pull_requests: 1340771338
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| id | node_id | number | state | locked | title | user | body | created_at | updated_at | closed_at | merged_at | merge_commit_sha | assignee | milestone | draft | head | base | author_association | auto_merge | repo | url | merged_by |
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| 1340771338 | PR_kwDOAMm_X85P6owK | 7821 | closed | 0 | Implement multidimensional initial guess and bounds for `curvefit` | 20118130 | - [x] Closes #7768 - [x] Tests added - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` With this PR, it's possible to pass an initial guess to `curvefit` that is a DataArray, which will be broadcast to the data dimensions. This way, the initial guess can vary with the data coordinates. I also added examples of using `curvefit` to the documentation, both a basic example and one with the multidimensional guess. I have a couple of questions: - Should we change the signature to `p0: dict[str, float | DataArray] | None`, instead of `dict[str, Any]` (and same for bounds)? scipy only optimizes over scalars, so I think it would be safe to assume that the values should either be those, or arrays that can be broadcast. - The usage example of curvefit is only in the docstring for DataArray, so now the docs differ between DA and dataset. But the example uses a DataArray only, so this should be ok, right? | 2023-05-06T13:09:49Z | 2023-06-01T15:51:40Z | 2023-05-31T12:43:07Z | 2023-05-31T12:43:07Z | 9909f90b4781be89e3f3ff7c87893928b3e3be6e | 0 | d081ee694273d67296b2860c87f60d378ab109fa | f45eb733b97e0a20f2981b6b20e8e8dcc815e529 | CONTRIBUTOR | 13221727 | https://github.com/pydata/xarray/pull/7821 |
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