issue_comments

Table actions

1 row where author_association = "NONE", issue = 654150730 and user = 22492773 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

  • pl-marasco · 1 ✖

issue 1

  • Xarray combine_by_coords return the monotonic global index error · 1 ✖

author_association 1

  • NONE · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
781553004 https://github.com/pydata/xarray/issues/4213#issuecomment-781553004 https://api.github.com/repos/pydata/xarray/issues/4213 MDEyOklzc3VlQ29tbWVudDc4MTU1MzAwNA== pl-marasco 22492773 2021-02-18T18:37:07Z 2021-02-19T07:38:04Z NONE

@TomNicholas I've landed on this discussion looking for a solution for what I consider the exact same problem. Indeed the overlapping is something that all the users of Sentinel 2 Level 1c will figure out. All the observations are deployed to users through a series of tiles following the MGRS grid system. Each tile has an overlapping area with the bordered once and is varying according to the position of the tile in relation to the reference system. Indeed the approach you are describing can solve the problem but would require the analysis of the bounding box and a consequential selection through the .sel(). In Rasterio this can be easily obtained through the .merge module. To have a quick example of how ti is used have a look here

You are right in pointing that there are multiple ways to treat the overlapping values but I would stick with the most common one that is as well reported in the link you mentioned. In other words (min, max, average, first, last) would be already a huge plus.

About dask, indeed is helping a lot to create a delayed object of the tiles (consider that at least for S2 data are in jp2 and we are forced to use open_rasterio instead of open_mfdataset) so the solution should be compatible with this kind of approach. If you need further explanation or I wasn't too clear please let me know.

About Pangeo, indeed a topic should be opened on it and eventually we can move there the discussion but, at least in my opinion, for the moment the right place to discuss is within xarray.

Seems that Sinergise for the AWS service has used the average algorithm to solve the same issue. Seems that all the users that will use the AWS S2 Products will not need to care about the overlap issue.

Edit: update on AWS service

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Xarray combine_by_coords return the monotonic global index error 654150730

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]);