push func to get logmean and logstddev

Author: lmartins88

GUI/fragility_vulnerability_base.py

@@ -158,4 +158,20 @@ def calculate_vul_curve_edps_to_loss(imls,edps,consequence_model,damage_threshol
 
       return vul_curve_array
 #%%
-      
+def residual_lognormal_dist(coeffs, imls, fractions):
+
+    mu = coeffs[0]
+    sigma = coeffs[1]
+    residual = abs(np.array(fractions) - stats.lognorm.cdf(imls, sigma, scale = math.exp(mu)))
+
+    return residual
+
+def get_logmean_logstddev(imls_array,poes_loss_ratio_array):
+      
+      x_vec=imls_array
+      y_vec=poes_loss_ratio_array
+      solution, _ = optimize.leastsq(residual_lognormal_dist,[0.1, 0.6],args=(x_vec, y_vec))
+      logmean=solution[0]
+      logstddev=solution[1]
+      
+      return logmean, logstddev

demand/CSM_implementation/csm_python_implementation.py

@@ -73,8 +73,8 @@ def get_target_spectrum(knownPer,selectionParams,indPer,rup):
     #%                                   =2 for Japan 
     #%                                   =3 for China or Turkey 
     #%                                   =4 for Italy
-    #%               .z1               : basin depth (km); depth from ground surface to the 1km/s shear-wave horizon, =999 if unknown
-    #%               .Vs30             : average shear wave velocity in the top 30m of the soil (m/s)
+    #%               .z1               : 
+    #%               .Vs30             : average shear wave velocity in the top 30m of the soil (m/s)basin depth (km); depth from ground surface to the 1km/s shear-wave horizon, =999 if unknown
     #%
     #%
     #% Outputs (these could be replaced by user-specified matrices if desired

fragility_vulnerability/fragility_vulnerability_base.py

@@ -158,4 +158,20 @@ def calculate_vul_curve_edps_to_loss(imls,edps,consequence_model,damage_threshol
 
       return vul_curve_array
 #%%
-      
+def residual_lognormal_dist(coeffs, imls, fractions):
+
+    mu = coeffs[0]
+    sigma = coeffs[1]
+    residual = abs(np.array(fractions) - stats.lognorm.cdf(imls, sigma, scale = math.exp(mu)))
+
+    return residual
+
+def get_logmean_logstddev(imls_array,poes_loss_ratio_array):
+      
+      x_vec=imls_array
+      y_vec=poes_loss_ratio_array
+      solution, _ = optimize.leastsq(residual_lognormal_dist,[0.1, 0.6],args=(x_vec, y_vec))
+      logmean=solution[0]
+      logstddev=solution[1]
+      
+      return logmean, logstddev