DOI

iplotx

.

Visualise networks and trees in Python, with style.

Supports: - networks: - networkx - igraph - graph-tool - zero-dependency - trees: - ETE4 - cogent3 - phyloframe - Biopython - scikit-bio - dendropy - zero-dependency

In addition to the above, any network or tree analysis library can register an entry point to gain compatibility with iplotx with no intervention from our side.

Installation

pip install iplotx

Quick Start

import networkx as nx
import matplotlib.pyplot as plt
import iplotx as ipx

g = nx.Graph([(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)])
layout = nx.layout.circular_layout(g)
ipx.plot(g, layout)

Documentation

See documentation and gallery.

Citation

If you use iplotx for publication figures, please cite:

F. Zanini. A universal tool for visualisation of networks and trees in Python. F1000Research 2025, 14:1377. https://doi.org/10.12688/f1000research.173131.1

Features

• Plot networks from multiple libraries including networkx, igraph and graph-tool, using Matplotlib. ✅
• Plot trees from multiple libraries such as cogent3, ETE4, phyloframe, skbio, biopython, and dendropy. ✅
• Flexible yet easy styling, including an internal library of styles ✅
• Interactive plotting, e.g. zooming and panning after the plot is created. ✅
• Store the plot to disk in many formats (SVG, PNG, PDF, GIF, etc.). ✅
• 3D network visualisation with depth shading. ✅
• Efficient plotting of large graphs (up to ~1 million nodes on a laptop). ✅
• Edit plotting elements after the plot is created, e.g. changing node colors, labels, etc. ✅
• Animations, e.g. showing the evolution of a network over time. ✅
• Mouse and keyboard interaction, e.g. hovering over nodes/edges to get information about them. ✅
• Node clustering and covers, e.g. showing communities in a network. ✅
• Edge tension, edge waypoints, and edge ports. ✅
• Choice of tree layouts and orientations. ✅
• Tree-specific options: cascades, subtree styling, split edges, etc. ✅
• (WIP) Support uni- and bi-directional communication between graph object and plot object.🏗️

Authors

Fabio Zanini (https://fabilab.org)