def test_too_short(self, q_series):
"""Missing if less than 20 values are present."""
q = q_series(np.random.rand(21))
out = land.fit(q, dist="norm")
assert not np.isnan(out.values[0])
q = q_series(np.random.rand(19))
out = land.fit(q, dist="norm")
assert np.isnan(out.values[0])
def test_nan(self, q_series):
r = np.random.rand(22)
r[0] = np.nan
q = q_series(r)
out = land.fit(q, dist="norm")
assert not np.isnan(out.values[0])
def test_options(self, q_series):
q = q_series(np.random.rand(19))
with set_options(missing_options={"at_least_n": {"n": 10}}):
out = land.fit(q, dist="norm")
np.testing.assert_array_equal(out.isnull(), False)
def test_ndim(self, ndq_series):
out = land.fit(ndq_series, dist="norm")
assert out.shape == (2, 2, 3)
np.testing.assert_array_equal(out.isnull(), False)
def test_simple(self, ndq_series):
ts = land.stats(ndq_series, freq="YS", op="max")
p = land.fit(ts, dist="gumbel_r")
assert p.attrs["estimator"] == "Maximum likelihood"
