NumPy provides the interp function, which is used for one-dimensional linear interpolation. The function takes four required arguments:

x: A 1-D array representing the x-coordinates of the data points.

xp: A 1-D array representing the x-coordinates of the data points at which the interpolated values are desired.

fp: A 1-D array representing the y-coordinates of the data points. The fp array should have the same length as xp.

left and right (optional): The values to be used for out-of-bounds extrapolation. If not given, left and right are set to fp[0] and fp[-1], respectively.

Here is an example of how you can use the interp function in NumPy:

import numpy as np x = np.array([0, 1, 2, 3, 4]) xp = np.array([1.5, 2.5, 3.5]) fp = np.array([2, 3, 4]) interpolated_values = np.interp(xp, x, fp) print(interpolated_values)

This will output:

[2.5 3.5 4. ]

This example demonstrates how the values at x-coordinates 1.5, 2.5, and 3.5 can be interpolated from the given data points (x, fp).