python-scipyHow do I use Python Scipy to fit a curve?

To use Python Scipy to fit a curve, you need to use the curve_fit function from the scipy.optimize package. This function takes a function that describes the curve you want to fit, and the data points you are fitting to.

Example code

from scipy.optimize import curve_fit
import numpy as np

def func(x, a, b):
    return a * np.exp(-b * x)

xdata = np.linspace(0, 4, 50)
y = func(xdata, 2.5, 1.3)
ydata = y + 0.2 * np.random.normal(size=len(xdata))

popt, pcov = curve_fit(func, xdata, ydata)

The output of this code is the optimized parameters popt and the covariance of the parameters pcov.

Code explanation

• from scipy.optimize import curve_fit: imports the curve_fit function from the scipy.optimize package
• def func(x, a, b):: defines the function that describes the curve you want to fit
• xdata = np.linspace(0, 4, 50): creates an array of x-values for the data points
• y = func(xdata, 2.5, 1.3): creates an array of y-values calculated from the function with the given parameters
• ydata = y + 0.2 * np.random.normal(size=len(xdata)): adds random noise to the y-values to simulate real data points
• popt, pcov = curve_fit(func, xdata, ydata): calls the curve_fit function to optimize the parameters of the function

Helpful links

• Scipy curve_fit documentation
• Python Scipy tutorial

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