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)