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')
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')
