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https://github.com/lukasmosser/digital_rocks_data
Download and load digital rocks datasets
https://github.com/lukasmosser/digital_rocks_data
Last synced: 6 days ago
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Download and load digital rocks datasets
- Host: GitHub
- URL: https://github.com/lukasmosser/digital_rocks_data
- Owner: LukasMosser
- License: apache-2.0
- Created: 2022-04-23T08:24:55.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-04-26T20:09:02.000Z (over 2 years ago)
- Last Synced: 2024-10-01T15:08:22.627Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 4.23 MB
- Stars: 28
- Watchers: 4
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Digital Rocks Data
### Make downloading and using Digital Rock Data great again!
[](https://github.com/lukasmosser/digital_rocks_data/actions/workflows/build.yaml)[](https://github.com/lukasmosser/digital_rocks_data/issues)[](https://lukasmosser.github.io/digital_rocks_data/index.html)
## Quick Start
### Installation from Github using pip
```bash
pip install drd
```
### Loading an Image Dataset
```python
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)](https://en.wikipedia.org/wiki/X-ray_microtomography).
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](https://www.digitalrocksportal.org/), [Zenodo](https://www.zenodo.org), 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](https://docs.xarray.dev/en/stable/) 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](https://www.digitalrocksportal.org/):
- [Eleven Sandstones Dataset](https://www.digitalrocksportal.org/projects/317)
- Berea
- Bandera Brown
- Bandera Gray
- Bentheimer
- Berea Sister Gray
- Berea Upper Gray
- Buff Berea
- Castle Gate
- Kirby
- Leopard
- Parker
[Imperial College London](https://www.imperial.ac.uk/earth-science/research/research-groups/pore-scale-modelling/micro-ct-images-and-networks/):
- MicroCT Images of Sandstones and Carbonates 2015
- [MicroCT Images of Sandstones and Carbonates 2009](https://figshare.com/projects/micro-CT_Images_-_2009/2275)
## 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.