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Merge branch 'develop' into feature/irregularMC

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Simon Kuberski 2021-11-17 17:21:15 +01:00
commit 913f682087
3 changed files with 70 additions and 19 deletions
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pyerrors/obs.py
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@ -155,20 +155,21 @@ class Obs:
return res
def gamma_method(self, **kwargs):
"""Calculate the error and related properties of the Obs.
"""Estimate the error and related properties of the Obs.
Parameters
----------
S : float
specifies a custom value for the parameter S (default 2.0), can be
a float or an array of floats for different ensembles
specifies a custom value for the parameter S (default 2.0).
If set to 0 it is assumed that the data exhibits no
autocorrelation. In this case the error estimates coincides
with the sample standard error.
tau_exp : float
positive value triggers the critical slowing down analysis
(default 0.0), can be a float or an array of floats for different
ensembles
(default 0.0).
N_sigma : float
number of standard deviations from zero until the tail is
attached to the autocorrelation function (default 1)
attached to the autocorrelation function (default 1).
fft : bool
determines whether the fft algorithm is used for the computation
of the autocorrelation function (default True)
@ -281,19 +282,26 @@ class Obs:
self.e_windowsize[e_name] = n
break
else:
# Standard automatic windowing procedure
g_w = 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) / g_w) - g_w / np.sqrt(np.arange(1, w_max) * e_N)
for n in range(1, w_max):
if n < w_max // 2 - 2:
_compute_drho(n + 1)
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)
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_windowsize[e_name] = n
break
if self.S[e_name] == 0.0:
self.e_tauint[e_name] = 0.5
self.e_dtauint[e_name] = 0.0
self.e_dvalue[e_name] = np.sqrt(e_gamma[e_name][0] / (e_N - 1))
self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt(0.5 / e_N)
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)
for n in range(1, w_max):
if n < w_max // 2 - 2:
_compute_drho(n + 1)
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)
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_windowsize[e_name] = n
break
self._dvalue += self.e_dvalue[e_name] ** 2
self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
@ -1547,6 +1555,25 @@ def load_object(path):
return pickle.load(file)
def import_jackknife(jacks, name):
"""Imports jackknife samples and returns an Obs
Parameters
----------
jacks : numpy.ndarray
numpy array containing the mean value as zeroth entry and
the N jackknife samples as first to Nth entry.
name : str
name of the ensemble the samples are defined on.
"""
length = len(jacks) - 1
prj = (np.ones((length, length)) - (length - 1) * np.identity(length))
samples = jacks[1:] @ prj
new_obs = Obs([samples], [name])
new_obs._value = jacks[0]
return new_obs
def merge_obs(list_of_obs):
"""Combine all observables in list_of_obs into one new observable