================
fixationmodel-py
================
A least-squares offline method to test if tracked gaze points resemble a fixation.
1. Install
==========
With `pip
<https://pypi.python.org/pypi/fixationmodel>`_::
$ 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 = fixationmodel.fit(rawdata)
>>> print(results)
{
'centroid': [344.682, 200.115],
'mean_squared_error': 0.000166802
}
3. API
======
3.1. fixationmodel.fit(gazepointlist)
-------------------------------------
Parameter:
- 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
'1.2.3'
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
<http://sqa.stackexchange.com/a/15257/14918>`_ and then either run quick tests::
$ python3.5 setup.py 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
<https://python-packaging-user-guide.readthedocs.org/en/latest/distributing/>`_:
1. Create an unpacked sdist: ``$ python setup.py sdist``
2. Create a universal wheel: ``$ python setup.py bdist_wheel --universal``
3. Go to `PyPI and register the project by filling the package form
<https://pypi.python.org/pypi?%3Aaction=submit_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.
5. Versioning
=============
`Semantic Versioning 2.0.0
<http://semver.org/>`_
6. License
==========
`MIT License
<http://github.com/axelpale/nudged-py/blob/master/LICENSE>`_
