Some software, scripts and toolboxes are available and may find your interest:

CRP Toolbox for MATLAB

^{®}allows for the creation of RPs as well as CRPs, quantification analysis of RPs and CRPs includes the new measures of complexity as LAM and TT, time scale alignment tool based on CRPs and further useful tools and methods of nonlinear time series analysis and data preparation are provided; platform independent (for MATLAB); both usage of graphical user interface as well as commandline call is possibleCommandline Recurrence Plots allows for the creation of RPs and their quantification analysis for really long data series; commandline based; currently for Unix/ Linux and Dos/ Windows

COPRA – Constructing Proxy Records From Age Models for MATLAB

^{®}is a depth-age modeling program that creates chronologies with uncertainties and can transform age-uncertainties to proxy-uncertainties.Makeinstall tool for MATLAB

^{®}creates a single, executable install file from comprehensive MATLAB toolboxes; allows a user friendly installation of toolboxes, and simplifies their distribution

The Makeinstall tool was selected on Oct. 3, 2014, as the Mathworks File Exchange Pick of the Week!further useful toolboxes from nonlinear time series analysis can be found in TOCSY (Toolbox for complex systems) or at Zenodo

Data and MATLAB Code for Reproducing RPs and RQA of Westerhold et al, Science, 369, 2020

The data file `CENOGRID_Loess_20.txt` contains the astronomically tuned deep-sea benthic foraminifer carbon (δ¹³C) and oxygen (δ¹⁸O) isotope reference records uniformly covering the entire Cenozoic. The first column is the tuned age in Ma, the second column the δ¹³C, and the third column the δ¹⁸O record.

The original calculations were performed using the CRP Toolbox for MATLAB. In order to avoid installing the toolbox and for better performance, the functions for calculating RP and RQA were here reimplemented, providing identical result.

To reproduce the RPs in Fig. 2, use the script `perform_rp.m`, for reproducing the determinism values and upper confidence bounds, use the script `perform_rqa.m`.

Published in

**T. Westerhold, N. Marwan, A. J. Drury, D. Liebrand, C. Agnini, E. Anagnostou, J. S. K. Barnet, S. M. Bohaty, D. De Vleeschouwer, F. Florindo, T. Frederichs, D. A. Hodell, A. E. Holbourn, D. Kroon, V. Lauretano, K. Littler, L. J. Lourens, M. Lyle, H. Pälike, U. Röhl, J. Tian, R. H. Wilkens, P. A. Wilson, J. C. Zachos**: An astronomically dated record of Earth's climate and its predictability over the last 66 million years, Science,**369**(6509), 1383–1387 (2020). DOI:10.1126/science.aba6853Edit distance based recurrence plot for event time series

Julia code to calculate recurrence plots of the Rössler system:

- calculated from the original continuous data (regular recurrence plot) and
- from the events series representing the maxima of the x-component (edit distance recurrence plot).

Published in

**N. Marwan**: Challenges and perspectives in recurrence analyses of event time series, Frontiers in Applied Mathematics and Statistics,**9**, 1129105 (2023). DOI:10.3389/fams.2023.1129105Difference recurrence plots for structural inspection using guided ultrasonic waves

Code and data source to reproduce figures in

**C. Brandt, N. Marwan**: Difference recurrence plots for structural inspection using guided ultrasonic waves – A new approach for evaluation of small signal differences, European Physical Journal – Special Topics,**232**, 69–81 (2023). DOI:10.1140/epjs/s11734-022-00701-8Power spectral estimate for discrete data

Data and Julia code for reproducing figures of

**N. Marwan, T. Braun:**Power spectral estimate for discrete data,*Chaos*,**33**(5), 053118 (2023). DOI:10.1063/5.0143224Acquisition and analysis of grey scale data from stalagmites using ImageJ software

We provide three scripts for MATLAB and Octave to extract grey values from scanned images (extract_greyvalues.m), to concatenate grey value tracks (combine_tracks.m), and for interpolation and uncertainty estimation of the grey value record chronology_with_uncert.m. Use for Octave was tested with version 6.2.0 and for MATLAB with version 2023.

Published in

**S. F. M. Breitenbach, N. Marwan**: Acquisition and analysis of greyscale data from stalagmites using ImageJ software, Cave and Karst Science,**50**(2), 69–78 (2023)hkraemer/PECUZAL_python: PECUZAL embedding algorithm

Minor bugfixes

Trends in recurrence analysis of dynamical system

Scripts to reproduce the figures in

**N. Marwan, K. H. Kraemer**: Trends in recurrence analysis of dynamical systems, European Physical Journal – Special Topics,**232**, 5–27 (2023). DOI:10.1140/epjs/s11734-022-00739-8pucicu/rp: Recurrence plot and recurrence quantification analysis implementation for MATLAB

change from pdist to pdist2 which allows better performance/ calculation speed for the RP

hkraemer/PECUZAL_Matlab: Sample_size keyword works properly now

In order to speed up computation it is possible to only consider a fraction of all points in the trajectory. The

`sample_size`

-keyword handles these situations and now also affects the computation of the`L`

-statistic.hkraemer/Recurrence_Spike_Spectra: Spike spectra for recurrences

Fully reproducible code base for the the paper Kraemer et al. 2022, Spike spectra for recurrences, published in Entropy.

Recurrence Flow measure of nonlinear dependence

In this Python implementation of the

**recurrence flow**measure, subroutines for computing a recurrence plot, non-uniformly embedding a uni-/multivariate time series and conducting recurrence flow based analysis, i.e. nonlinear correlation analysis and recurrence flow based delay selection, are provided. It corresponds to the first release of the respective Git repository:

https://github.com/ToBraun/RECFLOWFor any request, please get in touch with Tobias Braun (tobraun@pik-potsdam.de).

Interpolation and sampling effects on recurrence quantification measures

These are the jupyter notebooks, which are used to create the figures for the paper: Interpolation and sampling effects on recurrence quantification measures

To use the scripts the following python packages have to be installed:

- numpy
- matplotlib
- scipy
- jupyterlab
- tqdm
- ipywidgets

Nonlinear time series analysis of palaeoclimate proxy records

Jupyter notebook for nonlinear time series analysis of the palaeoclimate proxy records used in the paper. It calculates the number of potential wells, the entropy of the data, the order pattern (permutation) entropy, the recurrence quantification/network measures, DET, LAM, transitivity, and average path length as well as the visibility graph based irreversibility test statsitics p(k) and p(C).

The repository also contains the data sets used in the analysis in the folder Data.

Published in

**N. Marwan, J. F. Donges, R. V. Donner, D. Eroglu**: Nonlinear time series analysis of palaeoclimate proxy records, Quaternary Science Reviews,**274**, 107245 (2021). DOI:10.1016/j.quascirev.2021.107245Code of a two-dimensional neuron model exposed to an externally applied extremely low frequency (ELF) sinusoidal electric field, allowing the study of phase synchronization of neurons weakly coupled with gap junction. The analysis is performed using recurrence plots.

Published in

**A. M. Nkomidio, E. J. Ngamga, B. R. N. Nbendjo, J. Kurths, N. Marwan**: Recurrence-Based Synchronization Analysis of Weakly Coupled Bursting Neurons Under External ELF Fields, Entropy,**24**(2), 235 (2022). DOI:10.3390/e24020235