Scanpath Studio

Scanpath Studio shows you how people read. Load eye-tracking-while-reading data and watch each reading unfold over the text, exactly where it sat on the screen — then compare readers, analyse a corpus, and export figures ready for a paper.

Get started

• In the browser: the live demo at https://scanpath-studio.streamlit.app.

• With pip (Python 3.11–3.14):

  pip install scanpath-studio
  scanpath-studio      # opens the app in your browser
  

• As a desktop app: Windows · macOS (Apple silicon) · Linux.

The pip and desktop installs keep your data on your own machine; the hosted demo runs on Streamlit Community Cloud.

What you can do

• See the reading: fixations, saccades, heatmaps and raw gaze over the text at its true on-screen position, with fixations colored by any column.
• Replay it in real time or faster, and export it as HTML, GIF or MP4.
• Compare readers: overlay two trials or place them side by side — even from two different datasets.
• Analyse a corpus per text, sentence, reader or group, with every measure documented in the computation register.
• Triage, export and share: tag and filter trials, export one figure or a zip for every trial, and share a link that reopens the exact view.

A reading, replayed fixation by fixation	Two readers of one paragraph, overlaid (animated)

The app has three views: 🗺️ Scanpath for one trial at a time, 📊 Corpus Analysis for the whole dataset, and 🗂️ Data for loading and configuring datasets. The feature guides walk through each one.

Your data

Load word, fixation and raw-gaze tables in CSV, Parquet, Excel or another common format. Scanpath Studio adapts to how your study was recorded, so there is rarely anything to reformat first — see Loading public and own data.

Command line & Python API

Everything the app draws is also available headless — same pipeline, same figure. These run as-is on the bundled demo:

scanpath-studio render --sample --list-trials          # the demo's trials
scanpath-studio render --sample -o scanpath.html       # one trial, interactive HTML
scanpath-studio render --sample --animate -o replay.html
scanpath-studio render --sample -p l37_1129 -t l37_1129_2_1_1_Ele_r0 \
  --compare-with l7_1090:l7_1090_2_1_1_Ele_r0 -o compare.html

import scanpath_studio as sps

words, fixations = sps.load_sample_data()
print(sps.list_trials(words, fixations).head())
fig = sps.plot_scanpath(words, fixations, "l37_1129", "l37_1129_2_1_1_Ele_r0")
sps.save_figure(fig, "scanpath.html")
measures = sps.compute_word_metrics(words, fixations)  # FFD, FPRT, RPD, TFD, …

For your own files, pass --words ia.csv --fixations fix.csv to render, or use sps.load_scanpath_data("ia.csv", "fix.csv"). HTML output needs nothing else; PNG, SVG, PDF, GIF and MP4 go through Kaleido, which needs Chrome once: plotly_get_chrome -y. The CLI reference and the Python API reference list every flag and parameter.

Where next

The full documentation is at https://lacclab.github.io/scanpath-studio/:

• Getting started: install, launch and a first trial
• Tutorials: task walk-throughs, from checking a pilot to a figure for a paper
• Feature guides: every view and control
• Loading public and own data: what the loader accepts and how to map it
• CLI and Python API: scripting and batch rendering
• Gallery: the figures it draws, each with the code that makes it
• Computation register: how each measure is derived
• FAQ · Cite

Contributing

git clone https://github.com/lacclab/scanpath-studio.git
cd scanpath-studio
pip install -e ".[test]"          # or: uv sync --extra test --extra lint
streamlit run streamlit_app.py --server.address 127.0.0.1
pytest -n auto

CONTRIBUTING.md covers setup, the checks that gate CI, and how work is tracked in GitHub Issues; AGENTS.md is the architectural map. To preview the docs site locally, run pip install -e ".[docs]" and then mkdocs serve.

Taking part means following the Code of Conduct.

Citation

A paper is in preparation. Until then, cite the software by its DOI, 10.5281/zenodo.22933884 (GitHub's Cite this repository button formats it as APA or BibTeX). If you use the bundled demo, a subset of OneStop Eye Movements, please also cite:

@article{berzak2025onestop,
  title     = {{OneStop}: A 360-Participant {E}nglish Eye Tracking Dataset
               with Different Reading Regimes},
  author    = {Berzak, Yevgeni and Malmaud, Jonathan and Shubi, Omer
               and Meiri, Yoav and Lion, Ella and Levy, Roger},
  journal   = {Scientific Data},
  year      = {2025},
  publisher = {Nature Publishing Group},
  doi       = {10.1038/s41597-025-06272-2},
  url       = {https://www.nature.com/articles/s41597-025-06272-2},
}

AI-assisted software

Scanpath Studio was built with AI assistance. Cross-check results before publishing. If something looks wrong — or if you have a feature request or suggestion — report it.