def test_abs(self, tmp_path):
# TEST the absolute threshold
in_path = self.create_test_consec_data(tmp_path)
e = EventDetector(in_path)
# BELOW Q10
e.detect(
variable="precip", time_period="month", hilo="low", method="abs", value=0.3
)
assert (
len(e.thresh.month) == 12
), f"Expected the threshold \
calculation to be 12 (one for each month)"
# all monthly thresholds should be 10
got = e.thresh.precip.mean(dim=["lat", "lon"]).values
assert all(
got == 0.3
), f"\
Expected all monthly threshold values in e.thresh to be 0.3. Got: {got}"
# longest run should be 4 (below 0.3)
runs = e.calculate_runs()
assert runs.max().values == 4, f"Expected the longest run to be 4 (below 0.3)"
def test_q90(self, tmp_path):
# TEST the mean threshold
in_path = self.create_test_consec_data(tmp_path)
e = EventDetector(in_path)
# ABOVE Q90
e.detect(variable="precip", time_period="month", hilo="high", method="q90")
assert (
len(e.thresh.month) == 12
), f"Expected the threshold \
calculation to be 12 (one for each month)"
# all monthly thresholds should be 10
got = e.thresh.precip.mean(dim=["lat", "lon"]).values
assert all(
got == 10
), f"\
Expected all monthly threshold values in e.thresh to be 10. Got: {got}"
# longest run should be 0 (exceeding Q90)
runs = e.calculate_runs()
assert (
runs.max().values == 0
), f"Expected the longest run to be 0 (exceeding q90)"
def test_number_of_consecutive_events(self, tmp_path):
in_path = self.create_test_consec_data(tmp_path)
e = EventDetector(in_path)
e.detect(variable='precip',
time_period='dayofyear',
hilo='low',
method='std')
# check boolean dtype
assert e.exceedences.dtype == np.dtype('bool'), f"Expected a\
boolean array but got: {e.exceedences.dtype}"
# check the threshold calculation
assert e.thresh.precip.max().values == 10, f"Expected max to be 10\
got: {e.thresh.precip.max().values}"
# test run size
runs = e.calculate_runs()
assert runs.max().values == 4, f"Expected max run length to be 4\
Got: {runs.max().values}"
# ensure they are of the correct time slice
max_run_time = runs.where(runs == runs.max(), drop=True).squeeze()
expected = pd.to_datetime('2000-04-30')
got = pd.to_datetime(max_run_time.time.values)
assert got == expected, f"Expected\
the maximum time slice to be: {expected} Got: {got}"
# ensure the second largest timeslice is
second_largest_run_time = runs.where(runs == 3, drop=True).squeeze()
expected = pd.to_datetime('2000-11-30')
got = pd.to_datetime(second_largest_run_time.time.values)
assert got == expected, f"Expected\
def test_initialise_event_detector(self, tmp_path):
_create_dummy_precip_data(tmp_path)
data_dir = tmp_path / 'data' / 'interim'
precip_dir = data_dir / 'chirps_preprocessed' / 'chirps_kenya.nc'
e = EventDetector(precip_dir)
assert 'precip' in [v for v in e.ds.variables], f"\
Expected precip to be a variable.\
Got: {[v for v in e.ds.variables]}"
assert e.ds.precip.shape == (36, 30, 30), f"\
def test_q10(self, tmp_path):
# TEST the q10 threshold
in_path = self.create_test_consec_data(tmp_path)
e = EventDetector(in_path)
# BELOW Q10
e.detect(variable='precip',
time_period='month',
hilo='low',
method='q10')
assert len(e.thresh.month) == 12, f"Expected the threshold \
calculation to be 12 (one for each month)"
# all monthly thresholds should be 10
got = e.thresh.precip.mean(dim=['lat', 'lon']).values
assert all(got == 10), f"\
Expected all monthly threshold values in e.thresh to be 10. Got: {got}"
# longest run should be 0 (below Q10)
runs = e.calculate_runs()
assert runs.max(
).values == 4, f"Expected the longest run to be 4 (below q10)"
def test_initialise_event_detector(self, tmp_path):
_create_dummy_precip_data(tmp_path)
data_dir = tmp_path / "data" / "interim"
precip_dir = data_dir / "chirps_preprocessed" / "data_kenya.nc"
e = EventDetector(precip_dir)
assert "precip" in [
v for v in e.ds.variables
], f"\
Expected precip to be a variable.\
Got: {[v for v in e.ds.variables]}"
assert e.ds.precip.shape == (
36,
30,
30,
), f"\
from pathlib import Path
import sys
sys.path.append('..')
from src.analysis import EventDetector
data_dir = Path('data')
chirps_dir = data_dir / 'interim' / 'chirps_preprocessed' / 'chirps_kenya.nc'
e = EventDetector(chirps_dir)
variable = 'precip'
hilo = 'low'
method = 'std'
e.detect(variable=variable, time_period='dayofyear', hilo=hilo, method=method)
# plot the output
exceed = e.reapply_mask_to_boolean_xarray('precip', e.exceedences)
fig, ax = plt.subplots()
df = exceed.sum(dim=['lat', 'lon']).to_dataframe()
df.plot(ax=ax, label=f'{variable}_{hilo}_{method}', legend=True)
df.rolling(24).mean().plot(ax=ax, label='24mth mean')
ax.set_title(f'Number of Pixels with {variable} \
{"below" if hilo == "low" else "above"} {method}')
fig.savefig('.png')
from pathlib import Path
from src.analysis import EventDetector
data_dir = Path("data")
interim_dir = data_dir / "interim"
p_dir = interim_dir / "chirps_preprocessed" / "chirps_19812019_kenya.nc"
v_dir = interim_dir / "vhi_preprocessed" / "vhi_preprocess_kenya.nc"
e = EventDetector(p_dir)
e.detect(variable="precip", time_period="dayofyear", hilo="low", method="std")
runs = e.calculate_runs()