Closed hz-xiaxz closed 1 month ago
How to perform jackknife reasonably on complex number datasets is a question. I will try to find some reference later, or we should find another suitable way to resample complex numbers.
Ah, thanks forgot to test that feature!
-------- Original Message -------- On 7/28/24 12:01, LeoXia wrote:
Error occurs when evaluating complex observables.
ERROR
:
LoadError
:
MethodError
:
no method matching Carlo
.
Evaluable
(
::
Int64
,
::
Int64
,
::
Int64
,
::
Vector{ComplexF64}
,
::
Vector{ComplexF64}
)
Closest candidates are
:
Carlo
.
Evaluable
(
::
Int64
,
::
Int64
,
::
Int64
,
!
Matched
::
Vector{T}
,
!
Matched
::
Vector{T}
)
where
T
<:
AbstractFloat
@ Carlo
~
/
.
julia
/
dev
/
Carlo
/
src
/
evaluable
.
jl
:
3
this is because of the definition of struct Evaluable
struct
Evaluable{T
<:
AbstractFloat
} internal_bin_length
::
Int64
rebin_length
::
Int64
rebin_count
::
Int64
mean
::
Vector{T}
error
::
Vector{T}
end
where
julia> ComplexF64
isa
AbstractFloat false
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True, if you find a reference or a straightforward way to do it, you're welcome to open a pr.
I think a similar question appears already when computing the variance. But here I go with the definition of summing the real and imag variance just like the Julia functions do.
-------- Original Message -------- On 7/28/24 12:08, LeoXia wrote:
How to perform jackknife reasonably on complex number datasets is a question. I will try to find some reference later, or we should find another suitable way to resample complex numbers.
— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you are subscribed to this thread.Message ID: @.***>
Error occurs when evaluating complex observables.
this is because of the definition of struct
Evaluable
where