issue_comments: 365697240
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/1882#issuecomment-365697240 | https://api.github.com/repos/pydata/xarray/issues/1882 | 365697240 | MDEyOklzc3VlQ29tbWVudDM2NTY5NzI0MA== | 244887 | 2018-02-14T18:17:53Z | 2018-02-14T18:17:53Z | CONTRIBUTOR |
Nice title! I know xarray has its origins and most of its current users in the earth science domains, and so I would expect much of the core of an xarray tutorial to involve various geo* flavored data, but since SciPy has attendees from so many different backgrounds it could be useful to try to survey the scope of work being done with xarray right now. I imagine there must be other users in astronomy, physics, biology and perhaps even quantitative civics/demography that could have interesting snippets to share. For my part, I am using xarray to work with microscopy data in a biological context, and would be happy to share a snippet or two. |
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