grainsizetools-jwestraadt 3.2.1

Maintained by Marco A. Lopez-Sanchez - This website was last modified: 2024-06-26

GrainSizeTools is a free, open-source, cross-platform script written in Python that provides tools for (1) quantifying and visualising grain size populations, (2) applying stereological methods to approximate the true grain size distribution from 2D sections and (3) estimating differential stress for different mineral phases via paleopiezometry. The script has been designed to be accessible to users with no previous experience of the Python programming language and focuses on scientific reproducibility by including several ready-made templates for the different use cases. For users with programming skills, the script is organised in a modular (functional) way to facilitate reuse and code extension.

Latest release: v3.2.0
Date: 2024-06-26
See notes at https://github.com/marcoalopez/GrainSizeTools/releases/tag/v3.2.0 View project on GitHub

Features at a glance

• Import and manipulate tabular data sets including text, CSV, or Excel formats via Pandas.

• Fully automated descriptive statistics of grain size populations including:

  • Multiple averages (arithmetic and geometric means, median, and frequency peak ("mode") via Gaussian Kernel Density Estimator)
  • Estimation of robust confidence intervals (including some specific methods for lognormal populations such as the modified Cox or the GCI method)
  • Measures of dispersion and population shape
  • Normality and lognormality tests

• Estimation of differential stress using palaeopiezometers. It includes piezometric relations for quartz, olivine, calcite and feldspar (more to come!)

• Ready-to-publish plots in bitmap or vector format (see screenshots below), including:

  • Histograms and kernel density estimates
  • Area or volume-weighted plots
  • Normalized plots
  • Quantile-quantile plots, and more

• Stereology methods (approximating the true 3D grain size distribution from data collected in flat sections):

  • Saltykov method
  • Two-step method (log-normal fitting, shape characterization)
  • Estimate the volume fraction occupied by any grain size range

Download

https://github.com/marcoalopez/GrainSizeTools/releases

Installation

GrainSizeTools uses uv for dependency and environment management.

1. Install uv

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Or via pip: pip install uv

2. Clone the repository

git clone https://github.com/marcoalopez/GrainSizeTools.git
cd GrainSizeTools

3. Create the environment and install dependencies

uv sync --all-groups

This creates a .venv virtual environment with Python 3.11 and installs all dependencies (numpy, scipy, matplotlib, pandas, pyyaml) along with JupyterLab for running notebooks.

4. Launch JupyterLab

uv run jupyter lab

Then open any of the template notebooks in grain_size_tools/ or the examples in grain_size_tools/example_notebooks/.

Running scripts directly

Any Python script or command can be run inside the managed environment using uv run:

uv run python your_script.py

Usage

GrainSizeTools is a proper Python package and can be imported directly:

import grain_size_tools as gst

# or import submodules individually
from grain_size_tools import summarize, plot, stereology, piezometers

Describe a grain size population

import numpy as np
import grain_size_tools as gst

# load your grain diameter data (in microns)
data = np.loadtxt('my_data.txt')

# print descriptive statistics with confidence intervals
gst.summarize(data)

# visualize the distribution
fig, ax = gst.plot.distribution(data)

Stereological correction (2D → 3D)

# Saltykov method
fig, axes = gst.stereology.Saltykov(data, numbins=12)

# Two-step method (returns lognormal parameters)
fig, ax = gst.stereology.two_step(data)

Paleopiezometry

# load the piezometric database
version, metadata, db = gst.piezometers.load_piezometers_from_yaml(None)

# list available piezometers
gst.piezometers.list_piezometers(db)

# estimate differential stress (MPa) from a grain size (microns)
gst.piezometers.calc_diffstress(db.quartz.__dict__['Stipp_Tullis'], grain_size=40.0)

Documentation

https://github.com/marcoalopez/GrainSizeTools/wiki

Screenshots

Citation guidelines

If you have used the script, please consider citing the following paper:

Lopez-Sanchez, Marco A. (2018). GrainSizeTools: a Python script for grain size analysis and paleopiezometry based on grain size. Journal of Open Source Software 3, 863, https://doi.org/10.21105/joss.00863

By citing this paper, you are giving proper credit to the author and acknowledging his work.

License

GrainSizeTools script is licensed under the Apache License, Version 2.0 (the "License")

The documentation of GrainSizeTools script is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

Community guidelines

The GitHub site where the project is hosted offers several options (you'll need a GitHub account, it's free!)::

• Open a discussion: This is a place to:
  • Ask questions you are wondering about.
  • Requests for specific features or share ideas.
  • Interact with the developers.
• Open an issue: This is a place to report or track bugs.
• Make a pull request: You have modified, corrected or added a feature to one of the notebooks and send it to one of the developers to review it and add it to the main page.

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Copyright © 2017-2024 Marco A. Lopez-Sanchez

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