Added wpm documentation in fit_routine

src/juobs.jl

@@ -8,7 +8,7 @@ include("juobs_tools.jl")
 include("juobs_obs.jl")
 
 export read_mesons, read_ms1, read_ms, read_md
-export get_matrix, uwgevp_tot, energies, uwdot, uweigvals, uweigvecs, uweigen, invert, getall_eigvals, getall_eigvecs
+export get_matrix, energies, uwdot, uweigvals, uweigvecs, uweigen, invert, getall_eigvals, getall_eigvecs
 export corr_obs, plat_av, lin_fit, x_lin_fit, y_lin_fit, fit_routine
 export meff, dec_const_pcvc, comp_t0
 

src/juobs_linalg.jl

@@ -63,7 +63,6 @@ function get_matrix(corr_diag::Vector{Vector{uwreal}}, corr_upper::Vector{Vector
     aux = Matrix{uwreal}(undef, n, n)
     for t in 1:time  
         count=0
-        #testing = corr_upper[1][1]
         for i in range(n-1,1, step=-1)
             for j in range(n, i+1, step=-1)
                 aux[i,j] = corr_upper[d-count][t]
@@ -91,7 +90,6 @@ function energies(evals::Union{Vector{Vector{uwreal}},Array{Array{uwreal}} })
     eff_en = Array{Array{uwreal}}(undef, n)
     aux_en = Vector{uwreal}(undef, time-1)
     for i in 1:n
-        #aux_en = Vector{uwreal}(undef, time-1)
         for t in 1:time-1
             ratio = evals[t][i] / evals[t+1][i]
             aux_en[t] = 0.5*log(ratio * ratio)

src/juobs_tools.jl

@@ -145,11 +145,19 @@ Computes the results of a linear interpolation/extrapolation in the y axis
 y_lin_fit(par::Vector{uwreal}, x::Union{uwreal, Float64}) = par[1] + par[2] * x
 
 @doc raw"""
-fit_routine(model::Function, xdata::Array{<:Real}, ydata::Array{uwreal}, param::Int64=3)
+fit_routine(model::Function, xdata::Array{<:Real}, ydata::Array{uwreal}, param::Int64=3; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing )
 
 Given a model function with a number param of parameters and an array of uwreal,
 this function fit ydata with the given model and print  fit information
 The method return an array upar with the best fit parameters with their errors.
+The flag wpm is an optional array of Float64 of lenght 4. The first three paramenters specify the criteria to determine
+the summation windows:
+vp[1]: The autocorrelation function is summed up to t = round(vp[1]).
+vp[2]: The sumation window is determined using U. Wolff poposal with S_\tau = wpm[2]
+vp[3]: The autocorrelation function Γ(t) is summed up a point where its error δΓ(t) is a factor vp[3] times larger than the signal.
+An additional fourth parameter vp[4], tells ADerrors to add a tail to the error with \tau_{exp} = wpm[4].
+Negative values of wpm[1:4] are ignored and only one component of wpm[1:3] needs to be positive.
+
 '''@example
 @. model(x,p) = p[1] + p[2] * exp(-(p[3]-p[1])*x)
 fit_routine(model, ydata, param=3)