dhcp238:SigCalc3 glencowan$ more inputPar.txt
#  Example file for significance calculation with runSigCalc
#
#  option (0 = rest of info is data, 1 = rest is parameter set)
#
1
#
# mu       s
#
1            6
#
#  b_i        tau_i           a_i             xi_i
#
10             1.0             5                1
dhcp238:SigCalc3 glencowan$ more inputPar.txt
dhcp238:SigCalc3 glencowan$ ./runSigCalc inputPar.txt
Generating data set from input parameters: 
mu, s, btot, n = 1  6  10  19
bkg#, a, xi = 0  5  1
sigmaW, lambda, sigmaL = 1.23851  -0.883801  1.44338
m, S1, S2, = 5  -3.25697  9.96524
1  10  5  1
nObs = 19
m, S1, S2 = 5  -3.25697  9.96524
corresponding lambda, sigma_l = -0.883801  1.44338
numBkg = 1
n = 19
s = 6
prelim. i,  bHat_i = 0  6.94835
prelim. estimated total background = 6.94835

prelim. i,  bHat_i = 0  6.94835
Asymptotic discovery significance Z   = 1.06032
Corresponding p-value  = 0.1445
muHatObs = 2.21475
i, bHatObs, lambdaHatObs, sigmaHatObs = 0  5.7112  -0.651443  1.2524
i, bHatHatObs, lambdaHatHatObs, sigmaHatHatObs = 0  18.307  -0.256544  1.688
q0Obs    = 1.12428
bHatHatObsTot = 18.307
generated experiment 0
generated experiment 5000
generated experiment 10000
generated experiment 15000
generated experiment 20000
generated experiment 25000
generated experiment 30000
generated experiment 35000
generated experiment 40000
generated experiment 45000
9964 events rejected out of 50000 generated.
MC Z0 (data ~ log-normal weights) = 0.844196
generated experiment 0
generated experiment 5000
generated experiment 10000
generated experiment 15000
generated experiment 20000
generated experiment 25000
generated experiment 30000
generated experiment 35000
generated experiment 40000
generated experiment 45000
with exponentially distributed weights: 
7841 events rejected out of 50000 generated.
MC Z0 (data ~ exponential weights) = 1.00761