home / github / pull_requests

Menu
  • Search all tables
  • GraphQL API

pull_requests: 349097383

This data as json

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
349097383 MDExOlB1bGxSZXF1ZXN0MzQ5MDk3Mzgz 3596 closed 0 Add DataArray.pad, Dataset.pad, Variable.pad 12862013 Hello all, This is my first PR to a pydata project. This pull request is still very much a work in progress and I could really use your input on a couple of things. - [x] Closes #2605 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API 1. I moved the custom dask pad method into dask_array_compat to ensure backwards compatability to Dask versions that do not have dask.pad yet. We could chose to drop this support if we wanted to. 2. I'm still in doubt about the function signature, numpy as dask use optional kwargs, but that kinda interferes with the `**pad_width_kwargs`. I chose the signature that I thought looked least awkward. 3. The default behaviour of pad with `mode=constant` pads with NaN's converting the array to float in the process. This goes against the default behaviour of numpy as dask. 4. How should the coordinates of a DataArray be padded? I chose default padding except for modes "edge", "reflect", "symmetric", "wrap". 5. How should we handle inconsistencies between numpy.pad and Dask.pad, it turns out there are a couple [5303](https://github.com/dask/dask/issues/5303) Dataset.pad is coming up. 2019-12-04T21:18:41Z 2020-03-21T11:50:44Z 2020-03-19T14:41:50Z 2020-03-19T14:41:49Z e7d6e12662ae113a57eaf38eb2a19ab9ff92b9a8     0 f781f72ff5e88c49993d0b791f6a93cebb62739c 9fbb4170c1732fe2f3cd57b2b96d770a5bac50ed CONTRIBUTOR   13221727 https://github.com/pydata/xarray/pull/3596  

Links from other tables

  • 0 rows from pull_requests_id in labels_pull_requests
Powered by Datasette · Queries took 2.097ms