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    "# Julia objectives\n",
    "\n",
    "This notebook demonstrates the application of pyPESTO to objective functions defined in [Julia](https://julialang.org/)."
   ]
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   "source": [
    "pyPESTO's Julia interface is the class :class:`JuliaObjective <pypesto.objective.julia.JuliaObjective>`."
   ]
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    "As demonstration example, we use an [SIR disease dynamcis model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology). For simulation, we use [DifferentialEquations.jl](https://diffeq.sciml.ai/stable/).\n",
    "\n",
    "The code consists of multiple functions in the file `model_julia/SIR.jl`, wrapped in the namespace of a module `SIR`. We first speficy the reaction network via `Catalyst` (this is optional, one can also directly start with the differential equations), and then translate them to an ordinary differential equation (ODE) model in `OrdinaryDiffEq`. Then, we create synthetic observed `data` with normal noise. After that, we define a quadratic cost function `fun` (the negative log-likelihood under an additive normal noise model). We use backwards automatic differentiation via `Zygote` to derive also the gradient `grad` (there exist various derivative calculation methods, including forward and adjoint, and continuous and discrete sensitivities, see [SciMLSensitivity.jl](https://sensitivity.sciml.ai/dev/)):"
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       ".highlight .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"c\"># ODE model of SIR disease dynamics</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"k\">module</span><span class=\"w\"> </span><span class=\"n\">SIR</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># Install dependencies</span><span class=\"w\"></span>\n",
       "<span class=\"k\">import</span><span class=\"w\"> </span><span class=\"n\">Pkg</span><span class=\"w\"></span>\n",
       "<span class=\"n\">Pkg</span><span class=\"o\">.</span><span class=\"n\">add</span><span class=\"p\">([</span><span class=\"s\">&quot;Catalyst&quot;</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;OrdinaryDiffEq&quot;</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;Zygote&quot;</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;SciMLSensitivity&quot;</span><span class=\"p\">])</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># Define reaction network</span><span class=\"w\"></span>\n",
       "<span class=\"k\">using</span><span class=\"w\"> </span><span class=\"n\">Catalyst</span><span class=\"o\">:</span><span class=\"w\"> </span><span class=\"nd\">@reaction_network</span><span class=\"w\"></span>\n",
       "<span class=\"n\">sir_model</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"nd\">@reaction_network</span><span class=\"w\"> </span><span class=\"k\">begin</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"n\">r1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">S</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">I</span><span class=\"w\"> </span><span class=\"o\">--&gt;</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"n\">I</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"n\">r2</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">I</span><span class=\"w\"> </span><span class=\"o\">--&gt;</span><span class=\"w\"> </span><span class=\"n\">R</span><span class=\"w\"></span>\n",
       "<span class=\"k\">end</span><span class=\"w\"> </span><span class=\"n\">r1</span><span class=\"w\"> </span><span class=\"n\">r2</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># Ground truth parameter</span><span class=\"w\"></span>\n",
       "<span class=\"n\">p_true</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">[</span><span class=\"mf\">0.0001</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"mf\">0.01</span><span class=\"p\">]</span><span class=\"w\"></span>\n",
       "<span class=\"c\"># Initial state</span><span class=\"w\"></span>\n",
       "<span class=\"n\">u0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">[</span><span class=\"mi\">999</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"w\"></span>\n",
       "<span class=\"c\"># Time span</span><span class=\"w\"></span>\n",
       "<span class=\"n\">tspan</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"mf\">0.0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"mf\">250.0</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># Formulate as ODE problem</span><span class=\"w\"></span>\n",
       "<span class=\"k\">using</span><span class=\"w\"> </span><span class=\"n\">OrdinaryDiffEq</span><span class=\"o\">:</span><span class=\"w\"> </span><span class=\"n\">ODEProblem</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">solve</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">Tsit5</span><span class=\"w\"></span>\n",
       "<span class=\"n\">prob</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">ODEProblem</span><span class=\"p\">(</span><span class=\"n\">sir_model</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">u0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">tspan</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">p_true</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># True trajectory</span><span class=\"w\"></span>\n",
       "<span class=\"n\">sol_true</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">solve</span><span class=\"p\">(</span><span class=\"n\">prob</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">Tsit5</span><span class=\"p\">(),</span><span class=\"w\"> </span><span class=\"n\">saveat</span><span class=\"o\">=</span><span class=\"mi\">25</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># Observed data</span><span class=\"w\"></span>\n",
       "<span class=\"k\">using</span><span class=\"w\"> </span><span class=\"n\">Random</span><span class=\"o\">:</span><span class=\"w\"> </span><span class=\"n\">randn</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">MersenneTwister</span><span class=\"w\"></span>\n",
       "<span class=\"n\">sigma</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">40.0</span><span class=\"w\"></span>\n",
       "<span class=\"n\">rng</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">MersenneTwister</span><span class=\"p\">(</span><span class=\"mi\">1234</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "<span class=\"n\">data</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">sol_true</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">sigma</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"n\">rng</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">size</span><span class=\"p\">(</span><span class=\"n\">sol_true</span><span class=\"p\">))</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"k\">using</span><span class=\"w\"> </span><span class=\"n\">SciMLSensitivity</span><span class=\"o\">:</span><span class=\"w\"> </span><span class=\"n\">remake</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># Define log-likelihood</span><span class=\"w\"></span>\n",
       "<span class=\"k\">function</span><span class=\"w\"> </span><span class=\"n\">fun</span><span class=\"p\">(</span><span class=\"n\">p</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"c\"># untransform parameters</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"n\">p</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">10.0</span><span class=\"w\"> </span><span class=\"o\">.^</span><span class=\"w\"> </span><span class=\"n\">p</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"c\"># simulate</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"n\">_prob</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">remake</span><span class=\"p\">(</span><span class=\"n\">prob</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">p</span><span class=\"o\">=</span><span class=\"n\">p</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"n\">sol_sim</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">solve</span><span class=\"p\">(</span><span class=\"n\">_prob</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">Tsit5</span><span class=\"p\">(),</span><span class=\"w\"> </span><span class=\"n\">saveat</span><span class=\"o\">=</span><span class=\"mi\">25</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"c\"># calculate log-likelihood</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"mf\">0.5</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">log</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"nb\">pi</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">sigma</span><span class=\"o\">^</span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">sum</span><span class=\"p\">((</span><span class=\"n\">sol_sim</span><span class=\"w\"> </span><span class=\"o\">.-</span><span class=\"w\"> </span><span class=\"n\">data</span><span class=\"p\">)</span><span class=\"o\">.^</span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"w\"> </span><span class=\"o\">/</span><span class=\"w\"> </span><span class=\"n\">sigma</span><span class=\"o\">^</span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "<span class=\"k\">end</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"c\"># Define gradient and Hessian</span><span class=\"w\"></span>\n",
       "<span class=\"k\">using</span><span class=\"w\"> </span><span class=\"n\">Zygote</span><span class=\"o\">:</span><span class=\"w\"> </span><span class=\"n\">gradient</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">hessian</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"k\">function</span><span class=\"w\"> </span><span class=\"n\">grad</span><span class=\"p\">(</span><span class=\"n\">p</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"n\">gradient</span><span class=\"p\">(</span><span class=\"n\">fun</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">p</span><span class=\"p\">)[</span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"></span>\n",
       "<span class=\"k\">end</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"k\">function</span><span class=\"w\"> </span><span class=\"n\">hess</span><span class=\"p\">(</span><span class=\"n\">p</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
       "<span class=\"w\">    </span><span class=\"n\">hessian</span><span class=\"p\">(</span><span class=\"n\">fun</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"n\">p</span><span class=\"p\">)[</span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"></span>\n",
       "<span class=\"k\">end</span><span class=\"w\"></span>\n",
       "\n",
       "<span class=\"k\">end</span><span class=\"w\">  </span><span class=\"c\"># module</span><span class=\"w\"></span>\n",
       "</pre></div>\n"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pypesto.objective.julia import display_source_ipython\n",
    "\n",
    "display_source_ipython(\"model_julia/SIR.jl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693d13ef-a268-4f78-9b04-15b47f2f78a7",
   "metadata": {},
   "source": [
    "We make the cost function and gradient known to `JuliaObjective`. Importing module and dependencies, and initializing some operations, can take some time due to pre-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96df9672-94ad-4671-9ff4-d6e426e321ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pypesto\n",
    "from pypesto.objective.julia import JuliaObjective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "724abb5f-3ce2-4ac5-89fe-2b48f89d8733",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 42s, sys: 3.78 s, total: 1min 46s\n",
      "Wall time: 1min 48s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "obj = JuliaObjective(\n",
    "    module=\"SIR\",\n",
    "    source_file=\"model_julia/SIR.jl\",\n",
    "    fun=\"fun\",\n",
    "    grad=\"grad\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74cb4837-24fd-48a5-8160-a6c0818ff493",
   "metadata": {},
   "source": [
    "That's it -- now we have an objective function that we can simply use in pyPESTO like any other. Internally, it delegates all calls to Julia and translates results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d771a36-3af0-4027-b120-1f93d542d1d6",
   "metadata": {},
   "source": [
    "## Comments\n",
    "\n",
    "Before continuing with the workflow, some comments:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e7b5f4-9603-4e86-b7c0-f401f47e7ace",
   "metadata": {},
   "source": [
    "When calling a function for the first time, Julia performs some internal pre-processing. Subsequent function calls are much more efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8579d6f5-193e-444f-a8ff-44ae90293470",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.12228487889986\n",
      "CPU times: user 1.36 s, sys: 12.5 ms, total: 1.38 s\n",
      "Wall time: 1.37 s\n",
      "23.12228487889986\n",
      "CPU times: user 208 µs, sys: 11 µs, total: 219 µs\n",
      "Wall time: 215 µs\n",
      "[-38.6806348  19.9557434]\n",
      "CPU times: user 1min 42s, sys: 2.21 s, total: 1min 44s\n",
      "Wall time: 1min 43s\n",
      "[-38.6806348  19.9557434]\n",
      "CPU times: user 1.17 ms, sys: 0 ns, total: 1.17 ms\n",
      "Wall time: 1.13 ms\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([-4.0, -2.0])\n",
    "\n",
    "%time print(obj.get_fval(x))\n",
    "%time print(obj.get_fval(x))\n",
    "%time print(obj.get_grad(x))\n",
    "%time print(obj.get_grad(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea509992-7938-415d-bb09-ae8061318c21",
   "metadata": {},
   "source": [
    "The outputs are already numpy arrays (in custom Julia objectives, it needs to be made sure that return objects can be parsed to floats and numpy arrays in Python)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38c8c4aa-3faf-4f96-bf9d-271fb8a8d8b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(obj.get_grad(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef22f29-db86-4e8b-89a1-b3b91f4f5dcf",
   "metadata": {},
   "source": [
    "Here, we use backward automatic differentiation to calculate the gradient. We can verify its correctness via finite difference checks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a43d18f-ccf8-4c3f-a30a-2ad824e1e87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-38.75164238,  19.9661208 ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pypesto import FD\n",
    "\n",
    "fd = FD(obj, grad=True)\n",
    "\n",
    "fd.get_grad(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c146e002-37c1-43d0-8a10-a04f0e85c41f",
   "metadata": {},
   "source": [
    "Further, we can use the `JuliaObjective.get()` function to directly access any variable in the Julia module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b826499-e4c7-443e-9a59-d8d90e10d3b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sol_true = np.asarray(obj.get(\"sol_true\"))\n",
    "data = obj.get(\"data\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i, label in zip(range(3), [\"S\", \"I\", \"R\"]):\n",
    "    plt.plot(sol_true[i], color=f\"C{i}\", label=label)\n",
    "    plt.plot(data[i], \"x\", color=f\"C{i}\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8f7a25-bda1-4fa1-a7c1-76b479931d8e",
   "metadata": {},
   "source": [
    "## Inference problem\n",
    "\n",
    "Let us define an inference problem by specifying parameter bounds. Note that we use a log10-transformation of parameters in `fun`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d38e3540-9693-4b24-a734-cabdb5159d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypesto import Problem\n",
    "\n",
    "# parameter boundaries\n",
    "lb, ub = [-5.0, -3.0], [-3.0, -1.0]\n",
    "\n",
    "# estimation problem\n",
    "problem = Problem(obj, lb=lb, ub=ub)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad70b6b4-5ce2-40dc-804c-bb0fad505bfc",
   "metadata": {},
   "source": [
    "## Optimization\n",
    "\n",
    "Let us perform an optimization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2267d70-85fc-46cd-bb81-5bf7742c8d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 77.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.38 s, sys: 88.2 ms, total: 1.47 s\n",
      "Wall time: 1.47 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# optimize\n",
    "from pypesto import optimize\n",
    "\n",
    "result = optimize.minimize(problem)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c96d72-ecfc-425d-932b-fd30e5387415",
   "metadata": {},
   "source": [
    "The objective function evaluations are quite fast!\n",
    "\n",
    "We can also use parallelization, by passing a `pypesto.engine.MultiProcessEngine` to the `minimize` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b929224-6fa6-4f63-8892-37b2475b3738",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n",
      "2022-09-07 00:49:47,222 - pypesto.engine.multi_process - WARNING - Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n",
      "Performing parallel task execution on 8 processes.\n",
      "2022-09-07 00:49:47,233 - pypesto.engine.multi_process - INFO - Performing parallel task execution on 8 processes.\n",
      "100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 130.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 285 ms, sys: 177 ms, total: 462 ms\n",
      "Wall time: 1.73 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from pypesto.engine import MultiProcessEngine\n",
    "\n",
    "engine = MultiProcessEngine()\n",
    "result = optimize.minimize(problem, engine=engine, n_starts=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05888c10-1027-4580-aa0a-1076410579c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pypesto import visualize\n",
    "\n",
    "visualize.waterfall(result);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ce3918-062c-48ed-bf2a-d5622223ec96",
   "metadata": {},
   "source": [
    "## Sampling\n",
    "\n",
    "Last, let us perform sampling from the log-posterior with implicitly defined uniform prior via the parameter bounds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b88b414b-598a-40f1-876e-ef1255a50c93",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████| 10000/10000 [00:08<00:00, 1234.51it/s]\n",
      "Elapsed time: 8.135626907000017\n",
      "2022-09-07 00:49:57,692 - pypesto.sample.sample - INFO - Elapsed time: 8.135626907000017\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.87 s, sys: 320 ms, total: 8.18 s\n",
      "Wall time: 8.15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from pypesto import sample\n",
    "\n",
    "sampler = sample.AdaptiveParallelTemperingSampler(\n",
    "    internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=3\n",
    ")\n",
    "\n",
    "result = sample.sample(\n",
    "    problem, n_samples=10000, sampler=sampler, result=result\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1fa22993-8100-4ec4-9b75-64ea745e77f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Geweke burn-in index: 0\n",
      "2022-09-07 00:49:57,807 - pypesto.sample.diagnostics - INFO - Geweke burn-in index: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pypesto.sample.geweke_test(result)\n",
    "visualize.sampling_parameter_traces(\n",
    "    result, use_problem_bounds=False, size=(12, 5)\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e64acdf3-b4dd-4b9a-9bc3-aa5a219f937a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize.sampling_scatter(result, size=[13, 6]);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python-jl",
   "language": "python",
   "name": "python-jl"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}