def totalEfficiencies(self, org, dir=None, flavor=''):
recoLow, recoHigh, acceptance, denom = None, None, None, None
for step in org.steps:
for plotName in sorted(step.keys()):
if 'LowNX' + flavor == plotName: recoLow = step[plotName]
if 'HighNX' + flavor == plotName: recoHigh = step[plotName]
if 'AccNX' + flavor == plotName: acceptance = step[plotName]
if 'NX' + flavor == plotName: denom = step[plotName]
acc = tuple([
r.TGraphAsymmErrors(n, d, "cl=0.683 n")
for n, d in zip(acceptance, denom)
])
efflow = tuple([
r.TGraphAsymmErrors(n, d, "cl=0.683 n")
for n, d in zip(recoLow, denom)
])
effhigh = tuple([
r.TGraphAsymmErrors(n, d, "cl=0.683 n")
for n, d in zip(recoHigh, denom)
])
effacclow = tuple([
r.TGraphAsymmErrors(n, d, "cl=0.683 n")
for n, d in zip(recoLow, acceptance)
])
effacchigh = tuple([
r.TGraphAsymmErrors(n, d, "cl=0.683 n")
for n, d in zip(recoHigh, acceptance)
])
#fs = [0.4,0.6,1.,1.4] old factors for the record if not approved
fs = [0.1, 0.2, 0.3, 0.6, 1., 2., 3., 6., 10.]
allfs = [0.1 * a for a in fs]
allfs += fs
allfs += [10 * a for a in fs]
allfs = [round(a, 5) for a in allfs]
N = len(allfs)
forbidden = [7, 8, 9, 16, 17, 18]
f = 0.89
sysmap = {
'1000350': 0.075,
'1000150': 0.075,
'400150': 0.096,
'40050': 0.091,
'20050': 0.10
}
for i, sample in enumerate(org.samples):
digits = re.findall(r'\d+', sample['name'])
H, X = digits[0], digits[2]
name = 'H_' + str(H) + '_X_' + str(X)
sys = sysmap[H + X]
ctau = self.ctau[self.sig_names.index(name)]
data = {}
for factor in set(allfs):
data[factor] = []
for j in range(N):
if j in forbidden: continue
x, y = r.Double(0), r.Double(0)
eff = effhigh
effacc = effacchigh
if j < N / 3:
eff = efflow
effacc = effacclow
eff[i].GetPoint(j, x, y)
e = f * float(y)
eErr = f * eff[i].GetErrorY(j)
effacc[i].GetPoint(j, x, y)
ea = f * float(y)
eaErr = f * effacc[i].GetErrorY(j)
acc[i].GetPoint(j, x, y)
a = float(y)
aErr = acc[i].GetErrorY(j)
#if e > 0. : eErr = e*math.sqrt(sys*sys+pow(eErr/e,2))
#else : eErr = 0.
#if ea > 0. : eaErr = ea*math.sqrt(sys*sys+pow(eaErr/ea,2))
#else : eaErr = 0.
factor = allfs[j]
#print H,X,factor,a,aErr,e,eErr,ea,eaErr
output = [(a, aErr), (e, eErr), [ea, eaErr]]
data[factor].append(output)
#pickle.dump(output,open(supy.whereami()+'/../results/'+dir+'/efficiencies/'+name+'_'+str(factor)+'_'+str(j)+'.pkl','w'))
for factor in data.keys():
list = data[factor]
ac = [obj[0][0] for obj in list if obj[0][0] > 0]
acErr = [obj[0][1] for obj in list if obj[0][0] > 0]
ef = [obj[1][0] for obj in list if obj[1][0] > 0]
efErr = [obj[1][1] for obj in list if obj[1][0] > 0]
efac = [obj[2][0] for obj in list if obj[2][0] > 0]
efacErr = [obj[2][1] for obj in list if obj[2][0] > 0]
if len(ef) == 0: continue
e, eErr = weightedAvg(ef, efErr)
a, aErr = weightedAvg(ac, acErr)
ea, eaErr = weightedAvg(efac, efacErr)
#eErr = e*math.sqrt(sys*sys+pow(eErr/e,2))
#eaErr = ea*math.sqrt(sys*sys+pow(eaErr/ea,2))
print H, X, factor, a, aErr, e, eErr, ea, eaErr
output = [(a, aErr), (e, eErr), [ea, eaErr]]
pickle.dump(
output,
open(
supy.whereami() + '/../results/' + dir +
'/efficiencies/' + name + '_' + str(factor) + '.pkl',
'w'))
eval(file[:-4].split('_')[4]))
)
files2=sorted(os.listdir(dir2),key=lambda file: (eval(file[:-4].split('_')[1]),
eval(file[:-4].split('_')[3]),
eval(file[:-4].split('_')[4]))
)
x,w=[],[]
H,X=0,0
for file1,file2 in zip(files1,files2):
data1,data2=pickle.load(open(dir1+'/'+file1)),pickle.load(open(dir2+'/'+file2))
strings=file1[:-4].split('_')
if ((H!=strings[1] or X!=strings[3]) and H!=0):
avg,err=weightedAvg(x,w)
print round(avg,5),round(err,5)
x,w=[],[]
H,X,factor = strings[1],strings[3],eval(strings[4])
if factor != 0.1 and factor!=1 and factor!=10 : continue
e1,e2 = data1[dict[option][1]],data2[dict[option][1]]
if e1[0]==e2[0]==0 : continue
diff = 100* 2*(e1[0]-e2[0])/(e1[0]+e2[0])
n1=pow(e1[0]/e1[1],2)
n2=pow(e2[0]/e2[1],2)
if n1-n2==0: continue
diffe = diff*1./math.sqrt(abs(n1-n2))
#diffe=1
#diffe = 100* 4/pow(e1[0]+e2[0],2)*math.sqrt(pow(e2[0]*e1[1],2)+pow(e1[0]*e2[1],2))
x.append(diff)
w.append(diffe)
