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fix: removed _filter_zeroes to prevent unintended removal of

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configurations from idls.
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Fabian Joswig 2023-01-06 13:20:48 +01:00
parent 64034747c0
commit f76c24b5e8
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1 changed files with 2 additions and 38 deletions
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pyerrors/obs.py
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@ -57,7 +57,6 @@ class Obs:
tau_exp_dict = {} tau_exp_dict = {}
N_sigma_global = 1.0 N_sigma_global = 1.0
N_sigma_dict = {} N_sigma_dict = {}
filter_eps = 1e-10
def __init__(self, samples, names, idl=None, **kwargs): def __init__(self, samples, names, idl=None, **kwargs):
""" Initialize Obs object. """ Initialize Obs object.
@ -1102,35 +1101,6 @@ def _expand_deltas_for_merge(deltas, idx, shape, new_idx):
return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))]) return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))])
def _filter_zeroes(deltas, idx, eps=Obs.filter_eps):
"""Filter out all configurations with vanishing fluctuation such that they do not
contribute to the error estimate anymore. Returns the new deltas and
idx according to the filtering.
A fluctuation is considered to be vanishing, if it is smaller than eps times
the mean of the absolute values of all deltas in one list.
Parameters
----------
deltas : list
List of fluctuations
idx : list
List or ranges of configs on which the deltas are defined.
eps : float
Prefactor that enters the filter criterion.
"""
new_deltas = []
new_idx = []
maxd = np.mean(np.fabs(deltas))
for i in range(len(deltas)):
if abs(deltas[i]) > eps * maxd:
new_deltas.append(deltas[i])
new_idx.append(idx[i])
if new_idx:
return np.array(new_deltas), new_idx
else:
return deltas, idx
def derived_observable(func, data, array_mode=False, **kwargs): def derived_observable(func, data, array_mode=False, **kwargs):
"""Construct a derived Obs according to func(data, **kwargs) using automatic differentiation. """Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.
@ -1287,14 +1257,8 @@ def derived_observable(func, data, array_mode=False, **kwargs):
new_names_obs = [] new_names_obs = []
for name in new_names: for name in new_names:
if name not in new_covobs: if name not in new_covobs:
if is_merged[name]: new_samples.append(new_deltas[name])
filtered_deltas, filtered_idl_d = _filter_zeroes(new_deltas[name], new_idl_d[name]) new_idl.append(new_idl_d[name])
else:
filtered_deltas = new_deltas[name]
filtered_idl_d = new_idl_d[name]
new_samples.append(filtered_deltas)
new_idl.append(filtered_idl_d)
new_means.append(new_r_values[name][i_val]) new_means.append(new_r_values[name][i_val])
new_names_obs.append(name) new_names_obs.append(name)
final_result[i_val] = Obs(new_samples, new_names_obs, means=new_means, idl=new_idl) final_result[i_val] = Obs(new_samples, new_names_obs, means=new_means, idl=new_idl)