Team alignment health check

The exact vocabulary the automated check enforces: keys, tokens, entry points, and worked examples. Generated from the verification source.

{
 "constants": {},
 "entryName": "score_alignment",
 "expectNote": "expect is the comparison-space value (your return value goes through normalize first when one is published)",
 "hiddenCaseCount": 9,
 "hiddenCaseNames": [
  "single_member",
  "exactly_neutral_not_below",
  "high_divergence",
  "fractional_rounding",
  "all_below_neutral",
  "tie_weakest_lowest_index",
  "mixed_means_and_spread",
  "empty_matrix",
  "ragged_row"
 ],
 "inputKeys": [
  "scale_max",
  "scale_min",
  "votes"
 ],
 "normalizeSource": "def _align_normalize(r):\n    try:\n        if isinstance(r, dict) and \"error\" in r:\n            return {\"error\": str(r[\"error\"])}\n        return {\"means\": [int(x) for x in r[\"means\"]], \"below_neutral\": [int(x) for x in r[\"below_neutral\"]],\n                \"divergence\": [int(x) for x in r[\"divergence\"]], \"weakest\": int(r[\"weakest\"])}\n    except Exception:\n        return repr(r)\n",
 "returnShapes": [
  [
   "below_neutral",
   "divergence",
   "means",
   "weakest"
  ],
  [
   "error"
  ]
 ],
 "signature": "def score_alignment(inp):",
 "submission": "python exposing the entry function; inp is one input object; graded on held-out cases",
 "tier": "calculator",
 "visibleCases": [
  {
   "expect": {
    "below_neutral": [
     3
    ],
    "divergence": [
     0,
     0,
     0,
     0
    ],
    "means": [
     200,
     100,
     0,
     -100
    ],
    "weakest": 3
   },
   "input": {
    "scale_max": 2,
    "scale_min": -2,
    "votes": [
     [
      2,
      1,
      0,
      -1
     ],
     [
      2,
      1,
      0,
      -1
     ]
    ]
   },
   "name": "even_split_one_gap"
  }
 ],
 "vocabulary": [
  "below_neutral",
  "divergence",
  "error",
  "invalid_matrix",
  "means",
  "votes",
  "weakest"
 ]
}