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/3564#issuecomment-1439022617,https://api.github.com/repos/pydata/xarray/issues/3564,1439022617,IC_kwDOAMm_X85Vxb4Z,1839645,2023-02-21T20:01:04Z,2023-02-21T20:01:04Z,MEMBER,"Oops I think the url just changed
https://chrisholdgraf.com/blog/2019/2019-10-22-xarray-neuro/","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-1438976297,https://api.github.com/repos/pydata/xarray/issues/3564,1438976297,IC_kwDOAMm_X85VxQkp,7991816,2023-02-21T19:18:26Z,2023-02-21T19:18:26Z,NONE,"> In case it's helpful for inspiration, we took a similar approach with the MNE-Python package (neuro electrophysiology package):
>
> https://mne.tools/stable/index.html
>
> Maybe there are at least 3 levels in there, actually:
>
> * **Examples** - short vignettes that highlight one very specific piece of functionality, key-words for the example should be `ctrl-f`able in the title
>
> * **Tutorials** - in-depth guides through a common part of workflow that xarray wishes to enable, with more explanation and detail
>
> * **Domain use-cases** - examples of how xarray can facilitate use-cases in particular fields. Probably cover at a high-level many of the steps that multiple tutorials cover in-depth. More for ""inspiration and buy-in"" than in-depth learning.
>
>
> Does that make sense?
@choldgraf seems like this page is down (https://predictablynoisy.com/xarray-explore-ieeg). Are these examples available elsewhere?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-1190061811,https://api.github.com/repos/pydata/xarray/issues/3564,1190061811,IC_kwDOAMm_X85G7ubz,703554,2022-07-20T09:44:40Z,2022-07-20T09:44:40Z,CONTRIBUTOR,"Hi folks,
Just to mention that we've created a short tutorial on xarray which is meant as a gentle intro to folks coming from the malaria genetics field, who mostly have never heard of xarray before. We illustrate xarray first using outputs from a geostatistical model of how insecticide-treated bednets are used in Africa. We then give a couple of brief examples of how we use xarray for genomic data. There's video walkthroughs in French and English:
https://anopheles-genomic-surveillance.github.io/workshop-5/module-1-xarray.html
Please feel free to link to this in the xarray tutorial site if you'd like to :)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-1109951313,https://api.github.com/repos/pydata/xarray/issues/3564,1109951313,IC_kwDOAMm_X85CKINR,2448579,2022-04-26T15:39:16Z,2022-04-26T15:39:16Z,MEMBER,We've started discussing how to reorganize the xarray-tutorial repository here: https://github.com/xarray-contrib/xarray-tutorial/issues/53 . Comments are welcome!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-815887972,https://api.github.com/repos/pydata/xarray/issues/3564,815887972,MDEyOklzc3VlQ29tbWVudDgxNTg4Nzk3Mg==,2448579,2021-04-08T14:51:18Z,2022-04-21T20:29:35Z,MEMBER,@hafez-ahmad can you ask this question in Discussions? https://github.com/pydata/xarray/discussions,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-815880491,https://api.github.com/repos/pydata/xarray/issues/3564,815880491,MDEyOklzc3VlQ29tbWVudDgxNTg4MDQ5MQ==,20365917,2021-04-08T14:41:59Z,2022-04-21T20:29:30Z,NONE,"Hey everyone !
is there any way to change or reorder month names [
'DJF' 'JJA' 'MAM' 'SON'] during seasonal grouping? I like to change
'DJF' 'JJA' 'MAM' 'SON' combination and find out winter season Dec+Jan+Feb+Mar=winter season.
Your assistant highly appreciated.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-812669474,https://api.github.com/repos/pydata/xarray/issues/3564,812669474,MDEyOklzc3VlQ29tbWVudDgxMjY2OTQ3NA==,2448579,2021-04-02T19:05:59Z,2021-04-02T19:05:59Z,MEMBER,"Hi @apkrelling thanks for offering to help!
I think we can still add more domain-specific examples for meteorology and oceanography. @rabernat had some plans for this, maybe he can describe them.
> how to migrate from numpy to xarray, if that is still needed.
This would be totally great!
","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-812212847,https://api.github.com/repos/pydata/xarray/issues/3564,812212847,MDEyOklzc3VlQ29tbWVudDgxMjIxMjg0Nw==,74330736,2021-04-01T22:33:57Z,2021-04-01T22:33:57Z,CONTRIBUTOR,"Hello everyone, is this issue still relevant?
I could add a domain-use case for oceanography or meteorology, but it seems like that has already been done under
- getting started -> examples -> **ROMS Ocean Model Example**
- getting started -> examples -> **Calculating Seasonal Averages from Time Series of Monthly Means**
1) So there's no need to work on domain-use cases for oceanography or meteorology, is that correct?
2) Also, I'd be happy to contribute with something about **how to migrate from numpy to xarray**, if that is still needed.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-565605904,https://api.github.com/repos/pydata/xarray/issues/3564,565605904,MDEyOklzc3VlQ29tbWVudDU2NTYwNTkwNA==,1839645,2019-12-13T20:57:01Z,2019-12-13T20:58:09Z,MEMBER,"> For larger datasets, rather than storing them in github, a good approach is to create an archive on zenodo.org from which the data can be pulled.
Another note from MNE - we have a ""datasets"" sub-module that knows how to pull a few datasets from various online repositories (and in different structures). These store in a local folder (by default, `~/mne_data` I believe) and then they get fast-loaded after the first download. Many of the datasets are then stored in online repositories like OSF (https://osf.io/rxvq7/).
For datasets that aren't gigantic it's a pretty nice system. https://mne.tools/stable/overview/datasets_index.html?highlight=datasets","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-565539415,https://api.github.com/repos/pydata/xarray/issues/3564,565539415,MDEyOklzc3VlQ29tbWVudDU2NTUzOTQxNQ==,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](https://www.divio.com/blog/documentation/) 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](http://xarray.pydata.org/en/stable/computation.html#wrapping-custom-computation) 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](http://xarray.pydata.org/en/stable/internals.html#extending-xarray) 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.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-565516039,https://api.github.com/repos/pydata/xarray/issues/3564,565516039,MDEyOklzc3VlQ29tbWVudDU2NTUxNjAzOQ==,1197350,2019-12-13T16:50:45Z,2019-12-13T16:50:45Z,MEMBER,"> if we're uploading real data for these, how big can/should the files be? It might affect what dataset I use.
This is a good question. We need the tutorials to be able to run and build within a CI environment. That's the main constraint.
For larger datasets, rather than storing them in github, a good approach is to create an archive on https://zenodo.org/ from which the data can be pulled.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-565505147,https://api.github.com/repos/pydata/xarray/issues/3564,565505147,MDEyOklzc3VlQ29tbWVudDU2NTUwNTE0Nw==,14808389,2019-12-13T16:21:37Z,2019-12-13T16:21:37Z,MEMBER,https://www.divio.com/blog/documentation/ might be a useful reference for this?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-561227888,https://api.github.com/repos/pydata/xarray/issues/3564,561227888,MDEyOklzc3VlQ29tbWVudDU2MTIyNzg4OA==,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.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-557633282,https://api.github.com/repos/pydata/xarray/issues/3564,557633282,MDEyOklzc3VlQ29tbWVudDU1NzYzMzI4Mg==,1839645,2019-11-22T18:04:50Z,2019-11-22T18:04:50Z,MEMBER,"In case it's helpful for inspiration, we took a similar approach with the MNE-Python package (neuro electrophysiology package):
https://mne.tools/stable/index.html
Maybe there are at least 3 levels in there, actually:
* **Examples** - short vignettes that highlight one very specific piece of functionality, key-words for the example should be `ctrl-f`able in the title
* **Tutorials** - in-depth guides through a common part of workflow that xarray wishes to enable, with more explanation and detail
* **Domain use-cases** - examples of how xarray can facilitate use-cases in particular fields. Probably cover at a high-level many of the steps that multiple tutorials cover in-depth. More for ""inspiration and buy-in"" than in-depth learning.
Does that make sense?","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165