Performing linear interpolation in Python can involve different methods depending on the specific use case, but one common way to do so is to use the numpy library. Here’s an example of how to perform 1D linear interpolation on an array of input data:
import numpy as np
# generate some example data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])
# create an interpolation function
interp_func = np.interp(x=[2.5, 3.5], xp=x, fp=y)
# output the interpolated values
print(interp_func)
In this example, we first generate some example data in the form of two arrays (x and y). We then use numpy.interp() to create an interpolation function for the values of y that correspond to x=[2.5, 3.5]. Finally, we output the resulting interpolated values to the console.
Note that the numpy.interp() method assumes that the input data is uniformly spaced, and uses a piecewise linear function to interpolate the missing values. Other methods for interpolation, such as cubic splines, may be more appropriate for non-uniformly-spaced data or more complex functions.