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[ pre-commit ] : https://github.com/pre-commit/pre-commit
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[ black ] : https://github.com/psf/black
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- ** _ "MDIO"_ ** is a library to work with large multi-dimensional energy datasets.
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- The primary motivation behind ** MDIO** is to represent multi-dimensional
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- time series data in a format that makes it easier to use in resource assesment ,
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+ ** _ "MDIO"_ ** is a library to work with large multidimensional energy datasets.
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+ The primary motivation behind ** MDIO** is to represent multidimensional
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+ time series data in a format that makes it easier to use in resource assessment ,
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machine learning, and data processing workflows.
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- ## Features
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+ # Features
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** Shared Features**
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@@ -43,18 +43,16 @@ machine learning, and data processing workflows.
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- Oil & Gas Data
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- Import and export 2D - 5D seismic data types stored in SEG-Y.
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- - Import seismic interpretation, horizon, data (experimental).
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- - Optimized chunking logic for various seismic types.
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+ - Import seismic interpretation, horizon, data. ** FUTURE **
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+ - Optimized chunking logic for various seismic types. ** FUTURE **
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- Wind Resource Assessment
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- - Numerical weather prediction models with arbitrary metadata.
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- - Optimized chunking logic for time-series analysis and mapping.
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- - [ Xarray] [ xarray ] interface.
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+ - Numerical weather prediction models with arbitrary metadata. ** FUTURE **
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+ - Optimized chunking logic for time-series analysis and mappin. ** FUTURE **
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+ - [ Xarray] [ xarray ] interface. ** FUTURE **
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- ## Requirements
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+ The features marked as ** FUTURE ** will be open-sourced at a later date.
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- - TODO
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-
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- ## Installation
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+ # Installation
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You can install _ MDIO_ via [ pip] from [ PyPI] :
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@@ -73,30 +71,52 @@ pip install multidimio[lossy]
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` distributed ` installs [ Dask] [ dask ] for parallel, distributed processing.\
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` cloud ` installs [ fsspec] [ fsspec ] backed I/O libraries for [ AWS' S3] [ s3fs ] ,
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[ Google's GCS] [ gcsfs ] , and [ Azure ABS] [ adlfs ] .\
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- ` lossy ` will install the ZFPY library for lossy chunk compression.
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+ ` lossy ` will install the [ ZFPY] [ zfp ] library for lossy chunk compression.
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- ## Usage
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+ # Usage
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Please see the [ Command-line Reference] for details.
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- ## Contributing
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+ For Python API please see the [ API Reference] for details.
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+
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+ # Requirements
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+
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+ ### Minimal
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+
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+ Chunked storage and parallelization: ` zarr ` , ` dask ` , ` numba ` , and ` psutil ` .\
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+ SEG-Y Parsing: ` segyio ` \
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+ CLI and Progress Bars: ` click ` , ` click-params ` , and ` tqdm ` .
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+
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+ ### Optional
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+
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+ Distributed computing ` [distributed] ` : ` distributed ` and ` bokeh ` .\
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+ Cloud Object Store I/O ` [cloud] ` : ` s3fs ` , ` gcsfs ` , and ` adlfs ` .\
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+ Lossy Compression ` [lossy] ` : ` zfpy `
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+
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+ # Contributing
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Contributions are very welcome.
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To learn more, see the [ Contributor Guide] .
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- ## License
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+ # License
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Distributed under the terms of the [ Apache 2.0 license] [ license ] ,
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_ MDIO_ is free and open source software.
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- ## Issues
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+ # Issues
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If you encounter any problems,
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please [ file an issue] along with a detailed description.
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- ## Credits
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+ # Credits
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+
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+ This project was established at [ TGS] ( https://www.tgs.com/ ) . Original authors
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+ and current maintainers are [ Altay Sansal] ( https://github.com/tasansal ) and
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+ [ Sri Kainkaryam] ( https://github.com/srib ) ; with the support of many more great
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+ colleagues.
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- This project was generated from [ @cjolowicz ] 's [ Hypermodern Python Cookiecutter] template.
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+ This project template is based on [ @cjolowicz ] 's [ Hypermodern Python Cookiecutter]
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+ template.
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[ @cjolowicz ] : https://github.com/cjolowicz
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[ pypi ] : https://pypi.org/
@@ -119,3 +139,4 @@ This project was generated from [@cjolowicz]'s [Hypermodern Python Cookiecutter]
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[ license ] : https://github.com/TGSAI/mdio-python/blob/main/LICENSE
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[ contributor guide ] : https://github.com/TGSAI/mdio-python/blob/main/CONTRIBUTING.md
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[ command-line reference ] : https://mdio-python.readthedocs.io/en/latest/usage.html
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+ [ api reference ] : https://mdio-python.readthedocs.io/en/latest/reference.html
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