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feat: the correlation for two observables with different idls is now based on

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the intersection of two instead of the union. Tests added.
This commit is contained in:
Fabian Joswig 2022-04-08 11:44:01 +01:00
parent 934d091249
commit eacc9b19a3
2 changed files with 36 additions and 9 deletions
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19
pyerrors/obs.py
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@ -1472,8 +1472,8 @@ def _covariance_element(obs1, obs2):
"""Estimates the covariance of two Obs objects, neglecting autocorrelations."""
def calc_gamma(deltas1, deltas2, idx1, idx2, new_idx):
deltas1 = _expand_deltas_for_merge(deltas1, idx1, len(idx1), new_idx)
deltas2 = _expand_deltas_for_merge(deltas2, idx2, len(idx2), new_idx)
deltas1 = _collapse_deltas_for_merge(deltas1, idx1, len(idx1), new_idx)
deltas2 = _collapse_deltas_for_merge(deltas2, idx2, len(idx2), new_idx)
return np.sum(deltas1 * deltas2)
if set(obs1.names).isdisjoint(set(obs2.names)):
@ -1493,29 +1493,30 @@ def _covariance_element(obs1, obs2):
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
idl_d[r_name] = _merge_idx([obs1.idl[r_name], obs2.idl[r_name]])
idl_d[r_name] = _intersection_idx([obs1.idl[r_name], obs2.idl[r_name]])
gamma = 0.0
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
if len(idl_d[r_name]) == 0:
continue
gamma += calc_gamma(obs1.deltas[r_name], obs2.deltas[r_name], obs1.idl[r_name], obs2.idl[r_name], idl_d[r_name])
if gamma == 0.0:
continue
gamma_div = 0.0
e_N = 0
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
gamma_div += calc_gamma(np.ones(obs1.shape[r_name]), np.ones(obs2.shape[r_name]), obs1.idl[r_name], obs2.idl[r_name], idl_d[r_name])
e_N += len(idl_d[r_name])
gamma /= max(gamma_div, 1.0)
if len(idl_d[r_name]) == 0:
continue
gamma_div += np.sqrt(calc_gamma(obs1.deltas[r_name], obs1.deltas[r_name], obs1.idl[r_name], obs1.idl[r_name], idl_d[r_name]) * calc_gamma(obs2.deltas[r_name], obs2.deltas[r_name], obs2.idl[r_name], obs2.idl[r_name], idl_d[r_name]))
gamma /= gamma_div
# Bias correction hep-lat/0306017 eq. (49)
dvalue += (1 + 1 / e_N) * gamma / e_N
dvalue += gamma
for e_name in obs1.cov_names:

26
tests/obs_test.py
View file

@ -749,6 +749,32 @@ def test_covariance_idl():
pe.covariance([obs1, obs2])
def test_correlation_intersection_of_idls():
range1 = range(1, 2000, 2)
range2 = range(2, 2001, 2)
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
obs2_a = 0.4 * pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1]) + 0.6 * obs1
obs1.gamma_method()
obs2_a.gamma_method()
cov1 = pe.covariance([obs1, obs2_a])
corr1 = pe.covariance([obs1, obs2_a], correlation=True)
obs2_b = obs2_a + pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
obs2_b.gamma_method()
cov2 = pe.covariance([obs1, obs2_b])
corr2 = pe.covariance([obs1, obs2_b], correlation=True)
assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
assert cov1[0, 1] > cov2[0, 1]
obs2_c = pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
obs2_c.gamma_method()
assert np.isclose(0, pe.covariance([obs1, obs2_c])[0, 1], atol=1e-14)
def test_empty_obs():
o = pe.Obs([np.random.rand(100)], ['test'])
q = o + pe.Obs([], [], means=[])