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 1797233538,I_kwDOAMm_X85rH5uC,7971,Pint errors on python 3.11 and windows,14371165,closed,0,,,2,2023-07-10T17:44:51Z,2024-02-26T17:52:50Z,2024-02-26T17:52:50Z,MEMBER,,,,"### What happened? The CI seems to consistently crash on `test_units.py` now: ``` =========================== short test summary info =========================== FAILED xarray/tests/test_units.py::TestVariable::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestVariable::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[float64-function_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[float64-function_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-function_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-function_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_unary_operations[float64-round] - TypeError: no implementation found for 'numpy.round' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_unary_operations[int32-round] - TypeError: no implementation found for 'numpy.round' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataset::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataset::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] = 12 failed, 14880 passed, 1649 skipped, 146 xfailed, 68 xpassed, 574 warnings in 737.19s (0:12:17) = ``` For more details: https://github.com/pydata/xarray/actions/runs/5438369625/jobs/9889561685?pr=7955 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7971/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1691206894,I_kwDOAMm_X85kzcTu,7802,Mypy errors with matplotlib 3.8,14371165,closed,0,,,6,2023-05-01T19:03:51Z,2023-09-17T05:03:00Z,2023-09-17T05:02:59Z,MEMBER,,,,"Matplotlib has started to support typing in main (https://github.com/matplotlib/matplotlib/issues/20504) and mypy is throwing a few errors:
``` xarray/core/options.py:12: error: Cannot assign to a type [misc] xarray/core/options.py:12: error: Incompatible types in assignment (expression has type ""Type[str]"", variable has type ""Type[Colormap]"") [assignment] xarray/plot/utils.py:808: error: Argument 1 to ""set_xticks"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Iterable[float]"" [arg-type] xarray/plot/utils.py:810: error: Argument 1 to ""set_yticks"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Iterable[float]"" [arg-type] xarray/plot/utils.py:813: error: Argument 1 to ""set_xlim"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Union[float, Tuple[float, float], None]"" [arg-type] xarray/plot/utils.py:815: error: Argument 1 to ""set_ylim"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Union[float, Tuple[float, float], None]"" [arg-type] Generated Cobertura report: /home/runner/work/xarray/xarray/mypy_report/cobertura.xml Installing missing stub packages: /home/runner/micromamba-root/envs/xarray-tests/bin/python -m pip install types-Pillow types-PyYAML types-Pygments types-babel types-colorama types-paramiko types-psutil types-pytz types-pywin32 types-setuptools types-urllib3 Generated Cobertura report: /home/runner/work/xarray/xarray/mypy_report/cobertura.xml Found 154 errors in 10 files (checked 138 source files) xarray/plot/utils.py:1349: error: Unsupported operand types for * (""_SupportsArray[dtype[Any]]"" and ""float"") [operator] xarray/plot/utils.py:1349: error: Unsupported operand types for * (""_NestedSequence[_SupportsArray[dtype[Any]]]"" and ""float"") [operator] xarray/plot/utils.py:1349: error: Unsupported operand types for * (""_NestedSequence[Union[bool, int, float, complex, str, bytes]]"" and ""float"") [operator] xarray/plot/utils.py:1349: note: Left operand is of type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" xarray/plot/utils.py:1349: error: Unsupported operand types for * (""str"" and ""float"") [operator] xarray/plot/utils.py:1349: error: Unsupported operand types for * (""bytes"" and ""float"") [operator] xarray/plot/utils.py:1350: error: Item ""_SupportsArray[dtype[Any]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""_NestedSequence[_SupportsArray[dtype[Any]]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""int"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""float"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""complex"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""str"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""bytes"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""_NestedSequence[Union[bool, int, float, complex, str, bytes]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""_SupportsArray[dtype[Any]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""_NestedSequence[_SupportsArray[dtype[Any]]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""int"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""float"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""complex"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""str"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""bytes"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""_NestedSequence[Union[bool, int, float, complex, str, bytes]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/facetgrid.py:684: error: ""FigureCanvasBase"" has no attribute ""get_renderer"" [attr-defined] xarray/plot/accessor.py:182: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:182: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:309: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:309: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:428: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:428: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:433: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:433: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:552: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:552: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:557: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:557: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:676: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:676: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:681: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:681: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:800: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:800: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:948: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:948: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:1075: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:1075: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:1190: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:1190: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:324: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:324: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:478: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:478: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""x"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""y"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""u"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""v"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""density"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""linewidth"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""color"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""cmap"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""norm"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""arrowsize"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""arrowstyle"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""minlength"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""transform"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""zorder"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""start_points"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""maxlength"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""integration_direction"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""broken_streamlines"" [misc] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[float, Tuple[float, float]]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Colormap, None]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Normalize, None]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""float"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, ArrowStyle]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[Transform]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[float]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Literal['forward', 'backward', 'both']"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""bool"" [arg-type] xarray/plot/dataset_plot.py:751: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:751: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:718: error: Incompatible return value type (got ""Tuple[Union[ndarray[Any, Any], List[ndarray[Any, Any]]], ndarray[Any, Any], Union[BarContainer, Polygon, List[Union[BarContainer, Polygon]]]]"", expected ""Tuple[ndarray[Any, Any], ndarray[Any, Any], BarContainer]"") [return-value] xarray/plot/dataarray_plot.py:996: error: ""Axes"" has no attribute ""view_init"" [attr-defined] xarray/plot/dataarray_plot.py:1106: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1106: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[Sequence[Union[Union[Tuple[float, float, float], str], Union[str, Tuple[float, float, float, float], Tuple[Union[Tuple[float, float, float], str], float], Tuple[Tuple[float, float, float, float], float]]]], Union[Union[Tuple[float, float, float], str], Union[str, Tuple[float, float, float, float], Tuple[Union[Tuple[float, float, float], str], float], Tuple[Tuple[float, float, float, float], float]]], None]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[Union[str, Path, MarkerStyle]]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Colormap, None]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Normalize, None]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[float]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[float, Sequence[float], None]"" [arg-type] xarray/plot/dataarray_plot.py:1615: error: ""Axes"" has no attribute ""set_zlabel"" [attr-defined] xarray/plot/dataarray_plot.py:1655: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1655: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1874: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1874: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2010: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2010: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2146: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2146: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2464: error: ""Axes"" has no attribute ""plot_surface"" [attr-defined] xarray/tests/test_plot.py:427: error: Value of type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" is not indexable [index] xarray/tests/test_plot.py:443: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:457: error: ""None"" not callable [misc] xarray/tests/test_plot.py:462: error: ""None"" not callable [misc] xarray/tests/test_plot.py:465: error: ""None"" not callable [misc] xarray/tests/test_plot.py:471: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:477: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:482: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:486: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:493: error: ""None"" not callable [misc] xarray/tests/test_plot.py:498: error: ""None"" not callable [misc] xarray/tests/test_plot.py:501: error: ""None"" not callable [misc] xarray/tests/test_plot.py:931: error: Module has no attribute ""magma"" [attr-defined] xarray/tests/test_plot.py:933: error: Module has no attribute ""magma"" [attr-defined] xarray/tests/test_plot.py:1173: error: Module has no attribute ""RdBu"" [attr-defined] xarray/tests/test_plot.py:1746: error: Item ""Colormap"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1746: error: Item ""None"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1747: error: Item ""Colormap"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1747: error: Item ""None"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1749: error: Item ""Colormap"" of ""Optional[Colormap]"" has no attribute ""_rgba_over"" [union-attr] xarray/tests/test_plot.py:1749: error: Item ""None"" of ""Optional[Colormap]"" has no attribute ""_rgba_over"" [union-attr] xarray/tests/test_plot.py:1801: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""size"" [union-attr] xarray/tests/test_plot.py:1952: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""min"" [union-attr] xarray/tests/test_plot.py:1952: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""max"" [union-attr] xarray/tests/test_plot.py:1968: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""dtype"" [union-attr] xarray/tests/test_plot.py:1969: error: Value of type ""Optional[ndarray[Any, Any]]"" is not indexable [index] xarray/tests/test_plot.py:2125: error: ""Artist"" has no attribute ""get_clim"" [attr-defined] xarray/tests/test_plot.py:2135: error: ""Colorbar"" has no attribute ""vmin"" [attr-defined] xarray/tests/test_plot.py:2136: error: ""Colorbar"" has no attribute ""vmax"" [attr-defined] xarray/tests/test_plot.py:2202: error: ""Artist"" has no attribute ""get_clim"" [attr-defined] xarray/tests/test_plot.py:2218: error: ""Artist"" has no attribute ""norm"" [attr-defined] xarray/tests/test_plot.py:2747: error: Item ""_AxesBase"" of ""Optional[_AxesBase]"" has no attribute ""legend_"" [union-attr] xarray/tests/test_plot.py:2747: error: Item ""None"" of ""Optional[_AxesBase]"" has no attribute ""legend_"" [union-attr] xarray/tests/test_plot.py:2754: error: Item ""None"" of ""Optional[_AxesBase]"" has no attribute ""get_legend"" [union-attr] xarray/tests/test_plot.py:2775: error: Item ""None"" of ""Optional[FigureBase]"" has no attribute ""axes"" [union-attr] xarray/tests/test_plot.py:2775: error: Argument 1 to ""len"" has incompatible type ""Union[_AxesBase, None, Any]""; expected ""Sized"" [arg-type] xarray/tests/test_plot.py:2803: error: Module has no attribute ""dates"" [attr-defined] xarray/tests/test_plot.py:2812: error: Module has no attribute ""dates"" [attr-defined] xarray/tests/test_plot.py:2831: error: Item ""None"" of ""Optional[_AxesBase]"" has no attribute ""xaxis"" [union-attr] xarray/tests/test_plot.py:2831: error: Module has no attribute ""dates"" [attr-defined] xarray/tests/test_groupby.py:715: error: Argument 1 to ""groupby"" of ""Dataset"" has incompatible type ""ndarray[Any, dtype[signedinteger[Any]]]""; expected ""Union[Hashable, DataArray, IndexVariable]"" [arg-type] xarray/tests/test_groupby.py:715: note: Following member(s) of ""ndarray[Any, dtype[signedinteger[Any]]]"" have conflicts: xarray/tests/test_groupby.py:715: note: __hash__: expected ""Callable[[], int]"", got ""None"" xarray/tests/test_dataset.py:6964: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataset.py:6965: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataset.py:7007: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataset.py:7008: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6687: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6689: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6735: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6737: error: ""PlainQuantity[Any]"" not callable [operator] ```
Some guidance how to solve these: > - `[xy]ticks` in mpl is currently overly narrowly type hinted because I was following the docstring, but I agree that `ArrayLike` is a better type hint for that, plan on updating (including the docstring) upstream > - `[xy]lim` originally neglected the case of passing `set_xlim((min, max))` as a tuple, but that has been updated. xarray has that type hinted as array like, but mpl has it hinted as a 2-tuple (I think it is currently still of floats, but may be expanded as we more directly address units/categoricals/etc). Willing to debate here, but my starting position is that the ""exactly 2 values"" is valuable info here, and I think `tuple` is the only way to do that. > - `get_renderer` is not actually available on all of our backends, we should maybe see if there is a more preferred way of doing what you are doing here that will work for all backends, but haven't looked into it too closely. > - `Module has no attribute ` is another instance of dynamically generated behavior which can't be statically type checked (elegantly, at least), can probably be replaced by `mpl.colormaps[""""]` in many cases, which is statically typecheckable > - Anything to do with 3D Axes is not type hinted, perhaps ignore for now (or help us get that type hinted adequately, but it is relatively low priority currently) > - `Module has no attribute ""dates""` we don't currently type hint dates/units things, but it is on my mind, not sure yet if it will be in first release or not though (may at least put a placeholder that gets rid of this error, but treats everything as ""Any""). _Originally posted by @ksunden in https://github.com/pydata/xarray/issues/7787#issuecomment-1523743471_ > The suggestion from mpl (specifically @tacaswell) was to use [constrained layout](https://matplotlib.org/stable/tutorials/intermediate/constrainedlayout_guide.html) for the purpose that xarray currently uses `get_renderer`, this will ensure that the `facetgrid` works with all mpl backends. _Originally posted by @ksunden in https://github.com/pydata/xarray/issues/7787#issuecomment-1528091492_ > I'm also relatively sure that if you are willing to put a floor on the version of Matplotlib you support `get_window_extent` will use it's internally cached renderer (and when we make it uniformly optional we also fixed the cache invalidation logic). _Originally posted by @tacaswell in https://github.com/pydata/xarray/issues/7787#issuecomment-1528096647_ ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7802/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1795519181,I_kwDOAMm_X85rBXLN,7969,Upstream CI is failing,14371165,closed,0,,,2,2023-07-09T18:51:41Z,2023-07-10T17:34:12Z,2023-07-10T17:33:12Z,MEMBER,,,,"### What happened? The upstream CI has been failing for a while. Here's the latest: https://github.com/pydata/xarray/actions/runs/5501368493/jobs/10024902009#step:7:16 ```python Traceback (most recent call last): File """", line 1, in File ""/home/runner/work/xarray/xarray/xarray/__init__.py"", line 1, in from xarray import testing, tutorial File ""/home/runner/work/xarray/xarray/xarray/testing.py"", line 7, in import numpy as np ModuleNotFoundError: No module named 'numpy' ``` Digging a little in the logs ``` Installing build dependencies: started Installing build dependencies: finished with status 'error' error: subprocess-exited-with-error × pip subprocess to install build dependencies did not run successfully. │ exit code: 1 ╰─> [3 lines of output] Looking in indexes: https://pypi.anaconda.org/scipy-wheels-nightly/simple ERROR: Could not find a version that satisfies the requirement meson-python==0.13.1 (from versions: none) ERROR: No matching distribution found for meson-python==0.13.1 [end of output] ``` Might be some numpy problem? Should the CI be robust enough to handle these kinds of errors? Because I suppose we would like to get the automatic issue created anyway?","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7969/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1125040125,I_kwDOAMm_X85DDr_9,6244,Get pyupgrade to update the typing,14371165,closed,0,,,2,2022-02-05T21:56:56Z,2023-03-12T15:38:37Z,2023-03-12T15:38:37Z,MEMBER,,,,"### Is your feature request related to a problem? Use more up-to-date typing styles on all files. Will reduce number of imports and avoids big diffs when doing relatively minor changes because pre-commit/pyupgrade has been triggered somehow. Related to #6240 ### Describe the solution you'd like Add `from __future__ import annotations` on files with a lot of typing. Let pyupgrade do the rest. ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6244/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1318800553,I_kwDOAMm_X85Om0yp,6833,Require a pull request before merging to main,14371165,closed,0,,,4,2022-07-26T22:09:55Z,2023-01-13T16:51:03Z,2023-01-13T16:51:03Z,MEMBER,,,,"### Is your feature request related to a problem? I was making sure the test in #6832 failed on main, when it did I wrote a few lines in the `what's new` file but forgot switching back to the other branch and accidentally pushed directly to main. :( ### Describe the solution you'd like I think it's best if we require a pull request for merging. We seem to pretty much do this anyway. Seems to be this setting if I understand correctly: ![image](https://user-images.githubusercontent.com/14371165/181120776-dd2dc5c8-6467-41c3-8edf-c332151355cc.png) ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6833/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1376776178,I_kwDOAMm_X85SD-_y,7049,Backend entrypoints not public?,14371165,closed,0,,,0,2022-09-17T13:41:13Z,2022-10-26T16:01:06Z,2022-10-26T16:01:06Z,MEMBER,,,,"### What is your issue? As I've understood it `ZarrBackendEntrypoint` is the engine used when loading zarr-files. But for some reason we show `ZarrStore` in `xr.backends`. I believe the `ZarrStore` class is supposed to be just a implementation detail, right? ```python # The available engines: xr.backends.list_engines() Out[23]: {'netcdf4': , 'h5netcdf': , 'scipy': , 'pseudonetcdf': , 'pydap': , 'store': , 'zarr': } # The public class is ZarrStore instead of ZarrBackendEntrypoint, how come? dir(xr.backends) Out[22]: ['AbstractDataStore', 'BackendArray', 'BackendEntrypoint', 'CachingFileManager', 'CfGribDataStore', 'DummyFileManager', 'FileManager', 'H5NetCDFStore', 'InMemoryDataStore', 'NetCDF4DataStore', 'NioDataStore', 'PseudoNetCDFDataStore', 'PydapDataStore', 'ScipyDataStore', 'ZarrStore', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'api', 'cfgrib_', 'common', 'file_manager', 'h5netcdf_', 'list_engines', 'locks', 'lru_cache', 'memory', 'netCDF4_', 'netcdf3', 'plugins', 'pseudonetcdf_', 'pydap_', 'pynio_', 'rasterio_', 'scipy_', 'store', 'zarr'] ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7049/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1046458609,I_kwDOAMm_X84-X7Dx,5945,Start using `|` instead of `Union` or `Optional` when typing,14371165,closed,0,,,1,2021-11-06T08:12:57Z,2022-06-04T04:26:03Z,2022-06-04T04:26:03Z,MEMBER,,,," **Is your feature request related to a problem? Please describe.** To make it easier reading the typing it is now possible to use `|` instead of `Union` or `Optional`. Here's an example how it looks like in pandas: https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_core.py#L116-L134 **Describe the solution you'd like** Replace for example: * `Union[str, int]` with `str | int` * `Optional[str]` with `None | str` This would likely require adding `from __future__ import annotations` at the top of the file. References https://www.python.org/dev/peps/pep-0604/ ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5945/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1182697604,I_kwDOAMm_X85GfoiE,6416,xr.concat removes datetime information,14371165,closed,0,,,2,2022-03-27T23:19:30Z,2022-03-28T16:05:01Z,2022-03-28T16:05:01Z,MEMBER,,,,"### What happened? xr.concat removes datetime information and can't concatenate the arrays because they don't have compatible types anymore. ### What did you expect to happen? Succesful concatenation with the same type. ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr from datetime import datetime month = np.arange(1, 13, 1) data = np.sin(2 * np.pi * month / 12.0) darray = xr.DataArray(data, dims=[""time""]) darray.coords[""time""] = np.array([datetime(2017, m, 1) for m in month]) darray_nan = np.nan * darray.isel(**{""time"": -1}) darray = xr.concat([darray, darray_nan], dim=""time"") ``` ### Relevant log output ```Python Traceback (most recent call last): File """", line 2, in darray = xr.concat([darray, darray_nan], dim=""time"") File ""c:\users\j.w\documents\github\xarray\xarray\core\concat.py"", line 244, in concat return f( File ""c:\users\j.w\documents\github\xarray\xarray\core\concat.py"", line 642, in _dataarray_concat ds = _dataset_concat( File ""c:\users\j.w\documents\github\xarray\xarray\core\concat.py"", line 555, in _dataset_concat combined_idx = indexes[0].concat(indexes, dim, positions) File ""c:\users\j.w\documents\github\xarray\xarray\core\indexes.py"", line 318, in concat coord_dtype = np.result_type(*[idx.coord_dtype for idx in indexes]) File ""<__array_function__ internals>"", line 5, in result_type TypeError: The DType could not be promoted by . This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (, ) ``` ### Anything else we need to know? Similar to #6384. Happens around here: https://github.com/pydata/xarray/blob/728b648d5c7c3e22fe3704ba163012840408bf66/xarray/core/concat.py#L535 ### Environment
INSTALLED VERSIONS ------------------ commit: None python: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:37:25) [MSC v.1916 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: 1.10.6 libnetcdf: 4.7.4 xarray: 0.16.3.dev99+gc19467fb pandas: 1.3.1 numpy: 1.21.5 scipy: 1.7.1 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 2.10.0 Nio: None zarr: 2.8.3 cftime: 1.5.0 nc_time_axis: 1.3.1 PseudoNetCDF: installed rasterio: 1.2.6 cfgrib: None iris: 3.0.4 bottleneck: 1.3.2 dask: 2021.10.0 distributed: 2021.10.0 matplotlib: 3.4.3 cartopy: 0.19.0.post1 seaborn: 0.11.1 numbagg: 0.2.1 fsspec: 2021.11.1 cupy: None pint: 0.17 sparse: 0.12.0 setuptools: 49.6.0.post20210108 pip: 21.2.4 conda: None pytest: 6.2.4 IPython: 7.31.0 sphinx: 4.3.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6416/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1174585854,I_kwDOAMm_X85GAsH-,6384,xr.concat adds an extra array around elements,14371165,closed,0,,,1,2022-03-20T15:25:49Z,2022-03-21T04:49:23Z,2022-03-21T04:49:23Z,MEMBER,,,,"### What happened? When concatenating dataarrays with `pd.Interval` along a dim the `pd.Interval` is wrapped with a numpy array and appended instead of without like it it was before #5692. ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr shape = (2, 3, 4) darray = xr.DataArray(np.linspace(0, 1, num=np.prod(shape)).reshape(shape)) bins = [-1, 0, 1, 2] a = darray.groupby_bins(""dim_0"", bins).mean(...) a_nan = np.nan * a.isel(**{""dim_0_bins"": -1}) out = xr.concat([a, a_nan], dim=""dim_0_bins"") print(out[""dim_0_bins""]) ``` ### Relevant log output Current result: ```Python array([Interval(-1, 0, closed='right'), Interval(0, 1, closed='right'), Interval(1, 2, closed='right'), array(Interval(1, 2, closed='right'), dtype=object)], dtype=object) Coordinates: * dim_0_bins (dim_0_bins) object (-1, 0] (0, 1] (1, 2] (1, 2] ``` Should be: ```python array([Interval(-1, 0, closed='right'), Interval(0, 1, closed='right'), Interval(1, 2, closed='right'), Interval(1, 2, closed='right')], dtype=object) Coordinates: * dim_0_bins (dim_0_bins) object (-1, 0] (0, 1] (1, 2] (1, 2] ``` ### Anything else we need to know? _No response_ ### Environment
xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:37:25) [MSC v.1916 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: 1.10.6 libnetcdf: 4.7.4 xarray: 0.16.3.dev99+gc19467fb pandas: 1.3.1 numpy: 1.21.5 scipy: 1.7.1 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 2.10.0 Nio: None zarr: 2.8.3 cftime: 1.5.0 nc_time_axis: 1.3.1 PseudoNetCDF: installed rasterio: 1.2.6 cfgrib: None iris: 3.0.4 bottleneck: 1.3.2 dask: 2021.10.0 distributed: 2021.10.0 matplotlib: 3.4.3 cartopy: 0.19.0.post1 seaborn: 0.11.1 numbagg: 0.2.1 fsspec: 2021.11.1 cupy: None pint: 0.17 sparse: 0.12.0 setuptools: 49.6.0.post20210108 pip: 21.2.4 conda: None pytest: 6.2.4 IPython: 7.31.0 sphinx: 4.3.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6384/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 931796211,MDU6SXNzdWU5MzE3OTYyMTE=,5546,Limit number of displayed dimensions in repr,14371165,closed,0,,,1,2021-06-28T17:25:18Z,2022-01-03T17:38:48Z,2022-01-03T17:38:48Z,MEMBER,,,," **What happened**: Dimension doesn't seem to be limited when there are too many of them. See example below. This slows down the repr significantly and is quite unreadable to me. **What you expected to happen**: To be limited so that it aligns with whatever the maximum line length is for variables. It's also fine if it continues on a couple of rows below in similar fashion to variables. **Minimal Complete Verifiable Example**: This is probably a bit of an edge case. My real datasets usually have around 12 ""dimensions"" and coords, +2000 variables, 50 attrs. ```python a = np.arange(0, 2000) data_vars = dict() for i in a: data_vars[f""long_variable_name_{i}""] = xr.DataArray( name=f""long_variable_name_{i}"", data=np.array([3, 4]), dims=[f""long_coord_name_{i}_x""], coords={f""long_coord_name_{i}_x"": np.array([0, 1])}, ) ds0 = xr.Dataset(data_vars) ds0.attrs = {f""attr_{k}"": 2 for k in a} ``` ```python Dimensions: (long_coord_name_0_x: 2, long_coord_name_1000_x: 2, long_coord_name_1001_x: 2, long_coord_name_1002_x: 2, long_coord_name_1003_x: 2, long_coord_name_1004_x: 2, long_coord_name_1005_x: 2, long_coord_name_1006_x: 2, long_coord_name_1007_x: 2, long_coord_name_1008_x: 2, long_coord_name_1009_x: 2, long_coord_name_100_x: 2, long_coord_name_1010_x: 2, long_coord_name_1011_x: 2, long_coord_name_1012_x: 2, long_coord_name_1013_x: 2, long_coord_name_1014_x: 2, long_coord_name_1015_x: 2, long_coord_name_1016_x: 2, long_coord_name_1017_x: 2, long_coord_name_1018_x: 2, long_coord_name_1019_x: 2, long_coord_name_101_x: 2, long_coord_name_1020_x: 2, long_coord_name_1021_x: 2, long_coord_name_1022_x: 2, long_coord_name_1023_x: 2, long_coord_name_1024_x: 2, long_coord_name_1025_x: 2, long_coord_name_1026_x: 2, long_coord_name_1027_x: 2, long_coord_name_1028_x: 2, long_coord_name_1029_x: 2, long_coord_name_102_x: 2, long_coord_name_1030_x: 2, long_coord_name_1031_x: 2, long_coord_name_1032_x: 2, long_coord_name_1033_x: 2, long_coord_name_1034_x: 2, long_coord_name_1035_x: 2, long_coord_name_1036_x: 2, long_coord_name_1037_x: 2, long_coord_name_1038_x: 2, long_coord_name_1039_x: 2, long_coord_name_103_x: 2, long_coord_name_1040_x: 2, long_coord_name_1041_x: 2, long_coord_name_1042_x: 2, long_coord_name_1043_x: 2, long_coord_name_1044_x: 2, long_coord_name_1045_x: 2, long_coord_name_1046_x: 2, long_coord_name_1047_x: 2, long_coord_name_1048_x: 2, long_coord_name_1049_x: 2, long_coord_name_104_x: 2, long_coord_name_1050_x: 2, 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long_coord_name_834_x: 2, long_coord_name_835_x: 2, long_coord_name_836_x: 2, long_coord_name_837_x: 2, long_coord_name_838_x: 2, long_coord_name_839_x: 2, long_coord_name_83_x: 2, long_coord_name_840_x: 2, long_coord_name_841_x: 2, long_coord_name_842_x: 2, long_coord_name_843_x: 2, long_coord_name_844_x: 2, long_coord_name_845_x: 2, long_coord_name_846_x: 2, long_coord_name_847_x: 2, long_coord_name_848_x: 2, long_coord_name_849_x: 2, long_coord_name_84_x: 2, long_coord_name_850_x: 2, long_coord_name_851_x: 2, long_coord_name_852_x: 2, long_coord_name_853_x: 2, long_coord_name_854_x: 2, long_coord_name_855_x: 2, long_coord_name_856_x: 2, long_coord_name_857_x: 2, long_coord_name_858_x: 2, long_coord_name_859_x: 2, long_coord_name_85_x: 2, long_coord_name_860_x: 2, long_coord_name_861_x: 2, long_coord_name_862_x: 2, long_coord_name_863_x: 2, long_coord_name_864_x: 2, long_coord_name_865_x: 2, long_coord_name_866_x: 2, long_coord_name_867_x: 2, long_coord_name_868_x: 2, long_coord_name_869_x: 2, long_coord_name_86_x: 2, long_coord_name_870_x: 2, long_coord_name_871_x: 2, long_coord_name_872_x: 2, long_coord_name_873_x: 2, long_coord_name_874_x: 2, long_coord_name_875_x: 2, long_coord_name_876_x: 2, long_coord_name_877_x: 2, long_coord_name_878_x: 2, long_coord_name_879_x: 2, long_coord_name_87_x: 2, long_coord_name_880_x: 2, long_coord_name_881_x: 2, long_coord_name_882_x: 2, long_coord_name_883_x: 2, long_coord_name_884_x: 2, long_coord_name_885_x: 2, long_coord_name_886_x: 2, long_coord_name_887_x: 2, long_coord_name_888_x: 2, long_coord_name_889_x: 2, long_coord_name_88_x: 2, long_coord_name_890_x: 2, long_coord_name_891_x: 2, long_coord_name_892_x: 2, long_coord_name_893_x: 2, long_coord_name_894_x: 2, long_coord_name_895_x: 2, long_coord_name_896_x: 2, long_coord_name_897_x: 2, long_coord_name_898_x: 2, long_coord_name_899_x: 2, long_coord_name_89_x: 2, long_coord_name_8_x: 2, long_coord_name_900_x: 2, long_coord_name_901_x: 2, long_coord_name_902_x: 2, long_coord_name_903_x: 2, long_coord_name_904_x: 2, long_coord_name_905_x: 2, long_coord_name_906_x: 2, long_coord_name_907_x: 2, long_coord_name_908_x: 2, long_coord_name_909_x: 2, long_coord_name_90_x: 2, long_coord_name_910_x: 2, long_coord_name_911_x: 2, long_coord_name_912_x: 2, long_coord_name_913_x: 2, long_coord_name_914_x: 2, long_coord_name_915_x: 2, long_coord_name_916_x: 2, long_coord_name_917_x: 2, long_coord_name_918_x: 2, long_coord_name_919_x: 2, long_coord_name_91_x: 2, long_coord_name_920_x: 2, long_coord_name_921_x: 2, long_coord_name_922_x: 2, long_coord_name_923_x: 2, long_coord_name_924_x: 2, long_coord_name_925_x: 2, long_coord_name_926_x: 2, long_coord_name_927_x: 2, long_coord_name_928_x: 2, long_coord_name_929_x: 2, long_coord_name_92_x: 2, long_coord_name_930_x: 2, long_coord_name_931_x: 2, long_coord_name_932_x: 2, long_coord_name_933_x: 2, long_coord_name_934_x: 2, long_coord_name_935_x: 2, long_coord_name_936_x: 2, long_coord_name_937_x: 2, long_coord_name_938_x: 2, long_coord_name_939_x: 2, long_coord_name_93_x: 2, long_coord_name_940_x: 2, long_coord_name_941_x: 2, long_coord_name_942_x: 2, long_coord_name_943_x: 2, long_coord_name_944_x: 2, long_coord_name_945_x: 2, long_coord_name_946_x: 2, long_coord_name_947_x: 2, long_coord_name_948_x: 2, long_coord_name_949_x: 2, long_coord_name_94_x: 2, long_coord_name_950_x: 2, long_coord_name_951_x: 2, long_coord_name_952_x: 2, long_coord_name_953_x: 2, long_coord_name_954_x: 2, long_coord_name_955_x: 2, long_coord_name_956_x: 2, long_coord_name_957_x: 2, long_coord_name_958_x: 2, long_coord_name_959_x: 2, long_coord_name_95_x: 2, long_coord_name_960_x: 2, long_coord_name_961_x: 2, long_coord_name_962_x: 2, long_coord_name_963_x: 2, long_coord_name_964_x: 2, long_coord_name_965_x: 2, long_coord_name_966_x: 2, long_coord_name_967_x: 2, long_coord_name_968_x: 2, long_coord_name_969_x: 2, long_coord_name_96_x: 2, long_coord_name_970_x: 2, long_coord_name_971_x: 2, long_coord_name_972_x: 2, long_coord_name_973_x: 2, long_coord_name_974_x: 2, long_coord_name_975_x: 2, long_coord_name_976_x: 2, long_coord_name_977_x: 2, long_coord_name_978_x: 2, long_coord_name_979_x: 2, long_coord_name_97_x: 2, long_coord_name_980_x: 2, long_coord_name_981_x: 2, long_coord_name_982_x: 2, long_coord_name_983_x: 2, long_coord_name_984_x: 2, long_coord_name_985_x: 2, long_coord_name_986_x: 2, long_coord_name_987_x: 2, long_coord_name_988_x: 2, long_coord_name_989_x: 2, long_coord_name_98_x: 2, long_coord_name_990_x: 2, long_coord_name_991_x: 2, long_coord_name_992_x: 2, long_coord_name_993_x: 2, long_coord_name_994_x: 2, long_coord_name_995_x: 2, long_coord_name_996_x: 2, long_coord_name_997_x: 2, long_coord_name_998_x: 2, long_coord_name_999_x: 2, long_coord_name_99_x: 2, long_coord_name_9_x: 2) Coordinates: (12/2000) * long_coord_name_0_x (long_coord_name_0_x) int32 0 1 * long_coord_name_1_x (long_coord_name_1_x) int32 0 1 * long_coord_name_2_x (long_coord_name_2_x) int32 0 1 * long_coord_name_3_x (long_coord_name_3_x) int32 0 1 * long_coord_name_4_x (long_coord_name_4_x) int32 0 1 * long_coord_name_5_x (long_coord_name_5_x) int32 0 1 ... * long_coord_name_1994_x (long_coord_name_1994_x) int32 0 1 * long_coord_name_1995_x (long_coord_name_1995_x) int32 0 1 * long_coord_name_1996_x (long_coord_name_1996_x) int32 0 1 * long_coord_name_1997_x (long_coord_name_1997_x) int32 0 1 * long_coord_name_1998_x (long_coord_name_1998_x) int32 0 1 * long_coord_name_1999_x (long_coord_name_1999_x) int32 0 1 Data variables: (12/2000) long_variable_name_0 (long_coord_name_0_x) int32 3 4 long_variable_name_1 (long_coord_name_1_x) int32 3 4 long_variable_name_2 (long_coord_name_2_x) int32 3 4 long_variable_name_3 (long_coord_name_3_x) int32 3 4 long_variable_name_4 (long_coord_name_4_x) int32 3 4 long_variable_name_5 (long_coord_name_5_x) int32 3 4 ... long_variable_name_1994 (long_coord_name_1994_x) int32 3 4 long_variable_name_1995 (long_coord_name_1995_x) int32 3 4 long_variable_name_1996 (long_coord_name_1996_x) int32 3 4 long_variable_name_1997 (long_coord_name_1997_x) int32 3 4 long_variable_name_1998 (long_coord_name_1998_x) int32 3 4 long_variable_name_1999 (long_coord_name_1999_x) int32 3 4 Attributes: (12/2000) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_1994: 2 attr_1995: 2 attr_1996: 2 attr_1997: 2 attr_1998: 2 attr_1999: 2 ``` **Anything else we need to know?**: **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 15:50:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 byteorder: little LC_ALL: None LANG: en libhdf5: 1.10.6 libnetcdf: None xarray: 0.18.2 pandas: 1.2.4 numpy: 1.20.3 scipy: 1.6.3 netCDF4: None pydap: None h5netcdf: None h5py: 3.2.1 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.05.0 distributed: 2021.05.0 matplotlib: 3.4.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 49.6.0.post20210108 pip: 21.1.2 conda: 4.10.1 pytest: 6.2.4 IPython: 7.24.1 sphinx: 4.0.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5546/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1042698589,I_kwDOAMm_X84-JlFd,5928,Relax GitHub Actions first time contributor approval?,14371165,closed,0,,,2,2021-11-02T18:45:16Z,2021-11-02T21:44:54Z,2021-11-02T21:44:54Z,MEMBER,,,,"A while back GitHub made it so that new contributors cannot trigger GitHub Actions workflows and a maintainer has to hit ""Approve and Run"" every time they push a commit to their PR. This is rather annoying for both the contributor and the maintainer as the back and forth takes time. It however seems possible to relax this constraint: https://twitter.com/metcalfc/status/1448414192285806592?t=maeChQZTSUh2Ph0YFk-hGA&s=19 Shall we relax this constraint? ref: https://github.com/dask/community/issues/191","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5928/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 778083748,MDU6SXNzdWU3NzgwODM3NDg=,4761,Dataset.interp drops boolean variables,14371165,closed,0,,,0,2021-01-04T13:09:56Z,2021-05-13T15:28:15Z,2021-05-13T15:28:15Z,MEMBER,,,," **What happened**: `Dataset.interp` silently drops boolean variables. **What you expected to happen**: If I'm interpolating a group of variables I expect to get all of them back in the correct shape with relevant values in them. If the variables are boolean or object arrays I don't expect it to do linear interpolation because it doesn't make sense but stepwise interpolation like nearest or zero order interpolation should be fine to expect. **Minimal Complete Verifiable Example**: ```python import numpy as np a = np.arange(0, 5) b = np.core.defchararray.add(""long_variable_name"", a.astype(str)) coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=np.array([0, 1]).astype(bool), dims=[""time""], coords=coords, ) ds1 = xr.Dataset(data_vars) # Print raw data: print(ds1) Out[3]: Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: long_variable_name0 (time) bool False True long_variable_name1 (time) bool False True long_variable_name2 (time) bool False True long_variable_name3 (time) bool False True long_variable_name4 (time) bool False True # Interpolate: ds1 = ds1.interp( time=da.array([0, 0.5, 1, 2]), assume_sorted=True, method=""nearest"", kwargs=dict(fill_value=""extrapolate""), ) # Print interpolated data: Dimensions: (time: 4) Coordinates: * time (time) float64 0.0 0.5 1.0 2.0 Data variables: *empty* ``` **Anything else we need to know?**: `ds.interp_like `use `ds.reindex` in these cases which seems like a good choice in `ds.interp` as well. But I think that both `ds.interp` and `ds.interp_like` should fill by default with nearest value instead of np.nan because we're still requesting interpolation. **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.1.5 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4761/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 775875024,MDU6SXNzdWU3NzU4NzUwMjQ=,4739,Slow initilization of dataset.interp,14371165,closed,0,,,2,2020-12-29T12:46:05Z,2021-05-05T12:26:01Z,2021-05-05T12:26:01Z,MEMBER,,,," **What happened**: When interpolating a dataset with >2000 dask variables a lot of time is spent in `da.unifying_chunks` because `da.unifying_chunks` forces all variables and **coordinates** to a dask array. xarray on the other hand forces coordinates to pd.Index even if the coordinates was dask.array when the dataset was first created. **What you expected to happen**: If the coords of the dataset was initialized as dask arrays they should stay lazy. **Minimal Complete Verifiable Example**: ```python import xarray as xr import numpy as np import dask.array as da a = np.arange(0, 2000) b = np.core.defchararray.add(""long_variable_name"", a.astype(str)) coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=da.array([3, 4]), dims=[""time""], coords=coords ) ds0 = xr.Dataset(data_vars) ds0 = ds0.interp( time=da.array([0, 0.5, 1]), assume_sorted=True, kwargs=dict(fill_value=None), ) ``` **Anything else we need to know?**: Some thoughts: * Why can't coordinates be lazy? * Can we use dask.dataframe.Index instead of pd.Index when creating IndexVariables? * There's no time saved converting to dask arrays in `missing.interp_func`. But some time could be saved if we could convert them to dask arrays in `xr.Dataset.interp` before the variable loop starts. * Can we still store the dask array in IndexVariable and use a to_dask_array()-method to quickly get it? * Initializing the dataarrays will still be slow though since it still has to force the dask array to pd.Index. **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.1.5 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4739/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 792639470,MDU6SXNzdWU3OTI2Mzk0NzA=,4839,Coordinate attributes are dropped when interpolating datasets,14371165,closed,0,,,3,2021-01-23T20:05:33Z,2021-04-27T07:00:08Z,2021-04-27T07:00:08Z,MEMBER,,,," **What happened**: When I was interpolating datasets I noticed that the coordinate variables disappeared. **What you expected to happen**: Coordinate attributes should be retained just like variables are. **Minimal Complete Verifiable Example**: ```python import numpy as np import xarray as xr names = np.core.defchararray.add(""long_variable_name"", np.arange(0, 2).astype(str)) coords = dict(time=np.array([0, 1])) data_vars = dict() for v in names: data_vars[v] = xr.Variable( ""time"", np.array([0, 1], dtype=int), attrs=dict(unit=""kg"") ) ds1 = xr.Dataset(data_vars=data_vars, coords=coords) ds1.attrs = { k: 2 for k in np.core.defchararray.add(""attr_"", np.arange(0, 3).astype(str)) } ds1.time.attrs.update(unit=""s"") # Print time: ds1.time Out[115]: array([0, 1]) Coordinates: * time (time) int32 0 1 Attributes: unit: s # Interpolate: ds1 = ds1.interp( time=np.array([0, 0.5, 1, 2]), assume_sorted=True, method=""linear"", kwargs=dict(fill_value=""extrapolate""), ) # Print interpolated time, units are lost: ds1.time Out[117]: array([0. , 0.5, 1. , 2. ]) Coordinates: * time (time) float64 0.0 0.5 1.0 2. ``` **Anything else we need to know?**: **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel byteorder: little libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.2.0 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.1.2.post20210112 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.3
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4839/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 791725552,MDU6SXNzdWU3OTE3MjU1NTI=,4838,Simplify adding custom backends,14371165,closed,0,,,0,2021-01-22T06:02:53Z,2021-04-15T02:02:03Z,2021-04-15T02:02:03Z,MEMBER,,,," **Is your feature request related to a problem? Please describe.** I've been working on opening custom hdf formats in xarray, reading up on the apiv2 it is currently only possible to declare a new external plugin in setup.py but that doesn't seem easy or intuitive to me. **Describe the solution you'd like** Why can't we simply be allowed to add functions to the engine parameter? Example: ```python from custom_backend import engine ds = xr.load_dataset(filename, engine=engine) ``` This seems like a small function change to me from my initial _quick_ look because there's mainly a bunch of string checks in the normal case until we get to the registered backend functions, if we send in a function instead in the engine-parameter we can just bypass those checks. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4838/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 775322346,MDU6SXNzdWU3NzUzMjIzNDY=,4736,Limit number of data variables shown in repr,14371165,closed,0,,,2,2020-12-28T10:15:26Z,2021-01-04T02:13:52Z,2021-01-04T02:13:52Z,MEMBER,,,," **What happened**: xarray feels very unresponsive when using datasets with >2000 data variables because it has to print all the 2000 variables everytime you print something to console. **What you expected to happen**: xarray should limit the number of variables printed to console. Maximum maybe 25? Same idea probably apply to dimensions, coordinates and attributes as well, pandas only shows 2 for reference, the first and last variables. **Minimal Complete Verifiable Example**: ```python import numpy as np import xarray as xr a = np.arange(0, 2000) b = np.core.defchararray.add(""long_variable_name"", a.astype(str)) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=[3, 4], dims=[""time""], coords=dict(time=[0, 1]) ) ds = xr.Dataset(data_vars) # Everything above feels fast. Printing to console however takes about 13 seconds for me: print(ds) ``` **Anything else we need to know?**: Out of scope brainstorming: Though printing 2000 variables is probably madness for most people it is kind of nice to show all variables because you sometimes want to know what happened to a few other variables as well. Is there already an easy and fast way to create subgroup of the dataset, so we don' have to rely on the dataset printing everything to the console everytime? **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.1.5 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4736/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 683954433,MDU6SXNzdWU2ODM5NTQ0MzM=,4367,Should __repr__ and __str__ be PEP8 compliant?,14371165,closed,0,,,7,2020-08-22T08:10:15Z,2020-11-25T23:25:44Z,2020-11-25T23:25:44Z,MEMBER,,,," **Is your feature request related to a problem? Please describe.** When creating docs with examples it would be nice if you could simply use `print(ds)` without being concerned about line lengths. **Describe the solution you'd like** Limit line length so that a method docstring in a class can use `print(ds)` without breaking PEP8 conventions. So maximum line length for the `ds.__repr__`/`ds.__str__` would be 79 - 4 - 4 = 71. **Example code** Example where print(ds) creates lines longer than 79: ```python import numpy as np import pandas as pd import xarray as xr class foo(): """""" Test class. (...) """""" def bar(): """""" Return 1. Examples -------- Create data: >>> np.random.seed(0) >>> temperature = 15 + 8 * np.random.randn(2, 2, 3) >>> precipitation = 10 * np.random.rand(2, 2, 3) >>> lon = [[-99.83, -99.32], [-99.79, -99.23]] >>> lat = [[42.25, 42.21], [42.63, 42.59]] >>> time = pd.date_range(""2014-09-06"", periods=3) >>> reference_time = pd.Timestamp(""2014-09-05"") Initialize a dataset with multiple dimensions: >>> ds = xr.Dataset( ... { ... ""temperature"": ([""x"", ""y"", ""time""], temperature), ... ""precipitation"": ([""x"", ""y"", ""time""], precipitation), ... }, ... coords={ ... ""lon"": ([""x"", ""y""], lon), ... ""lat"": ([""x"", ""y""], lat), ... ""time"": time, ... ""reference_time"": reference_time, ... }, ... ) Print results: >>> print(ds.temperature.values[0]) [[29.11241877 18.20125767 22.82990387] [32.92714559 29.94046392 7.18177696]] >>> print(ds) Dimensions: (time: 3, x: 2, y: 2) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Data variables: temperature (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63 precipitation (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805 >>> ds Dimensions: (time: 3, x: 2, y: 2) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Data variables: temperature (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63 precipitation (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805 """""" return 1 if __name__ == '__main__': import doctest doctest.testmod() ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4367/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 679575175,MDU6SXNzdWU2Nzk1NzUxNzU=,4345,Improve Dataset documentation,14371165,closed,0,,,9,2020-08-15T13:27:33Z,2020-10-27T19:47:51Z,2020-10-27T19:47:51Z,MEMBER,,,,"**Is your feature request related to a problem? Please describe.** As a new user I find it difficult to get a new dataset initialized because the necessary parameters are not shown in the docstring. I have to google ""xarray dataset"" to get to http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html to figure it out. In the figure below xarray.Dataset does not show the necessary parameters in the help pane: ![image](https://user-images.githubusercontent.com/14371165/90313058-aab66000-df09-11ea-9f3a-d2d3f2698b66.png) Compare to pandas.DataFrame that includes it: ![image](https://user-images.githubusercontent.com/14371165/90313083-e94c1a80-df09-11ea-8013-a7ffe90ab4d8.png) **Describe the solution you'd like** Looking at https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L428 the `xr.Dataset.__init__.__doc__` does contain the necessary parameters so the suggestion is to simply move or copy that information up one level to` xr.Dataset.__doc__` For reference pandas does not use a docstring for the __init__ method: https://github.com/pandas-dev/pandas/blob/v1.1.0/pandas/core/frame.py#L339-L9257 The pandas docs also includes a few simple copy/pasteable examples on how to initialize. So a xarray example would be: ```python >>> import numpy as np >>> import xarray as xr >>> x = np.arange(4) >>> y = 2*x >>> ds = xr.Dataset({'y': (['x'], y)}, ... coords={'x': x}) >>> print(ds) Dimensions: (x: 4) Coordinates: * x (x) int32 0 1 2 3 Data variables: y (x) int32 0 2 4 6 ``` Or take some examples from http://xarray.pydata.org/en/stable/quick-overview.html#datasets or http://xarray.pydata.org/en/stable/data-structures.html#dataset although I found those a little bit confusing as they were dependent on previous results or rather complex with many dimensions. **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 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: None.None libhdf5: 1.10.4 libnetcdf: None xarray: 0.15.0 pandas: 1.0.3 numpy: 1.18.1 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2.14.0 distributed: 2.22.0 matplotlib: 3.1.3 cartopy: None seaborn: 0.10.0 numbagg: None setuptools: 49.2.1.post20200807 pip: 20.2.1 conda: 4.8.3 pytest: 6.0.1 IPython: 7.17.0 sphinx: 3.2.0 C:\ProgramData\Anaconda3\lib\site-packages\setuptools\distutils_patch.py:26: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first. ""Distutils was imported before Setuptools. This usage is discouraged ""
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4345/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue