RFC: Add lombscargle based surrogate for irregular ts

Project.toml

@@ -8,18 +8,20 @@ version = "1.0.0"
 AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"
 DSP = "717857b8-e6f2-59f4-9121-6e50c889abd2"
 DelayEmbeddings = "5732040d-69e3-5649-938a-b6b4f237613f"
+Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 InplaceOps = "505f98c9-085e-5b2c-8e89-488be7bf1f34"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LombScargle = "fc60dff9-86e7-5f2f-a8a0-edeadbb75bd9"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Wavelets = "29a6e085-ba6d-5f35-a997-948ac2efa89a"
 
 [compat]
 AbstractFFTs = "= 0.4.1, 0.5"
-DelayEmbeddings = "1.5.1"
 DSP = "^0.5.2, 0.6, ^1"
+DelayEmbeddings = "1.5.1"
 Distributions = "0.21, 1, 0.23"
 InplaceOps = "^0.3.0, ^1"
 Interpolations = "^0.12, ^1"

src/TimeseriesSurrogates.jl

@@ -4,6 +4,7 @@ module TimeseriesSurrogates
 # the global usage here. It makes it easier to understand how each algorithm works
 # and what it needs
 using Distributions
+using Distances # Will be used by the LombScargle method
 using StatsBase
 using InplaceOps
 using AbstractFFTs
@@ -29,6 +30,9 @@ include("methods/truncated_fourier.jl")
 include("methods/wavelet_based.jl")
 include("methods/pseudoperiodic.jl")
 
+# Methods for irregular time series
+include("methods/lombscargle.jl")
+
 # Visualization routine for time series + surrogate + periodogram/acf/histogram
 using Requires
 function __init__()

src/api.jl

@@ -41,3 +41,14 @@ function surrogate(x, method::Surrogate)
     sg = surrogenerator(x, method)
     sg()
 end
+
+"""
+    surrogate(x,t, method::Surrogate) → s
+Create a  single timeseries `s` from the irregular time series `x` with time steps `t`
+based on the given `method`.
+If you want to generate more than one surrogate from `x`, you should use a [`surrogenerator`](@ref)
+"""
+function surrogate(x, t, method::Surrogate)
+    sg = surrogenerator(x,t,method)
+    sg()
+end

src/methods/lombscargle.jl

@@ -0,0 +1,60 @@
+using LombScargle
+
+export LS
+"""
+
+Compute a surrogate of the irregular time series x with supporting time steps t
+based on the simulated annealing method described in
+
+Testing for nonlinearity in unevenly sampled time seriesAndreas Schmitz and Thomas Schreiber
+"""
+struct LS <: Surrogate end
+
+function loss(ls1, signal2,taxis;q=1)
+    #lsplan1 = LombScargle.plan(taxis, signal1)
+    lsplan2 = LombScargle.plan(taxis, signal2)
+    #ls1 = lombscargle(lsplan1)
+    ls2 = lombscargle(lsplan2)
+    #@show ls1.power .- ls2.power
+    return minkowski(ls1.power, ls2.power,q)
+end
+
+
+
+function surrogate(signal,taxis,sg::LS; N_total=10000, N_acc=2000, tol=1)
+    n = length(signal)
+    surr = surrogate(signal, RandomShuffle())
+    lsorg = lombscargle(taxis, signal)
+    lossold = loss(lsorg,surr, taxis)
+    i = j = 1
+    while i < N_total && j<N_acc
+        if mod(i,500) ==0
+            @show i, j,lossold
+        end
+        k,l = sample(1:n,2, replace=false)
+        #@show k,l
+        newsurr = copy(surr)
+        newsurr[[k,l]] = surr[[l,k]]
+        lossnew = loss(lsorg, newsurr, taxis)
+        if lossnew < lossold
+            lossnew <= tol && break
+            #@show "update"
+            surr, lossold = newsurr, lossnew
+            j += 1
+        #=else
+            ## Implement drawing with a probability p
+            lossdiff = lossnew - lossold
+            @show lossdiff
+            T = 1
+            p = exp(-lossdiff/log(i)) # Where does T come from?
+            if rand() < p
+                @show p
+                surr, lossold = newsurr, lossnew
+                j += 1
+            end
+        =#
+        end
+        i+=1
+    end
+    return surr
+end