def test_fit_nan(self):
da = self.da.copy()
da[0, 0, 0] = np.nan
out_nan = generic.fit(da, "lognorm")
out_censor = generic.fit(da[1:], "lognorm")
np.testing.assert_array_equal(out_nan.values[:, 0, 0],
out_censor.values[:, 0, 0])
def test_fit(self):
p = generic.fit(self.da, "lognorm")
assert p.dims[0] == "dparams"
assert p.get_axis_num("dparams") == 0
p0 = lognorm.fit(self.da.values[:, 0, 0])
np.testing.assert_array_equal(p[:, 0, 0], p0)
# Check that we can reuse the parameters with scipy distributions
cdf = lognorm.cdf(0.99, *p.values)
assert cdf.shape == (self.nx, self.ny)
assert p.attrs["estimator"] == "Maximum likelihood"
def test_pwm_fit(self, dist):
"""Test that the fitted parameters match parameters used to generate a random sample."""
pytest.importorskip("lmoments3")
n = 500
dc = generic.get_dist(dist)
par = self.params[dist]
da = xr.DataArray(
dc(**par).rvs(size=n),
dims=("time", ),
coords={"time": xr.cftime_range("1980-01-01", periods=n)},
)
out = generic.fit(da, dist=dist, method="PWM").compute()
# Check that values are identical to lmoments3's output dict
l3dc = generic.get_lm3_dist(dist)
expected = l3dc.lmom_fit(da.values)
for key, val in expected.items():
np.testing.assert_array_equal(out.sel(dparams=key), val, 1)
def test_synth(self):
mu = 23
sigma = 2
n = 10000
per = 0.9
d = norm(loc=mu, scale=sigma)
r = xr.DataArray(
d.rvs(n),
dims=("time", ),
coords={"time": xr.cftime_range(start="1980-01-01", periods=n)},
attrs={"history": "Mosquito bytes per minute"},
)
expected = d.ppf(per)
p = generic.fit(r, dist="norm")
q = generic.parametric_quantile(p=p, q=per)
np.testing.assert_array_almost_equal(q, expected, 1)
assert "quantile" in q.coords
def test_dims_order(self):
da = self.da.transpose()
p = generic.fit(da)
assert p.dims[-1] == "dparams"
def test_empty(self):
da = self.da.copy()
da[:, 0, 0] = np.nan
out = generic.fit(da, "lognorm").values
assert np.isnan(out[:, 0, 0]).all()
def test_genextreme_fit(self):
"""Check ML fit with a series that leads to poor values without good initial conditions."""
p = generic.fit(self.genextreme, "genextreme")
np.testing.assert_allclose(p, (0.20949, 297.954091, 75.7911863), 1e-5)
def test_weibull_min_fit(self):
"""Check ML fit with a series that leads to poor values without good initial conditions."""
p = generic.fit(self.weibull_min, "weibull_min")
np.testing.assert_allclose(p, (1.7760067, -322.092552, 4355.262679),
1e-5)
