Skip to main content

A Python package for analyzing reading behavior using eyetracking data

Project description

Eyekit is a Python package for analyzing reading behavior using eyetracking data. Eyekit aims to be entirely independent of any particular eyetracker hardware, experiment software, or data formats. It has an object-oriented style that defines three core objects – the TextBlock, InterestArea, and FixationSequence – that you bring into contact with a bit of coding.

Is Eyekit the Right Tool for Me?

  • You want to analyze which parts of a text someone is looking at and for how long.

  • You need convenient tools for extracting areas of interest from texts, such as specific words, phrases, or letter combinations.

  • You want to calculate common reading measures, such as gaze duration or initial landing position.

  • You need support for arbitrary fonts, multiline passages, right-to-left text, or non-alphabetical scripts.

  • You want the flexibility to define custom reading measures and to build your own reproducible processing pipeline.

  • You would like tools for dealing with noise and calibration issues, and for discarding fixations according to your own criteria.

  • You want to share your data in an open format and produce publication-ready vector graphics.

Installation

Eyekit may be installed using pip:

$ pip install eyekit

Eyekit is compatible with Python 3.8+. Its main dependency is the graphics library Cairo, which you might also need to install if it's not already on your system. Many Linux distributions have Cairo built in. On a Mac, it can be installed using Homebrew: brew install cairo. On Windows, it can be installed using Anaconda: conda install -c anaconda cairo.

Full documentation and a usage guide is available here

Contributing

This project is in the stable/maintenance phase and is not under active development. I am only aiming to fix bugs and keep it compatible with current versions of Python so that it remains useful to the community. With that in mind, contributions are very welcome, but please read the CONTRIBUTING file before submitting a pull request.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

eyekit-0.7.1.tar.gz (967.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

eyekit-0.7.1-py3-none-any.whl (69.1 kB view details)

Uploaded Python 3

File details

Details for the file eyekit-0.7.1.tar.gz.

File metadata

  • Download URL: eyekit-0.7.1.tar.gz
  • Upload date:
  • Size: 967.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.0

File hashes

Hashes for eyekit-0.7.1.tar.gz
Algorithm Hash digest
SHA256 56eab623e8c9dd46e14ffbbf0d5cae58563881ecfb69f919a7785bde61d971b1
MD5 f0f054efbd03872b57d0f7f16c0c621e
BLAKE2b-256 2dd80825dcaf05986f7244bf6423fefd52c36ecc109d88fcb6a9462fc38d68c7

See more details on using hashes here.

File details

Details for the file eyekit-0.7.1-py3-none-any.whl.

File metadata

  • Download URL: eyekit-0.7.1-py3-none-any.whl
  • Upload date:
  • Size: 69.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.13.0

File hashes

Hashes for eyekit-0.7.1-py3-none-any.whl
Algorithm Hash digest
SHA256 1bb59ee660fe5fbfee54246d03f235585b477914cf2cf69da69e897d121e59b1
MD5 e65d52c0e44b8a0e36d5704fd16ca33e
BLAKE2b-256 5a902c0edd4e39227dca902e4e0c6a1edc6863182fe5625fa664922d2ab44d45

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page