updated documentation on pvalue. Slight changes to make it more intuitive to call pvalue

src/juobs_obs.jl

@@ -183,7 +183,7 @@ function mpcac(a0p::Corr, pp::Corr, plat::Vector{Int64}; ca::Float64=0.0, pl::Bo
     
     if a0p.kappa == pp.kappa || a0p.mu == pp.mu
         if a0p.mu == [0.0, 0.0]
-            return mpcac(a0p.obs, pp.obs, plat, ca=ca, pl=pl, data=data, kappa=a0p.kappa, mu=nothing, wpm=wpm,savepl=davepl, label=label)
+            return mpcac(a0p.obs, pp.obs, plat, ca=ca, pl=pl, data=data, kappa=a0p.kappa, mu=nothing, wpm=wpm,savepl=savepl, label=label)
         else
             return mpcac(a0p.obs, pp.obs, plat, ca=ca, pl=pl, data=data, mu=a0p.mu, kappa=nothing, wpm=wpm,savepl=savepl,label=label)
         end
@@ -191,7 +191,7 @@ function mpcac(a0p::Corr, pp::Corr, plat::Vector{Int64}; ca::Float64=0.0, pl::Bo
         error("mu or kappa values does not match")
     end
 end
-#= 
+
 @doc raw"""
     mpcac_ren(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64}, za_zp::uwreal; ca::Float64=0.0,ba_bp_z::uwreal=0.0,pl::Bool=true, data::Bool=false,
             kappa::Union{Vector{Float64}, Nothing}=nothing, mu::Union{Vector{Float64}, Nothing}=nothing, 
@@ -313,7 +313,7 @@ function mpcac_ren(a0p::Corr, pp::Corr, plat::Vector{Int64}; ca::Float64=0.0,ba_
         error("mu or kappa values does not match")
     end
 end
- =#
+
 ## Decay constants
 @doc raw"""
     dec_const(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64}, m::uwreal, y0::Int64; ca::Float64=0.0, pl::Bool=true, data::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)

src/juobs_tools.jl

@@ -1490,18 +1490,25 @@ function inv_covar_multi_id(obs::Vector{uwreal}; wpm::Union{Dict{Int64,Vector{Fl
 end
 
 @doc raw"""
-    pvalue(chisq::Function,
+    function pvalue(chisq::Function,
         chi2::Float64,
         xp::Vector{Float64}, 
         data::Vector{uwreal},
+        W::Union{Vector{Float64},Nothing} = nothing,
         wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing} = Dict{Int64,Vector{Float64}}();
-    W::Union{Vector{Float64},Array{Float64,2}} = Vector{Float64}(),
+        nmc::Int64 = 5000)
+
+    function pvalue(chisq::Function,
+        chi2::Float64,
+        xp::Vector{Float64}, 
+        data::Vector{uwreal},
+        W::Array{Float64,2};
+        wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing} =  Dict{Int64,Vector{Float64}}(),
         nmc::Int64 = 5000)
 
 Computes the p-value of a previously done fit, using as input the `\chi^2` observed from the fit, the fit parameters and the fitted data. 
 The p-value for a given `\chi^2` is the probability of, given the data you have, finding such a `\chi^2` or worse from a fit, and still
 have the data well described by the fit function. `nmc` is the number of MC samples used to estimate the p-value integral, default is 5000.
-By now it only works with a vector for weights (containing the diagonal of W)
 
 ```@example
 function fit_defs(f::Function,x,W)
@@ -1517,16 +1524,16 @@ fit = curve_fit(fun, x, value.(y), 1.0 ./ err.(y) .^ 2, [0.5, 0.5])
 wpm = Dict{Int64,Vector{Float64}}()
 wpm[1] = [-1.0,-1.0,4-0,-1.0]
 Q = pvalue(chisq, chi2, value.(up), y, wpm; W = 1.0 ./ err.(y) .^ 2, nmc=10000)
-#Q = pvalue(chisq, chi2, value.(up), y; W = 1.0 ./ err.(y) .^ 2, nmc=10000)
+#Q = pvalue(chisq, chi2, va(Nx − NA )/2(Nx − NA )/2lue.(up), y; W = 1.0 ./ err.(y) .^ 2, nmc=10000)
 #Q = pvalue(chisq, chi2, value.(up), y)
 ```
 """
-
 function pvalue(chisq::Function,
     chi2::Float64,
     xp::Vector{Float64}, 
-    data::Vector{uwreal};
-    wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing} =  Dict{Int64,Vector{Float64}}(),
+    data::Vector{uwreal},
+    W::Union{Vector{Float64},Nothing} = nothing;
+    wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing} =  nothing,
     nmc::Int64 = 5000)
 
     n = length(xp)   # Number of fit parameters
@@ -1550,18 +1557,19 @@ function pvalue(chisq::Function,
     ForwardDiff.hessian!(hess, ccsq, xav, cfg)
 
     if (m-n > 0)
+        if !isnothing(W)
             W = zeros(Float64, m)
             for i in 1:m
                 if (data[i].err == 0.0)
-                    #isnothing(wpm) ? wuerr(data[i]) : uwerr(data[i], wpm)
-                    uwerr(data[i], wpm)
+                    isnothing(wpm) ? wuerr(data[i]) : uwerr(data[i], wpm)
+                    #uwerr(data[i], wpm)
                     if (data[i].err == 0.0)
                         error("Zero error in fit data")
                     end
                 end
-                global W[i] = 1.0 / data[i].err^2
+                W[i] = 1.0 / data[i].err^2
+            end
         end
-
         m = length(data)
         n = size(hess, 1) - m