Move SparseArrays and Statistics to extensions

Project.toml

@@ -8,19 +8,28 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Primes = "27ebfcd6-29c5-5fa9-bf4b-fb8fc14df3ae"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+
+[weakdeps]
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
+[extensions]
+SparseArraysExt = "SparseArrays"
+StatisticsExt = "Statistics"
+
 [compat]
 ExplicitImports = "1.13.2"
 Primes = "0.4, 0.5"
+SparseArrays = "<0.0.1, 1"
 Statistics = "<0.0.1, 1"
 julia = "1.10"
 
 [extras]
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["ExplicitImports", "Test", "SpecialFunctions"]
+test = ["ExplicitImports", "SparseArrays", "SpecialFunctions", "Statistics", "Test"]

ext/SparseArraysExt.jl

@@ -0,0 +1,14 @@
+module SparseArraysExt
+
+using DistributedArrays: DArray, localpart
+using DistributedArrays.Distributed: remotecall_fetch
+using SparseArrays: SparseArrays, nnz
+
+function SparseArrays.nnz(A::DArray)
+    B = asyncmap(A.pids) do p
+        remotecall_fetch(nnz∘localpart, p, A)
+    end
+    return reduce(+, B)
+end
+
+end

ext/StatisticsExt.jl

@@ -0,0 +1,8 @@
+module StatisticsExt
+
+using DistributedArrays: DArray
+using Statistics: Statistics
+
+Statistics._mean(f, A::DArray, region) = sum(f, A, dims = region) ./ prod((size(A, i) for i in region))
+
+end

src/DistributedArrays.jl

@@ -7,8 +7,6 @@ using Distributed: Distributed, RemoteChannel, Future, myid, nworkers, procs, re
 using LinearAlgebra: LinearAlgebra, Adjoint, Diagonal, I, Transpose, adjoint, adjoint!, axpy!, dot, lmul!, mul!, norm, rmul!, transpose, transpose!
 using Random: Random, rand!
 using Serialization: Serialization, AbstractSerializer, deserialize, serialize
-using SparseArrays: SparseArrays, nnz
-using Statistics: Statistics
 
 using Primes: factor
 

src/mapreduce.jl

@@ -117,13 +117,6 @@ function Base.count(f, A::DArray)
     return sum(B)
 end
 
-function SparseArrays.nnz(A::DArray)
-    B = asyncmap(A.pids) do p
-        remotecall_fetch(nnz∘localpart, p, A)
-    end
-    return reduce(+, B)
-end
-
 function Base.extrema(d::DArray)
     r = asyncmap(procs(d)) do p
         remotecall_fetch(p) do
@@ -133,8 +126,6 @@ function Base.extrema(d::DArray)
     return reduce((t,s) -> (min(t[1], s[1]), max(t[2], s[2])), r)
 end
 
-Statistics._mean(f, A::DArray, region) = sum(f, A, dims = region) ./ prod((size(A, i) for i in region))
-
 # Unary vector functions
 Base.:(-)(D::DArray) = map(-, D)