Tremor_Figures

In this file, we store all the plots associated with our nonlinear analysis of tremors.
As shown in the supplementary, we plotted the trajectories in phase-space for 4 values of the interval of the time lag defined by the ADF and AMI estimates.

Note:
* The names of the files are given in alphabetic order.
* If in the Local-Scaling-Exponent, a linear scaling region was not detected, then the plots CorrSum-LinearRegion, Linear-Region and Surrogate-sample# are not included.

1) 2D-phase-space: contains the four 2d-phase-spaces, one for each time lag.
2) 3D-phase-space-1: contains two 3D-phase-spaces, one for the maximum value of the time lag, and another for a smaller value near the maximum.
3) 3D-phase-space-2: contains two 3D-phase-spaces, one for the minimum value of the time lag, and another corresponding to a slightly larger value near the minimum.
4) ACF-large: shows the ACF function for large values of the time lag.
5) ACF-short: shows the ACF functions for short lags, here the 1/e decay and the first 0 are pointed out.
6) AMI-large: contains the AMI function for large lags.
7) AMI-short: shows the AMI function for short lags and point out the first minimum value of this function.
8) Cao-max-lag: contains the plot that shows the minimum embedding dimension for the maximum lag value.
9) Cao-min-lag: contains the plot that shows the minimum embedding dimension for the minimum lag value. If some of these plots show an NA for the embedding dimension, another value for the time lag inside the interval is considered.
10) CorrelationSum: shows the Correlation Sum for embedding dimensions m = 1,...,15.
11) CorrSum-ScalingRegion: if a local scaling region is detected, we plot the linear scaling region of the correlation sum.
12) Largest-LyapunovExp: displays the largest Lyapunov Exponent curves for embedding dimensions m = 7,...,10 and different radii eps.
13) CorrDim-LinearRegion: shows a view of the linear scaling region detected in the Local-Scaling-Exponents.
14) Local-Scaling-Exponets: shows the local scaling exponent plots for embedding dimensions m = 1,...,15 and is used to detect the correlation dimension.
15) Power-Spectrum: contains the power spectrum of the signal
16) Space-time-separation: shows the contour lines that help to detect non-stationarity and to detect time correlations in the time series.
17) Surrogate-sample#: we include two samples of the surrogated time series. The number of the sample is indicated in the name of the plot.
18) Time-series: shows the time series of the signal.