{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41a68d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from iminuit import Minuit\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Using the lognormal from numba_stats\n",
    "# instead of scipy_stats speeds things\n",
    "# up by a big factor.\n",
    "# Be careful however because the call sequences\n",
    "# are not exactly the sameand not all\n",
    "# functions are implemented.\n",
    "# from scipy.stats import lognorm\n",
    "from numba_stats import lognorm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181ae8c9",
   "metadata": {},
   "source": [
    "### This is fake data with 3 point\n",
    "We are going to fit Data = mu*S + B with some uncertainty on B <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "592d317b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is some fake data in 3 bins\n",
    "data = np.array([12, 7, 4])\n",
    "sig  = np.array([ 2,  2, 2])  # signal predicted with mu=1\n",
    "bg   = np.array([10,  5, 2])  # bg predicted\n",
    "err  = 0.2*bg                 # bg uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "face4bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check that lognormal makese sense, compare with gaussian\n",
    "color = ('red', 'green', 'blue')\n",
    "ax=plt.subplot(111)\n",
    "for b,e,c in zip(bg, err, color):\n",
    "    x = np.linspace(0.01, 20., 400)\n",
    "    ax.plot(x, lognorm.pdf(x, np.log(1+e/b), 0, scale=b), color=c, \n",
    "            label='numba logn  $\\mu$ = '+str(b) + '   $\\sigma$ = ' + str(e))\n",
    "    ax.plot(x, norm.pdf(x,    scale=e, loc=b), color=c, linestyle='dashed', \n",
    "            label='gaus  $\\mu$ = '+str(b) + '   $\\sigma$ = ' + str(e))\n",
    "\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "ax.set_xlim([0, 20.])\n",
    "_ = ax.set_ylim(0,1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "133fd0c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot it\n",
    "x = np.array([1.,2.,3.])\n",
    "b = np.array([0.5, 1.5, 2.5, 3.5, 4.5])\n",
    "ax = plt.subplot(111)\n",
    "ax.hist(x, b, weights=bg,  alpha=0.1, color='blue', label='BG', histtype='stepfilled')\n",
    "\n",
    "n1,b1,p1 = ax.hist(x, b, weights=bg+err, histtype='stepfilled',\n",
    "                   facecolor='none', edgecolor='none', linestyle='--')\n",
    "n2,b2,p2 = ax.hist(x, b, weights=bg-err, histtype='stepfilled', \n",
    "                   facecolor='none', edgecolor='none', linestyle='--')\n",
    "ax.bar(x=b1[:-1], height=n2-n1, bottom=n1, width=np.diff(b1),\n",
    "       align='edge', linewidth=0, edgecolor='blue', color='none', zorder=-1, \n",
    "       label='bg uncertainty', hatch='\\\\\\\\\\\\')\n",
    "\n",
    "ax.hist(x, b, weights=sig, color='red', label='sig $\\mu$=1', histtype='step',\n",
    "        linestyle='dotted')\n",
    "ax.errorbar(x, data, yerr=np.sqrt(data), fmt='o', color='black', label='data')\n",
    "# ax.bar(x, sig, align='center',width=1, color='None', edgecolor='red', label='sig ($\\mu$=1)')\n",
    "\n",
    "ax.set_xlim((0.5, 3.5))\n",
    "ax.legend()\n",
    "_ = ax.set_ylim((0,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2214450",
   "metadata": {},
   "outputs": [],
   "source": [
    "class myFit:\n",
    "    \"\"\"\n",
    "    Simplest example of wrapper around a minuit function\n",
    "    NLL fit to bins of data = mu*signal + background\n",
    "    including sigma of background (absolute)\n",
    "    \n",
    "    Sample usage:\n",
    "    Example of 3 bins, np.arrays are to be passed.\n",
    "    The fit parameters ordered for pinit are\n",
    "       mu, 1st bin bg, 2bd bin bg, 3rd bin bg\n",
    "    \n",
    "    f = myFit(data, signal, background, sigma)\n",
    "    m = Minuit(f.myNLL, pinit, name=(\"mu\", \"b1\", \"b2\", 'b3'))\n",
    "    m.errordef = Minuit.LIKELIHOOD\n",
    "    m.simplex()\n",
    "    m.migrad()\n",
    "    m.minos()\n",
    "    \n",
    "    After defining \"f\" can change data members as\n",
    "    f.d  = newData\n",
    "    f.b  = newBG\n",
    "    f.s  = newSignal\n",
    "    f.eb = newBGsigma\n",
    "    \"\"\"\n",
    "    def __init__(self, d, s, b, eb):\n",
    "        self.d     = d\n",
    "        self.b     = b\n",
    "        self.eb    = eb\n",
    "        self.s     = s\n",
    "        self.blah  = np.log(1+eb/b)\n",
    "\n",
    "    # This should really protect against logs of negative numbers\n",
    "    def myNLL(self, p):\n",
    "        mu = p[0]\n",
    "        fitbg = p[1:]\n",
    "        temp1 = (-self.d * np.log(mu*self.s + fitbg) + mu*self.s + fitbg).sum()\n",
    "        temp2 = 0\n",
    "        for bb,ee,fit in zip(self.b, self.eb, fitbg):\n",
    "            # pdf = lognorm.pdf(fit, np.log(1+ee/bb), 0, scale=bb)\n",
    "            # temp2 = temp2 - np.log(pdf)\n",
    "            # My pull request to add lognorm.logpdf \n",
    "            # https://github.com/HDembinski/numba-stats/pull/31\n",
    "            # is now included, so let's use it (faster and better)\n",
    "            temp2 = temp2 - lognorm.logpdf(fit, np.log(1+ee/bb), 0, scale=bb)\n",
    "        return temp1+temp2\n",
    "        \n",
    "        # return temp1 - (np.log(lognorm.pdf(fitbg, self.blah, 0, self.b))).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ba40106",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minuit.LIKELIHOOD =  0.5\n"
     ]
    }
   ],
   "source": [
    "# Initialize a myFit object that contains the\n",
    "# data, the signal model (mu=1), the pre-fit bg\n",
    "# prediction, its uncertainty, as well as the\n",
    "# negative log-likelihood function\n",
    "thisFit = myFit(data, sig, bg, err)\n",
    "\n",
    "# Note: 1 sigma in a negative-log-likelihood fit \n",
    "# corresponds to a change of 0.5.\n",
    "# Check the Minuit.LIKELIHOOD value (trust but verify)\n",
    "print(\"Minuit.LIKELIHOOD = \", Minuit.LIKELIHOOD)\n",
    "\n",
    "\n",
    "# initialize a Minuit object with pinit as the initial guess\n",
    "pinit     = np.array([1, bg[0], bg[1], bg[2]])\n",
    "mData = Minuit(thisFit.myNLL, pinit, name=(\"mu\", \"b1\", \"b2\", 'b3'))\n",
    "mData.errordef = Minuit.LIKELIHOOD\n",
    "mData.limits['mu'] = (-0.5, None)   # force mu>-0.5\n",
    "#mData.simplex()\n",
    "#mData.migrad()\n",
    "#mData.minos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39692610",
   "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 = -23.78 </td>\n",
       "        <td style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 59 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.84e-05 (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> mu </td>\n",
       "        <td> 1.1 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> -0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> b1 </td>\n",
       "        <td> 9.7 </td>\n",
       "        <td> 1.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> b2 </td>\n",
       "        <td> 4.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> b3 </td>\n",
       "        <td> 1.93 </td>\n",
       "        <td> 0.35 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> mu </th>\n",
       "        <th> b1 </th>\n",
       "        <th> b2 </th>\n",
       "        <th> b3 </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu </th>\n",
       "        <td> 0.579 </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.2 <strong>(-0.201)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.1 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.03 <strong>(-0.127)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b1 </th>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.2 <strong>(-0.201)</strong> </td>\n",
       "        <td> 2.59 </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0 <strong>(0.036)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.01 <strong>(0.025)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b2 </th>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.1 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0 <strong>(0.036)</strong> </td>\n",
       "        <td> 0.725 </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.01 <strong>(0.023)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b3 </th>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.03 <strong>(-0.127)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.01 <strong>(0.025)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.01 <strong>(0.023)</strong> </td>\n",
       "        <td> 0.122 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = -23.78                     │              Nfcn = 59               │\n",
       "│ EDM = 1.84e-05 (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 │ mu   │    1.1    │    0.8    │            │            │  -0.5   │         │       │\n",
       "│ 1 │ b1   │    9.7    │    1.6    │            │            │         │         │       │\n",
       "│ 2 │ b2   │    4.8    │    0.9    │            │            │         │         │       │\n",
       "│ 3 │ b3   │   1.93    │   0.35    │            │            │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌────┬─────────────────────────┐\n",
       "│    │    mu    b1    b2    b3 │\n",
       "├────┼─────────────────────────┤\n",
       "│ mu │ 0.579  -0.2  -0.1 -0.03 │\n",
       "│ b1 │  -0.2  2.59   0.0  0.01 │\n",
       "│ b2 │  -0.1   0.0 0.725  0.01 │\n",
       "│ b3 │ -0.03  0.01  0.01 0.122 │\n",
       "└────┴─────────────────────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The standard minimizer\n",
    "mData.migrad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f25f2d91",
   "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 = -23.78 </td>\n",
       "        <td style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 519 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.84e-05 (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> mu </td>\n",
       "        <td> 1.1 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td> -0.7 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td> -0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> b1 </td>\n",
       "        <td> 9.7 </td>\n",
       "        <td> 1.6 </td>\n",
       "        <td> -1.5 </td>\n",
       "        <td> 1.7 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> b2 </td>\n",
       "        <td> 4.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td> -0.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> b3 </td>\n",
       "        <td> 1.93 </td>\n",
       "        <td> 0.35 </td>\n",
       "        <td> -0.32 </td>\n",
       "        <td> 0.38 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> mu </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b1 </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b2 </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b3 </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Lower and upper minos error of the parameter\"> Error </th>\n",
       "        <td> -0.7 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td> -1.5 </td>\n",
       "        <td> 1.7 </td>\n",
       "        <td> -0.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td> -0.32 </td>\n",
       "        <td> 0.38 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Validity of lower/upper minos error\"> Valid </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Did scan hit limit of any parameter?\"> At Limit </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Did scan hit function call limit?\"> Max FCN </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"New minimum found when doing scan?\"> New Min </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> mu </th>\n",
       "        <th> b1 </th>\n",
       "        <th> b2 </th>\n",
       "        <th> b3 </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu </th>\n",
       "        <td> 0.579 </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.2 <strong>(-0.201)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.1 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.03 <strong>(-0.127)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b1 </th>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.2 <strong>(-0.201)</strong> </td>\n",
       "        <td> 2.59 </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0 <strong>(0.036)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.01 <strong>(0.025)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b2 </th>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.1 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0 <strong>(0.036)</strong> </td>\n",
       "        <td> 0.725 </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.01 <strong>(0.023)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b3 </th>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.03 <strong>(-0.127)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.01 <strong>(0.025)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.01 <strong>(0.023)</strong> </td>\n",
       "        <td> 0.122 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = -23.78                     │              Nfcn = 519              │\n",
       "│ EDM = 1.84e-05 (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 │ mu   │    1.1    │    0.8    │    -0.7    │    0.8     │  -0.5   │         │       │\n",
       "│ 1 │ b1   │    9.7    │    1.6    │    -1.5    │    1.7     │         │         │       │\n",
       "│ 2 │ b2   │    4.8    │    0.9    │    -0.8    │    0.9     │         │         │       │\n",
       "│ 3 │ b3   │   1.93    │   0.35    │   -0.32    │    0.38    │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n",
       "│          │          mu           │          b1           │          b2           │          b3           │\n",
       "├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n",
       "│  Error   │   -0.7    │    0.8    │   -1.5    │    1.7    │   -0.8    │    0.9    │   -0.32   │   0.38    │\n",
       "│  Valid   │   True    │   True    │   True    │   True    │   True    │   True    │   True    │   True    │\n",
       "│ At Limit │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "│ Max FCN  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "│ New Min  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "└──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘\n",
       "┌────┬─────────────────────────┐\n",
       "│    │    mu    b1    b2    b3 │\n",
       "├────┼─────────────────────────┤\n",
       "│ mu │ 0.579  -0.2  -0.1 -0.03 │\n",
       "│ b1 │  -0.2  2.59   0.0  0.01 │\n",
       "│ b2 │  -0.1   0.0 0.725  0.01 │\n",
       "│ b3 │ -0.03  0.01  0.01 0.122 │\n",
       "└────┴─────────────────────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Aymmetric Errors\n",
    "mData.minos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7cefd334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 1.066 +/- 0.754\n",
      "Minos errors:\n",
      "-0.696 +0.826\n"
     ]
    }
   ],
   "source": [
    "# You can get the results of the fit from the \n",
    "# Minuit object.  For example, the for the 0th parameter, ie, mu\n",
    "print(\"mu =\", round(mData.values[0],3), \"+/-\", round(mData.errors[0],3))\n",
    "print(\"Minos errors:\")\n",
    "print(round(mData.merrors[\"mu\"].lower,3), \" +\", \n",
    "      round(mData.merrors[\"mu\"].upper,3), sep=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c876f54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show a profile log likelihood scan (this is just for fun)\n",
    "mumin = mData.values[0] - 2*mData.errors[0]\n",
    "mumax = mData.values[0] + 3*mData.errors[0]\n",
    "mu, loglik= mData.draw_mnprofile(\"mu\",size=100, bound=[mumin,mumax], subtract_min=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95602b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\Delta$log-lik')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It does not look so pretty, but we can redraw it\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(mu,loglik)\n",
    "ax.set_xlim(mu[0], mu[-1])\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.grid()\n",
    "ax.plot([0,0],[0,ax.get_ylim()[1]], color='black',linestyle='dotted')\n",
    "ax.plot([mu[0], mu[-1]], [0.5, 0.5], color='red', linestyle='dashed')\n",
    "ax.plot([mu[0], mu[-1]], [2.0, 2.0], color='red', linestyle='dashed')\n",
    "ax.set_xlabel(\"Signal Strength\", size=15)\n",
    "ax.set_ylabel(\"$\\Delta$log-lik\", size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca9ce738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.ContourSet at 0x123c7be50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# an example of a countour plot\n",
    "mData.draw_mncontour(\"mu\",\"b1\", cl=[0.68,0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6ca5da32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x123d45e50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yadMmunXrhkajeeDs5uah23stGvXaCahidwKg92uArs/XhPvWJdzEWU1FfI8t2Ri0hQXsWvEdLe2jUUetxqkwhRpJK6mRtBJ9UHMM9YagrzUAHNyVDmsy1vU9vnd78/PziYuLw9nZGXt7e4USGu+5557j1KlTLFiwgICAABYtWsTAgQPZu3cvNWrUQKPR0KNHD+bOnVvyHDs7u5LPuG+//ZYTJ06we/du1q9fz3PPPUdiYiKSJBEdHc3ChQs5cOCAyQqMh5Gfn4+DgwPt27e/7XuVmppq8vczu2Jo586dXLlyhZCQG9sv6HQ6Xn/9dWbOnHnH/VZA/oGws7O77X6NRvPAvyCMeY45e+D26vWw73t5kHRRHtg4QOf3ULV4EZXaPH7kxPfYcqU618TQawKS4SuIWgvHlsCFLfKptPgDqDe+BzV6QoNh8l53asv4d7Gm7zHcub06nQ5JklCpVKhU5nV6NC8vj2XLlrFy5cqS3pwpU6awZs0a5s6dy+eff44kSdjb2991UtGZM2fo168f9erVo2rVqrz11lukpaXh4+PDyy+/zGeffVZhdqpXqVRIknTH72NZ/BybxyfTTUaNGkXXrl1vua9Hjx6MGjWKp556SqFUQon8TFjxIpyRxwYR3h76fi0vkCcIFYmt440ZaZmJcOJPuTC6EgmRK+SLs6+8AXDTMeDso3RioSwYDDfWNytvGsdSTxwpKipCp9Pd1kvi4ODAvn37Sr7etm0blSpVwsPDg86dOzN9+nS8vLwAaNCgAQsWLCAvL48NGzbg7++Pt7c3ixYtwt7enoEDB5qubWamQhZD2dnZnD9/vuTr6OhoIiIi8PT0JCQkpOQbW0yj0eDn50eNGmKbBkVdOQN/jJSnNqtt4ZFP5A8SMUtMqOhc/aHNq/Jq50kn5KLoxJ/yrMdtH8trGNV7DFq8AP71lU4rmJI2Fz5WaHmWdy+XemV9FxcXWrVqxbRp06hVqxa+vr4sWbKEvXv3Urmy/MfmI488wqBBgwgPD+fChQu8++679OzZk71796JWqxkzZgzHjx+ndu3aeHt7s3TpUq5du8akSZPYtm0b77//Pr///jtVqlRh7ty5BAYGlmXrK5QKWQwdOnSITp06lXxdPNZn9OjRzJs3T6FUwj1FroQVL0FhNrgGwpAFENRE6VSC8GAkSS52/OtDt6nyz/W+7yHhMEQski9h7eSiqEZPsSioUK4WLFjAmDFjCAwMRK1W07hxY4YOHcqhQ4cAGDp0aMmx9erVo379+lSpUoVt27bRpUsXNBoN33333S2v+dRTT/Hqq69y9OhRVqxYwbFjx/j888959dVX+fvvv8u1fUqqkMVQx44deZBJbncbJySUA10RbJkKu7+Wvw5rB4N/FacUBPOn1tw4jRZ3UC6KIldCzE754h4qF0WNRoK98gNOBSNpHOUeGqXe+wFUqVKF7du3k5OTQ2ZmJv7+/gwZMoTQ0NA7Hl+5cmW8vb05f/48Xbp0ue3xrVu3curUKX755RfefPNNevXqhZOTE0OGDOHbb781qknmqkIWQ4KZyEmFv56C6O3y163HQpfJYCaDpAWh1IKbQfCvkBEPB3+BQ79C+iXYMBG2fgyNRkDz58CritJJhQclSWa3CbSTkxNOTk5cu3aNjRs3Mnny5DseFx8fT2pqKv7+/rc9lp+fz8svv8yiRYtQq9XodLqSTgitVotOpyvLJlQ45jWcXqg4Lh+FnzrIhZDGCQbPhe7TRSEkWDa3IOg6GSachj4zwLsGFGbB/h9gVhNYMgyid4g90YQysWHDBtavX090dDSbNm2iU6dO1KxZkxEjRpCdnc2bb77Jvn37iImJ4d9//6V///5UrVqVHj163PZa06ZNo1evXjRq1AiANm3asGzZMo4fP863335LmzZtyrt5ihKfXMKDO7oQ1kwAXYG8W/jjC8G3ttKpBKH82DrKM8yaPAUXtsC+2XB+kzxVP2ot+NaVT6HVHwI2ty/pIQjGyMjIYOLEicTHx+Pp6cmjjz7KtGnTkCQJtVrN8ePHmT9/Punp6QQEBNC9e3emTZt227IyJ0+eZOnSpURERJTcN3jwYLZt20a7du2oUaMGixcvLufWKUsUQ0LpFRXC+rfh0PUFvar3hIE/WNQCdYLwQCRJXouoahe4elbuITq2BJJPwqpXYOtH0PJFuWgS44qEhzRkyBCGDBlyy316vZ7MzEwcHBzYsGFDqV6nbt26nDt37pb7VCoV33//Pd9//73J8poTcZpMKJ3MyzCv1/VCSIJO78HQxaIQEoRiPtWhz1cwIRK6TgEXf8hKhE2TYEYd2PQhZCUpnVIQhDsQxZBwX1LsHvixPcQfBHs3GL4UOrwFZraCqyCUCwcPaDsexh2D/t+Bd3UoyITdM2FmPVj1KqScv9+rCIJQjsSnmXBPoSlbUS8cCDlX5XEQz22D6t2VjiUIFZ+NnTzt/qX9ci9qUHPQFcKR+fBtU/hjFMQfVjqlIAiIYki4B9XeWTSM+xXJoJNX3316o9hWQxAelEoFNXvDM5vgqfVQ/RHAAKdXwS+dYV4fOLdZzEATBAWJAdTC7QwG2PoR6h1fAKBr/Rrqbh+KbTUE4WGFtpIvyZGwZxacWHpjEUffetBmHNQZKJaoEIRyJnqGhFvp9bD+HbheCJ0KGIK+03uiEBIEU/KtDQNny+OKWr4sr9WVfAKWPQPfNJInKui0SqcUBKshiiHhBr0OVo+VpwcDuh6fc963j8KhBMGCuQXBIx/Dayeh8/vg6A0ZsbDmNfi2GRxfKv+/FAShTIliSJAVFcJfY+QFFSUVDPgBfdMxSqcSBOvg6Ant35SLokc+AycfuBYNy56FH9rCmX/EmCJBKEOiGBJAmwd/jIDIFaDSwGPzoeEwpVMJgvXROEDLF+TTZ10myUtZXImE34fDL13g4jalEwqCRRLFkLUryIKFg+HcRrBxgOG/Q+1+SqcSBOtm6wTtXpeLonavy7ubJxyG3/rD/L4Qd1DphEI5MxgMPPfcc3h6eiJJEhEREXTs2JHXXnut3LPExMSUZLAUohiyZrlp8i/XS7vA1gVGLYOqXZVOJQhCMQcPuYdo3DF5rzO1rbwR7Jyu8qawSSeVTiiUk/Xr1zNv3jzWrFlDYmIidevWZdmyZUydOrXkmLCwMGbOnKlcSDMmiiFrlZUsr2+ScBgcPGH0KghtrXQqQRDuxLkS9PwMxh6BRqPkcX1Ra+XxRH8/A6kXlE5odXQ6Hdu2bWPJkiVs27YNna5sB7pfuHABf39/WrdujZ+fHzY2Nnh6euLi4lKm72stRDFkjdLj4NeecOUUOPvCU2shsLHSqQRBuB/3YOj/Lbx8AOoMAgxw4k955tnqcWLvs3KybNkywsLC6NSpE8OHD6dTp06EhYWxbNmyMnm/J598krFjxxIbG4skSYSFhQHccpqsc+fOXLp0iddeew1JkpDushzK8OHDefzxx2+5T6vV4u3tzW+//QbIvVBt27bF3d0dLy8v+vTpw4ULdy+4582bh7u7+y33rVix4rYMK1eupHHjxtjb21O5cmWmTJlCUVHRg/xTlBlRDFmbwlxYOAjSLoBbCDy1DirVUjqVIAgPwrsaPPYrPL8TqvUAgw4Oz4NvGsO2T6EwR+mEFmvZsmUMHjyY+Pj4W+5PSEhg8ODBZVIQff3110ydOpWgoCASExM5ePD2MWN//fUXQUFBTJ06lcTERBITE+/4WiNGjGD16tVkZ2eX3LdhwwZyc3MZOHAgADk5OUyYMIFDhw7x77//olKpGDhwIHq93ug27Ny5kyeeeIJx48YRGRnJjz/+yLx58/joo4+Mfk1TEsWQtdk0CVLOgrMfjFkHXlWUTiQIgrH868OIpfI2H4FNQZsD2z6Ri6LD88UaRSam0+kYN24chjssc1B83/jx401+yszNzQ0XFxfUajV+fn74+PjcdoynpydqtRoXFxf8/Pzw8/O742v16NEDJycnli9fXnLf4sWL6devX8kpt0cffZRBgwZRtWpVGjZsyNy5czlx4gSRkZFGt2HKlCm88847jB49msqVK9OtWzemTZvGjz/+aPRrmpIohqzJuc1w8Gf59oDv5AXfBEEwf6Gt4JnN8Ng88AiD7CRY/Sr80BbpvNj3zFR27tx5W4/QzQwGA3FxcezcubMcUz0YGxsbhgwZwqJFiwC5F2jlypWMGDGi5Jhz584xbNgwKleujKura8lpudjYWKPf99ixY0ydOhVnZ+eSy7PPPktiYiK5ubkP1SZTEBvgWIucVFj5kny7+fNi1pggWBpJkvc1q9ELDs6B7Z/BlUhs/hhKa+fakBQCwWJs4MO426knY49TyogRI+jQoQNXrlxh06ZNODg48Mgjj5Q83rdvX0JDQ/n5558JCAhAr9dTt25dCgsL7/h6KpXqtt4yrfbW7WSys7OZMmUKgwYNuu359vb2JmjVwxHFkDUwGGDNOMhOBu8a0G2K0okEQSgrNnbQ6iV54dSd/8Ow/0d8siMxzOkMDYbK236IXmGj+Pv7m/Q4U7O1tS3VKbrWrVsTHBzMH3/8wbp163jsscfQaDQApKamEhUVxc8//0y7du0A2LVr1z1fz8fHh6ysLHJycnBycgK4bQ2ixo0bExUVRdWqVY1oWdkTp8msQcQiOL1aXl160E/yKreCIFg2Bw/oPp2iF/YS79ESCQMcWwKzmsC/UyE/U+mEZqddu3YEBQXddaaWJEkEBweXFBHlLSwsjB07dpCQkEBKSso9jx0+fDg//PADmzZtuuUUmYeHB15eXvz000+cP3+eLVu2MGHChHu+VosWLXB0dOTdd9/lwoULLF68mHnz5t1yzKRJk/jtt9+YMmUKp06d4vTp0/z++++8//77RrfXlEQxZOnSomHd2/LtTu9CQENF4wiCUM7cQzkc9hJFT22E0DZQlA87/wffNIIDP4OuYkxtNgdqtZqvv/4a4LaCqPjrmTNnolaryz0bwNSpU4mJiaFKlSp3HGR9sxEjRhAZGUlgYCBt2rQpuV+lUvH7779z+PBh6taty2uvvcYXX3xxz9fy9PRk4cKFrF27lnr16rFkyRImT558yzE9evRgzZo1bNy4kWbNmtGyZUtmzJhBaGio0e01JXGazJLpimD5C1CYDSGtoc04pRMJgqAQQ0BjePIfebHGTR9C6jlY+4ZcEHWfDtW6yeOOhHsaNGgQf/31F+PGjbtlMHVQUBAzZ86845gYUxg/fjzjx4+/5b5t27ah1+vJzJR7+Vq2bMmxY8dK9Xq1atW646w4gK5du942c+zmY8PCwm577oABAxgwYMAt9z377LO3fN2jRw969OhRqnzlTRRDlmz3DIjbJ2+1MfAHUCnz14ogCBWEJEHN3lCtOxz6VZ6GnxIFix+Dyp2gx0fgW0fplBXeoEGD6N+/Pzt37iQxMRF/f3/atWunWI+Q8PBEMWSpEo7Ii68B9PoCPCpGV6QgCBWAWgMtnoP6Q+RTZvt/gItb5e09Go2CTu+Bi6/SKSs0tVpNx44dlY4hmIgYM2SJCnNh2XOgL4La/eUZJIIgCP/l4A7dp8nbe9QeAAY9HJkPsxrDji9Am6d0QkEoF6IYskSbPpDHA7j4Q5+ZYhyAIAj35hkOQ+bDmA0Q2EQeZ7hlOsxqCsf+gIfYhkEQzIEohixN1Ho4+It8e8D34OipbB5BEMxHSEt4ejM8OgfcgiEzHpY/B790hkt7lE5nMncbOCxUHOX9PRLFkCXJvgIrX5Zvt3wZqnRWNo8gCOZHpYJ6g+GVg9BlEtg6w+Wj8GtPWDpa/j1jpooXFqwI2z8I91b8PSr+npW1CjmAeseOHXzxxRccPnyYxMREli9ffsuUvcmTJ/P7778TFxeHra0tTZo04aOPPqJFixbKhVaawSAXQrkp4FtX/iUmCIJgLI0DtHtdHlC99WN5LFHkCojeAb2/hDqDzO4UvFqtxt3dnStX5ILO0dHxrgsomgu9Xk9hYSH5+fmoVObfv2EwGMjNzeXKlSu4u7uX2wy9ClkM5eTk0KBBA8aMGXPHNRuqV6/Ot99+S+XKlcnLy2PGjBl0796d8+fP33ehKYt18Bc4txHUdvDoL6BRfq8XQRAsgHMl6DsTmo6BFS9B8gn4awycWgG9vwJn8/qdW7ybe3FBZO4MBgN5eXk4ODiYfWF3M3d395LvVXmokMVQz5496dmz510fHz58+C1ff/XVV8yZM4fjx4/TpUuXOz6noKCAgoKCkq+LF6nSarW3bSh3N8XHlfb4cnM1CpuN7yMBui6T0XtUBRNkrLDtLUPW1mbRXstnsjZ714KnNqDaPQPV7hlIp1dhiNmF7pHPMdQe8PBBTaQ07fX29sbDw4OioiKzHz9UVFTEnj17aN26NTY2FfIj/YFIkoSNjQ1qtZqiojuvjl4W/38lQwX/SZAk6bbTZDcrLCzkm2++Yfr06Zw/fx5vb+87Hjd58mSmTLl9g9LFixfj6OhoysjlSqXX0v7sFNzyYkl2qc++Kq+bXde1IAjmxS03hkaXfsYtPw6ABPdmHA8aTaHGVeFkgjXIzc1l+PDhZGRk4Opqmp85sy2G1qxZw9ChQ8nNzcXf358VK1bQrFmzu77OnXqGgoODSUlJKfU/plarZdOmTXTr1q3cBnXdj2rzJNT7v8fg6EXRszvA2XQLpVXE9pY1a2uzaK/lK7M26wpR7foK1Z6ZSPoiDI5eci9Rrf6mew8jWNv32NraC5Camoq/v79JiyGz7VPr1KkTERERpKSk8PPPPzNkyBD2799PpUqV7ni8nZ0ddnZ2t92v0Wge+AfImOeUiQtbYf/3AEj9v0PjEVQmb1Nh2luOrK3Nor2Wz+Rt1mig6wdQpx8sfxHpyilslj0NtVfJY4mc7txLX16s7XtsTe0ti3Y+VDGUm5vLoUOHSExMvKXX5b+eeOKJh3mbO3JycqJq1apUrVqVli1bUq1aNebMmcPEiRNN/l4VUm4arHhRvt10DNS4+xgrQRCEMuPfAJ7bJq9YvfN/ELkSYnbJBVGdAUqnE4RSMboYmjRpEjNmzLjneg0GgwFJksqkGPovvV5/z4LMohgMsPpVyEoEr2rQ/SOlEwmCYM1sbKHze1Czlzzj7Eok/DkaIgdCry8V7yUShPsxqhj6/PPPmT59Omq1mt69e1O9enVcXFxMFio7O5vz58+XfB0dHU1ERASenp54eXnx0Ucf0a9fP/z9/UlJSeG7774jISGBxx57zGQZKrTz/8Lp1aDSyNPobc13ALggCBYkoJHcS7T9c9g1A04th+id0OcreZ9EQaigjCqGfv75ZxwcHNi5cyeNGzc2dSYOHTpEp06dSr6eMGECAKNHj+aHH37gzJkzzJ8/n5SUFLy8vGjWrBk7d+6kTp06Js9SIe2eKV83fxYCGiqZRBAE4VY2dtDlA6jZW14I9kokLH0C6gyEXv8DJy+lEwrCbYwqhuLi4ujcuXOZFEIAHTt2vOfaD8uWLSuT9zUL8YchZieobKDVy0qnEQRBuLPAxqKXSDAbRq3d7efnh5OTk6mzCKVR3CtUbwi4lc3sMUEQBJMo7iV6ZjP41JK3C1r6hLyCdU6q0ukEoYRRxdDQoUPZtm0bOTk5ps4j3EvKeXmsEECbV5XNIgiCUFqBjeH57fJeZ5IaTv4N37e48ftMEBRmVDE0efJkatWqRb9+/W4Z6CyUsT3fAAao/ghUqqV0GkEQhNKzsZM3kC7uJcq5Cn+MlHuJctOUTidYOaPGDPXq1Qu9Xs+2bduoVasWoaGhBAUF3XHHXEmS+Pfffx86qNXLSoJjS+TbbcYrGkUQBMFoxb1E2z+DXTPlXqKYXTDge6jaVel0gpUyqhjatm1byW2dTsfFixe5ePHiHY+1pF10FbX/B9AVQlBzCGmpdBpBEATjFfcS1ewDy1+AlChY+Ci0eAG6TgaNg9IJBStjVDEUHR1t6hzCveRnwsG58u2248VGrIIgWIbiXqJNH8KBH+U/+i5uh0d/Br96SqcTrIhRxVBoaKipcwj3cvhXKMgA7+pQXWy7IQiCBdE4QK/PoVo3efXqq6fh585yz1HLl+EOwy8EwdTET1lFV1QAe+XNWGn9qvjFIAiCZarWDV7aCzV6yUMCNr4PCwZARoLSyQQrID5ZK7pjSyA7CVz8of4QpdMIgiCUHSdvGLoY+swEjSNEb4fZreUFGwWhDJWqGFKpVNjY2HD27FkA1Gp1qS82NkbvBSsUFcKOL+XbrcfKgw4FQRAsmSRB06fg+Z3yXmf56fDnk7D8RXn8pCCUgVJVKiEhIUiShEajASA4OFjMEisPEQshIw6cfaHpGKXTCIIglB/vqvD0Jtj2Kez6Co4thku7YdDPENJC6XSChSlVMRQTE3PPr4UyUFQIO/4n3277mphqKgiC9VFr5O08qnaBZc9D+iX49RFo9wZ0eEt+XBBMQIwZqqiOLoDMeHD2gyZPKp1GEARBOaGt4cVdUH8oGPSw43OY+wikXlA6mWAhRDFUERUVwM7rvULtJoheIUEQBHs3GPQjPDoH7Nwg4RD80A6O/AYGg9LpBDP3UMVQREQEzz33HLVq1cLNzQ03Nzdq1arFc889x5EjR0yV0foc+Q0yE8AlABqPVjqNIAhCxVFvMLy4G0LbgjYHVo2FpaPE/mbCQzF6qtfUqVOZNm0aOp3ulvujoqKIiopi7ty5vP/++0yePPlhM1oXbT7s/Eq+3W4CaOyVzSOUG73eQGJmPjEpOeQW6tDp9ej0oDMYSm7r9QZ0BgNFegN6vQGDwYBaJaFSSagkCbUkIUmgVklo1Cq8nG2p5GKHj4s9rvY2YuKDYBncg2H0Knnz6i0fwenVEH9I3t8spJ3S6QQzZFQxtGDBAiZPnoyzszMvv/wyw4YNIywsDIBLly6xZMkSvvvuO6ZNm0aVKlUYNWqUKTNbtqMLIOsyuAZC4yeUTiOUgYw8LdEpOVy8ms3FqzlEp+Rw4Wo2Mak55Gv1Zfa+tjYqfJzt8HGRL76udvg42ZJ0RcLlfApBns74utjj6iCKJsEMqNTy5JLKneDvZyD1HCwYiKr5C6j0TZVOJ5gZo4qhmTNnotFo2Lp1K02aNLnlsXr16lGvXj0effRRWrduzcyZM0UxVFra/FvHCol1hcyaVqfn4tUcIhMziLycSWRiJlFJWaRkF971ORq1RLCnI672GtQqSb5IUknvj01xD5BK7v2RkNAbDOj0BvQG0BsMJV8XFOlJzS7galYBmflFFBbpSUjPIyE97z/vqmbJhRuntR00aqr7OlPL35XaAa7U8nelpp8LLvZi5o5QAQU0hOd3yCtWH5qD+sAPtLcPhha1IKCu0ukEM2FUMXT69Gk6dep0WyF0syZNmtC5c2e2b99udDirc2Q+ZCWCaxA0EgWkOSks0nMsPp1TCRmcTsySC5/kLAqL7tzTU8nFjso+TlT2caaytxOVfZwI93Ym2MMBG7Xp5zXka3WkXC+MrmYVkJxVwJXMfBLT8zh5IQ6DvSvJWQWk52rJ0+o4Fp/BsfiMW14jxNOROgGu1Atyo0GQO3UD3XBzEAWSUAHYOkKfr6BadwwrX8YtNw7DvB4weC5U76F0OsEMGFUMubq64uHhcd/j3NzccHV1NeYtrNOh6zvTt3tN9AqZgbxCHdvPXmXDqSQ2n04mK7/otmOc7Wyo5e9C7eu9LDX9XKlSyRlnu/Jdmd1eoybIw5EgD8db7tdqtaxde4levVqj0WjI1+pISM8jKimLyMuZnE6UL5cz8olNyyU2LZd1J5NKnh/m5Ui9IHfqB7pRN9CNOoGuuIoeJEEpNR6h6NkdZMwZhHf2GVj8OHSfDq1elle2FoS7MOo38iOPPMLGjRvJy8vDweHO077z8vLYsWMHPXqIqrxUUi/A1TOgsoG6g5VOI9xFVr6WLWeusP5kEtuirpKnvTGBwNvZlobB7iWFT21/N4I8HFCpzOeXsL1GTRUfZ6r4ONOrnn/J/ddyCjmdmMnJy3KP0Yn4DGLTcolJlS+rj10uOTbc20nuQQp0o16gG41DPbDXqJVojmCNnCuxp8pb9JG2oopYABvfk3+39v4KbGyVTidUUEYVQ59++inbtm1j0KBBzJo1i6pVq97y+IULFxg7diy2trZ89tlnJglq8c78I1+HtQUHd0WjCLfK1+rYcuYKy47Es+NsCoW6G6e+At0d6FnXj571/GgU7GFWhc+D8HCypXVVb1pX9S6571pOIScSMjiRkMHx+HROJmSSkJ5HdIo8KHzN8URAHoPUvro33Wv70blmJTycxAeSULYMKht0Pb9C5VtbLoaOLoC0izBkATh5KR1PqIBKVQyNGXP7vlgNGjRg9erV1KpVi4YNGxIaGgrIs8kiIiLQ6/X06dOH9957jzlz5pg2tSWKWitf1+itbA4BAIPBwOFL11h2JJ7Vxy6TedMpsMo+TnIBVNefOgGuVjvzysPJlvbVfWhf3afkvtTsAk5ezuRkQgYnEzI4GptOUmY+G04ls+FUMmqVRNNQD7rX8aN7bV+CPR3v8Q6C8BAkCVq9BN7V4M+n5H3NfukMw/6ASjWVTidUMKUqhubNm3fXx3Q6HYcPH+bw4cO3PbZ69WokSRLF0P1kX4W4/fLtGj2VzWLlEjPy2RAvMePr3cSk5pbc7+9mz8BGgQxoFEh1XxcFE1ZsXs52dKjuQ4frBZLBYODU5Uw2Riaz8VQSZ5Ky2B+dxv7oNKatiaSmnwvda/vSrbYfdQOtt7AUylC1bvDMJnn80LUYmNNNHlhdrZvSyYQKpFTF0NatW8s6h3U7u17eb8e/gbyYmFDu4q/l8v22C/x5KA6tTg3k4qBR07OuH482CaJlZS/UFnoKrCxJkkTd64OrJ3SrTlxaLhsjk9kUmcTBmGucScriTFIW32w5j7+bPT3q+NG7vj9NQiz3lKOggEq14Nmt8MdIiN0Di4dAj4+hxQtiYLUAlLIY6tChQ1nnsG7iFJli4tJy+X7bef46HI9WJ+9vVNnFwPPd6tKnQRBO5Tzry9IFezrydNtwnm4bzrWcQracucKmyGS2n71KYkY+8/bEMG9PDL6udvSs60+vev40DRWFkWACTl7wxEr45zU4uhDWvwNXTkOvL8XAasH47TgEEynMgQtb5Ns1RTFUXhLS8/h681mWHUmgSC8XQW2revNihzBSIvfRq1EgGo3471GWPJxsebRJEI82CSJfq2PnuRTWnUhkU2QyyZkFJYVRJRc7etb1kwujME/RQycYz8YW+n0LPrXkRRqPzJdn8j6+ABw9lU4nKEj8tlfaha1QlA/uIeBbR+k0Fq+wSM8vuy7yzb/nSra+aF/dh3FdqtIk1FNedydS4ZBWyF6jplttX7rV9qWgSMeucyn8c70wupJVwPy9l5i/9xI+LnY8UsePAY0CaRziLsYYCQ9OkqD1K+BdHf4aA5d2wc+dYNjv8uk0wSqJYkhpN58iE7/Yy9Se8yl8sPIkF67mANA83JN3etakccj9FxAVyo+djZoutXzpUksujPacT+WfE4lsPJXE1awCFuy7xIJ9l6jl78rIliEMaBgoTmcKD65691sHVv/SDQbPEStWWynxG0RJuiKIWiffFqfIysyVzHym/3OaVdcXBvR2tuO93jUZ0DBQ9CxUcHY2ajrVrESnmpUoHFiP3RdSWH3sMv8cT+R0YibvLT/JJ2vP8GjjQEa2DKWamOknPIjigdVLR8lT7xc/Dt2nQatXxB+nVkYUQ0qK3g55aeDgCSGtlE5jcYp0en7be4mvNp0lu6AIlQSjWoYyoXsNsaeWGbK1UdGpRiU61ajEpD61+etwPIv2xxKdklNyGq1FuCcjW4bSo44ftjam3+NNsEBOXjBqBax9HY78Jo8lunJG3utMbItkNSrkb4sdO3bQt29fAgICkCSJFStWlDym1Wp5++23qVevHk5OTgQEBPDEE09w+fLlu79gRXX8D/m67qOgFnWpKR2+lEbfb3czdU0k2QVFNAh2Z9UrbZnSv64ohCyAu6Mtz7SrzL8TOrDw6Rb0qOOLWiWxPzqNsUuO0vrTLfxvYxTJmflKRxXMgY0t9P0GHvkUJBVELITf+strwAlWoUIWQzk5OTRo0IDvvvvutsdyc3M5cuQIH3zwAUeOHGHZsmVERUXRr18/BZI+hIJsOL1avl3/cWWzWJCMXC1v/XWMR2fv5XRiJu6OGj4ZVI/lL7ambqCb0vEEE1OpJNpW8+bHUU3Z9XYnXu1SjUoudqRkFzBry3nafLqFV5cc5WjsNaWjChWdJEHLF2H4n2DnCrF74efOkHRS6WRCOTCqO6Jz58488sgjvPXWW/c87ssvv2Tt2rVs2bLlgV6/Z8+e9Ox555WY3dzc2LRp0y33ffvttzRv3pzY2FhCQkLu+LyCggIKCgpKvs7MzATkniatVluqXMXHlfb4e5EiV2GjzcXgEU6RbwMwwWuaminbWx6OxqYzfulxLmfIvQGPNQnkjW7V8HSyRacrQqe7zwtgfm1+WJbUXm9HG8Z2DOeFdqFsPn2F3/bFcuhSOquOXWbVscs0CHJjZPNA1HrLaG9pWdL3uDQeur1hHeDJ9dgsHYF0LRrD3B7oHp2HoXJH04U0IWv7/kLZtFUyGAyGB32SSqXiySefZO7cufc87tlnn2Xu3LnoSvMpdLeAksTy5csZMGDAXY/ZvHkz3bt3Jz09HVdX1zseM3nyZKZMmXLb/YsXL8bRsfz3R2p5/kt8s45zxm8AUf6Dyv39LYneAFsvS6yJVaFHwtvOwMhqOsLFWFqrF58D2xNVHE6R0BnkAbFuGgNt/fS09jXgLM6YCnehKcqiefQsvLPPoEdNRMgY4rzaKR1LQD5DNHz4cDIyMu76mf+gynSgSn5+PjY2ZTsWJj8/n7fffpthw4bd8x9l4sSJTJgwoeTrzMxMgoOD6d69e6n/MbVaLZs2baJbt25oNA/xWzQ7GZsIueu1ysB3qeJZ2fjXKkMma28ZSs0p5O2/T7I9NgWA3vX8mNavNi72xv3cmUObTcka2vsckJJdwJKD8Sw+EEdKdiH/xKnZlKhiQAN/XugQTrCH5W4Yaw3f45uZtL1FA9CvGYvq1DIax/5MgzAv9G1fr1Azzazt+wuQmppq8tcss0olMzOTPXv24O/vX1ZvgVarZciQIRgMBmbPnn3PY+3s7LCzu31mgEajeeAfIGOec4szq+S9yIKaofGtYfzrlJOHbm8Z2XcxlXG/HyU5swA7GxVT+tXh8WbBJpkuX1HbXFYsvb3+HhomdK/J8+3C+WTRBo7leXAiIZOlhxNYdvQyjzUN4uVOVQmy4KLI0r/H/2WS9mo08OgceVHc3TNR7/gUdfZl6P0VqCvWv6U1fX/Lop2lLoYqV7619+Kvv/5i27Ztdzy2qKiI5ORkioqKeOWVVx4q4N0UF0KXLl1iy5YtJusqKxfFs8jEwGmj6PQGvtt6npmbz6I3QNVKznw7vBE1/czoZ0BQhK2NimY+Bib1bMHxy9l8/e85dp5LYcmBOP46HM/jzYJ5uVNV/N0clI4qVBQqFXSbAm5BsO4tefp95mV4bB7YiXPxlqLUxVBMTEzJbUmSyM7OJjs7+47HajQaAgIC6NevH5988slDh/yv4kLo3LlzbN26FS8vL5O/R5m5GgWJEaCygTpirNCDSsku4NUlR9lzQe4mHdwkiKn96+BoK5YmEEpPkiSahnmy4OkWHIpJY8bms+w+n8rCfbEsPRjPsObBvNSpKr6u9kpHFSqK5s+Ca6C8hcf5zfBrLxjxJ7j4KZ1MMIFSf4Lo9fqS26UdQG2s7Oxszp8/X/J1dHQ0EREReHp64u/vz+DBgzly5Ahr1qxBp9ORlJQEgKenJ7a2FXz34eNL5euq3eTFvoRSy8jVMvKX/ZxJysLRVs20/nV5tEmQ0rEEM9c0zJNFz7Rk/8VUvtp0lv3Raczfe4klB+MY0SKEVzpVxctZLL4nADV7wZP/wOIhkHRc3sJj5F/gU/GHOwj3ZtQ6Q7/++itPP/20qbOUOHToEI0aNaJRo0YATJgwgUaNGjFp0iQSEhJYtWoV8fHxNGzYEH9//5LLnj17yiyTyZz4U76uP0TZHGYmr1DHmPkHOZOURSUXO1a90kYUQoJJtajsxR/Pt2Lxsy1oFuZBYZGeX3fH0OGLbXzz7zlyCoqUjihUBEFN5D3NPKtARizM6QYxu5VOJTwko84tjB49+p6Pp6Sk4O7ubvRMso4dO3KvGf9GrAZQMWQlQ/oleYVTsRlgqWl1el5cdJjDl67ham/Db083p2olca5eKButq3jTqrIXu86n8Pn6KE4kZPDVprP8tvcS47pUZWjzEDTqCrlerVBePCvD05tgyVCIPwALBsCA2VBvsNLJBCMZ9T/60KFDTJ06lcjIyFvuX758OX5+fvj6+uLl5cXXX39tkpAWI+m4fO1VDWydlM1iJvR6A2/8eYxtUVex16j49almYqC0UOYkSaJdNR9WvtyGb4c3IszLkZTsAj5YeYpuX21n9bHL6PVm+keZYBpOXjB6FdTqC7pC+Ptp2DUDzPWPdStnVDE0a9YsPv74Y3x9fUvui46OZujQoVy5cgU/Pz9ycnKYMGHCXWecWaXiYsivnrI5zITBYGDK6lOsjLiMjUpi9sgmNAn1VDqWYEVUKok+9QPYNKED0wbUxdvZjpjUXMYuOUr/73az61yK0hEFJWkc4LH50PIl+evNk+Gf10EnTqmaG6OKoX379tGoUaNbZnHNnTsXrVbLl19+SUJCAvv370elUoneoZslnZCvRTFUKl//e475ey8hSfC/IQ3oVKOS0pEEK6VRqxjVMpTtb3ZkQrfqONmqOZGQwcg5+xnxyz6OxaUrHVFQikoNj3wib/KKBIfmwB8joDBH6WTCAzCqGEpOTr5tD7BNmzbh5ORUsq5QkyZNaNeuHceOHXv4lJYi8XrPkH99ZXOYgQX7LjFz8zkApvSrQ/+GgQonEgRwsrPh1S7V2PFWJ55qE4atWsXu86n0/243Ly48zPkrd15uRLACLV+ExxeAjT2cXS9Pvc9KVjqVUEpGFUM6nY6iohvdgNnZ2Rw5coQ2bdrcMrU9ICCgZNq71SvIgrSL8m1f0TN0LxtOJfHhSnm7knFdqvFEqzBlAwnCf3g52/Fh3zr8+3oHHm0chCTBupNJdJ+xnbf/Ok5iRp7SEQUl1OoLo9eAo5e8ntycrvLackKFZ1QxFBISwuHDh0u+/ueffygqKqJr1663HJeZmYmbm9vDJbQUyZGAAVz8wdlH6TQV1uFLaby65Ch6AwxrHsz4rtWUjiQIdxXs6cj/hjRg/bj2dKvti94AfxyKo8MX2/h47WmyxXR86xPcTJ5p5lkZ0mNhTncx9d4MGFUM9e3bl9jYWAYNGsSsWbN44403UKlU9O/f/5bjjh49SmhoqEmCmr2SwdPiFNndnL+SzdPzD1FQpKdLzUpM61/XJPuMCUJZq+Hnws9PNOXvF1vTPNyTwiI9P+24SM+vd3AgOk3peEJ586oCT2+GoOaQnw4LB0HMLqVTCfdgVDH0xhtvEBYWxooVKxg3bhwJCQmMHz+eatVu/BW/f/9+EhISaN++vcnCmjUxk+yermTmM3ruAdJztTQMdmfW8EbYiLVcBDPTJNSDP55rya9PNSPIw4G4tDwe/2kvn6w9Tb5Wp3Q8oTwVT72v1gOK8mHx4xB/+P7PExRh1KeNt7c3x48fZ+7cuXz22Wds3ryZL7/88pZjkpKSGDduHCNHjjRJULMnZpLdVVa+lid/PUhCeh5hXo7MGd1U7DUmmC1JkuhUoxLrxrXj8abBGAzw446L9P92N6cuZygdTyhPGgcY8huEd4DCbLmHKOmk0qmEOzDqE+f48eMl+5PdTf/+/W87bWa1igqvjxlCFEP/odcbeHnxUSITM/F2tmX+mOZiHyjBIrjYa/hscH261vZl4rLjRCVnMeC73YzvWp3n21cWPZ/WQmMPQxfLhVDcfnm16qfWgbcYD1mRGPW/sWHDhowdO9bUWSzX5aOgK5BnGHhWVjpNhbL0UBw7zl7FQaNm7pPNCPUSK3MLlqVbbV82jG9P99q+aHUGvtgQxeAf9nL+SpbS0YTyYucMw5fKY0ZzrsJv/eHaJaVTCTcxqhjy9PQkICDA1Fks16XrA+dCW4MYEFwiLaeQT9efAeD17tWpH+SubCBBKCNeznb8OKoJ/3usAS52NkTEpdPrm13M3naBIp1e6XhCeXBwh1HLwbsGZCbIBVGWWHqmojCqGGrZsiUnTpwwdRbLVTytMrStsjkqmM/XnyE9V0tNPxdGtw5TOo4glClJkni0SRAbJ7SnYw0fCov0fLb+DI/O3sPZZNFLZBWcvOGJFeAeCtei5YIoJ1XpVAJGFkMffvghUVFR/O9//zN1HsujK5LPE4PcMyQAcCT2Gr8fjANg2oC6YhdwwWr4uznw65PN+GJwfVzsbTgWn0Gfb3bx3dbzopfIGrgGyLPMXALg6hlYOBDyxcB6pRk1gPr06dOMHDmSt956i4ULF9K7d29CQkKwt7e/4/FPPPHEQ4U0a0nH5FkE9m7gW0fpNBWCTm/ggxXyjIpHGwfRLExsvipYF0mSeKxpMO2q+fDu8hNsOXOFLzZEseFUEjMeb0gVH2elIwplySMMnlgJv/aExGOw6DH5FJqtGDOpFKOKoSeffBJJkjAYDBw7doxjx47dcXE8g8GAJEnWXQwVnyILaS1v6CewcN8lTl3OxNXehom9aiodRxAU4+dmz5zRTVl+NIHJq05x/Hov0eR+tRnSNFgsOmrJfKrLBdD8PvLZgyXD5EHWmjt3Kghly6hiaNKkSeI/aWldul4MhbVRNkcFcSUzny83yHv1vPlITbzFNHrBykmSxKDGQbSu4s2EpRHsuZDK23+fYPvZq3wysD5ujhqlIwplxb8+jPhbnm4fvR3+HA1DFoCN7X2fKpiWUcXQ5MmTTRzDQul1cGmvfFuMFwJg2j+nySooon6QG8ObhygdRxAqDD83exY+3YKfdl7kyw1RrD2RRERsOjMeb0iLyl5KxxPKSnAzGPY7LBos73a/7FkYPFecSShnYtRqWUo+CQUZYOsCfg2UTqO4HWevsvrYZVQSfDywHmqV6F0UhJupVBIvdKjC3y+2JszLkcsZ+Qz7eR9fbohCKwZXW67wdvD4IlBpIHIFrHwF9OL7XZ5EMVSWSsYLtQC1dW8vka/V8cFKedD06NZh1A10UziRIFRcDYLd+efVdjzWJAi9Ab7dep7Bs/dw4Wq20tGEslKtKzz2K0hqOLYY1r0JBoPSqaxGqT6hx4wZgyRJfPzxx/j6+jJmzJhSv4EkScyZM8fogGYt4ZB8HdJK2RwVwPdbz3MpNRdfVzsmdKuudBxBqPCc7Gz44rEGtK/uw3vLT3AsPoPe3+zkvd61GdkiRIzbtES1+sKA2bD8eTj4C2gcodtUsVhvOShVMTRv3jwkSeLtt9/G19eXefPmlfoNrLoYyr4iX3uEKRpDaeevZDN7+wUAPuxbBxd7MSBUEEqrb4MAmoZ58Mafx9h9PpUPVpxky+lkPhtcn0ouYuaRxWnwOGhzYc142PMN2LtC+zeVTmXxSlUMbd26FYCQkJBbvhbuI/f6yqKO1j34cdqaSLQ6A51q+NCzrp/ScQTB7Pi7ObBgTAvm7Ynh0/Vn2Bp1lUdm7uSTQfXoUUf8n7I4TZ+SC6IN78KW6fICjY1GKJ3KopWqGAoPD8fZ2blkUcUOHTqUaSiLkZMiX1txMXThajbbz15FJcHkfnVE174gGEmlkhjTNpy21bwZ/3sEkYmZPL/gMM+3r8zbj9REJSYkWJZWL8tnF3bPhFVjwbkSVOumdCqLVaoB1OHh4bz55o1uujFjxjB37twyC2UR9PobPUNO3spmUdCS/bEAdKpRSexILwgmUN3XheUvt+b5DpUB+HHHRV7/8xiFRWL2kcXp8iHUfxwMOlg6GhKOKJ3IYpWqGDIYDOhvmuY3b948du3aVWahLEJ+uvwDDFbbM5Sv1fHXkXgARrQUawoJgqnY2aiZ2LMWXz7WALVKYvnRBJ6ef5DsgiKlowmmpFJBv2+hckfQ5sDiIZB2UelUFqlUxZCbmxtxcXFlncWy5KbJ13auYGOdqyyvO5lIeq6WQHcHOlSvpHQcQbA4g5sEMWd0Uxxt1ew8l8LQn/ZyNatA6ViCKdnYyqtS+9WDnKuw8NEbQzAEkynVmKFmzZqxZcsWnnrqKcLDwwGIiIhg6tSp932uJEl88MEHD5fSHOUWjxey3k1IF+2TT5ENbRYsFlgUhDLSsUYlljzbkjHzDnIyIZNHZ+9h/pjmhHuL09IWw95V3rZjTle5Z2jxEBi9WmzsakKlKoY+/fRT+vbty/z580vui4iIICIi4r7PtdpiqGTwtHWOF4pKyuLQpWuoVRKPNwtWOo4gWLQGwe78/WJrnph7gNi0XAZ+v5ufRjWlebj1/jFmcVx8YeQymNMNEg7Dn0/B0MVKp7IYpSqGGjduTFRUFAcPHiQuLo4nn3yStm3b8vTTT5d1PvNl5YOnf9sbA0C3Wr5UchVroQhCWQvzduLvF1vzzG+HOBaXzshf9vP54Pr0ritOUVsM72ryzvbz+8K5DfJaRD2/UjqVRSj1HhHOzs506tQJgCeffJKqVasyevToMgtm9nKtt2coI1fLsiMJgLz1hiAI5cPHxY7fn23JhKURrDuZxPg/Irh4pQqVxa4OliO4OQz+Ff4YAUcXoHKqBIi9Lx+WUXuTRUdH88UXX5g6S4kdO3bQt29fAgICkCSJFStW3PL4smXL6N69O15eXkiSVKrTdeUuK1m+drK+mWRLD8WRp9VR08+FlpVFN70glCcHWzXfDW9cMvX+m60XWHheRYGYem85avaC3nKPkHrX/wi7+q/CgcyfUcVQaGgoXl5l9yGfk5NDgwYN+O677+76eNu2bfnss8/KLMNDuxYtX3uEK5ujnOn0BuZfP0X2ZOswsciiIChApZKY2LMWnwyqh1olcShFxTO/HRZT7y1J06eg40QA6sf/hnRmjcKBzFuF3Eq9Z8+e9OzZ866Pjxo1CoCYmJhSv2ZBQQEFBTemnGZmZgKg1WrRarWleo3i40pzvE3qeSSgyC0UQylfv6J5kPYW23z6CvHX8nB30NCrTqUHem5FYEybzZlor2Ub3MgfHwcVL/8ewb7oawz7aS+/jGqMp5Ot0tHKjFV9j1tPgGtxaI4tRL3ieYocvTBYwcbgZfG9lQwGQ4U+myxJEsuXL2fAgAG3PRYTE0N4eDhHjx6lYcOG93ydyZMnM2XKlNvuX7x4MY6OjiZKK5MMOvpEPIMKHRvqzCDf1npOlX17SsW5TBVdAvT0CxXd8oJQEcRlw+zTanKKJHwdDLxUS4e7dS5/ZnEkg45m0bPwzzhCodqRXdXeJ8shSOlYZSo3N5fhw4eTkZGBq6urSV6zQvYMlYWJEycyYcKEkq8zMzMJDg6me/fupf7H1Gq1bNq0iW7duqHR3GPn9WvRqCJ0GGzs6dx/BEhGnY1UXKnbe93l9DzO7d2JSoJJwzoQ4O5QDilN60HbbO5Eey1fcZt/f7YFzy46RlJmAT9ddGbek00Is8Atcqzte6zVavl3g45eKT9im3CQTgmzKHpyPbgGKh2tzKSmppr8Na2mGLKzs8PO7vY/hTQazQP/h7nvczLkxQYlj3A0tub/51dp/40OxcqDxusFuRPqY5pqXSnG/FyYM9Fey1czwJ2/XmzNqDkHiE7JYdgvB5k/pjl1AtyUjlYmrOl7rFfZon98Merf+iClRKFZMgSe3gAOHkpHKxNl8X01zy6Liq547xivKsrmKGf7o+VqvaVY6E0QKqQgD0eWPt+K2v6upGQX8viP+9hzXmztYBEcPGDk3+ASAClRsHo8VOxRMBWKKIbKQtoF+dqzsrI5ytn+aHk/thZiOr0gVFg+LnYsea4lLcI9yS4oYvSvB1h17LLSsQRTcA+GoYtAZQORK+D4H0onMhsVshjKzs6+ZbuP6OhoIiIiiI2VTz+lpaURERFBZGQkAFFRUURERJCUlKRU5FsV9wxZUTGUlJHPpdRcVBI0DRPFkCBUZG4OGuaPaU7vev5odQZeXXKUX3aK3dAtQmBj6PiOfPufN+DaJWXzmIkyK4Y6d+5Mjx49WLx4MQ86Ye3QoUM0atSIRo0aATBhwgQaNWrEpEmTAFi1ahWNGjWid+/eAAwdOpRGjRrxww8/mLYRxkq93jNkRafJik+R1Q5wxdXeOs7TC4I5s9eomTWsEU+1CQNg+j+nmb4mEr1enFoxe20nQHBLKMyC5c+DXqd0ogqvzIqhbdu2sWnTJkaNGkWdOnVYunRpqZ/bsWNHDAbDbZd58+YB8nYgd3p88uTJZdOYB6ErgvTrlbgV9QyVnCILt55lBATB3KlUEpP61GZiz5oA/LIrmqlrIhVOJTw0lRoG/Qi2zhC7F3Z/rXSiCq/MiqFff/2VOXPmMHbsWGxsbBg+fHhZvVXFkp8O+uurvLr4KxqlvBgMBnacvQpA6yqiGBIEcyJJEs93qML/HpP3t5q3J6Zko2XBjHmEQc/P5dtbP4LLEUqmqfDKbGr9fzdxLYt1ASok1U3/pHqdXKFbuOiUHOKv5aFRS7SsLIohQTBHjzYJIjkrn8/XRzF51SlCPB3pWEPseG/WGg6Hs+vg9GpY9iw8tx1sTbvIsKUotwHUZbmXWYWivmmZe12hcjnKUXGvULMwT5zsrGbpKkGwOC92qMJjTYLQG+CVxUeJSspSOpLwMCQJ+nwNzn6QchY2TVI6UYVVIWeTmTVrLIbOyeuUtK/uo3ASQRAehiRJfDSwXsm0+zHzDnIlK1/pWMLDcPKCAdc3PT/4M5zbpGyeCqpUf8bv2LHjod6kffv2D/V8s6K2kbffMOhBZ/kbBRYU6dh7QT4F2kEUQ4Jg9mxtVPw4qgkDv99DdEoOY+Yd5PfnWuEsen3NV9Wu0Px5OPAjrHwZXtwrF0lCiVL9dHfs2BFJkox+E53Oyqb1qW2hKB90BUonKXOHYq6Rp9VRycWOmn4uSscRBMEE3B1t+fXJZjw6ew8nEzJ5adER5oxuikYtTiaYrW5T4OK266tTvwqPL5RPowlAKYuhJ5544rZiKC0tjdWrVyNJEg0aNCAsLAyAS5culSyW2KdPHzw9rXABvpJiyPJ7ho7HZwDQsrLXQxXMgiBULGHeTsx5shnDftrHjrNXefvv4/zvsQbi/7m50jjAoz/Dz13gzBo4uhAaj1I6VYVRqmKoeH2fYsnJybRo0YLOnTsza9YsatWqdcvjZ86cYezYsRw/fpy9e/eaLKzZUF9fdNAKxgwlZeQBEORhfjvUC4Jwbw2D3fluRCOe/e0wy44k4O9mz5s9aiodSzCWfwPo/D5s/hDWvQ1hbaxqPbx7MarP85133qGgoIBVq1bdVggB1KxZkxUrVpCfn88777zz0CHNTvEgaisohhIz5MGV/m72CicRBKEsdK7py8cD6wLw3dYL/H4gVuFEwkNpPRZC24A2B5Y9Jy8ULBhXDK1fv54OHTrg6Hj39QqcnJzo0KEDGzZsMDqc2Sophiz/NFlyplwM+bmJniFBsFSPNwthXJdqAHyw8iSHYtIUTiQYTaWGgT+AnSvEH4Sd/1M6UYVgVDGUkZFBRkaGyY6zOMXbcZzfrGyOclDcM+TnKnqGBMGSje9ajZ51/dDqDLyw8AiJ10+RC2bIPQR6Xy+Ctn8GCYeVzVMBGFUMVa9ena1bt3L8+PG7HnP8+HG2bNlCjRo1jA5n9gx6pROUKa1Oz9VsecacnzhNJggWTZIkvnysATX9XEjJLuCFBYfJ11rZTGFLUu8xqDMIDDpYOdYqzmTci1HF0KuvvkphYSEdO3Zk6tSpREVFkZ+fT35+PlFRUUybNo1OnTpRVFTE2LFjTZ3ZfFTtqnSCMnUlqwCDATRqCS8n2/s/QRAEs+ZkZ8PPTzTF3VHDsfgM3l1+AoNB7HJvliQJen0BDp5w5RTsmaV0IkUZVQyNGTOGt956i4yMDKZMmULt2rVxcnLCycmJ2rVrM3nyZK5du8Ybb7zBmDFjTJ254tM4yddO3srmKGMxKTkABHs4olKJ6baCYA2CPR35bnhj1CqJZUcS+GH7RaUjCcZy8oYeH8u3t38GqReUzaMgo1fQ+vTTT9mzZw8jR44kLCwMW1tbbG1tCQ0NZeTIkezevZvPPvvMlFnNg14vj9IHsLXsRQijrxdD4d5OCicRBKE8tanqzYd9awPw2fozrD2RqHAiwWgNhkLljvLaeGteAyvt6Xuo9dVbtGhBixYtTJXFMhRm37ht56xcjnJQXAyFiWJIEKzOE63CuHg1h3l7YnjtjwgC3B1oGOyudCzhQUkS9JkB37eC6O1wbIm8272VEWurm1pxMSSpwcayBxXHiJ4hQbBqH/SpTeealSgo0vPM/EPEX8tVOpJgDM/K0PH6moAb3oWcFGXzKOChi6GioiKSk5OJjY2968WqFFwvhuycLX7fF3GaTBCsm1ol8c2wRtTydyUlu4Bn5h+ioEjMMDNLrV4B33qQdw3WT1Q6TbkzuhjavHkzHTt2xNnZmYCAAMLDw+94qVzZypb6LsySry18vFCRTk9smvxXoCiGBMF6OdvZMPfJpng723ImKYs5u6KVjiQYQ62Bfl8DEpxYahXr5N3MqDFDa9asYeDAgeh0Ojw8PAgPD8fFxbI//Eut4HoxZOHjhbQ6A0V6eaCdm4NG4TSCICjJ382B93rX4rU/jjHr3/MMaBhIgLtYld7sBDaBFi/A/tnyYOqX9oGtdfyxa1QxNGXKFPR6PTNmzOCVV15BrVabOpf5Kj5NZmvZxZCN+sYpwCKddc4+EAThhgENA1m8P5aDMdf4aO1pvhveWOlIgjE6vy/vap8eC1s/hh4fKZ2oXBh1muzUqVO0atWKcePGiULovwpvGjNkwWxuWldIq7fslbYFQbg/SZKY0q8uKgn+OZ7InvPWNwjXItg5y7PLAPZ9bzVbdRhVDDk7OxMSEmLqLJah+DSZhfcMSZKE5nrvkFYniiFBEKB2gCujWoYC8oauYjC1marWTd6uw6CHVeOsYqsOo4qhrl27cujQIVNnsQza61NLNY7K5igHNir5x0dbJE6TCYIgm9CtBt7Odly4msPsbda7orHZ6/EJOHhA8gmr2KrDqGLos88+IzMzk7fffpuioiJTZzJvzn7ydWaCsjnKQd71TRovpGTf50hBEKyFm6OGyf3k1am/33qB81eyFE4kGMXZRy6IwCq26jBqAPWvv/5Kz549+fLLL/n777/p2LEjQUFBqFS311aSJPHBBx88dFCz4VNDvr56Rtkc5SgrXxTEgiDc0LueP8tqJrDlzBUmLjvBH8+1EvsXmqMGQ+H4H3BxK6weB6NXW+z6eUYVQ5MnT0aSJAwGAxcvXuTixbtv1Gd1xZB3dUCC3FR5FU8L36wVINTT8k8JCoJQepIkMW1AXfZ9tZ2DMddYcjCWES1ClY4lPKibt+qI2Qkn/oT6Q5ROVSaM7hkS7sLWEdxDIP0SXI2y6GLIRiVRpDdQydVO6SiCIFQwge4OvNG9BlPXRPLlhij61A8Qa5KZI89waPc6bJ0ujx2q95hF9g4ZVQyNHj3a1Dksi0+N68XQGQhro3SaMqHT31h00c5GLK8gCMLtnmgVyuIDsZy/ks33284zsWctpSMJxmj2NOz8EpKOQ/xBCG6udCKTExu1loXicUMpZ5XNUYYKi25Mp7e1ET9GgiDczkatYmLPmgD8ujtGbORqrhw9oe5g+faBn5TNUkbEp1hZ8JH/81vyIOqbiyE7UQwJgnAXnWtWomVlTwqL9Hy5IUrpOIKxmj8rX59aAVnJikYpC0Z9ilWuXLnUlypVqjzw6+/YsYO+ffsSEBCAJEmsWLHilscNBgOTJk3C398fBwcHunbtyrlz54xpStnwLp5RZrn/8Qt08rR6Sbp1NWpBEISbSZLEe73kqfYrIi5zPD5d2UCCcQIaQlBz0GvhyHyl05icUcVQTExMqS/R0Q++g3FOTg4NGjTgu+++u+Pjn3/+Od988w0//PAD+/fvx8nJiR49epCfn29Mc0zPp7p8nZUI+RnKZikjBVq5Z8jORoVkgYPpBEEwnXpBbgxsFAjApJWn0OvFQq1mqflz8vWhuRa3KrVRA6j1d9mLymAwEBcXx8aNG/nwww8ZOXIkn3322QO/fs+ePenZs+dd32PmzJm8//779O/fH4DffvsNX19fVqxYwdChQ+/4vIKCAgoKCkq+zszMBECr1aLVlu6bWnzcfY9XO2Lj4o+UlUhRUiSGwKalev2K5l7t1RbJ9+kNkF9QiNpCeodK/T22EKK9lq+itPn1rlXYeCqJiLh0/jh4icGNA8vkfSpKe8tLuba3ei9snCrJn22nVmKo1b/s3/MOyqKtksFgKJMSPSIiglatWvHNN9/w7LPPGv06kiSxfPlyBgwYAMDFixepUqUKR48epWHDhiXHdejQgYYNG/L111/f8XUmT57MlClTbrt/8eLFODqafp2cVuc/p1LWSY6GPE2sVweTv77S9AZ464AarV7i3YZF+DoonUgQhIpuy2WJlZfUONsYeK+RDkej/hwXlFQz8W9qJK0kxbkGu6u9p0iG3Nxchg8fTkZGBq6uriZ5zTL7UWzYsCHNmzdn1qxZD1UM/VdSUhIAvr6+t9zv6+tb8tidTJw4kQkTJpR8nZmZSXBwMN27dy/1P6ZWq2XTpk1069YNjebe62WoNu6Cgyep729H3a69SvX6Fc392js/YT8RcRl4V2tEr/r+CiQ0vQf5HlsC0V7LV5Ha3E2n5+R3e7lwNYdIdWUm9app8veoSO0tD+Xe3sxGGL5dg3d2FL2ahkOl8l8uITU11eSvWaZ1ube3NwcPHizLtyg1Ozs77OxuXxxQo9E88A9QqZ5zfdyQOiMWtZn/h7xbe+sFuhMRl0FUcg6DzLyN/2XMz4U5E+21fBWhzRoNTO1flxG/7GfR/lhe7FiVAPey6VauCO0tT+XWXq8QqNkbTq9CEzEfev+v7N/zP8qinWU2JzotLY3du3fj7u5u0tf185M3Qk1OvnVqX3JycsljFYKjp3ydl65ojLJUN1DuUTt52TIHiQuCYHptqnrTLMwDvQHWn7x7b75QgTV7Rr4+9jsUWMZGvEb1DO3YseOuj2VnZ3P27Flmz57N1atXeeGFF4wOdyfh4eH4+fnx77//lowZyszMZP/+/bz44osmfa+HYu8uX+enK5miTNUJcAPgZEImBoNBzCoTBKFUetXz52DMNdaeSGRM23Cl4wgPKrw9eFWD1HPyRq7FxZEZM6oY6tix430/+AwGAx06dODTTz994NfPzs7m/PnzJV9HR0cTERGBp6cnISEhjB8/nunTp1OtWjXCw8P54IMPCAgIKBlkXSE4eMjXedeUzVGGqvu6oFFLZORpib+WR7DYsFUQhFJ4pK4fU1ZHcujSNZIy8vFzs1c6kvAgJEneomP9O3BwDjR92uz3KzOqGHriiSfuWgzZ2tri7+9Phw4d6NSpk1GhDh06dMtziwc+jx49mnnz5vHWW2+Rk5PDc889R3p6Om3btmX9+vXY21eg/1AO7vK1BZ8ms7VRUd3XhVOXMzl1OVMUQ4IglIq/mwNNQj04fOkaG04lMbp1mNKRhAfVYBhsngJXIiF2L4S2VjrRQzGqGJo3b56JY9yqY8eO3GvGvyRJTJ06lalTp5ZpjodSfJpMmwNFhWBjq2icslInwJVTlzOJTMzkkboVaMyW8NB0Oh07d+4kMTERf39/2rVrh1otNuUVTKNnXT8OX7rGqmOXRTFkjhzcof5jcOQ3OPiL2RdDYlOpsmLvduO2BY8bCvd2BiAuTWzAaEmWLVtGWFgYnTp1Yvjw4XTq1ImwsDCWLVumdDTBQvRtEIBKgsOXrhGbKn5/mKWmT8vXkasg+6qyWR7SQxdDCQkJLF26lBkzZjBjxgyWLl1KQkKCKbKZN5X6RkFkwafKgj3labGiGLIcy5YtY/DgwcTHx99yf0JCAoMHDxYFkWASvq72tKnqDcDyo+IzwywFNISARvJ+ZadXKp3moRhdDF29epUhQ4YQFhbGsGHDeOONN3jjjTcYNmwYoaGhPP7441y9at6V4kOzghllIdfHCcWKYsgi6HQ6xo0bd8fT1MX3jR8/Ht31jXoF4WEU71e2/Gj8PYdGCBVYnYHy9akVisZ4WEYVQxkZGbRv356//voLW1tb+vXrx6uvvsq4cePo378/dnZ2/Pnnn7Rv356MDCteg8YKBlEHe8jF0JWsAvK14gPS3O3cufO2HqGbFe8/uHPnznJMJViqHnX8cNCoiUnN5WhcutJxBGPUHiBfX9oN2VcUjfIwjCqGPv30U6KionjssceIiYlh+fLlzJgxg6+++oply5Zx6dIlhgwZQlRUlFEbtVqM4p4hC55e7+6owdlOHocff030Dpm7xMREkx4nCPfiZGdDt9ry1kqbI5Pvc7RQIXmEQkBjMOjh9Cql0xjNqGJo+fLlBAcHs3DhQnx8fG573NvbmwULFhAcHMzff//90CHNVvFaQxZ8mkySJII8iscN5SmcRnhY/v6l22OutMcJwv10rCF/huw4Z+XDKsyZBZwqM6oYunTpEm3atLnn/iAajYY2bdoQGxtrdDizV3yaLDdN0RhlrXjcUJzoGTJ77dq1Iygo6K7riEmSRHBwMO3atSvnZIKlaltNHkR9MiGT1OwChdMIRqndX74241NlRhVDDg4OpKSk3Pe4lJQUHBzKZhM+s+BdQ76+tFvZHGWsuBiKSrKMPWqsmVqt5uuvvwa4rSAq/nrmzJlivSHBZCq52FPLX97ncNf5+3+uCBWQRygENjHrU2VGFUNNmjRh+/btHDp06K7HHD58mG3bttG0aVOjw5m9mr3l60u7Icdy/5O3uf6X3doTiRQW6RVOIzysQYMG8ddffxEYGHjL/UFBQfz1118MGjRIoWSCpWpfXf4dsj1KnCozW8UDqc30VJlRxdBrr72GVqulS5cufPDBB5w+fZq8vDzy8vI4c+YMH374IV27dkWn0/Haa6+ZOrP58AgF/wZytXzmH6XTlJn21XzwdbXjWq6Wf0+LQZCWYNCgQcTExLB161YWL17M1q1biY6OFoWQUCY6Vq8EwJaoKxTpxB9UZqnOAPnaTE+VGVUM9erVi48++oicnBw+/vhj6tati7OzM87OztSpU4fp06eTlZXF9OnT6dmzp6kzm5da/eRrM+06LA21SmJQ4yAA/jx892nZgnlRq9V07NiRYcOG0bFjR3FqTCgzzcI88HDUkJ6r5UCMZY+xtFjuITfNKlutdJoHZvSiixMnTmTfvn2MHDmSsLAwbG1tsbW1JSwsjFGjRrF3714mTpxoyqzmqbgYurjdotcbeqyJXAxti7rClcx8hdMIgmBObNQqutaSp9hvPCV6l81Wce9Q5AolUxjFqGJo1apVrFu3jqZNmzJ//nwuXLhQcprswoULzJs3j2bNmpk6q3nyqQ4+NeXlys9uUDpNmans40yTUA/0BlgmltYXBOEB9agjb/S88VSSWI3aXBXPKovZZXbjZI0qhgYOHMg333xj6iyWq1Zf+dqCT5XBjd6hPw/FiV9mgiA8kLbVvHHQqLmckc+FqzlKxxGM4REG/g3N8lSZUcWQj48PHh4eps5iuYpPlZ3/Fwot9z957/r+2GtUXLiaI5bWFwThgdhr1Hg52wKQXVCkcBrBaMW9Q5HmtXGrUcVQx44dOXDggPjrv7T86oF7KBTlwfnNSqcpMy72GnrVlVcm/lsMpBYE4QHZquWPJK2YUWa+iouh6B2Qk6pslgdgVDE0bdo0UlJSeO2118jPF4Nl70uSoPb13qFIyz5V1q9hAACbTyej14tiWRCE0tMUF0NivTLz5VVF7gAw6CDKfJaUsTHmSUuWLKFXr17MmjWL33//na5duxISEoK9vf1tx0qSxAcffPDQQc1erX6wZ5Y8iLqoAGzslE5UJlpV8cLJVk1yZgEnEjJoEOyudCRBEMyExkZe5bxQ9AyZt/AOkHQCrpxROkmpGVUMTZ48GUmSMBgMXLlyhcWLF9/1WFEMXRfYFFz8ISsRLm6D6j2UTlQm7GzUdKjhw9oTSWw+nSyKIUEQSq34NFm+VhRDZs1JXlGcPPNZM8qoYujXX381dQ7Lp1LJ23Mc/AXObbTYYgigW21f1p5IYlNkMq93r6F0HEEQzISTnfyRlFsoBlCbNQdP+dqMNik3qhgaPXq0qXNYh9DWcjGUcFjpJGWqU41KqFUSZ5KyiEvLJfj6Rq6CIAj34mQrfyTliNlk5s3xejFkRj1DRq9ALRghoLF8nXRSHjdkodwdbWkWJi+9sClSrCYrCELpFPcMZRfoFE4iPJTinqG8a8rmeACiGCpPHmHyD4leC8knlU5TpoqX1t8sNm4VBKGUnO3k/e9Ez5CZc7SS02SVK1cu1XG2trZ4eXnRqFEjRowYQatWrYx5O8shSRDQCC78CwlHILCJ0onKTPfafkz/5zT7o9NISM8j0N1B6UiCIFRwGrHOkGUo7hnKTwe9Xh4zW8EZVQzFxMQ80PF79+5l9uzZjB07lpkzZxrzlpYjsLFcDF0+qnSSMhXi5UiLcE/2R6cxfU0ks0dabuEnCIJpFF1fm8xGLSmcRHgoDtd3qDDo5YKouKeoAjOqXNPr9YwbNw43Nzfee+89jh8/Tnp6Ounp6Zw4cYL3338fd3d3xo4dS2xsLIsXLyYgIKBkXSKrVjxuKOGIsjnKweR+dVCrJNadTGL72atKxxEEoYIr7hGyMYOeBOEebGxB4yTfzk9XNEppGfUT9/333zN79my2bt3KtGnTqFu3Lq6urri6ulKnTh2mTp3K1q1b+eGHH1ixYgVDhw5l9erVSJLEL7/8Yuo2mJfA68VQShQUZCubpYzV8ndldKswACavOkVBkRgUKQjC3RXp5J4hjegZMn92zvK1mezHaVQxNHv2bDp27EjDhg3vekyDBg3o3LkzP/zwAwANGzakSZMmHD1q2aeH7svFD1wC5O7DxGNKpylzr3Wrho+LHdEpOfy846LScQRBqMC0+us9Q2rRM2T2bK8XQ2byR79RP3EXLlwo1a717u7uXLx44wMwPDyc7Gzz+IcpU8W9Q5ct/1SZi72G93vXAuDbreeJS8tVOJEgCBWV9nrPkI1K9AyZvZKeIfP4zDeqGPLw8GDXrl0UFhbe9ZjCwkJ27dp1S9GUmZmJu7u7MW9pWQIayddWMG4IoF+DAFpW9iRfq2fqmkil4wiCUEFdvCp/cAaI2afmz9ZFvi7IUjZHKRlVDPXr14+EhASGDh1KbGzsbY/Hx8czbNgwLl++TP/+/UvuP336dKmn5Vs0K+oZAnl/uqn962KjktgUmcyWM2LtIUEQblVQpONssvzBWS/QTeE0wkOzhp6h6dOnU7VqVVasWEHVqlVp06YNQ4cOZejQobRp04YqVaqwfPlyqlSpwrRp0wA4cuQISUlJ9OrVyyTBs7KyGD9+PKGhoTg4ONC6dWsOHjxoktcuc8U9Q9diICdF0SjlpbqvC2PahgPw4apT5BWKwdSCINwQlZSFVmfA3VFDkIfoGTJ71jBmyMvLi7179zJmzBhsbGzYu3cvS5cuZenSpezduxe1Ws3TTz/N3r178fLyAqBx48bk5eWZbAf7Z555hk2bNrFgwQJOnDhB9+7d6dq1KwkJCSZ5/TLl4AF+9eTb+39QNks5erVLNfzd7IlLy+Prf88pHUcQhArkREIGIPcKSZIYM2T2bK9PrTeTniGjFl0EuSD65Zdf+Prrrzl8+DCJiYkA+Pv706RJE5ycnEwW8r/y8vL4+++/WblyJe3btwdg8uTJrF69mtmzZzN9+vTbnlNQUEBBwY39wDIzMwHQarVotdpSvW/xcaU9/l6kNm9g8/doDHu/p6jRU+Bc6aFf09RM2V4AOxV82LsmLyyO4OedF+lZx4fa/q4meW1TMXWbKzrRXstnLm0+FivvY1XH3+WhsppLe02lorZXpdKgBnSF+ehNnK0s2ioZDAaDMUE0Gk2pjo2NjSUkJOSBg91LVlYWrq6ubN68mS5dupTc37ZtW2xsbNi2bdttz5k8eTJTpky57f7Fixfj6KjAruoGA+3PTsEj9yIXfbpxImhU+WdQyK9RKiLSVAQ7GZhQT4eYOCIIwmfH1FzOlXiquo6GXg/8sSRUMA1i5xCWup3T/oM569fPpK+dm5vL8OHDycjIwNXVNH9QG1UMDR48mD///PO+XZkxMTF07tz5lun1ptK6dWtsbW1ZvHgxvr6+LFmyhNGjR1O1alWioqJuO/5OPUPBwcGkpKSU+h9Tq9WyadMmunXrVupi8F6kmB3YLBqEQaWh6MV94B760K9pSqZub7ErWQU88s1usvKLeK9XDZ5sVXHaXVZtrqhEey2fObQ5LaeQFp9uA2Dv2x3wdrYz+rXMob2mVFHbq175AqqTf6HrOhV9i5dM+tqpqan4+/ubtBgy6jTZsmXLeP755/npp5/uekx0dDQdO3YkPj7e6HD3smDBAsaMGUNgYCBqtZrGjRszbNgwDh8+fMfj7ezssLO7/T+YRqN54B8gY55zR9W6QOWOSBe3odn1JQysmOOHTNbe6wI9NbzTsybvLT/JjM3n6VU/sMJt5GrqNld0or2WryK3+UicPJGkuq8z/h7OJnnNitzeslDh2quTOx/Udk6oTZyrLNpp1ADqRx99lDlz5jBx4sQ7Pn7x4kU6duxIXFwcX3755UMFvJsqVaqwfft2srOziYuL48CBA2i1WvObut9lknx97HdItp41eIY1C6FZmAe5hTo+WHESIzooBUGwEHsvpgLQqrKXwkkEk9Hmydc2FesP3bsxqhhavHgxnTt35vPPP+eLL7645bHz58/ToUMH4uLimDlzJq+99ppJgt6Nk5MT/v7+XLt2jQ0bNtyyrpFZCGwCtfoBBthy+8BvS6VSSXwyqB4atcSWM1dYeyJJ6UiCIChk74XrxVAVUQxZDG2+fK2xVzZHKRlVDGk0GlasWEHTpk155513mDt3LgBRUVF07NiRhIQEvvnmG1599VWThr3Zhg0bWL9+PdHR0WzatIlOnTpRs2ZNnnrqqTJ7zzLT+X2QVBD1D8SZyVpJJlC1kgsvdawKwKSVJ0nMyFM4kSAI5e1qVgHnrmQjSdAiXBRDFqPICnqGQO6RWbduHTVr1uT555/nyy+/pHPnzly+fJlvv/2WV155xZQ5b5ORkcHLL79MzZo1eeKJJ2jbti0bNmyoWOdMS8unBjQcLt/+dwpY0SmjlzpVoba/K6k5hby06AiFRXqlIwmCUI7+PBwHQN0ANzycbBVOI5iMNfQMFfP09GTTpk0EBgby9ttvk5SUxOzZs3npJdOOHL+TIUOGcOHCBQoKCkhMTOTbb7/Fzc2Ml3Dv8A6obSFmJ1zYonSacmNno+aHkU1wtbfhaGw60/+xnnFTgmDt8rU65u6KAeDJ1mGKZhFMrKRnyDyKoVLNJtuxY8c9H3///fd56aWXGDp0KLVq1brt+OKFEYV7cA+GZs/Cvu/k3qHKnUD1ULWq2QjxcmTm0IaMmXeI3/ZeolGIOwMbBSkdSxCEMvb3kXhSsgsIcLOnX8MApeMIpqLX39hqyt48OilKVQx17NjxvmsKGQwGFi1axKJFi257TKcT+1CVSrsJcGQ+JB6D0yuhzkClE5WbzjV9ebVzVb7Zcp6Jy05Q08+VWhVsdWpBEExHpzfw0w55Dbpn2lVGo7aOP/6swtUzUJAJGifwrqF0mlIpVTH0xBNPiL1iyoOTN7QeC9s+gX+nQc0+oDbDMVBGGte1OhHxGew4e5UXFx5m5SttcXOwnvYLgjVZdzKRS6m5uDtqGNo8WOk4ginFH5CvAxuD2uhdv8pVqVLOmzevjGMIJVq9DAd+grQLELEImjypdKJyo1ZJfP14Q/rM2kVMai6vLz3GT6OaoBL7dQiCRdHrDXy/9QIAo1uF4WhrHh+YQinFXS+Ggpsrm+MBiH7JisbOBdq/Kd/e9umNhaushIeTLbNHNsbWRsXm08nM3n5B6UiCIJjYmhOJRCZm4mxnw2gxcNrylBRDLZTN8QCMKoays7M5fvw4KSkpdz0mJSWF48ePk5OTY3Q4q9V0DLiFQFYi7P9R6TTlrn6QO1P71QHgy41R/Hs6WeFEgiCYSmGRni83yPtHPt++Mp5iOr1lyU2D1HPy7aBmymZ5AEYVQ1999RWNGjXiwoW7/9V+4cIFGjVqxNdff210OKtlYwed3pVv7/oK8q4pm0cBQ5uHMLxFCAYDvLrkKGeSMpWOJAiCCfx+MJbYtFy8ne14ul240nEEU4u/vnCwVzVw9FQ2ywMwqhhavXo1VatWpUWLu3eBtWjRgipVqrBixQpjs1m3+kPApxbkZ8Dub5ROo4gp/erQuooXOYU6np53iJTsAqUjCYLwEHIKivjmX7nXYFzXamKskCUyw/FCYGQxdPHiRWrWrHnf42rVqkV0dLQxbyGo1Dc2cd03GzITlc2jAI1axfcjGhPm5UhCeh7PLzhMQZFYpkEQzNUvO6NJyS4kzMuRoc3EDDKLFLdfvraGYigvLw8Hh/vvN+Lg4EB2drYxbyEA1OgpD0AryoMdnyudRhHujrb8MroZLvY2HL50jWfmHyK3sEjpWIIgPKCIuHS+23oegDd61BDrClmiwlyIPyTfNqPB02BkMRQcHMzBg/ffUPTgwYMEBIhVRY0mSdB1snz78HxItc6ZVVUrOfPjqCY4aNTsPJfCiF/2k55bqHQsQRBK6UpWPi8sOEyhTk+32r70ruevdCShLFzYIv/x7h4CPvc/e1SRGFUM9ejRg5iYGGbMmHHXY77++muio6N55JFHjA4nAKGtoVp3MOhgy3Sl0yimdRVvFj3bAjcHDUdj0xny416SMvKVjiUIwn0UFul5edERkjLzqeLjxFdDGohFfC3VmX/k65p95D/mzYhRxdBbb72Fq6srb7zxBn369GHVqlWcOnWKU6dOsWrVKvr06cOECRNwdXXlrbfeMnVm69PlQ0CCU8vgcoTSaRTTOMSDP19oha+rHWeTsxn8wx6iU8TSDYJQkU3/J5KDMddwsbPhpyea4mIvVpW3SLoiOLtOvl2zt7JZjGBUMRQUFMSqVavw8vJi7dq1DBw4kPr161O/fn0GDhzI2rVr8fLyYuXKlYSGhpo6s/Xxqwv1HpNv/ztV2SwKq+7rwl8vtCbc24n4a3kM+XEvZ5OzlI4lCMIdLD0Ux297LwEw4/GGVPFxVjiRUGZi98jLwDh6QXBLpdM8MKNHsLVr146oqCg+/fRTunbtSo0aNahRowZdu3bls88+Iyoqig4dOpgyq3Xr9C6oNHDhXzi/Wek0igr2dOTPF1pRy9+Vq1kFDP1pHycTMpSOJQjCTQ5fSuP9FScBeK1rdbrW9lU4kVCmik+RVe9pNvuR3eyhhvN7eHjw1ltvsWHDBiIjI4mMjGTDhg28+eabeHh4mCqjAOAZDs2flW+vGievP2TFvJ3tWPJsCxoEuZGWU8jwn/dxNNb6FqcUhIro/JUsxsw7RGGRPGB6bOeqSkcSypLBcNN4IfM7RQZibzLz0vl98AiHzHjY8K7SaRTn7mjLwmda0DTUg8z8IkbNOcD+i6lKxxIEq5aUkc/ouQfJyNPSKMSdb4Y2EpstW7rEY5ARBxpHqNJJ6TRGMUkxlJ6eTlxcHLGxsXe8CCZi6wQDvgckOLoQzm5UOpHiXOw1zB/TnNZVvMguKGLknP3M3RWNwWBQOpogWJ2MPC1P/nqAhPQ8Kns7MWd0Mxxs1UrHEspaca9Q1S6guf8ahBWR0cVQUlISzzzzDJUqVcLLy4uwsDDCw8Nvu1SuXNmUeYXQ1tDyJfn26letct+y/3Kys2Huk83oWdcPrc7A1DWRPL/gMBm5WqWjCYLVKCjS8fyCQ5xJysLHxY75Y5qLTVitQWEuHF0g367ZV9ksD8GoYigxMZGmTZsyd+5c7Ozs8PHxwWAw0LJlSypVqlTyV3mrVq1o166dSQMLQJcP5E3wshJh3dtKp6kQ7DVqvh/RmCn96mCrVrExMple3+wU44gEoRxodXom/HGMfRfTcLazYd5TzQj2dFQ6llAe9n0nfxa5h0CdAUqnMZpRxdD06dO5fPkyU6dOJS4ujp49eyJJErt37yYxMZFt27ZRs2ZNJEli3bp1ps4saBxgwGyQVHD8Dzi9RulEFYIkSYxuHcbfL7Ym9Pp+Zo/9sJefd1wUp80EoYykZhcw8pf9/HMiEY1a4sdRTagT4KZ0LKE8ZF+FXV/Lt7t8CDZ2yuZ5CEYVQ+vXryc8PJz333//jo+3b9+ejRs3cvToUaZNm/ZQAYW7CG4GrV+Vb68ZDzli4HCxekFurBnblt71/SnSG/ho7WleWHiYzHxx2kwQTOlkQgb9vt3N/ug0nGzVzB7RhDZVvZWOJZSXHZ9DYRb4N4Q6g5RO81CMKoYSEhJo2LBhyddqtTxArqCgoOS+wMBAOnXqxNKlSx8uoXB3nd4Fn1qQcxXWvq50mgrFxV7Dt8MaMW1AXWzVKjacSqbfrF2cTsxUOpogWIQVRxN4dPYeEtLzCPd2YsXLbcRaQtYk9QIcmivf7j4NVOY9Od2o9K6urrd87e7uDshF0s3s7e1vu08wIRs7GDgbJDWcWg4nlymdqEKRJIlRLUP584VWBLo7EJOay8Dvd7PsSLzS0QTBbBXp9Hz0TyTj/4igoEhPxxo+rHi5DdV8XZSOJpSnzZNBXyTvnRneXuk0D82oYigkJOSWKfN169YFYO3atSX35ebmsnv3bvz9xe7EZSqgEbS73iv0z+uQfUXZPBVQg2B3Vo9tS7tq3uRr9UxYeoyJy06I02aC8ICu5RTy5K8H+XlnNAAvd6rCnNHNcHMQ+41ZlbgDcHqVPG616xSl05iEUcVQ586dOX78OFevXgWgX79+ODk58eabb/LOO+8wa9YsOnXqRHJyMj179jRpYOEO2r8JvvUgLw3WvCavBircwtPJlnlPNWdcl2pIEiw5EEvnL7fz9+F49Hrx7yUI93MgOo3e3+xk1/kUHDRqvhvemDd71EQtFlS0LgYDbPxAvt1wBPjWVjaPiRhVDI0YMYJBgwYRGRkJgKenJz/++CMGg4HPP/+c8ePHc/DgQWrXrs1HH31k0sDCHdjYyqfLVBo4swaOi3Fad6JWSbzWrToLn25BZW8nUrILeP3PYwz+YY/Y20wQ7qJIp2fm5rMM/WkvlzPyCfNyZPnLreldX/T6W6UzayBuH9g4yONWLYRRu6k1aNCAJUuW3HLfsGHDaNOmDWvXruXatWtUr16dfv36odGI7tNy4VcPOrwNW6fD2jcgtJW87oNwmzZVvVk/vj1zd0fzzb/nOBKbTt9vdzG8eQjjOotFQgWh2OX0PMb/HsGBmDQAHm0cxJT+dXC2M7+NOAUTyM+4sbZdq5fANUDZPCZk0p/okJAQXnjhBVO+pPAg2r4G5zZC/AFY9jw8uQZUYin8O7G1UfFChyoMaBjIJ+tOszLiMov2x/LP8UR6+Es8Ik6dCVZu/ckk3v77OBl5Wpxs1Xw0sB4DGgUqHUtQ0sYPIDMBPMJujFW1EOY9F064ldoGBv0Ets4Quwd2zVA6UYXn52bP10Mb8ftzLanp50J6npY/LqoZ+ssBIi+LafiC9cnX6nh/xQleWHiYjDwtDYLcWDuunSiErN2FrXBkvny7/3fyXpkW5IGKIZ1Ox/Hjxzly5AiZmbd+UJw7d44JEybQt29fhg0bxsKFC00a9L85PvjgA8LDw3FwcKBKlSpMmzZNrDIM4BkOvb6Qb2/7BBKOKJvHTLSs7MWasW15t2cN7FQGjsZl0PfbXUxbE0l2QZHS8QShzBkMBjaeSqL3NztZuE+eLfx8+8r8+UJrQr0s64NPeEAFWbBqrHy7+XMQ1lbZPGWg1KfJfv/9d1599VVSU+WVjjUaDS+99BJfffUV69evp3///hQVFZUUJEuXLuXvv/9m+fLlJg/92WefMXv2bObPn0+dOnU4dOgQTz31FG5ubrz66qsmfz+z02AYnN0AkSvg72fghZ0WV8WXBRu1iqdah2KXfIq9BYGsP5XMnF3RrDl+mUl96tCrnh+SJGbOCJbFYDCw81wK/9sYxbF4eSKBt7MdXw1pQPvqPgqnEyqETZMgIw7cQ+VtNyxQqYqhvXv3MmLECAwGAzY2Nri5uZGamsrXX39N1apVmTJlCvb29jz//POEh4dz8eJFfvrpJ1atWsX8+fMZPXq0SUPv2bOH/v3707t3bwDCwsJYsmQJBw4cMOn7mC1Jgj4zIP4gpF2A9ROh3zdKpzIb7nYwa2ADdl+8xqSVp4hNy+XlxUeo6efCc+0r07dBABq1OMMsmL+DMWl8sSGKA9HyAGkHjZqn2oTxfPsquDmKyS8CcHHbjZWm+38Lds6KxikrpSqG/ve//2EwGHjnnXeYMmUKGo2G6OhoHn/8cd59911ycnI4dOgQDRo0KHnO8OHDadasGb/99pvJi6HWrVvz008/cfbsWapXr86xY8fYtWsXX3311V2fU1BQcMt2IcWn+bRaLVpt6RbfKz6utMcrSuOC1Pdb1IsGIR2Zj86rGvrmDza43azaayI3t7lNZQ/+eaUVP+6IZu6eS5xJymLC0mN8sSGKJ1uFMKRpkNnPqrG277G1tRfu3OYTCRnM2Hyeneflnn5bGxXDmwXxQvtwvJztbjvenFjb97hM21uQhc3KV5AAXZMx6INaQQX4dy2LtkqGUgy0CQwMxMnJibNnz95y/65du2jfvj2tW7dm165dtz2vbdu2REVFlSzOaCp6vZ53332Xzz//HLVajU6n46OPPmLixIl3fc7kyZOZMuX2lTIXL16Mo6OjSfNVJNUTV1ArSd6m42TgMC5UEotgGiO3CHYlSexIUpGllU+VOagNtPE10N5fj5utwgEFoRQu58K6OBXH0+SeTZVkoKWPgR5BetzNd8NxoYzUj5tPeMq/5Np6s6Xmx+jU9kpHAuQdLoYPH05GRsZt24MZq1R/1l69epXWrVvfdn+jRo0ACA0NvePzQkND2b9//0PEu7OlS5eyaNEiFi9eTJ06dYiIiGD8+PEEBATctRdq4sSJTJgwoeTrzMxMgoOD6d69e6n/MbVaLZs2baJbt27ms36SoSe6HeGod/2PuglLqFWjOvpWY0v1VLNs70O6V5sHAwVaHSuPJfLLrhiiU3PZfFlie7KagQ0DeLZdGGFmNtDU2r7H1tZekNs8b8UmjhcFsOH0FQwG+Ux6//r+vNK5CqGelvXHoLV9j8uqvdLFbdgc/RcA28E/0aMC7T9WPHbZlEpVDBUVFeHicvsmfE5O8i9+O7s7/0lha2uLXq9/iHh3Vrztx9ChQwGoV68ely5d4pNPPrlrMWRnZ3fHnBqN5oF/gIx5jqK6TgK1BrZ/inrLFHn5/LavlfrpZtdeE7hbmzUaDSNahTOsRRibTyfz046LHLp0jaWHE/jrSAK96vnzUseq1A4wzV8r5cXavsfW0t7Dl9KY9e85tp21AeR9C3vW9WNCt+oWv7GqtXyPi5m0vbH74a/rn6VNnsKmehfTvK6JlMX31SwHPOTm5qJS3TqAVa1Wl0nhZTE6TZQ31dv28fXdhnXQ/g2lU5ktlUqiex0/utfx41BMGt9vu8CWM1dYczyRNccT6VTDh5c7VaVpmKfSUQUrUzw77Lut59l/fWC0hIFe9fx5uVM1syvUhXKWcAQWDQZtDlTuBI98qnSicmGWxVDfvn356KOPCAkJoU6dOhw9epSvvvqKMWPGKB2tYuv4NqhUsGU6bJkGBj10eEvpVGavaZgnc5/0JPJyJrO3X+Cf45fZGnWVrVFXaR7myQsdK9O+mg82YgaaUIb0egMbI5P5ftt5jl+fIq9RSwxsGEA13SWefLS+VfWUCEZIOgELBkJBJoS2gaGLQVMxxgmVtVIXQ/Pnz2f+/Pm33S9J0l0fKyuzZs3igw8+4KWXXuLKlSsEBATw/PPPM2nSpHLLYLbavyn3EP07FbZ+JN8WPUQmUTvAlVnDGvF6t+r8uOMCfx9O4EBMGgfmpeHpZEuPOr70qudPq8peojASTEar07P62GW+33aB81eyAbDXqBjWPIRn21XGx8mGtWsvKZxSqPCunIHf+kN+OgQ1h+F/gK1ljSe7l1IXQ8au7lwWi9S5uLgwc+ZMZs6cafLXtgrtXgdJDZs/lHuIHL2g6VNKp7IYYd5OfDKoPuO6VOeXnRdZdjSBtJxClhyIY8mBODwcNTxS108URoLRMvO1bI+6yr+nk9kadZWMPHmqsYu9DaNbhfFUmzCznyIvlKOU8/BbP8hNhYBGMPIvsLPsMWX/VapiSIzFsUBtx8tLrO/8Ev6ZAI6eULu/0qksip+bPe/3qc07PWuyPzqNNccT2XAq6bbCqEcdP3rXF4WRcG+xqblsOp3Mv6eTORCdRtFNmwl7O9vyVJtwRrUKxdVenAoTHkBaNMzvC9nJ4FsXRi4DezelU5U7sxwzJJhI5/chNwUOz5O37XDwgAo0fdJS2KhVtKnqTZuq3kzrX4f90Wn8cyKR9Sflwuj3g3H8fjAOb2c7+tT3p3/DABoGu4utP6ycwWDg3JVs1p1IYt3JRM4kZd3yeBUfJ7rW8qVLLV8ah7iLQlp4cOlxco9Q1mXwrgFPrJT/MLZCohiyZpIEvb+Su0ZPr4Ylw+HJNRDQUOlkFuvmwmhqv1sLo5TsAubtiWHenhhCvRzp3yCAfg0DqVrJMpe/F25nMBg4dTmTdScTWXcyiYtXc0oeU6skmod50qVWJbrW8iXM27zWtBIqmMxEuRBKjwXPyjB6FTh5K51KMaIYsnYqNQz6RZ5KGbMTFj4KT28ErypKJ7N4NxdGU/rVYde5FFZGJLAxMplLqbl8s+U832w5T50AVwY0DKRXfX8C3R2Uji2YWL5Wx94LqWw+ncyWM1dIzMgvecxWraJtNW8eqetHt1q+eDiJpc4FE7h2CRYOgrSL4B4Co1eDi5/SqRQliiFBnjo5dDHM6w1Jx2HBAHhyLThZ93+O8qRRq+hUsxKdalYit7CITZHJrIy4zI6zVzl1OZNTlzP5aO1p/N3saRjsTsNgdxoEu1Mv0A0nM98fzRolZ+az9cwVNp++wu7zKeRpdSWP2WtUdKxeiZ71/OhcsxIuYgyQYEqJx+U/frOTwS1YLoTcgpROpTjxW1SQ2bvCyL9hbg/5r4Uf2yMN+FHpVFbJ0daG/g0D6d8wkLScQv45kciqiAQOX7pGYkY+iRlJrDuZBIBKguq+LrcUSNV9XeRVxoUKIV+r42RCBhFx6RyNTSciLp2E9LxbjvFztadLrUp0qVWJ1lW8sdeoFUorWLSL2+H3EVCYBZXqyLPGXAOUTlUhiGJIuMG5EjyxCv4YAYnHUC8ZQnW/gWB4ROlkVsvTyZZRLUMZ1TKUnIIiTlz/UI2ITedYfDqJGfmcScriTFIWvx+MA8DRVk3DYHdaV/GiVRVvGgS5icG15cRgMBCblsvhS9c4EnuNiLh0ziRm3TLzC+ThevUD3ehSy5fONStRJ8BVDJgXytbJv2HZ86DXQmhbGLoIHNyVTlVhiGJIuJV7MIzZCOvfRjo8j1pJy9D/ngGDf7HaWQYVhZOdDS0re9GyslfJfcmZ+XJxdL1AOh6fTk6hjj0XUtlzIRU4i7OdDc3DPWldxYvWVbyp6eeCSvQcmURBkY6TCZkcuXSNQ5fSOHwpnZTsgtuO83GxK+m9axTsTr0gN3H6Syg/+2bD+nfk27X7w8CfrGZl6dISxZBwO4099P2aooCmsGYCNhf/hR/bw2PzIaiJ0umEm/i62tOjjh896sjju3R6A+evZHMgWi6G9l5MJT1Xy5YzV9hyRt6o08NRQ6vrhVHjIFfyioxfVNUa5Gt1xF/LJe5aHvFp8nVcWi5x13I5m5xNYdGt67Bp1BJ1A91oEuJBoxAPGoa4E+BmL3p+hPKn18uL6+75Rv66+XPyXmMqcRr2v0QxJNyVof5Qdp1Lp2PyHKRr0fJ4okc+gWbPyP38QoWjVknU8HOhhp8Lo1qFodcbiEzMZO+FVHZfSOFAdBrXcrWsPZHE2hNJ159lwwdHNuPtbIe3sx1ezrZ4Odnh7WKLt5P8dfH9xcdY2pikgiIdsam5XLiaQ3RKDhevZhOdksOltFyuZt3e03MzTydbmoR6lFzqBbqJMT+C8ooKYdUrcPwP+esuH0Lb18Tv7rsQxZBwT5kOIRSN+RfN2nHyWkRr34C4A9B3JtiKdU4qOpVK7qWoG+jGs+0ro9XpOR6fzu7zqey5kMKJhAxyCnRodYbrg7Pz7/uaGrVEgLsDwR6OBHs6Eux543aIpyMejppS9YIYDIYy6S3R6w1k5mvJzCsiq0BLVn4R2flFZBcUkZ5bwKEEidObzpFdqCMuLY/olBzir+Wiv0fnmLOdDUEeDgR7OsrX19tbtZIzYV6OotdHqFgKsmDpE3Bhi7z1Uv9voeFwpVNVaKIYEu7P3hWGLIC938KmD+HEUkiJgmG/i5kIZkajVtEk1JMmoZ682qUaWq2WFavX0rxdJ9Lz9aTmFJCSXUhKdgGp2YWkZt/4OiW7kLScArQ6A5dSc7mUmnvH93CyVV8vGuRCSSVJpOUU3nbJ0+pwsbPB1UGDq4MGNwcbXO3l23Y2KmxUEiqVhI1KQq1SoVaBWiXfD5CZp+Varpb03EKu5RaSnqvlWm4hGXnaexY2oIbY6NvudbazobKPE5W9nQj3dibcx4kwL0eCPRxxL2WBJwiKS4+FP0ZBYgRoHGHIb1Ctm9KpKjxRDAmlI0nQeiwENIaloyDxGPzUCYYtgcDGSqcTHoKtGgLcHQjV3H9Ar05vIDkzn7i0XGLTboyjib0+hiY5s4CcQl3JDLf7ySooIqug6Lap5qbgoFHjYm+Di70NzvYaXOxscLJVkX41iZpVw3BzsMXf3UEufnyc8HG2EwWPYL70Otg/FzZPAW2OvAH38D/FOM9SEsWQ8GDC2sCzW2DxULh6Gn7tBQN/gDoDlE4mlAO1Sj5FFuDuQIubZrUVkwcb5xF3LbdksLGEPK7m5ouXkx0Otmqy8rVk5heRkaclM08rX+drKSzSo9cbKNIb0N10XXzbYDDg6qDB3VGDh6MtHo4a3B1tS267OWqws7l93I5Wq2Xt2rX06lUTzf/bu+/wqKr0gePfmXRII5SEQBIIgdCL1ASWhI40QQQXkA0rRRT8yVpWlHVBdBdlXVhdkUVRUNFFxCAWWpAWKaEvHSmhk0RaEggpzJzfHyeFmEL6ZDLv53nuM5N7z73zvnMZ5p17zz23CMWfENbANfUydp8Ngst79Az/EBj6gb7NhigSKYZE8dVooG/ZsfJJOB0FX0fAtb9A9xelc56Nc3awI6iOa5Hvp1bbzamcIxKiCruXjvHneYSf+AdGdQ8cXaHP69D+STDK2GLFIe+WKBlnd91nqMsz+u/Nb0LkRMh4cAdcIYQQpXR5H3wYjt3WOdipe5iD+sCUGH21rxRCxSbvmCg5O3t9qf2g+WC0h8Nfw6eD4HaCpSMTQoiqKT0F1s+Axb0h4SjKxYu9AZMxjfxS7jFWClIMidLr8CQ8EQnOHnBpDyzoBHsW6w59QgghysbZrbAwRF/Zq8zQagT3ntrBZa9Q6aJQSlIMibIRGAYTNoF3S7h7E358ARaFwfkdlo5MCCGs291b8N2z8NkQuHkO3OvB6BUwfDFUr2Xp6KoEKYZE2akVBJO2wsP/0EeJ4g/Dkodh5XhIumLp6IQQwrooBUe/hQWdYf9nel6H8fDMLmjSz6KhVTVSDImyZWcPnSfBsweg/R8BAxxZCf/uANH/hHuF39pACCEEEH8UPh2sr9a9HQc1g2DcGhg0T1/AIsqUFEOifFSvqW/ZMWkL+HXWg4D9NFv/wjm5ztLRCSFE5ZRyA358Ef7TDc5Fg50TdP8zTN6ux3kT5ULGGRLly7ctPLleX2m24TW4GQv/fRyC+sDDb0PNRpaOUAghLM90D/Ytgc1/0/0uAZoNgb5vQo0Ay8ZmA+TIkCh/BgO0HgnP7oWu08DooAdrXBiqT52ZMiwdoRBCWE5sNCzqrm+Effcm1GkOf/gOHv9cCqEKIsWQqDhObnp01CkxENgD7qXqU2cfhsOlfZaOTgghKtatC/ru8p8OgoSj4OwJA96Bp6L1FbqiwkgxJCpezUYwdhUMWwQuXhB/BBb3grXTIe3BN/cUQgirlp4Cm/8O73eEY6vBYNQjR//fAeg0UV+IIiqUFEPCMgwGaPN7mLoXWv8eUBCzEBZ0gV/WWzo6IYQoe0rBkW90EbT1bX10vMHv9JGggf+Eal6WjtBmSTEkLKt6TXh0kR7B2jMAki7BlyPh63GQHG/p6IQQovSUgvM7YelAfYPrpEvg4Q8jPoWI78GnpaUjtHlyLE5UDkG94JmdsOUt2LkAjq6CM5ugxwxo9wQ4Vrd0hEIIUTypiXBoBez9BBKO6Xn2LtDtT9D1/8DBxbLxiWxSDInKw7E69H0DWj2mh56/+j9Y+2d9qelDf4COE+XKCiFE5XflIOz9GA6vhIwUPc/eBVqP0GMGefpZNDyRlxRDovKp20bf52zfEtj1Adw4Czv+rY8YBQ+AzpOhQTe5MaEQovJIT9H9gfZ+Alf258yv3VTfzLr14+DiabHwROGsthhq0KAB58+fzzP/mWeeYcGCBRaISJQpO3t9VUWH8XpMol0L4exmOPGDnuq0gM5P6fGL5FCzEMJSEk7oH24H/wtpiXqenSM0f0QXQf4h8sPNClhtMbRnzx5MJlP230eOHKFPnz6MGDHCglGJMmc06hsSNumn/9PZvQj+t1yPyfH9/8HGmdB+nL4s1aO+paMVQtiCe2lw/Ht9FOj89pz5NRroezK2e0LuJm9lrLYYql27dq6/33rrLRo1akRYmAxUVWXVaQqD5kOvv8L+z2H3R5B4AX6eD9vfg2aDocsz4N/Z0pEKIaqiG7GwbykcWAYp1/Q8g1Gfvu/wRwjsqX/ACatjtcXQ/dLT01m2bBnPP/88hgIOR6alpZGWlnPH9KSkJAAyMjLIyCja7SCy2hW1vbWrtPnau0Knp6HDJAy/rMO490OM57fDsW/h2LeY/bpgDn0O1ah3sQ9PV9qcy4nkW/XZWs5lnq8pHcPpjRj3f4rh7CYMKACUW13MbcdibvsEuPtmtjXpqQLZ2v6F8snVoJRSZb7VCrZixQpGjx7NhQsX8PX1zbfNrFmzeP311/PM//LLL6lWrVp5hyjKmfvdCwQmbMDv5naMSv9nlOjizynvQVzx7Igy2Fk4QiGE1VBmat7+hfo3d+J7azeOpjvZi+LdWnGuVk/iPdrK/ysWkpKSwujRo0lMTMTd3b1MtlkliqF+/frh6OjI999/X2Cb/I4M+fn5ce3atSK/mRkZGURFRdGnTx8cHBxKHXdlZ5X5Jl3FuPsDjPs/w5Ch/wNTNRpi7jIFc+vfg71zoatbZc6lIPlWfbaWc4nzVQriDmE8FonxaCSG5Ks5i6rXwdxqJOaHIqBGw3KIuuRsbf8CXL9+nbp165ZpMWT1p8nOnz/Pxo0biYyMLLSdk5MTTk5OeeY7ODgU+x9QSdaxZlaVb01/ePgtCPuz7lMU8x8MN2OxW/sidtHvQMgz+goPJ7dCN2NVOZcBybfqs7Wci5zv9TN6PKDDX8P1UznznTyg+RBo9RiGBr/DzmhHZT4OZEv7tzzytPpiaMmSJdSpU4eBAwdaOhRRmVTzgvCXIXQq7PsUdr4PSZch6q8Q/U/oNEmPVyRXfAhhe0wZ+gapuz+EizE58+2doUl/aDUCGvcB+7w/oEXVZNXFkNlsZsmSJURERGBvb9WpiPLiWF0fDeo4AQ6vgJ//pX/9bfsH7HgfWg7Xl+036vHAo0VCCCt3O0FfDbb3E8g6DWaw05//ViP0VWHOZXPaRVgXq64gNm7cyIULF3jyySctHYqo7Owd9dgfbUbpQRuj58HVg3BwmZ6MDhAQijGoD9VTHS0drRCiLF3er48CHfkGTOl6nqu3HtS1/Thw87ZoeMLyrLoY6tu3L1Wg/7eoSEY7PTJssyF6sLTjP8Cp9fqWH7FbsYvdSm9ALfyPPlzepB/4h+piSghhPVJuwIVoiFkEl3bnzK/XQZ8ib/6IfK5FNqsuhoQoMYNB39+sQTfd4fraafhlHeZf1sG5HRhvnNX3Rdv1ATi6QaNwaNwPGveVX5FCVDbpd/SNnS/vx+7SXnqf3o7DgYSc5UYHaPkodHoK6re3XJyi0pJiSAiAWkFQayqmjk+x4ftv6NfYEfszP8GpDXAnQQ+9fzxz6AbfdrowCu4PddvKfYeEqEimDIg/Cpf36RuiXj4Avx4HZQbACFTPauvVSN+/sP0f5UeMKJQUQ0L8xj07F1TTAdDqUTCbdd+iX9br02lXDuRMW9/SY460GKZ/dXq3lMJIiLJmyoBLe/WNms9ugSsHwZSWt51bXajXHpNPG2IuZtDxkUk4uNfO206IfEgxJERhjEao95CeerwCyfFwOgp+WQenf4KbsfDzPD3VbKyLohbDoE4zS0cuhHVSSo/9c2aTLoBioyE9OXcbZw/wzfxc1muvn7vXBcCckcGva9aAi2fFxy6slhRDQhSHm7e+Kq3dE7qfwi/r4EgknIrSl+xvfVtPtZtlFkaP6lNwQoiCpdzQR33ObNKPiRdzL3fxgsBwfQl8QFfwCpSjsKJMSTEkREk5VtfjFLUcDmnJcHKtLoxOb9R9GDb/TU8+rfTRohaPglflGspfCItIuQHnd+grOs/9DHGHgfuuDLZzBP8uENhDF0A+beRu8KJcSTEkRFlwctMdNVuPhLu34MSPcDRS/8qNO6ynn2brztfNh+rD+3WaywjYwjbcTsgsfLbrx4RjedvUaaELn8AeEBAKjnIDbVFxpBgSoqy5eEK7MXpKuQHHv4OjqyB2W07n6yzVaun+RVlT7WZQpym41LBY+EKUilL6NNfF3fqoz/ntcO2XvO1qN9VFT0BXPcSFm0/FxypEJimGhChP1bz0CLftx8HtX+H4at3xOuEY3DwHKdfgXLSe7udW977iKKtQCpZbhojKw2yCW+fh15Pw64nMx5O68Em/nbe9d8vMwqerHsjUVa70EpWHFENCVBTX2voeaR0n6L/T7+gvj4Tjuo9RwnFIOAFJl/R9k5Kv6g6l9/PwzyyOmurTbPU7SmdSUb7MJj1C+68n7it6TsC1U3AvNf91jA7g3RwCumUWPyH6h4EQlZQUQ0JYimP1nMv275eamFMk3V8o3Y6HxAt6OrU+p331OuDfGfy66C+duq3BzqFicxFVQ3qKPmoZdyinr1v8UchIyb+9nRPUaqKPWtZumvPo1VD+DQqrIsWQEJWNswf4ddLT/VJu3FccndBfWFcO5B0h294F6nfQV+P4dQG/jnqbQtzvzrWcoudq5uP1U9kjOedi75K34KkdDDUa6Pv9CWHlpBgSwlpU89KnHBp0zZmXkaoLoou74MIuuBgDd2/+ph+SAbxb5BRH/l3A088iKYgKYDbD3RuZp1rjMdy6RJO4aIzrtujCOTkOEi/B7bj8169eG3xa6yEhfFrp5zUbSdEjqjQphoSwZg7OEBCiJ9BfhNd+ySmOLuzSo2THH9HTnsW6nXs98O+C0acNftcvYjihoHoNcHYHp8zJ2R3snSyXm8hLKUi5DjfPw61zcOuCLmyS47KLH27Hgfle9ir2QDOAq/lsz6uRLnjqts4pgFy9pQ+asDlSDAlRlRiNmZ2rm+or2EB/UV6MySyOdupTIkmX4cg32B35hocALnyU//bsHHMKIye3zOce9z13z/3c2RPcfXWx5exeMTlXNXdv6au0bp7Xxc6tzMesvzPuFG071WqBW13Mrt5cvJlO/WYdsfOoq69UdKsLtZvI1YlCZJJiSIiqzs0Hmj+iJ9BXsV3aCxdjMMcd4ddLsdR2d8KYnqxH0k5NyrkXlCldX/6fcq34r+vkrosij3qZj/X1o7tvzvOqMrDevXRIvaVPUWZNacn6vc5I0R2T02/n/zzjjm6XngJpSXoqlEEXM57+UCMAPPz0fblcfTILHW/dqd7eEQBTRgYH16zBN3wAdg7SqVmI/EgxJIStcawOgWEQGIYpI4Nda9YwYMAAjPd/UZpN+ss8LVl/Oacm3fc8sYD5SbqvStLlzDZJ8GuS7vBdEJca4F7/voKpnv7b3VdPoO9abkoHc0bOc1Pmc3PW3/cyH9P1KaIC2hiVmeaXz2PcvB/sHcBgB0Z7fUQt+7ld5nO7nH4yqYm5C53s6ZZ+zG9cndKoXhs8A3IKnuznDXQhKacvhShTUgwJIfIy2umRtEt65++027ooSryU+XhZP97/PP12TlERf7gsoy+QHdAYIGFNOWzdoE8hutTQ75uTuy48Harpx6zp/r9/+9zJrWodMRPCSkgxJIQoe06umZdgB+e/XCl9tCW7OLoESVdynide1uMqGYz6aI2dox63xs5BPzc65P7bzqGQeVnP7TGZzcSeOUXDBv7YofQRMPM9UCbd+Tz7+b3MZSYdb3aRk9/kqR+dPeSKKyGslBRDQoiKZzDkHHnyblFhL2vOyOBo6hoC+kj/GSFEDqOlAxBCCCGEsCQphoQQQghh06QYEkIIIYRNk2JICCGEEDZNiiEhhBBC2DQphoQQQghh06QYEkIIIYRNk2JICCGEEDZNiiEhhBBC2DQphoQQQghh06QYEkIIIYRNk2JICCGEEDZNiiEhhBBC2DQphoQQQghh0+wtHYClKKUASEpKKvI6GRkZpKSkkJSUhIODQ3mFVmnYWr5gezlLvlWfreUs+VZ9ycnJQM73eFmw2WIo68308/OzcCRCCCGEKK7r16/j4eFRJtsyqLIsrayI2WzmypUruLm5YTAYirROUlISfn5+XLx4EXd393KO0PJsLV+wvZwl36rP1nKWfKu+xMRE/P39uXnzJp6enmWyTZs9MmQ0Gqlfv36J1nV3d7eZf3Rge/mC7eUs+VZ9tpaz5Fv1GY1l1+1ZOlALIYQQwqZJMSSEEEIImybFUDE4OTkxc+ZMnJycLB1KhbC1fMH2cpZ8qz5by1nyrfrKI2eb7UAthBBCCAFyZEgIIYQQNk6KISGEEELYNCmGhBBCCGHTpBgSQgghhE2TYugBbty4wZgxY3B3d8fT05Px48dz+/btQtcJDw/HYDDkmiZPnlxBERfPggULaNCgAc7OznTu3Jndu3cX2v7rr7+madOmODs706pVK9asWVNBkZad4uS8dOnSPPvS2dm5AqMtnW3btjF48GB8fX0xGAx8++23D1xny5YtPPTQQzg5OREUFMTSpUvLPc6yUtx8t2zZkmf/GgwG4uLiKibgUpozZw4dO3bEzc2NOnXqMHToUE6ePPnA9az1c1ySfK39M7xw4UJat26dPahiSEgIa9euLXQda92/UPx8y2r/SjH0AGPGjOHo0aNERUXxww8/sG3bNiZNmvTA9SZOnMjVq1ezp7lz51ZAtMXz1Vdf8fzzzzNz5kz2799PmzZt6NevHwkJCfm237FjB6NGjWL8+PEcOHCAoUOHMnToUI4cOVLBkZdccXMGPbLr/fvy/PnzFRhx6dy5c4c2bdqwYMGCIrWPjY1l4MCB9OjRg4MHDzJt2jQmTJjA+vXryznSslHcfLOcPHky1z6uU6dOOUVYtrZu3cqUKVPYtWsXUVFRZGRk0LdvX+7cuVPgOtb8OS5JvmDdn+H69evz1ltvsW/fPvbu3UvPnj155JFHOHr0aL7trXn/QvHzhTLav0oU6NixYwpQe/bsyZ63du1aZTAY1OXLlwtcLywsTD333HMVEGHpdOrUSU2ZMiX7b5PJpHx9fdWcOXPybT9y5Eg1cODAXPM6d+6snnrqqXKNsywVN+clS5YoDw+PCoqufAFq1apVhbb585//rFq0aJFr3uOPP6769etXjpGVj6Lku3nzZgWomzdvVkhM5S0hIUEBauvWrQW2qQqf4yxFybcqfYaz1KhRQy1evDjfZVVp/2YpLN+y2r9yZKgQO3fuxNPTkw4dOmTP6927N0ajkZiYmELX/eKLL6hVqxYtW7bklVdeISUlpbzDLZb09HT27dtH7969s+cZjUZ69+7Nzp07811n586dudoD9OvXr8D2lU1Jcga4ffs2AQEB+Pn5PfAXirWz9n1cUm3btqVu3br06dOH7du3WzqcEktMTATAy8urwDZVaR8XJV+oOp9hk8nE8uXLuXPnDiEhIfm2qUr7tyj5QtnsX5u9UWtRxMXF5Tlcbm9vj5eXV6F9CkaPHk1AQAC+vr4cOnSIl19+mZMnTxIZGVneIRfZtWvXMJlMeHt755rv7e3NiRMn8l0nLi4u3/bW0r+iJDkHBwfzySef0Lp1axITE3nnnXcIDQ3l6NGjJb7Rb2VW0D5OSkri7t27uLi4WCiy8lG3bl3+85//0KFDB9LS0li8eDHh4eHExMTw0EMPWTq8YjGbzUybNo2uXbvSsmXLAttZ++c4S1HzrQqf4cOHDxMSEkJqaiqurq6sWrWK5s2b59u2Kuzf4uRbVvvXJouh6dOn8/bbbxfa5vjx4yXe/v19ilq1akXdunXp1asXZ86coVGjRiXerqh4ISEhuX6RhIaG0qxZMxYtWsQbb7xhwchEWQgODiY4ODj779DQUM6cOcP8+fP5/PPPLRhZ8U2ZMoUjR47w888/WzqUClHUfKvCZzg4OJiDBw+SmJjIypUriYiIYOvWrQUWCNauOPmW1f61yWLohRdeYNy4cYW2CQwMxMfHJ0/H2nv37nHjxg18fHyK/HqdO3cG4PTp05WmGKpVqxZ2dnbEx8fnmh8fH19gbj4+PsVqX9mUJOffcnBwoF27dpw+fbo8QrS4gvaxu7t7lTsqVJBOnTpZXUExderU7As8HvRr2No/x1C8fH/LGj/Djo6OBAUFAdC+fXv27NnDu+++y6JFi/K0rQr7tzj5/lZJ969N9hmqXbs2TZs2LXRydHQkJCSEW7dusW/fvux1N23ahNlszi5wiuLgwYOAPiRfWTg6OtK+fXt++umn7Hlms5mffvqpwHOzISEhudoDREVFFXoutzIpSc6/ZTKZOHz4cKXal2XJ2vdxWTh48KDV7F+lFFOnTmXVqlVs2rSJhg0bPnAda97HJcn3t6rCZ9hsNpOWlpbvMmvevwUpLN/fKvH+LXUX7Cquf//+ql27diomJkb9/PPPqnHjxmrUqFHZyy9duqSCg4NVTEyMUkqp06dPq9mzZ6u9e/eq2NhYtXr1ahUYGKi6d+9uqRQKtHz5cuXk5KSWLl2qjh07piZNmqQ8PT1VXFycUkqpsWPHqunTp2e33759u7K3t1fvvPOOOn78uJo5c6ZycHBQhw8ftlQKxVbcnF9//XW1fv16debMGbVv3z71+9//Xjk7O6ujR49aKoViSU5OVgcOHFAHDhxQgJo3b546cOCAOn/+vFJKqenTp6uxY8dmtz979qyqVq2aeumll9Tx48fVggULlJ2dnVq3bp2lUiiW4uY7f/589e2336pTp06pw4cPq+eee04ZjUa1ceNGS6VQLE8//bTy8PBQW7ZsUVevXs2eUlJSsttUpc9xSfK19s/w9OnT1datW1VsbKw6dOiQmj59ujIYDGrDhg1Kqaq1f5Uqfr5ltX+lGHqA69evq1GjRilXV1fl7u6u/vjHP6rk5OTs5bGxsQpQmzdvVkopdeHCBdW9e3fl5eWlnJycVFBQkHrppZdUYmKihTIo3L///W/l7++vHB0dVadOndSuXbuyl4WFhamIiIhc7VesWKGaNGmiHB0dVYsWLdSPP/5YwRGXXnFynjZtWnZbb29vNWDAALV//34LRF0yWZeO/3bKyjEiIkKFhYXlWadt27bK0dFRBQYGqiVLllR43CVV3Hzffvtt1ahRI+Xs7Ky8vLxUeHi42rRpk2WCL4H8cgVy7bOq9DkuSb7W/hl+8sknVUBAgHJ0dFS1a9dWvXr1yi4MlKpa+1ep4udbVvvXoJRSxTuWJIQQQghRddhknyEhhBBCiCxSDAkhhBDCpkkxJIQQQgibJsWQEEIIIWyaFENCCCGEsGlSDAkhhBDCpkkxJIQQQgibJsWQEEIIIWyaFENCWIHNmzczfPhw6tWrh6OjIzVq1CA4OJgRI0bw/vvvk5iYmKt9eHg4BoOBc+fOWSbgEihJzGazmU8++YQePXrg5eWFg4MD3t7etGnThokTJ/LFF1+UX8BWaNasWRgMBpYuXWrpUISoVKQYEqKSmz17Nj179iQyMhIPDw8GDRpE3759cXFxITIykmeffZbjx49bOswKl56ezsCBAxk/fjzbtm0jODiY4cOHExoaSnJyMosXL2b8+PG51jl37hwGg4Hw8HDLBF3Oxo0bh8FgYMuWLZYORQirYm/pAIQQBdu3bx+zZs3CwcGBFStWMHTo0FzL4+LiWLZsGZ6enrnmf/bZZ6SkpFCvXr2KC7aCvf/++6xbtw4/Pz82bNhA06ZNcy0/evQon376qYWiE0JYEymGhKjEIiMjUUoxcuTIPIUQgI+PDy+++GKe+f7+/hUQnWV98803APz1r3/NUwgBtGjRgrlz51Z0WEIIKySnyYSoxH799VcAateuXaz1Cut/s3XrVnr27Imbmxs1atRgwIAB7N27l6VLl2IwGJg1a1aB2/r222/p0qUL1atXx8vLi1GjRnHp0qU8r3H16lXmzp1LWFhYdj8nHx8fHn30Ufbs2VOsXApS3Pdm1qxZNGzYENDvgcFgyJ7GjRuX3c5gMNCgQQPS09OZPXs2TZs2xcnJKVcxmpKSwpw5c2jXrh2urq64urrSpUuXAo9EZW3TZDLx9ttv06RJE5ycnPDz8+Pll18mLS0t3/UOHTrE4MGD8fT0xM3Nje7duxMVFcWWLVvyjTvr9Xv06JErv/z+HRw+fJghQ4ZQo0YNqlevTlhYGDt27CjSeylEVSPFkBCVmJ+fH6CPgiQkJJR6e5GRkfTq1YvNmzfTsmVL+vfvz4ULF+jWrRsxMTGFrvvBBx/w2GOP4eLiwoABA3B1dWX58uX07NmTu3fv5mq7evVqXn75ZeLj42ndujXDhg3D19eXVatW0bVrVzZs2FDqXLLem8WLF5ORkfHA9m3btmX48OEAeHt7ExERkT1169YtV1uz2czQoUOZO3cujRo14pFHHqFu3boAJCQkEBISwquvvkpcXBxhYWF0796dEydOMG7cOJ599tkCYxg9ejRvvvkmwcHB9O3bl+TkZObOnZunbxPAzp07CQkJ4YcffiAgIIBBgwaRmppK//79iYyMzNM+IiKCRo0aAdCvX79c+bm6uuZqu3fvXrp06cK5c+fo168fjRs3Ztu2bfTq1YsjR4488L0UospRQohK68yZM8rFxUUBys3NTUVERKiPPvpI7d+/X927d6/A9cLCwhSgYmNjs+clJiYqLy8vBagvvvgiV/vXXntNAQpQM2fOzHdb1apVUzt27Mief+fOHRUaGqoA9fHHH+da59ChQ+rIkSN54lq3bp1ydHRUjRo1Umaz+YExF+a///1vdsz+/v5q2rRp6quvvlKnT58ucJ3Y2FgFqLCwsALbZG0zKChIXbp0Kc/yAQMGKEA999xzKjU1NXt+XFyc6tChgwLU2rVr891ms2bN1NWrV7Pnnz17Vnl6eiogV9wmk0k1adJEAepvf/tbrm0tXrw4e3sRERG5lkVERChAbd68Od/cZs6cmb3uu+++m2vZtGnTFKDGjh1b4HsjRFUlxZAQldzGjRuVn59f9pdY1uTp6amefvppdeXKlTzr5FdYfPTRRwpQvXr1ytM+IyNDBQQEFFoMzZgxI896K1euzPdLuTBjxoxRgDp06NADY36QefPmqerVq+d5bxo0aKDmzJmj7t69m6t9cYqhr7/+Os+yAwcOKEB17NhRmUymPMv379+vADVkyJB8txkVFZVnnalTpypALVmyJHteVFSUAlTjxo3zfZ2uXbuWqhjq2rVrnmXXrl1TgAoICMh3XSGqMjlNJkQl16tXL06fPk1kZCSTJ0/moYcewt7enlu3brFw4ULatm3LyZMnH7id7du3AzBixIg8y+zt7bNPIRWkb9++eeY1adIE0H2EfistLY3Vq1czY8YMJk2axLhx4xg3bhyHDx8G4NSpUw+M+UH+9Kc/ceHCBT788ENGjx5N48aNAX0J/SuvvEKPHj3ynMIrCoPBwODBg/PMzzq9N3ToUIzGvP99ZvUh2r17d55lDg4O9OjRI8/8/N7DrH01fPjwfF/n8ccfL2Im+ctvX9asWRMvL69896UQVZ1cTSaEFXB0dGTYsGEMGzYMgFu3brF8+XJeffVVEhISmDp1KlFRUYVuI+tLLquvzW896Aq0+vXr55nn5uYGkKcDcFbn3MIGUExOTi709YrKy8uLiRMnMnHiRADOnz/PggULmDdvHrt27WLevHnMmDGjWNusU6cOTk5OeeZn5TNjxoxCt5mamppnno+PD3Z2dnnm5/celnZfPUh++zIrlhs3bpRq20JYIymGhLBCnp6eTJ48GV9fXx555BE2b95MSkoK1apVK7fXzO8IRX5U5lAA586dY/LkyUyePJnAwEBcXV0xGAy8+uqrzJkzB6VUucQZEBDA3LlzuXfvHvPnz+fHH38sdjHk7Oyc73yz2QxAt27dsjsrF1VR37+KUJliEaIykGJICCvWs2dPAEwmE7du3Sq0GMq6GurixYv5Li9ofnGdOHGCEydO0KFDBxYuXJhn+dmzZ8vkdR6kZ8+ezJ8/n2vXrpXZNrOOqAwdOpQXXnihzLb7WxW1r4QQmvw8EKISe9DRk9OnTwP6NFqtWrUKbdu1a1cgZ7DC+5lMpnwv1y6JmzdvAvmfirl58+YDT+cVVVHfm/tH4XZ0dATg3r17JXrNPn36ALBq1aoSrV9UWftq1apV+ea5YsWKfNcrbX5C2CophoSoxF577TVeeuklzpw5k2fZ5cuXeeqppwAYMmRI9hdhQUaMGIGXlxdRUVEsX74817I333yT2NjYMok5KCgIo9HIpk2bcnWSTk1NZfLkyWXWJ2XIkCG89957+W4vJiaGN954A4DHHnsse36tWrVwcHDgzJkzmEymYr9m586d6dOnD9u3b2fKlCkkJSXlafO///2PdevWFXvb9+vZsyeNGzfm5MmTeUbRXrp0KdHR0fmu5+vrC1CkDvVCiBxSDAlRid2+fZt33nmHoKAggoODGTZsGKNGjeJ3v/sdDRs2ZPfu3QQFBfGvf/3rgdvy8PDgo48+ws7OjlGjRhEaGsro0aNp1aoVf//735k0aRLAA4uqB6lTpw7jx48nKSmJNm3aMGjQIEaMGEGDBg3YtGlTrlGTS+PixYs899xzeHt707FjR0aOHMljjz1Gu3bt6NKlCzdu3GDw4MHZBSPo3Pr3709cXBxt2rThD3/4AxMmTGDJkiVFft1ly5bRrl07PvjgAwICAujRowdjxoxh0KBB+Pv707Zt21IXQ0ajkU8//ZRq1aoxffp02rZty+jRo+ncuTNPPvkkU6ZMyc7nfoMHD8ZgMPDiiy8ydOhQJkyYwIQJE7h+/Xqp4hGiqpNiSIhK7C9/+Quff/45TzzxBE5OTkRHR7Ny5UqOHTtGp06dmDt3LgcPHizyDVkfffRRNm7cSHh4OIcOHeLHH3/E19eX6Ojo7CuUatasWeq4Fy5cyD//+U8aNmzITz/9RHR0NL1792bv3r0EBASUevsAK1eu5L333uPhhx8mKSmJtWvX8t133xEfH8/AgQP58ssvWb16Nfb2ubtGLl68mLFjx3L9+nW+/PJLPv74Y7Zu3Vrk161Tpw47duzgvffeo3nz5hw4cICVK1dy6NAhAgMD+cc//pHv/eKKKyQkhB07djBo0CBiY2P57rvvcHBwYM2aNYSEhAB591X79u1ZtmwZzZs3Z8OGDXz88cd8/PHHZXblnhBVlUGV1yUdQgir0r9/f9avX8+uXbvo3LmzpcMRhZg8eTKLFi1i+fLlpR5zSAghxZAQNuXy5cvY29vj7e2dPc9sNvPuu+/y/PPP06RJE06cOIHBYLBglALgxo0bJCUl0aBBg1zzv/rqK8aMGYObmxuXLl2ievXqlglQiCpELq0XwoZER0fzxBNP0K5dOwICAkhLS+PIkSOcO3eOatWqsXjxYimEKolffvmFkJAQWrduTWBgIADHjx/n5MmT2NnZsWjRIimEhCgjcmRICBty6tQp5syZQ3R0NPHx8aSmpuLj40N4eDjTp0+nefPmlg5RZEpISGD27Nls2rSJK1eucOfOHWrVqkVoaCgvvvhidr8hIUTpSTEkhBBCCJsmV5MJIYQQwqZJMSSEEEIImybFkBBCCCFsmhRDQgghhLBpUgwJIYQQwqZJMSSEEEIImybFkBBCCCFsmhRDQgghhLBp/w+u2X47Neb78wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make it prettier\n",
    "p68    = mData.mncontour(\"mu\",\"b1\",cl=0.68)\n",
    "p95    = mData.mncontour(\"mu\",\"b1\",cl=0.95)\n",
    "# close the boundaries\n",
    "x68 = p68[:,0]\n",
    "y68 = p68[:,1]\n",
    "x68 = np.append(x68,x68[0])\n",
    "y68 = np.append(y68,y68[0])\n",
    "x95 = p95[:,0]\n",
    "y95 = p95[:,1]\n",
    "x95 = np.append(x95,x95[0])\n",
    "y95 = np.append(y95,y95[0])\n",
    "\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(x68,y68, label=\"68%\")\n",
    "ax.plot(x95,y95, label=\"95%\")\n",
    "ax.grid()\n",
    "ax.scatter(mData.values[0], mData.values[1], color='black', label=\"fit value\")\n",
    "ax.set_xlabel(\"Signal Strength\", size=15)\n",
    "ax.set_ylabel(\"Background, first bin\", size=15)\n",
    "ax.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}