Update bayesian_av to plot weights

src/juobs_tools.jl

@@ -368,7 +368,7 @@ function bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64},
                 chisq = gen_chisq(fun,x,dy)
                 fit = curve_fit(fun,x,value.(yy),W,p00)
                 isnothing(wpm) ? (up,chi_exp) = fit_error(chisq,coef(fit),yy) : (up,chi_exp) = fit_error(chisq,coef(fit),yy,wpm)
-                uwerr(up[1],wpm)
+                isnothing(wpm) ? uwerr(up[1]) : uwerr(up[1],wpm)
                 chi2 = sum(fit.resid.^2) * dof(fit) / chi_exp
                 push!(AIC, chi2 + 2*k + 2*Ncut)
                 push!(chi2chi2exp, chi2 / dof(fit))
@@ -401,6 +401,13 @@ function bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64},
         xlabel(L"model")
     
         display(gcf())
+
+        figure()
+        bar(x, weight_model, color="green")
+        ylabel(L"$weight$")
+        xlabel(L"model")
+    
+        display(gcf())
     end
 
     if !data                   
@@ -438,7 +445,7 @@ function bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_arra
                 chisq = gen_chisq(fun1,x,dy)
                 fit = curve_fit(fun1,x,value.(yy),W,p00)
                 isnothing(wpm) ? (up,chi_exp) = fit_error(chisq,coef(fit),yy) : (up,chi_exp) = fit_error(chisq,coef(fit),yy,wpm)
-                uwerr(up[1],wpm)
+                isnothing(wpm) ? uwerr(up[1]) : uwerr(up[1],wpm)
                 chi2 = sum(fit.resid.^2) * dof(fit) / chi_exp
                 push!(AIC, chi2 + 2*k1 + 2*Ncut)
                 push!(chi2chi2exp, chi2 / dof(fit))
@@ -482,6 +489,13 @@ function bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_arra
         xlabel(L"model")
     
         display(gcf())
+
+        figure()
+        bar(x, weight_model, color="green")
+        ylabel(L"$weight$")
+        xlabel(L"model")
+    
+        display(gcf())
     end
 
     if !data                   
@@ -489,7 +503,7 @@ function bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_arra
     else               
         return (p1_mean, systematic_err, p1, weight_model)    
     end 
-    
+
 end