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=2, scale=0.1)
yd = uniform(loc=4, scale=0.1)
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)
QM = EmpiricalQuantileMapping(kind=kind,
group="time.month",
nquantiles=5)
QM.train(ref, hist)
p = QM.adjust(sim)
mqm = QM.ds.af.mean(dim="quantiles")
expected = apply_correction(mon_triangular, 2, kind)
np.testing.assert_array_almost_equal(mqm, expected, 1)
# Test predict
np.testing.assert_array_almost_equal(p, ref, 2)
def test_add_dims(self, use_dask):
if use_dask:
chunks = {"location": -1}
else:
chunks = None
ref = (open_dataset(
"sdba/ahccd_1950-2013.nc",
chunks=chunks,
drop_variables=["lat",
"lon"]).sel(time=slice("1981", "2010")).tasmax)
ref = convert_units_to(ref, "K")
ref = ref.isel(location=1, drop=True).expand_dims(location=["Amos"])
dsim = open_dataset("sdba/CanESM2_1950-2100.nc",
chunks=chunks,
drop_variables=["lat", "lon"]).tasmax
hist = dsim.sel(time=slice("1981", "2010"))
sim = dsim.sel(time=slice("2041", "2070"))
# With add_dims, "does it run" test
group = Grouper("time.dayofyear", window=5, add_dims=["location"])
EQM = EmpiricalQuantileMapping.train(ref, hist, group=group)
EQM.adjust(sim).load()
# Without, sanity test.
group = Grouper("time.dayofyear", window=5)
EQM2 = EmpiricalQuantileMapping.train(ref, hist, group=group)
scen2 = EQM2.adjust(sim).load()
assert scen2.sel(location=["Kugluktuk", "Vancouver"]).isnull().all()
def test_simple(self, c_thresh, q_thresh, frac, power):
n = 45 * 365
def gen_testdata(c, s):
base = np.clip(norm.rvs(loc=0, scale=s, size=(n, )), 0, None)
qv = np.quantile(base[base > 1], q_thresh)
base[base > qv] = genpareto.rvs(c,
loc=qv,
scale=s,
size=base[base > qv].shape)
return xr.DataArray(
base,
dims=("time", ),
coords={
"time":
xr.cftime_range("1990-01-01", periods=n, calendar="noleap")
},
attrs={
"units": "mm/day",
"thresh": qv
},
)
ref = jitter_under_thresh(gen_testdata(-0.1, 2), 1e-3)
hist = jitter_under_thresh(gen_testdata(-0.1, 2), 1e-3)
sim = gen_testdata(-0.15, 2.5)
EQM = EmpiricalQuantileMapping(group="time.dayofyear",
nquantiles=15,
kind="*")
EQM.train(ref, hist)
scen = EQM.adjust(sim)
EX = ExtremeValues(c_thresh, q_thresh=q_thresh)
EX.train(ref, hist)
qv = (ref.thresh + hist.thresh) / 2
np.testing.assert_allclose(EX.ds.fit_params, [-0.1, qv, 2],
atol=0.5,
rtol=0.1)
np.testing.assert_allclose(EX.ds.thresh, qv, atol=0.15, rtol=0.01)
scen2 = EX.adjust(scen, sim, frac=frac, power=power)
# What to test???
# Test if extreme values of sim are still extreme
exval = sim > EX.ds.thresh
assert (scen2.where(exval) >
EX.ds.thresh).sum() > (scen.where(exval) > EX.ds.thresh).sum()
# ONLY extreme values have been touched (but some might not have been modified)
assert (((scen != scen2) | exval) == exval).all()
def test_real_data(self):
dsim = open_dataset("sdba/CanESM2_1950-2100.nc").chunk()
dref = open_dataset("sdba/ahccd_1950-2013.nc").chunk()
ref = convert_units_to(dref.sel(time=slice("1950", "2009")).pr, "mm/d")
hist = convert_units_to(
dsim.sel(time=slice("1950", "2009")).pr, "mm/d")
quantiles = np.linspace(0.01, 0.99, num=50)
with xr.set_options(keep_attrs=True):
ref = ref + uniform_noise_like(ref, low=1e-6, high=1e-3)
hist = hist + uniform_noise_like(hist, low=1e-6, high=1e-3)
EQM = EmpiricalQuantileMapping.train(ref,
hist,
group=Grouper("time.dayofyear",
window=31),
nquantiles=quantiles)
scen = EQM.adjust(hist, interp="linear", extrapolation="constant")
EX = ExtremeValues.train(ref,
hist,
cluster_thresh="1 mm/day",
q_thresh=0.97)
new_scen = EX.adjust(scen, hist, frac=0.000000001)
new_scen.load()
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(
kind=kind,
group="time",
nquantiles=50,
)
QM.train(ref, hist)
p = QM.adjust(sim, interp="linear")
q = QM.ds.coords["quantiles"]
expected = get_correction(xd.ppf(q), yd.ppf(q), kind)
# 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_stack_variables():
ds1 = open_dataset("sdba/CanESM2_1950-2100.nc")
ds2 = open_dataset("sdba/ahccd_1950-2013.nc")
da1 = stack_variables(ds1)
da2 = stack_variables(ds2)
assert list(da1.multivar.values) == ["pr", "tasmax"]
assert da1.multivar.attrs["_standard_name"] == [
"precipitation_flux",
"air_temperature",
]
assert da2.multivar.attrs["is_variables"]
assert da1.multivar.equals(da2.multivar)
da1p = da1.sortby("multivar", ascending=False)
with pytest.raises(ValueError, match="Inputs have different multivariate"):
EmpiricalQuantileMapping.train(da1p, da2)
ds1p = unstack_variables(da1)
xr.testing.assert_equal(ds1, ds1p)
def test_dask_julia(self):
dsim = open_dataset("sdba/CanESM2_1950-2100.nc").chunk()
dref = open_dataset("sdba/ahccd_1950-2013.nc").chunk()
dexp = open_dataset("sdba/adjusted_external.nc")
ref = convert_units_to(dref.sel(time=slice("1950", "2009")).pr, "mm/d")
hist = convert_units_to(
dsim.sel(time=slice("1950", "2009")).pr, "mm/d")
quantiles = np.linspace(0.01, 0.99, num=50)
EQM = EmpiricalQuantileMapping(group=Grouper("time.dayofyear",
window=31),
nquantiles=quantiles)
with xr.set_options(keep_attrs=True):
ref = ref + uniform_noise_like(ref, low=1e-6, high=1e-3)
hist = hist + uniform_noise_like(hist, low=1e-6, high=1e-3)
EQM.train(ref, hist)
scen = EQM.adjust(hist, interp="linear", extrapolation="constant")
EX = ExtremeValues(cluster_thresh="1 mm/day", q_thresh=0.97)
EX.train(ref, hist)
new_scen = EX.adjust(scen, hist, frac=0.000000001)
new_scen.load()
exp_scen = dexp.extreme_values_julia
xr.testing.assert_allclose(
new_scen.where(new_scen != scen).transpose("time", "location"),
exp_scen.where(new_scen != scen).transpose("time", "location"),
atol=0.005,
rtol=2e-3,
)
def cannon_2015_figure_2():
n = 10000
ref, hist, sim = tu.cannon_2015_rvs(n, random=False)
QM = EmpiricalQuantileMapping(kind="*", group="time", interp="linear")
QM.train(ref, hist)
sim_eqm = QM.predict(sim)
DQM = DetrendedQuantileMapping(kind="*", group="time", interp="linear")
DQM.train(ref, hist)
sim_dqm = DQM.predict(sim, degree=0)
QDM = QuantileDeltaMapping(kind="*", group="time", interp="linear")
QDM.train(ref, hist)
sim_qdm = QDM.predict(sim)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))
x = np.linspace(0, 105, 50)
ax1.plot(x, gaussian_kde(ref)(x), color="r", label="Obs hist")
ax1.plot(x, gaussian_kde(hist)(x), color="k", label="GCM hist")
ax1.plot(x, gaussian_kde(sim)(x), color="blue", label="GCM simure")
ax1.plot(x, gaussian_kde(sim_qdm)(x), color="lime", label="QDM future")
ax1.plot(x,
gaussian_kde(sim_eqm)(x),
color="darkgreen",
ls="--",
label="QM future")
ax1.plot(x,
gaussian_kde(sim_dqm)(x),
color="lime",
ls=":",
label="DQM future")
ax1.legend(frameon=False)
ax1.set_xlabel("Value")
ax1.set_ylabel("Density")
tau = np.array([0.25, 0.5, 0.75, 0.95, 0.99]) * 100
bc_gcm = (scoreatpercentile(sim, tau) -
scoreatpercentile(hist, tau)) / scoreatpercentile(hist, tau)
bc_qdm = (scoreatpercentile(sim_qdm, tau) -
scoreatpercentile(ref, tau)) / scoreatpercentile(ref, tau)
bc_eqm = (scoreatpercentile(sim_eqm, tau) -
scoreatpercentile(ref, tau)) / scoreatpercentile(ref, tau)
bc_dqm = (scoreatpercentile(sim_dqm, tau) -
scoreatpercentile(ref, tau)) / scoreatpercentile(ref, tau)
ax2.plot([0, 1], [0, 1], ls=":", color="blue")
ax2.plot(bc_gcm, bc_gcm, "-", color="blue", label="GCM")
ax2.plot(bc_gcm, bc_qdm, marker="o", mfc="lime", label="QDM")
ax2.plot(
bc_gcm,
bc_eqm,
marker="o",
mfc="darkgreen",
ls=":",
color="darkgreen",
label="QM",
)
ax2.plot(
bc_gcm,
bc_dqm,
marker="s",
mec="lime",
mfc="w",
ls="--",
color="lime",
label="DQM",
)
for i, s in enumerate(tau / 100):
ax2.text(bc_gcm[i],
bc_eqm[i],
f"{s} ",
ha="right",
va="center",
fontsize=9)
ax2.set_xlabel("GCM relative change")
ax2.set_ylabel("Bias adjusted relative change")
ax2.legend(loc="upper left", frameon=False)
ax2.set_aspect("equal")
plt.tight_layout()
return fig
def test_default_grouper_understood(tas_series):
ref = tas_series(np.arange(730).astype(float))
EQM = EmpiricalQuantileMapping.train(ref, ref)
EQM.adjust(ref)
assert EQM.group.dim == "time"
def test_raise_on_multiple_chunks(tas_series):
ref = tas_series(np.arange(730).astype(float)).chunk({"time": 365})
with pytest.raises(ValueError):
EmpiricalQuantileMapping.train(ref, ref, group=Grouper("time.month"))