def test_array_coverage(empty_ds):
da = empty_ds["CO2"]
da[:] = np.nan
da.name = None
da.pr.loc[{"time": "2001", "area": "COL"}] = 12.0 * ureg("Gg CO2 / year")
da.pr.loc[{"time": "2002", "area": "COL"}] = 13.0 * ureg("Gg CO2 / year")
expected = pd.DataFrame(
index=da["area (ISO3)"].values,
columns=da["time"].to_index(),
data=np.zeros((len(da["area (ISO3)"]), len(da["time"])), dtype=np.int32),
)
expected.loc["COL", "2001"] = 1
expected.loc["COL", "2002"] = 1
expected.index.name = "area (ISO3)"
expected.columns.name = "time"
pd.testing.assert_frame_equal(
expected.astype(np.int32), da.pr.coverage("area", "time").astype(np.int32)
)
pd.testing.assert_frame_equal(
expected.T.astype(np.int32),
da.pr.coverage("time", "area (ISO3)").astype(np.int32),
)
def partly_nan_ds(self, empty_ds):
empty_ds["CO2"][:] = 1 * ureg("Gg CO2 / year")
empty_ds["SF6"][:] = 1 * ureg("Gg SF6 / year")
empty_ds["CH4"][:] = 1 * ureg("Gg CH4 / year")
empty_ds["CH4"].loc[{
"area (ISO3)": "COL"
}] = np.nan * ureg("Gg CH4 / year")
return empty_ds
def partly_filled_ds(self, partly_nan_ds):
partly_nan_ds["KYOTOGHG (AR4GWP100)"][:] = 1 * ureg("Gg CO2 / year")
partly_nan_ds["KYOTOGHG (AR4GWP100)"].loc[{
"area (ISO3)": "COL"
}] = np.nan * ureg("Gg CO2 / year")
partly_nan_ds["KYOTOGHG (AR4GWP100)"].loc[{
"area (ISO3)": "BOL",
"time": "2020"
}] = np.nan * ureg("Gg CO2 / year")
return partly_nan_ds
def test_contents_sum_skipna(self, partly_nan_ds):
summed = partly_nan_ds.pr.gas_basket_contents_sum(
basket="KYOTOGHG (AR4GWP100)",
basket_contents=["CO2", "SF6", "CH4"],
skipna=False,
)
expected = partly_nan_ds["KYOTOGHG (AR4GWP100)"].copy()
expected[:] = (1 + self.sf6 + self.ch4) * ureg("Gg CO2 / year")
# NaNs not skipped
expected.loc[{"area (ISO3)": "COL"}] = np.nan * ureg("Gg CO2 / year")
assert_equal(summed, expected, equal_nan=True)
def test_contents_sum_default(self, partly_nan_ds):
summed = partly_nan_ds.pr.gas_basket_contents_sum(
basket="KYOTOGHG (AR4GWP100)",
basket_contents=["CO2", "SF6", "CH4"],
)
expected = partly_nan_ds["KYOTOGHG (AR4GWP100)"].copy()
expected[:] = (1 + self.sf6 + self.ch4) * ureg("Gg CO2 / year")
# NaN counted as 0
expected.loc[{
"area (ISO3)": "COL"
}] = (1 + self.sf6) * ureg("Gg CO2 / year")
assert_equal(summed, expected)
def test_fill_na_from_contents_skipna(self, partly_filled_ds):
filled = partly_filled_ds.pr.fill_na_gas_basket_from_contents(
basket="KYOTOGHG (AR4GWP100)",
basket_contents=["CO2", "SF6", "CH4"],
skipna=False,
)
expected = partly_filled_ds["KYOTOGHG (AR4GWP100)"].copy()
expected.loc[{"area (ISO3)": "COL"}] = np.nan * ureg("Gg CO2 / year")
expected.loc[{
"area (ISO3)": "BOL",
"time": "2020"
}] = (1 + self.sf6 + self.ch4) * ureg("Gg CO2 / year")
assert_equal(filled, expected, equal_nan=True)
def test_fill_na_from_contents_sel(self, partly_filled_ds):
filled = partly_filled_ds.pr.fill_na_gas_basket_from_contents(
basket="KYOTOGHG (AR4GWP100)",
basket_contents=["CO2", "SF6", "CH4"],
sel={"area (ISO3)": ["BOL"]},
skipna_evaluation_dims=("time", ),
)
expected = partly_filled_ds["KYOTOGHG (AR4GWP100)"].copy()
expected.loc[{
"area (ISO3)": "BOL",
"time": "2020"
}] = (1 + self.sf6 + self.ch4) * ureg("Gg CO2 / year")
assert_equal(filled, expected, equal_nan=True)
with pytest.raises(
ValueError,
match=
"The dimension of the selection doesn't match the dimension",
):
partly_filled_ds.pr.fill_na_gas_basket_from_contents(
basket="KYOTOGHG (AR4GWP100)",
basket_contents=["CO2", "SF6", "CH4"],
sel={"area (ISO3)": "BOL"},
skipna_evaluation_dims=("time", ),
)
def test_automatic_unit_conversion(
self, da: xr.DataArray, ts: np.ndarray, co2, new
):
actual = da.pr.set(
"area", "COL", ts * ureg("Mg CO2 / year"), existing="overwrite", **new
)
expected = da
expected.loc[{"area (ISO3)": "COL"}] = 1e-3 * ts[..., np.newaxis] * co2
assert_aligned_equal(actual, expected)
def test_contents_sum_skipna_evaluation_dims(self, partly_nan_ds):
partly_nan_ds["CH4"].loc[{
"area (ISO3)": "ARG",
"time": "2012"
}] = np.nan * ureg("Gg CH4 / year")
summed = partly_nan_ds.pr.gas_basket_contents_sum(
basket="KYOTOGHG (AR4GWP100)",
basket_contents=["CO2", "SF6", "CH4"],
skipna_evaluation_dims=("time", ),
)
expected = partly_nan_ds["KYOTOGHG (AR4GWP100)"].copy()
expected[:] = (1 + self.sf6 + self.ch4) * ureg("Gg CO2 / year")
# NaN only skipped where all time points NaN
expected.loc[{
"area (ISO3)": "COL"
}] = (1 + self.sf6) * ureg("Gg CO2 / year")
expected.loc[{
"area (ISO3)": "ARG",
"time": "2012"
}] = np.nan * ureg("Gg CO2 / year")
assert_equal(summed, expected, equal_nan=True)
def test_downscale_timeseries(empty_ds):
for key in empty_ds:
empty_ds[key][:] = np.nan
t = empty_ds.loc[{"area (ISO3)": "BOL"}].copy()
t["area (ISO3)"] = ["CAMB"] # here, the sum of COL, ARG, MEX, and BOL
ds = xr.concat([empty_ds, t], dim="area (ISO3)")
da: xr.DataArray = ds["CO2"]
da.loc[{
"area (ISO3)": ["COL", "ARG", "MEX"],
"time": "2002"
}] = 1 * ureg("Gg CO2 / year")
da.loc[{"area (ISO3)": "BOL", "time": "2002"}] = 3 * ureg("Gg CO2 / year")
da.loc[{"area (ISO3)": "CAMB", "time": "2002"}] = 6 * ureg("Gg CO2 / year")
da.loc[{
"area (ISO3)": ["COL", "ARG", "MEX", "BOL"],
"time": "2012"
}] = 2 * ureg("Gg CO2 / year")
da.loc[{
"area (ISO3)": "CAMB",
"source": "RAND2020"
}] = np.concatenate(
[np.array([6] * 11),
np.stack([8, 8]),
np.linspace(8, 10, 8)]) * ureg("Gg CO2 / year")
downscaled = da.pr.downscale_timeseries(
dim="area (ISO3)",
basket="CAMB",
basket_contents=["COL", "ARG", "MEX", "BOL"])
expected = da.copy()
expected.loc[{
"area (ISO3)": ["COL", "ARG", "MEX"],
"source": "RAND2020"
}] = np.broadcast_to(
np.concatenate([
np.array([1, 1]),
np.linspace(1 / 6, 2 / 8, 11) * np.array([6] * 9 + [8] * 2),
np.linspace(2, 2 * 10 / 8, 8),
]),
(3, 21),
).T * ureg("Gg CO2 / year")
expected.loc[{
"area (ISO3)": "BOL",
"source": "RAND2020"
}] = np.concatenate([
np.array([3, 3]),
np.linspace(3 / 6, 2 / 8, 11) * np.array([6] * 9 + [8] * 2),
np.linspace(2, 2 * 10 / 8, 8),
]) * ureg("Gg CO2 / year")
# we need a higher atol, because downscale_timeseries actually does the
# downscaling using a proper calendar while here we use a calendar where all years
# have the same length.
assert_equal(downscaled, expected, equal_nan=True, atol=0.01)
allclose(
downscaled.loc[{
"area (ISO3)": "CAMB"
}],
downscaled.loc[{
"area (ISO3)": ["COL", "ARG", "MEX", "BOL"]
}].sum(dim="area (ISO3)"),
)
downscaled_ds = ds.pr.downscale_timeseries(
dim="area (ISO3)",
basket="CAMB",
basket_contents=["COL", "ARG", "MEX", "BOL"])
assert_equal(downscaled_ds["CO2"], expected, equal_nan=True, atol=0.01)
da.loc[{"area (ISO3)": "BOL", "time": "2002"}] = 2 * ureg("Gg CO2 / year")
with pytest.raises(
ValueError,
match="To continue regardless, set check_consistency=False"):
da.pr.downscale_timeseries(
dim="area (ISO3)",
basket="CAMB",
basket_contents=["COL", "ARG", "MEX", "BOL"],
)
with pytest.raises(
ValueError,
match="To continue regardless, set check_consistency=False"):
ds.pr.downscale_timeseries(
dim="area (ISO3)",
basket="CAMB",
basket_contents=["COL", "ARG", "MEX", "BOL"],
)
downscaled = da.pr.downscale_timeseries(
dim="area (ISO3)",
basket="CAMB",
basket_contents=["COL", "ARG", "MEX", "BOL"],
check_consistency=False,
)
expected = da.copy()
expected.loc[{
"area (ISO3)": ["COL", "ARG", "MEX"],
"source": "RAND2020"
}] = np.broadcast_to(
np.concatenate([
np.array([1.2, 1.2, 1]),
(np.linspace(1 / 5, 2 / 8, 11) * np.array([6] * 9 + [8] * 2))[1:],
np.linspace(2, 2 * 10 / 8, 8),
]),
(3, 21),
).T * ureg("Gg CO2 / year")
expected.loc[{
"area (ISO3)": "BOL",
"source": "RAND2020"
}] = np.concatenate([
np.array([2.4, 2.4, 2]),
(np.linspace(2 / 5, 2 / 8, 11) * np.array([6] * 9 + [8] * 2))[1:],
np.linspace(2, 2 * 10 / 8, 8),
]) * ureg("Gg CO2 / year")
assert_equal(downscaled, expected, equal_nan=True, atol=0.01)
downscaled = da.pr.downscale_timeseries(
dim="area (ISO3)",
basket="CAMB",
basket_contents=["COL", "ARG", "MEX", "BOL"],
check_consistency=False,
sel={"time": slice("2005", "2020")},
)
expected = da.copy()
expected.loc[{
"area (ISO3)": ["COL", "ARG", "MEX", "BOL"],
"source": "RAND2020"
}] = np.broadcast_to(
np.concatenate([
np.array([
np.nan,
np.nan,
1,
np.nan,
np.nan,
6 / 4,
6 / 4,
6 / 4,
6 / 4,
6 / 4,
6 / 4,
2,
2,
]),
np.linspace(2, 2 * 10 / 8, 8),
]),
(4, 21),
).T * ureg("Gg CO2 / year")
expected.loc[{
"area (ISO3)": "BOL",
"time": "2002"
}] = 2 * ureg("Gg CO2 / year")
assert_equal(downscaled, expected, equal_nan=True, atol=0.01)
def test_downscale_gas_timeseries(empty_ds):
for key in empty_ds:
empty_ds[key][:] = np.nan
empty_ds["CO2"].loc[{"time": "2002"}] = 1 * ureg("Gg CO2 / year")
empty_ds["SF6"].loc[{"time": "2002"}] = 1 * ureg("Gg SF6 / year")
empty_ds["CH4"].loc[{"time": "2002"}] = 1 * ureg("Gg CH4 / year")
sf6 = 22_800
ch4 = 25
empty_ds["KYOTOGHG (AR4GWP100)"][:] = (1 + sf6 +
ch4) * ureg("Gg CO2 / year")
empty_ds["KYOTOGHG (AR4GWP100)"].loc[{
"time": "2020"
}] = (2 * (1 + sf6 + ch4) * ureg("Gg CO2 / year"))
downscaled = empty_ds.pr.downscale_gas_timeseries(
basket="KYOTOGHG (AR4GWP100)", basket_contents=["CO2", "SF6", "CH4"])
expected = empty_ds.copy()
expected["CO2"][:] = 1 * ureg("Gg CO2 / year")
expected["SF6"][:] = 1 * ureg("Gg SF6 / year")
expected["CH4"][:] = 1 * ureg("Gg CH4 / year")
expected["CO2"].loc[{"time": "2020"}] = 2 * ureg("Gg CO2 / year")
expected["SF6"].loc[{"time": "2020"}] = 2 * ureg("Gg SF6 / year")
expected["CH4"].loc[{"time": "2020"}] = 2 * ureg("Gg CH4 / year")
xr.testing.assert_identical(downscaled, expected)
empty_ds["SF6"].loc[{"time": "2002"}] = 2 * ureg("Gg SF6 / year")
with pytest.raises(
ValueError,
match="To continue regardless, set check_consistency=False"):
empty_ds.pr.downscale_gas_timeseries(
basket="KYOTOGHG (AR4GWP100)",
basket_contents=["CO2", "SF6", "CH4"])
def test_incompatible_units(self, da: xr.DataArray, ts: np.ndarray, new):
with pytest.raises(pint.errors.DimensionalityError, match="Cannot convert"):
da.pr.set("area", "COL", ts * ureg("kg"), existing="overwrite", **new)
def co2() -> pint.Unit:
return ureg("Gg CO2 / year")
