What happened?

I have xarray, zarr installed, but not dask, and am trying to call to_zarr in an async routine. I am looking for something I can await. The doc claims that a Dask.Delayed is returned. I understand that if I have a dask client open with asynchronous=True I can await the result.

However, not using Dask. Is there some way to get an awaitable from this object without a dask client?

What did you expect to happen?

I should get something back I can await in my async routine.

Minimal Complete Verifiable Example

```Python import xarray as xr from asyncio import get_event_loop

ds = xr.Dataset(data_vars = dict(x = ('x', [1, 2]))) deld = ds.to_zarr("bar.zarr", compute=False) loop.run_until_complete(deld. ...?) ```

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.8.5 (default, Sep 4 2020, 02:22:02) [Clang 10.0.0 ] python-bits: 64 OS: Darwin OS-release: 20.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.10.4 libnetcdf: 4.7.3

xarray: 2022.3.0 pandas: 1.4.1 numpy: 1.22.3 scipy: 1.6.2 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.11.1 cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.10.0 distributed: 2021.10.0 matplotlib: 3.1.3 cartopy: None seaborn: None numbagg: None fsspec: 2022.02.0 cupy: None pint: None sparse: None setuptools: 60.7.1 pip: 22.0.3 conda: 4.11.0 pytest: 7.1.1 IPython: 7.31.1 sphinx: None

What happened:

I have an dataarray with dtype np.bool_. When I write it using netcdf (engine h5netcdf, or default) and then read in a copy, the copy has dtype int8.

What you expected to happen:

The loaded data array should have dtype bool

Minimal Complete Verifiable Example:

I have had a hard time reducing this to a sample. The data array comes from a larger dataset which exhibits the same problem. I can copy the dataarray using copy() and it still exhibits the problem; however if I build a new data array using the constructor, the new array doesn't exhibit the problem. As far as I can tell, though, the original and the rebuilt dataarray are otherwise identical.

```python

in a pdb session

(Pdb) ci <xarray.DataArray 'cut_inclusive' (cut: 15)> array([False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]) Dimensions without coordinates: cut (Pdb) ci.to_netcdf('foo_ci.nca', engine="h5netcdf") (Pdb) csi = xr.read_dataarray('foo_ci_nca', engine="h5netcdf"); csi <xarray.DataArray 'cut_inclusive' (cut: 15)> array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8) Dimensions without coordinates: cut (Pdb) ci2 = xr.DataArray(ci, dims=('cut', )) (Pdb) ci2.equals(ci) True (Pdb) ci2.to_netcdf('foo_ci2.nca', engine="h5netcdf") (Pdb) csi2 = xr.open_dataarray('foo_ci2.nca', engine="h5netcdf"); csi2 <xarray.DataArray 'cut_inclusive' (cut: 15)> array([False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]) Dimensions without coordinates: cut (Pdb) ci3 = ci.copy() (Pdb) ci3.to_netcdf('foo_ci3.nca', engine="h5netcdf") (Pdb) csi3 = xr.open_dataarray('foo_ci3.nca', engine="h5netcdf"); csi3 <xarray.DataArray 'cut_inclusive' (cut: 15)> array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8) Dimensions without coordinates: cut ``` Anything else we need to know?:

I am at a loss how to investigate why ci and ci3 don't survive round-trip, but ci2 does. Unfortunately, I also have been unable to produce a free-standing example -- whenever I try I get an object that survives round trip intact. I suspect that xarray internals is somewhere/somehow keeping a cache to the original ci (presumably still linked to the overall dataset from which ci came), and this is what is causing the problem, but I don't know where to look. (Suggestions welcome!)

Environment:

Is your feature request related to a problem? Please describe.

I am trying to drop particular indices from a dimension that doesn't have coordinates.

Following: drop_sel() documentation, but leaving out the coordinate labels: python data = np.random.randn(2, 3) ds = xr.Dataset({"A": (["x", "y"], data)}) ds.drop_sel(y=[1]) gives me an error.

Describe the solution you'd like

I would think drop_isel should exist and work in analogy to drop_sel as isel does to sel.

Describe alternatives you've considered

As far as I know, I could either create coordinates especially to in order to drop, or rebuild a new dataset. Both are not congenial. (I'd be grateful to know if there is actually a straightforward way to do this I've overlooked.