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

To fit a curve with Python and SciPy, you can use the curve_fit function from the scipy.optimize module. This function takes a function that you want to fit to the data, as well as the x and y data. It then returns the parameters of the fitted curve.

For example:

from scipy.optimize import curve_fit
import numpy as np

# Define function to fit
def func(x, a, b):
    return a * np.exp(-b * x)

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

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

print(popt)
# Output: [2.55423706 1.35190947]

The curve_fit function returns an array of parameters, popt, that can be used to generate a fitted curve.

Parts of the code:

• from scipy.optimize import curve_fit: imports the curve_fit function from the scipy.optimize module
• def func(x, a, b):: defines the function to fit to the data
• xdata = np.linspace(0, 4, 50): creates an array of x values
• ydata = y + 0.2 * np.random.normal(size=len(xdata)): creates an array of y values with noise
• popt, pcov = curve_fit(func, xdata, ydata): fits the function to the data
• print(popt): prints the parameters of the fitted curve

Helpful links

• Scipy curve_fit
• Numpy linspace
• Numpy random normal

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