From b2c3c65e710ad4605089a6eecceb585e6f04b53e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=BAlio=20Hoffimann?= <julio.hoffimann@gmail.com>
Date: Tue, 15 Oct 2024 16:51:07 -0300
Subject: [PATCH] Refactor EmpiricalVariogram

---
 src/empirical/algorithms.jl |  6 +++---
 src/empirical/variogram.jl  | 12 ++++++------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/empirical/algorithms.jl b/src/empirical/algorithms.jl
index 05457d0..247eecc 100644
--- a/src/empirical/algorithms.jl
+++ b/src/empirical/algorithms.jl
@@ -49,9 +49,9 @@ function accumulate(data, var₁, var₂, estimator::Estimator, algo::AccumAlgor
 
   # lag sums and counts
   ℒ = Meshes.lentype(𝒫)
+  ns = zeros(Int, nlags)
   Σx = zeros(ℒ, nlags)
   Σy = zeros(V, nlags)
-  ns = zeros(Int, nlags)
 
   # loop over points inside ball
   @inbounds for j in 1:nelements(𝒫)
@@ -80,9 +80,9 @@ function accumulate(data, var₁, var₂, estimator::Estimator, algo::AccumAlgor
       lag == 0 && @warn "duplicate coordinates found, consider using `UniqueCoords`"
 
       if 0 < lag ≤ nlags && !ismissing(v)
+        ns[lag] += 1
         Σx[lag] += h
         Σy[lag] += v
-        ns[lag] += 1
       end
     end
   end
@@ -101,7 +101,7 @@ function accumulate(data, var₁, var₂, estimator::Estimator, algo::AccumAlgor
   ys = @. ordfun(Σy, ns)
   ys[ns .== 0] .= zero(eltype(ys))
 
-  xs, ys, ns
+  ns, xs, ys
 end
 
 include("algorithms/fullsearch.jl")
diff --git a/src/empirical/variogram.jl b/src/empirical/variogram.jl
index 3fabd55..44a58cd 100644
--- a/src/empirical/variogram.jl
+++ b/src/empirical/variogram.jl
@@ -47,9 +47,9 @@ See also: [`DirectionalVariogram`](@ref), [`PlanarVariogram`](@ref),
   (https://www.sciencedirect.com/science/article/pii/S0098300419302936)
 """
 struct EmpiricalVariogram{ℒ<:Len,V,D,E}
+  counts::Vector{Int}
   abscissa::Vector{ℒ}
   ordinate::Vector{V}
-  counts::Vector{Int}
   distance::D
   estimator::E
 end
@@ -75,9 +75,9 @@ function EmpiricalVariogram(
   estim, algo = estimalgo(𝒟, nlags, maxlag, distance, estimator, algorithm)
 
   # accumulate data with chosen algorithm
-  abscissa, ordinate, counts = accumulate(𝒮, var₁, var₂, estim, algo)
+  counts, abscissa, ordinate = accumulate(𝒮, var₁, var₂, estim, algo)
 
-  EmpiricalVariogram(abscissa, ordinate, counts, distance, estim)
+  EmpiricalVariogram(counts, abscissa, ordinate, distance, estim)
 end
 
 """
@@ -160,12 +160,12 @@ assuming that both variograms have the same number of lags, distance
 and estimator.
 """
 function merge(γα::EmpiricalVariogram{V,D,E}, γβ::EmpiricalVariogram{V,D,E}) where {V,D,E}
+  nα = γα.counts
+  nβ = γβ.counts
   xα = γα.abscissa
   xβ = γβ.abscissa
   yα = γα.ordinate
   yβ = γβ.ordinate
-  nα = γα.counts
-  nβ = γβ.counts
 
   # copy distance and estimator
   d = γα.distance
@@ -183,7 +183,7 @@ function merge(γα::EmpiricalVariogram{V,D,E}, γβ::EmpiricalVariogram{V,D,E})
   x[n .== 0] .= xα[n .== 0]
   y[n .== 0] .= 0
 
-  EmpiricalVariogram(x, y, n, d, e)
+  EmpiricalVariogram(n, x, y, d, e)
 end
 
 # -----------