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 937508115,MDU6SXNzdWU5Mzc1MDgxMTU=,5581,Error slicing CFTimeIndex with Pandas 1.3,161133,closed,0,,,4,2021-07-06T04:28:00Z,2021-07-23T21:53:51Z,2021-07-23T21:53:51Z,CONTRIBUTOR,,,," **What happened**: Slicing a DataArray with a CFTime time axis gives an error `TypeError: _maybe_cast_slice_bound() missing 1 required positional argument: 'kind'` **What you expected to happen**: The slice should return elements 31 to 180 **Minimal Complete Verifiable Example**: ```python import xarray as xr import cftime import numpy as np units = 'days since 2000-01-01 00:00' time_365 = cftime.num2date(np.arange(0, 10 * 365), units, '365_day') da = xr.DataArray(np.arange(time_365.size), coords = [time_365], dims = 'time') da.sel(time=slice('2000-02','2000-06')) ``` **Anything else we need to know?**: It appears to be a compatibility issue between Pandas 1.3.0 and Xarray 0.18.2, with Pandas 1.2.5 and Xarray 0.18.2 the slice behaves normally. Possibly there has been an interface change that has broken CFTimeIndex. Using a pure Pandas time axis works fine ```python import xarray as xr import pandas as pd import numpy as np time = pd.date_range('20000101','20100101', freq='D') da = xr.DataArray(np.arange(time.size), coords = [time], dims = 'time') da.sel(time=slice('2000-02','2000-06')) ``` **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.9.5 | packaged by conda-forge | (default, Jun 19 2021, 00:32:32) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 4.18.0-305.7.1.el8.nci.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.18.2 pandas: 1.3.0 numpy: 1.21.0 scipy: 1.7.0 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 3.3.0 Nio: None zarr: 2.8.3 cftime: 1.4.1 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.2.6 cfgrib: 0.9.9.0 iris: 2.4.0 bottleneck: 1.3.2 dask: 2021.06.2 distributed: 2021.06.2 matplotlib: 3.4.2 cartopy: 0.19.0.post1 seaborn: 0.11.1 numbagg: None pint: 0.17 setuptools: 52.0.0.post20210125 pip: 21.1.3 conda: 4.10.3 pytest: 6.2.4 IPython: 7.25.0 sphinx: 4.0.3
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5581/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 440988633,MDU6SXNzdWU0NDA5ODg2MzM=,2943,Rolling operations loose chunking with dask and bottleneck,161133,closed,0,,,1,2019-05-07T01:52:05Z,2019-05-07T02:01:13Z,2019-05-07T02:01:13Z,CONTRIBUTOR,,,,"#### Code Sample, a copy-pastable example if possible A ""Minimal, Complete and Verifiable Example"" will make it much easier for maintainers to help you: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports ```python import bottleneck import xarray import dask data = dask.array.ones((100,), chunks=(10,)) da = xarray.DataArray(data, dims=['time']) rolled = da.rolling(time=15).mean() # Expect the 'rolled' dataset to be chunked approximately the same as 'data', # however there is only one chunk in 'rolled' instead of 10 assert len(rolled.chunks[0]) > 1 ``` #### Problem description Rolling operations loose chunking over the rolled dimension when using dask datasets with bottleneck installed, which is a problem for large datasets where we don't want to load the entire thing. The issue appears to be caused by `xarray.core.dask_array_ops.dask_rolling_wrapper` calling `dask.array.overlap.overlap` on a DataArray instead of a Dask array. Possibly #2940 is related? #### Expected Output Chunks should be preserved through `.rolling().mean()` #### Output of ``xr.show_versions()``
INSTALLED VERSIONS ------------------ commit: None python: 3.6.7 | packaged by conda-forge | (default, Feb 28 2019, 09:07:38) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-862.14.4.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_AU.utf8 LANG: C LOCALE: en_AU.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.3 scipy: 1.2.1 netCDF4: 1.5.0.1 pydap: installed h5netcdf: None h5py: 2.9.0 Nio: None zarr: 2.3.1 cftime: 1.0.3.4 nc_time_axis: 1.2.0 PseudonetCDF: None rasterio: None cfgrib: None iris: 2.2.0 bottleneck: 1.2.1 dask: 1.2.0 distributed: 1.27.1 matplotlib: 3.0.3 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 41.0.1 pip: 19.1 conda: None pytest: 4.4.1 IPython: 7.5.0 sphinx: None
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