mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 06:40:24 +01:00
refactor!: covariance3 removed
This commit is contained in:
parent
f223b12cc2
commit
370cd34e0f
1 changed files with 0 additions and 64 deletions
|
@ -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
|
||||
|
||||
|
|
Loading…
Add table
Reference in a new issue