LumenManufaktur
Welcome to the LumenManufaktur.jl repository!
Documentation
Check out the Latest Documention which also includes tutorials and examples.
Installation
LumenManufaktur.jl is not an officially registered Julia package but it's available via
the KM3NeT Julia registry. To add
the KM3NeT Julia registry to your local Julia registry list, follow the
instructions there. After that, you can add LumenManufaktur.jl just like any other Julia package:
julia> import Pkg; Pkg.add("LumenManufaktur")Quickstart
julia> using LumenManufakturBenchmarks
Below are some benchmarks made on a MacBook Air M1 2020.
julia> using LumenManufaktur
julia> params = LMParameters(dispersion_model=BaileyDispersion(240))
LMParameters:
minimum distance = 0.1 m
module_radius = 0.25 m
lambda min / max = 300.0 nm / 700.0 nm
degree of Legendre polynomials = 5
dispersion model = BaileyDispersion(240.0, 1.3201, 1.4e-5, 16.2566, -4383.0, 1.1455e6)
scattering model = LumenManufaktur.Kopelevich()
scattering probability model = LumenManufaktur.Scatteringp00075()
absorption model = LumenManufaktur.DefaultAbsorption()
julia> @benchmark directlightfrommuon(params, LumenManufaktur.KM3NeTPMT, R, θ, ϕ) setup=begin; R=(rand()+1)*300; θ=rand()*2π; ϕ=rand()*2π; end
BenchmarkTools.Trial: 10000 samples with 200 evaluations.
Range (min … max): 403.125 ns … 2.225 μs ┊ GC (min … max): 0.00% … 77.28%
Time (median): 422.500 ns ┊ GC (median): 0.00%
Time (mean ± σ): 428.128 ns ± 29.850 ns ┊ GC (mean ± σ): 0.08% ± 1.08%
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403 ns Histogram: log(frequency) by time 507 ns <
Memory estimate: 64 bytes, allocs estimate: 4.
julia> @benchmark scatteredlightfrommuon(params, LumenManufaktur.KM3NeTPMT, D, cd, θ, ϕ, Δt) setup=begin; D=rand(1:100); cd=rand(); θ=rand()*2π; ϕ=rand()*2π; Δt=rand()*100; end
BenchmarkTools.Trial: 10000 samples with 7 evaluations.
Range (min … max): 4.435 μs … 16.631 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 4.881 μs ┊ GC (median): 0.00%
Time (mean ± σ): 4.928 μs ± 256.698 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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4.43 μs Histogram: frequency by time 5.62 μs <
Memory estimate: 80 bytes, allocs estimate: 5.Notice that the allocation estimate for a single call is not accurate, in fact, the calculation itself is non-allocating. The example below where demonstrates that calling the function 1000 times with random values in a tight for-loop only requires 2 allocations in total and has a memory estimate of 192 bytes:
julia> function manycalls()
params = LMParameters(dispersion_model=DispersionARCA)
for i in 1:1000
directlightfrommuon(params, LumenManufaktur.PMTKM3NeT, rand()*300+1, rand()*2π, rand()*2π)
end
end
manycalls (generic function with 1 method)
julia> @benchmark manycalls()
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 387.208 μs … 651.334 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 387.875 μs ┊ GC (median): 0.00%
Time (mean ± σ): 391.571 μs ± 11.914 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
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387 μs Histogram: log(frequency) by time 425 μs <
Memory estimate: 192 bytes, allocs estimate: 2.