pyerrors/pyerrors/misc.py

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import pickle
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import numpy as np
from .obs import Obs
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def dump_object(obj, name, **kwargs):
"""Dump object into pickle file.
Parameters
----------
obj : object
object to be saved in the pickle file
name : str
name of the file
path : str
specifies a custom path for the file (default '.')
"""
if 'path' in kwargs:
file_name = kwargs.get('path') + '/' + name + '.p'
else:
file_name = name + '.p'
with open(file_name, 'wb') as fb:
pickle.dump(obj, fb)
def load_object(path):
"""Load object from pickle file.
Parameters
----------
path : str
path to the file
"""
with open(path, 'rb') as file:
return pickle.load(file)
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def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
""" Generate observables with given covariance and autocorrelation times.
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Parameters
----------
means : list
list containing the mean value of each observable.
cov : numpy.ndarray
covariance matrix for the data to be generated.
name : str
ensemble name for the data to be geneated.
tau : float or list
can either be a real number or a list with an entry for
every dataset.
samples : int
number of samples to be generated for each observable.
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"""
assert len(means) == cov.shape[-1]
tau = np.asarray(tau)
if np.min(tau) < 0.5:
raise Exception('All integrated autocorrelations have to be >= 0.5.')
a = (2 * tau - 1) / (2 * tau + 1)
rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
# Normalize samples such that sample variance matches input
norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm))
data = [rand[0]]
for i in range(1, samples):
data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
corr_data = np.array(data) - np.mean(data, axis=0) + means
return [Obs([dat], [name]) for dat in corr_data.T]