pyerrors.misc

  1import pickle
  2import numpy as np
  3from .obs import Obs
  4
  5
  6def dump_object(obj, name, **kwargs):
  7    """Dump object into pickle file.
  8
  9    Parameters
 10    ----------
 11    obj : object
 12        object to be saved in the pickle file
 13    name : str
 14        name of the file
 15    path : str
 16        specifies a custom path for the file (default '.')
 17    """
 18    if 'path' in kwargs:
 19        file_name = kwargs.get('path') + '/' + name + '.p'
 20    else:
 21        file_name = name + '.p'
 22    with open(file_name, 'wb') as fb:
 23        pickle.dump(obj, fb)
 24
 25
 26def load_object(path):
 27    """Load object from pickle file.
 28
 29    Parameters
 30    ----------
 31    path : str
 32        path to the file
 33    """
 34    with open(path, 'rb') as file:
 35        return pickle.load(file)
 36
 37
 38def pseudo_Obs(value, dvalue, name, samples=1000):
 39    """Generate an Obs object with given value, dvalue and name for test purposes
 40
 41    Parameters
 42    ----------
 43    value : float
 44        central value of the Obs to be generated.
 45    dvalue : float
 46        error of the Obs to be generated.
 47    name : str
 48        name of the ensemble for which the Obs is to be generated.
 49    samples: int
 50        number of samples for the Obs (default 1000).
 51    """
 52    if dvalue <= 0.0:
 53        return Obs([np.zeros(samples) + value], [name])
 54    else:
 55        for _ in range(100):
 56            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
 57            deltas -= np.mean(deltas)
 58            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
 59            deltas += value
 60            res = Obs(deltas, [name])
 61            res.gamma_method(S=2, tau_exp=0)
 62            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
 63                break
 64
 65        res._value = float(value)
 66
 67        return res
 68
 69
 70def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
 71    """ Generate observables with given covariance and autocorrelation times.
 72
 73    Parameters
 74    ----------
 75    means : list
 76        list containing the mean value of each observable.
 77    cov : numpy.ndarray
 78        covariance matrix for the data to be generated.
 79    name : str
 80        ensemble name for the data to be geneated.
 81    tau : float or list
 82        can either be a real number or a list with an entry for
 83        every dataset.
 84    samples : int
 85        number of samples to be generated for each observable.
 86    """
 87
 88    assert len(means) == cov.shape[-1]
 89    tau = np.asarray(tau)
 90    if np.min(tau) < 0.5:
 91        raise Exception('All integrated autocorrelations have to be >= 0.5.')
 92
 93    a = (2 * tau - 1) / (2 * tau + 1)
 94    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
 95
 96    # Normalize samples such that sample variance matches input
 97    norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
 98    rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm))
 99
100    data = [rand[0]]
101    for i in range(1, samples):
102        data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
103    corr_data = np.array(data) - np.mean(data, axis=0) + means
104    return [Obs([dat], [name]) for dat in corr_data.T]
105
106
107def _assert_equal_properties(ol, otype=Obs):
108    otype = type(ol[0])
109    for o in ol[1:]:
110        if not isinstance(o, otype):
111            raise Exception("Wrong data type in list.")
112        for attr in ["is_merged", "reweighted", "e_content", "idl"]:
113            if hasattr(ol[0], attr):
114                if not getattr(ol[0], attr) == getattr(o, attr):
115                    raise Exception(f"All Obs in list have to have the same state '{attr}'.")