Remove all @inbounds annotations (#970)

* Remove `@inbounds`

* Bump version

Author: devmotion

Project.toml

@@ -1,7 +1,7 @@
 name = "StatsBase"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 authors = ["JuliaStats"]
-version = "0.34.6"
+version = "0.34.7"
 
 [deps]
 AliasTables = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"

src/counts.jl

@@ -31,7 +31,7 @@ function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitR
     m1 = last(levels)
     b = m0 - firstindex(levels) # firstindex(levels) == 1 because levels::UnitRange{<:Integer}
 
-    @inbounds for xi in x
+    for xi in x
         if m0 <= xi <= m1
             r[xi - b] += 1
         end
@@ -53,7 +53,7 @@ function addcounts!(r::AbstractArray, x::AbstractArray{<:Integer}, levels::UnitR
     m1 = last(levels)
     b = m0 - 1
 
-    @inbounds for i in eachindex(xv, wv)
+    for i in eachindex(xv, wv)
         xi = xv[i]
         if m0 <= xi <= m1
             r[xi - b] += wv[i]
@@ -287,9 +287,9 @@ function addcounts_dict!(cm::Dict{T}, x) where T
     for v in x
         index = ht_keyindex2!(cm, v)
         if index > 0
-            @inbounds cm.vals[index] += 1
+            cm.vals[index] += 1
         else
-            @inbounds Base._setindex!(cm, 1, v, -index)
+            Base._setindex!(cm, 1, v, -index)
         end
     end
     return cm
@@ -323,17 +323,17 @@ end
 function _addcounts!(::Type{T}, cm::Dict{T}, x; alg = :ignored) where T <: Union{UInt8, UInt16, Int8, Int16}
     counts = zeros(Int, 2^(8sizeof(T)))
 
-    @inbounds for xi in x
+    for xi in x
         counts[Int(xi) - typemin(T) + 1] += 1
     end
 
     for (i, c) in zip(typemin(T):typemax(T), counts)
         if c != 0
             index = ht_keyindex2!(cm, i)
             if index > 0
-                @inbounds cm.vals[index] += c
+                cm.vals[index] += c
             else
-                @inbounds Base._setindex!(cm, c, i, -index)
+                Base._setindex!(cm, c, i, -index)
             end
         end
     end
@@ -354,7 +354,7 @@ function _addcounts_radix_sort_loop!(cm::Dict{T}, sx::AbstractVector{T}) where T
 
     # now the data is sorted: can just run through and accumulate values before
     # adding into the Dict
-    @inbounds for i in start_i+1:lastindex(sx)
+    for i in start_i+1:lastindex(sx)
         sxi = sx[i]
         if !isequal(last_sx, sxi)
             cm[last_sx] = get(cm, last_sx, 0) + i - start_i
@@ -397,8 +397,8 @@ function addcounts!(cm::Dict{T}, x::AbstractArray{T}, wv::AbstractVector{W}) whe
     z = zero(W)
 
     for i in eachindex(xv, wv)
-        @inbounds xi = xv[i]
-        @inbounds wi = wv[i]
+        xi = xv[i]
+        wi = wv[i]
         cm[xi] = get(cm, xi, z) + wi
     end
     return cm

src/cov.jl

@@ -6,7 +6,7 @@ function _symmetrize!(a::DenseMatrix)
     m, n = size(a)
     m == n || error("a must be a square matrix.")
     for j = 1:n
-        @inbounds for i = j+1:n
+        for i = j+1:n
             vl = a[i,j]
             vr = a[j,i]
             a[i,j] = a[j,i] = middle(vl, vr)

src/deprecates.jl

@@ -71,7 +71,7 @@ function make_alias_table!(w::AbstractVector, wsum,
 
     ac = n / wsum
     for i = 1:n
-        @inbounds a[i] = w[i] * ac
+        a[i] = w[i] * ac
     end
 
     larges = Vector{Int}(undef, n)
@@ -80,7 +80,7 @@ function make_alias_table!(w::AbstractVector, wsum,
     ks = 0  # actual number of smalls
 
     for i = 1:n
-        @inbounds ai = a[i]
+        ai = a[i]
         if ai > 1.0
             larges[kl+=1] = i  # push to larges
         elseif ai < 1.0
@@ -91,8 +91,8 @@ function make_alias_table!(w::AbstractVector, wsum,
     while kl > 0 && ks > 0
         s = smalls[ks]; ks -= 1  # pop from smalls
         l = larges[kl]; kl -= 1  # pop from larges
-        @inbounds alias[s] = l
-        @inbounds al = a[l] = (a[l] - 1.0) + a[s]
+        alias[s] = l
+        al = a[l] = (a[l] - 1.0) + a[s]
         if al > 1.0
             larges[kl+=1] = l  # push to larges
         else
@@ -102,7 +102,7 @@ function make_alias_table!(w::AbstractVector, wsum,
 
     # this loop should be redundant, except for rounding
     for i = 1:ks
-        @inbounds a[smalls[i]] = 1.0
+        a[smalls[i]] = 1.0
     end
     nothing
 end

src/deviation.jl

@@ -13,7 +13,7 @@ function counteq(a::AbstractArray, b::AbstractArray)
     length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
     c = 0
     for i in eachindex(a, b)
-        @inbounds if a[i] == b[i]
+        if a[i] == b[i]
             c += 1
         end
     end
@@ -32,7 +32,7 @@ function countne(a::AbstractArray, b::AbstractArray)
     length(b) == n || throw(DimensionMismatch("Inconsistent lengths."))
     c = 0
     for i in eachindex(a, b)
-        @inbounds if a[i] != b[i]
+        if a[i] != b[i]
             c += 1
         end
     end
@@ -51,7 +51,7 @@ function sqL2dist(a::AbstractArray{T}, b::AbstractArray{T}) where T<:Number
     length(b) == n || throw(DimensionMismatch("Input dimension mismatch"))
     r = 0.0
     for i in eachindex(a, b)
-        @inbounds r += abs2(a[i] - b[i])
+        r += abs2(a[i] - b[i])
     end
     return r
 end
@@ -79,7 +79,7 @@ function L1dist(a::AbstractArray{T}, b::AbstractArray{T}) where T<:Number
     length(b) == n || throw(DimensionMismatch("Input dimension mismatch"))
     r = 0.0
     for i in eachindex(a, b)
-        @inbounds r += abs(a[i] - b[i])
+        r += abs(a[i] - b[i])
     end
     return r
 end
@@ -98,7 +98,7 @@ function Linfdist(a::AbstractArray{T}, b::AbstractArray{T}) where T<:Number
     length(b) == n || throw(DimensionMismatch("Input dimension mismatch"))
     r = 0.0
     for i in eachindex(a, b)
-        @inbounds v = abs(a[i] - b[i])
+        v = abs(a[i] - b[i])
         if r < v
             r = v
         end
@@ -119,8 +119,8 @@ function gkldiv(a::AbstractArray{T}, b::AbstractArray{T}) where T<:AbstractFloat
     n = length(a)
     r = 0.0
     for i in eachindex(a, b)
-        @inbounds ai = a[i]
-        @inbounds bi = b[i]
+        ai = a[i]
+        bi = b[i]
         if ai > 0
             r += (ai * log(ai / bi) - ai + bi)
         else

src/hist.jl

@@ -312,15 +312,15 @@ function push!(h::Histogram{T,N},xs::NTuple{N,Real},w::Real) where {T,N}
     h.isdensity && error("Density histogram must have float-type weights")
     idx = binindex(h, xs)
     if checkbounds(Bool, h.weights, idx...)
-        @inbounds h.weights[idx...] += w
+        h.weights[idx...] += w
     end
     h
 end
 
 function push!(h::Histogram{T,N},xs::NTuple{N,Real},w::Real) where {T<:AbstractFloat,N}
     idx = binindex(h, xs)
     if checkbounds(Bool, h.weights, idx...)
-        @inbounds h.weights[idx...] += h.isdensity ? w / binvolume(h, idx) : w
+        h.weights[idx...] += h.isdensity ? w / binvolume(h, idx) : w
     end
     h
 end
@@ -329,14 +329,14 @@ push!(h::AbstractHistogram{T,N},xs::NTuple{N,Real}) where {T,N} = push!(h,xs,one
 
 
 function append!(h::AbstractHistogram{T,N}, vs::NTuple{N,AbstractVector}) where {T,N}
-    @inbounds for i in eachindex(vs...)
+    for i in eachindex(vs...)
         xs = _multi_getindex(i, vs...)
         push!(h, xs, one(T))
     end
     h
 end
 function append!(h::AbstractHistogram{T,N}, vs::NTuple{N,AbstractVector}, wv::AbstractVector) where {T,N}
-    @inbounds for i in eachindex(wv, vs...)
+    for i in eachindex(wv, vs...)
         xs = _multi_getindex(i, vs...)
         push!(h, xs, wv[i])
     end
@@ -434,7 +434,7 @@ Calculate the norm of histogram `h` as the absolute value of its integral.
         SumT = norm_type(h)
         v_0 = 1
         s_0 = zero(SumT)
-        @inbounds @nloops(
+        @nloops(
             $N, i, weights,
             d -> begin
                 v_{$N-d+1} = v_{$N-d} * _edge_binvolume(SumT, edges[d], i_d)
@@ -493,7 +493,7 @@ arrays appropriately. See description of `normalize` for details. Returns `h`.
                     # Divide weights by bin volume, for :pdf also divide by sum of weights
                     SumT = norm_type(h)
                     vs_0 = (mode == :pdf) ? sum(SumT, weights) : one(SumT)
-                    @inbounds @nloops $N i weights d->(vs_{$N-d+1} = vs_{$N-d} * _edge_binvolume(SumT, edges[d], i_d)) begin
+                    @nloops $N i weights d->(vs_{$N-d+1} = vs_{$N-d} * _edge_binvolume(SumT, edges[d], i_d)) begin
                         (@nref $N weights i) /= $(Symbol("vs_$N"))
                         for A in aux_weights
                             (@nref $N A i) /= $(Symbol("vs_$N"))

src/misc.jl

@@ -29,7 +29,7 @@ function rle(v::AbstractVector{T}) where T
     cl = 1
 
     i = 2
-    @inbounds while i <= n
+    while i <= n
         vi = v[i]
         if isequal(vi, cv)
             cl += 1
@@ -67,7 +67,7 @@ function inverse_rle(vals::AbstractVector{T}, lens::AbstractVector{<:Integer}) w
 
     r = Vector{T}(undef, n)
     p = 0
-    @inbounds for i = 1 : m
+    for i = 1 : m
         j = lens[i]
         j >= 0 || throw(ArgumentError("lengths must be non-negative"))
         v = vals[i]
@@ -89,7 +89,7 @@ the index of its first occurrence in `a`.
 function indexmap(a::AbstractArray{T}) where T
     d = Dict{T,Int}()
     for i = 1 : length(a)
-        @inbounds k = a[i]
+        k = a[i]
         if !haskey(d, k)
             d[k] = i
         end
@@ -108,7 +108,7 @@ function levelsmap(a::AbstractArray{T}) where T
     d = Dict{T,Int}()
     index = 1
     for i = 1 : length(a)
-        @inbounds k = a[i]
+        k = a[i]
         if !haskey(d, k)
             d[k] = index
             index += 1
@@ -171,7 +171,7 @@ function _indicatormat_dense(x::AbstractArray{T}, c::AbstractArray{T}) where T
     n = length(x)
     r = zeros(Bool, m, n)
     o = 0
-    @inbounds for i = 1 : n
+    for i = 1 : n
         xi = x[i]
         r[o + d[xi]] = true
         o += m
@@ -187,7 +187,7 @@ function _indicatormat_sparse(x::AbstractArray{T}, c::AbstractArray{T}) where T
     n = length(x)
 
     rinds = Vector{Int}(undef, n)
-    @inbounds for i = 1 : n
+    for i = 1 : n
         rinds[i] = d[x[i]]
     end
     return sparse(rinds, 1:n, true, m, n)