PCA is useful to extract important features from a high-dimensional dataset and reduce its dimensionality. The algorithm seeks a linear combination of variables such that the maximum variance is extracted from the input features. It then removes this variance and seeks a second linear combination which explains the maximum proportion of the remaining variance, and so on. One can then use the first d principal components to embed a D-dimensional manifold into a d-dimensional one (with d << D). To be fully agnostic and as general as possible, we chose to discard any prior knowledge about different fibers and train our PCA on the full dataset containing the SOAP vectors computed from all the three systems. However, it is interesting to check how the PCA trained on a single fiber performs in embedding the high-dimensional features from other fibers. Figure ,