Installing#

pip install iplotx

To also install ilayoutx to compute network layouts before visualising them:

pip install iplotx ilayoutx

Quick Start#

import igraph as ig
import iplotx as ipx

g = ig.Graph.Ring(5)
layout = g.layout("circle").coords
ipx.network(g, layout)

import networkx as nx
import iplotx as ipx

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

Either way, the result is the same:

graph_basic

Features#

iplotx’s features’ include:

  • per-edge and per-vertex styling using sequences or dictionaries

  • labels

  • arrows

  • tunable offset for parallel (i.e. multi-) edges

  • ports (a la Graphviz)

  • curved edges and self-loops with controllable tension

  • tree layouts

  • label-based vertex autoscaling

  • node label margins and padding

  • export to many formats (e.g. PNG, SVG, PDF, EPS) thanks to matplotlib

  • compatibility with many GUI frameworks (e.g. Qt, GTK, Tkinter) thanks to matplotlib

  • data-driven axes autoscaling

  • consistent behaviour upon zooming and panning

  • correct HiDPI scaling (e.g. retina screens) including for vertex sizes, arrow sizes, and edge offsets

  • a consistent matplotlib artist hierarchy

  • post-plot editability (e.g. for animations)

  • interoperability with other charting tools (e.g. seaborn)

  • chainable style contexts

  • vertex clusterings and covers with convex hulls and rounding

  • a plugin mechanism for additional libraries

  • animations (see Animations)

  • 3D visualisations

  • mouse/keyboard interaction and events (e.g. hover, click, see Mouse event handling)

  • … and many more.

Rationale#

We believe graph analysis, graph layouting, and graph visualisation to be three separate tasks. iplotx currently focuses on visualisation. It can also compute simple tree layouts and might expand towards network layouts in the future.

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

Contributing#

Open an issue on GitHub to request features, report bugs, or show intention in contributing. Pull requests are very welcome.

Important

If you are the maintainer of a network/graph/tree analysis library and would like to propose improvements or see support for it, please reach out with an issue/PR on GitHub!

Local development#

To develop iplotx locally:

  • Clone the repository

  • Set up the code infrastructure using uv.

  • Install the test dependency group: uv sync --group test

  • Run the test suite with .venv/bin/pytest.

Release new version (maintainers only)#

To release a new version:

  • Change version in iplotx/version.py using semantic versioning (e.g. 1.2.3).

  • Commit and push changes to GitHub.

  • Wait to confirm all CI tests pass.

  • Create a new release on GitHub. The title must be v1.2.3 and the linked tag must be 1.2.3. When you hit “publish”, a new GitHub action is triggered, which will release to PyPI. In case of discrepancies, the release title wins: a “v” is stripped from the title and the rest is pasted into iplotx/version.py if it’s a valid semantic version.