issue_comments

Table actions

2 rows where issue = 374025325 and user = 2448579 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

  • dcherian · 2 ✖

issue 1

  • Array indexing with dask arrays · 2 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
992699334 https://github.com/pydata/xarray/issues/2511#issuecomment-992699334 https://api.github.com/repos/pydata/xarray/issues/2511 IC_kwDOAMm_X847K2PG dcherian 2448579 2021-12-13T17:21:20Z 2021-12-13T17:21:20Z MEMBER

IIUC this cannot work lazily in most cases if you have dimension coordinate variables. When xarray constructs the output after indexing, it will try to index those coordinate variables so that it can associate the right timestamp (for e.g) with the output.

The example from @ulijh should work though (it has no dimension coordinate or indexed variables)

python import xarray as xr import dask.array as da import numpy as np da = xr.DataArray(np.random.rand(3*4*5).reshape((3,4,5))).chunk(dict(dim_0=1)) idcs = da.argmax('dim_2') da[dict(dim_2=idcs)]

The example by @rafa-guedes (thanks for that one!) could be made to work I think.

``` python import numpy as np import dask.array as da import xarray as xr

darr = xr.DataArray(data=[0.2, 0.4, 0.6], coords={"z": range(3)}, dims=("z",)) good_indexer = xr.DataArray( data=np.random.randint(0, 3, 8).reshape(4, 2).astype(int), coords={"y": range(4), "x": range(2)}, dims=("y", "x") ) bad_indexer = xr.DataArray( data=da.random.randint(0, 3, 8).reshape(4, 2).astype(int), coords={"y": range(4), "x": range(2)}, dims=("y", "x") )

In [5]: darr
Out[5]: <xarray.DataArray (z: 3)> array([0.2, 0.4, 0.6]) Coordinates: * z (z) int64 0 1 2

In [6]: good_indexer
Out[6]: <xarray.DataArray (y: 4, x: 2)> array([[0, 1], [2, 2], [1, 2], [1, 0]]) Coordinates: * y (y) int64 0 1 2 3 * x (x) int64 0 1

In [7]: bad_indexer
Out[7]: <xarray.DataArray 'reshape-417766b2035dcb1227ddde8505297039' (y: 4, x: 2)> dask.array<reshape, shape=(4, 2), dtype=int64, chunksize=(4, 2), chunktype=numpy.ndarray> Coordinates: * y (y) int64 0 1 2 3 * x (x) int64 0 1

In [8]: darr[good_indexer]
Out[8]: <xarray.DataArray (y: 4, x: 2)> array([[0.2, 0.4], [0.6, 0.6], [0.4, 0.6], [0.4, 0.2]]) Coordinates: z (y, x) int64 0 1 2 2 1 2 1 0 * y (y) int64 0 1 2 3 * x (x) int64 0 1 ```

We can copy the dimension coordinates of the output (x,y) directly from the indexer. And the dimension coordinate on the input (z) should be a dask array in the output (since z is not a dimension coordinate in the output, this should be fine)

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Array indexing with dask arrays 374025325
568107398 https://github.com/pydata/xarray/issues/2511#issuecomment-568107398 https://api.github.com/repos/pydata/xarray/issues/2511 MDEyOklzc3VlQ29tbWVudDU2ODEwNzM5OA== dcherian 2448579 2019-12-20T22:14:34Z 2019-12-20T22:14:34Z MEMBER

I don't think any one is working on it. We would appreciate it if you could try to fix it.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Array indexing with dask arrays 374025325

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]);