issues: 977544678

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977544678 MDU6SXNzdWU5Nzc1NDQ2Nzg= 5733 Shoudn't `assert_allclose` transpose datasets? 14314623 open 0     16 2021-08-23T22:55:12Z 2024-05-01T15:27:21Z   CONTRIBUTOR      

I am trying to compare two datasets, one of which has possibly transposed dimensions on a data variable.

python import xarray as xr import numpy as np data = np.random.rand(4,6) da = xr.DataArray(data, dims=['x','y']) ds1 = xr.Dataset({'data':da}) ds2 = xr.Dataset({'data':da}).transpose('y','x') What happened: In my mind this should pass python xr.testing.assert_allclose(ds1, ds2) but instead it fails

```

AssertionError Traceback (most recent call last) <ipython-input-7-58cd53174a1e> in <module> ----> 1 xr.testing.assert_allclose(ds1, ds2)

[... skipping hidden 1 frame]

/srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/testing.py in assert_allclose(a, b, rtol, atol, decode_bytes) 169 a.variables, b.variables, compat=compat_variable 170 ) --> 171 assert allclose, formatting.diff_dataset_repr(a, b, compat=equiv) 172 else: 173 raise TypeError("{} not supported by assertion comparison".format(type(a)))

AssertionError: Left and right Dataset objects are not close

Differing data variables: L data (x, y) float64 0.8589 0.09264 0.0264 ... 0.1039 0.3685 0.3983 R data (y, x) float64 0.8589 0.8792 0.8433 0.6952 ... 0.3664 0.2214 0.3983 ```

Simply transposing ds2 to the same dimensions of ds1 fixes this (since the data is the same after all)

python xr.testing.assert_allclose(ds1, ds2.transpose('x','y'))

Since most of the other xarray operations are 'transpose-safe' ((ds1+ds2) = (ds1 + ds2.transpose('x','y')), shouldnt this one be too?

Environment:

Output of <tt>xr.show_versions()</tt> INSTALLED VERSIONS ------------------ commit: None python: 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 5.4.109+ machine: x86_64 processor: x86_64 byteorder: little LC_ALL: C.UTF-8 LANG: C.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.19.0 pandas: 1.2.4 numpy: 1.20.2 scipy: 1.6.2 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 3.2.1 Nio: None zarr: 2.7.1 cftime: 1.4.1 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.2.2 cfgrib: 0.9.9.0 iris: None bottleneck: 1.3.2 dask: 2021.04.1 distributed: 2021.04.1 matplotlib: 3.4.1 cartopy: 0.19.0 seaborn: None numbagg: None pint: 0.17 setuptools: 49.6.0.post20210108 pip: 20.3.4 conda: None pytest: None IPython: 7.22.0 sphinx: None 
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