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feat!: covariance replaced by covariance2, window altered to minimum of

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the window of the two observables. Tests adjusted.
This commit is contained in:
Fabian Joswig 2021-12-13 17:06:03 +00:00
parent 06f4caf579
commit ec20ee38a6
4 changed files with 10 additions and 79 deletions
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2
tests/covobs_test.py
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@ -40,7 +40,7 @@ def test_covobs():
[o.gamma_method() for o in cl]
assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])
assert(pe.covariance(cl[0], cl[1]) == cov[1][0])
do = cl[0] * cl[1]
assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))

3
tests/fits_test.py
View file

@ -83,6 +83,8 @@ def test_least_squares():
assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(betac[0], betac[1]), pcov[0, 1], abs_tol=1e-3)
def test_correlated_fit():
num_samples = 400
N = 10
@ -101,7 +103,6 @@ def test_least_squares():
c = cholesky(r, lower=True)
y = np.dot(c, x)
x = np.arange(N)
for linear in [True, False]:
data = []

12
tests/obs_test.py
View file

@ -555,7 +555,7 @@ def test_gamma_method_irregular():
assert((ae.e_tauint['a'] + 4 * ae.e_dtauint['a'] > ao.e_tauint['a']))
def test_covariance2_symmetry():
def test_covariance_symmetry():
value1 = np.random.normal(5, 10)
dvalue1 = np.abs(np.random.normal(0, 1))
test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't')
@ -564,8 +564,8 @@ def test_covariance2_symmetry():
dvalue2 = np.abs(np.random.normal(0, 1))
test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
test_obs2.gamma_method()
cov_ab = pe.covariance2(test_obs1, test_obs2)
cov_ba = pe.covariance2(test_obs2, test_obs1)
cov_ab = pe.covariance(test_obs1, test_obs2)
cov_ba = pe.covariance(test_obs2, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
@ -578,10 +578,10 @@ def test_covariance2_symmetry():
idx = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr], ['t'], idl=[idx])
a.gamma_method()
assert np.isclose(a.dvalue**2, pe.covariance2(a, a), atol=100, rtol=1e-4)
assert np.isclose(a.dvalue**2, pe.covariance(a, a), atol=100, rtol=1e-4)
cov_ab = pe.covariance2(test_obs1, a)
cov_ba = pe.covariance2(a, test_obs1)
cov_ab = pe.covariance(test_obs1, a)
cov_ba = pe.covariance(a, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)