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Commit 7d0ff853 authored by Alessandro 's avatar Alessandro
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Including Mrat covariances in juobs_const. 

Study and preliminary tests  on correlated fits
parent 0012cf43
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Showing with 90 additions and 31 deletions
constants/juobs_const.jl
View file @ 7d0ff853
......@@ -24,6 +24,14 @@ const t0_error = [11, 16, 18, 29] .* 1e-3
const t0_ph_value = [0.413]
const t0_ph_error = ones(1,1) .* 5e-3
## taking into account correlations in zm_tm
C = [[0.375369e-6, 0.429197e-6, -0.186896e-5] [0.429197e-6, 0.268393e-4, 0.686776e-4] [-0.186896e-5, 0.686776e-4, 0.212386e-3]]
z = cobs([0.348629, 0.020921, 0.070613], C, "z")
ZP(b) = z[1] + z[2] * (b - 3.79) + z[3] * (b - 3.79)^2
Mrat = uwreal([.9148, 0.0088], "M/mhad")
zm_tm_covar(b) = Mrat / ZP(b)
##
#Cov matrices
const C1 = zeros(5, 5)
const C2 = zeros(5, 5)
......
src/juobs.jl
View file @ 7d0ff853
......@@ -10,7 +10,7 @@ include("juobs_tools.jl")
include("juobs_obs.jl")
export read_mesons, read_ms1, read_ms, read_md, truncate_data!
export get_matrix, energies, uwdot, uweigvals, uweigvecs, uweigen, invert, getall_eigvals, getall_eigvecs, hess_reduce, uwcholesky, transpose, tridiag_reduction
export get_matrix, energies, uwdot, uweigvals, uweigvecs, uweigen, invert, getall_eigvals, getall_eigvecs, hess_reduce, uwcholesky, transpose, tridiag_reduction, make_positive_def, invert_covar_matrix
export corr_obs, corr_sym, md_sea, md_val, plat_av, lin_fit, x_lin_fit, y_lin_fit, fit_routine, global_fit_routine
export meff, mpcac, dec_const, dec_const_pcvc, comp_t0
......
src/juobs_linalg.jl
View file @ 7d0ff853
......@@ -741,6 +741,33 @@ function idty(n::Int64)
return res
end
function make_positive_def(A::Matrix)
B = 0.5 * (A + A')
U, S, V = svd(B)
H = V * Diagonal(S) * V'
A_hat = 0.5 * (B + H)
A_hat = 0.5 * ( A_hat + A_hat')
k = 0
while !isposdef(A_hat)
mineig = minimum(eigvals(A_hat))
eps = 2.220446049250313e-16
A_hat = A_hat + (-mineig*k^2 .+ eps*Matrix(I, size(A)))
k+=1
end
return A_hat
end
function invert_covar_matrix(A::Matrix)
F = svd(A)
cov_inv = F.V * Diagonal(1 ./F.S) * F.U'
return cov_inv
end
function more_penrose_inv(A::Matrix)
F = svd(A)
end
"""
......
src/juobs_obs.jl
View file @ 7d0ff853
......@@ -569,7 +569,7 @@ function comp_t0(Y::YData, plat::Vector{Int64}; L::Int64, pl::Bool=false,
model(x, p) = get_model(x, p, npol)
par = fit_routine(model, x, t2E, npol)
par, chi = fit_routine(model, x, t2E, npol)
fmin(x, p) = model(x, p) .- 0.3
t0 = root_error(fmin, t[nt0], par)
if pl
......@@ -661,7 +661,7 @@ function comp_t0(Y::Vector{YData}, plat::Vector{Int64}; L::Int64, pl::Bool=false
model(x, p) = get_model(x, p, npol)
par = fit_routine(model, x, t2E, npol)
par, chi = fit_routine(model, x, t2E, npol)
fmin(x, p) = model(x, p) .- 0.3
t0 = root_error(fmin, t[nt0], par)
if pl
......
src/juobs_tools.jl
View file @ 7d0ff853
......@@ -382,25 +382,44 @@ fit_routine(model, xdata, ydata, param=3)
fit_routine(model, xdata, ydata, param=3, covar=true)
```
"""
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)
function fit_routine(model::Function, xdata::Array{<:Real}, ydata::Array{uwreal}, param::Int64=3; info::Bool=false, wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing}=nothing, correlated_fit::Bool=false)
isnothing(wpm) ? uwerr.(ydata) : [uwerr(yaux, wpm) for yaux in ydata]
yval = value.(ydata)
yer = err.(ydata)
# Generate chi2 + solver
if !correlated_fit
chisq = gen_chisq(model, xdata, yer)
fit = curve_fit(model, xdata, yval, 1.0 ./ yer.^2, fill(0.5, param))
(upar, chi_exp) = isnothing(wpm) ? fit_error(chisq, coef(fit), ydata) : fit_error(chisq, coef(fit), ydata, wpm)
else
covar_inv = make_positive_def(invert_covar_matrix(cov(ydata)))
#covar_inv = (covar_inv + covar_inv')/2
chisq(par,dat) = gen_chisq_correlated(model, xdata, covar_inv, par, dat)
fit = curve_fit(model, xdata, yval, covar_inv, fill(0.5, param))
println(chisq(coef(fit), ydata))
(upar, chi_exp) = isnothing(wpm) ? fit_error(chisq, coef(fit), ydata, W=covar_inv) : fit_error(chisq, coef(fit), ydata, wpm, W=covar_inv)
end
chi2_fit_res = sum(fit.resid.^2 )
println("chi2 from fit residual = ", chi2_fit_res)
println("chi2 from chi2 function = ", chisq(coef(fit), ydata))
#Info
if info
for i = 1:length(upar)
isnothing(wpm) ? uwerr(upar[i]) : uwerr(upar[i], wpm)
print("\n Fit parameter: ", i, ": ")
details(upar[i])
end
chis2_corrected = (length(yval) - param) * chisq(coef(fit), ydata) / chi_exp
println("Chisq / chiexp: ", chisq(coef(fit), ydata), " / ", chi_exp, " (dof: ", length(yval) - param,")")
end
#chis2_corrected = (length(yval) - param) * chisq(coef(fit), ydata) / chi_exp
chis2_corrected = (length(yval) - param) * chi2_fit_res / chi_exp
#println("Chisq / chiexp: ", chisq(coef(fit), ydata), " / ", chi_exp, " (dof: ", length(yval) - param,")")
println("Chisq / chiexp: ", chi2_fit_res, " / ", chi_exp, " (dof: ", length(yval) - param,")")
println("Chisq corrected: ", chis2_corrected)
return upar, value(chis2_corrected)
return upar, chi2_fit_res / chi_exp
end
function fit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}, param::Int64=3;
......@@ -457,6 +476,8 @@ function fit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}
println("Chisq / chiexp: ", min_fun_cov(sol.minimizer), " / ", chi2_exp, " (dof: ", length(ydata) - param,")")
chis2_corrected = (length(ydata) - param) * min_fun_cov(sol.minimizer) / chi2_exp
println("Chisq corrected: ", chis2_corrected)
return upar, min_fun_cov(sol.minimizer) / chi2_exp
else
chisq_full(p, d) = get_chi2(model, d, ddat, p, Nalpha)
min_fun(t) = chisq_full(t, dat)
......@@ -466,6 +487,7 @@ function fit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}
println("Chisq / chiexp: ", min_fun(sol.minimizer), " / ", chi2_exp, " (dof: ", length(ydata) - param,")")
chis2_corrected = (length(ydata) - param) * min_fun(sol.minimizer) / chi2_exp
println("Chisq corrected: ", chis2_corrected)
return upar, min_fun(sol.minimizer) / chi2_exp
end
#### chisq_full, min_fun out of conditional ->
......@@ -477,7 +499,6 @@ function fit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}
# print("\n Fit parameter: ", i, ": ")
# details(upar[i])
#end
return upar, chis2_corrected
end
......@@ -505,7 +526,6 @@ function global_fit_routine(models::Vector{Function}, xdata::Vector{Array{Float6
fx = similar(x_flat)
for ii in 1:N_sets
mod = models[ii]
#par = param[idx[ii]]
fx[chunk_idx[ii]] = mod(x_flat[chunk_idx[ii]], param)
end
return fx
......@@ -514,26 +534,13 @@ function global_fit_routine(models::Vector{Function}, xdata::Vector{Array{Float6
chi = 0.0
for ii in 1:N_sets
mod = models[ii]
#par = param[idx[ii]]
# uncorrelated chi2
chi += sum((data[chunk_idx[ii]] .- mod(x_flat[chunk_idx[ii]], param)).^2 ./ err.(y_flat[chunk_idx[ii]]).^2 )
# correlated chi2
#cov_mat = cov(data[chunk_idx[ii]])
#chi += sum(
# juobs.transpose(data[chunk_idx[ii]] .- mod(x_flat[chunk_idx[ii]], param)) * cov_mat^(-1) * (data[chunk_idx[ii]] .- mod(x_flat[chunk_idx[ii]], param))
#)
end
return chi
end
#tmp_par = [0.1, 0.6, 1, 1, 0.5, 0.75, 1, 1, 1, 1, 1, 1]
#tmp_par = [1, 0.1, 1, 0.6]
#tmp_par = [1, 0.5, 1, 0.75]
#tmp = flat_chisq(tmp_par, y_flat)
#println("chi2 with correct values = ", tmp)
fit = curve_fit(flat_model, x_flat, value.(y_flat), 1.0 ./ err.(y_flat).^2, guess_param )
#W = cov(y_flat)^(-1)
#println(typeof(W))
#fit = curve_fit(flat_model, x_flat, value.(y_flat), W, guess_param )
#uncorrelated chi2exp
(upar, chi2_exp) = isnothing(wpm) ? fit_error(flat_chisq, coef(fit) , y_flat) : fit_error(flat_chisq, coef(fit) , y_flat, wpm)
#correlated chi2exp
......@@ -541,7 +548,6 @@ function global_fit_routine(models::Vector{Function}, xdata::Vector{Array{Float6
chisq = value(flat_chisq(coef(fit), y_flat))
if log
println("Converged param: ", fit.converged)
#println("wt param: ", fit.wt)
println("Chisq / chiexp: ", chisq, " / ", chi2_exp, " (dof: ", dof(fit),")")
chis2_corrected = (dof(fit)) * chisq / chi2_exp
println("Chisq corrected: ", chis2_corrected)
......@@ -553,6 +559,24 @@ function gen_chisq(f::Function, x::Array{<:Real}, err::Vector{Float64}) #constra
chisq(par, dat) = sum((dat .- f(x,par)).^2 ./err.^2)
return chisq
end
function gen_chisq_correlated(f::Function, x::Array{<:Real}, covar_inv::Matrix, par, dat )
chi2 = 0.0
#delta = dat - f(x, par)
#chi2 = (reshape(delta, 1,:) * covar_inv * delta )[1]
for i in 1:length(dat)
for j in 1:length(dat)
#println(f(x,par)[i])
#println(f(x,par)[j])
#println(f(x[j,:],par))
#println(f(x[i,:],par))
chi2 = chi2 + (dat[i] - f(x,par)[i]) * covar_inv[i,j] * (dat[j] - f(x,par)[j])
end
end
#chisq(par, dat) = sum(
# transpose((dat .- f(x,par))) * covar_inv * (dat .- f(x,par))
#)
return chi2
end
function get_chi2(f::Function, data, ddata, par, Nalpha) #full
chi2 = 0.0
......
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