Bump version after first batch of refactoring

Project.toml

@@ -1,7 +1,7 @@
 name = "Unfolding"
 uuid = "c5d2ca1b-4127-462e-b5fa-7f6e5ca16f83"
 authors = ["Rafael Moniz Caixeta"]
-version = "0.1.6"
+version = "0.1.7"
 
 [deps]
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"

src/data_handling.jl

@@ -32,7 +32,7 @@ names are passed in `colnames` array.
 function to_csv(input_matrix::AbstractArray,outname::String,colnames)
 
 	out = length(size(input_matrix))==1 ? input_matrix : input_matrix'
-	open(string(outname,".csv"); write=true) do f
+	open("$outname.csv"; write=true) do f
 		write(f, string(join(colnames,","),"\n"))
 		writedlm(f, out, ',')
 	end

src/landmark_isomap.jl

@@ -15,8 +15,8 @@ with the unfolded points.
 * `anchors` - number of anchors/landmark points for the dimensionality reduction.
 """
 function landmark_isomap(coords::AbstractMatrix; search="knn", neighval=16,
-	                     anchors=1500, dim=2)
-	n = size(coords, 2)
+	                     anchors=1500)
+	n, dim = size(coords, 2), 2
 	n < anchors && (anchors = n)
 
 	g, ianchors = graph_and_anchors(coords, search, neighval, anchors)
@@ -36,7 +36,7 @@ function landmark_isomap(coords::AbstractMatrix; search="knn", neighval=16,
 		tcoords[:,iothers] .= otcoords
 	else
 		M = fit(MDS, ADM, maxoutdim=dim, distances=true)
-		tcoords = dim == 2 ? vcat(transform(M),zeros(1, n)) : transform(M)
+		tcoords = vcat(transform(M),zeros(1, n))
 	end
  	tcoords
 end
@@ -66,7 +66,7 @@ function graph_and_anchors(ref_coords::AbstractMatrix, nhood, neigh_val, nanchor
 
 	g = SimpleWeightedGraph(Int.(src), Int.(dest), dwgt)
 	comps = length(connected_components(g))
-	@assert comps==1 string("$comps subgroups of isolated vertices, need to increase number of neighbors")
+	@assert comps==1 "$comps subgroups of isolated vertices, need to increase number of neighbors"
 
 	wgt = nhood=="inrange" ? [1/length(x) for x in idxs] : [mean(x) for x in dists]
 	ianchors = n > nanchors ? sort!(sample(1:n, Weights(wgt), nanchors, replace=false)) : collect(1:n)
@@ -93,7 +93,7 @@ function anchors_mds(ADM, dims)
     EM = (F.vectors[:,sorti])[:,1:dims]
     sq_eigenvals = sortλ[1:dims].^0.5
     AM = Diagonal(sq_eigenvals)
-    atcoords = dims == 2 ? vcat(permutedims(EM*AM),zeros(1,nx)) : permutedims(EM*AM)
+    atcoords = vcat(permutedims(EM*AM),zeros(1,nx))
 
 	# to use later for another points allocations
 	M1 = permutedims(EM)
@@ -106,5 +106,5 @@ end
 
 function triangulation(g::SimpleWeightedGraph, i, j, M1, M3)
 	M2 = dijkstra_shortest_paths(g, i).dists[j] .^ 2
-	size(M1,1) == 2 ? vcat(-0.5*M1*(M2-M3),0) : -0.5*M1*(M2-M3)
+	vcat(-0.5*M1*(M2-M3),0)
 end

test/runtests.jl

@@ -27,7 +27,7 @@ using Test
         df_block[:,c] = unf_block[i,:]
     end
 
-    to_vtk(unf_samps,"test_out_vtk",[(string("test",x),[rand() for i in 1:size(unf_samps,2)]) for x in 1:4])
+    to_vtk(unf_samps,"test_out_vtk",[("test$x",[rand() for i in 1:size(unf_samps,2)]) for x in 1:4])
     to_csv(unf_samps,"test_out_csv",["X","Y","Z"])
     error = error_dists(hcat(input_samps,input_block), hcat(unf_samps,unf_block), nneigh=8)