causality_paper/notebooks/example5_nonlinear.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 5 - Measuring non-linear dependencies\n",
    "\n",
    "Here we demonstrate that causal analysis is not limited to using linear regression methods\n",
    "to estimate causal effects. This example investigates the relationship between the Indian Ocean Dipole (IOD), ENSO and precipitation in Australia (AU) over the months Sept, Oct, Nov. \n",
    "\n",
    "<img src=\"../images/ex5.png\" width=\"500\" height=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import os\n",
    "import iris\n",
    "import iris.quickplot as qplt\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from scipy import signal\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1) Load the data + plot the time-series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "enso   = iris.load_cube('../sample_data/enso_son.nc')\n",
    "iod    = iris.load_cube('../sample_data/iod_son.nc')\n",
    "au     = iris.load_cube('../sample_data/precip_au_son.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nZiMY95cXxp0LJ/DO/9yD87ORku7PDSkXcwC1b/46Oi+tZVA2TtuG2jAyE67qMzTuj5ZdVnFhbvE9qXfOKVepB5TfmTyRyjTFiAvjZECOjPnxxMkZ/P7oVLOXopDJMITiKdlzkmbDNKMQdzGsJ0mNW0WxxxiTw3qLFwufs3rv5vBoAA6rCTuWtWcd9zmtVcuFp4NxHJEFA8cngmU99sGXJvDkqRk8e2a2pPvzMgWH1awcc9nNCNUyrLcQhd1iQrccWt065ANjwEvj+aIIfySJ7zx7rmh/wj/97vP46/sOl7UOtXFaiNavhZK+ceKeU2nv7S8OjeE1X3kSz5+fr+n6iiGMkwHh+ZxGtlc5NRXC3Y+f1j0vSU+lsAtX/zRDsad4Trb6tLepF8F4Col0RrkwAlJNT7U753OzEazsdGeF9IDahPWeOLnY3f/4ZHnG6bET0mMnS/S4/JHF1kUcj92CSA03H2MLMQy1OyG18YSi2NPyCv7poRP47M8O49DFBd3nS6QyeGnMjzFVS6RS4GIIu6U+Y1MAaTOk1VcPADxlCiJOT4cAAN966mzN1lcKwjgZkElZCddImeuP91/A539zTPcDu5iwtqDXx41TYxV7mQxDIJZEuyyIAFrHc1JaF2UZJ2vVcu9zs2Gs7HLlHZfCetU992MnptHtsaHPZy/Lc0qlM3ji5AwAYKJEVae6IznHZTPXdPNxcSGKoY7Fdk69Xju6Pfa8DuVz4QR+sPcCAG2vinN6OoRkmil/21I5PxdBu8uKwXZn3dR6gVgKiVQmr8YJWAzrlfrd4Z7eb18cx7i/PENcDcI4GZBJOVx2aiqEeKoxngFXMul90Xj+os1pleXQjQ/rce/N14I5p1legOtWhfUc1Y3NyGQYzs1FMNztzjsnhfWqe+4nTs7g+nU92NjvK8s4HbiwoISMJqswTh57jQUR81EMti0aJyLCtiFfXoTi20+fRTSZht1iKrhBPDYhnZsNx8sSjEjerquu05D1WhcBgMdmAVHpar0L81Gs7nEjwxi+88y5mq6zEMI4GRAeLktlGE5Ohhrymjw0MRvWNjhqNZXVbEKny9bwsJ56DYpxqjIn4Y8m8be/PIJHj0/Vdf7RjI7nVM3FaTIYQyKVwYrOfM/J67AgEK1can94zI+5cAI3ru/Bxn4vTk2HSm6189jxaZhNhMtWtFfpOVlqJiWPJdOYCcWzPCdACu2dnAohlpQ2geF4Ct9+5ixu3dyHncvbC3pOR8clg51Ms7I84AtzESzvdKG9ScbJZCJ4bKV3Jr84F8FVqzpxy6Y+fH/veeW9qjfCOBmQyUBMyes0KrTHZbYzOp6TP0dNJbUwamxYT22cahXWe/LkDL711Fm891vP4RVffgzffPJMzZqxquHKRrW0t81pRTSZrnja69kZKdwy3KXhOTmsSKQziFf43I8dnwYRcP26bqzv8yKRyuBsicq7x05M47IV7VjX68WEv7QNjJZxctstNSvC5WrD3C7tWwbbkM4wHJWN0A+eu4CFSBJ/dtMabB704dh4UBlumctRleGa0dnU5ZLOMFycj2JFp6tuAyeBxb56ud0hOKX214skUpgNJ7Csw4X3XbsK85EkfnZgtKZr1aNuxomI+ojoNfJPb71epxF88f5j+PUL4w17velgHFcMd8JtMxfcudWKRCqjeEG6Yb2cOpRen6PhnhNXNrW7bHDzkQpVGqe5iPScn3vtZrS7rPjfv3oJV3/+IfzT709Ut9gc+PvKO4YDi4a+UmPISw00c05VPvdjJ6axbagNXR47NvR7AQAnShBFzITieHHUjxvX96CvzYHZcLykejheQ6fGbTPXzHNSF+Cq2Tokd4oYCyCRyuA/nxjBVas6cdmKDmwe8CGaTOOsTknH0fEgBtok5WqpeadxfxSpDMOKThfaXda65ZwW++o5NM97HaWFfflwxuWdLly9uhObBnz45lNnGlKYXRfjRER3AdgL4M0A7gKwh4jeVI/XqjeMMXzrqTP44b4LDXvNyUAMfT4HNg3kx8PrwYQ/Bv5Z06tdyi2S7PXaq8o5Mcbw3m/txed+caTkx2SF9Wy1aW8zLw8AfMfVK3HfB6/FLz58LS5f2YF/+v3JmhZIzobjaHNaYbMsfuWU5q8V7p7PzUVgNZNSt6Ommv56/mgSBy4s4AZ51tHaXg9MBBwrIe/EFX43behFv88BxoqrOtMZhqBqlhPHbbcgkkjXJNw6uiB5fbme01C7Ex0uK46M+vHzg6MY98fwZzetAQBsllscHdGIXkwH45gJxXHd2m4AiznFYnClHvecArFkXcLJ52bDcKl6OOZSqufExRDLOiSV4/uuHcaJyRCePl1aiUA11Mtz+msAVzDG3sMYezeAKwF8tk6vVVcWIknEkhmcKLPOo1JC8RQiiTT6fHZsHvTh6HiwrrkQIHs6qN6XzB9NwmwiZZRBr9eOmVC84rX97OAoHj0+jT1n5kp+jNo4meS1VO05hRPwOiywyi1eti9rxx9etwrAYsK7FqhbF3Gqbf56bjaM5R0umE35Mzqr6QLw9KkZpDMMN26QjJPDasZwl7uk78BjxyWF3+YBH/rbpJDSRBE5eTCWvfHhcO84UoMcx+h8FCYC+tuyPQkiwtahNhy66Md/PD6CTQM+3Lhe+r3X9XphNZNmaJ2H9K5bJxmnmRJHvvOi5BVdknFirD5zt548NYOrVnUqsvlcPI7SxCbcOC3vkLzz1+0YRJfbhm89daZ2i9WhXsbJxBhTV5DO1vG16sqYLJ2cCMTq2mqEw9VNvT47Ng/4EIqncGG+tFh/pXDjZLOYdL9kgZiUE+Af9l6vHakMU8Ji5RCKp/D//eYYAJQlTc313tw1GDg4F07kNcbc2C/tmEvxFEplJhRHtzs7/l9t89ezMxHNkB5QXf+0x05Mw+uwYNfyduXYhn5v0VqnTIbh8ZMzuGFdD0wmQp9PMgTFFHu5f1dOLRWZFxei6PM5lE2Imi2DbTg6HsCpqRD+7KY1ymfcZjFhba9XM7TONy4vW1O+52QxEQbanDVt/qtmdCGKkekwrisw5VcK65VgnOajcFrNSn2ew2rGO65agYeOTeHsTH072NTLYNxPRA8Q0XuJ6L0Afg3gN4UeQETLiegRIjpKREeI6CPy8R8S0UH55ywRHazTmjUZV40zLrcQsRJ4qKzP6ygYVqglXKm3qd9bwHPKDrv0yheeShrAfuWhk5gKxnHr5j4sRJKIltgfzx9JKk1NgdpIjecjiaw8EAD0+aR5S7U0TrPhfM+pmplOjDGcn4tgpYYYAlDlnMq88DHG8NiJaVy3tjurYej6Pi/OzoYLKrVeHJUVfrLH1S9/Rop5TrrGyVY74zQmDxnUguedVnS6cMfW/qxzWwZ9Op5TEP0+B3q8dnS4rCXnnM7PRbCswwmzidAuf+5q3SXiSTm0er3s1WkhhfVKyTlFlJAe551Xr4TFRPivp89WvdZC1MU4McY+CeA/AGwHsAPA3YyxvyrysBSATzDGNgG4GsCHiGgzY+wtjLGdjLGdAH4K4N56rFkP9c6+IcYpyD0nB9b3eWE2aYcVasnofBQ9XjsG2py6XzJ/NKnkMQBU3CXi1FQI33zqDO7avQx3bJMuBGMlek9c0cW/KG57bcJ6uZ4TEWFjvxfHaxrWy2/CyUNvleycZ8MJhOIpXc/JV2aLGs7JqRDG/THcsD57172x3wvGULC04bETksKP52E63TbYzKbSPSeXnudUg7BeTgGumstWdMBiInzo5WvyOnhvHvBhJhTPKzg/Oh7ApgFJKNLlseuWYOTCZeRA9Z6zHk+cnEGfz451vR7d+3hLLNK+MBdV1svp9Tnwmu2DuO/AaF3rMOsliHAD+Dlj7OMAvgYgTUTWQo9hjI0zxp6XbwcBHAUwpHpOgiSu+H491qzHmD8Gq1nqIF3Li5Ue6rCew2rG2h5P3RV7Y35pOmiXx4ZZvbBeNFtNxesnypnrxBjD3/7yCBxWM/7yto0YkAsi1d5pIXLlxm5b9TOd5jWMEwDZONUm35dKZzAfSWrknCoXRHClnpaMHFCH9cp77sfltkO5xmm9rNgrtEF77MQ0tssKP0Ay8r0+e9FaJ/2wnqzIrFKxl84wjC/ENIUjgNQIds9nXoG3XLEi7xyPXqg3iIlUBqemQtg4IJ3rctt0SzByOT8XUerS2mVjXK5i7/hEULfmLJNheOrUDK5b26ObbwLkUoNUpqhxuSB7Trl8/Nb1+M1HrofdYtZ4VG2oV1jvcQB2IhoC8HsA7wPwX6U+mIiGAewCsEd1+HoAk4yxkzqP+QAR7SOifdPT01p3qYhxOVa9od+LExP1L4idCsThtJrhlXeNmwfrr9gbXYhiWbsTXR475iMJzQ9+IMcwKM1fy/CcfvfSJJ44OYOP3bIe3R67Uq1fam+yXONUi7DeXETbOG3o9yGcSGeJRXIJx1MlFabyvFxXjufksJpht5gqNE6LiXUtXDYzzCYq+7kfOzGNdb2evBDYcJcbNotJV07ujyRx4Py8Iibg9PscJYf1uLfHqVVYbyoYQyrDdMN6QP7fhrNJNkDqDeKpqRBSGaac6/bYS8o58VEZK6rwnGZCcdzxL0/g3x/V7oN5ZCyA+UiyYEgPKK0zuT+SRDCWUsQQapZ3ugq+n7WgXsaJGGMRAH8A4CuMsTcA2FzSA4k8kMJ3H2WMqV2Gt6GA18QYu5sxtpsxtrunRz8RWC5j/hgG25xYLyeE9fT9U4EYPnPfi1VXT08G4+j12ZVdz5ZBHyYD8bqNp2CMYWwhisF2B3o8NjAGTZGDP8dzctokA1pqzimWTOPvfvUSNvR58e5rVgIA+mQ1V6lhvYWI1FeP45YHDlZKNJFGLJnJyzkBwEY5ZKOXd0pnGG758mP44v3Hir4OD5V2axjBSlvYnJ2NwETQ3NUCktfiK1EuzIkkUtgzMpdnYABp/tS6Xo/u+/HkqRlkGJR8E6evrXg9XFFBRJUzu/jmRy+sV4g2pxXLOpxZeV+u1NushPX0Iw5quFKPh2IrMU4j02GkMwz37DmvuTF64pS0Mb92bfXGiQuxlnfW1wjpUTfjRETXAHgHJDEEAGgL7rMfZIVkmO5hjN2rOm6BZOh+WIe1FmTcH8VAuwMb+rzwR5NK37tc7jswiu/tOY8Xq5x7MhWIoc+7KHfdLO/OjtYptDcXTiCWzGBI9pyA/IJCxpii1lPT47OX3Pz1v585i4vzUXzudVuUuL7dYka3x155WK9KtR7PE3S68yPO6/tk46Tzvh+bCGDcH8P3914o6r0tNn3N353zWpdyOT8bxmC7s2BYxeso77mfHZlFIp3JC+lxNvR5deXkj52Ygs9hyRvdwT2nQkWb/mgSNvOi0IVTq0JrXki6rMKd/uYBH46qjNOxiQDsFpMSUu1y27EQSRYtNlZk2bLnxD3ncowTV8hNBGKaI3WePDmDjf1ezbZFajx26TMfKmCcLs7zGidt77ze1Ms4fQTApwHcxxg7QkSrATxS6AFyTukbAI4yxr6cc/oWAMcYYxfrslodMhmGCX8MA21OpUpeL+a+V67XKbd9fi5TwTh6fIsfLB46qJdij4etBtudyiC8XOMUTaaRTLM841ROIe5Tp2axsd+La9Z0ZR0fbHeU7Dnl5r08dnNVYb35sHRR0PKcPHYLlnc6cUzn771nRPp7h+Kpou1cuNebm3MCpGLZSj0nPTGE8txOS1lhvQcOT8Jjt+Cq1Z2a5zf0ezVLKlLpDB49Po3r1/XkCQr6fQ5Ek+mCyXf+d83NkdRKSq7+jFfClsE2nJldHEp4dDyI9X1e5Xflf9f5It7T+RzjBEDuElG6Wu/sbBgWE2GwzYHvPpvdhDWaSGPf2fmiIT1A7Tnpfz4uzC12h2gG9VLrPc4Yex1j7Ivy/0cYY/+jyMOuBfAuADerpON3yOfeigYLIQDpopJMMwy2O5SdtNbOMZ1h2HtWuliVOzE0l1zPqcNtw2Cbo26KPXXIQ/GccpRHejmBXm9pLYwYYzg86sc2eX6OmsE2Z0nvWSqdQTCeyvOcYslMyQ1Jc1nMBeUbDQAFu3HvOTOL5Z1ObBn04bvPnivoGSh99dzanlMlxkkalaEthuD4SqxlAaT398Gjk7h5Y6+uN6Ynirj3+VFMBeN4/a6hvMf0tRWvdZI84vzAisvKPafqwnqj81G0uxabBZfL5kFpKOGxCSmsr1bqAVBqgIqJIs7PRdDhsmZ9j8r9+5+bldR+b79qBZ48NYOR6cU8+J4zkud7fYH6Jk4pdXAX5iPwOix5m9JGUS+13noiupuIfkdED/OfQo9hjD3JGCPG2HYuHWeM/UY+917G2NfqsdZCjMkXzcE2JzrdNvR47Zox96PjAeUiMF6F5xSKpxCWu0Oo2TzYVjfFHg95DLU7db9kvAWOpucULByyAaQRILPhhDLcTc1AuwPjC9Giz8G/RLmCCKDynATf6Wp5ToCk2Dszk1/bwxjD3jNzuGpVF9519Uocmwhi3zn9KaGz4QQsJtJsJVPJTCd/VEqsDxfxnLxljGp/7uw85sIJ3JZT56NmIzdOE9nKtX9+6CR2LGvDLZvyW2iWUuuk1fQVACxyqK9atV6hGqdSUBR74wFMh6TPMo9oANDd1OWiVupx2p22stR6Z2bCGO5y4a4rlsNiItyz57xy7smTM7BZTLhylbbnq6aUDiIX5iKaYohGUa+w3o8BHADwNwA+qfppKbihGWiXvmAb+ryaaiXegqfTbavKc5pSycjVbB70YWQ6VHKxajmMLcTgtpnR5pR2dBYT5SmP9BLWvT47YknJoykEz8NpGafBNifCicJhH/Ua2l3ZnhNQedhnTjZOWmo9QApjpTMMp6ayVZonp0KYjyRx1apOvG7nILwOS8E5N7OhOLo8Nk1pbyWCCKUFTmftPKf7D4/DbjHhpg36u+5+nwNehyXLc/rhvgsYXYjiY7eu1/z9FONU1HPS3p1L5QLVh/WqMU6DbQ60Oa14aSygjMngXUQA6IbDczmvqnHilPP3Z4wpHnOv14HbtvbjJ/svKteFJ0/N4IrhjqxR93qUJoiINk0MAdTPOKUYY//OGNvLGNvPf+r0WnVD7TkB0sXq5FR+C/29cohn21BbVcZpUtUdQs3mAR8yrLa93jijCxEMyqOrTSZCl8eWpwxcHJeRvfNX5ORF8k6HR/0wEbJCIRxu+Ivl6rQMZLXGaT6SgInyw5UcfgHKDe3tGZGaXl61qgsumwVvunwZfnt4XFe5OBtKZA0ZVNMmDwUsp56Kd8ke7i7mOZU2qj2TYXjgyCRuXN8Dl00/9LVYnCy9H7FkGv/68CnsXtmhqfADFjdahca1B6L5TV851YpeGGMYndcvwC0FIsLmAR9eGg+olHr5nlMhRW0qncHofDQvT9juKt04zYQSCCfSisf8zqtXwh9N4pcvjGEqEMOxiSCuW1uaUplHHfSME2MMF+eXpuf0SyL6IBENEFEn/6nTa9WN8YUoHFaTslvf0OdFLJlREpuA9MXmIZ7BdkdVY4wXu0NkX8i2DObXWtSKsZzixC63PW8HmDsug7PYJaKwQT486seaHo/mhU8pxC3yvmkZJ4+s5qpUFDEblloXmTQapwLAcJcLNospb1Ow58wcBtocyq7ynVevRDLN8MPnzms9DWY0Whdx2pxWZBgQKiN0pe5sXQif04JwIl00J3fo4gImAjHcvk0/pMdZ3ycZJ8YYvrfnPCYCMXz8ldpeEyAp0jpc1oo9J5fNXJWU3B9NIpxIV12TI812CuDwqF/ypFQevM9hgdVMBeXk4/6YMipDTTk5p8VNieQxX7WqE+t6Pbjn2XN48tQMgMIti9RYzCY4rWbdsN5MSFLxNksMAdTPOL0HUhjvaQD75Z99dXqtujEu1zjxL56SEFbtpNUhnoE2J2ZCiYpbenAPhPet4yzrcMLrsNRFsTeW09aly2PLa/5aKKwHFO+vd3hMWwwBLI4wGCsiJ+eKptwOEUDlCfP5cAIdOiE9QPoC59b2MMaw58xcVsfnNT0eXLe2G9/TqT3Ral3EUVoYlZF3ODsTRq/XXtDLUT93MeN9/+EJWEyEmzf2FX3tDf1eBGIpnJkJ498ePYVrVncpzU/16PM5dAURmYx2mQLHU6XnpM6pVsPmAR/iqQweOTaVlW8CJM9K2tTpfw9yZeScdqcVkURpAye5jJxL2IkI77x6JQ5d9OPrT5xBp9uW5dEVw1ugM3mza5yA+qn1Vmn8rK7Ha9WTMbnGibO+T+pVpc477T2zGOLh7fgnS5z+mctkIJbVHYKjhBVqbJyiiTRmw4msL65WtTs3Tt6c8FdPCWG9qWAMk4E4tugYpx6vHRYTFfWcFO9NI+dUqeek1Vcvl1zF3pmZMKaDcVy5KlsS/86rV2LMH8PDx/JrT6SwnvbrVDIU8NxsRLdtkRpvCTOdGGO4/8gEXra2uyRV1gZZtfrZnx/GTCiBT7xyfdHH9Lc5dD2nYDwFxpA3aJDjsluq8pyqKcBVs0VuDhtOpJUCbTVdHlvBnJOet8s/z6V4T+dmIzCbKOt3ecNlQ3BazTg6HsC1a7t1owBaFJrptDjHaYl4TkT0l6rbb8459/lavlYjGF+IKWEnAHDZLFjR6cq6WD2rCvEo7XgqDO1N5XSHULNlsA3HJgK6I6Mrga9zKCusl/8lC8SS8NoteXODfA4L7BZTwbDekVHJoOp5TmZ5tEKxQlztsF71OadOHaUeZ2O/F1PBuCKe4OKX3FqgWzb1YqDNge/k1J5EEilEk2l06xRFVjLT6dxcuGiNk/TcxQ3fsYkgzs1GcHsBlZ4aXu/31KlZ3Li+B7uHi0frpUJc7Q2MXsiY46myuS+vcarWc1rT44FNrmvK9ZwAKe9UaKYTHwypvp4A6i4RxWudzsyGsbzDmTX2w+ewKhL+64t0hcilUJG2UrhcpVGvhlp7Tm9V3f50zrnbavxadSWZzmAqGMNgznCy9X2Lc20YY9gzshji4V5WpXmnyZwaJzW7VrQjlsxgz5naTaDko6uzck4eO6LJdNZ47NzWRRwiQo/XXrDW6bCs1ONyXC0G2ooX4vqjSTispqwanMX2NpV6TsmCYT1A3cZIMrJ7z8yh22PH6u5sz8ViNuHtV67AEydn8NDRSUUar3SH0HmdcqfhRhIpTAbipRknR3HjdP/hCRABt24uHtIDgHaXTSl1+Pitxb0mQArr6Y1r1wsZc1w2CyLVGKd5KW9czEMuhtVswvp+KXKiZZy63baCYT1pVEb+YMhyWhjp1bZ94IbVuHF9D24p8W/IKeQ5XZyPoNtjKxo6rie1Nk6kc1vr/4ZmMhBDhgEDOTsuXvsST6VxZiaMmdBiiGegjSvPKlPsTed0h1Bzy6Y+eB0W/Oi52o2L1wp58FontfeU25lBTa/XXjDn9OKoH6u73YqXo8VAu7Poe6aVNPdUEdZjjEmek0brIjUbVHlGaTMyqzth9G1XrcCyDif+8Nv78Pp/exq/OzKhGG69nNOicSrtd+DhoWIFuEBpYb37D0/giuFO3fVpcduWfrxl93LsUA0jLERfgXHtxYxTtc19z84uqlGrZdtQG9w2s2ZItVhY74KGjByAMtOpmHFijOHcTESztm1Vtxvffv+VZRtgqdRA+3UvzEWbGtIDam+cmM5trf8bGi4Jz215sl6ufTk9Fc4L8bhsUjV1sS7MehTynJw2M+7cOYjfHp6o2fyX0YWoFFZThZz4RWpatQuUpL7axqVYl4gjYwHN+iY1g21S/7VCcmqp6Wv2l89hNcFElYX1AtEU0hmmW4DL6fHY0em24dh4EBfnoxjzx3Tb+3R77HjoEzfi//uDbZgPJ/CB7+zHH337OQD6XSh8ZeycgcVu5KXknNqKhPVGpkM4PhksOaTH+ds7t+KLb9pe8v0LjWvXm+XEcdnMiCTSRYu0tZgJxfG4PDixFnzs1vW454+vzvN+AO2IgxqpADc/RMb/RsUKcWfDCQTjKUWpVwsK5pzmtY1pI6m1cdpBRAEiCgLYLt/m/99W49eqK9yryA3r8YTwickg9ozM5oV4Btoqk5Pz7hC5MnI1b9m9AvFUBr84WLiXW6mMLkTR73Nk9UPr0vCcCkl9e3123ZlOc+EERheiyqRRPQbbnUikMwWluFprICK5Dqb8hDlvXVRst8lre45NBhc3IzliCDV2ixlvu3IFHv7Ejfjnt+5Ej9cOm8Wkm/Pw2CwwUemCCD7HSW9UhppiAwcfODIJAHjVlvKMU7kUGteu1xqL47ZbkMowxEtQs+Xyw+cuIJHO4N3XDJf9WC16vQ7s1PEWCxXi+qNJLKhGZahpL3FzUmx+VyV47NrGKZ2RJhU0M98E1Ng4McbMjDEfY8zLGLPIt/n/m9OgqUK455Qb1lvV7YbVTDg2IV2srlqdHeIZLCFEpQW/wOe2LlKzdciHzQM+/KBGob3ReWlUhprFzuSL3pA0BVc/rBeIpTRHhRwu0BlCDQ+HFjLqenmvSsM+xbpDqJFmeQXxzOlZtLusBSeMcixmE+7cOYT7P3IDnvvMLbrzgkwmKqtLwNlZqT9bKco6jxLW037u+w+PY8eytoobopZKoRZGpYT1ACBSpmIvlc7gu8+ew3Vru7G2hL9XtXQXKMS9UKAuzVei53R2JnvcRi3wOqyIJvPr4CYCMSTTrKkFuED96pxanvGFKLwOS16uxGYxYXW3Bw8fm8S4P4arcvpYVeo56XWHUENEeMsVy3FkLKBc+HN58uRMyZ0kxvz5bV2UHaDKiylUh8K7RGjlnQ7LQxK3DBYJ65VQ65Q77JBTaQeB+TKM08Z+L6LJNO4/PI4rhzvLkuuaTKQbsuL4HKUbp/OzkZLyTYCkhNTbHc+E4jh00Y9X1tlrAgqPa/dHk7CYCC6bdssdfrzcv/HvXpK+n+952XDZ660ErYgDh9fJre3Nl6CbTQRvCZ3pz86GYTZRTfNAPCeZu7m7ONf8GidAGCdd+JBBLdb3e3FiUuq3lhviGWhzYD6SLHvooF53iFxev3MINosJP9qX7z09OzKLd39zD/7H9w8UjdHrja52WM3w2C3KDjCZziCSSOsaJy7g0JKTHx71Y0Wnq+guvxTPaSGazOqrx3FX6jlFCjd9VcPbGIUTaVy1Wj+kVylS89fSuwSUs3vWa/7K+wXqSfxrSaFx7TxcqydY8FSoyPyvp89iWYcTN2/Mb0ZbDwo1fz02Ls1/WqWTLyqlS8TZ2QiG2p2wWWp3ydbrr3dBVvEKz8mgjOcU4Krh3Zk7NEI8i+14ygvt6XWHyKXNZcXtW/tx34HRLAM4HYzjf3z/AKxmE05MhvD8ef0u2fz+qQzTDOl0q5RHxRLWvIXRqIbXc3g0UNLFr9Ntg91i0n3PChnISutgyvGc1vd5wa+duZ5yLfA5S5vplEhlMLYQLdlzAiSvTMvw8W4DehfMWqM1rp0xhmdHZguG3VwV1LIdHQ9g75k5vOvqlZrihXrAIw5aYzOOTgSwod+ru5ZS+uudmw3XVAwBLBbV525eLsxFQFT5/KtaIYyTDrkFuGr4bKcrNEI8Sq1TmaMzpoIxOKymvO4QWrxl93IEYyncf3gCgOQFfeyHB+GPJvG9P74aHrslq5W+FqMFKue7PHZlBxgokrBe0+NBv8+Brzx0MstY+iNJnJ+LKJX1hSAiDLQ5lDXlUigvIXWtrkAQEU7AZjHphpPUOG1mrOx0weuwaNa4VEup/dUuzEeQYSg6KkONz6kd1js7KxWFNuoCpDWu/cVRP0amw5pzoDiL/RNL/xv/9zNn4bCa8JYrlle22ArgEQetKdJHx4PY1K//uWlzFh44yBhTRmXUEp+u5xTBgM9RUy+tEoRx0iCWlNr65Cr1OJsHfSACrtWQqC52iSjPc5oMxNHnc5RUj3H16i6s6HThh7Iw4qsPn8KTp2bwv+/cgstXduDOnYP49QvjBfu1Faqc73LbMBPM8Zx0QnMOqxlfeOM2nJwK4Z9+f1I5fkTON5UaNhpsd+oa9EJrqEYQ0enSHmOhxZsuX1a3nbg0qr3w75DOMPz9r4/CbCJszxmFXgi9LgBnZ8JY3plfFFovtMa133dgFDazCXdsHdB9HC8CLbUQdyGSwH0HRvH6nUNKDVGj6PLY8sJ603J3Ea2WR5x2p63g5mQ+kkQwlirLYy4Fr46a86IBapwAYZw0mdBR6nGG2p34xYeuw9uvWpF3jvfXmyhTFDEV1K9xysVkIty1exmeGZnF9/eexz89dAJ/sGsId+2Wdopvu1KSnN93QH+q/ViB0dVqz2lxXIZ+3uimDb14y+7luPvx00o4sVQxBGegwERcrmTSCi267ZaKOkRIBbilX7w+fPM6/OVtG8t+nVIoRRDx978+ioePTeFzr9tSlvrMp5NzOjsbxqoaX+wKkTuuPZXO4JeHxnDzxt6CgpFyC61/vO8iYsnaycfLQav111FZDFHI4y6m1uTdyFcVGZFSLh6dUe0X5yNY1mQxBGAg40REy4noESI6SkRHiOgjqnN/TkTH5eNfqvdaFi/c+sZi27K2rB5XHIfVjE63rWzPaSqg3x1CizddvhwmAj5974tY0+PB/3nDVsUL2DrUhh3L2vC9ved1hRGj81G0Oa2anRu6PTbMhRNIZ5hqAm3hcONfv2YT+n0OfPLHhxBLpvHiaABD7c6SDcBgu9S5Wqurd6H+ax5HZWq9Upq+Ngqf04pEKqMrovnOs+fwzafO4P3XrsK7rl5Z9nPn7oyloXWlq/5qQe649idPzWAmlCgY0gMW1XqlSMnTGYb/fvYsrhzuLNguq150eex5UnI+/6lQWI/nnPS+qzw/WHvPKd/wJ1IZjAdiTRdDAAYyTgBSAD7BGNsE4GoAHyKizUT0cgB3AtjOGNsC4B/qvZDcIYPlMtDmKDvnVKg7hBb9bQ7cvLEPTqsZ//aOy/J6YL3tyhUFhRGFRld3uW3IMClEUornBEi7/y++aTtOT4fx5QdP4Miov2jxrZqBNicyFbS48dgtSKZZ2WNK5iPF++o1ikL99R47MY3P/eIIXrGxF3/96k1lP7fXYUEg58I3FYwjmkzXfCdeiNxap/sOjKLNacXLNxYejldO5/lHj0/hwly0YfLxXLo9trxC8qPjgbz5T7m0Oa1IppmuAT47G4GJaq+e48ZpZDqMXxwaw9/+8gje9LWnwVjxWWGNwDDGiTE2zhh7Xr4dBHAUwBCAPwPwBcZYXD6XP5OgxnDD0q+TcypGoRCVFqV0h9DiH9+yA7/72A2KQEPNa3cMwmO34Ht7tAt2Rxeiuslw3kF7NpwoKohQc/26Hrz9qhX4+hMjGJkJY2uJIT0ABZvmFhZE8DqY8oyTlHMyRl24XgujE5NBfPie57G+z4t/ftuuivJDPoc0zFA9duIMnwvUIKUekD2uPRRP4YEjE3j19oGsRr5a2C0mmE2k2xZIza9fGEeHy4pXbimvAWqt6HLbMRdOZLXhOjYexMYiIppiXSLOzoQx1FFbGTkgdTNxWE34r6fP4n98/wC+v/c8HFYzPnjTGtzapPdQTfNazhaAiIYB7AKwB8D/BXA9Ef09gBiAv2CMPafxmA8A+AAArFiRnwsqhzF/DF1uGxzW4kouLQbaHHju7FzJ9y+lO4QWXoc1b8YSx2234M6dg/jJ/ov4n6/ZnLdzG12I4mqdmh0+UnwmFEcgmoTdYir5vfjMHZvw2PFpqW3RstKNE/fiRhdiuDwncqXknHSKcAFJalxqmC6ZzsAfNaDnpIr9+6NJvP+/noPTZsY33rO7YOPcQnDDF4wllefIHVrXCNTj2n93ZAKxZAZvKBLSA+QWVTZz0c1HJsPw2Ilp3LC+RzPc3gi6PDakMwwL0SQ63TbEU2mcng7hls2Fa63U/fW0NoznZsN1+1t98Y3b4Y8mcdmKDmzo9zbtvdPCOCuRISIPgJ8C+ChjLADJgHZACvV9EsCPSENixRi7mzG2mzG2u6encKigGIVqnEphoN0BfzRZ0m4PWAxl9ZYR1isFPWFEICapf/Ryarwz+UwoUbCvnhYeuwX/+JaduGK4A5ev7Cj5cUohrkY41B9Nwm0za35xKulMzo2dYXJOjvyZTp/92WGM+2P42rsur0rurdWZvNEyciB7XPt9B0axrMOJy1eU9vkopQvI4TE/ZsMJ3LShuu9+NeS2/jo1FUIqw5Qibj2KDRw8OxupadsiNXfuHMK7rxnG1iHtHHozMdRqiMgKyTDdwxi7Vz58EcC9TGIvgAyA2rQZ1qFQjVMpKHLyEnvsTVboORWDCyO+v/eCknOYCyfw+5ekhp9D7dofePWXrFzjBABXrurEj//0ZSWFAjlehxVeu0UzHFpoDe4KijTnS2z62ihyx2b87MAofnFoDB99xTpcVuIFXI/F5q+LF75Gy8g5fT4HXhz146lTM3j9zqGS20CVosh89Pg0iIAb1jXPOHXnFOIeHS+u1AMKDxycD0sbxEZ6uUbBMGE92Rv6BoCjjLEvq079DMDNAB4lovUAbABm6rmWMX8UV+uMRSgFdTueUmS/vC9dse4QlfC2K1fgU/e+iLd9/VmcmQkrPfwsJsKGfu21tTutMJHUJywQ05/lVGsG2h2KUlKNXtNXoLJR7UrT1wbXweihHjh3YS6Cz/7sMHav7MAHX7626udWPCe1cWqwjJzT3+bAo8enAaCoSk9NKWG9R49PYftQm26D3UaQ28KIty0qVjxbaKbT2Tp0I28VDGOcAFwL4F0AXiSig/KxzwD4JoBvEtFhAAkA72GVDHcpkVA8hWAspVvjVArltjCaDJTeHaJcXrtjEN986gwWIkm8bE03Ng14sWnAhy2Dbbqeg8lE6HRLtU7+aLLm4UY99IQk/miiaNdqrYvXvz16Cqu73bgtp8iTty4ySs6JG965cAKf+NEhMAD/+JadNfFsfDleGZeRaxWQ1xsuiti+rK2sWq1iYb2FSAIHLyzgwzevq3qN1ZDb/JW3LbIUCZcVmumkzO9qoLLSKBjGODHGnoT+tNx3NmodPOcxUKFSDwD65OFq4yWG9aaCpXeHKBe33YLffezGsh/X7bEpOad1Gt2U68Fgu0PpLKHGH03q9oBz27W7VmcyDF99+BQ2D/jyjFOps5wahdUstVH6zrPnMBdO4P+9eUfNBr3lhvUmA5KMvNatcEqBz3V6/c7SvSZA6hIxH9EvzXji5AwyDLhxffNCeoDURJhICofztkW3biquenPbzLCYSNNzOjMTBhGaPvivGRgq52QECrX1KRW7xYxuj73k0RmTgZjSQNUodHvsmA3FEYimlIR9vRlsc2ImlMgrRi2Uc9ITRIwHYogk0nhx1I9EzqC6OXlnq9XlvFm0Oa2YCyfw6u0D+IPLyrt4F2IxrCe9P0qYqIEycs72ZZK3/todg2U9rlhz30ePT6PdZdUdBNgozCZCp8uGmXCipLZFHCKS+utpGKdzs2EMtjmLSu6XIsI45aA3ZLBcBtsdJXeJmArE65JvqoYujw3ToXjBWU61hr/nud2rKxFEnJyUktHxVCZvvtVcJAGP3WKoL3yXx4Z+nwN///qtNfWgHVYzbBaTUq/WDBk55xWb+rD/b25BT5kbsUJhPS4hv35dT8MFHlp0eWyYDcVLalukpk2nM/nZ2cglGdIDhHHKY3whChMBfVV6MlKjy+KeE2PMkJ5Tl9uOsYUYGCveHaJW8Ea7Y6r3LZZMI5bM6Bonq9kEm8WEUI6ai88rAoAD5xeyzs0bqHUR58t37cSP/uSaujQr9TmsKs8pApvZ1LRxCJUY3kJqvZfGA5gJxXFTk0N6nC63HbOhRElti9S0O615jZpjyTSOTQQ0i+wvBYRxymHMH0Ov11E0iVkMqct2cc9pIhBDOJHG6iaEWQrBCwqBxhkn7jmp3zelr16Bi7ZHY2d9aiqETrcNfT47DuS0cJozUOsizvo+L1bUKQ+kbv4qycidhvAySsVtsyCWzGj2XXz0uNQw5gajGCe5hVEpbYvUaI1NeXZkFrFkpum5tGZhGEGEURhoc+Bla6qfdjrQ5kAwnkIwltTt4gAsjnDeUOIOq1HwQlxAf1xGrRloc4AI+OzPD+PrT4xguTxDqdga3PZ8qfGpqRDW9XrQ4bLhwIWFrHPz4UTW77fU8aom7Z6tY7eBesFFL5FkGr6cTeOjx6exbait7FBhveiWm7+W0rZITbvLhlPToaxjjx6fht1i0u3kstQRnlMOn3jlBnz5LTurfh7FCyiSdzrOjZPBXPduVb1Io4yTw2rGV962C3ftXo6hdifOzYbx2xcnYCIU9CzdtuyZTowxnJwKYW2vB7tWtOPcbESp2gckybbRPKd64nNIAwcZY5JxMpiXXgy9vKI/ksTz5+eb2hUily63DcFYCqemQ9hUghiC06YR1nv0+BSuWdNVcRu1Vkd4TnVisRA3VjBmfHwiiH5f6e5/o1AXM5bT6aFaXrN9EK/ZvqjmYowhnsoU/ILmhvUWJfAebJabzx68sIBXyLJePmjwUsHnsGJ0IYrJQByxZKYpMvJqcOk0933ylCQhN5Rxkr836RLaFqnhAyfTGQaziXB2JoyzsxG879pV9Vqq4RGeU50o1CtOzfGJIDb0G8trAqQdIKeZhpOIiu4cc9VcJ6ckb3RtrxfbhtpgNpEyOiSaSCOaTF9anpPTgkA01VQZeTV4dDynR49Poc1pxY4yJgPXmy5VuLhUpR6wGJ3g9Wg8l2Ykw9tohHGqE1JRbeFx7al0BqemQ9hoROPUhJxTpeSOaj8tK/XW9XngtJmxacCrKPZ4X72uS8g4eR1WBGPJpsrIq4HPKlMr9hiTJOTXreuuWrxUS3gus5S2RWp4zR3vEvHI8Wms6nY3dCCk0TDOX3WJYTWb0OOxF/Sczs6GkUhlDOk5uWwWuGxmmE2kzEwyKrmCiJNTIXjtFkWev2t5Bw5dWEA6w5S+epeU5+SwIJ7K4MRkqKky8krJbVEVS6bxtcdGMBU0joScw8fNlNK2SI26v2IsmcazI7OXrEqPI3JOdWSg3YmJgL7ntKjUM55xAiTvKRRL1aWtUi3JC+tNhrC2z6Ose9eKdnzn2XM4ORU0XEfyRsBLAV4cXWg5GTkAuGS13kIkge8+ew5fffgUJgIxXLe2G7dvGyjy6MbCIw7lRkMUzymaxDMjs4inMnj5xsJzoJY6wjjVkcE2B07InQq0OD4RhNlEZTXBbCTdHjvMBjdMgCyISEhqNCLCqelQ1o56lzx24sD5BSW53nEJCSK4HP/waADXrm09WTL3nP76vsNIpDO4fGUHvnzXDrysCc1ri+GxW/DWK5aX1XUdyPacnj83D4fVhKtWVT4ZYSkgjFMdGWx34pHjU0ilM5ou/rGJIFZ1uw3VRkfNxn4vpgLx4ndsMm67BRkGRJNpJFMM08E41vUtGvzhLhc6XFYcOD+vJKkvKc9JVltGk+mWzGG0Oa1od1mxrMOJT7xyA25a32NYb56I8IU3bi/7cW1OeWxGJIFHjk/hmtWXroScI4xTHbl8ZQe+8eQZHLq4gMtX5u+Cjk8Esa2MUeaN5v+8fhvqOJ2kZqhnOl2Yk0YMqL1RIsKuFR04cH4B/T4HTGR8kUctUXf4aDWlHiDVv+39zC2wmsmwRqla+Ofx4AU/zs1G8P5LWELOEYKIOnLtmm6YCHj8RP5sxHA8hfNzEWw0WPGtGrOJDKWE0sNjX6yDOTkpK/VyxnzsWt6Ok1MhnJ2NoN1la7m8SzV4VV3lW63GiWOzmJasYQKk389lM+OBIxMAgJdvuLTzTYAwTnWlzWXFjuXtePzkdN45notab1AxRCvhti3WwZyaCsFhNeWNPOF5p8dPTqPDYAXP9UZdRN1qMvJLiTanFaF4Cqu73XXrs9hKCONUZ65f14NDFxbyWpPwtkVGrHFqNdQznU5OhbCmxwNTjme0fXkbiKQ6kksp3wQshvVaUUZ+KcFDezdewoW3aoRxqjM3rOtGhgFPn84O7R2fDMJlM2N5h9ghVYu699opuadeLj6HFevk45eSUg+QJq2aCC0pI7+U4MbpJhHSA2Ag40REy4noESI6SkRHiOgj8vHPEdEoER2Uf+5o9lrLYcfydnjtlrzQ3vGJINb1efN2+ILy4cZpOhjH6EJUMUK57FouhfYuNc+JiOB1WHVH3QuMQbvLKiTkKoyk1ksB+ARj7Hki8gLYT0QPyuf+kTH2D01cW8VYzSZcs6YLj5+YUepwAMk43SI3IhVUBw/rHbroBwDdurFdK9rxw30XLjnjBABvv2oFtg0ZVxkqAN5/7Sq8cnP/JS8h5xjGODHGxgGMy7eDRHQUQHmVbAblhvU9+N1LkzgzE8bqHg+mg3HMhhOG7QzRavB5P4fkuU1re7XfVy6KuBSN01/dtrHZSxAU4arVXbiq2YswEIYJ66khomEAuwDskQ99mIheIKJvElGHzmM+QET7iGjf9HS+Oq6Z3LBOSnA+cVLKOwkxRG3har3jk0FYzYSVOkqn9X0e/N2dW/C6nYOa5wUCgXEwnHEiIg+AnwL4KGMsAODfAawBsBOSZ/X/tB7HGLubMbabMba7p8dYapcVXS6s7HLh8ROS0Tw2EQBg3J56rYbJRHDZzEhnGIa73LDq1GYREd51zTB6vY4Gr1AgEJSLoYwTEVkhGaZ7GGP3AgBjbJIxlmaMZQB8HcCVzVxjpVy/rhvPjMwikcrg+EQQ3R571kA/QXVwUYS6bZFAIGhdDGOcSFIKfAPAUcbYl1XH1W2H3wDgcKPXVgtuWNeDSCKN58/P4/hkUIT0agwXRaztEcZJIFgKGEYQAeBaAO8C8CIRHZSPfQbA24hoJwAG4CyAP2nG4qrlmjVdMJsIjx6fxonJIN5+5cpmL2lJwUURaw3cDkogEJSOYYwTY+xJAFpFP79p9FrqgddhxWUr2vGT/RcQS2aE51RjuChCeE4CwdLAMGG9S4Hr1/VgJiQNuxNiiNrisVtgImB1jyg0FQiWAsI4NZAb5AF4RMB6EX6qKQPtDmzo94kCRoFgiWCYsN6lwLahNrQ5rehwWeG0iYtoLfnMHZsQT2aavQyBQFAjhHFqIGYT4UMvXwOzSTistcZls+AS6+cqECxphHFqMB+4YU2zlyAQCASGR2zhBQKBQGA4hHESCAQCgeEQxkkgEAgEhkMYJ4FAIBAYDmKMNXsNNYeIpgGcq+IpugHMFL2XcRHrby6tvP5WXjsg1t9syl3/SsaY5hiJJWmcqoWI9jHGdjd7HZUi1t9cWnn9rbx2QKy/2dRy/SKsJxAIBALDIYyTQCAQCAyHME7a3N3sBVSJWH9zaeX1t/LaAbH+ZlOz9Yuck0AgEAgMh/CcBAKBQGA4hHESCAQCgeG4JIwTEX2TiKaI6LDq2A4ieoaIXiSiXxKRTz4+TERRIjoo/3xN9ZjL5fufIqJ/ISKtyb1NXb98brt87oh83tEq6yeid6je+4NElCGinS20fisRfVs+fpSIPq16TCus30ZE35KPHyKim5q5fiJaTkSPyO/lESL6iHy8k4geJKKT8r8dqsd8Wl7jcSJ6VSutn4i65PuHiOirOc/VCuu/lYj2y+vcT0Q3V7x+xtiS/wFwA4DLABxWHXsOwI3y7fcD+Dv59rD6fjnPsxfANZDGyf8WwO0GXL8FwAsAdsj/7wJgbpX15zxuG4CRFnv/3w7gB/JtF4CzAIZbaP0fAvAt+XYvgP0ATM1aP4ABAJfJt70ATgDYDOBLAD4lH/8UgC/KtzcDOATADmAVgNPN/PxXsH43gOsA/CmAr+Y8VyusfxeAQfn2VgCjla6/7l8Mo/wgx+gACGBRELIcwEta98v5Ix1T/f9tAP7DgOu/A8B3W3X9OY/5PIC/b6X1y+v6JaRNQpf8Ze5sofX/K4B3qu73EIArm71+1ev+HMCtAI4DGFB9No7Ltz8N4NOq+z8gXxBbYv2q+70XKuPUauuXjxOAWUgbhbLXf0mE9XQ4DOB18u03Q/qCclYR0QEieoyIrpePDQG4qLrPRflYs9Bb/3oAjIgeIKLniegv5eOtsn41bwHwffl2q6z/JwDCAMYBnAfwD4yxObTO+g8BuJOILES0CsDl8rmmr5+IhiHtzPcA6GOMjQOA/G+vfLchABdUD+PrbJX169GK638jgAOMsTgqWP+lbJzeD+BDRLQfkruakI+PA1jBGNsF4OMAvifH47Xio83U4eut3wIpLPAO+d83ENEr0DrrBwAQ0VUAIowxnidplfVfCSANYBBSWOkTRLQarbP+b0K6cOwD8E8AngaQQpPXT0QeAD8F8FHGWKDQXTWOsQLHG0IZ69d9Co1jhl0/EW0B8EUAf8IPadyt4Pov2Um4jLFjAF4JAES0HsCr5eNxAHH59n4iOg3JG7kIYJnqKZYBGGvkmtXorR/SOh9jjM3I534DKd/wXbTG+jlvxaLXBLTO+/92APczxpIApojoKQC7ATyBFlg/YywF4GP8fkT0NICTAObRpPUTkRXShfEexti98uFJIhpgjI0T0QCAKfn4RWR74XydTfv8lLl+PVpm/US0DMB9AN7NGDstHy57/Zes50REvfK/JgB/A+Br8v97iMgs314NYB2kpPw4gCARXS2rTN4NKf7aFPTWDynGvp2IXERkAXAjpHxCq6yfH3szgB/wYy20/vMAbiYJN4CrIcXaW2L98ufGLd++FUCKMda0z4/8Wt8AcJQx9mXVqV8AeI98+z2qtfwCwFuJyC6HJdcB2NtC69ekVdZPRO0Afg0p7/dUVetvdEKtGT+QduDjAJKQLPgfAvgIpGT1CQBfwGJy+I0AjkCKvT8P4LWq59kNKVZ/GsBX+WOMtH75/u+Uf4fDAL7Uguu/CcCzGs9j+PUD8AD4sfz+vwTgky22/mFIye6jAH4PaaRB09YPKTTNIClQD8o/d0ASmzwEyat7CECn6jF/La/xOFSKsBZa/1kAcwBC8t9rc6usH9JGJ6y670EAvZWsX7QvEggEAoHhuGTDegKBQCAwLsI4CQQCgcBwCOMkEAgEAsMhjJNAIBAIDIcwTgKBQCAwHMI4CQRNRK6HepKIblcdu4uI7m/mugSCZiOk5AJBkyGirZBqo3YBMEOqDbmNLVbXl/NcZsZYurYrFAgajzBOAoEBIKIvQSpedMv/roQ0MsQC4HOMsZ/LjTe/I98HAD7MGHuapJlL/wtSoe1Oxtjmxq5eIKg9wjgJBAZAbhn0PKQGrL8CcIQx9l25HcxeSF4VA5BhjMWIaB2A7zPGdsvG6dcAtjLGzjRj/QJBrblkG78KBEaCMRYmoh9CallzF4DXEtFfyKcdAFZAapT5VZImA6chNSTm7BWGSbCUEMZJIDAOGfmHALyRMXZcfZKIPgdgEsAOSGKmmOp0uEFrFAgaglDrCQTG4wEAfy53bwYR7ZKPtwEYZ4xlALwLknhCIFiSCOMkEBiPvwNgBfACER2W/w8A/wbgPUT0LKSQnvCWBEsWIYgQCAQCgeEQnpNAIBAIDIcwTgKBQCAwHMI4CQQCgcBwCOMkEAgEAsMhjJNAIBAIDIcwTgKBQCAwHMI4CQQCgcBwCOMkEAgEAsMhjJNAIBAIDIcwTgKBQCAwHMI4CQQCgcBwCOMkEAgEAsMhjJNAIBAIDIcwTgJBEyGis0R0i3x7GRHdQ0SzRBQmor1E9Jqc+zP5XEi+30NE9JbmrF4gqB/COAkEBoCIOgE8CSABYAuAbgD/COB7RPSmnLvvYIx5AGwA8F+QRrf/rwYuVyCoO2Kek0DQRIjoLIA/AnAjgDcA2C5PuuXn/wrABwEMM8YYETEA6xhjp1T3eROA7wIYYozNNnL9AkG9EJ6TQGAMbgXwU7VhkvkRgBWQJt/q8XMAFgBX1mltAkHDEcZJIDAG3QDGNY6Pq85rwhhLApgB0FmHdQkETUEYJ4HAGMwAGNA4PqA6rwkRWQH0AJirw7oEgqYgjJNAYAx+D+CNRJT7nbwLwAUAJwo89k4AKQB767Q2gaDhCOMkEBiDfwTgA/ANIuonIgcRvQ3AXwP4JNNQLhFRJxG9A8C/AviiEEMIlhKWZi9AIBAAjLFZIroOwBcBvATALv/7LsbYz3PufkhW7SUAHALwMcbY9xq6YIGgzggpuUAgEAgMhwjrCQQCgcBwCOMkEAgEAsMhjJNAIBAIDIcwTgKBQCAwHEtSrdfd3c2Gh4ebvQyBQCAQFGD//v0zjLEerXNL0jgNDw9j3759zV6GQCAQCApAROf0zomwnkAgEAgMhzBOAoFAIDAcwjgJBAKBoCyCsSRS6dzpLrVFGCeBQGAo/JEkIolUs5chKMCXHzyBl33hYaQz9eswJIyTQCAwFO//9nP4P78+2uxlCArw9KlZbOj3wmyiur2GME4CgcBQTPhjmPDHmr0MgQ7TwTiOTwZxzZquur5OU40TEX2TiKaI6LDOeSKifyGiU0T0AhFd1ug1CgSCxhJJpBBNpJu9DIEOz4xIk1muXaM7nLkmNNtz+i8AtxU4fzuAdfLPBwD8ewPWJBAImkg0mUYkKYyTUXnm9Ay8Dgu2DrXV9XWaapwYY4+j8GjpOwH8N5N4FkA7EWmNshYIBEuATIYhlswgKgQRhuWpU7O4enVXXfNNQPM9p2IMQRpRzbkoH8uDiD5ARPuIaN/09HRDFicQCGpLLCV5TBER1jMkF+YiOD8XwcvqnG8CjG+ctEyzpnaRMXY3Y2w3Y2x3T49mqybBEiGdYZgOxpu9DEEd4LmmmAjrGZJnTsv5prX1zTcBxjdOFwEsV/1/GYCxJq1FYBB+fnAUN/7fRxCOi9DPUiOaFJ6TkXnq9Ay6PXas6/XU/bWMbpx+AeDdsmrvagB+xth4sxclaC4X5qKIJNKYCQnvaanBPadoMg3G6lfgKSgfxhiePj2Ll63pAlF9801Ak7uSE9H3AdwEoJuILgL4XwCsAMAY+xqA3wC4A8ApABEA72vOSgVGIhBLAgD80WSTVyKoNdxzYgyIJTNw2sxNXpGAc2oqhOlgHNeurX++CWiycWKMva3IeQbgQw1azpIklc7gLXc/i4/esg7Xr1saubigbJwWIsI4LTXU4bxoMi2Mk4F4Ws43vazO9U0co4f1BFWyEE1i/7l5PHemkGK/tQjGpFyT8JyWHlGVEEL01zMWT52awfJOJ5Z3uhryesI4LXEicenLPhtONHkltYMbpwVhnJYcMbXnJEQRhiGdYXh2ZBYvW90YrwkQxmnJE5Z3n3NLyDjxnFNAGKclhzqsJxR7xuHImB+BWAova1C+CRDGacnDQyNL0nOKLJ3fSSCRHdYTxsko8HxTvZu9qhHGaYkTlsN6S8lzEoKIpYs6lCcKcY3DU6dmsL7Pg16vo2GvKYzTEofvPpeScQoIQcSSRXhOxiOeSuO5s3MNU+lxhHFa4vCw3nwkUdeplY0ilkwjkZLGQwtBxNJDqPWMx8HzC4glMw3pp6dGGKclTjixWNS4FHI0PN8ECEHEUiSaU+ckaD4vjvoBALuHOxv6usI4LXEiqv5zSyG0x/NNdotJ5JyWINFEGj6HRbktaD4jM2F0uKzodNsa+rrCOC1xwqoveKMUe7OhOP7hgeNIpTM1f26eb1rW4cRCtPWNrSCbSDKNDvkiKHJOxuDMdBirut0Nf11hnJY46qFtjfKcfvfSJL76yCkcnwzW/Lm557S804VYMiMUXUuMaCINl80Ch9UkwnoG4cxMGKu669+FPBdhnJY44UQafGBlIc8pnkpjbCFak9ec8McA1McY8pzT8g6phYrIOy0tYsk0XDYzXDaLEEQYgHA8hYlADKt7hOckqDGReAr9Pqk2YS6kbyy+88w53PrlxxQlXDVMBSXjNFvg9Spl0XNyAhBy8qVGJJGC02qG02pGNFH7sLCgPM7OhgFAhPUEtSecSMPntKLNacVcWH/+0enpMMKJdE3yOJMB6XXqMW8pEOU5J8lzEnLypUU0mYHDaobTZkY0KTynZnNmRhgnQZ2IJFJw2y3octsKhvUmA5K3UwsFHA/r1UOAEYwlQQQMtkuek1DsLS0Ww3pmIYgwAGemJeM03CWMk6DGRBLSl73TbSsYZuMGZb4GBoWH9QqFESslEEvBY7eg0yUpukRYb2mhDuvVyzg9cGQCp6ZCdXnupcaZmTAG2xxNmasljNMSJxJfNE6FBArcc5qv0hNJpjOYkY3SbIEwYqUEYkn4HFa0uawAlkZh8VLEH03iXd/YgwtzkbIeF01IAwZdNnPdlJgf/+FBfP3xkbo891JjZCaMVU0QQwDCOC15wokU3DYLujz6Yb14Kq2cq/ZiPxVcNEgzdRFEpOB1WOC1W0Ak1HpG5eh4AE+cnMGeModc8um3zjqF9cLxFMKJtOLdV8vDxybx9KmZmjyX0WCMYWQ61JR8EyCM05InkkjDZZc8p/lIAhmN/nrTKoNSrefEPbA2p7UunlNQ9pxMJkKb0yoEEQaFe+nliGKS6QySaSaH9Sx16RDBN0/TNRLrfOn+4/jC/cdq8lxGYz6SRCCWwuom1DgBwjgtecJxyXPqdNuRzjBlUJ8ablCAGnhO8nNtHvDVJefEPSdAMoAi52RMuCeu3vgUg4fxFgURtVfr8fVMBWpjnGbDCRyfCNalG0qzOTMj5eVEWE9Qc9IZhngqA6fNjC65JYxWaG/Cr/acqjMoXEa+edCHcCJd891vIJZUjFO70yrUegaFb0zKMU78s+KwSsapHh0i+Hpmw9V36WeMYT6cQDyVUeqBlhIjslJvtQjrCWoN33lKnpNknLREEROyt9Pvc1Qd1psIxGA1E9b1SqGAWof2grEUfE5JDNHmsrVkWO/ZkVn85xNLOyHPa+rKMk6yMXJazXBYzYglM5ph6GqYlnNN6QyruoNJIJpCSl7fS+O1b9XVbM7MhGE1E4bkso1GI4zTEoYnlHnOCdDu2jAZiMFmMWG421V1WG8yEEOv14Fujx1AbVsYMcbywnqtKIj4wd7z+NL99WmMaxSUsF4ZuR3l8yqH9YDaj81Qr6ccw6mFeuN1dDxQ1XMZkZHpMFZ0umAxN8dMCOO0hAnHFz2nLk8Bz8kfQ7/PgQ6XrWrPaSoQR6/PrrxeLVsYRRJppDMMXofkOUlhvdaTkk8F40ikM7gwX5tehkZkroKcEzdEDpVxqrViT72eakUR6hD5UjROzWr4yhHGaQmj3okuhvXyv5ATAck4tbtsVV/sJwIx9Kk8p1q2MOJNX3MFEbUO/dQbfoE8vYQLQblx8keTiKdKMzAx/nmVw3oAal7rNB2MK/OipgLVycn5xmtDn3fJGadMhuHMbLgpDV85TTVORHQbER0nolNE9CmN8zcRkZ+IDso//7MZ62xVFM/JboHdYobHbtEUREwGYuhrc6DDJQkMGKv8Yj8ZiKG/zbHoOdUwrMebvvq45+SyIsOAUIt1r+Y79tPTS9c4zYYTsMjt8Eutd+ObKafclVx9rFZMh+LYPOhTblcDN8DXru3GZCC+JIZ5csb8USRSmabVOAFNNE5EZAbwrwBuB7AZwNuIaLPGXZ9gjO2Uf/53QxfZ4kSSi192AJpdIhhjkkHx2dHhsiGVYQjGK7vYRxIpBGMp9PrsykyeWn5hAzmeExdG+FtIsRdPpRWF4VJtocNVbGt6pJDQTImhPbUgYjGsV9uNx1QgjhWdLnjtlqrl5DwKce3aLgCNCe0xxvDT/Rfrbgib2fCV00zP6UoApxhjI4yxBIAfALizietZckTi0pfdLe9CtYxTIJpCLJlBn8+Bdt4SKFzZxZ7LyPu80oiOLre9pmE9XqOlzjkBrdVfT+1FLFXPiavYNvR7AZSed4qqNlN8Q1XLUoR0hmE2nECP144er70mOSev3YIdy9sBNMY47T83j0/8+BB++NyFur4ON07NkpEDzTVOQwDU7/BF+Vgu1xDRISL6LRFtaczSlgZhedfJd6FdGs1fuYy8TxZEAJXXOvFi3v42yTh1ewo3my0XnnPiOYN2eb21rnV68aIf//jgiZo+J4dfqAfaHDg9Ha4qhGpUuIpNMU4lGgFuiHjjV6C2ar35iFTb1OOxo9trr1qtNxdOoNNjQ7dHMnYvNcA4/fT5UQD139iMTIfhtpnR47XX9XUK0UzjRBrHcr+pzwNYyRjbAeArAH6m+2REHyCifUS0b3p6unarbGEiqpwToO05TagMSodb8kSqNU59PrvyerWsc1JyTrzOqU6e00/2X8A/P3Sy6oS5Fvw5r1ndBX80WZf+g82Gf8bW91XmOblslrqo9fg6en0O9NbAOM2GEorQaNOAD0frXOsUS6bx6xfGAFRmnH53ZAK/fXG8pPuekRu+EmldphtDM43TRQDLVf9fBmBMfQfGWIAxFpJv/waAlYi6tZ6MMXY3Y2w3Y2x3T09PvdbcUoRVaj0A6PRIxkm9W5/0LxbgVuuJcOPUK0/e7fLYa9rCKFetp4QhazAgUc3ogvR7vHDRX9PnBRa9iKvXSHmKpRja4yKYfjlUXKoR4IbIbjHVJazH16GE9aquc0oonVc2DXhxaipYk0nSejxybAqBWAqrut0YqcDr/tIDx/GVh0+VdN9my8iB5hqn5wCsI6JVRGQD8FYAv1DfgYj6STbdRHQlpPXONnylLUo0kYaJpC87IIX1EukMQirBw4RiUOw1COvF4bKZ4ZU9tS6PDTM5xrAaAtEkzCZSQj7cc6p1WG/cL9UfvXBxoabPC0gXSCLgqlWdAJamceKeU5fHhh5P6UYglkzDYTXBZCKVWq92ggjFOMlhuFA8VdXzz4Xjiue0ecCHZJrV9e9574FR9HrtePuVK+CPJssSRYTiKZyeDpWUA46n0rg4H2lqvgloonFijKUAfBjAAwCOAvgRY+wIEf0pEf2pfLc3AThMRIcA/AuAt7KlGKSvE3xcBnfNO935XRsmAjF0um2wW8xoc1pBVHln8slADH0+h/J63W47EqlsY1gNvDsEf36H1Qy7xVTzLhFjC5JxOlQHz2kqGEeny4blHS44rWacnlp6Pdn456vTbStLeBBNpBWjtJhzqp0nwtfR47WjVxbtVOo9MSa1P+qS6/k2DUjy9HqJIubCCTx6fAp37hzEuj7Jozk9Xfpn5/CoH4xJ3l6xusALcxFkGJpa4wQAlma+uByq+03Osa+pbn8VwFcbva6lQiQujcvgqJu/rpTHLk/6JYMCAGYTweeovOuC1LpoMYGq7ufHFXbVwMdlqGl31bb5aySRwnxEGgX/4qgfjLGaxt2ng3H0eO0wmQire9w4tQQ9p9lQAm6bVEjb47XjwPmFkh4XSaQVo+SwmkAERGvsOblsZrjtFiXRPx2MK9+FcgjGU0immfKdWt3ths1iqptx+tULY0imGd6wa5kS1h6ZDuFK2QMvBo8CpDMMC9Gk8t3Ugjd8baaMHBAdIpY03HPiKMYilO059fsWDUqHy1qF5xRXlHoAlELcWiX9A6q+epxaj80Yk/NNu1d2YC6cwMUatxiako0TAKzt9VTdJWImFC85yd0o5sJxdMp/+x5P6eUEPKwHAERU81Ht6veeb6KmKvScuAqVf6csZhPW93nqJoq49/lRbOz3YvOgD4PtTtgtprJCiOooQLG/B5eRDwvjJKgXfNAgR6sz+WQgrnhOACpuYcSLedXPxVsYzdao1imoGpfBaXfaaiqI4CG927YOAKi9KGJGdYFc0+PB6EK0qqT/158YwZ/d83zdduzHJgK4Z8+5sh4zG04oIeRurx2RRFrpVlKISCKlhPUAScgTqaGUfDq46NmrPadK4AW4ag9k84APR8cDNS8PGJkO4eCFBfzBZVKljdlEiiiiVF64uIBuecNQ7Hc+MxNGt8eeF6VoNMI4LWFyv+y5LYWS6Qxmw9nGSfKcyr/YSz3UMpphvVq1MArGUnlfGJ/TCn+0dqEfbpxu3tgLq5nwwuhCzZ6bMaaE9QAoHRSqSaLvGZHGoP9438XqF6jBPc+ex9/87HDJ/fEAafPDw109ntKNQDS5GNYDpJxirMZqPf7ed7psMJuoYuPEPacu9+LnfdOAD7PhRNUqwFx+dmAUJgLu3LlYBrqmx1Py52YunMCFuShevqEXQHHPaWQm3HQxBCCM05ImkkgrMnIAqpZC8jTQYByMISsU1+GyYb6CDhG8O4T6uQrNkKoESRCRn3Py17Az+dhCFCYClnc4sWnAhxcu1M5z8keTSKQzSjJ+bW91xikcT+HwqB8mAn52cLQuMubJQAyMoazw5lx4sf5H8VBK8J6jyYwiIQdkz6nWxkk2liYTodtjw1Swslo2RfThWfScuCiilsW4mQzDvQdGce3a7qxN5OoeNy7MR0vaNPB80ys2ScapFM+p2fkmQBinJQ0f0a6my21XPJkJVY0Tp9Kw3qSq0wTHYZVk5bVqYRSIaoX1rDUdODi6IIUmLWYTti9rw+FRf826nqvrbABgZZcLJipPdaXmwPkFpDIM775mGHPhBB4+NlWTdaqZlNd8fjZS0v0ZY1n1P+WEz6KJVJbn5LRZahbWiyXTCMRSWR0Pqql14t+hLlVYb1M/V+zVLu+079w8Ls5H8YZd2c1z1vR4kM6wkv4uL170gwh42dpuWM1UMAccjCUxHYw3bTS7GmGcljC5nhOQ3SVCy6B0uKwIJ9Jl78KVNkheR9bxrhq1MMpkGEKJlNK6iNPmtCJSwXr1GFuIYlCe/Ll9WTuC8RTO1GgEt7rOBpCM9/JOV8We094zszAR8LFb1qPXa8dP9te+3xrvaHGuxPeAf3byPKdSw3pqz8lqrplabyaUvTEAgF6vo2JBxFx4UZHIaXNZMdTurGn+774DF+GymfGqLf1Zx7nMu5SNzaGLfqzudsPnsBbtd3lONnbDXa4qVl0bhHFawoTjKaV1EUdtnBTPSRWKa3fzLhHlGZQpVTFv7uvVooVRKJECY4utizi8S0StFHtjfrVxagNQu2JcfiFUv0dreypX7O05M4etQ21oc1nxB5ctwyPHpysOU2mRyTBlzefnSgvrzeWo2DrKyO1EE9nGyWkz16y3Xq7XCqCsAuFcZkPxrJAeZ9NAbWc7PXFyBjdt6Mn7Hq8uI1/5wsUFbF/WDgDo9toKGid+TRhoa85odjXCOC1htDwndfPXyUAMNrMJHa7FCz6/Xa6cfDIQR7vLmrWTBKQWRrXwnHihbW5Yz1fD/nqZDMP4QgyD7XJOqMcDp9WMQzXKO2ldINf0ejAyE0a6zNBhPJXGgQsLuHJYqnN58+5lSGcY7pMbg9aC2XBCWdf5udI8J74R4eIbs4nQ5S58QeREE+mcsF7tck6LXuviRqzHa8/6HctBrUhUs2nAh5GZcM2GJM6GEhjUMBQeuwV9PntRxd6EP4apYFzZaHUXkfaPB7hxcujep1EI47RESaQySGVYwbDeRCCGXp89q8i00hZGfAJuLt0eW03Ueot99XI9J2m9/hrIyWfC0vj0IdlzsphN2Drkq5nnNB2Kw24xKe2dAGBNjxuJVAajZdZTvXDRj0QqoxRhrunx4PKVHfjx/os1kzLzsK/NbFLCPcVY7A5RXm6HMYZoMkfAYzXXrLeeltfa67MjnWEVCXbUikQ1mwZ8SGcYTk5WX1wdS6YRTabRoVMwW4pi75D82VU8J48dM0H933fSH4PFRErni2YijNMSJaKMy8gJ63lsiCbTiCbS8pDBbIOiNFPVMU4PH5vU/EJMydN0c+HGsFpRQW7TV2W9Neyvxwtw1TvVbUPtODIWQCpdfU5rSmMzwBV7p6bLS6LvGZFaTF4xvNgh4M2XL8OpKakmphZwg7J9WRvOz0VK+htqCQW6PcVbGMVTGWQYsjzvWqr1eE9DdV1SOTL3XNSKRDW1bGPEP9PtLu16o9U9boxMhwpuRl64uACLibBFnv4reYtx3b/luNwxxmxqXjdyTkHjRER/UOinUYtsBQ6P+nHtFx6ueY1DpfCO5G57flgPkMIvk4F4nkFZ9JzyL/aZDMOHv3cAn/3Z4bxzk4E4+jRmv3S5pd1ptWG33BHtnFqOzeA1TjznBAA7lrchnsrgRA12wtOhRSkzZ7Xc+bncHnt7zsxhY783a1f96u0DcFhN+PH+2tQ8cc/pilWdiKcyJcnB1X31OKV4TjHVFFyOo5Y5p5DU09BqXrzk9ShdIsrL0ymKRI2c08pOFzpcVjx+svqxPTx6wb+Tuazp8SAQSxVU371w0Y/1fV7F6Hd77Eim9b+PE4FoVg66mRTznF4r//whgG8AeIf8858A3lnfpbUWz5yexehCFMcn6jvTpVT4LKc8z8nNuzYkMOHP95wKhfUmgzFEEmk8fXo2S8KazjBMh7KLeTm5hb+VsjgFN8dzctXSc5KM05DKOPFwSC1Ce+oiUE6H24Yut60sxV4qncH+c/N5fdW8Divu2DqAXx4cq0k4jNeuXb6iAwBKCu3NhROwW0xZ4bker5TnKOR5Lc5yUof1LEikMhXlhHLReu8rbf4aiqeQSGU0w3omE+E12wfx4EuTyoaqUvh3UN9zkjY2IzqfHcYYXrjox47lbcqxbqWlmPbvPO6PtYZxYoy9jzH2PkhDADczxt7IGHsjADGRNgd+ceHjFpqNnufEd7RnZ8OIJtN5xslpkzp9a13sz6iSrz9WyZZnQ3GkM0wzrFerFkZ6OSf+/1p4TqMLUbhtZviciwZwuMsFr8OCF0arF0VMBePKBVFNOdX+AHBkLIBIIq3Z9PNNu5chGE/hgSMTVa0VkDYjXW6bEnosRU4+G5JyMerQZU+R3TqwOMsptwhXOle9nFzLOJVTIKxGK6+m5g2XDSGeyuC3h6v7G/DvoL7nVFhOfn4uAn80iW1D7coxJZSp8TszxjDhj2FAY5PZDErNOQ0zxtTdJScBrK/DegzJ535xBD87UFgFxS8uk3WYnloJ/AvttOYW4UofdF7FrmVQpC4R+Z4Or/fZ2O/FT/ZfVHa0fIetFdarVQsjvZyT1EndUrOw3mC7M+vCSkTYvqytas8pnkpjIZLUHHu9pteDU2XIyfeekVoWXTmcb5yuXtWFZR1O/PT56kN7Uo7MgaEOJ8wmwvm5UjynfIl1KUaAe3qOHLWe+lw1qLtDqJ/fa7dgKpC/rkyG4bDOhkQrr6Zm1/J2DHe5il4zilEsrDfY5oTDatL1nHizV67UA6Reh4B2M+ZALIVIIt0anpOKR4noASJ6LxG9B8CvATxSx3UZhkQqg+8+e67ol51LOsf9BjFOcR3PSb5wvDQmGadczwmQwghaOacz02HYLSb8+c3rMO6PKXH1CY1iXk4tw3o2iylPqi6tt7KuFrmMLcSy8k2c7cvacWw8WJU8mMvpe7WMU48b85HSh8ftOTOHVd1uZeKwGpOJcOP6Hrxw0V+1ak9qCmyH1WzCYLujROOUL7EupRBXK6y3ONOptPf96VMz+MSPDuWFDxmTws49vvz3Xm/e1E+ev4jXfOVJzQt/bi1XLkSE1+8awjMjs1VFUooJIkwmwqpufa/7hQsLsFtM2NDvVY7xSMaMxt/CSDVOQInGiTH2YQBfA7ADwE4AdzPG/ryO6zIMZ2fDSGVYwVzSfDiR1xKo2YR11HpeuwVWMylqoj6NL2yHzsX+7KzUc+vWzX3odNvwo+ek0B73FrV2XJ3yrq/asF4gmt8dglOrsRnq7hBqdixrQyrDcKyKfOKURo0TZ00ZPfYyGYbnzs5pek2c1T2esielajGpKg9Y0ekqKec0qyGx7lF268U9J6dVK6xX3Dil0hn8zc8O46fPX8Txyey/UyAm5YhyPSdA8iSmNTynx45LG6/DY/mqOy3RRy6v3zkExoCfHxwrunY95sMJOK1mzQ0ZZ3WPGyMz2mG9Fy76sXnQlyUCaXdaYTaR5t+CG9JW85wA4GkADwN4CMBT9VmO8Tghf9CngnHNUBcAjMxIFxWbxaR4Ec0mopNzIiJ0um2KW6/l7XS4tTuTj8yEMdwlDVV7w64h/P7oJGZDcUwFYjCRdpjDIhf5ahXifu2x0/jus6WNY5DGZWjvINtd1ffXiyXTmA0nMNSe/35sq4EoQqsAl7NWTmyXEto7MRWEP5osOGSOt7bRu2iVQiqdwUwormxeVnS6y/Ccsj8H3SVItrl3lNshAijNON13YFT5fZ86NZN1blpW42m9970anlMmw/DUaek5TmhsSJSwnoZajzPc7cauFe1VhfbmItpydTVrejy4MBfJawCbzjAcHvNjh/zZ5ZgKFEUvek4tZJyI6C4AeyGNTb8LwB4ielM9F2YU1BLiE5PaO2eekLxiuMM4npOOWg9YTORqdXSQjtvyBBGpdAYX5iJKQ8i3XLEcyTTDfQdGMRGIodtjh8Ws/XHSamEUS6bxz78/WYZx0vecfDXwnLRk5JzBNge6PbaqOkVwubKWIGKID48rwTgp+aYCxmlNd2EVVynMhhPIMCihw5VdLsyFEwUVaLFkGpFEOu+C6nNYYLOYChsnTc/JknVOj0Qqg39+6CS2DbVhdY8bT5zMNk6FvFYtmftL4wHl86/lLc+G4nBazZrfLTV/sGsIxyaCSgi9XBYiSd2QHmdNjxsZlq+kPDUVQiSRzso3caQuEfmbxXG/tMnUep+aQame018DuIIx9h7G2LsBXAngs/VblnE4ORlUkvD6xikEm9mEy1d2YjacqFnrkmrgX+jcDhHAooejlW8CpBZGC9FkVs5idCGKZJphlTzSen2fFzuXt+NH+y5gImcCbt7rabQweurUDKLJNE5Ph5AsocA1UMhzclrhr1JKrhTgahgnIsKuFR14dmS24jwOvwBq7balke2ekka27zkzh8E2B5Z16OcFhjqcsFlMZQ2jy4WHanmObGWn1Ai0kPekJxQgoqJ97DQ9pxJzTj/adwEX56P4xCvX4/q13dh7Zi7Lk+Cvq5Xv6/U6EIqnshSBT8qe15XDnZrfeb0C3FxevX0QFhPhZwcr857mIwldMQRnjY6cfLEzhIZx8mq3MJrwx9DjtWeFAZtJqaswMcbU/fhny3hsS3NiMoirV3fB57DkxbI5p6fCGO52KRcMLfVPowkn0rCZTZofNP7F0grpAVLOKZ1hCMQWv7B8dLO6lf5brliOE5Mh7Ds7p+kRcLRaGD340iQAIJlmOFtC+CmoMaKd0+6SPKdqBABaNU5qbt3Uh9GFKA6PVrYLng7G0em26X7x15ag2GOMYc/IHK5a3ZWlKMzFbCIMd7kqHsUBLH6G+WdkOTdOBfJOhYQCesIDDg/duVTqUmcJUvJYMo2vPHwSu1d24Mb1Pbh2bTeiyTQOnF9Q7qPVV0+9LvV9AGnjtKHPi+vWdeP8XCRviq9eAW4unW4bbtrQi58fHK2oVqsUz4nPXVL/rc/NhvEPDxzHYJtDKfJWo7dRGA/E0G8QMQRQuoG5X6XWey8ktd5v6rcsYxBPpXF2NoL1fR5s6PfqiiJGZkJY3e1RYrVGyDtFEqmsEe1qOot4TrxfnVoUwY3TcNeicXrN9gE4rVKLGS1hhfr11IKITIbh90enlPqZUrovaI1o57Q5rUhlmFLbVQmjC1EQ6RvsWzf3wWwi/PbwuOb5Ykg1Tvrv0bpeaWR7oQvxmZkwZkLxgiE9zupuj5ILrYTJYLYCc6U8QuFcQc9J3zss1iWCRxsctsVLkqsEKfk9e85jMhDHJ165AUSEq9d0wWyirLzTdCgOm8WUVb+mXhewaJxiyTT2npnDtWu7sb5PUrmdzNk0lOo5AcAbdg1hMhDHM6dnS7q/mlI8J7fdgoE2hyKmGV2I4u1f34NkOoNvve9KmDTaEHV7pebPuZu5CX/UMDVOQOlqvU8CuBvAdkiKvbsZY39Vz4UZgTNyt+j1fV6s75OMU+4fNJnO4PxsBGt63crF3giFuOF4Om/QIIeHXbRqnADtzuRnZsLw2i1KhTkgFcC+evsAAH1DJ72eHfORpNKf7sCFBcyE4vjADathIuh6pGq0RrRz2p2VjflQM7YQRa/XDptF+yvR4bbh6tWduP/wREUemlYRqJq1vR4whoKhuBflupudy9uLvt7qHjfOz0ZKCplqMRmQetHxv7fXYUWn21ZQsVeoOLWYcYom0jCR1GSWU0ytF46n8O+PnsK1a7twzZouAFJ7qx3L2rLyTrzGScvb5BsGnpfaf24e8VQG163rwkZZgn18IttbLsc4vWJTL7x2C+49UF7dGW/51VHEcwKkv/Xp6TCmAjG84+vPIhBL4jt/eFWWhFxNj8eORDqDQDR7I2Sk7hBAGaE5xthPGWMfZ4x9jDF2Xz0XZRT4jn5drxcb+70IxFJKwSnn3GwEqQzDmh6P8oc1gigikkhp5puAxVqnYp6TWp14ZiaM4W533hf8LVcsByDlOfTgFzhu7B58aRIWE+FVW/ox3OXWVESpSaYziCTSujmnWozNUM9x0uO2rQMYmQlX1GdPqwhUjdIAtkBo7/hEEBYTKXmGQqzu8SCVYbhQgsJOiykNkcuKTlfB5yskse7x2DEXSeg20JXGu1iyPl+OIjmnbz9zFjOhBD5+64as49et7cYLFxeUz0OhjUGu5/TkqRlYTIQrV3VheacLDqsJxycW/yaMMcyE4ooCsRgOqxl3bBvA/Ycnyto8BaJJMLb4XSzEGnkm2Dv+cw+mgnH81/uuxNah/FwTp1ujS0QonkIwljKMUg8o3vg1SEQBjZ8gEdVuopZBOTUZhImknQl38XN3+TwRuabHA6/DCo/dYohC3HAiDZe9iOekE4pb9JyyjROPb6u5YrgTP/zA1YoHpfl6vIWRHPZ58KUJXL26C21OK9b3eXWFJpyQTncIjjJwsApRhF4BrppXbekDEcoO7THGpAtkgdDncJcbZhMVNU5rejy63p0aRU5eYd5pMhDL+3ys6HThXIG5TrPhBKxm0lRV9njtYAy6tVfRZDpPOWq3mGAi7bBeIJbEfzw2gps39uLylR1Z565d240MA56VO7cXMk6d8jBErqZ86tQMdq1oh8dugdlEUsRkcvFSF0mkEVdN+i2Fu65YhmgyjSs//xD+9Dv78ZsXx4uKppTuEO4SPKduN0LxFM7PRfCN91yR937kohTiqoyT1uDRZlOst56XMebT+PEyxnyNWmSzODEZwnCXGw6redE45bj4PBHJLwb9bQ5DtDCKJlJw6RTv7R7uxO1b+3U/xLmdyeOpNEYXohjWME4AcNXqLtgt+oWCSgujUAIj0yGcng7jlk29AID1/V6cnS08nI23LsqdgsupdhouYwyjC1FdMQSn1+vA7pUduL/MnmmBaAqJtHYRKMdmMWFllwsnp/QN9bGJoG6oJhdFTl5h3knqMp99oVrZ5cLYQkw3VDgXknIkWuEzfkHUG4seS+YPxiQiuGwWzbDe3pE5+KNJ/PH1q/PO7VrRAafVrOSdChknk4nQ7bFhOhjHQiSBF0f9uG5tj3JeCucvvoelFODmcvnKTtz3wWvx9itXYN+5eXzwnudx+d89iE/f+6JuiHhe6Q5R/HV2D3ei22PHf7zrciW8WYhub37zV8U4tVrOqV4Q0W1EdJyIThHRpzTOExH9i3z+BSK6rJHrOzEVVMItHW4ber32rA8qIMnIe712JeTU73MYw3OKp/MKcDndHjv+/Z2X637wfU4riBZzOOdnI2BM2qFVQreqhRFX6d2yuQ8AsL7Pgwwr3B1BryM5h4/NqLQQdzacQCKVwWAJu8bbtg7g2ERQEYiUwnRIvwhUzdoefcVeIJbE6EK0ZOPU5rKiy22r2HOaCsby2iOt6HQhnWG6gxFnC+RiivXXiyRSWTVOHGlUe75IhE9sXd2T/5m0WUy4anUnnjw1g2Q6g7lIouDGgOfDnjk9C8aA69YtXuA39nsxE4orgp5iffX02Lm8HZ973Rbs+cwr8L0/ugpXrurE9/ee171WLBTpq6dm61Ab9v3NLbhpQ29Ja9FqYcTz5EZpXQQ00TgRkRnAvwK4HcBmAG8jos05d7sdwDr55wMA/r1R64un0jg3G1E8JgDY0J8fgjo9HcrKAfS3OQyUcypcJKiH2URocy52iVCUehUapy5lTEccD740ic0DPizrkNRfG+T3t1Bor5hxWhREVGacxgvUOOVy29Z+AOWF9rgsu5DcHgDW9XlwbjaCRCrfM+F5uU0DpRkngA+jK984JdMZzIQSmmE9QL/WaS4c15VY92pIttVEkxk4NHKkTp1puJP+GMwm0s39XLe2GyPTYRwe9YOxwhuDXq8DU8E4njw1A4/dooxJAZAXzp+TQ9PleE5qzCbCy9Z2413XrASgr+zlnlNnCcapXDpcNpgou/krv2b1Fgg9N5pmek5XAjjFGBthjCUA/ADAnTn3uRPAfzOJZwG0E5F+cqOGjExLSr11fYuGZ0OfFyengkrNAmMMI9PhrN3bQJsDU8FYTSanVkM4oe85lUKHy6Z8QZQap67KjFOb3M/rxGQQ+8/P41bZawIkg2c1U55HqkYJ6+kIIhxWE2xmU8VhvdEC3SFyGWp3YseytrJCe9xbKOo59UoiBq3RFLxTwYb+0qPplcrJuQHJldWvlP/+enJyraavnGItjPTC0HrTcCcCMfR47LoTW69b1w0ASvugQjJ+Xvfz5KkZXL26M6sWbVGxJ73/vJi8VEGEHvy9ndTZyHIxUnsJOadyMZsIne7sQtzxgDQepVAfv0bTTOM0BOCC6v8X5WPl3qcu8J282nNa3+9FLJlRFEuz4QT80WSe55Rh2i3pG0kkXrnnBMj96mTP6exsGJ1uG9pKkLVqYTIROlw2/OrQOBhDlnGymk1Y0+Mp6DkVM05EhDaXFf5oZe95odZFWty2dQAvXPQrRq0YhfrqqVnbI33WtEJ7xyekTiWlhB45q3vcmAklyjbaud0hOL1eO+wWE87rzHXis5y04OMp9D2ndFZ3CPXjtNR6k4GYbikEIG0kuz02/PIFycMt9N73eO2YCsZxbjaCa9d2551rd1mVz+dsBTknLXhuR99zSsBiInh1RE3VkivtnzCYjBxornHS2vLkZgdLuY90R6IPENE+Ito3PV39iOSTkyGYTZTlFfEQFN/F8l5ovKs0AEPUOjHGENFIMJeDNNNJuqiNTGsr9cqh22NDMJ7CULsTWwazd/+8hkyPQLRwWA+QWxhV6DmNLUThsJpKqikBgNvl0F6p3tNUMA67xaTbG5Czpld6j7WM07GJADb0eQt2hsil2KRUPSYD2p6TyUS63cnjqTSC8VTBi3ahLhHRhI5xsup4TkWG4hERrl3brQgYCob1VKGs63KMExFhQ59X+c5rTfqthE63DTazfqPoebk7RDl/73Lo9mQ3fx33xwwlIweaa5wuAliu+v8yALn95Uu5DwCAMXY3Y2w3Y2x3T0+P1l3K4sRkECu7XFkqNB7i47so3gVZLRQwQq1TLJkBY9pNX0sl13MarjCkx+G5iFs29eZ94db3Sd0RQnHt7gjcc/IUuLi3Oa0V55x4jVOpF4Lhbjc29ntxf4l5J64WK/b8LpsFQ+3OvI4EjLGylHqcSuXkvIu3Vv5hRadLM+fENzKFjFN3gULcaCKtKYhw2bRzTqXs9NVeUKEwHBdL9PnsigBKzYZ+L07IBfhak34rgYjQ67PrhvUWIomSlHqV0pPT/HXCHxWek4rnAKwjolVEZAPwVgC/yLnPLwC8W1btXQ3AnzORt26cmgphfW/2xcBls2BFp0tJjp6eCsFuMWVJkLnapZmKPT7LqRY5p3BcKjzWUkWVA89F3Lq5P++c0iZGJ7QXjCXhtJoLNqSUjGmlOadYURl5LrdvHcC+c/NKfUwhinWHUKPVY2/cH0MwlsLGgfKqN1Z0umAxUdl5p8lAHGYTKUKWrOfskoxTrgRaaV1UyHPyaDccBeSwnqZaz5IX1gvHUwjGU7qtpjjcOPkcloK5FP63uXZtt6bR2dDvRTiRxsX5qOak30rp9zkKhvVK9eQroVv2YhljiCXTmI8kDaXUA5ponBhjKQAfBvAAgKMAfsQYO0JEf0pEfyrf7TcARgCcAvB1AB9sxNpiyTTOzoaxvk9/FwVISr3VPZ6s/lUdLitsFlNTa534FNxqPKcOlxXRZFoJZ1Qb1lvV5UK3x46rVuf3heMegV7eKRhLafZFU7Osw4WRmVBek85SGFuIYrDML+bt2/rBGPDLQ8X3SlPBWMGEvJp1vdJkU3WjUB7y3Fim52Q1m7Ci01W25zRZQGywstOFSCKdl1Mtpf6nUAsjqUOEhudkNef1G5xQhlsWfk+H2p1Y3e0uujFY2eWGzWzCKzU2TkC2orSQ6KNc+toceR1nOFLT1/p5Tt0eGxKpDILxlCFrnIAm1zkxxn7DGFvPGFvDGPt7+djXGGNfk28zxtiH5PPbGGP7GrGukekwMgxY15d/MdjQ58XITBjxVBojM+E8j4KIml7rpHhOVcTF+RfjwPl5AKg6rPfhm9fh9x+/QdP7Wd6R3yZGTaFxGZxXbelHLJnBI8enCt4vl3gqjelgvGQxBGddrwdXrurE/33gGF68WHjOU7meUzyVyaolOioXfq/X+DwWoxI5+WQwrts9hCv2zud0ipgrYQBfj9eOYCyVV3CdyTDEUxlN78apodbjobBinhMAfOr2jfjzm9cVvE+P1459n71FKRPIZX3/Yq55JpRAd5ViCE6/Tyo70SrErbvnpKp1GjfYkEHOJTH2olx4lf46Dc9pfb8X6QzD0fEgLsxFNPucNbvWSRk/UIXShxf/HbiwAAAY7nZVtSabxaS7EzSZCOt69dsYFRqXwblylVQl/5sXy4v68r/ToMYE3EIQEf717Zehy23HH/33c7p/70Qqg/lIUnNcgxZKj73pxffi+EQQg20Opdi4HFb3eHBmNlzWyIapQH4BLme5Tq3TrDIuo1A9kXQuN6oQS+XPcuI4beY8Y6Z4TiUYp1du6cfrdxUX+OopQfm5wTaHynOqnXGKJtNZo2kAKcc4H0mWVIBbKYstjBKYCBhrPDtHGCcNTkwGYTaRZiiLu/i/OzKBDJMmUeYy0KYfS24EPAxSnVpP+rIeODePfp+jqhBhKUg9zPRzTsU8J7OJcNvWPjxybLrg2IlcRovMcSpEj9eOb773CoTjafzht5/TDCnyHEupxY1aDWCPTwTLzjdxVne7kcjxxIqh1VePs7zTCaL8yatz4QRMJKkm9eDNgXPXEikwGNNlNSOZZlktkxbDeo27mG7o9+LghQVEk+ma5Zy4FD7XWEeTaSRSmTqH9Rb7640bsK8eIIyTJlJPPZdmv7hV3W5YTITfyjJiTc+pgLveCMJx/S97qfAvxpg/VnW+qRQ29HswHYxndULnBAqMaFdzx7YBRJNpPHq89FKCszPSRZZ7BOWyod+Lr7xtF46OB/DRHx5EJsdDWRx0V5pxanfZ0O2x46Tc+TyZzuD0dKhspR6Hy8lPlyiKiKek5HhuXz2O3WLGgM+RZ5x46yKt+UGcZe3Se3wxxzhxNZ5eWA/IHpsx6Y/B67DUfcOkZn2/V/mdy21dpIdS65TjdSvdIepQgMtR99eb9MfQ5rQ29P0sBWGcNDg5GdSN79ssUtEo75qgpWLrb3Mgkc7odmAuxthCFLv/z4NFcxl6RJScU+UfNnXootK2ReWwvkAbo1I8JwC4alUXuj02/LqM0N7IdL7islxevrEXn33NZjz40iS+cP8xTPhjODzqx+MnppVNTDltYdb2upWR7SPTYSTTrGwxBKdcObnSaqnAercta8OvXhjDPXvOKcfmwvGi4a7+NgeIgIs5xcs8bKe1mXJqDBycCMQanrxXv/+1EkToFeIq3SHq6Dl1umwgkjZPRqxxAgBjmUoDEEumcX4ugtft1I9Tr++XQlCDbdrhLv6HHvfHlHER5bD3zBxmQgnsOTOLbcv057LoEVZyTtV4TovGoNKGr+WgNk5Xrc7urFyq52SWZ0Td+/yoblFnLiPyKJBCO/5SeO/LhnF6OoS7Hx/B3Y+PZJ1z2cxKX7pSWNfrxc8Ojsr1TZIYolLPqcttg89hKbkQd0qpcdK/WH3pjTvwkR8ewF/fdxiHR/343Ou2lJSLsVlM6PM6dMN6enVOQPZMp4lAvOEhKPVmtVY5J74ByK11mi+j6WulWMwmdLmlQtyJgPG6QwDCOOVxejqEDIOmjJyzoc+DXyK7M4SaflmWPBmIFRz6pcdheeLpSBmdr9VE4tV7Tg6rWWq6mUw3xHMaaHPAa7fk5Z1icvxdb1xGLq/eNoB79pzHo8encPu24m0YT0+HsHWw/L9RLkSEz712C7Yva0cqzdDlsaHLbUOn24Y+nwPuMsQpa3s9CMZSmArGcWwiCKuZsLq7+IBBvXWt7vGU7Dkp3SEKNKltc1nxjfdcgX988AS++sgpHB0PYsIfKzpHCJDyTqML2SFBbng065ys0vumziNO+mNY19udd996sqbHA7OJkM6wrGnQ1eCwmtHhsuZ7TnJYr55qPUDKO00HExj3x/K6thgBYZxy4LH+QrJdfk7Po1hsYVSZKOLImLRbLrftDKfQTrQcOlxWRP3phuSciAjr+704kSMnDxYZNJjLlas60eWWQnvFjFM8lcaFuQhet2OwskXnYDGbcNfu5cXvWAS1KKKcAYN6rO5xK7ONijEV4DLtwh6/2UT4i1dtwNYhHz7+o0OIJNIleRRD7U4cuDCfdYyH7LQ8XVdOWC+dYZgOxRse1nNYzRjucuH0dLhmnhMgyeFzBRG8M0s9w3qAZJwmAlHMhOLo9xmrABcQOac8TkxKo7AL1fVsHvSBSL9DdI9XKmCsRE7OGMPhMclzOl3hLB4+G6faUFW73Fq/nJBUNazv8+LEVDBLSBIsMi4jF4vZhFdt7cfDx6aKThs9PxuRFZeVeSX1Yl2Ocao0pMdZ0+PBZCCu2x5KzWQwDquZSg4p3bZ1AD/70LW4clWn0gm8EMs6nBhfiGVJ2xXPqUDOiW+4ZkJxpDOsKWGojf0+2MwmeGrYjLVfoxCXt4Jqr7vnZMOJiRAYM16NEyCMUx4nJkNY1e0uuFNd1uHCfR+8Fm+6fJnmebOJ0Ou1V+Q5XZiLIhhLYWWXC9PBuHJxLodqx2VwOtxWDHU4q9q1l8OGPg8WIklF4fbSWABfuv+4tJYydpF3bB1AJJHGo0UKcnOnGBuFHq8dXocFz5+fL2vAoB7cwz9TwmZnMhBDr9dR1sZmfZ8XP/qTa/CqLdpFrGqGOpxIZViWt8C9Ipc1/6LPvX9uwMab2M3gXdesxEdvXVfTZqxaLYzmIwl47ZaC7bpqQbfHjoQs0Rc5pxbgb+/cgrkSxl3sXN5e8Hyl49q51/Ta7YP46iOnMDIdxo4ir5VLteMyOH964xqEYuW3A6oUHi79wXMXsO/cPB4/MQ23zYw/uXE1rl9XejPfq1d3otNtw69fnMBtW/VDe3z6biPCluVARFjb68Hv5anBm8qY4aSF0p18JlRUYDMViNd14BxXRY4uRJWuHBHZ8Dhs+Rfj3LDeRBNrcq5e3YWrc8Q61dLnc2AmFEcynVGM0UIkUZc5Trl0q7qWCM+pBRhqd1akkMtFamFU/tiMw6N+WEyE27dJu9BKhsWFdfqUlcv163pKEhXUCt4m5ssPnsBLY3588lUb8PSnXoFP375Jd6icFhazCa/a0oeHj04WDO2NTIfR57OXJFNvNOt6PYrqslrPaWWXC0SlhYknA7GCYohqWaZRiBsrqNbjgoi0sj6gtNZFrUB/mwOMZQ9hrHd3CI66U7sRPSdhnOpEf5vUX6/cQtwjYwGs6/NiXa8XZhNVNGY7mkiXpQ4zCt0eOz5x63r8n9dvxZN/dTM+9PK1FQ84vGPbAMKJNB47oV+QOzITqlgFV2+4KMLrsFS9q3VYzdgy6MOPnrugJNv1KNQdohYMqjwnTiEBDz/G1XoTgRisZqpZIWyz4e+1OrRX73EZHK469NgthtygCeNUJwbaHIgkpAFsas7MhDXHcAOyGGLUj62DPtgsUkfp0wUUew++NImvPnwy73g4kaqJ59QM/vwV6/DOq1dWPS76mtVd6HBZ8cAR7YGAjDGcngoZLt/E4cZpY395Awb1+PwbtmEmFMdn7ntRd8MUTUh93grVOFWLy2ZBl9uW1SUimkzDZjbBopFj4YII7gFP+svPiRkZrXHt85EkOusshgAWPScjek2AME51Q6l1Un3o/JEk3vy1Z/An39mv+ZjJQByz4YRSG7W6u3BH6W88OYJ/fuhkXugqEq9NWK+VsZhNuHp1F547O6d5fjacQCCWUvIxRmOdPEus2pAeZ/uydnz8levxmxcn8JP9FzXvoxTglthBvVKGOpy4OL9Y6xRLpuGwal+KbBYTLCZSvKuJOnt2jUarS8R8gzwn/nc2Yr4JEMapbmjVOn3h/qOYCUmFlWc0Cmx58S0viFvd48aZmXBevzYASKUzeOGiH8k0w0vjgaxz4USqqgLcpcLlKztwYS6qKennRl+rca8RGGp34g27hvC6HcU7apfKn9ywBlet6sTnfnFE03vXG89ea4banTlhvcICHvXYDKN2M6iU3HHtyXQGwViqITmnTrfUwsio+TthnOoE343wC+OzI7P4/t4LeK1c8KkVbjo85gcRsGmAGyd5ts9CvrDixGRI+cIeksdacCKJdFWti5YKVwxLgw33ncv3nniBs9FqnDgmE+Ef37ITV67KH85YKWb5Oc0mwkd+cFDp9J1KZ/CDvefxP75/QLcbfy0ZandibCGqhBejyUzBVlNO6+Ko9kl/zLAX00rIHdfOpzl3NECtZzGb8Jbdy0sqAWgGwjjVCS7HHffHEEum8Zl7X8SyDie++MZt2L6sDfcf1jBOowGs7nYrYgZen6KVd+JV9g6rScM4Cc8JkIqlnVYz9p2dzzt3ejoEm8VU9pDBVmew3YnP/8E2HLywgK88dBK/fXEcr/ynx/Gpe1/EQLsD3//jqyvu0F4qQx1OxJIZzMoNTqNy0bgeLpvURisYSyKcSBtuYmu1qGudGtUdgvOFN27HrZv7GvJa5SKuYHXCbjGj22PDRCCGf3vkFEZmwvjv918Jl82CV23px/994DjG/VEMqMaDvzTmxxWqnTLv3TcyHcZNG7Kf/+D5BXS6bdi9sgMHVcYpnWGIFdmJXipYzSbsWtGu4zmFsarLXZZEfanwmu2DeOTYNP7l4VMAJNn63e+6HLdu7qtpgakeSq3TfBTdHjuiycJNep02CyKJtCIjX0phPUCa6/SS3LKsUX31WgHhOdWRPp8De87M4t8fO4037BrCDeulQlI+Dvp3RyaV+86G4hjzx7KakCodpTVqnQ5cWMCu5e3YuaIdZ2cjyo6rFuMylhK7V3bgpbFAXuuekZkw1vQaM9/UCP72zi1442XL8KU3bcf9H70Br9zS3xDDBEgdVoBFOXk0kS7Bc0phwi/lxJak5ySXnTSiI3mrIIxTHRloc2BkOgyP3YK/efUm5fiaHg/W9XqyQnu82euWocVuAHodpf3RJE5NhbBrRTt2LmsHAMV7itRgXMZSYvdwJzIMOHB+MbSXSGVwfi5i2BqnRuCxW/D/7tqBu3Yvb7j3yCficsVepMh4E6dVEkQ0YwJuI1CPa18M6wnPSRinOsK/RJ99zea8uU63be3HnjOzykBC3rZoy0B2d4rVPflycp5j2rWiA9uWtYEIOHRBeny4BuMylhK7VrTDRMBzqrzT+bkw0hlm2BqnpU6b0wqv3aJ0iYglC3tOTpskiJiQO64sJUEEkD2ufTGsJzwnYZzqyF27l+MvXrkeb9iVLwd+1ZZ+ZBiU/mlHRgNY3unM64iwpseDiUAsKyx14PwCiIDty9rgdVixtseDQxcXAKg8J5FzAgB4HVZsGvBhvyrvdFqRkV+6nlOzkeY6yWG9ZOG6PC6ImAjE0O6yVl2gbTTU49rnIwnYzCbx/YUwTnVl+7J2fPhm7S7GWwZ9WNbhxP2ypPzImF9z6B2vw1F3lD54YR7rej1Ky5Gdy9tx8MICGGMq4yQ8J87ulR04cH5BkU6PGLQb+aXEULtT6RIRSaQLGhyXXOc04W/8HKdGoC7EXQgn0e6yNiz/Z2SEcWoSRITbt/bjyZMzGPdHcXY2ojk1V91RGpDa7khiiMWpozuWt2MunMDF+SjCsiBC5JwW2T3ciUgijaNysfLp6ZA8lkLE9ZvFMpXnFCui1nPIdU5S37+lZ5zU49rnI8XH3V8qCOPURG7b2o9EOoOvypJerVHJK7tcMKk6SkvKvCR2rWhX7sPHdxy4sIBIXPKcRM5pkd3DkiHneaeR6ZDuFGNBYxjqcCIYS2E2FEcyzeAq6jmlpO4QS9A4qce1S62LxKYJEMapqexa3oEerx0/fO4CAGCLRljPbjFjWYdL6WjAVWc7VcZpQ78XdotUjKt4TiJmrTDQ5sSyDqeSd5Jk5CLf1EyG2iU5+akp6XNdyHNy2SzIyGMl+paYUo/Dx7U3alxGK9AU40REnUT0IBGdlP/t0LnfWSJ6kYgOEtG+Rq+z3phMhFdt6UMqw9Dns6NHp+Hm6h634jkdvLAAt82sNAYFpGLTbUNtOHRhARGu1mvBkRn15IrhTjx3dh6zoTgWIknhOTUZLic/JW+6CuWc1Eq+peg5AZKydyIQa9i4jFagWZ7TpwA8xBhbB+Ah+f96vJwxtpMxtrsxS2sst22RhvlpiSE4a3o8ODMTQibDcOD8AnYsb8+rTdmxvB0vjvoRiAnPSYvLV3ZgOhjHI8el+U5CqddceJcI7jkV+ryqvar+tqXTkVwNL8RdiCRFdwiZZhmnOwF8W779bQCvb9I6ms5Vqzsx3OVSukdosbrHjVgygzOzYRwdD2Tlmzg7l7cjnsrgwPl5mAiwW0TEVg1vAvujfVIIVSj1mku3xwa7xbQY1iuSc+IsRUEEwMe1J5DKMBHWk2lW7KePMTYOAIyxcSLq1bkfA/A7ImIA/oMxdrfeExLRBwB8AABWrFhR6/XWDavZhEc/+fKC9+GdDH5+cAypDMPO5flRUC6K2HtmDm6bRUhRc1jX64HPYcHeM3OwmU1KCx1BcyAiDHU4cXKyeM7pUgnrcYQgQqJu22si+j0RHdb4ubOMp7mWMXYZgNsBfIiIbtC7I2PsbsbYbsbY7p4efS+kFeG1Tvc+Lw2J44ZIzbIOJ7rcNoTFuAxNTCbCbtl7Gu52XZINX43GULtTaUlUrEMEANjMpiUrs1YbXeE5SdTNODHGbmGMbdX4+TmASSIaAAD53ymd5xiT/50CcB+AK+u1XiPT47XDY7fg4nwUyzudmsIJIsIO2WgJGbk2l6+UPM5LuaeekVjWsdiRv7BaTzrX12ZfshEBdbiyEbOcWoFmJSZ+AeA98u33APh57h2IyE1EXn4bwCsBHG7YCg0EESne0y6NkB5nh9wEVnhO2vC806XcjdxIDKlmaRUURFilzdZSDekBuWE94TkBzTNOXwBwKxGdBHCr/H8Q0SAR/Ua+Tx+AJ4noEIC9AH7NGLu/Kas1ALxThFZIj8Nrn1xW4TlpsWN5G27d3IdbNxtz8uelxpDKcyrWvghYumIIQJrfZJNFTJ3COAFokiCCMTYL4BUax8cA3CHfHgGwo8FLMyy8LkdLqcfZsUySowvPSRu7xYyvv3tJViS0JLwQFygt57SUPSciQp/PjovzUficIqwHiEm4LcPrdg5iIZrENo3+e5x2lw0b+73o8y7dL7Fg6aDOORVqVNzmtMJtM2N9n1f3PkuBfp8DwVhKiHVkhHFqEVZ2ufHZ12wuer97/ugq2JfYSAHB0qTP54DFREhlWMG6PIfVjMf+8uVLXsW2pseDaDLd7GUYBmGclhi5Qw0FAqNiNhH62xyYDSVgKuItdF8Cn+u/fvUmxFOZZi/DMAjjJBAImsZQu1OZQXap43VYsbQDl+UhjJNAIGgamwZ8CMZSxe8ouOQQxkkgEDSNT92+EfGkCGUJ8hHGSSAQNA2H1Vywxklw6SJaVwsEAoHAcAjjJBAIBALDIYyTQCAQCAwHMcaavYaaQ0TTAM5V8RTdAGZqtJxmINbfXFp5/a28dkCsv9mUu/6VjDHNGUdL0jhVCxHta+Wx8GL9zaWV19/KawfE+ptNLdcvwnoCgUAgMBzCOAkEAoHAcAjjpM3dzV5AlYj1N5dWXn8rrx0Q6282NVu/yDkJBAKBwHAIz0kgEAgEhkMYJ4FAIBAYjkvCOBHRN4loiogOq47tIKJniOhFIvolEfnk48NEFCWig/LP11SPuVy+/yki+hciasjIynLWL5/bLp87Ip93tMr6iegdqvf+IBFliGhnC63fSkTflo8fJaJPqx7TCuu3EdG35OOHiOimZq6fiJYT0SPye3mEiD4iH+8kogeJ6KT8b4fqMZ+W13iciF7VSusnoi75/iEi+mrOc7XC+m8lov3yOvcT0c0Vr58xtuR/ANwA4DIAh1XHngNwo3z7/QD+Tr49rL5fzvPsBXANAALwWwC3G3D9FgAvANgh/78LgLlV1p/zuG0ARlrs/X87gB/It10AzgIYbqH1fwjAt+TbvQD2AzA1a/0ABgBcJt/2AjgBYDOALwH4lHz8UwC+KN/eDOAQADuAVQBON/PzX8H63QCuA/CnAL6a81ytsP5dAAbl21sBjFa6/rp/MYzygxyjAyCARUHIcgAvad0v5490TPX/twH4DwOu/w4A323V9ec85vMA/r6V1i+v65eQNgld8pe5s4XW/68A3qm630MArmz2+lWv+3MAtwI4DmBA9dk4Lt/+NIBPq+7/gHxBbIn1q+73XqiMU6utXz5OAGYhbRTKXv8lEdbT4TCA18m33wzpC8pZRUQHiOgxIrpePjYE4KLqPhflY81Cb/3rATAieoCInieiv5SPt8r61bwFwPfl262y/p8ACAMYB3AewD8wxubQOus/BOBOIrIQ0SoAl8vnmr5+IhqGtDPfA6CPMTYOAPK/vfLdhgBcUD2Mr7NV1q9HK67/jQAOMMbiqGD9l7Jxej+ADxHRfkjuakI+Pg5gBWNsF4CPA/ieHI/Xio82U4evt34LpLDAO+R/30BEr0DrrB8AQERXAYgwxniepFXWfyWANIBBSGGlTxDRarTO+r8J6cKxD8A/AXgaQApNXj8ReQD8FMBHGWOBQnfVOMYKHG8IZaxf9yk0jhl2/US0BcAXAfwJP6Rxt4Lrv2SHDTLGjgF4JQAQ0XoAr5aPxwHE5dv7ieg0JG/kIoBlqqdYBmCskWtWo7d+SOt8jDE2I5/7DaR8w3fRGuvnvBWLXhPQOu//2wHczxhLApgioqcA7AbwBFpg/YyxFICP8fsR0dMATgKYR5PWT0RWSBfGexhj98qHJ4logDE2TkQDAKbk4xeR7YXzdTbt81Pm+vVomfUT0TIA9wF4N2PstHy47PVfsp4TEfXK/5oA/A2Ar8n/7yEis3x7NYB1kJLy4wCCRHS1rDJ5N6T4a1PQWz+kGPt2InIRkQXAjZDyCa2yfn7szQB+wI+10PrPA7iZJNwAroYUa2+J9cufG7d8+1YAKcZY0z4/8mt9A8BRxtiXVad+AeA98u33qNbyCwBvJSK7HJZcB2BvC61fk1ZZPxG1A/g1pLzfU1Wtv9EJtWb8QNqBjwNIQrLgfwjgI5CS1ScAfAGLyeE3AjgCKfb+PIDXqp5nN6RY/WkAX+WPMdL65fu/U/4dDgP4Uguu/yYAz2o8j+HXD8AD4Mfy+/8SgE+22PqHISW7jwL4PaSRBk1bP6TQNIOkQD0o/9wBSWzyECSv7iEAnarH/LW8xuNQKcJaaP1nAcwBCMl/r82tsn5IG52w6r4HAfRWsn7RvkggEAgEhuOSDesJBAKBwLgI4yQQCAQCwyGMk0AgEAgMhzBOAoFAIDAcwjgJBAKBwHAI4yQQNBG5HupJIrpddewuIrq/mesSCJqNkJILBE2GiLZCqo3aBcAMqTbkNrZYXV/Oc5kZY+narlAgaDzCOAkEBoCIvgSpeNEt/7sS0sgQC4DPMcZ+Ljfe/I58HwD4MGPsaZJmLv0vSIW2Oxljmxu7eoGg9gjjJBAYALll0POQGrD+CsARxth35XYweyF5VQxAhjEWI6J1AL7PGNstG6dfA9jKGDvTjPULBLXmkm38KhAYCcZYmIh+CKllzV0AXktEfyGfdgBYAalR5ldJmgychtSQmLNXGCbBUkIYJ4HAOGTkHwLwRsbYcfVJIvocgEkAOyCJmWKq0+EGrVEgaAhCrScQGI8HAPy53L0ZRLRLPt4GYJwxlgHwLkjiCYFgSSKMk0BgPP4OgBXAC0R0WP4/APwbgPcQ0bOQQnrCWxIsWYQgQiAQCASGQ3hOAoFAIDAcwjgJBAKBwHAI4yQQCAQCwyGMk0AgEAgMhzBOAoFAIDAcwjgJBAKBwHAI4yQQCAQCwyGMk0AgEAgMhzBOAoFAIDAcwjgJBAKBwHAI4yQQCAQCwyGMk0AgEAgMhzBOAoFAIDAcwjgJBAaDiB4lonkisucc+6Oc+91ERBcbv0KBoP4I4yQQGAgiGgZwPQAG4HXNXY1A0DyEcRIIjMW7ATwL4L8AvKe5SxEImoel2QsQCARZvBvAlwHsAfAsEfUxxiabvCaBoOEsWc+JiL5JRFPymOtaPF+aiA7KP7+oxXMKBGqI6DoAKwH8iDG2H8BpAG9v7qoEguawZI0TpLDIbTV8vihjbKf8I3IBgnrwHgC/Y4zNyP//HhZDeykA1pz7WwEkG7Q2gaChLNmwHmPscTm5rEBEawD8K4AeABEAf8wYO9aE5QkEWRCRE8BdAMxENCEftgNoJ6IdAM4DGM552CoA5xq2SIGggSxlz0mLuwH8OWPscgB/AeDfynisg4j2EdGzRPT6uqxOcCnzegBpAJsB7JR/NgF4AlIe6ocA3kdEV5LEegAfA/CDZixWIKg3S9ZzyoWIPABeBuDHRMQP2+VzfwDgf2s8bJQx9ir59grG2BgRrQbwMBG9yBg7Xe91Cy4Z3gPgW4yx8+qDRPRVAP8C4K8AfArAtwAsBzAF4D8hbbgEgiUHMcaavYa6IYf1fsUY20pEPgDHGWMDNXje/5Kf9yfVPpdAIBAI8rlkwnqMsQCAM0T0ZgCQQyM7SnksEXXwan0i6gZwLYCX6rZYgUAguMRZssaJiL4P4BkAG4joIhH9IYB3APhDIjoE4AiAO0t8uk0A9smPewTAFxhjwjgJBAJBnVjSYT2BQCAQtCZL1nMSCAQCQeuyJNV63d3dbHh4uNnLEAgEAkEB9u/fP8MY69E6tySN0/DwMPbt29fsZQgEAoGgAESkW0QuwnoCgUAgMBzCOAkEAoHAcAjjJBAIBALDIYyTQCAQGID95+bx6xfGm70MwyCMk0AgEBiAbzw5gr//tajt5wjjJBAIBAYgGEthLpJo9jIMgzBOAoFAYACCsRRiyQyiiXSzl2IIhHESCAQCAxCKp4D/v703j2/zrvL931+t3uR9t2NnsR1nT5M0SfekpYWW0tIBLqXMdKalLAUGZuDODAwzMD+238CFufeydKBAgUJp2UoXWkpLmzZdsq/O4jiOkzjebXmTvEiW9L1/PHpk2ZZk2ZYl2fm+Xy+9rDx6JH/ztf2c55zzOeeA8p78KOOkUCgUSYBzVDNOfUPKOEGCjZMQ4hEhRJcQ4kSY13cIIQaEEEf9jy/Ge40KhUIRDwKekzJOQOLbF/0M+B7waIRzXpdS3h6f5SgUCkX88flkwDj1qbAekGDPSUq5G+hN5BoUCoUi0Qy5PYHnynPSWAg5p6uEEMeEEH8SQqwJd5IQ4iNCiINCiIPd3d3xXJ9CoVDMCd1rApVz0kl243QYqJRSbgC+CzwV7kQp5cNSyi1Syi0FBSE7sCsUCkVSooshQKn1dJLaOEkpB6WUTv/z5wGzECI/wctSKBSKmOKY4DmNJXAlyUNSGychRLEQQvifb0Vbrz2xq1IoFIrY4vB7TiaDUDknPwlV6wkhHgd2APlCiBbgS4AZQEr5A+C9wINCCA8wAtwtpZQJWq5CoVDMC3pYrzwnVan1/CTUOEkpPzDN699Dk5orFArFosXp0kJ5S3LTONPhSPBqkoOkDuspFArF5YAe1qvITaNv2I0KECnjpFAoFgANnQ5cnsXbEFWXklfkpjHmlRMEEpcryjgpFIqkxunycPt33uAXey4meinzhnPUQ5rFSH6GFVC1TqCMk0KhSHK6Bkdxe32cbBtM9FLmDafLQ4bVRG66BVBdIkAZJ4VCkeToF+qGzsUrFHC4PNhSTOT4jZNS7CnjpFAokhy73zg1djnx+hanUMA56iEjxUxumu45qUJcZZwUCkVSo3tOLo+PS73DCV7N/OAYHcNmNZGTbgZUzgmUcVIoFElOcP5lsYb29JxThtWE2ShUfz2UcVIoFElOj9OFxahdqs52ORO8mvlBC+uZEEKQk2ZRnhOJHzaoUCgUEekdclOclYLH61u0npPD7zkB5KZblFoPZZwUCkWS0zvkJjfdQlaqmbOdi89zklKbgmtL0S7HOWkWpdZDhfUUCkWSY3e6yUu3UFOUwbnuxafYG3Z7kRLlOU1CGSeFQpHU6J5TdZENl8dHcxjF3iv1nfz1j/fhW2DGS29dlKF7Tulm+oaVlFwZJ4VCkbRIKbEPucjNsFBTZAPCK/Z+feASbzT20O10xXOJc0Zv+mpL0WTkuWkW+ofdi85DnCnKOCkUiqTF4fIw5pXkp1upLswA4GwI4+T1Sd46p80hbe0fiesa54pjVPOSbFbdc7LgkzA4cnl7T8o4KRSKpKXXqeVectMtpFtNlGWn0hBCFFHXOhDwQNoWmHGaHNYL9Ne7zEURyjgpFIqkRW9dlJuhXbCrizJChvXebOwJPF9wxslvVHVBRI6/hdHlXuukjJNCoUhadNVant+bqCmy0dQ9hMfrm3Dem409rCrJxGY10dY/Gvd1zgV9dlOwWg9UZ3JlnBQKRdJi94sb9At2dWEGbq+Pi0GKvRG3l4MX+ri2Ko/S7NQF6zkF6pxUZ3JAGSeFQpHE2AOekzaET1fsBYsiDl7sxe31cU1VPqXZKbQNLDDj5Pec0nXPSXUmB5RxUigUSUzvkJs0i5FUixGAKr9iL1gU8UZjD2ajYOuyXEqyUxdcWM/p8pBiNmD29w9MtRhJMRuU55ToBSgUCkU49AJcnXSrifKc1AmiiLca7VxRkUOaRVPz9Q65GXF7E7HcWeEY9QRqnHRy01SXCGWcFApF0mIfcgfEEDo1RbZAj72+ITcn2ga4tiofgNLsFIAFFdpzujyBGiednHTVmVwZJ4VCkbTYna4JnhNocvKmHidjXh97muxICdfoxikrFVhYcnLH6FigxkknN92i6pwSvQCFQqEIR++Qm7wM64RjNYU2xrySi/Yh3mjsIcNqYkN5FgCl2QvPODlHx8dl6OSosJ4yTgqFIjnR+uqFDuuBJop4q7GH7cvzMPnFBMVZKQjBghJFOF1TjZPqTK6Mk0KhSFKG3F7cHt+UsF5VYQZCwK76Li7Yh7mmKi/wmtlooMiWsqA8J4d/Cm4wOWkWHKMexiYVG19OKOOkUCiSkuC+esGkWowsyUnj6WNtAAExhM5Cq3UKJYjITdfUe5eznFwZJ4VCkZT0DGndIfIyLFNeqynKwO3xUWizBmqfdEoXUK2TPgV3iuekd4m4jAtxlXFSKBRJie456d0hgqn2552urcpHCDHhtbLsVFr7R5Ay+echjY758PokGdZJdU6qv54yTgqFIjnRL8yTw3qgeU4AV08K6QGUZKXg9vgCrY+SGYfLP8sphJQcVFhPoVAsANweH4/vb8btuTyS5IG+eiHCejeuLOLeqyp5+5qiKa8tJDn55KavOuP99ZRxUigUSc7TR1v5/JN1PFfXluilxIXeIRcpZgNpFtOU17LSzHz5zrVT2v7AwjJOjkmznHSyEzTTSUrJrvqupLgBSqhxEkI8IoToEkKcCPO6EEJ8RwjRKIQ4LoTYFO81KhTJwtNHNaP0+tmeac5cHGg1TlPzTdNRFjBOyS+KcLpCGyeLyYDNaop7l4jdZ3u472cHePJwS1y/byhiZpyEEMuFEM8KIXr8BudpIcTyad72M+AdEV6/Faj2Pz4C/HdsVqtQLCy6Bkd561wPRoPg9bM9CyLZP1fsTnfIkN50ZKeZSTUbF5bnlDLVO0xEf73H9zUDsP9Cb1y/byhi6Tn9CvgNUAyUAr8FHo/0BinlbiDSLtwJPCo19gLZQoiSGK1XoVgwPHu8HZ+EB65bRrfDRX3H1FHli43JHcmjRQixYGqddM/JZp0ansxJt9A7HD8peZdjlL+c7gTg0MW+uH3fcMTSOAkp5S+klB7/45fAXG/vyoBLQf9u8R+b+s2F+IgQ4qAQ4mB3d/ccv61CkVw8c7SVNaWZ3Hf1MgBeP7v4f8dna5xAyzu1LoSw3qhmfEJ5Trlp5rh6Tr871ILHJ/nA1iVctA/T5Ujs/sXSOO0SQnxOCLFUCFEphPhn4DkhRK4QIneWnylCHAtp8KSUD0spt0gptxQUFMzy2ykUycf5niGOtQzw7o1lFGelUFOUwe6GxZ93sg+5pvTVi5ayBTKuPVzOCfyeU5yMk88n+fWBS2xdlst7Ny8B4NCFxHpPU3dk9rzf//Wjk47fj2ZQpss/haIFWBL073Lg8pAqKRR+nj7aihDwrg2lAFxfXcCjey8y4vYGJsQuNobdHkbHfOTOQhABmufU7XDh8nixmpJ3jxwuD1aTAYtpqp+Qm2aJW53T3iY7F+3D/OPbalhblonFZODgxT5uXZe4LErMPCcp5bIIj9kYJoBngHv9qr3twICUsj1Wa1ZcfnQNjuLzJZ+Y4DO/OcpnfnN0ytqklDx9tI3ty/IoztIG6V1XU4Db40uKpPV8YXeGr3GKhhL/XnUMJHdozznqmVLjpJOTbmHY7WV0bP6n+v5qfzNZqWbesbYYq8nIxvJsDiY47zRn4ySEuNH/9a9CPaZ57+PAHmClEKJFCPEhIcTHhBAf85/yPNAENAI/Aj4+1/UqLl/sThfXfnMXvzuUeJnsZPY19fLk4VYeerVxwvG61gHO9wzx7itKA8e2Ls3FYjLwesPizTsFCnDnENYDaE2C0J6Ukjcbe0LeFDlCzHLSiVeXiN4hNy+e7OSuK8pIMWte5ualOZxsHUjouPtYeE43+L++K8Tj9khvlFJ+QEpZIqU0SynLpZQ/kVL+QEr5A//rUkr5CSnlCinlOinlwRisV3GZcqylH7fHx97z9kQvZQq9Q26sJgPffqmBV+o7A8efOtKGxWjgHWvHwyupFiNbl+ayexGLInr9TV/nIoiA5Kh1eqOxhw/+eB8v13dNeS1U01ednDh1iXjycAtur48PbK0IHNtSmYPHJzl6qX9ev3ck5mycpJRf8n+9L8Tj/rkvUaGIDXUtg/6vAwleyURG3F5Gxrx89PrlrC7J5NNPHOV8zxBen+TZ423srC0gK3Wi1Pj6mnwaOp1JH7aaLfYITV+jQQ+BtieB5/SXU9rNRkPnVPl/qCm4Orlx6EwupeRX+5vZVJHNymJb4PjmyhwADl1MXOg4lkW4XxdCZAf9O0cI8dVYfb5CMVfqWvsBaOx2MuRXSSUDdr+XUJqdyg//ZjNmo4EPP3qQl0510u1w8e6NU6snrqvWFKmLVVIeaPo6y5xTitlIfoY14bVOUkpeOaN5TE3dQ1Ned7g8UzqS6+gzneazS8SBC300dQ9xd5DXBFr7pKrCjITmnWIpJb9VStmv/0NK2QfcFsPPVyjmRF3rAPkZVqSEk22DiV5OAP3OODfdQnlOGt+75wrO9wzxqSeOYLOa2FlbOOU9tcU28jOs7F6krYzs/jBn+hzUiGXZKQmvdWrscnKpVzOQTT3OKa87XWPhBRFx6K/3xP5mbFYTt6+fqsq7cmkOhy/2JUxAFEvjZBRCBHxwIUQqMDufXKGIMV2Do3QOuvgfW8oBON7Sn9gFBWGfNFTv6hX5/Ottq3B7fLxjbXEgSR2MEILrq/N542x3UqoP54rd6SYv3TJlVtNMKE2CWqdX/HmmG2sLOd8z1XOKFNbLSjUjxPzlnLw+yQsnO7h9Q0nI5rqbK3MZHPVwtmuqUY0HsTROvwRe9ivu7gdeAn4ew89XKGZNXauWZ9qxspDSrJTAv5OB8blF4/dy91+zlO984Ar+5dbasO+7vqaAvuGxpPICY0XvkGvWIT0d3Tglsg/hy/VdrCrJ5OoVefQPj00wNOGm4OqYjAayUs3zpta7aB9i2O1lU0VOyNe3+PNOBxJUshDLOqdvAl8FVgFrgK/4jykU886uM1389M3zYV8/3jKAELCmNJN15VkcTyJRRKihekII7thQSn5G+ODDNf5Be4tRtae1Lppb4KU0O5Vht5eBkcSMOh8YHuPQxT5urC1gRYE2HLGpe9wLcXl8jHll2LAeQEGGdd5EL6fbNYHGqpLMkK9X5qWRn2FJWJ+9WI/MOA28IKX8LPC6EMI23RsUiljwiz0X+frzpwPtYCZzonWAFQUZpFtNrC/P5nzPUMIuWpOxD7kxGQSZES5SoSiwWVldksnuRVjvpI3LmKPn5FfsJarW6bWz3Xh9khtri1hekA5MFEWMN30N/3OvyE2juXd4XtZ3un0Qo0FQVZgR8nUhBJsrcziYIMVeLNV6HwZ+B/zQf6gMeCpWn69QRKKtf4Qxr+SNMAKB460DrC/LAmB9ufb1ZJKE9nqdbnJmmV+5riafQxf7GIhj9+p4oOec5kKia51eOd1JbrqFjUuyKc9Jw2wUnAsSRUQal6FTkacZp/kITZ5uH2RFQXrInKbOlspcLvWO0DUY/z2Mpef0CeAaYBBASnkWmCozSnL+cKSFH7/elOhlKGaIfnccXMCq0zk4SrfDxVq/cVrn/3o8WYzT8OwvxO9aX4rHJ3nqaGuMV5U49LqvWOScANoTICf3+iSvNnSzo6YAo0FgNAgq89I5H+w5BabghpaSA1TmpjHs9tLjjH3eqb7DQW1x6JCezpalWt4pEZLyWBonl5QysINCCBNzH5kRd3bVd/OLvRcTvQzFDBgcHcMx6kEI2HVmqnpNzy/pHlN2moWK3LSkKcady2iItWVZrCnN5IkDlxbNAMKAenGOnlNeugWLyZCQsN6R5j76h8cmlAEsz0+nKUix53D5x2VECOtV5mnhwObeqUq/uTAwPEZr/0jYfJPOmtIsrCYDBxPQoTyWxuk1IcS/AqlCiJvRhg0+G8PPjwuFNitdg65F84d+OdDuD9vsqCmg2+Gaol6rax3AIGB16fgf4rryLI4liZx8LsYJ4O4rl3C6fZATrYtDtRdKvTgbDAZBaVZKQsJ6r9R3YTQIrq8ZH9+zvCCDi/YhPF4fMO45RRJELMlNA+CiPbZ5p9Md2u/KqpLIsgCLycCGJdkJyTvF0jj9C9AN1KGNzXge+LcYfn5cKMpMYWTMiyOJOggoIqN3AbhnWyVCjNeW6NS19FNVmDGhlmN9WRYtfSNxm5cTCbvTNSfjdMfGMqwmA78+2BzDVSUOewj14mwpzU6ltW9+BAWReKW+iy2VORPaTi0vSGfMK2np035fI81y0lmSm4oQsTdO9e26cYrsOYEmKT/ZNsiwO77XxJgYJyGEAaiTUv5ISvk+KeV7/c8XnPtRmKndrXUNuhK8EkW06IWW68qy2FCeHWgXA1otSV3rYCDfpLO+PBsg4fVOY14fg6OeOV2Is1LN3LauhKePtCW0i3Ss0Pvq5c8x5wSacWqPc//B1v4R6jsc3LRqYsp9ha7Y84siAsYpgudkNRkpyUyJuWLvdLuD3HQLhbbpvdO1ZVl4fTJkEfF8EhPjJKX0AceEEBXTnpzkFNh047Q4G2ouRtr6RzAZBAU2KzfVFnLsUj/dDu3momNwlB6nK6DU01lbpt0x1iU4tKcXWM41v/L+K5fgcHn404mFP+5srh3JgynNTqVzcJQxfygtHgR3hQhmeb5e66Rd5ANqvQieE2iKvYv22BqG0x2DrCqxRaUQLc/RhCW6xxcvYhnWKwFOCiFeFkI8oz9i+PlxoShTq43ocijPaaHQ1j9KcVYKRoMIJKBf9XtPuhhiXflE42RLMbO8ID3hxbixyq9sW5bL0rw0njhwKRbLSij2ITcWo2Hai3Y0lGWn4JOaYjNe7KrvoiI3LVB4q5OTbiE7zRwQRThdHixGQ0QpN0BlbjrNvbEzDF6f5EwUSj2d8hwt7xVv4xTLMe3/Xww/K2Hobm48f5kVc6O1f4TSLO3ubk1pJkWZVnad6eJ9W5ZwQhdDlGRNed/6siz2NiV2mmyvMzb5FSEE/+PKJXzzhTM0dTtZXhC6sHIh0OvUBCJz6aunE1zrpF9k5xOP18ebjT3cfeWSkOtfnp8e6BLhHA3fuiiYirw0epwuhlwe0mNgsM/3DOHy+KLKNwHkpJlJsxhpXWiekxAiRQjxD8D7gFrgTSnla/pjrp8fbzKsJtIsRuU5LSDa+kcozdY8XiEEO1cW8npDD2NeH8dbBqgutJEaorv1uvJsOgZHExrCDUx8jUF+5b2byjEaBL85mHyTfmfCXNWLwYwbp/hcWNsHRnF5fBOUocEsL8gICuuNReUdVuZpRjVc3mlgZIxvvFAfdb7xdHt0Sj0dIQTlOam0xFlYEouw3s+BLWgqvVuBb8fgMxOGEIJCm1V5TgsEr0/SMTAauAgB7KwtxOHycOBCLydaB6aE9HT0uqdEiiL0sJ4+HmEuFGamsHNlIb871BLXHEus6XG6YmKsgYBHHa9aJ11Vp0vAJ7O8IJ0uhwvH6JjW9DUK41QxjZz8T3Xt/Per53j2WFtUazzdPogpQtuiUJTnpC3InNNqKeVfSyl/CLwXuC4Gn5lQCjNTlOe0QOh2uPD45ATjdG1VPhajgcf2NmMfcgc6QkxmTWkmBkFC807jxil8l4CZcPeVS+hxutgVYiR4rHjueHvUF8Jg2vpH+O7LZ3F5wt/hv3qmi2MtA6wpDf0zmympFiO56Za4eU4X/cWyevHsZHRRxPmeIRxRhvUqcyMX4uqdTp4+Fl2XkPoOBysKMrCaop+VtVA9p0BTLynloigO0gpxlee0ENDviMuCjFO61cS25bk871euhfOc0iwmqgttCfecstPMmIyx0SbtWFlAoc3KY/vmr+bp2y+e4YtPn5ixd/b7Qy18+6UGPvObYyFnULUPjPCPvz5KbbGNf3hbdayWS2l2StyMU3PvMBajgWK/sGoyupz8fM8QTpcnYtNXnaw0M1mp5rCekz6bbM85O12O6a9bp9sHow7p6ZRlpzI46olrs+RY/EVsEEIM+h8OYL3+XAixIEvWi/ye0wIs07rs0C86wZ4TaDJeKcFoEKyOkPjVxmf0T/uz7hty0+OMvTcdy/wKaDOA/u6apbzW0M2uM7H3nhyjYzT1DNE3PBa2yW446jsdmAyC546389XnTk/Y8zGvj7//1RHcHh/f/+CmaRVsM6E0KzVuXSKa7cOU56RiNIQWc1TkpWEQcK57KOIsp8lU5oXuTj465uVMh4ObVxfhk5pXG4n+YTftA6PURimG0NHFJPEURczZOEkpjVLKTP/DJqU0BT2f2Q4kCYU2K8Nub9jxC5cL//ulBr7wh7pELyMi48Zp4p2qXmNSXZgR8UK3vjyLHqebtmkKNT/+2GHu++mBOa52KvYh15xrnCbzoWuXsbwgnS89fZLRsdAhNJ9P8sfjbQyOzuxOOLg11NMzbDZb3z7IztpC7rtmKY+8eZ4fBTVY/taLZzh4sY+v/9W6KRLsuRKribg/fr2JO773RsRzmnuHqcgLrwq0moyU56TR1O2MOAV3MuFGZ9R3OBjzSt6zqYzVJZk8fTRyuHW6GU7hGK91il9oL9bznBYFqtZJ66zw6wOXeOZoW1J7kG39I9hSTNhSJuZsKvPS2VKZw46VkRvj61NA956zhz1ncHSM/Rd6qWsd4FKMK/Vj7TmBdgH86p1rae4d5qFXz4U859svneGTvzrC7w/NTNl3wh8CvXl1ES+e6oxaITY65uV8zxCrim38+ztX8871JXz9+XqeOtLKK/Wd/PC1Ju7ZVsGdG8tmtJ5oKMtOxeGae0jqxZOdHG8ZCNvySkpJs32YyjBiCJ3lBek0dQ/hcHmm/N6GozIvjda+kUBfPh09pLeuPJs7NpZy9FI/zRFaHc1UqaeTiEJcZZxCoGqdNGVQx+AoDpcnYcPaoqG1f3RCvimY3z14NZ+LMOYcNFFEoc0aMQT2VmMPXn+O5M8nO2a/2BDMh3ECuLoqnzs3lvKDV89NaTvzxP5mvr9LM1oNnc5Qbw9LXesAJVkp3HfNUobdXv5yeuqIklA0djnxSVhZnInBIPj2+zawbVku//S7Y/zDE0dZXZLJF29fPaO1REss5ORjXl+gUXBjV+g96xsew+HyhFXq6SzPz6Cx24nb44vY9DWYitw0PD45JTx5vGWA/AwLpVkpvGtDKQDPHg/vPZ1uHyQv3UJBhAnLochNt5BqNsb1WqCMUwgK/Z5T92XsOe1tGvck6v2hgGREq3EKbZyiQQjBjpUF7G7onnJXqvNaQzcZVhPVhRm8eDK6i3E0+HySvuGxeTFOAF+4bRVWk4EvPn0i4P2+1tDNF546wfU1BWyqyKaxa2Y/27rWAdaWZbFtWR5FmVaeiVK1V9+hfZ+Vxdode4rZyMP3bmFFQQY+CQ/FOM8UjB7ynYtxOtU2iMuj/X6cDbNnetgtnFJPZ3lBOm7/Z0Uf1tM+8+Ikxd7xln7WlWUhhKAsO5Url+ZEDLfWdzhYVZI54wLnRNQ6KeMUAr356+XsOe073xvoqFzfkby6lvaBkSn5ppmyc2Uhg6Mejlzqn/KalJLdDT1cU5XHretKOHCxN2bCiMHRMbw+OefWReEozEzhs7fU8PrZHp6ra+dU2yCfeOwwNUU2HvrgJlYWZ3K2yxl12Nbp8nC+Z4h1ZVkYDYLb15fy6pmuqKbwnukYxGoysDQoH5OVaubJj1/NXz5zA0vzI1/Q50JZDDynw83aPCOTQXA2jLep97+riCKspxOtcdILcYMVe0MuD41dzkATY4A7NpTS0OkM+Tfr8fo40+mYcUhPRzNOynNKKDariVSz8bLtTC6lZG+TnWur81mSm8rpjvh5TqNj3pAy41AMuz30DY/NyXMCuKY6H5NBhKwNOtc9RGv/CNfXFPD2NUVICX85FRvvKdAdYp48J4C/uWopa0oz+fKzp7j/ZwfIsJp45O+2BDzB/uGxwDqm42TrAFKOTxK+c2MpY17JCyenbzZb3+GguihjimQ+zWKiOGtuNxfTkZ9hxWwUtM5BsXe4uZ+SrBRWl2aGDevp+chpjVP+uOAjWrVecWYKFpNhgijiZNsgPgkbloyXSty2rgSjQfBMCGHE+Z4h3B5f1D31JhPvQlxlnEIghKAw07poBBGNXY6wqq1QNPcO0z4wyvbledQWZwZmv8w3o2Nerv3GKxNUXJHQ4+/hck7RkpliZnNlDrvOdE957bUG7dj11QWsLsmkPCeVF2NknHpjOLcoHEaD4KvvXku304XT5eGRv7uSEn/XBL1DQLiL7WT0erDgcfdL89KmVYhBdCPB5wuDQVCSNTfF3uGLfWyqyKGqMCPsfl20D1Nos4ZslRVMUaaVdP850dQ5gfZ/WJKTOqE7eUAMUZYdOJaXYeXaqnyeOTZVyHRqBjOcQlGWk8rAyNiMFZ6zRRmnMBTZUhZFWO9Icx83/+/d/OSN81G/R883XbU8l1XFNs73DM3IuM2WPU12epxufnMwupHj4WqcZsPO2kJOtw/SMUlSvruhm+UF6SzJTUMIwS2ri3njbE/UZQbne4b43O+Ph+yKYI9R09fpuKIih+9+4Ap++cC2CT3fZmqcTrQOUJyZEhgrI4Tgjo1l7GmyRyxatztddDtc1BbPLpwUC+ZSiNs5OEpr/whXVGRTXWijY3A05AX6Yu/wtF4TaPu2zB/ai9ZzAi2XFdyd/HjLAKVZ4z8PnTs2lNLSN8Lh5v4Jx+s7tDqzFYWzC6Hqir141Top4xSGgkzrghdEeLw+vvCHE0g5PkIiGvY29ZKfYWFFQQa1JZn4JGHj7LFED6ud6x4KJNAjoV9sSmIQFtrpl5y/1jC+T6NjXvadt3N99fio7bevKcLt9fFaCC8rFD978zxPHLgUcoS6Pstpvo0TwO3rS9m4JHvCsZKsFNItxhl5TpOHNt6xoRQp4dkIxZ9nJokhEsFcap0OX9TyTZsrc6iOYNAvTVPjFIwe2pvJWJCK3DSa7UOBG7e6MH0jb1lThNVk4HeHLvHWuR6++/JZ/vaR/fz8rQtUFc6sbVEw8R6doYxTGBZD89dH91zkVPsga8syOdzcH5U7LqVkX5OdbcvyEEIE7nZPz7MoQkrJK/VdbKrIxiCmr3QHzTgZxHhd2lyoKcqgNCuFXfXjRmf/+V5Gx3zcsHLcOG1ZmkteuiUqSbnPJ/nTCe28hs6pxjYeYb1ICCEihqmCcbo8NPnFEMFUFWawpjQzompPz1kmKqwHUJ6dSsfgaFhFZiQON/dhMRlYU5o17m1OulkbHfPSMTga6IM3Hctn4TlV5KYx5PZiH3IzMDLG+Z6hCWIIHVuKmZtWFfL4/kvc86N9fPulBtoHRrhzYylfu2tt1N9vMvEuxI3lPKdFRVFmCkP+LhGxGHoWbzoGRvn2i2e4oaaAB3es4O6H97LnnJ23rymO+L5LvSO0DYzy4PJcQAslpJgN8y4nb+xy0tI3woM7VpBmMfHH42189paaiJLX1v5RijJTMMegL50Qgh21hTxztA23x4fFZGB3QzcWk4Hty/IC5xkNgretKuL5uvbAeeE41NwXyFueCeEJ2p1u0i3GeZNQR0NVoY03Gqf3Ak+1DWpiiPKpBubOjaV8/fl6LvQMhVTdnenw19ZEMRJ8vijNTtWGDjpcM85RHrrYx7qyLCwmA0ty07CYDFPk5C19I0gJFXnRffb7tizBYjLMqN4oWLGnh9nXh+kb+dlbVrKyKJP15VlsqsghKwaNhfPSLaSYDSqsl2gKF8C49iGXh/aB0L8oX/njKTw+yZfvXMOmihzSLUZePzv9RUjPN21frl2QjQbByiLbvMvJ9dHWO1cWcvv6Ei7Yhye0ygnFXGucJrNzZSFOl4eDF7UBhK81dLN1ae6UBPcta4pwuDzsaQrfVQLg+bp2LCYDNUUZIY1T75CL3BiNhpgtVYUZdA66pvWqJ4shgrl9vVb8Gc57OtPhoHaW8uVYMdtCXJfHy4nWQTZXap1EjAbBioKp3qbeMbwiSs+pLDuVj++omlG90fhcp6FAQfD6IDFEMCsKMvj026rZWVsYE8MEeq1T/BR7yjiFIdlbGEkp+egvDnHtN3bxxadPTGipsutMF8/VtfPJnVVU5qVjMRm4akUer0fRqHNvk528dMuEWS+1xZmcbh+c1zZGr9R3UVtsozQ7lbevKcZkEBEr3QHaBmJrnK5ekYfFaODVM9209Y9wtsvJDTUFU867piqfdIsxYmjP55O8cKKDG2oK2FSRw5lOx5T9sw+5563GKVqiFUWcaB2gKNNKoW1qCLU0O5Vty3J56mjrlP+j1yc50+lgZVFi22zO1jidbBvE7fWxqSI7cKy6MIOzk/ZLrz+KRhAxW8pz0hBC+151LQNU5qXFzPBEv4ZUWvrjE9ZLqHESQrxDCHFGCNEohPhciNd3CCEGhBBH/Y8vxmttyd7C6KVTnbzR2MPmihwe29fMDf9rFz9+vYnB0TG+9PRJlhek85EblgfOv666gIv24QlS1MlIKdl3vpdty3Mn3NHVltjoGx6bN4HIwMgYBy/2cdMqTZSQk27hmqp8njveHtYg+nyS9v7RORfgBpNuNbF1WS676rsCXub1IYxTitnIjpWFvHSqM2xN1pFL/bQPjHLbumJqimz0DrnpcU6sJ+obdpMb54vLZCIl+IOpax0IOxcL4K4rymjqHpoyfqS5d5jRMV8SeE7a78lM2+/oYgi9ByNoe9bSN8Kwe1yx2dw7TJrFSP48esIpZiPFmSk024c53jIQMt8035Rlx68QN2HGSQhhBL6PNj13NfABIUSo5lqvSyk3+h9fjtf6krmFkcvj5WvPn6aqMIPHPryNFz59HVdU5PDV505z9f//Cs29w3z1zrUTVDn6RXZ3BO+ppW+E1v6RQEhPR09kR6Ogmw2vn+3G65OBTuIAt68voaVvhGNhBgH2DLlwe31zrnGazI6VBZztcvKr/Zcozkyhpih0h+xb1hTR7XCF7CoB2nRSi9HATauKAiq1yaG9XmfiPSc9hxLJOA25PJzrdoYM6encuq4Ei9HAH45MbJ2j18glUkYOWrFvTpp5xp7T4eY+yrJTA9cDGPc2z3WN3+g12zUZ+UzbAs2Uitw0jlzqp7V/hA1h8k3zSXlOGv3DYzjiUOuUSM9pK9AopWySUrqBJ4A7E7ieCWSmmLCaDEnpOf38rQtctA/z77evxmw0UF1k49H7t/LT+65kSW4af7O9kqur8ie8Z2leGuU5qbzeED7vtGdSvklHv7DMV97plfouctLMbFwyfnd6y5piLEYDfwyTx9ALcPUx3LFip99AHrvUz/U1+WEvNjtrCzEbBY/tuzjlNSk1ld511flkppjHjVOQYk9KiX3IHbNx5LPFaBAsz0+PaJxOtQ9O6AwRiqxUMzfWFvLssfYJirj6DgdCQHVhYo0T6HLymf09H77YH8g36VT7b1iCRRHNUdY4zZWK3LRAI99IP4/5IlDrFIcGsIk0TmXApaB/t/iPTeYqIcQxIcSfhBBrwn2YEOIjQoiDQoiD3d3R1aBEQggRGDqYTPQ4XXz35UZ2riyYkg/ZubKQP336Or7y7qlyUSEE11UXsOecPewE071NdnLTLYFQj05OuoXizJR5Uez5fJLXznRzQ03BhAFtWalmrq/J57m69pChs1gW4AazPD89cJEJFdLTyUwx88B1y3nycCsvTeoYcbxlgNb+EW5dVwJo7XPy0i2cCTLuw24vLo8vYTLyYKaTk9f5vdfpLobvvqKMHqeLN4PGj9R3DLIsL33argnxYKa1Tm39I3QMjk7IN4GmYDUZRGDPfD5Jc+9wQLAwn+jfQ4jQ4pT5JiAn713cxinULenkq9BhoFJKuQH4LvBUuA+TUj4spdwipdxSUBD+ojITkrHW6dsvNjAy5uXfZjFe4PrqfBwuD8fChKL2NfWybVluSG+htsQ2Lz32jrX0Yx9yBzyWYG5fX0r7wGig6WYw+kUm1mE9IQQ31hZiMgiuneR9TuYf31bD6pJMPvf74xPCv8/XtWM2Cm5eVRQ4trLYxpmg2phE1zgFU1WYwaW+4bBdQE60DlBos04IbYViZ20BmSkmngoK7Z3pcCS0+DaYsuzUGd3xH9LzTZM8J7PRwLL89IAoosvhwuXxUTFNN/JYoH+PqoIM0hNQ4jJeiDv/oohEGqcWYEnQv8uBCTEcKeWglNLpf/48YBZCRL5ixJBk85xOtQ3y6wPN3HvV0llNC726Kh+D0FryTOZS73DIfJNObXEmjV2OsF7XbNlV34VBEFIV97bVWqX7H0MU5Lb1j5JuMZKZGvs/0H+8uYbffuwqstMiGw6LycD/uXsjDpeHz/3+OFJKpJQ8f6Kda6ryJyipaopsnO10BLzA3jg0fY2W6kIbUsK57tDe03RiCB2rycg715fw55MdDLs9DLs9XOwdTmjxbTCl2Sk4Rj1R94Y73NxHitkQshddddG4t9kcZcPXWKAPMkyEGAIgP8OC1WSIiygikcbpAFAthFgmhLAAdwPPBJ8ghCgW/tt4IcRWtPVGLi6JIQU2a9J0JpdS8uU/niQr1cynb6qe1WdkpZrZuCQ7pChCH7YXzjitKrEx5pU0dU9U+w27PfzHMydnPSH2lTNdbK7MCWkIMqwmdq4s5Pm69sCwPx29xmk+EtBZqWauqMiZ/kQ0o/Mv76jl5fouHt9/iZNtg1zqHeG2tSUTzltZbGPY7Q38UevGKScJjFMkOfmwe3oxRDDv3ljGsNvLS6c6aeh0ImVi2xYFo4eA26PMOx1u7md9eXbIIu+qggwu2rWek7oCdroJuLFgeUE66RYjV68I/Xc63+hznRZ1zklK6QE+CfwZOA38Rkp5UgjxMSHEx/ynvRc4IYQ4BnwHuFvGcWZ4UWYKTpeHoSibfM4nrzZ0s7epl8/cXDOn2obrqgs43tJP//C4rPm1hm6++txpNizJnpJv0hlX7E0URXz7xQZ+9tYFnjjQPOO1dA2OcqJ1MGRIT+ed60vocrh48vDEceKxrnGaC/ddvZRrqvL4yh9P8d+vncNoENy8umjCOZNFEfEYlxEtS/PTMAblUII55R/LEG3y/cqluZRlp/KHI62BHNts5wfFmpnUOo2OeTnZOjBBQh5MVZENn9Qa+17qHcYgYp//DIUtxczef72Jv9oU+3H20VIWp0LchNY5SSmfl1LWSClXSCm/5j/2AynlD/zPvyelXCOl3CCl3C6lfCue6wt0iUiC0N7LpzvJsJq4e2vFnD7n+pp8fBLe8iet3zjbw0cePUhVQQY/v+9KDIbQnsjygnTMRsHpIFHEkeY+HnnzPELAq1E2Qg1G99ZujGCc3r6mmKuW5/H5J+smzFuKdXeIuWAwCL71vg2YjYLnjrdz9Yq8KR5RTZEuJ9cu2L1D2u9UMuScrCYjlblpIZv76nVLoRqMhsJgENyxsZTXz/bwRqOdNIuRJTnz71FEg56fjOau/83GHjw+OUUMoRNcH3axd5jS7NSIraxiiS3FPO+S9UjEayKu6hARgUCXiCQQRext6mXL0pw595HbUJ6NLcXE7oZu3jrXwwOPHmBZfjq/fGBbxByL2WigqnC8jZHb4+Nffn+c4swUHrxhBSfbBulyzGyfXqnvojQrhZVF4e+sLSYDD9+7mdoSGw8+doiDF3oZHfPS43RTOs9D6mZCSVYqX71rHQDv8rfzCSbDaqI8JzUgirAPubEYDUnTt7GqMIPGSTknj9fHr/Y1s6IgfUbNde+6ogyvT/LH421UF9nC3vDEmwL/0MFInpOUksf3N/PgY4cpy05le5jw2bL8dAwCznY5uWiPj1IvWSjPSaVveCzqsTGzRRmnCATGtc/Sc/L6ZEyK1XqcLhq7nGHzQTPBZDRwzYp8XjjZwYd+dpCK3DQee2BbVHfwq4ptATn5Q6820tDp5Gt3reWd67X8SrRjJEDbmz3n7FxfUzDtXaAtxczP7ttKaVYq9//sQMCDShbPSeeODaX86dPX8d7N5SFfX1lkG/ecnG5y0y0JvQMOpqowgws9QxMEL7891MLZLif/9PaVM/qsmiIbq0sykVL7nUkWDAZBcVb4uU4jbi//87fH+fyTdWxblsuzf38tmSmhQ+gpZiMVuWk0djm0URlxyDclC7pib74bwCrjFIG5Nn/9v39p4Lpv7poywG6m7GvSGpFuW5Y7p8/Rua4mn/7hMcpyUnnsge3kRdkZubZEG7S2r8nO93c1cufGUm6sLWJ1SSaFNiuvRijwnczp9kEGRz1cFWViNz/Dyi8e2Ea61cSnnjgCJJ9xAm3KaDhPoabYRlO3Niq7b9idFCE9narCDDw+GUjuD7s9/NdLDWyuzJm2k30o7rpCy4kkixhCpzQrdDL/Qs8Qdz30Jk8eaeHTN1Xzs/u2TvvzqSq0cbRZK4WItuHrYiBeozOUcYpAVqoZi8kwqxZGUkqePtZG//AY//bUiTk1Td3bZCfdYoxZ0d2dG8v41I1V/OqBbTMaY6CLIh587DC2FDNf9NdaCSG4oaaA1xu6o56Xs+dc6G4UkSjLTuUXH9oaCIXFusZpvqkttuHxSZp6nP6mr8ljnPQODroo4ke7z9PtcPGvt9XOyru7a1MZ11blB4Y4JgtlIbpEnG4f5F3fe4OOwVF++ndX8o8310woCA9HdVEGbf4bz8strAfzP3RQGacICCFmXYjb0KnFoteVZfGX0508Vzf98Lxw7DtvZ/PS3JjMLQIt//GZW1ZOW1Q5Gb15Z++Qmy+9a/UEj2vHykIGRz0cDVPgO5k9TXaW588slwHa3eovPrSNj16/PPBHslAYF0U46E0y46SP7j7b6aTLMcoPd5/j1rXFbK6cnbeen2Hllw9sCznfKZGUTho6aHe6eODnB0mzGHn2k9eyYwbGNFjZejmF9QoyrP5aJ+U5JZTZFuL++WQHQsCP7t3C+vIsvvT0SfqG3NO/cRJ2p4uGTifbl8cmpDcXCjKslGWn8rZVhdyxYWLS/9rqfIwGEZVqz+P1ceB8b9hk83SsLcvi87etSppEe7SsKMjAZBA0dDoCOadkIc1ioiw7lcZuJ//3L2dxe3z88ztqE72smFOanYrXJ+lyuHB7fHzsl4focbr40b1bWDJDAxPcLzDa8eyLASEEZXGodVLGaRpm6zm9eKqDTRU5FGel8I33rGdgZIyv/PHUjD9n/3k935SYortghBA888lreOiDm6eEerJSzWyqyObVhq4w7x7nZNsgDpeHq2Ig8FhIWExa25u6Vu3/nww1TsFUFWbw1jk7Txy4xAe3VbAsybyeWBA8OuPfnqrjwIU+vvW+DbPquKB7m9lp5rDCicVKPEZnKOM0DaE8Jyklb53rCduLrKVvmBOtg9ziL8RcVZLJgztW8OSR1kBtT7TsbbKTajaGHcccb/IyrGHrOW6oKeBE6/SScr37+bYk8AbjTU2xjYMXtBuORE/BnUxVYQbdDhepZiOfmmUXkmRHz1P+rz+f4TcHW/jUjVW8a8NU6X806N5mPDpDJBvxmIirjNM0FNisOEY9jLjHDdFP37zAPT/ax3/+qT7ke148qXWpviVI5fTJG6tYUZDOF56sm1F9wL7zsalvigd6vH53Q+SJu3ub7FQVZoScqrrYqS3S2hgB5E7Tuy/e6DmUB3esiFrBudAo8Run/ed7eceaYv7hbTVz+rxP3ljF/dcui8XSFhSf2LmCP3z86nn9Hsl/xUsw4+PaNW/gzcYevvb8adIsRn61vzmkTPzFUx3UFGVMCItYTUa++d71tA+O8q9P1uH2TK9q6x1yU9/hiEl9UzxYXZJJfoaVVyN4h2P+fNPlFtLTqQmSVidTzgng1rUlfObmGj60iC+2GVYTBTYrq0oy+a/3b5hz3vIDWyu4c2PiWgklivKcNCrnuQu7Mk7TMD6u3cWl3mE+8avDrChI58mPX43PJ3no1cYJ5/cOudl/vjdkbcjmylw+87YanjnWxvsf3jNt/dN4vmlhhL8MBr+k/GzPlEatOnWtAwy5vQvG4Maa4ImwiR40OJmsNDOfuqmaFHPiZy/NJ098ZDtPfGQ7aZbk6M6hCI0yTtOge04Xeob48KMH8fkkD//NFmqLM3nfliU8sf/ShIrzl0934pNwy+rQhYt/f1M1379nE2c6HNz+3dcD9T6h2NtkJ8VsSFh7/NmwY2UBAyNjYSXlewPTdheGwY01S3LSSDFrf3aJHtF+ubKiIIOs1MtLwLAQUcZpGnTP6avPnaKh08H37tkUqN345I1VSCTf3zXuPf35ZCelWSmsLQs/w+ad60t45pPXkJVq5q9/so+Hd58LWaS773wvmytz4tZQMhZcV63NjHotTGhvzzk7K4tsizanMR0Gg6CmyIZBQLa6QCoUYVk4V70EkZ1mxmI0MDjq4fO3rpowurssO5W7r6zgNwcvcal3mGG3h9fPdnPLmuJpq+qrCm08/clruWV1EV9/vp7P/ubYhL5m/cNu6jsG2Z4EEvKZkJ1m4YqKnJCtjNweHwcv9EXdsmixsq4si5Ks1AVXp6VQxBNlnKZBCMEVFdncfeUSHrhuaqL44ztXIITg+7sa2d3Qjcvj45Y1RSE+aSoZVhMPfXATn7m5hiePtPLRXxwKqAL3n+9FSti2AHMzO2oKON4ywKm2ibOfjrf0MzLmvWxDejr//I5aHv/w9kQvQ6FIapRxioInPrKd/3zP+pDeUElWKvdsreC3h1p45M0LZKeZ2bo0+ouvEIJP3VTN1+5ay64zXfzNT/YxMDzG3qZerCYDG5YkR33TTHjfliUUZ6Zw7yP7aAoaw7C3yY4QyVFQnEiyUs2XVUcBhWI2KOMUBdOF6B7csQKTQbD/fC831RZhmkVN0ge3VfK9D2ziWEs/7394D6+e6WJTRQ5W08JTThVnpfDLB7YhJXzwx/sCI9z3NNmpLc5MitHkCoUiuVHGKQYUZabw19srAXh7lCG9ULxzfQk//butNPcO09QztKDl1lWFGfzygW0Mu73c8+O9NNuHOXih77IP6SkUiuhQxilGfPpt1Xzx9tURR45Hw7XV+Tz+4e1ctTyPd20oidHqEsOqkkwevX8rfUNj3Pn9N3B5fJdt8a1CoZgZYi5zhpKVLVu2yIMHDyZ6GQo/+8/3cu8j+3B5fBz991vISlMSaoVCAUKIQ1LKLaFeUyXSinln67JcHntgO+e6ncowKRSKqFDGSREXNlfmsLkyJ9HLUCgUCwSVc1IoFApF0qGMk0KhUCiSDmWcFAqFQpF0KOOkUCgUiqRjUUrJhRDdwMVEryOO5AORx88q5hO1/4lF7X/imOveV0opC0K9sCiN0+WGEOJguFoBxfyj9j+xqP1PHPO59yqsp1AoFIqkQxknhUKhUCQdyjgtDh5O9AIuc9T+Jxa1/4lj3vZe5ZwUCoVCkXQoz0mhUCgUSYcyTgqFQqFIOpRxSlKEEI8IIbqEECeCjm0QQuwRQtQJIZ4VQmQGvbbe/9pJ/+sp/uOb/f9uFEJ8R0w31lcxo70XQpiFED/3Hz8thPh80HvU3s8CIcQSIcQu/36eFEJ82n88VwjxkhDirP9rTtB7Pu/f5zNCiLcHHVc/gxkw070XQtwshDjk3+NDQogbgz5rbnsvpVSPJHwA1wObgBNBxw4AN/if3w98xf/cBBwHNvj/nQcY/c/3A1cBAvgTcGui/2/J/pjh3t8DPOF/ngZcAJaqvZ/T/pcAm/zPbUADsBr4JvA5//HPAd/wP18NHAOswDLgnPr9j9veXwGU+p+vBVqDPmtOe688pyRFSrkb6J10eCWw2//8JeA9/ue3AMellMf877VLKb1CiBIgU0q5R2q/LY8C7573xS9wZrj3EkgXQpiAVMANDKq9nz1SynYp5WH/cwdwGigD7gR+7j/t54zv551oNwguKeV5oBHYqn4GM2emey+lPCKlbPMfPwmkCCGssdh7ZZwWFieAO/zP3wcs8T+vAaQQ4s9CiMNCiH/2Hy8DWoLe3+I/ppg54fb+d8AQ0A40A9+SUvai9j4mCCGWot2d7wOKpJTtoF1EgUL/aWXApaC36XutfgZzIMq9D+Y9wBEppYsY7L0yTguL+4FPCCEOobncbv9xE3At8EH/17uEEDehudOTUbUDsyPc3m8FvEApWkjps0KI5ai9nzNCiAzg98A/SCkHI50a4piMcFwxDTPYe/38NcA3gI/qh0KcNqO9V8ZpASGlrJdS3iKl3Aw8jhZbB+2u5DUpZY+Uchh4Hi1n0gKUB31EOdCGYsZE2Pt7gBeklGNSyi7gTWALau/nhBDCjHZxfExK+aT/cKc/XIT/a5f/eAvjniyM77X6GcyCGe49Qohy4A/AvVLK4GvSnPZeGacFhBCi0P/VAPwb8AP/S38G1gsh0vy5jxuAU3732yGE2O5XytwLPJ2ApS94Iux9M3Cj0EgHtgP1au9nj3+/fgKcllL+V9BLzwB/63/+t4zv5zPA3f5cxzKgGtivfgYzZ6Z7L4TIBp4DPi+lfFM/OSZ7n2h1iHqEVc08jpbHGEO7C/kQ8Gk09UwD8J/4O3z4z/9rtITkCeCbQce3+I+dA74X/B71mPveAxnAb/17fwr4J7X3c97/a9FCQMeBo/7HbWgq1JeBs/6vuUHv+YJ/n88QpApTP4P53Xu0G7WhoHOPAoWx2HvVvkihUCgUSYcK6ykUCoUi6VDGSaFQKBRJhzJOCoVCoUg6lHFSKBQKRdKhjJNCoVAokg5lnBSKBCCEyBNCHPU/OoQQrf7nTiHEQ4len0KRaJSUXKFIMEKI/wCcUspvJXotCkWyoDwnhSKJEELsEEL80f/8P/yzol4UQlwQQvyVEOKb/hk5L/jbzOhzc17zz9P5s95mRqFYyCjjpFAkNyuAd6KNLPglsEtKuQ4YAd7pN1DfBd4rtb5/jwBfS9RiFYpYYUr0AhQKRUT+JKUcE0LUAUbgBf/xOmAp2pyptcBL/kGjRrTWSwrFgkYZJ4UiuXEBSCl9QogxOZ4k9qH9/QrgpJTyqkQtUKGYD1RYT6FY2JwBCoQQV4E27sA/W0ehWNAo46RQLGCklG7gvcA3hBDH0LpCX53QRSkUMUBJyRUKhUKRdCjPSaFQKBRJhzJOCoVCoUg6lHFSKBQKRdKhjJNCoVAokg5lnBQKhUKRdCjjpFAoFIqkQxknhUKhUCQd/w+amFL695w6UAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,8)) \n",
    "\n",
    "\n",
    "plt.subplot(311)\n",
    "qplt.plot(enso)\n",
    "plt.title('ENSO')\n",
    "\n",
    "plt.subplot(312)\n",
    "qplt.plot(iod)\n",
    "plt.title('IOD')\n",
    "\n",
    "plt.subplot(313)\n",
    "qplt.plot(au)\n",
    "plt.title('AU')\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2) Data Processing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the data (zero mean, unit variance)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ENSO   = (enso - np.mean(enso.data))/np.std(enso.data)\n",
    "AU     = (au - np.mean(au.data))/np.std(au.data)\n",
    "IOD    = (iod - np.mean(iod.data))/np.std(iod.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detrend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ENSO   = signal.detrend(ENSO.data)\n",
    "AU     = signal.detrend(AU.data)\n",
    "IOD    = signal.detrend(IOD.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3) Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load into pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = np.stack((ENSO ,IOD, AU)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=DATA, columns=[\"ENSO\", \"IOD\", \"AU\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSO</th>\n",
       "      <th>IOD</th>\n",
       "      <th>AU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.568193</td>\n",
       "      <td>-0.244902</td>\n",
       "      <td>1.502270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.031345</td>\n",
       "      <td>0.728928</td>\n",
       "      <td>-0.518547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.036566</td>\n",
       "      <td>-0.239032</td>\n",
       "      <td>-0.293785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.700767</td>\n",
       "      <td>0.814033</td>\n",
       "      <td>-0.707653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.939703</td>\n",
       "      <td>-0.722722</td>\n",
       "      <td>-0.097123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2.304798</td>\n",
       "      <td>0.572842</td>\n",
       "      <td>-0.740667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>-0.861372</td>\n",
       "      <td>-1.269278</td>\n",
       "      <td>0.331561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>-0.831318</td>\n",
       "      <td>0.230555</td>\n",
       "      <td>0.812708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.586349</td>\n",
       "      <td>1.329264</td>\n",
       "      <td>-0.069035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.154989</td>\n",
       "      <td>1.952354</td>\n",
       "      <td>-1.687949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ENSO       IOD        AU\n",
       "0  -0.568193 -0.244902  1.502270\n",
       "1   1.031345  0.728928 -0.518547\n",
       "2  -0.036566 -0.239032 -0.293785\n",
       "3   0.700767  0.814033 -0.707653\n",
       "4  -0.939703 -0.722722 -0.097123\n",
       "..       ...       ...       ...\n",
       "65  2.304798  0.572842 -0.740667\n",
       "66 -0.861372 -1.269278  0.331561\n",
       "67 -0.831318  0.230555  0.812708\n",
       "68  0.586349  1.329264 -0.069035\n",
       "69  0.154989  1.952354 -1.687949\n",
       "\n",
       "[70 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AU'] = pd.qcut(df['AU'], 2,labels = ['low', 'high'])\n",
    "df['ENSO'] = pd.qcut(df['ENSO'], 3, labels = ['Nina', 'neut', 'Nino'])\n",
    "df['IOD'] = pd.qcut(df['IOD'], 3, labels = ['neg', 'zero', 'pos'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show contigency table (absolute numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>IOD</th>\n",
       "      <th colspan=\"3\" halign=\"left\">neg</th>\n",
       "      <th colspan=\"3\" halign=\"left\">zero</th>\n",
       "      <th colspan=\"3\" halign=\"left\">pos</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSO</th>\n",
       "      <th>Nina</th>\n",
       "      <th>neut</th>\n",
       "      <th>Nino</th>\n",
       "      <th>Nina</th>\n",
       "      <th>neut</th>\n",
       "      <th>Nino</th>\n",
       "      <th>Nina</th>\n",
       "      <th>neut</th>\n",
       "      <th>Nino</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "IOD   neg           zero            pos           All\n",
       "ENSO Nina neut Nino Nina neut Nino Nina neut Nino    \n",
       "AU                                                   \n",
       "low     2    6    0    2    4    5    0    3   13  35\n",
       "high   10    6    0    8    3    1    2    1    4  35\n",
       "All    12   12    0   10    7    6    2    4   17  70"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab( df['AU'], [ df['IOD'], df['ENSO']], margins=True,  dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 1b: Conditional probabilities for IOD and ENSO phases, P(IOD, ENSO). The default probability is 1/9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ENSO</th>\n",
       "      <th>Nina</th>\n",
       "      <th>neut</th>\n",
       "      <th>Nino</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>neg</th>\n",
       "      <td>0.171429</td>\n",
       "      <td>0.171429</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zero</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.242857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ENSO      Nina      neut      Nino\n",
       "IOD                               \n",
       "neg   0.171429  0.171429  0.000000\n",
       "zero  0.142857  0.100000  0.085714\n",
       "pos   0.028571  0.057143  0.242857"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab( df['IOD'], [df['ENSO']], normalize = \"all\", margins = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tabel 1a: Conditional probabilities for above-average AU. The default probability is 1/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) Conditional probabilities for below-average AU, P(AU- | IOD, ENSO) and above-average AU, i.e. P(AU+ | IOD, ENSO). The default probability is 1/2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>ENSO</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Nina</th>\n",
       "      <th colspan=\"3\" halign=\"left\">neut</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Nino</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOD</th>\n",
       "      <th>neg</th>\n",
       "      <th>zero</th>\n",
       "      <th>pos</th>\n",
       "      <th>neg</th>\n",
       "      <th>zero</th>\n",
       "      <th>pos</th>\n",
       "      <th>neg</th>\n",
       "      <th>zero</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.235294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ENSO      Nina           neut                 Nino                    \n",
       "IOD        neg zero  pos  neg      zero   pos  neg      zero       pos\n",
       "AU                                                                    \n",
       "low   0.166667  0.2  0.0  0.5  0.571429  0.75  0.0  0.833333  0.764706\n",
       "high  0.833333  0.8  1.0  0.5  0.428571  0.25  0.0  0.166667  0.235294"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab( df['AU'], [df['ENSO'], df['IOD']], normalize = 'columns', dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2)  marginal probability of ENSO (bottom of table 1a), i.e.  P(AU-|ENSO) and P(AU+|ENSO) respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ENSO</th>\n",
       "      <th>Nina</th>\n",
       "      <th>neut</th>\n",
       "      <th>Nino</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ENSO      Nina      neut      Nino  All\n",
       "AU                                     \n",
       "low   0.166667  0.565217  0.782609  0.5\n",
       "high  0.833333  0.434783  0.217391  0.5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab( df['AU'],df['ENSO'], margins=True, normalize = \"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) marginal probability of IOD (right side of table 1a), i.e. P(AU-|IOD) and P(AU+|IOD) respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>IOD</th>\n",
       "      <th>neg</th>\n",
       "      <th>zero</th>\n",
       "      <th>pos</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "IOD        neg      zero       pos  All\n",
       "AU                                     \n",
       "low   0.333333  0.478261  0.695652  0.5\n",
       "high  0.666667  0.521739  0.304348  0.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   ]
  }
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