removed ImageMorphopolgy requirement

Author: behinger

Project.toml

@@ -1,16 +1,14 @@
 name = "ClusterDepth"
 uuid = "c8d8bbfa-f476-4995-adff-2987f04015d1"
 authors = ["Benedikt V. Ehinger", "Maanik Marathe"]
-version = "0.2.0"
+version = "0.2.1"
 
 [deps]
-ImageMorphology = "787d08f9-d448-5407-9aad-5290dd7ab264"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
-ImageMorphology = "0.4"
 Random = "1"
 SparseArrays = "1"
 StatsBase = "0.33, 0.34"

src/ClusterDepth.jl

@@ -1,7 +1,7 @@
 module ClusterDepth
 
 using Random
-using ImageMorphology
+#using ImageMorphology
 using SparseArrays
 #using ExtendableSparse
 using StatsBase

src/cluster.jl

@@ -1,5 +1,6 @@
 """
 
+using Base: Stateful
 clusterdepth(rng,data::AbstractArray;τ=2.3, statfun=x->abs.(studentt(x)),permfun=sign_permute!,nperm=5000,pval_type=:troendle)
 
 calculate clusterdepth of given datamatrix. 
@@ -186,7 +187,7 @@ function calc_clusterdepth(d0, τ)
     end
 
     maxL = 1 + maximum(len) # go up only to max-depth
-
+    #    @debug maxL
     valCol_head = Vector{Float64}(undef, maxL)
     valCol_tail = Vector{Float64}(undef, maxL)
 
@@ -217,25 +218,38 @@ if the first and last cluster start on the first/last sample, we dont know their
 
 	Input is assumed to be a thresholded Array with only 0/1
 """
-function cluster(data)
-    label = label_components(Matrix(data))
-    K = maximum(label)
-    start = fill(0, K)
-    stop = fill(0, K)
-    for k = 1:K
-        #length[k] = sum(label.==k)
-        start[k] = findfirst(==(k), label)
-        stop[k] = findlast(==(k), label)
+function cluster(data::BitVector)
+
 
+    start = []
+    stop = []
+
+    state = false
+    for i in eachindex(data)
+        if data[i] == 1
+            if !state
+                append!(start, i)
+                state = true
+            end
+        else
+            if state
+                append!(stop, i - 1)
+                state = false
+            end
+        end
 
     end
-    len = stop .- start
+
+
+
+
 
     # if the first and last cluster start on the first/last sample, we dont know their real depth
-    if length(start) > 0 && start[end] + len[end] == length(data)
+    if length(start) == length(stop) + 1
         start = start[1:end-1]
-        len = len[1:end-1]
     end
+    len = stop .- start
+
     if length(start) > 0 && start[1] == 1
         start = start[2:end]
         len = len[2:end]

test/cluster.jl

@@ -1,13 +1,21 @@
 @testset "cluster" begin
     s, l = ClusterDepth.cluster(
-        [4.0 0.0 10.0 0.0 3.0 4.0 0 4.0 4.0 0.0 0.0 5.0] .> 0.9,
+        [4.0, 0.0, 10.0, 0.0, 3.0, 4.0, 0, 4.0, 4.0, 0.0, 0.0, 5.0] .> 0.9,
     )
+
     @test s == [3, 5, 8]
     @test l == [0, 1, 1]
 
-    s, l = ClusterDepth.cluster([0.0 0.0 0.0 0.0] .> 0.9)
+    s, l = ClusterDepth.cluster([0.0, 0.0, 0.0, 0.0] .> 0.9)
     @test s == []
     @test l == []
+
+
+    s, l = ClusterDepth.cluster(
+        [4.0, 0.0, 10.0, 0.0, 3.0, 4.0, 0, 4.0, 4.0] .> 0.9,
+    )
+    @test s == [3, 5]
+    @test l == [0, 1]
 end
 
 @testset "Tests for 2D data" begin

test/pvals.jl

@@ -22,7 +22,7 @@
             [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0, 0.0],
             cdm,
             0.1,
-            type = :naive,
+            type=:naive,
         )
         @test p[7] ≈ 1 / 1001
 
@@ -55,6 +55,8 @@
         p = ClusterDepth.pvals([0.0, 1.0, 2.0, 3.0, 4.0, 5.1, 2.0, 0], (cdm, cdm), 0.9)
         @test all((p .> 0.05) .== [1, 1, 1, 0, 0, 0, 1, 1])
 
+        start, len = ClusterDepth.cluster([0.0, 1.0, 2.0, 3.0, 4.0, 5.1, 2.0, 0] .> 0.9) # get observed clusters
+
 
         p = ClusterDepth.pvals([0.0, 1.0, 2.0, 3.0, 4.0, 2.0, 1.0, 0.0, 6], (cdm, cdm), 0.9)
         @test all((p .> 0.05) .== [1, 1, 1, 0, 0, 1, 1, 1, 1])