Source code for

# Copyright (c) 2013 Commissariat à l'énergie atomique et aux énergies alternatives (CEA)
# Copyright (c) 2013 Centre national de la recherche scientifique (CNRS)
# Copyright (c) 2020 Simons Foundation
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You may obtain a copy of the License at
# Authors: Olivier Parcollet, Nils Wentzell

from scipy.optimize import leastsq
import numpy as np, inspect as ins

[docs]class Fit: """ A simple general functional fit of a X,Y plot Given a function f(x, p0,p1,p2 ...) with parameters p0, ..., p2, and an init guess it adjust the parameters with least square method. The fitting is done at construction `self.param` is the tuple of adjusted parameters. The object is callable : `self(x) = f(x, *self.param)`, so it can be plotted e.g. """
[docs] def __init__ (self, x_array, y_array, fitter, p0 = None ) : """ :param x_array,y_array: curve to fit, as two 1d numpy arrays :param fitter: a tuple (F, name, init_value_default) where : * F is a function : `(x, *param_tuple)` -> y, which act on numpy arrays x and y * name is string for which name%param_tuple gives the TeX representation of the function * init_value_default is the default init point of the minimization :param p0: init guess of the fit. If None, uses the init_value_default of the function. """ self.function, self.fname, p00 = fitter assert len(ins.getargspec(self.function)[0])== len(p00) + 1, "error in number of parameters" assert len(y_array) == len(x_array) assert len(y_array) > len (p00) errfunc = lambda x : np.abs ( self.function(x_array,*x) - y_array[:]) self.param, success = leastsq(errfunc, p0 if p0 else p00 )
def __str__ (self) : return (self.fname%tuple(self.param) or 'Fit').replace("+ -","-") def __repr__ (self) : return str(self) def __repr_tex__ (self) : return str(self) def __call__ (self,x) : return self.function(x,*self.param)
# a collection of useful fit ... linear = lambda X, a,b : a * X + b, r"$%f x + %f$" , (1,1) quadratic = lambda X, a,b,c : (a * X + b)*X + c, r"$%f x^2 + %f x + %f$" , (0,1,1)