def test_gfwed_and_indicators():
# Also tests passing parameters as quantity strings
ds = open_dataset("FWI/GFWED_sample_2017.nc")
outs = fire_weather_indexes(
tas=ds.tas,
pr=ds.prbc,
snd=ds.snow_depth,
hurs=ds.rh,
sfcWind=ds.sfcwind,
lat=ds.lat,
season_method="GFWED",
overwintering=False,
dry_start="GFWED",
temp_condition_days=3,
snow_condition_days=3,
temp_start_thresh="6 degC",
temp_end_thresh="6 degC",
)
for exp, out in zip([ds.DC, ds.DMC, ds.FFMC, ds.ISI, ds.BUI, ds.FWI],
outs):
np.testing.assert_allclose(out.isel(loc=[0, 1]),
exp.isel(loc=[0, 1]),
rtol=0.03)
ds2 = ds.isel(time=slice(1, None))
with set_options(cf_compliance="log"):
mask = atmos.fire_season(
tas=ds2.tas,
snd=ds2.snow_depth,
method="GFWED",
temp_condition_days=3,
snow_condition_days=3,
temp_start_thresh="6 degC",
temp_end_thresh="6 degC",
)
# 3 first days are false by default assume same as 4th day.
mask = mask.where(mask.time > mask.time[2]).bfill("time")
outs = atmos.fire_weather_indexes(
tas=ds2.tas,
pr=ds2.prbc,
snd=ds2.snow_depth,
hurs=ds2.rh,
sfcWind=ds2.sfcwind,
lat=ds2.lat,
dc0=ds.DC.isel(time=0),
dmc0=ds.DMC.isel(time=0),
ffmc0=ds.FFMC.isel(time=0),
season_mask=mask,
overwintering=False,
dry_start="GFWED",
initial_start_up=False,
)
for exp, out in zip([ds2.DC, ds2.DMC, ds2.FFMC, ds2.ISI, ds2.BUI, ds2.FWI],
outs):
np.testing.assert_allclose(out, exp, rtol=0.03)
def test_cannon_and_diagnostics(self, cannon_2015_dist, cannon_2015_rvs):
ref, hist, sim = cannon_2015_rvs(15000, random=False)
# Quantile mapping
with set_options(sdba_extra_output=True):
QDM = QuantileDeltaMapping.train(ref,
hist,
kind="*",
group="time",
nquantiles=50)
scends = QDM.adjust(sim)
assert isinstance(scends, xr.Dataset)
sim_q_exp = sim.rank(dim="time", pct=True)
np.testing.assert_array_equal(sim_q_exp, scends.sim_q)
# Theoretical results
# ref, hist, sim = cannon_2015_dist
# u1 = equally_spaced_nodes(1001, None)
# u = np.convolve(u1, [0.5, 0.5], mode="valid")
# pu = ref.ppf(u) * sim.ppf(u) / hist.ppf(u)
# pu1 = ref.ppf(u1) * sim.ppf(u1) / hist.ppf(u1)
# pdf = np.diff(u1) / np.diff(pu1)
# mean = np.trapz(pdf * pu, pu)
# mom2 = np.trapz(pdf * pu ** 2, pu)
# std = np.sqrt(mom2 - mean ** 2)
bc_sim = scends.scen
np.testing.assert_almost_equal(bc_sim.mean(), 41.5, 1)
np.testing.assert_almost_equal(bc_sim.std(), 16.7, 0)
def test_degree_days_exceedance_date():
tas = open_dataset("FWI/GFWED_sample_2017.nc").tas
tas.attrs.update(cell_methods="time: mean within days",
standard_name="air_temperature")
out = atmos.degree_days_exceedance_date(
tas=tas,
thresh="4 degC",
op=">",
sum_thresh="200 K days",
)
np.testing.assert_array_equal(out, np.array([[153, 136, 9, 6]]).T)
assert "tmean > 4 degc" in out.attrs["description"]
with set_options(check_missing="skip"):
out = atmos.degree_days_exceedance_date(
tas=tas,
thresh="4 degC",
op=">",
sum_thresh="1500 K days",
start_date="07-02",
freq="YS",
)
np.testing.assert_array_equal(out,
np.array([[np.nan, 280, 241, 244]]).T)
def test_indicator_output(tas_series):
tas = tas_series(np.zeros(365))
with set_options(metadata_locales=["fr"]):
tgmean = atmos.tg_mean(tas, freq="YS")
assert "long_name_fr" in tgmean.attrs
assert (tgmean.attrs["description_fr"] ==
"Moyenne annuelle de la température journalière moyenne")
def test_cf(indname, atmosds):
with set_options(cf_compliance="warn"):
# skip when missing default values
ind = registry[indname].get_instance()
for name, param in ind.parameters.items():
if param["kind"] is not InputKind.DATASET and param[
"default"] is None:
pytest.skip(
f"Indicator {ind.identifier} has no default for {name}.")
ind(ds=atmosds)
def test_virtual_modules(virtual_indicator, atmosds):
with set_options(cf_compliance="warn"):
# skip when missing default values
mod, indname, ind = virtual_indicator
for name, param in ind.parameters.items():
if param.kind is not InputKind.DATASET and (
param.default in (None, _empty)
or (param.default == name and name not in atmosds)
):
pytest.skip(f"Indicator {mod}.{indname} has no default for {name}.")
ind(ds=atmosds)
def _adjust(
cls,
ref: xr.DataArray,
hist: xr.DataArray,
sim: xr.DataArray,
*,
base: TrainAdjust = QuantileDeltaMapping,
base_kws: Optional[Mapping[str, Any]] = None,
n_escore: int = 0,
n_iter: int = 20,
pts_dim: str = "multivar",
adj_kws: Optional[Mapping[str, Any]] = None,
rot_matrices: Optional[xr.DataArray] = None,
):
base_kws or {}
if "kind" in base_kws:
warn(
f'The adjustment kind cannot be controlled when using {cls.__name__}, it defaults to "+".'
)
base_kws.setdefault("kind", "+")
# Assuming sim has the same coords as hist
# We get the safest new name of the rotated dim.
rot_dim = xr.core.utils.get_temp_dimname(
set(ref.dims).union(hist.dims).union(sim.dims), pts_dim + "_prime"
)
# Get the rotation matrices
rot_matrices = rot_matrices or rand_rot_matrix(
ref[pts_dim], num=n_iter, new_dim=rot_dim
).rename(matrices="iterations")
# Call a map_blocks on the iterative function
# Sadly, this is a bit too complicated for map_blocks, we'll do it by hand.
escores_tmpl = xr.broadcast(
ref.isel({pts_dim: 0, "time": 0}),
hist.isel({pts_dim: 0, "time": 0}),
)[0].expand_dims(iterations=rot_matrices.iterations)
template = xr.Dataset(
data_vars={
"scenh": xr.full_like(hist, np.NaN).rename(time="time_hist"),
"scen": xr.full_like(sim, np.NaN),
"escores": escores_tmpl,
}
)
# Input data, rename time dim on sim since it can't be aligned with ref or hist.
ds = xr.Dataset(
data_vars={
"ref": ref.rename(time="time_hist"),
"hist": hist.rename(time="time_hist"),
"sim": sim,
"rot_matrices": rot_matrices,
}
)
kwargs = {
"base": base,
"base_kws": base_kws,
"n_escore": n_escore,
"n_iter": n_iter,
"pts_dim": pts_dim,
"adj_kws": adj_kws or {},
}
with set_options(sdba_extra_output=False):
out = ds.map_blocks(npdf_transform, template=template, kwargs=kwargs)
out = out.assign(rotation_matrices=rot_matrices)
out.scenh.attrs["units"] = hist.units
return out
def ufunc(request):
with set_options(run_length_ufunc=request.param):
yield request.param
