Random Mesh Color Pattern Generator

Description

Generate random hex control nodes and a shared-edge bilinear encoded-sRGB color grid via the existing interpolator, retaining its numeric precision and byte quantization; this is a rectangular mesh approximation, not a perceptual or linear-light blend.

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 mesh color pattern 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 controlRows, controlColumns, samplesPerCell 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 controlColors, colors. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Draw a rectangular grid of RGB control colors and bilinearly interpolate each encoded RGB channel across every cell at the requested sampling density. Convert interpolated channels to hexadecimal output colors.^[1]

Delegated operations used for this result: bilinear; rgb to hex. Their calculation or parsing conventions apply to the corresponding result fields.^{[2, 3]}

Input field	Meaning and units	Accepted input
controlRows	Control Rows	Required. Safe integer; minimum 2; maximum 8.
controlColumns	Control Columns	Required. Safe integer; minimum 2; maximum 8.
samplesPerCell	Samples Per Cell	Required. Safe integer; minimum 1; maximum 8.
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
controlColors	Control Colors	Required. Array of Array of Text; minimum UTF-16 length 7; maximum UTF-16 length 7.; minimum items 2; maximum items 8.; minimum items 2; maximum items 8.
colors	Colors	Required. Array of Array of Text; minimum UTF-16 length 7; maximum UTF-16 length 7.; minimum items 2; maximum items 57.; minimum items 2; maximum items 57.
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": {
    "controlRows": 2,
    "controlColumns": 2,
    "samplesPerCell": 2,
    "seed": 42
  },
  "output": {
    "controlColors": [
      [
        "#99E1EF",
        "#72C32B"
      ],
      [
        "#DA3B32",
        "#AB73B0"
      ]
    ],
    "colors": [
      [
        "#99E1EF",
        "#86D28D",
        "#72C32B"
      ],
      [
        "#BA8E91",
        "#A4957F",
        "#8F9B6E"
      ],
      [
        "#DA3B32",
        "#C35771",
        "#AB73B0"
      ]
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

This interpolates encoded color channels rather than linear-light intensities or a perceptual color space. It returns a color grid, not a rendered mesh image or physical illumination model.
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 Mesh Color Pattern Generator — tool.io
Calculation or validation: interpolation/bilinear — tool.io
Calculation or validation: convert/rgb-to-hex — tool.io

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