(py)ROOT Fit resampled histogram

[cvergis]

Hi everyone,

I am trying to create some toys so that I can measure the combination of several sources of uncertainties. The concept goes like this: I have a histogram which I call nominal and I fit on it a user-defined TF1 function (fnom). The nominal’s fit is ok and results to status converged.
However when I start producing my toys, there are resampled cases (a lot of them), in which the same function cannot give a converging fit.

h=dict()
Ntrials = 1000
toy = 0
while toy < Ntrials:
    h[toy] = resample ( hnom ) #resamples nominal histogram
    fun[toy] = function() #returns the TF1 function
    h[toy].Fit( fun[toy], "0r+" )
    if ROOT.gMinuit.fCstatu != "CONVERGED" : continue
    #else keep on doing my stuff

I was wondering whether my check for convergence makes sense or if it’s somewhat biasing the range in which the histogram bins are being randomly resampled. How else could I make the fits more successful (got several errors with Fit 0 : Minimization failed) ? at the moment the user-defined function is defined as:

def function():
    fun = ROOT.TF1("Log_power","[0]*pow(x,[1]+[2]*log(x))",400.,1150.)
    fun.SetParName(0,"N")
    fun.SetParName(1,"a")
    fun.SetParName(2,"b")
    fun.SetParLimits(2, -10.,10.)
    fun.SetParLimits(0,1000,10000000.)
    fun.SetParLimits(1 , 0., 100.)
    return fun