{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. The BlueiceExtendedModel: Fitting and Confidence Intervals\n",
    "In the previous tutorial we learned about the `BlueiceExtendedModel` and its rate and shape parameters. Now, we'll make use of this knowledge to fit the model to data and compute a confidence interval for the WIMP rate parameter."
   ]
  },
  {
   "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",
    "from copy import deepcopy\n",
    "\n",
    "from alea import BlueiceExtendedModel"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Initializing the model & generating data\n",
    "This is the same as in the previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing/loading models on one core: 100%|██████████| 5/5 [00:00<00:00, 661.79it/s]\n"
     ]
    }
   ],
   "source": [
    "# initialize\n",
    "config_path = \"unbinned_wimp_statistical_model_simple.yaml\"\n",
    "model = BlueiceExtendedModel.from_config(config_path)\n",
    "\n",
    "# generate and assign data\n",
    "data = model.generate_data()\n",
    "model.data = data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Fitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data is set, we can perform the unconditional fit as in the first example notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wimp_mass': 50.0,\n",
       " 'livetime': 2.0,\n",
       " 'wimp_rate_multiplier': 0.8886252867209539,\n",
       " 'er_rate_multiplier': 0.9659986044551202,\n",
       " 'er_band_shift': -0.09736695259158042}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_fit, max_ll = model.fit()\n",
    "best_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the expectation values under the best-fit parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'er': 386.3994446524198, 'wimp': 17.772505734419077}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_expectation_values(**best_fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we use [iminuit](https://iminuit.readthedocs.io/en/stable/index.html) as a backend for minimizing the likelihood, we can have a look at the very detailed iminuit output, which provides much more information about the fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 3040 </td>\n",
       "        <td style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 62 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.7e-07 (Goal: 0.0001) </td>\n",
       "        <td style=\"text-align:center\" title=\"Total run time of algorithms\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> No parameters at limit </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance accurate </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> wimp_mass </td>\n",
       "        <td> 50.0 </td>\n",
       "        <td> 0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> livetime </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> wimp_rate_multiplier </td>\n",
       "        <td> 0.89 </td>\n",
       "        <td> 0.27 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> er_rate_multiplier </td>\n",
       "        <td> 0.97 </td>\n",
       "        <td> 0.05 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> er_band_shift </td>\n",
       "        <td> -0.10 </td>\n",
       "        <td> 0.07 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> -2 </td>\n",
       "        <td> 2 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> wimp_mass </th>\n",
       "        <th> livetime </th>\n",
       "        <th> wimp_rate_multiplier </th>\n",
       "        <th> er_rate_multiplier </th>\n",
       "        <th> er_band_shift </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> wimp_mass </th>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> livetime </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> wimp_rate_multiplier </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td> 0.0705 </td>\n",
       "        <td style=\"background-color:rgb(238,238,250);color:black\"> -0.0012 <strong>(-0.096)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,233,233);color:black\"> 0.002 <strong>(0.111)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> er_rate_multiplier </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(238,238,250);color:black\"> -0.0012 <strong>(-0.096)</strong> </td>\n",
       "        <td> 0.00234 </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0001 <strong>(-0.029)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> er_band_shift </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,233,233);color:black\"> 0.002 <strong>(0.111)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0001 <strong>(-0.029)</strong> </td>\n",
       "        <td> 0.00557 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 3040                       │              Nfcn = 62               │\n",
       "│ EDM = 1.7e-07 (Goal: 0.0001)     │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │   Below EDM threshold (goal x 10)    │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│      No parameters at limit      │           Below call limit           │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│             Hesse ok             │         Covariance accurate          │\n",
       "└──────────────────────────────────┴──────────────────────────────────────┘\n",
       "┌───┬──────────────────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name                 │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────────────────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ wimp_mass            │   50.0    │    0.5    │            │            │         │         │  yes  │\n",
       "│ 1 │ livetime             │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 2 │ wimp_rate_multiplier │   0.89    │   0.27    │            │            │    0    │         │       │\n",
       "│ 3 │ er_rate_multiplier   │   0.97    │   0.05    │            │            │    0    │         │       │\n",
       "│ 4 │ er_band_shift        │   -0.10   │   0.07    │            │            │   -2    │    2    │       │\n",
       "└───┴──────────────────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│                      │            wimp_mass             livetime wimp_rate_multiplier   er_rate_multiplier        er_band_shift │\n",
       "├──────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│            wimp_mass │                    0                    0                 0.00               0.0000                0.000 │\n",
       "│             livetime │                    0                    0                 0.00               0.0000                0.000 │\n",
       "│ wimp_rate_multiplier │                 0.00                 0.00               0.0705              -0.0012                0.002 │\n",
       "│   er_rate_multiplier │               0.0000               0.0000              -0.0012              0.00234              -0.0001 │\n",
       "│        er_band_shift │                0.000                0.000                0.002              -0.0001              0.00557 │\n",
       "└──────────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.minuit_object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the parameters, which are not fittable are fixed in the fit to their nominal values. For comparison let's have a look at our parameter definition again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      nominal_value  fittable     ptype relative_uncertainty  uncertainty    blueice_anchors fit_limits parameter_interval_bounds  fit_guess                                               description\n",
      "wimp_mass                      50.0     False      None                 None          NaN               None       None                      None        NaN                                      WIMP mass in GeV/c^2\n",
      "livetime                        2.0     False  livetime                 None          NaN               None       None                      None        NaN                                         Livetime in years\n",
      "wimp_rate_multiplier            1.0      True      rate                 None          NaN               None  [0, None]                   [0, 50]        NaN                                                      None\n",
      "er_rate_multiplier              1.0      True      rate                 True          0.2               None  [0, None]                      None        1.0                                                      None\n",
      "er_band_shift                   0.0      True     shape                 None          NaN  [-2, -1, 0, 1, 2]    [-2, 2]                      None        NaN  ER band shape parameter (shifts the ER band up and down)\n"
     ]
    }
   ],
   "source": [
    "print(model.parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To perform a **conditional fit**, i.e. fix a parameter to a certain value, we can simply parse this value to the `fit` method with the value of the parameter we want to fix. For example we could perform the background-only fit by fixing the `wimp_rate_multiplier` to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wimp_mass': 50.0,\n",
       " 'livetime': 2.0,\n",
       " 'wimp_rate_multiplier': 0.0,\n",
       " 'er_rate_multiplier': 1.007873534571834,\n",
       " 'er_band_shift': -0.2540693824232261}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_fit_c, max_ll_c = model.fit(wimp_rate_multiplier=0.0)\n",
    "best_fit_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 3085 </td>\n",
       "        <td style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 67 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.6e-06 (Goal: 0.0001) </td>\n",
       "        <td style=\"text-align:center\" title=\"Total run time of algorithms\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> No parameters at limit </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance accurate </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> wimp_mass </td>\n",
       "        <td> 50.0 </td>\n",
       "        <td> 0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> livetime </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> wimp_rate_multiplier </td>\n",
       "        <td> 0.0 </td>\n",
       "        <td> 0.1 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> er_rate_multiplier </td>\n",
       "        <td> 1.01 </td>\n",
       "        <td> 0.05 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> er_band_shift </td>\n",
       "        <td> -0.25 </td>\n",
       "        <td> 0.07 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> -2 </td>\n",
       "        <td> 2 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> wimp_mass </th>\n",
       "        <th> livetime </th>\n",
       "        <th> wimp_rate_multiplier </th>\n",
       "        <th> er_rate_multiplier </th>\n",
       "        <th> er_band_shift </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> wimp_mass </th>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> livetime </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> wimp_rate_multiplier </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> er_rate_multiplier </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.00237 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> er_band_shift </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.00449 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 3085                       │              Nfcn = 67               │\n",
       "│ EDM = 1.6e-06 (Goal: 0.0001)     │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │   Below EDM threshold (goal x 10)    │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│      No parameters at limit      │           Below call limit           │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│             Hesse ok             │         Covariance accurate          │\n",
       "└──────────────────────────────────┴──────────────────────────────────────┘\n",
       "┌───┬──────────────────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name                 │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────────────────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ wimp_mass            │   50.0    │    0.5    │            │            │         │         │  yes  │\n",
       "│ 1 │ livetime             │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 2 │ wimp_rate_multiplier │    0.0    │    0.1    │            │            │    0    │         │  yes  │\n",
       "│ 3 │ er_rate_multiplier   │   1.01    │   0.05    │            │            │    0    │         │       │\n",
       "│ 4 │ er_band_shift        │   -0.25   │   0.07    │            │            │   -2    │    2    │       │\n",
       "└───┴──────────────────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│                      │            wimp_mass             livetime wimp_rate_multiplier   er_rate_multiplier        er_band_shift │\n",
       "├──────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│            wimp_mass │                    0                    0                    0               0.0000                0.000 │\n",
       "│             livetime │                    0                    0                    0               0.0000                0.000 │\n",
       "│ wimp_rate_multiplier │                    0                    0                    0               0.0000                0.000 │\n",
       "│   er_rate_multiplier │               0.0000               0.0000               0.0000              0.00237               0.0000 │\n",
       "│        er_band_shift │                0.000                0.000                0.000               0.0000              0.00449 │\n",
       "└──────────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.minuit_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'er': 403.1494216433639, 'wimp': 0.0}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_expectation_values(**best_fit_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can nicely see that in the constrained fit, the missing WIMP signal component is absorbed by a higher ER rate and a lower ER shift parameter (shifting the ER band down in cs2 towards the WIMP signals in our toy data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 The ancillary likelihood term"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The likelihood used in this model is the sum of a *science run* term and an *ancillary likelihood* term:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['science_run', 'ancillary']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.likelihood_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The science run term was discussed in some detail in the previous tutorial. Now we'll look at the ancillary likelihood term. You can find in the documentation that this term contains all constraints that you might have on some of the nuisance parameters from ancillary measurements.\n",
    "\n",
    "In our case we have an uncertainty on the ER rate of 20% on the nominal value. We take this into account by adding a normal distribution centered at 1 (nominal `er_rate_multiplier`) and with a standard deviation of 0.2 (20% uncertainty) to the ancillary likelihood term. Let's have a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "copied_data = {}\n",
    "copied_data[\"science_run\"] = deepcopy(model.data[\"science_run\"])\n",
    "copied_data[\"ancillary\"] = deepcopy(model.data[\"ancillary\"])\n",
    "# Set ancillary measurement of er_rate_multiplier to nominal value\n",
    "nominal_er = model.parameters.er_rate_multiplier.nominal_value\n",
    "copied_data[\"ancillary\"][\"er_rate_multiplier\"][0] = nominal_er\n",
    "\n",
    "# Assign data with nominal ancillary measurement\n",
    "model.data = copied_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x127fe1050>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1AQBXr17F3bt3JcdLTU1F7dq1kZaWhtjYWE2Xqvw/a+3atZGVlYXo6OgCtzdo0ADZ2dlFHt/e3h6ff/45du/ejTt37iA4OBhTpkxBo0aNkJCQgCVLlkimN9Unm729fZF/Z0Vlc3V1RUBAgM5rSpo1L0ubNm1Qvnx5JCYmYtasWQgJCcFvv/2GzMxM9OnTR/PaH3/8EYmJidiwYQNatmyJTz/9FIGBgXj48CG2b9+OpUuXomvXroWuZ0EKUnAxPFKAuayUSOaNK1uTqdJnZVkqnPa/77zHwYMHhRD/t+q09mPFihU6x9q/f7949dVXRcWKFYW9vb0oW7asCAkJEcuXL9dZ5bg4xy4sW3R0tGRV54K2563EXNj2/KtKaz+EEM89fp74+Hgxbtw48cILLwh7e3vh7Ows6tevLz7++GNx+vTpEmV7nqKyBQcHF/q6kmTN7+TJkyI4OFi4uroKFxcX0aBBA7FkyZIC32v79u2iZ8+ewsfHR9jZ2QlPT08RHBwsli1bprN6uDmyxJWtVULIPLcYmZTY2Fj4+voCAO7cuSNZdIbIUDIyMrB8+XJJ24gRI4qc5pHIGK5fv46cnBzY2dmhZs2aSschIiuiz+8fc/m8xq5NRGRwzs7OGDNmjNIxiIiISEYcbE1ERERERHpjIUFERERERHpjIUFERERERHpjIUFERERERHrjYGsiMqjs7GykpKQgOTkZycnJSEpKQkpKCsqWLYvAwEB4eXkpHZGIiIgMgIUEEZXK/fv3sWPHDmzbtg3Hjx/H48ePi9y/SpUqaNy4MRo3bowmTZqgWbNm8Pf3N05YIiIiMhgWEkSkt//++w9bt27Ftm3bcOLECeizHE1cXBzi4uKwe/duTVu7du3w4Ycfonfv3rC1tZUjMhERERkYCwkiKrYDBw5g8uTJOH36tEGPe+zYMRw7dgz+/v4YO3Ys3nrrLZQpU8ag70FERESGxcHWRPRcV69eRa9evdCpU6cSFxFOTk7P3ScmJgYTJ05E1apVMW7cOMTExJTovYiIiEh+LCSIqFCPHj3CuHHj0KBBA+zcubPQ/dzc3DBgwACsWbMGly5dQmRkpM4+N2/exKVLl7B27VpMmjQJL774IsqVK1fg8VJTU/HTTz+hXr16WLRokV5dp4iIiMg42LWJiHTk5ORgwYIFmDZtGpKSkgrcx8fHB6+88gr69OmD0NBQODo6arYlJCTo7G9nZ4e6deuibt26GDx4MIBnMzxt2rQJP/74I8LDw3Vek5GRgTFjxmD79u1Yvnw5qlSpYqCfkIiIiEqLdySISCIhIQGdOnXC+PHjCywifHx8sGTJEsTFxWHp0qXo1q2bpIjQh729PV577TWcOnUKx44dQ79+/WBjo/trae/evWjYsCHWr19fovchIiIiw2MhQUQa586dQ/PmzXHkyBGdbY6Ojvjkk09w/fp1jBo1CnZ2hruhqVKp0LZtW2zatAk3b97EqFGjdPZJSkrC4MGDMWjQIDx69Mhg701EREQlw0KCiAAAmzZtQtu2bXH79m2dbQMHDsSVK1cwc+ZMeHh4yJrDz88PS5YswZ49e1C5cmWd7Rs2bEDDhg0REREhaw4iIiIqGgsJIiunVqvx+eefY8CAAUhPT5dsq1q1Ko4ePYr169cbfdG4Ll26ICoqSjOeIr+7d+8iJCSkwDsnREREZBwsJIis2JMnT9C3b19Mnz5dZ1vbtm0RHh6Odu3aKZDsmbJly2Lt2rVYv349ypYtK9n25MkTdO3aFX/++adC6YiIlHH48GF4eXmhb9++nNWOFMVCgshKJSUloUOHDti+fbvOtrfffhv79+9HhQoVFEima+DAgYiKikLHjh0l7ZmZmXjllVewadMmZYIRkcVbuXIlVCqV0e/KFmXz5s1ITEzE1q1bZRkzFhMTA5VKBZVKVer1fPKOc+jQIYNkI9PCQoLICqWmpqJHjx44d+6cpN3W1hbz58/H0qVLSzwTk1wqV66MP//8E71795a0Z2dnY9CgQVi+fLkywYiIjOzdd99F27ZtMXnyZJQvX17pOGTFuI4EkZV5+vQp+vTpgxMnTkjay5Urh02bNiE0NFShZM/n5OSETZs2YcSIEfjtt9807Wq1Gm+99RYeP36MDz/8ULmARERGUL9+fRw7dkzpGEQsJIisSU5ODgYPHoy///5b0l65cmUcPnwYNWrUUChZ8dnZ2WHlypVwd3fHokWLJNvGjx+Pp0+f4uOPP1YoHRERkfVg1yYiK6FWq/H2229j69atkvby5ctj3759Bi0iXFxc8OWXX0oeLi4uBju+jY0NFixYgE8++URn2+TJkzlmgsiItm3bhpdffhkVK1aEvb09PD090axZM4wbNw6HDx8u9HVbt25Fz549UaFCBTg4OKBChQro2bMntm3bprNv/j77+fvcnz9/Hn379oW3tzecnJxQr149zJkzp9AByDk5OVizZg1CQkLg6+sLBwcHeHt7IyQkBN9//z3+++8/zb55YyPefPNNAMCtW7d0MsTExBSa7eLFixgyZAgqV64MOzs7zbY8sbGxWLBgAbp164YaNWrA2dkZHh4eaNasGaZNm4bHjx8X+zwY6jzlvT4gIEDTFhAQIDneypUrC/07zU/75w0JCZEc56uvvtJ5zbVr1zBq1ChUr14dTk5O8PDwQOPGjfHJJ5/g/v37hb5H/myxsbEYNWoU/Pz84ODgIPm7Kmj/GzduYPDgwfD29oa7uztatmyJzZs3a97j+vXrGDBgALy9veHi4oKWLVti165dxToHVkGQVblz544AIACIO3fuKB2HjEStVouxY8dq/u7zHu7u7uL06dNKxyuVb7/9VufncnZ2Nvufi+Rx7do1cenSJXHt2rUCt+fm5or4+HiLfOTm5hr0XGZmZop+/foJACIoKEhs3LhRhIeHi507d4rXXntN8+9x4cKFOq979dVXBQDRsmVLsXnzZhEeHi42b94sWrRoIQCIAQMGiKdPn2pek5WVJSIjI0VkZKTmuF9//bVo1qyZ2Lhxozh16pRYtmyZ8PLyEgDE5MmTC/y77dq1qwAghgwZIv78808RHh4utm3bJrp166Y5bp6kpCQRGRkppk+fLgCIypUrazLkPbKysgrMNn36dFGjRg2xdOlScfr0abFx40bh4+MjOX5wcLAAIIKDg8WWLVvE6dOnxY4dO8SQIUOESqUSL7zwgrh3757kZyjovQ4ePGiw85T3+j179mhev2fPHsnPnJSUVKzrQzvH8uXLJcd58OCBZP/ff/9dODg4CE9PT/HDDz+IY8eOid27d4vRo0cLlUolypcvL44fP17ge1SuXFkAEJ9++ql44YUXxJw5c8SpU6fErl27RM2aNQUAER0drbP/J598Ipo1aybWrVsnTp06JZYsWSLKlCkjAIhff/1VXL58WXTp0kVs27ZNnDhxQnzzzTfC3t5e2NjYiD179hTrPOT3vN8/+ZnL5zUWElbGXC5MMqwvvvhC58O2k5OTOHTokNLRDGL27Nk6P1+lSpV4jZOO5/1HHh8fr3MtWcojPj7eoOfynXfeEQBEkyZNREZGhs72Dz/8UAAQP/74o6R91KhRhb4uPT1dBAYGCgBi9OjRBb5v3s9TsWJFkZCQINkWFham+TLh8ePHkm3bt28XAESbNm10jqlWq0Xnzp0lH/TzrFixQgAQfn5+BeYpKJunp6e4efOmZNuMGTN0ColmzZqJrKwsneN88sknAoDo27fvc98rfyFR0HZ9z5MQQkRHR2teHx0dXcRP/HzPyymEEMeOHRN2dnbC3t5eREVF6WzP+x3v4+MjkpOTdbb7+fkJAMLBwUGcPHlSsm3NmjU6P0fe/m5ubjo/36pVqzSFY79+/XTe7/PPPxcARPv27Z//w2uxxEKCXZuILNxvv/2GadOmSdrs7OywefNmBAcHK5TKsCZMmICRI0dK2u7du4dXXnkFaWlpCqUislyXL1/G0qVLAQCff/45nJycdPaZNGlSga/75ZdfCn2ds7MzPvvsMwDA4sWLceXKlUIzvP766/Dy8pK0hYaGwsbGBhkZGYiIiJBsu3TpEgDAzc1N51gqlQpjxoxBjx49Cn0/fQwZMkTSPQgAPvjgA0RHR2ueDx8+HHPmzIG9vb3O69955x0AwPbt2wvs4qQPfc+TEv73v/8hJycHQ4cORf369XW2jx07Fq6uroiPj8eyZcsKPc6LL76Ili1bStr69u2L6OhoVK1aVWf/Hj166Ezr27lzZwDPFj594YUXUKZMGcn2Ll26AABOnjyJ7OzsYv18loyFBJEFu3jxIt59911Jm0qlwm+//Waw/zBNgUqlwsKFCxESEiJpP3PmDN544w2o1WqFkhFZpk2bNmn613fq1KnAfSpXrowDBw6gT58+er3uxRdfBPBsXFf+vuragoKCdNocHR01H5q1+9TXqlULALB3794CxyD07t3bYH3fC1rI083NTfKhdfjw4YV+mePn5wcAyM3NxfXr10uVRd/zZGx37tzByZMnAUBnraA8Dg4OqF69OgBg//79hR6roPPu5OQEf39/2Nnpzi/UrFkznTYfH58it1esWBHAs6nHHz58WGgWa8FCQmFPnz7FrFmz0KRJE7i7u8PT0xOtW7fG4sWL+eGHSiU1NRX9+vVDenq6pP3nn3/GoEGDFEolH3t7e2zevFln0Pgff/yBzz//XKFURJbpwoULAAAvLy94eHgUul9ISIjmQ3FxX1emTBmUK1cOAHD+/PlCj13Y+gnOzs4Ani1YmV/v3r3Rq1cvAMCXX36JChUqoFevXli8eDHi4uIKfZ+S8Pb2fu4+arUav//+O3r06IGqVavC2dkZdnZ2mkee1NTUUmXR9zwZW/6/4+HDh0vOQf5HZGQkAOD27duFHqs45z2/gs5N/nP/vO1Pnz7V6/0sEad/VdDDhw8RGhqKyMhIjBo1CvPnz0dWVhYWLFiA9957D5s2bcLu3bsLvGVMVBQhBN555x2dbgHvvvuu5pa5JSpXrhx27dqFVq1aITk5WdM+c+ZM1KlTB0OHDlUuHJmF8uXLIz4+XukYsjDkwmUpKSkA/u/DqKFf5+LigkePHmn2L4itra1e721jY4Nt27Zh06ZNWLp0KQ4ePIidO3di586dGD16NLp3744ffvhBc+eiNJ6XLTs7Gz169MC+fftQuXJlfPTRR2jSpImmgAKAhg0bAkChM1AZKovS8v8dL126VKdrkjYHB4dCt5XkmijNdmIhoaj+/fsjMjIS48aNw9y5czXtISEh6NOnD7Zv34733nsPK1asUC4kmaWlS5di7dq1kramTZvixx9/NMr7P3r0CO3bt5e0HT16VPKfpFxq166NTZs24aWXXkJubq6mfeTIkWjSpAkaNGggewYyXzY2Nnp/q2mN8vqNZ2RklOh12ndKteVt1+6fXloqlQoDBgzAgAEDEB8fj23btmH9+vU4ePAgdu/ejRMnTiAqKgqVKlUy6PtqW7RoEfbt2wc7Ozvs3bu3wHEB1iL/33H58uX5O9rMsNRSyJYtW3Do0CE4OTnpzKWsUqnwzTffAABWrVplEgOhyHycOXMGH3zwgaStTJky2LRpk9HubuXm5uLSpUuSR/4P9XLr3LkzFixYIGl7+vQphg4diqysLKPlILJUgYGBAJ7dWS9qMHBaWpqkaGjUqBEAIDExsdC7DSkpKXj06JHkfeTg4+ODUaNG4cCBAzh06BCcnZ3x6NEj/Prrr7K9Z568fv61atWy6iICkP4dFzW4Pjk5Gb/++iuOHDlijFhUTCwkFJL3iyo0NBSenp462+vWrYu6detCCIHly5cbOR2Zq5SUFPTv31/nw/KKFSs0A9WsxbvvvosxY8ZI2s6dO1fgIkhEpJ/+/ftrun38/fffBe7z33//wc3NDaNGjZK8Lm+RssJet3fvXgDP7g7179/fYJlnz55d6MKbwcHB6Nq1K4BnM77ll9cnXruL0Y4dO4pccK8oeWMgC+u2FBMTU6LjGkr+cQD5M548eVLvBT/zuhvlP05kZCTWr1+PzMxM+Pr6onXr1gBQ5GD33377DSNHjsS1a9f0en+SFwsJBWRlZWm+jShoNoU8edt2795tlFxk3oQQePPNN3Hz5k1J+/jx4yWzpliT2bNn63zbN2vWLPzzzz8KJSKyDHXq1NHMCDd9+vQCB+x++eWXUKlUGD16tKatbt26msLi66+/1nldZmYmZsyYAQB47733ULt2bYNlTk1NxY0bNwosYPLPjtSiRQvJtrxZevLukgDPxjgMHDiwxF2P87p+Xr16FadPn9bZvnDhwhId11C8vLw0BUD+n/uzzz4rcFrfohR0/ubNm4ehQ4dqCpbZs2fDzs4Ox48fx5YtW3SOcffuXcycORN+fn54/fXX9f55SD4cI6GAy5cva+Ye1p6/OL+8bbdu3UJKSkqx+orGxsYWuV37mxayHAsXLsTWrVslba1bt8asWbMUSqQ8Jycn/Pbbb2jZsqXm35xarcbQoUNx/vz5AueTJ6Li+fHHH/Hw4UNs3LgRwcHBmDRpEqpXr47Y2Fj88ssv2LVrF+bMmYM2bdpIXjdv3jw8fPgQW7ZsQXBwMD7++GP4+/sjJiYGs2bNwvnz59G/f3/88MMPktdFRUVJnkdHR8PLywsBAQFwdXXFtWvXkJWVpfm3HhcXh6ioKFStWhWenp6aOyEDBgzAhAkT0KZNG5QpUwZ37tzB4sWLcfHiRXTt2lVnUoY2bdqgfPnySExMxKxZsxASEoLffvsNmZmZmi9pCsvm4OBQ4ODt999/H+vWrcP58+fRvXt3fPrpp2jVqhUeP36M9evXS36X5/85HRwccPXq1QLfK+/nLO15Ap797uzSpQv+/PNPzJ49G+PHj8e5c+dw6NAhjB07VufnKUqvXr3w888/Y+HChahcuTJu376NjRs3omfPnppCok2bNvj9998xfPhwvPbaaxg3bhxefvll2NnZISIiAt988w2ys7Oxa9cuSRfdwn4WAAWOtXjez17YucsraK9evYq7d+9KjpeamoratWsXuB6IVVBqJTxrtmPHDs1qhbt27Sp0v/nz52v2K2ilx4Lk7V+chymvlEj6iYmJEa6urpK/3/Lly4vbt28rkqeg1YENvaquPmbOnKmTZ+TIkYrlIeXos7IsFc/WrVtFjx49hI+Pj7CzsxPlypUT3bp1E2FhYYW+Rq1Wiy1btohu3boJb29vYWdnJ7y9vUX37t3FH3/8UeBrCvu/LG/F5LzVirUfK1asEEIIkZmZKdavXy9ee+010ahRI+Hl5SVsbW1F+fLlRUhIiFi2bJnIyckp8L1PnjwpgoODhaurq3BxcRENGjQQS5YseW62olbDfvLkifj8889F3bp1haOjo3B0dBQ1a9YUY8aMETExMQX+nPlXnC7s5yztecpz7949MXDgQFG+fHlhb28vAgICxP/+9z+RlpZW6M9UkMePH4tRo0aJihUrCnt7e+Hr6yvefvttndW2hRDixo0bYsyYMaJmzZrCyclJODk5ibp164qJEyeKe/fu6exf2M9S2Mfb5/3shR0rOjq6yHNf3NW/LXFla5UQpZxXjPS2du1aDBkyBMCzPqKFLcrz66+/albr/eeffzR9CIuS941Lcdy5c6fAlR7JvAgh0KNHD/z555+S9rCwMHTr1k2RTAkJCZJFfQAgPj5esdlwcnNz0aFDB50uTTt37kTPnj0VyUTKuH79OnJycmBnZ4eaNWsqHYeIrIg+v39iY2Ph6+sLwLQ/r7FrkwnLX+MVt0C4c+dOkdvv3bun0/+TzNv69et1iohRo0YpVkSYIltbW6xevRqBgYFIS0vTtL/11luIioridJ9EREQlwEJCAe7u7po/F7WiZP4VE/O/piimWrGSPBITEzFu3DhJW6VKlax6XERhXnjhBcydO1dzlw94dpfknXfewZYtW/S6m0dERESctUkR1apV0/w5ISGh0P3yb2OBQAWZOHGizjW0YMGCAqcUpmd3IF5++WVJ29atW7FhwwaFEhEREZkvFhIKqFu3rmZ0f1FzRedt8/PzM/jqnmT+/v77b6xatUrS1rt3b/Tt21ehRKZPpVLhl19+gZeXl6R94sSJSE1NVSgVERGReWIhoQAHBwfNAOvw8PBC98ubW7pHjx5GyUXmIz09He+8846kzcPDQ2c1Z9JVoUIFLF68WNJ29+5dzdz1REREVDwsJBTy9ttvAwD279+PlJQUne1XrlzB5cuXoVKpMGLECGPHIxP31Vdf6Sw8N2vWLFSpUkWhROalb9++6NKli6Rtzpw5XDGViIhIDywkFPLqq6+iY8eOyMzMxNSpUyXbhBD49NNPAQDDhg1Ds2bNlIhIJurMmTM6CzW1a9dOs1osPZ9KpcK8efM0iyEBz1aq/fDDD8EZsYmIiIqHhYSCNm3ahIYNG+LHH3/Ee++9h+PHj+PgwYPo378/tm7ditDQUPz8889KxyQTolar8c477yA3N1fT5uDggKVLl8LGhv+c9VGnTh18+OGHkrY///wTu3btUiYQERGRmeH0rwry8vLC6dOnMXfuXKxbtw6//fYbbG1tUbduXSxatAjvvPMOPxySxNq1a3XG1UyZMgV169ZVKFHBnJycMHr0aJ02U/P5559jzZo1uHfvnqbtww8/xIsvvmiSeYmIiEwJV7a2MuayUiLpyszMRO3atXH79m1NW506dXD+/Hk4ODgomMy8/f777xg6dKikbfr06ZgyZYpCiUhON2/exNOnT6FSqVCrVi1+WUNERqFWq3Ht2jUIIeDo6Ijq1asXub+5fF7jb1AiMzF//nxJEQEA3333HYuIUhoyZAjatm0raZsxY4bOuSbLkHenSQjBKX+JyGhSU1M1Y/CcnZ0VTmM4JlVIREdHo02bNkrHIDI5iYmJOtOTBgcHo2fPngolshwqlQrz58+XrGydkZGB//3vfwqmIrl4eHho/nz//n08fvwYarVawUREZMnUajUeP36M+/fva9rc3d0VTGRYJjVGIj09Hf/++6/SMYhMzvTp03WmCf7+++8lH36p5Jo0aYJ33nlHsr7Epk2bcODAAYSGhiqYjAzN1dUVzs7OyMjIQG5uLuLi4qBSqWBra6t0NCKyQLm5uZLZAJ2dneHq6qpgIsMyaCFR2vUOkpOTDROEyILcvHkTCxculLQNGjQIQUFBCiWyTNOnT8fGjRvx6NEjTdukSZMQHh7Ogs2CqFQqVKtWDbdv30ZGRgaAZ92ccnJyFE5GRJbO2dkZ1apVs6j/UwxaSKxcuRIqlarAedjzn7T827XbLenkEhnCp59+iuzsbM1zBwcHzJw5U8FElql8+fKYPn26ZLapM2fOYNu2bejTp4+CycjQbGxs4Ofnh7S0NDx58kRzd4KIyNBsbW3h7OwMd3d3uLq6WtznXIN3berVqxc8PT0lbenp6diyZQvc3NzQvHlzVKxYEfb29sjOzsb9+/cRHh6Ox48fo1OnTiY7Kp1ICadOncKGDRskbe+//z4CAgIUSlQ8ycnJ6N27t6Rt27ZtOr8bTM3IkSMxb948XL16VdP2+eefo1evXuz6YmFUKhXc3Nzg5uamdBQiIrNl0OlfbWxsEBUVhXr16mna0tPT0b59e/Tt2xf/+9//4OjoqPO6p0+fYvbs2ViyZAnCw8Ph4+NjqEikxVymE6Nnd+iCg4Nx9OhRTZunpydu3LiBcuXKKZjs+RISEnT+HcfHx8Pb21uhRMW3fv16DB48WNK2du1anTYiIiK5mMvnNYPO2hQcHKwzgOSbb75Bu3btMGXKlAKLCABwdHTElClT0KdPH0ydOtWQkYjM1s6dOyVFBPBs8TlTLyLM3YABA9CgQQNJ21dffcU+9ERERFoMWkgcPHgQfn5+krYtW7agf//+xXp9//798ddffxkyEpFZysnJwccffyxp8/Pzw/vvv69QIuthY2ODr7/+WtJ27do1/P777wolIiIiMk2yryNx69YtzQJAz+Po6Ih79+7JnIjI9K1duxZXrlyRtM2cObPY/5aodF555RU0a9ZM0jZ16lRkZWUplIiIiMj0yF5IODk5Yf/+/cXad//+/Ra12h9RSeTm5uKbb76RtDVt2hSDBg1SKJH1UalUOnclYmJisGLFCoUSERERmR7ZC4mWLVti+vTpCAsLK3K/Xbt2YebMmWjVqpXckYhM2h9//KFzN2Lq1KmwsTGphegt3ksvvYQ2bdpI2r7++mtkZmYqlIiIiMi0yL6y9ccff4w9e/bg5ZdfRtOmTREaGgp/f384OzsjPT0dMTExOHDgAM6ePavZn8haCSEwY8YMSVtgYCB69OihUCLrpVKpMH36dMnK1nFxcViyZAnGjRunYDIiIiLTIHshERwcjHnz5uHDDz9EREQEzpw5o7OPEAI2NjaYO3cuOnToIHckIpO1e/dunD9/XtI2ZcoUi1vAxlyEhIQgJCQEBw8e1LR98803ePvtt3VmqCMiIrI2Rukr8f777+PYsWN46aWXYGdnByGE5mFnZ4fu3bvj+PHjnJGGrJoQAtOnT5e01alTB3379lUoEQHQGSvx4MEDLFy4UKE0REREpkP2OxJ5WrVqhbCwMGRmZuL69et4/PgxPDw8ULNmTc5EQwTgwIED+PfffyVtn376KVdUVljbtm3RrVs3/Pnnn5q27777Du+//z5cXFwUTEZERKQso4/edHJyQsOGDdG2bVs0bNiQRQTR/6d9NyIgIICrKZuIadOmSZ4nJiZyBiciIrJ6Ri8kcnJycPnyZZw8eRKXL1/marFEAI4fP45Dhw5J2iZPngw7O6PdNKQiNG/eHN26dZO0/fDDD8jNzVUoERERkfKMVkhER0fj9ddfh6enJxo0aIC2bduiQYMGKFu2LN544w1ER0cbKwqRydGeqalKlSoYNmyYQmmoIJMmTZI8v3nzJrZu3apQGiIiIuUZpZA4fPgwmjZtinXr1iE9PV0y2DotLQ1r1qxBs2bNcOTIEWPEITIpERERkv73wLMPrY6OjgolooJ07NgRTZs2lbR9//33EEIolIiIiEhZsvebePDgAfr06YOUlBTUr18fXbp0QUBAAFxcXJCeno6bN29i3759uHjxIvr27YtLly7Bx8dH7lhEJkP7boS3tzdGjhypUBrDcHBwQL9+/XTazJlKpcKkSZMk41ZOnTqFY8eOoX379gomIyIiUobshcQPP/yAjIwMrFu3DgMHDix0v3Xr1mHEiBH44Ycf8O2338odi8gkXLx4Uad7zMSJE81+NqAyZcpg06ZNSscwuH79+mHy5Mm4deuWpu37779nIUFERFZJ9q5NYWFh+Oijj4osIgBg8ODBmDRpEnbt2iV3JCKTMWfOHMnzsmXL4r333lMoDT2PnZ0dxo8fL2nbuXMnrly5olAiIiIi5cheSERHR6N79+7F2rd79+6IiYmRNxCRiUhISMDatWslbWPHjoWHh4dCiag43nrrLXh6ekratAtCIiIiayB7IaFSqYo9GFGlUsmchsh0LF26FE+fPtU8d3BwwOjRoxVMRMXh5uamc9do9erVuH//vkKJiIiIlCF7IeHv7489e/YUa9/du3fD399f3kBEJiA7OxuLFi2StA0ePBgVKlRQKBHpY+zYsZLB41lZWViwYIGCiYiIiIxP9kKiW7dumDVrFv74448i91u/fj1mz56NHj16yB2JSHF//PEH7t69K2kbO3asQmlIX5UqVcLQoUMlbYsWLUJqaqpCiYiIiIxPJWSeBP3BgweoXbs2njx5gkaNGqFz586oXr06nJ2dJdO/RkVFoUyZMrh8+TK/lZVRbGwsfH19AQB37txB1apVFU5kndq0aYMTJ05onrdt2xbHjh1TMJFhpaSk4O2335a0/frrryhTpoxCiQzv8uXLqFevnqTtp59+YkFIRESlZi6f12QvJABg//796NOnD1JTUwscByGEgLu7O7Zt24aQkBC541g1c7kwLdnp06fRokULSduGDRswYMAAhRIZXkJCgs56MPHx8fD29lYokTxefvllyUxz/v7+uH79OuzsZJ9Zm4iILJi5fF4zysrWnTp1QkREBAYMGAAnJyfJytZOTk4YNGgQIiIiWESQVZg/f77kedWqVdGnTx+F0lBp/O9//5M8j4mJwY4dOxRKQ0REZFxG+9qsZs2aWL9+PbKysnD9+nWkpKSgTJkyqFmzptmveEtUXPfv38f69eslbaNHj4a9vb1Ciag0OnTogKCgIJw+fVrTtnjxYvTt21fBVERERMZhlDsS+Tk4OKB+/fpo06YN6tevzyKCrMqSJUuQnZ2tee7k5ISRI0cqmIhKQ6VSYcyYMZK2ffv24fr16wolIiIiMh6jFxI5OTm4fPkyTp48icuXLyMnJ8fYEYgUkZWVhZ9//lnSNmTIEHh5eSmUiAxhwIABKFu2rKRt6dKlCqUhIiIyHqMVEtHR0Xj99dfh6emJBg0aoG3btmjQoAHKli2LN954A9HR0caKQqSIjRs34sGDB5I2zvBj/pydnfHmm29K2lasWIHMzEyFEhERERmHUQqJw4cPo2nTpli3bh3S09Mlg63T0tKwZs0aNGvWDEeOHDFGHCKjE0Jg3rx5krbg4GAEBgYqlIgM6Z133pE8T0xMxKZNmxRKQ0REZByyD7Z+8OAB+vTpg5SUFNSvXx9dunRBQEAAXFxcJOtIXLx4EX379sWlS5d0po0kMnf//vsvwsPDJW3jxo1TKA0ZWq1atdCpUyfs379f07Z48WKdReuIiIgsieyFxA8//ICMjAysW7cOAwcOLHS/devWYcSIEfjhhx/w7bffyh2LyKh++uknyXM/Pz+8/PLLCqUhObz33nuSQuKff/7BhQsX0KhRIwVTERERyUf2rk1hYWH46KOPiiwiAGDw4MGYNGmSZHEnIkuQmJiILVu2SNrGjBnDRcssTK9evVCpUiVJ2+LFixVKQ0REJD/ZC4no6Gh07969WPt2794dMTEx8gYiMrLff/8dWVlZmucODg4YMWKEgolIDvb29nj77bclbb/99huePHmiUCIiIiJ5yV5IqFQqCCGKvS+RJRFC4Ndff5W09e3bF+XLl1coEclp5MiRsLH5v1+rqampWLNmjYKJiIiI5CN7IeHv7489e/YUa9/du3fD399f3kBERnT69GlERUVJ2rS/tSbL4evri549e0raFi9eXOwvU4iIiMyJ7IVEt27dMGvWLPzxxx9F7rd+/XrMnj0bPXr0kDsSkdEsW7ZM8jwgIAAhISEKpSFjeO+99yTPz58/j5MnTyqUhoiISD6yj/acOHEili5div79+6NRo0bo3LkzqlevDmdnZ8n0r1FRUShTpgwmTJggdyQio0hLS8O6deskbSNGjJB0fbFU9vb2CA4O1mmzBnlTXOdfZHPx4sVo3bq1gqmIiIgMTyWMcM99//796NOnD1JTUwscByGEgLu7O7Zt28Zva2UWGxsLX19fAMCdO3dQtWpVhRNZrpUrV0pWPLaxscGtW7d4zq3ArFmzMHnyZM1zR0dHxMXFcWwMEREVi7l8XjPKV6OdOnVCREQEBgwYACcnJ8nK1k5OThg0aBAiIiJYRJBF0R5k/dJLL5nsLwIyrBEjRkjuwDx9+hSrVq1SMBEREZHhGa2PRc2aNbF+/XokJSUhMjISx44dQ2RkJJKSkrB27VrUqFHDWFGIZHf58mUcP35c0sZB1tbD29sb/fr1k7SxkCAiIktj9M7aDg4OqF+/Ptq0aYP69evDwcHB2BGIZLd8+XLJcx8fH53ZfMiyaReOFy5cwLlz55QJQ0REJAOTGvUZExOD0NBQpWMQlUpWVpbOt8/Dhg2zmsHG9EzHjh1RrVo1SRvvShARkSUxqUIiLS0Nhw8fVjoGUans2rULCQkJkjauZG19bGxsMHToUEnbmjVrkJ2drVAiIiIiw5J9+lcAyMzMxLp163D06FHcv38fmZmZBe6XlpZmjDhEstIeZN2uXTvUqVNHoTTKePLkiWTWIgD49ttv4e7urlAiZbzxxhuYMWOG5nlCQgL++usvvPzyywqmIiIiMgzZC4m7d++iY8eOuHHjBgA8d4XXgqaHJTIXd+7c0VnJ3RoHWWdmZmLRokWStq+++srqColatWqhdevWOHHihKZt1apVLCSIiMgiyF5IfPrpp/jvv/9QrVo1BAcHo1KlSnB2di5w3wcPHmDJkiVyRyKSzcqVK6FWqzXP3d3ddWbvIesybNgwSSGxc+dOPHr0COXKlVMwFRERUenJXkjs2bMHPXr0wPbt25+7om9UVBQWL14sdyQiWajVap3Zml577TW4uroqlIhMwcCBAzFu3Dg8ffoUwLPB+OvXr8fo0aMVTkZERFQ6sg+2TkpKwpgxY55bRABAhQoV8OWXX8odiUgWx44dQ0xMjKTtrbfeUiYMmQxPT0+88sorkjbO3kRERJZA9kLC19cXXl5exdrX29ubhQSZrbVr10qe169fH82bN1coDZmSN954Q/L81KlTuHLlikJpiIiIDEP2QmLAgAHYt29fsfa9e/cup8kks5SVlYVNmzZJ2oYMGcLJAwgA0LVrV1SoUEHSxrsSRERk7mQvJD777DP8+eef+P3335+7b1JSEv9zJbP0119/4dGjR5K2wYMHK5SGTI2dnR2GDBkiafvtt9+Qm5urUCIiIqLSM+hg68LuJlSpUgUjRozApEmT0KxZM5QvXx62trY6+yUnJxsyDpHRaHdratu2Lfz9/ZUJQyZp2LBh+OGHHzTP4+LicODAAbz44osKpiIiIio5gxYSK1euhEql0lkrIq/twYMHCAsLK/C1eftYQ1eQ7Oxs7NixA9u3b8epU6dw584d5ObmwsfHB0FBQRg+fDjnmTcjT548wY4dOyRt2t8+EzVq1AiNGzfGuXPnNG2rVq1iIUFERGbL4NO/9urVC56eniV6bXJyss4HMksTGxuLVq1aIS4uDtWqVcOkSZPQqFEjODo64tixY/jmm2/wxx9/oHfv3li/fj0cHR2VjkzPsXXrVmRkZGie29nZoX///gomIlM1bNgwSSHxxx9/4PHjx/Dw8FAuFBERUQkZvJCYMWMG6tWrV6LXRkVFWXwhkZycjLi4OFSpUgVnz56VLErVsmVLvPjii2jWrBm2bduGiRMnYsGCBQqmpeLQ7tbUtWvXYs9URtbltddew6RJk5CTkwMAyMjIwJYtW/Dmm28qnIyIiEh/Bh1sHRwcXKrFt8qVK6czTaKlmjBhQoEr2zZq1EgzSPfXX39FamqqsaORHh48eKAzKxm7NVFhfHx80K1bN0nb6tWrFUpDRERUOgYtJA4ePAg/P78Sv75y5cpYsWKFAROZHi8vL0ycOFFngar8AgMDAQBPnz7F1atXjRWNSmDDhg1Qq9Wa566urujVq5eCicjUDRs2TPL88OHDuHv3rkJpiIiISk726V/1ERMTg9DQUKVjyKpixYqYPXs2XnjhhUL3yT+jlZubmzFiUQlpd2vq3bt3qe7KkeXr0aMH3N3dNc+FENiyZYuCiYiIiErG4GMkSiMtLQ2HDx9WOobirl+/DgCoVKkSatSooddrY2Nji9x+7969Euciqf/++w///vuvpI3dmp6xtbXVGStV0JTP1sjJyQmvvPKKZG2dDRs2YOzYsQqmIiIi0p9BC4mrV68iLCwMr732mmYVV31WquY6EkBOTo7m28mJEyfq/eHL19dXjlhUAO27Ed7e3ujcubNCaUxLuXLlcPHiRaVjmKyBAwdKConjx4/jzp07/PdLRERmxaCFxIsvvoi4uDj89ddf2LNnD4DC15bQZk3rSBRl2bJlePDgAVq0aIEPPvhA6ThUCCGETiExYMAA2NvbK5SIzEmXLl3g6ekp+fJk06ZNmDBhgnKhiIiI9GTQQsLX1xexsbGoVq2apL24a0uY6joSq1ev1uvOirawsDB06dLluftdu3YNkyZNgo+PD9avX1+iD6V37twpcvu9e/fQokULvY9LUmfOnNEZCM9uTVRcDg4O6NOnj2RyiQ0bNrCQICIis2LQQuLAgQO4ePEiGjduLGkv7toSprqOhFqtRm5ubqle/zwPHjxAjx49YGtri7179yIgIKBE71W1atUSvY70s2bNGsnzgIAAtGrVSqE0ZI4GDhwoKSROnTqF6OjoEv/bJyIiMjaDztrk6OiIpk2bwsbm/w6rz9oSbm5u6NChgyEjGcTw4cMhhCjx46WXXiry+Pfv30doaCgSExOxd+9ezfSvZJpyc3Oxfv16Sdtrr71m9d3ySD+hoaEoX768pG3Tpk0KpSEiItKf7NO/6rO2hL+/Pw4ePChzItMSGxuL4OBgxMfH48CBAwgKClI6Ej3H4cOHdWa/Yrcm0pe9vT369u0raduwYYNCaYiIiPRnUtO/3r17F5999hmWL1+udBSjyFs3IzMzE4cPH9bp/hUTEwMvLy+uJWFiNm/eLHneuHFj1K1bV6E0piktLQ3ff/+9pG3SpElcY0PLwIED8csvv2ienzlzBv/995/e0z4TEREpwaQWpEtKSsKqVauUjmEU169fR4cOHZCTk1NgEQE863ev/aGVlJWbm4s//vhD0jZgwACF0piu9PR0TJ06VfJIT09XOpbJCQ4Oho+Pj6SNdyWIiMhcGPSOxLRp00r1+vj4eAMlMW2XLl1C586d4ezsjAMHDhS76xcp759//sGDBw8kba+++qpCacjc2dnZoV+/fli0aJGmbcOGDZgyZYqCqYiIiIrHoIXEV199VaoBp9awjsSNGzfQsWNHJCQkwMHBAfXr11c6Eukhb7HAPA0bNkStWrUUSkOWYODAgZJCIjIyEpcvX2Z3OSIiMnkGHyPRrFmzEveDTktLQ0REhIETmZbIyEgkJCQAALKyspCVlaVwIioutVqtU0jwbgSVVrt27VC5cmXcvXtX07ZhwwZ89dVXyoUiIiIqBoMXEitXrizWmhEFiYqKsvipT3v37v3cVb7JNJ0+fRqxsbGSNhYSVFo2Njbo378/5s2bp2nbuHEjvvzyS4u/Q0tERObNoIOt/fz84ODgUOLXOzo66qyKTWQqtO9G1K5dm13TyCC0B+xfvnwZUVFRCqUhIiIqHoMWEtHR0aWatrBmzZqIjo42YCIiwxBCFNitid8YkyG0atUKvr6+kjbO3kRERKZOkelfk5KSkJubq8RbE5XIuXPncPPmTUkbuzWRodjY2OjcldiwYQO7QRIRkUkzWiFx4sQJ9O7dG56envD29sbVq1cBPOsL/MYbb+DixYvGikKkN+27EQEBAWjSpIlCacgSDRw4UPL8v//+Q2RkpEJpiIiIns8ohcTPP/+MDh06YOfOnXj8+LHkW7acnBz8/vvvaNasmdUsRkfmh92aSG7NmzfXWVNm27ZtyoQhIiIqBtkLiQsXLuCDDz6AWq1G586dMXbsWMkHsNdeew3nz59H27ZtMXLkSJw/f17uSER6uXTpEq5cuSJpY7cmMjSVSoXevXtL2lhIEBGRKZO9kJg3bx5cXFzw77//Ys+ePZg3b57ON7kNGzbE3r170aRJE/zwww9yRyLSy+bNmyXPq1SpghYtWiiUhiyZdiFx9uxZ3Lp1S5kwREREzyF7IXHkyBFMmTIFzZs3L3I/W1tbjB8/HocPH5Y7EpFeCurWZGOjyDwFZOHatWuHcuXKSdq2b9+uUBoiIqKiyf5p6O7du2jdunWx9q1Zsybu378vcyKi4rt+/TouXLggaWO3JpKLnZ0dXn75ZUkbuzcREZGpkr2QsLGxQVZWVrH2TUhIgJOTk8yJiIpP+26Ej48P2rZtq1Aa86FSqeDl5SV5cHB68Wh3bzpy5AgSExOVCUNERFQE2QuJF154AX/99Vex9l2+fDlq1qwpcyKi4tMuJPr06QNbW1uF0pgPLy8vJCQkSB5eXl5KxzILXbp0gbOzs+Z5bm4udu/erWAiIiKigsleSLzyyiuYO3culi9fXug+qamp+OCDD7Blyxb07dtX7khExXLr1i2Eh4dL2vr166dQGrIWLi4u6NKli6SN3ZuIiMgUyV5IjB8/Hj4+Phg5ciTq1KmDkSNHQq1WY8GCBZgwYQK6d++OihUrYuHChfD19cX7778vdySiYvnjjz8kz8uVK4fg4GCF0pA10e7e9NdffyE9PV2ZMERERIWwk/sNPD09ERYWhh49euDatWu4fv06AGDJkiWafYQQqFq1Knbv3g13d3e5IxEVi/ZsOa+88grs7e0VSkPWpGfPnrCxsYFarQYAZGRk4O+//0avXr0UTkZERPR/jDKHZWBgIC5cuICPP/4YAQEBEEJoHgEBAfjkk09w/vx51K9f3xhxiJ4rKSkJx44dk7Rpf0tMJBcvLy+0b99e0sbuTUREZGpkvyORp1y5cvjmm2/wzTffIC0tDSkpKShTpgxcXV2NFYGo2P7880/k5uZqnjs5OaFz584KJiJr07t3b8m6Ojt27EBOTg7s7Iz2a5uIiKhIiqyq5erqisqVK+sUETExMQgNDVUiEpHEzp07Jc87deoEFxcXhdKYn4yMDCxcuFDyyMjIUDqWWXnllVckzxMTE/HPP/8olIaIiEiX7F9t2draIjIyEvXq1XvuvmlpaVzZmhSXnZ2NP//8U9KmvUgYFS01NVVn4oQBAwZIpjWlogUEBCAwMBDnz5/XtG3btg0dOnRQMBUREdH/kf2OhBCiWPulpaVh1qxZMqcher5jx44hJSVF0tazZ0+F0pA10x6Xs23btmL/TiUiIpKbUbo2PW8xpX///ReNGzfGmjVrjBGHqEja3ZqaNm2KKlWqKJSGrJl2IREdHY3IyEhlwhAREWkxSiExefLkAhekU6vVmDZtGtq3b48bN27A0dHRGHGICiWE0Ckk2K2JlBIYGAg/Pz9JG2dvIiIiU2GUQmLKlCkYPXo0tmzZommLjo5G+/btMXXqVOTk5KBz587YsWMHb9uToq5evYr//vtP0sZCgpSiUqkK7N5ERERkCmQfbO3n54c33ngDVatWxeuvvw43Nzfcv38fH3zwAVJTU2FnZ4fp06dj0qRJuHv3LoYNGyZ3JKJCad+NqFy5Mpo2bapQGqJn3ZvmzZuneX727FncunVL504FERGRscleSERHRwMAatSogeTkZPTo0UOzGN0LL7yAtWvXIigoCMCzD20rVqyQOxJRobQLiZ49e0KlUimUhgho164dypUrh0ePHmnadu7cqTMrFhERkbEZdR2Jjz76CB999BGEEOjevTvOnj2rKSIAICEhAdOmTTNmJCKNxMREHD9+XNLGbk2kNDs7O/To0UPSFhYWplAaIiKi/2P0BelmzpyJd999FxEREbh7965kW3x8PKZOnWrsSEQAnq1mrVarNc+dnZ3RqVMnBRMRPaNdSBw8eBDp6ekKpSEiInrGoF2bins3wcfHB0+fPkVoaChGjRqlaY+PjzdkHCK9aHdr6ty5MxdQI5PQpUsX2NjYaArdzMxMHDp0CN27d1c4GRERWTODFhJfffVVsfuTCyGQnJwsuQMhhGB/dFJEVlYW/vrrL0kbuzWRqShbtizatGmDY8eOadrCwsJYSBARkaIMPti6WbNmcHV1LdFr09LSEBERYeBERM939OhRPH78WNLG1azJlHTv3l1SSOzevRvz58/nly9ERKQYgxcSK1euRL169Ur02qioKAQGBho4EdHzaXdrCgoKQqVKlRRKQ6SrR48e+PTTTzXPY2JicOXKFdStW1fBVEREZM0MOti6SpUqsLe3L/Hr3dzc0KFDBwMmIno+rmZN5qBhw4aoUqWKpI2zNxERkZIMWkjcuXMHNWvWLPHr/f39cfDgQQMmInq+y5cv4+bNm5I2FhJkalQqlc6YCBYSRESkJKNP/1qUmJgYhIaGKh2DrIz23QhfX192sSslb29vzcKTeQ9vb2+lY5k97UKioLE9RERExmJShURaWhoOHz6sdAyyMrt27ZI852rWZKo6d+4s6T6anZ2Nv//+W8FERERkzQw62Prq1asICwvDa6+9hgoVKgAARowYUezXJycnGzIO0XMlJSXhn3/+kbRxtiYyVW5ubggODpYUD2FhYejbt6+CqYiIyFoZtJB48cUXERcXh7/++gt79uwB8GwWJ5VKBSFEka/N24ffBJMx7d+/X7KatZOTE0JCQhRMRFS07t276xQS/N1JRERKMGgh4evri9jYWFSrVk3S3qtXL3h6ej739cnJydixY4chIxEVKa/gzRMcHMzVrMmkde/eHRMmTNA8v3fvHs6fP4/GjRsrF4qIiKySQQuJAwcO4OLFizr/oc2YMaNYa0tERUWxkCCjEULorGbdtWtXhdIQFU+tWrVQvXp1yUxjYWFhLCSIiMjoDDrY2tHREU2bNoWNzf8dNjg4uNgrXXMdCTKmS5cuITY2VtL20ksvKZTGsjx9+hSbNm2SPJ4+fap0LIugUqnQo0cPSdvu3bsVSkNERNbM4Ctba9NnXQiuI0HGpN2tqVq1aqhTp45CaSzL48ePMWDAAElbfHw8p4A1kO7du2P+/Pma5ydPnkRiYiLKly+vYCoiIrI2JjX96927d/Wa5YmoNArq1sQBq2QOtMfyqNVq7N27V8FERERkjUyqkEhKSsKqVauUjkFWID09HUeOHJG0sVsTmQtnZ2edxTu5yjURERmbQbs2TZs2rVSvj4+PN1ASoqIdPnxY0mff1tYWnTp1UjARkX569OghGRvx119/ITc3F7a2tgqmIiIia2LQQuKrr74qVdcQzoVOxqLdral169YoU6aMQmmI9NetWzfJ84cPH+L06dNo1aqVQomIiMjaGHywdbNmzYo9S5O2tLQ0REREGDgRkS7tgdbs1kTmxt/fH/Xq1cOlS5c0bWFhYSwkiIjIaAxeSKxcubJYa0YUJCoqCoGBgQZORCQVExODq1evStq4fgSZo+7du0sKib1795a6iykREVFxGXSwtZ+fHxwcHEr8ekdHR51VsYkMTftuhJeXF5o2bapQGqKS0y6AT58+jaSkJIXSEBGRtTFoIREdHY0aNWqU+PU1a9ZEdHS0ARMR6Spo2tf8iygSmYt27drByclJ81ytVnMtHiIiMhp+eiKrkp2djf3790va2K2JzJWTkxPat28vadu3b59CaYiIyNqwkCCrcuLECTx58kTS1qVLF4XSEJXeiy++KHnOhemIiMhYWEiQVdHu1tS0aVNUqFBBoTREpaddCN+8eRM3b95UKA0REVkTFhJkVbQHWrNbE5m7hg0bwsfHR9LG7k1ERGQMLCTIajx48ABnzpyRtHH9CDJ3NjY26Ny5s6SNhQQRERkDCwmyGtp9x93d3dG6dWuF0hAZjvY4iQMHDiA3N1ehNEREZC0MviAdkanS7tbUqVMn2NvbK5TGspUvXx7x8fE6bSQP7UIiKSkJERERaNGihUKJiIjIGsh+R+LIkSPIyMiQ+22IiqRWq3UKCXZrko+NjQ28vb0lD67VIZ8qVaqgbt26kjZ2byIiIrnJ/j97SEgIYmJi5H4boiKdO3cODx8+lLRxoDVZEu3ZmzgNLBERyU32QkIIgc8++wzHjx+X+62ICqW9CF3NmjXh7++vTBgiGWh3bzpx4gRSU1MVSkNERNbAKH0NIiIi0L59ewQGBmLp0qVIT083xtuanX79+kGlUkGlUvEujoFpFxKdOnVSKAmRPIKDgyVjfrKzs3H48GEFExERkaUzSiERFhaGw4cPo169evjggw9QuXJljBs3DlevXjXG25uFTZs2YcuWLUrHsEhZWVk4evSopI2FBFkaNzc3nVnIOE6CiIjkJHshMWzYMJQtWxbt27fHunXrcOfOHUyaNAnbt29HvXr10LlzZ2zduhVqtVruKCbr4cOHeP/99+Hm5qZ0FIv077//Su6CqVQqhISEKJjI8mVlZeHQoUOSR1ZWltKxLJ529yYWEkREJCfZC4kVK1agUqVKmufe3t6YMmUKbt68ia1bt8LOzg79+/eHn58fpk+fjgcPHsgdyeSMHTsWT58+xSeffKJ0FIuk3a2pcePGnIpUZikpKQgJCZE8UlJSlI5l8bQLiUuXLiEuLk6hNEREZOkUm4/RxsYGvXr1wvLlyzF48GDExcXhyy+/RLVq1TB48GAcO3ZMqWhGtW3bNqxfvx5z5sxB5cqVlY5jkTg+gqxF8+bN4enpKWnjXQkiIpKL7IXE7du3C1xh9dChQxgwYAACAgKwdu1aAM+6nLz44ov477//0KFDBzRt2lRn7n9LkpSUhPfeew8vvvgi3nrrLaXjWKTU1FScPHlS0hYaGqpQGiJ52dra6hTKLCSIiEgusq9sHRAQgMjISNSrVw+pqalYtWoVFi1ahCtXrgB4Nj2st7c3RowYgXfffRd+fn4AgLNnz+LHH39Ez549sXz5cgwdOlTuqEY3btw4pKam4pdffjHYMWNjY4vcfu/ePYO9lzk4evQocnJyNM/t7OzQvn17BRMRyevFF1+UTNzw999/Q61Wc0FAIiIyONkLCSEELly4gPnz52PNmjVIS0uDEAIA0Lp1a4wePRr9+/eHg4OD5HVNmjTB6tWrERQUhJkzZ1pcIbF792789ttvmD9/vqZ4MgRfX1+DHcsSaHdratWqFQe1k0XTHicRHx+PyMhIBAYGKpSIiIgsleyFBAAMGTIEwLOiwtXVFa+99hrGjBmDRo0aPfe1rVu3xkcffSR3RKNKSUnBO++8g3bt2mHMmDFKx7FoHB9B1qZ69eqoXr06bt68qWnbt28fCwkiIjI4o9zrFkKgTp06mDdvHuLi4rBkyZJiFRHXrl3DrFmz4OzsbISUhVu9ejXs7OxK/Ni7d6/keBMnTkRiYiKWLVsGlUpl0Kx37twp8nHq1CmDvp8pe/jwIc6dOydpYyFB1oDTwBIRkTEY5Y7E2rVrMWjQIL1f99FHH2H37t2K92lXq9UFDhjX5/V59u7di2XLluG7775DrVq1DBFPomrVqgY/prk6ePCg5LmLiwtatmypUBoi4+nSpQuWLFmieX706FE8ffoUjo6OCqYiIiJLI/sdCT8/PwQFBeH27dt6Lzq3bds2ZGdn48CBAzKlK57hw4dDCFHix0svvQQAePLkCUaOHImgoCBMmDBB0Z/JGmh3a+rQoYPOWBwiS9SxY0fJ3c6MjAycPn1awURERGSJZC8kbt26hVq1aiEgIOC5MwpZuoiICNy+fRsRERFwdHTU6QKVfwrYGjVqFNhOxcfxEWStypUrpzMmQvsOHRERUWkZpWtTjRo18NVXX6FixYrGeDuTFRQUhMjIyEK3b9++HZ999hkAICwsTLNAXdmyZY2Sz5Lcvn0b//33n6SNhQRZk5CQEMkYoYMHD+Lzzz9XLhAREVkc2QsJe3t7zJ07F926dZP7rUyeq6srGjRoUOj28PBwzZ9r1aoFf39/I6SyTNp3I8qXL89Za8iqhISE4Mcff9Q8/+eff5CZmQknJycFUxERkSWRvWtTpUqV4OPjI/fbEEloFxIhISFckIusSvv27SXX/NOnT3VWeSciIioN2T9Zde3aFUePHi3WvhcvXoStra3MiUxLWloaoqKiEBUVhbi4OE37tWvXNO2kHyGETiERGhqqUBoiZXh6eqJJkyaSNo6TICIiQ5K9a9MXX3yBjh07ol27dmjevPlz989b9dpanD59GiEhITrtXbt21fzZ2s5JaV2+fBn379+XtHF8BFmjkJAQREREaJ4fOnRIuTBERGRxZC8k9u/fj4EDB6J9+/bo0qUL2rZtC29v7wLvPMTGxhp8gTZT17FjRxYKBqZ9N6Jq1aqoWbOmQmmsU9myZXXupnHSAOMLCQnB7NmzNc9PnjyJjIwMxRf5JCIiyyB7ITF8+HCoVCoIIbBr1y7s2rVL7rckK6e97kinTp2srkBVmp2dHerXr690DKvXrl072NraahbUzMrKwj///MM7dEREZBBGmf61UqVKsLe3f+5+2dnZuHfvnhESkaXKzc3V6b7BD01krTw8PNCsWTOcOnVK03bw4EH+myAiIoOQvZBQqVTYu3cv6tWr99x9o6KiOEUnlcqZM2eQnJwsaeOHJrJmISEhOoUEERGRIcg+a5M+/f8dHR1RrVo1GdOQpdP+kFSnTh3Nwn5E1kh7ModTp04hLS1NoTRERGRJZC8k1Gp1se5GAEDNmjURHR0tcyKyZIcPH5Y8L2hGLCJr0rZtW9jZ/d/N55ycHBw/flzBREREZClMaoWulJQUrF69WukYZKZyc3Nx7NgxSVtwcLBCaaxbTk4OLl68KHnk5OQoHcsqubm5ISgoSNLG7k1ERGQIJlVIxMbG4s0331Q6Bpmpc+fO4fHjx5K2Dh06KJTGuiUlJaFBgwaSR1JSktKxrJb2nTkWEkREZAhGmbUpz5UrV3D58mWkpqYWOHYiNjbWmHHIwmh3a6pZsyYqVaqkUBoi0xESEoKZM2dqnoeHh+PJkydwd3dXMBUREZk7oxQSV65cwRtvvCFZYZXI0I4cOSJ5zm5NRM+0adMG9vb2yM7OBvCsG+DRo0fRvXt3hZMREZE5k71r04MHDxAcHIzw8HDY29ujWrVqEEKgUqVKqFatGqpVqwY7OzsIIeDg4AA/Pz+5I5EFUqvVOHr0qKSNhQTRMy4uLmjVqpWkTXu9FSIiIn3JXkjMmTMHycnJWLJkCVJTUxEdHQ1bW1vs3bsX0dHRiI6ORlpaGubPnw87Ozvs3r1b7khkgaKiovDo0SNJGwsJov/TsWNHyXOOkyAiotKSvZD4888/8c4772DkyJGSKQjzs7Ozw5gxYzBq1Ch8//33ckciC6Q9PiIgIAC+vr4KpSEyPdoDrs+cOYOUlBSF0hARkSWQvZCIiYlBjx49irVvz549ebudSkR7fARnayKSat26NRwdHTXP1Wq1zr8bIiIifRhlZWsvLy9Jm6OjI+Lj43X2tbe3x7179+SORBZGCMGB1kTP4eTkhNatW0va2L2JiIhKQ/ZCwtfXF9euXZO0eXt748SJEzr7Hjx4ELa2tnJHIgtz5coVncKUhQSRLu1xErwDTEREpSF7IVGnTh18//33SE1N1bQ1b94cs2fPxv79+zVt27Ztw3fffYc6derIHYksjPb4iKpVqyIgIEChNESmS3ucxLlz53QmKSAiIiou2QuJXr164dy5c6hfvz527twJAHjjjTeQlJSELl26oEyZMvDw8MCrr76K9PR0vP7663JHIgujXUh06NABKpVKoTREpqtly5ZwcnLSPBdC4Pjx4womIiIic2aUQuKNN95AaGioZjXrl19+GcOGDYMQAk+ePNGsdN2lSxeMHTtW7khkQTg+gqj4HB0d0bJlS0mb9vorRERExSX7ytbly5fHypUrddpXrFiBPn36YP/+/VCr1Wjfvj369+/Pb5JJLzdu3MDdu3clbSwkiArXvn17yV08FhJERFRSshcSRenVqxd69eqlZAQyc9rdmipUqIBatWoplIbI9LVv317yPDw8HOnp6XBxcVEoERERmSvZuzbpIyUlBatXr1Y6BpkR7UIiODiYd7WIitC6dWvY2Pzfr/6cnBycOnVKwURERGSuTKqQiI2NxZtvvql0DDIjBQ20JuWVKVMGBw8elDzKlCmjdCwC4O7ujiZNmkja2L2JiIhKwqBdm0q7SurNmzcNlISswa1bt3D79m1JG8dHmAYHBwedNQvIdLRv3x4RERGa5ywkiIioJAxaSHTs2JHdSshotO9GlC9fHvXq1VMoDZH5aN++PebOnat5fuLECeTk5MDOTtFhc0REZGYM3rVJCFGqB1FxFdStKX/fbyIqWLt27STPU1NTce7cOWXCEBGR2TLopy6VSoWoqCio1eoSPS5cuGDIOGThOD6CqGR8fHxQu3ZtSRu7NxERkb4MWkiU9o6CSqXiXQkqlri4ONy4cUPSxvERRMWnfVeChQQREenLoIVEdHR0qebwr127NqKjow2YiCyV9sD+MmXKoFGjRgqlIW1qtRoJCQmSh1qtVjoW5aO9nsSxY8f4RQ4REenFoIWEn59fqQbrpaWl6XRXISqI9nXSvn172NraKpSGtCUmJsLHx0fySExMVDoW5aNdSCQkJODq1asKpSEiInNkUiNTuY4EFdexY8ckzzk+gkg/AQEBqFy5sqSN3ZuIiEgfXEeCzE5SUhIuXrwoadPu701ERVOpVGjfvj02bNigaTt69ChGjhypYCoiIjInXEeCzM6JEyckzx0dHdG0aVOF0hCZr4IKCSIiouLiOhJkdo4fPy55HhQUBEdHR4XSEJkv7XESMTExiI2NVSgNERGZG64jQWZHu5Bo27atQkmIzFuDBg3g6ekpadMef0RERFQYriNBZiUrKwunTp2StHF8BFHJ2NjY6BTi7N5ERETFxXUkyKycPXsWGRkZkrY2bdoolIbI/HFhOiIiKimTWkciKSkJq1atMmAisjTa3Zrq1q2LcuXKKZSGyPxpj5OIiopCUlKSQmmIiMicmNQ6EvHx8Zg6darSMciEcXwEkWE1b95cMlmBEELn3xkREVFBDDr9a1ZWFi5evIgmTZpo2lavXl3s13O2ECpKQR9wWEgQlY6joyNatmwpWQfo6NGj6Nmzp4KpiIjIHBi0kAgNDcWJEycwYsQI/PLLLwCA4cOHc20JMoibN2/iwYMHkjYWEkSl1759e51CgoiI6HkMWkjcunULQgjcvn1b0l6pUiXY29s/9/XZ2dm4d++eISORBdG+G+Hj44MaNWoolIbIcmiPkwgPD0dGRgacnZ0VSkRERObAoIXEvn37EBYWhsGDB2vaVCoV9u7di3r16j339VFRUQgMDDRkJLIg2vPbt23blne7iAygdevWsLGxgVqtBvDsS51///0XHTt2VDYYERGZNIMOtq5Tpw4mTJiASpUqadr0WReC60hQUTg+gkgeHh4eaNy4saSNA66JiOh5DHpHoiB533AVR/369fXan6zHo0ePcOnSJUkbCwnT5eHhgY0bN+q0kelq27Ytzpw5o3l+4sQJBdMQEZE5kL2QIDIE7Q81Tk5OaNq0qUJp6HkcHR3Rv39/pWOQHlq3bo358+drnp84cQJCCHYfJCKiQsm+joRarcbkyZMxYcIETJgwAdeuXZNsT09PR4cOHbBp0ya5o5AZ0+5mERQUBAcHB4XSEFke7RXiHz16pPP7moiIKD/Z70js2bMH3333HQDA3d0dgwYNkmy3sbFBeHg4Bg0ahH/++Qc//vij3JHIDGkXEu3atVMoCZFlqlatGipVqiSZOe+ff/5B7dq1FUxFRESmTPY7Etu2bYOdnR3mzp2Lhw8fokWLFpLtTk5OePDgAT7++GP89NNPWL9+vdyRyMxkZWXh1KlTkjaOjyAyLJVKpXNXguMkiIioKLIXEidPnsS7776LDz74oNC1JNzd3TFz5ky8/vrrWLp0qdyRyMycOXMGmZmZkrbWrVsrlIbIcmn/u2IhQURERZG9kIiJiUHPnj2Lte9rr72Gc+fOyRuIzI52t6Z69eqhXLlyCqUhslzadyQuXryIlJQUhdIQEZGpk72QyMzMhJeXV7H29fb2Rnp6usyJyNxw/Qjzk5CQAJVKJXkkJCQoHYueo2nTppJJDIQQ+PfffxVMREREpkz2QsLHxweXL18u1r6XLl0qdtFB1kEIwUKCyEgcHR3RrFkzSds///yjUBoiIjJ1shcSrVu3xtdff43k5OQi93v06BFmzJjB2XhI4saNG4iPj5e0sZAgkg/HSRARUXHJXkiMGTMG165dQ+PGjfHTTz/h4sWLyMjIAABkZGTg4sWLmDt3Lpo0aYJr165hzJgxckciM3Ls2DHJ8woVKuCFF15QKA2R5dMeJ3Hy5Emo1WqF0hARkSmTfR2J4OBgjB07FvPnz8f48eML3U8IgfHjx6N9+/ZyRyIzUlC3Jq60SyQf7TsSjx8/xqVLl9CgQQOFEhERkamS/Y4EAMybNw8zZ86Ei4sLhBA6D1dXV3z33XeYM2eOMeKQGeH4CCLjqly5Mvz8/CRtHCdBREQFkf2ORJ7Jkydj1KhR2L17Ny5cuICUlBSUKVMGgYGB6NGjB8qWLWusKGQmkpOTdQbqs5Agkl/r1q1x69YtzfMTJ05g1KhRCiYiIiJTZLRCAgDKlSuHoUOHGvMtyYxpr2bt6OiIJk2aKJSGyHq0adMG69ev1zznHQkiIiqIUbo2FVdKSgpWr16tdAyjunHjBiZOnIgGDRqgTJkycHV1RfXq1dGtWzfMmDEDDx48UDqiYk6ePCl5rj3HPRHJQ3ucxLVr1/Dw4UOF0hARkakyqUIiNjYWb775ptIxjObnn39GgwYNcP78eXz22WfYv38/du7cib59+2Lfvn347LPPcPbsWaVjKka7kGjVqpVCSYisS2BgIJydnSVt2v8eiYiIjNa1KS0tDREREbh//z4yMzML3Cc2NtZYcRS3cuVKjB49Gh9++CF+/PFHybbQ0FB4eXnhk08+USid8oQQLCSIFGJvb4+goCAcOXJE03bixAn07NlTwVRERGRqjFJITJkyBfPmzdOsH2Ht7t27h3HjxsHPzw+zZs0qcJ+RI0fC09MT9evXN3I603D9+nUkJSVJ2lhIEBlPmzZtJIUEx0kQEZE22QuJH374Ad988w0AwMbGBl5eXjq3zPNkZ2fj3r17ckdS3KJFi/D48WOMGTOm0D7/5cuXx7vvvmvkZKZD+25EpUqV4Ovrq1AaIuujPU7i1KlTyMnJgZ2dUefoICIiEyb7/wjLli2Dl5cXVq9ejdDQ0CIHy0ZFRSEwMFDuSIpbu3YtAH7DXpR///1X8rxly5ZciI7IiLQLifT0dERGRnLmNCIi0pB9sPXNmzfx3Xff4aWXXnrujDuOjo6oVq2a3JEU9fDhQ9y8eRMA4O/vj7///huvvPIKKlWqBBcXF1SrVg2DBw8ucTeC2NjYIh/mcseH4yOIlOXt7Y0aNWpI2ti9iYiI8pP9joS7uzsaNmxYrH1r1qyJ6OhomRMp6+LFi5o/T58+Hdu3b8fEiRPx0Ucfwc7ODvv27cM333yD9evXY9q0afj888/1Or4ldP9JT0/H+fPnJW0sJMyLm5sbFixYoNNG5qVNmzb477//NM9PnDiBMWPGKJiIiIhMieyFRPv27RETE4NmzZo9d9/09HSEh4ejQ4cOcsdSzKNHjzR/3rRpE37//XcMGTJE09ayZUs0b94c3bp1wxdffIH69eujb9++SkRVTEREBHJzczXPbWxs0Lx5cwUTkb6cnZ35gdMCtG7dWrK2D+9IEBFRfrJ3bfryyy8xa9YsnRl4ChIdHY2QkBC5I+lt9erVsLOzK/Fj7969mmOlpaVp/lyrVi1JEZHnpZdeQvv27QEAU6dO1SvrnTt3inxorxZtirS7NTVq1Aiurq4KpSGyXm3atJE8j46Oxv379xVKQ0REpkb2OxLJycl45ZVX0KBBAwwdOhTNmzdH+fLlYWtrq7Nv3tgBU6NWqyXfkJfk9Xnyz1hV1J2XkJAQHD16FBcuXMCDBw9QoUKFYr1X1apVS5zTVHB8BJFpqF+/Ptzd3fHkyRNN24kTJ9CnTx8FUxERkamQvZDo2LGjZrad77//Xu63k8Xw4cMxfPhwgxyrXLlymj8XVRxUqVJF8+fbt28Xu5Awd0IInDhxQtLGQoJIGba2tmjZsiX+/vtvTds///zDQoKIiAAYoWsT8OzDYXEflq5BgwaaPxd1l8MazkVBCppZioUEkXK0p4HVnpqZiIisl+yFhEqlQlRUFNRq9XMfFy5ckDuO4ry9vVGvXj0Az+40FCYuLg7As/Pn7+9vjGgmQbtbU9myZVGzZk2F0hCRdiEfERGBnJwchdIQEZEpkb2Q0OebdZVKZRXfxA8bNgwAcOjQIcn4ifwOHjwI4NksTt7e3kbLprSCFqKzsTHKjTMyoIcPH8Lb21vyePjwodKxqASCgoIkz9PT0yXTWBMRkfWS/RNadHQ0atWqVax969evX+gHa0syduxYVK9eHXfv3sXChQt1tv/11184duwYbGxs8M033yiQUDkcaG0ZhBB4+PCh5GENXxJYIm9vbwQEBEjazGH2NyIikp/shYSfnx/s7Io3pjs9PR1HjhyROZHynJ2dERYWhipVqmD8+PGYOHEijh8/jn///RczZszAq6++CgcHB/z666/o2LGj0nGNJisrCxEREZK2li1bKpSGiPJo/zvkOAkiIgKMNNi6uEx1HQk51K5dGxcvXsSnn36Kffv2oWvXrujYsSNWrVqFN954AxcuXMCbb76pdEyjunDhAjIzMyVtLVq0UCgNEeXR/nfIOxJERAQYYfrX/FJTU3H9+nWkpqYW2M3BVNeRkEuZMmUwbdo0TJs2TekoJkG7W1Pt2rUl0+USkTK070hcvHgRqampcHNzUygRERGZAqMUEvHx8RgzZgy2b99eqoXdyLJxfASRaWrSpAlsbW01v7/VajUiIiIQHByscDIiIlKS7F2bnjx5gnbt2mHLli3Iycmx+nUkqHAsJIhMk7OzMxo1aiRpY/cmIiKSvZCYO3cubty4gU8++QQxMTFQq9WwtbWVrC0RHR2NiRMnwtPTEzExMXJHIhOUkJCAGzduSNpYSBCZDg64JiIibbIXEtu3b8eQIUMwY8YMVKtWrcB9/Pz88P3336N3796YPXu23JHIBGl/KHFxcZGsAk5EyuKAayIi0iZ7IXH9+nUMHDiwWPsOGjQIe/bskTkRmSLtbk1BQUHFnjaYiOSnfUfizp07uHfvnkJpiIjIFMheSGRnZ6NSpUqSNnt7ezx69EhnXw8PD9y+fVvuSGSCtO9IsFsTkWmpXbs23N3dJW28K0FEZN1kLySqVKmiUxyUL18e586d09lX+1tpsg65ubksJIhMnK2tLYKCgiRtHCdBRGTdZC8katasiZ9++kky7WtgYCBmzZqFK1euaNoiIiIwc+ZMVK9eXe5IZGKuXLmCJ0+eSNq4ojWR6eE4CSIiyk/2QqJHjx44dOgQ2rRpg6NHjwJ4NhYiLi4OgYGBaNiwIRo0aIBWrVohMTER/fr1kzsSmZjTp09Lnvv6+up0hyMi5WkXEqdPn4ZarVYoDRERKU32QqJ3797o0KEDXFxcEB0dDQAYMmQIOnfujOzsbFy8eBGXLl1Cbm4uAgMD8dFHH8kdiUyMdiGh/WGFiEyD9p3Cx48f4+rVqwqlISIipck+LU6VKlVw6NAhSZtKpUJYWBgWLlyIAwcOQK1Wo3379nj//ffh4uIidyQyMdqFhHY/bDI/Li4u+PLLL3XayLxVrlwZVapUQVxcnKbt1KlTqFu3roKpiIhIKYrNr2lnZ4dx48Zh3LhxSkUgE5CVlYXz589L2lhImD9XV1d89dVXSscgGbRs2RJ//PGH5vm///6LYcOGKZiIiIiUInvXJqKiXLhwAVlZWZK2Zs2aKZSGiJ6HA66JiCgPCwlSlHa3ptq1a6NMmTIKpSGi59EuJM6fP4+MjAyF0hARkZJYSJCiOD6CyLw0b94cKpVK8zwnJ6fAdYGIiMjysZAgRbGQIDIv7u7uqFevnqSNC9MREVknFhKkmLS0NFy6dEnSxkKCyPRpTwPLcRJERNaJhQQp5syZM5LFrOzs7NC4cWPlApHBPHr0CPXr15c8Hj16pHQsMhAOuCYiIkDB6V+JtLs1NWjQAM7OzgqlIUPKzc3VuduUm5urUBoyNO1C4saNG3j48CG8vLwUSkRERErgHQlSDMdHEJmngop+7X/PRERk+WQvJI4cOcKpAalALCSIzJO9vT2aNm0qaeOAayIi6yN7IRESEoLo6Gi534bMzKNHj3Djxg1JGwsJIvPBAddERCT7GAkhBO7duwc3N7di7e/g4IDy5cvD3t5e5mSkpPDwcMlzJycn1K9fX6E0RKSvggZcCyEka0wQEZFlM8pg6y5duui1v62tLYKCgjBx4kT07dtXplSkJO1uTU2aNGHxSGRGtAuJxMRE3Lp1C/7+/soEIiIiozPKYGshhF6PnJwcnDhxAv3798enn35qjIhkZBwfQWTe/P39Ua5cOUmb9p1GIiKybLIXEtHR0XjttddQsWJFzJgxA0eOHMG1a9cQHR2Na9eu4ciRI5g+fTqqVKmC6dOn48aNG4iIiMCSJUtQp04dzJo1C4cOHZI7JhkZCwki86ZSqdCsWTNJW0REhEJpiIhICbJ3bTp16hQiIiIQFRWl8+0VANSoUQPt2rXDqFGj0KFDB7Rp0wYdO3ZEkyZNMGTIELRp0waLFi1Cx44d5Y5KRnL37l3cvXtX0sZCgsj8NG/eHPv27dM85x0JIiLrIvsdicWLF+Pzzz8vsIjIz8vLC1OmTMGsWbM0bS4uLhg/fjxOnDghd0wyIu27ER4eHqhZs6ZCaYiopAq6IyGEUCgNEREZm+yFxPnz51G3bt1i7VuvXj2db7QaNmyIhIQEOaKRQrQLiebNm8PGhmsjEpmb5s2bS54nJSVxum8iIisi+6e3tLQ03Lt3r1j73r17F6mpqZK2p0+fwsXFRY5opBCOjyCyDNWqVYOXl5ekjd2biIish+yFhJ+fH+bMmYPc3Nwi98vNzcWcOXNQrVo1Sfu5c+dQoUIFOSOSEQkhdD5osJAgMk8FDbhmIUFEZD1kLyT69++PgwcPokOHDggLC0N6erpke1paGnbt2oX27dvj8OHDGDhwoGbb7du38d1336FWrVpyxyQjuXnzJh49eiRpYyFBZL60uzdx5iYiIush+6xNkydPxtatW3HixAm8/PLLAJ4NrHZ2dkZ6ejoSExMBPPumun79+vj4448BAL/88gvGjh2L7OxsriVhQbS7Nfn4+MDX11ehNCQXJycnjB49WqeNLE9BhQRXuCYisg6yFxKurq44ePAghg8fjj///BMAChw83b17d6xYsQKurq4Ank0L+8knnwAA+vTpI3dMMpKCxkfwA4flcXd3x8KFC5WOQUag3bUpJSUFN27cQI0aNRRKRERExiJ7IQEA3t7e2L17N8LDw7F9+3ZcunQJjx8/hoeHB+rVq4dXXnlF51utkJAQhISEGCMeGREHWhNZlqpVq8LHxwfx8fGatvDwcBYSRERWwCiFRJ7mzZvrFAxkPXJzc3HmzBlJGwsJIvOmUqnQvHlzhIWFadrCw8MxaNAgBVMREZExcPJ+MprLly8jLS1N0sZCgsj8FbQwHRERWT6j3pG4du0aNm/ejPPnzyMlJQVlypRBYGAg+vfvz5WNrYB2tyY/Pz94e3srlIaIDKWgAddqtZoLTRIRWTijFBI5OTkYP348Fi9eDLVaLdm2efNmfPnllxg9ejTmzJkDOzuj1jZkRNrfUrKbG5Fl0P63/OTJE1y/fh21a9dWKBERERmDUT61Dx06FBs3boQQAgBQrlw5uLi4ID09HY8ePUJubi4WLFiA+Ph4rFu3zhiRSAHa4yO0u0OQ5UhOTkbv3r0lbdu2bYOnp6cieUhelStXRsWKFXH//n1NW0REBAsJIiILJ/t95+3bt2PDhg1o1KgRNm3ahOTkZDx8+BC3b9/Gw4cPkZycjA0bNqBhw4bYuHEjduzYIXckUkBOTg7OnTsnaWMhYbmys7Nx+PBhySM7O1vpWCQj7bsSXOGaiMjyyV5I/Prrr2jZsiVOnjyJV199FR4eHpLtHh4e6N+/P/79918EBQXhl19+kTsSKeDq1avIyMiQtDVp0kShNERkaCwkiIisj+yFxOnTp/HRRx/B0dGxyP0cHR3x0Ucf6QzIJcugPT7C19eXA62JLIj2HcazZ88iNzdXoTRERGQMshcSSUlJ8Pf3L9a+AQEBSEpKkjcQKYLjI4gsm/a/6dTUVFy7dk2hNEREZAyyFxJly5bFrVu3irVvTEwMypYtK3MiUoJ2IdG0aVOFkhCRHCpVqoQqVapI2ti9iYjIssleSAQFBeG7775DVlZWkfs9ffoUs2bN4pSgFkitVuPs2bOSNt6RILI8XJiOiMi6yF5IjBgxAidPnkTbtm2xY8cOpKamSrY/efIEW7duRevWrXH69Gm8/fbbckciI7t+/brO3zvvSBBZHg64JiKyLrKvI9GnTx/07dsXf/zxB/r06QMA8PLygrOzM9LT05GYmAgAEEKgX79+OnPPk/nT/layUqVKqFixokJpiEgu2oVE3oBrW1tbhRIREZGcZL8jAQBr167FyJEjATwrGBISEjTrSOQtUvfOO+/g999/N0YcMjIOtCayDtr/ttPT03HlyhWF0hARkdyMsrK1g4MDlixZgvHjx2PTpk24cOECUlJSUKZMGTRq1Aj9+/dHnTp1jBGFFMCB1kTWwcfHB76+vrhz546mLTw8HPXr11cwFRERycUohUSeOnXq4PPPPzfmW5LChBAsJIisSPPmzXUKiWHDhimYiIiI5GKUrk3FlZ6ejiNHjigdgwzo5s2bSElJkbSxaxOR5eLMTURE1sOkCono6GiEhIQoHYMMSPtDhLe3t85c80RkObQHXJ87dw45OTkKpSEiIjkZtGvT7du3S/X6u3fvGigJmYqCBlqrVCqF0hCR3LTvSGRkZODSpUto1KiRQomIiEguBi0k/P39+SGRJLTvSHB8hHVwcHBAv379dNrI8nl5eaFatWqSL5bOnj3LQoKIyAIZfLB13nSuJcVCxHJwoLX1KlOmDDZt2qR0DFJI06ZNdQoJDrgmIrI8Bi8k9u7di5o1a5botVevXkW3bt0MnIiUcvv2bTx69EjSxoHWRJavSZMm2LZtm+b52bNnlQtDRESyMXghUblyZfj5+ZXotampqaW+o0GmQ7tbU9myZUt8bRCR+WjSpInk+blz56BWq2FjY1LzexARUSkZ9Lf6wYMHERAQUOLXBwQE4ODBgwZMZNqOHTuG1157Df7+/nBycoKzszNq1qyJt956C+fPn1c6XqkV1K2JXdeILJ92IfH48WNER0crlIaIiORi0EIiODgYzs7OJX69i4sLgoODDZjIdH311Vdo3749/vjjDwwbNgx79uzBzp070b17d6xcuRLNmjXD4sWLlY5ZKtp3JNiticg6VKlSBV5eXpI27S8WiIjI/PE+swIOHTqEqVOnAgBWrlyJqVOnIjg4GJ07d8a8efMwffp05ObmYuzYsbh8+bLCaUtGCMEZm4islEql0vn3znESRESWh4WEAn777TcAgI+PDwYNGqSz/YMPPoBKpUJOTg42btxo7HgGcffuXSQkJEjaeEfCeqSkpKB///6Sh/YK52TZtLs3sZAgIrI8Bh9sTc8XFxcH4Nm6GwVxdXWFl5cXEhIScP/+fSMmMxztuxEeHh6oXr26QmnI2LKysrB582ZJ26JFixRKQ0pgIUFEZPl4R0IBeTMXFVYkZGdna6ZNrVGjhtFyGZJ2f+gmTZpwxhYiK6JdSDx48AD37t1TKA0REcmBn+wUMHToUKhUKty+fRvHjx/X2b5x40bk5uaiXLlyGDp0qF7Hjo2NLfJhrP/IOdCayLrVqFEDbm5ukjbelSAisiwsJBTQrl07zJ8/H46Ojhg0aBC2bduGx48fIzExEatWrcLYsWNRq1Yt/PXXX/Dx8dHr2L6+vkU+WrRoIdNPJcUVrYmsm42NDRo3bixp48xNRESWhYWEQsaMGYOoqCi0aNECffr0QZkyZeDl5YURI0agT58+2LNnD4KCgpSOWSL379/H3bt3JW0sJIisD8dJEBFZNhYSxbB69WrY2dmV+LF3717J8dRqNebOnYvGjRvj6NGj+PHHH3H48GH8/fffmDp1KjZu3IgaNWrgiy++QG5url5Z79y5U+Tj1KlThjw1BdL+1tHV1RW1atWS/X2JyLSwkCAismyctakY1Gq13h/otV+f3/jx4/HTTz/B09MT58+fR6VKlTTbOnXqhF69eqFZs2b4+uuvkZWVhW+//bbY71W1atUS5zQU7UKicePGsLW1VSgNESlFu5CIjo5GcnIyPD09lQlEREQGxTsSxTB8+HAIIUr8eOmllzTHunPnDhYsWADg2XoR+YuIPI0aNcLgwYMBAHPnzkVqaqpxflAD4UBrIgKAevXqwd7eXtJ27tw5ZcIQEZHBsZAwsn///Vdzh6JRo0aF7hcYGAgAePr0KS5dumSUbIai/UFB+1tJIrIODg4OaNCggaSNA66JiCwHCwkjE0Jo/qxSqYq1n52d+fRAS05ORkxMjKSNhQSR9dKeaIHjJIiILAcLCSPL/+3c+fPnC93vwoULAABHR0fUrl1b9lyGov0z2dvbo27dugqlISKlccA1EZHlYiFhZHXr1kVISAgA4KeffipwgbgLFy5g/fr1AIA333wTrq6uRs1YGtrdmurXrw8HBwdlwhCR4rQLiStXriAjI0OhNEREZEgsJBSwdu1aNGrUCMnJyQgMDMTcuXNx9OhRHDhwADNmzEDbtm2RnZ2NLl26YM6cOUrH1Yt2IaG9IBURWZdGjRpJunHm5uYiMjJSwURERGQo5tP53oJUrFgR4eHhWLVqFTZv3oxZs2bh0aNHUKlU8PHxQefOnTFkyBC8+uqrRY6jMEUsJIgoPzc3N9SqVQtXr17VtJ09exYtWrRQMBURERkCCwmF2Nvb4+2338bbb7+tdBSDycrKwsWLFyVtLCSsk729PYKDg3XayDo1bdpUUkhw5iYiIsvAQoIM5vLly8jOzpa05U1jS9bF09MThw4dUjoGmYgmTZpg3bp1mucccE1EZBk4RoIMRrtbk7+/P1ewJSKdAdeRkZHIyclRKA0RERkKCwkyGI6PIKKCaBcSmZmZuHLlikJpiIjIUFhIkMGwkCCigpQvXx6+vr6SNnZvIiIyfywkyCCEECwkiKhQXOGaiMjysJAgg7h9+zaSk5MlbSwkiCiPdvcmztxERGT+OGsTGYT23QhPT09Uq1ZNmTCkuCdPnmDy5MmStm+//Rbu7u4KJSKlaRcS586dgxDC7NbKISKi/8NCggyioG5N/IBgvTIzM7Fo0SJJ21dffcVCwoppFxIpKSmIjo5G9erVFUpERESlxa5NZBAcH0FERalatSrKly8vaeM4CSIi88ZCggyChQQRFUWlUukMuNb+vUFEROaFhQSVWnJyMmJiYiRtLCSISJv2Svfnz59XKAkRERkCCwkqNe0PA/b29qhbt65CaYjIVLGQICKyLCwkqNS0uyfUr18fDg4OyoQhIpOlXUjcvn0bSUlJCqUhIqLSYiFBpcbxEURUHHXq1NH5kuHChQsKpSEiotJiIUGlxkKCiIrD3t4e9erVk7SxexMRkfliIUGlkpWVhYsXL0raWEgQUWE4ToKIyHKwkKBSuXz5MrKzsyVt2h8UiIjysJAgIrIcLCSoVLS7Nfn7+8PT01ORLERk+rQLiaioKOTk5CiUhoiISoOFBJUKx0cQkT4aNWokef706VNcu3ZNoTRERFQaLCSoVFhIEJE+vLy8ULlyZUkbZ24iIjJPLCSoxIQQLCSISG8cJ0FEZBnslA5A5uv27dtITk6WtLGQIACwtbXVmebT1tZWoTRkagIDA/Hnn39qnrOQICIyTywkqMS070Z4enqiWrVqyoQhk1KuXDmdaYGJ8vCOBBGRZWDXJiqxgro1qVQqZcIQkdnQLiTu3r2Lhw8fKpSGiIhKioUElRjHRxBRSdSsWRNOTk6SNt6VICIyPywkqMRYSBBRSdjZ2aFBgwaSNhYSRETmh4UElUhKSgpiYmIkbVzRmoiKi+MkiIjMHwsJKpHIyEjJczs7O9StW1ehNERkblhIEBGZP87aRCWivYBUnTp14OjoqFAaMjVpaWn4/vvvJW2TJk2Cq6urQonI1GivcH3p0iVkZWXBwcFBoURERKQvFhJUItqFhPaHArJu6enpmDp1qqRtzJgxLCRIQ/t3RnZ2Nq5cucLfJUREZoRdm6hEWEgQUWmULVtWZ90Z7d8rRERk2lhIkN7UajWioqIkbSwkiEhfHCdBRGTeWEiQ3m7duoUnT55I2lhIEJG+WEgQEZk3FhKkN+3uB+XKlUPlypUVSkNE5oqFBBGReWMhQXrTLiQaNmwIlUqlUBoiMlfahUR8fDzu37+vUBoiItIXCwnSGwdaE5EhvPDCCzozefGuBBGR+WAhQXpjIUFEhmBjY4OGDRtK2lhIEBGZDxYSpJf09HRcv35d0sZCgohKiuMkiIjMFwsJ0sulS5cghNA8V6lUqF+/voKJiMicaX8RwUKCiMh8sJAgvWh3a6pRowZXKyaiEtO+I3HlyhVkZmYqlIaIiPTBQoL0wvERRGRI2r9DcnNzcenSJYXSEBGRPlhIkF4KmvqViKik3N3dUb16dUmb9u8ZIiIyTSwkqNiEELwjQUQGxwHXRETmiYUEFdu9e/eQmJgoaWMhQQVRqVTw8vKSPLhoIRWGhQQRkXmyUzoAmY/IyEjJc1dXVwQEBCiUhkyZl5cXEhISlI5BZkL7C4nIyEgIIVh8EhGZON6RoGIraHyEjQ0vISIqHe2xVg8fPkR8fLxCaYiIqLj4KZCKjeMjiEgO1atXh7Ozs6RN+w4oERGZHhYSVGwsJIhIDjY2NjoLW7KQICIyfSwkqFiysrJw+fJlSRsLCSIyFO3uTSwkiIhMHwsJKparV68iOztb0tagQQOF0hCRpWEhQURkfjhrExWLdrcmX19flC1bVqE0ZOoyMjKwfPlySduIESN0+sET5dEuJC5evIjc3FzY2toqlIiIiJ6HhQQVi/a3g+zWREVJTU3F+++/L2kbMGAACwkqlHYhkZGRgZs3b6JmzZoKJSIioudh1yYqFg60JiI5VahQAd7e3pK2qKgohdIQEVFxsJCgYmEhQURy0x53xXESRESmjYUEPVdiYiLi4uIkbSwkiMjQOOCaiMi8sJCg59L+z9zBwQG1atVSKA0RWSoWEkRE5oWFBD2Xdrem+vXrw86O4/SJyLC0C4nr168jIyNDoTRERPQ8LCToubQLCe3/7ImIDEF7dWu1Wq2zECYREZkOFhKlpFarsXDhQnh4eEClUiEmJqbYr01JScGUKVNQt25duLi4wMvLC6GhoVi/fr18gUuAU78SkTG4ubmhevXqkjZ2byIiMl0sJErh4sWLaNeuHd5//308efJEr9f+999/aNiwIb799lv06dMHBw4cwOrVq6FWqzF48GC8/vrrUKvVMiUvvtzcXJ0pGFlIEJFctO94cgpYIiLTxUKihL788ks0bdoUtra2mDx5sl6vffr0KXr27Ik7d+5gzpw5mDlzJlq1aoXu3btj7969aN68OdasWYOvv/5apvTFd/PmTaSnp0vaWEgQkVw4BSwRkflgIVFCc+fOxY8//ogjR46gdu3aer12wYIFuHr1KipXroyxY8dKtjk4OGDatGkAgFmzZuHu3bsGy1wS2v+Je3t7o0KFCgqlISJLx5mbiIjMBwuJErp06RJGjx4NlUql92uXLVsGAOjduzdsbW11tnfp0gXu7u7IyMjAmjVrSp21NLS7FXCgNRHJSft3zN27d/Ho0SOF0hARUVFYSJRQlSpVSvS66OhozSwkQUFBBe5ja2uLJk2aAAB2795dsoAGol1IaHc7ICIypJo1a8LBwUHSxrsSRESmiYsBGFn+qVT9/f0L3c/f3x9HjhzRmXr1eWJjY4vcfufOHc2f792799zjnT17VvK8SpUqz30PosTERJ22u3fv4unTpwqkIXPzwgsvSKZ9PXLkCF544QUFExERGVf+z2g5OTkKJikaCwkju337tubP3t7ehe6Xty0pKQlpaWlwdXUt1vF9fX2LnaVFixbF3jfPxx9/jI8//ljv1xE1btxY6Qhkpr744gt88cUXSscgIlJEQkJCkV8+K4ldm4ws/zSxTk5Ohe6Xf9vjx49lzUREREREpunBgwdKRyiUVdyRWL16NUaMGFHi14eFhaFLly4GTPR8QgjNn/UZ0J2/61JBoqOj0aFDBwDAP//8o9cdDGt27949zR2cU6dOoVKlSgonMn08ZyXD86Y/nrOS4XnTH89ZyfC86e/OnTto06YNAKBOnToKpymcVRQSarUaubm5pXq9obi7u2v+nJmZWeh++fuS53/N81StWrXY+/r6+uq1Pz1TqVIlnjc98ZyVDM+b/njOSobnTX88ZyXD86a/onqwKM0qujYNHz4cQogSP1566SWDZalWrZrmzwkJCYXul7etbNmyxR4fQURERERkLFZRSJiS/KtCx8TEFLpf3jauIk1EREREpoiFhJEFBARo+rqFh4cXuE9ubq5m2tUePXoYLRsRERERUXGxkFDA22+/DQDYtm1bgeMv9u3bhydPnsDJyQmvvfaaseMRERERET0XCwkFvP/++6hVqxbi4uKwYMECybbs7GzNfOmTJ08u8QraRERERERysopZm+QQHx+P+Ph4AEBcXJym/dq1a0hNTQXwrBtTQQOlHR0dsXv3boSGhmLChAmIj49Hz549kZSUhO+++w6nT5/GkCFD8PnnnxvnhyEiIiIi0hMLiRJatGgRpk6dqtPetWtXzZ8PHjyIjh07Fvj6GjVqIDIyEt999x22bNmCOXPmwMXFBYGBgVi3bh0GDRokV3QiIiIiolJTifwrnxERERERERUDx0gQEREREZHeWEgQEREREZHeWEgQEREREZHeWEgQEREREZHeWEgQEREREZHeWEgQEREREZHeWEgQEREREZHeWEgQEREREZHeWEgQEREREZHeWEiYsadPn2LWrFlo0qQJ3N3d4enpidatW2Px4sVQq9WlOnZKSgqmTJmCunXrwsXFBV5eXggNDcX69esNlF45cpy3mJgYqFSq5z5mz55t4J/GONRqNRYuXAgPDw+oVCrExMQY7NiWfK3Jcd4s9VrLzs7Gli1b8MYbb6BOnTpwdXWFk5MTqlWrhldffRU7d+4s9XtY4rUm53mz5Gvt4MGD+Oijj9C6dWuUL18ednZ2cHd3R6NGjfDhhx/ixo0bpXoPS73W5DpvlnqtFaZfv36an6m0/y8ofq0JMksJCQmiYcOGAoAYNWqUOHr0qNi/f7/o06ePACBCQ0NFRkZGiY59/fp14evrK2xsbMQnn3wiTpw4IXbv3i2Cg4MFADFkyBCRm5tr4J/IOOQ6b9HR0QKAcHFxEa6uroU+5s2bJ8NPJa+oqCjRunVrAUDziI6ONsixLflak+u8WeK1dufOHVGlShUBQFSrVk3Mnz9fHD58WJw8eVLMnj1blC9fXgAQvXv3FpmZmSV6D0u81uQ+b5Z4rQkhxJtvvikACA8PDzFlyhSxd+9ecerUKbF582bRpUsXAUA4OjqKrVu3luj4lnitCSHvebPUa60gGzduNNj/C6ZwrbGQMFMdO3YUAMS4ceMk7Wq1WrzyyisCgBg+fLjex83MzBS1a9cWAMSPP/4o2fb06VPRvHlzAUB89dVXpUivHLnOW94vQUN9wDYVX3zxhXBwcBDt2rUTkydPNugHYku+1uQ8b5Z4rUVGRgoAokqVKiIxMVFn+/nz54WdnZ0AIMaMGaP38S31WpP7vFnitSaEEMOGDRMAxOHDhwvc3rNnTwFAeHp6ivT0dL2ObanXmhDynjdLvda0JSQkCB8fH+Hm5lbq/xdM5VpjIWGGNm/eLAAIJycnkZSUpLP90qVLAoBQqVQiPDxcr2PPnj1bABCVK1cWOTk5OtvDwsIEAOHs7Czi4uJK+iMoQs7zZqm/BD08PMTChQuFWq0WK1asMOgHYku+1uQ8b5Z4reV9IJ4zZ06h+wwdOlTzjeeTJ0/0Or6lXmtynzdLvNaEEGLKlCni5ZdfLnT7+vXrNf9mT506pdexLfVaE0Le82ap15q2QYMGiTJlyogZM2aU+v8FU7nWOEbCDP36668AgNDQUHh6eupsr1u3LurWrQshBJYvX67XsZctWwYA6N27N2xtbXW2d+nSBe7u7sjIyMCaNWv0D68gOc+bpbp06RJGjx4NlUpl8GNb8rUm53mzRF5eXpg4cSJeeeWVQvcJDAwE8GyM09WrV/U6vqVea3KfN0s1ffp07Nixo9Dtjo6Omj+7u7vrdWxLvdYAec+bNdi2bRvWr1+POXPmoHLlyqU+nqlcaywkzExWVhb2798PAAgKCip0v7xtu3fvLvaxo6Ojcfny5SKPbWtriyZNmuh9bKXJed4sWZUqVWQ5riVfa4B8581SVaxYEbNnz8YLL7xQ6D75/6N0c3Mr9rEt+VqT87xZs3Xr1gEA2rZtizp16hT7dZZ8rRVHSc+bNUhKSsJ7772HF198EW+99Vapj2dK1xoLCTNz+fJlZGdnAwD8/f0L3S9v261bt5CSklKsY1+4cEHn9UUdO//+pk7O85bfX3/9hV69eqF69epwdXVFxYoV0blzZ/z888/IzMwsSXSLZMnXmrFY27V2/fp1AEClSpVQo0aNYr/O2q+1kp63/KzhWktNTcXx48cxcOBAbNy4Eb1798bWrVv1OoY1XmuGOG/5Weq1Nm7cOKSmpuKXX34xyPFM6VpjIWFmbt++rfmzt7d3ofvl3xYbGyvLsZOSkpCWllasYytNzvOW3//+9z80bNgQy5cvx5EjR/Djjz8iJSUFo0ePRvPmzQ06bao5s+RrzVis6VrLycnBli1bAAATJ04s8DZ+Yaz5WivNecvPkq+1GzduwNbWFu7u7mjXrh3OnDmDzZs3Y+vWrUVeLwWxpmvNkOctP0u81nbv3o3ffvsN33zzDfz8/AxyTFO61uxkOSrJ5smTJ5o/Ozk5Fbpf/m2PHz+W9diurq7FOr6S5Dxvea8LDQ3Fjz/+iEaNGmnamzVrhldffRUvvfQSDh48iO7du+Ps2bOSvqTWyJKvNblZ47W2bNkyPHjwAC1atMAHH3yg12ut+VorzXkDrONa8/X1xfnz55GRkYGrV69i6dKl6NevHzp37oxly5ahWrVqxT6WNV1rhjxvgOVeaykpKXjnnXfQrl07jBkzxmDHNaVrjXckLJQQQvNnQw/4lPPYSivpz1axYkXs379f8gswj4ODA+bOnQvgWRerFStWlDqntbDka62krO1au3btGiZNmgQfHx+sX78e9vb2sryPpV1rhjhv1nCtOTg4oEGDBggKCsLrr7+Ow4cP46233sLff/+Ndu3aISEhweDvaQnXmqHPm6VeaxMnTkRiYiKWLVumyN+1Ma41FhJmJv9MCEX1F3z69GmBr1Hq2EpT+mdr1KiRZpaGXbt2Gey45krpvw9LZknX2oMHD9CjRw/Y2tpi7969CAgI0PsY1nitGeK8FYclXWt5VCoVfvjhB7i6uuLOnTuYPn16sV9rjddantKct+Iwx2tt7969WLZsGaZNm4ZatWoZ9NimdK2xkDAz+W8XFlXx599WtWpVWY5dtmxZs7klK+d50zdDdHS0QY9rjiz5WjMFlnCt3b9/H6GhoUhMTMTevXs105jqy9quNUOdt+KyhGtNm4eHB1q1agUARU53qs3arjVtJT1vxWVO19qTJ08wcuRIBAUFYcKECQY/vildaxwjYWbq1q0Le3t7ZGdnFznoKG+bn58fypQpU6xj57+lWJxjF3QL0lTJed6KK/8tRmtnydeaKTD3ay02NhadOnXCo0ePcODAATRu3LjEx7Kma82Q5624zP1aK0yFChUAAHFxccV+jTVda4UpyXkrLnO61iIiInD79m3ExsYWOJ4j/8+Sfza1YcOGadaHKIopXWu8I2FmHBwc0KlTJwBAeHh4ofudPn0aANCjR49iHzsgIEAz93Nhx87NzcXZs2f1PrbS5DxvwLMFYZ43T3PeLAtFTdVmLSz5WpObpV9rMTEx6NChA548eYLDhw/rfBiOiYlBampqsY9nLdeaoc8bYJnXWlxcHOrUqYNjx44VuV/e9N/6fKFkydeanOcNsLxrLSgoCJGRkTh//jzOnTun85g2bZpm37CwsALbi2JS15psa2aTbDZv3iwACCcnJ5GcnKyz/fLlywKAUKlUIjw8XK9j5y25XqVKFZGbm6uz/c8//9S8d2xsbIl/BiXIed4AiDFjxhS6/ezZswKAACAWLlyod3ZTsGLFCs3PEB0dXerjWfK1lp+hz5slX2vXrl0Tvr6+wtfXV1y7dq3AfQCIFStW6HVcS7/W5DpvlnitRUdHCwDi+++/L3Sf9PR0Ua5cOQFAvPzyy3od31KvNbnPmyVea0UxxP8LpnKtsZAwUx07dhQAxPjx4yXtarVa9OnTRwAQw4cP13ndjh07hJeXl6hfv36BF29mZqaoVauWACDmzZsn2ZaVlSWCgoIEAPHVV18Z9OcxFrnOGwDh5uYm/vvvP51tmZmZIjg4WAAQNWrUEBkZGQb7eYxJ31981n6t5TH0ebPUa+3ixYuiUqVKonr16iImJqbQ/Qr6QGzN15qc580Sr7W8D8RVq1YV9+/fL3CfcePGab5UOnLkiGSbtV5rcp83S7zWilKc/xfM5VpjIWGmEhISRMOGDQUA8e6774pjx46JAwcOiFdffVUAEKGhoQX+Y+vZs6fm4p0zZ06Bx75+/brw9fUVtra2YsqUKeLEiRMiLCxM8yF8yJAhBVa/5kCu8+bu7i4AiLJly4rp06eLvXv3ivDwcPHbb7+JJk2aCACidu3a4vr168b4MQ3mwYMHIjIyUkRGRorp06drzsGePXs07ampqQW+1pqvNTnPmyVea//995/w9vYWAISDg4NwdXUt9FHQB2JrvdbkPm+WeK3FxcUJR0dHAUCUK1dOTJs2Tfz555/izJkz4o8//hDdunUTAISjo6P49ddfdV5vrdea3OfNEq81bampqc/9fyE/c7nWWEiYsczMTPHtt9+KwMBA4erqKjw8PETLli3FokWLCr1wduzYIf5fe/ceFFXZxwH8i+zislzEGyqpoLaISV5mUDN1EE2ZyswbNlQOOJmCiTYqac44mOMl3xzSmVQ0AQ01TIzBNC0voEaZU021NDWYkRhoJroBKgr4e//g3fOy7C5w1uWm38/MmbHznH1u56HZ39nnOU/nzp3liSeeqPfpqMlkkhUrVkhQUJDodDrp1KmThIWFyccff9xErWk+TdFv5eXlkpaWJq+88ooEBQWJh4eHaDQa6dq1q4wfP162bNkit2/fbuKWOV9CQoLyPzJ7R3Z2ts3PPspjrSn77WEca5mZmQ32V+3D1pP1R3GsNXW/PYxjTUTk+vXrkpSUJNOnTxeDwSB6vV5cXV3Fx8dHQkJCJD4+3uaTcZFHd6yJNG2/Paxjrbbs7OwG/0ZraytjzUWkDS2DJyIiIiKiVoFvbSIiIiIiItUYSBARERERkWoMJIiIiIiISDUGEkREREREpBoDCSIiIiIiUo2BBBERERERqcZAgoiIiIiIVGMgQUREREREqjGQICIiIiIi1RhIEBERERGRagwkiIiIiIhINQYSRERERESkGgMJIiIiIiJSjYEEERERERGpxkCCiIiIiIhUYyBBRERERESqMZAgIiJyUEBAAFxcXJRj1apVDuclIpg2bRq6dOmC06dPO6+SAMaOHWtRz+jo6GYtn4geTgwkiIjIgvnLZk5OTktXpdX78ssvYTQaERISUu915oBj165ddq+5ceMGMjMzUVJSgoyMDKfWMzU1FUajES+++GKLlE9EDydNS1eAiIiorQoMDAQAeHh4PHBenTt3xvLly3HmzBnExMQ8cH619enTBwDg4+PTIuUT0cOJgQQREVErsX79+ke6fCJqWzi1iYiIiIiIVGMgQURUi4ggIyMDzz77LLp27Qo3Nzf4+voiPDwcH330Eaqrq5Vrd+3aZbGA1cXFBQBw+PBhTJgwAV26dFHOjx071qH62FrMe/PmTSxZsgQGgwE6nc5qTUNpaSnS09MRGRmJ4OBgeHt7Q6/XIzAwELGxsfj9999tllW7DQAQFhbW4ELi/Px8zJ07F3379oVOp4O3tzeGDBmCt99+G1evXnWozbWtWrXKog4BAQEAgKSkJAwaNAju7u7o1asXYmJi8PfffwOouYdbt25FcHAwdDodevTogdjYWJhMJou8KyoqrO5f3XUhQUFBFun1rXGwxXz/Ll26BACYPXu2zUXP0dHRFufrjhdbi6WvXr2KBQsWoF+/ftDpdOjUqROee+45hxZKN1R+bcXFxVi8eDGCgoKg1+vh6emJAQMGIC4uDhcvXrTbB2rGMBG1DQwkiIj+5+7du4iIiEBERARKS0uxZcsWnD17Fps2bUJJSQmioqIQHh6O27dvAwCmTJkCo9GIlJQUJY/ExESsW7cO8+fPx7Fjx5CYmGjx5Vytuot5r1+/jtGjR8PLywtpaWn4/PPPMWLECIvPJCYmIjIyEt9++y0WLVqE48eP49ChQ5g5cybS0tIwZMgQZGdnW5VlNBphNBqV/05JSVHOGY1GzJ8/3+L6vXv34sknn8SBAwcQFxeHkydPIj09HaNGjcKGDRsQHByMr7/+2uG2A8D8+fNhNBqxZs0a5dyCBQuQl5eHpKQkHDp0CCEhIdi+fTvCwsJQXl6OxYsX48qVK0hJSUFGRgYCAgKQlJSEyZMnQ0SUfNq3b2/V5ro+++wzGI1G+Pn5OVR/8/0zf37NmjUWfbp27VoAwNq1a2E0GhEbG2szn7qLpQsLCxESEoJ27dohJSUFZ86cwdKlS3H69GmEhYXhgw8+UFXPhso3O3nyJAYMGIBt27YhMjISR48eRWZmJqZOnYodO3Zg0KBByMzMtNkHasYwEbURQkREIiISExMjAGTMmDFSVVVlkVZZWSlDhgwRADJv3jyLtOzsbAEgAGT48OFSUVFhkT5hwgQJDQ19oLqFhoYKAHF1dZUDBw5YpOXm5goAyc7OFhGRhIQE0ev1cvnyZat8jh07JgDEz89P7ty5Y7Msc1vM+dny1VdfiUajEa1WK3l5eVbpGzduFADi6+srJpOp8Q21IzU1VWl/TEyMRVp1dbX069dPAMjMmTPl3XfftUg3mUzi6ekpAOTkyZM282+ozf7+/gJAUlNTbaab709CQoJDnzdLSEgQAHbHS1RUlFLXuu0UEfn000+Vfjp37pzdz0dFRaku/8KFC+Ll5SUA5IsvvrBKz8jIEACi1+vl4sWLVulqxjARtQ38RYKICMBvv/2G7du3A6h5Ouvq6mqRrtFoEB8fD6DmSb15Gk1d8fHxaN++vcW5ffv2IT093Sn1DAoKwowZMyzODR8+HAUFBXjqqacA1EyD2bp1K3r27Gn1+fDwcAQEBKC4uBinTp1yuB5Lly5FVVUVZs2ahYEDB1qlx8XFwcPDA9euXUNycrLD5dRVXV2NRYsWWZxr164dxo0bBwDIzMy0eqreoUMH5Yn3mTNnnFaXluTt7Y0333zT6vzUqVMxePBgVFdXY/Xq1U4tc+XKlSgrK8O4ceMwceJEq/Tp06cjMDAQt2/fxqZNm+zm05gxTERtAwMJIiIABw4cgIhAp9PZnWYRFBQEAKisrLT7hXT06NFW57p06YLu3bs7pZ628tdoNAgICIBOpwNQE0hERUXZzcPf3x8A8OuvvzpUh8uXL+PcuXNKWba4ubmhb9++AGqmwziLh4eHch9q8/X1BQAYDAZ4e3tbpZv7/8qVK06rS0saNmyYVcBqNn78eADA8ePHUVFR4ZTy7t27h6ysLAD27zkA9O/fH0D997wxY5iI2gYGEkREAH766ScANQtw9Xo9NBqN1TF8+HDl+sLCQpv5dO3atUnr2dj8z549i1mzZsFgMMDLywtarVZph3kxbnl5uUN1MPcVULNI11ZfaTQaZe2Bvb5yRKdOnWye12hq3mbeuXPnetPv3r3rtLq0pPoCU/OC9MrKSly4cMEp5eXn5+POnTsAahbA27vnhw8fBlD/PW/qvxEiaj7cR4KICMC///4LAOjWrRtOnDjR4PXdunWzeb7ulChna0z+q1atwjvvvAOdToeFCxdi/Pjx6NGjh7Loe/bs2fjuu+8sFh6rYe4rANixY0eDC2Xd3NwcKseWdu3qf/7VUPrDwhwY2aLX65V/l5aWOqW82vc8ISEB06ZNq/f6+l4w0NR/I0TUfBhIEBGhZh49UPOLRHBwcAvXxnE//vijMjd+8+bNmDt3rtU1D7oLs7mvgJpfANpyfzVWVVVVS1fBQn31Mb9VDIDNaV6OqH3Pvb29H4l7TkQNezQe3RARNWDw4MEAap681rf/wfnz57Fz585WO9f+1KlTyi8NU6dObZIyzH0F1CxSt8dkMmHnzp1tYoGz+VcTW1OfRAQ3btxo7irVy95ifwD4888/AQBarRYGg8Ep5RkMBri7uwOo/55XVVUhOTkZR44ccUq5RNS6MZAgIgIQERGhTIsxz/O2JTY2FgsXLnzgp/pN5f79+8q/7U1dMn/RtMc89aT2541GI9LT01FRUYFevXph5MiRAOrvq7S0NLz++uvIz89vbPVbjHnNQXFxsVWa0WhU1gc4yjwVqXaf/vHHH0hPT0dJSYnq/M6fP293vYd5at7EiROdtni5ffv2mDJlCgDg6NGjFhsz1nb06FHMmTMH33zzjVPKJaLWjYEEERFq3sgUExMDoOb1r7a+3KWkpOCHH35AXFyc06aMONuYMWOUf+/Zs8cq/ciRI8ouy/aYv1TXfgq/efNmzJo1S/lCvHHjRmg0GuTm5uLgwYNWeRQXF2PdunXw9/fHq6++6lBbmtOoUaMAwOYrcd97770Hntdvq0/37duHyMhIlJWVqc6vvLzc5itWMzMz8fPPP8PV1RUrV650uL62rF69Gt7e3igsLLRZdnl5OZYvX44OHTpgwYIFTi2biFonrpEgIvqf999/HyUlJdi/fz9GjBiBFStWYPDgwbh+/TqysrKwY8cOhIeHK2sQbt26hYKCAhQUFCh55OXlAQA6duyIxx577IHrVFBQgFu3buHWrVsAgGvXrill9O/fH1qt1uL6ESNG4LXXXkNycjKWL1+OoqIiPP/889BqtcjOzsZ//vMfeHp6ory8XMnL19dXeX0qAEyePBnbtm3Dli1b4Ofnh8LCQnzyySeYNGmSEkg8/fTT2LNnD6Kjo/Hyyy9j0aJFeOGFF6DRaPD9999j/fr1qKysxOHDhx/oqbjJZMJff/2FoqIiADVvIsrLy4ObmxsCAwNx7do15QBq7kleXp7S/0VFRbh58yZMJpOSX+10s/j4eBw8eBB79+6Fr6+vsoN0amoqOnbsiJ49e+LSpUsoKiqy6LP8/Hzcu3fP6v7UzX/y5MnIzc3F7t27MWzYMJSVlSEpKQlDhw5F7969lXrWbYe5nXVNnz4d+/fvR1FREWbMmAF3d3ecOHFC2QF806ZNFovgzeOobj94eHigT58+jSr/8ccfR1ZWFqZNm4b4+Hjk5+fjpZdegqenJ3755Rds2LABhYWFOHjwoMVbpdSOYSJqQ1pwMzwiolYpKytLJk2aJL6+vqLRaMTHx0dCQ0MlOTlZqqurletq72hd97C3c7Ba5t2AbR0FBQU2P3P//n1JTk6WkSNHiqenp2i1WvHz85OIiAjJzc21yrPubsylpaUyd+5c6d69u2i1WunVq5fMmTNH/vnnH6uyLl68KG+88YYYDAbR6XSi0+lkwIABsmTJErly5coDt9+8o3Xdw9/fX0T+vxOzvf6vvRN0Q/cnNzdXnnnmGfHy8hJ3d3cZOnSofPjhhyLy/52p6/ZZ3fP28r93754sW7ZMevfuLRqNRnr06CEzZ85U7qG9eprbaVZ7Z+qysjJ56623lL738fGR8PBwycnJsWqbvXFk3sG6seWLiFy9elWWLVsmAwcOFL1eL25ubtKvXz+ZN2+eXLhwodFl1zeGiahtcBFx8P1/RERE1Kyio6Oxe/duREVFYdeuXS1dHSJ6xHGNBBERERERqcZAgoiIiIiIVONiayIiolauocXSREQtgWskiIiIWrmxY8fi9OnTVudDQ0ORk5PT/BUiIgKnNhERNYucnBy4uLioOh62L4gBAQGq2h8dHd3SVW41cnJyICJWx8M2RoiobeEvEkREzcC854Qaffr0abU7aDvCvOdCYzlrLw4iImoaDCSIiIiIiEg1Tm0iIiIiIiLVGEgQEREREZFqDCSIiIiIiEg1BhJERERERKQaAwkiIiIiIlKNgQQREREREanGQIKIiIiIiFRjIEFERERERKoxkCAiIiIiItUYSBARERERkWoMJIiIiIiISDUGEkREREREpBoDCSIiIiIiUo2BBBERERERqcZAgoiIiIiIVGMgQUREREREqjGQICIiIiIi1f4L0zz+me+8NQwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 4, 100)\n",
    "# y = model.likelihood_list[1].constraint_terms[\"er_rate_multiplier\"](x)\n",
    "y = model.likelihood_list[1].constraint_functions[\"er_rate_multiplier\"].logpdf(x)\n",
    "plt.axvline(nominal_er, c=\"k\", ls=\"--\", label=\"nominal rate\")\n",
    "plt.plot(x, y, c=\"k\", label=\"constraint term\")\n",
    "\n",
    "# Cosmetics\n",
    "plt.xlabel(\"er_rate_multiplier\")\n",
    "plt.ylabel(\"log ancillary likelihood\")\n",
    "plt.xlim(0, 4)\n",
    "plt.ylim(-10, 2)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In each generated toy, the ancillary measurement is varied according to the uncertainty (i.e. in this case measurements are drawn from the normal distribution with mean 1 and standard deviation 0.2). This then shifts the constraint term to the \"measured\" value. We can illustrate this by artificially assuming a measurement of 3.0 for the ancillary measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set ancillary measurement of er_rate_multiplier to 3\n",
    "copied_data[\"ancillary\"][\"er_rate_multiplier\"][0] = 3.0\n",
    "\n",
    "# Assign data with nominal ancillary measurement\n",
    "model.data = copied_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 2.0)"
      ]
     },
     "execution_count": 15,
     "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": [
    "x = np.linspace(0, 4, 100)\n",
    "y = model.likelihood_list[1].constraint_terms[\"er_rate_multiplier\"](x)\n",
    "plt.axvline(nominal_er, c=\"k\", ls=\"--\", label=\"nominal ancillary measurement\")\n",
    "plt.axvline(3.0, c=\"crimson\", ls=\"--\", label=\"'wrong' ancillary measurement\")\n",
    "plt.plot(x, y, c=\"crimson\", label=\"constraint term\")\n",
    "\n",
    "# Cosmetics\n",
    "plt.legend()\n",
    "plt.xlabel(\"er_rate_multiplier\")\n",
    "plt.ylabel(\"log ancillary likelihood\")\n",
    "plt.xlim(0, 4)\n",
    "plt.ylim(-10, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try fitting this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wimp_mass': 50.0,\n",
       " 'livetime': 2.0,\n",
       " 'wimp_rate_multiplier': 0.8276914651286785,\n",
       " 'er_rate_multiplier': 1.0992224805878552,\n",
       " 'er_band_shift': -0.10269293086202387}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_fit_anc, max_ll_ancc = model.fit()\n",
    "best_fit_anc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 3089 </td>\n",
       "        <td style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 69 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.27e-07 (Goal: 0.0001) </td>\n",
       "        <td style=\"text-align:center\" title=\"Total run time of algorithms\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> No parameters at limit </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance accurate </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> wimp_mass </td>\n",
       "        <td> 50.0 </td>\n",
       "        <td> 0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> livetime </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> wimp_rate_multiplier </td>\n",
       "        <td> 0.83 </td>\n",
       "        <td> 0.25 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> er_rate_multiplier </td>\n",
       "        <td> 1.10 </td>\n",
       "        <td> 0.05 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> er_band_shift </td>\n",
       "        <td> -0.10 </td>\n",
       "        <td> 0.07 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> -2 </td>\n",
       "        <td> 2 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> wimp_mass </th>\n",
       "        <th> livetime </th>\n",
       "        <th> wimp_rate_multiplier </th>\n",
       "        <th> er_rate_multiplier </th>\n",
       "        <th> er_band_shift </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> wimp_mass </th>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> livetime </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> wimp_rate_multiplier </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.00 </td>\n",
       "        <td> 0.0636 </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.0012 <strong>(-0.085)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,234,234);color:black\"> 0.002 <strong>(0.106)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> er_rate_multiplier </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.0012 <strong>(-0.085)</strong> </td>\n",
       "        <td> 0.00295 </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0.0001 <strong>(-0.026)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> er_band_shift </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,234,234);color:black\"> 0.002 <strong>(0.106)</strong> </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0.0001 <strong>(-0.026)</strong> </td>\n",
       "        <td> 0.00555 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 3089                       │              Nfcn = 69               │\n",
       "│ EDM = 2.27e-07 (Goal: 0.0001)    │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │   Below EDM threshold (goal x 10)    │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│      No parameters at limit      │           Below call limit           │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│             Hesse ok             │         Covariance accurate          │\n",
       "└──────────────────────────────────┴──────────────────────────────────────┘\n",
       "┌───┬──────────────────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name                 │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────────────────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ wimp_mass            │   50.0    │    0.5    │            │            │         │         │  yes  │\n",
       "│ 1 │ livetime             │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 2 │ wimp_rate_multiplier │   0.83    │   0.25    │            │            │    0    │         │       │\n",
       "│ 3 │ er_rate_multiplier   │   1.10    │   0.05    │            │            │    0    │         │       │\n",
       "│ 4 │ er_band_shift        │   -0.10   │   0.07    │            │            │   -2    │    2    │       │\n",
       "└───┴──────────────────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
       "│                      │            wimp_mass             livetime wimp_rate_multiplier   er_rate_multiplier        er_band_shift │\n",
       "├──────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│            wimp_mass │                    0                    0                 0.00                0.000                0.000 │\n",
       "│             livetime │                    0                    0                 0.00                0.000                0.000 │\n",
       "│ wimp_rate_multiplier │                 0.00                 0.00               0.0636              -0.0012                0.002 │\n",
       "│   er_rate_multiplier │                0.000                0.000              -0.0012              0.00295              -0.0001 │\n",
       "│        er_band_shift │                0.000                0.000                0.002              -0.0001              0.00555 │\n",
       "└──────────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.minuit_object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statistics of ER events in the science data set is sufficiently large to pull the ER rate parameter to a value that describes the science data well. Though we can see that due to the constraint term the parameter is pulled towards a larger value compared to the initial fit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we continue, let's revert to our originally generated data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.data = data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Constructing confidence intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data and know how to fit the model to it (with and without constraint), we can construct a confidence interval on our parameter of interest (POI), which is the `wimp_rate_multiplier` in this case.\n",
    "\n",
    "If we assume that a WIMP rate multiplier of 1 corresponds to a WIMP-nucleon cross-section of $10^{-45}\\;\\mathrm{cm^2}$, we can directly convert this into a confidence interval on the cross-section.\n",
    "\n",
    "Let's first see how we can compute the confidence interval with the `confidence_interval` method and then replicate the construction of the confidence interval by hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_xsec = 1e-45  # cm^2\n",
    "confidence_level = 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% CL confidence interval for WIMP-nucleon cross-section: [5.15e-46, 1.39e-45] cm2\n"
     ]
    }
   ],
   "source": [
    "lower_limit, upper_limit = model.confidence_interval(\n",
    "    poi_name=\"wimp_rate_multiplier\", confidence_level=confidence_level\n",
    ")\n",
    "lower_limit *= ref_xsec\n",
    "upper_limit *= ref_xsec\n",
    "\n",
    "print(\n",
    "    f\"90% CL confidence interval for WIMP-nucleon cross-section: [{lower_limit:.2e}, {upper_limit:.2e}] cm2\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a profile likelihood ratio test statistic to construct our confidence interval. This is defined as:  \n",
    "\n",
    "$q(s)\\equiv -2 \\cdot \\ln\\frac{\\mathcal{L}(s, \\hat{\\hat{\\boldsymbol{\\theta}}})}{\\mathcal{L}(\\hat{s}, \\hat{{\\boldsymbol{\\theta}}})}$,  \n",
    "\n",
    "where the likelihood $\\mathcal{L}$ is a function of the WIMP rate multiplier $s\\geq 0$ and a set of nuisance parameters $\\boldsymbol{\\theta}$, which in our case are the ER rate and ER band shift parameters.\n",
    "The single hat denotes the global maximum likelihood estimator of a parameter, while the double hat corresponds to the nuisance parameters that maximize the likelihood under the constraint that $s$ is fixed to a certain value.\n",
    "\n",
    "Let's start by computing the test statistic for a few values of $s$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unconstrained fit\n",
    "best_fit, ll_val = model.fit()\n",
    "\n",
    "# constrained fits\n",
    "s_vals = np.linspace(0, 2, 200)\n",
    "ll_vals_c = []\n",
    "for s in s_vals:\n",
    "    _, ll_val_c = model.fit(wimp_rate_multiplier=s)\n",
    "    ll_vals_c.append(ll_val_c)\n",
    "ll_vals_c = np.array(ll_vals_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$q(s)$')"
      ]
     },
     "execution_count": 22,
     "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": [
    "# plot the profile-likelihood scan\n",
    "plt.plot(s_vals, 2 * (ll_val - ll_vals_c))\n",
    "plt.axvline(best_fit[\"wimp_rate_multiplier\"], label=\"best fit\", ls=\":\", color=\"crimson\")\n",
    "\n",
    "plt.legend()\n",
    "plt.xlim(0)\n",
    "plt.ylim(0, 4)\n",
    "plt.xlabel(\"$s$\")\n",
    "plt.ylabel(\"$q(s)$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain the 90% CL confidence interval we use the fact that in the asymptotic limit, the distribution of $q(s)$ will follow a chi-square distribution with one degree of freedom (for more details refer to [this paper](https://arxiv.org/pdf/1007.1727.pdf) from Cowan et al.). Thus, the critical region for the 90% CL interval is given by the value at which the cumulative distribution function of the chi-square distribution is equal to 0.9. This value is given by the `chi2.ppf` function in `scipy.stats`.\n",
    "\n",
    "We also apply the linear conversion from the signal rate multiplier $s$ to cross-section $\\sigma$ here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$q(\\\\sigma)$')"
      ]
     },
     "execution_count": 23,
     "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": [
    "# plot the profile-likelihood scan\n",
    "\n",
    "plt.plot(s_vals * ref_xsec, 2 * (ll_val - ll_vals_c))\n",
    "plt.axhline(\n",
    "    stats.chi2(1).ppf(confidence_level),\n",
    "    label=\"Asymptotic critical value (90% CL)\",\n",
    "    c=\"orange\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "plt.axvline(lower_limit, c=\"orange\", label=\"Confidence interval\")\n",
    "plt.axvline(upper_limit, c=\"orange\")\n",
    "\n",
    "# Cosmetics\n",
    "plt.legend()\n",
    "plt.xlim(lower_limit * 0.5, upper_limit * 1.5)\n",
    "plt.semilogx()\n",
    "plt.ylim(0, 4)\n",
    "plt.xlabel(\"$\\sigma$\")\n",
    "plt.ylabel(\"$q(\\sigma)$\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "binf_env",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}