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fix: corrected bias correction of tau_int for irregular chains

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Simon Kuberski 2022-10-17 17:40:27 +02:00
parent fd4c866fdd
commit 89b0c37e76
2 changed files with 32 additions and 7 deletions
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20
pyerrors/obs.py
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@ -279,11 +279,17 @@ class Obs:
tmp = self.e_rho[e_name][i + 1:w_max] + np.concatenate([self.e_rho[e_name][i - 1::-1], self.e_rho[e_name][1:w_max - 2 * i]]) - 2 * self.e_rho[e_name][i] * self.e_rho[e_name][1:w_max - i]
self.e_drho[e_name][i] = np.sqrt(np.sum(tmp ** 2) / e_N)
# detect regular step size in irregular MC chain
gapsize = 1
for r_name in e_content[e_name][:1]:
if not isinstance(self.idl[r_name], range):
gapsize = np.min(np.diff(self.idl[r_name]))
gaps = []
for r_name in e_content[e_name]:
if isinstance(self.idl[r_name], range):
gaps.append(1)
else:
gaps.append(np.min(np.diff(self.idl[r_name])))
if not np.all([gi == gaps[0] for gi in gaps]):
raise Exception(f"Replica for ensemble {e_name} are not equally spaced.", gaps)
else:
gapsize = gaps[0]
_compute_drho(gapsize)
if self.tau_exp[e_name] > 0:
@ -295,7 +301,7 @@ class Obs:
_compute_drho(n + gapsize)
if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
# Bias correction hep-lat/0306017 eq. (49) included
self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N) + texp * np.abs(self.e_rho[e_name][n + 1]) # The absolute makes sure, that the tail contribution is always positive
self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n / gapsize + 1) / e_N) / (1 + 1 / e_N) + texp * np.abs(self.e_rho[e_name][n + 1]) # The absolute makes sure, that the tail contribution is always positive
self.e_dtauint[e_name] = np.sqrt(self.e_n_dtauint[e_name][n] ** 2 + texp ** 2 * self.e_drho[e_name][n + 1] ** 2)
# Error of tau_exp neglected so far, missing term: self.e_rho[e_name][n + 1] ** 2 * d_tau_exp ** 2
self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
@ -318,7 +324,7 @@ class Obs:
_compute_drho(gapsize * n + gapsize)
if g_w[n - 1] < 0 or n >= w_max - 1:
n *= gapsize
self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N) # Bias correction hep-lat/0306017 eq. (49)
self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n / gapsize + 1) / e_N) / (1 + 1 / e_N) # Bias correction hep-lat/0306017 eq. (49)
self.e_dtauint[e_name] = self.e_n_dtauint[e_name][n]
self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)

19
tests/obs_test.py
View file

@ -649,6 +649,25 @@ def test_gamma_method_irregular():
ol = [a, b]
o = (ol[0] - ol[1]) / (ol[1])
N = 1000
dat = gen_autocorrelated_array(np.random.normal(1, .2, size=N), .8)
idl_a = list(range(0, 1001, 1))
idl_a.remove(101)
oa = pe.Obs([dat], ["ens1"], idl=[idl_a])
oa.gamma_method()
tau_a = oa.e_tauint["ens1"]
idl_b = list(range(0, 10010, 10))
idl_b.remove(1010)
ob = pe.Obs([dat], ["ens1"], idl=[idl_b])
ob.gamma_method()
tau_b = ob.e_tauint["ens1"]
assert np.isclose(tau_a, tau_b)
def test_covariance_is_variance():
value = np.random.normal(5, 10)