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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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206247218 | MDExOlB1bGxSZXF1ZXN0MjA2MjQ3MjE4 | 2344 | closed | 0 | FutureWarning: creation of DataArrays w/ coords Dataset | hmaarrfk 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/2344 | |||||
393349721 | MDExOlB1bGxSZXF1ZXN0MzkzMzQ5NzIx | 3888 | closed | 0 | [WIP] [DEMO] Add tests for ZipStore for zarr | hmaarrfk 90008 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/3888 | |||||
476537397 | MDExOlB1bGxSZXF1ZXN0NDc2NTM3Mzk3 | 4395 | closed | 0 | WIP: Ensure that zarr.ZipStores are closed | hmaarrfk 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/4395 | |||||
477420193 | MDExOlB1bGxSZXF1ZXN0NDc3NDIwMTkz | 4400 | closed | 0 | [WIP] Support nano second time encoding. | hmaarrfk 90008 | <!-- Feel free to remove check-list items aren't relevant to your change --> 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/4400 | |||||
818499276 | PR_kwDOAMm_X84wyU7M | 6154 | closed | 0 | Use base ImportError not MoudleNotFoundError when testing for plugins | hmaarrfk 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. <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 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 | hmaarrfk 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:  Proposed:  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.  - [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 <dcherian@users.noreply.github.com>" } |
xarray 13221727 | https://github.com/pydata/xarray/pull/7172 | |||
1099657449 | PR_kwDOAMm_X85Bi3Dp | 7221 | closed | 0 | Remove debugging slow assert statement | hmaarrfk 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:  With this merge request:  ```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 | xarray 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 | hmaarrfk 90008 | In relation to my other PR. Without this PR  With the early return  <details><summary>Removing the frivolous copy (does not pass tests)</summary>  </details> <details><summary>Code for benchmark</summary> ```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) ``` </details> xref: https://github.com/pydata/xarray/pull/7221 <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/7222 | ||||
1100177522 | PR_kwDOAMm_X85Bk2By | 7223 | closed | 0 | Dataset insertion benchmark | hmaarrfk 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/7223 | ||||
1103975300 | PR_kwDOAMm_X85BzVOE | 7235 | closed | 0 | Fix type in benchmarks/merge.py | hmaarrfk 90008 | I don't think this affects what is displayed that is determined by param_names <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/7235 | ||||
1103984081 | PR_kwDOAMm_X85BzXXR | 7236 | closed | 0 | Expand benchmarks for dataset insertion and creation | hmaarrfk 90008 | Taken from discussions in https://github.com/pydata/xarray/issues/7224#issuecomment-1292216344 Thank you @Illviljan <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/7236 | ||||
1139443490 | PR_kwDOAMm_X85D6oci | 7334 | closed | 0 | Remove code used to support h5py<2.10.0 | hmaarrfk 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. <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 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 | hmaarrfk 90008 | Using `.data` accidentally tries to load the whole lazy arrays into memory. Sad. <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 <dcherian@users.noreply.github.com>" } |
xarray 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 | hmaarrfk 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. <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 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 | hmaarrfk 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. <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/8534 | ||||
1721098007 | PR_kwDOAMm_X85mld8X | 8736 | closed | 0 | Make list_chunkmanagers more resilient to broken entrypoints | hmaarrfk 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() <ipython-input-3-19326f4950bc>:1: UserWarning: Failed to load entrypoint MyChunkManager due to No module named 'my.array._chunkmanager'. Skipping. list_chunkmanagers() {'dask': <xarray.core.daskmanager.DaskManager at 0x7f5b826231c0>} ``` Thank you for considering. <!-- Feel free to remove check-list items aren't relevant to your change --> - [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 | xarray 13221727 | https://github.com/pydata/xarray/pull/8736 | ||||
1723028538 | PR_kwDOAMm_X85ms1Q6 | 8738 | closed | 0 | Don't break users that were already using ChunkManagerEntrypoint | hmaarrfk 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... <!-- Feel free to remove check-list items aren't relevant to your change --> - [ ] 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 | xarray 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 | hmaarrfk 90008 | xref: https://github.com/pydata/xarray/issues/8733 <details> ```python xarray/tests/test_variable.py F ================================================ FAILURES ================================================ ____________________________ TestAsCompatibleData.test_duck_array_with_chunks ____________________________ self = <xarray.tests.test_variable.TestAsCompatibleData object at 0x7f3d1b122e60> 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 = {<class 'xarray.tests.test_variable.TestAsCompatibleData.test_duck_array_with_chunks.<locals>.CustomArray'>} chunkmanagers = {'dask': <xarray.namedarray.daskmanager.DaskManager object at 0x7f3d1b1568f0>} def get_chunked_array_type(*args: Any) -> ChunkManagerEntrypoint[Any]: """ … | 2024-02-13T02:46:47Z | 2024-02-13T03:35:24Z | fbc348922aa26d8d1e01e69b8707656bd9b8ba88 | 0 | cc505c77930130bd527d330f43fe21bf9cd6c182 | d64460795e406bc4a998e2ddae0054a1029d52a9 | CONTRIBUTOR | xarray 13221727 | https://github.com/pydata/xarray/pull/8739 |
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