BP Designs - Algorithmic Pattern Generation

Generative system for creating natural, algorithmic patterns for physical fabrication.

Core Philosophy: Deterministic algorithms → organic emergence → physical craft

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What is it?

Beyond just "generating patterns," this project focuses on two core pillars:

1. Deep Composability

Patterns are not just static images; they are semantic building blocks.

• Functional Pipelines: A pattern produced by one generator (e.g., a Voronoi cell) can serve as the boundary or guidance field for another (e.g., a branching tree).
• Spatial Nesting: Using sub_canvas, patterns can be recursively nested and transformed within a global coordinate system.
• Semantic Preservation: We maintain the topology (e.g., parent-child relationships) until export, allowing for "smart" rendering like hierarchy-aware tapering.

2. Systematic Experimentation

We replace "random tweaking" with a rigorous experimentation framework.

• Parameter Grids: Define a search space and let the ExperimentRunner explore every combination.
• Data-Driven Gallery: Every experiment variant is saved with its metadata, allowing for visual comparison and discovery in the built-in gallery.
• Deterministic Results: Every pattern is seed-based, ensuring that "happy accidents" are always reproducible.

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Supported Algorithms

• Branching (Space Colonization): Organic, tree-like growth patterns with custom growth strategies and organic rendering.
• Cellular (Voronoi): Natural tiling and cellular structures (Phase 2: refinement in progress).
• Organs (Procedural Blossoms): Multi-ring, organic petal shapes for decorative elements.
• Primitives: 2D shapes (Ovals, Regular Polygons) as building blocks.

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1.1 The Design Lifecycle

The overall flow from experiment definition to visual discovery in the gallery follows a systematic pipeline:

graph TD
    subgraph Experiment
        Exp
        Grid
    end
    subgraph Review
        Met
        Out
        Gal
    end
    subgraph Composition
        Gen
        Pat
    end
    Exp[Experiment Script] --> Grid[Parameter Grid]
    Grid --> Gen[Generators]
    Grid --> Met[Metadata]
    Gen --> Pat[Patterns]
    Pat -- "Input to" --> Gen
    Composition --> Out[Image]
    Met --> Gal
    Out --> Gal[Web Gallery]

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1.2 Core API

graph TD
    subgraph Algorithm Layer
        G[Generator]
    end
    subgraph Semantic Layer
        P[Pattern]
    end
    subgraph Interchange Layer
        Ge[Geometry]
    end
    subgraph Output
        SVG[SVG Output]
    end

    G -- produces --> P
    P -- converts to --> Ge
    Ge -- renders to --> SVG
    P -- guides --> G

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Quick Start

Prerequisites

• Python 3.12
• Poetry

Setup

# Clone and install
poetry install

# Run tests
poetry run pytest

# Format and lint
poetry run ruff format
poetry run ruff check

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Experiment Workflow

1. Run an Experiment

# Generate parameter variations
poetry run python src/experiments/regular_polygon_basic.py

# Output: output/experiments/[experiment_name]/

2. View Results

# Option A: Direct (if browser allows file:// access)
open gallery/index.html

# Option B: HTTP server
python -m http.server 8000
# Then: http://localhost:8000/gallery/

3. Iterate

• Select experiment from dropdown
• Compare parameter effects visually
• Document findings in docs/exploration/[date]_[topic].md
• Update docs/exploration/LEARNINGS.md with insights

See: docs/ARCHITECTURE_GALLERY.md for technical details

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Design Goals

Algorithmic, Not AI

• Deterministic, rule-based generation (L-systems, space colonization, reaction-diffusion)
• No machine learning, no diffusion models
• Reproducible: same seed → same output

Composability

Patterns are building blocks:

• Layerable
• Combinable
• Transformable
• Maskable

Craft-Aware

Physical manufacturing constraints:

• Minimum line thickness
• Minimum spacing
• Adequate negative space
• Embossable depth and detail

See: docs/design_goals.md for full philosophy

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Testing

# Run tests
poetry run pytest

# With coverage
poetry run pytest --cov=src --cov-report=term-missing

# Run specific test
poetry run pytest tests/test_space_colonization.py

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