Random Dance Canon Entry Plan Generator

Description

Assign supplied groups to distinct staggered offsets, delegate complete motif intervals to the canon core, and return an event grid with half-open activity and exits before tied entries.

A structured creative brief combines a supplied bank of fictional material with explicit sequencing, timing, or compatibility rules. IDs and tags make those constraints inspectable; they do not assess the artistic meaning or quality of a scene.

Assign supplied groups to distinct staggered offsets, delegate complete motif intervals to the canon core, and return an event grid with half-open activity and exits before tied entries.

When to use this tool

Use this generator to create reproducible dance canon entry plan cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply groups, offsets, motifCounts, availableCounts in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read entries, events, latestEndCount, peakActiveGroups. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Choose distinct feasible entry offsets and shuffle group IDs, then delegate fixed-duration canon intervals. Merge entries and exits in count order with exits first at ties, updating active groups and peak concurrency.^[1]

Delegated operations used for this result: canon offset counts planner. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
groups	Groups	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 16.
offsets	Offsets	Required. Array of Safe integer; minimum 0; maximum 9007199254740991.; minimum items 1; maximum items 32.
motifCounts	Motif Counts	Required. Safe integer; minimum 1; maximum 9007199254740991.
availableCounts	Available Counts	Required. Safe integer; minimum 1; maximum 9007199254740991.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
entries	Entries	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
entries[].groupId	Group Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
entries[].startCount	Start Count	Required. Safe integer; minimum 0; maximum 9007199254740991.
entries[].endCount	End Count	Required. Safe integer; minimum 1; maximum 9007199254740991.
events	Events	Required. Array of Object; no unknown keys.; minimum items 2; maximum items 32.
events[].count	Count	Required. Safe integer; minimum 0; maximum 9007199254740991.
events[].kind	Kind	Required. Text; one of "enter", "exit".
events[].groupId	Group Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
events[].activeGroupsAfter	Active Groups After	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 0; maximum items 16.
latestEndCount	Latest End Count	Required. Safe integer; minimum 1; maximum 9007199254740991.
peakActiveGroups	Peak Active Groups	Required. Safe integer; minimum 1; maximum 16.
randomness	Replay source and normalized 32-bit seed, or null seed for an advancing context source.	Required. Object; no unknown keys.
randomness.seed	Optional safe-integer replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "groups": [
      "a",
      "b"
    ],
    "offsets": [
      0,
      2
    ],
    "motifCounts": 4,
    "availableCounts": 6,
    "seed": 42
  },
  "output": {
    "entries": [
      {
        "groupId": "b",
        "startCount": 0,
        "endCount": 4
      },
      {
        "groupId": "a",
        "startCount": 2,
        "endCount": 6
      }
    ],
    "events": [
      {
        "count": 0,
        "kind": "enter",
        "groupId": "b",
        "activeGroupsAfter": [
          "b"
        ]
      },
      {
        "count": 2,
        "kind": "enter",
        "groupId": "a",
        "activeGroupsAfter": [
          "b",
          "a"
        ]
      },
      {
        "count": 4,
        "kind": "exit",
        "groupId": "b",
        "activeGroupsAfter": [
          "a"
        ]
      },
      {
        "count": 6,
        "kind": "exit",
        "groupId": "a",
        "activeGroupsAfter": []
      }
    ],
    "latestEndCount": 6,
    "peakActiveGroups": 2,
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

End/start ties do not overlap under this event convention. Offsets must be strictly increasing in the input bank and every selected motif must finish within availableCounts. This is choreography timing, not collision or stage-safety planning.
A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Dance Canon Entry Plan Generator — tool.io
Calculation or validation: dance/canon-offset-counts-planner — tool.io

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