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

To use Python and SciPy to fit an exponential curve, you can use the curve_fit function from scipy.optimize. This function takes a user-defined function that models the data as well as the x and y data points as arguments. It then returns an array of optimized parameters that best fit the data.

Example code

import numpy as np
from scipy.optimize import curve_fit

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

xdata = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
ydata = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])

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

print(popt)

Output example

[ 1.5  -1. ]

Code explanation

1. import numpy as np: imports the numpy library as np, which is used to define the x and y data points.
2. from scipy.optimize import curve_fit: imports the curve_fit function from the scipy.optimize library, which is used to fit the data points to an exponential curve.
3. def func(x, a, b):: defines a user-defined function that takes x and two parameters (a and b) as arguments and returns an exponential function.
4. xdata = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]): defines the x data points as a numpy array.
5. ydata = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0]): defines the y data points as a numpy array.
6. popt, pcov = curve_fit(func, xdata, ydata): uses the curve_fit function to fit the data points to the user-defined exponential function. It returns an array of optimized parameters that best fit the data.
7. print(popt): prints the optimized parameters.

Helpful links

• scipy.optimize.curve_fit
• Python Curve Fitting

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