ops tweaks

Project.toml

@@ -35,8 +35,8 @@ ToeplitzMatrices = "c751599d-da0a-543b-9d20-d0a503d91d24"
 BlockArrays = "0.16"
 CompositeTypes = "0.1.2"
 DSP = "0.6, 0.7"
-DomainIntegrals = "0.3.6"
-DomainSets = "0.5.11"
+DomainIntegrals = "0.3.7"
+DomainSets = "0.5.12"
 FFTW = "1.3"
 FastGaussQuadrature = "0.4"
 FillArrays = "0.12,0.13"

src/bases/modifiers/derived_dict.jl

@@ -186,3 +186,5 @@ end
 # Implementing similardictionary is all it takes.
 
 similardictionary(dict::ConcreteDerivedDict, dict2::Dictionary) = ConcreteDerivedDict(dict2)
+
+modifiersymbol(d::ConcreteDerivedDict) = "alias"

src/bases/modifiers/operated_dict.jl

@@ -189,11 +189,17 @@ function _unsafe_eval_element_derivative(dict::OperatedDict, idxn, x, order, op,
     if isdiag(op)
         diag(op, idx) * unsafe_eval_element_derivative(src(dict), idxn, x, order)
     else
-        scratch_src[idx] = 1
-        apply!(op, scratch_dest, scratch_src)
-        scratch_src[idx] = 0
-		D = differentiation(superdict(dict))
-        eval_expansion(dest(D), D*scratch_dest, x)
+		if hasderivative(superdict(dict))
+		    scratch_src[idx] = 1
+		    apply!(op, scratch_dest, scratch_src)
+		    scratch_src[idx] = 0
+			D = differentiation(superdict(dict))
+		    eval_expansion(dest(D), D*scratch_dest, x)
+		else
+			A = matrix(op)
+			w = [unsafe_eval_element_derivative(src(dict), native_index(src(dict),k), x, order) for k in eachindex(src(dict))]
+			sum(A[k,idx] * w[k] for k in eachindex(dict))
+		end
     end
 end
 

src/bases/poly/ops/orthopoly.jl

@@ -80,15 +80,15 @@ function symmetric_jacobi_matrix(b::OPS)
             J[k,k+1] = sqrt(β[k+1])
         end
     end
-    J
+    SymTridiagonal(J)
 end
 
 function roots(b::OPS{T}) where {T<:Number}
     J = symmetric_jacobi_matrix(b)
     eigen(J).values
 end
 
-function roots(b::OPS{T}) where {T<:Union{BigFloat}}
+function roots(b::OPS{T}) where {T<:BigFloat}
     J = symmetric_jacobi_matrix(b)
     # assuming the user has imported GenericLinearAlgebra.jl
     sort(real(eigvals!(J)))
@@ -406,8 +406,8 @@ end
 See `monic_to_orthonormal_recurrence_coefficients`
 """
 function monic_to_orthonormal_recurrence_coefficients!(a::Array{T},b::Array{T},c::Array{T},α::Array{T},β::Array{T}) where {T}
-    a .= 1 ./ sqrt.(view(β,2:length(β)))
-    b .= -1.0.*view(α,1:length(α)-1)./sqrt.(view(β,2:length(β)))
-    c .= sqrt.(view(β,1:length(β)-1)./view(β,2:length(β)))
+    a .= 1 ./ sqrt.(β[2:length(β)])
+    b .= -one(T).*α[1:length(α)-1]./sqrt.(β[2:length(β)])
+    c .= sqrt.(β[1:length(β)-1]./β[2:length(β)])
     a,b,c
 end