{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models with censored observations\n",
    "\n",
    "In many practical applications, some of the observations in a timeseries\n",
    "will be censored. Left-censored observations are known to be on the left\n",
    "(lower than) a given threshold, and vice versa, right-censored values are above a given threshold,\n",
    "but the precise value of the observation is not known.\n",
    "\n",
    "A simplistic approach is to replace the censored observation with some \"intermediate\" value.\n",
    "For instance, if the observation is positive and left-censored, \n",
    "than we could use the mid point between $0$ and the threshold. However, this leads to all sorts\n",
    "of biases. Another approach is to impute the (partially) missing value, for instance with the EM algorithm,\n",
    "or by modeling the missing values as latent variables in a Bayesian model.\n",
    "\n",
    "A third approach is to \"integrate out\" the missing values. If the observation $X$ is modeled with \n",
    "a density function $f_X(x | \\theta)$, then for e.g. left-cencored values below threshold $t$, we replace\n",
    "the density with a cumulative density function (CDF) $F_X(t) = \\int_{-\\infty}^t f_X(x) dx$.\n",
    "\n",
    "In Stan, we can do this by replacing sampling statements like\n",
    "```\n",
    "x ~ normal(mu, sigma);\n",
    "```\n",
    "with statements that add the log-CDF to the `target' variable\n",
    "```\n",
    "target += normal_lcdf(x | mu, sigma);\n",
    "```\n",
    "In the case of right-censored observations, you would use `normal_lccdf` --- the complementary CDF or \"survival function\".\n",
    "\n",
    "A good example of data with many left-censored values is timeseries of HIV-1 viral load (VL) measurements.\n",
    "The VL measures the number of viral RNA copies in a mL of blood plasma, but the assay for detecting viral RNA\n",
    "typically has a limit of detection of 50 copies per mL (older assays had LODs of 200 copies per mL, and there are super-precise assays that have a lower LOD than 50).\n",
    "\n",
    "longitudinal VL measurement can to see if antiretroviral treatment is working, and with modeling such timeseries have been used to reveal some of the kinetic parameters of HIV-1. Here we simulate HIV-1 VL timeseries, assuming a simple model of an HIV-1 infection. At a certain time point, the simulated subjects are treated with ART, and their VL starts to decline. \n",
    "With the 50 copies per mL assay, the VL is not detectible early in the infection, and again after ART is controlling the virus.\n",
    "\n",
    "First we will generate some data. We'll use the `solve_ivp` function from `scipy.integrate` to generate \"typical\" viral load timeseries. Of course, we could use HBOMS to generate data for us, but we don't have to. If you want to test your model with random data, it can even help to use a different framework for simulation: this way you might catch some mistakes. You could even make the simulated data more complex than what is assumed in the HBOMS model to test robustness against model mis-specification. But that's a different subject. \n",
    "\n",
    "The model is a typical viral dynamic model of the form. \n",
    "\n",
    "$$ \\frac{dT}{dt} = \\lambda - d_T T - \\beta V T / T_0 $$\n",
    "$$ \\frac{dI}{dt} = \\beta V T / T_0 - d_I I $$\n",
    "\n",
    "Here $T$ is the concentration of the target cell population: for HIV-1 these are the helper T cells (or CD4+ T cells).\n",
    "The variable $I$ is the concentration of the infected cell population. The variable $V$ is the viral load, and for convenience we \n",
    "assume that this is proportional to $I$, and we rescale everything such that $V = I$. \n",
    "\n",
    "The error model for viral load is often assumed to be log-normal. Hence, we will generate noisy observations by sampling from a log-normal distribution with location parameter $\\log(I)$ and scale parameter $\\sigma$. Realizations that fall below the LOD of $50$ copies per mL are replaced by this threshold.\n",
    "\n",
    "HBOMS understands the following censor codes (using integer values):\n",
    "* uncensored observation have censor code 0, for these we use the log PDF\n",
    "* left censored observations have censor code -1, for these we use the log CDF\n",
    "* right censored observations have censor code 1, for these we use the log cCDF\n",
    "* missing observations have censor code 2, these are just ignored.\n",
    "\n",
    "In cases like this, missing values might as well be left ut of the data, but there are cases where we want to include missing observations explicitly.\n",
    "\n",
    "In addition to the observed values (the VL), we will also provide the HBOMS model with a censor code for each of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as sts\n",
    "from scipy.integrate import solve_ivp\n",
    "from scipy.special import expit\n",
    "\n",
    "# define \"ground truth\" parameter values\n",
    "\n",
    "T0_pop = 1e6 ## initial number of target cells\n",
    "d_T_pop = 0.05 ## death rate of target cells\n",
    "d_I_pop = 0.5 ## death rate of infected cells\n",
    "beta_pop = 1.7 ## infection rate\n",
    "lam_pop = T0_pop * d_T_pop ## production rate of target cells\n",
    "I0_pop = 0.1 ## initial number of infected cells\n",
    "sigma = 0.5 ## measurement noise\n",
    "lod = 50.0 ## limit of detection\n",
    "tau = 40.0 ## ART initiation time: 40 days post infection\n",
    "\n",
    "# define the ODE model. See equations above\n",
    "\n",
    "def vd_ode(t, y, T0, d_T, d_I, beta, lam, tau):\n",
    "    T, I = y\n",
    "    betat = beta * (1-expit(5*(t - tau)))\n",
    "    return np.array([\n",
    "        lam - d_T * T - betat * T * I / T0, \n",
    "        betat * T * I / T0 - d_I * I\n",
    "    ])\n",
    "\n",
    "# function to generate a VL timeseries with noise and censoring\n",
    "\n",
    "def gen_vd_data(params, N, tmax, lod=None):\n",
    "    T0, d_T, d_I, beta, lam, I0, sigma, tau = params\n",
    "    t_span = (0, tmax)\n",
    "    ObsTime = np.linspace(tmax/N, tmax, N)\n",
    "    y0 = [T0, I0]\n",
    "    sol = solve_ivp(\n",
    "        lambda t, y: vd_ode(t, y, T0, d_T, d_I, beta, lam, tau),\n",
    "        t_span, y0, dense_output=True, t_eval=ObsTime\n",
    "    )\n",
    "    Ihat = sol.y[1]\n",
    "    VL = sts.lognorm.rvs(scale=Ihat, s=sigma)\n",
    "    if lod is None:\n",
    "        CC = [0 for _ in VL] # censor code 0 means no censoring\n",
    "    else:\n",
    "        CC = [-1 if x < lod else 0 for x in VL] # censor code -1 means left-censored\n",
    "        VL = [lod if x < lod else x for x in VL]\n",
    "    return ObsTime, Ihat, VL, CC, sol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test python model\n",
    "\n",
    "Let's have a look at what the python simulations generate. We'll plot 20 data point\n",
    "using the above_defined ground-truth parameters. \n",
    "We'll plot the deterministic trajectories, as well as the VL measurements.\n",
    "\n",
    "In the VL graph, red dots correspond to censored observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 20\n",
    "tmax = 60\n",
    "\n",
    "param_vals = (T0_pop, d_T_pop, d_I_pop, beta_pop, lam_pop, I0_pop, sigma, tau)\n",
    "\n",
    "ObsTime, Ihat, VL, CC, sol = gen_vd_data(param_vals, N, tmax, lod=lod)\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, figsize=(7,5), sharex=True)\n",
    "ts = np.linspace(0, tmax, 1000)\n",
    "labs = [\"$T$\", \"$I$\"]\n",
    "for i in range(2):\n",
    "    axs[0].plot(ts, sol.sol(ts)[i], label=labs[i])\n",
    "axs[0].axvline(x=tau, label=\"start ART\", color='k')\n",
    "axs[0].legend(loc=1)\n",
    "axs[0].set_ylabel(\"cell concentration\\n(per mL blood)\")\n",
    "\n",
    "color = ['k' if c == 0 else 'r' for c in CC]\n",
    "axs[1].plot(ObsTime, VL, color='k', label=\"VL data\", marker='o', zorder=1)\n",
    "cens = [c != 0 for c in CC]\n",
    "axs[1].scatter(ObsTime[cens], np.array(VL)[cens], color='r', label='censored', zorder=2)\n",
    "axs[1].legend(loc=1)\n",
    "axs[1].set_xlabel(\"time post infection\")\n",
    "axs[1].set_ylabel(\"viral load\")\n",
    "\n",
    "fig.align_ylabels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate a panel of VL timeseries\n",
    "\n",
    "Next, we'll generate a number of timeseries, each with slightly different parameter values to simulate differences between individuals and viral fitness.\n",
    "We will also add some variation in the time when ART is initialted ($\\tau$), although we'll assume that this time is known and \n",
    "keep it constant.\n",
    "We also assume that $T_0$ is known and equal for everybody. This is unrealistic, but we'll do it to make things a bit simpler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for simulating parameter sets with random effects.\n",
    "\n",
    "def gen_vd_params(d_I, d_T, beta, I0, omega):\n",
    "    d_I_ran = d_I * sts.lognorm.rvs(s=omega)\n",
    "    d_T_ran = d_T * sts.lognorm.rvs(s=omega)\n",
    "    beta_ran = beta * sts.lognorm.rvs(s=omega)\n",
    "    I0_ran = I0 * sts.lognorm.rvs(s=omega)\n",
    "    return (d_I_ran, d_T_ran, beta_ran, I0_ran)\n",
    "\n",
    "R = 12 ## number of replicates\n",
    "omega = 0.15 ## amount of parameter variation between individuals\n",
    "\n",
    "\n",
    "# add variation in tau\n",
    "tau_indiv = sts.norm.rvs(loc=tau, scale=5, size=R)\n",
    "\n",
    "# lists that will contain the data\n",
    "VLs = []\n",
    "CCs = []\n",
    "ObsTimes = []\n",
    "\n",
    "# loop to generate data\n",
    "for r in range(R):\n",
    "    N = 20 # 20 measurements\n",
    "    tmax = 3*N # one measurement each 3 days\n",
    "    # generate inter-individual variation\n",
    "    d_I, d_T, beta, I0 = gen_vd_params(d_I_pop, d_T_pop, beta_pop, I0_pop, omega)\n",
    "    param_vals = (T0_pop, d_T, d_I, beta, d_T*T0_pop, I0, sigma, tau_indiv[r])\n",
    "    # generate observations with noise and censoring\n",
    "    ObsTime, Ihat, VL, CC, sol = gen_vd_data(param_vals, N, tmax, lod=lod)\n",
    "    ObsTimes.append(ObsTime)\n",
    "    VLs.append(VL)\n",
    "    CCs.append(CC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the data\n",
    "\n",
    "Have a look at each of the $R$ timeseries. Again, censored observations are indicated with red dots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'days post infection')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows = 3\n",
    "fig, axs = plt.subplots(nrows, R//nrows, figsize=(10,4), sharex=True, sharey=True)\n",
    "\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    t = ObsTimes[i]\n",
    "    VL = VLs[i]\n",
    "    CC = CCs[i]\n",
    "    ax.plot(t, VL, color='k', zorder=1)\n",
    "    color = ['k' if c == 0 else 'r' for c in CC]\n",
    "    ax.scatter(t, VL, s=5, color=color, zorder=2)\n",
    "    ax.set_yscale('log')\n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.text(0, 0.5, \"Viral Load\", rotation=90, va='center')\n",
    "fig.text(0.5, 0, \"days post infection\", ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define and compile the HBOMS model\n",
    "\n",
    "For defining the parameters, we will add random effects to $I_0$, $d_T$, $\\beta$ and $d_I$.\n",
    "We'll assume that $T_0$ is a global constant, and that $\\sigma$ is unknown, but the same for each participant.\n",
    "The ART initiation time is known, but can differ between individual. We therefore define it as `\"const_indiv\"`.\n",
    "\n",
    "In the ODEs, we'll use the expit function to switch from pre-ART to ART. In Stan, this function is called `inv_logit` (because expit is the inverse of logit). For these systems, it often helps to use a ODE solver for stiff systems. We can specify this using the options argument of the `HbomsModel` constructor.\n",
    "\n",
    "To specify that our observations (VL) can be censored, we use the `censored=True` option in the `Observation` constructor. \n",
    "If we do this, the HBOMS model expects that in addition to a `VL` field in the data, we also provide a `cc_VL` field. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01//EN\"\n",
       "   \"http://www.w3.org/TR/html4/strict.dtd\">\n",
       "<!--\n",
       "generated by Pygments <https://pygments.org/>\n",
       "Copyright 2006-2023 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "-->\n",
       "<html>\n",
       "<head>\n",
       "  <title></title>\n",
       "  <meta http-equiv=\"content-type\" content=\"text/html; charset=None\">\n",
       "  <style type=\"text/css\">\n",
       "/*\n",
       "generated by Pygments <https://pygments.org/>\n",
       "Copyright 2006-2023 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "*/\n",
       "pre { line-height: 125%; }\n",
       "td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "body .hll { background-color: #ffffcc }\n",
       "body { background: #f8f8f8; }\n",
       "body .c { color: #3D7B7B; font-style: italic } /* Comment */\n",
       "body .err { border: 1px solid #FF0000 } /* Error */\n",
       "body .k { color: #008000; font-weight: bold } /* Keyword */\n",
       "body .o { color: #666666 } /* Operator */\n",
       "body .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */\n",
       "body .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */\n",
       "body .cp { color: #9C6500 } /* Comment.Preproc */\n",
       "body .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */\n",
       "body .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */\n",
       "body .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */\n",
       "body .gd { color: #A00000 } /* Generic.Deleted */\n",
       "body .ge { font-style: italic } /* Generic.Emph */\n",
       "body .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */\n",
       "body .gr { color: #E40000 } /* Generic.Error */\n",
       "body .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       "body .gi { color: #008400 } /* Generic.Inserted */\n",
       "body .go { color: #717171 } /* Generic.Output */\n",
       "body .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       "body .gs { font-weight: bold } /* Generic.Strong */\n",
       "body .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       "body .gt { color: #0044DD } /* Generic.Traceback */\n",
       "body .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       "body .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       "body .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       "body .kp { color: #008000 } /* Keyword.Pseudo */\n",
       "body .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       "body .kt { color: #B00040 } /* Keyword.Type */\n",
       "body .m { color: #666666 } /* Literal.Number */\n",
       "body .s { color: #BA2121 } /* Literal.String */\n",
       "body .na { color: #687822 } /* Name.Attribute */\n",
       "body .nb { color: #008000 } /* Name.Builtin */\n",
       "body .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
       "body .no { color: #880000 } /* Name.Constant */\n",
       "body .nd { color: #AA22FF } /* Name.Decorator */\n",
       "body .ni { color: #717171; font-weight: bold } /* Name.Entity */\n",
       "body .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */\n",
       "body .nf { color: #0000FF } /* Name.Function */\n",
       "body .nl { color: #767600 } /* Name.Label */\n",
       "body .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
       "body .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       "body .nv { color: #19177C } /* Name.Variable */\n",
       "body .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
       "body .w { color: #bbbbbb } /* Text.Whitespace */\n",
       "body .mb { color: #666666 } /* Literal.Number.Bin */\n",
       "body .mf { color: #666666 } /* Literal.Number.Float */\n",
       "body .mh { color: #666666 } /* Literal.Number.Hex */\n",
       "body .mi { color: #666666 } /* Literal.Number.Integer */\n",
       "body .mo { color: #666666 } /* Literal.Number.Oct */\n",
       "body .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       "body .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       "body .sc { color: #BA2121 } /* Literal.String.Char */\n",
       "body .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       "body .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       "body .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       "body .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */\n",
       "body .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       "body .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */\n",
       "body .sx { color: #008000 } /* Literal.String.Other */\n",
       "body .sr { color: #A45A77 } /* Literal.String.Regex */\n",
       "body .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       "body .ss { color: #19177C } /* Literal.String.Symbol */\n",
       "body .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       "body .fm { color: #0000FF } /* Name.Function.Magic */\n",
       "body .vc { color: #19177C } /* Name.Variable.Class */\n",
       "body .vg { color: #19177C } /* Name.Variable.Global */\n",
       "body .vi { color: #19177C } /* Name.Variable.Instance */\n",
       "body .vm { color: #19177C } /* Name.Variable.Magic */\n",
       "body .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"kn\">functions</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* vector field */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">ode_fun</span><span class=\"p\">(</span><span class=\"kt\">real</span> <span class=\"n\">t</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">d_T</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">beta</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">d_I</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">tau</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack the state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">T</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">];</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">I</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare derivatives of state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">ddt_T</span><span class=\"p\">,</span> <span class=\"n\">ddt_I</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined ODEs */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">betat</span> <span class=\"o\">=</span> <span class=\"n\">beta</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mf\">1</span> <span class=\"o\">-</span> <span class=\"nb\">inv_logit</span><span class=\"p\">(</span><span class=\"mf\">5</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">t</span><span class=\"o\">-</span><span class=\"n\">tau</span><span class=\"p\">)));</span>\n",
       "        <span class=\"n\">ddt_T</span> <span class=\"o\">=</span> <span class=\"n\">T0</span> <span class=\"o\">*</span> <span class=\"n\">d_T</span> <span class=\"o\">-</span> <span class=\"n\">d_T</span> <span class=\"o\">*</span> <span class=\"n\">T</span> <span class=\"o\">-</span> <span class=\"n\">betat</span> <span class=\"o\">*</span> <span class=\"n\">I</span> <span class=\"o\">*</span> <span class=\"n\">T</span> <span class=\"o\">/</span> <span class=\"n\">T0</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">ddt_I</span> <span class=\"o\">=</span> <span class=\"n\">betat</span> <span class=\"o\">*</span> <span class=\"n\">I</span> <span class=\"o\">*</span> <span class=\"n\">T</span> <span class=\"o\">/</span> <span class=\"n\">T0</span> <span class=\"o\">-</span> <span class=\"n\">d_I</span> <span class=\"o\">*</span> <span class=\"n\">I</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with derivatives */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">ddt_T</span><span class=\"p\">,</span> <span class=\"n\">ddt_I</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* initial condition */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">gen_init</span><span class=\"p\">(</span><span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">I0</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* declare initial variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">T_0</span><span class=\"p\">,</span> <span class=\"n\">I_0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined initial condition */</span>\n",
       "        <span class=\"n\">T_0</span> <span class=\"o\">=</span> <span class=\"n\">T0</span><span class=\"p\">;</span>\n",
       "        <span class=\"n\">I_0</span> <span class=\"o\">=</span> <span class=\"n\">I0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* return literal vector with initival state variables */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">T_0</span><span class=\"p\">,</span> <span class=\"n\">I_0</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* IVP solver function */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">vector</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">data</span> <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">real</span> <span class=\"n\">Time</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">I0</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">d_T</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">beta</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">d_I</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">real</span> <span class=\"n\">tau</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"nb\">num_elements</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">);</span> <span class=\"cm\">/* number of time points */</span>\n",
       "        <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">init_state</span> <span class=\"o\">=</span> <span class=\"n\">gen_init</span><span class=\"p\">(</span><span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"n\">I0</span><span class=\"p\">);</span> <span class=\"cm\">/* generate initial state */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">sol</span><span class=\"p\">;</span> <span class=\"cm\">/* allocate space for solution */</span>\n",
       "        <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">ode_bdf_tol</span><span class=\"p\">(</span><span class=\"n\">ode_fun</span><span class=\"p\">,</span> <span class=\"n\">init_state</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">],</span> <span class=\"mf\">1e-06</span><span class=\"p\">,</span> <span class=\"mf\">1e-06</span><span class=\"p\">,</span> <span class=\"mf\">1000000</span><span class=\"p\">,</span> <span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"n\">d_T</span><span class=\"p\">,</span> <span class=\"n\">beta</span><span class=\"p\">,</span> <span class=\"n\">d_I</span><span class=\"p\">,</span> <span class=\"n\">tau</span><span class=\"p\">);</span> <span class=\"cm\">/* solve initial value problem */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">sol</span><span class=\"p\">;</span> <span class=\"cm\">/* return the solution */</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* auxiliary function for map_rect */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">map_rect_helper_fun</span><span class=\"p\">(</span><span class=\"kt\">vector</span> <span class=\"n\">ppar</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">upar</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">real</span> <span class=\"n\">rdat</span><span class=\"p\">,</span> <span class=\"n\">data</span> <span class=\"kt\">array</span><span class=\"p\">[]</span> <span class=\"kt\">int</span> <span class=\"n\">idat</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"kt\">int</span> <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"n\">idat</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">];</span> <span class=\"cm\">/* number of time points */</span>\n",
       "        <span class=\"cm\">/* solve initial value problem */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">sol</span> <span class=\"o\">=</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"o\">:</span><span class=\"n\">N</span> <span class=\"o\">+</span> <span class=\"mf\">2</span><span class=\"p\">],</span> <span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">],</span> <span class=\"n\">upar</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">],</span> <span class=\"n\">upar</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">],</span> <span class=\"n\">upar</span><span class=\"p\">[</span><span class=\"mf\">3</span><span class=\"p\">],</span> <span class=\"n\">upar</span><span class=\"p\">[</span><span class=\"mf\">4</span><span class=\"p\">],</span> <span class=\"n\">rdat</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]);</span>\n",
       "        <span class=\"cm\">/* concatenate solution into a vector */</span>\n",
       "        <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span> <span class=\"o\">*</span> <span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"n\">res</span><span class=\"p\">;</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span> <span class=\"p\">)</span> <span class=\"n\">res</span><span class=\"p\">[(</span><span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"mf\">2</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">n</span> <span class=\"o\">*</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">sol</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">];</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">res</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* log-likelihood function */</span>\n",
       "    <span class=\"kt\">vector</span> <span class=\"n\">loglik_fun</span><span class=\"p\">(</span><span class=\"kt\">real</span> <span class=\"n\">VL</span><span class=\"p\">,</span> <span class=\"kt\">int</span> <span class=\"n\">cc_VL</span><span class=\"p\">,</span> <span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">sigma</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack required state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">I</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare log-lik variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">ll_VL</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined log-likelihood */</span>\n",
       "        <span class=\"k\">if</span> <span class=\"p\">(</span> <span class=\"n\">cc_VL</span> <span class=\"o\">==</span> <span class=\"o\">-</span><span class=\"mf\">1</span> <span class=\"p\">)</span> <span class=\"n\">ll_VL</span> <span class=\"o\">=</span> <span class=\"nb\">lognormal_lcdf</span><span class=\"p\">(</span><span class=\"n\">VL</span> <span class=\"p\">|</span> <span class=\"nb\">log</span><span class=\"p\">(</span><span class=\"n\">I</span><span class=\"p\">),</span> <span class=\"n\">sigma</span><span class=\"p\">);</span> <span class=\"cm\">/* left-censored observation */</span>\n",
       "        <span class=\"k\">else</span> <span class=\"k\">if</span> <span class=\"p\">(</span> <span class=\"n\">cc_VL</span> <span class=\"o\">==</span> <span class=\"mf\">0</span> <span class=\"p\">)</span> <span class=\"n\">ll_VL</span> <span class=\"o\">=</span> <span class=\"nb\">lognormal_lpdf</span><span class=\"p\">(</span><span class=\"n\">VL</span> <span class=\"p\">|</span> <span class=\"nb\">log</span><span class=\"p\">(</span><span class=\"n\">I</span><span class=\"p\">),</span> <span class=\"n\">sigma</span><span class=\"p\">);</span> <span class=\"cm\">/* uncensored observation */</span>\n",
       "        <span class=\"k\">else</span> <span class=\"k\">if</span> <span class=\"p\">(</span> <span class=\"n\">cc_VL</span> <span class=\"o\">==</span> <span class=\"mf\">1</span> <span class=\"p\">)</span> <span class=\"n\">ll_VL</span> <span class=\"o\">=</span> <span class=\"nb\">lognormal_lccdf</span><span class=\"p\">(</span><span class=\"n\">VL</span> <span class=\"p\">|</span> <span class=\"nb\">log</span><span class=\"p\">(</span><span class=\"n\">I</span><span class=\"p\">),</span> <span class=\"n\">sigma</span><span class=\"p\">);</span> <span class=\"cm\">/* right-censored observation */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">ll_VL</span><span class=\"p\">]</span><span class=\"o\">&#39;</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* rng function */</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">VL_rng</span><span class=\"p\">(</span><span class=\"kt\">vector</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"kt\">real</span> <span class=\"n\">sigma</span><span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"cm\">/* unpack required state variables */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">I</span> <span class=\"o\">=</span> <span class=\"n\">state</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "        <span class=\"cm\">/* declare variable to-be-returned */</span>\n",
       "        <span class=\"kt\">real</span> <span class=\"n\">VL</span><span class=\"p\">;</span>\n",
       "        <span class=\"cm\">/* user-defined sampler */</span>\n",
       "        <span class=\"n\">VL</span> <span class=\"o\">=</span> <span class=\"nb\">lognormal_rng</span><span class=\"p\">(</span><span class=\"nb\">log</span><span class=\"p\">(</span><span class=\"n\">I</span><span class=\"p\">),</span> <span class=\"n\">sigma</span><span class=\"p\">);</span> <span class=\"cm\">/* random lognormal sample */</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">VL</span><span class=\"p\">;</span>\n",
       "    <span class=\"p\">}</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">data</span> <span class=\"p\">{</span>\n",
       "    <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">R</span><span class=\"p\">;</span> <span class=\"cm\">/* number of units */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">N</span><span class=\"p\">;</span> <span class=\"cm\">/* number of observations per unit */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">Time</span><span class=\"p\">;</span> <span class=\"cm\">/* observation times */</span>\n",
       "    <span class=\"cm\">/* observations */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">real</span> <span class=\"n\">VL</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"k\">upper</span><span class=\"p\">=</span><span class=\"mf\">2</span><span class=\"o\">&gt;</span> <span class=\"n\">cc_VL</span><span class=\"p\">;</span> <span class=\"cm\">/* censor codes for VL */</span>\n",
       "    <span class=\"kt\">int</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0</span><span class=\"o\">&gt;</span> <span class=\"n\">NSim</span><span class=\"p\">;</span> <span class=\"cm\">/* number of simulation time points */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"n\">NSim</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">TimeSim</span><span class=\"p\">;</span> <span class=\"cm\">/* simulation time points */</span>\n",
       "    <span class=\"cm\">/* constants */</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">T0</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">tau</span><span class=\"p\">;</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">transformed data</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* declarations */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"kt\">real</span> <span class=\"n\">rdats</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"kt\">int</span> <span class=\"n\">idats</span><span class=\"p\">;</span> <span class=\"cm\">/* integer data */</span>\n",
       "    <span class=\"cm\">/* definitions */</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">3</span><span class=\"o\">:</span><span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Time</span><span class=\"p\">;</span>\n",
       "    <span class=\"n\">idats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">N</span><span class=\"p\">;</span>\n",
       "    <span class=\"cm\">/* add constants to rdats */</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">rep_array</span><span class=\"p\">(</span><span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"n\">R</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">rdats</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tau</span><span class=\"p\">;</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">parameters</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* individual parameters (and their hyper-parameters) */</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">I0</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_I0</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_I0</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">d_T</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_d_T</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_d_T</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">beta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_beta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_beta</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">d_I</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span> <span class=\"n\">loc_d_I</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">scale_d_I</span><span class=\"p\">;</span>\n",
       "    <span class=\"cm\">/* fixed parameters */</span>\n",
       "    <span class=\"kt\">real</span><span class=\"o\">&lt;</span><span class=\"k\">lower</span><span class=\"p\">=</span><span class=\"mf\">0.0</span><span class=\"o\">&gt;</span> <span class=\"n\">sigma</span><span class=\"p\">;</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">transformed parameters</span> <span class=\"p\">{</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">4</span><span class=\"p\">]</span> <span class=\"n\">upars</span><span class=\"p\">;</span> <span class=\"cm\">/* prepare data structure for map_rect */</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"n\">ppar</span><span class=\"p\">;</span> <span class=\"cm\">/* no population parameters required for map_rect */</span>\n",
       "    <span class=\"cm\">/* assign unit-parameters to array of vectors */</span>\n",
       "    <span class=\"n\">upars</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">I0</span><span class=\"p\">;</span>\n",
       "    <span class=\"n\">upars</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">d_T</span><span class=\"p\">;</span>\n",
       "    <span class=\"n\">upars</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">3</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">beta</span><span class=\"p\">;</span>\n",
       "    <span class=\"n\">upars</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"p\">,</span> <span class=\"mf\">4</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">d_I</span><span class=\"p\">;</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">model</span> <span class=\"p\">{</span>\n",
       "    <span class=\"cm\">/* solve ODEs in parallel */</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">concat_res</span> <span class=\"o\">=</span> <span class=\"nb\">map_rect</span><span class=\"p\">(</span><span class=\"n\">map_rect_helper_fun</span><span class=\"p\">,</span> <span class=\"n\">ppar</span><span class=\"p\">,</span> <span class=\"n\">upars</span><span class=\"p\">,</span> <span class=\"n\">rdats</span><span class=\"p\">,</span> <span class=\"n\">idats</span><span class=\"p\">);</span>\n",
       "    <span class=\"cm\">/* compute log-likelihood of observations */</span>\n",
       "    <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">r</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">R</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "            <span class=\"cm\">/* extract state */</span>\n",
       "            <span class=\"kt\">int</span> <span class=\"n\">idx</span> <span class=\"o\">=</span> <span class=\"mf\">2</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"n\">r</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">;</span>\n",
       "            <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">state</span> <span class=\"o\">=</span> <span class=\"n\">concat_res</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"o\">:</span><span class=\"n\">idx</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">];</span>\n",
       "            <span class=\"cm\">/* compute likelihood of observation given state */</span>\n",
       "            <span class=\"k\">target +=</span> <span class=\"n\">loglik_fun</span><span class=\"p\">(</span><span class=\"n\">VL</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">cc_VL</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">sigma</span><span class=\"p\">);</span>\n",
       "        <span class=\"p\">}</span>\n",
       "    <span class=\"p\">}</span>\n",
       "    <span class=\"cm\">/* prior */</span>\n",
       "    <span class=\"n\">I0</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">lognormal</span><span class=\"p\">(</span><span class=\"n\">loc_I0</span><span class=\"p\">,</span> <span class=\"n\">scale_I0</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">loc_I0</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">scale_I0</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">d_T</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">lognormal</span><span class=\"p\">(</span><span class=\"n\">loc_d_T</span><span class=\"p\">,</span> <span class=\"n\">scale_d_T</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">loc_d_T</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">scale_d_T</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">beta</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">lognormal</span><span class=\"p\">(</span><span class=\"n\">loc_beta</span><span class=\"p\">,</span> <span class=\"n\">scale_beta</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">loc_beta</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">scale_beta</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">d_I</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">lognormal</span><span class=\"p\">(</span><span class=\"n\">loc_d_I</span><span class=\"p\">,</span> <span class=\"n\">scale_d_I</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">loc_d_I</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">scale_d_I</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "    <span class=\"n\">sigma</span> <span class=\"o\">~</span><span class=\"w\"> </span><span class=\"nb\">student_t</span><span class=\"p\">(</span><span class=\"mf\">3.0</span><span class=\"p\">,</span> <span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">2.5</span><span class=\"p\">);</span>\n",
       "<span class=\"p\">}</span>\n",
       "\n",
       "<span class=\"kn\">generated quantities</span> <span class=\"p\">{</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"n\">NSim</span><span class=\"p\">]</span> <span class=\"kt\">real</span> <span class=\"n\">T_sim</span><span class=\"p\">,</span> <span class=\"n\">I_sim</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)]</span> <span class=\"kt\">real</span> <span class=\"n\">VL_sim</span><span class=\"p\">;</span>\n",
       "    <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">log_lik</span><span class=\"p\">;</span> <span class=\"cm\">/* vector of log-likelihoods for model comparison */</span>\n",
       "    <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">r</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">R</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">NSim</span><span class=\"p\">]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">u_sim</span> <span class=\"o\">=</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">TimeSim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"n\">I0</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">d_T</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">beta</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">d_I</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">tau</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]);</span> <span class=\"cm\">/* solve ODEs at simulation times */</span>\n",
       "        <span class=\"kt\">array</span><span class=\"p\">[</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]]</span> <span class=\"kt\">vector</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">u_sim_obs</span> <span class=\"o\">=</span> <span class=\"n\">solve_ivp</span><span class=\"p\">(</span><span class=\"n\">Time</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]],</span> <span class=\"n\">T0</span><span class=\"p\">,</span> <span class=\"n\">I0</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">d_T</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">beta</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">d_I</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">],</span> <span class=\"n\">tau</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]);</span> <span class=\"cm\">/* solve ODEs at observation times */</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">NSim</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "            <span class=\"n\">T_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">u_sim</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">1</span><span class=\"p\">];</span>\n",
       "            <span class=\"n\">I_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">u_sim</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"mf\">2</span><span class=\"p\">];</span>\n",
       "        <span class=\"p\">}</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span> <span class=\"n\">n</span> <span class=\"k\">in</span> <span class=\"mf\">1</span><span class=\"o\">:</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">]</span> <span class=\"p\">)</span> <span class=\"p\">{</span>\n",
       "            <span class=\"kt\">int</span> <span class=\"n\">idx</span> <span class=\"o\">=</span> <span class=\"mf\">1</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">[</span><span class=\"o\">:</span><span class=\"n\">r</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mf\">1</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"mf\">1</span><span class=\"p\">;</span>\n",
       "            <span class=\"n\">log_lik</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"o\">:</span><span class=\"n\">idx</span> <span class=\"o\">+</span> <span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">loglik_fun</span><span class=\"p\">(</span><span class=\"n\">VL</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">cc_VL</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">u_sim_obs</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">sigma</span><span class=\"p\">);</span> <span class=\"cm\">/* record log-likelihood of each observation */</span>\n",
       "            <span class=\"n\">VL_sim</span><span class=\"p\">[</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">VL_rng</span><span class=\"p\">(</span><span class=\"n\">u_sim_obs</span><span class=\"p\">[</span><span class=\"n\">n</span><span class=\"p\">],</span> <span class=\"n\">sigma</span><span class=\"p\">);</span> <span class=\"cm\">/* simulate data at observation times */</span>\n",
       "        <span class=\"p\">}</span>\n",
       "    <span class=\"p\">}</span>\n",
       "<span class=\"p\">}</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import hboms.utilities as util\n",
    "import hboms\n",
    "\n",
    "params = [\n",
    "    hboms.Parameter(\"T0\", 1e6, \"const\"),\n",
    "    hboms.Parameter(\"I0\", 0.1, \"random\"),\n",
    "    hboms.Parameter(\"d_T\", 0.05, \"random\"), \n",
    "    hboms.Parameter(\"beta\", 1.7, \"random\"),\n",
    "    hboms.Parameter(\"d_I\", 0.5, \"random\"),\n",
    "    hboms.Parameter(\"sigma\", 0.5, \"fixed\"),\n",
    "    hboms.Parameter(\"tau\", list(tau_indiv), \"const_indiv\")\n",
    "]\n",
    "\n",
    "# use inv_logit to switch to the ART phase\n",
    "\n",
    "odes = \"\"\"\n",
    "real betat = beta * (1 - inv_logit(5*(t-tau)));\n",
    "ddt_T = T0 * d_T - d_T * T - betat * I * T / T0;\n",
    "ddt_I = betat * I * T / T0 - d_I * I;\n",
    "\"\"\"\n",
    "\n",
    "init = \"\"\"\n",
    "T_0 = T0;\n",
    "I_0 = I0;\n",
    "\"\"\"\n",
    "\n",
    "# use Stan's BDF integrator (backward-differentiation formula)\n",
    "\n",
    "options = {\n",
    "    \"integrator\" : \"ode_bdf_tol\"\n",
    "}\n",
    "\n",
    "# the VL gets a log-normal distribution and contains censored values\n",
    "\n",
    "hbm = hboms.HbomsModel(\n",
    "    name=\"vd_model\", \n",
    "    odes=odes, \n",
    "    init=init, \n",
    "    dists=[\n",
    "        hboms.StanDist(\"lognormal\", \"VL\", params=[\"log(I)\", \"sigma\"])\n",
    "    ], \n",
    "    params=params, \n",
    "    state=[\n",
    "        hboms.Variable(\"T\"), \n",
    "        hboms.Variable(\"I\")\n",
    "    ], \n",
    "    obs=[\n",
    "        hboms.Observation(\"VL\", censored=True)\n",
    "    ],\n",
    "    options=options\n",
    ")\n",
    "\n",
    "util.show_stan_model(hbm.model_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gather data and check initial parameter guess\n",
    "\n",
    "We now put all data in a python dictionary with the correct keys, and as usual, \n",
    "we want to see if our initial parameter guesses are any good. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:39:25 - cmdstanpy - INFO - CmdStan start processing\n",
      "16:39:25 - cmdstanpy - INFO - Chain [1] start processing\n",
      "16:39:25 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    \"VL\" : VLs,\n",
    "    \"cc_VL\" : CCs,\n",
    "    \"Time\" : ObsTimes\n",
    "}\n",
    "\n",
    "fig = hbm.init_check(data, yscale=\"log\", state_var_names=[\"I\", \"T\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the model\n",
    "\n",
    "Then, finally, we are ready to fit the model to the simulated data.\n",
    "We'll use $R$ CPU threads.\n",
    "\n",
    "When the fit tis complete, we'll look at the posterior predictive checks.\n",
    "In this graph, left-lensored values are indicated by a downward-pointing triangle. Uncensored values are indicated by circles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:39:45 - cmdstanpy - INFO - CmdStan start processing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b5d816734ee430a8ece8a51ed2b45fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:43:24 - cmdstanpy - INFO - CmdStan done processing.\n",
      "16:43:24 - cmdstanpy - WARNING - Non-fatal error during sampling:\n",
      "Exception: Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in 'vd_model.stan', line 52, column 31 to column 74) (in 'vd_model.stan', line 132, column 12 to column 70)\n",
      "\tException: Exception: lognormal_lcdf: Location parameter is -nan, but must be finite! (in 'vd_model.stan', line 51, column 27 to column 70) (in 'vd_model.stan', line 132, column 12 to column 70)\n",
      "Consider re-running with show_console=True if the above output is unclear!\n",
      "16:43:24 - cmdstanpy - WARNING - Some chains may have failed to converge.\n",
      "\tChain 1 had 3 divergent transitions (1.5%)\n",
      "\tUse the \"diagnose()\" method on the CmdStanMCMC object to see further information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hbm.sample(\n",
    "    data=data,\n",
    "    iter_sampling=200, iter_warmup=200, \n",
    "    refresh=1, show_progress=\"notebook\",\n",
    "    chains=1, threads_per_chain=R,\n",
    "    step_size=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = hbm.post_pred_check(data, yscale=\"log\", state_var_names=[\"I\", \"T\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What happens if we ignore censoring?\n",
    "\n",
    "Let's see what would happen to the fit if we decide to just ignore the fact that 50 copies per mL is the limit of detection, \n",
    "and treat these values as actual observed viral loads. For this, we set all the censor codes to zero, and re-fit the model.\n",
    "\n",
    "As expected, the fits looks pretty bad, and the measurement error is much larger than before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:46:28 - cmdstanpy - INFO - CmdStan start processing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "565562081e3449278e07cb4313c75508",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:49:11 - cmdstanpy - INFO - CmdStan done processing.\n",
      "16:49:11 - cmdstanpy - WARNING - Some chains may have failed to converge.\n",
      "\tChain 1 had 3 divergent transitions (1.5%)\n",
      "\tUse the \"diagnose()\" method on the CmdStanMCMC object to see further information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "bad_data = data.copy()\n",
    "bad_data[\"cc_VL\"] = [[0 for x in xs] for xs in bad_data[\"cc_VL\"]]\n",
    "\n",
    "bad_sam = hbm.sample(\n",
    "    data=bad_data,\n",
    "    iter_sampling=200, iter_warmup=200, \n",
    "    refresh=1, show_progress=\"notebook\",\n",
    "    chains=1, threads_per_chain=R,\n",
    "    step_size=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = hbm.post_pred_check(data, yscale=\"log\", state_var_names=[\"I\", \"T\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}