lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-05-14 19:43:41 +02:00
Code Issues Projects Releases Packages Wiki Activity

fix: covariance now works again for observables with covobs or non

Browse source 
overlapping idls
This commit is contained in:
Fabian Joswig 2022-04-28 15:12:08 +01:00
parent 9cfe56074d
commit 4be56514e5
1 changed files with 28 additions and 25 deletions
Download patch file Download diff file Expand all files Collapse all files

53
pyerrors/obs.py
View file

@ -1423,9 +1423,9 @@ 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). 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]) names = set([item for subnames in [set(o.names) - set(o.cov_names) for o in obs] for item in subnames])
idl_d = {} idl_d = {}
for name in mc_names: for name in names:
idl_d[name] = _intersection_idx([o.idl.get(name) for o in obs if o.idl.get(name) is not None]) idl_d[name] = _intersection_idx([o.idl.get(name) for o in obs if o.idl.get(name) is not None])
length = len(obs) length = len(obs)
@ -1495,35 +1495,38 @@ def _covariance_element(obs1, obs2, idl_d):
if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'): 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.') raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0.0 if obs1 is obs2:
dvalue = len(obs1.mc_names)
else:
dvalue = 0.0
for e_name in obs1.mc_names: for e_name in obs1.mc_names:
if e_name not in obs2.mc_names: if e_name not in obs2.mc_names:
continue
gamma = 0.0
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue 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: gamma = 0.0
continue
gamma_div = 0.0 for r_name in obs1.e_content[e_name]:
for r_name in obs1.e_content[e_name]: if r_name not in obs2.e_content[e_name]:
if r_name not in obs2.e_content[e_name]: continue
continue if len(idl_d[r_name]) == 0:
if len(idl_d[r_name]) == 0: continue
continue gamma += calc_gamma(obs1.deltas[r_name], obs2.deltas[r_name], obs1.idl[r_name], obs2.idl[r_name], idl_d[r_name])
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
dvalue += gamma if gamma == 0.0:
continue
gamma_div = 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_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
dvalue += gamma
for e_name in obs1.cov_names: for e_name in obs1.cov_names: