Make_interp_spline - Python

make_interp_spline is a function in the scipy.interpolate module in Python that constructs a cubic spline interpolating function.

What is cubic spline interpolation?

Cubic spline interpolation is a method of interpolating data points by fitting piecewise-defined cubic polynomials to the data points. These cubic polynomials smoothly connect the data points by ensuring that the first and second derivatives of the polynomials are continuous everywhere. This ensures that the interpolating function is smooth and has no sharp changes or irregularities.

How to use make_interp_spline?

The make_interp_spline function takes two arrays, x and y, that represent the x and y coordinates of the data points. It also optionally takes an integer k that specifies the degree of the spline fit. The default value of k is 3, which corresponds to cubic spline interpolation.

import numpy as np
from scipy.interpolate import make_interp_spline

# create some sample data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 1])

# create a cubic spline interpolating function
spline_function = make_interp_spline(x, y)

# evaluate the interpolating function at some x values
x_new = np.linspace(1, 5, num=100)
y_new = spline_function(x_new)

In the example above, we first create some sample data represented by the arrays x and y. We then use make_interp_spline to create a cubic spline interpolating function spline_function from the data. Finally, we evaluate the interpolating function at 100 evenly spaced x values between 1 and 5 using the linspace function from the numpy module.

Conclusion

make_interp_spline is a useful function in the scipy.interpolate module that can be used to create smooth interpolating functions from data points using cubic spline interpolation. By adjusting the k parameter, users can create interpolating functions of varying degrees of smoothness.