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. 2011 Sep 21;9(70):957–971. doi: 10.1098/rsif.2011.0431

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Figure 5. — Eigenvalue visualizations for experimentally derived distributions of return map matrices at phases ϕ = π/4, π/2, π (columns 2,3,4), from two datasets (rows; §2). The visualization shows probability contours for the distribution of eigenvalues obtained from the matrices produced by algorithm 1 step 9; if all matrices generated were identical, the distribution would consist of 27 delta functions. The leftmost column shows eigenvalues for surrogate based control _S [ϕ] (see §3.8.1 for details). While these controls seem similar to a circular law distribution, map data show distinct structures with large real eigenvalues. Despite some superficial similarities, the eigenvalue distributions at different phases are clearly distinct. (Online version in colour.)

Inline graphic — Eigenvalue visualizations for experimentally derived distributions of return map matrices at phases ϕ = π/4, π/2, π (columns 2,3,4), from two datasets (rows; §2). The visualization shows probability contours for the distribution of eigenvalues obtained from the matrices produced by algorithm 1 step 9; if all matrices generated were identical, the distribution would consist of 27 delta functions. The leftmost column shows eigenvalues for surrogate based control _S [ϕ] (see §3.8.1 for details). While these controls seem similar to a circular law distribution, map data show distinct structures with large real eigenvalues. Despite some superficial similarities, the eigenvalue distributions at different phases are clearly distinct. (Online version in colour.)