Datasets:

harryzhang1018
/

TestRandomization

#!/usr/bin/env python3
"""Case 1 physical runs and nominal simulations: per-run MAE (Tables II-III) and per-run start metadata.
Input: the folder TestingData of https://github.com/uwsbel/sbel-reproducibility/tree/master/2024/PathFollowingSim2real
(sub-folders rd_path = Path 1, syn_path = Path 2, lot17 = Path 3; five physical runs per policy and path, and one
nominal simulation log sim_<policy>.csv per policy and path) and the reference waypoints of the Docker image
(path_iros_1.csv, path_iros_2.csv, lot17_sinsquare.csv; copied to data/waypoints/).
Error definition (each logged sample): nearest reference waypoint (zero lookahead); lateral error = body-frame lateral
component of the displacement to that waypoint; heading error = wrapped difference between waypoint heading and
vehicle heading. Run MAE = mean of the absolute errors over all logged samples (no trimming or filtering).
Pose columns: nine-column logs (0, 1, 2); fourteen-column logs (Path 3 NN-MPC and NN-HD physical runs) (2, 3, 10).
The derived start quantities (initial pose, initial errors, motion onset, stale-pose fraction, pose outliers,
stationary segments) are proxies computed from the logged samples; the logs carry no timestamps.
Outputs (OUT_DIR):
  physical_runs.csv      60 physical runs (rounded as in the released table)
  nominal_sim_runs.csv   12 nominal simulation runs (full precision)
  tables_II_III.json     per path and policy: mean and sample SD (ddof = 1) of the five run MAEs, nominal sim MAE
  tables_II_III.tex      the rows of Tables II (lateral, m) and III (heading, rad)
  physical_pair_bootstrap.json  per path and metric: mean MAE difference of each controller pair and its 95 % run
                         bootstrap interval (20000 replicates, seed 20261019)
  physical_summary.json  start spread of the physical runs (max start distance, max |initial lateral error|)
Usage: python physical_runs.py TESTING_DATA_DIR WAYPOINTS_DIR OUT_DIR
"""
import csv
import glob
import hashlib
import itertools
import json
import math
import os
import sys

import numpy as np

PATHS = {'rd_path': 'path_iros_1', 'syn_path': 'path_iros_2', 'lot17': 'lot17_sinsquare'}
PATH_KEY = {'rd_path': 'P1', 'syn_path': 'P2', 'lot17': 'P3'}
POLICY = {'mpc': 'MPC', 'nn_mpc': 'NN-MPC', 'pid': 'PID', 'nn_mc': 'NN-HD', 'mpc_bb': 'NN-MPC', 'mc_bb': 'NN-HD'}
CTRL_ORDER = ['MPC', 'NN-MPC', 'PID', 'NN-HD']
N_BOOT, BOOT_SEED = 20000, 20261019


def sha(p):
    return hashlib.sha256(open(p, 'rb').read()).hexdigest()


def wrap(a):
    return np.arctan2(np.sin(a), np.cos(a))


def errors(x, y, th, ref, lookahead=0.0):
    ax, ay = x + np.cos(th) * lookahead, y + np.sin(th) * lookahead
    idx = ((ax[:, None] - ref[None, :, 0]) ** 2 + (ay[:, None] - ref[None, :, 1]) ** 2).argmin(1)
    dx, dy = ref[idx, 0] - x, ref[idx, 1] - y
    return -np.sin(th) * dx + np.cos(th) * dy, wrap(ref[idx, 2] - th), idx


def runs_of(mask):
    out, start = [], None
    for i, m in enumerate(mask):
        if m and start is None:
            start = i
        if not m and start is not None:
            out.append((start, i - 1)); start = None
    if start is not None:
        out.append((start, len(mask) - 1))
    return out


def analyse(f, ref, data):
    a = np.loadtxt(f, delimiter=',')
    cols = (0, 1, 2) if a.shape[1] == 9 else (2, 3, 10)
    x, y, th = a[:, cols[0]], a[:, cols[1]], a[:, cols[2]]
    lat, head, idx = errors(x, y, th, ref)
    step = np.r_[0.0, np.hypot(np.diff(x), np.diff(y))]
    moving = np.where(step > 0.01)[0]
    onset = int(moving[0]) if len(moving) else None
    # isolated outliers: sample far from both neighbours while neighbours are close to each other
    out_rows = []
    for t in range(1, len(a) - 1):
        d_prev = np.hypot(x[t] - x[t - 1], y[t] - y[t - 1]); d_next = np.hypot(x[t + 1] - x[t], y[t + 1] - y[t])
        d_skip = np.hypot(x[t + 1] - x[t - 1], y[t + 1] - y[t - 1])
        if d_prev > 0.5 and d_next > 0.5 and d_skip < 0.5:
            out_rows.append(t)
    big_steps = [int(t) for t in np.where(step > 0.5)[0] if t not in out_rows and t - 1 not in out_rows]
    stationary = [(s, e) for s, e in runs_of(step < 0.01) if onset is not None and s > onset and e - s + 1 >= 20 and e < len(a) - 1]
    n_ref = len(ref)
    return dict(
        file=os.path.relpath(f, data), sha256=sha(f), columns=a.shape[1], rows=len(a),
        initial_pose=[float(x[0]), float(y[0]), float(th[0])],
        initial_zero_lookahead_lateral_m=float(lat[0]), initial_heading_error_rad=float(head[0]),
        initial_nearest_waypoint=int(idx[0]),
        motion_onset_row=onset,
        stale_pose_fraction=float(np.mean(step[1:] < 1e-9)),
        max_progress_waypoint_fraction=float(idx.max() / (n_ref - 1)),
        isolated_pose_outlier_rows=out_rows,
        other_pose_steps_gt_0p5m_rows=big_steps,
        stationary_segments_after_onset_ge_2s=[[int(s), int(e)] for s, e in stationary],
        max_abs_lateral_m=float(np.abs(lat).max()),
        mae=np.abs(np.column_stack((lat, head))).mean(axis=0).tolist())


def main():
    data, wp, out = sys.argv[1:4]
    os.makedirs(out, exist_ok=True)
    runs = []
    for folder, stem in PATHS.items():
        ref = np.loadtxt(os.path.join(wp, stem + '.csv'), delimiter=',')
        for f in sorted(glob.glob(os.path.join(data, folder, '*.csv'))):
            name = os.path.basename(f)[:-4]
            pol = name[4:] if name.startswith('sim_') else name.rsplit('_', 1)[0]
            r = analyse(f, ref, data)
            r.update(path=folder, policy=POLICY[pol], kind='nominal_sim' if name.startswith('sim_') else 'physical')
            runs.append(r)

    phys = [r for r in runs if r['kind'] == 'physical']
    assert len(phys) == 60
    rows = []
    for r in sorted(phys, key=lambda r: (PATH_KEY[r['path']], CTRL_ORDER.index(r['policy']), r['file'])):
        run_idx = int(os.path.splitext(os.path.basename(r['file']))[0].split('_')[-1])
        rows.append({
            'path': PATH_KEY[r['path']], 'controller': r['policy'], 'run': run_idx, 'log_file': 'TestingData/' + r['file'],
            'log_sha256': r['sha256'], 'log_columns': r['columns'], 'rows': r['rows'],
            'start_distance_from_local_origin_m': round(math.hypot(*r['initial_pose'][:2]), 4),
            'start_yaw_local_frame_rad': round(r['initial_pose'][2], 4),
            'initial_lateral_error_m': round(r['initial_zero_lookahead_lateral_m'], 4),
            'initial_heading_error_rad': round(r['initial_heading_error_rad'], 4),
            'initial_nearest_waypoint': r['initial_nearest_waypoint'],
            'motion_onset_row': r['motion_onset_row'],
            'max_progress_waypoint_fraction': round(r['max_progress_waypoint_fraction'], 4),
            'stale_pose_fraction': round(r['stale_pose_fraction'], 4),
            'pose_steps_gt_0p5m': len(r['other_pose_steps_gt_0p5m_rows']),
            'isolated_pose_outliers': len(r['isolated_pose_outlier_rows']),
            'stationary_segments_ge_2s_after_onset': len(r['stationary_segments_after_onset_ge_2s']),
            'run_mae_lateral_m': round(r['mae'][0], 5), 'run_mae_heading_rad': round(r['mae'][1], 5),
            'max_abs_lateral_error_m': round(r['max_abs_lateral_m'], 3),
        })
    with open(os.path.join(out, 'physical_runs.csv'), 'w', newline='') as f:
        w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
        w.writeheader()
        w.writerows(rows)

    nom = sorted([r for r in runs if r['kind'] == 'nominal_sim'], key=lambda r: (PATH_KEY[r['path']], CTRL_ORDER.index(r['policy'])))
    assert len(nom) == 12
    with open(os.path.join(out, 'nominal_sim_runs.csv'), 'w', newline='') as f:
        w = csv.writer(f)
        w.writerow(['path', 'controller', 'log_file', 'log_sha256', 'log_columns', 'rows', 'run_mae_lateral_m', 'run_mae_heading_rad'])
        for r in nom:
            w.writerow([PATH_KEY[r['path']], r['policy'], 'TestingData/' + r['file'], r['sha256'], r['columns'], r['rows'],
                        repr(r['mae'][0]), repr(r['mae'][1])])

    # Tables II-III: mean and sample SD (ddof = 1) of the five run MAEs (full precision); one nominal simulation run
    tab = {}
    for folder in PATHS:
        p = PATH_KEY[folder]
        for c in CTRL_ORDER:
            v = np.array([r['mae'] for r in phys if r['path'] == folder and r['policy'] == c])
            s = next(r for r in nom if r['path'] == folder and r['policy'] == c)
            tab[f'{p}|{c}'] = {'n_runs': len(v), 'real_mean': v.mean(axis=0).tolist(), 'real_sd': v.std(axis=0, ddof=1).tolist(),
                               'nominal_sim': s['mae']}
    json.dump({'metrics': ['lateral_m', 'heading_rad'], 'cells': tab}, open(os.path.join(out, 'tables_II_III.json'), 'w'), indent=1)
    lines = []
    for mi, (name, unit) in enumerate((('Table II: lateral error', 'm'), ('Table III: heading error', 'rad'))):
        lines += [f'% {name} ({unit}); columns Path 1, Path 2, Path 3', r'Policy & Path 1 & Path 2 & Path 3 \\']
        for c in CTRL_ORDER:
            cells = [tab[f'{p}|{c}'] for p in ('P1', 'P2', 'P3')]
            lines.append(f'{c}-Sim & ' + ' & '.join(f"${x['nominal_sim'][mi]:.3f}$" for x in cells) + r' \\')
            lines.append(f'{c}-Real & ' + ' & '.join(f"${x['real_mean'][mi]:.3f}\\pm{x['real_sd'][mi]:.3f}$" for x in cells) + r' \\')
        lines.append('')
    open(os.path.join(out, 'tables_II_III.tex'), 'w').write('\n'.join(lines))

    # Run bootstrap of the mean-MAE difference of each controller pair: resample the five runs of each controller
    # independently (with replacement), 20000 replicates, seed 20261019; one draw array per path (Paths 1, 2, 3 in
    # this order), shared by both metrics.
    rng = np.random.default_rng(BOOT_SEED)
    pairs = {}
    for folder in PATHS:
        p = PATH_KEY[folder]
        run_mae = np.array([[r['mae'] for r in phys if r['path'] == folder and r['policy'] == c] for c in CTRL_ORDER])
        draws = rng.integers(0, 5, size=(4, N_BOOT, 5))
        for mi, m in enumerate(('lateral', 'heading')):
            v = run_mae[:, :, mi]
            boot = np.stack([v[k][draws[k]].mean(axis=1) for k in range(4)], axis=1)
            point = v.mean(axis=1)
            pairs[f'{p}_{m}'] = [{'pair': f'{CTRL_ORDER[a]} - {CTRL_ORDER[b]}', 'mean_difference': float(point[a] - point[b]),
                                  'bootstrap_95': np.quantile(boot[:, a] - boot[:, b], [.025, .975]).tolist()}
                                 for a, b in itertools.combinations(range(4), 2)]
    json.dump({'replicates': N_BOOT, 'seed': BOOT_SEED, 'pairs': pairs},
              open(os.path.join(out, 'physical_pair_bootstrap.json'), 'w'), indent=1)

    summ = {'n_physical_runs': len(phys), 'n_nominal_sim_runs': len(nom),
            'max_start_distance_from_local_origin_m': round(max(math.hypot(*r['initial_pose'][:2]) for r in phys), 4),
            'max_abs_initial_lateral_error_m': round(max(abs(r['initial_zero_lookahead_lateral_m']) for r in phys), 4)}
    json.dump(summ, open(os.path.join(out, 'physical_summary.json'), 'w'), indent=1)
    print(json.dumps(summ))
    print('\n'.join(lines))


if __name__ == '__main__':
    main()

	#!/usr/bin/env python3
	"""Case 1 physical runs and nominal simulations: per-run MAE (Tables II-III) and per-run start metadata.

	Input: the folder TestingData of https://github.com/uwsbel/sbel-reproducibility/tree/master/2024/PathFollowingSim2real
	(sub-folders rd_path = Path 1, syn_path = Path 2, lot17 = Path 3; five physical runs per policy and path, and one
	nominal simulation log sim_<policy>.csv per policy and path) and the reference waypoints of the Docker image
	(path_iros_1.csv, path_iros_2.csv, lot17_sinsquare.csv; copied to data/waypoints/).

	Error definition (each logged sample): nearest reference waypoint (zero lookahead); lateral error = body-frame lateral
	component of the displacement to that waypoint; heading error = wrapped difference between waypoint heading and
	vehicle heading. Run MAE = mean of the absolute errors over all logged samples (no trimming or filtering).
	Pose columns: nine-column logs (0, 1, 2); fourteen-column logs (Path 3 NN-MPC and NN-HD physical runs) (2, 3, 10).
	The derived start quantities (initial pose, initial errors, motion onset, stale-pose fraction, pose outliers,
	stationary segments) are proxies computed from the logged samples; the logs carry no timestamps.

	Outputs (OUT_DIR):
	physical_runs.csv 60 physical runs (rounded as in the released table)
	nominal_sim_runs.csv 12 nominal simulation runs (full precision)
	tables_II_III.json per path and policy: mean and sample SD (ddof = 1) of the five run MAEs, nominal sim MAE
	tables_II_III.tex the rows of Tables II (lateral, m) and III (heading, rad)
	physical_pair_bootstrap.json per path and metric: mean MAE difference of each controller pair and its 95 % run
	bootstrap interval (20000 replicates, seed 20261019)
	physical_summary.json start spread of the physical runs (max start distance, max \|initial lateral error\|)

	Usage: python physical_runs.py TESTING_DATA_DIR WAYPOINTS_DIR OUT_DIR
	"""
	import csv
	import glob
	import hashlib
	import itertools
	import json
	import math
	import os
	import sys

	import numpy as np

	PATHS = {'rd_path': 'path_iros_1', 'syn_path': 'path_iros_2', 'lot17': 'lot17_sinsquare'}
	PATH_KEY = {'rd_path': 'P1', 'syn_path': 'P2', 'lot17': 'P3'}
	POLICY = {'mpc': 'MPC', 'nn_mpc': 'NN-MPC', 'pid': 'PID', 'nn_mc': 'NN-HD', 'mpc_bb': 'NN-MPC', 'mc_bb': 'NN-HD'}
	CTRL_ORDER = ['MPC', 'NN-MPC', 'PID', 'NN-HD']
	N_BOOT, BOOT_SEED = 20000, 20261019


	def sha(p):
	return hashlib.sha256(open(p, 'rb').read()).hexdigest()


	def wrap(a):
	return np.arctan2(np.sin(a), np.cos(a))


	def errors(x, y, th, ref, lookahead=0.0):
	ax, ay = x + np.cos(th) * lookahead, y + np.sin(th) * lookahead
	idx = ((ax[:, None] - ref[None, :, 0]) 2 + (ay[:, None] - ref[None, :, 1]) 2).argmin(1)
	dx, dy = ref[idx, 0] - x, ref[idx, 1] - y
	return -np.sin(th) * dx + np.cos(th) * dy, wrap(ref[idx, 2] - th), idx


	def runs_of(mask):
	out, start = [], None
	for i, m in enumerate(mask):
	if m and start is None:
	start = i
	if not m and start is not None:
	out.append((start, i - 1)); start = None
	if start is not None:
	out.append((start, len(mask) - 1))
	return out


	def analyse(f, ref, data):
	a = np.loadtxt(f, delimiter=',')
	cols = (0, 1, 2) if a.shape[1] == 9 else (2, 3, 10)
	x, y, th = a[:, cols[0]], a[:, cols[1]], a[:, cols[2]]
	lat, head, idx = errors(x, y, th, ref)
	step = np.r_[0.0, np.hypot(np.diff(x), np.diff(y))]
	moving = np.where(step > 0.01)[0]
	onset = int(moving[0]) if len(moving) else None
	# isolated outliers: sample far from both neighbours while neighbours are close to each other
	out_rows = []
	for t in range(1, len(a) - 1):
	d_prev = np.hypot(x[t] - x[t - 1], y[t] - y[t - 1]); d_next = np.hypot(x[t + 1] - x[t], y[t + 1] - y[t])
	d_skip = np.hypot(x[t + 1] - x[t - 1], y[t + 1] - y[t - 1])
	if d_prev > 0.5 and d_next > 0.5 and d_skip < 0.5:
	out_rows.append(t)
	big_steps = [int(t) for t in np.where(step > 0.5)[0] if t not in out_rows and t - 1 not in out_rows]
	stationary = [(s, e) for s, e in runs_of(step < 0.01) if onset is not None and s > onset and e - s + 1 >= 20 and e < len(a) - 1]
	n_ref = len(ref)
	return dict(
	file=os.path.relpath(f, data), sha256=sha(f), columns=a.shape[1], rows=len(a),
	initial_pose=[float(x[0]), float(y[0]), float(th[0])],
	initial_zero_lookahead_lateral_m=float(lat[0]), initial_heading_error_rad=float(head[0]),
	initial_nearest_waypoint=int(idx[0]),
	motion_onset_row=onset,
	stale_pose_fraction=float(np.mean(step[1:] < 1e-9)),
	max_progress_waypoint_fraction=float(idx.max() / (n_ref - 1)),
	isolated_pose_outlier_rows=out_rows,
	other_pose_steps_gt_0p5m_rows=big_steps,
	stationary_segments_after_onset_ge_2s=[[int(s), int(e)] for s, e in stationary],
	max_abs_lateral_m=float(np.abs(lat).max()),
	mae=np.abs(np.column_stack((lat, head))).mean(axis=0).tolist())


	def main():
	data, wp, out = sys.argv[1:4]
	os.makedirs(out, exist_ok=True)
	runs = []
	for folder, stem in PATHS.items():
	ref = np.loadtxt(os.path.join(wp, stem + '.csv'), delimiter=',')
	for f in sorted(glob.glob(os.path.join(data, folder, '*.csv'))):
	name = os.path.basename(f)[:-4]
	pol = name[4:] if name.startswith('sim_') else name.rsplit('_', 1)[0]
	r = analyse(f, ref, data)
	r.update(path=folder, policy=POLICY[pol], kind='nominal_sim' if name.startswith('sim_') else 'physical')
	runs.append(r)

	phys = [r for r in runs if r['kind'] == 'physical']
	assert len(phys) == 60
	rows = []
	for r in sorted(phys, key=lambda r: (PATH_KEY[r['path']], CTRL_ORDER.index(r['policy']), r['file'])):
	run_idx = int(os.path.splitext(os.path.basename(r['file']))[0].split('_')[-1])
	rows.append({
	'path': PATH_KEY[r['path']], 'controller': r['policy'], 'run': run_idx, 'log_file': 'TestingData/' + r['file'],
	'log_sha256': r['sha256'], 'log_columns': r['columns'], 'rows': r['rows'],
	'start_distance_from_local_origin_m': round(math.hypot(*r['initial_pose'][:2]), 4),
	'start_yaw_local_frame_rad': round(r['initial_pose'][2], 4),
	'initial_lateral_error_m': round(r['initial_zero_lookahead_lateral_m'], 4),
	'initial_heading_error_rad': round(r['initial_heading_error_rad'], 4),
	'initial_nearest_waypoint': r['initial_nearest_waypoint'],
	'motion_onset_row': r['motion_onset_row'],
	'max_progress_waypoint_fraction': round(r['max_progress_waypoint_fraction'], 4),
	'stale_pose_fraction': round(r['stale_pose_fraction'], 4),
	'pose_steps_gt_0p5m': len(r['other_pose_steps_gt_0p5m_rows']),
	'isolated_pose_outliers': len(r['isolated_pose_outlier_rows']),
	'stationary_segments_ge_2s_after_onset': len(r['stationary_segments_after_onset_ge_2s']),
	'run_mae_lateral_m': round(r['mae'][0], 5), 'run_mae_heading_rad': round(r['mae'][1], 5),
	'max_abs_lateral_error_m': round(r['max_abs_lateral_m'], 3),
	})
	with open(os.path.join(out, 'physical_runs.csv'), 'w', newline='') as f:
	w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
	w.writeheader()
	w.writerows(rows)

	nom = sorted([r for r in runs if r['kind'] == 'nominal_sim'], key=lambda r: (PATH_KEY[r['path']], CTRL_ORDER.index(r['policy'])))
	assert len(nom) == 12
	with open(os.path.join(out, 'nominal_sim_runs.csv'), 'w', newline='') as f:
	w = csv.writer(f)
	w.writerow(['path', 'controller', 'log_file', 'log_sha256', 'log_columns', 'rows', 'run_mae_lateral_m', 'run_mae_heading_rad'])
	for r in nom:
	w.writerow([PATH_KEY[r['path']], r['policy'], 'TestingData/' + r['file'], r['sha256'], r['columns'], r['rows'],
	repr(r['mae'][0]), repr(r['mae'][1])])

	# Tables II-III: mean and sample SD (ddof = 1) of the five run MAEs (full precision); one nominal simulation run
	tab = {}
	for folder in PATHS:
	p = PATH_KEY[folder]
	for c in CTRL_ORDER:
	v = np.array([r['mae'] for r in phys if r['path'] == folder and r['policy'] == c])
	s = next(r for r in nom if r['path'] == folder and r['policy'] == c)
	tab[f'{p}\|{c}'] = {'n_runs': len(v), 'real_mean': v.mean(axis=0).tolist(), 'real_sd': v.std(axis=0, ddof=1).tolist(),
	'nominal_sim': s['mae']}
	json.dump({'metrics': ['lateral_m', 'heading_rad'], 'cells': tab}, open(os.path.join(out, 'tables_II_III.json'), 'w'), indent=1)
	lines = []
	for mi, (name, unit) in enumerate((('Table II: lateral error', 'm'), ('Table III: heading error', 'rad'))):
	lines += [f'% {name} ({unit}); columns Path 1, Path 2, Path 3', r'Policy & Path 1 & Path 2 & Path 3 \\']
	for c in CTRL_ORDER:
	cells = [tab[f'{p}\|{c}'] for p in ('P1', 'P2', 'P3')]
	lines.append(f'{c}-Sim & ' + ' & '.join(f"${x['nominal_sim'][mi]:.3f}$" for x in cells) + r' \\')
	lines.append(f'{c}-Real & ' + ' & '.join(f"${x['real_mean'][mi]:.3f}\\pm{x['real_sd'][mi]:.3f}$" for x in cells) + r' \\')
	lines.append('')
	open(os.path.join(out, 'tables_II_III.tex'), 'w').write('\n'.join(lines))

	# Run bootstrap of the mean-MAE difference of each controller pair: resample the five runs of each controller
	# independently (with replacement), 20000 replicates, seed 20261019; one draw array per path (Paths 1, 2, 3 in
	# this order), shared by both metrics.
	rng = np.random.default_rng(BOOT_SEED)
	pairs = {}
	for folder in PATHS:
	p = PATH_KEY[folder]
	run_mae = np.array([[r['mae'] for r in phys if r['path'] == folder and r['policy'] == c] for c in CTRL_ORDER])
	draws = rng.integers(0, 5, size=(4, N_BOOT, 5))
	for mi, m in enumerate(('lateral', 'heading')):
	v = run_mae[:, :, mi]
	boot = np.stack([v[k][draws[k]].mean(axis=1) for k in range(4)], axis=1)
	point = v.mean(axis=1)
	pairs[f'{p}_{m}'] = [{'pair': f'{CTRL_ORDER[a]} - {CTRL_ORDER[b]}', 'mean_difference': float(point[a] - point[b]),
	'bootstrap_95': np.quantile(boot[:, a] - boot[:, b], [.025, .975]).tolist()}
	for a, b in itertools.combinations(range(4), 2)]
	json.dump({'replicates': N_BOOT, 'seed': BOOT_SEED, 'pairs': pairs},
	open(os.path.join(out, 'physical_pair_bootstrap.json'), 'w'), indent=1)

	summ = {'n_physical_runs': len(phys), 'n_nominal_sim_runs': len(nom),
	'max_start_distance_from_local_origin_m': round(max(math.hypot(*r['initial_pose'][:2]) for r in phys), 4),
	'max_abs_initial_lateral_error_m': round(max(abs(r['initial_zero_lookahead_lateral_m']) for r in phys), 4)}
	json.dump(summ, open(os.path.join(out, 'physical_summary.json'), 'w'), indent=1)
	print(json.dumps(summ))
	print('\n'.join(lines))


	if __name__ == '__main__':
	main()