"""Recompute the published SYNTHETIC coordinate audit. Python standard library only.
Run: python3 reproduce.py [directory containing dataset.json and results.json]
No photos, detector models, biometric data or network requests are used.
"""
import json
import math
import sys
from pathlib import Path


def quantize(value):
    # Match the engine's half-away-from-zero rounding of normalized input.
    return math.copysign(math.floor(abs(value) * 1_000_000 + 0.5), value) / 1_000_000


def measure(case, dataset, rounded=True):
    width, height = case['imageWidth'], case['imageHeight']
    points = {int(i): (quantize(p['x']) * width, quantize(p['y']) * height)
              if rounded else (p['x'] * width, p['y'] * height)
              for i, p in case['inputCoordinates'].items()}
    right, left = [points[i] for i in dataset['iris']]
    dx, dy = left[0] - right[0], left[1] - right[1]
    length = math.hypot(dx, dy)
    if length <= 0:
        raise ValueError('Coincident pupil coordinates')
    ox, oy = (right[0] + left[0]) / 2, (right[1] + left[1]) / 2
    points = {i: ((dx * (x - ox) + dy * (y - oy)) / length,
                  (-dy * (x - ox) + dx * (y - oy)) / length)
              for i, (x, y) in points.items()}
    mid = sum(points[i][0] for i in dataset['midline']) / len(dataset['midline'])
    cheek_r, cheek_l = [points[i] for i in dataset['cheeks']]
    face_width = math.dist(cheek_r, cheek_l)
    if face_width <= 0:
        raise ValueError('Zero face width')
    gaps = [math.dist(points[r], (2 * mid - points[l][0], points[l][1]))
            for r, l in dataset['pairs']]
    return sum(gaps) / (len(gaps) * face_width)


if __name__ == '__main__':
    root = Path(sys.argv[1]) if len(sys.argv) > 1 else Path(__file__).resolve().parent
    data = json.loads((root / 'dataset.json').read_text())
    results = json.loads((root / 'results.json').read_text())
    assert data['synthetic'] and results['humanSubjects'] == 0
    errors = []
    for case in data['cases']:
        value = measure(case, data)
        error = abs(value - case['asymmetryIndex'])
        assert error < 1e-12, (case['id'], error)
        if case['expected'] is not None:
            assert abs(value - case['expected']) < 1e-12
        assert abs(measure(case, data, False) - case['withoutInputQuantization']) < 1e-12
        errors.append(error)
    assert len(errors) == results['caseCount']
    print(json.dumps({'verifiedCases': len(errors), 'maxErrorAgainstPinnedEngine': max(errors),
                      'synthetic': True, 'photosProcessed': 0, 'detectorAccuracyTested': False}, indent=2))