issue_comments

Table actions

2 rows where issue = 1221848774 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

  • polyval: Use Horner's algorithm + support chunked inputs · 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
1118960785 https://github.com/pydata/xarray/pull/6548#issuecomment-1118960785 https://api.github.com/repos/pydata/xarray/issues/6548 IC_kwDOAMm_X85CsfyR dcherian 2448579 2022-05-05T19:17:24Z 2022-05-05T19:17:24Z MEMBER

Forcing the user to be explicit reduces bugs and user support requests :) so we like to do that.

Thanks again @headtr1ck this is a great PR!

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   polyval: Use Horner's algorithm + support chunked inputs 1221848774
1115117344 https://github.com/pydata/xarray/pull/6548#issuecomment-1115117344 https://api.github.com/repos/pydata/xarray/issues/6548 IC_kwDOAMm_X85Cd1cg dcherian 2448579 2022-05-02T16:52:59Z 2022-05-02T16:52:59Z MEMBER

Nice this looks like an improvement for everything other than dask arrays with only 100 elements, which is not a good use-case for dask.

I was slightly concerned that the recrusive algorithm wouldn't work well with dask but it does seem to work better.

``` python def other_polyval(coord, coeffs, degree_dim="degree"): x = coord.data

deg_coord = coeffs[degree_dim]
N = int(deg_coord.max()) + 1

lhs = xr.DataArray(
    np.stack([x ** (N - 1 - i) for i in range(N)], axis=1),
    dims=(coord.name, degree_dim),
    coords={
        coord.name: coord.data,
        degree_dim: np.arange(deg_coord.max() + 1)[::-1],
    },
)
return xr.dot(lhs, coeffs, dims=degree_dim)

coeffs = xr.DataArray(np.random.randn(2), dims="degree") da = xr.DataArray(dask.array.random.random((10**6), chunks=(10000)), dims=("x")) print(len(da.data.dask)) print(len(xr.polyval(da, coeffs).data.dask)) print(len(other_polyval(da, coeffs).data.dask)) 100 502 1005 ```

 
{
    "total_count": 1,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 1,
    "eyes": 0
}
   polyval: Use Horner's algorithm + support chunked inputs 1221848774

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]);