temp

julia/src/Thesis.jl

@@ -1,6 +1,6 @@
 module Thesis
 
-  using LinearAlgebra, ForwardDiff, PlotlyJS, SPICE
+  using LinearAlgebra, ForwardDiff, PlotlyJS, SPICE, Distributed
 
   try
     furnsh("../../SPICE/naif0012.tls")

julia/src/inner_loop/find_closest.jl

@@ -11,6 +11,12 @@ function mass_est(T)
   return ans/n
 end
 
+converged(x) = NLsolve.converged(x)
+
+function converged(_::String)
+  return false
+end
+
 function nlp_solve(start::Vector{Float64},
                    final::Vector{Float64},
                    craft::Sc,
@@ -25,6 +31,21 @@ function nlp_solve(start::Vector{Float64},
     F[1:6, 1] .= prop_nlsolve(tanh.(x), start, craft, μ, tf-t0) .- final
   end
 
-  return nlsolve(f!, atanh.(x0), ftol=tol, autodiff=:forward, iterations=1_000)
+  # return nlsolve(f!, atanh.(x0), ftol=tol, autodiff=:forward, iterations=1_000)
+  p = addprocs(1)
+  response = Channel(1)
+  @async  put!(response, remotecall_fetch(nlsolve, 2, f!, atanh.(x0), ftol=tol, autodiff=:forward, iterations=1_000))
+
+  start=time()
+  while !isready(response) && (time() - start) < 30.
+    sleep(0.1)
+  end
+
+  if isready(response)
+    return fetch(response)
+  else
+    rmprocs(p);
+    return "error"
+  end
 
 end

julia/src/inner_loop/monotonic_basin_hopping.jl

@@ -40,9 +40,7 @@ function mbh(start::AbstractVector,
     if verbose print("\r",i) end
     # TODO: Should this be two separate "impatience" values?
     impatience = 0
-    println("HERE")
     x_star = nlp_solve(start, final, craft, μ, t0, tf, new_x(n), tol=tol)
-    println("THERE")
     while converged(x_star) == false && impatience < patience_level
       impatience += 1
       x_star = nlp_solve(start, final, craft, μ, t0, tf, new_x(n), tol=tol)