Pass tuple as input argument for scipy.optimize.curve_fit

Question

I have the following code:

import numpy as np
from scipy.optimize import curve_fit


def func(x, p): return p[0] + p[1] + x


popt, pcov = curve_fit(func, np.ara         


        

Answer 1

You can define functions that return other functions (see Passing additional arguments using scipy.optimize.curve_fit? )

Working example :

import numpy as np
import random
from scipy.optimize import curve_fit
from matplotlib import pyplot as plt
import math

def funToFit(x):
    return 0.5+2*x-3*x*x+0.2*x*x*x+0.1*x*x*x*x


xx=[random.uniform(1,5) for i in range(30)]
yy=[funToFit(xx[i])+random.uniform(-1,1) for i in range(len(xx))]


a=np.zeros(5)
def make_func(numarg):
    def func(x,*a):
        ng=numarg
        v=0
        for i in range(ng):
            v+=a[i]*np.power(x,i)
        return v
    return func

leastsq, covar = curve_fit(make_func(len(a)),xx,yy,tuple(a))
print leastsq
def fFited(x):
    v=0
    for i in range(len(leastsq)):
        v+=leastsq[i]*np.power(x,i)
    return v


xfine=np.linspace(1,5,200)
plt.plot(xx,yy,".")
plt.plot(xfine,fFited(xfine))
plt.show()