simulations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evolution on rugged adaptive landscapes\n",
    "## Obolski, Ram & Hadany\n",
    "## Key Issues Review for Reports on Progress in Physics, 2017\n",
    "\n",
    "This notebook contains text and Python code as the supporting material for\n",
    "\n",
    "> Obolski, Ram & Hadany (2017) Evolution on rugged adaptive landscapes, Reports on Progress in Physics.\n",
    "\n",
    "The most updated version of this notebook is on [GitHub](https://github.com/yoavram/UnderTheRug).\n",
    "\n",
    "To run the notebook you will need Python (v.3.5), Jupyter notebook (v.4.3), NumPy (v1.11), Matplotlib (v.2.0), Seaborn (v.0.7), and Cython (v.0.25); see the [README](https://github.com/yoavram/UnderTheRug/blob/master/README.md) for more instructions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 1: Evolutionary simulations on rugged landscapes\n",
    "\n",
    "The notebook includes code to load fitness landscape data, simulate evolution on the landscape using a Wright-Fisher model, and visualize the results of simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import collections\n",
    "import itertools\n",
    "import glob\n",
    "import gzip\n",
    "try:\n",
    "    import ujson as json\n",
    "except ImportError:\n",
    "    print(\"Can't import ujson, using json instead\")\n",
    "    import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('ticks')\n",
    "sns.set_context('paper', font_scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Empirical fitness landscape: *A. niger*\n",
    "\n",
    "The fitness landscape is composed of 8 loci and 186 combinations of mutations in these loci that were viable and had measurable fitness in the fungus [_Aspergillus niger_](http://en.wikipedia.org/wiki/Aspergillus_niger)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read fitness landscape file\n",
    "The [fitness landscape file](https://gitlab.com/yoavram/EvolRugAdaptLand2016/blob/master/Franke2011.csv) was downloaded from [Franke et al. 2011](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002134) and converted to CSV using [Tabula](http://tabula.nerdpower.org/).\n",
    "\n",
    "Start by downloading it if it's not already on the local filesystem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def download(filename, url):\n",
    "    if not os.path.exists(filename):\n",
    "        import urllib.request\n",
    "        urllib.request.urlretrieve(url, filename)\n",
    "    return os.path.exists(filename)\n",
    "\n",
    "landscape_filename = 'Franke2011.csv'\n",
    "landscape_url = 'https://github.com/yoavram/EvolRugAdaptLand2016/raw/master/Franke2011.csv'\n",
    "download(landscape_filename, landscape_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mutation number,fwn,arg,pyr,leu,phe,lys,oli,crn,Relative W\r\n",
      "0,0,0,0,0,0,0,0,0,1\r\n",
      "1,1,0,0,0,0,0,0,0,0.751\r\n",
      "1,0,1,0,0,0,0,0,0,0.773\r\n",
      "1,0,0,1,0,0,0,0,0,0.698\r\n",
      "1,0,0,0,1,0,0,0,0,0.757\r\n",
      "1,0,0,0,0,1,0,0,0,0.826\r\n",
      "1,0,0,0,0,0,1,0,0,m\r\n",
      "1,0,0,0,0,0,0,1,0,0.598\r\n",
      "1,0,0,0,0,0,0,0,1,0.628\r\n"
     ]
    }
   ],
   "source": [
    "!head $landscape_filename"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the file using Pandas, and convert `m` values in the `Relative W` column (fitness) to 0, as they mean \"missing\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mutation number</th>\n",
       "      <th>fwn</th>\n",
       "      <th>arg</th>\n",
       "      <th>pyr</th>\n",
       "      <th>leu</th>\n",
       "      <th>phe</th>\n",
       "      <th>lys</th>\n",
       "      <th>oli</th>\n",
       "      <th>crn</th>\n",
       "      <th>Relative W</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mutation number  fwn  arg  pyr  leu  phe  lys  oli  crn  Relative W\n",
       "0                0    0    0    0    0    0    0    0    0       1.000\n",
       "1                1    1    0    0    0    0    0    0    0       0.751\n",
       "2                1    0    1    0    0    0    0    0    0       0.773\n",
       "3                1    0    0    1    0    0    0    0    0       0.698\n",
       "4                1    0    0    0    1    0    0    0    0       0.757"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(landscape_filename)\n",
    "missing_W = data['Relative W'] == 'm'\n",
    "data.loc[missing_W, 'Relative W'] = 0\n",
    "data['Relative W'] = data['Relative W'].astype(float)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column called `genotype` with the bit string of each genotype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mutation number</th>\n",
       "      <th>fwn</th>\n",
       "      <th>arg</th>\n",
       "      <th>pyr</th>\n",
       "      <th>leu</th>\n",
       "      <th>phe</th>\n",
       "      <th>lys</th>\n",
       "      <th>oli</th>\n",
       "      <th>crn</th>\n",
       "      <th>Relative W</th>\n",
       "      <th>genotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.751</td>\n",
       "      <td>10000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773</td>\n",
       "      <td>01000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.698</td>\n",
       "      <td>00100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757</td>\n",
       "      <td>00010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mutation number  fwn  arg  pyr  leu  phe  lys  oli  crn  Relative W  \\\n",
       "0                0    0    0    0    0    0    0    0    0       1.000   \n",
       "1                1    1    0    0    0    0    0    0    0       0.751   \n",
       "2                1    0    1    0    0    0    0    0    0       0.773   \n",
       "3                1    0    0    1    0    0    0    0    0       0.698   \n",
       "4                1    0    0    0    1    0    0    0    0       0.757   \n",
       "\n",
       "   genotype  \n",
       "0  00000000  \n",
       "1  10000000  \n",
       "2  01000000  \n",
       "3  00100000  \n",
       "4  00010000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loci_columns = 'fwn \targ \tpyr \tleu \tphe \tlys \toli \tcrn'.split() # as copied from the table above\n",
    "data['genotype'] = data[loci_columns].apply(\n",
    "    lambda row: str.join('', map(str, row)), axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fitness = collections.defaultdict(float)\n",
    "for i, row in data.iterrows():\n",
    "    fitness[row['genotype']] = row['Relative W']\n",
    "\n",
    "assert fitness['00000000'] == 1\n",
    "\n",
    "genotypes = tuple(fitness.keys())\n",
    "assert '00000000' in genotypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the `hamming` function for distance between genotypes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hamming(g1, g2='00000000'):\n",
    "    assert len(g1) == len(g2)\n",
    "    return sum(g1i != g2i for g1i, g2i in zip(g1, g2))\n",
    "\n",
    "assert hamming('00000000', '10000000') == 1\n",
    "assert hamming('00000000', '10001010') == 3\n",
    "\n",
    "def plot_matrix(M):\n",
    "    plt.imshow(M, norm=mpl.colors.LogNorm(), cmap=mpl.cm.viridis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "Standard Wright-Fisher simulation (Otto & Day, 2007, ch. 13.4) of asexual, haploid, constant size populations with non-overlapping generations. Selection, determined by the fitness landscape; mutaion, bi-directional and uniform rate; and random genetic drift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = len(next(iter(fitness)))\n",
    "mu = 1e-4 # mutation rate as in Szendro et al. 2013 \n",
    "m = len(genotypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the mutation matrix $u$ used to compute the linear transformation:\n",
    "$$\n",
    "p'_k = (1-\\sum_{j \\ne k}{M_{j,k}})p_k + \\sum_{j \\ne k}{M_{k,j} p_j}\n",
    "$$\n",
    "where $u_{i,j}$ is the mutation rate from genotype $j$ to $i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FOH/+PK5duxa3T0tLCwoLC0WrwIWuobQKW5MNAa7J0J3elud8uvyoTGLTFFNG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\ncSLlcDis5v8Dv7ZbuL1lINUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff64198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mutation_matrix(genotypes, mu):\n",
    "    m = len(genotypes)\n",
    "    M = np.diag([1.0] * m)\n",
    "    for i in range(m):\n",
    "        for j in range(m):    \n",
    "            if hamming(genotypes[i], genotypes[j]) == 1:\n",
    "                M[j, i] = mu\n",
    "                M[i, i] -= mu\n",
    "\n",
    "    assert np.allclose(M.sum(axis=1), 1)\n",
    "    return M\n",
    "\n",
    "M = mutation_matrix(genotypes, mu)\n",
    "plot_matrix(M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The selection matrix is used to compute the linear part of the natural selection transformation:\n",
    "$$\n",
    "p'_k = \\frac{\\omega_k p_k}{\\bar{\\omega}}\n",
    "$$\n",
    "as well as to find the mean population fitness by calculating the inner product with $p$:\n",
    "$$\n",
    "\\bar{\\omega} = \\sum_{k}{\\omega_k p_k}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZIiIizPOMRiMyMzNx5MgRNDc3Izo6GqtWrcLAgQOtfFoPNDQ04OLFi0hLS8P2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\nIiKHxpIiIofGkiIih8aSIiKH9v8Bum1+icH7KZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11024d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def selection_matrix(genotypes, fitness):\n",
    "    return np.diag([fitness[g] for g in genotypes])\n",
    "\n",
    "S = selection_matrix(genotypes, fitness)\n",
    "plot_matrix(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then calculate a single matrix for mutation and the linear part of selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WpaITs0LK63l2drroqrETAe2EHcWJUsR6WIlxU1fuLB212JWKE3q4VTPK7osw\nZoWUiK5WM0Ty7rASYIZzcKC+NXCuFLEWdqL5nbhGrL3HdufMqqWVWWRLK0kIpVn3CRNawVtbtRPN\nz+IaOV1aRZTf1Wk8K6RmX/lbW91iohURElgVeGir/6o3Dk9QY7VUix5qm5Motb5Ypi2JaAbxrJDa\n8rfl1BHnajVZBEMm76RjrWWBiDefFfLSG6jiqBTCq5JGgsZ2ImK3GJZpSyKaQTwrpMxQ9c0zwdbU\nIhgyeScday0LeN18WsZzu7YToxfJwGRzgoo6GdykFip6JU2RgnXtEHVCSq8WtdOttWMFLeO5XdsJ\nzYtkYHIDvmtKCb58Io4X9lKgOSYSihB2c7kX/iLQelmIXEHBDFEnpLRqUSs/qNlSLRKxmfmTm/GX\nD/5s+ji7LywRDNjhc2DpaeVhjojZ5qC0hnPlB6VdZolQwZElrI3HLGBRQ7zq4NNITK4zbUynQdTu\nQVrQGs5p46R4mCNitjmoE23WowEzxmOnoC2BQ2PfyktvsDudXlDPT4DKq3qG83CblFtxUnbxtJBy\nE/XNadZzJsKN7RR2vYpmllZNjSONd2KMonWI6D2VNikPwPPGUavEZj1nTpYU5t3uyMi756RLOyW1\n2RVBBbjnuhdROLLGtJCqrq6Gz+cL2fb5558jOzu719+TT543UHZ2dmLlypW48sor4fP5sHDhQrS0\ntNj/BhEQMebDaZSlAK+bmYd3zw4pqc2On9OpNCSt88TCPW5KSH366adYsqR3DMv+/fsxYMAAVFZW\nhvz913/9V3CfFStW4J133sHixYuxatUq7Nu3D/Pnz0d3d7f9b8EYdSMGswZUUStMinIzlwy61ZHz\nvHXQZ7wTI5wSyiJ4FbWgcWDZ8e5RFb07c+YMqaioIOPHjyf5+fkkLy8v5PPHHnuMzJgxQ/f4uro6\nkpOTQ957773gtpqaGpKdnU22b99OXfyKEH7NQb2Mf9RibmPzaJpK08zTSkG1cL6ut184T6Hk0geZ\njRWt+LPuM9yHW3PQjz/+GBUVFSgvL8fMmb01hf379yM7O1v3+D179gAACgsLg9syMzMxZswY7Nq1\ny6RYjU7CVXn/sAXUx/K0cbFoP88SMza20WnNIQ1I9aBZCmvF30XCqdbnIsFL06MSUrm5uaiursas\nWbMQFxfX6/OvvvoKzc3NuP766zFhwgQUFRVh27Ztwc9ramowZMgQDBgwIOS41NRU1NbW2vsGHkTL\nuxf+AwdGpMIlAAAcXElEQVSOvMh1Dv5xDzqSNqElAGhyzfSWp0EbG2UO5pi0ZsPCecq5WBQAVBKQ\nzc5Tog+VkBo+fDgGDhyo+VlLSwuOHz+Ouro6LFiwABUVFcjPz8fSpUvx9ttvAwDa29uRkJDQ69iE\nhAS0tbVZmjiPKghOBT063TA0/HuVXrIEgX+vcsRFHckWZud6m8nBHJzShE/r0o3HZFBBIjwB2U6u\nqMx8OMcFdgcYOHAgNm3ahOzsbAwbNgwAMHXqVBw5cgTPP/88brjhBhBCNDUwve0KigFeTWdnJ4Bz\nVRBY1zinDXosTb/HsYJvPBAhydr/47mOLiUnZdRjX0MyctK0C+i5idPXQnMOF88TNtjTtpDq378/\npk2b1mv7tGnTsGvXLrS3tyMxMRHt7e299uno6EBSUpLu2GVlZSgrKwvZphRydxMzAqp09P2ut1r3\ncsQ5S1gIKH/OUublWvSuRXh6VumoxdzsPqIKKIBBMGdNTQ1ee+21oIajcObMGfTr1w8DBgxAZmYm\nvv32W5w+fTpkn8bGRmRlZdmdgiWcCoLjJaCcis0xg9OR9FbPd6o5k+1EoggRg0NtC6mWlhY8+uij\n+Oijj4LbCCHYsWMHLr/8csTFxaGgoADd3d14//33g/vU1tbiwIEDKCgosDuFEPRKtYSjZysRvUaQ\ngtYb1e10G6c1Rqvn6z+yFkcsNnewokVZMZ5r3YdV3zzD/TcWJZ5OjW0hlZ+fj8mTJ2PFihX4y1/+\ngo8++gh33nkn9u/fj3vuOZcykZ6eDr/fj0ceeQSvv/46AoEA5s+fj+zsbFxzzTW2v0QkzHqwnKoR\nJOIbK5YYlnIIbYcyLB1rVou1YgPUuw+rDj5tux6WHuHtuITBbMDW2rVrewVzHj9+nDzyyCNk2rRp\nJDc3l5SVlZF//OMfIfu0t7eTZcuWkfz8fDJ58mRy9913k8OHD5s9vQzmFAx/2iK3pyAxScmw2107\nt5Xn17Nt1nkKKTd/xGjGyTbtCjRt1lm0dI8maDMYlKyA4sRfUY/NLeJcRGZf+Vvdz+wG0Hmp6B1N\ng05Rit4Fvqtw9HylyXdReTYzVUnJXlyGs45up7X1KfYr3nXdPCuktvxtue5nIsQB0UIrUPUM+jTh\nECKGINiB1s6ofG9aY/ORpmRThmPWThar47HsFiMinhVS0QKtQBWlxxtrrBhrzUbK02oGw1IOod5E\nPSrWv4mV8byo+ZklZoQUL4+ISLgdgqAgYgwXLempzZa9fnqEa8t278VYuJdDoLZeCQIPw7k/6z4u\nJUmiAdrrUnLRXPoxKUq1mIH1eIQQ8vfaDFvH85hTNBBThnMWBPunffOMa7lTomfJ014XM2kVZmwo\nNFoZD5vMFRm1htUTIsFyTl4JMOZFzAgpLRVZhEqHXjLya0HjXbSDmd+I9cM8OKWJ6XhWiWSrEskm\nxWuZHzNCKto7GDtVljcckapBRKtzIRIipbG4WvROdHi/zTXPKYiRWmH7yc1uT8EQ/+D5jp6PZXxY\nV/PoyOeyodF4qRGpGqe0uKgQUm68zZ1Kpo0me4TTwZws48MuHHkw2C7LTtcWreqfVuyhZpdWPGyf\nRt+Z1UspKoSUV1HbyfRuOq8uYViU4tWCRXS1VRd+SmozzjSPsrWsYVH909J5XbB9snopxYSQEjVu\nR20nM3vjR/pOjvWBi7DM5vUwqr1mVpfcynU3K6xKM+5F35Hf2PL6scKu/cc/8k5GM3EAjiERXGAZ\nJ0XTgkciHiVjlpz//7HlLs7EeXi2L3MCGSdlEp4hCP6L53EbW8GMnUEkV7VdAl/9PvjdWXptzS5R\n3Wjp7nYpaivYXfrHtJAKh6U3KDy4kYvh0oSdQW3kVM+Fl+2INzxsLGaXqCmpxu2yvA6L+8Pu0j9m\nhBSN/YFntQCRgjbVc3HLkBstDE5pMhRUZl5QRvep06EvItwfMSOkoj2Y0w7REubA83tEqp0/OKUJ\ntRGWfmZeUEb3qajLPSXcgEfD2agQUuFeptJLljjSnTda8GqYQzhmv4cZ717gy5URhaC6cF4sooQb\n8Gg4GxVCKjyYs+rrpxzpzssCERKMSzPuFTZMg8W89JZIZrVrGiEoUrssN17UPM4ZRwghzEfliNIc\ntLq6mnkHY4lEFEoz7uWu4ZamLXI898/K8+tpTaokaTbT8bS8e17Nq5Kcw6rH1u7v/nm9vReoE0tw\nVgKK9XMYjqeFlFEB+NK0RabUTy3vnlt1pryM1hLNVExX2L523OBWPbZ2f/fc9EbLDUiNcLsyZ/jy\nmXcjhpiMOPePvl//s/R7LI8rYQ+LrAA3o7SPNaUwHU9GnMcIkVzC0eLpcgMeMTwssgK03PaRDPJm\njb+RxroouRHHD7GxnZaOWixcCIITZZJiUkh5Af+wBW5PwTRWHyDN5SHnNJ5Iws90NxoDQXpRciMO\nNNCl0ERayrFK4/IPv4PJOECoZ11W5owCzDx4gSMvcpyJPpGy43kVrdN6+FgYda0Yza0KR6PjxqTR\nxVGFh0XwcOnzan4rK3MKgp0lDZMHj3N0eKD5Be3zpi3qVR/IjqrvhNfUitE80m8U6drT/rZmjele\niffjiRRSJglf0hhpF6ztNIrNzGw9KbvLJ62H0E5FVJG9pnraCwt75bCUQ5b7+okacMubmBFSdt22\nem5wo+qDvAydatU6XBDyWj45hdtR+Ly1l8TkOnx/KE3zMxqbFK8QBCvdpNVwE6IcvY1c4NEcVMIW\nrbABvbCPkuwHeE9HWFqb0rmOXzJmScRwGzeQIQgWoXkD8K67pDcHGhuUaJ1rNDU5VdiHuk55YN8T\njsxJRBKT61CvUT2B1e8Z+Or3QpUIsooUUjj3UBkZcnnX1dHzjNDYQewuKSMlGLMwcIcb2Hl0HKbB\njus9UqkWO6SnNmNPXWbINtFiodwmqoWUKZe/wIZc3lTVPWvZfawIIJblknnZpOy43gNfrmQ4k1Cu\nyKh1tBQxyzgpNdIm9QNW1rTR1HCBZdqOuqGBFfxpi0wfUzxwjq1z8sSfea+r529sGOHq+Xnhz7yX\n+JPvIoRYe36phNTZs2fJ5s2bid/vJxMnTiSlpaXkT3/6E+np6SGEENLT00PWr19P/uM//oNcdtll\nZPbs2eTrr78OGePMmTPk8ccfJ1OnTiV5eXnk7rvvJocPH6aeqIIdw7mbeU+iGTB54PW8MqvQvARL\nRtwR+m+dFwRvY7rbcBNSa9euJRMmTCDr168nu3fvJmvXriXjxo0jFRUVhBBC1q1bR3Jzc8nWrVvJ\nzp07yU033USuuuoq8v333wfHWLp0KZkyZQp58803SVVVFSkqKiLXXXcdOXv2LPcvqcZpYcHzwTXz\nXfypC6n2C3+YJHzR0ka/bUwO/r9dbVc0uAip7u5u4vP5yLPPPhuy/dFHHyVXXHEFaW1tJXl5eWTj\nxo3Bz06cOEF8Ph/ZvHkzIYSQuro6kpOTQ957773gPjU1NSQ7O5ts376derKE0H/JaPtxJWwR3QQQ\nrUs/LiEIra2tuOGGG1BcXByyPSsrC8eOHcOePXvQ0dGB6dOnBz8bNGgQpkyZgl27dgEA9uzZAwAo\nLCwM7pOZmYkxY8YE92ENy4A8L3R7LRl0q2Pn8mLks//ieSEGY549F1mQktqMfQ186lEpeKUXo6GQ\nGjRoEJYvX45LL700ZPsHH3yAESNGoKWlBQCQlhYaQZuamora2loAQE1NDYYMGYIBAwbo7sMbO1G6\nevlsolA6+n5sP7nZsfOxfMDtRjnTEji2iVtiLWuUOKmcNOspNIBxcjKPLARhusW88cYb2L17N+bN\nm4e2tjbEx8cjPj4+ZJ+EhAS0tbUBANrb25GQkNBrHPU+vGHV0srtwEmt4E47cTVuNwcVOZjTrd9a\n/XsmJtfhsMUKn1ZXE2a/t7p8sN45S1PutjQXALjA7AHvvvsuVqxYgZKSEsycORMbN25EXFyc5r7K\ndkKI5j562xUqKytRWVkZsq2zs9PslC3jH35Hr7ev24F2rIvy8Q5SdaPYPytY/NallyyxHfU9IuWQ\n7XmYwez3pikfXNW0zuJsTGpSW7ZsQXl5OQoLC/H0008jLi4OSUlJ6OzsRFdXV8i+7e3tSEpKAgAk\nJiaivb2913gdHR3BfbQoKyvDW2+9FfL34ovO1VnyyvJAjduaUTgiCigna4RXff1Ur+BUq+fn1dK9\nOP5mLuOyglpIrV69GqtWrcL111+PtWvXBpd3GRkZIISgsbExZP/GxkZkZWUBOGck//bbb3H69Gnd\nfZzA7aWaAs95iNAWW3QC+59kavyPWDZHQ5OyanqgaeluhR2drzEfkyVUQmrr1q3YuHEjZs2ahSee\neAIXXHB+lejz+dC3b1/s3LkzuO3kyZP45JNPUFBQAAAoKChAd3c33n///eA+tbW1OHDgQHAfluil\nVbi9VFNgsoz44cFwosY0LaK8BGio+uYZXY3GrPFXz5FQOmqx7lLPqoF5cEqTLWO6JzGKUWhpaSET\nJkwgP//5z8nevXt7/XV1dZEnn3ySjB8/nmzatIns3LmT/OIXvyDTpk0LCeZcuHAhmTRpEqmsrHQk\nmJNXnFSsRlWLiNO/hT/9HmFKy6gDPlnD87pyCeZ88803ydixY3X/vvvuO9LV1UWeeuqpYMrLnDlz\neqXFtLe3k2XLlpH8/HwyefJkV9JiJJJo4p+1adT78s60KLn4NqptVp5f2WbdJv7B8xE3oL9rBuLS\n9HtslfGNNuxej9K0RSD9+3qmtnht40hkptI1eRCBmGuzLgKB7ypc9WB5UUCpi96xOFa9ze71qGpY\n4xkBBQCZqc34V712KWK3YdVdSAopDezU2ynuP5PhTMTAjlDRwk7RO/WxiqHerSJ6opCX3mC6HpVe\nqhfLVBmj+v+0eFZIzb7yt9zGthMftePUKwxnYoyXvXtW+uKpEcVbSwPv/M8Uk0s+0VO91HhWSG35\n23K3pyAETiz3FE3FSH03KzSs9MUTFSMNxCtCwYzpwqkEZc8KqWjAychnFrBS39U4lWDMm4hNRS2U\nQ+bdBJYFTtlipZCygNUlVvibh1XSs+F5bS6reMI6wdju8peHdmAloJN1jqaXkULKAqJ41Gjf0NG0\nrFLQzSpg4N2zA2vN0E7pE70GpGYQIRdUCinOqI3JIibb0iJagTRR+8mx1gzthEMMTG6wLahEyAWV\nQioMLVuAHc8MTw8UzYPKKnzAywJWVJyIxxqY3MD9HLyJOSFltETSsgXY8cy4kXSrzspnFUMkmibl\nBqIZs80ESx4/5H52hlWiWkhpPVhOLxPciOXhUb/bSJOy2xw0UhCskV2EdbCpHqIZs814Wy9KbsSn\ndekcZ8OPqBZSbi5RWHb0DY7JqfMsCwLHNmluD39R6DWMiBQEa2QXYaYtCmAk5smkjHq3p2CJqBZS\nZgl31fuH3wH/j+daGkvvobWDFyuFhr8o1A0jRAuN4GkkNrJrOpk5wLsLDWukkFIR7qoPtKxH4MRL\nXM4lbTzeDY2wUtXTyK5pN3TCjJDLSTtkSVD5hy0wt3+sJxgb5e7xaK3DEi95y5yy+XgFp3r2mVne\nmxVyOWmHUG8yKTlwJLS/gGEqUKwnGBvl7nmp3IboiF5lIFqFKIvlfSRvdrrNOlQyLcYjsFJpJdYR\nXYiywsoyk8abHfjmfONfEc0QUS2kStMWMVn2RTJ6WlVpnboZRNAyRCknI7J3lAZey0z/qP8XDE8Q\n0QwRFUJK7yFQqiwGO6tYDKzUMnraCdIsHX2/YzdDJC3DKbudKLmO4csnN6tQiGYznZRRjzPNo9ye\nhjY2aq+7Qqw0YigZ+mu3p+AoRf1uodqvZMh82+cqGVtuewzR8Kctsnxs0YW/DP5/TcMIFtPRxcrz\n63lNyj/hYcvHsmwQaZfwuSieFNFSMXhBW9E0XDO0og2Fl8gR0Q6jRuseCL9f7Gir6uagmanN4qXQ\ncBSaXIgVTUoSGd4tmszgz7pP/zOB5skDsxpcTGpSbiCKITiWEalUSySDtp15esHQ74S9UQopmF/2\niWIIVmBRaUHvGlhNC+o1vokSulbK7boFzyoXduOkWBjnjzQJkEJjVr1zG7eXe7HaZt2futDtKThO\nrP7WPJHLPQeoOvi0a8s9L6j/tLDS0HgiesusWAkkjgkhZXY5ZyQMquqf01TzeXuJ3KyCwCKuyz94\nfrBUC6/E7VjCTm6clWqzX9anWD6fHWJCSJmN1KURBlpvWRGjdbVwy+YT+K4ipFRLrONmQKeVarPj\n05t6bXNiVRETQkoLReuRnjp2ON3BWHSMNHivJsF/1+SsRhWVQopGU1C0Ht6eOl7eHzuBqLzc99He\nwbg0415TLatoNHgWL0mn8zMHp5zXqGQIAgVaEedCxdBwMr46VdPICbQErpNR4DTn8o9/CFV1zzJv\nWcXiIbdaBcLuNa41WY/KKp4XUoEvHmc6ntfTUESo02067kxD4Dpp36M5V+DLlZbHFzXuy+o1VlYH\nmanNaHJAUHleSLFCcYmL1hGEFsVrJkIzRxG0vKI+M6j35aG1qZdgdjV70V6c6tVBSmozdtWO5nq+\nmBNSem81L7jEI4VGsPKa2WmEaji2g3E9f+15g3pfFlpbuKBjWYhP78UpSpzUtMyD+Fe9/ZbuelAJ\nqe7ubrz88ssoLS1FXl4err32WrzyyisghAAAPv/8c2RnZ/f6e/LJ89nmnZ2dWLlyJa688kr4fD4s\nXLgQLS0tfL5VBFjZq0SJk2LtnbTTCNVwbEY1r2lx0nto2JcwTPiHhx9Y+R2dvp6RyEtv4Of1owlL\nX7t2LZkwYQJZv3492b17N1m7di0ZN24cqaioIIQQ8sYbb5C8vDyyd+/ekL+mpqbgGEuXLiVTpkwh\nb775JqmqqiJFRUXkuuuuI2fPnrUdVm8mE1uErHSZbhF9KGlDJdkPMB23ZMwSpuPxprUpPeLnVtJi\nDIVUd3c38fl85Nlnnw3Z/uijj5IrrriCEELIY489RmbMmKE7Rl1dHcnJySHvvfdecFtNTQ3Jzs4m\n27dvp54sIe7n7kkkIsKq6B1vuOTutba24oYbbkBxcXHI9qysLBw7dgwdHR3Yv38/srOzdcfYs2cP\nAKCwsDC4LTMzE2PGjMGuXbss6oBioGcX4NHBWGJMrAbn0oQy6N2T6qJ3ImIopAYNGoTly5fj0ksv\nDdn+wQcfYMSIERgwYAC++uorNDc34/rrr8eECRNQVFSEbdu2BfetqanBkCFDMGDAgJAxUlNTUVtb\ny+abuISeXYBHB2OvoxbovBKMeQYXiuZlU64nbTCn+p7k6SBRUJo72MWSd++NN97A7t27MW/ePLS0\ntOD48eOoq6vDggULUFFRgfz8fCxduhRvv/02AKC9vR0JCQm9xklISEBbW5u9byAJQYQ4KUB7HmqB\n7gVvajiihaco1zOSJ1HXm83RQaIwKaOeyTimhdS7776LFStWoKSkBDNnzsTAgQOxadMmvPrqqygt\nLcXUqVOxcuVKTJs2Dc8/fy7tgRCCuLi4XmPpbVeorKzEjTfeGPK3YIG5Vs800AQfivLwG+FWnFT4\nUqKqZrVlb2f4gyXqtWetWfHQ1ETIvjhst3CeGaPXyy+/TLKzs8ntt99Ozpw5E3HfLVu2kLFjx5K2\ntjbyxBNPkGnTpvXaZ8GCBWTWrFlmpsDEcC6Chy8WcKJQnl7nl2vifsH93Kzxp99jaruX+Lr+XBca\nrkXvVq9ejVWrVuH666/H2rVrER8fD+Ccvem1115DZ2dnyP5nzpxBv379MGDAAGRmZuLbb7/F6dOn\nQ/ZpbGxEVlaWPSlrATNvF6u2E2k4dya1Jbzzi4KZYE5R0FtOirbMtMLoNOst3S+g2Wnr1q3YuHEj\nZs2ahYceeihkidbS0oJHH30UQ4YMQVFREYBzy7gdO3bg8ssvR1xcHAoKCtDd3Y33338f1157LQCg\ntrYWBw4cwF13mQu46+7uBgAcPnzY1HFazP7J49jyceSWWJu++A0aGxtNj73p/x61dJzEeWZP/Q22\n7F7h9jQ8za98D2HrXuP8RuW5VZ5jGgyF1JEjR/D0009j7Nix+NnPfobPPvss5PNJkyZh8uTJWLFi\nBU6ePImhQ4eisrIS+/fvx3//938DANLT0+H3+/HII4+gra0NAwcOxOrVq5GdnY1rrrmGerIAcPTo\nUQDALbfcYuo4TS4Epk9/3/44Em/TH5g+fbrbs/A2F5u7hkePHkVGRgbVvnGE/JDbosNbb72FBx98\nUPfzv//97+jTpw9Wr16NDz/8ECdOnMCll16K+++/H5dffnlwv46ODqxatQrbt29HT08Ppk6diocf\nfhjDhw+n/FrnOH36NL744gusWLECGzeyy4+S0LFgwQK8+OKLbk8jpoima97d3Y2jR49iwoQJ6Nev\nH9UxhkJKVG688Ua89dZbbk8j5pDX3Xli/ZrHXBUEiUTiLaSQkkgkQiOFlEQiERrPCqmysjK3pxCT\nyOvuPLF+zT1rOJdIJLGBZzUpiUQSG0ghJZFIhEYKKYlEIjRSSEkkEqHxnJB6/fXXUVxcjMsuuwxl\nZWXYu3ev21OKGqqrq+Hz+UK2EUKwYcMGFBYWYuLEiZgzZw4OHjwYso8onYC8hFEHJnndVbCtGsOX\nbdu2kZycHLJu3Try4Ycfkrlz5xKfz0fq6+vdnprn+ec//0l8Ph/Jy8sL2b5u3TqSm5tLtm7dSnbu\n3EluuukmctVVV5Hvv/8+uA+rTkCxhFEHJnndz+MZIdXT00N++tOfkuXLlwe3dXZ2kquvvpr87ne/\nc3Fm3ubMmTOkoqKCjB8/nuTn54cIqdbWVpKXl0c2btwY3HbixAni8/nI5s2bCSFsOwHFCkYdmOR1\nD8Uzy726ujo0NTXh6quvDm678MILUVhY6PmOM27y8ccfo6KiAuXl5Zg5c2bIZ5999hk6OjpCSnAM\nGjQIU6ZMCV7zaO4ExAujDkx79uyR112FZ4SU0lUmvAZNWloa6uvrTRXRkpwnNzcX1dXVmDVrVq96\n88o1T0sLbaGt7vITzZ2AeGHUgUmxK8nrfg7PCCmlq0x415mEhAT09PTg1KlTbkzL8wwfPhwDBw7U\n/KytrQ3x8fHBUtEK6i4/shMQG9QdmOR1D8UzQor84PUIf9vrbZfYh0To5qNs19sn0rGSUMI7MMnr\nHopnhFRSUhKAc28QNR0dHejTp08vtVdin6SkJHR2dqKrqytke3t7e/D3SExM7PWbAOd+F2UfiT5b\ntmxBeXk5CgsL8fTTTyMuLk5e9zA8I6QUW1RDQ0PI9oaGBmRlZUXd20MEMjIyQAjp1VBC3eVHtE5A\nXkKvA5O87qF4RkhlZmZi5MiR2LlzZ3BbV1cXPvzwQxQUFLg4s+jF5/Ohb9++Idf85MmT+OSTT4LX\nXN0JSEHpBCR/F33UHZieeOIJXHDB+Z4o8rqHQtXSSgTi4uJw22234Xe/+x0GDRqESZMm4ZVXXsHx\n48cxe/Zst6cXlSQkJGDmzJlYs2YN+vTpg8zMTLz44otITEzEjBkzALDtBBQrGHVgmjBhgrzuKjxX\nT2rz5s344x//iOPHj2PcuHF44IEHeqVySKyxbt06bN68OSTV6OzZs3juueewbds2dHR0wOfz4eGH\nH8bo0aOD+7DqBBQr0HRgGjhwoLzuP+A5ISWRSGILz9ikJBJJbCKFlEQiERoppCQSidBIISWRSIRG\nCimJRCI0UkhJJBKhkUJKIpEIjRRSEolEaKSQkkgkQvP/AZmQSE/xu6QBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10478c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = M.dot(S)\n",
    "assert E.shape == (m ,m)\n",
    "plot_matrix(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random genetic drift is simulated by drawing a random vector from a multinomial distribution with parameters $N$ (population size) and $p$ (genotype frequencies after mutation and selection).\n",
    "\n",
    "We simulate adaptation of the population, starting from a population of the maladapted genotype `11011111` and evolving until 99% of population is fully adapted (fitness = 1). At each generation we apply the selection, mutation and drift operators, and keep tab of the adjusted frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting genotype: 11011111\n",
      "starting fitness: 0.456\n",
      "# generations: 838\n"
     ]
    }
   ],
   "source": [
    "N = 1e9\n",
    "generations = 2**15\n",
    "\n",
    "def adaptation(genotypes, start='11011111', finish='00000000', verbose=True):    \n",
    "    g0i = genotypes.index(start) # index of starting point. 11011111 is also used in Szendro2013\n",
    "    if verbose:\n",
    "        print(\"starting genotype:\", genotypes[g0i])\n",
    "        print(\"starting fitness:\", fitness[genotypes[g0i]])\n",
    "    g1i = genotypes.index(finish) # index of most adapted genotype\n",
    "    \n",
    "    # p is the frequencies vector\n",
    "    p = np.empty((m, generations), dtype=float)\n",
    "    p[:, 0] = 0\n",
    "    p[g0i, 0] = 1.0\n",
    "    \n",
    "    # pom holds the index of the most frequent genotype at each time point\n",
    "    pom = [p[:, 0].argmax()]\n",
    "    \n",
    "    # W holds the population mean fitness at each time point\n",
    "    W = np.empty(generations, dtype=float)\n",
    "    W[0] = S.diagonal() @ p[:, 0]  # mean fitness calculation\n",
    "\n",
    "    for k in range(1, generations):\n",
    "        if verbose and k % (generations / 10) == 0: \n",
    "            print(k, end=\" \")\n",
    "        p[:, k] = E @ p[:, k - 1]  # selection and mutation are deterministic\n",
    "        p[:, k] /= p[:, k].sum()  # normalize frequencies vector\n",
    "        p[:, k] = np.random.multinomial(N, p[:, k]) / N  # random genetic drift is stochastic\n",
    "\n",
    "        W[k] = (S.diagonal().dot(p[:, k]))  # mean fitness calculation\n",
    "        \n",
    "        # update pom if most frequent changed\n",
    "        if p[:, k].argmax() != pom[-1]:     \n",
    "            pom.append(p[:, k].argmax())\n",
    "        \n",
    "        # break if reached fixation\n",
    "        if p[g1i, k] > 0.99:\n",
    "            break\n",
    "    return k, p, W, pom\n",
    "\n",
    "k, p, W, pom = adaptation(genotypes, verbose=True)\n",
    "print(\"# generations:\", k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Path of maxium (POM)\n",
    "\n",
    "We follow the path of maxium (see [Szendro et al. 2013](http://www.ncbi.nlm.nih.gov/pubmed/23267075)) during the population evolution - this is the sequence of the most frequent genotype at each generation.\n",
    "\n",
    "We can see that although 7 mutations accumulated during the adaptation, some of them never reached a high frequency on their own, and there were only 4 genotypes that were the most frequent at any time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[37, 187, 161, 7, 3]\n",
      "['11011111', '11011101', '00001101', '10001000', '00000000']\n",
      "[7, 6, 3, 2, 0]\n",
      "[0.456, 0.841, 0.928, 0.973, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print([gi for gi in pom])\n",
    "print( [genotypes[gi] for gi in pom])\n",
    "print( [sum(map(int, genotypes[gi])) for gi in pom])\n",
    "print( [fitness[genotypes[gi]] for gi in pom])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows the dynamics of the population mean fitness over time on the top, and the frequencies of the most frequent genotype at the bottom. \n",
    "\n",
    "Note how each step of the adaptation is smaller (in terms of change in population mean fitness) and wider apart (in terms of time between changes in fitness). This is because the effective increase in fitness due to each additional mutation is smaller (diminishing effects)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ceeWVvSKgA2zedgCrxWD88IxIlyIiIiIivUzYIT0vL4+dO3d2Zy1RwzRN3vmk\nhNHn9MEZd/Yv6xERERGR6BJ2SL/33nt57bXXWLVqFfX19d1ZU8R99EUFu0pq+M70oZEuRURERER6\nIcM0TTOcjt/+9rc5cOAANTU1ANhsNiyWthnfMAw+/fTTrq+yG5SWlpKfn8/atWsZNGgQAP6AyUdf\nlPObFVvJy03joX+frCu7iIiIiEiPC/vE0by8PPLyzr6TKJe88hlxCWUcqXdTdqie+kYvE0Zm8JNb\nJiqgi4iIiEhEhB3SH3300e6sI2Kamvw44gMMzEhg/IgMxo/oS15OmgK6iIiIiERMp25mdCIej4eP\nP/6Yiy++uKteskf8100TQstdRERERESiQdghvb6+nocffpgPPvgAl8tFIBAI7fP7/aE7kH711Vdd\nX6WIiIiISC8S9tVdHn/8cf785z+TlZXFhAkTcLvdfPOb3+T888/HarUSExPDk08+2Z21ioiIiIj0\nCmGH9PXr1zNr1ixWrFjBL3/5SwBuvvlmnn76aVauXInNZmP37t3dVqiIiIiISG8Rdkivqqrioosu\nAiAtLY2+ffuGLrc4YsQIrr/+et54441OF7By5UpmzZrF2LFjKSgoYOvWrWEfu2jRIkaMGNHpMUVE\nREREolnYIT0xMRGv1xvazs3NbXMH0nPOOYeysrJODb569WoefPBBrr76ahYtWkRiYiJz586lpKTk\npMfu3LmT3//+950aT0RERETkTBB2SB83bhyvvfYajY2NQHD2fNOmTaHgvn37duLj48Me2DRNnnzy\nSW644QbuvPNOpk2bxlNPPUVqairLli074bF+v5/777+ftLS0sMcTERERETlThB3Sv//977Njxw6m\nT59OTU0NBQUFlJaWcv3113PnnXfypz/9qVOXX9y3bx9lZWXMmDEj1Ga325k+fTobNmw44bFLly6l\nvr6em2++OezxRERERETOFGGH9LFjx7Jy5UquuOIKUlJSGDp0KL/4xS+oq6vjww8/5Jvf/CY//elP\nwx547969AGRnZ7dpz8rKori4OHRJx2Pt27ePxYsXs2DBAhwOR9jjiYiIiIicKTp1M6ORI0fy0EMP\nhbavuuoqrrrqqlMauL6+HgCn09mm3el0EggEaGxsJCEhoc0+0zR54IEHuPrqq5k4cSJffPFFWGMV\nFRVRVFTUps3j8ZxS3SIiIiIi3a3TdxzdtGkT69evp6KigjvuuIO4uDi2bt3KFVdcgd1uD/t1TNME\nwDCMsNo/9F94AAAgAElEQVQBVqxYwb59+3jqqac6VXNBQQEFBQVt2kpLS8nPz+/U64iIiIiI9ISw\nQ7rf72f+/Pn89a9/DbVdf/31VFdXM3/+fFasWMHvf/97EhMTw3q9ln4NDQ2kp6eH2l0uFxaL5biT\nUMvLy/nlL3/Jo48+SmxsLD6fLxTofT4fFosFiyXs1TsiIiIiIlEr7FT7u9/9jr/+9a/8/Oc/5+23\n3w4F5JkzZ3Lffffxj3/8g9/+9rdhD9yyFv3Yyy2WlJSQm5t73Ez6hx9+SENDA3fddRejRo1i1KhR\nPPbYYwCMGjWqU2OLiIiIiESzsGfSV61axXXXXceNN95IdXV1qN3hcDBnzhxKSkp4++23ue+++8J6\nvZycHDIzM1mzZg1Tp04FwOv1sn79eqZPn35c/0svvZSXX365Tdsbb7zBc889x8svv0xGRka434qI\niIiISFQLO6QfOHCA0aNHd7h/+PDhvPTSS2EPbBgG8+bNY8GCBSQnJzNhwgSWL19OdXU1c+bMAaC4\nuJiqqirGjRtHamoqqampbV7jk08+AWDMmDFhjysiIiIiEu3CDumZmZlt7jB6rM2bN9O/f/9ODX7T\nTTfhdrt5/vnnWbp0KXl5eTzzzDNkZWUBsGTJElatWsWOHTs69boiIiIiImeysNekf+c736GoqIjX\nX38dn88HBGfD3W43v/3tb3njjTdO6XKM//Zv/8b69ev57LPPWLFiBePHjw/te+yxx04Y0OfMmaMA\nLyIiIiJnnbBn0m+//Xa+/vpr7r33Xmy24GH/9V//RW1tLT6fj0suuYQ77rij2woVEREREektwg7p\nVquVJ554guuuu441a9ZQUlKC3+9nwIABTJ8+XdccFxERERHpIp2+mdGUKVOYMmVKd9QiIiIiIiKc\nIKRv3rz5lF7w/PPPP+ViRERERETkBCH9lltuaXNDoZabFx17k6HW+w3D4KuvvuriEkVEREREepcT\nLncxTZO0tDQuvfRSJk2aFDphVEREREREuk+Hqfutt95i7dq1rF27ltWrV/P2229zySWXMHPmTC65\n5BLi4+N7sk4RERERkV7DMFvWsZxAdXU169atY926dWzcuBG/38/kyZO57LLLuPTSS0lPT++JWrtU\naWkp+fn5rF27lkGDBkW6HBERERGRkLBCemtut5sNGzawbt061q9fT01NDWPHjmXmzJnMnDmTnJyc\nbiq1aymki4iIiEi06nRIb800TbZs2cKSJUvYuHEjhmGwbdu2rqyv2yiki4iIiEi0OqUzQd1uNx98\n8AHvvPMO7777LgcPHsTpdDJ16tSurk9EREREpNcJO6QfPHiQ9evXs27dOj766COampro378/+fn5\nzJgxg8mTJ2O327uzVhERERGRXuGEIX3btm2sW7eOd955h23btmGaJnl5ecydO5f8/HzOPffcnqpT\nRERERKTX6DCkT58+nQMHDmC327ngggv4+c9/Tn5+Pv369evJ+kREREREep0OQ3pFRQUAKSkp7N+/\nn+XLl7N8+fITvphhGLzxxhtdW6GIiIiISC/TYUg///zze7IOERERERFp1mFI/+Mf/9iTdYiIiIiI\nSDNLpAsQEREREZG2FNJFRERERKKMQrqIiIiISJRRSBcRERERiTIK6SIiIiIiUUYhXUREREQkykQ8\npK9cuZJZs2YxduxYCgoK2Lp16wn7b9myhVtuuYWJEycydepU5s+fz+HDh3uoWhERERGR7hfRkL56\n9WoefPBBrr76ahYtWkRiYiJz586lpKSk3f67d+9mzpw5OJ1OnnjiCX7yk5+wZcsW5s6di9fr7eHq\nRURERES6R4c3M+pupmny5JNPcsMNN3DnnXcCcOGFF3L55ZezbNkyHnjggeOOWb58OX379mXRokXY\n7XYAsrOzuf7669m4cSPTpk3r0e9BRERERKQ7RCyk79u3j7KyMmbMmBFqs9vtTJ8+nQ0bNrR7zNCh\nQxk6dGgooAMMGTIEgNLS0u4tWERERESkh0QspO/duxcIzoS3lpWVRXFxMX6/H6vV2mbfTTfddNzr\nrFu3Djga1kVEREREznQRC+n19fUAOJ3ONu1Op5NAIEBjYyMJCQknfI3y8nIef/xxRo8ezeTJkzvs\nV1RURFFRUZs2j8dzipWLiIiIiHSviK5JBzAMI6z2Y5WXlzNnzhwCgQC//vWvT9i/oKCAgoKCNm2l\npaXk5+efSukiIiIiIt0qYld3SUxMBKChoaFNu8vlwmKxEB8f3+GxO3fu5Lvf/S719fU8++yzDB48\nuFtrFRERERHpSREL6S1r0Y+93GJJSQm5ubkdzox/9tln3HzzzVitVl544QVGjhzZ7bWKiIiIiPSk\niIX0nJwcMjMzWbNmTajN6/Wyfv16pkyZ0u4xpaWlzJs3jz59+vDiiy+Sk5PTQ9WKiIiIiPSciK1J\nNwyDefPmsWDBApKTk5kwYQLLly+nurqaOXPmAFBcXExVVRXjxo0D4JFHHqG+vp7//u//pry8nPLy\n8tDrDRgwgIyMjEh8KyIiIiIiXSpiIR2Cl1R0u908//zzLF26lLy8PJ555hmysrIAWLJkCatWrWLH\njh14vV7ee+89/H4/99xzz3GvNX/+fObOndvT34KIiIiISJczzJbLqfQyLVd3Wbt2LYMGDYp0OSIi\nIiIiIRFbky4iIiIiIu1TSBcRERERiTIK6SIiIiIiUUYhXUREREQkyiiki4iIiIhEGYV0EREREZEo\no5AuIiIiIhJlFNJFRERERKKMQrqIiIiISJRRSBcRERERiTIK6SIiIiIiUUYhXUREREQkyiiki4iI\niIhEGYV0EREREZEoo5AuIiIiIhJlFNJFRERERKKMQrqIiIiISJRRSBcRERERiTIK6SIiIiIiUUYh\nXUREREQkyiiki4iIiIhEGYV0EREREZEoo5AuIiIiIhJlIh7SV65cyaxZsxg7diwFBQVs3br1hP13\n7tzJrbfeyvjx45k+fTqFhYWYptlD1YqIiIiIdL+IhvTVq1fz4IMPcvXVV7No0SISExOZO3cuJSUl\n7favrKzktttuwzAMFi5cyA033MDChQt59tlne7hyEREREZHuY4vUwKZp8uSTT3LDDTdw5513AnDh\nhRdy+eWXs2zZMh544IHjjnnhhRfw+Xw89dRTxMXFMW3aNDweD4WFhcyePRu73d7T34aIiIiISJeL\n2Ez6vn37KCsrY8aMGaE2u93O9OnT2bBhQ7vHbNy4kSlTphAXFxdqmzlzJjU1NXz++efdXrOIiIiI\nSE+I2Ez63r17AcjOzm7TnpWVRXFxMX6/H6vVetwxkyZNOq5/y74JEyaEPb7f7wegoqKis6WLiIiI\niHRa//79sdnCi98RC+n19fUAOJ3ONu1Op5NAIEBjYyMJCQnHHdNe/9av156ioiKKioratDU0NABw\n0003ndo3ICIiIiLSCWvXrmXQoEFh9Y3omnQAwzDCaj8Zi6XjlTsFBQUUFBS0aWtqauKaa66hsLDw\nuBl7Cc8dd9zB7373u0iXccbS+3d69P6dHr1/p0fv36nTe3d69P6dnki/f/379w+7b8RCemJiIhCc\n0U5PTw+1u1wuLBYL8fHxxx2TkJAQmgFv0bJ97Kz7ycTGxuJ0Oo9bbiPhczgcYf82KMfT+3d69P6d\nHr1/p0fv36nTe3d69P6dnjPp/YvYiaMt4fjYyy2WlJSQm5vb7kx6Tk4OpaWlx/UHGDJkSDdVKiIi\nIiLSsyIW0nNycsjMzGTNmjWhNq/Xy/r165kyZUq7x0yePJmNGzficrlCbWvWrCElJYWRI0d2e80i\nIiIiIj0hYstdDMNg3rx5LFiwgOTkZCZMmMDy5cuprq5mzpw5ABQXF1NVVcW4ceMAuPHGG1m+fDm3\n3347c+fOZfv27RQWFnLPPffgcDgi9a2IiIiIiHQp60MPPfRQpAYfO3Ys8fHxrFy5klWrVpGUlMTj\njz/OiBEjAHj00UdZsGABP/rRj4DglVymTJnCu+++y/PPP8+ePXuYN28e8+bNO+UaRo8e3SXfS2+l\n9+/06P07PXr/To/ev9Oj9+/U6b07PXr/Ts+Z8v4ZZsvlVEREREREJCpEbE26iIiIiIi0TyFdRERE\nRCTKKKSLiIiIiEQZhXQRERERkSijkC4iIiIiEmUU0kVEREREooxCuoiIiIhIlFFIFxERERGJMgrp\nIiIiIiJRRiFdRERERCTKKKSLiIiIiEQZhXQRERERkSijkC4iIiIiEmUU0kVEREREooxCuoiIiIhI\nlFFIFxERERGJMgrpIiIiIiJRRiFdRERERCTKKKSLiIiIiEQZhXQRERERkSijkC4iIiIiEmUU0kVE\nREREooxCuoiIiIhIlFFIFxERERGJMgrpIiIiIiJRRiFdRERERCTKKKSLiIiIiEQZhXQRERERkSij\nkC4iIiIiEmUU0kVEREREooxCuoiIiIhIlOm1Id3n81FaWorP54t0KSIiIiIibfTakF5RUUF+fj4V\nFRWRLkVEREREpI1eG9JFRERERKKVQrqIiIiISJSJmpC+du1axo8ff9J+O3fu5NZbb2X8+PFMnz6d\nwsJCTNPsgQpFRERERHqGLdIFAGzZsoV77733pP0qKyu57bbbGDZsGAsXLuTLL79k4cKFWK1W5s6d\n2wOVioiIiIh0v4iGdI/Hw7Jly/jNb35DfHw8Xq/3hP1feOEFfD4fTz31FHFxcUybNg2Px0NhYSGz\nZ8/Gbrf3UOUiIiIiIt0nostd3nvvPQoLC5k/fz4333zzSftv3LiRKVOmEBcXF2qbOXMmNTU1fP75\n591ZqoiIiIhIj4loSB8zZgxr165l9uzZGIZx0v579+4lOzu7TVtWVlZo36nQenYRERERiTYRXe7S\nr1+/TvWvr6/H6XS2aWvZrq+vP6Ua7v71u4wclsuI7FRmTcqmfx/nyQ86AdM08QV8uP0e/AE/hmHB\nggEGWLBgGAYGYBjB5xaMYJthYDQ/FxE5FWbAJBAwCQQCBAI0Pza3+VvvCz5ve/Cxm+1MYLTTZBgG\nhsXAYoBhCf4fZrEYGK23m/sYBs37Wo5p209ERI6KihNHu4LF0vGHAkVFRRQVFbVp83g8AORPzKKq\nycZfP9jDa+/u5vvXnsfMCwZ3auyaxiO8vmMNXxzcQWltBV7/idfWn8zR8N5xkD/aZmmzP/gLgXFM\nm6XN/vZe69hjWvaF+hgG0PxD9QT9j3utTo7Xss9qWLBbbdgsNhxWOzaLDbvFht1qx249+jzOFovT\nEY/THke8Ix6HVeclyJnB7w/gqvdQX+em0eXB3eTD3eSlqcmHu7H5scmLx+3D6w3g8/qDX74A3ubn\nR9sD+AOBdkP0mcJqtWC1GVgsFqw2C1arEWxr+Tpmn+WYfXa7Fbvdis0RfLS382izt21zOKzExNmx\n2Sz6JUFETlsg4CPgc+P3ufH7m5qfNxHwe0jsMwybPb5Tr3dGhfSEhAQaGhratLVsJyQkdHhcQUEB\nBQUFbdpKS0vJz8/n6kvOYdCgQTS6ffzu1X/w5Mqt9E2J47zhfcOqqbimjIfXL8QX8DNxwFguGnw+\nSTEJOKwObBYrJiamaRIwTUwCmKaJaQZnqQJm8zbB/TQ/Hm07uj94TOCY/SamGTjJfjNUg2maBJpr\nCDS3Y5oEaK/fyff5CTTPxrUzVsv3dIqvHwj48QZ8eAM+fH4fnoA3rKVJdouNBIeT1Lhk0uJT6ROX\nQnp8GgOT+jMoOZOM+D4n/IVOpCv4vH6O1DRSXeniSLWLmqpGjlQ3Ul/XREOduzmYt//LvNVmITbW\nRmycnZhYO46YYLiMjbNjS4oJBlG7FZvNit1uCT63B8OqxWK0+rJgsTbParfTZjGCn/C1Fk5Qbd0l\n+E/cxAwE/w8KhJ4HZ+pNk9B2sO3odrBv8Hi/P0DAH3z0+wP4fQH8ARO/L0Ag1GbiDzTv8wfw+028\nHj/ugBef7+gvK16vH6/HH3oMh8VqEBtrJybW1vxlJzbWRkycnZiY4J9FnNOB0+kgPsFBvNNBvDMG\nZ4IDm90a1hgicuYI+L14PXX4PA34vC78Xhc+bwM+jwtf8/Ngmwu/txG/rwm/340Z8HX4mtmjbiB9\n4PmdquOMCuk5OTmUlpa2aSspKQFgyJAhp/XacTE27ioYz/5D9Tz95y9Y+F/TsVpO/APL6/fyxMZC\nkmMS+fmld5MSm3RaNciJ+ZuDu88fDO8ev4dGrxuX10WDt5EGjwuXt5E6dwNVjTVUNVaz7eBODrqq\ncPvcADisdob1ySWv71Dy+g5jZPo52DX7LqfI5/VzsKKOQxV1rR5rqa1pOtrJgKTkWJJT40lMiqFv\nv0SciTEkJMbgTIjBmRhDXLw9GBLjbNhsCn1dxTTN4CcPnmBg9/n8oederx+P24e7yUdTk7f5k4zm\nTzMag4/1hxqat700Nnjx+wPHjeGIsR4N7YkxJCXHkpgcS1JyHEkpR5/HxJ5RP25FzkqmaeLzNOBp\nqsLdWI3XXYvXfQSvu675efDR72s8/mDDgs0ej83uDD464omLHYDNFofVFoPFFovVFovVGtO8HRPa\ntthisdnjjn/Nkzij/teYPHkyRUVFuFwu4uODHxmsWbOGlJQURo4cedqvb7UYzP7WufzsqQ/48p+H\nGTv0xLPpH5ZsobzuII/Pul8BvQdYLVasFivYYjp1XMAMUOWqobS2nL01pWw/vJs3d77Dy1/+lTh7\nLOcPOI+p2Rcwtv9ILIZm2aVjdbVN7NtdSdm+akr2VVNRdoSAP/gJT3JqHH37JzJ6/EDS0p2kpMWT\n2ieepOQ4rDb9vYoEwzi6DIbTO90I0zTxuH24Gjw01HtwNXhw1XtwNbhpqPfQ2BBculS6t5raI43H\nfVISE2sjOSWO1D7xpKY7SWv1lZQSh+Ukk0IiEh4z4MfdWEVTw0GaGg6FArmnMfhoBo7+2zQsNuyO\nROwxSdhjEol19sMec3Tb5kjAZgsGcos1pseXxUV1SC8uLqaqqopx48YBcOONN7J8+XJuv/125s6d\ny/bt2yksLOSee+7B4XB0yZijhvQhLSmGDz8vP2lIf+vr9xjTbwQ5qYO6ZGzpHhbDQrozjXRnGuMy\nRwHB4L6vpoyPS7fyYcknvLfvYwYm9efK4flMz5mMzRrV/zSkh/h8for/WcXuHYfYveMgB8vrgGAg\nH5SdypjxAxmQlULf/onExukTmbOZYRjExAaXIKWGcYEBr8dH7ZEm6o40UXukidqa4JKnqsMNbP+8\ngiPVLlpW8FmsBqlp8WRkJtG3fyL9MhPp2z+JtHSnwrtIB0wzgLuxCldtGY315TTVH6Sp4SBu12FM\nM7jUzWKx44hLIyYujcS0ofSJSyUmLi3YFpuK1R4f1eejRHUSWbJkCatWrWLHjh0AZGRk8Nxzz/HI\nI49w1113kZ6ezt13392ldxu1WAwmj87k4y8r+I/vjO2wX4PHxa7KPdw+8cYuG1t6jsWwkJuaRW5q\nFgWjr2LboV28sWMtf/j7n/jz9reYPe46Jg7s+M9fzl5+f4B/7jzEtk/3s/2LCtxNPpwJDoaM6MuF\nlw4ld2g6icmxkS5TopzdYaNP3wT69G3/fCm/L0BNtYuqww1UH3ZReaiegxV1bH5/T2gW3mqz0Ldf\nApkDUxgwOIUBWSlkZCZiteqTGeldTDNAU8MhXLWluGrLcNWV4aotI+APLmW1ORKIdWaQkJpL+qBJ\nxDkziHVmYI9NxjiDPyE3zF56ofCWE0fXrl3LoEFtZ8I3bC3j8eV/548PXU5KYvtLK7bs/5zHNizh\nN9/6v2QmZvREydID9laX8sfPXubzAzu4OPsC/m1CAU5H587GljNT1eEGtnxUzGebi2mo95CW7uTc\ncQPIG5NJ/wFJGJrRlB5gmib1dW4OltdxqKKWA+V1lJfWcKiiDtMEm81C/4HJDMxOIXtIH7LP6UNc\nfNd8kiwSLQIBH67aUuqr9wS/avaG1ok74tKITxxIfNLA5scB2GPOziXHUT2THik5A4J/2Hv2H2H8\niPYD+LZDX5MSm0T/hPCuAiNnhpzUQTww7T95d+9HPLd1JV9X7uXei+9gUFJmpEuTbmCaJvt2V/LB\nuq/ZveMQMbE2xn5jEOedn0XmoOSo/hhUzk6GYZCYFEtiUiznjDj688Xj9lFeeoSy4hr2l9Tw1Wfl\nfPzeHjCgf2YS2UPTyTknGNq19ErONKZp0lhfTu3hHdRW7qS+Zi9mwIfFYseZkk3G4ItISMkhPjmr\n05cxPJMppLdjQN8EHHYre/bXdhjS91QXMzQtRz/Ez0KGYTA9dwrD04fwy/d/x/1rHuenF9/JyL7n\nRLo06SKmabLrq4O8v2YXpfuqychM5KobzmPUuAE4YvTfokQfR4yN7OYQDsG/w9WVLvbtrmTv7sNs\n+2w/H7/3TywWg6zcNIbl9WPYuRmkZyTo55REJb+viSOHt4eCudddi2GxkZh6DgOHXk5Cai7xiQMx\nLL33ilf6adQOq8Ugu38ie/Yf6bBPRf0hJg0a34NVSU8bkNiPR/Ln8+iG3/LIe4v46cU/5NyMYZEu\nS05TeekR3vrzl+zbXcnAwSkU/Nv5DM/rp+UsckYxDCN0dZjxkwaHQvvX2w+y66sDvPO37az5yzZS\n+8Qzckwmo8YN0KdDEnE+r4sjh7ZRfeBzag/vwDT9xDr7kdr/PJL7jCAhdQgWXRY5RCG9A1n9Eik5\nUNfuPq/fy2FXlZa69ALxjjjun/YjHnvvtzz+/lP8f/n3MihZS1/ORA31bta9sZ2tm4vpk+7ku3Mv\nYFhehkKLnBVaQvsFU3O5YGouHrePPbsOs3PbAT7dVMyH63eT2ieeUeMHMnrcADIyz841vBJ9An4v\nNYe2Ubn/79RV7sI0/TiTBzNw2BWkZIwhJj4t0iVGLYX0DqSnxPHpzkPt7jvUUIlpmgrpvUSsLYZ7\np97Bz9f+ikffW8yjs35KUkzHd7iV6PPVP8p545V/EPCbfPOaUUy8MEdXyJCzmiPGxojR/Rkxuj/f\nunYMe3Yd5stP97P5/T28v2YXmYOSGT9pMKPHD9Qadulypmniqi2lcv9mqso/xe9rJD4pi4HDryS1\n3xgcsSmRLvGMoJDegfTkWKrrmvD5A9iO+WFeUR8M7/11VZdew+mI56eX/JCfvPUoSzY9z0+mfl8z\nsGeARpeHN1/9gi+2ljHs3H78y/VjSUzS5ROld7FaLQwdmcHQkRlced0Yvv7qIFs3lfDmq5/z1mtf\nkndeJhdMzWXg4NRIlypnuIDfS1X5Vg6WvE9jXTk2RwLpAy+gz8CJxCX0j3R5ZxyF9A70SYnDNKG6\n1k3f1La3cq2oP4TVYiU9Tv+h9SZ9nX34/gW38Mv3f8ffdq3niuGXRrokOYGy4hpefv7vNDV6uea7\n4xg7cZB+sZJez2azMnJMJiPHZFJ7pJHPNpey9eN9fP5JGYNyUpl8yRBGju6PRZ80SSd4mmo4VPIh\nh0o/wu91kZQ+kgHnXE5y+ohefeLn6VJI70B6cjCYVx5pPC6kVzceITU2GYtF/4n1NucPPI/LzrmY\nP/1jNRMHjqWvs0+kS5J2fPLhPv626gsyMhO59QcXkpLWey7ZJRKupOQ4Lp45jItmDGXHF+V89O4/\nefn5T0hJi+OCi4fwjcmDsTsUE6RjbtdhKvasp3L/3zEsNvoMnEhG1kXEOrUcuCvoX18H+jTfUfDw\nkcbj9tW660mOSezpkiRK3DT2O2wu+4zntr7E/Kl3RLocacUMmLz9l2189O4/mTB5MJd/ezQ2u2Zx\nRE7EYjHIGzuAvLEDKCuu5qN3/8nbr2/jg7W7uHDGUCZOyVZYlzaaGg5S/s+1VJVvxWaPJ/OcWWRk\nTcFqjzv5wRI2/avrQJLTgd1m4XBN03H7jrjrSIrViYO9VbwjjlvHX8dvPnyWLfu/YMKA0ZEuSQCv\n18/qP23lq8/LmXXNKCZfMiTSJYmccQYOTuXaW77BpYcb2LBmF2v+8hUb133NRTOGMvGiHGw2/dLb\nm3majlC++y0Ol23GHpPIoBFXkT5wElab7nrbHRTSO2AYBn2SY6mqPT6k1zbVMTBJJ0D0ZhdmTeSt\nr9/jxX+sZlzmuVgMLX2KJI/bR9FzmynZU8X1syeSN1aXyRQ5HWnpTq757jgunjmMDW/v5O3Xt7Hp\n/b3kX5nHuedl6vyOXsbnbeTA3nc4sG8DFquDQcOvpG/WhbqmeTdTsjiBhHgH9S7Pce217jpdgq+X\nMwyD7465mn1Hyviw5JNIl9OruZt8/OnpjyndV8335k1SQBfpQmnpTq753nj+455p9Mlw8sofP+G5\nRR9Quq860qVJDzDNAIfLNvHl+7/gwL4N9Mu+mNFT76NfzjQF9B6gmfQTSIyzU9/oPa79iLue5Fit\nSe/t8voO47z+57Lyi78wZdA3dCJxBHi9fl585mMqyo5w47xJZA/Ribwi3SEjM4mb5k1m946DvP3n\nbTz75PtMmDyY/CvziIvXUoezkau2jOKvVtFwZB+p/c9j0PB/0fXNe5hSxQkkxDuoO2Ym3e3z4Pa5\nSdKJowJcP+pKyusO8vf9/4h0Kb1OwB/glec/YX9JDTf+uwK6SE84Z0QGt98zjcu/PZovtpax5Bfv\n8PmWUkzTjHRp0kX8Pg8l21/jq49+g9/XyLBv3M6QsTcroEeAZtJPICHOTsmBujZtte7gtkK6AAxP\nH8LwPkN4Y+daLhg0LtLl9BqmafL6ys/Ytf0gBbedz2AFdJEeY7EYXHBxLiPH9Odvq79g1Qtb+Wxz\nKVfdcB7Jqbq6x5msrur/sXfncVWW+eP/X2dlO+wgsm+KO4qiuZCaa1lpqalpTpbatNmnmsb6TZbT\nZ/qW05R9Jm2ZzEnTTC3TtGxR0zQ1TRRQ3ABlE0FW4bCc9f79gZLIeuAcDuD1fDzO4xH3ue77fosS\n73Pd7+t9XSAjeTN63VUCu99Jl9CRyOUiVbQXMZPeCI1z3XKXUp0WQJS7CDXu6TGWM/mppBam2zuU\nW4xlkBQAACAASURBVMaB3SkkHstmysz+RPX2s3c4gnBLcvNwYsa8wcx8ZDBXckv56O19JMWLWfWO\n6Prs+fljH6FUu9B72HN0DR8jEnQ7E9/9Rmic6i4cLbuWpLuKhaPCNYMD++Pr7MUPqft42nuevcPp\n9M4kXWbfD+cYOT6K6Nhge4cjCLe8Hn27Ehzuxc4tJ9m24QTnTuVy9/RonF1ErXpHUFF6iQtJn6Ov\nKiYwahJ+oSORiY5l7YL4W2iEq7OKKr0Jo8lcc6zCUN2S0VnpaK+whHZGIVdwR8QIfss6ToWh7uZX\ngvXk5ZSy7YsT9OzXlVETouwdjiAI1zi7qJn+p0FMnTOQiykFfPT2PjLSCu0dltAISZLIzzrE2aMr\nkcuV9Br6LF3DRosEvR0RfxON0DhXtxfSVvxR8lJ5LQlzVIkkXfjD6LChGExGDmWKdoy2oqsy8uXa\nY3h6O3PfgzHI5KJPsyC0N30HBvL4C6Pw9Hbhs48Oc/DnVCSzKH9pb4yGSi4kriPzzFa8A2Lpedsi\nnDSidLC9EUl6IzTX2krd2OGl0liFWqFCKRe7rgl/8HHxIrprT/ZePGTvUDolSZL47qskykqrmP6n\nWNQOolJPENorNw8n/vTEMIaOjGDPd2fY+OnvVNaz54hgH5XaXM7+9m9Ki1KIiH6I0N7TRM/zdkok\n6Y3QOFX/oy2vvHEmvQonlVi9LtR1R/hwUgovcqk0196hdDonjmRy6sQl7pkejU8XsR5EENo7hULO\n+Ht7M/ORwWRdLGLVuwe4crnU3mHd8kqunOLskZXI5Ep6Df0fPLv2t3dIQiNEkt4I1/pm0g1Voh5d\nqFdsQDQOSgexA6mVFeZr+WHbKQYMDqbfoCB7hyMIggV69O3KwuduR61W8N8VB0k5k2fvkG5JkmTm\nctou0hLW4urVjZ63PY2js4+9wxKaIJL0Rjg7Vj9SL68y1hyrMFbhJOrRhXqolWoG+fflcNZxe4fS\naZjNEt9sTEDj6sid9/e1dziCILSAp7cLjywaQUiEFxtXH+W3X9JEm8Y2ZDYZuJi0gZy0n/CPGEfk\ngD+hEJONHYLdk/TNmzczYcIEoqOjmTlzJidOnGh0/PHjx3nwwQeJiYlh7NixrFy5EoPB0Og5LeWg\nrk7Sdfo/kvTqchfxj1uo39DggWRdzRElL1by2y8XyE4vZvKs/qIOXRA6MAdHFbMeHcKQ28P5aftp\nvvsqCfMNndME2zAaKkk5/gkl+clERD9EQLeJontLB2LXv6lt27axdOlSJk+ezIoVK3B1dWX+/Plk\nZWXVOz4zM5P58+fj7OzMihUrmDdvHqtWrWL58uU2iU8hl+GgVlCpM9UcqzRUipp0oUEx/n1xUKjF\nbLoVFOSVsfeHswy5PZywSPFYVhA6OrlcxsQpfbl7ejQnjmbx5WfxGA2mpk8UWkRfVcK5o+9TWXaZ\n7oMWivrzDshuSbokSbz33nvMmDGDp59+mlGjRvHhhx/i6enJ2rVr6z3nhx9+wGQysWLFCuLi4pg7\ndy4PP/wwmzZtstmjM0e1gqqbZ9KVDja5l9DxOSjVDAzox9Hsxp8ICY2TJInvtpzE1c2RsZN62jsc\nQRCsaNCwUGY8HEvq2St8vuoIVZW2eRp+K6vU5nL2yEpMxip6DHkSV88Ie4cktIDdkvSMjAwuXbrE\nmDFjao6pVCpGjx7NgQMH6j1Hr9ejVCpxdPyj3MTDw4OKigr0etu0d3JUK6nS3ZCkG3Wi3EVo1KCA\nfqSXZFNUUWLvUDqspPhsMtIKmTStHyq1KHMRhM6mR9+uPPTYUHIvXeWzDw+hLdPZO6ROo6I0m3O/\nf4hC6UjP257GSdPV3iEJLWS3JD09PR2A0NDQWseDg4PJzMzEZKr7CGzy5MkoFAreeecdSkpKSEpK\nYu3atYwfPx4HB9vMblfPpNcud3EW5S5CIwb490GGjBOXT9k7lA6pskLPrh2n6RXtT7eeXewdjiAI\nNhIa6c3DTw6nrFTHmpUHKS0ROza3VnlJJueP/QcHR096DH4CtaOHvUMSWsFuU1RarRYAFxeXWsdd\nXFwwm81UVlai0dTuhxwSEsLixYt59dVX+eSTTwDo06cPb775ZqP32rRpE5s2bap1rLkz744OSip1\nN3V3EauihUa4OWjo5h3G8cunGBsZZ+9wOpyfd57FaDAx8b4+9g5FEAQb6xroziNPj+CzDw+x9oND\nPPzkcNw8xERYS2iLL5JyfDVOGj+6DVyAUkwodnh2rUkHkMlkzToO8OWXX7JkyRIeeOAB1qxZw1tv\nvcXVq1d57LHHGk26Z86cyddff13r9dFHHzUrTie1Et21mXRJkkR3F6FZBvr3JSnvLAaTqLW0RF5O\nKcd/y2DUhB64uYtfMIJwK/DyceHhJ4djMplZ+8EhrhaLGXVLlRWlkXL8E5xdA+g+aKFI0DsJuyXp\nrq6uAJSXl9c6XlFRgVwux9nZuc45H3/8MaNGjeJ///d/GTZsGFOmTOHjjz8mPj6e7du32yROB7WC\nymsLR/UmA2bJLMpdhCbF+PdFZ9RxJj/V3qF0GJIk8dP2ZDy9XRgSF27vcARBaEOe3tWJutks8dmH\nIlG3hLb4IqnHV+PiFky3gQtED/ROxG5J+vVa9JvbLWZlZREeHl7vTPrly5fp3792C6HIyEg8PDxI\nS0uzSZxODn8sHK0yVgHVHTwEoTHhnsG4O7iSlHfW3qF0GOdP53ExpYBx9/RCoRR9fAXhVuPp7cKf\nnhhWk6iXXa2yd0jtXkVpNikn/ouTWyCRMY+iEPlJp2K334RhYWH4+/uze/fummMGg4F9+/YxbNiw\nes8JDw/n+PHa/aczMjIoKSkhKMg224U7OihrFo7qrpUuOChEC0ahcTKZjD5dokjOO2fvUDoEk8nM\n7h2nCY30pkdf0YlAEG5V1xN1o8HM5x//RmWFbTq3dQaV2jzOx6/C0dmH7iJB75TslqTLZDIWLlzI\nxo0beffdd/nll1948sknKS4uZt68eUD15kUJCQk15zz55JP8+uuvvPzyyxw+fJjt27ezcOFCAgMD\nmTJlik3idFQrambS9abq/1k4KFU2uZfQufT168mFkky0+vKmB9/iEn/PojC/nAmTe9f7FE0QhFuH\np7cLc/48lLLSKjZ8chT9Dc0bhGq6igJS4j9GpXal+8AFKEQZbqdk12fKc+bMYfHixXzzzTc888wz\nlJWVsXr1aoKDgwH44IMPmDlzZs34u+66ixUrVnD69GkWLlzI8uXLiY2N5csvv6zTCcZaHNXKms2M\n9MbqJF2tEJ9Whab19euBJEmiLr0JRoOJ/T+dp3d/f/yDRLswQRCgS1dXHlxwG1cul7J5ze8YjWJn\n0usMOi3n41chV6iJin0Mpdql6ZOEDsnuu4Q8+uijPProo/W+t2zZMpYtW1br2IQJE5gwYUJbhAbU\n7pOuuz6TLpJ0oRn8XHzwcfbiZN5ZBgeK7Zgbcvy3TMpKqxg1sYe9QxEEoR0JCvVk5iOD+eKTo2z9\n/ATT5g5CLr+1n7SZjHpST/wXs0lPzyGLUDm42TskwYbE6qwmOF5bOCpJEjpjdU26WtR9Cc0gk8no\n69eDU6IuvUEGvZEDe1LoNzAIXz9Xe4cjCEI7ExHly9SHBnLm5GV2f3va3uHYlSSZuXjyc6q0uXSL\neRQHZy97hyTYmN1n0ts7JwcFZgkMRvMfNekKUZMuNE8f3yj2XTxMqU6Lm4NtSrIAzAYDBQcPU5qc\njFlvwDk0BN/b43Dw9bHZPa3h94PpVJTrGTkhyt6hCILQTvWK9mfC5D789E11i9bBI8LsHVKbkySJ\nrLPbuJp/hsiYebi4B9s7JKENiCS9CQ7q6m9Rpc5Yk6SLmnShuXr6RgJwviCNWBuVvJSdT+H8O/9H\nVW4uziHByB0dKTx0mMzPvyDogWkEPzANmUJhk3u3hq7KwMGfU4kZEoyXj6ipFAShYbfdHk5xQTk/\nbD2Ju6cTUb397B1Sm8pL30d+1mFCek3Fw7e3vcMR2ohI0pvgdC1Jr9Kb0NUsHBUz6ULzdHHxwcPR\njbM2StKvnjzF6X+8gXNwEAP+vRyXsOr9B4wVFWR/9TVZGzdzNTGJXkv+P5Qu7SsR/v1gOjqdkdvH\ndbd3KIIgtHMymYyJU/pQUlTBlnXxzHtqBP5B7vYOq00U5SZyKWUnXcPH4Btcf4tqoXMSNelNUKuq\nv0V6gwmdSY9SrkQhb3+zkkL7JJPJ6OnTjXP51t9sqyo3lzNvvoVrVHf6vvGPmgQdQOnsTNifHqLv\n669Rnp7B6ddex1hRYfUYWspgMHHkwEX6DwrG3bPu7sKCIAg3kyvkTJs7CG9fFzauPkpZaeff7Kii\nNJv0U5vw7DqAgG532jscoY2JJL0JalV1Qq4zmNCbDKIeXbBYT99IUoszalp4WoMkSaS+/xEKJyd6\nvvRXFA71b7Dl3rcPfV57lYqsbM79820kU/toY5b4exblWh3D7oi0dyiCIHQgagcls+YPQZIkNq85\n1qlbMxp0ZaSeWIOTSxfC+jwg9pC4BYkkvQkO15J0vcGE3qQXnV0Ei/XwicRkNpFWnGG1a17Zs5er\nSSeJfOIxlE3sEeAa1Z0ef32eksQkMjdstFoMLWU2mTm0N41e/fzx6WK7xbSCIHRObu5OPDAvltzs\nq+zcchJJkuwdktWZzUbSEtciSSYiY+YhF2vhbkkiSW+C6sZyF6NeLBoVLBbmEYSD0oGzVip5MVVV\nkb52HT4j4/CKHdSsczwHxhD60Gyyv/qakoREq8TRUsmJOZQUVTBiTDe7xiEIQscVHObFpGn9SDia\nxe8H0+0djlVJkkTmma1UXM0msv+fUDuKTd5uVRYn6Xq99R7ZdwQ1M+lGMzqTXmxkJFhMIVfQzSuU\nlKJ0q1wv98ddGLVaQuc8aNF5gVPvw61vH1JWfGC3+nRJkjj4cyoRUT4EBItfPIIgtFzMbSEMiQvn\nx2+SuZhaYO9wrCY/6xCFl44S0msqGs9we4cj2JHF3V2GDRvGuHHjmDRpEiNGjECp7NwNYtQ3lrsY\nRU260DKRXmEcyDjS6uuYDQZytm3Hd2Qcjl27WnSuTC6n+zNPceKZ50lf8xndnny81fFYKuXMFa5c\nLmPilL5tfm9BEDqf8ZN7cyW3lK/WHuOx50fh7ulk75BaRVuSQfa5HfgGD8MnaIi9w2kxs9mMqZ2s\ngWprCoUCudw6hSoWX2Xq1KkcOXKEP//5z4wYMYJXXnmFw4cPd8qaMACV8o9yF1GTLrRUN69Qiiuv\nUlRR0qrrXNn7C/qiIoKmTW3R+Y5+foQ+9CB5P+2mLCW1VbG0xG+/pBEQ7E5YN+82v7cgCJ2P4lrH\nF6VKwZZ18ZhMZnuH1GIGvZYLietwcg0gqMdke4fTYuXl5eh0OnuHYTc6nY7y8nKrXMviafCXX36Z\nl19+mWPHjvHDDz/w008/8eWXX+Lt7c2ECRO4++67iY2NtUpw7YFaeb27S3W5i6hJF1oi0qu6PWJq\nUTpDnAe0+Dp5P+3GI2YAziEt323Of9Jd5O3aw4X/fEL0W28gs9In/qbk5ZSSnlrI/XNiRJcCQRCs\nxkXjwLS5g1j7wSF2f3uGiVP62Dski0mSmYsnN2A2G4jsPxe5vGNWKZjNZuRyOU5OHfuJRmuoVCoq\nKytrvhet0eKzY2NjWbJkCfv372f9+vXcd999xMfHM3fuXEaNGsU///lPzp0716rg2gO5XIZKKcdw\nbSZd1KQLLeHj7IW7gyuprahLL8/IRJuSgt/4sa2KRaZQELFwPtqUFPL3H2jVtSxx5MAFNG4O9I4O\naLN7CoJwawgJ92Lc3b04sv8CZ5Iu2zsci11O20VZYSoR/WajdvK0dzgtZjKZOn0ZdHMoFAqrlPu0\negrNbDZjNBoxmUwYDAYkScJoNLJjxw7uu+8+FixYQF5eXqsDtSe1SoHOYEJnNKBWipp0wXIymYxI\nr1DSilrehvHKnp9RumrwGjK41fG49+uL5+BBZH2xCbPB0OrrNaVcq+Pk8UsMHhGGQimaSgmCYH1D\nR0XQo29Xtm9KoKjAOuUGbeFqwTkuX9iDf+R43Hx62DscwQqs9bS4Rb8tzWYzhw4d4tVXXyUuLo5H\nHnmELVu2EBMTw3//+18OHDjA/v37ee+99zh+/DjPP/+8VYK1F7VSjt5gFjPpQqt08w4jrSgDs2R5\nzaTZaCR/3y/4jhqJXGWdD4qhc2ZTlZtH3u6frXK9xsQfrv5wMnBoaBMjBUEQWkYmkzFl1gCcXdR8\n9dkxjIb2v3DRoCsl/eQXuHl3xz+idU9Jhc7H4iR9yZIlDB8+nPnz57Njxw5uu+02VqxYwcGDB3nj\njTcYPnw4crkcuVzO+PHjiYuL48yZM7aIvc2oVYrqPumiJl1ohUivUCoMleRq8y0+t/RUMoarpfiO\nHmW1eFzCw/C5fQTZm7/CZMNFPiajmWOH0okeGISLpv6dUQVBEKzB0UnF9D8NIj9Py4/fJNs7nEZV\n16FvBJmMsL6zkMnEU8abJSYmMnfu3FrH3njjDb744ouarzdv3szUqVOZMWMGe/furTV2zZo1vP32\n27WOVVZWMmvWLNLSau9dUt+96rsfQFFRERMmTLD5AlmLC4e2bdtGXFwc99xzD2PHjm1yccDo0aO5\n4447Whxge6BWKdAbTegxoFKIWiuhZSK9wgBILUwnwNXPonMLfzuC2scHTbdIq8YU8uAsjj/9P+Tu\n/IHA+6dY9drXnU7KQVuqY8hI0e9XEATb8w/yYOKUPuzccpKIKF96RfvbO6R65aX/QllRCt0HLkDl\n4GrvcGzmckE55ZW1yypdnFT4+7g0et6qVavYvn17TZ5ZVFTE4sWLSU9PZ/78+QDk5+ezbt06tmzZ\ngk6nY/bs2YwYMQKz2cySJUtISkpiwoQJNdc8efIkS5curVOGffO9GrofwIEDB3jnnXcoKLB9b36L\nM86DBw/i7u6OTqfDweGPWbGsrCx8fHzqJO1Tp7asVVx7olZVl7sYZAYxky60mJuDhi4u3qQVZTAy\n7LZmnyeZzRT+dgSfEcOt3hXFKTCALmNGc2nrN3SddCcKB+vPdB/9NZ2wbt74+btZ/dqCIAj1GTQs\nlLSzV9ixOZHAEA/cPNpXt5Hykkwupf6AX9ioTl2HflWr4/FluzHf1KVbLoPP/n4n7o08XQ0JCWHF\nihUsXrwYqG7tuGjRIvbv318zJikpiZiYGNRqNWq1mpCQEM6ePUtoaCj33Xcfw4cP58KFCzXj9Xo9\n77//fs01G7pXQ/cDkMvlfPrpp0ybNs3i74elLH624u7uzrvvvsvQoUO5ePFizfH33nuP2267jY8/\n/tiqAbYHamV1uYvBbEQtZtKFVujmFWZxh5eyc+cxFJfgPWyoTWIKmno/htJSrvy8t+nBFsq9dJVL\nGcUMHhFm9WsLgiA0RCaTce+M/qhUCrZuOIH55izRjkyGSi6c/Bxn10ACut1p73Bsyl3jwEcvjePd\nZ0fVen300rhGE3SAiRMn1uoUExwcTP/+/WuN0Wq1uLr+8RTCxcUFrVaLu7s7cXFxda45aNAg/P3r\nPlm5+V4N3Q9gxIgReHq2TQcei5P0Tz75hP/85z9MmDABN7c/ZsYefvhhJk+ezLvvvsvmzZutGqS9\nOVzr7mIwGVHJRXcXoeUivcJIL87CZG7+gqbC346gcnfDrVdPm8TkFBiA9/ChXNr6DZKVd4iLP5yB\nxtWBqD6W7Y4qCILQWs4aB6Y8OICMC4Uc2tv2m7fVR5IkMs58jVFfTkT07A7bD90S/j4udAv2qPVq\nqtSluTQaTa2Ng8rLy2sl7R2dxUn6l19+yfTp0/nnP/+Jt/cfuwb27duX119/nSlTprBu3TqrBmlv\n1S0YjRjNRlQKkaQLLRfuGYTBbORSaW6zzymOP47noIHIFAqbxRU0bSq6vCsU/HrIatfUVRk5eTyb\nmNtCUCjEgihBENpeRJQvw0d3Y+8P58jOKLZ3OBTlxFOcm0Bo72k4OPvYO5wOLzo6mvj4eHQ6HWVl\nZaSlpREVFWXvsKzG4t+cubm59O3bt8H3BwwYQGZmZquCam9UKjl6Y/WiB9Ut8KlXsJ0wj+qdQtNL\nsps1XpdfQGVWNh4xLd+ltDk0kRF4DOhP9pavkSTrPBY+eTwbg97EwKEhVrmeIAhCS9xxZw/8A93Y\n+vlxdFW23xeiIbrKYjLPbsPLPwYv/xi7xdGZ+Pr6MnfuXGbPns3DDz/Mc889V2u9ZEdnccYZGBjI\nkSNHmDVrVr3vHz9+nC5durQ6sPbEQaVAdz1JFzPpQitoHFzwcfYivTirWYtHSxITQSbDY0Ddujhr\nC5x2P8mv/J2riUmtvp8kScQfzqBbLz/cPZ2tFKEgCILlFEo5988ZyMfL9/P916e4b3bbJ8iSZCb9\n1CYUSkeCe97f5vfvqIKCguqUUC9atKjW1zNmzGDGjBn1nt9Q85L6Kj7qu1d997vu559tv8eIxTPp\n06dP5/vvv+ef//wnWVlZNcdzcnJ499132bFjB9OnT2/29TZv3syECROIjo5m5syZnDhxotHx11vi\nDBkyhNjYWB5//PFacdiCWqVAZ9RX/7dYOCq0UphHULNn0ouPJ+ASEYHKzfadUdz79cU5NIScHd+1\n+lqXMkvIyyll0DCxeZEgCPbn7avhrvv7kRSfzakTl9r8/lcyDqAtTiOs70yUqvbVaUZovyzOOB95\n5BEuXLjAp59+ypo1a5DLq/N8s9mMJEncf//9PPbYY8261rZt21i6dClPPfUU/fr1Y926dcyfP59v\nvvmG4ODgOuMNBgOPPPIIOp2Of/zjHygUCt59910WLFjAjh07UKtt0x5RrZKjN4mZdME6wjyD+T5l\nL5IkNdpSUTKZuJqURNeJExocY00ymQz/e+4m7f0PqczJwSkgoMXXij+UjrunE916dq6naoIgdFz9\nBweRciaPnVtOEhrhjau7Y5vct7Isl0sp39MlJA437+5tck+hc7A4SZfJZLz++uvMnTuX/fv3c/ny\nZUwmE/7+/owaNYpevXo16zqSJPHee+8xY8YMnn76aQCGDx/OnXfeydq1a1myZEmdc7Zt20Z6ejrf\nf/89AdcSiMDAQBYuXMj58+cbrZVvDbVSgcFkBBDdXYRWC/MIolxfQWFFMT4uXg2O0164iLFMa/N6\n9Bv5jrqdjM/WcXnnD0QseLRF16is0JOckMPt47sjl1u3r7sgCEJLyWQyJk3rx0f/2seOzYk8uGCI\n1feeuJnZbOTiqQ04OHsT2H2STe8ldD4trt3o0aMHPXq0vAF/RkYGly5dYsyYMTXHVCoVo0eP5sCB\nA/Wes3v3bm6//faaBB2gV69e/Prrry2OoznUKsUNM+mi3EVonXDP6qdEF0uyGk3SS08lI1erce3R\ndivVFQ4O+I0fR+73PxIy+0GUzpY/lk08lo3ZLDFgiFgwKghC++KiceCeB/qz6dPfOXEkk4FDbVuS\ndzltF5XaPHretgi5eBIvWKhFGWdZWRk///wzhYWFmOrpqyyTyViwYEGj10hPTwcgNLT2D0hwcDCZ\nmZmYTCYUN7WcO3fuHJMnT2blypV88cUXXL16leHDh/P3v/+9VuJubQ4qOUbz9e4u4odMaB0fZy9c\n1M6kF2cxOLDhBZqlp8+gieqOXNW2/+b8J93JpW3byd+7F/+7LZv5kSSJE79lENXHD1e3tnmULAiC\nYIkefbvSPzaIn7YnE97dF09v2yxu15akk3txLwGR43FxC7LJPYTOzeIk/ciRIzz++ONUVVU12Kqt\nOUm6VqsFqneHupGLiwtms5nKyko0Gk2t94qKivj6668JDAzk//2//0dFRQVvv/02f/7zn9m6dWud\n3aKu27RpE5s2bap1TK/XNxrfjVRKBUazESVi4ajQejKZjDCPIC42snhUMpspPXOGrne1/W50Dr6+\neN82hJxvd9L1rjuRyZu/vjwnq4T8PC1j7+ltwwgFQRBaZ+J9fbmYWsD2TQn86fFhyKxcmmcy6rh4\n8gtc3IPpGj6m6RMEoR4Wd3d5++23cXZ25p133uH7779nz549dV67d+9u8jrXE/yb68EaOg5gNBox\nGAysWrWK0aNHM2nSJP79739z/vx5fvrppwbvNXPmTL7++utar48++qjZf2aVUo6J6icGYuGoYA1h\nHsFkFDfclagyOxtjmRa33s1b42Ft/vdOoirnMiUnEiw6L+FoFho3B7r18LVRZIIgCK3n6KRi8swB\nZKQVcuTXi1a/fvb5bzHoygjrOwuZ3HYb0XV2iYmJzJ07t9axN954gy+++KLm682bNzN16lRmzJjB\n3r17a41ds2YNb7/9dq1jlZWVzJo1i7S0tEbvlZGRwYMPPsjs2bNZunQpZrO51nv33HNPq/98TbE4\nST937hwLFixg0qRJhIeHExgYWO+rKde3bb1xO1eAiooK5HI5zs51Hz85OzsTHR2N2w3t6Pr164eb\nmxvnz5+39I/SbGqVHGTVfzkiSResIdwzmPyKIrS68nrfv5p8GuRyXFux7qM13Hr3xjkkmNwfdzX7\nHKPBxKkTl4geFIRc7DAqCEI7FxHly+ARYfz83RkK8sqsdt3SwhQKsn8jKGoSji5iwiJXm8+Fooxa\nr1xtfpPnrVq1iiVLlqDT6YDqaooFCxbU6k+en5/PunXr2LhxI6tXr2b58uXo9Xqqqqp44YUX2LBh\nQ61rnjx5kjlz5tRp3X3zvQDefPNNnn32WTZs2IAkSezZsweobmLy3HPPUVxs+x1sLf5N6uNjnW1s\nr9ei3/yNysrKIjw8vN6Z9JCQEAyGuruFGY1Gm67QVikVyOTXknSx46hgBWEe1fWJ6SX1z6aXnj6D\nJiK8RQs3rUEmk+E3cQJFvx9DV1jUrHPOnspFV2VkwOC67VMFQRDao7F398LNw4ltXyRgNpmbPqEJ\nJqOOjNNfofEIxzd4uBUi7NhKq8r4n51LeWnXslqv/9m5lNKqxj8YhYSEsGLFipqvy8vLWbRokKnq\n6wAAIABJREFUEVOmTKk5lpSURExMDGq1GldXV0JCQjh79iw6nY777ruPxx9/vNY19Xo977//PhER\nEY3eCyA5OZkhQ4YAMHLkSA4dOgSAu7s769evt/yb0QIWJ+kzZ85kw4YNlJaWturGYWFh+Pv71yqN\nMRgM7Nu3j2HDhtV7TlxcHMePHycvL6/m2NGjR6moqCAmxnY7iKmVcpCLmXTBegLcuqKUK0kvqbup\nhiRJlCafsVupy3VdRo9CrlRyZfeeZo1POJpFYKgnPn6uNo5MEATBOtQOSqbMGkBOdgmHf7nQ6utd\nSv0eg66U0D4PIJOJJ4pujq78e9JrLBv/Uq3Xvye9hptj478rJk6cWGutYXBwMP371262oNVqayoz\noHpdo1arxd3dnbi4uDrXHDRoEP7+/k3eC6i1l4mLiwtlZdUfKu644456qz1sweJpYQcHB4xGI+PH\nj2fIkCF4enrWbGh0nUwmY+nSpY1eRyaTsXDhQv7xj3/g7u7OwIEDWb9+PcXFxcybNw+AzMxMioqK\nGDCguk/0vHnz2LJlCwsXLuSZZ56hsrKSt956i5iYmHr/MqxFpVKA7FpNuphJF6xAKVcQ7OZPRj2L\nR3VX8tEXFuLW276LL5UaF3zihpP7026Cpk9Fpmi4rvJqcSUXUvK5e1p0G0YoCILQesHhXtx2ezj7\nfjxHj75d8emiafqkepQVXyA/8yBBUfeIMpcbdNXY7nuh0WhqlU2Xl5fXStpb48bctry8vFapdVux\nOONctmxZzX/v2lV/vWpzknSAOXPmoNPp+Oyzz1izZg29evVi9erVNbuNfvDBB2zdupVz584B4OXl\nxRdffMGyZcv461//ikqlYsyYMfztb3+r80HBmq7PpCtlSptvfCDcOkI9guotdylNPg2AW++ebR1S\nHX4TJ3Dl530Un0jAK3ZQg+OS4rNQKuT0GWC7VqiCIAi2MuaunpxPzmP7pgTmPTXC4o3YzCY9Gclf\n4uIeQpfQ220UpXCz6Oho/u///g+dToderyctLY2oKOvsLdK7d2+OHDnCbbfdxv79+xk6dKhVrmsJ\ni5P0s2fPWjWARx99lEcfrX9nw2XLltX6UADVdUMffPCBVWNoikopRyY3oxSz6IIVhXoE8mvm7xjN\nJpQ3rP4vPXMGp6BAVO7udoyummuPKJxDQ8j7cVeDSbokSST+nk3Pfv44OolyMEEQOh6VWsm9M/rz\n2YeH+f3Xi9w2MqLpk26Qk/oT+spiIgfME2UubcjX15e5c+cye/ZsJEniueeew8HBwSrXfvHFF3nl\nlVdYvnw5ERERTJw40SrXtUSrss7y8nLy8vLw9/dHrVbX2Xyos1CrFCATSbpgXaEeQRjNRnJKcwnx\n+KMjUtn5FLt1dbmZTCaj68TxXPjkU3QFhTj4eNcZk3WxiKKCciZN62eHCAVBEKwjrJsPscPD2LPz\nDN17++Hl49L0SUB5SSZ5GfsJ6HYnTho/G0d5awkKCmLz5s21ji1atKjW1zNmzGDGjBn1nj916tR6\nj69bt67Je4WHhze6QPTgwYMNvmctLfq4d/r0aebOncuQIUO4++67SUhI4MiRI0ycOLFWa5zOQnWt\n3EUh65wfQgT7CL2WmGfcsHjUpNNRkZmFpnukvcKqw3dU9QLSvAYWkCb8noW7pxPh3azT+UkQBMFe\nxt7dCxeNAzs2JyKZ69+w8UZms5H05E04uwbQNWxUG0Qo3EosTtJPnz7NnDlzyMnJYebMmTXHXVxc\n0Ol0LFq0qE0+XbQltVKBTG5CIRMz6YL1uDpo8HLyIOPqH4tHyy9cBLMZTbdudoysNqXGBZ/b48jb\ntQfJZKr1nl5n5HRiDtGxQVbfsU8QBKGtOTgqueeB/mSkFRL/W0aT4y+n7aaqooDQvjPEpkWC1Vmc\npL/zzjt07dqVb7/9lqeffrpmh9D+/fuzY8cOIiIi2rxm3NZU1zYzkreuOkgQ6gj1CKrV4UWbmopM\nqcQlLNSOUdXVdeJ49AUFFB8/Uev4mZOX0etM9I8VvdEFQegcInv4EjMkhN3fnqakqKLBcRWl2eSm\n78U/fCzOrmLRvGB9Fifpx48fZ9q0aTg5OdXpdOLq6srMmTNtuvunPaiVClHuIthEqEdgrXIXbUoa\nLmGhyFXtawGmJqp79QLSXbtrHU84mkVIhFezazcFQRA6gvGTe+PgqOLbLxNrJiNvZDYbST+1GSeX\nLnSNGGOHCIVbgcVJulwur9Pw/UYVFRX1/oPuyNSq6pp0OSJJF6wrzCOIkqpSrlZVbw5WlpKKpnv7\nKXW5TiaT4Td+HEW/x6Mvqt4KubiwnIy0QgYMDrFzdIIgCNbl6KTi7unRXDhfQMLRuq1ycy/upbI8\nj9C+M5GLphKCjVicpA8aNIitW7diNBrrvFdcXMzGjRttuvunPSgVcmQyMzJJJOmCdYV6BAHVi0eN\n2nKqcnLaVT36jXxHj0SmUHDl570AJP6ejUqtoHf/uru3CYIgdHRRvf2IHhTET9uTKb1aWXO8suwy\nuRf20DVsFC5uQXaMUOjsLE7Sn3/+eTIzM7n//vtZvXo1MpmM/fv38+6773L33XdTUFBQpz1ORyeT\nyZApzMjETLpgZV01vqgUKtJLstFeqN6Suj3OpAOoXF3xHjaUvF17MBtNJB7Lok//ANQOYhZJEITO\naeJ9fVCqFHz31UkkSUIym0hP3oyDszf+EePtHV6nZTabefXVV5k5cyZz584lIyODhIQEHnjgAWbN\nmsXKlSsbHAfUO/a6xMRE5s6dW+eeb7zxBl988UXN15s3b2bq1KnMmDGDvXv31hq7Zs0a3n77bWv/\nseuw+Ldrz549+fzzz3n99df55JNPAPj0008B6NWrF//+97+Jju58W4PL5SJJF6xPIVcQ4hZARkk2\n2kta5I6OOAcFNn2infiNH0vB/gMk74nnanEl/QeLBaOCIHReTs5q7p7Wj81rjnHy+CW6eKVQUXqJ\nHkOeRK5oX2uH2qvKy7mYystrHVO4uODk37XBc3bv3o1er2fTpk0kJCSwbNkycnJyWLFiBcHBwTz2\n2GMkJydz6dKlOuM+/PBDli5dWmdsnz59WLVqFdu3b8fJyanmXkVFRSxevJj09HTmz58PQH5+PuvW\nrWPLli3odDpmz57NiBEjMJvNLFmyhKSkJCZMmGCbb9gNWjQF1rt3bzZs2EBxcTFZWVmYzWb8/f3x\n8+u8TfxlCgmZWewiJlhfqEcgaUUZaFMK0ESEI2vHm4K59+uLo39Xjh9IxdPbj5AIL3uHJAiCYFM9\n+/nTu38A+384wvAhx+gSGofGI8zeYXUIhqtXOf7kIjCba78hlzNkzScN7qwdHx/P7bffDsCAAQM4\nduwYPj4+hIRUr4GKi4vj8OHD5Ofn1xp36tQptFoter2+ztg+ffoQEhLCihUrWLx4cc29ysvLWbRo\nEfv37685lpSURExMDGq1GrVaTUhICGfPniU0NJT77ruP4cOHc+Ha029batVzak9PTzw9Pa0VS7sm\nk5tB1KQLNhDqEcQvGUcoSzHgM3yovcNplEwmw3P0WLKOyIgb2qVOhydBEITO6M77e3P0x+/RGxwJ\niGz77eE7KpW7OwM/WFHvTHpDCTqAVqtFo9HUfF1WVkZw8B9Pbl1cXMjKyqozTqFQ1Dl2fSzAxIkT\nyc7+o+0xQHBwMMHBwbWSdK1Wi6ura61raLVa3N3diYuL4+uvv27ut6BVLE7SJ02a1KxxO3futDiY\n9qw6SRcz6YL1hXoEoa4woC8oaLf16Dcq8O2FWZZCQGUG0M/e4QiCINhcedEx3N2ucvhoNK4BhfQZ\nIPqiN1djZS0N0Wg0lN+Q2Lu5uVFVVVXzdXl5ec2xG8eZzeY6514f25r7l5eX10ra24rFWae3t3ed\nl6enJ2azmYsXL2IymRg2bJgtYrUvmRlEuYtgAyEeAfgVGoD2u2j0RqeSC/FTl1N+YE+na7cqCIJw\ns6qKAi6lfI9v8HB8A3vy/daTlGt19g6rUxs4cGDNzHZCQgLR0dGoVCoyMzORJIlff/2V2NjYOuOi\noqLQaDT1jrVEdHQ08fHx6HQ6ysrKSEtLIyoqyup/zqZYPJO+bt26Bt87e/Ysjz76qMXfjA5BbkIS\nSbpgAxq1C+FlKkyOahy7Wj7j0JYKrmjJzihm/LAgKtZ9hfZ8Cq492v5/XIIgCG1BksxkJH+JSq0h\nsPskJgVKfPjWPn7YeoppcwfZO7xOa/z48Rw8eJBZs2YhSRJvvPEGZWVlvPDCC5hMJuLi4ujfvz/9\n+vWrMw7gtddeqzPWEr6+vsydO5fZs2cjSRLPPfccDg4OtvijNkomWXkq7IMPPmDnzp18++231rys\n1WVnZzN27Fj27NlDUFDTfU5nffEs/lJf3p29oA2iE241O557DIwm7l2x2t6hNGrPd2c4diid514Z\nS+KTT+E5cCDdnn7C3mEJgiDYxJXMQ2Sd3Ur3QQtx866ekEg8lsU3XyQwY14sPfuJfSJuZDBUPxVW\ntbNds9uatb4PVp8adnd3JzMz09qXtTtJJmbSBduQJAn3vHKyPGjX5SNms0TSsWz6xgSidlTTZewY\n8g/8irGisumTBUEQOhhdZRGXUnbiEzikJkEHiB4URLdeXdi55SSVFXo7Rih0dlbNOs+fP89nn31G\naGioNS/bLkiYMBtFJwvB+vQFBSjKq8hylyipKrV3OA1KO3eFstKqmt7ofuPGYtbpKPj1oJ0jEwRB\nsC5JkshI/gqF0pGgqHtqvSeTybhnejQGg4kfv0m2U4TCrcDimvTo6Oh6264ZjUbM1/pgLl++vPWR\ntSMmswlkEmazSNIF6ytLSQUgz1tJRkk2nk4Nt6Wyp8Tfs/Dx0xAY4gGAo18XPPpHk7drN10njLNz\ndIIgCNZTcOkoZUUpdIuZj0LlVOd9Nw8nxt/bm2+/TKLPgAC69+q8+8QI9tOiFoz1JelyuRwfHx8m\nTZpEjx49rBJce2EwGwEwiZl0wQa0KamoPD0waBzJKLnEAP8+9g6pjsoKPedO5TH6zh61fv79Jozj\n3FvvUJ6egUtY53uCJgjCrUdfWUz2uR14B8Ti7tuzwXExt4WQnJDDt18m8cRfR+PodGvXYQvWZ3GS\nvmzZMlvE0a4ZTNULAMwmUZMuWJ82NQ1Nt26EeMjJKMlu+gQ7OHX8EmZJIjq29iJrryGDUbq5kbdr\nNxEL59spOkEQBOuQJImM01+hUDoQ1OPeRsfKZDLundGfD/+1j107TnPvDMs6iAhCU0TW2QwGk5hJ\nF2xDMpvRpqXh2r0boR5B7TZJT/g9i249fHF1c6x1XK5S0WXMaPL37cesFwuoBEHo2Apzfqe08Dwh\nvaehVDk3Od7Dy5lx9/TmxJFM0s7lt0GEwq3EajXpjZHJZCQkJFh6q3ZDb66eSRdJumBtVZdzMZVX\noOnejVDXUn6+eAi9yYBa0X4em+bllHI5+yoPPFx/T2C/cWPJ2badwt+O4Dvy9jaOThAEwTr0VSVk\nnduBl/9APHx7N/u82GGhnE7M4dsvE3n8hdE4OFqcWgkNSExM5O2332bdunVkZGTw0ksvIZPJ6N69\nO0uXLkUul7Ny5Ur27duHUqnkb3/7G9HR0Q2OBcjIyOCpp56qaRVeVFTECy+8QFVVFV26dOHNN9/E\nycmJzZs3s3HjRpRKJU888QR33HFHg2NtxeKZ9KeeegpfX18MBgPDhg1jzpw5PProo4wbN67mGzBx\n4kQmTZpU87rrrrusHnhbul7uYjCIJF2wruuLRjXdIgn1CMIsmcm+etnOUdWWcDQTZxc1Ub3r32jJ\nOTgI1149yftpdxtHJgiCYB3VZS5bkMtVBPecYtG5Mnl12Uu5Vsee787YKMKOq6ignJysklqvooLy\nJs9btWoVS5YsQaer3t31zTff5Nlnn2XDhg1IksSePXtITk7m6NGjfPnllyxfvpzXXnutwbEA27Zt\n47nnnqO4uLjmPh988AH33HMPGzZsoHfv3mzatIn8/HzWrVvHxo0bWb16NcuXL0ev19c71pZa9HFP\nq9WydevWOgtE8/LyePDBBwkICODZZ59t1rU2b97MJ598Qm5uLr169eKll14iJiamWeeuWLGClStX\ncu7cOYv/DJa4nqRfq3oRBKvRpqbi0KULKjc3QgzVs+eZVy8R4RVi58iqGY0mkuKziY4NRqFs+DN9\n1wnjSPn3SiovX8bJX2zuIQhCx1KUE09pwVkiBzzcrDKXm3n5uDBmUi9++iaZ3gP8CYv0sUGUHU+5\nVsf7y37m5i1AZDJ4/u8TcNE0vItnSEgIK1asYPHixQAkJyczZMgQAEaOHMnBgwcJDw8nLi4OmUxG\nQEAAJpOJoqKieseOHz8ed3d31q9fz/jx42vuEx8fz5///OeascuXLyc4OJiYmBjUajVqtZqQkBDO\nnj1b79h58+ZZ69tVh8Uz6evXr2fevHn1dnDx8/PjoYceavYni23btrF06VImT57MihUrcHV1Zf78\n+WRlZTV57vnz5/nPf/5jafgtor+WnV/bQEoQrEabmoameyQAzionurh4k96O6tLPJ+dRWWEgZkhw\no+O8hw9D4ezMld0/t1FkgiAI1qGvukrWuW/w6hqDR5e+Lb7OkLhwgsI82bEpEYNezOoBuGgceOql\nMSx49vZar6deGtNogg7VVRlK5R9zyZIk1ZRbu7i4UFZWhlarRaPR/HG/a8frGwtwxx134Oxc+0OY\nVqvF1dW1znWvH7t+XKvV1jvWlixO0isrK2vKWuqj1WoxGpv+xylJEu+99x4zZszg6aefZtSoUXz4\n4Yd4enqydu3aRs81mUy8/PLLeHl5WRp+ixiv1aQbRbmLYEWSyUR52gU03brVHGtvi0cTjmYREOxB\nF3+3RscpHB3xHRlH3p6fMTfj518QBKE9kCSJzNNbkMmVFpe53EwulzF5Rn9Kr1bx8/dnrRRhx+fl\n40JAsEetl5ePi8XXuTH3LC8vx83NDY1GQ3l5ea3jrq6u9Y5tyI3XaOq69Y21JYuT9NjYWP773/9y\n+vTpOu/Fx8ezdu1a7rjjjiavk5GRwaVLlxgzZkzNMZVKxejRozlw4ECj565ZswatVstDDz1kafgt\ncn0m3WySYTKZ2+SeQudXkZWFWa/HtfvNSfolpJufDdpBaUklaeeuMKCJWfTr/CaMx1BcQnH8cRtH\nJgiCYB1Fl49zteAMIb2molRbnjjezMfPldETe3DkwEXS0wqsEKFwXe/evTly5AgA+/fvJzY2loED\nB/Lrr79iNpvJycnBbDbj5eVV79iGDBw4kF9++aVm7KBBg4iOjiY+Ph6dTkdZWRlpaWlERUXVO9aW\nLK5Jf/HFF5k1axbTpk2jf//+BAcHVy+4yMjg1KlThIaG8tJLLzV5nfT0dABCQ2tvgBIcHExmZiYm\nkwmFQlHnvIyMDFauXMmqVas4deqUpeG3iOHaTLpkVqA3mnFSiM6VQutpU1JBJsMlMrLmWJhHEFp9\nOUWVJXg7e9oxOkg8lo1CIadvTGCzxmsiI3CJCCdv1268bxti4+gEQRBaR191layz3+Dp1x9Pv35W\nu+6w0ZGcS85l+8YE/vwX0e3FWl588UVeeeUVli9fTkREBBMnTkShUBAbG8vMmTMxm828+uqrDY5t\nyBNPPMGLL77I5s2b8fT05J133sHZ2Zm5c+cye/ZsJEniueeew8HBod6xtmTxv5zw8HC+/fZbPv74\nYw4cOMCZM9UrmUNCQnjiiSeYP38+Li5NfxrVarUAdca6uLhgNpuprKysVWcE1Y+llixZwuTJk4mN\njW12kr5p06Y6dfJ6C3o6X184ilmO3mDCyUH8wAmtV5aShlNgIErnP9o3hXhUJ8QZJZfsmqRLkkTC\n0Ux6RftbtIue3/hxXFi1Gl1BIQ4+3jaMUBAEoeUkSSIjeTMyuYLgXvdZ9dpyuYz7HozhP+/8wk/b\nk8UmR60QFBTE5s2bger8c/369XXGLFq0iEWLFtU61tDY6w4ePFjz3z4+PqxevbrOmBkzZjBjxoxa\nxxoaaystyjZ9fX15+eWXW3Xj64/zb+653tBxgI0bN5KRkcGHH35o0b1mzpzJzJkzax3Lzs5m7Nix\nzTr/erkLkhyDUZS7CNahTU1F0y2y1rEuLt44Kh3IKMlmYEDLFzC1VsaFQooLK7jnAct+ufiOvJ30\nT9dy5ee9BM+YbqPoBEEQWqcg+zClheeJjHkElVrT9AkW8vJxYfy9vdm55SQ9+nYlqref1e8hdH4t\nrts4evQob731Fn/5y184f/48WVlZbN++HUMzW6BcXx17Y2E+QEVFBXK5vM7q28uXL/Ovf/2Ll19+\nGUdHR4xGY01CbzQaMZttlzxXz6TLQJKJJF2wCrNeT0VGJpob6tEB5DI5oe6Bdl88mnA0Cw8vZ8Ii\nLZsNV2pc8B4xnLxde5Bs+DMpCILQUlXl+WSf+xafwCEWbVpkqUHDQons4cuOzYlUaHU2u4/QeVmc\npJtMJv7yl7/w8MMP8+mnn7Jz504KCws5efIkixcv5uGHH25WS5rrteg3t1vMysoiPDy8zkz64cOH\nKS8v55lnnqFPnz706dOHZcuWAdCnTx/ef/99S/8ozWYwG1HKlYAMvdFks/sIt47y9Awko7HWotHr\nri8etRddlYHTiTkMGBKMTG55R6OuE8ahu3KFq0knbRCdIAhCy0lmE+mnNqJ0cCWox702vZdMJuPe\nmf0xGc18t+Vku2gIIHQsFifpH330ETt37uSVV15h165dNf/oxo0bx0svvURSUlKzEuawsDD8/f3Z\nvfuPXQoNBgP79u1j2LBhdcbfcccdfPXVV7VejzzyCABfffVVnboha9Kb9ChlimsxitlBofW0KanI\nFApcwsPqvBfqEUSONg+9sfnrJqwpOSEHo9FM/9igFp3v2qsnToEB5IodSAVBaGdy0/dRfjWL8L6z\nUCgdbX4/N3cnJk3tx5mky5w6Yb/JF6FjsrgmfevWrUyfPp3Zs2fX2lZVrVYzb948srKy2LVrV5Md\nXmQyGQsXLuQf//gH7u7uDBw4kPXr11NcXFyze1NmZiZFRUUMGDAAT09PPD1rL6SLj48HoF8/663K\nro/BZEStqF48J2bSBWvQpqbiHBqCXK2u816oRyCSJJFVeplIr9B6zrat40cyiejug7un5bvuQfXP\ntt/4cWSs34ChtBSVjfvICoIgNEdFaTY5aT/hFzYajWd4m923T0wAZ0/l8v3XpwiN8MbNw6npkwSB\nFsyk5+Xl0bdvwwvaoqKiyM/Pb9a15syZw+LFi/nmm2945plnKCsrY/Xq1QQHV/dl/uCDD+os+LQH\nvUmP6lqSLmbSBWsoS0mtU49+XYh7ADJkdqlLv5x9lZzMEgYNa92HA987RgNwZe8vVohKEAShdcwm\nAxdPbsTJxY+AbhPa9N4ymYxJ0/qhVMr5ZmMCklmUvQjNY3GS7u/vz/nz5xt8//fff6dr167Nvt6j\njz7Kvn37SExMZOPGjcTExNS8t2zZMs6dO9fgufPmzWv0fWvRmwyoFdUznmImXWgtY0UFldmX6q1H\nB3BUOeKn8SHdDkn68d8y0Lg6ENWn+T/D9VF7uOM1ZDB5P+0WdZiCINjdpZTv0VUUENZvFnJ527dR\ndnZRM+XBAVxMKeDQvrQ2v39Hc73n+cyZM5k7dy4ZGRkkJCTwwAMPMGvWLFauXNngOKBNx9qSxf9S\n77//ft5//30GDBjA0KFDgepPiTqdjk8++YTvvvuOJ5980uqB2lN1kn5tJl10dxFaSZuaBpKEJiqq\nwTH2WDyqqzJy8ng2Q+LCUVhhwy6/8WM5/drrlJ09h1uvnlaIUBAEwXJX889wJfMAQVH34OwaYLc4\nInt0YeioCPZ+f5bw7j4EBHvYLZa2pKsowGisqnVMqXTEwdmnwXN2796NXq9n06ZNJCQksGzZMnJy\nclixYgXBwcE89thjJCcnc+nSpTrjPvzwQ5YuXdpmY/v06WOz753FSfpjjz1Gamoqf/3rX1Eqq09/\n/vnnKS0txWg0MnLkSB5//HGrB2pPepMeB+X1mXSRpAutoz2fgtzREeeghnfyDPUI4ttz1bPQ9e0Z\nYAunTlxCrzcxcKh16uA9BvTHoYsvebv2iCRdEAS70FddJf3UJty8e9Al9HZ7h8PYSb3ISCvk6/XH\neez5kag7+eaIBr2WU7++Bdz8RFVG9OhXG+xRHx8fz+23V/99DRgwgGPHjuHj40NISAgAcXFxHD58\nmPz8/FrjTp06hVarRa/Xt9nYdpWkKxQK3nnnHaZPn87u3bvJysrCZDIREBDA6NGjm71BUEeiNxlq\nknSDQZS7CK1Tdv48mm6RyBSKBseEeQRSYaiksKIYHxevNonr+G8ZdOvRBQ+vli0YvZlMLsdv3Fiy\nv/qasEf+hOra3giCIAhtQZLMpJ/aCDI5YX1nIZO1/glhaymUcu6fM5BV7+7n+62nmDJrgL1DsimV\nWkPfuMX1zqQ3tomUVquttet8WVlZzXpFqN6dPisrq844hUJR55itx9qSxUn6X//6VyZOnMi4cePq\nbZXYGRlMBhwUKuQyMZMutI4kSZSdT6HLtYWVDQnxqG5/mF6S3SZJek5WCZezrzLzkcFWva7fxPFk\nbf6KvF17CJpq3a23BUEQGpN7cS9lRWl0H7QAlYP1dxVtKZ8uGu68ry87NicS2cOXvjENP1XtDByc\nfXCw8ByNRlNrs0s3Nzeqqv5I9MvLy2uO3TjObDbXOdfWY23J4o+VP/74I3l5ebaIpd26XpOuUikw\niIWjQivoCwoxFJfgGtW90XG+zl44q5zarMNL/KEMXN0d6d6ri1Wvq/bwwCduBLk7v0cyiZ8dQRDa\nhrYkvabdopt3w+t/7GXAkGB69/fnu6+SKCmqsHc47c7AgQPZv38/UL1YMzo6GpVKRWZmJpIk8euv\nvxIbG1tnXFRUFBqNpk3H2pLFM+k9evQgOTnZFrG0W9e7u6iVcvSiBaPQCmXnUwDQNJGlmY+fAAAg\nAElEQVSky2QyQj0C22TxaFWlgVMJlxg2KhK5FRaM3izg3rvJ3/cLhUeO4jP81nj6JgiC/RgNlVxM\n2oCLWxCB3SbaO5x6yWQy7p4ezcfL9/P158eZ9+Rwm/z/t6MaP348Bw8eZNasWUiSxBtvvEFZWRkv\nvPACJpOJuLg4+vfvT79+/eqMA3jttdfabKwtySQL+6OtX7+e5cuX061bNwYOHIiXl1edhW0ymYwF\nCxZYNVBry87OZuzYsezZs4egoMZ3Vnzhh9fp6RPJge+8mHBbKHPuFIvghJa5+OlaCvb/yuBPVzU5\n9r/xm0jMPc2/737NpjH9fjCdH7ae5JmXx+HuaZtNNpJe/BsyuZx+b75uk+sLgiBAdUnhxaT1XC08\nT++hz+Hg3DZreloq80Ihaz88zNCREYy/t7e9w2k1g8EAgEqlsnMk9mWt74PFM+mvv179SzYpKYmk\npKR6x3SEJN0SepMetUKFWiVHLxaOCq2gTUltchb9ulCPQH5M/YUqow5HpaUVfc0jSRLxh9Lp3svP\nZgk6VM+mn/vXcrQXLqCJiLDZfQRBuLUVZB+mOC+J8Og57T5BBwiJ8GbMXT3Z890ZgsM86dnP394h\nCe2IxUn6nj17bBFHu2YwGVErVTioFCJJF1pMMpnQpqYRPPOBZo0P9wxGQiK9OJuevpE2iSk9rZAr\nuWWMn2y7FlIAXkNvQ+3txeUdO+n+P0/b9F6CINyayq9mknV2O77Bw/Dq2nG6pgy/I5Ls9CK+2ZhA\nF383vHxc7B2S0ErWap/cZAFUr1692LFjR83XgYGBBAYG4u7ujr+/f83XN786E71Jj0quwkGtQCeS\ndKGFKjKzMOt0aBrYafRmIe6BqORKUosu2iymowcu4uOnISKq4U0lrEGuVOI/6S7y9x9AX1Ji03sJ\ngnDrMerLSUtch5NrAEE9Jts7HIvIZDKmPBiDk7Oar9Ye69CtnhUKBUaj0d5h2J3JZELRSJvl5moy\nSa+vZL24uJjBgwdz5MiRVgfQEdQsHFUp0Ok77g+PYF+lZ8+CXI5rM5N0pUJJuGcIKYXpNomnpKiC\n88m5DB4R3iYbJvlNGI9MoeDyju9sfi9BEG4dkmTmwsnPMZv0RPafi1ze8TYIcnRS8cDDseRf0fLD\n1lP2DqfF5HI5ZrOZyspK9Ho9BoPhlnrp9XoqKysxm83I5a1fCNziK1i43rTDkiSppgWjg0rMpAst\nV3r6LJqIcBROza/97uYdRmqhbWbSfz+YjtpBSf/YxhdOW4vKzRW/CeO4/P2PGCsq2+SegiB0fpfT\ndlFWmEpEv9monTztHU6L+Qe58/+3d+dxUVb7H8A/z+zMDAz7IqCgyCKLgIq4i6allZZZrpXKT7PN\n23a11azberulV0xTr1lXTdxS85pZrpm4g7soIMiigOzMDMx6fn+MjIzssgzI991rYuY825evA3zn\nPOc5z5gnQ5B0IhNnT2ZaO5z7JpPJIBaL2+xu2e0Jx3EQi8WQyVpmyFLH+7jZxvRGPRiYqUgX8aGu\noNM45P6UX7kCx+joJm3T08kHv147gJLKMthLWu6mCVqNHkknMhEe5d2mt6X2HP84bu3eg7w//oDn\n+I51SpoQ0v6U5F/Grev74NFjNOycA6wdTrNF9O+KrPQi/LrtAlw97NDF297aId0XHo/XIj3JnR1l\nsAE6g6kor7pwlHrSyf3Q3L4Nze0C2PVq2vSdPR19AaDFe9MvJOagslKHfoN8W3S/DRG7uMBl6GDc\n3LkLxjtTVBFCyP2oKM9F+oWfoHDpBY/uI60dTovgOA5jJ4bB1cMWm9aeQnlZZcMbkQcWFekN0Bq0\nAAARXwSxSEBj0sl9KbucDACwC2pake4ic4KdWI5rLVikM8Zw8q909Axys8osAp5PPgFtYRFu/3mk\nzY9NCHkw6LUqpJ5dC5GNA3xDp4DjHpxyRijk45mZ/cCMDJt/OA09dQ52Wo16V5eUlODmzZvmR25u\nLgCgqKjIor3640GhNZh6++6OSafhLqTpyq5cgcTDHSKHpo2X5DgOPZ18kdqCF4+mJufjdm45+g9p\n2170KjKfbnDoE4mc7TvBjHQHX0JI0xiNeqSd+y8M+kr4hc8EXyCxdkgtzk5hg2dm9kNudil2b7vQ\naa4DJJYaNRj1s88+M98Stbq33nqrzm2uXLly/1G1I1VFumkKRj00OioqSNOVXb4Cu6Cg+9q2p5Mv\ndl75vcWuFj92KA0eXgr49mzdaRfr4/nUE7j47kIUnTwFp+j+VouDENKxMMaQlbwTypIM+PeZ0yFu\nWHS/vLo54LGnw7Az/izcPGwRPax17pdB2q8Gi/RXXuncNx65O9xFCLGQ0XAX0mR6pRLqzCx0efzR\n+9rez9EHFfpK5JTnwlvRpVmx3MwqQUZqISZMj7Tqlfd2vXrBLrgXsuI3wzGqHzi6wIgQ0gi3sxJQ\nkH0cXYMmwNbxwS9ae/fzRt6tMvyx6zIcnGQICHG3dkikDVGR3gBt1YWjfCHEIgNdOEqarCz5KsAY\n7Hr1uq/t/Rx9wIFDSmFGs4v0hINpsHe0Qa8w6956muM4dJ06GRffW4iiEyfhNKBps94QQjqf0tvJ\nyLr6C1y8B8LFe4C1w2kzDz3WCyVFamxbfwbPvTgQXt067jSTpGmo+6oB5p50gelmRlqdAUYjjQ0j\njVd+JRlChR0kXe6vMJaKbOBp545rBWnNiqO4UIUr528iemgP8PjW/9FXhARDERqCzI2baGw6IaRe\nqtIsXD/3XyicAuDdwe4o2lw8Hocnp0XCrYsC8d+fRFGBytohkTZi/b/U7VylXgMAkAjEEAtNt3jV\n6qk3nTRe6YWLsOsV1KzhJYEufrhSkNqsOI4fvg6JjRDhUd7N2k9L8p4yCeobmShMOGbtUAgh7ZRG\nXYDUxDWQyN3hGzYdHK/5t1vvaIRCPibP6geJRIifVp+AWqmxdkikDVCR3gCLIl1k+sVA49JJY+nV\napSnpEIRFtas/fRy8cOt8nyUVJTe1/YqpQZJJzPRd5BPm968qCGK4F6wD++NzI2bwQz0c0UIsaTT\nKpGSuAZ8gQR+EbPAF4isHZLVyORiTJ3dH5UVOsR/fwo6Gn77wKMivQEavRYcOPMUjABoXDpptLKL\nlwCjEfa9m1ekB7r4AcB996YfO3QdHMcharB1pl2sj/eUSajIzsbtI0etHQohpB0x6DVIS1oLg64C\nfn3+D0Kx3NohWZ2jswyTY6OQe7MU29adgcFAQwUfZFYv0jdv3ozRo0cjLCwMkyZNQlJSUr3rJyYm\n4tlnn0Xfvn0xePBgzJ8/HwUFBa0WX6VeA7FABB7Ho5500mQl5y9A5OR03+PRqzhLHeEqc8KV/KYX\n6WqlBqeOpqPvQB/I5OJmxdEa7AID4NC3DzI3/ASjVmvtcAgh7YDRoENa0lpUKPPgFzkLEqn1poxt\nb7y6OWDic32ReiUfOzeepevkHmBWLdJ37NiBDz/8EOPGjUNcXBxsbW0RGxuLrKysWtdPS0vDjBkz\nIJPJ8PXXX2PBggVITExEbGwsdK10i/FKfSUkAlNhQz3ppKlKz1+Afe/QFpnuMMilJ67cTmnydsf/\nvA7GGAYMb7/Tlfk8/yw0BYW4tXuPtUMhhFhZ1c2KlKU34BcxEzJFV2uH1O7493LDk1MjcOlsDnZv\nPU83O3pAWa1IZ4xh6dKleOaZZ/DKK69g2LBhWLFiBRwcHPDjjz/Wus369evh4uKCuLg4DBs2DI8/\n/ji++eYbJCcnIyEhoVXiNPWk3ynS7/Ska6lIJ42gLSmB+kYmFGGhLbK/IJeeyCy9CaWm8Vf2V6i1\nOPlXBvoO9IHctv31oleRdvWG26iHkLVlG3Rl5dYOhxBiJcxoQPr5n1BemIIe4c93irnQ71dwhCce\nf6Y3kk5k4vedl6hQfwBZrUi/ceMGcnJyMGLECHObUCjE8OHDceTIkVq38fPzw6xZsyAUCs1t3bt3\nBwBkZ2e3SpyVek3NnnQa7kIaoSTpLAA0+6LRKiGu/mBguJh/tdHbHP/zOowGIwa24170Kl2nPANm\nMCBr8xZrh0IIsQLGjMi4tBklty+he+/pUDgHWjukdi88qiseeSIEJ46k48CvyVSoP2CsNs1DRkYG\nAKBbt24W7d7e3sjMzITBYACfbznN0rRp02rs58CBAwDuFustrdYinXrSSSMUn06ErLsvxE4tc9tq\nV7kz3OQuOJ+XjGjvyAbXr1BrcfJIOvoM6Aa5naRFYmhNIgcHeD31JLLiN8N99ChIu7afqSIJIa2L\nGQ1IvxiP4txz8A2dAnvXEGuH1GFEDfGFTmfA/t1XYDAYMerxXla9ozRpOVYr0pVKJQBAJpNZtMtk\nMhiNRlRUVEAur/9K7lu3buGf//wnQkJCEB1d9x0LN23ahE2bNlm0aRt5gZpGrzUX6ZI7U9dVaPSN\n2pZ0XsxgQHHSWXg8OqZF99vbLQhJuabTmg39Ek44lAaD3oiBMX4tGkNr8nxiHPL3H8D1Vf9B8D8W\n0R8aQjoBo1GP9PMbUHL7MnzDpsLRPdzaIXU4g0b4gc/n8Psvl2HQG/HIEyHgePT7s6OzWpFedUrm\n3j/CdbXf69atW5gxYwaMRiMWL15c7/qTJk3CpEmTLNqys7MxcuTIBuOs1GtgIzT1QkpEfHAcFemk\nYWXJV2FQqeDQp+Ee76YIcw/C72l/Ik95G+62rnWuV15aiRN/XkfUkO6wVbT/XvQqPJEIvrNjceUf\nn6HgyFG4DB1s7ZAIIa3IaNDh+rl1KCu8hh69n6Ue9GaIHtYDfAEfe36+AIPBiEefCqNCvYOz2ph0\nW1tbAIBKZXkRnFqtBo/Hg1QqrXPba9euYfLkyVAqlfj+++/RtWvrXfldfbgLx3GQigVQVbTOTDLk\nwVF8+gwEtraw7dmyvdjBrv7gcTycz7tS73qHf78KgYCPQSPa/1j0ezn27QPH/v2Q/v0P0KvV1g6H\nENJKDPpKpCatRVlRCnqEz6ACvQX0G+SDx5/pjcQTmdixMQkGPc2j3pFZrUivGot+73SLWVlZ8PX1\nrbNn/Ny5c5g+fTr4fD42bNiAwMDWvbCkepEOADYSIfWkkwYVnz4Dh8gIcPyWvX21TCSFn6MPzubW\nXaQX5JUj6WQWBo/0g420Y96dzzd2FgwqFW78d4O1QyGEtAKdpgzXTn0HVWkm/CJioXChi0RbSkT/\nrpgwNRKXzt3ExjUnoamkmqWjslqR7uPjAw8PD+zbt8/cptPpcOjQIQwYMKDWbbKzszF79mw4OTlh\n48aN8PHxafU4K/WV5ikYAUAmEUBNb3hSD3V2NtSZWXCMjmqV/Ud4BONC7hVo9bVfV3FgTzJsbcXo\n1w7vLtpYEjdXdHt2GnL3/IbSi5esHQ4hpAVVqvKRfGIZtJoyBPR7EXZOHee6mY4iJNITU/+vP7Jv\nFOG/KxKgLKu0dkjkPlitSOc4DrNnz0Z8fDwWL16Mw4cP46WXXkJxcTFmzJgBAMjMzMTZs2fN23z6\n6adQKpV4+eWXcevWLZw9e9b8yM/Pb5U47+1Jl0qEUFfScBdSt8KE4+CJxS0+Hr1KP8/e0Bi0tU7F\nmH2jGMkXcjHs4QAIhS3bi9/WPB4dA9uAAKTGLYdBo7F2OISQFqAsyUDyyW/B4wsR2P9VSO08rR3S\nA6u7vwuef2kQyksr8X3cURTeVlo7JNJEVr3j6LRp0zB//nzs3LkT8+bNQ3l5OdasWQNvb9PUa8uX\nLzdf8KnT6fDnn3/CYDDgzTffNF8MWvXYtWtXq8RYfXYXALChnnTSgMKE43DoGwm+uHVuHuSt6AJX\nmRNO5Zy3aGdGhr07LsLF3Ra9+3q1yrHbEsfnw2/eS9AUFuLGOhr2QkhHV3jzDK6dXgmJzBUBUS9D\nbONg7ZAeeB5eCsx8dTD4fA5r/v0X0q62TocmaR1Wm92lyqxZszBr1qxal33xxRf44osvAJhudHTp\nUtue9jYajdAYLIt0qViAMlXjpm8knU/FrVtQpafDa+KTrXYMjuPQ17M3EjJPw8imgMeZPmufO52N\nnMwSPDt3AHh8q37+bjFSLy90mz4VGWt/hENkBBwiI6wdEiGkiZjRgOyUX5F/4084ekSiW6+J4PGF\nDW9IWoSDkxSz5g3GtnWJ+Gn1CTz0eC9ED+1OU9x2AA/GX/JWUmkwnWKXCO5efCeVCKGi4S6kDgVH\njoInErXaUJcqfbuEoaSyDKmFGQCAygod9u++jKAwD/j2dG7VY7e1LuMeg314b6QsiYO2pMTa4RBC\nmkCvVSElcQ3ybxyBV8A4+IRMpgLdCmykIkz9vyhED+uBP365jJ0bz0JHN2Zs96hIr4daWwHANKNG\nFSkNdyF1YIwh/+AhOEb3B9/GplWPFeTiBzuxHAlZZwAAf/5xDRqNHqPH9WrV41oDx+Oh599eBWMM\nKUviwIw0pRghHYGqNAtXTiyFujwHPfv8H9y6DaHeWyvi8XkY9XgvPDE1ApfO3cTauL9onHo7R0V6\nPZRa0xzuctHdu6JKxQJUUJFOalGefBWVN2/BbWRMqx+Lz+NjoHdfJGSeRv6tUpw8ko7BI3tC4VD3\n/QU6MpGjA3r+7RWUJJ1F1qYt1g6HEFIPxozIyziM5JPLIBDYIKj/PNg5+Vs7LHJHWB8vzHxlEDSV\neqxe/CcuJGZbOyRSByrS61FrkW5Ds7uQ2uUfOAiRkxMUoW1zQ47B3fqhpKIMWzedgp29DQYO73g3\nLmoKx7594D35GWTFb0bhiZPWDocQUgudVonUpLXIvvY/uHYdhID+r0AsdbJ2WOQeXbztMeeNoegZ\n5IbtG5Kwa9M56LTUAdneUJFeD6XWdLdDefXhLmIBtHojdHQXL1KNobISBX8lwDVmWIvfwKguPZ18\n0a00CAVZFRj7VCgEHXzKxcbwnvQ0HKP6IWXxUqizqPeHkPak5PZlXDm2GKrSTPSImAnvgHHg8aw+\nPwWpg1gixITpkXjs6TBcSMzGqm/+RFZGkbXDItVQkV6Pqp50mbD6mHTTBS9011FSXf7BwzBUVMBt\n1Mg2O2ZZSSUU6b4oc7kJzx52bXZca+J4PPR8fR5ETo64/PGn0BYXWzskQjo9vU6N9AsbkZa0FjZy\nd/Qa8DrsXR6862MeRBzHITK6G2a/PhRiiQA/LDuKP3ZdpotK2wkq0uuh1KohE9qAx7ubJhuJqVdA\nVUFDXogJYwy3/vcrHKP6QuLu3mbH3L3tPCRiIXK8L+HIjc4z/EMglaLXwvdh1Glx+eNPoVdXWDsk\nQjqtkvxLuJzwNUpuX0a3Xk/DL/L/IJLYWzss0kQu7raY9epgxIwJxMkj6Vj19WFkplOvurVRkV4P\npVZlMR4dABQy03SMpSq6AyIxKT13HhXZ2fB47NE2O+aFM9lIvZKPxyb2RnjXQPye+icYY212fGuT\nuLmi18L3UXHzFpK/+CeMWrp3ASFtSaMuRGrSWqSd/QE2cg8ED3wTzl5RNHtLB8bj8zB4ZE/MfmMo\nxBIhflh2FL/En4WqnOoda6EivR5KTc0i3d7WdGOjUnrTkjtytu+EtFvXNrtgtLS4Ar/tuIRevT0Q\nGOqB0X5DkVmag6sF19vk+O2FvLsvgt6Zj7LLV3DlcyrUCWkLRoMWN1P34lLCv6Auy4Fv2DT4RcZS\n7/kDxNXdFrPmDcbYp0KRfDEX3355EKeOZsBo7DwdQe0FFen1UGrVkIstp7Szk5mK9BIlFQQEKLuS\njJKz5+A18ak26UEyGhm2/5QIoYiPsU+FAQB6u/eCm9wF/7u6r9WP397Yh/dG0LsLUHrhIpK/+IoK\ndUJaCWNGFN48g0tH/4Xc9INw7ToEwYP+Dkf3cOo9fwDxeBz6DvTBy2/HIDDUHXt+voBV3xxGypW8\nTnXW1tqoSK+HUquyuGgUAIQCHuQ2QpQqqSedAFnxm2Hj5QXnQQPa5Hh/7U9BZnoRnpgaAemdoVc8\njofxgaNxMucsskpvtkkc7YlDZASC3l2AkvMXcOmjT6BXqawdEiEPDMYYSm8n48qxJci4GA8bWw/0\nGvgGvPzHgi8QWzs80spkcjHGTQrHrHmDIRYLsPE/J/HfFceQfYMu2m8LVKTXQ6lV1xjuAgAKuZiK\ndILSCxdRcvYcvCc/0ybTLt5IK8Th369h8Ag/+Po5Wywb5tMfTjYO+PnynlaPoz1yiIxA8KIPoEpP\nx4V3P4CmkC54IqQ5GGMoK0zBtdPfITVpDXgCMQL6vQS/iJmQyFytHR5pY17dHDDjlUGYNKsf1EoN\nvl/6F+K/P4mczBJrh/ZAoyK9HqWVZbCTyGu029uKUUJj0js1ZjDg+uo1kPf0a5Ne9LKSCmz972l4\n+zhg2MMBNZYL+UKMCxyFhKwznbI3HQAUIcEI/ewT6MvKcf7vC1B+LcXaIRHS4TBmREn+JSSfiEPK\nmVUw6CvRI3wGAvq9BLmDr7XDI1bEcRwCgt3xwpvDMG5Sb9zOLceafx/BhlXHkXm90NrhPZCoSK+D\nzqBDqaYcTjYONZbZy8UooZ70Ti33t9+hvpGJ7rNjwfFa98dIrzNg84+nwePzMPG5vuDzaz/eyB6D\n4SJ1xH/Pbu20YwZlPt0Q9tUXEDk64cI77yNv3wFrh0RIh2A0aHE7+wQuH1uMtLM/gOPx4BcxC0HR\nr8HeNZjGnRMzHp+H8KiueHlBDJ6cGoHSkgr88G0Cvo/7C5fO3oTBQDd7bCl0K7A6FFaYTuE4yxxr\nLFPIRcjOL2/rkEg7obl9GzfW/wTXkSNgG+DfqsdijOF/W84jL6cMM14ZCLlt3WNARXwhng1/Cl8f\nXYXEWxfRp0toq8bWXomdnRD62cdI+241UuO+RcnZs+gxdw4E8ppnxQjp7DTqItzOSkBBzkkY9JVQ\nOAeia+ATkDt0p8Kc1IvH5yG0jxdCIjyRfDEXJ45cx7Z1Z2CrkKDvQB9E9u8KWT1/s0jDqEivQ4HK\nNKbVSVp7T3oxDXfplJjRiGtL4sC3kcB31vOtfrwDe5Jx/kw2npgSDs+uNd+L94ryDEewqz++T9yE\nXi49YSOUtHqM7RFPJILfqy9BERqM66vWIGne6+j5t1dh3zvM2qERYnUGvRYl+RdQePM0yotSwRNI\n4NylH1y6DoRE6tzwDgiphuNxCArzQFCYB25ll+LUX+n4849rOLz3KnoGuSI8qiv8glzrPAtM6kZF\neh0K1aYrl51rGe7i5iRFmUoLdaUOUomwrUMjVpS99WeUXbyE4I8WtnrP7Km/0nF0fypGPhqEsL7e\njdqG4zi80Hca/r73U/x4divm9pveqjG2ZxzHwTVmOOyCeyHl38twaeFHcB05At2emw6RvcLa4RHS\nphgzQlmcjsJbZ1Ccex5GgwZye1906zURDu7hNFMLaREeXgqMmxyOhx4LwoXEHJw9lYVNa09BKhch\nNNILoZGe8PBS0FmaRqIivQ6FFcWQCW0gqaUnsouzqTi7WaCCnxfdwKGzKDxxEpkbNsLr6adgH967\nVY+VePwG9uy4iKghvhgY06NJ27rbuuLZ8KfwnzMbEeERjP5eEa0UZccgcXVFyMcfIve333Fjw0YU\nHj+OrlMmw33Mw+AJ6FcgeXAxowHlxWkozjuPkvxL0GuVEErs4dptMJy69KVec9JqpHIx+g/tjv5D\nuyM3pxRnT2Xh/OksnPjzOhQONuaed6+uDuB4VLDXhf5C1aFAVQRnac3x6ADQxeVOkX5bSUV6J1F2\nJRnXvvk3HPv3Q9epk1v1WKeOZmDPzxfQZ0A3PDzu/i7YGtVjCC7kJWPZiR/hLndBN3uvVoi04+D4\nfHg8OgbOgwfixrqfkL5mLW79bze8np4Il+FDqVgnDwydRomywqsoK7yG0ttXYNBXQCSxh6NHBBxc\nQyGz7waOo2EHpO24eyrwiKcCox7vhYzUQlw5fxPnz2Tj+OHrsFVI4BfoCr9AF/j2dIHEhkYnVEd/\nmepwszwPbnKXWpfZyUSwlQpxs4BumtIZlF+9hssffQKZrw/8X/9bq83mwhjDkX0pOPTbVUQN8cXD\n4+9/RgWO4/By/+fxwf5/4Ysjy/FRzBtwlVOvmVChgN8rL8J97MPIit+M1Lhvkb1lK7o8MR6uw4eC\nb2Nj7RAJaRKDXgNV6Q2UF6WhrPAa1GXZAAAbuQdcvKNh7xoKqZ0XDS8gVsfn89AjwAU9AlwwdkIo\nMtOLkHwhF2lX85F0IhMcj4NnV3v4BbrCt6czunjZgy/o3B8oOdZJ52rLzs7GyJEjsX//fnh5WfYy\nGpkRM39+E+MCR+Gp4LG1bv/Wv/+Eh4sMb07t0xbhEispPHYC175ZApmvL3oteh8CqbThje6DTqvH\nL5vO4dLZmxgyqieGPxzQIn9UC9RFWHTgGzDGsDDmtTo/eHZWyrTryNq0BUUnT4FvYwPXEcPh/sjD\nkHp37jMPpP3Sa1VQlqRDWZyO8uJ0qMtzAGYEX2ADO2d/2DkFwM7JHyIJXXdBOo7iQjXSruYjLTkf\n6akF0GoMEAh58OzqgK6+jvD2dYS3jwPEnew6QCrSaynSb5bn4bVfF+GdoS8jwiOk1u2XbkpC8o1i\nLJ8/oi3CJW3MqNMh86d45GzfCafo/uj5+jzwxa1zYVXerTLs+CkJhbeVGD8pHMERni26/wJVET46\nuBgV+kq8NegFBLr4tej+HwSVeXnI3bMXefv2Q1+uhKxHd7gMHQLnIYMgdnKydnikk9JplFCXZUNd\nnmP6WpYDbaVpUgOh2A5yB1/I7X1h69AdErkbDWMhDwSD3ohbOaXIvF6IzPQiZKUXoUKtA8cBTq5y\neHgq4O6lgLunAh6eigd6iAwV6bUU6UczT+Hfx77HqvFfwl5iV+v2B05nYfHGRPx30cNwsO2c09w9\nqMquJOP6ytVQ3chE16mT4fXUk60yxMVgMOL44es49NtVODhJ8eS0CHi00jUOpZVl+ProKqQUpuPp\nkMcwPnA0+Dx+qxyrIzNoNCg+dRq3/zyC4jNJYHo9ZD26w6FPJBz6RMK2px84Pkk4WroAACAASURB\nVOWNtBzGGPRaFSpVeahU5ZsfFco86DSlAAAeXwypnSekdl6Q2nlCrugGkY0jDWEhnQIzMhTkK5GZ\nXoTcnFLcyi5F3q0yGPSmmybZO0rh1sUOzm5yuLjK4eRqC2dXOcSSjj+i2+rfwebNm/Gf//wHubm5\nCAoKwttvv42IiLpno7h27Ro+/fRTnD9/HgqFAlOnTsXs2bNb9JfVuVtX4CZ3qbNAB4AwP9P43oup\nhRjSwj2fxDrKr15D9rbtKDpxErLuvgj78jPY+vds8eMwxpB84RYO/JqMwgIV+g/pjhFjAyEUtl7x\np5DYYeHw1xB/cRc2XdyFY1mJeLb3BIS5B7XaMTsivlgM58GD4Dx4EHTl5Sg6eQrFZ5Jwa/evyN68\nFQK5HLaBAbALCoRtUADkfn6tdoaFPDiY0QCtphTaimJoKoqgrSiCpqIYmooCVCrzYdBX3FmTg1jq\nBInUBY4eEabC3NYTYqkT9ZKTTovjcXBxt4WLu625zWAwoiBfidzsUtzKKUX+rXKcP5WN8rJK8zq2\nCgmcXeVwcJJC4SCFvaMN7B2ksHeSQi4Xd4hZZazak75jxw688847ePnllxEaGop169YhMTERO3fu\nhLd3zXmhCwsLMW7cOPTs2RMzZ87EpUuXsGzZMrz55puIjY1t0rHr6knXGnSYvXM+xvQcjsmh4+vd\nx8tfHUAXZxnem9m/Sccm7YeurAwFR4/h9qHDKE++CkkXD3g99SRcY4a3eI+pVqPHhcQcnD6agbxb\nZfDt6YyHHgtqtd7zuqQUpuOHpC1IKUxHD8duGNMzBgO8IyHkP7inDJuLGQwov3oNxUlnUZ58FeXX\nUmCsrAR4PNh4doHMpxtkPj6Q+nSD1NsLYmdn6nHvBBgzQq9TQ6cpg05TDr2mHDqt6XlVm7ayBFpN\nKcDu3ipdIJJDJHEwFeQyV9jIXCGRuUIsdQaPfg4JuW+VFToU3laiIF+JgjwlCm8rUVKkRklRBSor\ndOb1+AIe7B1sYKuQQG4rgdxODFs701e5nQS2tqavYonAqmesrFakM8YwcuRIDBkyBB999BEAQKfT\n4ZFHHkFMTAzef//9GtssXboUGzZswKFDh2BzZxaGJUuWYOPGjfjrr78gFDb+l1tdRfqeawexNmkz\nvnlkIbwUHvXuY8+xDKzYdg5xb8Wgm3vdve6k/dArlVCmpqH0wkWUXriI8pRUAIB9WCjcRo2E04Do\nFi2ulOUaZKQW4OrFXKRcyYdWq4dfoCuih3ZHd3/rXcTJGMOZm+fx67WDuJh/FTZCCSI8QtC3SxiC\nXPxqvdMuuYsZDFClZ6D8WgrUN25AlWF6GCtNvTgcnw+xiwsk7m6QuLtB7OYGsbMThPb2EDk4QORg\nD75MRsMV2gFmNMBg0MCo18Bg0MKo10Cvr4BBp4ZepzZ9Nb+uuNt251G9+AYAnkACocgWQrEthGI7\niMQKiGwcIbZxgMjGESKJA/gCkZW+W0I6r8oKHUqK1SgpVKOkuAKlRWqUl1WivLQSynINyssqodfd\n8/PM5yCVimAjE8FGKoRUJoJUJoKNtOohhMRGAJFYCLFEcPchFkIk4je7t95qw11u3LiBnJwcjBhx\n98JLoVCI4cOH48iRI7Vuk5CQgAEDBpgLdAB46KGHsGLFCly4cAGRkZHNiul87hX8dGEnRnYf3GCB\nDgAj+npjx6FUfP7DKbw/KwperrYNbkNaj1GrhV6pgl6lgq60FJqCQmgLCqApLETlrVyob2RCW1QE\nABDY2kIREozus2PhFN0fIsf7L0oNBiMq1DqUFleguFCF4kI18m+VISezGCVFptPYbl3sED20O8L6\nesHRWdYi329zcByHvp690dezN7JKbyIh8wxO55xDQuZpAICT1AHd7L3QxdYNnrZucJU7QyG2hb2N\nAnKRFLxOfuqd4/Mh9+sBud/dG00xoxGa27ehzsqGJi8Plbl5qMzLQ9mVZFQePAyjRmO5D4EAIgd7\nCBUK8GUyCOQyCGSmh/m1VAaeWASeWAy+WAyeWAyeWFTtuRg8obDVpgVtbYwZTQ+jEWCGO68N1dqr\ntRkNYEY9jEYdjEY9mMH03NSmN381GnV3lunB7qxrNGhrFOJVrxkz1BMhB4FQCr5QCoHQBnyhFGIb\nRwjsvMAX2EAgkkIosjMX5EKxLXh8KsAJaY8kNkK42yjg3qX2mY8YY9BU6qEsq0R5uQbKskqoVVpU\nqHSoUGuhVpkeJUVqVKh1UKu00Gnr+/0BiMSmol0iEWD0+GD0CHBtUsxWK9IzMjIAAN26dbNo9/b2\nRmZmJgwGA/j39GhmZGSgf//+NdavWnY/Rfq+TcthbyeD3qiHwWjAk3wn2KsKcDB56T1rMounVa8m\nuhtRotTgr40nIeDzIOBx4DgO5g4yDuBYnXsCZ17YmE9bdZ304IB7T4g058NbjcNUa2jCfrlqmzZ4\nuqbGfk1JNu2DmV9XPbdoNzJwzFgzBwDA44Hx+WBufDDPnmB8IYwCIRhfgJsAUJwB7Mmo81tjd47D\nmOn4VV/BGJjx7sPymBz4Ah66+grgG8iHQCwAT1AEDTJw6nRDiWjqP13LnAhzADAKXtDbuaGSqaBh\nKmhLbkJbnI4UaJFiER8HHvjgwAOP44MH3p3nvDtLTf+h+v+56q9hsbe6cA0sb9xemqdZ+3W/8+jt\nCsAFPCMDT28AX68HX28Az2A0PTcYwBlKwDMWgWcwglduBK/ECJ7R2MAB7mIcZ/o1wHFgHEz55nGm\n9qqvHCyfm79WfaPVX3N3v/mqf77qy3F3O9Mi7m5b1eZVy3nVnnO1PG8BzAgY7/wKMD83AoxxMBoB\no6Haw8jdeW5aZjDw7742mLY16DkY9Kb1AR2A0jsP0unRya9OT3znYS8BmJiDkfFhBB+M8U3PGR8M\n1Z5X8FFZwUdurrzjFOlKpRIAIJNZ9irKZDIYjUZUVFRALpfX2Ka29avvrzabNm3Cpk2bLNq0Wi0A\nwNFZCWfH6oWODoC6jj3V/tMpcwbuLeJb6ye5SSVZO5i3hzEO93wsqb70nrUbn7Oa35rltoyrtma9\nPWUN7bfuw9z5qGDeps5/diMAbVP/7ZqTi/snuvMw7VcA068I6Z1PmkaAY2Bgd15XHdkAcAbAtKRm\nVFwtbY3UuC1aa79NV+9++Xced64zNTLTW8M0SpJ352GJw91Uc4yZX1d9SOVYtddV695Zhqp/omrP\nTfsy3g22+npVQVXfzrxetXUBU+Cs6sNy7fth7M7XO+vCWNVues6qElD1uto6d9et9tXAAD0Dq/aV\nGZmpnQFcjd/Btf84cuzuPwUhhLQFxgG5aQCG9WvSdlYr0quGwt87JrOu9obw6jndO2nSJEyaNMmi\nrWpMeuTI+TWmYCSEEEIIIcSarDaQ0dbWNH5bpVJZtKvVavB4PEhrubOjXC6vsX7V63t73QkhhBBC\nCOmorFakV41Fz8rKsmjPysqCr69vrT3pPj4+yM7OrrE+AHTv3r2VIiWEEEIIIaRtWa1I9/HxgYeH\nB/bt22du0+l0OHToEAYMGFDrNtHR0UhISIBafXfM+L59+2Bvb4/AwMBWj5kQQgghhJC2YLUx6RzH\nYfbs2fjHP/4BhUKByMhIrF+/HsXFxZgxYwYAIDMzE0VFRQgPDwcATJ06FevXr8ecOXMQGxuL5ORk\nrFq1Cm+++SZEIpr2ihBCCCGEPBisOrnutGnTMH/+fOzcuRPz5s1DeXk51qxZY55Wcfny5RYXfLq6\numLt2rXQ6/WYN28eNm/ejNdee63JdxslhBBCCCGkPbPaHUetra47jhJCCCGEEGJtVhvuYm0Gg2nu\n7NzcXCtHQgghhBBCOgN3d3cIBI0rvzttkX779m0ApiE3hBBCCCGEtLamjODotEV6SEgIfHx8sGrV\nKvD5dO+5+zF37lx899131g6jw6L8NQ/lr3kof81D+bt/lLvmofw1j7Xz5+7u3uh1O22RLpFIIJPJ\nzPO1k6YTiUQ0nr8ZKH/NQ/lrHspf81D+7h/lrnkof83TkfJn1dldCCGEEEIIITVRkU4IIYQQQkg7\nQ0U6IYQQQggh7Qx/0aJFi6wdhDWFhIRYO4QOjfLXPJS/5qH8NQ/lr3kof/ePctc8lL/m6Sj567Q3\nMyKEEEIIIaS9ouEuhBBCCCGEtDNUpBNCCCGEENLOUJFOCCGEEEJIO0NFOiGEEEIIIe0MFemEEEII\nIYS0M52ySN+8eTNGjx6NsLAwTJo0CUlJSdYOqd3Zv38/IiIiLNoYY1ixYgWGDx+O3r17Y+bMmUhL\nS7NYR6vV4rPPPsOgQYMQERGBefPmIS8vry1DtxqDwYC1a9dizJgxCA8Px9ixY7F+/XpUTaBE+auf\nVqvF4sWLERMTg/DwcDz33HO4dOmSeTnlr/G0Wi3GjBmDt99+29xG+atfcXExAgICajzmzZsHgPLX\nGMeOHcPTTz+NsLAwxMTEYOnSpTAYDAAof/U5ceJEre+9qkdOTg7lrwEGgwGrV6/GqFGjEBERgaef\nfhrHjh0zL++w+WOdzPbt21lgYCCLi4tjhw4dYrGxsSwiIoJlZmZaO7R248yZMywiIoKFh4dbtMfF\nxbHQ0FD2448/sn379rGnnnqKDR48mJWVlZnXefvtt1lUVBTbtm0b27NnDxs1ahQbN24c0+v1bf1t\ntLmlS5eykJAQtnz5cpaQkMCWLl3KgoKC2KpVqxhjlL+GLFq0iEVERLANGzawI0eOsDlz5rDIyEiW\nnZ3NGKP8NcXXX3/N/P392YIFC8xtlL/6JSQkMH9/f3bkyBGWlJRkfqSnpzPGKH8NOX36NAsODmYL\nFixgCQkJbPXq1SwkJITFxcUxxih/9SkvL7d4zyUlJbHjx4+zqKgoNnPmTGYwGCh/DVi5ciULCgpi\nK1asYEePHmVvvPEGCw4OZpcuXWKMddz3X6cq0o1GI4uJiWELFy40t2m1WjZixAj2j3/8w4qRtQ8a\njYatWrWKBQcHs379+lkU6eXl5Sw8PJytXLnS3FZSUsIiIiLY999/zxhj7MaNGywwMJDt3r3bvE56\nejoLCAhge/fubbtvxAoMBgOLiIhgixcvtmhftGgRi46Opvw1oKysjAUHB5tzwRhjFRUVLCwsjH37\n7beUvya4dOkSCw8PZ/379zcX6ZS/hq1du5YNHDiw1mWUv4ZNmTKFzZkzx6Ltq6++YtOnT6f83YdP\nPvmE9e/fnxUWFlL+GuGRRx5hf//7382v9Xo9GzZsGPvoo486dP461XCXGzduICcnByNGjDC3CYVC\nDB8+HEeOHLFiZO3Dn3/+iVWrVmH+/PmYPn26xbJz585BrVZj5MiR5jaFQoGoqChz7o4fPw4AGD58\nuHkdHx8f9OzZ84HPb3l5OZ544gmMHj3aot3X1xdFRUU4fvw45a8eNjY22Lx5MyZMmGBuEwgE4DgO\nWq2W3n+NpNfr8e677yI2NhZubm7mdspfw65evYqAgIBal1H+6ldUVITExEQ888wzFu1vvfUW1q1b\nR/lrotTUVGzYsAGvvfYaHB0dKX+NoNVqIZfLza/5fD5sbW1RWlraofPXqYr0jIwMAEC3bt0s2r29\nvZGZmWkeO9dZhYaGYv/+/XjuuefAcZzFsqrceXt7W7R7eXmZl6Wnp8PZ2RlSqbTOdR5UCoUCCxcu\nRK9evSzaDx48CHd3d/O4Nspf7QQCAXr16gWFQgGj0YisrCy8++674DgO48aNo/dfI61evRo6nQ5z\n5syxaKf8Nezq1auoqKjA5MmTERoaiqFDh2L16tVgjFH+GnD16lUwxiCVSjF37lyEhoZiwIABiIuL\ng9FopPw10eLFi+Hj42P+0EP5a9i0adOwc+dOHDt2DOXl5fjxxx+RkpKCsWPHduj8Cax2ZCtQKpUA\nAJlMZtEuk8lgNBpRUVFh8Umss6ne83YvpVIJkUgEkUhk0S6Tycx5ValUNXJbtU5ubm7LBtsBbNmy\nBQkJCXj//fcpf02wfPlyxMXFAQDmzZuH7t27448//qD8NSAtLQ3fffcdfvjhhxp5ovdf/YxGI9LS\n0mBjY4MFCxbAw8MDhw8fxjfffAONRgOhUEj5q0dxcTEAYP78+XjssccwY8YMnDp1CitWrIBYLAZj\njPLXSFlZWThw4AA+/vhj8HimflT6+W3YlClTcPz4ccyYMcPc9tprr2HkyJFYuXJlh81fpyrS2Z1Z\nNu7tJa6rndzFGKszP1Xtda1T37YPql9++QUffvghHn74YUyfPh0rV66k/DXSQw89hKioKJw4cQLL\nly+HTqeDRCKh/NXDaDTivffew8SJE2vMygTQz29DGGP47rvv0KVLF/OZ1ujoaKjVavznP//B3Llz\nKX/10Ol0AIDBgwdjwYIFAEz5Ky4uxooVKzBnzhzKXyNt2bIFdnZ2GD9+vLmNfn7rxxhDbGws0tLS\n8OGHH6JHjx5ISEjAt99+Czs7uw6dv05VpNva2gIwfWJydnY2t6vVavB4vBqnOchdtra20Gq10Ol0\nEAqF5naVSmXOq1wuh0qlqrGtWq02r9MZ/PDDD/jiiy8wYsQI/Otf/wLHcZS/JggMDAQAREVFQaVS\nYc2aNXjrrbcof/VYt24dbt68iZUrV0Kv15vbGWPQ6/X0/msAn8/HgAEDarQPGTIE8fHxsLGxofzV\no6oHcsiQIRbtAwcOxIYNG2BnZ0f5a6R9+/bhoYcesuj1pZ/f+p05cwZnzpzBkiVLMGbMGABA//79\nYTAY8NVXX+H111/vsPnrVGPSq3pIsrKyLNqzsrLg6+v7wH/abI5u3bqBMYbs7GyL9uzsbPj6+gIw\nXWRRUFCAysrKOtd50H3zzTf4/PPPMX78eCxdutT8i5byV7/bt29j27Zt5lOPVYKCgqDVaqFQKCh/\n9di3bx/y8vIQFRWF4OBgBAcHIzk5GTt27EBwcDAEAgHlrx55eXnYtGkTioqKLNo1Gg0A0PuvAV27\ndgVwt0e9StUHRnr/Nc7NmzeRlpZWYwIC+vtRv6rhKOHh4Rbtffr0QUVFBTiO67D561RFuo+PDzw8\nPLBv3z5zm06nw6FDh2rtRSF3RUREQCwWW+SutLQUJ0+eNOduwIABMBgMOHDggHmdjIwMpKSkdIr8\n/vjjj1i5ciWee+45fPHFFxAI7p6oovzVr6ysDO+++y727t1r0X706FE4OTnhoYceovzV46OPPsLW\nrVstHj4+PoiJicHWrVvx6KOPUv7qodVqsXDhQvzyyy8W7Xv37oWPjw9GjRpF+auHn58f3Nzc8Ntv\nv1m0Hz58GK6urvT+a6Tz588DAMLCwiza6e9H/Xx8fAAAiYmJFu3nzp2DQCDA6NGjO2z++IsWLVpk\ntaO3MY7jIBQKzeNctVotPv/8c1y/fh1ffvklFAqFtUNsN06ePImkpCTMnTsXACASiVBeXo5Vq1ZB\nLBajuLgYCxcuhE6nw6effgqxWAyFQoHU1FT8+OOPcHBwMM/Q4e7ujnfeecd8EcyDKD8/H3PnzkWP\nHj3wwgsvIC8vD7m5ueZHly5doFKpKH91cHR0REpKCjZt2mSeNmvNmjXYtm0bPvjgA4SHh9P7rx4O\nDg5wc3OzeGzduhXe3t6YNm0a/fw2QKFQICMjwzy0RalUYtWqVdi1axc+++wzBAQEUP7qwXEcHBwc\nsHr1ahQUFEAikWDz5s3YsGED5s+fj8jISMpfI+zZswcpKSl49dVXLdrp57d+rq6uuHjxIuLj4yGV\nSqFWq/Hzzz9j9erVeO655/DII4903Py17jTs7dOaNWvYsGHDWFhYGJs0aRJLTEy0dkjtztKlS2vc\ncVSn07GvvvqKDRw4kIWHh7OZM2ey1NRUi3VUKhV7//33Wb9+/VifPn3Yq6++ynJzc9sydKvYtm0b\n8/f3r/NRWFhI+WuAWq1m//znP1lMTAwLDg5m48ePZ3v27DEvp/w1zbhx4yzuOEr5q19FRQX7+uuv\nWUxMDAsJCWHjx49nv//+u3k55a9hu3btYo899hgLCQlho0aNYvHx8eZllL+Gffjhh2zUqFG1LqP8\n1a+iooJ9/vnnbPDgwSw0NJQ99thj7KeffmJGo5Ex1nHzxzF2Z2oTQgghhBBCSLvw4J7/IIQQQggh\npIOiIp0QQgghhJB2hop0QgghhBBC2hkq0gkhhBBCCGlnqEgnhBBCCCGknaEinRBCCCGEkHaGinRC\nCLEytVqN9evXY/LkyYiOjkZYWBgefvhhfPnllygsLLR2eC0iKyvL/Pznn39GQEAAzp49a8WICCGk\nfaN50gkhxIpycnIwd+5cpKamYtSoUejXrx8kEgkuX76M7du3QyqVYt26dejRo4e1Q71vs2bNgpeX\nFz7++GMApoI9MTERQ4YMgaOjo5WjI4SQ9omKdEIIsRKj0YhnnnkGaWlpWLlyJaKioiyWp6WlYerU\nqejevTs2btxopSibLyAgAJMmTTIX6YQQQhpGw10IIcRKdu7ciQsXLmDOnDk1CnQA6NGjB2bNmoXE\nxESkpaVZIUJCCCHWQkU6IYRYyf/+9z8IhUI8//zzda4zceJEbN26Fd27dwcA6PV6rFixAqNGjUJI\nSAhGjhyJZcuWQa/Xm7eJi4tD3759kZKSgpkzZyI8PBwDBgzAJ598gsrKSov9JycnY86cOYiMjERE\nRARiY2Nx6dIli3UCAgKwbNkyzJo1CyEhIZg6dSoAoKysDF9++aU5lsjISDz33HNISkoCAGRnZyMg\nIAAAsGnTJgQEBCA7O7vWMelFRUV47733MHDgQISGhuLxxx/H5s2bLeJ4++23MX78eJw+fRqTJk1C\nWFgYhg0bhri4OBiNRvN6OTk5ePHFFzFw4ECEhYVh/Pjx2LJlS6P/XQghpD0QWDsAQgjprE6fPo2Q\nkBBIpdI613FycoKTk5P59YIFC/Dbb7/h6aefRkBAAC5evIhly5YhLS0NixcvNq+n0WgwY8YMDBky\nBO+88w6OHj2KdevWQSKR4K233gIAXL58GdOmTYOXlxdeffVV6HQ6bNu2DdOmTcP69esREhJi3t+a\nNWsQHR2N9957DwKBAIwxzJkzB6mpqZg+fTo8PT2RmZmJn376CbGxsTh48CAcHR3xz3/+E/Pnz0d0\ndDQmTJhQ6xj04uJiTJo0Cfn5+Zg6dSo8PT2xb98+fPDBB8jOzsYbb7xhXjcvLw9z587FE088gQkT\nJmD37t1YtmwZnJ2dMWXKFOh0OsyePRuVlZWIjY2FXC7Hr7/+ivfffx9SqRSPPvpos/7NCCGkzTBC\nCCFtrri4mPn7+7O//e1vNZaVlZWxwsJCi4darWYJCQnM39+fbd++3WL99evXM39/f3bs2DHGGGNL\nly5l/v7+bMmSJRbrjRkzho0YMcL8esqUKWzs2LFMo9GY25RKJYuJiWHTpk0zt/n7+7NBgwYxnU5n\nbjt79izz9/dnO3bssDhGfHw88/f3Z4cOHbLY/oMPPjC/3rZtG/P392dJSUmMMca+/PJL5u/vz44e\nPWpex2g0shdffJEFBgay9PR0xhhjCxYsYP7+/mzLli3m9SorK1m/fv3Ys88+yxhj7Ny5c8zf35/9\n9ttv5nW0Wi2bMGFCjXwQQkh7Rj3phBBiBVXDM1gt1+7Hxsbi3LlzFm2vvPIKSkpKIBAIMHDgQBQV\nFZmXDRs2DBzH4fDhw4iOjja3P/zwwxb7CAwMxIEDBwCYhpecOXMGsbGxUCqVFusNGzYM8fHxUCqV\nkMvlAIDevXtDILj7J6N37944efIkZDKZuU2r1ZqH3ahUqkbn4uDBgwgODsbAgQPNbRzH4YUXXsD+\n/ftx8OBBzJw5s9bvSywWw9fX1zxVpaurKziOw6pVq2Bra4uoqCgIhUJs27at0fEQQkh7QEU6IYRY\ngYODA4RCIQoKCmose//9982Fc2lpKV577TUAQGZmJvR6PYYMGVLrPm/dulXjGNWJRCIYDAYApvHi\ngGkYy5o1a2rdX15enrlIr22YikAgwPr163Hy5Emkp6cjKysLOp0OQO0fPuqSk5ODhx56qEZ71bST\nN2/eNLcJhULY2trW+X25u7vjjTfewJIlSzBz5kzY2dlh8ODBGDduHGJiYhodEyGEWBsV6YQQYgUc\nx6F37964ePEiKioqYGNjY14WFhZmfn779m3zc6PRCAcHB3zzzTe17vPeQprHq3tugKqidubMmRg6\ndGit67i7u1vEW11BQQGeeeYZFBUVYeDAgRgzZgx69eoFAHj55ZfrPG5tGGM19l89RqFQWGcctZkz\nZw4ef/xx7N27F4cPH8Yff/yBX3/9FVOnTsWHH37YpNgIIcRaqEgnhBArefzxx3H69Gls3ry53hle\nqnh4eODYsWOIiIiwKOo1Gg32798PLy+vRh+7S5cuAEwFcPVhJgBw9uxZqFQqiESiOrePj49HTk4O\n4uPjERERYW7fvXt3o2Oo4unpievXr9doT09PBwC4ubk1el9lZWW4fPkyIiMjMWPGDMyYMQOlpaV4\n6aWXsGnTJixYsAASiaTJMRJCSFujKRgJIcRKJk6ciJCQECxZsgSHDx+usVylUiEuLs78evjw4TAY\nDDWGp2zcuBGvv/46EhMTG31sNzc3BAUFYcuWLRbj28vLy/Haa69h0aJFFmPQ71VSUgKO48xTQwKA\nTqdDfHw8AFhMCcnj8eod/hITE4PLly8jISHB3MYYw+rVq8FxHIYNG9bo7+vEiRN4/vnncfDgQXOb\nQqFA165dwXFcvWcXCCGkPaGedEIIsRKBQIDly5fjlVdewQsvvIBBgwZh0KBBkMvlSElJwe7du1FY\nWIj+/ftjwoQJ6NKlC4YOHYq4uDikp6ejX79+SElJMfdmjx07tknHf/fddzFr1iw89dRTmDx5MqRS\nKTZv3oy8vDzExcXVO7RkyJAhWLduHV544QWMHz8elZWV2L59OzIyMgBYXjjq6OiIY8eOYcuWLRg9\nenSNfc2ePRu//fYbXnzxRUybNs08BWNCQgJiY2Ph4+PT6O9p6NCh8PPzBPW/rQAAAW5JREFUw3vv\nvYfLly/Dy8sLly9fxo4dO/D000/Xe3aAEELaEyrSCSHEitzc3LBhwwbs2rULO3bswJo1a1BaWgoX\nFxcMGDAAEyZMwKBBg8zrf/vtt1ixYgV27dqFvXv3wtXVFVOnTsUrr7zS5AI0KioKGzZswNKlS7Fy\n5UpwHAd/f3+sXLmyznHqVYYNG4aPP/4Y33//PT7//HM4Ojqid+/e+Pe//40pU6bg5MmT5psevf76\n6/j666/xySef1FpwOzo6Ij4+HkuWLMHPP/8MtVqNHj164NNPP8XEiROb9D2JxWKsWbMGS5Yswfbt\n21FUVIQuXbrg1VdfxezZs5u0L0IIsSaONeUSfEIIIYQQQkiro8F5hBBCCCGEtDNUpBNCCCGEENLO\nUJFOCCGEEEJIO0NFOiGEEEIIIe0MFemEEEIIIYS0M1SkE0IIIYQQ0s5QkU4IIYQQQkg7Q0U6IYQQ\nQggh7QwV6YQQQgghhLQz/w9BsH5OWCzfSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110668f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_adaptation(k, p, W, pom, genotypes, ax=None):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(2, 1, figsize=(12, 8), sharex=True)\n",
    "\n",
    "    ax[0].plot(W[:k])\n",
    "    ax[0].set(xlabel='', ylabel='Mean fitness', ylim=(0, 1.1), xlim=(0, k))\n",
    "\n",
    "    ax[1].plot(p[pom, :k].T)\n",
    "    ax[1].set(xlabel='Generations', ylabel='Frequency')\n",
    "    ax[1].legend([genotypes[x] for x in pom], loc='center right', frameon=True, fontsize=10, handlelength=0.5)\n",
    "\n",
    "    sns.despine()\n",
    "    return ax\n",
    "    \n",
    "plot_adaptation(k, p, W, pom, genotypes);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line of descent (LOD)\n",
    "\n",
    "Line of descent ([Ostman et al. 2012](http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3223680); [Szendro et al. 2013](http://www.ncbi.nlm.nih.gov/pubmed/23267075)) is the sequence of genotypes that are the ancestors of the final population, assuming that the entire final population has a single phylogeny (likely in cases of strong sweeps, as we have in this asexual populations). We determine the LOD after the simulation ended using backward propagation: we draw a random individual in generation `t`, then draw its ancestor from generation `t-1` according to the genotype frequencies in generation `t-1`, the fitness of the different genotypes, and the mutation rates. This process is the reverse of the above simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def build_lod(p):\n",
    "    t = k - 1\n",
    "    lod = np.empty(k, dtype=int)\n",
    "    lod[t] = p[:, t].argmax() # start with the most frequent genotype\n",
    "    while t > 0:\n",
    "        gi = lod[t]\n",
    "        assert S.diagonal()[gi] > 0\n",
    "        t -= 1\n",
    "        q = p[:, t] * M[gi, :] * S.diagonal() # calculate parent probabilities\n",
    "        assert q.sum() > 0, gi\n",
    "        q /= q.sum()  # normalize probabilities vector\n",
    "        assert (q[S.diagonal() == 0] == 0).all(), q[S.diagonal() == 0]\n",
    "        gj = np.random.multinomial(1, q).argmax()  # draw a single parent & find its genotype index\n",
    "        assert S.diagonal()[gj] > 0\n",
    "        lod[t] = gj\n",
    "    return lod\n",
    "\n",
    "lod = build_lod(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want a clean LOD in which we remove consecutive duplicates. We write a [Cython](http://cython.org/) function to do this faster (Cython is an optimising static compiler for Python; we get ~4-fold increase of speed due to static type definitions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "def clean_lod(np.ndarray[long] lod):\n",
    "    cdef np.ndarray[char] take = np.ones_like(lod, dtype=np.uint8)\n",
    "    cdef int i = 1\n",
    "    cdef int n = len(lod)\n",
    "    \n",
    "    for i in range(1, n):\n",
    "        if lod[i] == lod[i - 1]:\n",
    "            take[i] = 0\n",
    "\n",
    "    return lod[take.view(dtype=np.bool)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the clean LOD has the 8 genotypes of the evolutionary trajectory (bridged by the 7 accumulated mutations), in contrast with the POM that only had 4 genotypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37, 187, 162, 204, 155,   7,  15,   3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_lod = clean_lod(lod)\n",
    "clean_lod(lod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S2N1DRKQWLg8pkydPxvLly7F3717cvHkT8fHxdiElNzcX8fHxuHLliqtLU0Th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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11762f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_lod(k, p, W, pom, genotypes):\n",
    "    fig, ax = plt.subplots(3, 1, figsize=(8, 10), sharex=True)\n",
    "\n",
    "    ax[0].plot([n - sum(map(int, genotypes[x])) for x in lod], label='LOD')\n",
    "    ax[0].plot([n - sum(map(int, genotypes[x])) for x in p[:,:k].argmax(axis=0)], label='POM')\n",
    "    ax[0].set(ylabel='# Mutations', ylim=(0, n+1))\n",
    "    ax[0].legend(loc='upper left')\n",
    "\n",
    "    ax[1].plot(lod, 'o', alpha=0.25, label='LOD')\n",
    "    ax[1].plot(p[:,:k].argmax(axis=0), 'o', alpha=0.25, label='POM')\n",
    "    ax[1].set(ylabel='Genotype')\n",
    "\n",
    "    ax[2].plot([p[lod[t], t] for t in range(k)])\n",
    "    ax[2].plot(p[:,:k].max(axis=0))\n",
    "    ax[2].set(xlabel='Time', ylabel='Freuqnecy')\n",
    "\n",
    "    fig.tight_layout()\n",
    "    sns.despine()\n",
    "    \n",
    "plot_lod(k, p, W, pom, genotypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaptive peaks\n",
    "\n",
    "Before proceeding, let's have a closer look at this adaptive landscape. \n",
    "Peaks are genotypes that are local maxima of the adaptive landscape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def flip_bit(bs, i):\n",
    "    return '{:08b}'.format(int(bs, base=2) ^ 2**i)\n",
    "\n",
    "assert flip_bit('10111110', 0) == '10111111'\n",
    "assert flip_bit('10111110', 1) == '10111100'\n",
    "assert flip_bit('00111110', 3) == '00110110'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks: 15\n"
     ]
    }
   ],
   "source": [
    "def find_peaks(genotypes, fitness):\n",
    "    peaks = collections.OrderedDict(fitness)\n",
    "    for g in genotypes:\n",
    "        for i in range(len(g)):\n",
    "            g2 = flip_bit(g, i)\n",
    "            if fitness[g2] > fitness[g]:\n",
    "                peaks.pop(g)\n",
    "                break\n",
    "    return peaks\n",
    "\n",
    "peaks = find_peaks(genotypes, fitness)\n",
    "peak_distances = [hamming(p) for p in peaks]\n",
    "print(\"Number of peaks:\", len(peaks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So out of 256 possible genotypes, 15 are peaks, or local maxima. That's almost 6%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaptive valleys\n",
    "\n",
    "Valleys are genotypes that are local minima."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of valleys: 84\n"
     ]
    }
   ],
   "source": [
    "def find_valleys(genotypes, fitness):\n",
    "    valleys = collections.OrderedDict(fitness)\n",
    "    for g in genotypes:\n",
    "        for i in range(len(g)):\n",
    "            g2 = flip_bit(g, i)        \n",
    "            if fitness[g2] and fitness[g2] < fitness[g]:\n",
    "                valleys.pop(g)\n",
    "                break\n",
    "    return valleys\n",
    "\n",
    "valleys = find_valleys(genotypes, fitness)\n",
    "valley_distances = [hamming(p) for p in valleys]\n",
    "print( \"Number of valleys:\", len(valleys))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "84 out of 256 genotypes are local minima - almost a third! However, this includes the non-vital genotypes.\n",
    "\n",
    "Let's plot all the peaks and valleys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NkTuI6erqoqqqqtHnCwoKaHAHIYSQNiF3EHNzc8Phw4fx/PnzBs89fvwYBw4c\nwPDhw5VSOUIIIaQpcs8TW7x4MXx9fTFp0iS4uLhAIBDg2LFjOHr0KJKSkqCrq0uTnQkhhLQJuVti\nlpaWOHbsGFxdXXH27FkwDIMff/wRycnJGDFiBOLi4tCvX7/WqCshhBDCIXdLDADMzc2xadMmMAyD\noqIi1NbWwsjISOpgD0I6kvT0dHz44YdITExETEwMsrOzUVpaipUrV0IkEmHDhg0wNDTEwIED4e/v\n3yCPkZERpwwACuchpCNQKIgBQG1tLUpKSsAwDDQ0NFBcXMx53tjYuMWVI4RP8vPzceTIEXTu3BlV\nVVW4du0avv76a1y5cgXx8fGoqqpCQEAAnJycEBwcjKlTpzbI884777BlAJBajix55s6dq8pLQUib\nkTuIFRcXY926dUhKSkJ1dXWj+e7fv9+iihGi7sJPb8a9/IcAAFFJFXJOPkQPox6oK36BgoICPH36\nFNHR0fD29sbjx49x7do1ZGdn4/79++jatSuio6ORnp6OJUuWYNasWXj8+DE+//xzGBoasj8KJct5\n+vQpKioqcOHCBfaHYlRUFKecp0+fquyaENLW5A5iGzZswIkTJzBixAjY2trScHrSYfnYT0bEmS0A\ngILfs9FjuBU2zg6HcEogfv31V3bfvSdPniAnJwfDhw/HO++8g507d0IkEqG0tBT9+vXD9OnTceTI\nEeTk5GDRokVwcnLC8OHDIRKJcPr0aU45+vr6DVph9+7d45RjZWWlmgtCiArIHcSSk5Ph5+eHiIiI\n1qgPIbxx5M5P7N+ismpoG+riyJ2f2En/6enpSEpKQmpqKnr27IkZM2bg22+/xePHj+Hj44PHjx/D\nxsYGR44cwe3bt+Hk5AQLCwsALxcOKC0txaxZs/D333+z5URERMDAwABCoRDAy56RHj16cMpZvny5\nSq4HIaog9+jEuro62NnZtUZdCOEVH/vJ7N86hroQPa+Cj/1kDBo0CF27dsW4ceMwZswYbN68Gb17\n94ZIJEJkZCT69OmD999/H8XFxZg9ezZ8fHzw9ttvo3fv3njy5AkAsGUA4JRjYGDAvubu3bthbGzc\noJz6efguPT0d06ZNA/By8EpERASWLFmCwsLCBs8rM09tbS127dqF6OjoVj9H0jJyt8Tc3Nxw7tw5\n+Pr6tkZ9COENO1NrDDYZiHv5D2H0Wk+Un8lDXOlBeHt7Q0uL+0/Lx8cHGzZsQEJCAvu8i4sLwsPD\nUVJSgoih/YyzAAAgAElEQVSICLx48aJBnuZIK4ev6t9jBABRaRWYW+WNDpRR1iAYyTwAEBcXh8rK\nSujp6bXtRSBykzuILVy4ECEhIVixYgXGjh0LIyMjaGg0bNA5ODgopYKEqDPxfTFtA11s2bIFdqbW\n7HMuLi5wcXEBAJiYmCAyMpJz7OzZszmPDQwMGuSRLEds9+7djZbDV/XvMQKAtoEuwj5djs0rvkBx\ncTE7bcDc3BxPnz6FiYkJlixZwulaVUYeAJg5cyauXr2KW7dutcm5E8XJHcTefPNNAEBCQgI7R6U+\nhmEgEAhodCLpEMStMfHfRHH1W7YAMNhkIHtNxd2mwMsBLqampg2OV1Yewi9yB7HPP/8cAoGgNepC\nCC/VvzdGWqZ+a6z+dZWl21RZeQi/CBjxrpYyysnJgZGRETp16iT1+ZKSEqSlpcHZ2VkpFWwtWVlZ\n8PLyQnJyMiwtLVVdHULI/ws/vfnl/z0/VnFNCB/I3RLz8vLCpk2bMHmy9F+fp06dwueff44bN260\nuHKE8EVeXh5nSanKykq5l4pSdFmq2tpaxMTEQCAQICQkRJWXQSmoZUvk0WwQy8rKQkxMDPuYYRh8\n//33uHbtWoO8dXV1uHLlCo3oIR3O4cOH2SWlAgMDoaOjg5iYGLlGySmyLBXQ/kbS0b1FIo9mg5il\npSUyMjJw6dIlAIBAIMDly5dx+fLlBnk1NDRgZGSExYsXy1yB+Ph4xMTE4MmTJ7C1tcXy5cvh6Ogo\n07Hbt2/Hjh078ODBA5lfjxBlqT8kPPNqKs50vomhxUPAMAw7YECeUXIikYid7Ny1a1eUlJTQSDpC\nmiFTd+KePXvYvwcNGoRNmzaxoxRbIjExEWFhYZg3bx6GDBmCAwcOQCgU4vjx480unZOWloZdu3a1\nuA6EKKr+IIT6k5236N5DQUEBAPlGydXV1eHJkyewsLDA8+fPYWpqSiPpCGmGQstOKWOFeoZhsG3b\nNvj6+mL+/PkAXk6kHj9+PPbt24fVq1c3emxtbS1WrVoFIyMj5OXltbguhCiiqcnOlZWVco+SkzbZ\nmUbSEdK0ZkcnfvPNN/D09MSAAQPYx80WKhBgzpw5TeZ5/Pgxxo0bh+joaIwaNYpNX7duHS5cuIBT\np041euzu3btx9OhRvPXWW4iMjFSoO5FGJxJluPs0jW2NhY3+iO7nENLGmm2JRUZGwtzcnA1i0lYU\nkCRrEAOAPn36cNKtrKyQkZGB2tpaqZtspqenY8eOHfjmm29w586dZutCSGuiyc6EqFazQUyy+zA5\nOVkpL1xWVgYA0NfX56Tr6+ujrq4OlZWV6NKlC+c5hmGwevVqTJkyBc7OzhTEiFqgIeGEqE6zQWzM\nmDGceWG9evUC8DII6enpSV03URbiXkzJ1T8aSwdeDmNOT09HVFSUXK8VFxeHuLg4TlpTG3oSIg9q\ngRGiOs0GMYZhIHnbrKioCG5ubtizZw9cXV0VemHxdhHl5eXo0aMHm15RUQENDY0Gc15yc3OxadMm\nrF+/Hp06dUJNTQ1br5qaGmhoaDQaUP38/ODn58dJE98TI4QQwl9yj04Uk3O1qgbE98IyMzM598Uy\nMzPRr1+/Bi2xy5cvo7y8HAsXLmxQlp2dHebPn48FCxa0qE6EEEL4ReEg1lJ9+/aFhYUFkpKS4O7u\nDgAQiURISUmBh4dHg/yjR4/G0aNHOWknTpzAt99+i6NHj9IcGkII6YBUFsQEAgGCg4Oxbt06GBoa\nwsnJCQcPHkRRURGCgoIAABkZGSgsLMTQoUPRvXt3dO/enVPGH3/8AQAYMmRIW1efEEKIGlBZEAMA\nf39/VFVVYf/+/di7dy9sbW2xe/dudrWOnTt3IiEhgZaVIoQQIlWzk50HDRqEVatWcQZBPH/+nJ1o\n3Ng6hz179lRuTZWMJjsTQgj/yRTEpA13F+/g3Bh139mZghghhPBfs92J4nUNCSGEEHVDQYwQQghv\nKbbcBiGEEKIGKIgRQkgLpaenY9q0aQBebhW1a9cuREdHAwDy8vLw0UcfITw8HLGxsVLzSJYha56Y\nmBhERERgyZIlKCwsbNVzVFcUxAghpAXy8/Nx5MgRdO7cGcDLtVorKyvZ5w8fPoyAgACEh4cjJSUF\nIpGoQR7JMqSVI5mnqqoK165dQ1hYGKZPn474+PjWPlW1REGMEELkEH56M3zjPmD/m3d6Dcqc/rfe\n68yZMzlryj579gwWFhYAgK5du6K0tLRBHhMTEyxZsoSzZmxzeYqLi2FkZAQAMDc3x9OnT1vvpNUY\nBTFCCJGDtK13mtqOx8LCAk+ePAHwco5t165dlVIPY2NjFBcXAwCePHnSYZfeU+mKHYQQwjfijVDv\n5T8EAAw2Gdjkdjw+Pj7YsGEDEhIS4O3tDS0t5XztamlpwcXFBeHh4SgpKUFERIRSyuWbZic7t1c0\n2ZkQoqi7T9MQcWYLACBs9Ee0p5wKUUuMEELkJG6Nif8mqkNBjBBCFNDUfTDSdiiIEUKIAqgFph5o\ndCIhHVD9SbPSJsw2N/FW2gReWcqhyblE2SiIEdLB1J80K23CrCwTbyUn8JaVlTVbDk3OJa2BuhMJ\naefCT29mh4OLDXYaCL37elInzIon1QqFQjb/zJkzcfXqVdy6dQtAwwm8JSUlzZZDk3NJa6CWGCHt\nXFOTcxWdMCs5gdfU1LTZcmhyLmkN1BIjpJ1ranKuohNmpU3gba4cmpxLWgNNdqbJzqQDoMm5pL2i\nlhghHQBNziXtFQUxQjoImpxL2iMKYoR0ENQCI+0RjU4khBDCWxTECCGE8BYFMUIIIbxFQYwQQghv\nURAjhBDCWyoPYvHx8fD29oaDgwP8/Pxw48aNJvNfv34dAQEBcHZ2hru7O5YuXYpnz561UW0JIYSo\nE5UGscTERISFhWHKlCnYvn07DAwMIBQKkZmZKTX/o0ePEBQUBH19fURGRmLZsmW4fv06hEIhRCJR\nG9eeEEKIqqlsnhjDMNi2bRt8fX0xf/58AICbmxvGjx+Pffv2YfXq1Q2OOXjwIExMTLB9+3Zoa2sD\nAPr06QMfHx9cunQJo0aNatNzIIQQoloqC2Lp6enIzs6Gp6cnm6atrQ0PDw+cP39e6jGvvPIKXnnl\nFTaAAUD//v0BvFwLkRBCSMeisiD2+PFjAC9bUvVZWVkhIyMDtbW10NTU5Dzn7+/foJzTp08D+F8w\nI4QQ0nGoLIiVlZUBAPT19Tnp+vr6qKurQ2VlJbp06dJkGbm5udi4cSPs7e0xfPjwRvPFxcUhLi6O\nk1ZdXa1gzQkhhKgLld4TAwCBQCBTuqTc3FwEBQWhrq4OW7ZsaTK/n58f/Pz8OGnirVgIIYTwl8pG\nJxoYGAAAysvLOekVFRXQ0NCAnp5eo8empaVhxowZKCsrw549e9C7d+9WrSshhBD1pLIgJr4XJjmc\nPjMzE/369Wu0ZXXr1i3MmjULmpqaiI2NxaBBg1q9roQQQtSTyoJY3759YWFhgaSkJDZNJBIhJSUF\nrq6uUo/JyspCcHAwjI2NcejQIfTt27eNaksIIUQdqeyemEAgQHBwMNatWwdDQ0M4OTnh4MGDKCoq\nQlBQEAAgIyMDhYWFGDp0KADgs88+Q1lZGdasWYPc3Fzk5uay5fXs2ROmpqaqOBVCCCEqotJNMf39\n/VFVVYX9+/dj7969sLW1xe7du2FlZQUA2LlzJxISEvDgwQOIRCKcO3cOtbW1WLx4cYOyli5dCqFQ\n2NanQAghRIUEjHg4YAcjHp2YnJwMS0tLVVeHEEKIAlS+ADAhhBCiKApihBBCeIuCGCGEEN6iIEYI\nIYS3KIgRQgjhLQpihBBCeIuCGCGEEN6iIEYIIYS3KIgRQgjhLQpihBBCeIuCGCGEEN6iIEYIIYS3\nKIgRQgjhLQpihBBCeIuCGCGEEN6iIEYIIYS3KIgRQgjhLQpihBBCeIuCGCGEEN6iIEYIIYS3KIgR\nQgjhLQpihBBCeIuCGCGEEN6iIEYIIYS3KIgRQgjhLQpihBBCeEvlQSw+Ph7e3t5wcHCAn58fbty4\n0WT+tLQ0zJ49G46OjvDw8EB0dDQYhmmj2hJCCFEnKg1iiYmJCAsLw5QpU7B9+3YYGBhAKBQiMzNT\nav6CggK89957EAgE2Lp1K3x9fbF161bs2bOnjWtOCCFEHWip6oUZhsG2bdvg6+uL+fPnAwDc3Nww\nfvx47Nu3D6tXr25wTGxsLGpqahAVFYXOnTtj1KhRqK6uRnR0NAIDA6Gtrd3Wp0EIIUSFVBbE0tPT\nkZ2dDU9PTzZNW1sbHh4eOH/+vNRjLl26BFdXV3Tu3JlNGzNmDKKionD79m04OTm1er35LPz0ZtzL\nf8hJG2wyEOGeH6uoRtLNiJ+HOqaOk6Yh0MBh369UVCPpfOM+kJoe7xfVxjVpGtVTuaieyiX5vSRv\n/VTWnfj48WMAQJ8+fTjpVlZWyMjIQG1trdRjpOWvXx5pnI/9ZJnSVG1sf3eZ0lRNAwKZ0kj70klT\nR6Y0VRvW00GmNFVr6XeQyoJYWVkZAEBfX5+Trq+vj7q6OlRWVko9Rlr++uWRxtmZWmOwyUD28WCT\ngbAztVZhjaQTOr8LDcH/PpoaAg0Ind9VYY2kO+y3U6Y0VZP2y1bdfo0D/Knn/un/kSlN1T4Z0bAl\nJi1N1SS/l+Sl0ntiACAQCGRKb46GRuPxOC4uDnFxcZy06upqucpvL3zsJyPizBb2b3U1tr87Tj06\nx/6trjQgQB0Y9m/SMXTS1MGL2mr2b3U1rKcDfs/5k/1bXdX/XpKXylpiBgYGAIDy8nJOekVFBTQ0\nNKCnp9fgmC5dujTIL37cpUuXRl/Lz88Px44d4/z39ddft/QUeEn8q0ddW2Fi4taYurbCxOq3vNSx\nFSZWv0Wjjq0bMb7Us37LSx1bYWL1W17q2AoTq/+9JC+VtcTE97YyMzM597kyMzPRr18/qS2xvn37\nIisri5MmHo7fv3//Vqxt+6LOLbD61LkFVh+1wDomdW6B1afOLbD6FP1eUlkQ69u3LywsLJCUlAR3\n95dfViKRCCkpKfDw8JB6zPDhwxEXF4eKigq2pZaUlIRu3bph0KBBbVV13lPnFlh96twCq0+dW2D1\nqXPLpj6+1FOdW2D1qXMLrD5Fv5dUFsQEAgGCg4Oxbt06GBoawsnJCQcPHkRRURGCgoIAABkZGSgs\nLMTQoUMBADNnzsTBgwcREhICoVCI1NRUREdHY/HixdDR4cevIkIIIcqj0hU7/P39sXTpUhw/fhwL\nFy5EaWkpdu/ezQ6b37lzJ/z8/Nj8pqam+Pbbb1FTU4OFCxciPj4eixYtglAoVNUpEEIIUSEB00EX\nHszKyoKXlxeSk5NhaWmp6uoQQghRgMoXACaEEEIURUGMEEIIb1EQI4QQwlsUxAghhPAWBTFCCCG8\nRUGMEEIIb1EQI4QQwlsUxAghhPAWBTFCCCG8RUGMEEIIb1EQI4QQwlsUxAghhPCWyrZiUbXa2loA\nwJMnT1RcE0IIIWLm5ubQ0pI9NHXYIJafnw/g5XYwhBBC1IO8O4t02K1YXrx4gTt37sDExASamppK\nK3fu3Ln4+uuvlVZeR0fXU7noeioXXU/lmjt3LhITE6klJotOnTrB2dlZ6eXq6OjQ/mRKRNdTueh6\nKhddT+XS0dGRK4ABNLCDEEIIj1EQI4QQwlsUxAghhPCWZnh4eLiqK9He2Nvbq7oK7QpdT+Wi66lc\ndD2VS97r2WFHJxJCCOE/6k4khBDCWxTECCGE8BYFMUIIIbxFQYwQQghvURBTkvj4eHh7e8PBwQF+\nfn64ceOGqqvEW7W1tfj2228xYcIEDB06FBMnTsTBgwdBY5Barrq6GhMmTMDy5ctVXRVeu3z5Mnx8\nfODg4IDRo0dj27Zt7KLiRD61tbX45ptvMHbsWDg6OsLHxweXL1+W+XgKYkqQmJiIsLAwTJkyBdu3\nb4eBgQGEQiEyMzNVXTVe2rlzJzZv3owpU6YgKioKEyZMwOeff46YmBhVV433duzYgb///lvV1eC1\nP/74A8HBwRgwYAB27doFf39/fPPNN4iKilJ11Xhp9+7d2LJlC9555x189dVX6N27N4KDg3Hv3j2Z\njqch9i3EMAy8vLwwYsQIREREAABEIhHGjx+P0aNHY/Xq1SquIb/U1dXB2dkZgYGBWLRoEZseERGB\nkydPyvULjXDdu3cP/v7+0NXVhYeHBzZs2KDqKvHSzJkzYWBggF27drFpX375JW7duoUDBw6osGb8\nNGHCBAwZMgQbN24E8LJl5uXlBU9PT6xZs6bZ4zvsAsDKkp6ejuzsbHh6erJp2tra8PDwwPnz51VY\nM34qLS3FtGnT4O3tzUnv168fCgsLUVFRAT09PRXVjr9qamqwcuVKCIVC/Prrr6quDm8VFhbi+vXr\n+OqrrzjpS5YsUVGN+K+6uhpdunRhH2tqasLAwADPnz+X6XjqTmyhx48fAwD69OnDSbeyskJGRgb1\nk8vJ0NAQa9asweDBgznpZ86cgbm5OQUwBX3zzTcQiUQICQlRdVV47cGDB2AYBnp6epg7dy6GDBkC\nV1dXbN++HXV1daquHi/5+/vj+PHjuHz5MkpLS7Fv3z48fPgQEydOlOl4aom1UFlZGQBAX1+fk66v\nr4+6ujpUVlZyfmUQ+R05cgSXLl2irlkFPXr0CF9//TX27t0LHR0dVVeH14qKigAAS5cuxeTJkxEU\nFITff/8dUVFR0NXVpR8JCnj33Xdx5coVBAUFsWmLFi2Cl5eXTMdTEGsh8S1FgUAgUzqRzw8//ICw\nsDCMGzcOs2bNUnV1eKeurg6rVq3C9OnT4ejoqOrq8J5IJAIAuLu7Y9myZQCA4cOHo6ioCFFRURAK\nhUrdZLe9YxgGQqEQjx49QlhYGAYMGIBLly7hq6++QteuXeHv799sGRTEWsjAwAAAUF5ejh49erDp\nFRUV0NDQoO6vFti7dy82bNgAT09PfPnll/SDQAEHDhxATk4Odu3ahZqaGjadYRjU1NTIvQFhRyfu\ncRkxYgQn3c3NDbGxscjOzkbv3r1VUTVe+uOPP/DHH39g69atmDBhAgDAxcUFtbW12LRpE6ZNm9ag\nl0sS3RNrIfG9MMnh9JmZmejXrx998Spo8+bNWL9+PaZOnYpt27ZRN5iCkpKSkJeXh9dffx12dnaw\ns7NDamoqEhMTYWdnh6ysLFVXkVfEAUrcIhMT/0Cgf+/yefLkCQBg6NChnPTXXnsNlZWVyM7ObrYM\n+hnWQn379oWFhQWSkpLg7u4O4OUHPCUlBR4eHqqtHE/t27cPu3btQmBgIFauXElfDC0QERGB8vJy\nTtqSJUvQr18/zJs3D6ampiqqGT+98sorMDMzw8mTJzF16lQ2/ezZszA1NUWvXr1UWDv+6du3LwDg\n+vXrmDRpEpt+69YtaGlpwdzcvNkyKIi1kEAgQHBwMNatWwdDQ0M4OTnh4MGDKCoq4tyoJLJ5+vQp\nvvzyS1hbW2PSpEm4desW53l7e3vqApND//79G6R16tQJ3bp1w5AhQ1RQI37T0NDAxx9/jGXLliEs\nLAzjx4/HpUuXkJCQgPDwcGhoUOeWPOzt7eHh4YGIiAgUFxdjwIAB+O233xATE4PAwEB07dq12TJo\nsrOS7NmzB/v370dRURFsbW2xbNkyupGugGPHjmHFihWNPn/58mUYGRm1YY3an6lTp8LW1pYmO7fA\nTz/9hF27duHx48ewsLCAUCiEn5+fqqvFSy9evMDWrVtx4sQJPH/+HH369MHMmTMxY8YMmXphKIgR\nQgjhLWr7EkII4S0KYoQQQniLghghhBDeoiBGCCGEtyiIEUII4S0KYoQQQniLglgrWr58eZMTSo8d\nOwYbGxtcvXq1DWulmKysLNjY2CA6OlrVVYGnpyeEQmGjj2XF5523s7Ky4O/vDwcHBwwfPlzmvZfa\nSnV1NZYvXw5HR0e89tprOHv2LGxsbBpscijtPWjN94XP77lYezgHZaKlD4hMjIyMsHHjxgb7fKmD\nlStXNrtIqKT3338flpaWWLt2bSvVqnV98cUXuHnzJhYsWABjY2MYGhqqukoccXFxSEhIwDvvvANH\nR0cMHjwYGzduRL9+/dg8n376KXJzcxETE8Omteb7cvToUWzYsAHXrl1TetltpT2cg7JRECMy0dPT\n46wVp07GjBkj9zEXL17k9QoLaWlpGDJkCObOnavqqkiVlpYGgUCA8PBwdvFmyc/PxYsXOUFNnNZa\n78u1a9dQVVXVKmW3lfZwDspG3YmE8JBIJJK79dmWRCIRtLW1afcB0uooiKmZvLw8rFmzBh4eHrC3\nt8frr7+OuXPn4tGjR2we8b20tLQ0zJ07F46OjnB3d0d0dDTq6uoQHR2NkSNHwtnZGQsWLEBhYSF7\nbEBAABYuXIiff/4ZkyZNgoODA95++23cunULeXl5mDdvHhwdHTF69Gjs2bOHPU7yntjVq1dhY2OD\nK1euYMWKFXBxcYGjoyNCQ0MbbO+Rk5ODDz/8EK+//jpcXFywdu1axMXFwcbGptmtQM6dO4fp06fj\n1VdfxcSJE3H58uUGeSTviaWmpiIoKAguLi4YOnQo/Pz8kJyczDkPAA3q8Pfff2Px4sVwd3eHvb09\nXF1dsXjxYuTl5bFlb9++Hc7Oznj48CHee+89DB06FK6urvj3v/+NFy9ecOr18OFDzJs3D6+//jpe\nf/11hISEIC0tjZPn6tWrmDVrFoYOHYphw4Zh4cKFyMjIaPR6iK97dnY2Lly4ABsbG2zfvp1NT0xM\nxPjx4+Hg4IDIyEgAL/e2++KLLzBq1CjY29tj3LhxiI6ORm1tLee8XFxckJqaioCAALz66qvw9PTE\n999/j+rqanzxxRcYPnw4XFxcsGrVKlRWVjZaRxsbGyQkJKC6uho2NjYICAhg08X3xCTPQfyZlva+\nSNZ/woQJiI2N5bxmdXU11q5dC09PT9jb27N70IlbLQEBAZw6bd++vdH6i/8NeXl5wcHBATNnzsT9\n+/cxePBgznE1NTWIiorC2LFjYW9vDy8vL+zYsYOzb5s8n5d79+4hJCQETk5OGDp0KAICAvD777+z\nz0s7h4ULF8LR0bFBWffv34eNjQ1++OEH9jN//PhxhIeHw9nZGS4uLlixYgXnu0HWa61uqDuxDUh+\nUMQqKio4j1+8eAF/f39UVVVh5syZ6NGjBx48eIC4uDg8ePAASUlJnF1j58yZA1dXVyxbtgyJiYmI\njIzE1atX8eTJE8yZMwfZ2dnYt28f9PT08MUXX7DH3bhxA7/99huCgoKgpaWFqKgoLFy4EF26dIGt\nrS2WL1+OhIQEfPHFFxg8eDCGDx/e6LktX74cVlZWWLhwIbKzs7F37148e/YM8fHxAIDS0lLMmjUL\nz58/R1BQEDp16oRDhw7hxIkTzV63CxcuYO7cubCxscHixYuRlZWF0NBQ1NbWNuiGqn+thUIhjI2N\nMX/+fGhqauLo0aOYN28evvvuOwwaNAgbN27E0qVLMXz4cLz99tswMjLC06dPMWPGDHTv3h3vv/8+\n9PX1cfPmTSQmJiInJweHDh1iX6OqqgpBQUEYMWIEVqxYgYsXL+LAgQPo1KkTlixZAgB49OgR/Pz8\noKenh/feew96enrYt28fAgMDkZiYCHNzc5w9exahoaF49dVXsXjxYpSUlODQoUPw8/PD999/j549\nezY4vwEDBmDjxo1Yv349TE1NIRQKYWNjww7siIiIwIwZM2Bqago7OztUV1fjvffew+3bt+Hj4wNr\na2tcvXoVkZGRePDgARvoxJ+/9957D5MmTcL48eMRGxuLVatW4aeffkJlZSUWLFiA27dv4+jRozAz\nM8PChQulvgcbN25EfHw8bt26hc8++4yzWWz9PPXPwdnZWer7UlNTg+DgYNy7dw/vvvsuLC0tceXK\nFaxduxa5ubns9Y6IiMDPP/+MwMBAWFpa4s8//8Q333yDkpISrF27FnPnzkVdXR1bJ3HAlGb9+vXY\nv38/Jk6cCGdnZ5w/fx6BgYGoq6vj5Fu2bBlOnjwJHx8f2NjY4M6dO9ixYwcePXqELVu2yPV5uXnz\nJgIDA2FsbIyQkBBoaWnhyJEjCAoKws6dOzFq1Cip52BtbY1Tp07h3Llz8Pb2Zl/zl19+ga6uLjw9\nPVFcXAwA2LJlC7S0tBAaGor8/HwcOHAAd+/exffffw9tbW2Zr7XaYUirWbZsGWNtbd3sf1euXGEY\nhmFOnDjBWFtbM7///junnMjISMba2pp5+PAhwzAM8/333zPW1tbMsmXL2DwZGRmMtbU14+TkxBQU\nFLDps2fPZt544w328axZszivyTAMs3XrVsba2pr5+OOP2bTc3FzG2tqaiYyMZBiGYTIzMxlra2tm\n165dDMMwzJUrVxhra2vG39+fqaurY49bs2YNY21tzWRnZzMMwzD/+c9/GGtra+b69etsnry8PMbR\n0ZGxtrZmMjMzG71+b731FjN27FimoqKCTUtISGCsra2Z999/n00bPXo0+1h8DW/fvs0+X1RUxIwb\nN4757rvv2DRra2vm008/ZR/v2rWLsbW1bVCfjz/+mLG2tmbKy8sZhmGYbdu2MdbW1szWrVs5+SZM\nmMB4enqyjxcsWMA4OTkxOTk5bNo///zDDBo0iNm2bRtTU1PDjB49mgkKCuJcvydPnjBOTk7M0qVL\nG70ukufMMP97PxYsWMDJd/DgQcba2pqJj4/npK9bt46xtrZmLl26xDmvbdu2sXkuXbrEWFtbM6NH\nj2aqqqrY9DFjxjB+fn5N1m/ZsmWMvb09J03ymkueg7Q88fHxjI2NDfPbb79x8m3YsIEZNGgQk56e\nzjAMwzg4ODBr167l5Fm5ciUjFAqbrJOk9PR0xtbWllMHhmGYhQsXcq6P+NokJCRw8omv9+XLlxmG\nkf3z8s477zDDhg3j/NstKSlhRo4cyYwePZqpqamReg4vXrxgnJycOP92GYZhxo4dy34WxP92hw0b\nxsyXuP4AAAyOSURBVDx79ozNc/ToUc5nQ9ZrrW6oO7GVaWtr49tvv5X6n+SwcHF3mbOzM5tWWVnJ\nbkcg2XLz9PRk/7aysoKWlhYcHR05W5VYWlri2bNnnOO6dOmC119/nX0s3pjOy8uLTTM3N4eWlhby\n8/ObPL+xY8dytkuwtbUFAPY1k5OT8eqrr3K2pTE1NcWUKVOaLLegoAB3797Fm2++ic6dO7PpU6ZM\nQbdu3Ro9TryJ3pYtW3Djxg3U1dWhW7duOHnyJN59991GjwsJCcGFCxdgaWnJppWWlkJbWxsAGnSf\njRs3jvN40KBBKCgoAPCyO+r8+fPw9PSEhYUFm6dv3774/vvvERgYiPv37yM7OxteXl4oKipCYWEh\nCgsLoa2tjWHDhiElJaXJ69OY1157jfP4zJkzMDIywttvv81JFw8IEXezitX/TIlbuyNGjODc27K0\ntGz2c6EsycnJMDc3x4ABA9hrVFhYCC8vL9TV1eHcuXMAXr7vv/zyC44fP46ysjIAwGeffcYZ+SiL\n06dPo7a2FrNnz+akS/5bTUpKgpaWFtzc3Dj1GjVqFAQCAc6ePcvJ39TnJT8/H7dv32Zbn2IGBgbw\n9/dHdnY2UlNTpdZXV1cXY8aMwZkzZ9iu07t37yI9PR0TJkzg5H3rrbdgbGzMPp42bRoMDQ3Zusp6\nrdUNdSe2MoFAADc3N6nPibfmlrRz507cvHkT//zzD7Kzs9l7F5LdGZL7amloaHA+pOI0RmK3HSMj\nI07gEW8yKa08yWMlSR4j/rIT1zUjI4MTHMUa6w4UE29LbmVl1aBO4i3ipXFycoK/vz9iY2Nx4cIF\nGBsbY9SoUXj77bcxbNiwJl/zxYsXiIyMxN27d/HPP/8gNzeXPX/Ja9+9e3fOYx0dHfZ9Ki4uRkVF\nBfr06dPgNcRTFC5evAgAWLduHdatW9dofTp16tRknSVJvh/Z2dno3bs3pxsaAHr06AFDQ0Pk5ORw\n0ut/fsTHyPKZai0ZGRnIzc2Fq6ur1Odzc3MBAGFhYfjwww+xdOlS9ofAuHHj8NZbb0FXV1eu1xMI\nBA0+d5Kf14yMDNTU1GDEiBFN1kusqc+L+D0Q/5isb8CAAWx5dnZ2Ul9r4sSJSExMxPnz5zFmzBj8\n8ssv0NPTw+jRo6WWJaapqQlLS0v235qs11rdUBBTI48ePcLMmTPBMAzc3Nzw9ttvw87ODpmZmVLn\nzUjb4ViWTeQkv9DkOVZSczvZ1tTUsK2Z+mT9YpE2nFgyoEhas2YNAgICcPLkSZw9exbHjx/HsWPH\n8Mknn2DOnDlSj7l69SqCg4PRtWtXuLm5wdXVFQ4ODrh48SJ27drVIH9T5y3+cmrqeorPYcmSJY1+\nOSmyg7VkvRiGabQetbW1Dd4baZ8NRT4XylJbW4uBAwdi5cqVUp8Xt3Td3Nxw5swZJCUlISUlBRcv\nXsSlS5dw+PBhHDlyROpnUJqamhoIBIIG+SU/r3V1dejevTs2b94stRxpPwgbI/5BIO06iz8nTdX/\njTfeQPfu3XHq1CmMGTMGJ0+ehKenZ4MfQNLKqP8ZkPVaqxsKYmokJiYG5eXlOHXqFHr16sWmq8Mq\nGYqysrJCenp6g3RpafVZWlpCIBDg8ePHnHSGYZCdnd1ol2JBQQHS0tLg6uqKDz74AB988AHy8vIQ\nGBiIvXv3NhrEduzYAQMDA5w4cYIzcViWASiSunfvjk6dOkldWWHjxo0wNTVlV3IxMDBo0FK/fPky\nNDU1FQpiknr16oV79+6htraWE6Dy8/NRVlYGMzOzFr9Ga+rZsycePnwIV1dXzpd8YWEhrl27Bj09\nPVRXV+P+/fswNzfHtGnTMG3aNIhEInz55ZfYu3cvrl69Cnd3d5lez8rKCnV1dcjMzOS0+CU/hxYW\nFrh8+TIcHR053d1VVVVITk7mdEs3R/xv/e+//27w3D///AMATb5PWlpaGDduHE6ePIm0tDRkZmZK\n3R1d8vNYU1OD7OxsjBw5EoBs11od0T0xNVJUVIQuXbpwPrBlZWVISEgAAM7QXb7w8vLCzZs3OX36\nz58/x08//dTkcUZGRnB0dERCQgJnSaWTJ0+iqKio0eN+/PFHBAUF4c6dO2yamZkZzMzMOEFBskus\nuLgYJiYmnACWl5eHX3/9FQA4w9GbI75Xcvr0ac79yMzMTOzfvx+FhYUYMmQIjI2NsX//fs7w6Nzc\nXISGhuKrr76S+fWaMnr0aBQWFuLYsWOcdPEPIw8PD6W8jiKkdUtKpnl4eCA/Px+JiYmcfDt37sSC\nBQuQmZmJsrIyvPvuu5wfe9ra2uz9WXHwlqUb1NPTEwKBgDMaFUCDYeYeHh6ora3F7t27OemHDh3C\nRx99hOvXrzf5OvWZmJjAzs4OCQkJnJHMZWVl+O6772Bubs6OpmzsHCZPnozi4mJ89dVXMDAwkNrN\nmZCQwLm3e+zYMZSWlrKLBchyrdURtcTUyMiRI3HmzBmEhoZizJgxKCwsxNGjR/H06VMAQHl5uYpr\nKD+hUIjjx48jICAAs2fPhp6eHuLi4tjA1FRX1fLlyxEQEABfX1+8++67KCgowIEDB5pcYunNN99E\nTEwM5s6dC39/f/xfe/cX0lQfBnD8u8AjFFlOlCVeCGEXUq2GsxtZF4bVUqOGcwqBRnljRiYKFWbU\nioxs6UhHpGTTZWWracG0IAii5kX0T0K86KJQlFARBElkXXV4fZdv2Vu54fO52+Gw85zfOezZOc9z\nficuLo6+vj4CgQAVFRXqelqtlufPn3Pnzh2ysrIwmUxcu3aNyspK0tPTGRoa4vbt2+qYL3Tsjx49\nitVqxWq1YrPZiIqKwu12ExsbS1FREYqicOzYMSorK8nLy2PPnj0Eg0Ha29uZnZ2dE+v/YbVa8Xq9\n1NTU0N/fr7bY+/1+duzYMW+99m/QarX09/fT0dGByWQiMTEx5LjYbDbu3bvHiRMnePXqFampqbx8\n+ZL79++TlZWlNkHt3r0bj8fD9PQ0mzZtYmRkBLfbTUpKiloL1Wq1zMzM0NjYSEZGBhs3bgyJae3a\nteTn59PS0sLo6CgGg4FAIKA22nw7XzMzMzGZTDidTj58+IDRaGRwcJCOjg42b96M2Wxe0FgcP36c\n4uJiLBYLNptNbbEfHR3F6XSq251vH9LS0tDpdPj9fvbu3fvdh8w/f/6MzWbDYrHw8eNHPB4P6enp\nagPIz451uJEkFkYKCgqYmJigs7OTFy9eEB8fz5YtWzhw4AC7du2ir6+PrVu3LnaYC7J69Wra2to4\nd+4czc3NREVFkZubi6Io6uf56PV6Wltbqaurw+FwEB8fz+nTp0P+Jf9TXFwc169f5/Lly7S1tTE5\nOUlycjInT56ksLBQXa+8vJy6ujrsdjvJyckcPnyYmZkZ/H4/vb296HQ6srOz2b59OwUFBQQCgZDC\n+H9JSUnh5s2bXLx4kaamJqKjozEajVRVVan1kpycHGJiYnC5XDQ0NKAoChs2bMDhcHz3B/ZXKIpC\na2sr9fX19PT0cPfuXZKSkqiqqqKoqOi3bONXlZaWUlNTw9mzZ1m+fDm5ubkhx8VoNHLjxg0aGhp4\n9OgRXq+XNWvWUFZWxsGDB9XvOnXqFDqdjocPH9LV1UVMTAzbtm2jvLxcvQLPz8/n2bNnNDY2MjIy\nMu8YV1dXExsbi9frpbe3F71ez6VLlygtLVXPV41Gw5UrV2hqaqK7u5uenh4SEhIoLCzk0KFDC56p\nJC0tjfb2durr63G5XCxbtgy9Xo/dbp+TPObbB41Gg9lspqWlZd4EWlJSwvDwMA6HgxUrVrBv3z6O\nHDmiJsjo6OifGutwown+rTYjsSSNjY2xatWqkIaBM2fO4PF4eP36tUxNJMLGtyvuf0/p9e7dOywW\nC3a7nby8vMUI7Ydqa2vx+Xw8ffp0zq3zT58+kZmZSUVFBSUlJYsY4Z8hNTHxR9XW1mIymfjy5Yu6\nbHp6midPnrBu3TpJYCKsvH37FoPBwOPHj+cs9/v9AKxfv34xwvqhqakpHjx4QHZ29m9pCIokS2tv\nxV+Xk5ODz+dj//797Ny5k9nZWXw+H8PDwyHvlhJisRkMBpKSkqiurmZgYICEhATevHlDZ2cnZrNZ\nbRYJF0NDQ1y4cIH3798zMTGhzlO5lEgSE39URkYGLpeLq1ev4nA4CAaDpKam0tzcvKhNBUJ8j6Io\nuN1unE4nt27dYnx8nMTERMrKysLyVtzKlSvVd4udP38+5CHtpUBqYkIIISKW1MSEEEJELEliQggh\nIpYkMSGEEBFLkpgQQoiIJUlMCCFExJIkJoQQImJ9BWWCz3SsFhS7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117635550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_peaks_valleys(peaks, valleys):\n",
    "    peak_distances = [hamming(p) for p in peaks]\n",
    "    valley_distances = [hamming(p) for p in valleys]\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(peak_distances, tuple(peaks.values()), '^')\n",
    "    for x,y,z in zip(peak_distances, list(peaks.values()), list(peaks.keys())):\n",
    "        ax.text(x, y, z, fontsize=8)\n",
    "\n",
    "    ax.plot(valley_distances, tuple(valleys.values()), 'v')\n",
    "    for x,y,z in zip(valley_distances, list(valleys.values()), list(valleys.keys())):\n",
    "        if y > 0:\n",
    "            ax.text(x, y, z, fontsize=8)\n",
    "    ax.set(xlim=(-0.5, n), \n",
    "           ylim=(-0.1, 1.1),\n",
    "           xlabel=\"Hamming distance from fittest genotype\",\n",
    "           ylabel=\"Fitness\"\n",
    "    )\n",
    "    sns.despine()\n",
    "    return fig, ax\n",
    "\n",
    "plot_peaks_valleys(peaks, valleys);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Landscape simulation plot\n",
    "\n",
    "We will now run many simulations to get some statistics on frequent trajectories (LODs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "repetitions = 1000\n",
    "results = (adaptation(genotypes, verbose=False) for _ in range(repetitions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lods = [None] * repetitions\n",
    "cleaned_lods = [None] * repetitions\n",
    "for i, (k, p, W, pom ) in enumerate(results):    \n",
    "    lods[i] = build_lod(p)\n",
    "    cleaned_lods[i] = clean_lod(lods[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genotype_freq = np.zeros(m)\n",
    "\n",
    "for lod in lods:\n",
    "    for gi in lod:\n",
    "        genotype_freq[gi] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MVFRUFP9+9Yy50nhp/VJYq1atHttuZWWFhg0bon///vjggw9Qq1atag1QG3zRgejpSKVS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\nRERERGTS/g+P9JgQvOsz0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179dc2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_genotype_freq(genotype_freq):\n",
    "    fig, ax = plt.subplots(1, 2, sharey=True, figsize=(10,4))\n",
    "\n",
    "    ax[0].plot(\n",
    "        np.log10(genotype_freq[genotype_freq > 0]), \n",
    "        '.k'\n",
    "    )\n",
    "    ax[0].set(\n",
    "        xlabel='genotype',\n",
    "        ylabel='log10 occurences',\n",
    "        xlim=(-1, n + 1),\n",
    "    )\n",
    "\n",
    "    ax[1].plot(\n",
    "        [hamming(genotypes[gi]) for gi, _ in enumerate(genotype_freq[genotype_freq > 0])], \n",
    "        np.log10(genotype_freq[genotype_freq > 0]), \n",
    "        '.k'\n",
    "    )\n",
    "\n",
    "    ax[1].set(\n",
    "        xlabel='# mutations',\n",
    "        xlim=(-1, n + 1),\n",
    "    )\n",
    "\n",
    "    fig.tight_layout()\n",
    "    sns.despine()\n",
    "    return fig, (ax)\n",
    "\n",
    "plot_genotype_freq(genotype_freq);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the above figure, on the left, the four genotypes that were very frequent and several other genotypes that were frequent, but we can see that this plot if very sparse, despite the log scale on the y-axis; most genotypes never reached even a moderate frequency.\n",
    "\n",
    "On the right we can see the genetic distances at which the populations spent most of their time. It seems like the populations spent a lot of time 5 mutations away from the global peak and less and less time at closer distances.\n",
    "\n",
    "Next we calculate the frequency of each trajectory/ancestry across all simulations. We see that two trajectories account for 25% of the simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genotype_freq = {genotypes[gi]:c for gi, c in enumerate(genotype_freq)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37, 187, 162, 204, 155, 7, 10, 3) 0.162\n",
      "(37, 187, 162, 204, 155, 7, 15, 3) 0.1\n",
      "(37, 187, 162, 122, 161, 197, 10, 3) 0.083\n",
      "(37, 187, 162, 122, 161, 245, 4, 3) 0.074\n",
      "(37, 187, 162, 74, 155, 7, 10, 3) 0.04\n"
     ]
    }
   ],
   "source": [
    "lods_freq = collections.defaultdict(int)\n",
    "for lod in cleaned_lods:\n",
    "    lods_freq[tuple(lod)] += 1\n",
    "\n",
    "top_lods_freq = sorted(lods_freq.items(), key=lambda x: x[1], reverse=True)[:5]\n",
    "for lod, cnt in top_lods_freq:\n",
    "    print(lod, cnt / repetitions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we produce a plot that summarizes the adaptive landscape topology and the characteristics of adaptation on this landscape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_landscape(top_lods_freq, genotypes, genotype_freq, fitness, peaks, valleys, ax=None):    \n",
    "    linestyles = 'solid dashed dashdot dotted'.split()\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(10, 7))\n",
    "    else:\n",
    "        fig = ax.figure\n",
    "    \n",
    "    # most frequent LOD\n",
    "    for lod, count in top_lods_freq[:len(linestyles)]:\n",
    "        linestyle = linestyles.pop(0)\n",
    "        ax.plot(\n",
    "            [hamming(genotypes[gi]) for gi in lod], \n",
    "            [fitness[genotypes[gi]] for gi in lod], \n",
    "            linewidth=5, \n",
    "            color='#404040', \n",
    "            linestyle=linestyle,\n",
    "            label='{:.2f}'.format(count / repetitions)\n",
    "        )\n",
    "\n",
    "    # markers for genotypes\n",
    "    size = lambda g: 3.5 + 1.0*genotype_freq[g] / repetitions\n",
    "    ax.scatter(\n",
    "        [hamming(g) for g, w in fitness.items() if w and g not in peaks and g not in valleys], \n",
    "        [w for g, w in fitness.items() if w and g not in peaks and g not in valleys], \n",
    "        c='k', \n",
    "        marker='o', \n",
    "        s=[size(g) for g, w in fitness.items() if w and g not in peaks and g not in valleys]\n",
    "    )\n",
    "    ax.scatter(\n",
    "        [hamming(g) for g, w in fitness.items() if w and g in peaks], \n",
    "        [w for g, w in fitness.items() if w and g in peaks], \n",
    "        c='k', \n",
    "        marker='^', \n",
    "        s=[size(g) for g, w in fitness.items() if w and g in peaks]\n",
    "    )\n",
    "    ax.scatter(\n",
    "        [hamming(g) for g, w in fitness.items() if w and g in valleys], \n",
    "        [w for g, w in fitness.items() if w and g in valleys], \n",
    "        c='k', \n",
    "        marker='v', \n",
    "        s=[size(g) for g, w in fitness.items() if w and g in valleys]\n",
    "    )\n",
    "\n",
    "    # lines for mutations\n",
    "    for g, w in ((g, w) for g, w in fitness.items() if w ):    \n",
    "        for i in range(len(g)):\n",
    "            g2 = flip_bit(g, i)\n",
    "            if fitness[g2] > 0:\n",
    "                ax.arrow(\n",
    "                    hamming(g), \n",
    "                    fitness[g], \n",
    "                    hamming(g2) - hamming(g), \n",
    "                    fitness[g2] - fitness[g], \n",
    "                    width=0.001,\n",
    "                    head_width=0,\n",
    "                    color='#404040', \n",
    "                    alpha=0.5, \n",
    "                    linestyle='dotted'\n",
    "                )\n",
    "\n",
    "    min_w = min((w for w in fitness.values() if w))\n",
    "    max_w = max((w for w in fitness.values() if w))\n",
    "    ax.set(\n",
    "        ylim=(min_w * 0.9, max_w * 1.1),\n",
    "        xlim=(-0.5, n + 0.5),\n",
    "        xlabel=\"# Mutations\",\n",
    "        ylabel=\"Fitness\"\n",
    "    )\n",
    "    ax.legend(title=\"Frequency\", loc='lower left', frameon=True)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Franke2011.tif,pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lt99+G3BgnUXKxAghRGj4PQZTUFBAdXV1q+1Hjx7ltttuCyiozmSvd8v4ixBChECHPllX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\nn1arJTc3F09PzwYTngKBQCBoWTr1HXbDhg1GVdVqtZpz586xZ88efH1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1eI4fP26YiFap\nVPz88884OztjbW1tNF4ul7N27Vq2bt3K9u3b+fzzz/nHP/7BzJkzcXZ2BiA0NJSxY8fi6enJ3r17\niYuL4/jx4+zatYulS5dy11138f7773dYQ1NWVsbBgwcZOHAgGo0GpVKJi4sL+fn5jB49moEDBxpk\nvb296dWrl+FvJRA0h3Y1MCkpKej1enr06GHYFhUVxYkTJ4xkMzMziYyMNLwPDQ1Fp9ORmJjYJrpe\nC71790alUqFSqQgKCiIhIYFu3bpRXV1Nbm4ujo6OPPfcc0bjSktL+eCDD9ola0ev17No0SKT8w7O\nzs4mw5LPPvusIcRUUlLCvn37Gg056fV63NzccHR0xMLCgsDAQH777Tf69evHpUuXMDMza2BkunTp\nwqeffmryeHl5ebz66qtkZ2fz0Ucf8eqrr7Jp0yby8vJYu3YtgwcPxt7e3mQDzWHDhjWYHwsPD8fD\nwwOdTsfGjRtZvnw5b731FlZWVhw8eBB/f3+CgoJYsmSJYYxOp+Pnn3/mrrvuYt26dSbnem5Eamtr\n2bx5MxkZGfTr14+jR4/SpUsXQkJC2Lp1K/379ycoKKjBGG9vbwoLC+nSpQsXLly4oR/sBDcO7Wpg\nCgoKcHFxaRCaqH+C+jt/317/5GoqO+hGwdraGk9PTxITE7GxseHgwYMAPPbYY9jZ2ZGfn4+bmxv9\n+/c3Gnvo0CE+/vjjtlaZb775hpiYGKPtjdWoDBw40FDrAnUhvsZSjQFcXV3RarXExsaSkZFBVFQU\nCQkJWFlZERwcTGBgILm5uQ1u1t26deOjjz7CysrK6HjZ2dk8/vjjbN++HahrlhkXF8dTTz2FnZ0d\nx48f5+LFi0bj6ueLLmf06NEUFRVx4sQJwsPDCQoKwtHRkUmTJpGUlMQff/zBpUuXjMbpdDq+/PJL\npk6dys8//3xDG5rs7Gx++uknoqOj6dGjB0eOHGHUqFEEBwcbul6fOXPG6G8ok8lQqVR4enri6OjY\n7EQLwc1NuxqYmpoaoxCRQqEw+ZQ8evRoVq5cSUZGBpWVlSxevBhzc/MmUyc3bNjAPffc0+D17LPP\ntvh1NEZlZSW///47//73v7nnnnv4/vvvOX36NLa2tpw/f57PP/+cd955h6FDh5q8gW/fvp39+/e3\nmb67d+9m7dq1RtsVCgULFizgyy+/ZPz48QZvQi6X88wzzxjk6sOAd999d4Pxc+fOJScnB6jz6kpK\nSqitrSU2NhaoW+5g27ZtREVF4eXlhZmZmZEX26tXL1auXGnIUrucy/9f9Ho9q1evNhSE/vjjj0by\n3bt3p2vXrkbb5XI5t99+O6dOnaKoqMiw3czMjEmTJjXYZors7Gzef/99HnvsMX755ZcbytDU1NQQ\nExODubk59957L87OzmzYsIGePXtiZWVFUVERSUlJDB06tNGVR62trbG0tCQ7O5suXbpQVlbWxlch\n6Gi0q4FRKpVGNQYajcZkzPzFF18kJCSEUaNGMXjwYHr06IGDg0OjNRMAU6ZMYdOmTQ1ejTVpbA3e\neusttmzZwtmzZw3X+fXXX/P444+zc+dOwyT1pk2bGl1Kua0K/k6fPt1od+E333yTbt264eTkRERE\nBJ999hmDBg1qUFQJdd7knXfe2SDFuKioiJ07d/LQQw+xd+9eXFxcqK2tZcSIEVRUVAB1Vf41NTVo\nNBqOHz9OQEAAaWlpRqnDfn5+fPTRR4ZOzY1RXl7Onj17yMjIMHiNl3Pfffc1OtbT05Pg4GD27dtn\n9L/57LPPMmfOHJOe1OVkZmaycOFC7rzzTj755JN2z3Y8d+4c69evp2/fvri7u1NcXMxPP/3EPffc\nY/iu/f7777z44ovU1NQYpdrXExYWRkpKCrfddhvl5eUkJyeL4ktBk7SrgfHw8KC4uLjBUrtFRUW4\nubkZydrY2PD5559z+PBhDhw4wKOPPkpRUZFRKuyNxODBg422paamGoVZLly4gF6vb5C1U49area1\n115rNR2hLtz4zjvvmCwonDp1qqEav6ioCIVCQdeuXVmwYAEvvfRSA9nDhw83SAyQJIldu3bRp08f\n7Ozs+PXXX1m7di2nTp1CLpc3+DuHh4cTGxtrCJE5OTmZzKYLCgpi8eLFTT5YPP300zz00ENs3LjR\naJ+npydDhgxBp9M1usTzoEGDUCqVJhuRDhs2jJ07d/Lcc89ha2vbqA5QN9exbds27rzzTpYsWdLm\nhqa6upozZ86gUql48sknsbGxobS0lNTUVO677z5DwsZvv/2GjY0NVVVVeHp6Nno8W1tb9Ho9rq6u\nODk5ERwczM8//9xWlyPogLSrgalfdOryifrjx4/Ts2dPI9lFixaxfft27O3tsbGxITY2FkdHR265\n5ZY20/dqMVXVX1JSYtIorlmzhkWLFpnMmDpz5gz/+c9/WkNFqqurmTlzpsm5rGHDhvHoo48a3qel\npTV4ur187qykpIQ//vijgZE8fPgwUVFRLFu2jPXr12Nvb09AQAClpaV8++23JCYm8scffwDg6+tL\ncXExdnZ22Nra4uPjg1arNZnJFhoaygcffGAyAw/qeoplZ2fzyy+/GO275557kMvlvP76643W+Mjl\ncgYOHIhKpWq0uPD+++/np59+4tlnn8XX19ekTD1arZatW7dy11138b///a9NQmcxMTHs2bOHwMBA\nIiIigLr08YMHD9KnTx+D3KVLl/jjjz8YPXo0sbGxhgy6xqhPW3dwcKC0tBQHBwdRGyNoFLO5c+fO\nba+TKxQKsrOz+eGHH4iMjGT//v2sXLmS2bNn4+rqanhiNjMz4+zZs3z99df06dOHjIwMZsyYweOP\nP06/fv2u6pzl5eWsWbOGRx999IqhluvFzs6OuLg4o9h9RESEkRdTXFxMz549iY6OZt++fUbHio+P\nZ8SIES2qs06nY968eSZrG8LCwpg/fz4KhQKZTEZOTg5yubzRosVjx47h4+NjMPjx8fGYm5sjSRJe\nXl5YWFhgY2NDTEwMkyZNIiEhAZ1OR3JyMsXFxaSlpWFlZYVaraZnz54olUqKioooKioiICCAM2fO\nUFpaargB5uXlIUmSyZRZtVrNjh07jGp4LC0t6d27N3Fxcfz000+cO3cOpVJJRkYGNjY2hrRyqGsj\nU15eTklJCdbW1o0mLvTo0YPo6GisrKwwMzNrkDb9d/R6Pfv37ycmJsZgbOvP11JotVri4uKwtrZm\n2LBhhgeW+nmlO+64w3BOvV7Pf/7zH5566imKi4spLy8nODi4yeMXFRVhbm6Ou7s7MTExjBw5kkuX\nLjXaDVtwc9OuBgYgOjqa48eP8+GHH5KQkMCMGTMYOnQo2dnZDBs2jIEDB+Lr60tERARpaWksXryY\nX3/9lSlTpvDcc89d9Re0LQ0M1E30Hzt2DC8vL2677Tbee+89goODOXv2rJHXkJGRwUsvvURycrJR\nEaEkSfzxxx9Mnjy5xW5Kq1atMhnicHV15ZNPPuHQoUN88sknBAQEkJycTHR0dJPnDgsLw8LCgsLC\nQrKzs8nOziYiIsIQ5/f09OTUqVMkJibStWtXxowZQ0xMDMOHD6dLly5kZWVhbW3NmTNnyMnJITg4\nmOzsbA4fPszChQuJi4vj1ltv5fTp04SFhTFs2DAUCgXHjx830sWUlzB27Fgef/xxDh8+zEMPPYST\nkxNjxowhKioKtVpNQkICsbGxFBQU4OPjg5+fH6mpqRQUFODp6dmox2Rvb0/v3r2JjIzExsaGS5cu\nNVkbU1ZWxr59+9i3bx+Ojo74+fk1mtZ9NRw9epT8/Hz69evXYNG7s2fPYm1t3aA3XL18ly5dDB0W\n+vXrd8X/LXd3d5KSkvDx8cHS0pKcnByUSiWFhYWNPnwIbl5k0k02S5eZmcmIESPYs2fPFUMbLUFZ\nWRlqtZqMjAx0Oh39+/dHkiRWr15tMmNr4cKF9O3bl0mTJpmc3B87dixvvPHGdeu1Y8cOPvzwQ6Pt\nlpaWLF26FH9/fx599FFDNlafPn2YPn26ydU6MzIyOHv2LHfccQclJSWkpKQwYMAAfv75Z8aOHdtA\ntry8nGXLliGTyQxdGQoLC7GwsCAoKIiwsDB2795NWloaw4YNY9++fRw+fNgw3sPDg+XLlzfIJvvq\nq6/47rvvmrxeuVzOd999Z5iTGDNmDPv370etVjNs2DDDjVWSJDIzMw2hufT0dFQqFc7OzowcOfKK\nN+Dc3Fy2bduGubk5mzdvblaChoWFBRMmTGDatGkmQ6RXorq6mp07d+Ln59dg8TZJkti5cyeBgYFG\n7W5ycnJYu3Ytr7/+OhUVFVRVVeHl5dWs8504ccJgrM6fP09ISAjr16/n3nvvRalUXrX+gs5Lu3sw\nbU1bezCWlpbY2Nggl8uprKzE3t4epVJJTU0NmZmZRuGzrKwsxo8fT69evUyueX/+/HkiIiKuK7nh\nxIkTzJ8/3+Qk9+zZs4mKiuK///1vg1BdTk4O27Zt44477jAKF+3YsYPq6mrCwsKIjY3ltttuo7q6\nmoCAAKPKf6VSiZOTE+fOnaOgoABra2vCwsLo168fFhYWHD58mDvuuANXV1e+//57kpKSGoyvqqpC\nr9fTr18/tFot+/btM4TfTNW71DN48GACAgLIyMhgwoQJAAYjlZOTg4eHB1BX7+Hg4ICXlxdeXl4G\nr6usrIzU1FRkMhkuLi6NGhpbW1t69+6Nu7s7I0aMwNfXl5SUlCYn+HU6HUlJSWzYsAFXV1e6dOnS\nLC9VkiQSEhKorKykX79+BAYGGvZptVp++eUXIiIijFZX1Wg0LF26lEceeQQHBwd27txJ3759m+0Z\nZ2Rk4O3tjUwmo7i4mJqaGvr27cvBgwcb6CAQCAPTRlhbW5ORkUFxcTE+Pj5YWVmh0+lksKY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be39z/0KtNoNJw+fZqWlhZ6e3vRaDRMTExc1eH7b7/P1+E3XcDVsVBgrgHq6+uZnp5Gp9OJJ/SS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h7iYAiEQcjJyWHZsmVoaWmh662eBFdXV+Tk5NDNwCYy2C+hvb0d1dXVsLOzQ1dXFwoKCvD5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\nNt516QAwrtMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11009b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('paper', font_scale=1.7)\n",
    "fig = plt.figure(figsize=(6, 10)) \n",
    "gs = mpl.gridspec.GridSpec(nrows=4, ncols=1, hspace=0.25)\n",
    "ax = [plt.subplot(gs[0, 0])]\n",
    "ax += [plt.subplot(gs[1, 0], sharex=ax[0]), plt.subplot(gs[2:, 0])]\n",
    "\n",
    "plot_adaptation(k, p, W, pom, genotypes, ax=ax[:2])\n",
    "ax[0].set_xticklabels([])\n",
    "plot_landscape(top_lods_freq, genotypes, genotype_freq, fitness, peaks, valleys, ax=ax[2])\n",
    "\n",
    "panel_label_size=plt.rcParams[ 'axes.titlesize']*1.5\n",
    "ax[0].annotate('D', (-0.16, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "ax[1].annotate('E', (-0.16, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "ax[2].annotate('F', (-0.16, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "\n",
    "sns.despine()\n",
    "fig.savefig(landscape_filename[:-4] + '.tif', dpi=300)\n",
    "fig.savefig(landscape_filename[:-4] + '.pdf', dpi=300, paper_size='A4')\n",
    "print(landscape_filename[:-4] + '.tif,pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 1e -4.0 N = 1e 9.0 generations = 2^ 15.0\n"
     ]
    }
   ],
   "source": [
    "print ('mu = 1e', np.log10(mu), 'N = 1e', np.log10(N), 'generations = 2^', np.log2(generations))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Empricial fitness landscape: TEM\n",
    "\n",
    "The fitness landscape is composed of 5 loci in the TEM gene and all 32 combinations of mutations in these loci. Fitness is measured as MIC ([minimum inhibitory concentration](https://en.wikipedia.org/wiki/Minimum_inhibitory_concentration)) in the bacteria [Escherichia_coli](https://en.wikipedia.org/wiki/Escherichia_coli)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read fitness landscape file\n",
    "The [fitness landscape file](https://gitlab.com/yoavram/EvolRugAdaptLand2016/blob/master/Weinreich2006.csv) was downloaded from [Weinreich et al. 2006](http://www.sciencemag.org/content/suppl/2006/04/05/312.5770.111.DC1/Weinreich_SOM.pdf) and converted to CSV using [Tabula](http://tabula.nerdpower.org/).\n",
    "\n",
    "Start by downloading it if it's not already on the local filesystem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landscape_filename = 'Weinreich2006.csv'\n",
    "landscape_url = 'https://github.com/yoavram/EvolRugAdaptLand2016/raw/master/Weinreich2006.csv'\n",
    "download(landscape_filename, landscape_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g4205a,A42G,E104K,M182T,G238S,Replicate 1,Replicate 2,Replicate 3\r\n",
      ",,,,,0.088,0.088,0.088\r\n",
      ",,,,+ ,1.4,1.4,1.4\r\n",
      ",,,+ ,,0.088,0.063,0.063\r\n",
      ",,,+ ,+ ,32,32,32\r\n",
      ",,+ ,,,0.13,0.088,0.18\r\n",
      ",,+ ,,+ ,360,360,360\r\n",
      ",,+ ,+ ,,0.18,0.13,0.18\r\n",
      ",,+ ,+ ,+ ,360,360,360\r\n",
      ",+ ,,,,0.088,0.088,0.088\r\n"
     ]
    }
   ],
   "source": [
    "!head $landscape_filename"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g4205a</th>\n",
       "      <th>A42G</th>\n",
       "      <th>E104K</th>\n",
       "      <th>M182T</th>\n",
       "      <th>G238S</th>\n",
       "      <th>Replicate 1</th>\n",
       "      <th>Replicate 2</th>\n",
       "      <th>Replicate 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>+</td>\n",
       "      <td>32.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>32.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  g4205a A42G E104K M182T G238S  Replicate 1  Replicate 2  Replicate 3\n",
       "0    NaN  NaN   NaN   NaN   NaN        0.088        0.088        0.088\n",
       "1    NaN  NaN   NaN   NaN    +         1.400        1.400        1.400\n",
       "2    NaN  NaN   NaN    +    NaN        0.088        0.063        0.063\n",
       "3    NaN  NaN   NaN    +     +        32.000       32.000       32.000\n",
       "4    NaN  NaN    +    NaN   NaN        0.130        0.088        0.180"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(landscape_filename)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert + to 1 and NaN to 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g4205a</th>\n",
       "      <th>A42G</th>\n",
       "      <th>E104K</th>\n",
       "      <th>M182T</th>\n",
       "      <th>G238S</th>\n",
       "      <th>Replicate 1</th>\n",
       "      <th>Replicate 2</th>\n",
       "      <th>Replicate 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>32.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   g4205a  A42G  E104K  M182T  G238S  Replicate 1  Replicate 2  Replicate 3\n",
       "0       0     0      0      0      0        0.088        0.088        0.088\n",
       "1       0     0      0      0      1        1.400        1.400        1.400\n",
       "2       0     0      0      1      0        0.088        0.063        0.063\n",
       "3       0     0      0      1      1       32.000       32.000       32.000\n",
       "4       0     0      1      0      0        0.130        0.088        0.180"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loci_columns = 'g4205a \tA42G \tE104K \tM182T \tG238S'.split() # as copied from the table above\n",
    "genotypes = data[loci_columns].as_matrix()\n",
    "genotypes[pd.isnull(genotypes)] = 0\n",
    "genotypes[genotypes != 0] = 1\n",
    "data['g4205a \tA42G \tE104K \tM182T \tG238S'.split()] = genotypes\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average resistance data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g4205a</th>\n",
       "      <th>A42G</th>\n",
       "      <th>E104K</th>\n",
       "      <th>M182T</th>\n",
       "      <th>G238S</th>\n",
       "      <th>Replicate 1</th>\n",
       "      <th>Replicate 2</th>\n",
       "      <th>Replicate 3</th>\n",
       "      <th>Resistance</th>\n",
       "      <th>Absolute W</th>\n",
       "      <th>Relative W</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088000</td>\n",
       "      <td>0.036629</td>\n",
       "      <td>0.010138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.380211</td>\n",
       "      <td>0.105237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.071333</td>\n",
       "      <td>0.029925</td>\n",
       "      <td>0.008283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.518514</td>\n",
       "      <td>0.420305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.132667</td>\n",
       "      <td>0.054102</td>\n",
       "      <td>0.014975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   g4205a  A42G  E104K  M182T  G238S  Replicate 1  Replicate 2  Replicate 3  \\\n",
       "0       0     0      0      0      0        0.088        0.088        0.088   \n",
       "1       0     0      0      0      1        1.400        1.400        1.400   \n",
       "2       0     0      0      1      0        0.088        0.063        0.063   \n",
       "3       0     0      0      1      1       32.000       32.000       32.000   \n",
       "4       0     0      1      0      0        0.130        0.088        0.180   \n",
       "\n",
       "   Resistance  Absolute W  Relative W  \n",
       "0    0.088000    0.036629    0.010138  \n",
       "1    1.400000    0.380211    0.105237  \n",
       "2    0.071333    0.029925    0.008283  \n",
       "3   32.000000    1.518514    0.420305  \n",
       "4    0.132667    0.054102    0.014975  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitness = data['Replicate 1 \tReplicate 2 \tReplicate 3'.split(' \t')]\n",
    "data['Resistance'] = fitness.mean(axis=1)\n",
    "data['Absolute W'] = np.log10(1 + data['Resistance']) # arbitrary transformation of resistance to fitness\n",
    "data['Relative W'] = data['Absolute W'] / data['Absolute W'].max()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create fitness map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g4205a</th>\n",
       "      <th>A42G</th>\n",
       "      <th>E104K</th>\n",
       "      <th>M182T</th>\n",
       "      <th>G238S</th>\n",
       "      <th>Replicate 1</th>\n",
       "      <th>Replicate 2</th>\n",
       "      <th>Replicate 3</th>\n",
       "      <th>Resistance</th>\n",
       "      <th>Absolute W</th>\n",
       "      <th>Relative W</th>\n",
       "      <th>genotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>2.557507</td>\n",
       "      <td>0.707884</td>\n",
       "      <td>11011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.066667</td>\n",
       "      <td>0.486667</td>\n",
       "      <td>0.134703</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1700.000000</td>\n",
       "      <td>3.230704</td>\n",
       "      <td>0.894216</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.380211</td>\n",
       "      <td>0.105237</td>\n",
       "      <td>11110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>4100.000000</td>\n",
       "      <td>3.612890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    g4205a  A42G  E104K  M182T  G238S  Replicate 1  Replicate 2  Replicate 3  \\\n",
       "27       1     1      0      1      1        360.0        360.0        360.0   \n",
       "28       1     1      1      0      0          1.4          2.8          2.0   \n",
       "29       1     1      1      0      1       1500.0       2100.0       1500.0   \n",
       "30       1     1      1      1      0          1.4          1.4          1.4   \n",
       "31       1     1      1      1      1       4100.0       4100.0       4100.0   \n",
       "\n",
       "     Resistance  Absolute W  Relative W genotype  \n",
       "27   360.000000    2.557507    0.707884    11011  \n",
       "28     2.066667    0.486667    0.134703    11100  \n",
       "29  1700.000000    3.230704    0.894216    11101  \n",
       "30     1.400000    0.380211    0.105237    11110  \n",
       "31  4100.000000    3.612890    1.000000    11111  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['genotype'] = data[loci_columns].apply(\n",
    "    lambda row: str.join('', map(str, row)), axis=1)\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fitness = collections.defaultdict(float)\n",
    "for i, row in data.iterrows():\n",
    "    fitness[row['genotype']] = row['Relative W']\n",
    "\n",
    "assert fitness['11111'] == 1\n",
    "\n",
    "genotypes = tuple(fitness.keys())\n",
    "assert '11111' in genotypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hamming(g1, g2='11111'):\n",
    "    assert len(g1) == len(g2)\n",
    "    return sum(g1i != g2i for g1i, g2i in zip(g1, g2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define variables for the simulations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = len(next(iter(fitness)))\n",
    "mu = 1e-8 # mutation rate\n",
    "m = len(genotypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we construct the selection and mutation matrices, as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11024ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M = mutation_matrix(genotypes, mu)\n",
    "plot_matrix(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rISFBkjRz5kx98sknqq2tVUlJyX3v43K55HK5/No8Hk8Y/wQAI01IweP1elVRUaFjx47J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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9fbb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = selection_matrix(genotypes, fitness)\n",
    "plot_matrix(S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pmM1mJTMzU6moqPB1SX6lubnZbR2PoijK3/72N2XOnDmK2WxWcnJylH/84x8+\nrNC33n77bWXs2LEP/Dd9+nRFUThW33f16lXlhRdeUJKTk5XMzEzFarW6+owcK+7HQ0TS+dVHLSIa\nGBg8RCQdg4eIpGPwEJF0DB4iko7BQ0TSMXiISDoGDxFJx+AhIun+DyyE6hmgfnN5AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1205a4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = M.dot(S)\n",
    "assert E.shape == (m ,m)\n",
    "plot_matrix(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "\n",
    "We start the simulations from the genotype farthest away from the optimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting genotype: 00000\n",
      "starting fitness: 0.010138392727488304\n",
      "# generations: 735\n"
     ]
    }
   ],
   "source": [
    "k, p, W, pom = adaptation(genotypes, start='00000', finish='11111', verbose=True)\n",
    "print(\"# generations:\", k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The POM includes 6 genotypes, each with a single additional mutation - this is in contrast to the _A. niger_ simulations that only had 4 genotypes at the POM, despite fixing more mutations. This can be attributed to the fact the the TEM landscape is much smoother then the _A. niger_ landscape, with a single peak (we analyse the peaks below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 14, 24, 23, 30, 25]\n",
      "['00000', '00001', '00101', '01101', '01111', '11111']\n",
      "[0, 1, 2, 3, 4, 5]\n",
      "[0.010138392727488304, 0.10523743208102061, 0.7078840942357442, 0.9196034932062663, 0.9583873159477904, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print([gi for gi in pom])\n",
    "print( [genotypes[gi] for gi in pom])\n",
    "print( [sum(map(int, genotypes[gi])) for gi in pom])\n",
    "print( [fitness[genotypes[gi]] for gi in pom])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, each adaptation step is smaller and takes more time, and fixation is very deterministic once the invading genotype managed to avoid fixation by random genetic drift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Q1VVjB49OthWXFyMHTt2dNl/+/bt2LZtGx588MGIzt/d5MCuKVysSURERETR\nEFHKXLlyJd5880089dRTMBqNAICZM2fi448/xvPPPx/Rherr6+FwOKDX64NtDocDdXV1nfr6fD48\n+eSTWLJkCcxmc0Tn724cESciIiKiaIpoasqbb76JxYsXo6SkBIsXLwYAXH/99RBFEb/85S+xYMGC\nbz2H2+3uNJVFFEX4fL5OfV988UWMGjUKU6dOxbZt2yIpEWVlZSgrKwtp6+rckZIVFYLAXVOIiIiI\nKDoiCuLV1dUoKCjo1D548OCI52+bTKZO01gkSYLVag1pO3jwIN544w1s3LgxovO2Ky0tRWlpaae6\np0+ffkHnaSfLKkfDiYiIiChqIgrigwcPxldffYX8/PyQ9k2bNmHw4MERXSg7OxuNjY1QVTW493hD\nQwMyMzND+r3//vtoamrC9ddfDwBQFAWKoqC4uBh//vOfMX78+Iiud6lkhUGciIiIiKInoiD+yCOP\nYMGCBTh8+DAURcHGjRtx9OhR/P3vf8ezzz4b0YUKCwsBAHv27AnuklJeXo4xY8aE9Lvzzjtxyy23\nBI937tyJxx57DBs2bEB2dnZE1+oOEoM4EREREUVRREnzuuuuw/PPP4+Kigro9Xr87W9/Q3V1NV5+\n+WV897vfjehCVqsVs2fPxpIlS1BZWYkNGzZg48aNuP3226EoCurr6+Hz+ZCamoqBAwcGv9rD98CB\nA2O6cFNRNIgGzg8nIiIiouiIeB/xq6++GldfffUlXWzRokX4xS9+gblz58LhcGDZsmUoKioKzuVe\ntWoVJk6ceEnX6C6cmkJERERE0RQ2iL/77rsRn6TjVJLzsdlseO655zq15+Xl4cCBA12+Z+LEiais\nrIy4lu4icbEmEREREUVR2CC+YMECCIJ/aoamaWFPIAhCxEG8N5EVFXoGcSIiIiKKkrBBfMqUKdi+\nfTvGjBmDmTNnYsaMGUhPT49lbXElKxpEBnEiIiIiipKwSfPVV1/FP//5T8yePRubN29GSUkJ7r33\nXrzxxhtobm6OZY1xIcsqDFysSURERERRct4h35SUFPzbv/0bXn31VXz00UeYMWMG/v73v2PatGn4\n8Y9/jPXr16O1tTVWtcaUrHKOOBERERFFT8RJMz09HaWlpfjrX/+Kjz/+GJMnT8avfvUrTJkyJZr1\nxQ3vrElERERE0RTx9oUA0Nrais2bN+O9997D559/jpSUFNx4443Rqi2uZEWFwcAgTkRERETR8a1B\nvKmpCR9SBrbdAAAgAElEQVR88AHef/99bN26Fenp6bjhhhvw2muvYdy4ccGdVfoaWdFg0DGIExER\nEVF0hA3i//Vf/4VNmzbhyy+/REZGBm644QY8+OCDGDduXCzrixtZVmG1XNAvDIiIiIiIIhY2af7y\nl7+EKIqYPHkyiouLIQgCvvzyS3z55Zed+j744INRLTIeJN5Zk4iIiIiiKGwQ79+/PwDg8OHDOHz4\ncNgTCILQJ4O4wiBORERERFEUNoh/+OGHsayjx5EVFSIXaxIRERFRlDBphiErGkfEiYiIiChqmDTD\nkGQVen3f3BGGiIiIiOKPQTwMWVEhckSciIiIiKKESTMMmYs1iYiIiCiKmDTDUBSNd9YkIiIioqhh\n0gyD+4gTERERUTQxaYbBqSlEREREFE1MmmHIsgoDd00hIiIioihhEO+CqmpQVO4jTkRERETRw6TZ\nBUVVAYBBnIiIiIiihkmzC5IcCOLcNYWIiIiIooRJswuKqgEAb+hDRERERFHDpNkFuX1EnIs1iYiI\niChKYhrE3W43Fi5ciHHjxqGkpAQbNmzosp+iKPj973+Pa665BuPHj8dDDz2EU6dOxaxOSfEHcT1H\nxImIiIgoSmKaNJcvX46qqiqsXbsW8+fPx+LFi7Fr165O/V577TWsX78ezzzzDMrKyqAoCubPnw9N\n02JSpxwI4pyaQkRERETRYojVhVwuF9avX4/Vq1ejsLAQhYWFKC8vx7p161BUVBTSd8OGDZg3bx4m\nTZoEAFi2bBmmTZuG6upq5OfnR71WmYs1iYiIiCjKYpY09+/fD1VVMXr06GBbcXExduzY0anv4sWL\nMX369OCxIPjnajudzugXCkBW/CPvnCNORERERNESsxHx+vp6OBwO6PX6YJvD4UBdXV2nvhMnTgw5\nXrNmDRwOBy677LKo1wmcnZrCfcSJiIiIKFpiFsTdbjdEUQxpE0URPp/vvO/75JNP8Je//AVPPfUU\nDIbw5ZaVlaGsrCyk7dvOHU4wiHNqChERERFFScyCuMlkgiRJIW2SJMFqtYZ9z5YtW/Dv//7vuPPO\nO3Hbbbed9/ylpaUoLS0Naauurg6Z4hIpjogTERERUbTFLGlmZ2ejsbERauD28QDQ0NCAzMzMLvt/\n9NFHmDdvHr73ve9h0aJFsSoTACDL7XPEGcSJiIiIKDpiljQLCwsBAHv27Am2lZeXY8yYMZ367t69\nG/Pnz8dtt92GJUuWxKrEoLMj4lysSURERETREbMgbrVaMXv2bCxZsgSVlZXYsGEDNm7ciNtvvx2K\noqC+vj44p3vx4sUYMWIEHn74YZw+fRr19fWor6/vNLUlWiSZU1OIiIiIKLpiNkccABYtWoRf/OIX\nmDt3LhwOB5YtW4aioqLgXO5Vq1YhNzcXlZWVAIBp06aFvH/t2rUYP3581OtsdfkgCIDdIn57ZyIi\nIiKiixDTIG6z2fDcc891as/Ly8OBAweCxx2fx0OT04skq5G3uCciIiKiqGHS7EJzqxcpdlO8yyAi\nIiKiPoxBvAtnnF6kJTGIExEREVH0MIh3odnJEXEiIiIiii4G8S40tXqRyhFxIiIiIooiBvEuNLV6\nkWI3xrsMIiIiIurDGMTPIckqnG4JqXZzvEshIiIioj6MQfwcLW1eAEAqR8SJiIiIKIoYxM9xpiUQ\nxDlHnIiIiIiiiEH8HJu/Ogar2YC8rKR4l0JEREREfVhM76zZU5UfqMP2vbX4pqYF+79pxO03DIeN\nt7cnIiIioihK+CB+tKYFS//yBTJTLSjIS8WdN43ArGlD4l0WEREREfVxCR/E//aPSmSnW/H/PVoC\no6iPdzlERERElCASfo74keomTB3TnyGciIiIiGIqoYO4V1LQ2OJFdrot3qUQERERUYJJ6CBe1+gC\nAOSkW+NcCRERERElmoQO4qcCQTzbwSBORERERLGV8EFcpxOQmWqJdylERERElGASPohnpFqg1yf0\nx0BEREREcZDQCbS2oY3zw4mIiIgoLhI6iJ9qdCGbQZyIiIiI4oBBnAs1iYiIiCgOEjaIO90S2twS\n9xAnIiIiorhI2CB+qqENAPcQJyIiIqL4SNwg3r6HOIM4EREREcVBTIO42+3GwoULMW7cOJSUlGDD\nhg1h+65fvx4lJSUoLi7GokWL4Ha7u7WWU40uGEU9UpNM3XpeIiIiIqJIxDSIL1++HFVVVVi7di3m\nz5+PxYsXY9euXZ36VVRUYNmyZViwYAHWrFmDvXv3YsWKFd1ay5HqZmSnWyAIQreel4iIiIgoEoZY\nXcjlcmH9+vVYvXo1CgsLUVhYiPLycqxbtw5FRUUhfV9//XXMmjULM2fOBAAsXboU99xzDxYsWACL\n5cLvgvnG5kNITW+GFjh2unz4pKIa988adanfFhERERHRRYnZiPj+/fuhqipGjx4dbCsuLsaOHTs6\n9d25cyeKi4uDx0VFRZBlGQcOHLioa/9r/yl8vvskvthdgy/21ODgsTO49eqhmH3V0Is6HxERERHR\npYrZiHh9fT0cDgf0en2wzeFwoK6urlPfuro6ZGdnB4/1ej1SU1O77Hs+iqIAAP7j+wXIycnp9PqJ\nEycu6HxEREREROHk5OTAYIg8XscsiLvdboiiGNImiiJ8Pl+nvh6PJ+K+7crKylBWVhbS1tbm36Lw\nhz/84cWWTUREREQUkc2bNyMvLy/i/jEL4iaTCZIkhbRJkgSrtfP2geH6nm9+eGlpKUpLS0PaPB4P\nZs+ejVdeeSVkJJ6i48EHH8TLL78c7zISAj/r2OFnHTv8rGOHn3Xs8LOOrXh/3l3NwDifmAXx7Oxs\nNDY2QlVV6HT+qekNDQ3IzMzssm99fX3wWJZlNDU1ddn3fMxmM2w2GwYOHHhpxVNEjEbjBf0USBeP\nn3Xs8LOOHX7WscPPOnb4WcdWb/u8Y7ZYs7CwEACwZ8+eYFt5eTnGjBnTqW9RUREqKiqCx7t27YIo\nisFzEBERERH1djEL4larFbNnz8aSJUtQWVmJDRs2YOPGjbj99tuhKArq6+uDc8BLS0vx1ltvYePG\njdi7dy+WLl2KOXPmwGg0xqpcIiIiIqKoiukNfRYtWoRBgwZh7ty5eOGFF7Bs2TIUFRWhpqYGU6dO\nDY6Cjx8/Hk888QRWrFiBH/3oRxgxYgQeffTRWJZKRERERBRVMZsjDgA2mw3PPfdcp/a8vLxOe4R3\ntfjyYnTHOSgy/Kxjh5917PCzjh1+1rHDzzp2+FnHVm/7vAVN07Rv70ZERERERN0pplNTiIiIiIjI\nj0GciIiIiCgOGMSJiIiIiOKAQZyIiIiIKA4YxImIiIiI4oBBnIiIiIgoDhjEiYiIiIjigEGciIiI\niCgOGMSJiIiIiOKAQZyIiIiIKA4YxImIiIiI4oBBnIiIiIgoDhjEiYiIiIjigEGciIiIiCgOGMSJ\niIiIiOKAQZyIiIiIKA4YxImIiIiI4oBBnIiIiIgoDhjEiYiIiIjigEGciIiIiCgOGMSJiIiIiOKA\nQZyIiIiIKA4YxImIiIiI4oBBnIiIiIgoDhjEiYiIiIjigEGciIiIiCgOGMSJiIiIiOKAQZyIiIiI\nKA4YxImIiIiI4oBBnIiIiIgoDhjEiYiIiIjioE8HcVmWUV1dDVmW410KEREREVGIPh3Ea2trMX36\ndNTW1sa7FCIiIiKiEH06iBMRERER9VQM4kREREREccAgTkREREQUB3EJ4k1NTZg0adJ5526vX78e\nJSUlKC4uxqJFi+B2u2NYIRERERFRdMU8iLe2tuKhhx5CY2Nj2D4VFRVYtmwZFixYgDVr1mDv3r1Y\nsWJFDKskIiIiIoqumAbxPXv2YM6cOWhpaTlvv9dffx2zZs3CzJkzMWrUKCxduhRvvfUWR8WJiIiI\nqM8wxPJiW7duxcyZMzF79mzceOONYfvt3LkT8+bNCx4XFRVBlmUcOHAAY8eOveDrHj/VCknXDAAQ\nDTpkp1shGvQhfVRNhUfywmq0dHkOTVEgtbZCdjqhycq5r3bxhjDFCAIEnQDodBAEnf9RJwTa9cHX\nBYMBeosFOkNM/xN1O03VIEkKZEmBJKlQVRVa4LPRNM3/PPCooePzcB+gnwAh3AsX0gwIYV85z3u6\n8RrhXwrT/wLfcN5rdP2CILR/CUDgUcA5xx0eASHkPee+r/05ERERhYppyvvxj38MAKiurj5vv7q6\nOmRnZweP9Xo9UlNTUVdXd1HXXfqXLyBa04PHjhQznvvZ1UhLMgMA3JIHiz5YjpOtp1CYMRQLpj6I\nJJM92N95+AgO/OZ38NTEfj9yndEIvcUCMS0V5uwsmLOzYR82DCmjR8KYlhbzeroiywqOHmlA9dEm\n1NW0oLnJjbZWL9pavZBlNd7lUQ8h+NP82cAO+H8IRWi41+kE6PT+R71eFzjWQR9s1wUf9Xrh7Osd\nXjMYdDAY9DCIOhhEPUTR/9z/qPe/fk670WSAyWyA2SxCb+A6diIiir4eOdzq8XggimJImyiK8Pl8\nYd9TVlaGsrKykLb2/ovuuQLZ2f0AAC6vjN+u/Rf+tH43HvvRBADAuwc+QF1bA+67vBR/2/EmPv1m\nG747fHrwPFWv/RWC3oDhCx+FmJwE4ZzazhVu9E/TNP+Ir6qGf1RV/0ixLENxu6G43JBdLvgaG+E9\nVYeGL7bh5Mb/AQQBaZcXo9/NM5FaPDYuI47NZ1zY8n+HsKfiBHxeBWaLiKx+ScjMsmNwQQZsdiNE\nowFiIAwZRD30eiF01BTo+vlFfD+aFmYU/cKa2092Ye8Je43zXOVC6zrfqcJ972H7h7/Iub+x0AK/\nrgg5hv+3HRq6+M1GsK92znnOOVf7a+rZ62qaBlXVoCoaFFWFqmhQg48aFEUNvq6qKpRAuywp8HrO\n9lUUNfCbGDXw2xj/c0WJ7AdDvUEHs9kAk1mEyWwIfImwWEVYbSbY7EbY7EZY7SZYbWefi6L+209O\nRES9nqxqaPFKaPXJaPFKAIDvZKVc8Hl6ZBA3mUyQJCmkTZIkWCxdTxsBgNLSUpSWloa0VVdXY/r0\n6Riam4K8vLMj4jdNHoT3tn4TPP646gtcM3gSZlx2Df51cje2n9gZDOKqJKH14CEMvOsOZEyZdOnf\nXDfwNjTgzJf/Qu17m1C59FdIv2ICCv79IYhJSTG5vqZq+OeHh/Dp+4dgNOkx8aohGPGdfsjun8wp\nCNSjtYf29qlSsuwP6JKkwOeV4fVI8HpkeDwyvJ6zx+3P62rcaHP60Ob0QpY6h3qT2YCUVAuSUy1I\nTjUjOdXiP06zwJFpQ1KymX9HiIh6AZ+ioq7NizqXB41uCY0eHxrdPjR6fDjjltDslULGxywGHV64\n4cKnT/fIIJ6dnY36+vrgsSzLaGpqQmZmZrecP81uQqtLgqZpEAQBTl8bcuwZAIArcsfiL+Wvo8Xr\nRLLJDufhI9AkCckjR3bLtbuDyeFAzowbkH3j9Wj4fCuOvPQn7FrwGEY/vQwmhyOq11YVFRte34E9\nO05g8jVDMe26y2Ayn/83BEQ9hU4nwGgywGi69H/6fF4ZrjZfMJi7nD44Wz1oPuNGS5MbJ442oXJn\nDTzus4MKolEPR6YNjkw7HFl2ODJtyO6XjIwsO3R6TochIoo1r6ygutWNYy1u1Dg9qHV6cKrNi0bP\n2VkYBp2ANLMR6RYROTYzRmYkI91sRIpJRLLJgGSTiGTjxf1/pUcG8aKiIlRUVGDWrFkAgF27dkEU\nRRQWFnbL+e1WI1RVg8sjw2LSwyN7YRX9o+2D0/KhaRpOtzUi2WRHS+U+6Ewm2AYP6pZrdydBEJAx\nZTJsQwZjzxNLULn0Vyh69hnozeaoXfP9dyuxd+dJ3PbDyzG6ODdq1yHq6doDfWq69bz9fF4ZTWfc\naKx3oqG+DQ31TjTUOVF16DRcbf5/6A0GHbL7J6NfXgpyclPQPz8VWf2SodNx9JziR1MVSN4WSD4n\nZF8bZJ8TkuSEInugKhJUxef/UmUAHZd/+zcf0OmN0OuN0OlN0BmMEEU7DCY7RGMSDEY7RKPdv0kB\nUYxIioqjzS4caWrD0WYXjre4cKrNCw2ATgCyrCbk2M2Y0D8V2TZz4MuEZKMhar/N7BFBXFEUNDY2\nIiUlBUajEaWlpbjvvvtQXFyMoUOHYunSpZgzZw6MRmO3XC/J6j9Pq8sHLfBvQHsQt4iBBZyyx/94\nsgaWvLwevXuJpV8/jFz8OHYtWISqv6xEwcPzvv1NF+HA3lps31KFG2ePYggnipDRZEBWThKycjpP\nHXO7fKg92YLa6mbUVDfjm8MN+GrrUUDzT3PJH5SOAUPSMWCIA7n5qVxESt1O0zTIvla4Wk/C3VoD\nr+s0vO5GeN2N8HmaEFjEEaTTidCLFuj0Ruj0InQ6/2OHEwbWf8j+kC77oCg+qLIHqho65RSCDiZL\nuv/LmgGTNQMWew6sybkwiOf/AZcoErKq4lBjG/Y3tOLwGSeqmtogqRqMOgH5yVYUOpJw/eBsDEi2\nIDfJAjEOv5nsEemypqYG06dPx6pVqzBx4kSMHz8eTzzxBFasWAGXy4Xrr78ejz76aLddL8nm/0ej\n1eWDzuz/R6ZTEJf8QVz1eqC3RG+EubvYBg3CoHvuwtevvIqs665FcuHwbj2/5JPxv2/txpBhmbhi\n2uBuPTdRorJYjRhckIHBBRnBNp9XxsnjTThW1YijRxqw5f8OQfLth9FkwNDhmbhsRBYKCrNgT+75\n/y5Rz6PIHjibjsJ5pgptzUfhbq2BLLUB8Idsky0TJks60rJGw2hJh9GS1mEE2wad/uIHxFTFB8nr\nhORrhexrhc/TBK+rAV5XA1obD+N09TZomn97YKM5FZakXNhS8mBPHQxbyoDQwE8URpPHh931Ldhd\n14zK063wKirsoh4F6XbcOqw/Lku3Iz/ZCkMP+Y1jXIJ4Xl4eDhw4EPYY6HrxZXc5OyIuQbT7fzXc\nHsAthtAgrni80JtMUamju+XMuBG17/8fjq1Zh9G/Wtqt5/5q61G0tnpx17xJXGxGFEVGkwGDCjIw\nqCADuB5QFBU11c04vL8Oh/edwsaynQCA/vmpGDW2P0aO6Y+UtPAL2SmxqaoM55kqNJ/eD+eZr+Fq\nOQFAg160wp46CJn5k2BJ6gdLUn+YLOn++1tEiU5vhMmaDlOH7YQ70lQFHlc9XC0n/CP0LSdQW/Ux\nVGUTBJ0BtpQBSHZchpTMkbDY+/H/RRTU4pXwVc0ZbK85gyNn2iAAGJxqw4wh2fhOVgryky3Q9dA/\nLz1iRDzW7IEg7nT5YJH9d+tsHxE3GYwQIASnpqheLwx2e9cn6mEEvR75P5iDA8/+Fi37D3TbqLgs\nK9j60REUXZ4LR2bv+CyI+gq9Xoe8gWnIG5iGa24cDmeLB4f312H/nlp8+I/9+ODdSgwYko6icXkY\nNTYXJnNC/rNOHSiyD831e9FUtwfNDQehyh4YjElITi9ARu4VsKcNgdmW1eOCrKDTw2LPgcWeAwfG\nAfCHc1frSTjPfI3WM1+jtuojnDy8CaI5FamZI5CWMxb21EFR/QGCeiZZ1bDzVBM+PX4a+063QhCA\nkRnJuLdoIL6TlYKki1w8GWu9o8puZjMboNMJaG3zwRYY+W4P4jpBB7No6jA1xQt9RnR3IulOjisn\nwpyTjdr/3dRtQfzg3lNwtnpx5TVDu+V8RHTx7MlmjL1iAMZeMQBulw8H9tRid/kJ/M+bu/D+xr34\nzuV5uPzKgeiXd+H72VLvpWkqWk4fRGNtOZrq9kJVfLAm5SJ74DSkZI6ENal/rwyrgk4PW0o+bCn5\nyB50NVRFQuuZr9FcX4mmur2oP74VRnMa0vsVw9F/HMy2rHiXTFHW6Pbhk2P1+OfxBrT4ZAxOteKO\nUfkY1y+t14Tvjnpfxd1AEATYLSJa3RKSfaEj4oB/eoo7MFKueL3Q9ZKpKYB/VDzr2hJUv/k25J8+\nAIP10he8lH9xDLkDUpHdL7kbKiSi7mKxGoOhvKnRhfJtx7Bj2zH8a+tRDBzqwOSSoSgo7Hkjn9R9\nJJ8TDSe2o/74Vvg8TTBZM5E96Bqk9yuG2Zrx7SfoZXR6ESkZw5GSMRz5hbPhPFOFxppy1B//HLVV\nHyLZMQxZA6YiOWN4r/zBg8Krcbrxv0dOYfvJRoh6Ha7sn46rBmQgP7l3L+xNyCAOAElWEa0uH1yS\nG4IgwGw4G7YtohluyQsAUD29K4gDQGbJ1Ti27r/Q+MU2ZF1bcknncjm9qDpUj5tu+043VUdE0ZCa\nbsW1NxXi6huGYf/uWnz+0WG8/pftyO6XjCnXFmDk2P7cDrEP8bTVobbqIzTW7gA0DWk5Y5GVPxnW\nlPyE+cFLEHRISh+KpPShyC+8FWdO7UTd0X/icMVrMFkcyB50NRy5E6DTJWzU6ROqW9x459BJ7DjV\njBSTiO8Nz8VV+Rmw9JE7GSfsn84kqxGtbf4gbjWE3u3OYjCfXazp9UR1X+5oMGdlwV4wFI3bv7rk\nIH6wsg6aBgwfldNN1RFRNOn1usAizn6oOnQan314GG+vLcdnHx7Gtd8t5Ah5L+d21qLm6804U7sT\noikJ/YZcj4y8KyAaE3v9jk4vwtF/PNL7jUNb81GcOroFx/atR03Vh+g3+FoG8l6o0e3DxkM1+Ly6\nAQ6LET/6zgBc2T89LlsMRlPC/qm0W41odUn+IC6G7jhgEc0dFmv6et2IOACkTRiPE+vfgSpJ0IkX\nv+XTgT01yB2QiqSU3vXDCFGiEwQBQ4ZlYsiwTBz7ugH/9/d9eP0v2zFwqAPX3zIS/fNT410iXQCf\npwknDv4DjbUVEE0pyC+cjYzcK7il3zkEQYA9dRDsqYPgbq1Fzdcf4Ni+t1Fb9RFyh30XadlF/EG0\nh/MpKv5+uBYfVJ2CSa9D6cg8XD0gAwZd3wrg7RI4iIuoPd0Gt+TpOohLHmiaBsXj6TXbF3aUPmE8\njr9ehpbKfUgdU3RR51AVFVWHGzCJizSJerUBQxy49+EpOLj3FDb/fR/+8vstmDB5EEpuKoTZwiDX\nkymyD6e++Qi133wCvd6IASO+B0fuFRzdjYAlKQdDxtwFd2sNThz6X1TtWoP6tCHIL5wNa1L/eJdH\nXaisb8GavcdwxiPh+kFZmDE0B9Y+MgUlnIT9m2w06CEpqn9E3HhOEDeYccbdDE2WAVWFztz7grht\n8CAY7Ha07K286CBec6IFPq+MQQW9Z9cYIuqaIAgYPjoHBSOysO3Tr/HJ+wexb3cNZtw6GiOKuCdz\nT9RUtwfH9m2A7HMia+BU9Bs8HXqRe8ZfKEtSPxRcfh+a6/fh+IGN2Lf1eWQNmIL+BTdBb+ieO3bT\npWn1ySirPI5tJ8/gsjQ7Hhk/FP3sifFnPWGDuE4nQFU1uCR38CY+7dpHxFWvf8FmbxwRF3Q6JI0Y\njua9lRd9jqNHTsNg0CF3AH+FTdRX6PU6TC4pwMgx/fHe+j14c9W/MHJMP3x3ThEsVoaSnkD2teHY\n/g04U7sDyY7hyB9xa5/cASXWUjJHIMlxGeqOfoqTRz5AU30lBo0qRVL6kHiXltAqT7fgtZ1HISkq\nfvSdAZiS5+ixN9+Jhr454SYCOgHBIN5pakpgsabi8QdxXS9brNkueeRIOA8egipJF/X+o0cakDco\nDQZD3/61EFEiSk23ovS+Cbjtzstx5EA9/vSbT1B1+HS8y0p4Z07txt7Pf4OW0wcwcNQPUHD5/Qzh\n3UinMyBn8LUYOelnEI12HPzqZVQf+B9oqhLv0hKOrKp4c381ntt+GDk2E5ZMG4Fp+RkJFcKBRB8R\n1zT4FAnGc3411b5Ys31EXGfsnaNEySNHQPX50PZ1FZKGD7ug92qahpPHm1A8cUCUqiOieBMEAaOL\nc5E/KA3r11Vg9ctbMfmaAlx703Do+tjOBD2dqsqoPvA/qD/+GVIyR2LAiNtgNPOmTNFitmVh+BUP\n4dQ3H+PE4U1wNlVhcNEPYbKkx7u0hNDg9uLl8ioca3Hhe8P6Y8bQ7IQL4O0S9l/a9qkpiqZCf86m\n/9ZAEJc9/pv69LbtC9vZBg8CdDo4v/76gt/b2uxBm9OHfnmclkLU16WkWfGjeZNRMqMQWz8+jDWv\nfIE2pzfeZSUMr6sRB7a/iNPVXyC/8FYMHXsPQ3gMCIIOOYOvxfAJ8yB5W7Bv6/NoPr0/3mX1eQcb\nW/Grzw6gxSvj55OGY2ZBTsKGcCCRg7ggQFUBVVOhOyeImw1maJoGr8sJAND3wsWagH9uuzU/D21f\nV13we08ebwIA9M/n/wyIEoFOJ2DadZfhzp9OQl1NK/783KfBfwcoeprr92HfF89D9rVh+BUPIWvA\nFC6cjTF76iCMmPQz2FIG4HD5azh1dAs0TYt3WX3SJ8fq8btth9DfbsYTU4ZjSKot3iXFXcIGcb1O\ngKJpXQZxU2CqitflAoBeuY94O9uQIXAeufAgXlPdDKvdiOTUxFi1TER+gy/LwAP/MQ02uwl/e+lz\nHNp3Kt4l9UmapgXuArkSttSBGDHpP2BLyY93WQnLIFpRcPl9yBo4DdUHNuJo5ZucN96NNE3D+gMn\nsGbPcUzNz8DPrrgMySZunQokcBDX6QSoitplEG8/VgJTU3Sm3jk1BQDsQwbDdfToBS/YrD3Zgpz+\nKRyZIUpAqelW3P3/JmPQUAf+67UvUbHtWLxL6lM0VcHx/Rtw/MA7yBowFQXF98IgWuNdVsITBB3y\nh9+CgaN+gIaTX+HIjr9BVXzxLqvXk1UNf911FP84cgrfH94fPxyVD4OO2aJdYgdxTYOqaZ3mJumD\nQTywfWEvnZoCANZBA6HJMjw1NRf0vtOnWpGZkxSlqoiopzOaDCi9dwLGTsjHu/+9E5+8f5C/ru8G\nqirj611rUX98K/ILv4f8wlkQhIT9X3GPlJE7AQVj70VL42Ec/NcrkCVXvEvqtSRFxcvlX2PbyUbc\nWzQQM4bmcIDvHAn7t19/njni7cey13+b+966awoAWPPzAACu4ycifo/kk3Gm0YXMbHu0yiKiXkCn\n17gXf8wAACAASURBVOHmfyvC1TcMwyebDuD9jZUM45dAVSQc2fE3NNdXYsiYu5A1YHK8S6IwUjIL\nMWz8T+Bx1uHgly9D9rXFu6ReR1JU/LH8a+w93YJ5lw/F5DzeHLArCRvEdToBitr1HPH2Y9Xrgc5o\nhKDrvR+TmJoKvc0G9//P3p3HR1lejf//3LNkm8mekH0hCRC2sEuVrYAsKj6gVUErXbStta1Lq199\nbBUqUuXp5lOxfdqfVuuCNlSB2ioiKCUIuAAJshgCJGTf15lJMjOZ+/79MSQwZpvALJnJ9X698mrn\nnuueOaEpnDk517kqKpy+p6HOCAqiIi4IApIksWDZOJatmsinecXs/pdIxi+HrauTM0dfxNB0jsxp\n3yUybrK3QxIGoY9IZ9yse7GYWyk68heRjA+B1Sbzp6PFfNlo4MczMpgSJwY/9Md3M8wr1NOaIsuo\nVf30iJvNPr1RE+z/iIYkJ9M+hES8vtY+LSY2TiTigiDYzZ6XwbKVE/lkn0jGh8rW1cmZIy/Q3lbJ\nmOnfIzxmnLdDEpwUHJrA2Jn3YOlsFW0qTrLJCn/JL+F0o4GfzMhkUqxIwgcychNxyT5HvK+KeHdi\nLlutqLS+v6s3OCWZjiG0ptTXGtCHBRIU7PvfuyAIrjN7vkjGh0q2WTmX/zc6jLWMnfkDcZy6DwoJ\nTbyQjLdw9uhfsXWJDZz9kRWFV46Xcry+lXumZTAxNszbIQ17IzYRV6u7N2sO0JrSZUPyg9PlQlKS\n6aisRJFlp9Y31ZuIjhX94YIg9DZ7fgZLLyTjB/ee83Y4w5oi2yj+4nWMraVkTfsuunBxUrGvCglN\nZMy0u+kwVFP8xWtitGEfFEXhrcJKDlU28Z2cNNGO4iTfzzIvk0qSsNn6qYhLagAUmw1JrfZGeC4V\nlJCAbLFgaW52an1zo4moaDFkXxCEvn1tfgZzFmfx4btf8sUR59veRhJFkTl/ciutDYVkTFlLaFSm\nt0MSrpAuIpWMqd+irbGI8ye3oijOFbdGio9K69ldUsfq8clcnSQ2Zjpr5CbiA4wv7H4syzbw4Y2a\n3YLi4wDorBn8YA5FUWhubCcyRsy0FQShf4uuyyZnRjLv/L2A4qJ6b4cz7FQU/Zum6nxGT1pDROwE\nb4cjuEh4TDbpE2+jqfoo1cV7vB3OsPFFXSu5pypYnB7LtaNHeTscn+L7WeZlUqn67xFXXVoRV/lB\nRTzO/n+KzpqaQde2myyYO7uIihEVcUEQ+idJEjfeNoX0rGi2/u0wNZWt3g5p2KgvP0Rd6X6Sx60g\nKmGat8MRXCw6cQYJmUuoPreb5ppj3g7H6yra2vn/8kuYFBvGbeOTvR2Oz/FoIt7R0cEjjzzCjBkz\nWLhwITt27OhznaIoPPfcc8ydO5fZs2fz85//HJPJtWODuqvetj4T8QsVcZvsFz3i6qAgtBERTlXE\nmxvtO8Ijo0VFXBCEgak1Km799iyiokP4+0ufYTKYvR2S17U1FlFWuIOY5K8xKnWet8MR3CQhYwmR\ncVMoOZGLqW3ktmcZLV08f6SY2JBAfjBtdK8OA2FwHs0yN23aRElJCVu2bOGBBx5g3bp1fPHFF73W\nvfXWW2zbto3//d//5eWXX+b48eP8/ve/d2ksqgvHq/Y9NcW/KuIAQXFxmGvrBl3X3GD/wBMpesQF\nQXBCYJCG1XfNoqtL5h+vHsbWNXL7ZjtNdRQfe43QqExSs1eJEwT9mCRJpE+6jWB9HOfy/4bVYvR2\nSB4nKwp/PXaeji4bP5qRQZDGP/IlT/NYIt7e3s727dt5/PHHyc7OZtWqVaxatYo33nij19q8vDyu\nu+46Zs6cyYQJE7jjjjv49NNPXRpPT9V7gKkpiuwfU1MAghLinGpNaW5qJzhEK0YXCoLgtPDIEG79\n9kwqzjfz/o4T3g7HK2xdZs4VvIomIJSMnLV+U8QR+qdSB5A59dvIspXzx98ccZs33ztbw4n6Nu6e\nkk5siG+fueJNHssyCwsLkWWZSZMm9VybNm0aBQUFvdZGRESQl5dHXV0dRqOR3bt3M378eJfGo1ZL\ngH0Gbu+pKRcScZt/bNYECBw1yqnWlNbmDsIjgz0QkSAI/iQtI5rrbp7EkUOl5H9a5u1wPEpRFEpP\nvYWlo4nMKd9CoxV/h44UAUERjJ50O22NRdSU7PV2OB5zsr6Nd85Uc0NWPDmjxJjCK6Hx1BvV19cT\nHR2N+pJxgNHR0dTV9W6XuPfee7nnnnuYN28eKpWKzMxMnnvuuQFfPzc3l9zcXIdrFkv/Q/dVkgRS\n34n4xYq47BfjCwGCRsVibW1FtlhQBQT0u661uYPwCPGPiCAIQzfj6nQqSlvYuf04SWmRjIofGafz\n1pcfpLmmgPTJtxMcGu/tcAQPC4/NJn70IqrO7kIfke73oyqbOiy8WFBCdnQo/zUmwdvh+DyPlXs7\nOjrQfuWUSq1W22eyXFVVhU6n44UXXuBvf/sbAM8888yAr7969Wq2bdvm8PXnP/+53/X2HvHuRPwr\n4wtVFyvikp9UxANiYgAwNzYOuK6tpYPwSLFRUxCEy3PdTZOIiAzh7VcPY7V0eTsctzO2lFJx+l/E\nplxDdMJ0b4cjeEli5lL0kaMp/mILVrPB2+G4TXdfuEal4vtT08XmTBfwWJYZGBiI1Wp1uGa1WgkJ\ncUz6FEXhkUce4e6772b+/PnMnj2bp59+mm3btvVZPb9cKtXIqogHdifi9Q39rlEUhdaWDsJERVwQ\nhMsUEKjhG9+aQXNjO+9vP+ntcNyqy9pO8bHXCAlLInncjd4OR/AiSaVm9OQ7AIXzJ3JRFMXbIbnF\n7pI6ipqM3DUljdBAsZfMFTyWiMfFxdHU1IR8yTHrjY2NxMbGOqxramqisrKSrKysnmvjx49HlmVq\nnNhs6KxLW1PUqv57xP2lIh4YYz/lytLQf0W8o92K1WITPeKCIFyRuIQwlt80ifzPyjjupydvKopC\n2ZfbsNnMjM65E5XKY52ewjAVEBRO2sRbaWs8TUPFJ94Ox+XK29rZfrqKa9NHMT4mzNvh+A2PZZnZ\n2dkAnDhxcUf90aNHmTJlisO68PBwtFotZ8+e7blWUlICQFJSksviUav636zZ81j2jzniAOrgYDR6\nPeaG/ivirc0dACIRFwThik2bncrEqYm8+/YXPecT+JOmmnyaa46ROv4mAoMjvR2OMExExE4gOmkW\nFUX/xtze/7+3vsZqk3mx4DxxukBuHpfo7XD8iseyzJCQEFauXMn69es5deoUO3bs4J133mHNmjXY\nbDbq6+uxWCxoNBpuvvlmfve735Gfn8+XX37JE088wfLly4mOjnZZPPbWlAv/vd+pKbLfTE0BCIiJ\nHjARb2u5kIiL1hRBEK6QJEnccEsOQUFa3sktQJH951f15o5myr7cTmT8VKLixcmZgqOUcf+FRquj\n5ESu34w03Ha6ilqTme9NTUfrJwXK4cKjf5qPPfYY6enp3H777Tz33HNs2LCBnJwcqqurmTt3Lvn5\n+QD8/Oc/Z+7cudx3333cddddZGVl8atf/cqlsTjdI+5Hs2ADY2KwDJKISxLoQsU8UEEQrlxQsJYb\nV0+h9Fwjn31c4u1wXEJRZM6f+DtqdSCp428Sh/YIvag1QaRPWo2p5Ty15/O8Hc4VO9NkZM/5OlaN\nTSAlTAxzcDWPNrXpdDqeffbZXteTk5M5ffp0z+OgoCAef/xxHn/8cbfFopYGaE3pTr5l/+kRB3uf\neNuXhf0+bzCY0YUG9pw6KgiCcKUyx41i5jVpfPjul2RmjyJmlN7bIV2R2vN5GJtLGDvzB2i0IikR\n+hYalcmo1HlUnX2f8NjxBOvjvB3SZbHaZF49Xkp6eAhLM3zzexju/CfLHCKVSkKS+hlfeOGxIit+\nMzUFQBsZiaW5pd/nTW1mQsOCPBiRIAgjwbUrJhAaHsQ//16AbPPdX9V3muqpOreLUalzCI3KGvwG\nYURLGnMdAcGRlJ1622dbVN47V0N9u5lvTU4VowrdZEQn4gO1pkhIIPvPyZoAAVGRdLW1IXf1PdvX\nYOhEL9pSBEFwsYBADf+1ZiqVZc0c2lfs7XAui6LIlJ56C21AKIlZ13k7HMEHqNRaUsffjLGlhIbK\nz70dzpBVGjrYea6WpRlxoiXFjUbsvCXVJa0paql3sq1SqcDPKuIBEREAWFtbCexj46vJYCY+URxV\nKwiC66VlRDN77mj27TrN+JwEomJ03g5pSBoqPsXYXMyYGd9Hren/dGJBuFRY9BiiEqZTWfQuEbHj\n0Qb6xtg/WVF47UQZ0cEBrMhyz+mZsixjs9nc8trupFarew5+dAX/KfcOkVrdf0W8+5o/zREHe2sK\ngLWf9hRDWyf6MFERFwTBPb6+PJsQXQDvvX3cpw48sXS2UnHmPaITZxIWPdbb4Qg+JnncjSBJlJ/+\nl7dDcVpeWQPnmk2snZxKgBumpJhMJsxms8tf1xPMZjMmk8llrzeyK+IDJOJqqbsi7j+JeHdF3NLS\nOxGXZQWTwYxe9IgLguAmgUEarrt5Mrkvf87JgiomTXPd2RDu0n1wj0qlEadnCpdFG6AneewKSk9u\npTVxBuEx2d4OaUBtZivbTlcxJzma7OhQl7++LMuoVCqCg31zVLJWq6Wjo6Pn+7hS/pNlDtGlk0H6\nq4gjy/5VEY+wt51Ym5t7PddusqAoiB5xQRDcatykeMZNimfXP0/S0W7xdjiDaqk7QWv9KVKzV4kp\nKcJli06ciT4yk7IvtyPbrN4OZ0A7iqoA3HZwj81mQ6Px7TqwWq12WVuN/2SZQzTQZk2wV8QVWfar\nHnGVVosmVN/n5BRjWyeAaE0RBMHtlq+ahNXSxUfv9T9OdTiwdVkoP/0OYdHjiIjL8XY4gg+TJInU\n8Tdh6WyhtnT4zhY/32Li4/JGVo5NICxQ6+1whi1Xnh8wchNxiUsS8d5/oCpJhST718maANqICCx9\nVMSNBnuvlj5UtKYIguBe4ZHBLFyezZFDpVSU9v77aLioKfmQLrOBlOyV4uAe4YoF6+MYlXINNSUf\nYels9XY4vciKwhunyknUB/H11FhvhzNi+FeWOQT21pQBNmv64dQUgIDIyD43a3ZXxENFRVwQBA+Y\nNXc0cYlhvL/9BIo8/DZudprqqT2/j7j0BQTpRFIiuEZC5hIklYbKM+95O5RePqlsoqSlnTUTU1D7\nyMF+siyzbt06Vq9ezdq1ayktLaWgoIBbb72VNWvW8Pzzz/e7DhjSWnfx7SadK+B4oM/I6BEHeyLe\nWVfX67qhzUxQsBaN1r8+eAiCMDypVBLLV03ilT8d5NjhcqZelertkHooikJ54T/RBIYSP3qxt8MR\n/IhGG0JS1nLKvtxGbMrV6CPSvR0SAO1WG28XVjIjPsItGzQHU91gwtTh2DuvC9aSMMiY0z179mCx\nWMjNzaWgoIBNmzZRVVXF5s2bSUlJ4Qc/+AEnT56ksrKy17r/+7//Y/369U6vdZcRm4irVaqLrSl9\nJNtqP03EtZERGE4X9bpuMpjFRk1BEDwqLTOaCVMS+fC9QrInJxAUPDx6UlvrT9LWeJqMKWvFzHDB\n5WKSZ1NfcYjywn+SPfs+pD6KgZ6281wNnV02bh2f7PH3bjWa+eGmPXz1F2MqCV795XLC9f3nJkeO\nHGHevHkATJ06lcOHDxMTE0Nqqv2D/dy5czl06BD19fUO606cOIHRaMRisTi11p1GbCJ+6YE+/VfE\nFb/rEQ+IiOhzfKGYIS4IgjcsuXE8f/yfveTtLmLpf030djjINivlhe8QGj2GiFGTvR2O4IckSUXK\nuFUUHf4/GquOEJM0y6vxNHZY2HO+jqWj44gO9vwHz3B9IH/+72v7rIgPlIQDGI1G9Hp9z2ODwUBK\nSsrF19DpKC8v77VOrVb3ujbQ2q6uLrdNehm5ibiKQaamqO0VcT/rEddGRiB3dmLr6EB9yQxPo8FM\neIRvzvQUBMF3hUeGMGdhFvv3nGH619KIGaUf/CY3qis7gKWzhazpd4sNmoLbhEZlEDFqMlVndxEV\nPwWV2nu/edlRVEWwRs3yjDivxTBYC0p/9Hq9w+E6YWFhdHZ29jw2mUw91y5dJ8tyr3sHWuvOcYv+\nVe4dgsHGF6okyb5Z0w8r4tD7UB+jqIgLguAl1yzMJDQ8iA/+edKrcXRZTNSUfEhM8myC9d5LSoSR\nIWnMdVgtBurKPvZaDGWt7Xxa2cSNYxII9sE9YtOnTycvzz4OsqCggJycHLRaLWVlZSiKwscff8zM\nmTN7rRs7dix6vd7pte40civiDq0pfYwvVKmR/HBqyqXH3AcnJAD2jUlGg1mMLhQEwSu0ARquvWE8\nb79+lOKiejLGemdKSVXxbhRFITFzqVfeXxhZgnSxxCbNpqZkLzFJs9EEXF5V+HIpisJbhZWM0gUy\nLyXGo+/tKkuWLOHAgQOsWbMGRVF4+umnMRgMPPzww9hsNubOncuUKVOYPHlyr3UATz75pNNr3cXp\nRPy+++7jpptuYsGCBaj9IDlVqyS4kH/3WxFX/LAiHnmhIn7JCEOL2YbVYhOjCwVB8JoJUxM5lFfM\nnn+d4vs/nY/k4fFpnaZ66ssPkZi5FG2g56dGCCNTQuYSGquPUF3yISnj/suj732yoY0vGw3cOz0D\njY+MK/wqlUrFhg0bel3funWrU+umTp3q9Fp3cTrLDAoK4qGHHmLevHk8/fTTFBYO7xPRBnNpa4q6\nnx5xyQ97xDV6PZJa7XCoj9Fg76fSiakpgiB4iSRJLLlxAjVVbRw/WuHx96888y7agFDi0uZ5/L2F\nkUsbGEpc2gLqyw5i7mjy2PvKF6rhWZE6psWFe+x9hd6cTsR/85vfcPDgQR599FHOnTvHN77xDVau\nXMkrr7xCU5PnfnhcZdADfaTuA338qyIuqVRoIyKwOiTi3adqikRcEATvScuIZtzEOD7aWUiX1eax\n9zU0FdNSd5KkMdd5ddOcMDLFpc9HrQ2m6uz7HnvPz6qaqTR08o3sJLEp2cuGlGUGBwezcuVK/vrX\nv7Jv3z6WL1/Os88+y/z58/nRj37EoUOH3BWny6kHOdBHrVIh+eH4QrC3p1zamtJutAAQMsiYIEEQ\nBHdbfMN4DG1mPt1f4pH3UxSZiqJ/ERKaRFTCNI+8pyBcSq0JIjFzCU3V+bQbqtz+fl2ywjtnqpkc\nG0ZWpHenFAmXMTWltbWVv//97zz44INs3ryZtLQ0HnroIdLS0vjxj3/Ms88+6444XU4lDTI1BQkJ\n/K41BUAbHo61ra3ncbvpQiIeMjwO0xAEYeSKiQtl+uxUPv7wTM/fTe7UXHOM9rYKksfdOCwOVhFG\nppik2QQER1F19gO3v9fBikbq282sGpvo9vcSBuf0Zs1du3bxzjvvkJeXh16vZ8WKFfziF79g/Pjx\nPWvS09PZtGkTP/3pT90SrCsN1pqiVuy/qvG3zZoAmrAwOqsufupuN5kJDtGi8rM2HEEQfNOCpWM5\nfrSC/XvOsGyl+w75UWQbVWd3ERaTTWhUptveRxAGI6nUJGRcS+nJrZhay9GFpwx+02Ww2mT+fbaa\nGfERpIaHuOU9hKFxOvN66KGHUBSFZ599lv379/dKwgEyMzNZs2aNy4N0B0mSkFT9jy9U052I+2FF\nPCzUsSJutBCiE32RgiAMD/qwIL42P5PDB8/T1tLhtvdpqPocc0cjSVnL3fYeguCs6ITpBIbEUnV2\nl9veI6+8gZZOKyvHJrjtPYShcToRz8vLY/PmzUybNq3nhKGjR49itV48knTmzJk8+uijro/STbrz\nb7XUO9nuroj7Y4+4NizMIRE3iURcEIRh5msLMtBq1eTtLnLL68s2K9Xn9hAZP4WQsCS3vIcgDIWk\nUpOYuYS2xtMYW867/PXNXTbeO1vD7KQoEvT+cZK2LMusW7eO1atXs3btWkpLSykoKODWW29lzZo1\nPP/88/2uA/pc2+3YsWOsXbvW7d+D01lma2srS5cu5cUXX+y59pOf/IQbb7yR8vJytwTnbt05dl87\nhtUX/mj8sUdcExaGzdSO3NUF2HvExUZNQRCGk6BgLXMWZVHwWTlNDabBbxii+vKDWC0GEjOXufy1\nBeFyRcZPIUgX55aq+Eel9ZisXdyYNfyq4TXGeoqbSh2+aoz1g963Z88eLBYLubm5PPTQQ2zatIn1\n69fzu9/9jjfffJNjx45x8uTJPtcBfa4FeOGFF3j88ccxm81u/b5hCIn4xo0bmThxIvfcc0/PtQ8+\n+IAxY8Y4fepQR0cHjzzyCDNmzGDhwoXs2LGj37Xvv/8+y5YtY+rUqXz3u9+lsrLS2VCdJkkKElI/\nifiFNf5YEQ8PA6DLYADsPeI6vaiIC4IwvFw1N50QXQD7dp126evaujqpLvmI6MSZBOm8c4qnIPRF\nklQkZi3F0HQWQ9NZl71uu9XG+8W1zEmOYZRueBXe2joNPPDeev579yaHrwfeW09bp2HAe48cOcK8\nefbZ/1OnTuXw4cNYLBZSU1ORJIm5c+dy6NChXutOnDiB0Wjscy1Aamoqmzdvdu83foHTmzXz8/PZ\ntm0bERERPdf0ej0PPvig033hmzZtoqSkhC1btlBYWMi6devIyMggJyfHYd3Ro0d5+OGHWbduHTNn\nzuTXv/41Dz/8MG+++aaz4TpFUnFhNkpvqu7Nmn64gVEbZk/Era1tBERGih5xQRCGJW2AhnnXjmHn\njhPMWZTFqIQwl7xubWkecpeZxIxrXfJ6guBKEaMmERyaROXZXYyblemSOd97ztdhscnckBXvgghd\nKywolD9c/yTtlnaH6yEBIYQFDXzKrdFoRK+/OILRYDCQknJxo6tOp6O8vLzXOrVa3eta91qAZcuW\nUVHhmYPFnM4yg4ODqaur63W9ubkZlRNV4/b2drZv387jjz9OdnY2q1atYtWqVbzxxhu91r700kus\nWrWK2267jYyMDH7xi19QW1uLwTDwJ6Ohss8R7/sHXOPPU1NC7T/Y1rY2FEXBJFpTBEEYpqZ/LY3w\niGD2vu+aqniXxUTt+TxiU64mIDjSJa8pCK7UXRU3tZynrfHK90h0WG18WFLHvJQYooKHZ9EtXh9L\nRlSaw1e8fvDfVun1ekymi61rYWFhdHZ29jw2mUyEhYX1WifLcq9r3Ws9zeksc+nSpfzyl7/k8OHD\nmM1mzGYzhw8f5sknn+TaawevKhQWFiLLMpMmTeq5Nm3aNAoKCnqt/fzzz1myZEnP45SUFD766CNC\nQwf+ZDRU9op4338Equ6pKX7YI97TmtLWhsVsw9Yli4q4IAjDklqjYsHScZw+UUNlWcvgNwyipmQv\noBA/etGVBycIbhIeMx5deCpVZ3ehKMoVvdZ/yuox22SWZcS5KLrhY/r06eTl5QH2jZc5OTlotVrK\nyspQFIWPP/6YmTNn9lo3duxY9Hp9n2s9zenWlIcffpgHHniAO++80+HXJIsWLeKxxx4b9P76+nqi\no6NRX5LYRkdH96qyt7W10dLSgtVq5Tvf+Q5nzpxh2rRpPPHEE8TF9f9DlJubS25ursM1i2XgwyAG\nak3R+PHUFI1OByoV1jbDxcN8RCIuCMIwlTMjiQMfnWHvzi+5856rL/t1rOY26soPEJc2H22gaws7\nguBKkiSRkHEtZ/NfwtBYRFjMuMt6HbNNZndJHdckRxE9TKvhV2LJkiUcOHCANWvWoCgKTz/9NAaD\ngYcffhibzcbcuXOZMmUKkydP7rUO4Mknn+y11tOcTsR1Oh0vvvgixcXFFBUVodVqyczMJD093an7\nOzo60GodT27UarW9kuX2dnuP0K9+9St+9rOfkZaWxh/+8Afuvfde3nrrrX7bYFavXs3q1asdrlVU\nVLB48eJ+Y1JJij0b7+u5Cx9A/bEiLqlUaEP1WNvakE32HcFis6YgCMOVSq3i68uzefu1I5QWN5KW\nEX1Zr1Nz/j9Ikpq49AUujlAQXC8sJpuQsGSqiz8kNHrsZfWK7y9rwGjpYnnG8OsNdwWVSsWGDRt6\nXd+6datT66ZOndprbbfk5OR+n3OlIZd7MzIyWL58OYsXL3Y6CQcIDAx0mDkOYLVaCQlxPNmpu2J+\n++23c+ONN5KTk8Nvf/tbCgsLOXHixFDDHdCAmzXx3x5xsI8w7Gprw2TsroiLHnFBEIavCTkJjIoP\nJe+Dy+uZtZrbqC//hFFpc9FoxYmCwvBnr4ovxthSgrG5eMj3W20yu4pruSoxcthNShEucroiXlxc\nzMaNG8nPz++VUAODJslxcXE0NTUhy3JPVbuxsZHYWMdm/MjISLRaLRkZGQ7XIiIiqK6u7jVh5Ypc\nGF/Yl54j7v2wIg4XD/Xp6E7ERUVcEIRhTFJJzF86lrdePUJZcSOpQ6yK15zfhySpiEub76YIBcH1\nwmMnEKyPp7p4D6FRmUO692BlIy1mK9dn+mc13F84nYg/8cQTtLa28uCDD17WrtLs7GzAnrB3J9NH\njx7t1Y+j0WiYOHEip06d6tkE2tTURHNzM0lJrj39TJIApb/xhRfW+GlF3J6IG2g3mdEGqNFq/fMD\nhyAI/mP85ARi40PZ90ERa3/ofK+41WygvvwQcWnzRDVc8CmSpCI+41pKvngdY3MJ+sjRTt3XJSvs\nPFfL9PgIEkP94xRNf+V0In78+HG2bt3ak1APVUhICCtXrmT9+vX86le/oqioiHfeeYfXXnsNm81G\nU1MT4eHhBAQE8N3vfpfHHnuMcePGkZWVxW9+8xsmTpzoMHHFFQYaX+jPR9yDvTWls6aGdpNF9IcL\nguATJJXE/CVjefu1I5SVNJE6Osqp+2pFNVzwYZFxk6nWjaK6eA9jZnzfqXs+q2qiscPCj6ZnDL5Y\n8Cqns8yYmJgrHqHz2GOPkZ6ezu23385zzz3Hhg0byMnJobq6mrlz55Kfnw/A8uXL+e///m82bdrE\nzTffjKIo/PGPf7yi9+6Lfd9Dfz3iF9b44YE+ANqwUKxtbRcO8xG9Y4Ig+IYJOQnExunJ+8C51V+2\n6wAAIABJREFUueJWs5H68oOMSr0GTYDOzdEJguvZq+KLaWsswtRaNuh6WVHYea6GybFhpIaL3wAN\nd05XxH/4wx/y9NNPs2HDBtLS0pw6xOerdDodzz77bK/rycnJnD7t+JdqX1NQXE1S9V8R7zlZU+Wf\nLRva8DCsrW2YjGbRHy4Igs/oqYq/ftSpqnht6T6QJOLSxKQUwXdFxU2h+txuqos/JGvadwdce6Sm\nhRqTme/kpHkoOuFKOJ1Nv/zyy+Tn53P99dczadKkXl8+SVLo74/A33vENaFhKF1dmAyd6MQMcUEQ\nfMj4KYlOVcW7LCbqyw8SmyKq4YJvk1Rq4kcvorX+FO1tlf2uUxSF987WkB0dSmakvt91/kKWZdat\nW8fq1atZu3YtpaWlFBQUcOutt7JmzRqef/55h/XHjh1j7dq1PY9LS0u5/fbbueOOO1i/fj2yLDs8\nt2LFCrd/D05XxH/wgx+4Mw6vGKg1Re3Hc8Th4uma7UYzwSIRFwTBh6hUEvOWjGXb60cpL2kipZ+q\neG3pPlAUMTdc8AvRCdN7quKZU7/V55ov6lqpMHTw0OwxHo7uynRU12C75Lh5ALVOR3DCwBNf9uzZ\ng8ViITc3l4KCAjZt2kRVVRWbN28mJSWFH/zgB5w8eZKJEyfywgsv8M477xAcfHHz6jPPPMODDz7I\n7NmzWbduHR9++CFLlixhx44dvPrqqzQ3N7vl+72U04n4TTfd5M44vEKSFJRBpqb462ZN7YXJN+3t\nVnR60SMuCIJvmTAlkbzdRez7oIg77/lar+e7LCbqyuzVcG2A/1cGBf9nr4ovpOzLbXQYawjWOyap\niqLw7tkaMiN0jIvynZ95a2srR390H1xSjQZApeKqv72INjy833uPHDnCvHnzAPvhPIcPHyYmJobU\n1FQA5s6dy6FDh5g4cSKpqals3ryZRx55pOf+kydPctVVVwEwf/58Dhw4wJIlSwgPD+f1119nyZIl\nLv5ue3M6EQf4/PPP+ctf/kJxcTGvvfYa27ZtIyUlhVWrVrkrPvcaYGpKT4+4H2/WlFFhscjieHtB\nEHyOSiUx/9qxbNtylPLzTaSkO1bFa0vzUBRZVMMFvxKdNIvq4g+pKf6I0Tl3ODz3ZYOBktZ27p+Z\neVmncHqLNjyc6X/a3GdFfKAkHMBoNKLXX/zQYTAYSElJ6Xms0+koLy8HYNmyZVRUVDjcryhKz5+V\nTqfDYDAAsHDhwsv/hobI6Sxz3759fO973yMhIYGGhgZkWUaSJH7xi1/w9ttvuzNGtxl4jri9JO6v\nmzU1YWFY1fZKuEjEBUHwRROmJhIzSt/rtE17NfwAsSlXow0M9VJ0guB6KpWGuPQFNNUU0Nne4PDc\nu+dqSA0LZlLs0M968bbghHj0WZkOX4O1pQDo9XpMlyTwYWFhdHZ29jw2mUwDnn1z6eCRwda6i9OJ\n+PPPP88jjzzCU0891XMM/U9+8hMeffRRXnrpJbcF6F79n6zp7xVxdWAgXcH2HzgxNUUQBF9k7xUf\nw7nT9VSUXuzlrC3dj6LIxItquOCHYpNno9GGUFOyt+daUZORoiYjN2TF+1Q1/EpNnz6dvLw8AAoK\nCsjJyUGr1VJWVoaiKHz88cfMnDmz3/snTJjAp59+CkBeXt6Aa93F6Szz7NmzzJ/f+zCEhQsX9pT9\nfc1AB/pIPRVx/0zEAeTQSEBUxAVB8F0TpyYRHatj34UJKl3WdurKPiY25WtoA32vMigIg1GpA4hL\nX0Bj1WEsHfYPoO+drSFBH8TUuAgvR+dZS5YsISAggDVr1vDMM8/w2GOP8eSTT/Lwww9zyy23MGHC\nhF4nuF/q0UcfZfPmzaxevRqr1cqyZcs8GL2d0z3ikZGRlJeXO/TegP3I+piYGJcH5hGS0m9rivrC\nngF/nZoCYAux916JzZqCIPgq1YW54tvfyKeyrBnJ8hmKYiM+/eveDk0Q3CY25WpqSvZSc/4/2BKW\ncrKhjbunpKMaQdVwsLeWbNiwodf1rVu39rk+OTnZ4bnRo0fz+uuv9/v6Bw4cuPIgB+F0ufe2227j\nySefZN++fQCUlZXx1ltv8dRTT/nuRJUBesQlP5+aAtAVFIqEQmDQkPbsCoIgDCsTp9mr4vt3n6C2\n7GNik0U1XPBvak0Qo9Lm0lD5Gf8uqiA2JJBZCZHeDku4DE5nYPfccw8Gg4H77rsPi8XC3XffjUaj\n4bvf/S4/+tGP3Bmj+ww4NcX+n4off7rsCtARYLaOqH4yQRD8j0olMe/aMZz8bAeyzUqcqIYLI8Co\n1LmcKi7giwYT35qcilol/i33RU4n4pIk8f/+3//jxz/+MefOnUOr1ZKenk5QUJA743MzedCKuOzH\nP9dWTRABstnbYQiCIFyx7ElRdNRV0dw2moCggUeeCYI/0GhDOBk4B721nVmxvpyLjWxOJ+JHjx51\neGy1Wjl16lTP4+nTp7suKk+RAKXvp7rHF/pzRdwiBaDpMng7DEEQhCvWUHkQjUbmyJFoJlzVQmLK\nyNq0Jow8NcZOThqDmaPOp7HCSFLWcm+HJFwGpxPxO+64A0mSUJSLmaskSUiShEql4sSJE24J0K0G\nOFmzuyJuk+Q+n/cHFjRozSYUm82vN6UKguDfbNYO6kr3E5M8mxB9OHm7i1hz11XeDksQ3GrnuRpC\nAzTMS4ylruwAcWkL0GiDB79RGFacTsQ//PBDh8c2m42SkhL+8Ic/8PDDD7s8MM9QQOl7M6ZKVrBJ\noPRXMvcDZpuKINmM1WAkIEL8KlcQBN9UV/Yxss1CQsYi5l3bxju5x6iuaCUhWfy9JvinhnYzn1Y1\ncdO4JJITU2mpPEh9+UESMhZ7OzRhiJweCZKUlOTwlZqayoIFC/j5z3/OM888484Y3UdS+m1NkRR7\nf7gs+29FvNMCWlsnXW1t3g5FEAThsti6Oqkt3U9M0lUEBEUweUYyEVEh5O0uGvxmQfBRu4prCdKo\nWZAaQ0BQBNFJs6gtzcPWNbL2fcmyzLp161i9ejVr166ltLQUsBeL77///p7DfrqVlpayYsWKnsdN\nTU3cdddd3HHHHTz44IN0dHQ4PLd06VLMZvf+mV7xbL7o6Oieb9z39D9HXFIUFAlkxT8TcUVR6DDL\nBNg6sYpEXBAEH1VXdgDZZiE+YxEAarWKedeO4fSJGmqqWr0cnSC4XkunhY8rGrk2fRRBGntbaXz6\nQmxdnTRUfOLl6C5PU4OJqvIWh6+mBtOg9+3ZsweLxUJubi4PPfQQmzZtoqysjDvvvJPjx487rN2x\nYwc//elPaW6+eArvn/70J1asWMEbb7zBhAkTyM3NBWD//v3cddddNDQ0uPYb7cNlb9YEMBqNvPLK\nK4wZM8alQXmMpKD0VxGXQVZJ2Pw0ETd3diHLClqbWSTigiD4JFtXJ7Xn9/VUw7vlzExm/54i8j4o\n4rbvzPJihILgeruK69CqJBalx/ZcCwyJIip+GjXn9xGbcg0qtdaLEQ6NyWjmj5s+6pWPSRL87JdL\nBzx08MiRI8ybNw+AqVOncuLECdrb29m4cSMvvPCCw9rw8HBef/11lixZ4nD/PffcA8D8+fP5/e9/\nz3e+8x1UKhUvv/wy3/jGN1z0XfbvijZrgr1l5Te/+Y3LA/OMwSvi/pqIt5ssAGhlM9ZWkYgLguB7\neqrhoxc6XFerVcxdPIZ//+MLaqvaiEsUh/sI/sFgtpJX3sC16bGEaB1TuISMRTRVH6Wh8nNGpV7j\npQiHTqcP5Mf/vYjODqvD9aBg7aAnfxuNRvR6fc9jtVpNVlYWGk3v9HbhwoW9rhmNRkJDQ+1x6HQY\nDPZJcnPmzBny93G5LnuzJoBWq2XUqFEuDciTFBSUfg70kWTF3iPu54l4cKBK9IgLguBz7L3heUQn\nzSIguPeJglNmprB/zxnydhdx67dneiFCQXC93efrALg2vXfuFaQbRWRcDrXn9xKTfBUqle+cmh0V\no7us+/R6PSbTxRYWWZb7TMIHuz8oKAiTyURYmOc/tF/2Zs2kpCSfTsIBe2tKPyf2SIp9hrgs2zwc\nlGd0J+IhugDRmiIIgs+pKzuI3GUmYfSiPp9Xa1TMXZzFl19UU1ct/o4TfJ/J0sXe0noWpMYQGth3\n60l8xiIsnS00VfduJ/ZH06dP79mQWVBQwNixY4d8/759+wDIy8tjxowZLo9xME5/bFi7dq3TR6G/\n+uqrlx2QZw3cmiKrQO6vidzHtRvtibguNEgk4oIg+BR7NXxfv9XwblNnpV6oip/hlm95/h9YQXCl\nD8/X0SUrLB0d1++akNBEwmMnUFOyl+jEmUjSFc/kGNaWLFnCgQMHWLNmDYqi8PTTTw/p/nvvvZdH\nH32UrVu3EhkZye9+9zs3Rdo/pxPx6dOn8/LLL5Odnc3MmTPRarWcOHGCAwcOsGTJEsLDfW9eq8JA\nmzW7e8T9tSJuJiBQTVBYqOgRFwTBp3RXw+P7qYZ3s1fFx/DetuPU14wlNj7UQxEKgmt1WG18eL6e\neSkxRAQNvBEzIWMxhZ9uprnmGFEJ0zwUoXeoVCo2bNjQ53ObNm3q8/qBAwd6/ntMTAx//etf+339\njz766MoCdILTiXhpaSnf/OY3efTRRx2u/+Uvf6GgoICNGze6PDj3k/s/WVNWkCXJfyviJgshugA0\nYeF01tR6OxxBEASnXKyGzyRwgGp4t6lXpfDxhV7xb6wVVXHBN31UWo/ZJrM8o/9qeDddeCqh0WOo\nLv6QyPgpfl8V93VO/6+zb98+brvttl7Xly1bxsGDB10alKc4VRH31x5xo4UQXSBaUREXBMGH1Jcf\nxNbVSfxo504Q1GjUzFmUxcljVdTXGtwcnSC4XmeXjT0ltcxJjiYqOMCpexJGX0unqZaWupNujk64\nUk4n4tHR0Rw+fLjX9by8POLj4516jY6ODh555BFmzJjBwoUL2bFjx6D3vPfee0yYMMHZMIdE4cLx\nmX0+2d0j7r9TU0J0AWjDw7C2tfUaSykIgjDc2LrM1JzfR0zSLKeq4d2mzU4lNDSI/bvPuDE6QXCP\nfWUNdHTZuC5z8Gp4t9CoDPQRo6kp/lD8+z7MOd2actddd/Hkk09SUFDApEmTUBSF/Px8du7cyf/8\nz/849RqbNm2ipKSELVu2UFhYyLp168jIyCAnJ6fP9W1tbUNuvB+SAQ/08e+TNdtNFqJidWjDwlG6\nurB1dKAJCfF2WIIgCP26WA0fuDf8qzRae1V81z9PMH/pWGJG6Qe/SRCGAYtN5oPiWr6WFE1MyMAz\ntb8qIWMxZ46+SFvjacJjst0UoXClhnSgT0hICFu2bOG9994jKCiIsWPH8te//pXZs2cPen97ezvb\nt2/ntddeIzs7m+zsbI4ePcobb7zRbyL+61//mpSUFJqampz/jobA3poywNQUSfLrRDw5PRJtuH1m\nprW1TSTigiAMW7Yus/0UzcRZBAZHDfn+6V9L5eOPzrB/TxE33THdDREKguvtL2/AYOni+iFUw7uF\nRo8lJCyF6uI9hEWPc3ryneBZQ+rgX7VqFf/4xz/Iz8/n0KFDvPLKK04l4QCFhYXIssykSZN6rk2b\nNo2CgoI+13/22Wd8+umn/PCHPxxKiEOiINNvnm2T/f5kTftmTXsiLg71EQRhOKsrO0BXV8eQq+Hd\nNFo1cxZmceJoJY31RhdHJwiuZ7XJvF9cy1WJkYzSBQ35fkmSSMhYhKmlFGNzsRsiFFxhSIl4aWkp\n69evZ+3atdTW1rJlyxY++eQTp+6tr68nOjoatVrdcy06Opq6urpeay0WC0888QTr168nKMi5H77c\n3Fxuvvlmh69Bk3il/4o4iv+erGmzyXR2WO094mHdFfFWL0clCILQty5rB7Xn/0Ns0mwCQ4ZeDe82\n/eo0dKGB5H1Q5MLoBME9DlQ00tpp5YYs5/bh9SU8dgLB+niqi/e4MLLhQ5Zl1q1bx+rVq1m7di2l\npaUA2Gw27r///p7DfrqVlpayYsWKXq/zt7/9jd/+9rcO1zo6OlizZg3nzp1z3zfAEBLxY8eOsXLl\nSsrLy8nPz8disXD27Fnuvvtu9u7dO+j9HR0daLWOsy+1Wi0Wi6XX2j/+8Y9MnDiRuXPnOhseq1ev\nZtu2bQ5ff/7znwe8R0ZGHqhHXCVhk/0vEe+45FTNntaUNjFNQBCE4an2/D5k2Up8hnOTUvqj1aqZ\nd+1YjudXUitO2xSGMatNZue5GqbHR5CgD77s15EkFfEZizE0ncXYUurCCF3L3N6Aqa3C4cvc3jDo\nfXv27MFisZCbm8tDDz3Epk2bKCsr48477+T48eMOa3fs2MFPf/pTmpube651dnby8MMP88Ybbzis\nPX78ON/85jcpLy93zTc4AKcT8d/+9rd8//vf56WXXupJqNevX8/3vvc9Nm/ePOj9gYGBWK1Wh2tW\nq5WQr/QlFxUV8Y9//IOf//znzoZ2BZT+W1Nk2W8r4pceb6/SalEHB4vTNQVBGJasZiN1ZfsZlTKH\ngKArPzhu+uxUIiKD+c/OQhdEJwjusb+8geZOKzeOSbji14qMyyEwJHbYVsWtFiMnPv41hZ/8weHr\nxMe/xmoZuI3syJEjzJs3D4CpU6dy4sQJ2tvb2bhxY6/W6fDwcF5//XWHa2azmVWrVvXqoLBYLPzx\nj38kIyPDBd/hwJzerHnq1Kk+D+255ZZbeOWVVwa9Py4ujqamJmRZRqWy5/+NjY3ExsY6rPvggw9o\naWlhyZIlgP3XCzabjWnTpvHCCy8wc+ZMZ0Me1ECbNfHjqSmm7kRcb9+BrQ0PE60pgiAMSzUlHwES\n8aMXuuT11BoVC5aN459vFlBR2kxymvNjEAXBEyw2mffO1TIrMZKk0MuvhneTJBUJGYs5f+LvGFtK\n0UekuSBK19EG6Jk09xG6ujodrms0QWgDBp5wZDQa0esvrlGr1WRlZaHR9E5vFy7s/XdIeHg4c+fO\nZdu2bQ7XZ8zw3OFfTlfEg4ODaWxs7HW9pKTE4Q+hP9nZ9tE5J06c6Ll29OhRpkyZ4rDuzjvvZOfO\nnezYsYMdO3awceNG1Go1O3bscNjo6QrKABVxSZb9dmrKpa0pAJrQMLFZUxCEYcfS2UJ9+UHi0uaj\nCdC57HUnT08mJk7PXlEVF4ah/5TW02a2cmPWlVfDu0UlTCNIN4qqs++77DVdKTAkBl1YssNXYEjM\noPfp9XpMJlPPY1mW+0zChzOnE/EVK1bwzDPPcO7cOSRJwmw2c+jQIZ566imWL18+6P0hISGsXLmS\n9evXc+rUKXbs2ME777zDmjVrsNls1NfXY7FYiIiIIC0trecrLs4+sictLc3pjZvOUhT7Efd9Drvv\nPllT8b+TNdtNFiQJgoPtLUb2irhIxAVBGF6qz+1BpQkkLn2+S19XpZJYuDybkjMNFBfVu/S1BeFK\ndHbZeL+4lquToojXuy7nkSQViZnLMDSdxdB01mWv623Tp0/v2ZBZUFDA2LFjvRzR0DmdiP/sZz8j\nKSmJG264gfb2dm688UbuuusuJk+ezEMPPeTUazz22GOkp6dz++2389xzz7FhwwZycnKorq5m7ty5\n5OfnX/Y3cjlkFFAk5D52bCqybD9Zs7/dnD7MZLQQrAtAUtnbcrRhYWKzpiAIw0qnqZ6Gqs+JT1+I\nWuPaIgxA9uR4EpLD2buzUJw8KAwbe0vrabd2scIFveFfFRE3ieDQJCrPvu83P/NLliwhICCANWvW\n8Mwzz/DYY495O6Qhc7p+f+7cOZ599ll+9rOfcerUKbRaLWPGjCE1NdXpN9PpdDz77LO9ricnJ3P6\n9Ok+75k9ezanTp1y+j2GQkEGJGRFQf3VJ2X7gT7+WBHvuDBDvJs2PAzrSdEjLgjC8FF17gO0AXpG\npV7jlteXJIlF12ez5f/7lKKTtYybdPkj4gTBFdqt9mr4nOQYYod4iqYzJElFUtYyzua/RFtDIeGx\n413+Hp6mUqnYsGFDn89t2rSpz+sHDhzode3mm2/uc+1rr712+cE5yemK+N13383x48dJTU1l+fLl\nLF68eEhJ+HDT0/utSNhsfbWmyBc2a/rHp8ZLmYxmh0RcEyZaUwRBGD7a2yporikgIWMxKnXA4Ddc\npoyxsaRlRrN3Z6Ff/vZT8C17ztdhsclXNDd8MGEx2ejC06g8u8tvquK+zulEPCwsrM+Z376qJ8FW\npL6T7e7WFD+siLd/tSIeFobc2YnsR//7CoLgmxRFoaLoXQJDYolJcu7k5svVXRWvqzFw7HP3zwsW\nhP6YLF3sKallXkoMUcHu+/ApSRJJY5bTYaikpe744DcIbud0a8rChQv5/ve/z6JFi0hJSem1cdKd\nR9G7w6XTUPqshMgyip9OTTEZzUTHXpxAcOmhPoEx0d4KSxAEgbaG0xiazpI59dtIql5Ngy6Xkh7F\nhCkJ7H2/kIlTEwkI9K2JC4J/2Flcg01WuD4zzu3vFRqVRWhUFlVnPyBi1CQkaUiHrAsu5vTfOLt2\n7SIyMpL8/PxemyolSfLdRFyRsPW1WdMm++3JmkaDGV3oxQ9Slx5zLxJxQRC8RVFkKs68iz5iNOGx\nEz32vouuH8+ffr2XQ/85x4Jl4zz2voIA0NRh4cPz9SwdPYqIIPdVwy+VmLWc0589T1P1UaITXXc+\nizB0AybiW7Zs4ZZbbiEwMJCPPvrIUzF5xKWJeF+tKYpsAz+siNtsMu0mC/rQixtBtJERAFguOfZV\nEATB0xqrDtNprGHcVT9Bkvo5bM0NomJ0XDV3NAf/c47pV6cRGub6KS2C0J9/FlURpFaxLMNzG4b1\nEWlEjJpI5dldRMZNQaXWeuy9BUcD/j5i48aNGI2Ox4s+8cQTNDU1uTUoT7iYYPc/vlBRSdj8LBFv\nN1pAwSERD4iwJ+LWlhZvhSUIwghn67JQdXYXkXE5Xjn5b961Y1CrVfxnZ98TvATBHSraOjhU2cQN\nWQmEaN3finWppDHXYzW3UVf2sUffV3A0YCLe147ad9991+EUI18lX2g5UfptTbGByv8q4kaD/QhZ\nfdjFRFwVEIBGr8fSJCrigiB4R13pProsJhLHXOeV9w8OCWD+kjHkf15GbZWYIiV4xrbTlUQHB7Ag\ndfBTJF0tSDeK2KTZVJd8RJfFd/O6Y8eOsXbtWodrTz/9NG+++abDtaamJpYuXYrZbHa4vnv37l7n\n4dhsNu6///6ew4Lcacgd+v4y7sahNaW/zZoqld8l4oY2+w/gpRVxsLeniIq4IAjeYOlsobpkL7Gp\ncwhy4lhrd5k1ZzSRUSHs/pd7zq4QhEudbjRwvL6NVeMS0aq9s2EyIXMJKArVxXu88v7d6kxmSlvb\nHb7qTOZB73vhhRd4/PHHe5LrpqYmvve97/Vqp96/fz933XUXDQ0NDtc3btzI7373u57iLEBZWRl3\n3nknx497ZqrMiN0qe7EvvJ8ecT+tiJsM9h9W3VcS8YDISFERFwTBKyqK3kWtCSQx41qvxqHWqFh8\nw3iKi+o5W1jn1VgE/yYrCm8VVpIWFsKshEivxaENDCV+9NepLz+Eub1h8BvcwGC28vi+k2w8UOjw\n9fi+kxjM1gHvTU1NZfPmzT2PTSYT9913HytXrnRYp1KpePnll4m40Irbbfr06fzyl790uNbe3s7G\njRuZPdu941O7DTo1xZMbZjxpsIq4Isvgh1NTjIZOgoK1aDSOvWgBkZF01tZ6KSpBEEYqQ3MxzTUF\npE24FbU22NvhMD4ngZTRUezacYLRD38dtWbE1qsEN/qksonzre08NHsMKi/nWXFp86kvP0TlmZ1k\nTFk7+A0uFhqoZeOCiXR0OZ7bEqxRExo48CbSZcuWUVFR0fM4JSWFlJSUXi0lc+bM6fP+66+/nk8/\n/dThWnZ29lDCv2KDJuLPPPOMw8xwq9XK73//e/R6vcO6p556yvXRudGgibhNBsn/WlOMbWZCw3of\nnauNjMBwWmxSEgTBcxRFprzwn4SEJROdNDxGqEmSxHU3TeKFZ/P4JK+YOYuyvB2S4Gc6u2xsO13F\n9PgIsqNDvR0OKnUAiVnLKD35DwzNxYRGZng8hlG63nnJSDFgIj5r1ixqamocrk2bNo2GhgaHPhtf\nrJo7TE3po0XcX6emfHWGeLfu1hRFUXzyf09BEHxPQ+VndBiqGHfVj4fVoSLxSeHMvCadvN1FTJ6e\nRFiE9yv1gv9472wNJmsXt2YneTuUHtGJM6kvP0R54Q7Gf+3BYfX/R383YCL+2muveSoOj+tJsJW+\nT9ZUbDYklcb/KuIGM+F9/KMSEBmJbLFga29Ho9P1cacgCILrdFnbqTrzPlEJ09BHpHs7nF6+vnwc\nJ49Vsftfp/jG2hneDkfwE3UmM7vP17EsI46YkOFTBZYkFSnZqzj92fM0VHxCbMo13g5pxBixZ/k6\nnqzZR7Ity6BSIcu23s/5MJPBTFJqRK/rFw/1aRGJuCAIbldZ9B6y3EXSmBu8HUqfgkMCWHz9eP61\n9RjTr05jdJb3prkI/uMfhRXoAzRcl+H+o+yHSh+RRnTijJ5DfjQBvpELJCcns3XrVodr9913X59r\n+zqccvbs2X1uzNy0aZNrAhzEiP3dw+AH+thApfLD1pTOXqMLwV4RB7CK0zUFQXAzY3MJDZWfkjRm\nOQFB4d4Op19TZ6WQlBrB+9uOY7P5178Fguedqm+joLaVW8YlEajx7OE9zkoacz2KbKPq7C5vhzJi\njOBE3J58K4pEXwVxRZZR1P41vtBi7sJitvWTiItj7gVBcD9Z7qL01NuEhCUP+19/SyqJ626eTEOd\nkYN7z3k7HMGHWW0yW06WkxWp46pE740rHIw2MIzEzCXUV3xCe1ult8MZtlx5ps4ITsQv3azZ99QU\nSfKvirixZ4Z4782aap0OSavF0iwO9REEwX1qz++j01RH2oRbfGJDWGJKBLPnZ5C3u4iGOqO3wxF8\n1HvnamjsMHPnpNRhPxBhVOpcgnSxlH25DcUNOZBaraarq8vlr+tJNpsNtdo1v9UQPeK8IqmhAAAg\nAElEQVT9ji+0gdq/xhca23ofb99NkiQCIiNFa4ogCG7T2d5AdfEeRqXNIyRs+EyMGMzXl42j8HgN\n//7HMb597zVIquGdSAnDS7Wxg53nalmaEUdS6PCfwCOp1KSOv5miw3+mvvwQo1L7nsF9uVQqFbIs\n09HRgVqtHvYfTC6lKAo2mw1ZllGpXFNIGP7lCDfp2aA5wBH3kqRyOPbU13VXxPtqTQF7e4qoiAuC\n4A6KolB26m00AXoSM5d6O5whCQjUsOLWHMqKmzj6aam3wxF8iKwovHa8nKhgLSuyErwdjtNCozKJ\nSbqKyjM7sXS6Pi/Q6XQEBgb6VBIO9qJlYGAgOhcOtRAVcUXC1t8R9/5WETeYkVQSISEBfT6vjYwU\nPeKCILhFQ8UhDE1nyZp2F2rN8Bnb5qyMsbFMmZXCnn9/yZgJcYSFD//KpuB9ByoaOdNs5MFZWQSo\nfav2mTT2BlrqT1H25XYyp37H5UmzSqVyWVXZl43YP4FLE+yvVsSV7iq4n01NMRrM6PWB/f5aNSAy\nQrSmCILgcub2BipO/5uYpKsIjx3v7XAu29L/moBGq+bdt467dLOW4J/azFbeKqzkqsRIJsaGeTuc\nIdNoQ0jNXkVr/Slaar/wdjh+SyTifbSmKDb77HBJrfaririhtZPQ8P4rUQGRkaI1RRAEl1IUmfMn\ntqIJ0JE87kZvh3NFgkMCuOEbkzlzqpb8T8u8HY4wjCmKwqvHy1ABq8cnezucyxYRl0N47ATKCnfQ\nZW33djh+aQQn4heSb0XC1k9FXPKzI+7bWjoGPKpZGxlJl8GAbLV6MCpBEPxZXel+jC0lpE1cjVrT\ne2KTr8menMCUWSns+udJmhpM3g5HGKYOVjZxrK6VOyelEhao9XY4l02SJFLH34Qsd1H25XZvh+OX\nRnAi3v/4QqX74AY/q4gPlogHxkQDYGls9FRIgiD4sQ5jLZVn3yc2dQ5h0VneDsdllq+aiE4fyPYt\nR5HFQT/CVzR2WMg9Vc5ViZHMSBi+M8OdFRAUQWr2SpprCmiqzvd2OH7Ho4l4R0cHjzzyCDNmzGDh\nwoXs2LGjz3U2m40//OEPfP3rX2fmzJn8+Mc/pra21qWxdCfYSl9TU3oq4v5zxL2iKLS2dAy4wSgw\nNhYAc32Dp8ISBMFPyTYrJV+8TkBQJElZ13s7HJcKDNKy6o5pVJW3sP/Ds94ORxhGZEXhlS9KCVSr\nuWNiirfDcZmohBlExOVQ9uU2t0xRGck8mohv2rSJkpIStmzZwgMPPMC6dev44oveGwBeeukltm/f\nzjPPPENubi42m40HHnjApZtjBuwRv5B8SypVn4f9+KIOk4Uuq0x45OAVcZGIC4JwpcpPv0NnewMZ\nOd9Erel7UpMvSx0dxZxFWeTtLqKiVGxyF+z+U1rPl40Gvp2Tik7rP4PpJEkibfzNqNQBnD/+d7cc\n9DNSeSwRb29vZ/v27Tz++ONkZ2ezatUqVq1axRtvvNFr7Y4dO7j33nu5+uqryczMZMOGDeTn51NR\nUeGyeC5tTemvRxy1GpviHxXx1hb7YT5hEf33aKqDg9GE6jE3iERcEITL11RTQEPFJ6SMu9GnDu4Z\nqgVLx5GQFM7brx2h3WTxdjiCl1W0dfBWYSXzU2KYFBvu7XBcThOgI33SagzN56gr3e/tcPyGxxLx\nwsJCZFlm0qRJPdemTZtGQUFBr7Xr1q1j8eLFPY+7Z1caja47XtiZqSkqP6qIt7V0ABA+QI84QGBM\nLOb6ek+EJAiCHzK3N1B68i0i43KISb7a2+G4lVqj4pZvzcDc2cX2N46i9HU4nDAidHbZ+Et+MbEh\ngdw2wXenpAwmLHoso1LnUnlmJ6bWcm+H4xc89nuT+vp6oqOjUavVPdeio6Opq6vrtXb27NkOj19/\n/XWio6MZM2ZMv6+fm5tLbm6uwzWLpf8KhUMi/tVku2eOuNpvesTbWjqQVBL6sIGnFgTGxojWFEEQ\nLotss1L8xRY0ATrSJtzic6fmXY6IqBBu+uY03vzrZ+TtOcOCpWO9HZLgBW+cLKepw8Iv5mQT6GMH\n9wxV0pjrMbacp/jYq4z/2oNoAlx3yuRI5LFEvKOjA63WcYSPVqsdMFkG2LdvHy+++CJPPfUUGk3/\n4a5evZrVq1c7XKuoqHCorF/q0uS7/zniKmQ/aU1pae4gLDwIVT+H+XQLjI2h5dhxD0UlCIK/UBSF\nsi/fpsNYw7hZP0KtHTknT44ZH8e8a8ew74PTJKdFkDlulLdDEjzoQEUjhyqb+M7kNBJD/f/nXqXW\nkjFlLV8e+l9Kjr9J1vS7kCT//vDhTh77kwsMDMT6lfnUVquVkJCQfu/Zv38/999/P3feeSc333yz\nS+PprohLfY0v7J6aolb7zRzx5kYTkdGDf2oNjLW3pohT4wRBGIq6so9prDpC2vhvoAv3n2kRzlqw\ndBwZY2LY9vpRmhvFfPGR4nyLiddPlHF1UhTXJEd5OxyPCQyOYvTkO2hrLKK6+ENvh+PTPJaIx8XF\n0dTUhCxfTGwbGxuJvTAy76v27t3Lvffey0033cRjj/3/7d15eFNl+jfw78metOle0n2FtkCbtpSt\ngrIqmwiiDiDIzxkURPGnjDP6Osrm6AgvLgwob0HUSwU3VBYFRUXADRcobYGWpQu0dN+37Oc87x9p\nA6EsBdKkae/PdYUkzzknuXOTntx58pznPOvwePi2ISciiC7TI26N0TpGvGcU4g21Ovj6X/lLTztF\nkAaC0QhzA01PRAjpnKbaMzh/+iv0ibgV/qGDXR2OS4hEHGbMGQS5QoqP3v4DBj2dGK2nazKasSGz\nEKGeSsxNjOgVQ7Eu5h2YgOCYcSgv+A6NNadcHY7bclohnpCQAAA4fvy4rS0zMxPJyckd1j127Bie\neOIJzJgxA8uXL++SeNp7wUWiyxTiPaxHnDGG+rpOFuLBQQAAQ3lFV4dFCOkB9C0VKMz+AGrfGITF\nTXF1OC6l8pRj9kND0dxowLb3DoOnk/30WBaBYePRIlgEhkfTYiDr4ePCryQ49nZ4+fdDUc4W6Fsc\ne76X3sJp7xyVSoVp06Zh+fLlyM3NxY4dO7Br1y7MmjULPM+jurraNl582bJl6N+/PxYvXoyamhpU\nV1ejurq6w9CWmyEwARw4iESijtMXto8RF/WMM2vqdWYYDZZODU1RaDQAAEMFFeKEkKszGRpx5shm\nSOVeiEl+AJxIfO2NerhAjRp/eXAIzhXUYs/nx2iYXw/EGMOW48UoqG/BI4Oi4afsefPkdxbHiRCt\nnQup3Av5R9+B2eS42e16C6d+hXv22WcRFRWF2bNnY926dXjhhReg1WpRXl6OkSNH2uYKz83NRVZW\nFm699VaMHDnSdsnOznZYLAITIOI4iEVXObOmWGw3lMZd1dfqAKBTPeJipRJSHx/oqUecEHIVvFmP\n/MzNAIB+aQ9BIr32/qW3iO4XgCn3anH092L88gOdebOn2Z1fgV/O1+KBpEjE+aldHY7LSaRK9E39\nGwSLEQVZ70HgaVjW9XDqaZ88PDzw+uuvd2gPCwvDqVMXxhddfLurWAtxEcBd5mDN9nnExWIIlp5Q\niFsPHOpMIQ5Yh6dQjzgh5EoEwYKCrPdgMjQgfsijkCl8XB1St5M6LAL1ta34Yc9JqDxkGDQ80tUh\nEQf49Xwtdp4px9S+QRgR5u/qcLoNucofsakP4vThjSg69qH1FzKaSaVTem2WBMYg4kQQXaZH/MIY\ncVGPGCNeW90KpUoKhVJ67ZUBKIKCYCinsV6EkI4EwYKinC1oaTiL2JQHoVQHuzqkbmvMpASkpUfi\nq89ycPxoqavDITcpu7IR7x87h/RQP0ztR+/7S3n6RCFGOwcNVSdQnPs5DcvqpF5ciAsQia5UiLef\nWVPSI05xX1PZjIA+np0+olsZEgx9WSn9ERFC7LQX4Y3VJxGT/ADUfrGuDqlb4zgOk2YkITElFDs+\nPIpTx+mXRneVW9OEjKOFSAz0xrykyF43Q0pn+fRJROTA+1BT+gdKz3xNdUQn9NpCnGe8tUec48B3\nGJpi7QWXSKWw8BZXhOdQNZUtCNB0fhybKiIcfKsOprq6LoyKEOJOrEX4VlsR7tNnoKtDcgsiEYdp\ns1PQb4AG2947jBPUM+528uta8OaRQsT5eWJhajQk1zgxXm8XEDoEoXFTUHl2P8oK9lIxfg29thC/\n2tCU9oM1ZRIZDPzVz/zZ3QkCQ011CwI0np3eRhVuPRmHvuR8V4VFCHEjF4rwPMQkz6Ui/DqJxSLc\nOy8N/bXB+GJrJrL+KHF1SKSTTtU2Y+2f+Yj0UuHRtFhIe+k0hdcrKGo0QvpOQkXhPpTlU8/41Tj1\nYM3upP1gTe4qY8RlMgXMvBmCYB3G4o4a6nTgLQIC+nS+EFcEacBJJNCVlMAnpeM874SQ3oO3GFCQ\n9R5a6ovaivBEV4fklsRiEe6eMwhSmRi7PsmC2cxjyIgoV4dFruJ4dSM2HClErK8nHkuLgZyK8OsS\nHDMWHCdC6ZndEAQeYXFT6ADOy+jlhTgHJuKuOI+4TCIHAJh4ExQihdNjdITqymYA1rltO4sTi6EM\nDYGOesQJ6dXMxiacyXwHRl0N+qU9BLVfX1eH5NZEIg5T70uGTCbB118cQ0uTAaMnxtN4427ocHk9\nNmedxYAANR4Z1HtP2HOzgqJHgxOJcf7ULlhMLYga+Bc638AlenkhLgK73PSFth5xayFu4E1QSN2z\nEK8sa4JcIYG3r/K6tlNFhEN3triLoiKEdHf65nLkH30XgmBB/JBFUHmFujqkHoETcZgwfSA8veT4\nYc9J1NfqcNfMZEikVJx0B4wx7C2sxOenyjAk2Bd/S46ExE1/Ee8uNJG3QiLzwNnjn8BiakFM8jyI\n2zo6CY0Rv/ysKW094nKZtfg2WoxOj89Rys83IijU+7p7XDxiYtBaVGTLBSGk92ioOo6Tf7wBsUSO\nhGGLqQh3MI7jMHJcP8yYOwh5x8rxfsYhNDXqXR1Wr2cRGLYcL8Hnp8owMUaDh1KiqAh3EP/gQeiX\n+je0NJzDqT/ehFFHk0G067XvMEHgIeZElz2zpmCwFt5ypXVctdHivgdsVpQ2IjjM+7q38+wbC8Fk\ngu48HeFPSG/BBB6l+d+gIOs9ePnHIX7oYsiVfq4Oq8dKTA3FvEfS0VCnw1uv/Yii/BpXh9RrNRrN\neP2PM/j5fA0eSIzAPQmhENGQIYfyCohH/NBHwVsMyPv9v2iuo7POAr25EG8bmiLiOFw6aQqv14ET\ni6FQWs9EaXTTmVN0LUY01usRHHoDhXhMDACgJZ/+UAjpDUyGBpw+vBEVhT8gOPZ2xCQ/QD8fO0F4\ntB8WLLkNARo1tmQcwk/fnwG79EOJdKn8+ha8+PNJlLcY8Peh/XBbRICrQ+qxVOoQ9B/+BFSewTh9\n5C1UFP0A1gNOnHgzenEh3jY0RcyB5+3fBBadHmKV0jYu3F2HppSWNADADfWISzw9oAgOQkt+gaPD\nIoR0Mw1Vx5H762sw6usQN3ghQmLvoNkNnMjTS4EHFg7HLWP6Yv/XJ7Fl029orNe5OqwejxcYvjxT\njjW/nYa/UoalIxMQ79/5iQ3IjZHIPNAv7WFoIm9D6ZmvcebIWzAZGl0dlsv0+oM1ZTIJ9Eb7k/bw\nej3ESiXkYhkAwMibXRHiTSsuqoPKQwb/65i68GLq+Dg0nzzl4KgIId2FxdSKklNfoq78CLwD+iMq\ncSYkMg9Xh9UricQijJvSHxExfvjq0xz8vzUHMWHaQKQMDadZVbpAZasBb2efxdkGHSbFajC1XzCN\nB3ciTiRGWNwUePn3Q9Gxj5F76DVEDbyvV06P2ssLcQ4qhQQ6wyWFuK6tEJe0FeJu2iNeXFiHiBi/\nG96JeycORPXBn2BpaYHE88aKeUJI98MYQ31FFkpO7gRjPCIG3IOA0GFU8HUD/fpr8Mg/R2HvzhP4\n8tNs5OaUYfKMJPj60xckR+AFhgPF1fjiVBm85RI8PTwOff3o881VvPzjMOCWv+Pc8U9RkPUefDVa\nhCdMg1Tu5erQnKbXfv1r7xH3UErRarDv8eb1urZCvG36Qjc8WNNi5lFW3ICIGP8bfgyvxESAMTSe\nyHNgZIQQV9K3VCI/820UHfsQnr7RGHDLPxAYNpyK8G5EqZJh+uxUzPzrEFSVNWPD/z2A/d+chNlk\nufbG5IoK6lvw0q8n8XHueQwP8cOykf2pCO8GpDJPxKb+FVGJs9BUl48Tv6xBdcmhXjN2vNf2iBt4\nE+QS2eV7xPV6iFUqSERiiDkRTG54sObZglrwvICovjdeiCuCNJD5+6MxJwf+w4Y4MDpCiLOZjS0o\nK9iLmtI/IJN7IyZ5Hnw1Sa4Oi1xFfGIQovoG4Od9Z/DLD/nI/rME46cMwMCUEHAi+uLUWQ0GM3ac\nLsMv52sR7qXE/0mPQ6wvFeDdCcdx8A9Jg3dAAs6f/hLFeV+gpvQPhMVN6fEnEuu1hXirqRWeMg+o\nFFLoLu0R1+kh8bL+LCKXyGFww6EpZ3Ir4eWtgCb4xn/e4TgOvmmpqPvzMKIf+hv1mBHihixmHarO\n/YTKcz+B4ziE9p2EPhEjIBJLXR0a6QS5QoJxU/ojZWg4vt2Viy+2ZuLnfWcwakI8EpKCaL98FS0m\nC74prMT+s1UQi0SYPSAMoyMDaVrCbkwi80BU4iz4hwzB+dNf4fThjfAKSEBYv8lQqoNdHV6X6LWF\neItRhwgfX3goJB0KcYtOD7lGAwCQS2RuN484YwyncyvRb4DmpnfSfsOGovLb76E7VwyPqEgHRUgI\n6WpmYzMqz/3Y9hMvj8Cw4QiOGU8HY7op/0BPzJ4/FMWFtTiw9xS2vXcYQaFeGDmuHxISgyCiU7Db\nNJss2H+uGt8XVYJnwLjoPpgQo4GHtNeWPG5H7ReLhGGPo74iB6X5XyP30Ovw0SQhKHoMPLzCXB2e\nQ/Xad2WzqQVquQdUvBR6Iw9eYBC3/dTH6/WQqKynhJeLZW43j3hpcQMa6/WITwy66cfy0SZBpFCg\n9tdDVIgT4gZaG0tQXfIr6iqywHFiBIanQxN5G6RympatJ4iI8ce8RbegKL8GP357Gp+9fwTevkoM\nHRmN1GERUCh77y8dla0GfF9UhV/P14IBuDU8AJP7BsFb3ntz4s44TgS/4BT4aBJRW/oHKs4exMnf\n/gu1Xz8ERY2C2r9fj5hmtdcW4i0mHTxlHvAQrH+geoMZnirrLCntB2sC1qEp7jZrSvafJVB7KRAT\nF3jTjyWSyRAw4hZU/bAf4TPvAycWOyBCQogj8RYD6iuPobrkEHRNJZDKvREcMx6BYcOpB7yHiu4b\ngOi+ASgracDvPxZi3+48HNh7CgOTQ5A8NBwR0Tc+Y5Y7sQgCcqoa8XNJLY5XN8FDJsGEGA1GRwbC\niwrwHkEkkiAw/BYEhA5DfWUOKor240zmZsiV/ggIGwb/kCGQyt13zH+vLMR5gYfOrIenTAUVZ01B\nq8FyoRDXWQ/WBACF2L2GphgNZhw/Woq09CiIHHQwj+b2caja9wPqj2bBb3CaQx6TEHJzBMGCptrT\nqCvPREPVCTDBArVfX8Qkz4NP4ABwIvrS3BuEhPvg7jmDMO7O/sj8rRjZf5Yg688S+AV4ICktDAOS\ngxGo6Vm/hjDGcLZRh8Pl9ThUWodmkwWRXirMTYzA8FA/yGiYTo/EicTwC06Fb1AKWuoLUX3+N5Tl\n70VZ/l54BybANygVPoH9IWo7B4y76JWFeKvJesYyT5kHPNoOWGofJ854HoLRCHHb0BSZxL2Gphz+\n9RzMZh5DRkQ57DHVCfHwiI1B6efb4Zs2qFf0shDSHVlMrWisOYnG6lw01pyCwBuh8NAgOOZ2+AWn\nQq70dXWIxEW8vJUYPSEeo26PQ1F+DbL+KMGv+/NxcO8pBGo8kaANRtwADYLDfBzWSeNMAmMorG/F\nkYoGZFY0oM5ggodUjGEhfhgZ7o9wL5WrQyROwnEc1H6xUPvFwmxqQV3ZEdSWH0VRzhaIxDJ4Bw6A\nT+BAeAXEQSLt/u+LXlmIt5haAVgLcSVr6xHXWwtxXm8AAIgV7jc0Ra8z4dCBAiSnhcPbV+mwx+U4\nDuEz78PJ/6xGw9Es+A5KddhjE0KuTODNaG08h+a6AjTXFaCl8RzABKi8wqCJGgWfPgOh9AymL8fE\nhhNxiIkLRExcIMwmCwpOVSM3uxy//1iEn747A6VKipi4QMTGW9fx8nHcZ4Wj1eqNyK1pRl5NM07W\nNqPZZIFaJkGqxgdpQT6I81dD4oZfKojjSGWe0ESNgiZqFAytVairyEJ95THUV2QB4ODhEwnvgAR4\n+cdBpQ7plr8U9tJC/KIeca6tR7ztNPe8Xg8Ath5xhViG2rbCvbvbtzsPFguPURPjHP7YfkOHwGvg\nABRufAsp616HWC53+HMQ0psxxmAyNEDXVAJd03m0NJxDa8M5MMZDLFHC0zcaEQnT4R3YHzKFj6vD\nJW5AKpMgISkYCUnB4C0CSs7VoeBUNQpPVeNEVhkAwMtHgbBIP4RF+SIs0hfBod4QS5w/tENgDGXN\nehQ0tKKwvhX59a2o0hnBAYjwUmFEmD8SA73Qz8+Tph8kl6Xw6IOQ2DsQEnsHTPp666+HNSdRUbgP\nZfnfQCSWw8MnAmqfaHj6xkDlFQqxROHqsHtnId7cVlir5R5Qiqwp0Nl6xK1FuqRtjLhcIofObHBB\nlNcn58h5ZP5WjEl3J8LL2/E9HBzHIfbRhche8k/kr38TcU8toV44Qm4QbzHBoKuCoaUShtYq6JpK\noWs6D4vZum+SyNTw8I5AaNwUqH1joFQH94jZAYjriCUiRMUGICo2AOMm90drsxHnCmtRcrYe58/V\n4+RX5RB4BrFEhD5BavQJ9kKfYDX6BHlBE6yGh1rusH2+3syjtEWP0mY9SpsNKG3W41yjDkZeAAcg\nVK1Egr8ad8eHIN5fDbWsV5Yq5CbIlL4IDE9HYHh62y+LJWhpKEJLfSEqzh6EUPAtAECu9IfSKxQq\ndQhU6hAo1cGQyr2cur916rtbr9dj+fLl2LdvH7y8vPDEE09g+vTpl113+/btWLduHRoaGjBx4kQs\nW7YMSqVjCswW44WhKTKxBCLOerAmAFharMvaZ00J9w7GgbOHYLKYIJN0zwMAjh8txa6Ps5CUForB\nDhwbfilVWBj6Pv4YTr/6OsQKJWIeeRgiCe0gCbkUYwwWUytMhjqY9PUw6uthMtTDqKuBobUKJkOD\nbV2JTA2VVygCw9Oh8gqDyisMMoW3C6MnvYGHWo4BySEYkBwCALCYeZSfb8T54npUljWhsqwRx4+W\ngrdYTzOuUErhF6CCr78HfP3tr9Ve8g7zmOvNPKp1RlTrjKjRm1DVakSN3ojKViNq9dbjrjgAfTzk\nCFUrMTk2CDG+HojyVkEh6X7DB4j7EomlUPvFQO0XA2AcmMBD31IBXXMZ9M1l0DWVoqJmPwTeOgxZ\nJJJC7hEAhSoQclUgFKoAyJS+kCm8IVX4QCRybN3j1Cpq1apVKCoqwtatW3Hy5EksW7YMMTEx0Gq1\ndusdPXoUL7zwAl566SVERkbi2WefxZo1a7Bs2TKHxNFiaoVULIW8rbBWXnR2zcbjJyCSy6EIsZ7B\nqX9gP/ACjzN1ZzGwj+OHfNyM5kYDDuw9haO/FyMxNRR3zUzp8l7qwNtGQjAZkf9mBlrPnkXUg/Pg\nnTiwS5+TkO6ACTwsFj14sx68RQ+zsQUWUzPMpuYLt43NMJtaYDI0gAkXThQmEkkhU/pBrvSDryYZ\nCs8+UHj0gcJDA4m0+47RJb2HRCpGeLQfwqP9bG0CL6CuVoeq8iZUVzajprYVNY0GnC5vRLPRDEEm\nBi8XgZeLwXlIwZQS8DIxzBIO5os+iqQcBz+5FIEqOVL7eCPMW4UwtRLBngqa4YQ4HScSQ+UVCpVX\nqK2NMQFGfR0MLVUw6KpgbK2BQVeN5voiWEzNdttLZJ6QKdoKc7k3pHJPSGRqKFSBbcX+9XFaIa7T\n6bB9+3Z88MEHSEhIQEJCAjIzM/Hhhx92KMQ/+ugj3HXXXZg8eTIAYOXKlXjwwQfxz3/+0yG94uXN\nVVBfNLduoI8SuUV1AIDa336H76BU2xjoSO9QKKUK5FWfcXkhzvMCGup0KC6sw5m8Spw6UQmZTIyJ\ndydiyIgopw0V0YwfB2VoKAo3bsbx55ZBHR8Hv+HD4J04EKrICBo/TpyOMcF6ESwXrgUeAuPBBB5M\nsEAQzBB4U9vFbLvmeRME4eJ2I3izAbzFAItZB95iAG/RQ7jC7EliqQpSmRpSmSekCm9bj7a1B8UX\ncqUfxFIVDeUiXY4xBovAwLddW28LMPPWNhMvwMgLMFoEmHjeepsXLmkXYOB56M08dGYerWYLWs08\ndDwP3huAtwLAhXG1YgAqkQgyAZBaBIgNPFi9BUKzCXy9ARK9BSKTdchJY9ulQCqCSiWDUiWDXCmB\nTC6BTCaBTC6GXC6BVC6BTCa2tcsVYkikYkgkYoglIkjaLtbb4gu3pSKIxSL6WyPXjeNEUKgCoFAF\nABhgt4y3GGAyNMJkaIDJ0ABz27XJ0ABDaxXMphbwZh04ToxBt6+6/udmjDEHvY6ryszMxLx585Cd\nnQ1x20lhtm/fjo0bN+Kbb76xW3fChAlYtGiRbdgKz/PQarXYunUrUlJSOv2c58+fx7hx4zB7yRKo\nfXzR/kItggUSkQQysRSMMfA8g8nMQyLmIDKbIChVECRyoG0LI9NDYDzEnBQcLvyBd8WfOrM9KgMY\nB1vQAgDhomcXMzAZA2TCZQO53thu6E3AAJnRBKneCInZAq7trcREIggiEZiIA+OsFwDW6w6BcWCX\nC/aaL+DGs3/Db/ib/g93rw+H9vfidf9XcJfcvGS53Wckd/mHuOJjcBfuc9xlll8nxqzvBya03WZc\n2/VFbQIgCNyFNgEQGMAEDoKAm3hDwfa34V6cH/Nl9xGd5pocs7Y3K0PbPpDjOiJpWWIAABXnSURB\nVLZdcn3xcnAXt3Ft2wDgRBA4ERjXtp+96D4TXX/vMicIEAkWiAULxDzfdpuHmDdDYjFBajFB0nZb\nwpsgsZht1zKzAWLefMUMM8ZBYGIITAKeSSBcdLHel0JgYjCIrNeX3BYgxo39/wngIIDjGC7syRg4\nMIBru76o/XJtF7a9+A/8wm3rcnRYxl2yXvsy26vg2OXbL7vdlVx9PZftVTrkpPfgOAaZUoRHnl90\n3ds6rUe8uroa/v7+tiIcAPz9/VFVVdVh3aqqKmg0Gtt9sVgMHx+fy67b7pNPPsEnn3xi12YyWXuw\nznuEQKkOuNmXcF24m/l0vvoD46Y++R2Jg7VjRNG2A2IdF9t0QchdluNruvHn7dId5BXCckmemO2f\nqyy/ynaXW273+cU6tLdvyl26fftddtGKl338Tuapvda5aBjrTf2/OqcvpIObel/c8KYu+ttxQY45\nxto6Jxg43lrQcUwAJ7TfZuDavsm1377Qbr8+Lt6WMYgFC0Q8D1FbEX3hNm+98DzEbde2NoGH2GKB\nxGKG2NJWUJvNEDHB6bnpLOsrF4EXSSFwkrYvHNYC3Xa77cI4UVt7223OWtRbv8yILvoyI2orr623\nL3wJEtm+IF1ufasLJX17D8CF8vuiL0uA7f6F13JRL0L7fVz0GNwl96+o6z5FLv1a4NindkHnmZOJ\nW27snDNOK8T1ej2kUvvTzUqlUluxfDGDwdDpddvNnDkTM2fOtGtr7xFfMWYQwsLCbiJ6QgghhBBC\nHMtpR0nI5XKYzWa7NrPZDJWq41mPrrSuo2ZNIYQQQgghxNWcVohrNBrU1dVBEC78DFZbW4vAwMDL\nrltdXW27b7FY0NDQcNl1CSGEEEIIcUdOK8QTEhIAAMePH7e1ZWZmIjk5ucO6Wq0WR48etd3PycmB\nVCq1PQYhhBBCCCHuzmmFuEqlwrRp07B8+XLk5uZix44d2LVrF2bNmgWe51FdXW0bAz5z5kx8/vnn\n2LVrF06cOIGVK1fi3nvvhUzWPU+oQwghhBBCyPVy6kz6zz77LKKiojB79mysW7cOL7zwArRaLcrL\nyzFy5EhbL/jgwYPx3HPPYc2aNZg3bx769++Pf/zjH84MlRBCCCGEkC7ltHnEXaF91pR9+/bRrCmE\nEEIIIaRbceop7p2N53kAQEVFhYsjIYQQQgghPV1QUBAkks6X1z26EG+feWXOnDkujoQQQgghhPR0\n1zsKo0cX4omJiYiKisKmTZvszuhJusYjjzyCjIwMV4fRK1CunYdy7TyUa+ehXDsP5dq5XJ3voKCg\n61q/RxfiCoUCHh4eiIyMdHUovYJMJqOx+E5CuXYeyrXzUK6dh3LtPJRr53K3fDt11hRCCCGEEEKI\nFRXihBBCCCGEuAAV4oQQQgghhLiAeMWKFStcHURXS0xMdHUIvQbl2nko185DuXYeyrXzUK6dh3Lt\nXO6U7x59Qh9CCCGEEEK6KxqaQgghhBBCiAtQIU4IIYQQQogLUCFOCCGEEEKIC1AhTgghhBBCiAtQ\nIU4IIYQQQogL9NhCXK/X4+mnn0ZaWhrGjBmDHTt2uDokt9fQ0ID09HRUVFTY2nJycjBjxgxotVrM\nnTsXxcXFdtts2rQJI0aMwJAhQ7B69WoIguDssN1KSUkJFi5ciMGDB2Ps2LHYuHEj2ic2olw71unT\npzF37lykpKRgwoQJdvsIynXXWbBgAZ577jnb/YMHD2LixIlITk7GokWLUFdXZ1smCAL+85//YNiw\nYbjllluwefNmV4TsVnbs2IH4+Hi7yyuvvAKAcu1oJpMJK1euxNChQ5Geno5XX32V9tdd5Isvvujw\nvo6Pj0dCQgIAN88366GWLVvG7r33XpaXl8e2b9/OkpKSWHZ2tqvDcltNTU3s/vvvZ3Fxcay8vJwx\nxlhLSwtLT09na9euZWfOnGFPPfUUu/POO5kgCIwxxnbv3s2GDRvGfv75Z/bnn3+y2267jW3evNmV\nL6NbM5vN7M4772RPPvkky8/PZwcPHmTDhw9nH330EeXawYxGIxs1ahRbsWIFO3fuHNu5cycbOHAg\ny8rKolx3oa+++orFxcWxf/3rX4wxxkpKSlhKSgrbsmULO3nyJHvwwQfZggULbOtv3LiRjR8/nmVl\nZbEffviBpaWlsa+//tpV4buF1157jT311FOsqqrKdmlpaaFcd4GlS5eyCRMmsOzsbPbLL7+wYcOG\nsU8//ZT2IV1Ar9fbvafLysrYHXfcwV566SW3z3ePLMRbW1tZUlISy8rKsrUtXbqUPfPMMy6Myn0d\nO3aM3XHHHezOO++0K8S3bdvGJk6caFvPYDCwQYMGsd9++40xxtj999/PNm7caFv+5ZdfsjFjxjg3\neDeSnZ3NBg4cyFpaWmxtGRkZbM6cOZRrByspKWFLlixhJpPJ1nb33Xezd955h3LdRerr69ltt93G\n7rnnHlshvnbtWjZ//nzbOhUVFSw+Pp4VFxczxhgbNWoU2717t215RkYGmzt3rnMDdzOPPfYY27Rp\nU4d2yrVj1dfXswEDBrDDhw/b2jZu3MiWLl1K+xAnaP/iaDQa3T7fPXJoysmTJyEIgt2ZlVJTU5GV\nleXCqNzXoUOHMHnyZKxfv96uPTs7G6mpqbb7crkcAwYMQHZ2Nniex7FjxzBo0CDb8tTUVJSWlqK6\nutppsbuT8PBwbNq0CR4eHnbtzc3NlGsHCwsLw2uvvQapVArGGA4ePIiioiKkpaVRrrvI6tWrMW3a\nNPTt29fWdmmuNRoNQkJCkJOTg8rKSpSXl9stT01NRU5Oju3nf9JRQUEBoqOjO7RTrh3ryJEj8Pb2\nRlpamq1twYIFeOGFF2gf0sWamprw1ltv4amnnoJMJnP7fPfIQry6uhr+/v4Qi8W2Nn9/f1RVVbkw\nKvf18MMP44knnoBEIrFrr66uRp8+fezaAgICUFVVhaamJhiNRrvlAQEBAED/D1fg6+uLW265xXbf\nZDLhs88+Q3p6OuW6izDGkJKSggULFmDatGnQarWU6y5w6NAhHD58GI8++qhd+9Vy3f4hGRgYaFvm\n7+8Pg8GAxsbGrg/aDZnNZpSUlOCbb77B2LFjMXHiRGzevBmMMcq1g5WUlCAsLAzbtm3DhAkTMH78\neGRkZFwz17QPuXkff/wxAgMDMXHiRADuX4tIrr2K+9Hr9ZBKpXZtUqkUJpPJRRH1TFfLs8FgsN2/\neBkA+n/oBMYYnnvuObS2tuLhhx/G3//+d8p1FxAEAVu2bEFJSQlWrFiByMhIel87mNFoxIoVK7Bs\n2TIoFAq7ZdfKtUgksusAkMlkACjXV1JcXAyz2Qy1Wo0NGzbg9OnTePHFFyGVSinXDqbT6VBQUIBd\nu3Zh9erVqKqqwrJly6BSqWgf0oUYY9i2bRv++te/2trcPd89shCXy+Uwm812bWazGSqVykUR9UxX\nyrOvry/kcrnt/sXLAECpVDovSDckCAKWLl2K7777Du+++y78/f0p111ELBYjKSkJSUlJqKiowNat\nWxETE0O5dqA33ngDAwcOxK233tph2ZXe10qlEjKZDIIgwGKx2ArE9g9O2pdfXmxsLH777Tf4+voC\nABISElBbW4uPP/6Ycu1gEokELS0teO2112y/JJSVleHjjz9GWFgY7UO6SF5eHsrKyjBlyhRbm7t/\nPvbIoSkajQZ1dXV209PU1tba/exGbp5Go0FNTY1dW01NDQIDA+Hj4wO5XG43Bqt93Ut/QiIXCIKA\nZ555Brt370ZGRoZt3Bvl2rEqKytx4MABu7bY2FjU19dTrh1s9+7d+P7775GamorU1FR8+eWX2LFj\nB6ZMmXLVXGs0GgDWfXe72tpaqFSqDsdRkAvai/B2MTExqKqqolw7WGBgILy8vOzqiujoaJSXl9M+\npAv99NNPGDx4MLy9vW1t7p7vHlmIt88refz4cVtbZmYmkpOTXRVSj6TVanH06FHbfaPRiNzcXKSk\npEAkEiExMdFueWZmJsLDw+Hn5+eKcN3CmjVr8O233yIjIwPDhw+3tVOuHauwsBCLFy9GU1OTrS03\nNxd9+/alXDvYBx98YCu+d+zYgbFjx2L8+PHYtGlTh1y3HzSYnJwMjUaDoKAgZGZm2pZnZmZCq9WC\n4zhXvJRub//+/Rg5ciQsFoutLS8vD9HR0ZRrB0tJSUFTUxNKSkpsbYWFhQgLC6N9SBfKzs62O0AW\n6AGfj66bsKVrPffcc2z69OnsxIkTbPv27Uyr1dI84jeppKTEbvrCpqYmNnToULZmzRrb3J1Tp061\nzd25c+dONmTIEHbgwAH2559/slGjRrF33nnHlS+hW8vNzWXx8fHsnXfesZsvtba2lnLtYCaTiU2e\nPJktXryYFRQUsN27d7PU1FT2/fffU6672DPPPGObvrC4uJglJSWxd9991za39cKFC23rbtiwgY0Z\nM4YdOXKE7d+/n6WlpbG9e/e6KvRur6GhgaWnp7Pnn3+eFRUVsd27d7NBgwaxPXv2UK67wIIFC9js\n2bNZXl4e+/HHH9nw4cPZli1baB/ShcaPH8+++uoruzZ3z3ePLcRbWlrYk08+ybRaLRszZgzbsWOH\nq0Nye5cW4owxduTIEXbnnXcyrVbL5s6dy86dO2e3zfr169nQoUPZ0KFD2apVqxjP884O222sW7eO\nxcXFdbiMHz+eMUa5drTi4mI2f/58lpKSwsaMGcM++eQT2zLKdde5uBBnjLHvvvuOjRs3jqWkpLBH\nHnmE1dbW2paZzWa2cuVKlpqaykaMGNGtTsLRXR0/fpzdf//9LDk5mY0ZM4Zt3brVtoxy7VhNTU3s\nH//4B0tJSWHp6enszTfftBV/tA/pGikpKezHH3/s0O7O+eYYo0lCCSGEEEIIcbYeOUacEEIIIYSQ\n7o4KcUIIIYQQQlyACnFCCCGEEEJcgApxQgghhBBCXIAKcUIIIYQQQlyACnFCCCGEEEJcgApxQghx\nIUEQ8NFHH2HmzJkYMmQItFotpk6dioyMDBiNRleHd035+fk4cOCA7f7YsWOxYcMG1wVECCFuhOYR\nJ4QQF7FYLFi4cCFyc3Px2GOPIT09HXK5HEePHsXatWsRHh6Od999t1ufZvyOO+7A1KlT8fjjjwMA\n6urqoFAooFKpXBwZIYR0fxJXB0AIIb3VO++8g99//x2ff/454uPjbe1hYWFITk7GpEmTcPDgQYwe\nPdp1QV7DpX05fn5+LoqEEELcDw1NIYQQF2CM4cMPP8T06dPtivB2ERER2LNnD0aNGgUAOHz4MGbN\nmgWtVotx48bh1VdftRu6Eh8fj88++wxz5sxBUlISRo8ejU8++cTuMT/99FNMmDDBNvxl+/bttmW/\n//47kpKSsGHDBgwdOhQPPPAAAGDv3r245557oNVqkZycjFmzZiEnJwcA8MADD6C4uBhvvPEGxo4d\nC6Dj0JR9+/ZhxowZSE5OxujRo7F+/XpYLBa75/z+++8xceJEJCYmYvr06Th8+LBt+wMHDmD69OnQ\narUYOXIk/v3vf7vFkB1CCOkMKsQJIcQFzp8/j/LycgwfPvyK60RGRoLjOOTl5WH+/Pm4/fbb8eWX\nX+LFF1/E/v37sWLFCrv1X3nlFcyZMwd79uzB7bffjhUrVqC0tBQA8OGHH+L111/HkiVL8NVXX+Gh\nhx7CSy+9ZFeMm0wm/P7779i2bRuef/555OTk4Mknn8SMGTOwZ88efPDBBwCApUuXAgDWr1+P0NBQ\n/O1vf8Nnn33WIf5vv/0Wjz/+OCZNmoQdO3bg6aefxgcffICXX37Zto7ZbMYbb7yBF198ETt37oRa\nrca//vUvMMZQV1eHxYsXY9asWfj666+xZs0a7NmzB2+99dYN550QQroTGppCCCEuUFNTAwDw9fW1\na7/rrrtQUlJiuz916lTodDqMGjUK8+fPB2At0FeuXIn7778fS5YsQZ8+fQAA99xzDyZPngwAePzx\nx/H+++8jJycHoaGhyMjIwOLFizFx4kQA1h73srIyZGRk4O6777Y930MPPYTIyEgAQF5eHpYvX45Z\ns2YBsA6Zue+++/D8888DAHx8fCAWi6FSqS47JGXTpk2YNGkSHn74YQBAdHQ0Ghoa8NJLL+HJJ58E\nYP1lYMmSJRg8eDAA4H/+53/w2GOPob6+HhUVFTCbzQgKCkJoaChCQ0OxefNmGn9OCOkxqBAnhBAX\n8PHxAQA0NjbatWdkZMBsNgMAnnnmGZhMJuTl5eHcuXNITU21rdc+NrugoMBWiEdFRdmWe3l5AbD2\nONfV1aGyshKrV6/GK6+8YlvHYrGA53mYTCZbW3h4uO12//79oVarsXHjRuTn5+PcuXPIy8uDIAid\neo1nzpyxK/IBYMiQIbBYLCgsLLS1RUdH226r1Wpb3P3798ekSZOwcOFCBAUFYcSIERg/fjzGjBnT\nqecnhJDujgpxQghxgYiICAQEBODw4cO2XmwACAkJsd1WKBQAAKlUiunTp9t6li8WGBhouy2TyTos\nZ4xBKpUCsA4pGTp0aId1JJILHwXtzwkAhw4dwoIFCzBu3DgMGjQI99xzD86ePYvly5d36jVe/Fjt\neJ7v8JxXipvjOKxduxaLFy/GwYMH8fPPP2Px4sWYNm2a3fAWQghxVzRGnBBCXEAsFmPOnDn44osv\nUFBQ0GG5yWRCXV0dAKBv374oKChAZGSk7VJXV4fVq1ejtbX1ms+lVquh0Whw/vx5u8f49ddf8fbb\nb0MkuvxHwXvvvYcRI0Zg7dq1mDdvHoYPH24bc97eI3+1qRVjY2Nx5MgRu7YjR45AKpUiIiLimnEf\nO3YML7/8Mvr27Yv58+fj3XffxZIlS7Bnz55rbksIIe6AesQJIcRFFixYgGPHjmH27NlYtGgRRo4c\nCYVCgaysLGzatAlFRUV44IEHoNVqMWPGDLz88sv4y1/+gtraWjz//PPQaDR2PeJXs2jRIqxatQoh\nISFIT09HdnY2Vq1ahYceeuiK2wQFBeHgwYPIysqCv78/Dhw4gPfeew+A9YuCXC6Hh4cHzp49i8rK\nSmg0mg7PuWDBAvTv3x+333478vLysG7dOtx33322IShXo1arsXXrVsjlctx7771obW3F/v37odVq\nO/WaCSGku6NCnBBCXEQikWDDhg3YuXMnvvjiC2RkZECn0yEkJAQjR47E+vXrbeO+N27ciP/+97/4\n8MMPoVarMWbMGDz99NOdfq7Zs2fDZDLh7bffxr///W9oNBo8+uijWLBgwRW3+d///V9UVVVh/vz5\nEIvFiI+Px6pVq7BkyRIcO3YMgwcPxoMPPogXX3wRP//8Mw4dOmS3/a233orVq1fbYu/Tpw/mzZuH\nhQsXdirmqKgovPnmm1i3bh3ef/99SKVS3HrrrXj22Wc7/boJIaQ7ozNrEkIIIYQQ4gI0RpwQQggh\nhBAXoEKcEEIIIYQQF6BCnBBCCCGEEBegQpwQQgghhBAXoEKcEEIIIYQQF6BCnBBCCCGEEBegQpwQ\nQgghhBAXoEKcEEIIIYQQF/j/lkDlIyIC2oEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca5cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_adaptation(k, p, W, pom, genotypes);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the LOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 14, 14, 14, 14])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lod = build_lod(p)\n",
    "lod[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 14, 24, 23, 30, 25])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_lod = clean_lod(lod)\n",
    "clean_lod(lod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WycgI0FSmUf3EH2EEaFkRCyM9evRQjx49dNZZZ+npp5/WokWLVFlZqXXr1unW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\npKp/AG6cO3mYuBPdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca64748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lod(k, p, W, pom, genotypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peaks and valleys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def flip_bit(bs, i):\n",
    "    return '{:05b}'.format(int(bs, base=2) ^ 2**i)\n",
    "\n",
    "assert flip_bit('11110', 0) == '11111'\n",
    "assert flip_bit('11110', 1) == '11100'\n",
    "assert flip_bit('11110', 3) == '10110'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This landscape only has a single peak - so it's much less rugged than the _A. niger_ landscape - and has 5/32 valleys, all with very low fitness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks: 1\n"
     ]
    }
   ],
   "source": [
    "peaks = find_peaks(genotypes, fitness)\n",
    "peak_distances = [hamming(p) for p in peaks]\n",
    "print(\"Number of peaks:\", len(peaks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of valleys: 5\n"
     ]
    }
   ],
   "source": [
    "valleys = find_valleys(genotypes, fitness)\n",
    "valley_distances = [hamming(p) for p in valleys]\n",
    "print( \"Number of valleys:\", len(valleys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pU2m6gYEBmjRpAhcXlzovlIiIGqYag8rQ0BBvvvkmAKB169bo1q0bnnuu1rcHlJSWliI0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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120fa2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_peaks_valleys(peaks, valleys):\n",
    "    peak_distances = [hamming(p) for p in peaks]\n",
    "    valley_distances = [hamming(p) for p in valleys]\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(peak_distances, tuple(peaks.values()), '^')\n",
    "    for x,y,z in zip(peak_distances, list(peaks.values()), list(peaks.keys())):\n",
    "        ax.text(x, y, z, fontsize=8)\n",
    "\n",
    "    ax.plot(valley_distances, tuple(valleys.values()), 'v')\n",
    "    for x,y,z in zip(valley_distances, list(valleys.values()), list(valleys.keys())):\n",
    "        if y > 0:\n",
    "            ax.text(x, y, z, fontsize=8)\n",
    "    ax.set(xlim=(-0.5, n), \n",
    "           ylim=(-0.1, 1.1),\n",
    "           xlabel=\"Hamming distance from fittest genotype\",\n",
    "           ylabel=\"Fitness\"\n",
    "    )\n",
    "    sns.despine()\n",
    "    return fig, ax\n",
    "\n",
    "plot_peaks_valleys(peaks, valleys);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "repetitions = 1000\n",
    "results = (adaptation(genotypes, '00000', '11111', verbose=False) for _ in range(repetitions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lods = [None] * repetitions\n",
    "cleaned_lods = [None] * repetitions\n",
    "for i, (k, p, W, pom ) in enumerate(results):    \n",
    "    lods[i] = build_lod(p)\n",
    "    cleaned_lods[i] = clean_lod(lods[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genotype_freq = np.zeros(m)\n",
    "\n",
    "for lod in lods:\n",
    "    for gi in lod:\n",
    "        genotype_freq[gi] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SljaUkJCg8PBw/fzzz661I0eOqFatWhajAgAAADljqez26tVLkydPVmxsrOLj4xUfH6+N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\nLgAAAPwWZRcAAAB+6/8BzPkfe/yCCwAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1205a9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_genotype_freq(genotype_freq);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genotype_freq = {genotypes[gi]:c for gi, c in enumerate(genotype_freq)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13, 14, 24, 23, 30, 25) 0.976\n",
      "(13, 14, 6, 23, 30, 25) 0.023\n",
      "(13, 14, 27, 9, 5, 25) 0.001\n"
     ]
    }
   ],
   "source": [
    "lods_freq = collections.defaultdict(int)\n",
    "for lod in cleaned_lods:\n",
    "    lods_freq[tuple(lod)] += 1\n",
    "\n",
    "top_lods_freq = sorted(lods_freq.items(), key=lambda x: x[1], reverse=True)[:5]\n",
    "for lod, cnt in top_lods_freq:\n",
    "    print(lod, cnt / repetitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weinreich2006.tif,pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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FRUWRPLUQQoh2JqIJJhAIAKAoSr0XDRNCCHHmiGiCWblyJQAjR47E4XBE8tRC\nCCHambCrKft8PoqKinjnnXd46qmnsNvt3H///ZGITQghRDvW7AQzceLEBu9zOBw899xzDB48OKyg\nhBBCtH9h74PRdZ2SkhL27duHx+Ph9ttvZ86cOVx44YURDVQIIUT70qTrwdTcyb9y5cp698GUlZWx\naNEi/v73v6NpGkuWLCEnJyfyEQshhGgXIjbJHxMTw6xZszj33HPRdZ2nn346UqcWQgjRDkW82OX5\n558PwLZt2yJ9aiGEEO1IxBOMqgZPKVdiFkKIM1vEE8yqVasAOOussyJ9aiGEEO1IxBKM1+vlqaee\n4tNPPwXgxz/+caROHVGBQIC8vLxQ1QEhhBDR0exlyrfffnudUvxer5c9e/ZQUVEBwE033cT48eOb\ndd7i4mIuvvhili9fTnp6er1tli9fzlNPPUVxcTEXXXQRs2fPxul0Nut5Dh8+zMSJExtcDSeEECIy\nmp1gtm7dWuu2oig4nU46d+7M0KFDufrqqxk+fHizzllaWsott9xyygKZubm5zJ07l1//+tf06NGD\nWbNmsWDBAmbPnt3clyCEEKIVNCnBjBo1iq+//joqAWzdupW77rqr0QuUvfzyy1x66aVMnToVgDlz\n5nDDDTdwzz33NLsXI4QQIvoiPsnfXGvWrGHq1KksWrTolO02bdpEdnZ26HZWVhaBQCBqiU8IIUR4\nwi52Ga6f//znAOTl5Z2yXX5+PmlpaaHbmqaRkJBAfn5+i5537nOfM3rEQK64sDtPr/07iqLwy5HX\nk+CMp3zvPg6+sYLKAwfQvV4wDEzDANMEVUVRVRRNQ1HVE7ctFqzxcTi7dCFxeDaxAzNRFKXZceUf\nLmXntsMUHCml9LgHv99AD+joAYM6C7/rWQne2PLwUEw1f1UdC0Ubuk+p3a7qj5NfV/BmPeeturPu\neWs8Z41T1X/e4CNUVUGzqGiagqapaJqK2sDfNruG3WHBbrdic1iw2y3YHRYcTivuWDtWq3bK90hE\njmmaGAEPAX8lgUAFut+DafgxDD+GEcDUAxiGH9MIYGJW/Zuu+29YUS2oqoaiWlFVS9VtC6pmQ7M6\nsVhdaBYXqmZt0f93IjraPME0lcfjwWq11jpmtVrx+XwNPmbZsmUsW7as1rHq9m6Hldc/+hZL+l42\nHv4KBYX/fvMR3087l8333I81Po64QQPRnM5QIgEllGxMQ8fUjRO3AwF8xcUc37yFvFdfJ35oFgPu\n/j+scbH8gukNAAAgAElEQVRNen26bvDf17aQu24fVptGWuc4YuMdxNs0LFYNTVOp9/+bev9fauh/\nMLPur+q/T0pMZo07a95Vb7vQ+eo7v1nzEMHPkJPaNXZ+TAzDRA8Y+LwGuu5H1w0M3UTXjVp/BwIG\nfl8An1dv4D0Au8NCTKydmDgH8QlOklLdJKfGkJzqJqVTDBZJQE1mGjreykIqy47gKS/A5zmGz1OM\n31OM31tKIFAJpnHqkygqqmrhxDeQGl9KUAj+99cxjQD1fquqeSpFw2JzY3MkYHMkBn87E3HGpOGM\n6YzF5g7r9Z4JTNPkmMfPgdJKDpRWUlDh42ill57xbq4Y0KVZ52o3CcZut+P3+2sd8/v9p5x/mT59\nOtOnT691LC8vj4kTJ/Kj7w1i9uKv2FW0l35JPUl0JfBN4R4KvtZBURj2h99jcTf/H6Op6xSt/5Jv\nFz3N9sfmc9av56JojX9g/fe1LWxav5+LrxhC9qju8iEXJtMw8fkCeD0BvN7gb0+ln/JSL6Ulnqrf\nXo4WlPH1tsN4PcFl66qqkN41jq7dE8nokUjv/qm4Y+1t/GpOHwFfOaXHdlN2bA9lxXuoLDtc9cEP\nmsUR+mB3J/TEZo9Ds7qxWF1YrE40qwvN4kDVrKiqNdQLUdSm/Vs3TRNM40Tvxwhg6D4C/gp0f2Xw\nd6ASv7cMn6cYn6eY8pL9+DzFoSRntcfhissgNqkvsUl9cMakoyhtPlPQpnTDZF9JBTsKS9lRWMre\n4xWU+4Nf0GyaSqrLRorTTid38/8/aDcJJi0tjYKCgtDtQCBAcXExqampLTqf2xHsDXn8PmJinMTZ\nYsgvO0rp9u3EDcxsUXIBUDSN5FFnY4lxs/XBhzn83vt0vviiUz5m765CctftY+qVZ5EzumeLnlfU\npqgKdocVu8PaaFvTNKko91GYX8bhAyUc2HeMb3fk88Xq70CBrt0SGDAknawRGcQlnHkLSnye4xw7\nspniI1soK/4OMLE6EohN7EVS52ycMek4Y9Kx2GKjOjylKAooGpqq0ZyvX6ah46kooLL0EBWlhyg/\nvpcDO9/CNHUstlgS07NISh+GO77HGTO8Zpgm3xSVse7gMb48fIxyv45NVeibFMMFPVLJiHOREesk\nxWVDDeM9aTcJJisri9zcXC699FIANm/ejNVqJTMzs0Xnc9o1VAU8AR+Jagxum4tyfyW+Yi8xfXqH\nHW/84MEkn3M2h/79FulTJgeH2Rqw9pPdpKbFMOKcHmE/r2g+RVFwx9hxx9jp3jsZ6AVAaYmHb7fn\ns/OrI3z83k4+ensHvQekcs64PvTun9KhP4xM06Ts2C7y962muCA4hByb1Jfug64kLrkfdmdSW4fY\nZIqqhZJgUufgQiFD91F2bA/FR7dz7PAmCvatxhGTTqfuY0nuPBxVa/yLSXvkCeiszitk5Xf5FFT4\nSHJYGdstmaxO8fSKd2PVItubO20TjK7rFBUVER8fj81mY/r06dx4441kZ2fTp08f5syZw1VXXdXo\n8uaGKIqC22nDF/Bh06zBBOOrIFBaiSUmJiKvIX3qxWx76BFKv95J3MD6E2F5mZed245w4bSBKGrH\n/cBqj2LjHGSP6k72qO54Kv1s23iALz/by4t//Zwu3RKYcPEA+gzo1NZhRlzpsd0c/OYdyor34HB3\notuAS0nqnI3F6mrr0CJG1WzEpQwgLmUA3QZcSknhNxTsW82+r17j0K736dJ3CsldRnSY4bOAYbJq\nXwH/+eYQ5X6dEZ0TuCGrJ30T3WH1UBpz2iaYQ4cOMXHiRJYsWcKoUaPIycnhwQcfZMGCBVRUVDBp\n0iTuvvvusJ4jxmXFp/uxalbcVheVfg+B0lIssU2bmG9M/OBBaC4XxRs3NZhg9uw8imGYDBrWvMkz\n0bocTisjzu3J8HN68O2OfD55/xte/OtaBgxJZ8plg0lIav8fvn5vCfu2v0Fx/hacMZ3pM+wnxKcO\n7NA9NQBFUYlPGUB8ygA85fkc+OYd9m57hfx9n9Jz8A9wxXVt6xDDsrOolKVb9nGk3MvojGSm9U0n\nxdU684qnTYLJyMiotafl5NtQ/6R9OJx2CyVmINSDUQwDvbISa2xkejCKphF/1mCKN22m+zX1x71v\nTyGJyS7i4s+8sf32SFEU+g1Mo29mJ7ZuOMD7//6KPy34H1O/fxZZORnt9sO46FAu+7YvB0Wh55Dp\nJHUe3mG+vTeHw92JPsN+RFnxd+z96nW2r32KLn0mkd7rgnb3fgQMgzd2HuS93fn0TnDz/8b0ont8\n634ROm0STFtQVQWDAFbNisNix+4LLoG0xESmBwMQf9ZZfLf4Hxh+P6q17rjuvj1FVeP+oj1RFIWz\nRmTQf3Aab/9rK2/+cyO7vyngkquHtqsVgIYRIO/rFRTsX0NiWhbdMq/Aao/MF6z2LCahJwPPuZ1D\nu97j4LfvUn58P72GzECzNu2LoGEY6HrDS+Wjrczr5x9b9rG3pIIr+6Vxfo9UVEWpsxK3PpqmhS67\nEq4zOsFoioJu6tg0K5qqYg0EE4zqiFz30d27J6auU3ngAO6ePWvd5/cFyD9cysgxvSL2fKJ12R1W\nLr82m179U/jPq5spLqxg+k9G4oo5/Zc26/5Kvs1dTNnxvXTLvILUbue22x5YNKiqha79puKO78me\nLS+xY93T9M+5Cas97pSPKy8vR1VVLJa2+Xj16wYlvgCXD+hMstOOw9K8LzxerxfDMHC3cCVtTWd0\nglEUMNGxaTY0RUU1ggmmKftWmsrVrTsAFfv210kwhQXlYEJqeuR6TKJtDM3pRlKym38+v47Ff1zN\nj28eTUyco63DapDfV8Y3X/4NX2Uh/XN+QWxi+CsnO6qEToPIHHUb33z5V77+4k/0H3ETNmdivW0N\nw0BV1Tarj+jXDQor/VisVjq57C1aFWa1WqmsrAy9lnC0r0HFCFNVBZ0ANs2CqmioVZuE1Qh+87DG\nxWJNTKBi77469xXmlwGQkiq7izuCbr2SuPFXY/F6Ayz98xrKS71tHVK9Av4Kdq7/C35PMf1zZkpy\naQJnTBr9R/4S0wgE3ztvWb3tdF1v057L4XIPqqKQ5m5ZcqmmaVpEhvjO+ARjEgj2YNTo9GAAXN26\nUbFvf53jR/PLcLqs7WI4RTRNcmoM1888l4pyHy8++zk+7+l1YTtD97Mr9+/4PcfpP3Jmu18h1Zoc\nrhT658xED3j4Nvd5DL3hMlWtTTdM8iu8KFXJxRJmzyNSQ6VndIJRFBNTMbGqFjRFQ60qmRTpBOPs\n2gXP4cN1jh/NLyOlk0yodjSpabFc+/NzKCwo542XczGNU9fPai2mabJn6z8pL8mj7/AbccbUf2E/\n0TC7K5m+w2+ksuwwe7b8s9Hisq3BNE0KKrzohkknV/jJJZJOn0jaQPWqQ4tqQVXU0BBZpBOMPSUF\n79HCOsePFZaTlCoJpiPqnBHPFddms2PLYf733ulxSYkj362i+Mhmep01g5iEnm0dTrvlju9OryEz\nKM7fQsG+1VF7HsMwmD17NtOnT+f6669n7969bNy4kauvvpoZM2bwxz/+EYCiSi+Pz5vLXT+/gZ/e\n8GP27t0LUG/b+s4ZTWf0JH91olcUJZhgojREZktJQa+oIFBRgcV1Yh16SbGHPpkdbye4CMo8qzPj\npwxg1Xtf07NvCr36prRZLKVFuzjwzX9J6zGexLSsNoujo0hMzyK1eAx5O/+DO6EH7vhuEX+ODz74\nAJ/Px7Jly9i4cSPz58/n4MGDLFq0iG7dunHTTTexYdMWvtqzB3Q/r77ySqjdn/70Jx5++OFabbdt\n28aBAwfqnPNPf/pTxGOvdmb3YKqGGVVFCc7BRKsHkxrc5+I7ejR0TNcNysq8xJ7GK41E+M67sB/d\neyXxxku5VJS3zZi97q9kz5aXiUnoSdd+F7dJDB1RRv9pOGM7s2fLyxh6w/tLDh0t59v9xbV+Dh0t\nb/T8X375Jeeddx4Aw4YNY/369fh8Prp3746iKIwZM5YPP/2UHZs3ccH48aF2W7dupaysrFbbsWPH\nsmbNmjrn3Lp1awTeiYad2T2Y0OUnlNrLlJu5brwx9pTgN1dvwVFc3YPLlstKvGBCbLwkmI5MVRWu\nuHY4f/n9Kv77+mau+lFOq8ewf+e/0QMeep41o8ml8UXjVNVCzyHT2b7mDxza/UG9yft4mZeZ8z/g\n5Gk4VYElj1xE/CkW+JSVlRFToy5iaWkp3brV6CnZ7JQfzsfwVBJbo7yVpml1Hut2u9m/f3+d45qm\nEQgEorby7cxOMFVdFgUVVa05yR/Zt8WWlASKgrdGD6a0xANAnCSYDi8+0clFVwzhjZdy2fnVEfoP\nSmv8QRFy/OgOCg98QfeB329XFZDbC2dMOum9LuDQnpUkpmVhddYe8o6PsfPn+y+kvLJ2D8fttJ4y\nuQDExMRQXn6ipxMXF4fHE/zc8AR0jpWWkpIYT4kZqNXOMIw6jy0vLw89/uS20VxWfWYPkVV1YdTq\nHkxoiCyyb4tqtWJNiK810V96vBKAWKlBdkY4a3hXevVL4e1/bWm1pcuG7mff9uXEJvYhJeOcVnnO\nM1F67wtwuFLZt315vavKOqe46dstodZP55TG974NHz6cjz/+GAhO2GdlZWG1Wtm7dy9HK7zkrl3D\nmFFn12nXv39/YmJisFqt7Nu3D9M0+fTTT8nJyam3bTSd2T2YGkNktSf5I/+22BIT8R8rDt0uOe5B\n1RTc7pZdbkC0L4qiMPXKs/jz71bx8fvfcOG0gVF/ziN7P8bnKaZv9o1SAiaKVNVCt8xL+ebLZyku\n2EZipyEROe+kSZNYvXo1M2bMwDRNHnvsMUpLS/m/u+7GFwgw7ryxDBs2DMMw6rQDmDNnDnfffTe6\nrjN27FiGDh3KWWedVW/baAnrk/S2227jiiuuYPz48WgRnhhvDdXLlBWq52Cqbke4BwNgjYvDX3I8\ndLv0eHCCX64Bc+ZITo1h9Pl9+Ox/u8gZ3SOqJf59nmIO715Jp25jcMa03pDcmSouuT9xKZkc3r2S\nhNRBETmnqqrMnTu31jHdMPnD8//AadFCJffrawfBSfxXXnml0XNGU1ifpA6Hg7vuuovzzjuPxx57\njB07dkQqrlZRcxWZqmqoZvUkf+R7MNb4ePzHS0K3K8q9uGUH/xln9IS+OBwWPno7uv+vHPjmbVTN\nRuc+k6L6POKEjP7T8HtLCfgaXyHWUiVeP4YJCU24FPjpIKwEs2DBAj777DPuu+8+du3axZVXXsll\nl13GP/7xD4qKiiIVY9Sc2Aej1u7BRGEnrCUuDn9JzQTjwyXDY2ccu8PC+CkD2LLhAAf3Fzf+gBao\nLDtC0aFcOve+EEsTy8uL8Dlj0kjqPAy/rwzTNCJ+/oARrJIcZ7ecVrv1TyXsKJ1OJ5dddhnPPfcc\nq1at4qKLLmLhwoWMGzeOm2++mTVr1kQizqio7sGEhsiqJ/mj0oOJO6kH48Ppbh/fQkRkZY/qTnKq\nO2q9mEO73sdqjyMlY1RUzi8altptLGAS8NVfDDMcJd4AChBnaz+fGxFJg8ePH+ef//wnd9xxB4sW\nLaJHjx7cdddd9OjRg1tuuYWFCxdG4mkirs4QmWFiqkpUJkSt8fHo5eUYgeAKokrpwZyxNE1l3KT+\n7Pq6gAP7jkX03BWlBzl2ZBOde09E1drPB1FHYXcmoFmc+L2lEe3F6IZBqS9ArN2C1o7mbcP6qv7u\nu++yYsUKPv74Y2JiYpg2bRoPPvggAweeWCHTs2dP5s+fz5133hl2sJGmqoBRcxUZJ8bNIswaH7xI\nUaCkFFtSogyRneEGD+vCqvd28sn73zDjp2dH7LwHv30PmyOR5K4jI3ZO0TxWeywBbyEBX1mjFydr\nquPtsPcCYSaYu+66i3HjxrFw4ULOP//8ejfs9OnThxkzZoTzNNFTY4hMVRQUE4jCCjIIriID8Jcc\nx5KQQGWlXxLMGUzVVMZO7MeKZRs5lHeczhnxYZ+zsuwwxwu20WPQ1ajqGb0DoU2pqgWL1YXfV4bF\nFhv2iIhumJT5AsTY2lfvBcIcIvv4449ZtGgR2dnZoeSyYcOGWtd9zsnJ4b777gsvyihRaw6RKSqa\nSdR7MP7jJXgqfGAiCeYMd9aIriQkOVn94bcROd+R7z7Gao8jqcvwiJxPtJzFFoNp6Oj+ihafo7ry\n8Q+mT+eumT/j2OEDzaqQXF/baps2beL6669v+QtsorC+5hw/fpyrr76ayZMnh5LIrbfeSlxcHM8+\n+2ztujmnodAkf9WGGIuhYEYrwYR6MKUEqooeOiXBnNE0TWXUeb15799fcfxYBfGJLd8X4/eWUHRo\nA136Tpbey2lA02xoFgd+Xxma1dWiXswHH3yA1+vlyef+wTdfbeF3jz9ep5ryqSok11dNefDgwTz7\n7LOsWLGiVS7rHNan6bx58xg8eDC/+MUvQsfee+89+vXrF/UdopFQc6MlgAYnujURprlcoCjo5eWh\nqrout+yDOdMNO7s7NpvGuk+/C+s8+ftWo6ialIQ5jVhsMRi6j0PHD7K7aG+tn8NlBY0+/ssvv2TU\n6DEETIPROSPqVFM+VYXkhqopA3Tv3p1FixZF9bVXC+urTm5uLv/6179ISEgIHYuJieGOO+44fedd\naqi5igxANaPXg1FUFc3lJFAzwbja14SdiDy7w0L2qO5s+Hwv4yb1x+5o/v+SesBDwf41pHQdhcUa\nveoAonk0i4OygI87P/g9JrVrlCmKwrOX/pY4R2wDjw5WU1YdLuyaht2i1ammfKoKyQ1VUwaYMmUK\neXl5kXqZpxRWgnE6neTn59OzZ89ax48dO4baDjYC1axFBqCihCb+o8HidhMoL8dTVVnVIQlGAGeP\n7cXaT/aw8Yt9jDqvd7MfX3hwA3rAQ6ceY6MQnWgpRVFIik1j/rhfotvcqMqJcloum+uUyQXA6XJz\nvLSUOFvwY7pmNWU4dYXkhqopt7awssDkyZN55JFHWL9+PV6vF6/Xy/r165kzZw4XXnhhpGKMHqW6\nXH/VEJkJZhSLAlrcbvTycjyeABaLiiXC150R7VNCkouBZ3Vm3Sd7ME++cEgjTNOkIG8N8akDpRz/\nachidZHmTqK7O5neST1CP+kxqY0+NvOsLNZ/thqXVatVTbkpFZIbqqbc2sLqwdx9993cfvvt/PCH\nP6w1iXXBBRcwa9assIOLtuo5GLXqDxUlqhcw0Kp6MN5KP3an9F7ECSPH9uQfT3/G7m+O0mdA4x8+\n1cqLv8NTdpiM/t+LYnSipRRFRbM6CfjLsdrjmjzZb5gmOeedz/q1n3PNNdfUqqbc1ArJ9VVTbm1h\nJRi3283f/vY3du/ezc6dO7FarfTp06fOkNnpqjqXVP9HV1Ci3oMJlFfg8fhxtGCsXXRc3XslkZIW\nw4bP9zYrwRTkrcHmTCIuObrX9RAtZ7HGEPCVowc8Ta4NV+4PgKrw6Ny5WE/am9fUCsn1VVOulpGR\n0eB9kRSRT7nevXvTu3fzx47b2smryBSTEzP/UWBxu6nIy8NbGZAejKhFURSGn9ODD/79FWUlHmLi\nGr/Sqd9XxrHDm+nSd0poqb04/aiaFVWzEfCVNTnBlPl0HJpWJ7m0N2ElmN27dzNv3jxyc3Nrba6s\ntnXr1nBOH3V1V5FB80bAm0cLzcH4cbSTctui9QzNyWDlW9vZ+MV+xk7s12j7wgNfAEhZmNOcoihY\nbG58lcUYRqDRfUo+3cCr66Q62/82hrASzEMPPcTx48e544472mSFQtiqJ/lDQ2REbR8MgCWmaois\nMoBLKimLkzhdNgYN7cyGz/cxZkLfU16MzjQNCvI+D14H3hbTYDtxerBYXfg8xQT8FdgaqU9W5gug\nKQpOa/tfBBRWgtmyZQuvvPIKmZmZkYqnVdUs1w/BORkzqsuUXcFJfo+fxGTZryDqGnFOD7Z8eYBd\nOwvom9mpwXZlx/bgqywiZfAPWjE60VKKomKxONF95ZinqE9mmCZl/gAxVktoZKU9C2uALyUlBdOM\n5qBSlIWGyIJvQ7BDE905GNPvx1PhwyFzMKIe3XolkZoWQ+7afadsV3hwPTZHIjGJvVopMhEui82N\nYQQwdF+DbSoDOoZpEmPrGIuAwkowM2fO5LHHHmPPnj0YRuSv4BZtyslDZKaCGdVlysGhDI/H36Id\n26LjUxSFoSO7s3PbkVDFh5PpAR/HjmwhucsImdxvR1TNjqJqBE5RALPcp2NTVWztfHK/WlivYvHi\nxeTm5jJ16lSGDBlS5+d0p9TZyR/ljZYxLkzA6wlID0Y06KwRXTFMk225B+q9vzh/C4buJbnLiFaO\nTIRDURQsVje6v6Lei5HphkllQMdd1Xupr0pycyok7927l2uuuYZrr72Whx9+uFYnYO/evUybNi1K\nr/SEsL5G33TTTZGKo21UD5HVXKYcRRa3G0PRMAxkH4xoUGycgz4DUtm0Po+RY+sOgRUeXE9MQi/s\nrpQ2iE6Ew2J14feWVO2JqT0PW+EPYGLirprc/+CDD+pUSa6vmnJDFZJ/85vfcMcddzBq1Chmz57N\nypUrmTRpEm+88QZLlizh2LHIXk213tcbzoOvuOKKSMXRRoIZpeYcTNQ3WqrBEv2yD0acyrCR3Xht\nyZcUHC4lNf1EzSqfp5jSol30GHRlG0YnmqPy0GH0GnXBvJVFBNxlxPceWKtduT+498VSVcfx5CrJ\n69evJyUlhe7duwOEKiQPHjw4VCH53nvvDZ1v27ZtnH128Gqp48aNY/Xq1UyaNIn4+HheeOEFJk2a\nFNXXDRHYaPnFF1/wl7/8hd27d7N06VL+9a9/0a1bNy6//PJIxBdVykldFtWM7ioyrUaCkX0w4lT6\nD0rD4bSyaf1+Lpw2KHS88OCXKKpGYlpWG0Ynmsp//Dgbbr4NTp6jVhRyFj+LPTEx2E438Og6KTX2\nvpxcEbmhaspQf4Vk0zRDw/9ut5vS0lIAJkyYELkX2IiwEsyqVav41a9+xaWXXsq6deswDANFUXjw\nwQfRdZ0rr2wn37JqZJVo9mA0h4OAJfgPyO6UITLRMItVY0h2FzZ/mccFF2eiaiqmaVJ48EsSOg1B\na+KOcNG2rPHxDH9mUa0ejGka+ClHdZ34DCj36ygouGoUwD25InJD1ZQbUrOifbuspvzHP/6Re++9\nl0cffRRNC74xt956K/fddx/PP/98RAKMpuoejMmJnfzRLNevqCqGMzjcIT0Y0ZisnG6UlXjZ/c1R\nAMqP78VbUUByl9aviitaztk5nZi+fUI/sf364eqaEVpNZpom5f4ALquGWmNz7clVkhuqptyQQYMG\nsXbtWiB4eft2V03522+/Zdy4cXWOT5gwgd/97ndNOkdlZSUPP/wwK1euJC4ujttvv73e4TVd1xk6\ndGitkjQpKSmsXr265S+g+r+leeJmNHswAIYz2OWVVWSiMV27J5DSKYZNX+ynb2YnCg9+idUeR1xy\n42VkxOnNYnXhrSjE0P34TRW/YZB40pfOSZMm1amSXF815Ybcd999PPTQQzzxxBP07t2bKVOmRPtl\n1RFWgklMTGT//v21xgUhWIMsJaVpK1zmz5/Pnj17ePHFF9mxYwezZ8+md+/eZGXVHmOuHmv85JNP\nQuOK1b2mSFGiPAcDYNjdgIndLkNk4tQURSErJ4NV7+2koryCY4c3kZIxSva+dACaxYGiqAT8FZSb\nzmBpmJOuD9VQleSmVkju1asXL7zwQoMxhPXlvInC+pT7wQ9+wJw5c3jggQcA2LdvH2vXruWJJ57g\nmmuuafTxFRUVLF++nKVLl5KZmUlmZiYbNmzgpZdeqpNgdu3aRffu3enUqeHyGc0WmuSvXqZsRj3B\n6DYnVt04ZZ0pIapl5WTw4ds72P7laghUyvBYB3HiOjEVlJtWXFZLk68V056ElWB+8YtfUFpaym23\n3YbP5+OnP/0pFouFn/zkJ9x8882NPn7Hjh0YhlFrU2Z2djZ/+ctf6rTdtWtX1K4zo9SYg4nmTn4A\n3erAquvRfRLRYcTFO+ndL4WSo6tJ75KBMyatrUMSEWKxuqnw+dAxiekAhS3rE1aCURSFe+65h1tu\nuYVdu3ZhtVrp2bMnDkfj17IAKCgoIDk5udZQV3JyMvn5+XXa7t69m+LiYmbMmEFeXh45OTk88MAD\n4fVoqif5W2kVGYCu2bGYdS9tIERDsoYn4S08giPu4rYORUSQqtnw4sCC2WFKw5wsrASzYcOGWrf9\nfj9fffVV6Pbw4cNP+fjKykqs1toTW1arFZ+vbg2m3bt3U1FRwezZs7FYLCxcuJCZM2fy6quvNjgX\ns2zZMpYtW1brWM1zn5xKWmMOxq/asBiV0X0S0aGkJB3kQKHCd3uT6DWo8faifTABL1ZceAAXUV3C\n2kbCSjDXXnstiqLUqqisKAqKoqCqaqMXHLPb7XUuVOb3+3G56payX7p0KYZhhEohPPnkk4wZM4at\nW7c2uJJi+vTpTJ8+vdaxvLw8Jk6cCIBZvXys6le0LzgGEMCCFvBG+VlER2GaJsVHNuANZLB541HO\nn2LK/F0HUeHXMQE73npLx3QEYSWYlStX1rqt6zp79uzhySef5O6772708WlpaRQVFWEYRmhTUGFh\nIampda9JbrfXvrpbUlISCQkJ9Q6nNVlomfKJWmRGtHswaGh+T+MNhQAqSw9SWXaYTt2v5PhHBXy3\nq5Be/aQGWUdQ7g9g1zSsaAR8FR0ywYQ18Ne1a9daP927d2f8+PE88MAD/OY3v2n08dUXKqvZ09mw\nYUOdHolhGEyYMIF33303dOzIkSMcO3aMXr1afj0Mpb7+SpTnYPyGisVfiREIRPV5RMdQeHA9Fqub\nvoNySEx2sWn9/rYOSURAwDCClZOtGharC133YBi1F//UV00Zgl/kf/WrX4U2YVY7uUJyUVERN954\nI9deey133HEHlZWVte6bPHkyXm90R1OiMrOUnJwcejNOxeVycdlll/Hwww/z1Vdf8cYbb7BixQpm\nzBODKiEAACAASURBVJiBrusUFBTg8/lQVZWxY8fyxBNPkJuby1dffcWdd97JuHHj6Nu3b4vjNE/a\nya8YZtR7MD5dwWL40CsaviaEEACGEaDoUC5JnYejWixkjchg++ZD+Lzy5aS9q/DrKIDbakGr6rno\nJ10npmY15bvuuov58+ezb98+fvjDH7Jly5Zabd944w3uvPPOWhWSn3nmGaZNm8ZLL73EoEGDQvPR\nn3zyCTfeeCNHjx6N7oskzASzYcOGOj8ff/wx8+bNo1+/pu02njVrFj179uSaa67hqaeeYu7cuWRl\nZXHo0CHGjh1Lbm5uqN25557LzTffzHXXXUeXLl1YsGBBOOGHRsiU6iEyoj/J7w2YWAwfgXJJMOLU\nSo7uIOAvD133JSunG36fzvbNh9o4MtEcRUfLObi/uPbP4RKcFg1NVVBVDU1z1LkQ2cnVlLdu3UpF\nRQXz5s1j1KhRtdpWV0hu6PHjxo3js88+A4IbOBcvXkxCQkK0XnJIxCf5ITh01tQPf7fbzcKFC+sc\nz8jI4Ouvvw7ddrlcPPLIIzzyyCPhhFxL9SR/aLtllCf5Dd3A7zelByOapPDgepyxnXHFdQUgMdlF\n995JbFq/n6EjuzXyaHE6KC/z8vT8Dzn5yvKKAjMfuADcwdsW24nSMaoWXFl7cjVlTdPo27cvFkvd\nj+36KiSXlZURGxusfVizmvKYMWMi8dKaJKKT/BBcZhzR3fbRVL2Tv8YkfzR7MN6qoQ2L7iNQo0qq\nECfz+8ooLthORv/v1To+NKcb/35lE8VFFSQkdbxJ4Y7GHWPnlvsvwFN5YrVsqc+PX1VITjzx369m\n6RibFg/UraZsGEa9yaUh1Y93OBzts5ryyZP8Xbt2bT/JBTi5vxLtUjHV/8ishg9dhsjEKRw7tBGA\npPTae8kGDe2Mxaqy+cu8+h4mTkNJKW66dEugS7cEOmfE406NIT0tFrXGgqKapWOqR4ROrqbcv3//\nZj3v8OHDWbVqFRCspjxiROtfYjusHsz111/f5Po5S5YsCeeposY0waj6DxrtHkx1ggnOwUgPRjSs\n8OB64lMysdpjah23O6wMPKszm9fncd6F/Tpk/aqOzKsbBEyDGJutzn0Wq4uArxxD96FZ7PVWU26O\nX/7yl9x333288sorJCYm8vvf/z5SL6PJwkoww4cPZ/HixWRmZpKTk4PVamXr1q21Ls15egtmlND4\nqGlinLJ9eDyVwSEym2bKHIxoUGXpISpKD9C798R678/K6caWDQfI++4Y3XoltXJ0IhxlvgAWRcVe\nT2kYVbOjqBoBfzmaxd5gNWUIVqGvT80KySkpKTz33HMNxvLhhx82M/rmCyvB7N27l+uuu4777ruv\n1vG//OUvbNy4kXnz5oUVXLQFJ/kVDKO1ejDBMjV2h0V6MKJBhQfXo1ldxKcOrPf+Xv1SiI13sGn9\nfkkw7YhhmlT4dWLt9VdOVhQFi9VNwFeG6TA6xGUZwnoFq1at4gc/+EGd41OmTAktiTudmaEeTHWC\nMaO6iqy6B+NwWmWZsqiXYQQoPPglSenZqGr93/9UVSFrRAbbNh7E75fK3O1FhV/HwCTG2vD3eovV\nhWka6IGOUe0jrASTnJzM+vXr6xz/+OOPSU9PD+fUraS6mvKJm9Geg7HaNKwulwyRiXoV528j4C8n\nNWPUKdsNzcnA6wnw9ZbDrRSZCFeZr6o0zCkqJ6uaFVWz1dkT016FNUR24403MmfOHDZu3MiQIUMw\nTZPc3Fzefvttfvvb30Yqxqip7sGEJvmJfoJxOP8/e/cdV3X1/wH89bmbe9nrgoBMGYIo7q1kzkzM\nTBo2zLScZcNvw5FpiqXmaNhQW+jXypG/MvuqOXMiiCLgYO8974W7Puf3B3LzygUucAfkeT4ePITP\nPJ8r3Pc963344AnEtImM0qs09wIkdt6wsnFv8ThnqQ28fBxw5UIWwvp6mKl0VHupNCzqNRo4Wwlb\nPZbHF0OpqAJhNWA4XXudmA5PtBSLxYiNjcXhw4chEokQGBiIHTt2NJlp2hk19sE01mAY1tSd/A0B\nhkvEUNfUmvBOVFdULytBTfkd+IRGt34wgP5DfXBgdwJKCmvg4mZj4tJRHVGrUoMDBmIDFhbj8sVA\nfRXUqromowi7mg4vDD916lRMnTrVGGUxOwJWpwYDmHgeTP3dGgwjQX1hkeluRHVJpXkXweVZwcFN\n//IT9wsJd8eRg0m4ciELE6aGtX4CZRGEENQq1ZDwuTpzX5rD4XDB5YmgVtWCJ5B06aHoHR6mkJWV\nhZUrV+LZZ59FUVERYmNjceHCBWOUzeSadvKbNl1/fZ26oQYjFtOJlpQOllWjLC8Ojt36alOFtIbH\n56LPwO5IvJxDE2B2YnVqDTSEwFpg+Od5nkACtUqBFSuWtzubcqNvv/0WGzZs0C1TXR2efPJJpKWl\nteOJDNehAJOYmIioqCjk5OQgISEBSqUSd+7cwezZs3HixAljldGEdDv5GdbUo8hUEFnxwJNIaB8M\npaOyKOlu5/7gNp3Xb4g3FPVq3Liab6KSUR1Vq1RDwOG0aVlkLk+Ek6cvoL5O1u5syvX19XjzzTex\ne/dunWOvX7+OZ555Bjk5pl/6oUMBZsOGDZgzZw527typXfp45cqVeOmll7Bt2zajFNCUCAjIvU1k\nBGAZ04WY+joVRCI+eJKGUWT3JwmlHlzF2Wdhbe8LK+u2jb50dJbAP8gFcecyTVMwqkNUGhY1tSXg\nKYogr8mDrDoXsupcKOQtp8pnGAbXrqdiyOAIEFbTrmzKCoUCU6dOxSuvvKKzXalU4rPPPoOfn59x\nHrIFHeqDSU5O1juZcvr06fjuu+86cmkzudvJ39izb/J5MHc7+UViEI0GrEIBrkhkwjtSXUFtZRZk\nVVnw6/1cu87vP9QHe3ddRk5mObx86MTLzqSytgqVidtQ2eSdhUH46BXgC5rvxJfJFbCWSKBWycAX\n2rY5m7KdnR2GDx+O/fv362w3Z06yDgUYKysrlJWVwdvbW2d7RkaGTprpzoolup38DckuTVyDseKD\nZ90w4kddU0sDDIXirDMQWDnC3jW0Xef36CmFo7ME50+mwesFGmA6C5YQ1DFCePZfAmue7oRYHk/U\nYnABABsbG9QrNFApZeAJbNqcTbkz6FAT2eTJk7Fu3TqkpaWBYRgoFAqcP38eq1evxoQJE4xVRpNp\n6OSHzkx+1kQjNjQaFiqlBiIrPvh312hQ3V2fgXpwKesqUFF8HdLuI9qdGoTDYTB4lB9SkwpRXkr7\n9joLmVINlhA42kkhsfXU+RKKnVs9v2/fvvj73GUQVo34K5fanE25M+hQgHn99dfh4eGBRx55BHK5\nHI8++ihefPFF9OrVC2+88Yaxymgy98+DAUugMVENpjGTssiKB97ddRnU1dUmuRfVdRRn/w0OVwAn\nj/4duk7v/p4QiwW4cMq0o4IowxBCUKNUw4rX8sz9lowdOxZCkRVeeOlVxMSsxzvvvGPkUppeh+pb\naWlp+OSTT/D6668jOTkZfD4fPXr0QPfu3Y1VPhO7bya/hjXZMGV5bUOiS7FECL5tQ7OYqprWYB5k\napUcJbkX4OI5GFxex5pK+QIe+g/zwbm/7mDU+CBIrFufMU6ZTr2ahZJlIe1AE3hjNmW1Sg6FvAwi\nyT9rbRmSTbnRtGnT9B77ww8/tLtshupQDWb27Nm4fv06unfvjgkTJmDMmDFdKLjcnWh5TzZlsAQa\njmkijKxWAQAQWwvAFYvBcLlQ19AazIOsOOssCNFA6jPSKNcbMMwHAHDpTIZRrke1X5VCBQGHAxGv\n4xmRuTwrcDg8qJRd7wNph57e1tYWSqXSWGUxO0J014NhWBasicaRyWUNr5NEImhIy21jQ2swDzCN\nqg7F2Wfh7DkIfKFxlrKVWAvRb6gPLp7JgPzuBxrK/OrVGtRrNLAT8Y0yC59hGPCENtCo6sFqVK2f\n0Il0qIksMjISc+bMwUMPPQQvLy+I7qsO3j/+urNp7ORnCQEhBIwJ+2BktUowDGAlbljJjm9rAxXt\ng3lgFeecA6tRws1ntFGvO/yhAMRfyMK5k2l4eHJPo16bMkyNUgU+hwcxz3iJKnl8MVSKaqiUNRBa\ndZ2Rgh0KMH/++SccHByQkJCAhIQEnX0Mw3SBANPQREYIAdiGyTBqxjTpLuW1ClhJBGDuNsHxbG2h\npjWYB5JaJUdR5ik4eQyAQGRv1GtLbIQYMMwXl//OwOBR/rC2oX0x5pRZKQeXA7jaGKf20ohhOOAL\nbKCsrwJfYGNwOiFLa3OAiY2NxfTp0yEUCs2y5KYpNdRcGprIiDbAmOZecpkSEsk/63DzbWzoMOUH\nVEH6cRCiQTf/sSa5/tBIf8Sdy8Tfx29jPE2CaTaEEBxOK8BjQd0gMSBrclvxBBKolDVQKqohEjsZ\n/fqm0OY+mDVr1qC2VjfV/PLly1FeXm60QpmLdslkQkDUDckCNSZK2C+rVUJ8z8iehhoMbSJ70Cjk\n5SjJ/htS71FG63u5n1giwJBRfrh8LhNlJXRZCHNJKqlGeqUMdkLj1l4aMQwHfKEtNCo5NJqu0ffd\n5gCjL3/W77//DlkXTN7YkK6/4ZmI5m4NxmSd/ApIrO+pwdjZQllRaZJ7UZ1X3p3D4PKtIPUZZdL7\nDI1saB773683THofqgFLCPbfzIePnRhWRux7ARqSCj/77LMAAB5fAg6Hj7VrPsCePXt0jisvL8e4\nceOgUOgO8Dh69GiTeYnNZWQ2to6PoYP+oNMVNI4iYwlA2IZUDhoOaUghY2TyWiXEkn9qMEInJ6iq\nqsCqaZr1B0V16U1UFCbCo8dEcHmm7RvhC3gYNyUUt1OKcSuZrj1kaiezSpBbU4dHAtyNWnv5+uuv\nsWzZMm3QqKiowMLX3sXJU2fB3lOLOXPmDF588UWUluom0VyzZg02btwIlv3nPa25jMymYJQA01UR\n8k8nP9E0BBiWYaBhNS2f2A7VVfWwtv3nTUXg5AgQAhWtxTwQWI0KWSn7Ye3gB6duA8xyz5Bwd3j7\nO+HIgSS6XowJVSlUOHgrH8M8neBrL2myv1imQFaVXOerWGbYMPLu3bvrZKaXyWRYvPhVTJ48EWpV\n3d33sIZJmbt27YK9ve6gkb59++L999/X2dZcRmZTaNcosq68wtq92MYFx1hom8gIA2hYDfhGHKWh\nVKhRX6eCnb2VdpvAqWGoobK8HEKX1vMSUV1bQfoxqOqrEBDxotn+fhiGweQnwvHlhlM4/nsKJk7r\nZZb7Pmh+SskFh2HweLAHcF8Te41ChWWnbujJpQxsHNMLNsKW32fGjx+P3Nxc7c9eXl7w8vLCqZMn\nARCo6qshsLLHsGHD9J4/adIkXLx4UWdbcHCwYQ9mBO0KMOvWrdOZ86JSqbBp06YmGZRXr17dsdKZ\nmE4nf2MNhgOoiXFrMNWVdQAAW4d7AoxjwygQRVkZ6Grq/241FekozDgBd/+HYWUtNeu9nVysETkp\nGEcPJSMk3B0+AfTDjDHFF1biUn4FnuvVHTYCHlQq3YmQNkI+1owKRZ1a9z3FisdtNbi0hOFwwOWJ\noFLWgMsXdTjVkKm0OcAMGDAAhYWFOtsiIiJQWlqq0/7XFWo5hNzTyc+aroms6m6AubcGw7e1AcPj\nQVnW9UbfUYZTq+TIuL4HEvvucPcdY5EyDBrhh9RrBTi4OwFzXh9J85QZSWW9Et9fz0JvVzsM92x+\n2LCrxDSvN5cnApcnhKKuAlYSKRhO5+vxaHOAMUeCNHNh7+3kV/9Tg9EYuZO/urIeAGBr98+nDIbD\ngcDRAcouOLybMgwhLLJu/ASNuh6+veaB4Rh/boQhOBwG02b2xVebTuNAbDyenjMYHBPl3HtQsIRg\nZ2IWuAyD53t1t9gHaoHIEfWyIijqKzrlDP+utXqNkd2b7PKfPhjT1GAk1gLw7pt8JXR2hqKkxKj3\nojqP/Dv/Q2XxDfj3ed7if/x2DmJMm9kXsV9fxIk/UjHmkRCLlqer25eah9SyGrw6IKBDTV2G8PT0\nxE8//aSzbdGiRdrvBVYOUMjLoOby9U5+HzRokN4O/eYyMhtT56tTmRG5O5NfrWH/aSLjwOgBprqi\nDrb3NI81EnVzR11evlHvRXUOZflxKMw4Do8eE2Hv2jlm0/sHuWLMpBD8/dcdXD5LMy6317ncMvwv\noxjTgz0Q6mKaybJtweOLwRfaQllfDbWqztLF0fFABxgWBByGgVKt0dZgWMb4nfwlxbVwcmm6PKqV\nhwfq8vK1aWqof4eKwkRkJv0Ep279IfVpula6JQ2N9MfA4b7442ASkhLyLF2cLudqUSW+v56FYZ5O\nGOvr2voJZsIX2oLLF0FRVwaNur7D1zPW3MYHOsAQwoLDcKBUsfeMIjNuExkhBCWFNXBxazpWTOzp\nAVahgLKszGj3oyyrojAR6dd3w8GtN7xDn+h0g10YhsH4qFCE9fHAgdh4XL2UbekidRnXi6uwPT4D\n4a52mBmmv9+Fy+VCbYHJ0wzDQGjlCC5XAIW8DBp1x5Zr0Gg04HI73mf4QPfBsITcDTC6NRhjBpjq\nyjooFWq46gkwVh4eAIC6vHwIXVyMdk/K/AghKM46jdxbv8NBGg7fsCfBMJ3z8xvDYTD16QjwBVwc\n2psIWa0SQyP9O10w7Ez+zi3DD9ezEOpii7kRvuA1M0iCw+GAZVnU1dWBy+Wa/TXl8O2gUpejtroQ\nApEDePymTfMtIYRAo9GAZVlwjDAq7QEPMA0v4r0BhjDGHUVWXNiQMVlfDUbkJgXD50OWmQX7Pr2N\ndk/KvDRqJXJSD6AsPw5Sn1Hw6DGp0waXRhxOwyRMsUSA47+noCC3ClOie0MgfKDfEprQsAS/3srH\nH+lFGObphJlh3ZsNLo0kEglYloVGY/yMIK1hGA4EQjtkpxxAdelNuPlFwsVrqMG/jwzDQCgUGiW4\nADTAgMtwoVSbromsILcKAiEXDo7iJvsYLhc2PQJQnZIKj6lTjHZPynxkldnISPovlPWV8A59As4e\nAy1dJIMxDIMxj4TArZstDv2UiC83nsLkJ3rDtwedjAkAJXIFdlzNRHqlDNOCumGCn9TgGgmHwzHa\nm3Sb8fkI6PMU8m4fQWHaYcgr78A7dAYEIjuzF+WBDjAawoLL8O/WYBonWgJqIwaY9Fsl8PZ31i40\ndj/bniEoOnqsYUVN2kTRZaiUtci7/QfK8i7BysYDIYNfNfssfWMJjfCAm6cdDu1NxA/bz6PPQC+M\nnhAEW7u2Na/8W6g0LP6XUYzDdwpgLeDjrcGB6OHYdJBOZ8YwHHgGToKNgy8yk/bixrkN8AiYABfP\nwWadj/VABxi5qg48xhZKlQYamRwAoBAw0BhpFJlSoUZuVgXGtrB0rW3PEOT+sh/yrGxIfLyNcl/K\ndFSKahRlnUZJzgUwDAOv4Ci4eA6x2CRKY3FyscYL84ci7lwmThy5iaT4PPQf5oPBI/30DrH/N1Jp\nWJzNLcORtCJUKpR4yNsVj/Zwh9gEi4eZi51LCEKHvYW824eRk3oQxVln4O73MBzdI8zyO2vxAFNX\nV4eVK1fi+PHjsLW1xauvvoqpU6fqPfbAgQPYunUrKisrMWHCBKxYsQJWVu3/5a9VyiBhhFCqWaiq\nq8FwuVDxGMiUxhlLnppUCFZD4B/c/HBGu15h4EokKDl1GhKfZ41yX8q4CKtBdfltlOXFobI4CQyH\nBxevIXcXDetan2xbwnAYDBjui179PHHuZBounk7HxTMZCAqVImJQd/j2cAbPyGuddAaFtfU4m1uG\n87llqFGq0d/dAY8E+MPD5t8RWHkCCbxDn4CL1xDkpx1F5o29yLvzB5w9BsLJY4BJJwFbPMDExMQg\nIyMDsbGxSE1NxYoVK+Dn54fw8HCd4xISEvDBBx/gww8/hLe3N9555x18/PHHWLFiRbvuyxIWdap6\nOHBEUKo0UNfUgGdrAy6Xh6r6jq80SQjBpTMZ8AlwgrNr829CHIEALiNHoPj4CXg+MR088b/jl7or\nI4RApahCbUUGqkpSUFWaCo26DiKJK7oFjIez5yDw+E371P4tRFZ8PDQxGENH+yMxLgdxf2dizzeX\nIBTxENhTCr9AF3j7O8HOwapLNuuqNCwyq+S4XlKFa8VVyKuphxWPi0HdHPCQjyvcrTtn4siOEtt6\nIiBiFuTVeSjJOY+irNMoSD8GKxsP2Lv2hK1TEMS2HuBwjBcWLBpg5HI5Dhw4gB9++AHBwcEIDg5G\nfHw8du/e3STA7NmzB1OmTMGkSZMAAKtWrcILL7yAt956q121GLmqYTKSkGMFlYqFSl0Dvq0t7IVW\nqDRCgLl8NhP5OZV4ek7ray54TH0Uxcf/Qua338N/3twu+UfbVWnUSijkJaiXl0IhL4G8Og+yqmyo\nFA2/A1bWbnDxGgJ7l1CI7bweqP8bkRUfg0b4YeBwXxQVVCMlsQA3kwpxPb5hgqatvQjunvZwdbeB\nq5stnKXWsHcQQyiy+OdWAA0fFKoUahTJ6lEoq0dBbT3SK2XIqa6DmiUQ87kIc7HFI/5u6C21h4Db\nuUf+GYvY1gPeodPhGTQZlSXJqCpORlHmaRSkHQXD4UFi6wWxnSdEEimsJK4QSVzBEzRd58YQFv1N\nSE1NBcuyCAv7J5VGREQEvvzyyybHJiYmYt68edqfw8PDoVarcfPmTfTp06fN95YrG5Z4lgjEKC6s\nh4qtAs/GBvYiMSrqqtrxNACrYVFcWIPEyzm4eCYDA4b5IKCF5rFGIjc3+Mx6Hulffg11TQ3cJ0+C\ndYA/uEKa9VafhhQ/GhBWo/dfjVoBjVoBVlN/9/vGf+ugUtRArayBSlHd8L3qn6W+uTwrWNm4w9G9\nLyR23SGx626RkTedDcMwcOtmB7dudoicGAxZrQI5GeXISi9HUX41rpzPgrz2n9UVRVZ82NlbwcZe\nBLFYACsJH1ZiAcQSAURWfPAFXAgEPPAFXPD53IZ/737P4TBgOAAYDjSEhQYNQ4XVLIGGEKhZFvVq\nFvVqDeru/tv4fa1SjSqFSvtVWa+C4u70AwaAs1gAHzsJBro7ws9BAm9bMbgPcNJPLk8EJ/e+cHLv\nC5ZVN3y4qsxEbWUmKotvQFl3Fo3r23B4Ijh164fuwfq7L5pj0QBTUlICJycnnRmjTk5OKC4ubnJs\ncXExpNJ/RulwuVzY29vrPdYQOy/egY/jE6hlAHEwsBN9QRgGUAIZJcBbv51o13UBgPAAPGSNY0wJ\njh3WTT7H4P4liRoJwMxdAA4hQHohkF4I0oHffQIj/+Hc98ndOIkkWi5jk3swume06RkJD4TYAMQa\nIO4gBNqlGsA2Lp8NQJtUofTuVxvLqKfMHTmg9esb4/+5lTK0tFsKQCoBIAEB0/A3hIavhrIrQYgK\nkNUBshauwzAgDEA4DNDWN32WgKNhwVVqwFWowVOowVWqIVGoYS9Xgi9Xgi9TgssSVIDgCoAr/zzd\nPU/fNZd+Nz4GgAc4HHeIrRWQSBQQiZVQXrmFZ9u4VplFA0xdXR34fN1MpHw+H0qlssmx9fX1Bh/b\naO/evdi7d6/OtsbjbZXVsFHywWEYaFjS8MfB5YEAYNFaqgei/ycGAENAuARofMPS7mj+/Pt3cTUa\ncDUsOGzDuyBD7j+l5T8EvX+eRsotxNxz7yZXJAa8n7aE6L3qP7sJtK9r4+Po/HtP0CAs/vnq6Gtn\n+OmGHNDuc5kO/xd27AKMEX6HCDh339Ib/m344gCEAUMAhiUN92HZu98DuPsvwzb8x3M0LDhqDbga\nDThqDRiWBXP3f66hhPf/LzINSbE4jWXQs5/SSyEHFHIuACtIpG3vm7JogBEKhU1WgFOpVBCLm3ag\nNndsS/0v0dHRiI6O1tmWm5uLMWPGYP7E8fD09OxA6SmKoqiWWLRXSyqVory8HOw92YTLysrgoicv\nl1QqRck9a6eo1WpUVlbqPZaiKIqyPIsGmODghga9pKQk7bb4+Hj07t00L1d4eDgSEhK0P1+7dg18\nPl97DYqiKKpzsWgTmVgsRlRUFFauXIkPP/wQt27dwqFDh/DDDz9Ao9GgvLwcdnZ2EAgEiI6Oxosv\nvoiIiAj4+/tj1apVmD59OgQCQZvu2ZiArrCw0BSPRFEU9a/m5uYGHs+w0MEQY60s004ymQzLli3D\nX3/9BScnJ7z66quIiorS9pV8//332uU+9+7di08//RRyuRxjx47F+++/D5GobR1PcXFxeOaZZ0zx\nKBRFUf96x48fN7j/2uIBxtzq6+sRFRWFr776yigL6jxIXnnlFWzfvt3Sxehy6OvWPvR1ax9Tv25t\nqcF0jim3ZiQSiSCRSODtTRNLtpVAIKAj79qBvm7tQ1+39ulMr9uDkRuBoiiKMjsaYCiKoiiToAGG\noiiKMgnu+++//76lC2EJ9ybYpAxHX7f2oa9b+9DXrX06y+v2wI0ioyiKosyDNpFRFEVRJkEDDEVR\nFGUSNMBQFEVRJkEDDEVRFGUSNMBQFEVRJkEDDEVRFGUSNMBQFEVRJkEDDEVRFGUSbc6mXF5ejp9/\n/hmnTp3CnTt3IJPJIJFIEBAQgDFjxiA6OhrW1tamKCtFGQ3Lsti7dy8OHjyI9PR0KBQKeHt745FH\nHsGsWbMgFAotXcQW3blzB7m5uRg9ejQA4KGHHsL06dMxf/58yxaMou7Rppn8hw4dwsqVKyGXy8Ew\nDJydneHq6orCwkKUlZUBAFxdXbF582b069fPZIWmqI5Qq9V4+eWXkZycjAULFmDIkCEQCoVISEjA\n5s2b4eXlhV27doFhGEsXtVnjxo3Do48+ikWLFgFo+OAnEokgFostXDKK+ofBNZht27bh008/BZfL\nxfPPP4+XXnoJrq6u2v3Xrl3D2rVrkZCQgNmzZ2P37t3o2bOnSQpNUR2xc+dOXLx4Efv27UNQUJB2\nu6enJ3r37o2JEyfi1KlT2tpBZ3T/50JHR0cLlYSiWkAMcP78eRIcHEyCg4PJr7/+2uxxCoWCnb00\n6gAAIABJREFUPPbYYyQwMJBMnTqVsCxryOUpymxYliWjRo0i7733XrPHZGZman93L1++TKKjo0mv\nXr3IQw89RDZs2EDq6+u1xwYGBpKff/6ZPP300yQsLIyMGjWK/Pe//9W53t69e8m4ceNIr169yOTJ\nk8n+/fu1+y5cuEDCwsLIZ599RgYMGEBmzpxJCCHkyJEjZNq0aaRXr14kPDycREdHk8TEREIIITNn\nziSBgYEkMDCQREZGEkIIiYyMJJ999pn2useOHSOPPfYYCQ8PJ6NGjSJbt24lKpVK555Hjx4l48eP\nJ6GhoSQqKopcvnxZe/6JEydIVFQU6dWrFxk2bBj54IMPdJ6bogzRaoBhWZaMGzeOBAYGkqVLl7Z6\nwUuXLml/+c+dO2eUQlKUsWRnZ5PAwEDyf//3f60em5ycTMLDw8k333xDMjMzyblz58gjjzxC3n77\nbe0xgYGBZNCgQeT3338n2dnZZM2aNSQ4OJjk5uYSQgiJjY0lgwcPJn/88QfJysoiBw8eJP369dMG\nmQsXLpDAwEDy3HPPkczMTJKamkoSExNJcHAw+fHHH0lOTg5JTEwk0dHRZMqUKYQQQioqKkhkZCSJ\niYkhZWVlhBDdAPPnn3+SkJAQ8tVXX5H09HTy+++/kwEDBpAPPvhAe8+goCBtULlz5w6ZOXMmGTt2\nLGFZlpSVlZHQ0FCyZ88ekpubS86dO0cGDx5Mtm3bZrz/COqB0GqAuXjxojZg3Lx506CL/vzzzyQl\nJYXWYKhOJz4+ngQGBpKzZ8/qbH/00UdJnz59tF/Lly8nb7zxBlm0aJHOcXFxcSQwMJAUFRURQhoC\nzEcffaTdX1VVRQIDA8nhw4cJIYSMGDGC/PjjjzrX+Pzzz8m4ceMIIf8EmNOnT2v3Jycnkz179uic\n88svv5Dg4GDtzw8//DDZunWr9ud7A8zjjz9OXn/9dZ3zY2NjSc+ePUl1dbX2nidPntTuP3r0KAkM\nDCRlZWXkxo0bJDAwkJw4cUK7PykpiaSnp+t7SSmqWa32wZw/fx4A4OzsjMDAQIOa3aZPn96xdjuK\nMhF7e3sAQFVVlc727du3Q6VSAQD+85//QKlUIiUlBVlZWYiIiNAeR+72faSlpWn7IH18fLT7bW1t\nAQAqlQrl5eUoKirC+vXrsWHDBu0xarUaGo0GSqVSu83Ly0v7fUhICGxsbPDll1/izp07yMrKQkpK\nCliWNegZb9++jccee0xn24ABA6BWq5Genq7d5uvrq/3exsZGW+6QkBBMnDgRL7/8Mtzc3DBs2DA8\n/PDDiIyMNOj+FNWo1QDT+AtpaHChqM6se/fucHZ2RlxcHCZNmqTd3q1bN+33IpEIAMDn8zF16lTM\nmTOnyXVcXFy03wsEgib7CSHg8/kAgOXLl2PgwIFNjuHx/vnza7wn0PChbu7cuRgzZgz69u2Lxx9/\nHJmZmVi5cqVBz3jvtRppNJom92yu3AzDYPPmzVi4cCFOnTqFs2fPYuHChYiKisK6desMKgNFAQZM\ntKyurgYAODg4mLwwFGVqXC4XzzzzDPbv34+0tLQm+5VKJcrLywEAAQEBSEtLg7e3t/arvLwc69ev\nh0wma/VeNjY2kEqlyM3N1bnGuXPnsGPHDnA4+v/8vvvuOwwbNgybN2/Gc889h8GDByMvLw/APzWo\nloZQ+/v748qVKzrbrly5Aj6fj+7du7da7uvXr2PdunUICAjA7NmzsWvXLixZsgSHDx9u9VyKuler\nNZjGT0NqtdrkhaEoc5g7dy6uX7+Op556CvPmzcPw4cMhEolw9epVfPXVV8jIyMCzzz6L8PBwTJs2\nDevWrcOMGTNQVlaGZcuWQSqV6tRgWjJv3jzExMSgW7duGDJkCBITExETE4OXXnqp2XPc3Nxw6tQp\nXL16FU5OTjh58iS+++47AA0BUCgUQiKRIDMzE0VFRZBKpU3uOXfuXISEhGDs2LFISUnB1q1b8cQT\nT2ibwlpiY2OD2NhYCIVCTJ8+HTKZDCdOnEB4eLhBz0xRjVoNMI1/SBUVFSYvDEWZA4/Hw+eff45f\nf/0V+/fvx/bt2yGXy9GtWzcMHz4c27Zt0/arfPnll9iyZQt2794NGxsbREZGYunSpQbf66mnnoJS\nqcSOHTuwevVqSKVSzJ8/H3Pnzm32nMWLF6O4uBizZ88Gl8tFUFAQYmJisGTJEly/fh39+/fHCy+8\ngDVr1uDs2bPaftJGI0aMwPr167Vld3V1xXPPPYeXX37ZoDL7+Pjgs88+w9atW/H999+Dz+djxIgR\neOeddwx+booCDJjJ/+2332LdunVwcnLCuXPnDLpoRkYGXFxcaMoYiqKoB1irfTCNs5nLyspw8+ZN\ngy66ZMkSDBo0CFu2bOlQ4UxBrVYjNzeXNvlRFEWZWKsBxsfHR5tOY8eOHa1e8OrVq0hJSYFarUZo\naGjHS2hkhYWFGDNmDAoLCy1dFIqiqH81g9L1NybUO3ToEI4cOdLscbW1tVi+fDkAICgoCGPGjDFC\nESmKoqiuyKAAM3bsWEyfPh2EECxZsgQff/wxiouLdY65fPkynnrqKdy6dQtisRgfffRRp85GS1EU\nRZmWwen6NRoN1q1bhx9//BGEEHC5XLi7u8PR0RH5+fkoLS0FALi7u+OTTz7Rmf3cmeTm5mLMmDE4\nfvw4PD09LV0ciqKof602rQcDACkpKfj5558RFxeH3Nxc1NfXw8bGBj169NDWdCQSianK22E0wFAU\nRZlHm1e0DAkJwYoVK0xRFoqiKOpfxKA+GIqiKIpqKxpgKIqiKJOgAYaiKIoyCRpgKIqiKJOgAYai\nKIoyCRpgKIqiKJOgAcYMGlcTpCiKepDQAGNiarUakydPptmbKYp64NAAY2JffPEFjhw5gu3bt1u6\nKBRFUWZFA4wJqdVqfPvttwCAXbt20VoMRVEPFBpgTOiLL75AfHw8ACA+Pp7WYiiKeqDQAGMi99Ze\nGtFaDEVRD5JOE2AqKysxZMiQFleaPHDgACIjIxEREYF33nkHdXV1Zixh29xbe2lEazEURT1IOkWA\nqampwYIFC1BeXt7sMQkJCfjggw/w1ltv4ccff8SNGzfw8ccfm7GUhtNXe2lEazEURT0oLB5gkpKS\nMH36dFRXV7d43J49ezBlyhRMmjQJoaGhWLVqFfbt29cpazGNtReGYSASiSAUCrX7aC2GoqgHRZvX\ngzG28+fPY9KkSYiKisL48eObPS4xMRHz5s3T/hweHg61Wo2bN2+iT58+5iiqQRprLyKRCL169dIu\nvsayLBQKBWQyGfbs2QOpVIrhw4fD3d3dwiWmKIoyDYsHmDlz5gBoWGmyJcXFxZBKpdqfuVwu7O3t\nUVxcbNLytVVj7SU4OFhnZU8OhwMrKytYWVkBAD7//HN8/vnn4HA4sLGxgbu7OwICAhAREYGBAwfC\n2traUo9AURRlFBYPMIaqr68Hn8/X2cbn86FUKps9Z+/evdi7d6/OtpaO76h7+17ubRZrCcuyqKqq\nQlVVFVJTU/Hbb78BAHg8HhwcHODl5YWgoCCMHDkSgYGB4HAs3qpJURRlkC4TYIRCIVQqlc42lUql\nrRHoEx0djejoaJ1tubm5GDNmjEnKeO/IsZKSEjg4OLT7Wmq1GiUlJSgpKUF8fDz27NkDKysr+Pj4\nwM/PD76+vvDz84OnpydcXFyM9QgURVFG02UCjFQqRUlJifZntVqNysrKTvPmev/Isfz8fACAv78/\nuFyuUe5RV1eHlJQUpKSk6GxnGAbW1tZwc3NDQEAA+vTpg8GDB8PW1tYo972XWq3GDz/8gF9++QW3\nbt1CXV0dxGIxQkJCMGPGDDz55JNGe16Korq2LhNgwsPDkZCQgClTpgAArl27Bj6fj+DgYAuXrIG+\neS/5+fnIz8+Hq6sr7O3tIZVKTfLmSwhBTU0NampqcPv2bfzxxx8AGprZ7O3t4enpiaCgIPTv3x/h\n4eEQCATtus9///tfrF69GsnJyU323b59G4cOHcKGDRuwevVqTJ48uUPPRFFU19dpA4xGo0F5eTns\n7OwgEAgQHR2NF198EREREfD398eqVaswffr0dr9ZGlNL816Ahv6jW7duoaCgADY2NrCysoKrq6vB\n/TQdKVdpaSlKS0tx9epVbX+USCTCsGHDEBQUpG1uc3R0bPFaX331Fd544w3U1ta2eNzVq1fx3HPP\n4dNPP8XTTz9ttGehKKrr6bQBpqCgAGPGjMH333+PQYMGoX///njvvffw8ccfQy6XY+zYsXjzzTct\nXUwA+msv96qvr4etrS2qq6tRX18PjUaDtLQ02NnZwcXFBf7+/lAoFDrnDB8+HJWVlSgqKoJSqYRK\npYJcLjdKeevr63H8+HEcP35cu83e3h4+Pj6QyWTw8/ND7969MXjwYDg4OODYsWP4z3/+02pwaVRR\nUYElS5bA398fgwYNMkqZKYrqehhCCLF0IcypsZP/+PHj8PT07PD11Go1Bg0a1GKAAQAnJyeUlZUB\nABwcHCCXy7VBpW/fvvjyyy9x+vRpnD59GkqlEs8++yxCQkLQu3dv5ObmIjk5GUqlEvv370dmZmaH\ny20oLpcLlmVRUVGBmpoaVFRUoKKiAob82syaNQs7d+40QykpiuqMOm0NpqtorfbS6N4aSkVFBXx8\nfLSBIj4+HhcuXMDrr7+OxYsXIycnB76+vigoKMCNGzdQWlqKYcOGQaPRYMuWLTrX3bp1KyoqKhAf\nH4/bt28jPz8f1dXVYFnWKM/XuBqnvb097O3t4eXlBUIIWJaFUqmETCZDVVUViouLm9TC/vjjD5SW\nlsLZ2dkoZaEoqmuhAaYDWut7uRchBAzDaD/5Z2dnw9raWtvstGvXLrzyyivg8Xjw9fUFALi7u8Pd\n3R1KpRKHDx9Genq6zvLLrq6uKCsrg0ajwfTp03VqZM8//zzKy8vh4OAAlUqF6upqozWxMQwDLper\nnTjq7OwMf39/aDQayGQyyOVyyGQyKBQKbNiwAR988EGn6CujKMq8aIDpAENrL0BDv4ednR0qKysB\nNEywlMvlkEgkkMlk2hxlCxcubHKuQCDA1KlTkZiYiOLiYsTHx0OlUmH8+PEYPXo0AODGjRu4ffs2\nOBwOxGIxsrOzAUAbwKRSKSZPngw/Pz/w+XwUFRUhIyMD6enpyM7ObjLHqD24XC5sbW11hkdfvHgR\n48ePh0AggIODAzw8PNCjRw/06dMHffr0gUgk6vB9KYrqnGgfTDtpNBr069cPqampBp/j4OCAiooK\nnW22trbgcrmoqKhAcHAwrly50upQ5vr6esTGxsLe3h4RERHw8fHRzvBXq9XYtGmTdqiyPt26dUNk\nZCQiIyPh5+cHjUaD3Nxc/O9//8P169eRl5eH6upqndqSqfD5fNjb26Nbt24ICAhA7969ERERQVPl\nUNS/AA0w7UQIQWlpaZvOSU1N1Ttv59SpU+jbty9sbGzg7OwMhmEMvmZFRQWuXr0KmUyGCRMmgMvl\nYtasWcjKyjLo/O7du2P06NGIjIyEj4+Pzr7S0lIsXboU58+fh0QigUgkAo/Ha1P52oNhGHh6esLH\nxwfe3t7aL09PzxYzN1AU1bnQAGNGt27dgqenJ8RicZN9+fn5cHFxaZJvzVAVFRVITEyEtbU1du/e\njWvXrrW5BuLr66ut2TS+NpcuXcLIkSN1OvAlEgkcHR1hZ2cHsVgMtVoNa2trs+RJa2yGk0ql8PPz\nQ69evTBgwAA4OTmZ/N4URbUNDTBmpFKpcP78eYwcObLJPo1Gg4MHD+Kxxx7r0Bs1y7KIj49HSkoK\nuFwuEhISEB8f3+ZRZT169EBkZCRGjx6NWbNm4c8//2z22MY5Pv7+/qirq9P2A3Xr1q3dz9FWjVmp\npVIpfHx8EBYWhgEDBsDNzc1sZaAoShcNMGZ28uRJbcf8/TIzM3Ht2jVtOpyOIIQgISEBPB4P1dXV\nKC8vx8mTJ3H16lWD5rDcy8XFBZcvX0ZBQUGTocgA4OHhgby8PACAj48PCgoKwOVy8fPPP2PChAnI\nzMxEXFwckpOTkZWVhdLSUshksjaXoz0YhkFQUBBCQkK0TW4+Pj6ws7Nr9dyamhrU1tbC1dWV5lej\nqHagAcbMEhMT0bt372b3Z2dnw97e3qiJKgsKCvD333+je/fu8PPzw5kzZ3DixAlcv369zdeqqqpq\n0vzm4OCgMyigR48eiI6OxurVq1u8Vk5ODuLi4nDjxg1kZmaiuLgY9fX1RhnR1hp7e3swDAM7Ozt4\ne3sjJCQE/fv3h6+vL+Li4vDRRx/hr7/+Qm1tLQIDA/H4449jxYoVNNBQVBvQAGNmmZmZcHBwaPET\ndG5uLjQaDby9vY167+LiYhQWFqKiokK7IujJkydx4sSJJhmam1NXV4eLFy/qbGMYBjweDyqVCi4u\nLli6dCkIIZg1a1a7JlnW1tYiKysLmZmZyMrKQlZWFm7fvt1kBJ6psCyLuro6yOVyVFVVIS8vD4QQ\nPP/88wbPe6IoigYYs9+fZVlcuHABQ4cObfYYQgh27NiBGTNmmCzl/p9//gmhUIiIiAg4OTmhsLBQ\nG2xu3brV7LkjRozQTvy8efMmlEolhEIhwsLCMHnyZMybNw9SqRRKpRKbN2/GnDlzOrQuzr3Ky8sR\nFxeHa9euIT09HYWFhWYZTs2yLK5fvw6ZTIZjx45hxIgRJr0fRf1b0ABjAadOncKoUaNaPEapVOLv\nv/9GZGSkycqh0WiQk5ODjIwM2Nvbo0+fPmAYBnl5eThx4gROnDiB9PR0nXO2bt2KyspKKJVKXLx4\nEfv27cObb74JsVgMqVSKwYMHa4+tra3FV199hfnz55t0QmV1dbU28KSlpaGgoABVVVVQq9VGu0dj\nkHnhhRewadMmo12Xov7NaICxgHPnzmHIkCGtzidRqVQoLi6Gh4eHycuUmJiIkpISeHt7w8/PT9vX\nkJ2drQ02QqEQX375pfac+fPn49y5c+jduzf8/Pxw8uRJPP3003jppZe0z5aXl4e9e/di8eLF4PHM\nmziitrYWmZmZKCkpQXZ2trbJLScnp13Bh2VZeHh4YPfu3SYoLUX9+9AAYwFlZWWQy+Xw8vJq9di0\ntDSkp6dj7NixZihZQ20gISEBVVVVGDdunLbmQQiBTCbTmWHfmGusqKhIZ8DAuHHj8NZbb2kDSn5+\nPn7++WcsWLDALEFGo9FApVJBpVIhPT0dSUlJcHV1RVBQEAoKCpCeno6ysjJt/0pRURFqa2uhVCoN\nuv4rr7yCGTNmmHzCKUV1dTTAWAAhBImJiejTp49Bxx8+fBihoaFG7/RvSW1tLeLi4uDo6AipVAqp\nVKr3uH379uHTTz/V2ebg4IDt27fD1dVVuy0zMxO//fYb5s+f36F5PiqVCjU1NQCA9PR03L59G3Z2\ndvDz84NKpUJGRgYUCgUCAgLg5eUFhUIBZ2dngxZ3UyqVWLVqFXbv3g0nJyfY2Ng0e+z06dMxb948\ns0wupaiuiia7tACGYdo0ImrSpEnIzs6GWq02WzOTtbU1Ro8ere17OH36NAYMGNAknUxOTo7OzxwO\nB2vXrtUJLkDD/JihQ4dix44dmDNnTpP7NdaQioqKIBKJUFJSgvT0dDAMA29vb9jZ2eHOnTsoKytD\nSEgI/Pz8EBwcjIiICJ2hw7169Wr3MwsEAqxZswZyuRy7du1CeHh4s8OSf/nlF5SVleHtt9+mmaIp\nqhm0BmMhp0+fxpAhQwxODUMIwcmTJzFixAiz92U03j8pKQkajQaEEPTq1Qs8Hg+EEGzduhUHDx4E\nl8vFqFGj4OLigjlz5mjfnAkhKCoqQl5eHrKzs3Hr1i2EhYWhuroaXl5e8Pb2RlpaGoqKihAWFgZf\nX18IBAJwuVyLNUMlJSVh7dq1KC4uBp/PR319vd7j+vTpg9WrV9PknBSlBw0wFiKTyZCfn48ePXoY\nfE5ZWRkOHTqEWbNmmbBkrSstLcXp06dx584dHDp0COPHjwePx4OjoyN8fX2RmZmJo0ePYtq0aRg+\nfDjy8vJQVVUFPz8/+Pj44PLly8jIyMAzzzxj0edoTXV1NUQiERITE7Fv374m838a+fn5ISYmBi4u\nLmYuIUV1brQB2UIkEkmzn4qb4+TkhJEjR2rTsliKs7Mzpk2bhoKCAtTW1iIxMRFeXl6YM2cOhgwZ\ngrlz52LdunU4duwYampqMHjwYJSVlaG8vBx8Ph9Dhw6Fk5MTfv31V4s+R2tsbW0hEAgQFBSEcePG\nYdq0aXqPS09Px6JFiwzOYE1RDwoaYCyorKyszef4+/vD2tra4kEGAD766CM8//zzeO6557Rp9G/d\nuoWamhoEBATgiy++wF9//YXt27fj66+/xrvvvosjR44AgHZpgUOHDlnyEQxia2sLa2tr9OvXD++8\n847efpmioiIsWrQISUlJFighRXVONMBYkJWVFaqrq9t8np2dHS5duoTc3FwTlMpwfD4fixYtgouL\nC0JCQrS5uo4ePQqgodM8PDwce/fuBdAwj2T9+vX48ccfQQjB5MmTwbJsi5maOxNnZ2eEhYVh7dq1\nekel1dTU4I033sDZs2ctUDqK6nxogLGgfv36NRmFZaioqCicP3/eLKtOtoTH40EqlYLD4WD27Nn4\n7rvvtE1/N2/exNq1a5ucs2PHDmzZsgUajQZTp05FaWlpp39TdnNzg0gkwvHjx3HgwAFoNBq9+eSU\nSiVWrlzZJWpmFGVqNMBYUOMorPbgcDiIiopCSUmJkUvVdlVVVfDy8kJSUhI2btyIwMBAXL16FZ6e\nnggNDdV7zq+//opVq1ZBoVDgmWeeQVpaGs6dO2fmkhsuPT0db7/9Nnbu3IkLFy5ArVZj5MiRerMs\nsCyLTz75BDt37jTLkgQU1VnRAGNhHQkQAoEAQqEQx48fN2KJ2s7f3x+XL1+Gi4sLOBwOXFxckJ2d\njffeew/vv/8+HnroIb3nnTlzBm+++Saqq6vx/PPPIzU1FVevXjVz6Q2TmZmJgoICnW2nTp3Chg0b\n9C6DDQA//PADPv74Y6PmRKOoroQGGAtzc3PrUF9KY6biuLg4YxWpzWxtbbUTIgHA09MTSqUSc+fO\nxRdffIFXX30VM2bM0HtuUlISFi9ejKKiIrzwwgv4+++/cfv2bXMW3yBTpkxp0rlfXV2NS5cuYdOm\nTRg0aJDe8/744w8sW7YMdXV15igmRXUqNMBYWFBQEEpLSzt0jTFjxsDKysrgXFqm4O7urs3EzOVy\n4eDggJ49e+LNN9/ErVu30LNnT8ybN0/vuVlZWVi4cCEyMzMxb948HD58GNnZ2WZ+gpY5OjrqrYn9\n8ssvEAqFWLNmDSZMmKD33IsXL+L1119HZWWlqYtJUZ0KDTAWxuFwjLJKYmhoKG7fvm2xT8ouLi6Q\ny+W4cuUKACAsLAxAw/P1798fiYmJSE1NxfLly/VmIigtLcXixYtx7do1LFiwAPv27UN+fr5Zn6E1\n+mphOTk5uHz5Mng8HpYuXYqZM2fqPTc1NRWLFi3qdM9EUaZEA0wnUFtbC5ZlO3ydgIAA7RBgc3Nw\ncMCdO3e0C6RJJBKduTqLFy/GyJEjwTAMYmJiIBaLm1xDJpNh6dKlOHPmDBYtWoTdu3e3a66QqQQE\nBCAgIKDJ9l9++QVAQ4652bNn47XXXtOb4iY3NxcLFy5scUE3ivo3oQGmE5BKpcjIyOjwdYRCISZM\nmIDLly8boVRtx+Px4OvrC6AhWWZ8fLzO/tGjR2PYsGGIj4/H3Llz4eTk1OQaKpUKq1evxsGDB/HK\nK69g586dkMvlZim/ISZPntxkW1xcnM7/X1RUFFatWqU3z1xFRQVee+01i/0fUZQ50QDTCfj5+aGo\nqMgo1/Ly8kJISAjKy8uNcr22CA0NhVqt1vaf6PsULxAIMGvWLJw9exZTpkzRuyYOIQSfffYZfvzx\nR8yaNQufffaZRfuX7vXoo4/qDYyNtZhGI0aMwMaNG/Wm/K+rq8M777yjnZBKUf9WNMB0Evom7bWX\njY0NUlJSkJqaarRrGsLV1RX19fVISUkBgGaH7/J4PHz88ccIDg7GokWLmp0rs3fvXnz55Zd45pln\n8Omnn3aK4b4cDgfDhg1rsv3o0aNNlmDo1asXtm7d2mTpAqBhUbS1a9diz549dK4M9a9FA0wn0ZgG\n31iGDh2KixcvQqFQGO2arWEYRqcm5uPj02LNbODAgXB3dwefz0e/fv30HnPs2DFs2rQJEydOxBdf\nfGH0MrfH8OHDIZFIdLapVCr83//9X5NjfXx88Omnn8LPz0/vtb766its27bN4hkZKMoUaIDpJJyd\nnXHjxg2jXY9hGDz99NNmT4ppb28PiUQClmXB4/F0llLWx9PTE++99x44HA5Gjx6t95grV67gk08+\nweDBgztFkAkPD8eAAQOabD948KDepjwXFxds2bIFvXv31nu9AwcOYPXq1Z2mGZCijIUGmE6iW7du\nHZ4Pcz8+nw9PT89m1zExhYiICIhEIhQWFgKAQSt3Ojs746OPPkJ0dDQefvhhvcfcvn0bn3zyCfz9\n/fHNN98YtcxtJRQKMWTIkCbLJWs0mmZT9ltbW+Ojjz7CqFGj9O4/deoUli5ditraWqOXl6IshQaY\nTsQUC1YJBALw+XxtmnxTYxgGdnZ22tn4QUFBBvedBAcHo1evXvDy8tK71n1BQQE0Gg3CwsIQGxtr\n1HK3lYODA0aOHAmgoRYWHR2NvXv3triAnEAgwIoVK/D444/r3Z+YmIjFixd3ivxyFGUMFg8wdXV1\nWLp0Kfr164fIyEgcPHhQ73GNS/MOHz4cgwYNwrvvvguZTGbm0poWh8OBSqUy+nX79u0LR0fHdi0N\n0B4VFRXavF29evVq08JqU6ZMwRtvvIHhw4c3SYn/6KOPYuLEiRg8eDDc3Ny0ywBYgqenJyIjI7F+\n/Xp8//33CA8Ph0gkavU8DoeDBQsW4OWXX9a7PyMjAwsWLDDKsHWKsjSLB5iYmBhkZGQSH+nOAAAg\nAElEQVQgNjYWr776KlasWIFr1641Oe6XX37B/v37sXnzZuzatQvXr1/Hpk2bLFBi0/Hw8EBCQoJJ\nrj1w4EDIZDKzBOXw8HDIZDIolUowDNPmBJa9e/fGypUrMXPmTO1CZuPHj0dOTg6Sk5MBNKTHsbe3\nx/79+41efkO4u7vDysoKAwcOBMMwcHNzMzi9DcMwePLJJ/Huu+/qzeJQUlKCxYsXt9p/RVGdnUUD\njFwux4EDB7Bs2TIEBwdj6tSpmDp1Knbv3t3k2NOnT2PixIno378/evbsiaefftqsfQvmYGtra9JJ\nhW5ubti3b59Jakn3EolECAsL0/a/tCevGIfDwcyZMxEREQEfHx+89dZb2LBhA/766y+cPn0aQEPQ\n4fP5+O2334xafkPdO+rPz8+vzUlLx44di5iYGG0QvVdtbS3eeOMN7bNSVFdk0QCTmpoKlmW1eauA\nhk5ifZ947e3tcfr0aRQXF6O2thZHjx5FSEiIOYtrFvom8RkLwzCYOHEifv/9d5Pdo5Grq6u2madn\nz57tbp778MMPMWfOHFy5cgUMw2DevHnYv38/Nm7cCKCh3+bo0aM4duyY0cpuKBsbG51UNiKRqM3P\n2b9/f2zevFmbFfteKpUK77//frPNxhTV2Vk0wJSUlMDJyUmnmcDJyQnFxcVNjp03bx44HA5GjBiB\nAQMGoLi4GCtXrjRncc1CIBC0qc+irVxcXDBq1ChUVVWZ7B5AQ/bhxiUEwsPD9XbaG2ro0KEICwvD\nxx9/jMTERGzevBlCoRBbt27Ff/7zH1y7dg0//PADTpw4YaziGyQoKAhpaWnan8PDw3H9+nUcOXIE\nixYtQk1NjUHXCQwMxKeffgpPT88m+wgh2LJlC7755hs6IZPqciwaYOrq6prka+Lz+XrnA+Tn50Mi\nkeDrr7/Gt99+CwBYt25di9ffu3cvpk2bpvP1yiuvGK38puDr62vyPFUODg4oLy83aRu/nZ0duFwu\nampqwOFwdN6I20MsFmPJkiWIjY3FiRMnMHPmTJw+fVqnGW779u1mXXqZw+Fo0+FUVFQgNjYWMTEx\nWL9+PZKSknD48GGDr9WtWzds27at2ewHsbGx+OijjzpFNgOKMpRFA4xQKGzSH6BSqZpk2iWEYOnS\npZg9ezZGjhyJQYMGYe3atdi/f7/e2k6j6Oho7N+/X+dr+/btJnkWYxEIBEbJrNwaX19fJCQkGC0H\nmj5hYWHaeR23b9/u8CdwgUCAjRs3wsHBAa+99lqTTMujRo1CcnKyUSesGkKtViM2NhbffvutzjyW\nAwcOtGmGvr29PTZt2oTBgwfr3X/kyBG89957dPEyqsuwaICRSqUoLy/XeUMtKytrMh+kvLwceXl5\nOqnSQ0JCwLKsdkLfv4m+BImm8OyzzyInJ8dkTS99+vTBzZs3ATT0lRgrq0CfPn0wcOBAnW0uLi6Y\nM2cO5s6dixMnTpgtJX5gYCBSU1Px2GOPNUnuWVRU1OZOeisrK6xZswYTJ07Uu//SpUtYsmSJQRNY\nKcrSLBpgGpsDkpKStNvi4+ObpNSws7MDn8/HnTt3tNsaO5A9PDzMUFLzcnR0NMvqhwzDoG/fvkhN\nTTVJkLGxsdE2w4WGhupNX99e8+fPx8KFC8EwDEJDQ7Ft2zYsW7YMVVVVeOWVV3D06NFmZ9Ubk52d\nHeRyOTw8PDBkyJAm++/PsmwILpeLt956C88995ze/Tdv3sTChQvNngaIotrKogFGLBYjKioKK1eu\nRHJyMg4ePIhDhw7hySefhEajQUlJCZRKJXg8HqZNm4aNGzciISEBKSkpWL58OSZMmGDSUVeW0r17\nd+0nf1PjcDiQSCQmG6kkFApRUFAAhmEM7vQ21OOPP46tW7di1qxZuH79OhYuXIj58+cjOTkZL7/8\nMvbt29fmocMd8cQTTzTZlpycrJ270xYMw2DWrFlYsmSJ3gES+fn5WLhwodl+TyiqPSw+0fKdd96B\nj48PnnrqKWzduhUffPABwsPDUVBQgOHDh2snHr777rsYPnw4Fi1ahBdffBEBAQH48MMPLVx60+Bw\nOGZtZ+/evbvOsGJjGjVqlLaD//bt20afgxMWFoZ+/fqhoKAA27Ztw44dO7B7927k5+dj+vTpmDt3\nLn788Uej3vN+Dg4OKCoqQu/evVtc8bI9pkyZglWrVkEgEDTZV1lZiddeew2XLl1q9/UpypQY8oCN\nfczNzcWYMWNw/PhxvcNCO4uzZ89i2LBhehftMpXS0lJIJBK9E/864ujRoxg7diyysrJQWlrabGr+\njoqLi0NWVhYeeeQRbNiwAdeuXdPm9Zo6dSoWLVrUoeHSzWFZFnFxcRg4cCD+/PNPxMTE6OzncDjY\nvXs3pFJpu++RlJSE9957T+88Gy6XizfffBMTJkxo9/UpyhQsXoOh9OvZsydycnLMek9nZ2ccP37c\n6NkEsrOzwbIsvL29YW9vb9Rr36t///547LHHcOjQIdy5c0cnaeTBgwexevVqk2Qx4HA42oEqkZGR\nTSZNsiyLAwcOdOgeYWFh2Lp1q94gpdFosH79esTGxtK5MlSnQgNMJ+Xg4GDyyZD6REZG4qeffjLq\nNR0cHPD3338DQJMh6MbWODdFXwf/yZMn8fbbb5skH5tAIIBKpYJAIMDUqVOb7P/tt9863Ozp7e3d\n4uJl33zzDbZu3UoXL6M6DRpgOimGYVBeXm72+0okEkyaNEmbDdkYoqKikJ6eDgBIT083eeCcNm0a\npkyZondffHw8Xn311SZzaDoqICBAOzR6ypQpTUbMyWQyoyyZ4OzsjC1btiAiIkLv/oMHD2LVqlV0\n8TKqU6ABphPjcDgWmbnt6uoKKysro41Q4nK56N69O4CGpp7G2oypcLlcvPbaa5g9e7be/WlpaViw\nYEG7knA2x9bWVjvJ0t7eHmPHjm1yzP79+40yidba2hoxMTGIjIzUu//MmTN46623jD5qj6LaigaY\nTiwiIsJi64LY29sjNTUVqampRrleSUkJampqYGdnZ5bBFQzDYObMmVi6dKnejv2ioiIsWrTIqLP+\n773P9OnTm+zPzc3V5mfrKIFAgGXLlum9DwBcu3YNixcvbjHTBUWZGg0wnZi1tTUUCoXF7h8VFYXs\n7Gyj1KL69++vXYXS19e3w9cz1MSJE/Hhhx/qXQysuroab7zxBs6dO2eUezk5OWmbFn19fdG/f3/t\nvp49eyImJkZnW0c1Ll42b948vfszMzPp4mWURdEA08mVlpZa9P4PP/wwioqKOjw6yc/PT/sJPzc3\n16yfrAcPHoxNmzbBzs6uyT6FQoHly5cbZQkDX19fnYmdM2bMwCOPPIKdO3fi6aefxqBBg0wyTHrG\njBl47733wOPxmuwrLS3FokWL2rzoG0UZAw0wnZxYLLZoWzqHw4GtrS327dvX4WsFBARAo9GgR48e\n+Ouvv4xQOsOFhITgs88+Q7du3ZrsY1kWGzZswHfffdehQHr/nKUBAwbgzTffhK+vL2xtbU06aOPh\nhx9GTEyM3lF6MpkMS5cuxalTp0x2f4rShwaYTi4iIsLs82HuZ2Njg+7du3d4BVGxWIybN2+Cx+PB\ny8vLSKUznIeHB7Zt24YePXro3f/tt99i48aNHRrmy+fz9c4jCgsLM3lal379+mHLli1wdHRssk+l\nUmHVqlUWW2KaejDRANPJ8fn8TjF5buDAgf/P3nlHNXX+f/x9syHMsPfeyhJEUVQcUEcrxVq1tWqL\n21rbah3fqijuqtVqrda2rtaqrVVqXah1FGdFFFBEtICiKIIsQUYI+f3Byf0R7w0kjAy9r3NyDjz3\nucmTEO7nPp/x/sDe3r5NtRx+fn44ceIEgMbmXJpAJBJh/fr1CA0NpT1+5MgRLFy4sNVN33x8fGgT\nIwiCAIvF6tBmckDjLnHTpk20BlwqlWLjxo3YunWrVnynGF59GAOjA2hLJpCdnR1u377dalePoaEh\n2Gw2pFIp6urq1KJ2TIe+vj6WL1+OqKgo2uMXL17EzJkzW1Wvw+fzFe6AAgMDkZ6ejvLycuzZs6fD\nalWsra2xceNG+Pr60h7fs2cPVqxYwTQvY+hwGAOjA5ibm2uNNHtAQAD27dvX6otT3759kZOTAzMz\nsw7v3NkcHA4Hc+fOxXvvvUd7PDMzE9OnT291vyG6epfHjx9j//79ePfdd7F161acOnWqVc+tDMbG\nxli7di3Cw8Npj588eRLz5s1rd1kgBoamMAZGB/Dz85PT1dIkbDYbI0eOlOvhowpCoZAsSHxZs0vd\nEASBCRMm4JNPPqEVFc3Pz8e0adPk+hApg729PSU1eM+ePfjwww9x9epVcufyxx9/dKirSiAQICEh\nAYMHD6Y9npKSgs8++0wjihEMrweMgdEBWCxWuzbraiumpqbw8fFplYvLzs4OZ86cAdAY+NYG3n77\nbcTHx9N+xiUlJZgxY4ZKBac2NjaUizadtEtOTg5SU1NVX7AKsNlszJw5E+PGjaM9np2djfHjxzNt\nmBk6BPaiRYsWaXoR6qSiogK7du3C2LFjYWRkpOnlKE1OTo5WtRfgcDjYuHEjZsyYgefPn6OiogKV\nlZUwMTFBXl4erl69iuLiYhgaGuL58+e4ffs2qUf26NEjVFVVobCwEPfu3YOVlRUqKytx7949Mt7E\n4/FQXFyM3NxcVFZWwtjYGPX19cjPz0dpaSlYLBYEAgFKS0vx5MkTvHjxAkZGRpBIJLh//z7Kysog\nEAjA5XJRWFiIoqIi1NfXQygUorq6Gg8fPkRZWRm5i2Kz2XB3d8f169cpistisRgWFhawsrICm83G\nkydPcO/ePVRXV0NfXx/l5eXIzc1FSUkJOBwOGhoakJWVBYlEgtraWujr60MgECAlJYVieAoLC9G1\na1cydiN7LzweD2w2G2VlZSgvL0dDQwP4fD7q6upQXFyMqqoqGBgYAGisdXn+/Dl5I1JZWYnS0lLU\n1dVBT08PUqkUtra2MDU1xY0bNyi7ppqaGrBYLIX6ZgwMrYVamcWglZiZmSEnJ0ehkq4mkEgkqK6u\nxtWrVyEWi2FlZYXr168jLy8P1dXVcHBwwMOHD3H79m0UFxfD2toa9vb2ePz4MS5fvgxjY2M0NDSg\nqKgIN2/eRGlpKezt7eHm5oa7d+/iyZMn0NfXh7OzM/h8PjIzM/H8+XO4uLjAy8sLqampePr0Kayt\nreHm5oaSkhLk5eVBIpHA2dkZdnZ2uHHjBp49ewZ7e3syw6uwsBD6+vpwcXEBj8dDVlYWKisr4eLi\ngjfffBMHDx4k3VhSqRRisRgCgQCZmZkoLy9HYWEhAMDW1hb379/Hf//9h7KyMjg4OMDW1hZ5eXm4\nc+cO7ty5A3d3d7DZbNy7dw9CoZDyGaalpSEpKQlsNhv379+HRCKBi4sLrK2tyX42Tk5OcHNzQ05O\nDh4/fgyhUAhnZ2dwuVxkZ2ejoqICzs7OcHNzQ0ZGBgoLC2FjYwMnJyeUlZXhwYMH5PMOHz4c+/fv\npyQiHDt2DGPHjgWbze74Lw7DawPTcEyHOH/+PHr27KnpZZA0NDTg9OnT8PHxgZ2dndLnFRcXIy8v\nDyEhIThy5IjCGIGmePr0KWbPng02m42cnBzo6enhr7/+UuniKxaLkZaWJicNI5FIMHr0aEriwNCh\nQ/Hpp5+22/pb4tatW5g+fTplJ7N8+XJ0795dbetgePVhYjA6hKaD4i/DYrHQv39/lTPczMzMkJGR\ngcrKSnh7e2td/xJLS0t8/fXX2Lx5M5YtW4bq6mp88sknKj0Hl8ulZJKx2WzExsZS5iYlJdF2quwo\n/Pz8aLPL2kMuh4GhKYyB0SEkEolWFsipKkFPEAQsLCxw8eJFuLq6ak2GXFNEIhF4PB7Cw8NhY2MD\ngUCAxYsXk8eVDYq//NkMHDiQ0pK6pqZG7Rd3ul3jpUuX2r1PDsPrDWNgdAiRSITbt29rehkUrK2t\nVZazcXFxAZ/PB0EQrU55VhcVFRVwdHREdnY29uzZg+PHj2PUqFEtqhQ7Ozvjv//+kxszMDDAoEGD\nKHMPHjyo1sLHrl27wtzcXG6soaGhXZqiMTDIYAyMDmFvb681Vf1NcXZ2RkFBgUrnuLm5QV9fH2Kx\nGKWlpR20svZh2bJlSExMxIIFC7Bnzx6sWrUK5eXlmD9/frPV/paWlrQ7gtjYWErdTVFREf755592\nX7si2Gw2Bg4cSBk/evRouzRFY2AAGAOjc7x816mrCAQCVFRU4N69e+jUqZNWV5QHBARAX18f3333\nnZyydUFBARYvXtzsBZkuMcDW1pY2WeP3339Xqwt00KBBFENXUFDASPsztBuMgdExCILQSg0pc3Nz\nlXcxlZWVyM/Ph4+Pj9bfNb/c6wVorNUZPHhwsz1eDAwMaHdow4YNo4xlZWW1a4fNlrC2tkaXLl0o\n40ywn6G9YAyMjmFnZ9fh1d+twdXVVeUe905OTuBwOJBIJEhPT++glbUPcXFxqKysJIsbORwOQkND\n0a9fv2bP8/DwoMRhgEY16aZtAzgcDt544w21ZwoOGTKEMpacnNwqoU8GhpdhDIyOYWJiopXuJIIg\naPW8msPb2xtubm548OAB8vLytDJDTkZQUBDYbDacnZ3h5+eH+Ph41NfXY9euXc2eJ6vsfxmCIDB8\n+HCYmppi7Nix+OKLLzBnzhyV6onag/DwcJiYmMiNicVinDx5Uq3rYHg1YQyMDmJmZqbpJdAiq2JX\nFoFAgKdPn6K6uhohISFkP3ttpWfPnigtLcXGjRvRs2dPdO3aFenp6Th8+HCz57FYLFq3ZmRkJPbu\n3Ytx48ZBJBKhqqqqo5auEC6Xi+joaMr44cOHtdrgM+gGjIHRQbhcLmprazW9DAr29vakjIqyFBQU\n4MmTJ/Dw8NB6bbhRo0aBw+GQO7WYmBiYmJggOTmZrOW5c+cOZYfp6uqKu3fvUp6Pw+GAx+MBaEwk\n0JSbkC5t+v79+8jMzNTAahheJRgDo4O4uLggJSVF08ugQBAEpFKpSgF7Kysr2Nra4ubNm6QYprbi\n6uqK2tpayPRhWSwWxo8fD0NDQ6xYsQJJSUmYPn06li1bJvcZiEQiskWBImRKzppIdnB0dKTtMNrS\nzoyBoSUYA6OD8Pl8iuKvtuDs7KySoQgJCYGZmRkaGhpw7949rcyQa4qTkxMuX75M/m5tbY2oqChI\nJBKsXLkSYrEYFy9exE8//SR3njLuJl9fX2RkZABo7EWjTgkdusr+s2fPasRtx/DqwBgYHcXQ0FDT\nS6DF2tpaJbkRDodDqiaHhoaq1HdFE8yaNQv6+vo4cOAAOZaUlERxb/3666/4+++/yd9NTU1b7I5p\nZGSEtLQ0zJ07F2PGjMGlS5fad/HN0KtXL4rac01Njdx7YGBQFcbA6ChmZmZam0oqc5UpS15eHurq\n6sBisbS+kNTc3Byurq5y4pR9+vShnfvVV1+RBlOWLaeIq1evIi4uDtu3b8eVK1cANHa8VBcCgQAD\nBgygjB89elRta2B49WizgWnaRCkxMRGTJk3Cd999p/WFc7qOo6Oj1t7t29ratqjT1RQLCwvY29sj\nPT1dK1OwX0YqleLy5cukOy8iIgIffvghZV5dXR0WLFiA4uJisFisZtO4pVIp5TO7ceOGyu2a2wKd\nm+zOnTtqXQPDq0WbDMzu3bsxZswY3L17F5mZmZg7dy6kUil+/fVXfPPNN+21RgYaWCyW1ra5VVUz\nLTg4GBUVFbC0tEROTo7W7sxkLFiwAM+ePcOECRPIsQ8++IB2J1NcXIwFCxagtrYWbDZbYfZfaGgo\nnJycKOO///57u627Jdzd3eHl5UUZZ4L9DK2lTQZm165dSEhIQFhYGA4fPgxfX19s3boVq1evxqFD\nh9prjQwKYLPZWluroIqbjMfjoaamBhYWFggICMC1a9c6eHVtw8TEBN7e3nKJFgRBYM6cOXLV+TKy\nsrKwdu1aeHp6KlTDJgiCVj7m9OnTapXQp0tZPnXqFGpqatS2BoZXhzYZmIKCAvTo0QNAY7fFXr16\nAWjMtGH6SnQ8vr6+KsvkqwsnJyeVXCt5eXmwsbFBTk4OHB0dO3Bl7YOBgQHc3NxQXFxMjgkEAixd\nupRSGQ8AJ0+exF9//UW2YqYjKiqKUgtUX1+PP//8s/0W3gL9+vWDQCCQG6uqqsK5c+fUtgaGV4c2\nGRgrKys8ePAADx48QHZ2NqkQe+3aNdjY2Cj1HNXV1Zg9eza6dOmCyMhIJCYmKpx7/PhxREdHIzAw\nEB9++KHKnRRfNUQikda6k1TNJjMxMcGTJ0/w7Nkzrc2Qa8rEiRNRWFiIbdu2yY1bWlpi6dKl4HA4\nlHN++OEH3L59W+HOjs/n46233qKMHzp0SG2FtUKhEJGRkZRxJtjP0BraZGDeffddfPLJJ3j//ffh\n4eGBkJAQ7N69G/Pnz8fIkSOVeo6VK1ciNzcXu3fvxowZM7Bw4ULaiubU1FTMmjULcXFxOHDgAPh8\nPmbNmtWW5es8BEGQCRbaCEEQSid7BAcH4/nz5zA3N0d+fj5FuVjbMDU1hVQqxbVr1yjuIz8/P8yc\nOZNyjlQqxd69e+XqaF5m6NChFONUXl6OU6dOtc/ClYAu2J+enq6ymCkDQ5sMzMSJE7FixQrExcVh\n586dABr/8RYvXkybVfMyL168wMGDBzF//nx4e3sjJiYGMTEx+PXXXylzt23bhpiYGLz77rtwdXXF\nl19+icLCQrn+HK8jBEFoXU97GV5eXkrLjbDZbBgbGyMwMBCmpqb4999/O3h1bcfDwwPOzs5kZX9T\n3njjDQwfPpwyXlNTg7Vr1yrceZqbm9PuIPbv36+2eJuvry+cnZ0p44yMP4OqtDlNuW/fvqRYn1gs\nhoODA/r376/UuVlZWWhoaECnTp3IsaCgINqGR1evXpXL03dwcMDp06d1wp3SkQQHB2utxIqqys85\nOTngcDgoLS2Fu7t7B66sffj444/B5/MVio9OmjQJAQEBlPFnz55h8eLFClUL3nnnHcpYXl6e2uSB\nCIKg3cUkJSVprYIEg3bSJgPz6NEjjBs3Dunp6aiurkZMTAyGDx+Ofv36kZIXzVFUVAQzMzO5rn9m\nZmaUFNeKigqUlZVBLBZj3Lhx6NGjBz7++GOVhRVfRQwMDLQ2XVmGsm4yc3NzpKWlobi4GI6Ojlqb\nISdDIBCguLgYVlZWyM7Ophxns9lYunQprQG6fv06Nm3aRPu8np6etNpg6iy8HDBgAKmPJqO8vBwX\nLlxQ2xoYdJ82GZjly5dDLBbD3NwcR44cQWFhIX777TcMHDgQX331VYvnV1dXU77EXC6Xkmkjuwte\ntmwZhg0bhs2bN6O6uhpTpkxhCjoBrY7DeHh4KN2l0dvbGwDQpUsXpKSk0Dbq0jZsbGxQXV2NhIQE\n2uMGBgZYv349bdA/MTFRYTo/3S7mypUruH//ftsWrCTGxsaIiIigjDNuMgZVaJOBuXLlCuLj42Fr\na4uzZ8+id+/e8Pf3x4cffoibN2+2eD6daKNYLIa+vr7cmGyHM2rUKLz55pvw9/fHmjVrkJWV1ezr\n7Nu3D7GxsXKPyZMnt+KdajcCgaBFtV5NYWpqqvTaCIKAjY0NLCws4OjoqBNxmHnz5iE7Oxt1dXUK\nXV729vZISEigreTfsGEDrUs4PDwctra2cmPGxsZqzZykc5Ndu3atRU01BgYZbTIwUqkUenp6kEgk\nuHz5MsLDwwE0BjJlfS6aw8rKCiUlJXK7kGfPnsHCwkJunqmpKbhcLlxdXeXGTExMmm1SNWLECBw4\ncEDusWXLFlXfptYTHBys1VlXsrbIylBQUACxWIyamhqdqIfhcDgwNDTEmDFjmq147969O21mpUQi\nQXx8POV7zGaz8fbbbwNoVKh+++23sW/fPvJ/TB0EBgZSjJxUKmVSlhmUpk0GJjAwED/88AM2bNiA\nmpoaREZGorCwEOvWrUNQUFCL58tcIk13IampqZTAKIfDgZ+fn1xGUklJCUpLS9XeYlYb4fF4Wi1z\n7+7urnQ2mY2NDS5evAiBQAA9Pb0OXln7INultVT3M2HCBNrYSkVFBb788ktKQsTAgQOxevVqbNu2\nDaGhoWCx1KtNy2KxaCv7jx07prWZiwzaRZu+sfPnz8fNmzexe/duzJ07FyKRCD/88ANycnIwd+7c\nFs/X19fH0KFDER8fj8zMTNInPXLkSEgkEhQVFZHxmA8//BDbt29HUlIS/vvvP/zvf/+Dn5+fXAba\n64w2x2FMTU2V7itiY2MDDocDT09PPH36FHfu3Ong1bWdSZMm4cSJE7h69SquXr2qcB5BEFizZg2M\njY0px3Jzc7F8+XK53bxQKERISAgIgtBYx8s33niDYtiKi4t1wn3JoHnaZGBcXFxw4MABpKSkYPTo\n0QAaUzeTkpJo8+jpmDdvHpydnTFq1Chs2LABCQkJ8Pf3x+PHj9GzZ09cv34dQOMXfe7cuVi5ciVi\nY2MhlUoVZuG8jhgbG6skMKlu2Gy20rssNzc3AI2K0crE8jSNQCCAiYkJZs6cibVr1zY7l8vlYt26\ndbS7M2tra4WZc/r6+hppk21mZobu3btTxplgP4MyUFNbVKS8vBz79u1Dbm4uZs2ahatXr8LT01Mu\nXtIcQqEQ69ato4zb29tT7l5HjBiBESNGtHXJrySdOnVCWloaLC0tNb0UWmTdGpVxnT5//hzGxsYw\nNDSkzb7SRsLCwnD37l1ascuXcXFxwccff4x169ahvr4eLBYLfn5++Pjjj5s9z8nJCdnZ2fD09CQN\nUXMtANqLwYMHU9KTL126hGfPnimsAWJgANq4g8nNzcXAgQPxxx9/4K+//sKLFy9w4sQJDBs2DKmp\nqe21RgYlYLPZ4PP5ml6GQoRCYbNCj02xtrZGcnIy7O3tUV9fr/X1MAAwbNgwnDx5EmPGjMGJEyda\nnD9o0CB06dIFenp6WL9+Pfz9/VsUtbSzs8Pjx49x9OhRjB8/Xqlas/aga9eulBZTJa8AACAASURB\nVEZwDQ0NOH78uFpen0F3aZOBWbFiBaKjo5GUlETWs6xZswZvvPFGi64ChvZHW1OVZbBYLKXcPIaG\nhuByuWCxWDA0NFSpeZmm4HA4KC8vh5OTE/bu3avUOcuXL4erqyt8fHwwfvx4XLlyRWG1fklJCXbs\n2IG1a9di9erVyMnJwf79+9vzLSiEzWZj4MCBlPGjR48ydWgMzdImA5OWlkbGXsgnZLEwceJEpbOG\nGNoPY2NjrRYk7NSpk9JFl7IYnoODA9LS0jpwVe1H//79sXXrVlRUVCjVRoHFYuGtt94iDdKSJUvw\n888/o6ioCABQVlZG7vquX7+OnTt3ymnvXbhwodk0/faELpusoKCAtoaHgUFGm/Me6e5Inz17plQd\nDEP74unpiby8PE0vQyF6enpKu8n4fD7Ky8vh6emp1RlyTXnvvfdQWlqK9evXK11vFRwcDBaLhUuX\nLoHNZmP+/PlYt24d7t27h8mTJ2PNmjWQSqXo3bs3pT6soaEBBw4c6Ii3QsHa2hrBwcGUcSbYz9Ac\nbTIwffv2xfr16+VSUPPz87F8+XLa9rEMHQuLxYJIJNL0MpqFw+Eo5SaztLRESkoK2Gw2DA0NdUZk\nsaysDObm5ujWrZtS883NzeHm5oZ//vkHBQUFsLCwgJ2dHSZNmoTCwkKcPHkS+/btA4fDIQsvm3L0\n6FGlU8DbCl2vmuTkZK3tScSgedpkYObNm4fy8nKEhYWhuroaw4cPR1RUFHg8HubMmdNea2RQAalU\nqtVB8ZcLZhXR1BC1pNigTYSEhGDZsmW4ffs29uzZo9Q5BEHgs88+w65du/DLL79g//79crGNrVu3\nIiMjA4MHD6Z0m3zx4oXaKuvDw8MhFArlxsRiMU6ePKmW12fQPdpkYIyMjLBv3z5s3boVX3zxBaZO\nnYpt27Zh7969Wn8n/apiZGSk1cWJenp6StdzODk5AQAiIiLw999/d+Sy2o1Ro0ahtLQUn3/+udLB\nfhMTE5SXl+O9996jlfyJiYmBj48PjIyMEBUVRTl+4MABtVTWc7lcREdHU8YPHz6s1Tc1DJqjXbQn\nwsPDERcXhzFjxtAWZTGoDycnJ60XI+TxeEr1iXFwcEB5eblKRkkbsLS0hEQigYuLC6XbJR3u7u7I\ny8uDo6MjXFxc0KNHDwCN2VseHh6IiYkh64GGDRtGOf/Jkydqk9EfOnQoZez+/ftMUg8DLW0yMDk5\nOfjoo48QFBSETp06UR4MmkHbi998fX2V2mUZGhqS8iiWlpZkdpW2ExwcjB9//BHz588nlSiag8Vi\nkS6xESNGgMPhoE+fPlizZg2+/vprrFy5koxzODo6IiwsjPIc6kpZdnR0hI+PD2W8OaFPhteXNhmY\nBQsW4OnTp/j000+xZMkSyoNBM7BYLK0WvxQIBErtSAiCIAUku3TpgqysrI5eWrswaNAgpKamwtzc\nHDt27FDqHA6HQ/7NZs2aBScnJwQGBsLAwADz5s3DkiVLSDcYXSvmjIwMtblG6YL9Z8+eVVuyAYPu\n0CYDk5GRgTVr1mDs2LF4++23KQ8GzWBra6v19QnK9rCRdbZ0cnLS6thSU1gsFoRCIQoLC1FbW4vv\nv/++xXPc3d3JrpgGBgbw9vbGP//8A6DRVTh48GB89913AABXV1dYWVlRnkNdu5g+ffpQyhBqamp0\nJk7GoD7aZGDMzc2Z4J4WYmpqqlSMQ5P4+fkptSPx9PQk3UO1tbVavTNrSmxsLA4ePIgdO3YopYJs\nbGyMiooK8vdu3bohNTUVOTk5AIDIyEiwWCxs3rwZU6dOpXUXnjlzRi1uRIFAQOumY2piGF6mTQZm\n8uTJWL58OXJzcxnJCC3D1NRU00toFi6Xq1Tmk4GBAdk6uVu3brhy5UpHL61d6NWrF2kcRowYoZRh\nfFkW/+OPP8bu3bvJcwMCArB//348efIEDQ0NFKFLiUSitl3EmDFjKGPZ2dm4e/euWl6fQTdok4HZ\nvn07rl+/jkGDBjFBfi2Dy+UqXTWvKXg8nlJ+e1nqbnBwsFa3JGgKi8VCTU0NsrOzUVtbixUrVrR4\njpmZmVwGIIfDwbRp03Dw4EFcunQJCxYskLuRa+o98PT0xKpVq9SmNu7u7k7pdgkwuxgGedqkhT5x\n4sT2WgdDO+Pi4oKUlBS1tthVlc6dO+P69esIDQ1tdp65uTkaGhrAYrHk3EjazvDhw7Ft2zasXLkS\nGzdubHG+q6srUlJSYG1tTY6JRCIYGxvj+fPn6NSpE21/nODgYMTGxqJr167tuv6WCAgIQEFBgdzY\nqVOnMHnyZEpBKMPrSZt2MARBYPDgwZTgfnR0NCMfoWH4fL7Wy6twOBylXKuBgYEoKysD0Pid05Wq\n/oiICNLt1aVLF5w+fbrZ+QRB0MY0o6KicO3aNUyePJm2309qaipSU1PVXosyefJkUkVdRlVVFc6d\nO6fWdTBoL22WiqHLBMrJyWHk+rWAl2U9tBF9ff0WdyVCoZC8Ux4yZEiLF2ptQk9PD/n5+fjyyy+V\nag6mSKvts88+w/nz57FkyRLa3cGBAwfIrDN1YWRkhIiICMq4uqRrGLQflQ3Mzp074ePjAx8fH0il\nUvTo0YP8XfYYPnw4AgMDO2K9DCpgYWGB0tJSTS+jWXx8fMj03OaQqUSLRCLKXbM2069fP2zevBkc\nDgeHDh1qMdjv6elJm44tEAjQs2dP5OTkYO7cubTnHjx4UO27B7pssvT0dK1uG8GgPlSOwYwePRoi\nkQgNDQ2YM2cO5s+fD0NDQ/I4QRAQCoW0XzwG9eLg4IBr1661GOPQJBwOR+lssrq6OvB4PLBYLFRV\nVenEDi08PBzbt28H0NgPZ9KkSfjpp58UzjcwMFBYhOrn54fz58+jW7duGDduHKWIs66uDuvWrYOD\ngwPOnz+PXr16kX11OooBAwZgy5YtlBuZI0eOYMqUKR362gzaj8oGhs1m48033wQA2NjYIDg4WGf6\npr9uyC7E2o6BgUGL/d27du2Kp0+fwt7eHnZ2drhy5Qr69u2rxlW2HltbW9y9exdxcXFKpVk3V1s2\nadIkLFu2DDNmzEBOTg7FLVZeXo7x48dDKpXi6dOnmDVrVpvX3xwEQaBbt244duyY3HhSUhLGjx+v\nU7tNhvZHZRfZli1bSAG/1NRU/Pjjj9iyZQvtg0HzsNlsrS+G9fHxIWtdFKGvr0/e2YeEhLQ4X5t4\n5513yAvwm2++2aIAprW1dbMdMadPn45Dhw5h7ty5cHd3pxyX/b2TkpLI5IiOZOTIkRRDUl5erjYB\nTgbtRWUD89tvv6G6upr8WdHj999/b/fFMqiOr68vrQS8NsFisZQygvfv3wfQWOMjFAp1pqq/c+fO\nZAHigAEDsGjRombnOzo6NpspZ2RkBE9PTyQnJ2Pp0qUKi2rr6+vx119/tXrdyuLo6Ehb98bUxDCo\nbGDi4uKgr68PADh9+rTCB6NLpB2IRCKtD/QDjRdNZWROZO+lS5cuOlPVDzQaxXPnzkEgECA7O7tF\n49hSxllISAhu3bqFkpISLF68WKGb+rffflNLwa2joyNl7Nq1a1rfOoKhY1HZwCxdupSSmrxgwQKd\n6Zv+ukEQhE78bby8vEhpFUX07NkThYWFABqzqnRFvh9orBmRdX4cMGAA1q1b1+x8Pp/fohjo9OnT\nceLECfj4+OCzzz6jnVNZWakWb0L//v0pNTpSqZRJWX7NUdnA0Lkyjhw5ohPB5NcVFoullo6HbUEZ\nNxmPx4ORkRGAxjvmR48eaX18SYanpyf4fD4AYMqUKbCxsWl2vpeXV4vq0RwOB8OHD8ehQ4cwaNAg\nvPPOO7Tzdu7c2eGFz35+fujTpw9l/NixY1r/3WPoONqlo6Wu/JO/rgQGBiI3N1fTy2gRExOTFrXG\nZMF9giDg5eWlMz1igMb/E1ngOy8vr1n3EZ/PV+rCbGtri7q6OlKiha4ZmFgsxuzZszv0Qk8QBDp3\n7kwR7CwuLsa///7bYa/LoN20i4Fh0G6MjIzIxAxtRhk3GZfLJY1l9+7dW5yvTYwaNQo///wzAGDc\nuHH49NNPm52vSDrmZUaOHInr16+jtLQUX331FaVXC9CodCzrJ9NR2Nvbw9vbmzLOBPtfX1plYJSR\nvGDQLmSdIbUZZb5XoaGhZKC/vr5ep1yzHh4eEAgEaGhogL29PekyU4SDg4PS6dgzZszAn3/+CQMD\nA8TFxdHOOXDgQIde7J2dneHn50cZv3Tpkk58/xjan1ZVSK5YsUJOD0ksFuPrr7+GgYGB3DymbbL2\nwOfzUV1dDT09PU0vpVnMzMzw6NEj2NnZ0R5ns9lk/MLY2BhlZWUoKyuDiYmJOpfZalxdXXH79m34\n+flhyJAhyMrKor3rBxrrYa5cuUJb6/IyPB4P3bt3x+HDhzFs2DD8+uuvtHGX9evXw9HREZ07d27z\ne6EjOjoaZ86cQXFxMTnW0NCA48eP4/333++Q12TQXlTewYSGhuLJkyfIy8sjH0FBQSguLpYbk9Us\nMGgHQUFBOvE3cXd3b7bIEAAeP35Muo58fX3VriLcFoYMGYI9e/YAaJTzP3jwYLs9t6+vL+7fv4+0\ntDSMHDmSdo6Hhwfs7e3b7TVfxs7OjrZFxNGjR5mmhK8hKu9gZD5kBt1CIBDoRDaPMm4yPT09pKen\nIyAgAAEBAUhOTlbDytoHV1dXFBQUoL6+HhwOBzdv3kROTg5cXV1p5xsaGqq0Q5s2bRpWr15NapW9\nrGsWEhLSod1OBQIBbGxsKPGjgoICXL9+HV26dOmw12bQPpgg/2uErvjBLS0tm93FeHt74/nz5wAa\njU1tba1O1PrI8PHxweHDhwE0piwvX75c4VxPT0/cu3dPpeefPn06Tp8+jYEDB5JjLBYLffr0QUpK\nSusWrQIGBga0hoSpiXn9YAzMa4ShoaFOFCfK7vIVQRAE3NzcAPy/GrM6NLfai9GjR5MtCnr27Nls\nPETZpmxNEQgEMDU1hZGREczMzDB27FjMnj0bCxcuxIgRIzBv3rw2rb8lQkND0atXL8p4cnIy04jw\nNYMxMK8RnTt3bjG+oS20lJ5bVlZGyq2YmJjohByODBsbGxQVFZHr9/Pzo6gRN4UgCJWNTFRUFMrL\ny7Fy5UqMGzcOnTp1wr1799C7d2907doVGzZsAABIJBKl+vGogpWVFTp16kRx64nFYlLNgOH1gDEw\nrxEcDqfF1FhtwdrautmmVSKRCJcuXQLQaDifPXuGFy9eqGt5bcba2ho//PADAKBPnz745ZdfFM51\ndnZulRH45JNPcPbsWTQ0NMDOzo50kb799ttgs9nYu3cvZs6ciU8++URlN1xL5ObmYsCAAZTxw4cP\nM4XZrxEaNzDV1dWYPXs2unTpgsjISCQmJrZ4ztGjR+Hr66uG1b166MpF2NnZuVlFYSsrK4jFYvJn\ne3t7pKWlqWt5beaDDz4gpWBkbjBF2mMWFhZkzEkVWCwW+vXrhz///BNAY7xK5kp88803sWXLFqSl\npaG2thYJCQnt+t3w8PCAtbU1Zfz+/fs6lfXH0DY0bmBWrlyJ3Nxc7N69GzNmzMDChQuRnp6ucH5F\nRUWzQVGG5tHT08OjR480vQylae5u18nJifw5Nze32YZl2oalpSWMjY1JN9nChQvx66+/Kpzf2rt+\nFxcXlJaW4vLly+jcuTNu3bqFO3fuYPLkyXIZe/n5+di0aVOrXoMOBwcHuLq6wt/fn3JMluDA8Oqj\nUQPz4sULHDx4EPPnz4e3tzdiYmIQExPT7D/aV199BQcHhw5dV0NDA8Ri8Sv58PDwQE5Ojlpfs7X1\nD7a2ts3W7vB4PLJ5l1QqRW1trU65X2xtbcl+LT4+Ps2u3cjISK54URU++ugjnDt3DuXl5cjNzcWW\nLVso0kFubm4YNWpUq55fEfb29nKZbDLOnj2rUwoMDK1HowYmKysLDQ0Ncs2KgoKCcOPGDdr5//77\nL65cuYLJkyd32Jpqampa7Dioy7BYLAQEBKj1Nevq6vD8+XOVDY2Dg0OzgpAikQhXr14F0CjoaWho\nqFM9YoYNG4akpCTy95ycHOzYsYN2rqenZ5u6eA4ZMgQfffQRfvrpJ8r/V0BAAKZOndruBZh6enoQ\nCoUQCoVy4zU1NUy/qNeEVknFtBdFRUUwMzMDm80mx8zMzGgVdevq6rBgwQLEx8d3WJ9vqVSK+vp6\niuTNq4ZYLAaHw1GbphyXyyX7mxgaGrbb8wqFQjLt1d7eHoWFhToTYwIAc3NzsNlslJeXw9jYGIsX\nL8YHH3yAcePGUea+rFKsKra2trQxnjfeeANxcXHYvXs3TE1N4eLi0qbXaYpQKMSjR48wYMAASmz1\nyJEjeOutt9rttRi0E43uYKqrqynGgsvl0nbg27RpE/z8/NCzZ88OW49EIukw46VNsNlsSoV3R0MQ\nBLhcrsq7GEdHx2bv3J2dncmfb926BWtra51ykwUFBeHcuXMAGutXmkteYbFYZGKDqpiamtJmdd26\ndQsikQhdu3bFzZs32/2zc3V1xaBBgyjj2dnZZBtphlcXjRoYPp9P+YcRi8VkS2YZ2dnZ+P333/G/\n//1Ppefft28fYmNj5R7NudekUmmb7xR1AbrPXR20pvGZra1tswoE1tbWZDyhpKQE5ubmzWafaRvv\nvvsu6eYDGiv79+3bRzu3rf1v6BqS5efn48qVK+jatSuMjIzIz04qlSIvL6/VryUjMDAQFhYW8PLy\nohxjZPxffTR6NbWyskJJSYncXe2zZ89gYWEhN+/EiRMoKyvDgAEDEBQUhAkTJkAikSAoKKhZ6YsR\nI0bgwIEDco8tW7Z02PvRJRT1cO9I2uKSU7TzMTY2RmpqKgCgV69eqKmp0akmZEZGRigvLydjTdbW\n1govvG3t6+Pq6org4GDK+P79+8Fms2FsbIzCwkKcOHECS5YswcSJE9u8y+DxeMjMzMTgwYMpx06d\nOvVKxzsZNGxgZDLlN2/eJMdSU1MpQejRo0fj2LFjSExMRGJiIpYuXQo2m43ExES5BAEG1dAVV5KD\ng4PCxmJcLpfMrrK0tISZmRmZ+qsr+Pn5yRVaNjQ0kG6zl2lr3IxuF5OamorMzEwEBATgzp07+O67\n73DmzBmIxWIsXry4zRlfhYWFCA8Pl2vxAQBVVVUK3yfDq4FGDYy+vj6GDh2K+Ph4ZGZmIjExEYcO\nHcLIkSMhkUhQVFSEuro6mJiYwMnJiXxYWVkBaKyDePlL21HMnTsXXl5eCh8ZGRlqWUd7weVydSYg\nbmNj02yKrq2tLflzeno6vL29W53SqwlGjRolt94dO3YolI6xsLDAw4cPW/1aYWFhtGn+8fHx+Oef\nf7Bt2zY5vbBHjx5h7dq1bboZ6d+/P3JzcxEZGUk5xrjJXm00HnCYN28enJ2dMWrUKGzYsAEJCQnw\n9/fH48eP0bNnT1y/fl3TSySxtrbGvn37aB8y8UVdoTUiipqkufbBbm5uZIbU48ePYWpqqlPil0ZG\nRhAKhaQiNIfDQVhYGO1cR0fHNsWYWCwWhg0bRhkvLi7Grl27aI3PmTNncOjQoVa/pqGhIZydnTFk\nyBDKsYyMjGYlgRh0G40bGKFQiHXr1iEtLQ2nT5/G0KFDATSmnd65c4f2Hy0sLEwjchM8Hg+BgYG0\nj5cTE3SBpunh2k5zelympqbksf79+yM7O7tZNWZtxMXFRa7XUk1NDa1iBYvFavONwcCBA2n7z+Tk\n5MDIyIjWK7Bp06Y2xWOePHkCR0dHuaw/Gcwu5tVF4wbmVcLLywubN29GTEwM/P39yaK5rKwsxMXF\nISgoCKGhoZg5cyZFNj8jIwMffPABAgMDERUVhePHj2PIkCHYuHEjgMZ+6l5eXpTCQ19fX3IO0JhJ\n9b///Q/dunWDv78/Ro8eLSe98/DhQ3h5eeHkyZP49NNPERgYiO7du2PVqlVysYu6ujqsW7cOffv2\nRUBAAGJiYnDq1CkAwKpVqxAUFEQJOC9ZsgRRUVFt/yBpsLKyUqiYTBAEeRdsZGQET09PEAShU26y\nmJgYub/TqFGj8O+//9LOlbW/bi08Hg/Lly+HSCSiHLtx4waCgoIo42KxGIsWLVKol9YSnp6eSEpK\nog32JyUlaSSrkaHjYQyMitTX11MeTV03mzdvxrBhw7Bu3TpERkbiv//+w6hRo1BVVYXVq1dj4cKF\nSEtLw9ixY8mLxMOHDzFmzBjU1dXh66+/xocffoj4+HiV78Jra2sxduxYnD9/HrNnz8Y333wDPp+P\nMWPGUO4+58+fDy8vL3z99deIjY3Ftm3bsH//fvL4zJkzsWvXLrz//vvYsmUL/Pz8MH36dFy+fBmx\nsbF48eKFXDW2WCzGkSNHEBMT05qPVWkU3b031SHLy8tDjx49dKJFtAwDAwNYWVnJuYt8fX1pd22e\nnp5tlti3srLC8uXLKera5ubm+Oijj2jdWQUFBVizZk2r4jE8Hg8ikQgDBgyg1JqVl5fjwoULKj8n\ng/bDGBgVePDgAfz8/CiPpv7p0NBQfPDBB+jXrx+cnJzw3XffwdDQENu3b0f//v3x5ptvYvv27bh/\n/z7++OMPAMDOnTsBAN9//z369u2LUaNGISEhQeXsncTERNy9exfff/89YmNjERkZie+//x42NjZy\nuxwA6Nu3L2bOnInQ0FB88cUXcHZ2xtmzZwE01h2dOHECc+bMQVxcHLp3746lS5ciJCQEly9fhoeH\nBzp37iz3vs+dO4eysjLSxdkReHl54fbt27THfH19yc+rtLQU+fn5tHfo2kxQUJBczDEhIYHWSOrr\n67dLoayXlxe+/PJLMjPN0tISU6ZMgbu7O6ZPnw47OzvKOefOnSPVmVXFx8cHBgYGiIiIoBxj3GSv\nJoyBUQFra2vs37+f8ujduzc552WpjcuXLyM8PBxcLpfc8djY2MDLywsXL14EALJXedMGTVFRUdDT\n01NpfZcvX4atrS08PDzI1wKA3r17k68lIzAwEMD/S5BYWVmRWWXXrl0DAPTr14+cTxAEfv75Z3z6\n6acAgNjYWFy4cIEMTB86dAhdu3alvSi1F6ampgqNrpmZGVkY2L17d+Tm5uLp06e0qhDayptvvokz\nZ86Qv3M4HOzfv5827bq9ZH4iIiIwefJkhIeHY/To0TA2NsbDhw/B4/GwYsUK8Hg8yjnfffcd2WpA\nFaysrHDr1i1aN1lKSopOFcgyKAdjYFSAx+Ohc+fOlEdTw/DyXXNZWRkOHjxI2fXcunWL1FyrqKig\nnEcQBKXgtCXKysrw6NEjymtt374dz58/l/PbywK5HA4H9fX1YLFYpOtDloHV3A5gyJAhYLFYOHLk\nCCoqKnDmzBnExsaqtN7WoshNJjMwXC4XXl5e8Pb21inXi0AggEQikUt5NzU1xdy5cylzrays2s0F\nOHz4cCxZsgTR0dEwNDREbm4uiouL4eDggAkTJlDmi8ViJCQkqByPIQgCOTk5CAwMlEstl9FcV08G\n3USjYpevAwYGBujVqxfGjBlDOSbboZiamtLKoTRNtZXdsTa9uNbW1spJrxgaGsLNzQ2rVq2iXQvd\n3SiPx6PUwxgZGQFoTBhoauSysrJQX1+PTp06wcjICP3798fx48dhYGAADofTYQH+pnh5eSEzM5O2\nwJbP56OhoQEsFgvV1dWwtrbWiGJBW4iMjMTZs2fRuXNnAI39kqZPn06Z5+DggJSUFLmeOK2FIAgQ\nBAEej4e6ujq4uLjgxo0b6N69O9555x0cP36cogcni8fEx8ertJsyMzNDVVUVBg8eTHb0lHHs2DGM\nHTtWp7IbGZqH2cF0MF27dsV///0HPz8/csfj5eWFTZs2kdLy3bp1w7Vr1+SMTEpKCioqKsjfZQrP\nhYWF5JjMlSUjNDQUBQUFsLS0lNthHT16FL///jvtPy5dv/cuXboAgJy7BmhsitVUaic2Nhapqan4\n/fffER0drZZUbWNjY4VusuDgYDI7r6GhAenp6bC3t9cZxQIAeOuttyg1PGPGjKHsFpqrC2oLfn5+\nuHjxIiIiInDp0iVs2bIF//33H23q8rlz55TqQNuU8PBwlJaWIjo6mqL7V1xcrDBzjkE3YQxMBzNt\n2jTcu3cPU6dOxZkzZ3D27FlMnDgR58+fJ5Vzx44dCwMDA8TFxeHkyZP4888/MXPmTLnn6datG/T0\n9LBixQokJycjMTERCxYskGstEBsbCysrK3z00Uc4dOgQLl26hISEBGzbto227kER3t7eiIqKwsqV\nK7Fz505cunQJ8+fPR2ZmJj766CNyXo8ePWBpaYlr167h7bffbuMnpTwsFos2LiESicjdmKurK/Lz\n82FqaqpTqr0cDgcSiUQumUEkEmHRokWUuXw+v90bdxEEgejoaKSkpODUqVOk8GZNTQ3tbnDz5s0q\nxWPYbDZqampgZmaG8PBwynEm2P9qwRiYDsbb2xu//PIL6urq8Pnnn2PmzJmQSCTYtm0bWW8gEomw\ne/duWFpaYtasWVi/fj0+/fRTuR2HoaEhvvnmG1RXV2PKlCn46aefsHDhQtjY2JBzhEIhfvnlF3Tu\n3BnLly/HpEmTcPXqVSxZsoS2x4gMPT09yi5m7dq1GDFiBH788UdMnjwZd+7cwdatW+XEElksFiIi\nImBnZ4euXbu20yfWMl5eXrh16xbtMVkchsViwcvLCyYmJm1q1KUJgoKCsGvXLvJ3Nzc32ou4t7d3\nhwh7GhkZYdeuXZTmbXRGXaZXpko8prKyErm5ubQy/pcuXWpWPZtBtyCkuuQ/aAcePnyIfv364e+/\n/6Z08JMVe2lLTxhfX19MmTKF1gffnkilUlRXV6vs4pJIJBgwYACGDRuGadOmtTi/PT/fK1eu0Ko8\nHDt2DJGRkRAIBCguLoapqSmSk5PRu3dvtTVYayv19fX47LPPsGHDBnLNP/zwA6RSKSZOnCg39+rV\nqwgNDW33NZw5cwYJCQlKz4+IiMDixYuV+owlEgmSkpIQHR2NUaNGUYqOW1hZVAAAIABJREFU4+Li\nMHr0aJXXzKB9MDsYBhAEoZIC8fPnz/Htt99iwoQJKC0txciRIztwdfSw2Wza6u+ePXuSYpBsNhtX\nr16Fv79/s62XtQ0OhwNnZ2e5LLEJEybA09OTMlcqlXaIplxkZCSt8jJAnyKdnJyMgwcPKvXcbDYb\nrq6uYLPZGDhwIOX4sWPHdEonj0ExjIFhAKBa0JjP52PPnj3Izs7GV199JVdFry68vb1p3WSGhoZk\nXMrU1BQPHjyASCSi3CVrO35+fpSCxsTERIorytHRUWErg7YydepUWtenVCqlNTKbN29W2mVnZ2eH\np0+fYuDAgZTnKigo0CqRW4bWwxgYLSYzM7PD3WMy9PT0lK4O5/F4uHDhAs6fP0/bhlcdGBgYKGxW\n1TTmIrvrb5p9pwtERUUhPT1d7k5+9OjRlFiatbU1Weza3hAEgYSEBHh4eFCO0d2M1NfXKx2PMTAw\nQHp6OqytrRESEkI5fvTo0dYtmkGrYAwMAwDdk+8HFLvJqquryYuup6cnJBIJLCwsdKpSnMViwcnJ\niZTvAYCQkBCYm5tT5nZkbInP52PixIlkD6aWePLkCTZs2NDiPIIg8Pz5cwCgrexPTk6W60vDoJsw\nBoaBRBc7QdI1eouIiCBFI9lsNtklVdeyk8LDwynV+u+++y4lnmRgYNCh7y0kJAQxMTEwNjZuca6b\nmxs++OADpZ63c+fOqKioQHh4uJwaBtCYEHLy5MlWrZdBe2AMDANJe/QaUSf6+vq0Oxg+n0/ecfP5\nfNy9excEQVAK+7Sdvn374vbt23LuqL59+2LNmjVy8zw8PJCbm9uha4mNjcXEiRObzQCMjo7GkiVL\naJuW0eHu7o7q6mpwuVxER0dTjh8+fFinimQZqOjWfxxDh9JeKr3qhMPh0ApaNo25ODo6AmhUWZa5\nZXQBFosFExMT7N27V248JydHrtpfHe5NHo8HFxcXTJkyhXKMzWYjJiYGc+fOxYMHD8haJGXIysqC\nVCqlrYm5f/++wnonBt2AMTAMJLp2hw801grRuclqa2vJu/qAgADU19ejS5cuOidF0q1bN0r31kmT\nJmHJkiVyY4rUDdoTb29viMViOTUHPp+PxYsXY8aMGQAapZH+/vtvpRuICYVCZGZmwtHREf7+/pTj\nTGW/bqN7VxSGDkXX4jB6enq0O5iQkBCyHsbAwABZWVkQCAS0QXJtpm/fvpBIJHI7lOjoaEpxpbu7\ne4dL4hAEgf79+8Pd3R1vvPEGHB0dsXz5cty5c4cMyBMEgXfffRf5+fkAGrPNmkuj7tKlCyl3Qxfs\nP3v2bLvL4TCoD8bAvMJUV1dj9uzZ6NKlCyIjIxUKE0qlUmzevBl9+vRBv379sGTJErmL9uPHjzFh\nwgQEBwcjKipKZYHDjkZPT4+iCM1ms+Hm5gag8aInk1rR19eXU6DWBezs7HDu3Dm5scLCQly9epX8\n3cTERE4ctaMQiUSwsrLC1KlTsWnTJgQHB8PFxQUXLlwgdy2GhoawtLTEzZs3sWTJEkycOJGyC5NB\nEATc3NwglUrRu3dvCIVCueM1NTVynVMZdAvGwLzCrFy5Erm5udi9ezdmzJiBhQsXyvV9l/HLL7/g\nxx9/xLx587B9+3Zcu3YNy5YtI49//vnnMDIywoEDBzBt2jTMnz+f9nk0ha+vL22ny7KyMjJILMuA\nsra2RmpqqlrX11YCAwNx4sQJubFJkyZh5cqVGlmPi4sLLl68SBa0duvWDfr6+khOTibnpKamYsGC\nBThz5gwkEgkSEhIUGsD6+nrcuXMHfD6ftq6KcZPpLoyBeUV58eIFDh48iPnz58Pb2xsxMTGIiYnB\nr7/+Spm7d+9exMXFITo6Gn5+fvjyyy/x+++/o7KyEpWVlUhNTcWkSZPg7OyMoUOHwtfXV+7uWdPw\neDyF2WSy+ExgYCBqampgaGioc1X93bt3R0lJidwuTSAQwNDQUK7Y1MzMjGxi15EQBAF3d3eyS6qe\nnh6EQiFcXFzwzz//YOfOnYiPj5dLRCgsLMQ333xD+3xWVlbkDQudmyw7O1unFLEZ/h/GwLyiZGVl\noaGhQa4xV1BQEG7cuEGZ+/DhQ7kAq6urKyQSCW7evAkejwd9fX0kJiaivr4eGRkZuHv3LtlqQFvg\ncrkUX72rqyuZTWZubk4WWlpZWelUOjaLxYKfnx/Onz8vNz5p0iQ5xWM3Nzey/qejcXNzA4/HIw1c\nly5dkJ+fj7S0NOzYsYN2/ocffqjw+WSttt3d3eHl5UU5zuxidBPdavenJZSWlmLVqlVITU1VOlum\nrXC5XAQHB2POnDkwNTVtcX5RURHMzMzkJP8V3eG+PC77ubS0FDweD/Hx8Vi8eDF27NgBiUSCqVOn\nonv37u3wrtoPf39/3LhxQy74TRAEXFxcyN8zMzPh4uICLy8vPHnyhLZtr7YSFhaGlJQUua6h3bt3\nx9KlS9G7d28A9M3jOpLg4GDcvHkT/v7+IAgCx48fx6VLlyjzgoKC8NZbb1HUy5sSEBCAyspKGBgY\nYPDgwZT2BKdOncLkyZNpG58xaC/MDqYVrFq1CleuXFGbcQEaK5uvXLmisB3yy8gK2JrC5XJpM66i\no6Px/fff48GDB6isrMS3334LNptNZpT9999/iI6Oxm+//YaFCxdi+/btlF4hmobL5dJeXHk8Hvk+\nJBIJpFIpDAwMVKrV0AbCwsKQlZVFqePJy8uTS7pgsVhq+16yWCzweDxkZGSAIAh4e3vTzsvOzkZ2\ndjaZWUaHLNMPAPr160cxJFVVVZREBwbthzEwrUCTxV/Kvjafz6dcaMRiMW3Pl48//hju7u6IiopC\njx490KlTJxgbG0MoFOLy5cvYv38/EhIS0KlTJ7z//vt4++23Kf3UtQGBQEC5AAuFQqSlpQFolF6R\naZSVlpbqXJW4k5MTdu/eLTe2Zs0aHDhwgPzdw8OjQ5qQKcLb2xu5ubkQi8X44IMPEBERQZlTVVWF\ns2fPIj8/v9nPPC8vDxKJBPr6+oiMjKQcZ9xkugdjYFqBn5+f1r+2lZUVSkpK5O7qnz17BgsLC8pc\noVCITZs24d9//8XFixcxduxYlJSUwNbWFhkZGXBycgKPxyPn+/j4oKCgoO1vpp3x8/OjZJOZmZmR\nazU3NydTlP38/Cg6X9pOr169UFxcLDdmYmIi13jN2NgY1dXVal1XdHQ0srKyQBAE5s+fDycnJ8qc\nx48fY/fu3WTShVQqxb179+TmhIWFITs7GwAwZMgQynNkZGSoLcbE0D4wBqYVzJkzB2FhYWrtfMnl\nchEWFoY5c+YoNV/mrrh58yY5JhN9fJmVK1fi8OHDMDIyglAoxPnz52FiYgIHBwdYWFggNzdXrgAz\nNze3WX+6puBwOLR3yE3jMLILnLOzs87tYLp27QqCIChy+H379sWPP/6ooVU17pb5fD4KCwvB4/Gw\natUq2h5BmZmZ2LlzJw4ePIiEhARMmjRJ7vvp4OBAil76+PjA2dmZ8hzMLka3YIL8rcDU1FRjNQjK\noq+vj6FDhyI+Ph7Lli1DdnY2Dh06hJ9//hkSiQQlJSUwNjYGj8eDpaUlNm7cCFdXV1RXVyMhIQFx\ncXGQSCTo27cvVq9eTY7dunULe/fuxfr16zX9FmkxMDBAaWmpXCKEpaUlJBIJ2Gw2Kioq8OLFC+jr\n66OkpETO+OgCIpEIf/75J95//31yzMfHB4sWLcL48ePJOU+ePIG1tbXa1uXh4YE//vgDsbGxsLKy\nwvjx47Fp0yaKMUxNTUVmZiaZfbZkyRJs3bqVrFN69uwZLC0twWazMXjwYGzatEnu/KSkJIwfP15r\n2pozNA+zg3mFmTdvHpydnTFq1Chs2LABCQkJ8Pf3x+PHj9GzZ0+ya+CYMWMQHh6OcePGYcaMGRgx\nYgQmTJgADocDIyMjbNu2Dbm5uYiJicGGDRsQHx9PZi5pG97e3pSaCQMDA7K4MioqinSziMVinWql\nDDRmjl2+fJkyrq+vT9aSuLq6qt2VRBAEIiMjydqYAQMG4N1336U1BE1rd54+fYqVK1eSrlxbW1uc\nPn0aQOPf6uXzy8vLceHChY56GwztDLODeYURCoVYt24dZdze3l4uDZTD4SA+Ph7x8fFy82RtlL28\nvPDzzz93+HrbAzabTckm09fXJ2tghEIhKSfftWtXnD9/Xq13+m0lMDAQenp6lB3KunXrsGPHDvj7\n+4PFYnVoEzJFmJmZwdraGtXV1dDT00O3bt3AZrNbTAi5fPkyfvvtN4wcORIikYhcu5GRESIiIkiD\nI+PIkSPo06dPR70NhnaE2cEwKITD4VA0vnQBY2NjSjC8ac2LLKuMxWLplHGR4enpKdcWGmgM9je9\n2+dyuQpbSnckbm5u5A7Lw8MD9vb2GDt2bIvn/fDDD2R8rGmLZrpgf0pKik51J32dYQwMg0IU1ZZo\nO15eXhQFX0dHR7IGqLKykqzJqK+v17keOOHh4Th+/DjtsUWLFgFoNELqTFeWQRAEbG1tSTeWr68v\nAgIC0K9fv2bPa2howJIlS1BeXg57e3vSzRkQEEBbEHvs2LH2XzxDu8MYGIZmaaoEoCuwWCxKhphI\nJCIvuJGRkaSBkQk36hK+vr4oLi6m7GKmTZtGqhbr6+vTFtWqAy8vL7BYLFRXV8Pc3BxVVVX49NNP\n0alTJwiFQnz11Vfo1q0b5byioiKsWLECBEGQBobFYtHqkx07dkznVLFfRxgDw9As6pYfaS+MjIzI\nokqg0d0nq97ncrmkRpuenp5OXqiCg4Pxzz//UMb9/f1J15gshqYJwsLCyF1kSEgI0tPTsXTpUnz7\n7bcIDQ3FnDlzaFPdr1y5gn379gEA+XeJjo6mNMMrLi7WueZxryOMgWFoFh6Pp5NxGC8vL0o2WdPU\n5QcPHpAXXzs7O7XK/rQHERERZJOvpsyaNQt79uwB0JjMIevqqW5YLBbYbDZu3boFgUAAgUCA+vp6\nsrbFxMQEX3zxBfT09Cjn/vjjj3IFsmZmZggPD6fMO3z4cIe+B4a2o3EDo2xTLIlEgm+++QZ9+vRB\nSEgIpk2bJtd3naFjYLPZOleQCNC3f/b09JS7u5cF+52cnHSuQtzb2xtFRUUUTTWBQIAzZ84AAGxs\nbPDs2TMNrK4RWcp4fX09AgMDKTJH/v7+cu2XZTQ0NGDNmjVyBpTOTXb58mVKMgeDdqFxA6NsU6xt\n27bh4MGDWLFiBfbt2weJRIIZM2bo5MVP19BEymt7YGpqKtf7xdLSknTb+Pj4kBcnfX19nZONARoz\ntuj6+5iYmJAFipp2/w0cOBAZGRlgsViwtbWlxI3eeecduVYRMoqLi/HNN9+Q2nKhoaEUmaOGhgaF\nyQ4M2oFGDYwqTbESExMxZcoUdO/eHW5ubkhISMD169fJvusMHQeXy5WTitEV3N3d5bLJCIIgDQmL\nxZLraVNfX69zsaaePXuipKSEcpO1YcMG0r1kaGiollbKiuDz+TAxMcHTp0/h7u5O+X/9448/yPTk\nl7l37x7Wrl0LoHEnPXDgQMqcY8eO6dzf7XVCowZGlaZYCxculEt1lN1VvyxFwfD/KOt+BICDBw8i\nMjISQUFBmDdvnpxg4tOnTzFp0iSEhISgb9+++P7773Vi50iXTcbh/H9tsVgsJu/wZQ2zdAlPT0+I\nRCJa4VFZB0+6WJS6cXZ2xoULFyCVSuHn50f+f0ulUty6davZ71JycjLp0Rg4cCBlN11QUEAqUjBo\nHxo1MKo0xQoLC4O5uTn5+y+//AIzMzO5oiwGeZR1P16/fh0JCQn44osv8Msvv+DWrVtYvXo1gMY7\n+6lTp0JPTw/79u3DokWLsGPHDjLTR9uxtLTEo0ePyN99fX1J376hoSFZr2FmZqaxtN624ODgQNsn\nxcPDA3PmzAGHw9H47pMgCPTo0QPnz58n05Zra2tBEATmzp3brEJ4Q0MDFi9ejLKyMlhbWyMkJIQy\n5+jRox25fIY2oFEDo0pTrKacO3cOP/74I2bNmiV3R/oy+/btQ2xsrNxj8uTJ7bJ2bUcV9+OePXvw\n1ltvYdCgQfDz88PixYvxxx9/oLq6GpmZmcjNzcWCBQvg6uqKXr16Ydy4cTqTwePi4iLnlrGzsyPT\nl0UikVwgWRtbELREZGQkkpOTKbuAwMBAMnGBxWJp3I1kaWkJKysrvHjxAqGhobh27RqAxizFpUuX\nwsbGRuG5JSUlWL58ORoaGmiD/cnJybQZdQyaR6MGRpWmWDKSk5PxySefYPTo0YiNjW32+UeMGIED\nBw7IPbZs2dIua9d2VHE/pqWlISgoiPzd398f9fX1uHPnDhwcHLB161aIRCK5v9XLjb20FboEhaYB\n/aZdGE1NTSk9SrQde3t7iEQiWgHMPn364O+//4aTkxMluK4JPDw8cO3aNfB4POjr65NJFiYmJlix\nYgWEQqHCc69evYpff/0V4eHhpKS/DLFYjBMnTnTo2hlah0bFLps2xZKllSpqigUAZ86cwfTp0/HO\nO+9g3rx56lwqLTt27MDOnTtVPm/s2LEYN26cSs+n6BxFqOJ+fPr0KaysrMjf2Ww2GZgNDAxEeHg4\npFIpampqUFdXh/3797co/aFNmJmZ4eHDh2RhX0VFBSnfb25ujtraWvD5fHTq1IlUXdYlvL29af+u\nM2bMwJEjR2BpaakVWXIEQcDKygoXL15EeHg4/vnnH/Tq1QtAY6p4QkICZs+erTDzbfv27ejcuTOi\no6MpLtojR47gnXfe0dmMx1cVje5gVGmKlZGRgRkzZiA2Npai+stARRX3Y01NTYtzCYKAWCzGl19+\niaqqKkyYMKFjFt4BuLu7y8VhunfvTrrD9PT0cOXKFQCNriS6RlnaTnR0NOlyeplDhw6hsrJSa5Iy\nPD09IRaLUV1dDUdHR7kEhODgYHz++ecKz21oaMDSpUvRs2dPyrH79+9rtJU5Az0aNTBNm2JlZmYi\nMTERhw4dwsiRIyGRSFBUVERe5BYuXAgfHx98/PHHKC4uRlFREYqKinSuAltdqOJ+VDS3aZW17J/7\n5MmT2LRpk85diJve2VpYWJCxO4FAINcT5vnz5ygrK1P7+tqCrEEXXbfHyMhITJ06Ffr6+loTp4iI\niMD9+/fh7OyMx48fyxm/QYMG4b333lN4bnFxMX777Tfa2hmm26X2ofFCS2WaYj18+BCZmZm4ceMG\nIiIi0LNnT/Ihq8ZmkKep+1GGIvejlZWVXEFifX09ysrKyLkNDQ2YM2cOTpw4gY0bN8rFa3QFc3Nz\nuTTkphXwnp6e5M8+Pj608Qxtx8XFhfYOfuTIkTA2Noa3t7dcDyBNInOH37lzB506daLEBePi4hQ2\ntDMyMsKECRNog/1nz55FVVVV+y+YodVo3MDImmKlpaXh9OnTGDp0KID/b4oVFhZG/kz3oEtbZFDN\n/ejv7y9XS5Ceng4ul0s+x+rVq3HixAls2bJFZz9vV1dXOTdZaWkpWevj7e1N7uC4XC4MDAw0ssa2\nEBUVhaqqKlpX2MSJE3H//n2tik94e3vj5s2bMDIyQnV1tZzeHYvFwrx58+Dj4yN3jrm5OdasWQMH\nBwf07t2b8neqqanB33//rZb1MyiJ9DUjPz9f6unpKc3Pz6ccq6urk9bV1WlgVR3Dl19+KY2JiZHe\nunVL+n/t3Xd4VHXa8PHvTDJppCeTOiGkkUAagVBkYemEIqLsurqPz4tiAXRVBLsgglhg5dFVV6Qs\n4IoVfURwETCCKB0CJJDeSZuZlEmfJDOZOe8fvDlvYgIkkJAQzue6vHYzc+bMb07C3OfX7nvXrl1C\nVFSUkJSUJDQ3NwulpaVCU1OTIAiCcObMGSEyMlLYvXu3kJycLNx1113CmjVrBEEQhNTUVCE0NFTY\ntm2bUFpaKuTk5AilpaVCRUVFl9vT29f35MmTgtlsFgRBEKqqqoTk5GRBEATBZDIJJ06cEI/Ly8sT\njEZjr7TxRnz44YfCyZMn2z3e0NAgLFq0SDh9+rRgMpl6oWUda2hoEE6fPi00NTUJR48ebfd8RUWF\ncP/99wsTJ04UVq5cKezatUvYv3+/kJmZKQiCILz//vvCxIkT2/y3cOHCm/0xJFfR6z0YSc/pzPAj\nXE6nvnz5ct555x3mz5/PkCFDeO655wD4+eefEQSBtWvXMm7cOGbOnMm4ceO47777evOjXRc/Pz8x\ndYyTk5M4jySXy9uklPH29r4lJ4xdXV07rFdvY2NDcXExvr6+fWaYDC63y8vLi+rqahwdHduthHN1\ndeXtt99mzZo1rF69GqVSSXNzM+fPn8dsNjNr1qx258zMzOz1zAWS/08mCH1keclNUlRUxJQpUzh4\n8GC7ehSth0kkHTOZTBiNRmxsbLr82r5wfU+dOsXo0aOBy/t/WoYMjx07xh/+8AfxuH379nWY+6ov\n0+l0vPzyy3z44YdYWVm1ee7gwYMcO3aMGTNmMGrUqF5qYcd++OEHZs+ezbFjxxg/fvwVjzObzWze\nvJnRo0fj5eWFt7c3ixYtIjMzs81xc+fO5ZlnnunpZks6QerBSLrkVk3f35GKigpxs19MTEybZdkD\nBgzo9UzEXeXq6srgwYM7LEQ2ZcoUJk2a1KfmYVrExsZy5MgR/Pz8rtr7kMvlDBkyBLPZjFarZf/+\n/R0mu/3555/FsgyS3iUFGEmX9XZuqxsxcOBA8Y73D3/4g/j/7ezs2twJx8bG3nLJL+FyKpwrfbm2\n7IlRq9U3uVVX5+3tjYeHB+7u7mg0mqumtQkPDyc6OpoTJ07w97//vcNiePX19R3mZ5PcfFKAkXSZ\nXC6/ZYOMt7c3lZWVwOX9PwMHDhSfaz0/YWdnd8ukw2lt1qxZHDx4sMP9YcuXL+fdd9/tkyUuWlaV\nRUVFXTWbgru7O5s2bWLnzp1X7UlLe2L6BinASLrM1taWpqam3m5Gt2gdRJycnNp8aWk0mltuONDR\n0RGlUsmOHTvaPefs7IxKpeqTNwcymQw3NzdSUlIwGAxtykW0JggCR48eveb5Ll68eMtVKe2PpAAj\n6TK5XN5hSeJbResUJY2NjWKerpEjR7apL3To0CEmT57cJxJFdkVQUNAVM5IvXryY0tLSPnmDEBIS\nQkNDA0OHDiUhIaHDY2QyGQsXLsTFxeWa55N6Mb3v1v2WkPSq3k7/fiNa16oPDw/nzJkzHDt2jIyM\nDC5cuMChQ4fYsWMHiYmJVFRUsGLFCr788kuSkpLIzc3l0KFDxMfHo9Vq+2QPZ+bMmeh0ug57AdHR\n0cTHx/ep5cqtTZw4EY1Gg6ura5uNsb8/5tlnn8Xa2vqq5zpw4MAtWeOnP+nVbMqSW5vRaOzzS7oN\nBgMajUbMGF1SUkJubi6lpaU4ODhgYWGBwWDA2dmZ0NBQfvrpJ3F/xcyZM7lw4QJDhgzhzJkzXLhw\ngVmzZjF69Gg0Gg1lZWU0NjZSUFBAQUEBTk5ODBkyhKamJmpra/H29sbX17dNRuubwdHREQcHB/bv\n388999zT7nmtVktiYmKH+bx6m4WFBRYWFigUCnJycvD19W13TMtw2uuvv87LL798xZud6upqjh8/\nzsSJE3u41ZIrkfbBtNIX9mncKgRBoK6uDgcHh06/pruvr/D/SghoNBosLS2prq4mKysLo9FIYGAg\n7u7u5ObmYjQaGT58OO7u7uIy3fLycsrLywkLC6OgoAA/Pz9kMhk//vgjU6ZM6fDuuKioiI0bN+Lv\n78/8+fOveAdtNBrJy8ujoKAANzc3XFxcyMzMpKysDB8fHwIDAzGZTNjb27crqdBdvvnmGxITE3nz\nzTfbPZednc3q1as7nKfpK3bu3MmIESMwmUxtcsW1yMnJoba2loKCAt57770rnic2Nlasziq5+aQA\n04oUYLqmoaGhTcbla+nq9RUEgYaGBurr69Hr9WRkZFBaWoqfnx8BAQEUFhZSXFxMUFAQYWFhVy1Y\n1ZGTJ08yZswYLl26RENDA2FhYdTX16PT6fDz87vi67KyskhLS6O+vp4JEybg4+PTpfc1m80UFxeT\nlJSEIAgMHjwYQRDIzMykrq6OoKAggoODsbW1xdra+roCUF1dHatWreLll1/uMPP1v/71L+Li4q76\nOXuTXq8nKSmJ5uZm/vCHP3Q457d3715mz57Nhg0b+Oabb654ri+++OKqFTMlPUcaIuvHGhoaeO21\n1zh48CCOjo4sWbKEu+++u8Njd+3axQcffEBVVRUzZsxg5cqVYvDoynm6QhAEDAYDjY2NZGVlkZ6e\njpubG0OGDKG6uprs7GwUCgXR0dH4+PigUqnafNm2XmJ8PVqWWw8cOJAffvhBDFId7a1oLSQkhJCQ\nEDIyMti0aRNhYWHMmzfvmnMCrd/Xz8+v3Zd7WFgYgiBgNpupra3l3LlzFBYWolQqCQkJoby8nMzM\nTOzt7cVrYmFh0eHmSXt7e0JDQ8nOzu4wwDg7O7Njxw5eeeWVTrX5ZrOzs0OlUmEymUhISOgw+0BI\nSAgAixYtoqSkpMM0OQBfffUVS5cu7dH2Sjom9WBa6W89mJY6O2vWrCE9PZ2VK1fy2WeftRt7P3/+\nPA8//DBvvvkm/v7+vPzyy8TGxrJy5cqrnmfw4MHIZLIrfrG2/GkZjUb0ej3Nzc2o1Wqsra3JzMyk\npqaGwMBAgoODxSzGN3O+oqqqiqKiIiIiIrhw4YJ4XX755RcmTZrUqXMIgkBSUhIlJSXU1dUxfPhw\ngoODe6zNLb26xsZGqqqqSEtLo7q6Gn9/f1QqFXl5eeh0OoKCgsjOzkatVvPkk092eK7Zs2f3+ZVW\nP/74Iy4uLkRHR7erZdTU1ERZWRkqlYqGhgYWL17c4dJkmUzGsGHDWLx4cYfDbZKeIwWYVvpTgNHr\n9YwZM4YdO3aI+bZWrlyJwWBg7dq1bY594YUXsLW1ZfXq1cDlgPPQQw9x8uRJBEG44nmef/55SktL\ncXV1xdraGktLSxQKBU1NTRiNRiwtLcWcZRYWFpjNZmQyWZ+6vi0xzPb4AAAgAElEQVTDZGq1Gi8v\nL2QyGfHx8URERHR5WOXcuXPs37+fqKgopk6del352m6UIAhUV1dTWVlJc3Mzr7/+OhMmTMDZ2Zng\n4GAcHBwoKirCwcGBRYsWkZqaytmzZ/Hw8MDe3h6ZTCb+/qysrLC1tRUrUFpaWoq9WoPBgMlkQqFQ\nYGVlhdlspqmpSbzhkMvl4jEtfxcmkwmDwYBMJhOvTVNTE2azGSsrKywsLGhubsZoNGJhYYGVlRVF\nRUUkJydjZWXFxIkT25338OHDTJ48GYPBQHl5OQ8//PBV9/m4urpy9913c//99/epv8P+Shoi66fS\n09Mxm81ERESIj8XExLBp06Z2xyYlJfH444+LP0dFRdHc3ExGRgZms/mK52lsbKS5uZnq6mqsra1x\ncXFBEATkcjnW1tbY2tq26ZH01eqjZrMZGxsbkpOTiYyMZPLkyaSkpHQ5wAwfPpyoqCgSEhI4dOgQ\nDQ0NBAcHd1iDp6fIZDKcnZ1xdnYGLpeHblmModFoqKio4PDhw2RmZmI0Ghk2bBhPPPGE+L8VFRVU\nV1ejUChwc3NDoVCg1Wqpra3F09MTd3d3SkpKqKiowNnZGaVSSWNjI6WlpRiNRry8vHB2dqaoqAid\nToeHhwfe3t6UlpZSXl6OjY0Nnp6eKBQK1Go11dXVeHp64u3tTVFRERUVFbi4uODh4SGet6CgADs7\nO44dO4bRaKSyshKlUom3tzcXLlygsbERa2trlEolTzzxBB988MEVr49Op2Pbtm1s374db29vli1b\nxogRI27Wr+e2IwWYG/DJJ5/w73//u8fO/+CDD/LQQw9d12vLysrarVByc3NrlxIdEJfwtrCwsMDZ\n2ZnS0lIEQbjieXx8fHB2dsbOzo6mpibq6upoamrCysoKS0tL9Ho9CoUCs9ksBqPS0lJ8fX2xtbXt\nlTv83wsNDSU1NZWIiAh+++03IiMjsbCwICgo6LrOZ2lpyZgxYzCbzRw7doxDhw5RWVnJiBEjurTi\nriuam5vR6/UUFRXx22+/kZOTg6enJyNHjkStVlNcXIxKpRJLK8fFxbFkyRKSkpLQarVi2fLNmzfT\n0NDAihUr2vw9/L7wV2Bg4DXb1DI/0qKjYcOhQ4d2+rwJCQnU19czefLkNo//9a9/xdLSEg8PD+Dy\nzU9hYSG7du26avsEQaCkpITnnnsOKysrJk+ezNKlS9tloZbcGCnA9FMNDQ3thgAUCkWHG88aGxuv\neGxzc/MVn5PJZOLKLRsbG2xsbDCbzVRVVYnvb21tjY2NDXZ2dhgMBhwcHKipqeHMmTOUl5fj7++P\nt7c3JSUl6HQ6wsPDCQgIuGmZAlxcXMRNh617LBcvXmT06NHXnX1YLpczfvx4hg0bxrlz5zh58iT1\n9fV4enpyxx13XNc5a2pq2LNnD5cuXWLEiBEoFAqOHz9OXV0dkZGRjB8/nvnz57eZqxg1ahTLli2j\nuroaW1tbwsLCxM80ZcoU8bj169cjCAIbN27kwQcfxM7Ojvnz53fLYo7u4OLigkajIT09Xay0CuDj\n44NGo2lz7NNPP42bmxvffPMN1dXV1zy3wWBg//797N+/H5VKxbJly27JsuB9kRRg+ilra+t2Q1JG\no7HdROnVjrW1tcVgMHT6PHD5i9XV1RW4fGddU1ODwWAQA42lpSWenp5Mnz69zesGDRqETqcTN0Lq\n9XoyMzMxm82Ehobi7OyMpaUlSqWy2+8y5XI5JpOJgQMHYjKZsLCwQC6Xk5SUxLBhw27o3A4ODkyY\nMIHGxkaOHDlCamoqcPnu3cnJqc2xZrMZtVpNXl4eMpmMhIQECgsL8fX1JTY2luLiYuzt7Xn00UfF\nHkbrINFaeno6ly5dws/Pj/DwcKqqqtr1RH5PJpPx+OOP8/jjj9PU1MSWLVv4y1/+Alxe6BEeHn5D\n1+JGBAUFkZubS1FREcHBwVha/v+vroyMDDw8PNrclDzwwAM88MADpKamsmnTJpKTkzuVfaKoqIhl\ny5ZhbW3N1KlTefrpp6VezQ2QAkw/5enpiU6nw2w2i//wKioqUCqVHR5bVlYm/tzc3ExVVZVYQbCz\n5/k9S0tLXF1dMZlM1NfXU1NTQ0JCAo6OjgwfPrzNvhW5XI67uzvu7u7iY61XuzU1NXHx4kWOHj2K\nl5cXfn5+aDQaCgsLUalUxMTEXDHoXUtoaCgpKSkMHjyYjIwMhg4dSmxsLMePH7+u83XExsaGadOm\noVarSUlJ4fPPP+fs2bNYWVlxzz33IJPJOH78ODKZjFmzZjFs2LA2BdA6o76+nvz8fLRaLXZ2dkyb\nNg25XE5WVhbJycldOpe1tTVPPvkkf/vb3/jHP/7BqlWr8Pf3Z/r06UyaNKlXJsgnT57MuXPnOHv2\nrFg0DkClUnH8+HHGjRvX7jVDhw7l/fffx2Aw8Omnn7J79+42+eaupKmpib1797J37178/Px47rnn\n+mTmg75OCjD9VMswQksKdLi8yqmjCeeoqCjOnz/PXXfdBcCFCxdQKBSEhYWJK3I6c54rsbCwwNHR\nEVtbW0JCQkhJSeHjjz9m8ODB+Pv7ExERcc3lydbW1sTGxhIbGys+1rIjvqKiAp1OR15eHmlpaZjN\nZoKCgnB1daW6uhoPDw88PT2v+B5OTk7U19djY2NDdnY2Q4cORS6X39AXSn5+Pr/99hvW1tb4+PiQ\nkJCAWq1GpVIxfPhwIiIicHV1xdbWFldXVwICApg2bdp1vdelS5c4ffo0Xl5ejBkzpl1PY/bs2SQl\nJVFeXt4mgHeGTCZj6dKlLF26FJ1Ox4svvsh7772Hm5sbq1evJjAw8KYVMbOwsMDd3Z2LFy9SW1sr\nzmkFBQWJJRiuxMrKikcffZRHH32UX3/9lY8++qjNTdXVFBYWsmTJEgICAnjyySeJiYnpk4Xb+iJp\nmXIr/WmZMsCKFStISUnhzTffJDMzk9dee40dO3YQHh6OTqfDyckJKysrEhISePjhh3njjTcICgri\nlVdeYeTIkaxYseKq5+nqF3Dr62symUhJSRFXF3l5eaFSqQgKCuq2f7yCIFBcXExycjJ2dnb4+vpS\nWlpKfn4+Dg4OREdH4+XlhVwuJzExkcjISE6ePMkf//hHAFJTUwkNDe0wMJnNZjGwHT9+nLS0NBwd\nHRk7diyNjY0kJycTEhJCXFzcVXtWKSkpVFVViUtybWxsiIuLu+YcVH19PYmJiTg5OWEymYiKirrq\nddu6dSuurq4d5ibrKrPZzK5duzh9+jRlZWWMHz+eOXPmdDl4Xa+dO3fi4eHRJsdYZWUljo6OXdpH\nVVdXx7p16zh58mSXShioVCruvPNOZsyY0W6YU9KWFGBa6W8Bpr6+nhUrVnDo0CHc3NxYsmQJc+fO\nFa/Bp59+Kg41fP311/zzn/9Er9czbdo0Vq1aJa7yutJ5uqqj6ysIAhcvXhSXRLesWBsyZAheXl7d\ncBWu3JaGhga0Wi0pKSnU1NSgUCjw9/fHYDBgZ2fHt99+y5kzZ5g7dy61tbUMGTIEpVJJQkICGo2G\nsLAwpk6dilKpxMbG5roXJgiCgFqt5syZM+ISXh8fHzw9PducUxAEysvLOXv2LAaDgalTp3Z6WPDQ\noUP8/PPPvPnmm916952ens4rr7yCyWQiMjKSRx55hEGDBvXoHX5dXR27d+9m4sSJYjLMmpoasrOz\nGT58+HWd89ChQ3z88cdiCe3OUCgU3HHHHbi6uvLkk0/e9KSmtwIpwLTS3wJMX3Ot62symcjIyCA3\nN5fKykp8fX2xsbEhOjq6y3nGrsepU6cYMWIEqampmEwmVqxYQXZ2NtOnT2fWrFl4eXkxaNCgTtUi\nuV7Hjh0Tl4mnpqZiYWHB1KlT+fXXX7G3tycgIACVStXlL3Cz2czzzz/P/Pnze2RfjiAInD17lg0b\nNqDT6Rg3bhwPPPBAj+UAKywsJDk5mRkzZojX4uuvv+a+++67ofPW1dXx9ttvc+rUKUwmU5deO2jQ\nIJ5//vl2y69vZ1KAaUUKMD2rs9dXEAQxsaWrqyvl5eWYzWacnJyIiorqsd/PmTNniI6O5pdffiEu\nLg6dTsfFixeZMGECgiBQWlpKWloacLmqZ3NzM76+vqhUqjarmm6U2WwmJyeHI0eO4OXlRVFREaNG\njSIqKuqGlm9v374dpVLJnXfe2W1t7chvv/3Gp59+yoABAwgJCSE8PJyxY8d2OldbZ3399deEh4eL\nm4CPHz/OmDFjum2J+88//8zGjRvR6XRdqvvj5eXFvffey913331LF+brDlKAaUUKMD3req5vbW0t\nJ0+epLy8HD8/P0wmE2azGW9v7zb7IbpDQ0MDaWlplJWVERcXd83jm5qaSE5ORq/Xi18kTk5O+Pj4\niEu1u6q5uZmjR4+i0+mIiooiOzsbd3d3fHx8xFVts2fP7lIW6xaHDx8mLS2tTdaGniQIAuvXr+f4\n8eMIgsC8efOYOHHiDScpbVFQUMAPP/zA4sWLsbCwoLGxkaampm6fFzEajRw7dowffviBc+fOdfp1\nCoWC0aNHs3jx4g7r2twOpADTihRgetaNXt/CwkLq6+vFdCIajQa5XE5YWFi3zdecOnUKpVKJp6dn\nl4flzGYzWq2W7OxsTCYTAwYMENOn+Pv7X3WMvmWy387Ort0wnMFgIC0tjaKiIsaMGYNGo8Ha2ho/\nP78u9QrMZjOvvvoqd95553Vv9rwegiCQlpbG7t27KSoqwsnJifHjxzNy5MgbXhhw4sQJ6uvrmTp1\nKnC5Fzpy5MjuaHaHiouL+c9//sP+/fupqqrq9Ovs7e2ZNWsWCxcuvK3maqQA04oUYHpWd13fiooK\nccFBZGQkRUVFVFdXI5fLiYyMvKE5koSEBKKjo9FoNN1SK6Wljk1tbS02NjY0NjZiZ2dHaGgoCoWC\n7Oxs8vLy8PPzu+amzoaGBn766ScCAwPx8/Pj4MGDmEwmZs2ahb29fafas337dsrKynjhhRdu+LNd\nj8rKSl599VVKSkrw8vJi3rx5ODk5MXz48Ov+4t25cyfTp0/H2dmZ//3f/2X27Nk9nobIYDCwZs0a\nTpw40eW5msDAQNasWdPlOkK3IinAtCIFmJ7Vkl6mO6+vRqPh4sWL1NTUMGnSJLKysqivr8fe3p7h\nw4d3eW6kdUBoWa7cncxmM0eOHCExMRF3d3dcXFxwcnLCy8ur0ylyqqurycjIoL6+nrFjx1JQUIDJ\nZEKlUl0z0MTHx5OcnMwzzzzT63s51Go1H330EZmZmTg6OjJ//nwCAwPb/bu8ltzcXH799VcWLFhA\nZWUlubm5NzWB5d69e9m2bRs6na5Lr/Px8eH+++9n9uzZ/XauRgowrbSkHL+e8W3Jten1+htazns1\nVVVVXLx4EU9PT5ycnLC0tOTChQvIZDI8PT2vmSaltVOnTqHRaLjrrru67Uu4oaGB9PR0DAYDCoWi\nzWa92tpaLl26REVFhdjLsbGxISoq6qp/i1qtlhMnThAREYFKpWLfvn0YjUbxbr4jzc3NbNiwgUmT\nJhEZGdktn+1GFRUV8fHHH1NTU4NKpWLw4ME4OTkxcuTITicI3bJlC5MmTSI4OJjS0lIx+eXNVFdX\nx9atW4mPj6e+vr7Tr7OysuKOO+7g8ccfb5NktD+QAszv1NfXI5fLr1gpUNI1giAgCEK7+jA9+X7n\nz58nPT0dHx8fRo8ejVarFfN7+fv7X3OfRmJiItbW1nh4eHRYDbIr1Go1p06dwtbWlkmTJnUqr5XJ\nZEKr1XLp0iXkcjkNDQ2YTCZCQ0Px9fVt1/aSkhLy8/OxtrZm2LBhFBQUUFdXh5eXV4cpff7973/T\n1NTEwoULb+iz9YT4+Hi2b9+OXq9n2LBhTJo0CV9f32sWcTMajXzxxRfMnz+f9PR0AgICejVb98mT\nJ9m2bRtZWVldep2dnR333HMPCxYs6BdzNVKA6YDZbO7yuKrkyuRyOXK5/KYH7Pr6erRaLQUFBeL+\nkczMTEpKSrCysiImJqbDISWDwUBSUhJ+fn7XtXigvr6eI0eO4O7ujpOTE0FBQTfca6uvryc3N5eG\nhgb0ej0GgwFPT0/CwsLEif709HSys7MZMWIEHh4e7Nu3D71ez5QpU9oEyiNHjvDdd9/xzjvvdOvy\n6u6k1WrZvXs3paWlFBcXM27cOPz8/AgNDb3iXX5iYiKlpaWMHz+e+Ph4MfVRb9LpdLzxxhskJSV1\nKtlmC5lMRkhICC+//DKDBg3quQb2MCnASPo9s9nMoUOHMJlMBAUFERQURFNTE+fPn6ehoQEbGxti\nY2Pb9C5Onz6NyWTq0morjUZDXl4eFRUVTJ48+bqTb3ZGS22dlhLBjY2NmEwmIiIiqK2tpbCwEH9/\nfwICAiguLqa8vBwXFxf8/f0BePvttxk7diwTJkzosTZ2B4PBwLZt24iPj8fT05O4uDhsbW1xdnYm\nKiqqXS/lnXfe4amnnuLo0aPiyrK+YteuXezevZtLly516XUDBgzgT3/6E/Pnz7/lejVSgJHcVhob\nG8U5ltjYWIKCgqiurub8+fMIgoCrqytDhw4lLS2N1NRU/vznP1/1Lt9oNIrBKzw8HD8/v16bsNXr\n9WRlZYnF39LS0rC1tWXOnDm4ublx8OBBqqurGTduHPv27SM4OJjx48f3SluvR2VlJd988w1HjhzB\nbDYzb948lEolQ4cOFZc7q9Vq9uzZw8yZM1GpVH1y8rympobNmzfzyy+/oNfrO/06mUzG4MGDWb58\nebescLwZpAAjuS2ZzWZSU1PFEs9Dhw5FJpOh1WrFyfhdu3aRn5/Ppk2b2v2DTk9Pp7GxEb1eT1hY\n2HVvrOxJZrOZwsJCzpw5Q0lJCZ6envj7++Pi4sLZs2fZsmULcHneo68OlXWkpqaGL7/8kosXLxIa\nGoqdnZ24eXPUqFGcOHGCkJAQjEZjt2/G7W5Hjhxh69at19Wruffee3nwwQd7qGXdQwowktueWq3m\nyJEj2NvbExUVhUqlwmw2M336dIqLiwkLC8PPz4+AgABx8l0mkxEYGMiQIUNITExEq9WiVCoJDw+n\npKSE4uJiAAYOHEhAQADZ2dlotVoGDhxIdHQ0OTk5FBUV4eLiQmBgIEajkYKCAmpqasT3SktLo6Sk\nBG9vbwYPHkxJSQklJSXI5XJ8fX1xd3cnKyuL0tJSVCoVYWFhZGZmUlRUhKurK4MGDaK5uZlLly5R\nXV2NIAi4uLhQU1ODRqMhJSUFo9GIUqnEw8ODoUOHUlJSglqtBhCzWycnJ6PT6fDx8SE2NpbMzEyK\ni4vF/TxOTk7k5eVRVVVFYGAgMTEx5OfnU1hYiIuLC0OGDMFoNKJWqzEYDHh5eREUFCS2w8nJSZyU\nr6mpobm5GUdHR9zc3BAEoU111JbicC1zesXFxaxdu5aCggLCwsKYNm0aFhYWHD58mPz8fMaOHcui\nRYt688+rU7RaLW+99VanC6O1cHR0ZMOGDX02U4AUYCSS/8dsNlNeXk5WVhYWFhbs3LmT7OxsHn30\nUbGui0qluuZGToPBIFbGFASB6upqSkpKqKurw8HBAWdnZy5duoRWq8XR0RFvb29MJhMajQa9Xo+H\nhwc+Pj4UFhaKGxJVKhXl5eWUlZVhYWGBp6cnbm5uNDQ0UFdXh4uLC97e3tjZ2Ymp5y0tLduN2bek\nt6mrq+Opp55CrVZTUFAgLoduaGigsbFRnI+qqKhArVZTXV2Ni4sLLi4uZGdno9FocHd3x8PDA7Va\nLQ7NeXt74+Xlxblz5ygpKREDX0uwtLa2FntRaWlpVFRUMGjQICIjIzl//jxFRUUolUqCgoJobGyk\noKCAxsZG/Pz8GDp0KBkZGWJvrCUgFhcX09zcLJb4zsrKQq/XU1lZiVarZf369SiVSgYOHIharUaj\n0eDo6IiXl5e4Yq/lunt7e3Pp0iXKyspQKpX4+vpSXV1NeXk5giCIRfHKysooLS1FqVTi7+9Pc3Oz\nWJ7Zzc0NFxcXcduDlZUVAwYMQC6Xi3vtWqqmduTrr7/m888/p7a2tlN/t/7+/mzfvr1Prnq9dfrF\n3aRlddjv63hLJC38/f3Jz8+nvr4etVrNrl278PPzw8/Pj+bmZkpKStDr9Xh6ejJo0CCysrLQarW4\nu7sTFBSEVqultLRU3IPj7u5OTk4OFRUVeHl5ERISQl5eHmq1GicnJ3x9fTGbzRQXF1NfXy9mbc7K\nyhK/yIOCgigtLUWtVosBxsHBgfT0dHQ6HUqlkpCQEJKTk9FoNDg4OIhffIWFhej1erHnkJqaikaj\nwcrKCj8/P+655x6cnZ3FtpeXlyOTyXB3d0epVJKbm0t1dTVubm5iL6miooIBAwbg6+tLc3MzGo2G\nxsZGXF1dCQ4OFtul0+nE1XwtqfBra2txcnIiNzeX2tpadDqdmG6/qqqK4uJitFotRqMRrVaLwWAg\nPz+fvLw8cnJy0Ov12NrakpaWRmVlJVVVVeL8mYeHR5sg6eDgwJ49e3B3dyciIoK8vDyKioqwtbVl\n4MCBWFhYkJ+fT11dHZ6engQFBZGRkYFWq8XFxYXBgwej1WpRq9XIZDK8vb1xcXEhJyeHyspK8bqn\npaWh1WrF36der6e4uFjsIapUKtLT06mpqcHJyYng4GBx8YVcLkepVIp/J7W1tbi6uhIREcHp06dp\namq66t9rTk4OKSkpV9z71N28vLw6PaR62/VgEhISeOCBB3q7GRKJRHJL6sroz20XYFqqDSqVypu2\n5G/x4sVs3LjxprzXzdafPxtIn+9WJ32+7teVHsxtN0TWsufhZrKysuq38z39+bOB9PluddLn6119\nb5G4RCKRSPoFKcBIJBKJpEdIAUYikUgkPcJi1apVq3q7EbeDlrrh/VF//mwgfb5bnfT5es9tt4pM\nIpFIJDeHNEQmkUgkkh4hBRiJRCKR9AgpwEgkEomkR0gBRiKRSCQ9QgowPaShoYEXXniBESNGMGnS\nJL7//vveblKPqKqq4o477uh3yUMLCwtZtGgRsbGxTJ48mU2bNtGf1sNkZmby3//93wwbNoy4uLh+\n+/e5cOFCli9f3tvN6Fbff/89oaGhbf5bv359bzerQ7ddqpibZe3ateTl5fH555+Tnp7OypUrCQwM\nJCoqqreb1m1qa2v529/+hk6n6+2mdKvm5maeeOIJgoOD+frrrykuLubFF1/EycmJ+++/v7ebd8MM\nBgMLFy5k0qRJvPXWWyQmJvLKK68QEBBAdHR0bzev2+zdu5dff/2VP//5z73dlG6Vl5fHnDlzePHF\nF8XHerI8942QAkwP0Ov17Nq1ix07dhAWFkZYWBjnzp3jiy++6DcBJjk5mWeffbZNHfv+IjU1lby8\nPL766isGDBhAUFAQDz30EP/5z3/6RYApLS1l+PDhvPLKKygUCgYOHMgnn3zCuXPn+k2Aqaqq4u9/\n/zuRkZG93ZRul5OTQ3R0NEqlsrebck3SEFkPSE9Px2w2t9kAFRMTQ2JiYi+2qnudOHGCWbNm8eGH\nH/Z2U7qdn58fmzdvZsCAAW0e72wBqL5OpVLx7rvvolAoEASBX3/9lby8PEaMGNHbTes269atY+7c\nuQQHB/d2U7pdTk4OAQEBvd2MTpECTA8oKyvDzc2tTTkANzc3SktLe7FV3euxxx5jyZIlt1Qt985y\ncXFh7Nix4s8Gg4Fvv/2WO+64oxdb1f0EQWDYsGEsXLiQuXPn9pve9YkTJ0hISOCJJ57o7aZ0O6PR\nSGFhIfv372fy5MnMmDGDf/3rX312frD/fTv0AS01xFtTKBQYDIZeapHkegmCwPLly6mvr+exxx7r\n7eZ0K7PZzGeffUZhYSGrVq3C39+fBQsW9HazbkhTUxOrVq1i5cqV2NjY9HZzul1BQQFGoxEHBwc2\nbNhAZmYmb7zxBgqFggcffLC3m9eOFGB6gLW1tVh7u4XRaOyzE3GSjpnNZl599VXi4+PZvn07bm5u\nvd2kbmVhYUFkZCSRkZFoNBo+//zzWz7A/POf/yQ8PJzx48f3dlN6RFBQECdPnsTFxQWAsLAwKioq\n+Oqrr6QAc7vw9PREp9NhNpuRyy+PQlZUVNwSk3KSy8xmMy+++CLx8fFs3LiRmJiY3m5St9FqtaSl\npTFx4kTxsaCgICorK3uvUd1k7969lJeXi7+vllGDxMRE9u7d25tN6zYtwaVFYGBgnx1+lwJMDwgL\nCwMur7RqGdfuTyt0bgfvvPMOP/30E5s2bWLMmDG93ZxulZuby5NPPsnx48dxdHQELq+c6w8T4jt2\n7KC5uVn8ef369cjlcl544YVebFX3+eWXX3j11Vc5fPiwOP+ZlpbWZyf9pUn+HmBnZ8fcuXN57bXX\nSE1N5fvvv2fPnj39Yonr7SAtLY3t27fzzDPPEBQURFlZGWVlZf1mv09sbCz+/v4sX76c3Nxcfvzx\nR7Zs2cLChQt7u2k3zNfXF39/f/G/AQMGYG9vj6+vb283rVsMHz4cs9nM6tWryc/PF393jzzySG83\nrUNSD6aHvPzyy6xYsYK//vWvuLm58frrr/ebVTr93c8//4wgCKxdu5a1a9eKjw8cOJD4+PhebFn3\nUCgUbNy4kdWrV/OnP/0JFxcXXnrpJaZMmdLbTZNcg5OTE1u2bOGtt97i7rvvxtXVlWeffZaZM2f2\ndtM6JNWDkUgkEkmPkIbIJBKJRNIjpAAjkUgkkh4hBRiJRCKR9AgpwEgkEomkR0gBRiKRSCQ9Qgow\nEolEIukRUoCR3LaKioqIiYnBZDIB8Mgjj/DNN990eOx3331HaGgo48aNw2w2t3s+MTGR0NBQpk2b\n1un3b2ho4PPPP+9Smw8fPkx2drbY/tDQUBISErp0DonkZpECjOS2deHCBcLDw8WyCq1T+3REJpNR\nWVnJuXPn2j23b98+ZDJZl97/k08+YevWrZ0+XqvVsmjRIioqKgDw9vbm6NGjUgoiSZ8lBRjJbevC\nhQtixcP8/HwMBsNV83HJ5XJGjRrF/v372zwuCAIHDhzock6THnoAAAWbSURBVMGuru5x/v3xFhYW\nKJXKdqUhJJK+QgowktvO5MmTCQ0NZfv27Wzbto3Q0FDi4uLQ6/UMHTqUoqKiK752xowZxMfHt/my\nP3/+PEajkZEjR7Y5NjQ0lN27d3f42Hfffcf7779PcXExoaGhnDp1CrPZzIYNG5g+fToRERHExsby\n1FNPiTnQJkyYAMD8+fN56aWX2g2RNTc3s2XLFqZPn05kZCRz5szhxx9/FN/7ww8/5JFHHuGjjz5i\n3LhxREVFsXDhQrRarXjM5s2bmTJlChEREcTFxXV5CE8iaU0KMJLbzrfffsuRI0ewtbVl586dHD16\nlPvuu4/77ruPo0eP4u3tfcXXTps2jbKyMpKSksTH9u3bR1xcXJsKptcya9YsHnvsMby8vDh69Cgx\nMTFs376dTz/9lBUrVnDgwAH+53/+h7Nnz/Lxxx8DsGvXLuByoFi+fHm7c65du5atW7eybNky9uzZ\nw+zZs1m2bBkHDhwQjzl16hQZGRlicE1NTeWDDz4A4NChQ2zdupU33niDAwcO8Oijj7JmzRrOnDnT\n6c8lkbQmBRjJbcfV1ZXGxkYAIiMjUSqVFBYWEh0djVKpvGqgcHV1ZdSoUeKXdsvw2KxZs7rUBhsb\nG+zs7MRhLisrKwICAli3bh1//OMf8fX1ZcKECYwfP57MzEzxveFywkMHB4c256urq+PLL79k6dKl\nzJgxg4CAABYvXsyMGTPYvHmzeJwgCLz11luEhIQQGxvLzJkzSUxMBC5XS1QoFPj4+ODr68u9997L\n9u3bCQwM7NJnk0haSAFGcltKT08nJCRELAiXnp7OkCFDOvXaGTNmiAHm7NmzAF2ef+nI5MmTcXR0\n5L333uPpp5/mzjvvZM+ePR2uWvu93NxcmpubGT58eJvHR44cSVZWlvizu7s79vb24s+Ojo5i9dU5\nc+bg7OzM9OnTmTNnDuvWrcPR0bHfVfKU3DxSgJHcdlqGjlJSUoiJiSEmJgadTsd//dd/ERMTQ0lJ\nyVVfP336dDQaDRcvXmT//v3MmDGjUyvIWhfC6siGDRt4+OGHqaurY/z48axbt4677rqrU5/Jysqq\nw8dNJpNYmOpKx7XMJ7m5ubFnzx4+++wzJk+ezIkTJ5g3bx7fffddp9ogkfyeVA9GctvZvHkzzz77\nLFOnTmXatGkcOXKEb7/9lvfffx8ADw+Pq77e1dWVkSNHcuDAAX766SdxDuP3FAoFdXV14s+XLl1q\n8/zvg9KWLVt4+umnWbBgQZvXtASIqwWxQYMGoVAoOHv2LCEhIeLjZ8+e7XSlyh9//JHKykoeeOAB\nRo4cydKlS3nsscfYt28f8+bN69Q5JJLWpB6M5Lbj4+NDTk4OkyZNwt/fH7VaLVZ59Pf3b3PHfyUz\nZszgiy++wNLS8or7UIYNG8bOnTtJT08nJSWF1157rU0PYsCAAVRXV5Obm0tTU5O4ryUnJ4esrCxe\nf/11zp8/L9aVHzBgAAAZGRlUVla2eS8bGxsWLFjAP/7xD/bv309+fj6bN2/mp59+ahOwrsZgMLBu\n3Tr27NlDcXExJ06cIDU1VdpnI7luUoCR3HZyc3MBxMnrCxcuEBMT06VzTJ8+ncbGRuLi4q7Ys1i1\nahUODg7ce++9PP300/zlL3/By8tLfD4uLg5fX1/uuusuDh8+zLp166ipqeGee+5hwYIFVFVV8eyz\nz5KdnU1DQwP29vb8n//zf1i/fj0rVqxo935Llizhvvvu46233hKXKL/77rudrnZ49913s2TJEj78\n8EPi4uJ46aWXmDdvHosXL+7StZFIWkgVLSUSiUTSI6QejEQikUh6hBRgJBKJRNIjpAAjkUgkkh4h\nBRiJRCKR9AgpwEgkEomkR0gBRiKRSCQ9QgowEolEIukRUoCRSCQSSY+QAoxEIpFIesT/BYk5TD5X\nvvwaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1214ab710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 10)) \n",
    "gs = mpl.gridspec.GridSpec(nrows=4, ncols=1, hspace=0.25)\n",
    "ax = [plt.subplot(gs[0, 0])]\n",
    "ax += [plt.subplot(gs[1, 0], sharex=ax[0]), plt.subplot(gs[2:, 0])]\n",
    "\n",
    "plot_adaptation(k, p, W, pom, genotypes, ax=ax[:2])\n",
    "ax[0].set_xticklabels([])\n",
    "plot_landscape(top_lods_freq, genotypes, genotype_freq, fitness, peaks, valleys, ax=ax[2])\n",
    "\n",
    "panel_label_size=plt.rcParams[ 'axes.titlesize']*1.5\n",
    "ax[0].annotate('A', (-0.16, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "ax[1].annotate('B', (-0.16, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "ax[2].annotate('C', (-0.16, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "\n",
    "sns.despine()\n",
    "fig.savefig(landscape_filename[:-4] + '.tif', dpi=300)\n",
    "fig.savefig(landscape_filename[:-4] + '.pdf', dpi=300, paper_size='A4')\n",
    "print(landscape_filename[:-4] + '.tif,pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 1e -8.0 N = 1e 9.0 generations = 2^ 15.0\n"
     ]
    }
   ],
   "source": [
    "print('mu = 1e', np.log10(mu), 'N = 1e', np.log10(N), 'generations = 2^', np.log2(generations))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge the two empirical landscape figures\n",
    "\n",
    "To merge the two figures into one, we use [`convert`](https://imagemagick.org/script/convert.php).\n",
    "Install on mac with homebrew: `brew install imagemagick`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!convert Weinreich2006.tif Franke2011.tif +append empirical_landscapes.tif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you require PDF, you can use [`pdfjam`](http://freecode.com/projects/pdfjam).\n",
    "Install on mac with homebrew: `brew install homebrew/tex/pdfjam`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ----\r\n",
      "  pdfjam: This is pdfjam version 2.08.\r\n",
      "  pdfjam: Reading any site-wide or user-specific defaults...\r\n",
      "          (none found)\r\n",
      "  pdfjam: Effective call for this run of pdfjam:\r\n",
      "          /Users/yoavram/homebrew/bin/pdfjam --nup '2x1' --landscape --outfile empirical_landscapes.pdf -- Weinreich2006.pdf - Franke2011.pdf - \r\n",
      "  pdfjam: Calling pdflatex...\r\n",
      "  pdfjam: Finished.  Output was to 'empirical_landscapes.pdf'.\r\n"
     ]
    }
   ],
   "source": [
    "!pdfjam --nup 2x1 --landscape --outfile empirical_landscapes.pdf Weinreich2006.pdf Franke2011.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-locus adaptive landscape\n",
    "\n",
    "The next plot explores adaptive peak shifts in a two-loci rugged landscape. Two bi-allelic loci give us four genotypes:\n",
    "- _ab_ with fitness 1; this is the initial genotype in the population\n",
    "- _aB_ and _Ab_ are the adaptive valleys with fitnes _1-s = 0.95_\n",
    "- _AB_ is the adaptive peak with _1+sH = 1.1_. This is the final destination of adaptation.\n",
    "\n",
    "We simulate a single adaptation to show the dynamics, as well as collate results from many simulation to explore the statsitcs of the adaptaion event. The simulation code is in `simulation.py` which is modified from [simulation2.py](https://github.com/yoavram/ruggedsim/blob/master/stochastic/simulation2.py); both the original code and the collated data are from from [Ram & Hadany, _PRSB_ 2016](http://rspb.royalsocietypublishing.org/content/281/1792/20141025.abstract). The data is deposited on [dryad](http://dx.doi.org/10.5061/dryad.3066j). We use the dataset labeld `adaptation_weak_mutation_15JUL14.csv.gz (424.1 Kb)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = 0.05 # selection coefficient\n",
    "H = 2 # double mutant advantage \n",
    "U = 0.0004 # mutation rate, Wielgoss et al. 2011\n",
    "u = U * 0.2 / 1000 \n",
    "beta=0.0002 # beneficial mutation rate, beta=0.0002, Gordo, Perfeito, and Sousa 2011\n",
    "N = pop_size = 1e6 # population size\n",
    "G = 25 # max number of deleterious mutations\n",
    "pi = 1 # see original paper (Ram & Hadany, 2013); however pi = 1 means you can ignore this value\n",
    "tau = 1.0 # same as previous line\n",
    "\n",
    "red,blue,green = sns.color_palette('Set1', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run a simulation and save it's result to files. **Run it again** if it resulted in extinction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulation\n",
      "Tick 0\n",
      "MSB reached at tick 5000 with mean fitness 0.9996\n",
      "Tick 1000000\n",
      "Tick 2000000\n",
      "Tick 3000000\n",
      "Tick 4000000\n",
      "Tick 5000000\n",
      "Tick 6000000\n",
      "Tick 7000000\n",
      "Double mutant appeared at tick 7778083 with mean fitness 0.9996\n",
      "AB/0 1e-06, AB/1 0, AB/2 0, AB/3 0\n",
      "Fixation at tick 7778364 with mean fitness 1.1 and AB frequency 1\n",
      "AB/0 0.9918, AB/1 0.008159, AB/2 1.6e-05, AB/3 0\n",
      "Simulation finished, 7778364 ticks\n"
     ]
    }
   ],
   "source": [
    "ps, Ws = simulation.run(N, s, H, U, beta, G, pi, tau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ps = np.array(ps)\n",
    "Ws = np.array(Ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('Ws.npz', 'wb') as f:\n",
    "    Ws = np.save(f, Ws)\n",
    "with open('ps.npz', 'wb') as f:\n",
    "    ps = np.save(f, ps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load simulation results from file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('Ws.npz', 'rb') as f:\n",
    "    Ws = np.load(f)\n",
    "with open('ps.npz', 'rb') as f:\n",
    "    ps = np.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data from the Ram & Hadany 2013 paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('df_all_15JUL14.csv') as f:\n",
    "    df = pd.read_csv(f)\n",
    "df = df[df.s == s]\n",
    "df = df[df.tau==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot everything together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GEhgYSOfOnQEYNmwYS5YsYezYsYwf77xb89y5cwkLC3Nd3yOEEEKIq0t2oZUJ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oL2w49gPX4cwttvZ4RFtmU4j6f4fJh37oT10GGEfvYJpG+8Xuf1VXP/japnn3PZRolECP38M4ju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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b0757b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8)) \n",
    "gs = mpl.gridspec.GridSpec(nrows=2, ncols=3)\n",
    "\n",
    "ax0 = plt.subplot(gs[0, :])\n",
    "ax1 = plt.subplot(gs[1, 1])\n",
    "ax2 = plt.subplot(gs[1, 2])\n",
    "ax3 = plt.subplot(gs[1, 0])\n",
    "\n",
    "alpha = 0.5\n",
    "tmax = Ws.size\n",
    "t = np.arange(tmax - 500, tmax)\n",
    "red, blue, green = sns.color_palette('Set1', 3)\n",
    "colors = [red, green, blue]\n",
    "lines_styles = ['solid', 'dashed', 'dotted']\n",
    "panel_label_size=plt.rcParams[ 'axes.titlesize']*1.5\n",
    "\n",
    "# frequency dynamics\n",
    "\n",
    "genotypes = ['ab', 'aB+Ab', 'AB']\n",
    "\n",
    "for load in range(3):\n",
    "    for g in range(3):\n",
    "        ax0.plot(np.arange(t.size), ps[t,g,load], color=colors[g], ls=lines_styles[load])\n",
    "\n",
    "ax0.set(\n",
    "    xlabel='Time (since generation {:,})'.format(tmax-500),\n",
    "    ylabel='Frequency',\n",
    "    yscale='log',\n",
    "    ylim=(1/N, 1.1)\n",
    ")\n",
    "ax0.legend(genotypes, loc='upper left', title='Genotype', frameon=True)\n",
    "\n",
    "# adaptation statistics\n",
    "\n",
    "sns.distplot(df.app_time[df.fix], bins=100, kde=False, norm_hist=True, hist_kws=dict(log=True, alpha=alpha), color=blue, ax=ax1)\n",
    "sns.distplot(df.app_time[~df.fix], bins=100, kde=False, norm_hist=True, hist_kws=dict(log=True, alpha=alpha), color=red, ax=ax1)\n",
    "\n",
    "ax1.set(\n",
    "    xlim=(0, 2e7),\n",
    "    xticks=[0, 0.5e7, 1e7, 1.5e7, 2e7],\n",
    "    xticklabels=r'0, 0.5, 1, 1.5, 2'.split(','),\n",
    "    xlabel='Appearance time (x$10^7$)',\n",
    "    ylabel=''\n",
    ")\n",
    "\n",
    "sns.distplot(df.fix_time[df.fix], bins=50, kde=False, norm_hist=True, hist_kws=dict(log=True, alpha=alpha), color=blue, ax=ax2)\n",
    "sns.distplot(df.fix_time[~df.fix], bins=50, kde=False, norm_hist=True, hist_kws=dict(log=True, alpha=alpha), color=red, ax=ax2)\n",
    "ax2.set(\n",
    "    xlim=(0, 400),\n",
    "    xlabel='Fixation time',\n",
    "    ylabel=''\n",
    ")\n",
    "ax2.legend(['Fixations', 'Extinctions'], loc='best')\n",
    "\n",
    "fix = df.fix.mean()\n",
    "ext = (~df.fix).mean()\n",
    "assert np.allclose(fix + ext, 1)\n",
    "\n",
    "ax3.bar([0, 1], [ext, fix], color=[red, blue], alpha=alpha)\n",
    "ax3.set(\n",
    "    xticks=[0, 1],\n",
    "    xticklabels=['Extinctions', 'Fixations'],\n",
    "    xlabel='',\n",
    "    ylabel='Frequency'\n",
    ")                     \n",
    "\n",
    "ax0.annotate('A', (-0.085, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "ax3.annotate('B', (-0.3, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "ax1.annotate('C', (-0.14, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "ax2.annotate('D', (-0.14, 0.98), xycoords='axes fraction', fontsize=panel_label_size)\n",
    "\n",
    "fig.tight_layout()\n",
    "sns.despine()\n",
    "fig.savefig('twolocus.tif', dpi=300)\n",
    "fig.savefig('twolocus.pdf', dpi=300, paper_size='A4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3246, 578, 2668)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fix.size, df.fix.sum(), (~df.fix).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# License\n",
    "\n",
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>."
   ]
  }
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