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BoxLeastSquares.jl
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Tophats at ludicrous speeds.
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BoxLeastSquares.jl
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# BoxLeastSquares.jl [](https://JuliaAstro.github.io/BoxLeastSquares/stable) [](https://JuliaAstro.github.io/BoxLeastSquares.jl/dev) [](https://github.com/JuliaAstro/BoxLeastSquares.jl/actions/workflows/CI.yml) [](https://juliaci.github.io/NanosoldierReports/pkgeval_badges/report.html) [](https://codecov.io/gh/JuliaAstro/BoxLeastSquares.jl) [](https://opensource.org/licenses/MIT) Box-least-squares (BLS) periodograms in pure Julia. ## Installation To install use [Pkg](https://julialang.github.io/Pkg.jl/v1/managing-packages/). From the REPL, press `]` to enter Pkg-mode ```julia pkg> add BoxLeastSquares ``` If you want to use the most up-to-date version of the code, check it out from `main` ```julia pkg> add BoxLeastSquares#main ``` ## Usage First, import the package, optionally aliasing the package name ```julia julia> using BoxLeastSquares julia> import BoxLeastSquares as BLS ``` now, load some data. If you don't have an estimate of the y error it will default to 1. ```julia julia> t, y, yerr = # produce data ``` The primary interface is through the `BLS` method ```julia julia> result = BLS(t, y, yerr; duration=0.16) BLSPeriodogram ============== input dim: 1000 output dim: 1820 period range: 0.32 - 5.014724142709022 duration range: 0.16 - 0.16 objective: likelihood parameters ---------- period: 1.99930396919953 duration: 0.16 t0: 0.5001330656464655 depth: 0.19594118110109113 ± 0.0008688097746093883 snr: 225.52828804117118 log-likelihood: 27396.365214805144 ``` The transit parameters at the maximum power can be retrieved using `BoxLeastSquares.params` ```julia julia> BoxLeastSquares.params(result) (power = 27396.365214805144, period = 1.99930396919953, duration = 0.16, t0 = 0.5001330656464655, depth = 0.19594118110109113, depth_err = 0.0008688097746093883, snr = 225.52828804117118, loglike = 27396.365214805144) ``` The period grid was automatically determined using `autoperiod`, but you can supply your own, too: ```jldoctest usage julia> periods = exp.(range(log(2) - 0.1, log(2) + 0.1, length=1000)); julia> result_fine = BLS(t, y, yerr; duration=0.12:0.01:0.20, periods=periods) BLSPeriodogram ============== input dim: 1000 output dim: 1000 period range: 1.809674836071919 - 2.210341836151295 duration range: 0.12 - 0.2 objective: likelihood parameters ---------- index: 503 period: 2.001001251543549 duration: 0.168 t0: 0.4961330656464656 depth: 0.19466955969052016 ± 0.0008627202098527317 snr: 225.64622628204188 log-likelihood: 27457.6383039924 ``` ### Unitful.jl BoxLeastSquares.jl is fully compatible with `Unitful.jl` (although it is not a dependency of the library). For example ```jldoctest usage julia> using Unitful julia> tu = t * u"d"; julia> results_units = BLS(tu, y, yerr; duration=(2:0.1:4)u"hr") BLSPeriodogram ============== input dim: 1000 output dim: 3343 period range: 0.3333333333333333 d - 4.988348864592586 d duration range: 2.0 hr - 4.0 hr objective: likelihood parameters ---------- index: 2986 period: 2.0019235780121827 d duration: 3.8000000000000003 hr t0: 0.4916330656464656 d depth: 0.19445716575012517 ± 0.0008692454825826517 snr: 223.70799693127577 log-likelihood: 26953.643422397385 ``` ## Contributing and Support If you would like to contribute, feel free to open a [pull request](https://github.com/JuliaAstro/BoxLeastSquares.jl/pulls). If you want to discuss something before contributing, head over to [discussions](https://github.com/JuliaAstro/BoxLeastSquares.jl/discussions) and join or open a new topic. If you're having problems with something, open an [issue](https://github.com/JuliaAstro/BoxLeastSquares.jl/issues).
