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xarray-einstats

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Stats, linear algebra and einops for xarray

Installation

To install, run

(.venv) $ pip install xarray-einstats

See the docs for more extensive install instructions.

Overview

As stated in their website:

xarray makes working with multi-dimensional labeled arrays simple, efficient and fun!

The code is often more verbose, but it is generally because it is clearer and thus less error prone and more intuitive. Here are some examples of such trade-off where we believe the increased clarity is worth the extra characters:

numpyxarray
a[2, 5]da.sel(drug="paracetamol", subject=5)
a.mean(axis=(0, 1))da.mean(dim=("chain", "draw"))
a.reshape((-1, 10))da.stack(sample=("chain", "draw"))
a.transpose(2, 0, 1)da.transpose("drug", "chain", "draw")

In some other cases however, using xarray can result in overly verbose code that often also becomes less clear. xarray_einstats provides wrappers around some numpy and scipy functions (mostly numpy.linalg and scipy.stats) and around einops with an api and features adapted to xarray. Continue at the getting started page.

Contributing

xarray-einstats is in active development and all types of contributions are welcome! See the contributing guide for details on how to contribute.

Relevant links

Similar projects

Here we list some similar projects we know of. Note that all of them are complementary and don't overlap:

Cite xarray-einstats

If you use this software, please cite it using the following template and the version specific DOI provided by Zenodo. Click on the badge to go to the Zenodo page and select the DOI corresponding to the version you used DOI

  • Oriol Abril-Pla. (2022). arviz-devs/xarray-einstats <version>. Zenodo. <version_doi>

or in bibtex format:

@software{xarray_einstats2022,
  author       = {Abril-Pla, Oriol},
  title        = {{xarray-einstats}},
  year         = 2022,
  url          = {https://github.com/arviz-devs/xarray-einstats}
  publisher    = {Zenodo},
  version      = {<version>},
  doi          = {<version_doi>},
}