The usual preamble....
In [1]:
%pylab inline
%config InlineBackend.figure_format = 'retina'
Populating the interactive namespace from numpy and matplotlib
One new package today:
pip install daft), from Dan Foreman-Mackey, for drawing graphical probabilistic models. (See below.)
In [2]:
import corner
import daft
import pystan
import scipy.special as sp
import scipy.signal as ss
import scipy.stats as st
import seaborn as sns
In [3]:
sns.set_context('talk')
sns.set_style('ticks')
sns.set_palette('colorblind')
This has come up a lot in the discussions this week. Here is an example of a "hierarchical" model and its interpretation as (semi)stacking. I first created this model to convince Dave Tsang that it might be fruitful to search for resonant shattering flares using semi-stacking; you can see the toy model I constructed, which is similar to this one, at https://github.com/farr/ShatteringFlares.
For some real work on semi-stacking that I have been involved in, see Lieu, et al. (2017).
In [4]:
def exp_sigmoid(x, x0, dx):
return 1.0/(1.0 + exp(-(x-x0)/dx))
def flare_profile(ts, tcent, tscale):
return exp_sigmoid(ts, tcent, tscale/10.0)*(1.0 - exp_sigmoid(ts, tcent, tscale))
In [5]:
ts = linspace(0, 10, 100)
plot(ts, flare_profile(ts, 3.0, 2.0))
Out[5]:
[<matplotlib.lines.Line2D at 0x1199d8c18>]
In [6]:
def draw_poisson_lc(ts, bg_rate, A, t0, dt):
total_rate = bg_rate + A*flare_profile(ts, t0, dt)
cts = 0.5*diff(ts)*(total_rate[:-1] + total_rate[1:])
return np.random.poisson(lam=cts)
In [7]:
ts = linspace(0, 10, 21)
cts = draw_poisson_lc(ts, 1.0, 20.0, 3.0, 2.0)
errorbar(0.5*(ts[:-1]+ts[1:]), cts, where(cts < 1, 1.0, sqrt(cts)), fmt='.')
Out[7]:
<Container object of 3 artists>
The parameters of a flare are then $A$, the amplitude, $t_0$ the peak time, and $\tau$, the decay timescale. If we imagine these come from a population, then we can conduct a hierarchical analysis.
Here is a daft model of our population:
In [8]:
pgm = daft.PGM([4.5, 4.5], origin=[0,-1])
pgm.add_node(daft.Node("amp_pop", r'$\mu_A$, $\sigma_A$', 0.5, 3, scale=1.75))
pgm.add_node(daft.Node('time_pop', r'$\mu_t$, $\sigma_t$', 2, 3, scale=1.75))
pgm.add_node(daft.Node('tau_pop', r'$\mu_\tau$, $\sigma_\tau$', 3.5, 3, scale=1.75))
pgm.add_node(daft.Node('amp', r'$A^{(i)}$', 0.5, 1.5, scale=1.25))
pgm.add_node(daft.Node('time', r'$t_0^{(i)}$', 2, 1.5, scale=1.25))
pgm.add_node(daft.Node('scale', r'$\tau^{(i)}$', 3.5, 1.5, scale=1.25))
pgm.add_node(daft.Node('counts', r'$\vec{n}^{(i)}$', 1.25, 0.5, observed=True))
pgm.add_node(daft.Node('background', r'$\lambda_\mathrm{bg}^{(i)}$', 2.75, 0.3, fixed=True))
pgm.add_plate(daft.Plate([0.0, -0.5, 4.0, 2.75], label=r'observations $i$'))
pgm.add_edge("amp_pop", "amp")
pgm.add_edge('time_pop', 'time')
pgm.add_edge('tau_pop', 'scale')
pgm.add_edge('amp', 'counts')
pgm.add_edge('time', 'counts')
pgm.add_edge('scale', 'counts')
pgm.add_edge('background', 'counts')
pgm.render()
Out[8]:
<matplotlib.axes._axes.Axes at 0x11a513b38>
Now we draw some "observations":
In [40]:
mu_A = log(4.0)
sigma_A = 0.5
mu_t = 0.0
sigma_t = 2.0
mu_tau = log(1.0)
sigma_tau = 0.25
mu_bg = log(1.0)
sigma_bg = 0.25
no = 100
nbin = 20
ts_bin = linspace(-5, 5, nbin+1)
bgs = []
As = []
ts = []
taus = []
counts = []
for i in range(no):
bg = random.lognormal(mu_bg, sigma_bg)
A = random.lognormal(mu_A, sigma_A)
t = random.normal(mu_t, sigma_t)
tau = random.lognormal(mu_tau, sigma_tau)
cts = draw_poisson_lc(ts_bin, bg, A, t, tau)
bgs.append(bg)
As.append(A)
ts.append(t)
taus.append(tau)
counts.append(cts)
In [41]:
t = linspace(-5, 5, 1000)
for i in range(no):
plot(t, bgs[i]+As[i]*flare_profile(t, ts[i], taus[i]), alpha=0.2)
In [16]:
model = pystan.StanModel(file='hierarchical_shattering.stan')
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_6a8877b13036685abaf485ee54e1d1e7 NOW.
In [42]:
data = {
'no': no,
'nc': nbin,
'ts_bin': ts_bin,
'bg_rate': bgs,
'counts': counts
}
In [43]:
fit = model.sampling(data=data)
fit
Out[43]:
Inference for Stan model: anon_model_6a8877b13036685abaf485ee54e1d1e7.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
mu_A 1.16 0.03 0.28 0.6 0.97 1.15 1.35 1.72 68 1.05
sigma_A 0.45 0.03 0.22 0.06 0.29 0.46 0.61 0.87 62 1.06
mu_t -0.43 0.05 0.58 -1.57 -0.82 -0.43 -0.05 0.67 157 1.03
sigma_t 2.3 0.07 0.6 1.45 1.91 2.18 2.55 3.95 84 1.03
mu_tau 0.27 0.03 0.31 -0.37 0.06 0.27 0.48 0.89 94 1.04
sigma_tau 0.56 0.03 0.31 0.04 0.31 0.56 0.77 1.19 110 1.04
norm_As[0] -0.14 0.06 0.93 -1.93 -0.78 -0.13 0.47 1.77 244 1.02
norm_As[1] -0.42 0.04 0.92 -2.19 -1.03 -0.41 0.16 1.49 526 1.01
norm_As[2] 0.6 0.05 0.88 -1.21 -0.02 0.64 1.22 2.21 297 1.01
norm_As[3] 0.35 0.08 1.07 -2.12 -0.31 0.47 1.11 2.17 181 1.03
norm_As[4] 0.42 0.08 0.98 -1.61 -0.15 0.46 1.08 2.22 143 1.02
norm_As[5] 0.15 0.05 0.94 -1.8 -0.47 0.15 0.79 1.98 383 1.01
norm_As[6] 1.43 0.1 0.97 -0.88 0.95 1.55 2.08 3.01 90 1.04
norm_As[7] -0.09 0.08 1.0 -1.94 -0.85 -0.1 0.57 1.93 177 1.03
norm_As[8] 0.44 0.06 0.91 -1.38 -0.12 0.44 1.07 2.2 239 1.01
norm_As[9] -0.32 0.05 0.96 -2.13 -0.98 -0.3 0.29 1.67 325 1.02
norm_As[10] -0.05 0.07 0.96 -1.87 -0.72 -0.04 0.62 1.83 194 1.02
norm_As[11] 0.05 0.05 0.91 -1.61 -0.63 0.05 0.69 1.82 295 1.02
norm_As[12] -0.4 0.04 0.92 -2.17 -1.05 -0.42 0.21 1.39 504 1.0
norm_As[13] -0.48 0.04 0.92 -2.28 -1.09 -0.48 0.17 1.31 481 1.01
norm_As[14] -0.13 0.1 1.0 -2.02 -0.83 -0.13 0.46 1.85 105 1.04
norm_As[15] -0.1 0.04 0.92 -1.88 -0.74 -0.09 0.5 1.67 631 1.01
norm_As[16] -0.13 0.07 0.93 -2.0 -0.76 -0.12 0.48 1.64 186 1.02
norm_As[17] -0.23 0.04 0.92 -2.07 -0.85 -0.21 0.43 1.47 462 1.0
norm_As[18] -0.05 0.06 0.96 -1.92 -0.7 -0.04 0.66 1.74 264 1.01
norm_As[19] -0.22 0.04 0.9 -2.11 -0.81 -0.21 0.38 1.57 525 1.01
norm_As[20] -0.35 0.03 0.9 -2.19 -0.94 -0.34 0.24 1.44 792 1.0
norm_As[21] -0.05 0.05 0.92 -1.9 -0.62 -0.08 0.54 1.73 394 1.01
norm_As[22] -0.36 0.05 0.91 -2.27 -0.96 -0.34 0.28 1.35 359 1.01
norm_As[23] 0.07 0.06 0.92 -1.72 -0.55 0.03 0.7 1.92 245 1.02
norm_As[24] -0.02 0.04 0.95 -1.96 -0.62 0.01 0.64 1.69 498 1.01
norm_As[25] -0.27 0.04 0.93 -2.12 -0.88 -0.28 0.37 1.52 453 1.01
norm_As[26] -0.42 0.04 0.91 -2.2 -1.01 -0.44 0.18 1.39 426 1.01
norm_As[27] 0.62 0.11 1.04 -1.83 -0.05 0.73 1.39 2.34 99 1.05
norm_As[28] -0.27 0.05 0.92 -2.08 -0.91 -0.23 0.39 1.5 363 1.01
norm_As[29] -0.4 0.06 0.96 -2.11 -1.13 -0.4 0.27 1.47 264 1.02
norm_As[30] -0.17 0.05 0.99 -2.12 -0.82 -0.19 0.48 1.81 472 1.01
norm_As[31] 0.35 0.04 0.91 -1.55 -0.2 0.34 0.94 2.1 489 1.0
norm_As[32] -0.18 0.09 0.99 -2.14 -0.85 -0.16 0.42 1.89 125 1.04
norm_As[33] -0.19 0.13 1.05 -2.15 -0.86 -0.22 0.42 2.28 68 1.06
norm_As[34] 0.07 0.06 0.97 -1.78 -0.59 0.02 0.72 1.96 252 1.02
norm_As[35] -0.21 0.04 0.91 -2.07 -0.81 -0.18 0.43 1.48 535 1.01
norm_As[36] -0.51 0.06 0.94 -2.35 -1.12 -0.51 0.09 1.41 236 1.02
norm_As[37] 0.97 0.07 0.96 -1.11 0.36 1.06 1.66 2.65 182 1.02
norm_As[38] -0.21 0.04 0.9 -2.08 -0.77 -0.18 0.39 1.52 441 1.01
norm_As[39] -0.11 0.05 0.92 -2.03 -0.72 -0.07 0.53 1.56 287 1.01
norm_As[40] 0.08 0.05 0.94 -1.85 -0.54 0.1 0.73 1.8 302 1.02
norm_As[41] 0.07 0.03 0.85 -1.73 -0.47 0.1 0.64 1.65 882 1.0
norm_As[42] -0.31 0.09 1.01 -2.38 -0.99 -0.31 0.41 1.64 141 1.03
norm_As[43] -0.04 0.05 0.9 -1.83 -0.64 -0.01 0.57 1.62 312 1.01
norm_As[44] -0.16 0.04 0.87 -1.8 -0.75 -0.14 0.44 1.5 476 1.0
norm_As[45] -0.41 0.04 0.89 -2.17 -1.03 -0.39 0.18 1.31 603 1.0
norm_As[46] 0.23 0.05 0.88 -1.57 -0.32 0.28 0.84 1.83 351 1.01
norm_As[47] -0.19 0.06 0.98 -2.23 -0.81 -0.16 0.49 1.63 235 1.0
norm_As[48] 0.18 0.08 0.96 -1.9 -0.44 0.22 0.87 1.95 131 1.02
norm_As[49] 0.42 0.08 0.89 -1.55 -0.11 0.46 1.04 2.01 126 1.03
norm_As[50] -0.25 0.03 0.88 -1.96 -0.85 -0.28 0.39 1.42 669 1.0
norm_As[51] -0.24 0.04 0.89 -2.07 -0.84 -0.21 0.35 1.48 402 1.0
norm_As[52] 0.14 0.04 0.88 -1.69 -0.44 0.21 0.75 1.75 460 1.01
norm_As[53] -0.29 0.05 0.93 -2.04 -0.9 -0.36 0.31 1.65 323 1.02
norm_As[54] -0.27 0.05 0.94 -2.12 -0.88 -0.28 0.31 1.76 327 1.02
norm_As[55] -0.31 0.03 0.88 -2.14 -0.86 -0.3 0.28 1.35 642 1.01
norm_As[56] -0.05 0.04 0.85 -1.85 -0.61 -0.02 0.56 1.5 504 1.01
norm_As[57] -0.25 0.04 0.92 -2.14 -0.82 -0.25 0.35 1.56 525 1.01
norm_As[58] 0.13 0.03 0.88 -1.63 -0.46 0.16 0.72 1.81 691 1.01
norm_As[59] 0.59 0.05 0.93 -1.32 -0.04 0.64 1.25 2.3 338 1.02
norm_As[60] 0.45 0.1 0.99 -1.58 -0.22 0.56 1.15 2.23 100 1.04
norm_As[61] 0.28 0.04 0.94 -1.58 -0.38 0.3 0.93 2.04 562 1.0
norm_As[62] -0.09 0.05 0.96 -1.96 -0.75 -0.12 0.55 1.77 357 1.01
norm_As[63] -0.06 0.07 0.91 -1.88 -0.68 -0.02 0.58 1.71 170 1.02
norm_As[64] 0.29 0.08 0.99 -1.77 -0.31 0.28 0.96 2.29 161 1.02
norm_As[65] -0.26 0.07 0.96 -2.35 -0.86 -0.2 0.39 1.47 200 1.01
norm_As[66] 0.26 0.04 0.93 -1.66 -0.31 0.28 0.85 2.01 445 1.0
norm_As[67] -0.28 0.04 0.93 -2.13 -0.92 -0.26 0.37 1.47 457 1.01
norm_As[68] 0.56 0.08 1.05 -1.6 -0.18 0.64 1.31 2.41 187 1.02
norm_As[69] -1.6e-3 0.03 0.89 -1.81 -0.56 0.04 0.6 1.67 801 1.0
norm_As[70] 0.01 0.09 0.95 -1.73 -0.63 -0.02 0.58 2.08 104 1.05
norm_As[71] 0.46 0.03 0.83 -1.27 -0.09 0.54 1.02 1.98 917 1.0
norm_As[72] 0.28 0.08 1.0 -1.66 -0.4 0.33 0.99 2.13 140 1.02
norm_As[73] -0.41 0.06 0.94 -2.23 -1.09 -0.4 0.25 1.3 290 1.01
norm_As[74] 0.17 0.05 0.92 -1.7 -0.43 0.22 0.81 1.85 417 1.01
norm_As[75] 0.19 0.07 0.96 -1.82 -0.41 0.25 0.83 1.93 170 1.03
norm_As[76] -0.31 0.05 0.95 -2.27 -0.92 -0.27 0.35 1.47 396 1.01
norm_As[77] 0.01 0.05 0.92 -1.86 -0.59 0.06 0.66 1.66 409 1.01
norm_As[78] -0.24 0.04 0.93 -2.2 -0.84 -0.2 0.41 1.48 558 1.01
norm_As[79] 0.48 0.05 0.95 -1.52 -0.12 0.54 1.15 2.12 319 1.01
norm_As[80] -0.29 0.05 0.95 -2.11 -0.94 -0.29 0.35 1.54 398 1.0
norm_As[81] -0.31 0.07 0.93 -2.06 -0.95 -0.3 0.35 1.43 194 1.02
norm_As[82] 1.13 0.05 0.89 -0.87 0.59 1.22 1.75 2.65 289 1.0
norm_As[83] -0.03 0.07 0.96 -1.78 -0.66 -0.07 0.56 2.34 188 1.03
norm_As[84] 0.87 0.06 0.93 -1.08 0.28 0.94 1.52 2.53 250 1.01
norm_As[85] -0.03 0.05 0.93 -2.04 -0.66 0.03 0.59 1.7 419 1.0
norm_As[86] -0.16 0.04 0.91 -2.01 -0.78 -0.12 0.46 1.58 567 1.01
norm_As[87] -0.02 0.05 0.94 -1.94 -0.65 0.02 0.63 1.7 390 1.01
norm_As[88] -0.1 0.04 0.96 -1.96 -0.77 -0.05 0.58 1.72 491 1.01
norm_As[89] -0.37 0.04 0.9 -2.19 -0.95 -0.4 0.22 1.41 515 1.01
norm_As[90] 0.02 0.06 0.95 -1.87 -0.64 0.03 0.73 1.75 262 1.02
norm_As[91] -0.19 0.05 0.88 -1.94 -0.77 -0.18 0.39 1.65 308 1.01
norm_As[92] 0.52 0.05 0.99 -1.55 -0.12 0.55 1.23 2.29 349 1.01
norm_As[93] -0.06 0.06 0.94 -1.96 -0.68 -0.03 0.54 1.82 216 1.02
norm_As[94] -0.37 0.06 0.98 -2.33 -1.0 -0.39 0.33 1.47 259 1.01
norm_As[95] -0.14 0.06 0.93 -1.99 -0.8 -0.14 0.5 1.69 210 1.02
norm_As[96] 0.21 0.06 0.95 -1.7 -0.36 0.24 0.83 1.99 266 1.01
norm_As[97] -0.4 0.04 0.9 -2.13 -1.01 -0.38 0.19 1.4 514 1.01
norm_As[98] 0.24 0.09 0.98 -2.03 -0.35 0.32 0.93 1.95 112 1.04
norm_As[99] -0.34 0.04 0.92 -2.19 -0.93 -0.34 0.23 1.52 592 1.0
norm_ts[0] -0.3 0.04 0.77 -1.55 -0.78 -0.38 0.02 1.59 403 1.01
norm_ts[1] -4.7e-3 0.05 1.08 -2.02 -0.64 -0.15 0.5 2.51 406 1.01
norm_ts[2] 0.51 0.03 0.47 -0.36 0.23 0.49 0.79 1.51 224 1.02
norm_ts[3] -1.4 0.08 1.03 -2.81 -2.09 -1.69 -0.92 0.99 155 1.03
norm_ts[4] -0.85 0.04 0.62 -1.99 -1.24 -0.89 -0.5 0.55 281 1.02
norm_ts[5] -0.31 0.03 0.65 -1.64 -0.62 -0.31 -0.03 1.26 566 1.01
norm_ts[6] 0.47 0.03 0.39 -0.33 0.22 0.46 0.73 1.23 237 1.02
norm_ts[7] -0.39 0.04 0.79 -1.88 -0.81 -0.44 -0.06 1.54 426 1.01
norm_ts[8] 0.83 0.06 0.85 -1.2 0.49 1.0 1.41 2.12 228 1.01
norm_ts[9] 0.57 0.06 1.01 -2.13 0.1 0.71 1.2 2.26 269 1.0
norm_ts[10] 0.02 0.03 0.79 -2.06 -0.31 0.18 0.52 1.25 708 1.0
norm_ts[11] 0.55 0.03 0.66 -1.07 0.22 0.58 0.95 1.73 397 1.0
norm_ts[12] 0.3 0.07 1.1 -2.17 -0.32 0.35 1.03 2.28 218 1.01
norm_ts[13] -0.12 0.09 1.21 -2.21 -1.01 -0.28 0.68 2.35 171 1.03
norm_ts[14] -0.4 0.13 1.19 -2.26 -1.29 -0.6 0.26 2.34 79 1.07
norm_ts[15] 0.42 0.06 0.85 -1.72-3.2e-3 0.47 0.95 1.88 200 1.02
norm_ts[16] 0.23 0.03 0.76 -1.49 -0.19 0.28 0.68 1.73 694 1.0
norm_ts[17] -0.21 0.07 0.92 -2.05 -0.75 -0.27 0.22 1.9 191 1.03
norm_ts[18] 0.59 0.05 0.95 -1.52 -0.02 0.81 1.25 2.05 425 1.01
norm_ts[19] -0.44 0.06 1.04 -2.07 -1.21 -0.69 0.27 1.83 256 1.02
norm_ts[20] 0.33 0.06 1.08 -1.92 -0.41 0.45 1.08 2.21 323 1.0
norm_ts[21] 0.16 0.04 0.8 -1.1 -0.33-3.4e-3 0.58 1.92 440 1.01
norm_ts[22] 0.27 0.08 1.08 -2.11 -0.42 0.38 1.01 2.24 172 1.03
norm_ts[23] 0.05 0.03 0.74 -1.31 -0.38 -0.02 0.42 1.84 452 1.01
norm_ts[24] -0.35 0.07 1.07 -2.64 -0.85 -0.09 0.29 1.42 249 1.01
norm_ts[25] 0.57 0.05 0.97 -1.57 -0.02 0.58 1.31 2.24 437 1.01
norm_ts[26] 3.8e-3 0.06 1.12 -1.95 -0.73 -0.13 0.64 2.43 384 1.01
norm_ts[27] -0.07 0.02 0.41 -0.93 -0.32 -0.05 0.2 0.69 348 1.01
norm_ts[28] 0.2 0.08 1.25 -2.1 -0.86 0.31 1.18 2.36 231 1.02
norm_ts[29] 0.03 0.12 1.1 -2.4 -0.56 0.15 0.65 2.11 91 1.04
norm_ts[30] -0.47 0.12 1.23 -2.32 -1.33 -0.87 0.37 2.07 102 1.03
norm_ts[31] 0.4 0.03 0.68 -1.16 0.02 0.48 0.84 1.55 409 1.01
norm_ts[32] 0.11 0.07 0.92 -1.76 -0.44 0.09 0.67 1.93 158 1.02
norm_ts[33] -0.32 0.03 0.92 -2.2 -0.9 -0.26 0.2 1.63 838 1.01
norm_ts[34] 0.09 0.03 0.69 -1.3 -0.31 0.08 0.5 1.52 515 1.0
norm_ts[35] 0.1 0.05 1.04 -2.09 -0.55 0.17 0.82 2.0 479 1.01
norm_ts[36] 0.18 0.07 1.22 -2.25 -0.58 0.08 1.11 2.49 288 1.01
norm_ts[37] -0.97 0.02 0.33 -1.7 -1.17 -0.96 -0.75 -0.36 358 1.01
norm_ts[38] 0.17 0.04 0.94 -1.66 -0.44 0.1 0.75 2.14 578 1.01
norm_ts[39] 0.57 0.03 0.74 -1.16 0.18 0.56 1.01 1.99 675 1.0
norm_ts[40] -0.13 0.04 0.68 -1.56 -0.5 -0.06 0.23 1.27 353 1.01
norm_ts[41] -0.2 0.04 0.88 -1.7 -0.79 -0.32 0.29 1.74 464 1.01
norm_ts[42] 0.6 0.08 1.07 -1.69 -0.11 0.6 1.44 2.38 168 1.03
norm_ts[43] -0.3 0.05 0.77 -1.85 -0.76 -0.33 0.12 1.4 205 1.01
norm_ts[44] -9.9e-4 0.04 0.94 -1.49 -0.72 -0.13 0.67 1.97 445 1.01
norm_ts[45] -0.1 0.09 1.13 -2.24 -0.8 -0.16 0.62 2.18 146 1.03
norm_ts[46] 0.41 0.07 0.58 -0.61 0.08 0.36 0.68 1.82 78 1.06
norm_ts[47] 0.25 0.06 1.03 -1.81 -0.53 0.41 0.98 2.12 277 1.01
norm_ts[48] 0.16 0.02 0.57 -1.13 -0.1 0.17 0.46 1.32 630 1.0
norm_ts[49] -0.21 0.06 0.87 -1.88 -0.84 -0.21 0.4 1.49 247 1.01
norm_ts[50] 0.47 0.05 1.04 -1.77 -0.26 0.61 1.23 2.23 427 1.0
norm_ts[51] 0.17 0.06 0.89 -1.6 -0.27 0.2 0.65 2.25 246 1.01
norm_ts[52] 0.61 0.08 0.96 -2.22 0.34 0.78 1.16 2.03 150 1.02
norm_ts[53] 0.02 0.08 1.26 -2.28 -1.02 0.07 1.0 2.21 222 1.02
norm_ts[54] -0.06 0.07 1.02 -1.9 -0.73 -0.18 0.64 2.04 223 1.02
norm_ts[55] -0.25 0.06 0.88 -1.88 -0.76 -0.31 0.14 1.96 207 1.02
norm_ts[56] 0.22 0.05 0.84 -1.55 -0.2 0.37 0.73 1.7 311 1.01
norm_ts[57] -0.16 0.07 1.16 -1.84 -1.07 -0.52 0.83 2.12 314 1.01
norm_ts[58] -0.07 0.05 0.97 -1.94 -0.79 0.03 0.67 1.66 359 1.01
norm_ts[59] -0.56 0.04 0.59 -1.85 -0.91 -0.47 -0.16 0.35 213 1.02
norm_ts[60] -0.47 0.03 0.64 -1.69 -0.83 -0.49 -0.15 1.13 390 1.01
norm_ts[61] -0.88 0.06 0.82 -2.03 -1.37 -1.04 -0.6 1.62 190 1.03
norm_ts[62] -0.13 0.04 0.91 -1.54 -0.74 -0.36 0.45 1.88 475 1.01
norm_ts[63] -0.29 0.03 0.76 -1.72 -0.74 -0.35 0.08 1.46 794 1.0
norm_ts[64] 0.59 0.05 0.62 -0.69 0.24 0.59 0.94 1.88 147 1.02
norm_ts[65] 0.24 0.07 0.93 -1.49 -0.44 0.27 0.95 1.99 188 1.02
norm_ts[66] -0.98 0.06 0.86 -2.21 -1.48 -1.12 -0.71 1.63 211 1.01
norm_ts[67] -0.46 0.07 1.16 -2.17 -1.23 -0.8 0.25 2.17 276 1.01
norm_ts[68] -0.54 0.03 0.5 -1.39 -0.81 -0.57 -0.32 0.66 230 1.03
norm_ts[69] -0.53 0.06 1.04 -2.28 -1.31 -0.63 0.15 1.71 312 1.01
norm_ts[70] 0.06 0.03 0.67 -1.47 -0.24 0.11 0.42 1.32 458 1.0
norm_ts[71] -0.09 0.02 0.42 -0.87 -0.37 -0.1 0.18 0.78 313 1.02
norm_ts[72] 0.36 0.1 0.89 -1.73 0.06 0.59 0.95 1.55 86 1.06
norm_ts[73] 0.05 0.07 1.24 -2.4 -0.88 0.18 0.96 2.26 274 1.01
norm_ts[74] 0.03 0.05 0.75 -1.45 -0.49 0.03 0.52 1.5 245 1.02
norm_ts[75] 0.17 0.05 0.85 -1.67 -0.3 0.32 0.7 1.65 263 1.01
norm_ts[76] -0.02 0.09 1.06 -2.03 -0.67 -0.16 0.58 2.29 138 1.02
norm_ts[77] -0.11 0.05 0.95 -1.9 -0.73 -0.29 0.51 1.87 316 1.0
norm_ts[78] 0.55 0.07 0.92 -1.46 0.13 0.65 1.14 2.11 158 1.03
norm_ts[79] -0.17 0.03 0.49 -1.1 -0.48 -0.2 0.11 0.9 255 1.01
norm_ts[80] -0.09 0.11 1.2 -2.17 -1.12 0.02 0.84 2.02 125 1.04
norm_ts[81] -0.11 0.08 1.18 -2.16 -1.13 0.05 0.71 2.19 234 1.01
norm_ts[82] 1.19 0.03 0.47 0.34 0.87 1.16 1.5 2.15 230 1.02
norm_ts[83] 0.03 0.1 1.04 -2.05 -0.7 0.14 0.79 1.8 108 1.04
norm_ts[84] 0.43 0.03 0.53 -0.78 0.13 0.46 0.78 1.4 309 1.0
norm_ts[85] -0.47 0.12 1.07 -2.19 -1.29 -0.62 0.31 1.7 75 1.05
norm_ts[86] -0.51 0.06 0.87 -1.85 -1.02 -0.65 -0.23 1.75 214 1.01
norm_ts[87] 0.64 0.03 0.72 -1.18 0.34 0.7 1.06 1.86 481 1.02
norm_ts[88] -0.18 0.04 0.87 -1.93 -0.68 -0.25 0.31 1.68 433 1.01
norm_ts[89] 0.21 0.08 1.26 -2.02 -0.77 0.11 1.31 2.42 268 1.0
norm_ts[90] -0.4 0.07 0.86 -1.89 -0.96 -0.52 -0.04 1.59 142 1.03
norm_ts[91] -0.22 0.06 0.94 -1.79 -0.86 -0.41 0.47 1.73 241 1.03
norm_ts[92] -0.47 0.03 0.46 -1.31 -0.74 -0.49 -0.24 0.7 315 1.02
norm_ts[93] -0.03 0.14 1.07 -1.83 -0.86 0.02 0.54 2.42 62 1.07
norm_ts[94] 0.48 0.03 0.85 -1.38 0.07 0.51 0.96 2.16 591 1.0
norm_ts[95] 0.13 0.04 0.98 -2.02 -0.42 0.15 0.82 1.89 496 1.01
norm_ts[96] 0.6 0.04 0.75 -0.92 0.09 0.65 1.1 1.93 423 1.01
norm_ts[97] -0.35 0.07 1.16 -2.19 -1.21 -0.58 0.46 2.27 300 1.02
norm_ts[98] 0.1 0.03 0.59 -0.97 -0.3 0.08 0.48 1.36 347 1.02
norm_ts[99] 0.26 0.05 1.1 -1.98 -0.43 0.3 1.0 2.27 465 1.01
norm_taus[0] 0.06 0.04 0.93 -1.8 -0.52 0.05 0.68 1.86 682 1.0
norm_taus[1] -0.44 0.03 0.94 -2.26 -1.1 -0.46 0.18 1.48 750 1.0
norm_taus[2] 0.51 0.04 0.9 -1.32 -0.08 0.56 1.12 2.27 595 1.01
norm_taus[3] 0.14 0.03 0.87 -1.66 -0.43 0.19 0.7 1.8 668 1.0
norm_taus[4] 0.36 0.04 0.91 -1.57 -0.22 0.41 0.94 2.05 410 1.01
norm_taus[5] -0.17 0.02 0.86 -1.88 -0.72 -0.16 0.37 1.58 1217 1.0
norm_taus[6] 0.64 0.05 0.86 -1.21 0.1 0.65 1.22 2.23 315 1.01
norm_taus[7] -0.3 0.05 0.94 -2.1 -0.94 -0.3 0.34 1.52 416 1.01
norm_taus[8] 0.62 0.04 0.96 -1.34 0.01 0.66 1.27 2.41 527 1.02
norm_taus[9] -0.12 0.05 0.98 -1.91 -0.8 -0.19 0.57 1.85 342 1.01
norm_taus[10] 0.03 0.03 0.93 -1.76 -0.58 0.02 0.66 1.89 951 1.0
norm_taus[11] 0.05 0.03 0.85 -1.6 -0.52 0.03 0.64 1.72 756 1.0
norm_taus[12] -0.38 0.07 0.98 -2.17 -1.11 -0.39 0.28 1.58 210 1.02
norm_taus[13] -0.46 0.05 0.95 -2.3 -1.07 -0.47 0.18 1.42 380 1.0
norm_taus[14] -0.23 0.05 0.95 -1.96 -0.92 -0.28 0.41 1.79 332 1.02
norm_taus[15] 6.3e-4 0.03 0.93 -1.85 -0.63 0.04 0.6 1.77 773 1.0
norm_taus[16] -0.11 0.04 0.95 -1.97 -0.71 -0.11 0.51 1.82 700 1.0
norm_taus[17] -0.12 0.04 0.88 -1.88 -0.72 -0.14 0.47 1.58 602 1.0
norm_taus[18] 0.21 0.07 1.02 -1.79 -0.46 0.23 0.88 2.17 216 1.02
norm_taus[19] -0.09 0.03 0.93 -1.88 -0.73 -0.11 0.54 1.84 953 1.01
norm_taus[20] -0.21 0.04 0.98 -2.1 -0.91 -0.2 0.44 1.72 729 1.0
norm_taus[21] 0.13 0.03 0.95 -1.8 -0.5 0.17 0.76 1.91 777 1.01
norm_taus[22] -0.28 0.04 0.97 -2.11 -0.97 -0.32 0.38 1.66 623 1.01
norm_taus[23] 0.28 0.04 0.94 -1.54 -0.38 0.25 0.93 2.12 539 1.0
norm_taus[24] -0.13 0.03 0.87 -1.84 -0.75 -0.14 0.44 1.55 634 1.0
norm_taus[25] -0.11 0.05 0.97 -1.98 -0.76 -0.11 0.58 1.73 372 1.01
norm_taus[26] -0.47 0.05 0.92 -2.27 -1.09 -0.48 0.15 1.37 369 1.0
norm_taus[27] 0.3 0.04 0.81 -1.4 -0.22 0.34 0.86 1.76 331 1.01
norm_taus[28] -0.29 0.08 1.04 -2.12 -1.01 -0.38 0.33 2.03 158 1.03
norm_taus[29] -0.32 0.04 0.93 -2.06 -0.94 -0.35 0.28 1.57 649 1.01
norm_taus[30] -0.35 0.04 0.95 -2.17 -1.01 -0.34 0.28 1.64 613 1.0
norm_taus[31] 0.39 0.05 0.99 -1.61 -0.29 0.42 1.1 2.25 408 1.01
norm_taus[32] -0.16 0.04 0.96 -2.08 -0.79 -0.17 0.48 1.71 574 1.0
norm_taus[33] -0.19 0.05 0.93 -2.08 -0.82 -0.18 0.48 1.51 406 1.01
norm_taus[34] 0.04 0.03 0.93 -1.94 -0.52 0.06 0.62 1.88 1052 1.0
norm_taus[35] -0.15 0.04 0.97 -2.05 -0.8 -0.18 0.47 1.8 489 1.01
norm_taus[36] -0.52 0.06 0.98 -2.32 -1.2 -0.57 0.12 1.53 237 1.02
norm_taus[37] 9.8e-3 0.05 0.8 -1.61 -0.5 0.02 0.52 1.53 309 1.01
norm_taus[38] -0.2 0.04 0.96 -2.11 -0.8 -0.18 0.4 1.79 498 1.01
norm_taus[39] 0.03 0.05 0.99 -2.07 -0.63 0.07 0.72 1.89 410 1.01
norm_taus[40] -0.1 0.05 0.91 -1.88 -0.72 -0.09 0.51 1.64 405 1.01
norm_taus[41] 0.31 0.05 0.96 -1.71 -0.3 0.35 0.96 2.06 454 1.01
norm_taus[42] -0.11 0.07 1.02 -2.08 -0.84 -0.09 0.6 1.88 192 1.02
norm_taus[43] 0.06 0.05 0.94 -1.85 -0.56 0.02 0.66 2.12 297 1.01
norm_taus[44] 0.02 0.04 0.98 -1.91 -0.65 0.03 0.67 1.94 608 1.01
norm_taus[45] -0.49 0.06 1.02 -2.43 -1.2 -0.56 0.19 1.57 288 1.01
norm_taus[46] 0.27 0.08 0.93 -1.48 -0.39 0.3 0.91 2.04 127 1.03
norm_taus[47] -0.1 0.03 0.91 -1.95 -0.71 -0.06 0.5 1.64 967 1.0
norm_taus[48] 0.09 0.07 0.91 -1.88 -0.49 0.1 0.68 1.85 187 1.02
norm_taus[49] 0.88 0.05 0.98 -1.24 0.26 0.95 1.53 2.59 469 1.01
norm_taus[50] -0.07 0.08 1.09 -2.3 -0.78 -0.05 0.69 1.96 193 1.02
norm_taus[51] -0.26 0.05 0.99 -2.46 -0.86 -0.21 0.36 1.64 346 1.01
norm_taus[52] 0.16 0.05 0.94 -1.7 -0.49 0.17 0.81 1.96 376 1.01
norm_taus[53] -0.2 0.04 0.97 -2.12 -0.85 -0.18 0.42 1.66 468 1.01
norm_taus[54] -0.19 0.04 0.96 -2.14 -0.84 -0.17 0.46 1.7 500 1.0
norm_taus[55] -0.37 0.04 0.91 -2.12 -0.97 -0.42 0.21 1.51 411 1.01
norm_taus[56] 0.12 0.03 0.95 -1.81 -0.48 0.11 0.74 2.0 1206 1.0
norm_taus[57] -0.33 0.03 0.91 -2.15 -0.94 -0.33 0.25 1.53 1240 1.0
norm_taus[58] 0.38 0.04 0.99 -1.58 -0.27 0.4 1.01 2.41 512 1.01
norm_taus[59] 0.39 0.06 0.95 -1.56 -0.24 0.39 1.03 2.13 277 1.01
norm_taus[60] 0.42 0.05 0.95 -1.55 -0.19 0.48 1.07 2.2 437 1.01
norm_taus[61] 0.03 0.05 0.91 -1.85 -0.56 0.07 0.62 1.78 363 1.01
norm_taus[62] -0.14 0.05 1.0 -2.1 -0.83 -0.13 0.58 1.73 368 1.01
norm_taus[63] -0.1 0.03 0.89 -1.81 -0.7 -0.08 0.44 1.74 777 1.0
norm_taus[64] 0.28 0.07 0.92 -1.59 -0.33 0.3 0.9 2.06 181 1.02
norm_taus[65] -0.03 0.04 1.01 -2.02 -0.72 -0.07 0.69 1.9 770 1.01
norm_taus[66] 0.1 0.03 0.91 -1.66 -0.52 0.09 0.71 1.94 780 1.01
norm_taus[67] -0.36 0.03 0.93 -2.15 -0.99 -0.37 0.23 1.56 966 1.0
norm_taus[68] 0.18 0.04 0.88 -1.6 -0.41 0.22 0.76 1.85 603 1.01
norm_taus[69] -0.05 0.05 0.97 -1.91 -0.7 -0.06 0.61 1.76 454 1.01
norm_taus[70]-9.8e-3 0.04 0.93 -1.88 -0.61 0.01 0.56 1.81 502 1.02
norm_taus[71] 0.3 0.03 0.85 -1.5 -0.25 0.33 0.9 1.89 610 1.01
norm_taus[72] 0.25 0.05 0.93 -1.64 -0.37 0.27 0.88 2.12 414 1.01
norm_taus[73] -0.33 0.04 1.0 -2.23 -1.01 -0.35 0.33 1.75 602 1.0
norm_taus[74] 0.29 0.04 0.92 -1.61 -0.29 0.32 0.89 2.09 562 1.01
norm_taus[75] 0.49 0.04 1.01 -1.57 -0.18 0.55 1.15 2.45 611 1.01
norm_taus[76] -0.27 0.08 0.99 -2.12 -0.93 -0.3 0.34 1.91 156 1.03
norm_taus[77] -0.01 0.07 1.03 -2.05 -0.69 7.7e-4 0.68 1.98 190 1.03
norm_taus[78] -0.13 0.03 0.91 -1.95 -0.74 -0.13 0.48 1.66 695 1.01
norm_taus[79] 0.47 0.05 0.89 -1.39 -0.05 0.5 0.98 2.34 357 1.01
norm_taus[80] -0.18 0.04 0.97 -2.11 -0.84 -0.2 0.42 1.79 546 1.0
norm_taus[81] -0.28 0.04 0.93 -2.07 -0.9 -0.28 0.33 1.58 641 1.01
norm_taus[82] 0.8 0.07 0.89 -1.06 0.23 0.8 1.42 2.47 164 1.02
norm_taus[83] 0.15 0.06 1.0 -1.77 -0.49 0.12 0.79 2.08 328 1.01
norm_taus[84] 0.83 0.05 0.93 -1.15 0.27 0.92 1.43 2.49 302 1.0
norm_taus[85]-6.8e-3 0.04 0.95 -1.9 -0.66-3.1e-3 0.66 1.83 611 1.01
norm_taus[86] -0.34 0.03 0.88 -2.17 -0.91 -0.32 0.2 1.43 1014 1.0
norm_taus[87] -0.06 0.05 0.93 -1.88 -0.69 -0.08 0.52 1.95 427 1.01
norm_taus[88] -0.12 0.03 0.92 -1.93 -0.73 -0.14 0.47 1.8 811 1.01
norm_taus[89] -0.23 0.05 0.97 -2.1 -0.86 -0.23 0.42 1.63 441 1.0
norm_taus[90] -0.01 0.09 1.02 -2.06 -0.73 0.06 0.71 1.84 135 1.03
norm_taus[91] -0.12 0.05 0.94 -2.0 -0.76 -0.1 0.5 1.81 406 1.01
norm_taus[92]-9.0e-3 0.04 0.91 -1.92 -0.63 -0.02 0.62 1.77 409 1.01
norm_taus[93] 0.05 0.06 0.99 -1.83 -0.64 0.03 0.69 2.17 315 1.0
norm_taus[94] -0.04 0.03 0.92 -1.86 -0.69 3.5e-3 0.59 1.76 934 1.0
norm_taus[95] -0.16 0.07 0.98 -2.13 -0.82 -0.13 0.52 1.78 199 1.02
norm_taus[96] 0.45 0.04 0.97 -1.56 -0.15 0.49 1.12 2.23 549 1.0
norm_taus[97] -0.42 0.03 0.89 -2.15 -1.0 -0.46 0.13 1.48 951 1.0
norm_taus[98] 0.43 0.04 0.94 -1.44 -0.18 0.45 1.09 2.18 457 1.01
norm_taus[99] -0.36 0.08 1.0 -2.39 -1.01 -0.38 0.3 1.69 160 1.03
As[0] 3.43 0.08 1.58 1.03 2.32 3.22 4.32 7.14 348 1.01
As[1] 3.01 0.17 1.67 0.64 1.8 2.78 3.96 7.31 97 1.03
As[2] 4.89 0.07 2.28 1.68 3.37 4.48 5.78 10.7 933 1.0
As[3] 4.45 0.08 2.35 1.07 2.88 4.04 5.54 10.24 818 1.0
As[4] 4.51 0.1 2.2 1.39 3.09 4.14 5.39 10.27 527 1.01
As[5] 3.86 0.12 2.09 0.95 2.47 3.49 4.92 8.55 315 1.02
As[6] 7.54 0.27 3.84 2.9 4.77 6.63 9.54 16.9 198 1.03
As[7] 3.44 0.15 1.92 0.81 2.14 3.16 4.24 8.54 166 1.02
As[8] 4.42 0.06 2.09 1.54 3.07 4.05 5.25 9.81 1107 1.0
As[9] 3.13 0.14 1.62 0.71 1.96 2.93 4.07 6.73 140 1.03
As[10] 3.64 0.07 1.86 0.96 2.39 3.38 4.55 8.12 666 1.01
As[11] 3.71 0.08 1.71 1.07 2.52 3.55 4.74 7.41 414 1.0
As[12] 3.03 0.14 1.65 0.68 1.85 2.81 3.94 6.76 148 1.02
As[13] 2.94 0.13 1.54 0.64 1.77 2.8 3.84 6.23 141 1.02
As[14] 3.28 0.14 1.66 0.79 2.13 3.09 4.14 7.08 138 1.03
As[15] 3.41 0.12 1.71 0.83 2.18 3.19 4.42 7.29 206 1.02
As[16] 3.31 0.16 1.63 0.8 2.12 3.1 4.23 7.07 101 1.04
As[17] 3.22 0.13 1.61 0.8 2.06 3.0 4.13 6.82 164 1.02
As[18] 3.6 0.13 1.81 0.87 2.33 3.33 4.53 8.32 208 1.01
As[19] 3.25 0.14 1.58 0.8 2.13 3.09 4.09 6.98 132 1.03
As[20] 3.06 0.12 1.49 0.72 1.92 2.92 4.0 6.14 145 1.02
As[21] 3.55 0.1 1.75 0.94 2.4 3.36 4.4 7.65 323 1.01
As[22] 3.02 0.12 1.5 0.71 1.91 2.83 3.98 6.1 149 1.02
As[23] 3.82 0.2 2.13 1.03 2.5 3.49 4.62 8.79 119 1.04
As[24] 3.62 0.12 1.84 0.9 2.39 3.28 4.62 7.91 219 1.01
As[25] 3.22 0.12 1.66 0.76 2.03 3.04 4.15 6.71 199 1.02
As[26] 2.98 0.16 1.57 0.64 1.79 2.77 3.92 6.4 101 1.03
As[27] 5.23 0.13 2.7 1.82 3.37 4.55 6.31 11.92 448 1.01
As[28] 3.18 0.15 1.69 0.72 1.93 2.96 4.13 7.14 120 1.02
As[29] 3.09 0.13 1.6 0.71 1.94 2.89 3.85 7.08 148 1.02
As[30] 3.38 0.14 1.83 0.72 2.09 3.09 4.29 7.58 170 1.02
As[31] 4.36 0.09 2.22 1.35 2.91 3.94 5.34 10.02 599 1.01
As[32] 3.32 0.18 1.76 0.78 2.13 3.07 4.17 7.53 94 1.04
As[33] 3.22 0.16 1.61 0.77 2.03 3.01 4.1 6.93 96 1.04
As[34] 3.73 0.16 1.91 0.99 2.42 3.41 4.65 8.24 138 1.03
As[35] 3.25 0.14 1.64 0.74 2.06 3.07 4.15 6.61 137 1.03
As[36] 2.84 0.17 1.57 0.57 1.65 2.59 3.77 6.17 83 1.05
As[37] 6.07 0.18 3.13 2.16 3.83 5.27 7.5 14.14 306 1.01
As[38] 3.21 0.14 1.58 0.76 2.08 3.01 4.15 6.57 120 1.03
As[39] 3.44 0.13 1.71 0.93 2.31 3.17 4.26 7.53 186 1.03
As[40] 3.76 0.15 2.0 0.91 2.45 3.45 4.65 8.62 180 1.02
As[41] 3.66 0.09 1.59 1.13 2.55 3.54 4.59 7.28 325 1.02
As[42] 3.23 0.12 1.82 0.74 1.98 2.93 4.11 8.31 212 1.02
As[43] 3.5 0.13 1.67 0.98 2.32 3.28 4.33 7.37 172 1.02
As[44] 3.34 0.1 1.56 0.9 2.24 3.2 4.16 6.94 253 1.01
As[45] 3.02 0.13 1.55 0.7 1.88 2.87 3.89 6.66 137 1.03
As[46] 4.04 0.08 1.8 1.3 2.81 3.76 4.99 8.43 528 1.01
As[47] 3.26 0.14 1.62 0.8 2.01 3.08 4.24 6.66 133 1.02
As[48] 3.94 0.06 1.87 1.09 2.71 3.67 4.86 8.77 837 1.0
As[49] 4.21 0.07 1.71 1.49 3.08 3.97 5.13 8.1 610 1.0
As[50] 3.25 0.13 1.67 0.82 2.03 3.04 4.21 7.0 153 1.03
As[51] 3.23 0.12 1.61 0.85 2.09 3.05 4.1 6.73 173 1.02
As[52] 3.78 0.14 1.88 0.94 2.5 3.57 4.78 7.78 192 1.02
As[53] 3.1 0.14 1.56 0.73 1.94 2.92 4.02 6.47 128 1.03
As[54] 3.16 0.15 1.64 0.75 1.98 2.93 4.07 6.9 113 1.03
As[55] 3.13 0.1 1.53 0.77 2.01 3.0 4.01 6.61 212 1.01
As[56] 3.55 0.09 1.64 0.93 2.39 3.32 4.53 7.46 364 1.01
As[57] 3.2 0.11 1.59 0.67 2.06 3.02 4.17 6.74 222 1.01
As[58] 3.79 0.14 1.83 1.07 2.58 3.53 4.66 8.49 163 1.02
As[59] 4.84 0.12 2.4 1.55 3.2 4.36 5.9 10.75 429 1.02
As[60] 4.53 0.1 2.17 1.47 3.03 4.14 5.51 9.89 519 1.01
As[61] 4.2 0.09 2.19 1.14 2.76 3.8 5.22 9.81 589 1.01
As[62] 3.42 0.15 1.78 0.88 2.15 3.19 4.38 7.45 150 1.02
As[63] 3.5 0.15 1.69 0.85 2.29 3.26 4.5 7.44 122 1.03
As[64] 4.05 0.12 1.93 1.18 2.74 3.76 5.04 9.14 280 1.01
As[65] 3.28 0.14 1.55 0.88 2.17 3.07 4.19 6.91 122 1.03
As[66] 4.12 0.15 2.12 1.1 2.78 3.77 5.06 10.04 210 1.02
As[67] 3.2 0.11 1.69 0.73 2.02 2.99 4.11 6.98 234 1.02
As[68] 5.05 0.16 2.9 1.45 3.24 4.3 6.02 13.03 325 1.02
As[69] 3.55 0.13 1.71 0.9 2.32 3.37 4.53 7.45 165 1.02
As[70] 3.53 0.12 1.67 0.94 2.38 3.36 4.37 7.26 191 1.02
As[71] 4.42 0.1 2.02 1.51 3.08 4.06 5.36 9.43 389 1.01
As[72] 4.37 0.08 2.28 1.26 2.9 3.95 5.3 10.68 844 1.0
As[73] 2.97 0.14 1.55 0.7 1.8 2.79 3.83 6.26 118 1.03
As[74] 3.88 0.08 1.83 1.02 2.69 3.61 4.89 8.2 522 1.0
As[75] 4.1 0.08 2.02 1.1 2.8 3.79 5.0 9.1 628 1.01
As[76] 3.12 0.12 1.57 0.68 1.93 3.02 4.04 6.67 178 1.02
As[77] 3.59 0.13 1.82 0.91 2.31 3.34 4.57 7.71 200 1.02
As[78] 3.23 0.14 1.66 0.73 2.03 3.05 4.17 7.11 133 1.03
As[79] 4.53 0.09 2.22 1.59 3.08 4.05 5.53 10.26 549 1.01
As[80] 3.2 0.12 1.63 0.7 2.04 3.01 4.08 7.23 186 1.02
As[81] 3.14 0.15 1.68 0.7 1.91 2.97 4.11 6.63 126 1.03
As[82] 6.23 0.13 2.98 2.43 4.16 5.6 7.49 14.0 544 1.01
As[83] 3.47 0.17 1.82 0.96 2.29 3.18 4.36 7.78 121 1.04
As[84] 5.64 0.15 2.83 2.04 3.66 4.95 6.77 13.17 364 1.01
As[85] 3.57 0.11 1.82 0.83 2.29 3.38 4.55 7.61 278 1.02
As[86] 3.33 0.17 1.74 0.74 2.06 3.13 4.24 7.55 111 1.03
As[87] 3.52 0.17 1.86 0.84 2.25 3.22 4.44 8.16 115 1.03
As[88] 3.42 0.15 1.79 0.8 2.16 3.1 4.42 7.49 142 1.02
As[89] 3.06 0.11 1.61 0.66 1.92 2.85 3.93 6.64 201 1.01
As[90] 3.67 0.08 1.86 0.89 2.43 3.35 4.66 8.0 556 1.01
As[91] 3.23 0.13 1.62 0.81 2.11 2.99 4.15 6.53 144 1.02
As[92] 4.61 0.12 2.4 1.31 2.99 4.2 5.77 10.61 402 1.01
As[93] 3.51 0.13 1.74 0.92 2.31 3.25 4.46 7.23 185 1.02
As[94] 3.14 0.1 1.63 0.76 1.94 2.93 4.04 6.6 289 1.01
As[95] 3.32 0.17 1.71 0.83 2.11 3.06 4.16 7.38 107 1.03
As[96] 3.89 0.1 1.8 1.17 2.66 3.56 4.79 8.11 310 1.02
As[97] 3.02 0.14 1.57 0.65 1.85 2.84 3.89 6.64 128 1.03
As[98] 4.14 0.07 1.93 1.31 2.83 3.82 5.11 8.82 699 1.01
As[99] 3.11 0.16 1.67 0.71 1.92 2.89 3.95 7.06 116 1.04
ts[0] -1.11 0.08 1.78 -3.75 -2.18 -1.25 -0.56 3.09 510 1.01
ts[1] -0.41 0.12 2.6 -5.54 -1.86 -0.76 0.57 5.7 439 1.01
ts[2] 0.67 0.04 0.87 -1.15 0.22 0.7 1.16 2.21 493 1.01
ts[3] -3.54 0.22 2.27 -6.27 -5.0 -4.45 -2.59 1.82 103 1.04
ts[4] -2.34 0.07 1.29 -4.37 -3.09 -2.64 -1.61 0.39 369 1.01
ts[5] -1.11 0.06 1.5 -4.04 -1.64 -1.02 -0.63 2.4 614 1.01
ts[6] 0.57 0.04 0.69 -1.05 0.17 0.7 1.08 1.54 338 1.02
ts[7] -1.3 0.12 1.99 -5.02 -2.25 -1.43 -0.68 4.1 271 1.01
ts[8] 1.43 0.18 1.94 -3.22 0.84 2.1 2.62 4.05 114 1.03
ts[9] 0.89 0.17 2.45 -5.49 -0.23 1.17 2.2 5.07 214 1.0
ts[10] -0.41 0.08 1.83 -5.33 -1.18 0.07 0.78 2.1 592 1.01
ts[11] 0.78 0.07 1.41 -2.97 0.2 0.89 1.67 2.75 441 1.01
ts[12] 0.17 0.24 2.84 -7.23 -1.22 0.35 1.77 5.17 142 1.03
ts[13] -0.75 0.29 2.98 -6.29 -2.81 -1.21 1.04 5.35 108 1.04
ts[14] -1.25 0.36 2.93 -5.72 -3.3 -1.89-3.8e-3 5.33 65 1.08
ts[15] 0.46 0.22 2.24 -5.52 -0.38 0.6 1.79 3.79 106 1.04
ts[16] 0.1 0.07 1.81 -3.88 -0.86 0.26 1.07 3.97 620 1.01
ts[17] -0.89 0.19 2.3 -5.63 -2.12 -1.1 -0.06 4.96 141 1.03
ts[18] 0.96 0.14 2.21 -3.7 -0.6 1.58 2.49 4.39 265 1.02
ts[19] -1.46 0.23 2.57 -6.9 -3.04 -2.16 0.05 4.01 122 1.04
ts[20] 0.19 0.21 2.74 -6.23 -1.32 0.65 1.9 4.86 178 1.02
ts[21] -0.04 0.14 1.88 -2.81 -1.14 -0.55 0.8 4.2 191 1.02
ts[22] 0.14 0.22 2.67 -5.48 -1.33 0.44 1.78 5.28 143 1.03
ts[23] -0.33 0.08 1.65 -3.31 -1.29 -0.49 0.43 3.78 438 1.01
ts[24] -1.32 0.18 2.56 -6.5 -2.37 -0.58 0.19 2.48 213 1.03
ts[25] 0.95 0.14 2.39 -4.17 -0.42 0.7 2.67 5.21 276 1.02
ts[26] -0.35 0.15 2.77 -5.04 -2.08 -0.79 0.91 6.34 331 1.01
ts[27] -0.6 0.03 0.78 -2.31 -1.09 -0.51 -0.03 0.62 735 1.0
ts[28] 0.13 0.21 3.06 -5.47 -2.36 0.23 2.49 5.61 206 1.02
ts[29] -0.5 0.36 3.0 -7.97 -1.71 -0.16 0.94 5.27 68 1.06
ts[30] -1.29 0.34 3.08 -5.68 -3.2 -2.5 0.35 5.99 80 1.04
ts[31] 0.46 0.08 1.53 -3.31 -0.5 0.83 1.57 2.62 355 1.02
ts[32] -0.09 0.26 2.41 -4.47 -1.38 -0.29 0.99 6.15 85 1.05
ts[33] -1.22 0.12 2.25 -5.88 -2.53 -0.91 0.04 3.24 354 1.01
ts[34] -0.26 0.07 1.57 -3.23 -1.21 -0.16 0.63 2.67 460 1.01
ts[35] -0.16 0.12 2.49 -5.38 -1.72 5.1e-3 1.49 4.58 439 1.01
ts[36] -2.8e-4 0.23 3.17 -6.62 -1.66 -0.29 2.03 5.93 184 1.02
ts[37] -2.59 0.06 0.66 -4.33 -2.76 -2.47 -2.22 -1.82 118 1.03
ts[38] -0.01 0.11 2.29 -4.4 -1.42 -0.18 1.06 5.21 448 1.01
ts[39] 0.84 0.07 1.66 -2.99 0.06 0.79 1.76 4.13 642 1.01
ts[40] -0.77 0.12 1.64 -4.98 -1.53 -0.44 0.02 2.56 174 1.03
ts[41] -0.93 0.09 1.99 -4.19 -2.26 -1.24 0.17 3.45 480 1.0
ts[42] 0.92 0.22 2.63 -4.61 -0.78 0.68 3.24 5.04 139 1.04
ts[43] -1.23 0.2 2.01 -6.4 -2.0 -1.11 -0.25 2.65 101 1.02
ts[44] -0.35 0.12 2.22 -3.61 -2.04 -0.91 1.28 4.4 343 1.01
ts[45] -0.72 0.27 2.81 -6.61 -2.18 -0.91 0.89 5.07 107 1.04
ts[46] 0.52 0.21 1.39 -1.84 -0.08 0.34 0.81 4.39 44 1.11
ts[47] 0.06 0.21 2.56 -6.04 -1.71 0.65 1.7 4.67 149 1.03
ts[48] -0.08 0.04 1.23 -2.98 -0.45-2.7e-3 0.4 2.56 836 1.0
ts[49] -0.94 0.14 1.94 -4.4 -2.39 -0.99 0.44 2.95 188 1.02
ts[50] 0.67 0.15 2.51 -4.82 -1.04 1.04 2.49 5.05 290 1.0
ts[51] -0.01 0.13 2.06 -3.86 -0.92-1.7e-3 0.84 5.19 261 1.01
ts[52] 0.96 0.18 2.16 -5.21 0.35 1.51 2.14 3.96 149 1.02
ts[53] -0.33 0.3 3.2 -5.93 -2.92 -0.29 1.82 6.51 114 1.04
ts[54] -0.52 0.2 2.47 -4.78 -2.05 -0.92 0.81 4.79 155 1.03
ts[55] -0.93 0.18 2.15 -4.87 -2.05 -1.17 -0.21 5.02 144 1.03
ts[56] 0.02 0.15 2.03 -4.55 -0.85 0.56 1.17 3.47 184 1.02
ts[57] -0.69 0.22 2.79 -4.49 -2.76 -1.75 1.51 5.18 168 1.02
ts[58] -0.59 0.16 2.37 -5.17 -2.38 -0.28 1.25 3.58 211 1.02
ts[59] -1.74 0.11 1.36 -4.73 -2.68 -1.32 -0.76 0.04 150 1.03
ts[60] -1.49 0.1 1.46 -4.12 -2.34 -1.46 -0.82 2.75 197 1.02
ts[61] -2.4 0.16 1.96 -4.57 -3.44 -2.89 -2.06 4.12 157 1.02
ts[62] -0.67 0.11 2.19 -3.82 -2.05 -1.4 0.6 4.67 417 1.01
ts[63] -1.09 0.07 1.78 -5.05 -2.05 -1.29 -0.3 3.25 747 1.01
ts[64] 0.85 0.16 1.38 -2.05 0.16 0.94 1.59 4.2 72 1.04
ts[65] 0.14 0.24 2.36 -4.57 -1.54 0.22 1.65 5.18 101 1.04
ts[66] -2.59 0.14 1.98 -5.01 -3.7 -2.98 -2.23 4.0 188 1.02
ts[67] -1.47 0.2 2.84 -5.97 -3.18 -2.42 0.14 5.24 204 1.02
ts[68] -1.65 0.1 1.13 -3.45 -2.16 -1.67 -1.23 1.06 116 1.05
ts[69] -1.69 0.16 2.5 -5.85 -3.57 -1.97 -0.14 4.16 237 1.01
ts[70] -0.31 0.07 1.49 -4.01 -0.85 -0.09 0.46 2.31 481 1.0
ts[71] -0.62 0.03 0.81 -2.06 -1.18 -0.7 -0.05 0.92 617 1.0
ts[72] 0.35 0.22 1.95 -4.17 -0.2 1.09 1.58 2.68 78 1.06
ts[73] -0.35 0.25 3.21 -7.24 -2.35 0.04 1.71 5.66 163 1.03
ts[74] -0.34 0.1 1.63 -3.08 -1.45 -0.42 0.65 3.56 245 1.01
ts[75] -0.08 0.17 2.09 -4.5 -1.1 0.48 1.15 3.57 151 1.02
ts[76] -0.37 0.29 2.73 -5.54 -1.91 -0.99 1.02 5.87 89 1.04
ts[77] -0.64 0.12 2.21 -4.27 -1.98 -1.28 0.82 4.11 325 1.0
ts[78] 0.91 0.24 2.36 -4.0 -0.07 1.08 2.07 6.27 96 1.05
ts[79] -0.8 0.06 1.0 -2.49 -1.41 -0.98 -0.34 1.93 262 1.01
ts[80] -0.62 0.29 2.95 -5.86 -3.32 -0.4 1.63 5.05 100 1.05
ts[81] -0.72 0.19 2.81 -5.54 -3.37 -0.29 1.16 4.92 227 1.0
ts[82] 2.15 0.03 0.77 0.48 1.79 2.19 2.56 3.52 537 1.0
ts[83] -0.42 0.28 2.56 -5.92 -1.92 -0.02 1.18 3.71 81 1.06
ts[84] 0.5 0.07 1.14 -2.34 -0.05 0.58 1.3 2.2 297 1.0
ts[85] -1.42 0.34 2.62 -4.97 -3.58 -1.82 0.32 4.48 60 1.07
ts[86] -1.5 0.14 2.1 -4.27 -2.61 -1.91 -1.11 4.85 222 1.01
ts[87] 1.0 0.08 1.64 -3.25 0.38 1.25 1.91 3.53 427 1.03
ts[88] -0.84 0.13 2.18 -5.16 -1.84 -1.07 0.19 3.97 294 1.01
ts[89] 0.2 0.22 3.09 -5.44 -2.11 -0.32 3.19 5.4 198 1.01
ts[90] -1.36 0.19 2.08 -5.72 -2.48 -1.61 -0.64 3.06 119 1.03
ts[91] -0.93 0.18 2.27 -4.77 -2.32 -1.41 0.7 3.58 167 1.04
ts[92] -1.48 0.05 0.94 -3.35 -1.88 -1.54 -1.19 1.08 397 1.01
ts[93] -0.44 0.43 2.9 -5.55 -2.33 -0.28 0.62 7.78 46 1.1
ts[94] 0.68 0.1 2.0 -3.5 -0.22 0.75 1.58 4.93 420 1.01
ts[95] -0.14 0.11 2.3 -5.24 -1.31 -0.17 1.6 3.91 410 1.01
ts[96] 0.97 0.11 1.75 -2.45 -0.32 1.05 2.32 3.81 251 1.02
ts[97] -1.3 0.21 2.84 -6.42 -3.18 -1.8 0.5 5.01 179 1.02
ts[98] -0.19 0.08 1.34 -2.54 -1.02 -0.31 0.57 2.88 290 1.02
ts[99] 0.17 0.15 2.68 -5.38 -1.58 0.16 1.83 5.37 312 1.01
taus[0] 1.66 0.06 1.42 0.33 0.95 1.41 1.97 4.65 479 1.02
taus[1] 1.22 0.04 0.92 0.2 0.66 1.06 1.53 3.47 453 1.01
taus[2] 2.2 0.08 1.63 0.58 1.29 1.79 2.57 6.51 420 1.02
taus[3] 1.63 0.04 0.95 0.47 1.03 1.46 1.99 4.04 556 1.01
taus[4] 1.99 0.07 1.66 0.54 1.16 1.64 2.28 5.46 579 1.0
taus[5] 1.4 0.05 0.94 0.36 0.84 1.21 1.71 3.55 398 1.01
taus[6] 2.33 0.09 1.7 0.8 1.4 1.89 2.7 6.48 351 1.02
taus[7] 1.31 0.04 0.92 0.21 0.75 1.19 1.64 3.19 474 1.01
taus[8] 2.7 0.22 3.35 0.59 1.27 1.79 2.78 10.93 229 1.04
taus[9] 1.55 0.08 1.35 0.26 0.78 1.26 1.85 5.19 257 1.02
taus[10] 1.67 0.07 1.71 0.41 0.94 1.35 1.94 4.64 536 1.0
taus[11] 1.58 0.04 1.0 0.4 0.94 1.38 1.93 4.0 524 1.01
taus[12] 1.29 0.08 1.04 0.23 0.63 1.11 1.66 3.67 180 1.02
taus[13] 1.2 0.06 0.85 0.16 0.61 1.05 1.54 3.26 214 1.01
taus[14] 1.44 0.1 1.58 0.27 0.74 1.2 1.71 3.95 274 1.02
taus[15] 1.58 0.05 1.22 0.35 0.96 1.38 1.91 4.02 500 1.01
taus[16] 1.49 0.06 1.13 0.24 0.82 1.29 1.84 4.05 422 1.01
taus[17] 1.44 0.06 0.96 0.34 0.81 1.26 1.78 3.8 283 1.01
taus[18] 2.07 0.21 3.82 0.26 1.05 1.56 2.24 6.14 327 1.01
taus[19] 1.52 0.07 1.38 0.31 0.84 1.25 1.8 4.31 379 1.01
taus[20] 1.42 0.06 1.16 0.23 0.76 1.21 1.73 4.05 398 1.01
taus[21] 1.77 0.07 1.38 0.38 1.0 1.46 2.1 5.76 372 1.01
taus[22] 1.34 0.08 1.07 0.27 0.7 1.14 1.69 3.78 193 1.02
taus[23] 2.19 0.3 3.6 0.46 1.06 1.55 2.28 6.98 149 1.02
taus[24] 1.42 0.05 0.88 0.33 0.88 1.26 1.76 3.62 310 1.01
taus[25] 1.49 0.05 1.08 0.24 0.84 1.26 1.85 4.04 444 1.02
taus[26] 1.18 0.05 0.78 0.22 0.64 1.03 1.55 3.19 228 1.01
taus[27] 1.83 0.04 1.16 0.65 1.19 1.6 2.17 4.37 761 1.0
taus[28] 1.36 0.1 1.15 0.21 0.68 1.13 1.68 4.41 144 1.03
taus[29] 1.29 0.05 0.99 0.22 0.72 1.13 1.64 3.31 345 1.01
taus[30] 1.3 0.05 1.03 0.21 0.7 1.11 1.62 3.51 373 1.01
taus[31] 2.29 0.17 2.67 0.41 1.15 1.61 2.54 7.92 236 1.01
taus[32] 1.41 0.04 0.89 0.26 0.84 1.26 1.78 3.52 416 1.01
taus[33] 1.39 0.03 0.81 0.28 0.83 1.27 1.76 3.25 614 1.01
taus[34] 1.62 0.05 1.25 0.35 0.94 1.39 1.95 4.3 583 1.01
taus[35] 1.54 0.11 1.53 0.29 0.77 1.22 1.81 5.42 180 1.03
taus[36] 1.17 0.08 0.9 0.17 0.59 1.01 1.52 3.26 142 1.02
taus[37] 1.54 0.1 1.0 0.55 1.01 1.34 1.77 4.1 107 1.04
taus[38] 1.39 0.07 0.95 0.18 0.8 1.2 1.73 3.82 201 1.01
taus[39] 1.66 0.1 1.35 0.25 0.94 1.38 2.0 4.82 185 1.02
taus[40] 1.51 0.06 1.2 0.29 0.83 1.29 1.83 4.11 362 1.01
taus[41] 1.98 0.07 1.5 0.42 1.13 1.64 2.3 5.83 516 1.01
taus[42] 1.57 0.07 1.34 0.27 0.81 1.31 1.91 4.67 378 1.02
taus[43] 1.62 0.06 1.21 0.39 0.95 1.35 1.91 4.75 389 1.01
taus[44] 1.64 0.06 1.37 0.27 0.9 1.38 1.95 4.74 485 1.01
taus[45] 1.21 0.07 0.98 0.16 0.56 1.04 1.57 3.55 187 1.01
taus[46] 1.86 0.09 1.39 0.41 1.11 1.58 2.17 5.21 229 1.02
taus[47] 1.48 0.05 1.15 0.32 0.86 1.28 1.77 3.89 514 1.01
taus[48] 1.73 0.06 1.72 0.3 1.0 1.43 2.02 4.87 764 1.01
taus[49] 3.02 0.13 2.88 0.73 1.49 2.23 3.51 10.99 507 1.01
taus[50] 1.75 0.16 2.09 0.19 0.84 1.34 1.93 7.71 163 1.03
taus[51] 1.38 0.06 1.14 0.16 0.78 1.18 1.69 3.87 405 1.02
taus[52] 1.85 0.1 1.73 0.33 1.01 1.45 2.12 6.93 284 1.01
taus[53] 1.4 0.06 1.0 0.25 0.79 1.24 1.74 3.79 306 1.02
taus[54] 1.43 0.07 1.28 0.26 0.81 1.22 1.7 3.91 366 1.0
taus[55] 1.24 0.07 0.9 0.24 0.68 1.11 1.59 3.12 169 1.02
taus[56] 1.76 0.08 2.0 0.39 0.97 1.42 2.0 5.42 623 1.01
taus[57] 1.27 0.04 0.8 0.3 0.73 1.12 1.62 3.26 325 1.01
taus[58] 2.3 0.16 2.79 0.45 1.13 1.62 2.45 9.01 307 1.02
taus[59] 2.19 0.15 2.34 0.57 1.16 1.65 2.44 7.31 237 1.01
taus[60] 2.17 0.1 2.18 0.45 1.19 1.68 2.45 6.92 444 1.01
taus[61] 1.55 0.04 1.35 0.35 1.0 1.37 1.86 3.61 1218 1.0
taus[62] 1.5 0.05 1.17 0.2 0.84 1.28 1.79 4.33 492 1.01
taus[63] 1.46 0.06 1.17 0.35 0.83 1.25 1.72 3.97 408 1.01
taus[64] 2.0 0.23 1.91 0.44 1.06 1.55 2.2 7.95 66 1.06
taus[65] 1.57 0.08 1.15 0.29 0.83 1.34 1.97 4.47 231 1.02
taus[66] 1.76 0.08 1.85 0.38 0.99 1.42 2.02 5.23 568 1.01
taus[67] 1.27 0.06 1.1 0.24 0.72 1.1 1.56 3.21 385 1.01
taus[68] 1.8 0.08 1.86 0.42 1.08 1.5 2.09 4.2 535 1.01
taus[69] 1.6 0.06 1.82 0.28 0.85 1.33 1.87 4.69 786 1.0
taus[70] 1.54 0.05 1.11 0.36 0.94 1.32 1.84 3.91 552 1.01
taus[71] 1.82 0.06 1.06 0.56 1.17 1.57 2.15 4.49 284 1.0
taus[72] 2.12 0.23 2.61 0.53 1.06 1.48 2.16 7.96 126 1.03
taus[73] 1.29 0.06 1.05 0.18 0.7 1.13 1.65 3.4 357 1.02
taus[74] 1.98 0.08 1.5 0.46 1.15 1.59 2.26 6.16 351 1.01
taus[75] 2.49 0.14 2.63 0.48 1.21 1.74 2.67 9.99 360 1.02
taus[76] 1.39 0.11 1.18 0.25 0.74 1.16 1.69 4.3 123 1.03
taus[77] 1.72 0.11 1.87 0.24 0.89 1.35 1.99 5.82 274 1.02
taus[78] 1.46 0.07 1.05 0.26 0.81 1.24 1.78 4.18 241 1.02
taus[79] 2.15 0.1 1.94 0.64 1.28 1.73 2.44 5.88 359 1.01
taus[80] 1.49 0.11 1.4 0.26 0.79 1.2 1.74 4.83 157 1.03
taus[81] 1.34 0.05 1.01 0.23 0.75 1.17 1.64 3.66 339 1.01
taus[82] 2.56 0.17 1.67 0.87 1.48 2.08 3.1 7.13 100 1.04
taus[83] 2.01 0.24 2.44 0.34 0.99 1.46 2.13 8.26 99 1.05
taus[84] 2.79 0.14 2.33 0.83 1.54 2.12 3.19 9.01 267 1.01
taus[85] 1.58 0.05 1.18 0.31 0.9 1.33 1.9 4.79 510 1.0
taus[86] 1.25 0.04 0.98 0.27 0.74 1.09 1.56 3.15 736 1.01
taus[87] 1.55 0.05 1.27 0.3 0.86 1.31 1.89 4.38 557 1.0
taus[88] 1.46 0.07 1.02 0.31 0.83 1.26 1.78 3.81 225 1.02
taus[89] 1.47 0.13 1.35 0.26 0.75 1.2 1.72 4.87 106 1.03
taus[90] 1.68 0.08 1.67 0.25 0.93 1.39 1.97 5.12 406 1.01
taus[91] 1.48 0.06 1.19 0.31 0.83 1.25 1.75 4.29 398 1.01
taus[92] 1.51 0.05 0.96 0.29 0.94 1.36 1.86 3.73 411 1.01
taus[93] 1.82 0.11 2.14 0.33 0.91 1.38 2.04 5.96 355 1.0
taus[94] 1.5 0.04 0.98 0.34 0.91 1.33 1.81 4.0 711 1.0
taus[95] 1.52 0.1 1.34 0.17 0.81 1.27 1.82 4.42 191 1.02
taus[96] 2.22 0.1 2.07 0.5 1.2 1.74 2.53 6.95 391 1.01
taus[97] 1.25 0.06 1.3 0.25 0.63 1.07 1.57 3.19 428 1.01
taus[98] 2.12 0.09 1.58 0.51 1.19 1.7 2.51 6.44 317 1.01
taus[99] 1.32 0.07 1.36 0.16 0.64 1.12 1.65 3.62 355 1.01
flare[0,0] 7.7e-3 2.4e-3 0.081.9e-552.9e-199.3e-12 3.8e-7 0.01 1237 1.0
flare[1,0] 0.02 2.9e-3 0.111.5e-1201.0e-311.9e-188.8e-11 0.25 1580 1.0
flare[2,0] 9.8e-4 4.0e-4 0.023.9e-487.1e-201.0e-13 3.0e-9 1.8e-3 1547 1.0
flare[3,0] 0.58 0.04 0.98.8e-49 6.1e-8 0.07 0.93 3.01 560 1.01
flare[4,0] 0.02 4.3e-3 0.131.4e-301.4e-10 1.8e-6 3.2e-4 0.18 930 1.0
flare[5,0] 9.6e-3 2.3e-3 0.092.0e-546.9e-224.5e-14 3.0e-9 0.02 1434 1.0
flare[6,0] 9.5e-4 3.4e-4 0.011.8e-335.0e-187.1e-13 2.0e-8 2.8e-3 949 1.01
flare[7,0] 0.03 5.5e-3 0.173.0e-1125.8e-233.1e-13 4.0e-8 0.29 1003 1.0
flare[8,0] 0.01 6.8e-3 0.091.4e-531.8e-242.3e-16 1.2e-8 0.13 174 1.02
flare[9,0] 0.03 0.01 0.219.1e-961.2e-323.9e-202.3e-12 0.56 206 1.01
flare[10,0] 0.05 7.5e-3 0.251.4e-592.4e-241.5e-15 9.5e-9 0.68 1076 1.0
flare[11,0] 0.01 3.9e-3 0.133.8e-691.1e-276.1e-181.9e-12 1.6e-4 1014 1.0
flare[12,0] 0.02 9.1e-3 0.157.2e-1172.3e-365.4e-201.4e-11 0.32 278 1.01
flare[13,0] 0.05 0.01 0.236.6e-1312.1e-305.8e-17 1.1e-8 0.86 363 1.01
flare[14,0] 0.07 7.2e-3 0.276.5e-778.9e-247.9e-13 1.1e-5 1.07 1444 1.0
flare[15,0] 0.01 9.6e-3 0.11.8e-893.2e-272.2e-171.6e-11 0.11 104 1.04
flare[16,0] 0.01 2.4e-3 0.112.4e-931.2e-282.8e-174.4e-11 7.9e-3 1999 1.0
flare[17,0] 0.04 0.02 0.231.7e-641.9e-232.9e-14 2.3e-8 0.82 88 1.04
flare[18,0] 9.1e-3 2.7e-3 0.092.2e-741.2e-251.2e-161.7e-10 0.01 1207 1.0
flare[19,0] 0.02 6.4e-3 0.143.7e-553.0e-203.3e-11 4.8e-6 0.25 495 1.01
flare[20,0] 0.06 0.02 0.262.8e-1036.6e-303.4e-196.2e-11 1.0 126 1.03
flare[21,0] 6.5e-3 1.9e-3 0.087.6e-569.9e-232.4e-14 3.5e-9 3.0e-3 1629 1.0
flare[22,0] 0.05 0.02 0.241.2e-971.1e-315.3e-194.0e-11 0.74 172 1.03
flare[23,0] 4.2e-3 1.2e-3 0.031.0e-507.3e-215.9e-13 5.6e-8 0.03 848 1.0
flare[24,0] 0.23 0.03 0.581.6e-841.0e-221.2e-13 1.3e-5 2.0 338 1.02
flare[25,0] 0.01 2.5e-3 0.11.6e-1062.5e-332.4e-201.7e-12 0.01 1601 1.0
flare[26,0] 0.02 3.7e-3 0.111.3e-953.1e-322.0e-187.1e-11 0.09 929 1.0
flare[27,0] 4.8e-4 1.4e-4 8.7e-31.2e-343.1e-176.2e-12 3.2e-8 8.2e-4 3770 1.0
flare[28,0] 0.03 4.1e-3 0.194.0e-1292.4e-322.2e-186.9e-10 0.46 2106 1.0
flare[29,0] 0.02 3.9e-3 0.155.6e-852.6e-302.1e-175.1e-10 0.28 1504 1.0
flare[30,0] 0.06 6.9e-3 0.263.8e-982.0e-252.7e-12 5.7e-6 1.02 1376 1.0
flare[31,0] 9.9e-3 2.4e-3 0.099.0e-624.3e-231.1e-14 5.0e-8 0.04 1325 1.0
flare[32,0] 0.01 2.7e-3 0.117.8e-1024.9e-282.0e-166.9e-10 0.03 1649 1.0
flare[33,0] 0.08 0.02 0.327.0e-698.0e-232.0e-13 2.6e-7 1.25 285 1.01
flare[34,0] 7.8e-3 2.2e-3 0.091.8e-733.8e-236.3e-15 1.2e-9 3.8e-3 1625 1.0
flare[35,0] 0.03 4.8e-3 0.179.6e-861.8e-297.6e-17 2.9e-9 0.42 1276 1.0
flare[36,0] 0.04 0.01 0.173.1e-1213.7e-362.3e-217.3e-12 0.57 228 1.01
flare[37,0] 0.03 0.02 0.184.6e-222.8e-11 3.7e-8 6.9e-6 0.15 92 1.04
flare[38,0] 0.01 3.1e-3 0.126.2e-1161.1e-282.4e-177.8e-11 0.02 1548 1.0
flare[39,0] 4.6e-3 1.3e-3 0.062.0e-1173.6e-285.0e-188.0e-12 5.2e-4 2298 1.0
flare[40,0] 0.03 0.01 0.185.6e-712.3e-247.1e-15 8.6e-9 0.35 153 1.03
flare[41,0] 0.02 2.7e-3 0.137.9e-532.9e-173.7e-10 5.7e-6 0.11 2510 1.0
flare[42,0] 0.02 5.6e-3 0.137.5e-806.1e-322.8e-195.7e-12 0.02 584 1.01
flare[43,0] 0.04 0.02 0.215.8e-512.7e-186.6e-12 3.3e-7 0.6 109 1.03
flare[44,0] 7.4e-3 1.9e-3 0.072.2e-784.1e-255.3e-14 3.6e-8 0.01 1422 1.0
flare[45,0] 0.05 0.02 0.231.3e-1241.7e-325.3e-17 4.6e-9 0.86 182 1.04
flare[46,0] 3.7e-3 3.2e-3 0.061.2e-911.3e-218.8e-152.9e-10 3.9e-4 302 1.01
flare[47,0] 0.03 0.01 0.192.4e-712.3e-278.7e-182.6e-10 0.44 319 1.01
flare[48,0] 2.4e-3 7.2e-4 0.032.4e-751.3e-223.0e-153.9e-10 2.8e-3 2264 1.0
flare[49,0] 0.04 4.3e-3 0.159.8e-397.8e-15 2.0e-7 2.0e-3 0.42 1184 1.0
flare[50,0] 0.02 4.1e-3 0.142.5e-1085.9e-301.9e-187.5e-11 0.14 1172 1.0
flare[51,0] 6.5e-3 1.8e-3 0.071.6e-1016.0e-295.1e-187.7e-12 3.3e-3 1421 1.0
flare[52,0] 0.02 8.3e-3 0.152.6e-671.3e-278.3e-186.2e-11 0.26 305 1.01
flare[53,0] 0.07 7.4e-3 0.284.6e-766.0e-287.4e-16 8.7e-8 1.06 1452 1.0
flare[54,0] 0.02 3.4e-3 0.139.8e-1161.2e-262.6e-15 1.9e-8 0.13 1446 1.0
flare[55,0] 0.02 3.5e-3 0.142.5e-883.4e-293.6e-15 3.4e-9 0.21 1639 1.0
flare[56,0] 0.02 7.9e-3 0.121.2e-593.6e-248.3e-16 2.5e-9 0.11 237 1.02
flare[57,0] 0.02 2.8e-3 0.131.2e-915.2e-283.4e-14 4.5e-8 0.05 2099 1.0
flare[58,0] 0.06 0.02 0.269.8e-565.9e-203.7e-11 3.4e-5 1.04 245 1.02
flare[59,0] 0.04 0.02 0.26.7e-344.3e-15 4.9e-9 2.3e-4 0.55 99 1.04
flare[60,0] 0.01 2.1e-3 0.095.4e-391.6e-14 4.9e-9 5.0e-5 0.15 1889 1.0
flare[61,0] 0.02 4.7e-3 0.162.0e-531.5e-11 9.1e-7 2.1e-4 0.21 1173 1.0
flare[62,0] 8.9e-3 1.9e-3 0.11.5e-1149.4e-246.5e-14 1.9e-8 9.2e-3 2608 1.0
flare[63,0] 0.03 0.01 0.172.0e-638.3e-223.9e-13 1.6e-8 0.35 233 1.02
flare[64,0] 2.2e-3 6.0e-4 0.035.2e-654.0e-262.7e-161.4e-10 8.6e-3 3032 1.0
flare[65,0] 0.01 9.2e-3 0.117.0e-982.4e-273.4e-16 1.8e-9 0.08 146 1.02
flare[66,0] 0.06 9.5e-3 0.251.5e-521.7e-11 4.3e-6 2.0e-3 0.72 671 1.01
flare[67,0] 0.05 0.02 0.246.0e-902.0e-237.2e-12 2.1e-6 0.89 249 1.01
flare[68,0] 5.3e-3 1.8e-3 0.061.1e-422.2e-14 1.4e-9 3.3e-6 0.01 1141 1.0
flare[69,0] 0.08 0.01 0.322.0e-837.5e-20 1.1e-9 2.6e-4 1.18 676 1.0
flare[70,0] 8.8e-3 2.5e-3 0.085.6e-624.8e-231.1e-152.6e-10 0.01 1090 1.0
flare[71,0] 2.7e-4 1.1e-4 4.0e-31.1e-385.5e-174.3e-12 2.1e-8 6.4e-4 1409 1.0
flare[72,0] 0.02 4.5e-3 0.111.9e-506.7e-245.3e-16 1.1e-8 0.16 600 1.01
flare[73,0] 0.07 8.9e-3 0.289.4e-1099.3e-333.8e-18 8.5e-9 0.99 1030 1.0
flare[74,0] 7.7e-3 2.5e-3 0.081.6e-555.2e-192.2e-12 9.8e-8 0.01 1067 1.0
flare[75,0] 0.04 0.02 0.213.1e-551.5e-192.0e-12 2.8e-6 0.39 108 1.04
flare[76,0] 0.03 6.5e-3 0.25.5e-862.1e-282.1e-168.8e-10 0.56 902 1.01
flare[77,0] 0.02 3.6e-3 0.141.8e-937.9e-238.2e-13 1.7e-7 0.04 1415 1.0
flare[78,0] 8.5e-3 2.1e-3 0.083.8e-891.5e-321.2e-204.2e-13 4.7e-3 1373 1.0
flare[79,0] 2.0e-3 5.8e-4 0.021.6e-334.8e-152.2e-10 5.3e-7 6.6e-3 1345 1.0
flare[80,0] 0.05 0.01 0.231.5e-866.5e-261.9e-14 1.5e-6 0.69 329 1.01
flare[81,0] 0.04 7.3e-3 0.221.2e-822.3e-271.6e-15 7.0e-7 0.7 890 1.0
flare[82,0] 2.0e-4 1.1e-4 3.8e-35.3e-372.4e-215.0e-157.2e-10 4.0e-4 1222 1.0
flare[83,0] 0.06 0.02 0.251.0e-787.9e-228.8e-13 1.4e-6 0.86 253 1.02
flare[84,0] 5.8e-3 2.1e-3 0.042.1e-341.0e-162.0e-11 5.7e-7 0.04 421 1.01
flare[85,0] 0.04 5.6e-3 0.25.1e-631.4e-195.2e-10 8.7e-5 0.54 1262 1.0
flare[86,0] 0.01 2.6e-3 0.111.1e-673.3e-225.7e-13 7.3e-8 0.03 1800 1.0
flare[87,0] 4.2e-3 1.8e-3 0.064.5e-987.6e-322.8e-203.8e-13 4.2e-4 971 1.01
flare[88,0] 0.04 0.01 0.241.4e-585.6e-233.3e-14 1.4e-8 0.7 276 1.01
flare[89,0] 0.02 4.2e-3 0.151.3e-852.1e-291.9e-17 1.1e-9 0.41 1227 1.0
flare[90,0] 0.04 0.02 0.28.7e-683.7e-196.5e-11 1.8e-6 0.7 100 1.04
flare[91,0] 0.03 0.01 0.177.7e-654.4e-241.4e-13 8.6e-8 0.17 136 1.03
flare[92,0] 5.3e-3 1.5e-3 0.076.2e-659.4e-173.4e-11 7.7e-8 4.7e-3 2234 1.0
flare[93,0] 0.05 0.03 0.255.3e-982.7e-233.8e-13 7.3e-7 0.87 88 1.06
flare[94,0] 5.6e-3 1.5e-3 0.073.7e-797.7e-291.6e-181.7e-12 1.0e-3 1974 1.0
flare[95,0] 0.04 9.2e-3 0.247.3e-1235.1e-282.2e-16 1.3e-9 0.64 668 1.01
flare[96,0] 2.4e-3 9.6e-4 0.041.7e-569.2e-231.1e-14 6.2e-9 3.9e-3 1360 1.0
flare[97,0] 0.03 6.6e-3 0.177.4e-1004.3e-251.0e-13 5.4e-7 0.54 700 1.0
flare[98,0] 2.7e-3 1.2e-3 0.049.4e-542.0e-188.7e-12 1.2e-7 5.4e-3 1194 1.0
flare[99,0] 0.03 4.7e-3 0.178.2e-1461.7e-341.6e-191.5e-11 0.5 1370 1.0
flare[0,1] 0.01 2.2e-3 0.092.7e-501.0e-163.8e-10 4.9e-6 0.06 1592 1.0
flare[1,1] 0.02 2.9e-3 0.126.2e-1121.4e-282.4e-16 3.3e-9 0.29 1586 1.0
flare[2,1] 1.6e-3 6.5e-4 0.021.7e-443.8e-181.6e-12 2.2e-8 3.7e-3 1110 1.0
flare[3,1] 0.95 0.06 1.051.3e-44 2.7e-6 0.73 1.6 3.46 355 1.02
flare[4,1] 0.04 8.0e-3 0.193.4e-27 9.4e-9 3.6e-5 3.0e-3 0.48 548 1.01
flare[5,1] 0.02 3.1e-3 0.127.1e-493.3e-192.6e-12 6.3e-8 0.1 1422 1.0
flare[6,1] 1.5e-3 5.3e-4 0.017.9e-311.7e-169.7e-12 1.3e-7 6.4e-3 670 1.01
flare[7,1] 0.04 6.5e-3 0.199.0e-1023.6e-202.2e-11 8.6e-7 0.56 870 1.01
flare[8,1] 0.02 8.5e-3 0.117.6e-509.4e-234.1e-15 8.2e-8 0.23 155 1.03
flare[9,1] 0.03 0.01 0.178.7e-884.8e-301.8e-184.1e-11 0.6 181 1.02
flare[10,1] 0.06 0.01 0.272.0e-543.9e-224.9e-14 1.1e-7 0.99 693 1.01
flare[11,1] 0.01 4.7e-3 0.136.2e-642.3e-252.1e-162.7e-11 9.3e-4 760 1.0
flare[12,1] 0.03 8.0e-3 0.161.2e-1063.0e-333.5e-184.2e-10 0.46 382 1.01
flare[13,1] 0.06 9.7e-3 0.232.3e-1191.8e-271.5e-14 4.5e-7 0.82 577 1.01
flare[14,1] 0.1 8.3e-3 0.341.7e-712.6e-217.9e-11 3.1e-4 1.27 1636 1.0
flare[15,1] 0.01 7.9e-3 0.091.5e-838.2e-258.6e-162.5e-10 0.08 129 1.03
flare[16,1] 0.01 2.8e-3 0.122.8e-865.3e-261.4e-156.5e-10 0.05 1798 1.0
flare[17,1] 0.05 0.03 0.247.0e-588.7e-211.6e-12 4.0e-7 0.89 63 1.06
flare[18,1] 0.01 2.8e-3 0.11.5e-671.4e-233.2e-15 2.2e-9 0.07 1357 1.0
flare[19,1] 0.03 5.5e-3 0.171.2e-508.7e-18 2.5e-9 1.1e-4 0.42 975 1.0
flare[20,1] 0.06 0.02 0.263.3e-971.4e-272.6e-17 1.1e-9 1.04 127 1.03
flare[21,1] 8.9e-3 2.5e-3 0.112.5e-511.4e-207.4e-13 4.9e-8 0.01 1845 1.0
flare[22,1] 0.05 0.02 0.232.2e-901.1e-283.4e-17 1.1e-9 0.83 156 1.03
flare[23,1] 7.9e-3 1.7e-3 0.052.9e-466.9e-191.5e-11 6.3e-7 0.09 955 1.0
flare[24,1] 0.26 0.04 0.589.1e-791.7e-203.6e-12 9.7e-5 1.92 269 1.02
flare[25,1] 0.02 6.0e-3 0.152.5e-961.6e-301.3e-184.0e-11 0.1 622 1.01
flare[26,1] 0.02 2.7e-3 0.111.9e-871.3e-282.2e-16 2.9e-9 0.18 1592 1.0
flare[27,1] 10.0e-4 2.2e-4 0.012.1e-311.8e-151.3e-10 3.4e-7 3.0e-3 3196 1.0
flare[28,1] 0.04 4.9e-3 0.23.5e-1201.2e-292.3e-16 3.5e-8 0.71 1672 1.0
flare[29,1] 0.03 4.5e-3 0.172.5e-781.3e-271.1e-15 1.4e-8 0.55 1401 1.0
flare[30,1] 0.1 0.01 0.355.0e-881.9e-223.7e-10 1.8e-4 1.32 1121 1.0
flare[31,1] 0.01 3.0e-3 0.116.4e-573.1e-212.2e-13 3.6e-7 0.1 1303 1.0
flare[32,1] 0.02 3.4e-3 0.152.9e-952.4e-251.5e-14 1.0e-8 0.17 1881 1.0
flare[33,1] 0.09 0.02 0.334.2e-612.5e-201.2e-11 4.8e-6 1.27 362 1.01
flare[34,1] 9.3e-3 2.4e-3 0.095.6e-681.0e-202.5e-13 1.7e-8 0.02 1474 1.0
flare[35,1] 0.04 5.3e-3 0.24.9e-791.4e-266.8e-15 6.6e-8 0.64 1464 1.0
flare[36,1] 0.03 5.5e-3 0.151.7e-1085.2e-335.3e-193.2e-10 0.43 707 1.01
flare[37,1] 0.04 0.02 0.232.1e-18 3.5e-9 1.4e-6 1.2e-4 0.74 112 1.03
flare[38,1] 0.02 2.8e-3 0.129.5e-1074.6e-261.4e-15 1.8e-9 0.19 1935 1.0
flare[39,1] 6.5e-3 1.6e-3 0.081.4e-1086.5e-261.9e-161.1e-10 2.1e-3 2193 1.0
flare[40,1] 0.03 0.01 0.183.0e-659.8e-223.1e-13 1.3e-7 0.36 211 1.02
flare[41,1] 0.03 4.5e-3 0.191.4e-481.8e-15 9.3e-9 5.9e-5 0.29 1763 1.0
flare[42,1] 0.01 5.0e-3 0.116.0e-731.1e-299.2e-181.3e-10 0.1 493 1.01
flare[43,1] 0.04 0.02 0.21.4e-464.2e-162.1e-10 4.6e-6 0.71 145 1.02
flare[44,1] 9.7e-3 1.9e-3 0.082.0e-699.0e-232.4e-12 6.5e-7 0.04 1631 1.0
flare[45,1] 0.06 0.01 0.231.0e-1103.6e-295.1e-15 1.2e-7 0.84 264 1.03
flare[46,1] 4.4e-3 3.6e-3 0.063.2e-861.0e-192.0e-13 2.7e-9 1.2e-3 312 1.01
flare[47,1] 0.03 8.3e-3 0.161.6e-666.8e-254.2e-16 6.1e-9 0.45 365 1.01
flare[48,1] 4.3e-3 9.5e-4 0.058.1e-711.9e-201.1e-13 4.3e-9 0.01 2520 1.0
flare[49,1] 0.07 7.1e-3 0.223.4e-362.6e-13 1.6e-6 8.6e-3 0.75 922 1.0
flare[50,1] 0.03 6.0e-3 0.191.3e-1002.5e-271.1e-16 1.6e-9 0.44 1025 1.0
flare[51,1] 9.6e-3 2.2e-3 0.095.1e-832.0e-263.4e-161.7e-10 0.02 1579 1.0
flare[52,1] 0.03 4.8e-3 0.168.6e-621.5e-252.1e-167.0e-10 0.43 1147 1.0
flare[53,1] 0.09 0.01 0.329.5e-691.8e-254.6e-14 2.6e-6 1.23 692 1.0
flare[54,1] 0.03 4.4e-3 0.161.3e-1053.5e-241.9e-13 3.7e-7 0.47 1391 1.0
flare[55,1] 0.03 4.1e-3 0.152.6e-803.8e-263.6e-13 8.5e-8 0.53 1405 1.0
flare[56,1] 0.02 6.2e-3 0.122.1e-544.4e-223.0e-14 3.0e-8 0.23 394 1.01
flare[57,1] 0.02 3.2e-3 0.131.1e-842.4e-253.2e-12 1.9e-6 0.27 1763 1.0
flare[58,1] 0.09 0.02 0.321.0e-514.1e-187.1e-10 3.2e-4 1.32 299 1.01
flare[59,1] 0.06 0.02 0.255.6e-303.0e-13 8.8e-8 1.8e-3 0.95 118 1.03
flare[60,1] 0.03 3.6e-3 0.166.7e-348.8e-1310.0e-8 3.7e-4 0.44 1925 1.0
flare[61,1] 0.05 6.1e-3 0.221.5e-48 2.1e-9 3.6e-5 3.7e-3 0.72 1293 1.0
flare[62,1] 0.01 2.0e-3 0.15.2e-1033.0e-214.0e-12 4.2e-7 0.06 2602 1.0
flare[63,1] 0.03 0.01 0.171.5e-573.5e-192.6e-11 3.1e-7 0.42 248 1.02
flare[64,1] 4.0e-3 9.9e-4 0.054.4e-605.3e-246.9e-15 1.5e-9 0.02 2951 1.0
flare[65,1] 0.01 5.5e-3 0.091.4e-905.0e-251.1e-14 3.3e-8 0.1 273 1.01
flare[66,1] 0.13 0.01 0.364.9e-47 2.4e-9 1.2e-4 0.03 1.35 744 1.0
flare[67,1] 0.06 0.01 0.241.3e-802.1e-20 1.1e-9 7.9e-5 0.95 359 1.01
flare[68,1] 8.9e-3 2.0e-3 0.086.3e-381.9e-12 3.4e-8 3.4e-5 0.05 1414 1.0
flare[69,1] 0.14 0.02 0.43.3e-761.2e-17 6.7e-8 5.6e-3 1.46 376 1.01
flare[70,1] 0.01 4.2e-3 0.12.7e-561.1e-204.7e-14 4.2e-9 0.07 593 1.0
flare[71,1] 9.7e-4 4.8e-4 0.037.8e-353.5e-151.1e-10 2.3e-7 2.0e-3 3698 1.0
flare[72,1] 0.03 7.2e-3 0.148.1e-476.0e-221.1e-14 1.4e-7 0.35 395 1.01
flare[73,1] 0.08 9.5e-3 0.291.2e-979.9e-302.6e-16 2.2e-7 1.11 958 1.01
flare[74,1] 9.7e-3 3.0e-3 0.096.7e-514.2e-175.3e-11 9.9e-7 0.04 797 1.0
flare[75,1] 0.05 0.02 0.237.3e-515.8e-182.8e-11 2.2e-5 0.72 124 1.03
flare[76,1] 0.04 6.5e-3 0.192.3e-762.3e-251.4e-14 1.5e-8 0.68 827 1.01
flare[77,1] 0.02 4.8e-3 0.151.7e-851.7e-204.1e-11 2.2e-6 0.25 981 1.0
flare[78,1] 0.01 2.1e-3 0.099.1e-835.3e-307.4e-197.2e-12 0.04 1872 1.0
flare[79,1] 3.7e-3 9.9e-4 0.043.7e-302.4e-13 4.1e-9 4.1e-6 0.02 1409 1.0
flare[80,1] 0.08 0.02 0.287.4e-771.4e-237.3e-13 1.0e-4 1.06 271 1.01
flare[81,1] 0.07 0.01 0.286.8e-766.3e-251.0e-13 5.0e-5 1.0 513 1.0
flare[82,1] 3.0e-4 1.5e-4 5.3e-31.6e-348.2e-205.6e-14 3.6e-9 8.2e-4 1174 1.0
flare[83,1] 0.08 0.02 0.283.1e-725.3e-202.2e-11 1.4e-5 1.06 211 1.02
flare[84,1] 9.0e-3 3.2e-3 0.066.7e-322.2e-152.2e-10 2.7e-6 0.08 346 1.01
flare[85,1] 0.09 8.9e-3 0.311.2e-561.7e-17 3.3e-8 2.4e-3 1.19 1228 1.0
flare[86,1] 0.02 2.4e-3 0.121.5e-614.2e-195.3e-11 2.7e-6 0.18 2297 1.0
flare[87,1] 6.3e-3 2.2e-3 0.079.5e-912.0e-291.4e-187.2e-12 2.5e-3 1098 1.01
flare[88,1] 0.06 0.02 0.261.1e-543.4e-201.6e-12 3.4e-7 1.04 226 1.01
flare[89,1] 0.03 4.4e-3 0.163.7e-804.6e-271.9e-15 2.7e-8 0.55 1339 1.0
flare[90,1] 0.05 0.02 0.234.9e-618.1e-17 3.1e-9 2.4e-5 0.98 93 1.04
flare[91,1] 0.04 0.02 0.24.1e-591.3e-211.2e-11 2.3e-6 0.6 126 1.03
flare[92,1] 0.01 2.7e-3 0.15.3e-581.5e-14 1.3e-9 1.1e-6 0.02 1375 1.0
flare[93,1] 0.06 0.02 0.261.3e-936.9e-211.2e-11 8.6e-6 0.94 119 1.04
flare[94,1] 8.4e-3 2.2e-3 0.085.6e-742.0e-267.0e-173.3e-11 5.9e-3 1448 1.0
flare[95,1] 0.06 0.01 0.289.3e-1121.7e-258.8e-15 2.3e-8 0.88 418 1.01
flare[96,1] 3.1e-3 8.6e-4 0.041.4e-524.3e-212.3e-13 4.8e-8 8.5e-3 1738 1.0
flare[97,1] 0.04 5.1e-3 0.21.8e-911.7e-221.8e-11 2.8e-5 0.73 1546 1.0
flare[98,1] 3.3e-3 1.2e-3 0.042.8e-492.4e-161.5e-10 1.1e-6 0.01 1095 1.0
flare[99,1] 0.04 4.5e-3 0.184.7e-1291.7e-311.3e-174.2e-10 0.61 1623 1.0
flare[0,2] 0.02 2.9e-3 0.131.5e-462.0e-14 1.6e-8 7.7e-5 0.26 1950 1.0
flare[1,2] 0.03 4.4e-3 0.141.2e-1021.8e-252.7e-14 1.3e-7 0.39 930 1.0
flare[2,2] 2.5e-3 1.0e-3 0.039.0e-411.9e-162.8e-11 1.5e-7 7.9e-3 1041 1.0
flare[3,2] 1.07 0.07 1.011.0e-40 8.0e-5 1.03 1.74 3.35 198 1.02
flare[4,2] 0.1 0.01 0.34.8e-24 4.5e-7 8.7e-4 0.03 1.02 498 1.01
flare[5,2] 0.03 3.6e-3 0.141.5e-431.3e-161.6e-10 1.2e-6 0.42 1588 1.0
flare[6,2] 2.4e-3 8.5e-4 0.023.8e-285.3e-151.3e-10 8.8e-7 0.01 503 1.01
flare[7,2] 0.06 0.01 0.232.7e-912.6e-17 1.4e-9 2.3e-5 0.87 506 1.01
flare[8,2] 0.03 0.01 0.142.7e-464.9e-216.8e-14 5.5e-7 0.42 162 1.02
flare[9,2] 0.03 9.8e-3 0.142.1e-781.9e-278.8e-175.4e-10 0.48 209 1.01
flare[10,2] 0.07 0.01 0.282.8e-495.4e-202.0e-12 1.1e-6 1.08 676 1.01
flare[11,2] 0.01 4.8e-3 0.111.0e-583.8e-237.0e-154.2e-10 4.7e-3 569 1.0
flare[12,2] 0.03 6.3e-3 0.164.4e-983.9e-302.2e-16 9.7e-9 0.51 637 1.0
flare[13,2] 0.07 9.2e-3 0.257.3e-1081.5e-241.9e-12 3.4e-5 0.95 734 1.0
flare[14,2] 0.15 0.01 0.396.5e-655.6e-19 7.3e-9 8.4e-3 1.41 1401 1.0
flare[15,2] 0.01 6.5e-3 0.082.6e-771.3e-223.3e-14 3.9e-9 0.12 172 1.02
flare[16,2] 0.02 3.3e-3 0.134.0e-781.3e-236.9e-14 1.1e-8 0.17 1576 1.0
flare[17,2] 0.06 0.03 0.251.3e-512.4e-181.8e-10 8.2e-6 0.94 63 1.07
flare[18,2] 0.02 3.5e-3 0.135.6e-631.4e-219.1e-14 3.4e-8 0.23 1426 1.0
flare[19,2] 0.06 6.7e-3 0.234.0e-462.7e-15 2.1e-7 2.4e-3 0.88 1213 1.0
flare[20,2] 0.06 0.02 0.232.7e-883.9e-251.6e-15 2.1e-8 0.87 168 1.02
flare[21,2] 10.0e-3 2.5e-3 0.15.9e-471.8e-183.0e-11 5.6e-7 0.03 1630 1.0
flare[22,2] 0.07 0.02 0.252.0e-837.8e-261.8e-15 3.1e-8 0.95 148 1.03
flare[23,2] 0.02 3.0e-3 0.15.7e-429.7e-173.9e-10 6.9e-6 0.2 1007 1.0
flare[24,2] 0.22 0.03 0.481.5e-723.5e-181.2e-10 6.3e-4 1.58 267 1.02
flare[25,2] 0.02 4.9e-3 0.151.6e-875.1e-285.9e-176.5e-10 0.3 933 1.0
flare[26,2] 0.02 2.7e-3 0.135.2e-783.5e-254.3e-14 1.9e-7 0.34 2227 1.0
flare[27,2] 2.6e-3 5.2e-4 0.034.8e-281.0e-13 2.9e-9 3.4e-6 0.01 2730 1.0
flare[28,2] 0.05 5.6e-3 0.221.5e-1107.8e-272.1e-14 1.2e-6 0.81 1552 1.0
flare[29,2] 0.06 6.9e-3 0.241.3e-711.4e-247.1e-14 3.2e-7 0.77 1166 1.0
flare[30,2] 0.16 0.01 0.421.9e-793.3e-19 7.0e-8 4.7e-3 1.47 909 1.01
flare[31,2] 0.02 3.8e-3 0.131.2e-512.1e-194.9e-12 2.6e-6 0.22 1131 1.0
flare[32,2] 0.03 4.0e-3 0.171.7e-878.1e-239.7e-13 1.6e-7 0.47 1808 1.0
flare[33,2] 0.11 0.02 0.323.0e-558.5e-186.3e-10 8.6e-5 1.19 421 1.01
flare[34,2] 0.01 2.6e-3 0.12.2e-621.4e-181.0e-11 2.6e-7 0.08 1485 1.0
flare[35,2] 0.05 5.7e-3 0.222.2e-731.2e-234.5e-13 1.2e-6 0.78 1560 1.0
flare[36,2] 0.03 4.0e-3 0.143.3e-968.0e-304.9e-17 1.4e-8 0.5 1226 1.0
flare[37,2] 0.07 0.03 0.311.2e-14 4.0e-7 5.2e-5 1.9e-3 1.26 140 1.02
flare[38,2] 0.03 4.4e-3 0.176.6e-972.0e-237.7e-14 3.7e-8 0.51 1509 1.0
flare[39,2] 7.6e-3 1.6e-3 0.086.9e-1019.9e-247.2e-15 1.7e-9 8.2e-3 2175 1.0
flare[40,2] 0.03 7.6e-3 0.195.5e-583.6e-191.3e-11 1.9e-6 0.5 603 1.01
flare[41,2] 0.06 6.9e-3 0.246.3e-441.2e-13 1.9e-7 5.8e-4 0.7 1254 1.0
flare[42,2] 0.02 8.7e-3 0.162.5e-664.0e-273.8e-16 3.2e-9 0.19 348 1.01
flare[43,2] 0.06 0.01 0.248.5e-434.7e-14 7.2e-9 6.4e-5 0.89 280 1.01
flare[44,2] 0.02 2.5e-3 0.112.6e-622.3e-201.1e-10 1.1e-5 0.15 1922 1.0
flare[45,2] 0.06 0.01 0.241.6e-978.8e-269.8e-13 4.1e-6 0.93 401 1.01
flare[46,2] 4.0e-3 2.6e-3 0.059.8e-819.2e-184.9e-12 2.7e-8 3.3e-3 352 1.01
flare[47,2] 0.03 5.5e-3 0.174.9e-611.8e-221.8e-14 1.6e-7 0.52 934 1.01
flare[48,2] 8.8e-3 1.8e-3 0.081.1e-642.9e-183.7e-12 5.3e-8 0.04 2146 1.0
flare[49,2] 0.12 0.01 0.329.2e-348.3e-12 1.4e-5 0.04 1.28 728 1.01
flare[50,2] 0.04 7.6e-3 0.212.0e-896.2e-254.9e-15 3.1e-8 0.71 748 1.0
flare[51,2] 0.01 3.3e-3 0.12.3e-721.5e-232.4e-14 5.2e-9 0.12 957 1.0
flare[52,2] 0.04 6.4e-3 0.197.8e-571.8e-235.7e-15 7.6e-9 0.67 847 1.01
flare[53,2] 0.1 0.01 0.311.5e-623.8e-233.6e-1210.0e-5 1.16 629 1.0
flare[54,2] 0.06 6.2e-3 0.226.4e-961.4e-211.2e-11 8.8e-6 0.87 1306 1.0
flare[55,2] 0.04 4.4e-3 0.181.4e-724.9e-233.7e-11 2.3e-6 0.67 1692 1.0
flare[56,2] 0.03 4.5e-3 0.167.2e-504.7e-2010.0e-13 5.6e-7 0.46 1304 1.0
flare[57,2] 0.04 4.1e-3 0.182.6e-781.1e-224.2e-10 9.1e-5 0.57 1985 1.0
flare[58,2] 0.13 0.02 0.392.0e-471.9e-16 1.0e-8 3.2e-3 1.52 339 1.01
flare[59,2] 0.11 0.02 0.312.6e-262.1e-11 1.7e-6 0.01 1.34 169 1.02
flare[60,2] 0.07 9.3e-3 0.261.3e-296.5e-11 2.1e-6 2.9e-3 0.98 796 1.01
flare[61,2] 0.17 0.02 0.418.9e-44 2.4e-7 1.2e-3 0.07 1.5 745 1.0
flare[62,2] 0.02 2.7e-3 0.146.7e-939.5e-192.7e-10 7.6e-6 0.24 2550 1.0
flare[63,2] 0.03 9.3e-3 0.161.8e-527.4e-17 1.4e-9 6.2e-6 0.5 294 1.01
flare[64,2] 5.5e-3 1.3e-3 0.063.2e-555.6e-221.6e-13 1.5e-8 0.04 2093 1.0
flare[65,2] 0.02 4.0e-3 0.122.4e-841.8e-225.0e-13 4.6e-7 0.23 944 1.0
flare[66,2] 0.3 0.02 0.572.0e-42 3.6e-7 3.6e-3 0.31 1.98 588 1.0
flare[67,2] 0.09 0.01 0.295.7e-716.1e-18 1.5e-7 2.5e-3 1.06 588 1.01
flare[68,2] 0.02 5.4e-3 0.134.0e-331.8e-10 9.3e-7 3.6e-4 0.18 583 1.01
flare[69,2] 0.24 0.03 0.521.7e-681.7e-15 3.0e-6 0.1 1.88 276 1.01
flare[70,2] 0.02 6.1e-3 0.138.3e-511.6e-182.1e-12 6.7e-8 0.2 484 1.01
flare[71,2] 1.9e-3 6.5e-4 0.043.7e-312.4e-13 2.5e-9 2.4e-6 6.5e-3 3098 1.0
flare[72,2] 0.06 0.02 0.248.1e-435.3e-202.2e-13 1.7e-6 0.87 208 1.02
flare[73,2] 0.09 9.5e-3 0.294.6e-851.1e-261.7e-14 5.9e-6 1.06 931 1.01
flare[74,2] 0.01 3.5e-3 0.11.1e-462.4e-15 1.4e-9 1.1e-5 0.1 782 1.0
flare[75,2] 0.08 0.02 0.291.0e-452.3e-163.8e-10 2.4e-4 1.2 167 1.02
flare[76,2] 0.04 6.4e-3 0.25.4e-682.6e-229.1e-13 4.4e-7 0.72 933 1.01
flare[77,2] 0.06 0.02 0.284.8e-772.1e-18 2.3e-9 3.0e-5 0.83 274 1.01
flare[78,2] 0.01 2.2e-3 0.18.3e-772.9e-274.2e-171.5e-10 0.13 2265 1.0
flare[79,2] 6.8e-3 1.6e-3 0.062.5e-271.2e-11 7.5e-8 3.4e-5 0.05 1237 1.0
flare[80,2] 0.17 0.04 0.435.6e-702.9e-213.0e-11 5.6e-3 1.52 142 1.03
flare[81,2] 0.15 0.03 0.381.4e-681.7e-225.3e-12 4.7e-3 1.52 225 1.01
flare[82,2] 4.6e-4 2.1e-4 7.2e-34.2e-322.5e-186.5e-13 1.8e-8 1.6e-3 1132 1.0
flare[83,2] 0.1 0.02 0.322.0e-673.3e-184.0e-10 2.0e-4 1.22 262 1.02
flare[84,2] 0.01 4.9e-3 0.093.1e-295.4e-14 2.1e-9 1.2e-5 0.13 311 1.01
flare[85,2] 0.23 0.03 0.529.2e-521.5e-15 1.2e-6 0.08 1.84 418 1.01
flare[86,2] 0.04 5.6e-3 0.192.2e-554.0e-16 6.6e-9 8.3e-5 0.6 1181 1.0
flare[87,2] 7.6e-3 1.9e-3 0.073.0e-831.6e-266.0e-171.4e-10 0.02 1323 1.0
flare[88,2] 0.08 0.02 0.291.2e-509.0e-189.4e-11 5.6e-6 1.15 206 1.02
flare[89,2] 0.05 6.5e-3 0.26.3e-741.3e-241.8e-13 9.6e-7 0.79 978 1.0
flare[90,2] 0.06 0.02 0.241.0e-541.0e-14 1.2e-7 3.6e-4 1.01 101 1.04
flare[91,2] 0.06 0.02 0.261.2e-535.1e-197.3e-10 5.6e-5 0.91 146 1.03
flare[92,2] 0.02 4.9e-3 0.158.9e-512.7e-12 4.7e-8 1.6e-5 0.1 913 1.0
flare[93,2] 0.08 0.02 0.297.3e-891.5e-183.8e-10 1.3e-4 1.06 182 1.02
flare[94,2] 0.01 2.8e-3 0.112.7e-685.4e-243.1e-156.0e-10 0.04 1493 1.0
flare[95,2] 0.07 0.02 0.31.6e-1007.6e-234.2e-13 3.3e-7 1.06 268 1.02
flare[96,2] 5.3e-3 1.1e-3 0.058.0e-492.1e-193.9e-12 4.1e-7 0.02 1995 1.0
flare[97,2] 0.09 0.01 0.36.4e-868.3e-20 4.1e-9 1.2e-3 1.11 513 1.01
flare[98,2] 5.1e-3 1.5e-3 0.051.9e-441.6e-14 2.7e-9 8.0e-6 0.03 1019 1.0
flare[99,2] 0.05 5.3e-3 0.221.5e-1161.3e-288.2e-16 1.7e-8 0.75 1697 1.0
flare[0,3] 0.07 9.4e-3 0.266.1e-423.7e-12 6.7e-7 1.4e-3 0.98 752 1.0
flare[1,3] 0.04 7.0e-3 0.193.0e-921.8e-224.2e-12 5.6e-6 0.66 730 1.0
flare[2,3] 3.7e-3 1.3e-3 0.044.8e-378.5e-154.6e-10 1.0e-6 0.02 1001 1.0
flare[3,3] 0.95 0.06 0.851.3e-36 3.5e-3 0.92 1.5 2.83 201 1.02
flare[4,3] 0.27 0.02 0.547.5e-21 2.7e-5 0.02 0.25 1.89 592 1.0
flare[5,3] 0.05 5.5e-3 0.23.0e-383.4e-14 8.1e-9 2.7e-5 0.74 1329 1.0
flare[6,3] 4.4e-3 1.4e-3 0.031.5e-251.9e-13 1.7e-9 5.6e-6 0.03 470 1.01
flare[7,3] 0.08 0.02 0.271.0e-801.0e-14 1.0e-7 5.4e-4 1.08 185 1.02
flare[8,3] 0.04 0.01 0.189.1e-432.1e-191.0e-12 3.2e-6 0.64 227 1.02
flare[9,3] 0.03 6.5e-3 0.121.8e-726.7e-254.9e-15 5.9e-9 0.39 359 1.01
flare[10,3] 0.08 0.01 0.274.7e-449.1e-186.6e-11 1.6e-5 1.03 691 1.0
flare[11,3] 0.01 5.2e-3 0.115.2e-546.7e-212.4e-13 6.1e-9 0.03 450 1.01
flare[12,3] 0.03 4.6e-3 0.162.8e-895.6e-272.1e-14 2.1e-7 0.51 1176 1.0
flare[13,3] 0.11 0.01 0.33.9e-981.0e-212.2e-10 2.4e-3 1.15 722 1.01
flare[14,3] 0.23 0.02 0.483.9e-581.6e-16 9.3e-7 0.18 1.58 941 1.0
flare[15,3] 0.01 5.2e-3 0.099.8e-712.6e-201.4e-12 5.2e-8 0.15 321 1.01
flare[16,3] 0.02 3.5e-3 0.141.7e-696.7e-213.6e-12 1.9e-7 0.28 1525 1.0
flare[17,3] 0.07 0.03 0.251.1e-457.0e-16 1.3e-8 1.8e-4 0.97 81 1.05
flare[18,3] 0.03 4.0e-3 0.152.0e-581.3e-192.2e-12 4.9e-7 0.45 1398 1.0
flare[19,3] 0.14 9.3e-3 0.361.1e-413.0e-13 1.9e-5 0.04 1.32 1457 1.0
flare[20,3] 0.06 0.01 0.213.6e-809.2e-238.6e-14 3.7e-7 0.79 272 1.01
flare[21,3] 0.01 2.7e-3 0.113.9e-434.5e-16 1.0e-9 6.2e-6 0.08 1658 1.0
flare[22,3] 0.07 0.02 0.266.2e-763.4e-231.5e-13 5.6e-7 0.94 132 1.03
flare[23,3] 0.03 5.4e-3 0.156.1e-381.1e-14 1.1e-8 6.3e-5 0.46 819 1.0
flare[24,3] 0.18 0.02 0.397.0e-665.2e-16 3.1e-9 4.7e-3 1.31 274 1.02
flare[25,3] 0.03 3.9e-3 0.143.0e-771.7e-252.8e-15 1.2e-8 0.44 1317 1.0
flare[26,3] 0.05 4.3e-3 0.21.0e-704.6e-227.9e-12 1.1e-5 0.68 2143 1.0
flare[27,3] 7.4e-3 1.4e-3 0.066.8e-256.2e-12 5.5e-8 3.6e-5 0.04 2163 1.0
flare[28,3] 0.08 6.6e-3 0.266.4e-1037.1e-241.1e-12 5.5e-5 0.94 1626 1.0
flare[29,3] 0.09 0.01 0.39.3e-649.1e-224.9e-12 4.9e-6 1.1 476 1.01
flare[30,3] 0.21 0.02 0.461.5e-692.0e-16 2.0e-5 0.11 1.57 838 1.01
flare[31,3] 0.03 5.1e-3 0.156.8e-471.3e-171.2e-10 1.7e-5 0.39 861 1.0
flare[32,3] 0.05 5.7e-3 0.219.5e-792.0e-205.6e-11 2.8e-6 0.72 1404 1.0
flare[33,3] 0.13 0.01 0.343.7e-491.4e-15 2.9e-8 1.6e-3 1.19 571 1.01
flare[34,3] 0.02 3.0e-3 0.125.4e-572.3e-163.7e-10 3.9e-6 0.2 1663 1.0
flare[35,3] 0.08 8.2e-3 0.293.8e-673.0e-212.5e-11 2.3e-5 1.01 1206 1.0
flare[36,3] 0.04 4.8e-3 0.176.7e-857.0e-279.8e-15 4.4e-7 0.59 1301 1.0
flare[37,3] 0.14 0.03 0.418.0e-11 4.4e-5 2.0e-3 0.03 1.49 240 1.01
flare[38,3] 0.04 8.8e-3 0.229.9e-858.5e-214.9e-12 8.0e-7 0.6 605 1.01
flare[39,3] 0.01 2.1e-3 0.11.4e-921.6e-212.9e-13 2.1e-8 0.04 2160 1.0
flare[40,3] 0.05 8.0e-3 0.221.0e-501.1e-166.2e-10 2.8e-5 0.74 719 1.01
flare[41,3] 0.11 0.01 0.341.1e-386.9e-12 4.0e-6 6.8e-3 1.27 1118 1.0
flare[42,3] 0.03 6.7e-3 0.155.1e-611.4e-242.3e-14 7.9e-8 0.34 484 1.0
flare[43,3] 0.09 0.01 0.38.1e-384.3e-12 2.1e-7 8.0e-4 1.19 503 1.0
flare[44,3] 0.04 4.2e-3 0.181.1e-513.6e-18 5.2e-9 2.2e-4 0.47 1741 1.0
flare[45,3] 0.09 0.01 0.282.9e-841.4e-228.8e-11 7.1e-5 1.06 463 1.0
flare[46,3] 5.5e-3 3.1e-3 0.064.7e-768.9e-161.1e-10 2.6e-7 0.01 433 1.01
flare[47,3] 0.05 6.5e-3 0.218.9e-564.0e-209.9e-13 3.8e-6 0.63 1066 1.0
flare[48,3] 0.02 3.2e-3 0.127.7e-594.3e-161.2e-10 7.4e-7 0.15 1315 1.0
flare[49,3] 0.2 0.02 0.443.7e-312.2e-10 1.0e-4 0.13 1.66 579 1.01
flare[50,3] 0.04 7.7e-3 0.192.7e-813.2e-222.9e-13 5.7e-7 0.68 643 1.0
flare[51,3] 0.03 4.9e-3 0.151.3e-641.5e-201.7e-12 1.5e-7 0.47 947 1.0
flare[52,3] 0.04 8.7e-3 0.196.8e-522.3e-211.3e-13 8.5e-8 0.73 476 1.01
flare[53,3] 0.13 0.02 0.333.6e-587.7e-211.6e-10 5.0e-3 1.19 451 1.0
flare[54,3] 0.09 9.5e-3 0.312.5e-887.0e-197.8e-10 1.6e-4 1.11 1087 1.0
flare[55,3] 0.06 5.8e-3 0.231.1e-656.4e-20 3.1e-9 6.3e-5 0.91 1638 1.0
flare[56,3] 0.05 6.4e-3 0.215.3e-455.5e-182.7e-11 7.9e-6 0.73 1111 1.0
flare[57,3] 0.09 6.7e-3 0.281.4e-713.7e-20 6.0e-8 4.0e-3 1.05 1761 1.0
flare[58,3] 0.2 0.03 0.472.3e-431.1e-14 1.5e-7 0.03 1.67 301 1.01
flare[59,3] 0.22 0.03 0.481.2e-22 1.5e-9 2.5e-5 0.1 1.65 196 1.02
flare[60,3] 0.13 0.02 0.381.0e-24 3.7e-9 3.3e-5 0.02 1.49 533 1.01
flare[61,3] 0.44 0.04 0.722.2e-38 2.5e-5 0.04 0.68 2.32 363 1.01
flare[62,3] 0.04 4.7e-3 0.24.5e-833.1e-16 2.3e-8 1.5e-4 0.65 1877 1.0
flare[63,3] 0.04 7.7e-3 0.172.0e-472.8e-14 7.8e-8 1.3e-4 0.62 476 1.01
flare[64,3] 8.2e-3 1.9e-3 0.061.2e-505.5e-203.8e-12 1.5e-7 0.07 1111 1.01
flare[65,3] 0.03 4.6e-3 0.169.7e-782.1e-192.2e-11 8.4e-6 0.41 1259 1.0
flare[66,3] 0.56 0.03 0.782.0e-36 4.3e-5 0.07 1.01 2.52 623 1.0
flare[67,3] 0.18 0.01 0.424.4e-649.0e-15 2.3e-5 0.08 1.39 914 1.01
flare[68,3] 0.06 0.01 0.233.2e-27 1.7e-8 2.5e-5 3.5e-3 0.74 370 1.01
flare[69,3] 0.38 0.04 0.642.3e-622.3e-13 9.3e-5 0.61 2.07 214 1.01
flare[70,3] 0.03 5.7e-3 0.142.0e-453.5e-168.4e-11 1.0e-6 0.36 629 1.0
flare[71,3] 3.8e-3 9.9e-4 0.041.3e-271.8e-11 6.8e-8 2.5e-5 0.02 1964 1.0
flare[72,3] 0.1 0.03 0.343.8e-393.6e-184.9e-12 1.9e-5 1.41 105 1.04
flare[73,3] 0.1 9.8e-3 0.32.4e-767.2e-241.2e-12 1.5e-4 1.11 955 1.01
flare[74,3] 0.02 4.2e-3 0.131.4e-421.3e-13 3.6e-8 1.2e-4 0.24 992 1.0
flare[75,3] 0.15 0.03 0.421.1e-4110.0e-15 5.2e-9 1.4e-3 1.55 215 1.01
flare[76,3] 0.05 6.0e-3 0.22.7e-612.1e-198.6e-11 1.4e-5 0.73 1167 1.0
flare[77,3] 0.08 0.02 0.31.4e-674.1e-16 1.0e-7 4.5e-4 1.11 225 1.01
flare[78,3] 0.02 2.9e-3 0.141.1e-701.6e-243.0e-15 3.7e-9 0.31 2342 1.0
flare[79,3] 0.01 2.5e-3 0.082.7e-245.9e-10 1.4e-6 2.6e-4 0.15 937 1.0
flare[80,3] 0.25 0.05 0.523.1e-634.4e-198.7e-10 0.09 1.81 106 1.04
flare[81,3] 0.26 0.04 0.532.2e-623.2e-202.5e-10 0.2 1.82 193 1.01
flare[82,3] 9.8e-4 3.8e-4 0.021.1e-298.0e-177.8e-12 1.0e-7 3.1e-3 2246 1.0
flare[83,3] 0.12 0.02 0.331.1e-613.3e-16 8.6e-9 2.7e-3 1.22 334 1.02
flare[84,3] 0.02 7.2e-3 0.129.0e-271.6e-12 2.1e-8 6.0e-5 0.24 256 1.02
flare[85,3] 0.41 0.06 0.711.2e-461.7e-13 2.6e-5 0.68 2.29 153 1.03
flare[86,3] 0.1 9.2e-3 0.32.5e-482.6e-13 7.7e-7 2.6e-3 1.07 1045 1.01
flare[87,3] 0.01 3.3e-3 0.11.3e-764.2e-242.6e-15 2.3e-9 0.1 883 1.0
flare[88,3] 0.1 0.02 0.334.2e-461.7e-15 5.5e-9 1.2e-4 1.27 198 1.02
flare[89,3] 0.06 0.01 0.256.1e-684.5e-221.5e-11 2.8e-5 0.87 431 1.0
flare[90,3] 0.09 0.02 0.291.4e-481.4e-12 4.2e-6 4.8e-3 1.22 160 1.02
flare[91,3] 0.1 0.02 0.321.1e-489.7e-17 5.3e-8 1.2e-3 1.22 187 1.02
flare[92,3] 0.03 5.9e-3 0.182.9e-445.0e-10 1.7e-6 2.4e-4 0.37 935 1.0
flare[93,3] 0.12 0.02 0.354.4e-832.5e-16 9.7e-9 1.8e-3 1.37 278 1.01
flare[94,3] 0.02 3.3e-3 0.121.3e-621.8e-211.3e-13 1.1e-8 0.23 1437 1.0
flare[95,3] 0.08 0.02 0.271.2e-892.2e-201.7e-11 4.7e-6 1.0 298 1.02
flare[96,3] 0.01 1.8e-3 0.084.2e-451.2e-177.7e-11 3.6e-6 0.06 1974 1.0
flare[97,3] 0.18 0.02 0.426.2e-802.6e-17 4.8e-7 0.04 1.46 611 1.0
flare[98,3] 9.5e-3 2.3e-3 0.071.1e-391.0e-12 6.1e-8 6.1e-5 0.07 1027 1.0
flare[99,3] 0.06 7.4e-3 0.231.1e-1022.1e-255.8e-14 5.7e-7 0.89 975 1.0
flare[0,4] 0.14 0.01 0.383.7e-364.3e-10 2.1e-5 0.02 1.37 1374 1.0
flare[1,4] 0.06 7.0e-3 0.225.3e-823.4e-195.4e-10 2.3e-4 0.83 955 1.0
flare[2,4] 5.2e-3 1.4e-3 0.042.0e-333.9e-13 7.4e-9 6.6e-6 0.04 1081 1.0
flare[3,4] 0.81 0.04 0.691.1e-32 0.07 0.79 1.27 2.28 279 1.02
flare[4,4] 0.63 0.03 0.81.6e-17 7.0e-4 0.25 1.09 2.64 682 1.0
flare[5,4] 0.08 7.4e-3 0.272.3e-321.4e-11 4.0e-7 5.5e-4 1.0 1339 1.0
flare[6,4] 8.4e-3 2.6e-3 0.065.6e-235.5e-12 2.3e-8 3.5e-5 0.07 557 1.01
flare[7,4] 0.14 0.03 0.382.7e-703.4e-12 6.1e-6 0.01 1.38 189 1.02
flare[8,4] 0.07 0.02 0.241.8e-398.8e-181.3e-11 1.9e-5 0.92 258 1.02
flare[9,4] 0.03 4.6e-3 0.148.3e-661.6e-222.2e-13 9.7e-8 0.44 921 1.0
flare[10,4] 0.08 9.6e-3 0.266.1e-391.5e-15 2.4e-9 1.9e-4 0.98 729 1.0
flare[11,4] 0.02 5.2e-3 0.123.0e-491.2e-187.8e-12 7.8e-8 0.21 518 1.0
flare[12,4] 0.04 4.3e-3 0.162.1e-826.1e-241.9e-12 6.1e-6 0.56 1372 1.0
flare[13,4] 0.17 0.02 0.41.7e-877.2e-19 3.6e-8 0.06 1.41 415 1.02
flare[14,4] 0.34 0.02 0.585.1e-522.5e-14 1.3e-4 0.57 1.82 573 1.01
flare[15,4] 0.02 4.3e-3 0.12.4e-642.7e-184.8e-11 9.7e-7 0.24 531 1.0
flare[16,4] 0.03 3.8e-3 0.153.3e-623.0e-182.1e-10 3.8e-6 0.4 1460 1.0
flare[17,4] 0.1 0.02 0.283.1e-401.3e-13 1.1e-6 4.8e-3 1.04 144 1.03
flare[18,4] 0.05 5.5e-3 0.211.9e-531.1e-175.1e-11 7.6e-6 0.77 1447 1.0
flare[19,4] 0.3 0.02 0.532.1e-372.6e-11 1.3e-3 0.44 1.73 857 1.01
flare[20,4] 0.06 7.4e-3 0.22.7e-712.4e-204.4e-12 8.0e-6 0.68 716 1.01
flare[21,4] 0.02 3.0e-3 0.124.6e-385.3e-14 4.3e-8 8.7e-5 0.2 1604 1.0
flare[22,4] 0.07 0.02 0.241.6e-682.0e-201.1e-11 1.2e-5 0.9 176 1.03
flare[23,4] 0.06 9.4e-3 0.235.0e-341.2e-12 3.3e-7 5.4e-4 0.84 590 1.01
flare[24,4] 0.15 0.02 0.336.6e-597.8e-14 9.2e-8 0.03 1.14 301 1.01
flare[25,4] 0.04 4.5e-3 0.175.3e-676.2e-231.6e-13 2.4e-7 0.59 1380 1.0
flare[26,4] 0.1 8.8e-3 0.37.4e-632.5e-19 1.6e-9 5.8e-4 1.12 1154 1.0
flare[27,4] 0.02 3.5e-3 0.121.1e-213.5e-10 1.3e-6 3.9e-4 0.15 1273 1.0
flare[28,4] 0.13 0.01 0.364.4e-947.1e-218.0e-11 3.4e-3 1.3 985 1.0
flare[29,4] 0.1 0.02 0.35.2e-575.0e-193.5e-10 6.7e-5 1.1 329 1.01
flare[30,4] 0.36 0.02 0.586.3e-629.4e-14 5.3e-3 0.57 1.94 614 1.01
flare[31,4] 0.04 6.7e-3 0.182.5e-428.8e-16 2.8e-9 1.1e-4 0.59 701 1.0
flare[32,4] 0.07 9.9e-3 0.276.9e-716.7e-18 3.8e-9 6.2e-5 1.04 735 1.01
flare[33,4] 0.16 0.01 0.387.1e-442.9e-13 1.1e-6 0.03 1.33 954 1.01
flare[34,4] 0.03 4.1e-3 0.174.1e-512.8e-14 1.2e-8 5.6e-5 0.51 1668 1.0
flare[35,4] 0.1 8.4e-3 0.311.7e-611.1e-18 2.4e-9 4.0e-4 1.11 1357 1.0
flare[36,4] 0.05 5.5e-3 0.193.2e-756.5e-241.2e-12 2.1e-5 0.72 1245 1.0
flare[37,4] 0.38 0.03 0.65 4.8e-7 4.2e-3 0.06 0.47 2.08 618 1.0
flare[38,4] 0.06 0.01 0.251.0e-743.4e-183.1e-10 1.7e-5 0.89 427 1.01
flare[39,4] 0.02 4.1e-3 0.138.0e-852.3e-198.5e-12 2.5e-7 0.2 999 1.0
flare[40,4] 0.07 9.5e-3 0.256.3e-444.9e-14 3.0e-8 4.1e-4 0.98 719 1.01
flare[41,4] 0.21 0.02 0.496.2e-353.8e-10 7.3e-5 0.07 1.75 472 1.01
flare[42,4] 0.03 5.0e-3 0.172.8e-555.9e-221.2e-12 1.7e-6 0.58 1092 1.0
flare[43,4] 0.12 0.01 0.341.2e-324.6e-10 7.7e-6 7.7e-3 1.32 522 1.0
flare[44,4] 0.08 6.8e-3 0.272.0e-456.4e-16 2.8e-7 4.5e-3 1.02 1577 1.0
flare[45,4] 0.1 0.01 0.281.6e-767.1e-20 6.2e-9 1.1e-3 1.04 494 1.0
flare[46,4] 7.1e-3 2.9e-3 0.073.9e-718.7e-14 2.7e-9 2.7e-6 0.03 523 1.01
flare[47,4] 0.08 9.7e-3 0.271.4e-505.4e-184.9e-11 1.2e-4 0.97 792 1.0
flare[48,4] 0.03 4.9e-3 0.158.8e-536.4e-14 3.9e-9 8.7e-6 0.41 982 1.0
flare[49,4] 0.29 0.03 0.541.7e-28 4.6e-9 9.0e-4 0.36 1.88 441 1.01
flare[50,4] 0.05 7.6e-3 0.24.3e-756.3e-201.7e-11 1.0e-5 0.74 693 1.0
flare[51,4] 0.06 9.6e-3 0.257.7e-581.2e-171.2e-10 4.0e-6 0.92 688 1.0
flare[52,4] 0.04 9.4e-3 0.199.5e-472.6e-193.1e-12 7.8e-7 0.72 393 1.02
flare[53,4] 0.2 0.02 0.428.1e-521.0e-18 5.9e-9 0.13 1.5 421 1.0
flare[54,4] 0.13 0.01 0.361.6e-803.6e-16 4.5e-8 3.6e-3 1.32 774 1.01
flare[55,4] 0.1 8.5e-3 0.31.4e-591.4e-16 2.3e-7 1.9e-3 1.09 1234 1.0
flare[56,4] 0.08 8.7e-3 0.292.3e-404.9e-167.9e-10 1.1e-4 1.09 1110 1.01
flare[57,4] 0.22 0.01 0.461.7e-651.1e-17 1.3e-5 0.15 1.61 952 1.01
flare[58,4] 0.26 0.03 0.531.6e-396.6e-13 2.0e-6 0.15 1.8 273 1.02
flare[59,4] 0.36 0.05 0.656.9e-19 8.0e-8 3.4e-4 0.44 2.11 174 1.02
flare[60,4] 0.24 0.02 0.521.4e-20 2.4e-7 6.1e-4 0.14 1.87 593 1.01
flare[61,4] 0.81 0.04 0.911.6e-34 1.8e-3 0.51 1.38 2.98 427 1.01
flare[62,4] 0.08 7.6e-3 0.281.6e-739.9e-14 1.9e-6 3.0e-3 1.12 1344 1.0
flare[63,4] 0.06 6.8e-3 0.223.9e-435.6e-12 4.4e-6 3.1e-3 0.79 1039 1.0
flare[64,4] 0.01 2.6e-3 0.095.2e-466.4e-189.0e-11 1.6e-6 0.12 1286 1.01
flare[65,4] 0.05 4.9e-3 0.27.0e-704.6e-178.9e-10 1.4e-4 0.66 1745 1.0
flare[66,4] 0.78 0.03 0.834.1e-33 4.1e-3 0.58 1.36 2.62 599 1.0
flare[67,4] 0.33 0.02 0.565.2e-576.9e-12 3.8e-3 0.52 1.84 568 1.0
flare[68,4] 0.14 0.02 0.397.8e-22 1.3e-6 5.9e-4 0.03 1.58 367 1.01
flare[69,4] 0.47 0.05 0.681.0e-562.4e-11 2.3e-3 0.89 2.13 189 1.01
flare[70,4] 0.04 4.5e-3 0.178.7e-405.9e-14 3.3e-9 1.3e-5 0.54 1414 1.0
flare[71,4] 9.3e-3 1.9e-3 0.065.6e-24 1.2e-9 1.8e-6 2.6e-4 0.09 1117 1.0
flare[72,4] 0.16 0.05 0.432.3e-352.7e-161.1e-10 1.4e-4 1.66 69 1.07
flare[73,4] 0.11 0.01 0.37.4e-673.7e-217.7e-11 1.9e-3 1.17 469 1.01
flare[74,4] 0.04 6.2e-3 0.183.8e-398.4e-12 8.2e-7 1.8e-3 0.57 882 1.0
flare[75,4] 0.2 0.03 0.51.1e-374.3e-13 7.5e-8 7.4e-3 1.76 239 1.01
flare[76,4] 0.08 6.8e-3 0.253.5e-551.7e-16 8.8e-9 4.3e-4 0.92 1354 1.0
flare[77,4] 0.1 0.02 0.321.6e-597.2e-14 4.0e-6 5.8e-3 1.14 331 1.01
flare[78,4] 0.04 5.2e-3 0.191.7e-645.7e-221.5e-13 9.6e-8 0.6 1373 1.0
flare[79,4] 0.03 5.4e-3 0.131.9e-21 3.0e-8 2.7e-5 2.1e-3 0.37 604 1.0
flare[80,4] 0.22 0.04 0.452.0e-576.0e-17 2.4e-8 0.2 1.52 133 1.03
flare[81,4] 0.28 0.04 0.523.0e-562.3e-17 1.0e-8 0.39 1.72 193 1.01
flare[82,4] 3.2e-3 1.4e-3 0.052.9e-272.3e-159.1e-11 5.5e-7 7.4e-3 1345 1.0
flare[83,4] 0.15 0.02 0.372.8e-542.6e-14 2.0e-7 0.01 1.38 279 1.03
flare[84,4] 0.03 0.01 0.152.6e-243.7e-11 2.2e-7 2.7e-4 0.42 219 1.02
flare[85,4] 0.44 0.05 0.71.2e-422.6e-11 5.7e-4 0.8 2.37 203 1.03
flare[86,4] 0.2 0.02 0.453.4e-421.5e-10 1.0e-4 0.06 1.56 642 1.01
flare[87,4] 0.02 8.1e-3 0.159.7e-7010.0e-221.5e-13 3.6e-8 0.32 335 1.01
flare[88,4] 0.11 0.02 0.332.2e-414.4e-13 3.0e-7 1.9e-3 1.24 244 1.01
flare[89,4] 0.09 0.01 0.282.9e-621.0e-19 1.2e-9 8.8e-4 0.96 395 1.0
flare[90,4] 0.16 0.03 0.392.4e-433.5e-10 1.4e-4 0.05 1.31 220 1.01
flare[91,4] 0.15 0.02 0.383.2e-421.9e-14 4.2e-6 0.02 1.48 249 1.02
flare[92,4] 0.06 9.3e-3 0.243.2e-36 9.4e-8 6.7e-5 3.4e-3 0.81 635 1.01
flare[93,4] 0.17 0.02 0.423.3e-763.4e-14 3.3e-7 0.02 1.47 396 1.01
flare[94,4] 0.03 4.8e-3 0.145.2e-565.3e-195.5e-12 2.5e-7 0.44 905 1.01
flare[95,4] 0.08 0.01 0.263.3e-794.6e-188.0e-10 8.3e-5 0.93 465 1.01
flare[96,4] 0.02 2.4e-3 0.11.0e-415.7e-16 1.6e-9 2.7e-5 0.16 1921 1.0
flare[97,4] 0.27 0.02 0.511.7e-715.7e-15 2.9e-5 0.33 1.67 569 1.0
flare[98,4] 0.02 2.8e-3 0.093.0e-355.2e-11 1.2e-6 5.5e-4 0.19 1101 1.0
flare[99,4] 0.08 0.01 0.288.4e-911.9e-225.9e-12 2.3e-5 1.03 535 1.0
flare[0,5] 0.24 0.01 0.481.2e-31 6.4e-8 7.1e-4 0.21 1.64 1080 1.0
flare[1,5] 0.11 9.0e-3 0.319.7e-742.8e-16 7.0e-8 7.4e-3 1.12 1207 1.0
flare[2,5] 8.5e-3 1.7e-3 0.063.3e-302.0e-11 1.1e-7 4.8e-5 0.09 1109 1.0
flare[3,5] 0.72 0.03 0.67.2e-29 0.17 0.68 1.08 2.09 312 1.01
flare[4,5] 1.03 0.04 0.973.8e-14 0.01 0.96 1.66 3.18 523 1.0
flare[5,5] 0.12 0.01 0.347.3e-27 4.4e-9 2.0e-5 8.7e-3 1.26 1123 1.0
flare[6,5] 0.01 4.1e-3 0.093.3e-201.8e-10 3.1e-7 2.3e-4 0.16 507 1.01
flare[7,5] 0.22 0.02 0.461.1e-61 1.6e-9 3.1e-4 0.16 1.54 549 1.01
flare[8,5] 0.09 0.02 0.36.1e-363.6e-161.7e-10 1.2e-4 1.14 228 1.02
flare[9,5] 0.03 4.0e-3 0.152.4e-593.9e-209.4e-12 1.9e-6 0.49 1315 1.0
flare[10,5] 0.09 9.2e-3 0.275.2e-342.1e-13 8.3e-8 2.8e-3 0.99 872 1.0
flare[11,5] 0.02 5.3e-3 0.135.4e-441.8e-162.6e-10 1.1e-6 0.36 602 1.0
flare[12,5] 0.05 5.6e-3 0.21.2e-738.1e-219.7e-11 1.4e-4 0.69 1257 1.0
flare[13,5] 0.2 0.02 0.392.7e-752.1e-16 4.9e-6 0.19 1.32 378 1.01
flare[14,5] 0.38 0.03 0.574.7e-476.3e-12 9.7e-3 0.67 1.86 413 1.01
flare[15,5] 0.03 5.8e-3 0.162.4e-582.7e-16 1.7e-9 1.5e-5 0.52 713 1.0
flare[16,5] 0.04 4.6e-3 0.182.8e-561.2e-15 8.9e-9 6.8e-5 0.51 1420 1.0
flare[17,5] 0.17 0.02 0.42.3e-354.5e-11 5.4e-5 0.07 1.39 514 1.01
flare[18,5] 0.08 7.2e-3 0.283.2e-477.3e-16 1.0e-9 1.1e-4 1.0 1460 1.01
flare[19,5] 0.47 0.03 0.643.9e-33 2.3e-9 0.04 0.85 2.03 528 1.01
flare[20,5] 0.06 6.0e-3 0.26.0e-632.9e-182.5e-10 1.4e-4 0.72 1100 1.0
flare[21,5] 0.04 4.2e-3 0.168.4e-348.8e-12 1.5e-6 1.0e-3 0.47 1452 1.0
flare[22,5] 0.08 0.02 0.241.2e-616.3e-185.3e-10 3.2e-4 0.88 221 1.02
flare[23,5] 0.1 0.01 0.311.3e-301.6e-10 8.1e-6 6.5e-3 1.18 466 1.01
flare[24,5] 0.13 0.02 0.292.1e-521.3e-11 2.7e-6 0.06 1.03 354 1.01
flare[25,5] 0.04 5.8e-3 0.186.8e-612.7e-201.0e-11 4.1e-6 0.65 1010 1.0
flare[26,5] 0.15 0.02 0.381.3e-561.6e-16 2.0e-7 0.02 1.38 394 1.01
flare[27,5] 0.05 7.1e-3 0.221.6e-18 2.1e-8 2.6e-5 3.7e-3 0.58 973 1.0
flare[28,5] 0.18 0.02 0.418.8e-861.7e-18 1.1e-8 0.09 1.44 738 1.01
flare[29,5] 0.1 0.01 0.288.7e-502.1e-16 1.3e-8 7.6e-4 1.01 456 1.0
flare[30,5] 0.54 0.04 0.711.7e-551.2e-11 0.2 0.9 2.32 251 1.01
flare[31,5] 0.06 9.0e-3 0.211.0e-376.3e-14 5.2e-8 6.0e-4 0.69 554 1.01
flare[32,5] 0.1 0.01 0.323.3e-632.5e-15 1.6e-7 1.1e-3 1.14 691 1.01
flare[33,5] 0.2 0.01 0.43.4e-385.2e-11 3.9e-5 0.17 1.39 939 1.0
flare[34,5] 0.07 6.8e-3 0.251.0e-444.9e-12 4.4e-7 9.0e-4 0.83 1333 1.0
flare[35,5] 0.12 0.01 0.338.1e-563.2e-16 1.3e-7 9.2e-3 1.16 732 1.0
flare[36,5] 0.07 6.5e-3 0.2210.0e-653.0e-211.4e-10 3.6e-4 0.79 1161 1.0
flare[37,5] 1.17 0.03 1.05 1.4e-3 0.28 1.0 1.74 3.78 1432 1.0
flare[38,5] 0.07 0.01 0.261.2e-639.7e-16 2.2e-8 4.3e-4 0.96 524 1.0
flare[39,5] 0.02 5.0e-3 0.133.6e-762.9e-173.1e-10 2.9e-6 0.33 716 1.0
flare[40,5] 0.13 0.02 0.382.9e-371.6e-11 1.2e-6 6.0e-3 1.29 607 1.01
flare[41,5] 0.32 0.03 0.61.1e-31 2.7e-8 1.6e-3 0.34 2.09 331 1.01
flare[42,5] 0.05 5.2e-3 0.22.7e-501.5e-195.4e-11 3.8e-5 0.73 1503 1.0
flare[43,5] 0.16 0.01 0.391.1e-28 3.4e-8 1.7e-4 0.05 1.36 833 1.0
flare[44,5] 0.19 0.01 0.433.9e-401.0e-13 1.3e-5 0.08 1.55 1097 1.0
flare[45,5] 0.11 0.01 0.297.0e-713.2e-17 5.8e-7 0.01 1.04 609 1.0
flare[46,5] 0.01 3.6e-3 0.097.0e-656.5e-12 6.5e-8 2.7e-5 0.11 681 1.01
flare[47,5] 0.12 0.01 0.351.4e-457.5e-16 1.8e-9 3.8e-3 1.31 748 1.0
flare[48,5] 0.05 8.3e-3 0.214.5e-487.6e-12 1.2e-7 1.0e-4 0.79 654 1.0
flare[49,5] 0.41 0.04 0.646.1e-26 1.1e-7 8.6e-3 0.74 2.09 299 1.02
flare[50,5] 0.06 6.3e-3 0.212.4e-695.4e-188.2e-10 2.1e-4 0.8 1168 1.0
flare[51,5] 0.1 0.01 0.334.9e-515.1e-15 6.6e-9 8.2e-5 1.19 659 1.0
flare[52,5] 0.04 9.2e-3 0.177.1e-422.0e-176.3e-11 5.2e-6 0.67 360 1.02
flare[53,5] 0.22 0.03 0.451.5e-471.9e-16 1.7e-7 0.25 1.52 314 1.01
flare[54,5] 0.18 0.03 0.436.2e-721.1e-13 2.7e-6 0.05 1.49 287 1.02
flare[55,5] 0.14 9.4e-3 0.347.8e-549.9e-14 1.6e-5 0.04 1.3 1336 1.0
flare[56,5] 0.13 0.01 0.371.4e-354.6e-14 1.7e-8 1.6e-3 1.37 646 1.01
flare[57,5] 0.4 0.03 0.623.2e-592.3e-15 1.4e-3 0.71 2.1 444 1.01
flare[58,5] 0.29 0.03 0.546.9e-362.7e-11 2.6e-5 0.39 1.8 258 1.02
flare[59,5] 0.49 0.06 0.772.7e-15 4.6e-6 5.1e-3 0.89 2.45 155 1.02
flare[60,5] 0.39 0.03 0.674.3e-17 1.4e-5 0.01 0.57 2.23 531 1.02
flare[61,5] 1.07 0.05 0.984.3e-31 0.07 1.0 1.59 3.36 375 1.01
flare[62,5] 0.16 0.01 0.393.1e-591.7e-11 1.4e-4 0.07 1.43 992 1.0
flare[63,5] 0.15 8.5e-3 0.357.0e-38 1.1e-9 2.7e-4 0.07 1.26 1680 1.0
flare[64,5] 0.02 3.8e-3 0.111.7e-417.1e-16 2.1e-9 1.7e-5 0.24 829 1.01
flare[65,5] 0.07 6.6e-3 0.257.1e-645.7e-15 5.4e-8 1.9e-3 0.91 1431 1.0
flare[66,5] 0.93 0.04 0.798.9e-30 0.19 0.89 1.44 2.72 471 1.0
flare[67,5] 0.51 0.03 0.671.4e-51 1.6e-9 0.18 0.89 2.09 422 1.01
flare[68,5] 0.3 0.04 0.574.8e-16 1.0e-4 0.01 0.3 1.86 262 1.01
flare[69,5] 0.47 0.04 0.636.4e-50 1.9e-9 0.04 0.86 1.98 280 1.01
flare[70,5] 0.06 6.3e-3 0.231.5e-339.4e-12 1.3e-7 1.8e-4 0.87 1286 1.0
flare[71,5] 0.03 4.3e-3 0.122.3e-20 7.4e-8 4.8e-5 2.6e-3 0.32 757 1.0
flare[72,5] 0.19 0.06 0.491.3e-312.2e-14 2.3e-9 1.0e-3 1.85 63 1.08
flare[73,5] 0.11 0.01 0.294.3e-592.9e-18 4.7e-9 0.02 1.09 591 1.01
flare[74,5] 0.12 0.03 0.361.1e-354.8e-10 1.7e-5 0.02 1.28 201 1.02
flare[75,5] 0.21 0.03 0.481.7e-332.0e-11 9.5e-7 0.03 1.7 233 1.01
flare[76,5] 0.13 8.2e-3 0.355.7e-497.8e-14 8.8e-7 0.01 1.23 1804 1.0
flare[77,5] 0.17 0.02 0.393.7e-511.2e-11 1.9e-4 0.08 1.42 515 1.01
flare[78,5] 0.06 0.01 0.241.7e-581.3e-197.6e-12 1.7e-6 0.82 572 1.01
flare[79,5] 0.07 0.01 0.235.2e-19 1.4e-6 5.3e-4 0.02 0.79 477 1.01
flare[80,5] 0.18 0.02 0.361.2e-518.6e-15 7.3e-7 0.2 1.22 291 1.02
flare[81,5] 0.23 0.03 0.428.1e-513.1e-15 4.7e-7 0.32 1.38 183 1.01
flare[82,5] 3.7e-3 1.6e-3 0.048.2e-256.5e-14 1.0e-9 2.7e-6 0.02 634 1.0
flare[83,5] 0.17 0.03 0.381.9e-471.9e-12 3.5e-6 0.06 1.39 214 1.03
flare[84,5] 0.05 0.01 0.197.8e-228.2e-10 2.2e-6 1.2e-3 0.69 200 1.02
flare[85,5] 0.41 0.03 0.631.8e-37 1.8e-9 0.01 0.71 2.1 352 1.02
flare[86,5] 0.34 0.03 0.567.8e-37 6.4e-8 7.6e-3 0.52 1.84 427 1.01
flare[87,5] 0.03 7.5e-3 0.152.9e-632.4e-197.2e-12 4.9e-7 0.38 403 1.01
flare[88,5] 0.13 0.01 0.322.1e-379.8e-11 1.6e-5 0.03 1.19 545 1.01
flare[89,5] 0.13 0.01 0.332.8e-551.7e-17 1.2e-7 0.03 1.12 726 1.0
flare[90,5] 0.3 0.03 0.541.5e-37 5.9e-8 3.8e-3 0.4 1.84 406 1.01
flare[91,5] 0.23 0.02 0.463.3e-376.6e-12 2.6e-4 0.23 1.64 364 1.01
flare[92,5] 0.13 0.02 0.351.3e-30 1.6e-5 2.4e-3 0.05 1.31 393 1.01
flare[93,5] 0.25 0.03 0.54.2e-715.1e-12 8.4e-6 0.24 1.68 339 1.01
flare[94,5] 0.04 6.1e-3 0.191.9e-498.4e-172.1e-10 4.4e-6 0.63 942 1.01
flare[95,5] 0.09 8.9e-3 0.272.7e-688.8e-16 4.6e-8 1.3e-3 0.94 885 1.01
flare[96,5] 0.03 3.2e-3 0.142.3e-382.8e-14 3.0e-8 2.1e-4 0.42 1809 1.0
flare[97,5] 0.29 0.02 0.476.0e-628.3e-13 1.3e-3 0.48 1.52 597 1.0
flare[98,5] 0.05 0.01 0.233.1e-31 2.4e-9 2.6e-5 4.1e-3 0.61 268 1.01
flare[99,5] 0.1 0.01 0.283.8e-761.8e-195.7e-10 6.5e-4 0.97 694 1.0
flare[0,6] 0.34 0.02 0.541.1e-27 6.0e-6 0.01 0.59 1.79 962 1.0
flare[1,6] 0.17 0.01 0.374.5e-652.5e-13 9.3e-6 0.08 1.32 910 1.0
flare[2,6] 0.02 2.9e-3 0.081.1e-267.6e-10 1.7e-6 3.4e-4 0.2 820 1.0
flare[3,6] 0.6 0.03 0.522.7e-25 0.15 0.55 0.91 1.79 359 1.01
flare[4,6] 1.11 0.04 0.917.4e-11 0.15 1.12 1.68 3.08 484 1.0
flare[5,6] 0.2 0.02 0.429.0e-22 1.6e-610.0e-4 0.13 1.48 773 1.0
flare[6,6] 0.03 6.1e-3 0.121.8e-17 5.6e-9 4.4e-6 1.4e-3 0.32 417 1.01
flare[7,6] 0.33 0.02 0.554.7e-54 8.1e-7 0.01 0.49 1.77 607 1.01
flare[8,6] 0.12 0.03 0.351.2e-321.6e-14 2.2e-9 6.4e-4 1.3 152 1.03
flare[9,6] 0.04 4.4e-3 0.163.1e-531.0e-173.5e-10 3.4e-5 0.52 1271 1.0
flare[10,6] 0.13 0.01 0.327.8e-293.5e-11 2.7e-6 0.03 1.14 890 1.0
flare[11,6] 0.03 5.5e-3 0.152.4e-392.8e-14 7.8e-9 1.6e-5 0.42 706 1.0
flare[12,6] 0.08 6.8e-3 0.241.1e-634.4e-18 5.6e-9 1.5e-3 0.93 1259 1.0
flare[13,6] 0.21 0.02 0.381.2e-659.8e-14 6.1e-4 0.29 1.29 528 1.01
flare[14,6] 0.37 0.03 0.57.5e-39 1.3e-9 0.07 0.64 1.66 374 1.01
flare[15,6] 0.06 9.8e-3 0.223.4e-523.6e-14 6.6e-8 2.3e-4 0.79 492 1.0
flare[16,6] 0.06 6.0e-3 0.228.2e-512.2e-13 3.9e-7 1.1e-3 0.79 1296 1.0
flare[17,6] 0.29 0.02 0.531.2e-31 7.3e-9 2.2e-3 0.41 1.77 691 1.01
flare[18,6] 0.12 0.01 0.363.5e-425.2e-14 1.8e-8 1.0e-3 1.25 717 1.01
flare[19,6] 0.49 0.03 0.611.5e-29 1.4e-7 0.2 0.9 1.93 379 1.0
flare[20,6] 0.08 6.5e-3 0.234.6e-561.1e-15 1.5e-8 4.2e-3 0.82 1200 1.0
flare[21,6] 0.08 6.2e-3 0.259.6e-30 1.0e-9 5.9e-5 0.01 0.96 1580 1.0
flare[22,6] 0.09 0.02 0.252.1e-561.3e-15 2.9e-8 5.0e-3 0.94 283 1.01
flare[23,6] 0.17 0.02 0.41.4e-26 1.5e-8 2.3e-4 0.06 1.53 449 1.01
flare[24,6] 0.13 0.01 0.284.4e-46 1.8e-9 9.0e-5 0.11 1.02 423 1.01
flare[25,6] 0.06 6.6e-3 0.218.2e-539.9e-184.3e-10 7.7e-5 0.74 986 1.0
flare[26,6] 0.19 0.02 0.384.6e-516.8e-14 2.9e-5 0.13 1.34 511 1.0
flare[27,6] 0.12 0.01 0.342.8e-15 1.2e-6 4.7e-4 0.04 1.21 893 1.01
flare[28,6] 0.23 0.02 0.455.5e-771.9e-16 5.5e-7 0.29 1.51 399 1.01
flare[29,6] 0.11 8.8e-3 0.279.8e-457.2e-14 3.5e-7 7.5e-3 0.96 914 1.0
flare[30,6] 0.51 0.03 0.616.8e-519.6e-10 0.3 0.84 2.06 391 1.01
flare[31,6] 0.08 0.01 0.253.7e-334.6e-12 1.0e-6 4.1e-3 0.84 451 1.01
flare[32,6] 0.14 0.01 0.364.7e-558.1e-13 6.9e-6 0.02 1.29 659 1.01
flare[33,6] 0.23 0.01 0.413.4e-32 6.8e-9 9.2e-4 0.31 1.37 935 1.0
flare[34,6] 0.12 0.01 0.321.4e-386.4e-10 1.6e-5 0.01 1.17 806 1.01
flare[35,6] 0.16 0.01 0.363.2e-508.1e-14 5.4e-6 0.05 1.26 657 1.0
flare[36,6] 0.08 5.8e-3 0.241.5e-551.5e-18 1.9e-8 5.0e-3 0.86 1689 1.0
flare[37,6] 2.12 0.07 1.23 0.37 1.29 1.85 2.67 5.22 354 1.01
flare[38,6] 0.11 0.01 0.317.4e-534.5e-13 1.9e-6 0.01 1.23 729 1.0
flare[39,6] 0.03 4.6e-3 0.143.7e-656.0e-15 1.2e-8 3.9e-5 0.45 873 1.0
flare[40,6] 0.2 0.02 0.464.0e-31 3.0e-9 4.4e-5 0.06 1.52 389 1.01
flare[41,6] 0.42 0.03 0.633.3e-28 1.1e-6 0.03 0.72 2.1 353 1.0
flare[42,6] 0.08 0.01 0.261.3e-452.4e-17 2.3e-9 6.3e-4 0.99 572 1.0
flare[43,6] 0.23 0.01 0.452.1e-24 4.1e-6 4.1e-3 0.25 1.56 1420 1.0
flare[44,6] 0.33 0.02 0.565.2e-361.0e-11 7.0e-4 0.48 1.87 717 1.0
flare[45,6] 0.14 9.3e-3 0.328.0e-612.3e-14 2.1e-5 0.09 1.15 1225 1.0
flare[46,6] 0.02 5.1e-3 0.134.3e-605.7e-10 1.6e-6 2.8e-4 0.27 652 1.01
flare[47,6] 0.18 0.02 0.422.0e-401.0e-13 8.2e-8 0.05 1.55 508 1.0
flare[48,6] 0.08 0.01 0.271.1e-418.6e-10 3.5e-6 1.0e-3 1.02 635 1.0
flare[49,6] 0.55 0.05 0.741.8e-23 2.1e-6 0.06 1.06 2.36 228 1.02
flare[50,6] 0.1 7.9e-3 0.291.1e-604.9e-16 2.3e-8 2.4e-3 1.0 1348 1.0
flare[51,6] 0.11 0.01 0.326.1e-461.8e-12 3.7e-7 1.3e-3 1.13 746 1.0
flare[52,6] 0.05 0.01 0.185.1e-372.3e-15 1.5e-9 3.7e-5 0.66 272 1.02
flare[53,6] 0.18 0.02 0.367.8e-442.6e-14 3.6e-6 0.21 1.22 403 1.01
flare[54,6] 0.23 0.03 0.452.4e-653.3e-11 1.5e-4 0.25 1.56 270 1.01
flare[55,6] 0.24 0.01 0.437.6e-472.0e-10 1.2e-3 0.31 1.45 1424 1.0
flare[56,6] 0.18 0.02 0.431.6e-314.6e-12 3.6e-7 9.4e-3 1.55 388 1.01
flare[57,6] 0.45 0.03 0.61.5e-534.6e-13 0.06 0.83 1.93 326 1.01
flare[58,6] 0.32 0.03 0.532.9e-32 1.3e-9 3.5e-4 0.54 1.72 271 1.02
flare[59,6] 0.58 0.07 0.88.8e-12 2.5e-4 0.06 1.1 2.47 133 1.03
flare[60,6] 0.58 0.03 0.782.3e-14 7.1e-4 0.13 1.04 2.5 528 1.01
flare[61,6] 0.98 0.04 0.838.4e-27 0.3 0.93 1.41 2.84 339 1.01
flare[62,6] 0.35 0.02 0.585.2e-50 3.0e-9 9.9e-3 0.53 1.89 704 1.0
flare[63,6] 0.34 0.02 0.571.1e-32 2.0e-7 0.01 0.52 1.97 1006 1.01
flare[64,6] 0.03 5.4e-3 0.152.0e-367.5e-14 4.9e-8 1.4e-4 0.43 775 1.01
flare[65,6] 0.14 0.01 0.367.2e-588.1e-13 2.7e-6 0.03 1.28 1171 1.0
flare[66,6] 0.98 0.04 0.732.3e-26 0.48 0.95 1.39 2.66 361 1.01
flare[67,6] 0.53 0.04 0.64.7e-47 9.1e-8 0.37 0.91 1.95 255 1.03
flare[68,6] 0.64 0.04 0.822.5e-12 7.6e-3 0.24 1.13 2.71 497 1.01
flare[69,6] 0.44 0.03 0.565.2e-45 1.2e-7 0.14 0.77 1.77 310 1.01
flare[70,6] 0.1 8.7e-3 0.35.9e-29 1.2e-9 4.1e-6 2.3e-3 1.14 1205 1.0
flare[71,6] 0.08 0.01 0.251.3e-16 4.1e-6 1.2e-3 0.03 0.92 546 1.0
flare[72,6] 0.19 0.06 0.468.6e-281.5e-12 4.4e-8 4.8e-3 1.61 67 1.08
flare[73,6] 0.13 0.01 0.291.3e-521.0e-15 1.9e-7 0.07 1.02 687 1.01
flare[74,6] 0.22 0.04 0.482.3e-32 2.5e-8 3.9e-4 0.13 1.76 175 1.02
flare[75,6] 0.22 0.03 0.476.0e-308.7e-10 1.1e-5 0.11 1.62 236 1.01
flare[76,6] 0.19 0.01 0.42.2e-421.7e-11 9.2e-5 0.16 1.43 1429 1.0
flare[77,6] 0.31 0.02 0.534.5e-43 2.9e-9 7.4e-3 0.44 1.75 921 1.01
flare[78,6] 0.08 0.01 0.251.1e-542.5e-173.4e-10 2.7e-5 0.93 510 1.01
flare[79,6] 0.17 0.02 0.371.8e-16 6.1e-5 8.5e-3 0.14 1.36 546 1.01
flare[80,6] 0.16 0.02 0.312.5e-461.1e-12 1.5e-5 0.2 1.07 371 1.02
flare[81,6] 0.19 0.02 0.341.8e-444.1e-13 1.3e-5 0.27 1.17 187 1.01
flare[82,6] 5.9e-3 2.3e-3 0.062.8e-221.7e-12 1.1e-8 1.2e-5 0.03 590 1.0
flare[83,6] 0.18 0.02 0.378.2e-411.2e-10 5.1e-5 0.14 1.28 279 1.02
flare[84,6] 0.08 0.02 0.252.8e-19 1.9e-8 2.2e-5 5.7e-3 1.03 196 1.02
flare[85,6] 0.43 0.04 0.571.9e-33 1.4e-7 0.13 0.74 1.89 255 1.02
flare[86,6] 0.53 0.05 0.662.1e-32 3.4e-5 0.23 0.91 2.18 188 1.02
flare[87,6] 0.03 8.6e-3 0.162.7e-565.1e-172.8e-10 5.9e-6 0.49 327 1.02
flare[88,6] 0.19 0.01 0.393.1e-33 2.3e-8 1.2e-3 0.15 1.38 1074 1.0
flare[89,6] 0.22 0.02 0.441.9e-501.1e-15 7.2e-6 0.23 1.52 645 1.0
flare[90,6] 0.47 0.03 0.658.7e-32 7.2e-6 0.07 0.84 2.03 436 1.01
flare[91,6] 0.34 0.03 0.523.6e-33 1.1e-9 0.01 0.62 1.72 408 1.0
flare[92,6] 0.38 0.03 0.592.9e-25 2.2e-3 0.08 0.55 2.04 499 1.01
flare[93,6] 0.33 0.04 0.575.3e-665.1e-10 1.5e-4 0.53 1.84 256 1.01
flare[94,6] 0.06 6.9e-3 0.221.5e-441.3e-14 7.6e-9 6.7e-5 0.78 962 1.01
flare[95,6] 0.11 9.1e-3 0.31.1e-551.3e-13 2.5e-6 0.01 1.1 1123 1.0
flare[96,6] 0.06 6.2e-3 0.216.2e-351.4e-12 4.6e-7 2.0e-3 0.75 1101 1.0
flare[97,6] 0.33 0.02 0.481.3e-541.8e-10 0.03 0.54 1.58 535 1.0
flare[98,6] 0.11 0.02 0.32.4e-27 1.1e-7 4.4e-4 0.03 1.19 285 1.01
flare[99,6] 0.12 0.01 0.327.5e-671.1e-16 3.3e-8 0.01 1.07 801 1.0
flare[0,7] 0.48 0.02 0.611.7e-23 5.6e-4 0.17 0.84 1.98 848 1.0
flare[1,7] 0.21 0.01 0.394.8e-572.3e-10 1.1e-3 0.25 1.37 942 1.0
flare[2,7] 0.04 6.3e-3 0.143.4e-23 3.4e-8 2.6e-5 2.4e-3 0.46 517 1.01
flare[3,7] 0.49 0.02 0.447.3e-22 0.12 0.42 0.76 1.5 387 1.0
flare[4,7] 1.05 0.03 0.78 6.1e-8 0.42 1.06 1.53 2.71 506 1.0
flare[5,7] 0.36 0.02 0.563.1e-17 3.4e-4 0.04 0.59 1.85 897 1.0
flare[6,7] 0.05 9.2e-3 0.189.2e-15 1.9e-7 6.2e-5 7.7e-3 0.69 404 1.01
flare[7,7] 0.52 0.04 0.675.3e-44 1.6e-4 0.21 0.88 2.29 297 1.02
flare[8,7] 0.16 0.04 0.415.5e-296.9e-13 3.0e-8 3.1e-3 1.5 111 1.03
flare[9,7] 0.06 6.2e-3 0.21.3e-471.2e-15 1.6e-8 5.5e-4 0.7 1002 1.0
flare[10,7] 0.18 0.01 0.391.2e-23 4.4e-9 7.6e-5 0.13 1.37 829 1.0
flare[11,7] 0.04 5.6e-3 0.171.4e-343.3e-12 2.4e-7 2.0e-4 0.55 867 1.0
flare[12,7] 0.11 7.7e-3 0.35.9e-551.6e-15 3.0e-7 0.01 1.09 1505 1.0
flare[13,7] 0.25 0.02 0.41.9e-571.6e-11 8.9e-3 0.38 1.37 543 1.01
flare[14,7] 0.33 0.02 0.439.2e-33 2.7e-7 0.13 0.57 1.47 441 1.01
flare[15,7] 0.08 0.01 0.252.1e-465.6e-12 1.7e-6 3.6e-3 0.96 339 1.01
flare[16,7] 0.11 9.2e-3 0.35.3e-444.6e-11 1.6e-5 0.02 1.09 1047 1.0
flare[17,7] 0.38 0.02 0.562.4e-28 1.8e-6 0.05 0.64 1.87 822 1.01
flare[18,7] 0.14 0.01 0.371.4e-362.4e-12 3.3e-7 9.2e-3 1.3 679 1.01
flare[19,7] 0.42 0.02 0.51.3e-25 6.6e-6 0.21 0.73 1.61 412 1.0
flare[20,7] 0.13 0.01 0.325.8e-501.7e-13 4.0e-7 0.03 1.16 848 1.0
flare[21,7] 0.19 0.01 0.48.2e-26 1.7e-7 2.4e-3 0.15 1.4 1441 1.0
flare[22,7] 0.1 0.01 0.283.0e-502.8e-13 1.2e-6 0.02 0.95 489 1.01
flare[23,7] 0.28 0.02 0.512.2e-23 1.1e-6 5.4e-3 0.32 1.69 505 1.01
flare[24,7] 0.16 0.01 0.317.6e-39 2.5e-7 2.7e-3 0.18 1.13 498 1.01
flare[25,7] 0.07 6.0e-3 0.214.6e-472.4e-15 2.3e-8 1.2e-3 0.8 1273 1.0
flare[26,7] 0.26 0.02 0.451.3e-451.9e-11 1.2e-3 0.37 1.62 537 1.01
flare[27,7] 0.29 0.02 0.553.1e-12 6.4e-5 8.8e-3 0.29 1.92 717 1.01
flare[28,7] 0.23 0.02 0.421.9e-682.1e-14 1.7e-5 0.34 1.45 307 1.01
flare[29,7] 0.12 8.3e-3 0.282.5e-382.2e-11 1.3e-5 0.04 1.02 1151 1.0
flare[30,7] 0.38 0.02 0.466.2e-46 5.7e-8 0.22 0.63 1.55 468 1.01
flare[31,7] 0.12 0.01 0.328.9e-292.7e-10 1.8e-5 0.02 1.11 457 1.01
flare[32,7] 0.21 0.02 0.444.1e-482.0e-10 2.7e-4 0.17 1.55 663 1.01
flare[33,7] 0.27 0.01 0.431.1e-27 9.2e-7 0.02 0.42 1.48 967 1.0
flare[34,7] 0.23 0.02 0.463.9e-33 6.7e-8 4.6e-4 0.19 1.57 439 1.02
flare[35,7] 0.19 9.7e-3 0.381.4e-442.0e-11 1.7e-4 0.19 1.36 1556 1.0
flare[36,7] 0.11 9.7e-3 0.295.7e-491.5e-15 1.8e-6 0.04 1.05 880 1.0
flare[37,7] 1.85 0.06 0.95 0.53 1.19 1.64 2.3 4.2 287 1.02
flare[38,7] 0.21 0.02 0.441.4e-427.0e-11 1.2e-4 0.17 1.57 431 1.01
flare[39,7] 0.04 5.1e-3 0.172.1e-588.4e-13 3.9e-7 5.9e-4 0.55 1115 1.0
flare[40,7] 0.25 0.03 0.52.6e-25 6.9e-7 1.3e-3 0.22 1.66 389 1.01
flare[41,7] 0.55 0.03 0.683.1e-25 4.5e-5 0.21 1.0 2.12 433 1.0
flare[42,7] 0.11 9.7e-3 0.294.7e-413.5e-15 7.3e-8 5.8e-3 1.12 907 1.0
flare[43,7] 0.38 0.01 0.562.2e-20 1.8e-4 0.05 0.62 1.83 1445 1.0
flare[44,7] 0.43 0.03 0.612.7e-31 1.3e-9 0.02 0.81 1.99 443 1.0
flare[45,7] 0.22 0.01 0.422.9e-549.6e-12 4.8e-4 0.28 1.45 1307 1.0
flare[46,7] 0.04 5.9e-3 0.187.4e-55 4.1e-8 3.5e-5 2.5e-3 0.58 926 1.01
flare[47,7] 0.19 0.02 0.41.1e-358.0e-12 2.3e-6 0.13 1.39 438 1.0
flare[48,7] 0.1 0.01 0.33.9e-35 9.6e-8 1.1e-4 0.01 1.17 724 1.0
flare[49,7] 0.67 0.06 0.795.5e-21 3.9e-5 0.26 1.26 2.49 202 1.02
flare[50,7] 0.14 0.02 0.378.9e-548.4e-14 4.6e-7 0.03 1.37 428 1.01
flare[51,7] 0.11 9.2e-3 0.283.6e-403.0e-10 1.2e-5 0.01 1.03 946 1.0
flare[52,7] 0.06 0.01 0.21.8e-322.2e-13 3.4e-8 2.8e-4 0.72 274 1.02
flare[53,7] 0.16 0.01 0.311.0e-397.1e-12 8.2e-5 0.2 1.05 641 1.01
flare[54,7] 0.28 0.03 0.485.6e-57 4.1e-9 8.7e-3 0.42 1.69 338 1.01
flare[55,7] 0.37 0.02 0.522.9e-41 1.7e-7 0.05 0.64 1.7 935 1.01
flare[56,7] 0.19 0.02 0.423.6e-274.3e-10 8.1e-6 0.03 1.44 340 1.01
flare[57,7] 0.39 0.02 0.52.6e-487.1e-11 0.14 0.68 1.61 422 1.01
flare[58,7] 0.36 0.03 0.551.1e-28 5.4e-8 4.6e-3 0.64 1.76 309 1.01
flare[59,7] 0.72 0.07 0.8 5.9e-8 0.01 0.46 1.25 2.56 144 1.03
flare[60,7] 0.81 0.03 0.844.0e-12 0.03 0.6 1.36 2.81 690 1.01
flare[61,7] 0.82 0.04 0.661.4e-23 0.32 0.76 1.17 2.36 339 1.01
flare[62,7] 0.56 0.03 0.712.8e-40 7.3e-7 0.24 0.99 2.2 408 1.01
flare[63,7] 0.53 0.03 0.698.4e-28 3.0e-5 0.15 0.95 2.16 547 1.01
flare[64,7] 0.06 7.5e-3 0.211.1e-318.4e-12 1.1e-6 1.4e-3 0.74 756 1.01
flare[65,7] 0.26 0.03 0.518.4e-525.2e-11 7.1e-5 0.29 1.75 280 1.01
flare[66,7] 0.84 0.04 0.632.2e-23 0.4 0.79 1.17 2.26 279 1.01
flare[67,7] 0.41 0.02 0.467.9e-42 3.2e-6 0.28 0.68 1.45 419 1.01
flare[68,7] 1.17 0.04 1.04 2.4e-9 0.29 1.04 1.7 3.8 868 1.0
flare[69,7] 0.4 0.02 0.493.9e-40 1.4e-5 0.19 0.68 1.66 390 1.01
flare[70,7] 0.14 0.01 0.361.1e-24 1.6e-7 1.3e-4 0.03 1.25 1219 1.0
flare[71,7] 0.27 0.02 0.496.4e-13 1.6e-4 0.02 0.29 1.71 657 1.0
flare[72,7] 0.18 0.04 0.418.1e-249.1e-11 9.5e-7 0.02 1.46 85 1.06
flare[73,7] 0.13 8.2e-3 0.285.6e-472.5e-13 7.6e-6 0.11 1.0 1193 1.0
flare[74,7] 0.36 0.03 0.616.8e-29 1.0e-6 8.2e-3 0.56 2.15 544 1.01
flare[75,7] 0.24 0.03 0.468.1e-26 4.1e-8 1.3e-4 0.23 1.57 272 1.01
flare[76,7] 0.31 0.02 0.495.3e-37 1.5e-9 6.9e-3 0.48 1.63 628 1.01
flare[77,7] 0.53 0.03 0.73.8e-36 3.7e-7 0.16 0.94 2.27 685 1.0
flare[78,7] 0.08 0.01 0.251.9e-496.1e-15 1.1e-8 3.5e-4 0.92 500 1.01
flare[79,7] 0.46 0.02 0.635.4e-14 2.0e-3 0.14 0.74 2.16 933 1.0
flare[80,7] 0.15 0.01 0.291.6e-408.9e-11 3.9e-4 0.19 1.04 583 1.01
flare[81,7] 0.16 0.02 0.32.1e-395.7e-11 2.1e-4 0.23 1.02 242 1.01
flare[82,7] 8.0e-3 2.3e-3 0.064.2e-205.2e-11 1.2e-7 6.1e-5 0.06 693 1.0
flare[83,7] 0.22 0.02 0.413.6e-34 6.9e-9 6.7e-4 0.28 1.38 378 1.02
flare[84,7] 0.12 0.02 0.331.1e-16 4.4e-7 2.1e-4 0.03 1.22 214 1.02
flare[85,7] 0.5 0.04 0.644.7e-29 8.1e-6 0.27 0.83 2.03 310 1.01
flare[86,7] 0.65 0.06 0.661.3e-29 0.01 0.51 1.05 2.19 124 1.02
flare[87,7] 0.04 6.0e-3 0.156.0e-511.1e-14 1.1e-8 7.4e-5 0.51 664 1.01
flare[88,7] 0.36 0.02 0.562.0e-29 2.4e-6 0.03 0.58 1.92 652 1.0
flare[89,7] 0.27 0.02 0.466.4e-458.5e-14 2.5e-4 0.42 1.54 451 1.0
flare[90,7] 0.63 0.04 0.736.6e-26 6.7e-4 0.41 1.05 2.37 418 1.01
flare[91,7] 0.46 0.03 0.579.2e-29 2.2e-7 0.19 0.79 1.85 435 1.01
flare[92,7] 1.03 0.04 0.994.3e-18 0.16 0.85 1.58 3.26 520 1.01
flare[93,7] 0.34 0.03 0.554.2e-61 4.4e-8 1.8e-3 0.6 1.79 268 1.01
flare[94,7] 0.08 9.1e-3 0.258.8e-392.3e-12 2.5e-7 1.1e-3 0.93 739 1.01
flare[95,7] 0.16 0.01 0.363.9e-463.2e-11 6.7e-5 0.11 1.26 1305 1.0
flare[96,7] 0.1 0.01 0.31.3e-316.1e-11 7.2e-6 0.01 1.08 681 1.01
flare[97,7] 0.32 0.02 0.441.3e-47 1.5e-8 0.09 0.52 1.5 619 1.0
flare[98,7] 0.23 0.02 0.463.2e-24 4.4e-6 6.7e-3 0.21 1.71 443 1.01
flare[99,7] 0.15 0.01 0.351.1e-564.8e-14 2.0e-6 0.06 1.22 883 1.0
flare[0,8] 0.65 0.03 0.651.6e-20 0.03 0.51 1.05 2.11 510 1.01
flare[1,8] 0.3 0.02 0.461.1e-50 1.2e-7 0.04 0.46 1.62 646 1.0
flare[2,8] 0.08 0.01 0.252.0e-19 1.4e-6 4.0e-4 0.02 0.85 458 1.01
flare[3,8] 0.41 0.02 0.393.1e-18 0.09 0.33 0.63 1.33 543 1.0
flare[4,8] 1.02 0.02 0.68 1.2e-5 0.53 0.98 1.37 2.53 811 1.0
flare[5,8] 0.68 0.02 0.712.4e-14 0.05 0.49 1.11 2.38 818 1.0
flare[6,8] 0.11 0.01 0.34.1e-12 6.0e-6 7.8e-4 0.04 1.09 436 1.01
flare[7,8] 0.68 0.05 0.723.2e-34 0.02 0.49 1.08 2.39 246 1.01
flare[8,8] 0.21 0.05 0.482.3e-252.7e-11 3.8e-7 0.01 1.63 96 1.03
flare[9,8] 0.09 9.9e-3 0.261.8e-435.2e-13 6.9e-7 5.5e-3 1.0 682 1.0
flare[10,8] 0.24 0.01 0.466.6e-19 4.0e-7 1.8e-3 0.29 1.56 948 1.0
flare[11,8] 0.06 7.3e-3 0.229.9e-304.5e-10 7.3e-6 2.8e-3 0.74 868 1.0
flare[12,8] 0.14 0.01 0.329.2e-464.8e-13 9.7e-6 0.07 1.18 880 1.0
flare[13,8] 0.26 0.02 0.46.1e-49 1.8e-9 0.02 0.39 1.42 545 1.0
flare[14,8] 0.33 0.02 0.426.6e-29 5.8e-5 0.17 0.52 1.47 436 1.02
flare[15,8] 0.13 0.02 0.312.5e-414.3e-10 4.0e-5 0.03 1.12 371 1.01
flare[16,8] 0.21 0.02 0.431.7e-37 8.0e-9 4.6e-4 0.18 1.55 620 1.01
flare[17,8] 0.49 0.02 0.582.3e-25 3.5e-4 0.27 0.81 1.94 611 1.01
flare[18,8] 0.15 0.01 0.359.0e-321.3e-10 6.0e-6 0.05 1.21 778 1.01
flare[19,8] 0.36 0.02 0.432.8e-22 1.5e-4 0.21 0.62 1.37 613 1.0
flare[20,8] 0.17 0.01 0.361.0e-432.5e-11 1.3e-5 0.11 1.32 791 1.0
flare[21,8] 0.43 0.03 0.611.0e-22 2.3e-5 0.06 0.74 1.94 538 1.01
flare[22,8] 0.11 0.01 0.281.0e-435.9e-11 4.0e-5 0.05 0.98 544 1.01
flare[23,8] 0.45 0.03 0.622.9e-20 5.7e-5 0.09 0.79 1.99 465 1.01
flare[24,8] 0.26 0.01 0.438.5e-32 3.4e-5 0.03 0.34 1.48 855 1.01
flare[25,8] 0.1 6.3e-3 0.252.0e-406.8e-13 2.0e-6 0.02 0.95 1625 1.0
flare[26,8] 0.29 0.02 0.451.2e-40 3.6e-9 0.02 0.45 1.59 603 1.01
flare[27,8] 0.58 0.03 0.79 5.3e-9 2.9e-3 0.16 0.98 2.63 768 1.0
flare[28,8] 0.19 0.02 0.333.1e-611.6e-12 2.3e-4 0.29 1.16 329 1.01
flare[29,8] 0.15 8.7e-3 0.324.5e-33 6.3e-9 3.3e-4 0.14 1.19 1381 1.0
flare[30,8] 0.29 0.02 0.354.1e-41 5.5e-6 0.15 0.47 1.19 340 1.01
flare[31,8] 0.21 0.02 0.431.9e-24 1.6e-8 4.0e-4 0.17 1.49 569 1.01
flare[32,8] 0.3 0.02 0.518.8e-42 4.2e-8 8.3e-3 0.45 1.76 630 1.0
flare[33,8] 0.35 0.03 0.57.2e-23 1.1e-4 0.1 0.54 1.86 356 1.01
flare[34,8] 0.4 0.04 0.655.2e-28 5.4e-6 9.8e-3 0.64 2.21 342 1.01
flare[35,8] 0.24 0.01 0.436.0e-39 2.5e-9 2.3e-3 0.32 1.54 942 1.0
flare[36,8] 0.17 10.0e-3 0.358.4e-435.4e-13 1.2e-4 0.15 1.19 1209 1.0
flare[37,8] 1.39 0.03 0.69 0.36 0.93 1.26 1.72 3.07 432 1.01
flare[38,8] 0.27 0.02 0.474.7e-37 1.1e-8 3.3e-3 0.41 1.59 824 1.0
flare[39,8] 0.07 6.9e-3 0.232.0e-501.5e-10 1.1e-5 6.3e-3 0.85 1113 1.0
flare[40,8] 0.32 0.02 0.528.5e-20 1.1e-4 0.03 0.48 1.74 697 1.01
flare[41,8] 0.66 0.03 0.73.9e-22 1.4e-3 0.49 1.15 2.22 532 1.0
flare[42,8] 0.13 9.3e-3 0.294.1e-366.0e-13 4.0e-6 0.05 1.08 1000 1.0
flare[43,8] 0.58 0.03 0.682.6e-17 3.4e-3 0.35 1.0 2.23 514 1.01
flare[44,8] 0.46 0.03 0.591.1e-27 8.8e-8 0.09 0.85 1.91 336 1.0
flare[45,8] 0.29 0.02 0.489.1e-50 1.6e-9 9.2e-3 0.44 1.6 904 1.0
flare[46,8] 0.09 8.9e-3 0.272.0e-49 3.1e-6 7.1e-4 0.02 0.98 935 1.0
flare[47,8] 0.19 0.02 0.373.2e-315.8e-10 4.7e-5 0.19 1.29 557 1.0
flare[48,8] 0.16 0.01 0.354.3e-28 1.3e-5 3.1e-3 0.09 1.28 954 1.01
flare[49,8] 0.75 0.06 0.791.6e-18 6.2e-4 0.57 1.37 2.48 198 1.02
flare[50,8] 0.18 0.03 0.428.0e-489.4e-12 7.9e-6 0.11 1.34 221 1.02
flare[51,8] 0.15 9.2e-3 0.328.1e-39 4.9e-8 4.4e-4 0.08 1.14 1250 1.0
flare[52,8] 0.07 9.4e-3 0.231.7e-281.9e-11 6.3e-7 2.7e-3 0.83 617 1.02
flare[53,8] 0.17 10.0e-3 0.315.6e-368.0e-10 1.8e-3 0.22 1.1 956 1.0
flare[54,8] 0.4 0.03 0.546.7e-51 5.2e-7 0.13 0.69 1.75 326 1.01
flare[55,8] 0.46 0.02 0.565.8e-36 1.5e-4 0.24 0.77 1.82 620 1.01
flare[56,8] 0.18 0.02 0.381.6e-22 3.9e-8 1.6e-4 0.1 1.34 378 1.01
flare[57,8] 0.32 0.02 0.428.0e-43 3.8e-9 0.14 0.54 1.38 496 1.01
flare[58,8] 0.39 0.03 0.551.1e-24 1.9e-6 0.03 0.7 1.77 344 1.01
flare[59,8] 1.02 0.04 0.85 6.7e-5 0.31 0.95 1.47 2.97 581 1.01
flare[60,8] 1.1 0.04 0.92.9e-10 0.34 1.02 1.62 3.21 645 1.01
flare[61,8] 0.71 0.03 0.551.2e-20 0.3 0.64 1.0 1.99 405 1.01
flare[62,8] 0.6 0.04 0.652.2e-32 2.5e-5 0.45 1.01 2.09 297 1.01
flare[63,8] 0.6 0.03 0.652.9e-23 3.6e-3 0.38 1.03 2.14 533 1.01
flare[64,8] 0.1 0.01 0.281.4e-278.4e-10 2.7e-5 0.01 1.07 701 1.01
flare[65,8] 0.29 0.03 0.55.4e-45 2.7e-9 1.1e-3 0.44 1.69 327 1.01
flare[66,8] 0.66 0.02 0.483.9e-21 0.29 0.61 0.94 1.68 475 1.01
flare[67,8] 0.3 0.01 0.352.7e-36 1.2e-4 0.19 0.5 1.16 609 1.0
flare[68,8] 1.6 0.06 1.14 1.9e-7 0.86 1.4 2.04 4.63 381 1.02
flare[69,8] 0.39 0.02 0.467.1e-35 7.8e-4 0.23 0.65 1.56 528 1.01
flare[70,8] 0.22 0.01 0.449.6e-20 1.6e-5 3.1e-3 0.2 1.49 1063 1.0
flare[71,8] 0.64 0.02 0.75 1.1e-9 6.0e-3 0.32 1.12 2.41 899 1.0
flare[72,8] 0.18 0.03 0.383.3e-20 5.6e-9 1.7e-5 0.09 1.3 123 1.04
flare[73,8] 0.14 9.8e-3 0.38.6e-427.4e-11 1.9e-4 0.13 1.08 965 1.0
flare[74,8] 0.53 0.03 0.73.8e-25 3.5e-5 0.08 0.98 2.23 517 1.01
flare[75,8] 0.26 0.03 0.472.1e-21 1.3e-6 1.6e-3 0.34 1.59 318 1.01
flare[76,8] 0.39 0.03 0.547.5e-33 1.4e-7 0.08 0.67 1.82 458 1.01
flare[77,8] 0.61 0.03 0.695.2e-31 1.6e-5 0.39 1.04 2.24 407 1.01
flare[78,8] 0.09 9.6e-3 0.243.8e-461.8e-12 3.7e-7 4.3e-3 0.94 654 1.01
flare[79,8] 0.93 0.04 0.942.3e-11 0.05 0.8 1.46 3.31 477 1.01
flare[80,8] 0.18 0.01 0.345.1e-34 1.1e-8 5.3e-3 0.21 1.2 1001 1.0
flare[81,8] 0.16 0.01 0.299.1e-34 7.2e-9 2.3e-3 0.21 1.02 487 1.0
flare[82,8] 0.01 2.6e-3 0.078.4e-18 1.2e-9 1.3e-6 2.9e-4 0.11 740 1.0
flare[83,8] 0.26 0.02 0.454.2e-30 6.0e-7 6.9e-3 0.39 1.46 442 1.02
flare[84,8] 0.2 0.03 0.433.6e-14 8.6e-6 2.1e-3 0.12 1.5 268 1.01
flare[85,8] 0.48 0.03 0.581.1e-25 2.1e-4 0.29 0.76 1.92 301 1.01
flare[86,8] 0.64 0.05 0.593.2e-26 0.12 0.53 0.97 2.06 156 1.02
flare[87,8] 0.06 6.0e-3 0.21.2e-442.2e-12 4.7e-7 8.7e-4 0.73 1161 1.0
flare[88,8] 0.52 0.03 0.663.7e-25 1.0e-4 0.27 0.88 2.22 615 1.0
flare[89,8] 0.25 0.02 0.413.4e-397.4e-12 3.2e-3 0.4 1.35 556 1.0
flare[90,8] 0.74 0.05 0.754.8e-21 0.01 0.64 1.14 2.51 218 1.02
flare[91,8] 0.53 0.03 0.597.4e-25 1.9e-5 0.41 0.87 1.93 294 1.02
flare[92,8] 1.4 0.04 0.993.2e-11 0.75 1.3 1.91 3.8 500 1.01
flare[93,8] 0.32 0.03 0.491.3e-55 2.8e-6 0.02 0.56 1.66 278 1.02
flare[94,8] 0.1 9.8e-3 0.278.9e-343.4e-10 8.9e-6 0.01 1.03 738 1.01
flare[95,8] 0.25 0.01 0.456.4e-34 3.6e-9 2.6e-3 0.33 1.56 1120 1.0
flare[96,8] 0.18 0.02 0.394.2e-28 2.0e-9 9.8e-5 0.09 1.32 370 1.0
flare[97,8] 0.29 0.02 0.46.3e-42 8.8e-7 0.11 0.46 1.37 619 1.0
flare[98,8] 0.46 0.03 0.675.3e-21 1.6e-4 0.08 0.75 2.18 651 1.01
flare[99,8] 0.16 0.01 0.344.1e-541.2e-11 7.7e-5 0.14 1.25 889 1.0
flare[0,9] 0.76 0.04 0.651.1e-17 0.2 0.68 1.17 2.14 301 1.01
flare[1,9] 0.42 0.03 0.553.5e-44 1.4e-5 0.17 0.69 1.91 331 1.01
flare[2,9] 0.17 0.02 0.364.8e-16 5.2e-5 6.0e-3 0.11 1.28 505 1.01
flare[3,9] 0.35 0.01 0.351.1e-14 0.07 0.26 0.52 1.19 620 1.0
flare[4,9] 0.98 0.03 0.68 1.1e-3 0.53 0.9 1.28 2.51 444 1.01
flare[5,9] 1.04 0.04 0.836.2e-12 0.43 0.93 1.47 3.02 487 1.01
flare[6,9] 0.26 0.02 0.53 1.6e-9 1.7e-4 0.01 0.25 1.83 461 1.01
flare[7,9] 0.71 0.04 0.681.4e-30 0.14 0.58 1.08 2.22 239 1.02
flare[8,9] 0.24 0.06 0.512.6e-22 1.1e-9 4.8e-6 0.04 1.73 84 1.04
flare[9,9] 0.12 0.01 0.292.5e-379.0e-11 1.9e-5 0.03 1.02 770 1.01
flare[10,9] 0.33 0.02 0.521.1e-14 4.4e-5 0.03 0.5 1.77 1021 1.0
flare[11,9] 0.11 8.8e-3 0.294.9e-25 4.4e-8 2.2e-4 0.03 1.08 1084 1.0
flare[12,9] 0.17 0.01 0.344.8e-391.6e-10 2.9e-4 0.19 1.19 991 1.0
flare[13,9] 0.23 0.01 0.362.6e-40 3.7e-7 0.03 0.35 1.31 1045 1.0
flare[14,9] 0.35 0.02 0.452.7e-24 2.1e-3 0.18 0.52 1.54 328 1.02
flare[15,9] 0.2 0.02 0.394.7e-36 3.7e-8 1.1e-3 0.18 1.37 520 1.0
flare[16,9] 0.33 0.02 0.524.1e-32 9.9e-7 9.0e-3 0.52 1.76 474 1.01
flare[17,9] 0.55 0.02 0.581.3e-22 0.01 0.39 0.9 1.89 576 1.01
flare[18,9] 0.18 0.02 0.379.5e-28 4.9e-9 8.0e-5 0.15 1.25 488 1.01
flare[19,9] 0.35 0.02 0.444.1e-19 1.4e-3 0.2 0.55 1.41 503 1.01
flare[20,9] 0.2 0.02 0.43.5e-38 1.7e-9 2.9e-4 0.22 1.36 329 1.01
flare[21,9] 0.67 0.05 0.766.5e-20 8.4e-4 0.47 1.14 2.4 253 1.02
flare[22,9] 0.15 0.01 0.359.3e-38 1.2e-810.0e-4 0.11 1.26 622 1.0
flare[23,9] 0.66 0.04 0.722.3e-17 3.5e-3 0.43 1.16 2.27 369 1.01
flare[24,9] 0.41 0.02 0.593.8e-27 2.0e-3 0.13 0.65 2.09 823 1.01
flare[25,9] 0.16 0.01 0.331.6e-356.9e-11 1.3e-4 0.13 1.19 704 1.0
flare[26,9] 0.3 0.02 0.439.4e-37 8.4e-7 0.07 0.49 1.5 643 1.0
flare[27,9] 1.02 0.05 1.02 6.8e-6 0.09 0.86 1.57 3.79 405 1.01
flare[28,9] 0.16 0.01 0.291.4e-541.3e-10 1.9e-3 0.22 1.02 415 1.01
flare[29,9] 0.22 0.01 0.391.1e-28 7.8e-7 9.8e-3 0.28 1.35 686 1.01
flare[30,9] 0.24 0.02 0.35.9e-37 1.0e-4 0.12 0.37 1.07 402 1.01
flare[31,9] 0.37 0.03 0.62.4e-20 8.2e-7 5.3e-3 0.58 2.06 481 1.01
flare[32,9] 0.4 0.03 0.561.6e-36 3.9e-6 0.09 0.68 1.92 389 1.01
flare[33,9] 0.46 0.03 0.586.9e-20 5.1e-3 0.24 0.71 1.97 280 1.01
flare[34,9] 0.51 0.04 0.683.4e-22 3.4e-4 0.12 0.88 2.21 338 1.01
flare[35,9] 0.27 0.01 0.442.6e-33 2.7e-7 0.01 0.4 1.47 915 1.0
flare[36,9] 0.22 0.01 0.393.3e-374.2e-10 3.0e-3 0.29 1.4 966 1.0
flare[37,9] 1.02 0.02 0.53 0.19 0.66 0.95 1.27 2.26 690 1.01
flare[38,9] 0.33 0.02 0.491.8e-31 1.5e-6 0.04 0.53 1.67 916 1.0
flare[39,9] 0.14 0.02 0.341.9e-43 2.1e-8 3.7e-4 0.06 1.25 402 1.01
flare[40,9] 0.51 0.02 0.621.2e-13 7.8e-3 0.28 0.84 2.07 963 1.01
flare[41,9] 0.69 0.03 0.664.8e-19 0.02 0.58 1.12 2.12 527 1.0
flare[42,9] 0.17 0.01 0.337.3e-324.7e-11 1.0e-4 0.18 1.2 934 1.0
flare[43,9] 0.7 0.04 0.72.0e-14 0.03 0.55 1.14 2.26 273 1.01
flare[44,9] 0.42 0.03 0.531.2e-24 3.1e-6 0.15 0.74 1.68 336 1.0
flare[45,9] 0.3 0.02 0.449.4e-45 8.1e-8 0.04 0.5 1.44 707 1.0
flare[46,9] 0.21 0.01 0.422.4e-44 2.0e-4 0.01 0.2 1.52 943 1.0
flare[47,9] 0.2 0.01 0.381.4e-26 4.0e-8 6.5e-4 0.25 1.35 795 1.0
flare[48,9] 0.33 0.01 0.511.0e-21 1.4e-3 0.07 0.5 1.7 1515 1.0
flare[49,9] 0.8 0.05 0.775.8e-16 0.01 0.76 1.39 2.37 206 1.02
flare[50,9] 0.19 0.02 0.371.9e-425.6e-10 1.8e-4 0.22 1.28 539 1.01
flare[51,9] 0.24 0.01 0.413.7e-38 7.0e-6 0.01 0.32 1.47 1426 1.0
flare[52,9] 0.11 0.02 0.311.3e-24 1.1e-9 1.3e-5 0.02 1.2 371 1.01
flare[53,9] 0.19 0.01 0.341.4e-32 4.2e-8 9.6e-3 0.23 1.25 772 1.0
flare[54,9] 0.47 0.03 0.565.3e-44 3.7e-5 0.27 0.79 1.88 323 1.01
flare[55,9] 0.51 0.02 0.555.3e-31 0.02 0.35 0.82 1.9 599 1.01
flare[56,9] 0.2 0.02 0.372.6e-18 3.1e-6 2.5e-3 0.22 1.31 486 1.01
flare[57,9] 0.26 0.02 0.351.6e-37 1.3e-7 0.12 0.42 1.18 522 1.01
flare[58,9] 0.42 0.03 0.546.6e-21 8.6e-5 0.15 0.73 1.73 451 1.01
flare[59,9] 1.39 0.03 0.93 0.02 0.77 1.24 1.83 3.74 813 1.01
flare[60,9] 1.23 0.04 0.86 1.1e-8 0.65 1.14 1.66 3.19 496 1.0
flare[61,9] 0.6 0.02 0.483.3e-18 0.25 0.52 0.84 1.74 424 1.01
flare[62,9] 0.53 0.03 0.566.3e-28 2.8e-4 0.42 0.87 1.84 278 1.01
flare[63,9] 0.67 0.03 0.646.0e-19 0.07 0.56 1.06 2.18 368 1.0
flare[64,9] 0.18 0.02 0.44.4e-24 8.9e-8 5.3e-4 0.09 1.44 646 1.01
flare[65,9] 0.3 0.02 0.465.2e-39 1.0e-7 0.01 0.51 1.56 386 1.01
flare[66,9] 0.51 0.02 0.45.5e-19 0.2 0.45 0.74 1.42 458 1.01
flare[67,9] 0.24 0.01 0.292.7e-32 1.6e-3 0.14 0.38 1.0 476 1.0
flare[68,9] 1.38 0.04 0.9 9.8e-6 0.8 1.25 1.77 3.75 532 1.01
flare[69,9] 0.42 0.02 0.51.5e-28 0.02 0.27 0.66 1.65 677 1.01
flare[70,9] 0.36 0.02 0.531.2e-15 1.0e-3 0.06 0.58 1.8 894 1.0
flare[71,9] 1.02 0.04 0.88 1.4e-6 0.13 0.98 1.57 2.99 563 1.01
flare[72,9] 0.19 0.02 0.399.1e-17 2.8e-7 3.0e-4 0.16 1.35 238 1.02
flare[73,9] 0.16 9.6e-3 0.324.9e-38 6.0e-9 1.7e-3 0.17 1.12 1093 1.0
flare[74,9] 0.62 0.04 0.721.1e-21 1.1e-3 0.29 1.12 2.2 407 1.01
flare[75,9] 0.31 0.03 0.498.6e-18 4.2e-5 0.02 0.49 1.59 360 1.01
flare[76,9] 0.37 0.02 0.481.0e-28 5.8e-6 0.15 0.62 1.64 539 1.01
flare[77,9] 0.57 0.03 0.611.4e-26 4.1e-4 0.41 0.95 2.01 317 1.01
flare[78,9] 0.1 9.1e-3 0.263.7e-422.6e-10 1.4e-5 0.02 0.95 835 1.01
flare[79,9] 1.22 0.05 0.99 4.1e-9 0.47 1.16 1.7 3.51 348 1.01
flare[80,9] 0.22 0.01 0.386.7e-28 6.1e-7 0.02 0.27 1.32 960 1.0
flare[81,9] 0.18 0.01 0.348.6e-30 4.9e-7 9.5e-3 0.22 1.24 565 1.0
flare[82,9] 0.02 4.5e-3 0.111.3e-15 3.3e-8 1.3e-5 1.3e-3 0.21 572 1.01
flare[83,9] 0.3 0.02 0.452.2e-25 2.2e-5 0.03 0.49 1.47 461 1.01
flare[84,9] 0.34 0.03 0.587.9e-12 1.8e-4 0.02 0.43 1.95 393 1.01
flare[85,9] 0.41 0.03 0.491.2e-22 2.0e-3 0.25 0.66 1.63 375 1.01
flare[86,9] 0.5 0.02 0.461.6e-22 0.11 0.41 0.75 1.62 469 1.01
flare[87,9] 0.09 7.7e-3 0.276.7e-382.6e-10 1.4e-5 0.01 0.99 1236 1.0
flare[88,9] 0.57 0.03 0.635.7e-22 1.4e-3 0.4 0.94 2.1 602 1.0
flare[89,9] 0.22 0.01 0.355.7e-353.7e-10 0.01 0.34 1.2 737 1.0
flare[90,9] 0.71 0.03 0.673.7e-18 0.08 0.59 1.06 2.39 420 1.02
flare[91,9] 0.46 0.02 0.495.9e-21 3.8e-4 0.36 0.75 1.62 421 1.02
flare[92,9] 1.22 0.04 0.81 1.2e-7 0.67 1.13 1.65 3.12 444 1.01
flare[93,9] 0.33 0.03 0.452.5e-50 2.3e-4 0.1 0.57 1.52 195 1.02
flare[94,9] 0.13 0.01 0.33.9e-29 3.7e-8 2.7e-4 0.07 1.09 803 1.0
flare[95,9] 0.35 0.02 0.534.8e-28 4.0e-7 0.04 0.56 1.85 605 1.0
flare[96,9] 0.28 0.03 0.516.2e-25 7.1e-8 1.4e-3 0.36 1.81 319 1.0
flare[97,9] 0.27 0.02 0.394.8e-36 1.8e-5 0.1 0.41 1.34 644 1.0
flare[98,9] 0.71 0.04 0.814.0e-18 3.8e-3 0.46 1.2 2.64 404 1.02
flare[99,9] 0.21 0.01 0.47.8e-46 1.6e-9 1.7e-3 0.26 1.4 892 1.0
flare[0,10] 0.68 0.03 0.552.4e-15 0.23 0.61 1.02 1.91 270 1.01
flare[1,10] 0.41 0.03 0.493.5e-36 4.7e-4 0.23 0.67 1.66 300 1.02
flare[2,10] 0.38 0.02 0.572.8e-12 1.6e-3 0.08 0.58 1.92 638 1.01
flare[3,10] 0.3 0.01 0.323.7e-11 0.06 0.2 0.44 1.12 717 1.0
flare[4,10] 0.83 0.02 0.58 3.5e-3 0.42 0.75 1.12 2.2 533 1.01
flare[5,10] 0.89 0.03 0.669.3e-10 0.4 0.82 1.25 2.41 593 1.01
flare[6,10] 0.57 0.04 0.84 6.5e-7 4.6e-3 0.12 0.96 2.8 564 1.01
flare[7,10] 0.58 0.03 0.555.4e-26 0.14 0.48 0.89 1.83 340 1.01
flare[8,10] 0.25 0.06 0.521.1e-18 4.3e-8 6.6e-5 0.11 1.78 79 1.04
flare[9,10] 0.15 0.01 0.331.1e-32 1.5e-8 5.4e-4 0.12 1.16 736 1.01
flare[10,10] 0.47 0.02 0.65.2e-11 2.8e-3 0.19 0.79 1.92 1476 1.0
flare[11,10] 0.23 0.01 0.422.2e-20 3.4e-6 5.9e-3 0.27 1.53 1166 1.0
flare[12,10] 0.21 0.01 0.371.7e-32 3.2e-8 4.8e-3 0.28 1.29 1014 1.0
flare[13,10] 0.22 9.6e-3 0.353.8e-32 1.6e-5 0.05 0.3 1.19 1324 1.0
flare[14,10] 0.3 0.02 0.382.6e-20 3.9e-3 0.14 0.45 1.32 401 1.02
flare[15,10] 0.3 0.02 0.492.4e-30 2.9e-6 0.02 0.46 1.76 539 1.01
flare[16,10] 0.4 0.03 0.551.9e-25 8.8e-5 0.09 0.68 1.77 460 1.01
flare[17,10] 0.52 0.02 0.529.6e-20 0.04 0.4 0.85 1.72 609 1.0
flare[18,10] 0.19 0.02 0.361.3e-23 1.9e-7 7.5e-4 0.21 1.25 438 1.01
flare[19,10] 0.35 0.03 0.452.5e-16 4.5e-3 0.19 0.53 1.51 173 1.02
flare[20,10] 0.22 0.02 0.41.6e-32 1.6e-7 3.1e-3 0.29 1.42 354 1.01
flare[21,10] 0.71 0.04 0.691.5e-17 0.01 0.6 1.17 2.25 289 1.01
flare[22,10] 0.2 0.02 0.391.4e-31 9.5e-7 8.1e-3 0.23 1.4 653 1.01
flare[23,10] 0.8 0.04 0.797.7e-15 0.09 0.68 1.25 2.52 487 1.01
flare[24,10] 0.55 0.03 0.632.1e-22 0.03 0.31 0.9 2.15 359 1.01
flare[25,10] 0.24 0.01 0.432.3e-31 6.7e-9 4.1e-3 0.33 1.47 852 1.01
flare[26,10] 0.3 0.02 0.417.3e-33 2.7e-5 0.1 0.47 1.41 679 1.0
flare[27,10] 1.45 0.04 1.03 4.7e-3 0.77 1.31 1.95 4.03 803 1.01
flare[28,10] 0.15 9.4e-3 0.271.4e-47 8.0e-9 5.0e-3 0.18 0.96 813 1.01
flare[29,10] 0.35 0.03 0.511.9e-25 2.9e-5 0.08 0.56 1.67 411 1.01
flare[30,10] 0.2 0.01 0.273.9e-33 4.2e-4 0.09 0.3 0.96 423 1.01
flare[31,10] 0.52 0.04 0.731.4e-16 3.4e-5 0.05 0.94 2.38 315 1.02
flare[32,10] 0.48 0.03 0.594.8e-32 2.9e-4 0.25 0.82 1.86 359 1.01
flare[33,10] 0.54 0.04 0.615.0e-16 0.04 0.35 0.85 2.14 194 1.02
flare[34,10] 0.6 0.04 0.661.6e-17 0.02 0.42 1.01 2.23 347 1.01
flare[35,10] 0.3 0.01 0.451.1e-27 2.0e-5 0.04 0.48 1.53 891 1.0
flare[36,10] 0.26 0.02 0.417.1e-30 3.8e-8 0.02 0.38 1.45 486 1.01
flare[37,10] 0.75 0.02 0.43 0.09 0.45 0.7 0.99 1.77 769 1.0
flare[38,10] 0.4 0.03 0.522.8e-26 8.7e-5 0.16 0.66 1.76 310 1.02
flare[39,10] 0.26 0.02 0.462.0e-37 2.3e-6 0.01 0.33 1.65 429 1.0
flare[40,10] 0.84 0.04 0.78 2.8e-9 0.19 0.72 1.25 2.71 430 1.0
flare[41,10] 0.68 0.03 0.62.5e-16 0.08 0.61 1.08 1.98 410 1.01
flare[42,10] 0.23 0.02 0.43.4e-28 1.6e-9 1.5e-3 0.34 1.32 476 1.01
flare[43,10] 0.67 0.04 0.622.9e-12 0.09 0.55 1.06 2.17 248 1.02
flare[44,10] 0.37 0.03 0.464.3e-21 1.0e-4 0.17 0.62 1.49 337 1.01
flare[45,10] 0.3 0.02 0.4310.0e-40 1.4e-6 0.09 0.48 1.44 609 1.0
flare[46,10] 0.5 0.02 0.644.4e-39 0.01 0.21 0.83 2.14 725 1.01
flare[47,10] 0.22 0.01 0.398.3e-23 2.2e-6 6.8e-3 0.3 1.38 827 1.0
flare[48,10] 0.75 0.02 0.741.3e-13 0.07 0.59 1.2 2.54 1256 1.0
flare[49,10] 0.87 0.05 0.731.6e-13 0.13 0.84 1.38 2.3 213 1.02
flare[50,10] 0.21 0.01 0.377.8e-38 3.4e-8 2.4e-3 0.28 1.28 637 1.0
flare[51,10] 0.41 0.03 0.552.9e-36 4.0e-4 0.15 0.68 1.82 449 1.01
flare[52,10] 0.15 0.01 0.353.6e-21 5.6e-8 2.6e-4 0.1 1.26 776 1.01
flare[53,10] 0.2 0.01 0.344.1e-29 1.5e-6 0.02 0.25 1.26 940 1.0
flare[54,10] 0.43 0.02 0.482.1e-36 1.0e-3 0.28 0.7 1.62 388 1.01
flare[55,10] 0.51 0.02 0.521.0e-26 0.06 0.38 0.79 1.75 671 1.01
flare[56,10] 0.26 0.01 0.421.0e-14 1.9e-4 0.03 0.38 1.42 1087 1.0
flare[57,10] 0.22 0.01 0.319.1e-33 6.7e-6 0.09 0.33 1.06 727 1.01
flare[58,10] 0.51 0.02 0.599.4e-18 2.2e-3 0.29 0.86 1.94 742 1.0
flare[59,10] 1.33 0.04 0.84 0.1 0.76 1.18 1.74 3.4 358 1.02
flare[60,10] 1.14 0.03 0.74 1.6e-7 0.63 1.06 1.53 2.87 499 1.0
flare[61,10] 0.47 0.02 0.391.6e-15 0.17 0.39 0.67 1.44 469 1.01
flare[62,10] 0.43 0.03 0.461.7e-24 2.7e-3 0.32 0.69 1.59 292 1.01
flare[63,10] 0.64 0.03 0.593.4e-15 0.13 0.54 0.97 2.07 381 1.0
flare[64,10] 0.3 0.02 0.521.3e-19 8.3e-6 0.01 0.42 1.69 483 1.01
flare[65,10] 0.32 0.02 0.451.8e-32 3.9e-6 0.06 0.54 1.52 405 1.01
flare[66,10] 0.41 0.02 0.371.8e-16 0.13 0.32 0.6 1.32 358 1.02
flare[67,10] 0.19 9.3e-3 0.263.6e-28 3.2e-3 0.1 0.29 0.89 758 1.01
flare[68,10] 1.09 0.03 0.7 1.3e-4 0.61 0.99 1.44 2.82 543 1.01
flare[69,10] 0.44 0.02 0.523.9e-24 0.03 0.27 0.67 1.85 697 1.01
flare[70,10] 0.62 0.02 0.661.7e-11 0.04 0.42 1.04 2.19 809 1.01
flare[71,10] 1.16 0.04 0.8 8.8e-4 0.61 1.12 1.6 2.93 512 1.01
flare[72,10] 0.24 0.02 0.431.9e-13 1.7e-5 5.3e-3 0.29 1.44 432 1.01
flare[73,10] 0.18 8.3e-3 0.342.8e-34 4.6e-7 0.01 0.22 1.22 1696 1.0
flare[74,10] 0.67 0.03 0.672.6e-18 0.03 0.55 1.12 2.2 404 1.01
flare[75,10] 0.4 0.02 0.556.8e-15 1.2e-3 0.11 0.69 1.77 490 1.01
flare[76,10] 0.34 0.02 0.444.6e-25 1.2e-4 0.17 0.55 1.53 389 1.01
flare[77,10] 0.49 0.03 0.535.8e-22 2.4e-3 0.34 0.81 1.78 276 1.02
flare[78,10] 0.14 0.01 0.314.3e-38 3.8e-8 4.3e-4 0.09 1.14 816 1.01
flare[79,10] 1.27 0.05 0.86 6.2e-7 0.71 1.19 1.72 3.32 296 1.01
flare[80,10] 0.24 0.02 0.45.6e-24 2.1e-5 0.04 0.31 1.39 604 1.0
flare[81,10] 0.2 0.01 0.342.6e-24 2.8e-5 0.03 0.24 1.26 667 1.0
flare[82,10] 0.04 7.9e-3 0.172.1e-13 9.2e-7 1.4e-4 6.6e-3 0.46 475 1.01
flare[83,10] 0.36 0.02 0.474.7e-21 4.7e-4 0.12 0.59 1.54 440 1.01
flare[84,10] 0.57 0.03 0.75 1.7e-9 3.2e-3 0.2 0.98 2.51 640 1.0
flare[85,10] 0.36 0.02 0.435.2e-20 6.9e-3 0.21 0.56 1.44 458 1.01
flare[86,10] 0.36 0.01 0.351.9e-19 0.07 0.29 0.55 1.24 925 1.01
flare[87,10] 0.15 0.01 0.352.4e-30 4.6e-8 3.8e-4 0.08 1.22 1021 1.01
flare[88,10] 0.53 0.02 0.569.4e-19 0.01 0.4 0.85 1.91 569 1.01
flare[89,10] 0.21 0.01 0.342.1e-30 1.4e-8 0.02 0.32 1.17 557 1.01
flare[90,10] 0.59 0.03 0.551.9e-15 0.1 0.48 0.9 1.93 460 1.01
flare[91,10] 0.36 0.02 0.391.8e-17 2.5e-3 0.27 0.58 1.31 496 1.01
flare[92,10] 0.96 0.03 0.65 5.1e-5 0.49 0.89 1.32 2.46 497 1.01
flare[93,10] 0.43 0.02 0.532.9e-46 7.7e-3 0.24 0.69 1.79 486 1.01
flare[94,10] 0.2 0.01 0.382.3e-25 3.4e-6 6.3e-3 0.24 1.34 1199 1.0
flare[95,10] 0.4 0.03 0.552.2e-23 2.9e-5 0.12 0.65 1.82 452 1.0
flare[96,10] 0.38 0.03 0.593.7e-21 2.5e-6 0.01 0.64 1.95 362 1.0
flare[97,10] 0.23 0.01 0.321.1e-30 2.4e-4 0.09 0.35 1.11 755 1.0
flare[98,10] 0.88 0.04 0.832.8e-14 0.06 0.77 1.37 2.77 381 1.01
flare[99,10] 0.25 0.01 0.41.4e-40 1.2e-7 0.01 0.37 1.34 770 1.0
flare[0,11] 0.55 0.02 0.461.9e-12 0.17 0.47 0.84 1.65 415 1.01
flare[1,11] 0.34 0.02 0.431.9e-32 2.2e-3 0.18 0.52 1.52 381 1.01
flare[2,11] 0.79 0.03 0.83 1.0e-8 0.05 0.61 1.26 2.79 1013 1.0
flare[3,11] 0.29 0.02 0.35 5.6e-8 0.05 0.17 0.4 1.26 528 1.02
flare[4,11] 0.67 0.02 0.49 5.2e-3 0.3 0.6 0.94 1.87 530 1.0
flare[5,11] 0.66 0.02 0.5 1.3e-7 0.28 0.58 0.94 1.85 795 1.0
flare[6,11] 1.15 0.04 1.18 1.9e-4 0.11 0.88 1.83 4.16 1128 1.0
flare[7,11] 0.43 0.02 0.415.8e-20 0.09 0.34 0.66 1.41 498 1.01
flare[8,11] 0.26 0.06 0.511.8e-15 1.6e-6 7.8e-4 0.23 1.82 78 1.05
flare[9,11] 0.21 0.02 0.395.1e-26 1.2e-6 7.4e-3 0.25 1.42 450 1.01
flare[10,11] 0.67 0.02 0.71 5.3e-8 0.05 0.48 1.06 2.31 1174 1.0
flare[11,11] 0.44 0.02 0.611.0e-15 2.3e-4 0.1 0.75 2.02 986 1.0
flare[12,11] 0.28 0.02 0.431.1e-26 5.0e-6 0.04 0.42 1.44 714 1.0
flare[13,11] 0.2 0.01 0.343.6e-26 1.1e-4 0.05 0.29 1.13 985 1.0
flare[14,11] 0.24 0.01 0.325.5e-18 4.3e-3 0.11 0.35 1.14 474 1.01
flare[15,11] 0.44 0.02 0.572.8e-25 1.4e-4 0.14 0.73 1.91 547 1.0
flare[16,11] 0.47 0.02 0.551.2e-20 6.3e-3 0.26 0.78 1.79 507 1.01
flare[17,11] 0.46 0.02 0.472.1e-17 0.05 0.35 0.72 1.62 604 1.0
flare[18,11] 0.2 0.02 0.372.5e-19 5.9e-6 5.8e-3 0.25 1.27 571 1.01
flare[19,11] 0.31 0.03 0.411.4e-13 6.3e-3 0.16 0.46 1.38 243 1.01
flare[20,11] 0.23 0.02 0.396.7e-26 8.4e-6 0.02 0.33 1.3 517 1.01
flare[21,11] 0.67 0.03 0.628.8e-15 0.06 0.56 1.07 2.08 319 1.01
flare[22,11] 0.28 0.02 0.441.1e-26 3.0e-5 0.03 0.43 1.56 544 1.01
flare[23,11] 0.86 0.05 0.771.3e-12 0.26 0.76 1.25 2.63 212 1.02
flare[24,11] 0.66 0.04 0.651.3e-17 0.08 0.52 1.05 2.17 219 1.03
flare[25,11] 0.34 0.03 0.534.0e-27 3.5e-7 0.03 0.56 1.77 319 1.01
flare[26,11] 0.28 0.01 0.393.2e-29 3.6e-4 0.11 0.43 1.38 945 1.0
flare[27,11] 1.64 0.03 0.94 0.3 1.04 1.45 2.06 3.93 870 1.0
flare[28,11] 0.14 7.2e-3 0.278.9e-41 2.7e-7 8.7e-3 0.17 0.98 1388 1.01
flare[29,11] 0.41 0.03 0.533.5e-22 5.7e-4 0.18 0.68 1.78 405 1.02
flare[30,11] 0.18 0.01 0.272.0e-28 4.6e-4 0.07 0.24 0.97 446 1.01
flare[31,11] 0.59 0.05 0.759.4e-13 1.4e-3 0.21 1.03 2.42 274 1.02
flare[32,11] 0.5 0.03 0.564.7e-28 4.5e-3 0.32 0.83 1.92 394 1.01
flare[33,11] 0.55 0.05 0.591.4e-13 0.07 0.38 0.85 2.18 125 1.03
flare[34,11] 0.76 0.03 0.73.0e-13 0.17 0.63 1.14 2.39 673 1.0
flare[35,11] 0.36 0.02 0.52.9e-22 4.0e-4 0.12 0.55 1.72 834 1.0
flare[36,11] 0.22 0.01 0.352.5e-26 1.1e-6 0.03 0.34 1.2 915 1.0
flare[37,11] 0.56 0.01 0.35 0.04 0.29 0.51 0.76 1.38 661 1.0
flare[38,11] 0.44 0.04 0.525.7e-23 1.6e-3 0.24 0.72 1.73 153 1.03
flare[39,11] 0.44 0.02 0.574.3e-30 1.8e-4 0.14 0.78 1.85 941 1.0
flare[40,11] 0.91 0.05 0.73 2.9e-7 0.33 0.82 1.32 2.54 234 1.02
flare[41,11] 0.65 0.03 0.572.2e-13 0.12 0.56 1.01 1.93 296 1.01
flare[42,11] 0.27 0.02 0.42.5e-24 5.1e-8 9.7e-3 0.45 1.32 320 1.01
flare[43,11] 0.61 0.03 0.54 1.5e-9 0.15 0.52 0.95 1.85 296 1.01
flare[44,11] 0.36 0.02 0.441.6e-17 2.2e-3 0.2 0.6 1.46 427 1.01
flare[45,11] 0.27 0.01 0.371.2e-37 3.3e-5 0.1 0.42 1.27 774 1.0
flare[46,11] 0.95 0.06 0.823.0e-34 0.21 0.86 1.44 2.8 214 1.02
flare[47,11] 0.25 0.02 0.413.8e-19 8.2e-5 0.03 0.37 1.41 685 1.01
flare[48,11] 1.12 0.03 0.83 1.8e-8 0.5 1.07 1.62 2.95 621 1.0
flare[49,11] 0.99 0.04 0.694.4e-11 0.44 1.0 1.43 2.4 309 1.01
flare[50,11] 0.23 0.02 0.382.1e-35 1.6e-6 0.02 0.34 1.38 371 1.0
flare[51,11] 0.56 0.06 0.624.1e-34 6.3e-3 0.37 0.93 2.03 116 1.03
flare[52,11] 0.23 0.01 0.438.7e-18 3.1e-6 4.4e-3 0.26 1.55 898 1.0
flare[53,11] 0.2 0.01 0.348.5e-26 2.4e-5 0.03 0.26 1.21 1006 1.0
flare[54,11] 0.36 0.02 0.416.1e-29 5.7e-3 0.23 0.58 1.39 489 1.01
flare[55,11] 0.42 0.01 0.431.5e-22 0.06 0.31 0.64 1.52 882 1.01
flare[56,11] 0.43 0.02 0.551.1e-11 7.1e-3 0.18 0.69 1.86 1230 1.0
flare[57,11] 0.19 0.01 0.292.0e-28 1.1e-4 0.07 0.27 1.07 742 1.0
flare[58,11] 0.58 0.03 0.631.8e-14 0.02 0.39 0.97 2.06 544 1.0
flare[59,11] 1.08 0.03 0.66 0.11 0.62 0.97 1.42 2.64 589 1.01
flare[60,11] 0.94 0.03 0.61 1.9e-6 0.5 0.89 1.29 2.34 532 1.0
flare[61,11] 0.37 0.01 0.342.3e-13 0.11 0.28 0.52 1.21 566 1.0
flare[62,11] 0.37 0.02 0.414.4e-20 0.01 0.25 0.57 1.43 360 1.01
flare[63,11] 0.56 0.03 0.558.2e-12 0.13 0.45 0.83 1.93 263 1.02
flare[64,11] 0.49 0.03 0.645.1e-16 5.7e-4 0.14 0.87 2.1 448 1.01
flare[65,11] 0.37 0.02 0.489.4e-28 8.6e-5 0.14 0.61 1.6 570 1.01
flare[66,11] 0.34 0.02 0.336.2e-14 0.09 0.24 0.49 1.19 345 1.02
flare[67,11] 0.16 8.1e-3 0.238.3e-25 3.6e-3 0.07 0.23 0.8 792 1.01
flare[68,11] 0.85 0.02 0.56 1.0e-3 0.45 0.77 1.15 2.2 553 1.01
flare[69,11] 0.41 0.02 0.481.2e-19 0.04 0.25 0.62 1.73 889 1.01
flare[70,11] 0.84 0.05 0.69 1.0e-8 0.26 0.74 1.29 2.3 185 1.02
flare[71,11] 1.24 0.04 0.72 0.05 0.77 1.16 1.63 2.96 415 1.01
flare[72,11] 0.34 0.02 0.523.8e-10 7.8e-4 0.05 0.51 1.73 956 1.0
flare[73,11] 0.23 0.02 0.391.5e-30 7.0e-6 0.03 0.3 1.41 638 1.0
flare[74,11] 0.79 0.03 0.683.4e-14 0.18 0.71 1.19 2.3 663 1.01
flare[75,11] 0.59 0.02 0.673.6e-12 0.03 0.35 0.98 2.26 912 1.0
flare[76,11] 0.31 0.02 0.46.6e-21 1.7e-3 0.15 0.49 1.37 331 1.01
flare[77,11] 0.42 0.03 0.471.1e-18 8.8e-3 0.28 0.67 1.6 278 1.02
flare[78,11] 0.22 9.9e-3 0.42.2e-32 3.8e-6 9.4e-3 0.27 1.4 1679 1.01
flare[79,11] 1.19 0.05 0.73 6.4e-5 0.71 1.11 1.56 2.97 248 1.01
flare[80,11] 0.27 0.02 0.442.9e-20 2.5e-4 0.05 0.36 1.46 414 1.01
flare[81,11] 0.25 0.02 0.44.6e-20 3.9e-4 0.06 0.34 1.44 657 1.0
flare[82,11] 0.08 0.01 0.252.1e-11 2.3e-5 1.3e-3 0.03 0.89 487 1.01
flare[83,11] 0.44 0.02 0.521.4e-17 4.8e-3 0.26 0.74 1.73 495 1.01
flare[84,11] 0.99 0.04 1.0 2.5e-7 0.05 0.86 1.55 3.54 603 1.01
flare[85,11] 0.32 0.02 0.389.1e-17 0.01 0.18 0.49 1.29 582 1.0
flare[86,11] 0.27 8.4e-3 0.283.1e-16 0.05 0.19 0.4 1.0 1103 1.0
flare[87,11] 0.26 0.01 0.461.2e-23 5.3e-6 7.8e-3 0.36 1.57 1146 1.01
flare[88,11] 0.46 0.02 0.482.8e-15 0.04 0.34 0.73 1.67 635 1.0
flare[89,11] 0.2 0.01 0.324.9e-26 2.5e-7 0.03 0.28 1.11 698 1.01
flare[90,11] 0.47 0.03 0.461.2e-12 0.07 0.37 0.74 1.58 318 1.02
flare[91,11] 0.29 0.01 0.331.6e-14 8.8e-3 0.2 0.46 1.16 618 1.01
flare[92,11] 0.74 0.02 0.52 1.4e-4 0.35 0.68 1.04 1.95 550 1.01
flare[93,11] 0.58 0.04 0.649.6e-43 0.05 0.38 0.9 2.19 299 1.01
flare[94,11] 0.33 0.01 0.472.1e-22 2.8e-4 0.08 0.53 1.61 1204 1.0
flare[95,11] 0.38 0.02 0.52.6e-19 5.4e-4 0.14 0.63 1.63 636 1.01
flare[96,11] 0.47 0.03 0.637.9e-18 5.1e-5 0.09 0.84 2.09 407 1.0
flare[97,11] 0.21 0.01 0.31.7e-25 1.0e-3 0.08 0.3 1.06 797 1.0
flare[98,11] 0.97 0.04 0.782.2e-11 0.31 0.93 1.43 2.65 467 1.0
flare[99,11] 0.26 0.01 0.417.3e-36 5.6e-6 0.04 0.4 1.44 889 1.0
flare[0,12] 0.45 0.02 0.46.6e-10 0.12 0.37 0.7 1.44 656 1.0
flare[1,12] 0.27 0.02 0.353.2e-27 4.5e-3 0.14 0.4 1.25 528 1.0
flare[2,12] 1.29 0.03 0.98 7.0e-6 0.53 1.25 1.81 3.63 813 1.0
flare[3,12] 0.27 0.03 0.35 1.6e-5 0.04 0.14 0.35 1.35 139 1.03
flare[4,12] 0.54 0.02 0.43 3.9e-3 0.22 0.46 0.76 1.61 473 1.0
flare[5,12] 0.49 0.01 0.41 4.5e-6 0.18 0.4 0.72 1.48 849 1.0
flare[6,12] 2.03 0.05 1.35 0.04 1.13 1.84 2.74 5.23 739 1.01
flare[7,12] 0.33 0.01 0.341.5e-17 0.06 0.23 0.5 1.17 582 1.01
flare[8,12] 0.29 0.06 0.516.0e-13 6.4e-5 9.2e-3 0.38 1.79 84 1.05
flare[9,12] 0.29 0.01 0.442.1e-21 6.7e-5 0.06 0.44 1.5 950 1.0
flare[10,12] 0.83 0.03 0.74 1.6e-5 0.22 0.71 1.23 2.57 595 1.01
flare[11,12] 0.66 0.03 0.722.3e-11 0.01 0.45 1.13 2.36 640 1.0
flare[12,12] 0.33 0.03 0.476.8e-23 8.9e-5 0.1 0.52 1.68 300 1.01
flare[13,12] 0.2 0.01 0.345.4e-22 3.2e-4 0.05 0.27 1.21 825 1.0
flare[14,12] 0.19 0.01 0.281.1e-15 3.8e-3 0.07 0.26 0.98 776 1.0
flare[15,12] 0.55 0.03 0.637.0e-20 2.9e-3 0.35 0.93 2.04 622 1.01
flare[16,12] 0.62 0.03 0.621.1e-14 0.08 0.47 0.96 2.13 379 1.01
flare[17,12] 0.38 0.02 0.44.2e-15 0.04 0.27 0.59 1.4 652 1.0
flare[18,12] 0.24 0.01 0.391.9e-15 1.1e-4 0.03 0.33 1.37 896 1.0
flare[19,12] 0.27 0.01 0.351.8e-11 9.4e-3 0.13 0.39 1.2 547 1.01
flare[20,12] 0.28 0.02 0.426.8e-20 2.5e-4 0.06 0.42 1.45 524 1.01
flare[21,12] 0.58 0.03 0.532.0e-12 0.09 0.48 0.93 1.83 414 1.01
flare[22,12] 0.35 0.02 0.496.5e-22 2.2e-4 0.08 0.58 1.65 664 1.01
flare[23,12] 0.8 0.04 0.671.6e-10 0.28 0.72 1.14 2.39 266 1.01
flare[24,12] 0.58 0.03 0.567.7e-13 0.07 0.46 0.91 1.96 273 1.02
flare[25,12] 0.36 0.03 0.536.2e-23 7.8e-6 0.06 0.62 1.74 384 1.01
flare[26,12] 0.28 0.02 0.413.4e-26 1.5e-3 0.11 0.41 1.37 682 1.0
flare[27,12] 1.41 0.02 0.77 0.32 0.89 1.26 1.77 3.29 956 1.0
flare[28,12] 0.15 6.7e-3 0.281.2e-35 8.4e-6 0.01 0.17 1.0 1731 1.0
flare[29,12] 0.4 0.02 0.492.3e-18 4.9e-3 0.2 0.65 1.63 509 1.01
flare[30,12] 0.15 0.01 0.242.8e-24 4.9e-4 0.05 0.2 0.9 408 1.02
flare[31,12] 0.69 0.04 0.72 5.7e-9 0.04 0.5 1.14 2.37 341 1.01
flare[32,12] 0.48 0.02 0.525.4e-23 0.02 0.32 0.77 1.82 454 1.01
flare[33,12] 0.46 0.04 0.495.7e-12 0.06 0.31 0.72 1.82 125 1.03
flare[34,12] 0.8 0.03 0.67 8.4e-9 0.28 0.68 1.16 2.39 663 1.0
flare[35,12] 0.4 0.02 0.522.7e-18 1.6e-3 0.19 0.62 1.77 709 1.01
flare[36,12] 0.2 9.8e-3 0.322.3e-23 1.1e-5 0.04 0.29 1.1 1067 1.0
flare[37,12] 0.42 0.01 0.3 0.02 0.19 0.37 0.59 1.11 555 1.0
flare[38,12] 0.43 0.03 0.52.7e-19 6.4e-3 0.26 0.69 1.68 247 1.02
flare[39,12] 0.59 0.02 0.641.1e-21 4.9e-3 0.44 1.01 2.05 722 1.0
flare[40,12] 0.71 0.03 0.57 2.4e-6 0.27 0.62 1.02 1.98 440 1.01
flare[41,12] 0.6 0.03 0.533.1e-10 0.15 0.51 0.92 1.8 300 1.01
flare[42,12] 0.26 0.02 0.392.9e-20 5.6e-7 0.02 0.44 1.28 453 1.01
flare[43,12] 0.55 0.03 0.48 3.8e-8 0.14 0.45 0.84 1.6 365 1.02
flare[44,12] 0.4 0.02 0.477.0e-14 0.02 0.24 0.64 1.67 683 1.01
flare[45,12] 0.24 0.01 0.351.8e-34 1.6e-4 0.08 0.36 1.26 999 1.0
flare[46,12] 1.13 0.06 0.781.5e-29 0.59 1.1 1.58 2.87 165 1.02
flare[47,12] 0.34 0.02 0.493.2e-16 1.6e-3 0.11 0.51 1.74 926 1.0
flare[48,12] 1.05 0.04 0.74 3.1e-6 0.52 0.99 1.47 2.78 372 1.0
flare[49,12] 1.05 0.03 0.7 1.0e-8 0.55 1.04 1.45 2.61 413 1.01
flare[50,12] 0.26 0.02 0.41.8e-31 4.1e-5 0.05 0.37 1.39 417 1.0
flare[51,12] 0.56 0.04 0.597.7e-32 0.02 0.41 0.9 2.0 197 1.03
flare[52,12] 0.34 0.02 0.534.9e-15 1.7e-4 0.04 0.52 1.84 1057 1.0
flare[53,12] 0.21 0.01 0.356.3e-23 2.2e-4 0.04 0.29 1.22 729 1.01
flare[54,12] 0.31 0.02 0.374.1e-25 9.4e-3 0.18 0.48 1.25 582 1.01
flare[55,12] 0.31 0.01 0.331.2e-18 0.04 0.21 0.48 1.15 1029 1.01
flare[56,12] 0.66 0.03 0.66 2.6e-9 0.08 0.49 1.08 2.15 489 1.0
flare[57,12] 0.19 0.02 0.321.9e-24 6.6e-4 0.06 0.24 1.18 412 1.01
flare[58,12] 0.58 0.02 0.592.7e-11 0.06 0.41 0.94 1.93 642 1.0
flare[59,12] 0.85 0.02 0.53 0.06 0.46 0.76 1.16 2.08 715 1.0
flare[60,12] 0.76 0.02 0.52 1.1e-5 0.37 0.71 1.07 1.89 529 1.0
flare[61,12] 0.29 0.01 0.32.1e-11 0.07 0.2 0.41 1.03 505 1.0
flare[62,12] 0.3 0.02 0.364.0e-16 0.02 0.18 0.46 1.26 450 1.0
flare[63,12] 0.46 0.02 0.45 2.5e-8 0.11 0.34 0.67 1.59 356 1.01
flare[64,12] 0.69 0.04 0.736.3e-12 0.02 0.51 1.17 2.4 352 1.01
flare[65,12] 0.42 0.02 0.514.8e-21 1.4e-3 0.22 0.7 1.76 548 1.01
flare[66,12] 0.27 0.02 0.38.6e-12 0.05 0.17 0.39 1.05 330 1.02
flare[67,12] 0.14 7.4e-3 0.225.1e-20 2.4e-3 0.05 0.19 0.79 887 1.01
flare[68,12] 0.66 0.02 0.47 4.4e-3 0.32 0.59 0.93 1.76 576 1.01
flare[69,12] 0.37 0.02 0.434.1e-16 0.04 0.21 0.53 1.55 835 1.0
flare[70,12] 0.86 0.05 0.62 5.7e-7 0.37 0.79 1.23 2.24 161 1.03
flare[71,12] 1.12 0.04 0.66 0.14 0.67 1.02 1.45 2.77 323 1.01
flare[72,12] 0.54 0.02 0.66 6.9e-7 0.02 0.26 0.9 2.16 1678 1.0
flare[73,12] 0.25 0.02 0.419.4e-28 4.8e-5 0.04 0.33 1.49 628 1.0
flare[74,12] 0.87 0.03 0.715.5e-11 0.33 0.79 1.27 2.55 666 1.01
flare[75,12] 0.87 0.03 0.824.6e-10 0.17 0.74 1.32 2.84 667 1.0
flare[76,12] 0.28 0.02 0.372.0e-17 4.4e-3 0.13 0.41 1.32 434 1.01
flare[77,12] 0.4 0.02 0.451.8e-15 0.02 0.25 0.6 1.58 416 1.01
flare[78,12] 0.37 0.02 0.531.8e-28 1.4e-4 0.08 0.59 1.8 594 1.01
flare[79,12] 1.05 0.03 0.63 2.9e-3 0.61 0.98 1.39 2.55 555 1.01
flare[80,12] 0.27 0.02 0.424.0e-16 1.1e-3 0.06 0.38 1.5 404 1.01
flare[81,12] 0.33 0.02 0.54.6e-17 2.5e-3 0.1 0.48 1.66 518 1.01
flare[82,12] 0.18 0.02 0.37 2.9e-9 5.9e-4 0.02 0.17 1.38 566 1.01
flare[83,12] 0.52 0.03 0.572.6e-14 0.02 0.33 0.84 1.91 349 1.01
flare[84,12] 1.4 0.05 1.15 7.1e-5 0.42 1.28 1.99 4.17 555 1.01
flare[85,12] 0.31 0.02 0.389.7e-14 0.02 0.17 0.46 1.35 535 1.0
flare[86,12] 0.21 6.6e-3 0.241.7e-14 0.03 0.13 0.31 0.86 1309 1.0
flare[87,12] 0.42 0.02 0.586.0e-17 5.8e-4 0.1 0.71 1.91 1205 1.01
flare[88,12] 0.42 0.02 0.457.1e-12 0.04 0.29 0.64 1.55 828 1.0
flare[89,12] 0.17 9.9e-3 0.291.8e-22 4.0e-6 0.03 0.24 0.99 862 1.0
flare[90,12] 0.39 0.02 0.392.3e-11 0.05 0.28 0.6 1.36 331 1.02
flare[91,12] 0.25 0.01 0.318.0e-12 0.01 0.15 0.37 1.07 807 1.01
flare[92,12] 0.59 0.02 0.44 3.8e-4 0.25 0.52 0.84 1.63 647 1.01
flare[93,12] 0.58 0.04 0.61.8e-39 0.07 0.42 0.93 2.04 231 1.02
flare[94,12] 0.53 0.03 0.69.1e-19 5.5e-3 0.33 0.86 2.03 305 1.01
flare[95,12] 0.36 0.02 0.451.2e-14 3.0e-3 0.16 0.58 1.49 698 1.01
flare[96,12] 0.56 0.03 0.671.9e-14 1.1e-3 0.3 0.99 2.11 504 1.0
flare[97,12] 0.18 7.7e-3 0.276.3e-21 1.8e-3 0.07 0.25 0.94 1256 1.01
flare[98,12] 1.01 0.03 0.74 1.4e-8 0.47 0.95 1.42 2.7 494 1.0
flare[99,12] 0.29 0.01 0.428.3e-30 1.1e-4 0.07 0.45 1.44 866 1.0
flare[0,13] 0.38 0.01 0.37 1.4e-8 0.08 0.28 0.58 1.28 669 1.0
flare[1,13] 0.23 0.01 0.324.8e-25 4.3e-3 0.1 0.32 1.14 481 1.0
flare[2,13] 1.5 0.03 0.93 3.3e-3 0.91 1.42 1.97 3.65 923 1.0
flare[3,13] 0.23 0.03 0.32 7.1e-5 0.03 0.11 0.3 1.18 94 1.05
flare[4,13] 0.44 0.02 0.38 3.1e-3 0.15 0.35 0.64 1.45 439 1.0
flare[5,13] 0.37 0.01 0.34 3.7e-5 0.11 0.28 0.56 1.26 750 1.0
flare[6,13] 2.68 0.09 1.43 0.84 1.69 2.34 3.4 6.28 231 1.02
flare[7,13] 0.25 0.01 0.286.5e-15 0.04 0.16 0.38 0.99 602 1.01
flare[8,13] 0.4 0.05 0.552.9e-10 2.0e-3 0.1 0.66 1.9 125 1.04
flare[9,13] 0.41 0.03 0.534.1e-18 1.0e-3 0.17 0.69 1.81 417 1.01
flare[10,13] 0.8 0.03 0.67 2.0e-4 0.29 0.69 1.15 2.31 674 1.01
flare[11,13] 0.8 0.03 0.7 6.2e-8 0.15 0.71 1.26 2.35 557 1.01
flare[12,13] 0.31 0.02 0.433.0e-19 4.0e-4 0.11 0.47 1.53 513 1.01
flare[13,13] 0.18 0.01 0.314.0e-18 3.7e-4 0.04 0.23 1.09 942 1.0
flare[14,13] 0.16 7.5e-3 0.267.0e-14 3.1e-3 0.05 0.2 0.96 1207 1.0
flare[15,13] 0.57 0.02 0.593.6e-15 0.02 0.41 0.94 1.97 678 1.01
flare[16,13] 0.67 0.03 0.619.4e-11 0.15 0.54 1.01 2.1 387 1.02
flare[17,13] 0.32 0.02 0.381.1e-12 0.03 0.2 0.47 1.32 413 1.0
flare[18,13] 0.35 0.02 0.483.8e-12 1.6e-3 0.11 0.55 1.64 872 1.0
flare[19,13] 0.24 0.01 0.324.5e-10 0.01 0.11 0.36 1.12 803 1.01
flare[20,13] 0.35 0.02 0.477.2e-16 2.0e-3 0.13 0.53 1.64 463 1.01
flare[21,13] 0.51 0.02 0.483.6e-10 0.09 0.41 0.82 1.72 449 1.01
flare[22,13] 0.35 0.02 0.453.8e-18 8.3e-4 0.11 0.59 1.51 710 1.01
flare[23,13] 0.68 0.02 0.56 1.8e-8 0.24 0.6 0.99 2.03 535 1.0
flare[24,13] 0.45 0.03 0.45 9.6e-9 0.06 0.34 0.7 1.6 283 1.01
flare[25,13] 0.34 0.02 0.464.9e-19 1.0e-4 0.1 0.57 1.52 436 1.01
flare[26,13] 0.25 0.02 0.371.3e-22 1.6e-3 0.09 0.34 1.29 371 1.01
flare[27,13] 1.1 0.02 0.59 0.22 0.69 1.0 1.4 2.54 1124 1.0
flare[28,13] 0.17 9.3e-3 0.326.6e-29 8.8e-5 0.02 0.18 1.17 1208 1.0
flare[29,13] 0.36 0.02 0.444.8e-16 0.01 0.2 0.57 1.57 622 1.01
flare[30,13] 0.13 0.01 0.221.5e-20 5.7e-4 0.04 0.16 0.8 461 1.01
flare[31,13] 0.97 0.03 0.79 1.2e-5 0.33 0.87 1.41 2.78 670 1.01
flare[32,13] 0.44 0.02 0.491.4e-19 0.03 0.3 0.68 1.7 391 1.01
flare[33,13] 0.36 0.03 0.391.3e-10 0.05 0.24 0.56 1.46 165 1.02
flare[34,13] 0.73 0.02 0.6 2.5e-6 0.27 0.62 1.06 2.18 659 1.0
flare[35,13] 0.39 0.02 0.51.6e-14 4.4e-3 0.19 0.61 1.72 708 1.01
flare[36,13] 0.16 8.0e-3 0.281.1e-20 2.4e-5 0.03 0.23 0.94 1188 1.0
flare[37,13] 0.31 0.01 0.25 6.6e-3 0.12 0.26 0.45 0.95 444 1.01
flare[38,13] 0.37 0.02 0.441.4e-16 0.01 0.22 0.59 1.52 714 1.01
flare[39,13] 0.65 0.02 0.621.4e-17 0.05 0.56 1.05 2.1 764 1.0
flare[40,13] 0.53 0.02 0.47 3.8e-6 0.16 0.45 0.78 1.65 492 1.0
flare[41,13] 0.54 0.03 0.5 2.3e-8 0.13 0.44 0.81 1.74 368 1.01
flare[42,13] 0.23 0.02 0.351.1e-16 6.7e-6 0.03 0.37 1.17 466 1.0
flare[43,13] 0.47 0.02 0.43 6.7e-8 0.11 0.37 0.71 1.46 437 1.01
flare[44,13] 0.44 0.03 0.51.9e-12 0.03 0.26 0.71 1.75 299 1.02
flare[45,13] 0.22 0.01 0.339.6e-31 4.9e-4 0.07 0.31 1.17 1016 1.0
flare[46,13] 1.03 0.06 0.672.1e-24 0.57 1.01 1.43 2.43 145 1.03
flare[47,13] 0.45 0.02 0.567.8e-13 0.01 0.21 0.72 1.88 582 1.0
flare[48,13] 0.87 0.04 0.62 7.4e-5 0.42 0.81 1.23 2.33 281 1.01
flare[49,13] 0.98 0.03 0.65 2.6e-6 0.5 0.96 1.35 2.4 425 1.01
flare[50,13] 0.31 0.01 0.441.4e-23 6.3e-4 0.09 0.49 1.51 1012 1.0
flare[51,13] 0.5 0.03 0.512.9e-30 0.04 0.38 0.78 1.71 213 1.03
flare[52,13] 0.5 0.02 0.621.4e-12 7.8e-3 0.21 0.84 2.15 873 1.01
flare[53,13] 0.24 0.02 0.381.4e-19 7.0e-4 0.05 0.34 1.31 631 1.01
flare[54,13] 0.26 0.01 0.336.5e-22 0.01 0.14 0.39 1.13 708 1.01
flare[55,13] 0.24 7.4e-3 0.285.2e-14 0.02 0.14 0.35 0.95 1396 1.0
flare[56,13] 0.79 0.04 0.68 1.0e-7 0.19 0.72 1.22 2.34 357 1.01
flare[57,13] 0.2 0.02 0.363.6e-21 1.7e-3 0.05 0.23 1.31 347 1.01
flare[58,13] 0.6 0.02 0.58 1.3e-8 0.1 0.44 0.96 1.96 804 1.0
flare[59,13] 0.67 0.02 0.45 0.04 0.33 0.59 0.94 1.73 619 1.0
flare[60,13] 0.62 0.02 0.45 9.0e-6 0.27 0.56 0.9 1.63 511 1.0
flare[61,13] 0.22 9.9e-3 0.246.5e-10 0.04 0.15 0.32 0.87 571 1.0
flare[62,13] 0.27 0.02 0.341.4e-13 0.02 0.14 0.4 1.2 498 1.0
flare[63,13] 0.39 0.02 0.4 8.2e-7 0.09 0.27 0.57 1.44 607 1.01
flare[64,13] 0.93 0.04 0.75 1.7e-9 0.31 0.87 1.38 2.7 436 1.01
flare[65,13] 0.45 0.02 0.531.1e-15 5.9e-3 0.24 0.75 1.75 717 1.01
flare[66,13] 0.22 0.01 0.275.8e-10 0.03 0.13 0.32 0.96 349 1.02
flare[67,13] 0.13 7.8e-3 0.221.7e-16 2.0e-3 0.04 0.15 0.78 772 1.01
flare[68,13] 0.52 0.02 0.4 2.7e-3 0.22 0.45 0.75 1.43 548 1.01
flare[69,13] 0.33 0.02 0.412.7e-12 0.03 0.18 0.46 1.49 420 1.0
flare[70,13] 0.72 0.04 0.55 5.6e-6 0.29 0.64 1.07 2.01 232 1.02
flare[71,13] 0.9 0.03 0.55 0.1 0.51 0.82 1.2 2.17 311 1.01
flare[72,13] 0.94 0.05 0.88 3.4e-4 0.22 0.8 1.37 3.06 372 1.01
flare[73,13] 0.26 0.02 0.411.5e-25 2.1e-4 0.05 0.37 1.46 549 1.0
flare[74,13] 0.8 0.02 0.64 9.9e-9 0.31 0.71 1.17 2.37 676 1.01
flare[75,13] 1.03 0.04 0.87 2.8e-8 0.32 0.95 1.48 3.14 485 1.0
flare[76,13] 0.24 0.01 0.331.3e-14 5.2e-3 0.1 0.34 1.15 565 1.01
flare[77,13] 0.37 0.02 0.449.1e-13 0.03 0.22 0.57 1.54 480 1.01
flare[78,13] 0.49 0.03 0.616.3e-24 2.9e-3 0.24 0.8 2.06 444 1.01
flare[79,13] 0.89 0.02 0.55 0.02 0.48 0.83 1.19 2.17 557 1.0
flare[80,13] 0.27 0.02 0.432.9e-12 2.1e-3 0.06 0.38 1.55 316 1.01
flare[81,13] 0.35 0.03 0.512.2e-14 5.8e-3 0.11 0.53 1.72 367 1.01
flare[82,13] 0.43 0.02 0.59 3.4e-7 0.01 0.14 0.66 2.01 782 1.0
flare[83,13] 0.51 0.03 0.542.7e-12 0.03 0.36 0.84 1.82 318 1.0
flare[84,13] 1.62 0.04 1.07 9.7e-3 0.89 1.5 2.2 4.11 608 1.01
flare[85,13] 0.3 0.02 0.421.3e-11 0.02 0.14 0.43 1.41 594 1.01
flare[86,13] 0.17 6.6e-3 0.222.9e-12 0.02 0.09 0.24 0.78 1100 1.0
flare[87,13] 0.62 0.04 0.693.0e-12 0.03 0.42 1.0 2.29 280 1.01
flare[88,13] 0.36 0.02 0.423.8e-10 0.04 0.24 0.53 1.42 704 1.0
flare[89,13] 0.16 9.7e-3 0.282.4e-19 2.5e-5 0.03 0.19 1.01 868 1.0
flare[90,13] 0.34 0.02 0.367.0e-10 0.05 0.23 0.52 1.22 362 1.01
flare[91,13] 0.24 0.01 0.32 1.3e-9 0.01 0.12 0.34 1.11 584 1.0
flare[92,13] 0.46 0.02 0.38 2.7e-4 0.16 0.4 0.67 1.4 642 1.0
flare[93,13] 0.49 0.03 0.51.8e-36 0.06 0.35 0.79 1.68 247 1.01
flare[94,13] 0.6 0.04 0.611.4e-15 0.03 0.43 0.96 2.1 231 1.01
flare[95,13] 0.37 0.02 0.441.7e-11 0.01 0.2 0.58 1.5 512 1.01
flare[96,13] 0.65 0.03 0.691.0e-11 0.02 0.49 1.06 2.23 597 1.0
flare[97,13] 0.18 0.01 0.313.0e-16 2.9e-3 0.06 0.22 1.1 886 1.01
flare[98,13] 0.95 0.03 0.68 3.3e-6 0.45 0.9 1.34 2.5 487 1.0
flare[99,13] 0.3 0.01 0.431.9e-24 5.6e-4 0.1 0.47 1.49 991 1.0
flare[0,14] 0.33 0.01 0.35 3.2e-8 0.06 0.22 0.49 1.24 656 1.01
flare[1,14] 0.19 0.02 0.293.2e-23 2.6e-3 0.07 0.25 1.03 360 1.01
flare[2,14] 1.47 0.03 0.81 0.16 0.94 1.37 1.85 3.35 1024 1.0
flare[3,14] 0.2 0.04 0.31 6.1e-5 0.02 0.09 0.25 1.04 57 1.07
flare[4,14] 0.36 0.02 0.35 2.1e-3 0.1 0.27 0.52 1.3 422 1.0
flare[5,14] 0.29 0.01 0.31 7.9e-5 0.07 0.2 0.43 1.13 477 1.01
flare[6,14] 2.43 0.08 1.25 0.81 1.55 2.16 3.05 5.48 224 1.02
flare[7,14] 0.2 8.9e-3 0.247.5e-13 0.02 0.11 0.29 0.87 740 1.01
flare[8,14] 0.64 0.03 0.68 4.2e-8 0.04 0.44 1.05 2.25 440 1.02
flare[9,14] 0.46 0.03 0.554.8e-15 3.7e-3 0.24 0.77 1.76 282 1.01
flare[10,14] 0.66 0.02 0.55 4.1e-4 0.24 0.54 0.95 1.96 813 1.0
flare[11,14] 0.91 0.03 0.67 1.4e-4 0.39 0.83 1.3 2.36 390 1.0
flare[12,14] 0.28 0.02 0.411.3e-15 1.3e-3 0.08 0.39 1.44 337 1.01
flare[13,14] 0.15 7.7e-3 0.262.3e-15 3.1e-4 0.03 0.19 0.91 1131 1.0
flare[14,14] 0.15 0.01 0.277.3e-12 2.1e-3 0.04 0.16 0.99 676 1.01
flare[15,14] 0.57 0.02 0.571.2e-10 0.08 0.43 0.9 1.9 644 1.01
flare[16,14] 0.58 0.02 0.52 1.0e-8 0.14 0.46 0.89 1.77 546 1.01
flare[17,14] 0.26 0.01 0.332.5e-10 0.02 0.15 0.38 1.18 543 1.0
flare[18,14] 0.51 0.02 0.618.5e-10 0.02 0.25 0.85 2.1 682 1.0
flare[19,14] 0.24 0.01 0.34 5.9e-9 0.01 0.09 0.33 1.21 852 1.01
flare[20,14] 0.39 0.02 0.495.5e-14 5.6e-3 0.17 0.64 1.65 478 1.01
flare[21,14] 0.46 0.02 0.45 4.6e-8 0.07 0.34 0.71 1.63 497 1.0
flare[22,14] 0.34 0.02 0.432.2e-14 1.8e-3 0.14 0.55 1.45 493 1.01
flare[23,14] 0.58 0.02 0.49 8.7e-7 0.18 0.49 0.86 1.76 602 1.0
flare[24,14] 0.36 0.02 0.38 2.2e-6 0.04 0.25 0.54 1.32 476 1.01
flare[25,14] 0.34 0.01 0.456.2e-16 1.1e-3 0.15 0.54 1.48 903 1.0
flare[26,14] 0.2 0.02 0.311.9e-19 1.7e-3 0.07 0.27 1.11 374 1.01
flare[27,14] 0.85 0.01 0.48 0.13 0.51 0.78 1.1 1.99 1193 1.01
flare[28,14] 0.2 0.01 0.378.8e-23 3.6e-4 0.03 0.23 1.32 1050 1.0
flare[29,14] 0.33 0.02 0.422.8e-15 9.6e-3 0.17 0.49 1.44 540 1.01
flare[30,14] 0.12 7.4e-3 0.217.3e-18 6.0e-4 0.03 0.13 0.8 831 1.01
flare[31,14] 1.2 0.04 0.89 2.5e-3 0.56 1.09 1.66 3.32 558 1.01
flare[32,14] 0.39 0.02 0.459.1e-17 0.04 0.25 0.58 1.57 393 1.01
flare[33,14] 0.29 0.02 0.34 7.6e-9 0.03 0.18 0.43 1.21 248 1.01
flare[34,14] 0.62 0.02 0.52 4.7e-6 0.21 0.51 0.91 1.86 530 1.0
flare[35,14] 0.37 0.02 0.471.9e-11 9.9e-3 0.18 0.58 1.61 681 1.01
flare[36,14] 0.14 7.0e-3 0.256.5e-18 3.0e-5 0.02 0.19 0.86 1226 1.0
flare[37,14] 0.24 0.01 0.22 2.7e-3 0.07 0.18 0.34 0.81 309 1.01
flare[38,14] 0.33 0.01 0.49.3e-15 0.02 0.17 0.5 1.41 753 1.0
flare[39,14] 0.68 0.02 0.64.9e-14 0.17 0.58 1.02 2.11 697 1.01
flare[40,14] 0.41 0.02 0.38 2.8e-6 0.1 0.32 0.6 1.35 500 1.0
flare[41,14] 0.48 0.02 0.45 4.9e-7 0.12 0.37 0.71 1.54 424 1.01
flare[42,14] 0.23 0.01 0.371.4e-13 8.8e-5 0.05 0.34 1.19 607 1.0
flare[43,14] 0.38 0.02 0.38 1.7e-7 0.08 0.29 0.58 1.32 469 1.01
flare[44,14] 0.43 0.03 0.491.6e-10 0.04 0.25 0.69 1.62 349 1.02
flare[45,14] 0.2 0.01 0.322.4e-26 5.6e-4 0.06 0.27 1.19 571 1.01
flare[46,14] 0.87 0.05 0.571.9e-19 0.46 0.85 1.23 2.11 144 1.03
flare[47,14] 0.52 0.03 0.611.5e-11 0.02 0.29 0.85 2.11 433 1.01
flare[48,14] 0.7 0.02 0.53 9.8e-5 0.31 0.63 1.01 1.88 477 1.01
flare[49,14] 0.86 0.03 0.57 5.7e-4 0.42 0.84 1.22 2.13 431 1.01
flare[50,14] 0.37 0.02 0.488.9e-22 3.1e-3 0.14 0.59 1.67 989 1.0
flare[51,14] 0.43 0.02 0.452.0e-25 0.05 0.32 0.67 1.5 429 1.01
flare[52,14] 0.74 0.04 0.715.4e-11 0.08 0.59 1.17 2.35 289 1.02
flare[53,14] 0.28 0.02 0.433.8e-17 1.1e-3 0.06 0.41 1.48 549 1.01
flare[54,14] 0.23 0.01 0.315.0e-18 0.01 0.11 0.33 1.12 697 1.0
flare[55,14] 0.18 6.8e-3 0.231.7e-11 0.01 0.09 0.27 0.79 1151 1.0
flare[56,14] 0.73 0.03 0.62 1.6e-6 0.2 0.67 1.12 2.13 322 1.01
flare[57,14] 0.2 0.02 0.355.2e-18 2.2e-3 0.05 0.23 1.22 364 1.01
flare[58,14] 0.63 0.03 0.62 1.9e-5 0.12 0.47 0.96 2.2 600 1.01
flare[59,14] 0.53 0.02 0.4 0.02 0.23 0.45 0.75 1.49 468 1.0
flare[60,14] 0.51 0.02 0.41 3.0e-5 0.19 0.43 0.75 1.47 502 1.01
flare[61,14] 0.17 8.6e-3 0.2 9.3e-9 0.03 0.1 0.24 0.74 549 1.0
flare[62,14] 0.27 0.01 0.372.3e-12 0.02 0.12 0.36 1.37 908 1.0
flare[63,14] 0.32 0.01 0.37 4.2e-6 0.06 0.2 0.46 1.36 686 1.0
flare[64,14] 1.1 0.04 0.79 6.6e-8 0.53 1.0 1.52 3.02 363 1.0
flare[65,14] 0.45 0.02 0.521.8e-11 0.02 0.26 0.75 1.75 710 1.01
flare[66,14] 0.18 0.01 0.24 1.1e-8 0.02 0.09 0.25 0.86 422 1.02
flare[67,14] 0.13 0.01 0.255.3e-14 2.1e-3 0.03 0.14 0.9 428 1.01
flare[68,14] 0.42 0.02 0.35 9.9e-4 0.15 0.33 0.6 1.28 529 1.01
flare[69,14] 0.29 0.03 0.39 1.8e-9 0.02 0.14 0.39 1.49 171 1.02
flare[70,14] 0.57 0.03 0.47 1.5e-5 0.21 0.48 0.84 1.72 320 1.01
flare[71,14] 0.72 0.03 0.46 0.05 0.37 0.64 0.98 1.77 305 1.01
flare[72,14] 1.21 0.07 0.97 2.9e-3 0.51 1.05 1.69 3.71 211 1.02
flare[73,14] 0.26 0.02 0.42.1e-22 6.9e-4 0.05 0.37 1.38 523 1.0
flare[74,14] 0.69 0.02 0.56 9.7e-7 0.25 0.6 1.02 2.08 632 1.01
flare[75,14] 0.97 0.04 0.79 1.4e-6 0.31 0.91 1.4 2.75 461 1.0
flare[76,14] 0.2 0.01 0.315.5e-13 5.3e-3 0.08 0.28 1.07 729 1.01
flare[77,14] 0.35 0.02 0.441.9e-10 0.02 0.17 0.5 1.55 402 1.01
flare[78,14] 0.54 0.03 0.61.6e-18 0.02 0.35 0.88 1.97 470 1.0
flare[79,14] 0.75 0.02 0.49 0.03 0.38 0.69 1.02 1.87 399 1.0
flare[80,14] 0.26 0.02 0.47.5e-10 3.7e-3 0.06 0.37 1.4 517 1.01
flare[81,14] 0.31 0.02 0.443.0e-13 5.8e-3 0.11 0.48 1.51 379 1.01
flare[82,14] 0.99 0.03 0.94 2.9e-5 0.17 0.81 1.54 3.31 1171 1.0
flare[83,14] 0.48 0.03 0.514.6e-10 0.05 0.32 0.78 1.67 308 1.01
flare[84,14] 1.77 0.04 1.01 0.26 1.07 1.59 2.26 4.31 637 1.01
flare[85,14] 0.27 0.01 0.372.9e-10 0.01 0.11 0.37 1.34 652 1.0
flare[86,14] 0.14 7.2e-3 0.27.9e-11 0.01 0.06 0.18 0.7 762 1.01
flare[87,14] 0.78 0.03 0.69 7.4e-9 0.19 0.68 1.17 2.38 548 1.01
flare[88,14] 0.31 0.02 0.49.4e-10 0.03 0.19 0.45 1.32 497 1.01
flare[89,14] 0.15 9.6e-3 0.283.1e-16 8.6e-5 0.02 0.17 1.04 864 1.0
flare[90,14] 0.31 0.02 0.36 2.7e-9 0.04 0.2 0.45 1.26 409 1.01
flare[91,14] 0.25 0.02 0.36 1.7e-7 0.02 0.12 0.32 1.35 566 1.0
flare[92,14] 0.36 0.01 0.32 6.2e-5 0.11 0.29 0.52 1.17 623 1.01
flare[93,14] 0.4 0.02 0.413.2e-33 0.05 0.27 0.63 1.4 303 1.01
flare[94,14] 0.55 0.03 0.555.6e-13 0.06 0.41 0.86 1.87 276 1.01
flare[95,14] 0.41 0.02 0.49 1.3e-9 0.02 0.23 0.64 1.61 513 1.01
flare[96,14] 0.71 0.03 0.67 1.1e-8 0.1 0.6 1.13 2.29 699 1.0
flare[97,14] 0.19 0.01 0.322.3e-13 2.6e-3 0.05 0.21 1.18 669 1.01
flare[98,14] 0.85 0.02 0.61 2.4e-4 0.37 0.77 1.19 2.23 621 1.0
flare[99,14] 0.3 0.01 0.417.2e-21 1.3e-3 0.11 0.45 1.43 943 1.0
flare[0,15] 0.29 0.01 0.35 9.5e-8 0.04 0.17 0.42 1.2 570 1.01
flare[1,15] 0.15 0.01 0.252.8e-19 2.0e-3 0.05 0.19 0.88 488 1.0
flare[2,15] 1.27 0.02 0.7 0.16 0.8 1.18 1.63 2.95 907 1.0
flare[3,15] 0.16 0.03 0.25 3.8e-5 0.01 0.07 0.19 0.83 88 1.05
flare[4,15] 0.3 0.02 0.33 1.1e-3 0.07 0.2 0.42 1.19 432 1.0
flare[5,15] 0.24 0.02 0.29 3.6e-5 0.04 0.14 0.33 1.02 288 1.01
flare[6,15] 2.0 0.06 0.97 0.64 1.29 1.8 2.5 4.37 257 1.02
flare[7,15] 0.16 7.1e-3 0.211.5e-10 0.01 0.08 0.22 0.77 903 1.0
flare[8,15] 1.02 0.03 0.83 4.1e-6 0.32 0.92 1.52 2.91 1113 1.0
flare[9,15] 0.42 0.03 0.492.9e-12 8.3e-3 0.23 0.69 1.66 229 1.02
flare[10,15] 0.52 0.02 0.46 2.9e-4 0.16 0.41 0.76 1.69 744 1.0
flare[11,15] 0.86 0.03 0.63 2.0e-3 0.38 0.77 1.22 2.23 454 1.0
flare[12,15] 0.25 0.02 0.375.1e-13 2.1e-3 0.07 0.35 1.31 439 1.01
flare[13,15] 0.14 7.2e-3 0.251.0e-13 2.7e-4 0.02 0.16 0.88 1198 1.0
flare[14,15] 0.13 0.01 0.263.8e-10 1.4e-3 0.03 0.13 0.96 464 1.01
flare[15,15] 0.59 0.02 0.59 1.3e-7 0.13 0.44 0.89 2.05 600 1.01
flare[16,15] 0.45 0.02 0.43 1.2e-7 0.1 0.34 0.69 1.49 697 1.01
flare[17,15] 0.22 0.01 0.3 6.3e-9 0.02 0.12 0.31 1.08 681 1.0
flare[18,15] 0.66 0.04 0.72 1.3e-7 0.04 0.45 1.09 2.5 267 1.01
flare[19,15] 0.23 0.01 0.36 7.6e-9 7.9e-3 0.08 0.3 1.25 644 1.01
flare[20,15] 0.37 0.02 0.481.3e-12 6.2e-3 0.17 0.61 1.6 515 1.0
flare[21,15] 0.41 0.02 0.42 2.5e-6 0.06 0.28 0.62 1.48 485 1.0
flare[22,15] 0.34 0.02 0.433.8e-12 2.8e-3 0.16 0.54 1.5 471 1.01
flare[23,15] 0.49 0.02 0.45 2.5e-5 0.13 0.38 0.73 1.59 513 1.0
flare[24,15] 0.3 0.02 0.37 2.4e-6 0.03 0.19 0.42 1.36 261 1.02
flare[25,15] 0.37 0.02 0.472.5e-13 4.4e-3 0.17 0.57 1.61 738 1.01
flare[26,15] 0.16 0.02 0.261.2e-18 1.3e-3 0.05 0.2 0.91 280 1.01
flare[27,15] 0.66 0.01 0.4 0.08 0.36 0.6 0.87 1.62 1148 1.01
flare[28,15] 0.29 0.04 0.512.5e-18 8.7e-4 0.04 0.36 1.75 187 1.02
flare[29,15] 0.27 0.01 0.366.8e-15 8.0e-3 0.13 0.39 1.28 595 1.01
flare[30,15] 0.12 9.0e-3 0.233.2e-15 5.5e-4 0.02 0.11 0.88 680 1.0
flare[31,15] 1.07 0.03 0.77 2.6e-3 0.51 1.0 1.46 2.94 662 1.01
flare[32,15] 0.35 0.02 0.446.5e-14 0.03 0.21 0.51 1.49 433 1.01
flare[33,15] 0.24 0.02 0.32 1.5e-8 0.02 0.13 0.34 1.14 248 1.01
flare[34,15] 0.48 0.02 0.42 7.4e-6 0.15 0.38 0.72 1.52 680 1.0
flare[35,15] 0.37 0.02 0.466.8e-10 0.02 0.18 0.55 1.65 488 1.01
flare[36,15] 0.13 6.9e-3 0.241.8e-15 6.1e-5 0.02 0.15 0.88 1236 1.0
flare[37,15] 0.18 0.01 0.19 1.2e-3 0.05 0.13 0.26 0.7 253 1.01
flare[38,15] 0.3 0.01 0.391.2e-12 0.01 0.15 0.43 1.34 743 1.0
flare[39,15] 0.64 0.02 0.58 5.3e-9 0.17 0.52 0.96 2.0 737 1.0
flare[40,15] 0.32 0.01 0.34 1.2e-6 0.06 0.22 0.48 1.17 529 1.0
flare[41,15] 0.43 0.02 0.42 9.0e-6 0.1 0.31 0.63 1.48 365 1.02
flare[42,15] 0.25 0.02 0.423.4e-11 9.6e-4 0.06 0.35 1.38 389 1.0
flare[43,15] 0.31 0.02 0.33 2.8e-7 0.05 0.21 0.46 1.16 455 1.01
flare[44,15] 0.41 0.03 0.49 1.7e-9 0.03 0.22 0.63 1.7 275 1.02
flare[45,15] 0.18 8.8e-3 0.35.4e-22 5.8e-4 0.04 0.23 1.06 1171 1.0
flare[46,15] 0.74 0.04 0.521.3e-14 0.36 0.69 1.04 1.9 194 1.02
flare[47,15] 0.51 0.03 0.58 1.8e-9 0.03 0.3 0.82 2.0 292 1.02
flare[48,15] 0.57 0.02 0.46 3.8e-5 0.22 0.49 0.82 1.63 341 1.01
flare[49,15] 0.78 0.02 0.52 0.01 0.39 0.73 1.1 1.91 716 1.0
flare[50,15] 0.41 0.02 0.516.1e-19 7.2e-3 0.19 0.66 1.69 765 1.0
flare[51,15] 0.36 0.02 0.393.8e-24 0.04 0.25 0.54 1.31 569 1.01
flare[52,15] 0.88 0.08 0.771.9e-10 0.21 0.8 1.36 2.57 102 1.03
flare[53,15] 0.27 0.02 0.424.8e-15 1.4e-3 0.06 0.4 1.44 510 1.01
flare[54,15] 0.22 0.02 0.331.5e-1410.0e-3 0.09 0.28 1.18 264 1.01
flare[55,15] 0.15 8.5e-3 0.247.8e-10 8.1e-3 0.07 0.21 0.77 773 1.01
flare[56,15] 0.62 0.03 0.54 3.7e-6 0.15 0.53 0.93 1.88 290 1.02
flare[57,15] 0.2 0.02 0.361.7e-14 2.5e-3 0.04 0.22 1.25 339 1.01
flare[58,15] 0.63 0.03 0.62 3.7e-5 0.12 0.46 0.95 2.18 416 1.01
flare[59,15] 0.43 0.02 0.36 0.01 0.16 0.34 0.61 1.37 358 1.01
flare[60,15] 0.43 0.02 0.38 5.8e-5 0.13 0.33 0.63 1.39 522 1.01
flare[61,15] 0.14 7.1e-3 0.18 1.5e-7 0.02 0.07 0.19 0.62 603 1.0
flare[62,15] 0.27 0.01 0.45.9e-11 0.02 0.1 0.35 1.47 701 1.01
flare[63,15] 0.25 0.01 0.31 3.5e-6 0.04 0.14 0.35 1.16 766 1.0
flare[64,15] 0.96 0.03 0.68 2.4e-6 0.46 0.87 1.37 2.54 425 1.0
flare[65,15] 0.45 0.02 0.514.3e-10 0.03 0.27 0.73 1.79 639 1.01
flare[66,15] 0.15 9.7e-3 0.22 1.7e-7 0.01 0.07 0.2 0.81 525 1.02
flare[67,15] 0.14 0.02 0.297.4e-12 1.4e-3 0.02 0.12 1.11 151 1.02
flare[68,15] 0.33 0.01 0.31 3.1e-4 0.1 0.25 0.48 1.14 510 1.01
flare[69,15] 0.24 0.03 0.34 5.9e-8 0.02 0.1 0.32 1.21 175 1.02
flare[70,15] 0.45 0.02 0.4 3.6e-5 0.14 0.35 0.66 1.49 319 1.01
flare[71,15] 0.57 0.02 0.4 0.03 0.26 0.48 0.79 1.5 295 1.01
flare[72,15] 1.06 0.06 0.82 2.0e-3 0.47 0.92 1.49 3.16 210 1.02
flare[73,15] 0.25 0.02 0.395.5e-20 1.1e-3 0.05 0.35 1.36 556 1.0
flare[74,15] 0.58 0.02 0.49 3.8e-5 0.19 0.49 0.85 1.76 577 1.01
flare[75,15] 0.85 0.03 0.68 1.3e-5 0.27 0.79 1.25 2.44 514 1.0
flare[76,15] 0.19 0.01 0.37.4e-12 5.5e-3 0.07 0.23 1.07 630 1.01
flare[77,15] 0.31 0.03 0.42 6.6e-9 0.02 0.13 0.45 1.46 244 1.02
flare[78,15] 0.53 0.03 0.579.0e-16 0.04 0.36 0.86 1.89 383 1.01
flare[79,15] 0.63 0.03 0.46 0.02 0.28 0.55 0.86 1.79 216 1.01
flare[80,15] 0.29 0.02 0.44 5.7e-9 5.2e-3 0.08 0.4 1.47 631 1.01
flare[81,15] 0.26 0.02 0.381.1e-11 5.5e-3 0.08 0.4 1.33 374 1.01
flare[82,15] 1.73 0.04 1.17 1.1e-3 0.97 1.6 2.32 4.52 1015 1.0
flare[83,15] 0.45 0.03 0.48 1.6e-8 0.05 0.3 0.7 1.69 259 1.01
flare[84,15] 1.65 0.03 0.94 0.37 1.0 1.5 2.08 4.01 847 1.01
flare[85,15] 0.24 0.02 0.34 3.1e-9 0.01 0.09 0.31 1.22 469 1.0
flare[86,15] 0.11 6.3e-3 0.18 1.1e-9 6.6e-3 0.04 0.14 0.63 833 1.01
flare[87,15] 0.77 0.03 0.63 1.1e-6 0.27 0.67 1.14 2.28 409 1.0
flare[88,15] 0.28 0.02 0.39 1.0e-9 0.02 0.15 0.38 1.34 370 1.01
flare[89,15] 0.15 9.7e-3 0.33.7e-13 2.7e-4 0.02 0.16 1.11 941 1.0
flare[90,15] 0.3 0.01 0.4 1.5e-9 0.03 0.16 0.41 1.38 774 1.01
flare[91,15] 0.3 0.04 0.46 4.6e-7 0.02 0.11 0.36 1.73 118 1.05
flare[92,15] 0.28 0.01 0.27 2.0e-5 0.07 0.2 0.41 0.96 601 1.01
flare[93,15] 0.35 0.02 0.393.6e-30 0.04 0.22 0.54 1.38 444 1.0
flare[94,15] 0.5 0.03 0.494.3e-11 0.08 0.37 0.76 1.69 276 1.01
flare[95,15] 0.44 0.03 0.54 4.5e-9 0.03 0.23 0.67 1.94 347 1.01
flare[96,15] 0.78 0.02 0.67 4.2e-6 0.2 0.67 1.19 2.36 824 1.0
flare[97,15] 0.17 0.01 0.316.2e-12 1.9e-3 0.04 0.18 1.09 703 1.0
flare[98,15] 0.72 0.02 0.54 4.2e-4 0.29 0.64 1.04 1.97 626 1.0
flare[99,15] 0.28 0.01 0.414.8e-17 1.8e-3 0.1 0.4 1.38 1151 1.0
flare[0,16] 0.26 0.02 0.35 2.1e-7 0.03 0.13 0.36 1.25 463 1.01
flare[1,16] 0.13 8.1e-3 0.241.0e-14 1.5e-3 0.04 0.15 0.8 846 1.0
flare[2,16] 1.04 0.02 0.61 0.09 0.62 0.96 1.38 2.41 830 1.0
flare[3,16] 0.12 0.01 0.19 2.3e-5 9.4e-3 0.05 0.14 0.69 192 1.02
flare[4,16] 0.25 0.01 0.3 5.7e-4 0.05 0.15 0.35 1.09 468 1.0
flare[5,16] 0.19 0.01 0.25 1.8e-5 0.02 0.1 0.25 0.91 333 1.01
flare[6,16] 1.62 0.04 0.79 0.45 1.05 1.5 2.03 3.51 356 1.01
flare[7,16] 0.13 5.8e-3 0.191.7e-10 6.2e-3 0.05 0.17 0.68 1065 1.0
flare[8,16] 1.23 0.03 0.89 3.2e-4 0.58 1.16 1.71 3.36 767 1.0
flare[9,16] 0.4 0.03 0.462.8e-10 0.01 0.22 0.64 1.55 268 1.02
flare[10,16] 0.4 0.02 0.38 1.6e-4 0.1 0.29 0.58 1.37 626 1.01
flare[11,16] 0.7 0.02 0.53 2.0e-3 0.3 0.62 1.0 2.0 553 1.0
flare[12,16] 0.23 0.01 0.351.4e-11 1.8e-3 0.06 0.34 1.23 654 1.0
flare[13,16] 0.12 7.7e-3 0.244.1e-12 2.5e-4 0.02 0.13 0.87 946 1.0
flare[14,16] 0.12 0.01 0.24 2.4e-9 1.2e-3 0.02 0.11 0.94 454 1.01
flare[15,16] 0.56 0.03 0.55 3.5e-6 0.12 0.4 0.84 2.04 372 1.01
flare[16,16] 0.36 0.01 0.37 6.5e-7 0.06 0.25 0.53 1.29 760 1.0
flare[17,16] 0.2 0.01 0.29 1.2e-8 0.01 0.09 0.26 1.04 750 1.0
flare[18,16] 0.73 0.04 0.71 8.5e-7 0.09 0.58 1.14 2.46 354 1.01
flare[19,16] 0.21 0.01 0.35 7.8e-9 6.0e-3 0.06 0.26 1.25 596 1.01
flare[20,16] 0.33 0.02 0.431.4e-11 6.5e-3 0.14 0.52 1.47 589 1.0
flare[21,16] 0.37 0.02 0.42 7.1e-6 0.05 0.24 0.55 1.47 427 1.01
flare[22,16] 0.31 0.02 0.421.4e-10 2.7e-3 0.13 0.47 1.42 593 1.01
flare[23,16] 0.42 0.02 0.41 1.0e-4 0.09 0.3 0.62 1.47 461 1.0
flare[24,16] 0.22 0.01 0.28 1.2e-6 0.02 0.13 0.32 0.98 550 1.01
flare[25,16] 0.39 0.02 0.53.3e-11 0.01 0.18 0.58 1.69 728 1.01
flare[26,16] 0.13 0.01 0.231.5e-16 8.3e-4 0.03 0.16 0.8 252 1.01
flare[27,16] 0.51 0.01 0.35 0.04 0.25 0.45 0.7 1.34 1070 1.01
flare[28,16] 0.32 0.04 0.521.1e-15 1.3e-3 0.04 0.45 1.88 151 1.02
flare[29,16] 0.21 0.01 0.37.3e-15 5.5e-3 0.09 0.3 1.07 731 1.01
flare[30,16] 0.11 7.7e-3 0.234.4e-14 4.5e-4 0.01 0.09 0.79 878 1.0
flare[31,16] 0.88 0.02 0.64 2.0e-3 0.4 0.81 1.21 2.42 785 1.0
flare[32,16] 0.3 0.02 0.392.9e-12 0.03 0.15 0.42 1.4 366 1.01
flare[33,16] 0.2 0.01 0.28 5.9e-9 0.02 0.09 0.27 0.98 372 1.01
flare[34,16] 0.37 0.01 0.36 1.6e-5 0.09 0.27 0.55 1.29 649 1.0
flare[35,16] 0.37 0.02 0.48 6.2e-9 0.02 0.16 0.54 1.65 376 1.01
flare[36,16] 0.12 6.0e-3 0.244.2e-14 7.7e-5 0.01 0.13 0.89 1616 1.0
flare[37,16] 0.14 0.01 0.17 4.5e-4 0.03 0.09 0.2 0.61 212 1.02
flare[38,16] 0.26 0.01 0.373.2e-12 0.01 0.11 0.37 1.28 713 1.01
flare[39,16] 0.56 0.02 0.5 2.9e-6 0.15 0.43 0.85 1.74 869 1.0
flare[40,16] 0.25 0.01 0.29 6.5e-7 0.04 0.16 0.38 1.03 525 1.0
flare[41,16] 0.4 0.02 0.43 1.6e-5 0.08 0.26 0.56 1.48 304 1.01
flare[42,16] 0.29 0.02 0.431.2e-10 4.0e-3 0.09 0.41 1.51 497 1.01
flare[43,16] 0.25 0.01 0.29 6.4e-7 0.03 0.15 0.36 1.01 431 1.01
flare[44,16] 0.36 0.02 0.44 9.1e-9 0.03 0.18 0.55 1.49 379 1.01
flare[45,16] 0.15 8.0e-3 0.271.6e-27 5.5e-4 0.03 0.18 1.0 1149 1.0
flare[46,16] 0.62 0.03 0.481.0e-10 0.26 0.54 0.87 1.71 374 1.02
flare[47,16] 0.44 0.04 0.51 1.2e-8 0.03 0.25 0.69 1.78 187 1.02
flare[48,16] 0.46 0.02 0.41 4.0e-5 0.15 0.37 0.67 1.43 400 1.01
flare[49,16] 0.74 0.02 0.54 0.02 0.35 0.67 1.04 1.96 626 1.0
flare[50,16] 0.43 0.02 0.521.8e-13 0.01 0.21 0.7 1.73 517 1.0
flare[51,16] 0.29 0.01 0.335.4e-24 0.02 0.18 0.43 1.2 540 1.0
flare[52,16] 0.81 0.07 0.67 1.1e-9 0.23 0.72 1.24 2.21 92 1.04
flare[53,16] 0.26 0.02 0.418.5e-14 1.5e-3 0.06 0.38 1.4 460 1.01
flare[54,16] 0.21 0.03 0.347.7e-12 7.6e-3 0.07 0.24 1.24 175 1.03
flare[55,16] 0.13 0.01 0.23 4.6e-9 4.4e-3 0.05 0.16 0.78 460 1.01
flare[56,16] 0.5 0.02 0.46 3.2e-6 0.11 0.4 0.76 1.6 419 1.01
flare[57,16] 0.19 0.02 0.361.4e-11 2.4e-3 0.04 0.19 1.35 319 1.01
flare[58,16] 0.57 0.03 0.62 6.1e-5 0.09 0.39 0.87 2.05 413 1.01
flare[59,16] 0.35 0.02 0.33 4.8e-3 0.11 0.25 0.49 1.25 305 1.01
flare[60,16] 0.36 0.02 0.36 8.6e-5 0.09 0.26 0.53 1.33 499 1.01
flare[61,16] 0.11 6.0e-3 0.16 2.8e-7 0.01 0.05 0.15 0.56 765 1.0
flare[62,16] 0.26 0.02 0.4 3.6e-9 0.01 0.09 0.33 1.46 512 1.01
flare[63,16] 0.2 8.9e-3 0.26 2.3e-6 0.02 0.1 0.26 0.95 853 1.0
flare[64,16] 0.77 0.03 0.58 3.2e-5 0.34 0.68 1.11 2.14 336 1.01
flare[65,16] 0.44 0.03 0.51 2.3e-9 0.04 0.27 0.68 1.82 344 1.01
flare[66,16] 0.13 8.0e-3 0.21 9.0e-7 7.5e-3 0.05 0.16 0.79 698 1.01
flare[67,16] 0.12 0.02 0.2710.0e-12 9.8e-4 0.02 0.1 1.0 185 1.02
flare[68,16] 0.27 0.01 0.27 7.6e-5 0.07 0.18 0.39 1.0 494 1.01
flare[69,16] 0.19 0.02 0.29 1.7e-7 0.01 0.08 0.25 1.07 210 1.02
flare[70,16] 0.35 0.02 0.36 2.9e-5 0.09 0.25 0.5 1.36 296 1.01
flare[71,16] 0.45 0.02 0.35 0.01 0.18 0.37 0.63 1.33 288 1.01
flare[72,16] 0.83 0.04 0.66 1.1e-3 0.34 0.74 1.18 2.39 237 1.02
flare[73,16] 0.25 0.02 0.395.6e-17 1.0e-3 0.05 0.35 1.37 367 1.01
flare[74,16] 0.49 0.02 0.4410.0e-5 0.15 0.39 0.72 1.56 754 1.0
flare[75,16] 0.74 0.03 0.6 1.6e-4 0.22 0.67 1.09 2.13 553 1.0
flare[76,16] 0.18 0.02 0.317.9e-11 4.3e-3 0.05 0.2 1.12 404 1.0
flare[77,16] 0.29 0.03 0.41 3.9e-8 0.01 0.11 0.4 1.44 250 1.02
flare[78,16] 0.49 0.02 0.534.2e-13 0.04 0.33 0.79 1.77 460 1.01
flare[79,16] 0.53 0.03 0.42 0.02 0.21 0.43 0.73 1.63 264 1.01
flare[80,16] 0.32 0.02 0.49 1.1e-8 4.6e-3 0.08 0.44 1.69 602 1.01
flare[81,16] 0.22 0.02 0.341.7e-11 4.3e-3 0.06 0.31 1.16 475 1.0
flare[82,16] 2.05 0.04 1.16 0.05 1.32 1.91 2.57 4.88 832 1.0
flare[83,16] 0.43 0.04 0.5 1.4e-7 0.05 0.27 0.65 1.77 187 1.02
flare[84,16] 1.41 0.03 0.78 0.26 0.84 1.29 1.81 3.29 941 1.0
flare[85,16] 0.22 0.02 0.35 7.0e-9 9.4e-3 0.08 0.28 1.31 461 1.01
flare[86,16] 0.09 5.8e-3 0.16 5.4e-9 3.7e-3 0.03 0.1 0.55 790 1.01
flare[87,16] 0.65 0.03 0.54 4.7e-6 0.2 0.56 0.97 1.9 445 1.01
flare[88,16] 0.24 0.02 0.36 7.5e-9 0.02 0.11 0.31 1.24 428 1.01
flare[89,16] 0.17 0.01 0.323.9e-11 1.0e-3 0.03 0.18 1.16 811 1.0
flare[90,16] 0.27 0.01 0.388.1e-10 0.02 0.12 0.34 1.36 675 1.01
flare[91,16] 0.3 0.05 0.48 4.4e-7 0.01 0.08 0.36 1.68 93 1.06
flare[92,16] 0.22 9.6e-3 0.23 4.1e-6 0.04 0.15 0.32 0.82 576 1.01
flare[93,16] 0.31 0.02 0.373.5e-27 0.03 0.17 0.45 1.27 393 1.0
flare[94,16] 0.46 0.03 0.48 3.3e-9 0.07 0.33 0.7 1.64 312 1.01
flare[95,16] 0.41 0.03 0.524.3e-10 0.02 0.2 0.63 1.76 253 1.01
flare[96,16] 0.83 0.03 0.69 2.0e-4 0.27 0.71 1.24 2.45 574 1.01
flare[97,16] 0.15 9.4e-3 0.281.1e-1110.0e-4 0.03 0.16 0.96 860 1.0
flare[98,16] 0.61 0.02 0.49 5.9e-4 0.22 0.51 0.89 1.81 501 1.0
flare[99,16] 0.25 0.02 0.382.9e-16 2.4e-3 0.08 0.34 1.35 606 1.0
flare[0,17] 0.22 0.01 0.32 2.8e-7 0.02 0.1 0.3 1.14 507 1.01
flare[1,17] 0.12 7.6e-3 0.247.9e-14 9.2e-4 0.03 0.12 0.82 989 1.0
flare[2,17] 0.85 0.02 0.54 0.05 0.46 0.77 1.16 2.06 658 1.01
flare[3,17] 0.09 6.1e-3 0.15 9.4e-6 6.2e-3 0.03 0.11 0.57 643 1.01
flare[4,17] 0.21 0.01 0.27 2.8e-4 0.03 0.11 0.28 1.0 480 1.0
flare[5,17] 0.15 0.01 0.22 7.3e-6 0.01 0.07 0.19 0.78 393 1.01
flare[6,17] 1.31 0.03 0.67 0.29 0.83 1.21 1.65 2.95 502 1.01
flare[7,17] 0.1 4.9e-3 0.177.5e-10 3.9e-3 0.04 0.13 0.6 1169 1.0
flare[8,17] 1.18 0.03 0.82 4.5e-3 0.6 1.11 1.61 3.18 757 1.0
flare[9,17] 0.38 0.03 0.46 8.9e-9 0.01 0.2 0.58 1.58 339 1.01
flare[10,17] 0.3 0.01 0.32 6.3e-5 0.06 0.2 0.44 1.14 599 1.0
flare[11,17] 0.53 0.02 0.44 8.9e-4 0.2 0.44 0.76 1.63 528 1.01
flare[12,17] 0.23 0.01 0.368.8e-11 1.2e-3 0.06 0.31 1.29 854 1.0
flare[13,17] 0.12 9.5e-3 0.261.0e-11 2.0e-4 0.01 0.12 0.92 721 1.0
flare[14,17] 0.12 0.01 0.27 2.7e-9 9.7e-4 0.02 0.1 0.93 391 1.01
flare[15,17] 0.46 0.02 0.48 1.1e-5 0.09 0.32 0.69 1.74 492 1.01
flare[16,17] 0.28 0.01 0.31 3.9e-7 0.04 0.18 0.42 1.08 788 1.0
flare[17,17] 0.17 0.01 0.29 1.5e-8 7.6e-3 0.06 0.21 1.02 764 1.0
flare[18,17] 0.7 0.03 0.66 2.9e-7 0.11 0.57 1.08 2.28 433 1.01
flare[19,17] 0.19 0.01 0.33 8.2e-9 3.9e-3 0.04 0.22 1.14 564 1.01
flare[20,17] 0.29 0.02 0.41.5e-10 6.4e-3 0.11 0.45 1.37 617 1.0
flare[21,17] 0.37 0.03 0.45 6.6e-6 0.04 0.2 0.51 1.58 310 1.02
flare[22,17] 0.27 0.02 0.395.0e-10 2.4e-3 0.1 0.39 1.33 635 1.01
flare[23,17] 0.37 0.02 0.4 2.2e-4 0.06 0.24 0.55 1.39 399 1.01
flare[24,17] 0.16 8.1e-3 0.22 4.1e-7 0.01 0.09 0.23 0.8 727 1.01
flare[25,17] 0.39 0.02 0.511.6e-10 0.02 0.17 0.58 1.7 547 1.0
flare[26,17] 0.11 0.01 0.211.1e-14 5.2e-4 0.02 0.12 0.75 291 1.01
flare[27,17] 0.4 0.01 0.31 0.02 0.17 0.34 0.56 1.14 922 1.0
flare[28,17] 0.28 0.03 0.4510.0e-14 1.3e-3 0.04 0.39 1.54 204 1.01
flare[29,17] 0.17 9.1e-3 0.261.3e-14 3.4e-3 0.06 0.23 0.95 829 1.01
flare[30,17] 0.1 9.0e-3 0.244.6e-14 3.7e-4 0.01 0.07 0.83 725 1.0
flare[31,17] 0.7 0.02 0.53 1.2e-3 0.29 0.63 1.0 1.97 754 1.0
flare[32,17] 0.23 0.01 0.322.1e-11 0.02 0.11 0.32 1.11 471 1.01
flare[33,17] 0.16 8.9e-3 0.24 2.2e-9 9.4e-3 0.06 0.21 0.82 722 1.0
flare[34,17] 0.29 0.01 0.31 6.7e-6 0.06 0.19 0.43 1.12 550 1.0
flare[35,17] 0.33 0.02 0.45 7.5e-9 0.01 0.13 0.47 1.57 332 1.02
flare[36,17] 0.13 7.6e-3 0.263.9e-14 8.7e-5 0.01 0.12 0.93 1183 1.01
flare[37,17] 0.11 0.01 0.15 1.8e-4 0.02 0.06 0.15 0.53 183 1.02
flare[38,17] 0.23 0.01 0.351.1e-11 8.1e-3 0.08 0.31 1.28 773 1.0
flare[39,17] 0.46 0.02 0.45 1.2e-5 0.11 0.35 0.71 1.54 776 1.0
flare[40,17] 0.2 0.01 0.26 2.4e-7 0.02 0.11 0.29 0.93 566 1.0
flare[41,17] 0.36 0.02 0.42 5.9e-6 0.06 0.21 0.5 1.44 372 1.01
flare[42,17] 0.38 0.02 0.531.3e-10 9.8e-3 0.14 0.54 1.78 509 1.01
flare[43,17] 0.2 0.01 0.26 4.0e-7 0.02 0.1 0.28 0.89 410 1.01
flare[44,17] 0.31 0.02 0.4 5.5e-9 0.02 0.14 0.47 1.38 485 1.01
flare[45,17] 0.14 8.8e-3 0.26 0.0 4.2e-4 0.02 0.15 0.92 850 1.01
flare[46,17] 0.51 0.02 0.44 2.3e-7 0.19 0.43 0.72 1.57 440 1.01
flare[47,17] 0.36 0.02 0.43 3.3e-8 0.02 0.19 0.56 1.46 401 1.02
flare[48,17] 0.37 0.02 0.37 2.6e-5 0.1 0.27 0.54 1.31 560 1.01
flare[49,17] 0.66 0.02 0.51 0.01 0.28 0.57 0.95 1.83 701 1.0
flare[50,17] 0.43 0.02 0.513.6e-12 0.02 0.23 0.71 1.73 507 1.0
flare[51,17] 0.23 9.9e-3 0.297.5e-24 0.02 0.13 0.33 1.03 869 1.0
flare[52,17] 0.69 0.06 0.58 4.1e-9 0.19 0.61 1.07 1.96 106 1.03
flare[53,17] 0.25 0.02 0.393.3e-13 1.2e-3 0.05 0.35 1.4 465 1.01
flare[54,17] 0.19 0.03 0.34 6.3e-9 6.1e-3 0.05 0.21 1.27 164 1.03
flare[55,17] 0.11 0.01 0.22 6.4e-9 2.5e-3 0.03 0.12 0.69 329 1.01
flare[56,17] 0.41 0.02 0.41 2.2e-6 0.08 0.3 0.61 1.42 497 1.01
flare[57,17] 0.19 0.02 0.364.2e-11 2.0e-3 0.03 0.18 1.37 342 1.01
flare[58,17] 0.48 0.02 0.52 9.9e-5 0.07 0.31 0.73 1.76 559 1.01
flare[59,17] 0.29 0.02 0.3 2.0e-3 0.07 0.19 0.41 1.15 276 1.01
flare[60,17] 0.31 0.02 0.34 1.0e-4 0.07 0.2 0.45 1.25 412 1.01
flare[61,17] 0.1 5.9e-3 0.16 4.0e-7 7.0e-3 0.04 0.12 0.52 726 1.0
flare[62,17] 0.24 0.02 0.38 5.7e-9 7.8e-3 0.07 0.29 1.36 514 1.01
flare[63,17] 0.15 7.5e-3 0.22 7.5e-7 0.01 0.07 0.2 0.8 865 1.0
flare[64,17] 0.62 0.04 0.5 1.6e-4 0.23 0.52 0.88 1.79 190 1.02
flare[65,17] 0.36 0.02 0.43 7.0e-9 0.03 0.2 0.56 1.49 798 1.01
flare[66,17] 0.12 8.8e-3 0.23 1.0e-6 5.7e-3 0.04 0.14 0.81 673 1.01
flare[67,17] 0.11 0.02 0.264.0e-11 7.0e-4 0.01 0.08 0.94 275 1.02
flare[68,17] 0.22 0.01 0.24 2.8e-5 0.04 0.13 0.31 0.9 477 1.01
flare[69,17] 0.15 0.01 0.23 1.1e-7 7.4e-3 0.05 0.2 0.85 345 1.01
flare[70,17] 0.27 0.02 0.3 1.1e-5 0.06 0.18 0.39 1.15 270 1.01
flare[71,17] 0.36 0.02 0.31 5.4e-3 0.12 0.28 0.51 1.19 283 1.01
flare[72,17] 0.65 0.03 0.54 7.0e-4 0.23 0.57 0.95 1.94 272 1.03
flare[73,17] 0.24 0.02 0.46.2e-16 8.0e-4 0.04 0.32 1.42 437 1.0
flare[74,17] 0.42 0.02 0.41 1.7e-4 0.11 0.3 0.6 1.49 646 1.0
flare[75,17] 0.64 0.02 0.55 2.0e-4 0.18 0.55 0.96 1.96 534 1.0
flare[76,17] 0.17 0.02 0.313.6e-10 3.0e-3 0.04 0.16 1.21 332 1.0
flare[77,17] 0.28 0.02 0.43 4.6e-8 0.01 0.09 0.38 1.5 573 1.01
flare[78,17] 0.41 0.02 0.451.4e-12 0.04 0.26 0.65 1.56 472 1.01
flare[79,17] 0.43 0.02 0.36 7.9e-3 0.15 0.34 0.61 1.32 274 1.01
flare[80,17] 0.3 0.02 0.47 2.5e-9 3.4e-3 0.06 0.42 1.65 591 1.01
flare[81,17] 0.18 0.01 0.296.1e-12 3.0e-3 0.05 0.24 1.03 468 1.0
flare[82,17] 2.02 0.04 1.04 0.44 1.32 1.86 2.46 4.64 828 1.0
flare[83,17] 0.42 0.06 0.55 1.9e-7 0.04 0.21 0.6 1.94 78 1.05
flare[84,17] 1.17 0.02 0.67 0.18 0.68 1.08 1.56 2.8 956 1.0
flare[85,17] 0.21 0.02 0.35 3.5e-9 6.1e-3 0.06 0.23 1.34 386 1.01
flare[86,17] 0.07 6.2e-3 0.16 7.4e-9 2.0e-3 0.02 0.07 0.49 664 1.01
flare[87,17] 0.51 0.02 0.45 5.2e-6 0.14 0.41 0.78 1.57 575 1.0
flare[88,17] 0.21 0.01 0.33 1.8e-8 0.01 0.08 0.25 1.11 543 1.01
flare[89,17] 0.22 0.02 0.394.0e-10 1.7e-3 0.04 0.25 1.42 527 1.0
flare[90,17] 0.22 0.01 0.323.5e-10 0.01 0.09 0.29 1.13 658 1.01
flare[91,17] 0.25 0.04 0.41 1.9e-7 7.4e-3 0.06 0.3 1.41 110 1.05
flare[92,17] 0.17 8.2e-3 0.19 8.9e-7 0.02 0.1 0.24 0.71 569 1.01
flare[93,17] 0.25 0.01 0.342.6e-24 0.02 0.13 0.35 1.16 561 1.0
flare[94,17] 0.39 0.02 0.43 2.7e-8 0.05 0.26 0.58 1.49 390 1.01
flare[95,17] 0.33 0.03 0.438.5e-11 0.02 0.15 0.49 1.49 264 1.01
flare[96,17] 0.85 0.04 0.7 1.4e-3 0.28 0.7 1.23 2.53 324 1.02
flare[97,17] 0.12 8.1e-3 0.2510.0e-12 6.1e-4 0.02 0.11 0.85 917 1.0
flare[98,17] 0.52 0.02 0.46 5.4e-4 0.17 0.4 0.76 1.66 419 1.01
flare[99,17] 0.24 0.02 0.414.4e-14 2.7e-3 0.07 0.3 1.43 401 1.02
flare[0,18] 0.18 0.01 0.2710.0e-8 0.01 0.07 0.24 0.98 558 1.01
flare[1,18] 0.12 8.5e-3 0.261.7e-13 6.1e-4 0.02 0.1 0.93 948 1.0
flare[2,18] 0.7 0.02 0.49 0.03 0.33 0.61 0.97 1.8 578 1.01
flare[3,18] 0.07 4.2e-3 0.13 4.5e-6 3.9e-3 0.02 0.08 0.46 928 1.01
flare[4,18] 0.18 0.01 0.25 1.4e-4 0.02 0.08 0.23 0.91 474 1.0
flare[5,18] 0.12 7.8e-3 0.19 1.9e-6 7.4e-3 0.05 0.14 0.66 559 1.01
flare[6,18] 1.06 0.02 0.6 0.18 0.64 0.97 1.37 2.56 742 1.0
flare[7,18] 0.09 7.2e-3 0.174.8e-10 2.4e-3 0.03 0.1 0.6 529 1.01
flare[8,18] 1.06 0.03 0.75 9.1e-3 0.54 0.95 1.43 2.74 832 1.0
flare[9,18] 0.35 0.02 0.46 1.9e-8 0.01 0.18 0.53 1.57 401 1.01
flare[10,18] 0.24 0.01 0.27 2.6e-5 0.04 0.14 0.34 0.96 581 1.0
flare[11,18] 0.41 0.02 0.37 4.8e-4 0.12 0.31 0.59 1.37 423 1.01
flare[12,18] 0.22 0.01 0.361.2e-10 1.0e-3 0.05 0.28 1.26 739 1.01
flare[13,18] 0.12 0.01 0.262.7e-12 1.4e-4 0.01 0.1 0.94 666 1.01
flare[14,18] 0.13 0.02 0.32 2.2e-9 6.6e-4 0.01 0.09 1.1 339 1.02
flare[15,18] 0.37 0.02 0.41 7.5e-6 0.06 0.23 0.54 1.48 622 1.01
flare[16,18] 0.22 9.9e-3 0.27 1.5e-7 0.02 0.12 0.32 0.93 718 1.01
flare[17,18] 0.15 0.01 0.28 3.6e-8 4.6e-3 0.04 0.16 0.97 773 1.0
flare[18,18] 0.62 0.03 0.62 7.4e-8 0.09 0.47 0.96 2.18 427 1.01
flare[19,18] 0.16 0.01 0.3 2.9e-9 2.4e-3 0.03 0.18 1.04 584 1.01
flare[20,18] 0.27 0.02 0.42.3e-10 4.8e-3 0.1 0.39 1.36 534 1.01
flare[21,18] 0.35 0.04 0.47 3.7e-6 0.03 0.16 0.48 1.61 135 1.03
flare[22,18] 0.23 0.01 0.358.8e-10 1.7e-3 0.07 0.32 1.23 634 1.01
flare[23,18] 0.33 0.02 0.4 1.6e-4 0.04 0.19 0.48 1.33 316 1.01
flare[24,18] 0.13 6.5e-3 0.18 1.8e-7 6.9e-3 0.05 0.17 0.65 779 1.0
flare[25,18] 0.39 0.03 0.511.6e-10 0.02 0.16 0.58 1.79 390 1.0
flare[26,18] 0.1 0.01 0.218.0e-14 3.7e-4 0.01 0.09 0.78 321 1.01
flare[27,18] 0.32 9.4e-3 0.27 0.01 0.12 0.25 0.45 0.99 840 1.0
flare[28,18] 0.25 0.02 0.41.3e-11 1.1e-3 0.04 0.34 1.37 282 1.01
flare[29,18] 0.14 8.4e-3 0.241.4e-14 2.0e-3 0.04 0.18 0.83 847 1.0
flare[30,18] 0.11 0.01 0.273.5e-14 2.7e-4 8.8e-3 0.07 0.98 667 1.0
flare[31,18] 0.56 0.02 0.47 8.7e-4 0.2 0.48 0.81 1.67 659 1.01
flare[32,18] 0.17 0.01 0.251.3e-10 0.01 0.07 0.24 0.88 554 1.01
flare[33,18] 0.13 7.1e-3 0.29.3e-10 5.6e-3 0.04 0.16 0.71 823 1.0
flare[34,18] 0.24 0.01 0.29 4.1e-6 0.04 0.14 0.34 0.97 557 1.0
flare[35,18] 0.26 0.02 0.38 3.4e-9 8.5e-3 0.09 0.36 1.39 260 1.02
flare[36,18] 0.14 0.01 0.33.1e-14 9.3e-5 0.01 0.12 1.11 558 1.01
flare[37,18] 0.09 0.01 0.13 6.6e-5 0.01 0.04 0.11 0.47 161 1.03
flare[38,18] 0.22 0.01 0.361.8e-11 5.6e-3 0.06 0.27 1.29 795 1.0
flare[39,18] 0.4 0.02 0.42 3.4e-6 0.07 0.27 0.61 1.43 751 1.0
flare[40,18] 0.16 9.2e-3 0.22 8.7e-8 0.01 0.08 0.23 0.8 581 1.0
flare[41,18] 0.31 0.02 0.39 3.9e-6 0.04 0.16 0.42 1.34 502 1.01
flare[42,18] 0.48 0.03 0.662.9e-11 0.01 0.18 0.75 2.17 365 1.02
flare[43,18] 0.17 0.01 0.23 2.3e-7 0.01 0.07 0.22 0.83 376 1.01
flare[44,18] 0.26 0.01 0.35 3.2e-9 0.01 0.1 0.38 1.24 603 1.01
flare[45,18] 0.13 8.8e-3 0.26 0.0 3.1e-4 0.02 0.13 0.97 884 1.01
flare[46,18] 0.44 0.02 0.41 6.0e-6 0.14 0.34 0.62 1.54 456 1.0
flare[47,18] 0.3 0.02 0.38 1.3e-8 0.01 0.14 0.45 1.33 494 1.01
flare[48,18] 0.3 0.01 0.33 5.2e-6 0.06 0.2 0.43 1.18 571 1.01
flare[49,18] 0.56 0.02 0.45 8.6e-3 0.21 0.47 0.82 1.65 679 1.0
flare[50,18] 0.44 0.03 0.558.5e-12 0.02 0.23 0.68 1.88 408 1.01
flare[51,18] 0.19 8.2e-3 0.267.1e-20 9.8e-3 0.09 0.25 0.91 1040 1.0
flare[52,18] 0.6 0.03 0.54 5.6e-9 0.16 0.49 0.92 1.96 275 1.02
flare[53,18] 0.24 0.02 0.45.7e-13 8.9e-4 0.04 0.32 1.41 383 1.0
flare[54,18] 0.2 0.03 0.37 1.4e-8 4.2e-3 0.04 0.21 1.33 146 1.03
flare[55,18] 0.09 9.2e-3 0.2 4.8e-9 1.4e-3 0.02 0.09 0.66 448 1.01
flare[56,18] 0.34 0.02 0.38 1.2e-6 0.05 0.23 0.49 1.29 520 1.0
flare[57,18] 0.19 0.02 0.361.7e-10 1.6e-3 0.02 0.16 1.36 372 1.01
flare[58,18] 0.4 0.02 0.45 8.8e-5 0.05 0.24 0.6 1.55 513 1.01
flare[59,18] 0.24 0.02 0.28 9.7e-4 0.05 0.14 0.33 1.07 260 1.01
flare[60,18] 0.28 0.02 0.34 4.7e-5 0.05 0.15 0.38 1.26 384 1.01
flare[61,18] 0.09 7.4e-3 0.17 5.0e-7 4.6e-3 0.03 0.09 0.59 538 1.0
flare[62,18] 0.2 0.01 0.34 1.1e-9 5.0e-3 0.05 0.22 1.2 613 1.01
flare[63,18] 0.13 6.5e-3 0.19 5.6e-7 7.9e-3 0.05 0.16 0.71 896 1.0
flare[64,18] 0.51 0.03 0.45 2.0e-4 0.17 0.39 0.74 1.66 174 1.02
flare[65,18] 0.29 0.01 0.36 1.4e-8 0.02 0.15 0.44 1.27 1074 1.01
flare[66,18] 0.13 0.02 0.29 5.8e-7 4.0e-3 0.03 0.12 0.93 180 1.02
flare[67,18] 0.11 0.01 0.278.7e-11 4.6e-4 8.4e-3 0.07 0.98 661 1.01
flare[68,18] 0.18 9.9e-3 0.22 8.7e-6 0.03 0.1 0.25 0.79 476 1.01
flare[69,18] 0.12 9.9e-3 0.21 6.4e-8 4.7e-3 0.04 0.15 0.78 455 1.01
flare[70,18] 0.22 0.02 0.26 5.1e-6 0.04 0.12 0.3 1.02 264 1.01
flare[71,18] 0.29 0.02 0.28 2.4e-3 0.08 0.21 0.4 1.05 275 1.01
flare[72,18] 0.52 0.03 0.45 3.8e-4 0.15 0.42 0.75 1.61 284 1.03
flare[73,18] 0.2 0.01 0.351.1e-15 6.0e-4 0.03 0.27 1.32 568 1.0
flare[74,18] 0.36 0.02 0.39 1.3e-4 0.08 0.23 0.5 1.36 634 1.01
flare[75,18] 0.54 0.02 0.5 2.3e-4 0.13 0.44 0.81 1.84 478 1.01
flare[76,18] 0.15 0.02 0.317.7e-10 1.9e-3 0.03 0.14 1.2 305 1.01
flare[77,18] 0.28 0.03 0.47 7.5e-9 6.9e-3 0.08 0.35 1.67 283 1.01
flare[78,18] 0.33 0.02 0.393.1e-12 0.03 0.19 0.51 1.32 552 1.01
flare[79,18] 0.35 0.02 0.32 4.2e-3 0.1 0.26 0.5 1.2 282 1.01
flare[80,18] 0.25 0.02 0.42 1.3e-9 2.1e-3 0.05 0.35 1.43 599 1.01
flare[81,18] 0.16 0.01 0.284.4e-12 2.2e-3 0.03 0.19 1.04 403 1.0
flare[82,18] 1.8 0.03 0.96 0.42 1.16 1.64 2.23 4.22 848 1.0
flare[83,18] 0.37 0.06 0.5 1.1e-7 0.03 0.16 0.49 1.86 73 1.06
flare[84,18] 0.98 0.02 0.59 0.11 0.54 0.89 1.32 2.35 875 1.0
flare[85,18] 0.18 0.02 0.33 1.4e-9 4.0e-3 0.04 0.18 1.25 306 1.02
flare[86,18] 0.06 6.6e-3 0.16 6.2e-9 1.1e-3 0.01 0.05 0.49 563 1.01
flare[87,18] 0.4 0.01 0.38 3.7e-6 0.09 0.3 0.6 1.33 669 1.0
flare[88,18] 0.17 0.01 0.31 1.1e-8 7.2e-3 0.06 0.2 1.07 596 1.01
flare[89,18] 0.28 0.03 0.466.7e-10 2.1e-3 0.04 0.36 1.62 327 1.01
flare[90,18] 0.18 0.01 0.276.2e-11 9.0e-3 0.07 0.23 0.96 697 1.01
flare[91,18] 0.21 0.03 0.35 6.5e-8 4.6e-3 0.05 0.24 1.18 123 1.04
flare[92,18] 0.13 7.0e-3 0.17 2.0e-7 0.01 0.07 0.19 0.61 573 1.01
flare[93,18] 0.21 0.01 0.32.1e-21 0.01 0.09 0.27 1.02 529 1.0
flare[94,18] 0.32 0.02 0.37 1.6e-7 0.04 0.2 0.46 1.35 407 1.01
flare[95,18] 0.26 0.02 0.365.1e-12 0.01 0.11 0.38 1.27 236 1.01
flare[96,18] 0.75 0.03 0.65 2.4e-3 0.23 0.6 1.11 2.37 345 1.02
flare[97,18] 0.1 7.1e-3 0.227.5e-12 3.3e-4 0.01 0.09 0.77 970 1.0
flare[98,18] 0.43 0.02 0.41 3.0e-4 0.12 0.32 0.65 1.49 408 1.01
flare[99,18] 0.21 0.02 0.364.6e-13 2.2e-3 0.05 0.26 1.25 493 1.01
flare[0,19] 0.15 9.2e-3 0.23 8.5e-8 7.4e-3 0.05 0.19 0.82 630 1.01
flare[1,19] 0.11 9.8e-3 0.274.1e-13 4.5e-4 0.01 0.08 0.95 773 1.0
flare[2,19] 0.57 0.02 0.44 0.01 0.24 0.47 0.81 1.63 506 1.01
flare[3,19] 0.06 3.6e-3 0.11 2.0e-6 2.5e-3 0.02 0.06 0.4 980 1.0
flare[4,19] 0.15 0.01 0.23 6.8e-5 0.01 0.06 0.19 0.82 474 1.0
flare[5,19] 0.09 6.3e-3 0.16 5.3e-7 4.3e-3 0.03 0.11 0.57 633 1.01
flare[6,19] 0.86 0.02 0.54 0.1 0.48 0.77 1.14 2.18 766 1.0
flare[7,19] 0.07 6.0e-3 0.162.5e-11 1.4e-3 0.02 0.08 0.5 680 1.01
flare[8,19] 0.91 0.02 0.69 5.8e-3 0.43 0.78 1.24 2.44 802 1.0
flare[9,19] 0.34 0.03 0.47 2.0e-8 9.9e-3 0.15 0.49 1.7 314 1.01
flare[10,19] 0.19 9.8e-3 0.23 1.1e-5 0.02 0.1 0.26 0.83 563 1.0
flare[11,19] 0.31 0.02 0.32 2.4e-4 0.08 0.22 0.46 1.15 396 1.01
flare[12,19] 0.21 0.02 0.385.1e-11 6.4e-4 0.04 0.23 1.3 618 1.01
flare[13,19] 0.11 0.01 0.251.2e-1310.0e-5 7.7e-3 0.08 0.89 610 1.01
flare[14,19] 0.15 0.04 0.378.6e-10 4.5e-4 0.01 0.08 1.34 74 1.07
flare[15,19] 0.29 0.01 0.35 4.4e-6 0.04 0.17 0.41 1.22 755 1.0
flare[16,19] 0.18 8.5e-3 0.24 3.9e-8 0.01 0.08 0.26 0.86 813 1.0
flare[17,19] 0.12 8.6e-3 0.24 1.6e-8 2.8e-3 0.03 0.12 0.8 767 1.0
flare[18,19] 0.53 0.03 0.57 1.7e-8 0.06 0.37 0.82 2.01 326 1.01
flare[19,19] 0.14 0.01 0.27 1.5e-9 1.5e-3 0.02 0.14 0.98 609 1.01
flare[20,19] 0.25 0.02 0.45.9e-11 3.2e-3 0.07 0.33 1.36 591 1.0
flare[21,19] 0.32 0.04 0.47 1.5e-6 0.02 0.12 0.42 1.67 133 1.03
flare[22,19] 0.2 0.02 0.333.5e-10 1.2e-3 0.05 0.27 1.16 461 1.01
flare[23,19] 0.29 0.02 0.4 8.1e-5 0.03 0.14 0.41 1.27 361 1.01
flare[24,19] 0.1 5.2e-3 0.15 1.7e-7 3.8e-3 0.04 0.12 0.55 877 1.0
flare[25,19] 0.37 0.03 0.517.9e-11 0.01 0.14 0.55 1.75 400 1.01
flare[26,19] 0.1 0.01 0.241.5e-14 2.8e-4 0.01 0.07 0.85 541 1.0
flare[27,19] 0.25 8.5e-3 0.24 5.0e-3 0.08 0.18 0.36 0.88 809 1.0
flare[28,19] 0.24 0.02 0.416.2e-11 8.2e-4 0.03 0.33 1.46 300 1.01
flare[29,19] 0.13 7.6e-3 0.256.2e-15 1.2e-3 0.03 0.14 0.82 1061 1.01
flare[30,19] 0.12 0.02 0.329.7e-14 1.8e-4 6.9e-3 0.06 1.16 278 1.01
flare[31,19] 0.46 0.02 0.42 3.8e-4 0.14 0.36 0.67 1.46 555 1.01
flare[32,19] 0.14 9.8e-3 0.222.5e-10 6.6e-3 0.05 0.18 0.79 524 1.01
flare[33,19] 0.1 6.1e-3 0.183.5e-10 3.5e-3 0.03 0.12 0.61 924 1.0
flare[34,19] 0.19 0.01 0.26 1.7e-6 0.02 0.1 0.26 0.89 562 1.0
flare[35,19] 0.22 0.02 0.359.5e-10 5.1e-3 0.06 0.28 1.27 229 1.03
flare[36,19] 0.16 0.02 0.347.1e-15 7.1e-5 9.6e-3 0.12 1.26 346 1.01
flare[37,19] 0.07 9.8e-3 0.12 2.7e-5 6.7e-3 0.03 0.09 0.42 145 1.03
flare[38,19] 0.2 0.01 0.363.1e-12 3.7e-3 0.04 0.23 1.33 751 1.0
flare[39,19] 0.33 0.01 0.38 4.6e-7 0.05 0.2 0.49 1.33 821 1.0
flare[40,19] 0.13 8.1e-3 0.2 2.6e-8 7.3e-3 0.05 0.17 0.72 600 1.0
flare[41,19] 0.26 0.02 0.35 1.9e-6 0.03 0.12 0.35 1.22 533 1.01
flare[42,19] 0.48 0.04 0.698.3e-12 9.3e-3 0.16 0.73 2.27 248 1.03
flare[43,19] 0.14 0.01 0.22 9.7e-8 8.2e-3 0.05 0.17 0.76 356 1.01
flare[44,19] 0.23 0.01 0.347.0e-10 9.6e-3 0.08 0.32 1.2 754 1.0
flare[45,19] 0.12 9.7e-3 0.28 0.0 1.7e-4 0.01 0.1 0.98 817 1.01
flare[46,19] 0.37 0.02 0.39 7.9e-6 0.1 0.26 0.5 1.37 449 1.0
flare[47,19] 0.24 0.01 0.33 6.6e-9 9.9e-3 0.1 0.34 1.17 513 1.01
flare[48,19] 0.24 0.01 0.29 1.7e-6 0.04 0.14 0.34 1.07 606 1.01
flare[49,19] 0.48 0.02 0.41 5.3e-3 0.16 0.38 0.7 1.46 617 1.0
flare[50,19] 0.37 0.02 0.473.1e-12 0.01 0.18 0.57 1.61 481 1.01
flare[51,19] 0.16 8.4e-3 0.25 0.0 6.3e-3 0.06 0.19 0.84 900 1.0
flare[52,19] 0.49 0.03 0.48 6.8e-9 0.11 0.37 0.74 1.75 256 1.01
flare[53,19] 0.22 0.02 0.384.2e-13 5.7e-4 0.03 0.27 1.34 323 1.0
flare[54,19] 0.18 0.03 0.38 1.3e-8 2.7e-3 0.03 0.17 1.37 175 1.03
flare[55,19] 0.08 7.9e-3 0.18 1.9e-9 7.9e-4 0.01 0.07 0.56 503 1.01
flare[56,19] 0.27 0.01 0.34 5.5e-7 0.03 0.16 0.39 1.16 581 1.0
flare[57,19] 0.17 0.02 0.351.9e-10 9.4e-4 0.02 0.13 1.3 356 1.01
flare[58,19] 0.34 0.02 0.42 5.0e-5 0.04 0.19 0.48 1.45 368 1.01
flare[59,19] 0.2 0.02 0.27 4.6e-4 0.03 0.1 0.27 1.02 250 1.01
flare[60,19] 0.24 0.02 0.33 1.4e-5 0.03 0.12 0.32 1.26 376 1.01
flare[61,19] 0.08 0.01 0.21 2.5e-7 3.0e-3 0.02 0.07 0.6 351 1.01
flare[62,19] 0.17 0.01 0.31.1e-10 3.1e-3 0.04 0.18 1.07 710 1.01
flare[63,19] 0.11 5.8e-3 0.19 2.4e-7 4.6e-3 0.03 0.12 0.66 1100 1.0
flare[64,19] 0.44 0.04 0.46 4.9e-4 0.12 0.3 0.62 1.65 154 1.03
flare[65,19] 0.24 0.01 0.33 4.9e-9 0.01 0.1 0.34 1.16 980 1.01
flare[66,19] 0.14 0.03 0.37 2.2e-7 2.5e-3 0.02 0.1 1.1 131 1.02
flare[67,19] 0.1 0.01 0.281.5e-11 2.7e-4 6.0e-3 0.05 0.99 691 1.01
flare[68,19] 0.14 9.2e-3 0.19 2.5e-6 0.02 0.07 0.2 0.71 441 1.01
flare[69,19] 0.11 9.7e-3 0.22 1.9e-8 2.8e-3 0.03 0.11 0.72 516 1.0
flare[70,19] 0.17 0.01 0.23 2.3e-6 0.02 0.09 0.22 0.9 261 1.01
flare[71,19] 0.23 0.02 0.25 1.1e-3 0.06 0.15 0.32 0.94 270 1.01
flare[72,19] 0.41 0.02 0.4 1.9e-4 0.1 0.31 0.6 1.43 271 1.03
flare[73,19] 0.18 0.01 0.321.7e-15 3.8e-4 0.02 0.22 1.15 680 1.0
flare[74,19] 0.3 0.02 0.35 4.7e-5 0.05 0.17 0.42 1.25 553 1.01
flare[75,19] 0.47 0.02 0.47 2.4e-4 0.1 0.34 0.69 1.68 500 1.01
flare[76,19] 0.14 0.02 0.33.3e-10 1.2e-3 0.02 0.11 1.15 239 1.02
flare[77,19] 0.26 0.02 0.46 1.6e-9 4.3e-3 0.06 0.3 1.51 447 1.01
flare[78,19] 0.26 0.01 0.331.2e-11 0.02 0.13 0.4 1.14 925 1.0
flare[79,19] 0.29 0.02 0.29 2.1e-3 0.07 0.2 0.41 1.08 284 1.01
flare[80,19] 0.2 0.01 0.354.2e-10 1.2e-3 0.03 0.27 1.19 603 1.01
flare[81,19] 0.14 0.01 0.273.9e-12 1.3e-3 0.03 0.15 0.99 354 1.01
flare[82,19] 1.53 0.03 0.85 0.3 0.95 1.39 1.91 3.64 836 1.0
flare[83,19] 0.3 0.05 0.45 3.2e-8 0.02 0.12 0.39 1.83 81 1.05
flare[84,19] 0.82 0.02 0.54 0.07 0.42 0.72 1.13 2.04 763 1.0
flare[85,19] 0.15 0.02 0.3 1.1e-9 2.7e-3 0.03 0.14 1.14 285 1.02
flare[86,19] 0.06 10.0e-3 0.17 1.7e-9 6.7e-4 7.9e-3 0.04 0.52 290 1.01
flare[87,19] 0.31 0.01 0.33 2.3e-6 0.05 0.21 0.46 1.17 756 1.0
flare[88,19] 0.14 0.01 0.27 1.0e-8 4.5e-3 0.04 0.15 0.94 550 1.01
flare[89,19] 0.32 0.04 0.532.0e-10 1.5e-3 0.04 0.41 1.81 189 1.01
flare[90,19] 0.15 8.7e-3 0.241.3e-11 5.6e-3 0.05 0.18 0.88 736 1.01
flare[91,19] 0.17 0.02 0.3 1.7e-8 2.9e-3 0.03 0.19 1.08 243 1.02
flare[92,19] 0.11 6.1e-3 0.15 4.5e-8 8.3e-3 0.05 0.14 0.53 583 1.0
flare[93,19] 0.17 0.01 0.277.9e-18 8.0e-3 0.06 0.21 0.93 459 1.0
flare[94,19] 0.26 0.02 0.34 1.7e-7 0.03 0.14 0.36 1.24 458 1.01
flare[95,19] 0.21 0.02 0.329.0e-13 6.8e-3 0.07 0.29 1.14 222 1.02
flare[96,19] 0.63 0.02 0.57 1.4e-3 0.17 0.48 0.93 2.03 539 1.01
flare[97,19] 0.09 6.9e-3 0.212.3e-12 1.7e-4 7.6e-3 0.07 0.71 899 1.0
flare[98,19] 0.36 0.02 0.37 1.7e-4 0.08 0.24 0.52 1.34 398 1.01
flare[99,19] 0.21 0.03 0.441.7e-12 1.6e-3 0.04 0.21 1.31 228 1.02
flare[0,20] 0.12 7.7e-3 0.21 3.1e-8 4.6e-3 0.04 0.15 0.72 737 1.01
flare[1,20] 0.1 9.9e-3 0.263.9e-13 2.8e-4 0.01 0.06 0.92 707 1.0
flare[2,20] 0.47 0.02 0.4 6.6e-3 0.17 0.37 0.69 1.46 470 1.02
flare[3,20] 0.05 3.4e-3 0.11 8.5e-7 1.6e-3 0.01 0.05 0.35 973 1.0
flare[4,20] 0.13 9.7e-3 0.21 3.0e-5 9.3e-3 0.04 0.15 0.75 481 1.0
flare[5,20] 0.08 5.9e-3 0.15 1.4e-7 2.4e-3 0.02 0.08 0.52 695 1.01
flare[6,20] 0.71 0.02 0.5 0.06 0.35 0.61 0.95 1.94 870 1.0
flare[7,20] 0.06 6.0e-3 0.162.4e-12 8.2e-4 0.01 0.06 0.45 677 1.01
flare[8,20] 0.77 0.02 0.63 3.7e-3 0.33 0.63 1.07 2.23 716 1.01
flare[9,20] 0.31 0.03 0.45 3.6e-9 6.8e-3 0.11 0.41 1.61 260 1.01
flare[10,20] 0.15 8.8e-3 0.2 3.7e-6 0.01 0.07 0.2 0.72 535 1.0
flare[11,20] 0.25 0.01 0.27 1.2e-4 0.05 0.15 0.36 0.98 446 1.01
flare[12,20] 0.2 0.02 0.387.1e-12 3.7e-4 0.03 0.19 1.41 491 1.01
flare[13,20] 0.12 0.02 0.32.9e-15 5.5e-5 5.6e-3 0.07 1.06 245 1.02
flare[14,20] 0.17 0.07 0.422.7e-10 2.6e-4 8.1e-3 0.07 1.66 40 1.13
flare[15,20] 0.23 9.9e-3 0.3 1.7e-6 0.02 0.12 0.32 1.04 954 1.0
flare[16,20] 0.15 8.6e-3 0.23 1.3e-8 7.5e-3 0.06 0.2 0.79 729 1.0
flare[17,20] 0.1 7.7e-3 0.21 6.4e-9 1.7e-3 0.02 0.1 0.69 718 1.0
flare[18,20] 0.45 0.04 0.53 3.9e-9 0.05 0.28 0.69 1.92 190 1.02
flare[19,20] 0.12 9.7e-3 0.256.1e-10 8.6e-4 0.01 0.11 0.88 693 1.01
flare[20,20] 0.22 0.02 0.391.1e-11 1.8e-3 0.05 0.27 1.41 641 1.0
flare[21,20] 0.27 0.03 0.43 5.0e-7 0.01 0.09 0.34 1.49 227 1.02
flare[22,20] 0.18 0.02 0.351.2e-10 7.2e-4 0.04 0.21 1.12 430 1.01
flare[23,20] 0.25 0.02 0.38 3.1e-5 0.02 0.11 0.34 1.17 404 1.01
flare[24,20] 0.08 4.6e-3 0.14 1.1e-7 2.2e-3 0.02 0.09 0.49 965 1.0
flare[25,20] 0.33 0.02 0.491.3e-11 0.01 0.12 0.46 1.69 400 1.01
flare[26,20] 0.11 0.01 0.282.3e-15 1.5e-4 7.5e-3 0.06 1.1 529 1.0
flare[27,20] 0.2 7.7e-3 0.22 2.4e-3 0.05 0.13 0.28 0.79 782 1.0
flare[28,20] 0.24 0.03 0.441.9e-11 5.3e-4 0.03 0.27 1.6 219 1.02
flare[29,20] 0.12 0.01 0.272.2e-15 7.1e-4 0.02 0.11 0.94 415 1.01
flare[30,20] 0.12 0.02 0.344.7e-14 1.1e-4 4.7e-3 0.05 1.26 226 1.01
flare[31,20] 0.38 0.02 0.39 2.1e-4 0.09 0.27 0.56 1.34 391 1.01
flare[32,20] 0.12 7.9e-3 0.232.0e-10 4.0e-3 0.03 0.15 0.75 874 1.01
flare[33,20] 0.08 5.3e-3 0.171.1e-10 2.1e-3 0.02 0.09 0.52 982 1.0
flare[34,20] 0.16 9.8e-3 0.25 6.4e-7 0.01 0.07 0.21 0.79 636 1.0
flare[35,20] 0.18 0.02 0.313.3e-10 3.0e-3 0.04 0.21 1.16 182 1.04
flare[36,20] 0.17 0.02 0.393.8e-15 5.2e-5 7.8e-3 0.12 1.38 295 1.01
flare[37,20] 0.06 9.3e-3 0.11 1.2e-5 4.1e-3 0.02 0.06 0.37 133 1.03
flare[38,20] 0.18 0.02 0.364.8e-13 2.4e-3 0.03 0.19 1.34 529 1.01
flare[39,20] 0.27 0.01 0.34 5.5e-8 0.03 0.14 0.39 1.2 856 1.0
flare[40,20] 0.11 7.1e-3 0.18 8.2e-9 4.1e-3 0.04 0.13 0.62 667 1.0
flare[41,20] 0.22 0.01 0.32 7.9e-7 0.02 0.09 0.29 1.08 570 1.01
flare[42,20] 0.42 0.04 0.62.1e-12 6.1e-3 0.14 0.6 2.05 187 1.03
flare[43,20] 0.12 0.01 0.22 3.3e-8 5.0e-3 0.04 0.14 0.73 330 1.0
flare[44,20] 0.21 0.01 0.361.1e-10 5.8e-3 0.06 0.26 1.14 752 1.0
flare[45,20] 0.12 0.01 0.3 0.0 9.5e-5 9.3e-3 0.08 1.03 793 1.01
flare[46,20] 0.3 0.02 0.35 3.5e-5 0.07 0.2 0.41 1.18 504 1.0
flare[47,20] 0.2 0.01 0.3 2.8e-9 6.3e-3 0.08 0.26 1.02 501 1.01
flare[48,20] 0.2 0.01 0.26 4.1e-7 0.02 0.1 0.27 0.97 626 1.01
flare[49,20] 0.41 0.02 0.37 3.1e-3 0.12 0.3 0.6 1.39 531 1.0
flare[50,20] 0.3 0.02 0.411.2e-13 7.6e-3 0.13 0.45 1.47 339 1.01
flare[51,20] 0.13 7.9e-3 0.25 0.0 3.8e-3 0.04 0.15 0.81 969 1.0
flare[52,20] 0.4 0.02 0.44 2.7e-9 0.07 0.26 0.59 1.44 319 1.01
flare[53,20] 0.19 0.02 0.351.6e-13 3.8e-4 0.02 0.21 1.2 399 1.0
flare[54,20] 0.16 0.03 0.33 7.8e-9 1.7e-3 0.02 0.13 1.19 135 1.03
flare[55,20] 0.06 7.0e-3 0.173.4e-10 4.7e-4 9.5e-3 0.05 0.5 587 1.01
flare[56,20] 0.23 0.01 0.31 2.4e-7 0.02 0.12 0.31 1.04 682 1.0
flare[57,20] 0.15 0.02 0.343.3e-10 5.7e-4 0.01 0.11 1.21 360 1.01
flare[58,20] 0.29 0.02 0.38 2.4e-5 0.03 0.14 0.39 1.31 326 1.01
flare[59,20] 0.17 0.02 0.25 2.0e-4 0.02 0.08 0.22 0.95 244 1.01
flare[60,20] 0.21 0.02 0.32 4.4e-6 0.02 0.09 0.27 1.18 409 1.01
flare[61,20] 0.07 0.01 0.21 8.9e-8 1.9e-3 0.01 0.06 0.61 327 1.01
flare[62,20] 0.15 0.01 0.311.8e-11 1.9e-3 0.03 0.15 1.01 684 1.01
flare[63,20] 0.09 5.4e-3 0.19 6.3e-8 2.6e-3 0.02 0.1 0.6 1246 1.0
flare[64,20] 0.38 0.04 0.45 2.5e-4 0.08 0.23 0.51 1.63 145 1.03
flare[65,20] 0.2 10.0e-3 0.38.0e-10 7.0e-3 0.07 0.27 1.07 910 1.01
flare[66,20] 0.12 0.03 0.32 6.9e-8 1.6e-3 0.02 0.08 0.99 155 1.02
flare[67,20] 0.1 0.01 0.293.2e-12 1.5e-4 4.3e-3 0.04 1.04 606 1.01
flare[68,20] 0.12 8.8e-3 0.18 9.3e-7 0.01 0.05 0.16 0.65 413 1.01
flare[69,20] 0.1 0.01 0.23 5.1e-9 1.6e-3 0.02 0.09 0.7 450 1.0
flare[70,20] 0.14 0.01 0.21 8.4e-7 0.01 0.06 0.17 0.8 270 1.01
flare[71,20] 0.19 0.01 0.23 4.2e-4 0.04 0.11 0.25 0.86 265 1.01
flare[72,20] 0.33 0.02 0.36 1.0e-4 0.07 0.22 0.48 1.32 236 1.03
flare[73,20] 0.16 0.01 0.331.6e-15 2.4e-4 0.02 0.18 1.12 820 1.0
flare[74,20] 0.25 0.02 0.33 2.1e-5 0.04 0.13 0.34 1.17 447 1.01
flare[75,20] 0.41 0.02 0.45 1.1e-4 0.07 0.27 0.58 1.62 398 1.01
flare[76,20] 0.14 0.03 0.321.3e-10 6.6e-4 0.01 0.09 1.1 99 1.04
flare[77,20] 0.22 0.02 0.412.3e-10 2.6e-3 0.04 0.25 1.37 516 1.01
flare[78,20] 0.22 0.01 0.322.0e-11 9.8e-3 0.09 0.31 1.09 1000 1.0
flare[79,20] 0.24 0.02 0.27 1.0e-3 0.05 0.15 0.33 0.97 262 1.01
flare[80,20] 0.17 0.01 0.318.6e-11 6.9e-4 0.02 0.21 1.05 549 1.01
flare[81,20] 0.13 0.02 0.296.8e-13 7.8e-4 0.02 0.12 1.05 302 1.01
flare[82,20] 1.29 0.03 0.76 0.21 0.76 1.16 1.65 3.11 664 1.01
flare[83,20] 0.26 0.05 0.42 6.3e-9 0.01 0.09 0.3 1.76 86 1.05
flare[84,20] 0.69 0.02 0.49 0.04 0.32 0.59 0.97 1.85 666 1.0
flare[85,20] 0.13 0.02 0.274.2e-10 1.7e-3 0.02 0.11 1.05 283 1.02
flare[86,20] 0.07 0.02 0.223.4e-10 3.6e-4 5.2e-3 0.03 0.72 202 1.02
flare[87,20] 0.25 9.9e-3 0.3 6.6e-7 0.03 0.15 0.35 1.04 897 1.0
flare[88,20] 0.12 0.01 0.25 3.7e-9 2.7e-3 0.03 0.12 0.85 547 1.01
flare[89,20] 0.3 0.04 0.538.3e-11 1.0e-3 0.03 0.36 1.73 171 1.01
flare[90,20] 0.12 7.8e-3 0.211.7e-12 3.5e-3 0.04 0.14 0.75 719 1.01
flare[91,20] 0.14 0.01 0.26 4.5e-9 1.8e-3 0.02 0.15 0.92 362 1.01
flare[92,20] 0.08 5.2e-3 0.13 9.4e-9 4.9e-3 0.03 0.11 0.44 592 1.0
flare[93,20] 0.15 0.01 0.251.9e-15 4.8e-3 0.04 0.17 0.89 387 1.0
flare[94,20] 0.22 0.01 0.31 5.8e-8 0.02 0.1 0.28 1.11 846 1.0
flare[95,20] 0.18 0.02 0.295.1e-14 4.0e-3 0.05 0.22 1.04 212 1.02
flare[96,20] 0.52 0.02 0.51 7.9e-4 0.12 0.37 0.78 1.76 541 1.01
flare[97,20] 0.08 7.2e-3 0.211.0e-12 8.7e-5 4.9e-3 0.05 0.65 815 1.0
flare[98,20] 0.3 0.02 0.34 6.2e-5 0.06 0.18 0.43 1.24 383 1.01
flare[99,20] 0.18 0.02 0.372.0e-13 1.0e-3 0.03 0.17 1.38 408 1.01
lp__ -1942 0.74 16.49 -1976 -1953 -1941 -1931 -1912 498 1.01
Samples were drawn using NUTS at Fri Jul 14 13:34:08 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
In [44]:
chains = fit.extract(permuted=True)
Corner plot of the population parameters:
In [45]:
corner.corner(
column_stack([chains[key] for key in ['mu_A', 'sigma_A', 'mu_t', 'sigma_t', 'mu_tau', 'sigma_tau']]),
labels=[r'$\mu_A$', r'$\sigma_A$', r'$\mu_t$', r'$\sigma_t$', r'$\mu_\tau$', r'$\sigma_\tau$'],
truths=[mu_A, sigma_A, mu_t, sigma_t, mu_tau, sigma_tau]
);
In [89]:
i = argmax(As)
plot(t, As[i]*flare_profile(t, ts[i], taus[i]), '-k')
plot(0.5*(ts_bin[:-1] + ts_bin[1:]), counts[i]/(diff(ts_bin)), '--k')
axhline(bgs[i], ls=':', color='k')
for j in range(10):
k = randint(chains['flare'].shape[0])
plot(ts_bin, chains['flare'][k,i,:], alpha=0.5, color=sns.color_palette()[0])
In [55]:
def population_amplitude(As, mu_A, sigma_A):
return st.lognorm(sigma_A, scale=exp(mu_A)).pdf(As)
A = linspace(0, 10, 1000)
pop_amps = zeros(1000)
for mu, s in zip(chains['mu_A'], chains['sigma_A']):
pop_amps += population_amplitude(A, mu, s)
pop_amps /= len(chains['mu_A']) # Take Average
plot(A, pop_amps, label='Fitted Amplitude Distribution')
plot(A, st.lognorm(sigma_A, scale=exp(mu_A)).pdf(A), '-k', label='True Amplitude Distribution')
legend(loc='best')
Out[55]:
<matplotlib.legend.Legend at 0x12d935a90>
Discussion/derivation of the "fundamental equation of populations": $$ p\left( \left\{ d^{(i)}, \boldsymbol{\theta^{(i)}} \right\} \mid \boldsymbol{\lambda} \right) = \left[ \prod_{i} P_\mathrm{det}\left( d^{(i)} \mid \boldsymbol{\lambda} \right) p\left( d^{(i)} | \boldsymbol{\theta}^{(i)} \right) \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}}\left( \boldsymbol{\theta}^{(i)} \mid \boldsymbol{\lambda} \right) \right] \exp\left[ - \int \mathrm{d} d \mathrm{d}\boldsymbol{\theta} P_\mathrm{det}\left( d \mid \boldsymbol \lambda \right) p\left( d \mid \boldsymbol{\theta} \right) \frac{\mathrm{d}N}{\mathrm{d} \boldsymbol{\theta}}\left( \boldsymbol{\theta} \mid \lambda \right) \right] $$
Since I didn't get a chance to talk about this due to time constraints, I will try to write something here. Let's work up to that equation. First, consider trying to measure a rate function parameterised by some parameters $\boldsymbol{\lambda}$: $$ \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}}\left( \boldsymbol{\theta} \mid \boldsymbol{\lambda} \right) $$ from a large number of measurements of $\boldsymbol{\theta}$. What is the appropriate likelihood to use for the measurements? Well, if we are willing to assume
Then our samples come from an "inhomogeneous Poisson process" (inhomogeneous because the rate, $\mathrm{d} N/\mathrm{d} \boldsymbol{\theta}$, is not constant, Poisson reflects the properties above, and process because $\boldsymbol{\theta}$ is a continuous variable and can take on uncountably many different values). The likelihood for such a process follows from considering a large number of short intervals; each short interval contains either zero or one event, and the probabilities are Poisson. The events in each interval are independent of any other interval's events. Thus, the likelihood is a product of $\mathrm{Poisson}(0)$ over intervals without a measurement (here indexed by $i$) and $\mathrm{Poisson}(1)$ over intervals with a measurement (here indexed by $j$): $$ p\left( \left\{ \boldsymbol{\theta}^{(i)} \right\} \mid \lambda \right) \prod_i \mathrm{d} \boldsymbol{\theta}_i = \prod_j \exp\left( - \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta} } \left( \boldsymbol{\theta_j} \mid \boldsymbol{\lambda} \right) \mathrm{d} \boldsymbol{\theta}_j \right) \prod_i \left[ \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta} } \left( \boldsymbol{\theta^{(i)}} \mid \boldsymbol{\lambda} \right) \mathrm{d} \boldsymbol{\theta}_i \exp\left( - \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta} } \left( \boldsymbol{\theta}^{(i)} \mid \boldsymbol{\lambda} \right) \mathrm{d} \boldsymbol{\theta}_i \right) \right] $$ ($j$ indexes intervals where there are no samples, and $i$ indexes the (finite number) of intervals where there are samples.) Taking the limit as the width of the intervals goes to zero (the number of non-measurement intervals goes to infinity, but the number of measurement intervals is fixed by the number of measurements) the exponentials combine and we can cancel the $\mathrm{d} \boldsymbol{\theta}_i$ terms because the likelihood should be a density, yielding $$ p\left( \left\{ \boldsymbol{\theta}^{(j)} \right\} \mid \lambda \right) = \prod_j \left[ \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}} \left( \boldsymbol{\theta}^{(j)} \mid \boldsymbol{\lambda} \right) \right] \exp\left( - \int \mathrm{d} \boldsymbol{\theta} \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}}\left(\boldsymbol{\theta} \mid \boldsymbol{\lambda}\right) \right) = \prod_j \left[ \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}} \left( \boldsymbol{\theta}^{(j)} \mid \boldsymbol{\lambda} \right) \right] \exp\left( - N\left(\boldsymbol{\lambda}\right) \right) $$ Those who notice the resemblance to the Poisson likelihood may be asking "where is the factorial?" It is not needed here because the measurements, $\theta^{(j)}$, are distinguishable, unlike for the standrad Poisson likelihood (see also the discussion of "time ordering" in Farr, et al. (2015)).
We will now do a worked example, beginning with this equation and adding various effects to our model until we arrive at the first equation above.
Let's try fitting some rates. Suppose a hypothetical LIGO detector can see GW merger events distributed in redshift as $$ \frac{\mathrm{d}N}{\mathrm{d} z} = N_0 \left( 1+z \right)^\alpha \exp\left(-\frac{z}{\beta} \right), $$ where $N_0$, $\alpha$, and $\beta$ are parameters, so $\boldsymbol{\lambda} = \left\{ N_0, \alpha, \beta \right\}$, and the distribution is one-dimensional (i.e. $\boldsymbol{\theta} = z$). Suppose further that this LIGO detector measures the redshift of detected events perfectly (so that we are directly measuring $\boldsymbol{\theta}$; we do not have intermediate "data" from which we must infer---imperfectly---$\boldsymbol{\theta}$). Let's try to fit the parameters to a population of events. (Note that the current horizon of aLIGO, even for high-mass binary black holes is $z \lesssim 0.3$; so-called "third generation" detectors will, however, be able to see heavy BBH across the universe (Vitale & Evans 2017).
In [4]:
Ntrue = 10
alphatrue=2
betatrue=2
def dNdz(zs, N0, alpha, beta):
return N0*(1+zs)**alpha*exp(-zs/beta)
zs = linspace(0, 10, 1000)
Nextrue = trapz(dNdz(zs, Ntrue, alphatrue, betatrue), zs)
print('I expect {:.0f} events'.format(Nextrue))
I expect 237 events
It is not a crazy distribution in redshift:
In [5]:
plot(zs, dNdz(zs, Ntrue, alphatrue, betatrue))
Out[5]:
[<matplotlib.lines.Line2D at 0x113d71b00>]
We will need to draw from this distribution; the code below uses the Python yield statement to produce a generator that generates events from this distribution. We draw the events using von Neumann rejection sampling, and restrict the redshift range to $0 \leq z \leq 10$.
In [6]:
def draw_zs(N0, alpha, beta):
zs = linspace(0, 10, 1000)
dndzs = dNdz(zs, N0, alpha, beta)
ymax = np.max(dndzs)
Nex = trapz(dndzs, zs)
Ndraw = random.poisson(Nex)
igen = 0
while igen < Ndraw:
y = random.uniform(low=0, high=ymax)
z = random.uniform(low=0, high=10)
if y < dNdz(z, N0, alpha, beta):
igen += 1
yield z
Let's check that the distribution of draws is correct:
In [7]:
zs = linspace(0, 10, 100)
plot(zs, dNdz(zs, Ntrue, alphatrue, betatrue)/Nextrue)
sns.distplot([z for z in draw_zs(Ntrue, alphatrue, betatrue)])
Out[7]:
<matplotlib.axes._subplots.AxesSubplot at 0x1117bcf98>
Now we build the Stan model that uses the above likelihood. There is a trick to get stan to compute the expected number of events: Stan doesn't (directly) do integrals like $$ N\left( \boldsymbol{\lambda} \right) = \int \mathrm{d} \boldsymbol{\theta} \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}}\left(\boldsymbol{\theta} \mid \boldsymbol{\lambda}\right), $$ but it does know how to solve ODEs, so we set up an ODE whose solution is the integral and get Stan to solve it.
Also, we use the generated quantities block to generate the rate function $\mathrm{d}N/\mathrm{d}\boldsymbol{\theta}$ at chosen values of $z$ every time a set of parameters are output (this is distinguished from transformed parameters by not being computed for every internal step of the Stan sampler, but only when values are being output).
In [8]:
model_direct = pystan.StanModel(file='zmodel_direct.stan')
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_91e0d6e30fee11794c6105178d0d5f4f NOW.
In [9]:
ztrues = [z for z in draw_zs(Ntrue, alphatrue, betatrue)]
In [10]:
data_direct = {
'nobs': len(ztrues),
'zobs': ztrues,
'nmodel': 100,
'zs_model': linspace(0, 10, 100)
}
In [11]:
fit_direct = model_direct.sampling(data=data_direct)
In [12]:
fit_direct.plot()
fit_direct
Out[12]:
Inference for Stan model: anon_model_91e0d6e30fee11794c6105178d0d5f4f.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
N0 12.88 0.13 3.89 6.62 10.03 12.35 15.31 21.43 856 1.01
alpha 1.65 0.02 0.43 0.83 1.34 1.64 1.93 2.52 812 1.01
beta 2.44 0.02 0.66 1.6 2.02 2.32 2.7 3.91 704 1.01
dNdz_model[0] 12.88 0.13 3.89 6.62 10.03 12.35 15.31 21.43 856 1.01
dNdz_model[1] 14.33 0.13 3.89 7.92 11.51 13.88 16.8 22.87 886 1.01
dNdz_model[2] 15.74 0.13 3.86 9.24 12.97 15.39 18.24 24.19 919 1.01
dNdz_model[3] 17.12 0.12 3.81 10.6 14.37 16.8 19.61 25.35 958 1.0
dNdz_model[4] 18.46 0.12 3.73 11.96 15.79 18.17 20.95 26.45 1003 1.0
dNdz_model[5] 19.75 0.11 3.65 13.28 17.15 19.49 22.22 27.42 1057 1.0
dNdz_model[6] 20.98 0.11 3.55 14.63 18.49 20.73 23.39 28.35 1121 1.0
dNdz_model[7] 22.16 0.1 3.45 15.84 19.72 21.94 24.5 29.21 1198 1.0
dNdz_model[8] 23.28 0.09 3.35 17.1 20.9 23.1 25.56 30.19 1290 1.0
dNdz_model[9] 24.34 0.09 3.26 18.32 22.04 24.2 26.54 31.03 1401 1.0
dNdz_model[10] 25.34 0.08 3.17 19.45 23.11 25.23 27.46 31.78 1531 1.0
dNdz_model[11] 26.27 0.08 3.09 20.52 24.1 26.16 28.36 32.61 1678 1.0
dNdz_model[12] 27.14 0.07 3.02 21.47 25.02 27.07 29.17 33.29 1841 1.0
dNdz_model[13] 27.95 0.07 2.97 22.35 25.87 27.86 29.97 34.03 2014 1.0
dNdz_model[14] 28.69 0.06 2.92 23.21 26.63 28.57 30.66 34.66 2185 1.0
dNdz_model[15] 29.38 0.06 2.89 23.92 27.35 29.27 31.32 35.32 2307 1.0
dNdz_model[16] 30.0 0.06 2.88 24.62 27.99 29.89 31.92 35.89 2404 1.0
dNdz_model[17] 30.56 0.06 2.87 25.15 28.56 30.44 32.46 36.48 2468 1.0
dNdz_model[18] 31.06 0.06 2.87 25.77 29.07 30.95 32.9 36.93 2496 1.0
dNdz_model[19] 31.51 0.06 2.88 26.3 29.48 31.4 33.35 37.38 2492 1.0
dNdz_model[20] 31.9 0.06 2.89 26.62 29.87 31.77 33.78 37.87 2461 1.0
dNdz_model[21] 32.23 0.06 2.91 26.93 30.18 32.08 34.14 38.3 2348 1.0
dNdz_model[22] 32.52 0.06 2.93 27.21 30.46 32.37 34.44 38.7 2256 1.0
dNdz_model[23] 32.75 0.06 2.94 27.4 30.68 32.58 34.64 38.97 2164 1.0
dNdz_model[24] 32.94 0.06 2.96 27.58 30.82 32.75 34.83 39.22 2076 1.0
dNdz_model[25] 33.07 0.07 2.98 27.7 30.99 32.9 34.97 39.33 1996 1.0
dNdz_model[26] 33.17 0.07 2.99 27.74 31.11 32.99 35.1 39.44 1924 1.0
dNdz_model[27] 33.22 0.07 3.0 27.78 31.14 33.07 35.14 39.52 1860 1.0
dNdz_model[28] 33.23 0.07 3.0 27.78 31.15 33.1 35.15 39.55 1805 1.0
dNdz_model[29] 33.21 0.07 3.0 27.74 31.11 33.07 35.15 39.51 1758 1.0
dNdz_model[30] 33.14 0.07 3.0 27.66 31.06 32.99 35.09 39.4 1719 1.0
dNdz_model[31] 33.05 0.07 2.99 27.59 30.99 32.89 34.98 39.33 1686 1.0
dNdz_model[32] 32.92 0.07 2.97 27.47 30.89 32.75 34.86 39.22 1659 1.0
dNdz_model[33] 32.76 0.07 2.96 27.36 30.75 32.59 34.69 38.98 1638 1.0
dNdz_model[34] 32.57 0.07 2.93 27.18 30.58 32.39 34.47 38.75 1621 1.0
dNdz_model[35] 32.35 0.07 2.91 27.0 30.38 32.16 34.24 38.48 1609 1.0
dNdz_model[36] 32.11 0.07 2.87 26.83 30.16 31.91 33.99 38.13 1601 1.0
dNdz_model[37] 31.85 0.07 2.84 26.62 29.92 31.62 33.7 37.84 1599 1.0
dNdz_model[38] 31.57 0.07 2.8 26.44 29.65 31.33 33.39 37.42 1601 1.0
dNdz_model[39] 31.26 0.07 2.76 26.24 29.37 31.04 33.06 37.07 1608 1.0
dNdz_model[40] 30.94 0.07 2.71 26.0 29.07 30.73 32.73 36.66 1620 1.0
dNdz_model[41] 30.6 0.07 2.67 25.75 28.76 30.41 32.36 36.25 1636 1.0
dNdz_model[42] 30.24 0.06 2.62 25.5 28.46 30.06 31.95 35.77 1658 1.0
dNdz_model[43] 29.87 0.06 2.57 25.22 28.13 29.7 31.54 35.3 1685 1.0
dNdz_model[44] 29.49 0.06 2.52 24.94 27.79 29.32 31.11 34.82 1718 1.0
dNdz_model[45] 29.09 0.06 2.47 24.62 27.43 28.95 30.7 34.3 1758 1.0
dNdz_model[46] 28.69 0.06 2.42 24.26 27.06 28.54 30.26 33.81 1804 1.0
dNdz_model[47] 28.27 0.05 2.36 23.9 26.68 28.16 29.81 33.28 1859 1.0
dNdz_model[48] 27.85 0.05 2.31 23.57 26.29 27.75 29.36 32.77 1921 1.0
dNdz_model[49] 27.42 0.05 2.26 23.24 25.9 27.33 28.91 32.25 1994 1.0
dNdz_model[50] 26.98 0.05 2.22 22.89 25.48 26.9 28.44 31.72 2076 1.0
dNdz_model[51] 26.54 0.05 2.17 22.52 25.05 26.46 27.96 31.14 2169 1.0
dNdz_model[52] 26.1 0.04 2.12 22.15 24.66 26.01 27.47 30.59 2273 1.0
dNdz_model[53] 25.65 0.04 2.08 21.77 24.23 25.57 27.0 30.04 2389 1.0
dNdz_model[54] 25.2 0.04 2.04 21.4 23.81 25.12 26.52 29.5 2517 1.0
dNdz_model[55] 24.74 0.04 2.0 20.98 23.4 24.67 26.05 28.97 2687 1.0
dNdz_model[56] 24.29 0.04 1.97 20.57 22.96 24.21 25.58 28.41 2899 1.0
dNdz_model[57] 23.83 0.04 1.94 20.18 22.53 23.77 25.1 27.85 3013 1.0
dNdz_model[58] 23.38 0.03 1.91 19.78 22.09 23.34 24.61 27.3 3124 1.0
dNdz_model[59] 22.93 0.03 1.88 19.36 21.66 22.87 24.14 26.74 3226 1.0
dNdz_model[60] 22.47 0.03 1.86 18.94 21.22 22.43 23.68 26.26 3316 1.0
dNdz_model[61] 22.02 0.03 1.84 18.52 20.78 21.98 23.23 25.76 3388 1.0
dNdz_model[62] 21.57 0.03 1.82 18.12 20.33 21.52 22.78 25.28 3438 1.0
dNdz_model[63] 21.13 0.03 1.81 17.71 19.9 21.08 22.35 24.79 3453 1.0
dNdz_model[64] 20.69 0.03 1.8 17.29 19.47 20.62 21.89 24.3 3449 1.0
dNdz_model[65] 20.25 0.03 1.79 16.84 19.04 20.19 21.45 23.86 3426 1.0
dNdz_model[66] 19.81 0.03 1.79 16.45 18.59 19.76 20.99 23.47 3385 1.0
dNdz_model[67] 19.38 0.03 1.78 16.01 18.15 19.33 20.56 23.06 3316 1.0
dNdz_model[68] 18.95 0.03 1.78 15.57 17.74 18.91 20.12 22.64 3231 1.0
dNdz_model[69] 18.53 0.03 1.78 15.16 17.31 18.49 19.7 22.24 3135 1.0
dNdz_model[70] 18.12 0.03 1.79 14.73 16.88 18.08 19.29 21.84 3033 1.0
dNdz_model[71] 17.7 0.03 1.79 14.35 16.46 17.66 18.89 21.42 2927 1.0
dNdz_model[72] 17.3 0.03 1.8 13.94 16.04 17.25 18.49 21.01 2821 1.0
dNdz_model[73] 16.9 0.03 1.8 13.54 15.62 16.85 18.1 20.6 2717 1.0
dNdz_model[74] 16.5 0.04 1.81 13.14 15.22 16.46 17.71 20.21 2538 1.0
dNdz_model[75] 16.11 0.04 1.82 12.74 14.81 16.07 17.34 19.85 2425 1.0
dNdz_model[76] 15.73 0.04 1.82 12.34 14.42 15.68 16.95 19.49 2320 1.0
dNdz_model[77] 15.35 0.04 1.83 11.96 14.03 15.31 16.57 19.09 2223 1.0
dNdz_model[78] 14.98 0.04 1.84 11.59 13.66 14.92 16.21 18.74 2133 1.0
dNdz_model[79] 14.62 0.04 1.85 11.23 13.29 14.54 15.84 18.38 2051 1.0
dNdz_model[80] 14.26 0.04 1.86 10.87 12.94 14.16 15.48 18.01 1976 1.0
dNdz_model[81] 13.91 0.04 1.86 10.52 12.58 13.81 15.12 17.66 1907 1.0
dNdz_model[82] 13.56 0.04 1.87 10.18 12.22 13.46 14.79 17.36 1844 1.0
dNdz_model[83] 13.22 0.04 1.88 9.83 11.88 13.11 14.45 17.03 1775 1.0
dNdz_model[84] 12.89 0.05 1.89 9.5 11.54 12.77 14.13 16.73 1719 1.0
dNdz_model[85] 12.56 0.05 1.89 9.15 11.2 12.45 13.8 16.42 1668 1.0
dNdz_model[86] 12.24 0.05 1.9 8.85 10.89 12.13 13.48 16.13 1622 1.0
dNdz_model[87] 11.93 0.05 1.9 8.54 10.56 11.81 13.17 15.84 1579 1.0
dNdz_model[88] 11.62 0.05 1.9 8.23 10.25 11.49 12.85 15.55 1539 1.0
dNdz_model[89] 11.32 0.05 1.91 7.94 9.96 11.18 12.56 15.27 1503 1.0
dNdz_model[90] 11.02 0.05 1.91 7.65 9.66 10.88 12.25 14.99 1469 1.0
dNdz_model[91] 10.74 0.05 1.91 7.38 9.36 10.59 11.97 14.74 1438 1.0
dNdz_model[92] 10.45 0.05 1.91 7.11 9.08 10.31 11.67 14.47 1409 1.0
dNdz_model[93] 10.18 0.05 1.91 6.86 8.8 10.03 11.4 14.22 1382 1.0
dNdz_model[94] 9.91 0.05 1.91 6.59 8.53 9.75 11.12 13.97 1357 1.0
dNdz_model[95] 9.64 0.05 1.91 6.32 8.27 9.48 10.84 13.72 1333 1.0
dNdz_model[96] 9.39 0.05 1.9 6.07 8.02 9.22 10.57 13.49 1311 1.0
dNdz_model[97] 9.13 0.05 1.9 5.84 7.77 8.96 10.3 13.24 1291 1.0
dNdz_model[98] 8.89 0.05 1.9 5.62 7.52 8.72 10.05 13.01 1271 1.0
dNdz_model[99] 8.65 0.05 1.89 5.41 7.28 8.48 9.79 12.75 1253 1.0
lp__ 499.49 0.05 1.41 496.0 498.87 499.85 500.49 501.04 937 1.01
Samples were drawn using NUTS at Tue Jul 18 22:50:20 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
In [13]:
chain_direct = fit_direct.extract(permuted=True)
How does the fit look? Pretty good. I realise this may not seem like a big deal, but it is fairly rare in the astro literature to see a fit like this without binning (right?).
In [14]:
corner.corner(
column_stack([chain_direct[key] for key in ['N0', 'alpha', 'beta']]),
labels=[r'$N_0$', r'$\alpha$', r'$\beta$'],
truths=[Ntrue, alphatrue, betatrue]
);
OK. Now let's be a bit more realistic. First, it is not reasonble to expect our hypothetical LIGO to measure the redshift perfectly. How should we handle this? We can introduce a $z_\mathrm{obs}$ for each event which will become the data (before the data was the true redshift, $z$), and let's imagine they are related by $$ \log z_\mathrm{obs} \sim N\left( \log z , \sigma_z \right), $$ with $\sigma_z = 0.2$ (i.e. our hypothetical IFO has 20% uncertainty in its measurements of redshift).
What should the likelihood look like now? Well, the rate still applies to the true redshift, $z$, but we now need a term that gives $p\left( z_\mathrm{obs} \mid z \right)$, or, in $z = \boldsymbol{\theta}$, $z_\mathrm{obs} = d$ language: $$ p\left( \left\{ d^{(i)}, \boldsymbol{\theta}^{(i)} \right\} \mid \lambda \right) = \prod_i \left[ p\left( d^{(i)} \mid \boldsymbol{\theta}^{(i)} \right) \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}} \left( \boldsymbol{\theta}^{(i)} \mid \boldsymbol{\lambda} \right) \right] \exp\left( - \int \mathrm{d} \boldsymbol{\theta} \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}}\left(\boldsymbol{\theta} \mid \boldsymbol{\lambda}\right) \right) $$ The term $p\left( d^{(i)} \mid \boldsymbol{\theta}^{(i)} \right)$ (which, in our case, is a log-normal distribution for $z_\mathrm{obs}$ given $z$ and $\sigma$) is the likelihood function that would figure prominently in a "parameter estimation" analysis.
Let's fit this new model. (By the way, notice how the dimensionality of the model has gone up significantly---each observation introduces a new parameter---which can make sampling very hard for some samplers. Fortunately, Stan tends to perform well in high-dimensional sampling problems.)
In [15]:
model_errors = pystan.StanModel(file='zmodel_errors.stan')
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_2c8aab45b74c291580bf52d0ab9ca26b NOW.
Let's draw some "observations" from the same set of true redshifts we had before:
In [16]:
zobs = []
sigma = []
for z in ztrues:
s = 0.2
zo = exp(log(z) + s*randn())
zobs.append(zo)
sigma.append(s)
In [17]:
data_errors = {
'nobs': len(zobs),
'zobs': zobs,
'sigmaobs': sigma,
'nmodel': 100,
'zs_model': linspace(0,10,101)[1:] # The code will give NaNs at z == 0.
}
In [18]:
fit_errors = model_errors.sampling(data=data_errors)
In [19]:
fit_errors
Out[19]:
Inference for Stan model: anon_model_2c8aab45b74c291580bf52d0ab9ca26b.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
N0 11.66 0.09 3.59 5.94 9.08 11.2 13.71 19.84 1500 1.0
alpha 1.78 0.01 0.45 0.9 1.47 1.78 2.09 2.67 1345 1.0
beta 2.31 0.02 0.6 1.53 1.9 2.18 2.57 3.79 1213 1.0
ztrue[0] 4.55 0.01 0.87 3.08 3.94 4.47 5.06 6.44 4000 1.0
ztrue[1] 2.01 6.3e-3 0.4 1.32 1.73 1.97 2.26 2.91 4000 1.0
ztrue[2] 1.48 4.6e-3 0.29 0.99 1.27 1.46 1.66 2.12 4000 1.0
ztrue[3] 1.9 6.1e-3 0.38 1.27 1.63 1.86 2.12 2.75 4000 1.0
ztrue[4] 8.18 0.02 1.09 5.95 7.4 8.25 9.04 9.9 4000 1.0
ztrue[5] 4.54 0.01 0.87 3.09 3.92 4.45 5.09 6.46 4000 1.0
ztrue[6] 1.71 5.5e-3 0.35 1.12 1.45 1.67 1.93 2.48 4000 1.0
ztrue[7] 3.59 0.01 0.72 2.39 3.05 3.51 4.04 5.11 4000 1.0
ztrue[8] 4.16 0.01 0.82 2.72 3.58 4.09 4.67 5.96 4000 1.0
ztrue[9] 3.04 9.4e-3 0.6 2.04 2.61 2.99 3.39 4.34 4000 1.0
ztrue[10] 0.26 8.5e-4 0.05 0.17 0.22 0.26 0.29 0.38 4000 1.0
ztrue[11] 2.22 6.9e-3 0.44 1.47 1.91 2.18 2.49 3.15 4000 1.0
ztrue[12] 8.18 0.02 1.08 5.94 7.42 8.27 9.04 9.87 4000 1.0
ztrue[13] 1.16 3.6e-3 0.23 0.78 1.0 1.14 1.3 1.67 4000 1.0
ztrue[14] 1.13 3.6e-3 0.23 0.74 0.96 1.11 1.26 1.63 4000 1.0
ztrue[15] 5.43 0.02 1.08 3.67 4.65 5.34 6.08 7.78 4000 1.0
ztrue[16] 1.02 3.3e-3 0.21 0.68 0.87 1.0 1.15 1.47 4000 1.0
ztrue[17] 5.48 0.02 1.06 3.66 4.74 5.4 6.12 7.8 4000 1.0
ztrue[18] 8.36 0.02 1.04 6.18 7.64 8.47 9.2 9.91 4000 1.0
ztrue[19] 2.51 7.8e-3 0.49 1.69 2.17 2.48 2.8 3.61 4000 1.0
ztrue[20] 3.76 0.01 0.75 2.52 3.23 3.68 4.19 5.43 4000 1.0
ztrue[21] 0.82 2.6e-3 0.17 0.54 0.7 0.8 0.93 1.18 4000 1.0
ztrue[22] 1.62 5.2e-3 0.33 1.08 1.38 1.59 1.81 2.39 4000 1.0
ztrue[23] 1.09 3.6e-3 0.23 0.71 0.93 1.07 1.23 1.6 4000 1.0
ztrue[24] 1.23 3.9e-3 0.25 0.82 1.06 1.21 1.38 1.77 4000 1.0
ztrue[25] 5.48 0.02 1.06 3.76 4.74 5.37 6.11 7.98 4000 1.0
ztrue[26] 8.43 0.02 1.02 6.15 7.74 8.55 9.27 9.91 4000 1.0
ztrue[27] 2.58 8.1e-3 0.51 1.71 2.22 2.53 2.89 3.68 4000 1.0
ztrue[28] 5.83 0.02 1.11 3.91 5.05 5.74 6.5 8.31 4000 1.0
ztrue[29] 4.42 0.01 0.86 2.99 3.84 4.33 4.92 6.34 4000 1.0
ztrue[30] 2.72 8.6e-3 0.54 1.79 2.34 2.66 3.03 3.96 4000 1.0
ztrue[31] 3.38 0.01 0.67 2.25 2.89 3.32 3.8 4.83 4000 1.0
ztrue[32] 8.24 0.02 1.06 6.01 7.48 8.36 9.09 9.89 4000 1.0
ztrue[33] 3.58 0.01 0.7 2.4 3.07 3.5 4.02 5.1 4000 1.0
ztrue[34] 3.5 0.01 0.7 2.34 3.0 3.42 3.93 5.07 4000 1.0
ztrue[35] 3.25 0.01 0.64 2.17 2.8 3.2 3.63 4.67 4000 1.0
ztrue[36] 5.26 0.02 1.02 3.53 4.54 5.17 5.87 7.53 4000 1.0
ztrue[37] 4.22 0.01 0.81 2.82 3.65 4.16 4.72 6.0 4000 1.0
ztrue[38] 3.32 0.01 0.63 2.24 2.86 3.27 3.7 4.72 4000 1.0
ztrue[39] 5.09 0.02 0.96 3.45 4.39 5.01 5.67 7.2 4000 1.0
ztrue[40] 1.83 5.8e-3 0.37 1.21 1.57 1.79 2.05 2.65 4000 1.0
ztrue[41] 4.11 0.01 0.79 2.77 3.54 4.05 4.62 5.87 4000 1.0
ztrue[42] 2.14 6.7e-3 0.42 1.43 1.84 2.1 2.4 3.09 4000 1.0
ztrue[43] 6.91 0.02 1.2 4.73 6.04 6.86 7.74 9.36 4000 1.0
ztrue[44] 1.9 6.0e-3 0.38 1.27 1.63 1.86 2.13 2.72 4000 1.0
ztrue[45] 2.63 8.2e-3 0.52 1.74 2.26 2.59 2.95 3.81 4000 1.0
ztrue[46] 5.48 0.02 1.03 3.71 4.74 5.41 6.11 7.82 4000 1.0
ztrue[47] 1.01 3.1e-3 0.19 0.67 0.87 0.99 1.13 1.43 4000 1.0
ztrue[48] 2.95 9.4e-3 0.59 1.94 2.52 2.9 3.31 4.24 4000 1.0
ztrue[49] 6.56 0.02 1.17 4.5 5.72 6.49 7.33 9.06 4000 1.0
ztrue[50] 1.33 4.2e-3 0.27 0.9 1.14 1.3 1.5 1.95 4000 1.0
ztrue[51] 4.04 0.01 0.82 2.66 3.46 3.97 4.55 5.81 4000 1.0
ztrue[52] 4.38 0.01 0.83 2.94 3.79 4.3 4.89 6.24 4000 1.0
ztrue[53] 4.54 0.01 0.88 3.06 3.93 4.46 5.08 6.48 4000 1.0
ztrue[54] 4.24 0.01 0.84 2.85 3.62 4.16 4.75 6.08 4000 1.0
ztrue[55] 7.18 0.02 1.19 4.96 6.33 7.12 8.01 9.59 4000 1.0
ztrue[56] 1.77 5.4e-3 0.34 1.19 1.54 1.74 1.98 2.52 4000 1.0
ztrue[57] 5.45 0.02 1.02 3.7 4.72 5.36 6.09 7.67 4000 1.0
ztrue[58] 5.84 0.02 1.08 4.01 5.07 5.74 6.49 8.23 4000 1.0
ztrue[59] 4.17 0.01 0.82 2.78 3.59 4.09 4.65 6.04 4000 1.0
ztrue[60] 3.43 0.01 0.67 2.3 2.95 3.38 3.84 4.9 4000 1.0
ztrue[61] 1.87 6.0e-3 0.38 1.23 1.59 1.83 2.11 2.72 4000 1.0
ztrue[62] 3.88 0.01 0.74 2.64 3.36 3.81 4.34 5.45 4000 1.0
ztrue[63] 3.35 0.01 0.67 2.24 2.89 3.29 3.75 4.88 4000 1.0
ztrue[64] 5.06 0.01 0.94 3.46 4.37 5.0 5.67 7.07 4000 1.0
ztrue[65] 1.89 6.3e-3 0.4 1.22 1.61 1.85 2.12 2.77 4000 1.0
ztrue[66] 5.05 0.01 0.94 3.42 4.37 4.96 5.62 7.14 4000 1.0
ztrue[67] 6.88 0.02 1.2 4.69 6.02 6.83 7.7 9.38 4000 1.0
ztrue[68] 1.27 4.0e-3 0.25 0.84 1.08 1.24 1.42 1.86 4000 1.0
ztrue[69] 4.34 0.02 0.87 2.91 3.71 4.25 4.87 6.25 3108 1.0
ztrue[70] 4.94 0.02 0.97 3.28 4.26 4.86 5.52 7.12 4000 1.0
ztrue[71] 5.95 0.02 1.15 3.97 5.13 5.86 6.68 8.48 4000 1.0
ztrue[72] 0.24 7.5e-4 0.05 0.16 0.2 0.23 0.26 0.34 4000 1.0
ztrue[73] 2.84 8.8e-3 0.56 1.89 2.43 2.79 3.18 4.04 4000 1.0
ztrue[74] 7.09 0.02 1.22 4.79 6.21 7.09 7.97 9.46 4000 1.0
ztrue[75] 2.01 6.4e-3 0.4 1.34 1.73 1.98 2.25 2.95 4000 1.0
ztrue[76] 3.84 0.01 0.78 2.54 3.28 3.77 4.3 5.52 4000 1.0
ztrue[77] 1.96 6.4e-3 0.4 1.26 1.67 1.92 2.2 2.85 4000 1.0
ztrue[78] 4.33 0.01 0.85 2.9 3.72 4.25 4.84 6.23 4000 1.0
ztrue[79] 2.33 7.1e-3 0.45 1.58 2.0 2.3 2.62 3.33 4000 1.0
ztrue[80] 5.41 0.02 1.02 3.65 4.69 5.3 6.04 7.69 4000 1.0
ztrue[81] 8.77 0.01 0.88 6.76 8.2 8.91 9.48 9.95 4000 1.0
ztrue[82] 1.77 5.4e-3 0.34 1.18 1.53 1.74 1.98 2.51 4000 1.0
ztrue[83] 6.12 0.02 1.14 4.16 5.31 6.02 6.84 8.55 4000 1.0
ztrue[84] 6.76 0.02 1.21 4.6 5.9 6.68 7.58 9.22 4000 1.0
ztrue[85] 3.25 0.01 0.64 2.17 2.8 3.18 3.64 4.66 4000 1.0
ztrue[86] 8.73 0.01 0.89 6.73 8.17 8.88 9.45 9.94 4000 1.0
ztrue[87] 3.0 9.5e-3 0.6 2.01 2.57 2.94 3.38 4.31 4000 1.0
ztrue[88] 4.76 0.01 0.92 3.22 4.1 4.67 5.33 6.75 4000 1.0
ztrue[89] 2.87 9.0e-3 0.57 1.91 2.47 2.82 3.22 4.15 4000 1.0
ztrue[90] 4.6 0.01 0.9 3.08 3.97 4.51 5.13 6.67 4000 1.0
ztrue[91] 5.21 0.02 1.0 3.5 4.48 5.11 5.82 7.5 4000 1.0
ztrue[92] 7.76 0.02 1.17 5.47 6.91 7.77 8.68 9.81 4000 1.0
ztrue[93] 7.69 0.02 1.2 5.33 6.81 7.71 8.59 9.81 4000 1.0
ztrue[94] 5.59 0.02 1.05 3.73 4.84 5.49 6.25 7.96 4000 1.0
ztrue[95] 4.67 0.01 0.89 3.1 4.03 4.6 5.22 6.66 4000 1.0
ztrue[96] 5.25 0.02 0.99 3.58 4.57 5.17 5.84 7.46 4000 1.0
ztrue[97] 5.88 0.02 1.13 3.95 5.05 5.79 6.62 8.36 4000 1.0
ztrue[98] 2.17 6.8e-3 0.43 1.46 1.87 2.13 2.43 3.12 4000 1.0
ztrue[99] 3.59 0.01 0.7 2.42 3.09 3.53 4.02 5.16 4000 1.0
ztrue[100] 5.58 0.02 1.06 3.77 4.83 5.5 6.23 7.91 4000 1.0
ztrue[101] 2.42 7.5e-3 0.47 1.62 2.08 2.37 2.71 3.47 4000 1.0
ztrue[102] 6.56 0.03 1.22 4.41 5.66 6.47 7.34 9.24 1642 1.0
ztrue[103] 4.77 0.01 0.94 3.21 4.1 4.69 5.33 6.85 4000 1.0
ztrue[104] 1.94 6.1e-3 0.39 1.28 1.66 1.9 2.17 2.8 4000 1.0
ztrue[105] 5.66 0.02 1.07 3.8 4.88 5.59 6.37 7.99 4000 1.0
ztrue[106] 3.98 0.01 0.76 2.67 3.44 3.91 4.44 5.66 4000 1.0
ztrue[107] 2.49 7.8e-3 0.49 1.66 2.14 2.44 2.81 3.57 4000 1.0
ztrue[108] 0.63 2.0e-3 0.13 0.42 0.54 0.62 0.72 0.92 4000 1.0
ztrue[109] 2.54 8.1e-3 0.51 1.67 2.18 2.49 2.86 3.68 4000 1.0
ztrue[110] 0.53 1.7e-3 0.11 0.35 0.45 0.52 0.59 0.77 4000 1.0
ztrue[111] 4.83 0.01 0.92 3.22 4.18 4.74 5.4 6.85 4000 1.0
ztrue[112] 5.64 0.02 1.06 3.78 4.9 5.55 6.31 7.95 4000 1.0
ztrue[113] 9.31 9.2e-3 0.58 7.86 8.99 9.46 9.77 9.97 4000 1.0
ztrue[114] 6.04 0.02 1.11 4.11 5.22 5.95 6.78 8.37 4000 1.0
ztrue[115] 5.69 0.02 1.07 3.85 4.94 5.6 6.35 8.06 4000 1.0
ztrue[116] 1.29 4.2e-3 0.26 0.84 1.09 1.26 1.46 1.86 4000 1.0
ztrue[117] 0.95 3.0e-3 0.19 0.63 0.82 0.93 1.07 1.38 4000 1.0
ztrue[118] 2.31 7.2e-3 0.46 1.54 1.98 2.28 2.59 3.33 4000 1.0
ztrue[119] 1.76 5.6e-3 0.35 1.17 1.5 1.72 1.98 2.53 4000 1.0
ztrue[120] 4.89 0.02 0.97 3.24 4.21 4.79 5.49 7.1 4000 1.0
ztrue[121] 0.13 4.2e-4 0.03 0.09 0.11 0.13 0.15 0.19 4000 1.0
ztrue[122] 3.5 0.01 0.69 2.31 3.01 3.42 3.93 5.01 4000 1.0
ztrue[123] 7.52 0.02 1.22 5.2 6.63 7.51 8.45 9.76 4000 1.0
ztrue[124] 2.63 8.2e-3 0.52 1.77 2.26 2.58 2.94 3.79 4000 1.0
ztrue[125] 0.49 1.6e-3 0.1 0.32 0.42 0.48 0.55 0.71 4000 1.0
ztrue[126] 1.29 4.1e-3 0.26 0.87 1.11 1.27 1.45 1.88 4000 1.0
ztrue[127] 3.07 9.7e-3 0.61 2.04 2.64 3.01 3.43 4.45 4000 1.0
ztrue[128] 6.73 0.02 1.17 4.63 5.87 6.66 7.52 9.22 4000 1.0
ztrue[129] 8.99 0.01 0.78 7.1 8.55 9.15 9.61 9.97 4000 1.0
ztrue[130] 6.24 0.02 1.16 4.2 5.41 6.16 6.98 8.76 4000 1.0
ztrue[131] 8.62 0.01 0.95 6.47 7.99 8.77 9.39 9.94 4000 1.0
ztrue[132] 7.56 0.02 1.22 5.27 6.68 7.55 8.47 9.8 4000 1.0
ztrue[133] 3.48 0.01 0.65 2.38 3.03 3.41 3.86 4.97 4000 1.0
ztrue[134] 3.07 9.8e-3 0.62 2.02 2.63 3.01 3.44 4.48 4000 1.0
ztrue[135] 8.92 0.01 0.82 6.98 8.42 9.09 9.58 9.96 4000 1.0
ztrue[136] 2.95 9.2e-3 0.58 1.99 2.54 2.89 3.29 4.23 4000 1.0
ztrue[137] 1.59 5.1e-3 0.32 1.05 1.36 1.55 1.79 2.28 4000 1.0
ztrue[138] 3.73 0.01 0.75 2.44 3.19 3.67 4.21 5.36 4000 1.0
ztrue[139] 7.89 0.02 1.15 5.61 7.05 7.94 8.77 9.83 4000 1.0
ztrue[140] 1.85 5.7e-3 0.36 1.24 1.59 1.81 2.07 2.64 4000 1.0
ztrue[141] 2.95 9.3e-3 0.59 1.97 2.53 2.88 3.32 4.28 4000 1.0
ztrue[142] 6.72 0.02 1.19 4.58 5.87 6.65 7.54 9.15 4000 1.0
ztrue[143] 2.24 7.1e-3 0.45 1.5 1.91 2.2 2.5 3.24 4000 1.0
ztrue[144] 4.7 0.01 0.93 3.13 4.05 4.6 5.23 6.78 4000 1.0
ztrue[145] 4.69 0.01 0.92 3.17 4.03 4.61 5.27 6.62 4000 1.0
ztrue[146] 7.81 0.02 1.17 5.52 6.98 7.85 8.71 9.8 4000 1.0
ztrue[147] 5.67 0.02 1.04 3.86 4.92 5.6 6.33 7.89 3251 1.0
ztrue[148] 4.41 0.01 0.86 2.98 3.79 4.34 4.93 6.29 4000 1.0
ztrue[149] 0.52 1.7e-3 0.11 0.34 0.44 0.51 0.59 0.76 4000 1.0
ztrue[150] 1.38 4.5e-3 0.29 0.91 1.18 1.35 1.55 2.03 4000 1.0
ztrue[151] 3.33 0.01 0.64 2.24 2.89 3.26 3.69 4.74 4000 1.0
ztrue[152] 8.19 0.02 1.11 5.83 7.41 8.27 9.08 9.92 4000 1.0
ztrue[153] 3.9 0.01 0.78 2.62 3.34 3.84 4.38 5.65 4000 1.0
ztrue[154] 5.22 0.02 0.99 3.51 4.51 5.13 5.82 7.45 4000 1.0
ztrue[155] 4.91 0.01 0.93 3.32 4.24 4.84 5.52 6.9 4000 1.0
ztrue[156] 1.7 5.3e-3 0.33 1.14 1.47 1.67 1.91 2.46 4000 1.0
ztrue[157] 5.45 0.02 1.02 3.73 4.73 5.36 6.06 7.72 4000 1.0
ztrue[158] 7.42 0.02 1.21 5.09 6.56 7.38 8.31 9.66 4000 1.0
ztrue[159] 5.41 0.02 1.06 3.58 4.67 5.3 6.06 7.74 4000 1.0
ztrue[160] 8.26 0.02 1.06 6.01 7.52 8.37 9.11 9.91 4000 1.0
ztrue[161] 7.49 0.02 1.21 5.18 6.63 7.46 8.41 9.72 4000 1.0
ztrue[162] 5.69 0.04 1.13 3.91 4.9 5.57 6.34 8.25 773 1.0
ztrue[163] 6.82 0.02 1.19 4.68 5.97 6.74 7.62 9.31 4000 1.0
ztrue[164] 8.77 0.01 0.9 6.63 8.21 8.94 9.51 9.95 4000 1.0
ztrue[165] 1.47 4.6e-3 0.29 0.97 1.26 1.44 1.65 2.11 4000 1.0
ztrue[166] 3.94 0.01 0.76 2.63 3.41 3.88 4.41 5.67 4000 1.0
ztrue[167] 7.89 0.02 1.15 5.58 7.06 7.93 8.79 9.84 4000 1.0
ztrue[168] 5.36 0.02 1.01 3.63 4.64 5.27 5.97 7.59 4000 1.0
ztrue[169] 5.01 0.02 0.95 3.36 4.35 4.92 5.59 7.11 4000 1.0
ztrue[170] 8.82 0.01 0.85 6.92 8.26 8.99 9.5 9.95 4000 1.0
ztrue[171] 4.15 0.01 0.82 2.76 3.56 4.07 4.66 5.95 4000 1.0
ztrue[172] 5.77 0.02 1.12 3.88 4.95 5.68 6.47 8.21 4000 1.0
ztrue[173] 1.75 5.4e-3 0.34 1.18 1.5 1.72 1.96 2.48 4000 1.0
ztrue[174] 2.53 7.8e-3 0.5 1.7 2.17 2.49 2.83 3.62 4000 1.0
ztrue[175] 3.47 0.01 0.69 2.33 2.98 3.41 3.88 5.01 4000 1.0
ztrue[176] 5.6 0.02 1.07 3.76 4.83 5.52 6.28 7.92 4000 1.0
ztrue[177] 6.93 0.02 1.2 4.78 6.07 6.87 7.79 9.38 4000 1.0
ztrue[178] 2.52 7.8e-3 0.49 1.68 2.18 2.48 2.82 3.61 4000 1.0
ztrue[179] 3.85 0.01 0.74 2.61 3.32 3.79 4.29 5.53 4000 1.0
ztrue[180] 1.24 4.0e-3 0.25 0.82 1.07 1.22 1.4 1.81 4000 1.0
ztrue[181] 7.08 0.02 1.22 4.84 6.18 7.03 7.96 9.47 4000 1.0
ztrue[182] 3.23 9.8e-3 0.62 2.19 2.8 3.16 3.6 4.59 4000 1.0
ztrue[183] 7.68 0.02 1.16 5.42 6.84 7.72 8.56 9.74 4000 1.0
ztrue[184] 4.75 0.01 0.91 3.15 4.09 4.67 5.33 6.74 4000 1.0
ztrue[185] 1.12 3.6e-3 0.23 0.74 0.96 1.1 1.25 1.63 4000 1.0
ztrue[186] 7.15 0.02 1.23 4.88 6.27 7.08 8.01 9.54 4000 1.0
ztrue[187] 2.01 6.2e-3 0.39 1.34 1.74 1.98 2.26 2.88 4000 1.0
ztrue[188] 2.87 8.8e-3 0.56 1.88 2.48 2.82 3.21 4.1 4000 1.0
ztrue[189] 1.93 6.1e-3 0.39 1.28 1.65 1.9 2.17 2.78 4000 1.0
ztrue[190] 4.99 0.01 0.94 3.37 4.32 4.89 5.59 7.03 4000 1.0
ztrue[191] 5.33 0.02 1.02 3.59 4.58 5.26 5.98 7.51 4000 1.0
ztrue[192] 6.95 0.02 1.2 4.81 6.08 6.9 7.78 9.4 4000 1.0
ztrue[193] 3.17 0.01 0.65 2.15 2.72 3.11 3.56 4.64 4000 1.0
ztrue[194] 8.83 0.01 0.85 6.86 8.29 9.0 9.53 9.95 4000 1.0
ztrue[195] 7.61 0.02 1.18 5.32 6.74 7.61 8.49 9.72 4000 1.0
ztrue[196] 3.98 0.01 0.77 2.7 3.42 3.91 4.48 5.67 4000 1.0
ztrue[197] 0.4 1.2e-3 0.08 0.27 0.34 0.39 0.44 0.58 4000 1.0
ztrue[198] 7.86 0.02 1.16 5.53 7.02 7.9 8.77 9.83 4000 1.0
ztrue[199] 4.43 0.01 0.89 2.98 3.78 4.35 4.96 6.49 4000 1.0
ztrue[200] 5.84 0.02 1.11 3.95 5.04 5.74 6.53 8.29 4000 1.0
ztrue[201] 2.56 8.0e-3 0.51 1.72 2.2 2.51 2.85 3.72 4000 1.0
ztrue[202] 2.86 8.9e-3 0.56 1.93 2.46 2.81 3.2 4.12 4000 1.0
ztrue[203] 4.21 0.01 0.83 2.8 3.62 4.13 4.71 6.02 4000 1.0
ztrue[204] 3.09 9.7e-3 0.61 2.06 2.65 3.03 3.47 4.45 4000 1.0
ztrue[205] 4.93 0.01 0.94 3.36 4.26 4.84 5.51 7.0 4000 1.0
ztrue[206] 8.69 0.01 0.93 6.57 8.09 8.84 9.45 9.95 4000 1.0
ztrue[207] 8.69 0.01 0.93 6.51 8.1 8.82 9.44 9.95 4000 1.0
ztrue[208] 2.24 6.9e-3 0.44 1.51 1.93 2.19 2.49 3.25 4000 1.0
ztrue[209] 6.28 0.02 1.16 4.26 5.47 6.19 6.99 8.84 4000 1.0
ztrue[210] 4.35 0.01 0.85 2.86 3.74 4.29 4.86 6.19 4000 1.0
ztrue[211] 4.71 0.01 0.93 3.16 4.03 4.62 5.28 6.71 4000 1.0
ztrue[212] 3.37 0.01 0.64 2.29 2.93 3.31 3.74 4.78 4000 1.0
ztrue[213] 3.69 0.01 0.72 2.49 3.19 3.61 4.12 5.32 4000 1.0
ztrue[214] 4.62 0.01 0.88 3.17 3.99 4.53 5.15 6.58 4000 1.0
ztrue[215] 1.9 6.0e-3 0.38 1.25 1.63 1.87 2.12 2.76 4000 1.0
ztrue[216] 6.28 0.02 1.17 4.24 5.46 6.18 7.02 8.88 2371 1.0
ztrue[217] 0.46 1.5e-3 0.09 0.3 0.4 0.45 0.51 0.68 4000 1.0
ztrue[218] 1.73 5.3e-3 0.34 1.16 1.49 1.71 1.93 2.51 4000 1.0
ztrue[219] 7.54 0.02 1.19 5.28 6.67 7.52 8.44 9.71 4000 1.0
ztrue[220] 6.7 0.02 1.2 4.58 5.85 6.61 7.5 9.3 4000 1.0
ztrue[221] 1.07 3.5e-3 0.22 0.71 0.91 1.06 1.21 1.57 4000 1.0
ztrue[222] 4.76 0.01 0.94 3.19 4.09 4.67 5.32 6.85 4000 1.0
ztrue[223] 2.51 8.0e-3 0.51 1.65 2.14 2.46 2.82 3.64 4000 1.0
ztrue[224] 0.04 1.1e-4 7.3e-3 0.02 0.03 0.03 0.04 0.05 4000 1.0
ztrue[225] 8.6 0.02 0.96 6.48 7.99 8.76 9.37 9.96 4000 1.0
ztrue[226] 5.54 0.02 1.07 3.73 4.77 5.46 6.2 7.94 4000 1.0
ztrue[227] 6.81 0.02 1.24 4.62 5.91 6.72 7.68 9.36 4000 1.0
dNdz_model[0] 13.09 0.09 3.62 7.16 10.47 12.68 15.22 21.14 1569 1.0
dNdz_model[1] 14.5 0.09 3.62 8.43 11.93 14.18 16.66 22.4 1648 1.0
dNdz_model[2] 15.89 0.09 3.6 9.71 13.33 15.63 18.09 23.72 1740 1.0
dNdz_model[3] 17.25 0.08 3.55 10.97 14.72 17.02 19.45 24.81 1846 1.0
dNdz_model[4] 18.57 0.08 3.5 12.33 16.07 18.38 20.78 25.92 1972 1.0
dNdz_model[5] 19.85 0.07 3.43 13.64 17.41 19.66 22.07 26.94 2123 1.0
dNdz_model[6] 21.08 0.07 3.35 14.95 18.74 20.94 23.28 28.03 2304 1.0
dNdz_model[7] 22.26 0.07 3.28 16.2 19.96 22.14 24.39 29.06 2519 1.0
dNdz_model[8] 23.39 0.06 3.21 17.41 21.12 23.33 25.49 30.08 2771 1.0
dNdz_model[9] 24.45 0.05 3.14 18.6 22.24 24.41 26.54 30.94 4000 1.0
dNdz_model[10] 25.46 0.05 3.09 19.74 23.29 25.42 27.49 31.79 4000 1.0
dNdz_model[11] 26.41 0.05 3.04 20.8 24.29 26.36 28.37 32.61 4000 1.0
dNdz_model[12] 27.29 0.05 3.01 21.75 25.2 27.26 29.24 33.49 4000 1.0
dNdz_model[13] 28.12 0.05 2.99 22.65 26.02 28.11 30.06 34.27 4000 1.0
dNdz_model[14] 28.88 0.05 2.98 23.41 26.78 28.85 30.83 35.02 4000 1.0
dNdz_model[15] 29.58 0.05 2.98 24.11 27.48 29.49 31.55 35.76 4000 1.0
dNdz_model[16] 30.22 0.05 2.98 24.71 28.07 30.11 32.2 36.36 4000 1.0
dNdz_model[17] 30.8 0.05 3.0 25.29 28.66 30.68 32.79 36.94 4000 1.0
dNdz_model[18] 31.32 0.05 3.02 25.69 29.18 31.18 33.31 37.49 4000 1.0
dNdz_model[19] 31.78 0.05 3.04 26.12 29.66 31.65 33.8 38.04 4000 1.0
dNdz_model[20] 32.19 0.05 3.07 26.47 30.04 32.05 34.22 38.48 4000 1.0
dNdz_model[21] 32.54 0.05 3.1 26.77 30.36 32.38 34.57 38.86 4000 1.0
dNdz_model[22] 32.84 0.05 3.12 27.02 30.64 32.67 34.89 39.11 4000 1.0
dNdz_model[23] 33.08 0.06 3.15 27.19 30.88 32.92 35.18 39.37 2839 1.0
dNdz_model[24] 33.28 0.06 3.17 27.31 31.07 33.11 35.4 39.6 2716 1.0
dNdz_model[25] 33.43 0.06 3.18 27.45 31.2 33.27 35.56 39.8 2611 1.0
dNdz_model[26] 33.54 0.06 3.19 27.54 31.3 33.38 35.68 39.89 2521 1.0
dNdz_model[27] 33.6 0.06 3.2 27.6 31.36 33.44 35.74 39.96 2445 1.0
dNdz_model[28] 33.62 0.07 3.2 27.61 31.39 33.46 35.73 40.03 2380 1.0
dNdz_model[29] 33.6 0.07 3.2 27.58 31.39 33.42 35.71 39.94 2326 1.0
dNdz_model[30] 33.54 0.07 3.19 27.53 31.33 33.33 35.64 39.93 2280 1.0
dNdz_model[31] 33.45 0.07 3.17 27.48 31.23 33.25 35.54 39.82 2243 1.0
dNdz_model[32] 33.32 0.07 3.15 27.41 31.14 33.13 35.42 39.75 2213 1.0
dNdz_model[33] 33.16 0.07 3.13 27.28 30.99 32.98 35.23 39.58 2190 1.0
dNdz_model[34] 32.97 0.07 3.1 27.14 30.83 32.82 35.02 39.35 2172 1.0
dNdz_model[35] 32.76 0.07 3.06 26.99 30.63 32.6 34.76 39.11 2161 1.0
dNdz_model[36] 32.51 0.07 3.02 26.89 30.41 32.38 34.5 38.8 2154 1.0
dNdz_model[37] 32.25 0.06 2.98 26.71 30.17 32.11 34.21 38.42 2154 1.0
dNdz_model[38] 31.96 0.06 2.93 26.54 29.93 31.82 33.88 38.04 2158 1.0
dNdz_model[39] 31.65 0.06 2.88 26.3 29.63 31.53 33.54 37.6 2168 1.0
dNdz_model[40] 31.32 0.06 2.83 26.04 29.34 31.19 33.19 37.17 2183 1.0
dNdz_model[41] 30.97 0.06 2.78 25.78 29.04 30.85 32.83 36.76 2204 1.0
dNdz_model[42] 30.6 0.06 2.72 25.58 28.71 30.49 32.44 36.26 2231 1.0
dNdz_model[43] 30.22 0.06 2.67 25.3 28.35 30.12 32.01 35.77 2263 1.0
dNdz_model[44] 29.83 0.05 2.61 25.02 27.99 29.74 31.56 35.27 2300 1.0
dNdz_model[45] 29.43 0.05 2.55 24.73 27.64 29.35 31.13 34.77 2344 1.0
dNdz_model[46] 29.01 0.05 2.5 24.39 27.28 28.95 30.65 34.23 2396 1.0
dNdz_model[47] 28.58 0.05 2.44 24.07 26.88 28.54 30.18 33.66 2457 1.0
dNdz_model[48] 28.15 0.05 2.39 23.72 26.48 28.11 29.72 33.1 2526 1.0
dNdz_model[49] 27.71 0.05 2.34 23.37 26.08 27.67 29.25 32.57 2606 1.0
dNdz_model[50] 27.26 0.04 2.28 23.0 25.67 27.23 28.74 32.05 2695 1.0
dNdz_model[51] 26.81 0.04 2.24 22.62 25.24 26.79 28.27 31.46 2796 1.0
dNdz_model[52] 26.35 0.04 2.19 22.2 24.81 26.32 27.78 30.87 3028 1.0
dNdz_model[53] 25.89 0.04 2.15 21.82 24.38 25.86 27.28 30.3 3131 1.0
dNdz_model[54] 25.42 0.04 2.11 21.45 23.96 25.4 26.8 29.74 3243 1.0
dNdz_model[55] 24.95 0.04 2.07 21.03 23.52 24.94 26.32 29.15 3362 1.0
dNdz_model[56] 24.49 0.03 2.03 20.63 23.08 24.45 25.85 28.61 3485 1.0
dNdz_model[57] 24.02 0.03 2.0 20.16 22.63 23.98 25.36 28.05 3611 1.0
dNdz_model[58] 23.55 0.03 1.98 19.76 22.19 23.51 24.87 27.55 4000 1.0
dNdz_model[59] 23.08 0.03 1.95 19.35 21.75 23.05 24.37 27.06 4000 1.0
dNdz_model[60] 22.62 0.03 1.94 18.91 21.29 22.58 23.89 26.5 4000 1.0
dNdz_model[61] 22.15 0.03 1.92 18.47 20.83 22.12 23.43 25.97 4000 1.0
dNdz_model[62] 21.69 0.03 1.91 18.04 20.4 21.64 22.98 25.5 4000 1.0
dNdz_model[63] 21.24 0.03 1.9 17.62 19.95 21.19 22.51 25.02 4000 1.0
dNdz_model[64] 20.78 0.03 1.89 17.17 19.5 20.73 22.03 24.56 4000 1.0
dNdz_model[65] 20.33 0.03 1.89 16.74 19.04 20.28 21.56 24.1 4000 1.0
dNdz_model[66] 19.88 0.03 1.88 16.3 18.6 19.85 21.12 23.66 4000 1.0
dNdz_model[67] 19.44 0.03 1.89 15.87 18.15 19.39 20.67 23.21 4000 1.0
dNdz_model[68] 19.0 0.03 1.89 15.43 17.72 18.94 20.24 22.74 4000 1.0
dNdz_model[69] 18.57 0.03 1.89 14.99 17.27 18.51 19.8 22.34 4000 1.0
dNdz_model[70] 18.14 0.03 1.9 14.54 16.85 18.08 19.37 21.95 4000 1.0
dNdz_model[71] 17.72 0.03 1.91 14.06 16.43 17.66 18.96 21.56 4000 1.0
dNdz_model[72] 17.3 0.03 1.91 13.64 15.99 17.23 18.55 21.16 4000 1.0
dNdz_model[73] 16.89 0.03 1.92 13.21 15.57 16.81 18.16 20.78 4000 1.0
dNdz_model[74] 16.48 0.03 1.93 12.8 15.16 16.41 17.78 20.42 4000 1.0
dNdz_model[75] 16.09 0.03 1.94 12.37 14.76 16.01 17.37 20.01 4000 1.0
dNdz_model[76] 15.69 0.03 1.95 11.98 14.37 15.62 16.98 19.64 4000 1.0
dNdz_model[77] 15.31 0.03 1.96 11.59 13.99 15.22 16.6 19.3 4000 1.0
dNdz_model[78] 14.93 0.04 1.97 11.19 13.6 14.85 16.21 18.93 3041 1.0
dNdz_model[79] 14.56 0.04 1.98 10.83 13.22 14.48 15.84 18.61 2948 1.0
dNdz_model[80] 14.19 0.04 1.99 10.47 12.84 14.1 15.47 18.3 2862 1.0
dNdz_model[81] 13.83 0.04 2.0 10.11 12.47 13.74 15.12 17.94 2781 1.0
dNdz_model[82] 13.48 0.04 2.01 9.77 12.13 13.38 14.76 17.61 2707 1.0
dNdz_model[83] 13.13 0.04 2.01 9.44 11.77 13.02 14.42 17.3 2638 1.0
dNdz_model[84] 12.79 0.04 2.02 9.11 11.43 12.68 14.09 16.97 2574 1.0
dNdz_model[85] 12.46 0.04 2.03 8.79 11.08 12.34 13.75 16.69 2514 1.0
dNdz_model[86] 12.13 0.04 2.03 8.47 10.75 12.01 13.41 16.44 2459 1.0
dNdz_model[87] 11.82 0.04 2.03 8.14 10.41 11.68 13.1 16.16 2407 1.0
dNdz_model[88] 11.5 0.04 2.04 7.82 10.1 11.37 12.79 15.89 2359 1.0
dNdz_model[89] 11.2 0.04 2.04 7.52 9.79 11.06 12.47 15.62 2315 1.0
dNdz_model[90] 10.9 0.04 2.04 7.26 9.48 10.75 12.17 15.33 2273 1.0
dNdz_model[91] 10.61 0.04 2.04 6.99 9.19 10.45 11.89 15.05 2234 1.0
dNdz_model[92] 10.32 0.04 2.04 6.73 8.9 10.16 11.59 14.8 2198 1.0
dNdz_model[93] 10.04 0.04 2.04 6.47 8.62 9.88 11.31 14.53 2163 1.0
dNdz_model[94] 9.77 0.04 2.04 6.22 8.36 9.6 11.02 14.25 2130 1.0
dNdz_model[95] 9.5 0.04 2.03 5.98 8.09 9.32 10.75 13.98 2099 1.0
dNdz_model[96] 9.24 0.04 2.03 5.74 7.82 9.06 10.48 13.72 2069 1.0
dNdz_model[97] 8.98 0.04 2.02 5.52 7.57 8.79 10.21 13.43 2042 1.0
dNdz_model[98] 8.74 0.04 2.02 5.31 7.33 8.54 9.95 13.17 2016 1.0
dNdz_model[99] 8.49 0.05 2.01 5.1 7.09 8.28 9.69 12.93 1991 1.0
lp__ 491.28 0.35 12.23 466.36 483.15 491.78 499.61 514.09 1199 1.0
Samples were drawn using NUTS at Tue Jul 18 22:53:11 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
In [20]:
fit_errors.plot()
Out[20]:
In [21]:
chain_errors = fit_errors.extract(permuted=True)
Let's have a look at the inference for a random $z$. The solid line is $z_\mathrm{obs}$, the dashed line is $z_\mathrm{true}$, and the distribution is $p\left( z_\mathrm{true} \mid z_\mathrm{obs}, N_0, \alpha, \beta \right)$.
In [22]:
i = randint(len(zobs))
sns.distplot(chain_errors['ztrue'][:,i])
axvline(zobs[i])
axvline(ztrues[i], ls=':')
Out[22]:
<matplotlib.lines.Line2D at 0x1150d1a58>
And how about the inference for the population parameters? It looks pretty good, though the uncertainties are a bit larger than in the previous fit, since we don't measure the redshifts perfectly any more.
In [23]:
corner.corner(
column_stack([chain_errors[key] for key in ['N0', 'alpha', 'beta']]),
labels=[r'$N_0$', r'$\alpha$', r'$\beta$'],
truths=[Ntrue, alphatrue, betatrue]
);
The whole reason we generated the $\mathrm{d}N/\mathrm{d}z$ curves was to make this plot. We have in black the true redshift distribution, and in blue the fitted one. The line gives the posterior median value, and the dark and light bands give $1\sigma$ and $2\sigma$ (68% and 95%) credible intervals.
In [24]:
plot(data_errors['zs_model'], median(chain_errors['dNdz_model'], axis=0))
fill_between(data_errors['zs_model'], percentile(chain_errors['dNdz_model'], 84, axis=0), percentile(chain_errors['dNdz_model'], 16, axis=0), color=sns.color_palette()[0], alpha=0.25)
fill_between(data_errors['zs_model'], percentile(chain_errors['dNdz_model'], 97.5, axis=0), percentile(chain_errors['dNdz_model'], 2.5, axis=0), color=sns.color_palette()[0], alpha=0.25)
plot(data_errors['zs_model'], dNdz(data_errors['zs_model'], Ntrue, alphatrue, betatrue), '-k')
Out[24]:
[<matplotlib.lines.Line2D at 0x11b39f5f8>]
Let us suppose that our LIGO detector cannot measure signals at all redshifts (see above regarding the current range of Advanced LIGO; and note that even at design sensitivity the horizon distance is $z \simeq 1$ for BBH). To model this effect, we imagine that the detector can see an event only if $z_\mathrm{obs} \leq 6$ (obviously this is optimistic for aLIGO, but bear with me).
Note that the selection is applied to the data, not to the parameters. This is a realistic model for the functioning of a real detector; we filter data streams to find triggers for events. In fact, I cannot think of a pipeline that would use the true parameters to filter events---we don't know the true parameters, so how can we filter on them. The seemingly simple idea that the selection depends on the data leads to some counter-intuitive consequences in the likelihood function that we will use; for a fuller explanation of this effect, see Loredo (2004), Mandel, Farr & Gair (2016), or the appendices of Abbott, et al. (2016).
Our selection function will do three things:
Putting everything together, we finally arrive at the equation referenced above, the "fundamental equation of populations": $$ p\left( \left\{ d^{(i)}, \boldsymbol{\theta^{(i)}} \right\} \mid \boldsymbol{\lambda} \right) = \left[ \prod_{i} P_\mathrm{det}\left( d^{(i)} \mid \boldsymbol{\lambda} \right) p\left( d^{(i)} | \boldsymbol{\theta}^{(i)} \right) \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}}\left( \boldsymbol{\theta}^{(i)} \mid \boldsymbol{\lambda} \right) \right] \exp\left[ - \int \mathrm{d} d \mathrm{d}\boldsymbol{\theta} P_\mathrm{det}\left( d \mid \boldsymbol \lambda \right) p\left( d \mid \boldsymbol{\theta} \right) \frac{\mathrm{d}N}{\mathrm{d} \boldsymbol{\theta}}\left( \boldsymbol{\theta} \mid \lambda \right) \right]. $$
Something to note, because it is subtle: one might ask, why don't we just fit for the downselected distribution of source parameters, $$ \bar{P}_\mathrm{det} \left( \boldsymbol{\theta} \right) \frac{\mathrm{d} N}{\mathrm{d} \boldsymbol{\theta}}, $$ using one of the formalisms above (either with or without errors)? After all, that is what the observable distribution of $\theta$ "looks like." The reason is that this corresponds to a "just so" story where our pipelines select on the true parameters, $\boldsymbol{\theta}$. But these parameters are almost never accessible to our pipelines; rather, they look at the data, and make a decision whether to flag something as a "detection" or not. Since the selection is done on the data, it is not appropriate to try to modify the distribution of parameters appearing in the likelihood.
OK. Let's get fitting.
The probability that a source at redshift ztrue will be detected:
In [25]:
def Pbar_det(ztrue):
return 0.5*sp.erfc(5*(log(ztrue)-log(6))/sqrt(2))
Compile our Stan model:
In [27]:
model_selected = pystan.StanModel(file='zmodel_selected.stan')
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_819686cb66fbfca4816662b30d71cdeb NOW.
Now we down-select our data set:
In [28]:
zobs_selected = []
sigma_selected = []
for zo, s in zip(zobs, sigma):
if zo < 6:
zobs_selected.append(zo)
sigma_selected.append(s)
print('Selected {:d} out of {:d} sources'.format(len(zobs_selected), len(zobs)))
Selected 175 out of 228 sources
Just to check that the distribution is reasonable:
In [29]:
sns.distplot(zobs_selected)
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x1159ca588>
It is helpful, in considering what our fit might look like now (i.e. what uncertainties we might find, etc), to plot the distribution of true redshifts and the distribution of redshifts that would be found in selected sources. These differ by a factor of $\bar{P}_\mathrm{dec}(z)$. (Again, note that we aren't fitting for the down-selected population directly.) For example, if the location of the peak moved signifcantly due to the selection, we might have trouble fitting for the parameter $\beta$ since it controls the location of the peak.
Note that the expected number of detected events matches well with our expectation above.
In [30]:
zs = linspace(0, 10, 1000)[1:]
dNdz_true = dNdz(zs, Ntrue, alphatrue, betatrue)
dNdz_selected = dNdz_true*Pbar_det(zs)
plot(zs, dNdz_true, '-k', label='True')
plot(zs, dNdz_selected, label='Selected')
print('I expected {:.0f} sources in total, but to detect only {:.0f}.'.format(trapz(dNdz_true, zs), trapz(dNdz_selected, zs)))
I expected 237 sources in total, but to detect only 174.
In [31]:
data_selected = {
'nobs': len(zobs_selected),
'zobs': zobs_selected,
'sigmaobs': sigma_selected,
'nmodel': 100,
'zs_model': linspace(0,10,101)[1:]
}
In [32]:
fit_selected = model_selected.sampling(data=data_selected)
In [33]:
fit_selected
Out[33]:
Inference for Stan model: anon_model_819686cb66fbfca4816662b30d71cdeb.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
N0 12.16 0.11 3.91 5.89 9.36 11.62 14.52 21.02 1270 1.0
alpha 1.51 0.02 0.57 0.56 1.08 1.47 1.87 2.76 725 1.01
beta 3.91 0.08 2.95 1.43 2.26 3.03 4.53 11.43 1232 1.01
ztrue[0] 4.64 0.01 0.94 3.07 3.96 4.54 5.22 6.64 4000 1.0
ztrue[1] 2.01 6.1e-3 0.39 1.35 1.74 1.97 2.23 2.89 4000 1.0
ztrue[2] 1.49 4.8e-3 0.31 0.99 1.27 1.46 1.68 2.17 4000 1.0
ztrue[3] 1.89 6.0e-3 0.38 1.26 1.62 1.84 2.11 2.74 4000 1.0
ztrue[4] 4.6 0.01 0.9 3.13 3.95 4.51 5.14 6.6 4000 1.0
ztrue[5] 1.71 5.5e-3 0.35 1.13 1.46 1.67 1.93 2.47 4000 1.0
ztrue[6] 3.62 0.01 0.72 2.42 3.1 3.56 4.07 5.21 4000 1.0
ztrue[7] 4.18 0.01 0.82 2.8 3.61 4.1 4.66 6.03 4000 1.0
ztrue[8] 3.05 9.5e-3 0.6 2.05 2.61 2.99 3.42 4.44 4000 1.0
ztrue[9] 0.26 8.3e-4 0.05 0.17 0.22 0.26 0.29 0.38 4000 1.0
ztrue[10] 2.21 6.9e-3 0.44 1.47 1.9 2.17 2.46 3.2 4000 1.0
ztrue[11] 1.15 3.7e-3 0.24 0.76 0.99 1.13 1.29 1.69 4000 1.0
ztrue[12] 1.12 3.5e-3 0.22 0.75 0.96 1.1 1.25 1.64 4000 1.0
ztrue[13] 5.5 0.02 1.08 3.64 4.76 5.4 6.13 7.85 4000 1.0
ztrue[14] 1.02 3.2e-3 0.2 0.68 0.87 1.0 1.13 1.46 4000 1.0
ztrue[15] 5.55 0.02 1.06 3.75 4.78 5.46 6.2 7.9 4000 1.0
ztrue[16] 2.52 7.9e-3 0.5 1.67 2.18 2.47 2.82 3.65 4000 1.0
ztrue[17] 3.79 0.01 0.77 2.5 3.23 3.73 4.27 5.48 4000 1.0
ztrue[18] 0.82 2.6e-3 0.17 0.54 0.7 0.8 0.92 1.19 4000 1.0
ztrue[19] 1.64 5.4e-3 0.34 1.08 1.39 1.61 1.84 2.41 4000 1.0
ztrue[20] 1.09 3.5e-3 0.22 0.71 0.93 1.07 1.22 1.59 4000 1.0
ztrue[21] 1.23 3.9e-3 0.25 0.82 1.06 1.21 1.38 1.77 4000 1.0
ztrue[22] 5.56 0.02 1.04 3.79 4.83 5.46 6.18 7.99 4000 1.0
ztrue[23] 2.6 8.3e-3 0.52 1.71 2.23 2.56 2.93 3.72 4000 1.0
ztrue[24] 5.92 0.02 1.13 3.98 5.1 5.82 6.65 8.41 4000 1.0
ztrue[25] 4.48 0.01 0.87 2.99 3.87 4.41 5.02 6.36 4000 1.0
ztrue[26] 2.73 8.7e-3 0.55 1.81 2.34 2.67 3.07 3.93 4000 1.0
ztrue[27] 3.39 0.01 0.67 2.28 2.91 3.35 3.81 4.85 4000 1.0
ztrue[28] 3.61 0.01 0.72 2.37 3.09 3.55 4.06 5.25 4000 1.0
ztrue[29] 3.53 0.01 0.69 2.37 3.02 3.46 3.95 5.06 4000 1.0
ztrue[30] 3.29 0.01 0.65 2.19 2.82 3.24 3.69 4.7 4000 1.0
ztrue[31] 5.32 0.02 1.02 3.58 4.6 5.21 5.97 7.56 4000 1.0
ztrue[32] 4.26 0.01 0.84 2.84 3.65 4.19 4.78 6.13 4000 1.0
ztrue[33] 3.35 0.01 0.66 2.25 2.88 3.3 3.76 4.79 4000 1.0
ztrue[34] 5.17 0.02 1.01 3.43 4.46 5.09 5.78 7.46 4000 1.0
ztrue[35] 1.84 5.7e-3 0.36 1.23 1.58 1.8 2.06 2.62 4000 1.0
ztrue[36] 4.19 0.01 0.83 2.81 3.61 4.11 4.68 6.06 4000 1.0
ztrue[37] 2.16 6.8e-3 0.43 1.45 1.86 2.12 2.43 3.12 4000 1.0
ztrue[38] 1.9 6.1e-3 0.38 1.26 1.63 1.86 2.12 2.76 4000 1.0
ztrue[39] 2.67 8.5e-3 0.53 1.76 2.31 2.61 2.97 3.91 4000 1.0
ztrue[40] 5.61 0.02 1.11 3.79 4.82 5.52 6.27 8.09 4000 1.0
ztrue[41] 1.0 3.1e-3 0.2 0.66 0.86 0.99 1.13 1.43 4000 1.0
ztrue[42] 2.94 9.3e-3 0.59 1.96 2.52 2.9 3.3 4.23 4000 1.0
ztrue[43] 1.33 4.5e-3 0.28 0.86 1.13 1.3 1.5 1.95 4000 1.0
ztrue[44] 4.09 0.01 0.81 2.74 3.54 4.01 4.57 5.96 4000 1.0
ztrue[45] 4.41 0.01 0.83 2.99 3.82 4.33 4.92 6.22 4000 1.0
ztrue[46] 4.61 0.01 0.91 3.09 3.96 4.51 5.2 6.62 4000 1.0
ztrue[47] 4.25 0.01 0.85 2.79 3.66 4.17 4.78 6.15 4000 1.0
ztrue[48] 1.77 5.6e-3 0.35 1.16 1.52 1.74 1.99 2.57 4000 1.0
ztrue[49] 5.55 0.02 1.05 3.75 4.79 5.46 6.21 7.86 4000 1.0
ztrue[50] 5.98 0.02 1.2 3.93 5.14 5.86 6.68 8.71 4000 1.0
ztrue[51] 4.2 0.01 0.85 2.82 3.59 4.11 4.69 6.05 4000 1.0
ztrue[52] 3.46 0.01 0.68 2.33 2.96 3.4 3.88 5.0 4000 1.0
ztrue[53] 1.86 5.7e-3 0.36 1.26 1.61 1.83 2.08 2.68 4000 1.0
ztrue[54] 3.95 0.01 0.78 2.66 3.4 3.89 4.44 5.72 4000 1.0
ztrue[55] 3.38 0.01 0.69 2.23 2.9 3.33 3.81 4.86 4000 1.0
ztrue[56] 5.15 0.04 1.05 3.47 4.45 5.02 5.72 7.44 556 1.01
ztrue[57] 1.89 6.2e-3 0.39 1.23 1.61 1.86 2.13 2.78 4000 1.0
ztrue[58] 5.11 0.02 0.98 3.41 4.43 5.03 5.73 7.21 4000 1.0
ztrue[59] 1.26 3.9e-3 0.25 0.84 1.09 1.24 1.41 1.82 4000 1.0
ztrue[60] 4.36 0.01 0.87 2.93 3.74 4.27 4.88 6.31 4000 1.0
ztrue[61] 4.99 0.02 1.0 3.3 4.29 4.92 5.6 7.19 4000 1.0
ztrue[62] 6.06 0.02 1.13 4.13 5.25 5.94 6.8 8.52 4000 1.0
ztrue[63] 0.24 7.7e-4 0.05 0.15 0.2 0.23 0.27 0.34 4000 1.0
ztrue[64] 2.83 9.0e-3 0.57 1.88 2.44 2.78 3.17 4.08 4000 1.0
ztrue[65] 2.04 6.6e-3 0.42 1.34 1.74 1.99 2.29 2.96 4000 1.0
ztrue[66] 3.88 0.01 0.77 2.6 3.33 3.81 4.38 5.55 4000 1.0
ztrue[67] 1.96 6.1e-3 0.39 1.32 1.67 1.92 2.2 2.8 4000 1.0
ztrue[68] 4.36 0.01 0.84 2.97 3.79 4.28 4.84 6.27 4000 1.0
ztrue[69] 2.33 7.4e-3 0.47 1.54 2.0 2.29 2.6 3.42 4000 1.0
ztrue[70] 5.51 0.02 1.05 3.69 4.76 5.42 6.17 7.81 4000 1.0
ztrue[71] 1.77 5.4e-3 0.34 1.18 1.52 1.74 1.98 2.52 4000 1.0
ztrue[72] 3.29 0.01 0.66 2.18 2.83 3.22 3.66 4.8 4000 1.0
ztrue[73] 3.04 9.5e-3 0.6 2.03 2.62 2.99 3.41 4.37 4000 1.0
ztrue[74] 4.86 0.01 0.93 3.3 4.19 4.79 5.46 6.85 4000 1.0
ztrue[75] 2.92 9.3e-3 0.59 1.93 2.51 2.85 3.28 4.25 4000 1.0
ztrue[76] 4.65 0.01 0.88 3.15 4.02 4.58 5.2 6.6 4000 1.0
ztrue[77] 5.33 0.03 1.11 3.51 4.55 5.22 5.96 7.84 1237 1.0
ztrue[78] 5.67 0.02 1.08 3.85 4.93 5.56 6.31 8.08 4000 1.0
ztrue[79] 4.76 0.01 0.92 3.21 4.1 4.68 5.32 6.76 4000 1.0
ztrue[80] 5.35 0.02 1.03 3.61 4.61 5.26 5.99 7.63 4000 1.0
ztrue[81] 5.94 0.02 1.14 4.0 5.14 5.83 6.62 8.5 4000 1.0
ztrue[82] 2.18 6.9e-3 0.44 1.45 1.87 2.13 2.45 3.12 4000 1.0
ztrue[83] 3.64 0.01 0.73 2.42 3.1 3.58 4.1 5.24 4000 1.0
ztrue[84] 5.67 0.02 1.08 3.81 4.89 5.58 6.35 8.0 4000 1.0
ztrue[85] 2.43 7.7e-3 0.49 1.63 2.08 2.38 2.73 3.54 4000 1.0
ztrue[86] 4.83 0.01 0.94 3.23 4.14 4.74 5.41 6.88 4000 1.0
ztrue[87] 1.93 6.0e-3 0.38 1.28 1.66 1.89 2.16 2.78 4000 1.0
ztrue[88] 5.83 0.02 1.13 3.91 5.0 5.72 6.51 8.35 4000 1.0
ztrue[89] 4.02 0.01 0.79 2.68 3.46 3.95 4.52 5.74 4000 1.0
ztrue[90] 2.5 8.2e-3 0.52 1.62 2.13 2.45 2.82 3.63 4000 1.0
ztrue[91] 0.64 2.0e-3 0.13 0.42 0.54 0.62 0.71 0.92 4000 1.0
ztrue[92] 2.54 8.0e-3 0.51 1.69 2.17 2.49 2.84 3.7 4000 1.0
ztrue[93] 0.53 1.7e-3 0.11 0.35 0.45 0.52 0.59 0.76 4000 1.0
ztrue[94] 4.87 0.01 0.95 3.28 4.19 4.78 5.46 7.02 4000 1.0
ztrue[95] 5.77 0.02 1.14 3.86 4.95 5.65 6.42 8.36 4000 1.0
ztrue[96] 5.81 0.02 1.16 3.86 5.01 5.68 6.48 8.41 4000 1.0
ztrue[97] 1.29 4.1e-3 0.26 0.84 1.11 1.26 1.44 1.87 4000 1.0
ztrue[98] 0.95 3.0e-3 0.19 0.64 0.82 0.94 1.07 1.37 4000 1.0
ztrue[99] 2.34 7.4e-3 0.47 1.55 2.0 2.3 2.64 3.38 4000 1.0
ztrue[100] 1.76 5.7e-3 0.36 1.15 1.51 1.72 1.96 2.59 4000 1.0
ztrue[101] 4.9 0.02 0.96 3.31 4.21 4.82 5.48 6.99 4000 1.0
ztrue[102] 0.13 4.1e-4 0.03 0.09 0.11 0.13 0.15 0.19 4000 1.0
ztrue[103] 3.53 0.01 0.7 2.31 3.05 3.46 3.93 5.1 4000 1.0
ztrue[104] 2.63 8.5e-3 0.54 1.73 2.26 2.58 2.95 3.84 4000 1.0
ztrue[105] 0.49 1.6e-3 0.1 0.33 0.42 0.48 0.55 0.72 4000 1.0
ztrue[106] 1.3 4.1e-3 0.26 0.87 1.12 1.28 1.45 1.89 4000 1.0
ztrue[107] 3.07 9.9e-3 0.63 2.06 2.62 3.01 3.44 4.45 4000 1.0
ztrue[108] 3.5 0.01 0.69 2.34 3.01 3.43 3.91 5.05 4000 1.0
ztrue[109] 3.08 9.8e-3 0.62 2.01 2.64 3.02 3.45 4.5 4000 1.0
ztrue[110] 2.99 9.9e-3 0.63 1.96 2.54 2.92 3.35 4.43 4000 1.0
ztrue[111] 1.6 5.1e-3 0.32 1.05 1.37 1.57 1.79 2.31 4000 1.0
ztrue[112] 3.76 0.01 0.74 2.51 3.25 3.7 4.22 5.46 4000 1.0
ztrue[113] 1.86 5.9e-3 0.38 1.24 1.59 1.82 2.08 2.69 4000 1.0
ztrue[114] 2.95 9.2e-3 0.58 1.99 2.54 2.89 3.29 4.27 4000 1.0
ztrue[115] 2.24 7.0e-3 0.44 1.49 1.94 2.2 2.51 3.26 4000 1.0
ztrue[116] 4.74 0.01 0.9 3.22 4.09 4.67 5.31 6.7 4000 1.0
ztrue[117] 4.75 0.01 0.94 3.19 4.09 4.66 5.31 6.8 4000 1.0
ztrue[118] 5.78 0.02 1.12 3.85 4.96 5.69 6.47 8.33 4000 1.0
ztrue[119] 4.49 0.01 0.9 3.02 3.84 4.4 5.04 6.43 4000 1.0
ztrue[120] 0.53 1.7e-3 0.11 0.35 0.45 0.51 0.59 0.77 4000 1.0
ztrue[121] 1.38 4.3e-3 0.27 0.92 1.19 1.35 1.54 1.99 4000 1.0
ztrue[122] 3.37 0.01 0.69 2.23 2.88 3.3 3.8 4.95 4000 1.0
ztrue[123] 3.95 0.01 0.78 2.64 3.41 3.87 4.39 5.79 4000 1.0
ztrue[124] 5.29 0.02 1.02 3.56 4.55 5.19 5.93 7.53 4000 1.0
ztrue[125] 4.96 0.02 0.96 3.33 4.24 4.85 5.55 7.12 4000 1.0
ztrue[126] 1.71 5.3e-3 0.33 1.15 1.47 1.68 1.91 2.43 4000 1.0
ztrue[127] 5.56 0.02 1.13 3.74 4.74 5.44 6.23 8.13 4000 1.0
ztrue[128] 5.44 0.02 1.08 3.63 4.68 5.33 6.11 7.73 4000 1.0
ztrue[129] 5.8 0.02 1.17 3.78 4.91 5.72 6.57 8.3 4000 1.0
ztrue[130] 1.48 4.8e-3 0.31 0.98 1.26 1.45 1.66 2.18 4000 1.0
ztrue[131] 4.01 0.01 0.8 2.65 3.44 3.93 4.5 5.78 4000 1.0
ztrue[132] 5.42 0.02 1.06 3.66 4.65 5.32 6.09 7.77 4000 1.0
ztrue[133] 5.09 0.02 1.02 3.35 4.37 5.0 5.7 7.35 4000 1.0
ztrue[134] 4.2 0.01 0.85 2.77 3.6 4.11 4.7 6.08 4000 1.0
ztrue[135] 5.86 0.02 1.14 3.91 5.06 5.76 6.58 8.32 4000 1.0
ztrue[136] 1.75 5.6e-3 0.35 1.16 1.5 1.71 1.96 2.54 4000 1.0
ztrue[137] 2.54 8.0e-3 0.5 1.7 2.19 2.49 2.84 3.66 4000 1.0
ztrue[138] 3.52 0.01 0.7 2.35 3.03 3.44 3.95 5.06 4000 1.0
ztrue[139] 5.68 0.02 1.08 3.82 4.92 5.56 6.36 8.06 4000 1.0
ztrue[140] 2.54 7.9e-3 0.5 1.68 2.18 2.51 2.85 3.65 4000 1.0
ztrue[141] 3.89 0.01 0.78 2.58 3.35 3.8 4.35 5.65 4000 1.0
ztrue[142] 1.25 4.0e-3 0.25 0.82 1.06 1.22 1.4 1.82 4000 1.0
ztrue[143] 3.23 0.01 0.64 2.16 2.78 3.17 3.61 4.65 4000 1.0
ztrue[144] 4.82 0.01 0.94 3.21 4.15 4.74 5.41 6.85 4000 1.0
ztrue[145] 1.12 3.6e-3 0.23 0.75 0.96 1.11 1.26 1.64 4000 1.0
ztrue[146] 2.01 6.3e-3 0.4 1.34 1.72 1.98 2.26 2.89 4000 1.0
ztrue[147] 2.89 9.2e-3 0.58 1.9 2.49 2.84 3.24 4.2 4000 1.0
ztrue[148] 1.93 6.3e-3 0.4 1.27 1.64 1.9 2.17 2.83 4000 1.0
ztrue[149] 5.07 0.02 1.02 3.32 4.33 4.98 5.69 7.35 4000 1.0
ztrue[150] 5.41 0.02 1.04 3.71 4.67 5.3 6.03 7.73 4000 1.0
ztrue[151] 3.15 9.7e-3 0.62 2.12 2.71 3.09 3.51 4.52 4000 1.0
ztrue[152] 4.02 0.01 0.77 2.73 3.47 3.95 4.48 5.72 4000 1.0
ztrue[153] 0.4 1.3e-3 0.08 0.26 0.34 0.39 0.45 0.58 4000 1.0
ztrue[154] 4.44 0.01 0.84 3.03 3.84 4.37 4.97 6.19 4000 1.0
ztrue[155] 5.94 0.02 1.13 4.0 5.11 5.85 6.67 8.44 4000 1.0
ztrue[156] 2.55 8.0e-3 0.51 1.73 2.18 2.5 2.86 3.69 4000 1.0
ztrue[157] 2.88 9.0e-3 0.57 1.91 2.48 2.83 3.22 4.16 4000 1.0
ztrue[158] 4.28 0.01 0.88 2.82 3.66 4.18 4.81 6.2 4000 1.0
ztrue[159] 3.1 9.7e-3 0.61 2.07 2.66 3.03 3.47 4.42 4000 1.0
ztrue[160] 5.01 0.02 1.0 3.37 4.31 4.91 5.62 7.25 4000 1.0
ztrue[161] 2.24 7.1e-3 0.45 1.47 1.92 2.19 2.53 3.22 4000 1.0
ztrue[162] 4.39 0.01 0.87 2.91 3.77 4.31 4.93 6.27 4000 1.0
ztrue[163] 4.8 0.02 0.97 3.18 4.11 4.69 5.38 7.05 4000 1.0
ztrue[164] 3.41 0.01 0.7 2.28 2.91 3.35 3.84 4.98 4000 1.0
ztrue[165] 3.72 0.01 0.75 2.46 3.19 3.65 4.18 5.41 4000 1.0
ztrue[166] 4.69 0.01 0.95 3.16 4.04 4.59 5.22 6.84 4000 1.0
ztrue[167] 1.9 6.1e-3 0.39 1.27 1.62 1.86 2.14 2.76 4000 1.0
ztrue[168] 0.46 1.4e-3 0.09 0.3 0.39 0.45 0.51 0.66 4000 1.0
ztrue[169] 1.75 5.6e-3 0.35 1.15 1.5 1.71 1.96 2.52 4000 1.0
ztrue[170] 1.08 3.4e-3 0.22 0.71 0.92 1.06 1.21 1.57 4000 1.0
ztrue[171] 4.81 0.01 0.94 3.23 4.15 4.73 5.38 6.85 4000 1.0
ztrue[172] 2.5 8.1e-3 0.51 1.65 2.13 2.44 2.8 3.66 4000 1.0
ztrue[173] 0.04 1.1e-4 7.1e-3 0.02 0.03 0.03 0.04 0.05 4000 1.0
ztrue[174] 5.62 0.02 1.09 3.77 4.84 5.51 6.29 8.05 4000 1.0
dNdz_model[0] 13.42 0.11 3.86 7.13 10.66 12.98 15.79 22.04 1339 1.0
dNdz_model[1] 14.67 0.1 3.79 8.37 11.97 14.27 17.03 23.12 1431 1.0
dNdz_model[2] 15.9 0.09 3.7 9.59 13.26 15.59 18.21 24.07 1552 1.0
dNdz_model[3] 17.1 0.09 3.61 10.91 14.58 16.79 19.33 25.01 1767 1.0
dNdz_model[4] 18.26 0.08 3.51 12.13 15.84 17.99 20.46 25.92 1955 1.0
dNdz_model[5] 19.39 0.07 3.42 13.37 17.05 19.15 21.56 26.7 2195 1.0
dNdz_model[6] 20.49 0.05 3.34 14.51 18.23 20.29 22.59 27.6 4000 1.0
dNdz_model[7] 21.54 0.05 3.26 15.61 19.31 21.34 23.6 28.48 4000 1.0
dNdz_model[8] 22.56 0.05 3.21 16.63 20.38 22.34 24.59 29.27 4000 1.0
dNdz_model[9] 23.53 0.05 3.16 17.73 21.39 23.33 25.56 30.13 4000 1.0
dNdz_model[10] 24.45 0.05 3.14 18.72 22.34 24.28 26.47 31.06 4000 1.0
dNdz_model[11] 25.33 0.05 3.13 19.56 23.21 25.13 27.39 31.92 4000 1.0
dNdz_model[12] 26.16 0.05 3.13 20.45 24.02 25.95 28.22 32.63 4000 1.0
dNdz_model[13] 26.95 0.05 3.15 21.23 24.81 26.72 29.03 33.44 4000 1.0
dNdz_model[14] 27.69 0.05 3.17 21.9 25.53 27.45 29.79 34.26 4000 1.0
dNdz_model[15] 28.38 0.05 3.21 22.51 26.2 28.15 30.49 35.13 4000 1.0
dNdz_model[16] 29.03 0.05 3.24 23.11 26.79 28.79 31.13 35.9 4000 1.0
dNdz_model[17] 29.63 0.05 3.28 23.69 27.36 29.4 31.73 36.57 4000 1.0
dNdz_model[18] 30.19 0.07 3.31 24.25 27.89 29.97 32.28 37.19 2365 1.0
dNdz_model[19] 30.7 0.08 3.34 24.74 28.35 30.48 32.8 37.76 1862 1.0
dNdz_model[20] 31.17 0.08 3.37 25.19 28.79 30.9 33.31 38.27 1752 1.0
dNdz_model[21] 31.6 0.08 3.39 25.61 29.19 31.33 33.72 38.72 1668 1.0
dNdz_model[22] 31.98 0.09 3.41 25.95 29.57 31.74 34.12 39.09 1604 1.0
dNdz_model[23] 32.33 0.09 3.42 26.29 29.89 32.09 34.48 39.53 1559 1.0
dNdz_model[24] 32.63 0.09 3.42 26.56 30.2 32.44 34.76 39.81 1528 1.0
dNdz_model[25] 32.9 0.09 3.41 26.84 30.47 32.7 35.03 40.06 1511 1.0
dNdz_model[26] 33.13 0.09 3.4 27.08 30.72 32.94 35.26 40.21 1507 1.0
dNdz_model[27] 33.33 0.09 3.38 27.3 30.93 33.15 35.44 40.33 1513 1.0
dNdz_model[28] 33.49 0.09 3.36 27.53 31.12 33.32 35.56 40.41 1531 1.0
dNdz_model[29] 33.62 0.08 3.33 27.69 31.28 33.46 35.73 40.54 1648 1.0
dNdz_model[30] 33.72 0.08 3.3 27.79 31.41 33.54 35.83 40.48 1685 1.0
dNdz_model[31] 33.79 0.08 3.26 27.86 31.49 33.62 35.92 40.45 1849 1.0
dNdz_model[32] 33.83 0.07 3.22 27.9 31.57 33.67 35.96 40.45 1908 1.0
dNdz_model[33] 33.84 0.07 3.19 27.98 31.6 33.68 35.95 40.38 1983 1.0
dNdz_model[34] 33.83 0.07 3.15 28.07 31.63 33.66 35.91 40.3 2077 1.0
dNdz_model[35] 33.79 0.07 3.11 28.06 31.63 33.65 35.84 40.17 2191 1.0
dNdz_model[36] 33.73 0.06 3.08 28.01 31.58 33.58 35.75 40.07 2504 1.0
dNdz_model[37] 33.64 0.06 3.05 27.99 31.51 33.52 35.63 39.97 2636 1.0
dNdz_model[38] 33.54 0.06 3.03 27.84 31.44 33.41 35.47 39.72 2785 1.0
dNdz_model[39] 33.41 0.06 3.02 27.76 31.31 33.29 35.32 39.56 2946 1.0
dNdz_model[40] 33.27 0.05 3.01 27.57 31.18 33.14 35.18 39.43 3114 1.0
dNdz_model[41] 33.11 0.05 3.01 27.4 31.03 32.97 35.04 39.29 3280 1.0
dNdz_model[42] 32.93 0.05 3.03 27.24 30.85 32.8 34.85 39.11 3625 1.0
dNdz_model[43] 32.74 0.05 3.05 27.02 30.63 32.61 34.69 38.99 3700 1.0
dNdz_model[44] 32.53 0.05 3.08 26.72 30.43 32.42 34.52 38.89 3752 1.0
dNdz_model[45] 32.31 0.05 3.12 26.42 30.17 32.2 34.33 38.79 3777 1.0
dNdz_model[46] 32.08 0.05 3.18 26.15 29.92 31.94 34.16 38.58 4000 1.0
dNdz_model[47] 31.83 0.05 3.24 25.79 29.63 31.68 33.94 38.45 4000 1.0
dNdz_model[48] 31.57 0.05 3.31 25.46 29.31 31.47 33.76 38.37 4000 1.0
dNdz_model[49] 31.31 0.06 3.39 25.03 28.98 31.21 33.56 38.21 3586 1.0
dNdz_model[50] 31.03 0.06 3.48 24.6 28.66 30.95 33.35 38.1 3478 1.0
dNdz_model[51] 30.75 0.06 3.58 24.06 28.31 30.67 33.13 38.02 3363 1.0
dNdz_model[52] 30.46 0.07 3.68 23.54 27.92 30.39 32.91 37.92 3011 1.0
dNdz_model[53] 30.16 0.07 3.78 23.03 27.54 30.11 32.7 37.71 2881 1.0
dNdz_model[54] 29.86 0.07 3.9 22.49 27.14 29.8 32.48 37.75 2760 1.0
dNdz_model[55] 29.55 0.08 4.01 22.01 26.75 29.48 32.25 37.71 2647 1.0
dNdz_model[56] 29.23 0.08 4.13 21.45 26.34 29.14 32.02 37.6 2545 1.0
dNdz_model[57] 28.91 0.09 4.25 20.89 25.96 28.83 31.77 37.56 2453 1.0
dNdz_model[58] 28.59 0.09 4.37 20.29 25.55 28.53 31.53 37.52 2369 1.0
dNdz_model[59] 28.26 0.09 4.49 19.7 25.11 28.22 31.28 37.48 2294 1.0
dNdz_model[60] 27.93 0.1 4.62 19.15 24.7 27.89 31.02 37.43 2226 1.0
dNdz_model[61] 27.6 0.1 4.74 18.64 24.29 27.52 30.74 37.39 2160 1.0
dNdz_model[62] 27.27 0.11 4.86 18.08 23.88 27.18 30.49 37.28 2101 1.0
dNdz_model[63] 26.93 0.11 4.98 17.54 23.47 26.83 30.24 37.19 2048 1.0
dNdz_model[64] 26.6 0.11 5.1 16.99 23.06 26.52 29.93 37.03 2000 1.0
dNdz_model[65] 26.26 0.12 5.22 16.45 22.61 26.16 29.65 36.91 1958 1.0
dNdz_model[66] 25.92 0.12 5.34 15.91 22.2 25.8 29.37 36.81 1919 1.0
dNdz_model[67] 25.59 0.13 5.46 15.38 21.77 25.42 29.11 36.7 1884 1.0
dNdz_model[68] 25.25 0.13 5.57 14.9 21.35 25.06 28.85 36.61 1853 1.01
dNdz_model[69] 24.91 0.13 5.68 14.38 20.94 24.71 28.58 36.54 1824 1.01
dNdz_model[70] 24.58 0.16 5.79 13.93 20.51 24.35 28.28 36.48 1380 1.01
dNdz_model[71] 24.24 0.16 5.9 13.46 20.07 24.01 28.02 36.46 1365 1.01
dNdz_model[72] 23.91 0.16 6.0 13.0 19.65 23.64 27.74 36.31 1352 1.01
dNdz_model[73] 23.58 0.17 6.11 12.54 19.24 23.28 27.48 36.23 1340 1.01
dNdz_model[74] 23.25 0.17 6.2 12.11 18.83 22.92 27.2 36.13 1329 1.01
dNdz_model[75] 22.92 0.17 6.3 11.7 18.43 22.56 26.93 36.01 1320 1.01
dNdz_model[76] 22.6 0.18 6.39 11.24 18.01 22.21 26.68 35.9 1311 1.01
dNdz_model[77] 22.27 0.19 6.48 10.77 17.62 21.83 26.4 35.85 1209 1.01
dNdz_model[78] 21.95 0.19 6.57 10.35 17.24 21.47 26.15 35.78 1203 1.01
dNdz_model[79] 21.64 0.19 6.65 9.97 16.88 21.13 25.86 35.68 1197 1.01
dNdz_model[80] 21.32 0.19 6.73 9.6 16.51 20.78 25.57 35.56 1193 1.01
dNdz_model[81] 21.01 0.2 6.81 9.23 16.12 20.45 25.28 35.45 1188 1.01
dNdz_model[82] 20.7 0.2 6.88 8.89 15.75 20.11 25.01 35.38 1185 1.01
dNdz_model[83] 20.39 0.2 6.95 8.52 15.4 19.78 24.73 35.27 1181 1.01
dNdz_model[84] 20.09 0.2 7.02 8.17 15.04 19.45 24.47 35.2 1179 1.01
dNdz_model[85] 19.79 0.21 7.08 7.85 14.66 19.13 24.19 35.1 1176 1.01
dNdz_model[86] 19.49 0.21 7.14 7.55 14.31 18.81 23.91 34.96 1174 1.01
dNdz_model[87] 19.2 0.21 7.2 7.23 13.96 18.46 23.65 34.9 1172 1.01
dNdz_model[88] 18.91 0.21 7.26 6.94 13.6 18.12 23.37 34.87 1171 1.01
dNdz_model[89] 18.62 0.21 7.31 6.65 13.27 17.8 23.11 34.74 1169 1.01
dNdz_model[90] 18.34 0.22 7.36 6.4 12.94 17.49 22.82 34.63 1150 1.01
dNdz_model[91] 18.06 0.22 7.41 6.12 12.64 17.18 22.56 34.56 1149 1.01
dNdz_model[92] 17.78 0.22 7.45 5.87 12.31 16.88 22.29 34.46 1148 1.01
dNdz_model[93] 17.51 0.22 7.49 5.62 12.02 16.57 22.05 34.36 1148 1.01
dNdz_model[94] 17.24 0.22 7.53 5.38 11.7 16.26 21.79 34.26 1147 1.01
dNdz_model[95] 16.97 0.22 7.57 5.15 11.4 15.96 21.54 34.12 1147 1.01
dNdz_model[96] 16.71 0.22 7.6 4.92 11.1 15.67 21.26 33.98 1147 1.01
dNdz_model[97] 16.45 0.23 7.63 4.71 10.81 15.38 20.98 33.87 1147 1.01
dNdz_model[98] 16.2 0.23 7.66 4.5 10.52 15.09 20.72 33.76 1147 1.01
dNdz_model[99] 15.95 0.23 7.69 4.31 10.24 14.8 20.47 33.64 1147 1.01
lp__ 420.93 0.33 10.63 399.16 413.91 421.51 428.41 440.8 1050 1.01
Samples were drawn using NUTS at Tue Jul 18 22:56:38 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
In [34]:
fit_selected.plot()
Out[34]:
In [35]:
chain_selected = fit_selected.extract(permuted=True)
In [36]:
corner.corner(
column_stack([chain_selected[key] for key in ['N0', 'alpha', 'beta']]),
labels=[r'$N_0$', r'$\alpha$', r'$\beta$'],
truths=[Ntrue, alphatrue, betatrue]
);
In [37]:
plot(data_selected['zs_model'], median(chain_selected['dNdz_model'], axis=0))
fill_between(data_selected['zs_model'], percentile(chain_selected['dNdz_model'], 84, axis=0), percentile(chain_selected['dNdz_model'], 16, axis=0), color=sns.color_palette()[0], alpha=0.25)
fill_between(data_selected['zs_model'], percentile(chain_selected['dNdz_model'], 97.5, axis=0), percentile(chain_selected['dNdz_model'], 2.5, axis=0), color=sns.color_palette()[0], alpha=0.25)
plot(data_selected['zs_model'], dNdz(data_selected['zs_model'], Ntrue, alphatrue, betatrue), '-k')
Out[37]:
[<matplotlib.lines.Line2D at 0x11ec81278>]
Now suppose that we forget that the down-selection was done for $z_\mathrm{obs} < 6$, and want to know the threshold redshift. We can introduce a new parameter into $\boldsymbol{\lambda} = \left\{ N_0, \alpha, \beta, z_\mathrm{th} \right\}$ where $$ P_\mathrm{det} \left( z_\mathrm{obs} | \boldsymbol{\lambda} \right) = \begin{cases} 1 & z_\mathrm{obs} \leq z_\mathrm{th} \\ 0 & z_\mathrm{obs} > z_\mathrm{th} \end{cases} $$ (One could imagine modelling the selection with a smoother curve, perhaps because it depends on some additional data not included in the model such as binary orientation, or detector state, or some such. But here we will keep it "simple.")
Note that if $z_\mathrm{th} < z_\mathrm{obs}^{(i)}$ for some actually observed system (number $i$) the likelihood becomes zero (this is why the $P_\mathrm{det}$ term is still required, even though it is "always" 1 for the observed systems); thus $z_\mathrm{th}$ is constrained to be greater than the largest $z_\mathrm{obs}$. We will actually implement the $P_\mathrm{det}$ term in the product by including this constraint in the $z_\mathrm{th}$ parameter (i.e. it won't appear explicitly in the part of the Stan model that iterates over detections) because Stan is not happy if the likelihood suddenly drops to zero. ($P_\mathrm{det}$ still does appear in the integral to compute the expected number of events!)
In [40]:
model_selected_fit = pystan.StanModel(file='zmodel_selected_fit.stan')
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_279285176a01bbaa7c3b3ef7e5af3959 NOW.
We can fit the same data set as before; we just have a new parameter:
In [48]:
fit_selected_fit = model_selected_fit.sampling(data=data_selected, iter=4000, thin=2)
In [49]:
fit_selected_fit
Out[49]:
Inference for Stan model: anon_model_279285176a01bbaa7c3b3ef7e5af3959.
4 chains, each with iter=4000; warmup=2000; thin=2;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
N0 12.2 0.07 3.74 5.99 9.5 11.83 14.52 20.55 2626 1.0
alpha 1.44 0.01 0.54 0.56 1.05 1.38 1.77 2.64 2442 1.0
beta 4.31 0.07 3.63 1.55 2.45 3.38 4.94 12.58 2877 1.0
zth 5.94 5.9e-4 0.04 5.9 5.91 5.93 5.95 6.03 3688 1.0
ztrue[0] 4.65 0.02 0.91 3.12 3.97 4.58 5.22 6.59 3250 1.0
ztrue[1] 2.01 6.9e-3 0.4 1.34 1.71 1.96 2.25 2.91 3408 1.0
ztrue[2] 1.5 5.2e-3 0.3 0.99 1.28 1.47 1.68 2.17 3490 1.0
ztrue[3] 1.88 6.3e-3 0.38 1.25 1.61 1.84 2.11 2.72 3662 1.0
ztrue[4] 4.62 0.02 0.9 3.11 3.98 4.53 5.18 6.62 3295 1.0
ztrue[5] 1.71 5.9e-3 0.34 1.14 1.47 1.68 1.92 2.46 3261 1.0
ztrue[6] 3.62 0.01 0.71 2.43 3.11 3.57 4.04 5.21 3427 1.0
ztrue[7] 4.2 0.01 0.84 2.79 3.61 4.1 4.69 6.15 3322 1.0
ztrue[8] 3.05 0.01 0.61 2.01 2.61 2.99 3.42 4.38 3429 1.0
ztrue[9] 0.26 8.9e-4 0.05 0.17 0.22 0.25 0.29 0.38 3513 1.0
ztrue[10] 2.22 7.7e-3 0.44 1.5 1.9 2.17 2.48 3.24 3311 1.0
ztrue[11] 1.16 4.1e-3 0.24 0.76 0.99 1.14 1.31 1.68 3364 1.0
ztrue[12] 1.12 4.0e-3 0.23 0.74 0.96 1.09 1.25 1.61 3216 1.0
ztrue[13] 5.56 0.02 1.07 3.73 4.79 5.45 6.21 7.9 3220 1.0
ztrue[14] 1.02 3.7e-3 0.21 0.67 0.87 1.0 1.14 1.48 3091 1.0
ztrue[15] 5.58 0.02 1.07 3.78 4.81 5.49 6.26 7.95 3078 1.0
ztrue[16] 2.52 8.7e-3 0.5 1.69 2.17 2.47 2.83 3.64 3316 1.0
ztrue[17] 3.82 0.01 0.76 2.59 3.29 3.74 4.31 5.52 3196 1.0
ztrue[18] 0.82 2.7e-3 0.16 0.55 0.7 0.8 0.91 1.17 3459 1.0
ztrue[19] 1.63 5.6e-3 0.32 1.1 1.4 1.6 1.83 2.34 3365 1.0
ztrue[20] 1.09 3.7e-3 0.22 0.73 0.94 1.07 1.23 1.57 3543 1.0
ztrue[21] 1.24 4.5e-3 0.25 0.82 1.05 1.21 1.39 1.81 3140 1.0
ztrue[22] 5.62 0.02 1.1 3.71 4.83 5.51 6.29 8.07 3465 1.0
ztrue[23] 2.61 9.3e-3 0.52 1.71 2.24 2.56 2.92 3.78 3209 1.0
ztrue[24] 5.96 0.02 1.14 4.03 5.15 5.86 6.67 8.5 2942 1.0
ztrue[25] 4.5 0.02 0.91 3.01 3.85 4.43 5.05 6.52 3012 1.0
ztrue[26] 2.74 9.5e-3 0.55 1.83 2.34 2.68 3.07 4.0 3427 1.0
ztrue[27] 3.4 0.01 0.67 2.28 2.92 3.34 3.81 4.94 3318 1.0
ztrue[28] 3.61 0.01 0.71 2.43 3.09 3.54 4.05 5.15 3505 1.0
ztrue[29] 3.55 0.01 0.71 2.34 3.04 3.48 3.98 5.14 3071 1.0
ztrue[30] 3.29 0.01 0.65 2.19 2.83 3.23 3.68 4.74 3494 1.0
ztrue[31] 5.35 0.02 1.04 3.57 4.62 5.27 5.96 7.61 3011 1.0
ztrue[32] 4.29 0.02 0.86 2.87 3.67 4.2 4.82 6.21 2763 1.0
ztrue[33] 3.37 0.01 0.69 2.23 2.89 3.32 3.79 4.94 2949 1.0
ztrue[34] 5.18 0.02 1.02 3.49 4.43 5.09 5.78 7.46 3141 1.0
ztrue[35] 1.83 6.1e-3 0.36 1.23 1.57 1.8 2.05 2.61 3490 1.0
ztrue[36] 4.19 0.01 0.82 2.83 3.62 4.12 4.68 6.08 3298 1.0
ztrue[37] 2.16 7.6e-3 0.43 1.43 1.85 2.11 2.42 3.12 3259 1.0
ztrue[38] 1.91 6.9e-3 0.39 1.26 1.62 1.87 2.13 2.78 3251 1.0
ztrue[39] 2.67 9.7e-3 0.54 1.75 2.29 2.61 3.0 3.85 3119 1.0
ztrue[40] 5.62 0.02 1.1 3.73 4.83 5.53 6.31 8.01 2950 1.0
ztrue[41] 1.01 3.5e-3 0.2 0.65 0.86 0.99 1.13 1.44 3388 1.0
ztrue[42] 2.95 0.01 0.6 1.98 2.52 2.89 3.3 4.29 3340 1.0
ztrue[43] 1.33 4.5e-3 0.26 0.87 1.15 1.31 1.48 1.89 3365 1.0
ztrue[44] 4.1 0.01 0.81 2.72 3.52 4.04 4.59 5.85 3095 1.0
ztrue[45] 4.42 0.01 0.86 3.0 3.81 4.32 4.93 6.34 3465 1.0
ztrue[46] 4.62 0.02 0.91 3.09 3.97 4.53 5.17 6.63 3342 1.0
ztrue[47] 4.28 0.01 0.82 2.85 3.7 4.22 4.8 6.02 3492 1.0
ztrue[48] 1.76 6.2e-3 0.36 1.15 1.51 1.72 1.98 2.57 3367 1.0
ztrue[49] 5.59 0.02 1.1 3.75 4.8 5.51 6.25 7.99 2968 1.0
ztrue[50] 5.96 0.02 1.13 4.07 5.15 5.85 6.65 8.55 2807 1.0
ztrue[51] 4.2 0.01 0.83 2.83 3.6 4.13 4.69 6.07 3137 1.0
ztrue[52] 3.45 0.01 0.71 2.27 2.95 3.41 3.86 5.04 3013 1.0
ztrue[53] 1.88 6.3e-3 0.37 1.24 1.61 1.84 2.11 2.69 3508 1.0
ztrue[54] 3.96 0.01 0.78 2.65 3.41 3.9 4.42 5.69 3374 1.0
ztrue[55] 3.4 0.01 0.68 2.24 2.91 3.34 3.82 4.91 3408 1.0
ztrue[56] 5.15 0.02 1.04 3.46 4.41 5.03 5.76 7.5 2655 1.0
ztrue[57] 1.89 7.1e-3 0.39 1.24 1.62 1.85 2.13 2.76 3023 1.0
ztrue[58] 5.12 0.02 1.0 3.45 4.4 5.02 5.74 7.32 3247 1.0
ztrue[59] 1.27 4.4e-3 0.26 0.83 1.08 1.24 1.43 1.85 3557 1.0
ztrue[60] 4.35 0.02 0.86 2.9 3.74 4.26 4.88 6.23 3227 1.0
ztrue[61] 4.99 0.02 1.0 3.33 4.29 4.89 5.55 7.24 3126 1.0
ztrue[62] 6.05 0.02 1.17 4.05 5.21 5.95 6.74 8.75 2861 1.0
ztrue[63] 0.23 8.4e-4 0.05 0.15 0.2 0.23 0.26 0.34 3209 1.0
ztrue[64] 2.84 9.9e-3 0.58 1.87 2.43 2.78 3.17 4.11 3350 1.0
ztrue[65] 2.03 7.0e-3 0.41 1.36 1.75 1.99 2.29 2.98 3445 1.0
ztrue[66] 3.89 0.01 0.78 2.6 3.35 3.8 4.35 5.64 3287 1.0
ztrue[67] 1.96 6.8e-3 0.39 1.32 1.68 1.92 2.21 2.81 3314 1.0
ztrue[68] 4.4 0.02 0.89 2.93 3.75 4.3 4.93 6.38 3013 1.0
ztrue[69] 2.35 8.1e-3 0.47 1.57 2.02 2.3 2.63 3.4 3322 1.0
ztrue[70] 5.54 0.02 1.06 3.72 4.79 5.44 6.19 7.92 3037 1.0
ztrue[71] 1.77 6.0e-3 0.35 1.19 1.52 1.74 1.98 2.57 3434 1.0
ztrue[72] 3.28 0.01 0.67 2.18 2.81 3.2 3.68 4.75 3170 1.0
ztrue[73] 3.03 0.01 0.63 2.0 2.58 2.96 3.42 4.42 3169 1.0
ztrue[74] 4.86 0.02 0.94 3.28 4.19 4.76 5.44 6.99 2944 1.0
ztrue[75] 2.9 0.01 0.59 1.92 2.48 2.85 3.26 4.25 3329 1.0
ztrue[76] 4.68 0.02 0.92 3.13 4.03 4.61 5.25 6.71 2915 1.0
ztrue[77] 5.32 0.02 1.01 3.62 4.59 5.21 5.96 7.55 2997 1.0
ztrue[78] 5.68 0.02 1.1 3.77 4.92 5.56 6.35 8.11 2897 1.0
ztrue[79] 4.74 0.02 0.94 3.19 4.08 4.65 5.31 6.85 2640 1.0
ztrue[80] 5.35 0.02 1.04 3.58 4.62 5.22 6.0 7.67 3349 1.0
ztrue[81] 5.95 0.02 1.12 4.03 5.14 5.86 6.68 8.39 3017 1.0
ztrue[82] 2.16 7.7e-3 0.43 1.42 1.86 2.12 2.43 3.1 3139 1.0
ztrue[83] 3.65 0.01 0.72 2.45 3.14 3.57 4.09 5.25 3223 1.0
ztrue[84] 5.7 0.02 1.12 3.79 4.91 5.61 6.37 8.21 3076 1.0
ztrue[85] 2.42 9.0e-3 0.49 1.62 2.08 2.37 2.72 3.53 3013 1.0
ztrue[86] 4.82 0.02 0.92 3.24 4.18 4.73 5.39 6.87 3446 1.0
ztrue[87] 1.94 6.5e-3 0.39 1.3 1.66 1.91 2.18 2.82 3592 1.0
ztrue[88] 5.82 0.02 1.14 3.92 5.02 5.71 6.52 8.39 2894 1.0
ztrue[89] 4.04 0.01 0.79 2.68 3.48 3.98 4.51 5.76 3473 1.0
ztrue[90] 2.51 8.7e-3 0.51 1.68 2.15 2.45 2.8 3.66 3414 1.0
ztrue[91] 0.64 2.1e-3 0.13 0.43 0.55 0.63 0.72 0.93 3598 1.0
ztrue[92] 2.55 9.0e-3 0.52 1.68 2.18 2.49 2.88 3.73 3335 1.0
ztrue[93] 0.53 1.8e-3 0.1 0.35 0.45 0.52 0.59 0.76 3297 1.0
ztrue[94] 4.92 0.02 0.96 3.28 4.24 4.84 5.48 7.03 3313 1.0
ztrue[95] 5.84 0.02 1.19 3.85 5.01 5.71 6.53 8.6 2345 1.0
ztrue[96] 5.79 0.02 1.11 3.89 4.99 5.7 6.49 8.21 3150 1.0
ztrue[97] 1.3 4.4e-3 0.26 0.85 1.11 1.27 1.46 1.87 3473 1.0
ztrue[98] 0.95 3.5e-3 0.19 0.64 0.82 0.93 1.07 1.37 2989 1.0
ztrue[99] 2.33 8.2e-3 0.47 1.55 2.01 2.28 2.61 3.37 3243 1.0
ztrue[100] 1.75 6.4e-3 0.37 1.15 1.5 1.72 1.97 2.57 3291 1.0
ztrue[101] 4.95 0.02 0.96 3.33 4.26 4.86 5.53 7.09 3055 1.0
ztrue[102] 0.13 4.3e-4 0.03 0.09 0.11 0.13 0.15 0.19 3764 1.0
ztrue[103] 3.55 0.01 0.7 2.41 3.06 3.48 3.96 5.14 3184 1.0
ztrue[104] 2.63 8.8e-3 0.53 1.71 2.25 2.59 2.95 3.79 3655 1.0
ztrue[105] 0.49 1.7e-3 0.1 0.33 0.42 0.48 0.55 0.71 3422 1.0
ztrue[106] 1.3 4.6e-3 0.26 0.85 1.11 1.27 1.47 1.87 3277 1.0
ztrue[107] 3.09 0.01 0.61 2.06 2.66 3.02 3.47 4.43 3705 1.0
ztrue[108] 3.52 0.01 0.7 2.35 3.02 3.45 3.93 5.09 3441 1.0
ztrue[109] 3.09 0.01 0.61 2.1 2.66 3.02 3.44 4.44 3054 1.0
ztrue[110] 2.99 0.01 0.61 2.0 2.55 2.93 3.36 4.36 3320 1.0
ztrue[111] 1.59 5.6e-3 0.31 1.06 1.36 1.56 1.79 2.27 3176 1.0
ztrue[112] 3.78 0.01 0.75 2.49 3.25 3.71 4.22 5.39 3625 1.0
ztrue[113] 1.86 6.3e-3 0.38 1.25 1.59 1.82 2.09 2.71 3500 1.0
ztrue[114] 2.97 0.01 0.59 1.99 2.55 2.91 3.33 4.26 3414 1.0
ztrue[115] 2.25 7.9e-3 0.46 1.47 1.92 2.21 2.52 3.27 3288 1.0
ztrue[116] 4.76 0.02 0.95 3.17 4.09 4.68 5.34 6.85 2840 1.0
ztrue[117] 4.76 0.02 0.93 3.23 4.11 4.69 5.31 6.9 3348 1.0
ztrue[118] 5.78 0.02 1.09 3.94 5.01 5.68 6.47 8.14 3009 1.0
ztrue[119] 4.5 0.02 0.9 2.98 3.86 4.43 5.04 6.51 3084 1.0
ztrue[120] 0.53 1.8e-3 0.11 0.35 0.45 0.51 0.59 0.76 3478 1.0
ztrue[121] 1.38 4.7e-3 0.28 0.92 1.18 1.36 1.55 2.01 3396 1.0
ztrue[122] 3.38 0.01 0.67 2.26 2.91 3.32 3.79 4.84 3426 1.0
ztrue[123] 3.97 0.01 0.77 2.67 3.41 3.91 4.45 5.65 3232 1.0
ztrue[124] 5.34 0.02 1.03 3.64 4.6 5.24 5.96 7.61 3160 1.0
ztrue[125] 5.03 0.02 0.98 3.39 4.33 4.94 5.62 7.22 2954 1.0
ztrue[126] 1.7 5.6e-3 0.34 1.12 1.46 1.67 1.9 2.45 3670 1.0
ztrue[127] 5.57 0.02 1.08 3.74 4.81 5.48 6.24 7.96 3056 1.0
ztrue[128] 5.49 0.02 1.08 3.65 4.71 5.39 6.16 7.88 3280 1.0
ztrue[129] 5.78 0.02 1.11 3.87 4.97 5.69 6.48 8.23 3151 1.0
ztrue[130] 1.46 5.1e-3 0.3 0.97 1.25 1.43 1.64 2.14 3345 1.0
ztrue[131] 4.02 0.01 0.81 2.66 3.45 3.94 4.53 5.78 3489 1.0
ztrue[132] 5.44 0.02 1.06 3.66 4.69 5.34 6.09 7.83 2806 1.0
ztrue[133] 5.11 0.02 1.01 3.38 4.4 5.04 5.74 7.31 3070 1.0
ztrue[134] 4.16 0.01 0.81 2.79 3.59 4.07 4.66 5.96 3344 1.0
ztrue[135] 5.86 0.02 1.13 3.91 5.06 5.74 6.57 8.37 2785 1.0
ztrue[136] 1.75 5.9e-3 0.35 1.16 1.5 1.73 1.96 2.54 3392 1.0
ztrue[137] 2.54 9.2e-3 0.5 1.67 2.19 2.5 2.85 3.67 2989 1.0
ztrue[138] 3.51 0.01 0.69 2.34 3.01 3.44 3.93 4.97 3412 1.0
ztrue[139] 5.71 0.02 1.12 3.79 4.92 5.62 6.42 8.17 2888 1.0
ztrue[140] 2.54 8.9e-3 0.51 1.68 2.19 2.5 2.85 3.66 3288 1.0
ztrue[141] 3.94 0.01 0.79 2.57 3.39 3.87 4.41 5.71 3261 1.0
ztrue[142] 1.24 4.3e-3 0.25 0.81 1.07 1.22 1.39 1.78 3314 1.0
ztrue[143] 3.26 0.01 0.64 2.19 2.79 3.19 3.64 4.68 3473 1.0
ztrue[144] 4.85 0.02 0.94 3.3 4.16 4.77 5.41 6.93 3080 1.0
ztrue[145] 1.13 3.9e-3 0.22 0.75 0.97 1.11 1.26 1.62 3365 1.0
ztrue[146] 2.03 7.1e-3 0.41 1.34 1.73 1.98 2.28 2.97 3400 1.0
ztrue[147] 2.88 0.01 0.58 1.91 2.47 2.83 3.22 4.15 3228 1.0
ztrue[148] 1.93 6.6e-3 0.38 1.3 1.66 1.9 2.16 2.77 3321 1.0
ztrue[149] 5.1 0.02 0.98 3.43 4.41 5.01 5.69 7.32 2922 1.0
ztrue[150] 5.48 0.02 1.08 3.67 4.7 5.36 6.14 7.9 2894 1.0
ztrue[151] 3.16 0.01 0.63 2.1 2.71 3.09 3.53 4.58 3398 1.0
ztrue[152] 4.04 0.01 0.81 2.65 3.47 3.96 4.54 5.92 3257 1.0
ztrue[153] 0.4 1.4e-3 0.08 0.26 0.34 0.39 0.45 0.58 3508 1.0
ztrue[154] 4.49 0.02 0.87 2.98 3.88 4.41 5.03 6.41 3364 1.0
ztrue[155] 5.91 0.02 1.14 3.97 5.1 5.8 6.61 8.38 2973 1.0
ztrue[156] 2.58 9.3e-3 0.51 1.72 2.22 2.52 2.89 3.72 3089 1.0
ztrue[157] 2.88 0.01 0.58 1.9 2.47 2.83 3.24 4.16 3135 1.0
ztrue[158] 4.23 0.02 0.85 2.79 3.63 4.16 4.74 6.08 3163 1.0
ztrue[159] 3.1 0.01 0.61 2.05 2.66 3.05 3.49 4.46 3447 1.0
ztrue[160] 5.06 0.02 0.99 3.38 4.33 4.96 5.65 7.26 2838 1.0
ztrue[161] 2.25 8.0e-3 0.45 1.5 1.92 2.21 2.53 3.26 3234 1.0
ztrue[162] 4.41 0.01 0.86 2.98 3.8 4.35 4.94 6.33 3349 1.0
ztrue[163] 4.79 0.02 0.96 3.16 4.12 4.69 5.4 6.91 3291 1.0
ztrue[164] 3.41 0.01 0.67 2.28 2.94 3.35 3.82 4.92 3295 1.0
ztrue[165] 3.72 0.01 0.74 2.48 3.18 3.65 4.19 5.37 3509 1.0
ztrue[166] 4.71 0.02 0.9 3.16 4.06 4.64 5.29 6.72 3157 1.0
ztrue[167] 1.91 6.8e-3 0.38 1.26 1.65 1.88 2.15 2.75 3201 1.0
ztrue[168] 0.46 1.6e-3 0.09 0.3 0.39 0.45 0.51 0.67 3486 1.0
ztrue[169] 1.75 6.1e-3 0.35 1.16 1.5 1.72 1.97 2.55 3303 1.0
ztrue[170] 1.08 4.2e-3 0.22 0.71 0.91 1.05 1.21 1.56 2719 1.0
ztrue[171] 4.8 0.02 0.95 3.25 4.12 4.7 5.37 6.95 2968 1.0
ztrue[172] 2.51 8.7e-3 0.51 1.66 2.16 2.46 2.81 3.64 3427 1.0
ztrue[173] 0.04 1.3e-4 7.2e-3 0.02 0.03 0.03 0.04 0.05 3202 1.0
ztrue[174] 5.62 0.02 1.09 3.73 4.87 5.52 6.29 8.06 3048 1.0
dNdz_model[0] 13.44 0.07 3.7 7.13 10.78 13.14 15.76 21.51 2668 1.0
dNdz_model[1] 14.66 0.07 3.64 8.35 12.01 14.41 16.99 22.38 2719 1.0
dNdz_model[2] 15.85 0.07 3.57 9.57 13.27 15.65 18.17 23.38 2779 1.0
dNdz_model[3] 17.02 0.07 3.49 10.81 14.53 16.85 19.29 24.41 2851 1.0
dNdz_model[4] 18.15 0.06 3.41 12.03 15.73 17.99 20.38 25.22 2935 1.0
dNdz_model[5] 19.25 0.06 3.34 13.23 16.87 19.07 21.42 26.06 3030 1.0
dNdz_model[6] 20.31 0.06 3.26 14.47 18.0 20.15 22.41 27.16 3135 1.0
dNdz_model[7] 21.34 0.06 3.2 15.61 19.06 21.2 23.45 27.95 3247 1.0
dNdz_model[8] 22.33 0.05 3.15 16.69 20.08 22.18 24.43 28.92 3362 1.0
dNdz_model[9] 23.27 0.05 3.12 17.73 21.08 23.12 25.36 29.88 3514 1.0
dNdz_model[10] 24.17 0.05 3.09 18.63 22.0 24.04 26.23 30.74 3573 1.0
dNdz_model[11] 25.03 0.05 3.08 19.48 22.88 24.89 27.02 31.58 3617 1.0
dNdz_model[12] 25.85 0.05 3.08 20.32 23.71 25.71 27.87 32.38 3650 1.0
dNdz_model[13] 26.63 0.05 3.09 21.04 24.46 26.48 28.63 33.09 3672 1.0
dNdz_model[14] 27.36 0.05 3.11 21.74 25.2 27.2 29.35 33.95 3684 1.0
dNdz_model[15] 28.05 0.05 3.13 22.43 25.89 27.86 30.05 34.7 3688 1.0
dNdz_model[16] 28.69 0.05 3.16 22.97 26.5 28.48 30.7 35.51 3684 1.0
dNdz_model[17] 29.3 0.05 3.19 23.61 27.1 29.06 31.32 36.16 3677 1.0
dNdz_model[18] 29.86 0.05 3.21 24.17 27.65 29.6 31.91 36.82 3666 1.0
dNdz_model[19] 30.38 0.05 3.24 24.67 28.14 30.11 32.45 37.38 3655 1.0
dNdz_model[20] 30.86 0.05 3.26 25.16 28.6 30.57 32.95 37.96 3644 1.0
dNdz_model[21] 31.31 0.05 3.28 25.59 29.01 31.04 33.38 38.44 3634 1.0
dNdz_model[22] 31.71 0.05 3.29 26.01 29.41 31.44 33.8 38.9 3626 1.0
dNdz_model[23] 32.08 0.06 3.3 26.39 29.75 31.79 34.18 39.36 3503 1.0
dNdz_model[24] 32.41 0.06 3.3 26.67 30.06 32.12 34.5 39.72 3479 1.0
dNdz_model[25] 32.7 0.06 3.3 26.95 30.36 32.38 34.79 39.85 3459 1.0
dNdz_model[26] 32.97 0.06 3.29 27.23 30.66 32.64 35.04 40.08 3445 1.0
dNdz_model[27] 33.19 0.06 3.28 27.41 30.91 32.89 35.26 40.25 3436 1.0
dNdz_model[28] 33.39 0.06 3.26 27.64 31.13 33.11 35.46 40.32 3433 1.0
dNdz_model[29] 33.56 0.06 3.24 27.78 31.29 33.32 35.59 40.38 3435 1.0
dNdz_model[30] 33.7 0.05 3.22 27.96 31.46 33.46 35.73 40.41 3442 1.0
dNdz_model[31] 33.81 0.05 3.2 28.09 31.6 33.57 35.85 40.51 3456 1.0
dNdz_model[32] 33.89 0.05 3.17 28.18 31.69 33.68 35.95 40.52 3474 1.0
dNdz_model[33] 33.94 0.05 3.15 28.2 31.78 33.73 35.97 40.53 3498 1.0
dNdz_model[34] 33.97 0.05 3.13 28.2 31.82 33.77 36.01 40.49 3527 1.0
dNdz_model[35] 33.98 0.05 3.11 28.2 31.85 33.8 35.98 40.43 3561 1.0
dNdz_model[36] 33.97 0.05 3.09 28.17 31.86 33.8 35.98 40.41 3598 1.0
dNdz_model[37] 33.93 0.05 3.08 28.11 31.85 33.8 35.91 40.38 3638 1.0
dNdz_model[38] 33.87 0.05 3.07 28.04 31.78 33.75 35.83 40.32 3979 1.0
dNdz_model[39] 33.8 0.05 3.07 27.89 31.67 33.7 35.76 40.25 4000 1.0
dNdz_model[40] 33.7 0.05 3.08 27.76 31.58 33.64 35.67 40.05 4000 1.0
dNdz_model[41] 33.59 0.05 3.09 27.6 31.49 33.54 35.56 39.96 4000 1.0
dNdz_model[42] 33.46 0.05 3.12 27.42 31.34 33.42 35.46 39.9 4000 1.0
dNdz_model[43] 33.32 0.05 3.15 27.22 31.18 33.24 35.3 39.87 4000 1.0
dNdz_model[44] 33.16 0.05 3.2 27.02 30.98 33.08 35.21 39.84 4000 1.0
dNdz_model[45] 32.98 0.05 3.25 26.73 30.78 32.92 35.06 39.74 4000 1.0
dNdz_model[46] 32.8 0.05 3.31 26.5 30.51 32.71 34.92 39.68 4000 1.0
dNdz_model[47] 32.6 0.05 3.38 26.15 30.28 32.49 34.79 39.58 4000 1.0
dNdz_model[48] 32.39 0.05 3.46 25.81 30.04 32.29 34.65 39.57 3969 1.0
dNdz_model[49] 32.17 0.06 3.55 25.46 29.78 32.05 34.54 39.56 3928 1.0
dNdz_model[50] 31.94 0.06 3.64 25.04 29.49 31.82 34.38 39.5 3884 1.0
dNdz_model[51] 31.71 0.06 3.74 24.59 29.14 31.59 34.21 39.46 3839 1.0
dNdz_model[52] 31.46 0.06 3.85 24.15 28.83 31.35 34.02 39.38 3678 1.0
dNdz_model[53] 31.2 0.07 3.96 23.68 28.46 31.1 33.86 39.27 3638 1.0
dNdz_model[54] 30.94 0.07 4.07 23.26 28.1 30.83 33.69 39.24 3598 1.0
dNdz_model[55] 30.68 0.07 4.19 22.82 27.73 30.56 33.49 39.26 3559 1.0
dNdz_model[56] 30.4 0.07 4.31 22.25 27.38 30.27 33.28 39.23 3519 1.0
dNdz_model[57] 30.12 0.08 4.43 21.79 27.02 29.99 33.09 39.18 3482 1.0
dNdz_model[58] 29.84 0.08 4.56 21.27 26.67 29.68 32.9 39.06 3445 1.0
dNdz_model[59] 29.55 0.08 4.68 20.76 26.29 29.35 32.71 39.01 3410 1.0
dNdz_model[60] 29.26 0.08 4.81 20.25 25.91 29.08 32.51 39.0 3376 1.0
dNdz_model[61] 28.96 0.09 4.94 19.72 25.51 28.77 32.33 38.99 3345 1.0
dNdz_model[62] 28.66 0.09 5.07 19.16 25.12 28.47 32.11 38.91 3315 1.0
dNdz_model[63] 28.36 0.09 5.19 18.59 24.73 28.19 31.9 38.81 3286 1.0
dNdz_model[64] 28.06 0.09 5.32 18.07 24.34 27.89 31.66 38.77 3259 1.0
dNdz_model[65] 27.75 0.1 5.45 17.58 23.92 27.54 31.41 38.71 3234 1.0
dNdz_model[66] 27.44 0.1 5.57 17.07 23.51 27.23 31.17 38.63 3210 1.0
dNdz_model[67] 27.13 0.1 5.69 16.57 23.11 26.92 30.92 38.58 3188 1.0
dNdz_model[68] 26.82 0.1 5.81 16.13 22.7 26.63 30.68 38.62 3166 1.0
dNdz_model[69] 26.51 0.11 5.93 15.73 22.3 26.32 30.42 38.66 3146 1.0
dNdz_model[70] 26.2 0.11 6.05 15.28 21.89 26.0 30.2 38.6 3127 1.0
dNdz_model[71] 25.89 0.11 6.16 14.81 21.46 25.66 29.96 38.58 3109 1.0
dNdz_model[72] 25.58 0.11 6.28 14.36 21.05 25.32 29.75 38.52 3092 1.0
dNdz_model[73] 25.28 0.12 6.39 13.9 20.64 25.01 29.53 38.51 3076 1.0
dNdz_model[74] 24.97 0.12 6.49 13.45 20.25 24.67 29.27 38.54 3061 1.0
dNdz_model[75] 24.66 0.12 6.6 13.03 19.83 24.34 29.03 38.54 3047 1.0
dNdz_model[76] 24.35 0.12 6.7 12.63 19.45 24.03 28.76 38.49 3033 1.0
dNdz_model[77] 24.05 0.12 6.8 12.21 19.05 23.71 28.53 38.47 3020 1.0
dNdz_model[78] 23.74 0.13 6.89 11.8 18.64 23.38 28.29 38.37 3008 1.0
dNdz_model[79] 23.44 0.13 6.99 11.36 18.25 23.09 28.02 38.29 2996 1.0
dNdz_model[80] 23.14 0.13 7.08 10.99 17.88 22.77 27.76 38.29 2985 1.0
dNdz_model[81] 22.84 0.13 7.16 10.62 17.48 22.47 27.51 38.26 2974 1.0
dNdz_model[82] 22.54 0.13 7.25 10.23 17.11 22.11 27.29 38.17 2964 1.0
dNdz_model[83] 22.25 0.13 7.33 9.86 16.75 21.8 27.03 38.05 2955 1.0
dNdz_model[84] 21.95 0.14 7.41 9.51 16.4 21.48 26.81 37.99 2945 1.0
dNdz_model[85] 21.66 0.14 7.48 9.18 16.04 21.17 26.56 37.92 2937 1.0
dNdz_model[86] 21.38 0.14 7.55 8.86 15.7 20.86 26.31 37.88 2928 1.0
dNdz_model[87] 21.09 0.14 7.62 8.55 15.36 20.55 26.06 37.82 2920 1.0
dNdz_model[88] 20.81 0.14 7.69 8.23 15.01 20.23 25.82 37.66 2912 1.0
dNdz_model[89] 20.53 0.14 7.75 7.94 14.68 19.89 25.56 37.5 2905 1.0
dNdz_model[90] 20.25 0.15 7.82 7.63 14.35 19.59 25.29 37.41 2898 1.0
dNdz_model[91] 19.98 0.15 7.87 7.36 14.05 19.29 25.03 37.24 2891 1.0
dNdz_model[92] 19.7 0.15 7.93 7.09 13.71 18.99 24.77 37.1 2885 1.0
dNdz_model[93] 19.43 0.15 7.98 6.84 13.37 18.67 24.5 37.05 2878 1.0
dNdz_model[94] 19.17 0.15 8.03 6.59 13.07 18.33 24.24 37.01 2872 1.0
dNdz_model[95] 18.9 0.15 8.08 6.32 12.74 18.04 24.0 36.99 2867 1.0
dNdz_model[96] 18.64 0.15 8.13 6.07 12.45 17.75 23.76 36.94 2861 1.0
dNdz_model[97] 18.39 0.15 8.17 5.84 12.14 17.45 23.5 36.86 2856 1.0
dNdz_model[98] 18.13 0.15 8.21 5.62 11.85 17.16 23.22 36.79 2851 1.0
dNdz_model[99] 17.88 0.15 8.25 5.41 11.56 16.89 22.95 36.67 2846 1.0
lp__ 418.41 0.23 10.41 396.56 411.61 418.82 425.75 437.22 2047 1.0
Samples were drawn using NUTS at Tue Jul 18 23:07:48 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
In [50]:
fit_selected_fit.plot()
Out[50]:
In [51]:
chain_selected_fit = fit_selected_fit.extract(permuted=True)
In [52]:
corner.corner(
column_stack([chain_selected_fit[key] for key in ['N0', 'alpha', 'beta', 'zth']]),
labels=[r'$N_0$', r'$\alpha$', r'$\beta$', r'$z_\mathrm{th}$'],
truths=[Ntrue, alphatrue, betatrue, 6]
);
WARNING:root:Too few points to create valid contours
In [ ]:
Content source: farr/GWDataAnalysisSummerSchool
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