# pyerrors.misc API documentation

# pyerrors.misc

View Source

      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    if not isinstance(ol[0], otype):
    109        raise Exception("Wrong data type in list.")
    110    for o in ol[1:]:
    111        if not isinstance(o, otype):
    112            raise Exception("Wrong data type in list.")
    113        if not ol[0].is_merged == o.is_merged:
    114            raise Exception("All Obs in list have to have the same state 'is_merged'.")
    115        if not ol[0].reweighted == o.reweighted:
    116            raise Exception("All Obs in list have to have the same property 'reweighted'.")
    117        if not ol[0].e_content == o.e_content:
    118            raise Exception("All Obs in list have to be defined on the same set of configs.")
    119        if not ol[0].idl == o.idl:
    120            raise Exception("All Obs in list have to be defined on the same set of configurations.")

def dump_object(obj, name, **kwargs) View Source

     7def dump_object(obj, name, **kwargs):
     8    """Dump object into pickle file.
     9
    10    Parameters
    11    ----------
    12    obj : object
    13        object to be saved in the pickle file
    14    name : str
    15        name of the file
    16    path : str
    17        specifies a custom path for the file (default '.')
    18    """
    19    if 'path' in kwargs:
    20        file_name = kwargs.get('path') + '/' + name + '.p'
    21    else:
    22        file_name = name + '.p'
    23    with open(file_name, 'wb') as fb:
    24        pickle.dump(obj, fb)

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 '.')

def load_object(path) View Source

    27def load_object(path):
    28    """Load object from pickle file.
    29
    30    Parameters
    31    ----------
    32    path : str
    33        path to the file
    34    """
    35    with open(path, 'rb') as file:
    36        return pickle.load(file)

Load object from pickle file.

###### Parameters

- path (str): path to the file

def pseudo_Obs(value, dvalue, name, samples=1000) View Source

    39def pseudo_Obs(value, dvalue, name, samples=1000):
    40    """Generate an Obs object with given value, dvalue and name for test purposes
    41
    42    Parameters
    43    ----------
    44    value : float
    45        central value of the Obs to be generated.
    46    dvalue : float
    47        error of the Obs to be generated.
    48    name : str
    49        name of the ensemble for which the Obs is to be generated.
    50    samples: int
    51        number of samples for the Obs (default 1000).
    52    """
    53    if dvalue <= 0.0:
    54        return Obs([np.zeros(samples) + value], [name])
    55    else:
    56        for _ in range(100):
    57            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
    58            deltas -= np.mean(deltas)
    59            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
    60            deltas += value
    61            res = Obs(deltas, [name])
    62            res.gamma_method(S=2, tau_exp=0)
    63            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
    64                break
    65
    66        res._value = float(value)
    67
    68        return res

Generate an Obs object with given value, dvalue and name for test purposes

###### Parameters

- value (float): central value of the Obs to be generated.
- dvalue (float): error of the Obs to be generated.
- name (str): name of the ensemble for which the Obs is to be generated.
- samples (int): number of samples for the Obs (default 1000).

def gen_correlated_data(means, cov, name, tau=0.5, samples=1000) View Source

     71def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
     72    """ Generate observables with given covariance and autocorrelation times.
     73
     74    Parameters
     75    ----------
     76    means : list
     77        list containing the mean value of each observable.
     78    cov : numpy.ndarray
     79        covariance matrix for the data to be generated.
     80    name : str
     81        ensemble name for the data to be geneated.
     82    tau : float or list
     83        can either be a real number or a list with an entry for
     84        every dataset.
     85    samples : int
     86        number of samples to be generated for each observable.
     87    """
     88
     89    assert len(means) == cov.shape[-1]
     90    tau = np.asarray(tau)
     91    if np.min(tau) < 0.5:
     92        raise Exception('All integrated autocorrelations have to be >= 0.5.')
     93
     94    a = (2 * tau - 1) / (2 * tau + 1)
     95    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
     96
     97    # Normalize samples such that sample variance matches input
     98    norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
     99    rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm))
    100
    101    data = [rand[0]]
    102    for i in range(1, samples):
    103        data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
    104    corr_data = np.array(data) - np.mean(data, axis=0) + means
    105    return [Obs([dat], [name]) for dat in corr_data.T]

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.