Beispiel #1
0
def output(bdt=0.976, probk=0.53, probmu=1.41, probe=-0.80, countoutput=False, text=False, graph=False, sigma=5):
    #Fit to find expected background count and exponential distribution parameter
    
    expcount, aval = f.run(file1, t1, CommonSelection, lower, upper, lowercut, uppercut, bdt, probk, probmu, probe, text=text, graph=graph)
    
    if expcount != None:
        
        #Construct full selection for efficiency calculations
        partselection = ("(BDT >"  + str(bdt) + ") && (Kplus_PIDK_corrected > " + str(probk) + ") && (muplus_PIDmu_corrected > " + 
                        str(probmu) + ") && (eminus_PIDe_corrected > " + str(probe) +
                        ")&& (Kplus_isMuonLoose == 0) && (Kplus_InAccMuon==1) && (Psi_M < 3000 || Psi_M >3200) && (B_BKGCAT == 10)")
    
        selection = CommonSelection + "(B_M < " + str(uppercut) + ") && (B_M > " + str(lowercut) + " ) && " + partselection
        
        #Determine the required Chisquare percentage for 2 sigma or 5 sigma Branching Ratio
        
        if int(sigma) == 2:
            percentage = 0.95
        
        elif int(sigma) == 5:
            percentage = 0.999999426697        
        
        #Using the expected background count and required percentage, calculate the required signal count through Poisson Statistics
        
        signal = p.getExpectedLimit(expcount, percentage)
        
        #Pass the signal count and selection to the branching ratio calculator
        
        ratio, error = br.run(signal, file2, t2, selection)     
                    
        #return ratio and error for the purpose of a csv output and graph
        if countoutput == True:
            return ratio, error
        #return just the ratio, for the purpose of minimisation
        else:
            return ratio
    #If the fit has not converged, or expected count is 0, return error
    else:
        return float('nan')
Beispiel #2
0
def output(bdt=0.96, probk=-2.0, probmu=2.0, probe=2.0, countoutput=False, text=False, dynamic=False, graph =False, random=False):
    #Fit to find expected background count and exponential distribution parameter
    
    expcount, aval = f.run(file1, t1, CommonSelection, lower, upper, lowercut, uppercut, bdt, probk, probmu, probe, text=text, graph=graph)
    count = 17
    significance = False
    
    maxrandomcount = 100
    
    if random:
        randomcount=0
    
    else:
        randomcount = maxrandomcount - 1
        
    ratioarray = []
    errorarray = []
    
    if expcount != None:
        
        #Calculates the Branmching Ratio 100 times, and calculates a mean
        
        while randomcount != maxrandomcount:
            
            #iteratively add counts to the signal peak until it has a 5 sigma significance
            
            while significance != True:
                sig, entries, significance, peak, sigma, selection = s.run(file2, t2, CommonSelection, var, lower, upper, lowercut, uppercut, bdt, expcount, aval, count, probk, probmu, probe, text=text, graph=graph)
                if (dynamic == True) & (peak < 3.0):
                    if sigma < (expcount * 0.2):
                        count += (int(sigma) +1)
                    else:
                        count +=20
                elif significance != True:
                    count += int(sigma*0.01) +1
            
            ratio, error = br.run(sig, entries, file2, t2, selection)
            randomcount += 1    
            
            ratioarray.append(ratio)
            errorarray.append(error)
            significance = False
            count += -5
        
        averageratio = np.mean(ratioarray)
        
        if random:
            averageerror = np.mean(errorarray)/math.sqrt(float(randomcount))
            print time.asctime(time.localtime()), "Final Branching Ratio is", ufloat(averageratio, averageerror)
            
            
        else:
            averageerror = np.mean(errorarray)
            
                    
        #return ratio and error for the purpose of a csv output and graph
        if countoutput == True:
            return averageratio, averageerror
        #return just the ratio, for the purpose of minimisation
        else:
            return averageratio
    #If the fit has not converged, or expected count is 0, return error
    else:
        return float('nan')