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@@ -5,6 +5,7 @@ version = "0.1.0"
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[deps]
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[deps]
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Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
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Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
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Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
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ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
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NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
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@@ -1,6 +1,6 @@
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module Thesis
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module Thesis
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using LinearAlgebra, ForwardDiff, PlotlyJS, SPICE
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using LinearAlgebra, ForwardDiff, PlotlyJS, SPICE, Distributed
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try
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try
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furnsh("../../SPICE/naif0012.tls")
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furnsh("../../SPICE/naif0012.tls")
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@@ -11,6 +11,12 @@ function mass_est(T)
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return ans/n
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return ans/n
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end
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end
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converged(x) = NLsolve.converged(x)
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function converged(_::String)
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return false
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end
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function nlp_solve(start::Vector{Float64},
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function nlp_solve(start::Vector{Float64},
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final::Vector{Float64},
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final::Vector{Float64},
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craft::Sc,
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craft::Sc,
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@@ -25,6 +31,21 @@ function nlp_solve(start::Vector{Float64},
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F[1:6, 1] .= prop_nlsolve(tanh.(x), start, craft, μ, tf-t0) .- final
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F[1:6, 1] .= prop_nlsolve(tanh.(x), start, craft, μ, tf-t0) .- final
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end
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end
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return nlsolve(f!, atanh.(x0), ftol=tol, autodiff=:forward, iterations=1_000)
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# return nlsolve(f!, atanh.(x0), ftol=tol, autodiff=:forward, iterations=1_000)
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p = addprocs(1)
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response = Channel(1)
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@async put!(response, remotecall_fetch(nlsolve, 2, f!, atanh.(x0), ftol=tol, autodiff=:forward, iterations=1_000))
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start=time()
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while !isready(response) && (time() - start) < 30.
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sleep(0.1)
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end
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if isready(response)
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return fetch(response)
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else
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rmprocs(p);
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return "error"
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end
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end
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end
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@@ -40,9 +40,7 @@ function mbh(start::AbstractVector,
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if verbose print("\r",i) end
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if verbose print("\r",i) end
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# TODO: Should this be two separate "impatience" values?
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# TODO: Should this be two separate "impatience" values?
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impatience = 0
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impatience = 0
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println("HERE")
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x_star = nlp_solve(start, final, craft, μ, t0, tf, new_x(n), tol=tol)
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x_star = nlp_solve(start, final, craft, μ, t0, tf, new_x(n), tol=tol)
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println("THERE")
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while converged(x_star) == false && impatience < patience_level
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while converged(x_star) == false && impatience < patience_level
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impatience += 1
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impatience += 1
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x_star = nlp_solve(start, final, craft, μ, t0, tf, new_x(n), tol=tol)
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x_star = nlp_solve(start, final, craft, μ, t0, tf, new_x(n), tol=tol)
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@@ -19,7 +19,7 @@ end
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# include("inner_loop/laguerre-conway.jl")
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# include("inner_loop/laguerre-conway.jl")
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# include("inner_loop/propagator.jl")
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# include("inner_loop/propagator.jl")
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# include("inner_loop/find_closest.jl")
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# include("inner_loop/find_closest.jl")
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# include("inner_loop/monotonic_basin_hopping.jl")
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include("inner_loop/monotonic_basin_hopping.jl")
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include("inner_loop/inner_loop.jl")
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include("inner_loop/inner_loop.jl")
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end
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end
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