Random Color Vision Deficiency Palette Generator

Description

Generate distinct sRGB hex colors separated by a caller threshold in OKLab after each existing full-severity protan/deutan/tritan simulation, with a finite attempt limit and no individual accessibility guarantee.

Procedural artwork expresses a visual idea through coordinates, repeated motifs, colors, or explicit placement constraints. A coordinate plan can be checked and reproduced before rendering, while physical material behavior and fabrication require separate information.

When to use this tool

Use this generator to create reproducible color vision deficiency palette 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 count, minimumDistance, maximumAttempts 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 colors, pairDistances, minimumPairDistance, attempts. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Draw distinct RGB candidates, simulate protan, deutan, and tritan views, and convert each simulated view to Oklab. Accept a candidate only when its minimum Euclidean distance to every accepted color across all three simulations meets minimumDistance.^[1]

Input field	Meaning and units	Accepted input
count	Count	Required. Safe integer; minimum 2; maximum 32.
minimumDistance	Minimum Distance	Required. Finite number; minimum 0.
maximumAttempts	Maximum Attempts	Required. Safe integer; minimum 2; maximum 8192.
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
colors	Colors	Required. Array of Text; minimum UTF-16 length 7; maximum UTF-16 length 7.; minimum items 2; maximum items 32.
pairDistances	Pair Distances	Required. Array of Array of Finite number; minimum 0.; minimum items 2; maximum items 32.; minimum items 2; maximum items 32.
minimumPairDistance	Minimum Pair Distance	Required. Finite number; minimum 0.
attempts	Attempts	Required. Safe integer; minimum 2; maximum 8192.
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": {
    "count": 3,
    "minimumDistance": 0.08,
    "maximumAttempts": 256,
    "seed": 42
  },
  "output": {
    "colors": [
      "#99E1EF",
      "#72C32B",
      "#DA3B32"
    ],
    "pairDistances": [
      [
        0,
        0.1304929930087915,
        0.29617685717787195
      ],
      [
        0.1304929930087915,
        0,
        0.13717839825762446
      ],
      [
        0.29617685717787195,
        0.13717839825762446,
        0
      ]
    ],
    "minimumPairDistance": 0.1304929930087915,
    "attempts": 3,
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Simulation is an approximation, not a vision assessment or a guarantee for every observer. Pair distances concern the simulated metric, not contrast against a background; finite attempt limits can exhaust the search.
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 Color Vision Deficiency Palette Generator — tool.io

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