issues: 354923742

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354923742 MDU6SXNzdWUzNTQ5MjM3NDI= 2388 Test equality of DataArrays up to transposition 35968931 closed 0     2 2018-08-28T22:13:01Z 2018-10-08T12:25:46Z 2018-10-08T12:25:46Z MEMBER      

While writing some unit tests to check I had wrapped np.gradient correctly with xr.apply_ufunc, I came unstuck because my results were equivalent except for transposed dimensions. It took me a while to realise that xarray.testing.asset_equal considers two DataArrays equal only if their dimensions are in the same order, because intuitively that shouldn't matter in the context of xarray's data model.

A simple example to demonstrate what I mean: ```python

Create two functionally-equivalent dataarrays

data = np.random.randn(4, 3) da1 = xr.DataArray(data, dims=('x', 'y')) da2 = xr.DataArray(data.T, dims=('y', 'x'))

This test will fail

xarray.tests.assert_equal(da1, da2) This test fails, with output E AssertionError: <xarray.DataArray (x: 4, y: 3)> E array([[ 0.761038, 0.121675, 0.443863], E [ 0.333674, 1.494079, -0.205158], E [ 0.313068, -0.854096, -2.55299 ], E [ 0.653619, 0.864436, -0.742165]]) E Coordinates: E * x (x) int64 5 7 9 11 E * y (y) int64 1 4 6 E <xarray.DataArray (y: 3, x: 4)> E array([[ 0.761038, 0.333674, 0.313068, 0.653619], E [ 0.121675, 1.494079, -0.854096, 0.864436], E [ 0.443863, -0.205158, -2.55299 , -0.742165]]) E Coordinates: E * x (x) int64 5 7 9 11 E * y (y) int64 1 4 6 ``` even though these two DataArrays are functionally-equivalent for all xarray operations you could perform with them.

It would make certain types of unit tests simpler and clearer to have a function like python xarray.tests.assert_equivalent(da1, da2) which would return true if one DataArray can be formed from the other by transposition.

I would have thought that a test that does this would just transpose one into the shape of the other before comparison?

 
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