pyerrors.obs

   1import warnings
   2import hashlib
   3import pickle
   4import numpy as np
   5import autograd.numpy as anp  # Thinly-wrapped numpy
   6import scipy
   7from autograd import jacobian
   8import matplotlib.pyplot as plt
   9from scipy.stats import skew, skewtest, kurtosis, kurtosistest
  10import numdifftools as nd
  11from itertools import groupby
  12from .covobs import Covobs
  13
  14# Improve print output of numpy.ndarrays containing Obs objects.
  15np.set_printoptions(formatter={'object': lambda x: str(x)})
  16
  17
  18class Obs:
  19    """Class for a general observable.
  20
  21    Instances of Obs are the basic objects of a pyerrors error analysis.
  22    They are initialized with a list which contains arrays of samples for
  23    different ensembles/replica and another list of same length which contains
  24    the names of the ensembles/replica. Mathematical operations can be
  25    performed on instances. The result is another instance of Obs. The error of
  26    an instance can be computed with the gamma_method. Also contains additional
  27    methods for output and visualization of the error calculation.
  28
  29    Attributes
  30    ----------
  31    S_global : float
  32        Standard value for S (default 2.0)
  33    S_dict : dict
  34        Dictionary for S values. If an entry for a given ensemble
  35        exists this overwrites the standard value for that ensemble.
  36    tau_exp_global : float
  37        Standard value for tau_exp (default 0.0)
  38    tau_exp_dict : dict
  39        Dictionary for tau_exp values. If an entry for a given ensemble exists
  40        this overwrites the standard value for that ensemble.
  41    N_sigma_global : float
  42        Standard value for N_sigma (default 1.0)
  43    N_sigma_dict : dict
  44        Dictionary for N_sigma values. If an entry for a given ensemble exists
  45        this overwrites the standard value for that ensemble.
  46    """
  47    __slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
  48                 'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
  49                 'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
  50                 'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
  51                 'idl', 'tag', '_covobs', '__dict__']
  52
  53    S_global = 2.0
  54    S_dict = {}
  55    tau_exp_global = 0.0
  56    tau_exp_dict = {}
  57    N_sigma_global = 1.0
  58    N_sigma_dict = {}
  59
  60    def __init__(self, samples, names, idl=None, **kwargs):
  61        """ Initialize Obs object.
  62
  63        Parameters
  64        ----------
  65        samples : list
  66            list of numpy arrays containing the Monte Carlo samples
  67        names : list
  68            list of strings labeling the individual samples
  69        idl : list, optional
  70            list of ranges or lists on which the samples are defined
  71        """
  72
  73        if kwargs.get("means") is None and len(samples):
  74            if len(samples) != len(names):
  75                raise ValueError('Length of samples and names incompatible.')
  76            if idl is not None:
  77                if len(idl) != len(names):
  78                    raise ValueError('Length of idl incompatible with samples and names.')
  79            name_length = len(names)
  80            if name_length > 1:
  81                if name_length != len(set(names)):
  82                    raise ValueError('Names are not unique.')
  83                if not all(isinstance(x, str) for x in names):
  84                    raise TypeError('All names have to be strings.')
  85            else:
  86                if not isinstance(names[0], str):
  87                    raise TypeError('All names have to be strings.')
  88            if min(len(x) for x in samples) <= 4:
  89                raise ValueError('Samples have to have at least 5 entries.')
  90
  91        self.names = sorted(names)
  92        self.shape = {}
  93        self.r_values = {}
  94        self.deltas = {}
  95        self._covobs = {}
  96
  97        self._value = 0
  98        self.N = 0
  99        self.idl = {}
 100        if idl is not None:
 101            for name, idx in sorted(zip(names, idl)):
 102                if isinstance(idx, range):
 103                    self.idl[name] = idx
 104                elif isinstance(idx, (list, np.ndarray)):
 105                    dc = np.unique(np.diff(idx))
 106                    if np.any(dc < 0):
 107                        raise ValueError("Unsorted idx for idl[%s]" % (name))
 108                    if len(dc) == 1:
 109                        self.idl[name] = range(idx[0], idx[-1] + dc[0], dc[0])
 110                    else:
 111                        self.idl[name] = list(idx)
 112                else:
 113                    raise TypeError('incompatible type for idl[%s].' % (name))
 114        else:
 115            for name, sample in sorted(zip(names, samples)):
 116                self.idl[name] = range(1, len(sample) + 1)
 117
 118        if kwargs.get("means") is not None:
 119            for name, sample, mean in sorted(zip(names, samples, kwargs.get("means"))):
 120                self.shape[name] = len(self.idl[name])
 121                self.N += self.shape[name]
 122                self.r_values[name] = mean
 123                self.deltas[name] = sample
 124        else:
 125            for name, sample in sorted(zip(names, samples)):
 126                self.shape[name] = len(self.idl[name])
 127                self.N += self.shape[name]
 128                if len(sample) != self.shape[name]:
 129                    raise ValueError('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
 130                self.r_values[name] = np.mean(sample)
 131                self.deltas[name] = sample - self.r_values[name]
 132                self._value += self.shape[name] * self.r_values[name]
 133            self._value /= self.N
 134
 135        self._dvalue = 0.0
 136        self.ddvalue = 0.0
 137        self.reweighted = False
 138
 139        self.tag = None
 140
 141    @property
 142    def value(self):
 143        return self._value
 144
 145    @property
 146    def dvalue(self):
 147        return self._dvalue
 148
 149    @property
 150    def e_names(self):
 151        return sorted(set([o.split('|')[0] for o in self.names]))
 152
 153    @property
 154    def cov_names(self):
 155        return sorted(set([o for o in self.covobs.keys()]))
 156
 157    @property
 158    def mc_names(self):
 159        return sorted(set([o.split('|')[0] for o in self.names if o not in self.cov_names]))
 160
 161    @property
 162    def e_content(self):
 163        res = {}
 164        for e, e_name in enumerate(self.e_names):
 165            res[e_name] = sorted(filter(lambda x: x.startswith(e_name + '|'), self.names))
 166            if e_name in self.names:
 167                res[e_name].append(e_name)
 168        return res
 169
 170    @property
 171    def covobs(self):
 172        return self._covobs
 173
 174    def gamma_method(self, **kwargs):
 175        """Estimate the error and related properties of the Obs.
 176
 177        Parameters
 178        ----------
 179        S : float
 180            specifies a custom value for the parameter S (default 2.0).
 181            If set to 0 it is assumed that the data exhibits no
 182            autocorrelation. In this case the error estimates coincides
 183            with the sample standard error.
 184        tau_exp : float
 185            positive value triggers the critical slowing down analysis
 186            (default 0.0).
 187        N_sigma : float
 188            number of standard deviations from zero until the tail is
 189            attached to the autocorrelation function (default 1).
 190        fft : bool
 191            determines whether the fft algorithm is used for the computation
 192            of the autocorrelation function (default True)
 193        """
 194
 195        e_content = self.e_content
 196        self.e_dvalue = {}
 197        self.e_ddvalue = {}
 198        self.e_tauint = {}
 199        self.e_dtauint = {}
 200        self.e_windowsize = {}
 201        self.e_n_tauint = {}
 202        self.e_n_dtauint = {}
 203        e_gamma = {}
 204        self.e_rho = {}
 205        self.e_drho = {}
 206        self._dvalue = 0
 207        self.ddvalue = 0
 208
 209        self.S = {}
 210        self.tau_exp = {}
 211        self.N_sigma = {}
 212
 213        if kwargs.get('fft') is False:
 214            fft = False
 215        else:
 216            fft = True
 217
 218        def _parse_kwarg(kwarg_name):
 219            if kwarg_name in kwargs:
 220                tmp = kwargs.get(kwarg_name)
 221                if isinstance(tmp, (int, float)):
 222                    if tmp < 0:
 223                        raise Exception(kwarg_name + ' has to be larger or equal to 0.')
 224                    for e, e_name in enumerate(self.e_names):
 225                        getattr(self, kwarg_name)[e_name] = tmp
 226                else:
 227                    raise TypeError(kwarg_name + ' is not in proper format.')
 228            else:
 229                for e, e_name in enumerate(self.e_names):
 230                    if e_name in getattr(Obs, kwarg_name + '_dict'):
 231                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_dict')[e_name]
 232                    else:
 233                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_global')
 234
 235        _parse_kwarg('S')
 236        _parse_kwarg('tau_exp')
 237        _parse_kwarg('N_sigma')
 238
 239        for e, e_name in enumerate(self.mc_names):
 240            gapsize = _determine_gap(self, e_content, e_name)
 241
 242            r_length = []
 243            for r_name in e_content[e_name]:
 244                if isinstance(self.idl[r_name], range):
 245                    r_length.append(len(self.idl[r_name]) * self.idl[r_name].step // gapsize)
 246                else:
 247                    r_length.append((self.idl[r_name][-1] - self.idl[r_name][0] + 1) // gapsize)
 248
 249            e_N = np.sum([self.shape[r_name] for r_name in e_content[e_name]])
 250            w_max = max(r_length) // 2
 251            e_gamma[e_name] = np.zeros(w_max)
 252            self.e_rho[e_name] = np.zeros(w_max)
 253            self.e_drho[e_name] = np.zeros(w_max)
 254
 255            for r_name in e_content[e_name]:
 256                e_gamma[e_name] += self._calc_gamma(self.deltas[r_name], self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
 257
 258            gamma_div = np.zeros(w_max)
 259            for r_name in e_content[e_name]:
 260                gamma_div += self._calc_gamma(np.ones((self.shape[r_name])), self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
 261            gamma_div[gamma_div < 1] = 1.0
 262            e_gamma[e_name] /= gamma_div[:w_max]
 263
 264            if np.abs(e_gamma[e_name][0]) < 10 * np.finfo(float).tiny:  # Prevent division by zero
 265                self.e_tauint[e_name] = 0.5
 266                self.e_dtauint[e_name] = 0.0
 267                self.e_dvalue[e_name] = 0.0
 268                self.e_ddvalue[e_name] = 0.0
 269                self.e_windowsize[e_name] = 0
 270                continue
 271
 272            self.e_rho[e_name] = e_gamma[e_name][:w_max] / e_gamma[e_name][0]
 273            self.e_n_tauint[e_name] = np.cumsum(np.concatenate(([0.5], self.e_rho[e_name][1:])))
 274            # Make sure no entry of tauint is smaller than 0.5
 275            self.e_n_tauint[e_name][self.e_n_tauint[e_name] <= 0.5] = 0.5 + np.finfo(np.float64).eps
 276            # hep-lat/0306017 eq. (42)
 277            self.e_n_dtauint[e_name] = self.e_n_tauint[e_name] * 2 * np.sqrt(np.abs(np.arange(w_max) + 0.5 - self.e_n_tauint[e_name]) / e_N)
 278            self.e_n_dtauint[e_name][0] = 0.0
 279
 280            def _compute_drho(i):
 281                tmp = (self.e_rho[e_name][i + 1:w_max]
 282                       + np.concatenate([self.e_rho[e_name][i - 1:None if i - (w_max - 1) // 2 <= 0 else (2 * i - (2 * w_max) // 2):-1],
 283                                         self.e_rho[e_name][1:max(1, w_max - 2 * i)]])
 284                       - 2 * self.e_rho[e_name][i] * self.e_rho[e_name][1:w_max - i])
 285                self.e_drho[e_name][i] = np.sqrt(np.sum(tmp ** 2) / e_N)
 286
 287            if self.tau_exp[e_name] > 0:
 288                _compute_drho(1)
 289                texp = self.tau_exp[e_name]
 290                # Critical slowing down analysis
 291                if w_max // 2 <= 1:
 292                    raise Exception("Need at least 8 samples for tau_exp error analysis")
 293                for n in range(1, w_max // 2):
 294                    _compute_drho(n + 1)
 295                    if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
 296                        # Bias correction hep-lat/0306017 eq. (49) included
 297                        self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N) + texp * np.abs(self.e_rho[e_name][n + 1])  # The absolute makes sure, that the tail contribution is always positive
 298                        self.e_dtauint[e_name] = np.sqrt(self.e_n_dtauint[e_name][n] ** 2 + texp ** 2 * self.e_drho[e_name][n + 1] ** 2)
 299                        # Error of tau_exp neglected so far, missing term: self.e_rho[e_name][n + 1] ** 2 * d_tau_exp ** 2
 300                        self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
 301                        self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
 302                        self.e_windowsize[e_name] = n
 303                        break
 304            else:
 305                if self.S[e_name] == 0.0:
 306                    self.e_tauint[e_name] = 0.5
 307                    self.e_dtauint[e_name] = 0.0
 308                    self.e_dvalue[e_name] = np.sqrt(e_gamma[e_name][0] / (e_N - 1))
 309                    self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt(0.5 / e_N)
 310                    self.e_windowsize[e_name] = 0
 311                else:
 312                    # Standard automatic windowing procedure
 313                    tau = self.S[e_name] / np.log((2 * self.e_n_tauint[e_name][1:] + 1) / (2 * self.e_n_tauint[e_name][1:] - 1))
 314                    g_w = np.exp(- np.arange(1, len(tau) + 1) / tau) - tau / np.sqrt(np.arange(1, len(tau) + 1) * e_N)
 315                    for n in range(1, w_max):
 316                        if g_w[n - 1] < 0 or n >= w_max - 1:
 317                            _compute_drho(n)
 318                            self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N)  # Bias correction hep-lat/0306017 eq. (49)
 319                            self.e_dtauint[e_name] = self.e_n_dtauint[e_name][n]
 320                            self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
 321                            self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
 322                            self.e_windowsize[e_name] = n
 323                            break
 324
 325            self._dvalue += self.e_dvalue[e_name] ** 2
 326            self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
 327
 328        for e_name in self.cov_names:
 329            self.e_dvalue[e_name] = np.sqrt(self.covobs[e_name].errsq())
 330            self.e_ddvalue[e_name] = 0
 331            self._dvalue += self.e_dvalue[e_name]**2
 332
 333        self._dvalue = np.sqrt(self._dvalue)
 334        if self._dvalue == 0.0:
 335            self.ddvalue = 0.0
 336        else:
 337            self.ddvalue = np.sqrt(self.ddvalue) / self._dvalue
 338        return
 339
 340    gm = gamma_method
 341
 342    def _calc_gamma(self, deltas, idx, shape, w_max, fft, gapsize):
 343        """Calculate Gamma_{AA} from the deltas, which are defined on idx.
 344           idx is assumed to be a contiguous range (possibly with a stepsize != 1)
 345
 346        Parameters
 347        ----------
 348        deltas : list
 349            List of fluctuations
 350        idx : list
 351            List or range of configurations on which the deltas are defined.
 352        shape : int
 353            Number of configurations in idx.
 354        w_max : int
 355            Upper bound for the summation window.
 356        fft : bool
 357            determines whether the fft algorithm is used for the computation
 358            of the autocorrelation function.
 359        gapsize : int
 360            The target distance between two configurations. If longer distances
 361            are found in idx, the data is expanded.
 362        """
 363        gamma = np.zeros(w_max)
 364        deltas = _expand_deltas(deltas, idx, shape, gapsize)
 365        new_shape = len(deltas)
 366        if fft:
 367            max_gamma = min(new_shape, w_max)
 368            # The padding for the fft has to be even
 369            padding = new_shape + max_gamma + (new_shape + max_gamma) % 2
 370            gamma[:max_gamma] += np.fft.irfft(np.abs(np.fft.rfft(deltas, padding)) ** 2)[:max_gamma]
 371        else:
 372            for n in range(w_max):
 373                if new_shape - n >= 0:
 374                    gamma[n] += deltas[0:new_shape - n].dot(deltas[n:new_shape])
 375
 376        return gamma
 377
 378    def details(self, ens_content=True):
 379        """Output detailed properties of the Obs.
 380
 381        Parameters
 382        ----------
 383        ens_content : bool
 384            print details about the ensembles and replica if true.
 385        """
 386        if self.tag is not None:
 387            print("Description:", self.tag)
 388        if not hasattr(self, 'e_dvalue'):
 389            print('Result\t %3.8e' % (self.value))
 390        else:
 391            if self.value == 0.0:
 392                percentage = np.nan
 393            else:
 394                percentage = np.abs(self._dvalue / self.value) * 100
 395            print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self._dvalue, self.ddvalue, percentage))
 396            if len(self.e_names) > 1:
 397                print(' Ensemble errors:')
 398            e_content = self.e_content
 399            for e_name in self.mc_names:
 400                gap = _determine_gap(self, e_content, e_name)
 401
 402                if len(self.e_names) > 1:
 403                    print('', e_name, '\t %3.6e +/- %3.6e' % (self.e_dvalue[e_name], self.e_ddvalue[e_name]))
 404                tau_string = " \N{GREEK SMALL LETTER TAU}_int\t " + _format_uncertainty(self.e_tauint[e_name], self.e_dtauint[e_name])
 405                tau_string += f" in units of {gap} config"
 406                if gap > 1:
 407                    tau_string += "s"
 408                if self.tau_exp[e_name] > 0:
 409                    tau_string = f"{tau_string: <45}" + '\t(\N{GREEK SMALL LETTER TAU}_exp=%3.2f, N_\N{GREEK SMALL LETTER SIGMA}=%1.0i)' % (self.tau_exp[e_name], self.N_sigma[e_name])
 410                else:
 411                    tau_string = f"{tau_string: <45}" + '\t(S=%3.2f)' % (self.S[e_name])
 412                print(tau_string)
 413            for e_name in self.cov_names:
 414                print('', e_name, '\t %3.8e' % (self.e_dvalue[e_name]))
 415        if ens_content is True:
 416            if len(self.e_names) == 1:
 417                print(self.N, 'samples in', len(self.e_names), 'ensemble:')
 418            else:
 419                print(self.N, 'samples in', len(self.e_names), 'ensembles:')
 420            my_string_list = []
 421            for key, value in sorted(self.e_content.items()):
 422                if key not in self.covobs:
 423                    my_string = '  ' + "\u00B7 Ensemble '" + key + "' "
 424                    if len(value) == 1:
 425                        my_string += f': {self.shape[value[0]]} configurations'
 426                        if isinstance(self.idl[value[0]], range):
 427                            my_string += f' (from {self.idl[value[0]].start} to {self.idl[value[0]][-1]}' + int(self.idl[value[0]].step != 1) * f' in steps of {self.idl[value[0]].step}' + ')'
 428                        else:
 429                            my_string += f' (irregular range from {self.idl[value[0]][0]} to {self.idl[value[0]][-1]})'
 430                    else:
 431                        sublist = []
 432                        for v in value:
 433                            my_substring = '    ' + "\u00B7 Replicum '" + v[len(key) + 1:] + "' "
 434                            my_substring += f': {self.shape[v]} configurations'
 435                            if isinstance(self.idl[v], range):
 436                                my_substring += f' (from {self.idl[v].start} to {self.idl[v][-1]}' + int(self.idl[v].step != 1) * f' in steps of {self.idl[v].step}' + ')'
 437                            else:
 438                                my_substring += f' (irregular range from {self.idl[v][0]} to {self.idl[v][-1]})'
 439                            sublist.append(my_substring)
 440
 441                        my_string += '\n' + '\n'.join(sublist)
 442                else:
 443                    my_string = '  ' + "\u00B7 Covobs   '" + key + "' "
 444                my_string_list.append(my_string)
 445            print('\n'.join(my_string_list))
 446
 447    def reweight(self, weight):
 448        """Reweight the obs with given rewighting factors.
 449
 450        Parameters
 451        ----------
 452        weight : Obs
 453            Reweighting factor. An Observable that has to be defined on a superset of the
 454            configurations in obs[i].idl for all i.
 455        all_configs : bool
 456            if True, the reweighted observables are normalized by the average of
 457            the reweighting factor on all configurations in weight.idl and not
 458            on the configurations in obs[i].idl. Default False.
 459        """
 460        return reweight(weight, [self])[0]
 461
 462    def is_zero_within_error(self, sigma=1):
 463        """Checks whether the observable is zero within 'sigma' standard errors.
 464
 465        Parameters
 466        ----------
 467        sigma : int
 468            Number of standard errors used for the check.
 469
 470        Works only properly when the gamma method was run.
 471        """
 472        return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue
 473
 474    def is_zero(self, atol=1e-10):
 475        """Checks whether the observable is zero within a given tolerance.
 476
 477        Parameters
 478        ----------
 479        atol : float
 480            Absolute tolerance (for details see numpy documentation).
 481        """
 482        return np.isclose(0.0, self.value, 1e-14, atol) and all(np.allclose(0.0, delta, 1e-14, atol) for delta in self.deltas.values()) and all(np.allclose(0.0, delta.errsq(), 1e-14, atol) for delta in self.covobs.values())
 483
 484    def plot_tauint(self, save=None):
 485        """Plot integrated autocorrelation time for each ensemble.
 486
 487        Parameters
 488        ----------
 489        save : str
 490            saves the figure to a file named 'save' if.
 491        """
 492        if not hasattr(self, 'e_dvalue'):
 493            raise Exception('Run the gamma method first.')
 494
 495        for e, e_name in enumerate(self.mc_names):
 496            fig = plt.figure()
 497            plt.xlabel(r'$W$')
 498            plt.ylabel(r'$\tau_\mathrm{int}$')
 499            length = int(len(self.e_n_tauint[e_name]))
 500            if self.tau_exp[e_name] > 0:
 501                base = self.e_n_tauint[e_name][self.e_windowsize[e_name]]
 502                x_help = np.arange(2 * self.tau_exp[e_name])
 503                y_help = (x_help + 1) * np.abs(self.e_rho[e_name][self.e_windowsize[e_name] + 1]) * (1 - x_help / (2 * (2 * self.tau_exp[e_name] - 1))) + base
 504                x_arr = np.arange(self.e_windowsize[e_name] + 1, self.e_windowsize[e_name] + 1 + 2 * self.tau_exp[e_name])
 505                plt.plot(x_arr, y_help, 'C' + str(e), linewidth=1, ls='--', marker=',')
 506                plt.errorbar([self.e_windowsize[e_name] + 2 * self.tau_exp[e_name]], [self.e_tauint[e_name]],
 507                             yerr=[self.e_dtauint[e_name]], fmt='C' + str(e), linewidth=1, capsize=2, marker='o', mfc=plt.rcParams['axes.facecolor'])
 508                xmax = self.e_windowsize[e_name] + 2 * self.tau_exp[e_name] + 1.5
 509                label = e_name + r', $\tau_\mathrm{exp}$=' + str(np.around(self.tau_exp[e_name], decimals=2))
 510            else:
 511                label = e_name + ', S=' + str(np.around(self.S[e_name], decimals=2))
 512                xmax = max(10.5, 2 * self.e_windowsize[e_name] - 0.5)
 513
 514            plt.errorbar(np.arange(length)[:int(xmax) + 1], self.e_n_tauint[e_name][:int(xmax) + 1], yerr=self.e_n_dtauint[e_name][:int(xmax) + 1], linewidth=1, capsize=2, label=label)
 515            plt.axvline(x=self.e_windowsize[e_name], color='C' + str(e), alpha=0.5, marker=',', ls='--')
 516            plt.legend()
 517            plt.xlim(-0.5, xmax)
 518            ylim = plt.ylim()
 519            plt.ylim(bottom=0.0, top=max(1.0, ylim[1]))
 520            plt.draw()
 521            if save:
 522                fig.savefig(save + "_" + str(e))
 523
 524    def plot_rho(self, save=None):
 525        """Plot normalized autocorrelation function time for each ensemble.
 526
 527        Parameters
 528        ----------
 529        save : str
 530            saves the figure to a file named 'save' if.
 531        """
 532        if not hasattr(self, 'e_dvalue'):
 533            raise Exception('Run the gamma method first.')
 534        for e, e_name in enumerate(self.mc_names):
 535            fig = plt.figure()
 536            plt.xlabel('W')
 537            plt.ylabel('rho')
 538            length = int(len(self.e_drho[e_name]))
 539            plt.errorbar(np.arange(length), self.e_rho[e_name][:length], yerr=self.e_drho[e_name][:], linewidth=1, capsize=2)
 540            plt.axvline(x=self.e_windowsize[e_name], color='r', alpha=0.25, ls='--', marker=',')
 541            if self.tau_exp[e_name] > 0:
 542                plt.plot([self.e_windowsize[e_name] + 1, self.e_windowsize[e_name] + 1 + 2 * self.tau_exp[e_name]],
 543                         [self.e_rho[e_name][self.e_windowsize[e_name] + 1], 0], 'k-', lw=1)
 544                xmax = self.e_windowsize[e_name] + 2 * self.tau_exp[e_name] + 1.5
 545                plt.title('Rho ' + e_name + r', tau\_exp=' + str(np.around(self.tau_exp[e_name], decimals=2)))
 546            else:
 547                xmax = max(10.5, 2 * self.e_windowsize[e_name] - 0.5)
 548                plt.title('Rho ' + e_name + ', S=' + str(np.around(self.S[e_name], decimals=2)))
 549            plt.plot([-0.5, xmax], [0, 0], 'k--', lw=1)
 550            plt.xlim(-0.5, xmax)
 551            plt.draw()
 552            if save:
 553                fig.savefig(save + "_" + str(e))
 554
 555    def plot_rep_dist(self):
 556        """Plot replica distribution for each ensemble with more than one replicum."""
 557        if not hasattr(self, 'e_dvalue'):
 558            raise Exception('Run the gamma method first.')
 559        for e, e_name in enumerate(self.mc_names):
 560            if len(self.e_content[e_name]) == 1:
 561                print('No replica distribution for a single replicum (', e_name, ')')
 562                continue
 563            r_length = []
 564            sub_r_mean = 0
 565            for r, r_name in enumerate(self.e_content[e_name]):
 566                r_length.append(len(self.deltas[r_name]))
 567                sub_r_mean += self.shape[r_name] * self.r_values[r_name]
 568            e_N = np.sum(r_length)
 569            sub_r_mean /= e_N
 570            arr = np.zeros(len(self.e_content[e_name]))
 571            for r, r_name in enumerate(self.e_content[e_name]):
 572                arr[r] = (self.r_values[r_name] - sub_r_mean) / (self.e_dvalue[e_name] * np.sqrt(e_N / self.shape[r_name] - 1))
 573            plt.hist(arr, rwidth=0.8, bins=len(self.e_content[e_name]))
 574            plt.title('Replica distribution' + e_name + ' (mean=0, var=1)')
 575            plt.draw()
 576
 577    def plot_history(self, expand=True):
 578        """Plot derived Monte Carlo history for each ensemble
 579
 580        Parameters
 581        ----------
 582        expand : bool
 583            show expanded history for irregular Monte Carlo chains (default: True).
 584        """
 585        for e, e_name in enumerate(self.mc_names):
 586            plt.figure()
 587            r_length = []
 588            tmp = []
 589            tmp_expanded = []
 590            for r, r_name in enumerate(self.e_content[e_name]):
 591                tmp.append(self.deltas[r_name] + self.r_values[r_name])
 592                if expand:
 593                    tmp_expanded.append(_expand_deltas(self.deltas[r_name], list(self.idl[r_name]), self.shape[r_name], 1) + self.r_values[r_name])
 594                    r_length.append(len(tmp_expanded[-1]))
 595                else:
 596                    r_length.append(len(tmp[-1]))
 597            e_N = np.sum(r_length)
 598            x = np.arange(e_N)
 599            y_test = np.concatenate(tmp, axis=0)
 600            if expand:
 601                y = np.concatenate(tmp_expanded, axis=0)
 602            else:
 603                y = y_test
 604            plt.errorbar(x, y, fmt='.', markersize=3)
 605            plt.xlim(-0.5, e_N - 0.5)
 606            plt.title(e_name + f'\nskew: {skew(y_test):.3f} (p={skewtest(y_test).pvalue:.3f}), kurtosis: {kurtosis(y_test):.3f} (p={kurtosistest(y_test).pvalue:.3f})')
 607            plt.draw()
 608
 609    def plot_piechart(self, save=None):
 610        """Plot piechart which shows the fractional contribution of each
 611        ensemble to the error and returns a dictionary containing the fractions.
 612
 613        Parameters
 614        ----------
 615        save : str
 616            saves the figure to a file named 'save' if.
 617        """
 618        if not hasattr(self, 'e_dvalue'):
 619            raise Exception('Run the gamma method first.')
 620        if np.isclose(0.0, self._dvalue, atol=1e-15):
 621            raise Exception('Error is 0.0')
 622        labels = self.e_names
 623        sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
 624        fig1, ax1 = plt.subplots()
 625        ax1.pie(sizes, labels=labels, startangle=90, normalize=True)
 626        ax1.axis('equal')
 627        plt.draw()
 628        if save:
 629            fig1.savefig(save)
 630
 631        return dict(zip(labels, sizes))
 632
 633    def dump(self, filename, datatype="json.gz", description="", **kwargs):
 634        """Dump the Obs to a file 'name' of chosen format.
 635
 636        Parameters
 637        ----------
 638        filename : str
 639            name of the file to be saved.
 640        datatype : str
 641            Format of the exported file. Supported formats include
 642            "json.gz" and "pickle"
 643        description : str
 644            Description for output file, only relevant for json.gz format.
 645        path : str
 646            specifies a custom path for the file (default '.')
 647        """
 648        if 'path' in kwargs:
 649            file_name = kwargs.get('path') + '/' + filename
 650        else:
 651            file_name = filename
 652
 653        if datatype == "json.gz":
 654            from .input.json import dump_to_json
 655            dump_to_json([self], file_name, description=description)
 656        elif datatype == "pickle":
 657            with open(file_name + '.p', 'wb') as fb:
 658                pickle.dump(self, fb)
 659        else:
 660            raise Exception("Unknown datatype " + str(datatype))
 661
 662    def export_jackknife(self):
 663        """Export jackknife samples from the Obs
 664
 665        Returns
 666        -------
 667        numpy.ndarray
 668            Returns a numpy array of length N + 1 where N is the number of samples
 669            for the given ensemble and replicum. The zeroth entry of the array contains
 670            the mean value of the Obs, entries 1 to N contain the N jackknife samples
 671            derived from the Obs. The current implementation only works for observables
 672            defined on exactly one ensemble and replicum. The derived jackknife samples
 673            should agree with samples from a full jackknife analysis up to O(1/N).
 674        """
 675
 676        if len(self.names) != 1:
 677            raise Exception("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
 678
 679        name = self.names[0]
 680        full_data = self.deltas[name] + self.r_values[name]
 681        n = full_data.size
 682        mean = self.value
 683        tmp_jacks = np.zeros(n + 1)
 684        tmp_jacks[0] = mean
 685        tmp_jacks[1:] = (n * mean - full_data) / (n - 1)
 686        return tmp_jacks
 687
 688    def export_bootstrap(self, samples=500, random_numbers=None, save_rng=None):
 689        """Export bootstrap samples from the Obs
 690
 691        Parameters
 692        ----------
 693        samples : int
 694            Number of bootstrap samples to generate.
 695        random_numbers : np.ndarray
 696            Array of shape (samples, length) containing the random numbers to generate the bootstrap samples.
 697            If not provided the bootstrap samples are generated bashed on the md5 hash of the enesmble name.
 698        save_rng : str
 699            Save the random numbers to a file if a path is specified.
 700
 701        Returns
 702        -------
 703        numpy.ndarray
 704            Returns a numpy array of length N + 1 where N is the number of samples
 705            for the given ensemble and replicum. The zeroth entry of the array contains
 706            the mean value of the Obs, entries 1 to N contain the N import_bootstrap samples
 707            derived from the Obs. The current implementation only works for observables
 708            defined on exactly one ensemble and replicum. The derived bootstrap samples
 709            should agree with samples from a full bootstrap analysis up to O(1/N).
 710        """
 711        if len(self.names) != 1:
 712            raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
 713
 714        name = self.names[0]
 715        length = self.N
 716
 717        if random_numbers is None:
 718            seed = int(hashlib.md5(name.encode()).hexdigest(), 16) & 0xFFFFFFFF
 719            rng = np.random.default_rng(seed)
 720            random_numbers = rng.integers(0, length, size=(samples, length))
 721
 722        if save_rng is not None:
 723            np.savetxt(save_rng, random_numbers, fmt='%i')
 724
 725        proj = np.vstack([np.bincount(o, minlength=length) for o in random_numbers]) / length
 726        ret = np.zeros(samples + 1)
 727        ret[0] = self.value
 728        ret[1:] = proj @ (self.deltas[name] + self.r_values[name])
 729        return ret
 730
 731    def __float__(self):
 732        return float(self.value)
 733
 734    def __repr__(self):
 735        return 'Obs[' + str(self) + ']'
 736
 737    def __str__(self):
 738        return _format_uncertainty(self.value, self._dvalue)
 739
 740    def __format__(self, format_type):
 741        if format_type == "":
 742            significance = 2
 743        else:
 744            significance = int(float(format_type.replace("+", "").replace("-", "")))
 745        my_str = _format_uncertainty(self.value, self._dvalue,
 746                                     significance=significance)
 747        for char in ["+", " "]:
 748            if format_type.startswith(char):
 749                if my_str[0] != "-":
 750                    my_str = char + my_str
 751        return my_str
 752
 753    def __hash__(self):
 754        hash_tuple = (np.array([self.value]).astype(np.float32).data.tobytes(),)
 755        hash_tuple += tuple([o.astype(np.float32).data.tobytes() for o in self.deltas.values()])
 756        hash_tuple += tuple([np.array([o.errsq()]).astype(np.float32).data.tobytes() for o in self.covobs.values()])
 757        hash_tuple += tuple([o.encode() for o in self.names])
 758        m = hashlib.md5()
 759        [m.update(o) for o in hash_tuple]
 760        return int(m.hexdigest(), 16) & 0xFFFFFFFF
 761
 762    # Overload comparisons
 763    def __lt__(self, other):
 764        return self.value < other
 765
 766    def __le__(self, other):
 767        return self.value <= other
 768
 769    def __gt__(self, other):
 770        return self.value > other
 771
 772    def __ge__(self, other):
 773        return self.value >= other
 774
 775    def __eq__(self, other):
 776        return (self - other).is_zero()
 777
 778    def __ne__(self, other):
 779        return not (self - other).is_zero()
 780
 781    # Overload math operations
 782    def __add__(self, y):
 783        if isinstance(y, Obs):
 784            return derived_observable(lambda x, **kwargs: x[0] + x[1], [self, y], man_grad=[1, 1])
 785        else:
 786            if isinstance(y, np.ndarray):
 787                return np.array([self + o for o in y])
 788            elif y.__class__.__name__ in ['Corr', 'CObs']:
 789                return NotImplemented
 790            else:
 791                return derived_observable(lambda x, **kwargs: x[0] + y, [self], man_grad=[1])
 792
 793    def __radd__(self, y):
 794        return self + y
 795
 796    def __mul__(self, y):
 797        if isinstance(y, Obs):
 798            return derived_observable(lambda x, **kwargs: x[0] * x[1], [self, y], man_grad=[y.value, self.value])
 799        else:
 800            if isinstance(y, np.ndarray):
 801                return np.array([self * o for o in y])
 802            elif isinstance(y, complex):
 803                return CObs(self * y.real, self * y.imag)
 804            elif y.__class__.__name__ in ['Corr', 'CObs']:
 805                return NotImplemented
 806            else:
 807                return derived_observable(lambda x, **kwargs: x[0] * y, [self], man_grad=[y])
 808
 809    def __rmul__(self, y):
 810        return self * y
 811
 812    def __sub__(self, y):
 813        if isinstance(y, Obs):
 814            return derived_observable(lambda x, **kwargs: x[0] - x[1], [self, y], man_grad=[1, -1])
 815        else:
 816            if isinstance(y, np.ndarray):
 817                return np.array([self - o for o in y])
 818            elif y.__class__.__name__ in ['Corr', 'CObs']:
 819                return NotImplemented
 820            else:
 821                return derived_observable(lambda x, **kwargs: x[0] - y, [self], man_grad=[1])
 822
 823    def __rsub__(self, y):
 824        return -1 * (self - y)
 825
 826    def __pos__(self):
 827        return self
 828
 829    def __neg__(self):
 830        return -1 * self
 831
 832    def __truediv__(self, y):
 833        if isinstance(y, Obs):
 834            return derived_observable(lambda x, **kwargs: x[0] / x[1], [self, y], man_grad=[1 / y.value, - self.value / y.value ** 2])
 835        else:
 836            if isinstance(y, np.ndarray):
 837                return np.array([self / o for o in y])
 838            elif y.__class__.__name__ in ['Corr', 'CObs']:
 839                return NotImplemented
 840            else:
 841                return derived_observable(lambda x, **kwargs: x[0] / y, [self], man_grad=[1 / y])
 842
 843    def __rtruediv__(self, y):
 844        if isinstance(y, Obs):
 845            return derived_observable(lambda x, **kwargs: x[0] / x[1], [y, self], man_grad=[1 / self.value, - y.value / self.value ** 2])
 846        else:
 847            if isinstance(y, np.ndarray):
 848                return np.array([o / self for o in y])
 849            elif y.__class__.__name__ in ['Corr', 'CObs']:
 850                return NotImplemented
 851            else:
 852                return derived_observable(lambda x, **kwargs: y / x[0], [self], man_grad=[-y / self.value ** 2])
 853
 854    def __pow__(self, y):
 855        if isinstance(y, Obs):
 856            return derived_observable(lambda x: x[0] ** x[1], [self, y])
 857        else:
 858            return derived_observable(lambda x: x[0] ** y, [self])
 859
 860    def __rpow__(self, y):
 861        if isinstance(y, Obs):
 862            return derived_observable(lambda x: x[0] ** x[1], [y, self])
 863        else:
 864            return derived_observable(lambda x: y ** x[0], [self])
 865
 866    def __abs__(self):
 867        return derived_observable(lambda x: anp.abs(x[0]), [self])
 868
 869    # Overload numpy functions
 870    def sqrt(self):
 871        return derived_observable(lambda x, **kwargs: np.sqrt(x[0]), [self], man_grad=[1 / 2 / np.sqrt(self.value)])
 872
 873    def log(self):
 874        return derived_observable(lambda x, **kwargs: np.log(x[0]), [self], man_grad=[1 / self.value])
 875
 876    def exp(self):
 877        return derived_observable(lambda x, **kwargs: np.exp(x[0]), [self], man_grad=[np.exp(self.value)])
 878
 879    def sin(self):
 880        return derived_observable(lambda x, **kwargs: np.sin(x[0]), [self], man_grad=[np.cos(self.value)])
 881
 882    def cos(self):
 883        return derived_observable(lambda x, **kwargs: np.cos(x[0]), [self], man_grad=[-np.sin(self.value)])
 884
 885    def tan(self):
 886        return derived_observable(lambda x, **kwargs: np.tan(x[0]), [self], man_grad=[1 / np.cos(self.value) ** 2])
 887
 888    def arcsin(self):
 889        return derived_observable(lambda x: anp.arcsin(x[0]), [self])
 890
 891    def arccos(self):
 892        return derived_observable(lambda x: anp.arccos(x[0]), [self])
 893
 894    def arctan(self):
 895        return derived_observable(lambda x: anp.arctan(x[0]), [self])
 896
 897    def sinh(self):
 898        return derived_observable(lambda x, **kwargs: np.sinh(x[0]), [self], man_grad=[np.cosh(self.value)])
 899
 900    def cosh(self):
 901        return derived_observable(lambda x, **kwargs: np.cosh(x[0]), [self], man_grad=[np.sinh(self.value)])
 902
 903    def tanh(self):
 904        return derived_observable(lambda x, **kwargs: np.tanh(x[0]), [self], man_grad=[1 / np.cosh(self.value) ** 2])
 905
 906    def arcsinh(self):
 907        return derived_observable(lambda x: anp.arcsinh(x[0]), [self])
 908
 909    def arccosh(self):
 910        return derived_observable(lambda x: anp.arccosh(x[0]), [self])
 911
 912    def arctanh(self):
 913        return derived_observable(lambda x: anp.arctanh(x[0]), [self])
 914
 915
 916class CObs:
 917    """Class for a complex valued observable."""
 918    __slots__ = ['_real', '_imag', 'tag']
 919
 920    def __init__(self, real, imag=0.0):
 921        self._real = real
 922        self._imag = imag
 923        self.tag = None
 924
 925    @property
 926    def real(self):
 927        return self._real
 928
 929    @property
 930    def imag(self):
 931        return self._imag
 932
 933    def gamma_method(self, **kwargs):
 934        """Executes the gamma_method for the real and the imaginary part."""
 935        if isinstance(self.real, Obs):
 936            self.real.gamma_method(**kwargs)
 937        if isinstance(self.imag, Obs):
 938            self.imag.gamma_method(**kwargs)
 939
 940    def is_zero(self):
 941        """Checks whether both real and imaginary part are zero within machine precision."""
 942        return self.real == 0.0 and self.imag == 0.0
 943
 944    def conjugate(self):
 945        return CObs(self.real, -self.imag)
 946
 947    def __add__(self, other):
 948        if isinstance(other, np.ndarray):
 949            return other + self
 950        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 951            return CObs(self.real + other.real,
 952                        self.imag + other.imag)
 953        else:
 954            return CObs(self.real + other, self.imag)
 955
 956    def __radd__(self, y):
 957        return self + y
 958
 959    def __sub__(self, other):
 960        if isinstance(other, np.ndarray):
 961            return -1 * (other - self)
 962        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 963            return CObs(self.real - other.real, self.imag - other.imag)
 964        else:
 965            return CObs(self.real - other, self.imag)
 966
 967    def __rsub__(self, other):
 968        return -1 * (self - other)
 969
 970    def __mul__(self, other):
 971        if isinstance(other, np.ndarray):
 972            return other * self
 973        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 974            if all(isinstance(i, Obs) for i in [self.real, self.imag, other.real, other.imag]):
 975                return CObs(derived_observable(lambda x, **kwargs: x[0] * x[1] - x[2] * x[3],
 976                                               [self.real, other.real, self.imag, other.imag],
 977                                               man_grad=[other.real.value, self.real.value, -other.imag.value, -self.imag.value]),
 978                            derived_observable(lambda x, **kwargs: x[2] * x[1] + x[0] * x[3],
 979                                               [self.real, other.real, self.imag, other.imag],
 980                                               man_grad=[other.imag.value, self.imag.value, other.real.value, self.real.value]))
 981            elif getattr(other, 'imag', 0) != 0:
 982                return CObs(self.real * other.real - self.imag * other.imag,
 983                            self.imag * other.real + self.real * other.imag)
 984            else:
 985                return CObs(self.real * other.real, self.imag * other.real)
 986        else:
 987            return CObs(self.real * other, self.imag * other)
 988
 989    def __rmul__(self, other):
 990        return self * other
 991
 992    def __truediv__(self, other):
 993        if isinstance(other, np.ndarray):
 994            return 1 / (other / self)
 995        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 996            r = other.real ** 2 + other.imag ** 2
 997            return CObs((self.real * other.real + self.imag * other.imag) / r, (self.imag * other.real - self.real * other.imag) / r)
 998        else:
 999            return CObs(self.real / other, self.imag / other)
1000
1001    def __rtruediv__(self, other):
1002        r = self.real ** 2 + self.imag ** 2
1003        if hasattr(other, 'real') and hasattr(other, 'imag'):
1004            return CObs((self.real * other.real + self.imag * other.imag) / r, (self.real * other.imag - self.imag * other.real) / r)
1005        else:
1006            return CObs(self.real * other / r, -self.imag * other / r)
1007
1008    def __abs__(self):
1009        return np.sqrt(self.real**2 + self.imag**2)
1010
1011    def __pos__(self):
1012        return self
1013
1014    def __neg__(self):
1015        return -1 * self
1016
1017    def __eq__(self, other):
1018        return self.real == other.real and self.imag == other.imag
1019
1020    def __str__(self):
1021        return '(' + str(self.real) + int(self.imag >= 0.0) * '+' + str(self.imag) + 'j)'
1022
1023    def __repr__(self):
1024        return 'CObs[' + str(self) + ']'
1025
1026
1027def _format_uncertainty(value, dvalue, significance=2):
1028    """Creates a string of a value and its error in paranthesis notation, e.g., 13.02(45)"""
1029    if dvalue == 0.0 or (not np.isfinite(dvalue)):
1030        return str(value)
1031    if not isinstance(significance, int):
1032        raise TypeError("significance needs to be an integer.")
1033    if significance < 1:
1034        raise ValueError("significance needs to be larger than zero.")
1035    fexp = np.floor(np.log10(dvalue))
1036    if fexp < 0.0:
1037        return '{:{form}}({:1.0f})'.format(value, dvalue * 10 ** (-fexp + significance - 1), form='.' + str(-int(fexp) + significance - 1) + 'f')
1038    elif fexp == 0.0:
1039        return f"{value:.{significance - 1}f}({dvalue:1.{significance - 1}f})"
1040    else:
1041        return f"{value:.{max(0, int(significance - fexp - 1))}f}({dvalue:2.{max(0, int(significance - fexp - 1))}f})"
1042
1043
1044def _expand_deltas(deltas, idx, shape, gapsize):
1045    """Expand deltas defined on idx to a regular range with spacing gapsize between two
1046       configurations and where holes are filled by 0.
1047       If idx is of type range, the deltas are not changed if the idx.step == gapsize.
1048
1049    Parameters
1050    ----------
1051    deltas : list
1052        List of fluctuations
1053    idx : list
1054        List or range of configs on which the deltas are defined, has to be sorted in ascending order.
1055    shape : int
1056        Number of configs in idx.
1057    gapsize : int
1058        The target distance between two configurations. If longer distances
1059        are found in idx, the data is expanded.
1060    """
1061    if isinstance(idx, range):
1062        if (idx.step == gapsize):
1063            return deltas
1064    ret = np.zeros((idx[-1] - idx[0] + gapsize) // gapsize)
1065    for i in range(shape):
1066        ret[(idx[i] - idx[0]) // gapsize] = deltas[i]
1067    return ret
1068
1069
1070def _merge_idx(idl):
1071    """Returns the union of all lists in idl as range or sorted list
1072
1073    Parameters
1074    ----------
1075    idl : list
1076        List of lists or ranges.
1077    """
1078
1079    if _check_lists_equal(idl):
1080        return idl[0]
1081
1082    idunion = sorted(set().union(*idl))
1083
1084    # Check whether idunion can be expressed as range
1085    idrange = range(idunion[0], idunion[-1] + 1, idunion[1] - idunion[0])
1086    idtest = [list(idrange), idunion]
1087    if _check_lists_equal(idtest):
1088        return idrange
1089
1090    return idunion
1091
1092
1093def _intersection_idx(idl):
1094    """Returns the intersection of all lists in idl as range or sorted list
1095
1096    Parameters
1097    ----------
1098    idl : list
1099        List of lists or ranges.
1100    """
1101
1102    if _check_lists_equal(idl):
1103        return idl[0]
1104
1105    idinter = sorted(set.intersection(*[set(o) for o in idl]))
1106
1107    # Check whether idinter can be expressed as range
1108    try:
1109        idrange = range(idinter[0], idinter[-1] + 1, idinter[1] - idinter[0])
1110        idtest = [list(idrange), idinter]
1111        if _check_lists_equal(idtest):
1112            return idrange
1113    except IndexError:
1114        pass
1115
1116    return idinter
1117
1118
1119def _expand_deltas_for_merge(deltas, idx, shape, new_idx):
1120    """Expand deltas defined on idx to the list of configs that is defined by new_idx.
1121       New, empty entries are filled by 0. If idx and new_idx are of type range, the smallest
1122       common divisor of the step sizes is used as new step size.
1123
1124    Parameters
1125    ----------
1126    deltas : list
1127        List of fluctuations
1128    idx : list
1129        List or range of configs on which the deltas are defined.
1130        Has to be a subset of new_idx and has to be sorted in ascending order.
1131    shape : list
1132        Number of configs in idx.
1133    new_idx : list
1134        List of configs that defines the new range, has to be sorted in ascending order.
1135    """
1136
1137    if type(idx) is range and type(new_idx) is range:
1138        if idx == new_idx:
1139            return deltas
1140    ret = np.zeros(new_idx[-1] - new_idx[0] + 1)
1141    for i in range(shape):
1142        ret[idx[i] - new_idx[0]] = deltas[i]
1143    return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))]) * len(new_idx) / len(idx)
1144
1145
1146def derived_observable(func, data, array_mode=False, **kwargs):
1147    """Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.
1148
1149    Parameters
1150    ----------
1151    func : object
1152        arbitrary function of the form func(data, **kwargs). For the
1153        automatic differentiation to work, all numpy functions have to have
1154        the autograd wrapper (use 'import autograd.numpy as anp').
1155    data : list
1156        list of Obs, e.g. [obs1, obs2, obs3].
1157    num_grad : bool
1158        if True, numerical derivatives are used instead of autograd
1159        (default False). To control the numerical differentiation the
1160        kwargs of numdifftools.step_generators.MaxStepGenerator
1161        can be used.
1162    man_grad : list
1163        manually supply a list or an array which contains the jacobian
1164        of func. Use cautiously, supplying the wrong derivative will
1165        not be intercepted.
1166
1167    Notes
1168    -----
1169    For simple mathematical operations it can be practical to use anonymous
1170    functions. For the ratio of two observables one can e.g. use
1171
1172    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])
1173    """
1174
1175    data = np.asarray(data)
1176    raveled_data = data.ravel()
1177
1178    # Workaround for matrix operations containing non Obs data
1179    if not all(isinstance(x, Obs) for x in raveled_data):
1180        for i in range(len(raveled_data)):
1181            if isinstance(raveled_data[i], (int, float)):
1182                raveled_data[i] = cov_Obs(raveled_data[i], 0.0, "###dummy_covobs###")
1183
1184    allcov = {}
1185    for o in raveled_data:
1186        for name in o.cov_names:
1187            if name in allcov:
1188                if not np.allclose(allcov[name], o.covobs[name].cov):
1189                    raise Exception('Inconsistent covariance matrices for %s!' % (name))
1190            else:
1191                allcov[name] = o.covobs[name].cov
1192
1193    n_obs = len(raveled_data)
1194    new_names = sorted(set([y for x in [o.names for o in raveled_data] for y in x]))
1195    new_cov_names = sorted(set([y for x in [o.cov_names for o in raveled_data] for y in x]))
1196    new_sample_names = sorted(set(new_names) - set(new_cov_names))
1197
1198    reweighted = len(list(filter(lambda o: o.reweighted is True, raveled_data))) > 0
1199
1200    if data.ndim == 1:
1201        values = np.array([o.value for o in data])
1202    else:
1203        values = np.vectorize(lambda x: x.value)(data)
1204
1205    new_values = func(values, **kwargs)
1206
1207    multi = int(isinstance(new_values, np.ndarray))
1208
1209    new_r_values = {}
1210    new_idl_d = {}
1211    for name in new_sample_names:
1212        idl = []
1213        tmp_values = np.zeros(n_obs)
1214        for i, item in enumerate(raveled_data):
1215            tmp_values[i] = item.r_values.get(name, item.value)
1216            tmp_idl = item.idl.get(name)
1217            if tmp_idl is not None:
1218                idl.append(tmp_idl)
1219        if multi > 0:
1220            tmp_values = np.array(tmp_values).reshape(data.shape)
1221        new_r_values[name] = func(tmp_values, **kwargs)
1222        new_idl_d[name] = _merge_idx(idl)
1223
1224    if 'man_grad' in kwargs:
1225        deriv = np.asarray(kwargs.get('man_grad'))
1226        if new_values.shape + data.shape != deriv.shape:
1227            raise Exception('Manual derivative does not have correct shape.')
1228    elif kwargs.get('num_grad') is True:
1229        if multi > 0:
1230            raise Exception('Multi mode currently not supported for numerical derivative')
1231        options = {
1232            'base_step': 0.1,
1233            'step_ratio': 2.5}
1234        for key in options.keys():
1235            kwarg = kwargs.get(key)
1236            if kwarg is not None:
1237                options[key] = kwarg
1238        tmp_df = nd.Gradient(func, order=4, **{k: v for k, v in options.items() if v is not None})(values, **kwargs)
1239        if tmp_df.size == 1:
1240            deriv = np.array([tmp_df.real])
1241        else:
1242            deriv = tmp_df.real
1243    else:
1244        deriv = jacobian(func)(values, **kwargs)
1245
1246    final_result = np.zeros(new_values.shape, dtype=object)
1247
1248    if array_mode is True:
1249
1250        class _Zero_grad():
1251            def __init__(self, N):
1252                self.grad = np.zeros((N, 1))
1253
1254        new_covobs_lengths = dict(set([y for x in [[(n, o.covobs[n].N) for n in o.cov_names] for o in raveled_data] for y in x]))
1255        d_extracted = {}
1256        g_extracted = {}
1257        for name in new_sample_names:
1258            d_extracted[name] = []
1259            ens_length = len(new_idl_d[name])
1260            for i_dat, dat in enumerate(data):
1261                d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas.get(name, np.zeros(ens_length)), o.idl.get(name, new_idl_d[name]), o.shape.get(name, ens_length), new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
1262        for name in new_cov_names:
1263            g_extracted[name] = []
1264            zero_grad = _Zero_grad(new_covobs_lengths[name])
1265            for i_dat, dat in enumerate(data):
1266                g_extracted[name].append(np.array([o.covobs.get(name, zero_grad).grad for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (new_covobs_lengths[name], 1)))
1267
1268    for i_val, new_val in np.ndenumerate(new_values):
1269        new_deltas = {}
1270        new_grad = {}
1271        if array_mode is True:
1272            for name in new_sample_names:
1273                ens_length = d_extracted[name][0].shape[-1]
1274                new_deltas[name] = np.zeros(ens_length)
1275                for i_dat, dat in enumerate(d_extracted[name]):
1276                    new_deltas[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
1277            for name in new_cov_names:
1278                new_grad[name] = 0
1279                for i_dat, dat in enumerate(g_extracted[name]):
1280                    new_grad[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
1281        else:
1282            for j_obs, obs in np.ndenumerate(data):
1283                for name in obs.names:
1284                    if name in obs.cov_names:
1285                        new_grad[name] = new_grad.get(name, 0) + deriv[i_val + j_obs] * obs.covobs[name].grad
1286                    else:
1287                        new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name])
1288
1289        new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad}
1290
1291        if not set(new_covobs.keys()).isdisjoint(new_deltas.keys()):
1292            raise Exception('The same name has been used for deltas and covobs!')
1293        new_samples = []
1294        new_means = []
1295        new_idl = []
1296        new_names_obs = []
1297        for name in new_names:
1298            if name not in new_covobs:
1299                new_samples.append(new_deltas[name])
1300                new_idl.append(new_idl_d[name])
1301                new_means.append(new_r_values[name][i_val])
1302                new_names_obs.append(name)
1303        final_result[i_val] = Obs(new_samples, new_names_obs, means=new_means, idl=new_idl)
1304        for name in new_covobs:
1305            final_result[i_val].names.append(name)
1306        final_result[i_val]._covobs = new_covobs
1307        final_result[i_val]._value = new_val
1308        final_result[i_val].reweighted = reweighted
1309
1310    if multi == 0:
1311        final_result = final_result.item()
1312
1313    return final_result
1314
1315
1316def _reduce_deltas(deltas, idx_old, idx_new):
1317    """Extract deltas defined on idx_old on all configs of idx_new.
1318
1319    Assumes, that idx_old and idx_new are correctly defined idl, i.e., they
1320    are ordered in an ascending order.
1321
1322    Parameters
1323    ----------
1324    deltas : list
1325        List of fluctuations
1326    idx_old : list
1327        List or range of configs on which the deltas are defined
1328    idx_new : list
1329        List of configs for which we want to extract the deltas.
1330        Has to be a subset of idx_old.
1331    """
1332    if not len(deltas) == len(idx_old):
1333        raise Exception('Length of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
1334    if type(idx_old) is range and type(idx_new) is range:
1335        if idx_old == idx_new:
1336            return deltas
1337    if _check_lists_equal([idx_old, idx_new]):
1338        return deltas
1339    indices = np.intersect1d(idx_old, idx_new, assume_unique=True, return_indices=True)[1]
1340    if len(indices) < len(idx_new):
1341        raise Exception('Error in _reduce_deltas: Config of idx_new not in idx_old')
1342    return np.array(deltas)[indices]
1343
1344
1345def reweight(weight, obs, **kwargs):
1346    """Reweight a list of observables.
1347
1348    Parameters
1349    ----------
1350    weight : Obs
1351        Reweighting factor. An Observable that has to be defined on a superset of the
1352        configurations in obs[i].idl for all i.
1353    obs : list
1354        list of Obs, e.g. [obs1, obs2, obs3].
1355    all_configs : bool
1356        if True, the reweighted observables are normalized by the average of
1357        the reweighting factor on all configurations in weight.idl and not
1358        on the configurations in obs[i].idl. Default False.
1359    """
1360    result = []
1361    for i in range(len(obs)):
1362        if len(obs[i].cov_names):
1363            raise Exception('Error: Not possible to reweight an Obs that contains covobs!')
1364        if not set(obs[i].names).issubset(weight.names):
1365            raise Exception('Error: Ensembles do not fit')
1366        for name in obs[i].names:
1367            if not set(obs[i].idl[name]).issubset(weight.idl[name]):
1368                raise Exception('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
1369        new_samples = []
1370        w_deltas = {}
1371        for name in sorted(obs[i].names):
1372            w_deltas[name] = _reduce_deltas(weight.deltas[name], weight.idl[name], obs[i].idl[name])
1373            new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name]))
1374        tmp_obs = Obs(new_samples, sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
1375
1376        if kwargs.get('all_configs'):
1377            new_weight = weight
1378        else:
1379            new_weight = Obs([w_deltas[name] + weight.r_values[name] for name in sorted(obs[i].names)], sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
1380
1381        result.append(tmp_obs / new_weight)
1382        result[-1].reweighted = True
1383
1384    return result
1385
1386
1387def correlate(obs_a, obs_b):
1388    """Correlate two observables.
1389
1390    Parameters
1391    ----------
1392    obs_a : Obs
1393        First observable
1394    obs_b : Obs
1395        Second observable
1396
1397    Notes
1398    -----
1399    Keep in mind to only correlate primary observables which have not been reweighted
1400    yet. The reweighting has to be applied after correlating the observables.
1401    Currently only works if ensembles are identical (this is not strictly necessary).
1402    """
1403
1404    if sorted(obs_a.names) != sorted(obs_b.names):
1405        raise Exception(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
1406    if len(obs_a.cov_names) or len(obs_b.cov_names):
1407        raise Exception('Error: Not possible to correlate Obs that contain covobs!')
1408    for name in obs_a.names:
1409        if obs_a.shape[name] != obs_b.shape[name]:
1410            raise Exception('Shapes of ensemble', name, 'do not fit')
1411        if obs_a.idl[name] != obs_b.idl[name]:
1412            raise Exception('idl of ensemble', name, 'do not fit')
1413
1414    if obs_a.reweighted is True:
1415        warnings.warn("The first observable is already reweighted.", RuntimeWarning)
1416    if obs_b.reweighted is True:
1417        warnings.warn("The second observable is already reweighted.", RuntimeWarning)
1418
1419    new_samples = []
1420    new_idl = []
1421    for name in sorted(obs_a.names):
1422        new_samples.append((obs_a.deltas[name] + obs_a.r_values[name]) * (obs_b.deltas[name] + obs_b.r_values[name]))
1423        new_idl.append(obs_a.idl[name])
1424
1425    o = Obs(new_samples, sorted(obs_a.names), idl=new_idl)
1426    o.reweighted = obs_a.reweighted or obs_b.reweighted
1427    return o
1428
1429
1430def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
1431    r'''Calculates the error covariance matrix of a set of observables.
1432
1433    WARNING: This function should be used with care, especially for observables with support on multiple
1434             ensembles with differing autocorrelations. See the notes below for details.
1435
1436    The gamma method has to be applied first to all observables.
1437
1438    Parameters
1439    ----------
1440    obs : list or numpy.ndarray
1441        List or one dimensional array of Obs
1442    visualize : bool
1443        If True plots the corresponding normalized correlation matrix (default False).
1444    correlation : bool
1445        If True the correlation matrix instead of the error covariance matrix is returned (default False).
1446    smooth : None or int
1447        If smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue
1448        smoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the
1449        largest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely
1450        small ones.
1451
1452    Notes
1453    -----
1454    The error covariance is defined such that it agrees with the squared standard error for two identical observables
1455    $$\operatorname{cov}(a,a)=\sum_{s=1}^N\delta_a^s\delta_a^s/N^2=\Gamma_{aa}(0)/N=\operatorname{var}(a)/N=\sigma_a^2$$
1456    in the absence of autocorrelation.
1457    The error covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite
1458    $$\sum_{i,j}v_i\Gamma_{ij}(0)v_j=\frac{1}{N}\sum_{s=1}^N\sum_{i,j}v_i\delta_i^s\delta_j^s v_j=\frac{1}{N}\sum_{s=1}^N\sum_{i}|v_i\delta_i^s|^2\geq 0\,,$$ for every $v\in\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.
1459    For observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.
1460    $$\tau_{\mathrm{int}, ij}=\sqrt{\tau_{\mathrm{int}, i}\times \tau_{\mathrm{int}, j}}$$
1461    This construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).
1462    '''
1463
1464    length = len(obs)
1465
1466    max_samples = np.max([o.N for o in obs])
1467    if max_samples <= length and not [item for sublist in [o.cov_names for o in obs] for item in sublist]:
1468        warnings.warn(f"The dimension of the covariance matrix ({length}) is larger or equal to the number of samples ({max_samples}). This will result in a rank deficient matrix.", RuntimeWarning)
1469
1470    cov = np.zeros((length, length))
1471    for i in range(length):
1472        for j in range(i, length):
1473            cov[i, j] = _covariance_element(obs[i], obs[j])
1474    cov = cov + cov.T - np.diag(np.diag(cov))
1475
1476    corr = np.diag(1 / np.sqrt(np.diag(cov))) @ cov @ np.diag(1 / np.sqrt(np.diag(cov)))
1477
1478    if isinstance(smooth, int):
1479        corr = _smooth_eigenvalues(corr, smooth)
1480
1481    if visualize:
1482        plt.matshow(corr, vmin=-1, vmax=1)
1483        plt.set_cmap('RdBu')
1484        plt.colorbar()
1485        plt.draw()
1486
1487    if correlation is True:
1488        return corr
1489
1490    errors = [o.dvalue for o in obs]
1491    cov = np.diag(errors) @ corr @ np.diag(errors)
1492
1493    eigenvalues = np.linalg.eigh(cov)[0]
1494    if not np.all(eigenvalues >= 0):
1495        warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
1496
1497    return cov
1498
1499
1500def _smooth_eigenvalues(corr, E):
1501    """Eigenvalue smoothing as described in hep-lat/9412087
1502
1503    corr : np.ndarray
1504        correlation matrix
1505    E : integer
1506        Number of eigenvalues to be left substantially unchanged
1507    """
1508    if not (2 < E < corr.shape[0] - 1):
1509        raise Exception(f"'E' has to be between 2 and the dimension of the correlation matrix minus 1 ({corr.shape[0] - 1}).")
1510    vals, vec = np.linalg.eigh(corr)
1511    lambda_min = np.mean(vals[:-E])
1512    vals[vals < lambda_min] = lambda_min
1513    vals /= np.mean(vals)
1514    return vec @ np.diag(vals) @ vec.T
1515
1516
1517def _covariance_element(obs1, obs2):
1518    """Estimates the covariance of two Obs objects, neglecting autocorrelations."""
1519
1520    def calc_gamma(deltas1, deltas2, idx1, idx2, new_idx):
1521        deltas1 = _reduce_deltas(deltas1, idx1, new_idx)
1522        deltas2 = _reduce_deltas(deltas2, idx2, new_idx)
1523        return np.sum(deltas1 * deltas2)
1524
1525    if set(obs1.names).isdisjoint(set(obs2.names)):
1526        return 0.0
1527
1528    if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
1529        raise Exception('The gamma method has to be applied to both Obs first.')
1530
1531    dvalue = 0.0
1532
1533    for e_name in obs1.mc_names:
1534
1535        if e_name not in obs2.mc_names:
1536            continue
1537
1538        idl_d = {}
1539        for r_name in obs1.e_content[e_name]:
1540            if r_name not in obs2.e_content[e_name]:
1541                continue
1542            idl_d[r_name] = _intersection_idx([obs1.idl[r_name], obs2.idl[r_name]])
1543
1544        gamma = 0.0
1545
1546        for r_name in obs1.e_content[e_name]:
1547            if r_name not in obs2.e_content[e_name]:
1548                continue
1549            if len(idl_d[r_name]) == 0:
1550                continue
1551            gamma += calc_gamma(obs1.deltas[r_name], obs2.deltas[r_name], obs1.idl[r_name], obs2.idl[r_name], idl_d[r_name])
1552
1553        if gamma == 0.0:
1554            continue
1555
1556        gamma_div = 0.0
1557        for r_name in obs1.e_content[e_name]:
1558            if r_name not in obs2.e_content[e_name]:
1559                continue
1560            if len(idl_d[r_name]) == 0:
1561                continue
1562            gamma_div += np.sqrt(calc_gamma(obs1.deltas[r_name], obs1.deltas[r_name], obs1.idl[r_name], obs1.idl[r_name], idl_d[r_name]) * calc_gamma(obs2.deltas[r_name], obs2.deltas[r_name], obs2.idl[r_name], obs2.idl[r_name], idl_d[r_name]))
1563        gamma /= gamma_div
1564
1565        dvalue += gamma
1566
1567    for e_name in obs1.cov_names:
1568
1569        if e_name not in obs2.cov_names:
1570            continue
1571
1572        dvalue += np.dot(np.transpose(obs1.covobs[e_name].grad), np.dot(obs1.covobs[e_name].cov, obs2.covobs[e_name].grad)).item()
1573
1574    return dvalue
1575
1576
1577def import_jackknife(jacks, name, idl=None):
1578    """Imports jackknife samples and returns an Obs
1579
1580    Parameters
1581    ----------
1582    jacks : numpy.ndarray
1583        numpy array containing the mean value as zeroth entry and
1584        the N jackknife samples as first to Nth entry.
1585    name : str
1586        name of the ensemble the samples are defined on.
1587    """
1588    length = len(jacks) - 1
1589    prj = (np.ones((length, length)) - (length - 1) * np.identity(length))
1590    samples = jacks[1:] @ prj
1591    mean = np.mean(samples)
1592    new_obs = Obs([samples - mean], [name], idl=idl, means=[mean])
1593    new_obs._value = jacks[0]
1594    return new_obs
1595
1596
1597def import_bootstrap(boots, name, random_numbers):
1598    """Imports bootstrap samples and returns an Obs
1599
1600    Parameters
1601    ----------
1602    boots : numpy.ndarray
1603        numpy array containing the mean value as zeroth entry and
1604        the N bootstrap samples as first to Nth entry.
1605    name : str
1606        name of the ensemble the samples are defined on.
1607    random_numbers : np.ndarray
1608        Array of shape (samples, length) containing the random numbers to generate the bootstrap samples,
1609        where samples is the number of bootstrap samples and length is the length of the original Monte Carlo
1610        chain to be reconstructed.
1611    """
1612    samples, length = random_numbers.shape
1613    if samples != len(boots) - 1:
1614        raise ValueError("Random numbers do not have the correct shape.")
1615
1616    if samples < length:
1617        raise ValueError("Obs can't be reconstructed if there are fewer bootstrap samples than Monte Carlo data points.")
1618
1619    proj = np.vstack([np.bincount(o, minlength=length) for o in random_numbers]) / length
1620
1621    samples = scipy.linalg.lstsq(proj, boots[1:])[0]
1622    ret = Obs([samples], [name])
1623    ret._value = boots[0]
1624    return ret
1625
1626
1627def merge_obs(list_of_obs):
1628    """Combine all observables in list_of_obs into one new observable
1629
1630    Parameters
1631    ----------
1632    list_of_obs : list
1633        list of the Obs object to be combined
1634
1635    Notes
1636    -----
1637    It is not possible to combine obs which are based on the same replicum
1638    """
1639    replist = [item for obs in list_of_obs for item in obs.names]
1640    if (len(replist) == len(set(replist))) is False:
1641        raise Exception('list_of_obs contains duplicate replica: %s' % (str(replist)))
1642    if any([len(o.cov_names) for o in list_of_obs]):
1643        raise Exception('Not possible to merge data that contains covobs!')
1644    new_dict = {}
1645    idl_dict = {}
1646    for o in list_of_obs:
1647        new_dict.update({key: o.deltas.get(key, 0) + o.r_values.get(key, 0)
1648                        for key in set(o.deltas) | set(o.r_values)})
1649        idl_dict.update({key: o.idl.get(key, 0) for key in set(o.deltas)})
1650
1651    names = sorted(new_dict.keys())
1652    o = Obs([new_dict[name] for name in names], names, idl=[idl_dict[name] for name in names])
1653    o.reweighted = np.max([oi.reweighted for oi in list_of_obs])
1654    return o
1655
1656
1657def cov_Obs(means, cov, name, grad=None):
1658    """Create an Obs based on mean(s) and a covariance matrix
1659
1660    Parameters
1661    ----------
1662    mean : list of floats or float
1663        N mean value(s) of the new Obs
1664    cov : list or array
1665        2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
1666    name : str
1667        identifier for the covariance matrix
1668    grad : list or array
1669        Gradient of the Covobs wrt. the means belonging to cov.
1670    """
1671
1672    def covobs_to_obs(co):
1673        """Make an Obs out of a Covobs
1674
1675        Parameters
1676        ----------
1677        co : Covobs
1678            Covobs to be embedded into the Obs
1679        """
1680        o = Obs([], [], means=[])
1681        o._value = co.value
1682        o.names.append(co.name)
1683        o._covobs[co.name] = co
1684        o._dvalue = np.sqrt(co.errsq())
1685        return o
1686
1687    ol = []
1688    if isinstance(means, (float, int)):
1689        means = [means]
1690
1691    for i in range(len(means)):
1692        ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad)))
1693    if ol[0].covobs[name].N != len(means):
1694        raise Exception('You have to provide %d mean values!' % (ol[0].N))
1695    if len(ol) == 1:
1696        return ol[0]
1697    return ol
1698
1699
1700def _determine_gap(o, e_content, e_name):
1701    gaps = []
1702    for r_name in e_content[e_name]:
1703        if isinstance(o.idl[r_name], range):
1704            gaps.append(o.idl[r_name].step)
1705        else:
1706            gaps.append(np.min(np.diff(o.idl[r_name])))
1707
1708    gap = min(gaps)
1709    if not np.all([gi % gap == 0 for gi in gaps]):
1710        raise Exception(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
1711
1712    return gap
1713
1714
1715def _check_lists_equal(idl):
1716    '''
1717    Use groupby to efficiently check whether all elements of idl are identical.
1718    Returns True if all elements are equal, otherwise False.
1719
1720    Parameters
1721    ----------
1722    idl : list of lists, ranges or np.ndarrays
1723    '''
1724    g = groupby([np.nditer(el) if isinstance(el, np.ndarray) else el for el in idl])
1725    if next(g, True) and not next(g, False):
1726        return True
1727    return False
class Obs:
 19class Obs:
 20    """Class for a general observable.
 21
 22    Instances of Obs are the basic objects of a pyerrors error analysis.
 23    They are initialized with a list which contains arrays of samples for
 24    different ensembles/replica and another list of same length which contains
 25    the names of the ensembles/replica. Mathematical operations can be
 26    performed on instances. The result is another instance of Obs. The error of
 27    an instance can be computed with the gamma_method. Also contains additional
 28    methods for output and visualization of the error calculation.
 29
 30    Attributes
 31    ----------
 32    S_global : float
 33        Standard value for S (default 2.0)
 34    S_dict : dict
 35        Dictionary for S values. If an entry for a given ensemble
 36        exists this overwrites the standard value for that ensemble.
 37    tau_exp_global : float
 38        Standard value for tau_exp (default 0.0)
 39    tau_exp_dict : dict
 40        Dictionary for tau_exp values. If an entry for a given ensemble exists
 41        this overwrites the standard value for that ensemble.
 42    N_sigma_global : float
 43        Standard value for N_sigma (default 1.0)
 44    N_sigma_dict : dict
 45        Dictionary for N_sigma values. If an entry for a given ensemble exists
 46        this overwrites the standard value for that ensemble.
 47    """
 48    __slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
 49                 'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
 50                 'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
 51                 'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
 52                 'idl', 'tag', '_covobs', '__dict__']
 53
 54    S_global = 2.0
 55    S_dict = {}
 56    tau_exp_global = 0.0
 57    tau_exp_dict = {}
 58    N_sigma_global = 1.0
 59    N_sigma_dict = {}
 60
 61    def __init__(self, samples, names, idl=None, **kwargs):
 62        """ Initialize Obs object.
 63
 64        Parameters
 65        ----------
 66        samples : list
 67            list of numpy arrays containing the Monte Carlo samples
 68        names : list
 69            list of strings labeling the individual samples
 70        idl : list, optional
 71            list of ranges or lists on which the samples are defined
 72        """
 73
 74        if kwargs.get("means") is None and len(samples):
 75            if len(samples) != len(names):
 76                raise ValueError('Length of samples and names incompatible.')
 77            if idl is not None:
 78                if len(idl) != len(names):
 79                    raise ValueError('Length of idl incompatible with samples and names.')
 80            name_length = len(names)
 81            if name_length > 1:
 82                if name_length != len(set(names)):
 83                    raise ValueError('Names are not unique.')
 84                if not all(isinstance(x, str) for x in names):
 85                    raise TypeError('All names have to be strings.')
 86            else:
 87                if not isinstance(names[0], str):
 88                    raise TypeError('All names have to be strings.')
 89            if min(len(x) for x in samples) <= 4:
 90                raise ValueError('Samples have to have at least 5 entries.')
 91
 92        self.names = sorted(names)
 93        self.shape = {}
 94        self.r_values = {}
 95        self.deltas = {}
 96        self._covobs = {}
 97
 98        self._value = 0
 99        self.N = 0
100        self.idl = {}
101        if idl is not None:
102            for name, idx in sorted(zip(names, idl)):
103                if isinstance(idx, range):
104                    self.idl[name] = idx
105                elif isinstance(idx, (list, np.ndarray)):
106                    dc = np.unique(np.diff(idx))
107                    if np.any(dc < 0):
108                        raise ValueError("Unsorted idx for idl[%s]" % (name))
109                    if len(dc) == 1:
110                        self.idl[name] = range(idx[0], idx[-1] + dc[0], dc[0])
111                    else:
112                        self.idl[name] = list(idx)
113                else:
114                    raise TypeError('incompatible type for idl[%s].' % (name))
115        else:
116            for name, sample in sorted(zip(names, samples)):
117                self.idl[name] = range(1, len(sample) + 1)
118
119        if kwargs.get("means") is not None:
120            for name, sample, mean in sorted(zip(names, samples, kwargs.get("means"))):
121                self.shape[name] = len(self.idl[name])
122                self.N += self.shape[name]
123                self.r_values[name] = mean
124                self.deltas[name] = sample
125        else:
126            for name, sample in sorted(zip(names, samples)):
127                self.shape[name] = len(self.idl[name])
128                self.N += self.shape[name]
129                if len(sample) != self.shape[name]:
130                    raise ValueError('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
131                self.r_values[name] = np.mean(sample)
132                self.deltas[name] = sample - self.r_values[name]
133                self._value += self.shape[name] * self.r_values[name]
134            self._value /= self.N
135
136        self._dvalue = 0.0
137        self.ddvalue = 0.0
138        self.reweighted = False
139
140        self.tag = None
141
142    @property
143    def value(self):
144        return self._value
145
146    @property
147    def dvalue(self):
148        return self._dvalue
149
150    @property
151    def e_names(self):
152        return sorted(set([o.split('|')[0] for o in self.names]))
153
154    @property
155    def cov_names(self):
156        return sorted(set([o for o in self.covobs.keys()]))
157
158    @property
159    def mc_names(self):
160        return sorted(set([o.split('|')[0] for o in self.names if o not in self.cov_names]))
161
162    @property
163    def e_content(self):
164        res = {}
165        for e, e_name in enumerate(self.e_names):
166            res[e_name] = sorted(filter(lambda x: x.startswith(e_name + '|'), self.names))
167            if e_name in self.names:
168                res[e_name].append(e_name)
169        return res
170
171    @property
172    def covobs(self):
173        return self._covobs
174
175    def gamma_method(self, **kwargs):
176        """Estimate the error and related properties of the Obs.
177
178        Parameters
179        ----------
180        S : float
181            specifies a custom value for the parameter S (default 2.0).
182            If set to 0 it is assumed that the data exhibits no
183            autocorrelation. In this case the error estimates coincides
184            with the sample standard error.
185        tau_exp : float
186            positive value triggers the critical slowing down analysis
187            (default 0.0).
188        N_sigma : float
189            number of standard deviations from zero until the tail is
190            attached to the autocorrelation function (default 1).
191        fft : bool
192            determines whether the fft algorithm is used for the computation
193            of the autocorrelation function (default True)
194        """
195
196        e_content = self.e_content
197        self.e_dvalue = {}
198        self.e_ddvalue = {}
199        self.e_tauint = {}
200        self.e_dtauint = {}
201        self.e_windowsize = {}
202        self.e_n_tauint = {}
203        self.e_n_dtauint = {}
204        e_gamma = {}
205        self.e_rho = {}
206        self.e_drho = {}
207        self._dvalue = 0
208        self.ddvalue = 0
209
210        self.S = {}
211        self.tau_exp = {}
212        self.N_sigma = {}
213
214        if kwargs.get('fft') is False:
215            fft = False
216        else:
217            fft = True
218
219        def _parse_kwarg(kwarg_name):
220            if kwarg_name in kwargs:
221                tmp = kwargs.get(kwarg_name)
222                if isinstance(tmp, (int, float)):
223                    if tmp < 0:
224                        raise Exception(kwarg_name + ' has to be larger or equal to 0.')
225                    for e, e_name in enumerate(self.e_names):
226                        getattr(self, kwarg_name)[e_name] = tmp
227                else:
228                    raise TypeError(kwarg_name + ' is not in proper format.')
229            else:
230                for e, e_name in enumerate(self.e_names):
231                    if e_name in getattr(Obs, kwarg_name + '_dict'):
232                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_dict')[e_name]
233                    else:
234                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_global')
235
236        _parse_kwarg('S')
237        _parse_kwarg('tau_exp')
238        _parse_kwarg('N_sigma')
239
240        for e, e_name in enumerate(self.mc_names):
241            gapsize = _determine_gap(self, e_content, e_name)
242
243            r_length = []
244            for r_name in e_content[e_name]:
245                if isinstance(self.idl[r_name], range):
246                    r_length.append(len(self.idl[r_name]) * self.idl[r_name].step // gapsize)
247                else:
248                    r_length.append((self.idl[r_name][-1] - self.idl[r_name][0] + 1) // gapsize)
249
250            e_N = np.sum([self.shape[r_name] for r_name in e_content[e_name]])
251            w_max = max(r_length) // 2
252            e_gamma[e_name] = np.zeros(w_max)
253            self.e_rho[e_name] = np.zeros(w_max)
254            self.e_drho[e_name] = np.zeros(w_max)
255
256            for r_name in e_content[e_name]:
257                e_gamma[e_name] += self._calc_gamma(self.deltas[r_name], self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
258
259            gamma_div = np.zeros(w_max)
260            for r_name in e_content[e_name]:
261                gamma_div += self._calc_gamma(np.ones((self.shape[r_name])), self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
262            gamma_div[gamma_div < 1] = 1.0
263            e_gamma[e_name] /= gamma_div[:w_max]
264
265            if np.abs(e_gamma[e_name][0]) < 10 * np.finfo(float).tiny:  # Prevent division by zero
266                self.e_tauint[e_name] = 0.5
267                self.e_dtauint[e_name] = 0.0
268                self.e_dvalue[e_name] = 0.0
269                self.e_ddvalue[e_name] = 0.0
270                self.e_windowsize[e_name] = 0
271                continue
272
273            self.e_rho[e_name] = e_gamma[e_name][:w_max] / e_gamma[e_name][0]
274            self.e_n_tauint[e_name] = np.cumsum(np.concatenate(([0.5], self.e_rho[e_name][1:])))
275            # Make sure no entry of tauint is smaller than 0.5
276            self.e_n_tauint[e_name][self.e_n_tauint[e_name] <= 0.5] = 0.5 + np.finfo(np.float64).eps
277            # hep-lat/0306017 eq. (42)
278            self.e_n_dtauint[e_name] = self.e_n_tauint[e_name] * 2 * np.sqrt(np.abs(np.arange(w_max) + 0.5 - self.e_n_tauint[e_name]) / e_N)
279            self.e_n_dtauint[e_name][0] = 0.0
280
281            def _compute_drho(i):
282                tmp = (self.e_rho[e_name][i + 1:w_max]
283                       + np.concatenate([self.e_rho[e_name][i - 1:None if i - (w_max - 1) // 2 <= 0 else (2 * i - (2 * w_max) // 2):-1],
284                                         self.e_rho[e_name][1:max(1, w_max - 2 * i)]])
285                       - 2 * self.e_rho[e_name][i] * self.e_rho[e_name][1:w_max - i])
286                self.e_drho[e_name][i] = np.sqrt(np.sum(tmp ** 2) / e_N)
287
288            if self.tau_exp[e_name] > 0:
289                _compute_drho(1)
290                texp = self.tau_exp[e_name]
291                # Critical slowing down analysis
292                if w_max // 2 <= 1:
293                    raise Exception("Need at least 8 samples for tau_exp error analysis")
294                for n in range(1, w_max // 2):
295                    _compute_drho(n + 1)
296                    if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
297                        # Bias correction hep-lat/0306017 eq. (49) included
298                        self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N) + texp * np.abs(self.e_rho[e_name][n + 1])  # The absolute makes sure, that the tail contribution is always positive
299                        self.e_dtauint[e_name] = np.sqrt(self.e_n_dtauint[e_name][n] ** 2 + texp ** 2 * self.e_drho[e_name][n + 1] ** 2)
300                        # Error of tau_exp neglected so far, missing term: self.e_rho[e_name][n + 1] ** 2 * d_tau_exp ** 2
301                        self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
302                        self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
303                        self.e_windowsize[e_name] = n
304                        break
305            else:
306                if self.S[e_name] == 0.0:
307                    self.e_tauint[e_name] = 0.5
308                    self.e_dtauint[e_name] = 0.0
309                    self.e_dvalue[e_name] = np.sqrt(e_gamma[e_name][0] / (e_N - 1))
310                    self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt(0.5 / e_N)
311                    self.e_windowsize[e_name] = 0
312                else:
313                    # Standard automatic windowing procedure
314                    tau = self.S[e_name] / np.log((2 * self.e_n_tauint[e_name][1:] + 1) / (2 * self.e_n_tauint[e_name][1:] - 1))
315                    g_w = np.exp(- np.arange(1, len(tau) + 1) / tau) - tau / np.sqrt(np.arange(1, len(tau) + 1) * e_N)
316                    for n in range(1, w_max):
317                        if g_w[n - 1] < 0 or n >= w_max - 1:
318                            _compute_drho(n)
319                            self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N)  # Bias correction hep-lat/0306017 eq. (49)
320                            self.e_dtauint[e_name] = self.e_n_dtauint[e_name][n]
321                            self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
322                            self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
323                            self.e_windowsize[e_name] = n
324                            break
325
326            self._dvalue += self.e_dvalue[e_name] ** 2
327            self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
328
329        for e_name in self.cov_names:
330            self.e_dvalue[e_name] = np.sqrt(self.covobs[e_name].errsq())
331            self.e_ddvalue[e_name] = 0
332            self._dvalue += self.e_dvalue[e_name]**2
333
334        self._dvalue = np.sqrt(self._dvalue)
335        if self._dvalue == 0.0:
336            self.ddvalue = 0.0
337        else:
338            self.ddvalue = np.sqrt(self.ddvalue) / self._dvalue
339        return
340
341    gm = gamma_method
342
343    def _calc_gamma(self, deltas, idx, shape, w_max, fft, gapsize):
344        """Calculate Gamma_{AA} from the deltas, which are defined on idx.
345           idx is assumed to be a contiguous range (possibly with a stepsize != 1)
346
347        Parameters
348        ----------
349        deltas : list
350            List of fluctuations
351        idx : list
352            List or range of configurations on which the deltas are defined.
353        shape : int
354            Number of configurations in idx.
355        w_max : int
356            Upper bound for the summation window.
357        fft : bool
358            determines whether the fft algorithm is used for the computation
359            of the autocorrelation function.
360        gapsize : int
361            The target distance between two configurations. If longer distances
362            are found in idx, the data is expanded.
363        """
364        gamma = np.zeros(w_max)
365        deltas = _expand_deltas(deltas, idx, shape, gapsize)
366        new_shape = len(deltas)
367        if fft:
368            max_gamma = min(new_shape, w_max)
369            # The padding for the fft has to be even
370            padding = new_shape + max_gamma + (new_shape + max_gamma) % 2
371            gamma[:max_gamma] += np.fft.irfft(np.abs(np.fft.rfft(deltas, padding)) ** 2)[:max_gamma]
372        else:
373            for n in range(w_max):
374                if new_shape - n >= 0:
375                    gamma[n] += deltas[0:new_shape - n].dot(deltas[n:new_shape])
376
377        return gamma
378
379    def details(self, ens_content=True):
380        """Output detailed properties of the Obs.
381
382        Parameters
383        ----------
384        ens_content : bool
385            print details about the ensembles and replica if true.
386        """
387        if self.tag is not None:
388            print("Description:", self.tag)
389        if not hasattr(self, 'e_dvalue'):
390            print('Result\t %3.8e' % (self.value))
391        else:
392            if self.value == 0.0:
393                percentage = np.nan
394            else:
395                percentage = np.abs(self._dvalue / self.value) * 100
396            print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self._dvalue, self.ddvalue, percentage))
397            if len(self.e_names) > 1:
398                print(' Ensemble errors:')
399            e_content = self.e_content
400            for e_name in self.mc_names:
401                gap = _determine_gap(self, e_content, e_name)
402
403                if len(self.e_names) > 1:
404                    print('', e_name, '\t %3.6e +/- %3.6e' % (self.e_dvalue[e_name], self.e_ddvalue[e_name]))
405                tau_string = " \N{GREEK SMALL LETTER TAU}_int\t " + _format_uncertainty(self.e_tauint[e_name], self.e_dtauint[e_name])
406                tau_string += f" in units of {gap} config"
407                if gap > 1:
408                    tau_string += "s"
409                if self.tau_exp[e_name] > 0:
410                    tau_string = f"{tau_string: <45}" + '\t(\N{GREEK SMALL LETTER TAU}_exp=%3.2f, N_\N{GREEK SMALL LETTER SIGMA}=%1.0i)' % (self.tau_exp[e_name], self.N_sigma[e_name])
411                else:
412                    tau_string = f"{tau_string: <45}" + '\t(S=%3.2f)' % (self.S[e_name])
413                print(tau_string)
414            for e_name in self.cov_names:
415                print('', e_name, '\t %3.8e' % (self.e_dvalue[e_name]))
416        if ens_content is True:
417            if len(self.e_names) == 1:
418                print(self.N, 'samples in', len(self.e_names), 'ensemble:')
419            else:
420                print(self.N, 'samples in', len(self.e_names), 'ensembles:')
421            my_string_list = []
422            for key, value in sorted(self.e_content.items()):
423                if key not in self.covobs:
424                    my_string = '  ' + "\u00B7 Ensemble '" + key + "' "
425                    if len(value) == 1:
426                        my_string += f': {self.shape[value[0]]} configurations'
427                        if isinstance(self.idl[value[0]], range):
428                            my_string += f' (from {self.idl[value[0]].start} to {self.idl[value[0]][-1]}' + int(self.idl[value[0]].step != 1) * f' in steps of {self.idl[value[0]].step}' + ')'
429                        else:
430                            my_string += f' (irregular range from {self.idl[value[0]][0]} to {self.idl[value[0]][-1]})'
431                    else:
432                        sublist = []
433                        for v in value:
434                            my_substring = '    ' + "\u00B7 Replicum '" + v[len(key) + 1:] + "' "
435                            my_substring += f': {self.shape[v]} configurations'
436                            if isinstance(self.idl[v], range):
437                                my_substring += f' (from {self.idl[v].start} to {self.idl[v][-1]}' + int(self.idl[v].step != 1) * f' in steps of {self.idl[v].step}' + ')'
438                            else:
439                                my_substring += f' (irregular range from {self.idl[v][0]} to {self.idl[v][-1]})'
440                            sublist.append(my_substring)
441
442                        my_string += '\n' + '\n'.join(sublist)
443                else:
444                    my_string = '  ' + "\u00B7 Covobs   '" + key + "' "
445                my_string_list.append(my_string)
446            print('\n'.join(my_string_list))
447
448    def reweight(self, weight):
449        """Reweight the obs with given rewighting factors.
450
451        Parameters
452        ----------
453        weight : Obs
454            Reweighting factor. An Observable that has to be defined on a superset of the
455            configurations in obs[i].idl for all i.
456        all_configs : bool
457            if True, the reweighted observables are normalized by the average of
458            the reweighting factor on all configurations in weight.idl and not
459            on the configurations in obs[i].idl. Default False.
460        """
461        return reweight(weight, [self])[0]
462
463    def is_zero_within_error(self, sigma=1):
464        """Checks whether the observable is zero within 'sigma' standard errors.
465
466        Parameters
467        ----------
468        sigma : int
469            Number of standard errors used for the check.
470
471        Works only properly when the gamma method was run.
472        """
473        return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue
474
475    def is_zero(self, atol=1e-10):
476        """Checks whether the observable is zero within a given tolerance.
477
478        Parameters
479        ----------
480        atol : float
481            Absolute tolerance (for details see numpy documentation).
482        """
483        return np.isclose(0.0, self.value, 1e-14, atol) and all(np.allclose(0.0, delta, 1e-14, atol) for delta in self.deltas.values()) and all(np.allclose(0.0, delta.errsq(), 1e-14, atol) for delta in self.covobs.values())
484
485    def plot_tauint(self, save=None):
486        """Plot integrated autocorrelation time for each ensemble.
487
488        Parameters
489        ----------
490        save : str
491            saves the figure to a file named 'save' if.
492        """
493        if not hasattr(self, 'e_dvalue'):
494            raise Exception('Run the gamma method first.')
495
496        for e, e_name in enumerate(self.mc_names):
497            fig = plt.figure()
498            plt.xlabel(r'$W$')
499            plt.ylabel(r'$\tau_\mathrm{int}$')
500            length = int(len(self.e_n_tauint[e_name]))
501            if self.tau_exp[e_name] > 0:
502                base = self.e_n_tauint[e_name][self.e_windowsize[e_name]]
503                x_help = np.arange(2 * self.tau_exp[e_name])
504                y_help = (x_help + 1) * np.abs(self.e_rho[e_name][self.e_windowsize[e_name] + 1]) * (1 - x_help / (2 * (2 * self.tau_exp[e_name] - 1))) + base
505                x_arr = np.arange(self.e_windowsize[e_name] + 1, self.e_windowsize[e_name] + 1 + 2 * self.tau_exp[e_name])
506                plt.plot(x_arr, y_help, 'C' + str(e), linewidth=1, ls='--', marker=',')
507                plt.errorbar([self.e_windowsize[e_name] + 2 * self.tau_exp[e_name]], [self.e_tauint[e_name]],
508                             yerr=[self.e_dtauint[e_name]], fmt='C' + str(e), linewidth=1, capsize=2, marker='o', mfc=plt.rcParams['axes.facecolor'])
509                xmax = self.e_windowsize[e_name] + 2 * self.tau_exp[e_name] + 1.5
510                label = e_name + r', $\tau_\mathrm{exp}$=' + str(np.around(self.tau_exp[e_name], decimals=2))
511            else:
512                label = e_name + ', S=' + str(np.around(self.S[e_name], decimals=2))
513                xmax = max(10.5, 2 * self.e_windowsize[e_name] - 0.5)
514
515            plt.errorbar(np.arange(length)[:int(xmax) + 1], self.e_n_tauint[e_name][:int(xmax) + 1], yerr=self.e_n_dtauint[e_name][:int(xmax) + 1], linewidth=1, capsize=2, label=label)
516            plt.axvline(x=self.e_windowsize[e_name], color='C' + str(e), alpha=0.5, marker=',', ls='--')
517            plt.legend()
518            plt.xlim(-0.5, xmax)
519            ylim = plt.ylim()
520            plt.ylim(bottom=0.0, top=max(1.0, ylim[1]))
521            plt.draw()
522            if save:
523                fig.savefig(save + "_" + str(e))
524
525    def plot_rho(self, save=None):
526        """Plot normalized autocorrelation function time for each ensemble.
527
528        Parameters
529        ----------
530        save : str
531            saves the figure to a file named 'save' if.
532        """
533        if not hasattr(self, 'e_dvalue'):
534            raise Exception('Run the gamma method first.')
535        for e, e_name in enumerate(self.mc_names):
536            fig = plt.figure()
537            plt.xlabel('W')
538            plt.ylabel('rho')
539            length = int(len(self.e_drho[e_name]))
540            plt.errorbar(np.arange(length), self.e_rho[e_name][:length], yerr=self.e_drho[e_name][:], linewidth=1, capsize=2)
541            plt.axvline(x=self.e_windowsize[e_name], color='r', alpha=0.25, ls='--', marker=',')
542            if self.tau_exp[e_name] > 0:
543                plt.plot([self.e_windowsize[e_name] + 1, self.e_windowsize[e_name] + 1 + 2 * self.tau_exp[e_name]],
544                         [self.e_rho[e_name][self.e_windowsize[e_name] + 1], 0], 'k-', lw=1)
545                xmax = self.e_windowsize[e_name] + 2 * self.tau_exp[e_name] + 1.5
546                plt.title('Rho ' + e_name + r', tau\_exp=' + str(np.around(self.tau_exp[e_name], decimals=2)))
547            else:
548                xmax = max(10.5, 2 * self.e_windowsize[e_name] - 0.5)
549                plt.title('Rho ' + e_name + ', S=' + str(np.around(self.S[e_name], decimals=2)))
550            plt.plot([-0.5, xmax], [0, 0], 'k--', lw=1)
551            plt.xlim(-0.5, xmax)
552            plt.draw()
553            if save:
554                fig.savefig(save + "_" + str(e))
555
556    def plot_rep_dist(self):
557        """Plot replica distribution for each ensemble with more than one replicum."""
558        if not hasattr(self, 'e_dvalue'):
559            raise Exception('Run the gamma method first.')
560        for e, e_name in enumerate(self.mc_names):
561            if len(self.e_content[e_name]) == 1:
562                print('No replica distribution for a single replicum (', e_name, ')')
563                continue
564            r_length = []
565            sub_r_mean = 0
566            for r, r_name in enumerate(self.e_content[e_name]):
567                r_length.append(len(self.deltas[r_name]))
568                sub_r_mean += self.shape[r_name] * self.r_values[r_name]
569            e_N = np.sum(r_length)
570            sub_r_mean /= e_N
571            arr = np.zeros(len(self.e_content[e_name]))
572            for r, r_name in enumerate(self.e_content[e_name]):
573                arr[r] = (self.r_values[r_name] - sub_r_mean) / (self.e_dvalue[e_name] * np.sqrt(e_N / self.shape[r_name] - 1))
574            plt.hist(arr, rwidth=0.8, bins=len(self.e_content[e_name]))
575            plt.title('Replica distribution' + e_name + ' (mean=0, var=1)')
576            plt.draw()
577
578    def plot_history(self, expand=True):
579        """Plot derived Monte Carlo history for each ensemble
580
581        Parameters
582        ----------
583        expand : bool
584            show expanded history for irregular Monte Carlo chains (default: True).
585        """
586        for e, e_name in enumerate(self.mc_names):
587            plt.figure()
588            r_length = []
589            tmp = []
590            tmp_expanded = []
591            for r, r_name in enumerate(self.e_content[e_name]):
592                tmp.append(self.deltas[r_name] + self.r_values[r_name])
593                if expand:
594                    tmp_expanded.append(_expand_deltas(self.deltas[r_name], list(self.idl[r_name]), self.shape[r_name], 1) + self.r_values[r_name])
595                    r_length.append(len(tmp_expanded[-1]))
596                else:
597                    r_length.append(len(tmp[-1]))
598            e_N = np.sum(r_length)
599            x = np.arange(e_N)
600            y_test = np.concatenate(tmp, axis=0)
601            if expand:
602                y = np.concatenate(tmp_expanded, axis=0)
603            else:
604                y = y_test
605            plt.errorbar(x, y, fmt='.', markersize=3)
606            plt.xlim(-0.5, e_N - 0.5)
607            plt.title(e_name + f'\nskew: {skew(y_test):.3f} (p={skewtest(y_test).pvalue:.3f}), kurtosis: {kurtosis(y_test):.3f} (p={kurtosistest(y_test).pvalue:.3f})')
608            plt.draw()
609
610    def plot_piechart(self, save=None):
611        """Plot piechart which shows the fractional contribution of each
612        ensemble to the error and returns a dictionary containing the fractions.
613
614        Parameters
615        ----------
616        save : str
617            saves the figure to a file named 'save' if.
618        """
619        if not hasattr(self, 'e_dvalue'):
620            raise Exception('Run the gamma method first.')
621        if np.isclose(0.0, self._dvalue, atol=1e-15):
622            raise Exception('Error is 0.0')
623        labels = self.e_names
624        sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
625        fig1, ax1 = plt.subplots()
626        ax1.pie(sizes, labels=labels, startangle=90, normalize=True)
627        ax1.axis('equal')
628        plt.draw()
629        if save:
630            fig1.savefig(save)
631
632        return dict(zip(labels, sizes))
633
634    def dump(self, filename, datatype="json.gz", description="", **kwargs):
635        """Dump the Obs to a file 'name' of chosen format.
636
637        Parameters
638        ----------
639        filename : str
640            name of the file to be saved.
641        datatype : str
642            Format of the exported file. Supported formats include
643            "json.gz" and "pickle"
644        description : str
645            Description for output file, only relevant for json.gz format.
646        path : str
647            specifies a custom path for the file (default '.')
648        """
649        if 'path' in kwargs:
650            file_name = kwargs.get('path') + '/' + filename
651        else:
652            file_name = filename
653
654        if datatype == "json.gz":
655            from .input.json import dump_to_json
656            dump_to_json([self], file_name, description=description)
657        elif datatype == "pickle":
658            with open(file_name + '.p', 'wb') as fb:
659                pickle.dump(self, fb)
660        else:
661            raise Exception("Unknown datatype " + str(datatype))
662
663    def export_jackknife(self):
664        """Export jackknife samples from the Obs
665
666        Returns
667        -------
668        numpy.ndarray
669            Returns a numpy array of length N + 1 where N is the number of samples
670            for the given ensemble and replicum. The zeroth entry of the array contains
671            the mean value of the Obs, entries 1 to N contain the N jackknife samples
672            derived from the Obs. The current implementation only works for observables
673            defined on exactly one ensemble and replicum. The derived jackknife samples
674            should agree with samples from a full jackknife analysis up to O(1/N).
675        """
676
677        if len(self.names) != 1:
678            raise Exception("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
679
680        name = self.names[0]
681        full_data = self.deltas[name] + self.r_values[name]
682        n = full_data.size
683        mean = self.value
684        tmp_jacks = np.zeros(n + 1)
685        tmp_jacks[0] = mean
686        tmp_jacks[1:] = (n * mean - full_data) / (n - 1)
687        return tmp_jacks
688
689    def export_bootstrap(self, samples=500, random_numbers=None, save_rng=None):
690        """Export bootstrap samples from the Obs
691
692        Parameters
693        ----------
694        samples : int
695            Number of bootstrap samples to generate.
696        random_numbers : np.ndarray
697            Array of shape (samples, length) containing the random numbers to generate the bootstrap samples.
698            If not provided the bootstrap samples are generated bashed on the md5 hash of the enesmble name.
699        save_rng : str
700            Save the random numbers to a file if a path is specified.
701
702        Returns
703        -------
704        numpy.ndarray
705            Returns a numpy array of length N + 1 where N is the number of samples
706            for the given ensemble and replicum. The zeroth entry of the array contains
707            the mean value of the Obs, entries 1 to N contain the N import_bootstrap samples
708            derived from the Obs. The current implementation only works for observables
709            defined on exactly one ensemble and replicum. The derived bootstrap samples
710            should agree with samples from a full bootstrap analysis up to O(1/N).
711        """
712        if len(self.names) != 1:
713            raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
714
715        name = self.names[0]
716        length = self.N
717
718        if random_numbers is None:
719            seed = int(hashlib.md5(name.encode()).hexdigest(), 16) & 0xFFFFFFFF
720            rng = np.random.default_rng(seed)
721            random_numbers = rng.integers(0, length, size=(samples, length))
722
723        if save_rng is not None:
724            np.savetxt(save_rng, random_numbers, fmt='%i')
725
726        proj = np.vstack([np.bincount(o, minlength=length) for o in random_numbers]) / length
727        ret = np.zeros(samples + 1)
728        ret[0] = self.value
729        ret[1:] = proj @ (self.deltas[name] + self.r_values[name])
730        return ret
731
732    def __float__(self):
733        return float(self.value)
734
735    def __repr__(self):
736        return 'Obs[' + str(self) + ']'
737
738    def __str__(self):
739        return _format_uncertainty(self.value, self._dvalue)
740
741    def __format__(self, format_type):
742        if format_type == "":
743            significance = 2
744        else:
745            significance = int(float(format_type.replace("+", "").replace("-", "")))
746        my_str = _format_uncertainty(self.value, self._dvalue,
747                                     significance=significance)
748        for char in ["+", " "]:
749            if format_type.startswith(char):
750                if my_str[0] != "-":
751                    my_str = char + my_str
752        return my_str
753
754    def __hash__(self):
755        hash_tuple = (np.array([self.value]).astype(np.float32).data.tobytes(),)
756        hash_tuple += tuple([o.astype(np.float32).data.tobytes() for o in self.deltas.values()])
757        hash_tuple += tuple([np.array([o.errsq()]).astype(np.float32).data.tobytes() for o in self.covobs.values()])
758        hash_tuple += tuple([o.encode() for o in self.names])
759        m = hashlib.md5()
760        [m.update(o) for o in hash_tuple]
761        return int(m.hexdigest(), 16) & 0xFFFFFFFF
762
763    # Overload comparisons
764    def __lt__(self, other):
765        return self.value < other
766
767    def __le__(self, other):
768        return self.value <= other
769
770    def __gt__(self, other):
771        return self.value > other
772
773    def __ge__(self, other):
774        return self.value >= other
775
776    def __eq__(self, other):
777        return (self - other).is_zero()
778
779    def __ne__(self, other):
780        return not (self - other).is_zero()
781
782    # Overload math operations
783    def __add__(self, y):
784        if isinstance(y, Obs):
785            return derived_observable(lambda x, **kwargs: x[0] + x[1], [self, y], man_grad=[1, 1])
786        else:
787            if isinstance(y, np.ndarray):
788                return np.array([self + o for o in y])
789            elif y.__class__.__name__ in ['Corr', 'CObs']:
790                return NotImplemented
791            else:
792                return derived_observable(lambda x, **kwargs: x[0] + y, [self], man_grad=[1])
793
794    def __radd__(self, y):
795        return self + y
796
797    def __mul__(self, y):
798        if isinstance(y, Obs):
799            return derived_observable(lambda x, **kwargs: x[0] * x[1], [self, y], man_grad=[y.value, self.value])
800        else:
801            if isinstance(y, np.ndarray):
802                return np.array([self * o for o in y])
803            elif isinstance(y, complex):
804                return CObs(self * y.real, self * y.imag)
805            elif y.__class__.__name__ in ['Corr', 'CObs']:
806                return NotImplemented
807            else:
808                return derived_observable(lambda x, **kwargs: x[0] * y, [self], man_grad=[y])
809
810    def __rmul__(self, y):
811        return self * y
812
813    def __sub__(self, y):
814        if isinstance(y, Obs):
815            return derived_observable(lambda x, **kwargs: x[0] - x[1], [self, y], man_grad=[1, -1])
816        else:
817            if isinstance(y, np.ndarray):
818                return np.array([self - o for o in y])
819            elif y.__class__.__name__ in ['Corr', 'CObs']:
820                return NotImplemented
821            else:
822                return derived_observable(lambda x, **kwargs: x[0] - y, [self], man_grad=[1])
823
824    def __rsub__(self, y):
825        return -1 * (self - y)
826
827    def __pos__(self):
828        return self
829
830    def __neg__(self):
831        return -1 * self
832
833    def __truediv__(self, y):
834        if isinstance(y, Obs):
835            return derived_observable(lambda x, **kwargs: x[0] / x[1], [self, y], man_grad=[1 / y.value, - self.value / y.value ** 2])
836        else:
837            if isinstance(y, np.ndarray):
838                return np.array([self / o for o in y])
839            elif y.__class__.__name__ in ['Corr', 'CObs']:
840                return NotImplemented
841            else:
842                return derived_observable(lambda x, **kwargs: x[0] / y, [self], man_grad=[1 / y])
843
844    def __rtruediv__(self, y):
845        if isinstance(y, Obs):
846            return derived_observable(lambda x, **kwargs: x[0] / x[1], [y, self], man_grad=[1 / self.value, - y.value / self.value ** 2])
847        else:
848            if isinstance(y, np.ndarray):
849                return np.array([o / self for o in y])
850            elif y.__class__.__name__ in ['Corr', 'CObs']:
851                return NotImplemented
852            else:
853                return derived_observable(lambda x, **kwargs: y / x[0], [self], man_grad=[-y / self.value ** 2])
854
855    def __pow__(self, y):
856        if isinstance(y, Obs):
857            return derived_observable(lambda x: x[0] ** x[1], [self, y])
858        else:
859            return derived_observable(lambda x: x[0] ** y, [self])
860
861    def __rpow__(self, y):
862        if isinstance(y, Obs):
863            return derived_observable(lambda x: x[0] ** x[1], [y, self])
864        else:
865            return derived_observable(lambda x: y ** x[0], [self])
866
867    def __abs__(self):
868        return derived_observable(lambda x: anp.abs(x[0]), [self])
869
870    # Overload numpy functions
871    def sqrt(self):
872        return derived_observable(lambda x, **kwargs: np.sqrt(x[0]), [self], man_grad=[1 / 2 / np.sqrt(self.value)])
873
874    def log(self):
875        return derived_observable(lambda x, **kwargs: np.log(x[0]), [self], man_grad=[1 / self.value])
876
877    def exp(self):
878        return derived_observable(lambda x, **kwargs: np.exp(x[0]), [self], man_grad=[np.exp(self.value)])
879
880    def sin(self):
881        return derived_observable(lambda x, **kwargs: np.sin(x[0]), [self], man_grad=[np.cos(self.value)])
882
883    def cos(self):
884        return derived_observable(lambda x, **kwargs: np.cos(x[0]), [self], man_grad=[-np.sin(self.value)])
885
886    def tan(self):
887        return derived_observable(lambda x, **kwargs: np.tan(x[0]), [self], man_grad=[1 / np.cos(self.value) ** 2])
888
889    def arcsin(self):
890        return derived_observable(lambda x: anp.arcsin(x[0]), [self])
891
892    def arccos(self):
893        return derived_observable(lambda x: anp.arccos(x[0]), [self])
894
895    def arctan(self):
896        return derived_observable(lambda x: anp.arctan(x[0]), [self])
897
898    def sinh(self):
899        return derived_observable(lambda x, **kwargs: np.sinh(x[0]), [self], man_grad=[np.cosh(self.value)])
900
901    def cosh(self):
902        return derived_observable(lambda x, **kwargs: np.cosh(x[0]), [self], man_grad=[np.sinh(self.value)])
903
904    def tanh(self):
905        return derived_observable(lambda x, **kwargs: np.tanh(x[0]), [self], man_grad=[1 / np.cosh(self.value) ** 2])
906
907    def arcsinh(self):
908        return derived_observable(lambda x: anp.arcsinh(x[0]), [self])
909
910    def arccosh(self):
911        return derived_observable(lambda x: anp.arccosh(x[0]), [self])
912
913    def arctanh(self):
914        return derived_observable(lambda x: anp.arctanh(x[0]), [self])

Class for a general observable.

Instances of Obs are the basic objects of a pyerrors error analysis. They are initialized with a list which contains arrays of samples for different ensembles/replica and another list of same length which contains the names of the ensembles/replica. Mathematical operations can be performed on instances. The result is another instance of Obs. The error of an instance can be computed with the gamma_method. Also contains additional methods for output and visualization of the error calculation.

Attributes
  • S_global (float): Standard value for S (default 2.0)
  • S_dict (dict): Dictionary for S values. If an entry for a given ensemble exists this overwrites the standard value for that ensemble.
  • tau_exp_global (float): Standard value for tau_exp (default 0.0)
  • tau_exp_dict (dict): Dictionary for tau_exp values. If an entry for a given ensemble exists this overwrites the standard value for that ensemble.
  • N_sigma_global (float): Standard value for N_sigma (default 1.0)
  • N_sigma_dict (dict): Dictionary for N_sigma values. If an entry for a given ensemble exists this overwrites the standard value for that ensemble.
Obs(samples, names, idl=None, **kwargs)
 61    def __init__(self, samples, names, idl=None, **kwargs):
 62        """ Initialize Obs object.
 63
 64        Parameters
 65        ----------
 66        samples : list
 67            list of numpy arrays containing the Monte Carlo samples
 68        names : list
 69            list of strings labeling the individual samples
 70        idl : list, optional
 71            list of ranges or lists on which the samples are defined
 72        """
 73
 74        if kwargs.get("means") is None and len(samples):
 75            if len(samples) != len(names):
 76                raise ValueError('Length of samples and names incompatible.')
 77            if idl is not None:
 78                if len(idl) != len(names):
 79                    raise ValueError('Length of idl incompatible with samples and names.')
 80            name_length = len(names)
 81            if name_length > 1:
 82                if name_length != len(set(names)):
 83                    raise ValueError('Names are not unique.')
 84                if not all(isinstance(x, str) for x in names):
 85                    raise TypeError('All names have to be strings.')
 86            else:
 87                if not isinstance(names[0], str):
 88                    raise TypeError('All names have to be strings.')
 89            if min(len(x) for x in samples) <= 4:
 90                raise ValueError('Samples have to have at least 5 entries.')
 91
 92        self.names = sorted(names)
 93        self.shape = {}
 94        self.r_values = {}
 95        self.deltas = {}
 96        self._covobs = {}
 97
 98        self._value = 0
 99        self.N = 0
100        self.idl = {}
101        if idl is not None:
102            for name, idx in sorted(zip(names, idl)):
103                if isinstance(idx, range):
104                    self.idl[name] = idx
105                elif isinstance(idx, (list, np.ndarray)):
106                    dc = np.unique(np.diff(idx))
107                    if np.any(dc < 0):
108                        raise ValueError("Unsorted idx for idl[%s]" % (name))
109                    if len(dc) == 1:
110                        self.idl[name] = range(idx[0], idx[-1] + dc[0], dc[0])
111                    else:
112                        self.idl[name] = list(idx)
113                else:
114                    raise TypeError('incompatible type for idl[%s].' % (name))
115        else:
116            for name, sample in sorted(zip(names, samples)):
117                self.idl[name] = range(1, len(sample) + 1)
118
119        if kwargs.get("means") is not None:
120            for name, sample, mean in sorted(zip(names, samples, kwargs.get("means"))):
121                self.shape[name] = len(self.idl[name])
122                self.N += self.shape[name]
123                self.r_values[name] = mean
124                self.deltas[name] = sample
125        else:
126            for name, sample in sorted(zip(names, samples)):
127                self.shape[name] = len(self.idl[name])
128                self.N += self.shape[name]
129                if len(sample) != self.shape[name]:
130                    raise ValueError('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
131                self.r_values[name] = np.mean(sample)
132                self.deltas[name] = sample - self.r_values[name]
133                self._value += self.shape[name] * self.r_values[name]
134            self._value /= self.N
135
136        self._dvalue = 0.0
137        self.ddvalue = 0.0
138        self.reweighted = False
139
140        self.tag = None

Initialize Obs object.

Parameters
  • samples (list): list of numpy arrays containing the Monte Carlo samples
  • names (list): list of strings labeling the individual samples
  • idl (list, optional): list of ranges or lists on which the samples are defined
S_global = 2.0
S_dict = {}
tau_exp_global = 0.0
tau_exp_dict = {}
N_sigma_global = 1.0
N_sigma_dict = {}
names
shape
r_values
deltas
N
idl
ddvalue
reweighted
tag
value
dvalue
e_names
cov_names
mc_names
e_content
covobs
def gamma_method(self, **kwargs):
175    def gamma_method(self, **kwargs):
176        """Estimate the error and related properties of the Obs.
177
178        Parameters
179        ----------
180        S : float
181            specifies a custom value for the parameter S (default 2.0).
182            If set to 0 it is assumed that the data exhibits no
183            autocorrelation. In this case the error estimates coincides
184            with the sample standard error.
185        tau_exp : float
186            positive value triggers the critical slowing down analysis
187            (default 0.0).
188        N_sigma : float
189            number of standard deviations from zero until the tail is
190            attached to the autocorrelation function (default 1).
191        fft : bool
192            determines whether the fft algorithm is used for the computation
193            of the autocorrelation function (default True)
194        """
195
196        e_content = self.e_content
197        self.e_dvalue = {}
198        self.e_ddvalue = {}
199        self.e_tauint = {}
200        self.e_dtauint = {}
201        self.e_windowsize = {}
202        self.e_n_tauint = {}
203        self.e_n_dtauint = {}
204        e_gamma = {}
205        self.e_rho = {}
206        self.e_drho = {}
207        self._dvalue = 0
208        self.ddvalue = 0
209
210        self.S = {}
211        self.tau_exp = {}
212        self.N_sigma = {}
213
214        if kwargs.get('fft') is False:
215            fft = False
216        else:
217            fft = True
218
219        def _parse_kwarg(kwarg_name):
220            if kwarg_name in kwargs:
221                tmp = kwargs.get(kwarg_name)
222                if isinstance(tmp, (int, float)):
223                    if tmp < 0:
224                        raise Exception(kwarg_name + ' has to be larger or equal to 0.')
225                    for e, e_name in enumerate(self.e_names):
226                        getattr(self, kwarg_name)[e_name] = tmp
227                else:
228                    raise TypeError(kwarg_name + ' is not in proper format.')
229            else:
230                for e, e_name in enumerate(self.e_names):
231                    if e_name in getattr(Obs, kwarg_name + '_dict'):
232                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_dict')[e_name]
233                    else:
234                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_global')
235
236        _parse_kwarg('S')
237        _parse_kwarg('tau_exp')
238        _parse_kwarg('N_sigma')
239
240        for e, e_name in enumerate(self.mc_names):
241            gapsize = _determine_gap(self, e_content, e_name)
242
243            r_length = []
244            for r_name in e_content[e_name]:
245                if isinstance(self.idl[r_name], range):
246                    r_length.append(len(self.idl[r_name]) * self.idl[r_name].step // gapsize)
247                else:
248                    r_length.append((self.idl[r_name][-1] - self.idl[r_name][0] + 1) // gapsize)
249
250            e_N = np.sum([self.shape[r_name] for r_name in e_content[e_name]])
251            w_max = max(r_length) // 2
252            e_gamma[e_name] = np.zeros(w_max)
253            self.e_rho[e_name] = np.zeros(w_max)
254            self.e_drho[e_name] = np.zeros(w_max)
255
256            for r_name in e_content[e_name]:
257                e_gamma[e_name] += self._calc_gamma(self.deltas[r_name], self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
258
259            gamma_div = np.zeros(w_max)
260            for r_name in e_content[e_name]:
261                gamma_div += self._calc_gamma(np.ones((self.shape[r_name])), self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
262            gamma_div[gamma_div < 1] = 1.0
263            e_gamma[e_name] /= gamma_div[:w_max]
264
265            if np.abs(e_gamma[e_name][0]) < 10 * np.finfo(float).tiny:  # Prevent division by zero
266                self.e_tauint[e_name] = 0.5
267                self.e_dtauint[e_name] = 0.0
268                self.e_dvalue[e_name] = 0.0
269                self.e_ddvalue[e_name] = 0.0
270                self.e_windowsize[e_name] = 0
271                continue
272
273            self.e_rho[e_name] = e_gamma[e_name][:w_max] / e_gamma[e_name][0]
274            self.e_n_tauint[e_name] = np.cumsum(np.concatenate(([0.5], self.e_rho[e_name][1:])))
275            # Make sure no entry of tauint is smaller than 0.5
276            self.e_n_tauint[e_name][self.e_n_tauint[e_name] <= 0.5] = 0.5 + np.finfo(np.float64).eps
277            # hep-lat/0306017 eq. (42)
278            self.e_n_dtauint[e_name] = self.e_n_tauint[e_name] * 2 * np.sqrt(np.abs(np.arange(w_max) + 0.5 - self.e_n_tauint[e_name]) / e_N)
279            self.e_n_dtauint[e_name][0] = 0.0
280
281            def _compute_drho(i):
282                tmp = (self.e_rho[e_name][i + 1:w_max]
283                       + np.concatenate([self.e_rho[e_name][i - 1:None if i - (w_max - 1) // 2 <= 0 else (2 * i - (2 * w_max) // 2):-1],
284                                         self.e_rho[e_name][1:max(1, w_max - 2 * i)]])
285                       - 2 * self.e_rho[e_name][i] * self.e_rho[e_name][1:w_max - i])
286                self.e_drho[e_name][i] = np.sqrt(np.sum(tmp ** 2) / e_N)
287
288            if self.tau_exp[e_name] > 0:
289                _compute_drho(1)
290                texp = self.tau_exp[e_name]
291                # Critical slowing down analysis
292                if w_max // 2 <= 1:
293                    raise Exception("Need at least 8 samples for tau_exp error analysis")
294                for n in range(1, w_max // 2):
295                    _compute_drho(n + 1)
296                    if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
297                        # Bias correction hep-lat/0306017 eq. (49) included
298                        self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N) + texp * np.abs(self.e_rho[e_name][n + 1])  # The absolute makes sure, that the tail contribution is always positive
299                        self.e_dtauint[e_name] = np.sqrt(self.e_n_dtauint[e_name][n] ** 2 + texp ** 2 * self.e_drho[e_name][n + 1] ** 2)
300                        # Error of tau_exp neglected so far, missing term: self.e_rho[e_name][n + 1] ** 2 * d_tau_exp ** 2
301                        self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
302                        self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
303                        self.e_windowsize[e_name] = n
304                        break
305            else:
306                if self.S[e_name] == 0.0:
307                    self.e_tauint[e_name] = 0.5
308                    self.e_dtauint[e_name] = 0.0
309                    self.e_dvalue[e_name] = np.sqrt(e_gamma[e_name][0] / (e_N - 1))
310                    self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt(0.5 / e_N)
311                    self.e_windowsize[e_name] = 0
312                else:
313                    # Standard automatic windowing procedure
314                    tau = self.S[e_name] / np.log((2 * self.e_n_tauint[e_name][1:] + 1) / (2 * self.e_n_tauint[e_name][1:] - 1))
315                    g_w = np.exp(- np.arange(1, len(tau) + 1) / tau) - tau / np.sqrt(np.arange(1, len(tau) + 1) * e_N)
316                    for n in range(1, w_max):
317                        if g_w[n - 1] < 0 or n >= w_max - 1:
318                            _compute_drho(n)
319                            self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N)  # Bias correction hep-lat/0306017 eq. (49)
320                            self.e_dtauint[e_name] = self.e_n_dtauint[e_name][n]
321                            self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
322                            self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
323                            self.e_windowsize[e_name] = n
324                            break
325
326            self._dvalue += self.e_dvalue[e_name] ** 2
327            self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
328
329        for e_name in self.cov_names:
330            self.e_dvalue[e_name] = np.sqrt(self.covobs[e_name].errsq())
331            self.e_ddvalue[e_name] = 0
332            self._dvalue += self.e_dvalue[e_name]**2
333
334        self._dvalue = np.sqrt(self._dvalue)
335        if self._dvalue == 0.0:
336            self.ddvalue = 0.0
337        else:
338            self.ddvalue = np.sqrt(self.ddvalue) / self._dvalue
339        return

Estimate the error and related properties of the Obs.

Parameters
  • S (float): specifies a custom value for the parameter S (default 2.0). If set to 0 it is assumed that the data exhibits no autocorrelation. In this case the error estimates coincides with the sample standard error.
  • tau_exp (float): positive value triggers the critical slowing down analysis (default 0.0).
  • N_sigma (float): number of standard deviations from zero until the tail is attached to the autocorrelation function (default 1).
  • fft (bool): determines whether the fft algorithm is used for the computation of the autocorrelation function (default True)
def gm(self, **kwargs):
175    def gamma_method(self, **kwargs):
176        """Estimate the error and related properties of the Obs.
177
178        Parameters
179        ----------
180        S : float
181            specifies a custom value for the parameter S (default 2.0).
182            If set to 0 it is assumed that the data exhibits no
183            autocorrelation. In this case the error estimates coincides
184            with the sample standard error.
185        tau_exp : float
186            positive value triggers the critical slowing down analysis
187            (default 0.0).
188        N_sigma : float
189            number of standard deviations from zero until the tail is
190            attached to the autocorrelation function (default 1).
191        fft : bool
192            determines whether the fft algorithm is used for the computation
193            of the autocorrelation function (default True)
194        """
195
196        e_content = self.e_content
197        self.e_dvalue = {}
198        self.e_ddvalue = {}
199        self.e_tauint = {}
200        self.e_dtauint = {}
201        self.e_windowsize = {}
202        self.e_n_tauint = {}
203        self.e_n_dtauint = {}
204        e_gamma = {}
205        self.e_rho = {}
206        self.e_drho = {}
207        self._dvalue = 0
208        self.ddvalue = 0
209
210        self.S = {}
211        self.tau_exp = {}
212        self.N_sigma = {}
213
214        if kwargs.get('fft') is False:
215            fft = False
216        else:
217            fft = True
218
219        def _parse_kwarg(kwarg_name):
220            if kwarg_name in kwargs:
221                tmp = kwargs.get(kwarg_name)
222                if isinstance(tmp, (int, float)):
223                    if tmp < 0:
224                        raise Exception(kwarg_name + ' has to be larger or equal to 0.')
225                    for e, e_name in enumerate(self.e_names):
226                        getattr(self, kwarg_name)[e_name] = tmp
227                else:
228                    raise TypeError(kwarg_name + ' is not in proper format.')
229            else:
230                for e, e_name in enumerate(self.e_names):
231                    if e_name in getattr(Obs, kwarg_name + '_dict'):
232                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_dict')[e_name]
233                    else:
234                        getattr(self, kwarg_name)[e_name] = getattr(Obs, kwarg_name + '_global')
235
236        _parse_kwarg('S')
237        _parse_kwarg('tau_exp')
238        _parse_kwarg('N_sigma')
239
240        for e, e_name in enumerate(self.mc_names):
241            gapsize = _determine_gap(self, e_content, e_name)
242
243            r_length = []
244            for r_name in e_content[e_name]:
245                if isinstance(self.idl[r_name], range):
246                    r_length.append(len(self.idl[r_name]) * self.idl[r_name].step // gapsize)
247                else:
248                    r_length.append((self.idl[r_name][-1] - self.idl[r_name][0] + 1) // gapsize)
249
250            e_N = np.sum([self.shape[r_name] for r_name in e_content[e_name]])
251            w_max = max(r_length) // 2
252            e_gamma[e_name] = np.zeros(w_max)
253            self.e_rho[e_name] = np.zeros(w_max)
254            self.e_drho[e_name] = np.zeros(w_max)
255
256            for r_name in e_content[e_name]:
257                e_gamma[e_name] += self._calc_gamma(self.deltas[r_name], self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
258
259            gamma_div = np.zeros(w_max)
260            for r_name in e_content[e_name]:
261                gamma_div += self._calc_gamma(np.ones((self.shape[r_name])), self.idl[r_name], self.shape[r_name], w_max, fft, gapsize)
262            gamma_div[gamma_div < 1] = 1.0
263            e_gamma[e_name] /= gamma_div[:w_max]
264
265            if np.abs(e_gamma[e_name][0]) < 10 * np.finfo(float).tiny:  # Prevent division by zero
266                self.e_tauint[e_name] = 0.5
267                self.e_dtauint[e_name] = 0.0
268                self.e_dvalue[e_name] = 0.0
269                self.e_ddvalue[e_name] = 0.0
270                self.e_windowsize[e_name] = 0
271                continue
272
273            self.e_rho[e_name] = e_gamma[e_name][:w_max] / e_gamma[e_name][0]
274            self.e_n_tauint[e_name] = np.cumsum(np.concatenate(([0.5], self.e_rho[e_name][1:])))
275            # Make sure no entry of tauint is smaller than 0.5
276            self.e_n_tauint[e_name][self.e_n_tauint[e_name] <= 0.5] = 0.5 + np.finfo(np.float64).eps
277            # hep-lat/0306017 eq. (42)
278            self.e_n_dtauint[e_name] = self.e_n_tauint[e_name] * 2 * np.sqrt(np.abs(np.arange(w_max) + 0.5 - self.e_n_tauint[e_name]) / e_N)
279            self.e_n_dtauint[e_name][0] = 0.0
280
281            def _compute_drho(i):
282                tmp = (self.e_rho[e_name][i + 1:w_max]
283                       + np.concatenate([self.e_rho[e_name][i - 1:None if i - (w_max - 1) // 2 <= 0 else (2 * i - (2 * w_max) // 2):-1],
284                                         self.e_rho[e_name][1:max(1, w_max - 2 * i)]])
285                       - 2 * self.e_rho[e_name][i] * self.e_rho[e_name][1:w_max - i])
286                self.e_drho[e_name][i] = np.sqrt(np.sum(tmp ** 2) / e_N)
287
288            if self.tau_exp[e_name] > 0:
289                _compute_drho(1)
290                texp = self.tau_exp[e_name]
291                # Critical slowing down analysis
292                if w_max // 2 <= 1:
293                    raise Exception("Need at least 8 samples for tau_exp error analysis")
294                for n in range(1, w_max // 2):
295                    _compute_drho(n + 1)
296                    if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
297                        # Bias correction hep-lat/0306017 eq. (49) included
298                        self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N) + texp * np.abs(self.e_rho[e_name][n + 1])  # The absolute makes sure, that the tail contribution is always positive
299                        self.e_dtauint[e_name] = np.sqrt(self.e_n_dtauint[e_name][n] ** 2 + texp ** 2 * self.e_drho[e_name][n + 1] ** 2)
300                        # Error of tau_exp neglected so far, missing term: self.e_rho[e_name][n + 1] ** 2 * d_tau_exp ** 2
301                        self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
302                        self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
303                        self.e_windowsize[e_name] = n
304                        break
305            else:
306                if self.S[e_name] == 0.0:
307                    self.e_tauint[e_name] = 0.5
308                    self.e_dtauint[e_name] = 0.0
309                    self.e_dvalue[e_name] = np.sqrt(e_gamma[e_name][0] / (e_N - 1))
310                    self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt(0.5 / e_N)
311                    self.e_windowsize[e_name] = 0
312                else:
313                    # Standard automatic windowing procedure
314                    tau = self.S[e_name] / np.log((2 * self.e_n_tauint[e_name][1:] + 1) / (2 * self.e_n_tauint[e_name][1:] - 1))
315                    g_w = np.exp(- np.arange(1, len(tau) + 1) / tau) - tau / np.sqrt(np.arange(1, len(tau) + 1) * e_N)
316                    for n in range(1, w_max):
317                        if g_w[n - 1] < 0 or n >= w_max - 1:
318                            _compute_drho(n)
319                            self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N)  # Bias correction hep-lat/0306017 eq. (49)
320                            self.e_dtauint[e_name] = self.e_n_dtauint[e_name][n]
321                            self.e_dvalue[e_name] = np.sqrt(2 * self.e_tauint[e_name] * e_gamma[e_name][0] * (1 + 1 / e_N) / e_N)
322                            self.e_ddvalue[e_name] = self.e_dvalue[e_name] * np.sqrt((n + 0.5) / e_N)
323                            self.e_windowsize[e_name] = n
324                            break
325
326            self._dvalue += self.e_dvalue[e_name] ** 2
327            self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
328
329        for e_name in self.cov_names:
330            self.e_dvalue[e_name] = np.sqrt(self.covobs[e_name].errsq())
331            self.e_ddvalue[e_name] = 0
332            self._dvalue += self.e_dvalue[e_name]**2
333
334        self._dvalue = np.sqrt(self._dvalue)
335        if self._dvalue == 0.0:
336            self.ddvalue = 0.0
337        else:
338            self.ddvalue = np.sqrt(self.ddvalue) / self._dvalue
339        return

Estimate the error and related properties of the Obs.

Parameters
  • S (float): specifies a custom value for the parameter S (default 2.0). If set to 0 it is assumed that the data exhibits no autocorrelation. In this case the error estimates coincides with the sample standard error.
  • tau_exp (float): positive value triggers the critical slowing down analysis (default 0.0).
  • N_sigma (float): number of standard deviations from zero until the tail is attached to the autocorrelation function (default 1).
  • fft (bool): determines whether the fft algorithm is used for the computation of the autocorrelation function (default True)
def details(self, ens_content=True):
379    def details(self, ens_content=True):
380        """Output detailed properties of the Obs.
381
382        Parameters
383        ----------
384        ens_content : bool
385            print details about the ensembles and replica if true.
386        """
387        if self.tag is not None:
388            print("Description:", self.tag)
389        if not hasattr(self, 'e_dvalue'):
390            print('Result\t %3.8e' % (self.value))
391        else:
392            if self.value == 0.0:
393                percentage = np.nan
394            else:
395                percentage = np.abs(self._dvalue / self.value) * 100
396            print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self._dvalue, self.ddvalue, percentage))
397            if len(self.e_names) > 1:
398                print(' Ensemble errors:')
399            e_content = self.e_content
400            for e_name in self.mc_names:
401                gap = _determine_gap(self, e_content, e_name)
402
403                if len(self.e_names) > 1:
404                    print('', e_name, '\t %3.6e +/- %3.6e' % (self.e_dvalue[e_name], self.e_ddvalue[e_name]))
405                tau_string = " \N{GREEK SMALL LETTER TAU}_int\t " + _format_uncertainty(self.e_tauint[e_name], self.e_dtauint[e_name])
406                tau_string += f" in units of {gap} config"
407                if gap > 1:
408                    tau_string += "s"
409                if self.tau_exp[e_name] > 0:
410                    tau_string = f"{tau_string: <45}" + '\t(\N{GREEK SMALL LETTER TAU}_exp=%3.2f, N_\N{GREEK SMALL LETTER SIGMA}=%1.0i)' % (self.tau_exp[e_name], self.N_sigma[e_name])
411                else:
412                    tau_string = f"{tau_string: <45}" + '\t(S=%3.2f)' % (self.S[e_name])
413                print(tau_string)
414            for e_name in self.cov_names:
415                print('', e_name, '\t %3.8e' % (self.e_dvalue[e_name]))
416        if ens_content is True:
417            if len(self.e_names) == 1:
418                print(self.N, 'samples in', len(self.e_names), 'ensemble:')
419            else:
420                print(self.N, 'samples in', len(self.e_names), 'ensembles:')
421            my_string_list = []
422            for key, value in sorted(self.e_content.items()):
423                if key not in self.covobs:
424                    my_string = '  ' + "\u00B7 Ensemble '" + key + "' "
425                    if len(value) == 1:
426                        my_string += f': {self.shape[value[0]]} configurations'
427                        if isinstance(self.idl[value[0]], range):
428                            my_string += f' (from {self.idl[value[0]].start} to {self.idl[value[0]][-1]}' + int(self.idl[value[0]].step != 1) * f' in steps of {self.idl[value[0]].step}' + ')'
429                        else:
430                            my_string += f' (irregular range from {self.idl[value[0]][0]} to {self.idl[value[0]][-1]})'
431                    else:
432                        sublist = []
433                        for v in value:
434                            my_substring = '    ' + "\u00B7 Replicum '" + v[len(key) + 1:] + "' "
435                            my_substring += f': {self.shape[v]} configurations'
436                            if isinstance(self.idl[v], range):
437                                my_substring += f' (from {self.idl[v].start} to {self.idl[v][-1]}' + int(self.idl[v].step != 1) * f' in steps of {self.idl[v].step}' + ')'
438                            else:
439                                my_substring += f' (irregular range from {self.idl[v][0]} to {self.idl[v][-1]})'
440                            sublist.append(my_substring)
441
442                        my_string += '\n' + '\n'.join(sublist)
443                else:
444                    my_string = '  ' + "\u00B7 Covobs   '" + key + "' "
445                my_string_list.append(my_string)
446            print('\n'.join(my_string_list))

Output detailed properties of the Obs.

Parameters
  • ens_content (bool): print details about the ensembles and replica if true.
def reweight(self, weight):
448    def reweight(self, weight):
449        """Reweight the obs with given rewighting factors.
450
451        Parameters
452        ----------
453        weight : Obs
454            Reweighting factor. An Observable that has to be defined on a superset of the
455            configurations in obs[i].idl for all i.
456        all_configs : bool
457            if True, the reweighted observables are normalized by the average of
458            the reweighting factor on all configurations in weight.idl and not
459            on the configurations in obs[i].idl. Default False.
460        """
461        return reweight(weight, [self])[0]

Reweight the obs with given rewighting factors.

Parameters
  • weight (Obs): Reweighting factor. An Observable that has to be defined on a superset of the configurations in obs[i].idl for all i.
  • all_configs (bool): if True, the reweighted observables are normalized by the average of the reweighting factor on all configurations in weight.idl and not on the configurations in obs[i].idl. Default False.
def is_zero_within_error(self, sigma=1):
463    def is_zero_within_error(self, sigma=1):
464        """Checks whether the observable is zero within 'sigma' standard errors.
465
466        Parameters
467        ----------
468        sigma : int
469            Number of standard errors used for the check.
470
471        Works only properly when the gamma method was run.
472        """
473        return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue

Checks whether the observable is zero within 'sigma' standard errors.

Parameters
  • sigma (int): Number of standard errors used for the check.
  • Works only properly when the gamma method was run.
def is_zero(self, atol=1e-10):
475    def is_zero(self, atol=1e-10):
476        """Checks whether the observable is zero within a given tolerance.
477
478        Parameters
479        ----------
480        atol : float
481            Absolute tolerance (for details see numpy documentation).
482        """
483        return np.isclose(0.0, self.value, 1e-14, atol) and all(np.allclose(0.0, delta, 1e-14, atol) for delta in self.deltas.values()) and all(np.allclose(0.0, delta.errsq(), 1e-14, atol) for delta in self.covobs.values())

Checks whether the observable is zero within a given tolerance.

Parameters
  • atol (float): Absolute tolerance (for details see numpy documentation).
def plot_tauint(self, save=None):
485    def plot_tauint(self, save=None):
486        """Plot integrated autocorrelation time for each ensemble.
487
488        Parameters
489        ----------
490        save : str
491            saves the figure to a file named 'save' if.
492        """
493        if not hasattr(self, 'e_dvalue'):
494            raise Exception('Run the gamma method first.')
495
496        for e, e_name in enumerate(self.mc_names):
497            fig = plt.figure()
498            plt.xlabel(r'$W$')
499            plt.ylabel(r'$\tau_\mathrm{int}$')
500            length = int(len(self.e_n_tauint[e_name]))
501            if self.tau_exp[e_name] > 0:
502                base = self.e_n_tauint[e_name][self.e_windowsize[e_name]]
503                x_help = np.arange(2 * self.tau_exp[e_name])
504                y_help = (x_help + 1) * np.abs(self.e_rho[e_name][self.e_windowsize[e_name] + 1]) * (1 - x_help / (2 * (2 * self.tau_exp[e_name] - 1))) + base
505                x_arr = np.arange(self.e_windowsize[e_name] + 1, self.e_windowsize[e_name] + 1 + 2 * self.tau_exp[e_name])
506                plt.plot(x_arr, y_help, 'C' + str(e), linewidth=1, ls='--', marker=',')
507                plt.errorbar([self.e_windowsize[e_name] + 2 * self.tau_exp[e_name]], [self.e_tauint[e_name]],
508                             yerr=[self.e_dtauint[e_name]], fmt='C' + str(e), linewidth=1, capsize=2, marker='o', mfc=plt.rcParams['axes.facecolor'])
509                xmax = self.e_windowsize[e_name] + 2 * self.tau_exp[e_name] + 1.5
510                label = e_name + r', $\tau_\mathrm{exp}$=' + str(np.around(self.tau_exp[e_name], decimals=2))
511            else:
512                label = e_name + ', S=' + str(np.around(self.S[e_name], decimals=2))
513                xmax = max(10.5, 2 * self.e_windowsize[e_name] - 0.5)
514
515            plt.errorbar(np.arange(length)[:int(xmax) + 1], self.e_n_tauint[e_name][:int(xmax) + 1], yerr=self.e_n_dtauint[e_name][:int(xmax) + 1], linewidth=1, capsize=2, label=label)
516            plt.axvline(x=self.e_windowsize[e_name], color='C' + str(e), alpha=0.5, marker=',', ls='--')
517            plt.legend()
518            plt.xlim(-0.5, xmax)
519            ylim = plt.ylim()
520            plt.ylim(bottom=0.0, top=max(1.0, ylim[1]))
521            plt.draw()
522            if save:
523                fig.savefig(save + "_" + str(e))

Plot integrated autocorrelation time for each ensemble.

Parameters
  • save (str): saves the figure to a file named 'save' if.
def plot_rho(self, save=None):
525    def plot_rho(self, save=None):
526        """Plot normalized autocorrelation function time for each ensemble.
527
528        Parameters
529        ----------
530        save : str
531            saves the figure to a file named 'save' if.
532        """
533        if not hasattr(self, 'e_dvalue'):
534            raise Exception('Run the gamma method first.')
535        for e, e_name in enumerate(self.mc_names):
536            fig = plt.figure()
537            plt.xlabel('W')
538            plt.ylabel('rho')
539            length = int(len(self.e_drho[e_name]))
540            plt.errorbar(np.arange(length), self.e_rho[e_name][:length], yerr=self.e_drho[e_name][:], linewidth=1, capsize=2)
541            plt.axvline(x=self.e_windowsize[e_name], color='r', alpha=0.25, ls='--', marker=',')
542            if self.tau_exp[e_name] > 0:
543                plt.plot([self.e_windowsize[e_name] + 1, self.e_windowsize[e_name] + 1 + 2 * self.tau_exp[e_name]],
544                         [self.e_rho[e_name][self.e_windowsize[e_name] + 1], 0], 'k-', lw=1)
545                xmax = self.e_windowsize[e_name] + 2 * self.tau_exp[e_name] + 1.5
546                plt.title('Rho ' + e_name + r', tau\_exp=' + str(np.around(self.tau_exp[e_name], decimals=2)))
547            else:
548                xmax = max(10.5, 2 * self.e_windowsize[e_name] - 0.5)
549                plt.title('Rho ' + e_name + ', S=' + str(np.around(self.S[e_name], decimals=2)))
550            plt.plot([-0.5, xmax], [0, 0], 'k--', lw=1)
551            plt.xlim(-0.5, xmax)
552            plt.draw()
553            if save:
554                fig.savefig(save + "_" + str(e))

Plot normalized autocorrelation function time for each ensemble.

Parameters
  • save (str): saves the figure to a file named 'save' if.
def plot_rep_dist(self):
556    def plot_rep_dist(self):
557        """Plot replica distribution for each ensemble with more than one replicum."""
558        if not hasattr(self, 'e_dvalue'):
559            raise Exception('Run the gamma method first.')
560        for e, e_name in enumerate(self.mc_names):
561            if len(self.e_content[e_name]) == 1:
562                print('No replica distribution for a single replicum (', e_name, ')')
563                continue
564            r_length = []
565            sub_r_mean = 0
566            for r, r_name in enumerate(self.e_content[e_name]):
567                r_length.append(len(self.deltas[r_name]))
568                sub_r_mean += self.shape[r_name] * self.r_values[r_name]
569            e_N = np.sum(r_length)
570            sub_r_mean /= e_N
571            arr = np.zeros(len(self.e_content[e_name]))
572            for r, r_name in enumerate(self.e_content[e_name]):
573                arr[r] = (self.r_values[r_name] - sub_r_mean) / (self.e_dvalue[e_name] * np.sqrt(e_N / self.shape[r_name] - 1))
574            plt.hist(arr, rwidth=0.8, bins=len(self.e_content[e_name]))
575            plt.title('Replica distribution' + e_name + ' (mean=0, var=1)')
576            plt.draw()

Plot replica distribution for each ensemble with more than one replicum.

def plot_history(self, expand=True):
578    def plot_history(self, expand=True):
579        """Plot derived Monte Carlo history for each ensemble
580
581        Parameters
582        ----------
583        expand : bool
584            show expanded history for irregular Monte Carlo chains (default: True).
585        """
586        for e, e_name in enumerate(self.mc_names):
587            plt.figure()
588            r_length = []
589            tmp = []
590            tmp_expanded = []
591            for r, r_name in enumerate(self.e_content[e_name]):
592                tmp.append(self.deltas[r_name] + self.r_values[r_name])
593                if expand:
594                    tmp_expanded.append(_expand_deltas(self.deltas[r_name], list(self.idl[r_name]), self.shape[r_name], 1) + self.r_values[r_name])
595                    r_length.append(len(tmp_expanded[-1]))
596                else:
597                    r_length.append(len(tmp[-1]))
598            e_N = np.sum(r_length)
599            x = np.arange(e_N)
600            y_test = np.concatenate(tmp, axis=0)
601            if expand:
602                y = np.concatenate(tmp_expanded, axis=0)
603            else:
604                y = y_test
605            plt.errorbar(x, y, fmt='.', markersize=3)
606            plt.xlim(-0.5, e_N - 0.5)
607            plt.title(e_name + f'\nskew: {skew(y_test):.3f} (p={skewtest(y_test).pvalue:.3f}), kurtosis: {kurtosis(y_test):.3f} (p={kurtosistest(y_test).pvalue:.3f})')
608            plt.draw()

Plot derived Monte Carlo history for each ensemble

Parameters
  • expand (bool): show expanded history for irregular Monte Carlo chains (default: True).
def plot_piechart(self, save=None):
610    def plot_piechart(self, save=None):
611        """Plot piechart which shows the fractional contribution of each
612        ensemble to the error and returns a dictionary containing the fractions.
613
614        Parameters
615        ----------
616        save : str
617            saves the figure to a file named 'save' if.
618        """
619        if not hasattr(self, 'e_dvalue'):
620            raise Exception('Run the gamma method first.')
621        if np.isclose(0.0, self._dvalue, atol=1e-15):
622            raise Exception('Error is 0.0')
623        labels = self.e_names
624        sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
625        fig1, ax1 = plt.subplots()
626        ax1.pie(sizes, labels=labels, startangle=90, normalize=True)
627        ax1.axis('equal')
628        plt.draw()
629        if save:
630            fig1.savefig(save)
631
632        return dict(zip(labels, sizes))

Plot piechart which shows the fractional contribution of each ensemble to the error and returns a dictionary containing the fractions.

Parameters
  • save (str): saves the figure to a file named 'save' if.
def dump(self, filename, datatype='json.gz', description='', **kwargs):
634    def dump(self, filename, datatype="json.gz", description="", **kwargs):
635        """Dump the Obs to a file 'name' of chosen format.
636
637        Parameters
638        ----------
639        filename : str
640            name of the file to be saved.
641        datatype : str
642            Format of the exported file. Supported formats include
643            "json.gz" and "pickle"
644        description : str
645            Description for output file, only relevant for json.gz format.
646        path : str
647            specifies a custom path for the file (default '.')
648        """
649        if 'path' in kwargs:
650            file_name = kwargs.get('path') + '/' + filename
651        else:
652            file_name = filename
653
654        if datatype == "json.gz":
655            from .input.json import dump_to_json
656            dump_to_json([self], file_name, description=description)
657        elif datatype == "pickle":
658            with open(file_name + '.p', 'wb') as fb:
659                pickle.dump(self, fb)
660        else:
661            raise Exception("Unknown datatype " + str(datatype))

Dump the Obs to a file 'name' of chosen format.

Parameters
  • filename (str): name of the file to be saved.
  • datatype (str): Format of the exported file. Supported formats include "json.gz" and "pickle"
  • description (str): Description for output file, only relevant for json.gz format.
  • path (str): specifies a custom path for the file (default '.')
def export_jackknife(self):
663    def export_jackknife(self):
664        """Export jackknife samples from the Obs
665
666        Returns
667        -------
668        numpy.ndarray
669            Returns a numpy array of length N + 1 where N is the number of samples
670            for the given ensemble and replicum. The zeroth entry of the array contains
671            the mean value of the Obs, entries 1 to N contain the N jackknife samples
672            derived from the Obs. The current implementation only works for observables
673            defined on exactly one ensemble and replicum. The derived jackknife samples
674            should agree with samples from a full jackknife analysis up to O(1/N).
675        """
676
677        if len(self.names) != 1:
678            raise Exception("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
679
680        name = self.names[0]
681        full_data = self.deltas[name] + self.r_values[name]
682        n = full_data.size
683        mean = self.value
684        tmp_jacks = np.zeros(n + 1)
685        tmp_jacks[0] = mean
686        tmp_jacks[1:] = (n * mean - full_data) / (n - 1)
687        return tmp_jacks

Export jackknife samples from the Obs

Returns
  • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples for the given ensemble and replicum. The zeroth entry of the array contains the mean value of the Obs, entries 1 to N contain the N jackknife samples derived from the Obs. The current implementation only works for observables defined on exactly one ensemble and replicum. The derived jackknife samples should agree with samples from a full jackknife analysis up to O(1/N).
def export_bootstrap(self, samples=500, random_numbers=None, save_rng=None):
689    def export_bootstrap(self, samples=500, random_numbers=None, save_rng=None):
690        """Export bootstrap samples from the Obs
691
692        Parameters
693        ----------
694        samples : int
695            Number of bootstrap samples to generate.
696        random_numbers : np.ndarray
697            Array of shape (samples, length) containing the random numbers to generate the bootstrap samples.
698            If not provided the bootstrap samples are generated bashed on the md5 hash of the enesmble name.
699        save_rng : str
700            Save the random numbers to a file if a path is specified.
701
702        Returns
703        -------
704        numpy.ndarray
705            Returns a numpy array of length N + 1 where N is the number of samples
706            for the given ensemble and replicum. The zeroth entry of the array contains
707            the mean value of the Obs, entries 1 to N contain the N import_bootstrap samples
708            derived from the Obs. The current implementation only works for observables
709            defined on exactly one ensemble and replicum. The derived bootstrap samples
710            should agree with samples from a full bootstrap analysis up to O(1/N).
711        """
712        if len(self.names) != 1:
713            raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
714
715        name = self.names[0]
716        length = self.N
717
718        if random_numbers is None:
719            seed = int(hashlib.md5(name.encode()).hexdigest(), 16) & 0xFFFFFFFF
720            rng = np.random.default_rng(seed)
721            random_numbers = rng.integers(0, length, size=(samples, length))
722
723        if save_rng is not None:
724            np.savetxt(save_rng, random_numbers, fmt='%i')
725
726        proj = np.vstack([np.bincount(o, minlength=length) for o in random_numbers]) / length
727        ret = np.zeros(samples + 1)
728        ret[0] = self.value
729        ret[1:] = proj @ (self.deltas[name] + self.r_values[name])
730        return ret

Export bootstrap samples from the Obs

Parameters
  • samples (int): Number of bootstrap samples to generate.
  • random_numbers (np.ndarray): Array of shape (samples, length) containing the random numbers to generate the bootstrap samples. If not provided the bootstrap samples are generated bashed on the md5 hash of the enesmble name.
  • save_rng (str): Save the random numbers to a file if a path is specified.
Returns
  • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples for the given ensemble and replicum. The zeroth entry of the array contains the mean value of the Obs, entries 1 to N contain the N import_bootstrap samples derived from the Obs. The current implementation only works for observables defined on exactly one ensemble and replicum. The derived bootstrap samples should agree with samples from a full bootstrap analysis up to O(1/N).
def sqrt(self):
871    def sqrt(self):
872        return derived_observable(lambda x, **kwargs: np.sqrt(x[0]), [self], man_grad=[1 / 2 / np.sqrt(self.value)])
def log(self):
874    def log(self):
875        return derived_observable(lambda x, **kwargs: np.log(x[0]), [self], man_grad=[1 / self.value])
def exp(self):
877    def exp(self):
878        return derived_observable(lambda x, **kwargs: np.exp(x[0]), [self], man_grad=[np.exp(self.value)])
def sin(self):
880    def sin(self):
881        return derived_observable(lambda x, **kwargs: np.sin(x[0]), [self], man_grad=[np.cos(self.value)])
def cos(self):
883    def cos(self):
884        return derived_observable(lambda x, **kwargs: np.cos(x[0]), [self], man_grad=[-np.sin(self.value)])
def tan(self):
886    def tan(self):
887        return derived_observable(lambda x, **kwargs: np.tan(x[0]), [self], man_grad=[1 / np.cos(self.value) ** 2])
def arcsin(self):
889    def arcsin(self):
890        return derived_observable(lambda x: anp.arcsin(x[0]), [self])
def arccos(self):
892    def arccos(self):
893        return derived_observable(lambda x: anp.arccos(x[0]), [self])
def arctan(self):
895    def arctan(self):
896        return derived_observable(lambda x: anp.arctan(x[0]), [self])
def sinh(self):
898    def sinh(self):
899        return derived_observable(lambda x, **kwargs: np.sinh(x[0]), [self], man_grad=[np.cosh(self.value)])
def cosh(self):
901    def cosh(self):
902        return derived_observable(lambda x, **kwargs: np.cosh(x[0]), [self], man_grad=[np.sinh(self.value)])
def tanh(self):
904    def tanh(self):
905        return derived_observable(lambda x, **kwargs: np.tanh(x[0]), [self], man_grad=[1 / np.cosh(self.value) ** 2])
def arcsinh(self):
907    def arcsinh(self):
908        return derived_observable(lambda x: anp.arcsinh(x[0]), [self])
def arccosh(self):
910    def arccosh(self):
911        return derived_observable(lambda x: anp.arccosh(x[0]), [self])
def arctanh(self):
913    def arctanh(self):
914        return derived_observable(lambda x: anp.arctanh(x[0]), [self])
N_sigma
S
e_ddvalue
e_drho
e_dtauint
e_dvalue
e_n_dtauint
e_n_tauint
e_rho
e_tauint
e_windowsize
tau_exp
class CObs:
 917class CObs:
 918    """Class for a complex valued observable."""
 919    __slots__ = ['_real', '_imag', 'tag']
 920
 921    def __init__(self, real, imag=0.0):
 922        self._real = real
 923        self._imag = imag
 924        self.tag = None
 925
 926    @property
 927    def real(self):
 928        return self._real
 929
 930    @property
 931    def imag(self):
 932        return self._imag
 933
 934    def gamma_method(self, **kwargs):
 935        """Executes the gamma_method for the real and the imaginary part."""
 936        if isinstance(self.real, Obs):
 937            self.real.gamma_method(**kwargs)
 938        if isinstance(self.imag, Obs):
 939            self.imag.gamma_method(**kwargs)
 940
 941    def is_zero(self):
 942        """Checks whether both real and imaginary part are zero within machine precision."""
 943        return self.real == 0.0 and self.imag == 0.0
 944
 945    def conjugate(self):
 946        return CObs(self.real, -self.imag)
 947
 948    def __add__(self, other):
 949        if isinstance(other, np.ndarray):
 950            return other + self
 951        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 952            return CObs(self.real + other.real,
 953                        self.imag + other.imag)
 954        else:
 955            return CObs(self.real + other, self.imag)
 956
 957    def __radd__(self, y):
 958        return self + y
 959
 960    def __sub__(self, other):
 961        if isinstance(other, np.ndarray):
 962            return -1 * (other - self)
 963        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 964            return CObs(self.real - other.real, self.imag - other.imag)
 965        else:
 966            return CObs(self.real - other, self.imag)
 967
 968    def __rsub__(self, other):
 969        return -1 * (self - other)
 970
 971    def __mul__(self, other):
 972        if isinstance(other, np.ndarray):
 973            return other * self
 974        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 975            if all(isinstance(i, Obs) for i in [self.real, self.imag, other.real, other.imag]):
 976                return CObs(derived_observable(lambda x, **kwargs: x[0] * x[1] - x[2] * x[3],
 977                                               [self.real, other.real, self.imag, other.imag],
 978                                               man_grad=[other.real.value, self.real.value, -other.imag.value, -self.imag.value]),
 979                            derived_observable(lambda x, **kwargs: x[2] * x[1] + x[0] * x[3],
 980                                               [self.real, other.real, self.imag, other.imag],
 981                                               man_grad=[other.imag.value, self.imag.value, other.real.value, self.real.value]))
 982            elif getattr(other, 'imag', 0) != 0:
 983                return CObs(self.real * other.real - self.imag * other.imag,
 984                            self.imag * other.real + self.real * other.imag)
 985            else:
 986                return CObs(self.real * other.real, self.imag * other.real)
 987        else:
 988            return CObs(self.real * other, self.imag * other)
 989
 990    def __rmul__(self, other):
 991        return self * other
 992
 993    def __truediv__(self, other):
 994        if isinstance(other, np.ndarray):
 995            return 1 / (other / self)
 996        elif hasattr(other, 'real') and hasattr(other, 'imag'):
 997            r = other.real ** 2 + other.imag ** 2
 998            return CObs((self.real * other.real + self.imag * other.imag) / r, (self.imag * other.real - self.real * other.imag) / r)
 999        else:
1000            return CObs(self.real / other, self.imag / other)
1001
1002    def __rtruediv__(self, other):
1003        r = self.real ** 2 + self.imag ** 2
1004        if hasattr(other, 'real') and hasattr(other, 'imag'):
1005            return CObs((self.real * other.real + self.imag * other.imag) / r, (self.real * other.imag - self.imag * other.real) / r)
1006        else:
1007            return CObs(self.real * other / r, -self.imag * other / r)
1008
1009    def __abs__(self):
1010        return np.sqrt(self.real**2 + self.imag**2)
1011
1012    def __pos__(self):
1013        return self
1014
1015    def __neg__(self):
1016        return -1 * self
1017
1018    def __eq__(self, other):
1019        return self.real == other.real and self.imag == other.imag
1020
1021    def __str__(self):
1022        return '(' + str(self.real) + int(self.imag >= 0.0) * '+' + str(self.imag) + 'j)'
1023
1024    def __repr__(self):
1025        return 'CObs[' + str(self) + ']'

Class for a complex valued observable.

CObs(real, imag=0.0)
921    def __init__(self, real, imag=0.0):
922        self._real = real
923        self._imag = imag
924        self.tag = None
tag
real
imag
def gamma_method(self, **kwargs):
934    def gamma_method(self, **kwargs):
935        """Executes the gamma_method for the real and the imaginary part."""
936        if isinstance(self.real, Obs):
937            self.real.gamma_method(**kwargs)
938        if isinstance(self.imag, Obs):
939            self.imag.gamma_method(**kwargs)

Executes the gamma_method for the real and the imaginary part.

def is_zero(self):
941    def is_zero(self):
942        """Checks whether both real and imaginary part are zero within machine precision."""
943        return self.real == 0.0 and self.imag == 0.0

Checks whether both real and imaginary part are zero within machine precision.

def conjugate(self):
945    def conjugate(self):
946        return CObs(self.real, -self.imag)
def derived_observable(func, data, array_mode=False, **kwargs):
1147def derived_observable(func, data, array_mode=False, **kwargs):
1148    """Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.
1149
1150    Parameters
1151    ----------
1152    func : object
1153        arbitrary function of the form func(data, **kwargs). For the
1154        automatic differentiation to work, all numpy functions have to have
1155        the autograd wrapper (use 'import autograd.numpy as anp').
1156    data : list
1157        list of Obs, e.g. [obs1, obs2, obs3].
1158    num_grad : bool
1159        if True, numerical derivatives are used instead of autograd
1160        (default False). To control the numerical differentiation the
1161        kwargs of numdifftools.step_generators.MaxStepGenerator
1162        can be used.
1163    man_grad : list
1164        manually supply a list or an array which contains the jacobian
1165        of func. Use cautiously, supplying the wrong derivative will
1166        not be intercepted.
1167
1168    Notes
1169    -----
1170    For simple mathematical operations it can be practical to use anonymous
1171    functions. For the ratio of two observables one can e.g. use
1172
1173    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])
1174    """
1175
1176    data = np.asarray(data)
1177    raveled_data = data.ravel()
1178
1179    # Workaround for matrix operations containing non Obs data
1180    if not all(isinstance(x, Obs) for x in raveled_data):
1181        for i in range(len(raveled_data)):
1182            if isinstance(raveled_data[i], (int, float)):
1183                raveled_data[i] = cov_Obs(raveled_data[i], 0.0, "###dummy_covobs###")
1184
1185    allcov = {}
1186    for o in raveled_data:
1187        for name in o.cov_names:
1188            if name in allcov:
1189                if not np.allclose(allcov[name], o.covobs[name].cov):
1190                    raise Exception('Inconsistent covariance matrices for %s!' % (name))
1191            else:
1192                allcov[name] = o.covobs[name].cov
1193
1194    n_obs = len(raveled_data)
1195    new_names = sorted(set([y for x in [o.names for o in raveled_data] for y in x]))
1196    new_cov_names = sorted(set([y for x in [o.cov_names for o in raveled_data] for y in x]))
1197    new_sample_names = sorted(set(new_names) - set(new_cov_names))
1198
1199    reweighted = len(list(filter(lambda o: o.reweighted is True, raveled_data))) > 0
1200
1201    if data.ndim == 1:
1202        values = np.array([o.value for o in data])
1203    else:
1204        values = np.vectorize(lambda x: x.value)(data)
1205
1206    new_values = func(values, **kwargs)
1207
1208    multi = int(isinstance(new_values, np.ndarray))
1209
1210    new_r_values = {}
1211    new_idl_d = {}
1212    for name in new_sample_names:
1213        idl = []
1214        tmp_values = np.zeros(n_obs)
1215        for i, item in enumerate(raveled_data):
1216            tmp_values[i] = item.r_values.get(name, item.value)
1217            tmp_idl = item.idl.get(name)
1218            if tmp_idl is not None:
1219                idl.append(tmp_idl)
1220        if multi > 0:
1221            tmp_values = np.array(tmp_values).reshape(data.shape)
1222        new_r_values[name] = func(tmp_values, **kwargs)
1223        new_idl_d[name] = _merge_idx(idl)
1224
1225    if 'man_grad' in kwargs:
1226        deriv = np.asarray(kwargs.get('man_grad'))
1227        if new_values.shape + data.shape != deriv.shape:
1228            raise Exception('Manual derivative does not have correct shape.')
1229    elif kwargs.get('num_grad') is True:
1230        if multi > 0:
1231            raise Exception('Multi mode currently not supported for numerical derivative')
1232        options = {
1233            'base_step': 0.1,
1234            'step_ratio': 2.5}
1235        for key in options.keys():
1236            kwarg = kwargs.get(key)
1237            if kwarg is not None:
1238                options[key] = kwarg
1239        tmp_df = nd.Gradient(func, order=4, **{k: v for k, v in options.items() if v is not None})(values, **kwargs)
1240        if tmp_df.size == 1:
1241            deriv = np.array([tmp_df.real])
1242        else:
1243            deriv = tmp_df.real
1244    else:
1245        deriv = jacobian(func)(values, **kwargs)
1246
1247    final_result = np.zeros(new_values.shape, dtype=object)
1248
1249    if array_mode is True:
1250
1251        class _Zero_grad():
1252            def __init__(self, N):
1253                self.grad = np.zeros((N, 1))
1254
1255        new_covobs_lengths = dict(set([y for x in [[(n, o.covobs[n].N) for n in o.cov_names] for o in raveled_data] for y in x]))
1256        d_extracted = {}
1257        g_extracted = {}
1258        for name in new_sample_names:
1259            d_extracted[name] = []
1260            ens_length = len(new_idl_d[name])
1261            for i_dat, dat in enumerate(data):
1262                d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas.get(name, np.zeros(ens_length)), o.idl.get(name, new_idl_d[name]), o.shape.get(name, ens_length), new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
1263        for name in new_cov_names:
1264            g_extracted[name] = []
1265            zero_grad = _Zero_grad(new_covobs_lengths[name])
1266            for i_dat, dat in enumerate(data):
1267                g_extracted[name].append(np.array([o.covobs.get(name, zero_grad).grad for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (new_covobs_lengths[name], 1)))
1268
1269    for i_val, new_val in np.ndenumerate(new_values):
1270        new_deltas = {}
1271        new_grad = {}
1272        if array_mode is True:
1273            for name in new_sample_names:
1274                ens_length = d_extracted[name][0].shape[-1]
1275                new_deltas[name] = np.zeros(ens_length)
1276                for i_dat, dat in enumerate(d_extracted[name]):
1277                    new_deltas[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
1278            for name in new_cov_names:
1279                new_grad[name] = 0
1280                for i_dat, dat in enumerate(g_extracted[name]):
1281                    new_grad[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
1282        else:
1283            for j_obs, obs in np.ndenumerate(data):
1284                for name in obs.names:
1285                    if name in obs.cov_names:
1286                        new_grad[name] = new_grad.get(name, 0) + deriv[i_val + j_obs] * obs.covobs[name].grad
1287                    else:
1288                        new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name])
1289
1290        new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad}
1291
1292        if not set(new_covobs.keys()).isdisjoint(new_deltas.keys()):
1293            raise Exception('The same name has been used for deltas and covobs!')
1294        new_samples = []
1295        new_means = []
1296        new_idl = []
1297        new_names_obs = []
1298        for name in new_names:
1299            if name not in new_covobs:
1300                new_samples.append(new_deltas[name])
1301                new_idl.append(new_idl_d[name])
1302                new_means.append(new_r_values[name][i_val])
1303                new_names_obs.append(name)
1304        final_result[i_val] = Obs(new_samples, new_names_obs, means=new_means, idl=new_idl)
1305        for name in new_covobs:
1306            final_result[i_val].names.append(name)
1307        final_result[i_val]._covobs = new_covobs
1308        final_result[i_val]._value = new_val
1309        final_result[i_val].reweighted = reweighted
1310
1311    if multi == 0:
1312        final_result = final_result.item()
1313
1314    return final_result

Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.

Parameters
  • func (object): arbitrary function of the form func(data, **kwargs). For the automatic differentiation to work, all numpy functions have to have the autograd wrapper (use 'import autograd.numpy as anp').
  • data (list): list of Obs, e.g. [obs1, obs2, obs3].
  • num_grad (bool): if True, numerical derivatives are used instead of autograd (default False). To control the numerical differentiation the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
  • man_grad (list): manually supply a list or an array which contains the jacobian of func. Use cautiously, supplying the wrong derivative will not be intercepted.
Notes

For simple mathematical operations it can be practical to use anonymous functions. For the ratio of two observables one can e.g. use

new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])

def reweight(weight, obs, **kwargs):
1346def reweight(weight, obs, **kwargs):
1347    """Reweight a list of observables.
1348
1349    Parameters
1350    ----------
1351    weight : Obs
1352        Reweighting factor. An Observable that has to be defined on a superset of the
1353        configurations in obs[i].idl for all i.
1354    obs : list
1355        list of Obs, e.g. [obs1, obs2, obs3].
1356    all_configs : bool
1357        if True, the reweighted observables are normalized by the average of
1358        the reweighting factor on all configurations in weight.idl and not
1359        on the configurations in obs[i].idl. Default False.
1360    """
1361    result = []
1362    for i in range(len(obs)):
1363        if len(obs[i].cov_names):
1364            raise Exception('Error: Not possible to reweight an Obs that contains covobs!')
1365        if not set(obs[i].names).issubset(weight.names):
1366            raise Exception('Error: Ensembles do not fit')
1367        for name in obs[i].names:
1368            if not set(obs[i].idl[name]).issubset(weight.idl[name]):
1369                raise Exception('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
1370        new_samples = []
1371        w_deltas = {}
1372        for name in sorted(obs[i].names):
1373            w_deltas[name] = _reduce_deltas(weight.deltas[name], weight.idl[name], obs[i].idl[name])
1374            new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name]))
1375        tmp_obs = Obs(new_samples, sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
1376
1377        if kwargs.get('all_configs'):
1378            new_weight = weight
1379        else:
1380            new_weight = Obs([w_deltas[name] + weight.r_values[name] for name in sorted(obs[i].names)], sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
1381
1382        result.append(tmp_obs / new_weight)
1383        result[-1].reweighted = True
1384
1385    return result

Reweight a list of observables.

Parameters
  • weight (Obs): Reweighting factor. An Observable that has to be defined on a superset of the configurations in obs[i].idl for all i.
  • obs (list): list of Obs, e.g. [obs1, obs2, obs3].
  • all_configs (bool): if True, the reweighted observables are normalized by the average of the reweighting factor on all configurations in weight.idl and not on the configurations in obs[i].idl. Default False.
def correlate(obs_a, obs_b):
1388def correlate(obs_a, obs_b):
1389    """Correlate two observables.
1390
1391    Parameters
1392    ----------
1393    obs_a : Obs
1394        First observable
1395    obs_b : Obs
1396        Second observable
1397
1398    Notes
1399    -----
1400    Keep in mind to only correlate primary observables which have not been reweighted
1401    yet. The reweighting has to be applied after correlating the observables.
1402    Currently only works if ensembles are identical (this is not strictly necessary).
1403    """
1404
1405    if sorted(obs_a.names) != sorted(obs_b.names):
1406        raise Exception(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
1407    if len(obs_a.cov_names) or len(obs_b.cov_names):
1408        raise Exception('Error: Not possible to correlate Obs that contain covobs!')
1409    for name in obs_a.names:
1410        if obs_a.shape[name] != obs_b.shape[name]:
1411            raise Exception('Shapes of ensemble', name, 'do not fit')
1412        if obs_a.idl[name] != obs_b.idl[name]:
1413            raise Exception('idl of ensemble', name, 'do not fit')
1414
1415    if obs_a.reweighted is True:
1416        warnings.warn("The first observable is already reweighted.", RuntimeWarning)
1417    if obs_b.reweighted is True:
1418        warnings.warn("The second observable is already reweighted.", RuntimeWarning)
1419
1420    new_samples = []
1421    new_idl = []
1422    for name in sorted(obs_a.names):
1423        new_samples.append((obs_a.deltas[name] + obs_a.r_values[name]) * (obs_b.deltas[name] + obs_b.r_values[name]))
1424        new_idl.append(obs_a.idl[name])
1425
1426    o = Obs(new_samples, sorted(obs_a.names), idl=new_idl)
1427    o.reweighted = obs_a.reweighted or obs_b.reweighted
1428    return o

Correlate two observables.

Parameters
  • obs_a (Obs): First observable
  • obs_b (Obs): Second observable
Notes

Keep in mind to only correlate primary observables which have not been reweighted yet. The reweighting has to be applied after correlating the observables. Currently only works if ensembles are identical (this is not strictly necessary).

def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
1431def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
1432    r'''Calculates the error covariance matrix of a set of observables.
1433
1434    WARNING: This function should be used with care, especially for observables with support on multiple
1435             ensembles with differing autocorrelations. See the notes below for details.
1436
1437    The gamma method has to be applied first to all observables.
1438
1439    Parameters
1440    ----------
1441    obs : list or numpy.ndarray
1442        List or one dimensional array of Obs
1443    visualize : bool
1444        If True plots the corresponding normalized correlation matrix (default False).
1445    correlation : bool
1446        If True the correlation matrix instead of the error covariance matrix is returned (default False).
1447    smooth : None or int
1448        If smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue
1449        smoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the
1450        largest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely
1451        small ones.
1452
1453    Notes
1454    -----
1455    The error covariance is defined such that it agrees with the squared standard error for two identical observables
1456    $$\operatorname{cov}(a,a)=\sum_{s=1}^N\delta_a^s\delta_a^s/N^2=\Gamma_{aa}(0)/N=\operatorname{var}(a)/N=\sigma_a^2$$
1457    in the absence of autocorrelation.
1458    The error covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite
1459    $$\sum_{i,j}v_i\Gamma_{ij}(0)v_j=\frac{1}{N}\sum_{s=1}^N\sum_{i,j}v_i\delta_i^s\delta_j^s v_j=\frac{1}{N}\sum_{s=1}^N\sum_{i}|v_i\delta_i^s|^2\geq 0\,,$$ for every $v\in\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.
1460    For observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.
1461    $$\tau_{\mathrm{int}, ij}=\sqrt{\tau_{\mathrm{int}, i}\times \tau_{\mathrm{int}, j}}$$
1462    This construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).
1463    '''
1464
1465    length = len(obs)
1466
1467    max_samples = np.max([o.N for o in obs])
1468    if max_samples <= length and not [item for sublist in [o.cov_names for o in obs] for item in sublist]:
1469        warnings.warn(f"The dimension of the covariance matrix ({length}) is larger or equal to the number of samples ({max_samples}). This will result in a rank deficient matrix.", RuntimeWarning)
1470
1471    cov = np.zeros((length, length))
1472    for i in range(length):
1473        for j in range(i, length):
1474            cov[i, j] = _covariance_element(obs[i], obs[j])
1475    cov = cov + cov.T - np.diag(np.diag(cov))
1476
1477    corr = np.diag(1 / np.sqrt(np.diag(cov))) @ cov @ np.diag(1 / np.sqrt(np.diag(cov)))
1478
1479    if isinstance(smooth, int):
1480        corr = _smooth_eigenvalues(corr, smooth)
1481
1482    if visualize:
1483        plt.matshow(corr, vmin=-1, vmax=1)
1484        plt.set_cmap('RdBu')
1485        plt.colorbar()
1486        plt.draw()
1487
1488    if correlation is True:
1489        return corr
1490
1491    errors = [o.dvalue for o in obs]
1492    cov = np.diag(errors) @ corr @ np.diag(errors)
1493
1494    eigenvalues = np.linalg.eigh(cov)[0]
1495    if not np.all(eigenvalues >= 0):
1496        warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
1497
1498    return cov

Calculates the error covariance matrix of a set of observables.

WARNING: This function should be used with care, especially for observables with support on multiple ensembles with differing autocorrelations. See the notes below for details.

The gamma method has to be applied first to all observables.

Parameters
  • obs (list or numpy.ndarray): List or one dimensional array of Obs
  • visualize (bool): If True plots the corresponding normalized correlation matrix (default False).
  • correlation (bool): If True the correlation matrix instead of the error covariance matrix is returned (default False).
  • smooth (None or int): If smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue smoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the largest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely small ones.
Notes

The error covariance is defined such that it agrees with the squared standard error for two identical observables $$\operatorname{cov}(a,a)=\sum_{s=1}^N\delta_a^s\delta_a^s/N^2=\Gamma_{aa}(0)/N=\operatorname{var}(a)/N=\sigma_a^2$$ in the absence of autocorrelation. The error covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite $$\sum_{i,j}v_i\Gamma_{ij}(0)v_j=\frac{1}{N}\sum_{s=1}^N\sum_{i,j}v_i\delta_i^s\delta_j^s v_j=\frac{1}{N}\sum_{s=1}^N\sum_{i}|v_i\delta_i^s|^2\geq 0\,,$$ for every $v\in\mathbb{R}^M$, while such an identity does not hold for larger windows/lags. For observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements. $$\tau_{\mathrm{int}, ij}=\sqrt{\tau_{\mathrm{int}, i}\times \tau_{\mathrm{int}, j}}$$ This construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).

def import_jackknife(jacks, name, idl=None):
1578def import_jackknife(jacks, name, idl=None):
1579    """Imports jackknife samples and returns an Obs
1580
1581    Parameters
1582    ----------
1583    jacks : numpy.ndarray
1584        numpy array containing the mean value as zeroth entry and
1585        the N jackknife samples as first to Nth entry.
1586    name : str
1587        name of the ensemble the samples are defined on.
1588    """
1589    length = len(jacks) - 1
1590    prj = (np.ones((length, length)) - (length - 1) * np.identity(length))
1591    samples = jacks[1:] @ prj
1592    mean = np.mean(samples)
1593    new_obs = Obs([samples - mean], [name], idl=idl, means=[mean])
1594    new_obs._value = jacks[0]
1595    return new_obs

Imports jackknife samples and returns an Obs

Parameters
  • jacks (numpy.ndarray): numpy array containing the mean value as zeroth entry and the N jackknife samples as first to Nth entry.
  • name (str): name of the ensemble the samples are defined on.
def import_bootstrap(boots, name, random_numbers):
1598def import_bootstrap(boots, name, random_numbers):
1599    """Imports bootstrap samples and returns an Obs
1600
1601    Parameters
1602    ----------
1603    boots : numpy.ndarray
1604        numpy array containing the mean value as zeroth entry and
1605        the N bootstrap samples as first to Nth entry.
1606    name : str
1607        name of the ensemble the samples are defined on.
1608    random_numbers : np.ndarray
1609        Array of shape (samples, length) containing the random numbers to generate the bootstrap samples,
1610        where samples is the number of bootstrap samples and length is the length of the original Monte Carlo
1611        chain to be reconstructed.
1612    """
1613    samples, length = random_numbers.shape
1614    if samples != len(boots) - 1:
1615        raise ValueError("Random numbers do not have the correct shape.")
1616
1617    if samples < length:
1618        raise ValueError("Obs can't be reconstructed if there are fewer bootstrap samples than Monte Carlo data points.")
1619
1620    proj = np.vstack([np.bincount(o, minlength=length) for o in random_numbers]) / length
1621
1622    samples = scipy.linalg.lstsq(proj, boots[1:])[0]
1623    ret = Obs([samples], [name])
1624    ret._value = boots[0]
1625    return ret

Imports bootstrap samples and returns an Obs

Parameters
  • boots (numpy.ndarray): numpy array containing the mean value as zeroth entry and the N bootstrap samples as first to Nth entry.
  • name (str): name of the ensemble the samples are defined on.
  • random_numbers (np.ndarray): Array of shape (samples, length) containing the random numbers to generate the bootstrap samples, where samples is the number of bootstrap samples and length is the length of the original Monte Carlo chain to be reconstructed.
def merge_obs(list_of_obs):
1628def merge_obs(list_of_obs):
1629    """Combine all observables in list_of_obs into one new observable
1630
1631    Parameters
1632    ----------
1633    list_of_obs : list
1634        list of the Obs object to be combined
1635
1636    Notes
1637    -----
1638    It is not possible to combine obs which are based on the same replicum
1639    """
1640    replist = [item for obs in list_of_obs for item in obs.names]
1641    if (len(replist) == len(set(replist))) is False:
1642        raise Exception('list_of_obs contains duplicate replica: %s' % (str(replist)))
1643    if any([len(o.cov_names) for o in list_of_obs]):
1644        raise Exception('Not possible to merge data that contains covobs!')
1645    new_dict = {}
1646    idl_dict = {}
1647    for o in list_of_obs:
1648        new_dict.update({key: o.deltas.get(key, 0) + o.r_values.get(key, 0)
1649                        for key in set(o.deltas) | set(o.r_values)})
1650        idl_dict.update({key: o.idl.get(key, 0) for key in set(o.deltas)})
1651
1652    names = sorted(new_dict.keys())
1653    o = Obs([new_dict[name] for name in names], names, idl=[idl_dict[name] for name in names])
1654    o.reweighted = np.max([oi.reweighted for oi in list_of_obs])
1655    return o

Combine all observables in list_of_obs into one new observable

Parameters
  • list_of_obs (list): list of the Obs object to be combined
Notes

It is not possible to combine obs which are based on the same replicum

def cov_Obs(means, cov, name, grad=None):
1658def cov_Obs(means, cov, name, grad=None):
1659    """Create an Obs based on mean(s) and a covariance matrix
1660
1661    Parameters
1662    ----------
1663    mean : list of floats or float
1664        N mean value(s) of the new Obs
1665    cov : list or array
1666        2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
1667    name : str
1668        identifier for the covariance matrix
1669    grad : list or array
1670        Gradient of the Covobs wrt. the means belonging to cov.
1671    """
1672
1673    def covobs_to_obs(co):
1674        """Make an Obs out of a Covobs
1675
1676        Parameters
1677        ----------
1678        co : Covobs
1679            Covobs to be embedded into the Obs
1680        """
1681        o = Obs([], [], means=[])
1682        o._value = co.value
1683        o.names.append(co.name)
1684        o._covobs[co.name] = co
1685        o._dvalue = np.sqrt(co.errsq())
1686        return o
1687
1688    ol = []
1689    if isinstance(means, (float, int)):
1690        means = [means]
1691
1692    for i in range(len(means)):
1693        ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad)))
1694    if ol[0].covobs[name].N != len(means):
1695        raise Exception('You have to provide %d mean values!' % (ol[0].N))
1696    if len(ol) == 1:
1697        return ol[0]
1698    return ol

Create an Obs based on mean(s) and a covariance matrix

Parameters
  • mean (list of floats or float): N mean value(s) of the new Obs
  • cov (list or array): 2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
  • name (str): identifier for the covariance matrix
  • grad (list or array): Gradient of the Covobs wrt. the means belonging to cov.