Random Moire Line Interference Pattern Generator

Description

Draw two bounded parallel-line families and clip their geometry to a rectangle for app-rendered interference artwork without display-safety claims.

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.

Draw two bounded parallel-line families and clip their geometry to a rectangle for app-rendered interference artwork without display-safety claims.

When to use this tool

Use this generator to create reproducible moire line interference 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 width, height, families 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 families, lines, bounds. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

For each line family, draw an angle in turns, spacing, and offset. Clip its parallel hatch lines to the rectangular canvas and return family-labeled segments with canvas bounds.^[1]

Input field	Meaning and units	Accepted input
width	Width	Required. Finite number; strictly above 0.
height	Height	Required. Finite number; strictly above 0.
families	Families	Required. Array of Object; no unknown keys.; minimum items 2; maximum items 2.
families[].turns	Turns	Required. Array of Finite number.; minimum items 1; maximum items 16.
families[].spacings	Spacings	Required. Array of Finite number; strictly above 0.; minimum items 1; maximum items 16.
families[].offsets	Offsets	Required. Array of Finite number.; minimum items 1; maximum items 16.
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
families	Families	Required. Array of Object; no unknown keys.; minimum items 2; maximum items 2.
families[].turns	Turns	Required. Finite number.
families[].spacing	Spacing	Required. Finite number; strictly above 0.
families[].offset	Offset	Required. Finite number.
lines	Lines	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 128.
lines[].family	Family	Required. Safe integer; minimum 0; maximum 1.
lines[].from	From	Required. Array of Finite number.; minimum items 2; maximum items 2.
lines[].to	To	Required. Array of Finite number.; minimum items 2; maximum items 2.
bounds	Bounds	Required. Object; no unknown keys.
bounds.minX	Min X	Required. Finite number.
bounds.minY	Min Y	Required. Finite number.
bounds.maxX	Max X	Required. Finite number.
bounds.maxY	Max Y	Required. Finite number.
bounds.width	Width	Required. Finite number.
bounds.height	Height	Required. Finite number.
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": {
    "width": 2,
    "height": 2,
    "families": [
      {
        "turns": [
          0
        ],
        "spacings": [
          1
        ],
        "offsets": [
          0
        ]
      },
      {
        "turns": [
          0.25
        ],
        "spacings": [
          1
        ],
        "offsets": [
          0
        ]
      }
    ],
    "seed": 42
  },
  "output": {
    "families": [
      {
        "turns": 0,
        "spacing": 1,
        "offset": 0
      },
      {
        "turns": 0.25,
        "spacing": 1,
        "offset": 0
      }
    ],
    "lines": [
      {
        "from": [
          0,
          0
        ],
        "to": [
          2,
          0
        ],
        "family": 0
      },
      {
        "from": [
          0,
          1
        ],
        "to": [
          2,
          1
        ],
        "family": 0
      },
      {
        "from": [
          0,
          2
        ],
        "to": [
          2,
          2
        ],
        "family": 0
      },
      {
        "from": [
          2,
          0
        ],
        "to": [
          2,
          2
        ],
        "family": 1
      },
      {
        "from": [
          1,
          0
        ],
        "to": [
          1,
          2
        ],
        "family": 1
      },
      {
        "from": [
          0,
          0
        ],
        "to": [
          0,
          2
        ],
        "family": 1
      }
    ],
    "bounds": {
      "minX": 0,
      "minY": 0,
      "maxX": 2,
      "maxY": 2,
      "width": 2,
      "height": 2
    },
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

The output is geometric line artwork, not a wave-optics or display-aliasing simulation. Line-count work is bounded and consistent coordinate units are required.
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 Moire Line Interference Pattern Generator — tool.io

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