https://github.com/tbuli/symfit

Symbolic Fitting; fitting as it should be.
https://github.com/tbuli/symfit

curve-fitting least-squares python scipy sympy

Last synced: about 1 month ago
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Symbolic Fitting; fitting as it should be.

• Host: GitHub
• URL: https://github.com/tbuli/symfit
• Owner: tBuLi
• License: mit
• Created: 2014-09-13T20:48:28.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2024-01-08T13:39:52.000Z (11 months ago)
• Last Synced: 2024-10-14T01:21:16.391Z (about 1 month ago)
• Topics: curve-fitting, least-squares, python, scipy, sympy
• Language: Python
• Homepage: http://symfit.readthedocs.org
• Size: 3.39 MB
• Stars: 235
• Watchers: 9
• Forks: 17
• Open Issues: 82
• Metadata Files:
  • Readme: README.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Authors: AUTHORS.rst

Awesome Lists containing this project

README

        
.. image:: https://zenodo.org/badge/24005390.svg

   :target: https://zenodo.org/badge/latestdoi/24005390

.. image:: https://coveralls.io/repos/github/tBuLi/symfit/badge.svg?branch=master

   :target: https://coveralls.io/github/tBuLi/symfit?branch=master

Please cite this DOI if ``symfit`` benefited your publication. Building this has been a lot of work, and as young researchers your citation means a lot to us.

Martin Roelfs & Peter C Kroon, symfit. doi:10.5281/zenodo.1133336

Documentation

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

http://symfit.readthedocs.org

Project Goals

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

The goal of this project is simple: to make fitting in Python pythonic.

What does pythonic fitting look like? Well, there's a simple test. If I can

give you pieces of example code and don't have to use any additional words to

explain what it does, it's pythonic.

.. code-block:: python

  from symfit import parameters, variables, Fit, Model

  import numpy as np

   

  xdata = np.array([1.0, 2.0, 3.0, 4.0, 5.0])

  ydata = np.array([2.3, 3.3, 4.1, 5.5, 6.7])

  yerr = np.array([0.1, 0.1, 0.1, 0.1, 0.1])

  

  a, b = parameters('a, b')

  x, y = variables('x, y')

  model = Model({y: a * x + b})

  

  fit = Fit(model, x=xdata, y=ydata, sigma_y=yerr)

  fit_result = fit.execute()

Cool right? So now that we have done a fit, how do we use the results?

.. code-block:: python

  import matplotlib.pyplot as plt

  

  yfit = model(x=xdata, **fit_result.params)[y]

  plt.plot(xdata, yfit)

  plt.show()

.. figure:: http://symfit.readthedocs.org/en/latest/_images/linear_model_fit.png

  :width: 600px

  :alt: Linear Fit

Need I say more? How about I let another code example do the talking?

.. code-block:: python

  from symfit import parameters, Fit, Equality, GreaterThan

  

  x, y = parameters('x, y')

  model = 2 * x * y + 2 * x - x**2 - 2 * y**2

  constraints = [

      Equality(x**3, y),

      GreaterThan(y, 1),

  ]

  

  fit = Fit(- model, constraints=constraints)

  fit_result = fit.execute()

I know what you are thinking. "What if I need to fit to a system of Ordinary Differential Equations?"

.. code-block:: python

  from symfit import variables, Parameter, ODEModel, Fit, D

  import numpy as np

  

  tdata = np.array([10, 26, 44, 70, 120])

  adata = 10e-4 * np.array([44, 34, 27, 20, 14])

          

  a, b, t = variables('a, b, t')

  k = Parameter('k', 0.1)

  

  model_dict = {

      D(a, t): - k * a**2,

      D(b, t): k * a**2,

  }

  

  ode_model = ODEModel(model_dict, initial={t: 0.0, a: 54 * 10e-4, b: 0.0})

  

  fit = Fit(ode_model, t=tdata, a=adata, b=None)

  fit_result = fit.execute()

For more fitting delight, check the docs at http://symfit.readthedocs.org.