change md_val for TSM, seems to be working good, J501 a bit differetn phi4_sh,...

change md_val for TSM, seems to be working good, J501 a bit differetn phi4_sh, rework dec_const, carefull check

src/juobs_obs.jl

@@ -196,7 +196,7 @@ f = dec_const(corr_a0p[1], corr_pp[1], [50, 60], m, pl=true, ca=ca)
 f_ratio = dec_const(corr_a0pL, corr_a0pR, corr_ppL, corr_ppR, [50, 60], m, pl=true, ca=ca)
 ```
 """
-function 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,
+function juobs.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,
     kappa::Union{Vector{Float64}, Nothing}=nothing, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing)     
     
     corr_a0p = -a0p
@@ -208,7 +208,7 @@ function dec_const(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64},
         corr_a0p = corr_a0p[2:end-1] + ca * der_pp
         aux = exp.((collect(1:T-2) .- (T-1)/2) .* [m for k = 1:T-2])
     else
-        aux = exp.((collect(0:T-1) .- T/2) .* [m for k = 1:T])
+        aux = exp.((collect(0:T-1) .- (T-1)/2) .* [m for k = 1:T])
     end
 
     R = corr_a0p .* aux ./ [((corr_pp[T-y0])^2)^(1/4) for k = 1:length(corr_a0p)]
@@ -237,7 +237,7 @@ function dec_const(a0p::Vector{uwreal}, pp::Vector{uwreal}, plat::Vector{Int64},
             title(string(L"$\kappa_1 = $", kappa[1], L" $\kappa_2 = $", kappa[2]))
         end
         display(gcf())
-        close()
+        #close()
     end
     if !data
         return f

src/juobs_tools.jl

@@ -464,7 +464,6 @@ If `new_version=true`, then the sign of the valence derivatives in `derm` is fli
 also that for the new dat files version, no `_d1, _d2` is written by the reader, so 
 you must select by hand (looking at the log files e.g.) which are you derivative 
 correlators. In the log file, the derivative correlators are signaled by `seq_prop=some number`.
-If `TSM=true`, computes valence derivatives of TSM correlator.
 
 ``\frac{d <A>}{dm(val)} = \sum_i \frac{\partial <A>}{\partial <O_i>}  \frac{d <O_i>}{d m(val)}``
 
@@ -495,7 +494,7 @@ m_md1, m_md2 = md_val(m, corr_pp[1], derm[1])
 m_shifted = m + dm1 * m_md1 + dm2 * m_md2
 ```
 """
-function md_val(a::uwreal, obs::Corr, derm::Vector{Corr}; new_version::Bool=false, TSM::Bool=false)
+function md_val(a::uwreal, obs::Corr, derm::Vector{Corr}; new_version::Bool=false)
     nid = neid(a)
     if nid != 1
         error("Error: neid > 1")
@@ -533,59 +532,6 @@ function md_val(a::uwreal, obs::Corr, derm::Vector{Corr}; new_version::Bool=fals
         ow_data = ow_mean .+ ws.fluc[n_ow]
         =#
         der = length(a.der) >= maximum(n_ow) ? a.der[n_ow] * w_mean : 0.0
-    elseif all(count.(prop) .== 3)
-        if TSM == false
-            return nothing
-        else
-            corr_der = getfield.(corr, :der)
-            n = findall.(t-> t==1, prop)
-            n = vcat(n'...)
-            if all(n[:, 1] .== n[1, 1]) # find ow and w
-                n_w = n[1, 1]
-                n_ow1 = n[:, 2]
-                n_ow2 = n[:, 3]
-            elseif all(n[:, 2] .== n[1, 2])
-                n_w = n[1, 2]
-                n_ow1 = n[:, 1]
-                n_ow2 = n[:, 3]
-            elseif all(n[:, 3] .== n[1, 3])
-                n_w = n[1, 3]
-                n_ow1 = n[:, 1]
-                n_ow2 = n[:, 2]
-            end
-            w_mean = 1 / getindex.(corr_der, n_ow1)[1]
-            if 1 / getindex.(corr_der, n_ow1)[1] != 1 / getindex.(corr_der, n_ow2)[1]
-                error("w_mean with n_ow1 and with n_ow2 different")
-            end
-            der = length(a.der) >= maximum(n_ow1) && length(a.der) >= maximum(n_ow2) ? a.der[n_ow1] * w_mean : 0.0
-        end
-    elseif all(count.(prop) .== 4)
-        if TSM == false
-            return nothing
-        else
-            corr_der = getfield.(corr, :der)
-            n = findall.(t-> t==1, prop)
-            n = vcat(n'...)
-            if all(n[:, 1] .== n[1, 1]) # find ow and w
-                n_w = n[1, 1]
-                n_ow1 = n[:, 2]
-                n_ow2 = n[:, 3]
-            elseif all(n[:, 2] .== n[1, 2])
-                n_w = n[1, 2]
-                n_ow1 = n[:, 1]
-                n_ow2 = n[:, 3]
-            elseif all(n[:, 3] .== n[1, 3]) && all(n[:, 4] .== n[1, 4])
-                n_w1 = n[1, 4]
-                n_w2 = n[1, 3]
-                n_ow1 = n[:, 1]
-                n_ow2 = n[:, 2]
-            end
-            w_mean = 1 / getindex.(corr_der, n_ow1)[1]
-            if 1 / getindex.(corr_der, n_ow1)[1] != 1 / getindex.(corr_der, n_ow2)[1]
-                error("w_mean with n_ow1 and with n_ow2 different")
-            end
-            der = length(a.der) >= maximum(n_ow1) && length(a.der) >= maximum(n_ow2) ? a.der[n_ow1] * w_mean : 0.0
-        end
     else
         return nothing
     end