FAILURE MAP
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FA-10826 / Audio frame buffers / Open access

Deinterleave stereo stride · case 01

Halving the array treats interleaved input as planar.

Verified by executionVariant 1 · 4 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Halving the array treats interleaved input as planar.

VERIFIED REPAIR

Apply the explicit PCM/playback contract: Split an even-length interleaved stereo array into left/right planar arrays.

Unsuccessful approach: Correct strides with reversed destinations swap left and right.

Case contract

Split an even-length interleaved stereo array into left/right planar arrays.

Why this case matters

A pure Python local audio pipeline stage with explicit sample formats and frame conventions; no real-time device or signal-spectrum claims.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(samples):
    return [samples[:len(samples)//2],samples[len(samples)//2:]]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([1, 3, 2, 4],)),[[1, 2], [3, 4]])
check('fixture 2',solve(*([],)),[[], []])
check('fixture 3',solve(*([5, 6],)),[[5], [6]])
check('fixture 4',solve(*([-1, 0, 0, 1],)),[[-1, 0], [0, 1]])
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 fixtureActualExpectedOutcome
fixture 1[[1, 3], [2, 4]][[1, 2], [3, 4]]Failed
fixture 2[[], []][[], []]Passed
fixture 3[[5], [6]][[5], [6]]Passed
fixture 4[[-1, 0], [0, 1]][[-1, 0], [0, 1]]Passed

SHA-256 / 698f3d5cf32516d3cb45dd41681733a229b64cf85f6eb5aee69503bc7453535e

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(samples):
    return [samples[1::2],samples[::2]]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([1, 3, 2, 4],)),[[1, 2], [3, 4]])
check('fixture 2',solve(*([],)),[[], []])
check('fixture 3',solve(*([5, 6],)),[[5], [6]])
check('fixture 4',solve(*([-1, 0, 0, 1],)),[[-1, 0], [0, 1]])
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 fixtureActualExpectedOutcome
fixture 1[[3, 4], [1, 2]][[1, 2], [3, 4]]Failed
fixture 2[[], []][[], []]Passed
fixture 3[[6], [5]][[5], [6]]Failed
fixture 4[[0, 1], [-1, 0]][[-1, 0], [0, 1]]Failed

SHA-256 / 038fb33b56db2488d163b09e42529252e8d215f1505798d7183404a81b00ab02

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(samples):
    return [samples[::2],samples[1::2]]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([1, 3, 2, 4],)),[[1, 2], [3, 4]])
check('fixture 2',solve(*([],)),[[], []])
check('fixture 3',solve(*([5, 6],)),[[5], [6]])
check('fixture 4',solve(*([-1, 0, 0, 1],)),[[-1, 0], [0, 1]])
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 fixtureActualExpectedOutcome
fixture 1[[1, 2], [3, 4]][[1, 2], [3, 4]]Passed
fixture 2[[], []][[], []]Passed
fixture 3[[5], [6]][[5], [6]]Passed
fixture 4[[-1, 0], [0, 1]][[-1, 0], [0, 1]]Passed

SHA-256 / 524c079a84a0a6dc84954bb1d79bf0d67ba8ea253f6eff0c2618b66e94465d9e

Verification & scope

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 2026-09-29T14:38:42.532138+00:00.

Case digest / d8d6340cba32507abbc1984aa5040ba07b47824f04a13cd72891817c22cf28bb