Metadata-Version: 2.1
Name: dynamic-chunks
Version: 0.0.2
Summary: Determine appropriate chunk sizes for a given xarray dataset based on target chunk size and chunk aspect ratio
Author-email: Julius Busecke <julius@ldeo.columbia.edu>
License: Apache-2.0
Project-URL: Homepage, https://github.com/jbusecke/dynamic_chunks
Project-URL: Tracker, https://github.com/jbusecke/dynamic_chunks/issues
Classifier: Development Status :: 1 - Planning
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: dask
Requires-Dist: xarray
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'

# dynamic_chunks

Determine appropriate chunk sizes for a given xarray dataset based on target chunk size and 'chunk aspect ratio'

The chunk aspect ratio describes the amount of chunks along a given dimension. Take a dataset with two dimensions (`a` and `b`). A chunk aspect ratio `{'a':2, 'b':1}` means that the number of total chunks along `b` is twice that of `b`.
This concept was inspired by a discussion with [Rich Signell](https://github.com/rsignell-usgs) at Scipy '23. The idea is that one might want to optimize the chunking of a dataset to make e.g. an operation along time n times slower than an operation along spatial dimensions.

## Useage
TBW

## Developer Guide

Set up your development environment with `conda`:

```
conda create --name dynamic_rechunking python=3.10 pip
conda activate dynamic_rechunking
pip install -e ".[test]"
```
