issue_comments

Table actions

2 rows where author_association = "CONTRIBUTOR", issue = 1300534066 and user = 703554 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date)

user 1

  • alimanfoo · 2 ✖

issue 1

  • Explaining xarray in a single picture · 2 ✖

author_association 1

  • CONTRIBUTOR · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1190057727 https://github.com/pydata/xarray/issues/6771#issuecomment-1190057727 https://api.github.com/repos/pydata/xarray/issues/6771 IC_kwDOAMm_X85G7tb_ alimanfoo 703554 2022-07-20T09:40:41Z 2022-07-20T09:41:07Z CONTRIBUTOR

Hi @dcherian,

We are currently reworking https://tutorial.xarray.dev/intro.html and would love to either add your material or link to it if you're creating a consolidated collection of genetics-related material. xref (#3564). We don't have a "domain-specific" section yet but are planning to create one after SciPy.

FWIW we've created a short tutorial on xarray which is meant as a gentle intro to folks coming from the malaria genetics field. 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": 1,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 1,
    "rocket": 0,
    "eyes": 0
}
   Explaining xarray in a single picture 1300534066
1190052947 https://github.com/pydata/xarray/issues/6771#issuecomment-1190052947 https://api.github.com/repos/pydata/xarray/issues/6771 IC_kwDOAMm_X85G7sRT alimanfoo 703554 2022-07-20T09:36:10Z 2022-07-20T09:36:10Z CONTRIBUTOR

Hi @TomNicholas,

I would've thought that latitude and longitude would be 1-dimensional coordinate variables, yet they are drawn as 2-D arrays?

I think that if you assume that the axes of your grid data align with the cardinal directions (East-West / North-South) then you would expect latitude and longitude to be 1D, but if they don't align then the coordinates would need be 2D (i.e. if x and y are merely arbitrary lines along the Earth's surface).

I agree with you though that 2D lat/lon grids are unnecessarily confusing, especially for non-geoscience users.

Interesting, I hadn't considered that. Definitely a bit mind-bending though for us non-geoscientists :)

I like the second diagram you showed more (it's also a neater version of the labelled one I made here). I think it's debatable whether elevation and land_cover constitute coordinates or data variables, but I have no strong opinion on that.

As for improvements, I think it would be clearer to at least use the second image over the first, and perhaps we could improve it further.

SGTM. FWIW on the second diagram I would use "dimensions" instead of "indexes". Getting dimensions first then helps to explain how you can use a coordinate variable to index a dimension.

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Explaining xarray in a single picture 1300534066

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

CREATE TABLE [issue_comments] (
   [html_url] TEXT,
   [issue_url] TEXT,
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [updated_at] TEXT,
   [author_association] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
    ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
    ON [issue_comments] ([user]);