def test_time(self, kind, name, series):
n = 10000
u = np.random.rand(n)
xd = uniform(loc=2, scale=1)
x = xd.ppf(u)
hist = sim = series(x, name)
ref = series(apply_correction(x, 2, kind), name)
scaling = Scaling(group="time", kind=kind)
scaling.train(ref, hist)
np.testing.assert_array_almost_equal(scaling.ds.af, 2)
p = scaling.adjust(sim)
np.testing.assert_array_almost_equal(p, ref)
def test_mon_U(self, mon_series, series, mon_triangular, kind, name):
n = 10000
u = np.random.rand(n)
xd = uniform(loc=2, scale=1)
x = xd.ppf(u)
hist = sim = series(x, name)
ref = mon_series(apply_correction(x, 2, kind), name)
# Test train
scaling = Scaling(group="time.month", kind=kind)
scaling.train(ref, hist)
expected = apply_correction(mon_triangular, 2, kind)
np.testing.assert_array_almost_equal(scaling.ds.af, expected)
# Test predict
p = scaling.adjust(sim)
np.testing.assert_array_almost_equal(p, ref)
def test_add_dim(self, series, mon_series):
n = 10000
u = np.random.rand(n, 4)
xd = uniform(loc=2, scale=1)
x = xd.ppf(u)
hist = sim = series(x, "tas")
ref = mon_series(apply_correction(x, 2, "+"), "tas")
group = Grouper("time.month", add_dims=["lon"])
scaling = Scaling.train(ref, hist, group=group, kind="+")
assert "lon" not in scaling.ds
p = scaling.adjust(sim)
assert "lon" in p.dims
np.testing.assert_array_almost_equal(p.transpose(*ref.dims), ref)