{"abstract":"Purchase returns remove units from the oldest matching layer.","category":"Double-entry ledger accounting","checks":7,"contract":"x = [['buy', qty, unit_cost] | ['sell', qty, unit_price] | ['purchase_return', qty, unit_cost]]. Layers are kept oldest first; a buy at the same cost as the newest layer merges into it. A sale consumes layers oldest first (COGS) and records revenue; a sale exceeding on-hand quantity is rejected whole. A purchase return removes qty from the newest layer at that cost holding at least qty, else it is rejected. Emptied layers are removed. Return {'cogs', 'revenue', 'inventory': sum qty*cost, 'layers', 'rejected': event indexes}.","evaluation_group":"w2-double-entry-ledger-accounting-fifo-perpetual-inventory","failed_approach":"Decrementing the oldest layer while testing the newest one for removal leaves an emptied layer behind.","family":"w2-double-entry-ledger-accounting-fifo-perpetual-inventory-return-layer-selection","id":"FA-58101","implementations":{"attempt":{"sha256":"096a9808f1dfee6a4ce9362136444a180d091c69a6d0112d50a5c49ad8a5b692","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    layers = []\n    cogs = revenue = 0\n    short = []\n    for i, ev in enumerate(x):\n        if ev[0] == 'buy':\n            if layers and layers[-1][1] == ev[2]:\n                layers[-1][0] += ev[1]\n            else:\n                layers.append([ev[1], ev[2]])\n        elif ev[0] == 'sell':\n            qty = ev[1]\n            if qty > sum(l[0] for l in layers):\n                short.append(i)\n                continue\n            revenue += qty * ev[2]\n            while qty:\n                head = layers[0]\n                take = min(qty, head[0])\n                cogs += take * head[1]\n                head[0] -= take\n                qty -= take\n                if head[0] == 0:\n                    layers.pop(0)\n        elif ev[0] == 'purchase_return':\n            match = [l for l in layers if l[1] == ev[2] and l[0] >= ev[1]]\n            if not match:\n                short.append(i)\n                continue\n            match[0][0] -= ev[1]\n            if match[-1][0] == 0:\n                layers.remove(match[-1])\n    inventory = sum(q * c for q, c in layers)\n    return {'cogs': cogs, 'revenue': revenue, 'inventory': inventory, 'layers': layers, 'rejected': short}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: return layer selection', [['buy', 5, 100], ['buy', 10, 100], ['sell', 12, 200], ['buy', 10, 120], ['sell', 1, 250], ['buy', 1, 120], ['buy', 1, 100], ['buy', 2, 100], ['purchase_return', 1, 100], ['buy', 10, 150]], {'cogs': 1300, 'revenue': 2650, 'inventory': 3220, 'layers': [[2, 100], [11, 120], [2, 100], [10, 150]], 'rejected': []}], ['control 1', [['sell', 3, 250], ['buy', 2, 120], ['purchase_return', 1, 100], ['buy', 5, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 980, 'layers': [[2, 120], [5, 100], [2, 120]], 'rejected': [0, 2]}], ['control 2', [['buy', 5, 150], ['sell', 3, 250], ['sell', 2, 250], ['buy', 10, 100], ['buy', 10, 120], ['purchase_return', 2, 100], ['purchase_return', 5, 100], ['buy', 10, 150], ['buy', 2, 120], ['purchase_return', 5, 100]], {'cogs': 750, 'revenue': 1250, 'inventory': 3240, 'layers': [[3, 100], [10, 120], [10, 150], [2, 120]], 'rejected': [9]}], ['control 3', [['buy', 1, 120], ['sell', 12, 200], ['sell', 2, 250], ['sell', 2, 250]], {'cogs': 0, 'revenue': 0, 'inventory': 120, 'layers': [[1, 120]], 'rejected': [1, 2, 3]}], ['control 4', [['buy', 5, 100], ['sell', 3, 250], ['sell', 12, 200]], {'cogs': 300, 'revenue': 750, 'inventory': 200, 'layers': [[2, 100]], 'rejected': [2]}], ['control 5', [['buy', 10, 120], ['purchase_return', 1, 100], ['sell', 1, 200], ['purchase_return', 2, 100], ['sell', 2, 200], ['purchase_return', 1, 120], ['buy', 10, 120], ['buy', 5, 120]], {'cogs': 360, 'revenue': 600, 'inventory': 2520, 'layers': [[21, 120]], 'rejected': [1, 3]}], ['control 6', [['sell', 12, 250], ['sell', 12, 200], ['buy', 5, 100], ['sell', 12, 250], ['sell', 12, 200], ['purchase_return', 1, 100], ['sell', 3, 250]], {'cogs': 300, 'revenue': 750, 'inventory': 100, 'layers': [[1, 100]], 'rejected': [0, 1, 3, 4]}]], [['regression: return layer selection', [['buy', 10, 100], ['purchase_return', 1, 100], ['buy', 5, 150], ['buy', 10, 100], ['purchase_return', 2, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 2690, 'layers': [[9, 100], [5, 150], [8, 100], [2, 120]], 'rejected': []}], ['control 1', [['sell', 12, 200], ['sell', 6, 250], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2]}], ['control 2', [['buy', 10, 100], ['sell', 12, 250], ['sell', 2, 250], ['sell', 12, 200], ['sell', 6, 200], ['buy', 1, 150], ['buy', 2, 120]], {'cogs': 800, 'revenue': 1700, 'inventory': 590, 'layers': [[2, 100], [1, 150], [2, 120]], 'rejected': [1, 3]}], ['control 3', [['purchase_return', 2, 100], ['sell', 12, 250], ['buy', 10, 100], ['buy', 1, 100], ['sell', 6, 200]], {'cogs': 600, 'revenue': 1200, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [0, 1]}], ['control 4', [['buy', 10, 150], ['sell', 3, 250], ['sell', 12, 200], ['buy', 5, 120], ['sell', 3, 200], ['buy', 1, 100], ['sell', 1, 250], ['buy', 2, 100], ['sell', 12, 200], ['buy', 10, 100]], {'cogs': 1050, 'revenue': 1600, 'inventory': 2350, 'layers': [[3, 150], [5, 120], [13, 100]], 'rejected': [2, 8]}], ['control 5', [['sell', 2, 250], ['purchase_return', 1, 150], ['buy', 10, 100], ['buy', 10, 150], ['buy', 2, 150], ['buy', 2, 100], ['sell', 2, 250], ['sell', 3, 250], ['buy', 5, 120]], {'cogs': 500, 'revenue': 1250, 'inventory': 3100, 'layers': [[5, 100], [12, 150], [2, 100], [5, 120]], 'rejected': [0, 1]}], ['control 6', [['buy', 10, 120], ['sell', 1, 200], ['buy', 10, 120], ['buy', 2, 120], ['sell', 6, 250], ['buy', 1, 100], ['buy', 2, 150], ['sell', 1, 250]], {'cogs': 960, 'revenue': 1950, 'inventory': 2080, 'layers': [[14, 120], [1, 100], [2, 150]], 'rejected': []}]], [['regression: return layer selection', [['sell', 2, 250], ['sell', 3, 250], ['sell', 1, 200], ['buy', 5, 100], ['buy', 10, 150], ['buy', 10, 100], ['purchase_return', 5, 100], ['sell', 3, 250], ['buy', 1, 100]], {'cogs': 300, 'revenue': 750, 'inventory': 2300, 'layers': [[2, 100], [10, 150], [6, 100]], 'rejected': [0, 1, 2]}], ['control 1', [['purchase_return', 5, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 320, 'layers': [[2, 100], [1, 120]], 'rejected': [0, 1]}], ['control 2', [['sell', 1, 250], ['buy', 5, 100], ['buy', 10, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 10, 100], ['sell', 6, 200], ['buy', 1, 120], ['buy', 10, 150], ['buy', 10, 150]], {'cogs': 800, 'revenue': 1700, 'inventory': 5020, 'layers': [[19, 100], [1, 120], [20, 150]], 'rejected': [0]}], ['control 3', [['purchase_return', 1, 100], ['sell', 1, 200], ['sell', 1, 250], ['sell', 3, 200], ['sell', 6, 200], ['buy', 10, 120], ['buy', 2, 150], ['buy', 2, 100], ['purchase_return', 2, 150]], {'cogs': 0, 'revenue': 0, 'inventory': 1400, 'layers': [[10, 120], [2, 100]], 'rejected': [0, 1, 2, 3, 4]}], ['control 4', [['buy', 1, 100], ['purchase_return', 5, 100], ['sell', 3, 200], ['buy', 1, 150], ['buy', 1, 150], ['sell', 3, 200], ['buy', 2, 120], ['buy', 10, 120]], {'cogs': 400, 'revenue': 600, 'inventory': 1440, 'layers': [[12, 120]], 'rejected': [1, 2]}], ['control 5', [['purchase_return', 1, 100], ['buy', 2, 150], ['sell', 6, 200], ['sell', 2, 250], ['buy', 5, 120], ['buy', 5, 150], ['sell', 1, 250], ['purchase_return', 2, 100]], {'cogs': 420, 'revenue': 750, 'inventory': 1230, 'layers': [[4, 120], [5, 150]], 'rejected': [0, 2, 7]}], ['control 6', [['buy', 2, 120], ['purchase_return', 1, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 340, 'revenue': 600, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [1]}]], [['regression: return layer selection', [['buy', 5, 100], ['sell', 1, 250], ['buy', 1, 120], ['buy', 10, 100], ['buy', 5, 100], ['buy', 10, 100], ['purchase_return', 1, 100]], {'cogs': 100, 'revenue': 250, 'inventory': 2920, 'layers': [[4, 100], [1, 120], [24, 100]], 'rejected': []}], ['control 1', [['sell', 1, 250], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 120], ['buy', 5, 150], ['sell', 3, 200], ['sell', 1, 200], ['sell', 6, 250], ['buy', 2, 120]], {'cogs': 540, 'revenue': 800, 'inventory': 690, 'layers': [[3, 150], [2, 120]], 'rejected': [0, 1, 2, 7]}], ['control 2', [['buy', 1, 100], ['purchase_return', 2, 150], ['purchase_return', 1, 120], ['purchase_return', 2, 100], ['buy', 5, 100], ['buy', 10, 100], ['buy', 1, 150], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1870, 'layers': [[16, 100], [1, 150], [1, 120]], 'rejected': [1, 2, 3]}], ['control 3', [['buy', 2, 150], ['buy', 10, 120], ['sell', 1, 200], ['purchase_return', 5, 100], ['buy', 1, 100], ['buy', 2, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 540, 'revenue': 800, 'inventory': 1960, 'layers': [[8, 120], [1, 100], [2, 150], [6, 100]], 'rejected': [3]}], ['control 4', [['sell', 12, 200], ['sell', 12, 250], ['buy', 2, 150], ['sell', 12, 250], ['buy', 2, 150], ['buy', 1, 100], ['buy', 5, 120], ['buy', 10, 150], ['sell', 3, 200], ['buy', 2, 150]], {'cogs': 450, 'revenue': 600, 'inventory': 2650, 'layers': [[1, 150], [1, 100], [5, 120], [12, 150]], 'rejected': [0, 1, 3]}], ['control 5', [['buy', 1, 100], ['buy', 2, 120], ['buy', 2, 120], ['sell', 3, 200], ['sell', 12, 200], ['buy', 5, 120], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 100]], {'cogs': 700, 'revenue': 1200, 'inventory': 680, 'layers': [[4, 120], [2, 100]], 'rejected': [4, 7]}], ['control 6', [['sell', 3, 250], ['purchase_return', 1, 150], ['purchase_return', 2, 120], ['sell', 6, 200], ['purchase_return', 2, 100]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4]}]], [['regression: return layer selection', [['sell', 2, 200], ['buy', 2, 100], ['buy', 5, 100], ['buy', 10, 120], ['buy', 1, 100], ['purchase_return', 1, 100], ['buy', 2, 100], ['buy', 2, 150]], {'cogs': 0, 'revenue': 0, 'inventory': 2400, 'layers': [[7, 100], [10, 120], [2, 100], [2, 150]], 'rejected': [0]}], ['control 1', [['buy', 10, 100], ['buy', 10, 150], ['purchase_return', 5, 100], ['sell', 6, 200], ['sell', 3, 200], ['sell', 2, 200]], {'cogs': 1400, 'revenue': 2200, 'inventory': 600, 'layers': [[4, 150]], 'rejected': []}], ['control 2', [['purchase_return', 2, 100], ['sell', 1, 200], ['buy', 5, 100], ['buy', 5, 100], ['purchase_return', 1, 150], ['sell', 2, 250], ['buy', 1, 100], ['sell', 1, 200]], {'cogs': 300, 'revenue': 700, 'inventory': 800, 'layers': [[8, 100]], 'rejected': [0, 1, 4]}], ['control 3', [['purchase_return', 1, 100], ['buy', 1, 100], ['sell', 3, 200], ['buy', 10, 100], ['buy', 5, 100], ['sell', 2, 200], ['purchase_return', 2, 150]], {'cogs': 200, 'revenue': 400, 'inventory': 1400, 'layers': [[14, 100]], 'rejected': [0, 2, 6]}], ['control 4', [['buy', 10, 120], ['buy', 1, 120], ['purchase_return', 2, 150], ['sell', 2, 250], ['buy', 1, 100]], {'cogs': 240, 'revenue': 500, 'inventory': 1180, 'layers': [[9, 120], [1, 100]], 'rejected': [2]}], ['control 5', [['sell', 6, 250], ['purchase_return', 2, 100], ['sell', 12, 250], ['sell', 3, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3]}], ['control 6', [['sell', 6, 250], ['sell', 3, 250], ['sell', 12, 200], ['sell', 1, 250], ['sell', 3, 250], ['purchase_return', 5, 150], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4, 5, 6]}]]]\nfor label, args, expected in fixtures[N-1]:\n    try:\n        actual = solve(args)\n    except Exception as exc:\n        actual = 'raised ' + type(exc).__name__\n    check(label, actual, 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":"2bcd62ab5cf427f5f362cc3ce859eb3e98083fdba65408f578f4f4ee3998026b","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    layers = []\n    cogs = revenue = 0\n    short = []\n    for i, ev in enumerate(x):\n        if ev[0] == 'buy':\n            if layers and layers[-1][1] == ev[2]:\n                layers[-1][0] += ev[1]\n            else:\n                layers.append([ev[1], ev[2]])\n        elif ev[0] == 'sell':\n            qty = ev[1]\n            if qty > sum(l[0] for l in layers):\n                short.append(i)\n                continue\n            revenue += qty * ev[2]\n            while qty:\n                head = layers[0]\n                take = min(qty, head[0])\n                cogs += take * head[1]\n                head[0] -= take\n                qty -= take\n                if head[0] == 0:\n                    layers.pop(0)\n        elif ev[0] == 'purchase_return':\n            match = [l for l in layers if l[1] == ev[2] and l[0] >= ev[1]]\n            if not match:\n                short.append(i)\n                continue\n            match[0][0] -= ev[1]\n            if match[0][0] == 0:\n                layers.remove(match[0])\n    inventory = sum(q * c for q, c in layers)\n    return {'cogs': cogs, 'revenue': revenue, 'inventory': inventory, 'layers': layers, 'rejected': short}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: return layer selection', [['buy', 5, 100], ['buy', 10, 100], ['sell', 12, 200], ['buy', 10, 120], ['sell', 1, 250], ['buy', 1, 120], ['buy', 1, 100], ['buy', 2, 100], ['purchase_return', 1, 100], ['buy', 10, 150]], {'cogs': 1300, 'revenue': 2650, 'inventory': 3220, 'layers': [[2, 100], [11, 120], [2, 100], [10, 150]], 'rejected': []}], ['control 1', [['sell', 3, 250], ['buy', 2, 120], ['purchase_return', 1, 100], ['buy', 5, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 980, 'layers': [[2, 120], [5, 100], [2, 120]], 'rejected': [0, 2]}], ['control 2', [['buy', 5, 150], ['sell', 3, 250], ['sell', 2, 250], ['buy', 10, 100], ['buy', 10, 120], ['purchase_return', 2, 100], ['purchase_return', 5, 100], ['buy', 10, 150], ['buy', 2, 120], ['purchase_return', 5, 100]], {'cogs': 750, 'revenue': 1250, 'inventory': 3240, 'layers': [[3, 100], [10, 120], [10, 150], [2, 120]], 'rejected': [9]}], ['control 3', [['buy', 1, 120], ['sell', 12, 200], ['sell', 2, 250], ['sell', 2, 250]], {'cogs': 0, 'revenue': 0, 'inventory': 120, 'layers': [[1, 120]], 'rejected': [1, 2, 3]}], ['control 4', [['buy', 5, 100], ['sell', 3, 250], ['sell', 12, 200]], {'cogs': 300, 'revenue': 750, 'inventory': 200, 'layers': [[2, 100]], 'rejected': [2]}], ['control 5', [['buy', 10, 120], ['purchase_return', 1, 100], ['sell', 1, 200], ['purchase_return', 2, 100], ['sell', 2, 200], ['purchase_return', 1, 120], ['buy', 10, 120], ['buy', 5, 120]], {'cogs': 360, 'revenue': 600, 'inventory': 2520, 'layers': [[21, 120]], 'rejected': [1, 3]}], ['control 6', [['sell', 12, 250], ['sell', 12, 200], ['buy', 5, 100], ['sell', 12, 250], ['sell', 12, 200], ['purchase_return', 1, 100], ['sell', 3, 250]], {'cogs': 300, 'revenue': 750, 'inventory': 100, 'layers': [[1, 100]], 'rejected': [0, 1, 3, 4]}]], [['regression: return layer selection', [['buy', 10, 100], ['purchase_return', 1, 100], ['buy', 5, 150], ['buy', 10, 100], ['purchase_return', 2, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 2690, 'layers': [[9, 100], [5, 150], [8, 100], [2, 120]], 'rejected': []}], ['control 1', [['sell', 12, 200], ['sell', 6, 250], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2]}], ['control 2', [['buy', 10, 100], ['sell', 12, 250], ['sell', 2, 250], ['sell', 12, 200], ['sell', 6, 200], ['buy', 1, 150], ['buy', 2, 120]], {'cogs': 800, 'revenue': 1700, 'inventory': 590, 'layers': [[2, 100], [1, 150], [2, 120]], 'rejected': [1, 3]}], ['control 3', [['purchase_return', 2, 100], ['sell', 12, 250], ['buy', 10, 100], ['buy', 1, 100], ['sell', 6, 200]], {'cogs': 600, 'revenue': 1200, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [0, 1]}], ['control 4', [['buy', 10, 150], ['sell', 3, 250], ['sell', 12, 200], ['buy', 5, 120], ['sell', 3, 200], ['buy', 1, 100], ['sell', 1, 250], ['buy', 2, 100], ['sell', 12, 200], ['buy', 10, 100]], {'cogs': 1050, 'revenue': 1600, 'inventory': 2350, 'layers': [[3, 150], [5, 120], [13, 100]], 'rejected': [2, 8]}], ['control 5', [['sell', 2, 250], ['purchase_return', 1, 150], ['buy', 10, 100], ['buy', 10, 150], ['buy', 2, 150], ['buy', 2, 100], ['sell', 2, 250], ['sell', 3, 250], ['buy', 5, 120]], {'cogs': 500, 'revenue': 1250, 'inventory': 3100, 'layers': [[5, 100], [12, 150], [2, 100], [5, 120]], 'rejected': [0, 1]}], ['control 6', [['buy', 10, 120], ['sell', 1, 200], ['buy', 10, 120], ['buy', 2, 120], ['sell', 6, 250], ['buy', 1, 100], ['buy', 2, 150], ['sell', 1, 250]], {'cogs': 960, 'revenue': 1950, 'inventory': 2080, 'layers': [[14, 120], [1, 100], [2, 150]], 'rejected': []}]], [['regression: return layer selection', [['sell', 2, 250], ['sell', 3, 250], ['sell', 1, 200], ['buy', 5, 100], ['buy', 10, 150], ['buy', 10, 100], ['purchase_return', 5, 100], ['sell', 3, 250], ['buy', 1, 100]], {'cogs': 300, 'revenue': 750, 'inventory': 2300, 'layers': [[2, 100], [10, 150], [6, 100]], 'rejected': [0, 1, 2]}], ['control 1', [['purchase_return', 5, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 320, 'layers': [[2, 100], [1, 120]], 'rejected': [0, 1]}], ['control 2', [['sell', 1, 250], ['buy', 5, 100], ['buy', 10, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 10, 100], ['sell', 6, 200], ['buy', 1, 120], ['buy', 10, 150], ['buy', 10, 150]], {'cogs': 800, 'revenue': 1700, 'inventory': 5020, 'layers': [[19, 100], [1, 120], [20, 150]], 'rejected': [0]}], ['control 3', [['purchase_return', 1, 100], ['sell', 1, 200], ['sell', 1, 250], ['sell', 3, 200], ['sell', 6, 200], ['buy', 10, 120], ['buy', 2, 150], ['buy', 2, 100], ['purchase_return', 2, 150]], {'cogs': 0, 'revenue': 0, 'inventory': 1400, 'layers': [[10, 120], [2, 100]], 'rejected': [0, 1, 2, 3, 4]}], ['control 4', [['buy', 1, 100], ['purchase_return', 5, 100], ['sell', 3, 200], ['buy', 1, 150], ['buy', 1, 150], ['sell', 3, 200], ['buy', 2, 120], ['buy', 10, 120]], {'cogs': 400, 'revenue': 600, 'inventory': 1440, 'layers': [[12, 120]], 'rejected': [1, 2]}], ['control 5', [['purchase_return', 1, 100], ['buy', 2, 150], ['sell', 6, 200], ['sell', 2, 250], ['buy', 5, 120], ['buy', 5, 150], ['sell', 1, 250], ['purchase_return', 2, 100]], {'cogs': 420, 'revenue': 750, 'inventory': 1230, 'layers': [[4, 120], [5, 150]], 'rejected': [0, 2, 7]}], ['control 6', [['buy', 2, 120], ['purchase_return', 1, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 340, 'revenue': 600, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [1]}]], [['regression: return layer selection', [['buy', 5, 100], ['sell', 1, 250], ['buy', 1, 120], ['buy', 10, 100], ['buy', 5, 100], ['buy', 10, 100], ['purchase_return', 1, 100]], {'cogs': 100, 'revenue': 250, 'inventory': 2920, 'layers': [[4, 100], [1, 120], [24, 100]], 'rejected': []}], ['control 1', [['sell', 1, 250], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 120], ['buy', 5, 150], ['sell', 3, 200], ['sell', 1, 200], ['sell', 6, 250], ['buy', 2, 120]], {'cogs': 540, 'revenue': 800, 'inventory': 690, 'layers': [[3, 150], [2, 120]], 'rejected': [0, 1, 2, 7]}], ['control 2', [['buy', 1, 100], ['purchase_return', 2, 150], ['purchase_return', 1, 120], ['purchase_return', 2, 100], ['buy', 5, 100], ['buy', 10, 100], ['buy', 1, 150], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1870, 'layers': [[16, 100], [1, 150], [1, 120]], 'rejected': [1, 2, 3]}], ['control 3', [['buy', 2, 150], ['buy', 10, 120], ['sell', 1, 200], ['purchase_return', 5, 100], ['buy', 1, 100], ['buy', 2, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 540, 'revenue': 800, 'inventory': 1960, 'layers': [[8, 120], [1, 100], [2, 150], [6, 100]], 'rejected': [3]}], ['control 4', [['sell', 12, 200], ['sell', 12, 250], ['buy', 2, 150], ['sell', 12, 250], ['buy', 2, 150], ['buy', 1, 100], ['buy', 5, 120], ['buy', 10, 150], ['sell', 3, 200], ['buy', 2, 150]], {'cogs': 450, 'revenue': 600, 'inventory': 2650, 'layers': [[1, 150], [1, 100], [5, 120], [12, 150]], 'rejected': [0, 1, 3]}], ['control 5', [['buy', 1, 100], ['buy', 2, 120], ['buy', 2, 120], ['sell', 3, 200], ['sell', 12, 200], ['buy', 5, 120], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 100]], {'cogs': 700, 'revenue': 1200, 'inventory': 680, 'layers': [[4, 120], [2, 100]], 'rejected': [4, 7]}], ['control 6', [['sell', 3, 250], ['purchase_return', 1, 150], ['purchase_return', 2, 120], ['sell', 6, 200], ['purchase_return', 2, 100]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4]}]], [['regression: return layer selection', [['sell', 2, 200], ['buy', 2, 100], ['buy', 5, 100], ['buy', 10, 120], ['buy', 1, 100], ['purchase_return', 1, 100], ['buy', 2, 100], ['buy', 2, 150]], {'cogs': 0, 'revenue': 0, 'inventory': 2400, 'layers': [[7, 100], [10, 120], [2, 100], [2, 150]], 'rejected': [0]}], ['control 1', [['buy', 10, 100], ['buy', 10, 150], ['purchase_return', 5, 100], ['sell', 6, 200], ['sell', 3, 200], ['sell', 2, 200]], {'cogs': 1400, 'revenue': 2200, 'inventory': 600, 'layers': [[4, 150]], 'rejected': []}], ['control 2', [['purchase_return', 2, 100], ['sell', 1, 200], ['buy', 5, 100], ['buy', 5, 100], ['purchase_return', 1, 150], ['sell', 2, 250], ['buy', 1, 100], ['sell', 1, 200]], {'cogs': 300, 'revenue': 700, 'inventory': 800, 'layers': [[8, 100]], 'rejected': [0, 1, 4]}], ['control 3', [['purchase_return', 1, 100], ['buy', 1, 100], ['sell', 3, 200], ['buy', 10, 100], ['buy', 5, 100], ['sell', 2, 200], ['purchase_return', 2, 150]], {'cogs': 200, 'revenue': 400, 'inventory': 1400, 'layers': [[14, 100]], 'rejected': [0, 2, 6]}], ['control 4', [['buy', 10, 120], ['buy', 1, 120], ['purchase_return', 2, 150], ['sell', 2, 250], ['buy', 1, 100]], {'cogs': 240, 'revenue': 500, 'inventory': 1180, 'layers': [[9, 120], [1, 100]], 'rejected': [2]}], ['control 5', [['sell', 6, 250], ['purchase_return', 2, 100], ['sell', 12, 250], ['sell', 3, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3]}], ['control 6', [['sell', 6, 250], ['sell', 3, 250], ['sell', 12, 200], ['sell', 1, 250], ['sell', 3, 250], ['purchase_return', 5, 150], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4, 5, 6]}]]]\nfor label, args, expected in fixtures[N-1]:\n    try:\n        actual = solve(args)\n    except Exception as exc:\n        actual = 'raised ' + type(exc).__name__\n    check(label, actual, 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":"92ee1c9184bef5579deacc2c53c1f32ff21a7d3c908388d31ea1c5b12c5f4466","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    layers = []\n    cogs = revenue = 0\n    short = []\n    for i, ev in enumerate(x):\n        if ev[0] == 'buy':\n            if layers and layers[-1][1] == ev[2]:\n                layers[-1][0] += ev[1]\n            else:\n                layers.append([ev[1], ev[2]])\n        elif ev[0] == 'sell':\n            qty = ev[1]\n            if qty > sum(l[0] for l in layers):\n                short.append(i)\n                continue\n            revenue += qty * ev[2]\n            while qty:\n                head = layers[0]\n                take = min(qty, head[0])\n                cogs += take * head[1]\n                head[0] -= take\n                qty -= take\n                if head[0] == 0:\n                    layers.pop(0)\n        elif ev[0] == 'purchase_return':\n            match = [l for l in layers if l[1] == ev[2] and l[0] >= ev[1]]\n            if not match:\n                short.append(i)\n                continue\n            match[-1][0] -= ev[1]\n            if match[-1][0] == 0:\n                layers.remove(match[-1])\n    inventory = sum(q * c for q, c in layers)\n    return {'cogs': cogs, 'revenue': revenue, 'inventory': inventory, 'layers': layers, 'rejected': short}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: return layer selection', [['buy', 5, 100], ['buy', 10, 100], ['sell', 12, 200], ['buy', 10, 120], ['sell', 1, 250], ['buy', 1, 120], ['buy', 1, 100], ['buy', 2, 100], ['purchase_return', 1, 100], ['buy', 10, 150]], {'cogs': 1300, 'revenue': 2650, 'inventory': 3220, 'layers': [[2, 100], [11, 120], [2, 100], [10, 150]], 'rejected': []}], ['control 1', [['sell', 3, 250], ['buy', 2, 120], ['purchase_return', 1, 100], ['buy', 5, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 980, 'layers': [[2, 120], [5, 100], [2, 120]], 'rejected': [0, 2]}], ['control 2', [['buy', 5, 150], ['sell', 3, 250], ['sell', 2, 250], ['buy', 10, 100], ['buy', 10, 120], ['purchase_return', 2, 100], ['purchase_return', 5, 100], ['buy', 10, 150], ['buy', 2, 120], ['purchase_return', 5, 100]], {'cogs': 750, 'revenue': 1250, 'inventory': 3240, 'layers': [[3, 100], [10, 120], [10, 150], [2, 120]], 'rejected': [9]}], ['control 3', [['buy', 1, 120], ['sell', 12, 200], ['sell', 2, 250], ['sell', 2, 250]], {'cogs': 0, 'revenue': 0, 'inventory': 120, 'layers': [[1, 120]], 'rejected': [1, 2, 3]}], ['control 4', [['buy', 5, 100], ['sell', 3, 250], ['sell', 12, 200]], {'cogs': 300, 'revenue': 750, 'inventory': 200, 'layers': [[2, 100]], 'rejected': [2]}], ['control 5', [['buy', 10, 120], ['purchase_return', 1, 100], ['sell', 1, 200], ['purchase_return', 2, 100], ['sell', 2, 200], ['purchase_return', 1, 120], ['buy', 10, 120], ['buy', 5, 120]], {'cogs': 360, 'revenue': 600, 'inventory': 2520, 'layers': [[21, 120]], 'rejected': [1, 3]}], ['control 6', [['sell', 12, 250], ['sell', 12, 200], ['buy', 5, 100], ['sell', 12, 250], ['sell', 12, 200], ['purchase_return', 1, 100], ['sell', 3, 250]], {'cogs': 300, 'revenue': 750, 'inventory': 100, 'layers': [[1, 100]], 'rejected': [0, 1, 3, 4]}]], [['regression: return layer selection', [['buy', 10, 100], ['purchase_return', 1, 100], ['buy', 5, 150], ['buy', 10, 100], ['purchase_return', 2, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 2690, 'layers': [[9, 100], [5, 150], [8, 100], [2, 120]], 'rejected': []}], ['control 1', [['sell', 12, 200], ['sell', 6, 250], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2]}], ['control 2', [['buy', 10, 100], ['sell', 12, 250], ['sell', 2, 250], ['sell', 12, 200], ['sell', 6, 200], ['buy', 1, 150], ['buy', 2, 120]], {'cogs': 800, 'revenue': 1700, 'inventory': 590, 'layers': [[2, 100], [1, 150], [2, 120]], 'rejected': [1, 3]}], ['control 3', [['purchase_return', 2, 100], ['sell', 12, 250], ['buy', 10, 100], ['buy', 1, 100], ['sell', 6, 200]], {'cogs': 600, 'revenue': 1200, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [0, 1]}], ['control 4', [['buy', 10, 150], ['sell', 3, 250], ['sell', 12, 200], ['buy', 5, 120], ['sell', 3, 200], ['buy', 1, 100], ['sell', 1, 250], ['buy', 2, 100], ['sell', 12, 200], ['buy', 10, 100]], {'cogs': 1050, 'revenue': 1600, 'inventory': 2350, 'layers': [[3, 150], [5, 120], [13, 100]], 'rejected': [2, 8]}], ['control 5', [['sell', 2, 250], ['purchase_return', 1, 150], ['buy', 10, 100], ['buy', 10, 150], ['buy', 2, 150], ['buy', 2, 100], ['sell', 2, 250], ['sell', 3, 250], ['buy', 5, 120]], {'cogs': 500, 'revenue': 1250, 'inventory': 3100, 'layers': [[5, 100], [12, 150], [2, 100], [5, 120]], 'rejected': [0, 1]}], ['control 6', [['buy', 10, 120], ['sell', 1, 200], ['buy', 10, 120], ['buy', 2, 120], ['sell', 6, 250], ['buy', 1, 100], ['buy', 2, 150], ['sell', 1, 250]], {'cogs': 960, 'revenue': 1950, 'inventory': 2080, 'layers': [[14, 120], [1, 100], [2, 150]], 'rejected': []}]], [['regression: return layer selection', [['sell', 2, 250], ['sell', 3, 250], ['sell', 1, 200], ['buy', 5, 100], ['buy', 10, 150], ['buy', 10, 100], ['purchase_return', 5, 100], ['sell', 3, 250], ['buy', 1, 100]], {'cogs': 300, 'revenue': 750, 'inventory': 2300, 'layers': [[2, 100], [10, 150], [6, 100]], 'rejected': [0, 1, 2]}], ['control 1', [['purchase_return', 5, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 320, 'layers': [[2, 100], [1, 120]], 'rejected': [0, 1]}], ['control 2', [['sell', 1, 250], ['buy', 5, 100], ['buy', 10, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 10, 100], ['sell', 6, 200], ['buy', 1, 120], ['buy', 10, 150], ['buy', 10, 150]], {'cogs': 800, 'revenue': 1700, 'inventory': 5020, 'layers': [[19, 100], [1, 120], [20, 150]], 'rejected': [0]}], ['control 3', [['purchase_return', 1, 100], ['sell', 1, 200], ['sell', 1, 250], ['sell', 3, 200], ['sell', 6, 200], ['buy', 10, 120], ['buy', 2, 150], ['buy', 2, 100], ['purchase_return', 2, 150]], {'cogs': 0, 'revenue': 0, 'inventory': 1400, 'layers': [[10, 120], [2, 100]], 'rejected': [0, 1, 2, 3, 4]}], ['control 4', [['buy', 1, 100], ['purchase_return', 5, 100], ['sell', 3, 200], ['buy', 1, 150], ['buy', 1, 150], ['sell', 3, 200], ['buy', 2, 120], ['buy', 10, 120]], {'cogs': 400, 'revenue': 600, 'inventory': 1440, 'layers': [[12, 120]], 'rejected': [1, 2]}], ['control 5', [['purchase_return', 1, 100], ['buy', 2, 150], ['sell', 6, 200], ['sell', 2, 250], ['buy', 5, 120], ['buy', 5, 150], ['sell', 1, 250], ['purchase_return', 2, 100]], {'cogs': 420, 'revenue': 750, 'inventory': 1230, 'layers': [[4, 120], [5, 150]], 'rejected': [0, 2, 7]}], ['control 6', [['buy', 2, 120], ['purchase_return', 1, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 340, 'revenue': 600, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [1]}]], [['regression: return layer selection', [['buy', 5, 100], ['sell', 1, 250], ['buy', 1, 120], ['buy', 10, 100], ['buy', 5, 100], ['buy', 10, 100], ['purchase_return', 1, 100]], {'cogs': 100, 'revenue': 250, 'inventory': 2920, 'layers': [[4, 100], [1, 120], [24, 100]], 'rejected': []}], ['control 1', [['sell', 1, 250], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 120], ['buy', 5, 150], ['sell', 3, 200], ['sell', 1, 200], ['sell', 6, 250], ['buy', 2, 120]], {'cogs': 540, 'revenue': 800, 'inventory': 690, 'layers': [[3, 150], [2, 120]], 'rejected': [0, 1, 2, 7]}], ['control 2', [['buy', 1, 100], ['purchase_return', 2, 150], ['purchase_return', 1, 120], ['purchase_return', 2, 100], ['buy', 5, 100], ['buy', 10, 100], ['buy', 1, 150], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1870, 'layers': [[16, 100], [1, 150], [1, 120]], 'rejected': [1, 2, 3]}], ['control 3', [['buy', 2, 150], ['buy', 10, 120], ['sell', 1, 200], ['purchase_return', 5, 100], ['buy', 1, 100], ['buy', 2, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 540, 'revenue': 800, 'inventory': 1960, 'layers': [[8, 120], [1, 100], [2, 150], [6, 100]], 'rejected': [3]}], ['control 4', [['sell', 12, 200], ['sell', 12, 250], ['buy', 2, 150], ['sell', 12, 250], ['buy', 2, 150], ['buy', 1, 100], ['buy', 5, 120], ['buy', 10, 150], ['sell', 3, 200], ['buy', 2, 150]], {'cogs': 450, 'revenue': 600, 'inventory': 2650, 'layers': [[1, 150], [1, 100], [5, 120], [12, 150]], 'rejected': [0, 1, 3]}], ['control 5', [['buy', 1, 100], ['buy', 2, 120], ['buy', 2, 120], ['sell', 3, 200], ['sell', 12, 200], ['buy', 5, 120], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 100]], {'cogs': 700, 'revenue': 1200, 'inventory': 680, 'layers': [[4, 120], [2, 100]], 'rejected': [4, 7]}], ['control 6', [['sell', 3, 250], ['purchase_return', 1, 150], ['purchase_return', 2, 120], ['sell', 6, 200], ['purchase_return', 2, 100]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4]}]], [['regression: return layer selection', [['sell', 2, 200], ['buy', 2, 100], ['buy', 5, 100], ['buy', 10, 120], ['buy', 1, 100], ['purchase_return', 1, 100], ['buy', 2, 100], ['buy', 2, 150]], {'cogs': 0, 'revenue': 0, 'inventory': 2400, 'layers': [[7, 100], [10, 120], [2, 100], [2, 150]], 'rejected': [0]}], ['control 1', [['buy', 10, 100], ['buy', 10, 150], ['purchase_return', 5, 100], ['sell', 6, 200], ['sell', 3, 200], ['sell', 2, 200]], {'cogs': 1400, 'revenue': 2200, 'inventory': 600, 'layers': [[4, 150]], 'rejected': []}], ['control 2', [['purchase_return', 2, 100], ['sell', 1, 200], ['buy', 5, 100], ['buy', 5, 100], ['purchase_return', 1, 150], ['sell', 2, 250], ['buy', 1, 100], ['sell', 1, 200]], {'cogs': 300, 'revenue': 700, 'inventory': 800, 'layers': [[8, 100]], 'rejected': [0, 1, 4]}], ['control 3', [['purchase_return', 1, 100], ['buy', 1, 100], ['sell', 3, 200], ['buy', 10, 100], ['buy', 5, 100], ['sell', 2, 200], ['purchase_return', 2, 150]], {'cogs': 200, 'revenue': 400, 'inventory': 1400, 'layers': [[14, 100]], 'rejected': [0, 2, 6]}], ['control 4', [['buy', 10, 120], ['buy', 1, 120], ['purchase_return', 2, 150], ['sell', 2, 250], ['buy', 1, 100]], {'cogs': 240, 'revenue': 500, 'inventory': 1180, 'layers': [[9, 120], [1, 100]], 'rejected': [2]}], ['control 5', [['sell', 6, 250], ['purchase_return', 2, 100], ['sell', 12, 250], ['sell', 3, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3]}], ['control 6', [['sell', 6, 250], ['sell', 3, 250], ['sell', 12, 200], ['sell', 1, 250], ['sell', 3, 250], ['purchase_return', 5, 150], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4, 5, 6]}]]]\nfor label, args, expected in fixtures[N-1]:\n    try:\n        actual = solve(args)\n    except Exception as exc:\n        actual = 'raised ' + type(exc).__name__\n    check(label, actual, 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 bounded teaching model with stipulated toy bookkeeping rules stated in the contract; amounts are integer cents; it makes no claim of conformance to any accounting standard or product. 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-double-entry-ledger-accounting-fifo-perpetual-inventory-return-layer-selection","generated_at":"2026-09-29T14:46:23.459326+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Ledger software must keep debits equal to credits and apply normal-balance, period and cutoff rules exactly; small sign or boundary slips silently misstate financial statements.","repair":"Return goods against the newest layer at the returned cost.","root_cause":"The oldest qualifying layer is debited instead of the newest.","sha256":"fc5c1edd474dc8bc30caca80880ed68d8ce152f87b0ea27b61a7e9c299e4eeb0","title":"FIFO perpetual inventory journal: return layer selection · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":41.95,"exit_code":1,"observations":[{"actual":{"cogs":1300,"inventory":3220,"layers":[[1,100],[11,120],[3,100],[10,150]],"rejected":[],"revenue":2650},"check":"regression: return layer selection","expected":{"cogs":1300,"inventory":3220,"layers":[[2,100],[11,120],[2,100],[10,150]],"rejected":[],"revenue":2650},"passed":false},{"actual":{"cogs":0,"inventory":980,"layers":[[2,120],[5,100],[2,120]],"rejected":[0,2],"revenue":0},"check":"control 1","expected":{"cogs":0,"inventory":980,"layers":[[2,120],[5,100],[2,120]],"rejected":[0,2],"revenue":0},"passed":true},{"actual":{"cogs":750,"inventory":3240,"layers":[[3,100],[10,120],[10,150],[2,120]],"rejected":[9],"revenue":1250},"check":"control 2","expected":{"cogs":750,"inventory":3240,"layers":[[3,100],[10,120],[10,150],[2,120]],"rejected":[9],"revenue":1250},"passed":true},{"actual":{"cogs":0,"inventory":120,"layers":[[1,120]],"rejected":[1,2,3],"revenue":0},"check":"control 3","expected":{"cogs":0,"inventory":120,"layers":[[1,120]],"rejected":[1,2,3],"revenue":0},"passed":true},{"actual":{"cogs":300,"inventory":200,"layers":[[2,100]],"rejected":[2],"revenue":750},"check":"control 4","expected":{"cogs":300,"inventory":200,"layers":[[2,100]],"rejected":[2],"revenue":750},"passed":true},{"actual":{"cogs":360,"inventory":2520,"layers":[[21,120]],"rejected":[1,3],"revenue":600},"check":"control 5","expected":{"cogs":360,"inventory":2520,"layers":[[21,120]],"rejected":[1,3],"revenue":600},"passed":true},{"actual":{"cogs":300,"inventory":100,"layers":[[1,100]],"rejected":[0,1,3,4],"revenue":750},"check":"control 6","expected":{"cogs":300,"inventory":100,"layers":[[1,100]],"rejected":[0,1,3,4],"revenue":750},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: return layer selection\", \"actual\": {\"cogs\": 1300, \"revenue\": 2650, \"inventory\": 3220, \"layers\": [[1, 100], [11, 120], [3, 100], [10, 150]], \"rejected\": []}, \"expected\": {\"cogs\": 1300, \"revenue\": 2650, \"inventory\": 3220, \"layers\": [[2, 100], [11, 120], [2, 100], [10, 150]], \"rejected\": []}, \"passed\": false}, {\"check\": \"control 1\", \"actual\": {\"cogs\": 0, \"revenue\": 0, \"inventory\": 980, \"layers\": [[2, 120], [5, 100], [2, 120]], \"rejected\": [0, 2]}, \"expected\": {\"cogs\": 0, \"revenue\": 0, \"inventory\": 980, \"layers\": [[2, 120], [5, 100], [2, 120]], \"rejected\": [0, 2]}, \"passed\": true}, {\"check\": \"control 2\", \"actual\": {\"cogs\": 750, \"revenue\": 1250, \"inventory\": 3240, \"layers\": [[3, 100], [10, 120], [10, 150], [2, 120]], \"rejected\": [9]}, \"expected\": {\"cogs\": 750, \"revenue\": 1250, \"inventory\": 3240, \"layers\": [[3, 100], [10, 120], [10, 150], [2, 120]], \"rejected\": [9]}, \"passed\": true}, {\"check\": \"control 3\", \"actual\": {\"cogs\": 0, \"revenue\": 0, \"inventory\": 120, \"layers\": [[1, 120]], \"rejected\": [1, 2, 3]}, \"expected\": {\"cogs\": 0, \"revenue\": 0, \"inventory\": 120, \"layers\": [[1, 120]], \"rejected\": [1, 2, 3]}, \"passed\": true}, {\"check\": \"control 4\", \"actual\": {\"cogs\": 300, \"revenue\": 750, \"inventory\": 200, \"layers\": [[2, 100]], \"rejected\": [2]}, \"expected\": {\"cogs\": 300, \"revenue\": 750, \"inventory\": 200, \"layers\": [[2, 100]], \"rejected\": [2]}, \"passed\": true}, {\"check\": \"control 5\", \"actual\": {\"cogs\": 360, \"revenue\": 600, \"inventory\": 2520, \"layers\": [[21, 120]], \"rejected\": [1, 3]}, \"expected\": {\"cogs\": 360, \"revenue\": 600, \"inventory\": 2520, \"layers\": [[21, 120]], \"rejected\": [1, 3]}, \"passed\": true}, {\"check\": \"control 6\", \"actual\": {\"cogs\": 300, \"revenue\": 750, \"inventory\": 100, \"layers\": [[1, 100]], \"rejected\": [0, 1, 3, 4]}, \"expected\": {\"cogs\": 300, \"revenue\": 750, \"inventory\": 100, \"layers\": [[1, 100]], \"rejected\": [0, 1, 3, 4]}, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.763,"exit_code":1,"observations":[{"actual":{"cogs":1300,"inventory":3220,"layers":[[1,100],[11,120],[3,100],[10,150]],"rejected":[],"revenue":2650},"check":"regression: return layer selection","expected":{"cogs":1300,"inventory":3220,"layers":[[2,100],[11,120],[2,100],[10,150]],"rejected":[],"revenue":2650},"passed":false},{"actual":{"cogs":0,"inventory":980,"layers":[[2,120],[5,100],[2,120]],"rejected":[0,2],"revenue":0},"check":"control 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