more updates

src/BlockTensorKit.jl

@@ -17,44 +17,45 @@ using Strided
 using TupleTools: getindices, isperm
 using BlockArrays
 using TupleTools
+using Base: @propagate_inbounds
 
-using VectorInterface: VectorInterface
+import VectorInterface as VI
 import TensorKit as TK
 import TensorOperations as TO
 
 # Spaces
 include("sumspace.jl")
-export SumSpace
+export SumSpace, ProductSumSpace
 
 # TensorMaps
-include("blockarray.jl")
+# include("blocktensor.jl")
 include("blocktensor.jl")
-include("sparseblocktensor.jl")
 export BlockTensorMap
+# export blocktype
 
 # implementations
-include("indexmanipulations.jl")
-include("linalg.jl")
-include("vectorinterface.jl")
-include("tensoroperations.jl")
-
-function adjointtensormaptype(::Type{S}, N₁::Int, N₂::Int, ::Type{T}) where {S,T}
-    I = sectortype(S)
-    if T <: DenseMatrix
-        M = T
-    elseif T <: Number
-        M = Matrix{T}
-    else
-        throw(ArgumentError("the final argument of `tensormaptype` should either be the scalar or the storage type, i.e. a subtype of `Number` or of `DenseMatrix`"))
-    end
-    if I === Trivial
-        return AdjointTensorMap{S,N₁,N₂,I,M,Nothing,Nothing}
-    else
-        F₁ = fusiontreetype(I, N₁)
-        F₂ = fusiontreetype(I, N₂)
-        return AdjointTensorMap{S,N₁,N₂,I,SectorDict{I,M},F₁,F₂}
-    end
-end
-adjointtensormaptype(S, N₁, N₂=0) = adjointtensormaptype(S, N₁, N₂, Float64)
+# include("indexmanipulations.jl")
+# include("linalg.jl")
+# include("vectorinterface.jl")
+# include("tensoroperations.jl")
+
+# function adjointtensormaptype(::Type{S}, N₁::Int, N₂::Int, ::Type{T}) where {S,T}
+#     I = sectortype(S)
+#     if T <: DenseMatrix
+#         M = T
+#     elseif T <: Number
+#         M = Matrix{T}
+#     else
+#         throw(ArgumentError("the final argument of `tensormaptype` should either be the scalar or the storage type, i.e. a subtype of `Number` or of `DenseMatrix`"))
+#     end
+#     if I === Trivial
+#         return AdjointTensorMap{S,N₁,N₂,I,M,Nothing,Nothing}
+#     else
+#         F₁ = fusiontreetype(I, N₁)
+#         F₂ = fusiontreetype(I, N₂)
+#         return AdjointTensorMap{S,N₁,N₂,I,SectorDict{I,M},F₁,F₂}
+#     end
+# end
+# adjointtensormaptype(S, N₁, N₂=0) = adjointtensormaptype(S, N₁, N₂, Float64)
 
 end