.. 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 - x2 - 2 * y2 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 * a2, D(b, t): k * a2, }

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.