{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. The BlueiceExtendedModel: Sensitivity Studies\n",
    "In this tutorial, we'll learn how to use the `Runner` class to perform sensitivity studies. As an example we'll again use the `BlueiceExtendedModel` and perform a sensitivity study for a Dark Matter search experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "\n",
    "from alea import Runner\n",
    "from alea.utils import load_yaml\n",
    "from inference_interface import toyfiles_to_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just some plotting settings\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams[\"figure.dpi\"] = 200\n",
    "mpl.rcParams[\"figure.figsize\"] = [4, 3]\n",
    "mpl.rcParams[\"font.family\"] = \"serif\"\n",
    "mpl.rcParams[\"font.size\"] = 9\n",
    "mpl.rcParams[\"mathtext.fontset\"] = \"dejavuserif\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sensitivity of a direct WIMP detection experiment is commonly expressed in terms of the median of upper limits that one would be able to set on the WIMP-nucleon cross-section in case of a background-only dataset. Thus we would:\n",
    "* Generate background-only toy data (i.e. `wimp_rate_multiplier = 0`)\n",
    "* Construct the confidence interval on the WIMP-nucleon cross-section as we did in the previous tutorial\n",
    "* Repeat this procedure many times and store the upper limits\n",
    "* Take the median of the upper limits as the sensitivity\n",
    "\n",
    "By now you should be able to do this by hand but you will learn in the following how to do this in a more efficient way using the `Runner` class of alea."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we will use the same simple WIMP model with ER background as before, we'll start by loading the config file of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"unbinned_wimp_statistical_model_simple.yaml\"\n",
    "model_config = load_yaml(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can initialize the runner. Specifying `generate_values={'wimp_rate_multiplier': 0.0}` means that despite the default value of 1.0 in the model configuration file, it will always be set to zero in the data generation, which means we'll generate data without signal.\n",
    "\n",
    "We'll then parse the model configuration (`model_config`) to define the statistical model as before. We'll start with 100 toy experiments (`n_mc=100`), which may take about one minute to compute. But depending on your machine or patience, you can increase this number to get more precise results.\n",
    "\n",
    "Other parsed arguments are:\n",
    "- `statistical_model`: the statistical model to use. Here we want to use the `BlueiceExtendedModel` so we need to specify the path to the module containing the class.\n",
    "- `poi`: the parameter of interest. Here we want to use the `wimp_rate_multiplier` parameter since we want to test our sensitivity to the signal strength.\n",
    "- `hypotheses`: This is a list of hypotheses. Each hypothesis is a dictionary with the parameters to fix in the fit and the corresponding fixed value. Some important hypotheses are named: `\"free\"` means that all parameters are free in the fit (equivalent to `{}`). Other named hypotheses are `\"zero\"`, which sets the `poi` to zero and `\"true\"`, which sets it to the value specified in the `generate_values`.\n",
    "- `compute_confidence_interval`: Boolean to enable the computation of the confidence interval. Of course we have to set it to `True` if we want to compute the sensitivity.\n",
    "- `toydata_mode`: You could choose between `'read'` (read toy data from file), `'generate'` (generate toys but don't store them), `'generate_and_store'` (generate toy data and store them), `'no_toydata'` (no toy data generated). Note that this doesn't affect whether the results of the runner are stored -- this is always done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing/loading models on one core: 100%|██████████| 5/5 [00:00<00:00, 412.91it/s]\n"
     ]
    }
   ],
   "source": [
    "runner = Runner(\n",
    "    statistical_model=\"alea.models.blueice_extended_model.BlueiceExtendedModel\",\n",
    "    poi=\"wimp_rate_multiplier\",\n",
    "    hypotheses=[\"free\"],\n",
    "    n_mc=100,\n",
    "    generate_values={\"wimp_rate_multiplier\": 0.0},\n",
    "    compute_confidence_interval=True,\n",
    "    toydata_mode=\"generate\",\n",
    "    output_filename=\"out.ii.h5\",\n",
    "    **model_config\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the toy experiments..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:51<00:00,  1.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving out.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "results = runner.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and load the results from the output file. Apart from the upper limits, the best-fit results for an unconstrained fit (`hypothesis=['free']`) are stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = toyfiles_to_numpy(\"out.ii.h5\")\n",
    "uls = results[\"free\"][\"ul\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the distribution of upper limits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of toy experiments')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(uls, bins=20)\n",
    "\n",
    "plt.xlim(0)\n",
    "plt.xlabel(\"Upper limit on $s$\")\n",
    "plt.ylabel(\"Number of toy experiments\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert the upper limits on the `wimp_rate_multiplier` $s$ into values of WIMP-nucleon cross section and visualize the distribution as a cumulative distribution function (CDF). We then compute the median of the upper limits (CDF = 0.5) and the one- and two-sigma bands, which are usually used to quote the sensitivity of an experiment (\"Brazilian-flag plot\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fraction of ULs below $\\\\sigma$')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref_xsec = 1e-45  # cm^2\n",
    "uls_xsec = uls * ref_xsec\n",
    "median_ul = np.quantile(uls_xsec, stats.norm().sf(0))\n",
    "\n",
    "# plot CDF of upper limits\n",
    "plt.step(\n",
    "    np.sort(uls_xsec),\n",
    "    np.linspace(0, 1, len(uls_xsec), endpoint=False),\n",
    "    color=\"k\",\n",
    "    label=\"Upper limits (CDF)\",\n",
    ")\n",
    "\n",
    "# plot median and bands\n",
    "plt.axvline(\n",
    "    median_ul,\n",
    "    color=\"k\",\n",
    "    zorder=0,\n",
    "    ls=\"--\",\n",
    "    label=rf\"Median of upper limits: {median_ul*1e46:.2f} $\\times 10^{{-46}}\\;\\mathrm{{cm}}^2$\",\n",
    ")\n",
    "plt.axvspan(\n",
    "    np.quantile(uls_xsec, stats.norm().sf(-2)),\n",
    "    np.quantile(uls_xsec, stats.norm().sf(2)),\n",
    "    color=\"gold\",\n",
    "    zorder=-2,\n",
    "    label=\"Band containing 95% of upper limits\",\n",
    ")\n",
    "plt.axvspan(\n",
    "    np.quantile(uls_xsec, stats.norm().sf(-1)),\n",
    "    np.quantile(uls_xsec, stats.norm().sf(1)),\n",
    "    color=\"limegreen\",\n",
    "    zorder=-1,\n",
    "    label=\"Band containing 68% of upper limits\",\n",
    ")\n",
    "\n",
    "# Cosmetics\n",
    "plt.legend(bbox_to_anchor=(0.5, 1), loc=\"lower center\", frameon=False)\n",
    "plt.ylim(0, 1)\n",
    "plt.semilogx()\n",
    "plt.xlabel(\"WIMP-nucleon cross-section $\\sigma$ [cm$^2$]\")\n",
    "plt.ylabel(\"Fraction of ULs below $\\sigma$\")"
   ]
  }
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