Random Histogram Bin Edge Challenge Generator

Description

Generate observations exactly on or one supplied epsilon around explicit bin edges, retaining endpoint-policy membership and outside labels.

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 observations exactly on or one supplied epsilon around explicit bin edges, retaining endpoint-policy membership and outside labels.

When to use this tool

Use this generator to create reproducible histogram bin edge challenge 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 edges, epsilon, count, closed 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 observations, sorted, bins, counts. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Generate values at or around supplied histogram edges, classify each under the selected closure convention, and return per-observation bin indexes, counts, and sorted observations. Interior edges belong to the following bin in left-closed mode or preceding bin in right-closed mode; both outermost edges are included. A bin index of -1 means outside the edge span.^[1]

Delegated operations used for this result: sort numbers. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
edges	Edges	Required. Array of Safe integer.; minimum items 2; maximum items 17.
epsilon	Epsilon	Required. Safe integer; minimum 1.
count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 1; maximum 64.
closed	Closed	Required. Text; one of "left", "right".
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
observations	Observations	Required. Array of Safe integer.; minimum items 1; maximum items 64.
sorted	Sorted	Required. Array of Safe integer.; minimum items 1; maximum items 64.
bins	Bins	Required. Array of Safe integer.; minimum items 1; maximum items 64.
counts	Counts	Required. Array of Safe integer.; minimum items 1; maximum items 16.
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": {
    "edges": [
      0,
      10,
      20
    ],
    "epsilon": 1,
    "count": 2,
    "closed": "left",
    "seed": 42
  },
  "output": {
    "observations": [
      10,
      21
    ],
    "sorted": [
      10,
      21
    ],
    "bins": [
      1,
      -1
    ],
    "counts": [
      0,
      1
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Edge spacing and epsilon must keep comparisons meaningful. Out-of-range observations can be unbinned; counts need not sum to total draws. Read the explicit endpoint closure rather than assuming all bins include their right edges.
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 Histogram Bin Edge Challenge Generator — tool.io
Calculation or validation: list/sort-numbers — tool.io

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