{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ContinuousDP: Stochastic Optimal Growth Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using QuantEcon\n",
    "using BasisMatrices\n",
    "using ContinuousDPs\n",
    "using PyPlot\n",
    "using Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.4\n",
    "beta = 0.96\n",
    "mu = 0\n",
    "sigma = 0.1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f(s, x) = log(x)\n",
    "g(s, x, e) = (s - x)^alpha * e;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "shock_size = 250\n",
    "shocks = exp.(mu .+ sigma * randn(shock_size))\n",
    "weights = fill(1/shock_size, shock_size);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 dimensional Basis on the hypercube formed by (1.0e-5,) × (4.0,).\n",
       "Basis families are Cheb\n"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_max = 4.\n",
    "n = 30\n",
    "s_min, s_max = 1e-5, grid_max\n",
    "basis = Basis(ChebParams(n, s_min, s_max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_lb(s) = s_min\n",
    "x_ub(s) = s;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ab = alpha * beta\n",
    "c1 = log(1 - ab) / (1 - beta)\n",
    "c2 = (mu + alpha * log(ab)) / (1 - alpha)\n",
    "c3 = 1 / (1 - beta)\n",
    "c4 = 1 / (1 - ab)\n",
    "\n",
    "# True optimal policy\n",
    "c_star(y) = (1 - alpha * beta) * y\n",
    "\n",
    "# True value function\n",
    "v_star(y) = c1 + c2 * (c3 - c4) + c4 * log(y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "cdp = ContinuousDP(f, g, beta, shocks, weights, x_lb, x_ub, basis);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body\u001b[36m::ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)}\u001b[39m\n",
      "\u001b[90m\u001b[70G│  \u001b[1G\u001b[39m\u001b[90m60 \u001b[39m1 ─ %1 = invoke ContinuousDPs.Interp(_9::Basis{1,Tuple{ChebParams{Float64}}})\u001b[36m::ContinuousDPs.Interp{1,Array{Float64,1},Array{Float64,2},LinearAlgebra.LU{Float64,Array{Float64,2}}}\u001b[39m\n",
      "\u001b[90m\u001b[70G│╻╷ Type\u001b[1G\u001b[39m\u001b[90m61 \u001b[39m│   %2 = %new(ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)}, f, g, discount, shocks, weights, x_lb, x_ub, %1)\u001b[36m::ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)}\u001b[39m\n",
      "\u001b[90m\u001b[70G│  \u001b[1G\u001b[39m\u001b[90m62 \u001b[39m└──      return %2\n"
     ]
    }
   ],
   "source": [
    "@code_warntype ContinuousDP(f, g, beta, shocks, weights, x_lb, x_ub, basis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "C_star = cdp.interp.Phi \\ v_star.(cdp.interp.S)\n",
    "Tv = Array{Float64}(undef, cdp.interp.length)\n",
    "C = copy(C_star)\n",
    "bellman_operator!(cdp, C, Tv);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[64G│╻ getproperty\u001b[1G\u001b[39m\u001b[90m198 \u001b[39m1 ─ %1 = (Base.getfield)(cdp, :interp)\u001b[91m\u001b[1m::ContinuousDPs.Interp{1,Array{Float64,1},TM,TL} where TL<:LinearAlgebra.Factorization where TM<:(AbstractArray{T,2} where T)\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[64G││\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│   %2 = (Base.getfield)(%1, :S)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[64G│ \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│   %3 = invoke ContinuousDPs.s_wise_max!(_2::ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)}, %2::Array{Float64,1}, _3::Array{Float64,1}, _4::Array{Float64,1})\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[64G│╻ getproperty\u001b[1G\u001b[39m\u001b[90m199 \u001b[39m│   %4 = (Base.getfield)(cdp, :interp)\u001b[91m\u001b[1m::ContinuousDPs.Interp{1,Array{Float64,1},TM,TL} where TL<:LinearAlgebra.Factorization where TM<:(AbstractArray{T,2} where T)\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[64G││\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│   %5 = (Base.getfield)(%4, :Phi_lu)\u001b[91m\u001b[1m::LinearAlgebra.Factorization\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[64G│ \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│        (ContinuousDPs.ldiv!)(C, %5, %3)\n",
      "\u001b[90m\u001b[64G│ \u001b[1G\u001b[39m\u001b[90m200 \u001b[39m└──      return C\n"
     ]
    }
   ],
   "source": [
    "@code_warntype bellman_operator!(cdp, C, Tv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_size = 200\n",
    "grid_y = collect(range(s_min, stop=s_max, length=grid_size))\n",
    "V_approx = funeval(C, cdp.interp.basis, grid_y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 900x500 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(9, 5))\n",
    "ax[:set_ylim](-35, -24)\n",
    "ax[:plot](grid_y, V_approx, lw=2, alpha=0.6, label=L\"$Tv^*$\")\n",
    "ax[:plot](grid_y, v_star.(grid_y), lw=2, alpha=0.6, label=L\"$v^*$\")\n",
    "ax[:legend](loc=\"lower right\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.106662 seconds (1.21 M allocations: 54.788 MiB, 19.19% gc time)\n",
      "  0.083827 seconds (1.13 M allocations: 51.089 MiB, 11.62% gc time)\n",
      "  0.091252 seconds (1.20 M allocations: 54.406 MiB, 13.58% gc time)\n"
     ]
    }
   ],
   "source": [
    "@time bellman_operator!(cdp, C, Tv)\n",
    "@time bellman_operator!(cdp, C, Tv)\n",
    "@time bellman_operator!(cdp, C, Tv);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body\u001b[36m::Tuple{Float64,Float64}\u001b[39m\n",
      "\u001b[90m\u001b[56G│╻       getproperty\u001b[1G\u001b[39m\u001b[90m151 \u001b[39m1 ── %1  = (Base.getfield)(cdp, :shocks)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[56G│╻       size\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %2  = (Base.arraysize)(%1, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[56G││╻       Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %3  = $(Expr(:foreigncall, :(:jl_alloc_array_2d), Array{Float64,2}, svec(Any, Int64, Int64), :(:ccall), 3, Array{Float64,2}, :(%2), 1, 1, :(%2)))\u001b[36m::Array{Float64,2}\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m152 \u001b[39m│    %4  = %new(getfield(ContinuousDPs, Symbol(\"#objective#3\")){ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)},Float64,Array{Float64,1},Array{Float64,2}}, cdp, s, C, %3)\u001b[36m::getfield(ContinuousDPs, Symbol(\"#objective#3\")){ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)},Float64,Array{Float64,1},Array{Float64,2}}\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m161 \u001b[39m│    %5  = Optim.optimize\u001b[36m::Core.Compiler.Const(Optim.optimize, false)\u001b[39m\n",
      "\u001b[90m\u001b[56G│╻       x_lb\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %6  = Main.s_min\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %7  = (isa)(%6, Float64)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───       goto #12 if not %7\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m2 ── %9  = π (%6, \u001b[36mFloat64\u001b[39m)\n",
      "\u001b[90m\u001b[56G││╻       sqrt\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %10 = (Base.Math.sqrt_llvm)(2.220446049250313e-16)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[56G││╻       #optimize#78\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %11 = π (1, \u001b[36mInt64\u001b[39m)\n",
      "\u001b[90m\u001b[56G│││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %12 = π (%11, \u001b[36mInt64\u001b[39m)\n",
      "\u001b[90m\u001b[56G│││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %13 = π (false, \u001b[36mBool\u001b[39m)\n",
      "\u001b[90m\u001b[56G│││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %14 = π (%12, \u001b[36mInt64\u001b[39m)\n",
      "\u001b[90m\u001b[56G│││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│          invoke Core.kwfunc(Optim.optimize::Any)\n",
      "\u001b[90m\u001b[56G│││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %16 = Optim.optimize\u001b[36m::typeof(Optim.optimize)\u001b[39m\n",
      "\u001b[90m\u001b[56G│││╻╷╷╷╷   #optimize\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %17 = (Base.slt_int)(0, 1)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[56G││││┃│││    isempty\u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───       goto #4 if not %17\n",
      "\u001b[90m\u001b[56G│││││┃││     iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m3 ──       goto #5\n",
      "\u001b[90m\u001b[56G││││││┃│      iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m4 ──       invoke Base.getindex(()::Tuple{}, 1::Int64)\n",
      "\u001b[90m\u001b[56G│││││││┃       iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───       $(Expr(:unreachable))\n",
      "\u001b[90m\u001b[56G│││││││ \u001b[1G\u001b[39m\u001b[90m    \u001b[39m5 ┄─       goto #6\n",
      "\u001b[90m\u001b[56G│││││╻       iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m6 ──       goto #7\n",
      "\u001b[90m\u001b[56G│││││   \u001b[1G\u001b[39m\u001b[90m    \u001b[39m7 ──       goto #8\n",
      "\u001b[90m\u001b[56G││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m8 ── %25 = invoke Optim.:(#optimize#71)(%10::Float64, 2.220446049250313e-16::Float64, 1000::Int64, false::Bool, %13::Bool, nothing::Nothing, %14::Int64, false::Bool, %16::typeof(Optim.optimize), %4::getfield(ContinuousDPs, Symbol(\"#objective#3\")){ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)},Float64,Array{Float64,1},Array{Float64,2}}, %9::Float64, _3::Float64, $(QuoteNode(Optim.Brent()))::Optim.Brent)\u001b[91m\u001b[1m::Optim.UnivariateOptimizationResults{Float64,Float64,_1,_2,Float64,Optim.Brent} where _2 where _1\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[56G││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───       goto #9\n",
      "\u001b[90m\u001b[56G│││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m9 ──       goto #10\n",
      "\u001b[90m\u001b[56G││      \u001b[1G\u001b[39m\u001b[90m    \u001b[39m10 ─       goto #11\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m11 ─       goto #13\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m12 ─ %30 = (%5)(%4, %6, s)\u001b[91m\u001b[1m::Optim.UnivariateOptimizationResults{Float64,Float64,_1,_2,Float64,Optim.Brent} where _2 where _1\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───       goto #13\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m13 ┄ %32 = φ (#11 => %25, #12 => %30)\u001b[91m\u001b[1m::Optim.UnivariateOptimizationResults{Float64,Float64,_1,_2,Float64,Optim.Brent} where _2 where _1\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[56G│╻       getproperty\u001b[1G\u001b[39m\u001b[90m162 \u001b[39m│    %33 = (Base.getfield)(%32, :minimum)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│          (Core.typeassert)(%33, ContinuousDPs.Float64)\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %35 = π (%33, \u001b[36mFloat64\u001b[39m)\n",
      "\u001b[90m\u001b[56G│╻       -\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %36 = (Base.neg_float)(%35)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[56G│╻       getproperty\u001b[1G\u001b[39m\u001b[90m163 \u001b[39m│    %37 = (Base.getfield)(%32, :minimizer)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│          (Core.typeassert)(%37, ContinuousDPs.Float64)\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %39 = π (%37, \u001b[36mFloat64\u001b[39m)\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m164 \u001b[39m│    %40 = (Core.tuple)(%36, %39)\u001b[36m::Tuple{Float64,Float64}\u001b[39m\n",
      "\u001b[90m\u001b[56G│       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───       return %40\n"
     ]
    }
   ],
   "source": [
    "s = 2.\n",
    "@code_warntype ContinuousDPs._s_wise_max(cdp, s, C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.002056 seconds (28.31 k allocations: 1.281 MiB)\n",
      "  0.002404 seconds (28.29 k allocations: 1.278 MiB)\n",
      "  0.002289 seconds (28.29 k allocations: 1.278 MiB)\n"
     ]
    }
   ],
   "source": [
    "@time ContinuousDPs._s_wise_max(cdp, s, C)\n",
    "@time ContinuousDPs._s_wise_max(cdp, s, C)\n",
    "@time ContinuousDPs._s_wise_max(cdp, s, C);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 900x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_init_func(s) = 5 * log(s)\n",
    "w = v_init_func.(grid_y)\n",
    "n = 35\n",
    "\n",
    "fig, ax = subplots(figsize=(9, 6))\n",
    "ax[:set_ylim](-50, 10)\n",
    "ax[:set_xlim](minimum(grid_y), maximum(grid_y))\n",
    "lb = \"initial condition\"\n",
    "jet = ColorMap(\"jet\")\n",
    "ax[:plot](grid_y, w, color=jet(0), lw=2, alpha=0.6, label=lb)\n",
    "\n",
    "S = cdp.interp.S\n",
    "V = v_init_func.(S)\n",
    "for i in 1:n\n",
    "    C = cdp.interp.Phi \\ V\n",
    "    bellman_operator!(cdp, C, V)\n",
    "    w = funeval(C, cdp.interp.basis, grid_y)\n",
    "    ax[:plot](grid_y, w, color=jet(i / n), lw=2, alpha=0.6)\n",
    "end\n",
    "\n",
    "lb = \"true value function\"\n",
    "ax[:plot](grid_y, v_star.(grid_y), \"k-\", lw=2, alpha=0.8, label=lb)\n",
    "ax[:legend](loc=\"lower right\")\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve by policy iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute iterate 6 with error 1.0658141036401503e-13\n",
      "Converged in 6 steps\n"
     ]
    }
   ],
   "source": [
    "res = solve(cdp);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body\u001b[36m::ContinuousDPs.CDPSolveResult{PFI,1,Array{Float64,1},Array{Float64,1}}\u001b[39m\n",
      "\u001b[90m\u001b[70G│ \u001b[1G\u001b[39m\u001b[90m296 \u001b[39m1 ─ %1 = ContinuousDPs.PFI\u001b[36m::Core.Compiler.Const(PFI, false)\u001b[39m\n",
      "\u001b[90m\u001b[70G│╻ solve\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│   %2 = (Base.Math.sqrt_llvm)(2.220446049250313e-16)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[70G││\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│   %3 = invoke ContinuousDPs.:(#solve#7)(%2::Float64, 500::Int64, 2::Int64, 50::Int64, _1::Function, _2::ContinuousDP{1,Array{Float64,1},Array{Float64,1},typeof(f),typeof(g),typeof(x_lb),typeof(x_ub)}, %1::Type{PFI})\u001b[36m::ContinuousDPs.CDPSolveResult{PFI,1,Array{Float64,1},Array{Float64,1}}\u001b[39m\n",
      "\u001b[90m\u001b[70G│ \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└──      return %3\n"
     ]
    }
   ],
   "source": [
    "@code_warntype solve(cdp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute iterate 6 with error 1.0658141036401503e-13\n",
      "Converged in 6 steps\n",
      "  0.909546 seconds (11.46 M allocations: 571.261 MiB, 11.57% gc time)\n",
      "Compute iterate 6 with error 1.0658141036401503e-13\n",
      "Converged in 6 steps\n",
      "  0.914953 seconds (11.46 M allocations: 571.270 MiB, 11.24% gc time)\n",
      "Compute iterate 6 with error 1.0658141036401503e-13\n",
      "Converged in 6 steps\n",
      "  0.910241 seconds (11.46 M allocations: 571.270 MiB, 11.05% gc time)\n"
     ]
    }
   ],
   "source": [
    "@time res = solve(cdp)\n",
    "@time res = solve(cdp)\n",
    "@time res = solve(cdp);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_eval_nodes!(res, grid_y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body\u001b[36m::ContinuousDPs.CDPSolveResult{PFI,1,Array{Float64,1},Array{Float64,1}}\u001b[39m\n",
      "\u001b[90m\u001b[59G│╻╷ set_eval_nodes!\u001b[1G\u001b[39m\u001b[90m137 \u001b[39m1 ─ %1 = (Base.getfield)(s_nodes_coord, 1, true)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[59G││╻  setproperty!\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│        (Base.setfield!)(res, :eval_nodes, %1)\n",
      "\u001b[90m\u001b[59G││╻  setproperty!\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│        (Base.setfield!)(res, :eval_nodes_coord, s_nodes_coord)\n",
      "\u001b[90m\u001b[59G│╻  set_eval_nodes!\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│   %4 = invoke ContinuousDPs.evaluate!(_2::ContinuousDPs.CDPSolveResult{PFI,1,Array{Float64,1},Array{Float64,1}})\u001b[36m::ContinuousDPs.CDPSolveResult{PFI,1,Array{Float64,1},Array{Float64,1}}\u001b[39m\n",
      "\u001b[90m\u001b[59G│  \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└──      return %4\n"
     ]
    }
   ],
   "source": [
    "@code_warntype set_eval_nodes!(res, grid_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.575157 seconds (7.74 M allocations: 349.491 MiB, 11.62% gc time)\n",
      "  0.573510 seconds (7.74 M allocations: 349.491 MiB, 10.07% gc time)\n",
      "  0.592195 seconds (7.74 M allocations: 349.491 MiB, 9.38% gc time)\n"
     ]
    }
   ],
   "source": [
    "@time set_eval_nodes!(res, grid_y)\n",
    "@time set_eval_nodes!(res, grid_y)\n",
    "@time set_eval_nodes!(res, grid_y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 900x500 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(9, 5))\n",
    "ax[:set_ylim](-35, -24)\n",
    "ax[:plot](grid_y, res.V, lw=2, alpha=0.6, label=\"approximate value function\")\n",
    "ax[:plot](grid_y, v_star.(grid_y), lw=2, alpha=0.6, label=\"true value function\")\n",
    "ax[:legend](loc=\"lower right\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ou5Wi9EQyixZiKd8JmfNMTirTgQq8iIiIyCjF4wYfftzLwZprFF4/xtbB01iNGHarhYK0RHJyC7BVPAr5y8BiMTuuTBMq8CIiIiIjZBgGde0DvF3TQmr7B2wYqMIRD2C1QI7bRUFOJo7SB2DWRrCpbsnY0n9RIiIiIiPQ0ufnrep2gs0fscx3iKSoF4CM5ASKM1NxLdwG8++DhCSTk8p0NaLDRnfv3s3q1atJTU0lJyeHXbt20dDQ8Ceve/nllykvL8fpdFJeXs6rr7466sAiIiIiZugbCvO7k028uO8wRXX/wZrevSRFvaS67JTnu1mwfCuu+/9vKN+p8i7jakQF/tChQzz99NOcOHGC/fv3E41Guf/++xkaGvrSa6qqqnjyySf51re+xblz5/jWt77FE088wQcffHDH4UVERETGWyAc463z7fz7mydwnvvfbO7+/8gIt+Fy2FiQk0JZ5QpS7/u/YMW3ICnD7LgyA1gMwzBGe3F3dzc5OTkcOnSIzZs3f+FznnzySXw+H2+99dbw7IEHHiA9PZ0XX3zxtr6Oz+fD4/Hg9Xpxu92jjSsiIiJy26KxOCeu9nL0QiOzrx9h9tA5LMSxWy0UpieSkz8La8VOyCnXBlUZldF23DtaA+/13lzzlfHl/9qsqqriRz/60S2z7du38/zzz3/pNaFQiFAoNPzY5/PdSUwRERGR22YYBudbvew/30xGZxWbBj7AboSxWizkuRPJz83GXvYQFK8D64gWM4iMiVEXeMMweOaZZ9i4cSOVlZVf+ryOjg5yc3NvmeXm5tLR0fGl1+zevZvnnntutNFERERERuXjniHePN+KpeUUK31HSYwNYAEyU5wUZXlwLroP5m0Du9PsqDKDjbrA/+AHP6C6upqjR4/+yeda/tuvlQzD+Nzss5599lmeeeaZ4cc+n4/i4uLRRhURERH5Sj2DIfbVdNB99RyV3kO4I10AuF0OSjKTSZ6/ERY9AC6PyUlFRlngf/jDH7J3714OHz5MUVHRVz43Ly/vc++2d3V1fe5d+c9yOp04nfqXrYiIiIwvfzjK+/Vd1NbXUdZ/kAXBjwFISrBRnJ6EZ/YyLGWPgDvf3KAinzGiAm8YBj/84Q959dVXOXjwIHPmzPmT19x1113s37//lnXw77zzDuvXrx95WhEREZEx8MkG1WM1V5h7/RBb/DWAQYLNSmF6IlmF87BW7IKsBWZHFfmcERX4p59+mt/+9rfs2bOH1NTU4XfWPR4PiYmJAHz729+msLCQ3bt3A/A3f/M3bN68mX/6p39i586d7Nmzh3ffffe2lt6IiIiIjCXDMLjQ5mN/9TUyO46yefBDrEYUq8VCvieRvLx87OWPQOFKnSwjk9aICvzPf/5zALZu3XrL/Fe/+hXf/e53AWhqasL6mR3Z69ev53e/+x0//vGP+Yd/+AfmzZvHSy+9xNq1a+8suYiIiMgINPf6ebO6BUtTFSt9x3HG/Z9uUM3OwFm2HeZsBpvD7KgiX+mOzoGfKDoHXkREREarbyjM2zXtdF0+TYX3ECnRXuDGBtXirBRSFm6FhdshIdncoDLjmHIOvIiIiMhkFYzEONjQxYUL5ynrP8CcUAsAiQ4bxRlJpM1bg6XsYUjOMjmpyMiowIuIiMi0EosbnGzs5Xh1A3N6DrAxUA+Aw2qhMD2J7FllN+6gmj7b3KAio6QCLyIiItOCYRjUdwyw/+xVMtsPsWHoIyxG/OYdVJ3kF5Zgr9gJeYu1QVWmNBV4ERERmfLa+gO8da4Jy8dHWTFwAkc8CEBmcgJFuTm4Kh6EWevBajM5qcidU4EXERGRKcvrj/DOhXa6L1ZR1n+EpJgXgFSXneIsD6ll98L8e8HhMjmpyNhRgRcREZEpJxiJcfhiN3U1H1Had4CS8I1707jsNzaopi/agKX0QUhMNzmpyNhTgRcREZEpIx43OHWtj6pzF5jd9R7rglcAsFstFKQlkjN3KbaKneApNDmpyPhRgRcREZEp4WLnAO+euURm6/usG6oGDKwWyEl1kV8yj4TKXZBTanZMkXGnAi8iIiKTWqcvyL5z1+DKAZYNfIjNCAOQkZxAYV4eSYsfhaLV8Jk7wYtMZyrwIiIiMikNBCO8W9tO94XDlHqP4owPAZDitFOcnY578Q6YswXsCSYnFZlYKvAiIiIyqYSjcY5e6qK++iQLew9QEOkBwGm3UpSRQmb5ViyLHgBnqslJRcyhAi8iIiKTgmEYfNTUz4kzZ5nd+S4rQ00A2G5uUM1duBpb+aOQkmNyUhFzqcCLiIiI6a50D/L+6TrSW95llb8WAAuQ43aSP7sU5+LHIHOeuSFFJgkVeBERETFN10CQ/ecaMS69R+XgKaxGDID0JAeFhUUkL9kFBcvBYjE5qcjkoQIvIiIiE24wFOX9C61017zPIl8VjngAgOQEG0W5WaQtfRhmbQSbqorIf6f/K0RERGTCRGNxjl/uof7MEeb3HiQ32g9Agt1KUUYqWYvvxbLgfkhIMjmpyOSlAi8iIiLjzjAMLrT5qDr1IUXt+1kabgNubFDN97jILduIvfxhSMowOanI5KcCLyIiIuOqtT/AgVM1JDfuY0ngInBjg2p2qpP8eYtxLXkM0krMDSkyhajAi4iIyLjwBiIcqL5K8MIbzB88h4U4AG6Xg6KS2aQufxxyyrVBVWSEVOBFRERkTEVicY7Vt9Hy0T7m9FdhN8IAuBw2CnNzyFyxE0vJXWC1mpxUZGpSgRcREZExYRgG55r7qD7xHiXdB1gQGwDAbrWQn+kmd9kObAvuBbvT5KQiU5sKvIiIiNyxput+jp04RlbTPhZFuoBPbsSUSP6SrTjLHwKXx9yQItOECryIiIiMWt9QmMOnzmJteJ25wcbheVqig4KFK0ld/jVw55uYUGT6UYEXERGREQtGYhyruYL3zGsUDtYABgBJCTbySxaQteYJyFpgbkiRaUoFXkRERG5bPG5w5moHVz/YS1HvCVKMKAAOq4W8vHzy1nwda/EqnSwjMo5U4EVEROS2XO30cvbom2S1H6Qk7gfAaoHs9DTyVz2Kc8FWsDnMDSkyA6jAi4iIyFfqGQhyouoQiZffpDDaOzxPS3ZRsPQ+Upc8BAnJJiYUmVlU4EVEROQLBcIxTnz0EcHqV8gOtgzPkxNs5JauI3v11yE5y8SEIjOTCryIiIjcIh43OF13ma6TfyBroJakm/MEm5XsWYsoXP8Ulow5pmYUmclU4EVERGTYpZZOGo68TFbPB2QZcQCsFgsZOQUUb3iShMJl2qAqYjIVeBEREaGrb4AzR/aS3HSQ7HhweO7xpFO09jFSFm4Gq83EhCLyCRV4ERGRGWwoGOFU1bsYdW+QFvUOz5NcLvKWPUD28ofA4TIxoYj8dyrwIiIiM1A0Fufs2dN4T/+elGD78DzBbiOzdCPF6x7HkpRuYkIR+TIq8CIiIjOIYRhcvHKJlmP/RYq3gZSbc5vVgqe4ktmbn8KRXmxqRhH5airwIiIiM0R7Zxf1h14iqeNDUjAAsADJ2SXM2vQUKUWV5gYUkduiAi8iIjLN+QYHOX/4NSxXD5AUDw/PXSkZFKx7nOzSjWC1mphQREZCBV5ERGSaikSjnK96h8D517FHB4bn9oREspbtoGTVg1jsThMTishoqMCLiIhMM0Y8zsXzH9L94e9x+DuHX+xtVhspizYzf8Pj2JM8pmYUkdFTgRcREZlG2q5dpPHwi9j7LuO4ObMAzuJlzN/yFMkZBWbGE5ExoAIvIiIyDfiud1F/6EWMllO3vLjbMmYzZ9NTZM4qNy2biIwtFXgREZEpLBQYoO7wq4QuHYB49NNPJGWSt/brzKrcABaLeQFFZMypwIuIiExBRizCpZP76D/7OkbE/+nckUTq4ocoW7cDq93xFX+CiExVKvAiIiJTiWHQWneCtqr/Ij7U8+nYasMxbyvlmx8nMTnVxIAiMt5U4EVERKaIvpZ6Gg+/SKTn6i3zeMEKFm75JpnZ+SYlE5GJpAIvIiIyyQX7O7h86EUCTWeIG8bwPOyZS8nGp5g1r9TEdCIy0VTgRUREJql4wMeV46/gqz9INBYbnodd2WSs/jplS9ZhtekOqiIzjQq8iIjIZBOL0Hr6TbrOvEE4FBgeR2zJOCoeZvX67bgStEFVZKZSgRcREZksDIPehqO0nngZv+/68DhmcRCdvZXFmx8j3aMNqiIznQq8iIjIJBBou8C1Iy/i67zGp6vcLfhyVrFo8zcoKdAGVRG5QQVeRETERNG+Zq4dfYn+a9VE459Wd2/qQvLv+gZrFi3CohsxichnqMCLiIiYwAj00fbBK/TUHSEY+XSD6qAzj+Tlj7F1xToS7NqgKiKfpwIvIiIykSJBes+/RftHbzHo/3SDasDmJr7oIVatvxdPUoKJAUVkslOBFxERmQjxGP7LR2j54FX6+3qH17lHrU76C7eydNMjFGV5TI0oIlODCryIiMh4MgwibdW0Hn+Jns4WYjfXuRsWK10Zq5m34XHWz8rTOncRuW0q8CIiIuPE6PuY9qqX6Pq4llA0PjzvSikla9XjPFixELtuxCQiI6QCLyIiMtaGrtN7+mU66qsYCEaHx73OQmwVu9i2ehUpTr0Ei8jo6KeHiIjIWAkPMVTzFu1n3+H64KcbVIfs6fhm72Dd+q3kpSWaGFBEpgMVeBERkTsVixC+fJCOU6/T2dtH3Lixzj1sTaQ9dytLNuxgW36a1rmLyJhQgRcRERktw8BoPU3HyZfp6GgjfHOde9xipzltDcVrH+Xx+QXYrCruIjJ2VOBFRERGo+cyvad+T/u1iwyGPlnnbqE5uZLUpY/w8JIFJCXoZVZExt6It74fPnyYRx55hIKCAiwWC6+99tpXPv/gwYNYLJbPfdTX1486tIiIiGkGOhg68nMu7dnNpYu1w+W92zmb5sVPs+WJv2H7qjKVdxEZNyP+6TI0NMTSpUv53ve+x+OPP37b1zU0NOB2u4cfZ2dnj/RLi4iImCfoI1z3Jh3nD9DpDQyvc/c5sukouJ+77trI/JwUk0OKyEww4gK/Y8cOduzYMeIvlJOTQ1pa2oivExERMVU0RPzy+3SdfYu26/1EYjeKe9CWQmPmFsrX3Ms9szOxap27iEyQCfv93vLlywkGg5SXl/PjH/+Ybdu2felzQ6EQoVBo+LHP55uIiCIiIp+Kx6H5A/rOvEZLRyf+cAyAmCWBy5615C7bzjfLinA5bCYHFZGZZtwLfH5+Pv/xH//BypUrCYVC/Od//if33HMPBw8eZPPmzV94ze7du3nuuefGO5qIiMjnGQZ01TF09hVam6/S54/cGGPl4+SlWEt38PCyeWQkJ5gcVERmKoth3FzEN5qLLRZeffVVdu3aNaLrHnnkESwWC3v37v3Cz3/RO/DFxcV4vd5b1tGLiIiMqf5mwjWv0X65mk5fkE9eINsTF9Bbsp27V1YyOyvZ1IgiMn34fD48Hs+IO64pW+TXrVvHb37zmy/9vNPpxOl0TmAiERGZ0fy9xOtep6vuGK39AaLxG9W9LyGfj3PvYc3KNews1o2YRGRyMKXAnzlzhvz8fDO+tIiIyKciAYxL79BX8y4t1wcIRG6sc/fbPVxM38L8pZv43sJsnHatcxeRyWPEBX5wcJDLly8PP25sbOTs2bNkZGRQUlLCs88+S2trK7/+9a8BeP7555k9ezYVFRWEw2F+85vf8PLLL/Pyyy+P3XchIiIyErEoXDvGUM0bNHf24A3cWOcesbqoT12Pp2wr36wswpPkMDmoiMjnjbjAnzp16pYTZJ555hkAvvOd7/DCCy/Q3t5OU1PT8OfD4TB/93d/R2trK4mJiVRUVPDGG2/w4IMPjkF8ERGRETAMaD9HqGYvba1NdA+EMIC4xcaVlJUEZ21j+7K5FGckmZ1URORL3dEm1oky2gX+IiIiw3qvEqt5jc5r9bR5g8RurnNvSSqnNfduti0vpbLQrXXuIjJhptQmVhERkQkz2I1Rt5feK6do7vUTisYB6HEWczHjbpYvWcKj87Nw2KwmBxURuT0q8CIiMj2FBuDi2wxePETT9UEGglEABuyZ1KZtobh0FX9enkeqS+vcRWRqUYEXEZHpJRaBqwcJ1b9DS3cfPYPfcjyNAAAgAElEQVQ37isSsiZT59mIffZdPLGkkDyPy+SgIiKjowIvIiLTg2FAy4dE616nvaODDm+QuGEQszi4lLqa/ryNbF82i0W5qVrnLiJTmgq8iIhMfd0NGLV76Gm9SktfgHAsDlj4OHkp1zI3s3HxXNbOycRmVXEXkalPBV5ERKYuXxvU7sXXVE1Tr5+h8I0bMXW65lKbtpXFpaX8sDSHxATdiElEpg8VeBERmXoC/dDwFoHG4zRf99PnDwPgdeRQ49lG9pxK/nxxPlkpTpODioiMPRV4ERGZOiJBuPIe0Uvv03rdR9dAkLgBAZubWs8mYnkr2Lm0gHnZKWYnFREZNyrwIiIy+cVj0FRFvP4tuq730NoXIBo3iFqdNLjX0ZW5lnsrC1lRko5V69xFZJpTgRcRkcnLMKCzBqN2D97uNpp6/QQiMQyLlaspK7iatp41i0r4H4uycdq1zl1EZgYVeBERmZz6rkHtHvwdF2nq9eMNRABoTVxErWczC+fM4QcVuaQlJZgcVERkYqnAi4jI5DJ0HepfJ9J8mpY+P90DIQygN6GQ856tuAsW8J3F+RRnJJmdVETEFCrwIiIyOYSH4NI7xBqP0Nk/RFt/gFjcYMieTo1nC8GMch5YnM/iQo9uxCQiM5oKvIiImCsWhY8PY1x8h16vl5ZeP8FonLA1kbq0DbR7lrOlNI8N87Nw2KxmpxURMZ0KvIiImMMwoO0jqHudwf4umnr9DASjxC12Lqeu4ZJ7Lcvn5vFUWS6pLofZaUVEJg0VeBERmXg9l6H2NULXr9HSF6BnMARYaEqqpNa9kaKCAv5qcR75nkSzk4qITDoq8CIiMnEGOqDuj8Taz9PuDdLuDRI3DLqds6nxbCEho5gnFudTmpeqde4iIl9CBV5ERMZf0AcX92Fcq6JnIEBLX4BwLI7PkU2NZwuD7gXcU5bD2jmZ2HQjJhGRr6QCLyIi4ycagisH4Mr7eAcHae71MxSOEbSlUJu+idbkCu6an8PdpTkkJuhGTCIit0MFXkRExl48Ds0fQMObBAb7aO710+ePELMkcNG9nsspq1hUmMHfLs4nK8VpdloRkSlFBV5ERMaOYUBXHdTtJdLfRmu/ny5fiDhWPk5eTp17A1kZGXx3cT7zc1LMTisiMiWpwIuIyNjwtkDtHmLdDXR6g7R5g8TiBh2u+dR4tmB15/JoeR4rStK0QVVE5A6owIuIyJ3x90LDmxgtp7g+GKSlL0AoGqcvIZ8az1YGk2exZWE2G+ZnkWDXjZhERO6UCryIiIxOJACX9kPjIXz+IM29fgZDUfx2DxcyttCWtIjVczK4VzdiEhEZUyrwIiIyMrEoXDsGF98m4PfR3Bugzx8mYnVR79lEY8pyFuSl8TeL88l1u8xOKyIy7ajAi4jI7TEMaD8H9a8T8XXR2h+gyxckZrFxJXUNF1PXkZmWxveW5DE/J9XstCIi05YKvIiI/Gm9V6F2D/HeRjp9Idr6A0TjBi1J5dS6N2FPyeTR8lxWlKRj1Y2YRETGlQq8iIh8ucFuqP8jRts5ev1hmnv9hKJxepzF1Hi24U/MZ9OCbDYtzMJp142YREQmggq8iIh8XmgQLu6Da8cYCIRpurlBdcCeyYXMLXQmzmPlrAzuLc/Fk6gNqiIiE0kFXkREPhWLwNVDcHk/wcAQzX0BeofChKzJ1KXdzbXkJczLdfPE4jzyPYlmpxURmZFU4EVE5MYG1ZZTNzaoDvXR1h+gayBIBAeX3Ou5nLKaDI+b7yzOZ2Fuim7EJCJiIhV4EZGZrrsBavcS9zZ/ZoMqXEteQp17A/akNB4pz2PVLG1QFRGZDFTgRURmKl8b1O7F6Kqj1x+mpTdAMBqj0zWXGs9Wgq5sNs7PYvPCbFwObVAVEZksVOBFRGaaQD80vAXNH+ALRobvoOp15FCTtY2exFksK05je3keniRtUBURmWxU4EVEZopIEK68B1cOEAgGae7z0+ePELC5qc3YRHNiOfNyUnhycT6FadqgKiIyWanAi4hMd/E4NFVBw5uEAz5a+wJ0D4SIWJ00eLZwJWUl2Z5kvlupDaoiIlOBCryIyHRlGNBZA3V/JObroN0bpMMXJBq3cDVlJQ2pd+FKcfO18lyWF2uDqojIVKECLyIyHfVdg9o9xK9foXvgxsky4VictsSFXPBsIerKZOuibDbMyyLBbjU7rYiIjIAKvIjIdDJ0Hepfx2j9iD5/mJa+AIFIjN6EQs5nbMXrKmTt3EzuLs0hxamXABGRqUg/vUVEpoPwEFx6Bz4+yoD/xgbVgWCUIXs6NZlbaHctYHFRGv+zIpesFKfZaUVE5A6owIuITGWxKHx8GC7tJ+AfoKUvQO9QmLA1kfq0rTQmL2VWVip/VZlPSWaS2WlFRGQMqMCLiExFhgFtH0Hd60QGr9PaH6DLFyRmsXM5dR0XU9eS7k7l/6jMpyw/VSfLiIhMIyrwIiJTTc9lqNtDrPcaHb4g7d4gsTg0JVVS696IIyWDh0tzWD07QyfLiIhMQyrwIiJTxUAn1O3F6KihZzBES3+AcDROt3M2NZ4tBJPy2Tg/i00Ls3DabWanFRGRcaICLyIy2QV9cHEfxrUq+v0hWvr8+MMxfI5sarK20OOaw+o5mdxdloPb5TA7rYiIjDMVeBGRySoagqsH4fJ7+IaGaLl5skzQlkJd+kaakiopK/DwPyryyHG7zE4rIiITRAVeRGSyiceh5STUv8HQQB8tvX76AxFilgQuujdxOWUl+Zkevr84nzlZyWanFRGRCaYCLyIyWRgGdNVB3V6CvS209gfoGQxjYOXj5GXUuzfgScvgm+W5VBS4dbKMiMgMpQIvIjIZeFugdg/hznra+gN0+UIYQIdrPjWeLdjceTxSnsPy4nSdLCMiMsOpwIuImMnfCw1vEm36kHZvgA5vkLhh0JeQT41nK8HU2WxdlMPauRk4bFaz04qIyCSgAi8iYoZIAC6/S+zyATq9Q7T3B4jGDfx2DxfcW+hJLWPjgmw2LsjC5dCRkCIi8ikVeBGRiRSLQtNx4g376OntpfXmWe4Rq4t6z100u1ewel4O312UQ4pTP6JFROTz9OogIjIRDAPaz2HU/ZHe7jZa+gIEIzHiFhtXUtdwyb2Oyll5PF6WS3pygtlpRURkElOBFxEZb71XMWr34m27SEuvn6FwDICWxDJqPZuYXVLCX5fnkquz3EVE5DaowIuIjJfBbqj/IwONp2npC+ALRgDocRZT49lGRsE8vlORR0lmkslBRURkKlGBFxEZa6FBuLgP/6XDtPQO0ecPAzBoz6TGswVrbgWPLc5nQU6KznIXEZERU4EXERkrsQhcPUSwbh+tPf1cH7xxlnvImkydewODuau4ryKfxYUeFXcRERk1FXgRkTtlGNByivCFvbR3dNA1ECJuGMQsDi6lrqYz6y62lBezanYGNt2ESURE7pAKvIjInehuIFLzGp3NV+jwBYnFDcDCteQlNGZsZl3FXJ6am0mCXTdhEhGRsaECLyIyGr52Yhf20HXlDG03b8IE0OmaS0PGVpaUlfF/LsgmMUE3YRIRkbE14reEDh8+zCOPPEJBQQEWi4XXXnvtT15z6NAhVq5cicvlYu7cufziF78YVVgREdMF+omf+S3db/y/VJ+poqnXTzRu4HXkcDz7SYw1f8H/engj2yvyVN5FRGRcjPgd+KGhIZYuXcr3vvc9Hn/88T/5/MbGRh588EG+//3v85vf/IZjx47x13/912RnZ9/W9SIik0IkiHHlPXqr36H1updA5MZZ7gGbm1rPJjIW3MW3KvLISnGaHFRERKa7ERf4HTt2sGPHjtt+/i9+8QtKSkp4/vnnASgrK+PUqVP88z//swq8iEx+8Tg0VdF/dg+tnd0MhqIARC0JNLjvwjZ3M7sWF1OQlmhyUBERmSnGfQ18VVUV999//y2z7du388tf/pJIJILD4fjcNaFQiFAoNPzY5/ONd0wRkVsZBnTW4PvoFdpar+EN3LgJk4GVqynL8c+6h3uWzmVOVrLJQUVEZKYZ9wLf0dFBbm7uLbPc3Fyi0Sg9PT3k5+d/7prdu3fz3HPPjXc0EZEv1neNwTMv0954gd6h8PC4LXEhXYX3sXl5OaV5qTrLXURETDEhp9D89xc5wzC+cP6JZ599lmeeeWb4sc/no7i4ePwCiogADF3HX72HzvrjdA/cuAkTQG9CIU1597FmxQp2FqVh1VnuIiJionEv8Hl5eXR0dNwy6+rqwm63k5mZ+YXXOJ1OnE5tBBORCRL2E6rbR8e5/XR5h4jffJNhyJ7O1axtlK/YyJ/NycRu01nuIiJivnEv8HfddRd//OMfb5m98847rFq16gvXv4uITJhYlPCVQ3Se2ktnb9/NmzBB2JrIlfSNzFp+H99amIPTruMgRURk8hhxgR8cHOTy5cvDjxsbGzl79iwZGRmUlJTw7LPP0trayq9//WsA/vIv/5J/+7d/45lnnuH73/8+VVVV/PKXv+TFF18cu+9CRGQkDINoy2k6PvgD3Z3thGNxAOIWO42pq8lYtoNvlBWT7NS97kREZPIZ8avTqVOn2LZt2/DjT9aqf+c73+GFF16gvb2dpqam4c/PmTOHN998kx/96Ef89Kc/paCggJ/85Cc6QlJETGH0XKa96nd0N18iGI3fnFpoTq7AWfEwDy1ZiCdJvx0UEZHJy2J8sqN0EvP5fHg8HrxeL2632+w4IjIFGQMddH7we7ovn8Yfjg3Pu52ziSx6mA0rl5KT6jIxoYiIzDSj7bj6/bCITG9BH92n99B14SCDwU+PhBxwZOGd8yBrVq+nOFNnuYuIyNShAi8i01M0RO/5t+k88ya+waHhcdCWQk/hPSy96z7m53pMDCgiIjI6KvAiMr3E4/guHaPtg1fw9fcMn+UesyTQmbOBhXc9zObibN2ESUREpiwVeBGZHgyDoZbzNB/7Hb6u5uHibmClK30FRWt3sXN+iW7CJCIiU54KvIhMecGea3x85EUGW2qJfWZf/vWUhWSu+jo7Khbh0E2YRERkmlCBF5EpKzLYy9UjLzFw5QSxeHx4PuAqIHHp17h7xSpcDt2ESUREphcVeBGZcuIhP1eq9uCre5do5NOTZQIOD9ayR1i7dhupiQkmJhQRERk/KvAiMmUYsQiNp9+l98xeYqHB4XnE6iI67z6WbHyYjNQkExOKiIiMPxV4EZn0jHic5toTdJ78A/HB7uF53GIjXLSB0s2Pk5uZYWJCERGRiaMCLyKTWltjHS3HXsTobbxlHsxZytyNT1BcVGJSMhEREXOowIvIpNTd0czVwy9Bx7lb5hHPXArWP8mc+WU6y11ERGYkFXgRmVS8/X3UH/4DXDsKxqcny8SScshc/XUWLl6LxaojIUVEZOZSgReRScEfCHDh6B+JNryDNR4anhsJqbiXPsKiNfdjs+lISBERERV4ETFVOBKj5uS7+Kv34oj4+OS9dYstAVfZvSxa/yhOV7KpGUVERCYTFXgRMUU8blBTfYq+U/+Fy9+B4+bcYrHgnLOeBZu/QZI709SMIiIik5EKvIhMKMMwaLh0kbaql0j2XsJ1c24BEgoqmbvpKdy5OllGRETky6jAi8iEaWxp5cqR35Pa/RHJGMNzZ0YxxRufInP2YhPTiYiITA0q8CIy7tp6+qg98hpJLUdINSLDc2dKBrlrHye/fBPoSEgREZHbogIvIuPm+kCAs8fewnH5HVLjQ8PzBGcSWSseomj5A1jsCSYmFBERmXpU4EVkzA0Ewpw6eZR47V5SIteH5wl2O2nl25i17jGsrlQTE4qIiExdKvAiMmaCkRinzp5l6OwrpAWahucOqwX33FXM3vgkdneuiQlFRESmPhV4EbljsbjBR/VX6Dr5B7J8F0i7ObdZLbgLFjJr41M4c+aZmlFERGS6UIEXkVEzDIPapk6uHHuF7OsnyTJiwI0jIT1ZBRSt/wbJs1Zqg6qIiMgYUoEXkVG51u2l+ugbpLceIDceHJ573B7y1zyGZ9EWsOlHjIiIyFjTq6uIjEjPQJAPj7+H68pb5ES9w/OURBe5y7aTtewhcCSamFBERGR6U4EXkdsyGIrywamTRM+/Rka4fXjuctjJKl1PwdqvY0lKNzGhiIjIzKACLyJfKRyN82FNHf2nXyZr6NLwPMFmJWNWJcUbvok1vdjEhCIiIjOLCryIfKF43ODclSaaTrxKbt8ZsogDYLNYSM8roXjjN0nIq9AGVRERkQmmAi8itzAMg0vtvdQd3Ut251HyjDBw42SZtIwsCtc9TvLc9WC1mhtURERkhlKBF5Fh7f1DnDryNu6m/eTFBofn7pRk8lc8TFrlfWB3mphQREREVOBFhIFAmA9OHofaveREuofnyU4HORVbyVm1C1xuExOKiIjIJ1TgRWawaCzO6erz9J1+mXR/4/DcZbeSOW8lheufwJKaZ2JCERER+e9U4EVmIMMwqP+4iWtHf096XzXpGADYrBYyC+dTvOEp7DkLTE4pIiIiX0QFXmSGab/eS83BV0hpO0a6EQVubFD1ZOZStP4bJM9eo5NlREREJjEVeJEZYjAQ4uyRNzAu7cMdCwzPk5JTyV+9k6yKe8CmHwkiIiKTnV6tRaa5aDRG9ekjDJx5FVe4d3ie4HCQWXkvxWt3YklINjGhiIiIjIQKvMg0ZRgGlxtqaK/6Hc6BJlw35zarhdS5a5i76UkcqdmmZhQREZGRU4EXmYa6Opq5ePB3OLqq+eTUdgvgylvE3M1PkZI3z8x4IiIicgdU4EWmkaGBfmoP/YFY4xEcRnx4bnfnUrThm+TOX6kNqiIiIlOcCrzINBANh2g48QaDNW9BNMgnFd3iTCFr5WPMWXEPFqvN1IwiIiIyNlTgRaYwIx7n2vnDdJ18GSPQ/+knbAkklt5L2cadOJxJ5gUUERGRMacCLzJF9Xx8nmuHXyTa3/zp0GLBUryW0q1P4k7LMi+ciIiIjBsVeJEpJnC9mUsHf0uwtebm/VNviGSWMnfzUxQUzzUtm4iIiIw/FXiRKSLm7+PK0d/ju3iMWPzTDaqhpHxy1z3BwoqVWLRBVUREZNpTgReZ7KIhWk69QffZNwmHQ8PjsN1N0pJHWbHuXhx2bVAVERGZKVTgRSareJy++kO0fPAK/oFPN6hGLQlE593Hss2P4knRBlUREZGZRgVeZLIxDAKt1TQdfQlvV/PwOncDKwN5aynb/A2K8nQHVRERkZlKBV5kEon1XqP52Itcv1ZLNP7pFtU+dxnF659g7YJ5WucuIiIyw6nAi0wG/l46Pvg9XfVVBCKx4bHPVUjK8se5e/lKEuxWEwOKiIjIZKECL2KmsB/v+TdpO/M2Pn9weDxkTye66CHWrtuGJznBxIAiIiIy2ajAi5ghFiV0+RCtH75GT2//8Dr3iDWR64V3s2LTgxRnuU2NKCIiIpOTCrzIRDIM4q1naDvxezo7WofXucctdtoz1jJv/S42zM7VOncRERH5UirwIhPl+hWun/wv2q414A9/ss7dQltKBenLd/HQ4vk4dZ67iIiI/Akq8CLjbbCLgTMv097wIX3+yPC42zmLeNmj3L1qOZ4kh4kBRUREZCpRgRcZL0Ef4bq3aK9+ny6fn09OhRxwZNFdvJ0N6zZRkpVsbkYRERGZclTgRcZaNEz8ygG6zrxBW08/kZvNPWhL4Vr2FirX3sc9JRla5y4iIiKjogIvMlbicWg5Sd+ZPbS0tw+vc49ZErjqWUve8h08WVagde4iIiJyR1TgRcZCVx1DZ1+htenK8Dp3AyvXkpdgK93Bw8vnk5ak89xFRETkzqnAi9wJbwuRmtdov3yOTl9weJ17h2s+vbO2c/eqxczK1Dp3ERERGTsq8CKjEegjXvc6PfXHaekbIhK70dz7EvK5ln0Pq1ev4dHiNK1zFxERkTFnHc1FP/vZz5gzZw4ul4uVK1dy5MiRL33uCy+8gMVi+dxHMBj80mtEJq1IAOpex/vm/8OFk+/R2DNIJGbgt3k4k/Uo1o0/4rs772VFSbrKu4iIiIyLEb8D/9JLL/G3f/u3/OxnP2PDhg38+7//Ozt27KC2tpaSkpIvvMbtdtPQ0HDLzOVyjS6xiBniMbh2jEDNG7R09tDrDwMQsbqoT72LlEVbeXJxEenJWucuIiIi42vEBf5f/uVf+LM/+zP+/M//HIDnn3+et99+m5///Ofs3r37C6+xWCzk5eXdWVIRMxgGdFQTrdlDe1szHb4QccMgbrFxNXkFgyXb2L5sLrN1nruIiIhMkBEV+HA4zOnTp/n7v//7W+b3338/x48f/9LrBgcHmTVrFrFYjGXLlvGP//iPLF++/EufHwqFCIVCw499Pt9IYoqMjd5G4hf20NNcT0uff3ide0tiGU0529i8bJGWyoiIiMiEG1GB7+npIRaLkZube8s8NzeXjo6OL7ymtLSUF154gcWLF+Pz+fjXf/1XNmzYwLlz51iwYMEXXrN7926ee+65kUQTGTuD3VD/R3xXT9HU62fo5nnu151F1KXdTWXlYv5qYTYuh85zF5H/v737DovqTNsAfs8AQ2dAOgKCdFCwgKGoYCzEFmNMxJhYks8k7tqwxGiyrgaTzxKNLepq1ljyJdFN7LpRsWFQTJSADdZCUFBGsdAEaTPn+4NldKQOAsPA/bsurss5855znvPwcnzmzHveQ0TU/Bo0C83zVxwFQajxKmRwcDCCg4OVr8PCwtCtWzesWbMGq1evrnaduXPnYsaMGcrX+fn5cHJyakioRPVX8hi4fhhPrp9C5sPHyPnvOPfHupa4LO0NK7dueLezA9pxnDsRERFpkFoFvJWVFXR0dKpcbc/Ozq5yVb4mYrEYQUFBuH79eo1t9PX1oa+vr05oRA0nLwPS41B+9Qju3M9BdkHFfO4lYiOkmvVEqUMPDA1oj47WJpqOlIiIiEi9Al4ikaB79+6IjY3F8OHDlctjY2MxbNiwem1DEAQkJyejc+fO6kVK1NgEAbiTCCFlP7Lv38PtnCKUKwQoRLq4btoDMusQ9OvsjK5OFhCLOc6diIiIWga1h9DMmDEDY8aMQWBgIEJCQrBx40ZkZGRg4sSJAICxY8eiffv2yhlpPvvsMwQHB8PDwwP5+flYvXo1kpOTsXbt2sY9EiJ13L8GpO5FriwdGY+K8KRMDkCEW8adccO8FwK9XRHlZQ19XY5zJyIiopZF7QI+KioKDx8+RExMDGQyGTp16oR///vf6NChAwAgIyMDYvHT50Pl5ubigw8+wN27dyGVStG1a1ecOnUKPXr0aLyjIKqvfBmQug9Fty8h81ERcp+UAQCyDVxxWRqBjh3d8Vc/O5gbcZw7ERERtUwiQRAETQdRl/z8fEilUuTl5cHMzEzT4ZA2Ks4Drv6CspsJuP2oEPcLSiAAyNOzwWVpBAwdfDHE3x5O7Yw0HSkRERG1EQ2tcRs0Cw2R1igvAdKOQ3HjGO7lFOBO7hPIFQKe6JgixawXCqy64JVO9vB3lHI+dyIiItIKLOCpdVIogIwECFd/QU7OQ2Q+KkJxuQLlIgmuSYORad4Dvbzt0dPdCno64rq3R0RERNRCsICn1kUQgHtXgNT9ePzwNjIfFSG/uBwCxEg36YarZqHo7Noe0/1sYWqgp+loiYiIiNTGAp5aj9wMIGUviu9exe2cJ3hYWPEgpixDT1yRhsPO3hEfdLaHg7mhhgMlIiIiajgW8KT9ih4BqftRlpmIrNwnygcxPZI44LK0D8SWrni9kz187E05zp2IiIi0Hgt40l6lRcCNWMjT4nA39zFkecWQKwQU6lrgillv5Jv74GUfO/RwbQcdPoiJiIiIWgkW8KR95OXAzV8hXDuMBzm5uJ37BKXlCpSJDZFqHoossy4I9bRHLw8rGOjxQUxERETUurCAJ+0hCEBWEoTUA8h7eBeZOUUoKpVDIdLFDdMgXDcLRteOdhjpbQupIW9QJSIiotaJBTxph4dpQMoePL73JzIfPUF+cRkAETKN/JBi1gsuTu0x2c8ONmYGmo6UiIiIqEmxgKeW7XE2kLIXT25fwJ1nZpa5r98Bl6URMLV1wZjO9nC1MtZwoERERETNgwU8tUwlBcDVQyj5Mx5ZOYW4X1ACAUCBnhUuSyNQ3s4LgzvZo1N7M84sQ0RERG0KC3hqWcpLgT9PovTqEcge5iG7oAQKQUCxjglSzXrigXkAXva1Qw+XdtDlE1SJiIioDWIBTy2DQgHcPofy1AO4e+8e7uZXTAkpF0lwzawHMs17IMzbAePdOLMMERERtW0s4EnzslNRfnkv7melIyv3CcoVAgSIccs4ADcseqK7pzNGelrDSMLuSkRERMSKiDQn7w7kV/bgfvpFyHKLUSpXAADuGrgj1bw3fDw9MdnLhlNCEhERET2DBTw1vyc5KE89iAep8ZDlVTyECQByJPa4Yh4OJ3d/fOBji3bGEg0HSkRERNTysICn5lNWjPJrsbh/4TDu5hQor7gX6UiRIu0NM7ceeMfPDracy52IiIioRizgqekp5ChPj8e983uR/eChsnAvExvgqmkIDDzCMdzXAQ7mhhoOlIiIiKjlYwFPTUcQUHbnAmRn/4WH9+4oC3eFSAd/GneF2HMABnV2gb2UhTsRERFRfbGApyZRev9P3Dn9I3LvXFMW7gBw29AHgvcgvOzvxcKdiIiIqAFYwFOjKsm7i4zT/0JB+nmUyQXl8of6jijzfBVh3brCTsox7kREREQNxQKeGkVJUT7STu/Ck2txKJeXK5c/1rNEqcdgdA/qCVtecSciIiJ6YSzg6YWUlBTjasJBFKceglBWrFxeqmOEErdIdA55BbZSIw1GSERERNS6sICnBikpK8eVcyfw5OJe6JbmKZcrRLooc4mAd9gw2LQz12CERERERK0TC3hSS0m5HBeTzuFx0i4YFcuediCRCEL7HnDv+SasbWw1GSIRERFRq6G1uzIAACAASURBVMYCnuqltFyB5CupyEn8GeaPb6ByUIwIgJ6dL5x6joKVg6smQyQiIiJqE1jAU63K5AokXr2F7PO7YZubDHNUzCwjAmBo5QyH0ChYuvhrNkgiIiKiNoQFPFWrTK7A+TQZbp87iPaPzsJWKANQUbibmFvBrscItPMKA0QizQZKRNSKyeVylJWVaToMInoBEokEYrG4UbfJAp5UyBUCzqU/wPXzR9HhfhycFYXK96SmJrDtNhQWnfoDOnoajJKIqHUTBAF3795Fbm6upkMhohckFovh6uoKiUTSaNtkAU8AKv6zuJiZi6Tzp+F49yg8yx8q37Mw1od1576wCBgC6JtqMEoiorahsni3sbGBkZERRPy2k0grKRQKZGVlQSaTwdnZudH+llnAE25kF+D0+SRYZR6CT0mmcrmFkR5sPF+CebfXARNrDUZIRNR2yOVyZfFuaWmp6XCI6AVZW1sjKysL5eXl0NNrnBEMLODbsNs5RTiZdBWGfx6Cd1GKcrmZgR7sXbxhHvgG0K6jBiMkImp7Kse8GxnxIXhErUHl0Bm5XM4Cnhou70kZjl5Mx5OUw3B7nAixIAcAGEt0YO/ghHaBr0Nk34U3qBIRaRCHzRC1Dk3xt8wCvg0plytw+vo93Dp/GB1zz0BP8QQAYKArhoO1Jay6vwpRh56ADrsFERERUUvVuHPaUIskCAJSs/Kwfe9eKI5/Aa9Hx6CneAJdsQjOVmbo1Pt1WA/7HKKOESzeiYiozXJxccHKlSs1HUazunnzJkQiEZKTkwEAJ0+ehEgkatIZkP7zn/8gODgYBgYG6NKlS5Ptpz4WLFig8RgagtVaK5ddUIxfE87C+M9/w6NUBqBiLndrMwM4+PWEvt9QwKidZoMkIiJqAc6dOwdjY+Mm34+Liwuio6MRHR3d5PtSV2hoKGQyGaRSaZPtY/78+TA2NsbVq1dhYmLSZPt5nkgkwu7du/Haa68pl82aNQtTpkxpthgaCwv4Vqq4TI5fk1JQdGE3HJ9cVy43M9BDew9/mHV7A5A6ajBCIiKiFyeXyyESiRrlQTnW1pxxTSKRwM7Orkn3kZaWhsGDB6NDhw5Nup/6MDExadYPEY2FQ2haGUEQ8Mf1DPzy4xqYnl0Gu/8W7xJdMTq4uMF72EyYRUxj8U5ERI3q0KFD6NmzJ8zNzWFpaYkhQ4YgLS1N+X7lUI3t27cjNDQUBgYG8PPzw8mTJ5VtKodvHDx4EAEBATAwMMBLL72ES5cuKdts2bIF5ubmOHDgAHx9faGvr49bt25BoVAgJiYGjo6O0NfXR5cuXXDo0CHletu2bYOJiQmuX396UWvKlCnw9PREYWHFQwufH0IjEomwYcMGDBkyBEZGRvDx8UFCQgJu3LiBiIgIGBsbIyQkROU409LSMGzYMNja2sLExARBQUE4evSo8v2IiAjcunUL06dPh0gkUrnB8cyZM+jduzcMDQ3h5OSEqVOnKmOrTuXwjw0bNsDJyQlGRkZ48803VYa/1JWX51U3hOb06dMIDw+HkZERLCwsEBkZiZycHGzbtg2WlpYoKSlR2caIESMwduzYarcvEomQmJiImJgYiEQiLFiwoNp9JicnQyQS4ebNmwCe/t4PHz4MHx8fmJiY4JVXXoFMJlPZ/rfffgs/Pz/o6+vD3t4ekydPBlDxuwWA4cOHQyQSKV8/P4SmrnxV9uNdu3ahT58+MDIyQkBAABISEmrMaVNgAd+KZGTn4uDOrXhy6DM45CZCBAXEIhFsrW3QaeCHsBvyd4hs/Ti7DBERNbrCwkLMmDED586dw7FjxyAWizF8+HAoFAqVdh999BFmzpyJpKQkhIaG4tVXX8XDhw+rtFm2bBnOnTsHGxsbvPrqq8rpNQGgqKgIixYtwj//+U9cuXIFNjY2WLVqFZYvX45ly5bh4sWLiIyMxKuvvqos2MeOHYtBgwbh7bffRnl5OQ4dOoQNGzbg+++/r3XYzMKFCzF27FgkJyfD29sbo0ePxocffoi5c+fi/PnzAKAsEgHg8ePHGDRoEI4ePYqkpCRERkZi6NChyMjIAADs2rULjo6OiImJgUwmUxagly5dQmRkJF5//XVcvHgRO3bsQHx8vMq2q3Pjxg3861//wv79+3Ho0CEkJydj0qRJyvfryktdkpOT0bdvX/j5+SEhIQHx8fEYOnQo5HI53nzzTcjlcuzbt0/Z/sGDBzhw4ADefffdarcnk8ng5+eHmTNnQiaTYdasWfWKA6j4vS9btgzfffcdTp06hYyMDJX1169fj0mTJuGDDz7ApUuXsG/fPri7uwOoGB4FAJs3b4ZMJlO+fl598/Xpp59i1qxZSE5OhqenJ9566y2Ul5fX+1heFIfQtAJ5RSU4H38Yutd/gbX8sXK51NQE7bsPhqlvf0BXX4MREhHRi1p74gbyi8vqbtiIzAz0MKmPe73ajhgxQuX1pk2bYGNjg5SUFHTq1Em5fPLkycq269evx6FDh7Bp0ybMnj1b2Wb+/Pno378/AGDr1q1wdHTE7t27MXLkSAAVc+WvW7cOAQEBynWWLVuGjz/+GKNGjQIALFmyBCdOnMDKlSuxdu1aAMCGDRvg7++PqVOnYteuXZg/fz6CgoJqPa53331Xud+PP/4YISEhmDdvHiIjIwEA06ZNUylWAwICVOL6/PPPsXv3buzbtw+TJ09Gu3btoKOjA1NTU5WhKl9++SVGjx6tHBfv4eGB1atXIzw8HOvXr4eBgUG18RUXFytzBABr1qzB4MGDsXz5ctjZ2dUrL7VZunQpAgMDsW7dOuUyPz8/5b9Hjx6NzZs348033wQAfP/993B0dERERES127Ozs4Ouri5MTEzUHqpTVlaGf/zjH3BzcwNQ0ZdiYmKU73/++eeYOXMmpk2bplxW+futHB5lbm5e637rm69Zs2Zh8ODBAIDPPvsMfn5+uHHjBry9vdU6poZiAa/FyuUKJCUmoCBpN0xK7imXG0r0YNspAraBrwEGZhqMkIiIGkt+cRnynzTfFT51paWlYd68eTh79iwePHigvPKekZGhUsCHhIQo/62rq4vAwECkpqaqbOvZNu3atYOXl5dKG4lEAn9/f+Xr/Px8ZGVlISwsTGU7YWFhuHDhgvK1hYUFNm3ahMjISISGhmLOnDl1Htez+7G1tQUAdO7cWWVZcXEx8vPzYWZmhsLCQnz22Wc4cOCA8umbT548UV6Br0liYiJu3LiB77//XrlMEAQoFAqkp6fDx8en2vWcnZ2VxTtQkTuFQoGrV6/CyMioXnmpTXJysrI4r87777+PoKAg3LlzB+3bt8fmzZsxfvz4Jpn73MjISFm8A4C9vT2ys7MBANnZ2cjKykLfvn0bvP369iNAtV/Y29srY2ABTzUSBAE3blzD7dM/wig/DZVf/OmKRbDo2BUuYaMgltprNEYiImpcZgaN8wTHptrn0KFD4eTkhG+++QYODg5QKBTo1KkTSktL61y3PsXes20MDQ2rXef5ZYIgVFl26tQp6OjoICsrC4WFhTAzq/1C17NPzqzcVnXLKj+wfPTRRzh8+DCWLVsGd3d3GBoa4o033qgzDwqFAh9++CGmTp1a5T1nZ+da131WZTzPHnd98lITQ0PDWt/v2rUrAgICsG3bNkRGRuLSpUvYv39/veMFoLwBWRAE5bJnh0xVev4ppiKRSLlOXXGqoz75qq0PNAcW8Frmwf17SD35IySyP2CEik4rAmBi6wrX3qNhaN88n/yIiKh51XcoiyY8fPgQqamp2LBhA3r16gUAiI+Pr7bt2bNn0bt3bwBAeXk5EhMTq4zzPnv2rLJozcnJwbVr12q9smlmZgYHBwfEx8crtw1U3BTao0cPlddLly7F/v37MWfOHEyZMgVbt25t2EHX4Ndff8X48eMxfPhwABVj4itvxKwkkUggl8tVlnXr1g1XrlxRjtmur4yMDGRlZcHBwQEAkJCQALFYDE9Pz3rnpTb+/v44duwYPvvssxrbTJgwAStWrMCdO3fQr18/ODk5qXUMlcNbZDIZLCwsAEA5L319mZqawsXFBceOHUOfPn2qbaOnp1cl789qjHw1FxbwWqL4SSGuxO1C2fXjkAhPv0KVmFrCKXQkrDxDeHMqERFphIWFBSwtLbFx40bY29sjIyOjxuEpa9euhYeHB3x8fLBixQrk5OTgvffeU2kTExMDS0tL2Nra4tNPP4WVlZXK3N3V+eijjzB//ny4ubmhS5cu2Lx5M5KTk5VDUgoKCjBmzBhMmTIFAwcOhLOzMwIDAzFkyJBah4ioy93dHbt27cLQoUMhEokwb968KldmXVxccOrUKYwaNQr6+vqwsrLCxx9/jODgYEyaNAnvv/8+jI2NkZqaitjYWKxZs6bG/RkYGGDcuHFYtmwZ8vPzMXXqVIwcOVI5zruuvNRl7ty56Ny5M/76179i4sSJkEgkOHHiBN58801YWVkBAN5++23MmjUL33zzDbZt29agnDk5OWHBggX4/PPPcf36dSxfvlzt7SxYsAATJ06EjY0NBg4ciIKCApw+fVo5z3tlgR8WFgZ9fX3lh4VnvWi+mgsL+BZOkJfj2u9HkJO8D6KyQlSW6GKJESy7DIFr0CsQ6TT/16pERESVxGIxtm/fjqlTp6JTp07w8vLC6tWrq72RcfHixViyZAmSkpLg5uaGvXv3KgvBZ9tMmzYN169fR0BAAPbt2weJRFJrDFOnTkV+fj5mzpyJ7Oxs+Pr6Yt++ffDw8ABQcbOpsbEx/vd//xdAxY2YS5YswcSJExEaGor27ds3Si5WrFiB9957D6GhocrCPD8/X6VNTEwMPvzwQ7i5uaGkpASCIMDf3x9xcXH49NNP0atXLwiCADc3N0RFRdW6P3d3d7z++usYNGgQHj16hEGDBqnccFpXXuri6emJI0eO4JNPPkGPHj1gaGiIl156CW+99ZayjZmZGUaMGIGDBw/W+UGrOnp6evjxxx/xl7/8BQEBAQgKCsLnn3+u9gercePGobi4GCtWrMCsWbNgZWWFN954Q/n+8uXLMWPGDHzzzTdo3759lW9GgBfPV3MRCc8OOGqh8vPzIZVKkZeXV+dYtVZDECC7+jtun/kXFI+zny4X68LQIxzevUZAYmSqufiIiKhJFBcXIz09Ha6urjXOPKKNbt68CVdXVyQlJdX46PqTJ0+iT58+yMnJgbm5eTNHqH0WLFiAPXv2qD3cpCn0798fPj4+WL16taZDaXFq+5tuaI3LK/AtUMHd6/jz5A8ozr6hslxw6Arv8Ldgbs0bVImIiEjzHj16hCNHjuD48eP4+uuvNR1Om8ECvgUpzcvGn79ux+Ob5yFXPP1ipMTMBc49R8PFvfoppIiIiIg0oVu3bsjJycGSJUvg5eWl6XDaDA6haQGEkse4mbAbOSknUFb+dNqkEn1LmHUbAf9uYRDr8KG5RERtQWsdQkPUVnEITWsjL8Pd5CO498d+FD8pVC4u1TGCyGsguocNhLEhn6BKRERERE+xgNcEQUDO9QRknf0Zj3PvKxcrRLoocuyFzuGvw6Ydb94hIiIioqpYwDezvIwryDy9HUX3b+Lp2CURci27wr3Xm3BzdqxlbSIiIiJq61jAN5P87AzcjN+OkjuX8Mz9qcg1cYNNj5Ho7+sDsZgPYiIiIiKi2rGAb2KP8x7hRvxPKPvzNBTP3C9cZGAHoy7DEdHtJejr6mgwQiIiIiLSJizgm0hRUSH+c3ovyq4dA+SlyuVlembQ8xuK4Jf6wVCf6SciIiIi9XBuwkZWXFqGxFMHkLRlFspSf1EW7wqxPkTeQ9Bl3DK81PsVFu9EREQt0IIFC1SeFDt+/Hi89tprTbrPjRs3wsnJCWKxGCtXrmzSfdXFxcVF4zFQ3VhFNpKSsnJcTDyDogt7YFBy/2liRWLouPaEd88RkJq302SIRERETSYiIgJdunRpdcXfqlWr0JSPzMnPz8fkyZPx1VdfYcSIEZBKpU22r2dt2bIF0dHRyM3NVVl+7tw5GBsbN0sM1HAs4F+QIAi4knoZdxN2wLTwFiqn5xcB0G3fBR17RcHchjPLEBERCYIAuVwOXV3tKT+auqDOyMhAWVkZBg8eDHt7+ybdV31YW1trOgSqBw6heQEy2R3Ebf8KhUeXwrTwFoCKwt3Qxg3ur3+KbiNmsngnIqJWb/z48YiLi8OqVasgEokgEolw8+ZNnDx5EiKRCIcPH0ZgYCD09fXx66+/VjssJTo6GhEREcrXgiBg6dKl6NixIwwNDREQEICff/651jhcXFywcOFCjB49GiYmJnBwcMCaNWtU2mRkZGDYsGEwMTGBmZkZRo4ciXv37tV6bM/GqlAosGTJEri7u0NfXx/Ozs744osvAAAvv/wyJk+erLL+w4cPoa+vj+PHj1fZ9pYtW9C5c2cAQMeOHZV5q09+IiIiMHXqVMyePRvt2rWDnZ0dFixYoLJObm4uPvjgA9ja2sLAwACdOnXCgQMHcPLkSbz77rvIy8tT/r4q131+CE1d+aoccvTdd9/BxcUFUqkUo0aNQkFBQY05pRfHAr4BnhQW4PeDm3Hr509heD9ZudzI3BbuA6fAP2o+2jl6azBCIiKi5rNq1SqEhITg/fffh0wmg0wmg5OTk/L92bNnY9GiRUhNTYW/v3+9tvm3v/0Nmzdvxvr163HlyhVMnz4d77zzDuLi4mpd78svv4S/vz/++OMPzJ07F9OnT0dsbCyAig8Fr732Gh49eoS4uDjExsYiLS0NUVFR9T7WuXPnYsmSJZg3bx5SUlLwww8/wNbWFgAwYcIE/PDDDygpKVG2//777+Hg4IA+ffpU2VZUVBSOHj0KAPj999+r5K0uW7duhbGxMX777TcsXboUMTExymNVKBQYOHAgzpw5g//7v/9DSkoKFi9eDB0dHYSGhmLlypUwMzNT/r5mzZpVZfv1zVdaWhr27NmDAwcO4MCBA4iLi8PixYvrfRykvgZ9h7Vu3Tp8+eWXkMlk8PPzw8qVK9GrV68a2+/cuRPz5s1DWloa3Nzc8MUXX2D48OENDlpTBHkZrv12CLkXDgBlRaictV1H3xi2ga/BqUs/QEd7vhYkIiItcmoZUJLfvPvUNwN6Vy3snieVSiGRSGBkZAQ7O7sq78fExKB///713m1hYSG++uorHD9+HCEhIQAqrlDHx8djw4YNCA8Pr3HdsLAwzJkzBwDg6emJ06dPY8WKFejfvz+OHj2KixcvIj09XVkof/fdd/Dz88O5c+cQFBRUa1wFBQVYtWoVvv76a4wbNw4A4Obmhp49ewIARowYgSlTpmDv3r0YOXIkAGDz5s0YP348RKKqz3oxNDSEpaUlgIqhK9Xlrjb+/v6YP38+AMDDwwNff/01jh07pjzW33//HampqfD09ARQkcNKUqkUIpGo1n3WN18KhQJbtmyBqakpAGDMmDE4duyY8psJanxqV5s7duxAdHQ01q1bh7CwMGzYsAEDBw5ESkoKnJ2dq7RPSEhAVFQUFi5ciOHDh2P37t0YOXIk4uPj8dJLLzXKQTQ1QaHAzcuncf/3nRCKHiqXi8S6MPJ+Gd49h0PXwESDERIRUatXkg8U52k6igYJDAxUq31KSgqKi4urFP2lpaXo2rVrretWFvzPvq4cEpKamgonJyeVq9y+vr4wNzdHampqnQV8amoqSkpK0Ldv32rf19fXxzvvvINvv/0WI0eORHJyMi5cuIA9e/bUut2Gev7bDHt7e2RnZwMAkpOT4ejoqCzeG6K++XJxcVEW78/HQU1D7QL+q6++wv/8z/9gwoQJAICVK1fi8OHDWL9+PRYtWlSl/cqVK9G/f3/MnTsXQMVXT3FxcVi5ciV+/PHHFwy/6WXeuIw7Z7ZDlHtLZbnQPhA+EW/BzNJGQ5EREVGbom+mtft8flYTsVhcZWaXsrIy5b8VCgUA4ODBg2jfvr1qSPr6au+/8uq3IAjVXgmvafnzDA0N62wzYcIEdOnSBbdv38a3336Lvn37okOHDmrFW1d+Kunp6am8FolEytzVJ9a61DdftcVBTUOtAr60tBSJiYnKr6YqDRgwAGfOnKl2nYSEBEyfPl1lWWRkZK3TTJWUlKiMH8vPb+avDP8r534Wsn5ZCtEzf0RlFu5wChuFDh29NBITERG1UfUYyqJJEokEcrm8Xm2tra1x+fJllWXJycnKQtDX1xf6+vrIyMiodbhMdc6ePVvltbe3t3K7GRkZyMzMVF5VTklJQV5eHnx8fOrctoeHBwwNDXHs2DHlhcznde7cGYGBgfjmm2/www8/VLmJtj7qyk99+Pv74/bt27h27Vq1V+Hr8/t60XxR01HrJtYHDx5ALpcrb9aoZGtri7t371a7zt27d9VqDwCLFi2CVCpV/qhzQ0djsrB2gLh9dwCAwtgO7fpMQdjb81i8ExERPcfFxQW//fYbbt68iQcPHtR6Bfbll1/G+fPnsW3bNly/fh3z589XKVhNTU0xa9YsTJ8+HVu3bkVaWhqSkpKwdu1abN26tdY4Tp8+jaVLl+LatWtYu3YtfvrpJ0ybNg0A0K9fP/j7++Ptt9/GH3/8gd9//x1jx45FeHh4vYb5GBgY4OOPP8bs2bOxbds2pKWl4ezZs9i0aZNKuwkTJmDx4sWQy+UNuuevrvzUR3h4OHr37o0RI0YgNjYW6enp+OWXX3Do0CEAFb+vx48f49ixY3jw4AGKioqqbONF80VNp0Gz0Dz/dUpdXz2p237u3LnIy8tT/mRmZjYkzEbh3jsK0uB38NL4xfDo3AMiMSfuISIiet6sWbOgo6MDX19fWFtbIyMjo8a2kZGRmDdvHmbPno2goCAUFBRg7NixKm0WLlyIv//971i0aBF8fHwQGRmJ/fv3w9XVtdY4Zs6cicTERHTt2hULFy7E8uXLERkZCaCiHtmzZw8sLCzQu3dv9OvXDx07dsSOHTvqfZzz5s3DzJkz8fe//x0+Pj6IioqqMt77rbfegq6uLkaPHg0DA4MatlSz+uSnPnbu3ImgoCC89dZb8PX1xezZs5VX3UNDQzFx4kRERUXB2toaS5curbJ+Y+SLmoZIUOPxYqWlpTAyMsJPP/2k8oly2rRpSE5OrnZqJ2dnZ0yfPl1lGM2KFSuwcuVK3Lp1q0r76uTn50MqlSIvLw9mZhoYA0hERNRMiouLkZ6eDldX1wYVf22Zi4sLoqOjER0drdE4MjMz4eLignPnzqFbt24ajYU0r7a/6YbWuGpdTpZIJOjevbtyjtFKsbGxCA0NrXadkJCQKu2PHDlSY3siIiIibVRWVoaMjAx8/PHHCA4OZvFOTUbtWWhmzJiBMWPGIDAwECEhIdi4cSMyMjIwceJEAMDYsWPRvn175Yw006ZNQ+/evbFkyRIMGzYMe/fuxdGjRxEfH9+4R0JERESkQadPn0afPn3g6elZ51NjiV6E2gV8VFQUHj58iJiYGMhkMnTq1An//ve/lVMkZWRkQPzMOPHQ0FBs374df/vb3zBv3jy4ublhx44dWjMHPBEREWmHmzdvanT/ERERVaZ/JGoKao2B1xSOgScioraCY+CJWheNj4EnIiIiIiLNYgFPRETUAvFJlkStQ1MMdlF7DDwRERE1HYlEArFYjKysLFhbW0MikdT67BQiarkEQcD9+/chEonUepJuXVjAExERtSBisRiurq6QyWTIysrSdDhE9IJEIhEcHR2ho6PTaNtkAU9ERNTCSCQSODs7o7y8XPnkTCLSTnp6eo1avAMs4ImIiFqkyq/cG/NrdyJqHXgTKxERERGRFmEBT0RERESkRVjAExERERFpEa0YA185f2Z+fr6GIyEiIiIiahyVta26c8VrRQFfUFAAAHByctJwJEREREREjaugoABSqbTe7UVCUzweqpEpFApkZWXB1NRUIw+zyM/Ph5OTEzIzM2FmZtbs+9dmzF3DMXcNx9y9GOav4Zi7hmPuGo65ezGazJ8gCCgoKICDgwPE4vqPbNeKK/BisRiOjo6aDgNmZmb8w2gg5q7hmLuGY+5eDPPXcMxdwzF3DcfcvRhN5U+dK++VeBMrEREREZEWYQFPRERERKRFdBYsWLBA00FoAx0dHUREREBXVytGHbUozF3DMXcNx9y9GOav4Zi7hmPuGo65ezHalj+tuImViIiIiIgqcAgNEREREZEWYQFPRERERKRFWMATEREREWkRFvBERERERFqEBTyAdevWwdXVFQYGBujevTt+/fXXWtvv3LkTvr6+0NfXh6+vL3bv3t1MkbZM6uRvy5YtEIlEVX6Ki4ubMeKW4dSpUxg6dCgcHBwgEomwZ8+eOteJi4tD9+7dYWBggI4dO+If//hHM0Ta8qibu5MnT1bb7/7zn/80U8Qtx6JFixAUFARTU1PY2Njgtddew9WrV+tcj+e9huWO57wK69evh7+/v/JBOSEhIfjll19qXYd9roK6uWOfq9miRYsgEokQHR1daztt6HttvoDfsWMHoqOj8emnnyIpKQm9evXCwIEDkZGRUW37hIQEREVFYcyYMbhw4QLGjBmDkSNH4rfffmvmyFsGdfMHVDzpTCaTqfwYGBg0Y9QtQ2FhIQICAvD111/Xq316ejoGDRqEXr16ISkpCZ988gmmTp2KnTt3NnGkLY+6uat09epVlX7n4eHRRBG2XHFxcZg0aRLOnj2L2NhYlJeXY8CAASgsLKxxHZ73KjQkdwDPeQDg6OiIxYsX4/z58zh//jxefvllDBs2DFeuXKm2PfvcU+rmDmCfq865c+ewceNG+Pv719pOa/qe0Mb16NFDmDhxosoyb29vYc6cOdW2HzlypPDKK6+oLIuMjBRGjRrVZDG2ZOrmb/PmzYJUKm2O0LQKAGH37t21tpk9e7bg7e2tsuzDDz8UgoODmzK0Fq8+uTtx4oQAQMjJyWmmqLRHdna2AECIi4ursQ3Pe9WrT+54zquZhYWFqFkPCAAABcJJREFU8M9//rPa99jnaldb7tjnqiooKBA8PDyE2NhYITw8XJg2bVqNbbWl77XpK/ClpaVITEzEgAEDVJYPGDAAZ86cqXadhISEKu0jIyNrbN+aNSR/APD48WN06NABjo6OGDJkCJKSkpo61Fahpr53/vx5lJWVaSgq7dK1a1fY29ujb9++OHHihKbDaRHy8vIAAO3atauxDc971atP7gCe854nl8uxfft2FBYWIiQkpNo27HPVq0/uAPa5502aNAmDBw9Gv3796myrLX2vTRfwDx48gFwuh62trcpyW1tb3L17t9p17t69q1b71qwh+fP29saWLVuwb98+/PjjjzAwMEBYWBiuX7/eHCFrtZr6Xnl5OR48eKChqLSDvb09Nm7ciJ07d2LXrl3w8vJC3759cerUKU2HplGCIGDGjBno2bMnOnXqVGM7nveqqm/ueM576tKlSzAxMYG+vj4mTpyI3bt3w9fXt9q27HOq1Mkd+5yq7du3448//sCiRYvq1V5b+p52PC+2iYlEIpXXgiBUWfYi7Vs7dfIRHByM4OBg5euwsDB069YNa9aswerVq5s0ztagulxXt5xUeXl5wcvLS/k6JCQEmZmZWLZsGXr37q3ByDRr8uTJuHjxIuLj4+tsy/Oeqvrmjue8p7y8vJCcnIzc3Fzs3LkT48aNQ1xcXI2FKPvcU+rkjn3uqczMTEybNg1HjhxR6x4Abeh7bfoKvJWVFXR0dKp8qsrOzq7y6auSnZ2dWu1bs4bk73lisRhBQUFt9sqAOmrqe7q6urC0tNRQVNorODi4Tfe7KVOmYN++fThx4gQcHR1rbcvznip1cve8tnzOk0gkcHd3R2BgIBYtWoSAgACsWrWq2rbsc6rUyd3z2nKfS0xMRHZ2Nrp37w5dXV3o6uoiLi4Oq1evhq6uLuRyeZV1tKXvtekCXiKRoHv37oiNjVVZHhsbi9DQ0GrXCQkJqdL+yJEjNbZvzRqSv+cJgoDk5GTY29s3RYitSk19LzAwEHp6ehqKSnslJSW1yX4nCAImT56MXbt24fjx43B1da1zHZ73KjQkd9Vtg+e8CoIgoKSkpNr32OdqV1vuqmvbVvtc3759cenSJSQnJyt/AgMD8fbbbyM5ORk6OjpV1tGavtf89822LNu3bxf09PSETZs2CSkpKUJ0dLRgbGws3Lx5UxAEQRgzZozKjCqnT58WdHR0hMWLFwupqanC4sWLBV1dXeHs2bOaOgSNUjd/CxYsEA4dOiSkpaUJSUlJwrvvvivo6uoKv/32m6YOQWMKCgqEpKQkISkpSQAgfPXVV0JSUpJw69YtQRAEYc6cOcKYMWOU7f/880/ByMhImD59upCSkiJs2rRJ0NPTE37++WdNHYLGqJu7FStWCLt37xauXbsmXL58WZgzZ44AQNi5c6emDkFj/vKXvwhSqVQ4efKkIJPJlD9FRUXKNjzvVa8hueM5r8LcuXOFU6dOCenp6cLFixeFTz75RBCLxcKRI0cEQWCfq426uWOfq93zs9Boa99r8wW8IAjC2rVrhQ4dOggSiUTo1q2bypRg4eHhwrhx41Ta//TTT4KXl5egp6cneHt7t8ki4Fnq5C86OlpwdnYWJBKJYG1tLQwYMEA4c+aMBqLWvMqpDZ//qczXuHHjhPDwcJV1Tp48KXTt2lWQSCSCi4uLsH79+uYPvAVQN3dLliwR3NzcBAMDA8HCwkLo2bOncPDgQc0Er2HV5Q2AsHnzZmUbnveq15Dc8ZxX4b333lP+P2FtbS307dtXWYAKAvtcbdTNHftc7Z4v4LW174kE4b93wRERERERUYvXpsfAExERERFpGxbwRERERERahAU8EREREZEWYQFPRERERKRFWMATEREREWkRFvBERERERFqEBTwRERERkRZhAU9EREREpEVYwBMRERERaREW8EREREREWoQFPBERERGRFmEBT0RERESkRf4fRPBjbq4oyoEAAAAASUVORK5CYII=",
      "text/plain": [
       "Figure(PyObject <Figure size 900x500 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(9, 5))\n",
    "ax[:plot](grid_y, res.X, lw=2, alpha=0.6, label=\"approximate policy function\")\n",
    "ax[:plot](grid_y, c_star.(grid_y), lw=2, alpha=0.6, label=\"true policy function\")\n",
    "ax[:legend](loc=\"lower right\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 900x500 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(9, 5))\n",
    "ax[:plot](grid_y, res.resid, lw=2, alpha=0.6, label=\"residual\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Simulate the controlled Markov process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 900x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_init = 0.1\n",
    "ts_length = 100\n",
    "y = simulate(res, s_init, ts_length)\n",
    "\n",
    "fig, ax = subplots(figsize=(9, 6))\n",
    "ax[:plot](1:ts_length, y, lw=2, alpha=0.6, label=\"beta = $(cdp.discount)\" )\n",
    "ax[:legend](loc=\"lower right\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 900x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(9, 6))\n",
    "\n",
    "for beta in (0.9, 0.94, 0.98)\n",
    "    cdp.discount = beta\n",
    "    res = solve(cdp, verbose=0)\n",
    "    set_eval_nodes!(res, grid_y)\n",
    "    y = simulate(res, s_init, ts_length)\n",
    "    ax[:plot](1:ts_length, y, lw=2, alpha=0.6, label=\"beta = $(cdp.discount)\" )\n",
    "end\n",
    "\n",
    "ax[:legend](loc=\"lower right\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         size\u001b[1G\u001b[39m\u001b[90m335 \u001b[39m1 ── %1   = (Base.arraysize)(s_path, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getproperty\u001b[1G\u001b[39m\u001b[90m336 \u001b[39m│    %2   = (Base.getfield)(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1,Array{Float64,1},Array{Float64,1},Tf,Tg,Tlb,Tub} where Tub<:Function where Tlb<:Function where Tg<:Function where Tf<:Function\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %3   = (Base.getfield)(%2, :weights)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         cumsum\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           invoke Core.kwfunc(Base.cumsum::Any)\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %5   = Base.cumsum\u001b[36m::typeof(cumsum)\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷╷╷     #cumsum\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %6   = (Base.slt_int)(0, 1)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│││┃│││      isempty\u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #3 if not %6\n",
      "\u001b[90m\u001b[47G││││┃││       iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m2 ──        goto #4\n",
      "\u001b[90m\u001b[47G│││││┃│        iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m3 ──        invoke Base.getindex(()::Tuple{}, 1::Int64)\n",
      "\u001b[90m\u001b[47G││││││┃         iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        $(Expr(:unreachable))\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m4 ┄─        goto #5\n",
      "\u001b[90m\u001b[47G││││╻         iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m5 ──        goto #6\n",
      "\u001b[90m\u001b[47G││││      \u001b[1G\u001b[39m\u001b[90m    \u001b[39m6 ──        goto #7\n",
      "\u001b[90m\u001b[47G│││       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m7 ── %14  = invoke Base.:(#cumsum#572)(1::Int64, %5::Function, %3::Array{Float64,1})\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G│││       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #8\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m8 ──        goto #9\n",
      "\u001b[90m\u001b[47G│╻         -\u001b[1G\u001b[39m\u001b[90m337 \u001b[39m9 ── %17  = (Base.sub_int)(%1, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷╷      rand\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %18  = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Array{Float64,1}, svec(Any, Int64), :(:ccall), 2, Array{Float64,1}, :(%17), :(%17)))\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G│││╻╷        rand!\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %19  = (Base.arraylen)(%18)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││╻         rand!\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %20  = (Base.mul_int)(8, %19)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││╻         _rand!\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %21  = (Base.arraylen)(%18)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││╻         *\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %22  = (Base.mul_int)(8, %21)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││╻         <=\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %23  = (Base.sle_int)(%20, %22)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #11 if not %23\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m10 ─        goto #12\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m11 ─        nothing\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m12 ┄ %27  = φ (#10 => true, #11 => false)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #14 if not %27\n",
      "\u001b[90m\u001b[47G││││││╻         macro expansion\u001b[1G\u001b[39m\u001b[90m    \u001b[39m13 ─ %29  = $(Expr(:gc_preserve_begin, :(%18)))\n",
      "\u001b[90m\u001b[47G│││││││╻╷        pointer\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %30  = $(Expr(:foreigncall, :(:jl_array_ptr), Ptr{Float64}, svec(Any), :(:ccall), 1, :(%18)))\u001b[36m::Ptr{Float64}\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││││╻         Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %31  = %new(Random.UnsafeView{Float64}, %30, %19)\u001b[36m::Random.UnsafeView{Float64}\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││││   \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           invoke Random.rand!(_2::MersenneTwister, %31::Random.UnsafeView{Float64}, $(QuoteNode(Random.SamplerTrivial{Random.CloseOpen01{Float64},Float64}(Random.CloseOpen01{Float64}())))::Random.SamplerTrivial{Random.CloseOpen01{Float64},Float64})\n",
      "\u001b[90m\u001b[47G│││││││   \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           $(Expr(:gc_preserve_end, :(%29)))\n",
      "\u001b[90m\u001b[47G│││││││   \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #15\n",
      "\u001b[90m\u001b[47G││││││╻         Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m14 ─ %35  = %new(Core.AssertionError, \"sizeof(Float64) * n64 <= sizeof(T) * length(A) && isbitstype(T)\")\u001b[36m::AssertionError\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           (Base.throw)(%35)\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        $(Expr(:unreachable))\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m15 ┄        goto #16\n",
      "\u001b[90m\u001b[47G││││      \u001b[1G\u001b[39m\u001b[90m    \u001b[39m16 ─        goto #17\n",
      "\u001b[90m\u001b[47G│││       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m17 ─        goto #18\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m18 ─        goto #19\n",
      "\u001b[90m\u001b[47G│╻         -\u001b[1G\u001b[39m\u001b[90m338 \u001b[39m19 ─ %42  = (Base.sub_int)(%1, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻         Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %43  = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Array{Int64,1}, svec(Any, Int64), :(:ccall), 2, Array{Int64,1}, :(%42), :(%42)))\u001b[36m::Array{Int64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         -\u001b[1G\u001b[39m\u001b[90m339 \u001b[39m│    %44  = (Base.sub_int)(%1, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷╷      Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %45  = (Base.sle_int)(1, %44)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│││╻         unitrange_last\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           (Base.sub_int)(%44, 1)\n",
      "\u001b[90m\u001b[47G││││      \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %47  = (Base.ifelse)(%45, %44, 0)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷       isempty\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %48  = (Base.slt_int)(%47, 1)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #21 if not %48\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m20 ─        goto #22\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m21 ─        goto #22\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m22 ┄ %52  = φ (#20 => true, #21 => false)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %53  = φ (#21 => 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %54  = φ (#21 => 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %55  = (Base.not_int)(%52)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #38 if not %55\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m23 ┄ %57  = φ (#22 => %53, #37 => %92)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %58  = φ (#22 => %54, #37 => %93)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getindex\u001b[1G\u001b[39m\u001b[90m340 \u001b[39m│    %59  = (Base.arrayref)(true, %18, %57)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷╷╷     #searchsortedlast#5\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %60  = (Base.arraysize)(%14, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││╻╷╷╷      searchsortedlast\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %61  = (Base.slt_int)(%60, 0)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││││┃│││││    axes\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %62  = (Base.ifelse)(%61, 0, %60)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││╻╷        searchsortedlast\u001b[1G\u001b[39m\u001b[90m    \u001b[39m└─── %63  = (Base.add_int)(%62, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││╻         searchsortedlast\u001b[1G\u001b[39m\u001b[90m    \u001b[39m24 ┄ %64  = φ (#23 => 0, #28 => %78)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %65  = φ (#23 => %63, #28 => %79)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││╻         -\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %66  = (Base.sub_int)(%65, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││╻         <\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %67  = (Base.slt_int)(%64, %66)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #29 if not %67\n",
      "\u001b[90m\u001b[47G│││││╻         +\u001b[1G\u001b[39m\u001b[90m    \u001b[39m25 ─ %69  = (Base.add_int)(%64, %65)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││╻         >>>\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %70  = (Base.lshr_int)(%69, 0x0000000000000001)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││╻         <<\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %71  = (Base.shl_int)(%69, 0xffffffffffffffff)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││    \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %72  = (Base.ifelse)(true, %70, %71)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││╻         getindex\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %73  = (Base.arrayref)(false, %14, %72)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[47G││││││╻         isless\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %74  = (Base.fpislt)(%59, %73)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #27 if not %74\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m26 ─        goto #28\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m27 ─        nothing\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m28 ┄ %78  = φ (#26 => %64, #27 => %72)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %79  = φ (#26 => %72, #27 => %65)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #24\n",
      "\u001b[90m\u001b[47G│││││     \u001b[1G\u001b[39m\u001b[90m    \u001b[39m29 ─        goto #30\n",
      "\u001b[90m\u001b[47G││││      \u001b[1G\u001b[39m\u001b[90m    \u001b[39m30 ─        goto #31\n",
      "\u001b[90m\u001b[47G│││       \u001b[1G\u001b[39m\u001b[90m    \u001b[39m31 ─        goto #32\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m32 ─        goto #33\n",
      "\u001b[90m\u001b[47G│╻         +\u001b[1G\u001b[39m\u001b[90m    \u001b[39m33 ─ %85  = (Base.add_int)(%64, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         setindex!\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           (Base.arrayset)(true, %43, %85, %57)\n",
      "\u001b[90m\u001b[47G││╻         ==\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %87  = (%58 === %47)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #35 if not %87\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m34 ─        goto #36\n",
      "\u001b[90m\u001b[47G││╻         +\u001b[1G\u001b[39m\u001b[90m    \u001b[39m35 ─ %90  = (Base.add_int)(%58, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #36\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m36 ┄ %92  = φ (#35 => %90)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %93  = φ (#35 => %90)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %94  = φ (#34 => true, #35 => false)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %95  = (Base.not_int)(%94)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #38 if not %95\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m37 ─        goto #23\n",
      "\u001b[90m\u001b[47G│╻         getproperty\u001b[1G\u001b[39m\u001b[90m343 \u001b[39m38 ─ %98  = (Base.getfield)(res, :eval_nodes_coord)\u001b[36m::Tuple{Array{Float64,1}}\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻         getindex\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %99  = (Base.getfield)(%98, 1, true)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻         Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %100 = invoke LinParams{Array{Float64,1}}(%99::Array{Float64,1}, 0::Int64)\u001b[36m::LinParams{Array{Float64,1}}\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %101 = (Core.tuple)(%100)\u001b[36m::Tuple{LinParams{Array{Float64,1}}}\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷       _Basis\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %102 = %new(Basis{1,Tuple{LinParams{Array{Float64,1}}}}, %101)\u001b[36m::Basis{1,Tuple{LinParams{Array{Float64,1}}}}\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getproperty\u001b[1G\u001b[39m\u001b[90m344 \u001b[39m│    %103 = (Base.getfield)(res, :X)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻         Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %104 = invoke BasisMatrices.BasisMatrix(BasisMatrices.Nothing::Type{Nothing}, %102::Basis{1,Tuple{LinParams{Array{Float64,1}}}}, $(QuoteNode(Tensor()))::Tensor)\u001b[36m::BasisMatrix{Tensor,SparseArrays.SparseMatrixCSC{Float64,Int64}}\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻         Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %105 = invoke BasisMatrices.get_coefs(%102::Basis{1,Tuple{LinParams{Array{Float64,1}}}}, %104::BasisMatrix{Tensor,SparseArrays.SparseMatrixCSC{Float64,Int64}}, %103::Array{Float64,1})\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻╷        axes\u001b[1G\u001b[39m\u001b[90m346 \u001b[39m│    %106 = (Base.arraysize)(s_path, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷╷      map\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %107 = (Base.slt_int)(%106, 0)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│││┃││       Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           (Base.ifelse)(%107, 0, %106)\n",
      "\u001b[90m\u001b[47G│╻         getproperty\u001b[1G\u001b[39m\u001b[90m347 \u001b[39m│    %109 = (Base.getfield)(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1,Array{Float64,1},Array{Float64,1},Tf,Tg,Tlb,Tub} where Tub<:Function where Tlb<:Function where Tg<:Function where Tf<:Function\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %110 = (Base.getfield)(%109, :shocks)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻         size\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %111 = (Base.arraysize)(%110, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│││╻╷╷╷      Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %112 = (Base.slt_int)(%111, 0)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││││┃│        Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           (Base.ifelse)(%112, 0, %111)\n",
      "\u001b[90m\u001b[47G│╻         setindex!\u001b[1G\u001b[39m\u001b[90m348 \u001b[39m│           (Base.arrayset)(true, s_path, s_init, 1)\n",
      "\u001b[90m\u001b[47G│╻         -\u001b[1G\u001b[39m\u001b[90m349 \u001b[39m│    %115 = (Base.sub_int)(%1, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷╷      Type\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %116 = (Base.sle_int)(1, %115)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│││╻         unitrange_last\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           (Base.sub_int)(%115, 1)\n",
      "\u001b[90m\u001b[47G││││      \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %118 = (Base.ifelse)(%116, %115, 0)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G││╻╷╷       isempty\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %119 = (Base.slt_int)(%118, 1)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #40 if not %119\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m39 ─        goto #41\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m40 ─        goto #41\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m41 ┄ %123 = φ (#39 => true, #40 => false)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %124 = φ (#40 => 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %125 = φ (#40 => 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %126 = (Base.not_int)(%123)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #47 if not %126\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m42 ┄ %128 = φ (#41 => %124, #46 => %149)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %129 = φ (#41 => %125, #46 => %150)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getindex\u001b[1G\u001b[39m\u001b[90m350 \u001b[39m│    %130 = (Base.arrayref)(true, s_path, %128)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻╷╷╷╷╷╷   Interpoland\u001b[1G\u001b[39m\u001b[90m351 \u001b[39m│    %131 = $(Expr(:foreigncall, :(:jl_alloc_array_2d), Array{Float64,2}, svec(Any, Int64, Int64), :(:ccall), 3, Array{Float64,2}, 1, 1, 1, 1))\u001b[36m::Array{Float64,2}\u001b[39m\n",
      "\u001b[90m\u001b[47G││┃│││      Interpoland\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %132 = invoke Base.fill!(%131::Array{Float64,2}, %130::Float64)\u001b[36m::Array{Float64,2}\u001b[39m\n",
      "\u001b[90m\u001b[47G│││┃         funeval\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %133 = invoke BasisMatrices.funeval(%105::Array{Float64,1}, %102::Basis{1,Tuple{LinParams{Array{Float64,1}}}}, %132::Array{Float64,2}, 0::Int64)\u001b[36m::Array{Float64,2}\u001b[39m\n",
      "\u001b[90m\u001b[47G││││╻         getindex\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %134 = (Base.arrayref)(true, %133, 1)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getindex\u001b[1G\u001b[39m\u001b[90m352 \u001b[39m│    %135 = (Base.arrayref)(true, %43, %128)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getproperty\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %136 = (Base.getfield)(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1,Array{Float64,1},Array{Float64,1},Tf,Tg,Tlb,Tub} where Tub<:Function where Tlb<:Function where Tg<:Function where Tf<:Function\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %137 = (Base.getfield)(%136, :shocks)\u001b[36m::Array{Float64,1}\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getindex\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %138 = (Base.arrayref)(true, %137, %135)\u001b[36m::Float64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         +\u001b[1G\u001b[39m\u001b[90m353 \u001b[39m│    %139 = (Base.add_int)(%128, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         getproperty\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %140 = (Base.getfield)(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1,Array{Float64,1},Array{Float64,1},Tf,Tg,Tlb,Tub} where Tub<:Function where Tlb<:Function where Tg<:Function where Tf<:Function\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %141 = (Base.getfield)(%140, :g)\u001b[91m\u001b[1m::Function\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %142 = (%141)(%130, %134, %138)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│           (Base.setindex!)(s_path, %142, %139)\n",
      "\u001b[90m\u001b[47G││╻         ==\u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %144 = (%129 === %118)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #44 if not %144\n",
      "\u001b[90m\u001b[47G││        \u001b[1G\u001b[39m\u001b[90m    \u001b[39m43 ─        goto #45\n",
      "\u001b[90m\u001b[47G││╻         +\u001b[1G\u001b[39m\u001b[90m    \u001b[39m44 ─ %147 = (Base.add_int)(%129, 1)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│╻         iterate\u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #45\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m45 ┄ %149 = φ (#44 => %147)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %150 = φ (#44 => %147)\u001b[36m::Int64\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %151 = φ (#43 => true, #44 => false)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m│    %152 = (Base.not_int)(%151)\u001b[36m::Bool\u001b[39m\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m└───        goto #47 if not %152\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m    \u001b[39m46 ─        goto #42\n",
      "\u001b[90m\u001b[47G│         \u001b[1G\u001b[39m\u001b[90m356 \u001b[39m47 ─        return s_path\n"
     ]
    }
   ],
   "source": [
    "@code_warntype simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000365 seconds (3.56 k allocations: 291.109 KiB)\n",
      "  0.000315 seconds (3.55 k allocations: 289.953 KiB)\n",
      "  0.000329 seconds (3.55 k allocations: 289.953 KiB)\n"
     ]
    }
   ],
   "source": [
    "@time simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init)\n",
    "@time simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init)\n",
    "@time simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Julia 1.0.0",
   "language": "julia",
   "name": "julia-1.0"
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  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.0.0"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
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