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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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72
pyerrors/obs.py
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@ -1334,76 +1334,6 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
is constrained to the maximum value in order to make sure that covariance
matrices are positive semidefinite.
Parameters
----------
obs1 : Obs
First Obs
obs2 : Obs
Second Obs
correlation : bool
if true the correlation instead of the covariance is
returned (default False)
"""
if set(obs1.names).isdisjoint(set(obs2.names)):
return 0.
for name in sorted(set(obs1.names + obs2.names)):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
raise Exception('Shapes of ensemble', name, 'do not fit')
if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0
for e_name in obs1.mc_names:
if e_name not in obs2.e_names:
continue
gamma = 0
r_length = []
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
r_length.append(len(obs1.deltas[r_name]))
gamma += np.sum(obs1.deltas[r_name] * obs2.deltas[r_name])
e_N = np.sum(r_length)
tau_combined = (obs1.e_tauint[e_name] + obs2.e_tauint[e_name]) / 2
dvalue += gamma / e_N * (1 + 1 / e_N) / e_N * 2 * tau_combined
for e_name in obs1.cov_names:
if e_name not in obs2.cov_names:
continue
dvalue += float(np.dot(np.transpose(obs1.covobs[e_name].grad), np.dot(obs1.covobs[e_name].cov, obs2.covobs[e_name].grad)))
if np.abs(dvalue / obs1.dvalue / obs2.dvalue) > 1.0:
dvalue = np.sign(dvalue) * obs1.dvalue * obs2.dvalue
if correlation:
dvalue = dvalue / obs1.dvalue / obs2.dvalue
return dvalue
def covariance2(obs1, obs2, correlation=False, **kwargs):
"""Alternative implementation of the covariance of two observables.
covariance(obs, obs) is equal to obs.dvalue ** 2
The gamma method has to be applied first to both observables.
If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance
is constrained to the maximum value in order to make sure that covariance
matrices are positive semidefinite.
Keyword arguments
-----------------
correlation -- if true the correlation instead of the covariance is
@ -1503,7 +1433,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
# Make sure no entry of tauint is smaller than 0.5
e_n_tauint[e_name][e_n_tauint[e_name] < 0.5] = 0.500000000001
window = max(obs1.e_windowsize[e_name], obs2.e_windowsize[e_name])
window = min(obs1.e_windowsize[e_name], obs2.e_windowsize[e_name])
# Bias correction hep-lat/0306017 eq. (49)
e_dvalue[e_name] = 2 * (e_n_tauint[e_name][window] + obs1.tau_exp[e_name] * np.abs(e_rho[e_name][window + 1])) * (1 + (2 * window + 1) / e_N) * e_gamma[e_name][0] / e_N