issues

Problem description

Opening a file with open_mfdataset fails if dask is not installed: ```

xr.open_mfdataset('tests/sample-data/era5-levels-members.nc')

ModuleNotFoundError Traceback (most recent call last) ~/devel/MPY/xarray/xarray/backends/api.py in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, lock, data_vars, coords, autoclose, parallel, kwargs) 626 --> 627 datasets = [open_(p, open_kwargs) for p in paths] 628 file_objs = [getattr_(ds, '_file_obj') for ds in datasets] 629 if preprocess is not None:

~/devel/MPY/xarray/xarray/backends/api.py in open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs) 340 341 with close_on_error(store): --> 342 return maybe_decode_store(store) 343 else: 344 if engine is not None and engine != 'scipy':

~/devel/MPY/xarray/xarray/backends/api.py in maybe_decode_store(store, lock) 276 277 if chunks is not None: --> 278 from dask.base import tokenize 279 # if passed an actual file path, augment the token with 280 # the file modification time

ModuleNotFoundError: No module named 'dask.base' ```

Reason

The reason is that the test in maybe_decode_store is for None but open_mfdataset in line 614 sets chunks to {} if it evaluates to False.

In fact setting line https://github.com/pydata/xarray/blob/master/xarray/backends/api.py#L614 as follows solves the problem: open_kwargs = dict(engine=engine, chunks=chunks, lock=lock, autoclose=autoclose, **kwargs) I could not run the tests to see what breaks with this change as running pytest without dask fails with: ../cpython3/lib/python3.7/site-packages/py/_path/local.py:668: in pyimport __import__(modname) xarray/tests/__init__.py:105: in <module> if LooseVersion(dask.__version__) < '0.18': E AttributeError: module 'dask' has no attribute '__version__'

What happened?

The plugin interface warns with DeprecationWarning in python 3.10.

What did you expect to happen?

No warning to be issued.

Minimal Complete Verifiable Example

Python Running `pytest` on any *Xarray* importing test.

Relevant log output

Python .../python3.10/site-packages/xarray/backends/plugins.py:98: DeprecationWarning: SelectableGroups dict interface is deprecated. Use select. entrypoints = entry_points().get("xarray.backends", ())

Anything else we need to know?

According to the Compatibity note in:

https://docs.python.org/3/library/importlib.metadata.html#entry-points

the new API is entry_points(group='xarray.backends')

Environment

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

I started implementing a few backend plugins following the documentation so I noticed a few easily fixable quirks:

http://xarray.pydata.org/en/latest/internals/how-to-add-new-backend.html

The main one is the BackendEntrypoint is found deep into xarray.backends.common.

Describe the solution you'd like

I suggest to add an easier to find reference: * xarray.backends.BackendEntrypoint would do * but maybe even xarray.BackendEntrypoint.

Additional context

For the simplest plugins BackendEntrypoint would be the only import to connect to xarray.

See this dummy GDAL plugin: https://github.com/bopen/xarray-gdal/blob/main/xarray_gdal/xarray_plugin.py But also the rioxarray plugin in this PR: corteva/rioxarray/pull/281

What happened:

With current xarray master the rioxarray.open_rasterio crashes.

What you expected to happen:

With the released version 0.17.0 of xarray the open works as expected.

Minimal Complete Verifiable Example:

```python

import rioxarray rioxarray.open_rasterio("myfile.tif")

AttributeError Traceback (most recent call last) <ipython-input-7-fedc16472305> in <module> ----> 1 da = rioxarray.open_rasterio("../../../FLAIR-PROC/rome-lockdown-2020/georeferenced/20200309+0130+417_VV-20200109-20200115-EPSG:32632-5.human-activity.tif") 2 da

/usr/local/Caskroom/miniconda/base/envs/BOP/lib/python3.8/site-packages/rioxarray/_io.py in open_rasterio(filename, parse_coordinates, chunks, cache, lock, masked, mask_and_scale, variable, group, default_name, **open_kwargs) 820 821 da_name = attrs.pop("NETCDF_VARNAME", default_name) --> 822 data = indexing.LazilyOuterIndexedArray( 823 RasterioArrayWrapper( 824 manager,

AttributeError: module 'xarray.core.indexing' has no attribute 'LazilyOuterIndexedArray' ```

Anything else we need to know?:

LazilyOuterIndexedArray was recently renamed to LazilyIndexedArray by @aurghs in https://github.com/pydata/xarray/pull/4810 after a suggestion from @shoyer https://github.com/pydata/xarray/pull/4810#discussion_r586754165

Adding an alias would make the transition easier.

What happened:

When I open() a netCDF4 file and pass the file object to xr.open_dataset with engine='scipy' xarray silently ignores the value of the engine keyword and opens the file with h5netcdf. When opening an netCDF3 file with engine='h5netcdf' the scipy engine is used instead without warning.

What you expected to happen:

Silent autodetection of the engine is fine when passing engine=None, but it is very surprising when passing a value explicitly. I would have expected an exception or at least a warning.

Minimal Complete Verifiable Example:

```python

xr.open_dataset('netCDF4.nc', engine='scipy')

KeyError Traceback (most recent call last) [...] TypeError: Error: netCDF4.nc is not a valid NetCDF 3 file If this is a NetCDF4 file, you may need to install the netcdf4 library, e.g.,

        $ pip install netcdf4

fp = open('netCDF4.nc') xr.open_dataset(fp, engine='scipy') <xarray.Dataset> Dimensions: (line: 41587, pixel: 17751) Coordinates: * line (line) int64 0 1 2 3 4 5 ... 41582 41583 41584 41585 41586 * pixel (pixel) int64 0 1 2 3 4 5 ... 17746 17747 17748 17749 17750 Data variables: phase (line, pixel) float32 ... Attributes: Conventions: CF-1.7 [...] ```

The cfgrib project offers to open a GRIB file as an xarray.Dataset: ```

import cfgrib cfgrib.open_dataset('era5-levels-members.grib') <xarray.Dataset> Dimensions: (air_pressure: 2, latitude: 61, longitude: 120, number: 10, time: 4) Coordinates: * number (number) int64 0 1 2 3 4 5 6 7 8 9 * time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00 step timedelta64[ns] ... * air_pressure (air_pressure) float64 850.0 500.0 * latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0 * longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0 valid_time (time) datetime64[ns] ... Data variables: z (number, time, air_pressure, latitude, longitude) float32 ... t (number, time, air_pressure, latitude, longitude) float32 ... Attributes: GRIB_edition: 1 GRIB_centre: ecmf GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2... It would be very helpful to have a backend for it distributed with *xarray* to be selected via the `engine` keyword argument: import xarray as xr xr.open_dataset('era5-levels-members.grib', engine='cfgrib') <xarray.Dataset> Dimensions: (air_pressure: 2, latitude: 61, longitude: 120, number: 10, time: 4) Coordinates: * number (number) int64 0 1 2 3 4 5 6 7 8 9 * time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00 step timedelta64[ns] ... * air_pressure (air_pressure) float64 850.0 500.0 * latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0 * longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0 valid_time (time) datetime64[ns] ... Data variables: z (number, time, air_pressure, latitude, longitude) float32 ... t (number, time, air_pressure, latitude, longitude) float32 ... Attributes: GRIB_edition: 1 GRIB_centre: ecmf GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2... ```

cc @StephanSiemen @iainrussell