Normalised eigenvalues of the frequency-frequency covariance matrix of the data cube. Circles refer to the original cube, plus signs to the wavelet-filtered large-scale cube, and ‘x’ crosses for the small-scale cube. The large-scale eigenvalues drop down faster with N_fg: the PCA assumption holds better in this case, and a few modes are enough to describe the large-scale data set. These values correspond to mean-centred maps cropped on the conservative footprint, although the eigenvalues behaviour does not change significantly for the other cases.