def test_empirical_pdf(smi, u, log):
cop = EmpiricalCopula(4, smi, smoothing="beta")
pdf = cop.pdf(u, log) if log else np.log(cop.pdf(u))
expected = [-38.968738, -7.304777, -14.892165, -5.423080, -24.478464]
assert_almost_equal(pdf, expected, decimal=6)
def test_empirical_to_margins():
data = load_marginal_data()
cop = EmpiricalCopula(data)
test_input = pseudo_obs(data)[:6]
output = cop.to_marginals(test_input)
assert_almost_equal(output, data.iloc[:6].to_numpy())
def copula():
data = load_marginal_data()
return EmpiricalCopula(data)
def test_empirical_summary(smi):
cop = EmpiricalCopula(data=smi)
assert isinstance(cop.summary(), Summary)
def test_empirical_random(smi, seed):
cop = EmpiricalCopula(data=smi)
rvs = cop.random(5, seed)
assert rvs.shape == (5, smi.shape[1])
def test_empirical_param_returns_none(smi):
cop = EmpiricalCopula(data=smi)
assert cop.params is None
def test_empirical_cdf(u, smi, smoothing, expected):
cop = EmpiricalCopula(data=smi, smoothing=smoothing)
assert_almost_equal(cop.cdf(u), expected)
def test_empirical_non_applicable_methods_raises_error(smi, func):
cop = EmpiricalCopula(data=smi)
with pytest.raises(NotApplicableError):
eval(func)