issues

What is your issue?

I'm using xarray to store datasets that are computed on a distributed dask cluster. I'm making use of the to_netcdf to chunk the storage.

I get some warnings about coroutine not awaited with distributed. Just wanted to know if that is something to worry about, like locks not released ?

log INFO:distributed.scheduler:State start INFO:distributed.scheduler: Scheduler at: tcp://127.0.0.1:8786 INFO:distributed.scheduler: dashboard at: http://127.0.0.1:8787/status INFO:distributed.worker: Start worker at: tcp://127.0.0.1:44425 INFO:distributed.worker: Listening to: tcp://127.0.0.1:44425 INFO:distributed.worker: Worker name: 0 INFO:distributed.worker: dashboard at: 127.0.0.1:42587 INFO:distributed.worker:Waiting to connect to: tcp://127.0.0.1:8786 INFO:distributed.worker:------------------------------------------------- INFO:distributed.worker: Threads: 8 INFO:distributed.worker: Memory: 15.38 GiB INFO:distributed.worker: Local Directory: /tmp/dask-worker-space/worker-l2i3et3y INFO:distributed.worker:------------------------------------------------- INFO:distributed.scheduler:Register worker <WorkerState 'tcp://127.0.0.1:44425', name: 0, status: init, memory: 0, processing: 0> INFO:distributed.scheduler:Starting worker compute stream, tcp://127.0.0.1:44425 INFO:distributed.core:Starting established connection to tcp://127.0.0.1:57776 INFO:distributed.worker: Registered to: tcp://127.0.0.1:8786 INFO:distributed.worker:------------------------------------------------- INFO:distributed.core:Starting established connection to tcp://127.0.0.1:8786 INFO:distributed.scheduler:Receive client connection: Client-15a7ea83-eb4e-11ed-ab82-5b728e3afd73 INFO:distributed.core:Starting established connection to tcp://127.0.0.1:57790 /home/jules/Code/xarraystoreexample/.venv/lib/python3.9/site-packages/distributed/lock.py:174: RuntimeWarning: coroutine 'PooledRPCCall.__getattr__.<locals>.send_recv_from_rpc' was never awaited self.acquire() /home/jules/Code/xarraystoreexample/.venv/lib/python3.9/site-packages/distributed/lock.py:178: RuntimeWarning: coroutine 'PooledRPCCall.__getattr__.<locals>.send_recv_from_rpc' was never awaited self.release() INFO:distributed.scheduler:Remove client Client-15a7ea83-eb4e-11ed-ab82-5b728e3afd73 INFO:distributed.core:Received 'close-stream' from tcp://127.0.0.1:57790; closing. INFO:distributed.scheduler:Remove client Client-15a7ea83-eb4e-11ed-ab82-5b728e3afd73 INFO:distributed.scheduler:Close client connection: Client-15a7ea83-eb4e-11ed-ab82-5b728e3afd73 INFO:distributed.worker:Stopping worker at tcp://127.0.0.1:44425. Reason: worker-close INFO:distributed.core:Received 'close-stream' from tcp://127.0.0.1:57776; closing. INFO:distributed.scheduler:Remove worker <WorkerState 'tcp://127.0.0.1:44425', name: 0, status: closing, memory: 0, processing: 0> INFO:distributed.core:Removing comms to tcp://127.0.0.1:44425 INFO:distributed.scheduler:Lost all workers INFO:distributed.core:Connection to tcp://127.0.0.1:8786 has been closed. INFO:distributed.scheduler:Scheduler closing... INFO:distributed.scheduler:Scheduler closing all comms

Here is a minimal example :

```python import logging import xarray as xr import numpy as np

import dask.distributed as dd import dask.array as da import pandas as pd

async def main(): cluster = dd.LocalCluster(scheduler_port=8786, processes=False, asynchronous=True)

await cluster
time = pd.date_range("1990-01-01", "1995-01-01", freq="h")

ds = xr.Dataset(
    coords={"time": time, "x": np.linspace(0, 100), "y": np.linspace(0, 100)}
)

ds["data"] = (("time", "y", "x"), da.ones((len(ds.time), 50, 50)))

async with dd.Client(address="tcp://127.0.0.1:8786", asynchronous=True) as client:
    await client.compute(
        ds.data.to_netcdf(
            "data.nc",
            encoding={"data": {"zlib": True, "complevel": 6}},
            engine="h5netcdf",
            compute=False,
        )
    )
await cluster.close()
return True

import asyncio import os

if name == "main": logging.basicConfig() os.environ["HDF5_USE_FILE_LOCKING"] = "False" asyncio.run(main()) ```

```python import xarray as xr

xr.show_versions()

INSTALLED VERSIONS

commit: None python: 3.9.16 (main, Dec 7 2022, 01:12:08) [GCC 11.3.0] python-bits: 64 OS: Linux OS-release: 5.19.0-41-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.9.1

xarray: 2022.12.0 pandas: 1.5.3 numpy: 1.24.3 scipy: 1.10.1 netCDF4: 1.6.3 pydap: None h5netcdf: 1.1.0 h5py: 3.8.0 Nio: None zarr: None cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2023.4.0 distributed: 2023.4.0 matplotlib: 3.7.1 cartopy: None seaborn: 0.11.2 numbagg: None fsspec: 2023.4.0 cupy: None pint: 0.20.1 sparse: None flox: None numpy_groupies: None setuptools: 67.7.2 pip: 23.1.2 conda: None pytest: 6.2.5 mypy: 0.931 IPython: 8.12.1 sphinx: None ```

Documentation on this feature :

https://docs.xarray.dev/en/stable/user-guide/dask.html#reading-and-writing-data

feel free to close this issue, if that's unrelated with xarray.

What happened?

I have a simple DataArray with datetime[ns] inside, but when I try to convert this to a supported numpy datetime dtype, the results is still a datetime[ns].

Using the values attribute & converting it, returns the correct results

What did you expect to happen?

I excpected the DataArray to be converted in the correct dtype

Minimal Complete Verifiable Example

```Python

time = pd.date_range('2015-01-01 00:00:00', '2015-12-31 23:59:00', inclusive='left', freq='15min') da = xr.DataArray(time) da <xarray.DataArray (dim_0: 35040)> array(['2015-01-01T00:00:00.000000000', '2015-01-01T00:15:00.000000000', '2015-01-01T00:30:00.000000000', ..., '2015-12-31T23:15:00.000000000', '2015-12-31T23:30:00.000000000', '2015-12-31T23:45:00.000000000'], dtype='datetime64[ns]') Coordinates: * dim_0 (dim_0) datetime64[ns] 2015-01-01 ... 2015-12-31T23:45:00

da.astype("datetime64[Y]") <xarray.DataArray (dim_0: 35040)> array(['2015-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000', ..., '2015-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * dim_0 (dim_0) datetime64[ns] 2015-01-01 ... 2015-12-31T23:45:00 da.values.astype("datetime64[Y]") array(['2015', '2015', '2015', ..., '2015', '2015', '2015'], dtype='datetime64[Y]') ```

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.8.10 (default, Nov 26 2021, 20:14:08) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 5.13.0-37-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.1 libnetcdf: None

xarray: 0.20.2 pandas: 1.4.1 numpy: 1.22.3 scipy: 1.8.0 netCDF4: None pydap: None h5netcdf: None h5py: 3.6.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2022.03.0 distributed: 2022.3.0 matplotlib: 3.5.1 cartopy: None seaborn: None numbagg: None fsspec: 2022.02.0 cupy: None pint: 0.18 sparse: None setuptools: 60.6.0 pip: 22.0.3 conda: None pytest: 6.2.5 IPython: 8.1.1 sphinx: None