geost 0.4.0

The Python interface for geological point data in The Netherlands

GeoST - Geological Subsurface Toolbox

The Geological Subsurface Toolbox (GeoST) package is designed to be an easy-to-use Python interface for working with subsurface point data in The Netherlands (boreholes, well logs and CPT's). It provides selection, analysis and export methods that can be applied generically to the loaded data. It is designed to connect with other Deltares developments such as iMod and DataFusionTools.

The internal BoreholeCollection, LogCollection and CptCollection classes use Pandas for storing data and header information and Pandera for data validation. For spatial functions Geopandas is used. The package also supports reading/writing parquet and geoparquet files through Pandas and Geopandas respectively.

GeoST is a work-in-progress and aims to support an increasing number of data sources.

Installation (user)

In a Python >= 3.12 environment, install the latest stable release using pip:

pip install geost

Or the latest (experimental) version of the main branch directly from GitHub using:

pip install git+https://github.com/Deltares-research/geost.git

Installation (developer)

GeoST uses Pixi for package management and workflows.

With pixi installed, navigate to the folder of the cloned repository and run the following to install all GeoST dependencies and the package itself in editable mode:

pixi install

See the Pixi documentation for more information. Next open the Pixi shell by running:

pixi shell

Finally install the pre-commit hooks that enable automatic checks upon committing changes:

pre-commit install

Documentation

All documentation can be found on our GitHub pages

Examples

For an overview of examples, go directly to the examples on our GitHub pages.

We collect additional examples that make use of GeoST and other Subsurface Toolbox developments in the Deltares sst-examples repository.

Contributing

You can contribute by testing, raising issues and making pull requests. Some general guidelines:

• Use new branches for developing new features or bugfixes. Use prefixes such as feature/ bugfix/ experimental/ to indicate the type of branch
• Add unit tests (and test data) for new methods and functions using pytest.
• Add Numpy-style docstrings
• Use pre-commit (see installation for developers on this page)