issue_comments: 721360380
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/4562#issuecomment-721360380 | https://api.github.com/repos/pydata/xarray/issues/4562 | 721360380 | MDEyOklzc3VlQ29tbWVudDcyMTM2MDM4MA== | 20617032 | 2020-11-03T20:34:02Z | 2020-11-03T20:34:02Z | NONE |
To elaborate a little more on my particular use case, as it might give insight or an alternative solution: I often have time data taken under different experimental parameters, which are my coordinates. However, often the coordinate matrix is very sparse, meaning that my coordinate matrix might be 5x5x5, but I only have 10 data points or so somewhat randomly sampling this space. So being able to see all my 'test cases' with respect to hue/col etc is very useful to quickly examine the data and coordinate combinations, which helps once I want to unstack the array deal with all of the empty parameter space. |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
735523592 |