FA-79826 / Barcode symbology encoding / Open access

Pharmacode assigns wide bars to odd values · case 01

Every package code scans as a different product number.

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

ROOT CAUSE

The parity test is inverted, so odd values produce wide bars.

VERIFIED REPAIR

Even values produce wide bars.

Unsuccessful approach: Testing divisibility by four misclassifies values that are 2 mod 4.

Case contract

Encode an integer 3..131070 as a one-track Pharmacode: repeatedly, an even value adds a wide bar and becomes (n - 2) / 2, an odd value adds a narrow bar and becomes (n - 1) / 2, until zero; bars are read in the reverse order of generation (left to right). Width: narrow 1, wide 3, spaces 2 between bars. Return [pattern, width] or None outside the range.

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(n):
    if not isinstance(n, int) or n < 3 or n > 131070:
        return None
    bars = []
    while n > 0:
        if n % 2 == 1:
            bars.append('w')
            n = (n - 2) // 2
        else:
            bars.append('n')
            n = (n - 1) // 2
    bars.reverse()
    width = sum(3 if b == 'w' else 1 for b in bars) + 2 * (len(bars) - 1)
    return [''.join(bars), width]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[34, ['nnnww', 17]], [120, ['wwwnnw', 24]], [1, None], [2, None], [131071, None], [0, None], [-5, None], [306, ['nnwwnnww', 30]]], [[6, ['ww', 8]], [146, ['nnwnnww', 25]], [2, None], [131071, None], [0, None], [-5, None], [1, None], [85, ['nwnwwn', 22]]], [[142, ['nnnwwww', 27]], [86, ['nwnwww', 24]], [131071, None], [0, None], [-5, None], [1, None], [2, None], [396, ['wnnnwwnw', 30]]], [[229, ['wwnnwwn', 27]], [157, ['nnwwwwn', 27]], [240, ['wwwnnnw', 27]], [0, None], [-5, None], [1, None], [2, None], [143, ['nnwnnnn', 21]]], [[157, ['nnwwwwn', 27]], [228, ['wwnnwnw', 27]], [242, ['wwwnnww', 29]], [-5, None], [1, None], [2, None], [131071, None], [225, ['wwnnnwn', 25]]]]
labels = ["regression: even value bar type", "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: even value bar type 0	`['nwnn', 12]`	`['nnnww', 17]`	Failed
repair trap 1	`['wwwnwn', 24]`	`['wwwnnw', 24]`	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	`['nwwnwnn', 25]`	`['nnwwnnww', 30]`	Failed

SHA-256 / f199fbfaab66d67c75cef7cd33e64da4311c8515fa6e6888db753164c9ef958b

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(n):
    if not isinstance(n, int) or n < 3 or n > 131070:
        return None
    bars = []
    while n > 0:
        if n % 4 == 0:
            bars.append('w')
            n = (n - 2) // 2
        else:
            bars.append('n')
            n = (n - 1) // 2
    bars.reverse()
    width = sum(3 if b == 'w' else 1 for b in bars) + 2 * (len(bars) - 1)
    return [''.join(bars), width]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[34, ['nnnww', 17]], [120, ['wwwnnw', 24]], [1, None], [2, None], [131071, None], [0, None], [-5, None], [306, ['nnwwnnww', 30]]], [[6, ['ww', 8]], [146, ['nnwnnww', 25]], [2, None], [131071, None], [0, None], [-5, None], [1, None], [85, ['nwnwwn', 22]]], [[142, ['nnnwwww', 27]], [86, ['nwnwww', 24]], [131071, None], [0, None], [-5, None], [1, None], [2, None], [396, ['wnnnwwnw', 30]]], [[229, ['wwnnwwn', 27]], [157, ['nnwwwwn', 27]], [240, ['wwwnnnw', 27]], [0, None], [-5, None], [1, None], [2, None], [143, ['nnwnnnn', 21]]], [[157, ['nnwwwwn', 27]], [228, ['wwnnwnw', 27]], [242, ['wwwnnww', 29]], [-5, None], [1, None], [2, None], [131071, None], [225, ['wwnnnwn', 25]]]]
labels = ["regression: even value bar type", "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: even value bar type 0	`['nnnwn', 15]`	`['nnnww', 17]`	Failed
repair trap 1	`['nnnnnw', 18]`	`['wwwnnw', 24]`	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	`['nnwnnnwn', 26]`	`['nnwwnnww', 30]`	Failed

SHA-256 / 954b66b43c43a905fe2c1c3fced2c5f575bb398c2e2e9f14170c4558aef07659

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(n):
    if not isinstance(n, int) or n < 3 or n > 131070:
        return None
    bars = []
    while n > 0:
        if n % 2 == 0:
            bars.append('w')
            n = (n - 2) // 2
        else:
            bars.append('n')
            n = (n - 1) // 2
    bars.reverse()
    width = sum(3 if b == 'w' else 1 for b in bars) + 2 * (len(bars) - 1)
    return [''.join(bars), width]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[34, ['nnnww', 17]], [120, ['wwwnnw', 24]], [1, None], [2, None], [131071, None], [0, None], [-5, None], [306, ['nnwwnnww', 30]]], [[6, ['ww', 8]], [146, ['nnwnnww', 25]], [2, None], [131071, None], [0, None], [-5, None], [1, None], [85, ['nwnwwn', 22]]], [[142, ['nnnwwww', 27]], [86, ['nwnwww', 24]], [131071, None], [0, None], [-5, None], [1, None], [2, None], [396, ['wnnnwwnw', 30]]], [[229, ['wwnnwwn', 27]], [157, ['nnwwwwn', 27]], [240, ['wwwnnnw', 27]], [0, None], [-5, None], [1, None], [2, None], [143, ['nnwnnnn', 21]]], [[157, ['nnwwwwn', 27]], [228, ['wwnnwnw', 27]], [242, ['wwwnnww', 29]], [-5, None], [1, None], [2, None], [131071, None], [225, ['wwnnnwn', 25]]]]
labels = ["regression: even value bar type", "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: even value bar type 0	`['nnnww', 17]`	`['nnnww', 17]`	Passed
repair trap 1	`['wwwnnw', 24]`	`['wwwnnw', 24]`	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	`['nnwwnnww', 30]`	`['nnwwnnww', 30]`	Passed

SHA-256 / 5f4f0c2275245a342e80b10caaa5ba8e362655408c6f9fa0ae916dcb2c816f09

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 2026-09-29T14:49:48.210266+00:00.

Case digest / 126913fdb1fbf9ad4c6ffe0766278f2587dfcef2a6cac242be1d7a124f8bf71a