# pyerrors.misc API documentation

# pyerrors.misc

View Source

      1import platform
      2import numpy as np
      3import scipy
      4import matplotlib
      5import matplotlib.pyplot as plt
      6import pandas as pd
      7import pickle
      8from .obs import Obs
      9from .version import __version__
     10
     11
     12def print_config():
     13    """Print information about version of python, pyerrors and dependencies."""
     14    config = {"system": platform.system(),
     15              "python": platform.python_version(),
     16              "pyerrors": __version__,
     17              "numpy": np.__version__,
     18              "scipy": scipy.__version__,
     19              "matplotlib": matplotlib.__version__,
     20              "pandas": pd.__version__}
     21
     22    for key, value in config.items():
     23        print(f"{key : <10}\t {value}")
     24
     25
     26def errorbar(x, y, axes=plt, **kwargs):
     27    """pyerrors wrapper for the errorbars method of matplotlib
     28
     29    Parameters
     30    ----------
     31    x : list
     32        A list of x-values which can be Obs.
     33    y : list
     34        A list of y-values which can be Obs.
     35    axes : (matplotlib.pyplot.axes)
     36        The axes to plot on. default is plt.
     37    """
     38    val = {}
     39    err = {}
     40    for name, comp in zip(["x", "y"], [x, y]):
     41        if all(isinstance(o, Obs) for o in comp):
     42            if not all(hasattr(o, 'e_dvalue') for o in comp):
     43                [o.gamma_method() for o in comp]
     44            val[name] = [o.value for o in comp]
     45            err[name] = [o.dvalue for o in comp]
     46        else:
     47            val[name] = comp
     48            err[name] = None
     49
     50        if f"{name}err" in kwargs:
     51            err[name] = kwargs.get(f"{name}err")
     52            kwargs.pop(f"{name}err", None)
     53
     54    axes.errorbar(val["x"], val["y"], xerr=err["x"], yerr=err["y"], **kwargs)
     55
     56
     57def dump_object(obj, name, **kwargs):
     58    """Dump object into pickle file.
     59
     60    Parameters
     61    ----------
     62    obj : object
     63        object to be saved in the pickle file
     64    name : str
     65        name of the file
     66    path : str
     67        specifies a custom path for the file (default '.')
     68
     69    Returns
     70    -------
     71    None
     72    """
     73    if 'path' in kwargs:
     74        file_name = kwargs.get('path') + '/' + name + '.p'
     75    else:
     76        file_name = name + '.p'
     77    with open(file_name, 'wb') as fb:
     78        pickle.dump(obj, fb)
     79
     80
     81def load_object(path):
     82    """Load object from pickle file.
     83
     84    Parameters
     85    ----------
     86    path : str
     87        path to the file
     88
     89    Returns
     90    -------
     91    object : Obs
     92        Loaded Object
     93    """
     94    with open(path, 'rb') as file:
     95        return pickle.load(file)
     96
     97
     98def pseudo_Obs(value, dvalue, name, samples=1000):
     99    """Generate an Obs object with given value, dvalue and name for test purposes
    100
    101    Parameters
    102    ----------
    103    value : float
    104        central value of the Obs to be generated.
    105    dvalue : float
    106        error of the Obs to be generated.
    107    name : str
    108        name of the ensemble for which the Obs is to be generated.
    109    samples: int
    110        number of samples for the Obs (default 1000).
    111
    112    Returns
    113    -------
    114    res : Obs
    115        Generated Observable
    116    """
    117    if dvalue <= 0.0:
    118        return Obs([np.zeros(samples) + value], [name])
    119    else:
    120        for _ in range(100):
    121            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
    122            deltas -= np.mean(deltas)
    123            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
    124            deltas += value
    125            res = Obs(deltas, [name])
    126            res.gamma_method(S=2, tau_exp=0)
    127            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
    128                break
    129
    130        res._value = float(value)
    131
    132        return res
    133
    134
    135def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
    136    """ Generate observables with given covariance and autocorrelation times.
    137
    138    Parameters
    139    ----------
    140    means : list
    141        list containing the mean value of each observable.
    142    cov : numpy.ndarray
    143        covariance matrix for the data to be generated.
    144    name : str
    145        ensemble name for the data to be geneated.
    146    tau : float or list
    147        can either be a real number or a list with an entry for
    148        every dataset.
    149    samples : int
    150        number of samples to be generated for each observable.
    151
    152    Returns
    153    -------
    154    corr_obs : list[Obs]
    155        Generated observable list
    156    """
    157
    158    assert len(means) == cov.shape[-1]
    159    tau = np.asarray(tau)
    160    if np.min(tau) < 0.5:
    161        raise Exception('All integrated autocorrelations have to be >= 0.5.')
    162
    163    a = (2 * tau - 1) / (2 * tau + 1)
    164    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
    165
    166    # Normalize samples such that sample variance matches input
    167    norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
    168    rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm))
    169
    170    data = [rand[0]]
    171    for i in range(1, samples):
    172        data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
    173    corr_data = np.array(data) - np.mean(data, axis=0) + means
    174    return [Obs([dat], [name]) for dat in corr_data.T]
    175
    176
    177def _assert_equal_properties(ol, otype=Obs):
    178    otype = type(ol[0])
    179    for o in ol[1:]:
    180        if not isinstance(o, otype):
    181            raise Exception("Wrong data type in list.")
    182        for attr in ["reweighted", "e_content", "idl"]:
    183            if hasattr(ol[0], attr):
    184                if not getattr(ol[0], attr) == getattr(o, attr):
    185                    raise Exception(f"All Obs in list have to have the same state '{attr}'.")

def print_config(): View Source

    13def print_config():
    14    """Print information about version of python, pyerrors and dependencies."""
    15    config = {"system": platform.system(),
    16              "python": platform.python_version(),
    17              "pyerrors": __version__,
    18              "numpy": np.__version__,
    19              "scipy": scipy.__version__,
    20              "matplotlib": matplotlib.__version__,
    21              "pandas": pd.__version__}
    22
    23    for key, value in config.items():
    24        print(f"{key : <10}\t {value}")

Print information about version of python, pyerrors and dependencies.

def errorbar( x, y, axes=<module 'matplotlib.pyplot' from '/opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/matplotlib/pyplot.py'>, **kwargs): View Source

    27def errorbar(x, y, axes=plt, **kwargs):
    28    """pyerrors wrapper for the errorbars method of matplotlib
    29
    30    Parameters
    31    ----------
    32    x : list
    33        A list of x-values which can be Obs.
    34    y : list
    35        A list of y-values which can be Obs.
    36    axes : (matplotlib.pyplot.axes)
    37        The axes to plot on. default is plt.
    38    """
    39    val = {}
    40    err = {}
    41    for name, comp in zip(["x", "y"], [x, y]):
    42        if all(isinstance(o, Obs) for o in comp):
    43            if not all(hasattr(o, 'e_dvalue') for o in comp):
    44                [o.gamma_method() for o in comp]
    45            val[name] = [o.value for o in comp]
    46            err[name] = [o.dvalue for o in comp]
    47        else:
    48            val[name] = comp
    49            err[name] = None
    50
    51        if f"{name}err" in kwargs:
    52            err[name] = kwargs.get(f"{name}err")
    53            kwargs.pop(f"{name}err", None)
    54
    55    axes.errorbar(val["x"], val["y"], xerr=err["x"], yerr=err["y"], **kwargs)

pyerrors wrapper for the errorbars method of matplotlib

###### Parameters

- x (list): A list of x-values which can be Obs.
- y (list): A list of y-values which can be Obs.
- axes ((matplotlib.pyplot.axes)): The axes to plot on. default is plt.

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

    58def dump_object(obj, name, **kwargs):
    59    """Dump object into pickle file.
    60
    61    Parameters
    62    ----------
    63    obj : object
    64        object to be saved in the pickle file
    65    name : str
    66        name of the file
    67    path : str
    68        specifies a custom path for the file (default '.')
    69
    70    Returns
    71    -------
    72    None
    73    """
    74    if 'path' in kwargs:
    75        file_name = kwargs.get('path') + '/' + name + '.p'
    76    else:
    77        file_name = name + '.p'
    78    with open(file_name, 'wb') as fb:
    79        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 '.')

###### Returns

- None

def load_object(path): View Source

    82def load_object(path):
    83    """Load object from pickle file.
    84
    85    Parameters
    86    ----------
    87    path : str
    88        path to the file
    89
    90    Returns
    91    -------
    92    object : Obs
    93        Loaded Object
    94    """
    95    with open(path, 'rb') as file:
    96        return pickle.load(file)

Load object from pickle file.

###### Parameters

- path (str): path to the file

###### Returns

- object (Obs): Loaded Object

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

     99def pseudo_Obs(value, dvalue, name, samples=1000):
    100    """Generate an Obs object with given value, dvalue and name for test purposes
    101
    102    Parameters
    103    ----------
    104    value : float
    105        central value of the Obs to be generated.
    106    dvalue : float
    107        error of the Obs to be generated.
    108    name : str
    109        name of the ensemble for which the Obs is to be generated.
    110    samples: int
    111        number of samples for the Obs (default 1000).
    112
    113    Returns
    114    -------
    115    res : Obs
    116        Generated Observable
    117    """
    118    if dvalue <= 0.0:
    119        return Obs([np.zeros(samples) + value], [name])
    120    else:
    121        for _ in range(100):
    122            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
    123            deltas -= np.mean(deltas)
    124            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
    125            deltas += value
    126            res = Obs(deltas, [name])
    127            res.gamma_method(S=2, tau_exp=0)
    128            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
    129                break
    130
    131        res._value = float(value)
    132
    133        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).

###### Returns

- res (Obs): Generated Observable

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

    136def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
    137    """ Generate observables with given covariance and autocorrelation times.
    138
    139    Parameters
    140    ----------
    141    means : list
    142        list containing the mean value of each observable.
    143    cov : numpy.ndarray
    144        covariance matrix for the data to be generated.
    145    name : str
    146        ensemble name for the data to be geneated.
    147    tau : float or list
    148        can either be a real number or a list with an entry for
    149        every dataset.
    150    samples : int
    151        number of samples to be generated for each observable.
    152
    153    Returns
    154    -------
    155    corr_obs : list[Obs]
    156        Generated observable list
    157    """
    158
    159    assert len(means) == cov.shape[-1]
    160    tau = np.asarray(tau)
    161    if np.min(tau) < 0.5:
    162        raise Exception('All integrated autocorrelations have to be >= 0.5.')
    163
    164    a = (2 * tau - 1) / (2 * tau + 1)
    165    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
    166
    167    # Normalize samples such that sample variance matches input
    168    norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
    169    rand = rand @ np.diag(np.sqrt(np.diag(cov))) @ np.diag(1 / np.sqrt(norm))
    170
    171    data = [rand[0]]
    172    for i in range(1, samples):
    173        data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
    174    corr_data = np.array(data) - np.mean(data, axis=0) + means
    175    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.

###### Returns

- corr_obs (list[Obs]): Generated observable list