"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.
This is not an official TGS product.
Shared Features
Domain Specific Features
The features marked as FUTURE will be open-sourced at a later date.
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.
Please see the Command-line Usage for details.
For Python API please see the API Reference for details.
Chunked storage and parallelization: zarr
, dask
, numba
, and psutil
.\
SEG-Y Parsing: segyio
\
CLI and Progress Bars: click
, click-params
, and tqdm
.
Distributed computing [distributed]
: distributed
and bokeh
.\
Cloud Object Store I/O [cloud]
: s3fs
, gcsfs
, and adlfs
.\
Lossy Compression [lossy]
: zfpy
Contributions are very welcome. To learn more, see the Contributor Guide.
Distributed under the terms of the Apache 2.0 license, MDIO is free and open source software.
If you encounter any problems, please file an issue along with a detailed description.
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.