NO_STR_DATA_PATH,
'rc_shear_history.npy'))[service_time == display_yr, :].flatten()
rc_shear_str1 = np.load(os.path.join(
STR1_DATA_PATH,
'rc_shear_history.npy'))[service_time == display_yr, :].flatten()
rc_shear_str2 = np.load(os.path.join(
STR2_DATA_PATH,
'rc_shear_history.npy'))[service_time == display_yr, :].flatten()
# weight data
weights0 = np.load(os.path.join(NO_STR_DATA_PATH, 'likelihood_weighting.npy'))
weights1 = np.load(os.path.join(STR1_DATA_PATH, 'likelihood_weighting.npy'))
weights2 = np.load(os.path.join(STR2_DATA_PATH, 'likelihood_weighting.npy'))
## parameters and fitting distribution
mu0, sigma0 = weightedAvgAndStd(rc_shear_no_str, weights0)
mu1, sigma1 = weightedAvgAndStd(rc_shear_str1, weights1)
mu2, sigma2 = weightedAvgAndStd(rc_shear_str2, weights2)
lognormal0 = Lognormal('str0', mu0, sigma0)
lognormal1 = Lognormal('str1', mu1, sigma1)
lognormal2 = Lognormal('str2', mu2, sigma2)
print 'Before strengthening: mean = {}, std = {}'.format(mu0, sigma0)
print 'U-jacket with anchor: mean = {}, std = {}'.format(mu1, sigma1)
print 'U-jacket without anhor: mean = {}, std = {}'.format(mu2, sigma2)
## =======================================================================##
## generate and save figures
## =======================================================================##
plt.close('all')
plt.rc('font', family='serif', size=12)
service_time = np.loadtxt(datafile)[0,:]
display_yr = 78
# residual shear strength data
rc_shear_no_str = np.load( os.path.join(NO_STR_DATA_PATH, 'rc_shear_history.npy') )[service_time==display_yr, :].flatten()
rc_shear_str1 = np.load( os.path.join(STR1_DATA_PATH, 'rc_shear_history.npy') )[service_time==display_yr, :].flatten()
rc_shear_str2 = np.load( os.path.join(STR2_DATA_PATH, 'rc_shear_history.npy') )[service_time==display_yr, :].flatten()
# weight data
weights0 = np.load( os.path.join(NO_STR_DATA_PATH, 'likelihood_weighting.npy') )
weights1 = np.load( os.path.join(STR1_DATA_PATH, 'likelihood_weighting.npy') )
weights2 = np.load( os.path.join(STR2_DATA_PATH, 'likelihood_weighting.npy') )
## parameters and fitting distribution
mu0, sigma0 = weightedAvgAndStd(rc_shear_no_str, weights0)
mu1, sigma1 = weightedAvgAndStd(rc_shear_str1, weights1)
mu2, sigma2 = weightedAvgAndStd(rc_shear_str2, weights2)
lognormal0 = Lognormal('str0', mu0, sigma0)
lognormal1 = Lognormal('str1', mu1, sigma1)
lognormal2 = Lognormal('str2', mu2, sigma2)
print 'Before strengthening: mean = {}, std = {}'.format(mu0, sigma0)
print 'U-jacket with anchor: mean = {}, std = {}'.format(mu1, sigma1)
print 'U-jacket without anhor: mean = {}, std = {}'.format(mu2, sigma2)
## =======================================================================##
## generate and save figures
## =======================================================================##
plt.close('all')
plt.rc('font', family='serif', size=12)
# residual shear strength data
rc_shear_no_evidence = np.load( os.path.join(NO_EVIDENCE_DATA_PATH, 'rc_shear_history.npy') )[service_time==display_yr, :].flatten()
rc_shear_evidence2 = np.load( os.path.join(EVIDENCE2_DATA_PATH, 'rc_shear_history.npy') )[service_time==display_yr, :].flatten()
rc_shear_evidence3 = np.load( os.path.join(EVIDENCE3_DATA_PATH, 'rc_shear_history.npy') )[service_time==display_yr, :].flatten()
rc_shear_evidence4 = np.load( os.path.join(EVIDENCE4_DATA_PATH, 'rc_shear_history.npy') )[service_time==display_yr, :].flatten()
# weight data
weights1 = np.ones(np.shape(ds_no_evidence))
weights2 = np.load( os.path.join(EVIDENCE2_DATA_PATH, 'likelihood_weighting.npy') )
weights3 = np.load( os.path.join(EVIDENCE3_DATA_PATH, 'likelihood_weighting.npy') )
weights4 = np.load( os.path.join(EVIDENCE4_DATA_PATH, 'likelihood_weighting.npy') )
# fitting data with lognormal distribution
# for ds
ds_mu1, ds_sigma1 = weightedAvgAndStd(ds_no_evidence, weights1)
ds_mu2, ds_sigma2 = weightedAvgAndStd(ds_evidence2, weights2)
ds_mu3, ds_sigma3 = weightedAvgAndStd(ds_evidence3, weights3)
ds_mu4, ds_sigma4 = weightedAvgAndStd(ds_evidence4, weights4)
ds_lognormal1 = Lognormal('no_ev', ds_mu1, ds_sigma1)
ds_lognormal2 = Lognormal('ev2', ds_mu2, ds_sigma2)
ds_lognormal3 = Lognormal('ev3', ds_mu3, ds_sigma3)
ds_lognormal4 = Lognormal('ev4', ds_mu4, ds_sigma4)
# for flexure resistance
flex_mu1, flex_sigma1 = weightedAvgAndStd(rc_flex_no_evidence, weights1)
flex_mu2, flex_sigma2 = weightedAvgAndStd(rc_flex_evidence2, weights2)
flex_mu3, flex_sigma3 = weightedAvgAndStd(rc_flex_evidence3, weights3)
flex_mu4, flex_sigma4 = weightedAvgAndStd(rc_flex_evidence4, weights4)
flex_lognormal1 = Lognormal('no_ev', flex_mu1, flex_sigma1)
flex_lognormal2 = Lognormal('ev2', flex_mu2, flex_sigma2)
flex_lognormal3 = Lognormal('ev3', flex_mu3, flex_sigma3)
