Is it possible to compare multiple line graphs to give a sort of ' similarity rating'

Question

So I am trying to measure data from a smartphone ambient light sensor (ALS). My goal is to be able to be able to look at the data and be able to infer the location of the device.

To do this my plan is to take several 'walkthroughs' of a building and produce an average as there are lots of variables and inaccuracies that come from using the sensor. So, I want to try and find the total 'fingerprint' of all the rooms within the building. I have smaller data sets of just the individual rooms too which I want to use to compare with their subsequent 'fingerprints'.

So, is anyone able to offer some insight into how I mathematically compare similarities between the 'fingerprint' of the walk through and the 'fingerprints' of the individual rooms?

In the examples provided I have used Nyquist sampling with spine interpolation to account for the fact that the ALS sensor is non-uniform to make it easier to work on but also I included the original line.

I used Python Google Colab and the graphs are from ploty.

This just a reading from a single room not an average This is a just a reading from a single walk through

Ideally, I want to be able to produce a similarity rating for which part of the walk through 1 is from.

I have read about using the root-mean-square difference and I am very confused (mostly about how to search along the walk-through line graph to find the most similar section)