def test_values(self):
# Check values are OK. Values taken from pyproj test.
boston_lat = 42.0 + (15.0 / 60.0)
boston_lon = -71.0 - (7.0 / 60.0)
portland_lat = 45.0 + (31.0 / 60.0)
portland_lon = -123.0 - (41.0 / 60.0)
da = xr.DataArray(0,
coords={
"lon": [boston_lon],
"lat": [boston_lat]
},
dims=["lon", "lat"])
d = subset.distance(da, lon=portland_lon, lat=portland_lat)
np.testing.assert_almost_equal(d, 4164074.239, decimal=3)
def test_broadcasting(self):
# Check output dimensions match lons and lats.
lon = np.linspace(-180, 180, 20)
lat = np.linspace(-90, 90, 30)
da = xr.Dataset(data_vars=None, coords={"lon": lon, "lat": lat})
da["data"] = xr.DataArray(np.random.rand(lon.size, lat.size),
dims=["lon", "lat"])
d = subset.distance(da, lon=-34, lat=56).squeeze("site")
assert d.dims == da.data.dims
assert d.shape == da.data.shape
assert d.units == "m"
# Example of how to get the actual 2D indices.
k = d.argmin()
i, j = np.unravel_index(k, da.data.shape)
assert d[i, j] == d.min()