issue_comments

Table actions

3 rows where author_association = "MEMBER" and issue = 330918967 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 1

issue 1

  • DataArray.interp() : poor performance · 3 ✖

author_association 1

  • MEMBER · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
396049670 https://github.com/pydata/xarray/issues/2223#issuecomment-396049670 https://api.github.com/repos/pydata/xarray/issues/2223 MDEyOklzc3VlQ29tbWVudDM5NjA0OTY3MA== fujiisoup 6815844 2018-06-10T13:36:42Z 2018-06-10T13:49:58Z MEMBER

Thanks for your deeper analysis.

It seems everything's well with xarray.

Happy to hear that.

I first thought i'll get a 1D array which is not the case (this is often the behavior I want).

Our interp is working orthogonally by default, so passing two arrays sized 10,000 will result in interpolation of 100,000,000 values. In order to get a 1D array, you can pass two dataarrays with the same dimension, python new_tension = xr.DataArray(new_tension, dims='new_dim') new_resistance = xr.DataArray(new_resistance, dims='new_dim') arr.interp(tension=new_tension, resistance=new_resistance) which gives a 1d array with the new dimension new_dim. See here for the details.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   DataArray.interp() : poor performance 330918967
396050056 https://github.com/pydata/xarray/issues/2223#issuecomment-396050056 https://api.github.com/repos/pydata/xarray/issues/2223 MDEyOklzc3VlQ29tbWVudDM5NjA1MDA1Ng== fujiisoup 6815844 2018-06-10T13:42:59Z 2018-06-10T13:42:59Z MEMBER

I want to keep this issue open, as the performance can be increased for such a case.

In the above example, python arr.interp(tension=new_tension, resistance=new_resistance) and python arr.interp(tension=new_tension).interp(resistance=new_resistance) gives the same result (for 'linear' and 'nearest' methods), but the latter runs much faster. This difference looks similar to the difference between our orthogonal indexing and vectorized indexing. We may need orthogonal interpolation path, which would significantly increase the performance in some cases.

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   DataArray.interp() : poor performance 330918967
396002143 https://github.com/pydata/xarray/issues/2223#issuecomment-396002143 https://api.github.com/repos/pydata/xarray/issues/2223 MDEyOklzc3VlQ29tbWVudDM5NjAwMjE0Mw== fujiisoup 6815844 2018-06-09T22:09:27Z 2018-06-09T22:09:27Z MEMBER

@gwin-zegal , thank you for using our new feature and reporting the issue. I confirmed the poor performance of interp.

I will look inside later, whether problem is on our code or upstream (scipy.interpolate).

A possible workaround for your code is to change python arr.interp({'tension': new_tension, 'resistance': new_resistance}) to python arr.interp({tension': new_tension}).interp('resistance': new_resistance}) but it does not solve all the problems.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   DataArray.interp() : poor performance 330918967

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]);