
“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).
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][install] in the documentation.
Using MDIO#
Please see the [Command-line Reference][usage] for details.
For Python API please see the [API Reference][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. Original authors and current maintainers are Altay Sansal and Sri Kainkaryam; with the support of many more great colleagues.
This project template is based on @cjolowicz’s Hypermodern Python Cookiecutter template.