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A lite Python package for handling and visualizing eyetracking data
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eyekit Added initial_landing_positions() function Jan 20, 2020
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Eyekit is a lite Python package for handling and visualizing eyetracking data, with a particular emphasis on the reading of multiline passages presented in a fixed-width font.


  • Python 3
  • Numpy
  • Inkscape (optional; required for producing .pdf, .eps, or .png graphics)


pip install

Usage examples

Start by importing eyekit:

import eyekit

Eyekit makes use of two basic types of object: the Passage object and the FixationSequence object. Much of eyekit's functionality centers around bringing these two objects into contact; typically, we have a passage of text and we want to analyze which parts of the passage the participant is looking at.

The Passage object

A Passage object represents the passage of text. It can be created by referencing a .txt file or by passing in a list of strings (one string for each line of text). When you initialize the Passasge, it is necessary to specify the pixel position of the first character, the pixel spacing between characters, and the pixel spacing between lines:

passage = eyekit.Passage('example_passage.txt',
	first_character_position=(368, 155),

By assuming a fixed-width font, eyekit uses these details to place the passage of text on an imaginary grid, such that each character has a row and column index. Subsetting the passage with a row,column index, for example,


returns the character in that position along with its pixel coordinates:

('C', (368, 155))

The Passage object has three iterators: iter_words(), iter_chars(), and iter_ngrams(). Each of these can optionally accept a filtering function. For example, here we are printing all five letter words in the passage that begin with 'b', along with the pixel coordinates of their initial and final letters:

for word in passage.iter_words(lambda word : len(word) == 5 and word[0] == 'b'):
	print(word, word[0].xy, word[-1].xy)
[b, o, s, c, o] (1312, 155) (1376, 155)
[b, o, s, c, o] (768, 475) (832, 475)
[b, o, s, c, o] (1088, 539) (1152, 539)
[b, i, m, b, a] (672, 603) (736, 603)
[b, i, m, b, a] (720, 731) (784, 731)

The FixationSequence object

Raw fixation data can be stored in whatever format you want, but when you load in your data you will represent each passage reading as a FixationSequence. Creation of a FixationSequence expects an x-coordinate, y-coordinate, and duration for each of the fixations, for example [[368, 161, 208], [427, 159, 178], ...]. Here we will load in some example data from a json file:

import json
with open('example_data.json') as file:
	data = json.load(file)
fixation_sequence = eyekit.FixationSequence(data['fixations'])

A FixationSequence is, as you'd expect, a sequence of fixations, and it can be traversed, indexed, and sliced as expected. For example,


slices out fixations 10 through 14 into a new FixationSequence:

FixationSequence[Fixation[1394,187], ..., Fixation[688,232]]

Bringing a FixationSequence into contact with a Passage

The Passage object provides three methods for finding the nearest character, word, or ngram to a given fixation: nearest_word(), nearest_char(), and nearest_ngram(). For example, to retrieve the nearest word to each of the fixations in the sequence, you could do:

for fixation in fixation_sequence:
[e, r, a, n, o]
[v, o, l, t, a]
[o, r, s, i]
[v, i, v, e, v, a, n, o]

Visualizing the data

The Diagram object is used to create visualizations of a passage and associated fixation data. When creating a Diagram, you specify the width and height of the screen. You can then chose to render the text itself and/or an associated fixation sequence.

diagram1 = eyekit.Diagram(1920, 1080)
diagram1.render_passage(passage, fontsize=28)

The diagram can be saved as an .svg file. If you have Inkscape installed, you can also save as a .pdf, .eps, or .png file. The crop_to_passage option removes any margins around the passage:'example_diagrams/fixations.svg', crop_to_passage=True)

Analysis tools

Eyekit provides a number of tools for handling and analyzing eyetracking data.

Correcting vertical drift

As can be seen in visualization above, the raw data suffers from vertical drift – the fixations gradually become misaligned with the lines of text. The correct_vertical_drift function can be used to snap the fixations to the correct lines of the passage:

corrected_fixation_sequence = eyekit.correct_vertical_drift(passage, fixation_sequence)

We can then visually inspect this corrected fixation sequence like so:

diagram2 = eyekit.Diagram(1920, 1080)
diagram2.render_passage(passage, fontsize=28)
diagram2.render_fixations(corrected_fixation_sequence)'example_diagrams/corrected_fixations.svg', crop_to_passage=True)

Analyzing duration mass

On each fixation, the reader takes in information from several characters. We can visualize this by spreading the fixation data across the passage using the spread_duration_mass function:

duration_mass = eyekit.spread_duration_mass(passage, corrected_fixation_sequence)

diagram3 = eyekit.Diagram(1920, 1080)
diagram3.render_heatmap(passage, duration_mass)
diagram3.render_passage(passage, fontsize=28)'example_diagrams/duration_mass.svg', crop_to_passage=True)


Depending on the language you're working with and your particular assumptions, you may want to specify an alternative alphabet or how special characters should be treated. Any character in the passage that is not specified in the alphabet will be ignored (for example, when iterating over characters in the passage). Setting the special characters allows you to specifiy that certain characters should be treated as identical (for example, that à is the same as a or that an apostrophe is the same as a space).

eyekit.set_alphabet(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'à', 'á', 'è', 'é', 'ì', 'í', 'ò', 'ó', 'ù', 'ú', ' ', ''])
eyekit.set_special_characters({'à':'a', 'á':'a', 'è':'e', 'é':'e', 'ì':'i', 'í':'i', 'ò':'o', 'ó':'o', 'ù':'u', 'ú':'u', ' ':'_', '':'_'})


eyekit is licensed under the terms of the MIT License.

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