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 206247218,MDExOlB1bGxSZXF1ZXN0MjA2MjQ3MjE4,2344,closed,0,FutureWarning: creation of DataArrays w/ coords Dataset,90008,"Previously, this would raise a: FutureWarning: iteration over an xarray.Dataset will change in xarray v0.11 to only include data variables, not coordinates. Iterate over the Dataset.variables property instead to preserve existing behavior in a forwards compatible manner. - [ ] Closes #xxxx (remove if there is no corresponding issue, which should only be the case for minor changes) - [ ] Tests added (for all bug fixes or enhancements) - [ ] Tests passed (for all non-documentation changes) - [ ] 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) ",2018-08-05T16:34:59Z,2018-08-06T16:02:09Z,2018-08-06T16:02:09Z,,c44aae428b1867b22c51087d9e04b53933e9664c,,,0,d4dd9ee0c00223e6324d0e7807b4f4b1b1d8fa8b,56381ef444c5e699443e8b4e08611060ad5c9507,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/2344, 393349721,MDExOlB1bGxSZXF1ZXN0MzkzMzQ5NzIx,3888,closed,0,[WIP] [DEMO] Add tests for ZipStore for zarr,90008," - [ ] Related to #3815 - [ ] Tests added - [ ] Passes `isort -rc . && black . && mypy . && flake8` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API ",2020-03-25T02:29:20Z,2020-03-26T04:23:05Z,2020-03-25T21:57:09Z,,e88946e8d7ddfbff98f0af78f1eb0df00edb2521,,,0,37a6b0ddabcb81f4b39aa75038d04c2a824758e3,009aa66620b3437cf0de675013fa7d1ff231963c,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/3888, 476537397,MDExOlB1bGxSZXF1ZXN0NDc2NTM3Mzk3,4395,closed,0,WIP: Ensure that zarr.ZipStores are closed,90008,"ZipStores aren't always closed making it hard to use them as fluidly as regular zarr stores. - [ ] Closes #xxxx - [x] Tests added - [x] Passes `isort . && black . && mypy . && flake8` # master doesn't pass black - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2020-08-31T20:57:49Z,2023-01-31T21:39:15Z,2023-01-31T21:38:23Z,,5ac27926097b0a7f24c250b50e35f8f0dd9a2116,,,0,bbd2515502d7a42ccb94c0569132e7fadd921233,d1e4164f3961d7bbb3eb79037e96cae14f7182f8,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/4395, 477420193,MDExOlB1bGxSZXF1ZXN0NDc3NDIwMTkz,4400,closed,0,[WIP] Support nano second time encoding.,90008," Not too sure i have the bandwidth to complete this seeing as cftime and datetime don't have nanoseconds, but maybe it can help somebody. - [x] Closes #4183 - [x] Tests added - [ ] Passes `isort . && black . && mypy . && flake8` - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2020-09-02T00:16:04Z,2023-03-26T20:59:00Z,2023-03-26T20:08:50Z,,3b78de79321e29d7fb2fc548a03a125c6192a65b,,,0,74e9d72f970b0bfab4f473fc44bf6fe820decda1,d1e4164f3961d7bbb3eb79037e96cae14f7182f8,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/4400, 818499276,PR_kwDOAMm_X84wyU7M,6154,closed,0,Use base ImportError not MoudleNotFoundError when testing for plugins,90008,"Admittedly i had a pretty broken environment (I manually uninstalled C dependencies for python packages installed with conda), but I still expected xarray to ""work"" with a different backend. I hope the comments in the code explain why `ImportError` is preferred to `ModuleNotFoundError`. Thank you for considering. - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-01-11T09:48:36Z,2022-01-11T10:28:51Z,2022-01-11T10:24:57Z,2022-01-11T10:24:57Z,5c08ab296bf9bbcfb5bd3c262e3fdcce986d69ab,,,0,92fc8747305b3e0127ce49884d5fda1382560f69,9226c7ac87b3eb246f7a7e49f8f0f23d68951624,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/6154, 1088467433,PR_kwDOAMm_X85A4LHp,7172,closed,0,Lazy import dask.distributed to reduce import time of xarray,90008,"I was auditing the import time of my software and found that distributed added a non insignificant amount of time to the import of xarray: Using `tuna`, one can find that the following are sources of delay in import time for xarray: To audit, one can use the the command ``` python -X importtime -c ""import numpy as np; import pandas as pd; import dask.array; import xarray as xr"" 2>import.log && tuna import.lo ``` The command as is, breaks out the import time of numpy, pandas, and dask.array to allow you to focus on ""other"" costs within xarray. Main branch: ![image](https://user-images.githubusercontent.com/90008/196051640-8bb182a9-fbb0-4b83-a39d-a576fec25249.png) Proposed: ![image](https://user-images.githubusercontent.com/90008/196051596-34d87232-5cb9-4f3d-84f9-d2ec969c95ce.png) One would be tempted to think that this is due to xarray.testing and xarray.tutorial but those just move the imports one level down in tuna graphs. ![image](https://user-images.githubusercontent.com/90008/196051584-7895b64c-319a-4f9f-8327-b254b6571551.png) - [x] ~~Closes~~ - [x] ~~Tests added~~ - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [x] ~~New functions/methods are listed in `api.rst`~~",2022-10-16T18:25:31Z,2022-10-18T17:41:50Z,2022-10-18T17:06:34Z,2022-10-18T17:06:34Z,89f7de888468eb37979faa686e7d70dbe11fb83c,,,0,6b4aa3c401720e324ffa407c4da7bad6ecaf6fa2,9df2dfca57e1c672f6faf0f7945d2f38921a4bb2,CONTRIBUTOR,"{""enabled_by"": {""login"": ""dcherian"", ""id"": 2448579, ""node_id"": ""MDQ6VXNlcjI0NDg1Nzk="", ""avatar_url"": ""https://avatars.githubusercontent.com/u/2448579?v=4"", ""gravatar_id"": """", ""url"": ""https://api.github.com/users/dcherian"", ""html_url"": ""https://github.com/dcherian"", ""followers_url"": ""https://api.github.com/users/dcherian/followers"", ""following_url"": ""https://api.github.com/users/dcherian/following{/other_user}"", ""gists_url"": ""https://api.github.com/users/dcherian/gists{/gist_id}"", ""starred_url"": ""https://api.github.com/users/dcherian/starred{/owner}{/repo}"", ""subscriptions_url"": ""https://api.github.com/users/dcherian/subscriptions"", ""organizations_url"": ""https://api.github.com/users/dcherian/orgs"", ""repos_url"": ""https://api.github.com/users/dcherian/repos"", ""events_url"": ""https://api.github.com/users/dcherian/events{/privacy}"", ""received_events_url"": ""https://api.github.com/users/dcherian/received_events"", ""type"": ""User"", ""site_admin"": false}, ""merge_method"": ""squash"", ""commit_title"": ""Lazy import dask.distributed to reduce import time of xarray (#7172)"", ""commit_message"": ""* Lazy import testing and tutorial\r\n\r\n* Lazy import distributed to avoid a costly import\r\n\r\n* Revert changes to __init__\r\n\r\n* Explain why we lazy import\r\n\r\n* Add release note\r\n\r\n* dask.distritubed.lock now supports blocking argument\r\n\r\nCo-authored-by: Deepak Cherian ""}",13221727,https://github.com/pydata/xarray/pull/7172, 1099657449,PR_kwDOAMm_X85Bi3Dp,7221,closed,0,Remove debugging slow assert statement,90008,"We've been trying to understand why our code is slow. One part is that we use xarray.Datasets almost like dictionaries for our data. The following code is quite common for us ```python import xarray as xr dataset = xr.Dataset() dataset['a'] = 1 dataset['b'] = 2 ``` However, through benchmarks, it became obvious that the `merge_core` method of xarray was causing alot of slowdowns. `main` branch: ![image](https://user-images.githubusercontent.com/90008/197914741-c920046a-e957-4584-9e00-082575fd1f6c.png) With this merge request: ![image](https://user-images.githubusercontent.com/90008/197914642-9d9439a3-397b-4f04-abb2-ddc62c7b4849.png) ```python from tqdm import tqdm import xarray as xr from time import perf_counter import numpy as np N = 1000 # Everybody is lazy loading now, so lets force modules to get instantiated dummy_dataset = xr.Dataset() dummy_dataset['a'] = 1 dummy_dataset['b'] = 1 del dummy_dataset time_elapsed = np.zeros(N) dataset = xr.Dataset() for i in tqdm(range(N)): time_start = perf_counter() dataset[f""var{i}""] = i time_end = perf_counter() time_elapsed[i] = time_end - time_start # %% from matplotlib import pyplot as plt plt.plot(np.arange(N), time_elapsed * 1E3, label='Time to add one variable') plt.xlabel(""Number of existing variables"") plt.ylabel(""Time to add a variables (ms)"") plt.ylim([0, 50]) plt.grid(True) ``` - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-10-26T01:43:08Z,2022-10-28T02:49:44Z,2022-10-28T02:49:44Z,2022-10-28T02:49:44Z,040816a64f52974a79f631c55d920f4b6a4c22ec,,,0,1a58759ea804775564a4e074e28444d0241e9f2a,c000690c7aa6dd134b45e580f377681a0de1996c,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/7221, 1099665485,PR_kwDOAMm_X85Bi5BN,7222,closed,0,Actually make the fast code path return early for Aligner.align,90008,"In relation to my other PR. Without this PR ![image](https://user-images.githubusercontent.com/90008/197916473-0149747e-25b0-41d6-921d-1fad62a23699.png) With the early return ![image](https://user-images.githubusercontent.com/90008/197916546-9ea9a020-2683-4d62-805a-b386835d61c0.png)
Removing the frivolous copy (does not pass tests) ![image](https://user-images.githubusercontent.com/90008/197916632-dbc89c21-94a9-4b92-af11-5b1fa5f5cddd.png)
Code for benchmark ```python from tqdm import tqdm import xarray as xr from time import perf_counter import numpy as np N = 1000 # Everybody is lazy loading now, so lets force modules to get instantiated dummy_dataset = xr.Dataset() dummy_dataset['a'] = 1 dummy_dataset['b'] = 1 del dummy_dataset time_elapsed = np.zeros(N) dataset = xr.Dataset() # tqdm = iter for i in tqdm(range(N)): time_start = perf_counter() dataset[f""var{i}""] = i time_end = perf_counter() time_elapsed[i] = time_end - time_start # %% from matplotlib import pyplot as plt plt.plot(np.arange(N), time_elapsed * 1E3, label='Time to add one variable') plt.xlabel(""Number of existing variables"") plt.ylabel(""Time to add a variables (ms)"") plt.ylim([0, 10]) plt.grid(True) ```
xref: https://github.com/pydata/xarray/pull/7221 - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-10-26T01:59:09Z,2022-10-28T16:22:36Z,2022-10-28T16:22:35Z,2022-10-28T16:22:35Z,65bfa4d10a529f00a9f9b145d1cea402bdae83d0,,,0,f9e23d49244def9a01687d06e8c5ff26e5d68b9e,040816a64f52974a79f631c55d920f4b6a4c22ec,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/7222, 1100177522,PR_kwDOAMm_X85Bk2By,7223,closed,0,Dataset insertion benchmark,90008,"xref: https://github.com/pydata/xarray/pull/7221 - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-10-26T12:09:14Z,2022-10-27T15:38:09Z,2022-10-27T15:38:09Z,2022-10-27T15:38:09Z,c000690c7aa6dd134b45e580f377681a0de1996c,,,0,2fdf774d51cb5d7b9e7e20b58c601b3029a09b10,076bd8e15f04878d7b97100fb29177697018138f,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/7223, 1103975300,PR_kwDOAMm_X85BzVOE,7235,closed,0,Fix type in benchmarks/merge.py,90008,"I don't think this affects what is displayed that is determined by param_names - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-10-29T13:28:12Z,2022-10-29T15:52:45Z,2022-10-29T15:52:45Z,2022-10-29T15:52:45Z,2608c407d73551e0d6055d4b81060e321e905d95,,,0,62c3b918c96734a543a45f94f980a51a5a2091f2,e1936a98059ae29da2861f58a7aff4a56302aac1,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/7235, 1103984081,PR_kwDOAMm_X85BzXXR,7236,closed,0,Expand benchmarks for dataset insertion and creation,90008,"Taken from discussions in https://github.com/pydata/xarray/issues/7224#issuecomment-1292216344 Thank you @Illviljan - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-10-29T13:55:19Z,2022-10-31T15:04:13Z,2022-10-31T15:03:58Z,2022-10-31T15:03:58Z,bc35e39e5754c7a6c84c274815d95cb4130f0000,,,0,bab7cbb9fc9f7b7446b8dac3786c651bf5bc3d29,2608c407d73551e0d6055d4b81060e321e905d95,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/7236, 1139443490,PR_kwDOAMm_X85D6oci,7334,closed,0,Remove code used to support h5py<2.10.0,90008,"It seems that the relevant issue was fixed in 2.10.0 https://github.com/h5py/h5py/commit/466181b178c1b8a5bfa6fb8f217319e021f647e0 I'm not sure how far back you want to fix things. I'm hoping to test this on the CI. I found this since I've been auditing slowdowns in our codebase, which has caused me to review much of the reading pipeline. Do you want to add a test for h5py>=2.10.0? Or can we assume that users won't install things together. https://pypi.org/project/h5py/2.10.0/ I could for example set the backend to not be available if a version of h5py that is too old is detected. One could alternatively, just keep the code here. - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-11-29T19:34:24Z,2022-11-30T23:30:41Z,2022-11-30T23:30:41Z,2022-11-30T23:30:41Z,2fb22cf37b0de6c24ef8eef0f8398d34ee4e3ebb,,,0,84539d6bba8f4d425b53eecde62e229e4fa84257,3aa75c8d00a4a2d4acf10d80f76b937cadb666b7,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/7334, 1145695726,PR_kwDOAMm_X85ESe3u,7356,closed,0,Avoid loading entire dataset by getting the nbytes in an array,90008,"Using `.data` accidentally tries to load the whole lazy arrays into memory. Sad. - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2022-12-05T03:29:53Z,2023-03-17T17:31:22Z,2022-12-12T16:46:40Z,2022-12-12T16:46:40Z,021c73e12cccb06c017ce6420dd043a0cfbf9f08,,,0,02e3cb10c2d49569b888532f48ba8e47226c1e85,db68db6793bdd10f740e1ff7f68d821e853e3d73,CONTRIBUTOR,"{""enabled_by"": {""login"": ""dcherian"", ""id"": 2448579, ""node_id"": ""MDQ6VXNlcjI0NDg1Nzk="", ""avatar_url"": ""https://avatars.githubusercontent.com/u/2448579?v=4"", ""gravatar_id"": """", ""url"": ""https://api.github.com/users/dcherian"", ""html_url"": ""https://github.com/dcherian"", ""followers_url"": ""https://api.github.com/users/dcherian/followers"", ""following_url"": ""https://api.github.com/users/dcherian/following{/other_user}"", ""gists_url"": ""https://api.github.com/users/dcherian/gists{/gist_id}"", ""starred_url"": ""https://api.github.com/users/dcherian/starred{/owner}{/repo}"", ""subscriptions_url"": ""https://api.github.com/users/dcherian/subscriptions"", ""organizations_url"": ""https://api.github.com/users/dcherian/orgs"", ""repos_url"": ""https://api.github.com/users/dcherian/repos"", ""events_url"": ""https://api.github.com/users/dcherian/events{/privacy}"", ""received_events_url"": ""https://api.github.com/users/dcherian/received_events"", ""type"": ""User"", ""site_admin"": false}, ""merge_method"": ""squash"", ""commit_title"": ""Avoid loading entire dataset by getting the nbytes in an array (#7356)"", ""commit_message"": ""* Avoid instantiating entire dataset by getting the nbytes in an array\r\n\r\nUsing `.data` accidentally tries to load the whole lazy arrays into\r\nmemory.\r\n\r\nSad.\r\n\r\n* DOC: Add release note for bugfix.\r\n\r\n* Add test to ensure that number of bytes of sparse array is correctly\r\nreported\r\n\r\n* Add suggested test using InaccessibleArray\r\n\r\n* [pre-commit.ci] auto fixes from pre-commit.com hooks\r\n\r\nfor more information, see https://pre-commit.ci\r\n\r\n* Remove duplicate test\r\n\r\nCo-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>\r\nCo-authored-by: Deepak Cherian ""}",13221727,https://github.com/pydata/xarray/pull/7356, 1369630754,PR_kwDOAMm_X85Rougi,7883,closed,0,Avoid one call to len when getting ndim of Variables,90008,"I admit this is a super micro optimization but it avoids in certain cases the creation of a tuple, and a call to len on it. I hit this as I was trying to understand why Variable indexing was so much slower than numpy indexing. It seems that bounds checking in python is just slower than in C. Feel free to close this one if you don't want this kind of optimization. - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2023-05-29T23:37:10Z,2023-07-03T15:44:32Z,2023-07-03T15:44:31Z,,623aff94a679912d56e5fa38543f20856d368753,,,0,856419b0599c024405510cac5fd71ad8c00deca4,86f99337d803866a4288fc7550f9ee8c495baf87,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/7883, 1637698012,PR_kwDOAMm_X85hnUnc,8534,closed,0,Point users to where in their code they should make mods for Dataset.dims,90008,"Its somewhat annoying to get warnings that point to a line within a library where the warning is issued. It really makes it unclear what one needs to change. This points to the user's access of the `dims` attribute. - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2023-12-10T14:31:29Z,2023-12-10T18:50:10Z,2023-12-10T18:23:42Z,2023-12-10T18:23:42Z,8d168db533715767042676d0dfd1b4563ed0fb61,,,0,0654243249c6a988b5435529eb0fa4d918410ba4,9acc411bc7e99e61269eadf77e96b9ddd40aec9e,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/8534, 1721098007,PR_kwDOAMm_X85mld8X,8736,closed,0,Make list_chunkmanagers more resilient to broken entrypoints,90008,"As I'm a developing my custom chunk manager, I'm often checking out between my development branch and production branch breaking the entrypoint. This made xarray impossible to import unless I re-ran `pip install -e . -vv` which is somewhat tiring. This should help xarray be more resilient in other software's bugs in case they install malformed entrypoints Example: ```python >>> from xarray.core.parallelcompat import list_chunkmanagers >>> list_chunkmanagers() :1: UserWarning: Failed to load entrypoint MyChunkManager due to No module named 'my.array._chunkmanager'. Skipping. list_chunkmanagers() {'dask': } ``` Thank you for considering. - [x] Closes #xxxx - [x] Tests added - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [x] New functions/methods are listed in `api.rst` This is mostly a quality of life thing for developers, I don't see this as a user visible change.",2024-02-11T21:37:38Z,2024-03-13T17:54:02Z,2024-03-13T17:54:02Z,2024-03-13T17:54:02Z,a3f7774443862b1ee8822778a2f813b90cea24ef,,,0,b51a951cf7b6d5258318d590d093044f1fba2eb9,c919739fe6b2cdd46887dda90dcc50cb22996fe5,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/8736, 1723028538,PR_kwDOAMm_X85ms1Q6,8738,closed,0,Don't break users that were already using ChunkManagerEntrypoint,90008,"For example, you just broke cubed https://github.com/xarray-contrib/cubed-xarray/blob/main/cubed_xarray/cubedmanager.py#L15 Not sure how much you care, it didn't seem like anybody other than me ever tried this module on github... - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2024-02-13T02:17:55Z,2024-02-13T15:37:54Z,2024-02-13T03:21:32Z,,0b5b35994d84459ba815d129eb7214cb24aa8bbf,,,0,3003f9b281a9634a791a9b3052769f0bb340bffe,d64460795e406bc4a998e2ddae0054a1029d52a9,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/8738, 1723044865,PR_kwDOAMm_X85ms5QB,8739,open,0,Add a test for usability of duck arrays with chunks property,90008,"xref: https://github.com/pydata/xarray/issues/8733
```python xarray/tests/test_variable.py F ================================================ FAILURES ================================================ ____________________________ TestAsCompatibleData.test_duck_array_with_chunks ____________________________ self = def test_duck_array_with_chunks(self): # Non indexable type class CustomArray(NDArrayMixin, indexing.ExplicitlyIndexed): def __init__(self, array): self.array = array @property def chunks(self): return self.shape def __array_function__(self, *args, **kwargs): return NotImplemented def __array_ufunc__(self, *args, **kwargs): return NotImplemented array = CustomArray(np.arange(3)) assert is_chunked_array(array) var = Variable(dims=(""x""), data=array) > var.load() /home/mark/git/xarray/xarray/tests/test_variable.py:2745: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /home/mark/git/xarray/xarray/core/variable.py:936: in load self._data = to_duck_array(self._data, **kwargs) /home/mark/git/xarray/xarray/namedarray/pycompat.py:129: in to_duck_array chunkmanager = get_chunked_array_type(data) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (CustomArray(array=array([0, 1, 2])),), chunked_arrays = [CustomArray(array=array([0, 1, 2]))] chunked_array_types = {.CustomArray'>} chunkmanagers = {'dask': } def get_chunked_array_type(*args: Any) -> ChunkManagerEntrypoint[Any]: """""" Detects which parallel backend should be used for given set of arrays. Also checks that all arrays are of same chunking type (i.e. not a mix of cubed and dask). """""" # TODO this list is probably redundant with something inside xarray.apply_ufunc ALLOWED_NON_CHUNKED_TYPES = {int, float, np.ndarray} chunked_arrays = [ a for a in args if is_chunked_array(a) and type(a) not in ALLOWED_NON_CHUNKED_TYPES ] # Asserts all arrays are the same type (or numpy etc.) chunked_array_types = {type(a) for a in chunked_arrays} if len(chunked_array_types) > 1: raise TypeError( f""Mixing chunked array types is not supported, but received multiple types: {chunked_array_types}"" ) elif len(chunked_array_types) == 0: raise TypeError(""Expected a chunked array but none were found"") # iterate over defined chunk managers, seeing if each recognises this array type chunked_arr = chunked_arrays[0] chunkmanagers = list_chunkmanagers() selected = [ chunkmanager for chunkmanager in chunkmanagers.values() if chunkmanager.is_chunked_array(chunked_arr) ] if not selected: > raise TypeError( f""Could not find a Chunk Manager which recognises type {type(chunked_arr)}"" E TypeError: Could not find a Chunk Manager which recognises type .CustomArray'> /home/mark/git/xarray/xarray/namedarray/parallelcompat.py:158: TypeError ============================================ warnings summary ============================================ xarray/testing/assertions.py:9 /home/mark/git/xarray/xarray/testing/assertions.py:9: DeprecationWarning: Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0), (to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries) but was not found to be installed on your system. If this would cause problems for you, please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466 import pandas as pd -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html ======================================== short test summary info ========================================= FAILED xarray/tests/test_variable.py::TestAsCompatibleData::test_duck_array_with_chunks - TypeError: Could not find a Chunk Manager which recognises type - [ ] Closes #xxxx - [ ] Tests added - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` ",2024-02-13T02:46:47Z,2024-02-13T03:35:24Z,,,fbc348922aa26d8d1e01e69b8707656bd9b8ba88,,,0,cc505c77930130bd527d330f43fe21bf9cd6c182,d64460795e406bc4a998e2ddae0054a1029d52a9,CONTRIBUTOR,,13221727,https://github.com/pydata/xarray/pull/8739,