Spirograph Python Matplotlib

Spirograph Python Matplotlib

What this skill does

This skill creates beginner-friendly Python scripts for creatives to generate spirograph-inspired artwork using Matplotlib.

The scripts produced by this skill:

• Accept user inputs for geometry and style.
• Ask for both background colour and main colour inputs.
• Support multiple colours for each palette and gradient blending.
• Ensure each output is unique by combining deterministic parameters with a uniqueness mechanism.
• Always save a PNG output file.

When to use it

Use this skill when the user asks for:

• Spirograph or hypotrochoid/epitrochoid-style art in Python.
• Generative art scripts that are easy to run and modify.
• Colourful creative outputs with gradients.
• Script-first workflows where PNG exports are required.

Do not use this skill for:

• Real-time GPU rendering pipelines.
• 3D engines or game frameworks.
• Non-Python stacks unless explicitly requested.

Inputs the generated script should support

At minimum, the generated script should ask for:

• Geometry controls:
  • R (outer radius)
  • r (inner radius)
  • d (offset)
  • points (sampling density)
  • rotations (curve coverage)
• Uniqueness controls:
  • seed (optional deterministic repeatability)
  • variation_strength (small randomized perturbation)
  • output_name (PNG filename stem)
• Styling controls:
  • background_colors (one or more colours)
  • main_colors (one or more colours)
  • use_gradient (yes/no)
  • line_width
  • alpha
  • figure_size

Colour input can be hex (#1f77b4), CSS-like names (gold), or comma-separated lists.

Instructions

1. Confirm objective and constraints.

• Restate that the script will be easy to run, produce unique outputs, and save PNG files every run.

2. Generate a single-file Python script first.

• Prefer one script (for example, spirograph_art.py) with clear sections:
  • input parsing
  • colour parsing and gradient helpers
  • curve generation
  • rendering
  • PNG export

3. Implement spirograph math with stable defaults.

• Use a parametric curve (hypotrochoid or epitrochoid) and expose R, r, d, points, and rotations.
• Validate numeric ranges and provide beginner-safe defaults.

4. Enforce uniqueness per output.

• If seed is provided, use it for deterministic uniqueness.
• If no seed is provided, derive one from time + entropy.
• Apply controlled perturbations (variation_strength) to one or more geometry or style parameters.
• Include the seed in the output filename or metadata text on-canvas.

5. Implement background and main colour systems.

• Parse single or multi-colour input lists.
• If multiple colours are supplied and gradient is enabled:
  • interpolate smoothly across the background and/or stroke colours.
• If gradient is disabled:
  • cycle or randomly choose from provided palettes in a seed-controlled way.

6. Render for visual quality.

• Use a high DPI figure.
• Hide axes and margins.
• Use anti-aliased lines.
• Centre and scale composition for balanced framing.
• Prefer dark-on-light or light-on-dark contrast checks when possible.

7. Always export PNG.

• Save output on every run with bbox_inches='tight' and consistent DPI.
• Confirm save path in terminal output.
• Never skip file export, even if display fails.

8. Add usage instructions.

• Provide run command and one example input set.
• Keep explanation concise and creative-focused.

Quality checks

Before finalising, verify:

• Script runs with Python + Matplotlib only (unless user asks for extras).
• Both background and main colour prompts exist.
• Multi-colour and gradient behaviour works for both palettes.
• Two consecutive runs produce different outputs when no fixed seed is set.
• Same seed reproduces the same image.
• PNG file is always written.

Output format

When using this skill, produce:

1. File edits:

• A runnable Python script (or minimal set of scripts) in the requested location.

2. Short run instructions:

• Dependencies and command to execute.

3. Save confirmation behaviour:

• Explain where PNGs are written and how names are generated.

4. Optional creative presets:

• Include 2-3 input presets (for example: neon bloom, monochrome etch, sunset ribbon).

Example prompts

• "Create an easy Python spirograph script with gradient background and always save PNG."
• "Make a creative coding script for unique spirograph art; ask me for multiple background and line colours."
• "Build a Matplotlib spirograph generator with seed control and deterministic reruns."

Ambiguities to confirm with the user

If not specified, ask these before final implementation:

• Should this be CLI prompts, command-line arguments, or both?
• Should gradients apply to background only, main lines only, or both?
• Should uniqueness rely mostly on geometry variation, colour variation, or both?
• Preferred default canvas size and DPI?

If the user does not answer, default to:

• CLI prompts
• gradient support for both background and main lines
• mixed geometry + colour variation
• figsize=(8, 8), dpi=300