def run_minuit(ObjectName,Case,fitNum):
Object = cfp.Object(ObjectName)
Object.InitiateCase(Case)
if Object.fitMethod.lower() != 'minuit':
raise NameError("Fit method initiated %s is not Minuit" % Object.fitMethod)
Object.InitiateFitNum(fitNum)
x = cfp.chainPrep(Object)
tmcmc.runminuit.RunMinuit(Object.FuncName,x.ObservedData,\
x.ModelParams,x.NuisanceData,x.BoundParams,1e0,Object.OutFitFile)
def run_explore(ObjectName,Case,param,nsteps):
Object = cfp.Object(ObjectName)
Object.InitiateCase(Case)
if Object.fitMethod.lower() != 'mcmc':
raise NameError("given fit method (%s) is not MCMC" % Object.fitMethod)
x = cfp.chainPrep(Object)
x.setForExplore(param)
OutFile = Object.casePath+Object.name+'.'+Object.fitID+'.'+param+'.expmcmc'
tmcmc.mcmc.mcmc_mh_adapt(nsteps,x.FuncName,\
x.ObservedData,\
x.ModelParams,\
x.NuisanceData,\
x.BoundParams,\
False,True,\
OutFile,True)
def run_post_mcmc(ObjectName,Case,fitNum,Stage):
Object = cfp.Object(ObjectName)
Object.InitiateCase(Case)
Object.InitiateFitNum(fitNum)
Object.InitiateData()
x = cfp.chainPrep(Object)
# cropping
if Stage == 0 or Stage == 1:
print 'Cropping MCMC'
tmcmc.iopostmcmc.cropMCMC(Object.OutFitFile,Object.CroppedFileName,0.05,1e5)
# auto-correlation & statistics
if Stage == 0 or Stage == 2:
print 'Autocorrelation Stats'
lowtol = 0.01
jmax = 5000
makePlotsFlag = True
res = 1
tmcmc.postmcmc.autocorMCMC(Object.CroppedFileName,lowtol,jmax,\
Object.AutoCorStatsFile,makePlotsFlag,silent=False,\
ftag=Object.AutoCorFigRoot,resolution=res,dynamic=True)
# print trace figures
if Stage == 0 or Stage == 3:
print 'Trace Plots'
tmcmc.postmcmc.plotChain(Object.CroppedFileName, ftag=Object.TracePlotRoot)
#covariance statistics
if Stage == 0 or Stage == 4:
print 'Covariance Stats'
tmcmc.postmcmc.covcorStats(Object.CroppedFileName,Object.CovCorStatsRoot)
# get lowest chisq
if Stage == 0 or Stage == 5:
print 'Lowest Chisq and Errors'
tmcmc.iomcmc.WriteLowestChisq(Object.CroppedFileName,Object.ModelParams,\
Object.LowestChiSQFile,True)
tmcmc.iopostmcmc.printErrors(Object.CroppedFileName,Object.LowestChiSQFile,\
Object.ParErrorFile)
def run_mcmc(ObjectName, Case, fitNum, nsteps):
Object = cfp.Object(ObjectName)
Object.InitiateCase(Case)
Object.InitiateFitNum(fitNum)
Object.InitiateData()
x = cfp.chainPrep(Object)
OutFile = Object.OutFitFile
tmcmc.mcmc.mcmc_mh_adapt(
nsteps,
Object.FuncName,
Object.ObservedData,
x.ModelParams,
x.NuisanceData,
Object.BoundParams,
False,
False,
OutFile,
True,
)
