fit_routine + lin_fit generalized

input xdata Array{Real}

src/juobs_tools.jl

@@ -95,7 +95,7 @@ function plat_av(obs::Vector{uwreal}, plat::Vector{Int64})
     return av 
 end
 
-function lin_fit(x::Vector{Float64}, v::Vector{Float64}, e::Vector{Float64})
+function lin_fit(x::Vector{Real}, v::Vector{Float64}, e::Vector{Float64})
     sig2 = e .* e
     S = sum(1 ./ sig2)
     Sx = sum(x ./ sig2)
@@ -108,7 +108,7 @@ function lin_fit(x::Vector{Float64}, v::Vector{Float64}, e::Vector{Float64})
     return par
 end
 @doc raw"""
-lin_fit(x::Vector{Float64}, y::Vector{uwreal})
+lin_fit(x::Vector{Real}, y::Vector{uwreal})
 
 Computes a linear fit of uwreal data points y. This method return uwreal fit parameters and chisqexpected.
 
@@ -117,7 +117,7 @@ fitp, csqexp = lin_fit(phi2, m2)
 m2_phys = fitp[1] + fitp[2] * phi2_phys
 ```
 """
-function lin_fit(x::Vector{Float64}, y::Vector{uwreal})
+function lin_fit(x::Vector{Real}, y::Vector{uwreal})
     uwerr.(y)
     par = lin_fit(x, value.(y), err.(y))
 
@@ -150,10 +150,9 @@ The method return an array upar with the best fit parameters with their errors.
 @. model(x,p) = p[1] + p[2] * exp(-(p[3]-p[1])*x)
 fit_routine(model, ydata, param=3)
 """
-function fit_routine(model::Function, ydata::Array{uwreal}, param::Int64=3; wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing )
+function 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 )
     yval = value.(ydata)
     yer = err.(ydata)
-    xdata = collect(1:length(ydata))
     chisq = gen_chisq(model, xdata, yer)
     fit = curve_fit(model, xdata, yval, 1.0 ./ yer.^2, fill(0.5, param))
     isnothing(wpm) ? (upar, chi_exp) = fit_error(chisq, coef(fit), ydata) : (upar, chi_exp) = fit_error(chisq, coef(fit), ydata, wpm)
@@ -165,7 +164,7 @@ function fit_routine(model::Function, ydata::Array{uwreal}, param::Int64=3; wpm:
     println("Chisq / chiexp: ", chisq(coef(fit), ydata), " / ", chi_exp, " (dof: ", dof(fit),")")
     return upar
 end
-function gen_chisq(f::Function, x::Vector{Int64}, err::Vector{Float64})
+function gen_chisq(f::Function, x::Array{Real}, err::Vector{Float64})
     chisq(par, dat) = sum((dat .- f(x,par)).^2 ./err.^2)
     return chisq
 end