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Merge pull request #124 from s-kuberski/fix/irregular

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fix: bug in automatic window for irregular chains fixed
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Fabian Joswig 2022-10-19 11:02:22 +01:00 • committed by GitHub
commit 760916692d
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2 changed files with 52 additions and 10 deletions
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33
pyerrors/obs.py
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@ -279,21 +279,33 @@ 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)
_compute_drho(1)
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:
texp = self.tau_exp[e_name]
# Critical slowing down analysis
if w_max // 2 <= 1:
raise Exception("Need at least 8 samples for tau_exp error analysis")
for n in range(1, w_max // 2):
_compute_drho(n + 1)
for n in range(gapsize, w_max // 2, gapsize):
_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)
self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n / gapsize + 0.5) / e_N)
self.e_windowsize[e_name] = n
break
else:
@ -305,16 +317,17 @@ class Obs:
self.e_windowsize[e_name] = 0
else:
# Standard automatic windowing procedure
tau = self.S[e_name] / np.log((2 * self.e_n_tauint[e_name][1:] + 1) / (2 * self.e_n_tauint[e_name][1:] - 1))
g_w = np.exp(- np.arange(1, w_max) / tau) - tau / np.sqrt(np.arange(1, w_max) * e_N)
tau = self.S[e_name] / np.log((2 * self.e_n_tauint[e_name][gapsize::gapsize] + 1) / (2 * self.e_n_tauint[e_name][gapsize::gapsize] - 1))
g_w = np.exp(- np.arange(1, len(tau) + 1) / tau) - tau / np.sqrt(np.arange(1, len(tau) + 1) * e_N)
for n in range(1, w_max):
if n < w_max // 2 - 2:
_compute_drho(n + 1)
_compute_drho(gapsize * n + gapsize)
if g_w[n - 1] < 0 or n >= w_max - 1:
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)
n *= gapsize
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)
self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n / gapsize + 0.5) / e_N)
self.e_windowsize[e_name] = n
break

29
tests/obs_test.py
View file

@ -628,9 +628,19 @@ def test_gamma_method_irregular():
ao = pe.Obs([[carr[i] for i in range(len(carr)) if i % 2 == 1]], ['a'], idl=[[i for i in range(len(carr)) if i % 2 == 1]])
ao.gamma_method()
arrt = [carr[i] for i in range(len(carr)) if i % 2 == 1]
idlt = [i for i in range(len(carr)) if i % 2 == 1]
for el in [int(e) for e in N * np.random.uniform(size=10)]:
arrt = arrt[:el] + arrt[el + 1:]
idlt = idlt[:el] + idlt[el + 1:]
ai = pe.Obs([arrt], ['a'], idl=[idlt])
ai.gamma_method()
assert(ae.e_tauint['a'] < a.e_tauint['a'])
assert((ae.e_tauint['a'] - 4 * ae.e_dtauint['a'] < ao.e_tauint['a']))
assert((ae.e_tauint['a'] + 4 * ae.e_dtauint['a'] > ao.e_tauint['a']))
assert((ai.e_tauint['a'] - 4 * ai.e_dtauint['a'] < ao.e_tauint['a']))
assert((ai.e_tauint['a'] + 4 * ai.e_dtauint['a'] > ao.e_tauint['a']))
a = pe.pseudo_Obs(1, .1, 'a', samples=10)
a.idl['a'] = range(4, 15)
@ -639,6 +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)