list
vals
import numpy as np
str.zfill()
zfill(10)
n=5
print(f'{n:03}')
from MiscFuns import ODNested
print('abc',end='')
alpha = ODNested()
alpha['one']['two'] = 5
alpha.values()[0].values()[0]
from uncertainties import ufloat
this_val = ufloat(0.0005,0.0025)
print r'${:.1eSL}$'.format(this_val)
print 'a\rightarrow'.replace('\rightarrow','RIGHTARROW')
1.0 == 1.0000000000000001
xvalssize = (5,5)
xdata_1 = np.array([range(xvalssize[1]) for _ in range(xvalssize[0])])
xdata_2 = xdata_1.swapaxes(0,1).flatten()
xdata_1 = xdata_1.flatten()
import pandas as pa
import numpy as np
class AResAlpha(object):
"""
His results for alpha ratio
"""
def __init__(self,
Info={},
thissym='Not Set',
thiscol='Not Set',
thisshift=0.0,
name='',
LLname='',
Auto=True):
## A infront of variable is for Ahmed file convention
## Initialising plotting parameters
self.thisshift = thisshift
self.thiscol = thiscol
self.thissym = thissym
# Opboot { it } bs
self.alphaAvg = ODNested()
self.alphaStd = ODNested()
## kappa up down (in integer form)
if 'kud' in list(Info.keys()): self.kud = 'kud' + str(Info['kud'])
else: self.kud = ''
## kappa strange (in integer form)
if 'ks' in list(Info.keys()): self.ks = 'ks' + str(Info['ks'])
else: self.ks = ''
## Current obersvables are TopCharge and Weinberg
if 'Observable' in list(Info.keys()): self.Observ = Info['Observable']
else: self.Observ = 'TopCharge' ## default to TopCharge
self.latparams = mp.LatticeParameters(Info=Info)
self.latparams.LoadPickle()
if self.Observ == 'TopCharge':
self.thisconv = chitcoeff(self.latparams.nt, self.latparams.nxyz,
self.latparams.latspace, 'QQ')
else:
self.thisconv = chitcoeff(self.latparams.nt, self.latparams.nxyz,
self.latparams.latspace, 'WW')
## Ahmed only gave me a single flow time result, this will be changed when he gives me all flow times.
if 'flowtime' in list(Info.keys()): self.flowtime = Info['flowtime']
else: self.flowtime = 't_f6.00' ## default to read all flow times
self.kappafolder = 'Kud0' + self.kud.replace(
'kud', '') + 'Ks0' + self.ks.replace('ks', '')
self.AObserv = self.Observ.lower().replace('weinberg', 'weinopp')
self.Akud = self.kud.replace('kud', 'K')[:-2]
self.Aflowtime = self.flowtime.replace('t_f', '').replace('.', '')
self.Auto = Auto
self.SetCustomName(name, LLname)
thisdir = outputdir + '/AhmedResults/'
if self.Auto:
self.readdir = thisdir + 'ratio_' + self.AObserv + '_' + self.Akud + '_at_flow_time_' + self.Aflowtime + '/'
else:
self.readdir = thisdir + 'ratio_boot_' + self.AObserv + '_' + self.Akud + '_at_flow_time_' + self.Aflowtime + '/'
self.thisfile = 'Tauint_at_flow_time_' + self.Aflowtime + 'time_value_dvalue.txt'
def ReadAlpha(self):
print('reading ', self.readdir + self.thisfile)
with open(self.readdir + self.thisfile, 'r') as f:
## first line has info
f.readline()
for line in f:
fmtline = line.split()
self.alphaAvg['t' + str(fmtline[0])] = np.float(fmtline[1])
self.alphaStd['t' + str(fmtline[0])] = np.float(fmtline[2])
def PlotAlpha(self,
xlims=defxlimAlpha,
thiscol='PreDefine',
thissym='PreDefine',
thisshift='PreDefine'):
if thisshift != 'PreDefine':
xlen = np.abs(xlims[1] - xlims[0])
self.thisshift = thisshift * xlen
if thissym != 'PreDefine': self.thissym = thissym
if thiscol != 'PreDefine': self.thiscol = thiscol
xvals, yvals, yerr = [], [], []
for (it, iy), iyerr in zip(iter(self.alphaAvg.items()),
iter(self.alphaStd.values())):
itplot = untstr(it)
if xlims[0] - 1 < itplot < xlims[1] + 1:
xvals.append(itplot)
yvals.append(iy / self.thisconv)
yerr.append(iyerr / self.thisconv)
if len(list(self.alphaAvg.keys())) == 0:
raise EnvironmentError(
'Please read in Ahmend results before plotting with self.ReadAlpha()'
)
if self.thiscol == 'Not Set' or self.thissym == 'Not Set':
raise EnvironmentError(
'plotting color or symbol not set, please initialise class or set when plotting'
)
# pl.errorbar(np.array(xvals)+self.thisshift,yvals,yerr,label=self.LegLab,fmt=self.thissym,color=self.thiscol)
def SetCustomName(self, string='', stringLL=''):
if string == '':
self.name = self.filename = '_'.join(
[self.kappafolder, self.Observ])
else:
self.filename = self.name = string
if stringLL == '':
if self.Auto:
self.LegLab = '$' + '\ '.join([
self.latparams.GetPionMassLab(), self.Observ, 'Ahmed'
]) + '$ Auto' ## customise this how you like
else:
self.LegLab = '$' + '\ '.join([
self.latparams.GetPionMassLab(), self.Observ, 'Ahmed'
]) + '$' ## customise this how you like
else:
self.LegLab = stringLL
class AResChi2(object):
"""
His results for <Q^{2}> or <W^{2}> (WITHOUT COEFFICIENT)
"""
def __init__(self,
Info={},
thissym='Not Set',
thiscol='Not Set',
thisshift=0.0,
name='',
LLname=''):
## A infront of variable is for Ahmed file convention
## Initialising plotting parameters
self.thisshift = thisshift
self.thiscol = thiscol
self.thissym = thissym
# Opboot { it } bs
self.Chi2Avg = ODNested()
self.Chi2Std = ODNested()
## kappa up down (in integer form)
if 'kud' in list(Info.keys()): self.kud = 'kud' + str(Info['kud'])
else: self.kud = ''
## kappa strange (in integer form)
if 'ks' in list(Info.keys()): self.ks = 'ks' + str(Info['ks'])
else: self.ks = ''
## Current obersvables are TopCharge and Weinberg
if 'Observable' in list(Info.keys()): self.Observ = Info['Observable']
else: self.Observ = 'TopCharge' ## default to TopCharge
self.latparams = mp.LatticeParameters(Info=Info)
self.latparams.LoadPickle()
if 'TopCharge' in self.Observ:
self.thisconv = chitcoeff(self.latparams.nt, self.latparams.nxyz,
self.latparams.latspace, 'QQ')
else:
self.thisconv = chitcoeff(self.latparams.nt, self.latparams.nxyz,
self.latparams.latspace, 'WW')
# ## this is used display only these flowtimes, Still reads and writes all
# if 'tflowlist' in Info.keys(): self.tflowlist = Info['tflowlist']
# else: self.tflowlist = defxlimOp ## default to read all flow times
# listout = []
# for ict,itflow in enumerate(self.tflowlist):
# if 't_f' not in str(itflow):
# listout.append(tflowstr(itflow))
# else:
# listout.append(itflow)
# self.tflowfloat = [untflowstr(itflow) for itflow in listout]
# self.tflowlist = np.array(listout)
self.kappafolder = 'Kud0' + self.kud.replace(
'kud', '') + 'Ks0' + self.ks.replace('ks', '')
if 'topcharge' in self.Observ.lower().replace('weinberg', 'weinopp'):
self.AObserv = 'topcharge'
elif 'weinopp' in self.Observ.lower().replace('weinberg', 'weinopp'):
self.AObserv = 'weinopp'
self.Akud = self.kud.replace('kud', '')[:-2]
self.SetCustomName(name, LLname)
thisdir = outputdir + '/AhmedResults/'
self.readdir = thisdir + self.AObserv + self.Akud + '/'
self.thisfile = self.AObserv + self.Akud + '.txt'
def ReadChi2(self):
print('reading ', self.readdir + self.thisfile)
with open(self.readdir + self.thisfile, 'r') as f:
## first line has info
f.readline()
for line in f:
fmtline = line.split()
tflowkey = tflowstr(float(fmtline[0]) / 100)
self.Chi2Avg[tflowkey] = np.float(fmtline[1])
self.Chi2Std[tflowkey] = np.float(fmtline[2])
def CorrectAhmed(self, iy, iyerr):
if 'TopCharge' in self.Observ or 'Q' in self.Observ:
return iy**(1 / 4.), iy**(-3 / 4.) * iyerr / 4.
else:
return iy**(1 / 8.), iy**(-7 / 8.) * iyerr / 8.
# def CorrectAhmed(self,iy,iyerr):
# return iy,iyerr
def PlotChi2(self,
xlims='data',
thiscol='PreDefine',
thissym='PreDefine',
thisshift='PreDefine'):
if len(list(self.Chi2Avg.keys())) == 0:
raise EnvironmentError(
'Please read in Ahmend results before plotting with self.ReadChi2()'
)
if isinstance(xlims, str) and xlims == 'data':
xlims = list(self.Chi2Avg.keys())
if (not isinstance(xlims[0], str)) or 't_f' not in xlims[0]:
xlims = list(map(tflowstr, xlims))
if thisshift != 'PreDefine':
xlen = np.abs(xlims[1] - xlims[0])
self.thisshift = thisshift * xlen
if thissym != 'PreDefine': self.thissym = thissym
if thiscol != 'PreDefine': self.thiscol = thiscol
if self.thiscol == 'Not Set' or self.thissym == 'Not Set':
raise EnvironmentError(
'plotting color or symbol not set, please initialise class or set when plotting'
)
xvals, yvals, yerr = [], [], []
for (it, iy), iyerr in zip(iter(self.Chi2Avg.items()),
iter(self.Chi2Std.values())):
itplot = TflowToPhys(np.array([untflowstr(it)]),
self.latparams.latspace)[0]
if it in xlims:
iycorr, iyerrcorr = self.CorrectAhmed(iy, iyerr)
xvals.append(itplot)
yvals.append(iycorr * self.thisconv)
yerr.append(iyerrcorr * self.thisconv)
# pl.errorbar(np.array(xvals)+self.thisshift,yvals,yerr,label=self.LegLab,fmt=self.thissym,color=self.thiscol)
def SetCustomName(self, string='', stringLL=''):
if string == '':
self.name = self.filename = '_'.join(
[self.kappafolder, self.Observ])
else:
self.filename = self.name = string
if stringLL == '':
self.LegLab = '$' + '\ '.join([
self.latparams.GetPionMassLab(), self.Observ, 'Ahmed'
]) + '$' ## customise this how you like
else:
self.LegLab = stringLL
class FormFact(object):
"""
This class should produce all form factor combinations for a particular from factor type
(e.g. vector, pseudo-vector, tensor etc...)
"""
def Construct_FormFact_File(this_file):
return Construct_File_Object(this_file, FormFact)
def __init__(self,
currtype,
q2list,
pp2list=[0],
Info=defInfo,
tflowlist=[],
name=''):
if currtype not in CurrTypes:
raise IOError(
currtype +
' not recognised as a current type in FormFactors.py')
self.currtype = currtype
self.doflow = ('Top' in self.currtype) or ('Wein' in self.currtype)
self.fffun = FFFitFuns[currtype]
self.currOp = CurrOpps[currtype]
self.currOpNP = CurrOppsNoProj[currtype]
self.currOpNPFSig = CurrOppsNoProjSigBack[currtype]
self.npar = NoFFPars[currtype]
self.ffpars = FFParams[currtype]
self.kappafolder = ''
self.ppparams = mp.LatticeParameters(mom2list=pp2list, Info=Info)
self.ppparams.LoadPickle()
self.qparams = mp.LatticeParameters(mom2list=q2list, Info=Info)
self.qparams.LoadPickle()
self.nt = self.qparams.nt
self.nxyz = self.qparams.nxyz
self.dim_label = 'RC' + str(self.nxyz) + 'x' + str(self.nt)
self.dim_ll = str(self.nxyz) + r'^{3}\times ' + str(self.nt)
tracespin = CorrSpinTrace()
self.FFFun = tracespin[self.currtype]
self.tflowlist = tflowlist
# if not isinstance(mass,basestring):
# self.SetMass(mass)
# else:
# self.mass = 'Not Set'
# if not isinstance(alpha,basestring):
# self.SetAlpha(alpha,tflowlist)
# else:
# self.alpha = 'Not Set'
self.mass = 'Not Set'
self.alpha = 'Not Set'
self.SetCustomName(name)
# ## self.paramDict { icurrOp , ipkey , iqkey , imass (, tflow ) , ipar } bs
# ## (, tflow) if cp odd form factor with flow times
# self.paramDict = 'Not Set'
if self.doflow:
self.param_Col_Names = [
'current_operator', 'sink_momentum', 'current_momentum',
'mass_and_alpha', 'flow_time', 'form_factor'
]
self.paramRed_Col_Names = [
'current_operator', 'sink_momentum', 'current_momentum',
'mass_and_alpha', 'flow_time', 'form_factor'
]
else:
self.param_Col_Names = [
'current_operator', 'sink_momentum', 'current_momentum',
'mass', 'form_factor'
]
self.paramRed_Col_Names = [
'current_operator', 'sink_momentum', 'current_momentum',
'mass', 'form_factor'
]
self.param_Stats = pa.DataFrame()
'''
columns = FF FFAvg FFStd
rows are param_Col_Names
'''
self.paramRed_Stats = pa.DataFrame()
'''
columns = FF FFAvg FFStd
rows are param_Col_Names
'''
def AppendName(self):
this_str = self.name
if isinstance(self.mass, (dict, OrderedDict)):
this_str += '_' + list(self.mass.keys())[0]
if isinstance(self.alpha, (dict, OrderedDict)):
this_str += '_' + list(self.alpha.keys())[0] + '_' + list(
list(self.alpha.values())[0].keys())[0]
self.SetCustomName(string=this_str)
def SetCustomName(self, string=''):
if string == '':
# if len(self.tflowlist) > 0:
# if self.tflowlist[0] == 'none':
# self.name = '_'.join([ self.currtype,'q'+''.join(map(str,self.qparams.mom2list)),
# 'pp'+''.join(map(str,self.ppparams.mom2list))])
# else:
# self.name = '_'.join([ self.currtype,'q'+''.join(map(str,self.qparams.mom2list)),
# 'pp'+''.join(map(str,self.ppparams.mom2list)),self.tflowlist[0]])
# else:
self.name = '_'.join([
self.currtype, 'q' + ''.join(map(str, self.qparams.mom2list)),
'pp' + ''.join(map(str, self.ppparams.mom2list))
])
else:
self.name = string
self.FFcoeffDir = outputdir + '/' + self.dim_label + self.kappafolder + 'FFcoeffs/'
mkdir_p(self.FFcoeffDir + '/Pickle/')
mkdir_p(self.FFcoeffDir + '/Excel/')
self.HumanFile = self.FFcoeffDir + self.name + '.xml'
self.ExcelFile = self.FFcoeffDir + '/Excel/' + self.name + '.xlsx'
self.PickleFile = self.FFcoeffDir + '/Pickle/' + self.name + '.py3p'
def GetRedFFParms(self):
if len(self.param_Stats) == 0:
self.GetFFParams()
vall, keyl = [], []
vall_nd, qsqrdlist = [], []
Avgl, Stdl = [], []
keycombl, coeffl = [], []
for ikey, iFF in self.param_Stats['FF'].groupby(
self.paramRed_Col_Names[:-1]):
if isinstance(ikey, str):
this_key = list(iFF.index[0])
iq = this_key[2]
else:
this_key = ikey
iq = ikey[2]
thisqsqrd = self.qparams.Getpsqrdform(iq, pre='q')
checkbool = True
FFvals = iFF.values
FFvalsAvg = np.array([hold_val.Avg for hold_val in FFvals])
for ic, iffavg in enumerate(FFvalsAvg):
if abs(iffavg) > myeps:
break
elif ic == len(FFvalsAvg) - 1:
print('Warning, form factor is all zero for:')
print(ikey)
for ics, (pref_ff,
prev_qsqrd) in enumerate(zip(vall_nd, qsqrdlist)):
prev_ffAvg = np.array([hold_val.Avg for hold_val in pref_ff])
comp_rat = prev_ffAvg[ic] / FFvalsAvg[ic]
rat_list = []
for denomen, numer in zip(FFvalsAvg, prev_ffAvg):
if denomen == 0:
if numer != 0:
rat_list.append(False)
else:
rat_list.append(numer / denomen)
if all(rat_list == comp_rat) and prev_qsqrd == thisqsqrd:
checkbool = False
keycombl[ics].append(this_key)
coeffl[ics].append(comp_rat)
break
if checkbool:
keycombl.append([this_key])
coeffl.append([1.0])
vall += list(FFvals)
vall_nd.append(list(FFvals))
qsqrdlist.append(thisqsqrd)
Avgl += [ival.Avg for ival in FFvals]
Stdl += [ival.Std for ival in FFvals]
keyl += list(iFF.keys())
if len(keyl) > 0:
indicies = pa.MultiIndex.from_tuples(keyl,
names=self.paramRed_Col_Names)
self.paramRed_Stats.loc[:, 'FF'] = pa.Series(vall, index=indicies)
self.paramRed_Stats.loc[:, 'FFAvg'] = pa.Series(Avgl,
index=indicies)
self.paramRed_Stats.loc[:, 'FFStd'] = pa.Series(Stdl,
index=indicies)
self.combine_keys_forRF = keycombl
self.coeffs_forRF = coeffl
else:
print('Warning, error with reducing form factor list')
print(self.param_Stats['FF'])
## imass is input parameter since we can use bootstrapped values
## wrapped by MassFFParams()
def GetFFParms(self):
# if thismass != 'PreDef': self.SetMass(thismass)
# if thisalpha != 'PreDef' and self.doflow: self.SetAlpha(thisalpha,thistflowlist)
if self.mass == 'Not Set':
raise IOError(
'mass not set, either pass into MassFFParams() or use SetMass()'
)
if self.alpha == 'Not Set':
self.alpha = {}
for itflow in self.tflowlist:
if itflow == 'none': continue
self.alpha[itflow] = {'Test': alpha_guess}
plist, pAvg, pStd = [], [], []
ilist = []
if self.doflow and len(self.tflowlist) == 0:
raise IOError('no tflowlist present with flowed operators')
looplist = [
self.currOp, self.ppparams.momformlist, self.qparams.momformlist
]
thistimer = Timer(linklist=looplist, name='FF ' + self.name)
for icurrOp in self.currOp:
for ip in self.ppparams.momformlist:
for iq in self.qparams.momformlist:
ipvec, iqvec = list(self.ppparams.TOpvec(
ip, actual=True)), list(
self.qparams.TOpvec(iq, actual=True))
ipkey, iqkey = self.ppparams.TOpform(
ip, 'pp'), self.qparams.TOpform(iq, 'q')
hold, rcheck, ccheck = self.FFFun(
icurrOp, iqvec, ipvec,
list(self.mass.values())[0].Avg)
if ccheck == True and rcheck == True:
print(hold)
raise ArithmeticError(
'Operator ' + ' '.join(icurrOp +
('pp' + ip, 'q' + iq)) +
' has both real and imaginary components, check Form Factor coeff construction'
)
elif ccheck == True:
Opout = icurrOp + ('cmplx', )
else:
Opout = icurrOp
for imass, bootmass in self.mass.items():
if self.doflow:
for itflow in self.tflowlist:
for ialpha, bootalpha in self.alpha[
itflow].items():
holdlist = [[] for i in self.ffpars]
# print bootmass.Avg,iqvec,ipvec,icurrOp
# print rcheck, ccheck
# print hold
thisalpha = [bootalpha] * len(
bootmass.bootvals)
if isinstance(bootalpha, BootStrap):
thisalpha = bootalpha.bootvals
for bmass, balpha in zip(
bootmass.bootvals, thisalpha):
hold, rdump, cdump = self.FFFun(
icurrOp,
iqvec,
ipvec,
bmass,
alpha=balpha)
if rcheck:
for icpar, ipar in enumerate(
self.ffpars):
holdlist[icpar].append(
hold[icpar].real)
if ccheck:
for icpar, ipar in enumerate(
self.ffpars):
holdlist[icpar].append(
hold[icpar].imag)
for icpar, ipar in enumerate(self.ffpars):
if len(holdlist[icpar]
) != bootmass.nboot:
continue
ilist.append(
('_'.join(Opout), ipkey, iqkey,
'_'.join([imass,
ialpha]), itflow, ipar))
this_param = BootStrap(
bootmass.nboot,
name=ipar,
bootvals=holdlist[icpar])
plist.append(this_param)
pAvg.append(this_param.Avg)
pStd.append(this_param.Std)
else:
holdlist = [[] for i in self.ffpars]
for bmass in bootmass.bootvals:
hold, rdump, cdump = self.FFFun(
icurrOp,
iqvec,
ipvec,
bmass,
alpha=self.alpha)
if rcheck:
for icpar, ipar in enumerate(self.ffpars):
holdlist[icpar].append(
hold[icpar].real)
if ccheck:
for icpar, ipar in enumerate(self.ffpars):
holdlist[icpar].append(
hold[icpar].imag)
for icpar, ipar in enumerate(self.ffpars):
if len(holdlist[icpar]) != bootmass.nboot:
continue
ilist.append(('_'.join(Opout), ipkey, iqkey,
imass, ipar))
this_param = BootStrap(
bootmass.nboot,
name=ipar,
bootvals=holdlist[icpar])
plist.append(this_param)
pAvg.append(this_param.Avg)
pStd.append(this_param.Std)
thistimer.Lap()
if len(ilist) > 0:
indicies = pa.MultiIndex.from_tuples(ilist,
names=self.param_Col_Names)
self.param_Stats.loc[:, 'FF'] = pa.Series(plist, index=indicies)
self.param_Stats.loc[:, 'FFAvg'] = pa.Series(pAvg, index=indicies)
self.param_Stats.loc[:, 'FFStd'] = pa.Series(pStd, index=indicies)
## self.mass ends up as BootStrap class
def SetMass(self, mass):
self.mass = ODNested()
if isinstance(mass, TwoPointCorr):
if 'boot' not in mass.C2_Fit_Stats:
raise IOError(
'C2_Fit_Stats in TwoPointCorr not calculated, please run Fit() on TwoPointCorr instance'
)
for (istate, ip), fitdata in mass.C2_Fit_Stats['boot'].items():
if ip != 'p000': continue
ifitr, this_data = fitdata.GetMaxFitr()
self.mass['C2_' + '_'.join([istate, ifitr])] = this_data.iloc[
0].fit_data['Params']['Energy']
elif isinstance(mass, Fitting):
if 'Energy' not in list(mass.fit_data['Params'].keys()):
print(list(mass.fit_data['Params'].keys()))
raise IOError('Fitting class instance has no parameter Energy')
self.mass['C2_' + mass.name] = mass.fit_data['Params']['Energy']
elif isinstance(mass, BootStrap):
self.mass['C2_' + mass.name] = mass
elif mass == 'Not Set':
self.mass = 'Not Set'
else:
raise IOError(
'mass input type not recognisied (see FormFactors.py, SetMass())'
)
def GetFuns(self):
if not hasattr(self, 'FFFun'):
self.FFFun, self.fffun = ReadFuns(self.FFFun_name, self.fffun_name)
def RemoveFuns(self):
if hasattr(self, 'FFFun') and hasattr(self.FFFun, '__name__'):
self.FFFun_name = self.FFFun.__name__
self.fffun_name = self.fffun.__name__
WriteFuns(self.FFFun, self.fffun)
del self.FFFun
del self.fffun
## self.Alpha ends up as BootStrap class
def SetAlpha(self, alpha, tflowlist):
self.alpha = ODNested()
self.tflowlist = tflowlist
if isinstance(alpha, NNQFullCorr):
# if self.kappafolder not in ['',alpha.kappafolder+'/']:
# raise IOError('kappa folders used in FormFact (from FormFactors.py) have changed, check alpha and mass used.')
# self.kappafolder = alpha.kappafolder + '/'
# self.SetCustomName(string=self.name)
if 'boot' not in alpha.NNQFull_Fit_Stats:
raise IOError(
'No fits have been performed on the AlphaFull object passed to Form Factors'
)
for itflow in tflowlist:
if ('p000',
itflow) not in alpha.NNQFull_Fit_Stats['boot'].index:
print(alpha.NNQFull_Fit_Stats['boot'])
raise IOError(
'No zero momentum found in NNQFull_Fit_Stats in TwoPointCorr, try running Fit()'
)
ifitr, this_data = alpha.NNQFull_Fit_Stats.at[(
'p000', itflow), 'boot'].GetMaxFitr()
ifitr = ifitr.replace('fittwor', 'tsumfitr')
self.alpha[itflow][
'alpha_' +
ifitr] = this_data.iloc[0].fit_data['Params'].iloc[0]
return
elif isinstance(alpha, NNQCorr):
# if self.kappafolder not in ['',alpha.kappafolder+'/']:
# raise IOError('kappa folders used in FormFact (from FormFactors.py) have changed, check alpha and mass used.')
# self.kappafolder = alpha.kappafolder + '/'
# self.SetCustomName(string=self.name)
if 'boot' not in alpha.NNQ_Fit_Stats:
raise IOError(
'No fits have been performed on the Alpha object passed to Form Factors'
)
for itflow in tflowlist:
if ('p000', itflow) not in alpha.NNQ_Fit_Stats['boot'].index:
print(alpha.NNQ_Fit_Stats['boot'])
raise IOError(
'No zero momentum found in NNQ_Fit_Stats in TwoPointCorr, try running Fit()'
)
ifitr, this_data = alpha.NNQ_Fit_Stats.at[('p000', itflow),
'boot'].GetMaxFitr()
self.alpha[itflow][
'alpha_' +
ifitr] = this_data.iloc[0].fit_data['Params'].iloc[0]
return
try:
firstflow = list(alpha.keys())[0]
if isinstance(alpha[firstflow], Fitting):
for itflow in tflowlist:
if itflow not in list(alpha.keys()):
raise IOError(
'No tflow found in alpha.keys(), alpha can be dictionary with tflows'
)
if 'Energy' not in list(alpha.fit_data['Params'].keys()):
print(list(alpha[itflow].fit_data['Params'].keys()))
raise IOError(
'Fitting class instance has no parameter Energy')
self.alpha[itflow][
'alpha_' +
alpha.name] = alpha.fit_data['Params'].iloc[0]
elif isinstance(alpha[firstflow], BootStrap):
for itflow in tflowlist:
if itflow not in list(alpha.keys()):
raise IOError(
'No tflow found in alpha.keys(), alpha can be dictionary with tflows'
)
self.alpha[itflow]['alpha_' + alpha.name] = alpha
except Exception as err:
if alpha == 'No Alpha' or alpha == 'PreDef':
self.alpha = 'Not Set'
elif self.alpha != 'Not Set':
print(type(alpha))
if isinstance(alpha, str):
print(alpha)
raise IOError(
'alpha input type not recognisied (see FormFactors.py, SetAlpha())'
)
def __getitem__(self, ikey):
return self.param_Stats['FF'][ikey]
## if data is instance of FormFact (e.g. data = FormFact(...)
## for iOp,ipkey,iqkey,imass,ipar,data in data.items():
## etc.....
def items(self):
return list(self.param_Stats['FF'].items())
def iteritems(self):
return iter(self.param_Stats['FF'].items())
def iteritemsRed(self):
return iter(self.paramRed_Stats['FF'].items())
def itemsMass(self):
## self.paramDict { icurrOp , ipkey , iqkey , imass } bs
output = []
for icurrOp, currdata in self.paramDict.items():
for ipkey, pdata in currdata.items():
for iqkey, qdata in pdata.items():
output.append((icurrOp, ipkey, iqkey, qdata))
return output
def itemsParList(self):
## self.paramDict { icurrOp , ipkey , iqkey , imass } bs
output = []
if self.doflow:
this_levels = ('current_operator', 'sink_momentum',
'current_momentum', 'mass_and_alpha', 'flow_time')
for (icurrOp, ipkey, iqkey, imass,
itflow), flow_data in self.param_Stats['FF'].groupby(
level=this_levels):
this_fdata = flow_data[icurrOp, ipkey, iqkey, imass, itflow]
output.append((icurrOp, ipkey, iqkey, imass, itflow,
list(this_fdata.keys()), this_fdata.values))
else:
this_levels = ('current_operator', 'sink_momentum',
'current_momentum', 'mass')
for (icurrOp, ipkey, iqkey,
imass), flow_data in self.param_Stats['FF'].groupby(
level=this_levels):
this_fdata = flow_data[icurrOp, ipkey, iqkey, imass]
output.append((icurrOp, ipkey, iqkey, imass,
list(this_fdata.keys()), this_fdata.values))
return output
def itemsParListRed(self):
## self.paramDict { icurrOp , ipkey , iqkey , imass } bs
output = []
if self.doflow:
this_levels = ('current_operator', 'sink_momentum',
'current_momentum', 'mass_and_alpha', 'flow_time')
for (icurrOp, ipkey, iqkey, imass,
itflow), flow_data in self.paramRed_Stats['FF'].groupby(
level=this_levels):
this_fdata = flow_data[icurrOp, ipkey, iqkey, imass, itflow]
output.append((icurrOp, ipkey, iqkey, imass, itflow,
list(this_fdata.keys()), this_fdata.values))
else:
this_levels = ('current_operator', 'sink_momentum',
'current_momentum', 'mass')
for (icurrOp, ipkey, iqkey,
imass), flow_data in self.paramRed_Stats['FF'].groupby(
level=this_levels):
this_fdata = flow_data[icurrOp, ipkey, iqkey, imass]
output.append((icurrOp, ipkey, iqkey, imass,
list(this_fdata.keys()), this_fdata.values))
return output
def itemsAvgStd(self):
outlist = []
for ivals in self.param_Stats.itertuples():
outlist.append((ivals[0], ivals[2], ivals[3]))
return outlist
def values(self):
## self.paramDict { icurrOp , ipkey , iqkey , imass , ipar } bs
return self.param_Stats['FF'].values
def valuesAvgStd(self):
## self.paramDict { icurrOp , ipkey , iqkey , imass , ipar } bs
outlist = []
for ivals in self.param_Stats.itertuples():
outlist.append((ivals[2], ivals[3]))
return outlist
def keys(self):
return list(self.param_Stats.keys())
def keysOpMoms(self):
## self.paramDict { icurrOp , ipkey , iqkey , imass , ipar } bs
output = []
for (icurrOp, ipkey, iqkey), idata in self.param_Stats['FF'].groupby(
level=('current_operator', 'sink_momentum',
'current_momentum')):
output.append((icurrOp.split('_'), ipkey, iqkey))
return output
def keysOpMomsRed(self):
## self.paramDict { icurrOp , ipkey , iqkey , imass , ipar } bs
output = []
for (icurrOp, ipkey,
iqkey), idata in self.paramRed_Stats['FF'].groupby(
level=('current_operator', 'sink_momentum',
'current_momentum')):
output.append((icurrOp.split('_'), ipkey, iqkey))
return output
def Write(self):
def FixDictArray(thislist, ikey):
outDict = OrderedDict()
for ic, ilist in enumerate(thislist):
outDict[ikey + str(ic + 1)] = ilist
return outDict
self.outDict = ODNested()
self.outDict['Type'] = self.currtype
self.outDict['FFparams'] = FixDictArray(self.ffpars, 'FF')
self.outDict['tflowlist'] = FixDictArray(self.tflowlist, 'flow')
## TODO, output more information about momenta if you would like
self.outDict['nt'] = self.nt
self.outDict['nxyz'] = self.nxyz
self.outDict['q2max'] = self.qparams.mom2max
self.outDict['q2min'] = self.qparams.mom2min
self.outDict['pp2max'] = self.ppparams.mom2max
self.outDict['pp2min'] = self.ppparams.mom2min
excel_params = pa.Series(deepcopy(self.outDict))
firstkey = list(self.mass.keys())[0]
excel_params['massAvg_' + firstkey] = list(self.mass.values())[0].Avg
excel_params['massStd_' + firstkey] = list(self.mass.values())[0].Std
for icFF, iFF in excel_params['FFparams'].items():
excel_params[icFF] = iFF
for ictflow, itflow in excel_params['tflowlist'].items():
excel_params[ictflow] = itflow
del excel_params['FFparams']
del excel_params['tflowlist']
if self.doflow:
for itflow in self.tflowlist:
firstdata = list(self.alpha[itflow].values())[0]
if isinstance(firstdata, BootStrap):
excel_params['AlphaAvg_' + firstkey + '_' +
itflow] = firstdata.Avg
else:
excel_params['AlphaAvg_' + firstkey + '_' +
itflow] = firstdata
for itflow in self.tflowlist:
firstdata = list(self.alpha[itflow].values())[0]
if isinstance(firstdata, BootStrap):
excel_params['AlphaStd_' + firstkey + '_' +
itflow] = firstdata.Std
for ikey, massdata in self.mass.items():
if isinstance(massdata, BootStrap):
self.outDict['mass'][ikey] = AvgStdToFormat(
massdata.Avg, massdata.Std)
else:
self.outDict['mass'][ikey] = massdata
if self.doflow:
for itflow, alphatflow in self.alpha.items():
firstalpha = list(alphatflow.values())[0]
alpha_key = list(alphatflow.keys())[0]
if isinstance(firstalpha, BootStrap):
self.outDict['alpha'][alpha_key][itflow] = AvgStdToFormat(
firstalpha.Avg, firstalpha.Std)
else:
self.outDict['alpha'][alpha_key][itflow] = firstalpha
if self.doflow:
for (gammakey, ipp, iq, imass, itflow,
ipar), paramdata in self.items():
thisqsqrd = self.qparams.Getpsqrdform(iq, pre='q')
## TODO, this is incorrectly order with respect to qsqrd, need to write function to fix this somewhere
## also, you can play around with the ordering of keys to get the formating you like !!
# self.outDict[imass][ipp][itflow][self.qparams.Getpsqrdform(iq,pre='q')]['Coeff_of_'+ipar]['_'.join([gammakey,iq])] = AvgStdToFormat(paramdata.Avg,paramdata.Std)
try:
self.outDict['Equations'][imass][ipp][itflow][thisqsqrd][
'_'.join([gammakey, iq])] += ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
except Exception as err:
self.outDict['Equations'][imass][ipp][itflow][thisqsqrd][
'_'.join([gammakey, iq])] = ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
if ipar == self.ffpars[-1]:
self.outDict['Equations'][imass][ipp][itflow][thisqsqrd][
'_'.join([gammakey, iq])] = self.outDict['Equations'][
imass][ipp][itflow][thisqsqrd]['_'.join(
[gammakey, iq])][:-1]
else:
for (gammakey, ipp, iq, imass, ipar), paramdata in self.items():
thisqsqrd = self.qparams.Getpsqrdform(iq, pre='q')
## TODO, this is incorrectly order with respect to qsqrd, need to write function to fix this somewhere
## also, you can play around with the ordering of keys to get the formating you like !!
try:
self.outDict['Equations'][imass][ipp][thisqsqrd]['_'.join(
[gammakey, iq])] += ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
except Exception as err:
self.outDict['Equations'][imass][ipp][thisqsqrd]['_'.join(
[gammakey, iq])] = ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
if ipar == self.ffpars[-1]:
self.outDict['Equations'][imass][ipp][thisqsqrd]['_'.join([
gammakey, iq
])] = self.outDict['Equations'][imass][ipp][thisqsqrd][
'_'.join([gammakey, iq])][:-1]
if self.doflow:
for (gammakey, ipp, iq, imass, itflow,
ipar), paramdata in self.iteritemsRed():
thisqsqrd = self.qparams.Getpsqrdform(iq, pre='q')
## TODO, this is incorrectly order with respect to qsqrd, need to write function to fix this somewhere
## also, you can play around with the ordering of keys to get the formating you like !!
# self.outDict[imass][ipp][itflow][self.qparams.Getpsqrdform(iq,pre='q')]['Coeff_of_'+ipar]['_'.join([gammakey,iq])] = AvgStdToFormat(paramdata.Avg,paramdata.Std)
try:
self.outDict['Equations_Reduced'][imass][ipp][itflow][
thisqsqrd]['_'.join(
[gammakey, iq])] += ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
except Exception as err:
self.outDict['Equations_Reduced'][imass][ipp][itflow][
thisqsqrd]['_'.join(
[gammakey, iq])] = ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
if ipar == self.ffpars[-1]:
self.outDict['Equations_Reduced'][imass][ipp][itflow][
thisqsqrd]['_'.join([
gammakey, iq
])] = self.outDict['Equations_Reduced'][imass][ipp][
itflow][thisqsqrd]['_'.join([gammakey, iq])][:-1]
else:
for (gammakey, ipp, iq, imass,
ipar), paramdata in self.iteritemsRed():
thisqsqrd = self.qparams.Getpsqrdform(iq, pre='q')
## TODO, this is incorrectly order with respect to qsqrd, need to write function to fix this somewhere
## also, you can play around with the ordering of keys to get the formating you like !!
try:
self.outDict['Equations_Reduced'][imass][ipp][thisqsqrd][
'_'.join([gammakey, iq])] += ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
except Exception as err:
self.outDict['Equations_Reduced'][imass][ipp][thisqsqrd][
'_'.join([gammakey, iq])] = ' (' + AvgStdToFormat(
paramdata.Avg, paramdata.Std, dec=5,
numlen=8) + ') ' + ipar + ' +'
if ipar == self.ffpars[-1]:
self.outDict['Equations_Reduced'][imass][ipp][thisqsqrd][
'_'.join([gammakey,
iq])] = self.outDict['Equations_Reduced'][
imass][ipp][thisqsqrd]['_'.join(
[gammakey, iq])][:-1]
## Write human readable file with data
WriteXml(self.HumanFile, {'Results': self.outDict})
WriteExcel(self.ExcelFile, {'FF_results': deepcopy(self.param_Stats)},
params=excel_params)
## pickles rest of data for reading
self.RemoveFuns()
WritePickle(self.PickleFile, self.__dict__)
self.GetFuns()
def LoadKappaDir(self, mass, alpha='None'):
if isinstance(mass, TwoPointCorr):
if self.kappafolder.replace('/', '') not in ['', mass.kappafolder]:
raise IOError(
'kappa folders used in FormFact (from FormFactors.py) have changed, check alpha and mass used.'
)
self.kappafolder = mass.kappafolder + '/'
if isinstance(alpha, NNQFullCorr) or isinstance(alpha, NNQCorr):
if self.kappafolder.replace('/',
'') not in ['', alpha.NN.kappafolder]:
raise IOError(
'kappa folders used in FormFact (from FormFactors.py) have changed, check alpha and mass used.'
)
self.kappafolder = alpha.NN.kappafolder + '/'
self.SetCustomName(string=self.name)
def LoadPickle(self,
thismass,
thisalpha='No Alpha',
thistflowlist=[],
DefWipe=False):
# if self.doflow and thisalpha == 'No Alpha':
# raise EnvironmentError('alpha is needed for FormFact when using FO form factor.')
self.LoadKappaDir(thismass, alpha=thisalpha)
self.SetMass(thismass)
self.SetAlpha(thisalpha, thistflowlist)
self.AppendName()
# print 'loading ', self.PickleFile, 'with DefWipe ' , DefWipe
if os.path.isfile(self.PickleFile) and not DefWipe:
# print 'Loading Pickle for ' , self.name
thispickle = ReadPickleWrap(self.PickleFile)
# print thistflowlist, thispickle['tflowlist'],self.doflow
if len(thistflowlist
) == 0 or thistflowlist == thispickle['tflowlist'] or (
not self.doflow):
self.__dict__.update(thispickle)
self.Write()
else:
if os.path.isfile(self.PickleFile + '.bak'):
print('using backupfile for ' + self.name)
self.PickleFile = self.PickleFile + '.bak'
self.HumanFile = self.HumanFile + '.bak'
self.ExcelFile = self.ExcelFile.replace(
'.xlsx', '.bak.xlsx')
self.LoadPickle(thismass,
thisalpha=thisalpha,
thistflowlist=thistflowlist,
DefWipe=DefWipe)
return
self.GetFFParms()
self.GetRedFFParms()
self.Write()
else:
self.GetFFParms()
self.GetRedFFParms()
self.Write()