vizopt 0.0.2

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.

Installation

pip install vizopt

Quick Start

import networkx as nx
from vizopt.bubblejax import optimize_bubble_layout

# Create a graph
graph = nx.Graph()
graph.add_edges_from([("Munich", "Vienna"), ("Vienna", "Prague")])

# Define inclusion tree (cities in countries)
inclusion_tree = nx.DiGraph()
inclusion_tree.add_edges_from([
    ("Munich", "Germany"),
    ("Vienna", "Austria"),
    ("Prague", "Czechia"),
])

# Optimize layout
result = optimize_bubble_layout(
    graph=graph,
    inclusion_tree=inclusion_tree,
    node_radii={"Munich": 0.3, "Vienna": 0.3, "Prague": 0.3},
)

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

Examples

See examples/examples_with_bubbles.ipynb for detailed usage.

License

MIT