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A compact Python library for modeling, analyzing, and visualizing optical network topologies.

Project description

Topolib 🚀

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Topolib is a compact, modular Python library for modeling, analyzing, and visualizing optical network topologies.
Goal: Provide researchers and engineers with a simple, extensible toolkit for working with nodes, links, metrics, and map-based visualizations.

🌐 Model | 📊 Analyze | 🗺️ Visualize | 🧩 Extend


📂 Examples

Explore ready-to-run usage examples in the examples/ folder!


🧭 Overview

Topolib is organized into four main modules:

  • 🧱 Elements: Node, Link — basic building blocks
  • 🕸️ Topology: Topology, Path — manage nodes, links, paths, and adjacency
  • 📈 Analysis: Metrics, TrafficMatrix — compute node degree, link stats, connection matrices, and traffic demand matrices
  • 🖼️ Visualization: MapView — interactive maps with Folium and PyQt6, clean PNG exports

✨ Features

  • Modular, extensible design
  • Easy-to-use classes for nodes, links, and paths
  • Built-in metrics and analysis helpers
  • Traffic demand matrix generation with three models (gravitational, DC/IXP, distribution probability)
  • Returns NumPy arrays for efficient mathematical operations
  • Interactive map visualization with Folium and PyQt6
  • Clean PNG export without external dependencies (no Selenium required)
  • Resource caching for faster map rendering
  • JSON import/export and interoperability
  • Ready for Sphinx, Read the Docs, and PyPI

⚡ Quickstart

python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install topolib

📚 Documentation

Full documentation: https://topolib.readthedocs.io/


📝 Basic usage

Creating a topology

from topolib.elements.node import Node
from topolib.topology.topology import Topology

n1 = Node(1, 'A', 10.0, 20.0)
n2 = Node(2, 'B', 11.0, 21.0)
topo = Topology(nodes=[n1, n2])
# Add links, compute metrics, visualize, etc.

Generating traffic matrices

from topolib.topology import Topology
from topolib.analysis import TrafficMatrix

# Load a topology
topo = Topology.load_default_topology("Germany-14nodes")

# Generate traffic matrix using gravitational model
matrix = TrafficMatrix.gravitational(topo, rate=0.015)
# Returns NumPy array: matrix[i, j] = traffic from node i to j (Gbps)

# Export to CSV
TrafficMatrix.to_csv(matrix, topo, "traffic_matrix.csv")

# Export to JSON (list of demands with src, dst, required fields)
TrafficMatrix.to_json(matrix, topo, "traffic_matrix.json")

🛠️ Development

See CONTRIBUTING.md for development guidelines, commit message rules, and pre-commit setup.


📄 License

MIT — see LICENSE for details.

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