Ian Hunt-Isaak
I am working as an Xarray community developer at Earthmover. In this role I am focused on improving Xarray’s support and documentation for the biology/biomedical community. Prior to this I completed my PhD in which I extensively used Xarray, zarr and the Pydata stack to implement custom microscope control software and analyze multimodal timelapse single cell microscopy data. I loved the open source scientific software so much that now I get to work full time improving it and sharing it with others.
Sessions
Xarray provides data structures for multi-dimensional labeled arrays and a toolkit for scalable data analysis on large, complex datasets. Many real-world datasets fit this structure. However, a common roadblock for users is knowing how to load the data in Xarray and then how to best use Xarray’s tools to represent the structure of the data. In this hands-on tutorial we will showcase how to work with Xarray, various ways to get real-world data into Xarray (with examples from geosciences and biology) and finally how to easily make complex selections on data using community developed custom indexes.
Installation Instructions: https://tutorial.xarray.dev/workshops/scipy2026/index.html
In the past year Xarray has seen increased usage across various sub-fields of biology, revealing interesting challenges. It can be difficult to determine the best way to represent a data structure (e.g. anndata, NGFF-Zarr) as an Xarray object. Furthermore, some use cases such as whole brain imaging require the use of lesser known Xarray features such as custom indexes.
In this talk I will showcase examples of how to encode common biological data structures as Xarray objects. Finally, I will demonstrate how the custom index infrastructure has expanded what types of data can be usefully encoded in Xarray.