Divide some tests into sub-testsets (qutip#186)

ytdHuang · Jul 11, 2024 · 4ad239b · 4ad239b
2 parents b225542 + e351356
commit 4ad239b
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diff --git a/src/qobj/quantum_object.jl b/src/qobj/quantum_object.jl
@@ -324,11 +324,11 @@ Returns the length of the matrix or vector corresponding to the [`QuantumObject`
 Base.length(A::QuantumObject{<:AbstractArray{T}}) where {T} = length(A.data)
 
 Base.isequal(A::QuantumObject{<:AbstractArray{T}}, B::QuantumObject{<:AbstractArray{T}}) where {T} =
-    isequal(A.data, B.data) && isequal(A.type, B.type) && isequal(A.dims, B.dims)
-Base.isapprox(A::QuantumObject{<:AbstractArray{T}}, B::QuantumObject{<:AbstractArray{T}}) where {T} =
-    isapprox(A.data, B.data) && isequal(A.type, B.type) && isequal(A.dims, B.dims)
+    isequal(A.type, B.type) && isequal(A.dims, B.dims) && isequal(A.data, B.data)
+Base.isapprox(A::QuantumObject{<:AbstractArray{T}}, B::QuantumObject{<:AbstractArray{T}}; kwargs...) where {T} =
+    isequal(A.type, B.type) && isequal(A.dims, B.dims) && isapprox(A.data, B.data; kwargs...)
 Base.:(==)(A::QuantumObject{<:AbstractArray{T}}, B::QuantumObject{<:AbstractArray{T}}) where {T} =
-    (A.data == B.data) && (A.type == B.type) && (A.dims == B.dims)
+    (A.type == B.type) && (A.dims == B.dims) && (A.data == B.data)
 
 SparseArrays.sparse(A::QuantumObject{<:AbstractArray{T}}) where {T} = QuantumObject(sparse(A.data), A.type, A.dims)
 SparseArrays.nnz(A::QuantumObject{<:AbstractSparseArray}) = nnz(A.data)

diff --git a/test/aqua.jl b/test/aqua.jl
diff --git a/test/code_quality.jl b/test/code_quality.jl
@@ -0,0 +1,11 @@
+using Aqua, JET
+
+@testset "Code quality" verbose = true begin
+    @testset "Aqua.jl" begin
+        Aqua.test_all(QuantumToolbox; ambiguities = false)
+    end
+
+    @testset "JET.jl" begin
+        JET.test_package(QuantumToolbox; target_defined_modules = true, ignore_missing_comparison = true)
+    end
+end
diff --git a/test/cuda_ext.jl b/test/cuda_ext.jl
@@ -4,7 +4,7 @@ using CUDA.CUSPARSE
 QuantumToolbox.about()
 CUDA.versioninfo()
 
-@testset "CUDA Extension" begin
+@testset "CUDA Extension" verbose = true begin
     ψdi = Qobj(Int64[1, 0])
     ψdf = Qobj(Float64[1, 0])
     ψdc = Qobj(ComplexF64[1, 0])

diff --git a/test/jet.jl b/test/jet.jl
diff --git a/test/negativity_and_partial_transpose.jl b/test/negativity_and_partial_transpose.jl
@@ -1,32 +1,34 @@
-@testset "Negativity and Partial Transpose" begin
-    # tests for negativity
-    rho = (1 / 40) * Qobj(
-        [
-            15 1 1 15
-            1 5 -3 1
-            1 -3 5 1
-            15 1 1 15
-        ];
-        dims = [2, 2],
-    )
-    Neg = negativity(rho, 1)
-    @test Neg ≈ 0.25
-    @test negativity(rho, 2) ≈ Neg
-    @test negativity(rho, 1; logarithmic = true) ≈ log2(2 * Neg + 1)
-    @test_throws ArgumentError negativity(rho, 3)
+@testset "Negativity and Partial Transpose" verbose = true begin
+    @testset "negativity" begin
+        rho = (1 / 40) * Qobj(
+            [
+                15 1 1 15
+                1 5 -3 1
+                1 -3 5 1
+                15 1 1 15
+            ];
+            dims = [2, 2],
+        )
+        Neg = negativity(rho, 1)
+        @test Neg ≈ 0.25
+        @test negativity(rho, 2) ≈ Neg
+        @test negativity(rho, 1; logarithmic = true) ≈ log2(2 * Neg + 1)
+        @test_throws ArgumentError negativity(rho, 3)
+    end
 
-    # tests for partial transpose (PT)
-    # A (24 * 24)-matrix which contains number 1 ~ 576
-    A_dense = Qobj(reshape(1:(24^2), (24, 24)), dims = [2, 3, 4])
-    A_sparse = dense_to_sparse(A_dense)
-    PT = (true, false)
-    for s1 in PT
-        for s2 in PT
-            for s3 in PT
-                mask = [s1, s2, s3]
-                @test partial_transpose(A_dense, mask) == partial_transpose(A_sparse, mask)
+    @testset "partial_transpose" begin
+        # A (24 * 24)-matrix which contains number 1 ~ 576
+        A_dense = Qobj(reshape(1:(24^2), (24, 24)), dims = [2, 3, 4])
+        A_sparse = dense_to_sparse(A_dense)
+        PT = (true, false)
+        for s1 in PT
+            for s2 in PT
+                for s3 in PT
+                    mask = [s1, s2, s3]
+                    @test partial_transpose(A_dense, mask) == partial_transpose(A_sparse, mask)
+                end
             end
         end
+        @test_throws ArgumentError partial_transpose(A_dense, [true])
     end
-    @test_throws ArgumentError partial_transpose(rho, [true])
 end