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Javier Ugarrio
juobs
Commits
92f6a225
Commit
92f6a225
authored
Feb 23, 2021
by
Javier
Browse files
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Combined fits + full chi2 fit
get_chi2 + fit_routine restructured
parent
f7c87a98
Changes
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src/juobs_tools.jl
src/juobs_tools.jl
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src/juobs_tools.jl
View file @
92f6a225
...
...
@@ -249,17 +249,22 @@ 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.
'''@example
@. model(x,p) = p[1] + p[2] * exp(-(p[3]-p[1])*x)
@. model2(x,p) = p[1] + p[2] * x[:, 1] + (p[3] + p[4] * x[:, 1]) * x[:, 2]
fit_routine(model, xdata, ydata, param=3)
"""
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
)
isnothing
(
wpm
)
?
uwerr
.
(
ydata
)
:
uwerr
.
(
ydata
,
wpm
)
yval
=
value
.
(
ydata
)
yer
=
err
.
(
ydata
)
# Generate chi2 + solver
chisq
=
gen_chisq
(
model
,
xdata
,
yer
)
min_fun
(
t
)
=
chisq
(
t
,
yval
)
sol
=
optimize
(
min_fun
,
fill
(
0.5
,
param
),
method
=
LBFGS
())
par
=
Optim
.
minimizer
(
sol
)
# Info
(
upar
,
chi_exp
)
=
isnothing
(
wpm
)
?
fit_error
(
chisq
,
par
,
ydata
)
:
fit_error
(
chisq
,
par
,
ydata
,
wpm
)
for
i
=
1
:
length
(
upar
)
isnothing
(
wpm
)
?
uwerr
(
upar
[
i
])
:
uwerr
(
upar
[
i
],
wpm
)
...
...
@@ -269,15 +274,12 @@ function fit_routine(model::Function, xdata::Array{<:Real}, ydata::Array{uwreal}
println
(
"Chisq / chiexp: "
,
sol
.
minimum
,
" / "
,
chi_exp
,
" (dof: "
,
length
(
yval
)
-
param
,
")"
)
return
upar
end
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
#TODO: COMBINED FITS, UPDATE DOC
function
fit_routine
(
model
::
Function
,
xdata
::
Array
{
uwreal
},
ydata
::
Array
{
uwreal
},
param
::
Int64
=
3
;
wpm
::
Union
{
Dict
{
Int64
,
Vector
{
Float64
}},
Dict
{
String
,
Vector
{
Float64
}},
Nothing
}
=
nothing
,
covar
::
Bool
=
false
)
Nalpha
=
size
(
xdata
,
2
)
# number of x-variables
Ndata
=
size
(
xdata
,
1
)
# number of datapoints
if
isnothing
(
wpm
)
uwerr
.
(
ydata
)
uwerr
.
(
xdata
)
...
...
@@ -291,33 +293,53 @@ function fit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}
xval
=
value
.
(
xdata
)
xer
=
err
.
(
xdata
)
dat
=
vcat
(
xval
,
yval
)
ddat
=
vcat
(
xer
,
yer
)
data
=
vcat
(
xdata
,
ydata
)
dat
=
Vector
{
Float64
}(
undef
,
Ndata
*
(
Nalpha
+
1
)
)
ddat
=
Vector
{
Float64
}(
undef
,
Ndata
*
(
Nalpha
+
1
)
)
data
=
Vector
{
uwreal
}(
undef
,
Ndata
*
(
Nalpha
+
1
))
#guess
for
i
=
1
:
Nalpha
dat
[(
i
-
1
)
*
Ndata
+
1
:
i
*
Ndata
]
=
xval
[
:
,
i
]
ddat
[(
i
-
1
)
*
Ndata
+
1
:
i
*
Ndata
]
=
xer
[
:
,
i
]
data
[(
i
-
1
)
*
Ndata
+
1
:
i
*
Ndata
]
=
xdata
[
:
,
i
]
end
dat
[
Nalpha
*
Ndata
+
1
:
end
]
=
yval
ddat
[
Nalpha
*
Ndata
+
1
:
end
]
=
yer
data
[
Nalpha
*
Ndata
+
1
:
end
]
=
ydata
# Guess
chisq
=
gen_chisq
(
model
,
xval
,
yer
)
min_fun_cons
(
t
)
=
chisq
(
t
,
yval
)
sol_cons
=
optimize
(
min_fun_cons
,
fill
(
0.5
,
param
),
method
=
LBFGS
())
par_cons
=
Optim
.
minimizer
(
sol_cons
)
# Generate chi2 + solver
if
covar
C
=
[
ADerrors
.
cov
([
xdata
[
k
],
ydata
[
k
]])
for
k
=
1
:
length
(
ydata
)]
chisq_full_cov
(
p
,
d
)
=
get_chi2_cov
(
model
,
d
,
C
,
p
)
aux
=
Vector
{
Vector
{
uwreal
}}(
undef
,
Ndata
)
for
k
=
1
:
Ndata
aux
[
k
]
=
Vector
{
uwreal
}(
undef
,
Nalpha
+
1
)
for
i
=
1
:
Nalpha
aux
[
k
][
i
]
=
xdata
[
k
,
i
]
end
aux
[
k
][
Nalpha
+
1
]
=
ydata
[
k
]
end
C
=
isnothing
(
wpm
)
?
[
ADerrors
.
cov
(
aux
[
k
])
for
k
=
1
:
Ndata
]
:
[
ADerrors
.
cov
(
aux
[
k
],
wpm
)
for
k
=
1
:
Ndata
]
chisq_full_cov
(
p
,
d
)
=
get_chi2_cov
(
model
,
d
,
C
,
p
,
Nalpha
)
min_fun_cov
(
t
)
=
chisq_full_cov
(
t
,
dat
)
sol
=
optimize
(
min_fun_cov
,
vcat
(
par_cons
,
xval
),
method
=
LBFGS
())
sol
=
optimize
(
min_fun_cov
,
vcat
(
par_cons
,
dat
[
1
:
Nalpha
*
Ndata
]
),
method
=
LBFGS
())
(
upar
,
chi2_exp
)
=
isnothing
(
wpm
)
?
fit_error
(
chisq_full_cov
,
Optim
.
minimizer
(
sol
),
data
)
:
fit_error
(
chisq_full_cov
,
Optim
.
minimizer
(
sol
),
data
,
wpm
)
else
chisq_full
(
p
,
d
)
=
get_chi2
(
model
,
d
,
ddat
,
p
)
chisq_full
(
p
,
d
)
=
get_chi2
(
model
,
d
,
ddat
,
p
,
Nalpha
)
min_fun
(
t
)
=
chisq_full
(
t
,
dat
)
sol
=
optimize
(
min_fun
,
vcat
(
par_cons
,
xval
),
method
=
LBFGS
())
sol
=
optimize
(
min_fun
,
vcat
(
par_cons
,
dat
[
1
:
Nalpha
*
Ndata
]),
method
=
LBFGS
())
(
upar
,
chi2_exp
)
=
isnothing
(
wpm
)
?
fit_error
(
chisq_full
,
Optim
.
minimizer
(
sol
),
data
)
:
fit_error
(
chisq_full
,
Optim
.
minimizer
(
sol
),
data
,
wpm
)
end
#### chisq_full, min_fun out of conditional ->
#### COMPILER WARNING ** incremental compilation may be fatally broken for this module **
# Info
for
i
=
1
:
length
(
upar
)
isnothing
(
wpm
)
?
uwerr
(
upar
[
i
])
:
uwerr
(
upar
[
i
],
wpm
)
print
(
"
\n
Fit parameter: "
,
i
,
": "
)
...
...
@@ -328,113 +350,54 @@ function fit_routine(model::Function, xdata::Array{uwreal}, ydata::Array{uwreal}
end
function
get_chi2
(
f
::
Function
,
x
,
dx
,
y
,
dy
,
par
)
#full
function
gen_chisq
(
f
::
Function
,
x
::
Array
{
<:
Real
},
err
::
Vector
{
Float64
})
#constrained
chisq
(
par
,
dat
)
=
sum
((
dat
.-
f
(
x
,
par
))
.^
2
./
err
.^
2
)
return
chisq
end
function
get_chi2
(
f
::
Function
,
data
,
ddata
,
par
,
Nalpha
)
#full
chi2
=
0.0
Ndata
=
length
(
x
)
Npar
=
length
(
par
)
-
Ndata
Ndata
=
div
(
length
(
data
),
Nalpha
+
1
)
Npar
=
length
(
par
)
-
Ndata
*
Nalpha
p
=
par
[
1
:
Npar
]
for
k
=
1
:
Ndata
xx
=
par
[
Npar
+
k
]
yy
=
f
(
xx
,
p
)
xx
=
[
par
[
Npar
+
k
+
(
i
-
1
)
*
Ndata
]
for
i
=
1
:
Nalpha
]
Cinv
=
zeros
(
Nalpha
+
1
,
Nalpha
+
1
)
[
Cinv
[
i
,
i
]
=
1
/
ddata
[
k
+
(
i
-
1
)
*
Ndata
]
^
2
for
i
=
1
:
Nalpha
+
1
]
xx
=
[
par
[
Npar
+
k
+
(
i
-
1
)
*
Ndata
]
for
i
=
1
:
Nalpha
]
delta
=
[
data
[
k
+
(
i
-
1
)
*
Ndata
]
-
xx
[
i
]
for
i
=
1
:
Nalpha
]
yy
=
f
(
xx
'
,
p
)
push!
(
delta
,
data
[
k
+
Nalpha
*
Ndata
]
-
yy
[
1
])
delta
=
[
x
[
k
]
-
xx
,
y
[
k
]
-
yy
]
C
=
[[
1
/
dx
[
k
]
^
2
,
0.0
]
[
0.0
,
1
/
dy
[
k
]
^
2
]]
chi2
+=
delta
'
*
C
*
delta
chi2
+=
delta
'
*
Cinv
*
delta
end
return
chi2
end
function
get_chi2
(
f
::
Function
,
data
,
ddata
,
par
)
#full
Ndata
=
div
(
length
(
data
),
2
)
x
=
data
[
1
:
Ndata
]
y
=
data
[
Ndata
+
1
:
end
]
dx
=
ddata
[
1
:
Ndata
]
dy
=
ddata
[
Ndata
+
1
:
end
]
return
get_chi2
(
f
,
x
,
dx
,
y
,
dy
,
par
)
end
function
get_chi2_cov
(
f
::
Function
,
x
,
y
,
C
,
par
)
#full + cov
function
get_chi2_cov
(
f
::
Function
,
data
,
C
,
par
,
Nalpha
)
# full + cov
chi2
=
0.0
Ndata
=
length
(
x
)
Npar
=
length
(
par
)
-
Ndata
Ndata
=
div
(
length
(
data
),
Nalpha
+
1
)
Npar
=
length
(
par
)
-
Ndata
*
Nalpha
p
=
par
[
1
:
Npar
]
for
k
=
1
:
Ndata
xx
=
par
[
Npar
+
k
]
yy
=
f
(
xx
,
p
)
delta
=
[
x
[
k
]
-
xx
,
y
[
k
]
-
yy
]
if
det
(
C
[
k
])
>
1e-12
if
det
(
C
[
k
])
/
prod
(
diag
(
C
[
k
]))
>
1e-6
Cinv
=
inv
(
C
[
k
])
else
C11
=
C
[
k
][
1
,
1
]
C22
=
C
[
k
][
2
,
2
]
Cinv
=
[[
1
/
C11
,
0.0
]
[
0.0
,
1
/
C22
]]
Cinv
=
zeros
(
Nalpha
+
1
,
Nalpha
+
1
)
[
Cinv
[
i
,
i
]
=
1
/
C
[
k
][
i
,
i
]
for
i
=
1
:
Nalpha
+
1
]
end
xx
=
[
par
[
Npar
+
k
+
(
i
-
1
)
*
Ndata
]
for
i
=
1
:
Nalpha
]
delta
=
[
data
[
k
+
(
i
-
1
)
*
Ndata
]
-
xx
[
i
]
for
i
=
1
:
Nalpha
]
yy
=
f
(
xx
'
,
p
)
push!
(
delta
,
data
[
k
+
Nalpha
*
Ndata
]
-
yy
[
1
])
chi2
+=
delta
'
*
Cinv
*
delta
chi2
+=
delta
'
*
Cinv
*
delta
end
return
chi2
end
function
get_chi2_cov
(
f
::
Function
,
data
,
C
,
par
)
#full + cov
Ndata
=
div
(
length
(
data
),
2
)
x
=
data
[
1
:
Ndata
]
y
=
data
[
Ndata
+
1
:
end
]
return
get_chi2_cov
(
f
,
x
,
y
,
C
,
par
)
end
#=
using LsqFit
@doc raw"""
find_xmin(obs::Vector{uwreal}, y0::Int64; pl::Bool=false)
find_xmin(corr::Corr; pl::Bool=false)
Find the platau starting point for a given correlator.
The starting point xmin is determined
|C_2|^2 * exp(-M_2 * (xmin-y0)) / (2 * M_2) < dC(xmin, y0) / 4
where C_2 and M_2 are the matrix element and mass of the first excited state and dC is
the statistical error of the correlator
"""
function find_xmin(obs::Vector{uwreal}, y0::Int64; pl::Bool=false)
p0 = [0.5, 0.5, 1.0, 1.0]
@. model(t, p) = p[1] * exp(-p[2] * (t-y0)) + p[3] * exp(-p[4] * (t-y0))
x = Int64.(0:length(obs)-1)
uwerr.(obs)
y = value.(obs)
dy = err.(obs)
wt = 1 ./ dy.^2
fit = curve_fit(model, Float64.(x[y0+1:end]), y[y0+1:end], p0)
fit = curve_fit(model, Float64.(x[y0+1:end]), y[y0+1:end], wt[y0+1:end], fit.param)
par = fit.param
#println(par)
if par[2] > par[4]
p1 = par[1]
p2 = par[2]
else
p1 = par[3]
p2 = par[4]
end
@. f(t) = p1^2 * exp(-p2 * (t-y0)) / (2 * p2)
xmin = findfirst(t-> f(t) < 0.25*dy[t + 1], x)
if pl
errorbar(x, y, dy, fmt="x")
t = Int64.(y0:length(obs))
plot(t, model(t, par))
display(gcf())
end
return xmin
end
find_xmin(corr::Corr; pl::Bool=false) = find_xmin(corr.obs, corr.y0, pl=pl)
=#
\ No newline at end of file
end
\ No newline at end of file
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