Evaluate A Simulation

PyRecEst includes evaluation helpers for generating simulated scenarios, running filters, saving the raw evaluation output, and summarizing errors and runtimes.

This tutorial runs the built-in R2randomWalk scenario with a Kalman filter. The short run count keeps the example fast; larger experiments should use many more runs.

import tempfile
import warnings

from pyrecest.evaluation import (
    evaluate_for_simulation_config,
    summarize_filter_results,
)


filter_configs = [{"name": "kf", "parameter": None}]

with tempfile.TemporaryDirectory() as output_dir:
    (
        last_filter_states,
        runtimes,
        run_failed,
        groundtruths,
        measurements,
        scenario_config,
        expanded_filter_configs,
        evaluation_config,
    ) = evaluate_for_simulation_config(
        "R2randomWalk",
        filter_configs,
        n_runs=3,
        n_timesteps=5,
        initial_seed=1,
        save_folder=output_dir,
        auto_warning_on_off=False,
    )

    with warnings.catch_warnings():
        warnings.simplefilter("ignore", UserWarning)
        summaries = summarize_filter_results(
            scenario_config=scenario_config,
            filter_configs=expanded_filter_configs,
            runtimes=runtimes,
            groundtruths=groundtruths,
            run_failed=run_failed,
            last_filter_states=last_filter_states,
        )

for result in summaries:
    print(
        f"{result['name']}: "
        f"error_mean={float(result['error_mean']):.3f}, "
        f"error_std={float(result['error_std']):.3f}, "
        f"time_mean={float(result['time_mean']):.4f}, "
        f"failure_rate={float(result['failure_rate']):.3f}"
    )

What To Notice

• evaluate_for_simulation_config() accepts either a built-in scenario name or a scenario configuration dictionary.
• filter_configs use compact filter names such as kf for Kalman filter and pf for particle filter.
• A list or tuple in parameter expands into multiple filter configurations. For example, {"name": "pf", "parameter": [51, 81]} runs two particle-filter settings.
• The function saves a timestamped .npy file in save_folder and also returns the arrays needed for immediate analysis.
• summarize_filter_results() computes aggregate error, runtime, and failure statistics from the returned evaluation data.

Useful Variations

• Increase n_runs for more reliable summary statistics.
• Set extract_all_point_estimates=True when you need estimates at every time step instead of only final states.
• Use evaluate_for_file() when ground truth and measurements already exist in a saved data file.