python numpy/scipy curve fitting

Question

I have some points and I am trying to fit curve for this points. I know that there exist scipy.optimize.curve_fit function, but I do not understand documentation, i.e how to use this function.

My points: np.array([(1, 1), (2, 4), (3, 1), (9, 3)])

Can anybody explain how to do that?

Answer 1

You'll first need to separate your numpy array into two separate arrays containing x and y values.

x = [1, 2, 3, 9]
y = [1, 4, 1, 3]

curve_fit also requires a function that provides the type of fit you would like. For instance, a linear fit would use a function like

def func(x, a, b):
    return a*x + b

scipy.optimize.curve_fit(func, x, y) will return a numpy array containing two arrays: the first will contain values for a and b that best fit your data, and the second will be the covariance of the optimal fit parameters.

Here's an example for a linear fit with the data you provided.

import numpy as np
from scipy.optimize import curve_fit

x = np.array([1, 2, 3, 9])
y = np.array([1, 4, 1, 3])

def fit_func(x, a, b):
    return a*x + b

params = curve_fit(fit_func, x, y)

[a, b] = params[0]

This code will return a = 0.135483870968 and b = 1.74193548387

Here's a plot with your points and the linear fit... which is clearly a bad one, but you can change the fitting function to obtain whatever type of fit you would like.