polaris.analyze.kpi_comparator.KpiComparator

polaris.analyze.kpi_comparator.KpiComparator#

class polaris.analyze.kpi_comparator.KpiComparator#

Bases: object

This class provides an easy way to group together multiple runs of POLARIS and compare their outputs. Runs KPIs are added along with a string based name which is used as the label for that run in any subsequent plots which are generated.

from polaris.analyze.kpi_comparator import KpiComparator

results = KpiComparator()
results.add_run(ResultKPIs.from_iteration(ref_project_dir / f"{city}_iteration_2"), 'REF_iteration_2')
results.add_run(ResultKPIs.from_iteration(eval_project_dir / f"{city}_iteration_2"), 'EVAL_iteration_2')

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

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.

The object can also be used to generate a set of csv files for input into Excel (if you really have to use Excel):

results.dump_to_csvs(output_dir = "my_csv_dump_dir")
__init__()#

Methods

__init__()

across_iterations(cols, **kwargs)

add_iter()

add_run(kpi, run_id)

available_plots()

dump_to_csvs(output_dir[, metrics_to_dump])

has_run(run_id)

plot_act_dist([act_type])

plot_activity_start_time_distributions(**kwargs)

plot_calibration_destination(**kwargs)

plot_calibration_for_activity_generation(...)

plot_calibration_for_mode_share(**kwargs)

plot_calibration_timing(**kwargs)

plot_congestion_pricing(**kwargs)

plot_cpu_mem(**kwargs)

plot_everything(**kwargs)

plot_gaps(**kwargs)

plot_mode_share(**kwargs)

plot_multiple_gaps(kpi_results)

plot_network_gaps(**kwargs)

plot_pax_in_network(**kwargs)

plot_population(**kwargs)

plot_rmse_vs_observed(**kwargs)

plot_skim_stats([show_min_max])

plot_tnc(**kwargs)

plot_transit(**kwargs)

plot_trip_length_distributions([max_dist])

plot_vehicle_connectivity(**kwargs)

plot_vmt(**kwargs)

plot_vmt_by_link_type(**kwargs)

__init__()#
add_run(kpi: ResultKPIs, run_id: str)#
has_run(run_id)#
dump_to_csvs(output_dir, metrics_to_dump=None, **kwargs)#
plot_everything(**kwargs)#
classmethod available_plots()#
plot_mode_share(**kwargs)#
plot_population(**kwargs)#
plot_congestion_pricing(**kwargs)#
plot_transit(**kwargs)#
add_iter()#
across_iterations(cols, **kwargs)#
plot_act_dist(act_type: str | None = None, **kwargs)#
plot_vmt(**kwargs)#
plot_vehicle_connectivity(**kwargs)#
plot_gaps(**kwargs)#
static plot_multiple_gaps(kpi_results)#
plot_pax_in_network(**kwargs)#
plot_cpu_mem(**kwargs)#
plot_network_gaps(**kwargs)#
plot_skim_stats(show_min_max=False, **kwargs)#
plot_trip_length_distributions(max_dist=None, **kwargs)#
plot_activity_start_time_distributions(**kwargs)#
plot_tnc(**kwargs)#
plot_rmse_vs_observed(**kwargs)#
plot_calibration_for_activity_generation(**kwargs)#
plot_calibration_for_mode_share(**kwargs)#
plot_calibration_timing(**kwargs)#
plot_calibration_destination(**kwargs)#