def estimate(self):
"""
returns the health state of user("A"/"B","C","D")
"""
estimator = estimation(self.user_dict)
state, prefer = estimator.estimate()
self.prefer = prefer
return state
nBin = 500
Emin = 0.
Emax = 3.
mass = 5.81 #masa de Xe
purity = 0.91 #pureza
trun = 100. #dias
BgrRej = 1.
SigEff = 1.
rand = rt.TRandom3(0)
Nbb2n = rand.Poisson((round(st.estimation(mass,purity,trun)*.695197*SigEff))) #
texp = trun * 24 * 3600 #s
# Ratio act (mBq)
esperado ={60: {0 : int(round(3.935333e-2 * 0 /1000. *texp * BgrRej)),
1 : int(round(1.781900e-2 * 2.32e-1 /1000. *texp * BgrRej)),
2 : int(round(2.663240e-3 * 8.82 /1000. *texp * BgrRej)),
3 : int(round(6.060000e-5 * 8.4e-1 /1000. *texp * BgrRej)),
4 : int(round(3.945800e-2 * 2.27e-1 /1000. *texp * BgrRej)),
5 : int(round(4.147233e-2 * 9.66e-1 /1000. *texp * BgrRej)),
6 : int(round(1.525725e-3 * 2.02 /1000. *texp * BgrRej)),
7 : int(round(1.616075e-2 * 0 /1000. *texp * BgrRej)),
8 : int(round(8.496444e-3 * 2.52e1 /1000. *texp * BgrRej)),
9 : int(round(1.553625e-2 * 1.16e-1 /1000. *texp * BgrRej)),
from math import e
from random import random
from estimation import estimation, probability
def get_max():
s=random()
n=1
while s>=e**(-3):
s*=random()
n+=1
return n-1
N=[10*2,10**3,10**4,10**5,10**6]
print "PARTE A)\r\n"
for i in N:
print estimation(i,get_max)
print "\r\nPARTE B)\r\n"
for i in range(0,7):
print probability(i,10**6,get_max)
import estimation as st
rt.gStyle.SetOptStat(1111);
rt.gStyle.SetOptFit(1111111);
nBin = 500
Emin = 0.
Emax = 3.
mass = 5.81 #masa de Xe
purity = 0.91 #pureza
trun = 100. #dias
Nbb2n = int(st.estimation(mass,purity,trun)*.695197)
texp = trun * 24 * 3600 #s
elementdic = {60: 'Co60',
208: 'Tl208',
214: 'Bi214',
40: 'K40'}
filedic = {0 :'ANODE_QUARTZ ',
1 :'CARRIER_PLATE ',
2 :'DICE_BOARD ',
3 :'ENCLOSURE_BODY ',
4 :'ICS ',
5 :'PEDESTAL ',
from random import random
from tabulate import tabulate
from estimation import estimation
def get_min():
s=0.0
n=0
while s<1.0:
s+=random()
n+=1
return n
N=[10**2,10**3,10**4,10**5,10**6]
table=[]
k=0
headers=['N', 'E(N)']
for n in N:
table.append([n])
for i in N:
table[k].append(estimation(i,get_min))
k +=1
print tabulate(table, headers, tablefmt="orgtbl")
nBin = 500
Emin = 0.
Emax = 3.
mass = 5.81 # Xe mass in kg
purity = 0.91 # Ratio of 136Xe
trun = 800. # Data taking time in days
BgrRej = 1.0#0.2 # Background rejection factor
SigEff = 1.0#0.8 # Signal efficiency factor
rand = rt.TRandom3(0)
# Double beta efficiency and expected number of events
BBeff = 0.695197
Nbb2n = int( round( estimation(mass,purity,trun) * BBeff * SigEff ) )
if poissonize:
Nbb2n = rand.Poisson(Nbb2n)
texp = trun * 24 * 3600 # Exposition time in s
# "element" branch map
elementdic = { 60 : 'Co60',
208 : 'Tl208',
214 : 'Bi214',
40 : 'K40'}
# "part" branch map
partdic = { 0 :'ANODE_QUARTZ ',
1 :'CARRIER_PLATE ',
2 :'DICE_BOARD ',
