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feat: first version of strictly positive semi-definite covariance

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Fabian Joswig 2022-02-26 08:45:33 +00:00
parent bb3ddabfd0
commit 344da7a3d9
1 changed files with 2 additions and 8 deletions
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10
pyerrors/obs.py
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@ -1390,8 +1390,6 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
dvalue = 0
e_gamma = {}
e_dvalue = {}
e_n_tauint = {}
e_rho = {}
for e_name in obs1.mc_names:
@ -1435,14 +1433,10 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
e_N += np.sum(np.ones_like(idl_d[r_name]))
e_gamma[e_name] /= gamma_div[:w_max]
e_rho[e_name] = e_gamma[e_name][:w_max] / e_gamma[e_name][0]
e_n_tauint[e_name] = np.cumsum(np.concatenate(([0.5], e_rho[e_name][1:])))
# 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
tau_int = min(obs1.e_tauint[e_name], obs2.e_tauint[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
e_dvalue[e_name] = 2 * (tau_int) * (1 + 1 / e_N) * e_gamma[e_name][0] / e_N
dvalue += e_dvalue[e_name]