From f83d73449f444574fd84414dade3f9845afe1815 Mon Sep 17 00:00:00 2001
From: Connor <connor@richardconnorjohnstone.com>
Date: Thu, 7 Oct 2021 15:56:11 -0600
Subject: [PATCH] Update for meeting with Bosanac

---
 LaTeX/flowcharts/mbh.png            |  Bin 0 -> 105112 bytes
 LaTeX/flowcharts/mbh.svg            |  915 ++++++++++++++++++++
 LaTeX/flowcharts/nlp.png            |  Bin 0 -> 69209 bytes
 LaTeX/flowcharts/nlp.svg            | 1194 +++++++++++++++++++++++++++
 julia/src/Thesis.jl                 |    4 +-
 julia/src/errors.jl                 |    5 +
 julia/src/inner_loop/nlp_solver.jl  |  110 +++
 julia/src/inner_loop/phase.jl       |   60 --
 julia/src/mission.jl                |   46 ++
 julia/test/inner_loop/nlp_solver.jl |   36 +
 julia/test/inner_loop/phase.jl      |   74 --
 julia/test/runtests.jl              |   10 +-
 12 files changed, 2313 insertions(+), 141 deletions(-)
 create mode 100644 LaTeX/flowcharts/mbh.png
 create mode 100644 LaTeX/flowcharts/mbh.svg
 create mode 100644 LaTeX/flowcharts/nlp.png
 create mode 100644 LaTeX/flowcharts/nlp.svg
 create mode 100644 julia/src/inner_loop/nlp_solver.jl
 delete mode 100644 julia/src/inner_loop/phase.jl
 create mode 100644 julia/test/inner_loop/nlp_solver.jl
 delete mode 100644 julia/test/inner_loop/phase.jl

diff --git a/LaTeX/flowcharts/mbh.png b/LaTeX/flowcharts/mbh.png
new file mode 100644
index 0000000000000000000000000000000000000000..2e272c3e1b59fa025b84b806fdb56f9ae9402462
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+       x="84.681992"
+       y="0"><tspan
+         x="123.84493"
+         y="476.10424"
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+       x="84.681992"
+       y="0"><tspan
+         x="145.61151"
+         y="476.10424"
+         id="tspan295439">Thrust Profile
+</tspan><tspan
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+         y="494.62899"
+         id="tspan295441"> (per phase)</tspan></text>
+    <text
+       xml:space="preserve"
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+    <text
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+         id="tspan219811-1-1"
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+    <text
+       xml:space="preserve"
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+    <text
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+    <text
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diff --git a/julia/src/Thesis.jl b/julia/src/Thesis.jl
index 7ca2c0d..7f51b72 100644
--- a/julia/src/Thesis.jl
+++ b/julia/src/Thesis.jl
@@ -25,8 +25,8 @@ module Thesis
   include("./plotting.jl")
   include("./inner_loop/laguerre-conway.jl")
   include("./inner_loop/propagator.jl")
-  include("./inner_loop/phase.jl")
-  include("./inner_loop/monotonic_basin_hopping.jl")
+  include("./inner_loop/nlp_solver.jl")
+  # include("./inner_loop/monotonic_basin_hopping.jl")
   # include("./outer_loop.jl")
 
 end
diff --git a/julia/src/errors.jl b/julia/src/errors.jl
index 4b0a6b6..0b4f9ad 100644
--- a/julia/src/errors.jl
+++ b/julia/src/errors.jl
@@ -26,3 +26,8 @@ struct Mass_Error{T} <: Exception where T <: Real
   mass::T
 end
 Base.showerror(io::IO, e::Mass_Error) = print(io, "mass (", e.mass, ") got too low in propagation")
+
+struct Mission_Creation_Error{T} <: Exception where T <: Real
+  num_entries::T
+end
+Base.showerror(io::IO, e::Mission_Creation_Error) = print(io, "Tried to create a mission with $(e.num_entries) entries")
diff --git a/julia/src/inner_loop/nlp_solver.jl b/julia/src/inner_loop/nlp_solver.jl
new file mode 100644
index 0000000..eace95e
--- /dev/null
+++ b/julia/src/inner_loop/nlp_solver.jl
@@ -0,0 +1,110 @@
+using SNOW
+
+export solve_mission
+
+struct Result
+  converged::Bool
+  info::Symbol
+  sol::Matrix{Float64}
+end
+
+"""
+This function will take an initial guess to a mission profile and leverage Ipopt to solve the mission
+"""
+function solve_mission( guess::Mission_Guess;
+                        tol=1e-8,
+                        verbose::Bool=false )
+  # First we need to define a function that propagates the mission all the way through
+  # The function takes in a constraints vector (g) and an input vector (x) and spits out 
+  # the objective function. In my case, the objective will be constant.
+  #
+
+  # First we define our starting point
+  x0 = Vector(guess)
+  n = size(guess.phases[1].thrust_profile)[1]
+  flybys = [ p.planet for p in guess.phases ]
+
+  # And our NLP
+  function optimizer!(g,x)
+    # Establish initial conditions
+    mass = guess.start_mass
+    v∞_out = x[2:4]
+    current_planet = Earth
+    launch_date = Dates.unix2datetime(x[1])
+    time = utc2et(Dates.format(launch_date,"yyyy-mm-ddTHH:MM:SS"))
+    start = state(current_planet, time, v∞_out, mass)
+    
+    # Now, for each phase we must require:
+    # - That the ending state matches (unless final leg)
+    # - That the v∞_out == v∞_in (unless final leg)
+    # - That the minimum height is acceptable (unless final leg)
+    # - That the ending position matches (if final leg)
+    # - That the ending mass is acceptable (if final leg)
+    i = 0
+    for flyby in flybys
+      phase_params = x[ 5 + (3n+7) * i : 5 + (3n+7) * (i+1) - 1 ]
+      v∞_in = phase_params[1:3]
+      v∞_out = phase_params[4:6]
+      tof = phase_params[7]
+      thrusts = reshape(phase_params[8:end], (n,3))
+      current_planet = flyby
+      time += tof
+      goal = state(current_planet, time, v∞_in)
+      final = prop(reshape(thrusts, (n,3)), start, guess.sc, tof)[2]
+      if flyby != flybys[end]
+        g[1+8i:6+8i] .= final[1:6] .- goal[1:6]
+        g[7+8i] = norm(v∞_out) - norm(v∞_in)
+        δ = acos( ( v∞_in ⋅ v∞_out ) / ( norm(v∞_in) * norm(v∞_out) ) )
+        g[8+8i] = (current_planet.μ/(v∞_in ⋅ v∞_in)) * ( 1/sin(δ/2) - 1 )
+      else
+        g[8length(flybys)+1:8length(flybys)+3] .= final[1:3] .- goal[1:3]
+        g[8length(flybys)+4] = final[7]
+      end
+      start = state(current_planet, time, v∞_out, final[7])
+      i += 1
+      return 1.0
+    end
+  end
+  g = zeros(8length(flybys)+4)
+  optimizer!(g,x0)
+  return g
+
+
+
+  # n = size(x0)[1]
+
+  # function f!(g,x)
+    # try
+      # g[1:6] .= prop(reshape(x,(n,3)), start, craft, tof, primary)[2][1:6]
+      # return 1.
+    # catch e
+      # if isa(e,LaGuerreConway_Error) || isa(e, Mass_Error) || isa(e, PropOne_Error)
+        # return 10_000.
+      # else
+        # rethrow
+      # end
+    # end
+  # end
+
+  # lower_x = -1 * ones(3n)
+  # upper_x = ones(3n)
+  # num_constraints = 6
+  # filename = "ipopt_"*string(rand(UInt))
+  # ipopt_options = Dict("constr_viol_tol" => tol,
+                       # "acceptable_constr_viol_tol" => 1e-4,
+                       # "max_iter" => 10_000,
+                       # "max_cpu_time" => 30.,
+                       # "print_level" => 0,
+                       # "output_file" => filename)
+  # options = Options(solver=IPOPT(ipopt_options),
+                    # derivatives=ForwardAD())
+  # x, _, info = minimize(f!, vec(x0), num_constraints, lower_x, upper_x, final[1:6], final[1:6], options)
+  # rm(filename)
+  # if info in [:Solve_Succeeded, :Solved_To_Acceptable_Level]
+    # return Result(true, info, reshape(x,(n,3)))
+  # else
+    # if verbose println(info) end
+    # return Result(false, info, x0)
+  # end
+
+end
diff --git a/julia/src/inner_loop/phase.jl b/julia/src/inner_loop/phase.jl
deleted file mode 100644
index 09f9aad..0000000
--- a/julia/src/inner_loop/phase.jl
+++ /dev/null
@@ -1,60 +0,0 @@
-using SNOW
-
-export solve_phase
-
-struct Result
-  converged::Bool
-  info::Symbol
-  sol::Matrix{Float64}
-end
-
-"""
-This function will take a single phase (so an initial state, and a final state) and an initial guess
-to the thrust profile and use an NLP solver to find the nearest thrust profile to that initial guess
-that satisfies the final state condition
-"""
-function solve_phase( start::Vector{Float64},
-                      final::Vector{Float64},
-                      craft::Sc,
-                      tof::Float64,
-                      x0::Matrix{Float64},
-                      primary::Body=Sun;
-                      tol=1e-8,
-                      verbose::Bool=false )
-  n = size(x0)[1]
-
-  function f!(g,x)
-    try
-      g[1:6] .= prop(reshape(x,(n,3)), start, craft, tof, primary)[2][1:6]
-      return 1.
-    catch e
-      if isa(e,LaGuerreConway_Error) || isa(e, Mass_Error) || isa(e, PropOne_Error)
-        return 10_000.
-      else
-        rethrow
-      end
-    end
-  end
-
-  lower_x = -1 * ones(3n)
-  upper_x = ones(3n)
-  num_constraints = 6
-  filename = "ipopt_"*string(rand(UInt))
-  ipopt_options = Dict("constr_viol_tol" => tol,
-                       "acceptable_constr_viol_tol" => 1e-4,
-                       "max_iter" => 10_000,
-                       "max_cpu_time" => 30.,
-                       "print_level" => 0,
-                       "output_file" => filename)
-  options = Options(solver=IPOPT(ipopt_options),
-                    derivatives=ForwardAD())
-  x, _, info = minimize(f!, vec(x0), num_constraints, lower_x, upper_x, final[1:6], final[1:6], options)
-  rm(filename)
-  if info in [:Solve_Succeeded, :Solved_To_Acceptable_Level]
-    return Result(true, info, reshape(x,(n,3)))
-  else
-    if verbose println(info) end
-    return Result(false, info, x0)
-  end
-
-end
diff --git a/julia/src/mission.jl b/julia/src/mission.jl
index 250154b..65ab7c5 100644
--- a/julia/src/mission.jl
+++ b/julia/src/mission.jl
@@ -1,6 +1,7 @@
 export Phase, Mission_Guess, Mission, Bad_Mission
 export test_phase1, test_phase2
 export test_mg, test_mission
+export Vector
 
 struct Phase
   planet::Body
@@ -55,6 +56,51 @@ function Mission_Guess(sc::Sc, mass::Float64, date::DateTime, v∞::Vector{Float
   Mission_Guess(sc, mass, date, v∞, phases, false)
 end
 
+"""
+This is the opposite of the function below. It takes a vector and any other necessary information and outputs a mission guess
+"""
+function Mission_Guess(x::Vector{Float64}, sc::Sc, mass::Float64, flybys::Vector{Body})
+  # Variable mission params
+  launch_date = Dates.unix2datetime(x[1])
+  launch_v∞ = x[2:4]
+
+  # Try to intelligently determine n
+  three_n = ((length(x)-4)/length(flybys)) - 7
+  three_n % 3 == 0 || throw(Mission_Creation_Error(three_n))
+  n::Int = three_n/3
+
+  # Build the phases
+  i = 0
+  phases = Vector{Phase}()
+  for flyby in flybys
+    phase_params = x[ 5 + (3n+7) * i : 5 + (3n+7) * (i+1) - 1 ]
+    v∞_in = phase_params[1:3]
+    v∞_out = phase_params[4:6]
+    tof = phase_params[7]
+    thrusts = reshape(phase_params[8:end], (n,3))
+    push!(phases, Phase(flyby, v∞_in, v∞_out, tof, thrusts))
+    i += 1
+  end
+  return Mission_Guess(sc, mass, launch_date, launch_v∞, phases, false)
+end
+
+"""
+This is a function to convert a mission guess into an nlp-ready vector. It doesn't contain all
+information from the guess though.
+"""
+function Base.Vector(g::Mission_Guess)
+  result = Vector{Float64}()
+  push!(result, Dates.datetime2unix(g.launch_date))
+  push!(result, g.launch_v∞...)
+  for phase in g.phases
+    push!(result,phase.v∞_in...)
+    push!(result,phase.v∞_out...)
+    push!(result,phase.tof)
+    push!(result,phase.thrust_profile...)
+  end
+  return result
+end
+
 const test_mg = Mission_Guess(bepi, 12_000., DateTime(1992,11,19), [-3.4,1.2,0.1], test_phases)
             
 struct Mission
diff --git a/julia/test/inner_loop/nlp_solver.jl b/julia/test/inner_loop/nlp_solver.jl
new file mode 100644
index 0000000..49787f1
--- /dev/null
+++ b/julia/test/inner_loop/nlp_solver.jl
@@ -0,0 +1,36 @@
+@testset "Phase" begin
+
+  println("Testing NLP solver")
+
+  # We'll start by testing the mission_guess -> vector function
+  vec = Vector(test_mg)
+  @test typeof(vec) == Vector{Float64}
+
+  # Now we go in the other direction
+  flybys = [ p.planet for p in test_mg.phases ]
+  guess = Mission_Guess(vec, test_mg.sc, test_mg.start_mass, flybys)
+  @test typeof(guess) == Mission_Guess
+  @test guess.sc == test_mg.sc
+  @test guess.start_mass == test_mg.start_mass
+  @test guess.launch_date == test_mg.launch_date
+  @test guess.launch_v∞ == test_mg.launch_v∞
+  @test guess.converged == test_mg.converged
+  for i in 1:length(guess.phases)
+    @test guess.phases[i].planet == test_mg.phases[i].planet
+    @test guess.phases[i].v∞_in == test_mg.phases[i].v∞_in
+    @test guess.phases[i].v∞_out == test_mg.phases[i].v∞_out
+    @test guess.phases[i].tof == test_mg.phases[i].tof
+    @test guess.phases[i].thrust_profile == test_mg.phases[i].thrust_profile
+  end
+
+  # Now we test an example run of the basic "inner function"
+  leave, arrive = DateTime(1992,11,19), DateTime(1993,4,1)
+  earth_state = state(Earth, leave)
+  venus_state = state(Venus, arrive)
+  v∞_out, v∞_in, tof = Thesis.lamberts(Earth, Venus, leave, arrive)
+  phase = Phase(Venus, v∞_in, v∞_in, tof, zeros(20,3))
+  guess = Mission_Guess(bepi, 12_000., leave, v∞_out, [phase])
+  g = solve_mission(guess)
+  println(g)
+
+end
diff --git a/julia/test/inner_loop/phase.jl b/julia/test/inner_loop/phase.jl
deleted file mode 100644
index 3f428d3..0000000
--- a/julia/test/inner_loop/phase.jl
+++ /dev/null
@@ -1,74 +0,0 @@
-@testset "Phase" begin
-
-  println("Testing NLP solver")
-
-  using SNOW, PlotlyJS
-
-  # First we'll test an Earth case
-  # Initial Setup
-  T = rand( 8hour : second : day )
-  revs = 8
-  n = 10 * revs
-  start_mass = 10_000.
-
-  # A simple orbit raising
-  start = gen_orbit(T, start_mass, Earth)
-  thrust = spiral(0.9, n, start, test_sc, revs*T, Earth)
-  final = prop(thrust, start, test_sc, revs*T, Earth)[2]
-  new_T = 2π * √( xyz_to_oe(final, Earth.μ)[1]^3 / Earth.μ )
-
-  # This should be close enough to converge, but Earth orbits are picky
-  thrust_guess = spiral(0.89, n, start, test_sc, revs*T, Earth)
-  guess_final = prop(thrust_guess, start, test_sc, revs*T, Earth)[2]
-  result = solve_phase(start, final, test_sc, revs*T, thrust_guess, Earth, verbose=true)
-  calc_path, calc_final = prop(result.sol, start, test_sc, revs*T, Earth, interpolate=true)
-
-  # Test
-  @test norm(guess_final[1:6] - final[1:6]) > 1e-4
-  @test result.converged
-  @test norm(calc_final[1:6] - final[1:6]) < 1e-4
-
-  # Plot
-  paths = Pathlist()
-  push!(paths, prop(start, T, Earth),
-               calc_path,
-               prop(calc_final, T, Earth),
-               prop(final, T, Earth) )
-  fig = plot_orbits(paths, Earth,
-                    labels=["init", "transit", "post-transit", "final"],
-                    colors=["#FFF","#F44","#4F4","#44F"])
-  savefig(fig, "../plots/nlp_test_earth.html")
-
-  # Now the sun case
-  T = rand( 0.5year : hour : 1.5year )
-  tof = 0.7T
-  n = 20
-  start_mass = 12_000.
-
-  # A simple orbit raising again, to keep things simple
-  start = gen_orbit(T, start_mass)
-  thrust = spiral(0.6, n, start, bepi, tof)
-  final = prop(thrust, start, bepi, tof)[2]
-  new_T = 2π * √( xyz_to_oe(final, Sun.μ)[1]^3 / Sun.μ )
-
-  # This should be close enough to converge
-  thrust_guess = spiral(0.55, n, start, bepi, tof)
-  guess_final = prop(thrust_guess, start, bepi, tof)[2]
-  result = solve_phase(start, final, bepi, tof, thrust_guess, verbose=true)
-  calc_path, calc_final = prop(result.sol, start, bepi, tof, interpolate=true)
-
-  # Test
-  @test norm(guess_final[1:6] - final[1:6]) > 1e-4
-  @test result.converged
-  @test norm(calc_final[1:6] - final[1:6]) < 1e-4
-
-  # Plot
-  paths = Pathlist()
-  push!(paths, prop(start, T), calc_path, prop(calc_final, T), prop(final, T) )
-  fig = plot_orbits(paths,
-                    labels=["init", "transit", "post-transit", "final"],
-                    colors=["#FFF","#F44","#4F4","#44F"])
-  savefig(fig, "../plots/nlp_test_sun.html")
-
-
-end
diff --git a/julia/test/runtests.jl b/julia/test/runtests.jl
index 46ca670..841f503 100644
--- a/julia/test/runtests.jl
+++ b/julia/test/runtests.jl
@@ -14,9 +14,9 @@ catch
 end
 
 @testset "All Tests" begin
-  include("plotting.jl")
-  include("inner_loop/laguerre-conway.jl")
-  include("inner_loop/propagator.jl")
-  include("inner_loop/phase.jl")
-  include("inner_loop/monotonic_basin_hopping.jl")
+  # include("plotting.jl")
+  # include("inner_loop/laguerre-conway.jl")
+  # include("inner_loop/propagator.jl")
+  include("inner_loop/nlp_solver.jl")
+  # include("inner_loop/monotonic_basin_hopping.jl")
 end