vizopt 0.0.4

Mathematical optimization for data visualization

vizopt

Mathematical optimization for data visualization, specifically designed for graph layouts with hierarchical inclusion constraints ("bubble layouts").

Uses JAX for automatic differentiation and JIT compilation to efficiently optimize layouts via gradient descent.

Read the documentation https://spectalizer.github.io/vizopt/.

Installation

pip install vizopt

Quick Start

import numpy as np
from vizopt.templates import circle_packing

# Define circle radii
rng = np.random.default_rng(0)
radii = rng.uniform(0.1, 1.0, size=20).tolist()

# Pack circles to minimize overlap and bounding box size
positions = circle_packing.optimize_circle_packing(
    radii=radii,
    weight_total_size=10.0,
    collision_offset=0.05,
    optim_kwargs={"n_iters": 3000, "learning_rate": 0.01},
)
# positions is a list of (x, y) tuples, one per circle

Features

• Multi-objective optimization (edge lengths, compactness, collision avoidance, inclusion constraints)
• Efficient JAX-based gradient descent with JIT compilation
• Handles arbitrary hierarchical inclusion relationships
• NetworkX integration with a consistent DiGraph convention: parent → child edges ((u, v) means v ⊂ u)

Examples

See examples/examples_with_bubbles.ipynb for detailed usage.

License

MIT

For developers

Documentation

Using Zensical.

uv run zensical serve

uv run python scripts/nb_to_md.py --execute examples/circle_packing.ipynb docs/examples/from-notebook-circle-packing.md