python-scipyHow do I use scipy.optimize.curve_fit in Python?

scipy.optimize.curve_fit is a function in the SciPy library of Python used to fit a curve of the form f(x, *params) to data. It finds the parameters of the curve that best fit the given data points.

from scipy.optimize import curve_fit
import numpy as np

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

x_data = np.array([0, 1, 2, 3, 4, 5])
y_data = np.array([1, 3, 5, 7, 9, 11])

popt, pcov = curve_fit(func, x_data, y_data)

print(popt)

Output example

[2. 1.]

The code above uses curve_fit to fit a linear function f(x) = a*x + b to the given data points. The func function is defined as the model to fit the data to. The x_data and y_data are the x-coordinates and y-coordinates of the data points. The curve_fit function returns two values, popt and pcov which are the optimal parameters and the covariance matrix of the parameters respectively. In this case, the optimal parameters are a = 2 and b = 1.

Code explanation

1. from scipy.optimize import curve_fit: imports the curve_fit function from the SciPy library
2. import numpy as np: imports the NumPy library for array manipulation
3. def func(x, a, b): defines the model function to fit the data to
4. x_data and y_data: the x-coordinates and y-coordinates of the data points
5. popt, pcov = curve_fit(func, x_data, y_data): calls the curve_fit function to fit the data to the model function
6. print(popt): prints the optimal parameters

Helpful links

1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html
2. https://www.tutorialspoint.com/scipy/scipy_optimize.htm

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