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refactor!: covariance3 removed

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Fabian Joswig 2021-12-07 08:31:24 +00:00
parent f223b12cc2
commit 370cd34e0f
1 changed files with 0 additions and 64 deletions
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pyerrors/obs.py
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@ -1546,70 +1546,6 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
return dvalue
def covariance3(obs1, obs2, correlation=False, **kwargs):
"""Another alternative implementation of the covariance of two observables.
covariance2(obs, obs) is equal to obs.dvalue ** 2
Currently only works if ensembles are identical.
The gamma method has to be applied first to both observables.
If abs(covariance2(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
returned (default False)
plot -- if true, the integrated autocorrelation time for each ensemble is
plotted.
"""
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_names') or not hasattr(obs2, 'e_names'):
raise Exception('The gamma method has to be applied to both Obs first.')
tau_exp = []
S = []
for e_name in sorted(set(obs1.e_names + obs2.e_names)):
t_1 = obs1.tau_exp.get(e_name)
t_2 = obs2.tau_exp.get(e_name)
if t_1 is None:
t_1 = 0
if t_2 is None:
t_2 = 0
tau_exp.append(max(t_1, t_2))
S_1 = obs1.S.get(e_name)
S_2 = obs2.S.get(e_name)
if S_1 is None:
S_1 = Obs.S_global
if S_2 is None:
S_2 = Obs.S_global
S.append(max(S_1, S_2))
check_obs = obs1 + obs2
check_obs.gamma_method(tau_exp=tau_exp, S=S)
if kwargs.get('plot'):
check_obs.plot_tauint()
check_obs.plot_rho()
cov = (check_obs.dvalue ** 2 - obs1.dvalue ** 2 - obs2.dvalue ** 2) / 2
if np.abs(cov / obs1.dvalue / obs2.dvalue) > 1.0:
cov = np.sign(cov) * obs1.dvalue * obs2.dvalue
if correlation:
cov = cov / obs1.dvalue / obs2.dvalue
return cov
def pseudo_Obs(value, dvalue, name, samples=1000):
"""Generate a pseudo Obs with given value, dvalue and name