Metadata-Version: 1.1
Name: fixationmodel
Version: 0.1.1
Summary: A least-squares offline method to test if tracked gaze points resemble a fixation
Home-page: https://github.com/infant-cognition-tampere/fixationmodel-py
Author: Akseli Palen
Author-email: akseli.palen@gmail.com
License: MIT
Description: ================
        fixationmodel-py
        ================
        
        A least-squares offline method to test if tracked gaze points resemble a fixation.
        
        
        Install
        =======
        
        With `pip
        <https://pypi.python.org/pypi/fixationmodel>`_::
        
            $ pip install fixationmodel
        
        
        
        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
            }
        
        
        
        API
        ===
        
        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.
        
        
        fixationmodel.version
        ---------------------
        
        
        
        
        For developers
        ==============
        
        Use virtualenv::
        
            $ virtualenv -p python3.5 fixationmodel-py
            $ cd fixationmodel-py
            $ source bin/activate
            ...
            $ deactivate
        
        
        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
        
        
        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 sdist: ``$ gpg --detach-sign -a dist/fixa...tar.gz``
            2. Sign the wheel: ``$ gpg --detach-sign -a dist/fixa...whl``
            3. Upload: ``twine upload dist/*`` (will ask your PyPI password)
        5. Package published!
        
        Versioning
        ==========
        
        `Semantic Versioning 2.0.0
        <http://semver.org/>`_
        
        
        
        License
        =======
        
        `MIT License
        <http://github.com/axelpale/nudged-py/blob/master/LICENSE>`_
        
Keywords: fixation estimation eye-tracking
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
