def test_adapt_freq_add_dims(use_dask):
time = pd.date_range("1990-01-01", "2020-12-31", freq="D")
prvals = np.random.randint(0, 100, size=(time.size, 3))
pr = xr.DataArray(
prvals,
coords={
"time": time,
"lat": [0, 1, 2]
},
dims=("time", "lat"),
attrs={"units": "mm d-1"},
)
if use_dask:
pr = pr.chunk()
group = Grouper("time.month", add_dims=["lat"])
with xr.set_options(keep_attrs=True):
prsim = xr.where(pr < 20, pr / 20, pr)
prref = xr.where(pr < 10, pr / 20, pr)
sim_ad, pth, dP0 = adapt_freq(prref, prsim, thresh="1 mm d-1", group=group)
assert set(sim_ad.dims) == set(prsim.dims)
assert "lat" not in pth.dims
group = Grouper("time.dayofyear", window=5)
with xr.set_options(keep_attrs=True):
prsim = xr.where(pr < 20, pr / 20, pr)
prref = xr.where(pr < 10, pr / 20, pr)
sim_ad, pth, dP0 = adapt_freq(prref, prsim, thresh="1 mm d-1", group=group)
assert set(sim_ad.dims) == set(prsim.dims)
def test_adapt_freq(use_dask):
time = pd.date_range("1990-01-01", "2020-12-31", freq="D")
prvals = np.random.randint(0, 100, size=(time.size, 3))
pr = xr.DataArray(
prvals,
coords={
"time": time,
"lat": [0, 1, 2]
},
dims=("time", "lat"),
attrs={"units": "mm d-1"},
)
if use_dask:
pr = pr.chunk({"lat": 1})
group = Grouper("time.month")
with xr.set_options(keep_attrs=True):
prsim = xr.where(pr < 20, pr / 20, pr)
prref = xr.where(pr < 10, pr / 20, pr)
sim_ad, pth, dP0 = adapt_freq(prref, prsim, thresh="1 mm d-1", group=group)
# Where the input is considered zero
input_zeros = sim_ad.where(prsim <= 1)
# The proportion of corrected values (time.size * 3 * 0.2 is the theoritical number of values under 1 in prsim)
dP0_out = (input_zeros > 1).sum() / (time.size * 3 * 0.2)
np.testing.assert_allclose(dP0_out, 0.5, atol=0.1)
# Assert that corrected values were generated in the range ]1, 20 + tol[
corrected = (input_zeros.where(input_zeros > 1).stack(
flat=["lat", "time"]).reset_index("flat").dropna("flat"))
assert ((corrected < 20.1) & (corrected > 1)).all()
# Assert that non-corrected values are untouched
# Again we add a 0.5 tol because of randomness.
xr.testing.assert_equal(
sim_ad.where(prsim > 20.1),
prsim.where(prsim > 20.5).transpose("lat", "time"),
)
# Assert that Pth and dP0 are approx the good values
np.testing.assert_allclose(pth, 20, rtol=0.05)
np.testing.assert_allclose(dP0, 0.5, atol=0.14)
assert sim_ad.units == "mm d-1"
assert sim_ad.attrs["references"].startswith("Themeßl")
assert pth.units == "mm d-1"
def adapt_freq_graph():
"""
Create a graphic with the additive adjustment factors estimated after applying the adapt_freq method.
"""
n = 10000
x = tu.series(synth_rainfall(2, 2, wet_freq=0.25, size=n), "pr") # sim
y = tu.series(synth_rainfall(2, 2, wet_freq=0.5, size=n), "pr") # ref
xp = adapt_freq(x, y, thresh=0).sim_ad
fig, (ax1, ax2) = plt.subplots(2, 1)
sx = x.sortby(x)
sy = y.sortby(y)
sxp = xp.sortby(xp)
# Original and corrected series
ax1.plot(sx.values, color="blue", lw=1.5, label="x : sim")
ax1.plot(sxp.values, color="pink", label="xp : sim corrected")
ax1.plot(sy.values, color="k", label="y : ref")
ax1.legend()
# Compute qm factors
qm_add = QuantileDeltaMapping(kind="+", group="time").train(y, x).ds
qm_mul = QuantileDeltaMapping(kind="*", group="time").train(y, x).ds
qm_add_p = QuantileDeltaMapping(kind="+", group="time").train(y, xp).ds
qm_mul_p = QuantileDeltaMapping(kind="*", group="time").train(y, xp).ds
qm_add.cf.plot(ax=ax2, color="cyan", ls="--", label="+: y-x")
qm_add_p.cf.plot(ax=ax2, color="cyan", label="+: y-xp")
qm_mul.cf.plot(ax=ax2, color="brown", ls="--", label="*: y/x")
qm_mul_p.cf.plot(ax=ax2, color="brown", label="*: y/xp")
ax2.legend(loc="upper left", frameon=False)
return fig
