G = 1.12
lmd1 = -0.968
beta1 = -0.571 # w.r.t. mm
lmd2 = 0.122
beta2 = -0.305 # w.r.t. mm
lmd3 = 0.829
beta3 = -0.423 # w.r.t. mm
sigmae = 0.2 # mm
life = 5
lifearray = np.arange(life) + 1.
if __name__ == '__main__':
# random variables
rv_a0 = stats.norm(0.5, 0.5 * 0.1)
rv_m = stats.norm(3.0, 3.0 * 0.05)
[logmean, logstd] = lognstats(2.3e-12, 0.3 * 2.3e-12)
# [logmean, logstd] = lognstats(4.5e-13, 0.3*4.5e-13)
rv_C = stats.lognorm(logstd, scale=np.exp(logmean))
[wblscale, wblc] = wblstats(22.5, 0.1 * 22.5)
rv_Sre = stats.weibull_min(wblc, scale=wblscale)
[logmean, logstd] = lognstats(2e6, 0.1 * 2e6)
rv_Na = stats.lognorm(logstd, scale=np.exp(logmean))
# network model
# create nodes
node_m = Node("M", parents=None, rvname='normal', rv=rv_m)
node_k = Node("K", parents=[node_m], rvname='continuous')
node_a0 = Node('a0', parents=None, rvname='normal', rv=rv_a0)
aarray = [node_a0]
marray = []
for i in range(life):
from soliman2014_funcs import ksmp_mc, aismp_mc, msr2k, mc2k, mc2ai
trunclmd = 100.
if __name__ == '__main__':
# parameters
nsmp = 1e6
G = 1.12
lmd = 0.122; beta = -0.305 # w.r.t. mm
sigmae = 0.2 # mm
acrit = 30.
life=5; lifearray = np.arange(life)+1.
# random variables
rv_a0 = stats.norm(0.5, 0.5*0.1)
rv_m = stats.norm(3.0, 3.0*0.05)
[logmean, logstd] = lognstats(2.3e-12, 0.3*2.3e-12)
# [logmean, logstd] = lognstats(4.5e-13, 0.3*4.5e-13)
rv_C = stats.lognorm(logstd, scale=np.exp(logmean))
[wblscale, wblc] = wblstats(22.5, 0.1*22.5)
rv_Sre = stats.weibull_min(wblc, scale=wblscale)
[logmean, logstd] = lognstats(2e6, 0.1*2e6)
rv_Na = stats.lognorm(logstd, scale=np.exp(logmean))
# network model
# create nodes
node_m = Node("M", parents=None, rvname='normal', rv=rv_m)
node_k = Node("K", parents=[node_m], rvname='continuous')
node_a0 = Node('a0', parents=None, rvname='normal', rv=rv_a0)
aarray = [node_a0]
marray=[]
for i in range(life):
from soliman2014_funcs import lognstats, wblstats, ksmp_mc, rolnR
if __name__ == '__main__':
# crude MC for no evidence
nsmp = int(1e6)
G = 1.12
lmd = 0.122; beta = -0.305 # w.r.t. mm
sigmae = 0.2 # mm
acrit = 30.
life=5; lifearray = np.arange(life)+1.
# random variables
a0mean,a0std = 0.5, 0.5*0.1
rv_a0 = stats.norm(a0mean,a0std)
mmean,mstd = 3.0, 3.0*0.05
rv_m = stats.norm(mmean, mstd)
[logCmean, logCstd] = lognstats(2.3e-12, 0.3*2.3e-12)
# [logCmean, logCstd] = lognstats(4.5e-13, 0.3*4.5e-13)
rv_C = stats.lognorm(logCstd, scale=np.exp(logCmean))
[wblscale, wblc] = wblstats(22.5, 0.1*22.5)
rv_Sre = stats.weibull_min(wblc, scale=wblscale)
[logNamean, logNastd] = lognstats(2e6, 0.1*2e6)
rv_Na = stats.lognorm(logNastd, scale=np.exp(logNamean))
# crude MC
# correlate Csmp and msmp
msmp = rv_m.rvs(size=nsmp)
umsmp = (msmp-mmean)/mstd
uLogCsmp0 = stats.norm.rvs(size=nsmp)
uLogCsmp = -np.sqrt(rolnR**2)*umsmp-np.sqrt(1-rolnR**2)*uLogCsmp0 #correlated logC
Csmp = np.exp(logCmean+uLogCsmp*logCstd)
# other variables
Sresmp = rv_Sre.rvs(size=nsmp)
if __name__ == '__main__':
# crude MC for no evidence
nsmp = int(1e6)
G = 1.12
lmd = 0.122; beta = -0.305 # w.r.t. mm
sigmae = 0.2 # mm
life=20; lifearray = np.arange(life+1.)
lifearray1 = np.arange(0, life+1., 5.)
# random variables
a0mean,a0std = 0.5, 0.5*0.1
rv_a0 = stats.norm(a0mean,a0std)
mmean,mstd = 3.0, 3.0*0.1
rv_m = stats.norm(mmean, mstd)
[logCmean, logCstd] = lognstats(2.3e-12, 0.3*2.3e-12)
rv_C = stats.lognorm(logCstd, scale=np.exp(logCmean))
[wblscale, wblc] = wblstats(22.5, 0.1*22.5)
rv_Sre = stats.weibull_min(wblc, scale=wblscale)
[logNamean, logNastd] = lognstats(1e6, 0.1*1e6)
rv_Na = stats.lognorm(logNastd, scale=np.exp(logNamean))
[logNamean1, logNastd1] = lognstats(5e6, 0.1*5e6)
rv_Na1 = stats.lognorm(logNastd1, scale=np.exp(logNamean1))
# crude MC
# correlate Csmp and msmp
msmp = rv_m.rvs(size=nsmp)
umsmp = (msmp-mmean)/mstd
uLogCsmp0 = stats.norm.rvs(size=nsmp)
uLogCsmp = -np.sqrt(rolnR**2)*umsmp-np.sqrt(1-rolnR**2)*uLogCsmp0 #correlated logC
Csmp = np.exp(logCmean+uLogCsmp*logCstd)
# other variables
if __name__ == '__main__':
# crude MC for no evidence
nsmp = int(1e6)
G = 1.12
lmd = 0.122
beta = -0.305 # w.r.t. mm
sigmae = 0.2 # mm
life = 20
lifearray = np.arange(life + 1.)
lifearray1 = np.arange(0, life + 1., 5.)
# random variables
a0mean, a0std = 0.5, 0.5 * 0.1
rv_a0 = stats.norm(a0mean, a0std)
mmean, mstd = 3.0, 3.0 * 0.1
rv_m = stats.norm(mmean, mstd)
[logCmean, logCstd] = lognstats(2.3e-12, 0.3 * 2.3e-12)
rv_C = stats.lognorm(logCstd, scale=np.exp(logCmean))
[wblscale, wblc] = wblstats(22.5, 0.1 * 22.5)
rv_Sre = stats.weibull_min(wblc, scale=wblscale)
[logNamean, logNastd] = lognstats(1e6, 0.1 * 1e6)
rv_Na = stats.lognorm(logNastd, scale=np.exp(logNamean))
[logNamean1, logNastd1] = lognstats(5e6, 0.1 * 5e6)
rv_Na1 = stats.lognorm(logNastd1, scale=np.exp(logNamean1))
# crude MC
# correlate Csmp and msmp
msmp = rv_m.rvs(size=nsmp)
umsmp = (msmp - mmean) / mstd
uLogCsmp0 = stats.norm.rvs(size=nsmp)
uLogCsmp = -np.sqrt(
rolnR**2) * umsmp - np.sqrt(1 - rolnR**2) * uLogCsmp0 #correlated logC
Csmp = np.exp(logCmean + uLogCsmp * logCstd)
if __name__ == "__main__":
# crude MC for no evidence
nsmp = int(1e6)
G = 1.12
lmd = 0.122
beta = -0.305 # w.r.t. mm
sigmae = 0.2 # mm
acrit = 30.0
life = 5
lifearray = np.arange(life) + 1.0
# random variables
a0mean, a0std = 0.5, 0.5 * 0.1
rv_a0 = stats.norm(a0mean, a0std)
mmean, mstd = 3.0, 3.0 * 0.05
rv_m = stats.norm(mmean, mstd)
[logCmean, logCstd] = lognstats(2.3e-12, 0.3 * 2.3e-12)
# [logCmean, logCstd] = lognstats(4.5e-13, 0.3*4.5e-13)
rv_C = stats.lognorm(logCstd, scale=np.exp(logCmean))
[wblscale, wblc] = wblstats(22.5, 0.1 * 22.5)
rv_Sre = stats.weibull_min(wblc, scale=wblscale)
[logNamean, logNastd] = lognstats(2e6, 0.1 * 2e6)
rv_Na = stats.lognorm(logNastd, scale=np.exp(logNamean))
# crude MC
# correlate Csmp and msmp
msmp = rv_m.rvs(size=nsmp)
umsmp = (msmp - mmean) / mstd
uLogCsmp0 = stats.norm.rvs(size=nsmp)
uLogCsmp = -np.sqrt(rolnR ** 2) * umsmp - np.sqrt(1 - rolnR ** 2) * uLogCsmp0 # correlated logC
Csmp = np.exp(logCmean + uLogCsmp * logCstd)
# other variables
Sresmp = rv_Sre.rvs(size=nsmp)
