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Homepage & docs: http://marcoalopez.github.io/GrainSizeTools/
GrainSizeTools is a free, open-source, cross-platform script written in Python that provides several tools for (1) estimating average grain size in polycrystalline materials, (2) characterizing the nature of the distribution of grain sizes (either from apparent distributions or approximating 3D grain size distributions via stereology), and estimating differential stress via paleopizometers. The script requires as the input the areas of the grain profiles measured grain-by-grain on planar sections and does not require previous experience with Python programming language (see documentation below and FAQ). For users with coding skills, the script is organized in a modular way facilitating the reuse and code extension.
Lopez-Sanchez, MA (2018). GrainSizeTools: a Python script for grain size analysis and paleopiezometry based on grain size. Journal of Open Source Software, 3(30), 863, https://doi.org/10.21105/joss.00863
Features
- Extract data automatically from tabular-like files including txt, csv, or excel formats.
- Estimate different statistical descriptors to characterize grain size distributions. Average grain size measures include the arithmetic, geometric, RMS and area-weighted means, median, and frequency peak ("mode") using a Gaussian Kernel Density Estimator. Grain size can be represented in linear, logarithmic, and square root scales.
- Estimate normalized apparent grain size distributions to compare between different grain size populations.
- Estimate differential stress via paleopiezometers including multiple piezometric relations for quartz, olivine, calcite, and feldspar.
- Estimate robust confidence intervals using the student's t-Distribution
- Include several algorithms to estimate the optimal bin size of histograms and the optimal bandwidth of the Gaussian KDE based on population features.
- Approximate the actual 3D grain size distribution from data collected in plane sections (2D data) using the Saltykov method. This includes estimating the volume of a particular grain size fraction.Approximate the actual 3D grain size distribution from data collected in plane sections (2D data) using the Saltykov method. This includes estimating the volume of a particular grain size fraction.
- Approximate the lognormal shape of the 3D grain size distribution via the two-step method and characterize the shape using a single parameter (the MSD - Multiplicative Standard Deviation) .
- Check lognormality using quantile-quantile plots (new in v2.0.3!)
- Ready-to-publish plots in bitmap or vector format (see screenshots for examples).
Project Samples
