Random Treemap Hierarchy Dataset Generator

Description

Generate positive leaf weights and exact ancestor totals in a supplied rooted hierarchy; treemap packing belongs to the renderer.

A chart dataset contains values plus the labels, grouping, totals, or missing-value conventions needed to interpret them. Separating the numeric construction from plotting makes conservation rules and deliberate edge cases visible before a chart is rendered.

Generate positive leaf weights and exact ancestor totals in a supplied rooted hierarchy; treemap packing belongs to the renderer.

When to use this tool

Use this generator to create reproducible treemap hierarchy dataset 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 nodes, minimum, maximum 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 nodes. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Assign random positive values to leaves of the supplied rooted hierarchy and compute each ancestor as the exact sum of its descendant leaves. Return node records in supplied order with their resulting values.^[1]

Input field	Meaning and units	Accepted input
nodes	Nodes	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 32.
nodes[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
nodes[].parent	Parent	Required. Safe integer; minimum -1; maximum 30.
nodes[].leaf	Leaf	Required. Boolean.
minimum	Minimum	Required. Safe integer; minimum 1; maximum 9007199254740990.
maximum	Maximum	Required. Safe integer; minimum 1; maximum 9007199254740990.
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
nodes	Nodes	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 32.
nodes[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
nodes[].parent	Parent	Required. Safe integer.
nodes[].value	Value	Required. Safe integer.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric 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": {
    "nodes": [
      {
        "id": "root",
        "parent": -1,
        "leaf": false
      },
      {
        "id": "a",
        "parent": 0,
        "leaf": true
      },
      {
        "id": "b",
        "parent": 0,
        "leaf": true
      }
    ],
    "minimum": 2,
    "maximum": 5,
    "seed": 42
  },
  "output": {
    "nodes": [
      {
        "id": "root",
        "parent": -1,
        "value": 7
      },
      {
        "id": "a",
        "parent": 0,
        "value": 4
      },
      {
        "id": "b",
        "parent": 0,
        "value": 3
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

The hierarchy must satisfy the helper’s rooted-tree validation. Values do not determine treemap rectangles; area packing, ordering and visual legibility belong to the renderer.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
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 Treemap Hierarchy Dataset Generator — tool.io

Similar or alternative tools

Random Sunburst Hierarchy Dataset Generator
Generate multilevel synthetic hierarchy totals, depths and parent shares with explicit empty terminal branches; radial layout is surface-owned.
Random Heatmap Block Structure Dataset Generator
Construct labeled heatmap blocks with bounded integer noise and optional mirrored symmetry, retaining the planted block mask.