diff --git a/docs/pyerrors/input/openQCD.html b/docs/pyerrors/input/openQCD.html index 310bb933..10bf8d72 100644 --- a/docs/pyerrors/input/openQCD.html +++ b/docs/pyerrors/input/openQCD.html @@ -96,1227 +96,1198 @@
   1import os
    2import fnmatch
-   3import re
-   4import struct
-   5import warnings
-   6import numpy as np  # Thinly-wrapped numpy
-   7import matplotlib.pyplot as plt
-   8from matplotlib import gridspec
-   9from ..obs import Obs
-  10from ..fits import fit_lin
-  11from ..obs import CObs
-  12from ..correlators import Corr
+   3import struct
+   4import warnings
+   5import numpy as np  # Thinly-wrapped numpy
+   6import matplotlib.pyplot as plt
+   7from matplotlib import gridspec
+   8from ..obs import Obs
+   9from ..fits import fit_lin
+  10from ..obs import CObs
+  11from ..correlators import Corr
+  12from .utils import sort_names
   13
   14
-  15def _find_files(path, prefix, postfix, ext, known_files=[]):
-  16    found = []
-  17    files = []
-  18
-  19    if postfix != "":
-  20        if postfix[-1] != ".":
-  21            postfix = postfix + "."
-  22        if postfix[0] != ".":
-  23            postfix = "." + postfix
-  24
-  25    if ext[0] == ".":
-  26        ext = ext[1:]
-  27
-  28    pattern = prefix + "*" + postfix + ext
-  29
-  30    for (dirpath, dirnames, filenames) in os.walk(path + "/"):
-  31        found.extend(filenames)
-  32        break
-  33
-  34    if known_files != []:
-  35        for kf in known_files:
-  36            if kf not in found:
-  37                raise FileNotFoundError("Given file " + kf + " does not exist!")
-  38
-  39        return known_files
-  40
-  41    if not found:
-  42        raise FileNotFoundError(f"Error, directory '{path}' not found")
-  43
-  44    for f in found:
-  45        if fnmatch.fnmatch(f, pattern):
-  46            files.append(f)
-  47
-  48    if files == []:
-  49        raise Exception("No files found after pattern filter!")
-  50
-  51    files = _sort_names(files)
-  52    return files
-  53
-  54
-  55def _sort_names(ll):
-  56    r_pattern = r'r(\d+)'
-  57    id_pattern = r'id(\d+)'
-  58
-  59    # sort list by id first
-  60    if all([re.search(id_pattern, entry) for entry in ll]):
-  61        ll.sort(key=lambda x: int(re.findall(id_pattern, x)[0]))
-  62    # then by replikum
-  63    if all([re.search(r_pattern, entry) for entry in ll]):
-  64        ll.sort(key=lambda x: int(re.findall(r_pattern, x)[0]))
-  65    # as the rearrangements by one key let the other key untouched, the list is sorted now
+  15def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
+  16    """Read rwms format from given folder structure. Returns a list of length nrw
+  17
+  18    Parameters
+  19    ----------
+  20    path : str
+  21        path that contains the data files
+  22    prefix : str
+  23        all files in path that start with prefix are considered as input files.
+  24        May be used together postfix to consider only special file endings.
+  25        Prefix is ignored, if the keyword 'files' is used.
+  26    version : str
+  27        version of openQCD, default 2.0
+  28    names : list
+  29        list of names that is assigned to the data according according
+  30        to the order in the file list. Use careful, if you do not provide file names!
+  31    r_start : list
+  32        list which contains the first config to be read for each replicum
+  33    r_stop : list
+  34        list which contains the last config to be read for each replicum
+  35    r_step : int
+  36        integer that defines a fixed step size between two measurements (in units of configs)
+  37        If not given, r_step=1 is assumed.
+  38    postfix : str
+  39        postfix of the file to read, e.g. '.ms1' for openQCD-files
+  40    files : list
+  41        list which contains the filenames to be read. No automatic detection of
+  42        files performed if given.
+  43    print_err : bool
+  44        Print additional information that is useful for debugging.
+  45
+  46    Returns
+  47    -------
+  48    rwms : Obs
+  49        Reweighting factors read
+  50    """
+  51    known_oqcd_versions = ['1.4', '1.6', '2.0']
+  52    if not (version in known_oqcd_versions):
+  53        raise Exception('Unknown openQCD version defined!')
+  54    print("Working with openQCD version " + version)
+  55    if 'postfix' in kwargs:
+  56        postfix = kwargs.get('postfix')
+  57    else:
+  58        postfix = ''
+  59
+  60    if 'files' in kwargs:
+  61        known_files = kwargs.get('files')
+  62    else:
+  63        known_files = []
+  64
+  65    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
   66
-  67    else:
-  68        # fallback
-  69        sames = ''
-  70        if len(ll) > 1:
-  71            for i in range(len(ll[0])):
-  72                checking = ll[0][i]
-  73                for rn in ll[1:]:
-  74                    is_same = (rn[i] == checking)
-  75                if is_same:
-  76                    sames += checking
-  77                else:
-  78                    break
-  79            print(ll[0][len(sames):])
-  80        ll.sort(key=lambda x: int(re.findall(r'\d+', x[len(sames):])[0]))
-  81    return ll
-  82
+  67    replica = len(ls)
+  68
+  69    if 'r_start' in kwargs:
+  70        r_start = kwargs.get('r_start')
+  71        if len(r_start) != replica:
+  72            raise Exception('r_start does not match number of replicas')
+  73        r_start = [o if o else None for o in r_start]
+  74    else:
+  75        r_start = [None] * replica
+  76
+  77    if 'r_stop' in kwargs:
+  78        r_stop = kwargs.get('r_stop')
+  79        if len(r_stop) != replica:
+  80            raise Exception('r_stop does not match number of replicas')
+  81    else:
+  82        r_stop = [None] * replica
   83
-  84def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
-  85    """Read rwms format from given folder structure. Returns a list of length nrw
-  86
-  87    Parameters
-  88    ----------
-  89    path : str
-  90        path that contains the data files
-  91    prefix : str
-  92        all files in path that start with prefix are considered as input files.
-  93        May be used together postfix to consider only special file endings.
-  94        Prefix is ignored, if the keyword 'files' is used.
-  95    version : str
-  96        version of openQCD, default 2.0
-  97    names : list
-  98        list of names that is assigned to the data according according
-  99        to the order in the file list. Use careful, if you do not provide file names!
- 100    r_start : list
- 101        list which contains the first config to be read for each replicum
- 102    r_stop : list
- 103        list which contains the last config to be read for each replicum
- 104    r_step : int
- 105        integer that defines a fixed step size between two measurements (in units of configs)
- 106        If not given, r_step=1 is assumed.
- 107    postfix : str
- 108        postfix of the file to read, e.g. '.ms1' for openQCD-files
- 109    files : list
- 110        list which contains the filenames to be read. No automatic detection of
- 111        files performed if given.
- 112    print_err : bool
- 113        Print additional information that is useful for debugging.
- 114
- 115    Returns
- 116    -------
- 117    rwms : Obs
- 118        Reweighting factors read
- 119    """
- 120    known_oqcd_versions = ['1.4', '1.6', '2.0']
- 121    if not (version in known_oqcd_versions):
- 122        raise Exception('Unknown openQCD version defined!')
- 123    print("Working with openQCD version " + version)
- 124    if 'postfix' in kwargs:
- 125        postfix = kwargs.get('postfix')
- 126    else:
- 127        postfix = ''
- 128
- 129    if 'files' in kwargs:
- 130        known_files = kwargs.get('files')
- 131    else:
- 132        known_files = []
- 133
- 134    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
+  84    if 'r_step' in kwargs:
+  85        r_step = kwargs.get('r_step')
+  86    else:
+  87        r_step = 1
+  88
+  89    print('Read reweighting factors from', prefix[:-1], ',',
+  90          replica, 'replica', end='')
+  91
+  92    if names is None:
+  93        rep_names = []
+  94        for entry in ls:
+  95            truncated_entry = entry
+  96            suffixes = [".dat", ".rwms", ".ms1"]
+  97            for suffix in suffixes:
+  98                if truncated_entry.endswith(suffix):
+  99                    truncated_entry = truncated_entry[0:-len(suffix)]
+ 100            idx = truncated_entry.index('r')
+ 101            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+ 102    else:
+ 103        rep_names = names
+ 104
+ 105    rep_names = sort_names(rep_names)
+ 106
+ 107    print_err = 0
+ 108    if 'print_err' in kwargs:
+ 109        print_err = 1
+ 110        print()
+ 111
+ 112    deltas = []
+ 113
+ 114    configlist = []
+ 115    r_start_index = []
+ 116    r_stop_index = []
+ 117
+ 118    for rep in range(replica):
+ 119        tmp_array = []
+ 120        with open(path + '/' + ls[rep], 'rb') as fp:
+ 121
+ 122            t = fp.read(4)  # number of reweighting factors
+ 123            if rep == 0:
+ 124                nrw = struct.unpack('i', t)[0]
+ 125                if version == '2.0':
+ 126                    nrw = int(nrw / 2)
+ 127                for k in range(nrw):
+ 128                    deltas.append([])
+ 129            else:
+ 130                if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
+ 131                    raise Exception('Error: different number of reweighting factors for replicum', rep)
+ 132
+ 133            for k in range(nrw):
+ 134                tmp_array.append([])
  135
- 136    replica = len(ls)
- 137
- 138    if 'r_start' in kwargs:
- 139        r_start = kwargs.get('r_start')
- 140        if len(r_start) != replica:
- 141            raise Exception('r_start does not match number of replicas')
- 142        r_start = [o if o else None for o in r_start]
- 143    else:
- 144        r_start = [None] * replica
+ 136            # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
+ 137            nfct = []
+ 138            if version in ['1.6', '2.0']:
+ 139                for i in range(nrw):
+ 140                    t = fp.read(4)
+ 141                    nfct.append(struct.unpack('i', t)[0])
+ 142            else:
+ 143                for i in range(nrw):
+ 144                    nfct.append(1)
  145
- 146    if 'r_stop' in kwargs:
- 147        r_stop = kwargs.get('r_stop')
- 148        if len(r_stop) != replica:
- 149            raise Exception('r_stop does not match number of replicas')
- 150    else:
- 151        r_stop = [None] * replica
- 152
- 153    if 'r_step' in kwargs:
- 154        r_step = kwargs.get('r_step')
- 155    else:
- 156        r_step = 1
- 157
- 158    print('Read reweighting factors from', prefix[:-1], ',',
- 159          replica, 'replica', end='')
- 160
- 161    if names is None:
- 162        rep_names = []
- 163        for entry in ls:
- 164            truncated_entry = entry
- 165            suffixes = [".dat", ".rwms", ".ms1"]
- 166            for suffix in suffixes:
- 167                if truncated_entry.endswith(suffix):
- 168                    truncated_entry = truncated_entry[0:-len(suffix)]
- 169            idx = truncated_entry.index('r')
- 170            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
- 171    else:
- 172        rep_names = names
- 173
- 174    rep_names = _sort_names(rep_names)
- 175
- 176    print_err = 0
- 177    if 'print_err' in kwargs:
- 178        print_err = 1
- 179        print()
- 180
- 181    deltas = []
- 182
- 183    configlist = []
- 184    r_start_index = []
- 185    r_stop_index = []
- 186
- 187    for rep in range(replica):
- 188        tmp_array = []
- 189        with open(path + '/' + ls[rep], 'rb') as fp:
+ 146            nsrc = []
+ 147            for i in range(nrw):
+ 148                t = fp.read(4)
+ 149                nsrc.append(struct.unpack('i', t)[0])
+ 150            if version == '2.0':
+ 151                if not struct.unpack('i', fp.read(4))[0] == 0:
+ 152                    raise Exception("You are using the input for openQCD version 2.0, this is not correct.")
+ 153
+ 154            configlist.append([])
+ 155            while True:
+ 156                t = fp.read(4)
+ 157                if len(t) < 4:
+ 158                    break
+ 159                config_no = struct.unpack('i', t)[0]
+ 160                configlist[-1].append(config_no)
+ 161                for i in range(nrw):
+ 162                    if (version == '2.0'):
+ 163                        tmpd = _read_array_openQCD2(fp)
+ 164                        tmpd = _read_array_openQCD2(fp)
+ 165                        tmp_rw = tmpd['arr']
+ 166                        tmp_nfct = 1.0
+ 167                        for j in range(tmpd['n'][0]):
+ 168                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
+ 169                            if print_err:
+ 170                                print(config_no, i, j,
+ 171                                      np.mean(np.exp(-np.asarray(tmp_rw[j]))),
+ 172                                      np.std(np.exp(-np.asarray(tmp_rw[j]))))
+ 173                                print('Sources:',
+ 174                                      np.exp(-np.asarray(tmp_rw[j])))
+ 175                                print('Partial factor:', tmp_nfct)
+ 176                    elif version == '1.6' or version == '1.4':
+ 177                        tmp_nfct = 1.0
+ 178                        for j in range(nfct[i]):
+ 179                            t = fp.read(8 * nsrc[i])
+ 180                            t = fp.read(8 * nsrc[i])
+ 181                            tmp_rw = struct.unpack('d' * nsrc[i], t)
+ 182                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
+ 183                            if print_err:
+ 184                                print(config_no, i, j,
+ 185                                      np.mean(np.exp(-np.asarray(tmp_rw))),
+ 186                                      np.std(np.exp(-np.asarray(tmp_rw))))
+ 187                                print('Sources:', np.exp(-np.asarray(tmp_rw)))
+ 188                                print('Partial factor:', tmp_nfct)
+ 189                    tmp_array[i].append(tmp_nfct)
  190
- 191            t = fp.read(4)  # number of reweighting factors
- 192            if rep == 0:
- 193                nrw = struct.unpack('i', t)[0]
- 194                if version == '2.0':
- 195                    nrw = int(nrw / 2)
- 196                for k in range(nrw):
- 197                    deltas.append([])
- 198            else:
- 199                if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
- 200                    raise Exception('Error: different number of reweighting factors for replicum', rep)
- 201
- 202            for k in range(nrw):
- 203                tmp_array.append([])
- 204
- 205            # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
- 206            nfct = []
- 207            if version in ['1.6', '2.0']:
- 208                for i in range(nrw):
- 209                    t = fp.read(4)
- 210                    nfct.append(struct.unpack('i', t)[0])
- 211            else:
- 212                for i in range(nrw):
- 213                    nfct.append(1)
- 214
- 215            nsrc = []
- 216            for i in range(nrw):
- 217                t = fp.read(4)
- 218                nsrc.append(struct.unpack('i', t)[0])
- 219            if version == '2.0':
- 220                if not struct.unpack('i', fp.read(4))[0] == 0:
- 221                    raise Exception("You are using the input for openQCD version 2.0, this is not correct.")
- 222
- 223            configlist.append([])
- 224            while True:
- 225                t = fp.read(4)
- 226                if len(t) < 4:
- 227                    break
- 228                config_no = struct.unpack('i', t)[0]
- 229                configlist[-1].append(config_no)
- 230                for i in range(nrw):
- 231                    if (version == '2.0'):
- 232                        tmpd = _read_array_openQCD2(fp)
- 233                        tmpd = _read_array_openQCD2(fp)
- 234                        tmp_rw = tmpd['arr']
- 235                        tmp_nfct = 1.0
- 236                        for j in range(tmpd['n'][0]):
- 237                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
- 238                            if print_err:
- 239                                print(config_no, i, j,
- 240                                      np.mean(np.exp(-np.asarray(tmp_rw[j]))),
- 241                                      np.std(np.exp(-np.asarray(tmp_rw[j]))))
- 242                                print('Sources:',
- 243                                      np.exp(-np.asarray(tmp_rw[j])))
- 244                                print('Partial factor:', tmp_nfct)
- 245                    elif version == '1.6' or version == '1.4':
- 246                        tmp_nfct = 1.0
- 247                        for j in range(nfct[i]):
- 248                            t = fp.read(8 * nsrc[i])
- 249                            t = fp.read(8 * nsrc[i])
- 250                            tmp_rw = struct.unpack('d' * nsrc[i], t)
- 251                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
- 252                            if print_err:
- 253                                print(config_no, i, j,
- 254                                      np.mean(np.exp(-np.asarray(tmp_rw))),
- 255                                      np.std(np.exp(-np.asarray(tmp_rw))))
- 256                                print('Sources:', np.exp(-np.asarray(tmp_rw)))
- 257                                print('Partial factor:', tmp_nfct)
- 258                    tmp_array[i].append(tmp_nfct)
- 259
- 260            diffmeas = configlist[-1][-1] - configlist[-1][-2]
- 261            configlist[-1] = [item // diffmeas for item in configlist[-1]]
- 262            if configlist[-1][0] > 1 and diffmeas > 1:
- 263                warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
- 264                offset = configlist[-1][0] - 1
- 265                configlist[-1] = [item - offset for item in configlist[-1]]
- 266
- 267            if r_start[rep] is None:
- 268                r_start_index.append(0)
- 269            else:
- 270                try:
- 271                    r_start_index.append(configlist[-1].index(r_start[rep]))
- 272                except ValueError:
- 273                    raise Exception('Config %d not in file with range [%d, %d]' % (
- 274                        r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
- 275
- 276            if r_stop[rep] is None:
- 277                r_stop_index.append(len(configlist[-1]) - 1)
- 278            else:
- 279                try:
- 280                    r_stop_index.append(configlist[-1].index(r_stop[rep]))
- 281                except ValueError:
- 282                    raise Exception('Config %d not in file with range [%d, %d]' % (
- 283                        r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
- 284
- 285            for k in range(nrw):
- 286                deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step])
- 287
- 288    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
- 289        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
- 290    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
- 291    if np.any([step != 1 for step in stepsizes]):
- 292        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
- 293
- 294    print(',', nrw, 'reweighting factors with', nsrc, 'sources')
- 295    result = []
- 296    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
- 297
- 298    for t in range(nrw):
- 299        result.append(Obs(deltas[t], rep_names, idl=idl))
- 300    return result
+ 191            diffmeas = configlist[-1][-1] - configlist[-1][-2]
+ 192            configlist[-1] = [item // diffmeas for item in configlist[-1]]
+ 193            if configlist[-1][0] > 1 and diffmeas > 1:
+ 194                warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
+ 195                offset = configlist[-1][0] - 1
+ 196                configlist[-1] = [item - offset for item in configlist[-1]]
+ 197
+ 198            if r_start[rep] is None:
+ 199                r_start_index.append(0)
+ 200            else:
+ 201                try:
+ 202                    r_start_index.append(configlist[-1].index(r_start[rep]))
+ 203                except ValueError:
+ 204                    raise Exception('Config %d not in file with range [%d, %d]' % (
+ 205                        r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 206
+ 207            if r_stop[rep] is None:
+ 208                r_stop_index.append(len(configlist[-1]) - 1)
+ 209            else:
+ 210                try:
+ 211                    r_stop_index.append(configlist[-1].index(r_stop[rep]))
+ 212                except ValueError:
+ 213                    raise Exception('Config %d not in file with range [%d, %d]' % (
+ 214                        r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 215
+ 216            for k in range(nrw):
+ 217                deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step])
+ 218
+ 219    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
+ 220        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
+ 221    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+ 222    if np.any([step != 1 for step in stepsizes]):
+ 223        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
+ 224
+ 225    print(',', nrw, 'reweighting factors with', nsrc, 'sources')
+ 226    result = []
+ 227    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
+ 228
+ 229    for t in range(nrw):
+ 230        result.append(Obs(deltas[t], rep_names, idl=idl))
+ 231    return result
+ 232
+ 233
+ 234def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
+ 235    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
+ 236
+ 237    It is assumed that all boundary effects have
+ 238    sufficiently decayed at x0=xmin.
+ 239    The data around the zero crossing of t^2<E> - 0.3
+ 240    is fitted with a linear function
+ 241    from which the exact root is extracted.
+ 242
+ 243    It is assumed that one measurement is performed for each config.
+ 244    If this is not the case, the resulting idl, as well as the handling
+ 245    of r_start, r_stop and r_step is wrong and the user has to correct
+ 246    this in the resulting observable.
+ 247
+ 248    Parameters
+ 249    ----------
+ 250    path : str
+ 251        Path to .ms.dat files
+ 252    prefix : str
+ 253        Ensemble prefix
+ 254    dtr_read : int
+ 255        Determines how many trajectories should be skipped
+ 256        when reading the ms.dat files.
+ 257        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
+ 258    xmin : int
+ 259        First timeslice where the boundary
+ 260        effects have sufficiently decayed.
+ 261    spatial_extent : int
+ 262        spatial extent of the lattice, required for normalization.
+ 263    fit_range : int
+ 264        Number of data points left and right of the zero
+ 265        crossing to be included in the linear fit. (Default: 5)
+ 266    r_start : list
+ 267        list which contains the first config to be read for each replicum.
+ 268    r_stop : list
+ 269        list which contains the last config to be read for each replicum.
+ 270    r_step : int
+ 271        integer that defines a fixed step size between two measurements (in units of configs)
+ 272        If not given, r_step=1 is assumed.
+ 273    plaquette : bool
+ 274        If true extract the plaquette estimate of t0 instead.
+ 275    names : list
+ 276        list of names that is assigned to the data according according
+ 277        to the order in the file list. Use careful, if you do not provide file names!
+ 278    files : list
+ 279        list which contains the filenames to be read. No automatic detection of
+ 280        files performed if given.
+ 281    plot_fit : bool
+ 282        If true, the fit for the extraction of t0 is shown together with the data.
+ 283    assume_thermalization : bool
+ 284        If True: If the first record divided by the distance between two measurements is larger than
+ 285        1, it is assumed that this is due to thermalization and the first measurement belongs
+ 286        to the first config (default).
+ 287        If False: The config numbers are assumed to be traj_number // difference
+ 288
+ 289    Returns
+ 290    -------
+ 291    t0 : Obs
+ 292        Extracted t0
+ 293    """
+ 294
+ 295    if 'files' in kwargs:
+ 296        known_files = kwargs.get('files')
+ 297    else:
+ 298        known_files = []
+ 299
+ 300    ls = _find_files(path, prefix, 'ms', 'dat', known_files=known_files)
  301
- 302
- 303def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
- 304    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
- 305
- 306    It is assumed that all boundary effects have
- 307    sufficiently decayed at x0=xmin.
- 308    The data around the zero crossing of t^2<E> - 0.3
- 309    is fitted with a linear function
- 310    from which the exact root is extracted.
+ 302    replica = len(ls)
+ 303
+ 304    if 'r_start' in kwargs:
+ 305        r_start = kwargs.get('r_start')
+ 306        if len(r_start) != replica:
+ 307            raise Exception('r_start does not match number of replicas')
+ 308        r_start = [o if o else None for o in r_start]
+ 309    else:
+ 310        r_start = [None] * replica
  311
- 312    It is assumed that one measurement is performed for each config.
- 313    If this is not the case, the resulting idl, as well as the handling
- 314    of r_start, r_stop and r_step is wrong and the user has to correct
- 315    this in the resulting observable.
- 316
- 317    Parameters
- 318    ----------
- 319    path : str
- 320        Path to .ms.dat files
- 321    prefix : str
- 322        Ensemble prefix
- 323    dtr_read : int
- 324        Determines how many trajectories should be skipped
- 325        when reading the ms.dat files.
- 326        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
- 327    xmin : int
- 328        First timeslice where the boundary
- 329        effects have sufficiently decayed.
- 330    spatial_extent : int
- 331        spatial extent of the lattice, required for normalization.
- 332    fit_range : int
- 333        Number of data points left and right of the zero
- 334        crossing to be included in the linear fit. (Default: 5)
- 335    r_start : list
- 336        list which contains the first config to be read for each replicum.
- 337    r_stop : list
- 338        list which contains the last config to be read for each replicum.
- 339    r_step : int
- 340        integer that defines a fixed step size between two measurements (in units of configs)
- 341        If not given, r_step=1 is assumed.
- 342    plaquette : bool
- 343        If true extract the plaquette estimate of t0 instead.
- 344    names : list
- 345        list of names that is assigned to the data according according
- 346        to the order in the file list. Use careful, if you do not provide file names!
- 347    files : list
- 348        list which contains the filenames to be read. No automatic detection of
- 349        files performed if given.
- 350    plot_fit : bool
- 351        If true, the fit for the extraction of t0 is shown together with the data.
- 352    assume_thermalization : bool
- 353        If True: If the first record divided by the distance between two measurements is larger than
- 354        1, it is assumed that this is due to thermalization and the first measurement belongs
- 355        to the first config (default).
- 356        If False: The config numbers are assumed to be traj_number // difference
- 357
- 358    Returns
- 359    -------
- 360    t0 : Obs
- 361        Extracted t0
- 362    """
- 363
- 364    if 'files' in kwargs:
- 365        known_files = kwargs.get('files')
- 366    else:
- 367        known_files = []
- 368
- 369    ls = _find_files(path, prefix, 'ms', 'dat', known_files=known_files)
- 370
- 371    replica = len(ls)
- 372
- 373    if 'r_start' in kwargs:
- 374        r_start = kwargs.get('r_start')
- 375        if len(r_start) != replica:
- 376            raise Exception('r_start does not match number of replicas')
- 377        r_start = [o if o else None for o in r_start]
- 378    else:
- 379        r_start = [None] * replica
- 380
- 381    if 'r_stop' in kwargs:
- 382        r_stop = kwargs.get('r_stop')
- 383        if len(r_stop) != replica:
- 384            raise Exception('r_stop does not match number of replicas')
- 385    else:
- 386        r_stop = [None] * replica
- 387
- 388    if 'r_step' in kwargs:
- 389        r_step = kwargs.get('r_step')
- 390    else:
- 391        r_step = 1
+ 312    if 'r_stop' in kwargs:
+ 313        r_stop = kwargs.get('r_stop')
+ 314        if len(r_stop) != replica:
+ 315            raise Exception('r_stop does not match number of replicas')
+ 316    else:
+ 317        r_stop = [None] * replica
+ 318
+ 319    if 'r_step' in kwargs:
+ 320        r_step = kwargs.get('r_step')
+ 321    else:
+ 322        r_step = 1
+ 323
+ 324    print('Extract t0 from', prefix, ',', replica, 'replica')
+ 325
+ 326    if 'names' in kwargs:
+ 327        rep_names = kwargs.get('names')
+ 328    else:
+ 329        rep_names = []
+ 330        for entry in ls:
+ 331            truncated_entry = entry.split('.')[0]
+ 332            idx = truncated_entry.index('r')
+ 333            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+ 334
+ 335    Ysum = []
+ 336
+ 337    configlist = []
+ 338    r_start_index = []
+ 339    r_stop_index = []
+ 340
+ 341    for rep in range(replica):
+ 342
+ 343        with open(path + '/' + ls[rep], 'rb') as fp:
+ 344            t = fp.read(12)
+ 345            header = struct.unpack('iii', t)
+ 346            if rep == 0:
+ 347                dn = header[0]
+ 348                nn = header[1]
+ 349                tmax = header[2]
+ 350            elif dn != header[0] or nn != header[1] or tmax != header[2]:
+ 351                raise Exception('Replica parameters do not match.')
+ 352
+ 353            t = fp.read(8)
+ 354            if rep == 0:
+ 355                eps = struct.unpack('d', t)[0]
+ 356                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
+ 357            elif eps != struct.unpack('d', t)[0]:
+ 358                raise Exception('Values for eps do not match among replica.')
+ 359
+ 360            Ysl = []
+ 361
+ 362            configlist.append([])
+ 363            while True:
+ 364                t = fp.read(4)
+ 365                if (len(t) < 4):
+ 366                    break
+ 367                nc = struct.unpack('i', t)[0]
+ 368                configlist[-1].append(nc)
+ 369
+ 370                t = fp.read(8 * tmax * (nn + 1))
+ 371                if kwargs.get('plaquette'):
+ 372                    if nc % dtr_read == 0:
+ 373                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
+ 374                t = fp.read(8 * tmax * (nn + 1))
+ 375                if not kwargs.get('plaquette'):
+ 376                    if nc % dtr_read == 0:
+ 377                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
+ 378                t = fp.read(8 * tmax * (nn + 1))
+ 379
+ 380        Ysum.append([])
+ 381        for i, item in enumerate(Ysl):
+ 382            Ysum[-1].append([np.mean(item[current + xmin:
+ 383                             current + tmax - xmin])
+ 384                            for current in range(0, len(item), tmax)])
+ 385
+ 386        diffmeas = configlist[-1][-1] - configlist[-1][-2]
+ 387        configlist[-1] = [item // diffmeas for item in configlist[-1]]
+ 388        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
+ 389            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
+ 390            offset = configlist[-1][0] - 1
+ 391            configlist[-1] = [item - offset for item in configlist[-1]]
  392
- 393    print('Extract t0 from', prefix, ',', replica, 'replica')
- 394
- 395    if 'names' in kwargs:
- 396        rep_names = kwargs.get('names')
- 397    else:
- 398        rep_names = []
- 399        for entry in ls:
- 400            truncated_entry = entry.split('.')[0]
- 401            idx = truncated_entry.index('r')
- 402            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
- 403
- 404    Ysum = []
- 405
- 406    configlist = []
- 407    r_start_index = []
- 408    r_stop_index = []
- 409
- 410    for rep in range(replica):
- 411
- 412        with open(path + '/' + ls[rep], 'rb') as fp:
- 413            t = fp.read(12)
- 414            header = struct.unpack('iii', t)
- 415            if rep == 0:
- 416                dn = header[0]
- 417                nn = header[1]
- 418                tmax = header[2]
- 419            elif dn != header[0] or nn != header[1] or tmax != header[2]:
- 420                raise Exception('Replica parameters do not match.')
- 421
- 422            t = fp.read(8)
- 423            if rep == 0:
- 424                eps = struct.unpack('d', t)[0]
- 425                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
- 426            elif eps != struct.unpack('d', t)[0]:
- 427                raise Exception('Values for eps do not match among replica.')
+ 393        if r_start[rep] is None:
+ 394            r_start_index.append(0)
+ 395        else:
+ 396            try:
+ 397                r_start_index.append(configlist[-1].index(r_start[rep]))
+ 398            except ValueError:
+ 399                raise Exception('Config %d not in file with range [%d, %d]' % (
+ 400                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 401
+ 402        if r_stop[rep] is None:
+ 403            r_stop_index.append(len(configlist[-1]) - 1)
+ 404        else:
+ 405            try:
+ 406                r_stop_index.append(configlist[-1].index(r_stop[rep]))
+ 407            except ValueError:
+ 408                raise Exception('Config %d not in file with range [%d, %d]' % (
+ 409                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 410
+ 411    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
+ 412        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
+ 413    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+ 414    if np.any([step != 1 for step in stepsizes]):
+ 415        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
+ 416
+ 417    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
+ 418    t2E_dict = {}
+ 419    for n in range(nn + 1):
+ 420        samples = []
+ 421        for nrep, rep in enumerate(Ysum):
+ 422            samples.append([])
+ 423            for cnfg in rep:
+ 424                samples[-1].append(cnfg[n])
+ 425            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
+ 426        new_obs = Obs(samples, rep_names, idl=idl)
+ 427        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
  428
- 429            Ysl = []
- 430
- 431            configlist.append([])
- 432            while True:
- 433                t = fp.read(4)
- 434                if (len(t) < 4):
- 435                    break
- 436                nc = struct.unpack('i', t)[0]
- 437                configlist[-1].append(nc)
- 438
- 439                t = fp.read(8 * tmax * (nn + 1))
- 440                if kwargs.get('plaquette'):
- 441                    if nc % dtr_read == 0:
- 442                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
- 443                t = fp.read(8 * tmax * (nn + 1))
- 444                if not kwargs.get('plaquette'):
- 445                    if nc % dtr_read == 0:
- 446                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
- 447                t = fp.read(8 * tmax * (nn + 1))
- 448
- 449        Ysum.append([])
- 450        for i, item in enumerate(Ysl):
- 451            Ysum[-1].append([np.mean(item[current + xmin:
- 452                             current + tmax - xmin])
- 453                            for current in range(0, len(item), tmax)])
- 454
- 455        diffmeas = configlist[-1][-1] - configlist[-1][-2]
- 456        configlist[-1] = [item // diffmeas for item in configlist[-1]]
- 457        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
- 458            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
- 459            offset = configlist[-1][0] - 1
- 460            configlist[-1] = [item - offset for item in configlist[-1]]
- 461
- 462        if r_start[rep] is None:
- 463            r_start_index.append(0)
- 464        else:
- 465            try:
- 466                r_start_index.append(configlist[-1].index(r_start[rep]))
- 467            except ValueError:
- 468                raise Exception('Config %d not in file with range [%d, %d]' % (
- 469                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
- 470
- 471        if r_stop[rep] is None:
- 472            r_stop_index.append(len(configlist[-1]) - 1)
- 473        else:
- 474            try:
- 475                r_stop_index.append(configlist[-1].index(r_stop[rep]))
- 476            except ValueError:
- 477                raise Exception('Config %d not in file with range [%d, %d]' % (
- 478                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
- 479
- 480    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
- 481        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
- 482    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
- 483    if np.any([step != 1 for step in stepsizes]):
- 484        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
- 485
- 486    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
- 487    t2E_dict = {}
- 488    for n in range(nn + 1):
- 489        samples = []
- 490        for nrep, rep in enumerate(Ysum):
- 491            samples.append([])
- 492            for cnfg in rep:
- 493                samples[-1].append(cnfg[n])
- 494            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
- 495        new_obs = Obs(samples, rep_names, idl=idl)
- 496        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
- 497
- 498    zero_crossing = np.argmax(np.array(
- 499        [o.value for o in t2E_dict.values()]) > 0.0)
- 500
- 501    x = list(t2E_dict.keys())[zero_crossing - fit_range:
- 502                              zero_crossing + fit_range]
- 503    y = list(t2E_dict.values())[zero_crossing - fit_range:
- 504                                zero_crossing + fit_range]
- 505    [o.gamma_method() for o in y]
- 506
- 507    fit_result = fit_lin(x, y)
- 508
- 509    if kwargs.get('plot_fit'):
- 510        plt.figure()
- 511        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
- 512        ax0 = plt.subplot(gs[0])
- 513        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
- 514        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
- 515        [o.gamma_method() for o in ymore]
- 516        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
- 517        xplot = np.linspace(np.min(x), np.max(x))
- 518        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
- 519        [yi.gamma_method() for yi in yplot]
- 520        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
- 521        retval = (-fit_result[0] / fit_result[1])
- 522        retval.gamma_method()
- 523        ylim = ax0.get_ylim()
- 524        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
- 525        ax0.set_ylim(ylim)
- 526        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
- 527        xlim = ax0.get_xlim()
- 528
- 529        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
- 530        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
- 531        ax1 = plt.subplot(gs[1])
- 532        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
- 533        ax1.tick_params(direction='out')
- 534        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
- 535        ax1.axhline(y=0.0, ls='--', color='k')
- 536        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
- 537        ax1.set_xlim(xlim)
- 538        ax1.set_ylabel('Residuals')
- 539        ax1.set_xlabel(r'$t/a^2$')
- 540
- 541        plt.draw()
- 542    return -fit_result[0] / fit_result[1]
- 543
- 544
- 545def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
- 546    arr = []
- 547    if d == 2:
- 548        for i in range(n[0]):
- 549            tmp = wa[i * n[1]:(i + 1) * n[1]]
- 550            if quadrupel:
- 551                tmp2 = []
- 552                for j in range(0, len(tmp), 2):
- 553                    tmp2.append(tmp[j])
- 554                arr.append(tmp2)
- 555            else:
- 556                arr.append(np.asarray(tmp))
- 557
- 558    else:
- 559        raise Exception('Only two-dimensional arrays supported!')
+ 429    zero_crossing = np.argmax(np.array(
+ 430        [o.value for o in t2E_dict.values()]) > 0.0)
+ 431
+ 432    x = list(t2E_dict.keys())[zero_crossing - fit_range:
+ 433                              zero_crossing + fit_range]
+ 434    y = list(t2E_dict.values())[zero_crossing - fit_range:
+ 435                                zero_crossing + fit_range]
+ 436    [o.gamma_method() for o in y]
+ 437
+ 438    fit_result = fit_lin(x, y)
+ 439
+ 440    if kwargs.get('plot_fit'):
+ 441        plt.figure()
+ 442        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+ 443        ax0 = plt.subplot(gs[0])
+ 444        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+ 445        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+ 446        [o.gamma_method() for o in ymore]
+ 447        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
+ 448        xplot = np.linspace(np.min(x), np.max(x))
+ 449        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
+ 450        [yi.gamma_method() for yi in yplot]
+ 451        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
+ 452        retval = (-fit_result[0] / fit_result[1])
+ 453        retval.gamma_method()
+ 454        ylim = ax0.get_ylim()
+ 455        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
+ 456        ax0.set_ylim(ylim)
+ 457        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
+ 458        xlim = ax0.get_xlim()
+ 459
+ 460        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
+ 461        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
+ 462        ax1 = plt.subplot(gs[1])
+ 463        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+ 464        ax1.tick_params(direction='out')
+ 465        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+ 466        ax1.axhline(y=0.0, ls='--', color='k')
+ 467        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
+ 468        ax1.set_xlim(xlim)
+ 469        ax1.set_ylabel('Residuals')
+ 470        ax1.set_xlabel(r'$t/a^2$')
+ 471
+ 472        plt.draw()
+ 473    return -fit_result[0] / fit_result[1]
+ 474
+ 475
+ 476def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
+ 477    arr = []
+ 478    if d == 2:
+ 479        for i in range(n[0]):
+ 480            tmp = wa[i * n[1]:(i + 1) * n[1]]
+ 481            if quadrupel:
+ 482                tmp2 = []
+ 483                for j in range(0, len(tmp), 2):
+ 484                    tmp2.append(tmp[j])
+ 485                arr.append(tmp2)
+ 486            else:
+ 487                arr.append(np.asarray(tmp))
+ 488
+ 489    else:
+ 490        raise Exception('Only two-dimensional arrays supported!')
+ 491
+ 492    return arr
+ 493
+ 494
+ 495def _find_files(path, prefix, postfix, ext, known_files=[]):
+ 496    found = []
+ 497    files = []
+ 498
+ 499    if postfix != "":
+ 500        if postfix[-1] != ".":
+ 501            postfix = postfix + "."
+ 502        if postfix[0] != ".":
+ 503            postfix = "." + postfix
+ 504
+ 505    if ext[0] == ".":
+ 506        ext = ext[1:]
+ 507
+ 508    pattern = prefix + "*" + postfix + ext
+ 509
+ 510    for (dirpath, dirnames, filenames) in os.walk(path + "/"):
+ 511        found.extend(filenames)
+ 512        break
+ 513
+ 514    if known_files != []:
+ 515        for kf in known_files:
+ 516            if kf not in found:
+ 517                raise FileNotFoundError("Given file " + kf + " does not exist!")
+ 518
+ 519        return known_files
+ 520
+ 521    if not found:
+ 522        raise FileNotFoundError(f"Error, directory '{path}' not found")
+ 523
+ 524    for f in found:
+ 525        if fnmatch.fnmatch(f, pattern):
+ 526            files.append(f)
+ 527
+ 528    if files == []:
+ 529        raise Exception("No files found after pattern filter!")
+ 530
+ 531    files = sort_names(files)
+ 532    return files
+ 533
+ 534
+ 535def _read_array_openQCD2(fp):
+ 536    t = fp.read(4)
+ 537    d = struct.unpack('i', t)[0]
+ 538    t = fp.read(4 * d)
+ 539    n = struct.unpack('%di' % (d), t)
+ 540    t = fp.read(4)
+ 541    size = struct.unpack('i', t)[0]
+ 542    if size == 4:
+ 543        types = 'i'
+ 544    elif size == 8:
+ 545        types = 'd'
+ 546    elif size == 16:
+ 547        types = 'dd'
+ 548    else:
+ 549        raise Exception("Type for size '" + str(size) + "' not known.")
+ 550    m = n[0]
+ 551    for i in range(1, d):
+ 552        m *= n[i]
+ 553
+ 554    t = fp.read(m * size)
+ 555    tmp = struct.unpack('%d%s' % (m, types), t)
+ 556
+ 557    arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True)
+ 558    return {'d': d, 'n': n, 'size': size, 'arr': arr}
+ 559
  560
- 561    return arr
- 562
+ 561def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
+ 562    """Read the topologial charge based on openQCD gradient flow measurements.
  563
- 564def _read_array_openQCD2(fp):
- 565    t = fp.read(4)
- 566    d = struct.unpack('i', t)[0]
- 567    t = fp.read(4 * d)
- 568    n = struct.unpack('%di' % (d), t)
- 569    t = fp.read(4)
- 570    size = struct.unpack('i', t)[0]
- 571    if size == 4:
- 572        types = 'i'
- 573    elif size == 8:
- 574        types = 'd'
- 575    elif size == 16:
- 576        types = 'dd'
- 577    else:
- 578        raise Exception("Type for size '" + str(size) + "' not known.")
- 579    m = n[0]
- 580    for i in range(1, d):
- 581        m *= n[i]
- 582
- 583    t = fp.read(m * size)
- 584    tmp = struct.unpack('%d%s' % (m, types), t)
- 585
- 586    arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True)
- 587    return {'d': d, 'n': n, 'size': size, 'arr': arr}
- 588
- 589
- 590def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
- 591    """Read the topologial charge based on openQCD gradient flow measurements.
- 592
- 593    Parameters
- 594    ----------
- 595    path : str
- 596        path of the measurement files
- 597    prefix : str
- 598        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
- 599        Ignored if file names are passed explicitly via keyword files.
- 600    c : double
- 601        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
- 602    dtr_cnfg : int
- 603        (optional) parameter that specifies the number of measurements
- 604        between two configs.
- 605        If it is not set, the distance between two measurements
- 606        in the file is assumed to be the distance between two configurations.
- 607    steps : int
- 608        (optional) Distance between two configurations in units of trajectories /
- 609         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
- 610    version : str
- 611        Either openQCD or sfqcd, depending on the data.
- 612    L : int
- 613        spatial length of the lattice in L/a.
- 614        HAS to be set if version != sfqcd, since openQCD does not provide
- 615        this in the header
- 616    r_start : list
- 617        list which contains the first config to be read for each replicum.
- 618    r_stop : list
- 619        list which contains the last config to be read for each replicum.
- 620    files : list
- 621        specify the exact files that need to be read
- 622        from path, practical if e.g. only one replicum is needed
- 623    postfix : str
- 624        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
- 625    names : list
- 626        Alternative labeling for replicas/ensembles.
- 627        Has to have the appropriate length.
- 628    Zeuthen_flow : bool
- 629        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
- 630        for version=='sfqcd' If False, the Wilson flow is used.
- 631    integer_charge : bool
- 632        If True, the charge is rounded towards the nearest integer on each config.
- 633
- 634    Returns
- 635    -------
- 636    result : Obs
- 637        Read topological charge
- 638    """
- 639
- 640    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
- 641
- 642
- 643def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
- 644    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
- 645
- 646    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
- 647
- 648    Parameters
- 649    ----------
- 650    path : str
- 651        path of the measurement files
- 652    prefix : str
- 653        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
- 654        Ignored if file names are passed explicitly via keyword files.
- 655    c : double
- 656        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
- 657    dtr_cnfg : int
- 658        (optional) parameter that specifies the number of measurements
- 659        between two configs.
- 660        If it is not set, the distance between two measurements
- 661        in the file is assumed to be the distance between two configurations.
- 662    steps : int
- 663        (optional) Distance between two configurations in units of trajectories /
- 664         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
- 665    r_start : list
- 666        list which contains the first config to be read for each replicum.
- 667    r_stop : list
- 668        list which contains the last config to be read for each replicum.
- 669    files : list
- 670        specify the exact files that need to be read
- 671        from path, practical if e.g. only one replicum is needed
- 672    names : list
- 673        Alternative labeling for replicas/ensembles.
- 674        Has to have the appropriate length.
- 675    postfix : str
- 676        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
- 677    Zeuthen_flow : bool
- 678        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
- 679    """
- 680
- 681    if c != 0.3:
- 682        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
- 683
- 684    plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
- 685    C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
- 686    L = plaq.tag["L"]
- 687    T = plaq.tag["T"]
+ 564    Parameters
+ 565    ----------
+ 566    path : str
+ 567        path of the measurement files
+ 568    prefix : str
+ 569        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+ 570        Ignored if file names are passed explicitly via keyword files.
+ 571    c : double
+ 572        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+ 573    dtr_cnfg : int
+ 574        (optional) parameter that specifies the number of measurements
+ 575        between two configs.
+ 576        If it is not set, the distance between two measurements
+ 577        in the file is assumed to be the distance between two configurations.
+ 578    steps : int
+ 579        (optional) Distance between two configurations in units of trajectories /
+ 580         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 581    version : str
+ 582        Either openQCD or sfqcd, depending on the data.
+ 583    L : int
+ 584        spatial length of the lattice in L/a.
+ 585        HAS to be set if version != sfqcd, since openQCD does not provide
+ 586        this in the header
+ 587    r_start : list
+ 588        list which contains the first config to be read for each replicum.
+ 589    r_stop : list
+ 590        list which contains the last config to be read for each replicum.
+ 591    files : list
+ 592        specify the exact files that need to be read
+ 593        from path, practical if e.g. only one replicum is needed
+ 594    postfix : str
+ 595        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+ 596    names : list
+ 597        Alternative labeling for replicas/ensembles.
+ 598        Has to have the appropriate length.
+ 599    Zeuthen_flow : bool
+ 600        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+ 601        for version=='sfqcd' If False, the Wilson flow is used.
+ 602    integer_charge : bool
+ 603        If True, the charge is rounded towards the nearest integer on each config.
+ 604
+ 605    Returns
+ 606    -------
+ 607    result : Obs
+ 608        Read topological charge
+ 609    """
+ 610
+ 611    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
+ 612
+ 613
+ 614def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
+ 615    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
+ 616
+ 617    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
+ 618
+ 619    Parameters
+ 620    ----------
+ 621    path : str
+ 622        path of the measurement files
+ 623    prefix : str
+ 624        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+ 625        Ignored if file names are passed explicitly via keyword files.
+ 626    c : double
+ 627        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+ 628    dtr_cnfg : int
+ 629        (optional) parameter that specifies the number of measurements
+ 630        between two configs.
+ 631        If it is not set, the distance between two measurements
+ 632        in the file is assumed to be the distance between two configurations.
+ 633    steps : int
+ 634        (optional) Distance between two configurations in units of trajectories /
+ 635         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 636    r_start : list
+ 637        list which contains the first config to be read for each replicum.
+ 638    r_stop : list
+ 639        list which contains the last config to be read for each replicum.
+ 640    files : list
+ 641        specify the exact files that need to be read
+ 642        from path, practical if e.g. only one replicum is needed
+ 643    names : list
+ 644        Alternative labeling for replicas/ensembles.
+ 645        Has to have the appropriate length.
+ 646    postfix : str
+ 647        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+ 648    Zeuthen_flow : bool
+ 649        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
+ 650    """
+ 651
+ 652    if c != 0.3:
+ 653        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
+ 654
+ 655    plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
+ 656    C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
+ 657    L = plaq.tag["L"]
+ 658    T = plaq.tag["T"]
+ 659
+ 660    if T != L:
+ 661        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
+ 662
+ 663    if Zeuthen_flow is not True:
+ 664        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
+ 665
+ 666    t = (c * L) ** 2 / 8
+ 667
+ 668    normdict = {4: 0.012341170468270,
+ 669                6: 0.010162691462430,
+ 670                8: 0.009031614807931,
+ 671                10: 0.008744966371393,
+ 672                12: 0.008650917856809,
+ 673                14: 8.611154391267955E-03,
+ 674                16: 0.008591758449508,
+ 675                20: 0.008575359627103,
+ 676                24: 0.008569387847540,
+ 677                28: 8.566803713382559E-03,
+ 678                32: 0.008565541650006,
+ 679                40: 8.564480684962046E-03,
+ 680                48: 8.564098025073460E-03,
+ 681                64: 8.563853943383087E-03}
+ 682
+ 683    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
+ 684
+ 685
+ 686def _read_flow_obs(path, prefix, c, dtr_cnfg=1, version="openQCD", obspos=0, sum_t=True, **kwargs):
+ 687    """Read a flow observable based on openQCD gradient flow measurements.
  688
- 689    if T != L:
- 690        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
- 691
- 692    if Zeuthen_flow is not True:
- 693        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
- 694
- 695    t = (c * L) ** 2 / 8
- 696
- 697    normdict = {4: 0.012341170468270,
- 698                6: 0.010162691462430,
- 699                8: 0.009031614807931,
- 700                10: 0.008744966371393,
- 701                12: 0.008650917856809,
- 702                14: 8.611154391267955E-03,
- 703                16: 0.008591758449508,
- 704                20: 0.008575359627103,
- 705                24: 0.008569387847540,
- 706                28: 8.566803713382559E-03,
- 707                32: 0.008565541650006,
- 708                40: 8.564480684962046E-03,
- 709                48: 8.564098025073460E-03,
- 710                64: 8.563853943383087E-03}
- 711
- 712    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
- 713
- 714
- 715def _read_flow_obs(path, prefix, c, dtr_cnfg=1, version="openQCD", obspos=0, sum_t=True, **kwargs):
- 716    """Read a flow observable based on openQCD gradient flow measurements.
- 717
- 718    Parameters
- 719    ----------
- 720    path : str
- 721        path of the measurement files
- 722    prefix : str
- 723        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
- 724        Ignored if file names are passed explicitly via keyword files.
- 725    c : double
- 726        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
- 727    dtr_cnfg : int
- 728        (optional) parameter that specifies the number of measurements
- 729        between two configs.
- 730        If it is not set, the distance between two measurements
- 731        in the file is assumed to be the distance between two configurations.
- 732    steps : int
- 733        (optional) Distance between two configurations in units of trajectories /
- 734         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
- 735    version : str
- 736        Either openQCD or sfqcd, depending on the data.
- 737    obspos : int
- 738        position of the obeservable in the measurement file. Only relevant for sfqcd files.
- 739    sum_t : bool
- 740        If true sum over all timeslices, if false only take the value at T/2.
- 741    L : int
- 742        spatial length of the lattice in L/a.
- 743        HAS to be set if version != sfqcd, since openQCD does not provide
- 744        this in the header
- 745    r_start : list
- 746        list which contains the first config to be read for each replicum.
- 747    r_stop : list
- 748        list which contains the last config to be read for each replicum.
- 749    files : list
- 750        specify the exact files that need to be read
- 751        from path, practical if e.g. only one replicum is needed
- 752    names : list
- 753        Alternative labeling for replicas/ensembles.
- 754        Has to have the appropriate length.
- 755    postfix : str
- 756        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
- 757    Zeuthen_flow : bool
- 758        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
- 759        for version=='sfqcd' If False, the Wilson flow is used.
- 760    integer_charge : bool
- 761        If True, the charge is rounded towards the nearest integer on each config.
- 762
- 763    Returns
- 764    -------
- 765    result : Obs
- 766        flow observable specified
- 767    """
- 768    known_versions = ["openQCD", "sfqcd"]
- 769
- 770    if version not in known_versions:
- 771        raise Exception("Unknown openQCD version.")
- 772    if "steps" in kwargs:
- 773        steps = kwargs.get("steps")
- 774    if version == "sfqcd":
- 775        if "L" in kwargs:
- 776            supposed_L = kwargs.get("L")
- 777        else:
- 778            supposed_L = None
- 779        postfix = "gfms"
+ 689    Parameters
+ 690    ----------
+ 691    path : str
+ 692        path of the measurement files
+ 693    prefix : str
+ 694        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+ 695        Ignored if file names are passed explicitly via keyword files.
+ 696    c : double
+ 697        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+ 698    dtr_cnfg : int
+ 699        (optional) parameter that specifies the number of measurements
+ 700        between two configs.
+ 701        If it is not set, the distance between two measurements
+ 702        in the file is assumed to be the distance between two configurations.
+ 703    steps : int
+ 704        (optional) Distance between two configurations in units of trajectories /
+ 705         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 706    version : str
+ 707        Either openQCD or sfqcd, depending on the data.
+ 708    obspos : int
+ 709        position of the obeservable in the measurement file. Only relevant for sfqcd files.
+ 710    sum_t : bool
+ 711        If true sum over all timeslices, if false only take the value at T/2.
+ 712    L : int
+ 713        spatial length of the lattice in L/a.
+ 714        HAS to be set if version != sfqcd, since openQCD does not provide
+ 715        this in the header
+ 716    r_start : list
+ 717        list which contains the first config to be read for each replicum.
+ 718    r_stop : list
+ 719        list which contains the last config to be read for each replicum.
+ 720    files : list
+ 721        specify the exact files that need to be read
+ 722        from path, practical if e.g. only one replicum is needed
+ 723    names : list
+ 724        Alternative labeling for replicas/ensembles.
+ 725        Has to have the appropriate length.
+ 726    postfix : str
+ 727        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+ 728    Zeuthen_flow : bool
+ 729        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+ 730        for version=='sfqcd' If False, the Wilson flow is used.
+ 731    integer_charge : bool
+ 732        If True, the charge is rounded towards the nearest integer on each config.
+ 733
+ 734    Returns
+ 735    -------
+ 736    result : Obs
+ 737        flow observable specified
+ 738    """
+ 739    known_versions = ["openQCD", "sfqcd"]
+ 740
+ 741    if version not in known_versions:
+ 742        raise Exception("Unknown openQCD version.")
+ 743    if "steps" in kwargs:
+ 744        steps = kwargs.get("steps")
+ 745    if version == "sfqcd":
+ 746        if "L" in kwargs:
+ 747            supposed_L = kwargs.get("L")
+ 748        else:
+ 749            supposed_L = None
+ 750        postfix = "gfms"
+ 751    else:
+ 752        if "L" not in kwargs:
+ 753            raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.")
+ 754        else:
+ 755            L = kwargs.get("L")
+ 756        postfix = "ms"
+ 757
+ 758    if "postfix" in kwargs:
+ 759        postfix = kwargs.get("postfix")
+ 760
+ 761    if "files" in kwargs:
+ 762        known_files = kwargs.get("files")
+ 763    else:
+ 764        known_files = []
+ 765
+ 766    files = _find_files(path, prefix, postfix, "dat", known_files=known_files)
+ 767
+ 768    if 'r_start' in kwargs:
+ 769        r_start = kwargs.get('r_start')
+ 770        if len(r_start) != len(files):
+ 771            raise Exception('r_start does not match number of replicas')
+ 772        r_start = [o if o else None for o in r_start]
+ 773    else:
+ 774        r_start = [None] * len(files)
+ 775
+ 776    if 'r_stop' in kwargs:
+ 777        r_stop = kwargs.get('r_stop')
+ 778        if len(r_stop) != len(files):
+ 779            raise Exception('r_stop does not match number of replicas')
  780    else:
- 781        if "L" not in kwargs:
- 782            raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.")
- 783        else:
- 784            L = kwargs.get("L")
- 785        postfix = "ms"
- 786
- 787    if "postfix" in kwargs:
- 788        postfix = kwargs.get("postfix")
- 789
- 790    if "files" in kwargs:
- 791        known_files = kwargs.get("files")
- 792    else:
- 793        known_files = []
- 794
- 795    files = _find_files(path, prefix, postfix, "dat", known_files=known_files)
+ 781        r_stop = [None] * len(files)
+ 782    rep_names = []
+ 783
+ 784    zeuthen = kwargs.get('Zeuthen_flow', False)
+ 785    if zeuthen and version not in ['sfqcd']:
+ 786        raise Exception('Zeuthen flow can only be used for version==sfqcd')
+ 787
+ 788    r_start_index = []
+ 789    r_stop_index = []
+ 790    deltas = []
+ 791    configlist = []
+ 792    if not zeuthen:
+ 793        obspos += 8
+ 794    for rep, file in enumerate(files):
+ 795        with open(path + "/" + file, "rb") as fp:
  796
- 797    if 'r_start' in kwargs:
- 798        r_start = kwargs.get('r_start')
- 799        if len(r_start) != len(files):
- 800            raise Exception('r_start does not match number of replicas')
- 801        r_start = [o if o else None for o in r_start]
- 802    else:
- 803        r_start = [None] * len(files)
- 804
- 805    if 'r_stop' in kwargs:
- 806        r_stop = kwargs.get('r_stop')
- 807        if len(r_stop) != len(files):
- 808            raise Exception('r_stop does not match number of replicas')
- 809    else:
- 810        r_stop = [None] * len(files)
- 811    rep_names = []
- 812
- 813    zeuthen = kwargs.get('Zeuthen_flow', False)
- 814    if zeuthen and version not in ['sfqcd']:
- 815        raise Exception('Zeuthen flow can only be used for version==sfqcd')
- 816
- 817    r_start_index = []
- 818    r_stop_index = []
- 819    deltas = []
- 820    configlist = []
- 821    if not zeuthen:
- 822        obspos += 8
- 823    for rep, file in enumerate(files):
- 824        with open(path + "/" + file, "rb") as fp:
- 825
- 826            Q = []
- 827            traj_list = []
- 828            if version in ['sfqcd']:
- 829                t = fp.read(12)
- 830                header = struct.unpack('<iii', t)
- 831                zthfl = header[0]  # Zeuthen flow -> if it's equal to 2 it means that the Zeuthen flow is also 'measured' (apart from the Wilson flow)
- 832                ncs = header[1]  # number of different values for c in t_flow=1/8 c² L² -> measurements done for ncs c's
- 833                tmax = header[2]  # lattice T/a
+ 797            Q = []
+ 798            traj_list = []
+ 799            if version in ['sfqcd']:
+ 800                t = fp.read(12)
+ 801                header = struct.unpack('<iii', t)
+ 802                zthfl = header[0]  # Zeuthen flow -> if it's equal to 2 it means that the Zeuthen flow is also 'measured' (apart from the Wilson flow)
+ 803                ncs = header[1]  # number of different values for c in t_flow=1/8 c² L² -> measurements done for ncs c's
+ 804                tmax = header[2]  # lattice T/a
+ 805
+ 806                t = fp.read(12)
+ 807                Ls = struct.unpack('<iii', t)
+ 808                if (Ls[0] == Ls[1] and Ls[1] == Ls[2]):
+ 809                    L = Ls[0]
+ 810                    if not (supposed_L == L) and supposed_L:
+ 811                        raise Exception("It seems the length given in the header and by you contradict each other")
+ 812                else:
+ 813                    raise Exception("Found more than one spatial length in header!")
+ 814
+ 815                t = fp.read(16)
+ 816                header2 = struct.unpack('<dd', t)
+ 817                tol = header2[0]
+ 818                cmax = header2[1]  # highest value of c used
+ 819
+ 820                if c > cmax:
+ 821                    raise Exception('Flow has been determined between c=0 and c=%lf with tolerance %lf' % (cmax, tol))
+ 822
+ 823                if (zthfl == 2):
+ 824                    nfl = 2  # number of flows
+ 825                else:
+ 826                    nfl = 1
+ 827                iobs = 8 * nfl  # number of flow observables calculated
+ 828
+ 829                while True:
+ 830                    t = fp.read(4)
+ 831                    if (len(t) < 4):
+ 832                        break
+ 833                    traj_list.append(struct.unpack('i', t)[0])   # trajectory number when measurement was done
  834
- 835                t = fp.read(12)
- 836                Ls = struct.unpack('<iii', t)
- 837                if (Ls[0] == Ls[1] and Ls[1] == Ls[2]):
- 838                    L = Ls[0]
- 839                    if not (supposed_L == L) and supposed_L:
- 840                        raise Exception("It seems the length given in the header and by you contradict each other")
- 841                else:
- 842                    raise Exception("Found more than one spatial length in header!")
- 843
- 844                t = fp.read(16)
- 845                header2 = struct.unpack('<dd', t)
- 846                tol = header2[0]
- 847                cmax = header2[1]  # highest value of c used
- 848
- 849                if c > cmax:
- 850                    raise Exception('Flow has been determined between c=0 and c=%lf with tolerance %lf' % (cmax, tol))
- 851
- 852                if (zthfl == 2):
- 853                    nfl = 2  # number of flows
- 854                else:
- 855                    nfl = 1
- 856                iobs = 8 * nfl  # number of flow observables calculated
- 857
- 858                while True:
- 859                    t = fp.read(4)
- 860                    if (len(t) < 4):
- 861                        break
- 862                    traj_list.append(struct.unpack('i', t)[0])   # trajectory number when measurement was done
- 863
- 864                    for j in range(ncs + 1):
- 865                        for i in range(iobs):
- 866                            t = fp.read(8 * tmax)
- 867                            if (i == obspos):  # determines the flow observable -> i=0 <-> Zeuthen flow
- 868                                Q.append(struct.unpack('d' * tmax, t))
- 869
- 870            else:
- 871                t = fp.read(12)
- 872                header = struct.unpack('<iii', t)
- 873                # step size in integration steps "dnms"
- 874                dn = header[0]
- 875                # number of measurements, so "ntot"/dn
- 876                nn = header[1]
- 877                # lattice T/a
- 878                tmax = header[2]
+ 835                    for j in range(ncs + 1):
+ 836                        for i in range(iobs):
+ 837                            t = fp.read(8 * tmax)
+ 838                            if (i == obspos):  # determines the flow observable -> i=0 <-> Zeuthen flow
+ 839                                Q.append(struct.unpack('d' * tmax, t))
+ 840
+ 841            else:
+ 842                t = fp.read(12)
+ 843                header = struct.unpack('<iii', t)
+ 844                # step size in integration steps "dnms"
+ 845                dn = header[0]
+ 846                # number of measurements, so "ntot"/dn
+ 847                nn = header[1]
+ 848                # lattice T/a
+ 849                tmax = header[2]
+ 850
+ 851                t = fp.read(8)
+ 852                eps = struct.unpack('d', t)[0]
+ 853
+ 854                while True:
+ 855                    t = fp.read(4)
+ 856                    if (len(t) < 4):
+ 857                        break
+ 858                    traj_list.append(struct.unpack('i', t)[0])
+ 859                    # Wsl
+ 860                    t = fp.read(8 * tmax * (nn + 1))
+ 861                    # Ysl
+ 862                    t = fp.read(8 * tmax * (nn + 1))
+ 863                    # Qsl, which is asked for in this method
+ 864                    t = fp.read(8 * tmax * (nn + 1))
+ 865                    # unpack the array of Qtops,
+ 866                    # on each timeslice t=0,...,tmax-1 and the
+ 867                    # measurement number in = 0...nn (see README.qcd1)
+ 868                    tmpd = struct.unpack('d' * tmax * (nn + 1), t)
+ 869                    Q.append(tmpd)
+ 870
+ 871        if len(np.unique(np.diff(traj_list))) != 1:
+ 872            raise Exception("Irregularities in stepsize found")
+ 873        else:
+ 874            if 'steps' in kwargs:
+ 875                if steps != traj_list[1] - traj_list[0]:
+ 876                    raise Exception("steps and the found stepsize are not the same")
+ 877            else:
+ 878                steps = traj_list[1] - traj_list[0]
  879
- 880                t = fp.read(8)
- 881                eps = struct.unpack('d', t)[0]
- 882
- 883                while True:
- 884                    t = fp.read(4)
- 885                    if (len(t) < 4):
- 886                        break
- 887                    traj_list.append(struct.unpack('i', t)[0])
- 888                    # Wsl
- 889                    t = fp.read(8 * tmax * (nn + 1))
- 890                    # Ysl
- 891                    t = fp.read(8 * tmax * (nn + 1))
- 892                    # Qsl, which is asked for in this method
- 893                    t = fp.read(8 * tmax * (nn + 1))
- 894                    # unpack the array of Qtops,
- 895                    # on each timeslice t=0,...,tmax-1 and the
- 896                    # measurement number in = 0...nn (see README.qcd1)
- 897                    tmpd = struct.unpack('d' * tmax * (nn + 1), t)
- 898                    Q.append(tmpd)
- 899
- 900        if len(np.unique(np.diff(traj_list))) != 1:
- 901            raise Exception("Irregularities in stepsize found")
- 902        else:
- 903            if 'steps' in kwargs:
- 904                if steps != traj_list[1] - traj_list[0]:
- 905                    raise Exception("steps and the found stepsize are not the same")
- 906            else:
- 907                steps = traj_list[1] - traj_list[0]
- 908
- 909        configlist.append([tr // steps // dtr_cnfg for tr in traj_list])
- 910        if configlist[-1][0] > 1:
- 911            offset = configlist[-1][0] - 1
- 912            warnings.warn('Assume thermalization and that the first measurement belongs to the first config. Offset = %d configs (%d trajectories / cycles)' % (
- 913                offset, offset * steps))
- 914            configlist[-1] = [item - offset for item in configlist[-1]]
- 915
- 916        if r_start[rep] is None:
- 917            r_start_index.append(0)
- 918        else:
- 919            try:
- 920                r_start_index.append(configlist[-1].index(r_start[rep]))
- 921            except ValueError:
- 922                raise Exception('Config %d not in file with range [%d, %d]' % (
- 923                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
- 924
- 925        if r_stop[rep] is None:
- 926            r_stop_index.append(len(configlist[-1]) - 1)
- 927        else:
- 928            try:
- 929                r_stop_index.append(configlist[-1].index(r_stop[rep]))
- 930            except ValueError:
- 931                raise Exception('Config %d not in file with range [%d, %d]' % (
- 932                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 880        configlist.append([tr // steps // dtr_cnfg for tr in traj_list])
+ 881        if configlist[-1][0] > 1:
+ 882            offset = configlist[-1][0] - 1
+ 883            warnings.warn('Assume thermalization and that the first measurement belongs to the first config. Offset = %d configs (%d trajectories / cycles)' % (
+ 884                offset, offset * steps))
+ 885            configlist[-1] = [item - offset for item in configlist[-1]]
+ 886
+ 887        if r_start[rep] is None:
+ 888            r_start_index.append(0)
+ 889        else:
+ 890            try:
+ 891                r_start_index.append(configlist[-1].index(r_start[rep]))
+ 892            except ValueError:
+ 893                raise Exception('Config %d not in file with range [%d, %d]' % (
+ 894                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 895
+ 896        if r_stop[rep] is None:
+ 897            r_stop_index.append(len(configlist[-1]) - 1)
+ 898        else:
+ 899            try:
+ 900                r_stop_index.append(configlist[-1].index(r_stop[rep]))
+ 901            except ValueError:
+ 902                raise Exception('Config %d not in file with range [%d, %d]' % (
+ 903                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 904
+ 905        if version in ['sfqcd']:
+ 906            cstepsize = cmax / ncs
+ 907            index_aim = round(c / cstepsize)
+ 908        else:
+ 909            t_aim = (c * L) ** 2 / 8
+ 910            index_aim = round(t_aim / eps / dn)
+ 911
+ 912        Q_sum = []
+ 913        for i, item in enumerate(Q):
+ 914            if sum_t is True:
+ 915                Q_sum.append([sum(item[current:current + tmax])
+ 916                             for current in range(0, len(item), tmax)])
+ 917            else:
+ 918                Q_sum.append([item[int(tmax / 2)]])
+ 919        Q_top = []
+ 920        if version in ['sfqcd']:
+ 921            for i in range(len(Q_sum) // (ncs + 1)):
+ 922                Q_top.append(Q_sum[i * (ncs + 1) + index_aim][0])
+ 923        else:
+ 924            for i in range(len(Q) // dtr_cnfg):
+ 925                Q_top.append(Q_sum[dtr_cnfg * i][index_aim])
+ 926        if len(Q_top) != len(traj_list) // dtr_cnfg:
+ 927            raise Exception("qtops and traj_list dont have the same length")
+ 928
+ 929        if kwargs.get('integer_charge', False):
+ 930            Q_top = [round(q) for q in Q_top]
+ 931
+ 932        truncated_file = file[:-len(postfix)]
  933
- 934        if version in ['sfqcd']:
- 935            cstepsize = cmax / ncs
- 936            index_aim = round(c / cstepsize)
- 937        else:
- 938            t_aim = (c * L) ** 2 / 8
- 939            index_aim = round(t_aim / eps / dn)
- 940
- 941        Q_sum = []
- 942        for i, item in enumerate(Q):
- 943            if sum_t is True:
- 944                Q_sum.append([sum(item[current:current + tmax])
- 945                             for current in range(0, len(item), tmax)])
- 946            else:
- 947                Q_sum.append([item[int(tmax / 2)]])
- 948        Q_top = []
- 949        if version in ['sfqcd']:
- 950            for i in range(len(Q_sum) // (ncs + 1)):
- 951                Q_top.append(Q_sum[i * (ncs + 1) + index_aim][0])
- 952        else:
- 953            for i in range(len(Q) // dtr_cnfg):
- 954                Q_top.append(Q_sum[dtr_cnfg * i][index_aim])
- 955        if len(Q_top) != len(traj_list) // dtr_cnfg:
- 956            raise Exception("qtops and traj_list dont have the same length")
+ 934        if "names" not in kwargs:
+ 935            try:
+ 936                idx = truncated_file.index('r')
+ 937            except Exception:
+ 938                if "names" not in kwargs:
+ 939                    raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
+ 940            ens_name = truncated_file[:idx]
+ 941            rep_names.append(ens_name + '|' + truncated_file[idx:].split(".")[0])
+ 942        else:
+ 943            names = kwargs.get("names")
+ 944            rep_names = names
+ 945
+ 946        deltas.append(Q_top)
+ 947
+ 948    rep_names = sort_names(rep_names)
+ 949
+ 950    idl = [range(int(configlist[rep][r_start_index[rep]]), int(configlist[rep][r_stop_index[rep]]) + 1, 1) for rep in range(len(deltas))]
+ 951    deltas = [deltas[nrep][r_start_index[nrep]:r_stop_index[nrep] + 1] for nrep in range(len(deltas))]
+ 952    result = Obs(deltas, rep_names, idl=idl)
+ 953    result.tag = {"T": tmax - 1,
+ 954                  "L": L}
+ 955    return result
+ 956
  957
- 958        if kwargs.get('integer_charge', False):
- 959            Q_top = [round(q) for q in Q_top]
+ 958def qtop_projection(qtop, target=0):
+ 959    """Returns the projection to the topological charge sector defined by target.
  960
- 961        truncated_file = file[:-len(postfix)]
- 962
- 963        if "names" not in kwargs:
- 964            try:
- 965                idx = truncated_file.index('r')
- 966            except Exception:
- 967                if "names" not in kwargs:
- 968                    raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
- 969            ens_name = truncated_file[:idx]
- 970            rep_names.append(ens_name + '|' + truncated_file[idx:].split(".")[0])
- 971        else:
- 972            names = kwargs.get("names")
- 973            rep_names = names
- 974
- 975        deltas.append(Q_top)
- 976
- 977    rep_names = _sort_names(rep_names)
- 978
- 979    idl = [range(int(configlist[rep][r_start_index[rep]]), int(configlist[rep][r_stop_index[rep]]) + 1, 1) for rep in range(len(deltas))]
- 980    deltas = [deltas[nrep][r_start_index[nrep]:r_stop_index[nrep] + 1] for nrep in range(len(deltas))]
- 981    result = Obs(deltas, rep_names, idl=idl)
- 982    result.tag = {"T": tmax - 1,
- 983                  "L": L}
- 984    return result
- 985
+ 961    Parameters
+ 962    ----------
+ 963    path : Obs
+ 964        Topological charge.
+ 965    target : int
+ 966        Specifies the topological sector to be reweighted to (default 0)
+ 967
+ 968    Returns
+ 969    -------
+ 970    reto : Obs
+ 971        projection to the topological charge sector defined by target
+ 972    """
+ 973    if qtop.reweighted:
+ 974        raise Exception('You can not use a reweighted observable for reweighting!')
+ 975
+ 976    proj_qtop = []
+ 977    for n in qtop.deltas:
+ 978        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
+ 979
+ 980    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
+ 981    return reto
+ 982
+ 983
+ 984def read_qtop_sector(path, prefix, c, target=0, **kwargs):
+ 985    """Constructs reweighting factors to a specified topological sector.
  986
- 987def qtop_projection(qtop, target=0):
- 988    """Returns the projection to the topological charge sector defined by target.
- 989
- 990    Parameters
- 991    ----------
- 992    path : Obs
- 993        Topological charge.
- 994    target : int
- 995        Specifies the topological sector to be reweighted to (default 0)
- 996
- 997    Returns
- 998    -------
- 999    reto : Obs
-1000        projection to the topological charge sector defined by target
-1001    """
-1002    if qtop.reweighted:
-1003        raise Exception('You can not use a reweighted observable for reweighting!')
-1004
-1005    proj_qtop = []
-1006    for n in qtop.deltas:
-1007        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
-1008
-1009    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
-1010    return reto
-1011
-1012
-1013def read_qtop_sector(path, prefix, c, target=0, **kwargs):
-1014    """Constructs reweighting factors to a specified topological sector.
-1015
-1016    Parameters
-1017    ----------
-1018    path : str
-1019        path of the measurement files
-1020    prefix : str
-1021        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
-1022    c : double
-1023        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
-1024    target : int
-1025        Specifies the topological sector to be reweighted to (default 0)
-1026    dtr_cnfg : int
-1027        (optional) parameter that specifies the number of trajectories
-1028        between two configs.
-1029        if it is not set, the distance between two measurements
-1030        in the file is assumed to be the distance between two configurations.
-1031    steps : int
-1032        (optional) Distance between two configurations in units of trajectories /
-1033         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-1034    version : str
-1035        version string of the openQCD (sfqcd) version used to create
-1036        the ensemble. Default is 2.0. May also be set to sfqcd.
-1037    L : int
-1038        spatial length of the lattice in L/a.
-1039        HAS to be set if version != sfqcd, since openQCD does not provide
-1040        this in the header
-1041    r_start : list
-1042        offset of the first ensemble, making it easier to match
-1043        later on with other Obs
-1044    r_stop : list
-1045        last configurations that need to be read (per replicum)
-1046    files : list
-1047        specify the exact files that need to be read
-1048        from path, practical if e.g. only one replicum is needed
-1049    names : list
-1050        Alternative labeling for replicas/ensembles.
-1051        Has to have the appropriate length
-1052    Zeuthen_flow : bool
-1053        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-1054        for version=='sfqcd' If False, the Wilson flow is used.
-1055
-1056    Returns
-1057    -------
-1058    reto : Obs
-1059        projection to the topological charge sector defined by target
-1060    """
-1061
-1062    if not isinstance(target, int):
-1063        raise Exception("'target' has to be an integer.")
-1064
-1065    kwargs['integer_charge'] = True
-1066    qtop = read_qtop(path, prefix, c, **kwargs)
-1067
-1068    return qtop_projection(qtop, target=target)
-1069
+ 987    Parameters
+ 988    ----------
+ 989    path : str
+ 990        path of the measurement files
+ 991    prefix : str
+ 992        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
+ 993    c : double
+ 994        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
+ 995    target : int
+ 996        Specifies the topological sector to be reweighted to (default 0)
+ 997    dtr_cnfg : int
+ 998        (optional) parameter that specifies the number of trajectories
+ 999        between two configs.
+1000        if it is not set, the distance between two measurements
+1001        in the file is assumed to be the distance between two configurations.
+1002    steps : int
+1003        (optional) Distance between two configurations in units of trajectories /
+1004         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+1005    version : str
+1006        version string of the openQCD (sfqcd) version used to create
+1007        the ensemble. Default is 2.0. May also be set to sfqcd.
+1008    L : int
+1009        spatial length of the lattice in L/a.
+1010        HAS to be set if version != sfqcd, since openQCD does not provide
+1011        this in the header
+1012    r_start : list
+1013        offset of the first ensemble, making it easier to match
+1014        later on with other Obs
+1015    r_stop : list
+1016        last configurations that need to be read (per replicum)
+1017    files : list
+1018        specify the exact files that need to be read
+1019        from path, practical if e.g. only one replicum is needed
+1020    names : list
+1021        Alternative labeling for replicas/ensembles.
+1022        Has to have the appropriate length
+1023    Zeuthen_flow : bool
+1024        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+1025        for version=='sfqcd' If False, the Wilson flow is used.
+1026
+1027    Returns
+1028    -------
+1029    reto : Obs
+1030        projection to the topological charge sector defined by target
+1031    """
+1032
+1033    if not isinstance(target, int):
+1034        raise Exception("'target' has to be an integer.")
+1035
+1036    kwargs['integer_charge'] = True
+1037    qtop = read_qtop(path, prefix, c, **kwargs)
+1038
+1039    return qtop_projection(qtop, target=target)
+1040
+1041
+1042def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
+1043    """
+1044    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
+1045
+1046    Parameters
+1047    ----------
+1048    path : str
+1049        The directory to search for the files in.
+1050    prefix : str
+1051        The prefix to match the files against.
+1052    qc : str
+1053        The quark combination extension to match the files against.
+1054    corr : str
+1055        The correlator to extract data for.
+1056    sep : str, optional
+1057        The separator to use when parsing the replika names.
+1058    **kwargs
+1059        Additional keyword arguments. The following keyword arguments are recognized:
+1060
+1061        - names (List[str]): A list of names to use for the replicas.
+1062
+1063    Returns
+1064    -------
+1065    Corr
+1066        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
+1067    or
+1068    CObs
+1069        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
 1070
-1071def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
-1072    """
-1073    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
-1074
-1075    Parameters
-1076    ----------
-1077    path : str
-1078        The directory to search for the files in.
-1079    prefix : str
-1080        The prefix to match the files against.
-1081    qc : str
-1082        The quark combination extension to match the files against.
-1083    corr : str
-1084        The correlator to extract data for.
-1085    sep : str, optional
-1086        The separator to use when parsing the replika names.
-1087    **kwargs
-1088        Additional keyword arguments. The following keyword arguments are recognized:
+1071
+1072    Raises
+1073    ------
+1074    FileNotFoundError
+1075        If no files matching the specified prefix and quark combination extension are found in the specified directory.
+1076    IOError
+1077        If there is an error reading a file.
+1078    struct.error
+1079        If there is an error unpacking binary data.
+1080    """
+1081
+1082    # found = []
+1083    files = []
+1084    names = []
+1085
+1086    # test if the input is correct
+1087    if qc not in ['dd', 'ud', 'du', 'uu']:
+1088        raise Exception("Unknown quark conbination!")
 1089
-1090        - names (List[str]): A list of names to use for the replicas.
-1091
-1092    Returns
-1093    -------
-1094    Corr
-1095        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
-1096    or
-1097    CObs
-1098        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
-1099
-1100
-1101    Raises
-1102    ------
-1103    FileNotFoundError
-1104        If no files matching the specified prefix and quark combination extension are found in the specified directory.
-1105    IOError
-1106        If there is an error reading a file.
-1107    struct.error
-1108        If there is an error unpacking binary data.
-1109    """
+1090    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
+1091        raise Exception("Unknown correlator!")
+1092
+1093    if "files" in kwargs:
+1094        known_files = kwargs.get("files")
+1095    else:
+1096        known_files = []
+1097    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
+1098
+1099    if "names" in kwargs:
+1100        names = kwargs.get("names")
+1101    else:
+1102        for f in files:
+1103            if not sep == "":
+1104                se = f.split(".")[0]
+1105                for s in f.split(".")[1:-2]:
+1106                    se += "." + s
+1107                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
+1108            else:
+1109                names.append(prefix)
 1110
-1111    # found = []
-1112    files = []
-1113    names = []
-1114
-1115    # test if the input is correct
-1116    if qc not in ['dd', 'ud', 'du', 'uu']:
-1117        raise Exception("Unknown quark conbination!")
-1118
-1119    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
-1120        raise Exception("Unknown correlator!")
-1121
-1122    if "files" in kwargs:
-1123        known_files = kwargs.get("files")
-1124    else:
-1125        known_files = []
-1126    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
-1127
-1128    if "names" in kwargs:
-1129        names = kwargs.get("names")
-1130    else:
-1131        for f in files:
-1132            if not sep == "":
-1133                se = f.split(".")[0]
-1134                for s in f.split(".")[1:-2]:
-1135                    se += "." + s
-1136                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
-1137            else:
-1138                names.append(prefix)
-1139
-1140    names = sorted(names)
-1141    files = sorted(files)
-1142
-1143    cnfgs = []
-1144    realsamples = []
-1145    imagsamples = []
-1146    repnum = 0
-1147    for file in files:
-1148        with open(path + "/" + file, "rb") as fp:
-1149
-1150            t = fp.read(8)
-1151            kappa = struct.unpack('d', t)[0]
-1152            t = fp.read(8)
-1153            csw = struct.unpack('d', t)[0]
-1154            t = fp.read(8)
-1155            dF = struct.unpack('d', t)[0]
-1156            t = fp.read(8)
-1157            zF = struct.unpack('d', t)[0]
-1158
-1159            t = fp.read(4)
-1160            tmax = struct.unpack('i', t)[0]
-1161            t = fp.read(4)
-1162            bnd = struct.unpack('i', t)[0]
-1163
-1164            placesBI = ["gS", "gP",
-1165                        "gA", "gV",
-1166                        "gVt", "lA",
-1167                        "lV", "lVt",
-1168                        "lT", "lTt"]
-1169            placesBB = ["g1", "l1"]
-1170
-1171            # the chunks have the following structure:
-1172            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
-1173
-1174            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
-1175            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
-1176            cnfgs.append([])
-1177            realsamples.append([])
-1178            imagsamples.append([])
-1179            for t in range(tmax):
-1180                realsamples[repnum].append([])
-1181                imagsamples[repnum].append([])
+1111    names = sorted(names)
+1112    files = sorted(files)
+1113
+1114    cnfgs = []
+1115    realsamples = []
+1116    imagsamples = []
+1117    repnum = 0
+1118    for file in files:
+1119        with open(path + "/" + file, "rb") as fp:
+1120
+1121            t = fp.read(8)
+1122            kappa = struct.unpack('d', t)[0]
+1123            t = fp.read(8)
+1124            csw = struct.unpack('d', t)[0]
+1125            t = fp.read(8)
+1126            dF = struct.unpack('d', t)[0]
+1127            t = fp.read(8)
+1128            zF = struct.unpack('d', t)[0]
+1129
+1130            t = fp.read(4)
+1131            tmax = struct.unpack('i', t)[0]
+1132            t = fp.read(4)
+1133            bnd = struct.unpack('i', t)[0]
+1134
+1135            placesBI = ["gS", "gP",
+1136                        "gA", "gV",
+1137                        "gVt", "lA",
+1138                        "lV", "lVt",
+1139                        "lT", "lTt"]
+1140            placesBB = ["g1", "l1"]
+1141
+1142            # the chunks have the following structure:
+1143            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
+1144
+1145            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
+1146            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
+1147            cnfgs.append([])
+1148            realsamples.append([])
+1149            imagsamples.append([])
+1150            for t in range(tmax):
+1151                realsamples[repnum].append([])
+1152                imagsamples[repnum].append([])
+1153
+1154            while True:
+1155                cnfgt = fp.read(chunksize)
+1156                if not cnfgt:
+1157                    break
+1158                asascii = struct.unpack(packstr, cnfgt)
+1159                cnfg = asascii[0]
+1160                cnfgs[repnum].append(cnfg)
+1161
+1162                if corr not in placesBB:
+1163                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
+1164                else:
+1165                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
+1166
+1167                corrres = [[], []]
+1168                for i in range(len(tmpcorr)):
+1169                    corrres[i % 2].append(tmpcorr[i])
+1170                for t in range(int(len(tmpcorr) / 2)):
+1171                    realsamples[repnum][t].append(corrres[0][t])
+1172                for t in range(int(len(tmpcorr) / 2)):
+1173                    imagsamples[repnum][t].append(corrres[1][t])
+1174        repnum += 1
+1175
+1176    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
+1177    for rep in range(1, repnum):
+1178        s += ", " + str(len(realsamples[rep][t]))
+1179    s += " samples"
+1180    print(s)
+1181    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
 1182
-1183            while True:
-1184                cnfgt = fp.read(chunksize)
-1185                if not cnfgt:
-1186                    break
-1187                asascii = struct.unpack(packstr, cnfgt)
-1188                cnfg = asascii[0]
-1189                cnfgs[repnum].append(cnfg)
+1183    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
+1184
+1185    compObs = []
+1186
+1187    for t in range(int(len(tmpcorr) / 2)):
+1188        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
+1189                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
 1190
-1191                if corr not in placesBB:
-1192                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
-1193                else:
-1194                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
-1195
-1196                corrres = [[], []]
-1197                for i in range(len(tmpcorr)):
-1198                    corrres[i % 2].append(tmpcorr[i])
-1199                for t in range(int(len(tmpcorr) / 2)):
-1200                    realsamples[repnum][t].append(corrres[0][t])
-1201                for t in range(int(len(tmpcorr) / 2)):
-1202                    imagsamples[repnum][t].append(corrres[1][t])
-1203        repnum += 1
-1204
-1205    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
-1206    for rep in range(1, repnum):
-1207        s += ", " + str(len(realsamples[rep][t]))
-1208    s += " samples"
-1209    print(s)
-1210    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
-1211
-1212    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
-1213
-1214    compObs = []
-1215
-1216    for t in range(int(len(tmpcorr) / 2)):
-1217        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
-1218                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
-1219
-1220    if len(compObs) == 1:
-1221        return compObs[0]
-1222    else:
-1223        return Corr(compObs)
+1191    if len(compObs) == 1:
+1192        return compObs[0]
+1193    else:
+1194        return Corr(compObs)
 
@@ -1332,223 +1303,223 @@ -
 85def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
- 86    """Read rwms format from given folder structure. Returns a list of length nrw
- 87
- 88    Parameters
- 89    ----------
- 90    path : str
- 91        path that contains the data files
- 92    prefix : str
- 93        all files in path that start with prefix are considered as input files.
- 94        May be used together postfix to consider only special file endings.
- 95        Prefix is ignored, if the keyword 'files' is used.
- 96    version : str
- 97        version of openQCD, default 2.0
- 98    names : list
- 99        list of names that is assigned to the data according according
-100        to the order in the file list. Use careful, if you do not provide file names!
-101    r_start : list
-102        list which contains the first config to be read for each replicum
-103    r_stop : list
-104        list which contains the last config to be read for each replicum
-105    r_step : int
-106        integer that defines a fixed step size between two measurements (in units of configs)
-107        If not given, r_step=1 is assumed.
-108    postfix : str
-109        postfix of the file to read, e.g. '.ms1' for openQCD-files
-110    files : list
-111        list which contains the filenames to be read. No automatic detection of
-112        files performed if given.
-113    print_err : bool
-114        Print additional information that is useful for debugging.
-115
-116    Returns
-117    -------
-118    rwms : Obs
-119        Reweighting factors read
-120    """
-121    known_oqcd_versions = ['1.4', '1.6', '2.0']
-122    if not (version in known_oqcd_versions):
-123        raise Exception('Unknown openQCD version defined!')
-124    print("Working with openQCD version " + version)
-125    if 'postfix' in kwargs:
-126        postfix = kwargs.get('postfix')
-127    else:
-128        postfix = ''
-129
-130    if 'files' in kwargs:
-131        known_files = kwargs.get('files')
-132    else:
-133        known_files = []
-134
-135    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
+            
 16def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
+ 17    """Read rwms format from given folder structure. Returns a list of length nrw
+ 18
+ 19    Parameters
+ 20    ----------
+ 21    path : str
+ 22        path that contains the data files
+ 23    prefix : str
+ 24        all files in path that start with prefix are considered as input files.
+ 25        May be used together postfix to consider only special file endings.
+ 26        Prefix is ignored, if the keyword 'files' is used.
+ 27    version : str
+ 28        version of openQCD, default 2.0
+ 29    names : list
+ 30        list of names that is assigned to the data according according
+ 31        to the order in the file list. Use careful, if you do not provide file names!
+ 32    r_start : list
+ 33        list which contains the first config to be read for each replicum
+ 34    r_stop : list
+ 35        list which contains the last config to be read for each replicum
+ 36    r_step : int
+ 37        integer that defines a fixed step size between two measurements (in units of configs)
+ 38        If not given, r_step=1 is assumed.
+ 39    postfix : str
+ 40        postfix of the file to read, e.g. '.ms1' for openQCD-files
+ 41    files : list
+ 42        list which contains the filenames to be read. No automatic detection of
+ 43        files performed if given.
+ 44    print_err : bool
+ 45        Print additional information that is useful for debugging.
+ 46
+ 47    Returns
+ 48    -------
+ 49    rwms : Obs
+ 50        Reweighting factors read
+ 51    """
+ 52    known_oqcd_versions = ['1.4', '1.6', '2.0']
+ 53    if not (version in known_oqcd_versions):
+ 54        raise Exception('Unknown openQCD version defined!')
+ 55    print("Working with openQCD version " + version)
+ 56    if 'postfix' in kwargs:
+ 57        postfix = kwargs.get('postfix')
+ 58    else:
+ 59        postfix = ''
+ 60
+ 61    if 'files' in kwargs:
+ 62        known_files = kwargs.get('files')
+ 63    else:
+ 64        known_files = []
+ 65
+ 66    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
+ 67
+ 68    replica = len(ls)
+ 69
+ 70    if 'r_start' in kwargs:
+ 71        r_start = kwargs.get('r_start')
+ 72        if len(r_start) != replica:
+ 73            raise Exception('r_start does not match number of replicas')
+ 74        r_start = [o if o else None for o in r_start]
+ 75    else:
+ 76        r_start = [None] * replica
+ 77
+ 78    if 'r_stop' in kwargs:
+ 79        r_stop = kwargs.get('r_stop')
+ 80        if len(r_stop) != replica:
+ 81            raise Exception('r_stop does not match number of replicas')
+ 82    else:
+ 83        r_stop = [None] * replica
+ 84
+ 85    if 'r_step' in kwargs:
+ 86        r_step = kwargs.get('r_step')
+ 87    else:
+ 88        r_step = 1
+ 89
+ 90    print('Read reweighting factors from', prefix[:-1], ',',
+ 91          replica, 'replica', end='')
+ 92
+ 93    if names is None:
+ 94        rep_names = []
+ 95        for entry in ls:
+ 96            truncated_entry = entry
+ 97            suffixes = [".dat", ".rwms", ".ms1"]
+ 98            for suffix in suffixes:
+ 99                if truncated_entry.endswith(suffix):
+100                    truncated_entry = truncated_entry[0:-len(suffix)]
+101            idx = truncated_entry.index('r')
+102            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+103    else:
+104        rep_names = names
+105
+106    rep_names = sort_names(rep_names)
+107
+108    print_err = 0
+109    if 'print_err' in kwargs:
+110        print_err = 1
+111        print()
+112
+113    deltas = []
+114
+115    configlist = []
+116    r_start_index = []
+117    r_stop_index = []
+118
+119    for rep in range(replica):
+120        tmp_array = []
+121        with open(path + '/' + ls[rep], 'rb') as fp:
+122
+123            t = fp.read(4)  # number of reweighting factors
+124            if rep == 0:
+125                nrw = struct.unpack('i', t)[0]
+126                if version == '2.0':
+127                    nrw = int(nrw / 2)
+128                for k in range(nrw):
+129                    deltas.append([])
+130            else:
+131                if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
+132                    raise Exception('Error: different number of reweighting factors for replicum', rep)
+133
+134            for k in range(nrw):
+135                tmp_array.append([])
 136
-137    replica = len(ls)
-138
-139    if 'r_start' in kwargs:
-140        r_start = kwargs.get('r_start')
-141        if len(r_start) != replica:
-142            raise Exception('r_start does not match number of replicas')
-143        r_start = [o if o else None for o in r_start]
-144    else:
-145        r_start = [None] * replica
+137            # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
+138            nfct = []
+139            if version in ['1.6', '2.0']:
+140                for i in range(nrw):
+141                    t = fp.read(4)
+142                    nfct.append(struct.unpack('i', t)[0])
+143            else:
+144                for i in range(nrw):
+145                    nfct.append(1)
 146
-147    if 'r_stop' in kwargs:
-148        r_stop = kwargs.get('r_stop')
-149        if len(r_stop) != replica:
-150            raise Exception('r_stop does not match number of replicas')
-151    else:
-152        r_stop = [None] * replica
-153
-154    if 'r_step' in kwargs:
-155        r_step = kwargs.get('r_step')
-156    else:
-157        r_step = 1
-158
-159    print('Read reweighting factors from', prefix[:-1], ',',
-160          replica, 'replica', end='')
-161
-162    if names is None:
-163        rep_names = []
-164        for entry in ls:
-165            truncated_entry = entry
-166            suffixes = [".dat", ".rwms", ".ms1"]
-167            for suffix in suffixes:
-168                if truncated_entry.endswith(suffix):
-169                    truncated_entry = truncated_entry[0:-len(suffix)]
-170            idx = truncated_entry.index('r')
-171            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
-172    else:
-173        rep_names = names
-174
-175    rep_names = _sort_names(rep_names)
-176
-177    print_err = 0
-178    if 'print_err' in kwargs:
-179        print_err = 1
-180        print()
-181
-182    deltas = []
-183
-184    configlist = []
-185    r_start_index = []
-186    r_stop_index = []
-187
-188    for rep in range(replica):
-189        tmp_array = []
-190        with open(path + '/' + ls[rep], 'rb') as fp:
+147            nsrc = []
+148            for i in range(nrw):
+149                t = fp.read(4)
+150                nsrc.append(struct.unpack('i', t)[0])
+151            if version == '2.0':
+152                if not struct.unpack('i', fp.read(4))[0] == 0:
+153                    raise Exception("You are using the input for openQCD version 2.0, this is not correct.")
+154
+155            configlist.append([])
+156            while True:
+157                t = fp.read(4)
+158                if len(t) < 4:
+159                    break
+160                config_no = struct.unpack('i', t)[0]
+161                configlist[-1].append(config_no)
+162                for i in range(nrw):
+163                    if (version == '2.0'):
+164                        tmpd = _read_array_openQCD2(fp)
+165                        tmpd = _read_array_openQCD2(fp)
+166                        tmp_rw = tmpd['arr']
+167                        tmp_nfct = 1.0
+168                        for j in range(tmpd['n'][0]):
+169                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
+170                            if print_err:
+171                                print(config_no, i, j,
+172                                      np.mean(np.exp(-np.asarray(tmp_rw[j]))),
+173                                      np.std(np.exp(-np.asarray(tmp_rw[j]))))
+174                                print('Sources:',
+175                                      np.exp(-np.asarray(tmp_rw[j])))
+176                                print('Partial factor:', tmp_nfct)
+177                    elif version == '1.6' or version == '1.4':
+178                        tmp_nfct = 1.0
+179                        for j in range(nfct[i]):
+180                            t = fp.read(8 * nsrc[i])
+181                            t = fp.read(8 * nsrc[i])
+182                            tmp_rw = struct.unpack('d' * nsrc[i], t)
+183                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
+184                            if print_err:
+185                                print(config_no, i, j,
+186                                      np.mean(np.exp(-np.asarray(tmp_rw))),
+187                                      np.std(np.exp(-np.asarray(tmp_rw))))
+188                                print('Sources:', np.exp(-np.asarray(tmp_rw)))
+189                                print('Partial factor:', tmp_nfct)
+190                    tmp_array[i].append(tmp_nfct)
 191
-192            t = fp.read(4)  # number of reweighting factors
-193            if rep == 0:
-194                nrw = struct.unpack('i', t)[0]
-195                if version == '2.0':
-196                    nrw = int(nrw / 2)
-197                for k in range(nrw):
-198                    deltas.append([])
-199            else:
-200                if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
-201                    raise Exception('Error: different number of reweighting factors for replicum', rep)
-202
-203            for k in range(nrw):
-204                tmp_array.append([])
-205
-206            # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
-207            nfct = []
-208            if version in ['1.6', '2.0']:
-209                for i in range(nrw):
-210                    t = fp.read(4)
-211                    nfct.append(struct.unpack('i', t)[0])
-212            else:
-213                for i in range(nrw):
-214                    nfct.append(1)
-215
-216            nsrc = []
-217            for i in range(nrw):
-218                t = fp.read(4)
-219                nsrc.append(struct.unpack('i', t)[0])
-220            if version == '2.0':
-221                if not struct.unpack('i', fp.read(4))[0] == 0:
-222                    raise Exception("You are using the input for openQCD version 2.0, this is not correct.")
-223
-224            configlist.append([])
-225            while True:
-226                t = fp.read(4)
-227                if len(t) < 4:
-228                    break
-229                config_no = struct.unpack('i', t)[0]
-230                configlist[-1].append(config_no)
-231                for i in range(nrw):
-232                    if (version == '2.0'):
-233                        tmpd = _read_array_openQCD2(fp)
-234                        tmpd = _read_array_openQCD2(fp)
-235                        tmp_rw = tmpd['arr']
-236                        tmp_nfct = 1.0
-237                        for j in range(tmpd['n'][0]):
-238                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
-239                            if print_err:
-240                                print(config_no, i, j,
-241                                      np.mean(np.exp(-np.asarray(tmp_rw[j]))),
-242                                      np.std(np.exp(-np.asarray(tmp_rw[j]))))
-243                                print('Sources:',
-244                                      np.exp(-np.asarray(tmp_rw[j])))
-245                                print('Partial factor:', tmp_nfct)
-246                    elif version == '1.6' or version == '1.4':
-247                        tmp_nfct = 1.0
-248                        for j in range(nfct[i]):
-249                            t = fp.read(8 * nsrc[i])
-250                            t = fp.read(8 * nsrc[i])
-251                            tmp_rw = struct.unpack('d' * nsrc[i], t)
-252                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
-253                            if print_err:
-254                                print(config_no, i, j,
-255                                      np.mean(np.exp(-np.asarray(tmp_rw))),
-256                                      np.std(np.exp(-np.asarray(tmp_rw))))
-257                                print('Sources:', np.exp(-np.asarray(tmp_rw)))
-258                                print('Partial factor:', tmp_nfct)
-259                    tmp_array[i].append(tmp_nfct)
-260
-261            diffmeas = configlist[-1][-1] - configlist[-1][-2]
-262            configlist[-1] = [item // diffmeas for item in configlist[-1]]
-263            if configlist[-1][0] > 1 and diffmeas > 1:
-264                warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
-265                offset = configlist[-1][0] - 1
-266                configlist[-1] = [item - offset for item in configlist[-1]]
-267
-268            if r_start[rep] is None:
-269                r_start_index.append(0)
-270            else:
-271                try:
-272                    r_start_index.append(configlist[-1].index(r_start[rep]))
-273                except ValueError:
-274                    raise Exception('Config %d not in file with range [%d, %d]' % (
-275                        r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
-276
-277            if r_stop[rep] is None:
-278                r_stop_index.append(len(configlist[-1]) - 1)
-279            else:
-280                try:
-281                    r_stop_index.append(configlist[-1].index(r_stop[rep]))
-282                except ValueError:
-283                    raise Exception('Config %d not in file with range [%d, %d]' % (
-284                        r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
-285
-286            for k in range(nrw):
-287                deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step])
-288
-289    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
-290        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
-291    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
-292    if np.any([step != 1 for step in stepsizes]):
-293        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
-294
-295    print(',', nrw, 'reweighting factors with', nsrc, 'sources')
-296    result = []
-297    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
-298
-299    for t in range(nrw):
-300        result.append(Obs(deltas[t], rep_names, idl=idl))
-301    return result
+192            diffmeas = configlist[-1][-1] - configlist[-1][-2]
+193            configlist[-1] = [item // diffmeas for item in configlist[-1]]
+194            if configlist[-1][0] > 1 and diffmeas > 1:
+195                warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
+196                offset = configlist[-1][0] - 1
+197                configlist[-1] = [item - offset for item in configlist[-1]]
+198
+199            if r_start[rep] is None:
+200                r_start_index.append(0)
+201            else:
+202                try:
+203                    r_start_index.append(configlist[-1].index(r_start[rep]))
+204                except ValueError:
+205                    raise Exception('Config %d not in file with range [%d, %d]' % (
+206                        r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+207
+208            if r_stop[rep] is None:
+209                r_stop_index.append(len(configlist[-1]) - 1)
+210            else:
+211                try:
+212                    r_stop_index.append(configlist[-1].index(r_stop[rep]))
+213                except ValueError:
+214                    raise Exception('Config %d not in file with range [%d, %d]' % (
+215                        r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+216
+217            for k in range(nrw):
+218                deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step])
+219
+220    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
+221        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
+222    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+223    if np.any([step != 1 for step in stepsizes]):
+224        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
+225
+226    print(',', nrw, 'reweighting factors with', nsrc, 'sources')
+227    result = []
+228    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
+229
+230    for t in range(nrw):
+231        result.append(Obs(deltas[t], rep_names, idl=idl))
+232    return result
 
@@ -1605,246 +1576,246 @@ Reweighting factors read
-
304def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
-305    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
-306
-307    It is assumed that all boundary effects have
-308    sufficiently decayed at x0=xmin.
-309    The data around the zero crossing of t^2<E> - 0.3
-310    is fitted with a linear function
-311    from which the exact root is extracted.
+            
235def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
+236    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
+237
+238    It is assumed that all boundary effects have
+239    sufficiently decayed at x0=xmin.
+240    The data around the zero crossing of t^2<E> - 0.3
+241    is fitted with a linear function
+242    from which the exact root is extracted.
+243
+244    It is assumed that one measurement is performed for each config.
+245    If this is not the case, the resulting idl, as well as the handling
+246    of r_start, r_stop and r_step is wrong and the user has to correct
+247    this in the resulting observable.
+248
+249    Parameters
+250    ----------
+251    path : str
+252        Path to .ms.dat files
+253    prefix : str
+254        Ensemble prefix
+255    dtr_read : int
+256        Determines how many trajectories should be skipped
+257        when reading the ms.dat files.
+258        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
+259    xmin : int
+260        First timeslice where the boundary
+261        effects have sufficiently decayed.
+262    spatial_extent : int
+263        spatial extent of the lattice, required for normalization.
+264    fit_range : int
+265        Number of data points left and right of the zero
+266        crossing to be included in the linear fit. (Default: 5)
+267    r_start : list
+268        list which contains the first config to be read for each replicum.
+269    r_stop : list
+270        list which contains the last config to be read for each replicum.
+271    r_step : int
+272        integer that defines a fixed step size between two measurements (in units of configs)
+273        If not given, r_step=1 is assumed.
+274    plaquette : bool
+275        If true extract the plaquette estimate of t0 instead.
+276    names : list
+277        list of names that is assigned to the data according according
+278        to the order in the file list. Use careful, if you do not provide file names!
+279    files : list
+280        list which contains the filenames to be read. No automatic detection of
+281        files performed if given.
+282    plot_fit : bool
+283        If true, the fit for the extraction of t0 is shown together with the data.
+284    assume_thermalization : bool
+285        If True: If the first record divided by the distance between two measurements is larger than
+286        1, it is assumed that this is due to thermalization and the first measurement belongs
+287        to the first config (default).
+288        If False: The config numbers are assumed to be traj_number // difference
+289
+290    Returns
+291    -------
+292    t0 : Obs
+293        Extracted t0
+294    """
+295
+296    if 'files' in kwargs:
+297        known_files = kwargs.get('files')
+298    else:
+299        known_files = []
+300
+301    ls = _find_files(path, prefix, 'ms', 'dat', known_files=known_files)
+302
+303    replica = len(ls)
+304
+305    if 'r_start' in kwargs:
+306        r_start = kwargs.get('r_start')
+307        if len(r_start) != replica:
+308            raise Exception('r_start does not match number of replicas')
+309        r_start = [o if o else None for o in r_start]
+310    else:
+311        r_start = [None] * replica
 312
-313    It is assumed that one measurement is performed for each config.
-314    If this is not the case, the resulting idl, as well as the handling
-315    of r_start, r_stop and r_step is wrong and the user has to correct
-316    this in the resulting observable.
-317
-318    Parameters
-319    ----------
-320    path : str
-321        Path to .ms.dat files
-322    prefix : str
-323        Ensemble prefix
-324    dtr_read : int
-325        Determines how many trajectories should be skipped
-326        when reading the ms.dat files.
-327        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
-328    xmin : int
-329        First timeslice where the boundary
-330        effects have sufficiently decayed.
-331    spatial_extent : int
-332        spatial extent of the lattice, required for normalization.
-333    fit_range : int
-334        Number of data points left and right of the zero
-335        crossing to be included in the linear fit. (Default: 5)
-336    r_start : list
-337        list which contains the first config to be read for each replicum.
-338    r_stop : list
-339        list which contains the last config to be read for each replicum.
-340    r_step : int
-341        integer that defines a fixed step size between two measurements (in units of configs)
-342        If not given, r_step=1 is assumed.
-343    plaquette : bool
-344        If true extract the plaquette estimate of t0 instead.
-345    names : list
-346        list of names that is assigned to the data according according
-347        to the order in the file list. Use careful, if you do not provide file names!
-348    files : list
-349        list which contains the filenames to be read. No automatic detection of
-350        files performed if given.
-351    plot_fit : bool
-352        If true, the fit for the extraction of t0 is shown together with the data.
-353    assume_thermalization : bool
-354        If True: If the first record divided by the distance between two measurements is larger than
-355        1, it is assumed that this is due to thermalization and the first measurement belongs
-356        to the first config (default).
-357        If False: The config numbers are assumed to be traj_number // difference
-358
-359    Returns
-360    -------
-361    t0 : Obs
-362        Extracted t0
-363    """
-364
-365    if 'files' in kwargs:
-366        known_files = kwargs.get('files')
-367    else:
-368        known_files = []
-369
-370    ls = _find_files(path, prefix, 'ms', 'dat', known_files=known_files)
-371
-372    replica = len(ls)
-373
-374    if 'r_start' in kwargs:
-375        r_start = kwargs.get('r_start')
-376        if len(r_start) != replica:
-377            raise Exception('r_start does not match number of replicas')
-378        r_start = [o if o else None for o in r_start]
-379    else:
-380        r_start = [None] * replica
-381
-382    if 'r_stop' in kwargs:
-383        r_stop = kwargs.get('r_stop')
-384        if len(r_stop) != replica:
-385            raise Exception('r_stop does not match number of replicas')
-386    else:
-387        r_stop = [None] * replica
-388
-389    if 'r_step' in kwargs:
-390        r_step = kwargs.get('r_step')
-391    else:
-392        r_step = 1
+313    if 'r_stop' in kwargs:
+314        r_stop = kwargs.get('r_stop')
+315        if len(r_stop) != replica:
+316            raise Exception('r_stop does not match number of replicas')
+317    else:
+318        r_stop = [None] * replica
+319
+320    if 'r_step' in kwargs:
+321        r_step = kwargs.get('r_step')
+322    else:
+323        r_step = 1
+324
+325    print('Extract t0 from', prefix, ',', replica, 'replica')
+326
+327    if 'names' in kwargs:
+328        rep_names = kwargs.get('names')
+329    else:
+330        rep_names = []
+331        for entry in ls:
+332            truncated_entry = entry.split('.')[0]
+333            idx = truncated_entry.index('r')
+334            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+335
+336    Ysum = []
+337
+338    configlist = []
+339    r_start_index = []
+340    r_stop_index = []
+341
+342    for rep in range(replica):
+343
+344        with open(path + '/' + ls[rep], 'rb') as fp:
+345            t = fp.read(12)
+346            header = struct.unpack('iii', t)
+347            if rep == 0:
+348                dn = header[0]
+349                nn = header[1]
+350                tmax = header[2]
+351            elif dn != header[0] or nn != header[1] or tmax != header[2]:
+352                raise Exception('Replica parameters do not match.')
+353
+354            t = fp.read(8)
+355            if rep == 0:
+356                eps = struct.unpack('d', t)[0]
+357                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
+358            elif eps != struct.unpack('d', t)[0]:
+359                raise Exception('Values for eps do not match among replica.')
+360
+361            Ysl = []
+362
+363            configlist.append([])
+364            while True:
+365                t = fp.read(4)
+366                if (len(t) < 4):
+367                    break
+368                nc = struct.unpack('i', t)[0]
+369                configlist[-1].append(nc)
+370
+371                t = fp.read(8 * tmax * (nn + 1))
+372                if kwargs.get('plaquette'):
+373                    if nc % dtr_read == 0:
+374                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
+375                t = fp.read(8 * tmax * (nn + 1))
+376                if not kwargs.get('plaquette'):
+377                    if nc % dtr_read == 0:
+378                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
+379                t = fp.read(8 * tmax * (nn + 1))
+380
+381        Ysum.append([])
+382        for i, item in enumerate(Ysl):
+383            Ysum[-1].append([np.mean(item[current + xmin:
+384                             current + tmax - xmin])
+385                            for current in range(0, len(item), tmax)])
+386
+387        diffmeas = configlist[-1][-1] - configlist[-1][-2]
+388        configlist[-1] = [item // diffmeas for item in configlist[-1]]
+389        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
+390            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
+391            offset = configlist[-1][0] - 1
+392            configlist[-1] = [item - offset for item in configlist[-1]]
 393
-394    print('Extract t0 from', prefix, ',', replica, 'replica')
-395
-396    if 'names' in kwargs:
-397        rep_names = kwargs.get('names')
-398    else:
-399        rep_names = []
-400        for entry in ls:
-401            truncated_entry = entry.split('.')[0]
-402            idx = truncated_entry.index('r')
-403            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
-404
-405    Ysum = []
-406
-407    configlist = []
-408    r_start_index = []
-409    r_stop_index = []
-410
-411    for rep in range(replica):
-412
-413        with open(path + '/' + ls[rep], 'rb') as fp:
-414            t = fp.read(12)
-415            header = struct.unpack('iii', t)
-416            if rep == 0:
-417                dn = header[0]
-418                nn = header[1]
-419                tmax = header[2]
-420            elif dn != header[0] or nn != header[1] or tmax != header[2]:
-421                raise Exception('Replica parameters do not match.')
-422
-423            t = fp.read(8)
-424            if rep == 0:
-425                eps = struct.unpack('d', t)[0]
-426                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
-427            elif eps != struct.unpack('d', t)[0]:
-428                raise Exception('Values for eps do not match among replica.')
+394        if r_start[rep] is None:
+395            r_start_index.append(0)
+396        else:
+397            try:
+398                r_start_index.append(configlist[-1].index(r_start[rep]))
+399            except ValueError:
+400                raise Exception('Config %d not in file with range [%d, %d]' % (
+401                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+402
+403        if r_stop[rep] is None:
+404            r_stop_index.append(len(configlist[-1]) - 1)
+405        else:
+406            try:
+407                r_stop_index.append(configlist[-1].index(r_stop[rep]))
+408            except ValueError:
+409                raise Exception('Config %d not in file with range [%d, %d]' % (
+410                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+411
+412    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
+413        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
+414    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+415    if np.any([step != 1 for step in stepsizes]):
+416        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
+417
+418    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
+419    t2E_dict = {}
+420    for n in range(nn + 1):
+421        samples = []
+422        for nrep, rep in enumerate(Ysum):
+423            samples.append([])
+424            for cnfg in rep:
+425                samples[-1].append(cnfg[n])
+426            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
+427        new_obs = Obs(samples, rep_names, idl=idl)
+428        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
 429
-430            Ysl = []
-431
-432            configlist.append([])
-433            while True:
-434                t = fp.read(4)
-435                if (len(t) < 4):
-436                    break
-437                nc = struct.unpack('i', t)[0]
-438                configlist[-1].append(nc)
-439
-440                t = fp.read(8 * tmax * (nn + 1))
-441                if kwargs.get('plaquette'):
-442                    if nc % dtr_read == 0:
-443                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
-444                t = fp.read(8 * tmax * (nn + 1))
-445                if not kwargs.get('plaquette'):
-446                    if nc % dtr_read == 0:
-447                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
-448                t = fp.read(8 * tmax * (nn + 1))
-449
-450        Ysum.append([])
-451        for i, item in enumerate(Ysl):
-452            Ysum[-1].append([np.mean(item[current + xmin:
-453                             current + tmax - xmin])
-454                            for current in range(0, len(item), tmax)])
-455
-456        diffmeas = configlist[-1][-1] - configlist[-1][-2]
-457        configlist[-1] = [item // diffmeas for item in configlist[-1]]
-458        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
-459            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
-460            offset = configlist[-1][0] - 1
-461            configlist[-1] = [item - offset for item in configlist[-1]]
-462
-463        if r_start[rep] is None:
-464            r_start_index.append(0)
-465        else:
-466            try:
-467                r_start_index.append(configlist[-1].index(r_start[rep]))
-468            except ValueError:
-469                raise Exception('Config %d not in file with range [%d, %d]' % (
-470                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
-471
-472        if r_stop[rep] is None:
-473            r_stop_index.append(len(configlist[-1]) - 1)
-474        else:
-475            try:
-476                r_stop_index.append(configlist[-1].index(r_stop[rep]))
-477            except ValueError:
-478                raise Exception('Config %d not in file with range [%d, %d]' % (
-479                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
-480
-481    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
-482        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
-483    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
-484    if np.any([step != 1 for step in stepsizes]):
-485        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
-486
-487    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
-488    t2E_dict = {}
-489    for n in range(nn + 1):
-490        samples = []
-491        for nrep, rep in enumerate(Ysum):
-492            samples.append([])
-493            for cnfg in rep:
-494                samples[-1].append(cnfg[n])
-495            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
-496        new_obs = Obs(samples, rep_names, idl=idl)
-497        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
-498
-499    zero_crossing = np.argmax(np.array(
-500        [o.value for o in t2E_dict.values()]) > 0.0)
-501
-502    x = list(t2E_dict.keys())[zero_crossing - fit_range:
-503                              zero_crossing + fit_range]
-504    y = list(t2E_dict.values())[zero_crossing - fit_range:
-505                                zero_crossing + fit_range]
-506    [o.gamma_method() for o in y]
-507
-508    fit_result = fit_lin(x, y)
-509
-510    if kwargs.get('plot_fit'):
-511        plt.figure()
-512        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
-513        ax0 = plt.subplot(gs[0])
-514        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
-515        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
-516        [o.gamma_method() for o in ymore]
-517        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
-518        xplot = np.linspace(np.min(x), np.max(x))
-519        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
-520        [yi.gamma_method() for yi in yplot]
-521        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
-522        retval = (-fit_result[0] / fit_result[1])
-523        retval.gamma_method()
-524        ylim = ax0.get_ylim()
-525        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
-526        ax0.set_ylim(ylim)
-527        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
-528        xlim = ax0.get_xlim()
-529
-530        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
-531        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
-532        ax1 = plt.subplot(gs[1])
-533        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
-534        ax1.tick_params(direction='out')
-535        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
-536        ax1.axhline(y=0.0, ls='--', color='k')
-537        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
-538        ax1.set_xlim(xlim)
-539        ax1.set_ylabel('Residuals')
-540        ax1.set_xlabel(r'$t/a^2$')
-541
-542        plt.draw()
-543    return -fit_result[0] / fit_result[1]
+430    zero_crossing = np.argmax(np.array(
+431        [o.value for o in t2E_dict.values()]) > 0.0)
+432
+433    x = list(t2E_dict.keys())[zero_crossing - fit_range:
+434                              zero_crossing + fit_range]
+435    y = list(t2E_dict.values())[zero_crossing - fit_range:
+436                                zero_crossing + fit_range]
+437    [o.gamma_method() for o in y]
+438
+439    fit_result = fit_lin(x, y)
+440
+441    if kwargs.get('plot_fit'):
+442        plt.figure()
+443        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+444        ax0 = plt.subplot(gs[0])
+445        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+446        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+447        [o.gamma_method() for o in ymore]
+448        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
+449        xplot = np.linspace(np.min(x), np.max(x))
+450        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
+451        [yi.gamma_method() for yi in yplot]
+452        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
+453        retval = (-fit_result[0] / fit_result[1])
+454        retval.gamma_method()
+455        ylim = ax0.get_ylim()
+456        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
+457        ax0.set_ylim(ylim)
+458        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
+459        xlim = ax0.get_xlim()
+460
+461        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
+462        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
+463        ax1 = plt.subplot(gs[1])
+464        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+465        ax1.tick_params(direction='out')
+466        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+467        ax1.axhline(y=0.0, ls='--', color='k')
+468        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
+469        ax1.set_xlim(xlim)
+470        ax1.set_ylabel('Residuals')
+471        ax1.set_xlabel(r'$t/a^2$')
+472
+473        plt.draw()
+474    return -fit_result[0] / fit_result[1]
 
@@ -1925,57 +1896,57 @@ Extracted t0
-
591def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
-592    """Read the topologial charge based on openQCD gradient flow measurements.
-593
-594    Parameters
-595    ----------
-596    path : str
-597        path of the measurement files
-598    prefix : str
-599        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
-600        Ignored if file names are passed explicitly via keyword files.
-601    c : double
-602        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
-603    dtr_cnfg : int
-604        (optional) parameter that specifies the number of measurements
-605        between two configs.
-606        If it is not set, the distance between two measurements
-607        in the file is assumed to be the distance between two configurations.
-608    steps : int
-609        (optional) Distance between two configurations in units of trajectories /
-610         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-611    version : str
-612        Either openQCD or sfqcd, depending on the data.
-613    L : int
-614        spatial length of the lattice in L/a.
-615        HAS to be set if version != sfqcd, since openQCD does not provide
-616        this in the header
-617    r_start : list
-618        list which contains the first config to be read for each replicum.
-619    r_stop : list
-620        list which contains the last config to be read for each replicum.
-621    files : list
-622        specify the exact files that need to be read
-623        from path, practical if e.g. only one replicum is needed
-624    postfix : str
-625        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
-626    names : list
-627        Alternative labeling for replicas/ensembles.
-628        Has to have the appropriate length.
-629    Zeuthen_flow : bool
-630        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-631        for version=='sfqcd' If False, the Wilson flow is used.
-632    integer_charge : bool
-633        If True, the charge is rounded towards the nearest integer on each config.
-634
-635    Returns
-636    -------
-637    result : Obs
-638        Read topological charge
-639    """
-640
-641    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
+            
562def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
+563    """Read the topologial charge based on openQCD gradient flow measurements.
+564
+565    Parameters
+566    ----------
+567    path : str
+568        path of the measurement files
+569    prefix : str
+570        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+571        Ignored if file names are passed explicitly via keyword files.
+572    c : double
+573        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+574    dtr_cnfg : int
+575        (optional) parameter that specifies the number of measurements
+576        between two configs.
+577        If it is not set, the distance between two measurements
+578        in the file is assumed to be the distance between two configurations.
+579    steps : int
+580        (optional) Distance between two configurations in units of trajectories /
+581         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+582    version : str
+583        Either openQCD or sfqcd, depending on the data.
+584    L : int
+585        spatial length of the lattice in L/a.
+586        HAS to be set if version != sfqcd, since openQCD does not provide
+587        this in the header
+588    r_start : list
+589        list which contains the first config to be read for each replicum.
+590    r_stop : list
+591        list which contains the last config to be read for each replicum.
+592    files : list
+593        specify the exact files that need to be read
+594        from path, practical if e.g. only one replicum is needed
+595    postfix : str
+596        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+597    names : list
+598        Alternative labeling for replicas/ensembles.
+599        Has to have the appropriate length.
+600    Zeuthen_flow : bool
+601        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+602        for version=='sfqcd' If False, the Wilson flow is used.
+603    integer_charge : bool
+604        If True, the charge is rounded towards the nearest integer on each config.
+605
+606    Returns
+607    -------
+608    result : Obs
+609        Read topological charge
+610    """
+611
+612    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
 
@@ -2045,76 +2016,76 @@ Read topological charge
-
644def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
-645    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
-646
-647    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
-648
-649    Parameters
-650    ----------
-651    path : str
-652        path of the measurement files
-653    prefix : str
-654        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
-655        Ignored if file names are passed explicitly via keyword files.
-656    c : double
-657        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
-658    dtr_cnfg : int
-659        (optional) parameter that specifies the number of measurements
-660        between two configs.
-661        If it is not set, the distance between two measurements
-662        in the file is assumed to be the distance between two configurations.
-663    steps : int
-664        (optional) Distance between two configurations in units of trajectories /
-665         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-666    r_start : list
-667        list which contains the first config to be read for each replicum.
-668    r_stop : list
-669        list which contains the last config to be read for each replicum.
-670    files : list
-671        specify the exact files that need to be read
-672        from path, practical if e.g. only one replicum is needed
-673    names : list
-674        Alternative labeling for replicas/ensembles.
-675        Has to have the appropriate length.
-676    postfix : str
-677        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
-678    Zeuthen_flow : bool
-679        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
-680    """
-681
-682    if c != 0.3:
-683        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
-684
-685    plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
-686    C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
-687    L = plaq.tag["L"]
-688    T = plaq.tag["T"]
-689
-690    if T != L:
-691        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
-692
-693    if Zeuthen_flow is not True:
-694        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
-695
-696    t = (c * L) ** 2 / 8
-697
-698    normdict = {4: 0.012341170468270,
-699                6: 0.010162691462430,
-700                8: 0.009031614807931,
-701                10: 0.008744966371393,
-702                12: 0.008650917856809,
-703                14: 8.611154391267955E-03,
-704                16: 0.008591758449508,
-705                20: 0.008575359627103,
-706                24: 0.008569387847540,
-707                28: 8.566803713382559E-03,
-708                32: 0.008565541650006,
-709                40: 8.564480684962046E-03,
-710                48: 8.564098025073460E-03,
-711                64: 8.563853943383087E-03}
-712
-713    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
+            
615def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
+616    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
+617
+618    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
+619
+620    Parameters
+621    ----------
+622    path : str
+623        path of the measurement files
+624    prefix : str
+625        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+626        Ignored if file names are passed explicitly via keyword files.
+627    c : double
+628        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+629    dtr_cnfg : int
+630        (optional) parameter that specifies the number of measurements
+631        between two configs.
+632        If it is not set, the distance between two measurements
+633        in the file is assumed to be the distance between two configurations.
+634    steps : int
+635        (optional) Distance between two configurations in units of trajectories /
+636         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+637    r_start : list
+638        list which contains the first config to be read for each replicum.
+639    r_stop : list
+640        list which contains the last config to be read for each replicum.
+641    files : list
+642        specify the exact files that need to be read
+643        from path, practical if e.g. only one replicum is needed
+644    names : list
+645        Alternative labeling for replicas/ensembles.
+646        Has to have the appropriate length.
+647    postfix : str
+648        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+649    Zeuthen_flow : bool
+650        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
+651    """
+652
+653    if c != 0.3:
+654        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
+655
+656    plaq = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=6, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
+657    C2x1 = _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version="sfqcd", obspos=7, sum_t=False, Zeuthen_flow=Zeuthen_flow, integer_charge=False, **kwargs)
+658    L = plaq.tag["L"]
+659    T = plaq.tag["T"]
+660
+661    if T != L:
+662        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
+663
+664    if Zeuthen_flow is not True:
+665        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
+666
+667    t = (c * L) ** 2 / 8
+668
+669    normdict = {4: 0.012341170468270,
+670                6: 0.010162691462430,
+671                8: 0.009031614807931,
+672                10: 0.008744966371393,
+673                12: 0.008650917856809,
+674                14: 8.611154391267955E-03,
+675                16: 0.008591758449508,
+676                20: 0.008575359627103,
+677                24: 0.008569387847540,
+678                28: 8.566803713382559E-03,
+679                32: 0.008565541650006,
+680                40: 8.564480684962046E-03,
+681                48: 8.564098025073460E-03,
+682                64: 8.563853943383087E-03}
+683
+684    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
 
@@ -2170,30 +2141,30 @@ postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
-
 988def qtop_projection(qtop, target=0):
- 989    """Returns the projection to the topological charge sector defined by target.
- 990
- 991    Parameters
- 992    ----------
- 993    path : Obs
- 994        Topological charge.
- 995    target : int
- 996        Specifies the topological sector to be reweighted to (default 0)
- 997
- 998    Returns
- 999    -------
-1000    reto : Obs
-1001        projection to the topological charge sector defined by target
-1002    """
-1003    if qtop.reweighted:
-1004        raise Exception('You can not use a reweighted observable for reweighting!')
-1005
-1006    proj_qtop = []
-1007    for n in qtop.deltas:
-1008        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
-1009
-1010    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
-1011    return reto
+            
959def qtop_projection(qtop, target=0):
+960    """Returns the projection to the topological charge sector defined by target.
+961
+962    Parameters
+963    ----------
+964    path : Obs
+965        Topological charge.
+966    target : int
+967        Specifies the topological sector to be reweighted to (default 0)
+968
+969    Returns
+970    -------
+971    reto : Obs
+972        projection to the topological charge sector defined by target
+973    """
+974    if qtop.reweighted:
+975        raise Exception('You can not use a reweighted observable for reweighting!')
+976
+977    proj_qtop = []
+978    for n in qtop.deltas:
+979        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
+980
+981    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
+982    return reto
 
@@ -2229,62 +2200,62 @@ projection to the topological charge sector defined by target
-
1014def read_qtop_sector(path, prefix, c, target=0, **kwargs):
-1015    """Constructs reweighting factors to a specified topological sector.
-1016
-1017    Parameters
-1018    ----------
-1019    path : str
-1020        path of the measurement files
-1021    prefix : str
-1022        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
-1023    c : double
-1024        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
-1025    target : int
-1026        Specifies the topological sector to be reweighted to (default 0)
-1027    dtr_cnfg : int
-1028        (optional) parameter that specifies the number of trajectories
-1029        between two configs.
-1030        if it is not set, the distance between two measurements
-1031        in the file is assumed to be the distance between two configurations.
-1032    steps : int
-1033        (optional) Distance between two configurations in units of trajectories /
-1034         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-1035    version : str
-1036        version string of the openQCD (sfqcd) version used to create
-1037        the ensemble. Default is 2.0. May also be set to sfqcd.
-1038    L : int
-1039        spatial length of the lattice in L/a.
-1040        HAS to be set if version != sfqcd, since openQCD does not provide
-1041        this in the header
-1042    r_start : list
-1043        offset of the first ensemble, making it easier to match
-1044        later on with other Obs
-1045    r_stop : list
-1046        last configurations that need to be read (per replicum)
-1047    files : list
-1048        specify the exact files that need to be read
-1049        from path, practical if e.g. only one replicum is needed
-1050    names : list
-1051        Alternative labeling for replicas/ensembles.
-1052        Has to have the appropriate length
-1053    Zeuthen_flow : bool
-1054        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-1055        for version=='sfqcd' If False, the Wilson flow is used.
-1056
-1057    Returns
-1058    -------
-1059    reto : Obs
-1060        projection to the topological charge sector defined by target
-1061    """
-1062
-1063    if not isinstance(target, int):
-1064        raise Exception("'target' has to be an integer.")
-1065
-1066    kwargs['integer_charge'] = True
-1067    qtop = read_qtop(path, prefix, c, **kwargs)
-1068
-1069    return qtop_projection(qtop, target=target)
+            
 985def read_qtop_sector(path, prefix, c, target=0, **kwargs):
+ 986    """Constructs reweighting factors to a specified topological sector.
+ 987
+ 988    Parameters
+ 989    ----------
+ 990    path : str
+ 991        path of the measurement files
+ 992    prefix : str
+ 993        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
+ 994    c : double
+ 995        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
+ 996    target : int
+ 997        Specifies the topological sector to be reweighted to (default 0)
+ 998    dtr_cnfg : int
+ 999        (optional) parameter that specifies the number of trajectories
+1000        between two configs.
+1001        if it is not set, the distance between two measurements
+1002        in the file is assumed to be the distance between two configurations.
+1003    steps : int
+1004        (optional) Distance between two configurations in units of trajectories /
+1005         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+1006    version : str
+1007        version string of the openQCD (sfqcd) version used to create
+1008        the ensemble. Default is 2.0. May also be set to sfqcd.
+1009    L : int
+1010        spatial length of the lattice in L/a.
+1011        HAS to be set if version != sfqcd, since openQCD does not provide
+1012        this in the header
+1013    r_start : list
+1014        offset of the first ensemble, making it easier to match
+1015        later on with other Obs
+1016    r_stop : list
+1017        last configurations that need to be read (per replicum)
+1018    files : list
+1019        specify the exact files that need to be read
+1020        from path, practical if e.g. only one replicum is needed
+1021    names : list
+1022        Alternative labeling for replicas/ensembles.
+1023        Has to have the appropriate length
+1024    Zeuthen_flow : bool
+1025        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+1026        for version=='sfqcd' If False, the Wilson flow is used.
+1027
+1028    Returns
+1029    -------
+1030    reto : Obs
+1031        projection to the topological charge sector defined by target
+1032    """
+1033
+1034    if not isinstance(target, int):
+1035        raise Exception("'target' has to be an integer.")
+1036
+1037    kwargs['integer_charge'] = True
+1038    qtop = read_qtop(path, prefix, c, **kwargs)
+1039
+1040    return qtop_projection(qtop, target=target)
 
@@ -2353,159 +2324,159 @@ projection to the topological charge sector defined by target
-
1072def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
-1073    """
-1074    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
-1075
-1076    Parameters
-1077    ----------
-1078    path : str
-1079        The directory to search for the files in.
-1080    prefix : str
-1081        The prefix to match the files against.
-1082    qc : str
-1083        The quark combination extension to match the files against.
-1084    corr : str
-1085        The correlator to extract data for.
-1086    sep : str, optional
-1087        The separator to use when parsing the replika names.
-1088    **kwargs
-1089        Additional keyword arguments. The following keyword arguments are recognized:
+            
1043def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
+1044    """
+1045    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
+1046
+1047    Parameters
+1048    ----------
+1049    path : str
+1050        The directory to search for the files in.
+1051    prefix : str
+1052        The prefix to match the files against.
+1053    qc : str
+1054        The quark combination extension to match the files against.
+1055    corr : str
+1056        The correlator to extract data for.
+1057    sep : str, optional
+1058        The separator to use when parsing the replika names.
+1059    **kwargs
+1060        Additional keyword arguments. The following keyword arguments are recognized:
+1061
+1062        - names (List[str]): A list of names to use for the replicas.
+1063
+1064    Returns
+1065    -------
+1066    Corr
+1067        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
+1068    or
+1069    CObs
+1070        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
+1071
+1072
+1073    Raises
+1074    ------
+1075    FileNotFoundError
+1076        If no files matching the specified prefix and quark combination extension are found in the specified directory.
+1077    IOError
+1078        If there is an error reading a file.
+1079    struct.error
+1080        If there is an error unpacking binary data.
+1081    """
+1082
+1083    # found = []
+1084    files = []
+1085    names = []
+1086
+1087    # test if the input is correct
+1088    if qc not in ['dd', 'ud', 'du', 'uu']:
+1089        raise Exception("Unknown quark conbination!")
 1090
-1091        - names (List[str]): A list of names to use for the replicas.
-1092
-1093    Returns
-1094    -------
-1095    Corr
-1096        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
-1097    or
-1098    CObs
-1099        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
-1100
-1101
-1102    Raises
-1103    ------
-1104    FileNotFoundError
-1105        If no files matching the specified prefix and quark combination extension are found in the specified directory.
-1106    IOError
-1107        If there is an error reading a file.
-1108    struct.error
-1109        If there is an error unpacking binary data.
-1110    """
+1091    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
+1092        raise Exception("Unknown correlator!")
+1093
+1094    if "files" in kwargs:
+1095        known_files = kwargs.get("files")
+1096    else:
+1097        known_files = []
+1098    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
+1099
+1100    if "names" in kwargs:
+1101        names = kwargs.get("names")
+1102    else:
+1103        for f in files:
+1104            if not sep == "":
+1105                se = f.split(".")[0]
+1106                for s in f.split(".")[1:-2]:
+1107                    se += "." + s
+1108                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
+1109            else:
+1110                names.append(prefix)
 1111
-1112    # found = []
-1113    files = []
-1114    names = []
-1115
-1116    # test if the input is correct
-1117    if qc not in ['dd', 'ud', 'du', 'uu']:
-1118        raise Exception("Unknown quark conbination!")
-1119
-1120    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
-1121        raise Exception("Unknown correlator!")
-1122
-1123    if "files" in kwargs:
-1124        known_files = kwargs.get("files")
-1125    else:
-1126        known_files = []
-1127    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
-1128
-1129    if "names" in kwargs:
-1130        names = kwargs.get("names")
-1131    else:
-1132        for f in files:
-1133            if not sep == "":
-1134                se = f.split(".")[0]
-1135                for s in f.split(".")[1:-2]:
-1136                    se += "." + s
-1137                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
-1138            else:
-1139                names.append(prefix)
-1140
-1141    names = sorted(names)
-1142    files = sorted(files)
-1143
-1144    cnfgs = []
-1145    realsamples = []
-1146    imagsamples = []
-1147    repnum = 0
-1148    for file in files:
-1149        with open(path + "/" + file, "rb") as fp:
-1150
-1151            t = fp.read(8)
-1152            kappa = struct.unpack('d', t)[0]
-1153            t = fp.read(8)
-1154            csw = struct.unpack('d', t)[0]
-1155            t = fp.read(8)
-1156            dF = struct.unpack('d', t)[0]
-1157            t = fp.read(8)
-1158            zF = struct.unpack('d', t)[0]
-1159
-1160            t = fp.read(4)
-1161            tmax = struct.unpack('i', t)[0]
-1162            t = fp.read(4)
-1163            bnd = struct.unpack('i', t)[0]
-1164
-1165            placesBI = ["gS", "gP",
-1166                        "gA", "gV",
-1167                        "gVt", "lA",
-1168                        "lV", "lVt",
-1169                        "lT", "lTt"]
-1170            placesBB = ["g1", "l1"]
-1171
-1172            # the chunks have the following structure:
-1173            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
-1174
-1175            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
-1176            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
-1177            cnfgs.append([])
-1178            realsamples.append([])
-1179            imagsamples.append([])
-1180            for t in range(tmax):
-1181                realsamples[repnum].append([])
-1182                imagsamples[repnum].append([])
+1112    names = sorted(names)
+1113    files = sorted(files)
+1114
+1115    cnfgs = []
+1116    realsamples = []
+1117    imagsamples = []
+1118    repnum = 0
+1119    for file in files:
+1120        with open(path + "/" + file, "rb") as fp:
+1121
+1122            t = fp.read(8)
+1123            kappa = struct.unpack('d', t)[0]
+1124            t = fp.read(8)
+1125            csw = struct.unpack('d', t)[0]
+1126            t = fp.read(8)
+1127            dF = struct.unpack('d', t)[0]
+1128            t = fp.read(8)
+1129            zF = struct.unpack('d', t)[0]
+1130
+1131            t = fp.read(4)
+1132            tmax = struct.unpack('i', t)[0]
+1133            t = fp.read(4)
+1134            bnd = struct.unpack('i', t)[0]
+1135
+1136            placesBI = ["gS", "gP",
+1137                        "gA", "gV",
+1138                        "gVt", "lA",
+1139                        "lV", "lVt",
+1140                        "lT", "lTt"]
+1141            placesBB = ["g1", "l1"]
+1142
+1143            # the chunks have the following structure:
+1144            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
+1145
+1146            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
+1147            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
+1148            cnfgs.append([])
+1149            realsamples.append([])
+1150            imagsamples.append([])
+1151            for t in range(tmax):
+1152                realsamples[repnum].append([])
+1153                imagsamples[repnum].append([])
+1154
+1155            while True:
+1156                cnfgt = fp.read(chunksize)
+1157                if not cnfgt:
+1158                    break
+1159                asascii = struct.unpack(packstr, cnfgt)
+1160                cnfg = asascii[0]
+1161                cnfgs[repnum].append(cnfg)
+1162
+1163                if corr not in placesBB:
+1164                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
+1165                else:
+1166                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
+1167
+1168                corrres = [[], []]
+1169                for i in range(len(tmpcorr)):
+1170                    corrres[i % 2].append(tmpcorr[i])
+1171                for t in range(int(len(tmpcorr) / 2)):
+1172                    realsamples[repnum][t].append(corrres[0][t])
+1173                for t in range(int(len(tmpcorr) / 2)):
+1174                    imagsamples[repnum][t].append(corrres[1][t])
+1175        repnum += 1
+1176
+1177    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
+1178    for rep in range(1, repnum):
+1179        s += ", " + str(len(realsamples[rep][t]))
+1180    s += " samples"
+1181    print(s)
+1182    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
 1183
-1184            while True:
-1185                cnfgt = fp.read(chunksize)
-1186                if not cnfgt:
-1187                    break
-1188                asascii = struct.unpack(packstr, cnfgt)
-1189                cnfg = asascii[0]
-1190                cnfgs[repnum].append(cnfg)
+1184    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
+1185
+1186    compObs = []
+1187
+1188    for t in range(int(len(tmpcorr) / 2)):
+1189        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
+1190                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
 1191
-1192                if corr not in placesBB:
-1193                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
-1194                else:
-1195                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
-1196
-1197                corrres = [[], []]
-1198                for i in range(len(tmpcorr)):
-1199                    corrres[i % 2].append(tmpcorr[i])
-1200                for t in range(int(len(tmpcorr) / 2)):
-1201                    realsamples[repnum][t].append(corrres[0][t])
-1202                for t in range(int(len(tmpcorr) / 2)):
-1203                    imagsamples[repnum][t].append(corrres[1][t])
-1204        repnum += 1
-1205
-1206    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
-1207    for rep in range(1, repnum):
-1208        s += ", " + str(len(realsamples[rep][t]))
-1209    s += " samples"
-1210    print(s)
-1211    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
-1212
-1213    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
-1214
-1215    compObs = []
-1216
-1217    for t in range(int(len(tmpcorr) / 2)):
-1218        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
-1219                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
-1220
-1221    if len(compObs) == 1:
-1222        return compObs[0]
-1223    else:
-1224        return Corr(compObs)
+1192    if len(compObs) == 1:
+1193        return compObs[0]
+1194    else:
+1195        return Corr(compObs)
 
diff --git a/docs/pyerrors/input/sfcf.html b/docs/pyerrors/input/sfcf.html index 7bf47ec7..608a108d 100644 --- a/docs/pyerrors/input/sfcf.html +++ b/docs/pyerrors/input/sfcf.html @@ -81,11 +81,11 @@
3import re 4import numpy as np # Thinly-wrapped numpy 5from ..obs import Obs - 6from . import utils + 6from .utils import sort_names, check_idl 7 8 - 9def read_sfcf(path, prefix, name, quarks='.*', corr_type='bi', noffset=0, wf=0, wf2=0, version="1.0c", cfg_separator="n", **kwargs): - 10 """Read sfcf c format from given folder structure. + 9def read_sfcf(path, prefix, name, quarks='.*', corr_type='bi', noffset=0, wf=0, wf2=0, version="1.0c", cfg_separator="n", silent=False, **kwargs): + 10 """Read sfcf files from given folder structure. 11 12 Parameters 13 ---------- @@ -149,304 +149,379 @@ 71 else: 72 im = 0 73 part = 'real' - 74 if "replica" in kwargs: - 75 reps = kwargs.get("replica") - 76 if corr_type == 'bb': - 77 b2b = True - 78 single = True - 79 elif corr_type == 'bib': - 80 b2b = True - 81 single = False - 82 else: - 83 b2b = False - 84 single = False - 85 compact = True - 86 appended = False - 87 known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"] - 88 - 89 if version not in known_versions: - 90 raise Exception("This version is not known!") - 91 if (version[-1] == "c"): - 92 appended = False - 93 compact = True - 94 version = version[:-1] - 95 elif (version[-1] == "a"): - 96 appended = True - 97 compact = False - 98 version = version[:-1] - 99 else: -100 compact = False -101 appended = False -102 read = 0 -103 T = 0 -104 start = 0 -105 ls = [] -106 if "replica" in kwargs: -107 ls = reps -108 else: -109 for (dirpath, dirnames, filenames) in os.walk(path): -110 if not appended: -111 ls.extend(dirnames) -112 else: -113 ls.extend(filenames) -114 break -115 if not ls: -116 raise Exception('Error, directory not found') -117 # Exclude folders with different names -118 for exc in ls: -119 if not fnmatch.fnmatch(exc, prefix + '*'): -120 ls = list(set(ls) - set([exc])) -121 -122 if not appended: -123 if len(ls) > 1: -124 # New version, to cope with ids, etc. -125 ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) -126 replica = len(ls) -127 else: -128 replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls])) -129 print('Read', part, 'part of', name, 'from', prefix[:-1], -130 ',', replica, 'replica') -131 if 'names' in kwargs: -132 new_names = kwargs.get('names') -133 if len(new_names) != len(set(new_names)): -134 raise Exception("names are not unique!") -135 if len(new_names) != replica: -136 raise Exception('Names does not have the required length', replica) -137 else: -138 new_names = [] -139 if not appended: -140 for entry in ls: -141 try: -142 idx = entry.index('r') -143 except Exception: -144 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") -145 -146 if 'ens_name' in kwargs: -147 new_names.append(kwargs.get('ens_name') + '|' + entry[idx:]) -148 else: -149 new_names.append(entry[:idx] + '|' + entry[idx:]) -150 else: -151 -152 for exc in ls: -153 if not fnmatch.fnmatch(exc, prefix + '*.' + name): -154 ls = list(set(ls) - set([exc])) -155 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -156 for entry in ls: -157 myentry = entry[:-len(name) - 1] -158 try: -159 idx = myentry.index('r') -160 except Exception: -161 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") -162 -163 if 'ens_name' in kwargs: -164 new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:]) -165 else: -166 new_names.append(myentry[:idx] + '|' + myentry[idx:]) -167 idl = [] -168 if not appended: -169 for i, item in enumerate(ls): -170 sub_ls = [] -171 if "files" in kwargs: -172 sub_ls = kwargs.get("files") -173 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -174 else: -175 for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): -176 if compact: -177 sub_ls.extend(filenames) -178 else: -179 sub_ls.extend(dirnames) -180 break -181 if compact: -182 for exc in sub_ls: -183 if not fnmatch.fnmatch(exc, prefix + '*'): -184 sub_ls = list(set(sub_ls) - set([exc])) -185 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -186 else: -187 for exc in sub_ls: -188 if not fnmatch.fnmatch(exc, 'cfg*'): -189 sub_ls = list(set(sub_ls) - set([exc])) -190 sub_ls.sort(key=lambda x: int(x[3:])) -191 rep_idl = [] -192 no_cfg = len(sub_ls) -193 for cfg in sub_ls: -194 try: -195 if compact: -196 rep_idl.append(int(cfg.split(cfg_separator)[-1])) -197 else: -198 rep_idl.append(int(cfg[3:])) -199 except Exception: -200 raise Exception("Couldn't parse idl from directroy, problem with file " + cfg) -201 rep_idl.sort() -202 # maybe there is a better way to print the idls -203 print(item, ':', no_cfg, ' configurations') -204 idl.append(rep_idl) -205 # here we have found all the files we need to look into. -206 if i == 0: -207 # here, we want to find the place within the file, -208 # where the correlator we need is stored. -209 # to do so, the pattern needed is put together -210 # from the input values -211 if version == "0.0": -212 pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf) -213 # if b2b, a second wf is needed -214 if b2b: -215 pattern += ", wf_2 " + str(wf2) -216 qs = quarks.split(" ") -217 pattern += " : " + qs[0] + " - " + qs[1] -218 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") -219 for k, line in enumerate(file): -220 if read == 1 and not line.strip() and k > start + 1: -221 break -222 if read == 1 and k >= start: -223 T += 1 -224 if pattern in line: -225 read = 1 -226 start = k + 1 -227 print(str(T) + " entries found.") -228 file.close() -229 else: -230 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) -231 if b2b: -232 pattern += '\nwf_2 ' + str(wf2) -233 # and the file is parsed through to find the pattern -234 if compact: -235 file = open(path + '/' + item + '/' + sub_ls[0], "r") -236 else: -237 # for non-compactified versions of the files -238 file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r") -239 -240 content = file.read() -241 match = re.search(pattern, content) -242 if match: -243 start_read = content.count('\n', 0, match.start()) + 5 + b2b -244 end_match = re.search(r'\n\s*\n', content[match.start():]) -245 T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b -246 assert T > 0 -247 print(T, 'entries, starting to read in line', start_read) -248 file.close() -249 else: -250 file.close() -251 raise Exception('Correlator with pattern\n' + pattern + '\nnot found.') -252 -253 # we found where the correlator -254 # that is to be read is in the files -255 # after preparing the datastructure -256 # the correlators get parsed into... -257 deltas = [] -258 for j in range(T): -259 deltas.append([]) -260 -261 for t in range(T): -262 deltas[t].append(np.zeros(no_cfg)) -263 if compact: -264 for cfg in range(no_cfg): -265 with open(path + '/' + item + '/' + sub_ls[cfg]) as fp: -266 lines = fp.readlines() -267 # check, if the correlator is in fact -268 # printed completely -269 if (start_read + T > len(lines)): -270 raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?") -271 # and start to read the correlator. -272 # the range here is chosen like this, -273 # since this allows for implementing -274 # a security check for every read correlator later... -275 for k in range(start_read - 6, start_read + T): -276 if k == start_read - 5 - b2b: -277 if lines[k].strip() != 'name ' + name: -278 raise Exception('Wrong format', sub_ls[cfg]) -279 if (k >= start_read and k < start_read + T): -280 floats = list(map(float, lines[k].split())) -281 deltas[k - start_read][i][cfg] = floats[-2:][im] -282 else: -283 for cnfg, subitem in enumerate(sub_ls): -284 with open(path + '/' + item + '/' + subitem + '/' + name) as fp: -285 # since the non-compatified files -286 # are typically not so long, -287 # we can iterate over the whole file. -288 # here one can also implement the chekc from above. -289 for k, line in enumerate(fp): -290 if (k >= start_read and k < start_read + T): -291 floats = list(map(float, line.split())) -292 if version == "0.0": -293 deltas[k - start][i][cnfg] = floats[im - single] -294 else: -295 deltas[k - start_read][i][cnfg] = floats[1 + im - single] -296 -297 else: -298 if "files" in kwargs: -299 ls = kwargs.get("files") -300 else: -301 for exc in ls: -302 if not fnmatch.fnmatch(exc, prefix + '*.' + name): -303 ls = list(set(ls) - set([exc])) -304 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) -305 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) -306 if b2b: -307 pattern += '\nwf_2 ' + str(wf2) -308 for rep, file in enumerate(ls): -309 rep_idl = [] -310 with open(path + '/' + file, 'r') as fp: -311 content = fp.readlines() -312 data_starts = [] -313 for linenumber, line in enumerate(content): -314 if "[run]" in line: -315 data_starts.append(linenumber) -316 if len(set([data_starts[i] - data_starts[i - 1] for i in range(1, len(data_starts))])) > 1: -317 raise Exception("Irregularities in file structure found, not all runs have the same output length") -318 chunk = content[:data_starts[1]] -319 for linenumber, line in enumerate(chunk): -320 if line.startswith("gauge_name"): -321 gauge_line = linenumber -322 elif line.startswith("[correlator]"): -323 corr_line = linenumber -324 found_pat = "" -325 for li in chunk[corr_line + 1: corr_line + 6 + b2b]: -326 found_pat += li -327 if re.search(pattern, found_pat): -328 start_read = corr_line + 7 + b2b -329 break -330 endline = corr_line + 6 + b2b -331 while not chunk[endline] == "\n": -332 endline += 1 -333 T = endline - start_read -334 if rep == 0: -335 deltas = [] -336 for t in range(T): -337 deltas.append([]) -338 for t in range(T): -339 deltas[t].append(np.zeros(len(data_starts))) -340 # all other chunks should follow the same structure -341 for cnfg in range(len(data_starts)): -342 start = data_starts[cnfg] -343 stop = start + data_starts[1] -344 chunk = content[start:stop] -345 try: -346 rep_idl.append(int(chunk[gauge_line].split(cfg_separator)[-1])) -347 except Exception: -348 raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line) -349 -350 found_pat = "" -351 for li in chunk[corr_line + 1:corr_line + 6 + b2b]: -352 found_pat += li -353 if re.search(pattern, found_pat): -354 for t, line in enumerate(chunk[start_read:start_read + T]): -355 floats = list(map(float, line.split())) -356 deltas[t][rep][cnfg] = floats[im + 1 - single] -357 idl.append(rep_idl) -358 -359 if "check_configs" in kwargs: -360 print("Checking for missing configs...") -361 che = kwargs.get("check_configs") -362 if not (len(che) == len(idl)): -363 raise Exception("check_configs has to be the same length as replica!") -364 for r in range(len(idl)): -365 print("checking " + new_names[r]) -366 utils.check_idl(idl[r], che[r]) -367 print("Done") -368 result = [] -369 for t in range(T): -370 result.append(Obs(deltas[t], new_names, idl=idl)) -371 return result + 74 + 75 if corr_type == 'bb': + 76 b2b = True + 77 single = True + 78 elif corr_type == 'bib': + 79 b2b = True + 80 single = False + 81 else: + 82 b2b = False + 83 single = False + 84 + 85 known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"] + 86 + 87 if version not in known_versions: + 88 raise Exception("This version is not known!") + 89 if (version[-1] == "c"): + 90 appended = False + 91 compact = True + 92 version = version[:-1] + 93 elif (version[-1] == "a"): + 94 appended = True + 95 compact = False + 96 version = version[:-1] + 97 else: + 98 compact = False + 99 appended = False +100 ls = [] +101 if "replica" in kwargs: +102 ls = kwargs.get("replica") +103 else: +104 for (dirpath, dirnames, filenames) in os.walk(path): +105 if not appended: +106 ls.extend(dirnames) +107 else: +108 ls.extend(filenames) +109 break +110 if not ls: +111 raise Exception('Error, directory not found') +112 # Exclude folders with different names +113 for exc in ls: +114 if not fnmatch.fnmatch(exc, prefix + '*'): +115 ls = list(set(ls) - set([exc])) +116 +117 if not appended: +118 ls = sort_names(ls) +119 replica = len(ls) +120 +121 else: +122 replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls])) +123 if not silent: +124 print('Read', part, 'part of', name, 'from', prefix[:-1], ',', replica, 'replica') +125 +126 if 'names' in kwargs: +127 new_names = kwargs.get('names') +128 if len(new_names) != len(set(new_names)): +129 raise Exception("names are not unique!") +130 if len(new_names) != replica: +131 raise Exception('names should have the length', replica) +132 +133 else: +134 ens_name = kwargs.get("ens_name") +135 if not appended: +136 new_names = _get_rep_names(ls, ens_name) +137 else: +138 new_names = _get_appended_rep_names(ls, prefix, name, ens_name) +139 new_names = sort_names(new_names) +140 +141 idl = [] +142 if not appended: +143 for i, item in enumerate(ls): +144 rep_path = path + '/' + item +145 if "files" in kwargs: +146 files = kwargs.get("files") +147 else: +148 files = [] +149 sub_ls = _find_files(rep_path, prefix, compact, files) +150 rep_idl = [] +151 no_cfg = len(sub_ls) +152 for cfg in sub_ls: +153 try: +154 if compact: +155 rep_idl.append(int(cfg.split(cfg_separator)[-1])) +156 else: +157 rep_idl.append(int(cfg[3:])) +158 except Exception: +159 raise Exception("Couldn't parse idl from directroy, problem with file " + cfg) +160 rep_idl.sort() +161 # maybe there is a better way to print the idls +162 if not silent: +163 print(item, ':', no_cfg, ' configurations') +164 idl.append(rep_idl) +165 # here we have found all the files we need to look into. +166 if i == 0: +167 # here, we want to find the place within the file, +168 # where the correlator we need is stored. +169 # to do so, the pattern needed is put together +170 # from the input values +171 if version == "0.0": +172 file = path + '/' + item + '/' + sub_ls[0] + '/' + name +173 else: +174 if compact: +175 file = path + '/' + item + '/' + sub_ls[0] +176 else: +177 file = path + '/' + item + '/' + sub_ls[0] + '/' + name +178 +179 pattern = _make_pattern(version, name, noffset, wf, wf2, b2b, quarks) +180 start_read, T = _find_correlator(file, version, pattern, b2b, silent=silent) +181 +182 # preparing the datastructure +183 # the correlators get parsed into... +184 deltas = [] +185 for j in range(T): +186 deltas.append([]) +187 +188 if compact: +189 rep_deltas = _read_compact_rep(path, item, sub_ls, start_read, T, b2b, name, im) +190 +191 for t in range(T): +192 deltas[t].append(rep_deltas[t]) +193 else: +194 for t in range(T): +195 deltas[t].append(np.zeros(no_cfg)) +196 for cnfg, subitem in enumerate(sub_ls): +197 with open(path + '/' + item + '/' + subitem + '/' + name) as fp: +198 for k, line in enumerate(fp): +199 if (k >= start_read and k < start_read + T): +200 floats = list(map(float, line.split())) +201 if version == "0.0": +202 deltas[k - start_read][i][cnfg] = floats[im - single] +203 else: +204 deltas[k - start_read][i][cnfg] = floats[1 + im - single] +205 +206 else: +207 if "files" in kwargs: +208 ls = kwargs.get("files") +209 else: +210 for exc in ls: +211 if not fnmatch.fnmatch(exc, prefix + '*.' + name): +212 ls = list(set(ls) - set([exc])) +213 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +214 pattern = _make_pattern(version, name, noffset, wf, wf2, b2b, quarks) +215 deltas = [] +216 for rep, file in enumerate(ls): +217 rep_idl = [] +218 filename = path + '/' + file +219 T, rep_idl, rep_data = _read_append_rep(filename, pattern, b2b, cfg_separator, im, single) +220 if rep == 0: +221 for t in range(T): +222 deltas.append([]) +223 for t in range(T): +224 deltas[t].append(rep_data[t]) +225 idl.append(rep_idl) +226 +227 if "check_configs" in kwargs: +228 if not silent: +229 print("Checking for missing configs...") +230 che = kwargs.get("check_configs") +231 if not (len(che) == len(idl)): +232 raise Exception("check_configs has to be the same length as replica!") +233 for r in range(len(idl)): +234 if not silent: +235 print("checking " + new_names[r]) +236 check_idl(idl[r], che[r]) +237 if not silent: +238 print("Done") +239 result = [] +240 for t in range(T): +241 result.append(Obs(deltas[t], new_names, idl=idl)) +242 return result +243 +244 +245def _find_files(rep_path, prefix, compact, files=[]): +246 sub_ls = [] +247 if not files == []: +248 files.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +249 else: +250 for (dirpath, dirnames, filenames) in os.walk(rep_path): +251 if compact: +252 sub_ls.extend(filenames) +253 else: +254 sub_ls.extend(dirnames) +255 break +256 if compact: +257 for exc in sub_ls: +258 if not fnmatch.fnmatch(exc, prefix + '*'): +259 sub_ls = list(set(sub_ls) - set([exc])) +260 sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +261 else: +262 for exc in sub_ls: +263 if not fnmatch.fnmatch(exc, 'cfg*'): +264 sub_ls = list(set(sub_ls) - set([exc])) +265 sub_ls.sort(key=lambda x: int(x[3:])) +266 files = sub_ls +267 if len(files) == 0: +268 raise FileNotFoundError("Did not find files in", rep_path, "with prefix", prefix, "and the given structure.") +269 return files +270 +271 +272def _make_pattern(version, name, noffset, wf, wf2, b2b, quarks): +273 if version == "0.0": +274 pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf) +275 if b2b: +276 pattern += ", wf_2 " + str(wf2) +277 qs = quarks.split(" ") +278 pattern += " : " + qs[0] + " - " + qs[1] +279 else: +280 pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) +281 if b2b: +282 pattern += '\nwf_2 ' + str(wf2) +283 return pattern +284 +285 +286def _find_correlator(file_name, version, pattern, b2b, silent=False): +287 T = 0 +288 +289 file = open(file_name, "r") +290 +291 content = file.read() +292 match = re.search(pattern, content) +293 if match: +294 if version == "0.0": +295 start_read = content.count('\n', 0, match.start()) + 1 +296 T = content.count('\n', start_read) +297 else: +298 start_read = content.count('\n', 0, match.start()) + 5 + b2b +299 end_match = re.search(r'\n\s*\n', content[match.start():]) +300 T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b +301 if not T > 0: +302 raise ValueError("Correlator with pattern\n" + pattern + "\nis empty!") +303 if not silent: +304 print(T, 'entries, starting to read in line', start_read) +305 +306 else: +307 file.close() +308 raise ValueError('Correlator with pattern\n' + pattern + '\nnot found.') +309 +310 file.close() +311 return start_read, T +312 +313 +314def _read_compact_file(rep_path, config_file, start_read, T, b2b, name, im): +315 with open(rep_path + config_file) as fp: +316 lines = fp.readlines() +317 # check, if the correlator is in fact +318 # printed completely +319 if (start_read + T + 1 > len(lines)): +320 raise Exception("EOF before end of correlator data! Maybe " + rep_path + config_file + " is corrupted?") +321 corr_lines = lines[start_read - 6: start_read + T] +322 del lines +323 t_vals = [] +324 +325 if corr_lines[1 - b2b].strip() != 'name ' + name: +326 raise Exception('Wrong format in file', config_file) +327 +328 for k in range(6, T + 6): +329 floats = list(map(float, corr_lines[k].split())) +330 t_vals.append(floats[-2:][im]) +331 return t_vals +332 +333 +334def _read_compact_rep(path, rep, sub_ls, start_read, T, b2b, name, im): +335 rep_path = path + '/' + rep + '/' +336 no_cfg = len(sub_ls) +337 deltas = [] +338 for t in range(T): +339 deltas.append(np.zeros(no_cfg)) +340 for cfg in range(no_cfg): +341 cfg_file = sub_ls[cfg] +342 cfg_data = _read_compact_file(rep_path, cfg_file, start_read, T, b2b, name, im) +343 for t in range(T): +344 deltas[t][cfg] = cfg_data[t] +345 return deltas +346 +347 +348def _read_chunk(chunk, gauge_line, cfg_sep, start_read, T, corr_line, b2b, pattern, im, single): +349 try: +350 idl = int(chunk[gauge_line].split(cfg_sep)[-1]) +351 except Exception: +352 raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line) +353 +354 found_pat = "" +355 data = [] +356 for li in chunk[corr_line + 1:corr_line + 6 + b2b]: +357 found_pat += li +358 if re.search(pattern, found_pat): +359 for t, line in enumerate(chunk[start_read:start_read + T]): +360 floats = list(map(float, line.split())) +361 data.append(floats[im + 1 - single]) +362 return idl, data +363 +364 +365def _read_append_rep(filename, pattern, b2b, cfg_separator, im, single): +366 with open(filename, 'r') as fp: +367 content = fp.readlines() +368 data_starts = [] +369 for linenumber, line in enumerate(content): +370 if "[run]" in line: +371 data_starts.append(linenumber) +372 if len(set([data_starts[i] - data_starts[i - 1] for i in range(1, len(data_starts))])) > 1: +373 raise Exception("Irregularities in file structure found, not all runs have the same output length") +374 chunk = content[:data_starts[1]] +375 for linenumber, line in enumerate(chunk): +376 if line.startswith("gauge_name"): +377 gauge_line = linenumber +378 elif line.startswith("[correlator]"): +379 corr_line = linenumber +380 found_pat = "" +381 for li in chunk[corr_line + 1: corr_line + 6 + b2b]: +382 found_pat += li +383 if re.search(pattern, found_pat): +384 start_read = corr_line + 7 + b2b +385 break +386 else: +387 raise ValueError("Did not find pattern\n", pattern, "\nin\n", filename) +388 endline = corr_line + 6 + b2b +389 while not chunk[endline] == "\n": +390 endline += 1 +391 T = endline - start_read +392 +393 # all other chunks should follow the same structure +394 rep_idl = [] +395 rep_data = [] +396 +397 for cnfg in range(len(data_starts)): +398 start = data_starts[cnfg] +399 stop = start + data_starts[1] +400 chunk = content[start:stop] +401 idl, data = _read_chunk(chunk, gauge_line, cfg_separator, start_read, T, corr_line, b2b, pattern, im, single) +402 rep_idl.append(idl) +403 rep_data.append(data) +404 +405 data = [] +406 +407 for t in range(T): +408 data.append([]) +409 for c in range(len(rep_data)): +410 data[t].append(rep_data[c][t]) +411 return T, rep_idl, data +412 +413 +414def _get_rep_names(ls, ens_name=None): +415 new_names = [] +416 for entry in ls: +417 try: +418 idx = entry.index('r') +419 except Exception: +420 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") +421 +422 if ens_name: +423 new_names.append('ens_name' + '|' + entry[idx:]) +424 else: +425 new_names.append(entry[:idx] + '|' + entry[idx:]) +426 return new_names +427 +428 +429def _get_appended_rep_names(ls, prefix, name, ens_name=None): +430 new_names = [] +431 for exc in ls: +432 if not fnmatch.fnmatch(exc, prefix + '*.' + name): +433 ls = list(set(ls) - set([exc])) +434 ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) +435 for entry in ls: +436 myentry = entry[:-len(name) - 1] +437 try: +438 idx = myentry.index('r') +439 except Exception: +440 raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") +441 +442 if ens_name: +443 new_names.append('ens_name' + '|' + entry[idx:]) +444 else: +445 new_names.append(myentry[:idx] + '|' + myentry[idx:]) +446 return new_names
@@ -456,14 +531,14 @@
def - read_sfcf( path, prefix, name, quarks='.*', corr_type='bi', noffset=0, wf=0, wf2=0, version='1.0c', cfg_separator='n', **kwargs): + read_sfcf( path, prefix, name, quarks='.*', corr_type='bi', noffset=0, wf=0, wf2=0, version='1.0c', cfg_separator='n', silent=False, **kwargs):
-
 10def read_sfcf(path, prefix, name, quarks='.*', corr_type='bi', noffset=0, wf=0, wf2=0, version="1.0c", cfg_separator="n", **kwargs):
- 11    """Read sfcf c format from given folder structure.
+            
 10def read_sfcf(path, prefix, name, quarks='.*', corr_type='bi', noffset=0, wf=0, wf2=0, version="1.0c", cfg_separator="n", silent=False, **kwargs):
+ 11    """Read sfcf files from given folder structure.
  12
  13    Parameters
  14    ----------
@@ -527,308 +602,179 @@
  72    else:
  73        im = 0
  74        part = 'real'
- 75    if "replica" in kwargs:
- 76        reps = kwargs.get("replica")
- 77    if corr_type == 'bb':
- 78        b2b = True
- 79        single = True
- 80    elif corr_type == 'bib':
- 81        b2b = True
- 82        single = False
- 83    else:
- 84        b2b = False
- 85        single = False
- 86    compact = True
- 87    appended = False
- 88    known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"]
- 89
- 90    if version not in known_versions:
- 91        raise Exception("This version is not known!")
- 92    if (version[-1] == "c"):
- 93        appended = False
- 94        compact = True
- 95        version = version[:-1]
- 96    elif (version[-1] == "a"):
- 97        appended = True
- 98        compact = False
- 99        version = version[:-1]
-100    else:
-101        compact = False
-102        appended = False
-103    read = 0
-104    T = 0
-105    start = 0
-106    ls = []
-107    if "replica" in kwargs:
-108        ls = reps
-109    else:
-110        for (dirpath, dirnames, filenames) in os.walk(path):
-111            if not appended:
-112                ls.extend(dirnames)
-113            else:
-114                ls.extend(filenames)
-115            break
-116        if not ls:
-117            raise Exception('Error, directory not found')
-118        # Exclude folders with different names
-119        for exc in ls:
-120            if not fnmatch.fnmatch(exc, prefix + '*'):
-121                ls = list(set(ls) - set([exc]))
-122
-123    if not appended:
-124        if len(ls) > 1:
-125            # New version, to cope with ids, etc.
-126            ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
-127        replica = len(ls)
-128    else:
-129        replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls]))
-130    print('Read', part, 'part of', name, 'from', prefix[:-1],
-131          ',', replica, 'replica')
-132    if 'names' in kwargs:
-133        new_names = kwargs.get('names')
-134        if len(new_names) != len(set(new_names)):
-135            raise Exception("names are not unique!")
-136        if len(new_names) != replica:
-137            raise Exception('Names does not have the required length', replica)
-138    else:
-139        new_names = []
-140        if not appended:
-141            for entry in ls:
-142                try:
-143                    idx = entry.index('r')
-144                except Exception:
-145                    raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
-146
-147                if 'ens_name' in kwargs:
-148                    new_names.append(kwargs.get('ens_name') + '|' + entry[idx:])
-149                else:
-150                    new_names.append(entry[:idx] + '|' + entry[idx:])
-151        else:
-152
-153            for exc in ls:
-154                if not fnmatch.fnmatch(exc, prefix + '*.' + name):
-155                    ls = list(set(ls) - set([exc]))
-156            ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
-157            for entry in ls:
-158                myentry = entry[:-len(name) - 1]
-159                try:
-160                    idx = myentry.index('r')
-161                except Exception:
-162                    raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
-163
-164                if 'ens_name' in kwargs:
-165                    new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:])
-166                else:
-167                    new_names.append(myentry[:idx] + '|' + myentry[idx:])
-168    idl = []
-169    if not appended:
-170        for i, item in enumerate(ls):
-171            sub_ls = []
-172            if "files" in kwargs:
-173                sub_ls = kwargs.get("files")
-174                sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
-175            else:
-176                for (dirpath, dirnames, filenames) in os.walk(path + '/' + item):
-177                    if compact:
-178                        sub_ls.extend(filenames)
-179                    else:
-180                        sub_ls.extend(dirnames)
-181                    break
-182                if compact:
-183                    for exc in sub_ls:
-184                        if not fnmatch.fnmatch(exc, prefix + '*'):
-185                            sub_ls = list(set(sub_ls) - set([exc]))
-186                    sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
-187                else:
-188                    for exc in sub_ls:
-189                        if not fnmatch.fnmatch(exc, 'cfg*'):
-190                            sub_ls = list(set(sub_ls) - set([exc]))
-191                    sub_ls.sort(key=lambda x: int(x[3:]))
-192            rep_idl = []
-193            no_cfg = len(sub_ls)
-194            for cfg in sub_ls:
-195                try:
-196                    if compact:
-197                        rep_idl.append(int(cfg.split(cfg_separator)[-1]))
-198                    else:
-199                        rep_idl.append(int(cfg[3:]))
-200                except Exception:
-201                    raise Exception("Couldn't parse idl from directroy, problem with file " + cfg)
-202            rep_idl.sort()
-203            # maybe there is a better way to print the idls
-204            print(item, ':', no_cfg, ' configurations')
-205            idl.append(rep_idl)
-206            # here we have found all the files we need to look into.
-207            if i == 0:
-208                # here, we want to find the place within the file,
-209                # where the correlator we need is stored.
-210                # to do so, the pattern needed is put together
-211                # from the input values
-212                if version == "0.0":
-213                    pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf)
-214                    # if b2b, a second wf is needed
-215                    if b2b:
-216                        pattern += ", wf_2 " + str(wf2)
-217                    qs = quarks.split(" ")
-218                    pattern += " : " + qs[0] + " - " + qs[1]
-219                    file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r")
-220                    for k, line in enumerate(file):
-221                        if read == 1 and not line.strip() and k > start + 1:
-222                            break
-223                        if read == 1 and k >= start:
-224                            T += 1
-225                        if pattern in line:
-226                            read = 1
-227                            start = k + 1
-228                    print(str(T) + " entries found.")
-229                    file.close()
-230                else:
-231                    pattern = 'name      ' + name + '\nquarks    ' + quarks + '\noffset    ' + str(noffset) + '\nwf        ' + str(wf)
-232                    if b2b:
-233                        pattern += '\nwf_2      ' + str(wf2)
-234                    # and the file is parsed through to find the pattern
-235                    if compact:
-236                        file = open(path + '/' + item + '/' + sub_ls[0], "r")
-237                    else:
-238                        # for non-compactified versions of the files
-239                        file = open(path + '/' + item + '/' + sub_ls[0] + '/' + name, "r")
-240
-241                    content = file.read()
-242                    match = re.search(pattern, content)
-243                    if match:
-244                        start_read = content.count('\n', 0, match.start()) + 5 + b2b
-245                        end_match = re.search(r'\n\s*\n', content[match.start():])
-246                        T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b
-247                        assert T > 0
-248                        print(T, 'entries, starting to read in line', start_read)
-249                        file.close()
-250                    else:
-251                        file.close()
-252                        raise Exception('Correlator with pattern\n' + pattern + '\nnot found.')
-253
-254                # we found where the correlator
-255                # that is to be read is in the files
-256                # after preparing the datastructure
-257                # the correlators get parsed into...
-258                deltas = []
-259                for j in range(T):
-260                    deltas.append([])
-261
-262            for t in range(T):
-263                deltas[t].append(np.zeros(no_cfg))
-264            if compact:
-265                for cfg in range(no_cfg):
-266                    with open(path + '/' + item + '/' + sub_ls[cfg]) as fp:
-267                        lines = fp.readlines()
-268                        # check, if the correlator is in fact
-269                        # printed completely
-270                        if (start_read + T > len(lines)):
-271                            raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?")
-272                        # and start to read the correlator.
-273                        # the range here is chosen like this,
-274                        # since this allows for implementing
-275                        # a security check for every read correlator later...
-276                        for k in range(start_read - 6, start_read + T):
-277                            if k == start_read - 5 - b2b:
-278                                if lines[k].strip() != 'name      ' + name:
-279                                    raise Exception('Wrong format', sub_ls[cfg])
-280                            if (k >= start_read and k < start_read + T):
-281                                floats = list(map(float, lines[k].split()))
-282                                deltas[k - start_read][i][cfg] = floats[-2:][im]
-283            else:
-284                for cnfg, subitem in enumerate(sub_ls):
-285                    with open(path + '/' + item + '/' + subitem + '/' + name) as fp:
-286                        # since the non-compatified files
-287                        # are typically not so long,
-288                        # we can iterate over the whole file.
-289                        # here one can also implement the chekc from above.
-290                        for k, line in enumerate(fp):
-291                            if (k >= start_read and k < start_read + T):
-292                                floats = list(map(float, line.split()))
-293                                if version == "0.0":
-294                                    deltas[k - start][i][cnfg] = floats[im - single]
-295                                else:
-296                                    deltas[k - start_read][i][cnfg] = floats[1 + im - single]
-297
-298    else:
-299        if "files" in kwargs:
-300            ls = kwargs.get("files")
-301        else:
-302            for exc in ls:
-303                if not fnmatch.fnmatch(exc, prefix + '*.' + name):
-304                    ls = list(set(ls) - set([exc]))
-305                ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
-306        pattern = 'name      ' + name + '\nquarks    ' + quarks + '\noffset    ' + str(noffset) + '\nwf        ' + str(wf)
-307        if b2b:
-308            pattern += '\nwf_2      ' + str(wf2)
-309        for rep, file in enumerate(ls):
-310            rep_idl = []
-311            with open(path + '/' + file, 'r') as fp:
-312                content = fp.readlines()
-313                data_starts = []
-314                for linenumber, line in enumerate(content):
-315                    if "[run]" in line:
-316                        data_starts.append(linenumber)
-317                if len(set([data_starts[i] - data_starts[i - 1] for i in range(1, len(data_starts))])) > 1:
-318                    raise Exception("Irregularities in file structure found, not all runs have the same output length")
-319                chunk = content[:data_starts[1]]
-320                for linenumber, line in enumerate(chunk):
-321                    if line.startswith("gauge_name"):
-322                        gauge_line = linenumber
-323                    elif line.startswith("[correlator]"):
-324                        corr_line = linenumber
-325                        found_pat = ""
-326                        for li in chunk[corr_line + 1: corr_line + 6 + b2b]:
-327                            found_pat += li
-328                        if re.search(pattern, found_pat):
-329                            start_read = corr_line + 7 + b2b
-330                            break
-331                endline = corr_line + 6 + b2b
-332                while not chunk[endline] == "\n":
-333                    endline += 1
-334                T = endline - start_read
-335                if rep == 0:
-336                    deltas = []
-337                    for t in range(T):
-338                        deltas.append([])
-339                for t in range(T):
-340                    deltas[t].append(np.zeros(len(data_starts)))
-341                # all other chunks should follow the same structure
-342                for cnfg in range(len(data_starts)):
-343                    start = data_starts[cnfg]
-344                    stop = start + data_starts[1]
-345                    chunk = content[start:stop]
-346                    try:
-347                        rep_idl.append(int(chunk[gauge_line].split(cfg_separator)[-1]))
-348                    except Exception:
-349                        raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line)
-350
-351                    found_pat = ""
-352                    for li in chunk[corr_line + 1:corr_line + 6 + b2b]:
-353                        found_pat += li
-354                    if re.search(pattern, found_pat):
-355                        for t, line in enumerate(chunk[start_read:start_read + T]):
-356                            floats = list(map(float, line.split()))
-357                            deltas[t][rep][cnfg] = floats[im + 1 - single]
-358            idl.append(rep_idl)
-359
-360    if "check_configs" in kwargs:
-361        print("Checking for missing configs...")
-362        che = kwargs.get("check_configs")
-363        if not (len(che) == len(idl)):
-364            raise Exception("check_configs has to be the same length as replica!")
-365        for r in range(len(idl)):
-366            print("checking " + new_names[r])
-367            utils.check_idl(idl[r], che[r])
-368        print("Done")
-369    result = []
-370    for t in range(T):
-371        result.append(Obs(deltas[t], new_names, idl=idl))
-372    return result
+ 75
+ 76    if corr_type == 'bb':
+ 77        b2b = True
+ 78        single = True
+ 79    elif corr_type == 'bib':
+ 80        b2b = True
+ 81        single = False
+ 82    else:
+ 83        b2b = False
+ 84        single = False
+ 85
+ 86    known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"]
+ 87
+ 88    if version not in known_versions:
+ 89        raise Exception("This version is not known!")
+ 90    if (version[-1] == "c"):
+ 91        appended = False
+ 92        compact = True
+ 93        version = version[:-1]
+ 94    elif (version[-1] == "a"):
+ 95        appended = True
+ 96        compact = False
+ 97        version = version[:-1]
+ 98    else:
+ 99        compact = False
+100        appended = False
+101    ls = []
+102    if "replica" in kwargs:
+103        ls = kwargs.get("replica")
+104    else:
+105        for (dirpath, dirnames, filenames) in os.walk(path):
+106            if not appended:
+107                ls.extend(dirnames)
+108            else:
+109                ls.extend(filenames)
+110            break
+111        if not ls:
+112            raise Exception('Error, directory not found')
+113        # Exclude folders with different names
+114        for exc in ls:
+115            if not fnmatch.fnmatch(exc, prefix + '*'):
+116                ls = list(set(ls) - set([exc]))
+117
+118    if not appended:
+119        ls = sort_names(ls)
+120        replica = len(ls)
+121
+122    else:
+123        replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls]))
+124    if not silent:
+125        print('Read', part, 'part of', name, 'from', prefix[:-1], ',', replica, 'replica')
+126
+127    if 'names' in kwargs:
+128        new_names = kwargs.get('names')
+129        if len(new_names) != len(set(new_names)):
+130            raise Exception("names are not unique!")
+131        if len(new_names) != replica:
+132            raise Exception('names should have the length', replica)
+133
+134    else:
+135        ens_name = kwargs.get("ens_name")
+136        if not appended:
+137            new_names = _get_rep_names(ls, ens_name)
+138        else:
+139            new_names = _get_appended_rep_names(ls, prefix, name, ens_name)
+140        new_names = sort_names(new_names)
+141
+142    idl = []
+143    if not appended:
+144        for i, item in enumerate(ls):
+145            rep_path = path + '/' + item
+146            if "files" in kwargs:
+147                files = kwargs.get("files")
+148            else:
+149                files = []
+150            sub_ls = _find_files(rep_path, prefix, compact, files)
+151            rep_idl = []
+152            no_cfg = len(sub_ls)
+153            for cfg in sub_ls:
+154                try:
+155                    if compact:
+156                        rep_idl.append(int(cfg.split(cfg_separator)[-1]))
+157                    else:
+158                        rep_idl.append(int(cfg[3:]))
+159                except Exception:
+160                    raise Exception("Couldn't parse idl from directroy, problem with file " + cfg)
+161            rep_idl.sort()
+162            # maybe there is a better way to print the idls
+163            if not silent:
+164                print(item, ':', no_cfg, ' configurations')
+165            idl.append(rep_idl)
+166            # here we have found all the files we need to look into.
+167            if i == 0:
+168                # here, we want to find the place within the file,
+169                # where the correlator we need is stored.
+170                # to do so, the pattern needed is put together
+171                # from the input values
+172                if version == "0.0":
+173                    file = path + '/' + item + '/' + sub_ls[0] + '/' + name
+174                else:
+175                    if compact:
+176                        file = path + '/' + item + '/' + sub_ls[0]
+177                    else:
+178                        file = path + '/' + item + '/' + sub_ls[0] + '/' + name
+179
+180                pattern = _make_pattern(version, name, noffset, wf, wf2, b2b, quarks)
+181                start_read, T = _find_correlator(file, version, pattern, b2b, silent=silent)
+182
+183                # preparing the datastructure
+184                # the correlators get parsed into...
+185                deltas = []
+186                for j in range(T):
+187                    deltas.append([])
+188
+189            if compact:
+190                rep_deltas = _read_compact_rep(path, item, sub_ls, start_read, T, b2b, name, im)
+191
+192                for t in range(T):
+193                    deltas[t].append(rep_deltas[t])
+194            else:
+195                for t in range(T):
+196                    deltas[t].append(np.zeros(no_cfg))
+197                for cnfg, subitem in enumerate(sub_ls):
+198                    with open(path + '/' + item + '/' + subitem + '/' + name) as fp:
+199                        for k, line in enumerate(fp):
+200                            if (k >= start_read and k < start_read + T):
+201                                floats = list(map(float, line.split()))
+202                                if version == "0.0":
+203                                    deltas[k - start_read][i][cnfg] = floats[im - single]
+204                                else:
+205                                    deltas[k - start_read][i][cnfg] = floats[1 + im - single]
+206
+207    else:
+208        if "files" in kwargs:
+209            ls = kwargs.get("files")
+210        else:
+211            for exc in ls:
+212                if not fnmatch.fnmatch(exc, prefix + '*.' + name):
+213                    ls = list(set(ls) - set([exc]))
+214            ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
+215        pattern = _make_pattern(version, name, noffset, wf, wf2, b2b, quarks)
+216        deltas = []
+217        for rep, file in enumerate(ls):
+218            rep_idl = []
+219            filename = path + '/' + file
+220            T, rep_idl, rep_data = _read_append_rep(filename, pattern, b2b, cfg_separator, im, single)
+221            if rep == 0:
+222                for t in range(T):
+223                    deltas.append([])
+224            for t in range(T):
+225                deltas[t].append(rep_data[t])
+226            idl.append(rep_idl)
+227
+228    if "check_configs" in kwargs:
+229        if not silent:
+230            print("Checking for missing configs...")
+231        che = kwargs.get("check_configs")
+232        if not (len(che) == len(idl)):
+233            raise Exception("check_configs has to be the same length as replica!")
+234        for r in range(len(idl)):
+235            if not silent:
+236                print("checking " + new_names[r])
+237            check_idl(idl[r], che[r])
+238        if not silent:
+239            print("Done")
+240    result = []
+241    for t in range(T):
+242        result.append(Obs(deltas[t], new_names, idl=idl))
+243    return result
 
-

Read sfcf c format from given folder structure.

+

Read sfcf files from given folder structure.

Parameters
diff --git a/docs/pyerrors/input/utils.html b/docs/pyerrors/input/utils.html index 5068135e..f34fb46c 100644 --- a/docs/pyerrors/input/utils.html +++ b/docs/pyerrors/input/utils.html @@ -52,6 +52,9 @@

API Documentation

    +
  • + sort_names +
  • check_idl
  • @@ -71,47 +74,167 @@

    pyerrors.input.utils

    -

    Utilities for the input

    -
    - + -
     1"""Utilities for the input"""
    - 2
    +                        
     1import re
    + 2"""Utilities for the input"""
      3
    - 4def check_idl(idl, che):
    - 5    """Checks if list of configurations is contained in an idl
    - 6
    - 7    Parameters
    - 8    ----------
    - 9    idl : range or list
    -10        idl of the current replicum
    -11    che : list
    -12        list of configurations to be checked against
    -13
    -14    Returns
    -15    -------
    -16    miss_str : str
    -17        string with integers of which idls are missing
    -18    """
    -19    missing = []
    -20    for c in che:
    -21        if c not in idl:
    -22            missing.append(c)
    -23    # print missing configurations such that it can directly be parsed to slurm terminal
    -24    if not (len(missing) == 0):
    -25        print(len(missing), "configs missing")
    -26        miss_str = str(missing[0])
    -27        for i in missing[1:]:
    -28            miss_str += "," + str(i)
    -29        print(miss_str)
    -30    return miss_str
    + 4
    + 5def sort_names(ll):
    + 6    """Sorts a list of names of replika with searches for `r` and `id` in the replikum string.
    + 7    If this search fails, a fallback method is used,
    + 8    where the strings are simply compared and the first diffeing numeral is used for differentiation.
    + 9
    +10    Parameters
    +11    ----------
    +12    ll: list
    +13        list to sort
    +14
    +15    Returns
    +16    -------
    +17    ll: list
    +18        sorted list
    +19    """
    +20    if len(ll) > 1:
    +21        r_pattern = r'r(\d+)'
    +22        id_pattern = r'id(\d+)'
    +23
    +24        # sort list by id first
    +25        if all([re.search(id_pattern, entry) for entry in ll]):
    +26            ll.sort(key=lambda x: int(re.findall(id_pattern, x)[0]))
    +27        # then by replikum
    +28        if all([re.search(r_pattern, entry) for entry in ll]):
    +29            ll.sort(key=lambda x: int(re.findall(r_pattern, x)[0]))
    +30        # as the rearrangements by one key let the other key untouched, the list is sorted now
    +31
    +32        else:
    +33            # fallback
    +34            sames = ''
    +35            if len(ll) > 1:
    +36                for i in range(len(ll[0])):
    +37                    checking = ll[0][i]
    +38                    for rn in ll[1:]:
    +39                        is_same = (rn[i] == checking)
    +40                    if is_same:
    +41                        sames += checking
    +42                    else:
    +43                        break
    +44                print("Using prefix:", ll[0][len(sames):])
    +45            ll.sort(key=lambda x: int(re.findall(r'\d+', x[len(sames):])[0]))
    +46    return ll
    +47
    +48
    +49def check_idl(idl, che):
    +50    """Checks if list of configurations is contained in an idl
    +51
    +52    Parameters
    +53    ----------
    +54    idl : range or list
    +55        idl of the current replicum
    +56    che : list
    +57        list of configurations to be checked against
    +58
    +59    Returns
    +60    -------
    +61    miss_str : str
    +62        string with integers of which idls are missing
    +63    """
    +64    missing = []
    +65    for c in che:
    +66        if c not in idl:
    +67            missing.append(c)
    +68    # print missing configurations such that it can directly be parsed to slurm terminal
    +69    if not (len(missing) == 0):
    +70        print(len(missing), "configs missing")
    +71        miss_str = str(missing[0])
    +72        for i in missing[1:]:
    +73            miss_str += "," + str(i)
    +74        print(miss_str)
    +75    return miss_str
     
    +
    + +
    + + def + sort_names(ll): + + + +
    + +
     6def sort_names(ll):
    + 7    """Sorts a list of names of replika with searches for `r` and `id` in the replikum string.
    + 8    If this search fails, a fallback method is used,
    + 9    where the strings are simply compared and the first diffeing numeral is used for differentiation.
    +10
    +11    Parameters
    +12    ----------
    +13    ll: list
    +14        list to sort
    +15
    +16    Returns
    +17    -------
    +18    ll: list
    +19        sorted list
    +20    """
    +21    if len(ll) > 1:
    +22        r_pattern = r'r(\d+)'
    +23        id_pattern = r'id(\d+)'
    +24
    +25        # sort list by id first
    +26        if all([re.search(id_pattern, entry) for entry in ll]):
    +27            ll.sort(key=lambda x: int(re.findall(id_pattern, x)[0]))
    +28        # then by replikum
    +29        if all([re.search(r_pattern, entry) for entry in ll]):
    +30            ll.sort(key=lambda x: int(re.findall(r_pattern, x)[0]))
    +31        # as the rearrangements by one key let the other key untouched, the list is sorted now
    +32
    +33        else:
    +34            # fallback
    +35            sames = ''
    +36            if len(ll) > 1:
    +37                for i in range(len(ll[0])):
    +38                    checking = ll[0][i]
    +39                    for rn in ll[1:]:
    +40                        is_same = (rn[i] == checking)
    +41                    if is_same:
    +42                        sames += checking
    +43                    else:
    +44                        break
    +45                print("Using prefix:", ll[0][len(sames):])
    +46            ll.sort(key=lambda x: int(re.findall(r'\d+', x[len(sames):])[0]))
    +47    return ll
    +
    + + +

    Sorts a list of names of replika with searches for r and id in the replikum string. +If this search fails, a fallback method is used, +where the strings are simply compared and the first diffeing numeral is used for differentiation.

    + +
    Parameters
    + +
      +
    • ll (list): +list to sort
    • +
    + +
    Returns
    + +
      +
    • ll (list): +sorted list
    • +
    +
    + + +
    @@ -123,33 +246,33 @@
    -
     5def check_idl(idl, che):
    - 6    """Checks if list of configurations is contained in an idl
    - 7
    - 8    Parameters
    - 9    ----------
    -10    idl : range or list
    -11        idl of the current replicum
    -12    che : list
    -13        list of configurations to be checked against
    -14
    -15    Returns
    -16    -------
    -17    miss_str : str
    -18        string with integers of which idls are missing
    -19    """
    -20    missing = []
    -21    for c in che:
    -22        if c not in idl:
    -23            missing.append(c)
    -24    # print missing configurations such that it can directly be parsed to slurm terminal
    -25    if not (len(missing) == 0):
    -26        print(len(missing), "configs missing")
    -27        miss_str = str(missing[0])
    -28        for i in missing[1:]:
    -29            miss_str += "," + str(i)
    -30        print(miss_str)
    -31    return miss_str
    +            
    50def check_idl(idl, che):
    +51    """Checks if list of configurations is contained in an idl
    +52
    +53    Parameters
    +54    ----------
    +55    idl : range or list
    +56        idl of the current replicum
    +57    che : list
    +58        list of configurations to be checked against
    +59
    +60    Returns
    +61    -------
    +62    miss_str : str
    +63        string with integers of which idls are missing
    +64    """
    +65    missing = []
    +66    for c in che:
    +67        if c not in idl:
    +68            missing.append(c)
    +69    # print missing configurations such that it can directly be parsed to slurm terminal
    +70    if not (len(missing) == 0):
    +71        print(len(missing), "configs missing")
    +72        miss_str = str(missing[0])
    +73        for i in missing[1:]:
    +74            miss_str += "," + str(i)
    +75        print(miss_str)
    +76    return miss_str
     
    diff --git a/docs/search.js b/docs/search.js index f10327c2..c4d46c17 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oWhat is pyerrors?\n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n
      \n
    • automatic differentiation for exact linear error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
    • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
    • \n
    • coherent error propagation for data from different Markov chains.
    • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
    • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
    • \n
    \n\n

    More detailed examples can found in the GitHub repository \"badge\".

    \n\n

    If you use pyerrors for research that leads to a publication please consider citing:

    \n\n
      \n
    • Fabian Joswig, Simon Kuberski, Justus T. Kuhlmann, Jan Neuendorf, pyerrors: a python framework for error analysis of Monte Carlo data. [arXiv:2209.14371 [hep-lat]].
    • \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
    • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
    • \n
    \n\n

    and

    \n\n
      \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
    • \n
    \n\n

    where applicable.

    \n\n

    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.

    \n\n

    Installation

    \n\n

    Install the most recent release using pip and pypi:

    \n\n
    \n
    pip install pyerrors     # Fresh install\npip install -U pyerrors  # Update\n
    \n
    \n\n

    Install the most recent release using conda and conda-forge:

    \n\n
    \n
    conda install -c conda-forge pyerrors  # Fresh install\nconda update -c conda-forge pyerrors   # Update\n
    \n
    \n\n

    Install the current develop version:

    \n\n
    \n
    pip install git+https://github.com/fjosw/pyerrors.git@develop\n
    \n
    \n\n

    Basic example

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n
    \n\n

    The Obs class

    \n\n

    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble. It is crucial for the correct error propagation that observations from the same Monte Carlo history are labeled with the same name. See Multiple ensembles/replica for details.

    \n\n
    \n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n
    \n\n

    Error propagation

    \n\n

    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.

    \n\n

    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n
    \n\n

    Error estimation

    \n\n

    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.

    \n\n
    \n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.

    \n\n
    \n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_rho.

    \n\n

    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.

    \n\n

    Exponential tails

    \n\n

    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.

    \n\n
    \n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    For the full API see pyerrors.obs.Obs.gamma_method.

    \n\n

    Multiple ensembles/replica

    \n\n

    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.

    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    Observables from the same Monte Carlo chain have to be initialized with the same name for correct error propagation. If different names were used in this case the data would be treated as statistically independent resulting in loss of relevant information and a potential over or under estimate of the statistical error.

    \n\n

    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.

    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    Error estimation for multiple ensembles

    \n\n

    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.

    \n\n
    \n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n
    \n\n

    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.

    \n\n

    Irregular Monte Carlo chains

    \n\n

    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.

    \n\n
    \n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n
    \n\n

    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.

    \n\n

    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.

    \n\n

    For the full API see pyerrors.obs.Obs.

    \n\n

    Correlators

    \n\n

    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n
    \n\n

    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n
    \n\n

    The individual entries of a correlator can be accessed via slicing

    \n\n
    \n
    print(my_corr[3])\n> 0.3227(33)\n
    \n
    \n\n

    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.

    \n\n
    \n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n
    \n\n

    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:

    \n\n
      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
    • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
    • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
    • \n
    • Corr.plateau extracts a plateau value from the correlator in a given range.
    • \n
    • Corr.roll periodically shifts the correlator.
    • \n
    • Corr.reverse reverses the time ordering of the correlator.
    • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
    • \n
    • Corr.reweight reweights the correlator.
    • \n
    \n\n

    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).

    \n\n

    For the full API see pyerrors.correlators.Corr.

    \n\n

    Complex valued observables

    \n\n

    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.

    \n\n
    \n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n
    \n\n

    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.

    \n\n
    \n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n
    \n\n

    The Covobs class

    \n\n

    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.

    \n\n

    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.

    \n\n
    \n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n
    \n\n

    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.

    \n\n

    Correlated auxiliary data is defined similarly to above, e.g., via

    \n\n
    \n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n
    \n\n

    where RAP now is a list of two Obs that contains the two correlated parameters.

    \n\n

    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via

    \n\n
    \n
    o.covobs[k].grad\n
    \n
    \n\n

    Error propagation in iterative algorithms

    \n\n

    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least squares fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.

    \n\n

    Least squares fits

    \n\n

    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.

    \n\n

    Fit functions have to be of the following form

    \n\n
    \n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n
    \n\n

    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.

    \n\n

    Fits can then be performed via

    \n\n
    \n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n
    \n\n

    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.

    \n\n

    Data stored in Corr objects can be fitted directly using the Corr.fit method.

    \n\n
    \n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n
    \n\n

    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.

    \n\n

    For fit functions with multiple independent variables the fit function can be of the form

    \n\n
    \n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n
    \n\n

    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.

    \n\n

    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.

    \n\n

    Total least squares fits

    \n\n

    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only difference being that x also has to be a list or numpy.array of Obs.

    \n\n

    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.

    \n\n

    Matrix operations

    \n\n

    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:

    \n\n
      \n
    • inv for the matrix inverse.
    • \n
    • cholseky for the Cholesky decomposition.
    • \n
    • det for the matrix determinant.
    • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
    • \n
    • eig for eigenvalues of general matrices.
    • \n
    • pinv for the Moore-Penrose pseudoinverse.
    • \n
    • svd for the singular-value-decomposition.
    • \n
    \n\n

    For the full API see pyerrors.linalg.

    \n\n

    Export data

    \n\n

    \n\n

    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:

    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
    • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
    • \n
    • Who did write the file when and on which machine?
    • \n
    \n\n

    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example

    \n\n
    \n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n
    \n\n

    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.

    \n\n

    json.gz format specification

    \n\n

    The first entries of the file provide optional auxiliary information:

    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
    • \n
    • version is a string that specifies the version of the format.
    • \n
    • who is a string that specifies the user name of the creator of the file.
    • \n
    • date is a string and contains the creation date of the file.
    • \n
    • host is a string and contains the hostname of the machine where the file has been written.
    • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
    • \n
    \n\n

    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.

    \n\n

    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:

    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
    • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
    • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
    • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
    • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
    • \n
    • data is an array that contains the data from MC chains. We will define it below.
    • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
    • \n
    \n\n

    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:

    \n\n
      \n
    • id, a string that contains the name of the ensemble
    • \n
    • replica, an array that contains an entry per replica of the ensemble.
    • \n
    \n\n

    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.

    \n\n

    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.

    \n\n

    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:

    \n\n
      \n
    • id, a string that identifies the covariance matrix
    • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
    • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
    • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
    • \n
    \n\n

    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.

    \n\n

    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.

    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "kind": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "kind": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "kind": "function", "doc": "

    Initialize a Corr object.

    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
    • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
    • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
    • \n
    \n", "signature": "(data_input, padding=[0, 0], prange=None)"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.gm": {"fullname": "pyerrors.correlators.Corr.gm", "modulename": "pyerrors.correlators", "qualname": "Corr.gm", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "kind": "function", "doc": "

    We need to project the Correlator with a Vector to get a single value at each timeslice.

    \n\n

    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to

    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "kind": "function", "doc": "

    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.

    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
    • \n
    • j (int):\nSecond index to be picked.
    • \n
    \n", "signature": "(self, i, j):", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "kind": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "kind": "function", "doc": "

    Symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "kind": "function", "doc": "

    Anti-symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.is_matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.is_matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.is_matrix_symmetric", "kind": "function", "doc": "

    Checks whether a correlator matrices is symmetric on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "kind": "function", "doc": "

    Symmetrizes the correlator matrices on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "kind": "function", "doc": "

    Solve the generalized eigenvalue problem on the correlator matrix and returns the corresponding eigenvectors.

    \n\n

    The eigenvectors are sorted according to the descending eigenvalues, the zeroth eigenvector(s) correspond to the\nlargest eigenvalue(s). The eigenvector(s) for the individual states can be accessed via slicing

    \n\n
    \n
    C.GEVP(t0=2)[0]  # Ground state vector(s)\nC.GEVP(t0=2)[:3]  # Vectors for the lowest three states\n
    \n
    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for the right hand side of the GEVP according to $G(t)v_i=\\lambda_i G(t_0)v_i$
    • \n
    • ts (int):\nfixed time $G(t_s)v_i=\\lambda_i G(t_0)v_i$ if sort=None.\nIf sort=\"Eigenvector\" it gives a reference point for the sorting method.
    • \n
    • sort (string):\nIf this argument is set, a list of self.T vectors per state is returned. If it is set to None, only one vector is returned.\n
        \n
      • \"Eigenvalue\": The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
      • \n
      • \"Eigenvector\": Use the method described in arXiv:2004.10472 to find the set of v(t) belonging to the state.\nThe reference state is identified by its eigenvalue at $t=t_s$.
      • \n
    • \n
    \n\n
    Other Parameters
    \n\n
      \n
    • state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
    • \n
    \n", "signature": "(self, t0, ts=None, sort='Eigenvalue', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "kind": "function", "doc": "

    Determines the eigenvalue of the GEVP by solving and projecting the correlator

    \n\n
    Parameters
    \n\n
      \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
    • \n
    • All other parameters are identical to the ones of Corr.GEVP.
    • \n
    \n", "signature": "(self, t0, ts=None, state=0, sort='Eigenvalue'):", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "kind": "function", "doc": "

    Constructs an NxN Hankel matrix

    \n\n

    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))

    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
    • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
    • \n
    \n", "signature": "(self, N, periodic=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "kind": "function", "doc": "

    Periodically shift the correlator by dt timeslices

    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
    • \n
    \n", "signature": "(self, dt):", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "kind": "function", "doc": "

    Reverse the time ordering of the Corr

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "kind": "function", "doc": "

    Thin out a correlator to suppress correlations

    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
    • \n
    • offset (int):\nOffset the equal spacing
    • \n
    \n", "signature": "(self, spacing=2, offset=0):", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "kind": "function", "doc": "

    Correlate the correlator with another correlator or Obs

    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
    • \n
    \n", "signature": "(self, partner):", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "kind": "function", "doc": "

    Reweight the correlator.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
    • \n
    \n", "signature": "(self, weight, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "kind": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
    • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
    • \n
    \n", "signature": "(self, partner, parity=1):", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "kind": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "kind": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "kind": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)\nlogsym: uses the symmetric effective mass log(C(t-1) / C(t+1))/2
    • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
    • \n
    \n", "signature": "(self, variant='log', guess=1.0):", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "kind": "function", "doc": "

    Fits function to the data

    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
    • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
    • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
    • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "kind": "function", "doc": "

    Extract a plateau value from a Corr object

    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
    • \n
    • method (str):\nmethod to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.
    • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
    • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "kind": "function", "doc": "

    Sets the attribute prange of the Corr object.

    \n", "signature": "(self, prange):", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "kind": "function", "doc": "

    Plots the correlator using the tag of the correlator as label if available.

    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8].
    • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
    • \n
    • logscale (bool):\nSets y-axis to logscale.
    • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure.
    • \n
    • fit_res (Fit_result):\nFit_result object to be visualized.
    • \n
    • ylabel (str):\nLabel for the y-axis.
    • \n
    • save (str):\npath to file in which the figure should be saved.
    • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
    • \n
    • hide_sigma (float):\nHides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
    • \n
    • references (list):\nList of floating point values that are displayed as horizontal lines for reference.
    • \n
    • title (string):\nOptional title of the figure.
    • \n
    \n", "signature": "(\tself,\tx_range=None,\tcomp=None,\ty_range=None,\tlogscale=False,\tplateau=None,\tfit_res=None,\tylabel=None,\tsave=None,\tauto_gamma=False,\thide_sigma=None,\treferences=None,\ttitle=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "kind": "function", "doc": "

    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.

    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
    • \n
    \n", "signature": "(self, logscale=True):", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "kind": "function", "doc": "

    Dumps the Corr into a file of chosen type

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "kind": "function", "doc": "

    \n", "signature": "(self, print_range=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "kind": "function", "doc": "

    Project large correlation matrix to lowest states

    \n\n

    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.

    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
    • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
    • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
    • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
    • \n
    \n\n
    Notes
    \n\n

    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.

    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "kind": "module", "doc": "

    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "kind": "class", "doc": "

    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "kind": "function", "doc": "

    Initialize Covobs object.

    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
    • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(mean, cov, name, pos=None, grad=None)"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "kind": "function", "doc": "

    Return the variance (= square of the error) of the Covobs

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "kind": "module", "doc": "

    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "kind": "function", "doc": "

    Rank-3 epsilon tensor

    \n\n

    Based on https://codegolf.stackexchange.com/a/160375

    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k) of the epsilon tensor of rank 3
    • \n
    \n", "signature": "(i, j, k):", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "kind": "function", "doc": "

    Rank-4 epsilon tensor

    \n\n

    Extension of https://codegolf.stackexchange.com/a/160375

    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k,o) of the epsilon tensor of rank 4
    • \n
    \n", "signature": "(i, j, k, o):", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "kind": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "signature": "(gamma_tag):", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "kind": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "kind": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
    • \n
    • chisquare_by_dof (float):\nreduced chisquare.
    • \n
    • p_value (float):\np-value of the fit
    • \n
    • t2_p_value (float):\nHotelling t-squared p-value for correlated fits.
    • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.Fit_result.gm": {"fullname": "pyerrors.fits.Fit_result.gm", "modulename": "pyerrors.fits", "qualname": "Fit_result.gm", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x).\n ```

    \n\n
    Parameters
    \n\n
      \n
    • For an uncombined fit:
    • \n
    • x (list):\nlist of floats.
    • \n
    • y (list):\nlist of Obs.
    • \n
    • func (object):\nfit function, has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • OR For a combined fit:
    • \n
    • x (dict):\ndict of lists.
    • \n
    • y (dict):\ndict of lists of Obs.
    • \n
    • funcs (dict):\ndict of objects\nfit functions have to be of the form (here a[0] is the common fit parameter)\n```python\nimport autograd.numpy as anp\nfuncs = {\"a\": func_a,\n \"b\": func_b}

      \n\n

      def func_a(a, x):\n return a[1] * anp.exp(-a[0] * x)

      \n\n

      def func_b(a, x):\n return a[2] * anp.exp(-a[0] * x)

      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • priors (dict or list, optional):\npriors can either be a dictionary with integer keys and the corresponding priors as values or\na list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters. In case of correlated fits the guess is used to perform\nan uncorrelated fit which then serves as guess for the correlated fit.
    • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
    • \n
    • tol (float, optional):\ncan be used (only for combined fits and methods other than Levenberg-Marquard) to set the tolerance for convergence\nto a different value to either speed up convergence at the cost of a larger error on the fitted parameters (and possibly\ninvalid estimates for parameter uncertainties) or smaller values to get more accurate parameter values\nThe stopping criterion depends on the method, e.g. migrad: edm_max = 0.002 * tol * errordef (EDM criterion: edm < edm_max)
    • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
    • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
    • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
    • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
    • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
    • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
    • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
    • \n
    • func (object):\nfunc has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
    • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
    • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
    • \n
    \n\n
    Notes
    \n\n

    Based on the orthogonal distance regression module of scipy.

    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
    • \n
    \n", "signature": "(x, y, func, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "kind": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
    • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • fit_parameters (list[Obs]):\nLIist of fitted observables.
    • \n
    \n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\n check if the residuals of the fit are gaussian distributed.

    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(x, o_y, func, p, title=''):", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "kind": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n\n

    For uncorrelated data the residuals are expected to be distributed ~N(0,1).

    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(x, y, func, fit_res, title=''):", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "kind": "function", "doc": "

    Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n\n
    Returns
    \n\n
      \n
    • err (np.array(Obs)):\nError band for an array of sample values x
    • \n
    \n", "signature": "(x, func, beta):", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "kind": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.

    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "

    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.

    \n\n

    Jackknife samples

    \n\n

    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "kind": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "kind": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (List[Obs]):\nExtracted data
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "kind": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n\n
    Returns
    \n\n
      \n
    • success (int):\nreturns 0 is successful
    • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "kind": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (dict):\nExtracted meson data
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "kind": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.dobs": {"fullname": "pyerrors.input.dobs", "modulename": "pyerrors.input.dobs", "kind": "module", "doc": "

    \n"}, "pyerrors.input.dobs.create_pobs_string": {"fullname": "pyerrors.input.dobs.create_pobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_pobs_string", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML formatted string of the input data
    • \n
    \n", "signature": "(obsl, name, spec='', origin='', symbol=[], enstag=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_pobs": {"fullname": "pyerrors.input.dobs.write_pobs", "modulename": "pyerrors.input.dobs", "qualname": "write_pobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
    • \n
    • gz (bool):\nIf True, the output is a gzipped xml. If False, the output is an xml file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='',\torigin='',\tsymbol=[],\tenstag=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_pobs": {"fullname": "pyerrors.input.dobs.read_pobs", "modulename": "pyerrors.input.dobs", "qualname": "read_pobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
    • \n
    • separatior_insertion (str or int):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nNone (default): Replica names remain unchanged.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
    • \n
    • or
    • \n
    • res (dict):\nImported data and meta-data
    • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=None):", "funcdef": "def"}, "pyerrors.input.dobs.import_dobs_string": {"fullname": "pyerrors.input.dobs.import_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "import_dobs_string", "kind": "function", "doc": "

    Import a list of Obs from a string in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • content (str):\nXML string containing the data
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
    • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
    • \n
    • or
    • \n
    • res (dict):\nImported data and meta-data
    • \n
    \n", "signature": "(content, full_output=False, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_dobs": {"fullname": "pyerrors.input.dobs.read_dobs", "modulename": "pyerrors.input.dobs", "qualname": "read_dobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes XML file.
    • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
    • \n
    • or
    • \n
    • res (dict):\nImported data and meta-data
    • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.create_dobs_string": {"fullname": "pyerrors.input.dobs.create_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_dobs_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .xml.gz file according to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator |is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • who (str):\nProvide the name of the person that exports the data.
    • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML string generated from the data
    • \n
    \n", "signature": "(\tobsl,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_dobs": {"fullname": "pyerrors.input.dobs.write_dobs", "modulename": "pyerrors.input.dobs", "qualname": "write_dobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • who (str):\nProvide the name of the person that exports the data.
    • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
    • \n
    • gz (bool):\nIf True, the output is a gzipped XML. If False, the output is a XML file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "kind": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "kind": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
    • \n
    • gammas (tuple of strings):\nInstrad of a meson label one can also provide a tuple of two strings\nindicating the gamma matrices at source and sink.\n(\"Gamma5\", \"Gamma5\") corresponds to the pseudoscalar pseudoscalar\ntwo-point function. The gammas argument dominateds over meson.
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • corr (Corr):\nCorrelator of the source sink combination in question.
    • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_DistillationContraction_hd5": {"fullname": "pyerrors.input.hadrons.read_DistillationContraction_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_DistillationContraction_hd5", "kind": "function", "doc": "

    Read hadrons DistillationContraction hdf5 files in given directory structure

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the directories to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • diagrams (list):\nList of strings of the diagrams to extract, e.g. [\"direct\", \"box\", \"cross\"].
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (dict):\nextracted DistillationContration data
    • \n
    \n", "signature": "(path, ens_id, diagrams=['direct'], idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "kind": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
    • \n
    • shape (tuple of ints):\nShape of created array.
    • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
    • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
    • \n
    • offset (int, optional):\nOffset of array data in buffer.
    • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
    • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
    • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
    • \n
    • data (buffer):\nThe array's elements, in memory.
    • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
    • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
    • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
    • \n
    • imag (ndarray):\nImaginary part of the array.
    • \n
    • real (ndarray):\nReal part of the array.
    • \n
    • size (int):\nNumber of elements in the array.
    • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
    • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
    • \n
    • ndim (int):\nThe array's number of dimensions.
    • \n
    • shape (tuple of ints):\nShape of the array.
    • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
    • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
    • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view). The base array is where the\narray data is actually stored.
    • \n
    \n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
    2. \n
    3. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
    4. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    \n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n
    \n\n

    Second mode:

    \n\n
    \n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "kind": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.

    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "kind": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Npr_matrix):\nread Cobs-matrix
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "kind": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict[Npr_matrix]):\nextracted Bilinears
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "kind": "function", "doc": "

    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    • vertices (list):\nVertex functions to be extracted.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict):\nextracted fourquark matrizes
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV']):", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "kind": "module", "doc": "

    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • json_string (str):\nString for export to .json(.gz) file
    • \n
    \n", "signature": "(ol, description='', indent=1):", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • Null
    • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True):", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "kind": "function", "doc": "

    Reconstruct a list of Obs or structures containing Obs from a json string.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
    • \n
    • or
    • \n
    • result (Obs):\nonly one observable if the list only has one entry
    • \n
    • or
    • \n
    • result (dict):\nif full_output=True
    • \n
    \n", "signature": "(json_string, verbose=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "kind": "function", "doc": "

    Import a list of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
    • \n
    • or
    • \n
    • result (Obs):\nonly one observable if the list only has one entry
    • \n
    • or
    • \n
    • result (dict):\nif full_output=True
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "kind": "function", "doc": "

    Export a dict of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True):", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "kind": "function", "doc": "

    Import a dict of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (Obs / list / Corr):\nRead data
    • \n
    • or
    • \n
    • data (dict):\nRead data and meta-data
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "kind": "function", "doc": "

    Read pbp format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of observables read
    • \n
    \n", "signature": "(path, prefix, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "kind": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "kind": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
    • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
    • \n
    • version (str):\nversion of openQCD, default 2.0
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • rwms (Obs):\nReweighting factors read
    • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "kind": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.

    \n\n

    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
    • \n
    • prefix (str):\nEnsemble prefix
    • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
    • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
    • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
    • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
    • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
    • \n
    \n\n
    Returns
    \n\n
      \n
    • t0 (Obs):\nExtracted t0
    • \n
    \n", "signature": "(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "kind": "function", "doc": "

    Read the topologial charge based on openQCD gradient flow measurements.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Obs):\nRead topological charge
    • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_gf_coupling": {"fullname": "pyerrors.input.openQCD.read_gf_coupling", "modulename": "pyerrors.input.openQCD", "qualname": "read_gf_coupling", "kind": "function", "doc": "

    Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.

    \n\n

    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
    • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "kind": "function", "doc": "

    Returns the projection to the topological charge sector defined by target.

    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
    • \n
    \n", "signature": "(qtop, target=0):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "kind": "function", "doc": "

    Constructs reweighting factors to a specified topological sector.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
    • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
    • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_ms5_xsf": {"fullname": "pyerrors.input.openQCD.read_ms5_xsf", "modulename": "pyerrors.input.openQCD", "qualname": "read_ms5_xsf", "kind": "function", "doc": "

    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a Corr object containing the data.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nThe directory to search for the files in.
    • \n
    • prefix (str):\nThe prefix to match the files against.
    • \n
    • qc (str):\nThe quark combination extension to match the files against.
    • \n
    • corr (str):\nThe correlator to extract data for.
    • \n
    • sep (str, optional):\nThe separator to use when parsing the replika names.
    • \n
    • **kwargs: Additional keyword arguments. The following keyword arguments are recognized:

      \n\n
        \n
      • names (List[str]): A list of names to use for the replicas.
      • \n
    • \n
    \n\n
    Returns
    \n\n
      \n
    • Corr: A complex valued Corr object containing the data read from the files. In case of boudary to bulk correlators.
    • \n
    • or
    • \n
    • CObs: A complex valued CObs object containing the data read from the files. In case of boudary to boundary correlators.
    • \n
    \n\n
    Raises
    \n\n
      \n
    • FileNotFoundError: If no files matching the specified prefix and quark combination extension are found in the specified directory.
    • \n
    • IOError: If there is an error reading a file.
    • \n
    • struct.error: If there is an error unpacking binary data.
    • \n
    \n", "signature": "(path, prefix, qc, corr, sep='r', **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas": {"fullname": "pyerrors.input.pandas", "modulename": "pyerrors.input.pandas", "kind": "module", "doc": "

    \n"}, "pyerrors.input.pandas.to_sql": {"fullname": "pyerrors.input.pandas.to_sql", "modulename": "pyerrors.input.pandas", "qualname": "to_sql", "kind": "function", "doc": "

    Write DataFrame including Obs or Corr valued columns to sqlite database.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be written to the database.
    • \n
    • table_name (str):\nName of the table in the database.
    • \n
    • db (str):\nPath to the sqlite database.
    • \n
    • if exists (str):\nHow to behave if table already exists. Options 'fail', 'replace', 'append'.
    • \n
    • gz (bool):\nIf True the json strings are gzipped.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(df, table_name, db, if_exists='fail', gz=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.read_sql": {"fullname": "pyerrors.input.pandas.read_sql", "modulename": "pyerrors.input.pandas", "qualname": "read_sql", "kind": "function", "doc": "

    Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.

    \n\n
    Parameters
    \n\n
      \n
    • sql (str):\nSQL query to be executed.
    • \n
    • db (str):\nPath to the sqlite database.
    • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
    • \n
    \n", "signature": "(sql, db, auto_gamma=False, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.dump_df": {"fullname": "pyerrors.input.pandas.dump_df", "modulename": "pyerrors.input.pandas", "qualname": "dump_df", "kind": "function", "doc": "

    Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.

    \n\n

    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be dumped to a file.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • gz (bool):\nIf True, the output is a gzipped csv file. If False, the output is a csv file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(df, fname, gz=True):", "funcdef": "def"}, "pyerrors.input.pandas.load_df": {"fullname": "pyerrors.input.pandas.load_df", "modulename": "pyerrors.input.pandas", "qualname": "load_df", "kind": "function", "doc": "

    Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
    • \n
    \n", "signature": "(fname, auto_gamma=False, gz=True):", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "kind": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "kind": "function", "doc": "

    Read sfcf c format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to the sfcf files.
    • \n
    • prefix (str):\nPrefix of the sfcf files.
    • \n
    • name (str):\nName of the correlation function to read.
    • \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
    • \n
    • corr_type (str):\nType of correlation function to read. Can be\n
        \n
      • 'bi' for boundary-inner
      • \n
      • 'bb' for boundary-boundary
      • \n
      • 'bib' for boundary-inner-boundary
      • \n
    • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
    • \n
    • wf (int):\nID of wave function
    • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
    • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • ens_name (str):\nreplaces the name of the ensemble
    • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
    • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
    • \n
    • replica (list):\nlist of replica to be read, default is all
    • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
    • \n
    • check_configs (list[list[int]]):\nlist of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of Observables with length T, observable per timeslice.\nbb-type correlators have length 1.
    • \n
    \n", "signature": "(\tpath,\tprefix,\tname,\tquarks='.*',\tcorr_type='bi',\tnoffset=0,\twf=0,\twf2=0,\tversion='1.0c',\tcfg_separator='n',\t**kwargs):", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "kind": "module", "doc": "

    Utilities for the input

    \n"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "kind": "function", "doc": "

    Checks if list of configurations is contained in an idl

    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
    • \n
    • che (list):\nlist of configurations to be checked against
    • \n
    \n\n
    Returns
    \n\n
      \n
    • miss_str (str):\nstring with integers of which idls are missing
    • \n
    \n", "signature": "(idl, che):", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "kind": "module", "doc": "

    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "kind": "function", "doc": "

    Matrix multiply all operands.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
    • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "

    Matrix multiply both operands making use of the jackknife approximation.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • For large matrices this is considerably faster compared to matmul.
    • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "

    Wrapper for numpy.einsum

    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
    • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    \n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "kind": "function", "doc": "

    Cholesky decomposition of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "kind": "function", "doc": "

    Determinant of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "kind": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "kind": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "kind": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "kind": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.misc.print_config": {"fullname": "pyerrors.misc.print_config", "modulename": "pyerrors.misc", "qualname": "print_config", "kind": "function", "doc": "

    Print information about version of python, pyerrors and dependencies.

    \n", "signature": "():", "funcdef": "def"}, "pyerrors.misc.errorbar": {"fullname": "pyerrors.misc.errorbar", "modulename": "pyerrors.misc", "qualname": "errorbar", "kind": "function", "doc": "

    pyerrors wrapper for the errorbars method of matplotlib

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nA list of x-values which can be Obs.
    • \n
    • y (list):\nA list of y-values which can be Obs.
    • \n
    • axes ((matplotlib.pyplot.axes)):\nThe axes to plot on. default is plt.
    • \n
    \n", "signature": "(\tx,\ty,\taxes=<module 'matplotlib.pyplot' from '/opt/hostedtoolcache/Python/3.10.10/x64/lib/python3.10/site-packages/matplotlib/pyplot.py'>,\t**kwargs):", "funcdef": "def"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "kind": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
    • \n
    • name (str):\nname of the file
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(obj, name, **kwargs):", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "kind": "function", "doc": "

    Load object from pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
    • \n
    \n\n
    Returns
    \n\n
      \n
    • object (Obs):\nLoaded Object
    • \n
    \n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
    • \n
    • dvalue (float):\nerror of the Obs to be generated.
    • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
    • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nGenerated Observable
    • \n
    \n", "signature": "(value, dvalue, name, samples=1000):", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "kind": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
    • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
    • \n
    • name (str):\nensemble name for the data to be geneated.
    • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
    • \n
    • samples (int):\nnumber of samples to be generated for each observable.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • corr_obs (list[Obs]):\nGenerated observable list
    • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000):", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "kind": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "kind": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
    • \n
    • k (int):\nNumber of states to extract (default 1).
    • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • energy_levels (list[Obs]):\nExtracted energy levels
    • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "kind": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "kind": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
    • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
    • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
    • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
    • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "

    Initialize Obs object.

    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
    • \n
    • names (list):\nlist of strings labeling the individual samples
    • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
    • \n
    \n", "signature": "(samples, names, idl=None, **kwargs)"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
    • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
    • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
    • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
    • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.gm": {"fullname": "pyerrors.obs.Obs.gm", "modulename": "pyerrors.obs", "qualname": "Obs.gm", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
    • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
    • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
    • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
    • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "kind": "function", "doc": "

    Output detailed properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
    • \n
    \n", "signature": "(self, ens_content=True):", "funcdef": "def"}, "pyerrors.obs.Obs.reweight": {"fullname": "pyerrors.obs.Obs.reweight", "modulename": "pyerrors.obs", "qualname": "Obs.reweight", "kind": "function", "doc": "

    Reweight the obs with given rewighting factors.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
    • \n
    \n", "signature": "(self, weight):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
    • \n
    • Works only properly when the gamma method was run.
    • \n
    \n", "signature": "(self, sigma=1):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "kind": "function", "doc": "

    Checks whether the observable is zero within a given tolerance.

    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
    • \n
    \n", "signature": "(self, atol=1e-10):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "kind": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "kind": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "kind": "function", "doc": "

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

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "kind": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble

    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
    • \n
    \n", "signature": "(self, expand=True):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "kind": "function", "doc": "

    Export jackknife samples from the Obs

    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
    • \n
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "kind": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "kind": "function", "doc": "

    \n", "signature": "(real, imag=0.0)"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "kind": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "kind": "function", "doc": "

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

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
    • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
    • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
    • \n
    \n\n
    Notes
    \n\n

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

    \n\n

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

    \n", "signature": "(func, data, array_mode=False, **kwargs):", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "kind": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
    • \n
    \n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "

    Correlate two observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
    • \n
    • obs_b (Obs):\nSecond observable
    • \n
    \n\n
    Notes
    \n\n

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

    \n", "signature": "(obs_a, obs_b):", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "kind": "function", "doc": "

    Calculates the error covariance matrix of a set of observables.

    \n\n

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

    \n\n

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

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

    The error covariance is defined such that it agrees with the squared standard error for two identical observables\n$$\\operatorname{cov}(a,a)=\\sum_{s=1}^N\\delta_a^s\\delta_a^s/N^2=\\Gamma_{aa}(0)/N=\\operatorname{var}(a)/N=\\sigma_a^2$$\nin the absence of autocorrelation.\nThe 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\n$$\\sum_{i,j}v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor 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.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).

    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "kind": "function", "doc": "

    Imports jackknife samples and returns an Obs

    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
    • \n
    • name (str):\nname of the ensemble the samples are defined on.
    • \n
    \n", "signature": "(jacks, name, idl=None):", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "kind": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

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

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

    \n", "signature": "(list_of_obs):", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
    • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(means, cov, name, grad=None):", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "kind": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "kind": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
    • \n
    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:

      \n\n
      \n
      import autograd.numpy as anp\ndef root_func(x, d):\n   return anp.exp(-x ** 2) - d\n
      \n
    • \n
    • guess (float):\nInitial guess for the minimization.

    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nObs valued root of the function.
    • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs):", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "kind": "module", "doc": "

    \n"}}, "docInfo": {"pyerrors": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 8258}, "pyerrors.correlators": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 108}, "pyerrors.correlators.Corr.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 40, "bases": 0, "doc": 94}, "pyerrors.correlators.Corr.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 18, "bases": 0, "doc": 13}, "pyerrors.correlators.Corr.gm": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 18, "bases": 0, "doc": 13}, "pyerrors.correlators.Corr.projected": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 43, "bases": 0, "doc": 64}, "pyerrors.correlators.Corr.item": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 53}, "pyerrors.correlators.Corr.plottable": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 31}, "pyerrors.correlators.Corr.symmetric": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 9}, "pyerrors.correlators.Corr.anti_symmetric": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 10}, "pyerrors.correlators.Corr.is_matrix_symmetric": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 13}, "pyerrors.correlators.Corr.matrix_symmetric": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 10}, "pyerrors.correlators.Corr.GEVP": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 326}, "pyerrors.correlators.Corr.Eigenvalue": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 50, "bases": 0, "doc": 59}, "pyerrors.correlators.Corr.Hankel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 26, "bases": 0, "doc": 67}, "pyerrors.correlators.Corr.roll": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 26}, "pyerrors.correlators.Corr.reverse": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 9}, "pyerrors.correlators.Corr.thin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 31, "bases": 0, "doc": 43}, "pyerrors.correlators.Corr.correlate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 53}, "pyerrors.correlators.Corr.reweight": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 79}, "pyerrors.correlators.Corr.T_symmetry": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 26, "bases": 0, "doc": 51}, "pyerrors.correlators.Corr.deriv": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 47}, "pyerrors.correlators.Corr.second_deriv": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 45}, "pyerrors.correlators.Corr.m_eff": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 36, "bases": 0, "doc": 148}, "pyerrors.correlators.Corr.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 43, "bases": 0, "doc": 110}, "pyerrors.correlators.Corr.plateau": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 92}, "pyerrors.correlators.Corr.set_prange": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 11}, "pyerrors.correlators.Corr.show": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 149, "bases": 0, "doc": 241}, "pyerrors.correlators.Corr.spaghetti_plot": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 42}, "pyerrors.correlators.Corr.dump": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 38, "bases": 0, "doc": 69}, "pyerrors.correlators.Corr.print": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 22, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sqrt": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.log": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.exp": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sin": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.cos": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.tan": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.sinh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.cosh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr.tanh": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 3}, 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    What is pyerrors?

    \n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n
      \n
    • automatic differentiation for exact linear error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
    • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
    • \n
    • coherent error propagation for data from different Markov chains.
    • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
    • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
    • \n
    \n\n

    More detailed examples can found in the GitHub repository \"badge\".

    \n\n

    If you use pyerrors for research that leads to a publication please consider citing:

    \n\n
      \n
    • Fabian Joswig, Simon Kuberski, Justus T. Kuhlmann, Jan Neuendorf, pyerrors: a python framework for error analysis of Monte Carlo data. [arXiv:2209.14371 [hep-lat]].
    • \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
    • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
    • \n
    \n\n

    and

    \n\n
      \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
    • \n
    \n\n

    where applicable.

    \n\n

    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.

    \n\n

    Installation

    \n\n

    Install the most recent release using pip and pypi:

    \n\n
    \n
    pip install pyerrors     # Fresh install\npip install -U pyerrors  # Update\n
    \n
    \n\n

    Install the most recent release using conda and conda-forge:

    \n\n
    \n
    conda install -c conda-forge pyerrors  # Fresh install\nconda update -c conda-forge pyerrors   # Update\n
    \n
    \n\n

    Install the current develop version:

    \n\n
    \n
    pip install git+https://github.com/fjosw/pyerrors.git@develop\n
    \n
    \n\n

    Basic example

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n
    \n\n

    The Obs class

    \n\n

    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble. It is crucial for the correct error propagation that observations from the same Monte Carlo history are labeled with the same name. See Multiple ensembles/replica for details.

    \n\n
    \n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n
    \n\n

    Error propagation

    \n\n

    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.

    \n\n

    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.

    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n
    \n\n

    Error estimation

    \n\n

    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.

    \n\n
    \n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.

    \n\n
    \n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_rho.

    \n\n

    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.

    \n\n

    Exponential tails

    \n\n

    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.

    \n\n
    \n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n

    For the full API see pyerrors.obs.Obs.gamma_method.

    \n\n

    Multiple ensembles/replica

    \n\n

    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.

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    \n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    Observables from the same Monte Carlo chain have to be initialized with the same name for correct error propagation. If different names were used in this case the data would be treated as statistically independent resulting in loss of relevant information and a potential over or under estimate of the statistical error.

    \n\n

    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.

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    \n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n
    \n\n

    Error estimation for multiple ensembles

    \n\n

    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.

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    \n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n
    \n\n

    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.

    \n\n

    Irregular Monte Carlo chains

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    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.

    \n\n
    \n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n
    \n\n

    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.

    \n\n

    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.

    \n\n

    For the full API see pyerrors.obs.Obs.

    \n\n

    Correlators

    \n\n

    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n
    \n\n

    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.

    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n
    \n\n

    The individual entries of a correlator can be accessed via slicing

    \n\n
    \n
    print(my_corr[3])\n> 0.3227(33)\n
    \n
    \n\n

    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.

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    \n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n
    \n\n

    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:

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      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
    • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
    • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
    • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
    • \n
    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
    • \n
    • Corr.plateau extracts a plateau value from the correlator in a given range.
    • \n
    • Corr.roll periodically shifts the correlator.
    • \n
    • Corr.reverse reverses the time ordering of the correlator.
    • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
    • \n
    • Corr.reweight reweights the correlator.
    • \n
    \n\n

    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).

    \n\n

    For the full API see pyerrors.correlators.Corr.

    \n\n

    Complex valued observables

    \n\n

    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.

    \n\n
    \n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n
    \n\n

    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.

    \n\n
    \n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n
    \n\n

    The Covobs class

    \n\n

    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.

    \n\n

    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.

    \n\n
    \n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n
    \n\n

    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.

    \n\n

    Correlated auxiliary data is defined similarly to above, e.g., via

    \n\n
    \n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n
    \n\n

    where RAP now is a list of two Obs that contains the two correlated parameters.

    \n\n

    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via

    \n\n
    \n
    o.covobs[k].grad\n
    \n
    \n\n

    Error propagation in iterative algorithms

    \n\n

    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least squares fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.

    \n\n

    Least squares fits

    \n\n

    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.

    \n\n

    Fit functions have to be of the following form

    \n\n
    \n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n
    \n\n

    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.

    \n\n

    Fits can then be performed via

    \n\n
    \n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n
    \n\n

    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.

    \n\n

    Data stored in Corr objects can be fitted directly using the Corr.fit method.

    \n\n
    \n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n
    \n\n

    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.

    \n\n

    For fit functions with multiple independent variables the fit function can be of the form

    \n\n
    \n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n
    \n\n

    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.

    \n\n

    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.

    \n\n

    Total least squares fits

    \n\n

    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only difference being that x also has to be a list or numpy.array of Obs.

    \n\n

    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.

    \n\n

    Matrix operations

    \n\n

    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:

    \n\n
      \n
    • inv for the matrix inverse.
    • \n
    • cholseky for the Cholesky decomposition.
    • \n
    • det for the matrix determinant.
    • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
    • \n
    • eig for eigenvalues of general matrices.
    • \n
    • pinv for the Moore-Penrose pseudoinverse.
    • \n
    • svd for the singular-value-decomposition.
    • \n
    \n\n

    For the full API see pyerrors.linalg.

    \n\n

    Export data

    \n\n

    \n\n

    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:

    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
    • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
    • \n
    • Who did write the file when and on which machine?
    • \n
    \n\n

    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example

    \n\n
    \n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n
    \n\n

    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.

    \n\n

    json.gz format specification

    \n\n

    The first entries of the file provide optional auxiliary information:

    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
    • \n
    • version is a string that specifies the version of the format.
    • \n
    • who is a string that specifies the user name of the creator of the file.
    • \n
    • date is a string and contains the creation date of the file.
    • \n
    • host is a string and contains the hostname of the machine where the file has been written.
    • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
    • \n
    \n\n

    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.

    \n\n

    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:

    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
    • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
    • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
    • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
    • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
    • \n
    • data is an array that contains the data from MC chains. We will define it below.
    • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
    • \n
    \n\n

    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:

    \n\n
      \n
    • id, a string that contains the name of the ensemble
    • \n
    • replica, an array that contains an entry per replica of the ensemble.
    • \n
    \n\n

    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.

    \n\n

    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.

    \n\n

    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:

    \n\n
      \n
    • id, a string that identifies the covariance matrix
    • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
    • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
    • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
    • \n
    \n\n

    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.

    \n\n

    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.

    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "kind": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "kind": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "kind": "function", "doc": "

    Initialize a Corr object.

    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
    • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
    • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
    • \n
    \n", "signature": "(data_input, padding=[0, 0], prange=None)"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.gm": {"fullname": "pyerrors.correlators.Corr.gm", "modulename": "pyerrors.correlators", "qualname": "Corr.gm", "kind": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "kind": "function", "doc": "

    We need to project the Correlator with a Vector to get a single value at each timeslice.

    \n\n

    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to

    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "kind": "function", "doc": "

    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.

    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
    • \n
    • j (int):\nSecond index to be picked.
    • \n
    \n", "signature": "(self, i, j):", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "kind": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "kind": "function", "doc": "

    Symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "kind": "function", "doc": "

    Anti-symmetrize the correlator around x0=0.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.is_matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.is_matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.is_matrix_symmetric", "kind": "function", "doc": "

    Checks whether a correlator matrices is symmetric on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "kind": "function", "doc": "

    Symmetrizes the correlator matrices on every timeslice.

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "kind": "function", "doc": "

    Solve the generalized eigenvalue problem on the correlator matrix and returns the corresponding eigenvectors.

    \n\n

    The eigenvectors are sorted according to the descending eigenvalues, the zeroth eigenvector(s) correspond to the\nlargest eigenvalue(s). The eigenvector(s) for the individual states can be accessed via slicing

    \n\n
    \n
    C.GEVP(t0=2)[0]  # Ground state vector(s)\nC.GEVP(t0=2)[:3]  # Vectors for the lowest three states\n
    \n
    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for the right hand side of the GEVP according to $G(t)v_i=\\lambda_i G(t_0)v_i$
    • \n
    • ts (int):\nfixed time $G(t_s)v_i=\\lambda_i G(t_0)v_i$ if sort=None.\nIf sort=\"Eigenvector\" it gives a reference point for the sorting method.
    • \n
    • sort (string):\nIf this argument is set, a list of self.T vectors per state is returned. If it is set to None, only one vector is returned.\n
        \n
      • \"Eigenvalue\": The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
      • \n
      • \"Eigenvector\": Use the method described in arXiv:2004.10472 to find the set of v(t) belonging to the state.\nThe reference state is identified by its eigenvalue at $t=t_s$.
      • \n
    • \n
    \n\n
    Other Parameters
    \n\n
      \n
    • state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
    • \n
    \n", "signature": "(self, t0, ts=None, sort='Eigenvalue', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "kind": "function", "doc": "

    Determines the eigenvalue of the GEVP by solving and projecting the correlator

    \n\n
    Parameters
    \n\n
      \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
    • \n
    • All other parameters are identical to the ones of Corr.GEVP.
    • \n
    \n", "signature": "(self, t0, ts=None, state=0, sort='Eigenvalue'):", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "kind": "function", "doc": "

    Constructs an NxN Hankel matrix

    \n\n

    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))

    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
    • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
    • \n
    \n", "signature": "(self, N, periodic=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "kind": "function", "doc": "

    Periodically shift the correlator by dt timeslices

    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
    • \n
    \n", "signature": "(self, dt):", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "kind": "function", "doc": "

    Reverse the time ordering of the Corr

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "kind": "function", "doc": "

    Thin out a correlator to suppress correlations

    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
    • \n
    • offset (int):\nOffset the equal spacing
    • \n
    \n", "signature": "(self, spacing=2, offset=0):", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "kind": "function", "doc": "

    Correlate the correlator with another correlator or Obs

    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
    • \n
    \n", "signature": "(self, partner):", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "kind": "function", "doc": "

    Reweight the correlator.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
    • \n
    \n", "signature": "(self, weight, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "kind": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
    • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
    • \n
    \n", "signature": "(self, partner, parity=1):", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "kind": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "kind": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
    • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "kind": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)\nlogsym: uses the symmetric effective mass log(C(t-1) / C(t+1))/2
    • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
    • \n
    \n", "signature": "(self, variant='log', guess=1.0):", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "kind": "function", "doc": "

    Fits function to the data

    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
    • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
    • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
    • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "kind": "function", "doc": "

    Extract a plateau value from a Corr object

    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
    • \n
    • method (str):\nmethod to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.
    • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
    • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "kind": "function", "doc": "

    Sets the attribute prange of the Corr object.

    \n", "signature": "(self, prange):", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "kind": "function", "doc": "

    Plots the correlator using the tag of the correlator as label if available.

    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8].
    • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
    • \n
    • logscale (bool):\nSets y-axis to logscale.
    • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure.
    • \n
    • fit_res (Fit_result):\nFit_result object to be visualized.
    • \n
    • ylabel (str):\nLabel for the y-axis.
    • \n
    • save (str):\npath to file in which the figure should be saved.
    • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
    • \n
    • hide_sigma (float):\nHides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
    • \n
    • references (list):\nList of floating point values that are displayed as horizontal lines for reference.
    • \n
    • title (string):\nOptional title of the figure.
    • \n
    \n", "signature": "(\tself,\tx_range=None,\tcomp=None,\ty_range=None,\tlogscale=False,\tplateau=None,\tfit_res=None,\tylabel=None,\tsave=None,\tauto_gamma=False,\thide_sigma=None,\treferences=None,\ttitle=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "kind": "function", "doc": "

    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.

    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
    • \n
    \n", "signature": "(self, logscale=True):", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "kind": "function", "doc": "

    Dumps the Corr into a file of chosen type

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "kind": "function", "doc": "

    \n", "signature": "(self, print_range=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "kind": "function", "doc": "

    Project large correlation matrix to lowest states

    \n\n

    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.

    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
    • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
    • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
    • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
    • \n
    \n\n
    Notes
    \n\n

    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.

    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "kind": "module", "doc": "

    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "kind": "class", "doc": "

    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "kind": "function", "doc": "

    Initialize Covobs object.

    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
    • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(mean, cov, name, pos=None, grad=None)"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "kind": "function", "doc": "

    Return the variance (= square of the error) of the Covobs

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "kind": "module", "doc": "

    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "kind": "function", "doc": "

    Rank-3 epsilon tensor

    \n\n

    Based on https://codegolf.stackexchange.com/a/160375

    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k) of the epsilon tensor of rank 3
    • \n
    \n", "signature": "(i, j, k):", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "kind": "function", "doc": "

    Rank-4 epsilon tensor

    \n\n

    Extension of https://codegolf.stackexchange.com/a/160375

    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k,o) of the epsilon tensor of rank 4
    • \n
    \n", "signature": "(i, j, k, o):", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "kind": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "signature": "(gamma_tag):", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "kind": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "kind": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
    • \n
    • chisquare_by_dof (float):\nreduced chisquare.
    • \n
    • p_value (float):\np-value of the fit
    • \n
    • t2_p_value (float):\nHotelling t-squared p-value for correlated fits.
    • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.Fit_result.gm": {"fullname": "pyerrors.fits.Fit_result.gm", "modulename": "pyerrors.fits", "qualname": "Fit_result.gm", "kind": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x).\n ```

    \n\n
    Parameters
    \n\n
      \n
    • For an uncombined fit:
    • \n
    • x (list):\nlist of floats.
    • \n
    • y (list):\nlist of Obs.
    • \n
    • func (object):\nfit function, has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • OR For a combined fit:
    • \n
    • x (dict):\ndict of lists.
    • \n
    • y (dict):\ndict of lists of Obs.
    • \n
    • funcs (dict):\ndict of objects\nfit functions have to be of the form (here a[0] is the common fit parameter)\n```python\nimport autograd.numpy as anp\nfuncs = {\"a\": func_a,\n \"b\": func_b}

      \n\n

      def func_a(a, x):\n return a[1] * anp.exp(-a[0] * x)

      \n\n

      def func_b(a, x):\n return a[2] * anp.exp(-a[0] * x)

      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • priors (dict or list, optional):\npriors can either be a dictionary with integer keys and the corresponding priors as values or\na list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters. In case of correlated fits the guess is used to perform\nan uncorrelated fit which then serves as guess for the correlated fit.
    • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
    • \n
    • tol (float, optional):\ncan be used (only for combined fits and methods other than Levenberg-Marquard) to set the tolerance for convergence\nto a different value to either speed up convergence at the cost of a larger error on the fitted parameters (and possibly\ninvalid estimates for parameter uncertainties) or smaller values to get more accurate parameter values\nThe stopping criterion depends on the method, e.g. migrad: edm_max = 0.002 * tol * errordef (EDM criterion: edm < edm_max)
    • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
    • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
    • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
    • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
    • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
    • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "kind": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
    • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
    • \n
    • func (object):\nfunc has to be of the form

      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n

      For multiple x values func can be of the form

      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n

      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.

    • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
    • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
    • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
    • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
    • \n
    \n\n
    Notes
    \n\n

    Based on the orthogonal distance regression module of scipy.

    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
    • \n
    \n", "signature": "(x, y, func, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "kind": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
    • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • fit_parameters (list[Obs]):\nLIist of fitted observables.
    • \n
    \n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\n check if the residuals of the fit are gaussian distributed.

    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(x, o_y, func, p, title=''):", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "kind": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n\n

    For uncorrelated data the residuals are expected to be distributed ~N(0,1).

    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(x, y, func, fit_res, title=''):", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "kind": "function", "doc": "

    Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n\n
    Returns
    \n\n
      \n
    • err (np.array(Obs)):\nError band for an array of sample values x
    • \n
    \n", "signature": "(x, func, beta):", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "kind": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.

    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "

    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.

    \n\n

    Jackknife samples

    \n\n

    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "kind": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "kind": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (List[Obs]):\nExtracted data
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "kind": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
    • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    \n\n
    Returns
    \n\n
      \n
    • success (int):\nreturns 0 is successful
    • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "kind": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (dict):\nExtracted meson data
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "kind": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
    • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
    • \n
    • start (int):\nThe first configuration to be read (default 1)
    • \n
    • stop (int):\nThe last configuration to be read (default None)
    • \n
    • step (int):\nFixed step size between two measurements (default 1)
    • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
    • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.dobs": {"fullname": "pyerrors.input.dobs", "modulename": "pyerrors.input.dobs", "kind": "module", "doc": "

    \n"}, "pyerrors.input.dobs.create_pobs_string": {"fullname": "pyerrors.input.dobs.create_pobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_pobs_string", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML formatted string of the input data
    • \n
    \n", "signature": "(obsl, name, spec='', origin='', symbol=[], enstag=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_pobs": {"fullname": "pyerrors.input.dobs.write_pobs", "modulename": "pyerrors.input.dobs", "qualname": "write_pobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
    • \n
    • gz (bool):\nIf True, the output is a gzipped xml. If False, the output is an xml file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='',\torigin='',\tsymbol=[],\tenstag=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_pobs": {"fullname": "pyerrors.input.dobs.read_pobs", "modulename": "pyerrors.input.dobs", "qualname": "read_pobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen pobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
    • \n
    • separatior_insertion (str or int):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nNone (default): Replica names remain unchanged.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
    • \n
    • or
    • \n
    • res (dict):\nImported data and meta-data
    • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=None):", "funcdef": "def"}, "pyerrors.input.dobs.import_dobs_string": {"fullname": "pyerrors.input.dobs.import_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "import_dobs_string", "kind": "function", "doc": "

    Import a list of Obs from a string in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • content (str):\nXML string containing the data
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
    • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
    • \n
    • or
    • \n
    • res (dict):\nImported data and meta-data
    • \n
    \n", "signature": "(content, full_output=False, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_dobs": {"fullname": "pyerrors.input.dobs.read_dobs", "modulename": "pyerrors.input.dobs", "qualname": "read_dobs", "kind": "function", "doc": "

    Import a list of Obs from an xml.gz file in the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes XML file.
    • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
    • \n
    • or
    • \n
    • res (dict):\nImported data and meta-data
    • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.create_dobs_string": {"fullname": "pyerrors.input.dobs.create_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_dobs_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .xml.gz file according to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator |is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • who (str):\nProvide the name of the person that exports the data.
    • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML string generated from the data
    • \n
    \n", "signature": "(\tobsl,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_dobs": {"fullname": "pyerrors.input.dobs.write_dobs", "modulename": "pyerrors.input.dobs", "qualname": "write_dobs", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.

    \n\n

    Tags are not written or recovered automatically. The separator | is removed from the replica names.

    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • name (str):\nThe name of the observable.
    • \n
    • spec (str):\nOptional string that describes the contents of the file.
    • \n
    • origin (str):\nSpecify where the data has its origin.
    • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
    • \n
    • who (str):\nProvide the name of the person that exports the data.
    • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
    • \n
    • gz (bool):\nIf True, the output is a gzipped XML. If False, the output is a XML file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "kind": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "kind": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
    • \n
    • gammas (tuple of strings):\nInstrad of a meson label one can also provide a tuple of two strings\nindicating the gamma matrices at source and sink.\n(\"Gamma5\", \"Gamma5\") corresponds to the pseudoscalar pseudoscalar\ntwo-point function. The gammas argument dominateds over meson.
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • corr (Corr):\nCorrelator of the source sink combination in question.
    • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_DistillationContraction_hd5": {"fullname": "pyerrors.input.hadrons.read_DistillationContraction_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_DistillationContraction_hd5", "kind": "function", "doc": "

    Read hadrons DistillationContraction hdf5 files in given directory structure

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the directories to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • diagrams (list):\nList of strings of the diagrams to extract, e.g. [\"direct\", \"box\", \"cross\"].
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (dict):\nextracted DistillationContration data
    • \n
    \n", "signature": "(path, ens_id, diagrams=['direct'], idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "kind": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
    • \n
    • shape (tuple of ints):\nShape of created array.
    • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
    • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
    • \n
    • offset (int, optional):\nOffset of array data in buffer.
    • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
    • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
    • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
    • \n
    • data (buffer):\nThe array's elements, in memory.
    • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
    • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
    • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
    • \n
    • imag (ndarray):\nImaginary part of the array.
    • \n
    • real (ndarray):\nReal part of the array.
    • \n
    • size (int):\nNumber of elements in the array.
    • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
    • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
    • \n
    • ndim (int):\nThe array's number of dimensions.
    • \n
    • shape (tuple of ints):\nShape of the array.
    • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
    • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
    • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view). The base array is where the\narray data is actually stored.
    • \n
    \n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
    2. \n
    3. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
    4. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    \n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n
    \n\n

    Second mode:

    \n\n
    \n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "kind": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.

    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "kind": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Npr_matrix):\nread Cobs-matrix
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "kind": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict[Npr_matrix]):\nextracted Bilinears
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "kind": "function", "doc": "

    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
    • \n
    • filestem (str):\nnamestem of the files to read
    • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
    • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
    • \n
    • vertices (list):\nVertex functions to be extracted.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict):\nextracted fourquark matrizes
    • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV']):", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "kind": "module", "doc": "

    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "kind": "function", "doc": "

    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • json_string (str):\nString for export to .json(.gz) file
    • \n
    \n", "signature": "(ol, description='', indent=1):", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "kind": "function", "doc": "

    Export a list of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • Null
    • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True):", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "kind": "function", "doc": "

    Reconstruct a list of Obs or structures containing Obs from a json string.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
    • \n
    • or
    • \n
    • result (Obs):\nonly one observable if the list only has one entry
    • \n
    • or
    • \n
    • result (dict):\nif full_output=True
    • \n
    \n", "signature": "(json_string, verbose=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "kind": "function", "doc": "

    Import a list of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
    • \n
    • or
    • \n
    • result (Obs):\nonly one observable if the list only has one entry
    • \n
    • or
    • \n
    • result (dict):\nif full_output=True
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "kind": "function", "doc": "

    Export a dict of Obs or structures containing Obs to a .json(.gz) file

    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • description (str):\nOptional string that describes the contents of the json file.
    • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
    • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True):", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "kind": "function", "doc": "

    Import a dict of Obs or structures containing Obs from a .json(.gz) file.

    \n\n

    The following structures are supported: Obs, list, numpy.ndarray, Corr

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • verbose (bool):\nPrint additional information that was written to the file.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
    • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (Obs / list / Corr):\nRead data
    • \n
    • or
    • \n
    • data (dict):\nRead data and meta-data
    • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "kind": "function", "doc": "

    Read pbp format from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of observables read
    • \n
    \n", "signature": "(path, prefix, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "kind": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "kind": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
    • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
    • \n
    • version (str):\nversion of openQCD, default 2.0
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • rwms (Obs):\nReweighting factors read
    • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "kind": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.

    \n\n

    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
    • \n
    • prefix (str):\nEnsemble prefix
    • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
    • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
    • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
    • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
    • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
    • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
    • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
    • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
    • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
    • \n
    \n\n
    Returns
    \n\n
      \n
    • t0 (Obs):\nExtracted t0
    • \n
    \n", "signature": "(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "kind": "function", "doc": "

    Read the topologial charge based on openQCD gradient flow measurements.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Obs):\nRead topological charge
    • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_gf_coupling": {"fullname": "pyerrors.input.openQCD.read_gf_coupling", "modulename": "pyerrors.input.openQCD", "qualname": "read_gf_coupling", "kind": "function", "doc": "

    Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.

    \n\n

    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
    • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
    • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
    • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "kind": "function", "doc": "

    Returns the projection to the topological charge sector defined by target.

    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
    • \n
    \n", "signature": "(qtop, target=0):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "kind": "function", "doc": "

    Constructs reweighting factors to a specified topological sector.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
    • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
    • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
    • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
    • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
    • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
    • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
    • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
    • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
    • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
    • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
    • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_ms5_xsf": {"fullname": "pyerrors.input.openQCD.read_ms5_xsf", "modulename": "pyerrors.input.openQCD", "qualname": "read_ms5_xsf", "kind": "function", "doc": "

    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a Corr object containing the data.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nThe directory to search for the files in.
    • \n
    • prefix (str):\nThe prefix to match the files against.
    • \n
    • qc (str):\nThe quark combination extension to match the files against.
    • \n
    • corr (str):\nThe correlator to extract data for.
    • \n
    • sep (str, optional):\nThe separator to use when parsing the replika names.
    • \n
    • **kwargs: Additional keyword arguments. The following keyword arguments are recognized:

      \n\n
        \n
      • names (List[str]): A list of names to use for the replicas.
      • \n
    • \n
    \n\n
    Returns
    \n\n
      \n
    • Corr: A complex valued Corr object containing the data read from the files. In case of boudary to bulk correlators.
    • \n
    • or
    • \n
    • CObs: A complex valued CObs object containing the data read from the files. In case of boudary to boundary correlators.
    • \n
    \n\n
    Raises
    \n\n
      \n
    • FileNotFoundError: If no files matching the specified prefix and quark combination extension are found in the specified directory.
    • \n
    • IOError: If there is an error reading a file.
    • \n
    • struct.error: If there is an error unpacking binary data.
    • \n
    \n", "signature": "(path, prefix, qc, corr, sep='r', **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas": {"fullname": "pyerrors.input.pandas", "modulename": "pyerrors.input.pandas", "kind": "module", "doc": "

    \n"}, "pyerrors.input.pandas.to_sql": {"fullname": "pyerrors.input.pandas.to_sql", "modulename": "pyerrors.input.pandas", "qualname": "to_sql", "kind": "function", "doc": "

    Write DataFrame including Obs or Corr valued columns to sqlite database.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be written to the database.
    • \n
    • table_name (str):\nName of the table in the database.
    • \n
    • db (str):\nPath to the sqlite database.
    • \n
    • if exists (str):\nHow to behave if table already exists. Options 'fail', 'replace', 'append'.
    • \n
    • gz (bool):\nIf True the json strings are gzipped.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(df, table_name, db, if_exists='fail', gz=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.read_sql": {"fullname": "pyerrors.input.pandas.read_sql", "modulename": "pyerrors.input.pandas", "qualname": "read_sql", "kind": "function", "doc": "

    Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.

    \n\n
    Parameters
    \n\n
      \n
    • sql (str):\nSQL query to be executed.
    • \n
    • db (str):\nPath to the sqlite database.
    • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
    • \n
    \n", "signature": "(sql, db, auto_gamma=False, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.dump_df": {"fullname": "pyerrors.input.pandas.dump_df", "modulename": "pyerrors.input.pandas", "qualname": "dump_df", "kind": "function", "doc": "

    Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.

    \n\n

    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.

    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be dumped to a file.
    • \n
    • fname (str):\nFilename of the output file.
    • \n
    • gz (bool):\nIf True, the output is a gzipped csv file. If False, the output is a csv file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(df, fname, gz=True):", "funcdef": "def"}, "pyerrors.input.pandas.load_df": {"fullname": "pyerrors.input.pandas.load_df", "modulename": "pyerrors.input.pandas", "qualname": "load_df", "kind": "function", "doc": "

    Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.

    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
    • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
    • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
    • \n
    \n", "signature": "(fname, auto_gamma=False, gz=True):", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "kind": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "kind": "function", "doc": "

    Read sfcf files from given folder structure.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to the sfcf files.
    • \n
    • prefix (str):\nPrefix of the sfcf files.
    • \n
    • name (str):\nName of the correlation function to read.
    • \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
    • \n
    • corr_type (str):\nType of correlation function to read. Can be\n
        \n
      • 'bi' for boundary-inner
      • \n
      • 'bb' for boundary-boundary
      • \n
      • 'bib' for boundary-inner-boundary
      • \n
    • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
    • \n
    • wf (int):\nID of wave function
    • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
    • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
    • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
    • \n
    • ens_name (str):\nreplaces the name of the ensemble
    • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
    • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
    • \n
    • replica (list):\nlist of replica to be read, default is all
    • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
    • \n
    • check_configs (list[list[int]]):\nlist of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
    • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of Observables with length T, observable per timeslice.\nbb-type correlators have length 1.
    • \n
    \n", "signature": "(\tpath,\tprefix,\tname,\tquarks='.*',\tcorr_type='bi',\tnoffset=0,\twf=0,\twf2=0,\tversion='1.0c',\tcfg_separator='n',\tsilent=False,\t**kwargs):", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "kind": "module", "doc": "

    \n"}, "pyerrors.input.utils.sort_names": {"fullname": "pyerrors.input.utils.sort_names", "modulename": "pyerrors.input.utils", "qualname": "sort_names", "kind": "function", "doc": "

    Sorts a list of names of replika with searches for r and id in the replikum string.\nIf this search fails, a fallback method is used,\nwhere the strings are simply compared and the first diffeing numeral is used for differentiation.

    \n\n
    Parameters
    \n\n
      \n
    • ll (list):\nlist to sort
    • \n
    \n\n
    Returns
    \n\n
      \n
    • ll (list):\nsorted list
    • \n
    \n", "signature": "(ll):", "funcdef": "def"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "kind": "function", "doc": "

    Checks if list of configurations is contained in an idl

    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
    • \n
    • che (list):\nlist of configurations to be checked against
    • \n
    \n\n
    Returns
    \n\n
      \n
    • miss_str (str):\nstring with integers of which idls are missing
    • \n
    \n", "signature": "(idl, che):", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "kind": "module", "doc": "

    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "kind": "function", "doc": "

    Matrix multiply all operands.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
    • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "

    Matrix multiply both operands making use of the jackknife approximation.

    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    • For large matrices this is considerably faster compared to matmul.
    • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "

    Wrapper for numpy.einsum

    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
    • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
    • \n
    \n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "kind": "function", "doc": "

    Cholesky decomposition of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "kind": "function", "doc": "

    Determinant of Obs valued matrices.

    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "kind": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "kind": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "kind": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "kind": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "kind": "module", "doc": "

    \n"}, "pyerrors.misc.print_config": {"fullname": "pyerrors.misc.print_config", "modulename": "pyerrors.misc", "qualname": "print_config", "kind": "function", "doc": "

    Print information about version of python, pyerrors and dependencies.

    \n", "signature": "():", "funcdef": "def"}, "pyerrors.misc.errorbar": {"fullname": "pyerrors.misc.errorbar", "modulename": "pyerrors.misc", "qualname": "errorbar", "kind": "function", "doc": "

    pyerrors wrapper for the errorbars method of matplotlib

    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nA list of x-values which can be Obs.
    • \n
    • y (list):\nA list of y-values which can be Obs.
    • \n
    • axes ((matplotlib.pyplot.axes)):\nThe axes to plot on. default is plt.
    • \n
    \n", "signature": "(\tx,\ty,\taxes=<module 'matplotlib.pyplot' from '/opt/hostedtoolcache/Python/3.10.10/x64/lib/python3.10/site-packages/matplotlib/pyplot.py'>,\t**kwargs):", "funcdef": "def"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "kind": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
    • \n
    • name (str):\nname of the file
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n\n
    Returns
    \n\n
      \n
    • None
    • \n
    \n", "signature": "(obj, name, **kwargs):", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "kind": "function", "doc": "

    Load object from pickle file.

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
    • \n
    \n\n
    Returns
    \n\n
      \n
    • object (Obs):\nLoaded Object
    • \n
    \n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
    • \n
    • dvalue (float):\nerror of the Obs to be generated.
    • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
    • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nGenerated Observable
    • \n
    \n", "signature": "(value, dvalue, name, samples=1000):", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "kind": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
    • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
    • \n
    • name (str):\nensemble name for the data to be geneated.
    • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
    • \n
    • samples (int):\nnumber of samples to be generated for each observable.
    • \n
    \n\n
    Returns
    \n\n
      \n
    • corr_obs (list[Obs]):\nGenerated observable list
    • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000):", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "kind": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "kind": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
    • \n
    • k (int):\nNumber of states to extract (default 1).
    • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
    • \n
    \n\n
    Returns
    \n\n
      \n
    • energy_levels (list[Obs]):\nExtracted energy levels
    • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "kind": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "kind": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
    • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
    • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
    • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
    • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
    • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "

    Initialize Obs object.

    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
    • \n
    • names (list):\nlist of strings labeling the individual samples
    • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
    • \n
    \n", "signature": "(samples, names, idl=None, **kwargs)"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
    • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
    • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
    • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
    • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.gm": {"fullname": "pyerrors.obs.Obs.gm", "modulename": "pyerrors.obs", "qualname": "Obs.gm", "kind": "function", "doc": "

    Estimate the error and related properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
    • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
    • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
    • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
    • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "kind": "function", "doc": "

    Output detailed properties of the Obs.

    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
    • \n
    \n", "signature": "(self, ens_content=True):", "funcdef": "def"}, "pyerrors.obs.Obs.reweight": {"fullname": "pyerrors.obs.Obs.reweight", "modulename": "pyerrors.obs", "qualname": "Obs.reweight", "kind": "function", "doc": "

    Reweight the obs with given rewighting factors.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
    • \n
    \n", "signature": "(self, weight):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
    • \n
    • Works only properly when the gamma method was run.
    • \n
    \n", "signature": "(self, sigma=1):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "kind": "function", "doc": "

    Checks whether the observable is zero within a given tolerance.

    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
    • \n
    \n", "signature": "(self, atol=1e-10):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "kind": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "kind": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "kind": "function", "doc": "

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

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "kind": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble

    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
    • \n
    \n", "signature": "(self, expand=True):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
    • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
    • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
    • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
    • \n
    • path (str):\nspecifies a custom path for the file (default '.')
    • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "kind": "function", "doc": "

    Export jackknife samples from the Obs

    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
    • \n
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "kind": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "kind": "function", "doc": "

    \n", "signature": "(real, imag=0.0)"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "kind": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "kind": "function", "doc": "

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

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "kind": "function", "doc": "

    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
    • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
    • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
    • \n
    \n\n
    Notes
    \n\n

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

    \n\n

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

    \n", "signature": "(func, data, array_mode=False, **kwargs):", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "kind": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
    • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
    • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
    • \n
    \n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "

    Correlate two observables.

    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
    • \n
    • obs_b (Obs):\nSecond observable
    • \n
    \n\n
    Notes
    \n\n

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

    \n", "signature": "(obs_a, obs_b):", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "kind": "function", "doc": "

    Calculates the error covariance matrix of a set of observables.

    \n\n

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

    \n\n

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

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

    The error covariance is defined such that it agrees with the squared standard error for two identical observables\n$$\\operatorname{cov}(a,a)=\\sum_{s=1}^N\\delta_a^s\\delta_a^s/N^2=\\Gamma_{aa}(0)/N=\\operatorname{var}(a)/N=\\sigma_a^2$$\nin the absence of autocorrelation.\nThe 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\n$$\\sum_{i,j}v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor 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.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).

    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "kind": "function", "doc": "

    Imports jackknife samples and returns an Obs

    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
    • \n
    • name (str):\nname of the ensemble the samples are defined on.
    • \n
    \n", "signature": "(jacks, name, idl=None):", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "kind": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

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

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

    \n", "signature": "(list_of_obs):", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "kind": "function", "doc": "

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

    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
    • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
    • \n
    • name (str):\nidentifier for the covariance matrix
    • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
    • \n
    \n", "signature": "(means, cov, name, grad=None):", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "kind": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "kind": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
    • \n
    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:

      \n\n
      \n
      import autograd.numpy as anp\ndef root_func(x, d):\n   return anp.exp(-x ** 2) - d\n
      \n
    • \n
    • guess (float):\nInitial guess for the minimization.

    • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nObs valued root of the function.
    • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs):", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "kind": "module", "doc": "

    \n"}}, "docInfo": {"pyerrors": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 8258}, "pyerrors.correlators": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "pyerrors.correlators.Corr": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 108}, "pyerrors.correlators.Corr.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 40, "bases": 0, "doc": 94}, "pyerrors.correlators.Corr.gamma_method": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 18, "bases": 0, "doc": 13}, "pyerrors.correlators.Corr.gm": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 18, "bases": 0, "doc": 13}, "pyerrors.correlators.Corr.projected": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 43, "bases": 0, "doc": 64}, 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"pyerrors.input.openQCD.read_gf_coupling": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gm": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 12, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 3}}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.dobs.create_pobs_string": {"tf": 1}, "pyerrors.input.dobs.write_pobs": {"tf": 1}, "pyerrors.input.dobs.read_pobs": {"tf": 1}, "pyerrors.input.dobs.import_dobs_string": {"tf": 1}, "pyerrors.input.dobs.read_dobs": {"tf": 1}, "pyerrors.input.dobs.create_dobs_string": {"tf": 1}, "pyerrors.input.dobs.write_dobs": {"tf": 1}, "pyerrors.input.openQCD.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_gf_coupling": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_qtop_sector": {"tf": 1.4142135623730951}}, "df": 10}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; // mirrored in build-search-index.js (part 1) // Also split on html tags. this is a cheap heuristic, but good enough.