id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 1718410975,I_kwDOAMm_X85mbN7f,7856,Unrecognized chunk manager dask - must be one of: [],14371165,closed,0,,,11,2023-05-21T08:07:57Z,2024-03-27T19:09:18Z,2023-05-24T16:26:20Z,MEMBER,,,,"### What happened? I have just updated my development branch of xarray to latest main. No other changes. When using `.chunk()` on a Variable xarray crashes. ### What did you expect to happen? No crash ### Minimal Complete Verifiable Example ```Python import numpy as np import pandas as pd import xarray as xr t_size = 8000 t = np.arange(t_size) var = xr.Variable(dims=(""T"",), data=np.random.randn(t_size)).chunk() ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output ```Python Traceback (most recent call last): File ""C:\Users\J.W\AppData\Local\Temp\ipykernel_6480\4053253683.py"", line 8, in var = xr.Variable(dims=(""T"",), data=np.random.randn(t_size)).chunk() File ""C:\Users\J.W\Documents\GitHub\xarray\xarray\core\variable.py"", line 1249, in chunk chunkmanager = guess_chunkmanager(chunked_array_type) File ""C:\Users\J.W\Documents\GitHub\xarray\xarray\core\parallelcompat.py"", line 87, in guess_chunkmanager raise ValueError( ValueError: unrecognized chunk manager dask - must be one of: [] ``` ### Anything else we need to know? Likely from #7019. ### Environment
xr.show_versions() C:\Users\J.W\anaconda3\envs\xarray-tests\lib\site-packages\_distutils_hack\__init__.py:33: UserWarning: Setuptools is replacing distutils. warnings.warn(""Setuptools is replacing distutils."") INSTALLED VERSIONS ------------------ commit: None python: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:30:19) [MSC v.1929 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: ('Swedish_Sweden', '1252') libhdf5: 1.12.2 libnetcdf: 4.8.1 xarray: 2022.9.1.dev266+gbd01f9cc.d20221006 pandas: 1.5.2 numpy: 1.23.5 scipy: 1.9.3 netCDF4: 1.6.0 pydap: installed h5netcdf: 1.0.2 h5py: 3.7.0 Nio: None zarr: 2.13.2 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: 3.2.2 iris: 3.3.0 bottleneck: 1.3.5 dask: 2022.9.2 distributed: 2022.9.2 matplotlib: 3.6.2 cartopy: 0.21.0 seaborn: 0.13.0.dev0 numbagg: 0.2.1 fsspec: 2022.10.0 cupy: None pint: 0.19.2 sparse: 0.13.0 flox: 999 numpy_groupies: 0.9.14+22.g19c7601 setuptools: 65.5.1 pip: 22.3.1 conda: None pytest: 7.2.0 mypy: 1.2.0 IPython: 7.33.0 sphinx: 5.3.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7856/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 962647570,MDExOlB1bGxSZXF1ZXN0NzA1NDA2NDY0,5678,Add typing to the OPTIONS dict,14371165,closed,0,,,11,2021-08-06T11:21:02Z,2024-03-13T21:45:54Z,2021-08-19T22:15:23Z,MEMBER,,0,pydata/xarray/pulls/5678,"This adds typing to the dict values. Using variables as keys is apparently not permitted by mypy so that part has been removed. Attempts to fix issues found in #5662. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5678/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 957439114,MDExOlB1bGxSZXF1ZXN0NzAwODY1NDE3,5662,Limit and format number of displayed dimensions in repr,14371165,closed,0,,,11,2021-08-01T09:12:24Z,2022-08-12T09:07:49Z,2022-01-03T17:38:49Z,MEMBER,,0,pydata/xarray/pulls/5662,"When there's a lot of dims, create a new line and continue printing. If there's even more dims that even a few rows can't display them all then limit the number of dims displayed in similar fashion to coordinates. Questions: * Where should this be used? Datasets, dataarrays, dimensions without coords? * Should `dim_summary_limited` be a straight replacement for `dim_summary`? I'm not super familiar with all the places it is used so I'm unsure. * Should we print the number of dims shown and the total number of dims? If yes, then we need to rethink how the dimensions are displayed, as it's not possible with the current style. See the example with short names. - [x] Closes #5546 - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` Test case: ```python import numpy as np import xarray as xr # A few dims with long names: a = np.arange(0, 24) data_vars = dict() for i in a: data_vars[f""long_variable_name_{i}""] = xr.DataArray( name=f""long_variable_name_{i}"", data=np.arange(0, 20), dims=[f""long_coord_name_{i}_x""], coords={f""long_coord_name_{i}_x"": np.arange(0, 20) * 2}, ) ds0 = xr.Dataset(data_vars) ds0.attrs = {f""attr_{k}"": 2 for k in a} print(ds0) Dimensions: (long_coord_name_0_x: 20, long_coord_name_10_x: 20, long_coord_name_11_x: 20, long_coord_name_12_x: 20, long_coord_name_13_x: 20, long_coord_name_14_x: 20, long_coord_name_15_x: 20, long_coord_name_16_x: 20, long_coord_name_17_x: 20, long_coord_name_18_x: 20, long_coord_name_19_x: 20, long_coord_name_1_x: 20, long_coord_name_20_x: 20, long_coord_name_21_x: 20, long_coord_name_22_x: 20, long_coord_name_23_x: 20, long_coord_name_2_x: 20, long_coord_name_3_x: 20, long_coord_name_4_x: 20, long_coord_name_5_x: 20, long_coord_name_6_x: 20, long_coord_name_7_x: 20, long_coord_name_8_x: 20, long_coord_name_9_x: 20) Coordinates: (12/24) * long_coord_name_0_x (long_coord_name_0_x) int32 0 2 4 6 8 ... 32 34 36 38 * long_coord_name_1_x (long_coord_name_1_x) int32 0 2 4 6 8 ... 32 34 36 38 * long_coord_name_2_x (long_coord_name_2_x) int32 0 2 4 6 8 ... 32 34 36 38 * long_coord_name_3_x (long_coord_name_3_x) int32 0 2 4 6 8 ... 32 34 36 38 * long_coord_name_4_x (long_coord_name_4_x) int32 0 2 4 6 8 ... 32 34 36 38 * long_coord_name_5_x (long_coord_name_5_x) int32 0 2 4 6 8 ... 32 34 36 38 ... * long_coord_name_18_x (long_coord_name_18_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_19_x (long_coord_name_19_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_20_x (long_coord_name_20_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_21_x (long_coord_name_21_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_22_x (long_coord_name_22_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_23_x (long_coord_name_23_x) int32 0 2 4 6 ... 32 34 36 38 Data variables: (12/24) long_variable_name_0 (long_coord_name_0_x) int32 0 1 2 3 4 ... 16 17 18 19 long_variable_name_1 (long_coord_name_1_x) int32 0 1 2 3 4 ... 16 17 18 19 long_variable_name_2 (long_coord_name_2_x) int32 0 1 2 3 4 ... 16 17 18 19 long_variable_name_3 (long_coord_name_3_x) int32 0 1 2 3 4 ... 16 17 18 19 long_variable_name_4 (long_coord_name_4_x) int32 0 1 2 3 4 ... 16 17 18 19 long_variable_name_5 (long_coord_name_5_x) int32 0 1 2 3 4 ... 16 17 18 19 ... long_variable_name_18 (long_coord_name_18_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_19 (long_coord_name_19_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_20 (long_coord_name_20_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_21 (long_coord_name_21_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_22 (long_coord_name_22_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_23 (long_coord_name_23_x) int32 0 1 2 3 ... 16 17 18 19 Attributes: (12/24) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_18: 2 attr_19: 2 attr_20: 2 attr_21: 2 attr_22: 2 attr_23: 2 ``` ```python # Many dims with long names: a = np.arange(0, 200) data_vars = dict() for i in a: data_vars[f""long_variable_name_{i}""] = xr.DataArray( name=f""long_variable_name_{i}"", data=np.arange(0, 20), dims=[f""long_coord_name_{i}_x""], coords={f""long_coord_name_{i}_x"": np.arange(0, 20) * 2}, ) ds1 = xr.Dataset(data_vars) ds1.attrs = {f""attr_{k}"": 2 for k in a} print(ds1) Dimensions: (long_coord_name_0_x: 20, long_coord_name_100_x: 20, long_coord_name_101_x: 20, long_coord_name_102_x: 20, long_coord_name_103_x: 20, long_coord_name_104_x: 20, ... long_coord_name_94_x: 20, long_coord_name_95_x: 20, long_coord_name_96_x: 20, long_coord_name_97_x: 20, long_coord_name_98_x: 20, long_coord_name_99_x: 20, long_coord_name_9_x: 20) Coordinates: (12/200) * long_coord_name_0_x (long_coord_name_0_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_1_x (long_coord_name_1_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_2_x (long_coord_name_2_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_3_x (long_coord_name_3_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_4_x (long_coord_name_4_x) int32 0 2 4 6 ... 32 34 36 38 * long_coord_name_5_x (long_coord_name_5_x) int32 0 2 4 6 ... 32 34 36 38 ... * long_coord_name_194_x (long_coord_name_194_x) int32 0 2 4 6 ... 34 36 38 * long_coord_name_195_x (long_coord_name_195_x) int32 0 2 4 6 ... 34 36 38 * long_coord_name_196_x (long_coord_name_196_x) int32 0 2 4 6 ... 34 36 38 * long_coord_name_197_x (long_coord_name_197_x) int32 0 2 4 6 ... 34 36 38 * long_coord_name_198_x (long_coord_name_198_x) int32 0 2 4 6 ... 34 36 38 * long_coord_name_199_x (long_coord_name_199_x) int32 0 2 4 6 ... 34 36 38 Data variables: (12/200) long_variable_name_0 (long_coord_name_0_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_1 (long_coord_name_1_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_2 (long_coord_name_2_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_3 (long_coord_name_3_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_4 (long_coord_name_4_x) int32 0 1 2 3 ... 16 17 18 19 long_variable_name_5 (long_coord_name_5_x) int32 0 1 2 3 ... 16 17 18 19 ... long_variable_name_194 (long_coord_name_194_x) int32 0 1 2 3 ... 17 18 19 long_variable_name_195 (long_coord_name_195_x) int32 0 1 2 3 ... 17 18 19 long_variable_name_196 (long_coord_name_196_x) int32 0 1 2 3 ... 17 18 19 long_variable_name_197 (long_coord_name_197_x) int32 0 1 2 3 ... 17 18 19 long_variable_name_198 (long_coord_name_198_x) int32 0 1 2 3 ... 17 18 19 long_variable_name_199 (long_coord_name_199_x) int32 0 1 2 3 ... 17 18 19 Attributes: (12/200) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_194: 2 attr_195: 2 attr_196: 2 attr_197: 2 attr_198: 2 attr_199: 2 ``` ```python # Many dims with short names: data_vars = dict() for i in a: data_vars[f""n_{i}""] = xr.DataArray( name=f""n_{i}"", data=np.arange(0, 20), dims=[f""{i}_x""], coords={f""{i}_x"": np.arange(0, 20) * 2}, ) ds2 = xr.Dataset(data_vars) ds2.attrs = {f""attr_{k}"": 2 for k in a} print(ds2) Dimensions: (0_x: 20, 100_x: 20, 101_x: 20, 102_x: 20, 103_x: 20, 104_x: 20, 105_x: 20, 106_x: 20, 107_x: 20, 108_x: 20, 109_x: 20, 10_x: 20, 110_x: 20, 111_x: 20, 112_x: 20, 113_x: 20, 114_x: 20, 115_x: 20, 116_x: 20, 117_x: 20, 118_x: 20, 119_x: 20, 11_x: 20, 120_x: 20, 121_x: 20, 122_x: 20, 123_x: 20, 124_x: 20, 125_x: 20, 126_x: 20, 127_x: 20, 128_x: 20, 129_x: 20, 12_x: 20, 130_x: 20, 131_x: 20, ... 71_x: 20, 72_x: 20, 73_x: 20, 74_x: 20, 75_x: 20, 76_x: 20, 77_x: 20, 78_x: 20, 79_x: 20, 7_x: 20, 80_x: 20, 81_x: 20, 82_x: 20, 83_x: 20, 84_x: 20, 85_x: 20, 86_x: 20, 87_x: 20, 88_x: 20, 89_x: 20, 8_x: 20, 90_x: 20, 91_x: 20, 92_x: 20, 93_x: 20, 94_x: 20, 95_x: 20, 96_x: 20, 97_x: 20, 98_x: 20, 99_x: 20, 9_x: 20) Coordinates: (12/200) * 0_x (0_x) int32 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 * 1_x (1_x) int32 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 * 2_x (2_x) int32 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 * 3_x (3_x) int32 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 * 4_x (4_x) int32 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 * 5_x (5_x) int32 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 ... * 194_x (194_x) int32 0 2 4 6 8 10 12 14 16 ... 22 24 26 28 30 32 34 36 38 * 195_x (195_x) int32 0 2 4 6 8 10 12 14 16 ... 22 24 26 28 30 32 34 36 38 * 196_x (196_x) int32 0 2 4 6 8 10 12 14 16 ... 22 24 26 28 30 32 34 36 38 * 197_x (197_x) int32 0 2 4 6 8 10 12 14 16 ... 22 24 26 28 30 32 34 36 38 * 198_x (198_x) int32 0 2 4 6 8 10 12 14 16 ... 22 24 26 28 30 32 34 36 38 * 199_x (199_x) int32 0 2 4 6 8 10 12 14 16 ... 22 24 26 28 30 32 34 36 38 Data variables: (12/200) n_0 (0_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_1 (1_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_2 (2_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_3 (3_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_4 (4_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_5 (5_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ... n_194 (194_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_195 (195_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_196 (196_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_197 (197_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_198 (198_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n_199 (199_x) int32 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Attributes: (12/200) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_194: 2 attr_195: 2 attr_196: 2 attr_197: 2 attr_198: 2 attr_199: 2 ``` ```python # DataArray with many dimensions: dims = {f""dim_{v}"": 2 for v in np.arange(12)} a = xr.DataArray( name=""LongDataArrayName"", data=np.random.randn(*dims.values()), dims=dims.keys(), coords={k: np.arange(v) * (i + 1) for i, (k, v) in enumerate(dims.items())}, ) print(a) array([[[[[[[[[[[[ 8.28296160e-01, 2.08993090e-01], [ 8.70468836e-01, 8.90423004e-01]], [[ 7.34784427e-01, 2.05408058e-01], [-8.57071909e-02, 8.44265228e-01]]], [[[-9.35953498e-01, -1.28911601e+00], [ 1.10041466e+00, 6.65778297e-02]], [[-1.20951652e+00, 6.75763964e-01], [-4.71836513e-02, 9.06088516e-01]]]], [[[[ 1.59629635e+00, 7.32189004e-01], [-3.93944434e-01, 2.46067012e+00]], [[ 1.20534658e-01, -1.10855175e+00], [ 1.75768289e+00, 1.82771876e+00]]], ... [[[ 1.24664897e+00, 1.72548620e+00], [-7.64230130e-02, -7.96243220e-01]], [[-7.02358327e-01, 2.20921513e+00], [-7.45919399e-01, 8.16166442e-01]]]], [[[[-1.06278662e+00, -3.01061594e-01], [-2.68674730e-01, 7.61941899e-01]], [[-7.40916926e-01, 1.85122750e+00], [-5.42460065e-02, -7.57741769e-01]]], [[[-4.12356234e-02, 7.41777992e-01], [-1.36243505e+00, -1.25845181e+00]], [[-7.42535368e-01, 1.13262286e-01], [ 1.03699306e+00, -8.51127899e-01]]]]]]]]]]]]) Coordinates: * dim_0 (dim_0) int32 0 1 * dim_1 (dim_1) int32 0 2 * dim_2 (dim_2) int32 0 3 * dim_3 (dim_3) int32 0 4 * dim_4 (dim_4) int32 0 5 * dim_5 (dim_5) int32 0 6 * dim_6 (dim_6) int32 0 7 * dim_7 (dim_7) int32 0 8 * dim_8 (dim_8) int32 0 9 * dim_9 (dim_9) int32 0 10 * dim_10 (dim_10) int32 0 11 * dim_11 (dim_11) int32 0 12 ``` ```python # DataArray with many dimensions but no coords: dims = {f""dim_{v}"": 2 for v in np.arange(12)} a = xr.DataArray( name=""LongDataArrayName"", data=np.random.randn(*dims.values()), dims=dims.keys(), ) print(a) array([[[[[[[[[[[[ 2.53218063e-02, -2.01034380e+00], [ 3.07624042e-01, 1.82085569e-01]], [[ 1.23998647e+00, 2.80961964e-01], [ 5.22623248e-01, -2.10621456e-01]]], [[[ 1.55794218e+00, -1.32803310e+00], [-7.41474289e-01, -3.35995545e-01]], [[ 9.96489723e-03, -1.84197059e-01], [-1.24173835e+00, 4.94205388e-01]]]], [[[[-2.11962358e-01, 1.18012909e+00], [-4.62991218e-01, -9.49171994e-01]], [[ 3.90534280e-01, -2.63453002e+00], [ 3.57944636e-01, 2.16335768e-01]]], ... [[[-1.11275429e+00, -9.33574221e-01], [ 8.62574702e-01, 1.14185983e+00]], [[ 1.36795402e+00, 1.14331852e+00], [ 5.96785305e-01, 1.47307855e+00]]]], [[[[ 1.95270558e+00, -7.76150298e-01], [ 2.05301468e+00, -1.15633640e+00]], [[-9.45507288e-01, 1.21096830e+00], [ 1.59340121e+00, -3.60261023e-01]]], [[[ 2.25343528e+00, -2.84332626e-01], [ 1.86644712e-01, -2.78371182e-01]], [[-8.86245009e-01, -4.00356195e-01], [-2.44036388e-01, -1.53543170e+00]]]]]]]]]]]]) Dimensions without coordinates: dim_0, dim_1, dim_2, dim_3, dim_4, dim_5, dim_6, dim_7, dim_8, dim_9, dim_10, dim_11 ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5662/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull