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        "description": "Data Fitting with SciPy\n=======================\n\n|PyPI| |GitHub-license| |Requires.io| |CircleCI| |Codecov|\n\n    Built from `makenew/python-package <https://github.com/makenew/python-package>`__.\n\n.. |PyPI| image:: https://img.shields.io/pypi/v/scipy-data_fitting.svg\n   :target: https://pypi.python.org/pypi/scipy-data_fitting\n   :alt: PyPI\n.. |GitHub-license| image:: https://img.shields.io/github/license/razor-x/scipy-data_fitting.svg\n   :target: ./LICENSE.txt\n   :alt: GitHub license\n.. |Requires.io| image:: https://img.shields.io/requires/github/razor-x/scipy-data_fitting.svg\n   :target: https://requires.io/github/razor-x/scipy-data_fitting/requirements/\n   :alt: Requires.io\n.. |CircleCI| image:: https://img.shields.io/circleci/project/razor-x/scipy-data_fitting.svg\n   :target: https://circleci.com/gh/razor-x/scipy-data_fitting\n   :alt: CircleCI\n.. |Codecov| image:: https://img.shields.io/codecov/c/github/razor-x/scipy-data_fitting.svg\n   :target: https://codecov.io/gh/razor-x/scipy-data_fitting\n   :alt: Codecov\n\nDescription\n-----------\n\n|figure|\n\n.. |figure| image:: https://raw.github.com/razor-x/scipy-data_fitting/master/plot.png\n\nComplete pipeline for easy data fitting with Python 3.\n\nCheck out the `example fits on Fitalyzer`_.\nSee the `Fitalyzer README`_ for details on how to use Fitalyzer for\nvisualizing your fits.\n\n.. _example fits on Fitalyzer: http://io.evansosenko.com/fitalyzer/?firebase=scipy-data-fitting\n.. _Fitalyzer README: https://github.com/razor-x/fitalyzer\n\nInstallation\n------------\n\nThis package is registered on the `Python Package Index (PyPI)`_\nas scipy_data_fitting_.\n\nAdd this line to your application's requirements.txt\n\n::\n\n    scipy_data_fitting\n\nand install it with\n\n::\n\n    $ pip install -r requirements.txt\n\nIf you are writing a Python package which will depend on this,\nadd this to your requirements in ``setup.py``.\n\nAlternatively, install it directly using pip with\n\n::\n\n    $ pip install scipy_data_fitting\n\n.. _scipy_data_fitting: https://pypi.python.org/pypi/scipy-data_fitting\n.. _Python Package Index (PyPI): https://pypi.python.org/\n\nDocumentation\n-------------\n\nDocumentation is generated from source with `pdoc`_.\nThe latest version is hosted at `pythonhosted.org/scipy-data\\_fitting/`_.\n\nTo get started quickly, check out the `examples`_.\n\nThen, refer to the source documentation for details on how to use each class.\n\n.. _pdoc: https://pypi.python.org/pypi/pdoc/\n.. _pythonhosted.org/scipy-data\\_fitting/: https://pythonhosted.org/scipy-data_fitting/\n.. _examples: https://github.com/razor-x/scipy-data_fitting/tree/master/examples\n\nBasic Usage\n-----------\n\n.. code:: python\n\n    from scipy_data_fitting import Data, Model, Fit, Plot\n\n    # Load data from a CSV file.\n    data = Data('linear')\n    data.path = 'linear.csv'\n    data.error = (0.5, None)\n\n    # Create a linear model.\n    model = Model('linear')\n    model.add_symbols('t', 'v', 'x_0')\n    t, v, x_0 = model.get_symbols('t', 'v', 'x_0')\n    model.expressions['line'] = v * t + x_0\n\n    # Create the fit using the data and model.\n    fit = Fit('linear', data=data, model=model)\n    fit.expression = 'line'\n    fit.independent = {'symbol': 't', 'name': 'Time', 'units': 's'}\n    fit.dependent = {'name': 'Distance', 'units': 'm'}\n    fit.parameters = [\n        {'symbol': 'v', 'guess': 1, 'units': 'm/s'},\n        {'symbol': 'x_0', 'value': 1, 'units': 'm'},\n    ]\n\n    # Save the fit result to a json file.\n    fit.to_json(fit.name + '.json', meta=fit.metadata)\n\n    # Save a plot of the fit to an image file.\n    plot = Plot(fit)\n    plot.save(fit.name + '.svg')\n    plot.close()\n\nControlling the fitting process\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe above example will fit the line using the default algorithm\n``scipy.optimize.curve_fit``.\n\nFor a linear fit, it may be more desirable to use a more efficient\nalgorithm.\n\nFor example, to use ``numpy.polyfit``, one could set a\n``fit_function`` and allow both parameters to vary,\n\n.. code:: python\n\n    fit.parameters = [\n        {'symbol': 'v', 'guess': 1, 'units': 'm/s'},\n        {'symbol': 'x_0', 'guess': 1, 'units': 'm'},\n    ]\n    fit.options['fit_function'] = \\\n        lambda f, x, y, p0, **op: (numpy.polyfit(x, y, 1), )\n\nControlling the fitting process this way allows, for example,\nincorporating error values and computing and returning goodness of fit\ninformation.\n\nSee ``scipy_data_fitting.Fit.options`` for further details on how to\ncontrol the fit and also how to use `lmfit`_.\n\n.. _lmfit: http://lmfit.github.io/lmfit-py/\n\nDevelopment and Testing\n-----------------------\n\nSource Code\n~~~~~~~~~~~\n\nThe `scipy-data_fitting source`_ is hosted on GitHub.\nClone the project with\n\n::\n\n    $ git clone https://github.com/razor-x/scipy-data_fitting.git\n\n.. _scipy-data_fitting source: https://github.com/razor-x/scipy-data_fitting\n\nRequirements\n~~~~~~~~~~~~\n\nYou will need `Python 3`_ with pip_.\n\nInstall the development dependencies with\n\n::\n\n    $ pip install -r requirements.devel.txt\n\n.. _pip: https://pip.pypa.io/\n.. _Python 3: https://www.python.org/\n\nTests\n~~~~~\n\nLint code with\n\n::\n\n    $ python setup.py lint\n\n\nRun tests with\n\n::\n\n    $ python setup.py test\n\nor\n\n::\n\n    $ make test\n\nDocumentation\n~~~~~~~~~~~~~\n\nGenerate documentation with pdoc by running\n\n::\n\n    $ make docs\n\nExamples\n~~~~~~~~\n\nRun an example with\n\n::\n\n    $ python examples/example_fit.py\n\nor run all the examples with\n\n::\n\n    $ make examples\n\nContributing\n------------\n\nPlease submit and comment on bug reports and feature requests.\n\nTo submit a patch:\n\n1. Fork it (https://github.com/razor-x/scipy-data_fitting/fork).\n2. Create your feature branch (``git checkout -b my-new-feature``).\n3. Make changes. Write and run tests.\n4. Commit your changes (``git commit -am 'Add some feature'``).\n5. Push to the branch (``git push origin my-new-feature``).\n6. Create a new Pull Request.\n\nLicense\n-------\n\nThis Python package is licensed under the MIT license.\n\nWarranty\n--------\n\nThis software is provided \"as is\" and without any express or implied\nwarranties, including, without limitation, the implied warranties of\nmerchantibility and fitness for a particular purpose.",
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