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feat: obs.covariance now calculates the intersection of all ensembles

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for the full covariance matrix.
This commit is contained in:
Fabian Joswig 2022-04-28 15:02:28 +01:00
parent 3a8e21ef2d
commit 9cfe56074d
1 changed files with 7 additions and 8 deletions
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15
pyerrors/obs.py
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@ -1423,6 +1423,11 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
This construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).
'''
mc_names = set([item for subnames in [o.mc_names for o in obs] for item in subnames])
idl_d = {}
for name in mc_names:
idl_d[name] = _intersection_idx([o.idl.get(name) for o in obs if o.idl.get(name) is not None])
length = len(obs)
max_samples = np.max([o.N for o in obs])
@ -1432,7 +1437,7 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
cov = np.zeros((length, length))
for i in range(length):
for j in range(i, length):
cov[i, j] = _covariance_element(obs[i], obs[j])
cov[i, j] = _covariance_element(obs[i], obs[j], idl_d=idl_d)
cov = cov + cov.T - np.diag(np.diag(cov))
corr = np.diag(1 / np.sqrt(np.diag(cov))) @ cov @ np.diag(1 / np.sqrt(np.diag(cov)))
@ -1476,7 +1481,7 @@ def _smooth_eigenvalues(corr, E):
return vec @ np.diag(vals) @ vec.T
def _covariance_element(obs1, obs2):
def _covariance_element(obs1, obs2, idl_d):
"""Estimates the covariance of two Obs objects, neglecting autocorrelations."""
def calc_gamma(deltas1, deltas2, idx1, idx2, new_idx):
@ -1497,12 +1502,6 @@ def _covariance_element(obs1, obs2):
if e_name not in obs2.mc_names:
continue
idl_d = {}
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
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]: