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“MDIO” is a library to work with large multidimensional energy datasets. The primary motivation behind MDIO is to represent multidimensional time series data in a format that makes it easier to use in resource assessment, machine learning, and data processing workflows.

See the documentation for more information.

Features#

Shared Features

  • Abstractions for common energy data types (see below).

  • Cloud native chunked storage based on Zarr and fsspec.

  • Lossy and lossless data compression using Blosc and ZFP.

  • Distributed reads and writes using Dask.

  • Powerful command-line-interface (CLI) based on Click

Domain Specific Features

  • Oil & Gas Data

    • Import and export 2D - 5D seismic data types stored in SEG-Y.

    • Import seismic interpretation, horizon, data. FUTURE

    • Optimized chunking logic for various seismic types. FUTURE

  • Wind Resource Assessment

    • Numerical weather prediction models with arbitrary metadata. FUTURE

    • Optimized chunking logic for time-series analysis and mapping. FUTURE

    • Xarray interface. FUTURE

The features marked as FUTURE will be open-sourced at a later date.

Installing MDIO#

Simplest way to install MDIO via pip from PyPI:

$ pip install multidimio

or install MDIO via conda from conda-forge:

$ conda install -c conda-forge multidimio

Extras must be installed separately on Conda environments.

For details, please see the installation instructions in the documentation.

Using MDIO#

Please see the Command-line Usage for details.

For Python API please see the API Reference for details.

Requirements#

Minimal#

Chunked storage and parallelization: zarr, dask, numba, and psutil.
SEG-Y Parsing: segyio
CLI and Progress Bars: click, click-params, and tqdm.

Optional#

Distributed computing [distributed]: distributed and bokeh.
Cloud Object Store I/O [cloud]: s3fs, gcsfs, and adlfs.
Lossy Compression [lossy]: zfpy

Contributing to MDIO#

Contributions are very welcome. To learn more, see the Contributor Guide.

Licensing#

Distributed under the terms of the Apache 2.0 license, MDIO is free and open source software.

Issues#

If you encounter any problems, please file an issue along with a detailed description.

Credits#

This project was established at TGS. Current maintainer is Altay Sansal with the support of many more great colleagues.

This project template is based on @cjolowicz’s Hypermodern Python Cookiecutter template.