More dot syntax & update use of Compat

Revert changes of 29d641d and 8d90bae that avoided a::Number + r::Range
(related to https://discourse.julialang.org/t/broadcast-operations-with-ranges/10139,
which has been fixed in Julia 0.7-alpha).

Add using StatsBase
Remove @compat for Julia < 0.6, add Compat.SparseArrays.sparse

Author: heliosdrm

REQUIRE

@@ -1,3 +1,5 @@
 julia 0.6
 Distances 0.3.1
 Compat 0.62.0
+StatsBase
+

src/RecurrenceAnalysis.jl

@@ -1,6 +1,6 @@
 module RecurrenceAnalysis
 
-using Distances, Compat
+using Distances, StatsBase, Compat
 using Compat.SparseArrays, Compat.LinearAlgebra
 import Compat.String
 import Base.Meta.parse
@@ -47,7 +47,7 @@ end
 function colvals(x::SparseMatrixCSC)
     cv = zeros(Int,nnz(x))
     @inbounds for c=1:size(x,2)
-        cv[nzrange(x,c)] = c
+        cv[nzrange(x,c)] .= c
     end
     cv
 end
@@ -75,7 +75,7 @@ function embed_indices(x::AbstractMatrix, indices)
     dx = size(x)
     dxm = size(indices)
     ix_rep = repeat(indices, inner=[1,dx[2]])
-    ix_rep += repeat(dx[1]*(0:dx[2]-1)', outer=[dxm...])
+    ix_rep .+= repeat(dx[1].*(0:dx[2]-1)', outer=[dxm...])
     x[ix_rep]
 end
 
@@ -117,7 +117,7 @@ function recurrencematrix(x, radius; scale=1, kwargs...)
     argsdm = haskey(kwargs,:metric) ? (x, kwargs[:metric]) : (x,)
     dm = distancematrix(argsdm...)
     (typeof(scale) <: Function) && (scale = scale(dm))
-    sparse(dm .< radius*scale)
+    Compat.SparseArrays.sparse(dm .< radius*scale)
 end
 
 """
@@ -132,7 +132,7 @@ function crossrecurrencematrix(x, y, radius; scale=1, kwargs...)
     argsdm = haskey(kwargs,:metric) ? (x, y, kwargs[:metric]) : (x, y)
     dm = distancematrix(argsdm...)
     (typeof(scale) <: Function) && (scale = scale(dm))
-    sparse(dm .< radius*scale)
+    Compat.SparseArrays.sparse(dm .< radius*scale)
 end
 
 """

src/delay.jl

@@ -128,7 +128,7 @@ function gmi(x, delay::Integer, radius::Real)
     rm1 = rmfull[1:n, 1:n]
     h2 = -log2(nnz(rm1)/n^2)
     # Joint entropy
-    rmj = rm1 .* rmfull[delay+(1:n), delay+(1:n)]
+    rmj = rm1 .* rmfull[delay.+(1:n), delay.+(1:n)]
     h2tau = -log2(nnz(rmj)/n^2)
     # Maximum entropy for a given radius corresponds to uniform distribution
     maxh2 = log2((maximum(x)-minimum(x))/radius)
@@ -146,7 +146,7 @@ function gmi(x, delay::Union{Array, AbstractRange}, radius::Real)
     # Joint entropy
     rmj = spzeros(n, n)
     for d in delay
-        rmj = rm1 .* rmfull[(1+d):(n+d), (1+d):(n+d)]
+        rmj = rm1 .* rmfull[d.+(1:n), d.+(1:n)]
         h2tau[d+1] = -log2(nnz(rmj)/n^2)
     end
     # Maximum entropy for a given radius corresponds to uniform distribution

src/dimension.jl

@@ -3,8 +3,8 @@ function embedmatrix1(x::Array{T, 2}, delay, metric = "max") where T
     dist = getmetric(metric)
     fun = Dict(Euclidean()=>+, Chebyshev()=>max)[dist]
     nr, nc = size(x)
-    @compat x .= abs2.(x)
-    @compat y = sqrt.(fun.(x[1:nr-delay, 1:nc-delay], x[1+delay:nr, 1+delay:nc]))
+    x .= abs2.(x)
+    y = sqrt.(fun.(x[1:nr-delay, 1:nc-delay], x[1+delay:nr, 1+delay:nc]))
     Array{T,2}(y)
 end
 
@@ -29,7 +29,7 @@ function fnn(x, mbounds, delay, thresholds; metric="max")
     # Start with matrix embedded at m=m1
     dm = distancematrix(embed(x, m1, delay), metric)
     n = size(dm)[1]
-    dm[1:n+1:end] = maximum(dm)
+    dm[1:n+1:end] .= maximum(dm)
     for m = 1:length(nfnn)
         # Look for nearest neighbours within the first n-delay
         n -= delay
@@ -39,7 +39,7 @@ function fnn(x, mbounds, delay, thresholds; metric="max")
         nnv2 = dm[1:n,1:n][nnpos]
         fnn1 = ( ((nnv2.^2)./(nnv1.^2) .- 1) .> Rtol2 )
         fnn2 = ( (nnv2/Ra) .> Atol )
-        @compat nfnn[m] = sum(fnn1 .| fnn2)
+        nfnn[m] = sum(fnn1 .| fnn2)
     end
     nfnn
 end
@@ -58,7 +58,7 @@ function afnn(x, mbounds, delay; metric="max")
     m1, m2 = mbounds
     dm = distancematrix(embed(x, m1, delay), metric)
     n = size(dm)[1]
-    dm[1:n+1:end] = maximum(dm)
+    dm[1:n+1:end] .= maximum(dm)
     mean_ratio = zeros(m2-m1+2)
     mean_increment = zeros(m2-m1+2)
     for m = 1:length(mean_ratio)
@@ -71,7 +71,7 @@ function afnn(x, mbounds, delay; metric="max")
         # Project nnpos in original series
         nnx = [coord[2] for coord in nnpos]
         d = (m1+m-1)*delay
-        @compat mean_increment[m] = mean(abs.(x[(1+d):(n+d)] .- x[nnx .+ d]))
+        mean_increment[m] = mean(abs.(x[(1:n).+d] .- x[nnx .+ d]))
     end
     e1 = mean_ratio[2:end]./mean_ratio[1:end-1]
     e2 = mean_increment[2:end]./mean_increment[1:end-1]
@@ -92,7 +92,7 @@ function ffnn(x, mbounds, delay; metric="max")
     m1, m2 = mbounds
     dm = distancematrix(embed(x, m1, delay), metric)
     n = size(dm)[1]
-    dm[1:n+1:end] = maximum(dm)
+    dm[1:n+1:end] .= maximum(dm)
     fnn_ratio = zeros(m2-m1+1)
     for m = 1:length(fnn_ratio)
         n -= delay

src/radius.jl

@@ -7,7 +7,7 @@ function radius_mrr(x::AbstractVector, rr::Real)
     d = zeros(lx)
     for i=1:lx
         dxs = xs[max(1,i-nr+1):min(lx,i+nr-1)] - xs[i]
-        @compat d[i] = sort(abs.(dxs))[nr]
+        d[i] = sort(abs.(dxs))[nr]
     end
     median(d)
 end
@@ -39,7 +39,7 @@ function sorteddistances(x; theiler::Integer=0, scale=maximum, kwargs...)
     pos = 0
     for d = theiler:n-1
         tmp = dm[n*d+1 : n+1 : n^2]
-        distarray[(1+pos):(length(tmp)+pos)] = tmp
+        distarray[pos .+ (1:length(tmp))] = tmp
         pos += length(tmp)
     end
     sort(distarray), (1.:length(distarray))/length(distarray)

src/rqa.jl

@@ -37,7 +37,7 @@ function diagonalhistogram(x::AbstractMatrix{Bool}; theiler::Integer=1, kwargs..
     # Iterate over diagonals - excluding LOI and Theiler window
     # If the matrix is symmetric, examine only the upper triangle
     diag_collection = collect(theiler:n-2)
-    @compat xsym = issymmetric(x)
+    xsym = issymmetric(x)
     !xsym && prepend!(diag_collection, collect(-(m-2):-max(theiler,1)))
     for d in diag_collection
         increment = (xsym && d > 0) ? 2 : 1
@@ -74,15 +74,15 @@ function diagonalhistogram(x::SparseMatrixCSC{Bool}; theiler::Integer=1, kwargs.
     m,n=size(x)
     rv = rowvals(x)
     dv = colvals(x) - rowvals(x)
-    @compat if issymmetric(x)
+    if issymmetric(x)
         valid = (dv .>= theiler)
         f = 2
     else
-        @compat valid = (abs.(dv) .>= theiler)
+        valid = (abs.(dv) .>= theiler)
         f = 1
     end
-    vmat = sparse(rv[valid], dv[valid]+m+1, true)
-    f*verticalhistogram(vmat)
+    vmat = sparse(rv[valid], dv[valid] .+ (m+1), true)
+    f .* verticalhistogram(vmat)
 end
 
 """
@@ -154,7 +154,7 @@ function entropy(diag_hist::Vector; lmin=2, kwargs...)
     if lmin <= nbins
         prob_bins = diag_hist[lmin:nbins] ./ sum(diag_hist[lmin:nbins])
         prob_bins = prob_bins[findall(!iszero, prob_bins)]
-        @compat typeof(0.0)( -sum(prob_bins .* log.(prob_bins)) )
+        typeof(0.0)( -sum(prob_bins .* log.(prob_bins)) )
     else
         0.0
     end
@@ -184,7 +184,7 @@ end
 
 # Number of l-length sequences, based on diagonals
 function countsequences(diag_hist::Vector; lmin=2, kwargs...)
-    overlap = (1:length(diag_hist))' - lmin + 1
+    overlap = (1:length(diag_hist))' .- (lmin+1)
     overlap[overlap .< 0] = 0
     overlap * diag_hist
 end

src/windowed.jl

@@ -25,12 +25,12 @@ function ij_block_rmat(x, y, bsize, dindex, vargs...; kwargs...)
     rws = (Int)[]
     cls = (Int)[]
     for i=abs(dindex):n_fullblocks-1
-        ix = i*bsize + brange
-        iy = i*bsize + brange
+        ix = i*bsize .+ brange
+        iy = i*bsize .+ brange
         if dindex < 0
-            iy -= -dindex*bsize
+            iy = iy .+ dindex*bsize
         elseif dindex > 0
-            ix -= dindex*bsize
+            ix = ix .- dindex*bsize
         end
         rmat_b = crossrecurrencematrix(x[ix,:], y[iy,:], vargs...; kwargs...)
         append!(rws, rowvals(rmat_b) .+ix[1] .- 1)
@@ -94,10 +94,10 @@ macro windowed(ex, options...)
         if in(f, rqa_funs)
             @gensym w s nw i
             x = ex.args[2]
-            submat = :($x[(1+$i):($w+$i), (1+$i):($w+$i)])
+            submat = :($x[$i.+$w,$i.+$w])
             ex.args[2] = submat
             ret_ex = quote
-                $w = $(dict_op[:width])
+                $w = 1:$(dict_op[:width])
                 $s = $(dict_op[:step])
                 $nw = size($x,1) - $(dict_op[:width])
                 ($(rqa_types[f]))[$ex for $i=0:$s:$nw]
@@ -108,10 +108,10 @@ macro windowed(ex, options...)
         if f == :rqa
             @gensym w s nw ni rqa_dict i rqa_i k v
             x = ex.args[2]
-            submat = :($x[(($i-1)*$s+1):(($i-1)*$s+$w), (($i-1)*$s+1):(($i-1)*$s+$w)])
+            submat = :($x[($i-1)*$s.+$w,($i-1)*$s.+$w])
             ex.args[2] = submat
             ret_ex = quote
-                $w = $(dict_op[:width])
+                $w = 1:$(dict_op[:width])
                 $s = $(dict_op[:step])
                 $nw = size($x,1) - $(dict_op[:width])
                 $ni = div($nw, $s)+1
@@ -159,7 +159,7 @@ macro windowed(ex, options...)
                 $exd_upper
                 append!($i, $ii)
                 append!($j, $jj)
-                sparse($i,$j,true,size($x,1),size($y,1))
+                Compat.SparseArrays.sparse($i,$j,true,size($x,1),size($y,1))
             end
             return esc(ret_ex)
         elseif f == :recurrencematrix
@@ -180,7 +180,7 @@ macro windowed(ex, options...)
                 append!($i,$ii)
                 append!($j,$jj)
                 $n = size($x,1)
-                sparse($i,$j,true,$n,$n)
+                Compat.SparseArrays.sparse($i,$j,true,$n,$n)
             end
             return esc(ret_ex)
         elseif f == :jointrecurrencematrix
@@ -199,7 +199,7 @@ macro windowed(ex, options...)
             ret_ex = quote
                 $ex_rmx
                 $ex_rmy
-                @compat $rm1 .& $rm2
+                $rm1 .& $rm2
             end
             return esc(ret_ex)
         end

test/runtests.jl

@@ -67,7 +67,7 @@ rqapar = rqa(rmat, theiler=2, lmin=5, border=20)
 rmatw = @windowed recurrencematrix(xe, 1.5) 50
 crmatw = @windowed(crossrecurrencematrix(x, y, 1.5),30)
 @windowed jrmatw = jointrecurrencematrix(x, y, 1.5) 30
-@test jrmatw[(34:63),(34:63)] == jrmat[(34:63), (34:63)]
+@test jrmatw[33 .+ (1:30), 33 .+ (1:30)] == jrmat[33 .+ (1:30), 33 .+ (1:30)]
 @windowed(rrw = recurrencerate(rmatw), width=50, step=40)
 @windowed rqaw = rqa(rmatw) width=50 step=40
 @test rqaw["RR"] == rrw