From bb60ee482b27367d6103896702b60009ad4c9cde Mon Sep 17 00:00:00 2001 From: fjosw Date: Fri, 17 Jun 2022 15:37:35 +0000 Subject: [PATCH] Documentation updated --- docs/pyerrors/input/hadrons.html | 974 +++++++++++++++---------------- 1 file changed, 483 insertions(+), 491 deletions(-) diff --git a/docs/pyerrors/input/hadrons.html b/docs/pyerrors/input/hadrons.html index 55ae19cf..9be36b7f 100644 --- a/docs/pyerrors/input/hadrons.html +++ b/docs/pyerrors/input/hadrons.html @@ -268,294 +268,290 @@ 171 identifier = tuple(identifier) 172 # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case. 173 -174 check_traj = h5file["DistillationContraction/Metadata"].attrs.get("Traj")[0] -175 -176 assert check_traj == n_traj -177 -178 for diagram in diagrams: -179 real_data = np.zeros(Nt) -180 for x0 in range(Nt): -181 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)] -182 real_data += np.roll(raw_data[:]["re"].astype(np.double), -x0) -183 real_data /= Nt -184 -185 corr_data[diagram].append(real_data) -186 h5file.close() +174 for diagram in diagrams: +175 real_data = np.zeros(Nt) +176 for x0 in range(Nt): +177 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)] +178 real_data += np.roll(raw_data[:]["re"].astype(np.double), -x0) +179 real_data /= Nt +180 +181 corr_data[diagram].append(real_data) +182 h5file.close() +183 +184 res_dict[str(identifier)] = {} +185 +186 for diagram in diagrams: 187 -188 res_dict[str(identifier)] = {} +188 tmp_data = np.array(corr_data[diagram]) 189 -190 for diagram in diagrams: -191 -192 tmp_data = np.array(corr_data[diagram]) +190 l_obs = [] +191 for c in tmp_data.T: +192 l_obs.append(Obs([c], [ens_id], idl=[idx])) 193 -194 l_obs = [] -195 for c in tmp_data.T: -196 l_obs.append(Obs([c], [ens_id], idl=[idx])) -197 -198 corr = Corr(l_obs) -199 corr.tag = str(identifier) +194 corr = Corr(l_obs) +195 corr.tag = str(identifier) +196 +197 res_dict[str(identifier)][diagram] = corr +198 +199 return res_dict 200 -201 res_dict[str(identifier)][diagram] = corr -202 -203 return res_dict -204 -205 -206class Npr_matrix(np.ndarray): -207 -208 def __new__(cls, input_array, mom_in=None, mom_out=None): -209 obj = np.asarray(input_array).view(cls) -210 obj.mom_in = mom_in -211 obj.mom_out = mom_out -212 return obj +201 +202class Npr_matrix(np.ndarray): +203 +204 def __new__(cls, input_array, mom_in=None, mom_out=None): +205 obj = np.asarray(input_array).view(cls) +206 obj.mom_in = mom_in +207 obj.mom_out = mom_out +208 return obj +209 +210 @property +211 def g5H(self): +212 """Gamma_5 hermitean conjugate 213 -214 @property -215 def g5H(self): -216 """Gamma_5 hermitean conjugate -217 -218 Uses the fact that the propagator is gamma5 hermitean, so just the -219 in and out momenta of the propagator are exchanged. -220 """ -221 return Npr_matrix(self, -222 mom_in=self.mom_out, -223 mom_out=self.mom_in) -224 -225 def _propagate_mom(self, other, name): -226 s_mom = getattr(self, name, None) -227 o_mom = getattr(other, name, None) -228 if s_mom is not None and o_mom is not None: -229 if not np.allclose(s_mom, o_mom): -230 raise Exception(name + ' does not match.') -231 return o_mom if o_mom is not None else s_mom -232 -233 def __matmul__(self, other): -234 return self.__new__(Npr_matrix, -235 super().__matmul__(other), -236 self._propagate_mom(other, 'mom_in'), -237 self._propagate_mom(other, 'mom_out')) -238 -239 def __array_finalize__(self, obj): -240 if obj is None: -241 return -242 self.mom_in = getattr(obj, 'mom_in', None) -243 self.mom_out = getattr(obj, 'mom_out', None) +214 Uses the fact that the propagator is gamma5 hermitean, so just the +215 in and out momenta of the propagator are exchanged. +216 """ +217 return Npr_matrix(self, +218 mom_in=self.mom_out, +219 mom_out=self.mom_in) +220 +221 def _propagate_mom(self, other, name): +222 s_mom = getattr(self, name, None) +223 o_mom = getattr(other, name, None) +224 if s_mom is not None and o_mom is not None: +225 if not np.allclose(s_mom, o_mom): +226 raise Exception(name + ' does not match.') +227 return o_mom if o_mom is not None else s_mom +228 +229 def __matmul__(self, other): +230 return self.__new__(Npr_matrix, +231 super().__matmul__(other), +232 self._propagate_mom(other, 'mom_in'), +233 self._propagate_mom(other, 'mom_out')) +234 +235 def __array_finalize__(self, obj): +236 if obj is None: +237 return +238 self.mom_in = getattr(obj, 'mom_in', None) +239 self.mom_out = getattr(obj, 'mom_out', None) +240 +241 +242def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None): +243 """Read hadrons ExternalLeg hdf5 file and output an array of CObs 244 -245 -246def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None): -247 """Read hadrons ExternalLeg hdf5 file and output an array of CObs -248 -249 Parameters -250 ---------- -251 path : str -252 path to the files to read -253 filestem : str -254 namestem of the files to read -255 ens_id : str -256 name of the ensemble, required for internal bookkeeping -257 idl : range -258 If specified only configurations in the given range are read in. -259 """ +245 Parameters +246 ---------- +247 path : str +248 path to the files to read +249 filestem : str +250 namestem of the files to read +251 ens_id : str +252 name of the ensemble, required for internal bookkeeping +253 idl : range +254 If specified only configurations in the given range are read in. +255 """ +256 +257 files, idx = _get_files(path, filestem, idl) +258 +259 mom = None 260 -261 files, idx = _get_files(path, filestem, idl) -262 -263 mom = None -264 -265 corr_data = [] -266 for hd5_file in files: -267 file = h5py.File(path + '/' + hd5_file, "r") -268 raw_data = file['ExternalLeg/corr'][0][0].view('complex') -269 corr_data.append(raw_data) -270 if mom is None: -271 mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -272 file.close() -273 corr_data = np.array(corr_data) -274 -275 rolled_array = np.rollaxis(corr_data, 0, 5) -276 -277 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -278 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): -279 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) -280 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) -281 matrix[si, sj, ci, cj] = CObs(real, imag) -282 -283 return Npr_matrix(matrix, mom_in=mom) +261 corr_data = [] +262 for hd5_file in files: +263 file = h5py.File(path + '/' + hd5_file, "r") +264 raw_data = file['ExternalLeg/corr'][0][0].view('complex') +265 corr_data.append(raw_data) +266 if mom is None: +267 mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +268 file.close() +269 corr_data = np.array(corr_data) +270 +271 rolled_array = np.rollaxis(corr_data, 0, 5) +272 +273 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +274 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): +275 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) +276 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) +277 matrix[si, sj, ci, cj] = CObs(real, imag) +278 +279 return Npr_matrix(matrix, mom_in=mom) +280 +281 +282def read_Bilinear_hd5(path, filestem, ens_id, idl=None): +283 """Read hadrons Bilinear hdf5 file and output an array of CObs 284 -285 -286def read_Bilinear_hd5(path, filestem, ens_id, idl=None): -287 """Read hadrons Bilinear hdf5 file and output an array of CObs -288 -289 Parameters -290 ---------- -291 path : str -292 path to the files to read -293 filestem : str -294 namestem of the files to read -295 ens_id : str -296 name of the ensemble, required for internal bookkeeping -297 idl : range -298 If specified only configurations in the given range are read in. -299 """ -300 -301 files, idx = _get_files(path, filestem, idl) -302 -303 mom_in = None -304 mom_out = None -305 -306 corr_data = {} -307 for hd5_file in files: -308 file = h5py.File(path + '/' + hd5_file, "r") -309 for i in range(16): -310 name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8') -311 if name not in corr_data: -312 corr_data[name] = [] -313 raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex') -314 corr_data[name].append(raw_data) -315 if mom_in is None: -316 mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -317 if mom_out is None: -318 mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) +285 Parameters +286 ---------- +287 path : str +288 path to the files to read +289 filestem : str +290 namestem of the files to read +291 ens_id : str +292 name of the ensemble, required for internal bookkeeping +293 idl : range +294 If specified only configurations in the given range are read in. +295 """ +296 +297 files, idx = _get_files(path, filestem, idl) +298 +299 mom_in = None +300 mom_out = None +301 +302 corr_data = {} +303 for hd5_file in files: +304 file = h5py.File(path + '/' + hd5_file, "r") +305 for i in range(16): +306 name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8') +307 if name not in corr_data: +308 corr_data[name] = [] +309 raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex') +310 corr_data[name].append(raw_data) +311 if mom_in is None: +312 mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +313 if mom_out is None: +314 mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) +315 +316 file.close() +317 +318 result_dict = {} 319 -320 file.close() -321 -322 result_dict = {} -323 -324 for key, data in corr_data.items(): -325 local_data = np.array(data) -326 -327 rolled_array = np.rollaxis(local_data, 0, 5) -328 -329 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -330 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): -331 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) -332 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) -333 matrix[si, sj, ci, cj] = CObs(real, imag) +320 for key, data in corr_data.items(): +321 local_data = np.array(data) +322 +323 rolled_array = np.rollaxis(local_data, 0, 5) +324 +325 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +326 for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]): +327 real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx]) +328 imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx]) +329 matrix[si, sj, ci, cj] = CObs(real, imag) +330 +331 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) +332 +333 return result_dict 334 -335 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) -336 -337 return result_dict +335 +336def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]): +337 """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs 338 -339 -340def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]): -341 """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs -342 -343 Parameters -344 ---------- -345 path : str -346 path to the files to read -347 filestem : str -348 namestem of the files to read -349 ens_id : str -350 name of the ensemble, required for internal bookkeeping -351 idl : range -352 If specified only configurations in the given range are read in. -353 vertices : list -354 Vertex functions to be extracted. -355 """ -356 -357 files, idx = _get_files(path, filestem, idl) -358 -359 mom_in = None -360 mom_out = None +339 Parameters +340 ---------- +341 path : str +342 path to the files to read +343 filestem : str +344 namestem of the files to read +345 ens_id : str +346 name of the ensemble, required for internal bookkeeping +347 idl : range +348 If specified only configurations in the given range are read in. +349 vertices : list +350 Vertex functions to be extracted. +351 """ +352 +353 files, idx = _get_files(path, filestem, idl) +354 +355 mom_in = None +356 mom_out = None +357 +358 vertex_names = [] +359 for vertex in vertices: +360 vertex_names += _get_lorentz_names(vertex) 361 -362 vertex_names = [] -363 for vertex in vertices: -364 vertex_names += _get_lorentz_names(vertex) +362 corr_data = {} +363 +364 tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_' 365 -366 corr_data = {} -367 -368 tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_' -369 -370 for hd5_file in files: -371 file = h5py.File(path + '/' + hd5_file, "r") -372 -373 for i in range(32): -374 name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8')) -375 if name in vertex_names: -376 if name not in corr_data: -377 corr_data[name] = [] -378 raw_data = file[tree + str(i) + '/corr'][0][0].view('complex') -379 corr_data[name].append(raw_data) -380 if mom_in is None: -381 mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) -382 if mom_out is None: -383 mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) +366 for hd5_file in files: +367 file = h5py.File(path + '/' + hd5_file, "r") +368 +369 for i in range(32): +370 name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8')) +371 if name in vertex_names: +372 if name not in corr_data: +373 corr_data[name] = [] +374 raw_data = file[tree + str(i) + '/corr'][0][0].view('complex') +375 corr_data[name].append(raw_data) +376 if mom_in is None: +377 mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float) +378 if mom_out is None: +379 mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float) +380 +381 file.close() +382 +383 intermediate_dict = {} 384 -385 file.close() -386 -387 intermediate_dict = {} -388 -389 for vertex in vertices: -390 lorentz_names = _get_lorentz_names(vertex) -391 for v_name in lorentz_names: -392 if v_name in [('SigmaXY', 'SigmaZT'), -393 ('SigmaXT', 'SigmaYZ'), -394 ('SigmaYZ', 'SigmaXT'), -395 ('SigmaZT', 'SigmaXY')]: -396 sign = -1 +385 for vertex in vertices: +386 lorentz_names = _get_lorentz_names(vertex) +387 for v_name in lorentz_names: +388 if v_name in [('SigmaXY', 'SigmaZT'), +389 ('SigmaXT', 'SigmaYZ'), +390 ('SigmaYZ', 'SigmaXT'), +391 ('SigmaZT', 'SigmaXY')]: +392 sign = -1 +393 else: +394 sign = 1 +395 if vertex not in intermediate_dict: +396 intermediate_dict[vertex] = sign * np.array(corr_data[v_name]) 397 else: -398 sign = 1 -399 if vertex not in intermediate_dict: -400 intermediate_dict[vertex] = sign * np.array(corr_data[v_name]) -401 else: -402 intermediate_dict[vertex] += sign * np.array(corr_data[v_name]) +398 intermediate_dict[vertex] += sign * np.array(corr_data[v_name]) +399 +400 result_dict = {} +401 +402 for key, data in intermediate_dict.items(): 403 -404 result_dict = {} +404 rolled_array = np.moveaxis(data, 0, 8) 405 -406 for key, data in intermediate_dict.items(): -407 -408 rolled_array = np.moveaxis(data, 0, 8) -409 -410 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) -411 for index in np.ndindex(rolled_array.shape[:-1]): -412 real = Obs([rolled_array[index].real], [ens_id], idl=[idx]) -413 imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx]) -414 matrix[index] = CObs(real, imag) +406 matrix = np.empty((rolled_array.shape[:-1]), dtype=object) +407 for index in np.ndindex(rolled_array.shape[:-1]): +408 real = Obs([rolled_array[index].real], [ens_id], idl=[idx]) +409 imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx]) +410 matrix[index] = CObs(real, imag) +411 +412 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) +413 +414 return result_dict 415 -416 result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out) -417 -418 return result_dict +416 +417def _get_lorentz_names(name): +418 lorentz_index = ['X', 'Y', 'Z', 'T'] 419 -420 -421def _get_lorentz_names(name): -422 lorentz_index = ['X', 'Y', 'Z', 'T'] -423 -424 res = [] -425 -426 if name == "TT": -427 for i in range(4): -428 for j in range(i + 1, 4): -429 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[i] + lorentz_index[j])) -430 return res -431 -432 if name == "TTtilde": -433 for i in range(4): -434 for j in range(i + 1, 4): -435 for k in range(4): -436 for o in range(k + 1, 4): -437 fac = epsilon_tensor_rank4(i, j, k, o) -438 if not np.isclose(fac, 0.0): -439 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[k] + lorentz_index[o])) -440 return res -441 -442 assert len(name) == 2 +420 res = [] +421 +422 if name == "TT": +423 for i in range(4): +424 for j in range(i + 1, 4): +425 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[i] + lorentz_index[j])) +426 return res +427 +428 if name == "TTtilde": +429 for i in range(4): +430 for j in range(i + 1, 4): +431 for k in range(4): +432 for o in range(k + 1, 4): +433 fac = epsilon_tensor_rank4(i, j, k, o) +434 if not np.isclose(fac, 0.0): +435 res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[k] + lorentz_index[o])) +436 return res +437 +438 assert len(name) == 2 +439 +440 if 'S' in name or 'P' in name: +441 if not set(name) <= set(['S', 'P']): +442 raise Exception("'" + name + "' is not a Lorentz scalar") 443 -444 if 'S' in name or 'P' in name: -445 if not set(name) <= set(['S', 'P']): -446 raise Exception("'" + name + "' is not a Lorentz scalar") -447 -448 g_names = {'S': 'Identity', -449 'P': 'Gamma5'} -450 -451 res.append((g_names[name[0]], g_names[name[1]])) +444 g_names = {'S': 'Identity', +445 'P': 'Gamma5'} +446 +447 res.append((g_names[name[0]], g_names[name[1]])) +448 +449 else: +450 if not set(name) <= set(['V', 'A']): +451 raise Exception("'" + name + "' is not a Lorentz scalar") 452 -453 else: -454 if not set(name) <= set(['V', 'A']): -455 raise Exception("'" + name + "' is not a Lorentz scalar") +453 for ind in lorentz_index: +454 res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5', +455 'Gamma' + ind + (name[1] == 'A') * 'Gamma5')) 456 -457 for ind in lorentz_index: -458 res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5', -459 'Gamma' + ind + (name[1] == 'A') * 'Gamma5')) -460 -461 return res +457 return res @@ -725,36 +721,32 @@ If specified only configurations in the given range are read in. 172 identifier = tuple(identifier) 173 # "DistillationContraction/Metadata/DmfSuffix" contains info about different quarks, irrelevant in the SU(3) case. 174 -175 check_traj = h5file["DistillationContraction/Metadata"].attrs.get("Traj")[0] -176 -177 assert check_traj == n_traj -178 -179 for diagram in diagrams: -180 real_data = np.zeros(Nt) -181 for x0 in range(Nt): -182 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)] -183 real_data += np.roll(raw_data[:]["re"].astype(np.double), -x0) -184 real_data /= Nt -185 -186 corr_data[diagram].append(real_data) -187 h5file.close() +175 for diagram in diagrams: +176 real_data = np.zeros(Nt) +177 for x0 in range(Nt): +178 raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)] +179 real_data += np.roll(raw_data[:]["re"].astype(np.double), -x0) +180 real_data /= Nt +181 +182 corr_data[diagram].append(real_data) +183 h5file.close() +184 +185 res_dict[str(identifier)] = {} +186 +187 for diagram in diagrams: 188 -189 res_dict[str(identifier)] = {} +189 tmp_data = np.array(corr_data[diagram]) 190 -191 for diagram in diagrams: -192 -193 tmp_data = np.array(corr_data[diagram]) +191 l_obs = [] +192 for c in tmp_data.T: +193 l_obs.append(Obs([c], [ens_id], idl=[idx])) 194 -195 l_obs = [] -196 for c in tmp_data.T: -197 l_obs.append(Obs([c], [ens_id], idl=[idx])) -198 -199 corr = Corr(l_obs) -200 corr.tag = str(identifier) -201 -202 res_dict[str(identifier)][diagram] = corr -203 -204 return res_dict +195 corr = Corr(l_obs) +196 corr.tag = str(identifier) +197 +198 res_dict[str(identifier)][diagram] = corr +199 +200 return res_dict @@ -787,44 +779,44 @@ If specified only configurations in the given range are read in. - 
207class Npr_matrix(np.ndarray):
-208
-209    def __new__(cls, input_array, mom_in=None, mom_out=None):
-210        obj = np.asarray(input_array).view(cls)
-211        obj.mom_in = mom_in
-212        obj.mom_out = mom_out
-213        return obj
+            
203class Npr_matrix(np.ndarray):
+204
+205    def __new__(cls, input_array, mom_in=None, mom_out=None):
+206        obj = np.asarray(input_array).view(cls)
+207        obj.mom_in = mom_in
+208        obj.mom_out = mom_out
+209        return obj
+210
+211    @property
+212    def g5H(self):
+213        """Gamma_5 hermitean conjugate
 214
-215    @property
-216    def g5H(self):
-217        """Gamma_5 hermitean conjugate
-218
-219        Uses the fact that the propagator is gamma5 hermitean, so just the
-220        in and out momenta of the propagator are exchanged.
-221        """
-222        return Npr_matrix(self,
-223                          mom_in=self.mom_out,
-224                          mom_out=self.mom_in)
-225
-226    def _propagate_mom(self, other, name):
-227        s_mom = getattr(self, name, None)
-228        o_mom = getattr(other, name, None)
-229        if s_mom is not None and o_mom is not None:
-230            if not np.allclose(s_mom, o_mom):
-231                raise Exception(name + ' does not match.')
-232        return o_mom if o_mom is not None else s_mom
-233
-234    def __matmul__(self, other):
-235        return self.__new__(Npr_matrix,
-236                            super().__matmul__(other),
-237                            self._propagate_mom(other, 'mom_in'),
-238                            self._propagate_mom(other, 'mom_out'))
-239
-240    def __array_finalize__(self, obj):
-241        if obj is None:
-242            return
-243        self.mom_in = getattr(obj, 'mom_in', None)
-244        self.mom_out = getattr(obj, 'mom_out', None)
+215        Uses the fact that the propagator is gamma5 hermitean, so just the
+216        in and out momenta of the propagator are exchanged.
+217        """
+218        return Npr_matrix(self,
+219                          mom_in=self.mom_out,
+220                          mom_out=self.mom_in)
+221
+222    def _propagate_mom(self, other, name):
+223        s_mom = getattr(self, name, None)
+224        o_mom = getattr(other, name, None)
+225        if s_mom is not None and o_mom is not None:
+226            if not np.allclose(s_mom, o_mom):
+227                raise Exception(name + ' does not match.')
+228        return o_mom if o_mom is not None else s_mom
+229
+230    def __matmul__(self, other):
+231        return self.__new__(Npr_matrix,
+232                            super().__matmul__(other),
+233                            self._propagate_mom(other, 'mom_in'),
+234                            self._propagate_mom(other, 'mom_out'))
+235
+236    def __array_finalize__(self, obj):
+237        if obj is None:
+238            return
+239        self.mom_in = getattr(obj, 'mom_in', None)
+240        self.mom_out = getattr(obj, 'mom_out', None)
 

 
 
@@ -1075,44 +1067,44 @@ in and out momenta of the propagator are exchanged.
- 
247def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
-248    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
-249
-250    Parameters
-251    ----------
-252    path : str
-253        path to the files to read
-254    filestem : str
-255        namestem of the files to read
-256    ens_id : str
-257        name of the ensemble, required for internal bookkeeping
-258    idl : range
-259        If specified only configurations in the given range are read in.
-260    """
+            
243def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
+244    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
+245
+246    Parameters
+247    ----------
+248    path : str
+249        path to the files to read
+250    filestem : str
+251        namestem of the files to read
+252    ens_id : str
+253        name of the ensemble, required for internal bookkeeping
+254    idl : range
+255        If specified only configurations in the given range are read in.
+256    """
+257
+258    files, idx = _get_files(path, filestem, idl)
+259
+260    mom = None
 261
-262    files, idx = _get_files(path, filestem, idl)
-263
-264    mom = None
-265
-266    corr_data = []
-267    for hd5_file in files:
-268        file = h5py.File(path + '/' + hd5_file, "r")
-269        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
-270        corr_data.append(raw_data)
-271        if mom is None:
-272            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-273        file.close()
-274    corr_data = np.array(corr_data)
-275
-276    rolled_array = np.rollaxis(corr_data, 0, 5)
-277
-278    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-279    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
-280        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
-281        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
-282        matrix[si, sj, ci, cj] = CObs(real, imag)
-283
-284    return Npr_matrix(matrix, mom_in=mom)
+262    corr_data = []
+263    for hd5_file in files:
+264        file = h5py.File(path + '/' + hd5_file, "r")
+265        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
+266        corr_data.append(raw_data)
+267        if mom is None:
+268            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+269        file.close()
+270    corr_data = np.array(corr_data)
+271
+272    rolled_array = np.rollaxis(corr_data, 0, 5)
+273
+274    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+275    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
+276        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
+277        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
+278        matrix[si, sj, ci, cj] = CObs(real, imag)
+279
+280    return Npr_matrix(matrix, mom_in=mom)
 

 
 
@@ -1145,58 +1137,58 @@ If specified only configurations in the given range are read in.
- 
287def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
-288    """Read hadrons Bilinear hdf5 file and output an array of CObs
-289
-290    Parameters
-291    ----------
-292    path : str
-293        path to the files to read
-294    filestem : str
-295        namestem of the files to read
-296    ens_id : str
-297        name of the ensemble, required for internal bookkeeping
-298    idl : range
-299        If specified only configurations in the given range are read in.
-300    """
-301
-302    files, idx = _get_files(path, filestem, idl)
-303
-304    mom_in = None
-305    mom_out = None
-306
-307    corr_data = {}
-308    for hd5_file in files:
-309        file = h5py.File(path + '/' + hd5_file, "r")
-310        for i in range(16):
-311            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
-312            if name not in corr_data:
-313                corr_data[name] = []
-314            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
-315            corr_data[name].append(raw_data)
-316            if mom_in is None:
-317                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-318            if mom_out is None:
-319                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
+            
283def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
+284    """Read hadrons Bilinear hdf5 file and output an array of CObs
+285
+286    Parameters
+287    ----------
+288    path : str
+289        path to the files to read
+290    filestem : str
+291        namestem of the files to read
+292    ens_id : str
+293        name of the ensemble, required for internal bookkeeping
+294    idl : range
+295        If specified only configurations in the given range are read in.
+296    """
+297
+298    files, idx = _get_files(path, filestem, idl)
+299
+300    mom_in = None
+301    mom_out = None
+302
+303    corr_data = {}
+304    for hd5_file in files:
+305        file = h5py.File(path + '/' + hd5_file, "r")
+306        for i in range(16):
+307            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
+308            if name not in corr_data:
+309                corr_data[name] = []
+310            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
+311            corr_data[name].append(raw_data)
+312            if mom_in is None:
+313                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+314            if mom_out is None:
+315                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
+316
+317        file.close()
+318
+319    result_dict = {}
 320
-321        file.close()
-322
-323    result_dict = {}
-324
-325    for key, data in corr_data.items():
-326        local_data = np.array(data)
-327
-328        rolled_array = np.rollaxis(local_data, 0, 5)
-329
-330        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-331        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
-332            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
-333            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
-334            matrix[si, sj, ci, cj] = CObs(real, imag)
-335
-336        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
-337
-338    return result_dict
+321    for key, data in corr_data.items():
+322        local_data = np.array(data)
+323
+324        rolled_array = np.rollaxis(local_data, 0, 5)
+325
+326        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+327        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
+328            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
+329            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
+330            matrix[si, sj, ci, cj] = CObs(real, imag)
+331
+332        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
+333
+334    return result_dict
 

 
 
@@ -1229,85 +1221,85 @@ If specified only configurations in the given range are read in.
- 
341def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
-342    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
-343
-344    Parameters
-345    ----------
-346    path : str
-347        path to the files to read
-348    filestem : str
-349        namestem of the files to read
-350    ens_id : str
-351        name of the ensemble, required for internal bookkeeping
-352    idl : range
-353        If specified only configurations in the given range are read in.
-354    vertices : list
-355        Vertex functions to be extracted.
-356    """
-357
-358    files, idx = _get_files(path, filestem, idl)
-359
-360    mom_in = None
-361    mom_out = None
+            
337def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
+338    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
+339
+340    Parameters
+341    ----------
+342    path : str
+343        path to the files to read
+344    filestem : str
+345        namestem of the files to read
+346    ens_id : str
+347        name of the ensemble, required for internal bookkeeping
+348    idl : range
+349        If specified only configurations in the given range are read in.
+350    vertices : list
+351        Vertex functions to be extracted.
+352    """
+353
+354    files, idx = _get_files(path, filestem, idl)
+355
+356    mom_in = None
+357    mom_out = None
+358
+359    vertex_names = []
+360    for vertex in vertices:
+361        vertex_names += _get_lorentz_names(vertex)
 362
-363    vertex_names = []
-364    for vertex in vertices:
-365        vertex_names += _get_lorentz_names(vertex)
+363    corr_data = {}
+364
+365    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
 366
-367    corr_data = {}
-368
-369    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
-370
-371    for hd5_file in files:
-372        file = h5py.File(path + '/' + hd5_file, "r")
-373
-374        for i in range(32):
-375            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
-376            if name in vertex_names:
-377                if name not in corr_data:
-378                    corr_data[name] = []
-379                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
-380                corr_data[name].append(raw_data)
-381                if mom_in is None:
-382                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
-383                if mom_out is None:
-384                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
+367    for hd5_file in files:
+368        file = h5py.File(path + '/' + hd5_file, "r")
+369
+370        for i in range(32):
+371            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
+372            if name in vertex_names:
+373                if name not in corr_data:
+374                    corr_data[name] = []
+375                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
+376                corr_data[name].append(raw_data)
+377                if mom_in is None:
+378                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
+379                if mom_out is None:
+380                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
+381
+382        file.close()
+383
+384    intermediate_dict = {}
 385
-386        file.close()
-387
-388    intermediate_dict = {}
-389
-390    for vertex in vertices:
-391        lorentz_names = _get_lorentz_names(vertex)
-392        for v_name in lorentz_names:
-393            if v_name in [('SigmaXY', 'SigmaZT'),
-394                          ('SigmaXT', 'SigmaYZ'),
-395                          ('SigmaYZ', 'SigmaXT'),
-396                          ('SigmaZT', 'SigmaXY')]:
-397                sign = -1
+386    for vertex in vertices:
+387        lorentz_names = _get_lorentz_names(vertex)
+388        for v_name in lorentz_names:
+389            if v_name in [('SigmaXY', 'SigmaZT'),
+390                          ('SigmaXT', 'SigmaYZ'),
+391                          ('SigmaYZ', 'SigmaXT'),
+392                          ('SigmaZT', 'SigmaXY')]:
+393                sign = -1
+394            else:
+395                sign = 1
+396            if vertex not in intermediate_dict:
+397                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
 398            else:
-399                sign = 1
-400            if vertex not in intermediate_dict:
-401                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
-402            else:
-403                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
+399                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
+400
+401    result_dict = {}
+402
+403    for key, data in intermediate_dict.items():
 404
-405    result_dict = {}
+405        rolled_array = np.moveaxis(data, 0, 8)
 406
-407    for key, data in intermediate_dict.items():
-408
-409        rolled_array = np.moveaxis(data, 0, 8)
-410
-411        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
-412        for index in np.ndindex(rolled_array.shape[:-1]):
-413            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
-414            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
-415            matrix[index] = CObs(real, imag)
-416
-417        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
-418
-419    return result_dict
+407        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
+408        for index in np.ndindex(rolled_array.shape[:-1]):
+409            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
+410            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
+411            matrix[index] = CObs(real, imag)
+412
+413        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
+414
+415    return result_dict