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feat: Consolidate FEMBase.jl into JuliaFEM (Phase 1 complete)
MAJOR MILESTONE: FEMBase + FEMBasis fully consolidated, JuliaFEM loads! Consolidated files: - src/elements/ (3 files): elements.jl, elements_lagrange.jl, integrate.jl - src/fields/ (1 file): fields.jl (DCTI, DVTI, DCTV, DVTV, etc.) - src/sparse/ (1 file): sparse.jl (SparseMatrixCOO, SparseVectorCOO) - src/assembly/ (2 files): problems.jl, assembly.jl - src/solvers/ (1 file): solvers_base.jl - src/analysis.jl, src/core_types.jl (Node, IP, IntegrationPoint) Changes to JuliaFEM.jl: - Added dependencies: Tensors, Calculus - Removed @reexport using FEMBase (now consolidated) - Added 20+ include statements for consolidated files - Include order: fields → core_types → fembase_compat → sparse → elements Compatibility layer: - Created fembase_compat.jl: Minimal FEMBase submodule for vendor packages - Temporarily disabled vendor-specific Mortar2D functions in solvers_modal.jl Bug fixes: - Changed i == 1 → isequal(i, 1) in integrate.jl (== operator overridden by fields) - Resolved all FEMBasis. namespace references throughout codebase Result: - ✅ JuliaFEM loads successfully on Julia 1.12.1 - ✅ 134 exported symbols (was 171 with separate FEMBase) - ✅ Core types accessible: Seg2, Quad4, Problem, AbstractProblem, etc. - ⚠️ Vendor packages show FEMBase cache warnings (expected, harmless) TODO: - Re-enable Mortar2D functions after vendor consolidation - Field system == operator override needs redesign (Phase 4) - Continue Phase 2: Consolidate remaining vendor packages
This commit is contained in:
Jukka Aho
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# This file is a part of JuliaFEM.
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# License is MIT: see https://github.com/JuliaFEM/FEMBase.jl/blob/master/LICENSE
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using SparseArrays
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import SparseArrays: sparse, sparsevec
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mutable struct SparseMatrixCOO{T<:Real}
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I :: Vector{Int}
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J :: Vector{Int}
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V :: Vector{T}
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end
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const SparseVectorCOO = SparseMatrixCOO
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function SparseMatrixCOO()
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return SparseMatrixCOO{Float64}([], [], [])
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end
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function SparseVectorCOO(I::Vector, V::Vector)
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return SparseVectorCOO(I, fill!(similar(I), 1), V)
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end
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function convert(::Type{SparseMatrixCOO}, A::SparseMatrixCSC)
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return SparseMatrixCOO(findnz(A)...)
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end
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function convert(::Type{SparseVectorCOO}, A::SparseVector)
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return SparseVectorCOO(findnz(A)...)
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end
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function convert(::Type{SparseMatrixCOO}, A::Matrix)
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idx = findall(!iszero, A)
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I = getindex.(idx, 1)
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J = getindex.(idx, 2)
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V = [A[i] for i in idx]
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return SparseMatrixCOO(I, J, V)
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end
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function convert(::Type{SparseMatrixCOO}, b::Vector)
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I = findall(!iszero, b)
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J = fill(1, size(I))
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V = b[I]
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return SparseMatrixCOO(I, J, V)
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end
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SparseArrays.sparse(A::SparseMatrixCOO) = sparse(A.I, A.J, A.V)
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SparseArrays.sparse(A::SparseMatrixCOO, n::Int, m::Int) = sparse(A.I, A.J, A.V, n, m)
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SparseArrays.sparse(A::SparseMatrixCOO, n::Int, m::Int, f::Function) = sparse(A.I, A.J, A.V, n, m, f)
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Base.Matrix(A::SparseMatrixCOO) = Matrix(sparse(A))
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Base.Matrix(A::SparseMatrixCOO, n::Int, m::Int) = Matrix(sparse(A, n, m))
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SparseArrays.sparsevec(b::SparseVectorCOO) = sparsevec(b.I, b.V)
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SparseArrays.sparsevec(b::SparseVectorCOO, n::Int) = sparsevec(b.I, b.V, n)
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Base.Vector(b::SparseVectorCOO) = Vector(sparsevec(b))
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Base.Vector(b::SparseVectorCOO, n::Int) = Vector(sparsevec(b, n))
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function add!(A::SparseMatrixCOO, I::Int, J::Int, V::Float64)
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push!(A.I, I)
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push!(A.J, J)
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push!(A.V, V)
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return nothing
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end
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function add!(A::SparseMatrixCOO, I::Int, V::Float64)
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push!(A.I, I)
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push!(A.J, 1)
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push!(A.V, V)
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return nothing
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end
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function empty!(A::SparseMatrixCOO)
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empty!(A.I)
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empty!(A.J)
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empty!(A.V)
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return nothing
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end
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function append!(A::SparseMatrixCOO, B::SparseMatrixCOO)
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append!(A.I, B.I)
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append!(A.J, B.J)
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append!(A.V, B.V)
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return nothing
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end
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function isempty(A::SparseMatrixCOO)
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return isempty(A.I) && isempty(A.J) && isempty(A.V)
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end
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"""
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add!(K, dofs1, dofs2, ke)
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Add local element matrix `ke` to sparse matrix `K` for indices defined by `dofs1`
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and `dofs2`. This basically does `A[dofs1, dofs2] = A[dofs1, dofs2] + data`.
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# Examples
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```julia
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S = [3, 4]
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M = [6, 7, 8]
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ke = [5 6 7; 8 9 10]
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K = SparseMatrixCOO()
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add!(K, S, M, ke)
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Matrix(A)
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# output
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4x8 Array{Float64,2}:
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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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0.0 0.0 0.0 0.0 0.0 5.0 6.0 7.0
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0.0 0.0 0.0 0.0 0.0 8.0 9.0 10.0
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```
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"""
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function add!(A::SparseMatrixCOO, dofs1::AbstractVector{Int}, dofs2::AbstractVector{Int}, data)
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n, m = length(dofs1), length(dofs2)
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@assert length(data) == n*m
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k = 1
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for j=1:m
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for i=1:n
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add!(A, dofs1[i], dofs2[j], data[k])
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k += 1
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end
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end
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return nothing
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end
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""" Add sparse matrix of CSC to COO. """
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function add!(A::SparseMatrixCOO, B::SparseMatrixCSC)
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i, j, v = findnz(B)
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C = SparseMatrixCOO(i, j, v)
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append!(A, C)
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end
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""" Add new data to COO Sparse vector. """
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function add!(A::SparseMatrixCOO, dofs::Vector{Int}, data::Array{Float64}, dim::Int=1)
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if length(dofs) != length(data)
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@error("Dimension mismatch when adding data to sparse vector!", dofs, data)
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error("Simulation stopped.")
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end
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append!(A.I, dofs)
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append!(A.J, dim*ones(Int, length(dofs)))
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append!(A.V, vec(data))
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end
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""" Add SparseVector to SparseVectorCOO. """
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function add!(a::SparseVectorCOO, b::SparseVector)
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i, v = findnz(b)
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c = SparseVectorCOO(i, v)
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append!(a, c)
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return
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end
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"""
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get_nonzero_rows(A)
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Returns indices of all nonzero rows from a sparse matrix `A`.
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"""
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function get_nonzero_rows(A)
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return sort(unique(A.rowval))
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end
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"""
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get_nonzero_columns(A)
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Returns indices of all nonzero columns from a sparse matrix `A`.
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"""
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function get_nonzero_columns(A)
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return get_nonzero_rows(copy(transpose(A)))
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end
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function size(A::SparseMatrixCOO)
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isempty(A) && return (0, 0)
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return maximum(A.I), maximum(A.J)
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end
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function size(A::SparseMatrixCOO, idx::Int)
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return size(A)[idx]
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end
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""" Resize sparse matrix A to (higher) dimension n x m. """
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function resize_sparse(A, n, m)
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idx = findall(!iszero, A)
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I = getindex.(idx, 1)
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J = getindex.(idx, 2)
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V = [A[i] for i in idx]
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return sparse(I, J, V, n, m)
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end
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""" Resize sparse vector b to (higher) dimension n. """
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function resize_sparsevec(b, n)
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return sparsevec(b.nzind, b.nzval, n)
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end
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""" Approximative comparison of two matrices A and B. """
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function isapprox(A::SparseMatrixCOO, B::SparseMatrixCOO)
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A2 = sparse(A)
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B2 = sparse(B, size(A2)...)
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return isapprox(A2, B2)
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end
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isapprox(A::SparseMatrixCOO, B) = isapprox(Matrix(A), B)
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isapprox(A, B::SparseMatrixCOO) = isapprox(A, Matrix(B))
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