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)
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) +@@ -1605,246 +1576,246 @@ Reweighting factors read16def 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
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. +@@ -1925,57 +1896,57 @@ Extracted t0235def 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]
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) +@@ -2045,76 +2016,76 @@ Read topological charge562def 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)
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] +@@ -2170,30 +2141,30 @@ postfix of the file to read, e.g. '.gfms.dat' for openQCD-files615def 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]
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 +@@ -2229,62 +2200,62 @@ projection to the topological charge sector defined by target959def 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
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) +@@ -2353,159 +2324,159 @@ projection to the topological charge sector defined by target985def 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)
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: +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_names1043def 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)
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 resultRead 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
andid
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 +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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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_str0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return 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;e 1;){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();o What is pyerrors?\n\n \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...).
\nMore detailed examples can found in the GitHub repository
\n\n.
If you use
\n\npyerrors
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.
\nand
\n\n\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.
\nwhere applicable.
\n\nThere exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.
\n\nInstallation
\n\nInstall the most recent release using pip and pypi:
\n\n\n\n\n\npip install pyerrors # Fresh install\npip install -U pyerrors # Update\n
Install the most recent release using conda and conda-forge:
\n\n\n\n\n\nconda install -c conda-forge pyerrors # Fresh install\nconda update -c conda-forge pyerrors # Update\n
Install the current
\n\ndevelop
version:\n\n\n\npip install git+https://github.com/fjosw/pyerrors.git@develop\n
Basic example
\n\n\n\n\n\nimport 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
The
\n\nObs
class\n\n
pyerrors
introduces a new datatype,Obs
, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAnObs
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\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
Error propagation
\n\nWhen performing mathematical operations on
\n\nObs
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.The
\n\nObs
class is designed such that mathematical numpy functions can be used onObs
just as for regular floats.\n\n\n\nimport 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
Error estimation
\n\nThe error estimation within
\n\npyerrors
is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest thegamma_method
can be called as detailed in the following example.\n\n\n\nmy_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
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
\n\ngamma_method
as parameter.\n\n\n\nmy_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
The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods
\n\npyerrors.obs.Obs.plot_tauint
andpyerrors.obs.Obs.plot_rho
.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\nExponential tails
\n\nSlow 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
\n\ngamma_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\nmy_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
For the full API see
\n\npyerrors.obs.Obs.gamma_method
.Multiple ensembles/replica
\n\nError propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their
\n\nname
.\n\n\n\nobs1 = 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
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\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\nobs1 = 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
Error estimation for multiple ensembles
\n\nIn 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\n\n\npe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
In case the
\n\ngamma_method
is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to thegamma_method
still dominates over the dictionaries.Irregular Monte Carlo chains
\n\n\n\n
Obs
objects defined on irregular Monte Carlo chains can be initialized with the parameteridl
.\n\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
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.Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g.
\n\npyerrors.obs.Obs.plot_rho
orpyerrors.obs.Obs.plot_tauint
.For the full API see
\n\npyerrors.obs.Obs
.Correlators
\n\nWhen one is not interested in single observables but correlation functions,
\n\npyerrors
offers theCorr
class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize aCorr
objects one needs to arrange the data as a list ofObs
\n\n\n\nmy_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
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\n\n\nmy_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
The individual entries of a correlator can be accessed via slicing
\n\n\n\n\n\nprint(my_corr[3])\n> 0.3227(33)\n
Error propagation with the
\n\nCorr
class works very similar toObs
objects. Mathematical operations are overloaded andCorr
objects can be computed together with otherCorr
objects,Obs
objects or real numbers and integers.\n\n\n\nmy_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
\n\n
pyerrors
provides the user with a set of regularly used methods for the manipulation of correlator objects:\n
\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 asCorr
. Different discretizations of the numerical derivative are available.- \n
Corr.second_deriv
returns the second derivative of the correlator asCorr
. 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 anotherCorr
orObs
object.- \n
Corr.reweight
reweights the correlator.\n\n
pyerrors
can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (seepyerrors.correlators.Corr.GEVP
).For the full API see
\n\npyerrors.correlators.Corr
.Complex valued observables
\n\n\n\n
pyerrors
can handle complex valued observables via the classpyerrors.obs.CObs
.\nCObs
are initialized with a real and an imaginary part which both can beObs
valued.\n\n\n\nmy_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
Elementary mathematical operations are overloaded and samples are properly propagated as for the
\n\nObs
class.\n\n\n\nmy_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
The
\n\nCovobs
classIn 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
\n\nCovobs
class allows to define such quantities inpyerrors
. 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.This concept is built into the definition of
\n\nCovobs
. Inpyerrors
, 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 enterscov_Obs
, since the second argument of this function is the covariance matrix of theCovobs
.\n\n\n\nimport 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
The resulting object
\n\nmpi
is anObs
that contains aCovobs
. In the following, it may be handled as any otherObs
. The contribution of the covariance matrix to the error of anObs
is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of theObs
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.Correlated auxiliary data is defined similarly to above, e.g., via
\n\n\n\n\n\nRAP = 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
where
\n\nRAP
now is a list of twoObs
that contains the two correlated parameters.Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the
\n\nCovobs
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 anObs
o
with respect to a covariance matrix with the identifying stringk
may be accessed via\n\n\n\no.covobs[k].grad\n
Error propagation in iterative algorithms
\n\n\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.Least squares fits
\n\nStandard non-linear least square fits with errors on the dependent but not the independent variables can be performed with
\n\npyerrors.fits.least_squares
. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.Fit functions have to be of the following form
\n\n\n\n\n\nimport autograd.numpy as anp\n\ndef func(a, x):\n return a[1] * anp.exp(-a[0] * x)\n
It is important that numerical functions refer to
\n\nautograd.numpy
instead ofnumpy
for the automatic differentiation in iterative algorithms to work properly.Fits can then be performed via
\n\n\n\n\n\nfit_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
where x is a
\n\nlist
ornumpy.array
offloats
and y is alist
ornumpy.array
ofObs
.Data stored in
\n\nCorr
objects can be fitted directly using theCorr.fit
method.\n\n\n\nmy_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.
\n\nFor fit functions with multiple independent variables the fit function can be of the form
\n\n\n\n\n\ndef func(a, x):\n (x1, x2) = x\n return a[0] * x1 ** 2 + a[1] * x2\n
\n\n
pyerrors
also supports correlated fits which can be triggered via the parametercorrelated_fit=True
.\nDetails about how the required covariance matrix is estimated can be found inpyerrors.obs.covariance
.Direct visualizations of the performed fits can be triggered via
\n\nresplot=True
orqqplot=True
. For all available options seepyerrors.fits.least_squares
.Total least squares fits
\n\n\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, seepyerrors.fits.least_squares
. The syntax is identical to the standard least squares case, the only difference being thatx
also has to be alist
ornumpy.array
ofObs
.For the full API see
\n\npyerrors.fits
for fits andpyerrors.roots
for finding roots of functions.Matrix operations
\n\n\n\n
pyerrors
provides wrappers forObs
- andCObs
-valued matrix operations based onnumpy.linalg
. The supported functions include:\n
\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.For the full API see
\n\npyerrors.linalg
.Export data
\n\n\n\nThe preferred exported file format within
\n\npyerrors
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?
\nThis can be achieved by storing all information in one single file. The export routines of
\n\npyerrors
are written such that as much information as possible is written automatically as described in the following example\n\n\n\nmy_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
The format also allows to directly write out the content of
\n\nCorr
objects or lists and arrays ofObs
objects by passing the desired data topyerrors.input.json.dump_to_json
.json.gz format specification
\n\nThe first entries of the file provide optional auxiliary information:
\n\n\n
\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 inpyerrors
. 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.The only necessary entry of the file is the field\n-
\n\nobsdata
, an array that contains the actual data.Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of
\n\nObs
,list
,numpy.ndarray
,Corr
. AllObs
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 arrayobsdata
, are treated independently. Each entry of the arrayobsdata
has the following required entries:\n
\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. Thetag
of anObs
inpyerrors
is written here.- \n
reweighted
is a Bool that may be used to specify, whether theObs
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
inpyerrors
). We will define it below.The array
\n\ndata
contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:\n
\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.Each entry of
\n\nreplica
contains\nname
, a string that contains the name of the replica\ndeltas
, an array that contains the actual data.Each entry in
\n\ndeltas
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 eachObs
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.The array
\n\ncdata
contains information about the contribution of auxiliary observables, represented byCovobs
inpyerrors
, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:\n
\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 eachObs
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.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\nJulia 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\nEverything, 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\nThe 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\nParameters
\n\n\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": "- 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.
\nApply 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\nThe 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\nParameters
\n\n\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": "- i (int):\nFirst index to be picked.
\n- j (int):\nSecond index to be picked.
\nOutputs the correlator in a plotable format.
\n\nOutputs 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\nThe 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\n\n\nC.GEVP(t0=2)[0] # Ground state vector(s)\nC.GEVP(t0=2)[:3] # Vectors for the lowest three states\n
Parameters
\n\n\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\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 $t=t_s$.
\nOther Parameters
\n\n\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": "- state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
\nDetermines the eigenvalue of the GEVP by solving and projecting the correlator
\n\nParameters
\n\n\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": "- 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.
\nConstructs an NxN Hankel matrix
\n\nC(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\nParameters
\n\n\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": "- N (int):\nDimension of the Hankel matrix
\n- periodic (bool, optional):\ndetermines whether the matrix is extended periodically
\nPeriodically shift the correlator by dt timeslices
\n\nParameters
\n\n\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": "- dt (int):\nnumber of timeslices
\nReverse 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\nParameters
\n\n\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": "- spacing (int):\nKeep only every 'spacing'th entry of the correlator
\n- offset (int):\nOffset the equal spacing
\nCorrelate the correlator with another correlator or Obs
\n\nParameters
\n\n\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": "- 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.
\nReweight the correlator.
\n\nParameters
\n\n\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": "- 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.
\nReturn the time symmetry average of the correlator and its partner
\n\nParameters
\n\n\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": "- partner (Corr):\nTime symmetry partner of the Corr
\n- partity (int):\nParity quantum number of the correlator, can be +1 or -1
\nReturn the first derivative of the correlator with respect to x0.
\n\nParameters
\n\n\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": "- variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
\nReturn the second derivative of the correlator with respect to x0.
\n\nParameters
\n\n\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": "- variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
\nReturns the effective mass of the correlator as correlator object
\n\nParameters
\n\n\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": "- 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
\nFits function to the data
\n\nParameters
\n\n\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": "- 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.\n
\nfitrange=[4, 6]
corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.- silent (bool):\nDecides whether output is printed to the standard output.
\nExtract a plateau value from a Corr object
\n\nParameters
\n\n\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": "- 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
\nSets 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\nParameters
\n\n\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": "- 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.
\nProduces a spaghetti plot of the correlator suited to monitor exceptional configurations.
\n\nParameters
\n\n\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": "- logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
\nDumps the Corr into a file of chosen type
\n\nParameters
\n\n\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": "- 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 '.')
\nProject large correlation matrix to lowest states
\n\nThis 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\nParameters
\n\n\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.
\nNotes
\n\nWe 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\nParameters
\n\n\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": "- 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.
\nReturn 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\nBased on https://codegolf.stackexchange.com/a/160375
\n\nReturns
\n\n\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": "- elem (int):\nElement (i,j,k) of the epsilon tensor of rank 3
\nRank-4 epsilon tensor
\n\nExtension of https://codegolf.stackexchange.com/a/160375
\n\nReturns
\n\n\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": "- elem (int):\nElement (i,j,k,o) of the epsilon tensor of rank 4
\nReturns 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\nAttributes
\n\n\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": "- 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.
\nApply 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\nParameters
\n\n\n
\n\n- For an uncombined fit:
\n- x (list):\nlist of floats.
\n- y (list):\nlist of Obs.
\n- \n
func (object):\nfit function, has to be of the form
\n\n\n\n\n\nimport autograd.numpy as anp\n\ndef func(a, x):\n return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
For multiple x values func can be of the form
\n\n\n\n\n\ndef func(a, x):\n (x1, x2) = x\n return a[0] * x1 ** 2 + a[1] * x2\n
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- OR For a combined fit:
\n- x (dict):\ndict of lists.
\n- y (dict):\ndict of lists of Obs.
\n- \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\ndef func_a(a, x):\n return a[1] * anp.exp(-a[0] * x)
\n\ndef func_b(a, x):\n return a[2] * anp.exp(-a[0] * x)
\n\nIt is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- 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
\npyerrors.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).- 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).
\nReturns
\n\n\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": "- output (Fit_result):\nParameters and information on the fitted result.
\nPerforms a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
\n\nParameters
\n\n\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- \n
func (object):\nfunc has to be of the form
\n\n\n\n\n\nimport autograd.numpy as anp\n\ndef func(a, x):\n return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
For multiple x values func can be of the form
\n\n\n\n\n\ndef func(a, x):\n (x1, x2) = x\n return a[0] * x1 ** 2 + a[1] * x2\n
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- 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).
\nNotes
\n\nBased on the orthogonal distance regression module of scipy.
\n\nReturns
\n\n\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": "- output (Fit_result):\nParameters and information on the fitted result.
\nPerforms a linear fit to y = n + m * x and returns two Obs n, m.
\n\nParameters
\n\n\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.
\nReturns
\n\n\n
\n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "- fit_parameters (list[Obs]):\nLIist of fitted observables.
\nGenerates 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\nReturns
\n\n\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": "- None
\nGenerates a plot which compares the fit to the data and displays the corresponding residuals
\n\nFor uncorrelated data the residuals are expected to be distributed ~N(0,1).
\n\nReturns
\n\n\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": "- None
\nCalculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
\n\nReturns
\n\n\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": "- err (np.array(Obs)):\nError band for an array of sample values x
\nPerforms a Kolmogorov\u2013Smirnov test for the p-values of all fit object.
\n\nParameters
\n\n\n
\n\n- objects (list):\nList of fit results to include in the analysis (optional).
\nReturns
\n\n\n
\n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "- None
\n\n\n
pyerrors
includes aninput
submodule in which input routines and parsers for the output of various numerical programs are contained.Jackknife samples
\n\nFor comparison with other analysis workflows
\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": "pyerrors
can also generate jackknife samples from anObs
object or import jackknife samples into anObs
object.\nSeepyerrors.obs.Obs.export_jackknife
andpyerrors.obs.import_jackknife
for details.Extract generic MCMC data from a bdio file
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n
\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\nReturns
\n\n\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": "- data (List[Obs]):\nExtracted data
\nWrite Obs to a bdio file according to ADerrors conventions
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n
\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\nReturns
\n\n\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": "- success (int):\nreturns 0 is successful
\nExtract mesons data from a bdio file and return it as a dictionary
\n\nThe dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\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
\nReturns
\n\n\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": "- data (dict):\nExtracted meson data
\nExtract dSdm data from a bdio file and return it as a dictionary
\n\nThe dictionary can be accessed with a tuple consisting of (type, kappa)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\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": "- 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
\nExport a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.
\n\nTags are not written or recovered automatically. The separator | is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- xml_str (str):\nXML formatted string of the input data
\nExport a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.
\n\nTags are not written or recovered automatically. The separator | is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nImport a list of Obs from an xml.gz file in the Zeuthen pobs format.
\n\nTags are not written or recovered automatically.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- res (list[Obs]):\nImported data
\n- or
\n- res (dict):\nImported data and meta-data
\nImport a list of Obs from a string in the Zeuthen dobs format.
\n\nTags are not written or recovered automatically.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- res (list[Obs]):\nImported data
\n- or
\n- res (dict):\nImported data and meta-data
\nImport a list of Obs from an xml.gz file in the Zeuthen dobs format.
\n\nTags are not written or recovered automatically.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- res (list[Obs]):\nImported data
\n- or
\n- res (dict):\nImported data and meta-data
\nGenerate 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\nTags are not written or recovered automatically. The separator |is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- xml_str (str):\nXML string generated from the data
\nExport a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.
\n\nTags are not written or recovered automatically. The separator | is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nRead hadrons meson hdf5 file and extract the meson labeled 'meson'
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- corr (Corr):\nCorrelator of the source sink combination in question.
\nRead hadrons DistillationContraction hdf5 files in given directory structure
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result (dict):\nextracted DistillationContration data
\nndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)
\n\nAn 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\nArrays should be constructed using
\n\narray
,zeros
orempty
(refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)
) for instantiating an array.For more information, refer to the
\n\nnumpy
module and examine the\nmethods and attributes of an array.Parameters
\n\n\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.
\nAttributes
\n\n\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.,
\nx.flat = 3
(Seendarray.flat
for\nassignment examples; TODO).- 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.,
\nitemsize * size
.- 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
\n(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
).- 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
\nbase
\n(unless that array is also a view). Thebase
array is where the\narray data is actually stored.See Also
\n\n\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. itsdtype.type <numpy.dtype.type>
.Notes
\n\nThere are two modes of creating an array using
\n\n__new__
:\n
\n\n- If
\nbuffer
is None, then onlyshape
,dtype
, andorder
\nare used.- If
\nbuffer
is an object exposing the buffer interface, then\nall keywords are interpreted.No
\n\n__init__
method is needed because the array is fully initialized\nafter the__new__
method.Examples
\n\nThese examples illustrate the low-level
\n\nndarray
constructor. Refer\nto theSee Also
section above for easier ways of constructing an\nndarray.First mode,
\n\nbuffer
is None:\n\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
Second mode:
\n\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": "\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
Gamma_5 hermitean conjugate
\n\nUses 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\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result (Npr_matrix):\nread Cobs-matrix
\nRead hadrons Bilinear hdf5 file and output an array of CObs
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result_dict (dict[Npr_matrix]):\nextracted Bilinears
\nRead hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result_dict (dict):\nextracted fourquark matrizes
\nGenerate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- json_string (str):\nString for export to .json(.gz) file
\nExport a list of Obs or structures containing Obs to a .json(.gz) file
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- Null
\nReconstruct a list of Obs or structures containing Obs from a json string.
\n\nThe following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- 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
\nImport a list of Obs or structures containing Obs from a .json(.gz) file.
\n\nThe following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- 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
\nExport a dict of Obs or structures containing Obs to a .json(.gz) file
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nImport a dict of Obs or structures containing Obs from a .json(.gz) file.
\n\nThe following structures are supported: Obs, list, numpy.ndarray, Corr
\n\nParameters
\n\n\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]+.
\nReturns
\n\n\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": "- data (Obs / list / Corr):\nRead data
\n- or
\n- data (dict):\nRead data and meta-data
\nRead pbp format from given folder structure.
\n\nParameters
\n\n\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
\nReturns
\n\n\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": "- result (list[Obs]):\nlist of observables read
\nRead rwms format from given folder structure. Returns a list of length nrw
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- rwms (Obs):\nReweighting factors read
\nExtract t0 from given .ms.dat files. Returns t0 as Obs.
\n\nIt is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2
\n\n- 0.3\nis fitted with a linear function\nfrom which the exact root is extracted. 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\nParameters
\n\n\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
\nReturns
\n\n\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": "- t0 (Obs):\nExtracted t0
\nRead the topologial charge based on openQCD gradient flow measurements.
\n\nParameters
\n\n\n
\n\n- path (str):\npath of the measurement files
\n- prefix (str):\nprefix of the measurement files, e.g.
\n_id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files. - 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.
\nReturns
\n\n\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": "- result (Obs):\nRead topological charge
\nRead the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
\n\nNote: 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\nParameters
\n\n\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": "- path (str):\npath of the measurement files
\n- prefix (str):\nprefix of the measurement files, e.g.
\n_id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files. - 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.
\nReturns the projection to the topological charge sector defined by target.
\n\nParameters
\n\n\n
\n\n- path (Obs):\nTopological charge.
\n- target (int):\nSpecifies the topological sector to be reweighted to (default 0)
\nReturns
\n\n\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": "- reto (Obs):\nprojection to the topological charge sector defined by target
\nConstructs reweighting factors to a specified topological sector.
\n\nParameters
\n\n\n
\n\n- path (str):\npath of the measurement files
\n- prefix (str):\nprefix of the measurement files, e.g.
\n_id0_r0.ms.dat - 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.
\nReturns
\n\n\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": "- reto (Obs):\nprojection to the topological charge sector defined by target
\nRead data from files in the specified directory with the specified prefix and quark combination extension, and return a
\n\nCorr
object containing the data.Parameters
\n\n\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- \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.
\nReturns
\n\n\n
\n\n- Corr: A complex valued
\nCorr
object containing the data read from the files. In case of boudary to bulk correlators.- or
\n- CObs: A complex valued
\nCObs
object containing the data read from the files. In case of boudary to boundary correlators.Raises
\n\n\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": "- 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.
\nWrite DataFrame including Obs or Corr valued columns to sqlite database.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nExecute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
\nExports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
\n\nBefore making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nImports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
\nRead sfcf c format from given folder structure.
\n\nParameters
\n\n\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\n
- 'bi' for boundary-inner
\n- 'bb' for boundary-boundary
\n- 'bib' for boundary-inner-boundary
\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
\nReturns
\n\n\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": "- result (list[Obs]):\nlist of Observables with length T, observable per timeslice.\nbb-type correlators have length 1.
\nUtilities 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\nParameters
\n\n\n
\n\n- idl (range or list):\nidl of the current replicum
\n- che (list):\nlist of configurations to be checked against
\nReturns
\n\n\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": "- miss_str (str):\nstring with integers of which idls are missing
\nMatrix multiply all operands.
\n\nParameters
\n\n\n
\n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "- 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.
\nMatrix multiply both operands making use of the jackknife approximation.
\n\nParameters
\n\n\n
\n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "- 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.
\nWrapper for numpy.einsum
\n\nParameters
\n\n\n
\n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "- 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.
\nInverse 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\nParameters
\n\n\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": "- 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.
\nDump object into pickle file.
\n\nParameters
\n\n\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 '.')
\nReturns
\n\n\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": "- None
\nLoad object from pickle file.
\n\nParameters
\n\n\n
\n\n- path (str):\npath to the file
\nReturns
\n\n\n
\n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "- object (Obs):\nLoaded Object
\nGenerate an Obs object with given value, dvalue and name for test purposes
\n\nParameters
\n\n\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).
\nReturns
\n\n\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": "- res (Obs):\nGenerated Observable
\nGenerate observables with given covariance and autocorrelation times.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- corr_obs (list[Obs]):\nGenerated observable list
\nMatrix pencil method to extract k energy levels from data
\n\nImplementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)
\n\nParameters
\n\n\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).
\nReturns
\n\n\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": "- energy_levels (list[Obs]):\nExtracted energy levels
\nClass for a general observable.
\n\nInstances 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\nAttributes
\n\n\n
\n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "- 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.
\nInitialize Obs object.
\n\nParameters
\n\n\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": "- 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
\nEstimate the error and related properties of the Obs.
\n\nParameters
\n\n\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": "- 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)
\nEstimate the error and related properties of the Obs.
\n\nParameters
\n\n\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": "- 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)
\nOutput detailed properties of the Obs.
\n\nParameters
\n\n\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": "- ens_content (bool):\nprint details about the ensembles and replica if true.
\nReweight the obs with given rewighting factors.
\n\nParameters
\n\n\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": "- 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.
\nChecks whether the observable is zero within 'sigma' standard errors.
\n\nParameters
\n\n\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": "- sigma (int):\nNumber of standard errors used for the check.
\n- Works only properly when the gamma method was run.
\nChecks whether the observable is zero within a given tolerance.
\n\nParameters
\n\n\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": "- atol (float):\nAbsolute tolerance (for details see numpy documentation).
\nPlot integrated autocorrelation time for each ensemble.
\n\nParameters
\n\n\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": "- save (str):\nsaves the figure to a file named 'save' if.
\nPlot normalized autocorrelation function time for each ensemble.
\n\nParameters
\n\n\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": "- save (str):\nsaves the figure to a file named 'save' if.
\nPlot 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\nParameters
\n\n\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": "- expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
\nPlot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.
\n\nParameters
\n\n\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": "- save (str):\nsaves the figure to a file named 'save' if.
\nDump the Obs to a file 'name' of chosen format.
\n\nParameters
\n\n\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": "- 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 '.')
\nExport jackknife samples from the Obs
\n\nReturns
\n\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": "- 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).
\nClass 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\nParameters
\n\n\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.
\nNotes
\n\nFor simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use
\n\nnew_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\nParameters
\n\n\n
\n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "- 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.
\nCorrelate two observables.
\n\nParameters
\n\n\n
\n\n- obs_a (Obs):\nFirst observable
\n- obs_b (Obs):\nSecond observable
\nNotes
\n\nKeep 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\nWARNING: 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\nThe gamma method has to be applied first to all observables.
\n\nParameters
\n\n\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.
\nNotes
\n\nThe 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\nParameters
\n\n\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": "- 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.
\nCombine all observables in list_of_obs into one new observable
\n\nParameters
\n\n\n
\n\n- list_of_obs (list):\nlist of the Obs object to be combined
\nNotes
\n\nIt 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\nParameters
\n\n\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": "- 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.
\nFinds the root of the function func(x, d) where d is an
\n\nObs
.Parameters
\n\n\n
\n\n- d (Obs):\nObs passed to the function.
\n- \n
func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:
\n\n\n\nimport autograd.numpy as anp\ndef root_func(x, d):\n return anp.exp(-x ** 2) - d\n
- \n
guess (float):\nInitial guess for the minimization.
Returns
\n\n\n
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res (Obs):\n
\nObs
valued root of the function.What is pyerrors?
\n\n\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...).
\nMore detailed examples can found in the GitHub repository
\n\n.
If you use
\n\npyerrors
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.
\nand
\n\n\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.
\nwhere applicable.
\n\nThere exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.
\n\nInstallation
\n\nInstall the most recent release using pip and pypi:
\n\n\n\n\n\npip install pyerrors # Fresh install\npip install -U pyerrors # Update\n
Install the most recent release using conda and conda-forge:
\n\n\n\n\n\nconda install -c conda-forge pyerrors # Fresh install\nconda update -c conda-forge pyerrors # Update\n
Install the current
\n\ndevelop
version:\n\n\n\npip install git+https://github.com/fjosw/pyerrors.git@develop\n
Basic example
\n\n\n\n\n\nimport 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
The
\n\nObs
class\n\n
pyerrors
introduces a new datatype,Obs
, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAnObs
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\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
Error propagation
\n\nWhen performing mathematical operations on
\n\nObs
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.The
\n\nObs
class is designed such that mathematical numpy functions can be used onObs
just as for regular floats.\n\n\n\nimport 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
Error estimation
\n\nThe error estimation within
\n\npyerrors
is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest thegamma_method
can be called as detailed in the following example.\n\n\n\nmy_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
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
\n\ngamma_method
as parameter.\n\n\n\nmy_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
The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods
\n\npyerrors.obs.Obs.plot_tauint
andpyerrors.obs.Obs.plot_rho
.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\nExponential tails
\n\nSlow 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
\n\ngamma_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\nmy_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
For the full API see
\n\npyerrors.obs.Obs.gamma_method
.Multiple ensembles/replica
\n\nError propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their
\n\nname
.\n\n\n\nobs1 = 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
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\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\nobs1 = 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
Error estimation for multiple ensembles
\n\nIn 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\n\n\npe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
In case the
\n\ngamma_method
is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to thegamma_method
still dominates over the dictionaries.Irregular Monte Carlo chains
\n\n\n\n
Obs
objects defined on irregular Monte Carlo chains can be initialized with the parameteridl
.\n\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
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.Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g.
\n\npyerrors.obs.Obs.plot_rho
orpyerrors.obs.Obs.plot_tauint
.For the full API see
\n\npyerrors.obs.Obs
.Correlators
\n\nWhen one is not interested in single observables but correlation functions,
\n\npyerrors
offers theCorr
class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize aCorr
objects one needs to arrange the data as a list ofObs
\n\n\n\nmy_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
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\n\n\nmy_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
The individual entries of a correlator can be accessed via slicing
\n\n\n\n\n\nprint(my_corr[3])\n> 0.3227(33)\n
Error propagation with the
\n\nCorr
class works very similar toObs
objects. Mathematical operations are overloaded andCorr
objects can be computed together with otherCorr
objects,Obs
objects or real numbers and integers.\n\n\n\nmy_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
\n\n
pyerrors
provides the user with a set of regularly used methods for the manipulation of correlator objects:\n
\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 asCorr
. Different discretizations of the numerical derivative are available.- \n
Corr.second_deriv
returns the second derivative of the correlator asCorr
. 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 anotherCorr
orObs
object.- \n
Corr.reweight
reweights the correlator.\n\n
pyerrors
can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (seepyerrors.correlators.Corr.GEVP
).For the full API see
\n\npyerrors.correlators.Corr
.Complex valued observables
\n\n\n\n
pyerrors
can handle complex valued observables via the classpyerrors.obs.CObs
.\nCObs
are initialized with a real and an imaginary part which both can beObs
valued.\n\n\n\nmy_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
Elementary mathematical operations are overloaded and samples are properly propagated as for the
\n\nObs
class.\n\n\n\nmy_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
The
\n\nCovobs
classIn 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
\n\nCovobs
class allows to define such quantities inpyerrors
. 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.This concept is built into the definition of
\n\nCovobs
. Inpyerrors
, 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 enterscov_Obs
, since the second argument of this function is the covariance matrix of theCovobs
.\n\n\n\nimport 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
The resulting object
\n\nmpi
is anObs
that contains aCovobs
. In the following, it may be handled as any otherObs
. The contribution of the covariance matrix to the error of anObs
is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of theObs
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.Correlated auxiliary data is defined similarly to above, e.g., via
\n\n\n\n\n\nRAP = 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
where
\n\nRAP
now is a list of twoObs
that contains the two correlated parameters.Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the
\n\nCovobs
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 anObs
o
with respect to a covariance matrix with the identifying stringk
may be accessed via\n\n\n\no.covobs[k].grad\n
Error propagation in iterative algorithms
\n\n\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.Least squares fits
\n\nStandard non-linear least square fits with errors on the dependent but not the independent variables can be performed with
\n\npyerrors.fits.least_squares
. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.Fit functions have to be of the following form
\n\n\n\n\n\nimport autograd.numpy as anp\n\ndef func(a, x):\n return a[1] * anp.exp(-a[0] * x)\n
It is important that numerical functions refer to
\n\nautograd.numpy
instead ofnumpy
for the automatic differentiation in iterative algorithms to work properly.Fits can then be performed via
\n\n\n\n\n\nfit_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
where x is a
\n\nlist
ornumpy.array
offloats
and y is alist
ornumpy.array
ofObs
.Data stored in
\n\nCorr
objects can be fitted directly using theCorr.fit
method.\n\n\n\nmy_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.
\n\nFor fit functions with multiple independent variables the fit function can be of the form
\n\n\n\n\n\ndef func(a, x):\n (x1, x2) = x\n return a[0] * x1 ** 2 + a[1] * x2\n
\n\n
pyerrors
also supports correlated fits which can be triggered via the parametercorrelated_fit=True
.\nDetails about how the required covariance matrix is estimated can be found inpyerrors.obs.covariance
.Direct visualizations of the performed fits can be triggered via
\n\nresplot=True
orqqplot=True
. For all available options seepyerrors.fits.least_squares
.Total least squares fits
\n\n\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, seepyerrors.fits.least_squares
. The syntax is identical to the standard least squares case, the only difference being thatx
also has to be alist
ornumpy.array
ofObs
.For the full API see
\n\npyerrors.fits
for fits andpyerrors.roots
for finding roots of functions.Matrix operations
\n\n\n\n
pyerrors
provides wrappers forObs
- andCObs
-valued matrix operations based onnumpy.linalg
. The supported functions include:\n
\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.For the full API see
\n\npyerrors.linalg
.Export data
\n\n\n\nThe preferred exported file format within
\n\npyerrors
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?
\nThis can be achieved by storing all information in one single file. The export routines of
\n\npyerrors
are written such that as much information as possible is written automatically as described in the following example\n\n\n\nmy_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
The format also allows to directly write out the content of
\n\nCorr
objects or lists and arrays ofObs
objects by passing the desired data topyerrors.input.json.dump_to_json
.json.gz format specification
\n\nThe first entries of the file provide optional auxiliary information:
\n\n\n
\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 inpyerrors
. 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.The only necessary entry of the file is the field\n-
\n\nobsdata
, an array that contains the actual data.Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of
\n\nObs
,list
,numpy.ndarray
,Corr
. AllObs
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 arrayobsdata
, are treated independently. Each entry of the arrayobsdata
has the following required entries:\n
\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. Thetag
of anObs
inpyerrors
is written here.- \n
reweighted
is a Bool that may be used to specify, whether theObs
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
inpyerrors
). We will define it below.The array
\n\ndata
contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:\n
\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.Each entry of
\n\nreplica
contains\nname
, a string that contains the name of the replica\ndeltas
, an array that contains the actual data.Each entry in
\n\ndeltas
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 eachObs
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.The array
\n\ncdata
contains information about the contribution of auxiliary observables, represented byCovobs
inpyerrors
, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:\n
\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 eachObs
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.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\nJulia 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\nEverything, 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\nThe 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\nParameters
\n\n\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": "- 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.
\nApply 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\nThe 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\nParameters
\n\n\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": "- i (int):\nFirst index to be picked.
\n- j (int):\nSecond index to be picked.
\nOutputs the correlator in a plotable format.
\n\nOutputs 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\nThe 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\n\n\nC.GEVP(t0=2)[0] # Ground state vector(s)\nC.GEVP(t0=2)[:3] # Vectors for the lowest three states\n
Parameters
\n\n\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\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 $t=t_s$.
\nOther Parameters
\n\n\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": "- state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
\nDetermines the eigenvalue of the GEVP by solving and projecting the correlator
\n\nParameters
\n\n\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": "- 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.
\nConstructs an NxN Hankel matrix
\n\nC(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\nParameters
\n\n\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": "- N (int):\nDimension of the Hankel matrix
\n- periodic (bool, optional):\ndetermines whether the matrix is extended periodically
\nPeriodically shift the correlator by dt timeslices
\n\nParameters
\n\n\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": "- dt (int):\nnumber of timeslices
\nReverse 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\nParameters
\n\n\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": "- spacing (int):\nKeep only every 'spacing'th entry of the correlator
\n- offset (int):\nOffset the equal spacing
\nCorrelate the correlator with another correlator or Obs
\n\nParameters
\n\n\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": "- 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.
\nReweight the correlator.
\n\nParameters
\n\n\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": "- 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.
\nReturn the time symmetry average of the correlator and its partner
\n\nParameters
\n\n\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": "- partner (Corr):\nTime symmetry partner of the Corr
\n- partity (int):\nParity quantum number of the correlator, can be +1 or -1
\nReturn the first derivative of the correlator with respect to x0.
\n\nParameters
\n\n\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": "- variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
\nReturn the second derivative of the correlator with respect to x0.
\n\nParameters
\n\n\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": "- variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
\nReturns the effective mass of the correlator as correlator object
\n\nParameters
\n\n\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": "- 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
\nFits function to the data
\n\nParameters
\n\n\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": "- 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.\n
\nfitrange=[4, 6]
corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.- silent (bool):\nDecides whether output is printed to the standard output.
\nExtract a plateau value from a Corr object
\n\nParameters
\n\n\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": "- 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
\nSets 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\nParameters
\n\n\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": "- 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.
\nProduces a spaghetti plot of the correlator suited to monitor exceptional configurations.
\n\nParameters
\n\n\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": "- logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
\nDumps the Corr into a file of chosen type
\n\nParameters
\n\n\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": "- 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 '.')
\nProject large correlation matrix to lowest states
\n\nThis 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\nParameters
\n\n\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.
\nNotes
\n\nWe 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\nParameters
\n\n\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": "- 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.
\nReturn 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\nBased on https://codegolf.stackexchange.com/a/160375
\n\nReturns
\n\n\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": "- elem (int):\nElement (i,j,k) of the epsilon tensor of rank 3
\nRank-4 epsilon tensor
\n\nExtension of https://codegolf.stackexchange.com/a/160375
\n\nReturns
\n\n\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": "- elem (int):\nElement (i,j,k,o) of the epsilon tensor of rank 4
\nReturns 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\nAttributes
\n\n\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": "- 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.
\nApply 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\nParameters
\n\n\n
\n\n- For an uncombined fit:
\n- x (list):\nlist of floats.
\n- y (list):\nlist of Obs.
\n- \n
func (object):\nfit function, has to be of the form
\n\n\n\n\n\nimport autograd.numpy as anp\n\ndef func(a, x):\n return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
For multiple x values func can be of the form
\n\n\n\n\n\ndef func(a, x):\n (x1, x2) = x\n return a[0] * x1 ** 2 + a[1] * x2\n
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- OR For a combined fit:
\n- x (dict):\ndict of lists.
\n- y (dict):\ndict of lists of Obs.
\n- \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\ndef func_a(a, x):\n return a[1] * anp.exp(-a[0] * x)
\n\ndef func_b(a, x):\n return a[2] * anp.exp(-a[0] * x)
\n\nIt is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- 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
\npyerrors.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).- 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).
\nReturns
\n\n\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": "- output (Fit_result):\nParameters and information on the fitted result.
\nPerforms a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
\n\nParameters
\n\n\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- \n
func (object):\nfunc has to be of the form
\n\n\n\n\n\nimport autograd.numpy as anp\n\ndef func(a, x):\n return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
For multiple x values func can be of the form
\n\n\n\n\n\ndef func(a, x):\n (x1, x2) = x\n return a[0] * x1 ** 2 + a[1] * x2\n
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- 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).
\nNotes
\n\nBased on the orthogonal distance regression module of scipy.
\n\nReturns
\n\n\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": "- output (Fit_result):\nParameters and information on the fitted result.
\nPerforms a linear fit to y = n + m * x and returns two Obs n, m.
\n\nParameters
\n\n\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.
\nReturns
\n\n\n
\n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "- fit_parameters (list[Obs]):\nLIist of fitted observables.
\nGenerates 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\nReturns
\n\n\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": "- None
\nGenerates a plot which compares the fit to the data and displays the corresponding residuals
\n\nFor uncorrelated data the residuals are expected to be distributed ~N(0,1).
\n\nReturns
\n\n\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": "- None
\nCalculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
\n\nReturns
\n\n\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": "- err (np.array(Obs)):\nError band for an array of sample values x
\nPerforms a Kolmogorov\u2013Smirnov test for the p-values of all fit object.
\n\nParameters
\n\n\n
\n\n- objects (list):\nList of fit results to include in the analysis (optional).
\nReturns
\n\n\n
\n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "- None
\n\n\n
pyerrors
includes aninput
submodule in which input routines and parsers for the output of various numerical programs are contained.Jackknife samples
\n\nFor comparison with other analysis workflows
\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": "pyerrors
can also generate jackknife samples from anObs
object or import jackknife samples into anObs
object.\nSeepyerrors.obs.Obs.export_jackknife
andpyerrors.obs.import_jackknife
for details.Extract generic MCMC data from a bdio file
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n
\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\nReturns
\n\n\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": "- data (List[Obs]):\nExtracted data
\nWrite Obs to a bdio file according to ADerrors conventions
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n
\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\nReturns
\n\n\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": "- success (int):\nreturns 0 is successful
\nExtract mesons data from a bdio file and return it as a dictionary
\n\nThe dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\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
\nReturns
\n\n\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": "- data (dict):\nExtracted meson data
\nExtract dSdm data from a bdio file and return it as a dictionary
\n\nThe dictionary can be accessed with a tuple consisting of (type, kappa)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\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": "- 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
\nExport a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.
\n\nTags are not written or recovered automatically. The separator | is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- xml_str (str):\nXML formatted string of the input data
\nExport a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.
\n\nTags are not written or recovered automatically. The separator | is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nImport a list of Obs from an xml.gz file in the Zeuthen pobs format.
\n\nTags are not written or recovered automatically.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- res (list[Obs]):\nImported data
\n- or
\n- res (dict):\nImported data and meta-data
\nImport a list of Obs from a string in the Zeuthen dobs format.
\n\nTags are not written or recovered automatically.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- res (list[Obs]):\nImported data
\n- or
\n- res (dict):\nImported data and meta-data
\nImport a list of Obs from an xml.gz file in the Zeuthen dobs format.
\n\nTags are not written or recovered automatically.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- res (list[Obs]):\nImported data
\n- or
\n- res (dict):\nImported data and meta-data
\nGenerate 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\nTags are not written or recovered automatically. The separator |is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- xml_str (str):\nXML string generated from the data
\nExport a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.
\n\nTags are not written or recovered automatically. The separator | is removed from the replica names.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nRead hadrons meson hdf5 file and extract the meson labeled 'meson'
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- corr (Corr):\nCorrelator of the source sink combination in question.
\nRead hadrons DistillationContraction hdf5 files in given directory structure
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result (dict):\nextracted DistillationContration data
\nndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)
\n\nAn 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\nArrays should be constructed using
\n\narray
,zeros
orempty
(refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)
) for instantiating an array.For more information, refer to the
\n\nnumpy
module and examine the\nmethods and attributes of an array.Parameters
\n\n\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.
\nAttributes
\n\n\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.,
\nx.flat = 3
(Seendarray.flat
for\nassignment examples; TODO).- 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.,
\nitemsize * size
.- 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
\n(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
).- 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
\nbase
\n(unless that array is also a view). Thebase
array is where the\narray data is actually stored.See Also
\n\n\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. itsdtype.type <numpy.dtype.type>
.Notes
\n\nThere are two modes of creating an array using
\n\n__new__
:\n
\n\n- If
\nbuffer
is None, then onlyshape
,dtype
, andorder
\nare used.- If
\nbuffer
is an object exposing the buffer interface, then\nall keywords are interpreted.No
\n\n__init__
method is needed because the array is fully initialized\nafter the__new__
method.Examples
\n\nThese examples illustrate the low-level
\n\nndarray
constructor. Refer\nto theSee Also
section above for easier ways of constructing an\nndarray.First mode,
\n\nbuffer
is None:\n\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
Second mode:
\n\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": "\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
Gamma_5 hermitean conjugate
\n\nUses 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\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result (Npr_matrix):\nread Cobs-matrix
\nRead hadrons Bilinear hdf5 file and output an array of CObs
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result_dict (dict[Npr_matrix]):\nextracted Bilinears
\nRead hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- result_dict (dict):\nextracted fourquark matrizes
\nGenerate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- json_string (str):\nString for export to .json(.gz) file
\nExport a list of Obs or structures containing Obs to a .json(.gz) file
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- Null
\nReconstruct a list of Obs or structures containing Obs from a json string.
\n\nThe following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- 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
\nImport a list of Obs or structures containing Obs from a .json(.gz) file.
\n\nThe following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- 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
\nExport a dict of Obs or structures containing Obs to a .json(.gz) file
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nImport a dict of Obs or structures containing Obs from a .json(.gz) file.
\n\nThe following structures are supported: Obs, list, numpy.ndarray, Corr
\n\nParameters
\n\n\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]+.
\nReturns
\n\n\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": "- data (Obs / list / Corr):\nRead data
\n- or
\n- data (dict):\nRead data and meta-data
\nRead pbp format from given folder structure.
\n\nParameters
\n\n\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
\nReturns
\n\n\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": "- result (list[Obs]):\nlist of observables read
\nRead rwms format from given folder structure. Returns a list of length nrw
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- rwms (Obs):\nReweighting factors read
\nExtract t0 from given .ms.dat files. Returns t0 as Obs.
\n\nIt is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2
\n\n- 0.3\nis fitted with a linear function\nfrom which the exact root is extracted. 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\nParameters
\n\n\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
\nReturns
\n\n\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": "- t0 (Obs):\nExtracted t0
\nRead the topologial charge based on openQCD gradient flow measurements.
\n\nParameters
\n\n\n
\n\n- path (str):\npath of the measurement files
\n- prefix (str):\nprefix of the measurement files, e.g.
\n_id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files. - 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.
\nReturns
\n\n\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": "- result (Obs):\nRead topological charge
\nRead the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
\n\nNote: 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\nParameters
\n\n\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": "- path (str):\npath of the measurement files
\n- prefix (str):\nprefix of the measurement files, e.g.
\n_id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files. - 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.
\nReturns the projection to the topological charge sector defined by target.
\n\nParameters
\n\n\n
\n\n- path (Obs):\nTopological charge.
\n- target (int):\nSpecifies the topological sector to be reweighted to (default 0)
\nReturns
\n\n\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": "- reto (Obs):\nprojection to the topological charge sector defined by target
\nConstructs reweighting factors to a specified topological sector.
\n\nParameters
\n\n\n
\n\n- path (str):\npath of the measurement files
\n- prefix (str):\nprefix of the measurement files, e.g.
\n_id0_r0.ms.dat - 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.
\nReturns
\n\n\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": "- reto (Obs):\nprojection to the topological charge sector defined by target
\nRead data from files in the specified directory with the specified prefix and quark combination extension, and return a
\n\nCorr
object containing the data.Parameters
\n\n\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- \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.
\nReturns
\n\n\n
\n\n- Corr: A complex valued
\nCorr
object containing the data read from the files. In case of boudary to bulk correlators.- or
\n- CObs: A complex valued
\nCObs
object containing the data read from the files. In case of boudary to boundary correlators.Raises
\n\n\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": "- 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.
\nWrite DataFrame including Obs or Corr valued columns to sqlite database.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nExecute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
\nExports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
\n\nBefore making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- None
\nImports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
\nRead sfcf files from given folder structure.
\n\nParameters
\n\n\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\n
- 'bi' for boundary-inner
\n- 'bb' for boundary-boundary
\n- 'bib' for boundary-inner-boundary
\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
\nReturns
\n\n\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": "- result (list[Obs]):\nlist of Observables with length T, observable per timeslice.\nbb-type correlators have length 1.
\nSorts a list of names of replika with searches for
\n\nr
andid
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.Parameters
\n\n\n
\n\n- ll (list):\nlist to sort
\nReturns
\n\n\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": "- ll (list):\nsorted list
\nChecks if list of configurations is contained in an idl
\n\nParameters
\n\n\n
\n\n- idl (range or list):\nidl of the current replicum
\n- che (list):\nlist of configurations to be checked against
\nReturns
\n\n\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": "- miss_str (str):\nstring with integers of which idls are missing
\nMatrix multiply all operands.
\n\nParameters
\n\n\n
\n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "- 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.
\nMatrix multiply both operands making use of the jackknife approximation.
\n\nParameters
\n\n\n
\n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "- 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.
\nWrapper for numpy.einsum
\n\nParameters
\n\n\n
\n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "- 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.
\nInverse 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\nParameters
\n\n\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": "- 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.
\nDump object into pickle file.
\n\nParameters
\n\n\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 '.')
\nReturns
\n\n\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": "- None
\nLoad object from pickle file.
\n\nParameters
\n\n\n
\n\n- path (str):\npath to the file
\nReturns
\n\n\n
\n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "- object (Obs):\nLoaded Object
\nGenerate an Obs object with given value, dvalue and name for test purposes
\n\nParameters
\n\n\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).
\nReturns
\n\n\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": "- res (Obs):\nGenerated Observable
\nGenerate observables with given covariance and autocorrelation times.
\n\nParameters
\n\n\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.
\nReturns
\n\n\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": "- corr_obs (list[Obs]):\nGenerated observable list
\nMatrix pencil method to extract k energy levels from data
\n\nImplementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)
\n\nParameters
\n\n\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).
\nReturns
\n\n\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": "- energy_levels (list[Obs]):\nExtracted energy levels
\nClass for a general observable.
\n\nInstances 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\nAttributes
\n\n\n
\n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "- 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.
\nInitialize Obs object.
\n\nParameters
\n\n\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": "- 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
\nEstimate the error and related properties of the Obs.
\n\nParameters
\n\n\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": "- 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)
\nEstimate the error and related properties of the Obs.
\n\nParameters
\n\n\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": "- 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)
\nOutput detailed properties of the Obs.
\n\nParameters
\n\n\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": "- ens_content (bool):\nprint details about the ensembles and replica if true.
\nReweight the obs with given rewighting factors.
\n\nParameters
\n\n\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": "- 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.
\nChecks whether the observable is zero within 'sigma' standard errors.
\n\nParameters
\n\n\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": "- sigma (int):\nNumber of standard errors used for the check.
\n- Works only properly when the gamma method was run.
\nChecks whether the observable is zero within a given tolerance.
\n\nParameters
\n\n\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": "- atol (float):\nAbsolute tolerance (for details see numpy documentation).
\nPlot integrated autocorrelation time for each ensemble.
\n\nParameters
\n\n\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": "- save (str):\nsaves the figure to a file named 'save' if.
\nPlot normalized autocorrelation function time for each ensemble.
\n\nParameters
\n\n\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": "- save (str):\nsaves the figure to a file named 'save' if.
\nPlot 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\nParameters
\n\n\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": "- expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
\nPlot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.
\n\nParameters
\n\n\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": "- save (str):\nsaves the figure to a file named 'save' if.
\nDump the Obs to a file 'name' of chosen format.
\n\nParameters
\n\n\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": "- 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 '.')
\nExport jackknife samples from the Obs
\n\nReturns
\n\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": "- 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).
\nClass 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\nParameters
\n\n\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.
\nNotes
\n\nFor simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use
\n\nnew_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\nParameters
\n\n\n
\n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "- 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.
\nCorrelate two observables.
\n\nParameters
\n\n\n
\n\n- obs_a (Obs):\nFirst observable
\n- obs_b (Obs):\nSecond observable
\nNotes
\n\nKeep 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\nWARNING: 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\nThe gamma method has to be applied first to all observables.
\n\nParameters
\n\n\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.
\nNotes
\n\nThe 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\nParameters
\n\n\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": "- 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.
\nCombine all observables in list_of_obs into one new observable
\n\nParameters
\n\n\n
\n\n- list_of_obs (list):\nlist of the Obs object to be combined
\nNotes
\n\nIt 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\nParameters
\n\n\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": "- 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.
\nFinds the root of the function func(x, d) where d is an
\n\nObs
.Parameters
\n\n\n
\n\n- d (Obs):\nObs passed to the function.
\n- \n
func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:
\n\n\n\nimport autograd.numpy as anp\ndef root_func(x, d):\n return anp.exp(-x ** 2) - d\n
- \n
guess (float):\nInitial guess for the minimization.
Returns
\n\n\n
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"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.- res (Obs):\n
\nObs
valued root of the function.