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A least-squares offline method to test if tracked gaze points resemble a fixation

Project description

A least-squares offline method to test if tracked gaze points resemble a fixation.

1. Install

With pip:

$ pip install fixationmodel

2. Usage

A data structure we call pointlist is used for point sequences. It is a list of points, where each point is a list [x, y].

The usage is simple:

>>> import fixationmodel
>>> rawdata = [
    [130.012, 404.231],
    [129.234, 403.478],
    [None, None],
    [133.983, 450.044],
>>> results =
>>> print(results)
    'centroid': [344.682, 200.115],
    'mean_squared_error': 0.000166802

3. API



  • gazepointlist: a list of [x, y] points i.e. a list of lists.

Return dict with following keys:

  • centroid: a list [x, y], the most probable target of the fixation
  • mean_squared_error: the average squared error for a point.

3.2. fixationmodel.version

Gives the current version string:

>>> fixationmodel.version

4. For developers

4.1. Virtualenv

Use virtualenv:

$ virtualenv -p python3.5 fixationmodel-py
$ cd fixationmodel-py
$ source bin/activate
$ deactivate

4.2. Testing

Follow instructions to install pyenv and then either run quick tests:

$ python3.5 test

or comprehensive tests for multiple Python versions in tox.ini:

$ pyenv local 2.6.9 2.7.10 3.2.6 3.3.6 3.4.3 3.5.0
$ eval "$(pyenv init -)"
$ pyenv rehash
$ tox

4.3. Publishing to PyPI

Follow python packaging instructions:

  1. Create an unpacked sdist: $ python sdist
  2. Create a universal wheel: $ python bdist_wheel --universal
  3. Go to PyPI and register the project by filling the package form by uploading fixationmodel.egg-info/PKG_INFO file.
  4. Upload the package with twine:
    1. Sign the dist: $ gpg --detach-sign -a dist/fixa...0.1.2*
    2. Upload: twine upload dist/fixa...0.1.2* (will ask your PyPI password)
  5. Package published!

Updating the package takes same steps except the 3rd.

6. License

MIT License

Project details

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Files for fixationmodel, version 0.1.3
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