def test_quantiles(self, series, kind, name):
"""Train on
hist: U
ref: Normal
Predict on hist to get ref
"""
ns = 10000
u = np.random.rand(ns)
# Define distributions
xd = uniform(loc=10, scale=1)
yd = norm(loc=12, scale=1)
# Generate random numbers with u so we get exact results for comparison
x = xd.ppf(u)
y = yd.ppf(u)
# Test train
hist = sim = series(x, name)
ref = series(y, name)
DQM = DetrendedQuantileMapping(kind=kind,
group="time",
nquantiles=50,
interp="linear")
DQM.train(ref, hist)
p = DQM.adjust(sim)
q = DQM.ds.quantiles
ex = apply_correction(xd.ppf(q), invert(xd.mean(), kind), kind)
ey = apply_correction(yd.ppf(q), invert(yd.mean(), kind), kind)
expected = get_correction(ex, ey, kind)
# Results are not so good at the endpoints
np.testing.assert_array_almost_equal(DQM.ds.af[2:-2], expected[2:-2],
1)
# Test predict
# Accept discrepancies near extremes
middle = (x > 1e-2) * (x < 0.99)
np.testing.assert_array_almost_equal(p[middle], ref[middle], 1)
# Test with simure not equal to hist
ff = series(np.ones(ns) * 1.1, name)
sim2 = apply_correction(sim, ff, kind)
ref2 = apply_correction(ref, ff, kind)
p2 = DQM.adjust(sim2)
np.testing.assert_array_almost_equal(p2[middle], ref2[middle], 1)
# Test with actual trend in sim
trend = series(
np.linspace(-0.2, 0.2, ns) + (1 if kind == MULTIPLICATIVE else 0),
name)
sim3 = apply_correction(sim, trend, kind)
ref3 = apply_correction(ref, trend, kind)
p3 = DQM.adjust(sim3)
np.testing.assert_array_almost_equal(p3[middle], ref3[middle], 1)
def test_mon_U(self, mon_series, series, mon_triangular, add_dims, kind,
name, use_dask):
"""
Train on
hist: U
ref: U + monthly cycle
Predict on hist to get ref
"""
u = np.random.rand(10000)
# Define distributions
xd = uniform(loc=1, scale=1)
yd = uniform(loc=2, scale=2)
noise = uniform(loc=0, scale=1e-7)
# Generate random numbers
x = xd.ppf(u)
y = yd.ppf(u) + noise.ppf(u)
# Test train
ref = mon_series(y, name)
hist = sim = series(x, name)
if use_dask:
ref = ref.chunk({"time": -1})
hist = hist.chunk({"time": -1})
sim = sim.chunk({"time": -1})
if add_dims:
ref = ref.expand_dims(site=[0, 1, 2, 3, 4]).drop_vars("site")
hist = hist.expand_dims(site=[0, 1, 2, 3, 4]).drop_vars("site")
sim = sim.expand_dims(site=[0, 1, 2, 3, 4]).drop_vars("site")
sel = {"site": 0}
else:
sel = {}
QDM = QuantileDeltaMapping.train(ref,
hist,
kind=kind,
group="time.month",
nquantiles=40)
p = QDM.adjust(sim, interp="linear" if kind == "+" else "nearest")
q = QDM.ds.coords["quantiles"]
expected = get_correction(xd.ppf(q), yd.ppf(q), kind)
expected = apply_correction(mon_triangular[:, np.newaxis],
expected[np.newaxis, :], kind)
np.testing.assert_array_almost_equal(QDM.ds.af.sel(quantiles=q, **sel),
expected, 1)
# Test predict
np.testing.assert_allclose(p,
ref.transpose(*p.dims),
rtol=0.1,
atol=0.2)
def test_quantiles(self, series, kind, name):
"""Train on
x : U(1,1)
y : U(1,2)
"""
u = np.random.rand(10000)
# Define distributions
xd = uniform(loc=1, scale=1)
yd = uniform(loc=2, scale=4)
# Generate random numbers with u so we get exact results for comparison
x = xd.ppf(u)
y = yd.ppf(u)
# Test train
hist = sim = series(x, name)
ref = series(y, name)
QDM = QuantileDeltaMapping.train(
ref.astype("float32"),
hist.astype("float32"),
kind=kind,
group="time",
nquantiles=10,
)
p = QDM.adjust(sim.astype("float32"), interp="linear")
q = QDM.ds.coords["quantiles"]
expected = get_correction(xd.ppf(q), yd.ppf(q), kind)[np.newaxis, :]
# Results are not so good at the endpoints
np.testing.assert_array_almost_equal(QDM.ds.af, expected, 1)
# Test predict
# Accept discrepancies near extremes
middle = (u > 1e-2) * (u < 0.99)
np.testing.assert_array_almost_equal(p[middle], ref[middle], 1)
# Test dtype control of map_blocks
assert QDM.ds.af.dtype == "float32"
assert p.dtype == "float32"
def test_quantiles(self, series, kind, name):
"""Train on
hist: U
ref: Normal
Predict on hist to get ref
"""
u = np.random.rand(10000)
# Define distributions
xd = uniform(loc=10, scale=1)
yd = norm(loc=12, scale=1)
# Generate random numbers with u so we get exact results for comparison
x = xd.ppf(u)
y = yd.ppf(u)
# Test train
hist = sim = series(x, name)
ref = series(y, name)
QM = EmpiricalQuantileMapping.train(
ref,
hist,
kind=kind,
group="time",
nquantiles=50,
)
p = QM.adjust(sim, interp="linear")
q = QM.ds.coords["quantiles"]
expected = get_correction(xd.ppf(q), yd.ppf(q), kind)[np.newaxis, :]
# Results are not so good at the endpoints
np.testing.assert_array_almost_equal(QM.ds.af[:, 2:-2],
expected[:, 2:-2], 1)
# Test predict
# Accept discrepancies near extremes
middle = (x > 1e-2) * (x < 0.99)
np.testing.assert_array_almost_equal(p[middle], ref[middle], 1)
def test_interp_on_quantiles_monthly():
t = xr.cftime_range("2000-01-01",
"2030-12-31",
freq="D",
calendar="noleap")
ref = xr.DataArray(
(-20 * np.cos(2 * np.pi * t.dayofyear / 365) +
2 * np.random.random_sample((t.size, )) + 273.15 + 0.1 *
(t - t[0]).days / 365), # "warming" of 1K per decade,
dims=("time", ),
coords={"time": t},
attrs={"units": "K"},
)
sim = xr.DataArray(
(-18 * np.cos(2 * np.pi * t.dayofyear / 365) +
2 * np.random.random_sample((t.size, )) + 273.15 + 0.11 *
(t - t[0]).days / 365), # "warming" of 1.1K per decade
dims=("time", ),
coords={"time": t},
attrs={"units": "K"},
)
ref = ref.sel(time=slice(None, "2015-01-01"))
hist = sim.sel(time=slice(None, "2015-01-01"))
group = Grouper("time.month")
quantiles = u.equally_spaced_nodes(15, eps=1e-6)
ref_q = group.apply(nbu.quantile, ref, main_only=True, q=quantiles)
hist_q = group.apply(nbu.quantile, hist, main_only=True, q=quantiles)
af = u.get_correction(hist_q, ref_q, "+")
for interp in ["nearest", "linear", "cubic"]:
afi = u.interp_on_quantiles(sim,
hist_q,
af,
group="time.month",
method=interp,
extrapolation="constant")
assert afi.isnull().sum("time") == 0, interp
def test_mon_U(self, mon_series, series, mon_triangular, kind, name):
"""
Train on
hist: U
ref: U + monthly cycle
Predict on hist to get ref
"""
u = np.random.rand(10000)
# Define distributions
xd = uniform(loc=1, scale=1)
yd = uniform(loc=2, scale=2)
noise = uniform(loc=0, scale=1e-7)
# Generate random numbers
x = xd.ppf(u)
y = yd.ppf(u) + noise.ppf(u)
# Test train
hist = sim = series(x, name)
ref = mon_series(y, name)
QDM = QuantileDeltaMapping(kind=kind,
group="time.month",
nquantiles=40)
QDM.train(ref, hist)
p = QDM.adjust(sim)
q = QDM.ds.coords["quantiles"]
expected = get_correction(xd.ppf(q), yd.ppf(q), kind)
expected = apply_correction(mon_triangular[:, np.newaxis],
expected[np.newaxis, :], kind)
np.testing.assert_array_almost_equal(QDM.ds.af.sel(quantiles=q),
expected, 1)
# Test predict
np.testing.assert_array_almost_equal(p, ref, 1)
