FA-10641 / Raster clipping / Open access

Clip row both ends · case 01

Negative clipping coordinates are interpreted as Python indices from the row end.

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

ROOT CAUSE

Negative clipping coordinates are interpreted as Python indices from the row end.

VERIFIED REPAIR

Use the stage contract: Clip integer half-open horizontal coverage [start,end) against a row extent [0,len(row)); return covered pixels in order.

Unsuccessful approach: Adding one to the exclusive end shades an extra pixel.

Case contract

Clip integer half-open horizontal coverage [start,end) against a row extent [0,len(row)); return covered pixels in order.

Why this case matters

A deterministic software graphics stage with explicit channel and coordinate conventions; no hardware, device profile or API behavior is inferred.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(row, start, end):
    return row[start:end]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve(*([0, 1, 2, 3], -2, 2)), [0, 1])
check('fixture 2', solve(*([0, 1, 2, 3], 1, 3)), [1, 2])
check('fixture 3', solve(*([0, 1], 4, 8)), [])
check('fixture 4', solve(*([0, 1], 0, 0)), [])
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
fixture 1	`[]`	`[0, 1]`	Failed
fixture 2	`[1, 2]`	`[1, 2]`	Passed
fixture 3	`[]`	`[]`	Passed
fixture 4	`[]`	`[]`	Passed

SHA-256 / ffbc9966c88936cdfb836918801d9bf47acc19b2cf12530654b2d1567718adb5

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(row, start, end):
    return row[max(0,start):min(len(row),end)+1]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve(*([0, 1, 2, 3], -2, 2)), [0, 1])
check('fixture 2', solve(*([0, 1, 2, 3], 1, 3)), [1, 2])
check('fixture 3', solve(*([0, 1], 4, 8)), [])
check('fixture 4', solve(*([0, 1], 0, 0)), [])
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
fixture 1	`[0, 1, 2]`	`[0, 1]`	Failed
fixture 2	`[1, 2, 3]`	`[1, 2]`	Failed
fixture 3	`[]`	`[]`	Passed
fixture 4	`[0]`	`[]`	Failed

SHA-256 / 83f8d8ef3da21eef89441a3f5e43f81dd2cd415f3a2518712d433ce906e4b55d

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(row, start, end):
    return row[max(0,start):max(0,min(len(row),end))] if end>max(0,start) else []
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve(*([0, 1, 2, 3], -2, 2)), [0, 1])
check('fixture 2', solve(*([0, 1, 2, 3], 1, 3)), [1, 2])
check('fixture 3', solve(*([0, 1], 4, 8)), [])
check('fixture 4', solve(*([0, 1], 0, 0)), [])
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
fixture 1	`[0, 1]`	`[0, 1]`	Passed
fixture 2	`[1, 2]`	`[1, 2]`	Passed
fixture 3	`[]`	`[]`	Passed
fixture 4	`[]`	`[]`	Passed

SHA-256 / 63ff6062f0da64373ffae109210f0fa575ba0ae89cc388406d0e669d074781b3

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 2026-09-29T14:38:40.900005+00:00.

Case digest / e9cc7852de88f6e1cde2aa47ab59af19db14404f70d25e0a2c94a321b460d6db