generalized dispatch for symbolic computation

chakravala · Nov 28, 2019 · c829d3f · c829d3f
1 parent e7fd9e0
commit c829d3f
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Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/src/algebra.jl b/src/algebra.jl
@@ -1957,7 +1957,7 @@ function ^(v::T,i::Integer) where T<:TensorTerm
     for k ∈ 1:(i-1)%4
         out *= basis(v)
     end
-    return typeof(v)<:Basis ? out : out*value(v)^i
+    return typeof(v)<:Basis ? out : out*DirectSum.:^(value(v),i)
 end
 
 function Base.:^(v::T,i::S) where {T<:TensorAlgebra{V},S<:Integer} where V

diff --git a/src/composite.jl b/src/composite.jl
@@ -7,7 +7,7 @@ export exph, log_fast, logh_fast
 ## exponential & logarithm function
 
 @inline Base.expm1(t::Basis{V,0}) where V = Simplex{V}(ℯ-1)
-@inline Base.expm1(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}(expm1(value(t)))
+@inline Base.expm1(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}(DirectSum.expm1(value(t)))
 
 function Base.expm1(t::T) where T<:TensorAlgebra{V} where V
     S,term,f = t,(t^2)/2,norm(t)
@@ -108,7 +108,7 @@ end
 for (qrt,n) ∈ ((:sqrt,2),(:cbrt,3))
     @eval begin
         @inline Base.$qrt(t::Basis{V,0} where V) = t
-        @inline Base.$qrt(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}($qrt(value(t)))
+        @inline Base.$qrt(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}($Sym.$qrt(value(t)))
         @inline function Base.$qrt(t::T) where T<:TensorAlgebra
             isscalar(t) ? $qrt(scalar(t)) : exp(log(t)/$n)
         end
@@ -117,7 +117,7 @@ end
 
 ## trigonometric
 
-@inline Base.cosh(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}(cosh(value(t)))
+@inline Base.cosh(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}(DirectSum.cosh(value(t)))
 
 function Base.cosh(t::T) where T<:TensorAlgebra{V} where V
     τ = t^2
@@ -166,7 +166,7 @@ end
     end
 end
 
-@inline Base.sinh(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}(sinh(value(t)))
+@inline Base.sinh(t::T) where T<:TensorTerm{V,0} where V = Simplex{V}(DirectSum.sinh(value(t)))
 
 function Base.sinh(t::T) where T<:TensorAlgebra{V} where V
     τ,f = t^2,norm(t)