diff --git a/docs/pyerrors.html b/docs/pyerrors.html index a520da02..1f1f3ca0 100644 --- a/docs/pyerrors.html +++ b/docs/pyerrors.html @@ -3,7 +3,7 @@ - + pyerrors API documentation diff --git a/docs/pyerrors/correlators.html b/docs/pyerrors/correlators.html index d78bbcf1..f1deecac 100644 --- a/docs/pyerrors/correlators.html +++ b/docs/pyerrors/correlators.html @@ -3,7 +3,7 @@ - + pyerrors.correlators API documentation diff --git a/docs/pyerrors/covobs.html b/docs/pyerrors/covobs.html index afab0f72..8c1ba353 100644 --- a/docs/pyerrors/covobs.html +++ b/docs/pyerrors/covobs.html @@ -3,7 +3,7 @@ - + pyerrors.covobs API documentation diff --git a/docs/pyerrors/dirac.html b/docs/pyerrors/dirac.html index 8ec4e924..62fabbd2 100644 --- a/docs/pyerrors/dirac.html +++ b/docs/pyerrors/dirac.html @@ -3,7 +3,7 @@ - + pyerrors.dirac API documentation diff --git a/docs/pyerrors/fits.html b/docs/pyerrors/fits.html index a37db5bb..96b55029 100644 --- a/docs/pyerrors/fits.html +++ b/docs/pyerrors/fits.html @@ -3,7 +3,7 @@ - + pyerrors.fits API documentation diff --git a/docs/pyerrors/input.html b/docs/pyerrors/input.html index 9e8e68fe..59675da6 100644 --- a/docs/pyerrors/input.html +++ b/docs/pyerrors/input.html @@ -3,7 +3,7 @@ - + pyerrors.input API documentation diff --git a/docs/pyerrors/input/bdio.html b/docs/pyerrors/input/bdio.html index 0e74f042..6924fbb5 100644 --- a/docs/pyerrors/input/bdio.html +++ b/docs/pyerrors/input/bdio.html @@ -3,7 +3,7 @@ - + pyerrors.input.bdio API documentation diff --git a/docs/pyerrors/input/dobs.html b/docs/pyerrors/input/dobs.html index c4eb0f2f..d85a2e46 100644 --- a/docs/pyerrors/input/dobs.html +++ b/docs/pyerrors/input/dobs.html @@ -3,7 +3,7 @@ - + pyerrors.input.dobs API documentation diff --git a/docs/pyerrors/input/hadrons.html b/docs/pyerrors/input/hadrons.html index 97339c92..3c974861 100644 --- a/docs/pyerrors/input/hadrons.html +++ b/docs/pyerrors/input/hadrons.html @@ -3,7 +3,7 @@ - + pyerrors.input.hadrons API documentation @@ -55,6 +55,9 @@
  • read_meson_hd5
    • +
  • + extract_t0_hd5 +
  • read_DistillationContraction_hd5
    • @@ -105,489 +108,556 @@ 6from ..obs import Obs, CObs 7from ..correlators import Corr 8from ..dirac import epsilon_tensor_rank4 - 9 + 9from .misc import fit_t0 10 - 11def _get_files(path, filestem, idl): - 12 ls = os.listdir(path) - 13 - 14 # Clean up file list - 15 files = list(filter(lambda x: x.startswith(filestem + "."), ls)) - 16 - 17 if not files: - 18 raise Exception('No files starting with', filestem, 'in folder', path) - 19 - 20 def get_cnfg_number(n): - 21 return int(n.replace(".h5", "")[len(filestem) + 1:]) # From python 3.9 onward the safer 'removesuffix' method can be used. - 22 - 23 # Sort according to configuration number - 24 files.sort(key=get_cnfg_number) - 25 - 26 cnfg_numbers = [] - 27 filtered_files = [] - 28 for line in files: - 29 no = get_cnfg_number(line) - 30 if idl: - 31 if no in list(idl): - 32 filtered_files.append(line) - 33 cnfg_numbers.append(no) - 34 else: - 35 filtered_files.append(line) - 36 cnfg_numbers.append(no) - 37 - 38 if idl: - 39 if Counter(list(idl)) != Counter(cnfg_numbers): - 40 raise Exception("Not all configurations specified in idl found, configurations " + str(list(Counter(list(idl)) - Counter(cnfg_numbers))) + " are missing.") - 41 - 42 # Check that configurations are evenly spaced - 43 dc = np.unique(np.diff(cnfg_numbers)) - 44 if np.any(dc < 0): - 45 raise Exception("Unsorted files") - 46 if len(dc) == 1: - 47 idx = range(cnfg_numbers[0], cnfg_numbers[-1] + dc[0], dc[0]) - 48 elif idl: - 49 idx = idl - 50 else: - 51 raise Exception("Configurations are not evenly spaced. Provide an idl if you want to proceed with this set of configurations.") - 52 - 53 return filtered_files, idx - 54 + 11 + 12def _get_files(path, filestem, idl): + 13 ls = os.listdir(path) + 14 + 15 # Clean up file list + 16 files = list(filter(lambda x: x.startswith(filestem + "."), ls)) + 17 + 18 if not files: + 19 raise Exception('No files starting with', filestem, 'in folder', path) + 20 + 21 def get_cnfg_number(n): + 22 return int(n.replace(".h5", "")[len(filestem) + 1:]) # From python 3.9 onward the safer 'removesuffix' method can be used. + 23 + 24 # Sort according to configuration number + 25 files.sort(key=get_cnfg_number) + 26 + 27 cnfg_numbers = [] + 28 filtered_files = [] + 29 for line in files: + 30 no = get_cnfg_number(line) + 31 if idl: + 32 if no in list(idl): + 33 filtered_files.append(line) + 34 cnfg_numbers.append(no) + 35 else: + 36 filtered_files.append(line) + 37 cnfg_numbers.append(no) + 38 + 39 if idl: + 40 if Counter(list(idl)) != Counter(cnfg_numbers): + 41 raise Exception("Not all configurations specified in idl found, configurations " + str(list(Counter(list(idl)) - Counter(cnfg_numbers))) + " are missing.") + 42 + 43 # Check that configurations are evenly spaced + 44 dc = np.unique(np.diff(cnfg_numbers)) + 45 if np.any(dc < 0): + 46 raise Exception("Unsorted files") + 47 if len(dc) == 1: + 48 idx = range(cnfg_numbers[0], cnfg_numbers[-1] + dc[0], dc[0]) + 49 elif idl: + 50 idx = idl + 51 else: + 52 raise Exception("Configurations are not evenly spaced. Provide an idl if you want to proceed with this set of configurations.") + 53 + 54 return filtered_files, idx 55 - 56def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None): - 57 r'''Read hadrons meson hdf5 file and extract the meson labeled 'meson' - 58 - 59 Parameters - 60 ----------------- - 61 path : str - 62 path to the files to read - 63 filestem : str - 64 namestem of the files to read - 65 ens_id : str - 66 name of the ensemble, required for internal bookkeeping - 67 meson : str - 68 label of the meson to be extracted, standard value meson_0 which - 69 corresponds to the pseudoscalar pseudoscalar two-point function. - 70 gammas : tuple of strings - 71 Instrad of a meson label one can also provide a tuple of two strings - 72 indicating the gamma matrices at source and sink. - 73 ("Gamma5", "Gamma5") corresponds to the pseudoscalar pseudoscalar - 74 two-point function. The gammas argument dominateds over meson. - 75 idl : range - 76 If specified only configurations in the given range are read in. - 77 - 78 Returns - 79 ------- - 80 corr : Corr - 81 Correlator of the source sink combination in question. - 82 ''' - 83 - 84 files, idx = _get_files(path, filestem, idl) - 85 - 86 tree = meson.rsplit('_')[0] - 87 if gammas is not None: - 88 h5file = h5py.File(path + '/' + files[0], "r") - 89 found_meson = None - 90 for key in h5file[tree].keys(): - 91 if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]: - 92 found_meson = key - 93 break - 94 h5file.close() - 95 if found_meson: - 96 meson = found_meson - 97 else: - 98 raise Exception("Source Sink combination " + str(gammas) + " not found.") - 99 -100 corr_data = [] -101 infos = [] -102 for hd5_file in files: -103 h5file = h5py.File(path + '/' + hd5_file, "r") -104 if not tree + '/' + meson in h5file: -105 raise Exception("Entry '" + meson + "' not contained in the files.") -106 raw_data = h5file[tree + '/' + meson + '/corr'] -107 real_data = raw_data[:]["re"].astype(np.double) -108 corr_data.append(real_data) -109 if not infos: -110 for k, i in h5file[tree + '/' + meson].attrs.items(): -111 infos.append(k + ': ' + i[0].decode()) -112 h5file.close() -113 corr_data = np.array(corr_data) -114 -115 l_obs = [] -116 for c in corr_data.T: -117 l_obs.append(Obs([c], [ens_id], idl=[idx])) -118 -119 corr = Corr(l_obs) -120 corr.tag = r", ".join(infos) -121 return corr -122 + 56 + 57def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None): + 58 r'''Read hadrons meson hdf5 file and extract the meson labeled 'meson' + 59 + 60 Parameters + 61 ----------------- + 62 path : str + 63 path to the files to read + 64 filestem : str + 65 namestem of the files to read + 66 ens_id : str + 67 name of the ensemble, required for internal bookkeeping + 68 meson : str + 69 label of the meson to be extracted, standard value meson_0 which + 70 corresponds to the pseudoscalar pseudoscalar two-point function. + 71 gammas : tuple of strings + 72 Instrad of a meson label one can also provide a tuple of two strings + 73 indicating the gamma matrices at source and sink. + 74 ("Gamma5", "Gamma5") corresponds to the pseudoscalar pseudoscalar + 75 two-point function. The gammas argument dominateds over meson. + 76 idl : range + 77 If specified only configurations in the given range are read in. + 78 + 79 Returns + 80 ------- + 81 corr : Corr + 82 Correlator of the source sink combination in question. + 83 ''' + 84 + 85 files, idx = _get_files(path, filestem, idl) + 86 + 87 tree = meson.rsplit('_')[0] + 88 if gammas is not None: + 89 h5file = h5py.File(path + '/' + files[0], "r") + 90 found_meson = None + 91 for key in h5file[tree].keys(): + 92 if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]: + 93 found_meson = key + 94 break + 95 h5file.close() + 96 if found_meson: + 97 meson = found_meson + 98 else: + 99 raise Exception("Source Sink combination " + str(gammas) + " not found.") +100 +101 corr_data = [] +102 infos = [] +103 for hd5_file in files: +104 h5file = h5py.File(path + '/' + hd5_file, "r") +105 if not tree + '/' + meson in h5file: +106 raise Exception("Entry '" + meson + "' not contained in the files.") +107 raw_data = h5file[tree + '/' + meson + '/corr'] +108 real_data = raw_data[:]["re"].astype(np.double) +109 corr_data.append(real_data) +110 if not infos: +111 for k, i in h5file[tree + '/' + meson].attrs.items(): +112 infos.append(k + ': ' + i[0].decode()) +113 h5file.close() +114 corr_data = np.array(corr_data) +115 +116 l_obs = [] +117 for c in corr_data.T: +118 l_obs.append(Obs([c], [ens_id], idl=[idx])) +119 +120 corr = Corr(l_obs) +121 corr.tag = r", ".join(infos) +122 return corr 123 -124def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None): -125 """Read hadrons DistillationContraction hdf5 files in given directory structure -126 -127 Parameters -128 ----------------- -129 path : str -130 path to the directories to read -131 ens_id : str -132 name of the ensemble, required for internal bookkeeping -133 diagrams : list -134 List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"]. -135 idl : range -136 If specified only configurations in the given range are read in. -137 -138 Returns -139 ------- -140 result : dict -141 extracted DistillationContration data -142 """ -143 -144 res_dict = {} +124 +125def _extract_real_arrays(path, files, tree, keys): +126 corr_data = {} +127 for key in keys: +128 corr_data[key] = [] +129 for hd5_file in files: +130 h5file = h5py.File(path + '/' + hd5_file, "r") +131 for key in keys: +132 if not tree + '/' + key in h5file: +133 raise Exception("Entry '" + key + "' not contained in the files.") +134 raw_data = h5file[tree + '/' + key + '/data'] +135 real_data = raw_data[:].astype(np.double) +136 corr_data[key].append(real_data) +137 h5file.close() +138 for key in keys: +139 corr_data[key] = np.array(corr_data[key]) +140 return corr_data +141 +142 +143def extract_t0_hd5(path, filestem, ens_id, obs='Clover energy density', fit_range=5, idl=None, **kwargs): +144 r'''Read hadrons FlowObservables hdf5 file and extract t0 145 -146 directories, idx = _get_files(path, "data", idl) -147 -148 explore_path = Path(path + "/" + directories[0]) -149 -150 for explore_file in explore_path.iterdir(): -151 if explore_file.is_file(): -152 stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1] -153 else: -154 continue -155 -156 file_list = [] -157 for dir in directories: -158 tmp_path = Path(path + "/" + dir) -159 file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5") -160 -161 corr_data = {} -162 -163 for diagram in diagrams: -164 corr_data[diagram] = [] +146 Parameters +147 ----------------- +148 path : str +149 path to the files to read +150 filestem : str +151 namestem of the files to read +152 ens_id : str +153 name of the ensemble, required for internal bookkeeping +154 obs : str +155 label of the observable from which t0 should be extracted. +156 Options: 'Clover energy density' and 'Plaquette energy density' +157 fit_range : int +158 Number of data points left and right of the zero +159 crossing to be included in the linear fit. (Default: 5) +160 idl : range +161 If specified only configurations in the given range are read in. +162 plot_fit : bool +163 If true, the fit for the extraction of t0 is shown together with the data. +164 ''' 165 -166 try: -167 for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))): -168 h5file = h5py.File(hd5_file) -169 -170 if n_file == 0: -171 if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...": -172 raise Exception("Routine is only implemented for files containing inversions on all timeslices.") -173 -174 Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0] -175 -176 identifier = [] -177 for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1): -178 encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file)) -179 full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_") -180 my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3]) -181 identifier.append(my_tuple) -182 identifier = tuple(identifier) -183 # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case. -184 -185 for diagram in diagrams: -186 -187 if diagram == "triangle" and "Identity" not in str(identifier): -188 part = "im" -189 else: -190 part = "re" -191 -192 real_data = np.zeros(Nt) -193 for x0 in range(Nt): -194 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double) -195 real_data += np.roll(raw_data, -x0) -196 real_data /= Nt -197 -198 corr_data[diagram].append(real_data) -199 h5file.close() -200 -201 res_dict[str(identifier)] = {} -202 -203 for diagram in diagrams: +166 files, idx = _get_files(path, filestem, idl) +167 tree = "FlowObservables" +168 +169 h5file = h5py.File(path + '/' + files[0], "r") +170 obs_key = None +171 for key in h5file[tree].keys(): +172 if obs == h5file[tree][key].attrs["description"][0].decode(): +173 obs_key = key +174 break +175 h5file.close() +176 if obs_key is None: +177 raise Exception(f"Observable {obs} not found.") +178 +179 corr_data = _extract_real_arrays(path, files, tree, ["FlowObservables_0", obs_key]) +180 +181 if not np.allclose(corr_data["FlowObservables_0"][0], corr_data["FlowObservables_0"][:]): +182 raise Exception("Not all flow times were equal.") +183 +184 t2E_dict = {} +185 for t2, dat in zip(corr_data["FlowObservables_0"][0], corr_data[obs_key].T): +186 t2E_dict[t2] = Obs([dat], [ens_id], idl=[idx]) - 0.3 +187 +188 return fit_t0(t2E_dict, fit_range, plot_fit=kwargs.get('plot_fit')) +189 +190 +191def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None): +192 """Read hadrons DistillationContraction hdf5 files in given directory structure +193 +194 Parameters +195 ----------------- +196 path : str +197 path to the directories to read +198 ens_id : str +199 name of the ensemble, required for internal bookkeeping +200 diagrams : list +201 List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"]. +202 idl : range +203 If specified only configurations in the given range are read in. 204 -205 tmp_data = np.array(corr_data[diagram]) -206 -207 l_obs = [] -208 for c in tmp_data.T: -209 l_obs.append(Obs([c], [ens_id], idl=[idx])) +205 Returns +206 ------- +207 result : dict +208 extracted DistillationContration data +209 """ 210 -211 corr = Corr(l_obs) -212 corr.tag = str(identifier) -213 -214 res_dict[str(identifier)][diagram] = corr -215 except FileNotFoundError: -216 print("Skip", stem) -217 -218 return res_dict -219 -220 -221class Npr_matrix(np.ndarray): +211 res_dict = {} +212 +213 directories, idx = _get_files(path, "data", idl) +214 +215 explore_path = Path(path + "/" + directories[0]) +216 +217 for explore_file in explore_path.iterdir(): +218 if explore_file.is_file(): +219 stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1] +220 else: +221 continue 222 -223 def __new__(cls, input_array, mom_in=None, mom_out=None): -224 obj = np.asarray(input_array).view(cls) -225 obj.mom_in = mom_in -226 obj.mom_out = mom_out -227 return obj -228 -229 @property -230 def g5H(self): -231 """Gamma_5 hermitean conjugate +223 file_list = [] +224 for dir in directories: +225 tmp_path = Path(path + "/" + dir) +226 file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5") +227 +228 corr_data = {} +229 +230 for diagram in diagrams: +231 corr_data[diagram] = [] 232 -233 Uses the fact that the propagator is gamma5 hermitean, so just the -234 in and out momenta of the propagator are exchanged. -235 """ -236 return Npr_matrix(self, -237 mom_in=self.mom_out, -238 mom_out=self.mom_in) -239 -240 def _propagate_mom(self, other, name): -241 s_mom = getattr(self, name, None) -242 o_mom = getattr(other, name, None) -243 if s_mom is not None and o_mom is not None: -244 if not np.allclose(s_mom, o_mom): -245 raise Exception(name + ' does not match.') -246 return o_mom if o_mom is not None else s_mom -247 -248 def __matmul__(self, other): -249 return self.__new__(Npr_matrix, -250 super().__matmul__(other), -251 self._propagate_mom(other, 'mom_in'), -252 self._propagate_mom(other, 'mom_out')) +233 try: +234 for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))): +235 h5file = h5py.File(hd5_file) +236 +237 if n_file == 0: +238 if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...": +239 raise Exception("Routine is only implemented for files containing inversions on all timeslices.") +240 +241 Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0] +242 +243 identifier = [] +244 for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1): +245 encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file)) +246 full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_") +247 my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3]) +248 identifier.append(my_tuple) +249 identifier = tuple(identifier) +250 # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case. +251 +252 for diagram in diagrams: 253 -254 def __array_finalize__(self, obj): -255 if obj is None: -256 return -257 self.mom_in = getattr(obj, 'mom_in', None) -258 self.mom_out = getattr(obj, 'mom_out', None) -259 -260 -261def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None): -262 """Read hadrons ExternalLeg hdf5 file and output an array of CObs -263 -264 Parameters -265 ---------- -266 path : str -267 path to the files to read -268 filestem : str -269 namestem of the files to read -270 ens_id : str -271 name of the ensemble, required for internal bookkeeping -272 idl : range -273 If specified only configurations in the given range are read in. -274 -275 Returns -276 ------- -277 result : Npr_matrix -278 read Cobs-matrix -279 """ +254 if diagram == "triangle" and "Identity" not in str(identifier): +255 part = "im" +256 else: +257 part = "re" +258 +259 real_data = np.zeros(Nt) +260 for x0 in range(Nt): +261 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double) +262 real_data += np.roll(raw_data, -x0) +263 real_data /= Nt +264 +265 corr_data[diagram].append(real_data) +266 h5file.close() +267 +268 res_dict[str(identifier)] = {} +269 +270 for diagram in diagrams: +271 +272 tmp_data = np.array(corr_data[diagram]) +273 +274 l_obs = [] +275 for c in tmp_data.T: +276 l_obs.append(Obs([c], [ens_id], idl=[idx])) +277 +278 corr = Corr(l_obs) +279 corr.tag = str(identifier) 280 -281 files, idx = _get_files(path, filestem, idl) -282 -283 mom = None +281 res_dict[str(identifier)][diagram] = corr +282 except FileNotFoundError: +283 print("Skip", stem) 284 -285 corr_data = [] -286 for hd5_file in files: -287 file = h5py.File(path + '/' + hd5_file, "r") -288 raw_data = file['ExternalLeg/corr'][0][0].view('complex') -289 corr_data.append(raw_data) -290 if mom is None: -291 mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -292 file.close() -293 corr_data = np.array(corr_data) -294 -295 rolled_array = np.rollaxis(corr_data, 0, 5) -296 -297 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -298 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): -299 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) -300 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) -301 matrix[si, sj, ci, cj] = CObs(real, imag) -302 -303 return Npr_matrix(matrix, mom_in=mom) -304 -305 -306def read_Bilinear_hd5(path, filestem, ens_id, idl=None): -307 """Read hadrons Bilinear hdf5 file and output an array of CObs -308 -309 Parameters -310 ---------- -311 path : str -312 path to the files to read -313 filestem : str -314 namestem of the files to read -315 ens_id : str -316 name of the ensemble, required for internal bookkeeping -317 idl : range -318 If specified only configurations in the given range are read in. -319 -320 Returns -321 ------- -322 result_dict: dict[Npr_matrix] -323 extracted Bilinears -324 """ -325 -326 files, idx = _get_files(path, filestem, idl) +285 return res_dict +286 +287 +288class Npr_matrix(np.ndarray): +289 +290 def __new__(cls, input_array, mom_in=None, mom_out=None): +291 obj = np.asarray(input_array).view(cls) +292 obj.mom_in = mom_in +293 obj.mom_out = mom_out +294 return obj +295 +296 @property +297 def g5H(self): +298 """Gamma_5 hermitean conjugate +299 +300 Uses the fact that the propagator is gamma5 hermitean, so just the +301 in and out momenta of the propagator are exchanged. +302 """ +303 return Npr_matrix(self, +304 mom_in=self.mom_out, +305 mom_out=self.mom_in) +306 +307 def _propagate_mom(self, other, name): +308 s_mom = getattr(self, name, None) +309 o_mom = getattr(other, name, None) +310 if s_mom is not None and o_mom is not None: +311 if not np.allclose(s_mom, o_mom): +312 raise Exception(name + ' does not match.') +313 return o_mom if o_mom is not None else s_mom +314 +315 def __matmul__(self, other): +316 return self.__new__(Npr_matrix, +317 super().__matmul__(other), +318 self._propagate_mom(other, 'mom_in'), +319 self._propagate_mom(other, 'mom_out')) +320 +321 def __array_finalize__(self, obj): +322 if obj is None: +323 return +324 self.mom_in = getattr(obj, 'mom_in', None) +325 self.mom_out = getattr(obj, 'mom_out', None) +326 327 -328 mom_in = None -329 mom_out = None +328def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None): +329 """Read hadrons ExternalLeg hdf5 file and output an array of CObs 330 -331 corr_data = {} -332 for hd5_file in files: -333 file = h5py.File(path + '/' + hd5_file, "r") -334 for i in range(16): -335 name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8') -336 if name not in corr_data: -337 corr_data[name] = [] -338 raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex') -339 corr_data[name].append(raw_data) -340 if mom_in is None: -341 mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -342 if mom_out is None: -343 mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) -344 -345 file.close() -346 -347 result_dict = {} -348 -349 for key, data in corr_data.items(): -350 local_data = np.array(data) +331 Parameters +332 ---------- +333 path : str +334 path to the files to read +335 filestem : str +336 namestem of the files to read +337 ens_id : str +338 name of the ensemble, required for internal bookkeeping +339 idl : range +340 If specified only configurations in the given range are read in. +341 +342 Returns +343 ------- +344 result : Npr_matrix +345 read Cobs-matrix +346 """ +347 +348 files, idx = _get_files(path, filestem, idl) +349 +350 mom = None 351 -352 rolled_array = np.rollaxis(local_data, 0, 5) -353 -354 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -355 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): -356 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) -357 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) -358 matrix[si, sj, ci, cj] = CObs(real, imag) -359 -360 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) +352 corr_data = [] +353 for hd5_file in files: +354 file = h5py.File(path + '/' + hd5_file, "r") +355 raw_data = file['ExternalLeg/corr'][0][0].view('complex') +356 corr_data.append(raw_data) +357 if mom is None: +358 mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +359 file.close() +360 corr_data = np.array(corr_data) 361 -362 return result_dict +362 rolled_array = np.rollaxis(corr_data, 0, 5) 363 -364 -365def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]): -366 """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs -367 -368 Parameters -369 ---------- -370 path : str -371 path to the files to read -372 filestem : str -373 namestem of the files to read -374 ens_id : str -375 name of the ensemble, required for internal bookkeeping -376 idl : range -377 If specified only configurations in the given range are read in. -378 vertices : list -379 Vertex functions to be extracted. -380 -381 Returns -382 ------- -383 result_dict : dict -384 extracted fourquark matrizes -385 """ +364 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +365 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): +366 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) +367 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) +368 matrix[si, sj, ci, cj] = CObs(real, imag) +369 +370 return Npr_matrix(matrix, mom_in=mom) +371 +372 +373def read_Bilinear_hd5(path, filestem, ens_id, idl=None): +374 """Read hadrons Bilinear hdf5 file and output an array of CObs +375 +376 Parameters +377 ---------- +378 path : str +379 path to the files to read +380 filestem : str +381 namestem of the files to read +382 ens_id : str +383 name of the ensemble, required for internal bookkeeping +384 idl : range +385 If specified only configurations in the given range are read in. 386 -387 files, idx = _get_files(path, filestem, idl) -388 -389 mom_in = None -390 mom_out = None -391 -392 vertex_names = [] -393 for vertex in vertices: -394 vertex_names += _get_lorentz_names(vertex) -395 -396 corr_data = {} +387 Returns +388 ------- +389 result_dict: dict[Npr_matrix] +390 extracted Bilinears +391 """ +392 +393 files, idx = _get_files(path, filestem, idl) +394 +395 mom_in = None +396 mom_out = None 397 -398 tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_' -399 -400 for hd5_file in files: -401 file = h5py.File(path + '/' + hd5_file, "r") -402 -403 for i in range(32): -404 name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8')) -405 if name in vertex_names: -406 if name not in corr_data: -407 corr_data[name] = [] -408 raw_data = file[tree + str(i) + '/corr'][0][0].view('complex') -409 corr_data[name].append(raw_data) -410 if mom_in is None: -411 mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -412 if mom_out is None: -413 mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) -414 -415 file.close() -416 -417 intermediate_dict = {} +398 corr_data = {} +399 for hd5_file in files: +400 file = h5py.File(path + '/' + hd5_file, "r") +401 for i in range(16): +402 name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8') +403 if name not in corr_data: +404 corr_data[name] = [] +405 raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex') +406 corr_data[name].append(raw_data) +407 if mom_in is None: +408 mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +409 if mom_out is None: +410 mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) +411 +412 file.close() +413 +414 result_dict = {} +415 +416 for key, data in corr_data.items(): +417 local_data = np.array(data) 418 -419 for vertex in vertices: -420 lorentz_names = _get_lorentz_names(vertex) -421 for v_name in lorentz_names: -422 if v_name in [('SigmaXY', 'SigmaZT'), -423 ('SigmaXT', 'SigmaYZ'), -424 ('SigmaYZ', 'SigmaXT'), -425 ('SigmaZT', 'SigmaXY')]: -426 sign = -1 -427 else: -428 sign = 1 -429 if vertex not in intermediate_dict: -430 intermediate_dict[vertex] = sign * np.array(corr_data[v_name]) -431 else: -432 intermediate_dict[vertex] += sign * np.array(corr_data[v_name]) -433 -434 result_dict = {} -435 -436 for key, data in intermediate_dict.items(): -437 -438 rolled_array = np.moveaxis(data, 0, 8) -439 -440 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -441 for index in np.ndindex(rolled_array.shape[:-1]): -442 real = Obs([rolled_array[index].real], [ens_id], idl=[idx]) -443 imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx]) -444 matrix[index] = CObs(real, imag) -445 -446 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) +419 rolled_array = np.rollaxis(local_data, 0, 5) +420 +421 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +422 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): +423 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) +424 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) +425 matrix[si, sj, ci, cj] = CObs(real, imag) +426 +427 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) +428 +429 return result_dict +430 +431 +432def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]): +433 """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs +434 +435 Parameters +436 ---------- +437 path : str +438 path to the files to read +439 filestem : str +440 namestem of the files to read +441 ens_id : str +442 name of the ensemble, required for internal bookkeeping +443 idl : range +444 If specified only configurations in the given range are read in. +445 vertices : list +446 Vertex functions to be extracted. 447 -448 return result_dict -449 -450 -451def _get_lorentz_names(name): -452 lorentz_index = ['X', 'Y', 'Z', 'T'] +448 Returns +449 ------- +450 result_dict : dict +451 extracted fourquark matrizes +452 """ 453 -454 res = [] +454 files, idx = _get_files(path, filestem, idl) 455 -456 if name == "TT": -457 for i in range(4): -458 for j in range(i + 1, 4): -459 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[i] + lorentz_index[j])) -460 return res -461 -462 if name == "TTtilde": -463 for i in range(4): -464 for j in range(i + 1, 4): -465 for k in range(4): -466 for o in range(k + 1, 4): -467 fac = epsilon_tensor_rank4(i, j, k, o) -468 if not np.isclose(fac, 0.0): -469 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[k] + lorentz_index[o])) -470 return res -471 -472 assert len(name) == 2 -473 -474 if 'S' in name or 'P' in name: -475 if not set(name) <= set(['S', 'P']): -476 raise Exception("'" + name + "' is not a Lorentz scalar") -477 -478 g_names = {'S': 'Identity', -479 'P': 'Gamma5'} -480 -481 res.append((g_names[name[0]], g_names[name[1]])) -482 -483 else: -484 if not set(name) <= set(['V', 'A']): -485 raise Exception("'" + name + "' is not a Lorentz scalar") -486 -487 for ind in lorentz_index: -488 res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5', -489 'Gamma' + ind + (name[1] == 'A') * 'Gamma5')) -490 -491 return res +456 mom_in = None +457 mom_out = None +458 +459 vertex_names = [] +460 for vertex in vertices: +461 vertex_names += _get_lorentz_names(vertex) +462 +463 corr_data = {} +464 +465 tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_' +466 +467 for hd5_file in files: +468 file = h5py.File(path + '/' + hd5_file, "r") +469 +470 for i in range(32): +471 name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8')) +472 if name in vertex_names: +473 if name not in corr_data: +474 corr_data[name] = [] +475 raw_data = file[tree + str(i) + '/corr'][0][0].view('complex') +476 corr_data[name].append(raw_data) +477 if mom_in is None: +478 mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +479 if mom_out is None: +480 mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) +481 +482 file.close() +483 +484 intermediate_dict = {} +485 +486 for vertex in vertices: +487 lorentz_names = _get_lorentz_names(vertex) +488 for v_name in lorentz_names: +489 if v_name in [('SigmaXY', 'SigmaZT'), +490 ('SigmaXT', 'SigmaYZ'), +491 ('SigmaYZ', 'SigmaXT'), +492 ('SigmaZT', 'SigmaXY')]: +493 sign = -1 +494 else: +495 sign = 1 +496 if vertex not in intermediate_dict: +497 intermediate_dict[vertex] = sign * np.array(corr_data[v_name]) +498 else: +499 intermediate_dict[vertex] += sign * np.array(corr_data[v_name]) +500 +501 result_dict = {} +502 +503 for key, data in intermediate_dict.items(): +504 +505 rolled_array = np.moveaxis(data, 0, 8) +506 +507 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +508 for index in np.ndindex(rolled_array.shape[:-1]): +509 real = Obs([rolled_array[index].real], [ens_id], idl=[idx]) +510 imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx]) +511 matrix[index] = CObs(real, imag) +512 +513 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) +514 +515 return result_dict +516 +517 +518def _get_lorentz_names(name): +519 lorentz_index = ['X', 'Y', 'Z', 'T'] +520 +521 res = [] +522 +523 if name == "TT": +524 for i in range(4): +525 for j in range(i + 1, 4): +526 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[i] + lorentz_index[j])) +527 return res +528 +529 if name == "TTtilde": +530 for i in range(4): +531 for j in range(i + 1, 4): +532 for k in range(4): +533 for o in range(k + 1, 4): +534 fac = epsilon_tensor_rank4(i, j, k, o) +535 if not np.isclose(fac, 0.0): +536 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[k] + lorentz_index[o])) +537 return res +538 +539 assert len(name) == 2 +540 +541 if 'S' in name or 'P' in name: +542 if not set(name) <= set(['S', 'P']): +543 raise Exception("'" + name + "' is not a Lorentz scalar") +544 +545 g_names = {'S': 'Identity', +546 'P': 'Gamma5'} +547 +548 res.append((g_names[name[0]], g_names[name[1]])) +549 +550 else: +551 if not set(name) <= set(['V', 'A']): +552 raise Exception("'" + name + "' is not a Lorentz scalar") +553 +554 for ind in lorentz_index: +555 res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5', +556 'Gamma' + ind + (name[1] == 'A') * 'Gamma5')) +557 +558 return res @@ -603,72 +673,72 @@ - 
     57def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):
- 58    r'''Read hadrons meson hdf5 file and extract the meson labeled 'meson'
- 59
- 60    Parameters
- 61    -----------------
- 62    path : str
- 63        path to the files to read
- 64    filestem : str
- 65        namestem of the files to read
- 66    ens_id : str
- 67        name of the ensemble, required for internal bookkeeping
- 68    meson : str
- 69        label of the meson to be extracted, standard value meson_0 which
- 70        corresponds to the pseudoscalar pseudoscalar two-point function.
- 71    gammas : tuple of strings
- 72        Instrad of a meson label one can also provide a tuple of two strings
- 73        indicating the gamma matrices at source and sink.
- 74        ("Gamma5", "Gamma5") corresponds to the pseudoscalar pseudoscalar
- 75        two-point function. The gammas argument dominateds over meson.
- 76    idl : range
- 77        If specified only configurations in the given range are read in.
- 78
- 79    Returns
- 80    -------
- 81    corr : Corr
- 82        Correlator of the source sink combination in question.
- 83    '''
- 84
- 85    files, idx = _get_files(path, filestem, idl)
- 86
- 87    tree = meson.rsplit('_')[0]
- 88    if gammas is not None:
- 89        h5file = h5py.File(path + '/' + files[0], "r")
- 90        found_meson = None
- 91        for key in h5file[tree].keys():
- 92            if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]:
- 93                found_meson = key
- 94                break
- 95        h5file.close()
- 96        if found_meson:
- 97            meson = found_meson
- 98        else:
- 99            raise Exception("Source Sink combination " + str(gammas) + " not found.")
-100
-101    corr_data = []
-102    infos = []
-103    for hd5_file in files:
-104        h5file = h5py.File(path + '/' + hd5_file, "r")
-105        if not tree + '/' + meson in h5file:
-106            raise Exception("Entry '" + meson + "' not contained in the files.")
-107        raw_data = h5file[tree + '/' + meson + '/corr']
-108        real_data = raw_data[:]["re"].astype(np.double)
-109        corr_data.append(real_data)
-110        if not infos:
-111            for k, i in h5file[tree + '/' + meson].attrs.items():
-112                infos.append(k + ': ' + i[0].decode())
-113        h5file.close()
-114    corr_data = np.array(corr_data)
-115
-116    l_obs = []
-117    for c in corr_data.T:
-118        l_obs.append(Obs([c], [ens_id], idl=[idx]))
-119
-120    corr = Corr(l_obs)
-121    corr.tag = r", ".join(infos)
-122    return corr
+            
     58def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):
+ 59    r'''Read hadrons meson hdf5 file and extract the meson labeled 'meson'
+ 60
+ 61    Parameters
+ 62    -----------------
+ 63    path : str
+ 64        path to the files to read
+ 65    filestem : str
+ 66        namestem of the files to read
+ 67    ens_id : str
+ 68        name of the ensemble, required for internal bookkeeping
+ 69    meson : str
+ 70        label of the meson to be extracted, standard value meson_0 which
+ 71        corresponds to the pseudoscalar pseudoscalar two-point function.
+ 72    gammas : tuple of strings
+ 73        Instrad of a meson label one can also provide a tuple of two strings
+ 74        indicating the gamma matrices at source and sink.
+ 75        ("Gamma5", "Gamma5") corresponds to the pseudoscalar pseudoscalar
+ 76        two-point function. The gammas argument dominateds over meson.
+ 77    idl : range
+ 78        If specified only configurations in the given range are read in.
+ 79
+ 80    Returns
+ 81    -------
+ 82    corr : Corr
+ 83        Correlator of the source sink combination in question.
+ 84    '''
+ 85
+ 86    files, idx = _get_files(path, filestem, idl)
+ 87
+ 88    tree = meson.rsplit('_')[0]
+ 89    if gammas is not None:
+ 90        h5file = h5py.File(path + '/' + files[0], "r")
+ 91        found_meson = None
+ 92        for key in h5file[tree].keys():
+ 93            if gammas[0] == h5file[tree][key].attrs["gamma_snk"][0].decode() and h5file[tree][key].attrs["gamma_src"][0].decode() == gammas[1]:
+ 94                found_meson = key
+ 95                break
+ 96        h5file.close()
+ 97        if found_meson:
+ 98            meson = found_meson
+ 99        else:
+100            raise Exception("Source Sink combination " + str(gammas) + " not found.")
+101
+102    corr_data = []
+103    infos = []
+104    for hd5_file in files:
+105        h5file = h5py.File(path + '/' + hd5_file, "r")
+106        if not tree + '/' + meson in h5file:
+107            raise Exception("Entry '" + meson + "' not contained in the files.")
+108        raw_data = h5file[tree + '/' + meson + '/corr']
+109        real_data = raw_data[:]["re"].astype(np.double)
+110        corr_data.append(real_data)
+111        if not infos:
+112            for k, i in h5file[tree + '/' + meson].attrs.items():
+113                infos.append(k + ': ' + i[0].decode())
+114        h5file.close()
+115    corr_data = np.array(corr_data)
+116
+117    l_obs = []
+118    for c in corr_data.T:
+119        l_obs.append(Obs([c], [ens_id], idl=[idx]))
+120
+121    corr = Corr(l_obs)
+122    corr.tag = r", ".join(infos)
+123    return corr
 
    
 
 
@@ -704,6 +774,92 @@ Correlator of the source sink combination in question.
    + +
    + +
    + + def + extract_t0_hd5( path, filestem, ens_id, obs='Clover energy density', fit_range=5, idl=None, **kwargs): + + + +
    + + 
    144def extract_t0_hd5(path, filestem, ens_id, obs='Clover energy density', fit_range=5, idl=None, **kwargs):
+145    r'''Read hadrons FlowObservables hdf5 file and extract t0
+146
+147    Parameters
+148    -----------------
+149    path : str
+150        path to the files to read
+151    filestem : str
+152        namestem of the files to read
+153    ens_id : str
+154        name of the ensemble, required for internal bookkeeping
+155    obs : str
+156        label of the observable from which t0 should be extracted.
+157        Options: 'Clover energy density' and 'Plaquette energy density'
+158    fit_range : int
+159        Number of data points left and right of the zero
+160        crossing to be included in the linear fit. (Default: 5)
+161    idl : range
+162        If specified only configurations in the given range are read in.
+163    plot_fit : bool
+164        If true, the fit for the extraction of t0 is shown together with the data.
+165    '''
+166
+167    files, idx = _get_files(path, filestem, idl)
+168    tree = "FlowObservables"
+169
+170    h5file = h5py.File(path + '/' + files[0], "r")
+171    obs_key = None
+172    for key in h5file[tree].keys():
+173        if obs == h5file[tree][key].attrs["description"][0].decode():
+174            obs_key = key
+175            break
+176    h5file.close()
+177    if obs_key is None:
+178        raise Exception(f"Observable {obs} not found.")
+179
+180    corr_data = _extract_real_arrays(path, files, tree, ["FlowObservables_0", obs_key])
+181
+182    if not np.allclose(corr_data["FlowObservables_0"][0], corr_data["FlowObservables_0"][:]):
+183        raise Exception("Not all flow times were equal.")
+184
+185    t2E_dict = {}
+186    for t2, dat in zip(corr_data["FlowObservables_0"][0], corr_data[obs_key].T):
+187        t2E_dict[t2] = Obs([dat], [ens_id], idl=[idx]) - 0.3
+188
+189    return fit_t0(t2E_dict, fit_range, plot_fit=kwargs.get('plot_fit'))
+
    + + +

    Read hadrons FlowObservables hdf5 file and extract t0

    + +
    Parameters
    + + +
    + +
    @@ -716,101 +872,101 @@ Correlator of the source sink combination in question. - 
    125def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None):
-126    """Read hadrons DistillationContraction hdf5 files in given directory structure
-127
-128    Parameters
-129    -----------------
-130    path : str
-131        path to the directories to read
-132    ens_id : str
-133        name of the ensemble, required for internal bookkeeping
-134    diagrams : list
-135        List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"].
-136    idl : range
-137        If specified only configurations in the given range are read in.
-138
-139    Returns
-140    -------
-141    result : dict
-142        extracted DistillationContration data
-143    """
-144
-145    res_dict = {}
-146
-147    directories, idx = _get_files(path, "data", idl)
-148
-149    explore_path = Path(path + "/" + directories[0])
-150
-151    for explore_file in explore_path.iterdir():
-152        if explore_file.is_file():
-153            stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1]
-154        else:
-155            continue
-156
-157        file_list = []
-158        for dir in directories:
-159            tmp_path = Path(path + "/" + dir)
-160            file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5")
-161
-162        corr_data = {}
-163
-164        for diagram in diagrams:
-165            corr_data[diagram] = []
-166
-167        try:
-168            for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))):
-169                h5file = h5py.File(hd5_file)
-170
-171                if n_file == 0:
-172                    if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...":
-173                        raise Exception("Routine is only implemented for files containing inversions on all timeslices.")
-174
-175                    Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0]
-176
-177                    identifier = []
-178                    for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1):
-179                        encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file))
-180                        full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_")
-181                        my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3])
-182                        identifier.append(my_tuple)
-183                    identifier = tuple(identifier)
-184                    # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case.
-185
-186                for diagram in diagrams:
-187
-188                    if diagram == "triangle" and "Identity" not in str(identifier):
-189                        part = "im"
-190                    else:
-191                        part = "re"
-192
-193                    real_data = np.zeros(Nt)
-194                    for x0 in range(Nt):
-195                        raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double)
-196                        real_data += np.roll(raw_data, -x0)
-197                    real_data /= Nt
-198
-199                    corr_data[diagram].append(real_data)
-200                h5file.close()
-201
-202            res_dict[str(identifier)] = {}
-203
-204            for diagram in diagrams:
+            
    192def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None):
+193    """Read hadrons DistillationContraction hdf5 files in given directory structure
+194
+195    Parameters
+196    -----------------
+197    path : str
+198        path to the directories to read
+199    ens_id : str
+200        name of the ensemble, required for internal bookkeeping
+201    diagrams : list
+202        List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"].
+203    idl : range
+204        If specified only configurations in the given range are read in.
 205
-206                tmp_data = np.array(corr_data[diagram])
-207
-208                l_obs = []
-209                for c in tmp_data.T:
-210                    l_obs.append(Obs([c], [ens_id], idl=[idx]))
+206    Returns
+207    -------
+208    result : dict
+209        extracted DistillationContration data
+210    """
 211
-212                corr = Corr(l_obs)
-213                corr.tag = str(identifier)
-214
-215                res_dict[str(identifier)][diagram] = corr
-216        except FileNotFoundError:
-217            print("Skip", stem)
-218
-219    return res_dict
+212    res_dict = {}
+213
+214    directories, idx = _get_files(path, "data", idl)
+215
+216    explore_path = Path(path + "/" + directories[0])
+217
+218    for explore_file in explore_path.iterdir():
+219        if explore_file.is_file():
+220            stem = explore_file.with_suffix("").with_suffix("").as_posix().split("/")[-1]
+221        else:
+222            continue
+223
+224        file_list = []
+225        for dir in directories:
+226            tmp_path = Path(path + "/" + dir)
+227            file_list.append((tmp_path / stem).as_posix() + tmp_path.suffix + ".h5")
+228
+229        corr_data = {}
+230
+231        for diagram in diagrams:
+232            corr_data[diagram] = []
+233
+234        try:
+235            for n_file, (hd5_file, n_traj) in enumerate(zip(file_list, list(idx))):
+236                h5file = h5py.File(hd5_file)
+237
+238                if n_file == 0:
+239                    if h5file["DistillationContraction/Metadata"].attrs.get("TimeSources")[0].decode() != "0...":
+240                        raise Exception("Routine is only implemented for files containing inversions on all timeslices.")
+241
+242                    Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0]
+243
+244                    identifier = []
+245                    for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1):
+246                        encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file))
+247                        full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_")
+248                        my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3])
+249                        identifier.append(my_tuple)
+250                    identifier = tuple(identifier)
+251                    # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case.
+252
+253                for diagram in diagrams:
+254
+255                    if diagram == "triangle" and "Identity" not in str(identifier):
+256                        part = "im"
+257                    else:
+258                        part = "re"
+259
+260                    real_data = np.zeros(Nt)
+261                    for x0 in range(Nt):
+262                        raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:][part].astype(np.double)
+263                        real_data += np.roll(raw_data, -x0)
+264                    real_data /= Nt
+265
+266                    corr_data[diagram].append(real_data)
+267                h5file.close()
+268
+269            res_dict[str(identifier)] = {}
+270
+271            for diagram in diagrams:
+272
+273                tmp_data = np.array(corr_data[diagram])
+274
+275                l_obs = []
+276                for c in tmp_data.T:
+277                    l_obs.append(Obs([c], [ens_id], idl=[idx]))
+278
+279                corr = Corr(l_obs)
+280                corr.tag = str(identifier)
+281
+282                res_dict[str(identifier)][diagram] = corr
+283        except FileNotFoundError:
+284            print("Skip", stem)
+285
+286    return res_dict
 
    
 
 
@@ -850,44 +1006,44 @@ extracted DistillationContration data
    - 
    222class Npr_matrix(np.ndarray):
-223
-224    def __new__(cls, input_array, mom_in=None, mom_out=None):
-225        obj = np.asarray(input_array).view(cls)
-226        obj.mom_in = mom_in
-227        obj.mom_out = mom_out
-228        return obj
-229
-230    @property
-231    def g5H(self):
-232        """Gamma_5 hermitean conjugate
-233
-234        Uses the fact that the propagator is gamma5 hermitean, so just the
-235        in and out momenta of the propagator are exchanged.
-236        """
-237        return Npr_matrix(self,
-238                          mom_in=self.mom_out,
-239                          mom_out=self.mom_in)
-240
-241    def _propagate_mom(self, other, name):
-242        s_mom = getattr(self, name, None)
-243        o_mom = getattr(other, name, None)
-244        if s_mom is not None and o_mom is not None:
-245            if not np.allclose(s_mom, o_mom):
-246                raise Exception(name + ' does not match.')
-247        return o_mom if o_mom is not None else s_mom
-248
-249    def __matmul__(self, other):
-250        return self.__new__(Npr_matrix,
-251                            super().__matmul__(other),
-252                            self._propagate_mom(other, 'mom_in'),
-253                            self._propagate_mom(other, 'mom_out'))
-254
-255    def __array_finalize__(self, obj):
-256        if obj is None:
-257            return
-258        self.mom_in = getattr(obj, 'mom_in', None)
-259        self.mom_out = getattr(obj, 'mom_out', None)
+            
    289class Npr_matrix(np.ndarray):
+290
+291    def __new__(cls, input_array, mom_in=None, mom_out=None):
+292        obj = np.asarray(input_array).view(cls)
+293        obj.mom_in = mom_in
+294        obj.mom_out = mom_out
+295        return obj
+296
+297    @property
+298    def g5H(self):
+299        """Gamma_5 hermitean conjugate
+300
+301        Uses the fact that the propagator is gamma5 hermitean, so just the
+302        in and out momenta of the propagator are exchanged.
+303        """
+304        return Npr_matrix(self,
+305                          mom_in=self.mom_out,
+306                          mom_out=self.mom_in)
+307
+308    def _propagate_mom(self, other, name):
+309        s_mom = getattr(self, name, None)
+310        o_mom = getattr(other, name, None)
+311        if s_mom is not None and o_mom is not None:
+312            if not np.allclose(s_mom, o_mom):
+313                raise Exception(name + ' does not match.')
+314        return o_mom if o_mom is not None else s_mom
+315
+316    def __matmul__(self, other):
+317        return self.__new__(Npr_matrix,
+318                            super().__matmul__(other),
+319                            self._propagate_mom(other, 'mom_in'),
+320                            self._propagate_mom(other, 'mom_out'))
+321
+322    def __array_finalize__(self, obj):
+323        if obj is None:
+324            return
+325        self.mom_in = getattr(obj, 'mom_in', None)
+326        self.mom_out = getattr(obj, 'mom_out', None)
 
    
 
 
@@ -1130,49 +1286,49 @@ in and out momenta of the propagator are exchanged.
    - 
    262def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
-263    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
-264
-265    Parameters
-266    ----------
-267    path : str
-268        path to the files to read
-269    filestem : str
-270        namestem of the files to read
-271    ens_id : str
-272        name of the ensemble, required for internal bookkeeping
-273    idl : range
-274        If specified only configurations in the given range are read in.
-275
-276    Returns
-277    -------
-278    result : Npr_matrix
-279        read Cobs-matrix
-280    """
-281
-282    files, idx = _get_files(path, filestem, idl)
-283
-284    mom = None
-285
-286    corr_data = []
-287    for hd5_file in files:
-288        file = h5py.File(path + '/' + hd5_file, "r")
-289        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
-290        corr_data.append(raw_data)
-291        if mom is None:
-292            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-293        file.close()
-294    corr_data = np.array(corr_data)
-295
-296    rolled_array = np.rollaxis(corr_data, 0, 5)
-297
-298    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-299    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
-300        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
-301        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
-302        matrix[si, sj, ci, cj] = CObs(real, imag)
-303
-304    return Npr_matrix(matrix, mom_in=mom)
+            
    329def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
+330    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
+331
+332    Parameters
+333    ----------
+334    path : str
+335        path to the files to read
+336    filestem : str
+337        namestem of the files to read
+338    ens_id : str
+339        name of the ensemble, required for internal bookkeeping
+340    idl : range
+341        If specified only configurations in the given range are read in.
+342
+343    Returns
+344    -------
+345    result : Npr_matrix
+346        read Cobs-matrix
+347    """
+348
+349    files, idx = _get_files(path, filestem, idl)
+350
+351    mom = None
+352
+353    corr_data = []
+354    for hd5_file in files:
+355        file = h5py.File(path + '/' + hd5_file, "r")
+356        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
+357        corr_data.append(raw_data)
+358        if mom is None:
+359            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+360        file.close()
+361    corr_data = np.array(corr_data)
+362
+363    rolled_array = np.rollaxis(corr_data, 0, 5)
+364
+365    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+366    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
+367        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
+368        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
+369        matrix[si, sj, ci, cj] = CObs(real, imag)
+370
+371    return Npr_matrix(matrix, mom_in=mom)
 
    
 
 
@@ -1212,63 +1368,63 @@ read Cobs-matrix
    - 
    307def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
-308    """Read hadrons Bilinear hdf5 file and output an array of CObs
-309
-310    Parameters
-311    ----------
-312    path : str
-313        path to the files to read
-314    filestem : str
-315        namestem of the files to read
-316    ens_id : str
-317        name of the ensemble, required for internal bookkeeping
-318    idl : range
-319        If specified only configurations in the given range are read in.
-320
-321    Returns
-322    -------
-323    result_dict: dict[Npr_matrix]
-324        extracted Bilinears
-325    """
-326
-327    files, idx = _get_files(path, filestem, idl)
-328
-329    mom_in = None
-330    mom_out = None
-331
-332    corr_data = {}
-333    for hd5_file in files:
-334        file = h5py.File(path + '/' + hd5_file, "r")
-335        for i in range(16):
-336            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
-337            if name not in corr_data:
-338                corr_data[name] = []
-339            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
-340            corr_data[name].append(raw_data)
-341            if mom_in is None:
-342                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-343            if mom_out is None:
-344                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
-345
-346        file.close()
-347
-348    result_dict = {}
-349
-350    for key, data in corr_data.items():
-351        local_data = np.array(data)
-352
-353        rolled_array = np.rollaxis(local_data, 0, 5)
-354
-355        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-356        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
-357            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
-358            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
-359            matrix[si, sj, ci, cj] = CObs(real, imag)
-360
-361        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
-362
-363    return result_dict
+            
    374def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
+375    """Read hadrons Bilinear hdf5 file and output an array of CObs
+376
+377    Parameters
+378    ----------
+379    path : str
+380        path to the files to read
+381    filestem : str
+382        namestem of the files to read
+383    ens_id : str
+384        name of the ensemble, required for internal bookkeeping
+385    idl : range
+386        If specified only configurations in the given range are read in.
+387
+388    Returns
+389    -------
+390    result_dict: dict[Npr_matrix]
+391        extracted Bilinears
+392    """
+393
+394    files, idx = _get_files(path, filestem, idl)
+395
+396    mom_in = None
+397    mom_out = None
+398
+399    corr_data = {}
+400    for hd5_file in files:
+401        file = h5py.File(path + '/' + hd5_file, "r")
+402        for i in range(16):
+403            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
+404            if name not in corr_data:
+405                corr_data[name] = []
+406            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
+407            corr_data[name].append(raw_data)
+408            if mom_in is None:
+409                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+410            if mom_out is None:
+411                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
+412
+413        file.close()
+414
+415    result_dict = {}
+416
+417    for key, data in corr_data.items():
+418        local_data = np.array(data)
+419
+420        rolled_array = np.rollaxis(local_data, 0, 5)
+421
+422        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+423        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
+424            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
+425            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
+426            matrix[si, sj, ci, cj] = CObs(real, imag)
+427
+428        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
+429
+430    return result_dict
 
    
 
 
@@ -1308,90 +1464,90 @@ extracted Bilinears
    - 
    366def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
-367    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
-368
-369    Parameters
-370    ----------
-371    path : str
-372        path to the files to read
-373    filestem : str
-374        namestem of the files to read
-375    ens_id : str
-376        name of the ensemble, required for internal bookkeeping
-377    idl : range
-378        If specified only configurations in the given range are read in.
-379    vertices : list
-380        Vertex functions to be extracted.
-381
-382    Returns
-383    -------
-384    result_dict : dict
-385        extracted fourquark matrizes
-386    """
-387
-388    files, idx = _get_files(path, filestem, idl)
-389
-390    mom_in = None
-391    mom_out = None
-392
-393    vertex_names = []
-394    for vertex in vertices:
-395        vertex_names += _get_lorentz_names(vertex)
-396
-397    corr_data = {}
-398
-399    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
-400
-401    for hd5_file in files:
-402        file = h5py.File(path + '/' + hd5_file, "r")
-403
-404        for i in range(32):
-405            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
-406            if name in vertex_names:
-407                if name not in corr_data:
-408                    corr_data[name] = []
-409                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
-410                corr_data[name].append(raw_data)
-411                if mom_in is None:
-412                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-413                if mom_out is None:
-414                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
-415
-416        file.close()
-417
-418    intermediate_dict = {}
-419
-420    for vertex in vertices:
-421        lorentz_names = _get_lorentz_names(vertex)
-422        for v_name in lorentz_names:
-423            if v_name in [('SigmaXY', 'SigmaZT'),
-424                          ('SigmaXT', 'SigmaYZ'),
-425                          ('SigmaYZ', 'SigmaXT'),
-426                          ('SigmaZT', 'SigmaXY')]:
-427                sign = -1
-428            else:
-429                sign = 1
-430            if vertex not in intermediate_dict:
-431                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
-432            else:
-433                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
-434
-435    result_dict = {}
-436
-437    for key, data in intermediate_dict.items():
-438
-439        rolled_array = np.moveaxis(data, 0, 8)
-440
-441        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-442        for index in np.ndindex(rolled_array.shape[:-1]):
-443            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
-444            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
-445            matrix[index] = CObs(real, imag)
-446
-447        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
+            
    433def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
+434    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
+435
+436    Parameters
+437    ----------
+438    path : str
+439        path to the files to read
+440    filestem : str
+441        namestem of the files to read
+442    ens_id : str
+443        name of the ensemble, required for internal bookkeeping
+444    idl : range
+445        If specified only configurations in the given range are read in.
+446    vertices : list
+447        Vertex functions to be extracted.
 448
-449    return result_dict
+449    Returns
+450    -------
+451    result_dict : dict
+452        extracted fourquark matrizes
+453    """
+454
+455    files, idx = _get_files(path, filestem, idl)
+456
+457    mom_in = None
+458    mom_out = None
+459
+460    vertex_names = []
+461    for vertex in vertices:
+462        vertex_names += _get_lorentz_names(vertex)
+463
+464    corr_data = {}
+465
+466    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
+467
+468    for hd5_file in files:
+469        file = h5py.File(path + '/' + hd5_file, "r")
+470
+471        for i in range(32):
+472            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
+473            if name in vertex_names:
+474                if name not in corr_data:
+475                    corr_data[name] = []
+476                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
+477                corr_data[name].append(raw_data)
+478                if mom_in is None:
+479                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+480                if mom_out is None:
+481                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
+482
+483        file.close()
+484
+485    intermediate_dict = {}
+486
+487    for vertex in vertices:
+488        lorentz_names = _get_lorentz_names(vertex)
+489        for v_name in lorentz_names:
+490            if v_name in [('SigmaXY', 'SigmaZT'),
+491                          ('SigmaXT', 'SigmaYZ'),
+492                          ('SigmaYZ', 'SigmaXT'),
+493                          ('SigmaZT', 'SigmaXY')]:
+494                sign = -1
+495            else:
+496                sign = 1
+497            if vertex not in intermediate_dict:
+498                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
+499            else:
+500                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
+501
+502    result_dict = {}
+503
+504    for key, data in intermediate_dict.items():
+505
+506        rolled_array = np.moveaxis(data, 0, 8)
+507
+508        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+509        for index in np.ndindex(rolled_array.shape[:-1]):
+510            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
+511            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
+512            matrix[index] = CObs(real, imag)
+513
+514        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
+515
+516    return result_dict
 
    
 
 
diff --git a/docs/pyerrors/input/json.html b/docs/pyerrors/input/json.html
index d2ce8fec..93a60603 100644
--- a/docs/pyerrors/input/json.html
+++ b/docs/pyerrors/input/json.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.input.json API documentation
 
     
diff --git a/docs/pyerrors/input/misc.html b/docs/pyerrors/input/misc.html
index 529b4d49..1e1873f9 100644
--- a/docs/pyerrors/input/misc.html
+++ b/docs/pyerrors/input/misc.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.input.misc API documentation
 
     
@@ -52,6 +52,9 @@
 
             
    API Documentation
    
                 
      
+            
    • 
+                    fit_t0
+            
      • 
             
    • 
                     read_pbp
             
      • 
@@ -81,132 +84,246 @@
   3import re
   4import struct
   5import numpy as np  # Thinly-wrapped numpy
-  6from ..obs import Obs
-  7
-  8
-  9def read_pbp(path, prefix, **kwargs):
- 10    """Read pbp format from given folder structure.
+  6import matplotlib.pyplot as plt
+  7from matplotlib import gridspec
+  8from ..obs import Obs
+  9from ..fits import fit_lin
+ 10
  11
- 12    Parameters
- 13    ----------
- 14    r_start : list
- 15        list which contains the first config to be read for each replicum
- 16    r_stop : list
- 17        list which contains the last config to be read for each replicum
- 18
- 19    Returns
- 20    -------
- 21    result : list[Obs]
- 22        list of observables read
- 23    """
- 24
- 25    ls = []
- 26    for (dirpath, dirnames, filenames) in os.walk(path):
- 27        ls.extend(filenames)
- 28        break
- 29
- 30    if not ls:
- 31        raise Exception('Error, directory not found')
- 32
- 33    # Exclude files with different names
- 34    for exc in ls:
- 35        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
- 36            ls = list(set(ls) - set([exc]))
- 37    if len(ls) > 1:
- 38        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
- 39    replica = len(ls)
- 40
- 41    if 'r_start' in kwargs:
- 42        r_start = kwargs.get('r_start')
- 43        if len(r_start) != replica:
- 44            raise Exception('r_start does not match number of replicas')
- 45        # Adjust Configuration numbering to python index
- 46        r_start = [o - 1 if o else None for o in r_start]
- 47    else:
- 48        r_start = [None] * replica
- 49
- 50    if 'r_stop' in kwargs:
- 51        r_stop = kwargs.get('r_stop')
- 52        if len(r_stop) != replica:
- 53            raise Exception('r_stop does not match number of replicas')
- 54    else:
- 55        r_stop = [None] * replica
- 56
- 57    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
+ 12def fit_t0(t2E_dict, fit_range, plot_fit=False):
+ 13    zero_crossing = np.argmax(np.array(
+ 14        [o.value for o in t2E_dict.values()]) > 0.0)
+ 15
+ 16    x = list(t2E_dict.keys())[zero_crossing - fit_range:
+ 17                              zero_crossing + fit_range]
+ 18    y = list(t2E_dict.values())[zero_crossing - fit_range:
+ 19                                zero_crossing + fit_range]
+ 20    [o.gamma_method() for o in y]
+ 21
+ 22    fit_result = fit_lin(x, y)
+ 23
+ 24    if plot_fit is True:
+ 25        plt.figure()
+ 26        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+ 27        ax0 = plt.subplot(gs[0])
+ 28        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+ 29        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+ 30        [o.gamma_method() for o in ymore]
+ 31        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
+ 32        xplot = np.linspace(np.min(x), np.max(x))
+ 33        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
+ 34        [yi.gamma_method() for yi in yplot]
+ 35        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
+ 36        retval = (-fit_result[0] / fit_result[1])
+ 37        retval.gamma_method()
+ 38        ylim = ax0.get_ylim()
+ 39        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
+ 40        ax0.set_ylim(ylim)
+ 41        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
+ 42        xlim = ax0.get_xlim()
+ 43
+ 44        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
+ 45        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
+ 46        ax1 = plt.subplot(gs[1])
+ 47        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+ 48        ax1.tick_params(direction='out')
+ 49        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+ 50        ax1.axhline(y=0.0, ls='--', color='k')
+ 51        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
+ 52        ax1.set_xlim(xlim)
+ 53        ax1.set_ylabel('Residuals')
+ 54        ax1.set_xlabel(r'$t/a^2$')
+ 55
+ 56        plt.draw()
+ 57    return -fit_result[0] / fit_result[1]
  58
- 59    print_err = 0
- 60    if 'print_err' in kwargs:
- 61        print_err = 1
- 62        print()
- 63
- 64    deltas = []
- 65
- 66    for rep in range(replica):
- 67        tmp_array = []
- 68        with open(path + '/' + ls[rep], 'rb') as fp:
+ 59
+ 60def read_pbp(path, prefix, **kwargs):
+ 61    """Read pbp format from given folder structure.
+ 62
+ 63    Parameters
+ 64    ----------
+ 65    r_start : list
+ 66        list which contains the first config to be read for each replicum
+ 67    r_stop : list
+ 68        list which contains the last config to be read for each replicum
  69
- 70            t = fp.read(4)  # number of reweighting factors
- 71            if rep == 0:
- 72                nrw = struct.unpack('i', t)[0]
- 73                for k in range(nrw):
- 74                    deltas.append([])
- 75            else:
- 76                if nrw != struct.unpack('i', t)[0]:
- 77                    raise Exception('Error: different number of factors for replicum', rep)
- 78
- 79            for k in range(nrw):
- 80                tmp_array.append([])
- 81
- 82            # This block is necessary for openQCD1.6 ms1 files
- 83            nfct = []
- 84            for i in range(nrw):
- 85                t = fp.read(4)
- 86                nfct.append(struct.unpack('i', t)[0])
- 87            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
- 88
- 89            nsrc = []
- 90            for i in range(nrw):
- 91                t = fp.read(4)
- 92                nsrc.append(struct.unpack('i', t)[0])
- 93
- 94            # body
- 95            while True:
- 96                t = fp.read(4)
- 97                if len(t) < 4:
- 98                    break
- 99                if print_err:
-100                    config_no = struct.unpack('i', t)
-101                for i in range(nrw):
-102                    tmp_nfct = 1.0
-103                    for j in range(nfct[i]):
-104                        t = fp.read(8 * nsrc[i])
-105                        t = fp.read(8 * nsrc[i])
-106                        tmp_rw = struct.unpack('d' * nsrc[i], t)
-107                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
-108                        if print_err:
-109                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
-110                            print('Sources:', np.asarray(tmp_rw))
-111                            print('Partial factor:', tmp_nfct)
-112                    tmp_array[i].append(tmp_nfct)
-113
-114            for k in range(nrw):
-115                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
+ 70    Returns
+ 71    -------
+ 72    result : list[Obs]
+ 73        list of observables read
+ 74    """
+ 75
+ 76    ls = []
+ 77    for (dirpath, dirnames, filenames) in os.walk(path):
+ 78        ls.extend(filenames)
+ 79        break
+ 80
+ 81    if not ls:
+ 82        raise Exception('Error, directory not found')
+ 83
+ 84    # Exclude files with different names
+ 85    for exc in ls:
+ 86        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
+ 87            ls = list(set(ls) - set([exc]))
+ 88    if len(ls) > 1:
+ 89        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
+ 90    replica = len(ls)
+ 91
+ 92    if 'r_start' in kwargs:
+ 93        r_start = kwargs.get('r_start')
+ 94        if len(r_start) != replica:
+ 95            raise Exception('r_start does not match number of replicas')
+ 96        # Adjust Configuration numbering to python index
+ 97        r_start = [o - 1 if o else None for o in r_start]
+ 98    else:
+ 99        r_start = [None] * replica
+100
+101    if 'r_stop' in kwargs:
+102        r_stop = kwargs.get('r_stop')
+103        if len(r_stop) != replica:
+104            raise Exception('r_stop does not match number of replicas')
+105    else:
+106        r_stop = [None] * replica
+107
+108    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
+109
+110    print_err = 0
+111    if 'print_err' in kwargs:
+112        print_err = 1
+113        print()
+114
+115    deltas = []
 116
-117    rep_names = []
-118    for entry in ls:
-119        truncated_entry = entry.split('.')[0]
-120        idx = truncated_entry.index('r')
-121        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
-122    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
-123    result = []
-124    for t in range(nrw):
-125        result.append(Obs(deltas[t], rep_names))
-126
-127    return result
+117    for rep in range(replica):
+118        tmp_array = []
+119        with open(path + '/' + ls[rep], 'rb') as fp:
+120
+121            t = fp.read(4)  # number of reweighting factors
+122            if rep == 0:
+123                nrw = struct.unpack('i', t)[0]
+124                for k in range(nrw):
+125                    deltas.append([])
+126            else:
+127                if nrw != struct.unpack('i', t)[0]:
+128                    raise Exception('Error: different number of factors for replicum', rep)
+129
+130            for k in range(nrw):
+131                tmp_array.append([])
+132
+133            # This block is necessary for openQCD1.6 ms1 files
+134            nfct = []
+135            for i in range(nrw):
+136                t = fp.read(4)
+137                nfct.append(struct.unpack('i', t)[0])
+138            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
+139
+140            nsrc = []
+141            for i in range(nrw):
+142                t = fp.read(4)
+143                nsrc.append(struct.unpack('i', t)[0])
+144
+145            # body
+146            while True:
+147                t = fp.read(4)
+148                if len(t) < 4:
+149                    break
+150                if print_err:
+151                    config_no = struct.unpack('i', t)
+152                for i in range(nrw):
+153                    tmp_nfct = 1.0
+154                    for j in range(nfct[i]):
+155                        t = fp.read(8 * nsrc[i])
+156                        t = fp.read(8 * nsrc[i])
+157                        tmp_rw = struct.unpack('d' * nsrc[i], t)
+158                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
+159                        if print_err:
+160                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
+161                            print('Sources:', np.asarray(tmp_rw))
+162                            print('Partial factor:', tmp_nfct)
+163                    tmp_array[i].append(tmp_nfct)
+164
+165            for k in range(nrw):
+166                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
+167
+168    rep_names = []
+169    for entry in ls:
+170        truncated_entry = entry.split('.')[0]
+171        idx = truncated_entry.index('r')
+172        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+173    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
+174    result = []
+175    for t in range(nrw):
+176        result.append(Obs(deltas[t], rep_names))
+177
+178    return result
    +
    + +
    + + def + fit_t0(t2E_dict, fit_range, plot_fit=False): + + + +
    + + 
    13def fit_t0(t2E_dict, fit_range, plot_fit=False):
+14    zero_crossing = np.argmax(np.array(
+15        [o.value for o in t2E_dict.values()]) > 0.0)
+16
+17    x = list(t2E_dict.keys())[zero_crossing - fit_range:
+18                              zero_crossing + fit_range]
+19    y = list(t2E_dict.values())[zero_crossing - fit_range:
+20                                zero_crossing + fit_range]
+21    [o.gamma_method() for o in y]
+22
+23    fit_result = fit_lin(x, y)
+24
+25    if plot_fit is True:
+26        plt.figure()
+27        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
+28        ax0 = plt.subplot(gs[0])
+29        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+30        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
+31        [o.gamma_method() for o in ymore]
+32        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
+33        xplot = np.linspace(np.min(x), np.max(x))
+34        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
+35        [yi.gamma_method() for yi in yplot]
+36        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
+37        retval = (-fit_result[0] / fit_result[1])
+38        retval.gamma_method()
+39        ylim = ax0.get_ylim()
+40        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
+41        ax0.set_ylim(ylim)
+42        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
+43        xlim = ax0.get_xlim()
+44
+45        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
+46        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
+47        ax1 = plt.subplot(gs[1])
+48        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
+49        ax1.tick_params(direction='out')
+50        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
+51        ax1.axhline(y=0.0, ls='--', color='k')
+52        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
+53        ax1.set_xlim(xlim)
+54        ax1.set_ylabel('Residuals')
+55        ax1.set_xlabel(r'$t/a^2$')
+56
+57        plt.draw()
+58    return -fit_result[0] / fit_result[1]
+
    + + + + +
    @@ -218,125 +335,125 @@
    - 
     10def read_pbp(path, prefix, **kwargs):
- 11    """Read pbp format from given folder structure.
- 12
- 13    Parameters
- 14    ----------
- 15    r_start : list
- 16        list which contains the first config to be read for each replicum
- 17    r_stop : list
- 18        list which contains the last config to be read for each replicum
- 19
- 20    Returns
- 21    -------
- 22    result : list[Obs]
- 23        list of observables read
- 24    """
- 25
- 26    ls = []
- 27    for (dirpath, dirnames, filenames) in os.walk(path):
- 28        ls.extend(filenames)
- 29        break
- 30
- 31    if not ls:
- 32        raise Exception('Error, directory not found')
- 33
- 34    # Exclude files with different names
- 35    for exc in ls:
- 36        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
- 37            ls = list(set(ls) - set([exc]))
- 38    if len(ls) > 1:
- 39        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
- 40    replica = len(ls)
- 41
- 42    if 'r_start' in kwargs:
- 43        r_start = kwargs.get('r_start')
- 44        if len(r_start) != replica:
- 45            raise Exception('r_start does not match number of replicas')
- 46        # Adjust Configuration numbering to python index
- 47        r_start = [o - 1 if o else None for o in r_start]
- 48    else:
- 49        r_start = [None] * replica
- 50
- 51    if 'r_stop' in kwargs:
- 52        r_stop = kwargs.get('r_stop')
- 53        if len(r_stop) != replica:
- 54            raise Exception('r_stop does not match number of replicas')
- 55    else:
- 56        r_stop = [None] * replica
- 57
- 58    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
- 59
- 60    print_err = 0
- 61    if 'print_err' in kwargs:
- 62        print_err = 1
- 63        print()
- 64
- 65    deltas = []
- 66
- 67    for rep in range(replica):
- 68        tmp_array = []
- 69        with open(path + '/' + ls[rep], 'rb') as fp:
+            
     61def read_pbp(path, prefix, **kwargs):
+ 62    """Read pbp format from given folder structure.
+ 63
+ 64    Parameters
+ 65    ----------
+ 66    r_start : list
+ 67        list which contains the first config to be read for each replicum
+ 68    r_stop : list
+ 69        list which contains the last config to be read for each replicum
  70
- 71            t = fp.read(4)  # number of reweighting factors
- 72            if rep == 0:
- 73                nrw = struct.unpack('i', t)[0]
- 74                for k in range(nrw):
- 75                    deltas.append([])
- 76            else:
- 77                if nrw != struct.unpack('i', t)[0]:
- 78                    raise Exception('Error: different number of factors for replicum', rep)
- 79
- 80            for k in range(nrw):
- 81                tmp_array.append([])
- 82
- 83            # This block is necessary for openQCD1.6 ms1 files
- 84            nfct = []
- 85            for i in range(nrw):
- 86                t = fp.read(4)
- 87                nfct.append(struct.unpack('i', t)[0])
- 88            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
- 89
- 90            nsrc = []
- 91            for i in range(nrw):
- 92                t = fp.read(4)
- 93                nsrc.append(struct.unpack('i', t)[0])
- 94
- 95            # body
- 96            while True:
- 97                t = fp.read(4)
- 98                if len(t) < 4:
- 99                    break
-100                if print_err:
-101                    config_no = struct.unpack('i', t)
-102                for i in range(nrw):
-103                    tmp_nfct = 1.0
-104                    for j in range(nfct[i]):
-105                        t = fp.read(8 * nsrc[i])
-106                        t = fp.read(8 * nsrc[i])
-107                        tmp_rw = struct.unpack('d' * nsrc[i], t)
-108                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
-109                        if print_err:
-110                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
-111                            print('Sources:', np.asarray(tmp_rw))
-112                            print('Partial factor:', tmp_nfct)
-113                    tmp_array[i].append(tmp_nfct)
-114
-115            for k in range(nrw):
-116                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
+ 71    Returns
+ 72    -------
+ 73    result : list[Obs]
+ 74        list of observables read
+ 75    """
+ 76
+ 77    ls = []
+ 78    for (dirpath, dirnames, filenames) in os.walk(path):
+ 79        ls.extend(filenames)
+ 80        break
+ 81
+ 82    if not ls:
+ 83        raise Exception('Error, directory not found')
+ 84
+ 85    # Exclude files with different names
+ 86    for exc in ls:
+ 87        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
+ 88            ls = list(set(ls) - set([exc]))
+ 89    if len(ls) > 1:
+ 90        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
+ 91    replica = len(ls)
+ 92
+ 93    if 'r_start' in kwargs:
+ 94        r_start = kwargs.get('r_start')
+ 95        if len(r_start) != replica:
+ 96            raise Exception('r_start does not match number of replicas')
+ 97        # Adjust Configuration numbering to python index
+ 98        r_start = [o - 1 if o else None for o in r_start]
+ 99    else:
+100        r_start = [None] * replica
+101
+102    if 'r_stop' in kwargs:
+103        r_stop = kwargs.get('r_stop')
+104        if len(r_stop) != replica:
+105            raise Exception('r_stop does not match number of replicas')
+106    else:
+107        r_stop = [None] * replica
+108
+109    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
+110
+111    print_err = 0
+112    if 'print_err' in kwargs:
+113        print_err = 1
+114        print()
+115
+116    deltas = []
 117
-118    rep_names = []
-119    for entry in ls:
-120        truncated_entry = entry.split('.')[0]
-121        idx = truncated_entry.index('r')
-122        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
-123    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
-124    result = []
-125    for t in range(nrw):
-126        result.append(Obs(deltas[t], rep_names))
-127
-128    return result
+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                for k in range(nrw):
+126                    deltas.append([])
+127            else:
+128                if nrw != struct.unpack('i', t)[0]:
+129                    raise Exception('Error: different number of factors for replicum', rep)
+130
+131            for k in range(nrw):
+132                tmp_array.append([])
+133
+134            # This block is necessary for openQCD1.6 ms1 files
+135            nfct = []
+136            for i in range(nrw):
+137                t = fp.read(4)
+138                nfct.append(struct.unpack('i', t)[0])
+139            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
+140
+141            nsrc = []
+142            for i in range(nrw):
+143                t = fp.read(4)
+144                nsrc.append(struct.unpack('i', t)[0])
+145
+146            # body
+147            while True:
+148                t = fp.read(4)
+149                if len(t) < 4:
+150                    break
+151                if print_err:
+152                    config_no = struct.unpack('i', t)
+153                for i in range(nrw):
+154                    tmp_nfct = 1.0
+155                    for j in range(nfct[i]):
+156                        t = fp.read(8 * nsrc[i])
+157                        t = fp.read(8 * nsrc[i])
+158                        tmp_rw = struct.unpack('d' * nsrc[i], t)
+159                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
+160                        if print_err:
+161                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
+162                            print('Sources:', np.asarray(tmp_rw))
+163                            print('Partial factor:', tmp_nfct)
+164                    tmp_array[i].append(tmp_nfct)
+165
+166            for k in range(nrw):
+167                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
+168
+169    rep_names = []
+170    for entry in ls:
+171        truncated_entry = entry.split('.')[0]
+172        idx = truncated_entry.index('r')
+173        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+174    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
+175    result = []
+176    for t in range(nrw):
+177        result.append(Obs(deltas[t], rep_names))
+178
+179    return result
 
    
 
 
diff --git a/docs/pyerrors/input/openQCD.html b/docs/pyerrors/input/openQCD.html
index a2977331..f5b355d2 100644
--- a/docs/pyerrors/input/openQCD.html
+++ b/docs/pyerrors/input/openQCD.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.input.openQCD API documentation
 
     
@@ -99,1197 +99,1151 @@
        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 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 = []
+   6from ..obs import Obs
+   7from ..obs import CObs
+   8from ..correlators import Corr
+   9from .misc import fit_t0
+  10from .utils import sort_names
+  11
+  12
+  13def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
+  14    """Read rwms format from given folder structure. Returns a list of length nrw
+  15
+  16    Parameters
+  17    ----------
+  18    path : str
+  19        path that contains the data files
+  20    prefix : str
+  21        all files in path that start with prefix are considered as input files.
+  22        May be used together postfix to consider only special file endings.
+  23        Prefix is ignored, if the keyword 'files' is used.
+  24    version : str
+  25        version of openQCD, default 2.0
+  26    names : list
+  27        list of names that is assigned to the data according according
+  28        to the order in the file list. Use careful, if you do not provide file names!
+  29    r_start : list
+  30        list which contains the first config to be read for each replicum
+  31    r_stop : list
+  32        list which contains the last config to be read for each replicum
+  33    r_step : int
+  34        integer that defines a fixed step size between two measurements (in units of configs)
+  35        If not given, r_step=1 is assumed.
+  36    postfix : str
+  37        postfix of the file to read, e.g. '.ms1' for openQCD-files
+  38    files : list
+  39        list which contains the filenames to be read. No automatic detection of
+  40        files performed if given.
+  41    print_err : bool
+  42        Print additional information that is useful for debugging.
+  43
+  44    Returns
+  45    -------
+  46    rwms : Obs
+  47        Reweighting factors read
+  48    """
+  49    known_oqcd_versions = ['1.4', '1.6', '2.0']
+  50    if not (version in known_oqcd_versions):
+  51        raise Exception('Unknown openQCD version defined!')
+  52    print("Working with openQCD version " + version)
+  53    if 'postfix' in kwargs:
+  54        postfix = kwargs.get('postfix')
+  55    else:
+  56        postfix = ''
+  57
+  58    if 'files' in kwargs:
+  59        known_files = kwargs.get('files')
+  60    else:
+  61        known_files = []
+  62
+  63    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
   64
-  65    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
+  65    replica = len(ls)
   66
-  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
-  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
+  67    if 'r_start' in kwargs:
+  68        r_start = kwargs.get('r_start')
+  69        if len(r_start) != replica:
+  70            raise Exception('r_start does not match number of replicas')
+  71        r_start = [o if o else None for o in r_start]
+  72    else:
+  73        r_start = [None] * replica
+  74
+  75    if 'r_stop' in kwargs:
+  76        r_stop = kwargs.get('r_stop')
+  77        if len(r_stop) != replica:
+  78            raise Exception('r_stop does not match number of replicas')
+  79    else:
+  80        r_stop = [None] * replica
+  81
+  82    if 'r_step' in kwargs:
+  83        r_step = kwargs.get('r_step')
+  84    else:
+  85        r_step = 1
+  86
+  87    print('Read reweighting factors from', prefix[:-1], ',',
+  88          replica, 'replica', end='')
+  89
+  90    if names is None:
+  91        rep_names = []
+  92        for entry in ls:
+  93            truncated_entry = entry
+  94            suffixes = [".dat", ".rwms", ".ms1"]
+  95            for suffix in suffixes:
+  96                if truncated_entry.endswith(suffix):
+  97                    truncated_entry = truncated_entry[0:-len(suffix)]
+  98            idx = truncated_entry.index('r')
+  99            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+ 100    else:
+ 101        rep_names = names
+ 102
+ 103    rep_names = sort_names(rep_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()
+ 105    print_err = 0
+ 106    if 'print_err' in kwargs:
+ 107        print_err = 1
+ 108        print()
+ 109
+ 110    deltas = []
  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            # 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            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            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, postfix='ms', **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    postfix : str
- 267        Postfix of measurement file (Default: ms)
- 268    r_start : list
- 269        list which contains the first config to be read for each replicum.
- 270    r_stop : list
- 271        list which contains the last config to be read for each replicum.
- 272    r_step : int
- 273        integer that defines a fixed step size between two measurements (in units of configs)
- 274        If not given, r_step=1 is assumed.
- 275    plaquette : bool
- 276        If true extract the plaquette estimate of t0 instead.
- 277    names : list
- 278        list of names that is assigned to the data according according
- 279        to the order in the file list. Use careful, if you do not provide file names!
- 280    files : list
- 281        list which contains the filenames to be read. No automatic detection of
- 282        files performed if given.
- 283    plot_fit : bool
- 284        If true, the fit for the extraction of t0 is shown together with the data.
- 285    assume_thermalization : bool
- 286        If True: If the first record divided by the distance between two measurements is larger than
- 287        1, it is assumed that this is due to thermalization and the first measurement belongs
- 288        to the first config (default).
- 289        If False: The config numbers are assumed to be traj_number // difference
- 290
- 291    Returns
- 292    -------
- 293    t0 : Obs
- 294        Extracted t0
- 295    """
- 296
- 297    if 'files' in kwargs:
- 298        known_files = kwargs.get('files')
- 299    else:
- 300        known_files = []
+ 112    configlist = []
+ 113    r_start_index = []
+ 114    r_stop_index = []
+ 115
+ 116    for rep in range(replica):
+ 117        tmp_array = []
+ 118        with open(path + '/' + ls[rep], 'rb') as fp:
+ 119
+ 120            t = fp.read(4)  # number of reweighting factors
+ 121            if rep == 0:
+ 122                nrw = struct.unpack('i', t)[0]
+ 123                if version == '2.0':
+ 124                    nrw = int(nrw / 2)
+ 125                for k in range(nrw):
+ 126                    deltas.append([])
+ 127            else:
+ 128                if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
+ 129                    raise Exception('Error: different number of reweighting factors for replicum', rep)
+ 130
+ 131            for k in range(nrw):
+ 132                tmp_array.append([])
+ 133
+ 134            # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
+ 135            nfct = []
+ 136            if version in ['1.6', '2.0']:
+ 137                for i in range(nrw):
+ 138                    t = fp.read(4)
+ 139                    nfct.append(struct.unpack('i', t)[0])
+ 140            else:
+ 141                for i in range(nrw):
+ 142                    nfct.append(1)
+ 143
+ 144            nsrc = []
+ 145            for i in range(nrw):
+ 146                t = fp.read(4)
+ 147                nsrc.append(struct.unpack('i', t)[0])
+ 148            if version == '2.0':
+ 149                if not struct.unpack('i', fp.read(4))[0] == 0:
+ 150                    raise Exception("You are using the input for openQCD version 2.0, this is not correct.")
+ 151
+ 152            configlist.append([])
+ 153            while True:
+ 154                t = fp.read(4)
+ 155                if len(t) < 4:
+ 156                    break
+ 157                config_no = struct.unpack('i', t)[0]
+ 158                configlist[-1].append(config_no)
+ 159                for i in range(nrw):
+ 160                    if (version == '2.0'):
+ 161                        tmpd = _read_array_openQCD2(fp)
+ 162                        tmpd = _read_array_openQCD2(fp)
+ 163                        tmp_rw = tmpd['arr']
+ 164                        tmp_nfct = 1.0
+ 165                        for j in range(tmpd['n'][0]):
+ 166                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
+ 167                            if print_err:
+ 168                                print(config_no, i, j,
+ 169                                      np.mean(np.exp(-np.asarray(tmp_rw[j]))),
+ 170                                      np.std(np.exp(-np.asarray(tmp_rw[j]))))
+ 171                                print('Sources:',
+ 172                                      np.exp(-np.asarray(tmp_rw[j])))
+ 173                                print('Partial factor:', tmp_nfct)
+ 174                    elif version == '1.6' or version == '1.4':
+ 175                        tmp_nfct = 1.0
+ 176                        for j in range(nfct[i]):
+ 177                            t = fp.read(8 * nsrc[i])
+ 178                            t = fp.read(8 * nsrc[i])
+ 179                            tmp_rw = struct.unpack('d' * nsrc[i], t)
+ 180                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
+ 181                            if print_err:
+ 182                                print(config_no, i, j,
+ 183                                      np.mean(np.exp(-np.asarray(tmp_rw))),
+ 184                                      np.std(np.exp(-np.asarray(tmp_rw))))
+ 185                                print('Sources:', np.exp(-np.asarray(tmp_rw)))
+ 186                                print('Partial factor:', tmp_nfct)
+ 187                    tmp_array[i].append(tmp_nfct)
+ 188
+ 189            diffmeas = configlist[-1][-1] - configlist[-1][-2]
+ 190            configlist[-1] = [item // diffmeas for item in configlist[-1]]
+ 191            if configlist[-1][0] > 1 and diffmeas > 1:
+ 192                warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
+ 193                offset = configlist[-1][0] - 1
+ 194                configlist[-1] = [item - offset for item in configlist[-1]]
+ 195
+ 196            if r_start[rep] is None:
+ 197                r_start_index.append(0)
+ 198            else:
+ 199                try:
+ 200                    r_start_index.append(configlist[-1].index(r_start[rep]))
+ 201                except ValueError:
+ 202                    raise Exception('Config %d not in file with range [%d, %d]' % (
+ 203                        r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 204
+ 205            if r_stop[rep] is None:
+ 206                r_stop_index.append(len(configlist[-1]) - 1)
+ 207            else:
+ 208                try:
+ 209                    r_stop_index.append(configlist[-1].index(r_stop[rep]))
+ 210                except ValueError:
+ 211                    raise Exception('Config %d not in file with range [%d, %d]' % (
+ 212                        r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 213
+ 214            for k in range(nrw):
+ 215                deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step])
+ 216
+ 217    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
+ 218        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
+ 219    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+ 220    if np.any([step != 1 for step in stepsizes]):
+ 221        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
+ 222
+ 223    print(',', nrw, 'reweighting factors with', nsrc, 'sources')
+ 224    result = []
+ 225    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
+ 226
+ 227    for t in range(nrw):
+ 228        result.append(Obs(deltas[t], rep_names, idl=idl))
+ 229    return result
+ 230
+ 231
+ 232def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, postfix='ms', **kwargs):
+ 233    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
+ 234
+ 235    It is assumed that all boundary effects have
+ 236    sufficiently decayed at x0=xmin.
+ 237    The data around the zero crossing of t^2<E> - 0.3
+ 238    is fitted with a linear function
+ 239    from which the exact root is extracted.
+ 240
+ 241    It is assumed that one measurement is performed for each config.
+ 242    If this is not the case, the resulting idl, as well as the handling
+ 243    of r_start, r_stop and r_step is wrong and the user has to correct
+ 244    this in the resulting observable.
+ 245
+ 246    Parameters
+ 247    ----------
+ 248    path : str
+ 249        Path to .ms.dat files
+ 250    prefix : str
+ 251        Ensemble prefix
+ 252    dtr_read : int
+ 253        Determines how many trajectories should be skipped
+ 254        when reading the ms.dat files.
+ 255        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
+ 256    xmin : int
+ 257        First timeslice where the boundary
+ 258        effects have sufficiently decayed.
+ 259    spatial_extent : int
+ 260        spatial extent of the lattice, required for normalization.
+ 261    fit_range : int
+ 262        Number of data points left and right of the zero
+ 263        crossing to be included in the linear fit. (Default: 5)
+ 264    postfix : str
+ 265        Postfix of measurement file (Default: ms)
+ 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, postfix, 'dat', known_files=known_files)
  301
- 302    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
+ 302    replica = len(ls)
  303
- 304    replica = len(ls)
- 305
- 306    if 'r_start' in kwargs:
- 307        r_start = kwargs.get('r_start')
- 308        if len(r_start) != replica:
- 309            raise Exception('r_start does not match number of replicas')
- 310        r_start = [o if o else None for o in r_start]
- 311    else:
- 312        r_start = [None] * replica
- 313
- 314    if 'r_stop' in kwargs:
- 315        r_stop = kwargs.get('r_stop')
- 316        if len(r_stop) != replica:
- 317            raise Exception('r_stop does not match number of replicas')
- 318    else:
- 319        r_stop = [None] * replica
- 320
- 321    if 'r_step' in kwargs:
- 322        r_step = kwargs.get('r_step')
- 323    else:
- 324        r_step = 1
+ 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    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    print('Extract t0 from', prefix, ',', replica, 'replica')
- 327
- 328    if 'names' in kwargs:
- 329        rep_names = kwargs.get('names')
- 330    else:
- 331        rep_names = []
- 332        for entry in ls:
- 333            truncated_entry = entry.split('.')[0]
- 334            idx = truncated_entry.index('r')
- 335            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+ 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    Ysum = []
- 338
- 339    configlist = []
- 340    r_start_index = []
- 341    r_stop_index = []
+ 337    configlist = []
+ 338    r_start_index = []
+ 339    r_stop_index = []
+ 340
+ 341    for rep in range(replica):
  342
- 343    for rep in range(replica):
- 344
- 345        with open(path + '/' + ls[rep], 'rb') as fp:
- 346            t = fp.read(12)
- 347            header = struct.unpack('iii', t)
- 348            if rep == 0:
- 349                dn = header[0]
- 350                nn = header[1]
- 351                tmax = header[2]
- 352            elif dn != header[0] or nn != header[1] or tmax != header[2]:
- 353                raise Exception('Replica parameters do not match.')
- 354
- 355            t = fp.read(8)
- 356            if rep == 0:
- 357                eps = struct.unpack('d', t)[0]
- 358                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
- 359            elif eps != struct.unpack('d', t)[0]:
- 360                raise Exception('Values for eps do not match among replica.')
+ 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            Ysl = []
- 363
- 364            configlist.append([])
- 365            while True:
- 366                t = fp.read(4)
- 367                if (len(t) < 4):
- 368                    break
- 369                nc = struct.unpack('i', t)[0]
- 370                configlist[-1].append(nc)
- 371
- 372                t = fp.read(8 * tmax * (nn + 1))
- 373                if kwargs.get('plaquette'):
- 374                    if nc % dtr_read == 0:
- 375                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
- 376                t = fp.read(8 * tmax * (nn + 1))
- 377                if not kwargs.get('plaquette'):
- 378                    if nc % dtr_read == 0:
- 379                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
- 380                t = fp.read(8 * tmax * (nn + 1))
- 381
- 382        Ysum.append([])
- 383        for i, item in enumerate(Ysl):
- 384            Ysum[-1].append([np.mean(item[current + xmin:
- 385                             current + tmax - xmin])
- 386                            for current in range(0, len(item), tmax)])
- 387
- 388        diffmeas = configlist[-1][-1] - configlist[-1][-2]
- 389        configlist[-1] = [item // diffmeas for item in configlist[-1]]
- 390        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
- 391            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
- 392            offset = configlist[-1][0] - 1
- 393            configlist[-1] = [item - offset for item in configlist[-1]]
- 394
- 395        if r_start[rep] is None:
- 396            r_start_index.append(0)
- 397        else:
- 398            try:
- 399                r_start_index.append(configlist[-1].index(r_start[rep]))
- 400            except ValueError:
- 401                raise Exception('Config %d not in file with range [%d, %d]' % (
- 402                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
- 403
- 404        if r_stop[rep] is None:
- 405            r_stop_index.append(len(configlist[-1]) - 1)
- 406        else:
- 407            try:
- 408                r_stop_index.append(configlist[-1].index(r_stop[rep]))
- 409            except ValueError:
- 410                raise Exception('Config %d not in file with range [%d, %d]' % (
- 411                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
- 412
- 413    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
- 414        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
- 415    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
- 416    if np.any([step != 1 for step in stepsizes]):
- 417        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
- 418
- 419    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
- 420    t2E_dict = {}
- 421    for n in range(nn + 1):
- 422        samples = []
- 423        for nrep, rep in enumerate(Ysum):
- 424            samples.append([])
- 425            for cnfg in rep:
- 426                samples[-1].append(cnfg[n])
- 427            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
- 428        new_obs = Obs(samples, rep_names, idl=idl)
- 429        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
+ 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        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    return fit_t0(t2E_dict, fit_range, plot_fit=kwargs.get('plot_fit'))
  430
- 431    zero_crossing = np.argmax(np.array(
- 432        [o.value for o in t2E_dict.values()]) > 0.0)
- 433
- 434    x = list(t2E_dict.keys())[zero_crossing - fit_range:
- 435                              zero_crossing + fit_range]
- 436    y = list(t2E_dict.values())[zero_crossing - fit_range:
- 437                                zero_crossing + fit_range]
- 438    [o.gamma_method() for o in y]
- 439
- 440    fit_result = fit_lin(x, y)
- 441
- 442    if kwargs.get('plot_fit'):
- 443        plt.figure()
- 444        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
- 445        ax0 = plt.subplot(gs[0])
- 446        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
- 447        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
- 448        [o.gamma_method() for o in ymore]
- 449        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
- 450        xplot = np.linspace(np.min(x), np.max(x))
- 451        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
- 452        [yi.gamma_method() for yi in yplot]
- 453        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
- 454        retval = (-fit_result[0] / fit_result[1])
- 455        retval.gamma_method()
- 456        ylim = ax0.get_ylim()
- 457        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
- 458        ax0.set_ylim(ylim)
- 459        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
- 460        xlim = ax0.get_xlim()
- 461
- 462        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
- 463        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
- 464        ax1 = plt.subplot(gs[1])
- 465        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
- 466        ax1.tick_params(direction='out')
- 467        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
- 468        ax1.axhline(y=0.0, ls='--', color='k')
- 469        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
- 470        ax1.set_xlim(xlim)
- 471        ax1.set_ylabel('Residuals')
- 472        ax1.set_xlabel(r'$t/a^2$')
- 473
- 474        plt.draw()
- 475    return -fit_result[0] / fit_result[1]
+ 431
+ 432def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
+ 433    arr = []
+ 434    if d == 2:
+ 435        for i in range(n[0]):
+ 436            tmp = wa[i * n[1]:(i + 1) * n[1]]
+ 437            if quadrupel:
+ 438                tmp2 = []
+ 439                for j in range(0, len(tmp), 2):
+ 440                    tmp2.append(tmp[j])
+ 441                arr.append(tmp2)
+ 442            else:
+ 443                arr.append(np.asarray(tmp))
+ 444
+ 445    else:
+ 446        raise Exception('Only two-dimensional arrays supported!')
+ 447
+ 448    return arr
+ 449
+ 450
+ 451def _find_files(path, prefix, postfix, ext, known_files=[]):
+ 452    found = []
+ 453    files = []
+ 454
+ 455    if postfix != "":
+ 456        if postfix[-1] != ".":
+ 457            postfix = postfix + "."
+ 458        if postfix[0] != ".":
+ 459            postfix = "." + postfix
+ 460
+ 461    if ext[0] == ".":
+ 462        ext = ext[1:]
+ 463
+ 464    pattern = prefix + "*" + postfix + ext
+ 465
+ 466    for (dirpath, dirnames, filenames) in os.walk(path + "/"):
+ 467        found.extend(filenames)
+ 468        break
+ 469
+ 470    if known_files != []:
+ 471        for kf in known_files:
+ 472            if kf not in found:
+ 473                raise FileNotFoundError("Given file " + kf + " does not exist!")
+ 474
+ 475        return known_files
  476
- 477
- 478def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
- 479    arr = []
- 480    if d == 2:
- 481        for i in range(n[0]):
- 482            tmp = wa[i * n[1]:(i + 1) * n[1]]
- 483            if quadrupel:
- 484                tmp2 = []
- 485                for j in range(0, len(tmp), 2):
- 486                    tmp2.append(tmp[j])
- 487                arr.append(tmp2)
- 488            else:
- 489                arr.append(np.asarray(tmp))
+ 477    if not found:
+ 478        raise FileNotFoundError(f"Error, directory '{path}' not found")
+ 479
+ 480    for f in found:
+ 481        if fnmatch.fnmatch(f, pattern):
+ 482            files.append(f)
+ 483
+ 484    if files == []:
+ 485        raise Exception("No files found after pattern filter!")
+ 486
+ 487    files = sort_names(files)
+ 488    return files
+ 489
  490
- 491    else:
- 492        raise Exception('Only two-dimensional arrays supported!')
- 493
- 494    return arr
- 495
- 496
- 497def _find_files(path, prefix, postfix, ext, known_files=[]):
- 498    found = []
- 499    files = []
- 500
- 501    if postfix != "":
- 502        if postfix[-1] != ".":
- 503            postfix = postfix + "."
- 504        if postfix[0] != ".":
- 505            postfix = "." + postfix
- 506
- 507    if ext[0] == ".":
- 508        ext = ext[1:]
+ 491def _read_array_openQCD2(fp):
+ 492    t = fp.read(4)
+ 493    d = struct.unpack('i', t)[0]
+ 494    t = fp.read(4 * d)
+ 495    n = struct.unpack('%di' % (d), t)
+ 496    t = fp.read(4)
+ 497    size = struct.unpack('i', t)[0]
+ 498    if size == 4:
+ 499        types = 'i'
+ 500    elif size == 8:
+ 501        types = 'd'
+ 502    elif size == 16:
+ 503        types = 'dd'
+ 504    else:
+ 505        raise Exception("Type for size '" + str(size) + "' not known.")
+ 506    m = n[0]
+ 507    for i in range(1, d):
+ 508        m *= n[i]
  509
- 510    pattern = prefix + "*" + postfix + ext
- 511
- 512    for (dirpath, dirnames, filenames) in os.walk(path + "/"):
- 513        found.extend(filenames)
- 514        break
+ 510    t = fp.read(m * size)
+ 511    tmp = struct.unpack('%d%s' % (m, types), t)
+ 512
+ 513    arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True)
+ 514    return {'d': d, 'n': n, 'size': size, 'arr': arr}
  515
- 516    if known_files != []:
- 517        for kf in known_files:
- 518            if kf not in found:
- 519                raise FileNotFoundError("Given file " + kf + " does not exist!")
- 520
- 521        return known_files
- 522
- 523    if not found:
- 524        raise FileNotFoundError(f"Error, directory '{path}' not found")
- 525
- 526    for f in found:
- 527        if fnmatch.fnmatch(f, pattern):
- 528            files.append(f)
- 529
- 530    if files == []:
- 531        raise Exception("No files found after pattern filter!")
- 532
- 533    files = sort_names(files)
- 534    return files
- 535
- 536
- 537def _read_array_openQCD2(fp):
- 538    t = fp.read(4)
- 539    d = struct.unpack('i', t)[0]
- 540    t = fp.read(4 * d)
- 541    n = struct.unpack('%di' % (d), t)
- 542    t = fp.read(4)
- 543    size = struct.unpack('i', t)[0]
- 544    if size == 4:
- 545        types = 'i'
- 546    elif size == 8:
- 547        types = 'd'
- 548    elif size == 16:
- 549        types = 'dd'
- 550    else:
- 551        raise Exception("Type for size '" + str(size) + "' not known.")
- 552    m = n[0]
- 553    for i in range(1, d):
- 554        m *= n[i]
- 555
- 556    t = fp.read(m * size)
- 557    tmp = struct.unpack('%d%s' % (m, types), t)
- 558
- 559    arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True)
- 560    return {'d': d, 'n': n, 'size': size, 'arr': arr}
- 561
- 562
- 563def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
- 564    """Read the topologial charge based on openQCD gradient flow measurements.
- 565
- 566    Parameters
- 567    ----------
- 568    path : str
- 569        path of the measurement files
- 570    prefix : str
- 571        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
- 572        Ignored if file names are passed explicitly via keyword files.
- 573    c : double
- 574        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
- 575    dtr_cnfg : int
- 576        (optional) parameter that specifies the number of measurements
- 577        between two configs.
- 578        If it is not set, the distance between two measurements
- 579        in the file is assumed to be the distance between two configurations.
- 580    steps : int
- 581        (optional) Distance between two configurations in units of trajectories /
- 582         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
- 583    version : str
- 584        Either openQCD or sfqcd, depending on the data.
- 585    L : int
- 586        spatial length of the lattice in L/a.
- 587        HAS to be set if version != sfqcd, since openQCD does not provide
- 588        this in the header
- 589    r_start : list
- 590        list which contains the first config to be read for each replicum.
- 591    r_stop : list
- 592        list which contains the last config to be read for each replicum.
- 593    files : list
- 594        specify the exact files that need to be read
- 595        from path, practical if e.g. only one replicum is needed
- 596    postfix : str
- 597        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
- 598    names : list
- 599        Alternative labeling for replicas/ensembles.
- 600        Has to have the appropriate length.
- 601    Zeuthen_flow : bool
- 602        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
- 603        for version=='sfqcd' If False, the Wilson flow is used.
- 604    integer_charge : bool
- 605        If True, the charge is rounded towards the nearest integer on each config.
- 606
- 607    Returns
- 608    -------
- 609    result : Obs
- 610        Read topological charge
- 611    """
- 612
- 613    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
- 614
+ 516
+ 517def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
+ 518    """Read the topologial charge based on openQCD gradient flow measurements.
+ 519
+ 520    Parameters
+ 521    ----------
+ 522    path : str
+ 523        path of the measurement files
+ 524    prefix : str
+ 525        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+ 526        Ignored if file names are passed explicitly via keyword files.
+ 527    c : double
+ 528        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+ 529    dtr_cnfg : int
+ 530        (optional) parameter that specifies the number of measurements
+ 531        between two configs.
+ 532        If it is not set, the distance between two measurements
+ 533        in the file is assumed to be the distance between two configurations.
+ 534    steps : int
+ 535        (optional) Distance between two configurations in units of trajectories /
+ 536         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 537    version : str
+ 538        Either openQCD or sfqcd, depending on the data.
+ 539    L : int
+ 540        spatial length of the lattice in L/a.
+ 541        HAS to be set if version != sfqcd, since openQCD does not provide
+ 542        this in the header
+ 543    r_start : list
+ 544        list which contains the first config to be read for each replicum.
+ 545    r_stop : list
+ 546        list which contains the last config to be read for each replicum.
+ 547    files : list
+ 548        specify the exact files that need to be read
+ 549        from path, practical if e.g. only one replicum is needed
+ 550    postfix : str
+ 551        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+ 552    names : list
+ 553        Alternative labeling for replicas/ensembles.
+ 554        Has to have the appropriate length.
+ 555    Zeuthen_flow : bool
+ 556        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+ 557        for version=='sfqcd' If False, the Wilson flow is used.
+ 558    integer_charge : bool
+ 559        If True, the charge is rounded towards the nearest integer on each config.
+ 560
+ 561    Returns
+ 562    -------
+ 563    result : Obs
+ 564        Read topological charge
+ 565    """
+ 566
+ 567    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
+ 568
+ 569
+ 570def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
+ 571    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
+ 572
+ 573    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.
+ 574
+ 575    Parameters
+ 576    ----------
+ 577    path : str
+ 578        path of the measurement files
+ 579    prefix : str
+ 580        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+ 581        Ignored if file names are passed explicitly via keyword files.
+ 582    c : double
+ 583        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+ 584    dtr_cnfg : int
+ 585        (optional) parameter that specifies the number of measurements
+ 586        between two configs.
+ 587        If it is not set, the distance between two measurements
+ 588        in the file is assumed to be the distance between two configurations.
+ 589    steps : int
+ 590        (optional) Distance between two configurations in units of trajectories /
+ 591         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 592    r_start : list
+ 593        list which contains the first config to be read for each replicum.
+ 594    r_stop : list
+ 595        list which contains the last config to be read for each replicum.
+ 596    files : list
+ 597        specify the exact files that need to be read
+ 598        from path, practical if e.g. only one replicum is needed
+ 599    names : list
+ 600        Alternative labeling for replicas/ensembles.
+ 601        Has to have the appropriate length.
+ 602    postfix : str
+ 603        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+ 604    Zeuthen_flow : bool
+ 605        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
+ 606    """
+ 607
+ 608    if c != 0.3:
+ 609        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
+ 610
+ 611    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)
+ 612    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)
+ 613    L = plaq.tag["L"]
+ 614    T = plaq.tag["T"]
  615
- 616def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
- 617    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
+ 616    if T != L:
+ 617        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
  618
- 619    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.
- 620
- 621    Parameters
- 622    ----------
- 623    path : str
- 624        path of the measurement files
- 625    prefix : str
- 626        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
- 627        Ignored if file names are passed explicitly via keyword files.
- 628    c : double
- 629        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
- 630    dtr_cnfg : int
- 631        (optional) parameter that specifies the number of measurements
- 632        between two configs.
- 633        If it is not set, the distance between two measurements
- 634        in the file is assumed to be the distance between two configurations.
- 635    steps : int
- 636        (optional) Distance between two configurations in units of trajectories /
- 637         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
- 638    r_start : list
- 639        list which contains the first config to be read for each replicum.
- 640    r_stop : list
- 641        list which contains the last config to be read for each replicum.
- 642    files : list
- 643        specify the exact files that need to be read
- 644        from path, practical if e.g. only one replicum is needed
- 645    names : list
- 646        Alternative labeling for replicas/ensembles.
- 647        Has to have the appropriate length.
- 648    postfix : str
- 649        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
- 650    Zeuthen_flow : bool
- 651        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
- 652    """
- 653
- 654    if c != 0.3:
- 655        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
- 656
- 657    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)
- 658    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)
- 659    L = plaq.tag["L"]
- 660    T = plaq.tag["T"]
- 661
- 662    if T != L:
- 663        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
- 664
- 665    if Zeuthen_flow is not True:
- 666        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
- 667
- 668    t = (c * L) ** 2 / 8
- 669
- 670    normdict = {4: 0.012341170468270,
- 671                6: 0.010162691462430,
- 672                8: 0.009031614807931,
- 673                10: 0.008744966371393,
- 674                12: 0.008650917856809,
- 675                14: 8.611154391267955E-03,
- 676                16: 0.008591758449508,
- 677                20: 0.008575359627103,
- 678                24: 0.008569387847540,
- 679                28: 8.566803713382559E-03,
- 680                32: 0.008565541650006,
- 681                40: 8.564480684962046E-03,
- 682                48: 8.564098025073460E-03,
- 683                64: 8.563853943383087E-03}
- 684
- 685    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
- 686
- 687
- 688def _read_flow_obs(path, prefix, c, dtr_cnfg=1, version="openQCD", obspos=0, sum_t=True, **kwargs):
- 689    """Read a flow observable based on openQCD gradient flow measurements.
- 690
- 691    Parameters
- 692    ----------
- 693    path : str
- 694        path of the measurement files
- 695    prefix : str
- 696        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
- 697        Ignored if file names are passed explicitly via keyword files.
- 698    c : double
- 699        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
- 700    dtr_cnfg : int
- 701        (optional) parameter that specifies the number of measurements
- 702        between two configs.
- 703        If it is not set, the distance between two measurements
- 704        in the file is assumed to be the distance between two configurations.
- 705    steps : int
- 706        (optional) Distance between two configurations in units of trajectories /
- 707         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
- 708    version : str
- 709        Either openQCD or sfqcd, depending on the data.
- 710    obspos : int
- 711        position of the obeservable in the measurement file. Only relevant for sfqcd files.
- 712    sum_t : bool
- 713        If true sum over all timeslices, if false only take the value at T/2.
- 714    L : int
- 715        spatial length of the lattice in L/a.
- 716        HAS to be set if version != sfqcd, since openQCD does not provide
- 717        this in the header
- 718    r_start : list
- 719        list which contains the first config to be read for each replicum.
- 720    r_stop : list
- 721        list which contains the last config to be read for each replicum.
- 722    files : list
- 723        specify the exact files that need to be read
- 724        from path, practical if e.g. only one replicum is needed
- 725    names : list
- 726        Alternative labeling for replicas/ensembles.
- 727        Has to have the appropriate length.
- 728    postfix : str
- 729        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
- 730    Zeuthen_flow : bool
- 731        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
- 732        for version=='sfqcd' If False, the Wilson flow is used.
- 733    integer_charge : bool
- 734        If True, the charge is rounded towards the nearest integer on each config.
- 735
- 736    Returns
- 737    -------
- 738    result : Obs
- 739        flow observable specified
- 740    """
- 741    known_versions = ["openQCD", "sfqcd"]
- 742
- 743    if version not in known_versions:
- 744        raise Exception("Unknown openQCD version.")
- 745    if "steps" in kwargs:
- 746        steps = kwargs.get("steps")
- 747    if version == "sfqcd":
- 748        if "L" in kwargs:
- 749            supposed_L = kwargs.get("L")
- 750        else:
- 751            supposed_L = None
- 752        postfix = "gfms"
- 753    else:
- 754        if "L" not in kwargs:
- 755            raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.")
- 756        else:
- 757            L = kwargs.get("L")
- 758        postfix = "ms"
- 759
- 760    if "postfix" in kwargs:
- 761        postfix = kwargs.get("postfix")
- 762
- 763    if "files" in kwargs:
- 764        known_files = kwargs.get("files")
- 765    else:
- 766        known_files = []
- 767
- 768    files = _find_files(path, prefix, postfix, "dat", known_files=known_files)
- 769
- 770    if 'r_start' in kwargs:
- 771        r_start = kwargs.get('r_start')
- 772        if len(r_start) != len(files):
- 773            raise Exception('r_start does not match number of replicas')
- 774        r_start = [o if o else None for o in r_start]
- 775    else:
- 776        r_start = [None] * len(files)
- 777
- 778    if 'r_stop' in kwargs:
- 779        r_stop = kwargs.get('r_stop')
- 780        if len(r_stop) != len(files):
- 781            raise Exception('r_stop does not match number of replicas')
- 782    else:
- 783        r_stop = [None] * len(files)
- 784    rep_names = []
- 785
- 786    zeuthen = kwargs.get('Zeuthen_flow', False)
- 787    if zeuthen and version not in ['sfqcd']:
- 788        raise Exception('Zeuthen flow can only be used for version==sfqcd')
- 789
- 790    r_start_index = []
- 791    r_stop_index = []
- 792    deltas = []
- 793    configlist = []
- 794    if not zeuthen:
- 795        obspos += 8
- 796    for rep, file in enumerate(files):
- 797        with open(path + "/" + file, "rb") as fp:
- 798
- 799            Q = []
- 800            traj_list = []
- 801            if version in ['sfqcd']:
- 802                t = fp.read(12)
- 803                header = struct.unpack('<iii', t)
- 804                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)
- 805                ncs = header[1]  # number of different values for c in t_flow=1/8 c² L² -> measurements done for ncs c's
- 806                tmax = header[2]  # lattice T/a
- 807
- 808                t = fp.read(12)
- 809                Ls = struct.unpack('<iii', t)
- 810                if (Ls[0] == Ls[1] and Ls[1] == Ls[2]):
- 811                    L = Ls[0]
- 812                    if not (supposed_L == L) and supposed_L:
- 813                        raise Exception("It seems the length given in the header and by you contradict each other")
- 814                else:
- 815                    raise Exception("Found more than one spatial length in header!")
- 816
- 817                t = fp.read(16)
- 818                header2 = struct.unpack('<dd', t)
- 819                tol = header2[0]
- 820                cmax = header2[1]  # highest value of c used
- 821
- 822                if c > cmax:
- 823                    raise Exception('Flow has been determined between c=0 and c=%lf with tolerance %lf' % (cmax, tol))
- 824
- 825                if (zthfl == 2):
- 826                    nfl = 2  # number of flows
- 827                else:
- 828                    nfl = 1
- 829                iobs = 8 * nfl  # number of flow observables calculated
- 830
- 831                while True:
- 832                    t = fp.read(4)
- 833                    if (len(t) < 4):
- 834                        break
- 835                    traj_list.append(struct.unpack('i', t)[0])   # trajectory number when measurement was done
- 836
- 837                    for j in range(ncs + 1):
- 838                        for i in range(iobs):
- 839                            t = fp.read(8 * tmax)
- 840                            if (i == obspos):  # determines the flow observable -> i=0 <-> Zeuthen flow
- 841                                Q.append(struct.unpack('d' * tmax, t))
+ 619    if Zeuthen_flow is not True:
+ 620        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
+ 621
+ 622    t = (c * L) ** 2 / 8
+ 623
+ 624    normdict = {4: 0.012341170468270,
+ 625                6: 0.010162691462430,
+ 626                8: 0.009031614807931,
+ 627                10: 0.008744966371393,
+ 628                12: 0.008650917856809,
+ 629                14: 8.611154391267955E-03,
+ 630                16: 0.008591758449508,
+ 631                20: 0.008575359627103,
+ 632                24: 0.008569387847540,
+ 633                28: 8.566803713382559E-03,
+ 634                32: 0.008565541650006,
+ 635                40: 8.564480684962046E-03,
+ 636                48: 8.564098025073460E-03,
+ 637                64: 8.563853943383087E-03}
+ 638
+ 639    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
+ 640
+ 641
+ 642def _read_flow_obs(path, prefix, c, dtr_cnfg=1, version="openQCD", obspos=0, sum_t=True, **kwargs):
+ 643    """Read a flow observable based on openQCD gradient flow measurements.
+ 644
+ 645    Parameters
+ 646    ----------
+ 647    path : str
+ 648        path of the measurement files
+ 649    prefix : str
+ 650        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+ 651        Ignored if file names are passed explicitly via keyword files.
+ 652    c : double
+ 653        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+ 654    dtr_cnfg : int
+ 655        (optional) parameter that specifies the number of measurements
+ 656        between two configs.
+ 657        If it is not set, the distance between two measurements
+ 658        in the file is assumed to be the distance between two configurations.
+ 659    steps : int
+ 660        (optional) Distance between two configurations in units of trajectories /
+ 661         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 662    version : str
+ 663        Either openQCD or sfqcd, depending on the data.
+ 664    obspos : int
+ 665        position of the obeservable in the measurement file. Only relevant for sfqcd files.
+ 666    sum_t : bool
+ 667        If true sum over all timeslices, if false only take the value at T/2.
+ 668    L : int
+ 669        spatial length of the lattice in L/a.
+ 670        HAS to be set if version != sfqcd, since openQCD does not provide
+ 671        this in the header
+ 672    r_start : list
+ 673        list which contains the first config to be read for each replicum.
+ 674    r_stop : list
+ 675        list which contains the last config to be read for each replicum.
+ 676    files : list
+ 677        specify the exact files that need to be read
+ 678        from path, practical if e.g. only one replicum is needed
+ 679    names : list
+ 680        Alternative labeling for replicas/ensembles.
+ 681        Has to have the appropriate length.
+ 682    postfix : str
+ 683        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+ 684    Zeuthen_flow : bool
+ 685        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+ 686        for version=='sfqcd' If False, the Wilson flow is used.
+ 687    integer_charge : bool
+ 688        If True, the charge is rounded towards the nearest integer on each config.
+ 689
+ 690    Returns
+ 691    -------
+ 692    result : Obs
+ 693        flow observable specified
+ 694    """
+ 695    known_versions = ["openQCD", "sfqcd"]
+ 696
+ 697    if version not in known_versions:
+ 698        raise Exception("Unknown openQCD version.")
+ 699    if "steps" in kwargs:
+ 700        steps = kwargs.get("steps")
+ 701    if version == "sfqcd":
+ 702        if "L" in kwargs:
+ 703            supposed_L = kwargs.get("L")
+ 704        else:
+ 705            supposed_L = None
+ 706        postfix = "gfms"
+ 707    else:
+ 708        if "L" not in kwargs:
+ 709            raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.")
+ 710        else:
+ 711            L = kwargs.get("L")
+ 712        postfix = "ms"
+ 713
+ 714    if "postfix" in kwargs:
+ 715        postfix = kwargs.get("postfix")
+ 716
+ 717    if "files" in kwargs:
+ 718        known_files = kwargs.get("files")
+ 719    else:
+ 720        known_files = []
+ 721
+ 722    files = _find_files(path, prefix, postfix, "dat", known_files=known_files)
+ 723
+ 724    if 'r_start' in kwargs:
+ 725        r_start = kwargs.get('r_start')
+ 726        if len(r_start) != len(files):
+ 727            raise Exception('r_start does not match number of replicas')
+ 728        r_start = [o if o else None for o in r_start]
+ 729    else:
+ 730        r_start = [None] * len(files)
+ 731
+ 732    if 'r_stop' in kwargs:
+ 733        r_stop = kwargs.get('r_stop')
+ 734        if len(r_stop) != len(files):
+ 735            raise Exception('r_stop does not match number of replicas')
+ 736    else:
+ 737        r_stop = [None] * len(files)
+ 738    rep_names = []
+ 739
+ 740    zeuthen = kwargs.get('Zeuthen_flow', False)
+ 741    if zeuthen and version not in ['sfqcd']:
+ 742        raise Exception('Zeuthen flow can only be used for version==sfqcd')
+ 743
+ 744    r_start_index = []
+ 745    r_stop_index = []
+ 746    deltas = []
+ 747    configlist = []
+ 748    if not zeuthen:
+ 749        obspos += 8
+ 750    for rep, file in enumerate(files):
+ 751        with open(path + "/" + file, "rb") as fp:
+ 752
+ 753            Q = []
+ 754            traj_list = []
+ 755            if version in ['sfqcd']:
+ 756                t = fp.read(12)
+ 757                header = struct.unpack('<iii', t)
+ 758                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)
+ 759                ncs = header[1]  # number of different values for c in t_flow=1/8 c² L² -> measurements done for ncs c's
+ 760                tmax = header[2]  # lattice T/a
+ 761
+ 762                t = fp.read(12)
+ 763                Ls = struct.unpack('<iii', t)
+ 764                if (Ls[0] == Ls[1] and Ls[1] == Ls[2]):
+ 765                    L = Ls[0]
+ 766                    if not (supposed_L == L) and supposed_L:
+ 767                        raise Exception("It seems the length given in the header and by you contradict each other")
+ 768                else:
+ 769                    raise Exception("Found more than one spatial length in header!")
+ 770
+ 771                t = fp.read(16)
+ 772                header2 = struct.unpack('<dd', t)
+ 773                tol = header2[0]
+ 774                cmax = header2[1]  # highest value of c used
+ 775
+ 776                if c > cmax:
+ 777                    raise Exception('Flow has been determined between c=0 and c=%lf with tolerance %lf' % (cmax, tol))
+ 778
+ 779                if (zthfl == 2):
+ 780                    nfl = 2  # number of flows
+ 781                else:
+ 782                    nfl = 1
+ 783                iobs = 8 * nfl  # number of flow observables calculated
+ 784
+ 785                while True:
+ 786                    t = fp.read(4)
+ 787                    if (len(t) < 4):
+ 788                        break
+ 789                    traj_list.append(struct.unpack('i', t)[0])   # trajectory number when measurement was done
+ 790
+ 791                    for j in range(ncs + 1):
+ 792                        for i in range(iobs):
+ 793                            t = fp.read(8 * tmax)
+ 794                            if (i == obspos):  # determines the flow observable -> i=0 <-> Zeuthen flow
+ 795                                Q.append(struct.unpack('d' * tmax, t))
+ 796
+ 797            else:
+ 798                t = fp.read(12)
+ 799                header = struct.unpack('<iii', t)
+ 800                # step size in integration steps "dnms"
+ 801                dn = header[0]
+ 802                # number of measurements, so "ntot"/dn
+ 803                nn = header[1]
+ 804                # lattice T/a
+ 805                tmax = header[2]
+ 806
+ 807                t = fp.read(8)
+ 808                eps = struct.unpack('d', t)[0]
+ 809
+ 810                while True:
+ 811                    t = fp.read(4)
+ 812                    if (len(t) < 4):
+ 813                        break
+ 814                    traj_list.append(struct.unpack('i', t)[0])
+ 815                    # Wsl
+ 816                    t = fp.read(8 * tmax * (nn + 1))
+ 817                    # Ysl
+ 818                    t = fp.read(8 * tmax * (nn + 1))
+ 819                    # Qsl, which is asked for in this method
+ 820                    t = fp.read(8 * tmax * (nn + 1))
+ 821                    # unpack the array of Qtops,
+ 822                    # on each timeslice t=0,...,tmax-1 and the
+ 823                    # measurement number in = 0...nn (see README.qcd1)
+ 824                    tmpd = struct.unpack('d' * tmax * (nn + 1), t)
+ 825                    Q.append(tmpd)
+ 826
+ 827        if len(np.unique(np.diff(traj_list))) != 1:
+ 828            raise Exception("Irregularities in stepsize found")
+ 829        else:
+ 830            if 'steps' in kwargs:
+ 831                if steps != traj_list[1] - traj_list[0]:
+ 832                    raise Exception("steps and the found stepsize are not the same")
+ 833            else:
+ 834                steps = traj_list[1] - traj_list[0]
+ 835
+ 836        configlist.append([tr // steps // dtr_cnfg for tr in traj_list])
+ 837        if configlist[-1][0] > 1:
+ 838            offset = configlist[-1][0] - 1
+ 839            warnings.warn('Assume thermalization and that the first measurement belongs to the first config. Offset = %d configs (%d trajectories / cycles)' % (
+ 840                offset, offset * steps))
+ 841            configlist[-1] = [item - offset for item in configlist[-1]]
  842
- 843            else:
- 844                t = fp.read(12)
- 845                header = struct.unpack('<iii', t)
- 846                # step size in integration steps "dnms"
- 847                dn = header[0]
- 848                # number of measurements, so "ntot"/dn
- 849                nn = header[1]
- 850                # lattice T/a
- 851                tmax = header[2]
- 852
- 853                t = fp.read(8)
- 854                eps = struct.unpack('d', t)[0]
- 855
- 856                while True:
- 857                    t = fp.read(4)
- 858                    if (len(t) < 4):
- 859                        break
- 860                    traj_list.append(struct.unpack('i', t)[0])
- 861                    # Wsl
- 862                    t = fp.read(8 * tmax * (nn + 1))
- 863                    # Ysl
- 864                    t = fp.read(8 * tmax * (nn + 1))
- 865                    # Qsl, which is asked for in this method
- 866                    t = fp.read(8 * tmax * (nn + 1))
- 867                    # unpack the array of Qtops,
- 868                    # on each timeslice t=0,...,tmax-1 and the
- 869                    # measurement number in = 0...nn (see README.qcd1)
- 870                    tmpd = struct.unpack('d' * tmax * (nn + 1), t)
- 871                    Q.append(tmpd)
- 872
- 873        if len(np.unique(np.diff(traj_list))) != 1:
- 874            raise Exception("Irregularities in stepsize found")
- 875        else:
- 876            if 'steps' in kwargs:
- 877                if steps != traj_list[1] - traj_list[0]:
- 878                    raise Exception("steps and the found stepsize are not the same")
- 879            else:
- 880                steps = traj_list[1] - traj_list[0]
- 881
- 882        configlist.append([tr // steps // dtr_cnfg for tr in traj_list])
- 883        if configlist[-1][0] > 1:
- 884            offset = configlist[-1][0] - 1
- 885            warnings.warn('Assume thermalization and that the first measurement belongs to the first config. Offset = %d configs (%d trajectories / cycles)' % (
- 886                offset, offset * steps))
- 887            configlist[-1] = [item - offset for item in configlist[-1]]
- 888
- 889        if r_start[rep] is None:
- 890            r_start_index.append(0)
- 891        else:
- 892            try:
- 893                r_start_index.append(configlist[-1].index(r_start[rep]))
- 894            except ValueError:
- 895                raise Exception('Config %d not in file with range [%d, %d]' % (
- 896                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
- 897
- 898        if r_stop[rep] is None:
- 899            r_stop_index.append(len(configlist[-1]) - 1)
- 900        else:
- 901            try:
- 902                r_stop_index.append(configlist[-1].index(r_stop[rep]))
- 903            except ValueError:
- 904                raise Exception('Config %d not in file with range [%d, %d]' % (
- 905                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
- 906
- 907        if version in ['sfqcd']:
- 908            cstepsize = cmax / ncs
- 909            index_aim = round(c / cstepsize)
- 910        else:
- 911            t_aim = (c * L) ** 2 / 8
- 912            index_aim = round(t_aim / eps / dn)
+ 843        if r_start[rep] is None:
+ 844            r_start_index.append(0)
+ 845        else:
+ 846            try:
+ 847                r_start_index.append(configlist[-1].index(r_start[rep]))
+ 848            except ValueError:
+ 849                raise Exception('Config %d not in file with range [%d, %d]' % (
+ 850                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 851
+ 852        if r_stop[rep] is None:
+ 853            r_stop_index.append(len(configlist[-1]) - 1)
+ 854        else:
+ 855            try:
+ 856                r_stop_index.append(configlist[-1].index(r_stop[rep]))
+ 857            except ValueError:
+ 858                raise Exception('Config %d not in file with range [%d, %d]' % (
+ 859                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+ 860
+ 861        if version in ['sfqcd']:
+ 862            cstepsize = cmax / ncs
+ 863            index_aim = round(c / cstepsize)
+ 864        else:
+ 865            t_aim = (c * L) ** 2 / 8
+ 866            index_aim = round(t_aim / eps / dn)
+ 867
+ 868        Q_sum = []
+ 869        for i, item in enumerate(Q):
+ 870            if sum_t is True:
+ 871                Q_sum.append([sum(item[current:current + tmax])
+ 872                             for current in range(0, len(item), tmax)])
+ 873            else:
+ 874                Q_sum.append([item[int(tmax / 2)]])
+ 875        Q_top = []
+ 876        if version in ['sfqcd']:
+ 877            for i in range(len(Q_sum) // (ncs + 1)):
+ 878                Q_top.append(Q_sum[i * (ncs + 1) + index_aim][0])
+ 879        else:
+ 880            for i in range(len(Q) // dtr_cnfg):
+ 881                Q_top.append(Q_sum[dtr_cnfg * i][index_aim])
+ 882        if len(Q_top) != len(traj_list) // dtr_cnfg:
+ 883            raise Exception("qtops and traj_list dont have the same length")
+ 884
+ 885        if kwargs.get('integer_charge', False):
+ 886            Q_top = [round(q) for q in Q_top]
+ 887
+ 888        truncated_file = file[:-len(postfix)]
+ 889
+ 890        if "names" not in kwargs:
+ 891            try:
+ 892                idx = truncated_file.index('r')
+ 893            except Exception:
+ 894                if "names" not in kwargs:
+ 895                    raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
+ 896            ens_name = truncated_file[:idx]
+ 897            rep_names.append(ens_name + '|' + truncated_file[idx:].split(".")[0])
+ 898        else:
+ 899            names = kwargs.get("names")
+ 900            rep_names = names
+ 901
+ 902        deltas.append(Q_top)
+ 903
+ 904    rep_names = sort_names(rep_names)
+ 905
+ 906    idl = [range(int(configlist[rep][r_start_index[rep]]), int(configlist[rep][r_stop_index[rep]]) + 1, 1) for rep in range(len(deltas))]
+ 907    deltas = [deltas[nrep][r_start_index[nrep]:r_stop_index[nrep] + 1] for nrep in range(len(deltas))]
+ 908    result = Obs(deltas, rep_names, idl=idl)
+ 909    result.tag = {"T": tmax - 1,
+ 910                  "L": L}
+ 911    return result
+ 912
  913
- 914        Q_sum = []
- 915        for i, item in enumerate(Q):
- 916            if sum_t is True:
- 917                Q_sum.append([sum(item[current:current + tmax])
- 918                             for current in range(0, len(item), tmax)])
- 919            else:
- 920                Q_sum.append([item[int(tmax / 2)]])
- 921        Q_top = []
- 922        if version in ['sfqcd']:
- 923            for i in range(len(Q_sum) // (ncs + 1)):
- 924                Q_top.append(Q_sum[i * (ncs + 1) + index_aim][0])
- 925        else:
- 926            for i in range(len(Q) // dtr_cnfg):
- 927                Q_top.append(Q_sum[dtr_cnfg * i][index_aim])
- 928        if len(Q_top) != len(traj_list) // dtr_cnfg:
- 929            raise Exception("qtops and traj_list dont have the same length")
- 930
- 931        if kwargs.get('integer_charge', False):
- 932            Q_top = [round(q) for q in Q_top]
- 933
- 934        truncated_file = file[:-len(postfix)]
+ 914def qtop_projection(qtop, target=0):
+ 915    """Returns the projection to the topological charge sector defined by target.
+ 916
+ 917    Parameters
+ 918    ----------
+ 919    path : Obs
+ 920        Topological charge.
+ 921    target : int
+ 922        Specifies the topological sector to be reweighted to (default 0)
+ 923
+ 924    Returns
+ 925    -------
+ 926    reto : Obs
+ 927        projection to the topological charge sector defined by target
+ 928    """
+ 929    if qtop.reweighted:
+ 930        raise Exception('You can not use a reweighted observable for reweighting!')
+ 931
+ 932    proj_qtop = []
+ 933    for n in qtop.deltas:
+ 934        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
  935
- 936        if "names" not in kwargs:
- 937            try:
- 938                idx = truncated_file.index('r')
- 939            except Exception:
- 940                if "names" not in kwargs:
- 941                    raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
- 942            ens_name = truncated_file[:idx]
- 943            rep_names.append(ens_name + '|' + truncated_file[idx:].split(".")[0])
- 944        else:
- 945            names = kwargs.get("names")
- 946            rep_names = names
- 947
- 948        deltas.append(Q_top)
- 949
- 950    rep_names = sort_names(rep_names)
- 951
- 952    idl = [range(int(configlist[rep][r_start_index[rep]]), int(configlist[rep][r_stop_index[rep]]) + 1, 1) for rep in range(len(deltas))]
- 953    deltas = [deltas[nrep][r_start_index[nrep]:r_stop_index[nrep] + 1] for nrep in range(len(deltas))]
- 954    result = Obs(deltas, rep_names, idl=idl)
- 955    result.tag = {"T": tmax - 1,
- 956                  "L": L}
- 957    return result
- 958
- 959
- 960def qtop_projection(qtop, target=0):
- 961    """Returns the projection to the topological charge sector defined by target.
- 962
- 963    Parameters
- 964    ----------
- 965    path : Obs
- 966        Topological charge.
- 967    target : int
- 968        Specifies the topological sector to be reweighted to (default 0)
- 969
- 970    Returns
- 971    -------
- 972    reto : Obs
- 973        projection to the topological charge sector defined by target
- 974    """
- 975    if qtop.reweighted:
- 976        raise Exception('You can not use a reweighted observable for reweighting!')
- 977
- 978    proj_qtop = []
- 979    for n in qtop.deltas:
- 980        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
- 981
- 982    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
- 983    return reto
- 984
- 985
- 986def read_qtop_sector(path, prefix, c, target=0, **kwargs):
- 987    """Constructs reweighting factors to a specified topological sector.
+ 936    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
+ 937    return reto
+ 938
+ 939
+ 940def read_qtop_sector(path, prefix, c, target=0, **kwargs):
+ 941    """Constructs reweighting factors to a specified topological sector.
+ 942
+ 943    Parameters
+ 944    ----------
+ 945    path : str
+ 946        path of the measurement files
+ 947    prefix : str
+ 948        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
+ 949    c : double
+ 950        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
+ 951    target : int
+ 952        Specifies the topological sector to be reweighted to (default 0)
+ 953    dtr_cnfg : int
+ 954        (optional) parameter that specifies the number of trajectories
+ 955        between two configs.
+ 956        if it is not set, the distance between two measurements
+ 957        in the file is assumed to be the distance between two configurations.
+ 958    steps : int
+ 959        (optional) Distance between two configurations in units of trajectories /
+ 960         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+ 961    version : str
+ 962        version string of the openQCD (sfqcd) version used to create
+ 963        the ensemble. Default is 2.0. May also be set to sfqcd.
+ 964    L : int
+ 965        spatial length of the lattice in L/a.
+ 966        HAS to be set if version != sfqcd, since openQCD does not provide
+ 967        this in the header
+ 968    r_start : list
+ 969        offset of the first ensemble, making it easier to match
+ 970        later on with other Obs
+ 971    r_stop : list
+ 972        last configurations that need to be read (per replicum)
+ 973    files : list
+ 974        specify the exact files that need to be read
+ 975        from path, practical if e.g. only one replicum is needed
+ 976    names : list
+ 977        Alternative labeling for replicas/ensembles.
+ 978        Has to have the appropriate length
+ 979    Zeuthen_flow : bool
+ 980        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+ 981        for version=='sfqcd' If False, the Wilson flow is used.
+ 982
+ 983    Returns
+ 984    -------
+ 985    reto : Obs
+ 986        projection to the topological charge sector defined by target
+ 987    """
  988
- 989    Parameters
- 990    ----------
- 991    path : str
- 992        path of the measurement files
- 993    prefix : str
- 994        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
- 995    c : double
- 996        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
- 997    target : int
- 998        Specifies the topological sector to be reweighted to (default 0)
- 999    dtr_cnfg : int
-1000        (optional) parameter that specifies the number of trajectories
-1001        between two configs.
-1002        if it is not set, the distance between two measurements
-1003        in the file is assumed to be the distance between two configurations.
-1004    steps : int
-1005        (optional) Distance between two configurations in units of trajectories /
-1006         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-1007    version : str
-1008        version string of the openQCD (sfqcd) version used to create
-1009        the ensemble. Default is 2.0. May also be set to sfqcd.
-1010    L : int
-1011        spatial length of the lattice in L/a.
-1012        HAS to be set if version != sfqcd, since openQCD does not provide
-1013        this in the header
-1014    r_start : list
-1015        offset of the first ensemble, making it easier to match
-1016        later on with other Obs
-1017    r_stop : list
-1018        last configurations that need to be read (per replicum)
-1019    files : list
-1020        specify the exact files that need to be read
-1021        from path, practical if e.g. only one replicum is needed
-1022    names : list
-1023        Alternative labeling for replicas/ensembles.
-1024        Has to have the appropriate length
-1025    Zeuthen_flow : bool
-1026        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-1027        for version=='sfqcd' If False, the Wilson flow is used.
-1028
-1029    Returns
-1030    -------
-1031    reto : Obs
-1032        projection to the topological charge sector defined by target
-1033    """
-1034
-1035    if not isinstance(target, int):
-1036        raise Exception("'target' has to be an integer.")
+ 989    if not isinstance(target, int):
+ 990        raise Exception("'target' has to be an integer.")
+ 991
+ 992    kwargs['integer_charge'] = True
+ 993    qtop = read_qtop(path, prefix, c, **kwargs)
+ 994
+ 995    return qtop_projection(qtop, target=target)
+ 996
+ 997
+ 998def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
+ 999    """
+1000    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
+1001
+1002    Parameters
+1003    ----------
+1004    path : str
+1005        The directory to search for the files in.
+1006    prefix : str
+1007        The prefix to match the files against.
+1008    qc : str
+1009        The quark combination extension to match the files against.
+1010    corr : str
+1011        The correlator to extract data for.
+1012    sep : str, optional
+1013        The separator to use when parsing the replika names.
+1014    **kwargs
+1015        Additional keyword arguments. The following keyword arguments are recognized:
+1016
+1017        - names (List[str]): A list of names to use for the replicas.
+1018
+1019    Returns
+1020    -------
+1021    Corr
+1022        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
+1023    or
+1024    CObs
+1025        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
+1026
+1027
+1028    Raises
+1029    ------
+1030    FileNotFoundError
+1031        If no files matching the specified prefix and quark combination extension are found in the specified directory.
+1032    IOError
+1033        If there is an error reading a file.
+1034    struct.error
+1035        If there is an error unpacking binary data.
+1036    """
 1037
-1038    kwargs['integer_charge'] = True
-1039    qtop = read_qtop(path, prefix, c, **kwargs)
-1040
-1041    return qtop_projection(qtop, target=target)
-1042
-1043
-1044def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
-1045    """
-1046    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
-1047
-1048    Parameters
-1049    ----------
-1050    path : str
-1051        The directory to search for the files in.
-1052    prefix : str
-1053        The prefix to match the files against.
-1054    qc : str
-1055        The quark combination extension to match the files against.
-1056    corr : str
-1057        The correlator to extract data for.
-1058    sep : str, optional
-1059        The separator to use when parsing the replika names.
-1060    **kwargs
-1061        Additional keyword arguments. The following keyword arguments are recognized:
-1062
-1063        - names (List[str]): A list of names to use for the replicas.
-1064
-1065    Returns
-1066    -------
-1067    Corr
-1068        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
-1069    or
-1070    CObs
-1071        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
-1072
-1073
-1074    Raises
-1075    ------
-1076    FileNotFoundError
-1077        If no files matching the specified prefix and quark combination extension are found in the specified directory.
-1078    IOError
-1079        If there is an error reading a file.
-1080    struct.error
-1081        If there is an error unpacking binary data.
-1082    """
-1083
-1084    # found = []
-1085    files = []
-1086    names = []
-1087
-1088    # test if the input is correct
-1089    if qc not in ['dd', 'ud', 'du', 'uu']:
-1090        raise Exception("Unknown quark conbination!")
-1091
-1092    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
-1093        raise Exception("Unknown correlator!")
-1094
-1095    if "files" in kwargs:
-1096        known_files = kwargs.get("files")
-1097    else:
-1098        known_files = []
-1099    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
+1038    # found = []
+1039    files = []
+1040    names = []
+1041
+1042    # test if the input is correct
+1043    if qc not in ['dd', 'ud', 'du', 'uu']:
+1044        raise Exception("Unknown quark conbination!")
+1045
+1046    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
+1047        raise Exception("Unknown correlator!")
+1048
+1049    if "files" in kwargs:
+1050        known_files = kwargs.get("files")
+1051    else:
+1052        known_files = []
+1053    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
+1054
+1055    if "names" in kwargs:
+1056        names = kwargs.get("names")
+1057    else:
+1058        for f in files:
+1059            if not sep == "":
+1060                se = f.split(".")[0]
+1061                for s in f.split(".")[1:-2]:
+1062                    se += "." + s
+1063                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
+1064            else:
+1065                names.append(prefix)
+1066
+1067    names = sorted(names)
+1068    files = sorted(files)
+1069
+1070    cnfgs = []
+1071    realsamples = []
+1072    imagsamples = []
+1073    repnum = 0
+1074    for file in files:
+1075        with open(path + "/" + file, "rb") as fp:
+1076
+1077            t = fp.read(8)
+1078            kappa = struct.unpack('d', t)[0]
+1079            t = fp.read(8)
+1080            csw = struct.unpack('d', t)[0]
+1081            t = fp.read(8)
+1082            dF = struct.unpack('d', t)[0]
+1083            t = fp.read(8)
+1084            zF = struct.unpack('d', t)[0]
+1085
+1086            t = fp.read(4)
+1087            tmax = struct.unpack('i', t)[0]
+1088            t = fp.read(4)
+1089            bnd = struct.unpack('i', t)[0]
+1090
+1091            placesBI = ["gS", "gP",
+1092                        "gA", "gV",
+1093                        "gVt", "lA",
+1094                        "lV", "lVt",
+1095                        "lT", "lTt"]
+1096            placesBB = ["g1", "l1"]
+1097
+1098            # the chunks have the following structure:
+1099            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
 1100
-1101    if "names" in kwargs:
-1102        names = kwargs.get("names")
-1103    else:
-1104        for f in files:
-1105            if not sep == "":
-1106                se = f.split(".")[0]
-1107                for s in f.split(".")[1:-2]:
-1108                    se += "." + s
-1109                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
-1110            else:
-1111                names.append(prefix)
-1112
-1113    names = sorted(names)
-1114    files = sorted(files)
-1115
-1116    cnfgs = []
-1117    realsamples = []
-1118    imagsamples = []
-1119    repnum = 0
-1120    for file in files:
-1121        with open(path + "/" + file, "rb") as fp:
+1101            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
+1102            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
+1103            cnfgs.append([])
+1104            realsamples.append([])
+1105            imagsamples.append([])
+1106            for t in range(tmax):
+1107                realsamples[repnum].append([])
+1108                imagsamples[repnum].append([])
+1109
+1110            while True:
+1111                cnfgt = fp.read(chunksize)
+1112                if not cnfgt:
+1113                    break
+1114                asascii = struct.unpack(packstr, cnfgt)
+1115                cnfg = asascii[0]
+1116                cnfgs[repnum].append(cnfg)
+1117
+1118                if corr not in placesBB:
+1119                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
+1120                else:
+1121                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
 1122
-1123            t = fp.read(8)
-1124            kappa = struct.unpack('d', t)[0]
-1125            t = fp.read(8)
-1126            csw = struct.unpack('d', t)[0]
-1127            t = fp.read(8)
-1128            dF = struct.unpack('d', t)[0]
-1129            t = fp.read(8)
-1130            zF = struct.unpack('d', t)[0]
+1123                corrres = [[], []]
+1124                for i in range(len(tmpcorr)):
+1125                    corrres[i % 2].append(tmpcorr[i])
+1126                for t in range(int(len(tmpcorr) / 2)):
+1127                    realsamples[repnum][t].append(corrres[0][t])
+1128                for t in range(int(len(tmpcorr) / 2)):
+1129                    imagsamples[repnum][t].append(corrres[1][t])
+1130        repnum += 1
 1131
-1132            t = fp.read(4)
-1133            tmax = struct.unpack('i', t)[0]
-1134            t = fp.read(4)
-1135            bnd = struct.unpack('i', t)[0]
-1136
-1137            placesBI = ["gS", "gP",
-1138                        "gA", "gV",
-1139                        "gVt", "lA",
-1140                        "lV", "lVt",
-1141                        "lT", "lTt"]
-1142            placesBB = ["g1", "l1"]
-1143
-1144            # the chunks have the following structure:
-1145            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
+1132    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
+1133    for rep in range(1, repnum):
+1134        s += ", " + str(len(realsamples[rep][t]))
+1135    s += " samples"
+1136    print(s)
+1137    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
+1138
+1139    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
+1140
+1141    compObs = []
+1142
+1143    for t in range(int(len(tmpcorr) / 2)):
+1144        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
+1145                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
 1146
-1147            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
-1148            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
-1149            cnfgs.append([])
-1150            realsamples.append([])
-1151            imagsamples.append([])
-1152            for t in range(tmax):
-1153                realsamples[repnum].append([])
-1154                imagsamples[repnum].append([])
-1155
-1156            while True:
-1157                cnfgt = fp.read(chunksize)
-1158                if not cnfgt:
-1159                    break
-1160                asascii = struct.unpack(packstr, cnfgt)
-1161                cnfg = asascii[0]
-1162                cnfgs[repnum].append(cnfg)
-1163
-1164                if corr not in placesBB:
-1165                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
-1166                else:
-1167                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
-1168
-1169                corrres = [[], []]
-1170                for i in range(len(tmpcorr)):
-1171                    corrres[i % 2].append(tmpcorr[i])
-1172                for t in range(int(len(tmpcorr) / 2)):
-1173                    realsamples[repnum][t].append(corrres[0][t])
-1174                for t in range(int(len(tmpcorr) / 2)):
-1175                    imagsamples[repnum][t].append(corrres[1][t])
-1176        repnum += 1
-1177
-1178    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
-1179    for rep in range(1, repnum):
-1180        s += ", " + str(len(realsamples[rep][t]))
-1181    s += " samples"
-1182    print(s)
-1183    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
-1184
-1185    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
-1186
-1187    compObs = []
-1188
-1189    for t in range(int(len(tmpcorr) / 2)):
-1190        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
-1191                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
-1192
-1193    if len(compObs) == 1:
-1194        return compObs[0]
-1195    else:
-1196        return Corr(compObs)
+1147    if len(compObs) == 1:
+1148        return compObs[0]
+1149    else:
+1150        return Corr(compObs)
    @@ -1305,223 +1259,223 @@ - 
     16def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
- 17    """Read rwms format from given folder structure. Returns a list of length nrw
- 18
- 19    Parameters
- 20    ----------
- 21    path : str
- 22        path that contains the data files
- 23    prefix : str
- 24        all files in path that start with prefix are considered as input files.
- 25        May be used together postfix to consider only special file endings.
- 26        Prefix is ignored, if the keyword 'files' is used.
- 27    version : str
- 28        version of openQCD, default 2.0
- 29    names : list
- 30        list of names that is assigned to the data according according
- 31        to the order in the file list. Use careful, if you do not provide file names!
- 32    r_start : list
- 33        list which contains the first config to be read for each replicum
- 34    r_stop : list
- 35        list which contains the last config to be read for each replicum
- 36    r_step : int
- 37        integer that defines a fixed step size between two measurements (in units of configs)
- 38        If not given, r_step=1 is assumed.
- 39    postfix : str
- 40        postfix of the file to read, e.g. '.ms1' for openQCD-files
- 41    files : list
- 42        list which contains the filenames to be read. No automatic detection of
- 43        files performed if given.
- 44    print_err : bool
- 45        Print additional information that is useful for debugging.
- 46
- 47    Returns
- 48    -------
- 49    rwms : Obs
- 50        Reweighting factors read
- 51    """
- 52    known_oqcd_versions = ['1.4', '1.6', '2.0']
- 53    if not (version in known_oqcd_versions):
- 54        raise Exception('Unknown openQCD version defined!')
- 55    print("Working with openQCD version " + version)
- 56    if 'postfix' in kwargs:
- 57        postfix = kwargs.get('postfix')
- 58    else:
- 59        postfix = ''
- 60
- 61    if 'files' in kwargs:
- 62        known_files = kwargs.get('files')
- 63    else:
- 64        known_files = []
+            
     14def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
+ 15    """Read rwms format from given folder structure. Returns a list of length nrw
+ 16
+ 17    Parameters
+ 18    ----------
+ 19    path : str
+ 20        path that contains the data files
+ 21    prefix : str
+ 22        all files in path that start with prefix are considered as input files.
+ 23        May be used together postfix to consider only special file endings.
+ 24        Prefix is ignored, if the keyword 'files' is used.
+ 25    version : str
+ 26        version of openQCD, default 2.0
+ 27    names : list
+ 28        list of names that is assigned to the data according according
+ 29        to the order in the file list. Use careful, if you do not provide file names!
+ 30    r_start : list
+ 31        list which contains the first config to be read for each replicum
+ 32    r_stop : list
+ 33        list which contains the last config to be read for each replicum
+ 34    r_step : int
+ 35        integer that defines a fixed step size between two measurements (in units of configs)
+ 36        If not given, r_step=1 is assumed.
+ 37    postfix : str
+ 38        postfix of the file to read, e.g. '.ms1' for openQCD-files
+ 39    files : list
+ 40        list which contains the filenames to be read. No automatic detection of
+ 41        files performed if given.
+ 42    print_err : bool
+ 43        Print additional information that is useful for debugging.
+ 44
+ 45    Returns
+ 46    -------
+ 47    rwms : Obs
+ 48        Reweighting factors read
+ 49    """
+ 50    known_oqcd_versions = ['1.4', '1.6', '2.0']
+ 51    if not (version in known_oqcd_versions):
+ 52        raise Exception('Unknown openQCD version defined!')
+ 53    print("Working with openQCD version " + version)
+ 54    if 'postfix' in kwargs:
+ 55        postfix = kwargs.get('postfix')
+ 56    else:
+ 57        postfix = ''
+ 58
+ 59    if 'files' in kwargs:
+ 60        known_files = kwargs.get('files')
+ 61    else:
+ 62        known_files = []
+ 63
+ 64    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
  65
- 66    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
+ 66    replica = len(ls)
  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
+ 68    if 'r_start' in kwargs:
+ 69        r_start = kwargs.get('r_start')
+ 70        if len(r_start) != replica:
+ 71            raise Exception('r_start does not match number of replicas')
+ 72        r_start = [o if o else None for o in r_start]
+ 73    else:
+ 74        r_start = [None] * replica
+ 75
+ 76    if 'r_stop' in kwargs:
+ 77        r_stop = kwargs.get('r_stop')
+ 78        if len(r_stop) != replica:
+ 79            raise Exception('r_stop does not match number of replicas')
+ 80    else:
+ 81        r_stop = [None] * replica
+ 82
+ 83    if 'r_step' in kwargs:
+ 84        r_step = kwargs.get('r_step')
+ 85    else:
+ 86        r_step = 1
+ 87
+ 88    print('Read reweighting factors from', prefix[:-1], ',',
+ 89          replica, 'replica', end='')
+ 90
+ 91    if names is None:
+ 92        rep_names = []
+ 93        for entry in ls:
+ 94            truncated_entry = entry
+ 95            suffixes = [".dat", ".rwms", ".ms1"]
+ 96            for suffix in suffixes:
+ 97                if truncated_entry.endswith(suffix):
+ 98                    truncated_entry = truncated_entry[0:-len(suffix)]
+ 99            idx = truncated_entry.index('r')
+100            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+101    else:
+102        rep_names = names
+103
+104    rep_names = sort_names(rep_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()
+106    print_err = 0
+107    if 'print_err' in kwargs:
+108        print_err = 1
+109        print()
+110
+111    deltas = []
 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            # 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            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            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
+113    configlist = []
+114    r_start_index = []
+115    r_stop_index = []
+116
+117    for rep in range(replica):
+118        tmp_array = []
+119        with open(path + '/' + ls[rep], 'rb') as fp:
+120
+121            t = fp.read(4)  # number of reweighting factors
+122            if rep == 0:
+123                nrw = struct.unpack('i', t)[0]
+124                if version == '2.0':
+125                    nrw = int(nrw / 2)
+126                for k in range(nrw):
+127                    deltas.append([])
+128            else:
+129                if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
+130                    raise Exception('Error: different number of reweighting factors for replicum', rep)
+131
+132            for k in range(nrw):
+133                tmp_array.append([])
+134
+135            # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
+136            nfct = []
+137            if version in ['1.6', '2.0']:
+138                for i in range(nrw):
+139                    t = fp.read(4)
+140                    nfct.append(struct.unpack('i', t)[0])
+141            else:
+142                for i in range(nrw):
+143                    nfct.append(1)
+144
+145            nsrc = []
+146            for i in range(nrw):
+147                t = fp.read(4)
+148                nsrc.append(struct.unpack('i', t)[0])
+149            if version == '2.0':
+150                if not struct.unpack('i', fp.read(4))[0] == 0:
+151                    raise Exception("You are using the input for openQCD version 2.0, this is not correct.")
+152
+153            configlist.append([])
+154            while True:
+155                t = fp.read(4)
+156                if len(t) < 4:
+157                    break
+158                config_no = struct.unpack('i', t)[0]
+159                configlist[-1].append(config_no)
+160                for i in range(nrw):
+161                    if (version == '2.0'):
+162                        tmpd = _read_array_openQCD2(fp)
+163                        tmpd = _read_array_openQCD2(fp)
+164                        tmp_rw = tmpd['arr']
+165                        tmp_nfct = 1.0
+166                        for j in range(tmpd['n'][0]):
+167                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
+168                            if print_err:
+169                                print(config_no, i, j,
+170                                      np.mean(np.exp(-np.asarray(tmp_rw[j]))),
+171                                      np.std(np.exp(-np.asarray(tmp_rw[j]))))
+172                                print('Sources:',
+173                                      np.exp(-np.asarray(tmp_rw[j])))
+174                                print('Partial factor:', tmp_nfct)
+175                    elif version == '1.6' or version == '1.4':
+176                        tmp_nfct = 1.0
+177                        for j in range(nfct[i]):
+178                            t = fp.read(8 * nsrc[i])
+179                            t = fp.read(8 * nsrc[i])
+180                            tmp_rw = struct.unpack('d' * nsrc[i], t)
+181                            tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
+182                            if print_err:
+183                                print(config_no, i, j,
+184                                      np.mean(np.exp(-np.asarray(tmp_rw))),
+185                                      np.std(np.exp(-np.asarray(tmp_rw))))
+186                                print('Sources:', np.exp(-np.asarray(tmp_rw)))
+187                                print('Partial factor:', tmp_nfct)
+188                    tmp_array[i].append(tmp_nfct)
+189
+190            diffmeas = configlist[-1][-1] - configlist[-1][-2]
+191            configlist[-1] = [item // diffmeas for item in configlist[-1]]
+192            if configlist[-1][0] > 1 and diffmeas > 1:
+193                warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
+194                offset = configlist[-1][0] - 1
+195                configlist[-1] = [item - offset for item in configlist[-1]]
+196
+197            if r_start[rep] is None:
+198                r_start_index.append(0)
+199            else:
+200                try:
+201                    r_start_index.append(configlist[-1].index(r_start[rep]))
+202                except ValueError:
+203                    raise Exception('Config %d not in file with range [%d, %d]' % (
+204                        r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
+205
+206            if r_stop[rep] is None:
+207                r_stop_index.append(len(configlist[-1]) - 1)
+208            else:
+209                try:
+210                    r_stop_index.append(configlist[-1].index(r_stop[rep]))
+211                except ValueError:
+212                    raise Exception('Config %d not in file with range [%d, %d]' % (
+213                        r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
+214
+215            for k in range(nrw):
+216                deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep] + 1][::r_step])
+217
+218    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
+219        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
+220    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+221    if np.any([step != 1 for step in stepsizes]):
+222        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
+223
+224    print(',', nrw, 'reweighting factors with', nsrc, 'sources')
+225    result = []
+226    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
+227
+228    for t in range(nrw):
+229        result.append(Obs(deltas[t], rep_names, idl=idl))
+230    return result
 
    
 
 
@@ -1578,248 +1532,204 @@ Reweighting factors read
    - 
    235def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, postfix='ms', **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    postfix : str
-268        Postfix of measurement file (Default: ms)
-269    r_start : list
-270        list which contains the first config to be read for each replicum.
-271    r_stop : list
-272        list which contains the last config to be read for each replicum.
-273    r_step : int
-274        integer that defines a fixed step size between two measurements (in units of configs)
-275        If not given, r_step=1 is assumed.
-276    plaquette : bool
-277        If true extract the plaquette estimate of t0 instead.
-278    names : list
-279        list of names that is assigned to the data according according
-280        to the order in the file list. Use careful, if you do not provide file names!
-281    files : list
-282        list which contains the filenames to be read. No automatic detection of
-283        files performed if given.
-284    plot_fit : bool
-285        If true, the fit for the extraction of t0 is shown together with the data.
-286    assume_thermalization : bool
-287        If True: If the first record divided by the distance between two measurements is larger than
-288        1, it is assumed that this is due to thermalization and the first measurement belongs
-289        to the first config (default).
-290        If False: The config numbers are assumed to be traj_number // difference
-291
-292    Returns
-293    -------
-294    t0 : Obs
-295        Extracted t0
-296    """
-297
-298    if 'files' in kwargs:
-299        known_files = kwargs.get('files')
-300    else:
-301        known_files = []
+            
    233def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, postfix='ms', **kwargs):
+234    """Extract t0 from given .ms.dat files. Returns t0 as Obs.
+235
+236    It is assumed that all boundary effects have
+237    sufficiently decayed at x0=xmin.
+238    The data around the zero crossing of t^2<E> - 0.3
+239    is fitted with a linear function
+240    from which the exact root is extracted.
+241
+242    It is assumed that one measurement is performed for each config.
+243    If this is not the case, the resulting idl, as well as the handling
+244    of r_start, r_stop and r_step is wrong and the user has to correct
+245    this in the resulting observable.
+246
+247    Parameters
+248    ----------
+249    path : str
+250        Path to .ms.dat files
+251    prefix : str
+252        Ensemble prefix
+253    dtr_read : int
+254        Determines how many trajectories should be skipped
+255        when reading the ms.dat files.
+256        Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
+257    xmin : int
+258        First timeslice where the boundary
+259        effects have sufficiently decayed.
+260    spatial_extent : int
+261        spatial extent of the lattice, required for normalization.
+262    fit_range : int
+263        Number of data points left and right of the zero
+264        crossing to be included in the linear fit. (Default: 5)
+265    postfix : str
+266        Postfix of measurement file (Default: ms)
+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, postfix, 'dat', known_files=known_files)
 302
-303    ls = _find_files(path, prefix, postfix, 'dat', known_files=known_files)
+303    replica = len(ls)
 304
-305    replica = len(ls)
-306
-307    if 'r_start' in kwargs:
-308        r_start = kwargs.get('r_start')
-309        if len(r_start) != replica:
-310            raise Exception('r_start does not match number of replicas')
-311        r_start = [o if o else None for o in r_start]
-312    else:
-313        r_start = [None] * replica
-314
-315    if 'r_stop' in kwargs:
-316        r_stop = kwargs.get('r_stop')
-317        if len(r_stop) != replica:
-318            raise Exception('r_stop does not match number of replicas')
-319    else:
-320        r_stop = [None] * replica
-321
-322    if 'r_step' in kwargs:
-323        r_step = kwargs.get('r_step')
-324    else:
-325        r_step = 1
+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    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    print('Extract t0 from', prefix, ',', replica, 'replica')
-328
-329    if 'names' in kwargs:
-330        rep_names = kwargs.get('names')
-331    else:
-332        rep_names = []
-333        for entry in ls:
-334            truncated_entry = entry.split('.')[0]
-335            idx = truncated_entry.index('r')
-336            rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
+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    Ysum = []
-339
-340    configlist = []
-341    r_start_index = []
-342    r_stop_index = []
+338    configlist = []
+339    r_start_index = []
+340    r_stop_index = []
+341
+342    for rep in range(replica):
 343
-344    for rep in range(replica):
-345
-346        with open(path + '/' + ls[rep], 'rb') as fp:
-347            t = fp.read(12)
-348            header = struct.unpack('iii', t)
-349            if rep == 0:
-350                dn = header[0]
-351                nn = header[1]
-352                tmax = header[2]
-353            elif dn != header[0] or nn != header[1] or tmax != header[2]:
-354                raise Exception('Replica parameters do not match.')
-355
-356            t = fp.read(8)
-357            if rep == 0:
-358                eps = struct.unpack('d', t)[0]
-359                print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
-360            elif eps != struct.unpack('d', t)[0]:
-361                raise Exception('Values for eps do not match among replica.')
+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            Ysl = []
-364
-365            configlist.append([])
-366            while True:
-367                t = fp.read(4)
-368                if (len(t) < 4):
-369                    break
-370                nc = struct.unpack('i', t)[0]
-371                configlist[-1].append(nc)
-372
-373                t = fp.read(8 * tmax * (nn + 1))
-374                if kwargs.get('plaquette'):
-375                    if nc % dtr_read == 0:
-376                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
-377                t = fp.read(8 * tmax * (nn + 1))
-378                if not kwargs.get('plaquette'):
-379                    if nc % dtr_read == 0:
-380                        Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
-381                t = fp.read(8 * tmax * (nn + 1))
-382
-383        Ysum.append([])
-384        for i, item in enumerate(Ysl):
-385            Ysum[-1].append([np.mean(item[current + xmin:
-386                             current + tmax - xmin])
-387                            for current in range(0, len(item), tmax)])
-388
-389        diffmeas = configlist[-1][-1] - configlist[-1][-2]
-390        configlist[-1] = [item // diffmeas for item in configlist[-1]]
-391        if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
-392            warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
-393            offset = configlist[-1][0] - 1
-394            configlist[-1] = [item - offset for item in configlist[-1]]
-395
-396        if r_start[rep] is None:
-397            r_start_index.append(0)
-398        else:
-399            try:
-400                r_start_index.append(configlist[-1].index(r_start[rep]))
-401            except ValueError:
-402                raise Exception('Config %d not in file with range [%d, %d]' % (
-403                    r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
-404
-405        if r_stop[rep] is None:
-406            r_stop_index.append(len(configlist[-1]) - 1)
-407        else:
-408            try:
-409                r_stop_index.append(configlist[-1].index(r_stop[rep]))
-410            except ValueError:
-411                raise Exception('Config %d not in file with range [%d, %d]' % (
-412                    r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
-413
-414    if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
-415        raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
-416    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
-417    if np.any([step != 1 for step in stepsizes]):
-418        warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
-419
-420    idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]] + 1, r_step) for rep in range(replica)]
-421    t2E_dict = {}
-422    for n in range(nn + 1):
-423        samples = []
-424        for nrep, rep in enumerate(Ysum):
-425            samples.append([])
-426            for cnfg in rep:
-427                samples[-1].append(cnfg[n])
-428            samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep] + 1][::r_step]
-429        new_obs = Obs(samples, rep_names, idl=idl)
-430        t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
-431
-432    zero_crossing = np.argmax(np.array(
-433        [o.value for o in t2E_dict.values()]) > 0.0)
-434
-435    x = list(t2E_dict.keys())[zero_crossing - fit_range:
-436                              zero_crossing + fit_range]
-437    y = list(t2E_dict.values())[zero_crossing - fit_range:
-438                                zero_crossing + fit_range]
-439    [o.gamma_method() for o in y]
-440
-441    fit_result = fit_lin(x, y)
-442
-443    if kwargs.get('plot_fit'):
-444        plt.figure()
-445        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
-446        ax0 = plt.subplot(gs[0])
-447        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
-448        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
-449        [o.gamma_method() for o in ymore]
-450        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
-451        xplot = np.linspace(np.min(x), np.max(x))
-452        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
-453        [yi.gamma_method() for yi in yplot]
-454        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
-455        retval = (-fit_result[0] / fit_result[1])
-456        retval.gamma_method()
-457        ylim = ax0.get_ylim()
-458        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
-459        ax0.set_ylim(ylim)
-460        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
-461        xlim = ax0.get_xlim()
-462
-463        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
-464        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
-465        ax1 = plt.subplot(gs[1])
-466        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
-467        ax1.tick_params(direction='out')
-468        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
-469        ax1.axhline(y=0.0, ls='--', color='k')
-470        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
-471        ax1.set_xlim(xlim)
-472        ax1.set_ylabel('Residuals')
-473        ax1.set_xlabel(r'$t/a^2$')
-474
-475        plt.draw()
-476    return -fit_result[0] / fit_result[1]
+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        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    return fit_t0(t2E_dict, fit_range, plot_fit=kwargs.get('plot_fit'))
 
    
 
 
@@ -1902,57 +1812,57 @@ Extracted t0
    - 
    564def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
-565    """Read the topologial charge based on openQCD gradient flow measurements.
-566
-567    Parameters
-568    ----------
-569    path : str
-570        path of the measurement files
-571    prefix : str
-572        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
-573        Ignored if file names are passed explicitly via keyword files.
-574    c : double
-575        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
-576    dtr_cnfg : int
-577        (optional) parameter that specifies the number of measurements
-578        between two configs.
-579        If it is not set, the distance between two measurements
-580        in the file is assumed to be the distance between two configurations.
-581    steps : int
-582        (optional) Distance between two configurations in units of trajectories /
-583         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-584    version : str
-585        Either openQCD or sfqcd, depending on the data.
-586    L : int
-587        spatial length of the lattice in L/a.
-588        HAS to be set if version != sfqcd, since openQCD does not provide
-589        this in the header
-590    r_start : list
-591        list which contains the first config to be read for each replicum.
-592    r_stop : list
-593        list which contains the last config to be read for each replicum.
-594    files : list
-595        specify the exact files that need to be read
-596        from path, practical if e.g. only one replicum is needed
-597    postfix : str
-598        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
-599    names : list
-600        Alternative labeling for replicas/ensembles.
-601        Has to have the appropriate length.
-602    Zeuthen_flow : bool
-603        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-604        for version=='sfqcd' If False, the Wilson flow is used.
-605    integer_charge : bool
-606        If True, the charge is rounded towards the nearest integer on each config.
-607
-608    Returns
-609    -------
-610    result : Obs
-611        Read topological charge
-612    """
-613
-614    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
+            
    518def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
+519    """Read the topologial charge based on openQCD gradient flow measurements.
+520
+521    Parameters
+522    ----------
+523    path : str
+524        path of the measurement files
+525    prefix : str
+526        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+527        Ignored if file names are passed explicitly via keyword files.
+528    c : double
+529        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+530    dtr_cnfg : int
+531        (optional) parameter that specifies the number of measurements
+532        between two configs.
+533        If it is not set, the distance between two measurements
+534        in the file is assumed to be the distance between two configurations.
+535    steps : int
+536        (optional) Distance between two configurations in units of trajectories /
+537         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+538    version : str
+539        Either openQCD or sfqcd, depending on the data.
+540    L : int
+541        spatial length of the lattice in L/a.
+542        HAS to be set if version != sfqcd, since openQCD does not provide
+543        this in the header
+544    r_start : list
+545        list which contains the first config to be read for each replicum.
+546    r_stop : list
+547        list which contains the last config to be read for each replicum.
+548    files : list
+549        specify the exact files that need to be read
+550        from path, practical if e.g. only one replicum is needed
+551    postfix : str
+552        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+553    names : list
+554        Alternative labeling for replicas/ensembles.
+555        Has to have the appropriate length.
+556    Zeuthen_flow : bool
+557        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+558        for version=='sfqcd' If False, the Wilson flow is used.
+559    integer_charge : bool
+560        If True, the charge is rounded towards the nearest integer on each config.
+561
+562    Returns
+563    -------
+564    result : Obs
+565        Read topological charge
+566    """
+567
+568    return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
 
    
 
 
@@ -2022,76 +1932,76 @@ Read topological charge
    - 
    617def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
-618    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
+            
    571def read_gf_coupling(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):
+572    """Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
+573
+574    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.
+575
+576    Parameters
+577    ----------
+578    path : str
+579        path of the measurement files
+580    prefix : str
+581        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
+582        Ignored if file names are passed explicitly via keyword files.
+583    c : double
+584        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
+585    dtr_cnfg : int
+586        (optional) parameter that specifies the number of measurements
+587        between two configs.
+588        If it is not set, the distance between two measurements
+589        in the file is assumed to be the distance between two configurations.
+590    steps : int
+591        (optional) Distance between two configurations in units of trajectories /
+592         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+593    r_start : list
+594        list which contains the first config to be read for each replicum.
+595    r_stop : list
+596        list which contains the last config to be read for each replicum.
+597    files : list
+598        specify the exact files that need to be read
+599        from path, practical if e.g. only one replicum is needed
+600    names : list
+601        Alternative labeling for replicas/ensembles.
+602        Has to have the appropriate length.
+603    postfix : str
+604        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
+605    Zeuthen_flow : bool
+606        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
+607    """
+608
+609    if c != 0.3:
+610        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
+611
+612    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)
+613    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)
+614    L = plaq.tag["L"]
+615    T = plaq.tag["T"]
+616
+617    if T != L:
+618        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
 619
-620    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.
-621
-622    Parameters
-623    ----------
-624    path : str
-625        path of the measurement files
-626    prefix : str
-627        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat.
-628        Ignored if file names are passed explicitly via keyword files.
-629    c : double
-630        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
-631    dtr_cnfg : int
-632        (optional) parameter that specifies the number of measurements
-633        between two configs.
-634        If it is not set, the distance between two measurements
-635        in the file is assumed to be the distance between two configurations.
-636    steps : int
-637        (optional) Distance between two configurations in units of trajectories /
-638         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-639    r_start : list
-640        list which contains the first config to be read for each replicum.
-641    r_stop : list
-642        list which contains the last config to be read for each replicum.
-643    files : list
-644        specify the exact files that need to be read
-645        from path, practical if e.g. only one replicum is needed
-646    names : list
-647        Alternative labeling for replicas/ensembles.
-648        Has to have the appropriate length.
-649    postfix : str
-650        postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
-651    Zeuthen_flow : bool
-652        (optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
-653    """
-654
-655    if c != 0.3:
-656        raise Exception("The required lattice norm is only implemented for c=0.3 at the moment.")
-657
-658    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)
-659    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)
-660    L = plaq.tag["L"]
-661    T = plaq.tag["T"]
-662
-663    if T != L:
-664        raise Exception("The required lattice norm is only implemented for T=L at the moment.")
-665
-666    if Zeuthen_flow is not True:
-667        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
-668
-669    t = (c * L) ** 2 / 8
-670
-671    normdict = {4: 0.012341170468270,
-672                6: 0.010162691462430,
-673                8: 0.009031614807931,
-674                10: 0.008744966371393,
-675                12: 0.008650917856809,
-676                14: 8.611154391267955E-03,
-677                16: 0.008591758449508,
-678                20: 0.008575359627103,
-679                24: 0.008569387847540,
-680                28: 8.566803713382559E-03,
-681                32: 0.008565541650006,
-682                40: 8.564480684962046E-03,
-683                48: 8.564098025073460E-03,
-684                64: 8.563853943383087E-03}
-685
-686    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
+620    if Zeuthen_flow is not True:
+621        raise Exception("The required lattice norm is only implemented for the Zeuthen flow at the moment.")
+622
+623    t = (c * L) ** 2 / 8
+624
+625    normdict = {4: 0.012341170468270,
+626                6: 0.010162691462430,
+627                8: 0.009031614807931,
+628                10: 0.008744966371393,
+629                12: 0.008650917856809,
+630                14: 8.611154391267955E-03,
+631                16: 0.008591758449508,
+632                20: 0.008575359627103,
+633                24: 0.008569387847540,
+634                28: 8.566803713382559E-03,
+635                32: 0.008565541650006,
+636                40: 8.564480684962046E-03,
+637                48: 8.564098025073460E-03,
+638                64: 8.563853943383087E-03}
+639
+640    return t * t * (5 / 3 * plaq - 1 / 12 * C2x1) / normdict[L]
 
    
 
 
@@ -2147,30 +2057,30 @@ postfix of the file to read, e.g. '.gfms.dat' for openQCD-files
    - 
    961def qtop_projection(qtop, target=0):
-962    """Returns the projection to the topological charge sector defined by target.
-963
-964    Parameters
-965    ----------
-966    path : Obs
-967        Topological charge.
-968    target : int
-969        Specifies the topological sector to be reweighted to (default 0)
-970
-971    Returns
-972    -------
-973    reto : Obs
-974        projection to the topological charge sector defined by target
-975    """
-976    if qtop.reweighted:
-977        raise Exception('You can not use a reweighted observable for reweighting!')
-978
-979    proj_qtop = []
-980    for n in qtop.deltas:
-981        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
-982
-983    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
-984    return reto
+            
    915def qtop_projection(qtop, target=0):
+916    """Returns the projection to the topological charge sector defined by target.
+917
+918    Parameters
+919    ----------
+920    path : Obs
+921        Topological charge.
+922    target : int
+923        Specifies the topological sector to be reweighted to (default 0)
+924
+925    Returns
+926    -------
+927    reto : Obs
+928        projection to the topological charge sector defined by target
+929    """
+930    if qtop.reweighted:
+931        raise Exception('You can not use a reweighted observable for reweighting!')
+932
+933    proj_qtop = []
+934    for n in qtop.deltas:
+935        proj_qtop.append(np.array([1 if round(qtop.r_values[n] + q) == target else 0 for q in qtop.deltas[n]]))
+936
+937    reto = Obs(proj_qtop, qtop.names, idl=[qtop.idl[name] for name in qtop.names])
+938    return reto
 
    
 
 
@@ -2206,62 +2116,62 @@ projection to the topological charge sector defined by target
    - 
     987def read_qtop_sector(path, prefix, c, target=0, **kwargs):
- 988    """Constructs reweighting factors to a specified topological sector.
- 989
- 990    Parameters
- 991    ----------
- 992    path : str
- 993        path of the measurement files
- 994    prefix : str
- 995        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
- 996    c : double
- 997        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
- 998    target : int
- 999        Specifies the topological sector to be reweighted to (default 0)
-1000    dtr_cnfg : int
-1001        (optional) parameter that specifies the number of trajectories
-1002        between two configs.
-1003        if it is not set, the distance between two measurements
-1004        in the file is assumed to be the distance between two configurations.
-1005    steps : int
-1006        (optional) Distance between two configurations in units of trajectories /
-1007         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
-1008    version : str
-1009        version string of the openQCD (sfqcd) version used to create
-1010        the ensemble. Default is 2.0. May also be set to sfqcd.
-1011    L : int
-1012        spatial length of the lattice in L/a.
-1013        HAS to be set if version != sfqcd, since openQCD does not provide
-1014        this in the header
-1015    r_start : list
-1016        offset of the first ensemble, making it easier to match
-1017        later on with other Obs
-1018    r_stop : list
-1019        last configurations that need to be read (per replicum)
-1020    files : list
-1021        specify the exact files that need to be read
-1022        from path, practical if e.g. only one replicum is needed
-1023    names : list
-1024        Alternative labeling for replicas/ensembles.
-1025        Has to have the appropriate length
-1026    Zeuthen_flow : bool
-1027        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
-1028        for version=='sfqcd' If False, the Wilson flow is used.
-1029
-1030    Returns
-1031    -------
-1032    reto : Obs
-1033        projection to the topological charge sector defined by target
-1034    """
-1035
-1036    if not isinstance(target, int):
-1037        raise Exception("'target' has to be an integer.")
-1038
-1039    kwargs['integer_charge'] = True
-1040    qtop = read_qtop(path, prefix, c, **kwargs)
-1041
-1042    return qtop_projection(qtop, target=target)
+            
    941def read_qtop_sector(path, prefix, c, target=0, **kwargs):
+942    """Constructs reweighting factors to a specified topological sector.
+943
+944    Parameters
+945    ----------
+946    path : str
+947        path of the measurement files
+948    prefix : str
+949        prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
+950    c : double
+951        Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
+952    target : int
+953        Specifies the topological sector to be reweighted to (default 0)
+954    dtr_cnfg : int
+955        (optional) parameter that specifies the number of trajectories
+956        between two configs.
+957        if it is not set, the distance between two measurements
+958        in the file is assumed to be the distance between two configurations.
+959    steps : int
+960        (optional) Distance between two configurations in units of trajectories /
+961         cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
+962    version : str
+963        version string of the openQCD (sfqcd) version used to create
+964        the ensemble. Default is 2.0. May also be set to sfqcd.
+965    L : int
+966        spatial length of the lattice in L/a.
+967        HAS to be set if version != sfqcd, since openQCD does not provide
+968        this in the header
+969    r_start : list
+970        offset of the first ensemble, making it easier to match
+971        later on with other Obs
+972    r_stop : list
+973        last configurations that need to be read (per replicum)
+974    files : list
+975        specify the exact files that need to be read
+976        from path, practical if e.g. only one replicum is needed
+977    names : list
+978        Alternative labeling for replicas/ensembles.
+979        Has to have the appropriate length
+980    Zeuthen_flow : bool
+981        (optional) If True, the Zeuthen flow is used for Qtop. Only possible
+982        for version=='sfqcd' If False, the Wilson flow is used.
+983
+984    Returns
+985    -------
+986    reto : Obs
+987        projection to the topological charge sector defined by target
+988    """
+989
+990    if not isinstance(target, int):
+991        raise Exception("'target' has to be an integer.")
+992
+993    kwargs['integer_charge'] = True
+994    qtop = read_qtop(path, prefix, c, **kwargs)
+995
+996    return qtop_projection(qtop, target=target)
 
    
 
 
@@ -2330,159 +2240,159 @@ projection to the topological charge sector defined by target
    - 
    1045def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
-1046    """
-1047    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
-1048
-1049    Parameters
-1050    ----------
-1051    path : str
-1052        The directory to search for the files in.
-1053    prefix : str
-1054        The prefix to match the files against.
-1055    qc : str
-1056        The quark combination extension to match the files against.
-1057    corr : str
-1058        The correlator to extract data for.
-1059    sep : str, optional
-1060        The separator to use when parsing the replika names.
-1061    **kwargs
-1062        Additional keyword arguments. The following keyword arguments are recognized:
-1063
-1064        - names (List[str]): A list of names to use for the replicas.
-1065
-1066    Returns
-1067    -------
-1068    Corr
-1069        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
-1070    or
-1071    CObs
-1072        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
-1073
-1074
-1075    Raises
-1076    ------
-1077    FileNotFoundError
-1078        If no files matching the specified prefix and quark combination extension are found in the specified directory.
-1079    IOError
-1080        If there is an error reading a file.
-1081    struct.error
-1082        If there is an error unpacking binary data.
-1083    """
-1084
-1085    # found = []
-1086    files = []
-1087    names = []
-1088
-1089    # test if the input is correct
-1090    if qc not in ['dd', 'ud', 'du', 'uu']:
-1091        raise Exception("Unknown quark conbination!")
-1092
-1093    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
-1094        raise Exception("Unknown correlator!")
-1095
-1096    if "files" in kwargs:
-1097        known_files = kwargs.get("files")
-1098    else:
-1099        known_files = []
-1100    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
+            
     999def read_ms5_xsf(path, prefix, qc, corr, sep="r", **kwargs):
+1000    """
+1001    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a `Corr` object containing the data.
+1002
+1003    Parameters
+1004    ----------
+1005    path : str
+1006        The directory to search for the files in.
+1007    prefix : str
+1008        The prefix to match the files against.
+1009    qc : str
+1010        The quark combination extension to match the files against.
+1011    corr : str
+1012        The correlator to extract data for.
+1013    sep : str, optional
+1014        The separator to use when parsing the replika names.
+1015    **kwargs
+1016        Additional keyword arguments. The following keyword arguments are recognized:
+1017
+1018        - names (List[str]): A list of names to use for the replicas.
+1019
+1020    Returns
+1021    -------
+1022    Corr
+1023        A complex valued `Corr` object containing the data read from the files. In case of boudary to bulk correlators.
+1024    or
+1025    CObs
+1026        A complex valued `CObs` object containing the data read from the files. In case of boudary to boundary correlators.
+1027
+1028
+1029    Raises
+1030    ------
+1031    FileNotFoundError
+1032        If no files matching the specified prefix and quark combination extension are found in the specified directory.
+1033    IOError
+1034        If there is an error reading a file.
+1035    struct.error
+1036        If there is an error unpacking binary data.
+1037    """
+1038
+1039    # found = []
+1040    files = []
+1041    names = []
+1042
+1043    # test if the input is correct
+1044    if qc not in ['dd', 'ud', 'du', 'uu']:
+1045        raise Exception("Unknown quark conbination!")
+1046
+1047    if corr not in ["gS", "gP", "gA", "gV", "gVt", "lA", "lV", "lVt", "lT", "lTt", "g1", "l1"]:
+1048        raise Exception("Unknown correlator!")
+1049
+1050    if "files" in kwargs:
+1051        known_files = kwargs.get("files")
+1052    else:
+1053        known_files = []
+1054    files = _find_files(path, prefix, "ms5_xsf_" + qc, "dat", known_files=known_files)
+1055
+1056    if "names" in kwargs:
+1057        names = kwargs.get("names")
+1058    else:
+1059        for f in files:
+1060            if not sep == "":
+1061                se = f.split(".")[0]
+1062                for s in f.split(".")[1:-2]:
+1063                    se += "." + s
+1064                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
+1065            else:
+1066                names.append(prefix)
+1067
+1068    names = sorted(names)
+1069    files = sorted(files)
+1070
+1071    cnfgs = []
+1072    realsamples = []
+1073    imagsamples = []
+1074    repnum = 0
+1075    for file in files:
+1076        with open(path + "/" + file, "rb") as fp:
+1077
+1078            t = fp.read(8)
+1079            kappa = struct.unpack('d', t)[0]
+1080            t = fp.read(8)
+1081            csw = struct.unpack('d', t)[0]
+1082            t = fp.read(8)
+1083            dF = struct.unpack('d', t)[0]
+1084            t = fp.read(8)
+1085            zF = struct.unpack('d', t)[0]
+1086
+1087            t = fp.read(4)
+1088            tmax = struct.unpack('i', t)[0]
+1089            t = fp.read(4)
+1090            bnd = struct.unpack('i', t)[0]
+1091
+1092            placesBI = ["gS", "gP",
+1093                        "gA", "gV",
+1094                        "gVt", "lA",
+1095                        "lV", "lVt",
+1096                        "lT", "lTt"]
+1097            placesBB = ["g1", "l1"]
+1098
+1099            # the chunks have the following structure:
+1100            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
 1101
-1102    if "names" in kwargs:
-1103        names = kwargs.get("names")
-1104    else:
-1105        for f in files:
-1106            if not sep == "":
-1107                se = f.split(".")[0]
-1108                for s in f.split(".")[1:-2]:
-1109                    se += "." + s
-1110                names.append(se.split(sep)[0] + "|r" + se.split(sep)[1])
-1111            else:
-1112                names.append(prefix)
-1113
-1114    names = sorted(names)
-1115    files = sorted(files)
-1116
-1117    cnfgs = []
-1118    realsamples = []
-1119    imagsamples = []
-1120    repnum = 0
-1121    for file in files:
-1122        with open(path + "/" + file, "rb") as fp:
+1102            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
+1103            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
+1104            cnfgs.append([])
+1105            realsamples.append([])
+1106            imagsamples.append([])
+1107            for t in range(tmax):
+1108                realsamples[repnum].append([])
+1109                imagsamples[repnum].append([])
+1110
+1111            while True:
+1112                cnfgt = fp.read(chunksize)
+1113                if not cnfgt:
+1114                    break
+1115                asascii = struct.unpack(packstr, cnfgt)
+1116                cnfg = asascii[0]
+1117                cnfgs[repnum].append(cnfg)
+1118
+1119                if corr not in placesBB:
+1120                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
+1121                else:
+1122                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
 1123
-1124            t = fp.read(8)
-1125            kappa = struct.unpack('d', t)[0]
-1126            t = fp.read(8)
-1127            csw = struct.unpack('d', t)[0]
-1128            t = fp.read(8)
-1129            dF = struct.unpack('d', t)[0]
-1130            t = fp.read(8)
-1131            zF = struct.unpack('d', t)[0]
+1124                corrres = [[], []]
+1125                for i in range(len(tmpcorr)):
+1126                    corrres[i % 2].append(tmpcorr[i])
+1127                for t in range(int(len(tmpcorr) / 2)):
+1128                    realsamples[repnum][t].append(corrres[0][t])
+1129                for t in range(int(len(tmpcorr) / 2)):
+1130                    imagsamples[repnum][t].append(corrres[1][t])
+1131        repnum += 1
 1132
-1133            t = fp.read(4)
-1134            tmax = struct.unpack('i', t)[0]
-1135            t = fp.read(4)
-1136            bnd = struct.unpack('i', t)[0]
-1137
-1138            placesBI = ["gS", "gP",
-1139                        "gA", "gV",
-1140                        "gVt", "lA",
-1141                        "lV", "lVt",
-1142                        "lT", "lTt"]
-1143            placesBB = ["g1", "l1"]
-1144
-1145            # the chunks have the following structure:
-1146            # confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
+1133    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
+1134    for rep in range(1, repnum):
+1135        s += ", " + str(len(realsamples[rep][t]))
+1136    s += " samples"
+1137    print(s)
+1138    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
+1139
+1140    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
+1141
+1142    compObs = []
+1143
+1144    for t in range(int(len(tmpcorr) / 2)):
+1145        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
+1146                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
 1147
-1148            chunksize = 4 + (8 * 2 * tmax * 10) + (8 * 2 * 2)
-1149            packstr = '=i' + ('d' * 2 * tmax * 10) + ('d' * 2 * 2)
-1150            cnfgs.append([])
-1151            realsamples.append([])
-1152            imagsamples.append([])
-1153            for t in range(tmax):
-1154                realsamples[repnum].append([])
-1155                imagsamples[repnum].append([])
-1156
-1157            while True:
-1158                cnfgt = fp.read(chunksize)
-1159                if not cnfgt:
-1160                    break
-1161                asascii = struct.unpack(packstr, cnfgt)
-1162                cnfg = asascii[0]
-1163                cnfgs[repnum].append(cnfg)
-1164
-1165                if corr not in placesBB:
-1166                    tmpcorr = asascii[1 + 2 * tmax * placesBI.index(corr):1 + 2 * tmax * placesBI.index(corr) + 2 * tmax]
-1167                else:
-1168                    tmpcorr = asascii[1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr):1 + 2 * tmax * len(placesBI) + 2 * placesBB.index(corr) + 2]
-1169
-1170                corrres = [[], []]
-1171                for i in range(len(tmpcorr)):
-1172                    corrres[i % 2].append(tmpcorr[i])
-1173                for t in range(int(len(tmpcorr) / 2)):
-1174                    realsamples[repnum][t].append(corrres[0][t])
-1175                for t in range(int(len(tmpcorr) / 2)):
-1176                    imagsamples[repnum][t].append(corrres[1][t])
-1177        repnum += 1
-1178
-1179    s = "Read correlator " + corr + " from " + str(repnum) + " replika with " + str(len(realsamples[0][t]))
-1180    for rep in range(1, repnum):
-1181        s += ", " + str(len(realsamples[rep][t]))
-1182    s += " samples"
-1183    print(s)
-1184    print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF, "bnd:", bnd)
-1185
-1186    # we have the data now... but we need to re format the whole thing and put it into Corr objects.
-1187
-1188    compObs = []
-1189
-1190    for t in range(int(len(tmpcorr) / 2)):
-1191        compObs.append(CObs(Obs([realsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs),
-1192                            Obs([imagsamples[rep][t] for rep in range(repnum)], names=names, idl=cnfgs)))
-1193
-1194    if len(compObs) == 1:
-1195        return compObs[0]
-1196    else:
-1197        return Corr(compObs)
+1148    if len(compObs) == 1:
+1149        return compObs[0]
+1150    else:
+1151        return Corr(compObs)
 
    
 
 
diff --git a/docs/pyerrors/input/pandas.html b/docs/pyerrors/input/pandas.html
index 14b7940b..345f182c 100644
--- a/docs/pyerrors/input/pandas.html
+++ b/docs/pyerrors/input/pandas.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.input.pandas API documentation
 
     
diff --git a/docs/pyerrors/input/sfcf.html b/docs/pyerrors/input/sfcf.html
index 399fe256..8479d85a 100644
--- a/docs/pyerrors/input/sfcf.html
+++ b/docs/pyerrors/input/sfcf.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.input.sfcf API documentation
 
     
diff --git a/docs/pyerrors/input/utils.html b/docs/pyerrors/input/utils.html
index 0c79029c..35d788be 100644
--- a/docs/pyerrors/input/utils.html
+++ b/docs/pyerrors/input/utils.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.input.utils API documentation
 
     
diff --git a/docs/pyerrors/linalg.html b/docs/pyerrors/linalg.html
index 90c4b97e..ace6383d 100644
--- a/docs/pyerrors/linalg.html
+++ b/docs/pyerrors/linalg.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.linalg API documentation
 
     
diff --git a/docs/pyerrors/misc.html b/docs/pyerrors/misc.html
index 363a6d26..59a399e5 100644
--- a/docs/pyerrors/misc.html
+++ b/docs/pyerrors/misc.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.misc API documentation
 
     
diff --git a/docs/pyerrors/mpm.html b/docs/pyerrors/mpm.html
index 5c3b6efd..40eabfd8 100644
--- a/docs/pyerrors/mpm.html
+++ b/docs/pyerrors/mpm.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.mpm API documentation
 
     
diff --git a/docs/pyerrors/obs.html b/docs/pyerrors/obs.html
index 1d790a77..af2eb985 100644
--- a/docs/pyerrors/obs.html
+++ b/docs/pyerrors/obs.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.obs API documentation
 
     
diff --git a/docs/pyerrors/roots.html b/docs/pyerrors/roots.html
index c45c2525..5836a34c 100644
--- a/docs/pyerrors/roots.html
+++ b/docs/pyerrors/roots.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.roots API documentation
 
     
diff --git a/docs/pyerrors/version.html b/docs/pyerrors/version.html
index 24b90e43..fd00dbdd 100644
--- a/docs/pyerrors/version.html
+++ b/docs/pyerrors/version.html
@@ -3,7 +3,7 @@
 
     
     
-    
+    
     pyerrors.version API documentation
 
     
diff --git a/docs/search.js b/docs/search.js
index ddd5b65b..c67e8db8 100644
--- a/docs/search.js
+++ b/docs/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
 /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oWhat is pyerrors?\n\n
    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:
    \n\n
      \n
    • automatic differentiation for exact linear error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
      • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
      • \n
    • coherent error propagation for data from different Markov chains.
      • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
      • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
      • \n
    \n\n
    More detailed examples can found in the GitHub repository \"badge\".
    \n\n
    If you use pyerrors for research that leads to a publication please consider citing:
    \n\n
      \n
    • Fabian Joswig, Simon Kuberski, Justus T. Kuhlmann, Jan Neuendorf, pyerrors: a python framework for error analysis of Monte Carlo data. [arXiv:2209.14371 [hep-lat]].
      • \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
      • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
      • \n
    \n\n
    and
    \n\n
      \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
      • \n
    \n\n
    where applicable.
    \n\n
    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.
    \n\n
    Installation
    \n\n
    Install the most recent release using pip and pypi:
    \n\n
    \n
    pip install pyerrors     # Fresh install\npip install -U pyerrors  # Update\n
    \n
    \n\n
    Install the most recent release using conda and conda-forge:
    \n\n
    \n
    conda install -c conda-forge pyerrors  # Fresh install\nconda update -c conda-forge pyerrors   # Update\n
    \n
    \n\n
    Install the current develop version:
    \n\n
    \n
    pip install git+https://github.com/fjosw/pyerrors.git@develop\n
    \n
    \n\n
    Basic example
    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n
    \n\n
    The Obs class
    \n\n
    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble. It is crucial for the correct error propagation that observations from the same Monte Carlo history are labeled with the same name. See Multiple ensembles/replica for details.
    \n\n
    \n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n
    \n\n
    Error propagation
    \n\n
    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.
    \n\n
    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.
    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n
    \n\n
    Error estimation
    \n\n
    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.
    \n\n
    \n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n
    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.
    \n\n
    \n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n
    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_rho.
    \n\n
    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.
    \n\n
    Exponential tails
    \n\n
    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.
    \n\n
    \n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n
    For the full API see pyerrors.obs.Obs.gamma_method.
    \n\n
    Multiple ensembles/replica
    \n\n
    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.
    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n
    \n\n
    Observables from the same Monte Carlo chain have to be initialized with the same name for correct error propagation. If different names were used in this case the data would be treated as statistically independent resulting in loss of relevant information and a potential over or under estimate of the statistical error.
    \n\n
    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.
    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n
    \n\n
    Error estimation for multiple ensembles
    \n\n
    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.
    \n\n
    \n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n
    \n\n
    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.
    \n\n
    Irregular Monte Carlo chains
    \n\n
    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.
    \n\n
    \n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n
    \n\n
    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.
    \n\n
    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.
    \n\n
    For the full API see pyerrors.obs.Obs.
    \n\n
    Correlators
    \n\n
    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs
    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n
    \n\n
    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.
    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n
    \n\n
    The individual entries of a correlator can be accessed via slicing
    \n\n
    \n
    print(my_corr[3])\n> 0.3227(33)\n
    \n
    \n\n
    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.
    \n\n
    \n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n
    \n\n
    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:
    \n\n
      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
      • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
      • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
      • \n
    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
      • \n
    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
      • \n
    • Corr.plateau extracts a plateau value from the correlator in a given range.
      • \n
    • Corr.roll periodically shifts the correlator.
      • \n
    • Corr.reverse reverses the time ordering of the correlator.
      • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
      • \n
    • Corr.reweight reweights the correlator.
      • \n
    \n\n
    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).
    \n\n
    For the full API see pyerrors.correlators.Corr.
    \n\n
    Complex valued observables
    \n\n
    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.
    \n\n
    \n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n
    \n\n
    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.
    \n\n
    \n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n
    \n\n
    The Covobs class
    \n\n
    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.
    \n\n
    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.
    \n\n
    \n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n
    \n\n
    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.
    \n\n
    Correlated auxiliary data is defined similarly to above, e.g., via
    \n\n
    \n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n
    \n\n
    where RAP now is a list of two Obs that contains the two correlated parameters.
    \n\n
    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via
    \n\n
    \n
    o.covobs[k].grad\n
    \n
    \n\n
    Error propagation in iterative algorithms
    \n\n
    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least squares fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.
    \n\n
    Least squares fits
    \n\n
    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.
    \n\n
    Fit functions have to be of the following form
    \n\n
    \n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n
    \n\n
    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.
    \n\n
    Fits can then be performed via
    \n\n
    \n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n
    \n\n
    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.
    \n\n
    Data stored in Corr objects can be fitted directly using the Corr.fit method.
    \n\n
    \n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n
    \n\n
    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.
    \n\n
    For fit functions with multiple independent variables the fit function can be of the form
    \n\n
    \n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n
    \n\n
    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.
    \n\n
    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.
    \n\n
    Total least squares fits
    \n\n
    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only difference being that x also has to be a list or numpy.array of Obs.
    \n\n
    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.
    \n\n
    Matrix operations
    \n\n
    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:
    \n\n
      \n
    • inv for the matrix inverse.
      • \n
    • cholseky for the Cholesky decomposition.
      • \n
    • det for the matrix determinant.
      • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
      • \n
    • eig for eigenvalues of general matrices.
      • \n
    • pinv for the Moore-Penrose pseudoinverse.
      • \n
    • svd for the singular-value-decomposition.
      • \n
    \n\n
    For the full API see pyerrors.linalg.
    \n\n
    Export data
    \n\n
    \n\n
    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:
    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
      • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
      • \n
    • Who did write the file when and on which machine?
      • \n
    \n\n
    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example
    \n\n
    \n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n
    \n\n
    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.
    \n\n
    json.gz format specification
    \n\n
    The first entries of the file provide optional auxiliary information:
    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
      • \n
    • version is a string that specifies the version of the format.
      • \n
    • who is a string that specifies the user name of the creator of the file.
      • \n
    • date is a string and contains the creation date of the file.
      • \n
    • host is a string and contains the hostname of the machine where the file has been written.
      • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
      • \n
    \n\n
    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.
    \n\n
    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:
    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
      • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
      • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or  \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
      • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
      • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
      • \n
    • data is an array that contains the data from MC chains. We will define it below.
      • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
      • \n
    \n\n
    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:
    \n\n
      \n
    • id, a string that contains the name of the ensemble
      • \n
    • replica, an array that contains an entry per replica of the ensemble.
      • \n
    \n\n
    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.
    \n\n
    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.
    \n\n
    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:
    \n\n
      \n
    • id, a string that identifies the covariance matrix
      • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
      • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
      • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable  with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
      • \n
    \n\n
    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.
    \n\n
    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.
    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "kind": "module", "doc": "
    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "kind": "class", "doc": "
    The class for a correlator (time dependent sequence of pe.Obs).
    \n\n
    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.
    \n\n
    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.
    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "kind": "function", "doc": "
    Initialize a Corr object.
    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
      • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
      • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
      • \n
    \n", "signature": "(data_input, padding=[0, 0], prange=None)"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "kind": "function", "doc": "
    Apply the gamma method to the content of the Corr.
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.gm": {"fullname": "pyerrors.correlators.Corr.gm", "modulename": "pyerrors.correlators", "qualname": "Corr.gm", "kind": "function", "doc": "
    Apply the gamma method to the content of the Corr.
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "kind": "function", "doc": "
    We need to project the Correlator with a Vector to get a single value at each timeslice.
    \n\n
    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "kind": "function", "doc": "
    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.
    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
      • \n
    • j (int):\nSecond index to be picked.
      • \n
    \n", "signature": "(self, i, j):", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "kind": "function", "doc": "
    Outputs the correlator in a plotable format.
    \n\n
    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "kind": "function", "doc": "
    Symmetrize the correlator around x0=0.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "kind": "function", "doc": "
    Anti-symmetrize the correlator around x0=0.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.is_matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.is_matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.is_matrix_symmetric", "kind": "function", "doc": "
    Checks whether a correlator matrices is symmetric on every timeslice.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "kind": "function", "doc": "
    Symmetrizes the correlator matrices on every timeslice.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "kind": "function", "doc": "
    Solve the generalized eigenvalue problem on the correlator matrix and returns the corresponding eigenvectors.
    \n\n
    The eigenvectors are sorted according to the descending eigenvalues, the zeroth eigenvector(s) correspond to the\nlargest eigenvalue(s). The eigenvector(s) for the individual states can be accessed via slicing
    \n\n
    \n
    C.GEVP(t0=2)[0]  # Ground state vector(s)\nC.GEVP(t0=2)[:3]  # Vectors for the lowest three states\n
    \n
    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for the right hand side of the GEVP according to $G(t)v_i=\\lambda_i G(t_0)v_i$
      • \n
    • ts (int):\nfixed time $G(t_s)v_i=\\lambda_i G(t_0)v_i$ if sort=None.\nIf sort=\"Eigenvector\" it gives a reference point for the sorting method.
      • \n
    • sort (string):\nIf this argument is set, a list of self.T vectors per state is returned. If it is set to None, only one vector is returned.\n
        \n
      • \"Eigenvalue\": The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
        • \n
      • \"Eigenvector\": Use the method described in arXiv:2004.10472 to find the set of v(t) belonging to the state.\nThe reference state is identified by its eigenvalue at $t=t_s$.
        • \n
      • \n
    \n\n
    Other Parameters
    \n\n
      \n
    • state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
      • \n
    \n", "signature": "(self, t0, ts=None, sort='Eigenvalue', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "kind": "function", "doc": "
    Determines the eigenvalue of the GEVP by solving and projecting the correlator
    \n\n
    Parameters
    \n\n
      \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
      • \n
    • All other parameters are identical to the ones of Corr.GEVP.
      • \n
    \n", "signature": "(self, t0, ts=None, state=0, sort='Eigenvalue'):", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "kind": "function", "doc": "
    Constructs an NxN Hankel matrix
    \n\n
    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))
    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
      • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
      • \n
    \n", "signature": "(self, N, periodic=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "kind": "function", "doc": "
    Periodically shift the correlator by dt timeslices
    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
      • \n
    \n", "signature": "(self, dt):", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "kind": "function", "doc": "
    Reverse the time ordering of the Corr
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "kind": "function", "doc": "
    Thin out a correlator to suppress correlations
    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
      • \n
    • offset (int):\nOffset the equal spacing
      • \n
    \n", "signature": "(self, spacing=2, offset=0):", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "kind": "function", "doc": "
    Correlate the correlator with another correlator or Obs
    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
      • \n
    \n", "signature": "(self, partner):", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "kind": "function", "doc": "
    Reweight the correlator.
    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
      • \n
    \n", "signature": "(self, weight, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "kind": "function", "doc": "
    Return the time symmetry average of the correlator and its partner
    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
      • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
      • \n
    \n", "signature": "(self, partner, parity=1):", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "kind": "function", "doc": "
    Return the first derivative of the correlator with respect to x0.
    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "kind": "function", "doc": "
    Return the second derivative of the correlator with respect to x0.
    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "kind": "function", "doc": "
    Returns the effective mass of the correlator as correlator object
    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)\nlogsym: uses the symmetric effective mass log(C(t-1) / C(t+1))/2
      • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
      • \n
    \n", "signature": "(self, variant='log', guess=1.0):", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "kind": "function", "doc": "
    Fits function to the data
    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
      • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
      • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
      • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "kind": "function", "doc": "
    Extract a plateau value from a Corr object
    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
      • \n
    • method (str):\nmethod to extract the plateau.\n    'fit' fits a constant to the plateau region\n    'avg', 'average' or 'mean' just average over the given timeslices.
      • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
      • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "kind": "function", "doc": "
    Sets the attribute prange of the Corr object.
    \n", "signature": "(self, prange):", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "kind": "function", "doc": "
    Plots the correlator using the tag of the correlator as label if available.
    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8].
      • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
      • \n
    • logscale (bool):\nSets y-axis to logscale.
      • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure.
      • \n
    • fit_res (Fit_result):\nFit_result object to be visualized.
      • \n
    • fit_key (str):\nKey for the fit function in Fit_result.fit_function (for combined fits).
      • \n
    • ylabel (str):\nLabel for the y-axis.
      • \n
    • save (str):\npath to file in which the figure should be saved.
      • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
      • \n
    • hide_sigma (float):\nHides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
      • \n
    • references (list):\nList of floating point values that are displayed as horizontal lines for reference.
      • \n
    • title (string):\nOptional title of the figure.
      • \n
    \n", "signature": "(\tself,\tx_range=None,\tcomp=None,\ty_range=None,\tlogscale=False,\tplateau=None,\tfit_res=None,\tfit_key=None,\tylabel=None,\tsave=None,\tauto_gamma=False,\thide_sigma=None,\treferences=None,\ttitle=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "kind": "function", "doc": "
    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
      • \n
    \n", "signature": "(self, logscale=True):", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "kind": "function", "doc": "
    Dumps the Corr into a file of chosen type
    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "kind": "function", "doc": "
    \n", "signature": "(self, print_range=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "kind": "function", "doc": "
    Project large correlation matrix to lowest states
    \n\n
    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.
    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
      • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
      • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
      • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
      • \n
    \n\n
    Notes
    \n\n
    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.
    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "kind": "module", "doc": "
    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "kind": "class", "doc": "
    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "kind": "function", "doc": "
    Initialize Covobs object.
    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
      • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(mean, cov, name, pos=None, grad=None)"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "kind": "function", "doc": "
    Return the variance (= square of the error) of the Covobs
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "kind": "module", "doc": "
    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "kind": "function", "doc": "
    Rank-3 epsilon tensor
    \n\n
    Based on https://codegolf.stackexchange.com/a/160375
    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k) of the epsilon tensor of rank 3
      • \n
    \n", "signature": "(i, j, k):", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "kind": "function", "doc": "
    Rank-4 epsilon tensor
    \n\n
    Extension of https://codegolf.stackexchange.com/a/160375
    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k,o) of the epsilon tensor of rank 4
      • \n
    \n", "signature": "(i, j, k, o):", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "kind": "function", "doc": "
    Returns gamma matrix in Grid labeling.
    \n", "signature": "(gamma_tag):", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "kind": "module", "doc": "
    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "kind": "class", "doc": "
    Represents fit results.
    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
      • \n
    • chisquare_by_dof (float):\nreduced chisquare.
      • \n
    • p_value (float):\np-value of the fit
      • \n
    • t2_p_value (float):\nHotelling t-squared p-value for correlated fits.
      • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "kind": "function", "doc": "
    Apply the gamma method to all fit parameters
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.Fit_result.gm": {"fullname": "pyerrors.fits.Fit_result.gm", "modulename": "pyerrors.fits", "qualname": "Fit_result.gm", "kind": "function", "doc": "
    Apply the gamma method to all fit parameters
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "kind": "function", "doc": "
    Performs a non-linear fit to y = func(x).\n    ```
    \n\n
    Parameters
    \n\n
      \n
    • For an uncombined fit:
      • \n
    • x (list):\nlist of floats.
      • \n
    • y (list):\nlist of Obs.
      • \n
    • func (object):\nfit function, has to be of the form
      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n
      For multiple x values func can be of the form
      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n
      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
      • \n
    • OR For a combined fit:
      • \n
    • x (dict):\ndict of lists.
      • \n
    • y (dict):\ndict of lists of Obs.
      • \n
    • funcs (dict):\ndict of objects\nfit functions have to be of the form (here a[0] is the common fit parameter)\n```python\nimport autograd.numpy as anp\nfuncs = {\"a\": func_a,\n       \"b\": func_b}
      \n\n
      def func_a(a, x):\n   return a[1] * anp.exp(-a[0] * x)
      \n\n
      def func_b(a, x):\n   return a[2] * anp.exp(-a[0] * x)
      \n\n
      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
      • \n
    • priors (dict or list, optional):\npriors can either be a dictionary with integer keys and the corresponding priors as values or\na list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters. In case of correlated fits the guess is used to perform\nan uncorrelated fit which then serves as guess for the correlated fit.
      • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
      • \n
    • tol (float, optional):\ncan be used (only for combined fits and methods other than Levenberg-Marquard) to set the tolerance for convergence\nto a different value to either speed up convergence at the cost of a larger error on the fitted parameters (and possibly\ninvalid estimates for parameter uncertainties) or smaller values to get more accurate parameter values\nThe stopping criterion depends on the method, e.g. migrad: edm_max = 0.002 * tol * errordef (EDM criterion: edm < edm_max)
      • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
      • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
      • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
      • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
      • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "kind": "function", "doc": "
    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
      • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
      • \n
    • func (object):\nfunc has to be of the form
      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n
      For multiple x values func can be of the form
      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n
      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
      • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n\n
    Notes
    \n\n
    Based on the orthogonal distance regression module of scipy.
    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
      • \n
    \n", "signature": "(x, y, func, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "kind": "function", "doc": "
    Performs a linear fit to y = n + m * x and returns two Obs n, m.
    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
      • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • fit_parameters (list[Obs]):\nLIist of fitted observables.
      • \n
    \n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "
    Generates a quantile-quantile plot of the fit result which can be used to\n   check if the residuals of the fit are gaussian distributed.
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(x, o_y, func, p, title=''):", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "kind": "function", "doc": "
    Generates a plot which compares the fit to the data and displays the corresponding residuals
    \n\n
    For uncorrelated data the residuals are expected to be distributed ~N(0,1).
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(x, y, func, fit_res, title=''):", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "kind": "function", "doc": "
    Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
    \n\n
    Returns
    \n\n
      \n
    • err (np.array(Obs)):\nError band for an array of sample values x
      • \n
    \n", "signature": "(x, func, beta):", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "kind": "function", "doc": "
    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.
    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "
    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.
    \n\n
    Jackknife samples
    \n\n
    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.
    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "kind": "module", "doc": "
    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "kind": "function", "doc": "
    Extract generic MCMC data from a bdio file
    \n\n
    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (List[Obs]):\nExtracted data
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "kind": "function", "doc": "
    Write Obs to a bdio file according to ADerrors conventions
    \n\n
    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • success (int):\nreturns 0 is successful
      • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "kind": "function", "doc": "
    Extract mesons data from a bdio file and return it as a dictionary
    \n\n
    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
    \n\n
    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (dict):\nExtracted meson data
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "kind": "function", "doc": "
    Extract dSdm data from a bdio file and return it as a dictionary
    \n\n
    The dictionary can be accessed with a tuple consisting of (type, kappa)
    \n\n
    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.dobs": {"fullname": "pyerrors.input.dobs", "modulename": "pyerrors.input.dobs", "kind": "module", "doc": "
    \n"}, "pyerrors.input.dobs.create_pobs_string": {"fullname": "pyerrors.input.dobs.create_pobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_pobs_string", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.
    \n\n
    Tags are not written or recovered automatically. The separator | is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML formatted string of the input data
      • \n
    \n", "signature": "(obsl, name, spec='', origin='', symbol=[], enstag=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_pobs": {"fullname": "pyerrors.input.dobs.write_pobs", "modulename": "pyerrors.input.dobs", "qualname": "write_pobs", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.
    \n\n
    Tags are not written or recovered automatically. The separator | is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped xml. If False, the output is an xml file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='',\torigin='',\tsymbol=[],\tenstag=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_pobs": {"fullname": "pyerrors.input.dobs.read_pobs", "modulename": "pyerrors.input.dobs", "qualname": "read_pobs", "kind": "function", "doc": "
    Import a list of Obs from an xml.gz file in the Zeuthen pobs format.
    \n\n
    Tags are not written or recovered automatically.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str or int):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nNone (default): Replica names remain unchanged.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=None):", "funcdef": "def"}, "pyerrors.input.dobs.import_dobs_string": {"fullname": "pyerrors.input.dobs.import_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "import_dobs_string", "kind": "function", "doc": "
    Import a list of Obs from a string in the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically.
    \n\n
    Parameters
    \n\n
      \n
    • content (str):\nXML string containing the data
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(content, full_output=False, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_dobs": {"fullname": "pyerrors.input.dobs.read_dobs", "modulename": "pyerrors.input.dobs", "qualname": "read_dobs", "kind": "function", "doc": "
    Import a list of Obs from an xml.gz file in the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes XML file.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.create_dobs_string": {"fullname": "pyerrors.input.dobs.create_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_dobs_string", "kind": "function", "doc": "
    Generate the string for the export of a list of Obs or structures containing Obs\nto a .xml.gz file according to the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically. The separator |is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML string generated from the data
      • \n
    \n", "signature": "(\tobsl,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_dobs": {"fullname": "pyerrors.input.dobs.write_dobs", "modulename": "pyerrors.input.dobs", "qualname": "write_dobs", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically. The separator | is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped XML. If False, the output is a XML file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "kind": "module", "doc": "
    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "kind": "function", "doc": "
    Read hadrons meson hdf5 file and extract the meson labeled 'meson'
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
      • \n
    • gammas (tuple of strings):\nInstrad of a meson label one can also provide a tuple of two strings\nindicating the gamma matrices at source and sink.\n(\"Gamma5\", \"Gamma5\") corresponds to the pseudoscalar pseudoscalar\ntwo-point function. The gammas argument dominateds over meson.
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • corr (Corr):\nCorrelator of the source sink combination in question.
      • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_DistillationContraction_hd5": {"fullname": "pyerrors.input.hadrons.read_DistillationContraction_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_DistillationContraction_hd5", "kind": "function", "doc": "
    Read hadrons DistillationContraction hdf5 files in given directory structure
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the directories to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • diagrams (list):\nList of strings of the diagrams to extract, e.g. [\"direct\", \"box\", \"cross\"].
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (dict):\nextracted DistillationContration data
      • \n
    \n", "signature": "(path, ens_id, diagrams=['direct'], idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "kind": "class", "doc": "
    ndarray(shape, dtype=float, buffer=None, offset=0,\n        strides=None, order=None)
    \n\n
    An array object represents a multidimensional, homogeneous array\nof fixed-size items.  An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)
    \n\n
    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below).  The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.
    \n\n
    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.
    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
      • \n
    • shape (tuple of ints):\nShape of created array.
      • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
      • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
      • \n
    • offset (int, optional):\nOffset of array data in buffer.
      • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
      • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
      • \n
    • data (buffer):\nThe array's elements, in memory.
      • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
      • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
      • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator.  The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
      • \n
    • imag (ndarray):\nImaginary part of the array.
      • \n
    • real (ndarray):\nReal part of the array.
      • \n
    • size (int):\nNumber of elements in the array.
      • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
      • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
      • \n
    • ndim (int):\nThe array's number of dimensions.
      • \n
    • shape (tuple of ints):\nShape of the array.
      • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2).  This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
      • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
      • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view).  The base array is where the\narray data is actually stored.
      • \n
    \n\n
    See Also
    \n\n
    array:  Construct an array. 
    \nzeros:  Create an array, each element of which is zero. 
    \nempty:  Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\"). 
    \ndtype:  Create a data-type. 
    \nnumpy.typing.NDArray:  An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.  
    \n\n
    Notes
    \n\n
    There are two modes of creating an array using __new__:
    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
      2. \n
    2. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
      3. \n
    \n\n
    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.
    \n\n
    Examples
    \n\n
    These examples illustrate the low-level ndarray constructor.  Refer\nto the See Also section above for easier ways of constructing an\nndarray.
    \n\n
    First mode, buffer is None:
    \n\n
    \n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n
    \n\n
    Second mode:
    \n\n
    \n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "kind": "variable", "doc": "
    Gamma_5 hermitean conjugate
    \n\n
    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.
    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "kind": "function", "doc": "
    Read hadrons ExternalLeg hdf5 file and output an array of CObs
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Npr_matrix):\nread Cobs-matrix
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "kind": "function", "doc": "
    Read hadrons Bilinear hdf5 file and output an array of CObs
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict[Npr_matrix]):\nextracted Bilinears
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "kind": "function", "doc": "
    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    • vertices (list):\nVertex functions to be extracted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict):\nextracted fourquark matrizes
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV']):", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "kind": "module", "doc": "
    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "kind": "function", "doc": "
    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file
    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • json_string (str):\nString for export to .json(.gz) file
      • \n
    \n", "signature": "(ol, description='', indent=1):", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to a .json(.gz) file
    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • Null
      • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True):", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "kind": "function", "doc": "
    Reconstruct a list of Obs or structures containing Obs from a json string.
    \n\n
    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
      • \n
    • or
      • \n
    • result (Obs):\nonly one observable if the list only has one entry
      • \n
    • or
      • \n
    • result (dict):\nif full_output=True
      • \n
    \n", "signature": "(json_string, verbose=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "kind": "function", "doc": "
    Import a list of Obs or structures containing Obs from a .json(.gz) file.
    \n\n
    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
      • \n
    • or
      • \n
    • result (Obs):\nonly one observable if the list only has one entry
      • \n
    • or
      • \n
    • result (dict):\nif full_output=True
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "kind": "function", "doc": "
    Export a dict of Obs or structures containing Obs to a .json(.gz) file
    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True):", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "kind": "function", "doc": "
    Import a dict of Obs or structures containing Obs from a .json(.gz) file.
    \n\n
    The following structures are supported: Obs, list, numpy.ndarray, Corr
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (Obs / list / Corr):\nRead data
      • \n
    • or
      • \n
    • data (dict):\nRead data and meta-data
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "kind": "module", "doc": "
    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "kind": "function", "doc": "
    Read pbp format from given folder structure.
    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of observables read
      • \n
    \n", "signature": "(path, prefix, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "kind": "module", "doc": "
    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "kind": "function", "doc": "
    Read rwms format from given folder structure. Returns a list of length nrw
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
      • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
      • \n
    • version (str):\nversion of openQCD, default 2.0
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • rwms (Obs):\nReweighting factors read
      • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "kind": "function", "doc": "
    Extract t0 from given .ms.dat files. Returns t0 as Obs.
    \n\n
    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.
    \n\n
    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
      • \n
    • prefix (str):\nEnsemble prefix
      • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
      • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
      • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
      • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
      • \n
    • postfix (str):\nPostfix of measurement file (Default: ms)
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
      • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
      • \n
    \n\n
    Returns
    \n\n
      \n
    • t0 (Obs):\nExtracted t0
      • \n
    \n", "signature": "(\tpath,\tprefix,\tdtr_read,\txmin,\tspatial_extent,\tfit_range=5,\tpostfix='ms',\t**kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "kind": "function", "doc": "
    Read the topologial charge based on openQCD gradient flow measurements.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Obs):\nRead topological charge
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_gf_coupling": {"fullname": "pyerrors.input.openQCD.read_gf_coupling", "modulename": "pyerrors.input.openQCD", "qualname": "read_gf_coupling", "kind": "function", "doc": "
    Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
    \n\n
    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "kind": "function", "doc": "
    Returns the projection to the topological charge sector defined by target.
    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
      • \n
    \n", "signature": "(qtop, target=0):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "kind": "function", "doc": "
    Constructs reweighting factors to a specified topological sector.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
      • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
      • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_ms5_xsf": {"fullname": "pyerrors.input.openQCD.read_ms5_xsf", "modulename": "pyerrors.input.openQCD", "qualname": "read_ms5_xsf", "kind": "function", "doc": "
    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a Corr object containing the data.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nThe directory to search for the files in.
      • \n
    • prefix (str):\nThe prefix to match the files against.
      • \n
    • qc (str):\nThe quark combination extension to match the files against.
      • \n
    • corr (str):\nThe correlator to extract data for.
      • \n
    • sep (str, optional):\nThe separator to use when parsing the replika names.
      • \n
    • **kwargs: Additional keyword arguments. The following keyword arguments are recognized:
      \n\n
        \n
      • names (List[str]): A list of names to use for the replicas.
        • \n
      • \n
    \n\n
    Returns
    \n\n
      \n
    • Corr: A complex valued Corr object containing the data read from the files. In case of boudary to bulk correlators.
      • \n
    • or
      • \n
    • CObs: A complex valued CObs object containing the data read from the files. In case of boudary to boundary correlators.
      • \n
    \n\n
    Raises
    \n\n
      \n
    • FileNotFoundError: If no files matching the specified prefix and quark combination extension are found in the specified directory.
      • \n
    • IOError: If there is an error reading a file.
      • \n
    • struct.error: If there is an error unpacking binary data.
      • \n
    \n", "signature": "(path, prefix, qc, corr, sep='r', **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas": {"fullname": "pyerrors.input.pandas", "modulename": "pyerrors.input.pandas", "kind": "module", "doc": "
    \n"}, "pyerrors.input.pandas.to_sql": {"fullname": "pyerrors.input.pandas.to_sql", "modulename": "pyerrors.input.pandas", "qualname": "to_sql", "kind": "function", "doc": "
    Write DataFrame including Obs or Corr valued columns to sqlite database.
    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be written to the database.
      • \n
    • table_name (str):\nName of the table in the database.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • if exists (str):\nHow to behave if table already exists. Options 'fail', 'replace', 'append'.
      • \n
    • gz (bool):\nIf True the json strings are gzipped.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(df, table_name, db, if_exists='fail', gz=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.read_sql": {"fullname": "pyerrors.input.pandas.read_sql", "modulename": "pyerrors.input.pandas", "qualname": "read_sql", "kind": "function", "doc": "
    Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
    \n\n
    Parameters
    \n\n
      \n
    • sql (str):\nSQL query to be executed.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
      • \n
    \n", "signature": "(sql, db, auto_gamma=False, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.dump_df": {"fullname": "pyerrors.input.pandas.dump_df", "modulename": "pyerrors.input.pandas", "qualname": "dump_df", "kind": "function", "doc": "
    Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
    \n\n
    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.
    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be dumped to a file.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • gz (bool):\nIf True, the output is a gzipped csv file. If False, the output is a csv file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(df, fname, gz=True):", "funcdef": "def"}, "pyerrors.input.pandas.load_df": {"fullname": "pyerrors.input.pandas.load_df", "modulename": "pyerrors.input.pandas", "qualname": "load_df", "kind": "function", "doc": "
    Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
      • \n
    \n", "signature": "(fname, auto_gamma=False, gz=True):", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "kind": "module", "doc": "
    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "kind": "function", "doc": "
    Read sfcf files from given folder structure.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to the sfcf files.
      • \n
    • prefix (str):\nPrefix of the sfcf files.
      • \n
    • name (str):\nName of the correlation function to read.
      • \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
      • \n
    • corr_type (str):\nType of correlation function to read. Can be\n
        \n
      • 'bi' for boundary-inner
        • \n
      • 'bb' for boundary-boundary
        • \n
      • 'bib' for boundary-inner-boundary
        • \n
      • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
      • \n
    • wf (int):\nID of wave function
      • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
      • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • ens_name (str):\nreplaces the name of the ensemble
      • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
      • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
      • \n
    • replica (list):\nlist of replica to be read, default is all
      • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
      • \n
    • check_configs (list[list[int]]):\nlist of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of Observables with length T, observable per timeslice.\nbb-type correlators have length 1.
      • \n
    \n", "signature": "(\tpath,\tprefix,\tname,\tquarks='.*',\tcorr_type='bi',\tnoffset=0,\twf=0,\twf2=0,\tversion='1.0c',\tcfg_separator='n',\tsilent=False,\t**kwargs):", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "kind": "module", "doc": "
    \n"}, "pyerrors.input.utils.sort_names": {"fullname": "pyerrors.input.utils.sort_names", "modulename": "pyerrors.input.utils", "qualname": "sort_names", "kind": "function", "doc": "
    Sorts a list of names of replika with searches for r and id in the replikum string.\nIf this search fails, a fallback method is used,\nwhere the strings are simply compared and the first diffeing numeral is used for differentiation.
    \n\n
    Parameters
    \n\n
      \n
    • ll (list):\nlist to sort
      • \n
    \n\n
    Returns
    \n\n
      \n
    • ll (list):\nsorted list
      • \n
    \n", "signature": "(ll):", "funcdef": "def"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "kind": "function", "doc": "
    Checks if list of configurations is contained in an idl
    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
      • \n
    • che (list):\nlist of configurations to be checked against
      • \n
    \n\n
    Returns
    \n\n
      \n
    • miss_str (str):\nstring with integers of which idls are missing
      • \n
    \n", "signature": "(idl, che):", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "kind": "module", "doc": "
    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "kind": "function", "doc": "
    Matrix multiply all operands.
    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "
    Matrix multiply both operands making use of the jackknife approximation.
    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • For large matrices this is considerably faster compared to matmul.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "
    Wrapper for numpy.einsum
    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
      • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    \n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "
    Inverse of Obs or CObs valued matrices.
    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "kind": "function", "doc": "
    Cholesky decomposition of Obs valued matrices.
    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "kind": "function", "doc": "
    Determinant of Obs valued matrices.
    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "kind": "function", "doc": "
    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "kind": "function", "doc": "
    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "kind": "function", "doc": "
    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "kind": "function", "doc": "
    Computes the singular value decomposition of a matrix of Obs.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "kind": "module", "doc": "
    \n"}, "pyerrors.misc.print_config": {"fullname": "pyerrors.misc.print_config", "modulename": "pyerrors.misc", "qualname": "print_config", "kind": "function", "doc": "
    Print information about version of python, pyerrors and dependencies.
    \n", "signature": "():", "funcdef": "def"}, "pyerrors.misc.errorbar": {"fullname": "pyerrors.misc.errorbar", "modulename": "pyerrors.misc", "qualname": "errorbar", "kind": "function", "doc": "
    pyerrors wrapper for the errorbars method of matplotlib
    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nA list of x-values which can be Obs.
      • \n
    • y (list):\nA list of y-values which can be Obs.
      • \n
    • axes ((matplotlib.pyplot.axes)):\nThe axes to plot on. default is plt.
      • \n
    \n", "signature": "(\tx,\ty,\taxes=<module 'matplotlib.pyplot' from '/opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/matplotlib/pyplot.py'>,\t**kwargs):", "funcdef": "def"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "kind": "function", "doc": "
    Dump object into pickle file.
    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
      • \n
    • name (str):\nname of the file
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(obj, name, **kwargs):", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "kind": "function", "doc": "
    Load object from pickle file.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
      • \n
    \n\n
    Returns
    \n\n
      \n
    • object (Obs):\nLoaded Object
      • \n
    \n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "
    Generate an Obs object with given value, dvalue and name for test purposes
    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
      • \n
    • dvalue (float):\nerror of the Obs to be generated.
      • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
      • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nGenerated Observable
      • \n
    \n", "signature": "(value, dvalue, name, samples=1000):", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "kind": "function", "doc": "
    Generate observables with given covariance and autocorrelation times.
    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
      • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
      • \n
    • name (str):\nensemble name for the data to be geneated.
      • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
      • \n
    • samples (int):\nnumber of samples to be generated for each observable.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • corr_obs (list[Obs]):\nGenerated observable list
      • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000):", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "kind": "module", "doc": "
    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "kind": "function", "doc": "
    Matrix pencil method to extract k energy levels from data
    \n\n
    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)
    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
      • \n
    • k (int):\nNumber of states to extract (default 1).
      • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • energy_levels (list[Obs]):\nExtracted energy levels
      • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "kind": "module", "doc": "
    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "kind": "class", "doc": "
    Class for a general observable.
    \n\n
    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.
    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
      • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
      • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
      • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
      • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "
    Initialize Obs object.
    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
      • \n
    • names (list):\nlist of strings labeling the individual samples
      • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
      • \n
    \n", "signature": "(samples, names, idl=None, **kwargs)"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "kind": "function", "doc": "
    Estimate the error and related properties of the Obs.
    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.gm": {"fullname": "pyerrors.obs.Obs.gm", "modulename": "pyerrors.obs", "qualname": "Obs.gm", "kind": "function", "doc": "
    Estimate the error and related properties of the Obs.
    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "kind": "function", "doc": "
    Output detailed properties of the Obs.
    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
      • \n
    \n", "signature": "(self, ens_content=True):", "funcdef": "def"}, "pyerrors.obs.Obs.reweight": {"fullname": "pyerrors.obs.Obs.reweight", "modulename": "pyerrors.obs", "qualname": "Obs.reweight", "kind": "function", "doc": "
    Reweight the obs with given rewighting factors.
    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(self, weight):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "kind": "function", "doc": "
    Checks whether the observable is zero within 'sigma' standard errors.
    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
      • \n
    • Works only properly when the gamma method was run.
      • \n
    \n", "signature": "(self, sigma=1):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "kind": "function", "doc": "
    Checks whether the observable is zero within a given tolerance.
    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
      • \n
    \n", "signature": "(self, atol=1e-10):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "kind": "function", "doc": "
    Plot integrated autocorrelation time for each ensemble.
    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "kind": "function", "doc": "
    Plot normalized autocorrelation function time for each ensemble.
    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "kind": "function", "doc": "
    Plot replica distribution for each ensemble with more than one replicum.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "kind": "function", "doc": "
    Plot derived Monte Carlo history for each ensemble
    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
      • \n
    \n", "signature": "(self, expand=True):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "kind": "function", "doc": "
    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.
    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "kind": "function", "doc": "
    Dump the Obs to a file 'name' of chosen format.
    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "kind": "function", "doc": "
    Export jackknife samples from the Obs
    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
      • \n
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "kind": "class", "doc": "
    Class for a complex valued observable.
    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "kind": "function", "doc": "
    \n", "signature": "(real, imag=0.0)"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "kind": "function", "doc": "
    Executes the gamma_method for the real and the imaginary part.
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "kind": "function", "doc": "
    Checks whether both real and imaginary part are zero within machine precision.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "kind": "function", "doc": "
    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.
    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
      • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
      • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
      • \n
    \n\n
    Notes
    \n\n
    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use
    \n\n
    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])
    \n", "signature": "(func, data, array_mode=False, **kwargs):", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "kind": "function", "doc": "
    Reweight a list of observables.
    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "
    Correlate two observables.
    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
      • \n
    • obs_b (Obs):\nSecond observable
      • \n
    \n\n
    Notes
    \n\n
    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical (this is not strictly necessary).
    \n", "signature": "(obs_a, obs_b):", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "kind": "function", "doc": "
    Calculates the error covariance matrix of a set of observables.
    \n\n
    WARNING: This function should be used with care, especially for observables with support on multiple\n         ensembles with differing autocorrelations. See the notes below for details.
    \n\n
    The gamma method has to be applied first to all observables.
    \n\n
    Parameters
    \n\n
      \n
    • obs (list or numpy.ndarray):\nList or one dimensional array of Obs
      • \n
    • visualize (bool):\nIf True plots the corresponding normalized correlation matrix (default False).
      • \n
    • correlation (bool):\nIf True the correlation matrix instead of the error covariance matrix is returned (default False).
      • \n
    • smooth (None or int):\nIf smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue\nsmoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the\nlargest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely\nsmall ones.
      • \n
    \n\n
    Notes
    \n\n
    The error covariance is defined such that it agrees with the squared standard error for two identical observables\n$$\\operatorname{cov}(a,a)=\\sum_{s=1}^N\\delta_a^s\\delta_a^s/N^2=\\Gamma_{aa}(0)/N=\\operatorname{var}(a)/N=\\sigma_a^2$$\nin the absence of autocorrelation.\nThe error covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite\n$$\\sum_{i,j}v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).
    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "kind": "function", "doc": "
    Imports jackknife samples and returns an Obs
    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
      • \n
    • name (str):\nname of the ensemble the samples are defined on.
      • \n
    \n", "signature": "(jacks, name, idl=None):", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "kind": "function", "doc": "
    Combine all observables in list_of_obs into one new observable
    \n\n
    Parameters
    \n\n
      \n
    • list_of_obs (list):\nlist of the Obs object to be combined
      • \n
    \n\n
    Notes
    \n\n
    It is not possible to combine obs which are based on the same replicum
    \n", "signature": "(list_of_obs):", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "kind": "function", "doc": "
    Create an Obs based on mean(s) and a covariance matrix
    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
      • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(means, cov, name, grad=None):", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "kind": "module", "doc": "
    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "kind": "function", "doc": "
    Finds the root of the function func(x, d) where d is an Obs.
    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
      • \n
    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:
      \n\n
      \n
      import autograd.numpy as anp\ndef root_func(x, d):\n   return anp.exp(-x ** 2) - d\n
      \n
      • \n
    • guess (float):\nInitial guess for the minimization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nObs valued root of the function.
      • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs):", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "kind": "module", "doc": "
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+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"pyerrors": {"fullname": "pyerrors", "modulename": "pyerrors", "kind": "module", "doc": "
    What is pyerrors?
    \n\n
    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:
    \n\n
      \n
    • automatic differentiation for exact linear error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
      • \n
    • treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
      • \n
    • coherent error propagation for data from different Markov chains.
      • \n
    • non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
      • \n
    • real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
      • \n
    \n\n
    More detailed examples can found in the GitHub repository \"badge\".
    \n\n
    If you use pyerrors for research that leads to a publication please consider citing:
    \n\n
      \n
    • Fabian Joswig, Simon Kuberski, Justus T. Kuhlmann, Jan Neuendorf, pyerrors: a python framework for error analysis of Monte Carlo data. [arXiv:2209.14371 [hep-lat]].
      • \n
    • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
      • \n
    • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
      • \n
    \n\n
    and
    \n\n
      \n
    • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.
      • \n
    \n\n
    where applicable.
    \n\n
    There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.
    \n\n
    Installation
    \n\n
    Install the most recent release using pip and pypi:
    \n\n
    \n
    pip install pyerrors     # Fresh install\npip install -U pyerrors  # Update\n
    \n
    \n\n
    Install the most recent release using conda and conda-forge:
    \n\n
    \n
    conda install -c conda-forge pyerrors  # Fresh install\nconda update -c conda-forge pyerrors   # Update\n
    \n
    \n\n
    Install the current develop version:
    \n\n
    \n
    pip install git+https://github.com/fjosw/pyerrors.git@develop\n
    \n
    \n\n
    Basic example
    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method()                     # Estimate the statistical error\nprint(my_new_obs)                             # Print the result to stdout\n> 0.31498(72)\n
    \n
    \n\n
    The Obs class
    \n\n
    pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble. It is crucial for the correct error propagation that observations from the same Monte Carlo history are labeled with the same name. See Multiple ensembles/replica for details.
    \n\n
    \n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n
    \n\n
    Error propagation
    \n\n
    When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.
    \n\n
    The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.
    \n\n
    \n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
    \n
    \n\n
    Error estimation
    \n\n
    The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.
    \n\n
    \n
    my_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n
    We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.
    \n\n
    \n
    my_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n
    The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_rho.
    \n\n
    If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.
    \n\n
    Exponential tails
    \n\n
    Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.
    \n\n
    \n
    my_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1\n> 1000 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
    \n
    \n\n
    For the full API see pyerrors.obs.Obs.gamma_method.
    \n\n
    Multiple ensembles/replica
    \n\n
    Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.
    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 2 ensembles:\n>   \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n>   \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
    \n
    \n\n
    Observables from the same Monte Carlo chain have to be initialized with the same name for correct error propagation. If different names were used in this case the data would be treated as statistically independent resulting in loss of relevant information and a potential over or under estimate of the statistical error.
    \n\n
    pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.
    \n\n
    \n
    obs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result   2.00697958e+00\n> 1500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1'\n>     \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n>     \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
    \n
    \n\n
    Error estimation for multiple ensembles
    \n\n
    In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.
    \n\n
    \n
    pe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
    \n
    \n\n
    In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.
    \n\n
    Irregular Monte Carlo chains
    \n\n
    Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.
    \n\n
    \n
    # Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result         9.98319881e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result         9.99100712e-01\n> 500 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result         1.01718064e+00\n> 5 samples in 1 ensemble:\n>   \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
    \n
    \n\n
    Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.
    \n\n
    Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.
    \n\n
    For the full API see pyerrors.obs.Obs.
    \n\n
    Correlators
    \n\n
    When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs
    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0      0.7957(80)\n> 1      0.5156(51)\n> 2      0.3227(33)\n> 3      0.2041(21)\n
    \n
    \n\n
    In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.
    \n\n
    \n
    my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])\nprint(my_corr)\n> x0/a  Corr(x0/a)\n> ------------------\n> 0\n> 1      0.7957(80)\n> 2      0.5156(51)\n> 3      0.3227(33)\n> 4      0.2041(21)\n> 5\n
    \n
    \n\n
    The individual entries of a correlator can be accessed via slicing
    \n\n
    \n
    print(my_corr[3])\n> 0.3227(33)\n
    \n
    \n\n
    Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.
    \n\n
    \n
    my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr\n
    \n
    \n\n
    pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:
    \n\n
      \n
    • Corr.gamma_method applies the gamma method to all entries of the correlator.
      • \n
    • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
      • \n
    • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
      • \n
    • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
      • \n
    • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
      • \n
    • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
      • \n
    • Corr.plateau extracts a plateau value from the correlator in a given range.
      • \n
    • Corr.roll periodically shifts the correlator.
      • \n
    • Corr.reverse reverses the time ordering of the correlator.
      • \n
    • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
      • \n
    • Corr.reweight reweights the correlator.
      • \n
    \n\n
    pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).
    \n\n
    For the full API see pyerrors.correlators.Corr.
    \n\n
    Complex valued observables
    \n\n
    pyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.
    \n\n
    \n
    my_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
    \n
    \n\n
    Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.
    \n\n
    \n
    my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
    \n
    \n\n
    The Covobs class
    \n\n
    In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.
    \n\n
    This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.
    \n\n
    \n
    import pyerrors.obs as pe\n\nmpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')\nmpi.gamma_method()\nmpi.details()\n> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)\n>  pi^0 mass     5.00000000e-04\n> 0 samples in 1 ensemble:\n>   \u00b7 Covobs   'pi^0 mass'\n
    \n
    \n\n
    The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \\times M$ covariance matrix $\\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\\times M$ Jacobian matrix $J$, via\n$$s = \\sqrt{J^T \\Sigma J}\\,,$$\nwhere the Jacobian is computed for each derived quantity via automatic differentiation.
    \n\n
    Correlated auxiliary data is defined similarly to above, e.g., via
    \n\n
    \n
    RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')\nprint(RAP)\n> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]\n
    \n
    \n\n
    where RAP now is a list of two Obs that contains the two correlated parameters.
    \n\n
    Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via
    \n\n
    \n
    o.covobs[k].grad\n
    \n
    \n\n
    Error propagation in iterative algorithms
    \n\n
    pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least squares fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.
    \n\n
    Least squares fits
    \n\n
    Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.
    \n\n
    Fit functions have to be of the following form
    \n\n
    \n
    import autograd.numpy as anp\n\ndef func(a, x):\n    return a[1] * anp.exp(-a[0] * x)\n
    \n
    \n\n
    It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.
    \n\n
    Fits can then be performed via
    \n\n
    \n
    fit_result = pe.fits.least_squares(x, y, func)\nprint("\\n", fit_result)\n> Fit with 2 parameters\n> Method: Levenberg-Marquardt\n> `ftol` termination condition is satisfied.\n> chisquare/d.o.f.: 0.9593035785160936\n\n>  Goodness of fit:\n> \u03c7\u00b2/d.o.f. = 0.959304\n> p-value   = 0.5673\n> Fit parameters:\n> 0      0.0548(28)\n> 1      1.933(64)\n
    \n
    \n\n
    where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.
    \n\n
    Data stored in Corr objects can be fitted directly using the Corr.fit method.
    \n\n
    \n
    my_corr = pe.Corr(y)\nfit_result = my_corr.fit(func, fitrange=[12, 25])\n
    \n
    \n\n
    this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.
    \n\n
    For fit functions with multiple independent variables the fit function can be of the form
    \n\n
    \n
    def func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n
    \n
    \n\n
    pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True.\nDetails about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.
    \n\n
    Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.
    \n\n
    Total least squares fits
    \n\n
    pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only difference being that x also has to be a list or numpy.array of Obs.
    \n\n
    For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.
    \n\n
    Matrix operations
    \n\n
    pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:
    \n\n
      \n
    • inv for the matrix inverse.
      • \n
    • cholseky for the Cholesky decomposition.
      • \n
    • det for the matrix determinant.
      • \n
    • eigh for eigenvalues and eigenvectors of hermitean matrices.
      • \n
    • eig for eigenvalues of general matrices.
      • \n
    • pinv for the Moore-Penrose pseudoinverse.
      • \n
    • svd for the singular-value-decomposition.
      • \n
    \n\n
    For the full API see pyerrors.linalg.
    \n\n
    Export data
    \n\n
    \n\n
    The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:
    \n\n
      \n
    • What observables are stored? Possibly: How exactly are they defined.
      • \n
    • How does each single ensemble or external quantity contribute to the error of the observable?
      • \n
    • Who did write the file when and on which machine?
      • \n
    \n\n
    This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example
    \n\n
    \n
    my_obs = pe.Obs([samples], ["test_ensemble"])\nmy_obs.tag = "My observable"\n\npe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")\n# For a single observable one can equivalently use the class method dump\nmy_obs.dump("test_output_file", description="This file contains a test observable")\n\ncheck = pe.input.json.load_json("test_output_file")\n\nprint(my_obs == check)\n> True\n
    \n
    \n\n
    The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.
    \n\n
    json.gz format specification
    \n\n
    The first entries of the file provide optional auxiliary information:
    \n\n
      \n
    • program is a string that indicates which program was used to write the file.
      • \n
    • version is a string that specifies the version of the format.
      • \n
    • who is a string that specifies the user name of the creator of the file.
      • \n
    • date is a string and contains the creation date of the file.
      • \n
    • host is a string and contains the hostname of the machine where the file has been written.
      • \n
    • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
      • \n
    \n\n
    The only necessary entry of the file is the field\n-obsdata, an array that contains the actual data.
    \n\n
    Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:
    \n\n
      \n
    • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
      • \n
    • value is an array that contains the mean values of the Obs inside the structure.\nThe following entries are optional:
      • \n
    • layout is a string that specifies the layout of multi-dimensional structures. Examples are \"2, 2\" for a 2x2 dimensional matrix or  \"64, 4, 4\" for a Corr with $T=64$ and 4x4 matrices on each time slices. \"1\" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
      • \n
    • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
      • \n
    • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
      • \n
    • data is an array that contains the data from MC chains. We will define it below.
      • \n
    • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.
      • \n
    \n\n
    The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:
    \n\n
      \n
    • id, a string that contains the name of the ensemble
      • \n
    • replica, an array that contains an entry per replica of the ensemble.
      • \n
    \n\n
    Each entry of replica contains\nname, a string that contains the name of the replica\ndeltas, an array that contains the actual data.
    \n\n
    Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.
    \n\n
    The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:
    \n\n
      \n
    • id, a string that identifies the covariance matrix
      • \n
    • layout, a string that defines the dimensions of the $M\\times M$ covariance matrix (has to be \"M, M\" or \"1\").
      • \n
    • cov, an array that contains the $M\\times M$ many entries of the covariance matrix, stored in row-major format.
      • \n
    • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable  with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
      • \n
    \n\n
    A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.
    \n\n
    Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.
    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "kind": "module", "doc": "
    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "kind": "class", "doc": "
    The class for a correlator (time dependent sequence of pe.Obs).
    \n\n
    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with matrices.
    \n\n
    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nmatrix at every timeslice. Other dependency (eg. spatial) are not supported.
    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "kind": "function", "doc": "
    Initialize a Corr object.
    \n\n
    Parameters
    \n\n
      \n
    • data_input (list or array):\nlist of Obs or list of arrays of Obs or array of Corrs
      • \n
    • padding (list, optional):\nList with two entries where the first labels the padding\nat the front of the correlator and the second the padding\nat the back.
      • \n
    • prange (list, optional):\nList containing the first and last timeslice of the plateau\nregion indentified for this correlator.
      • \n
    \n", "signature": "(data_input, padding=[0, 0], prange=None)"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "kind": "function", "doc": "
    Apply the gamma method to the content of the Corr.
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.gm": {"fullname": "pyerrors.correlators.Corr.gm", "modulename": "pyerrors.correlators", "qualname": "Corr.gm", "kind": "function", "doc": "
    Apply the gamma method to the content of the Corr.
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "kind": "function", "doc": "
    We need to project the Correlator with a Vector to get a single value at each timeslice.
    \n\n
    The method can use one or two vectors.\nIf two are specified it returns v1@G@v2 (the order might be very important.)\nBy default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
    \n", "signature": "(self, vector_l=None, vector_r=None, normalize=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.item": {"fullname": "pyerrors.correlators.Corr.item", "modulename": "pyerrors.correlators", "qualname": "Corr.item", "kind": "function", "doc": "
    Picks the element [i,j] from every matrix and returns a correlator containing one Obs per timeslice.
    \n\n
    Parameters
    \n\n
      \n
    • i (int):\nFirst index to be picked.
      • \n
    • j (int):\nSecond index to be picked.
      • \n
    \n", "signature": "(self, i, j):", "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "kind": "function", "doc": "
    Outputs the correlator in a plotable format.
    \n\n
    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "kind": "function", "doc": "
    Symmetrize the correlator around x0=0.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "kind": "function", "doc": "
    Anti-symmetrize the correlator around x0=0.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.is_matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.is_matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.is_matrix_symmetric", "kind": "function", "doc": "
    Checks whether a correlator matrices is symmetric on every timeslice.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.matrix_symmetric": {"fullname": "pyerrors.correlators.Corr.matrix_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.matrix_symmetric", "kind": "function", "doc": "
    Symmetrizes the correlator matrices on every timeslice.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "kind": "function", "doc": "
    Solve the generalized eigenvalue problem on the correlator matrix and returns the corresponding eigenvectors.
    \n\n
    The eigenvectors are sorted according to the descending eigenvalues, the zeroth eigenvector(s) correspond to the\nlargest eigenvalue(s). The eigenvector(s) for the individual states can be accessed via slicing
    \n\n
    \n
    C.GEVP(t0=2)[0]  # Ground state vector(s)\nC.GEVP(t0=2)[:3]  # Vectors for the lowest three states\n
    \n
    \n\n
    Parameters
    \n\n
      \n
    • t0 (int):\nThe time t0 for the right hand side of the GEVP according to $G(t)v_i=\\lambda_i G(t_0)v_i$
      • \n
    • ts (int):\nfixed time $G(t_s)v_i=\\lambda_i G(t_0)v_i$ if sort=None.\nIf sort=\"Eigenvector\" it gives a reference point for the sorting method.
      • \n
    • sort (string):\nIf this argument is set, a list of self.T vectors per state is returned. If it is set to None, only one vector is returned.\n
        \n
      • \"Eigenvalue\": The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
        • \n
      • \"Eigenvector\": Use the method described in arXiv:2004.10472 to find the set of v(t) belonging to the state.\nThe reference state is identified by its eigenvalue at $t=t_s$.
        • \n
      • \n
    \n\n
    Other Parameters
    \n\n
      \n
    • state (int):\nReturns only the vector(s) for a specified state. The lowest state is zero.
      • \n
    \n", "signature": "(self, t0, ts=None, sort='Eigenvalue', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "kind": "function", "doc": "
    Determines the eigenvalue of the GEVP by solving and projecting the correlator
    \n\n
    Parameters
    \n\n
      \n
    • state (int):\nThe state one is interested in ordered by energy. The lowest state is zero.
      • \n
    • All other parameters are identical to the ones of Corr.GEVP.
      • \n
    \n", "signature": "(self, t0, ts=None, state=0, sort='Eigenvalue'):", "funcdef": "def"}, "pyerrors.correlators.Corr.Hankel": {"fullname": "pyerrors.correlators.Corr.Hankel", "modulename": "pyerrors.correlators", "qualname": "Corr.Hankel", "kind": "function", "doc": "
    Constructs an NxN Hankel matrix
    \n\n
    C(t) c(t+1) ... c(t+n-1)\nC(t+1) c(t+2) ... c(t+n)\n.................\nC(t+(n-1)) c(t+n) ... c(t+2(n-1))
    \n\n
    Parameters
    \n\n
      \n
    • N (int):\nDimension of the Hankel matrix
      • \n
    • periodic (bool, optional):\ndetermines whether the matrix is extended periodically
      • \n
    \n", "signature": "(self, N, periodic=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "kind": "function", "doc": "
    Periodically shift the correlator by dt timeslices
    \n\n
    Parameters
    \n\n
      \n
    • dt (int):\nnumber of timeslices
      • \n
    \n", "signature": "(self, dt):", "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "kind": "function", "doc": "
    Reverse the time ordering of the Corr
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.thin": {"fullname": "pyerrors.correlators.Corr.thin", "modulename": "pyerrors.correlators", "qualname": "Corr.thin", "kind": "function", "doc": "
    Thin out a correlator to suppress correlations
    \n\n
    Parameters
    \n\n
      \n
    • spacing (int):\nKeep only every 'spacing'th entry of the correlator
      • \n
    • offset (int):\nOffset the equal spacing
      • \n
    \n", "signature": "(self, spacing=2, offset=0):", "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "kind": "function", "doc": "
    Correlate the correlator with another correlator or Obs
    \n\n
    Parameters
    \n\n
      \n
    • partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
      • \n
    \n", "signature": "(self, partner):", "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "kind": "function", "doc": "
    Reweight the correlator.
    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
      • \n
    \n", "signature": "(self, weight, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "kind": "function", "doc": "
    Return the time symmetry average of the correlator and its partner
    \n\n
    Parameters
    \n\n
      \n
    • partner (Corr):\nTime symmetry partner of the Corr
      • \n
    • partity (int):\nParity quantum number of the correlator, can be +1 or -1
      • \n
    \n", "signature": "(self, partner, parity=1):", "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "kind": "function", "doc": "
    Return the first derivative of the correlator with respect to x0.
    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, forward, backward, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "kind": "function", "doc": "
    Return the second derivative of the correlator with respect to x0.
    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\ndecides which definition of the finite differences derivative is used.\nAvailable choice: symmetric, improved, log, default: symmetric
      • \n
    \n", "signature": "(self, variant='symmetric'):", "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "kind": "function", "doc": "
    Returns the effective mass of the correlator as correlator object
    \n\n
    Parameters
    \n\n
      \n
    • variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)\nlogsym: uses the symmetric effective mass log(C(t-1) / C(t+1))/2
      • \n
    • guess (float):\nguess for the root finder, only relevant for the root variant
      • \n
    \n", "signature": "(self, variant='log', guess=1.0):", "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "kind": "function", "doc": "
    Fits function to the data
    \n\n
    Parameters
    \n\n
      \n
    • function (obj):\nfunction to fit to the data. See fits.least_squares for details.
      • \n
    • fitrange (list):\nTwo element list containing the timeslices on which the fit is supposed to start and stop.\nCaution: This range is inclusive as opposed to standard python indexing.\nfitrange=[4, 6] corresponds to the three entries 4, 5 and 6.\nIf not specified, self.prange or all timeslices are used.
      • \n
    • silent (bool):\nDecides whether output is printed to the standard output.
      • \n
    \n", "signature": "(self, function, fitrange=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "kind": "function", "doc": "
    Extract a plateau value from a Corr object
    \n\n
    Parameters
    \n\n
      \n
    • plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
      • \n
    • method (str):\nmethod to extract the plateau.\n    'fit' fits a constant to the plateau region\n    'avg', 'average' or 'mean' just average over the given timeslices.
      • \n
    • auto_gamma (bool):\napply gamma_method with default parameters to the Corr. Defaults to None
      • \n
    \n", "signature": "(self, plateau_range=None, method='fit', auto_gamma=False):", "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "kind": "function", "doc": "
    Sets the attribute prange of the Corr object.
    \n", "signature": "(self, prange):", "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "kind": "function", "doc": "
    Plots the correlator using the tag of the correlator as label if available.
    \n\n
    Parameters
    \n\n
      \n
    • x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8].
      • \n
    • comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.\nThe tags of these correlators are used as labels if available.
      • \n
    • logscale (bool):\nSets y-axis to logscale.
      • \n
    • plateau (Obs):\nPlateau value to be visualized in the figure.
      • \n
    • fit_res (Fit_result):\nFit_result object to be visualized.
      • \n
    • fit_key (str):\nKey for the fit function in Fit_result.fit_function (for combined fits).
      • \n
    • ylabel (str):\nLabel for the y-axis.
      • \n
    • save (str):\npath to file in which the figure should be saved.
      • \n
    • auto_gamma (bool):\nApply the gamma method with standard parameters to all correlators and plateau values before plotting.
      • \n
    • hide_sigma (float):\nHides data points from the first value on which is consistent with zero within 'hide_sigma' standard errors.
      • \n
    • references (list):\nList of floating point values that are displayed as horizontal lines for reference.
      • \n
    • title (string):\nOptional title of the figure.
      • \n
    \n", "signature": "(\tself,\tx_range=None,\tcomp=None,\ty_range=None,\tlogscale=False,\tplateau=None,\tfit_res=None,\tfit_key=None,\tylabel=None,\tsave=None,\tauto_gamma=False,\thide_sigma=None,\treferences=None,\ttitle=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.spaghetti_plot": {"fullname": "pyerrors.correlators.Corr.spaghetti_plot", "modulename": "pyerrors.correlators", "qualname": "Corr.spaghetti_plot", "kind": "function", "doc": "
    Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
    \n\n
    Parameters
    \n\n
      \n
    • logscale (bool):\nDetermines whether the scale of the y-axis is logarithmic or standard.
      • \n
    \n", "signature": "(self, logscale=True):", "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "kind": "function", "doc": "
    Dumps the Corr into a file of chosen type
    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nName of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', **kwargs):", "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "kind": "function", "doc": "
    \n", "signature": "(self, print_range=None):", "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.correlators.Corr.prune": {"fullname": "pyerrors.correlators.Corr.prune", "modulename": "pyerrors.correlators", "qualname": "Corr.prune", "kind": "function", "doc": "
    Project large correlation matrix to lowest states
    \n\n
    This method can be used to reduce the size of an (N x N) correlation matrix\nto (Ntrunc x Ntrunc) by solving a GEVP at very early times where the noise\nis still small.
    \n\n
    Parameters
    \n\n
      \n
    • Ntrunc (int):\nRank of the target matrix.
      • \n
    • tproj (int):\nTime where the eigenvectors are evaluated, corresponds to ts in the GEVP method.\nThe default value is 3.
      • \n
    • t0proj (int):\nTime where the correlation matrix is inverted. Choosing t0proj=1 is strongly\ndiscouraged for O(a) improved theories, since the correctness of the procedure\ncannot be granted in this case. The default value is 2.
      • \n
    • basematrix (Corr):\nCorrelation matrix that is used to determine the eigenvectors of the\nlowest states based on a GEVP. basematrix is taken to be the Corr itself if\nis is not specified.
      • \n
    \n\n
    Notes
    \n\n
    We have the basematrix $C(t)$ and the target matrix $G(t)$. We start by solving\nthe GEVP $$C(t) v_n(t, t_0) = \\lambda_n(t, t_0) C(t_0) v_n(t, t_0)$$ where $t \\equiv t_\\mathrm{proj}$\nand $t_0 \\equiv t_{0, \\mathrm{proj}}$. The target matrix is projected onto the subspace of the\nresulting eigenvectors $v_n, n=1,\\dots,N_\\mathrm{trunc}$ via\n$$G^\\prime_{i, j}(t) = (v_i, G(t) v_j)$$. This allows to reduce the size of a large\ncorrelation matrix and to remove some noise that is added by irrelevant operators.\nThis may allow to use the GEVP on $G(t)$ at late times such that the theoretically motivated\nbound $t_0 \\leq t/2$ holds, since the condition number of $G(t)$ is decreased, compared to $C(t)$.
    \n", "signature": "(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):", "funcdef": "def"}, "pyerrors.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "kind": "module", "doc": "
    \n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "kind": "class", "doc": "
    \n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "kind": "function", "doc": "
    Initialize Covobs object.
    \n\n
    Parameters
    \n\n
      \n
    • mean (float):\nMean value of the new Obs
      • \n
    • cov (list or array):\n2d Covariance matrix or 1d diagonal entries
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(mean, cov, name, pos=None, grad=None)"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "kind": "function", "doc": "
    Return the variance (= square of the error) of the Covobs
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "kind": "module", "doc": "
    \n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "kind": "function", "doc": "
    Rank-3 epsilon tensor
    \n\n
    Based on https://codegolf.stackexchange.com/a/160375
    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k) of the epsilon tensor of rank 3
      • \n
    \n", "signature": "(i, j, k):", "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "kind": "function", "doc": "
    Rank-4 epsilon tensor
    \n\n
    Extension of https://codegolf.stackexchange.com/a/160375
    \n\n
    Returns
    \n\n
      \n
    • elem (int):\nElement (i,j,k,o) of the epsilon tensor of rank 4
      • \n
    \n", "signature": "(i, j, k, o):", "funcdef": "def"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "kind": "function", "doc": "
    Returns gamma matrix in Grid labeling.
    \n", "signature": "(gamma_tag):", "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "kind": "module", "doc": "
    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "kind": "class", "doc": "
    Represents fit results.
    \n\n
    Attributes
    \n\n
      \n
    • fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
      • \n
    • chisquare_by_dof (float):\nreduced chisquare.
      • \n
    • p_value (float):\np-value of the fit
      • \n
    • t2_p_value (float):\nHotelling t-squared p-value for correlated fits.
      • \n
    \n", "bases": "collections.abc.Sequence"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "kind": "function", "doc": "
    Apply the gamma method to all fit parameters
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.Fit_result.gm": {"fullname": "pyerrors.fits.Fit_result.gm", "modulename": "pyerrors.fits", "qualname": "Fit_result.gm", "kind": "function", "doc": "
    Apply the gamma method to all fit parameters
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "kind": "function", "doc": "
    Performs a non-linear fit to y = func(x).\n    ```
    \n\n
    Parameters
    \n\n
      \n
    • For an uncombined fit:
      • \n
    • x (list):\nlist of floats.
      • \n
    • y (list):\nlist of Obs.
      • \n
    • func (object):\nfit function, has to be of the form
      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n
      For multiple x values func can be of the form
      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n
      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
      • \n
    • OR For a combined fit:
      • \n
    • x (dict):\ndict of lists.
      • \n
    • y (dict):\ndict of lists of Obs.
      • \n
    • funcs (dict):\ndict of objects\nfit functions have to be of the form (here a[0] is the common fit parameter)\n```python\nimport autograd.numpy as anp\nfuncs = {\"a\": func_a,\n       \"b\": func_b}
      \n\n
      def func_a(a, x):\n   return a[1] * anp.exp(-a[0] * x)
      \n\n
      def func_b(a, x):\n   return a[2] * anp.exp(-a[0] * x)
      \n\n
      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
      • \n
    • priors (dict or list, optional):\npriors can either be a dictionary with integer keys and the corresponding priors as values or\na list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\nnon-linear fits with many parameters. In case of correlated fits the guess is used to perform\nan uncorrelated fit which then serves as guess for the correlated fit.
      • \n
    • method (str, optional):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
      • \n
    • tol (float, optional):\ncan be used (only for combined fits and methods other than Levenberg-Marquard) to set the tolerance for convergence\nto a different value to either speed up convergence at the cost of a larger error on the fitted parameters (and possibly\ninvalid estimates for parameter uncertainties) or smaller values to get more accurate parameter values\nThe stopping criterion depends on the method, e.g. migrad: edm_max = 0.002 * tol * errordef (EDM criterion: edm < edm_max)
      • \n
    • correlated_fit (bool):\nIf True, use the full inverse covariance matrix in the definition of the chisquare cost function.\nFor details about how the covariance matrix is estimated see pyerrors.obs.covariance.\nIn practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).\nThis procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
      • \n
    • expected_chisquare (bool):\nIf True estimates the expected chisquare which is\ncorrected by effects caused by correlated input data (default False).
      • \n
    • resplot (bool):\nIf True, a plot which displays fit, data and residuals is generated (default False).
      • \n
    • qqplot (bool):\nIf True, a quantile-quantile plot of the fit result is generated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
      • \n
    \n", "signature": "(x, y, func, priors=None, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "kind": "function", "doc": "
    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nlist of Obs, or a tuple of lists of Obs
      • \n
    • y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
      • \n
    • func (object):\nfunc has to be of the form
      \n\n
      \n
      import autograd.numpy as anp\n\ndef func(a, x):\n   return a[0] + a[1] * x + a[2] * anp.sinh(x)\n
      \n
      \n\n
      For multiple x values func can be of the form
      \n\n
      \n
      def func(a, x):\n   (x1, x2) = x\n   return a[0] * x1 ** 2 + a[1] * x2\n
      \n
      \n\n
      It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
      • \n
    • silent (bool, optional):\nIf true all output to the console is omitted (default False).
      • \n
    • initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
      • \n
    • expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
      • \n
    • num_grad (bool):\nUse numerical differentation instead of automatic differentiation to perform the error propagation (default False).
      • \n
    \n\n
    Notes
    \n\n
    Based on the orthogonal distance regression module of scipy.
    \n\n
    Returns
    \n\n
      \n
    • output (Fit_result):\nParameters and information on the fitted result.
      • \n
    \n", "signature": "(x, y, func, silent=False, **kwargs):", "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "kind": "function", "doc": "
    Performs a linear fit to y = n + m * x and returns two Obs n, m.
    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nCan either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.
      • \n
    • y (list):\nList of Obs, the dvalues of the Obs are used as yerror for the fit.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • fit_parameters (list[Obs]):\nLIist of fitted observables.
      • \n
    \n", "signature": "(x, y, **kwargs):", "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "kind": "function", "doc": "
    Generates a quantile-quantile plot of the fit result which can be used to\n   check if the residuals of the fit are gaussian distributed.
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(x, o_y, func, p, title=''):", "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "kind": "function", "doc": "
    Generates a plot which compares the fit to the data and displays the corresponding residuals
    \n\n
    For uncorrelated data the residuals are expected to be distributed ~N(0,1).
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(x, y, func, fit_res, title=''):", "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "kind": "function", "doc": "
    Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
    \n\n
    Returns
    \n\n
      \n
    • err (np.array(Obs)):\nError band for an array of sample values x
      • \n
    \n", "signature": "(x, func, beta):", "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "kind": "function", "doc": "
    Performs a Kolmogorov\u2013Smirnov test for the p-values of all fit object.
    \n\n
    Parameters
    \n\n
      \n
    • objects (list):\nList of fit results to include in the analysis (optional).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(objects=None):", "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "kind": "module", "doc": "
    pyerrors includes an input submodule in which input routines and parsers for the output of various numerical programs are contained.
    \n\n
    Jackknife samples
    \n\n
    For comparison with other analysis workflows pyerrors can also generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.
    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "kind": "module", "doc": "
    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "kind": "function", "doc": "
    Extract generic MCMC data from a bdio file
    \n\n
    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (List[Obs]):\nExtracted data
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "kind": "function", "doc": "
    Write Obs to a bdio file according to ADerrors conventions
    \n\n
    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path -- path to the bdio file
      • \n
    • bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • success (int):\nreturns 0 is successful
      • \n
    \n", "signature": "(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "kind": "function", "doc": "
    Extract mesons data from a bdio file and return it as a dictionary
    \n\n
    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
    \n\n
    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (dict):\nExtracted meson data
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "kind": "function", "doc": "
    Extract dSdm data from a bdio file and return it as a dictionary
    \n\n
    The dictionary can be accessed with a tuple consisting of (type, kappa)
    \n\n
    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to
    \n\n
    all:                bdio.o $(LIBDIR)\n            gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n            cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
    \n\n
    Parameters
    \n\n
      \n
    • file_path (str):\npath to the bdio file
      • \n
    • bdio_path (str):\npath to the shared bdio library libbdio.so (default ./libbdio.so)
      • \n
    • start (int):\nThe first configuration to be read (default 1)
      • \n
    • stop (int):\nThe last configuration to be read (default None)
      • \n
    • step (int):\nFixed step size between two measurements (default 1)
      • \n
    • alternative_ensemble_name (str):\nManually overwrite ensemble name
      • \n
    \n", "signature": "(file_path, bdio_path='./libbdio.so', **kwargs):", "funcdef": "def"}, "pyerrors.input.dobs": {"fullname": "pyerrors.input.dobs", "modulename": "pyerrors.input.dobs", "kind": "module", "doc": "
    \n"}, "pyerrors.input.dobs.create_pobs_string": {"fullname": "pyerrors.input.dobs.create_pobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_pobs_string", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to an xml string\naccording to the Zeuthen pobs format.
    \n\n
    Tags are not written or recovered automatically. The separator | is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML formatted string of the input data
      • \n
    \n", "signature": "(obsl, name, spec='', origin='', symbol=[], enstag=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_pobs": {"fullname": "pyerrors.input.dobs.write_pobs", "modulename": "pyerrors.input.dobs", "qualname": "write_pobs", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen pobs format.
    \n\n
    Tags are not written or recovered automatically. The separator | is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure have to be defined on the same ensemble.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • enstag (str):\nEnstag that is written to pobs. If None, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped xml. If False, the output is an xml file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='',\torigin='',\tsymbol=[],\tenstag=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_pobs": {"fullname": "pyerrors.input.dobs.read_pobs", "modulename": "pyerrors.input.dobs", "qualname": "read_pobs", "kind": "function", "doc": "
    Import a list of Obs from an xml.gz file in the Zeuthen pobs format.
    \n\n
    Tags are not written or recovered automatically.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str or int):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nNone (default): Replica names remain unchanged.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=None):", "funcdef": "def"}, "pyerrors.input.dobs.import_dobs_string": {"fullname": "pyerrors.input.dobs.import_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "import_dobs_string", "kind": "function", "doc": "
    Import a list of Obs from a string in the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically.
    \n\n
    Parameters
    \n\n
      \n
    • content (str):\nXML string containing the data
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(content, full_output=False, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.read_dobs": {"fullname": "pyerrors.input.dobs.read_dobs", "modulename": "pyerrors.input.dobs", "qualname": "read_dobs", "kind": "function", "doc": "
    Import a list of Obs from an xml.gz file in the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned as list.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes XML file.
      • \n
    • separatior_insertion (str, int or bool):\nstr: replace all occurences of \"separator_insertion\" within the replica names\nby \"|%s\" % (separator_insertion) when constructing the names of the replica.\nint: Insert the separator \"|\" at the position given by separator_insertion.\nTrue (default): separator \"|\" is inserted after len(ensname), assuming that the\nensemble name is a prefix to the replica name.\nNone or False: No separator is inserted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (list[Obs]):\nImported data
      • \n
    • or
      • \n
    • res (dict):\nImported data and meta-data
      • \n
    \n", "signature": "(fname, full_output=False, gz=True, separator_insertion=True):", "funcdef": "def"}, "pyerrors.input.dobs.create_dobs_string": {"fullname": "pyerrors.input.dobs.create_dobs_string", "modulename": "pyerrors.input.dobs", "qualname": "create_dobs_string", "kind": "function", "doc": "
    Generate the string for the export of a list of Obs or structures containing Obs\nto a .xml.gz file according to the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically. The separator |is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • xml_str (str):\nXML string generated from the data
      • \n
    \n", "signature": "(\tobsl,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None):", "funcdef": "def"}, "pyerrors.input.dobs.write_dobs": {"fullname": "pyerrors.input.dobs.write_dobs", "modulename": "pyerrors.input.dobs", "qualname": "write_dobs", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to a .xml.gz file\naccording to the Zeuthen dobs format.
    \n\n
    Tags are not written or recovered automatically. The separator | is removed from the replica names.
    \n\n
    Parameters
    \n\n
      \n
    • obsl (list):\nList of Obs that will be exported.\nThe Obs inside a structure do not have to be defined on the same set of configurations,\nbut the storage requirement is increased, if this is not the case.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • name (str):\nThe name of the observable.
      • \n
    • spec (str):\nOptional string that describes the contents of the file.
      • \n
    • origin (str):\nSpecify where the data has its origin.
      • \n
    • symbol (list):\nA list of symbols that describe the observables to be written. May be empty.
      • \n
    • who (str):\nProvide the name of the person that exports the data.
      • \n
    • enstags (dict):\nProvide alternative enstag for ensembles in the form enstags = {ename: enstag}\nOtherwise, the ensemble name is used.
      • \n
    • gz (bool):\nIf True, the output is a gzipped XML. If False, the output is a XML file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(\tobsl,\tfname,\tname,\tspec='dobs v1.0',\torigin='',\tsymbol=[],\twho=None,\tenstags=None,\tgz=True):", "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "kind": "module", "doc": "
    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "kind": "function", "doc": "
    Read hadrons meson hdf5 file and extract the meson labeled 'meson'
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
      • \n
    • gammas (tuple of strings):\nInstrad of a meson label one can also provide a tuple of two strings\nindicating the gamma matrices at source and sink.\n(\"Gamma5\", \"Gamma5\") corresponds to the pseudoscalar pseudoscalar\ntwo-point function. The gammas argument dominateds over meson.
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • corr (Corr):\nCorrelator of the source sink combination in question.
      • \n
    \n", "signature": "(path, filestem, ens_id, meson='meson_0', idl=None, gammas=None):", "funcdef": "def"}, "pyerrors.input.hadrons.extract_t0_hd5": {"fullname": "pyerrors.input.hadrons.extract_t0_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "extract_t0_hd5", "kind": "function", "doc": "
    Read hadrons FlowObservables hdf5 file and extract t0
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • obs (str):\nlabel of the observable from which t0 should be extracted.\nOptions: 'Clover energy density' and 'Plaquette energy density'
      • \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
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
      • \n
    \n", "signature": "(\tpath,\tfilestem,\tens_id,\tobs='Clover energy density',\tfit_range=5,\tidl=None,\t**kwargs):", "funcdef": "def"}, "pyerrors.input.hadrons.read_DistillationContraction_hd5": {"fullname": "pyerrors.input.hadrons.read_DistillationContraction_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_DistillationContraction_hd5", "kind": "function", "doc": "
    Read hadrons DistillationContraction hdf5 files in given directory structure
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the directories to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • diagrams (list):\nList of strings of the diagrams to extract, e.g. [\"direct\", \"box\", \"cross\"].
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (dict):\nextracted DistillationContration data
      • \n
    \n", "signature": "(path, ens_id, diagrams=['direct'], idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "kind": "class", "doc": "
    ndarray(shape, dtype=float, buffer=None, offset=0,\n        strides=None, order=None)
    \n\n
    An array object represents a multidimensional, homogeneous array\nof fixed-size items.  An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)
    \n\n
    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below).  The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.
    \n\n
    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.
    \n\n
    Parameters
    \n\n
      \n
    • (for the __new__ method; see Notes below)
      • \n
    • shape (tuple of ints):\nShape of created array.
      • \n
    • dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
      • \n
    • buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
      • \n
    • offset (int, optional):\nOffset of array data in buffer.
      • \n
    • strides (tuple of ints, optional):\nStrides of data in memory.
      • \n
    • order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • T (ndarray):\nTranspose of the array.
      • \n
    • data (buffer):\nThe array's elements, in memory.
      • \n
    • dtype (dtype object):\nDescribes the format of the elements in the array.
      • \n
    • flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
      • \n
    • flat (numpy.flatiter object):\nFlattened version of the array as an iterator.  The iterator\nallows assignments, e.g., x.flat = 3 (See ndarray.flat for\nassignment examples; TODO).
      • \n
    • imag (ndarray):\nImaginary part of the array.
      • \n
    • real (ndarray):\nReal part of the array.
      • \n
    • size (int):\nNumber of elements in the array.
      • \n
    • itemsize (int):\nThe memory use of each array element in bytes.
      • \n
    • nbytes (int):\nThe total number of bytes required to store the array data,\ni.e., itemsize * size.
      • \n
    • ndim (int):\nThe array's number of dimensions.
      • \n
    • shape (tuple of ints):\nShape of the array.
      • \n
    • strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous (3, 4) array of type\nint16 in C-order has strides (8, 2).  This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4).
      • \n
    • ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
      • \n
    • base (ndarray):\nIf the array is a view into another array, that array is its base\n(unless that array is also a view).  The base array is where the\narray data is actually stored.
      • \n
    \n\n
    See Also
    \n\n
    array:  Construct an array. 
    \nzeros:  Create an array, each element of which is zero. 
    \nempty:  Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\"). 
    \ndtype:  Create a data-type. 
    \nnumpy.typing.NDArray:  An ndarray alias :term:generic <generic type>\nw.r.t. its dtype.type <numpy.dtype.type>.  
    \n\n
    Notes
    \n\n
    There are two modes of creating an array using __new__:
    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
      2. \n
    2. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
      3. \n
    \n\n
    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.
    \n\n
    Examples
    \n\n
    These examples illustrate the low-level ndarray constructor.  Refer\nto the See Also section above for easier ways of constructing an\nndarray.
    \n\n
    First mode, buffer is None:
    \n\n
    \n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n
    \n\n
    Second mode:
    \n\n
    \n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n
    \n", "bases": "numpy.ndarray"}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"fullname": "pyerrors.input.hadrons.Npr_matrix.g5H", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix.g5H", "kind": "variable", "doc": "
    Gamma_5 hermitean conjugate
    \n\n
    Uses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.
    \n"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "kind": "function", "doc": "
    Read hadrons ExternalLeg hdf5 file and output an array of CObs
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Npr_matrix):\nread Cobs-matrix
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "kind": "function", "doc": "
    Read hadrons Bilinear hdf5 file and output an array of CObs
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict[Npr_matrix]):\nextracted Bilinears
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None):", "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "kind": "function", "doc": "
    Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the files to read
      • \n
    • filestem (str):\nnamestem of the files to read
      • \n
    • ens_id (str):\nname of the ensemble, required for internal bookkeeping
      • \n
    • idl (range):\nIf specified only configurations in the given range are read in.
      • \n
    • vertices (list):\nVertex functions to be extracted.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result_dict (dict):\nextracted fourquark matrizes
      • \n
    \n", "signature": "(path, filestem, ens_id, idl=None, vertices=['VA', 'AV']):", "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "kind": "module", "doc": "
    \n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "kind": "function", "doc": "
    Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file
    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • json_string (str):\nString for export to .json(.gz) file
      • \n
    \n", "signature": "(ol, description='', indent=1):", "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "kind": "function", "doc": "
    Export a list of Obs or structures containing Obs to a .json(.gz) file
    \n\n
    Parameters
    \n\n
      \n
    • ol (list):\nList of objects that will be exported. At the moment, these objects can be\neither of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • Null
      • \n
    \n", "signature": "(ol, fname, description='', indent=1, gz=True):", "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "kind": "function", "doc": "
    Reconstruct a list of Obs or structures containing Obs from a json string.
    \n\n
    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
    \n\n
    Parameters
    \n\n
      \n
    • json_string (str):\njson string containing the data.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
      • \n
    • or
      • \n
    • result (Obs):\nonly one observable if the list only has one entry
      • \n
    • or
      • \n
    • result (dict):\nif full_output=True
      • \n
    \n", "signature": "(json_string, verbose=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "kind": "function", "doc": "
    Import a list of Obs or structures containing Obs from a .json(.gz) file.
    \n\n
    The following structures are supported: Obs, list, numpy.ndarray, Corr\nIf the list contains only one element, it is unpacked from the list.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nreconstructed list of observables from the json string
      • \n
    • or
      • \n
    • result (Obs):\nonly one observable if the list only has one entry
      • \n
    • or
      • \n
    • result (dict):\nif full_output=True
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False):", "funcdef": "def"}, "pyerrors.input.json.dump_dict_to_json": {"fullname": "pyerrors.input.json.dump_dict_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_dict_to_json", "kind": "function", "doc": "
    Export a dict of Obs or structures containing Obs to a .json(.gz) file
    \n\n
    Parameters
    \n\n
      \n
    • od (dict):\nDict of JSON valid structures and objects that will be exported.\nAt the moment, these objects can be either of: Obs, list, numpy.ndarray, Corr.\nAll Obs inside a structure have to be defined on the same set of configurations.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • description (str):\nOptional string that describes the contents of the json file.
      • \n
    • indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in exported dict to be reps[0-9]+.
      • \n
    • gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(od, fname, description='', indent=1, reps='DICTOBS', gz=True):", "funcdef": "def"}, "pyerrors.input.json.load_json_dict": {"fullname": "pyerrors.input.json.load_json_dict", "modulename": "pyerrors.input.json", "qualname": "load_json_dict", "kind": "function", "doc": "
    Import a dict of Obs or structures containing Obs from a .json(.gz) file.
    \n\n
    The following structures are supported: Obs, list, numpy.ndarray, Corr
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • verbose (bool):\nPrint additional information that was written to the file.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    • full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
      • \n
    • reps (str):\nSpecify the structure of the placeholder in imported dict to be reps[0-9]+.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (Obs / list / Corr):\nRead data
      • \n
    • or
      • \n
    • data (dict):\nRead data and meta-data
      • \n
    \n", "signature": "(fname, verbose=True, gz=True, full_output=False, reps='DICTOBS'):", "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "kind": "module", "doc": "
    \n"}, "pyerrors.input.misc.fit_t0": {"fullname": "pyerrors.input.misc.fit_t0", "modulename": "pyerrors.input.misc", "qualname": "fit_t0", "kind": "function", "doc": "
    \n", "signature": "(t2E_dict, fit_range, plot_fit=False):", "funcdef": "def"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "kind": "function", "doc": "
    Read pbp format from given folder structure.
    \n\n
    Parameters
    \n\n
      \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of observables read
      • \n
    \n", "signature": "(path, prefix, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "kind": "module", "doc": "
    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "kind": "function", "doc": "
    Read rwms format from given folder structure. Returns a list of length nrw
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath that contains the data files
      • \n
    • prefix (str):\nall files in path that start with prefix are considered as input files.\nMay be used together postfix to consider only special file endings.\nPrefix is ignored, if the keyword 'files' is used.
      • \n
    • version (str):\nversion of openQCD, default 2.0
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • print_err (bool):\nPrint additional information that is useful for debugging.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • rwms (Obs):\nReweighting factors read
      • \n
    \n", "signature": "(path, prefix, version='2.0', names=None, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "kind": "function", "doc": "
    Extract t0 from given .ms.dat files. Returns t0 as Obs.
    \n\n
    It is assumed that all boundary effects have\nsufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3\nis fitted with a linear function\nfrom which the exact root is extracted.
    \n\n
    It is assumed that one measurement is performed for each config.\nIf this is not the case, the resulting idl, as well as the handling\nof r_start, r_stop and r_step is wrong and the user has to correct\nthis in the resulting observable.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to .ms.dat files
      • \n
    • prefix (str):\nEnsemble prefix
      • \n
    • dtr_read (int):\nDetermines how many trajectories should be skipped\nwhen reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
      • \n
    • xmin (int):\nFirst timeslice where the boundary\neffects have sufficiently decayed.
      • \n
    • spatial_extent (int):\nspatial extent of the lattice, required for normalization.
      • \n
    • fit_range (int):\nNumber of data points left and right of the zero\ncrossing to be included in the linear fit. (Default: 5)
      • \n
    • postfix (str):\nPostfix of measurement file (Default: ms)
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • r_step (int):\ninteger that defines a fixed step size between two measurements (in units of configs)\nIf not given, r_step=1 is assumed.
      • \n
    • plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
      • \n
    • names (list):\nlist of names that is assigned to the data according according\nto the order in the file list. Use careful, if you do not provide file names!
      • \n
    • files (list):\nlist which contains the filenames to be read. No automatic detection of\nfiles performed if given.
      • \n
    • plot_fit (bool):\nIf true, the fit for the extraction of t0 is shown together with the data.
      • \n
    • assume_thermalization (bool):\nIf True: If the first record divided by the distance between two measurements is larger than\n1, it is assumed that this is due to thermalization and the first measurement belongs\nto the first config (default).\nIf False: The config numbers are assumed to be traj_number // difference
      • \n
    \n\n
    Returns
    \n\n
      \n
    • t0 (Obs):\nExtracted t0
      • \n
    \n", "signature": "(\tpath,\tprefix,\tdtr_read,\txmin,\tspatial_extent,\tfit_range=5,\tpostfix='ms',\t**kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop": {"fullname": "pyerrors.input.openQCD.read_qtop", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop", "kind": "function", "doc": "
    Read the topologial charge based on openQCD gradient flow measurements.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nEither openQCD or sfqcd, depending on the data.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    • integer_charge (bool):\nIf True, the charge is rounded towards the nearest integer on each config.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (Obs):\nRead topological charge
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, version='openQCD', **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_gf_coupling": {"fullname": "pyerrors.input.openQCD.read_gf_coupling", "modulename": "pyerrors.input.openQCD", "qualname": "read_gf_coupling", "kind": "function", "doc": "
    Read the gradient flow coupling based on sfqcd gradient flow measurements. See 1607.06423 for details.
    \n\n
    Note: The current implementation only works for c=0.3 and T=L. The definition of the coupling in 1607.06423 requires projection to topological charge zero which is not done within this function but has to be performed in a separate step.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat.\nIgnored if file names are passed explicitly via keyword files.
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L.
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of measurements\nbetween two configs.\nIf it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • r_start (list):\nlist which contains the first config to be read for each replicum.
      • \n
    • r_stop (list):\nlist which contains the last config to be read for each replicum.
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length.
      • \n
    • postfix (str):\npostfix of the file to read, e.g. '.gfms.dat' for openQCD-files
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for the coupling. If False, the Wilson flow is used.
      • \n
    \n", "signature": "(path, prefix, c, dtr_cnfg=1, Zeuthen_flow=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.qtop_projection": {"fullname": "pyerrors.input.openQCD.qtop_projection", "modulename": "pyerrors.input.openQCD", "qualname": "qtop_projection", "kind": "function", "doc": "
    Returns the projection to the topological charge sector defined by target.
    \n\n
    Parameters
    \n\n
      \n
    • path (Obs):\nTopological charge.
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
      • \n
    \n", "signature": "(qtop, target=0):", "funcdef": "def"}, "pyerrors.input.openQCD.read_qtop_sector": {"fullname": "pyerrors.input.openQCD.read_qtop_sector", "modulename": "pyerrors.input.openQCD", "qualname": "read_qtop_sector", "kind": "function", "doc": "
    Constructs reweighting factors to a specified topological sector.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath of the measurement files
      • \n
    • prefix (str):\nprefix of the measurement files, e.g. _id0_r0.ms.dat
      • \n
    • c (double):\nSmearing radius in units of the lattice extent, c = sqrt(8 t0) / L
      • \n
    • target (int):\nSpecifies the topological sector to be reweighted to (default 0)
      • \n
    • dtr_cnfg (int):\n(optional) parameter that specifies the number of trajectories\nbetween two configs.\nif it is not set, the distance between two measurements\nin the file is assumed to be the distance between two configurations.
      • \n
    • steps (int):\n(optional) Distance between two configurations in units of trajectories /\n cycles. Assumed to be the distance between two measurements * dtr_cnfg if not given
      • \n
    • version (str):\nversion string of the openQCD (sfqcd) version used to create\nthe ensemble. Default is 2.0. May also be set to sfqcd.
      • \n
    • L (int):\nspatial length of the lattice in L/a.\nHAS to be set if version != sfqcd, since openQCD does not provide\nthis in the header
      • \n
    • r_start (list):\noffset of the first ensemble, making it easier to match\nlater on with other Obs
      • \n
    • r_stop (list):\nlast configurations that need to be read (per replicum)
      • \n
    • files (list):\nspecify the exact files that need to be read\nfrom path, practical if e.g. only one replicum is needed
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • Zeuthen_flow (bool):\n(optional) If True, the Zeuthen flow is used for Qtop. Only possible\nfor version=='sfqcd' If False, the Wilson flow is used.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • reto (Obs):\nprojection to the topological charge sector defined by target
      • \n
    \n", "signature": "(path, prefix, c, target=0, **kwargs):", "funcdef": "def"}, "pyerrors.input.openQCD.read_ms5_xsf": {"fullname": "pyerrors.input.openQCD.read_ms5_xsf", "modulename": "pyerrors.input.openQCD", "qualname": "read_ms5_xsf", "kind": "function", "doc": "
    Read data from files in the specified directory with the specified prefix and quark combination extension, and return a Corr object containing the data.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nThe directory to search for the files in.
      • \n
    • prefix (str):\nThe prefix to match the files against.
      • \n
    • qc (str):\nThe quark combination extension to match the files against.
      • \n
    • corr (str):\nThe correlator to extract data for.
      • \n
    • sep (str, optional):\nThe separator to use when parsing the replika names.
      • \n
    • **kwargs: Additional keyword arguments. The following keyword arguments are recognized:
      \n\n
        \n
      • names (List[str]): A list of names to use for the replicas.
        • \n
      • \n
    \n\n
    Returns
    \n\n
      \n
    • Corr: A complex valued Corr object containing the data read from the files. In case of boudary to bulk correlators.
      • \n
    • or
      • \n
    • CObs: A complex valued CObs object containing the data read from the files. In case of boudary to boundary correlators.
      • \n
    \n\n
    Raises
    \n\n
      \n
    • FileNotFoundError: If no files matching the specified prefix and quark combination extension are found in the specified directory.
      • \n
    • IOError: If there is an error reading a file.
      • \n
    • struct.error: If there is an error unpacking binary data.
      • \n
    \n", "signature": "(path, prefix, qc, corr, sep='r', **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas": {"fullname": "pyerrors.input.pandas", "modulename": "pyerrors.input.pandas", "kind": "module", "doc": "
    \n"}, "pyerrors.input.pandas.to_sql": {"fullname": "pyerrors.input.pandas.to_sql", "modulename": "pyerrors.input.pandas", "qualname": "to_sql", "kind": "function", "doc": "
    Write DataFrame including Obs or Corr valued columns to sqlite database.
    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be written to the database.
      • \n
    • table_name (str):\nName of the table in the database.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • if exists (str):\nHow to behave if table already exists. Options 'fail', 'replace', 'append'.
      • \n
    • gz (bool):\nIf True the json strings are gzipped.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(df, table_name, db, if_exists='fail', gz=True, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.read_sql": {"fullname": "pyerrors.input.pandas.read_sql", "modulename": "pyerrors.input.pandas", "qualname": "read_sql", "kind": "function", "doc": "
    Execute SQL query on sqlite database and obtain DataFrame including Obs or Corr valued columns.
    \n\n
    Parameters
    \n\n
      \n
    • sql (str):\nSQL query to be executed.
      • \n
    • db (str):\nPath to the sqlite database.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
      • \n
    \n", "signature": "(sql, db, auto_gamma=False, **kwargs):", "funcdef": "def"}, "pyerrors.input.pandas.dump_df": {"fullname": "pyerrors.input.pandas.dump_df", "modulename": "pyerrors.input.pandas", "qualname": "dump_df", "kind": "function", "doc": "
    Exports a pandas DataFrame containing Obs valued columns to a (gzipped) csv file.
    \n\n
    Before making use of pandas to_csv functionality Obs objects are serialized via the standardized\njson format of pyerrors.
    \n\n
    Parameters
    \n\n
      \n
    • df (pandas.DataFrame):\nDataframe to be dumped to a file.
      • \n
    • fname (str):\nFilename of the output file.
      • \n
    • gz (bool):\nIf True, the output is a gzipped csv file. If False, the output is a csv file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(df, fname, gz=True):", "funcdef": "def"}, "pyerrors.input.pandas.load_df": {"fullname": "pyerrors.input.pandas.load_df", "modulename": "pyerrors.input.pandas", "qualname": "load_df", "kind": "function", "doc": "
    Imports a pandas DataFrame from a csv.(gz) file in which Obs objects are serialized as json strings.
    \n\n
    Parameters
    \n\n
      \n
    • fname (str):\nFilename of the input file.
      • \n
    • auto_gamma (bool):\nIf True applies the gamma_method to all imported Obs objects with the default parameters for\nthe error analysis. Default False.
      • \n
    • gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • data (pandas.DataFrame):\nDataframe with the content of the sqlite database.
      • \n
    \n", "signature": "(fname, auto_gamma=False, gz=True):", "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "kind": "module", "doc": "
    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "kind": "function", "doc": "
    Read sfcf files from given folder structure.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nPath to the sfcf files.
      • \n
    • prefix (str):\nPrefix of the sfcf files.
      • \n
    • name (str):\nName of the correlation function to read.
      • \n
    • quarks (str):\nLabel of the quarks used in the sfcf input file. e.g. \"quark quark\"\nfor version 0.0 this does NOT need to be given with the typical \" - \"\nthat is present in the output file,\nthis is done automatically for this version
      • \n
    • corr_type (str):\nType of correlation function to read. Can be\n
        \n
      • 'bi' for boundary-inner
        • \n
      • 'bb' for boundary-boundary
        • \n
      • 'bib' for boundary-inner-boundary
        • \n
      • \n
    • noffset (int):\nOffset of the source (only relevant when wavefunctions are used)
      • \n
    • wf (int):\nID of wave function
      • \n
    • wf2 (int):\nID of the second wavefunction\n(only relevant for boundary-to-boundary correlation functions)
      • \n
    • im (bool):\nif True, read imaginary instead of real part\nof the correlation function.
      • \n
    • names (list):\nAlternative labeling for replicas/ensembles.\nHas to have the appropriate length
      • \n
    • ens_name (str):\nreplaces the name of the ensemble
      • \n
    • version (str):\nversion of SFCF, with which the measurement was done.\nif the compact output option (-c) was specified,\nappend a \"c\" to the version (e.g. \"1.0c\")\nif the append output option (-a) was specified,\nappend an \"a\" to the version
      • \n
    • cfg_separator (str):\nString that separates the ensemble identifier from the configuration number (default 'n').
      • \n
    • replica (list):\nlist of replica to be read, default is all
      • \n
    • files (list):\nlist of files to be read per replica, default is all.\nfor non-compact output format, hand the folders to be read here.
      • \n
    • check_configs (list[list[int]]):\nlist of list of supposed configs, eg. [range(1,1000)]\nfor one replicum with 1000 configs
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (list[Obs]):\nlist of Observables with length T, observable per timeslice.\nbb-type correlators have length 1.
      • \n
    \n", "signature": "(\tpath,\tprefix,\tname,\tquarks='.*',\tcorr_type='bi',\tnoffset=0,\twf=0,\twf2=0,\tversion='1.0c',\tcfg_separator='n',\tsilent=False,\t**kwargs):", "funcdef": "def"}, "pyerrors.input.utils": {"fullname": "pyerrors.input.utils", "modulename": "pyerrors.input.utils", "kind": "module", "doc": "
    \n"}, "pyerrors.input.utils.sort_names": {"fullname": "pyerrors.input.utils.sort_names", "modulename": "pyerrors.input.utils", "qualname": "sort_names", "kind": "function", "doc": "
    Sorts a list of names of replika with searches for r and id in the replikum string.\nIf this search fails, a fallback method is used,\nwhere the strings are simply compared and the first diffeing numeral is used for differentiation.
    \n\n
    Parameters
    \n\n
      \n
    • ll (list):\nlist to sort
      • \n
    \n\n
    Returns
    \n\n
      \n
    • ll (list):\nsorted list
      • \n
    \n", "signature": "(ll):", "funcdef": "def"}, "pyerrors.input.utils.check_idl": {"fullname": "pyerrors.input.utils.check_idl", "modulename": "pyerrors.input.utils", "qualname": "check_idl", "kind": "function", "doc": "
    Checks if list of configurations is contained in an idl
    \n\n
    Parameters
    \n\n
      \n
    • idl (range or list):\nidl of the current replicum
      • \n
    • che (list):\nlist of configurations to be checked against
      • \n
    \n\n
    Returns
    \n\n
      \n
    • miss_str (str):\nstring with integers of which idls are missing
      • \n
    \n", "signature": "(idl, che):", "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "kind": "module", "doc": "
    \n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "kind": "function", "doc": "
    Matrix multiply all operands.
    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • This implementation is faster compared to standard multiplication via the @ operator.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "kind": "function", "doc": "
    Matrix multiply both operands making use of the jackknife approximation.
    \n\n
    Parameters
    \n\n
      \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    • For large matrices this is considerably faster compared to matmul.
      • \n
    \n", "signature": "(*operands):", "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "kind": "function", "doc": "
    Wrapper for numpy.einsum
    \n\n
    Parameters
    \n\n
      \n
    • subscripts (str):\nSubscripts for summation (see numpy documentation for details)
      • \n
    • operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
      • \n
    \n", "signature": "(subscripts, *operands):", "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "kind": "function", "doc": "
    Inverse of Obs or CObs valued matrices.
    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "kind": "function", "doc": "
    Cholesky decomposition of Obs valued matrices.
    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "kind": "function", "doc": "
    Determinant of Obs valued matrices.
    \n", "signature": "(x):", "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "kind": "function", "doc": "
    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "kind": "function", "doc": "
    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "kind": "function", "doc": "
    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "kind": "function", "doc": "
    Computes the singular value decomposition of a matrix of Obs.
    \n", "signature": "(obs, **kwargs):", "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "kind": "module", "doc": "
    \n"}, "pyerrors.misc.print_config": {"fullname": "pyerrors.misc.print_config", "modulename": "pyerrors.misc", "qualname": "print_config", "kind": "function", "doc": "
    Print information about version of python, pyerrors and dependencies.
    \n", "signature": "():", "funcdef": "def"}, "pyerrors.misc.errorbar": {"fullname": "pyerrors.misc.errorbar", "modulename": "pyerrors.misc", "qualname": "errorbar", "kind": "function", "doc": "
    pyerrors wrapper for the errorbars method of matplotlib
    \n\n
    Parameters
    \n\n
      \n
    • x (list):\nA list of x-values which can be Obs.
      • \n
    • y (list):\nA list of y-values which can be Obs.
      • \n
    • axes ((matplotlib.pyplot.axes)):\nThe axes to plot on. default is plt.
      • \n
    \n", "signature": "(\tx,\ty,\taxes=<module 'matplotlib.pyplot' from '/opt/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/matplotlib/pyplot.py'>,\t**kwargs):", "funcdef": "def"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "kind": "function", "doc": "
    Dump object into pickle file.
    \n\n
    Parameters
    \n\n
      \n
    • obj (object):\nobject to be saved in the pickle file
      • \n
    • name (str):\nname of the file
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n\n
    Returns
    \n\n
      \n
    • None
      • \n
    \n", "signature": "(obj, name, **kwargs):", "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "kind": "function", "doc": "
    Load object from pickle file.
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\npath to the file
      • \n
    \n\n
    Returns
    \n\n
      \n
    • object (Obs):\nLoaded Object
      • \n
    \n", "signature": "(path):", "funcdef": "def"}, "pyerrors.misc.pseudo_Obs": {"fullname": "pyerrors.misc.pseudo_Obs", "modulename": "pyerrors.misc", "qualname": "pseudo_Obs", "kind": "function", "doc": "
    Generate an Obs object with given value, dvalue and name for test purposes
    \n\n
    Parameters
    \n\n
      \n
    • value (float):\ncentral value of the Obs to be generated.
      • \n
    • dvalue (float):\nerror of the Obs to be generated.
      • \n
    • name (str):\nname of the ensemble for which the Obs is to be generated.
      • \n
    • samples (int):\nnumber of samples for the Obs (default 1000).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nGenerated Observable
      • \n
    \n", "signature": "(value, dvalue, name, samples=1000):", "funcdef": "def"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "kind": "function", "doc": "
    Generate observables with given covariance and autocorrelation times.
    \n\n
    Parameters
    \n\n
      \n
    • means (list):\nlist containing the mean value of each observable.
      • \n
    • cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
      • \n
    • name (str):\nensemble name for the data to be geneated.
      • \n
    • tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
      • \n
    • samples (int):\nnumber of samples to be generated for each observable.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • corr_obs (list[Obs]):\nGenerated observable list
      • \n
    \n", "signature": "(means, cov, name, tau=0.5, samples=1000):", "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "kind": "module", "doc": "
    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "kind": "function", "doc": "
    Matrix pencil method to extract k energy levels from data
    \n\n
    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)
    \n\n
    Parameters
    \n\n
      \n
    • data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
      • \n
    • k (int):\nNumber of states to extract (default 1).
      • \n
    • p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
      • \n
    \n\n
    Returns
    \n\n
      \n
    • energy_levels (list[Obs]):\nExtracted energy levels
      • \n
    \n", "signature": "(corrs, k=1, p=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "kind": "module", "doc": "
    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "kind": "class", "doc": "
    Class for a general observable.
    \n\n
    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.
    \n\n
    Attributes
    \n\n
      \n
    • S_global (float):\nStandard value for S (default 2.0)
      • \n
    • S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
      • \n
    • tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
      • \n
    • tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    • N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
      • \n
    • N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
      • \n
    \n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "kind": "function", "doc": "
    Initialize Obs object.
    \n\n
    Parameters
    \n\n
      \n
    • samples (list):\nlist of numpy arrays containing the Monte Carlo samples
      • \n
    • names (list):\nlist of strings labeling the individual samples
      • \n
    • idl (list, optional):\nlist of ranges or lists on which the samples are defined
      • \n
    \n", "signature": "(samples, names, idl=None, **kwargs)"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "kind": "function", "doc": "
    Estimate the error and related properties of the Obs.
    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.gm": {"fullname": "pyerrors.obs.Obs.gm", "modulename": "pyerrors.obs", "qualname": "Obs.gm", "kind": "function", "doc": "
    Estimate the error and related properties of the Obs.
    \n\n
    Parameters
    \n\n
      \n
    • S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
      • \n
    • tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
      • \n
    • N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
      • \n
    • fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
      • \n
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "kind": "function", "doc": "
    Output detailed properties of the Obs.
    \n\n
    Parameters
    \n\n
      \n
    • ens_content (bool):\nprint details about the ensembles and replica if true.
      • \n
    \n", "signature": "(self, ens_content=True):", "funcdef": "def"}, "pyerrors.obs.Obs.reweight": {"fullname": "pyerrors.obs.Obs.reweight", "modulename": "pyerrors.obs", "qualname": "Obs.reweight", "kind": "function", "doc": "
    Reweight the obs with given rewighting factors.
    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(self, weight):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "kind": "function", "doc": "
    Checks whether the observable is zero within 'sigma' standard errors.
    \n\n
    Parameters
    \n\n
      \n
    • sigma (int):\nNumber of standard errors used for the check.
      • \n
    • Works only properly when the gamma method was run.
      • \n
    \n", "signature": "(self, sigma=1):", "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "kind": "function", "doc": "
    Checks whether the observable is zero within a given tolerance.
    \n\n
    Parameters
    \n\n
      \n
    • atol (float):\nAbsolute tolerance (for details see numpy documentation).
      • \n
    \n", "signature": "(self, atol=1e-10):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "kind": "function", "doc": "
    Plot integrated autocorrelation time for each ensemble.
    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "kind": "function", "doc": "
    Plot normalized autocorrelation function time for each ensemble.
    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "kind": "function", "doc": "
    Plot replica distribution for each ensemble with more than one replicum.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "kind": "function", "doc": "
    Plot derived Monte Carlo history for each ensemble
    \n\n
    Parameters
    \n\n
      \n
    • expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
      • \n
    \n", "signature": "(self, expand=True):", "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "kind": "function", "doc": "
    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.
    \n\n
    Parameters
    \n\n
      \n
    • save (str):\nsaves the figure to a file named 'save' if.
      • \n
    \n", "signature": "(self, save=None):", "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "kind": "function", "doc": "
    Dump the Obs to a file 'name' of chosen format.
    \n\n
    Parameters
    \n\n
      \n
    • filename (str):\nname of the file to be saved.
      • \n
    • datatype (str):\nFormat of the exported file. Supported formats include\n\"json.gz\" and \"pickle\"
      • \n
    • description (str):\nDescription for output file, only relevant for json.gz format.
      • \n
    • path (str):\nspecifies a custom path for the file (default '.')
      • \n
    \n", "signature": "(self, filename, datatype='json.gz', description='', **kwargs):", "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "kind": "function", "doc": "
    Export jackknife samples from the Obs
    \n\n
    Returns
    \n\n
      \n
    • numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
      • \n
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "kind": "class", "doc": "
    Class for a complex valued observable.
    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "kind": "function", "doc": "
    \n", "signature": "(real, imag=0.0)"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "kind": "function", "doc": "
    Executes the gamma_method for the real and the imaginary part.
    \n", "signature": "(self, **kwargs):", "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "kind": "function", "doc": "
    Checks whether both real and imaginary part are zero within machine precision.
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "kind": "function", "doc": "
    \n", "signature": "(self):", "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "kind": "function", "doc": "
    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.
    \n\n
    Parameters
    \n\n
      \n
    • func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
      • \n
    • data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
      • \n
    • man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
      • \n
    \n\n
    Notes
    \n\n
    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use
    \n\n
    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])
    \n", "signature": "(func, data, array_mode=False, **kwargs):", "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "kind": "function", "doc": "
    Reweight a list of observables.
    \n\n
    Parameters
    \n\n
      \n
    • weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
      • \n
    • obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
      • \n
    • all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl. Default False.
      • \n
    \n", "signature": "(weight, obs, **kwargs):", "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "kind": "function", "doc": "
    Correlate two observables.
    \n\n
    Parameters
    \n\n
      \n
    • obs_a (Obs):\nFirst observable
      • \n
    • obs_b (Obs):\nSecond observable
      • \n
    \n\n
    Notes
    \n\n
    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical (this is not strictly necessary).
    \n", "signature": "(obs_a, obs_b):", "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "kind": "function", "doc": "
    Calculates the error covariance matrix of a set of observables.
    \n\n
    WARNING: This function should be used with care, especially for observables with support on multiple\n         ensembles with differing autocorrelations. See the notes below for details.
    \n\n
    The gamma method has to be applied first to all observables.
    \n\n
    Parameters
    \n\n
      \n
    • obs (list or numpy.ndarray):\nList or one dimensional array of Obs
      • \n
    • visualize (bool):\nIf True plots the corresponding normalized correlation matrix (default False).
      • \n
    • correlation (bool):\nIf True the correlation matrix instead of the error covariance matrix is returned (default False).
      • \n
    • smooth (None or int):\nIf smooth is an integer 'E' between 2 and the dimension of the matrix minus 1 the eigenvalue\nsmoothing procedure of hep-lat/9412087 is applied to the correlation matrix which leaves the\nlargest E eigenvalues essentially unchanged and smoothes the smaller eigenvalues to avoid extremely\nsmall ones.
      • \n
    \n\n
    Notes
    \n\n
    The error covariance is defined such that it agrees with the squared standard error for two identical observables\n$$\\operatorname{cov}(a,a)=\\sum_{s=1}^N\\delta_a^s\\delta_a^s/N^2=\\Gamma_{aa}(0)/N=\\operatorname{var}(a)/N=\\sigma_a^2$$\nin the absence of autocorrelation.\nThe error covariance is estimated by calculating the correlation matrix assuming no autocorrelation and then rescaling the correlation matrix by the full errors including the previous gamma method estimate for the autocorrelation of the observables. The covariance at windowsize 0 is guaranteed to be positive semi-definite\n$$\\sum_{i,j}v_i\\Gamma_{ij}(0)v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i,j}v_i\\delta_i^s\\delta_j^s v_j=\\frac{1}{N}\\sum_{s=1}^N\\sum_{i}|v_i\\delta_i^s|^2\\geq 0\\,,$$ for every $v\\in\\mathbb{R}^M$, while such an identity does not hold for larger windows/lags.\nFor observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.\n$$\\tau_{\\mathrm{int}, ij}=\\sqrt{\\tau_{\\mathrm{int}, i}\\times \\tau_{\\mathrm{int}, j}}$$\nThis construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).
    \n", "signature": "(obs, visualize=False, correlation=False, smooth=None, **kwargs):", "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "kind": "function", "doc": "
    Imports jackknife samples and returns an Obs
    \n\n
    Parameters
    \n\n
      \n
    • jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
      • \n
    • name (str):\nname of the ensemble the samples are defined on.
      • \n
    \n", "signature": "(jacks, name, idl=None):", "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "kind": "function", "doc": "
    Combine all observables in list_of_obs into one new observable
    \n\n
    Parameters
    \n\n
      \n
    • list_of_obs (list):\nlist of the Obs object to be combined
      • \n
    \n\n
    Notes
    \n\n
    It is not possible to combine obs which are based on the same replicum
    \n", "signature": "(list_of_obs):", "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "kind": "function", "doc": "
    Create an Obs based on mean(s) and a covariance matrix
    \n\n
    Parameters
    \n\n
      \n
    • mean (list of floats or float):\nN mean value(s) of the new Obs
      • \n
    • cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
      • \n
    • name (str):\nidentifier for the covariance matrix
      • \n
    • grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
      • \n
    \n", "signature": "(means, cov, name, grad=None):", "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "kind": "module", "doc": "
    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "kind": "function", "doc": "
    Finds the root of the function func(x, d) where d is an Obs.
    \n\n
    Parameters
    \n\n
      \n
    • d (Obs):\nObs passed to the function.
      • \n
    • func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:
      \n\n
      \n
      import autograd.numpy as anp\ndef root_func(x, d):\n   return anp.exp(-x ** 2) - d\n
      \n
      • \n
    • guess (float):\nInitial guess for the minimization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • res (Obs):\nObs valued root of the function.
      • \n
    \n", "signature": "(d, func, guess=1.0, **kwargs):", "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "kind": "module", "doc": "
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