy, broken after hdf5 migration and not used very often.
metric_activity_distances()
metric_activity_duration_distribution()
metric_activity_rate_distribution()
metric_activity_start_distribution()
metric_activity_start_time_distributions()
metric_avo_by_tnc_operator()
metric_calibration_act_gen()
metric_calibration_boardings()
metric_calibration_destination()
metric_calibration_mode_share()
metric_calibration_timing()
metric_count_validation()
metric_distance_by_act_type()
metric_ev_charging()
metric_executed_activity_mode_share_by_income()
metric_executed_modes()
metric_file_sizes()
metric_flow_density_fd()
metric_gaps()
metric_mode_shares()
metric_network_gaps_by_hour()
metric_network_gaps_by_link_type()
metric_num_hh()
metric_num_hh_by_hh_size()
metric_num_persons_by_age_band_10()
metric_num_persons_by_age_band_5()
metric_planned_modes()
metric_polaris_exe()
metric_population()
metric_rmse_vs_observed()
metric_road_pricing()
metric_skim_stats()
metric_summary()
metric_tnc_request_stats()
metric_tnc_stats()
metric_tnc_trip_stats()
metric_transit_boardings()
metric_transit_experience()
metric_trip_length_distribution()
metric_tts()
metric_validation_speed()
metric_vehicle_technology()
metric_vmt_vht()
metric_vmt_vht_by_link()
network_gaps_by_x(x)
one_value(conn, query[, default])

Attributes

result_time_step

result_time_step = 3600#

__init__(inputs: PolarisInputs, cache_dir: Path, population_scale_factor: float)#

classmethod from_iteration(iteration: ConvergenceIteration, **kwargs)#

Create a KPI object from a ConvergenceIteration object.

classmethod from_dir(iteration_dir: Path, **kwargs)#

Create a KPI object from a given directory.

classmethod from_args(files: PolarisInputs, iteration_dir: Path, cache_name: str = 'kpi.cache', clear_cache=False, exit_if_no_cache=False, population_scale_factor=None)#

cache_all_available_metrics(verbose=1, metrics_to_cache=None, skip_cache=False)#

classmethod available_metrics()#

close()#

get_kpi_value(kpi_name)#

get_cached_kpi_value(kpi_name, skip_cache=False, force_cache=False)#

has_cached_kpi(kpi_name)#

cached_metrics()#

metric_summary()#

metric_file_sizes()#

metric_polaris_exe()#

metric_gaps()#

static one_value(conn, query, default=0)#

metric_population()#

metric_num_persons_by_age_band_5()#

metric_num_persons_by_age_band_10()#

metric_num_hh()#

metric_num_hh_by_hh_size()#

metric_tts()#

metric_distance_by_act_type()#

metric_planned_modes()#

metric_executed_modes()#

metric_mode_shares()#

metric_executed_activity_mode_share_by_income()#

metric_activity_start_distribution()#

metric_activity_rate_distribution()#

metric_activity_duration_distribution()#

metric_vmt_vht()#

metric_ev_charging()#

metric_vmt_vht_by_link()#

metric_flow_density_fd()#

metric_activity_distances()#

metric_vehicle_technology()#

metric_tnc_request_stats()#

metric_tnc_trip_stats()#

metric_tnc_stats()#

metric_avo_by_tnc_operator()#

metric_road_pricing()#

metric_transit_boardings()#

metric_transit_experience()#

network_gaps_by_x(x)#

metric_network_gaps_by_link_type()#

metric_network_gaps_by_hour()#

metric_skim_stats()#

metric_count_validation()#

metric_rmse_vs_observed()#

metric_calibration_act_gen()#

metric_calibration_mode_share()#

metric_calibration_timing()#

metric_calibration_destination()#

metric_calibration_boardings()#

metric_validation_speed()#

metric_trip_length_distribution()#

metric_activity_start_time_distributions()#

m_etric_average_vehicle_ownership()#

This metric retrieves the average household vehicle ownership from the synthetic population in the output

m_etric_traffic_cumulative_gap()#

This metric is slow, memory heavy, broken after hdf5 migration and not used very often. Renaming it to m_etric to remove it from auto-running.