FA-66741 / Airline fare rule evaluation / Open access
Prorated coupon values do not add up to the fare · case 01
Interline settlement is short by a few cents on most tickets.
ROOT CAUSE
The floor-division remainder is never assigned to any coupon.
VERIFIED REPAIR
Add cents minus the sum of shares to the designated coupon.
Unsuccessful approach: Adding a single cent is wrong when the remainder is larger than one cent.
Case contract
Input {'fare','tpm':[miles per coupon, 0 for surface]}. Work in integer cents: each coupon gets floor(cents*tpm/total); the rounding remainder goes to the last coupon with nonzero miles; surface coupons get 0. No miles -> 'ERR_NO_MILES'. Return coupon values in currency units.
Why this case matters
Prorating a through fare across coupons by mileage must conserve the fare to the cent and keep value off surface sectors.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(x):
tpm = x['tpm']
total = sum(tpm)
if total == 0: return 'ERR_NO_MILES'
cents = round(x['fare'] * 100)
shares = [cents * t // total for t in tpm]
return [s / 100 for s in shares]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['three coupons uneven', {'fare': 1001.0, 'tpm': [1200, 350, 2900]}, [269.93, 78.73, 652.34]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1501, 0]}, [270.41, 507.36, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 901]}, [0.0, 218.61, 281.4]], ['single coupon', {'fare': 123.45, 'tpm': [2475]}, [123.45]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 1565.87, 'tpm': [193, 5089]}, [57.21, 1508.66]], ['sampled case 2', {'fare': 583.22, 'tpm': [1271, 0, 0]}, [583.22, 0.0, 0.0]], ['sampled case 3', {'fare': 2079.37, 'tpm': [1800]}, [2079.37]], ['regression: rounding remainder allocation', {'fare': 1582.69, 'tpm': [3167, 752, 2410, 859]}, [697.32, 165.57, 530.64, 189.16]]], [['three coupons uneven', {'fare': 1002.0, 'tpm': [1200, 350, 2900]}, [270.2, 78.8, 653.0]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1502, 0]}, [270.29, 507.48, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 902]}, [0.0, 218.48, 281.53]], ['single coupon', {'fare': 246.9, 'tpm': [2475]}, [246.9]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 2915.76, 'tpm': [0, 5491, 214, 782]}, [0.0, 2468.08, 96.18, 351.5]], ['sampled case 2', {'fare': 2482.84, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 3', {'fare': 1177.57, 'tpm': [0, 0]}, 'ERR_NO_MILES']], [['three coupons uneven', {'fare': 1003.0, 'tpm': [1200, 350, 2900]}, [270.47, 78.88, 653.65]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1503, 0]}, [270.17, 507.6, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 903]}, [0.0, 218.34, 281.67]], ['single coupon', {'fare': 370.35, 'tpm': [2475]}, [370.35]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 110.22, 'tpm': [0]}, 'ERR_NO_MILES'], ['sampled case 2', {'fare': 836.64, 'tpm': [5298, 5970, 0, 5279]}, [267.87, 301.85, 0.0, 266.92]], ['sampled case 3', {'fare': 218.58, 'tpm': [5530]}, [218.58]], ['regression: rounding remainder allocation', {'fare': 2950.8, 'tpm': [0, 3274, 2611, 5594, 5187]}, [0.0, 579.67, 462.29, 990.44, 918.4]]], [['three coupons uneven', {'fare': 1004.0, 'tpm': [1200, 350, 2900]}, [270.74, 78.96, 654.3]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1504, 0]}, [270.05, 507.72, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 904]}, [0.0, 218.2, 281.81]], ['single coupon', {'fare': 493.8, 'tpm': [2475]}, [493.8]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 524.52, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 2', {'fare': 1224.53, 'tpm': [0, 1463]}, [0.0, 1224.53]], ['sampled case 3', {'fare': 2475.89, 'tpm': [0]}, 'ERR_NO_MILES'], ['regression: rounding remainder allocation', {'fare': 1160.8, 'tpm': [2634, 1797, 144, 4820, 0]}, [325.44, 222.02, 17.79, 595.55, 0.0]]], [['three coupons uneven', {'fare': 1005.0, 'tpm': [1200, 350, 2900]}, [271.01, 79.04, 654.95]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1505, 0]}, [269.94, 507.83, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 905]}, [0.0, 218.07, 281.94]], ['single coupon', {'fare': 617.25, 'tpm': [2475]}, [617.25]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 927.71, 'tpm': [5167, 2555]}, [620.75, 306.96]], ['sampled case 2', {'fare': 574.25, 'tpm': [3022]}, [574.25]], ['sampled case 3', {'fare': 1349.84, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['regression: rounding remainder allocation', {'fare': 1976.74, 'tpm': [4338, 5522, 3056, 4678, 3840]}, [400.06, 509.26, 281.83, 431.42, 354.17]]]]
for label, args, expected in fixtures[N-1]:
check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| three coupons uneven | [269.93, 78.73, 652.33] | [269.93, 78.73, 652.34] | Failed |
| surface sector last | [270.41, 507.35, 0.0] | [270.41, 507.36, 0.0] | Failed |
| surface sector first | [0.0, 218.61, 281.39] | [0.0, 218.61, 281.4] | Failed |
| single coupon | [123.45] | [123.45] | Passed |
| equal miles odd cents | [33.33, 33.33, 33.33] | [33.33, 33.33, 33.34] | Failed |
| all surface | ERR_NO_MILES | ERR_NO_MILES | Passed |
| sampled case 1 | [57.21, 1508.65] | [57.21, 1508.66] | Failed |
| sampled case 2 | [583.22, 0.0, 0.0] | [583.22, 0.0, 0.0] | Passed |
| sampled case 3 | [2079.37] | [2079.37] | Passed |
| regression: rounding remainder allocation | [697.32, 165.57, 530.64, 189.13] | [697.32, 165.57, 530.64, 189.16] | Failed |
SHA-256 / 95825722f597b847378062e030f8e239600f020ac723f378a67361503b17f22e
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(x):
tpm = x['tpm']
total = sum(tpm)
if total == 0: return 'ERR_NO_MILES'
cents = round(x['fare'] * 100)
shares = [cents * t // total for t in tpm]
last = max(i for i, t in enumerate(tpm) if t > 0)
shares[last] += 1 if cents != sum(shares) else 0
return [s / 100 for s in shares]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['three coupons uneven', {'fare': 1001.0, 'tpm': [1200, 350, 2900]}, [269.93, 78.73, 652.34]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1501, 0]}, [270.41, 507.36, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 901]}, [0.0, 218.61, 281.4]], ['single coupon', {'fare': 123.45, 'tpm': [2475]}, [123.45]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 1565.87, 'tpm': [193, 5089]}, [57.21, 1508.66]], ['sampled case 2', {'fare': 583.22, 'tpm': [1271, 0, 0]}, [583.22, 0.0, 0.0]], ['sampled case 3', {'fare': 2079.37, 'tpm': [1800]}, [2079.37]], ['regression: rounding remainder allocation', {'fare': 1582.69, 'tpm': [3167, 752, 2410, 859]}, [697.32, 165.57, 530.64, 189.16]]], [['three coupons uneven', {'fare': 1002.0, 'tpm': [1200, 350, 2900]}, [270.2, 78.8, 653.0]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1502, 0]}, [270.29, 507.48, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 902]}, [0.0, 218.48, 281.53]], ['single coupon', {'fare': 246.9, 'tpm': [2475]}, [246.9]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 2915.76, 'tpm': [0, 5491, 214, 782]}, [0.0, 2468.08, 96.18, 351.5]], ['sampled case 2', {'fare': 2482.84, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 3', {'fare': 1177.57, 'tpm': [0, 0]}, 'ERR_NO_MILES']], [['three coupons uneven', {'fare': 1003.0, 'tpm': [1200, 350, 2900]}, [270.47, 78.88, 653.65]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1503, 0]}, [270.17, 507.6, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 903]}, [0.0, 218.34, 281.67]], ['single coupon', {'fare': 370.35, 'tpm': [2475]}, [370.35]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 110.22, 'tpm': [0]}, 'ERR_NO_MILES'], ['sampled case 2', {'fare': 836.64, 'tpm': [5298, 5970, 0, 5279]}, [267.87, 301.85, 0.0, 266.92]], ['sampled case 3', {'fare': 218.58, 'tpm': [5530]}, [218.58]], ['regression: rounding remainder allocation', {'fare': 2950.8, 'tpm': [0, 3274, 2611, 5594, 5187]}, [0.0, 579.67, 462.29, 990.44, 918.4]]], [['three coupons uneven', {'fare': 1004.0, 'tpm': [1200, 350, 2900]}, [270.74, 78.96, 654.3]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1504, 0]}, [270.05, 507.72, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 904]}, [0.0, 218.2, 281.81]], ['single coupon', {'fare': 493.8, 'tpm': [2475]}, [493.8]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 524.52, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 2', {'fare': 1224.53, 'tpm': [0, 1463]}, [0.0, 1224.53]], ['sampled case 3', {'fare': 2475.89, 'tpm': [0]}, 'ERR_NO_MILES'], ['regression: rounding remainder allocation', {'fare': 1160.8, 'tpm': [2634, 1797, 144, 4820, 0]}, [325.44, 222.02, 17.79, 595.55, 0.0]]], [['three coupons uneven', {'fare': 1005.0, 'tpm': [1200, 350, 2900]}, [271.01, 79.04, 654.95]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1505, 0]}, [269.94, 507.83, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 905]}, [0.0, 218.07, 281.94]], ['single coupon', {'fare': 617.25, 'tpm': [2475]}, [617.25]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 927.71, 'tpm': [5167, 2555]}, [620.75, 306.96]], ['sampled case 2', {'fare': 574.25, 'tpm': [3022]}, [574.25]], ['sampled case 3', {'fare': 1349.84, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['regression: rounding remainder allocation', {'fare': 1976.74, 'tpm': [4338, 5522, 3056, 4678, 3840]}, [400.06, 509.26, 281.83, 431.42, 354.17]]]]
for label, args, expected in fixtures[N-1]:
check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| three coupons uneven | [269.93, 78.73, 652.34] | [269.93, 78.73, 652.34] | Passed |
| surface sector last | [270.41, 507.36, 0.0] | [270.41, 507.36, 0.0] | Passed |
| surface sector first | [0.0, 218.61, 281.4] | [0.0, 218.61, 281.4] | Passed |
| single coupon | [123.45] | [123.45] | Passed |
| equal miles odd cents | [33.33, 33.33, 33.34] | [33.33, 33.33, 33.34] | Passed |
| all surface | ERR_NO_MILES | ERR_NO_MILES | Passed |
| sampled case 1 | [57.21, 1508.66] | [57.21, 1508.66] | Passed |
| sampled case 2 | [583.22, 0.0, 0.0] | [583.22, 0.0, 0.0] | Passed |
| sampled case 3 | [2079.37] | [2079.37] | Passed |
| regression: rounding remainder allocation | [697.32, 165.57, 530.64, 189.14] | [697.32, 165.57, 530.64, 189.16] | Failed |
SHA-256 / f3a861dc85b48358863784575a33c5992bee38593eb88d0cc589714fcd8eb82b
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(x):
tpm = x['tpm']
total = sum(tpm)
if total == 0: return 'ERR_NO_MILES'
cents = round(x['fare'] * 100)
shares = [cents * t // total for t in tpm]
last = max(i for i, t in enumerate(tpm) if t > 0)
shares[last] += cents - sum(shares)
return [s / 100 for s in shares]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['three coupons uneven', {'fare': 1001.0, 'tpm': [1200, 350, 2900]}, [269.93, 78.73, 652.34]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1501, 0]}, [270.41, 507.36, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 901]}, [0.0, 218.61, 281.4]], ['single coupon', {'fare': 123.45, 'tpm': [2475]}, [123.45]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 1565.87, 'tpm': [193, 5089]}, [57.21, 1508.66]], ['sampled case 2', {'fare': 583.22, 'tpm': [1271, 0, 0]}, [583.22, 0.0, 0.0]], ['sampled case 3', {'fare': 2079.37, 'tpm': [1800]}, [2079.37]], ['regression: rounding remainder allocation', {'fare': 1582.69, 'tpm': [3167, 752, 2410, 859]}, [697.32, 165.57, 530.64, 189.16]]], [['three coupons uneven', {'fare': 1002.0, 'tpm': [1200, 350, 2900]}, [270.2, 78.8, 653.0]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1502, 0]}, [270.29, 507.48, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 902]}, [0.0, 218.48, 281.53]], ['single coupon', {'fare': 246.9, 'tpm': [2475]}, [246.9]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 2915.76, 'tpm': [0, 5491, 214, 782]}, [0.0, 2468.08, 96.18, 351.5]], ['sampled case 2', {'fare': 2482.84, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 3', {'fare': 1177.57, 'tpm': [0, 0]}, 'ERR_NO_MILES']], [['three coupons uneven', {'fare': 1003.0, 'tpm': [1200, 350, 2900]}, [270.47, 78.88, 653.65]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1503, 0]}, [270.17, 507.6, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 903]}, [0.0, 218.34, 281.67]], ['single coupon', {'fare': 370.35, 'tpm': [2475]}, [370.35]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 110.22, 'tpm': [0]}, 'ERR_NO_MILES'], ['sampled case 2', {'fare': 836.64, 'tpm': [5298, 5970, 0, 5279]}, [267.87, 301.85, 0.0, 266.92]], ['sampled case 3', {'fare': 218.58, 'tpm': [5530]}, [218.58]], ['regression: rounding remainder allocation', {'fare': 2950.8, 'tpm': [0, 3274, 2611, 5594, 5187]}, [0.0, 579.67, 462.29, 990.44, 918.4]]], [['three coupons uneven', {'fare': 1004.0, 'tpm': [1200, 350, 2900]}, [270.74, 78.96, 654.3]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1504, 0]}, [270.05, 507.72, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 904]}, [0.0, 218.2, 281.81]], ['single coupon', {'fare': 493.8, 'tpm': [2475]}, [493.8]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 524.52, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 2', {'fare': 1224.53, 'tpm': [0, 1463]}, [0.0, 1224.53]], ['sampled case 3', {'fare': 2475.89, 'tpm': [0]}, 'ERR_NO_MILES'], ['regression: rounding remainder allocation', {'fare': 1160.8, 'tpm': [2634, 1797, 144, 4820, 0]}, [325.44, 222.02, 17.79, 595.55, 0.0]]], [['three coupons uneven', {'fare': 1005.0, 'tpm': [1200, 350, 2900]}, [271.01, 79.04, 654.95]], ['surface sector last', {'fare': 777.77, 'tpm': [800, 1505, 0]}, [269.94, 507.83, 0.0]], ['surface sector first', {'fare': 500.01, 'tpm': [0, 700, 905]}, [0.0, 218.07, 281.94]], ['single coupon', {'fare': 617.25, 'tpm': [2475]}, [617.25]], ['equal miles odd cents', {'fare': 100.0, 'tpm': [500, 500, 500]}, [33.33, 33.33, 33.34]], ['all surface', {'fare': 50.0, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['sampled case 1', {'fare': 927.71, 'tpm': [5167, 2555]}, [620.75, 306.96]], ['sampled case 2', {'fare': 574.25, 'tpm': [3022]}, [574.25]], ['sampled case 3', {'fare': 1349.84, 'tpm': [0, 0]}, 'ERR_NO_MILES'], ['regression: rounding remainder allocation', {'fare': 1976.74, 'tpm': [4338, 5522, 3056, 4678, 3840]}, [400.06, 509.26, 281.83, 431.42, 354.17]]]]
for label, args, expected in fixtures[N-1]:
check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| three coupons uneven | [269.93, 78.73, 652.34] | [269.93, 78.73, 652.34] | Passed |
| surface sector last | [270.41, 507.36, 0.0] | [270.41, 507.36, 0.0] | Passed |
| surface sector first | [0.0, 218.61, 281.4] | [0.0, 218.61, 281.4] | Passed |
| single coupon | [123.45] | [123.45] | Passed |
| equal miles odd cents | [33.33, 33.33, 33.34] | [33.33, 33.33, 33.34] | Passed |
| all surface | ERR_NO_MILES | ERR_NO_MILES | Passed |
| sampled case 1 | [57.21, 1508.66] | [57.21, 1508.66] | Passed |
| sampled case 2 | [583.22, 0.0, 0.0] | [583.22, 0.0, 0.0] | Passed |
| sampled case 3 | [2079.37] | [2079.37] | Passed |
| regression: rounding remainder allocation | [697.32, 165.57, 530.64, 189.16] | [697.32, 165.57, 530.64, 189.16] | Passed |
SHA-256 / b747823c10215017dc8d978d1ec6691b64181a9827fd884034dfe8c383fd6bf6
Verification & scope
A stipulated toy fare-rule contract with invented constants; it is not an ATPCO or carrier tariff implementation and makes no claim of industry-standard conformance. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:47:46.596869+00:00.
Case digest / f7ab769ef50f385a1505a1687084fa2b44b4cb4b9c73ecbc3123f2c180fc84a9