FA-79526 / Barcode symbology encoding / Open access

ITF stop pattern is written narrow-narrow-wide · case 01

Scanners fail to find the stop pattern when reading right to left.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The stop elements are emitted in reverse order.

VERIFIED REPAIR

The stop is a wide bar, a narrow space, then a narrow bar.

Unsuccessful approach: Omitting the final narrow bar leaves the symbol ending on a space.

Case contract

Encode ASCII digits as Interleaved 2 of 5 element widths (narrow 1, wide = ratio in {2, 2.5, 3}). An odd-length input gets a leading 0. Start is narrow bar/space/bar/space; each digit pair interleaves the first digit's pattern in the bars with the second digit's pattern in the spaces; stop is wide bar, narrow space, narrow bar. Digit patterns use weights 1,2,4,7 plus parity (0 = NNWWN). Invalid input returns None.

Why this case matters

Retail, logistics, pharmacy and document workflows depend on encoders that produce exactly the module pattern, code-set switches, separators and quiet zones scanners expect; one misplaced module or separator makes a label unreadable or, worse, scan as different data.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(digits, ratio):
    if not digits or not all(c in '0123456789' for c in digits) or ratio not in (2, 2.5, 3):
        return None
    P = {'0': 'NNWWN', '1': 'WNNNW', '2': 'NWNNW', '3': 'WWNNN', '4': 'NNWNW',
         '5': 'WNWNN', '6': 'NWWNN', '7': 'NNNWW', '8': 'WNNWN', '9': 'NWNWN'}
    if len(digits) % 2:
        digits = '0' + digits
    wid = {'N': 1, 'W': ratio}
    out = [1, 1, 1, 1]
    for i in range(0, len(digits), 2):
        a, b = P[digits[i]], P[digits[i + 1]]
        for k in range(5):
            out.append(wid[a[k]])
            out.append(wid[b[k]])
    out += [1, 1, ratio]
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[('809220', 3), [1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]], [('2', 2.5), [1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1, 1]], [('596257', 4), None], [('37775', 4), None], [('683', 4), None], [('395208', 4), None], [('4528', 4), None], [('960081', 3), [1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1]]], [[('954', 3), [1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 3, 1, 1, 1, 3, 3, 1, 1, 1, 3, 3, 1, 1]], [('246919', 2), [1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1]], [('13', 4), None], [('296045', 4), None], [('402165', 4), None], [('044955', 4), None], [('803415', 4), None], [('5377', 2), [1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1]]], [[('486862', 3), [1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3, 3, 1, 1, 3, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1]], [('6', 3), [1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 3, 1, 1]], [('820280', 4), None], [('5694', 4), None], [('977', 4), None], [('80', 4), None], [('159055', 4), None], [('15', 3), [1, 1, 1, 1, 3, 3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 1]]], [[('6298', 3), [1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 1, 3, 3, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1]], [('74', 2.5), [1, 1, 1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 2.5, 2.5, 1, 1]], [('00', 4), None], [('909384', 4), None], [('4', 4), None], [('72', 4), None], [('69867', 4), None], [('17', 3), [1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1]]], [[('48', 3), [1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3, 3, 1, 3, 1, 1]], [('0', 2), [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1]], [('6', 4), None], [('2527', 4), None], [('156', 4), None], [('8', 4), None], [('460', 4), None], [('420', 3), [1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]]]]
labels = ["regression: stop pattern element order", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), 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
regression: stop pattern element order 0	`[1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 1, 1, 3]`	`[1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]`	Failed
repair trap 1	`[1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 1, 1, 2.5]`	`[1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1, 1]`	Failed
combined fault 2	`None`	`None`	Passed
control 3	`None`	`None`	Passed
control 4	`None`	`None`	Passed
boundary 5	`None`	`None`	Passed
boundary 6	`None`	`None`	Passed
control 7	`[1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3]`	`[1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1]`	Failed

SHA-256 / 96a2d827fe9607625cdc5dc0691e6fa2cc509f0934f242724ceebff42f91ae31

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(digits, ratio):
    if not digits or not all(c in '0123456789' for c in digits) or ratio not in (2, 2.5, 3):
        return None
    P = {'0': 'NNWWN', '1': 'WNNNW', '2': 'NWNNW', '3': 'WWNNN', '4': 'NNWNW',
         '5': 'WNWNN', '6': 'NWWNN', '7': 'NNNWW', '8': 'WNNWN', '9': 'NWNWN'}
    if len(digits) % 2:
        digits = '0' + digits
    wid = {'N': 1, 'W': ratio}
    out = [1, 1, 1, 1]
    for i in range(0, len(digits), 2):
        a, b = P[digits[i]], P[digits[i + 1]]
        for k in range(5):
            out.append(wid[a[k]])
            out.append(wid[b[k]])
    out += [ratio, 1]
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[('809220', 3), [1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]], [('2', 2.5), [1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1, 1]], [('596257', 4), None], [('37775', 4), None], [('683', 4), None], [('395208', 4), None], [('4528', 4), None], [('960081', 3), [1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1]]], [[('954', 3), [1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 3, 1, 1, 1, 3, 3, 1, 1, 1, 3, 3, 1, 1]], [('246919', 2), [1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1]], [('13', 4), None], [('296045', 4), None], [('402165', 4), None], [('044955', 4), None], [('803415', 4), None], [('5377', 2), [1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1]]], [[('486862', 3), [1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3, 3, 1, 1, 3, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1]], [('6', 3), [1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 3, 1, 1]], [('820280', 4), None], [('5694', 4), None], [('977', 4), None], [('80', 4), None], [('159055', 4), None], [('15', 3), [1, 1, 1, 1, 3, 3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 1]]], [[('6298', 3), [1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 1, 3, 3, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1]], [('74', 2.5), [1, 1, 1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 2.5, 2.5, 1, 1]], [('00', 4), None], [('909384', 4), None], [('4', 4), None], [('72', 4), None], [('69867', 4), None], [('17', 3), [1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1]]], [[('48', 3), [1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3, 3, 1, 3, 1, 1]], [('0', 2), [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1]], [('6', 4), None], [('2527', 4), None], [('156', 4), None], [('8', 4), None], [('460', 4), None], [('420', 3), [1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]]]]
labels = ["regression: stop pattern element order", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), 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
regression: stop pattern element order 0	`[1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1]`	`[1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]`	Failed
repair trap 1	`[1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1]`	`[1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1, 1]`	Failed
combined fault 2	`None`	`None`	Passed
control 3	`None`	`None`	Passed
control 4	`None`	`None`	Passed
boundary 5	`None`	`None`	Passed
boundary 6	`None`	`None`	Passed
control 7	`[1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1]`	`[1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1]`	Failed

SHA-256 / dad4a86df69863d51312b80cb346b52e90a9d62903fed6309137f3599893019c

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(digits, ratio):
    if not digits or not all(c in '0123456789' for c in digits) or ratio not in (2, 2.5, 3):
        return None
    P = {'0': 'NNWWN', '1': 'WNNNW', '2': 'NWNNW', '3': 'WWNNN', '4': 'NNWNW',
         '5': 'WNWNN', '6': 'NWWNN', '7': 'NNNWW', '8': 'WNNWN', '9': 'NWNWN'}
    if len(digits) % 2:
        digits = '0' + digits
    wid = {'N': 1, 'W': ratio}
    out = [1, 1, 1, 1]
    for i in range(0, len(digits), 2):
        a, b = P[digits[i]], P[digits[i + 1]]
        for k in range(5):
            out.append(wid[a[k]])
            out.append(wid[b[k]])
    out += [ratio, 1, 1]
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[('809220', 3), [1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]], [('2', 2.5), [1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1, 1]], [('596257', 4), None], [('37775', 4), None], [('683', 4), None], [('395208', 4), None], [('4528', 4), None], [('960081', 3), [1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1]]], [[('954', 3), [1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 3, 1, 1, 1, 3, 3, 1, 1, 1, 3, 3, 1, 1]], [('246919', 2), [1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1]], [('13', 4), None], [('296045', 4), None], [('402165', 4), None], [('044955', 4), None], [('803415', 4), None], [('5377', 2), [1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1]]], [[('486862', 3), [1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3, 3, 1, 1, 3, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1]], [('6', 3), [1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 3, 1, 1]], [('820280', 4), None], [('5694', 4), None], [('977', 4), None], [('80', 4), None], [('159055', 4), None], [('15', 3), [1, 1, 1, 1, 3, 3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 1]]], [[('6298', 3), [1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 1, 3, 3, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1]], [('74', 2.5), [1, 1, 1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 2.5, 2.5, 1, 1]], [('00', 4), None], [('909384', 4), None], [('4', 4), None], [('72', 4), None], [('69867', 4), None], [('17', 3), [1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1]]], [[('48', 3), [1, 1, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3, 3, 1, 3, 1, 1]], [('0', 2), [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1]], [('6', 4), None], [('2527', 4), None], [('156', 4), None], [('8', 4), None], [('460', 4), None], [('420', 3), [1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]]]]
labels = ["regression: stop pattern element order", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), 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
regression: stop pattern element order 0	`[1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]`	`[1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 3, 1, 3, 1, 1]`	Passed
repair trap 1	`[1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1, 1]`	`[1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 1, 2.5, 1, 1, 2.5, 2.5, 1, 1]`	Passed
combined fault 2	`None`	`None`	Passed
control 3	`None`	`None`	Passed
control 4	`None`	`None`	Passed
boundary 5	`None`	`None`	Passed
boundary 6	`None`	`None`	Passed
control 7	`[1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1]`	`[1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 3, 3, 1, 1]`	Passed

SHA-256 / cd080c5737bbf185e09e481c5810b57b0377ec7d94a6316f6d5014d218093ac2

Verification & scope

A deterministic bounded teaching model with a stipulated contract; it makes no claim of conformance to any published specification. 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 2026-09-29T14:49:45.374331+00:00.

Case digest / f8343aca028d9a8bd2dd019c8443360e7bb695eb352ea4b4ad08fe45ac34395c