Skip to main content

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

Project description

Topolib 🚀

Python Version License Issues Develop coverage Documentation Status

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

topolib-0.10.0.tar.gz (110.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

topolib-0.10.0-py3-none-any.whl (161.1 kB view details)

Uploaded Python 3

File details

Details for the file topolib-0.10.0.tar.gz.

File metadata

  • Download URL: topolib-0.10.0.tar.gz
  • Upload date:
  • Size: 110.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.2.1 CPython/3.11.14 Linux/5.15.154+

File hashes

Hashes for topolib-0.10.0.tar.gz
Algorithm Hash digest
SHA256 4ad06139c63dab936e72be2418e59cd2cde6b87809b122c127265d95af6bd680
MD5 7595890040921da939642d466cf24c1b
BLAKE2b-256 923d8858fcc863751e4e48e5c8061fe94f8726ee3284b8388e6feec2e80e8750

See more details on using hashes here.

File details

Details for the file topolib-0.10.0-py3-none-any.whl.

File metadata

  • Download URL: topolib-0.10.0-py3-none-any.whl
  • Upload date:
  • Size: 161.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.2.1 CPython/3.11.14 Linux/5.15.154+

File hashes

Hashes for topolib-0.10.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3a33d08ecab139d63c49fd71e3d5a36f5635ca8608d42fc2bc915d63378886bf
MD5 dde206f2749e9f6a9037f5d129bf63f4
BLAKE2b-256 56e999c15c023d441a4d3c3f815b9d682f18b4fd754227f8bf4cb147f6163837

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page