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  • TomNicholas · 2 ✖

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  • DOC: from examples to tutorials · 2 ✖

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565539415 https://github.com/pydata/xarray/issues/3564#issuecomment-565539415 https://api.github.com/repos/pydata/xarray/issues/3564 MDEyOklzc3VlQ29tbWVudDU2NTUzOTQxNQ== TomNicholas 35968931 2019-12-13T17:50:15Z 2019-12-13T17:50:15Z MEMBER

Maybe there are at least 3 levels in there, actually...

The article linked by @keewis is well worth reading in my opinion - it describes a similar breakdown of different types of documentation:

  • Tutorials - learning-oriented lessons to get newcomers started,
  • How-to guides - goal-oriented series of steps to solve a specific problem,
  • Explanation - understanding-oriented discussion providing background and context,
  • Reference - information-oriented description of technical machinery.

I think for xarray there is another type, like you suggest @choldgraf:

  • Domain use-cases (/inspiration/showing-off) - showcase-oriented examples of groups using xarray in anger to do something cool.

I personally think xarray in general has reference nailed, lots of good explanation, but is generally a bit weaker on tutorials and how-to guides, and doesn't have many examples of domain use-cases.

I have some ideas for how-to's (maybe these should all go in a separate issue?):

  • How to migrate from numpy to xarray - Huge numbers of numpy users need to shown exactly what code should be replaced with what, and what they can then stop worrying about.
  • How to apply your own analysis functions - i.e. apply_ufunc how-to. The existing documentation on that is more along the lines of an explanation in my opinion, and I've certainly found apply_ufunc to have a steep learning curve.
  • How to organise domain-specific functionality - In-depth guide to various tricks you can pull with accessors, and when you might want to go beyond that. The documentation we have on that only shows a couple of possible approaches.

We need the tutorials to be able to run and build within a CI environment.

So @rabernat for small datasets what might be an appropriate max filesize? I literally have no idea. ~1MB?

a good approach is to create an archive on https://zenodo.org/

I'll look into that.

 
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   DOC: from examples to tutorials 527323165
561227888 https://github.com/pydata/xarray/issues/3564#issuecomment-561227888 https://api.github.com/repos/pydata/xarray/issues/3564 MDEyOklzc3VlQ29tbWVudDU2MTIyNzg4OA== TomNicholas 35968931 2019-12-03T15:48:05Z 2019-12-03T15:48:05Z MEMBER

@rabernat I'm going to be making a simple plasma physics-oriented xarray tutorial to give at a workshop next week.

I was wondering - if we're uploading real data for these, how big can/should the files be? It might affect what dataset I use.

 
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   DOC: from examples to tutorials 527323165

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