def test_train_qdm(kind):
"""Test that train_qdm outputs store giving sdba.adjustment.QuantileDeltaMapping
Checks that output is consistent if we do "additive" or "multiplicative"
QDM kinds.
"""
# Setup input data.
n_years = 10
n = n_years * 365
model_bias = 2
ts = np.sin(np.linspace(-10 * 3.14, 10 * 3.14, n)) * 0.5
hist = _datafactory(ts + model_bias)
ref = _datafactory(ts)
output_key = "memory://test_train_qdm/test_output.zarr"
hist_key = "memory://test_train_qdm/hist.zarr"
ref_key = "memory://test_train_qdm/ref.zarr"
# Load up a fake repo with our input data in the place of big data and cloud
# storage.
repository.write(hist_key, hist)
repository.write(ref_key, ref)
train_qdm(
historical=hist_key,
reference=ref_key,
out=output_key,
variable="fakevariable",
kind=kind,
)
assert QuantileDeltaMapping.from_dataset(repository.read(output_key))
def test_train_qdm_isel_slice():
"""Test that train_qdm outputs subset data when passed isel_slice"""
# Setup input data.
n_years = 10
n = n_years * 365
# Lazy way to make fake data for 2 latitudes...
model_bias = 2
ts = np.sin(np.linspace(-10 * 3.14, 10 * 3.14, n)) * 0.5
hist1 = _datafactory(ts + model_bias)
hist2 = _datafactory(ts + model_bias).assign_coords(
{"lat": hist1["lat"].data + 1.0}
)
hist = xr.concat([hist1, hist2], dim="lat")
ref1 = _datafactory(ts)
ref2 = _datafactory(ts + model_bias).assign_coords({"lat": ref1["lat"].data + 1.0})
ref = xr.concat([ref1, ref2], dim="lat")
output_key = "memory://test_train_qdm_isel_slice/test_output.zarr"
hist_key = "memory://test_train_qdm_isel_slice/hist.zarr"
ref_key = "memory://test_train_qdm_isel_slice/ref.zarr"
repository.write(hist_key, hist)
repository.write(ref_key, ref)
train_qdm(
historical=hist_key,
reference=ref_key,
out=output_key,
variable="fakevariable",
kind="additive",
isel_slice={"lat": slice(0, 1)}, # select only 1 of 2 lats by idx...
)
# Check we can read output and it's the selected value, only.
ds_result = repository.read(output_key)
np.testing.assert_equal(ds_result["lat"].data, ref["lat"].data[0])
assert QuantileDeltaMapping.from_dataset(ds_result)