def test_maybe_convert_vertex_to_bounds():
# create a ds with verticies
lon = np.arange(0, 10)
lat = np.arange(20, 30)
data = np.random.rand(len(lon), len(lat))
ds = xr.DataArray(data, dims=["x", "y"], coords={
"x": lon,
"y": lat
}).to_dataset(name="test")
ds.coords["lon"] = ds.x * xr.ones_like(ds.y)
ds.coords["lat"] = xr.ones_like(ds.x) * ds.y
ds.coords["lon_verticies"] = (
xr.DataArray([-0.1, -0.1, 0.1, 0.1], dims=["vertex"]) + ds["lon"])
ds.coords["lat_verticies"] = (
xr.DataArray([-0.1, 0.1, 0.1, -0.1], dims=["vertex"]) + ds["lat"])
ds = promote_empty_dims(ds)
# create expected dataset
ds_expected = ds.copy()
for va in ["lon", "lat"]:
ds_expected.coords[va + "_bounds"] = (
xr.DataArray([-0.1, 0.1], dims=["bnds"]) + ds_expected[va])
ds_expected = promote_empty_dims(ds_expected)
ds_test = maybe_convert_vertex_to_bounds(ds)
xr.testing.assert_identical(ds_expected, ds_test)
# check that datasets that already conform to this are not changed
xr.testing.assert_identical(ds_expected,
maybe_convert_vertex_to_bounds(ds_expected))
assert np.all(ds_test.lon_bounds.diff("bnds") > 0)
assert np.all(ds_test.lat_bounds.diff("bnds") > 0)
def test_promote_empty_dims():
xlen, ylen, zlen = (10, 5, 6)
ds = create_test_ds("x", "y", "z", xlen, ylen, zlen)
ds = ds.drop(["x", "y", "z"])
ds_promoted = promote_empty_dims(ds)
assert set(["x", "y", "z"]).issubset(set(ds_promoted.coords))
