Scanpath Studio

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

Get started

• As a desktop app — recommended, and the easiest way to work with your own data. It updates itself from the app's About menu: Windows macOS (Apple silicon) Linux

• With pip (Python 3.11–3.14):

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

• In the browser, to try it on the bundled demo data: the live demo at https://scanpath-studio.streamlit.app.

The desktop and pip installs keep your data on your own machine, handle large datasets, and download the public corpora (PoTeC, OneStop) in one click. The hosted demo runs on Streamlit Community Cloud, with limited memory and no corpus downloads.

Your data

Scanpath Studio reads two tables, and either one alone also works:

Raw gaze, participant and text IDs, conditions and reading measures are optional and unlock more. Files can be CSV, TSV, Parquet, Feather, Excel or a .zip of them. Columns are detected from common EyeLink, Tobii, Gazepoint, SMI and Pupil Labs names, and you can correct any guess. See Loading public and own data and the data format.

_{Add your own data: EyeLink's fixation and interest-area reports, their columns detected for you}

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.

_{Style the plot: regressions in their own color, word boxes, the reading order, a palette}

Replay it

Play a trial back in real time or faster, and export it as HTML, GIF or MP4.

_{Replay the reading fixation by fixation, here at ×4}

Compare participants

Overlay two trials or place them side by side — even from two different datasets.

_{Compare two readers of a text side by side, stepping through the texts together}

Analyze a corpus

Per text, participant or group. Corpus Analysis uses the reading measures in your data (such as EyeLink's IA_* columns) and does not compute its own; each is defined in the computation register.

_{Corpus Analysis: a measure on the stimulus, against surprisal, and between two groups}

Find and tag trials

Narrow the trials by condition, participant or your own tags, and star the ones worth coming back to.

_{Find trials: narrow them by condition and participant, then step through what is left}

Export and share

Export one figure or a zip for every trial, and share a link that reopens the exact view.

_{Export a figure for a paper, 180 mm wide at 300 dpi}

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

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")

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 and PDF (and GIF/MP4 replays) go through Kaleido, which needs Chrome, Chromium or Edge, or run plotly_get_chrome -y once. 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,lint]"     # 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, please cite the software by its DOI. In a methods section you could write:

Scanpaths were visualized with Scanpath Studio (Shubi et al., 2026).

Shubi, O., Gruteke Klein, K., Grossman, M., Lion, E., Jakobi, D. N., Reich, D. R., Jäger, L., & Berzak, Y. (2026). Scanpath Studio [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.22933884

@software{shubi2026scanpathstudio,
  title     = {Scanpath Studio},
  author    = {Shubi, Omer and Gruteke Klein, Keren and Grossman, Maya
               and Lion, Ella and Jakobi, Deborah N. and Reich, David R.
               and J{\"a}ger, Lena and Berzak, Yevgeni},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.22933884},
  url       = {https://doi.org/10.5281/zenodo.22933884},
}

Add the version you used (scanpath-studio version prints it), e.g. "(Version 0.38.0)".

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 development

Scanpath Studio was developed with AI assistance. As with any research software, cross-check its results before relying on them in published work. Found a bug, or have a feature request? Open an issue.