FA-54976 / Astronomical coordinate conventions / Open access
Detector mosaic sections: First detector coordinate after a chip gap includes the wrong edge · case 01
The adapter reports an incorrect b first pixel while other fields remain valid.
ROOT CAUSE
First detector coordinate after a chip gap includes the wrong edge. Faulty expression: d['a']+1
THE FAILURE
First detector coordinate after a chip gap includes the wrong edge. Faulty expression: d['a']+1
Unsuccessful approach: A partial convention repair still uses d['a']+d['gap']
Case contract
Two readout amplifiers cover a focal plane with overscan removed. Amplifier B is physically reversed. Detector labels are one-based; chip gap has no samples. Serial overscan is measured in columns and parallel overscan in rows. Output fields are defined by: a_science_column = d['x']-d['serial']; b_detector_column = d['a']+d['gap']+d['b']+1-d['x']; science_row = d['y']-d['parallel']; b_first_pixel = d['a']+d['gap']+1; mosaic_extent = d['a']+d['gap']+d['b']; science_rows = d['rows']-d['parallel']
Why this case matters
Catalog, detector, sky-coordinate, and spectroscopy adapters must preserve the association between numeric coordinates and their declared reference conventions.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(d):
return {'a_science_column': d['x']-d['serial'], 'b_detector_column': d['a']+d['gap']+d['b']+1-d['x'], 'science_row': d['y']-d['parallel'], 'b_first_pixel': d['a']+1, 'mosaic_extent': d['a']+d['gap']+d['b'], 'science_rows': d['rows']-d['parallel']}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = {1: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 7, 'rows': 20}, {'a_science_column': 1, 'b_detector_column': 23, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 3, 'y': 7, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 23, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 7, 'rows': 20}, {'a_science_column': 1, 'b_detector_column': 20, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 1, 'y': 1, 'rows': 20}, {'a_science_column': -1, 'b_detector_column': 25, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 7, 'rows': 9}, {'a_science_column': 1, 'b_detector_column': 11, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 8, 'y': 20, 'rows': 20}, {'a_science_column': 6, 'b_detector_column': 18, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 2: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 7, 'rows': 20}, {'a_science_column': 2, 'b_detector_column': 22, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 4, 'y': 7, 'rows': 20}, {'a_science_column': 4, 'b_detector_column': 22, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 7, 'rows': 20}, {'a_science_column': 2, 'b_detector_column': 19, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 2, 'y': 1, 'rows': 20}, {'a_science_column': 0, 'b_detector_column': 24, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 7, 'rows': 9}, {'a_science_column': 2, 'b_detector_column': 10, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 9, 'y': 20, 'rows': 20}, {'a_science_column': 7, 'b_detector_column': 17, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 3: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 7, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 21, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 5, 'y': 7, 'rows': 20}, {'a_science_column': 5, 'b_detector_column': 21, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 7, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 18, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 1, 'rows': 20}, {'a_science_column': 1, 'b_detector_column': 23, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 7, 'rows': 9}, {'a_science_column': 3, 'b_detector_column': 9, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 10, 'y': 20, 'rows': 20}, {'a_science_column': 8, 'b_detector_column': 16, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 4: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 6, 'y': 7, 'rows': 20}, {'a_science_column': 4, 'b_detector_column': 20, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 6, 'y': 7, 'rows': 20}, {'a_science_column': 6, 'b_detector_column': 20, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 6, 'y': 7, 'rows': 20}, {'a_science_column': 4, 'b_detector_column': 17, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 1, 'rows': 20}, {'a_science_column': 2, 'b_detector_column': 22, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 6, 'y': 7, 'rows': 9}, {'a_science_column': 4, 'b_detector_column': 8, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 11, 'y': 20, 'rows': 20}, {'a_science_column': 9, 'b_detector_column': 15, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 5: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 7, 'y': 7, 'rows': 20}, {'a_science_column': 5, 'b_detector_column': 19, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 7, 'y': 7, 'rows': 20}, {'a_science_column': 7, 'b_detector_column': 19, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 7, 'y': 7, 'rows': 20}, {'a_science_column': 5, 'b_detector_column': 16, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 1, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 21, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 7, 'y': 7, 'rows': 9}, {'a_science_column': 5, 'b_detector_column': 7, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 12, 'y': 20, 'rows': 20}, {'a_science_column': 10, 'b_detector_column': 14, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})]}
for i, (record, expected) in enumerate(fixtures[N]):
check('astronomical fixture %s' % i, solve(record), 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 |
|---|---|---|---|
| astronomical fixture 0 | {'a_science_column': 1, 'b_detector_column': 23, 'b_first_pixel': 13, 'mosaic_extent': 25, 'science_row': 3, 'science_rows': 16} | {'a_science_column': 1, 'b_detector_column': 23, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': 3, 'science_rows': 16} | Failed |
| astronomical fixture 1 | {'a_science_column': 3, 'b_detector_column': 23, 'b_first_pixel': 13, 'mosaic_extent': 25, 'science_row': 7, 'science_rows': 20} | {'a_science_column': 3, 'b_detector_column': 23, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': 7, 'science_rows': 20} | Failed |
| astronomical fixture 2 | {'a_science_column': 1, 'b_detector_column': 20, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_row': 3, 'science_rows': 16} | {'a_science_column': 1, 'b_detector_column': 20, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_row': 3, 'science_rows': 16} | Passed |
| astronomical fixture 3 | {'a_science_column': -1, 'b_detector_column': 25, 'b_first_pixel': 13, 'mosaic_extent': 25, 'science_row': -3, 'science_rows': 16} | {'a_science_column': -1, 'b_detector_column': 25, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': -3, 'science_rows': 16} | Failed |
| astronomical fixture 4 | {'a_science_column': 1, 'b_detector_column': 11, 'b_first_pixel': 5, 'mosaic_extent': 13, 'science_row': 3, 'science_rows': 5} | {'a_science_column': 1, 'b_detector_column': 11, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_row': 3, 'science_rows': 5} | Failed |
| astronomical fixture 5 | {'a_science_column': 6, 'b_detector_column': 18, 'b_first_pixel': 13, 'mosaic_extent': 25, 'science_row': 16, 'science_rows': 16} | {'a_science_column': 6, 'b_detector_column': 18, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': 16, 'science_rows': 16} | Failed |
SHA-256 / ae263c8232b8cf3556990e741da25136e1b63fa5b24edd815b584fa175d9176e
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(d):
return {'a_science_column': d['x']-d['serial'], 'b_detector_column': d['a']+d['gap']+d['b']+1-d['x'], 'science_row': d['y']-d['parallel'], 'b_first_pixel': d['a']+d['gap'], 'mosaic_extent': d['a']+d['gap']+d['b'], 'science_rows': d['rows']-d['parallel']}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = {1: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 7, 'rows': 20}, {'a_science_column': 1, 'b_detector_column': 23, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 3, 'y': 7, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 23, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 7, 'rows': 20}, {'a_science_column': 1, 'b_detector_column': 20, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 1, 'y': 1, 'rows': 20}, {'a_science_column': -1, 'b_detector_column': 25, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 7, 'rows': 9}, {'a_science_column': 1, 'b_detector_column': 11, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 8, 'y': 20, 'rows': 20}, {'a_science_column': 6, 'b_detector_column': 18, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 2: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 7, 'rows': 20}, {'a_science_column': 2, 'b_detector_column': 22, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 4, 'y': 7, 'rows': 20}, {'a_science_column': 4, 'b_detector_column': 22, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 7, 'rows': 20}, {'a_science_column': 2, 'b_detector_column': 19, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 2, 'y': 1, 'rows': 20}, {'a_science_column': 0, 'b_detector_column': 24, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 7, 'rows': 9}, {'a_science_column': 2, 'b_detector_column': 10, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 9, 'y': 20, 'rows': 20}, {'a_science_column': 7, 'b_detector_column': 17, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 3: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 7, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 21, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 5, 'y': 7, 'rows': 20}, {'a_science_column': 5, 'b_detector_column': 21, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 7, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 18, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 3, 'y': 1, 'rows': 20}, {'a_science_column': 1, 'b_detector_column': 23, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 7, 'rows': 9}, {'a_science_column': 3, 'b_detector_column': 9, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 10, 'y': 20, 'rows': 20}, {'a_science_column': 8, 'b_detector_column': 16, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 4: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 6, 'y': 7, 'rows': 20}, {'a_science_column': 4, 'b_detector_column': 20, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 6, 'y': 7, 'rows': 20}, {'a_science_column': 6, 'b_detector_column': 20, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 6, 'y': 7, 'rows': 20}, {'a_science_column': 4, 'b_detector_column': 17, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 4, 'y': 1, 'rows': 20}, {'a_science_column': 2, 'b_detector_column': 22, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 6, 'y': 7, 'rows': 9}, {'a_science_column': 4, 'b_detector_column': 8, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 11, 'y': 20, 'rows': 20}, {'a_science_column': 9, 'b_detector_column': 15, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})], 5: [({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 7, 'y': 7, 'rows': 20}, {'a_science_column': 5, 'b_detector_column': 19, 'science_row': 3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 0, 'parallel': 0, 'x': 7, 'y': 7, 'rows': 20}, {'a_science_column': 7, 'b_detector_column': 19, 'science_row': 7, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 20}), ({'a': 12, 'b': 10, 'gap': 0, 'serial': 2, 'parallel': 4, 'x': 7, 'y': 7, 'rows': 20}, {'a_science_column': 5, 'b_detector_column': 16, 'science_row': 3, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_rows': 16}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 5, 'y': 1, 'rows': 20}, {'a_science_column': 3, 'b_detector_column': 21, 'science_row': -3, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16}), ({'a': 4, 'b': 6, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 7, 'y': 7, 'rows': 9}, {'a_science_column': 5, 'b_detector_column': 7, 'science_row': 3, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_rows': 5}), ({'a': 12, 'b': 10, 'gap': 3, 'serial': 2, 'parallel': 4, 'x': 12, 'y': 20, 'rows': 20}, {'a_science_column': 10, 'b_detector_column': 14, 'science_row': 16, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_rows': 16})]}
for i, (record, expected) in enumerate(fixtures[N]):
check('astronomical fixture %s' % i, solve(record), 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 |
|---|---|---|---|
| astronomical fixture 0 | {'a_science_column': 1, 'b_detector_column': 23, 'b_first_pixel': 15, 'mosaic_extent': 25, 'science_row': 3, 'science_rows': 16} | {'a_science_column': 1, 'b_detector_column': 23, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': 3, 'science_rows': 16} | Failed |
| astronomical fixture 1 | {'a_science_column': 3, 'b_detector_column': 23, 'b_first_pixel': 15, 'mosaic_extent': 25, 'science_row': 7, 'science_rows': 20} | {'a_science_column': 3, 'b_detector_column': 23, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': 7, 'science_rows': 20} | Failed |
| astronomical fixture 2 | {'a_science_column': 1, 'b_detector_column': 20, 'b_first_pixel': 12, 'mosaic_extent': 22, 'science_row': 3, 'science_rows': 16} | {'a_science_column': 1, 'b_detector_column': 20, 'b_first_pixel': 13, 'mosaic_extent': 22, 'science_row': 3, 'science_rows': 16} | Failed |
| astronomical fixture 3 | {'a_science_column': -1, 'b_detector_column': 25, 'b_first_pixel': 15, 'mosaic_extent': 25, 'science_row': -3, 'science_rows': 16} | {'a_science_column': -1, 'b_detector_column': 25, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': -3, 'science_rows': 16} | Failed |
| astronomical fixture 4 | {'a_science_column': 1, 'b_detector_column': 11, 'b_first_pixel': 7, 'mosaic_extent': 13, 'science_row': 3, 'science_rows': 5} | {'a_science_column': 1, 'b_detector_column': 11, 'b_first_pixel': 8, 'mosaic_extent': 13, 'science_row': 3, 'science_rows': 5} | Failed |
| astronomical fixture 5 | {'a_science_column': 6, 'b_detector_column': 18, 'b_first_pixel': 15, 'mosaic_extent': 25, 'science_row': 16, 'science_rows': 16} | {'a_science_column': 6, 'b_detector_column': 18, 'b_first_pixel': 16, 'mosaic_extent': 25, 'science_row': 16, 'science_rows': 16} | Failed |
SHA-256 / fc86d9b3098392705bc52c53416e7363e6261248c6b5ed009b924878b9541dcb
HELD IN THE MEMBER ARCHIVE
The verified repair and its recorded checks are member-only.
This mechanism has 6 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.
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Explicitly stipulated finite algebraic adapter; no standards conformance, physical accuracy, or production-library claim. Inputs are the documented finite valid model domain. 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 2026-09-29T14:45:53.400810+00:00.
Case digest / b097492ae6eed0d7f262b81be437697be3e1304fb45f03ad82670aac56ffc2a2