issue_comments: 366791162
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/1914#issuecomment-366791162 | https://api.github.com/repos/pydata/xarray/issues/1914 | 366791162 | MDEyOklzc3VlQ29tbWVudDM2Njc5MTE2Mg== | 5635139 | 2018-02-19T20:05:53Z | 2018-02-19T20:05:53Z | MEMBER | I think that this shouldn't be too hard to 'get done' but also that xarray may not give you much help natively. (I'm not sure though, so take this as hopefully helpful contribution rather than a definitive answer) Specifically, can you do (2) by generating a product of the coords? Either using numpy, stacking, or some simple python: ```python In [3]: list(product(*((data[x].values) for x in data.dims))) Out[3]: [(0.287706062977495, 0.065327131503921), (0.287706062977495, 0.17398282388217068), (0.287706062977495, 0.1455022501442349), (0.42398126102299216, 0.065327131503921), (0.42398126102299216, 0.17398282388217068), (0.42398126102299216, 0.1455022501442349), (0.13357153947234057, 0.065327131503921), (0.13357153947234057, 0.17398282388217068), (0.13357153947234057, 0.1455022501442349), (0.42347765161572537, 0.065327131503921), (0.42347765161572537, 0.17398282388217068), (0.42347765161572537, 0.1455022501442349)] ``` then distribute those out to a cluster if you need, and then unstack them back into a dataset? |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
297560256 |