PARSE is an open source package for representativity analysis of 3D binary images. It aims at representativity analysis for different scalar and vector metrics. Using PARSE library, one can estimate determenistic and statistical representative elementary volumes (dREV and sREV) for these metrics. Stationarity analysis and comparison of different images using vector metrics are also possible.
Currently, we provide the following metrics for REV analysis:
- Porosity.
- Permeability.
- Euler density.
- Correlation functions (two-point probabilty
$S_2$ , lineal path function$L_2$ , cluster function$C_2$ , surface-surface function$F_{ss}$ , surface-void function$F_{sv}$ , pore-size function$P$ , chord length function$p$ ). - Pore-network model characterstics (pore and throat numbers, pore and throat radii, connectivity, mean pore and throat radii, mean connectivity).
- Persistence diagrams.
Python 3.x and Julia 1.x with packages StatsBase.jl, LinearAlgebra.jl, CorrelationFunctions.jl (version=0.11.0) and EulerCharacteristic.jl should be installed.
To install the latest PyPI release as a library run
python3 -m pip install revanalyzer
or you can clone this repository and run from local folder
python3 -m pip install .
Documentation is available here on GitHub Pages.
To build the documentation locally clone this repository, then read /docs/README.md
Numerous Jupiter notebooks with examples which show the functionality of PARSE library are available here:
- REV analysis for porosity
- REV analysis for permeability
- REV analysis for Euler density
- REV analysis for correlation functions
- REV analysis for pore-network model characteristics
- REV analysis for persistence diagrams
- Comparison of two images using vector metric
- Stationarity analysis
Mathematical backgound for REV analysis, description of metrics used in 'REVAnalyzer' and application evamples with real porous image data:
Andrey S. Zubov, Center for Computational Physics, Landau School for Physics and Research, Moscow Institute of Physics and Technology.