netCDF + lazy backend: Error when sel is used with slice, reverse arrange

What happened?

import xarray as xr
data = xr.open_dataset('data_example.nc')

# wrong
a = data.sel(time='1979-1',isobaricInhPa=200).z[:,  ::10,::10][:, ::-1,:]

It seem that when use sel() , [: , ::-1 , :] and [: , ::10 , ::10] at the same time will cause the second coord will wrong like this. Why just when use sel and[: , ::10 , ::10][: , ::-1 , :] ,it will goes wrong in axis=2( the data DO NOT correspond to the coordinates) ?

What did you expect to happen?

The data correspond to the coordinates on Latitude.

Minimal Complete Verifiable Example

problem_example.zip

MVCE confirmation

• Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• Complete example — the example is self-contained, including all data and the text of any traceback.
• Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

---

commit: None python: 3.8.12 (default, Oct 12 2021, 03:01:40) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: AMD64 Family 25 Mod el 33 Stepping 0, AuthenticAMD byteorder: little LC_ALL: None LANG: en LOCALE: (‘Chinese (Simplified)_China’, ‘936’) libhdf5: 1.12.1 libnetcdf: 4.8.1 xarray: 2022.3.0 pandas: 1.4.2 numpy: 1.22.3 scipy: 1.7.3 netCDF4: 1.5.7 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.6.0 nc_time_axis: None PseudoNetCDF: None rasterio: 1.2.10 cfgrib: 0.9.10.1 iris: None bottleneck: None dask: None distributed: None matplotlib: 3.5.1 cartopy: 0.20.2 seaborn: None numbagg: None fsspec: None cupy: None pint: None sparse: None setuptools: 56.0.0 pip: 22.0.4 conda: None pytest: None IPython: 8.3.0 sphinx: 4.5.0

[jesieleo]

First of all, thank you for your help @dcherian @max-sixty . I finally reproduced this problem. I put the ds.isel(time=[0], isoBaricInhPa=1).z[:, ::10, :][:, ::-1, :] to ds.isel(time=[0], isoBaricInhPa=1).z[:, ::10, ::10][:, ::-1, :] can make a minimal example.

import numpy as np
import xarray as xr
ds = xr.Dataset(
    {
        "z": (
            ("time", "isoBaricInhPa", "latitude", "longitude"),
            np.ones((1, 5, 721, 1440)),
        )
    },
    coords={"latitude": np.linspace(-90, 90, 721)},
).to_netcdf("test.nc")
ds = xr.open_dataset('test.nc')
a = ds.isel(time=[0], isoBaricInhPa=1).z[:, ::10, ::10][:, ::-1, :]
print(a.sizes)
print(a.shape)
print(a)

Frozen({'time': 1, 'latitude': 73, 'longitude': 144})
(1, 73, 144)
<xarray.DataArray 'z' (time: 1, latitude: 72, longitude: 144)>
array([[[1., 1., ..., 1., 1.],
        [1., 1., ..., 1., 1.],
        ...,
        [1., 1., ..., 1., 1.],
        [1., 1., ..., 1., 1.]]])
Coordinates:
  * latitude  (latitude) float64 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0
Dimensions without coordinates: time, longitude

[max-sixty]

@dcherian thanks a lot for looking into this. @jesieleo thanks for your patience in us finding the bug.