issue_comments

Thanks @benbovy!

If everyone is happy with this I'm going to merge it.

Yes, please!

If everyone is happy with this I'm going to merge it.

I agree with @max-sixty A > C > B but only weakly.

Same opinion.

This looks v good @benbovy!

I'm just fixing int32 vs int64 issue with tests run on appveyor, then to me it's ready to merge unless someone else has objections.

This looks v good @benbovy!

I agree with @max-sixty A > C > B but only weakly.

I'm wondering which is the best among the options below:

TBH I think they're all good; I don't have a strong view. I'd low-confidence rank A->C->B

I think you should feel free to merge an MVP rather than perfecting, but check out https://docs.pytest.org/en/latest/example/simple.html#writing-well-integrated-assertion-helpers

I think that might be as simple as wrapping these in pytest.fail(...)

If you want to add that example as a test, that could be a good way of documenting the function. But I don't think it's strictly needed

Yes I could use those examples for the tests. I think it might be good to write shallow tests at least for diff_array_repr and diff_dataset_repr (in formatting.py).

I'm wondering which is the best among the options below:

A.

Differing data variables: L var1 (x, y) int64 1 2 3 4 5 7 R var1 (x) int64 1 2 L var2 (x, y) int64 4 5 6 7 8 9 description: variable 2 R var2 (x, y) int64 4 5 6 7 8 9

B.

Differing data variables: L var1 (x, y) int64 1 2 3 4 5 7 R var1 (x) int64 1 2 L var2 (x, y) int64 4 5 6 7 8 9 description: variable 2 R var2 (x, y) int64 4 5 6 7 8 9

C.

Differing data variables: L var1 (x, y) int64 1 2 3 4 5 7 var2 (x, y) int64 4 5 6 7 8 9 description: variable 2 R var1 (x) int64 1 2 var2 (x, y) int64 4 5 6 7 8 9

If you want to add that example as a test, that could be a good way of documenting the function. But I don't think it's strictly needed

This looks amazing! I'm excited to use it. Thanks for finishing it up

full comparison is done once again for formatting the report. I don't think there is an easy way to avoid that, but I don't think it's a big deal either.

i.e. there's a performance cost? Yeah that's def fine!

Thanks for reminding me this PR @max-sixty ! It deserves more than no commits since 1.5 years!

The assertion error messages now look like pytest's failure reports (see test_eq_dict). I've tried to reuse as much as possible the repr formatting that we already have for the xarray objects.

As examples:

```python ds_a = xr.Dataset(data_vars={'var1': (('x', 'y'), [[1, 2, 3], [4, 5, 7]]), 'var2': ('x', [3, 4])}, coords={'x': ['a', 'b'], 'y': [1, 2, 3]}, attrs={'units': 'm', 'description': 'desc'})

ds_b = xr.Dataset(data_vars={'var1': ('x', [1, 2])}, coords={'x': ('x', ['a', 'c'], {'source': 0}), 'label': ('x', [1, 2])}, attrs={'units': 'kg'})

xr.testing.assert_identical(ds_a, ds_b) AssertionError: Left and right Dataset objects are not identical Differing dimensions: (x: 2, y: 3) != (x: 2) Differing coordinates: L * x (x) <U1 'a' 'b' R * x (x) <U1 'a' 'c' source: 0 Left contains more coordinates: * y (y) int64 1 2 3 Right contains more coordinates: label (x) int64 1 2 Differing data variables: L var1 (x, y) int64 1 2 3 4 5 7 R var1 (x) int64 1 2 Left contains more data variables: var2 (x) int64 3 4 Differing attributes: L units: m R units: kg Left contains more attributes: description: desc ```

```python da_a = xr.DataArray([[1, 2, 3], [4, 5, 7]], dims=('x', 'y'), coords={'x': ['a', 'b'], 'y': [1, 2, 3]}, attrs={'units': 'm', 'description': 'desc'})

da_b = xr.DataArray([1, 2], dims='x', coords={'x': ['a', 'c'], 'label': ('x', [1, 2])}, attrs={'units': 'kg'})

xr.testing.assert_equal(da_a, da_b) AssertionError: Left and right DataArray objects are not equal Differing values: L array([[1, 2, 3], [4, 5, 7]]) R array([1, 2]) Differing coordinates: L * x (x) <U1 'a' 'b' R * x (x) <U1 'a' 'c' Left contains more coordinates: * y (y) int64 1 2 3 Right contains more coordinates: label (x) int64 1 2 ```

Those examples are probably not the most readable ones but it's for a full showcase.

A downside of this approach (i.e., report the exact differing) is that when a and b differs, full comparison is done once again for formatting the report. I don't think there is an easy way to avoid that, but I don't think it's a big deal either.

Any interest in pushing this over the line @benbovy ?

Indeed, exact differing variables/attributes would be much better here. I'll look into this next week.

pytest's failure reports and reports of numpy.testing functions may be good sources of inspiration.

This is certainly welcome. In an ideal world, we might point specifically to the exact differing variables/attributes, but this is a fine start.