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