mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 23:00:25 +01:00
72 lines
2 KiB
Python
72 lines
2 KiB
Python
import pickle
|
|
import numpy as np
|
|
from .obs import Obs
|
|
|
|
|
|
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)
|
|
|
|
|
|
def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
|
|
""" Generate observables with given covariance and autocorrelation times.
|
|
|
|
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.
|
|
"""
|
|
|
|
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]
|