Digital Rocks Data

Make downloading and using Digital Rock Data great again!

Quick Start

Installation from Github using pip

pip install drd

Loading an Image Dataset

from drd.datasets.eleven_sandstones import load_eleven_sandstones

# xarray DataArray with image data
img = load_eleven_sandstones("Berea", "Berea_2d25um_grayscale.raw") 

# Plot average over z dimension
img.mean(dim='z').plot()

About

Digital Rock Images are three-dimensional datasets of rocks and other porous media.
These are typically acquired using three-dimensional imaging techniques such as Micro-Computer Tomography (MicroCT).

They represent a rich dataset that form a basis for characterization of physical processes involving porous media.

Purpose of the drd library

Digital Rock Images are scattered throughout the web on various hosting sites such as the Digital Rocks Portal, Zenodo, or university specific sites. This library aims to make downloading these datasets easy through a python interface so they can be used in automated image processing workflows, reproducible research, or data science and machine learning worfklows.

Furthermore, these images are associated with metadata about their spatial dimensions which should be considered when loading these image datasets.
The library therefore requires these metadata to be available and creates an xarray DataArray which can keep spatial scale information when loading an image dataset.

Each dataset is linked in this library i.e. no hosting is done by the library itself.

Available Datasets

Digital Rocks Portal:

• Eleven Sandstones Dataset
  • Berea
  • Bandera Brown
  • Bandera Gray
  • Bentheimer
  • Berea Sister Gray
  • Berea Upper Gray
  • Buff Berea
  • Castle Gate
  • Kirby
  • Leopard
  • Parker

Imperial College London:

• MicroCT Images of Sandstones and Carbonates 2015
• MicroCT Images of Sandstones and Carbonates 2009

Contributing

Authors are encouraged to contribute their own datasets using the correct metadata.
See drd/datasets/eleven_sandstones.py for an example implementation.
Please add corresponding tests and an example to your pull request.

Creation

This package was created during the Transform 22 software sprint.