def test_spatial_analogs(method):
if method == "skezely_rizzo":
pytest.skip("Method not implemented.")
diss = open_dataset("SpatialAnalogs/dissimilarity")
data = open_dataset("SpatialAnalogs/indicators")
target = data.sel(lat=46.1875, lon=-72.1875, time=slice("1970", "1990"))
candidates = data.sel(time=slice("1970", "1990"))
out = xca.spatial_analogs(target, candidates, method=method)
np.testing.assert_allclose(diss[method], out, rtol=1e-3, atol=1e-3)
def test_spatial_analogs(method):
if method in [
"nearest_neighbor", "kldiv"
] and parse_version(__scipy_version__) < parse_version("1.6.0"):
pytest.skip("Method not supported in scipy<1.6.0")
diss = open_dataset("SpatialAnalogs/dissimilarity")
data = open_dataset("SpatialAnalogs/indicators")
target = data.sel(lat=46.1875, lon=-72.1875, time=slice("1970", "1990"))
candidates = data.sel(time=slice("1970", "1990"))
out = xca.spatial_analogs(target, candidates, method=method)
np.testing.assert_allclose(diss[method], out, rtol=1e-3, atol=1e-3)
def test_spatial_analogs_multi_index():
# Test multi-indexes
diss = open_dataset("SpatialAnalogs/dissimilarity")
data = open_dataset("SpatialAnalogs/indicators")
target = data.sel(lat=46.1875, lon=-72.1875, time=slice("1970", "1990"))
candidates = data.sel(time=slice("1970", "1990"))
target_stacked = target.stack(sample=["time"])
candidates_stacked = candidates.stack(sample=["time"])
method = "seuclidean"
out = xca.spatial_analogs(target_stacked,
candidates_stacked,
dist_dim="sample",
method=method)
np.testing.assert_allclose(diss[method], out, rtol=1e-3, atol=1e-3)
# Check that it works as well when time dimensions don't have the same length.
candidates = data.sel(time=slice("1970", "1991"))
xca.spatial_analogs(target_stacked,
candidates_stacked,
dist_dim="sample",
method=method)
def test_spatial_analogs_multidim():
diss = open_dataset("SpatialAnalogs/dissimilarity")
data = open_dataset("SpatialAnalogs/indicators")
targets = data.sel(lat=slice(46, 47),
lon=slice(-73, -72),
time=slice("1970", "1990"))
targets = targets.stack(locations=["lat", "lon"])
candidates = data.sel(time=slice("1970", "1990"))
out = xca.spatial_analogs(targets, candidates, method="seuclidean")
assert out.dims == ("locations", "lat", "lon")
np.testing.assert_array_almost_equal(
diss.seuclidean, out.sel(locations=(46.1875, -72.1875)), 5)
assert out.attrs["indices"] == "meantemp,totalpr"