clean imports

Author: ogauthe

test/Project.toml

@@ -8,7 +8,6 @@ DiagonalArrays = "74fd4be6-21e2-4f6f-823a-4360d37c7a77"
 GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 JLArrays = "27aeb0d3-9eb9-45fb-866b-73c2ecf80fcb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 SparseArraysBase = "0d5efcca-f356-4864-8770-e1ed8d78f208"
@@ -28,7 +27,6 @@ DiagonalArrays = "0.3"
 GPUArraysCore = "0.2"
 JLArrays = "0.2"
 LinearAlgebra = "1"
-Pkg = "1"
 Random = "1"
 SafeTestsets = "0.1"
 SparseArraysBase = "0.5"

test/test_basics.jl

@@ -4,12 +4,9 @@ using BlockArrays:
   BlockArrays,
   Block,
   BlockArray,
-  BlockIndexRange,
   BlockRange,
-  BlockSlice,
   BlockVector,
   BlockedOneTo,
-  BlockedUnitRange,
   BlockedArray,
   BlockedVector,
   blockedrange,
@@ -35,9 +32,8 @@ using BlockSparseArrays:
   view!
 using GPUArraysCore: @allowscalar
 using JLArrays: JLArray, JLMatrix
-using LinearAlgebra: Adjoint, Transpose, dot, mul!, norm
+using LinearAlgebra: Adjoint, Transpose, dot, norm
 using SparseArraysBase: SparseArrayDOK, SparseMatrixDOK, SparseVectorDOK, storedlength
-using TensorAlgebra: contract
 using Test: @test, @test_broken, @test_throws, @testset, @inferred
 using TestExtras: @constinferred
 using TypeParameterAccessors: TypeParameterAccessors, Position
@@ -1120,20 +1116,7 @@ arrayts = (Array, JLArray)
     @test a_dest[Block(2, 1)] == a1[Block(2, 1)]
     @test a_dest[Block(3, 4)] == a2[Block(1, 2)]
   end
-  @testset "TensorAlgebra" begin
-    a1 = dev(BlockSparseArray{elt}(undef, [2, 3], [2, 3]))
-    a1[Block(1, 1)] = dev(randn(elt, size(@view(a1[Block(1, 1)]))))
-    a2 = dev(BlockSparseArray{elt}(undef, [2, 3], [2, 3]))
-    a2[Block(1, 1)] = dev(randn(elt, size(@view(a1[Block(1, 1)]))))
-    # TODO: Make this work, requires customization of `TensorAlgebra.fusedims` and
-    # `TensorAlgebra.splitdims` in terms of `BlockSparseArrays.blockreshape`,
-    # and customization of `TensorAlgebra.:⊗` in terms of `GradedUnitRanges.tensor_product`.
-    a_dest, dimnames_dest = contract(a1, (1, -1), a2, (-1, 2))
-    @allowscalar begin
-      a_dest_dense, dimnames_dest_dense = contract(Array(a1), (1, -1), Array(a2), (-1, 2))
-      @test a_dest ≈ a_dest_dense
-    end
-  end
+
   @testset "blockreshape" begin
     a = dev(BlockSparseArray{elt}(undef, ([3, 4], [2, 3])))
     a[Block(1, 2)] = dev(randn(elt, size(@view(a[Block(1, 2)]))))