issue_comments: 373226888
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/1989#issuecomment-373226888 | https://api.github.com/repos/pydata/xarray/issues/1989 | 373226888 | MDEyOklzc3VlQ29tbWVudDM3MzIyNjg4OA== | 6815844 | 2018-03-15T01:10:37Z | 2018-03-15T01:15:33Z | MEMBER | I notice that bottleneck does the dtype conversion. I think in your environment bottleneck is installed. ```python In [9]: np.sum(a) # equivalent to a.sum(), using bottleneck Out[9]: <xarray.DataArray ()> array(499943.21875) In [10]: np.sum(a.data) # numpy native Out[10]: 499941.53 In [15]: bn.nansum(a.data) Out[15]: 499943.21875 In [11]: a.sum(dim=('x', 'y')) # multiple dims calls native np.sum Out[11]: <xarray.DataArray ()> array(499941.53, dtype=float32) ``` It might be an upstream issue. |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
305373563 |