def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
assert data_path.exists(), f"file not created correctly"
spi = SPI(data_path / "data_kenya.nc")
variable = "precip"
spi.fit(variable=variable)
coords = [c for c in spi.index.coords]
vars_ = [v for v in spi.index.variables if v not in coords]
assert "SPI3" in vars_, f"Expecting `v` variable in" "`self.index`"
assert spi.index.SPI3.isel(time=slice(0, 2)).isnull().all(), (
""
"The first two timesteps should be nan because using SPI3")
assert not spi.index.SPI3.isel(time=slice(0, 3)).isnull().all(), (
""
"The first two timesteps but not 3rd should be nan SPI3")
assert np.isclose(
spi.index.SPI3.mean(), 0,
atol=0.01), (""
"Expect the mean SPI value to be close to 0 because"
"converted to a standard normal distribution")
assert np.isclose(spi.index.SPI3.std(), 1, atol=0.01), (
""
"Expect the `std()` SPI value to be close to 1 because"
"converted to a standard normal distribution")
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
ci = ConditionIndex(data_path / "data_kenya.nc")
variable = "precip"
ci.fit(variable=variable)
coords = [c for c in ci.index.coords]
vars_ = [v for v in ci.index.variables if v not in coords]
assert "precip_condition_index_1" in vars_, (
f"Expecting `precip_condition_index_1` variable in"
"`self.index`")
# MIN-MAX of 0, 100
assert np.isclose(
float(ci.index[vars_[0]].max().values), 100
), f"Expected to have max value of 100. Got: {ci.index[vars_[0]].max()}"
assert np.isclose(
float(ci.index[vars_[0]].min().values), 0
), f"Expected to have min value of 0. Got: {ci.index[vars_[0]].min()}"
assert (np.isclose(
ci.index[vars_[0]].max(dim="time"),
100)).all(), "Expect all times to have a max value of 100"
assert (
np.isclose(ci.index[vars_[0]].max(dim=["lat", "lon"]), 100)
).all(), "Expect all pixels (lat, lon) to have a max value of 100"
assert (np.isclose(
ci.index[vars_[0]].min(dim="time"),
0)).all(), "Expect all times to have a min value of 0"
assert (np.isclose(
ci.index[vars_[0]].min(dim=["lat", "lon"]),
0)).all(), "Expect all pixels (lat, lon) to have a min value of 0"
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date='2000-01-01',
end_date='2010-01-01')
spi = SPI(data_path / 'chirps_kenya.nc')
variable = 'precip'
spi.fit(variable=variable)
coords = [c for c in spi.index.coords]
vars_ = [v for v in spi.index.variables if v not in coords]
assert "SPI3" in vars_, f"Expecting `v` variable in"\
"`self.index`"
assert spi.index.SPI3.isel(time=slice(0, 2)).isnull().all(), "" \
"The first two timesteps should be nan because using SPI3"
assert not spi.index.SPI3.isel(time=slice(0, 3)).isnull().all(), "" \
"The first two timesteps but not 3rd should be nan SPI3"
assert np.isclose(spi.index.SPI3.mean(), 0, atol=0.01), ""\
"Expect the mean SPI value to be close to 0 because" \
"converted to a standard normal distribution"
assert np.isclose(spi.index.SPI3.std(), 1, atol=0.01), ""\
"Expect the `std()` SPI value to be close to 0 because" \
"converted to a standard normal distribution"
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(
tmp_path, start_date="2000-01-01", end_date="2010-01-01"
)
di = DecileIndex(data_path / "chirps_kenya.nc")
variable = "precip"
di.fit(variable=variable)
coords = [c for c in di.index.coords]
vars_ = [v for v in di.index.variables if v not in coords]
assert "quintile" in vars_, f"Expecting `quintile` variable in" "`self.index`"
vals = np.unique(di.index.quintile.values)
assert all(np.isin(np.arange(1, 6), vals)), (
f"Expect quintiles to" f" be from 1 - 5. Got: {vals}"
)
assert di.index.rank_norm.min().values == 0, (
f"Expect minmum "
f"rank_norm to be 0. Got: {di.index.rank_norm.min().values}"
)
assert di.index.rank_norm.max().values == 100, (
f"Expect max "
f"rank_norm to be 100. Got: {di.index.rank_norm.max().values}"
)
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
b = BaseIndices(data_path / "chirps_kenya.nc")
assert isinstance(
b.ds,
xr.Dataset), (f"expected to have loaded"
f"`ds` as an attribute in `BaseIndices` object")
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
spi = SPI(data_path / "data_kenya.nc")
assert spi.name == "spi", f"Expected name" f"to be `spi` got: {spi.name}"
with pytest.raises(AttributeError):
# assert error raised because haven't fit
spi.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
ma = MovingAverage(data_path / 'chirps_kenya.nc')
assert ma.name == '3month_moving_average', f"Expected name"\
f"to be `3month_moving_average` got: {ma.name}"
with pytest.raises(AttributeError):
# assert error raised because haven't fit
ma.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
a = AnomalyIndex(data_path / 'chirps_kenya.nc')
assert a.name == 'rainfall_anomaly_index', f"Expected name"\
f"to be `rainfall_anomaly_index` got: {a.name}"
with pytest.raises(AttributeError):
# assert error raised because haven't fit
a.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
czi = ChinaZIndex(data_path / 'chirps_kenya.nc')
assert czi.name == 'china_z_index', f"Expected name"\
f"to be `china_z_index` got: {czi.name}"
with pytest.raises(AttributeError):
# assert error raised because haven't fit
czi.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
pni = PercentNormalIndex(data_path / 'chirps_kenya.nc')
assert pni.name == 'percent_normal_index', f"Expected name"\
f"to be `percent_normal_index` got: {pni.name}"
with pytest.raises(AttributeError):
# assert error raised because haven't fit
pni.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
dsi = DroughtSeverityIndex(data_path / 'chirps_kenya.nc')
assert dsi.name == 'drought_severity_index', f"Expected name"\
f"to be `drought_severity_index` got: {dsi.name}"
with pytest.raises(AttributeError):
# assert error raised because haven't fit
dsi.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
di = DecileIndex(data_path / "data_kenya.nc")
assert di.name == "decile_index", (
f"Expected name"
f"to be `decile_index` got: {di.name}")
with pytest.raises(AttributeError):
# assert error raised because haven't fit
di.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
zsi = ZScoreIndex(data_path / "chirps_kenya.nc")
assert zsi.name == "z_score_index", (
f"Expected name"
f"to be `z_score_index` got: {zsi.name}")
with pytest.raises(AttributeError):
# assert error raised because haven't fit
zsi.index
def test_initialisation(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
vdi = VegetationDeficitIndex(data_path / "data_kenya.nc")
assert vdi.name == "vegetation_deficit_index", (
"Expected name"
f"to be `vegetation_deficit_index` got: {vdi.name}")
with pytest.raises(AttributeError):
# assert error raised because haven't fit
vdi.index
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
ai = AnomalyIndex(data_path / "chirps_kenya.nc")
variable = "precip"
ai.fit(variable=variable)
coords = [c for c in ai.index.coords]
vars_ = [v for v in ai.index.variables if v not in coords]
assert "RAI" in vars_, f"Expecting `RAI` variable in `self.index`"
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date='2000-01-01',
end_date='2010-01-01')
ma = MovingAverage(data_path / 'chirps_kenya.nc')
variable = 'precip'
ma.fit(variable=variable)
vars_ = [v for v in ma.index.data_vars]
assert "precip_3month_moving_average" in vars_, \
"Expecting `precip_3month_moving_average` variable in"\
"`self.index`"
def test_apply_over_period(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
ds = xr.open_dataset(data_path / 'chirps_kenya.nc')
got = apply_over_period(ds,
xr.Dataset.mean,
in_variable='precip',
out_variable='mean',
time_str='month')
expected = (ds.groupby('time.month').mean(dim='time').rename(
{'precip': 'mean'}))
assert all(got == expected), f"Expected: {expected}\nGot: {got}"
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
ma = MovingAverage(data_path / "data_kenya.nc")
variable = "precip"
ma.fit(variable=variable)
vars_ = [v for v in ma.index.data_vars]
assert "precip_3month_moving_average" in vars_, (
"Expecting `precip_3month_moving_average` variable in"
"`self.index`")
def test_apply_over_period(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
ds = xr.open_dataset(data_path / "data_kenya.nc")
got = apply_over_period(
ds,
xr.Dataset.mean,
in_variable="precip",
out_variable="mean",
time_str="month",
)
expected = ds.groupby("time.month").mean(dim="time").rename(
{"precip": "mean"})
assert all(got == expected), f"Expected: {expected}\nGot: {got}"
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date='2000-01-01',
end_date='2010-01-01')
czi = ChinaZIndex(data_path / 'chirps_kenya.nc')
variable = 'precip'
czi.fit(variable=variable)
coords = [c for c in czi.index.coords]
vars_ = [v for v in czi.index.variables if v not in coords]
assert "CZI" in vars_, f"Expecting `CZI` variable in `self.index`"
assert np.isclose(czi.index.CZI.median().values, 0, atol=0.1), "" \
"Because values are normally distributed expect CZI median to be" \
"close to 0"
def test_save(self, tmp_path):
file_path = _create_dummy_precip_data(tmp_path)
data_path = tmp_path / "data"
if not data_path.exists():
data_path.mkdir(parents=True, exist_ok=True)
b = BaseIndices(file_path / "chirps_kenya.nc")
b.name = "SPI"
b.index = xr.open_dataset(file_path / "chirps_kenya.nc")
b.save(data_path)
assert (data_path / "analysis" / "indices" /
"SPI.nc").exists(), "Expected to have created a new `.nc` file"
def test_save(self, tmp_path):
file_path = _create_dummy_precip_data(tmp_path)
data_path = tmp_path / 'data'
if not data_path.exists():
data_path.mkdir(parents=True, exist_ok=True)
b = BaseIndices(file_path / 'chirps_kenya.nc')
b.name = 'SPI'
b.index = xr.open_dataset(file_path / 'chirps_kenya.nc')
b.save(data_path)
assert (data_path / 'analysis' / 'indices' / 'SPI.nc').exists(), \
"Expected to have created a new `.nc` file"
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(
tmp_path, start_date='2000-01-01', end_date='2010-01-01'
)
pni = PercentNormalIndex(data_path / 'chirps_kenya.nc')
variable = 'precip'
pni.fit(variable=variable)
coords = [c for c in pni.index.coords]
vars_ = [v for v in pni.index.variables if v not in coords]
assert "PNI" in vars_, f"Expecting `v` variable in"\
"`self.index`"
assert np.isclose(pni.index.PNI.mean().values, 100, atol=5), ""\
"Expect the mean PNI value to be 100 (100% is NORMAL)"
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
dsi = DroughtSeverityIndex(data_path / "chirps_kenya.nc")
variable = "precip"
dsi.fit(variable=variable)
coords = [c for c in dsi.index.coords]
vars_ = [v for v in dsi.index.variables if v not in coords]
assert "DSI" in vars_, f"Expecting `v` variable in" "`self.index`"
assert (dsi.index.DSI.max() <= 4) and (dsi.index.DSI.min() >= -4), (
""
"Range of valid DSI values from -4 to 4")
def test_fit_modified(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
czi = ChinaZIndex(data_path / "chirps_kenya.nc")
variable = "precip"
czi.fit(variable=variable, modified=True)
coords = [c for c in czi.index.coords]
vars_ = [v for v in czi.index.variables if v not in coords]
assert "MCZI" in vars_, f"Expecting `CZI` variable in `self.index`"
assert np.isclose(czi.index.MCZI.median().values, 0, atol=0.1), (
""
"Because values are normally distributed expect CZI median to be"
"close to 0")
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
vdi = VegetationDeficitIndex(data_path / "data_kenya.nc")
variable = "precip"
vdi.fit(variable=variable)
vars_ = [v for v in vdi.index.data_vars]
assert "VCI3M_index" in vars_, (f"Expecting `VCI3M_index` variable in"
"`self.index`")
vals = np.unique(vdi.index.VCI3M_index.values)
assert all(np.isin(np.arange(1, 6),
vals)), (f"Expect quintiles to"
f" be from 1 - 5. Got: {vals}")
def test_fit(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path,
start_date="2000-01-01",
end_date="2010-01-01")
zsi = ZScoreIndex(data_path / "chirps_kenya.nc")
variable = "precip"
zsi.fit(variable=variable)
coords = [c for c in zsi.index.coords]
vars_ = [v for v in zsi.index.variables if v not in coords]
assert "ZSI" in vars_, f"Expecting `ZSI` variable in" "`self.index`"
assert np.isclose(
zsi.index.ZSI.mean(), 0,
atol=0.01), (""
"Expect the mean ZScoreIndex value to be close to 0")
def test_rolling_cumsum(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
ds = xr.open_dataset(data_path / 'chirps_kenya.nc')
cumsum3 = rolling_cumsum(ds, rolling_window=3)
cumsum6 = rolling_cumsum(ds, rolling_window=6)
# assert start in 2nd month (CENTRED WINDOW)
got = pd.to_datetime(cumsum3.isel(time=0).time.values)
expected = pd.to_datetime('1999-02-28')
assert got == expected, "Expected minimum time of Cumsum to be:" \
f"{expected} because centered window with size=3. Got: {got}"
# assert start in 4th month
got = pd.to_datetime(cumsum6.isel(time=0).time.values)
expected = pd.to_datetime('1999-04-30')
assert got == expected, "Expected minimum time of Cumsum to be:"\
f"{expected} because centered window with size=6. Got: {got}"
def test_create_shape_aligned_climatology_season(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
ds = xr.open_dataset(data_path / "data_kenya.nc")
time_period = "season"
ds_window = rolling_cumsum(ds, rolling_window=3)
climatology = ds_window.groupby(f"time.{time_period}").mean(dim="time")
assert climatology.precip.shape[0] == 4, (f"There should be 4 seasons"
f"in the climatology")
clim = create_shape_aligned_climatology(ds_window,
climatology,
variable="precip",
time_period=time_period)
assert ds_window["precip"].shape == clim["precip"].shape, (
f"Expected"
f"the shape of `ds_window` ({ds_window['precip'].shape}) to =="
f"`clim` ({clim['precip'].shape})")
def test_create_shape_aligned_climatology_season(self, tmp_path):
data_path = _create_dummy_precip_data(tmp_path)
ds = xr.open_dataset(data_path / 'chirps_kenya.nc')
time_period = 'season'
ds_window = rolling_cumsum(ds, rolling_window=3)
climatology = (ds_window.groupby(f'time.{time_period}').mean(
dim='time'))
assert climatology.precip.shape[0] == 4, f"There should be 4 seasons" \
f"in the climatology"
clim = create_shape_aligned_climatology(ds_window,
climatology,
variable='precip',
time_period=time_period)
assert ds_window['precip'].shape == clim['precip'].shape, f"Expected" \
f"the shape of `ds_window` ({ds_window['precip'].shape}) to ==" \
f"`clim` ({clim['precip'].shape})"
