Why Is The Output Of Linspace And Interp1d Always The Same?

- 1 answer

So I was doing my assignment and we are required to use interpolation (linear interpolation) for the same. We have been asked to use the interp1d package from scipy.interpolate and use it to generate new y values given new x values and old coordinates (x1,y1) and (x2,y2).
To get new x coordinates (lets call this x_new) I used np.linspace between (x1,x2) and the new y coordinates (lets call this y_new) I found out using interp1d function on x_new.

However, I also noticed that applying np.linspace on (y1,y2) generates the exact same values of y_new which we got from interp1d on x_new.

Can anyone please explain to me why this is so? And if this is true, is it always true?
And if this is always true why do we at all need to use the interp1d function when we can use the np.linspace in it's place?

Here is the code I wrote:

import scipy.interpolate as ip
import numpy as np

x = [-1.5, 2.23]
y = [0.1, -11]

x_new = np.linspace(start=x[0], stop=x[-1], num=10)

y_new = np.linspace(start=y[0], stop=y[-1], num=10)

f = ip.interp1d(x, y)
y_new2 = f(x_new)
print(y_new2)  # y_new2 values always the same as y_new


The reason why you stumbled upon this is that you only use two points for an interpolation of a linear function. You have as an input two different x values with corresponding y values. You then ask interp1d to find a linear function f(x)=m*x +b that fits best your input data. As you only have two points as input data, there is an exact solution, because a linear function is exactly defined by two points. To see this: take piece of paper, draw two dots an then think about how many straight lines you can draw to connect these dots.

The linear function that you get from two input points is defined by the parameters m=(y1-y2)/(x1-x2) and b=y1-m*x1, where (x1,y1),(x2,y2) are your two inputs points (or elements in your x and y arrays in your code snippet.

So, now what does np.linspace(start, stop, num,...) do? It gives you num evenly spaced points between start and stop. These points are start, start + delta, ..., end. The step width delta is given by delta=(end-start)/(num - 1). The -1 comes from the fact that you want to include your endpoint. So the nth point in your interval will lie at xn=x1+n*(x2-x1)/(num-1). At what y values will these points end up after we apply our linear function from interp1d? Lets plug it in:

f(xn)=m*xn+b=(y1-y2)/(x1-x2)*(x1+n/(num-1)*(x2-x1)) + y1-(y1-y1)/(x1-x2)*x1. Simplifying this results in f(xn)=(y2-y1)*n/(num - 1) + y1. And this is exactly what you get from np.linspace(y1,y2,num), i.e. f(xn)=yn!

Now, does this always work? No! We made use of the fact that our linear function is defined by the two endpoints of the intervals we use in np.linspace. So this will not work in general. Try to add one more x value and one more y value in your input list and then compare the results.