def test_mae():
sim = (
open_dataset("sdba/CanESM2_1950-2100.nc").sel(time=slice("1950", "1953")).tasmax
)
ref = open_dataset("sdba/nrcan_1950-2013.nc").sel(time=slice("1950", "1953")).tasmax
test = sdba.measures.mae(sim, ref).values
np.testing.assert_array_almost_equal(test, [4.159672, 14.2148, 9.768536], 4)
def test_3d_data_with_nans(self):
# test with 3d data
pr = open_dataset(self.nc_file).pr
prMM = open_dataset(self.nc_file).pr
prMM.values *= 86400.0
prMM.attrs["units"] = "mm/day"
# put a nan somewhere
prMM.values[10, 1, 0] = np.nan
pr.values[10, 1, 0] = np.nan
pr_min = "5 mm/d"
out1 = atmos.wetdays(pr, thresh=pr_min, freq="MS")
out2 = atmos.wetdays(prMM, thresh=pr_min, freq="MS")
# test kg m-2 s-1
pr.attrs["units"] = "kg m-2 s-1"
out3 = atmos.wetdays(pr, thresh=pr_min, freq="MS")
np.testing.assert_array_equal(out1, out2)
np.testing.assert_array_equal(out1, out3)
# check some vector with and without a nan
x1 = prMM[:31, 0, 0].values
wd1 = (x1 >= int(pr_min.split(" ")[0])).sum()
assert wd1 == out1.values[0, 0, 0]
assert np.isnan(out1.values[0, 1, 0])
# make sure that vector with all nans gives nans whatever skipna
assert np.isnan(out1.values[0, -1, -1])
def test_DTR_3d_data_with_nans(self):
tasmax = open_dataset(self.nc_tasmax).tasmax
tasmax_C = open_dataset(self.nc_tasmax).tasmax
tasmax_C -= K2C
tasmax_C.attrs["units"] = "C"
tasmin = open_dataset(self.nc_tasmin).tasmin
tasmin_C = open_dataset(self.nc_tasmin).tasmin
tasmin_C -= K2C
tasmin_C.attrs["units"] = "C"
# put a nan somewhere
tasmin.values[32, 1, 0] = np.nan
tasmin_C.values[32, 1, 0] = np.nan
dtr = atmos.daily_temperature_range(tasmin, tasmax, freq="MS")
dtrC = atmos.daily_temperature_range(tasmin_C, tasmax_C, freq="MS")
min1 = tasmin.values[:, 0, 0]
max1 = tasmax.values[:, 0, 0]
dtr1 = max1 - min1
np.testing.assert_array_equal(dtr, dtrC)
assert dtr.attrs["units"] == "K"
assert np.allclose(dtr1[0:31].mean(), dtr.values[0, 0, 0])
assert np.isnan(dtr.values[1, 1, 0])
assert np.isnan(dtr.values[0, -1, -1])
dtr = atmos.max_daily_temperature_range(tasmin, tasmax, freq="MS")
dtrC = atmos.max_daily_temperature_range(tasmin_C, tasmax_C, freq="MS")
np.testing.assert_array_equal(dtr, dtrC)
assert dtr.attrs["units"] == "K"
assert np.allclose(dtr1[0:31].max(), dtr.values[0, 0, 0])
assert np.isnan(dtr.values[1, 1, 0])
assert np.isnan(dtr.values[0, -1, -1])
def test_3d_data_with_nans(self):
tasmax = open_dataset(self.nc_tasmax).tasmax
tasmin = open_dataset(self.nc_tasmin).tasmin
tasmaxC = open_dataset(self.nc_tasmax).tasmax
tasminC = open_dataset(self.nc_tasmin).tasmin
tasmaxC -= K2C
tasmaxC.attrs["units"] = "C"
tasminC -= K2C
tasminC.attrs["units"] = "C"
# put a nan somewhere
tasmin.values[180, 1, 0] = np.nan
tasminC.values[180, 1, 0] = np.nan
out = atmos.tx_tn_days_above(tasmin,
tasmax,
thresh_tasmax="25 C",
thresh_tasmin="18 C")
outC = atmos.tx_tn_days_above(tasminC,
tasmaxC,
thresh_tasmax="25 C",
thresh_tasmin="18 C")
np.testing.assert_array_equal(out, outC)
min1 = tasmin.values[:, 53, 76]
max1 = tasmax.values[:, 53, 76]
out1 = ((min1 > (K2C + 18)) * (max1 > (K2C + 25)) * 1.0).sum()
assert np.allclose(out1, out.values[0, 53, 76])
assert np.isnan(out.values[0, 1, 0])
assert np.isnan(out.values[0, -1, -1])
def test_with_different_phases(self):
# test with different phases
pr = open_dataset(self.nc_pr).pr # mm/s
tasmin = open_dataset(self.nc_tasmin).tasmin # K
out_tot = atmos.precip_accumulation(pr, freq="MS")
out_sol = atmos.solid_precip_accumulation(pr, tas=tasmin, freq="MS")
out_liq = atmos.liquid_precip_accumulation(pr, tas=tasmin, freq="MS")
np.testing.assert_array_almost_equal(out_liq + out_sol, out_tot, 4)
assert "solid" in out_sol.long_name
assert "liquid" in out_liq.long_name
assert out_sol.standard_name == "lwe_thickness_of_snowfall_amount"
# With a non-default threshold
out_sol = atmos.solid_precip_accumulation(pr,
tas=tasmin,
thresh="40 degF",
freq="MS")
out_liq = atmos.liquid_precip_accumulation(pr,
tas=tasmin,
thresh="40 degF",
freq="MS")
np.testing.assert_array_almost_equal(out_liq + out_sol, out_tot, 4)
def test_rmse():
sim = (
open_dataset("sdba/CanESM2_1950-2100.nc").sel(time=slice("1950", "1953")).tasmax
)
ref = open_dataset("sdba/nrcan_1950-2013.nc").sel(time=slice("1950", "1953")).tasmax
test = sdba.measures.rmse(sim, ref).values
np.testing.assert_array_almost_equal(test, [5.4499755, 18.124086, 12.387193], 4)
def test_3d_data_with_nans(self, prunits, prfac, tasunits, tasoffset, chunks):
pr = open_dataset(self.nc_pr).pr
pr2 = pr.copy()
pr2.values *= prfac
pr2.attrs["units"] = prunits
tasmax = open_dataset(self.nc_tasmax, chunks=chunks).tasmax
tasmin = open_dataset(self.nc_tasmin, chunks=chunks).tasmin
tas = 0.5 * (tasmax + tasmin)
tas.attrs = tasmax.attrs
tas2 = tas.copy()
if chunks:
tas2 = tas2.chunk(chunks)
if tasunits:
tas2.values -= tasoffset
tas2.attrs["units"] = tasunits
pr2.values[10, 1, 0] = np.nan
pr.values[10, 1, 0] = np.nan
outref = atmos.rain_on_frozen_ground_days(pr, tas, freq="MS")
out21 = atmos.rain_on_frozen_ground_days(pr2, tas, freq="MS")
out22 = atmos.rain_on_frozen_ground_days(pr2, tas2, freq="MS")
out12 = atmos.rain_on_frozen_ground_days(pr, tas2, freq="MS")
np.testing.assert_array_equal(outref, out21)
np.testing.assert_array_equal(outref, out22)
np.testing.assert_array_equal(outref, out12)
assert np.isnan(out22.values[0, 1, 0])
assert np.isnan(out22.values[0, -1, -1])
def test_3d_data_with_nans(self):
# test with 3d data
pr = open_dataset(self.nc_file).pr
prMM = open_dataset(self.nc_file).pr
prMM.values *= 86400.0
prMM.attrs["units"] = "mm/day"
# put a nan somewhere
prMM.values[10, 1, 0] = np.nan
pr.values[10, 1, 0] = np.nan
out1 = atmos.max_1day_precipitation_amount(pr, freq="MS")
out2 = atmos.max_1day_precipitation_amount(prMM, freq="MS")
# test kg m-2 s-1
pr.attrs["units"] = "kg m-2 s-1"
out3 = atmos.max_1day_precipitation_amount(pr, freq="MS")
np.testing.assert_array_almost_equal(out1, out2, 3)
np.testing.assert_array_almost_equal(out1, out3, 3)
x1 = prMM[:31, 0, 0].values
rx1 = x1.max()
assert np.allclose(rx1, out1.values[0, 0, 0])
# assert (np.allclose(di1, dis.values[0, 0, 0]))
assert np.isnan(out1.values[0, 1, 0])
# assert (np.allclose(di2, dis.values[0, 1, 0]))
assert np.isnan(out1.values[0, -1, -1])
def test_3d_data_with_nans(self):
# test with 3d data
tas = open_dataset(self.nc_file).tasmax
tasC = open_dataset(self.nc_file).tasmax
tasC -= K2C
tasC.attrs["units"] = "C"
# put a nan somewhere
tas.values[180, 1, 0] = np.nan
tasC.values[180, 1, 0] = np.nan
# compute with both skipna options
thresh = 273.16 + 25
fd = atmos.tx_days_above(tas, freq="YS")
fdC = atmos.tx_days_above(tasC, freq="YS")
x1 = tas.values[:, 0, 0]
fd1 = (x1[x1 > thresh]).size
np.testing.assert_array_equal(fd, fdC)
assert np.allclose(fd1, fd.values[0, 0, 0])
assert np.isnan(fd.values[0, 1, 0])
assert np.isnan(fd.values[0, -1, -1])
def test_3d_data_with_nans(self):
# test with 3d data
pr = open_dataset(self.nc_file).pr
prMM = open_dataset(self.nc_file).pr
prMM.values *= 86400.0
prMM.attrs["units"] = "mm/day"
# put a nan somewhere
prMM.values[10, 1, 0] = np.nan
pr.values[10, 1, 0] = np.nan
pr_min = "5 mm/d"
out1 = atmos.maximum_consecutive_dry_days(pr, thresh=pr_min, freq="MS")
out2 = atmos.maximum_consecutive_dry_days(prMM,
thresh=pr_min,
freq="MS")
# test kg m-2 s-1
pr.attrs["units"] = "kg m-2 s-1"
out3 = atmos.maximum_consecutive_dry_days(pr, thresh=pr_min, freq="MS")
np.testing.assert_array_equal(out1, out2)
np.testing.assert_array_equal(out1, out3)
# check some vector with and without a nan
x1 = prMM[:31, 0, 0] * 0.0 + 50.0
x1[5:10] = 0
x1.attrs["units"] = "mm/day"
cdd1 = atmos.maximum_consecutive_dry_days(x1, freq="MS")
assert cdd1 == 5
assert np.isnan(out1.values[0, 1, 0])
# make sure that vector with all nans gives nans whatever skipna
assert np.isnan(out1.values[0, -1, -1])
def test_3d_data_with_nans(self):
# test with 3d data
tasmin = open_dataset(self.nc_file).tasmin
tasminC = open_dataset(self.nc_file).tasmin
tasminC -= K2C
tasminC.attrs["units"] = "C"
# put a nan somewhere
tasmin.values[180, 1, 0] = np.nan
tasminC.values[180, 1, 0] = np.nan
# compute with both skipna options
thresh = 273.16
fd = atmos.frost_days(tasmin, freq="YS")
fdC = atmos.frost_days(tasminC, freq="YS")
# fds = xci.frost_days(tasmin, thresh=thresh, freq='YS', skipna=True)
x1 = tasmin.values[:, 0, 0]
fd1 = (x1[x1 < thresh]).size
np.testing.assert_array_equal(fd, fdC)
assert np.allclose(fd1, fd.values[0, 0, 0])
# assert (np.allclose(fd1, fds.values[0, 0, 0]))
assert np.isnan(fd.values[0, 1, 0])
# assert (np.allclose(fd2, fds.values[0, 1, 0]))
assert np.isnan(fd.values[0, -1, -1])
def test_convert_units(self):
tasmax = open_dataset(self.nc_tasmax).tasmax
tasmin = open_dataset(self.nc_tasmin).tasmin
tasmax.values -= K2C
tasmax.attrs["units"] = "C"
tasmin.values -= K2C
tasmin.attrs["units"] = "C"
# put a nan somewhere
tasmin.values[180, 1, 0] = np.nan
with pytest.warns(None) as record:
frzthw = atmos.daily_freezethaw_cycles(
tasmin,
tasmax,
thresh_tasmax="0 degC",
thresh_tasmin="0 degC",
freq="YS",
)
min1 = tasmin.values[:, 0, 0]
max1 = tasmax.values[:, 0, 0]
frzthw1 = ((min1 < 0) * (max1 > 0) * 1.0).sum()
assert (
"This index calculation will soon require user-specified thresholds."
not in [str(q.message) for q in record])
assert np.allclose(frzthw1, frzthw.values[0, 0, 0])
assert np.isnan(frzthw.values[0, 1, 0])
assert np.isnan(frzthw.values[0, -1, -1])
def test_Tmean_3d_data(self):
ds_tmax = open_dataset(self.nc_files[0])
ds_tmin = open_dataset(self.nc_files[1])
tas = atmos.tg(ds_tmin.tasmin, ds_tmax.tasmax)
tas_C = atmos.tg(ds_tmin.tasmin, ds_tmax.tasmax)
tas_C.values -= K2C
tas_C.attrs["units"] = "C"
# put a nan somewhere
tas.values[180, 1, 0] = np.nan
tas_C.values[180, 1, 0] = np.nan
tmmean = atmos.tg_mean(tas)
tmmeanC = atmos.tg_mean(tas_C)
x1 = tas.values[:, 0, 0]
tmmean1 = x1.mean()
# TODO: Investigate the differences between the two outputs.
# The conversion to K is done after / before the mean.
np.testing.assert_array_almost_equal(tmmeanC, tmmean, 3)
# test single point vs manual
assert np.allclose(tmmean1, tmmean.values[0, 0, 0],
tmmeanC.values[0, 0, 0])
# test single nan point
assert np.isnan(tmmean.values[0, 1, 0])
# test all nan point
assert np.isnan(tmmean.values[0, -1, -1])
def test_dtr_var_3d_data_with_nans(self):
tasmax = open_dataset(self.nc_tasmax).tasmax
tasmax_C = open_dataset(self.nc_tasmax).tasmax
tasmax_C -= K2C
tasmax_C.attrs["units"] = "C"
tasmin = open_dataset(self.nc_tasmin).tasmin
tasmin_C = open_dataset(self.nc_tasmin).tasmin
tasmin_C -= K2C
tasmin_C.attrs["units"] = "C"
# put a nan somewhere
tasmin.values[32, 1, 0] = np.nan
tasmin_C.values[32, 1, 0] = np.nan
etr = atmos.extreme_temperature_range(tasmin, tasmax, freq="MS")
etrC = atmos.extreme_temperature_range(tasmin_C, tasmax_C, freq="MS")
min1 = tasmin.values[:, 0, 0]
max1 = tasmax.values[:, 0, 0]
np.testing.assert_array_equal(etr, etrC)
etr1 = max1[0:31].max() - min1[0:31].min()
assert np.allclose(etr1, etr.values[0, 0, 0], etrC.values[0, 0, 0])
assert np.isnan(etr.values[1, 1, 0])
assert np.isnan(etr.values[0, -1, -1])
def test_dtr_var_3d_data_with_nans(self):
tasmax = open_dataset(self.nc_tasmax).tasmax
tasmax_C = open_dataset(self.nc_tasmax).tasmax
tasmax_C -= K2C
tasmax_C.attrs["units"] = "C"
tasmin = open_dataset(self.nc_tasmin).tasmin
tasmin_C = open_dataset(self.nc_tasmin).tasmin
tasmin_C -= K2C
tasmin_C.attrs["units"] = "C"
# put a nan somewhere
tasmin.values[32, 1, 0] = np.nan
tasmin_C.values[32, 1, 0] = np.nan
dtr = atmos.daily_temperature_range_variability(tasmin,
tasmax,
freq="MS")
dtrC = atmos.daily_temperature_range_variability(tasmin_C,
tasmax_C,
freq="MS")
min1 = tasmin.values[:, 0, 0]
max1 = tasmax.values[:, 0, 0]
assert dtr.attrs["units"] == "K"
dtr1a = max1 - min1
dtr1 = abs(np.diff(dtr1a))
np.testing.assert_array_equal(dtr, dtrC)
# first month jan use 0:30 (n==30) because of day to day diff
assert np.allclose(dtr1[0:30].mean(), dtr.values[0, 0, 0],
dtrC.values[0, 0, 0])
assert np.isnan(dtr.values[1, 1, 0])
assert np.isnan(dtr.values[0, -1, -1])
def get_snowfall(self):
dnr = xr.merge(
(open_dataset(self.pr_file), open_dataset(self.tasmin_file)))
return atmos.snowfall_approximation(dnr.pr,
tas=dnr.tasmin,
thresh="-0.5 degC",
method="binary")
def test_3d_data_with_nans(self):
# test with 3d data
tas = open_dataset(self.nc_file).tasmin
tasC = open_dataset(self.nc_file).tasmin
tasC -= K2C
tasC.attrs["units"] = "C"
# put a nan somewhere
tas.values[180, 1, 0] = np.nan
tasC.values[180, 1, 0] = np.nan
# compute with both skipna options
thresh = 273.16 + 20
out = atmos.tropical_nights(tas, freq="YS")
outC = atmos.tropical_nights(tasC, freq="YS")
# fds = xci.frost_days(tasmin, thresh=thresh, freq='YS', skipna=True)
x1 = tas.values[:, 0, 0]
out1 = (x1[x1 > thresh]).size
np.testing.assert_array_equal(out, outC)
assert np.allclose(out1, out.values[0, 0, 0])
assert np.isnan(out.values[0, 1, 0])
assert np.isnan(out.values[0, -1, -1])
def test_3d_data_with_nans(self):
# test with 3d data
pr = open_dataset(self.nc_pr).pr # mm/s
prMM = open_dataset(self.nc_pr).pr
prMM *= 86400
prMM.attrs["units"] = "mm/day"
# put a nan somewhere
prMM.values[10, 1, 0] = np.nan
pr.values[10, 1, 0] = np.nan
out1 = atmos.precip_accumulation(pr, freq="MS")
out2 = atmos.precip_accumulation(prMM, freq="MS")
# test kg m-2 s-1
pr.attrs["units"] = "kg m-2 s-1"
out3 = atmos.precip_accumulation(pr, freq="MS")
np.testing.assert_array_almost_equal(out1, out2, 3)
np.testing.assert_array_almost_equal(out1, out3, 5)
# check some vector with and without a nan
x1 = prMM[:31, 0, 0].values
prTot = x1.sum()
np.testing.assert_almost_equal(prTot, out1.values[0, 0, 0], 4)
assert np.isnan(out1.values[0, 1, 0])
assert np.isnan(out1.values[0, -1, -1])
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_3d_data_with_nans(self, units, factor, chunks):
# test with 3d data
pr1 = open_dataset(self.nc_file).pr
pr2 = open_dataset(self.nc_file, chunks=chunks).pr
pr2.values *= factor
pr2.attrs["units"] = units
# put a nan somewhere
pr2.values[10, 1, 0] = np.nan
pr1.values[10, 1, 0] = np.nan
wind = 3
out1 = atmos.max_n_day_precipitation_amount(pr1,
window=wind,
freq="MS")
out2 = atmos.max_n_day_precipitation_amount(pr2,
window=wind,
freq="MS")
np.testing.assert_array_almost_equal(out1, out2, 3)
x1 = pr1[:31, 0, 0].values * 86400
df = pd.DataFrame({"pr": x1})
rx3 = df.rolling(wind).sum().max()
assert np.allclose(rx3, out1.values[0, 0, 0])
assert np.isnan(out1.values[0, 1, 0])
assert np.isnan(out1.values[0, -1, -1])
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_3d_data_with_nans(self):
# test with 3d data
pr = open_dataset(self.nc_file).pr
prMM = open_dataset(self.nc_file).pr
prMM.values *= 86400.0
prMM.attrs["units"] = "mm/day"
# put a nan somewhere
prMM.values[10, 1, 0] = np.nan
pr.values[10, 1, 0] = np.nan
# compute with both skipna options
pr_min = "2 mm/d"
# dis = daily_pr_intensity(pr, pr_min=pr_min, freq='MS', skipna=True)
out1 = atmos.daily_pr_intensity(pr, thresh=pr_min, freq="MS")
out2 = atmos.daily_pr_intensity(prMM, thresh=pr_min, freq="MS")
# test kg m-2 s-1
pr.attrs["units"] = "kg m-2 s-1"
out3 = atmos.daily_pr_intensity(pr, thresh=pr_min, freq="MS")
np.testing.assert_array_almost_equal(out1, out2, 3)
np.testing.assert_array_almost_equal(out1, out3, 3)
x1 = prMM[:31, 0, 0].values
di1 = x1[x1 >= int(pr_min.split(" ")[0])].mean()
# buffer = np.ma.masked_invalid(x2)
# di2 = buffer[buffer >= pr_min].mean()
assert np.allclose(di1, out1.values[0, 0, 0])
# assert (np.allclose(di1, dis.values[0, 0, 0]))
assert np.isnan(out1.values[0, 1, 0])
# assert (np.allclose(di2, dis.values[0, 1, 0]))
assert np.isnan(out1.values[0, -1, -1])
def test_corn_heat_units():
tn = open_dataset("ERA5/daily_surface_cancities_1990-1993.nc").tasmin
tx = open_dataset("ERA5/daily_surface_cancities_1990-1993.nc").tasmax
with xr.set_options(keep_attrs=True):
tnC = tn - K2C
tnC.attrs["units"] = "C"
chu = atmos.corn_heat_units(tasmin=tn,
tasmax=tx,
thresh_tasmin="4.44 degC",
thresh_tasmax="10 degC")
chuC = atmos.corn_heat_units(tasmin=tnC,
tasmax=tx,
thresh_tasmin="4.44 degC",
thresh_tasmax="10 degC")
np.testing.assert_allclose(chu, chuC, rtol=1e-3)
np.testing.assert_allclose(chu[0, 180:185],
np.array(
[13.777, 12.368, 11.966, 14.674, 16.797]),
rtol=1e-4)
assert ("specific thresholds : tmin > 4.44 degc and tmax > 10 degc."
in chu.description)
def test_annual_cycle_correlation():
sim = (
open_dataset("sdba/CanESM2_1950-2100.nc").sel(time=slice("1950", "1953")).tasmax
)
ref = open_dataset("sdba/nrcan_1950-2013.nc").sel(time=slice("1950", "1953")).tasmax
test = (
sdba.measures.annual_cycle_correlation(sim, ref, window=31)
.sel(location="Vancouver")
.values
)
np.testing.assert_array_almost_equal(test, [0.94580488], 4)
def test_spatial_analogs(method):
if method == "skezely_rizzo":
pytest.skip("Method not implemented.")
diss = open_dataset("SpatialAnalogs/dissimilarity")
data = open_dataset("SpatialAnalogs/indicators")
target = data.sel(lat=46.1875, lon=-72.1875, time=slice("1970", "1990"))
candidates = data.sel(time=slice("1970", "1990"))
out = xca.spatial_analogs(target, candidates, method=method)
np.testing.assert_allclose(diss[method], out, rtol=1e-3, atol=1e-3)
def test_spatial_analogs(method):
if method in [
"nearest_neighbor", "kldiv"
] and parse_version(__scipy_version__) < parse_version("1.6.0"):
pytest.skip("Method not supported in scipy<1.6.0")
diss = open_dataset("SpatialAnalogs/dissimilarity")
data = open_dataset("SpatialAnalogs/indicators")
target = data.sel(lat=46.1875, lon=-72.1875, time=slice("1970", "1990"))
candidates = data.sel(time=slice("1970", "1990"))
out = xca.spatial_analogs(target, candidates, method=method)
np.testing.assert_allclose(diss[method], out, rtol=1e-3, atol=1e-3)
def test_raise(self):
da = open_dataset(self.nc_poslons).tas
with pytest.raises(ValueError):
subset.subset_bbox(
da,
lon_bnds=self.lonGCM,
lat_bnds=self.latGCM,
start_date="2056",
end_date="2055",
)
da = open_dataset(
self.nc_2dlonlat).tasmax.drop_vars(names=["lon", "lat"])
with pytest.raises(Exception):
subset.subset_bbox(da, lon_bnds=self.lon, lat_bnds=self.lat)
def test_wraps(self, tmp_netcdf_filename, vectorize):
ds = open_dataset(self.nc_file)
# Polygon crosses meridian, a warning should be raised
with pytest.warns(UserWarning):
sub = subset.subset_shape(ds, self.meridian_geojson)
# No time subsetting should occur.
assert len(sub.tas) == 12
# Average temperature at surface for region in January (time=0)
np.testing.assert_array_almost_equal(
float(np.mean(sub.tas.isel(time=0))), 285.064453)
self.compare_vals(ds, sub, "tas")
poly = gpd.read_file(self.meridian_multi_geojson)
subtas = subset.subset_shape(ds.tas, poly, vectorize=vectorize)
np.testing.assert_array_almost_equal(
float(np.mean(subtas.isel(time=0))), 281.091553)
assert sub.crs.prime_meridian_name == "Greenwich"
assert sub.crs.grid_mapping_name == "latitude_longitude"
sub.to_netcdf(tmp_netcdf_filename)
assert tmp_netcdf_filename.exists()
with xr.open_dataset(filename_or_obj=tmp_netcdf_filename) as f:
assert {"tas", "crs"}.issubset(set(f.data_vars))
subset.subset_shape(ds,
self.meridian_multi_geojson,
vectorize=vectorize)
def test_no_wraps(self, tmp_netcdf_filename, vectorize):
ds = open_dataset(self.nc_file)
with pytest.warns(None) as record:
sub = subset.subset_shape(ds,
self.poslons_geojson,
vectorize=vectorize)
self.compare_vals(ds, sub, "tas")
# No time subsetting should occur.
assert len(sub.tas) == 12
# Average temperature at surface for region in January (time=0)
np.testing.assert_array_almost_equal(
float(np.mean(sub.tas.isel(time=0))), 276.732483)
# Check that no warnings are raised for meridian crossing
assert (
'"Geometry crosses the Greenwich Meridian. Proceeding to split polygon at Greenwich."'
'" This feature is experimental. Output might not be accurate."'
not in [str(q.message) for q in record])
assert sub.crs.prime_meridian_name == "Greenwich"
assert sub.crs.grid_mapping_name == "latitude_longitude"
sub.to_netcdf(tmp_netcdf_filename)
assert tmp_netcdf_filename.exists()
with xr.open_dataset(filename_or_obj=tmp_netcdf_filename) as f:
assert {"tas", "crs"}.issubset(set(f.data_vars))
subset.subset_shape(ds, self.poslons_geojson, vectorize=vectorize)
def test_rotated_pole_with_time(self):
ds = open_dataset(self.lons_2d_nc_file)
with pytest.warns(None) as record:
sub = subset.subset_shape(
ds,
self.eastern_canada_geojson,
start_date="1984-06-01",
end_date="1984-06-15",
)
self.compare_vals(ds.sel(time=slice("1984-06-01", "1984-06-15")),
sub,
"tasmax",
flag_2d=True)
# Should only have 15 days of data.
assert len(sub.tasmax) == 15
# Average max temperature at surface for region on June 1st, 1984 (time=0)
np.testing.assert_allclose(float(np.mean(sub.tasmax.isel(time=0))),
289.634968)
# Check that no warnings are raised for meridian crossing
assert (
'"Geometry crosses the Greenwich Meridian. Proceeding to split polygon at Greenwich."'
'" This feature is experimental. Output might not be accurate."'
not in [str(q.message) for q in record])