def test_gaussian_constraint():
param_vals = [5, 6, 3]
observed = [3, 6.1, 4.3]
sigma = [1, 0.3, 0.7]
true_val = true_gauss_constr_value(x=observed, mu=param_vals, sigma=sigma)
assert true_val == true_gauss_constr_value(x=param_vals, mu=observed, sigma=sigma)
params = [zfit.Parameter(f"Param{i}", val) for i, val in enumerate(param_vals)]
constr = GaussianConstraint(params=params, observation=observed, uncertainty=sigma)
constr_np = constr.value().numpy()
assert constr_np == pytest.approx(true_val)
assert constr.get_cache_deps() == set(params)
param_vals[0] = 2
params[0].set_value(param_vals[0])
constr2_np = constr.value().numpy()
constr2_newtensor_np = constr.value().numpy()
assert constr2_newtensor_np == pytest.approx(constr2_np)
true_val2 = true_gauss_constr_value(x=param_vals, mu=observed, sigma=sigma)
assert constr2_np == pytest.approx(true_val2)
print(true_val2)
constr.observation[0].set_value(5)
observed[0] = 5
print("x: ", param_vals, [p.numpy() for p in params])
print("mu: ", observed, [p.numpy() for p in constr.observation])
print("sigma: ", sigma, np.sqrt([p for p in np.diag(constr.covariance)]))
true_val3 = true_gauss_constr_value(x=param_vals, mu=observed, sigma=sigma)
constr3_np = constr.value().numpy()
assert constr3_np == pytest.approx(true_val3)
def test_gaussian_constraint_matrix():
param1 = zfit.Parameter("Param1", 5)
param2 = zfit.Parameter("Param2", 6)
params = [param1, param2]
observed = [3., 6.1]
sigma = np.array([[1, 0.3],
[0.3, 0.5]])
trueval = true_multinormal_constr_value(x=zfit.run(params)[0], mean=observed, cov=sigma)
constr = GaussianConstraint(params=params, observation=observed, uncertainty=sigma)
constr_np = zfit.run(constr.value())
assert constr_np == pytest.approx(trueval)
#assert constr_np == pytest.approx(3.989638)
assert constr.get_cache_deps() == set(params)