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https://github.com/razor-x/scipy-data_fitting

Complete pipeline for easy data fitting with Python.
https://github.com/razor-x/scipy-data_fitting

Last synced: 3 months ago
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Complete pipeline for easy data fitting with Python.

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README 

        
Data Fitting with SciPy

=======================



|PyPI| |GitHub-license| |Requires.io| |CircleCI| |Codecov|



    Built from `makenew/python-package `__.



.. |PyPI| image:: https://img.shields.io/pypi/v/scipy-data_fitting.svg

   :target: https://pypi.python.org/pypi/scipy-data_fitting

   :alt: PyPI

.. |GitHub-license| image:: https://img.shields.io/github/license/razor-x/scipy-data_fitting.svg

   :target: ./LICENSE.txt

   :alt: GitHub license

.. |Requires.io| image:: https://img.shields.io/requires/github/razor-x/scipy-data_fitting.svg

   :target: https://requires.io/github/razor-x/scipy-data_fitting/requirements/

   :alt: Requires.io

.. |CircleCI| image:: https://img.shields.io/circleci/project/razor-x/scipy-data_fitting.svg

   :target: https://circleci.com/gh/razor-x/scipy-data_fitting

   :alt: CircleCI

.. |Codecov| image:: https://img.shields.io/codecov/c/github/razor-x/scipy-data_fitting.svg

   :target: https://codecov.io/gh/razor-x/scipy-data_fitting

   :alt: Codecov



Description

-----------



|figure|



.. |figure| image:: https://raw.github.com/razor-x/scipy-data_fitting/master/plot.png



Complete pipeline for easy data fitting with Python 3.



Check out the `example fits on Fitalyzer`_.

See the `Fitalyzer README`_ for details on how to use Fitalyzer for

visualizing your fits.



.. _example fits on Fitalyzer: http://io.evansosenko.com/fitalyzer/?firebase=scipy-data-fitting

.. _Fitalyzer README: https://github.com/razor-x/fitalyzer



Installation

------------



This package is registered on the `Python Package Index (PyPI)`_

as scipy_data_fitting_.



Add this line to your application's requirements.txt



::



    scipy_data_fitting



and install it with



::



    $ pip install -r requirements.txt



If you are writing a Python package which will depend on this,

add this to your requirements in ``setup.py``.



Alternatively, install it directly using pip with



::



    $ pip install scipy_data_fitting



.. _scipy_data_fitting: https://pypi.python.org/pypi/scipy-data_fitting

.. _Python Package Index (PyPI): https://pypi.python.org/



Documentation

-------------



Documentation is generated from source with `pdoc`_.

The latest version is hosted at `pythonhosted.org/scipy-data\_fitting/`_.



To get started quickly, check out the `examples`_.



Then, refer to the source documentation for details on how to use each class.



.. _pdoc: https://pypi.python.org/pypi/pdoc/

.. _pythonhosted.org/scipy-data\_fitting/: https://pythonhosted.org/scipy-data_fitting/

.. _examples: https://github.com/razor-x/scipy-data_fitting/tree/master/examples



Basic Usage

-----------



.. code:: python



    from scipy_data_fitting import Data, Model, Fit, Plot



    # Load data from a CSV file.

    data = Data('linear')

    data.path = 'linear.csv'

    data.error = (0.5, None)



    # Create a linear model.

    model = Model('linear')

    model.add_symbols('t', 'v', 'x_0')

    t, v, x_0 = model.get_symbols('t', 'v', 'x_0')

    model.expressions['line'] = v * t + x_0



    # Create the fit using the data and model.

    fit = Fit('linear', data=data, model=model)

    fit.expression = 'line'

    fit.independent = {'symbol': 't', 'name': 'Time', 'units': 's'}

    fit.dependent = {'name': 'Distance', 'units': 'm'}

    fit.parameters = [

        {'symbol': 'v', 'guess': 1, 'units': 'm/s'},

        {'symbol': 'x_0', 'value': 1, 'units': 'm'},

    ]



    # Save the fit result to a json file.

    fit.to_json(fit.name + '.json', meta=fit.metadata)



    # Save a plot of the fit to an image file.

    plot = Plot(fit)

    plot.save(fit.name + '.svg')

    plot.close()



Controlling the fitting process

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



The above example will fit the line using the default algorithm

``scipy.optimize.curve_fit``.



For a linear fit, it may be more desirable to use a more efficient

algorithm.



For example, to use ``numpy.polyfit``, one could set a

``fit_function`` and allow both parameters to vary,



.. code:: python



    fit.parameters = [

        {'symbol': 'v', 'guess': 1, 'units': 'm/s'},

        {'symbol': 'x_0', 'guess': 1, 'units': 'm'},

    ]

    fit.options['fit_function'] = \

        lambda f, x, y, p0, **op: (numpy.polyfit(x, y, 1), )



Controlling the fitting process this way allows, for example,

incorporating error values and computing and returning goodness of fit

information.



See ``scipy_data_fitting.Fit.options`` for further details on how to

control the fit and also how to use `lmfit`_.



.. _lmfit: http://lmfit.github.io/lmfit-py/



Development and Testing

-----------------------



Source Code

~~~~~~~~~~~



The `scipy-data_fitting source`_ is hosted on GitHub.

Clone the project with



::



    $ git clone https://github.com/razor-x/scipy-data_fitting.git



.. _scipy-data_fitting source: https://github.com/razor-x/scipy-data_fitting



Requirements

~~~~~~~~~~~~



You will need `Python 3`_ with pip_.



Install the development dependencies with



::



    $ pip install -r requirements.devel.txt



.. _pip: https://pip.pypa.io/

.. _Python 3: https://www.python.org/



Tests

~~~~~



Lint code with



::



    $ python setup.py lint



Run tests with



::



    $ python setup.py test



or



::



    $ make test



Documentation

~~~~~~~~~~~~~



Generate documentation with pdoc by running



::



    $ make docs



Examples

~~~~~~~~



Run an example with



::



    $ python examples/example_fit.py



or run all the examples with



::



    $ make examples



Contributing

------------



Please submit and comment on bug reports and feature requests.



To submit a patch:



1. Fork it (https://github.com/razor-x/scipy-data_fitting/fork).

2. Create your feature branch (``git checkout -b my-new-feature``).

3. Make changes. Write and run tests.

4. Commit your changes (``git commit -am 'Add some feature'``).

5. Push to the branch (``git push origin my-new-feature``).

6. Create a new Pull Request.



License

-------



This Python package is licensed under the MIT license.



Warranty

--------



This software is provided "as is" and without any express or implied

warranties, including, without limitation, the implied warranties of

merchantibility and fitness for a particular purpose.