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Cloud native, scalable storage engine for various types of energy data.

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[![PyPI](https://img.shields.io/pypi/v/multidimio.svg)][install_pip]
[![Conda](https://img.shields.io/conda/vn/conda-forge/multidimio)][install_conda]
[![Python Version](https://img.shields.io/pypi/pyversions/multidimio)][python version]
[![Status](https://img.shields.io/pypi/status/multidimio.svg)][status]
[![License](https://img.shields.io/pypi/l/multidimio)][apache 2.0 license]

[![Tests](https://github.com/TGSAI/mdio-python/workflows/Tests/badge.svg)][tests]
[![Codecov](https://codecov.io/gh/TGSAI/mdio-python/branch/main/graph/badge.svg)][codecov]
[![Read the documentation at https://mdio-python.readthedocs.io/](https://img.shields.io/readthedocs/mdio-python/latest.svg?label=Read%20the%20Docs)][read the docs]

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)][pre-commit]
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)][ruff]

[![PyPI Downloads](https://static.pepy.tech/personalized-badge/multidimio?period=total&units=international_system&left_color=grey&right_color=blue&left_text=PyPI%20downloads)][pypi_]
[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/multidimio?label=Conda%20downloads&style=flat)][conda-forge_]

[pypi_]: https://pypi.org/project/multidimio/
[conda-forge_]: https://anaconda.org/conda-forge/multidimio
[status]: https://pypi.org/project/multidimio/
[python version]: https://pypi.org/project/multidimio
[read the docs]: https://mdio-python.readthedocs.io/
[tests]: https://github.com/TGSAI/mdio-python/actions?workflow=Tests
[codecov]: https://app.codecov.io/gh/TGSAI/mdio-python
[pre-commit]: https://github.com/pre-commit/pre-commit
[ruff]: https://github.com/astral-sh/ruff
[install_pip]: https://mdio-python.readthedocs.io/en/latest/installation.html#using-pip-and-virtualenv
[install_conda]: https://mdio-python.readthedocs.io/en/latest/installation.html#using-conda

> 🎉 MDIO v1 is out. Ingestion and export for SEG-Y is fully functional with templates! However, there may still
> be minor issues. Please report any issues you encounter.

> 🚧👷🏻 We are actively working on updating the documentation and adding missing features to v1 release. Please
> check back later for more updates!

**_"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][read the docs] for more information.

This is not an official TGS product.

# Features

**Shared Features**

- Abstractions for common energy data types (see below).
- Cloud native chunked storage based on [Zarr][zarr] and [fsspec][fsspec].
- Standardized models for lossy and lossless data compression using [Blosc][blosc] and [ZFP][zfp].
- Distributed reads and writes using [Dask][dask].
- Powerful command-line-interface (CLI) based on [Click][click]

**Domain Specific Features**

- Oil & Gas Data
  - Import and export 2D - 5D seismic data types stored in SEG-Y.
  - Optimized chunking logic for various seismic types using MDIO templates.
  - Native [Xarray][xarray] data model and interface wrapper.
  - Import seismic interpretation, horizon, data. **FUTURE**

# Installing MDIO

Simplest way to install _MDIO_ via [pip] from [PyPI]:

```shell
$ pip install multidimio
```

or install _MDIO_ via [conda] from [conda-forge]:

```shell
$ 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: `TGSAI/segy`\
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](https://www.tgs.com/). The current maintainer is [Brian Michell](https://github.com/BrianMichell) with the support of
many more great colleagues.

[pypi]: https://pypi.org/
[conda-forge]: https://conda-forge.org/
[file an issue]: https://github.com/TGSAI/mdio-python/issues
[pip]: https://pip.pypa.io/
[conda]: https://docs.conda.io/
[dask]: https://www.dask.org/
[zarr]: https://zarr.dev/
[fsspec]: https://filesystem-spec.readthedocs.io/en/latest/
[s3fs]: https://s3fs.readthedocs.io/
[gcsfs]: https://gcsfs.readthedocs.io/
[adlfs]: https://github.com/fsspec/adlfs
[blosc]: https://www.blosc.org/
[zfp]: https://computing.llnl.gov/projects/zfp
[xarray]: https://xarray.dev/
[click]: https://palletsprojects.com/p/click/

[apache 2.0 license]: https://github.com/TGSAI/mdio-python/blob/main/LICENSE
[contributor guide]: https://github.com/TGSAI/mdio-python/blob/main/CONTRIBUTING.md
[command-line usage]: https://mdio-python.readthedocs.io/en/latest/usage.html
[api reference]: https://mdio-python.readthedocs.io/en/latest/reference.html
[installation instructions]: https://mdio-python.readthedocs.io/en/latest/installation.html

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