https://github.com/pucicu/rp_speed_test
Code to get the calculation speed for recurrence plots/ recurrence quantification analysis using MATLAB, R, Python, and Julia code.
https://github.com/pucicu/rp_speed_test
nonlinear-dynamics performance-testing recurrence-plot recurrence-quantification-analysis time-series-analysis
Last synced: 3 months ago
JSON representation
Code to get the calculation speed for recurrence plots/ recurrence quantification analysis using MATLAB, R, Python, and Julia code.
- Host: GitHub
- URL: https://github.com/pucicu/rp_speed_test
- Owner: pucicu
- License: gpl-3.0
- Created: 2022-02-17T15:56:20.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2026-02-27T06:50:38.000Z (4 months ago)
- Last Synced: 2026-02-27T12:55:28.048Z (4 months ago)
- Topics: nonlinear-dynamics, performance-testing, recurrence-plot, recurrence-quantification-analysis, time-series-analysis
- Language: Python
- Homepage: http://recurrence-plot.tk/
- Size: 926 KB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Comparison of calculation speed for recurrence plots/ recurrence quantification analysis for MATLAB, R, Python, and Julia
[](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/pucicu/RP_Speed_Test)
## General
Measuring the calculation time for creating a recurrence plot (RP)
and calculation of the standard recurrence quantification measures
for the Rössler system with the standard parameters (_a_ = 0.25, _b_ = 0.25, and _c_ = 4)
and a sampling time of _Δt_ = 0.05.
The RPs were calculated using Euclidean norm and a threshold of _ε_ = 1.2.
Selected RQA measures are calculated and compared for the used tools.
Only the _x_-component of the Rössler system is used, after removing the first 1,000
points as transients. A simple time delay embedding with _m_ = 3 and _τ_ = 6
is applied.
The RP and RQA calculations are implemented for MATLAB, R,
Julia, Python, and a C++-based tool using the following packages/ tools
Software | Package/ URL
---------|-------------
MATLAB | simple _rp.m_ v1.2 code
MATLAB | _CRP Toolbox_ v5.26(R36) code
R | _crqa_ v2.0.2
Julia | _DynamicalSystems.jl_ v1.4.0
Julia | _RecurrenceMicrostatesAnalysis.jl_ 0.2.24
Julia | _RQA_Samp_
Python | simple RP and RQA implementation (included)
Python | _pyunicorn_ v0.6.1
Python | _PyRQA_ v8.0.0
Python | _AccRQA_ v0.9.1
C++ | _RQA_OpenMP_ v1.414
## Requirements
Software | Requirements
---------|--------------
MATLAB | install the code from as a subfolder `rp`
MATLAB | install the code from by calling the installer file `install` from the MATLAB commandline
R | packages `nonlinearTseries`, `crqa`, `abind`, `tictoc`
Julia | packages `OrdinaryDiffEq`, `DelayEmbeddings`, `DynamicalSystems`, `DelimitedFiles`, `RecurrenceMicrostatesAnalysis`, and `RQA_Samp`
Python | packages `PyRQA`, `pyunicorn`, `accrqa`, `numpy`, `scipy`
OpenMP | download the code from to a subfolder `Libs` and compile it as specified in the `README.md`
For Python, see file `requirements.txt` (you can use `pip install -r requirements.txt` to get the required Python packages).
## Procedure
The recurrence analysis is performed on the time series obtained from the Rössler system with growing length, starting with _N_ = 200 (ending at max. _N_ = 500,000), increasing in steps to provide equidistant points along the _x_-axis in a log-log plot. The increase of length will be stopped when the calculation time exceeds 600 sec. For each selected length, the calculation time is measured 10 times and then averaged.
Not all RQA measures are available across all implementations (e.g., in simple Python code or in RQA_Samp). Network measure calculations were disabled in all examples.
The calculation time is measured for:
- Recurrence Plot (RP) computation (where applicable), and
- Recurrence Quantification Analysis (RQA).
__Note:__ If RQA calculations require a precomputed RP, the RQA time includes the RP calculation time.
For calculations using the different implementations, individual scripts are available. These scripts can be executed in batch via the shell script `run_software_test.sh`.
For high-performance computing (HPC), SLURM scripts are provided. To enable HPC submission, set the flag `SUBMIT_HPC=1` in the script `run_software_test.sh`.
## Results
The results presented here were primarily computed on a single node of the "Foote" high-performance cluster at PIK. Each node is equipped with:
- 1 × AMD EPYC 9554 Genoa processor (64 cores @ 3.1 GHz),
- 768 GB RAM.
For GPU-based calculations (using the AccRQA and PyRQA packages), we utilised:
- NVIDIA H100 HBM3 (80 GB, OpenCL 3.0, CUDA).
MPI-based calculations for AccRQA were executed across 128 compute nodes.
Recurrence plots (RPs) cannot be calculated using _RQA_OpenMP_, _RQA_Samp_, and _RecurrenceMicrostates_. Therefore, RP calculation times are not available for these implementations.
## Copyright
Norbert Marwan\
Potsdam Institute for Climate Impact Research\
3/2026
License: GPLv3+