{"abstract":"Pip-quoted premiums are thousands of times too large.","category":"Options payoff and settlement","checks":8,"contract":"Inputs quote, quote type, base notional, spot and pip size. pips: term premium = quote*pip*notional, base = term/spot. pct_base: base = quote/100*notional, term = base*spot. pct_term: term = quote/100*notional*spot, base = term/spot. Exact fractions; return [term, base] rounded to 2 as floats.","evaluation_group":"w2-options_payoff_and_settlement-fx-premium-conversion","failed_approach":"Hard-coding a pip of 0.0001 is wrong for yen pairs.","family":"w2-options_payoff_and_settlement-fx-premium-conversion-pip-scaling","id":"FA-61716","implementations":{"attempt":{"sha256":"dbaba026074ab711a4452311a1bcc160871168e1bcf5b5b5c83ac750c2993786","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(quote, quote_type, notional, spot, pip):\n    q = Fraction(str(quote))\n    s = Fraction(str(spot))\n    N = Fraction(notional)\n    pp = Fraction(str(pip))\n    if quote_type == 'pips':\n        term = q * Fraction('0.0001') * N\n        base = term / s\n    elif quote_type == 'pct_base':\n        base = q / 100 * N\n        term = base * s\n    else:\n        term = q / 100 * N * s\n        base = term / s\n    return [float(round(term, 2)), float(round(base, 2))]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression pip scaling 1', [0.85, 'pips', 250000, 1.2712, 0.0001], [21.25, 16.72]], ['regression pip scaling 2', [12.5, 'pips', 250000, 1.0845, 0.0001], [312.5, 288.15]], ['partial repair probe 1', [1.25, 'pips', 250000, 149.35, 0.01], [3125.0, 20.92]], ['partial repair probe 2', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['normal control 1', [0.85, 'pct_base', 5000000, 149.35, 0.01], [6347375.0, 42500.0]], ['normal control 2', [110.0, 'pct_base', 5000000, 1.0845, 0.0001], [5964750.0, 5500000.0]], ['normal control 3', [45.0, 'pct_base', 250000, 1.2712, 0.0001], [143010.0, 112500.0]], ['normal control 4', [1.25, 'pct_base', 1000000, 1.2712, 0.0001], [15890.0, 12500.0]]], [['regression pip scaling 1', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['regression pip scaling 2', [45.0, 'pips', 1000000, 1.2712, 0.0001], [4500.0, 3539.96]], ['partial repair probe 1', [110.0, 'pips', 250000, 149.35, 0.01], [275000.0, 1841.31]], ['partial repair probe 2', [110.0, 'pips', 5000000, 149.35, 0.01], [5500000.0, 36826.25]], ['normal control 1', [110.0, 'pct_term', 5000000, 149.35, 0.01], [821425000.0, 5500000.0]], ['normal control 2', [12.5, 'pct_base', 5000000, 1.0845, 0.0001], [677812.5, 625000.0]], ['normal control 3', [12.5, 'pct_term', 1000000, 1.0845, 0.0001], [135562.5, 125000.0]], ['normal control 4', [45.0, 'pct_term', 1000000, 1.0845, 0.0001], [488025.0, 450000.0]]], [['regression pip scaling 1', [45.0, 'pips', 1000000, 149.35, 0.01], [450000.0, 3013.06]], ['regression pip scaling 2', [110.0, 'pips', 1000000, 149.35, 0.01], [1100000.0, 7365.25]], ['partial repair probe 1', [45.0, 'pips', 250000, 149.35, 0.01], [112500.0, 753.26]], ['partial repair probe 2', [110.0, 'pips', 5000000, 149.35, 0.01], [5500000.0, 36826.25]], ['normal control 1', [12.5, 'pct_base', 1000000, 1.2712, 0.0001], [158900.0, 125000.0]], ['normal control 2', [1.25, 'pct_term', 250000, 1.2712, 0.0001], [3972.5, 3125.0]], ['normal control 3', [110.0, 'pct_term', 1000000, 1.0845, 0.0001], [1192950.0, 1100000.0]], ['normal control 4', [0.85, 'pct_term', 1000000, 1.2712, 0.0001], [10805.2, 8500.0]]], [['regression pip scaling 1', [12.5, 'pips', 250000, 1.2712, 0.0001], [312.5, 245.83]], ['regression pip scaling 2', [0.85, 'pips', 1000000, 149.35, 0.01], [8500.0, 56.91]], ['partial repair probe 1', [12.5, 'pips', 5000000, 149.35, 0.01], [625000.0, 4184.8]], ['partial repair probe 2', [45.0, 'pips', 5000000, 149.35, 0.01], [2250000.0, 15065.28]], ['normal control 1', [0.85, 'pct_base', 5000000, 1.2712, 0.0001], [54026.0, 42500.0]], ['normal control 2', [1.25, 'pct_term', 250000, 1.0845, 0.0001], [3389.06, 3125.0]], ['normal control 3', [45.0, 'pct_base', 1000000, 149.35, 0.01], [67207500.0, 450000.0]], ['normal control 4', [12.5, 'pct_base', 5000000, 149.35, 0.01], [93343750.0, 625000.0]]], [['regression pip scaling 1', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['regression pip scaling 2', [1.25, 'pips', 250000, 149.35, 0.01], [3125.0, 20.92]], ['partial repair probe 1', [45.0, 'pips', 250000, 149.35, 0.01], [112500.0, 753.26]], ['partial repair probe 2', [0.85, 'pips', 5000000, 149.35, 0.01], [42500.0, 284.57]], ['normal control 1', [0.85, 'pct_term', 5000000, 1.2712, 0.0001], [54026.0, 42500.0]], ['normal control 2', [45.0, 'pct_term', 1000000, 1.0845, 0.0001], [488025.0, 450000.0]], ['normal control 3', [1.25, 'pct_term', 5000000, 1.2712, 0.0001], [79450.0, 62500.0]], ['normal control 4', [1.25, 'pct_base', 5000000, 1.2712, 0.0001], [79450.0, 62500.0]]]]\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":"fc5b0c8da6170648e4e7b1630efe9035914296aef5ab831b325021944daf0e61","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(quote, quote_type, notional, spot, pip):\n    q = Fraction(str(quote))\n    s = Fraction(str(spot))\n    N = Fraction(notional)\n    pp = Fraction(str(pip))\n    if quote_type == 'pips':\n        term = q * N\n        base = term / s\n    elif quote_type == 'pct_base':\n        base = q / 100 * N\n        term = base * s\n    else:\n        term = q / 100 * N * s\n        base = term / s\n    return [float(round(term, 2)), float(round(base, 2))]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression pip scaling 1', [0.85, 'pips', 250000, 1.2712, 0.0001], [21.25, 16.72]], ['regression pip scaling 2', [12.5, 'pips', 250000, 1.0845, 0.0001], [312.5, 288.15]], ['partial repair probe 1', [1.25, 'pips', 250000, 149.35, 0.01], [3125.0, 20.92]], ['partial repair probe 2', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['normal control 1', [0.85, 'pct_base', 5000000, 149.35, 0.01], [6347375.0, 42500.0]], ['normal control 2', [110.0, 'pct_base', 5000000, 1.0845, 0.0001], [5964750.0, 5500000.0]], ['normal control 3', [45.0, 'pct_base', 250000, 1.2712, 0.0001], [143010.0, 112500.0]], ['normal control 4', [1.25, 'pct_base', 1000000, 1.2712, 0.0001], [15890.0, 12500.0]]], [['regression pip scaling 1', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['regression pip scaling 2', [45.0, 'pips', 1000000, 1.2712, 0.0001], [4500.0, 3539.96]], ['partial repair probe 1', [110.0, 'pips', 250000, 149.35, 0.01], [275000.0, 1841.31]], ['partial repair probe 2', [110.0, 'pips', 5000000, 149.35, 0.01], [5500000.0, 36826.25]], ['normal control 1', [110.0, 'pct_term', 5000000, 149.35, 0.01], [821425000.0, 5500000.0]], ['normal control 2', [12.5, 'pct_base', 5000000, 1.0845, 0.0001], [677812.5, 625000.0]], ['normal control 3', [12.5, 'pct_term', 1000000, 1.0845, 0.0001], [135562.5, 125000.0]], ['normal control 4', [45.0, 'pct_term', 1000000, 1.0845, 0.0001], [488025.0, 450000.0]]], [['regression pip scaling 1', [45.0, 'pips', 1000000, 149.35, 0.01], [450000.0, 3013.06]], ['regression pip scaling 2', [110.0, 'pips', 1000000, 149.35, 0.01], [1100000.0, 7365.25]], ['partial repair probe 1', [45.0, 'pips', 250000, 149.35, 0.01], [112500.0, 753.26]], ['partial repair probe 2', [110.0, 'pips', 5000000, 149.35, 0.01], [5500000.0, 36826.25]], ['normal control 1', [12.5, 'pct_base', 1000000, 1.2712, 0.0001], [158900.0, 125000.0]], ['normal control 2', [1.25, 'pct_term', 250000, 1.2712, 0.0001], [3972.5, 3125.0]], ['normal control 3', [110.0, 'pct_term', 1000000, 1.0845, 0.0001], [1192950.0, 1100000.0]], ['normal control 4', [0.85, 'pct_term', 1000000, 1.2712, 0.0001], [10805.2, 8500.0]]], [['regression pip scaling 1', [12.5, 'pips', 250000, 1.2712, 0.0001], [312.5, 245.83]], ['regression pip scaling 2', [0.85, 'pips', 1000000, 149.35, 0.01], [8500.0, 56.91]], ['partial repair probe 1', [12.5, 'pips', 5000000, 149.35, 0.01], [625000.0, 4184.8]], ['partial repair probe 2', [45.0, 'pips', 5000000, 149.35, 0.01], [2250000.0, 15065.28]], ['normal control 1', [0.85, 'pct_base', 5000000, 1.2712, 0.0001], [54026.0, 42500.0]], ['normal control 2', [1.25, 'pct_term', 250000, 1.0845, 0.0001], [3389.06, 3125.0]], ['normal control 3', [45.0, 'pct_base', 1000000, 149.35, 0.01], [67207500.0, 450000.0]], ['normal control 4', [12.5, 'pct_base', 5000000, 149.35, 0.01], [93343750.0, 625000.0]]], [['regression pip scaling 1', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['regression pip scaling 2', [1.25, 'pips', 250000, 149.35, 0.01], [3125.0, 20.92]], ['partial repair probe 1', [45.0, 'pips', 250000, 149.35, 0.01], [112500.0, 753.26]], ['partial repair probe 2', [0.85, 'pips', 5000000, 149.35, 0.01], [42500.0, 284.57]], ['normal control 1', [0.85, 'pct_term', 5000000, 1.2712, 0.0001], [54026.0, 42500.0]], ['normal control 2', [45.0, 'pct_term', 1000000, 1.0845, 0.0001], [488025.0, 450000.0]], ['normal control 3', [1.25, 'pct_term', 5000000, 1.2712, 0.0001], [79450.0, 62500.0]], ['normal control 4', [1.25, 'pct_base', 5000000, 1.2712, 0.0001], [79450.0, 62500.0]]]]\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":"fa7ab858db71984cd6146af995b5014c821e0a0fbd65da04108c368a4ffb9e17","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(quote, quote_type, notional, spot, pip):\n    q = Fraction(str(quote))\n    s = Fraction(str(spot))\n    N = Fraction(notional)\n    pp = Fraction(str(pip))\n    if quote_type == 'pips':\n        term = q * pp * N\n        base = term / s\n    elif quote_type == 'pct_base':\n        base = q / 100 * N\n        term = base * s\n    else:\n        term = q / 100 * N * s\n        base = term / s\n    return [float(round(term, 2)), float(round(base, 2))]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression pip scaling 1', [0.85, 'pips', 250000, 1.2712, 0.0001], [21.25, 16.72]], ['regression pip scaling 2', [12.5, 'pips', 250000, 1.0845, 0.0001], [312.5, 288.15]], ['partial repair probe 1', [1.25, 'pips', 250000, 149.35, 0.01], [3125.0, 20.92]], ['partial repair probe 2', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['normal control 1', [0.85, 'pct_base', 5000000, 149.35, 0.01], [6347375.0, 42500.0]], ['normal control 2', [110.0, 'pct_base', 5000000, 1.0845, 0.0001], [5964750.0, 5500000.0]], ['normal control 3', [45.0, 'pct_base', 250000, 1.2712, 0.0001], [143010.0, 112500.0]], ['normal control 4', [1.25, 'pct_base', 1000000, 1.2712, 0.0001], [15890.0, 12500.0]]], [['regression pip scaling 1', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['regression pip scaling 2', [45.0, 'pips', 1000000, 1.2712, 0.0001], [4500.0, 3539.96]], ['partial repair probe 1', [110.0, 'pips', 250000, 149.35, 0.01], [275000.0, 1841.31]], ['partial repair probe 2', [110.0, 'pips', 5000000, 149.35, 0.01], [5500000.0, 36826.25]], ['normal control 1', [110.0, 'pct_term', 5000000, 149.35, 0.01], [821425000.0, 5500000.0]], ['normal control 2', [12.5, 'pct_base', 5000000, 1.0845, 0.0001], [677812.5, 625000.0]], ['normal control 3', [12.5, 'pct_term', 1000000, 1.0845, 0.0001], [135562.5, 125000.0]], ['normal control 4', [45.0, 'pct_term', 1000000, 1.0845, 0.0001], [488025.0, 450000.0]]], [['regression pip scaling 1', [45.0, 'pips', 1000000, 149.35, 0.01], [450000.0, 3013.06]], ['regression pip scaling 2', [110.0, 'pips', 1000000, 149.35, 0.01], [1100000.0, 7365.25]], ['partial repair probe 1', [45.0, 'pips', 250000, 149.35, 0.01], [112500.0, 753.26]], ['partial repair probe 2', [110.0, 'pips', 5000000, 149.35, 0.01], [5500000.0, 36826.25]], ['normal control 1', [12.5, 'pct_base', 1000000, 1.2712, 0.0001], [158900.0, 125000.0]], ['normal control 2', [1.25, 'pct_term', 250000, 1.2712, 0.0001], [3972.5, 3125.0]], ['normal control 3', [110.0, 'pct_term', 1000000, 1.0845, 0.0001], [1192950.0, 1100000.0]], ['normal control 4', [0.85, 'pct_term', 1000000, 1.2712, 0.0001], [10805.2, 8500.0]]], [['regression pip scaling 1', [12.5, 'pips', 250000, 1.2712, 0.0001], [312.5, 245.83]], ['regression pip scaling 2', [0.85, 'pips', 1000000, 149.35, 0.01], [8500.0, 56.91]], ['partial repair probe 1', [12.5, 'pips', 5000000, 149.35, 0.01], [625000.0, 4184.8]], ['partial repair probe 2', [45.0, 'pips', 5000000, 149.35, 0.01], [2250000.0, 15065.28]], ['normal control 1', [0.85, 'pct_base', 5000000, 1.2712, 0.0001], [54026.0, 42500.0]], ['normal control 2', [1.25, 'pct_term', 250000, 1.0845, 0.0001], [3389.06, 3125.0]], ['normal control 3', [45.0, 'pct_base', 1000000, 149.35, 0.01], [67207500.0, 450000.0]], ['normal control 4', [12.5, 'pct_base', 5000000, 149.35, 0.01], [93343750.0, 625000.0]]], [['regression pip scaling 1', [0.85, 'pips', 250000, 149.35, 0.01], [2125.0, 14.23]], ['regression pip scaling 2', [1.25, 'pips', 250000, 149.35, 0.01], [3125.0, 20.92]], ['partial repair probe 1', [45.0, 'pips', 250000, 149.35, 0.01], [112500.0, 753.26]], ['partial repair probe 2', [0.85, 'pips', 5000000, 149.35, 0.01], [42500.0, 284.57]], ['normal control 1', [0.85, 'pct_term', 5000000, 1.2712, 0.0001], [54026.0, 42500.0]], ['normal control 2', [45.0, 'pct_term', 1000000, 1.0845, 0.0001], [488025.0, 450000.0]], ['normal control 3', [1.25, 'pct_term', 5000000, 1.2712, 0.0001], [79450.0, 62500.0]], ['normal control 4', [1.25, 'pct_base', 5000000, 1.2712, 0.0001], [79450.0, 62500.0]]]]\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 exchange or clearing rulebook. 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-options_payoff_and_settlement-fx-premium-conversion-pip-scaling","generated_at":"2026-09-29T14:46:57.853462+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Option expiry, exercise and settlement engines move cash and shares; a wrong branch misstates obligations.","repair":"Multiply the pip quote by the pair's pip size.","root_cause":"The pip quote is multiplied by notional without the pip size.","sha256":"10642fe74ffc01a38285d975831fcaf9041fb91477350b08f629ecbd5ec0f364","title":"FX option premium quote conversion: pip quotes are applied without the pip size · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":44.793,"exit_code":1,"observations":[{"actual":[21.25,16.72],"check":"regression pip scaling 1","expected":[21.25,16.72],"passed":true},{"actual":[312.5,288.15],"check":"regression pip scaling 2","expected":[312.5,288.15],"passed":true},{"actual":[31.25,0.21],"check":"partial repair probe 1","expected":[3125.0,20.92],"passed":false},{"actual":[21.25,0.14],"check":"partial repair probe 2","expected":[2125.0,14.23],"passed":false},{"actual":[6347375.0,42500.0],"check":"normal control 1","expected":[6347375.0,42500.0],"passed":true},{"actual":[5964750.0,5500000.0],"check":"normal control 2","expected":[5964750.0,5500000.0],"passed":true},{"actual":[143010.0,112500.0],"check":"normal control 3","expected":[143010.0,112500.0],"passed":true},{"actual":[15890.0,12500.0],"check":"normal control 4","expected":[15890.0,12500.0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression pip scaling 1\", \"actual\": [21.25, 16.72], \"expected\": [21.25, 16.72], \"passed\": true}, {\"check\": \"regression pip scaling 2\", \"actual\": [312.5, 288.15], \"expected\": [312.5, 288.15], \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": [31.25, 0.21], \"expected\": [3125.0, 20.92], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [21.25, 0.14], \"expected\": [2125.0, 14.23], \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": [6347375.0, 42500.0], \"expected\": [6347375.0, 42500.0], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [5964750.0, 5500000.0], \"expected\": [5964750.0, 5500000.0], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [143010.0, 112500.0], \"expected\": [143010.0, 112500.0], \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": [15890.0, 12500.0], \"expected\": [15890.0, 12500.0], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":44.556,"exit_code":1,"observations":[{"actual":[212500.0,167164.88],"check":"regression pip scaling 1","expected":[21.25,16.72],"passed":false},{"actual":[3125000.0,2881512.22],"check":"regression pip scaling 2","expected":[312.5,288.15],"passed":false},{"actual":[312500.0,2092.4],"check":"partial repair probe 1","expected":[3125.0,20.92],"passed":false},{"actual":[212500.0,1422.83],"check":"partial repair probe 2","expected":[2125.0,14.23],"passed":false},{"actual":[6347375.0,42500.0],"check":"normal control 1","expected":[6347375.0,42500.0],"passed":true},{"actual":[5964750.0,5500000.0],"check":"normal control 2","expected":[5964750.0,5500000.0],"passed":true},{"actual":[143010.0,112500.0],"check":"normal control 3","expected":[143010.0,112500.0],"passed":true},{"actual":[15890.0,12500.0],"check":"normal control 4","expected":[15890.0,12500.0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression pip scaling 1\", \"actual\": [212500.0, 167164.88], \"expected\": [21.25, 16.72], \"passed\": false}, {\"check\": \"regression pip scaling 2\", \"actual\": [3125000.0, 2881512.22], \"expected\": [312.5, 288.15], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [312500.0, 2092.4], \"expected\": [3125.0, 20.92], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [212500.0, 1422.83], \"expected\": [2125.0, 14.23], \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": [6347375.0, 42500.0], \"expected\": [6347375.0, 42500.0], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [5964750.0, 5500000.0], \"expected\": [5964750.0, 5500000.0], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [143010.0, 112500.0], \"expected\": [143010.0, 112500.0], \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": [15890.0, 12500.0], \"expected\": [15890.0, 12500.0], \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":45.112,"exit_code":0,"observations":[{"actual":[21.25,16.72],"check":"regression pip scaling 1","expected":[21.25,16.72],"passed":true},{"actual":[312.5,288.15],"check":"regression pip scaling 2","expected":[312.5,288.15],"passed":true},{"actual":[3125.0,20.92],"check":"partial repair probe 1","expected":[3125.0,20.92],"passed":true},{"actual":[2125.0,14.23],"check":"partial repair probe 2","expected":[2125.0,14.23],"passed":true},{"actual":[6347375.0,42500.0],"check":"normal control 1","expected":[6347375.0,42500.0],"passed":true},{"actual":[5964750.0,5500000.0],"check":"normal control 2","expected":[5964750.0,5500000.0],"passed":true},{"actual":[143010.0,112500.0],"check":"normal control 3","expected":[143010.0,112500.0],"passed":true},{"actual":[15890.0,12500.0],"check":"normal control 4","expected":[15890.0,12500.0],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression pip scaling 1\", \"actual\": [21.25, 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