Update in bayesian_av to return vector with data

src/juobs_tools.jl

@@ -307,11 +307,11 @@ function plat_av(obs::Vector{uwreal}, plat::Vector{Int64}, wpm::Union{Dict{Int64
 end
 
 @doc raw"""
-    bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k::Int64, pl::Bool; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
+    bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k::Int64, pl::Bool, data::Bool; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
 
-    bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k1::Int64, k2::Int64, pl::Bool; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
+    bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k1::Int64, k2::Int64, pl::Bool, data::Bool; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
 
-Computes bayesian average of data. For a given fit function, it explores choices of fit intervals, assigning each a weight. Then it does the weighted average and assigns a systematic. See https://arxiv.org/abs/2008.01069 
+Computes bayesian average of data. For a given fit function, it explores choices of fit intervals, assigning each of them a weight. The function saves the `first` fit parameter of your function, and then it does the weighted average of it and assigns a systematic. See https://arxiv.org/abs/2008.01069 
 
 The function takes as input the fit intervals to explore. 
 
@@ -323,14 +323,14 @@ The function takes as input the fit intervals to explore.
 
 You can also use as input two fit functions, and two values of `k`, one for each function. Then, for each fit interval choice, the function explores the two fit functions. This means that for each fit interval choice you get two results: one for the first fit funcction, and another for the second.
 
-The method returns two objects: first, the weighted average as an uwreal object, with mean value and statistichal error. The second object returned is the systematic error coming from the fit interval variation.
+The method returns two objects: first, the weighted average as an uwreal object, with mean value and statistichal error. The second object returned is the systematic error coming from the fit interval variation. If `data` is `true`, then returns 4 objects: weighted average, systematic error, a vector with the results of the fit for each fit interval choice, and a vector with the weights associated to each fit.
 
 ```@example
 @.fun(x,p) = p[1] * x ^0
 k = 1
 tmin_array = [10,11,12,13,14,15]
 tmax_array = [80,81,82,83,84,85]
-(average, systematics) = bayesian_av(fun,x,tmin_array,tmax_array,k,pl=true)
+(average, systematics, data, weights) = bayesian_av(fun,x,tmin_array,tmax_array,k,pl=true,data=true)
 
 @.fun1(x,p) = p[1] * x ^0
 @.fun2(x,p) = p[1] + p[2] * exp( - p[3] * (x))
@@ -341,7 +341,7 @@ tmax_array = [80,81,82,83,84,85]
 (average, systematics) = bayesian_av(fun1,fun2,x,tmin_array,tmax_array,k1,k2)
 ```
 """
-function bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k::Int64, pl::Bool=false; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
+function bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k::Int64, pl::Bool=false, data::Bool=false; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
 
     weight_model = Array{Float64,1}()
     AIC = Array{Float64,1}()
@@ -403,11 +403,15 @@ function bayesian_av(fun::Function, y::Array{uwreal}, tmin_array::Array{Int64},
         display(gcf())
     end
 
-    return (p1_mean, systematic_err)  
+    if !data                   
+        return (p1_mean, systematic_err)                                         
+    else               
+        return (p1_mean, systematic_err, p1, weight_model)    
+    end 
 
 end
 
-function bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k1::Int64, k2::Int64, pl::Bool=false; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
+function bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_array::Array{Int64}, tmax_array::Array{Int64}, k1::Int64, k2::Int64, pl::Bool=false, data::Bool=false; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)
 
     weight_model = Array{Float64,1}()
     AIC = Array{Float64,1}()
@@ -480,8 +484,12 @@ function bayesian_av(fun1::Function, fun2::Function, y::Array{uwreal}, tmin_arra
         display(gcf())
     end
 
-    return (p1_mean, systematic_err)  
-
+    if !data                   
+        return (p1_mean, systematic_err)                                         
+    else               
+        return (p1_mean, systematic_err, p1, weight_model)    
+    end 
+    
 end