gempy_abc_topo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Can not use cuDNN on context None: cannot compile with cuDNN. We got this error:\n",
      "b'C:\\\\Users\\\\r02as18\\\\AppData\\\\Local\\\\Temp\\\\try_flags_ml8lb4jb.c:4:19: fatal error: cudnn.h: No such file or directory\\r\\ncompilation terminated.\\r\\n'\n",
      "Mapped name None to device cuda: Quadro M1200 (0000:01:00.0)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import scipy.stats as ss\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sys.path.append(\"C:/Users/r02as18/git/gempy/\")\n",
    "import gempy as gp\n",
    "\n",
    "geo_data = gp.create_data([0,2000,0,2000,0,2000],\n",
    "                          [100,100,100],\n",
    "                          path_o = \"C:/Users/r02as18/git/gempy/notebooks/input_data/tut_chapter1/simple_fault_model_orientations.csv\", # importing orientation (foliation) data\n",
    "                          path_i = \"C:/Users/r02as18/git/gempy/notebooks/input_data/tut_chapter1/simple_fault_model_points.csv\") # importing point-positional interface data\n",
    "\n",
    "gp.set_series(geo_data, {\"Fault_Series\":'Main_Fault',\n",
    "                         \"Strat_Series\": ('Sandstone_2','Siltstone', 'Shale', 'Sandstone_1')},\n",
    "                          order_series = [\"Fault_Series\", 'Strat_Series'],\n",
    "                          order_formations=['Main_Fault',\n",
    "                                            'Sandstone_2','Siltstone', 'Shale', 'Sandstone_1'], verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling theano function...\n",
      "Compilation Done!\n",
      "Level of Optimization:  fast_compile\n",
      "Device:  cuda\n",
      "Precision:  float32\n",
      "Number of faults:  1\n"
     ]
    }
   ],
   "source": [
    "interp_data = gp.InterpolatorData(geo_data, u_grade=[1,1], output='geology', compile_theano=True, theano_optimizer='fast_compile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "geo_data_res_stoch = deepcopy(interp_data.geo_data_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create custom 2d grid\n",
    "nx = 50\n",
    "nz = 50\n",
    "geo_data.resolution = [nx, 1, nz]\n",
    "\n",
    "grid = []\n",
    "for x in np.linspace(0,2000,nx):\n",
    "    for y in [1000]:\n",
    "        for z in np.linspace(0,2000,nz):\n",
    "            grid.append([x,y,z])\n",
    "grid = np.array(grid)\n",
    "\n",
    "lb_init, fb_init = gp.compute_model_at(grid, interp_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load \"likelihood\" topology graph \n",
    "import pickle\n",
    "G_observed = pickle.load(open(\"G.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Priors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "priors = []\n",
    "priors_names = []\n",
    "\n",
    "for fmt in geo_data.formations.index:\n",
    "    if \"basement\" in fmt:\n",
    "        continue\n",
    "    priors.append(ss.norm(0,200))\n",
    "    priors_names.append(fmt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<scipy.stats._distn_infrastructure.rv_frozen at 0x1d144e58438>,\n",
       " <scipy.stats._distn_infrastructure.rv_frozen at 0x1d144e585c0>,\n",
       " <scipy.stats._distn_infrastructure.rv_frozen at 0x1d144e586d8>,\n",
       " <scipy.stats._distn_infrastructure.rv_frozen at 0x1d144e58828>,\n",
       " <scipy.stats._distn_infrastructure.rv_frozen at 0x1d144e589b0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "priors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABC-REJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████| 100/100 [02:18<00:00,  1.39s/it]\n"
     ]
    }
   ],
   "source": [
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "n_iter = 100  # number of desired posterior samples\n",
    "epsilon = 0.1  # error parameter (if 0.1 will accept jaccard indices of 0.9 or larger)\n",
    "\n",
    "# storage\n",
    "lbs = []\n",
    "fbs = []\n",
    "graphs = []\n",
    "centroids = []\n",
    "jaccards = []\n",
    "n_sim = 0\n",
    "prior_samples_storage = []\n",
    "\n",
    "for i in tqdm(range(n_iter)):\n",
    "    distance = epsilon + 1  # initiate/reset distance\n",
    "    while distance > epsilon:\n",
    "        # draw a new set of priors\n",
    "        prior_samples = []\n",
    "        for prior in priors:\n",
    "            prior_samples.append(prior.rvs())\n",
    "            \n",
    "        # change data in gempy interpolator\n",
    "        for i, fmt in enumerate(priors_names):\n",
    "            f = interp_data.geo_data_res.interfaces[\"formation\"] == fmt\n",
    "            interp_data.geo_data_res.interfaces.set_value(f, \"Z\", geo_data_res_stoch.interfaces[f][\"Z\"] + (prior_samples[i] / interp_data.rescaling_factor))\n",
    "        interp_data.update_interpolator()\n",
    "        \n",
    "        # compute gempy model\n",
    "        lb, fb = gp.compute_model_at(grid, interp_data)\n",
    "        G, C, *_ = gp.topology_compute(geo_data, lb[0], fb, filter_rogue=True)\n",
    "        \n",
    "        # compare graphs\n",
    "        j = gp.topology.compare_graphs(G_observed, G)\n",
    "        distance = 1 - j  # calc distance\n",
    "        n_sim += 1  # count total iterations needed to reach n_iter of accepted models\n",
    "        \n",
    "    # store accepted models\n",
    "    graphs.append(G)\n",
    "    centroids.append(C)\n",
    "    lbs.append(lb)\n",
    "    fbs.append(fb)\n",
    "    prior_samples_storage.append(prior_samples)\n",
    "    jaccards.append(j)\n",
    "\n",
    "lbs = np.array(lbs)\n",
    "prior_samples_storage = np.array(prior_samples_storage)\n",
    "acc_ratio = n_iter / n_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20449897750511248"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d150137d68>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(jaccards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w5sy8NTO/BHwNeP6oApUkSZKkWdazcRYR2wGvAl7StngH4KrMvKltWQI7Djc8SZIkSZoPizbOImID4OPA4Zl5fdtDmwJrO1ZfC6wcbniSJEmSNB969ZwdCVyYmV/sWL4W2KRj2UpgzbACkyRJkqbBPY7diM1P3njSYWgG9GqcPRt4TkTcEBE3AFsDnwQCeGBEbNS2bgCXjCZMSZIkSZpti06ln5k7tP8eEVcBhzZT6e8HvCUijgT2BJ4AvHQ0YUqSJEnSbOv7Pmdd7Av8A/DfwM+A52bmTwbdyD2O3YjNt9qo62M3HHLzMsKTRqOVs+anJElq16u00XMH9TJQ4ywzH9j2/6uBpww7IEmSJEmaR4PchFqSJEmSNCLLKWscuX5mvbF7WJNi6YIkSRqE57bqxZ4zSZIkSaqAjTNJkiRJqkDVZY39WKh72C5hTZqlC6qFM4xK0vTodv7g8Xt+2HMmSZIkSRWY+p6zhThZw3Ra6HNbc8165c56M8arY7Np85M3rjJnPS5K0nSqpRqnljhq1M97A73Pae05kyRJkqQK2DiTJEmSpArMbFljL+Mo7+m3e3PUcUzSMN6DeWPJQJ3mIZfNPUmaXoN8T43yWF5LHEs16e97e84kSZIkqQI2ziRJkiSpAnNb1tjLoF2ao+qWXWrXai3d1RqNcZcMzHqJrjndP2d8nE2tz7WWWUa9N580WoOUsY/yO3KU5xfT+t1uz5kkSZIkVcDGmSRJkiRVwLLGIamt67S2eGbJ636wOZtcu0nXxz74yOvHHE1vSy2BHHYOjaNE17zvbpw564yP9ZiFv4dpn/VN82Pazg36MS3HkGmJs1/2nEmSJElSBWycSZIkSVIFJl7WOIvdwJpfL7rg3j3XqTmvaywNqDGmWTKJnB33jI81znq7VP49LGyh96bGz1HzZdrPDTRefTXOImJ/4GjgAcDVwOsz87MRcS/gVGBP4FfA0Zl5yqiClSRJkqRZ1bOsMSK2Bz4MHJyZmwGHA2dExH2Ak4E1wCpgP+D4iNhphPFKkiRJ0kzq2XOWmf8REasyc01EbAZsCdwI3ALsA2yfmTcD50XE6cAhwGHDCM5uYM2iXnltTqs2487ZYczQN4zyP2+OOhu8cfpsqu3G6ctV47lBKybPS8arr7LGpmH2IOBHwHrAS4BtgVsz88r2VZmJPxFJkiRJGq9BJgT5CbAxsDvwOeB44KaOddYCK4cTWn9qvNIgLccke4wX2rd/R3fWz2d003Wdh8fZNcmcrb0Hqpb4+vmMoJ687TZZ2CRzaJi9a4PmxDz17NXy91KjGs8Nxh1H7YZ1nO27cZaZtzX/PTsiPg3sCnROs7iSMgZNkiRJkjSAfiYE2TsivtKxeEPgCmBFRGzdvjpwyRDjkyRJkqS50E/P2XeBXSPiQOA04E+BvYFHA1sDx0bEIcCOwAHNY9UYVzfwoF2+w9z3tLNEbHDjLued9fxe6utT/8zZ4ZrHnJ3kMIZhTFIzjn2PMo5BWJ44GbUM9Rnk+FTzMXdSx9mePWeZ+TPg6ZQp9G8A3gzsk5mXUWZmXAH8FPg0cERmfmd04UqSJEnSbOp3tsavU8aYdS6/Hth/2EFJkiRJ0rwZZLbGmTXJbuBZ6fptN4/lNrWpZWalSZaWmYfTZRi5MozP3JydXrXcG7WWkr5a4pgX237lFu6+sntBWj59ozFH012Nx5hxnAfX+LoX07OsUZIkSZI0ejbOJEmSJKkCEy9r7NYNXEv3b0st3aGW22hUavt8a4tH9aolV2qJQ4ureRjDtAxdmKRu7+E0zOYcZ/2m5zq1nfvWaF6Os/acSZIkSVIFbJxJkiRJUgUmXtbYTa/uX7t+BzMv3cCSJC1HzWWP44pj3DxHKWo59+2nBLPF8/HRsOdMkiRJkipg40ySJEmSKlBlWWMvtcx6U0sXtCRJGr1aSvAGjWMcZZC1vDezapTnvoOUMvbzvHk9/+33fbxx7S18Z5HH7TmTJEmSpApMZc9ZP2ro1Rr0SsS8XmnopvO963WVYZy63Zuvxc9QNWrlrPkpzSd7teZDDee+/cTRrubvpaX2KC6XPWeSJEmSVAEbZ5IkSZJUgZkta+xlqV2/o+zi7Lbtmrt7BzWp7uFxqmWyGqmbWkpeJEnjV+N52DhKIGt83Yux50ySJEmSKmDjTJIkSZIqMLdljb3U0gVae5lcLe/TNKmltGxWZlPS8NR+vNH8cFZcTZuzdz6KDTbfqutjT/vOy8cczWyal3POvhpnEfE44ARgB+CXwPGZ+cGIuBdwKrAn8Cvg6Mw8ZVTBSpIkSdKs6lnW2DTAPgecCNwL+HPg2Ih4EnAysAZYBewHHB8RO40uXEmSJEmaTf30nG0DfCEzT2t+/25EnAM8FtgH2D4zbwbOi4jTgUOAw0YSrbqal27eeTHK0rKl5kotpZi16Od9rOnG6cNUSy4sNZfnLVdnUe3lt+3xmW8C+MKjT+y5zrhLHxeKyRLMyevZOMvMC4EDW783PWm7A98Hbs3MK9tXB/YddpCSJEmSNOsGmq0xIu4JnAVcAJwD3NSxylpg5XBCkyRJkqT50fdsjRHxIODzwBXAs4GHApt0rLaSMgZN0gjVUlrWslA801LSY2nw8ExTyVnLtOTpoJaS17NSjlvLMdJZcdWvWsoMe5VgWvY4en31nEXELsB3gH8B9snMm4DLgRURsXX7qsAlQ49SkiRJkmZcz56ziFgFfBk4ITOPay3PzBsj4kzKzI2HADsCBwB7jypYSf2ppSdokleNa3kPdFe1fTbT1PNb23s3rYbdwzuMz6WW3r5a9Puezkpv70K69WQttfeqn4lJhrGNee1d6/f9ve2Ga+Dcpy34eD9ljQcD9wWOjIgj25a/hzIz40nATynljEdk5iz/jUiSJEnSSPQzW+MxwDGLrLL/8MKRJEmSpPnU94Qgkoqzdz6KDTbfqutj89qVPwyWa6k25uR8q63MsPbJdnrx72l4apk8ZCGzOKnIMEpC+zXQVPqSJEmSpNGwcSZJkiRJFbCsURqiGmcxqr38QZKmUY1leuOYIbfG161imDM7jlIt50rjLFUchD1nkiRJklQBG2eSJEmSVAHLGqUxq2UWo1rimEVfePSJPW8yKUmTZHnifKi1dK+XaY17GOw5kyRJkqQK2DiTJEmSpApY1ihVZhizKw6jHKCW2ZRqNM/lFtI4nL3zUWyw+VZdH6vluNN+HKglJknTz54zSZIkSaqAjTNJkiRJqoBljR2m5QZ+mj813kx6VmZ8tExRmh41llzXeHyWNJ3sOZMkSZKkCky856zboN/arjR5RUw1q7nXZ5DYhv33VPP7Imm0RtWrP+hxZVaqC2rUem+9p6RmjT1nkiRJklQBG2eSJEmSVIGJlzV2My1lAN7jRBoeyxAljUstwxVqnNykFn4naF4N1DiLiEcBn83MrZrf7wWcCuwJ/Ao4OjNPGXqUkiRJkjTj+iprjIj1IuKFwL8CG7Y9dDKwBlgF7AccHxE7DT1KSZIkSZpx/facvQ7YH3gr8H8BImIzYB9g+8y8GTgvIk4HDgEOG0GsvzPsMoBhdJ17f7TR+cKjT3Q2JknSyNVS7thuWoZ6DMKSRWlh/U4IcirwCOD8tmXbAbdm5pVtyxLYcUixSZIkSdLc6KvnLDOvBYiI9sWbAjd1rLoWWDmUyCRJkiRpjixntsa1wCYdy1ZSxqBNXKvLvMZShGksQRgXSx0kSbWp+btpkNhGef5R83skTZPl3OfscmBFRGzdtiyAS5YXkiRJkiTNnyU3zjLzRuBM4NiIWBkRuwEHAKcNKzhJkiRJmhfLvQn1IcBJwE8p5YxHZOZ3lh3VENXYzT6LMy/1o8bPQpoHZ+98FBtsvlXXx2o53lgGLo2e38NS/QZqnGXmauA+bb9fT5liX5IkSZK0DMvtOdMIDHplq5Yry16Rk6bPsO8bOWz2qEmS5slyJgSRJEmSJA2JjTNJkiRJqoBljTPAckJJozSq0sLlHLu6PddSx8F0ew9vu+EaOPdpE4hGkgT2nEmSJElSFWycSZIkSVIFLGuUJC1JbaWFzuy4MMvfJWk62HMmSZIkSRWwcSZJkiRJFbCsUZI0NO3lc7WUEw5S0ldLzIOwZFGSZoc9Z5IkSZJUARtnkiRJklQByxolSSMxjeV2tZRATuN7J0laPnvOJEmSJKkCNs4kSZIkqQKWNUqStASWHkqShs2eM0mSJEmqgI0zSZIkSarAsssaI2Jn4IPAjsDlwIsz89vL3a4kSZIkzZNl9ZxFxMbAWcCHgc2BE4HPRMSGQ4hNkiRJkubGcssanwj8NjM/kJm3ZuapwHXA05cfmiRJkiTNj+U2znYALulYlpQSR0mSJElSn5Y75mxTYG3HsrXAyj6euz7Abf/782WGoHnQlifrTzAMc1Z9M2c1jSrIW3NWAzFnNW165exyG2drgU06lq0E1vTx3C0BfvnhFy4zBM2ZLYErJrhvc1aDMmc1jSaVt+aslsqc1bTpmrPLbZxdChzasSyA0/t47vnA7sC1wO3LjEOzb31KEp8/wRjMWQ3CnNU0mnTemrMalDmrabNozq63bt26JW85IjYCrgTeBpwEHNj8/0GZ+eslb1iSJEmS5syyJgTJzN8ATwWeC1wPHAY8w4aZJEmSJA1mWT1nkiRJkqThWO5U+pIkSZKkIbBxJkmSJEkVsHEmSZIkSRWwcSZJkiRJFbBxJkmSJEkVsHEmSZIkSRWwcSZJkiRJFdhg0gGMU0SsA16SmSdFxHrA84BzMvO/Rrzf1cDjF3j4TZn55ma9FwCvBB4C/DfwKeCozFy7wHY/AuyQmY9ZQkwbA98GTsrMk3qs+4fAu4HHAP8LnNrEffug+9VgJpWzzb6fABwDPAz4JfAh4NjM/G2XdR8HfA3YNDNvXmSbq4GfZeZzlhDPfYCLgIMz88s91t0deDuwE/Bz4N2Z+Z5B96nBTTJn22JY8PgWEc8G3gBsC/wX8A/AOzKz600/zdnZN+Hj7L7AkcAOlOPsJ4A3ZubNEfFA4D8Xefo2mfnjLttcjTk78yact7sA7wB2BW4EzgJek5k3tK1zCPC3wP2BS4G/zcyvLLLNq4BPZuZrlhDPdsD3gF0z87Ie6+4DvBnYDrgKeHNmfmLQfY7SvPWcbQl8tPn/HsDHgLuPYb/7Nvtu//dJ4FrKCW+rYfYh4IOUA93LgOdSThwWcjiw96DBRMTvAV8AHt7HuvcCvko58D4KOBR4KSWxNXoTydmIeBTlc7+c0ij/K+Ag4P1d1n0C5cDcz/FkX+BFS4hnmyaerfpYdzvgy8A3gV2AvwPe1nxRaPQmdZwFFj++RcSfAKcBHwD+EHgT5Vh2+CKbNGdn36SOs0+iXIQ9A9gROBg4EGg1cH7CXc8ddqQ04v5ft4ZZw5ydD5PK2y2AfwN+BDwSOADYC/hI2zrPA94LHAvsDJwDfL652L+Q3Sh5NGg8OwFfATbtY909KH9zHwce0cT8sYh4yqD7HaW56jnLzJ+1/breGPd7ffvvEfEM4NnAnpl5TbP4MOCjmdk6+b0iIl4HfCQiXpSZv+6y3V8NGktE7A+8C7im17qNQyjv1Qsz8zfAJc2VtXdGxFsX6tXTcEwqZ4HXAD8A/rLpKfthRBwMrI6IYzLzxxGxCXACJUcuohykF9X5t9CPiDgUeAulodiPVwA/ysxXNb9fFhHbAq8DTh50/xrMBHO2n+PbvsBFbcfZKyNiP8pFrnd3e4I5O/smmLP3A96VmW9rfr8yIj4JPKOJ63agPTYi4v2UxtlfL7RRc3Y+TDBvnwjcG3hVZt4IXB4R7wXeHhHrNVUIrwc+mJmnNs95VUT8EfBqyoXeu8jMXwwaSES8lVJxdjGwdR9PeQ3wxcw8vvn9uIh4OPBa4F8G3f+ozFXjrNUFDFxGacUDXBoRR2fmURFxD+B44JnASsqHfWRmfrV5/kGUVv1plAPjpcAft7aVmU/oI4ZNKFfFTs3M1W0PvYLSO9VuHaU3YnPgLo2zzrLGppShVxz7ULqi3wfc0iteSjnmN5qGWcvZlCsUu1FK2TQiE8zZ7YHVHSWM/075Ang85QrdFsAfUA7UDwE+3MfrWU1buU1TxrA6Mw9a5Gl/BvwNcC5wZa99NPEnzDOuAAAgAElEQVR1HmTPBl4bEdtk5tV9bENLNOHjbK/j2y+AHZsei69SSnYfR+lJW+j1rMacnWmTytnMbPV60JSlParZx1kLxPlUSo4/vuM7uXO91ZizM2+Cx9pWI+rQiHg7paG2H/DtzFwXEfcFHkppCLU7m1J6udDruYq2ssbm9R2dmUct8jY8ldLZ8b9t78FC278bsDul4dgZ1/sjYkVm3rrYNsZlrhpnbb4FPAv4NOWDurA5MH6J0iDal5J8zwS+HBHPyMwvNc+9H+WEdFdgsyYR9x1g3y8Cfp8y5uF3MvPr7b9HxIaUKwwXDlA/3DOOzDygbR/9bPP+wOqOZa2r0g/oMy4t37hz9hpgm45lD25+bgHQfPk+ASAiHrLE17UbPS4SZOafNPt4YJ/bvD+lHKhde8560jAeYz/O9nF8O4FSYvNvwO3A+pQTk6MGeF3m7OyayLlBcxJ9HeWc7ELgjQuseixwZuf5Qh/M2dk21rzNzLMj4m2U4+ZbKMfRi4E9m1Xu3/zslh+DnDduCazpEcsu8LvhFb3cE9hsgbhWAKuAnw4Q38jMZeMsM2+JiFa3/y8zc01E7AU8FnhAZrY+nOOaQY9/S0nylrdk5hVt2+urhCAi1qdcmTq5ozu6c72NKIOCg4UnEun2ugYuZejDpkDnFbrWhA8bj2B/6mICOXsK8MmIeGnz//sCfw/cBmy0/Ff0uzgGLmPogzlbgUkdZ3u4PyWXX0m5yvoYyoQGR1HGn/Vkzs6uCebsbcCjKWO93g18JSIelW2TbjW9vQ8HXriE12XOzrBx521E3JNSXXMKZYK4LYF3AmdExJO5Y+xXt/xYPyI2yMzb+nhdC54nL9FicUFFeTuXjbMF7NL8vKzjiuuGwP90rNtvTXanPSm9EYuV0NyHcvVjV2CfzDxvifsalpu468l4K4EXvaKhkRtZzmbmGRGxFeVK7YmUkoEjKQfkgcc6jpk5W69xHGcX80/AP2fmu5rfL4yIFZQxtO8b0UlsP8zZeo08Z5ux298FvtucZH+TMsHCv7at9pfAeZn53aXsYwTM2bqNMm/fBmyZmc9qLWhKEr8PPJ07eqa65cdv+mmYjchNzc/q89bG2R3uRmlNP6LLY51Txt/UZZ1+PBP4XmZe2u3BKLMffQm4B/DEChpmUEoT7texrDWTUxXdv3NspDmbme+KiHdTror9AtiEMpZnFCfNw2TO1mscx9mumnEQOwCdx9VvUb4Lt+WOsRTjZs7Wa2Q5G2XmuFsz8/9rW3xR8/N+beutAJ7GwuWOk2DO1m2Ux9o9KKXhv5OZF0fEGsox9hvN4vtRprdv2YrJ5sb1lAZYt7y9mVJaXIV5bpx13tPmYkpr+p6ZeUFrYVNX+xv6LHnpYQ/gi90eiIgHUSbXWAM8JjP7GZA7DucCr+gYKLkXZYKSCxZ+mkZgbDkbES8DdsnMg2nGEUTE8yn3M/nmUrc7JudSJilptxdwZVt5h8ZjEsfZhayhjLvZGfhs2/KdmjgnOUbGnK3HOHP2UMq9lnZuW/ZHzc8ftC17OGW8zIL3iJoAc7Yu48zb67hzzrbOYTcDrsrMX0bEpZT8+Hzbanty1zkMxqYZT/d1Slzva3toL+CbtUwGAvPdOLux+fmIiLiWMuvQd4HTI+Iwyv0bDqDU5h642IYi4t6weJ1uM47soZQysW4+RulafRqwNiJ+v+2xX2QfN3zuJ44+trEJ5Uugtc9TgCOA0yKiddO+Y4B3ZuZQr2yrp3Hm7A+AEyPiAsoFhcdQZhk9Mrvc1mGpmt6MW3IJt4Vo28ZmlIHMrfr09wAXRZna9/2Uk52/odw7UOM11uPsYjLzpoj4e+CIiPgxZYauRwLHAR/OzGv72Y45O/PGmbMnAN+IiHdShjts1/z8bGae37bezpQLC4veXHeROMzZ2TfuvP1sRLyDcm/eVZSxkpcC/9yscxzwDxFxOaVB9leUPO77PnjNefCazFxyuWEzPm7DtpL144GvRsQbKPc7+zPKTJNPXeo+RmHebkLd7mJKEn2UMhjyduDJwNcpN6f7IbA/cGBmntZjW59p/i1mFeX9vku3aTPT3R8D96L8MV3b8W+7/l5SX3H08uxmnw8AyMzrgCdR4v93yk0F38dgs5tpOMaWs5n5NeAFlPvvXUopp3lF21idYTmfO264ulSvpuQsAJmZlAPtYyklFW8AXp133G9F4zPu42wvRwD/lzIhyA8o00i/k8Fu2GvOzrZxHme/AzyF0rC5EPgQ5YbUz+lYdUvgf/LOtzYZhDk7+8aZt2dyR95eQJnA7nvA7tnc4iHLbSJeRTnefo8ys/PTMvOHA7ymayl5txzvoeR/K/bVwHMpU/pfRLnn2gGZWVOvNOutW9fZE6ppEREfB7bOzD0mHYvUj4j4BqXs5fmTjkXqhzmraWPOahpFxE8p9wCuaWzlRMxzWePUakoP/4AyG08tMzdJC4qIzSkzPQYTrDmX+mXOatqYs5pGEbGKMpHIKuDHEw6nCvNc1jjNHkkZjLuOUj8r1W5vysnCFcBJkw1F6os5q2ljzmoaHUSZqfxs4B8nG0odLGuUJEmSpApMrKyxmb1wN8qAv54zEWrurU8ZFH1+a8DpuJmzGpA5q2k00bw1Z7UE5qymzaI5O8kxZ7tRZpGRBrE7d9zgcNzMWS2FOatpNKm8NWe1VOaspk3XnJ1k4+xagA8+eBu2WLFigmFoGvz3rbfyoiuvhrapfCfAnFXfzFlNowry1pzVQMxZTZteOTvJxtntAFusWMFWG244wTA0ZSZZMmDOainMWU2jSeWtOaulMmc1bbrmrLM1SpIkSVIFbJxJkiRJUgVsnEmSJElSBWycSZIkSVIFbJxJkiRJUgVsnEmSJElSBWycSZIkSVIFbJxJkiRJUgVsnEmSJElSBWycSZIkSVIFbJxJkiRJUgVsnEmSJElSBWycSZIkSVIFbJxJkiRJUgU2mHQAkqTRusexG7H5Vht1feyGQ24eczSSJGkh9pxJkiRJUgVsnEmSJElSBSxrlKQ5tvnJG/dcx9JHSZLGw54zSZIkSaqAjTNJkiRJqkDfZY0RsQq4GHhhZn4+Ih4InAI8CrgWeGVmfn4kUUqSJqZX6aNlj9Ov9RmvuWY92HfCweAMo5Lm1yA9Z6cAv9f2+6eA84B7A4cDp0fEfYcYmyRJkiTNjb56ziLixcCvgZ80vz8UeBiwR2beCnwpIr4GPB84YUSxSpIq5KQi9evnM5oW5ptq1OrtNfe0XD17ziJiO+BVwEvaFu8AXJWZN7UtS2DH4YYnSZIkSfNh0cZZRGwAfBw4PDOvb3toU2Btx+prgZXDDU+SJEmS5kOvssYjgQsz84sdy9cCm3QsWwmsGVZgUq0cqC4NbtyTiiy1jG9a/oZnqUxxFJzERpNi7mm5epU1Pht4TkTcEBE3AFsDnwQCeGBEtJ+hBnDJaMKUJEmSpNm2aM9ZZu7Q/ntEXAUc2kylvx/wlog4EtgTeALw0tGEKUmSJEmzre/7nHWxL/APwH8DPwOem5k/GUpU0pRyFjFpaWopQ5xkHJYqjs9C77XH5zot9rdRy735+uV5gnoZqHGWmQ9s+//VwFOGHZAkSZIkzaNBbkItSZIkSRqR5ZQ1DkW3me/sztUscyan2bf5yRtPXanNtKqlFLCWOLQ8kzw+11LaOw7+vSyu2/szjZ+zlsaeM0mSJEmqgI0zSZIkSarAxMsau7HsS/NskjM5OYNZfyzJkebTcv72x33cHmcMg8ahwdV4bjDuOGrX7/vUa9iDPWeSJEmSVIEqe8568R4Rmnfj7l0exhXRmv8mveIradRqOM7UEINGZxp7UT03uCt7ziRJkiSpAjbOJEmSJKkCU1nW2A8nFZlunZ+f94waTI2Dd8d9Dx/Ld+7wuh9szibXbnKnZR985PUTikaSNGm1fEeO49ygltfaL3vOJEmSJKkCNs4kSZIkqQIzW9bYyzjKHsddxlW7aetWnic1fzY1xzbNXnTBvRd93LJHSeqf5ePjNcvnBvacSZIkSVIFbJxJkiRJUgUmXtbYrRu4ZZLdwZO80fVyumrHXRI5y93K0jzrVfYIluxodBY7N2hnDqpmlo9rKew5kyRJkqQK2DiTJEmSpAr0VdYYEfsDRwMPAK4GXp+Zn42IewGnAnsCvwKOzsxThhVc7WU1NZb01RiTpNlkyc50Wuxzu+m6m8YYyfLVfp4gLab2/G3F59/QePXsOYuI7YEPAwdn5mbA4cAZEXEf4GRgDbAK2A84PiJ2GmG8kiRJkjSTejbOMvM/gFWZ+a2I2AzYErgRuAXYB3hTZt6cmecBpwOHjDJgSZIkSZpFfZU1ZuaaiHgQ8CNgPeAlwLbArZl5ZfuqwL5Dj3IRNZbV2A0822qdYVSqzSiPz/2UA41q37VZ7nsx67q9PzWUinWapZzU8Iz7PLfb/movv6xZt/euV/n4IFPp/wTYGNgd+BxwPNC59bXAygG2KUmSJEligMZZZt7W/PfsiPg0sCvQ2X2wkjIGrRqTbO0PejVzXq869PM+Tcsgda8uSf2b5DFyOb1N4/gbtjdstCaRe732WWMl0CT1+xnVcn6w7Vdu4e4r70Y+faOx7reWvKkljnEY9fG5nwlB9o6Ir3Qs3hC4AlgREVu3rw5cMsT4JEmSJGku9NNz9l1g14g4EDgN+FNgb+DRwNbAsRFxCLAjcEDzmCRJkiRpAD0bZ5n5s4h4OvAu4O+B/wD2yczLmkbZScBPKeWMR2TmdwYJYFLdwO1q6YqtJY7lshxncQ4Gl5amlmNLLXFofGr4fp72svl5+buJs36z6OO1lT2OS+1DfWp5n6D/2Rq/Thlj1rn8emD/YQclSZIkSfOm55gzSZIkSdLoDTKV/kjV1g3crpauzqXGMY5ZprR84yibmWQOzaqF3tNaZhCTNHq1fEcOEsdSj+u1vNZp1et8F8Z/zlvjOfg855k9Z5IkSZJUARtnkiRJklSBasoae6mxG7hde3yTjKObee4aniWTnC1sHKUytfDvRdI88FhXr9rKDGs/B69Zt/fuxrW3sNjU9vacSZIkSVIFbJxJkiRJUgWmpqyxH7V0A9cSh+ZLLSUqNc4IWct7I6k/237lFu6+8s7Xj/3ulIoaywy7xTSrf7P9vP/LYc+ZJEmSJFXAxpkkSZIkVWCmyhp7GXY38FK7NS17HPy96zWzzThZbjMalh5KWozfnVL/Rl16N4oYavkbnvR7Z8+ZJEmSJFVgrnrO+lHDlbkaB3oOYtJXHCahhryRpHlW43en3w1S/+bx/LEbe84kSZIkqQI2ziRJkiSpApY1DqiWLtelxtFPCUUtr3GWTLLcZpDP0xIbSbNsHGWGgxxzayzFrNFi71NNE4ZJw2DPmSRJkiRVwMaZJEmSJFWgr7LGiHgccAKwA/BL4PjM/GBE3As4FdgT+BVwdGaeMqpgtXyWLNZrmOU2o7oHXyfLbe7Q+d7VVGrjvfmk/tQ4u2KNMS2X5yLSwnr2nDUNsM8BJwL3Av4cODYingScDKwBVgH7AcdHxE6jC1eSJEmSZlM/PWfbAF/IzNOa378bEecAjwX2AbbPzJuB8yLidOAQ4LCRRCtJkiRJM6pn4ywzLwQObP3e9KTtDnwfuDUzr2xfHdh32EFKqrO0pcaYhmmWS29m/bOTRqHGY0ItMz7W+N5I02igCUEi4p7AWcAFwDnATR2rrAVWDic0SZIkSZoffTfOIuJBwLeA6ym9Y2uATTpWW9kslyRJkiQNoN/ZGncBvgx8HHh1Zv42Ii4HVkTE1pn549aqwCWjCVXSYmosKVkopkmWzNX4PtVo3KVSS/1cLL+UevO4N3pn73wUG2y+Vc/1nvadl48hGk2zno2ziFhFaZidkJnHtZZn5o0RcSZl5sZDgB2BA4C9RxWsJEmSJM2qfnrODgbuCxwZEUe2LX8PZWbGk4CfUsoZj8jMWm7tI6lSXsWdDTVMKuK9+Zau23tX0/35pFn0hUef2HOdYfau9bO/Ue1bS9PPbI3HAMcsssr+wwtHkiRJkubTQLM1SpIkSZJGo68JQSRJGlSN5avdYpqlUsca33NJg+lWijiucsNByiAtgRxM67297YZr4NynLbiePWeSJEmSVAEbZ5IkSZJUAcsaJUlzbdz3dBvUPJYqtu4ZZdmUVCxUbjjJv5FeJZDz9vc76MyYC7HnTJIkSZIqYONMkiRJkipgWaMkzbhWiVg3tZTE1F7+Mo+lhTWouWyqxjIzzZ9hldKNwrhvuD1sk3pv7TmTJEmSpArYOJMkSZKkCljWuIBpKreRpKWqpeykljg0XcadN/3sz3JHqX/jKF2uufSzG3vOJEmSJKkCNs4kSZIkqQITL2vsNotYbV3/lihImmfDLjtZaolJzTP3TbvWe3vbDdfAuU+bcDTD1S1vJpEr5u/ydXsPZzFndYdpK0kcBnvOJEmSJKkCE+8562aQVvKwrzQNsm8nDZGkeibzqCWOGs3j1efF1FgRY8+aeSqBPWeSJEmSVAUbZ5IkSZJUgYHKGiPiUcBnM3Or5vd7AacCewK/Ao7OzFOGHuUiailNqGXA8Sz6wqNPdMBvB/NN06aWcqVZLB2r5b2dBTW/l9NatlvzeyrVqK+es4hYLyJeCPwrsGHbQycDa4BVwH7A8RGx09CjlCRJkqQZ129Z4+uAw4G3thZExGbAPsCbMvPmzDwPOB04ZOhRSpIkSdKM67es8VTgGODxbcu2A27NzCvbliWw75BiW5ZWN3qNMy/VWHYwSZY8DIezh0rLN8nZgpcah9Ri3kjTr6/GWWZeCxAR7Ys3BW7qWHUtsHIokUmSJEnSHFnObI1rgU06lq2kjEGTJEmSJA1gOTehvhxYERFbZ+aPm2UBXLL8sIann9LCcZcB1FI2M26zUm5x9s5HscHmW91pWS032O217izlkzRps3JMkyTVY8k9Z5l5I3AmcGxErIyI3YADgNOGFZwkSZIkzYvl3oT6EGAF8FPg08ARmfmdZUclSZIkSXNmoLLGzFwN3Kft9+uB/Ycc01hMSznKoHHWcPPteTQtparO6ChJklSv5facSZIkSZKGYDkTgqhC9mTVr5bJOrrFYW/a0nW+n7fdcA2c+7QJRSNJkqaRPWeSJEmSVAEbZ5IkSZJUAcsapUrUUJJaS8ll7Wr4rCRJ0uyx50ySJEmSKmDjTJIkSZIqYFmjpJ6m5T5uw2DJoiRJmhR7ziRJkiSpAjbOJEmSJKkCljVKGqpBywJruPm2JElSDew5kyRJkqQK2DiTJEmSpApY1ihpoiwzlCRJKuw5kyRJkqQK2DiTJEmSpArYOJMkSZKkCtg4kyRJkqQK2DiTJEmSpAose7bGiNgZ+CCwI3A58OLM/PZytytJkiRJ82RZPWcRsTFwFvBhYHPgROAzEbHhEGKTJEmSpLmx3LLGJwK/zcwPZOatmXkqcB3w9OWHJkmSJEnzY7mNsx2ASzqWJaXEUZIkSZLUp+WOOdsUWNuxbC2wso/nrg9w2//+fJkhaB605cn6EwzDnFXfzFlNowry1pzVQMxZTZteObvcxtlaYJOOZSuBNX08d0uAX374hcsMQXNmS+CKCe7bnNWgzFlNo0nlrTmrpTJnNW265uxyG2eXAod2LAvg9D6eez6wO3AtcPsy49DsW5+SxOdPMAZzVoMwZzWNJp235qwGZc5q2iyas+utW7duyVuOiI2AK4G3AScBBzb/f1Bm/nrJG5YkSZKkObOsCUEy8zfAU4HnAtcDhwHPsGEmSZIkSYNZVs+ZJEmSJGk4ljuVviRJkiRpCGycSZIkSVIFbJxJkiRJUgVsnEmSJElSBWycSZIkSVIFbJxJkiRJUgVsnEmSJElSBTaYdADjFBHrgJdk5kkRsR7wPOCczPyvMez7scDfATsDvwHOBY7IzKvb1nk28AZgW+C/gH8A3pGZXW9GFxGrgZ9l5nOWEM99gIuAgzPzyz3W3R14O7AT8HPg3Zn5nkH3qcFNOGd3Ad4B7ArcCJwFvCYzb+iy7uOArwGbZubNi2xzNebsTJtwzp4EvKjLQ5tk5s0RcTdKLq/sePyMhXIyIq4CPpmZr1lCPNsB3wN2zczLeqy7D/BmYDvgKuDNmfmJQfeppak8bzcG3go8G/g94IfAWzLzzEW2eRXm7UyrPGdXAK8BXgBsCXwXODIzVy+yzaswZ4H56znbEvho8/89gI8Bdx/1TiPiocC/AQk8Fng6sAr4t4hY2azzJ8BpwAeAPwTeREmewxfZ9L50/+PoFc82wFeBrfpYdzvgy8A3gV0oDcy3RcQhg+5XSzKpnN2CkrM/Ah4JHADsBXyky7pPoDTc+jmemLOzbyI529gJeE8Tw+/+tV0weAilYfbYjnUWy8ndKDk0kIjYCfgKsGkf6+4BfAr4OPAIyt/ZxyLiKYPuV0tWc97+A/DnwF8COwKfAz4TEX+8yDbN29lXc86+C/i/wLGU3PgM8OXmQu5CzNnGXPWcZebP2n5db4y7PpjSE/bSVi9YRPwF8GPg8cCXKCetF2Xm+5vnXBkR+wF7A+/uttHMvH7QQCLiUOAtwOV9PuUVwI8y81XN75dFxLbA64CTB92/BjPBnH0icG/gVZl5I3B5RLwXeHtErJeZ6yJiE+AE4BBKj9Yje23UnJ19k8rZ5srxHwLv74ih3U6UyoXzMvP2frabmb9YQixvBV4JXAxs3cdTXgN8MTOPb34/LiIeDrwW+JdB96/B1Zq3EbE+cCulh+TfmsVvjohXAHtSLkLdhXk7+yrO2c2Bl1Kqw05pFr8rIh4GHE250HsX5uwd5qpx1uoCBi4DzmkWXxoRR2fmURFxD+B44JmUq6sXU7phv9o8/yBKq/404K+BS4E/bm0rM5+wwK5PBf6pozyx9f97Nz9/AewYEU+i9BA8DHgcpSdtodezmrYSsaZLeHVmHrTI2/BnwN9QyiqvXGS9lsdz14Q9G3htRGzTXpap4ZtgzrYOkodGxNspebof8O22PN4C+ANKQ+4hwIf7eD2rMWdn2gRz9kGUq8aXLBLeTsB/9Nswa+K5irZSm+b1HZ2ZRy3ytKdSStD+lzveg4W2fzdgd+D1HQ+dDbw/IlZk5q39xqulqTVvm1w9uC3OlZQLYptRqhsWej1XYd7OtFpzlnI+sB5lqEO7fweeFxHrdzsGm7N3mKvGWZtvAc8CPk35oC5srgR8idJo2pdycvpMSjfsMzLzS81z70c5Id0V2KzpQdh3sZ1lZrcEfj1wM6UhBqUHYmfKwfZ2YH3KH8xRA7yu3YBbesTyJwAR8cA+t3l/4Ccdy65pfj4A8ER3PMads2dHxNso+fcWSj5eTLlS21rnauAJABHxkCW+LnN2do01ZykNL4CDI+JTlO+3r1HGSV7Tts5vI+IsSu79nFLWcuIADbYtgTWLrZCZu8DvSn57uSflRLtbzq6glMD/tM/YtHw15i0AEfEy4L2UE9+jM/PbA7wu83Z21ZazrbzdhtIga3kwsCGwOXBdH69rbnN2LhtnmXlLRLTKq36ZmWsiYi/KOIQHZGbrwzkuyqQIf0tJ8pa3ZOYVbdsbqFQrIv4WeDFweFuX8P2B+1K6Z88BHkOZ0OAoyvizfl7XwF3CfdiUUgbUrlVTvPEI9qcuxp2zEXFPYHvgFErP75bAO4EzIuLJg/Q89Hhd5uyMmsBx9mGUE5HrgX0oJx1vBb4WETtn5ppmnY0ox9XXU3pZj6V8Kfc1CH2Rksmlao2TMGcrUGnetnyRUsb4p8DfRcQNmdl12EOX12XezqgKc/aaiPgycHxE/Cdl2MOTuKMHeKM+X9fc5uxcNs4WsEvz87KIaF++IfA/Hev2O/blTpra8XcCLwfemJkntj38T8A/Z+a7mt8vjDLbzTsj4n0jOontx03c9Q+plcCLXtHQyI0yZ99GGdz7rNaCpuTg+5QJbT474PbGyZyt1yhz9q2UMRCtK7IXR8T3KVdC9wX+H2WcxPptJ7zfj4hNgaMj4sgJlbTc1Pw0Z+s16bwFIDP/s/nvhRHxYMpJdl+NsxEwb+s26Zx9PmWM9/mUhtyFzfPeDvxqwP0Ny9TkrI2zO9yN0pp+RJfHOnsJbuqyzqIiYjPgDODJwCGZ+aG2x+4L7ACc1/G0b1E+o225YwzQuF1NuSrSrjVjXhXdv3NslDm7Bx3jGTLz4ohYQ8nVmpmz9RpZzmbmb+kolWmu4F5HKWclM7tt82LKCcsWlImbxu16yklBt5y9mf7KfzRaE8vbiLg7padsdcdF2ouAv4qIuzXbGDfztm6TPtb+AtinGSN598z8eUS8HLg2M389yP6GaGpydt6m0m/Xee+wiymt6Xtm5o9a/4C/Ag5azo4iYkPgC5QSmqe3N8waayjjbnbuWL5TE+ckx8icS5nwod1ewJVtXeUaj7HlLOUgdad8jIgHUeq1r1rmtkfNnK3HOI+zH4+Ib3QsezBwH+AHEXHviLguIl7c8dRHUb60r2ECmgl2vk73nP1mLQPU50w1eUsZX3Y65R5W7f4IuHRCDTPztj415SwRcVZEPCsz12bmz5vV/pxym5uJmKacneeesxubn4+IiGsps7t9Fzg9Ig6j3N/pAErZwIGLbSgi7g2L1um+idIT8TxKOcLvtz32q8y8KSL+HjgiIn5MmTnmkcBxwIcz89p+XlDTA3dLZi65y7jp4dusrdb3PcBFUaZRfz/lC+FvgJctdR9asnHm7AnAZyPiHcAHKWNy3k2Zzemfl/k62uMwZ2fbOHP2DODMiDiWMlby/pSc/TZwVmb+NiLOoUxDfg3lRr5Pbvb9yrzzbLqLxfH7wJqOsUADacZ0btjWE3I88NWIeAPlHjx/Rpkd9alL3YeWpba8/QDwxoi4mnLy+2zguZRJIPpi3s68anK2WecXlPuLXtv8/whKWfkL+n1B85yz89xzdjHlJPOjlMGQt1O+qL9OuTndD4H9gQMz87Qe2/pM86UJn9gAACAASURBVG8hrWQ8Dbi241/rsSMoN+x7JeXg+3eU8WmD3LD3fMqJ6XK8uokLgMxMStI+lnL39TcAr87MU5e5Hw1ubDmbmWcCT6E0bC4APkH5/HfPzM7BtMthzs62cebsWZQv2SdRSr4+Rcmvvdt6F15AmZ3xvZRpoA8DXp6ZC96ypItrKTm3HO9pYmvFvppysv28JvaDgAMy8yvL3I+Wpra8fRXl5PeEJrZ9gX0yc5Cxv+btbKstZ19O6Wj4Z8qMjQ8A9sjMfm6H0zK3ObveunV9XSxUhZpu5Ssz8/mTjkXqhzmraRMRPwVOzcw3TjoWqV/mraaNOXuHeS5rnFpR7r6+PRDA6slGI/VmzmraRMQqyuQ3q4AfTzgcqS/mraaNOXtX81zWOM32ppzgXgGcNNlQpL6Ys5o2B1HuBXQ28I+TDUXq20GYt5ouB2HO3olljZIkSZJUgYmVNUbERsBulAF/nfdckDqtD2wJnD/kCSn6Zs5qQOasptFE89ac1RKYs5o2i+bsJMec7UaZRUYaxO7A/8/e3cfNVpaFHv8hbGBvQDe+7SAFlODCMEIUNAtTtFI8GBGh0iE9cPZBTaRUKjGUlxDlhC9kCRJIHwU1jx4VDSsCfMkCDohiyJXCgSA2qXBAtnsjG+T8sWZyGOZ5Zs3rumfm9/189mc/s2Y9s65Zc839rHvd17rXV/quNRnmrIZhzmoWNZW35qyGZc5q1vTM2SY7Z+sAzn7qzjxxxYoGw9As+O6mTRx90y3QMWV6A8xZ1WbOahYVkLfmrAZizmrW9MvZJjtnDwI8ccUKdtxyywbD0IxpsmTAnNUwzFnNoqby1pzVsMxZzZqeOetsjZIkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUgCYnBJEkTcGjT9uK1Ttu1fO5u9feN+VoJEnSUhw5kyRJkqQC2DmTJEmSpAJY1ihJC2z1OVv3XcfSR0mSpsORM0mSJEkqgJ0zSZIkSSqAZY2SpGX1K3207HH2tT/j9bdvBoc0HIwkLTBHziRJkiSpAHbOJEmSJKkAljVKkkbijI/lq/MZSZKa58iZJEmSJBXAzpkkSZIkFcCyRknSxJU24+NS8cxK+aVlipI0nxw5kyRJkqQC2DmTJEmSpAJY1ihJatygZXqTKj8ctlxwHPFYqihJqt05i4g1wHXAkZn5uYjYBTgX2A9YB7wxMz83kSglSZIkac4NMnJ2LvC4jsefAC4BXgy8CPh4ROyamd8bY3ySJD1CkyNcvTjqNV6PPm0rVu+41cOWzcpkLZI0ilrXnEXEa4AfAre2Hj8N+Dng5MzclJkXA18EfmdSgUqSJEnSPOvbOYuI3YA3Aa/tWLwHcHNmbuxYlsCe4w1PkiRJkhbDsmWNEbEF8BHg2My8KyLaT20DbOhafQPwpLFHKBXGchtpdll+OLtKu1eeJE1Cv5GzE4BrM/NvupZvAFZ2LVsFrB9XYJIkSZK0SPp1zl4OvCIi7o6Iu4GdgI8BAewSEZ3DBwFcP5kwJUmSJGm+LVvWmJl7dD6OiJuB17em0j8UOCUiTgAOAJ4PvG4yYUpls9xGkppVp2TVtlhS6Ua5CfUhwAeB7wJ3AK/MzFvHEpUkSZIkLZiBOmeZuUvHz7cAvzbugCRJkiRpEY0yciapJstt1KTjv7malesePofT2c+8q6FopOb0aotte2dHr89v/e2bVbVc0pyodRNqSZIkSdJk2TmTJEmSpAJY1igVoskZH9vbtrxncEt9bqWX2hx99WOXfd6yRy2KJsvOB7kp+ry2z94YXno4R84kSZIkqQCNj5w9+rStWL3jVj2fm9ezRNIwpjGyNuwZzHn9ri7yGd1+I2vg6Jomp9ckNm1N5F0JI1yjtEfTbqMXue2URuXImSRJkiQVwM6ZJEmSJBWg8bLG5XhvKKm+JstILLdZTE4qMpuW+9w23rlxipEMZ6n4S8m3EtuYEmOaN+3LdDwu1agcOZMkSZKkAtg5kyRJkqQCFF3WWEeT94bSYmrPIlZKCc2ss9xmfjVZ9rioJZd1ZtmcV84wqhJ4XKpROXImSZIkSQWwcyZJkiRJBZj5ssZ+nPFxtiz1ea2/fTM4ZMrB9LGoZVPSuDRZhjZK+d+0v9uLXKo4biW0250x+HeinlmfYbSTx6Xqx5EzSZIkSSqAnTNJkiRJKkDjZY3tme86TXuYfxoz64xjRrpZHOZe5Jn4nDlMGt0gJX3T+j5ZZji/pl32OGwuzfrfDr9Dy3PGx/nU/lz7XapTq3MWEYcBJwFPBm4B3pqZn46I7YHzgAOAe4CTMvPckSKXJEmSpAXUt6wxInYHPgQclZnbAscCH4+IxwPnAOuBNcChwOkRsdcE45UkSZKkudR35Cwz/zUi1mTm+ojYFtgBuBe4HzgY2D0z7wOujIgLgbXAMaMEVcJsSp1KmVln2BLBcce2yKWK41ZKCc2sl8iU5uirHztzM4jNA0ulNGmD5lhn2zrO/Bwljknx+zc90zouLeW4s2STOCauVdbY6pg9BfgOsBnwWmBXYFNm3tS5KsVNeC5JkiRJ5RtkQpBbga2B/YHPAqcD3aeGNwCrxhPa0ko8+1/yaFLJsWl505pUZFEvSh+EZ4UlDaqUdqOUOOZZe4K7Uv4uDnLsV0qFVSkjbk0fN9funGXmA60fL42ITwLPAlZ2rbaK6ho0SZIkSdIA6kwIcmBEXNK1eEvgRmBFROzUuTpw/RjjkyRJkqSFUGfk7BrgWRFxBHAB8GLgQODZwE7AaRGxFtgTOLz1XCNKm0hE4+HkCstrslxl1sshLfWRJI3TLB6LNl3G11ZKHE3rO3KWmXcAB1FNoX83cDJwcGbeQDUz4wrgNuCTwHGZecXkwpUkSZKk+VR3tsYvU11j1r38LuCwcQclSZIkSYtmkNkaZ960Zr4bZHuTjqEklpCpzVyQJC2iaR+Lavb0LWuUJEmSJE2enTNJkiRJKsBClTXWMUi51aSGnQct+Wpy+NvyNEmSpPHpdWw1rWO9zm1bXtkMR84kSZIkqQB2ziRJkiSpAJY1jqCUkr5S4pAkSdL4TXKWx6Ve21nHB1N3f228c+OyzztyJkmSJEkFsHMmSZIkSQWwrFGSJBVl10vuZ7tVjyIP2qrpUKSZUcKM4yXFMaqmLhty5EySJEmSCtD4yFn77Fgnz5SpZOasZk2vnG0zd1WyuOhHyz5v/krDKWUyuUlOOlLKexyUI2eSJEmSVAA7Z5IkSZJUgMbLGnuxjEGzprSc7YzH74uW0y93wRxSuUrM36Vi8nskjc+slizW4ciZJEmSJBXAzpkkSZIkFaDIssZ+ZqWMwRIGtU0jZ5faRp1t92L+qq2UMq3Syoc1G6aRN3Xa2UHaYnO5t1778N4N93NFA7FIk1KrcxYRvwScAewBfB84PTPPjojtgfOAA4B7gJMy89xJBStJkiRJ86pvWWOrA/ZZ4Exge+C3gNMi4kXAOcB6YA1wKHB6ROw1uXAlSZIkaT7VGTnbGfh8Zl7QenxNRFwGPBc4GNg9M+8DroyIC4G1wDETiXYAJZS/DFpOZhnDT3Tvu0UoWyghZzuVWD5coqX2kzlrzpZquf20CHnbrcS8Ke27NS3DluGXYNdL7me7VY+a289G09O3c5aZ1wJHtB+3RtL2B74BbMrMmzpXBw4Zd5CSJEmSNO8Gmq0xIh4DXARcDVwGbOxaZQOwajyhSZIkSdLiqD1bY0Q8BfgccCPwcuBpwMqu1VZRXYNWvHGUDIx7+H3eZ3yc5XKFEpS4/+ax9KbE/TyrSpydzpydDZc+40S2WL0jL73iDU2HApS3j0uZQbWO0vbdpM1jG6PpqjVyFhH7AFcAfwscnJkbgW8DKyJip85VgevHHqUkSZIkzbm+I2cRsQb4AnBGZr6rvTwz742Iz1DN3LgW2BM4HDhwUsFOUylnekq8ULlTKftJZSolf83T8pXyGZmzZfn8s89c9vkmR9Y6Y3OET3U12cY4Ud1sqFPWeBTwBOCEiDihY/n7qGZmPAu4jaqc8bjMXLSJniRJkiRpZHVma3wH8I5lVjlsfOFIkiRJ0mKqPSGIymUZg2aZ+atZY86Wo1/ZI0yn5LCUODQfSplUpMRJnRbBQFPpS5IkSZImw86ZJEmSJBXAskZpQO3773QqZcawNstnJKlSShtZ8syTs6LXPnzg7tvhSy9tIJrmDDLj47TKsOf9Xr1LGWb/3rvhfpabPdGRM0mSJEkqgJ0zSZIkSSqAZY3SGEy7XKXf9pZ63rKZxVRaKa7UtGFnV6zze8Oy7HGy+3fRlDCrbJM33B5F0/vOkTNJkiRJKoCdM0mSJEkqQONljb3KbXopZTi/c8i9lJhUvlJuUGrZjNpKzgXLclWC0krsSvk7MqjS9qPKMmwJYZ1yyKbLE4flyJkkSZIkFcDOmSRJkiQVoPGyxrqaHM5fatuW3mic+t0odRqlIbNaNtOkpfbZrN8YtcRcKOVmwvOovW9nPW8X3bB/J+p8jyxPVElmtWSxDkfOJEmSJKkAMzNyVkcpZ1VLiWOWde/DRT2bW+KZypInkhi3Evd/ScbZ1g27r0sc4SuRuazlmB9SORw5kyRJkqQC2DmTJEmSpALMVVljL6VM2mHpzcNZQjG/Ss91c2+ySmlzO817Ka45Lambl9jMroE6ZxGxH/DpzNyx9Xh74DzgAOAe4KTMPHfsUUqSJEnSnKtV1hgRm0XEkcDfAVt2PHUOsB5YAxwKnB4Re409SkmSJEmac3VHzo4HDgNOBf4QICK2BQ4Gds/M+4ArI+JCYC1wzARiHat+pTdNlIkMss2Sh6YtsVE/5sjiKbnEZtB8LLEsXlKzLn3GiWyxeseHLSutreh8vpT2V49Ud0KQ84C9gas6lu0GbMrMmzqWJbDnmGKTJEmSpIVRa+QsM9cBRETn4m2AjV2rbgBWjSUySZIkSVogo8zWuAFY2bVsFdU1aDNrVspHplECOSv7QtJsmtU2ZlbjljRdJc8UW+LMuqqMcp+zbwMrImKnjmUBXD9aSJIkSZK0eIbunGXmvcBngNMiYlVE7AscDlwwruAkSZIkaVGMehPqtcBZwG1U5YzHZeYVI0elsbIER5IkqSx1js9KmPHRUsfxaO/bB+6+Hb700iXXG6hzlpmXA4/veHwX1RT7kiRJkqQRjDpyJkmSJGkC+o1kTaM6qsQRvhKN67MYZUIQSZIkSdKY2DmTJEmSpAJY1ihJkiTNiBIneiv5nm7jMM197siZJEmSJBXAzpkkSZIkFcCyRkmSJEkTM2hZYAn3d2uKI2eSJEmSVAA7Z5IkSZJUAMsaJUmSJBWjpDLDaXPkTJIkSZIKYOdMkiRJkgpg50ySJEmSCmDnTJIkSZIKYOdMkiRJkgpg50ySJEmSCmDnTJIkSZIKYOdMkiRJkgow8k2oI+IZwNnAnsC3gddk5j+P+rqSJEmStEhGGjmLiK2Bi4APAauBM4FPRcSWY4hNkiRJkhbGqGWNLwB+nJkfyMxNmXkecCdw0OihSZIkSdLiGLVztgdwfdeypCpxlCRJkiTVNOo1Z9sAG7qWbQBW1fjdzQEe+MF/jBiCFkFHnmzeYBjmrGozZzWLCshbc1YDMWc1a/rl7Kidsw3Ayq5lq4D1NX53B4Dvf+jIEUPQgtkBuLHBbZuzGpQ5q1nUVN6asxqWOatZ0zNnR+2cfQt4fdeyAC6s8btXAfsD64AHR4xD829zqiS+qsEYzFkNwpzVLGo6b81ZDcqc1axZNmc3e+ihh4Z+5YjYCrgJeCdwFnBE6+enZOYPh35hSZIkSVowI00Ikpk/Al4CvBK4CzgGeJkdM0mSJEkazEgjZ5IkSZKk8Rh1Kn1JkiRJ0hjYOZMkSZKkAtg5kyRJkqQC2DmTJEmSpALYOZMkSZKkAtg5kyRJkqQC2DmTJEmSpAJs0XQA0xQRDwGvzcyzImIz4LeByzLz36ew7bOAo3s8tTIz7+tadxvgGuCfMvPVy7zm5cAdmfmKIeJ5PPB14KjM/EKfdfcH/iewF/AfwHsz832DblODKz1nI+JFwDv4SW6cA5yamT1voGjOzr8mc7Yjhq2BfwbOysyzup57FfBG4GeA7wKfAE7MzA1LvNb5wB6Z+ZxxxtFj3acD7wWeA/wAOA94e2Y+OOh2NZiG29knAn8K/BeqE+aXAW/IzFs71tmntc5zgHuAjwFv6T526Fj/fMzZuddU3rb+jv/yEk+/PTNP7lp/N+BrwLMy84ZlXvd8zFtgwTpnwA5UDRvA84APA0+b0rb3At4HvLNz4RKN658BuwP/1Oc1DwEGTqaI2Bn4LLBjjXV3A74AnAW8GvhF4P0RsSEzzxl02xpYsTkbEc8FLm6t80pgP+AvgR8C71niNc3Z+ddkzhIRjwP+Gvj5Hs+9iipHjwX+FgjgbKq8+q9LvOSxwObjjKPHutsD/wBcQvU92qMV52bAWwfdtgbWSM5GxAqqPHwIeDGwCfgA8NmI2CczH4qIpwKXU7V/zwCeCpxP1ZE7domXNmcXQ1Nt7SHAll3L3kPVYfvLzoURsRdwEbBNjdc1b1sWqnOWmXd0PNxsWtttndF4OvAXXTH0Wvcw4FeB7Pe6mXnXELG8HjgF+HbNX/l94DuZ+abW4xsiYlfgeKpREk1Q4Tl7CnBJZr659fjGiPh54AUs0TkzZ+dfUzkL/9l+vge4fYlVjgH+KjP/ovX4xog4Hjg/Io7OzB92/0Jm3tO9bAxxdFtLta+OzMwfAde3RorfHRGnLjWqp/FoMGdfQXUwvXtm/htARLwW+AywG/CvVAeM/wa8qnVmPyPiZODIpV7UnF0MTeVt99/xiHgZ8HLggMy8vWP5qVRVCtcBO9V4XfO2ZaE6Z+0hYOAGqtIBgG9FxEmZeWJEPBo4HfgNYBVVQp2Qmf/Q+v1XA38CXAD8D+BbVGflLwPIzOcvsemnANsB1/eJb2eqs/2/CZxW4/1cTkeJWETcDFy+XCkk8OvA7wFfAm7qtw2qMyF/27XsUuAtEbFzZt5S4zU0pFJzNiJWUuXGqzqXZ+Yf9Xk/l2POzrUGcxbgYKryr/cD9/d4/vepylw7PUQ1CrGaatS3+/2cT0epTSuHR42j2y8DX2kdLLRdSnW2eV/gizVeQ0NqMGcPBL7c7pi11v0aDz+QPRB4f2fJVWb+OfDny7yf8zFn517DbW07hpVU1TPnZeblXU+/hKrT9oOO+JZ7rfMxb4EF65x1+CpVB+iTwP7Ata2Rgoup/lAfAnyPKqG/EBEvy8yLW7/708DPAs8Ctm2VHRzSZ3t7tf4/KiI+QbXfvwj8UfssQ0RsDlwIfDAzL4uIYd7XvvRJzsz8ldb2dqn5mk8Cbu1a1j5D8WTAA93pKC1nf4aq/OCHEfFh4FeA7wMfAt6TmT+u+b7M2fk17ZwlMw9v/9yrDc3ML3c+jogtgTcD1w5wncbIcfTwJKrStU6dOavpmHbO7g5cGRFvpDqjvx3VSag3Zea6iNgO+CnguxHxfqoD0Q3A/wJO6jrAXI45O9+m3tZ2OJoqR/+4+4nM3AcgIp4/zJtigfN2ITtnmXl/RLSHZb+fmesj4oXAc4EnZ+Ztrefe1boQ9w+okrztlMy8seP1+pVq/RzVF+Quqsb1p4FTgS9GxDMycz3wdmAr4IQR3tf3hv3dZWwDdP8BaF8nt/UEtqceSstZ4NGt9T5IdWbuDKqO1nuoDjBOrPm+zNk51UDODiQitgI+SnXd2VIXtz/CuONoMWcL0EDOPpqqtPGrVGWKWwPvAi6LiL35STv7Lqq29iCqDt2ZVNdJvrrm+zJn51hTbW1rUOH3gHP6XbIzjEXO24XsnC1hn9b/N3T1vrcE/l/XunWvfWk7leranTtbj6+LiG8AtwGHtEq7jgX2y8xNA772pG2k6jR2aifw+inHoodrLGepyigAPp6Zf9r6+dqI2AH4g1ZZRc8ZG6fAnC3XJHO2ttY1Bp+kOlt8cGZeOalt1WTOlmuSObup9e/QzNwIEBG/BdxM1RFrl1j9Y0fJ+NdaJxb+KiKOm9AJrjrM2bJNo609ANiZahKbWTETeWvn7CceRdWb3rvHc92zy20c5IVbJV53di27PSLupBpGfQFVb/7qji/RSuCZEXEo8LOdNelTdgvVqEmn9ox5t6EmNZmzl7QWf73rV79JlctPoJqmvAnmbLkmlrN1RTWb58VUoxIvKKBjBuZsySaZs7cBd7U7ZgCZeUtE3Et13e+nqM7q92pnaa3TVOfMnC3bNNra3wC+lpnfGvL3mzATebvIN6HuPqt/HVVv+jGZ+Z32P+C/U7N0YCkR8ZGI+ErXsqcCj6dqZP+QajrPvTv+fYNq6ty9qT8LzSR8iarz2OmFwE0dQ+WajmJytnXd2Xeo7hPSaS/gbro6dlNmzpZjajlbR0Q8hWo04sfAcwrpmEGVs78Y1dTqbS+kmqDk6mZCWljTzNkvAk+PiG3bC1rX1m5HNePsg8BX6N3OPgj83xG3PwpztixNtLXP4ycnamfFTOTtIo+c3dv6f++IWEc1u9s1wIURcQzVgefhVLW5Ryz3QhHxWFi2PvbjwGci4jTgXKoLEt9LdcO8i1qjFA8bZYiIHwH3tr5MtUTEE4D7c4jpSDteY1uqi0Lb9cPvA74eEX8G/AXwC1Q1xr877DY0tGJytrXO24APR8T1wKep6tvfDLwza97M0Zyde9PM2To+TFXC8lJgQ0T8VMdz36uTt+OIozXD2WM6tnkucBxwQVTTpO9GdXP3d3eOqmgqppmzZ1G1S38dEW+hKjn7M6pZcj/fWudk4NKIeCfV/ZieRnXvyfPrljSaswthqm1tq7T2adSYWXxYi5y3izxydh3wv4G/oroY8kGq+4t9GfgI8C/AYcARmXlBn9f6VOtfT5l5EXAo8CKq8oRPAFcBBw4wq10dV1EdmI7izcC69oPMTKrpUJ9LdYf3PwbenJnnjbgdDa6onM3MjwK/Dfw3qil4T6GaCOTUAd6TOTvfppaz/UTEz1BNE7091UHLuq5/u9V8qZHiaHl5a5tPBmhd2/kiYA3wf6gO0N9PzYl1NFbTbGfvBH6JqvzsK8DfU5Vdvag9E2NrhtEXA8+nqrQ5m2pW3NcN8J7M2fk37bZ2DVUfYpJVMgubt5s99FBT1+xrVK2ys5sy83eajkWqw5zVrImIjwA7Zebzmo5FqsOc1Swyb39ikcsaZ1ZErKaaTjd45P0apOKYs5o1rXKYn6Wa9eyahsOR+jJnNYvM20da5LLGWXYg1QHujVQ161LpzFnNmmdSXTz+ENW9/KTSmbOaReZtF8saJUmSJKkAjZU1tmZ62ZfqQr1aM7tpoW0O7ABc1b5QetrMWQ3InNUsajRvzVkNwZzVrFk2Z5u85mxfqllkpEHsTzWrVRPMWQ3DnNUsaipvzVkNy5zVrOmZs012ztYBnP3UnXniihX91tWC++6mTRx90y3QMWV6A8xZ1WbOahYVkLfmrAZizmrW9MvZJjtnDwI8ccUKdtxyywbD0IxpsmTAnNUwzFnNoqby1pzVsMxZzZqeOetsjZIkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUADtnkiRJklQAO2eSJEmSVIAt6q4YEWuA64AjM/NzEbELcC6wH7AOeGNmfm4iUUqSJEnSnBtk5Oxc4HEdjz8BXAk8FjgWuDAinjDG2CRJkiRpYdTqnEXEa4AfAre2Hj8N+Dng5MzclJkXA18EfmdSgUqSJEnSPOvbOYuI3YA3Aa/tWLwHcHNmbuxYlsCe4w1PkiRJkhbDstecRcQWwEeAYzPzrohoP7UNsKFr9Q3AkwYN4NGnbcXqHbd62LK719436MtIkiRJ0kzrN3J2AnBtZv5N1/INwMquZauA9eMKTJIkSZIWSb/O2cuBV0TE3RFxN7AT8DEggF0ionPIK4DrJxOmJEmSJM23ZcsaM3OPzscRcTPw+tZU+ocCp0TECcABwPOB140jqNXnbL3s85Y9qkm9SnHbzE1JkiQNq/Z9zno4BPgg8F3gDuCVmXnrWKKSJEmSpAUzUOcsM3fp+PkW4NfGHZAkSZIkLaJRRs4a06/sESwvUzPMTUmSJA2r1k2oJUmSJEmTZedMkiRJkgowk2WNdfQqL7OcbHZ0f37rb9+smoJmDjgbqaatPcOouSVJk+FMzhoXR84kSZIkqQBzO3LWS53JGtqaOMvRjm9ez7AMsv8XWSmTinTGMa85Oaw6n1GJo72O2mpW9BqFMD9VsuO/uZqV61Zy9jPvesRzpfxd12xw5EySJEmSCmDnTJIkSZIK0HhZY3sYuFOvIeFpG7QEb9jh6F7bGaX8b9rD4pYqNmPa5WnDfs6zUqZhHlcsvVHJLMvVLDj66scu+/xSx7il5Pe8/72fBY6cSZIkSVIB7JxJkiRJUgEaL2vsZdgh4SaVUhZVShxq1rBluePOnybLI/wuTEYppTdSN8tyNQv6HeNC7+Pc0vPbvw31jzv6zebsyJkkSZIkFcDOmSRJkiQVoMiyxn6GHRKWxqHXDKNts5p3pZUAlhaP6pt26U3ppT5NGuZ7VOLN08eptJluFzU3x6G9b+cxZyc14+M4tl0njl6Wiq3k70BTxyKOnEmSJElSAeycSZIkSVIBZrKssY5ZnPFRP9H9+W28c2NDkQzGklupv3GUeg1SbjJPN1W15HeySit7nOS2S2FOD26pY41hjy8GKWUc5PfqxDON78C0cqzufux3TFurcxYRhwEnAU8GbgHempmfjojtgfOAA4B7gJMy89xakUmSJEmS/lPfssaI2B34EHBUZm4LHAt8+wqOLgAAIABJREFUPCIeD5wDrAfWAIcCp0fEXhOMV5IkSZLmUt+Rs8z814hYk5nrI2JbYAfgXuB+4GBg98y8D7gyIi4E1gLHTDLocWiy7HFRSy6HHTafN4v6+Ut1lVLmNI44OstwSnlfGl6Ts4OOkj/TLolcxFzf9ZL72W7Vw8c88qCtphpDr+OLJo8pxn2pR2nliZNSq6yx1TF7CvAdYDPgtcCuwKbMvKlzVeZuQlNJkiRJmrxBJgS5Fdga2B/4LHA60H1F2wZg1XhCa1aTEzuM0mOf9hmSps8uzKMmcs/RvMHU+YxmZRIbTdYijiAsuhI/8xJjWgRx0Y+WfX4aI2ulH6cNG1+d45LS3/tSanfOMvOB1o+XRsQngWcB3XfiXUV1DZokSZIkaQB1JgQ5MCIu6Vq8JXAjsCIidupcHbh+jPFJkiRJ0kKoM3J2DfCsiDgCuAB4MXAg8GxgJ+C0iFgL7Akc3npuIZQ4XFpiTBq/aZchTrLsoATz/r05/purWbmuu9ChMiufkSTNm35ljzD9SUVmxTz/3e47cpaZdwAHUU2hfzdwMnBwZt5ANTPjCuA24JPAcZl5xeTClSRJkqT5VHe2xi9TXWPWvfwu4LBxByVJkiRJi2aQ2RolzYhShvubLIcsZR+UrsmZaaWlWIorVdqlj5Y3zr72Z3nvhvtZrsywb1mjJEmSJGny7JxJkiRJUgEsa5RUHEsSy1LKTco747C0bXFZiqtF1Dmz47RKHOvMJrmcRSjFHHUf9eLImSRJkiQVwM6ZJEmSJBXAskZJ0kgmWWa21GsPUvpqidvDLbfvNt65cYqRTM60S3EttdQ0LVVK16+McBIleOPcXpNlkNPeN8tx5EySJEmSCmDnTJIkSZIKYFmjJGnimpzxcR5LIJ3RdDRNlCHOYx5OW699OC+luOPQqzRvlmZMLKm0sEmOnEmSJElSARw5kwa06yX3s92qh5/XmKUzU1KJShkJGjaOcYx0lLIPSlBCO9vk5zHvo2zm+vQ4GjV7HDmTJEmSpALYOZMkSZKkAjRe1tguXbAsTLOsX9mA+a0mlVAiNu8s05q80trZznia/D41WQJp3kvj58iZJEmSJBXAzpkkSZIkFaDxssa20soVpHGqM1vSpHJ82Jma/M7NN9tczZtZaWdnpQRSUjNqdc4i4peAM4A9gO8Dp2fm2RGxPXAecABwD3BSZp47qWAlSZIkaV71LWtsdcA+C5wJbA/8FnBaRLwIOAdYD6wBDgVOj4i9JheuJEmSJM2nOiNnOwOfz8wLWo+viYjLgOcCBwO7Z+Z9wJURcSGwFjhm3IE2Wa4gTUNpZWZ+5+pZaj/du+F+rphyLONkiZjm0ajt7Dhu6Dvoa5jjvbX346y3tVK3vp2zzLwWOKL9uDWStj/wDWBTZt7UuTpwyLiDlCRJkqR5N9BsjRHxGOAi4GrgMmBj1yobgFXjCU2SJEmSFkft2Roj4inA54AbgZcDTwNWdq22iuoatEY0WRbmjHjj1b0/LVsor+wRyoxpVOMoW1oU4/z8h93vlogNr9e+W/S2dql8ajJvesU0T3lsmys9XK2Rs4jYB7gC+Fvg4MzcCHwbWBERO3WuClw/9iglSZIkac71HTmLiDXAF4AzMvNd7eWZeW9EfIZq5sa1wJ7A4cCBkwp2VCVeZD6PIw9L8ezYZJW4f0uZVKTEfbMIHIWYPnN9Mkrbr6W0rUspbX9Nw6XPOJEtVu/4sGUvveINDUWjWVanrPEo4AnACRFxQsfy91HNzHgWcBtVOeNxmbnIFRGSJEmSNJQ6szW+A3jHMqscNr5wJEmSJGkx1Z4QZNGUMiRfeulCp1L22aT1Kl1os4RhcIuSN/qJ0j7z0tvZ0vbXNJTWzn7+2Wcu+3yJbf8i5k1pZjFv1LyBptKXJEmSJE2GnTNJkiRJKoBljXPA0oVy9CthgOmUMZQShzQvbGfLUWL7tlRMtrNazrhzudfrmYOzx5EzSZIkSSqAnTNJkiRJKoBljdKUTWr2pjrlEdOIQ9W+feDu2+FLL206FKC8me/aLAXTpJSSW7azGtWoOTToscE4md+993+/4wNHziRJkiSpAHbOJEmSJKkAljVKhZmVchxY3JKFJstExq3Ez7nEmDQfBikRm8b33FxfWt39X1IJeRNK/ntUyvHMuE16nztyJkmSJEkFsHMmSZIkSQWwrFGaESWWB8zjTGQll4g0Zdyf8zj2sTdbHa/2/rRErLzvv+2s5s0sHs9MkyNnkiRJklSAxkfOlrv/TqdSzgx19qxLiUmLraSzPd0GiW2S36eS99E8KOUsqJMrPJx5vxhsZzUvzKGKI2eSJEmSVAA7Z5IkSZJUgMbLGusqsVyllFIeaR5YzjB/SvxM52VyhRL3rcpn3kjlG6hzFhH7AZ/OzB1bj7cHzgMOAO4BTsrMc8cepSRJkiTNuVpljRGxWUQcCfwdsGXHU+cA64E1wKHA6RGx19ijlCRJkqQ5V3fk7HjgMOBU4A8BImJb4GBg98y8D7gyIi4E1gLHTCDWviZ135tBywC8/87ouvfhot97R9J0DFv21cTfGknS/Kk7Ich5wN7AVR3LdgM2ZeZNHcsS2HNMsUmSJEnSwqg1cpaZ6wAionPxNsDGrlU3AKvGEpkkSZIkLZBRZmvcAKzsWraK6hq0YpQyo2KJs002yfIdSfPENk2SNA6j3Ofs28CKiNipY1kA148WkiRJkiQtnqE7Z5l5L/AZ4LSIWBUR+wKHAxeMKzhJkiRJWhSj3oR6LXAWcBtVOeNxmXnFyFFNQb9yxyZKVAbZZsklkJb3SJIkSYMbqHOWmZcDj+94fBfVFPuSJEmSpBGMOnI2d2Zl1Gcao2yzsi8kSZKkeTDKhCCSJEmSpDGxcyZJkiRJBbCscQFYnihJkiSVz5EzSZIkSSqAnTNJkiRJKoCdM0mSJEkqgJ0zSZIkSSqAnTNJkiRJKoCdM0mSJEkqgJ0zSZIkSSqAnTNJkiRJKoCdM0mSJEkqgJ0zSZIkSSqAnTNJkiRJKoCdM0mSJEkqgJ0zSZIkSSqAnTNJkiRJKsAWo75ARDwDOBvYE/g28JrM/OdRX1eSJEmSFslII2cRsTVwEfAhYDVwJvCpiNhyDLFJkiRJ0sIYtazxBcCPM/MDmbkpM88D7gQOGj00SZIkSVoco3bO9gCu71qWVCWOkiRJkqSaRr3mbBtgQ9eyDcCqGr+7OcADP/iPEUPQIujIk80bDMOcVW3mrGZRAXlrzmog5qxmTb+cHbVztgFY2bVsFbC+xu/uAPD9Dx05YghaMDsANza4bXNWgzJnNYuayltzVsMyZzVreubsqJ2zbwGv71oWwIU1fvcqYH9gHfDgiHFo/m1OlcRXNRiDOatBmLOaRU3nrTmrQZmzmjXL5uxmDz300NCvHBFbATcB7wTOAo5o/fyUzPzh0C8sSZIkSQtmpAlBMvNHwEuAVwJ3AccAL7NjJkmSJEmDGWnkTJIkSZI0HqNOpS9JkiRJGgM7Z5IkSZJUADtnkiRJklQAO2eSJEmSVAA7Z5IkSZJUADtnkiRJklQAO2eSJEmSVIAtmg5gmiLiIeC1mXlWRGwG/DZwWWb++xS2/VzgT4BnAD8CvgQcl5m3dKxzAxBdv3pFZj5nide8HLgjM18xRDyPB74OHJWZX+iz7v7A/wT2Av4DeG9mvm/QbWpwDefsIcAJwB7A94GPAm/LzPtaz28NnAq8HHgc8C/AKZn5mWVe82bgY5n5R0PEsxvwNeBZmXlDn3UPBk4GdgNuBk7OzI8Ouk0NrqmcbbWHv7zE02/PzJNbsS3llzPzSz1e93xgj6Xa4T4xbQ38M3BWZp7VZ92nA+8FngP8ADivFfeDg25Xgym5ne1a95eALwLb9Hq+Y73L8dhg7nlM+7Dfnau8XbSRsx2Av2r9/Dzgw8B2k95oRDwN+HsggecCBwFrgL+PiFWtdbYGfobqQHeHjn8HLvPShwBHDxHPzsA/ADvWWHc34AvAPwL7UH0Z3xkRawfdrobSVM6+CPgE8HFgT+Ao4AigswH7IPBbwH9rrfNZ4FMR8YvLvPS+VDk0aDx7AZcA29RY93mt2D8C7A2cD3w4In5t0O1qKI3kLFV7uEPXv48B64C/7Iit898uwLeBS4GvLvG6x7J8O9xTRDwO+Dzw8zXW3Z6qTf4PYD/g9cDrqE4waPJKbmfb6z4fuIh6x20eGywGj2mZz7xdqJGzzLyj4+FmU9z0UcC/A6/LzIcAIuK/Av9Gdab3YuDpwObAV7viXFJm3jVoIBHxeuAUqgOSOn4f+E5mvqn1+IaI2BU4Hjhn0O1rMA3m7E8D78nMd7Ye3xQRHwNeBhARmwObqM7a/X1rnZMj4veBA6gavkfIzO8NGkhEnAq8EbgO2KnGr/wR8DeZeXrr8bsi4ueBtwB/O+j2NZimcra7PYyIl1EdGByQmbf3iI2IeDewGnhuZj6wxOveM2gsEXEY8B7g9pq/spZqXx2ZmT8Crm+dCX53RJyamRsGjUH1ldrOAkTESuAMqhz5OvDMfi/qscFi8Jh2fvN2oTpn7SFg4Abgstbib0XESZl5YkQ8Gjgd+A1gFdXB4AmZ+Q+t3381VS/7AuB/AN8CfrH9Wpn5/CU2fR7wv9pJ3NL++bGt//cCfpCZtw3wfi6nYwi4VTJ2eWa+eplf+3Xg96iGoG+qsZlf5pEHtJcCb4mInTuHsDV+TeVsZrbPxtEql9ivtY2LWs8/SNVAt9dZRXXwsC3VGbWl3s/NdJQ1tt7fSZl54jK74SVUB9k/6NgHS73+o4D9gbd2PXUp8BcRsSIzNy33GhpNg+1sZwwrqUYfzsvMy5dY5+nAG4BXZ+b3l3mt8+koa2y1u/3iOBj4U+D9wP394qVqZ7/S6pi1XUo1UrwvVSmbJqTUdrblicDPAi+gGon4UI33czkeG8w9j2mBOc3bheqcdfgq8JvAJ6kO5K5tNYwXUyXYIcD3qBL6CxHxssy8uPW7P03VUD4L2DYzH2rVjC8pM6/vsfitwH1UQ7FQJfLdEfFRquHpe1rxnbpcbXmXfelzIJCZvwIQEbvUfM0nAbd2LWufDX4yUEQiL4Cp5mxbq3G/k6qtuBZ4W491fhf4M6ozdydl5j8P8L52ANYvt0Jm7tPazvNrvN5jqDqIvXJ2BVXpRe0/FhpJIznbcjTwU8AfL7POnwDfyMyPDPC6tOJeVmYe3v45ovuSi56eBFzetayzndV0FNfOtg4Wn99a72eGfF8eG8w3j2nnLG8XsnOWmfdHRHv49PuZuT4iXkhVO/vkjp7+uyJiH+APqJK87ZTMvLHj9QYaio2IPwBeAxzbMdz7c1QHlhcD76C6yPJPqc6UvbLm+xq4ZKyGbagu9uzU/mJtPYHtqYcGc/YB4NlUtdzvBS6JiP26Jin4G6oyxhcDfxIRd2fme2u+r1rlDgNoX5NmzjasqZxtldz+HnDOUvkVEbtTlY4dOtCbGiCOAdnOFqDwdnZoHhvMN49pBzITebuQnbMl7NP6/4auM51bAv+va926ta0P0zpoeDdVKc3bMvPMjqdfAqzsuL7huoi4H/hoRPxhZv7bMNscg43AVl3L2gm87IiHJm7iOdu61uUa4JpW4/+PwAuBv+tY5/+2frw2Ip5K1fDX6pxNwMbW/+ZsmSaes1TXPO4MfGCZdV4F3AEsObPolNnOlquIdrZA5mzZPKbtbSby1s7ZTzyKqje9d4/nus9ebeyxzrIiYluqGZl+FVibmX/Z+Xxm3s8jh2+va/3/ZKoLLZtwC9Wwd6f2jDiWhzVrYjkb1YyHmzLznzoWf731/09HxHZUI2WXd53d+jrw3yPiUZn540G2OSZ3UTWwvXL2PqrSITVnou1sy28AX8vMb/VZ52PjGpkYA9vZcjXWzg7yWg0wZ8vmMW1vM5G3i9w5677fzXVUvenHZObV7YUR8U6qBH/7sBuKiC2pplR+JnBQdt2DoTWJwXeAj2fmWzqe2o+q3CGH3fYYfInqQuROLwRuGuRCT43F1HKWairv3ahKEdp+ofX/N6muL7sQOI6Hj5L9AvCthjpmtOrlv0yVs+/veOqFwD86GcjUTTNn255HVWrbU2sWxKcBbx7DtsblS8Dvd01Y80Lgh8DVS/+aJqCkdrZkHhuUxWPaemYibxe5c3Zv6/+9I2Id1ewt1wAXRsQxVIl1OFWJ1hHLvVBEPBaWrdN9O9UBw29TlX79VMdz92Tmxoj4FHBsRFxPVdLwHKr63HcvN5NYVxxPAO7PIaZ+7niNbakuCm3XDb8P+HpE/BnwF1R/OH4P+N1ht6GhTTNnzwC+EtVU4x+gOoD4APDpzLyq9RofAN4WEbdQHUi8nKqW/DfrvqHWd2F9Zg5dThARjwG27BjBOx34h4j4Y6p7CP061bVFLxl2GxraNHOWiNiKquN12jIv1T4Q/voy64wUR43XWEl1Pcb3WqN351Kd6LggIto3T38HVfs/7AiihlNUOzsOHhssBI9pe7/GTObtot2EutN1wP+muoHfKa0/kL8KfJnq5rX/AhwGHJGZF/R5rU+1/i3lVa3/L6C6IWrnv/Zzf0R1r4a3AddTzST2p1T3ZqrrKnrcvHJAb27FBUBmJtVB7XOBr1HNfvbmzDxvxO1ocFPL2cy8Avg1qobrWqqb+H4ceEXHam+iGjU7oxXbIcDBmfnpAd7TOkYfwXgfVe4DkNXU6a+k+sPxdeDVwOGZecmI29HgptnOQjUb56NYvnx1h9b/w5a41omjn5dT5f6TATLzTuBFVPH/H6rZT98PnDjidjS40trZcfDYYP55TNvbTObtZg891D0SqlkREV+hGor9naZjkeqIiNuo7j31iCn5pRJFxEeAnTLzeU3HItXhsYFmkXn7E4tc1jizImI1sDsQPPLeOFJxImINsAfVyEBTFwJLtbVKD3+WatazaxoOR+rLYwPNIvP2kRa5rHGWHUiVwDcCZzUbilTLq6nud3Ip8NfNhiLV8kyqi8cforqOUSqdxwaaReZtF8saJUmSJKkAjZU1tmbV2pfqQr1S7jWjcm1OdSH/VZnZfXf3qTBnNSBzVrOo0bw1ZzUEc1azZtmcbfKas32pZpGRBrE/8JWGtm3OahjmrGZRU3lrzmpY5qxmTc+cbbJztg7g7KfuzBNXrGgwDM2C727axNE33QIdU6I2wJxVbeasZlEBeWvOaiDmrGZNv5xtsnP2IMATV6xgxy23bDAMzZgmSwbMWQ3DnNUsaipvzVkNy5zVrOmZs87WKEmSJEkFsHMmSZIkSQWwcyZJkiRJBbBzJkmSJEkFsHMmSZIkSQVocrZGSdIUPPq0rVi941Y9n7t77X1TjkaSJC3FkTNJkiRJKoCdM0mSJEkqgGWNkrTAVp+zdd91LH3UtLVLcc09SYvGkTNJkiRJKoCdM0mSJEkqgGWN0oAst9Gi6Vf66Hdh9rU/4/W3bwaHNBxMB3NP0qJx5EySJEmSCmDnTJIkSZIKYFmjNKQ6s9y1WXqjeeaMj+UbpL2aJeaepHnjyJkkSZIkFcDOmSRJkiQVwLJGaQqWKr2x3EaLorRZ92b9OzmvZYqT0GtfzcrnrKWVOsOoNCpHziRJkiSpAHbOJEmSJKkAtcsaI2INcB1wZGZ+LiJ2Ac4F9gPWAW/MzM9NJEppTpVW6qXhdX+WltoMZtAyvUl9N4YtFxxHPJYqTo+zPJbJ74A02MjZucDjOh5/ArgSeCxwLHBhRDxhjLFJkiRJ0sKoNXIWEa8Bfgjc2nr8NODngOdl5ibg4oj4IvA7wBkTilVaOKWc3XWEzzO6pRn2PoOT+hzNj/lTyr0sS4ljVH5HpHr6jpxFxG7Am4DXdizeA7g5Mzd2LEtgz/GGJ0mSJEmLYdnOWURsAXwEODYz7+p4ahtgQ9fqG4BV4w1PkiRJkhZDv7LGE4BrM/NvupZvAFZ2LVsFrB9XYJLqKaHkpZTJHAZlmc1i8HPWpI27HR42Z6cxoY3fJ2my+pU1vhx4RUTcHRF3AzsBHwMC2CUitupYN4DrJxOmJEmSJM23ZUfOMnOPzscRcTPw+tZU+ocCp0TECcABwPOB100mTEmSJEmab7Xvc9bDIcAHge8CdwCvzMxbxxKVpIkooQRy0DgkaZ6U2P6VGJO0qAbqnGXmLh0/3wL82rgDkiRJkqRFNMhNqCVJkiRJEzJKWeNYPPq0rVi941YPW1bKTG7SIrPMRZKkejye1bg4ciZJkiRJBbBzJkmSJEkFaLyssZd+5VQOE6tJx39zNSvXreTsZ97VdCiSJKlQHs9qGI6cSZIkSVIBihw566fORAWejdCkHX31Y5d93pE1lcLRXs2ads72Yh5rXng8q14cOZMkSZKkAtg5kyRJkqQCzGRZYx29hoodGp4d3Z/f+ts3g0MaCmZI/coewfIcTZeluJoHtq1aJB7PzrZen1+/Y1pHziRJkiSpAHbOJEmSJKkAc1vW2EuTs+LU2fakY2jaIPtgUVhmNp+OvvqxbLxzY9NhDMxyMc0L29b51/6MZ7GtHZXHs82a9PGsI2eSJEmSVAA7Z5IkSZJUgIUqa6xjnMO1ww57jjJcOu0hZEsVJ2up0pxJleTUKWubRhwlGnTfzCvLxeZHr89yUUrESinhnXYbP0tsc0fj8exgSjqedeRMkiRJkgpg50ySJEmSClCrrDEiDgNOAp4M3AK8NTM/HRHbA+cBBwD3ACdl5rmDBHD8N1ezct3Kns+VPqxf0hBoW4kxafwGKfeo8z0atnxk3HE0xfKZ8WmyXKyUUrVSmNejabKEd9jPruT8nvd89Hh2vEqMaVr6jpxFxO7Ah4CjMnNb4Fjg4xHxeOAcYD2wBjgUOD0i9ppgvJIkSZI0l/p2zjLzX4E1mfnViNgW2AG4F7gfOBh4e2bel5lXAhcCaycZsCRJkiTNo1pljZm5PiKeAnwH2Ax4LbArsCkzb+pcFThkXMFZoiKNrpRSkmmU6ZTyXrU8y8WGZ46XY9jZbSf5GU6j1NwcHJzHsxrEIFPp3wpsDewPfBY4Heiec3cDsGo8oUmSJEnS4qjdOcvMB1o/XhoRnwSeBXRf+biK6hq0qfGeO9J88yzt4inxMy8xJs2W0nKotHgWncezaqszIciBEXFJ1+ItgRuBFRGxU+fqwPVjjE+SJEmSFkKdkbNrgGdFxBHABcCLgQOBZwM7AadFxFpgT+Dw1nOSJEmSpAH07Zxl5h0RcRDwHuDPgX8FDs7MG1qdsrOA26jKGY/LzCsmGfCgmrgI06HpwdT5jDbe2X15oyRJ0mLweLZ8dUuF+x3T1p2t8ctU15h1L78LOKxWJJIkSZKkJfW95kySJEmSNHmDTKU/t6Y9bDvr99kZhLNBSZIkTZ7Hs5MzzeNZR84kSZIkqQB2ziRJkiSpAJY11lBKad4gcZQydD2Pdr3kfrZb1fu8Rh601ZSjkfpr56z5KUmLq5RjOY9nl+fImSRJkiQVwM6ZJEmSJBXAssY5NQvDtvMoLvpR33UsLVNT6uRnm3kqSWraIh7POnImSZIkSQWwcyZJkiRJBWi8rNFZxLRo+pWWTfK7YFmb6loqV8wLTUOvWXHNPZXM41mNiyNnkiRJklSAxkfO2pocTZBKUsqkIqXEUaI6++beDfdzxRRimbZS2mpHgcervT9LzttSck9ajnmqUTlyJkmSJEkFsHMmSZIkSQUopqyxnxJLrLxgXk0ZpGxikPKvScYxKya5vxbBJMsNh/1s5jFP61i0XJ6l44S2ec09LW2W8tT8bIYjZ5IkSZJUADtnkiRJklSAWmWNEfFLwBnAHsD3gdMz8+yI2B44DzgAuAc4KTPPnVSwfeMspHzAWcTGKy76UdEziJWolHKmJr8LpewDLa2Uz6jEMqOllLLPZtU0jhMG+YxmKfeatNx+msfjA49n51PdWXH7jpy1OmCfBc4Etgd+CzgtIl4EnAOsB9YAhwKnR8Reo4UuSZIkSYunzsjZzsDnM/OC1uNrIuIy4LnAwcDumXkfcGVEXAisBY6ZSLSSJEmSNKf6ds4y81rgiPbj1kja/sA3gE2ZeVPn6sAh4w5yXMZRPjDukpJShq6nbZZLcy59xolssXrHns+99Io3TDma+THLOaH5Z35OV7udnXabWuLf5BJjGpXfp9F4PFuOSeTyQBOCRMRjgIuAq4HLgI1dq2wAVo0nNEmSJElaHLU7ZxHxFOCrwF1Uo2PrgZVdq61qLZckSZIkDaDubI37AF8APgK8OTN/HBHfBlZExE6Z+W/tVYHrBwmgqdKFpZQ21D5LNwYsbd814fPPPrPvOtPI9VLikKRR9GvLSit7bEIpMz6WuG+myePZ5Xk8W1/fzllErKHqmJ2Rme9qL8/MeyPiM1QzN64F9gQOBw6cVLCSJEmSNK/qjJwdBTwBOCEiTuhY/j6qmRnPAm6jKmc8LjOHut1EaWfHStd0r17DWyrXp53jfucm5/PPPpMH7r4dvvTSpkOR5lopVQKlxLEUjxmmx7+tgzE3H6nObI3vAN6xzCqHjS8cSZIkSVpMA83WKEmSJEmajFoTgpSgyZKBOtueRhyab6WVQpReptOkQdsESc0ppW0tJY5Z1msfzloJucez6seRM0mSJEkqgJ0zSZIkSSrAzJQ11lFKyUApccyy7n04a2ULk9BvlscmSu3mMdctWZQWizPolsP2t1JKLpQSxywbphTXkTNJkiRJKoCdM0mSJEkqwFyVNfZj6UI5LF0Yn5L3ZSkzPpa8jySVqVe70dleTbtdKaU9HZTt7/h5PFuOSeS3I2eSJEmSVAA7Z5IkSZJUgIUqa1xKaUPuli5okZg3kmZF6e3VsPEtdUxR+vvVw5X2eXk8OxxHziRJkiSpAHbOJEmSJKkAljXOqKaHXCVJ0nzwmEJNMfceyZEzSZIkSSqAnTNJkiRJKoCdM0mSJEkqgJ0zSZIkSSqAnTNJkiRJKsBAszVGxH7ApzNzx9bj7YFgBSU+AAAHV0lEQVTzgAOAe4CTMvPcsUcpSZIkSXOu1shZRGwWEUcCfwds2fHUOcB6YA1wKHB6ROw19iglSZIkac7VLWs8HjgWOLW9ICK2BQ4G3p6Z92XmlcCFwNqxRylJkiRJc65u5+w8YG/gqo5luwGbMvOmjmUJ7Dmm2CRJkiRpYdS65iwz1wFEROfibYCNXatuAFaNJTJJkiRJWiADTQjSZQOwsmvZKqpr0CRJWtbnn33mf/780ive0GAkkiSVYZSp9L8NrIiInTqWBXD9aCFJkiRJ0uIZunOWmfcCnwFOi4hVEbEvcDhwwbiCkyRJkqRFMUpZI1QzM54F3EZVznhcZl4xclSSpLG59BknssXqHYsuHbTEUZKkATtnmXk58PiOx3cBh405JkmSJElaOKOOnP3/9u4dRO4qiuP4N8RHHoUvRGw0KeREg0IsLEQLxUbFIKJoilgoQoSIoFgG0cIXWGiTREnSaBDFFL4tRLSQJBIRBOUQDQHFFzFESTaGxKzFjDKO0Z3d/+ze//3v91Pt3pmdPWfnx8KZe+c/kqRKDO5OTWU2d6+mquO/bndHTZLUdU0uCCJJkiRJGhOHM0mSJElqAY81SpL+pY1HC09Vk0cdJUld4s6ZJEmSJLWAw5kkSZIktYDHGiVJI2vb0UI/H236/u9qmScOfQ8f3zyH1UiSBrlzJkmSJEkt4HAmSZIkSS3gsUZJUiOjHC2czgdgj6OO/9L1o49z8XeWJM0ed84kSZIkqQUcziRJkiSpBTzWKEkam7ZfPXE6x/5qr1+SVB93ziRJkiSpBRzOJEmSJKkFPNYoSZoVtR/BG3f9g8cka//bSJJmhztnkiRJktQC7pxJkjQH3C2TJE2l8XAWEauAzcBKYC+wLjN3Nn1cSZIkSZpPGh1rjIhFwJvANuBs4HlgR0ScMYbaJEmSJGneaPqes+uAk5m5MTOPZ+ZW4BfglualSZIkSdL80XQ4WwF8ObSW9I44SpIkSZJG1PQ9Z0uBiaG1CWDJCD+7EODEbz81LEHzwUBOFhYsw8xqZGZWNWpBbs2spsXMqjZTZbbpcDYBLB5aWwIcHuFnLwQ4sO2ehiVonrkQ+Kbg7zazmi4zqxqVyq2Z1UyZWdXmlJltOpx9BawfWgtg+wg/+ylwLfAD8EfDOtR9C+mF+NOCNZhZTYeZVY1K59bMarrMrGrzv5ldMDk5OeNHjogzgX3AU8AmYG3/6+WZeWTGDyxJkiRJ80yjC4Jk5jHgRmANcBB4AFjtYCZJkiRJ09No50ySJEmSNB5NL6UvSZIkSRoDhzNJkiRJagGHM0mSJElqAYczSZIkSWoBhzNJkiRJaoGmH0I9YxGxCtgMrAT2Ausyc2epesYhIq4BngVWAAeAZzJzc0ScA2wFrgd+BR7LzC3lKm0mIi4AvgDuycy3ImIZsAW4it6HMD6UmW8VLHFWmFkzWxsza2Zr1LXcmlkzWxszWzazRXbOImIR8CawDTgbeB7YERFnlKhnHPqBfYNeL+cAdwBPRsQNwIvAYeAC4HbgmYi4olStY7AFOG/g+9eA3cC5wIPA9og4v0Rhs8XMmtnamFkzW6Ou5dbMmtnamNnymS11rPE64GRmbszM45m5FfgFuKVQPeNwMfB2Zr6cmScz8zPgQ+Bq4Fbg0cz8PTN3A9uB+wrWOmMRsQ44Anzb//5S4HLg8f5z+S7wEXB3uSpnhZk1s7Uxs2a2Rl3LrZk1s7Uxs4UzW2o4WwF8ObSW9LaDq5SZn2fm2r++77/ycC2wADiemfsG706FvUbEJcDDwP0DyyuA/Zl5dGCtyv6mYGYr7NXMmtk5LrGxeZ5Z6FhuzayZrY2ZLZ/ZUsPZUmBiaG0CWFKglrGLiLPobXHvofdqw9Ghu1TXa0ScBrwEPJiZBwdu6vRzOaDTfZrZ+vobQaf7NLP19TeizvZqZuvrb0Sd7dXMlumv1HA2ASweWltC7xxr1SJiOfAJcBC4jV5PXeh1A/B5Zr4ztN7Z53JIZ/s0s0Cd/U2ls32aWaDO/kbRyV7NLFBnf6PoZK9mFijUX6nh7CsghtaCf28LVyUirgR2Ae8Dt/a3RvcCp0fERYN3pb5e7wTuiohDEXEIuAh4hV4vyyLizIH71tjfVMxsfb2aWTNbW6/zPbPQwdya2b/V2N8ozGxdWp/ZBZOTk3P9O+k3vg94CtgErO1/vTwzj8x5QWMwcDnOZzPz6aHbXgd+p/emyZXAe8BNmblrzgsdk4jYD6zvX3p0D/ABvVcjrgdeBS7LzG/LVTheZtbM1sbMmtkadS23ZtbMlqxtJsxs+cwW+ZyzzDwWETfSC/ETwNfA6hpDPOBe4HxgQ0RsGFh/jl6INwHf0dsefaTmIJ/CbcALwM/Aj8Carv3zNbNmtjZm1szWqIO5NbNmtjZmtnBmi+ycSZIkSZL+qdR7ziRJkiRJAxzOJEmSJKkFHM4kSZIkqQUcziRJkiSpBRzOJEmSJKkFHM4kSZIkqQUcziRJkiSpBRzOJEmSJKkF/gQcj/agZXKQhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=5, nrows=5, figsize=(15,15), sharex=True, sharey=True)\n",
    "\n",
    "for ax in axes.flatten():\n",
    "    i = np.random.choice(len(lbs))\n",
    "    ax.imshow(lbs[i][0].reshape(nx,nz).T, origin=\"lower\", cmap=gp.plotting.colors.cmap, norm=gp.plotting.colors.norm)\n",
    "    # networkx.draw_networkx(graphs[i], centroids[i], ax=ax)\n",
    "    ax.set_title(\"iter: \"+str(i)+\" j: \"+str(np.round(jaccards[i], 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1d1500ce550>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ie_post, iet_post = gp.posterior_analysis.calculate_ie(lbs[:,0,:].astype(\"int\"))\n",
    "plt.imshow(ie_post.reshape(nx,nz).T, origin=\"lower\", cmap=\"viridis\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}