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]