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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
78510209 MDExOlB1bGxSZXF1ZXN0Nzg1MTAyMDk= 917 closed 0 added to_dict function for xarray objects jsignell 4806877 After the conversation #432 2016-07-22T17:14:03Z 2016-10-17T20:33:02Z 2016-08-11T21:54:25Z 2016-08-11T21:54:25Z b708f71f05cccfc4c7063ea58c25c46cfe37dd8d     0 592f7bcfcb97871caa6ec982587a6c853a7b8198 9fe2aff89494f0f268dc98bcb549246837d0ae89 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/917  
89891374 MDExOlB1bGxSZXF1ZXN0ODk4OTEzNzQ= 1052 closed 0 catch numpy arrays in attrs before converting to dict jsignell 4806877 Makes it easier to dump to json (after conversation on #917) 2016-10-18T20:22:50Z 2016-10-25T18:19:50Z 2016-10-25T18:19:45Z 2016-10-25T18:19:45Z 293922eba4f5856e23c56a7f9c63482e1f47decb     0 f4ce8a8bfb3af90a8f7e5dedd2b50465a42e6b82 62a7f2c00caacb5b917ff320e28aca9cb4396649 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/1052  
148502414 MDExOlB1bGxSZXF1ZXN0MTQ4NTAyNDE0 1654 closed 0 [DOCS] PyNIO is now available on conda-forge jsignell 4806877 Just a docs change. Updated instructions for installing PyNIO to use conda-forge. 2017-10-24T20:19:28Z 2017-10-24T20:20:02Z 2017-10-24T20:19:59Z 2017-10-24T20:19:59Z 177b864333a61eb5c53b589c1a3cf48591d19b0d     0 ab4896ab911c88fa0b2a32dc88cb56dca625f156 423d3902ae432989a8a28bf9608621805d7095c5 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/1654  
209931263 MDExOlB1bGxSZXF1ZXN0MjA5OTMxMjYz 2375 closed 0 Make `dim` optional on unstack jsignell 4806877 - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed (for all non-documentation changes) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) Not sure if this is a desirable change but I thought it could be easily discussed as a PR. Just for context I was looking at flattening spatial data for machine learning pipelines and then reshaping after the output had been acquired. I have a NxM array called `b` and am flattening it like: ```python flat_input = b.stack(z=('y', 'x')) ``` Then I use that flat_input in my ML pipeline and get back an `np.array` (called `output`) which I want to unstack using all the metadata that went into stacking my original NxM array b. ```python xr.full_like(flat_input, output).unstack(dim='z') ``` This PR just makes the `dim` argument optional in unstack so that we can use ```python xr.full_like(flat_input, output).unstack() ``` As a follow on PR I was thinking of making a function called `xr.unstack_like(other: xr.DataArray|xr.Dataset, data: array_like)` to encompass this functionality. A couple questions: 1) Would something like `xr.unstack_like` be desirable? 2) Should we be using `xr.full_like` in this way? The documentation in xarray and numpy only mentions scalars, but arrays work fine in both. If this is a supported behavior I could add docs, and if not perhaps a copy and overwrite is a better approach? Here is a gist of the workflow using a tweaked datashader example and datashader example data https://gist.github.com/jsignell/79a6cf2da5c1458211d9dcf34d4417df 2018-08-21T19:29:06Z 2018-09-05T16:01:23Z 2018-09-05T15:19:07Z 2018-09-05T15:19:07Z 73f5b02a42a4003815d2bfc91e658195f5050be1     0 7ec32529652c9b19b18e747154a31b20d2c57b6e 69086b332c6c950587830b266df4e624c2106d89 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/2375  
211114053 MDExOlB1bGxSZXF1ZXN0MjExMTE0MDUz 2384 closed 0 Adding data kwarg to copy to create new objects with same structure as original jsignell 4806877 - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed (for all non-documentation changes) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) This is a PR for the follow on work set out in https://github.com/pydata/xarray/pull/2375#issuecomment-415227870 2018-08-27T13:42:28Z 2018-09-19T13:04:39Z 2018-09-19T01:19:08Z 2018-09-19T01:19:08Z b679f4a9edb2437d11f28bf6516fc7aa8d673acb     0 5f6ce936fac62c295ffede2470d5b4f04bfb315c 4de8dbc3b1de461c0c9d3b002e55d60b46d2e6d2 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/2384  
239651957 MDExOlB1bGxSZXF1ZXN0MjM5NjUxOTU3 2618 closed 0 Adding mask to open_rasterio jsignell 4806877 <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Discussed in #1736 - [x] Tests added - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Not sure if this is the right approach @snowman2 2018-12-18T22:24:04Z 2021-06-24T13:44:33Z 2021-06-23T16:14:28Z   c8a656c6e48b8bdbccc58fb8a001bf40e7c5d998     0 68bdc7f8a50cbfa3c12832fb82d2104fec8971ac 6d93a95d05bdbfc33fff24064f67d29dd891ab58 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/2618  
330694079 MDExOlB1bGxSZXF1ZXN0MzMwNjk0MDc5 3425 closed 0 Html repr jsignell 4806877 <!-- Feel free to remove check-list items aren't relevant to your change --> This PR supersedes #1820 - see that PR for original discussion. See [this gist](https://nbviewer.jupyter.org/gist/jsignell/2b7843a6e4852198bf963fbd299e8d46) to try out the new MultiIndex and options functionality. - [x] Closes #1627, closes #1820 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API TODO: - [x] Add support for Multi-indexes - [x] Probably good to have some opt-in or fail back system in case where we (or users) know that the rendering will not work - [x] Add some tests 2019-10-21T21:08:54Z 2019-10-25T07:00:26Z 2019-10-24T16:48:47Z 2019-10-24T16:48:46Z ba48fbcd6ee14e0bbd8887a970a1125fde6769f0     0 1d960933ab252e0f79f7e050e6c9261d55568057 652dd3ca77dd19bbd1ab21fe556340c1904ec382 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/3425  
332152088 MDExOlB1bGxSZXF1ZXN0MzMyMTUyMDg4 3443 closed 0 jupyterlab dark theme jsignell 4806877 <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] Closes #xxxx - [ ] Tests added - [x] Passes `black . && mypy . && flake8` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Follow on to #3425 to include support for jupyterlab dark theme. Note that this includes slight color changes. The most striking of which is that in jupyterlab light and regular notebook the even rows are white like the background. ## Jlab dark <img width="766" alt="Screen Shot 2019-10-24 at 12 57 56 PM" src="https://user-images.githubusercontent.com/4806877/67508281-b70dbb80-f65e-11e9-83a0-37407d0052c1.png"> ## Jlab light <img width="770" alt="Screen Shot 2019-10-24 at 12 58 34 PM" src="https://user-images.githubusercontent.com/4806877/67508283-b9701580-f65e-11e9-855c-17d3d5e5d136.png"> ## notebook <img width="775" alt="Screen Shot 2019-10-24 at 1 04 13 PM" src="https://user-images.githubusercontent.com/4806877/67508341-d6a4e400-f65e-11e9-8396-f584ee193eda.png"> 2019-10-24T17:08:27Z 2019-10-29T03:47:28Z 2019-10-29T03:47:28Z 2019-10-29T03:47:28Z 43d07b7b1d389a4bfc95c920149f4caa78653e81     0 60218fb5e7ec71ba4bc8398adf41e0ea8d4f61a6 ba48fbcd6ee14e0bbd8887a970a1125fde6769f0 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/3443  
332226754 MDExOlB1bGxSZXF1ZXN0MzMyMjI2NzU0 3444 closed 0 Escaping dtypes jsignell 4806877 <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] Closes #xxxx - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Follow-on to https://github.com/pydata/xarray/pull/3425 to make html_repr work with dtypes like '<U5' 2019-10-24T20:24:33Z 2019-10-24T21:51:18Z 2019-10-24T21:50:20Z 2019-10-24T21:50:20Z bb0a5a2b1c71f7c2622543406ccc82ddbb290ece     0 89fe8bbd42f5104d095d67c48feef6f8c4f558e9 ba48fbcd6ee14e0bbd8887a970a1125fde6769f0 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/3444  
381597390 MDExOlB1bGxSZXF1ZXN0MzgxNTk3Mzkw 3812 closed 0 Turn on html repr by default jsignell 4806877 <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3806 - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API 2020-02-28T21:12:43Z 2020-03-26T02:19:22Z 2020-03-02T23:01:44Z 2020-03-02T23:01:44Z b155853ff6e17172b1b6b16c0da31522718e9409     0 a225878a0912968c022bbf4887f1bb82e9dfc811 8512b7bf498c0c300f146447c0b05545842e9404 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/3812  
993569436 PR_kwDOAMm_X847OKqc 6774 closed 0 Make the `sel` error more descriptive when `method` is unset jsignell 4806877 - [x] Tests added This came up in the tutorial and I was wondering if the error could be made a little clearer. Not sure if the error message should hint that a user might want to use ``method``. ```python import numpy as np import pandas as pd import xarray as xr arr = xr.DataArray( data=np.arange(48).reshape(4, 2, 6), dims=("u", "v", "time"), coords={ "u": [-3.2, 2.1, 5.3, 6.5], "v": [-1, 2.6], "time": pd.date_range("2009-01-05", periods=6, freq="M"), }, ) arr.sel(u=5, time="2009-04-28") # I removed `method="nearest"` ``` Before this PR: ```python-traceback ... KeyError: 5.0 ``` After this PR: ```python-traceback ... KeyError: "not all values found in index 'u'. Did you mean to set `method=`?" ``` 2022-07-11T21:17:07Z 2022-07-13T14:49:24Z 2022-07-12T20:33:00Z 2022-07-12T20:33:00Z 4aae7fd0c39d4462d745dffc6c1eb880a5efa973     0 ecc3683ac13469301b6bccb4b7eb580b9fc464d7 7cc6cc991e586a6158bb656b8001234ccda25407 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/6774  
998210728 PR_kwDOAMm_X847f3yo 6792 closed 0 Raise an error if you pass an invalid key in `chunks` jsignell 4806877 - [x] Tests added This was a minor issue that came up at the Dask BOF. Currently if the key of chunks dict isn't included in the dims then it gets silently ignored. This PR makes xarray raise an error instead. I'm not sure if this is the right place to put this change. So just let me know if it should go somewhere else. I changed an existing test. I am _pretty_ sure it was not intentionally using a key that isn't in the dims. 2022-07-15T21:23:20Z 2022-08-02T15:57:50Z 2022-07-22T16:52:32Z   640ceb9fd05550e9fc8d75e1f2e28559f448f8b9     0 6300884e581848748846983475bf00ad930bb4ef 5678b758bff24db28b53217c70da00c2fc0340a3 CONTRIBUTOR   xarray 13221727 https://github.com/pydata/xarray/pull/6792  

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