argscape 0.7.1

ARGscape: interactive ARG visualization and analysis

ARGscape is a visualization and analysis toolkit for ancestral recombination graphs (ARGs) encoded as tree sequences. Use it as a Python library in Jupyter notebooks, as a command-line tool, or through the web application.

import argscape
import msprime

ts = msprime.sim_ancestry(samples=10, sequence_length=1e4, recombination_rate=1e-8)
viz = argscape.visualize(ts)
viz.show()  # Opens interactive visualization in browser

Links

• Web App: argscape.com
• Documentation: argscape.com/docs
• Paper: arxiv.org/abs/2510.07255

Installation

Python Package (Recommended)

# Basic installation (visualization only)
pip install argscape

# With spatial inference support
# Requires spatial dependencies pre-installed
# Conda installation recommended - see below
pip install argscape[spatial]

Conda Environment (Full Features)

For the complete toolkit including all inference methods:

# Download environment file from GitHub or argscape.com/install
conda env create -f environment.yml
conda activate argscape_local

Quick Start

Python API

import argscape

# Load and visualize a tree sequence
ts = tskit.load("example.trees")
viz = argscape.visualize(ts)
viz.display()  # Jupyter notebook
viz.show()     # Browser
viz.export("figure.png", dpi=300)  # Export

# Run spatial inference
result = argscape.infer(ts, method="fastgaia")
result.ts  # Tree sequence with inferred locations

Web Application

# Start the web app locally
argscape serve

# Or visit argscape.com for the hosted version

Command Line

# Run inference from terminal
argscape infer run --input data.trees --method fastgaia --output ./results

Features

Visualization

Feature	Description
2D Force Graph	Interactive force-directed ARG layout with D3.js
3D Spatial	Geographic visualization with Three.js for spatially-embedded data
Themes	Four built-in themes: liquid, tskit, paper, grayscale
Custom Colors	Override any theme color for publication figures
Filtering	Genomic range and temporal filtering with interactive sliders
Export	PNG, SVG, and PDF export at custom DPI

Spatial Inference

Estimate ancestral locations from sample coordinates:

Method	Description	Speed
midpoint	Weighted midpoint of descendants	Fast
fastgaia	Fast GAIA algorithm	Fast
gaia-quadratic	GAIA with quadratic cost	Medium
gaia-linear	GAIA with linear cost	Medium

# Infer locations and visualize in 3D
result = argscape.infer(ts, method="fastgaia")
viz = argscape.visualize(result.ts, mode="spatial_3d")
viz.show()

Interactive Controls

When using .show() or .display(show_controls=True):

Control	Key	Description
Nodes	N	Adjust sizes, toggle labels
Edges	E	Width and opacity
Mutations	M	Toggle markers
Layout	L	Sample ordering, spacing
Theme	T	Switch themes
Stats	I	View ARG statistics
Export	X	Save visualization

Gallery

2D Network Visualization

3D Spatial Visualization

CLI Reference

argscape - Web Application

argscape [--port PORT] [--no-browser] [--no-tsdate]

argscape infer - Spatial Inference

# Interactive mode
argscape infer

# Direct execution
argscape infer run --input data.trees --method fastgaia --output ./results

# Session management
argscape infer load --file data.trees --name mydata
argscape infer list

argscape load - Session Storage

argscape load load --file data.trees --name mydata
argscape load list
argscape load rm --name mydata

Development

# Clone repository
git clone https://github.com/chris-a-talbot/argscape.git
cd argscape

# Backend
conda env create -f argscape/api/environment.yml
conda activate argscape_local
pip install -e .
uvicorn argscape.api.main:app --reload --port 8000

# Frontend (separate terminal)
cd frontend && npm install && npm run dev

Citation

If you use ARGscape in your research, please cite:

Talbot, C., & Bradburd, G. (2025). ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs. arXiv. https://arxiv.org/abs/2510.07255

License

MIT License

Acknowledgments

• tskit team for the tree sequence toolkit and feedback
• Bradburd Lab for funding, support, and the GAIA algorithms
• James Kitchens and Coop Lab for testing and the SPARG and spacetrees algorithms
• Messer Lab for continued support

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Documentation: argscape.com/docs | Issues: GitHub