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        "author": "Akseli Palen",
        "author_email": "akseli.palen@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Human Machine Interfaces",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "================\nfixationmodel-py\n================\n\nA least-squares offline method to test if tracked gaze points resemble a fixation.\n\n\n1. Install\n==========\n\nWith `pip\n<https://pypi.python.org/pypi/fixationmodel>`_::\n\n    $ pip install fixationmodel\n\n\n\n2. Usage\n========\n\nA data structure we call **pointlist** is used for point sequences. It is a list of points, where each point is a list [x, y].\n\nThe usage is simple::\n\n    >>> import fixationmodel\n    >>> rawdata = [\n        [130.012, 404.231],\n        [129.234, 403.478],\n        [None, None],\n        [133.983, 450.044],\n        ...\n    ]\n    >>> results = fixationmodel.fit(rawdata)\n    >>> print(results)\n    {\n        'centroid': [344.682, 200.115],\n        'mean_squared_error': 0.000166802\n    }\n\n\n\n3. API\n======\n\n3.1. fixationmodel.fit(gazepointlist)\n-------------------------------------\n\nParameter:\n\n- gazepointlist: a list of [x, y] points i.e. a list of lists.\n\nReturn dict with following keys:\n\n- centroid: a list [x, y], the most probable target of the fixation\n- mean_squared_error: the average squared error for a point.\n\n\n3.2. fixationmodel.version\n--------------------------\n\nGives the current version string::\n\n    >>> fixationmodel.version\n    '1.2.3'\n\n\n\n4. For developers\n=================\n\n4.1. Virtualenv\n---------------\n\nUse virtualenv::\n\n    $ virtualenv -p python3.5 fixationmodel-py\n    $ cd fixationmodel-py\n    $ source bin/activate\n    ...\n    $ deactivate\n\n\n4.2. Testing\n------------\n\nFollow `instructions to install pyenv\n<http://sqa.stackexchange.com/a/15257/14918>`_ and then either run quick tests::\n\n    $ python3.5 setup.py test\n\nor comprehensive tests for multiple Python versions in ``tox.ini``::\n\n    $ pyenv local 2.6.9 2.7.10 3.2.6 3.3.6 3.4.3 3.5.0\n    $ eval \"$(pyenv init -)\"\n    $ pyenv rehash\n    $ tox\n\n\n\n4.3. Publishing to PyPI\n-----------------------\n\nFollow `python packaging instructions\n<https://python-packaging-user-guide.readthedocs.org/en/latest/distributing/>`_:\n\n1.  Create an unpacked sdist: ``$ python setup.py sdist``\n2.  Create a universal wheel: ``$ python setup.py bdist_wheel --universal``\n3.  Go to `PyPI and register the project by filling the package form\n    <https://pypi.python.org/pypi?%3Aaction=submit_form>`_ by uploading\n    ``fixationmodel.egg-info/PKG_INFO`` file.\n4.  Upload the package with twine:\n\n    1. Sign the dist: ``$ gpg --detach-sign -a dist/fixa...0.1.2*``\n    2. Upload: ``twine upload dist/fixa...0.1.2*`` (will ask your PyPI password)\n\n5. Package published!\n\nUpdating the package takes same steps except the 3rd.\n\n\n5. Versioning\n=============\n\n`Semantic Versioning 2.0.0\n<http://semver.org/>`_\n\n\n\n6. License\n==========\n\n`MIT License\n<http://github.com/axelpale/nudged-py/blob/master/LICENSE>`_",
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