gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "import pylangacq as pla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "eve = pla.read_chat(\"Brown/**/*.cha\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Age histogram')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEFCAYAAAAL/efAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCn8RERNS+IuImND/Ane2YbYti5r2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faad00455c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(list(eve.age(months=True).values())).to_frame(\"age in months\").plot.hist()\n",
    "plt.title(\"Age histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = tuple(\"what did\".split())\n",
    "# Find sentences with a matching prefix.\n",
    "sents = [sent for sent in eve.tagged_sents(participant=\"MOT\")\n",
    "         if len(sent) >= len(prefix)\n",
    "         and tuple(token[0].lower() for token in sent[:len(prefix)]) == prefix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "239"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('what', 'DET:INT', 'what', (1, 2, 'SUBJ')),\n",
       "  ('did', 'V', 'do&PAST', (2, 0, 'ROOT')),\n",
       "  ('the', 'DET:ART', 'the', (3, 4, 'DET')),\n",
       "  ('doggie', 'N', 'dog-DIM', (4, 5, 'SUBJ')),\n",
       "  ('write', 'V', 'write', (5, 2, 'COMP')),\n",
       "  ('?', '?', '', (6, 2, 'PUNCT'))],\n",
       " [('what', 'PRO:INT', 'what', (1, 4, 'LINK')),\n",
       "  ('did', 'MOD', 'do&PAST', (2, 4, 'AUX')),\n",
       "  ('you', 'PRO:PER', 'you', (3, 4, 'SUBJ')),\n",
       "  ('see', 'V', 'see', (4, 0, 'ROOT')),\n",
       "  ('in', 'PREP', 'in', (5, 4, 'JCT')),\n",
       "  ('the', 'DET:ART', 'the', (6, 7, 'DET')),\n",
       "  ('park', 'V', 'park', (7, 5, 'POBJ')),\n",
       "  ('this', 'PRO:DEM', 'this', (8, 9, 'DET')),\n",
       "  ('morning', 'N', 'morning', (9, 7, 'OBJ')),\n",
       "  ('?', '?', '', (10, 4, 'PUNCT'))],\n",
       " [('what', 'PRO:INT', 'what', (1, 4, 'LINK')),\n",
       "  ('did', 'MOD', 'do&PAST', (2, 4, 'AUX')),\n",
       "  ('he', 'PRO:SUB', 'he', (3, 4, 'SUBJ')),\n",
       "  ('climb', 'V', 'climb', (4, 0, 'ROOT')),\n",
       "  ('?', '?', '', (5, 4, 'PUNCT'))]]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sents[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Root verb analysis\n",
    "\n",
    "One easy way to get at the typical uses of what-did sentences is to look at the associated root verb. Let's gather frequency data on those verbs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the root verb from each sentence.\n",
    "def get_root_verb(sent):\n",
    "    for token in sent:\n",
    "        word, pos, lemma, (_, _, deprel) = token\n",
    "        if deprel.lower() == \"root\":\n",
    "            return token[0].lower()\n",
    "root_verbs = [get_root_verb(sent) for sent in sents]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faace676470>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAETCAYAAAArjI32AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRDr3/wBwz92xwgolnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faace674ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(root_verbs).value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>do</th>\n",
       "      <td>0.372385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>say</th>\n",
       "      <td>0.209205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>have</th>\n",
       "      <td>0.050209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>see</th>\n",
       "      <td>0.037657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>did</th>\n",
       "      <td>0.037657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            wd\n",
       "do    0.372385\n",
       "say   0.209205\n",
       "have  0.050209\n",
       "see   0.037657\n",
       "did   0.037657"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_freqs = (pd.Series(root_verbs).value_counts() / len(root_verbs)).to_frame(\"wd\")\n",
    "rel_freqs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLITIC</th>\n",
       "      <td>0.116776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is</th>\n",
       "      <td>0.048196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>want</th>\n",
       "      <td>0.037268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>have</th>\n",
       "      <td>0.029346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>see</th>\n",
       "      <td>0.027904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             all\n",
       "CLITIC  0.116776\n",
       "is      0.048196\n",
       "want    0.037268\n",
       "have    0.029346\n",
       "see     0.027904"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare this to corpus-wide verb frequencies.\n",
    "all_root_verbs = [word for word, _, _, rel in eve.tagged_words() if len(rel) == 3 and rel[2].lower() == \"root\"]\n",
    "all_root_verb_freqs = (pd.Series(all_root_verbs).value_counts() / len(all_root_verbs)).to_frame(\"all\")\n",
    "all_root_verb_freqs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'root verb')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABawAAAHsCAYAAAA+SepCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEgisAQAAAAAAAACW8H+GjghB95+fdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faace639780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Join freq tables together and graph.\n",
    "# NB we're doing a left join here -- that means we're ignoring verbs that appear in the corpus but not in any WD construction.\n",
    "plt.subplots(figsize=(25, 8))\n",
    "joined_freqs = rel_freqs.join(all_root_verb_freqs, how=\"left\")\n",
    "sns.barplot(data=joined_freqs.reset_index().melt(id_vars=(\"index\",), var_name=\"source\", value_name=\"relative frequency\"),\n",
    "            x=\"index\", y=\"relative frequency\", hue=\"source\")\n",
    "\n",
    "plt.title(\"Relative frequencies of root verbs in 'what did' phrases vs. all sentences (left join)\")\n",
    "plt.xlabel(\"root verb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'root verb')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABawAAAHsCAYAAAA+SepCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xt8zvX/x/HntWsnNsVU9G1h1Jwl\np9LMYRIxMedopUJ9pV+i5RDRSM4dptBJEkYaFaWyYojSSo1GX/LFWMhxG3a63r8/3FzfLbZryq59\n2ON+u3W77bo+7/f78/occ3t6781mjDECAAAAAAAAAKCEeZR0AQAAAAAAAAAASATWAAAAAAAAAACL\nILAGAAAAAAAAAFgCgTUAAAAAAAAAwBIIrAEAAAAAAAAAlkBgDQAAAAAAAACwBAJrAABQqJo1a6pz\n587q0qWLunbtqvbt26t79+5KSkpy2TcyMlKrV68utM3+/fv15JNPSpIOHTqkPn36XJa6c3Nz9e9/\n/1vt27fXBx98cFnGdJfFixfrzTffLPb9zJ49W61bt9aoUaOKZfwxY8Zo27ZtxTL2yJEj9c477/zt\n/q+++qpWrFhR5PbfffedatasWeT2NWvW1LFjxy6pprVr1+rVV1912e7YsWPOWuLj4zVx4sSLtgsP\nD9d3332nuLg4hYWFXVItRXl2r3bnr2FcXJwee+yxYt/frFmztGbNmmLfz9+1aNEiLVmy5G/3L+r9\nXZikpCT93//9n8t2Xbp00alTpwpt8957713SOwAAALiPZ0kXAAAArG/+/PkKCAhwfn7nnXc0ceLE\nfxRenHfw4EHt2bNHklSpUiXFxsb+4zGlc+H3hg0btHXrVtnt9ssyprvcf//9btnPsmXLNH36dDVp\n0qRYxv/222/Vu3fvYhn7n3rqqacuqX2ZMmVUpkyZYqrmnKSkJJ08efKS+rRt21Zt27YttI2vr2+x\n145/7rvvvtMtt9xS0mVc1IEDB7R8+XItXbr0b4/xd+7vv6pfv75ee+01l+0+/vhjl20iIyPVo0cP\nhYSE6Prrr/9HdQEAgMuLwBoAAFySnJwcpaam6tprr3V+N3v2bH355ZdyOBy66aabNG7cOFWqVClf\nvzlz5ig+Pl5nz57VmTNnNGLECIWFhWnMmDE6dOiQHn30Ub3wwgvq3LmzfvjhB4WFhen1119XvXr1\nJElDhw5Vs2bN1LdvX5f7S09P14ABA5STk6Nu3bopJiZGHTt2VNu2bbVjxw5Nnz5dZcuW1YsvvqgT\nJ04oNzfXGV5I52bffvrpp6pQoYKaNGmibdu2acGCBRo5cqRuvfVWPfroo5KU7/OhQ4cUHR2t1NRU\nZWdnq1OnTnr88ceVkpKi/v37q1WrVvr555916tQpRUVFqV27dsrJydG0adO0du1a2e123X777Ro3\nbpzmzp2r48eP6/nnny9w3JycHE2YMEE//vijvLy8FBgYqJdeekl+fn75zvsff/yh8ePH68CBAzLG\nqGvXrhowYICGDh2qQ4cO6bnnntNTTz2ljh07OvvExcVp2bJlOnPmjPz9/bVgwQK9/vrrWrVqlex2\nu4KCgjR27Fhdf/31BY7/8ssv6/Dhw3rmmWc0depU3Xbbbc7x+/Tpo4cffljt27eXJE2bNk2SFBUV\npQ8//FCLFy+Ww+FQ+fLlNXbsWNWoUUMjR47UiRMntH//frVu3VqSlJiYqC+++ELp6ekKCQnRiBEj\n5Onpqddee01fffWVvLy8VKFCBb300ku64YYb8p2XvNeufv36GjRokDZu3KjDhw9rwIAB6tu3b772\nNWrUUEhIiJKTk/X4449r3bp1kqRHH31U1113naZMmaKsrCyFhoY6Z8nGxMTo559/1okTJ/Too4+q\nX79+On36tMaPH6+9e/fqxIkT8vPz0/Tp05WWlqbY2Fjl5uaqXLlyevrpp/Pt/8svv9TLL7+sMmXK\nOJ+J89fqiy++0Ny5c7Vr1y6NHj1aZ86cUfXq1XX69GlJUq1atXTHHXfor+Li4rR69Wo5HA4dPHhQ\nlSpV0uTJk53PUnx8vN555x39+eefat68uSZOnKiDBw+qX79+qlGjhg4cOKAFCxYoLi7ugme7Xbt2\n2r17t5577jllZWXJGKMePXqoX79+kgp+Z3z55ZeaPXu2bDab7Ha7nn32WTVt2jRf3QXdP/3799eI\nESN0/PhxSVKrVq00dOjQC447rz179ig6OloZGRk6cuSIatWqpVdeeUU+Pj6F9pOkI0eOFLi/wu5j\nf39/7dy5U3/88Ydq1qypKVOmaMWKFdq2bZumTp0qu92uVq1aafr06dqyZYtyc3NVp04djRkzRv7+\n/goLC1NERIQ2bdqk1NRUdenSxbnfZcuWad68efLw8FCFChU0ZcoU3Xjjjfr66681e/ZsZWdny9fX\nVyNGjNDtt99e6DXKa+7cuerSpYtsNpskac2aNZo1a5YcDof8/Pw0atQoNWjQQDExMc53lyTn5y5d\nulxwfxf1WY+KinLW8d1332nChAlauXKl0tLS9MILL2jHjh2y2WwKDQ3VsGHD5OnpqZo1a2rTpk1a\nu3atvvrqK3l4eGjv3r3y9fXVlClTVKNGDdntdt1777166623NHr0aJfXGwAAuJEBAAAoRHBwsAkP\nDzfh4eEmJCTEhIWFmQkTJpg///zTGGPM8uXLzdChQ012drYxxpjY2FgzYMAAY4wxDzzwgPn8889N\nSkqKiYyMNGfOnDHGGLNy5UoTHh5ujDFm8+bNplOnTsYYY/bv328aNmxojDHm1VdfNS+88IIxxpgT\nJ06YZs2amVOnThW6v7zyjnX+OJYvX26MMSY7O9t07NjRbNu2zRhjzKlTp8y9995rfvrpJ/PFF1+Y\njh07mrS0NJOVlWUGDBhgHnjgAWOMMSNGjDBvv/22c8y8nyMjI018fLwxxpizZ8+ayMhIs2rVKrN/\n/34THBxsvv76a2OMMatXrzatW7c2xhgzf/58069fP3PmzBmTm5trnnrqKbN8+XLz2muvOY+9oHG3\nbNliOnToYBwOhzHGmKlTp5rExMQLzkO/fv3Mu+++6zzOzp07m5UrVxpjjGnTpo355ZdfLujz0Ucf\nmaZNm5q0tDRjjDHLli0zvXv3NhkZGcYYY1577TXzyCOP/O3xly1bZgYNGmSMMSYnJ8e0aNHC7Nmz\nx3z33Xemb9++5vTp08YYY9avX286dOjgPNcPPfRQvnMfERFhMjIyTGZmpnnggQfMwoULzcGDB02j\nRo1MZmamMcaYd955x3z11VcX1JD32gUHB5sFCxYYY4xJSkoy9erVM2fPnr2gz3lhYWFm586d5syZ\nM6Z169amZcuWxhhj1q5d67wXg4ODzTvvvGOMMWb79u2mXr16Jisry3z++edmwoQJzrHGjh1roqOj\nnef1/HXP68iRI6Zx48bmP//5jzHGmDlz5pjg4GBjzLlrdf5cdunSxSxdutQYY8wPP/xgatasaTZv\n3lzgcXz00UemYcOG5vfffzfGGDNt2jTz5JNPGmPOPbv//ve/TU5Ojjl9+rQJCQkxW7Zscd7PW7Zs\nMcaYQp/tUaNGmblz5xpjjDl8+LAZOnSoyc3NLfQZbtu2rfnpp5+MMeeuf0xMzAV1F3T/zJo1y4wd\nO9YYY0xGRoYZOnSoOXXqVIHHb4wxkydPNitWrDDGGJOVlWXCw8PN6tWrjTHnruHRo0fzneO8Ctqf\nq/u4d+/eJjMz02RlZZmuXbuaZcuWOc/5559/bowxJiYmxkyePNn5fM+YMcOMGzfOGHPuuZo8ebIx\nxpg//vjD1K9f3+zbt88kJyebO+64wxw8eNAYY8y8efPM2LFjzZ49e0x4eLg5duyYMcaY3377zYSE\nhJiMjIwCr1FeDofD3HHHHWb//v3GGGN27dpl7rrrLrNv3z5jjDHffvutCQkJMWlpaRfcw3k/5/35\nUp71vPL+/+LZZ581EyZMMA6Hw2RmZppHHnnEeSx5r13jxo1NamqqMcaY6Oho8+yzzzrH27Ztm/N9\nDAAArIMZ1gAAwKXzS4Js375dgwYN0h133KGKFStKkr755hslJSWpe/fukiSHw6EzZ87k63/TTTdp\n6tSp+vTTT7V37179/PPPysjIKHSf3bt3V48ePTRy5EitXLlSYWFhKleuXJH2V5DzS1/897//1b59\n+/LNqjt79qx+/fVX7dq1S+3atZO/v78kqXfv3po/f36h454+fVpbtmzRyZMnnWu0nj59Wjt27FCD\nBg3k5eWlVq1aSZLq1KmjEydOSDq3ZEaXLl3k6+srSXrllVcknZuV6GrcFi1ayG63q2fPnmrRooXa\nt2+vBg0aXFDXjz/+qHfffVeSVK5cOXXr1k0JCQnq1KlTocdUs2ZN5zlISEhQt27dVLZsWUnSgw8+\nqDlz5igtLe1vjd+xY0dNnTpVR44c0a+//qpq1aqpWrVqWrp0qfbu3ZtvHfNTp045z1fjxo3zjdOl\nSxdnTffdd5/WrVunPn36qFatWoqIiFDLli3VsmVLNW/evNBjleRcVqNu3brKysrS6dOnC5xl265d\nOyUkJOjWW2/VnXfeqZ07d+o///mP4uPjdc899zjbhYeHS5Jq166trKwspaenq0OHDrr55pu1YMEC\n7d27V99//71uv/32QmtLTExUcHCwc7mI3r17a+bMmfnaHD9+XDt37lTXrl2d5+rWW291edwhISEK\nCgqSJPXq1UtdunRxbuvYsaPsdrvKlCmjatWq6ejRo6pcubI8PT3VsGFDSYU/2+3atdOIESP0yy+/\nqHnz5hozZow8PDwKfYY7deqkIUOGqFWrVgoJCdHAgQMvqLmg+yc0NFSDBg1Samqq7rrrLg0fPlzl\nypUr9PijoqK0ceNGvfXWW/rvf/+rw4cPO2emu1LQ/tauXVvofRwaGipvb29JUnBw8EWXyVi7dq3S\n0tL07bffSpKys7Od71zpf/drpUqVVLFiRZ08eVJbtmxRixYtdOONN0o6N+NckhYuXKjDhw87P0uS\nzWbTvn37CrxGeR0/flxpaWkKDAyUJG3evFl33nmnbr75ZklS8+bNFRAQcEnr1bs6R3991i8mISFB\nixcvls1mk7e3t/r06aP58+dr0KBB+drVrVtXlStXlnTu/fvVV185twUGBurgwYPKzMws0qx6AADg\nHgTWAACgyOrWratRo0Zp5MiRql27tgIDA+VwOPItoZCVlXVBALN9+3YNHjxY/fv3V0hIiJo2baoX\nXnih0H3ddNNNqlOnjtauXau4uDhnuFyU/RXkfLh5/tfS865z+ueff6pcuXJ65ZVXZIxxfu/l5eX8\n2Waz5duWnZ3trMkYo9jYWOdawceOHZOPj4+OHz8uLy8vZwh0/lfqJcnTM/8fxf788085HA7n58LG\n9fPz08cff6wff/xRmzdv1tChQ53LTvy1f14Oh0M5OTlFPlfn++StO+8Yf2f8MmXKqH379lq5cqV+\n+ukn9ezZ09m3S5cuziUAHA6HDh8+7Fx+Jm9NkvKtTW6Mkaenpzw8PPTBBx8oKSlJmzZt0qRJkxQa\nGqpnn3220JrOh1Xnj/Ovx5XX3XffrVdffVWHDx9WSEiIKlasqA0bNighISHfUh7nr2/eMRctWqSl\nS5eqX79+6ty5s8qXL6+UlJRCa/trPX+9b/5Ou/PynkOHw5Hvc97+ee99b29v57bCnu02bdroiy++\n0LfffqtNmzbp9ddfV1xcXKHP8NNPP63u3btr48aNiouL07vvvqtly5blq7mg+6dBgwaKj4/Xpk2b\ntHnzZvXs2VNvvfVWviVU/mrYsGHKzc3Vvffeq9atWys1NbXQa59XQftzdR+f/wuqv57XvBwOh0aP\nHu38i66MjAxlZmY6t+cNV8+PYbfb8z2nZ8+e1YEDB+RwONS8eXPnX4hJUmpqqm644QbVqlXrotfo\nfMCbd3yHwyEPD48L3gfSufsuJyenwHfkxY7vUp71gsYo6L2UV2Hn28vLSzab7YLjAQAAJcvDdRMA\nAID/CQ8PV4MGDfTSSy9Jklq0aKFly5YpPT1d0rn1n/8aDm7ZskX16tXTww8/rGbNmik+Pl65ubmS\nzgVmBYUavXr10ltvvaUzZ844Z9wVZX+uBAUFydfX1xlYp6amKjw8XNu2bVPr1q21evVqnTx5Ug6H\nQytWrHD2q1ChgnMW4aFDh/T9999Lkvz9/dWwYUPNmzdP0rmZgvfff7/i4+MLraN58+ZauXKlsrKy\n5HA4NH78eK1atcq5vbBxv/nmG/Xv31+33367nnzySXXt2vWCGY7+/v667bbbtHDhQklSWlqaVqxY\nobvuuuuSzldoaKg++ugj58zTBQsWqGnTpipXrlyh49vt9gLD6169emn58uX68ccfnWsRt2jRQqtW\nrdLhw4clSYsXL9ZDDz1UYF2rVq1SVlaWMjMztXz5crVs2VI7duxQeHi4atSooccee0z9+/dXUlLS\nJR2vK40aNdL+/fu1du1a3XXXXQoJCdH8+fNVrVo1VahQodC+GzZsUEREhHr27KmgoCB9/fXX+Z6F\ni52vpk2bateuXdqxY4ekc2tP/1WFChVUt25dffjhh5LOBcm//faby2PZvHmzDh06JEmKjY1VmzZt\nXPbJq7Bne/jw4frss8/UqVMnjRs3Tv7+/tq3b1+Bz3BOTo7CwsJ05swZ3X///Ro3bpx27typrKys\nC/Z7sftn+vTpeuONN3T33Xfrueee0y233KL//Oc/hda/YcMGPfHEE8413H/++Wdn/a4UtL9LvY/P\ny3v9W7RooYULFzrfDWPHjr1gVv1f3XHHHdq0aZNzv7GxsZo2bZqaN2+ujRs3avfu3ZKkdevW6b77\n7tPZs2cLvEZ5VahQQddcc40OHDgg6dx7a8OGDdq/f78kOdfSvu2221ShQgVt375dxhilp6frm2++\nKfD4/s45yqtFixb64IMPZIxRVlaWli5desnvtv379yswMNA54x0AAFgDM6wBAMAlGzt2rO677z6t\nX79ePXv21KFDh9SrVy/ZbDbdeOONmjx5cr724eHh+vLLL3XvvffK4XCoTZs2OnnypNLT03XLLbfI\nx8dHPXr00Msvv5yvX1hYmF544YV8ywIUZX+ueHt764033tCLL76ot99+Wzk5OXrqqaecofiDDz6o\nvn37ysfHRzfddJOzX2RkpJ555hm1b99egYGBuvPOO53bpk+frgkTJqhz587KyspSeHi47rvvvkJn\nz/bp00cHDhxQt27dZIxRs2bNFBkZqdmzZ7scNzc3VwkJCQoPD1fZsmV17bXXasKECRfsY/r06YqO\njlZcXJyysrLUuXNndevW7ZLOV48ePZSamqqePXvK4XCoatWqmj59usvx27Vrp6ioKI0fP14tWrTI\nN2a9evVkt9vVoUMH52zRFi1aaODAgXrkkUdks9nk7++vWbNmFTj7MTAwUH379lVGRobatWuniIgI\n2Ww23XvvverevbvKli0rX19fjRkz5pKO1xUPDw+1bNlSSUlJCggIUOPGjXXy5Ml8y4EU5JFHHtHz\nzz/vnDXcsGFDZ7B855136plnntGECRM0duxYZ5+AgABNnz5dzzzzjLy8vC74RwjPmzlzpkaNGqXY\n2FhVqVJF1atXd1lPpUqVFBUVpSNHjuiWW25RdHR0UU6BU2HP9uDBg/Xcc89pyZIlstvtuvvuu9W0\naVM1adLkos+wp6enRo8erWeeeUaenp6y2WyaNGnSRcPEi90/Dz30kEaOHKnw8HB5e3urZs2azqVp\nunTpookTJ6p+/fr5xnn66af1xBNPqGzZsvL391fTpk0vCGwLUtD+vL29L+k+Pi8sLEwzZ85Udna2\nBg8erClTpigiIkK5ubmqXbu2Ro4cWWj/mjVrKioqSgMGDJAkXX/99Zo0aZIqVaqk6OhoDRs2zPmb\nCLNnz5afn1+B1+iv7rnnHq1fv159+/bVLbfconHjxmnIkCHKzc2Vr6+v5syZo3Llyjn/v3DPPfeo\nUqVKatasmXNG81/v779zjvIaM2aMJk6cqM6dOys7O1uhoaF6/PHHi9xfktavX68OHTpcUh8AAFD8\nbKaov/MGAABQCq1evVoLFy7UggULSroU4LKKi4vTF198oblz55Z0KbC4/fv366mnntJHH31Uostn\nxMfHa86cOc7fJPgncnNzFRERoXfffVfXXXfdZagOAABcLiwJAgAAAAAo0M0336yuXbsqNja2xGr4\n4IMP9Pzzz6tHjx6XZbwFCxbooYceIqwGAMCCmGENAAAAAAAAALAEZlgDAAAAAAAAACyBwBoAAAAA\nAAAAYAkE1gAAAAAAAAAAS/As6QL+ia1bt8rHx6ekywAAAAAAAAAAFCIzM1MNGzZ02e6KDqx9fHxU\nu3btki4DAAAAAAAAAFCI5OTkIrVjSRAAAAAAAAAAgCUQWAMAAAAAAAAALIHAGgAAAAAAAABgCVf0\nGtYXk52drZSUFJ09e7akS7lkvr6+CgwMlJeXV0mXAgAAAAAAAABud9UF1ikpKSpXrpyqVasmm81W\n0uUUmTFGR48eVUpKioKCgkq6HAAAAAAAAABwu6tuSZCzZ8+qYsWKV1RYLUk2m00VK1a8ImeGAwAA\nAAAAAMDlcNUF1pKuuLD6vCu1bgAAAAAAAAC4HK7KwBoAAAAAAAAAcOUhsAYAAAAAAAAAWMJV948u\nFsWePXs0atQoeXp6ym63a+rUqZo3b54SExMlSeHh4XrooYc0cuRIdezYUS1btlRCQoI+++wzTZ48\nWW3atFH16tVVvXp19evXT2PGjFF2drZ8fX318ssvKzMzU2PHjlVmZqZ8fHw0YcIE3XjjjSV81AAA\nAAAAAABgbaUysP72229Vt25djRw5Uj/88IO++uorpaSkaOnSpcrJyVHfvn115513Ftg/NTVVcXFx\nqlChgv79739r0KBBatmypT777DP9+uuvWrZsmSIjI9WqVStt2rRJ06dP14wZM9x4hAAAAAAAAABw\n5SmVgXWPHj301ltvacCAASpXrpxq166tJk2ayGazycvLS7fddpt2796dr48xxvlzhQoVVKFCBUnn\nZmvffvvtkqSOHTtKkiZNmqS5c+fq7bffljFGXl5ebjoyAAAAAAAAALhylcrAOj4+Xo0bN9aQIUO0\ncuVKzZw5U3Xr1lX//v2VnZ2tn376SREREfL29taRI0ckSb/++quzv4fH/5b+rlGjhpKSknTXXXfp\nk08+0cmTJ1W9enU98sgjatSokXbv3q0tW7a4/RgBAAAAAAAA4EpTKgPrevXqKSoqSjExMfLw8FBM\nTIxWrlyp3r17Kzs7Wx06dFDdunXVs2dPjR49Wp9++qmqVat20bGeffZZPf/885o9e7Z8fX01bdo0\ntW7dWuPHj1dmZqbOnj2r5557zr0HCAAAAAAAAABXIJvJu9bFFSY5OVm1a9d2+d2V5EqvHwAAAAAA\nAAD+qqi5p4fLFgAAAAAAAAAAuAGBNQAAAAAAAADAEgisAQAAAAAAAACWQGANAAAAAAAAALAEAmsA\nsIjM7NzL2g4AAAAAAOBK41nSBQAAzvHxsqtx1Psu2yVOe9AN1QAAAAAAALjfVT/D+nLPRLxc4/Xq\n1UspKSmXZSwAAAAAAAAAuBpc9TOsizpjsaiY2QgAAAAAAAAAxeOqn2HtbhERETp69Kiys7PVqFEj\n/frrr87vZ8yYoW7dumnw4ME6fvx4CVcKAAAAAAAAANZy1c+wdre2bdtq/fr1qly5sgIDA7Vx40Z5\ne3vr5ptvVmJiopYtW6bTp0/rnnvuKelSAQAAAAAAAMBSCKwvs3vuuUdz5szRjTfeqKeffloLFiyQ\nMUYdOnTQ1q1b5eHhIX9/fwUHB5d0qQAAAAAAAABgKSwJcpkFBwcrJSVFv/zyi1q1aqXTp08rPj5e\nN9xwg3755Rc5HA6dPn1au3btKulSAQAAAAAAAMBSmGFdDJo2baqUlBR5eHioadOm2rVrl5o0aaIO\nHTqoR48euuGGG1SxYsWSLhMAAAAAAAAALMVmjDElXcTflZycrNq1axf6XWZ2rny87Jdtn5d7vL+6\n2DEBKD0aR73vsk3itAfdUAkAAAAAAMDlU9Tc86pfEuRyh8vFGVYDAAAAAAAAQGl21QfWAAAAAAAA\nAIArA4E1AAAAAAAAAMASCKwBAAAAAAAAAJZAYA0AAAAAAAAAsAQCawAAAAAAAACAJVz1gbXJySzx\n8SIjI7V7927FxMRo8eLFl7UeAAAAAAAAALhaeJZ0AcXN5umjfdH1L9t4VZ5PumxjAQAAAAAAAAD+\n56oPrN0tPT1dzz33nNLS0nT8+HH17NmzpEsCAAAAAAAAgCsCgfVltnfvXnXq1En33HOPDh06pMjI\nSFWqVKmkywIAAAAAAAAAyyOwvsyuu+46zZ8/X19++aX8/f2Vk5NT0iUBAAAAAAAAwBWhWAJrh8Oh\n8ePHa+fOnfL29tbEiRNVtWpV5/aFCxcqLi5ONptNTzzxhNq0aSNjjFq2bKlq1apJkho2bKjhw4cX\nR3nF6t1331XDhg3Vt29fbd68WevWrSvpkgAAAAAAAADgilAsgfWaNWuUlZWlJUuWaOvWrZo8ebJm\nz54tSTp27JgWLVqkFStWKDMzU506dVLr1q21b98+1a1bV3PmzCmOktymTZs2Gj9+vD799FOVL19e\ndrtdWVlZJV0WAAAAAAAAAFhesQTWiYmJCg0NlXRupvS2bduc2wICAvTxxx/L09NTBw4c0DXXXCOb\nzabt27c713z29fXVqFGjVL169X9ci8nJVJXnk/7xOHnHs3n6FLj9zjvv1OrVqy+67cknn7xsdQAA\nAAAAAADA1cajOAZNT0+Xv7+/87Pdbs+3lrOnp6c++OAD9e7dW+3bt5ckXX/99Ro0aJAWLFigxx57\nTFFRUZellsLCZSuMBwAAAAAAAAA4p1hmWPv7+ysjI8P52eFwyNMz/64eeOAB9erVSwMHDtTmzZt1\n2223yW63S5KaNGmiQ4cOyRgjm81W4H4yMzOVnJyc77vs7GydOXPmMh6Ne2VnZ19wTABKh9q1axe5\nLe8JAAAAAABwNSqWwLpRo0b65ptv1LFjR23dulXBwcHObb///rtmzpypmJgYeXl5ydvbWx4eHpo1\na5bKly+vgQMHaseOHfrXv/4SMsZCAAAgAElEQVRVaFgtST4+PhcEPMnJySpTpkxxHJZbeHl5XVJo\nBaB04j0BAAAAAACuJEWdfFcsgXW7du20ceNG9enTR8YYTZo0SfPmzVOVKlXUtm1b1apVS71795bN\nZlNoaKiaNWummjVrKioqSuvWrZPdbtdLL730t/fvama2VRljSroEAAAAAAAAACgxNnMFp6TJyckX\nzDLcs2ePypUrp4oVK15RobUxRkePHlVaWpqCgoJKuhwAJaRx1Psu2yROe9ANlQAAAAAAAFw+F8ty\nL6ZYZliXpMDAQKWkpOjIkSMlXcol8/X1VWBgYEmXAQAAAAAAAAAl4qoLrL28vJihDAAAAAAAAABX\nII+SLgAAAAAAAAAAAInAGgAAAAAAAABgEQTWAAAAAAAAAABLILAGAAAAAAAAAFgCgTUAAAAAAAAA\nwBIIrAEAAAAAAAAAlkBgDQAAAAAAAACwBAJrAAAAAAAAAIAlEFgDAAAAAAAAACyBwBoAAAAAAAAA\nYAkE1gAAAAAAAAAASyCwBgAAAAAAAABYAoE1AAAAAAAAAMASCKwBAAAAAAAAAJZAYA0AAAAAAAAA\nsAQCawAAAAAAAACAJRBYAwAAAAAAAAAsgcAaAAAAAAAAAGAJBNYAAAAAAAAAAEsgsAYAAAAAAAAA\nWAKBNQAAAAAAAADAEgisAQAAAAAAAACWQGANAAAAAAAAALAEAmsAAAAAAAAAgCUQWAMAAAAAAAAA\nLIHAGgAAAAAAAABgCQTWAAAAAAAAAABLILAGAAAAAAAAAFgCgTUAAAAAAAAAwBIIrAEAAAAAAAAA\nlkBgDQAAAAAAAACwBAJrAAAAAAAAAIAlEFgDAAAAAAAAACyBwBoAAAAAAAAAYAkE1gAAAAAAAAAA\nSyCwBgAAAAAAAABYAoE1AAAAAAAAAMASCKwBAAAAAAAAAJZAYA0AAAAAAAAAsAQCawAAAAAAAACA\nJRBYAwAAAAAAAAAsgcAaAAAAAAAAAGAJBNYAAAAAAAAAAEsgsAYAAAAAAAAAWAKBNQAAAAAAAADA\nEgisAQAAAAAAAACW4FkcgzocDo0fP147d+6Ut7e3Jk6cqKpVqzq3L1y4UHFxcbLZbHriiSfUpk0b\nnT17VlFRUTp69Kj8/Pw0ZcoUBQQEFEd5AAAAAAAAAAALKpYZ1mvWrFFWVpaWLFmi4cOHa/Lkyc5t\nx44d06JFixQbG6v33ntP48ePlzFGixcvVnBwsBYtWqSuXbvqjTfeKI7SAAAAAAAAAAAWVSyBdWJi\nokJDQyVJDRs21LZt25zbAgIC9PHHH8vLy0t//vmnrrnmGtlstnx9WrZsqU2bNhVHaQAAAAAAAAAA\niyqWJUHS09Pl7+/v/Gy325WTkyNPz3O78/T01AcffKCYmBhFRkY6+5QrV06S5Ofnp7S0NJf7yczM\nVHJycjEcAQC4X+3atYvclncfAAAAAAC4GhVLYO3v76+MjAznZ4fD4Qyrz3vggQfUq1cvDRw4UJs3\nb87XJyMjQ9dcc43L/fj4+FxSwAMAVwvefQAAAAAA4EpS1Ml3xbIkSKNGjZSQkCBJ2rp1q4KDg53b\nfv/9dw0ZMkTGGHl5ecnb21seHh5q1KiR1q1bJ0lKSEhQ48aNi6M0AAAAAAAAAIBFFcsM63bt2mnj\nxo3q06ePjDGaNGmS5s2bpypVqqht27aqVauWevfuLZvNptDQUDVr1kz169fXiBEjdP/998vLy0sz\nZswojtIAAAAAAAAAABZlM8aYki7i70pOTubX4gFcVRpHve+yTeK0B91QCQAAAAAAwOVT1Cy3WJYE\nAQAAAAAAAADgUhFYAwAAAAAAAAAsgcAaAAAAAAAAAGAJBNYAAAAAAAAAAEsgsAYAAAAAAAAAWAKB\nNQAAAAAAAADAEgisAQAAAAAAAACWQGANAAAAAAAAALAEAmsAAAAAAAAAgCUQWAMAAAAAAAAALIHA\nGgAAAAAAAABgCQTWAAAAAAAAAABLILAGAAAAAAAAAFgCgTUAAAAAAAAAwBIIrAEAAAAAAAAAlkBg\nDQAAAAAAAACwBAJrAAAAAAAAAIAlEFgDAAAAAAAAACyBwBoAAAAAAAAAYAkE1gAAAAAAAAAASyCw\nBgAAAAAAAABYAoE1AAAAAAAAAMASCKwBAAAAAAAAAJZAYA0AAAAAAAAAsAQCawAAAAAAAACAJRBY\nAwAAAAAAAAAsgcAaAAAAAAAAAGAJBNYAAAAAAAAAAEsgsAYAAAAAAAAAWAKBNQAAAAAAAADAEgis\nAQAAAAAAAACWQGANAAAAAAAAALAEAmsAAAAAAAAAgCUQWAMAAAAAAAAALIHAGgAAAAAAAABgCQTW\nAAAAAAAAAABLILAGAAAAAAAAAFgCgTUAAAAAAAAAwBIIrAEAAAAAAAAAlkBgDQAAAAAAAACwBAJr\nAAAAAAAAAIAlEFgDAAAAAAAAACyBwBoAAAAAAAAAYAkE1gAAAAAAAAAASyCwBgAAAAAAAABYAoE1\nAAAAAAAAAMASCKwBAAAAAAAAAJZAYA0AAAAAAAAAsATP4hjU4XBo/Pjx2rlzp7y9vTVx4kRVrVrV\nuf29997TqlWrJEmtWrXSkCFDZIxRy5YtVa1aNUlSw4YNNXz48OIoDwAAAAAAAABgQcUSWK9Zs0ZZ\nWVlasmSJtm7dqsmTJ2v27NmSpP379+uTTz7Rhx9+KJvNpr59++ruu+9WmTJlVLduXc2ZM6c4SgIA\nAAAAAAAAWFyxLAmSmJio0NBQSedmSm/bts25rXLlynr77bdlt9vl4eGhnJwc+fj4aPv27Tp06JAi\nIyM1cOBA/f7778VRGgAAAAAAAADAooplhnV6err8/f2dn+12u3JycuTp6SkvLy8FBATIGKOpU6eq\nTp06CgoK0p9//qlBgwbp3nvv1Q8//KCoqCh99NFHhe4nMzNTycnJxXEIAOB2tWvXLnJb3n0AAAAA\nAOBqVCyBtb+/vzIyMpyfHQ6HPD3/t6vMzEyNHj1afn5+GjdunCSpXr16stvtkqQmTZro0KFDMsbI\nZrMVuB8fH59LCngA4GrBuw8AAAAAAFxJijr5rliWBGnUqJESEhIkSVu3blVwcLBzmzFGgwcPVs2a\nNRUdHe0MqWfNmqX58+dLknbs2KF//etfhYbVAAAAAAAAAICrS7HMsG7Xrp02btyoPn36yBijSZMm\nad68eapSpYocDoe+//57ZWVlaf369ZKkYcOGadCgQYqKitK6detkt9v10ksvFUdpAAAAAAAAAACL\nKpbA2sPDQ9HR0fm+q1GjhvPnpKSki/Z78803i6McAAAAAAAAAMAVoFiWBAEAAAAAAAAA4FIRWAMA\nAAAAAAAALIHAGgAAAAAAAABgCQTWAAAAAAAAAABLILAGAAAAAAAAAFgCgTUAAAAAAAAAwBIIrAEA\nAAAAAAAAlkBgDQAAAAAAAACwBAJrAAAAAAAAAIAlEFgDAAAAAAAAACyBwBoAAAAAAAAAYAkE1gAA\nAAAAAAAASyCwBgAAAAAAAABYAoE1AAAAAAAAAMASCKwBAAAAAAAAAJZAYA0AAAAAAAAAsASXgXV2\ndrY76gAAAAAAAAAAlHIuA+tu3brpxRdf1G+//eaOegAAAAAAAAAApZSnqwYff/yx1q9fr1mzZun4\n8eO677771LFjR/n5+bmjPgAAAAAAAABAKeFyhrWHh4datmyp7t27q3z58lqwYIEeffRRLVmyxB31\nAQAAAAAAAABKCZczrKdOnar4+Hg1a9ZMAwcOVIMGDeRwONStWzf17t3bHTUCAAAAAAAAAEoBl4F1\ntWrVtHz5cpUtW9b5DzB6eHho1qxZxV4cAAAAAAAAAKD0cLkkiDFGr7zyiiTpscce04oVKyRJgYGB\nxVsZAAAAAAAAAKBUcRlYx8bGavjw4ZKkuXPnavHixcVeFAAAAAAAAACg9CnSP7ro4+MjSfLy8pLN\nZiv2ogAAAAAAAAAApY/LNazbtm2rvn37qkGDBtq+fbvCwsLcURcAAAAAAAAAoJRxGVgPHjxYbdq0\n0Z49e9S1a1fVqlXLHXUBAAAAAAAAAEoZl0uCpKamasOGDfr999+1Zs0azZo1yx11AQAAAAAAAABK\nGZeB9VNPPaX09HRdd911zv8AAAAAAAAAALjcXC4J4ufnp6efftodtQAAAAAAAAAASjGXgfWtt96q\nVatWqXbt2rLZbJKkoKCgYi8MAAAAAAAAAFC6uAysk5OTlZyc7Pxss9n0/vvvF2tRAAAAAAAAAIDS\nx2VgvWDBAqWlpenAgQO6+eab5efn5466AAAAAAAAAACljMvA+osvvtDs2bOVm5urDh06yGazafDg\nwe6oDQAAAAAAAABQini4ajBv3jwtXbpU5cuX1+DBg7VmzRp31AUAAAAAAAAAKGVcBtYeHh7y9vaW\nzWaTzWZTmTJl3FEXAAAAAAAAAKCUcRlYN2nSRMOGDdOhQ4f0/PPPq379+u6oCwAAAAAAAABQyrhc\nw3rYsGFKSEhQnTp1VKNGDbVp08YddQEAAAAAAAAAShmXM6xXrFihY8eO6brrrtPJkye1YsUKd9QF\nAAAAAAAAAChlXM6w3r17tyTJGKPk5GSVL19eXbt2LfbCAAAAAAAAAACli8vAevjw4c6fjTF67LHH\nirUgAAAAAACAK1Fmdq58vOz/uA0AlGYuA+usrCznz0eOHFFKSkqxFgQAAAAAAHAl8vGyq3HU+4W2\nSZz2oJuqAYArk8vAukOHDrLZbDLGyNfXV48++qg76gIAAAAAAAAAlDIuA+uvv/7aHXUAAAAAAAAA\nAEo5l4H1gw8W/Ksq779f+K+5AAAAAAAAAABQVC4D61tvvVW333677rjjDiUlJWnFihUaOnSoO2oD\nAAAAAAAAAJQiHq4a7Nq1S+Hh4br++usVFhamU6dOqXr16qpevbo76gMAAAAAAAAAlBIuZ1gbY/Th\nhx+qQYMGSkxMVNmyZV0O6nA4NH78eO3cuVPe3t6aOHGiqlat6tz+3nvvadWqVZKkVq1aaciQITp7\n9qyioqJ09OhR+fn5acqUKQoICPgHhwYAAAAAAAAAuJK4nGE9Y8YM/frrr5oxY4ZSU1M1efJkl4Ou\nWbNGWVlZWrJkiYYPH56vz/79+/XJJ58oNjZWS5Ys0YYNG7Rjxw4tXrxYwcHBWrRokbp27ao33njj\nnx0ZAAAAAAAAAOCK4jKwvv7669WuXTvdfffd6tSpk3x8fFwOmpiYqNDQUElSw4YNtW3bNue2ypUr\n6+2335bdbpeHh4dycnLk4+OTr0/Lli21adOmv3tMAAAAAAAAAIArkMslQWbOnKk//vhDu3fvlpeX\nl958803NnDmz0D7p6eny9/d3frbb7crJyZGnp6e8vLwUEBAgY4ymTp2qOnXqKCgoSOnp6SpXrpwk\nyc/PT2lpaS6Lz8zMVHJysst2AHAlqF27dpHb8u4DAAAArKeof6bnz/MAUDCXgXViYqIWLlyoyMhI\nRUREaPHixS4H9ff3V0ZGhvOzw+GQp+f/dpWZmanRo0fLz89P48aNu6BPRkaGrrnmGpf78fHxuaSA\nBwCuFrz7AAAAgCsXf54HUBoV9S/rXC4Jkpubq8zMTNlsNuXm5srDw2UXNWrUSAkJCZKkrVu3Kjg4\n2LnNGKPBgwerZs2aio6Olt1ud/ZZt26dJCkhIUGNGzcu0gEAAAAAAAAAAK4OLmdY9+/fX926ddOx\nY8fUs2dPPfzwwy4HbdeunTZu3Kg+ffrIGKNJkyZp3rx5qlKlihwOh77//ntlZWVp/fr1kqRhw4bp\n/vvv14gRI3T//ffLy8tLM2bM+OdHBwAAAAAAAAC4YrgMrMuXL69FixZp7969CgwMVEBAgMtBPTw8\nFB0dne+7GjVqOH9OSkq6aL/XXnvN5dgAAAAAAAAAgKuTy/U9YmJidO2116pBgwZFCqsBAAAAAAAA\nAPg7XM6wttlseuKJJxQUFORcv3rYsGHFXhgAAAAAAAAAoHQpMLDes2ePgoKC1L17d3fWAwAAAAAA\nAAAopQoMrEeNGqXY2FitWbNGr7/+ujtrAgAAAAAAAACUQgUG1lWqVFFISIhOnjypFi1a5Nu2YcOG\nYi8MAAAAAAAAAFC6FBhYT506VZL0wgsvaNy4cW4rCAAAAAAAAABQOnm4akBYDQAAAAAAAABwB5eB\nNQAAAAAAAAAA7kBgDQAAAAAAAACwhALXsD7vt99+0/jx45WWlqbOnTvr1ltvVZs2bdxRGwAAAAAA\nAACgFHE5w/rFF1/USy+9pPLly6tHjx6KiYlxR10AAAAAAAAAgFKmSEuCVK1aVTabTQEBAfLz8yvu\nmgAAAAAAAAAApZDLwPraa69VbGyszpw5o1WrVumaa65xR10AAAAAAAAAgFLGZWA9adIkpaSkqEKF\nCtq2bZtefPFFd9QFAAAAAAAAAChlXP6ji6+99pp69eqlW265xR31AAAAAAAAAABKKZeBdaNGjTRt\n2jRlZGSoW7du6tixo3x9fd1RGwAAAAAAAACgFHG5JEiHDh00d+5czZw5U+vXr1eLFi3cURcAAAAA\nAAAAoJRxOcP64MGDWr58ub788kvVqVNHb731ljvqAgAAAAAAAACUMi4D6yeffFI9e/bUwoUL5e/v\n746aAAAAAAAAAAClUIGB9R9//KHKlStr2rRpstlsOnLkiI4cOSJJCgoKcluBAAAAAAAAAIDSocDA\net68eRo1apTGjRuX73ubzab333+/2AsDAAAAAAAAAJQuBQbWo0aNkiQ9/PDDCgsLc37/2WefFX9V\nAAAAAAAAAIBSp8DA+ptvvtGPP/6oVatWaevWrZIkh8Oh+Ph4dezY0W0FAgAAAAAAAABKhwID61q1\naunEiRPy8fFxrllts9nUqVMntxUHAAAAAAAAACg9Cgysb7zxRkVERKhLly7y8PBwfn/48GG3FAYA\nAAAAAAAAKF0KDKzPmzVrlhYtWqTs7GydPXtW1apV06pVq9xRGwAAAAAAAACgFPFw1SAhIUEJCQnq\n3LmzPvvsM1WqVMkddQEAAAAAAAAAShmXgXX58uXl7e2tjIwMVa1aVWfOnHFHXQAAAAAAAACAUsZl\nYF25cmUtW7ZMZcqU0YwZM5Senu6OugAAAAAAAAAApYzLNayjo6OVmpqqDh06aPny5XrllVfcURcA\nAAAAAAAAoJQpMLBesmTJBd95e3vrhx9+UI0aNYq1KAAAAAAAAABA6VNgYH3kyBF31gEAAAAAAAAA\nKOUKDKyHDBni/Pnbb79VSkqKGjRooKCgILcUBgAAAAAAAAAoXVyuYT1z5kz98ccf2r17t7y8vPTm\nm29q5syZ7qgNAAAAAAAAAFCKeLhqkJiYqKlTp6ps2bKKiIhQSkqKO+oCAAAAAAAAAJQyLgPr3Nxc\nZWZmymazKTc3Vx4eLrsAAAAAAAAAAHDJXC4J0r9/f3Xr1k3Hjh1Tz5499fDDD7ujLgAAAAAAAABA\nKeMysC5fvrwWLVqkvXv3KjAwUAEBAe6oCwAAAAAAAABQyrhc3yMmJkbXXnutGjRoQFgNAAAAAAAA\nACg2LmdY22w2PfHEEwoKCnKuXz1s2LBiLwwAAAAAAAAAULq4DKy7d+/ujjoAAAAAAAAAAKWcy8A6\nIiLCHXUAAAAAAAAAAEo5l2tYAwAAAAAAAADgDgTW/9/encdVVe3/H38zCA7gkJppiglKmmYGmrdy\niJytnDVRMSu1umkOOeUUGZpTVtrXsTTFIeyrVlZWan01LU3p+vM6ow2iXqfEAYjxrN8fPjgX5ACH\n4egmX8+/lLP3Z62z91prr/05ewAAAAAAAAAAWAIJawAAAAAAAACAJZCwBgAAAAAAAABYAglrAAAA\nAAAAAIAlkLAGAAAAAAAAAFiCpyuC2mw2hYeH6+jRo/Ly8lJERIRq1qyZZZlLly6pd+/e2rhxo7y9\nvWWMUYsWLXTPPfdIkho1aqRXX33VFdUDAAAAAAAAAFiQSxLWW7ZsUUpKiqKiorRv3z5Nnz5dCxYs\nsH/+ww8/6O2339bFixftfzt58qTq16+vhQsXuqJKAAAAAAAAAACLc8kjQaKjo9W8eXNJ16+UPnDg\nQNZC3d21bNkylS9f3v63gwcP6ty5cwoLC9OgQYP066+/uqJqAAAAAAAAAACLcskV1vHx8fLx8bH/\n38PDQ2lpafL0vF7co48+mm2dypUra/DgwerQoYP27t2r0aNHa926dbmWk5ycrMOHDxdt5QHgFqlX\nr57TyzL2AQAAANbj7Jye+TwA5MwlCWsfHx8lJCTY/2+z2ezJ6pw0aNBAHh4ekqTGjRvr3LlzMsbI\nzc0tx3W8vb3zleABgL8Lxj4AAACg+GI+D+B25OyPdS55JEhQUJC2b98uSdq3b58CAwPzXOf999/X\n8uXLJUlHjhxRtWrVck1WAwAAAAAAAAD+XlxyhXWbNm20c+dO9e7dW8YYTZs2TcuWLZOfn59atWrl\ncJ3Bgwdr9OjR2rZtmzw8PPTWW2+5omoAAAAAAAAAAItyScLa3d1dU6ZMyfK3gICAbMt999139n+X\nK1dOixcvdkV1AAAAAAAAAADFgEseCQIAAAAAAAAAQH6RsAYAAAAAAAAAWAIJawAAAAAAAACAJZCw\nBgAAAAAAAABYAglrAAAAAAAAAIAlkLAGAAAAAAAAAFgCCWsAAAAAAAAAgCWQsAYAAAAAAAAAWAIJ\nawAAAAAAAACAJZCwBgAAAAAAAABYAglrAAAAAAAAAIAlkLAGAAAAAAAAAFgCCWsAAAAAAAAAgCWQ\nsAYAAAAAAAAAWAIJawAAAAAAAACAJZCwBgAAAAAAAABYAglrAAAAAAAAAIAlkLAGAAAAAAAAAFgC\nCWsAAAAAAAAAgCWQsAYAAAAAAAAAWAIJawAAAAAAAACAJZCwBgAAAAAAAABYAglrAAAAAAAAAIAl\nkLAGAAAAAAAAAFgCCWsAAAAAAAAAgCWQsAYAAAAAAAAAWAIJawAAAAAAAACAJZCwBgAAAAAAAABY\nAglrAAAAAAAAAIAlkLAGAAAAAAAAAFgCCWsAAAAAAAAAgCWQsAYAAAAAAAAAWMJtk7A2aclFuhwA\nAAAAAAAAoGh53uoK3Cxunt46OeX+PJfzm/zvm1AbAAAAAAAAAMCNbpsrrAEAAAAAAAAA1kbCGgAA\nAAAAAABgCSSsAQAAAAAAAACWQMIaAAAAAAAAAGAJJKwBAAAAAAAAAJZAwhoAAAAAAAAAYAkkrAEA\nAAAAAAAAlkDCGgAAAAAAAABgCSSsAQAAAAAAAACWQMIaAAAAAAAAAGAJJKwBAAAAAAAAAJZAwhoA\nAAAAAAAAYAkkrAEAAAAAAAAAluCShLXNZtPkyZP19NNPKywsTH/88Ue2ZS5duqS2bdsqOTlZkpSU\nlKShQ4eqT58+GjRokC5duuSKqgEAAAAAAAAALMolCestW7YoJSVFUVFRevXVVzV9+vQsn//www96\n7rnndPHiRfvf1qxZo8DAQK1evVpdunTR/PnzXVE1AAAAAAAAAIBFuSRhHR0drebNm0uSGjVqpAMH\nDmQt1N1dy5YtU/ny5R2u06JFC/3000+uqBoAAAAAAAAAwKI8XRE0Pj5ePj4+9v97eHgoLS1Nnp7X\ni3v00UcdruPr6ytJKlOmjK5du+aKqgEAAAAAAAAALMolCWsfHx8lJCTY/2+z2ezJamfWSUhIUNmy\nZfMsJzk5WYcPH3aqTvXq1XNqOUlOxwSAosQ4BQAAABRvzs7pmc8DQM5ckrAOCgrS999/r44dO2rf\nvn0KDAx0ap1t27apYcOG2r59u4KDg/Ncx9vbO18JHme5IiYAFCXGKQAAAKD4Yj4P4Hbk7I91LklY\nt2nTRjt37lTv3r1ljNG0adO0bNky+fn5qVWrVg7XCQ0N1dixYxUaGqoSJUro7bffdkXVAAAAAAAA\nAAAW5ZKEtbu7u6ZMmZLlbwEBAdmW++677+z/LlWqlObOneuK6gAAAAAAAAAAigH3W10BAAAAAAAA\nAAAkEtYAAAAAAAAAAIsgYQ0AAAAAAAAAsAQS1gAAAAAAAAAASyBhDQAAAAAAAACwBBLWAAAAAAAA\nAABLIGENAAAAAAAAALAEEtYAAAAAAAAAAEsgYQ0AAAAAAAAAsAQS1gAAAAAAAAAASyBhDQAAAAAA\nAACwBBLWAAAAAAAAAABLIGENAAAAAAAAALAEEtYAAAAAAAAAAEsgYQ0AAAAAAAAAsAQS1gAAAAAA\nAAAASyBhDQAAAAAAAACwBBLWAAAAAAAAAABLIGENAAAAAAAAALAEEtYAAAAAAAAAAEsgYQ0AAAAA\nAAAAsAQS1gAAAAAAAAAASyBhDQAAAAAAAACwBBLWAAAAAAAAAABLIGENAAAAAAAAALAEEtYAAAAA\nAAAAAEsgYQ0AAAAAAAAAsAQS1gAAAAAAAAAASyBhDQAAAAAAAACwBBLWAAAAAAAAAABLIGENAAAA\nAAAAALAEEtYAAAAAAAAAAEsgYQ0AAAAAAAAAsAQS1gAAAAAAAAAASyBhDQAAAAAAAACwBBLWAAAA\nAAAAAABLIGENAAAAAAAAALAEEtYAAAAAAAAAAEsgYQ0AAAAAAAAAsAQS1gAAAAAAAAAASyBhDQAA\nAAAAAACwBBLWAAAAAAAAAABLIGENAAAAAAAAALAEEtYAAAAAAAAAAEsgYQ0AAAAAAAAAsAQS1gAA\nAAAAAAAASyBhDQAAAAAAAACwBBLWAAAAAAAAAABL8HRFUJvNpvDwcB09elReXl6KiIhQzZo17Z+v\nXbtWH3/8sTw9PfXSSy8pJCREly9fVrt27RQYGChJat26tZ555hlXVA8AAAAAAAAAYEEuSVhv2bJF\nKSkpioqK0r59+zR9+nQtWLBAknThwgVFRkZq3bp1Sk5OVp8+ffToo4/q0KFDevLJJzVp0iRXVAkA\nAAAAAAAAYHEueSRIdHS0mjdvLklq1KiRDhw4YP9s//79evDBB+Xl5SVfX1/5+fnpyJEjOnDggA4e\nPKh+/frplVde0fnz5xlK3qcAACAASURBVF1RNQAAAAAAAACARbnkCuv4+Hj5+PjY/+/h4aG0tDR5\nenoqPj5evr6+9s/KlCmj+Ph4+fv7q0GDBnrkkUf0+eefKyIiQnPnzs21nOTkZB0+fNipOtWrV8/p\n+jsbEwCKEuMUAOTN7x5/lSnlnedyCX8l6+Tvv96EGgEA8F/OzumZzwNAzlySsPbx8VFCQoL9/zab\nTZ6eng4/S0hIkK+vrxo2bKhSpUpJktq0aZNnslqSvL2985XgcZYrYgJAUWKcAnA7Cx69Is9lomf1\nZ6wEAFgWxygAtyNnf6xzySNBgoKCtH37dknSvn377C9SlKSGDRsqOjpaycnJunbtmk6cOKHAwEBN\nnDhR33zzjSTpp59+Uv369V1RNQAAAAAuYtKSi3Q5AAAA3H5ccoV1mzZttHPnTvXu3VvGGE2bNk3L\nli2Tn5+fWrVqpbCwMPXp00fGGI0YMULe3t569dVXNX78eK1Zs0alSpVSRESEK6oGAAAAwEXcPL11\ncsr9eS7nN/nfN6E2AAAAKI5ckrB2d3fXlClTsvwtICDA/u9evXqpV69eWT6vUaOGIiMjXVEdAAAA\nAAAAAEAx4JJHggAAAAAAAAAAkF8krAEAAAAAAAAAlkDCGgAAAAAAAABgCSSsAQAAAAAAAACWQMIa\nAAAAAAAAAGAJJKwBAAAAAAAAAJZAwhoAAAAAAAAAYAkkrAEAAAAAAAAAlkDCGgAAAAAAAABgCSSs\nASAfklPTi2QZAAAAAMgvzkeA7ExacpEuh1vP81ZXAACKE+8SHgoevSLXZaJn9b9JtQEAAABwO+F8\nBMjOzdNbJ6fcn+dyfpP/fRNqg6LAFdYAAAAAAAAAAEsgYQ0AAAAAAAAAsAQS1gAAAAAAAAAASyBh\nDQAAAAAAAACwBBLWAAAAAAAAAABLIGENAAAAAAAAALAEEtYAAAAAAAAAAEsgYQ0AAIC/HZOWXCTL\nAAAAALi5PG91BQAAAICi5ubprZNT7s91Gb/J/75JtQEAAADgLK6wBgAAAAAAxYqzd8ncjnfTcJcR\ngOKOK6wBAAAAQNcTOG6e3oVeBoDrOXMnjXR73k3DXUYAijsS1gAAAAAgkjwAAABWwCNBAABAjpJT\n04tkGQAAAAAAnMEV1gAAIEfeJTwUPHpFrstEz+p/k2oDAAAAAPi74wprAChmeIkKAAAAAAD4u+IK\nawAoZni+JqzG2ReQWfVFZbxkDQAAAACsg4Q1AAAoFGd+RJGs+0MKPwIBAAAAgHXwSBAAAAAAQLHj\n7Et/eTlw0eOlzLjdMN4ANxdXWAMAAAAAih1nXgws8XJgV+ClzLjdMN4ANxdXWAMAAAAAAAAALIGE\nNQAAgIuYtOQiXa4g6xU0NgAAAADcCjwSBAAAwEVc/UJKXhgJAAAA4O+GK6wBAAAAAAAAC3P1nXuA\nlXCFNQAAAAAAAGBhrr5zD7ASrrAuIjxDEgAAAAAAAAAKhyusiwjPkASKlklLlpund6GXAQAAgLU5\nO6cr6NyPeSUAAMULCWtYVnJqurxLeBTZcihe+BEIfyfOjFMFHcsYKwFkYDxAccULanEjxikAuL2R\nsIZleZfwUPDoFXkuFz2r/02oDQAUnDPjWUHHMsZKABkYDwD8Xbhy7gQAsD6eYQ3eNAsAAAAgV7yz\nB8DfAWMZUDxwhTV40yxQxFz9HEYAAFD8FPf5AY/VAPB3wFgGFA8krHHbcuUzZV2tOD+j0op1KmrF\n+UegwpxMF+c+hVujOI9lyF1xHg9cXffivG1QOMV5fgD8nTD/AADrI2GN21Zxfi5acX5GZXGu++2g\nMCfTxblP4dZgPPj7Ks7jgavrXpy3DazNmR+drXr1NnAzWWH+UdzvuABuNxxjc+aqbUPCGgAAAACK\nOW5zB4oP7rj4+yKx+ffEMTZnrto2JKwBAIClMfEHAABAcUBiEygaJKwBAHACt27eOkz8bw3aPAD8\n/fGjMADAikhY32LF/YUPvDjIMfbrrVOc646cWaFPcesmbje0edxsHMNhNa6ef1ihzfOjMDK7Hdr8\n7cgK+7Uw8YszXuZdcCSsbzErvPChMHhxkGPs11unONcdOSvufQoAkDeO4bAaV88/aPOwGtr835MV\n9mth4hdnvMy74FySsLbZbAoPD9fRo0fl5eWliIgI1axZ0/752rVr9fHHH8vT01MvvfSSQkJCdOnS\nJY0aNUpJSUm688479dZbb6lUqVKuqB4AAAAAAAAAwILcXRF0y5YtSklJUVRUlF599VVNnz7d/tmF\nCxcUGRmpjz/+WB9++KHmzJmjlJQUzZ8/X08++aRWr16t++67T1FRUa6oGgAAAAAAAADAolySsI6O\njlbz5s0lSY0aNdKBAwfsn+3fv18PPvigvLy85OvrKz8/Px05ciTLOi1atNCPP/7oiqoBAAAAAAAA\nACzKzRhjijrohAkT1LZtW7Vs2VKS9Nhjj2nLli3y9PTUZ599pmPHjmn06NGSpDFjxqhLly56/fXX\ntXHjRpUsWVKxsbEaM2aM1qxZk2s5+/btk7c3bysGAAAAAAAAACtLTk5Wo0aN8lzOJc+w9vHxUUJC\ngv3/NptNnp6eDj9LSEiQr6+v/e8lS5ZUQkKCypYtm2c5znxBAAAAAAAAAEDx4JJHggQFBWn79u2S\nrl8FHRgYaP+sYcOGio6OVnJysq5du6YTJ04oMDBQQUFB2rZtmyRp+/btCg4OdkXVAAAAAAAAAAAW\n5ZJHgthsNoWHh+vYsWMyxmjatGnavn27/Pz81KpVK61du1ZRUVEyxuiFF15Qu3btdPHiRY0dO1YJ\nCQmqUKGC3n77bZUuXbqoqwYAAAAAAAAAsCiXJKwBAAAAAAAAAMgvlzwSBAAAAAAAAACA/CJhDQAA\nAAAAAACwhGKXsI6JidHgwYMVFham7t27a+7cuYqNjVWvXr2yLTtv3jytWbNGn376qcLCwtSrVy8F\nBQUpLCxMYWFhOnfunMLCwnTixIkcY1vtiSnjxo2zv9CyINavX6/Zs2cXYY1yjjtixAilpKTozJkz\n+u677woVqyjr5cryCiOjLWa028I4evSo9uzZk+Pnjz/+uJKTk/OMk7FtLly4oPDwcKfKLor63wqn\nTp1yOI44Kzk5WY8//rimTp2qM2fOZPnsxIkTCgsLK2wV7WW4Yv387OOClmGlmEVl5cqVTi+bnp6u\n559/Xi1atNCGDRucWmf79u0aN25cjrFCQ0N15coVp+uQl+TkZH3yySdFEis/2+ZGjvpRcVNUfSq/\noqKilJqa6vTyBT0GPvroo7fs+Onqcm/29yrKOVRuMZ2xZ88eHTlyJN/rbd++XVFRUfleTyr67e3o\neG7FtlqQfetsvMcff1ynTp3KX2UzlTF58mT7+OXsnNGZuPndB0VVdmHqAGnNmjWaN2+e08sX9X7L\nr4LOP4YMGZLtb/n97n8nu3fv1ogRI25J2YcPH9b7778vSdq8ebPOnTvn9Lp5zWWLqn3mVc6jjz4q\nSVnyTc7Ey+lc2lH7zJDbeWxecW8nBZ3jFCeFzWkURMZ52+XLl7Vx48YijV2sEtZXr17VyJEjNX78\neEVGRmrt2rU6duyYduzYket6Xbp0UWRkpObMmaPatWsrMjJSkZGRqlKlSp6xP/74Y1d/rb+td955\nR15eXtq1a5d++eWXW12d28q3336r48ePF1m8ypUr35LES3E0YcIEVatW7VZXI9/YxwWzYMECp5e9\ncOGC4uLitH37dnXt2rVQ5WbEWrNmjcqVK1eoWDfGLaqEdX62zY2Kaz/K7Fb1qUWLFslms930clG0\nbtUcat26dTp//ny+12vRooWefvppF9To76uo923meFWqVFHFihULHKts2bLMCVBsFXT+kZEgxa1X\nr149e4J2xYoVio+Pd3rdopzL3sxy8opX0PZ5s7ZHcVDQOQ5yl3HedvTo0QJdZJEbzyKN5mJbt25V\n06ZNdc8990iSPDw8NGPGDJ0/f97pq9XyG7tEiRL2ZeLj4zVhwgRdu3ZNcXFx6tmzpzZt2qQKFSro\n6tWrWrx4scLDw/XHH3/IZrNp+PDhatq0qVPlO4ptjNGnn34qd3d3BQUFaezYsfbl/9//+3+KiIjQ\n3LlzVbVq1Xx913379umZZ55RfHy8hg4dqtKlS+udd96Rh4eHatSooSlTpmT53gWNO2XKFH3xxRda\nvHixkpKS9OCDD6p69eqKiIiQJJUvX17Tpk2Tr69vnrFmz56te+65R15eXnrjjTc0YcIExcXFSZIm\nTpyoe++9VytXrtS3336rtLQ0+fr6Zvk1/NKlS/rnP/+pYcOG6eGHH3aq7ps2bZK3t7dmz54tf39/\n/f7776pSpYr69u2rK1eu6Nlnn9X69evz3C5JSUl67bXXdObMGaWmpmrcuHFatWpVln3dp08fp7ax\nM7FatWqlDRs2qESJEqpfv77+/PNP+wHuvvvu0xtvvCFJCg8Pt1998/7776tcuXLZ4rdr107S9V/q\nRo4cqbVr1+qpp55S48aNdezYMdWqVUsVK1bU3r175eXlpcWLF0uStmzZok2bNikpKUkTJ05Uw4YN\ns8UeP368oqKiFBsbq/T0dD377LPq2LGjwsLCdO+99yomJkalS5dW48aNtWPHDl29elVLly5V6dKl\n9frrrzvsZ86WcejQIb355pvy8PCQt7e33nzzTfs2Tk9P17hx41SnTh0NHjw41/2RkJCgUaNG6erV\nq/Lz85N0/Vf08PBw+fr6atSoUTLGqHLlyk7tX2fLcFT/nJJ7zq5vs9my7OOHHnpIR48elZubm+bP\nny8fHx+98cYbOnDggCpVqqTTp09rwYIFql69eo7bIWNsnDdvniZOnJilnT744IN69913tWjRIvs4\n8fnnn2vv3r367LPPNG7cuFxj5jTefv3111q1apX9+7/33nuKiYnR4sWLVaJECZ09e1a9e/fWrl27\ndOTIEfXv3z9b/0tNTc3WxuLi4rLFjYqK0pUrVxQeHu7Uif2kSZP0+++/a/LkyapXr578/f21ZMkS\nlShRQqdOnVLHjh310ksv6cSJExo/frxKlSqlUqVKOUxIZ4517tw5xcfHKz093T7GPfnkk/Yxc+LE\niRo1apRSUlJUq1Yt7dq1S5s3b9bPP/+cbexfuHChjh8/rvfff9/hlRxJSUkaM2aMzp8/r6pVq2rP\nnj368MMPs43rK1eudHrbOIpZq1YthYeHa/To0Zo7d66qV6+uTZs2KTo6WsOGDXN4DHCGo7IWL17s\ndH/KT4zC9KmClrlz505duHBBI0aM0Pz5853aJtL1OcVzzz2nS5cuKTQ0VOXKlcvW3suVK6dJkybp\n+PHjqlGjhlJSUpxeNyYmRgsXLpS7u7suXLigp59+Wn379lVYWJhq1aql3377TcYYvfPOO6pcubLe\nfvtt7dmzR8YYDRgwQB06dFBYWJjq1q2rmJgYxcbGqmLFinmWu2jRItWtW1ddu3bVhQsX9MILL2j9\n+vUO4+e1TWrVqpWtvyQnJ2ebu/Xp0yfLWPXhhx/Kw8Mj3/sg89iYMYdq1aqV0/s0p5g3zm3uvvtu\nzZ49WyVKlNAjjzyiH374QQcPHlTt2rXz9aPR+vXr9euvv+r48eOKj49XUlKSRo8e7fQ8WPrvXK17\n9+7asWOHkpKSdPLkSQ0aNEjdunXL8dg3f/58bdmyRenp6QoNDVWzZs0kZT2eV6pUqcBlLFu2TMHB\nwWrfvr2ef/55NW/eXAMGDNCECRPUvXt3BQUF5fndIiMj9cUXX8jNzU0dO3ZU3759C7Vv84o3b948\nlSpVSqtWrcrxXCI3p0+fVq9evbR27Vr739asWaOdO3dqzpw52rdvX4HPHZYuXaovv/xSnp6eaty4\nsUaPHq2rV69q9OjR2Y5ljsr28vKSdL3Nff/990pKStKFCxfUv39/bd26VTExMRozZozOnj3r1HlB\n48aNHc4r84qfmpqqjz76SO7u7goODtaoUaN09uxZhYeHKzk5WZcvX9bLL7+s1q1bZzsGdOjQQbt3\n787S/qpXr26fryclJdnPQ0eMGKGqVavq1KlTeuKJJxQTE6NDhw7pscce08iRI3X06FGH51br16/X\ntm3bspRRv359h8s6GhP37t2radOmqVy5cnJ3d1ejRo0c7s/169dr69atio+PV1xcnF5++WX7Z8eO\nHdP06dNls9l09epVTZw4UYmJiVq7dq3mzp0rSerdu7fmzp2rO++8M9d246icGTNmZBvTzp07l6+5\n2fr167Vu3TrZbDb99ttv2rVrV47f/cZ+179//zzj//bbb3rttdfk6ekpDw8PzZw5U3PnztXZs2cV\nFxenFi1a6JVXXlG7du30ySefqHz58lq9erUSExM1cODAfMdeuXJltn2ZUxvJK1737t31xx9/aODA\ngbp06ZJCQkI0dOhQl4yTjsr+/vvv1blzZx0+fFhjx47V6tWr7f0/N5nnsgcOHMjWHzMUdkzLXM6x\nY8cKNC91FG///v1q1qyZvv76a12+fFnDhg3T448/rkcffVQ7d+7MMh+Kj4/Xe++9Z4/h6Dw2r7gh\nISHy9/eXv7+/nnnmGU2YMEFpaWlyc3PTxIkTtWvXLvudnZMnT7afW8yfP181atTQU089leV7ZMwH\nRo0apeTkZHXo0EHPP/98tmPRf/7zH02aNEnJycn2dpRTXqtr16764IMPVLZsWTVt2lQrV67Ufffd\np65du6pZs2Y6cOCAEhISFBAQoLfeekvz5s3TqVOn9Oeff+rMmTN67bXXVKFChVznOPktIzo6WjNm\nzJCnp6fKli2r2bNny8fHJ19179Kli7766qssY8q4ceN0+fJlXb58WYsWLdIHH3zgcM6aU9xu3brJ\nx8dH//znP3XhwgXde++9ioiIcLi909PT9eqrr+quu+5SbGys7r//fnuuKLcyHnroIVWsWFGVKlVS\nixYttH37doWHh2vhwoU6cuSIoqKi1KJFC6f3b65MMbJw4ULz0UcfZft7bGys6dmzZ7a/z50716xe\nvTrX5fr162eOHz+eY+zMDhw4YL755htjjDFnz541bdq0Mf369TPffvutMcaYVatWmZkzZxpjjLl0\n6ZLp2LGj09/NUexu3bqZf/3rX/bYqampZuzYsebdd981Tz/9tLl48aLT8TOsW7fODBw40NhsNnPx\n4kUTEhJi2rZta4/1zjvvmKioqCKJ27JlS5OUlGTWrVtnZs2aZYwxpmfPniYmJsYYY8zatWvNnDlz\nnI518OBBY4wxM2fONKtWrTLGGPPbb7+Z3r17m/T0dDNv3jyTnp5ujDHmueeeM3v37jXr1q0z48aN\nM7169TL79u3Ld92NMWbWrFlm3bp15uTJk6ZHjx7GGGNWrlxpli5d6tS2WbZsmf37Hz161CxdujTb\nvjbmv23xxnZbkFgZMVJTU01ISIh9/86bN8+cPn3ahISEmD179hhjjBk7dqz58ssvHcbP+H/mvhMS\nEmL27t1rjDGmXbt25v/+7/+MMcb07dvXHDp0yMydO9dMmjTJGGPMsWPHTJcuXRzG/p//+R8zdepU\nY4wx165dM23atDF//vmn6devn/nss8+MMdf348qVK40xxowZM8Zs3rw5137mbBldu3Y1hw4dMsYY\ns3nzZjN06FATGxtrunbtaoYPH24vMy+RkZH2Nrxv3z4TEhJi34/Tp0+396Uvv/zS9OvXz6mYzpTh\nqP6FXf/GfRwdHW2MMWbkyJHmiy++MJs3bzbDhg0zxhjz559/muDgYBMbG5vrdsgYGx2Nb8YY8+ST\nT5qkpCQzZswY06lTJ3PhwgUzY8YMs23btjxj5tQOFixYYBITE40xxkyaNMl89tlnZteuXaZjx44m\nJSXF/Otf/zItWrQwycnJ5uTJk6ZTp07Ztpmj2I7iGmPMI488ktcutMvYxhn9c9euXaZDhw4mNTXV\nJCQkmKCgIGOMMUOHDjU7duwwxhizaNEiM3bs2BxjTZ8+3X7sOnv2rAkJCTHp6ekmJCTEPmZOnTrV\n3qZ37NhhQkJCjM1mczj253Q8zfDRRx+ZGTNmGGOMOX78uKlbt26O47qz28ZRzIx+tGrVKjNv3jxj\njDGDBg0yR48edXgMcJajsvLTn/ITozB9qjD1DgkJsR+/nLFu3TozYMAAY7PZTGxsrOnQoYPD9v7d\nd9+ZkSNHGmOMOX36tKlfv77T62a09eTkZPPXX3+Z1q1bm4sXL5p+/fqZDRs2GGOuH1fffPNN83//\n939m+PDhxhhjkpKSTKdOncyVK1dMv379zOeff26MMWbw4MGmdevWeZZ7/PhxExYWZowxZsmSJSYy\nMjLH+Lltk/bt2zvsLzmNbZnHqoLug4z9mHkOlR+5xTTmv3ObXbt2maeeesq+3tixY822bdsKVN7Q\noUNN9+7dzbVr18zvv/9unx84s27mudq6devMc889Z4y53sfbtWtnjDEO2/zBgwfN008/bdLS0kxi\nYqJ58803zcmTJ7MdzwtTxs8//2xee+0189dff5kePXqYQYMGGZvNZrp06WJsNlue3+2VV14xvXv3\nNmlpaSY9Pd2EhYWZEydOFGjf5jeeo3MJZ8oYPnx4lvFryZIlZuTIkSYtLS3H44czcYcMGWJ69Ohh\nUlJSjM1mMy+//LL57rvvcj2WZS77xnjPPvusMcaYL774wvTo0cPYbDbz008/mRdeeMHp84Kc5hN5\nxe/QoYN9zBk1apTZsWOH2blzp9m1a5cxxpjo6GgzYMAA+zbMfAyYPHlytva3cuVKc/bsWWPM9bnM\n/PnzTWxsrGnatKm5evWqOX/+vLn//vtNXFycSUpKMg8//LAxJudzK0dt3NGyOY2J3bp1M7/++qsx\nxpjJkyebuXPn5rhfBwwYYNLT082FCxfMY489Zpo3b26SkpLMl19+aY4cOWKMMebzzz83EyZMMDab\nzbRr185cvnzZxMTEmBdffDHPtpNXOcb8d0wzJn9zs3Xr1tnrkLGeo+8eExPjsN/lZeXKlWbKlCkm\nJSXF/Pjjj+bo0aNm7dq1xpjr2/uhhx4yxhjz3nvv2cerp59+2ly4cCHfsVesWOFwXzpz/u0oXmRk\npOnYsaNJTk42iYmJ9roW9TiZU9kZ3yVjTuisjPlXbv2xKMa0jHJympdmtCdn65/5PGH8+PHGGGN2\n7dplBg4cmC1exnxozpw5ZtGiRbmex+YV99577zWXLl0yxlw/B9m8ebMxxphDhw6Zrl27mtOnT5tn\nnnnGXnZGPiQ0NNRcu3Yt2/fIfCxKSkoyISEhDo9Fw4YNs88TfvzxR/sc05F58+aZDRs2mJ9++sk8\n9dRTZvHixSYmJsa8/PLLZvHixcYYY9LT00379u3N2bNnzdy5c83EiRONMdfPfTLGwtzmOPktY/r0\n6Wbx4sUmPT3dbN682Zw+fTpfcXM6lo8dO9YsW7bMGGNynbPmFHf48OGmadOm5vLlyyY9Pd08/vjj\n5uLFiw63d2xsrHnooYfMtWvXTFpamnnsscfM+fPn86x7RhnJycn2dnH8+HGza9cue33zs39zU6yu\nsK5WrZoOHTqU5W+xsbE6e/asS2M3adJEklSpUiUtX75c3377rXx8fJSWliZJqlWrlqTrvyJHR0dr\n//79kqS0tDTFxcWpQoUKeZbvKPZbb72lpUuXavbs2WrUqJH9edo7d+5UQkKCPD0LtvuCg4Pl5uam\nihUrqmTJkjp16pSGDx8u6fov+hnPWypMXF9fX/3xxx/Zljlx4oT9V5vU1FT7tnMmVubtvGvXLm3a\ntEnS9ce5uLu7q0SJEho5cqRKly6ts2fP2vfPDz/8oMqVK+d6i3Rudc/Y7jVq1FCZMmV0/Phxbdy4\n0emr13799Ve1aNFCkhQYGKhy5crp7bffztaOXBErLi5OZcuWtd8WmvmKyQYNGki63vaSkpIcxj9w\n4IAuXryYrR7169eXdP2W0YCAAPu/M54HltFn6tSpowsXLjiMvWbNGj3yyCOSJB8fHwUEBCg2NjZb\n/Nq1a2eJn1s/c7aM8+fPq169eva6vv3225KuP/vbx8dHiYmJTu2PmJgYNW/eXJL0wAMPZOmTMTEx\n6ty5syQpKCiowM8Mc1RGTvUvyvXvu+8+SVLVqlWVnJys06dP268wueOOO+Tv75/ndsjoszmNnc2a\nNdPu3bv1n//8R0899ZR+/PFH7d27VyNGjNDWrVtzjZlTO6hYsaLGjh2rMmXK6Ndff7XXuU6dOipR\nooR8fX3l5+cnLy8vlStXzuEz7BzFdnd3dxi3sAIDA+Xp6SlPT0+VLFnSvj0bNmwo6Xrb+fXXX3Nc\n/8SJE/YrG6pUqSIfHx9dunRJ0n+31YkTJ+yPIGncuLGk61eXnT9/Pt9j/4kTJ+x9LCAgQHfccYfT\n43p+Ymbo1KmTQkND1bNnT8XHxyswMNDhMaAwZeWnPxUmRn76lCvqnZv77rtPbm5uqly5spKSkhz2\no8ztslq1avarJJxZV5IefPBB+5VRderU0cmTJyVJ//jHPyRdb+vfffedqlSpooMHD9qf+5+WlmZ/\nnnnGNixXrpwqVaqUZ7kBAQFKT0/X6dOn9dVXX+mjjz5SVFSUw/hly5bNcZucOXNG7u7u2fpLy5Yt\nHY5tkvLdD27cjkUht5gm0zta8lvXnPj5+SkkJEQjR45UWlpavt7dcONcrW7dupKu95eMq/kdtfnf\nfvtNDRs2lIeHh0qVKqWJEyfq1KlTDo/nBS0jODhYU6dO1e7du9W2bVt988032rt3rxo1aiQ3N7c8\nv9uBAweUlpamAQMGSJKuXLlib/8FkZ94OZ1L5NdPP/0kDw8PeXh46M8//yzQ8UO6/lzaxx57zH7l\nYuPGjRUTE5PrsSxz2TfK2Fe+vr4KCAiQm5ubypUrp9TUVKfPC3KaT+QWPzExUZcuXbJfwZiQkKDY\n2FgFBwdrwYIF+t///V+5ubllGRMyHwPi4uKytb8qVapo6tSpKl26tM6dO2e/IrVGjRry9fWVl5eX\nKlWqpPLly0uSqdgrigAAEZJJREFUve3ldgy+sQxHyx47dszhmHju3Dl7rKCgoFzbbJMmTeTu7q5K\nlSqpbNmy9uf13nnnnZo/f75KliyphIQE+fj4yM3NTZ06ddIXX3yhU6dOqUePHjnGdbYcSYV679SN\nY6Cj737s2DGdOXMmW7/L7dgtST169NCSJUs0cOBA+fr6asiQIfr3v/+tXbt2ycfHxz729OjRQyNG\njFCTJk1UqVIl+10h+Yldt25dh/vS2XnajfEeffRR1alTx37szpiLu2KcdFR2YVWuXDnH/lhUY5rk\nODdRWBnnwpnP0zPLGE/uuusu+7m6M+exjuJWqFDBnq86ceKE/Ty+Xr16Onv2rKpVq6akpCTt379f\nAQEBOnPmjPbv3y9fX1+HVxRnltEvHR2Ljh07Zr+C2BiT6xXtbdu21cKFC1W1alWNGDFCkZGRMsbo\niSee0P79++1jfWJiov09Lhlt9K677rL3s9zkt4wXX3xRCxcu1DPPPKMqVarY58jOxm3Xrp1mzJjh\n8Fie+Xw3pzlrTnHr16+vU6dO2e/QrVixov76668ct7efn599P1auXDnLuXFuZcTGxuZ6x0N+9m9u\nitUzrENCQvTDDz/Yd2RqaqqmT5+uY8eO3ZTYS5cuVaNGjTR79my1b9/e3gEzBmF/f3898cQTioyM\n1JIlS9S+fXunny3qKPbatWv1xhtvaOXKlTp8+LD+9a9/SbqedBwwYECBny3373//W9L15xklJyfr\n7rvv1vz58xUZGakXX3wxX7dv5hQ3MTHRPvC5u7vbJ4W1atXSjBkzFBkZqdGjR6tly5ZOx3J3v95c\n/f39NWDAAEVGRurdd9/VU089pSNHjmjLli169913NWnSJNlsNvv+6dKli2bNmmW/Dc2Z8qpUqaLz\n58/LGJPlwfy9evXSggULVKVKlSxJldwEBATY48fGxurNN9902I6KMpabm5tsNpsqVqyoq1ev6vLl\ny5KkiIgI+4Tc0eThxvhz5sxxWI+8Jh4ZZRw9etR+u82Nsb/88kvt3btX0vVH4hw7dszhrfA3yq2f\nOVvGnXfead+ve/bssT8KqH79+vZHUzjzQgZ/f3/t27dP0vXHbGSeCPn7+9v7bEadCsJRGTnVvyjX\nv3Ef16lTxx7nypUr+v3333MtI3OMnMbO1q1ba8mSJbr33nvVrFkzrVq1SjVr1lSJEiXyjOmoHXh6\nemru3Ll65513FBERIW9v72zjtDNujN2yZUutWLHCYdzCnCDlVK/MbefAgQO5rh8QEGBv4+fOndPV\nq1ftJ7MZY2ZgYKA9XsY2rVChgu66665sY3/m8dqRzLFOnjypuLi4HMd1Z7eNo5gZfHx81KBBA731\n1lvq1q2bffvceAxwlqOy8tOfChMjP32qMGVmjP/5kblu165dc9iPMvfJc+fO2V+A5My60vVEVXp6\nuv766y8dP35cNWvWlPTfNv7LL7+odu3a8vf3V9OmTRUZGanly5erQ4cOeR4bciu3R48emjVrlmrX\nrq2yZcs6HT/z98qpv+Q0tt24vjNyWj6vPpmfmF5eXg7nNhljRcY6BR3X/vjjDyUkJGjx4sWaPn16\nlsdt5SXzXO2vv/5yuD0ctXl/f38dOnRINptNqampevbZZ5WSkuLweF7QMtzd3dWgQQN98MEHatas\nmYKDgzVr1iy1bdvWqe9Wt25d1a5dWytWrFBkZKS6deumwMDAAu/b/MTL6Vwiv+bPn6+yZctqzZo1\nOfYHZ9SrV0/79+9XWlqajDH2R0DldizLXPaNcuo3qampTp8X5DavzCm+m5ubqlatqqVLlyoyMlL9\n+vXTAw88oPfee0+dO3fWrFmz1LRp01zHhBv/P3HiRE2bNk3Tp0/XnXfe6fT8JbdzqxvXdbRsTmNi\n5cqV7QnhvOaxBw8elCRdvHhR8fHx9gtlpk6dqldeeUUzZsxQYGCg/Tt1795dX3/9tfbs2ZPjuaAz\n5VSrVs3hmJbfMSzzGCjJ4Xf39/d32O/ysnXrVgUHB2v58uVq3769OnfubH8My3PPPaekpCQZY1St\nWjX5+vpq4cKFTifxb4y9fv16h/vS2fPvG+MtWbLkpo2TjsrOkN/jUsZYmFt/LIoxLaOcwsxLHcWT\n8j+HkHI+j80rbub2n3ksPnz4sP2Hk5YtW2rWrFlq1qyZmjVrpoiIiCyPV8nM29vbfsFaRp91dCzy\n9/fXqFGjFBkZqTfeeMP+KFJHAgMDderUKe3fv18tW7ZUYmKitm7dKi8vL/3nP//RnDlzNHLkSHt/\nyum75taW8lvGxo0b1bVrV0VGRqpOnTpZHqHlTNzcxpTM57s5zVlzituiRYsczy8dbe/c2lpuZdw4\nbkpZ21p+9m9uitUV1j4+Ppo+fbomTpwoY4wSEhIUEhKiFi1aaPbs2faTWUkaN25ckcTO/GzTkJAQ\nhYeHa+PGjSpfvrw8PDyy/FrTu3dvTZw4Uf369VN8fLz69OnjcEc64ih27dq11aNHD1WoUEFVqlTR\nAw88YH9mcs+ePfX1119r48aN+R4Uk5KS1L9/fyUmJioiIkLp6ekaPHiwjDEqU6aMZs6cma94juJO\nmTJFEyZMkHS9oS9YsED169dXeHi4xo4dq/T0dEnXJzL5iSVJL774oiZMmKC1a9cqPj5eQ4YMUc2a\nNVWqVCl169ZNXl5eqly5cpYH6teuXVudOnXSW2+95fAE6sbyTp8+rcGDB+vuu+/OctVV69atNWXK\nFM2aNcvp7dK7d2+NHz9e/fr1U3p6ulq1aqUVK1bk2I6KIlaDBg00c+ZMBQQE6PXXX9cLL7wgd3d3\n3Xfffbr//vudjv/ss89mSR4569SpU+rfv79SUlI0ZcoUh7E/+OADrVq1SqGhoUpOTtaQIUOcekFQ\nbv3M2TIiIiL05ptvyhgjDw8PTZs2zR6/ZMmS9nb6ySef5PrLYd++ffXaa68pNDRU/v7+WX45HDZs\nmEaMGKGvvvrKqUR8fsrIrf5FvX6Gxx57TNu3b1fv3r1VqVIllSxZ0v59c9sOUs5jZ1BQkH777TcN\nHDhQdevW1enTp+3P68srpqN24OPjo6CgIHXt2lWlS5dW2bJldf78+Xxvf0exg4ODs8WVrk/sRo0a\npdmzZ+erjNy8/vrrGjFihD788EPdcccd8vb2znHZF154QePHj9c333yjpKQkTZkyJdvdN4MGDdKY\nMWO0adMm3XnnnfL09JS7u7smTJiQbez38fFRamqqZs2apdGjR2crr0ePHho3bpz69u2ratWqydvb\nO8dx3dlt4yhmZj179tTAgQPt7dTRMcBZjsrKb38oihhS7n2qMGU2btxYgwcP1ooVKwp0wuPj46OG\nDRtma+/du3dXdHS0evbsqWrVqjm8eyyndatXr660tDQNGjRIly9f1ksvvWT/0XfDhg366KOPVKpU\nKc2cOVPly5fXzz//rD59+igxMVGtW7fO8wqenMqVpPbt22vq1Kn2l3A9/vjj+Y6fU39xc3PLdV5Y\nFDLPoZ544olCxRo4cKDDuU1mDzzwgGbPnq3q1avb76ByVs2aNfXzzz/r008/VYkSJfTKK6/ka/3M\nc7WMK44yc9Tma9SooebNmys0NFQ2m02hoaH243bm43nfvn0LXIYktWnTRq+99prq1q2rZs2a6dNP\nP7VfhZaXWrVqqXz58goNDVVKSooaNmyoKlWqFHjf5ifevffem+1coqAmTpyonj176uGHH3bYH5xR\ns2ZNBQUF2fdXcHCwWrdurSZNmuR6LMtcdl4/KkrXrwR19rxg0qRJ+T5/8/T01IABAxQWFqb09HTd\nfffd6tChg328WbRokf1Kamd17txZvXr1UtmyZVWpUiWnXwzm7LlVTsvec889DsfEWbNm2e9aKVOm\nTK4XYV28eFHPPPOMrl27ptdff91+UVWnTp30z3/+UxUrVtRdd91l3x5VqlRRmTJl1KhRo3zdMXxj\nOefPn3c4phV2bubou9etW1cPP/xwtn6XlwYNGmj06NGaN2+e3N3dtXr1aoWHhys6OlqlSpVSzZo1\ndf78eVWpUkW9evVSRESE0+eZN8aeO3euNm7cmG1fOttGbowXFhZmvwgpM1eMk7mV/eCDD2rMmDFa\nunSp/Yes3FSsWFGpqamKiYnJtT8WdkzLKCchIUGbNm0q0LzUUbzC3GXl6Dw2P3HHjBmjSZMmaenS\npUpLS7O3lbZt2+r999/XggULdP78eU2fPl0LFy50GKN58+Zas2aNQkNDVb9+fZUpU8bhsWjs2LH2\nZ/4nJSVlyfc40qRJE506dUru7u5q0qSJjh8/roYNG2r+/Pnq1auXvLy8VKNGjVzHzrzmOPkp4/77\n79e4ceNUunRplShRwp7zcLbuzowpec1ZHcUtXbq0wzrkd3sXpAw/Pz8dO3ZMH330UYHLu5GbKezl\nYcBt5K+//lK/fv30ySefOP1jBPB3cOLECR05ckRPPPGE4uLi9OSTT+r777936uUnuLW2bdumChUq\nqGHDhvrxxx+1cOFCrVixokCxfvnlFyUmJqpZs2b6/fffNXDgQG3ZsqVQ9XNFTFeWVVT1zU+fupnb\nyBV2796tjz/+WO+8806Wv2e8pDa/iVEAgDVkfsFafmT84J5xt42ryilOvvrqK8XExGjYsGG3uioA\nYAnF6gpr4Fb65Zdf9Prrr2v48OEkq3HbqVq1qmbPnq3ly5crPT1do0aNIlldTFSvXl3jx4+Xh4eH\nbDZbgX/hlq4/S3PkyJF6//33lZaWpsmTJxe6fq6I6cqyiqq++elTN3MbAQDgKklJSerTp4+aN2/u\ndLL6djBnzhzt3bvX6XckAcDtgCusAQAAAAAAAACWwGWiAAAAAAAAAABLIGENAAAAAAAAALAEEtYA\nAAAAAAAAAEsgYQ0AAADcIkePHtWePXuKJNbu3bs1YsSIIokFAAAA3CokrAEAAIBb5Ntvv9Xx48dv\ndTUAAAAAy/C81RUAAAAAirP169dr3bp1stlseuWVV3ThwgUtX75cXl5euueeezRlyhRJ0vjx4xUb\nG6v09HQ9++yzCg4O1oYNG1SiRAnVr19fDRs2lCStWLFCV69e1ZAhQ5SSkqJOnTrp888/V1RUlL74\n4gu5ubmpY8eO6t+/v8aNG6fLly/r8uXLev755/XHH3/o+eefV1xcnEJDQ9WzZ89buWkAAACAfCNh\nDQAAABRS2bJltWDBAsXFxWny5MnasGGDfHx8NG3aNEVFRUmSKlSooFmzZik+Pl7dunXTxx9/rK5d\nu6pSpUr2ZLUkde7cWX369NHLL7+srVu3KiQkRCdPntRXX32l1atXy83NTQMGDFCzZs0kSf/4xz80\nYMAA7d69W6mpqVqwYIFsNps6d+6sVq1a6Y477rgl2wQAAAAoCBLWAAAAQCHVqlVLkhQbG6vatWvL\nx8dHktSkSRPt2LFD7u7ueuSRRyRJPj4+CggIUGxsrMNY5cqVU7169RQdHa0NGzZo7NixOnr0qM6c\nOaMBAwZIkq5cuaKTJ09mKVuSGjVqJC8vL0lSQECATp06RcIaAAAAxQrPsAYAAAAKyd39+rS6evXq\nOnHihBITEyVJP//8s2rVqqWAgADt3btXkhQfH69jx46pevXqcnNzk81myxavV69eWr58uZKSkhQQ\nECB/f3/Vrl1bK1asUGRkpLp166bAwEBJkpubm329Q4cOKS0tTYmJiTpx4oT8/Pxc/dUBAACAIsUV\n1gAAAEARueOOOzR06FD1799f7u7u8vPz06hRo+Tm5qZJkyYpNDRUycnJGjJkiCpWrKgGDRpo5syZ\nCggI0D/+8Q97nIceekiTJk3SSy+9JEmqW7euHn74YYWGhiolJUUNGzZUlSpVspXv7e2tQYMG6erV\nqxo6dKjKly9/0747AAAAUBTcjDHmVlcCAAAAAAAAAAAeCQIAAAAAAAAAsAQS1gAAAAAAAAAASyBh\nDQAAAAAAAACwBBLWAAAAAAAAAABLIGENAAAAAAAAALAEEtYAAAAAAAAAAEsgYQ0AAAAAAAAAsAQS\n1gAAAAAAAAAAS/j/UUEx2lGz978AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faace9cab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Join freq tables together (outer join) and graph.\n",
    "plt.subplots(figsize=(25, 8))\n",
    "joined_freqs = rel_freqs.join(all_root_verb_freqs[all_root_verb_freqs[\"all\"] > 0.005], how=\"outer\")\n",
    "sns.barplot(data=joined_freqs.reset_index().melt(id_vars=(\"index\",), var_name=\"source\", value_name=\"relative frequency\"),\n",
    "            x=\"index\", y=\"relative frequency\", hue=\"source\")\n",
    "\n",
    "plt.title(\"Relative frequencies of root verbs in 'what did' phrases vs. all sentences (outer join)\")\n",
    "plt.xlabel(\"root verb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentence length analysis\n",
    "\n",
    "Let's compare the lengths of what-did sentences to the average sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_lengths = pd.DataFrame({\"wd\": tuple(token[0].lower() for token in sent[:len(prefix)]) == prefix,\n",
    "                             \"length\": len(sent)}\n",
    "                            for sent in eve.tagged_sents(participant=\"MOT\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Sentence lengths')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2sAAAJZCAYAAAA3cK2CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faad00fc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15, 10))\n",
    "bins = np.histogram(sent_lengths.length, bins=40)[1] #get the bin edges\n",
    "sns.distplot(sent_lengths[sent_lengths.wd].length, bins=bins, color=\"skyblue\", label=\"WD\", norm_hist=True, kde=False)\n",
    "sns.distplot(sent_lengths[~sent_lengths.wd].length, bins=bins, color=\"red\", label=\"All\", norm_hist=True, kde=False)\n",
    "plt.legend()\n",
    "plt.title(\"Sentence lengths\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}