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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 ScottWales 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 <tt>xr.show_versions()</tt> 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
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  completed xarray 13221727 issue
457771111 MDExOlB1bGxSZXF1ZXN0Mjg5NTExOTk2 3033 ENH: keepdims=True for xarray reductions ScottWales 161133 closed 0     3 2019-06-19T02:04:53Z 2019-06-23T09:18:42Z 2019-06-23T09:18:33Z CONTRIBUTOR   0 pydata/xarray/pulls/3033

Add new option keepdims to xarray reduce operations, following the behaviour of Numpy.

keepdims may be passed to reductions on either Datasets or DataArrays, and will result in the reduced dimensions being still present in the output with size 1.

Coordinates that depend on the reduced dimensions will be removed from the Dataset/DataArray

The name keepdims is used here to be consistent with Numpy, keep_dims was an alternative name proposed in #2170.

The functionality has only been added to Variable.reduce(), Dataarray.reduce() and DataSet.reduce() to start off with, it is not implemented for GroupBy, Rolling or Resample.

  • [x] Closes #2170
  • [x] Tests added
  • [x] Fully documented, including whats-new.rst for all changes and api.rst for new API
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    xarray 13221727 pull
440988633 MDU6SXNzdWU0NDA5ODg2MzM= 2943 Rolling operations loose chunking with dask and bottleneck ScottWales 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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  completed xarray 13221727 issue

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