issue_comments: 786813358
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| 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/2799#issuecomment-786813358 | https://api.github.com/repos/pydata/xarray/issues/2799 | 786813358 | MDEyOklzc3VlQ29tbWVudDc4NjgxMzM1OA== | 90008 | 2021-02-26T18:19:28Z | 2021-02-26T18:19:28Z | CONTRIBUTOR | I hope the following can help users that struggle with the speed of xarray: I've found that when doing numerical computation, I often use the xarray to grab all the metadata relevant to my computation. Scale, chromaticity, experimental information. Eventually, i create a function that acts as a barrier: - Xarray input (high level experimental data) - Computation parameters output (low level implementation detail relevant information). The low level implementation can operate on the fast numpy arrays. I've found this to be the struggle with creating high level APIs that do things like sanitize inputs (xarray routines like For the example that @nbren12 brought up originally, it might be better to create xarray routines (if they don't exist already) that can create fast iterators for the underlying numpy arrays given a set of dimensions that the user cares about. |
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