curve-fit.annealing 0.0.2

Curve fitting with global optimization routines

curve_fit.annealing

Most curve fitting algorithms rely on local optimization routines. These demand good estimates of the fit parameters.

Instead, this module allows to use global optimization routines of scipy.optimize_ to minimize the squared deviation function.

Installation

.. highlight:: none

This module can be installed from PyPI ::

pip3 install curve_fit.annealing

Example

Let us fit a beat signal with two sinus functions, with a total of 6 free parameters.

By default, the curve_fit function of this module will use the scipy.optimize.dual_annealing_ method to find the global optimum of the curve fitting problem. The dual annealing algorithm requires bounds for the fitting parameters. Other global optimization methods like scipy.optimize.basinhopping_ require an initial guess of the parameters instead.

.. highlight:: python

::

import numpy as np from matplotlib import pyplot as plt from curve_fit import annealing

def f(x,p): # Sum of two sinus functions return p[0]*np.sin(p[1]*x + p[2]) + p[3]*np.sin(p[4]*x+p[5])

xdata = np.linspace(-100,100,1000) ydata = f(xdata, [1, 1, 0, 1, 0.9, 0])

plt.plot(xdata, ydata, label='data') bounds=[[0,2],[0,2],[0,2np.pi],[0,2],[0,2],[0,2np.pi]]

result = annealing.curve_fit(f, xdata, ydata, bounds=bounds)

p_opt = result.x # optimal fit parameters ydata_res = f(xdata, p_opt) plt.plot(xdata, ydata_res, label='fit') plt.legend() plt.grid()

plt.show()

Or use scipy.optimize.basinhopping_ ::

result = annealing.curve_fit(f, xdata, ydata, method='basinhopping', x0=np.zeros(6))

API

curve_fit(f, xdata, ydata, [method='dual_annealing', args, kwargs])

Fit function f to data with selectable optimization method from scipy.optimize.

Parameters: f: callable The model function, f(xdata, p). The second argument holds the fitting parameters. xdata : array_like or object The independent variable where the data is measured. Should usually be an M-length sequence or an (k,M)-shaped array for functions with k predictors, but can actually be any object. ydata : array_like The dependent data, a length M array - nominally f(xdata, ...). method : str scipy.optimize method to use for non-linear least squares minimization. Default is 'dual_annealing'. args, kwargs : tuple and dict, optional Additional arguments passed to the optimization method.

Returns: Return OptimizeResult object. The x attribute holds the fitting parameters.

.. _scipy.optimize: https://docs.scipy.org/doc/scipy/reference/optimize.html .. _scipy.optimize.dual_annealing: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.dual_annealing.html#scipy.optimize.dual_annealing .. _scipy.optimize.basinhopping: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.basinhopping.html#scipy.optimize.basinhopping