def test_hydro(self):
fn = Path(TESTS_DATA, "Raven", "q_sim.nc")
ds = xr.open_dataset(fn)
miss = checks.missing_any(ds.q_sim, freq="YS")
np.testing.assert_array_equal(miss[:-1], False)
np.testing.assert_array_equal(miss[-1], True)
miss = checks.missing_any(ds.q_sim, freq="YS", season="JJA")
np.testing.assert_array_equal(miss, False)
def test_season(self, tasmin_series):
ts = tasmin_series(np.zeros(360))
miss = checks.missing_any(ts, freq="YS", season="MAM")
np.testing.assert_equal(miss, [False])
miss = checks.missing_any(ts, freq="YS", season="JJA")
np.testing.assert_array_equal(miss, [True, True])
miss = checks.missing_any(ts, freq="YS", season="SON")
np.testing.assert_equal(miss, [False])
def test_missing_days(self, tas_series):
a = np.arange(360.0)
a[5:10] = np.nan
ts = tas_series(a)
out = checks.missing_any(ts, freq="MS")
assert out[0]
assert not out[1]
def test_month(self, tasmin_series):
ts = tasmin_series(np.zeros(36))
miss = checks.missing_any(ts, freq="YS", month=7)
np.testing.assert_equal(miss, [False])
miss = checks.missing_any(ts, freq="YS", month=8)
np.testing.assert_equal(miss, [True])
with pytest.raises(ValueError, match=r"No data for selected period."):
miss = checks.missing_any(ts, freq="YS", month=1)
miss = checks.missing_any(ts, freq="YS", month=[7, 8])
np.testing.assert_equal(miss, [True])
ts = tasmin_series(np.zeros(76))
miss = checks.missing_any(ts, freq="YS", month=[7, 8])
np.testing.assert_equal(miss, [False])
def missing(self, *args, **kwds):
"""Return whether an output is considered missing or not."""
from functools import reduce
indexer = kwds["indexer"]
freq = kwds["freq"] or generic.default_freq(**indexer)
miss = (checks.missing_any(da, freq, **indexer) for da in args)
return reduce(np.logical_or, miss)
def missing(*args, **kwds):
"""Return whether an output is considered missing or not."""
from functools import reduce
freq = kwds.get("freq")
if freq is not None:
# We flag any period with missing data
miss = (checks.missing_any(da, freq) for da in args)
else:
# There is no resampling, we flag where one of the input is missing
miss = (da.isnull() for da in args)
return reduce(np.logical_or, miss)
def test_to_period_end(self, tasmin_series):
a = np.zeros(365) + K2C + 5.0
a[2] -= 20
ts = tasmin_series(a)
miss = checks.missing_any(ts, freq="A-JUN")
np.testing.assert_equal(miss, [False])
def test_missing_season(self):
n = 378
times = pd.date_range("2001-12-31", freq="1D", periods=n)
da = xr.DataArray(np.arange(n), [("time", times)])
miss = checks.missing_any(da, "Q-NOV")
np.testing.assert_array_equal(miss, [True, False, False, False, True])
def test_missing_season(self):
n = 378
times = pd.date_range('2001-12-31', freq='1D', periods=n)
da = xr.DataArray(np.arange(n), [('time', times)])
miss = checks.missing_any(da, 'Q-NOV')
np.testing.assert_array_equal(miss, [True, False, False, False, True])
