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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
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
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
56eab623e8c9dd46e14ffbbf0d5cae58563881ecfb69f919a7785bde61d971b1
|
|
| MD5 |
f0f054efbd03872b57d0f7f16c0c621e
|
|
| BLAKE2b-256 |
2dd80825dcaf05986f7244bf6423fefd52c36ecc109d88fcb6a9462fc38d68c7
|
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
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1bb59ee660fe5fbfee54246d03f235585b477914cf2cf69da69e897d121e59b1
|
|
| MD5 |
e65d52c0e44b8a0e36d5704fd16ca33e
|
|
| BLAKE2b-256 |
5a902c0edd4e39227dca902e4e0c6a1edc6863182fe5625fa664922d2ab44d45
|