This code implements the algorithm in Wheeler & Kipping 201?. Find posterior samples from the paper at github.com/ajwheeler/WDdata.

This code is tested with Julia 1.0, but it should be compatible with 0.6 as well. It requires the Interpolations, DataFrames, and FITSIO packages. To install them run these commands on the Julia REPL

Pkg.add("Interpolations")
Pkg.add("DataFrames")
Pkg.add("FITSIO")

Here is a brief usage example

push!(LOAD_PATH, "/directory/containing/this/code")
using WeirdDetector

#here's how you can get a Kepler light curve detrended like we did in the paper
getFITS(8462852, fitsdir="./") #download FITS files for Boyajian's star to the current directory
df = loadFITS(8462852, fitsdir="./") #load all quarters into single data frame, perform ourlier rejection and detrending
data = pointsify(df) #convert to data type taken by periodogram()

#Here's how to prep arbitary data
t = ...      # array of epochs
F = ...      # array of flux values
sigmaF = ... # array of 1-sigma uncertainties
data = Point.(t, F, sigmaF) #this constructs an array of Points with Julia's broadcast syntax

periods = optimal_periods(pmin=0.25, pmax=50) #construct array of periods used in paper.  Any array of Float32's will do.
output = periodogram(data, periods, parallel=true) #construct a DataFrame containing the chi-squared and kurtosis values for each period
                                                   #if you want to parallelize, start Julia with "julia -n <number of cores>"

#do some postprocessing 
output[:delt_chi2] = flatten(output[:chi2], periods) #calculate \Delta \chi^2 (see equation N)

null_output = scrambled_periodogram(data, periods)
null_output[:delt_chi2] = flatten(null_output[:chi2], periods)
sigma = movingstd((null_output[:kurtosis] .- 3) .* (null_output[:delt_chi2])

output[:zeta] = (output[:kurtosis] .- 3) .* output[:delt_chi2] ./ sigma

#plot the periodogram
using PyPlot
plot(periods, output[:zeta])