Eyekit

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General
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- Support for Python 3.13.

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Added
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- `InterestArea`s can now be serialized and written to JSON.

Changed
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- `TextBlock.zones()` is now `TextBlock.interest_areas()`.

Deprecated
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- `measure.landing_distances()` has been deprecated, since it does not return a single value and therefore does not play well with `measure.interest_area_report()`.
- `TextBlock.which_line()`, `TextBlock.which_word()`, and `TextBlock.which_character()` have been deprecated, since there is no obvious use case for finding an interest area given a fixation.

Removed
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- `io.read()`
- `io.write()`
- `tools.discard_short_fixations()`
- `tools.discard_out_of_bounds_fixations()`
- `tools.snap_to_lines()`
- `TextBlock.word_centers()`
- `TextBlock.which_zone()`

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Added
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- Added `interest_area_report()` convenience function, which allows you to create interest area reports for multiple measures across many trials.

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General
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- Modernized installation process with pyproject.toml.

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Added
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- Added `create_stimuli()` function for creating experimental PNG stimulus images.
- `segment()` method added to `FixationSequence` for segmenting fixation sequence at given time intervals (e.g. page turns or subtitle timings).

=======================

General
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- Eyekit is now licensed under the terms of the GPLv3.
- Dropped support for Python 3.7

Added
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- Ability to extract individual samples from an ASC file in addition to the fixations by setting `import_samples=True`.
- Ability to place a background image in an `Image` object, which is useful if you want to render participant fixations over a screenshot of the experimental screen.
- Added `render_frame()` method to `Image` for building videos and animations.
- `discard_long_fixations()` method added to `FixationSequence`.
- `shift_x()`, `shift_y()`, and `shift_time()` methods added to `Fixation` and `FixationSequence`.

Changed
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- The fixation tagging system now allows arbitrary key-value pairs.
- The `snap_to_lines()` method now returns two measures of data quality, delta and kappa.