issue_comments: 391441780
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/2175#issuecomment-391441780 | https://api.github.com/repos/pydata/xarray/issues/2175 | 391441780 | MDEyOklzc3VlQ29tbWVudDM5MTQ0MTc4MA== | 1200058 | 2018-05-23T17:58:55Z | 2018-05-24T15:34:27Z | NONE | In general, I'd work with data "lego blocks". Visualizations up to three dimensions would be self-explaining. One block = scalar, a row of blocks = vector, a plane of blocks = matrix, a cuboid of blocks = 3D array. Different variables can then be aligned along each dimension (similar to the red and orange planes aligned to the right side of the pink cuboid) More than three dimensions could be handled by placing multiple cuboid-blocks (like the blue and pink cuboid in the logo). The relational sizes of different dimensions should be chosen carefully, maybe with some non-linear scaling?
Or we could separate large dimensions in the middle:
However, I'm not sure how to realize that... |
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325661581 |
(just an illustration, drawing what I'd like to have in libreoffice is hard)