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 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())' )
list,vals = zip(*[[y,x] for y,x in sorted(zip(list,vals))]) 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()
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'
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 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
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 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())' )
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()