{"abstract":"Long-dated bills report a negative bond-equivalent yield.","category":"Bond day-count conventions","checks":8,"contract":"Inputs settlement and maturity [y,m,d] (1 to 364 days apart) and discount rate d. t = days. Price = 100*(1 - d*t/360). Year basis B is 366 if a 29 February lies in (settle, settle+365 days], else 365. For t <= B/2, BEY = B*d/(360 - d*t); otherwise BEY solves the quadratic with a = t/(2B) - 0.25, b = t/B, c = (price-100)/price, taking (-b + sqrt(b^2 - 4ac))/(2a). Return [price rounded 6, BEY rounded 8].","evaluation_group":"w2-bond_day_count_conventions-bill-discount-to-bey","failed_approach":"Flipping the sign inside the discriminant gives a positive but wrong yield.","family":"w2-bond_day_count_conventions-bill-discount-to-bey-quadratic-root-selection","id":"FA-61121","implementations":{"attempt":{"sha256":"068e7e92ac029df61547231f83e7587e00c9b9c6c8ad120ff2f83bcba8ce2b82","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nimport math\nN = 1\nobservations = []\ndef solve(settle, maturity, d):\n    S = datetime.date(*settle)\n    M = datetime.date(*maturity)\n    t = (M - S).days\n    def leap(y):\n        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0\n    horizon = S + datetime.timedelta(days=365)\n    basis = 366 if any(leap(y) and S < datetime.date(y, 2, 29) <= horizon for y in (S.year, S.year + 1)) else 365\n    price = 100 * (1 - d * t / 360)\n    if t <= basis / 2:\n        bey = basis * d / (360 - d * t)\n    else:\n        a = t / (2 * basis) - 0.25\n        b = t / basis\n        c = (price - 100) / price\n        bey = (-b + math.sqrt(b * b + 4 * a * c)) / (2 * a)\n    return [round(price, 6), round(bey, 8)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression quadratic root selection 1', [[2049, 2, 28], [2049, 8, 30], 0.01], [99.491667, 0.01019055]], ['regression quadratic root selection 2', [[2082, 11, 15], [2083, 5, 17], 0.0525], [97.33125, 0.05468459]], ['partial repair probe 1', [[2050, 11, 30], [2051, 11, 29], 0.08], [91.911111, 0.08638888]], ['partial repair probe 2', [[2089, 3, 11], [2089, 9, 10], 0.08], [95.933333, 0.08453969]], ['boundary control 1', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['boundary control 2', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['normal control 1', [[2023, 12, 15], [2024, 6, 13], 0.0435], [97.812917, 0.04521386]], ['normal control 2', [[2090, 7, 29], [2091, 1, 27], 0.0525], [97.345833, 0.05468048]]], [['regression quadratic root selection 1', [[2100, 12, 28], [2101, 6, 29], 0.025], [98.729167, 0.02567259]], ['regression quadratic root selection 2', [[2002, 11, 1], [2003, 5, 3], 0.0525], [97.33125, 0.05468459]], ['partial repair probe 1', [[2090, 3, 8], [2091, 3, 7], 0.0435], [95.601667, 0.04561452]], ['partial repair probe 2', [[2013, 8, 17], [2014, 2, 16], 0.08], [95.933333, 0.08453969]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2099, 3, 1], [2099, 8, 29], 0.08], [95.977778, 0.0845103]], ['normal control 2', [[2081, 5, 27], [2081, 9, 1], 0.0435], [98.827917, 0.04462724]]], [['regression quadratic root selection 1', [[2099, 3, 28], [2100, 3, 27], 0.01], [98.988889, 0.01021643]], ['regression quadratic root selection 2', [[2065, 8, 13], [2066, 8, 12], 0.0435], [95.601667, 0.04561452]], ['partial repair probe 1', [[2073, 4, 5], [2073, 10, 5], 0.01], [99.491667, 0.01019055]], ['partial repair probe 2', [[1998, 12, 30], [1999, 11, 28], 0.0435], [95.97625, 0.04548568]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2075, 1, 30], [2075, 7, 31], 0.0525], [97.345833, 0.05468048]], ['normal control 2', [[2077, 9, 11], [2078, 3, 12], 0.01], [99.494444, 0.01019041]]], [['regression quadratic root selection 1', [[2070, 4, 30], [2070, 10, 30], 0.08], [95.933333, 0.08453969]], ['regression quadratic root selection 2', [[2100, 12, 15], [2101, 6, 16], 0.08], [95.933333, 0.08453969]], ['partial repair probe 1', [[2074, 4, 20], [2074, 10, 21], 0.0435], [97.776667, 0.04509876]], ['partial repair probe 2', [[2071, 7, 14], [2072, 7, 12], 0.001], [99.898889, 0.00101744]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['boundary control 2', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['normal control 1', [[2032, 2, 5], [2032, 8, 6], 0.025], [98.729167, 0.02574383]], ['normal control 2', [[2027, 12, 1], [2028, 6, 1], 0.025], [98.729167, 0.02574383]]], [['regression quadratic root selection 1', [[2093, 10, 27], [2094, 5, 1], 0.025], [98.708333, 0.02567271]], ['regression quadratic root selection 2', [[2004, 2, 29], [2005, 2, 27], 0.08], [91.911111, 0.08638888]], ['partial repair probe 1', [[2100, 1, 28], [2100, 7, 31], 0.0435], [97.776667, 0.04509876]], ['partial repair probe 2', [[2099, 3, 1], [2100, 2, 28], 0.025], [97.472222, 0.02583812]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2095, 3, 5], [2095, 9, 3], 0.001], [99.949444, 0.00101718]], ['normal control 2', [[2045, 1, 1], [2045, 7, 1], 0.025], [98.743056, 0.02566988]]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"36e9af74dd0014505a03146bf9f291e995767429f7b5e04ae82b86fc195535c1","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nimport math\nN = 1\nobservations = []\ndef solve(settle, maturity, d):\n    S = datetime.date(*settle)\n    M = datetime.date(*maturity)\n    t = (M - S).days\n    def leap(y):\n        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0\n    horizon = S + datetime.timedelta(days=365)\n    basis = 366 if any(leap(y) and S < datetime.date(y, 2, 29) <= horizon for y in (S.year, S.year + 1)) else 365\n    price = 100 * (1 - d * t / 360)\n    if t <= basis / 2:\n        bey = basis * d / (360 - d * t)\n    else:\n        a = t / (2 * basis) - 0.25\n        b = t / basis\n        c = (price - 100) / price\n        bey = (-b - math.sqrt(b * b - 4 * a * c)) / (2 * a)\n    return [round(price, 6), round(bey, 8)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression quadratic root selection 1', [[2049, 2, 28], [2049, 8, 30], 0.01], [99.491667, 0.01019055]], ['regression quadratic root selection 2', [[2082, 11, 15], [2083, 5, 17], 0.0525], [97.33125, 0.05468459]], ['partial repair probe 1', [[2050, 11, 30], [2051, 11, 29], 0.08], [91.911111, 0.08638888]], ['partial repair probe 2', [[2089, 3, 11], [2089, 9, 10], 0.08], [95.933333, 0.08453969]], ['boundary control 1', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['boundary control 2', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['normal control 1', [[2023, 12, 15], [2024, 6, 13], 0.0435], [97.812917, 0.04521386]], ['normal control 2', [[2090, 7, 29], [2091, 1, 27], 0.0525], [97.345833, 0.05468048]]], [['regression quadratic root selection 1', [[2100, 12, 28], [2101, 6, 29], 0.025], [98.729167, 0.02567259]], ['regression quadratic root selection 2', [[2002, 11, 1], [2003, 5, 3], 0.0525], [97.33125, 0.05468459]], ['partial repair probe 1', [[2090, 3, 8], [2091, 3, 7], 0.0435], [95.601667, 0.04561452]], ['partial repair probe 2', [[2013, 8, 17], [2014, 2, 16], 0.08], [95.933333, 0.08453969]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2099, 3, 1], [2099, 8, 29], 0.08], [95.977778, 0.0845103]], ['normal control 2', [[2081, 5, 27], [2081, 9, 1], 0.0435], [98.827917, 0.04462724]]], [['regression quadratic root selection 1', [[2099, 3, 28], [2100, 3, 27], 0.01], [98.988889, 0.01021643]], ['regression quadratic root selection 2', [[2065, 8, 13], [2066, 8, 12], 0.0435], [95.601667, 0.04561452]], ['partial repair probe 1', [[2073, 4, 5], [2073, 10, 5], 0.01], [99.491667, 0.01019055]], ['partial repair probe 2', [[1998, 12, 30], [1999, 11, 28], 0.0435], [95.97625, 0.04548568]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2075, 1, 30], [2075, 7, 31], 0.0525], [97.345833, 0.05468048]], ['normal control 2', [[2077, 9, 11], [2078, 3, 12], 0.01], [99.494444, 0.01019041]]], [['regression quadratic root selection 1', [[2070, 4, 30], [2070, 10, 30], 0.08], [95.933333, 0.08453969]], ['regression quadratic root selection 2', [[2100, 12, 15], [2101, 6, 16], 0.08], [95.933333, 0.08453969]], ['partial repair probe 1', [[2074, 4, 20], [2074, 10, 21], 0.0435], [97.776667, 0.04509876]], ['partial repair probe 2', [[2071, 7, 14], [2072, 7, 12], 0.001], [99.898889, 0.00101744]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['boundary control 2', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['normal control 1', [[2032, 2, 5], [2032, 8, 6], 0.025], [98.729167, 0.02574383]], ['normal control 2', [[2027, 12, 1], [2028, 6, 1], 0.025], [98.729167, 0.02574383]]], [['regression quadratic root selection 1', [[2093, 10, 27], [2094, 5, 1], 0.025], [98.708333, 0.02567271]], ['regression quadratic root selection 2', [[2004, 2, 29], [2005, 2, 27], 0.08], [91.911111, 0.08638888]], ['partial repair probe 1', [[2100, 1, 28], [2100, 7, 31], 0.0435], [97.776667, 0.04509876]], ['partial repair probe 2', [[2099, 3, 1], [2100, 2, 28], 0.025], [97.472222, 0.02583812]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2095, 3, 5], [2095, 9, 3], 0.001], [99.949444, 0.00101718]], ['normal control 2', [[2045, 1, 1], [2045, 7, 1], 0.025], [98.743056, 0.02566988]]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"45c143426c677d1f7f711b5e827816abb60069b4beb79b4618e7d880cece5b04","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nimport math\nN = 1\nobservations = []\ndef solve(settle, maturity, d):\n    S = datetime.date(*settle)\n    M = datetime.date(*maturity)\n    t = (M - S).days\n    def leap(y):\n        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0\n    horizon = S + datetime.timedelta(days=365)\n    basis = 366 if any(leap(y) and S < datetime.date(y, 2, 29) <= horizon for y in (S.year, S.year + 1)) else 365\n    price = 100 * (1 - d * t / 360)\n    if t <= basis / 2:\n        bey = basis * d / (360 - d * t)\n    else:\n        a = t / (2 * basis) - 0.25\n        b = t / basis\n        c = (price - 100) / price\n        bey = (-b + math.sqrt(b * b - 4 * a * c)) / (2 * a)\n    return [round(price, 6), round(bey, 8)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression quadratic root selection 1', [[2049, 2, 28], [2049, 8, 30], 0.01], [99.491667, 0.01019055]], ['regression quadratic root selection 2', [[2082, 11, 15], [2083, 5, 17], 0.0525], [97.33125, 0.05468459]], ['partial repair probe 1', [[2050, 11, 30], [2051, 11, 29], 0.08], [91.911111, 0.08638888]], ['partial repair probe 2', [[2089, 3, 11], [2089, 9, 10], 0.08], [95.933333, 0.08453969]], ['boundary control 1', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['boundary control 2', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['normal control 1', [[2023, 12, 15], [2024, 6, 13], 0.0435], [97.812917, 0.04521386]], ['normal control 2', [[2090, 7, 29], [2091, 1, 27], 0.0525], [97.345833, 0.05468048]]], [['regression quadratic root selection 1', [[2100, 12, 28], [2101, 6, 29], 0.025], [98.729167, 0.02567259]], ['regression quadratic root selection 2', [[2002, 11, 1], [2003, 5, 3], 0.0525], [97.33125, 0.05468459]], ['partial repair probe 1', [[2090, 3, 8], [2091, 3, 7], 0.0435], [95.601667, 0.04561452]], ['partial repair probe 2', [[2013, 8, 17], [2014, 2, 16], 0.08], [95.933333, 0.08453969]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2099, 3, 1], [2099, 8, 29], 0.08], [95.977778, 0.0845103]], ['normal control 2', [[2081, 5, 27], [2081, 9, 1], 0.0435], [98.827917, 0.04462724]]], [['regression quadratic root selection 1', [[2099, 3, 28], [2100, 3, 27], 0.01], [98.988889, 0.01021643]], ['regression quadratic root selection 2', [[2065, 8, 13], [2066, 8, 12], 0.0435], [95.601667, 0.04561452]], ['partial repair probe 1', [[2073, 4, 5], [2073, 10, 5], 0.01], [99.491667, 0.01019055]], ['partial repair probe 2', [[1998, 12, 30], [1999, 11, 28], 0.0435], [95.97625, 0.04548568]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2075, 1, 30], [2075, 7, 31], 0.0525], [97.345833, 0.05468048]], ['normal control 2', [[2077, 9, 11], [2078, 3, 12], 0.01], [99.494444, 0.01019041]]], [['regression quadratic root selection 1', [[2070, 4, 30], [2070, 10, 30], 0.08], [95.933333, 0.08453969]], ['regression quadratic root selection 2', [[2100, 12, 15], [2101, 6, 16], 0.08], [95.933333, 0.08453969]], ['partial repair probe 1', [[2074, 4, 20], [2074, 10, 21], 0.0435], [97.776667, 0.04509876]], ['partial repair probe 2', [[2071, 7, 14], [2072, 7, 12], 0.001], [99.898889, 0.00101744]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 31], 0.05], [97.458333, 0.05215904]], ['boundary control 2', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['normal control 1', [[2032, 2, 5], [2032, 8, 6], 0.025], [98.729167, 0.02574383]], ['normal control 2', [[2027, 12, 1], [2028, 6, 1], 0.025], [98.729167, 0.02574383]]], [['regression quadratic root selection 1', [[2093, 10, 27], [2094, 5, 1], 0.025], [98.708333, 0.02567271]], ['regression quadratic root selection 2', [[2004, 2, 29], [2005, 2, 27], 0.08], [91.911111, 0.08638888]], ['partial repair probe 1', [[2100, 1, 28], [2100, 7, 31], 0.0435], [97.776667, 0.04509876]], ['partial repair probe 2', [[2099, 3, 1], [2100, 2, 28], 0.025], [97.472222, 0.02583812]], ['boundary control 1', [[2023, 5, 1], [2023, 10, 30], 0.05], [97.472222, 0.05215161]], ['boundary control 2', [[2023, 1, 1], [2023, 1, 2], 0.05], [99.986111, 0.05070149]], ['normal control 1', [[2095, 3, 5], [2095, 9, 3], 0.001], [99.949444, 0.00101718]], ['normal control 2', [[2045, 1, 1], [2045, 7, 1], 0.025], [98.743056, 0.02566988]]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic toy contract stated explicitly in the contract field; no claim of conformance to any published convention text. 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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-bond_day_count_conventions-bill-discount-to-bey-quadratic-root-selection","generated_at":"2026-09-29T14:46:52.131267+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.","repair":"Take the positive root (-b + sqrt(b^2 - 4ac))/(2a).","root_cause":"The quadratic solution subtracts the square root.","sha256":"a6f239d5b9122bec1b0236f3253021f05cc9daa3d21ecc574a755d19ff295d36","title":"Discount-basis bill price and bond-equivalent yield: the negative root of the quadratic is returned · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":42.777,"exit_code":1,"observations":[{"actual":[99.491667,-0.01019083],"check":"regression quadratic root selection 1","expected":[99.491667,0.01019055],"passed":false},{"actual":[97.33125,-0.05469276],"check":"regression quadratic root selection 2","expected":[97.33125,0.05468459],"passed":false},{"actual":[91.911111,-0.09028161],"check":"partial repair probe 1","expected":[91.911111,0.08638888],"passed":false},{"actual":[95.933333,-0.08455922],"check":"partial repair probe 2","expected":[95.933333,0.08453969],"passed":false},{"actual":[99.986111,0.05070149],"check":"boundary control 1","expected":[99.986111,0.05070149],"passed":true},{"actual":[97.458333,0.05215904],"check":"boundary control 2","expected":[97.458333,0.05215904],"passed":true},{"actual":[97.812917,0.04521386],"check":"normal control 1","expected":[97.812917,0.04521386],"passed":true},{"actual":[97.345833,0.05468048],"check":"normal control 2","expected":[97.345833,0.05468048],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression quadratic root selection 1\", \"actual\": [99.491667, -0.01019083], \"expected\": [99.491667, 0.01019055], \"passed\": false}, {\"check\": \"regression quadratic root selection 2\", \"actual\": [97.33125, -0.05469276], \"expected\": [97.33125, 0.05468459], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [91.911111, -0.09028161], \"expected\": [91.911111, 0.08638888], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [95.933333, -0.08455922], \"expected\": [95.933333, 0.08453969], \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": [99.986111, 0.05070149], \"expected\": [99.986111, 0.05070149], \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": [97.458333, 0.05215904], \"expected\": [97.458333, 0.05215904], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [97.812917, 0.04521386], \"expected\": [97.812917, 0.04521386], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [97.345833, 0.05468048], \"expected\": [97.345833, 0.05468048], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":45.814,"exit_code":1,"observations":[{"actual":[99.491667,-732.01019055],"check":"regression quadratic root selection 1","expected":[99.491667,0.01019055],"passed":false},{"actual":[97.33125,-732.05468459],"check":"regression quadratic root selection 2","expected":[97.33125,0.05468459],"passed":false},{"actual":[91.911111,-4.09740817],"check":"partial repair probe 1","expected":[91.911111,0.08638888],"passed":false},{"actual":[95.933333,-732.08453969],"check":"partial repair probe 2","expected":[95.933333,0.08453969],"passed":false},{"actual":[99.986111,0.05070149],"check":"boundary control 1","expected":[99.986111,0.05070149],"passed":true},{"actual":[97.458333,0.05215904],"check":"boundary control 2","expected":[97.458333,0.05215904],"passed":true},{"actual":[97.812917,0.04521386],"check":"normal control 1","expected":[97.812917,0.04521386],"passed":true},{"actual":[97.345833,0.05468048],"check":"normal control 2","expected":[97.345833,0.05468048],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression quadratic root selection 1\", \"actual\": [99.491667, -732.01019055], \"expected\": [99.491667, 0.01019055], \"passed\": false}, {\"check\": \"regression quadratic root selection 2\", \"actual\": [97.33125, -732.05468459], \"expected\": [97.33125, 0.05468459], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [91.911111, -4.09740817], \"expected\": [91.911111, 0.08638888], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [95.933333, -732.08453969], \"expected\": [95.933333, 0.08453969], 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