def writeHDF5(self):
def computeNuITD(fit_,gamma_,ri_):
# nNu = 0
# for mom in self.momAvg:
# dispListAvg = self.dispAvg[mTag]
# nNu += len(dispListAvg)
nuArr = []
for mom in self.momAvg:
mTag = tags.momString(mom)
dispListAvg = self.dispAvg[mTag]
for z3 in dispListAvg:
dkey_ = (mTag,z3,gamma_)
nu = mom[2]*z3*self.unitMom # Ioffe-time, nu = Pz*z3*unitMom
nuArr.append([nu,self.mean[fit_][dkey_][ri_][0],self.mean[fit_][dkey_][ri_][1]])
nuArr = np.array(nuArr,dtype=np.float64)
return nuArr
#---------------------------
h5_file = h5py.File(self.info['HDF5 Output File'],'w')
for fType in self.fitTypes.keys():
for fit in self.fitTypes[fType]:
# Write momentum and z3 dependence
for mom in self.momAvg:
mTag = tags.momString(mom)
mh5Tag = tags.momH5(mom)
dispListAvg = self.dispAvg[mTag]
for z3 in dispListAvg:
dispTag = tags.disp(z3)
for gamma in self.gammaList:
insTag = tags.insertion(gamma)
dkey = (mTag,z3,gamma)
for ri in self.RI:
group = '%s/%s/%s/%s/%s'%(fit,mh5Tag,dispTag,insTag,ri)
dset_name_bins = 'bins/' + group
dset_name_mean = 'mean/' + group
h5_file.create_dataset(dset_name_bins, data = self.bins[fit][dkey][ri])
h5_file.create_dataset(dset_name_mean, data = self.mean[fit][dkey][ri],dtype='f')
# End for momentum
# Write the nu-dependence of the ITD
for gamma in self.gammaList:
for ri in self.RI:
nuITD = computeNuITD(fit,gamma,ri)
group = '%s/%s/%s'%(fit,insTag,ri)
dset_name_nuMean = 'nuDep/' + group
h5_file.create_dataset(dset_name_nuMean, data = nuITD)
# End for fType
h5_file.close()
print('Reduced Ioffe-time distributions written in HDF5.')
def getDataHDF5():
print('\nWill read data from HDF5')
inputHDF5 = self.dataInfo['Input Data']['HDF5 File']
h5_file = h5py.File(inputHDF5, 'r')
for mom in self.moms:
mTag = tags.momString(mom)
mh5Tag = tags.momH5(mom)
t0List = self.dSetAttr[mTag]['t0']
tsepList = self.dSetAttr[mTag]['tsep']
dispList = self.dSetAttr[mTag]['disp']
Nrows = self.dSetAttr[mTag]['Nrows']
for ri in self.RI:
self.plainData[ri][mTag] = {}
for z3 in dispList:
dispTag = tags.disp(z3)
for tsep in tsepList:
tsepTag = tags.tsep(tsep)
for t0 in t0List:
t0Tag = tags.t0(t0)
for iop, opPair in enumerate(
self.dSetAttr[mTag]['intOpList']):
opTag = tags.src_snk(opPair)
for row in range(1, Nrows + 1):
rowTag = tags.row(row)
for gamma in self.gammaList:
insTag = tags.insertion(gamma)
dkey = (tsep, t0, z3, iop, row,
gamma)
dset = 'plain/%s/%s/%s/%s/%s/%s/%s/%s/data' % (
mh5Tag, dispTag, tsepTag,
t0Tag, opTag, rowTag, insTag,
ri)
self.plainData[ri][mTag][
dkey] = np.array(h5_file[dset])
print('Reading three-point data for momentum %s completed.' %
(mTag))
def dumpHDF5(fitSeq,h5_file):
fType = fitSeq['Type']
fLabel = fitSeq['Label']
for mom in self.momAvg:
mTag = tags.momString(mom)
mh5Tag = tags.momH5(mom)
tsepList = self.dSetAttr3pt[mTag]['tsep']
dispListAvg = self.dispAvg[mTag]
for z3 in dispListAvg:
dispTag = tags.disp(z3)
for gamma in self.gammaList:
insTag = tags.insertion(gamma)
for tsep in tsepList:
dkey = (tsep,z3,gamma)
tsepTag = tags.tsep(tsep)
fAttr = self.fitAttr[mTag][tsep]
for ri in self.RI:
# Write optimalFitValues
group = '%s/%s/%s/%s/%s'%(ri,mh5Tag,dispTag,insTag,tsepTag)
dset_name = 'OptimalFitRanges/' + group
h5_file.create_dataset(dset_name, data = np.array([self.optimalFit[fLabel][ri][mTag][dkey]]))
for nf in range(fAttr['Nfits']):
tstart = fAttr['nf=%d'%(nf)]['tstart']
tstop = fAttr['nf=%d'%(nf)]['tstop']
h5LabelNf = 'nf%d_%d-%d'%(nf,tstart,tstop)
group = '%s/%s/%s/%s/%s/%s'%(ri,mh5Tag,dispTag,insTag,tsepTag,h5LabelNf)
dset_name_Mbins = 'MatElem/bins/' + group
dset_name_Mmean = 'MatElem/mean/' + group
dset_name_chiBins = 'chiSquare/bins/' + group
dset_name_chiMean = 'chiSquare/mean/' + group
h5_file.create_dataset(dset_name_Mbins, data = self.Mbins[fLabel][ri][mTag][dkey][nf])
h5_file.create_dataset(dset_name_Mmean, data = self.Mmean[fLabel][ri][mTag][dkey][nf],dtype='f')
h5_file.create_dataset(dset_name_chiBins, data = self.chiBins[fLabel][ri][mTag][dkey][nf])
h5_file.create_dataset(dset_name_chiMean, data = self.chiMean[fLabel][ri][mTag][dkey][nf],dtype='f')
# End for momentum
print('Plateau fitting data for type = %s, label = %s written in HDF5.'%(fType,fLabel))
def writeHDF5(self):
h5_file = h5py.File(self.dataInfo['HDF5 Output File'], 'w')
for mom in self.momAvg:
mTag = tags.momString(mom)
mh5Tag = tags.momH5(mom)
tsepList = self.dSetAttr3pt[mTag]['tsep']
tsepList_rs = self.dSetAttr3pt[mTag]['tsep'][:-1]
dispListAvg = self.dispAvg[mTag]
Ntsep = len(tsepList)
Ntsep_rs = len(tsepList_rs)
for z3 in dispListAvg:
dispTag = tags.disp(z3)
for gamma in self.gammaList:
insTag = tags.insertion(gamma)
for ri in self.RI:
sumRatioH5 = (np.zeros(Ntsep), np.zeros(Ntsep),
np.zeros(Ntsep))
for its, tsep in enumerate(tsepList):
dkey = (tsep, z3, gamma)
tsepTag = tags.tsep(tsep)
# Write the plain ratio bins and mean
rType = 'plain'
group = '%s/%s/%s/%s/%s/%s' % (
rType, mh5Tag, tsepTag, dispTag, insTag, ri)
dset_name_bins = 'bins/' + group
dset_name_mean = 'mean/' + group
h5_file.create_dataset(
dset_name_bins,
data=self.bins[rType][ri][mTag][dkey])
h5_file.create_dataset(
dset_name_mean,
data=self.mean[rType][ri][mTag][dkey],
dtype='f')
#---------------------------------------------------------------
# Write the summed ratio bins
rType = 'sum'
group = '%s/%s/%s/%s/%s/%s' % (
rType, mh5Tag, tsepTag, dispTag, insTag, ri)
dset_name_bins = 'bins/' + group
h5_file.create_dataset(
dset_name_bins,
data=self.bins[rType][ri][mTag][dkey])
# Convert the summed ratio mean into arrays that depend on tsep
sumRatioH5[0][its] = tsep # tsep (x)
sumRatioH5[1][its] = self.mean[rType][ri][mTag][
dkey][0] # ratio mean (y)
sumRatioH5[2][its] = self.mean[rType][ri][mTag][
dkey][1] # ratio error (y-error)
# End for tsep
# Write the summed ratio means
rType = 'sum'
group = '%s/%s/%s/%s/%s' % (rType, mh5Tag, dispTag,
insTag, ri)
dset_name_mean = 'mean/' + group
h5_file.create_dataset(dset_name_mean,
data=sumRatioH5,
dtype='f')
#-----------------------------
# Reduced-summed ratio
rSumRatioH5 = (np.zeros(Ntsep_rs), np.zeros(Ntsep_rs),
np.zeros(Ntsep_rs))
for its, tsep in enumerate(tsepList_rs):
dkey = (tsep, z3, gamma)
tsepTag = tags.tsep(tsep)
# Write the summed ratio bins
rType = 'r-sum'
group = '%s/%s/%s/%s/%s/%s' % (
rType, mh5Tag, tsepTag, dispTag, insTag, ri)
dset_name_bins = 'bins/' + group
h5_file.create_dataset(
dset_name_bins,
data=self.bins[rType][ri][mTag][dkey])
# Convert the reduced-summed ratio mean into arrays that depend on tsep
rSumRatioH5[0][its] = tsep # tsep (x)
rSumRatioH5[1][its] = self.mean[rType][ri][mTag][
dkey][0] # ratio mean (y)
rSumRatioH5[2][its] = self.mean[rType][ri][mTag][
dkey][1] # ratio error (y-error)
# End for tsep
# Write the reduced-summed ratio means
rType = 'r-sum'
group = '%s/%s/%s/%s/%s' % (rType, mh5Tag, dispTag,
insTag, ri)
dset_name_mean = 'mean/' + group
h5_file.create_dataset(dset_name_mean,
data=rSumRatioH5,
dtype='f')
#-----------------------------
# End for momentum
h5_file.close()
print('Ratio data written in HDF5.')
def writeHDF5(self):
h5_file = h5py.File(self.dataInfo['HDF5 Output File'], 'w')
# Write the Pz- and z3-averaged data
for mom in self.momAvg:
mTag = tags.momString(mom)
mh5Tag = tags.momH5(mom)
tsepList = self.dSetAttr[mTag]['tsep']
dispListAvg = self.dispAvg[mTag]
for z3 in dispListAvg:
dispTag = tags.disp(z3)
for tsep in tsepList:
tsepTag = tags.tsep(tsep)
for gamma in self.gammaList:
insTag = tags.insertion(gamma)
dkeyAvg = (tsep, z3, gamma)
# Write the averaged data
for ri in self.RI:
avg_group = 'fullavg/%s/%s/%s/%s/%s' % (
mh5Tag, dispTag, tsepTag, insTag, ri)
dset_name_data = avg_group + '/data'
dset_name_bins = avg_group + '/bins'
dset_name_mean = avg_group + '/mean'
h5_file.create_dataset(
dset_name_data,
data=self.data[ri][mTag][dkeyAvg])
h5_file.create_dataset(
dset_name_bins,
data=self.bins[ri][mTag][dkeyAvg])
h5_file.create_dataset(
dset_name_mean,
data=self.mean[ri][mTag][dkeyAvg],
dtype='f')
#--------------------------------------
for mom in self.moms:
mTag = tags.momString(mom)
mh5Tag = tags.momH5(mom)
t0List = self.dSetAttr[mTag]['t0']
tsepList = self.dSetAttr[mTag]['tsep']
dispList = self.dSetAttr[mTag]['disp']
Nrows = self.dSetAttr[mTag]['Nrows']
for z3 in dispList:
dispTag = tags.disp(z3)
for tsep in tsepList:
tsepTag = tags.tsep(tsep)
for gamma in self.gammaList:
insTag = tags.insertion(gamma)
dkeyAvg = (tsep, z3, gamma)
# Write Avg data
for ri in self.RI:
avg_group = 'avg/%s/%s/%s/%s/%s' % (
mh5Tag, dispTag, tsepTag, insTag, ri)
dset_name_avgData = avg_group + '/data'
dset_name_avgBins = avg_group + '/bins'
dset_name_avgMean = avg_group + '/mean'
h5_file.create_dataset(
dset_name_avgData,
data=self.avgData[ri][mTag][dkeyAvg])
h5_file.create_dataset(
dset_name_avgBins,
data=self.avgBins[ri][mTag][dkeyAvg])
h5_file.create_dataset(
dset_name_avgMean,
data=self.avgMean[ri][mTag][dkeyAvg],
dtype='f')
for t0 in t0List:
t0Tag = tags.t0(t0)
for iop, opPair in enumerate(
self.dSetAttr[mTag]['intOpList']):
opTag = tags.src_snk(opPair)
for row in range(1, Nrows + 1):
rowTag = tags.row(row)
dkey = (tsep, t0, z3, iop, row, gamma)
# Write the plain data
for ri in self.RI:
plain_group = 'plain/%s/%s/%s/%s/%s/%s/%s/%s' % (
mh5Tag, dispTag, tsepTag, insTag,
t0Tag, opTag, rowTag, ri)
dset_name_plainData = plain_group + '/data'
dset_name_plainBins = plain_group + '/bins'
dset_name_plainMean = plain_group + '/mean'
h5_file.create_dataset(
dset_name_plainData,
data=self.plainData[ri][mTag]
[dkey])
h5_file.create_dataset(
dset_name_plainBins,
data=self.plainBins[ri][mTag]
[dkey])
h5_file.create_dataset(
dset_name_plainMean,
data=self.plainMean[ri][mTag]
[dkey],
dtype='f')
#--------------------------------------
h5_file.close()
print('Three-point function data written in HDF5.')
def dumpLinearFitsHDF5(fitSeq, h5_file):
fType = fitSeq['Type']
fLabel = fitSeq['Label']
tsepLowList = fitSeq['tsepLow']
for mom in self.momAvg:
mTag = tags.momString(mom)
mh5Tag = tags.momH5(mom)
dispListAvg = self.dispAvg[mTag]
for z3 in dispListAvg:
dispTag = tags.disp(z3)
for gamma in self.gammaList:
insTag = tags.insertion(gamma)
dkeyF = (z3, gamma)
for ri in self.RI:
for tL in tsepLowList:
sLTag = 'tL%d' % (tL)
tini = self.tsepFitX[fLabel][mTag][sLTag][0]
tfin = self.tsepFitX[fLabel][mTag][sLTag][-1]
h5LabelT = 'tsep_%d-%d' % (tini, tfin)
# Write Chi^2
group = '%s/%s/%s/%s/%s' % (
ri, mh5Tag, dispTag, insTag, h5LabelT)
dset_name_chiBins = 'chiSquare/bins/' + group
dset_name_chiMean = 'chiSquare/mean/' + group
h5_file.create_dataset(
dset_name_chiBins,
data=self.chiBins[fLabel][sLTag][ri][mTag]
[dkeyF])
h5_file.create_dataset(
dset_name_chiMean,
data=self.chiMean[fLabel][sLTag][ri][mTag]
[dkeyF],
dtype='f')
# Write fit bands
dset_name_fitBands = 'fitBands/' + group
h5_file.create_dataset(
dset_name_fitBands,
data=(self.fitBands[fLabel][sLTag][ri]
[mTag][dkeyF]['x'],
self.fitBands[fLabel][sLTag][ri]
[mTag][dkeyF]['v'],
self.fitBands[fLabel][sLTag][ri]
[mTag][dkeyF]['e']),
dtype='f')
# Write Fit parameters
for fP, fpH5 in zip(self.fitParams[fType],
self.fitParamsH5[fType]):
fpTag = fP + '_%s' % (sLTag)
dset_name_bins = '%s/bins/' % (
fpH5) + group
dset_name_mean = '%s/mean/' % (
fpH5) + group
h5_file.create_dataset(
dset_name_bins,
data=self.bins[fLabel][fpTag][ri][mTag]
[dkeyF])
h5_file.create_dataset(
dset_name_mean,
data=self.mean[fLabel][fpTag][ri][mTag]
[dkeyF],
dtype='f')
# End for momentum
print(
'Summation fitting data for type = %s, label = %s written in HDF5.'
% (fType, fLabel))