fonnesbeck/Data Summaries.ipynb

## Data Summaries.ipynb
{
 "metadata": {
  "name": "Data Summaries"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "pd.set_option('max_rows', 50)\n",
      "pd.set_option('max_columns', 30)\n",
      "from __future__ import division\n",
      "\n",
      "%load_ext save_on_exec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1141dae90>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lsl_dr = pd.read_csv(\"LSL_DR_dataset.csv\")\n",
      "lsl_dr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 6427 entries, 0 to 6426\n",
        "Data columns:\n",
        "Unnamed: 0                  6427  non-null values\n",
        "study_id                    6427  non-null values\n",
        "male                        6427  non-null values\n",
        "_race                       6427  non-null values\n",
        "prim_lang                   6379  non-null values\n",
        "sib                         6427  non-null values\n",
        "_mother_ed                  5540  non-null values\n",
        "_father_ed                  5472  non-null values\n",
        "premature_age               5578  non-null values\n",
        "onset_1                     30  non-null values\n",
        "age                         6427  non-null values\n",
        "tech_ad                     4619  non-null values\n",
        "tech_as                     4604  non-null values\n",
        "type_hl_ad                  4154  non-null values\n",
        "type_hl_as                  4146  non-null values\n",
        "degree_hl_ad                4238  non-null values\n",
        "degree_hl_as                4228  non-null values\n",
        "redcap_event_name           5650  non-null values\n",
        "redcap_data_access_group    5650  non-null values\n",
        "premature                   4928  non-null values\n",
        "onset                       5650  non-null values\n",
        "age_amp                     5435  non-null values\n",
        "race                        6427  non-null values\n",
        "non_english                 6379  non-null values\n",
        "mother_ed                   4470  non-null values\n",
        "father_ed                   4154  non-null values\n",
        "hearing_loss_birth          4910  non-null values\n",
        "tech_right                  4619  non-null values\n",
        "tech_left                   4604  non-null values\n",
        "degree_hl                   4219  non-null values\n",
        "baha                        4619  non-null values\n",
        "hearing_aid                 4619  non-null values\n",
        "cochlear                    4619  non-null values\n",
        "implants                    4619  non-null values\n",
        "implant_category            4275  non-null values\n",
        "age_test                    6386  non-null values\n",
        "domain                      6427  non-null values\n",
        "school                      6427  non-null values\n",
        "score                       6427  non-null values\n",
        "test_type                   6427  non-null values\n",
        "dtypes: float64(31), int64(2), object(7)"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1141dab10>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Test scores for top-5 and bottom-5 performing schools"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def best_and_worst(domain, count=5, data=lsl_dr):\n",
      "    \n",
      "    grouped_by_school = lsl_dr[lsl_dr.domain==domain].groupby(\"school\")\n",
      "    sorted_scores = np.round(sort(grouped_by_school['score'].mean()), 1).tolist()\n",
      "    \n",
      "    return sorted_scores[:count], sorted_scores[-count:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1141e7ed0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "domains = lsl_dr.domain.unique()\n",
      "extreme_scores = np.array([best_and_worst(dom) for dom in lsl_dr.domain.unique()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1141e7910>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots()\n",
      "plot(range(3), extreme_scores[:,0,:], 'ro')\n",
      "plot(range(3), extreme_scores[:,1,:], 'bo')\n",
      "xlim(-0.5, 2.5)\n",
      "xticks(range(3), domains.tolist(), rotation=90)\n",
      "ylabel('Mean score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x11460f9d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAFnCAYAAACrc064AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nihW2GGXSzX0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114609210>"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x11465add0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the plot above, the blue points indicate schools with the highest mean scores, while the red points are those with the lowest."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Proportion of students below, within and above 1 SD of mean"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calc_proportions(domain):\n",
      "    \n",
      "    subset = lsl_dr[lsl_dr.domain==domain]\n",
      "    \n",
      "    below = np.round(100.*subset.score[subset.score<85].count()/len(subset.score), 1)\n",
      "    above = np.round(100.*subset.score[subset.score>115].count()/len(subset.score), 1)\n",
      "        \n",
      "    print('{0}% of scores below 85, {1}% between 85 and 115 and {2}% above 115'.format(below, 100-below-above, above))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1141dacd0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "calc_proportions('Articulation')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "54.2% of scores below 85, 44.9% between 85 and 115 and 0.9% above 115\n"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x11464f750>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "calc_proportions('Expressive Vocabulary')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "44.5% of scores below 85, 46.8% between 85 and 115 and 8.7% above 115\n"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x114612590>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "calc_proportions('Receptive Vocabulary')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "47.2% of scores below 85, 47.5% between 85 and 115 and 5.3% above 115\n"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x114620310>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Progression of low-performance group"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Function to calculate difference in scores between first and second test."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calc_progression(dataset):\n",
      "\n",
      "    study_id = dataset[\"study_id\"].unique()\n",
      "    \n",
      "    # filter study ids with multiple tests\n",
      "    multiple_test = [i for i in study_id if sum(dataset['study_id']==i)>1]\n",
      "    \n",
      "    prog_data = []\n",
      "    for id in multiple_test:\n",
      "        \n",
      "        # Get records\n",
      "        recs = dataset[dataset['study_id']==id].sort_index(by='age')\n",
      "        # Grab variables from first record\n",
      "        prog_rec = recs.values[0].tolist()\n",
      "        # Calculate difference in score from first to second test\n",
      "        scores = recs[\"score\"].values\n",
      "        prog_rec += [scores[1] - scores[0]]\n",
      "        # Append record\n",
      "        prog_data.append(prog_rec)\n",
      "        \n",
      "    return pd.DataFrame(prog_data, columns=dataset.columns.tolist() + [\"difference\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1141e76d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot histograms of differences by domain."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def progression_histogram(domain):\n",
      "    calc_progression(lsl_dr[(lsl_dr.domain==domain) & (lsl_dr.score<85)]).difference.hist(bins=25)\n",
      "    xlabel(\"Difference in Scores\")\n",
      "    ylabel(\"Count\")\n",
      "    title(domain)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x114609590>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8,6))\n",
      "for i,domain in enumerate(lsl_dr.domain.unique()):\n",
      "    subplot(3,1,i)\n",
      "    progression_histogram(domain)\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGqCAYAAADp8VUdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtYVNX+P/D3DPfbzHARURFRcRIvWQjkNUeg1NSOSukx\nLKCbXzPLDNRjmWgqGWqhkBeOqalHPUpeu32PJCJqBnLJRBIUE5PLUWFABLnM+v3Bz/1l5DIbnIFh\n78/reXge9uw9a6/Pms34ca+115IwxhgIIYQQQgRG2tEVIIQQQggxBEpyCCGEECJIlOQQQgghRJAo\nySGEEEKIIFGSQwghhBBBoiSHEEIIIYJESQ4h5LFFRESgX79+HV2NdrNjxw6YmZk9djnXr1+HVCrF\n2bNn9VArQsijKMkhpB2EhIRAKpU2+pHJZB1dNb0IDw/H+fPnDXqOL774AlZWVigpKWly/wsvvIDR\no0cbtA6EkM6FkhxC2smzzz6LwsJCrZ9r164Z/Ly1tbUGP4eNjQ0cHBwMeo6QkBAAwK5duxrtu3Hj\nBv73f/8Xs2fPNmgdjBFjrF0+Y0I6I0pyCGkn5ubmcHZ21vpxcnICANy9exc9e/bE/PnzueOLi4vR\nrVs3fPzxxwCAxMRESKVSJCUlISAgAPb29njmmWfw66+/cu95eMyZM2cwYcIEODg4YNu2bQCAb775\nBl5eXrCzs4NSqcTq1atRV1fHvfeHH37AjBkz0LVrV67sjIwMAEBNTQ1WrlyJMWPGwMbGBt27d8fM\nmTO59zbsrsrJyYFUKsW5c+e04j9//jykUimuXr0KAKiqqsKSJUvQv39/2NnZwcvLC4cOHWq2/ezt\n7fHSSy8hLi6u0b6vv/4aCoUC06dPR11dHVatWgVPT0/IZDL4+vriu+++0zq+qqoKH3/8MQYMGABr\na2v07t0bkZGR3P6PPvoIAwYMgI2NDdzc3DBnzhyUlZU1Om9KSgpGjBgBhUIBPz8/5OTkcPua6tK6\nefMm9xk2R9e5H5abkZEBlUoFmUyGrVu3wsTEpFGbJyUlwdTUFPn5+c2ejxBBY4QQgwsODmYBAQEt\nHpOUlMTMzMzYsWPHmEajYc8//zwbOXIkq6urY4wxdvLkSSaRSJinpyf77rvvWHZ2NnvjjTeYjY0N\nKygo0Dpm4MCB7Pjx4+z69evs5s2bbN26dczFxYXFxMSw33//nW3ZsoW5uLiwpUuXMsYYKygoYGZm\nZmzRokXs4sWLLDs7m+3du5ddvHiRMcbYunXrmEwmY0ePHmV5eXksJSWFRUdHc3VftmwZ8/Dw4LZH\njBjB5syZoxXfnDlz2MiRI7ntyZMns2eeeYbt3buX/fbbb2zFihVMKpWyhISEFttIIpGwc+fOca/V\n1dWxnj17svnz5zPGGAsLC2MODg7s4MGDLCcnh61evbpRuZMmTWJ9+/ZlR44cYXl5eSw5OZlt27aN\n279y5UqWnJzMrl69ynbu3Mnc3d1ZcHAwt3/79u1MKpUyX19flpSUxH777Tc2adIk1qNHD1ZZWckd\nY2pqqlX//Px8JpFI2KlTpxhjjOXl5TGJRMLOnDnT6nMPHz6cJSYmsry8PPbf//6XjRs3joWGhmqd\nb9asWeyFF15otj0JETpKcghpB8HBwczU1JTZ2tpq/bz44otaxy1fvpw5OTmxBQsWMAcHB3bjxg1u\n38MEJiIignutoqKCWVhYcMnKw2M+/fRT7pjq6mpma2vL4uLitM61cuVKplAoGGOMXb16lUkkEvb7\n7783Wf9ly5YxX1/fZuN7NMnZvHkzc3BwYNXV1Ywxxh48eMAcHBzY1q1bGWOMnT17lpmamrLLly9r\nlRMQEMCmTJnS7HkYY8zT05O9/vrr3Pb333/PJBIJy8rKYhUVFczc3JwtXrxY6z1eXl7Mz8+PMcbY\n6dOnmUQiYRcuXGjxPA199dVXzMLCgtvevn07k0gkbMeOHdxrOTk5TCKRcMlSW5McvufetWuX1nHf\nfvsts7GxYWVlZYwxxkpKSpi1tTU7fPgw7zgJERrqriKknQwbNgyZmZlaP1u2bNE6ZunSpejXrx++\n+OILbN68GT179mxUTkBAAPe7tbU1RowYgaysLK1jxo8fz/2ek5ODiooKzJ8/H3Z2dtzPqlWrUFZW\nhjt37qBPnz6YOnUqhg0bhmnTpmHDhg24efMmV0ZwcDDy8vLg4eGBOXPm4Ntvv0VNTU2zsU6fPh33\n79/H8ePHAQDHjx/H/fv3MWPGDABAeno6NBoNfHx8tOqUlJSE3NzcFtvx7bffxr///W/cu3cPABAX\nF4dRo0bB09MTubm5qKmpwYQJExq1x6VLlwAAGRkZsLe3h5eXV7Pn+Pbbb/Hss8+iR48esLOzQ1hY\nGGpqalBYWKh1XMPPwsPDA+7u7o0+i9bie+5x48ZpbU+ePBlyuRx79uwBAOzevRsKhQKTJ09+rPoQ\n0plRkkNIO7G0tESfPn20flxcXLSOuXXrFq5cuQJTU1P88ccfvMpljDV6rXv37tzvD8fdHDx4UCvB\n+v3335GTkwN7e3sAQHx8PH7++Wf4+PggPj4eSqUSv/zyCwCgd+/eyMvLw9q1a2Fubo73338fzzzz\nTLN1sre3x+TJk/HNN98AqB8P9Le//Y17mqyurg5yubxR0nf58mX88MMPLcYbHByM2tpa7N69G0VF\nRTh+/Djefvttne0kkUh0HgPUjx16+eWX8dRTT+Hw4cNIT0/H+vXrwRhDdXU1rzIAQCqVNvpsysvL\n9XJuExMTdOnSReu9pqameOONN7gxS//85z8RGhoKqZS+5ol40dVPSDvR9Y+sRqNBUFAQnn76aezb\ntw8rVqxoNJAUAE6cOMH9fv/+fZw9exYDBgxottwnnngCNjY2uHr1aqMkq0+fPlr/CPr4+OAf//gH\nTp06haeffhpbt27l9tnY2GDKlCmIjo7Gzz//jIyMDJw+fbrZ8wYHB+P777/HlStX8MMPP+C1117j\n9g0dOhSlpaWorKxsVB9XV9cW26nhAOQdO3bAzs4O06dPB1B/N8XCwgLff/+91nt++OEHDBo0CADw\n1FNPoaSkBBcuXGiy/OTkZFhZWWH9+vXw8fGBh4eH1uDuhhp+Frm5ubh+/Tr3WTg7O0Oj0SAzM5M7\n5tixYy3G1ppzN+XNN99EZmYmNm/ejIsXL+LNN9/k/V5ChMi0oytAiFg8ePAARUVFjf53//BuzqpV\nq3D58mVkZmbCxcUFb7/9Nl555RVkZGRALpdzxx84cAC+vr5wd3fH+vXrYWZmhnfeeafZ85qbm2P1\n6tVYsmQJJBIJ/P39UVtbi4sXLyIjIwOfffYZzp49i4SEBIwbNw5OTk7IyMjAb7/9xnXHREVFoUeP\nHhgyZAg0Gg2+/vprmJqaNroT1dD48eNhb2+PGTNmwMHBQasLbcSIEZg6dSqmTZuGzz//HIMHD0ZJ\nSQnOnj0LKysrnf84v/322xgzZgyuX7+O1157Debm5gDqu+/ee+89xMXFwdvbG08++SR3B+thQjJq\n1ChMmDABM2bMwPr16zF48GDcunUL2dnZeOONN9C/f3/cv38fa9euxdSpU3H+/HkkJCQ0qoNEIsGW\nLVvQp08fKBQKfPTRR+jevTteeeUVAMAzzzwDOzs7LFu2DEuWLMGvv/7KdSU1h++5m+Pm5obx48dj\n/vz5CAgIgLu7O+/3EiJIHToiiBCRCAkJYRKJpNGPVCpld+7cYWfOnGFmZmbs+PHj3HuqqqrYkCFD\n2IwZMxhj/zeo+NSpU0ylUjG5XM58fX3ZL7/8wr3n5MmTTCqVsr/++qtRHQ4cOMCGDRvG5HI5s7e3\nZ8OGDWObN29mjDF26dIl9sILLzAXFxdmYWHBevXqxRYuXMhqamoYY4xt2bKFDR06lMlkMmZra8t8\nfX3Z0aNHubIjIiJYv379Gp3zgw8+YFKplC1YsKDRvurqarZq1So2aNAgZm1tzVxcXNiECRPYyZMn\nebWpp6cnk0qlLCsrS+v1uro6tmrVKvbEE08wW1tb5uPjo9WujDFWWVnJFi9ezJRKJbO0tGS9e/dm\na9as4fZ/+eWXbOjQoaxr165sxowZ7NixY0wqlbI///yTMVY/+NfMzIydP3+ePfPMM0wul7OxY8ey\nK1euaJ3nu+++Y56enkwul7Nx48axs2fPMqlUqjXwWCqVag085nvu5hw+fJhJJBJ28OBBXu1IiJBJ\nGGuiQ58QYnQSExPh5+eHmzdvao25IaShr776Cp9++iny8/Nhako364m4GfQvoKqqCtu2bcOff/6J\nmpoavPPOO3B1dUVMTAyKiorQtWtXzJs3D5aWloasBiGECF5FRQWuXLmCqKgozJ07lxIcQmDggcfb\ntm2Dp6cnPv/8c6xduxY9evTgntpYu3Yt+vXrh/j4eENWgRBB4fuEEBGfuXPnQqVSYdSoUQgPD+/o\n6hBiFAyW5Ny/fx+XL1+Gn58fgPpHHq2trZGamooxY8YAAFQqFVJSUgxVBUIERaVSoa6ujrqqSJN2\n7NgBtVqNXbt2wcLCoqOrQ4hRMNj9zOLiYshkMsTGxuLatWtQKpUICQmBWq2GQqEAAMjlcqjV6mbL\naM1TBYQQQggRPn9/f97HGizJqaurw9WrVzFt2jS89dZb2Lp1a6M5P/jcem9pVlJCCCGEiEdaWlqr\njjdYd5WjoyNsbW3h7e0Nc3NzjBw5EpmZmVAoFCgtLQUAlJSUaM3/IUbJyckdXYV2IYY4xRAjII44\nxRAjQHEKiRhibAuDJTkKhQIuLi7IycmBRqNBWloaBg0ahKFDhyIxMREAcOrUKfj4+BiqCoQQQggR\nMYPOk3Pr1i3ExsairKwMbm5umDdvHhhjvB8hT0hIoO4qQgghhACo765qzZgco54MkJIcQgghhDzU\n2iSHFujsYGLpRxVDnGKIERBHnGKIEaA4hUQMMbYFJTmEEEIIESTqriKEEEJIp0DdVYQQQgghoCSn\nw4mlH1UMcYohRkAccYohRoDiFBIxxNgWtEwtITwVlD1AcUV1s/s1Tu7ILCjnVZazjTm6yWh9IUII\nMSSDjsmZO3curKysIJVKYWJigsjISFRWVtI8OaRTyiwoR/h3uXopK2qiB4Z0s9NLWYQQIhatHZNj\n8Ds5ERERsLW15bbj4+OhVCoRHh6Ow4cPIz4+HkFBQYauBiGEEEJExuBjch69UZSamooxY8YAAFQq\nFVJSUgxdBaMmln5UscQpBmL4LMUQI0BxCokYYmwLgyY5EokEK1aswMKFC3HixAkAgFqthkKhAADI\n5XKo1eoWy2j4wSUnJwtu++LFi0ZVH9puflvXtdpaHR0PbdM2ff8IZ/vixYtGVR9Db/Nl0DE5JSUl\nsLe3x82bNxEZGYl3330Xn3/+ObZv384dExoaqrXdEI3JIcaExuQQQkjHMqp5cuzt7QEArq6u8PX1\nRW5uLuRyOUpLSwHUJ0FyudyQVSCEEEKISBksyXnw4AEqKysBAGVlZUhPT4ebmxu8vb2RmJgIADh1\n6hR8fHwMVYVOoS233zojscQpBmL4LMUQI0BxCokYYmwLU0MVrFarERUVBQCws7PDxIkTMWTIECiV\nSsTExCAsLIx7hJwQQgghRN9o7SpCeKIxOYQQ0rGMakwOIYQQQkhHoSSng4mlH1UscYqBGD5LMcQI\nUJxCIoYY28JgY3IIIc0zkUh4r3OlC62DRQghTaMxOYTwpM8xOcsCemP5iTy9lEXjewghYkFjcggh\nhBBCYOAkR6PRYOHChVizZg0AoLKyElFRUQgLC0NUVBSqqqoMefpOQSz9qGKJUwzE8FmKIUaA4hQS\nMcTYFgZNcr7//nu4urpy2w9XIF+7di369euH+Ph4Q56eEEIIISKmM8k5d+5ck6//8ssvLb7vzp07\nSE9Ph5+fH/carUDe2KhRozq6Cu1CLHGKgRg+SzHECFCcQiKGGNtCZ5KzadOmJl/fvHlzi+/buXMn\nZs2aBan0/07R2hXIARh0FVPapu3WbOt7FXJ96+j2oW3apm3abq9tvpp9uqqoqAiMMYSHh2Pt2rXc\n64wxXLt2Ddu3b0dcXFyThV64cAEZGRl44403cOnSJRw/fhyLFi1qtOJ4SyuQA+J4uio5OVkUGbgQ\n4qSnq+oJ4bPURQwxAhSnkIghRqD1T1c1O0/Oe++91+TvQP1dmOnTpzdb6JUrV5Camoq0tDTU1NSg\nsrISGzdu5FYgVygUtAI5IYQQQgxK5zw5y5Ytw/Lly9t8gqysLBw7dgyLFi3C7t27YWtriylTpuDw\n4cOoqKhAUFBQs+8Vw50c0nnQnRxCCOlYep8n53ESnEcFBgYiJycHYWFhyMnJQWBgoN7KJoQQQghp\nqNnuqoeKioqwb98+XL9+vdG8Ns0NSm5owIABGDBgAADAysoK4eHhbayqMImlH1UscYqBGD5LMcQI\nUJxCIoYY20JnkrNhwwbIZDK89NJL3JNRhBBCCCHGTmeSc+vWLSxatAgymaw96iM6Ysm8xRJnZ1dQ\n9gDFFdUtHmPXdwjvxUU76+KhYrleKU7hEEOMbaEzyRkyZAiys7Ph6+vbHvUhhHSg4opqvQ2uBuoH\nRXfGJIcQIgw6Bx7b29sjNjYWa9aswf79+7V+yONry+RGnZFY4iTCIJbrleIUDjHE2BY67+Tcu3eP\nu4tz584dAPUTAkokEsPWjBBCCCHkMehMcubOndvqQqurqxEREYGamhqYm5tj+PDhmDRpEiorKxET\nE4OioiJ07doV8+bNg6WlZZsqLhRi6UcVS5xEGMRyvVKcwiGGGNuC1yPkzenatWuTr5ubm2PZsmWw\nsLBATU0NFi9ejKFDhyIhIQFKpRLh4eE4fPgw4uPjW5wMkBBCCCGkrXSOyXnvvfea/WmJhUX9YMOq\nqipoNBqYmZnRKuRNEEs/qljiJMIgluuV4hQOMcTYFjrv5Dw6wDgvLw9Hjx7FiBEjWnyfRqPBokWL\nkJ+fj5CQEDg5ObVpFXJCCCGEkLbQeSfnUb1790ZQUBD+9a9/tVywVIqoqChs2LABP/30E/LytNfp\n4Ttw2ZiWdjfE9qOxdnR9DLU9atQoo6pPW7aNPSnXR7z6jrFheR39+YnteqXvH3F9ng9fM5b6tEe8\nfOhcoLMpmZmZ2LBhA7Zt28br+F27dsHBwQH/+c9/EBERwa1Cvnz5cnz55ZfNvo8W6CTGRAwLdOoz\nRoAWDyWE6FdrF+jU2V31ySefaG2r1WoUFhZixowZzb6nrKwMJiYmsLGxQXl5OdLT0xEaGgpvb28k\nJiZiypQpOHXqFHx8fHhXVKiSk8Wx3ohY4iTCIJbrleIUDjHE2BY6kxw/Pz+tbQsLC/Tq1Qvdu3dv\n9j2lpaWIjY2FRqOBQqHApEmTMHjwYHh4eCAmJgZhYWHcI+SEEEIIIYagM8lRqVStLtTNzQ1r1qxp\n9DqtQt6YWDJvscRJhEEs1yvFKRxiiLEtdCY5tbW1iI+PR1JSEkpKSuDg4IDRo0cjMDAQpqY6304I\nMTATiYT3gpm6VNe2eogeIYQYLZ1Zyp49e3DlyhVMmzYNffr0wdWrV3Hy5ElUVlYiJCSkHaoobGLp\nRxVLnB1BXVWrt0HMywJ666Wczk4s1yvFKRxiiLEtdCY5Z8+eRVRUFGQyGYD6R8h9fX0RHh5OSQ4h\nhBBCjFar58kh+iWWzFsscRJhEMv1SnEKhxhibAudd3KGDx+OyMhI+Pv7a3VXDRs2rD3qRwghhBDS\nJjrv5AQFBeGpp57CkSNH8Mknn+Do0aMYMmQIZs2a1R71E7y2zODYGYklTiIMYrleKU7hEEOMbdHs\nnZzs7GycO3cOoaGhmDFjhtbkf9u3b0deXh6USmWzBd++fRuxsbFQq9WQyWRQqVRQqVSorKxETEwM\nioqKuLlyLC0t9RsVIYQQQkSv2Ts5hw4dgqenZ5P7Bg4ciEOHDrVYsKmpKYKDg7F+/XosWLAAe/bs\nwc2bNxEfHw+lUom1a9eiX79+iI+Pf7wIOjmx9KOKJU4iDGK5XilO4RBDjG3R7J2ca9euYf78+U3u\nGzRoEOLi4losWKFQcCuOy2QyeHh44O7du0hNTUVERASA+okGIyIiEBQU1MbqE0LEoqDsAYorqvVS\nlrONObrJLPRSFiHEeDWb5DDGYGJi0uQ+ExMTaDQa3icpLCxEfn4+lEol1Go1l/zI5XKdqx43fPa/\n4YqyQtm+ePEi5syZYzT1MdR2w/5iY6hPW7aNfRVyfairq9VreWq1Gvj/C3Q+bvvn3PovVib/Vy/1\niprogau/pTR7PiFcr/T9I67Pc9OmTRg8eLDR1MdQ29bW1miNZlchX7ZsGSZOnAhfX99G+1JSUnD8\n+HEsX75c5wmqqqoQERGBwMBA+Pj4IDQ0FNu3b+f2P7rdkBhWIRfLBE5CiNNYVyE31rIA410hXVe9\nhHC98kFxCocYYgT0uAr5lClT8NVXX0Gj0cDX1xdSqRQajQa//vorvv76a7zzzjs6C6+trcW6desw\nevRobsVxuVyO0tJSKBQKlJSUQC6X866sEInhogQ6Lk59dnHQkgfiQX+XwiKGOMUQY1s0m+Q8/fTT\nmDFjBrZs2YLo6GjY2dmhvLwcVlZW3GPlLWGMYfPmzXB1dcXEiRO51729vZGYmIgpU6bg1KlTXPJD\niCEUV1Tr9e4LIYSQzqPFyQADAgIwcuRI5Ofn4/bt23ByckLPnj1hZWWls+A//vgDp0+fhpubGxYu\nXAgAeOWVVxAYGIiYmBiEhYVxj5CLmVhuMYolTqKtsy4eKpbrleIUDjHE2BY6Zzy2srKCUqlscU6c\npvTv3x/79+9vcl94eHiryiKEdE60eCghpCPR2lUdTCyZt1jiJMIgluuV4hQOMcTYFpTkEEIIIUSQ\nKMnpYGJZb0QscRJhEMv1SnEKhxhibAtKcgghhBAiSJTkdDCx9KOKJU4iDGK5XilO4RBDjG2h8+mq\nttq0aRPS0tIgk8mwbt06AKAVyAkhhBDSbgx2J0elUmHJkiVar9EK5I2JpR9VLHESYRDL9UpxCocY\nYmwLgyU5np6esLGx0XotNTUVY8aMAVCfBKWkpBjq9IQQQggRuXYdk9PaFcjFQCz9qGKJkwiDWK5X\nilM4xBBjWxhsTI4uEomE13ENp6o2lqXeaduw232f9EFxRTWXBD9cxLUt2yZm+hvzVVdXq7eyjJW+\nY9Rnefosy0QiQVL2TQCPd30BQHcnB1TU1OnlerWT1uFpZS8AxvP3SNu0bUzb1tbWaA0JY8xgC8IU\nFxdjzZo13MDj+fPnIyIigluBfPny5fjyyy+bfX9CQgK8vLwMVT2jIJb1RloTZ2ZBuV4X1dTnsgJU\nVseVJ4ayoiZ6YEg3O72UxQd9/wiHGGIEgLS0NPj7+/M+vl27qx6uQA6AViAnhBBCiEEZLMmJjo7G\n0qVLUVBQgDlz5uDkyZMIDAxETk4OwsLCkJOTg8DAQEOdvtMQQ+YNiCdOQjoTsfxdiiFOMcTYFgYb\nk/P+++83+TqtQE4IIU0zkUiQWVCul7KcbczRTWahl7II6aw6bOAxqSeWflSxxEnI41BX1ep1fI+u\nJEcsf5diiFMMMbYFLetACCGEEEGiOzkdTCiZd0HZAxRXVDe7367vEN634atrDfbAHyGkAaF8/+gi\nhjjFEGNbUJJD9KK4olqvj30TQgghj4u6qzoYrTdCCOkoYvn+EUOcYoixLSjJIYQQQoggdUh3VVZW\nFnbu3Im6ujr4+/tjwoQJHVENo9DaflRdY19agx4xJUTcxDKOQwxxiiHGtmj3JEej0WDTpk1YunQp\nHBwc8I9//AODBw+Gq6vrY5X7l7oK6qo6vdTR2dYMTjbmeilL3/Q59oXPI6aEEEJIZ9XuSU5ubi5c\nXFzg7OwMABg5ciRSU1MfO8kpKK/GvswifVQR7wx3hZONXorSqSPnNtDnxGP0RBQhnU9HfP90xN1o\nMcwhwzdGsfUGGHSBzqb88ssvyMzMxOzZswEASUlJyM3Nxeuvv97o2ISEhPasGiGEEEKMXGsW6DTq\nR8hbEwghhBBCSEPt/nSVg4MDbt++zW3fuXMHDg4O7V0NQgghhAhcuyc5ffv2RWFhIYqLi1FbW4uz\nZ8/C29u7vatBCCGEEIFr9zE5QP0j5Dt27OAeIX/hhRfauwqEEEIIEbgOSXIIIYQQQgyNZjwmhBBC\niCAZ3dNV+/fvR2pqKgDAzc0NISEhsLOzAwB8//33+Pnnn2FiYoLQ0FD079+/I6v6WHbt2oW0tDSY\nm5vD09MT06dPh7W1NQDhxHnu3DkcOHAAf/31FyIjI9GnTx9un1BifEios3hv2rQJaWlpkMlkWLdu\nHQCgsrISMTExKCoqQteuXTFv3jxYWlp2cE3b7vbt24iNjYVarYZMJoNKpYJKpRJUnNXV1YiIiEBN\nTQ3Mzc0xfPhwTJo0SVAxNqTRaLB48WI4Ojpi0aJFgoxz7ty5sLKyglQqhYmJCSIjIwUXZ1VVFbZt\n24Y///wTNTU1eOedd+Dq6tq6GJmRuX//Pvf7gQMH2L59+xhjjOXn57OwsDBWU1PDioqK2Lvvvsvq\n6uo6qpqPLTMzk9XV1bG6ujq2efNmtnv3bsaYsOK8efMm++uvv1hERAS7evUq97qQYmSMsbq6Ovbu\nu++yoqIiVlNTw8LCwlh+fn5HV0svsrKy2LVr19iCBQu413bt2sUOHz7MGGPs0KFD3LXbWZWUlLC8\nvDzGGGNqtZq9+eabLD8/X3BxVlVVMcYYq66uZgsWLGC3bt0SXIwPHTt2jEVHR7PPPvuMMSa8a5Yx\nxt555x1WXl6u9ZrQ4oyJiWEJCQmMMcZqa2tZRUVFq2M0uu4qKysrAEBdXR0ePHgAMzMzAEBKSgpG\njhwJU1NTODs7w8XFBbm5+lneoCM8+eSTkEqlkEqlGDJkCO7evQtAWHH26NED3bt3b/S6kGIEtGfx\nNjU15WbxFgJPT0/Y2GhP/52amooxY8YAAFQqFVJSUjqianqjUCjg7u4OAJDJZPDw8MDdu3cFF6eF\nRf3MtFVVVdBoNDAzMxNcjED9tCTp6enw8/PjXhNinADAHhlSK6Q479+/j8uXL3Ofo4mJCaytrVsd\no9F1VwEhB7piAAAgAElEQVTA3r17ceLECXTv3h3Lli0DAJSUlKBfv37cMY6Ojlxi0NklJCRwH6SQ\n43xIaDHevXsXTk5O3LaDg0OnTtp0UavVUCgUAAC5XA61Wt3BNdKfwsJC5OfnQ6lUCi5OjUaDRYsW\nIT8/HyEhIXBychJcjACwc+dOzJo1C5WVldxrQoxTIpFgxYoVkEgkeP755xEQECCoOIuLiyGTyRAb\nG4tr165BqVQiJCSk1TF2SJLz6aeforS0tNHrM2fOhLe3N2bOnIlp06Zh37592LNnD4KDg5ssRyKR\nGLqqj0VXnADw7bffwtLSEsOHD2+2HGOOk0+MfBhzjKR5Qvrcqqqq8OWXXyI4OLhRH78Q4pRKpYiK\nikJxcTEiIyPxxBNPaO0XQowXLlyAXC5H7969cenSpSaPEUKcQP13r729PW7evInIyEj06NFDa39n\nj7Ourg5Xr17FtGnT8NZbb2Hr1q04d+6c1jF8YuyQJGfp0qU6j7GwsMDYsWOxceNGAPX/O75z5w63\nvzPMlKwrzsTERKSnp2sd19ni5PNZPqqzxaiL2GbxlsvlKC0thUKhQElJCeRyeUdX6bHV1tZi3bp1\nGD16NHx8fAAIM04AcHZ2hpeXF7KysgQX45UrV5Camoq0tDTU1NSgsrISGzduFFycAGBvbw8AcHV1\nha+vL3JzcwUVp6OjI2xtbbn/LI8cORJJSUlQKBStitHoxuQUFBQAqM/izpw5Azc3NwCAt7c3zpw5\ng9raWhQXF6OwsBAeHh4dWdXHkpGRgaNHj2LhwoUwNzfnXhdanE0RWoxim8Xb29sbiYmJAIBTp05x\nSUFnxRjD5s2b4erqiokTJ3KvCynOsrIyVFRUAADKy8uRnp4ONzc3QcUI1N9B3rRpE2JjYzF//nwM\nGjQI8+bNE1ycDx484LrjysrKBPl5KhQKuLi4ICcnBxqNBmlpaRg0aBCGDh3aqhiNbjLAdevW4dat\nWzA3N8fAgQPx4osvQiaTAah/7DghIYF77NjT07ODa9t27733Hmpra2FrawsAUCqVePPNNwEIJ85f\nf/0V27dvR1lZGaytrdG7d28sWbIEgHBifEios3hHR0cjKysL5eXlkMvlmD59OoYNGyaox1Szs7Ox\nbNkyuLm5cbe/X3nlFTzxxBOCifPGjRuIjY2FRqOBQqHA8OHD4efnJ7hHjhvKysrCsWPHBPkIeXFx\nMaKiogAAdnZ2GD58OJ577jnBxXnr1i3ExsairKwMbm5umDdvHhhjrYrR6JIcQgghhBB9MLruKkII\nIYQQfaAkhxBCCCGCREkOIYQQQgSJkhxCCCGECBIlOYQIWFxcHOLj47nt06dPY9GiRQgODsa9e/eQ\nm5uL1atXIzg4uNMuRfHhhx8iKyuro6tBCDFC9HQVIZ3U3LlzoVarYWJiAltbWzg7O2PcuHEYNmxY\nk8fX1tYiODgYS5YswcCBAwEAK1asgKenJ15++eX2rLrRSElJwb///W8UFRXBzMwMvXr1wv/8z//A\n2dm5o6tGCNEDo1y7ihDCz+LFizFo0CBUVlbi0qVL2LFjB3JycvDqq682Ora0tBS1tbVa0/nfvn27\n0fT+fGk0GkilnfdmcGFhIb788kvMnTsX3t7e0Gg0yMzM1GtMD/8P2dmn2Ceks6IkhxABsLKygre3\nNxQKBZYuXYrnnnsOLi4uiI2NhZOTE5599lksXLgQAPD666/Dw8MD//3vf1FcXIzPP/8cJiYm2LZt\nG6qrq7Fz506kp6dDIpHAz88PL7/8MqRSKRITE5GQkIDBgwcjMTERY8aMQWBgIPbu3Ytz586hpqYG\nvr6+CA4Ohrm5OS5duoSNGzfipZdewuHDh1FTU4OZM2dCpVIBAKqrq7Fv3z6cP38eFRUVcHNzw8cf\nfwxzc3NcuXIF33zzDW7evIkuXbogNDQUAwYMaDL2uXPnYs6cORg0aBAOHDiAW7duwdbWFufOnYNc\nLsfcuXPRp0+fRu+7efMm7O3tMWLECO61Z555hvtdo9Hg8OHDOHnyJMrKytCtWzeEh4fD0dERf/zx\nB3bs2IGCggJ069YNoaGhUCqVAICIiAg8+eSTyMrKQk5ODj7//HPU1tbi66+/xrVr1yCXyzFjxgxu\nvbrs7Gx8++23yMnJgYWFBSZOnIjJkyfr5bogROwoySFEQDw8PGBvb49r167BxcWFu4PQrVs3rF+/\nHu+++y527NjB3a1omCAAwBdffAFzc3OsXLkS9+/fx8aNG+Ho6IiAgAAAQG5uLgYMGIC1a9fC1NQU\ne/bswY0bN/CPf/wDZmZm2LJlC+Lj4zFz5kwA9as/X79+HatXr0ZKSgq2bdsGX19fWFtbY9euXbhx\n4wZWrlwJuVyO3NxcSKVS3L17F6tWrUJoaCieeeYZ/Prrr/j888+xYcMGbvbzlpw/fx6vv/46/v73\nv2P37t3Ytm0bVq1a1ei4wYMHo7y8HFu2bMGIESPQr18/rZlTjx8/jtOnT2PJkiXo1q0bbty4AQsL\nC9y7dw+fffYZQkJCMHr0aJw5cwaRkZHYuHEjN4P5Tz/9hNmzZ2PIkCF48OABFixYgHHjxmHBggX4\n448/EB0dDTc3N/To0QM7d+7E1KlTsXjxYlRVVaGoqOgxrgBCSEOd914zIaRJ9vb2WguGPqRr+F1p\naSnS0tIQFBQEZ2dnuLu7w8/PD2fOnOGOMTExwcsvvwxra2uYmZnhxIkTmD59Onr27AkXFxdMmDBB\n63jGGKZPnw6ZTAaVSgWJRIJbt25Bo9Hg5MmTmDhxIuzt7SGVSqFUKmFqaoqkpCT069cPKpUKVlZW\nGDNmDJydnZGens4r/u7duyMgIAA2NjYYO3Ys/vzzzyaPs7CwwMcff4z79+8jOjoab7zxBr766itU\nV1cDABISEhAQEIBu3boBANzc3GBra4u0tDSYm5tjzJgxkEqlGD16NCwsLHDhwgWu7KeeegpeXl4w\nMTFBRkYGLC0tMXXqVNjY2MDLywuDBg3iVlTWaDQoKipCZWUlt/wJIUQ/6E4OIQJz9+5dODk5tfp9\nt2/fhkajQXh4OPeaRqPRKqtXr14wNa3/2igrK0N1dTU+++wzbj9jTCuZsre35+6+mJiYwM7ODlVV\nVSgvL0dNTQ369+/fZD0uX76M0NBQ7rW6ujqUlpbyiqNXr17c7wqFAjU1Nc2OH+rXrx8++OADAMBv\nv/2G2NhYJCcnw8/PD3fv3m1yvNLdu3cbJSJ9+/bF3bt3AdSPv+nXrx+372G3YMN4NBoNt1L9u+++\niyNHjuDdd99Fr169MGvWrE69YC0hxoSSHEIEJDc3F6WlpW26G+Do6AipVIr169fD3t6+yWNMTEy4\n3+3s7GBubo6PPvqo1f8o29nZwczMDNnZ2fD19dXa5+TkhIEDB3KLubaXJ598Ek8//TTS09Ph5+cH\nR0dHZGdnN4rNwcEBeXl5Wq9dvXpVK46GCZWTkxO6du2KL774osnz9uzZE++++y40Gg2OHj2Kbdu2\nITIyUo+RESJe1F1FSCf28K7J/fv3ceHCBURHR2P8+PFcF0trZoiwt7eHl5cX9uzZg5s3b0Kj0aCw\nsLDZOWikUin8/f2xf/9+XLt2DRqNBnfv3kVmZqbOc0mlUowdOxZHjhxBdnY2NBoNrly5gtraWowe\nPRpXrlzBqVOncO/ePVRXV+PSpUvcnRJ9yc7ORkJCAsrKylBbW4vs7GykpKRwg5T9/Pxw4sQJpKen\no66uDn/++Sfu3bsHLy8vVFdXIykpCXV1dUhOTsaDBw8wdOjQJs8zdOhQVFVV4ejRo9wTbrm5ufjr\nr79QW1uL06dP4/79+6irq4NUKkVZWZle4yREzOhODiGd2Jo1a2BiYgJra2u4uLhg5syZWk8LtfbR\n5blz5+Jf//oXVq1ahfv378PFxQV/+9vfmi0vKCgIBw8exPr161FeXg4HBweMGzcOQ4YM0XmuV199\nFXv37sWXX36JyspK9O7dG0uWLIGjoyM+/vhj7N69Gzt27ICJiQn69euHN954g1cMfGO2sbFBamoq\n9u3bh6qqKshkMvj7+3PxTpo0CbW1tdi2bRvKysrg6uqKsLAwODg4YNGiRdixYwe+/vprdOvWDYsX\nL+YGHT/K0tISS5cuxTfffIMjR44AANzd3fHaa68BqJ+g8euvv4apqSk8PT3x/vvv86o/IUQ3mgyQ\nEEIIIYJE3VWEEEIIESRKcgghhBAiSJTkEEIIIUSQKMkhhBBCiCBRkkMIIYQQQaIkhxBCCCGCREkO\nIYQQQgSJkhxCCCGECBIlOYSQVrt+/TqkUinOnj3b0VVpN1KpFP/6178euxyVSoW33npLDzUihOhC\nSQ4h7SwkJARSqRRSqRSmpqbo3r07Xn75ZeTk5HR01Zrk4eGB5cuXa73m5uaGwsLCRotr6lNxcTHM\nzc2xadOmJvf/+9//homJCa5evWqwOhiCRCJp9XIbhJC2oSSHkA7w7LPPorCwENevX8fGjRuRkpKC\nSZMmdXS1mtTUP8hSqRTOzs4wNTXc8nfOzs6YMmUK4uLimtwfFxeHsWPHom/fvgarg7Gqrq7u6CoQ\n0ilQkkNIBzA3N4ezszNcXV0RGBiI0NBQ5OTkoLy8nDvm/Pnz8Pf3h6OjI1xdXfH66683Won7xx9/\nxMiRI2Fvbw8nJye88MILKC0t5fZ/88038PLygp2dHZRKJVavXo26ujpuv7u7Oz799FN88MEH6Nmz\nJ9zd3REZGcntV6lUuHr1KpYvX87dfbpx40aj7qqRI0di9uzZjeL09PTEJ598wm1///33GDFiBBQK\nBXr37o0PP/wQ9+/fb7adZs+ejYyMDFy4cEHr9WvXruHnn3/mznn69GmMGjUKCoUCHh4eWLhwIR48\neMC7rf7zn/9ApVLB0dERCoUCKpUKKSkpjepz//59BAcHo2vXrnjiiSfw9ddfa+1vqksrICAAoaGh\nzcbI59xSqRTbtm3DW2+9BTc3N7z22msYO3ZsozZnjKFv375YtWpVs+cjRFQYIaRdBQcHs4CAAMYY\nY3V1dSwjI4MNHDiQ+fr6csf8+uuvzMzMjC1btoylpqayQ4cOMV9fXzZmzBjumAMHDjAzMzO2cuVK\ndvnyZfb777+zmJgYdvv2bcYYY+vWrWMuLi4sJiaG/f7772zLli3MxcWFLV26lCujV69ezMHBgX3y\nySfsypUrbNeuXczGxoZFR0czxhi7e/cu6927NwsPD2dFRUWsqKiI1dXVsby8PCaRSNiZM2cYY4xt\n3bqV2dvbswcPHnBlnz9/nkkkEpaTk8MYYyw+Pp7Z2NiwqKgo9ttvv7Hdu3czpVLJXn311Rbby8PD\ng82ePVvrtSVLlrCuXbuympoalpmZyUxMTNiCBQvYH3/8wX744QfWs2dPrXJ1tdWhQ4fYgQMH2JUr\nV9jJkydZYGAgc3BwYHfu3OHKkEgkzNnZmcXExLCcnBwWHR3NTExM2JEjR7SO2bNnj1ZdAwICWGho\nKLetUqnYW2+9xW235tyxsbHs2rVrLCcnh+3du5fZ2dmxe/fuccedOHGCmZqasoKCghbblBCxoCSH\nkHYWHBzMTE1Nma2tLbO0tGQSiYSNHz9e6x+1559/ngUFBWm9Lzk5mUkkEpaZmckYY6xv375s3rx5\nTZ6jurqa2drasri4OK3XV65cyRQKBbfdq1cv5u7urnVMUFAQ69mzJ7ft4eHBli9frnXMo0lOSUkJ\ns7KyYgcOHOCOmTt3LhsxYgS3rVQq2UcffaRVzu7du5lEImGlpaVNxsEYY2vWrGEymYxVVFQwxhir\nra1l3bt3ZwsXLmSMMTZr1izWrVs3rfds2LCBSaVSduPGDcZYy23VlNu3bzNra2uthEUikTCVSqV1\n3MiRI9no0aO1jmltksP33P7+/lrHVVVVsS5durB//vOf3Gt///vf2ZQpU3hGSYjwUXcVIR1g2LBh\nyMzMRFJSEhYsWICkpCRkZWVx+9PT0xEfHw87OzvuZ/z48ZBIJMjJycG9e/dw7do1PP/8802Wn5OT\ng4qKCsyfP1+rjFWrVqGsrAx37twBUD/eJiAgQOu9zz33HG7evIl79+7xjkehUODFF1/Erl27AAA1\nNTXYt28fXnvtNQBARUUFcnJy8MUXX2jVZ/bs2ZBIJMjNzW227NDQUFRVVWHfvn0AgO+++w6FhYV4\n++23AQCXLl3C+PHjtd4zfvx4MMaQlZWls60AIC8vD6+++ir69esHuVwOd3d3VFVV4caNG1rHNdVW\nly5d4tlKj3fuR2O0sLBASEgIN2bpzp07OHz4MD25RUgDhhs1SAhplqWlJfr06YM+ffrAx8cHhYWF\neOedd5CZmQmJRAKNRoPFixfj1VdfbfTerl27gjHWYvkPx90cPHgQSqWy0X57e3vud11l8fXaa69h\n6tSpuH37NpKTk1FRUYG///3vAACNRgMA2LBhA8aOHdvovT169Gi23C5duuBvf/sbtm7ditdff73R\ngGOJRPLYMUyaNAlmZmb46quv0LNnT5iamsLX17fVA3wlEonWmCcAKCsr08u5u3fv3ui9s2fPxrp1\n63Dx4kUkJCTA2dkZEyZMaFWdCREyupNDSAd49ImliIgIZGVlcXcrvLy88Pvvv3OJUMMfGxsb2Nra\nom/fvvjpp5+aLP+JJ56AjY0Nrl692mQZUmn9nz5jDAkJCVrv/c9//gNXV1fY2toCqB8k/eg/3E15\n/vnn4eDggH379uGbb77B5MmTIZfLAQB2dnbw8PBAdnZ2k/WxsLBosezZs2fj119/xQ8//IAff/xR\na8DtwIED8eOPP2od/8MPP0AikWDgwIE62+rOnTu4fPkyFi5ciOeeew79+/dHQUGB1gDuh06cONGo\nrQYOHMhtOzs7Iy0tjdsuLCxEampqs3G15txN6du3L/z8/BAXF4dt27bh9ddfp8fTCWmA7uQQ0gEe\nvfPg4eGBF198EZ9//jlmzpyJzz77DMOHD8eHH36IV199FXZ2dsjJycHBgwcRExMDS0tLrF69GkFB\nQejatSsCAwOh0Whw8uRJzJw5E46Ojli9ejWWLFkCiUQCf39/1NbW4uLFi8jIyMBnn33Gnbu8vBwR\nERF45ZVXkJqaiiNHjmg9ndO7d28kJSXh8uXL6NKlCxwdHZuMydTUFK+88gq++uorXLt2DfHx8Vr7\n161bh5deegn29vZ48cUXYWZmhsuXL+PHH3/E5s2bW2wvf39/9O3bF0FBQXB0dMTUqVO5feHh4di7\ndy8WLFiAt99+G9evX8e6deswa9YsuLq6AkCLbWVvb48uXbpg+/bteOKJJ1BdXY2PP/4YNjY2jepx\n+fJlxMbG4vnnn8ePP/6I8+fP4+DBg9z+gIAAHDx4ECNHjoRcLse6desgl8u1Pm9WPxYSAFp17ubM\nnj0bQUFB0Gg0ePPNN3m/jxBR6LDRQISIVEhICHvuuecavX727FkmlUrZTz/9xBhjLCMjg02cOJG5\nuLgwGxsb5unpyT744ANWW1vLvef48eNs2LBhzM7Ojjk6OrJJkyZpDeI9cOAAGzZsGJPL5cze3p4N\nGzaMbd68mdvv7u7OVqxYwd577z3Wo0cP1qtXL7Zy5UqteqWmpjIvLy9mZWXFpFIp+/PPP1leXh6T\nSqXcwOOHMjMzmUQiYV27dmV1dXWNYjx58iQbO3Ysc3JyYjKZjD311FPs008/5dVua9asYVKplBtw\n3NDp06fZqFGjmEwmY3369GELFy7UetJLV1udPXuWvfTSS1wbnThxotGAa4lEwuLi4lhQUBDr0qUL\nUyqVjQZ2FxYWssmTJzO5XM4GDx7M9u7dq3PgMd9zPzqg+aGamhrm7OzMJk2axKsdCRETCWN66pAn\nhHQ6vXv3xltvvYUlS5Z0dFVIG925cwc9e/bE/v37MXny5I6uDiFGxaDdVXPnzoWVlRWkUilMTEwQ\nGRmJyspKxMTEoKioCF27dsW8efNgaWlpyGoQQppB/8fpvGpra1FQUIDly5fD1dWVEhxCmmDwMTkR\nERHcAEYAiI+Ph1KpRHh4OA4fPoz4+HgEBQUZuhqEkCbQINXOKzk5Gf7+/vD19cXOnTs7ujqEGCWD\nJzmP/k8xNTUVERERAOqnjI+IiKAkh5AOkpeX19FVIG2kUql4PfVGiJgZNMmRSCRYsWIFJBIJnn/+\neQQEBECtVkOhUAAA5HI51Gp1s+9/9NFWQgghhIibv78/72MNmuR8+umnsLe3x82bNxEZGdlowi8+\nt8q9vLwMVT3BSE5OxqhRozq6GkaP2okfaid+qJ34oXbih9qJn4bzUPFh0MkAH86q6urqCl9fX+Tm\n5kIul3MTXZWUlHCThRFCCCGE6JPBkpwHDx6gsrISQP205unp6XBzc4O3tzcSExMBAKdOnYKPj4+h\nqiAalP3zQ+3ED7UTP9RO/FA78UPtZBgG665Sq9WIiooCUD+l+8SJEzFkyBAolUrExMQgLCyMe4Sc\nEEIIIUTfjHoywISEBBqTwwP15fIj1HYqKHuA4orWLSTZEk35XTyt7KW38oRKqNeTvlE78UPtxE9a\nWprxDDwmhBhecUU1wr/L1Vt5H4/qoreyCCGkI9Eq5AJA2T8/1E780MMA/ND1xA+1Ez/UToZBSQ4h\nhBBCBImSHAFITk7u6Cp0CtRO/LQ0QSf5P3Q98UPtxA+1k2FQkkMIIYQQQaIkRwCoL5cfaid+aEwO\nP3Q98UPtxA+1k2FQkkMIIYQQQaIkRwCoL5cfaid+aEwOP3Q98UPtxA+1k2EYdJ4cjUaDxYsXw9HR\nEYsWLUJlZSViYmJQVFTEzXZsaWlpyCoQQgghRKQMeifn+++/h6urK7cdHx8PpVKJtWvXol+/foiP\njzfk6UWD+nL5oXbih8bk8EPXEz/UTvxQOxmGwZKcO3fuID09HX5+ftxrqampGDNmDABApVIhJSXF\nUKcnhBBCiMgZLMnZuXMnZs2aBan0/06hVquhUCgA1P9vkU/ff8N+yuTkZNpuYvvha8ZSH2Pd3rRp\nk1HVR9/b+vLw77Kj4zH2baFfT/rafviasdTHWLfpejLM951BFui8cOECMjIy8MYbb+DSpUs4fvw4\nFi1ahNDQUGzfvp077tHtR9ECnfwkJ9PCbnwItZ0yC8r1vnbVs/1ddR8ockK9nvSN2okfaid+jGKB\nzitXriA1NRVpaWmoqalBZWUlNm7cCLlcjtLSUigUCpSUlFDfv57QHwY/1E780N8lP3Q98UPtxA+1\nk2EYJMmZOXMmZs6cCQDIysrCsWPHMG/ePOzevRuJiYmYMmUKTp06BR8fH0OcnhBCCCHEsI+QPyow\nMBAxMTEICwvjHiEnj49uc/JD7cSPWq0GutnprbyCsgcorqjWS1nONuboJrPQS1mPi64nfqid+KF2\nMgyDJzkDBgzAgAEDAABWVlYIDw839CkJIUakuKJab2OGoiZ6GE2SQwgxfjTjsQBQ9s8PtRM/NCaH\nH7qe+KF24ofayTAoySGEEEKIIFGSIwBtmTtAjKid+KG1q/ih64kfaid+qJ0Mg5IcQgghhAiSziTn\n3LlzTb7+yy+/6L0ypG2oL5cfaid+aEwOP3Q98UPtxA+1k2HoTHI2bdrU5OubN2/We2UIIYQQQvSl\n2SSnqKgIhYWFYIyhqKiI+yksLMTZs2dhZmbWnvUkLaC+XH6onfihMTn80PXED7UTP9ROhtHsPDnv\nvfdek78D9bezp0+fbrhaEUIIIYQ8pmaTnP379wMAli1bhuXLl7eq0OrqakRERKCmpgbm5uYYPnw4\nJk2ahMrKSsTExKCoqIib8djS0vLxIiDUl8uTMbWTPmcBrq7V7xq7NCaHH2O6nowZtRM/1E6GoXPG\n49YmOABgbm6OZcuWwcLCAjU1NVi8eDGGDh2KhIQEKJVKhIeH4/Dhw4iPj0dQUFCbKk5IZ6bPWYCX\nBfTWSzmEECI0OgceFxUVITo6Gh988AHmzJmj9dMSC4v6qderqqqg0WhgZmaG1NRUjBkzBgCgUqmQ\nkpKihxAI9eXyQ+3ED43J4YeuJ36onfihdjIMnXdyNmzYAJlMhpdeegkKhYJ3wRqNBosWLUJ+fj5C\nQkLg5OQEtVrNlSGXy3l9mTZctOzhRUDb2tsN28oY6mOs2xcvXjSq+hg7fcVr13eI3urUcPHQjv78\njO16Mtbth4ylPsa6TdcTv21ra2u0hoQx1mKHfmhoKKKjoyGTyVpV8EPFxcWIjIzEe++9hxUrVmD7\n9u1aZTfcflRCQgK8vLzadF5CjFlmQbleu6uWn8jTS1lA/SKYQ/S4Crk+Y9V33QghnUtaWhr8/f15\nH6+zu2rIkCHIzs5uc4WcnZ3h5eWFrKwsyOVylJaWAgBKSkpogCMhhBBCDEZnkmNvb4/Y2FisWbMG\n+/fv1/ppTllZGSoqKgAA5eXlSE9Ph5ubG7y9vZGYmAgAOHXqFHx8fPQThchRXy4/1E780Jgcfuh6\n4ofaiR9qJ8PQOSbn3r178PX1BQDcuXMHAMAYg0QiafY9paWliI2NhUajgUKhwKRJkzB48GB4eHgg\nJiYGYWFh3CPkhBDSUR7nUX6NkzsyC8q5bWcbc3STWeiraoQQPdCZ5MydO7fVhbq5uWHNmjWNXrey\nskJ4eHiryyMt6ywDWTsatRM/YupGfvxH+f/L/RY10YOSnCbQ3x0/1E6GoTPJKSoqanZf165d9VoZ\nQgghhBB90ZnkPLqkQ0Mtjcsh7afhY/akedRO/DR8TJuQx0V/d/xQOxmGziTn0UQmLy8PR48exYgR\nIwxWKUIIIYSQx6UzyXlU7969ERQUhFWrVtHTUUaCsn9+qJ34MeYxOSYSidZg38el73W/SGP0d8cP\ntZNhtDrJAYC//voLZWVl+q4LIYS0SF1Vq9eJD2ndL0KETWeS88knn2htq9VqFBYWYsaMGQarFGkd\n6svlh9qJHxqTQ/SJ/u74oXYyDJ1Jjp+fn9a2hYUFevXqhe7duxusUoQQQgghj0tnkqNSqdqhGuRx\nUKuv/CkAACAASURBVPbPD7UTP8Y8Jod0PvR3xw+1k2HoTHJqa2sRHx+PpKQklJSUwMHBAaNHj0Zg\nYCBMTds0pIcQQgghxOB0Zil79uzBlStXMG3aNPTp0wdXr17FyZMnUVlZiZCQkGbfd/v2bcTGxkKt\nVkMmk0GlUkGlUqGyshIxMTEoKirilnawtLTUZ0yiQ325/FA78UNjcog+0d8dP9ROhqEzyTl79iyi\noqIgk8kA1D9C7uvri/Dw8BaTHFNTUwQHB8Pd3R1lZWX48MMP4eHhgcTERCiVSoSHh+Pw4cOIj49H\nUFCQ3gIihBBCCAF4rELeVgqFAu7u7gAAmUwGDw8P3L17F6mpqRgzZgyA+vE+KSkphqqCaFD2zw+1\nEz80JofoE/3d8UPtZBg67+QMHz4ckZGR8Pf31+quGjZsGO+TFBYWIj8/H0qlEmq1GgqFAkD9l6la\nrW7xvQ1v4T1cip62aVsI28ZOX/Ha9R2itzrV1dXqrSx9l6dWq5F8NdNori/apm0hbltbW6M1JIyx\nFqf8rKmpwbfffovk5GSUlJTA3t4eo0aNwrRp02BmZqbzBFVVVYiIiEBgYCB8fHwQGhqK7du3c/sf\n3W4oISEBXl5erQpIjKgvlx9jaqfMgvLHXP36/ywL6K3XCfI+HtUFz/Z31Vt5xhyrPsuLmuiBITSW\nqRFj+rszZtRO/KSlpcHf35/38c3eycnOzsa5c+cQGhqKGTNmaE3+t337duTl5UGpVLZYeG1tLdat\nW4fRo0dzS0DI5XKUlpZCoVCgpKSEbo0TQgghxCCaTXIOHTqEsWPHNrlv4MCBOHToEBYtWtRswYwx\nbN68Ga6urpg4cSL3ure3NxITEzFlyhScOnWK1r/SA8r++aF24sdBoaD1oYje0N8dP9ROhtFsknPt\n2jXMnz+/yX2DBg1CXFxciwX/8ccfOH36NNzc3LBw4UIAwCuvvILAwEDExMQgLCyMe4ScEGI8aH0o\nQohQNJvkMMZgYmLS5D4TExNoNJoWC+7fvz/279/f5L7w8PBWVJHoQn25/FA78aPvwb1ioe8V0p1t\nzNFNZqG38joK/d3xQ+1kGM0mOT169EBGRgZ8fX0b7fvtt9/g6qq/gYmEENLZ6fsOWNRED0EkOYR0\npGbnyZkyZQri4uLwyy+/cHdtNBoNfvnlF8TFxWHq1KntVknSMsr++aF24sfEhJZrIfpDf3f8UDsZ\nRrPfZk8//TRmzJiBLVu2IDo6GnZ2digvL4eVlRWCgoLw1FNPtWc9CSGEEEJapcX/sgUEBGDkyJHI\nz8/H7du34eTkhJ49e8LKyqq96kd4oL5cfqid+KExOUSf6O+OH2onw9B5X9rKygpKpVLnnDiEGJuC\nsgcorqjmtjVO7m0eGCqUQaCEECIm1PkuAJT9N624orqJmXb/26ayxDQIlMbkEH2i7yd+qJ0Mg77N\nCOFB348H0wR5hBBieJTkCAD15RqemCbIozE5RJ/o+4kfaifDMFiSs2nTJqSlpUEmk2HdunUAgMrK\nSsTExKCoqIib7djS0tJQVSCEEEKIiBksyVGpVBg/fjxiYmK41+Lj46FUKhEeHo7Dhw8jPj4eQUFB\nhqqCaFD2T/SJxuQYB312kXbkwHn6fuKH2skwDPZt5unpieLiYq3XUlNTERERAaA+CYqIiKAkhxBC\nmqDPLlIxDZwnpKF2/S+bWq2GQqEAAMjlcqjVap3vadhPmZycDAC0/cj2w9eMpT7Gss3n+iKNGfOY\nHH3XTZ/lGXPdAPp+MvbtTZs2YfDgwUZTH2Pdtra2RmtIGGMGe8yjuLgYa9as4cbkhIaGYvv27dz+\nR7cflZCQAC8vL0NVTzBowFrTMgvKm3iEvG2WBfTW+8BjfZWn77p9PLYnVp7M11t5xhyrWOoWNdED\nQ7rZ6aWs1qLvJ36onfhJS0uDv78/7+Pb9U6OXC5HaWkpFAoFSkpKIJfL2/P0gkV/GESfaEyO8HTk\nCun0/cQPtZNhtOu3mbe3NxITEzFlyhScOnUKPj4+7Xl6QggRJVohnYhVs6uQP67o6GgsXboUBQUF\nmDNnDk6ePInAwEDk5OQgLCwMOTk5CAwMNNTpRaVh3zchj8uYx+SQzoe+n/ihdjIMg93Jef/995t8\nPTw83FCnJIQQInKPrln3OGjNus6POt8FgPpyiT7RmByiT+39/dT0mnVt057dcvQ9bhj0bUaMhj7/\nBwbQ+lCEECJ2lOQIgFAePdTn/8AA414fypjRmByiT0L5fjI0aifDMNjAY0IIIYSQjkR3cjoBXd04\ndn2H8J4Dw8bMBBU1dfqqGg3MEyAak0N0ac28O7q+n/T9ndRZu6npLo5h0LdZJ6DPbhx9z8pK82UQ\nIj76nHfHEDNFE/IQdVcRQrTQmBxC2h/Nk2MYlOQQQgghRJA6pLsqKysLO3fuRF1dHfz9/THh/7V3\n52FRV/sDx9/OsAjITghquAGJopShuVwT0X7W1VuWqZl2EW0X614TNZek0tzSVCQ1Q3FLvYbhcu3X\n/ckN9xIE7T4SCmJuCKTIooAwM9/fHzzOFQVZGmCc+byex+fhu8yZcz5+Z/jwPed7znPPNUU1hAEY\nck2ch7Uv3dTImBwhKjTmml91HZNj6Ck3DDk2ypjGajb6t5lOp2PVqlXMnj0bFxcXPvzwQ7p27Uqb\nNm3qVM7VwtuUaXUGqVNzCxUt7Y3jP+RhY+i+eSGEMBbGvOZXQ0y5YchV7802ycnIyMDDwwN3d3cA\n+vbtS1JSUp2TnP0ZeWxKzjZInT4c0NagSY5MaiceZjImR4jGJ/PkNIxmiqI06m/Qn376iVOnTvHW\nW28BcPDgQTIyMhg/fvx958bHxzdm1YQQQghh5AYOHFjrc426870uDRFCCCGEuFujP13l4uLCtWvX\n9NvXr1/HxcWlsashhBBCCBPX6ElOx44dyc7OJjc3F41Gw9GjRwkMDGzsagghhBDCxDX6mByoeIQ8\nJiZG/wj5n//858aughBCCCFMXJMkOUIIIYQQDU1mPBZCCCGESTK6p6u2b99OUlISAF5eXowbNw57\ne3sA9u3bx7///W/UajWhoaF06tSpKavapDZt2kRycjJWVlb4+fkxcuRIbG1tAYnT3Y4dO8aOHTu4\ncuUK8+fPp0OHDvpjEqfKZCbyqq1atYrk5GQcHBxYsmQJACUlJaxcuZKcnBxatmzJpEmTaN68eRPX\ntGldu3aNqKgoCgoKcHBwICgoiKCgIInVPcrKyoiIiKC8vBwrKyt69+7N0KFDJU7V0Ol0TJ8+HVdX\nV6ZNm1b3OClGpri4WP/zjh07lG3btimKoiiXLl1SpkyZopSXlys5OTlKWFiYotVqm6qaTe7UqVOK\nVqtVtFqtsnr1amXz5s2Kokic7nX58mXlypUrSkREhHLu3Dn9folTZVqtVgkLC1NycnKU8vJyZcqU\nKcqlS5eaulpGITU1VcnMzFQmT56s37dp0yYlLi5OURRF+e677/SfP3N248YN5fz584qiKEpBQYHy\n+uuvK5cuXZJYVaG0tFRRFEUpKytTJk+erGRlZUmcqrFnzx5l+fLlyoIFCxRFqftnz+i6q2xsbADQ\narXcvn0bS0tLABITE+nbty8WFha4u7vj4eFBRobhprR+2HTr1g2VSoVKpSIgIIC8vDxA4nSv1q1b\n06pVq/v2S5wqu3smcgsLC/1M5AL8/Pyws7OrtC8pKYn+/fsDEBQURGJiYlNUzag4OTnRrl07ABwc\nHPD29iYvL09iVQVr64oZ9ktLS9HpdFhaWkqcqnD9+nVSUlIIDg7W76trnIwuyQHYunUrb775Jmlp\naTz//PMA3LhxA1dXV/05rq6u+l/s5i4+Pl7/GL7EqXYkTpXl5eXh5uam33ZxcTHreNSkoKAAJycn\nABwdHSkoKGjiGhmX7OxsLl26hK+vr8SqCjqdjvDwcN544w0GDx6Mm5ubxKkKGzZsYOzYsahU/01V\n6hqnJhmT8+mnn5Kfn3/f/tGjRxMYGMjo0aN56aWX2LZtG1u2bCEkJKTKcpo1a9bQVW1SNcUJYOfO\nnTRv3pzevXtXW47EqXZMPU6iYch1U1lpaSnLli0jJCTkvrESEqsKKpWKxYsXk5uby/z583nssccq\nHZc4wYkTJ3B0dKR9+/acPn26ynNqE6cmSXJmz55d4znW1tYMGDCAyMhIoOIvy+vXr+uPm8NMyTXF\nKSEhgZSUlErnSZxqxxzj9CAyE3ndODo6kp+fj5OTEzdu3MDR0bGpq2QUNBoNS5YsoV+/fvTo0QOQ\nWD2Iu7s73bt3JzU1VeJ0j7Nnz5KUlERycjLl5eWUlJQQGRlZ5zgZXXfV1atXgYoxOUeOHMHLywuA\nwMBAjhw5gkajITc3l+zsbLy9vZuyqk3q5MmT7N69m6lTp2JlZaXfL3GqHYlTZTITed0EBgaSkJAA\nwIEDB/S/0M2ZoiisXr2aNm3aMGTIEP1+iVVlhYWF3Lp1C4CioiJSUlLw8vKSON1j9OjRrFq1iqio\nKP72t7/h7+/PpEmT6hwno5sMcMmSJWRlZWFlZUWXLl14/vnncXBwACoe+Y2Pj9c/8uvn59fEtW06\n7733HhqNhhYtWgDg6+vL66+/Dkic7nb8+HHWr19PYWEhtra2tG/fnhkzZgASp3vJTORVW758Oamp\nqRQVFeHo6MjIkSPp1auXPO57j7S0NObMmYOXl5e+G+HVV1/lsccek1jd5eLFi0RFRaHT6XBycqJ3\n794EBwfLI+QPkJqayp49e+r1CLnRJTlCCCGEEIZgdN1VQgghhBCGIEmOEEIIIUySJDlCCCGEMEmS\n5AghhBDCJEmSI4QJW7t2LbGxsfrtQ4cOMW3aNEJCQrh58yYZGRl89tlnhISEPLTLOHzwwQekpqY2\ndTWEEEZInq4S4iE1ceJECgoKUKvVtGjRAnd3dwYPHkyvXr2qPF+j0RASEsKMGTPo0qULAJ988gl+\nfn6MGDGiMatuNBITE/nHP/5BTk4OlpaWtG3blrfffht3d/emrpoQwgCaZMZjIYRhTJ8+HX9/f0pK\nSjh9+jQxMTGkp6fz2muv3Xdufn4+Go2m0hTy165du29K+drS6XSV1pR52GRnZ7Ns2TImTpxIYGAg\nOp2OU6dOGbRNd/6GlGn6hWgakuQIYQJsbGwIDAzEycmJ2bNn88wzz+Dh4UFUVBRubm48/fTTTJ06\nFYDx48fj7e3N77//Tm5uLosWLUKtVhMdHU1ZWRkbNmwgJSWFZs2aERwczIgRI1CpVCQkJBAfH0/X\nrl1JSEigf//+DB8+nK1bt3Ls2DHKy8vp2bMnISEhWFlZcfr0aSIjI3n55ZeJi4ujvLyc0aNHExQU\nBEBZWRnbtm3j559/5tatW3h5eTFr1iysrKw4e/YsGzdu5PLlyzzyyCOEhobSuXPnKts+ceJE3nnn\nHfz9/dmxYwdZWVm0aNGCY8eO4ejoyMSJE+nQocN9r7t8+TLOzs706dNHv++pp57S/6zT6YiLi+PH\nH3+ksLAQT09PwsPDcXV15cyZM8TExHD16lU8PT0JDQ3F19cXgIiICLp160Zqairp6eksWrQIjUbD\nunXryMzMxNHRkVGjRunXm0tLS2Pnzp2kp6djbW3NkCFD+Mtf/mKQ60IIcydJjhAmxNvbG2dnZzIz\nM/Hw8NDfQfD09GTp0qWEhYURExOjv1txd4IA8MUXX2BlZcXcuXMpLi4mMjISV1dXBg0aBEBGRgad\nO3fm888/x8LCgi1btnDx4kU+/PBDLC0tWbNmDbGxsYwePRqoWDH4t99+47PPPiMxMZHo6Gh69uyJ\nra0tmzZt4uLFi8ydOxdHR0cyMjJQqVTk5eUxb948QkNDeeqppzh+/DiLFi1ixYoV+tnPH+Tnn39m\n/PjxvPLKK2zevJno6GjmzZt333ldu3alqKiINWvW0KdPH3x8fCrNnLp3714OHTrEjBkz8PT05OLF\ni1hbW3Pz5k0WLFjAuHHj6NevH0eOHGH+/PlERkbqZyD/4YcfeOuttwgICOD27dtMnjyZwYMHM3ny\nZM6cOcPy5cvx8vKidevWbNiwgRdffJHp06dTWlpKTk7OH7gChBB3e3jvNQshquTs7Fxpsc07ahp+\nl5+fT3JyMmPGjMHd3Z127doRHBzMkSNH9Oeo1WpGjBiBra0tlpaW7N+/n5EjR/Loo4/i4eHBc889\nV+l8RVEYOXIkDg4OBAUF0axZM7KystDpdPz4448MGTIEZ2dnVCoVvr6+WFhYcPDgQXx8fAgKCsLG\nxob+/fvj7u5OSkpKrdrfqlUrBg0ahJ2dHQMGDODChQtVnmdtbc2sWbMoLi5m+fLlTJgwgS+//JKy\nsjIA4uPjGTRoEJ6engB4eXnRokULkpOTsbKyon///qhUKvr164e1tTUnTpzQl/3444/TvXt31Go1\nJ0+epHnz5rz44ovY2dnRvXt3/P39OXbsGFBxxygnJ4eSkhL90iNCCMOQOzlCmJi8vDzc3Nzq/Lpr\n166h0+kIDw/X79PpdJXKatu2LRYWFV8bhYWFlJWVsWDBAv1xRVEqJVPOzs76uy9qtRp7e3tKS0sp\nKiqivLycTp06VVmPX3/9ldDQUP0+rVZLfn5+rdrRtm1b/c9OTk6Ul5dXO37Ix8eHv//97wD88ssv\nREVFcfjwYYKDg8nLy6tyvFJeXt59iUjHjh3Jy8sDKsbf+Pj46I/d6Ra8uz06nU6/yntYWBi7du0i\nLCyMtm3bMnbsWLNeLFYIQ5IkRwgTkpGRQX5+fr3uBri6uqJSqVi6dCnOzs5VnqNWq/U/29vbY2Vl\nxcyZM+v8S9ne3h5LS0vS0tLo2bNnpWNubm506dJFv5BqY+nWrRtPPPEEKSkpBAcH4+rqSlpa2n1t\nc3Fx4fz585X2nTt3rlI77k6o3NzcaNmyJV988UWV7/voo48SFhaGTqdj9+7dREdHM3/+fAO2TAjz\nJd1VQjzE7tw1KS4u5sSJEyxfvpxnn31W38VSlxkinJ2d6d69O1u2bOHy5cvodDqys7OrnYNGpVIx\ncOBAtm/fTmZmJjqdjry8PE6dOlXje6lUKgYMGMCuXbtIS0tDp9Nx9uxZNBoN/fr14+zZsxw4cICb\nN29SVlbG6dOn9XdKDCUtLY34+HgKCwvRaDSkpaWRmJioH6QcHBzM/v37SUlJQavVcuHCBW7evEn3\n7t0pKyvj4MGDaLVaDh8+zO3bt3nyySerfJ8nn3yS0tJSdu/erX/CLSMjgytXrqDRaDh06BDFxcVo\ntVpUKhWFhYUGbacQ5kzu5AjxEFu4cCFqtRpbW1s8PDwYPXp0paeF6vro8sSJE/nmm2+YN28excXF\neHh48MILL1Rb3pgxY/j2229ZunQpRUVFuLi4MHjwYAICAmp8r9dee42tW7eybNkySkpKaN++PTNm\nzMDV1ZVZs2axefNmYmJiUKvV+Pj4MGHChFq1obZttrOzIykpiW3btlFaWoqDgwMDBw7Ut3fo0KFo\nNBqio6MpLCykTZs2TJkyBRcXF6ZNm0ZMTAzr1q3D09OT6dOn6wcd36t58+bMnj2bjRs3smvXLgDa\ntWvHX//6V6BigsZ169ZhYWGBn58f77//fq3qL4SomUwGKIQQQgiTJN1VQgghhDBJkuQIIYQQwiRJ\nkiOEEEIIkyRJjhBCCCFMkiQ5QgghhDBJkuQIIYQQwiRJkiOEEEIIkyRJjhBCCCFMkiQ5QgiDS0hI\nQKVSkZWVZbAyf/vtN1QqFUePHv3DZY0bN45nnnnGALUSQhgzSXKEEHpXrlzBwsKC1q1bo9Vqa/Ua\nCwsLNm7cWGlf3759yc7O1q+h1VQ2b95c5erjkZGRfPvtt01QIyFEY5IkRwihFx0dzWOPPUZJSQl7\n9ux54Lnl5eVAxVpR964OY2lpibu7e53Xzmos9vb2ODo6NnU1hBANTJIcIQQAOp2OdevWMWnSJMaM\nGcNXX31V6Xi7du349NNPmTZtGj4+Pjz99NO0b98erVZLaGgoKpUKtVoNVN1dlZWVxWuvvUa7du2w\ns7MjICCAf/7znwDExMRgaWlZ6f0uX76MSqXi4MGD1dZ55syZdO7cGTs7O7y8vHjnnXf0q3gnJCTo\nF8FUqVSoVCrGjx8PVN1d9dVXX9GtWzfs7e0JCAhg/fr197V/7ty5zJw5k/bt2+Ph4cHkyZNrfcdL\nCNH4ZBVyIQQA33//PXl5eYwdO5bz58/zxBNPcOHCBdq2bas/Z9myZbz//vt8//33aDQa3Nzc8PT0\nZOnSpYwaNarasgsKCujZsyedO3fmm2++oVWrVpw+fVqfFNWXra0ta9euxdPTk8OHDzNnzhxKSkqI\niYmhb9++rFy5krCwMLKzswGwsbHRv/buu0xRUVGEh4ezYsUKBgwYwP79+3n33XdRFEWfGN1p/yuv\nvMKOHTs4fPgwH3zwAf7+/pXOEUIYD0lyhBBAxZ2MV199lRYtWtC1a1d69erF119/zaeffqo/x9nZ\nmY8++ui+1zo6OuLu7l5t2ZGRkeh0Onbt2qVPNNq1a/eH6zxz5kz9zx06dODWrVv8/e9/198ZcnBw\nAKiybnd3sS1YsIAXXniB119/HYCOHTvyr3/9i3nz5lVKYDw9PVm5ciUAgYGBxMTEsH//fklyhDBS\n0l0lhODKlSvs27ePt99+W7/vzTffZN26deh0OqDizsezzz5br/JPnjxJnz59Kt1JMYSdO3fy9NNP\n07p1a+zt7ZkyZQrl5eX6Oze1UVhYyJUrV3juuecq7X/22Wf57bffKC0tBSraP3To0ErndO/enZyc\nnD/eECFEg5AkRwhBdHQ0Wq2WHj16YGlpiaWlJRMmTCA7O5vdu3frz2vVqlW9yq9qcPLdVCrVfceL\niooeWObPP//MiBEjePzxx4mLiyMlJYWlS5eiKAplZWX1qmdN7O3tK22rVCp9EiiEMD6S5Ahh5nQ6\nHdHR0cycOZNTp07p/508eZJXXnnlvgHI97Kysqpx8O3jjz/O0aNHKS4urvK4u7s7Op2OU6dO6ffV\n9HTX4cOHsbGxYenSpfTo0QNvb2+OHz9+X92AByZYDg4OtGnThn379lXa//3339OhQweaN2/+wHoI\nIYyXJDlCmLnvv/+ey5cv89Zbb9G5c2f9vy5dujBu3Dj+9a9/ceHChWoThfbt2/PDDz+QmZnJtWvX\nqjxn0qRJqFQqXnjhBY4ePcr58+fZu3cv//u//wtAz549sbe3Z86cORw/fpyVK1eyZcuWB9a7U6dO\nFBcX8/nnn3PmzBk2btxIfHz8fXUD2LJlCzk5Ody6davKsj788EN2797N119/TXp6OmvWrOH7779n\nxowZ+nMelCgJIYyTJDlCmLm1a9fSq1cv2rRpc9+xAQMG4OLiwtdff13tnDdLlizhl19+wc/Pj5Yt\nW+r3332+g4MDx48f55FHHmHUqFH4+/sze/Zs/XEXFxe2bt3K2bNn+Z//+R/27t3L6tWr73vPu7eH\nDBnCF198wbfffkv//v3Zt28fK1eurHROjx49eP/99/nggw/w9PRk0qRJ+nLuPu+dd95h2bJlrFix\ngieeeIIvv/ySL7/8ktDQ0Crf++59xjoXkBACminy54kQQgghTFCDPkJeWlpKdHQ0Fy5coLy8nHff\nfZc2bdqwcuVKcnJyaNmyJZMmTZI+byGEEEIYXIPeyYmKisLPz4/g4GC0Wi23b99m586d2Nvb88IL\nLxAXF8etW7cYM2ZMQ1VBCCGEEGaqwcbkFBcX8+uvvxIcHAyAWq3G1taWpKQk+vfvD0BQUBCJiYkN\nVQUhhBBCmLEG667Kzc3FwcGBqKgoMjMz8fX1Zdy4cRQUFODk5ARUzJJaUFBQbRn3PikhhBBCCPM2\ncODAWp/bYEmOVqvl3LlzvPTSS7zxxht89dVXHDt2rNI5tXkqoXv37g1VRSGEEEI8RJKTk+t0foMl\nOa6urrRo0YLAwEAA+vbty8GDB3FyciI/Px8nJydu3LiBo6NjQ1XBJBw+fJg//elPTV2NJmPu7QeJ\nAUDK2Quo7F0MUpa7nRWeDtYGKauxyDUgMQCJQX00WJLj5OSEh4cH6enpdOzYkeTkZPz9/XF1dSUh\nIYFhw4Zx4MABevTo0VBVEEKYiCKdmrn/zDBIWYuHeD90SY4Qon4a9OmqrKwsoqKiKCwsxMvLi0mT\nJqEoSq0fIY+Pj5fuKiEEp64WEW7AJCfA077mE4UQRic5Odk4xuRAxWJ+8+bNu29/eHh4Q76tEEII\nIYQs62DsDh8+3NRVaFLm3n6QGAAPfArTHMg1IDEAiUF9SJIjhBBCCJMkSY6RM/eR9ObefpAYAGb/\nFKZcAxIDkBjUhyQ5QgghhDBJkuQYOXPvgzX39oPEAGRMjlwDEgOQGNRHgz5dNXHiRGxsbFCpVKjV\naubPn09JSYmsQi6EEEKIBtegSQ5AREQELVq00G/Hxsbi6+tLeHg4cXFxxMbGyirkD2DufbDm3n6Q\nGMCdMTm/N3U1moxcAxIDkBjUR4N3V90716CsQi6EEEKIxtCgSU6zZs345JNPmDp1Kvv37weo0yrk\nQvpgzb39IDEAGZMj14DEACQG9dGg3VWffvopzs7OXL58mfnz59O6detKx2uzCvndC5Ld+Q82p+3/\n/Oc/RlUfaX/jb99hLPVpqm1Da+r2yHbdtv/zn/8YVX3k+7Bptm1tbamLBl276m4bNmzAxcWF+Ph4\nIiIi9KuQf/zxxyxbtqzK18jaVUIIkLWrhBAV6rp2VYN1V92+fZuSkhIACgsLSUlJwcvLi8DAQBIS\nEgBkFXIhhBBCNJgGS3IKCgr46KOPCA8PZ9myZQwZMoSAgACGDx9Oeno6U6ZMIT09neHDhzdUFUyC\nuffBmnv7QWIAMiZHrgGJAUgM6qPBxuS4u7uzePHi+/bb2NjIKuRCCCGEaHAy47GRM/d5Ecy9/SAx\nAFm7Sq4BiQFIDOpDkhwhhBBCmCRJcoycuffBmnv7QWIAMiZHrgGJAUgM6kOSHCGEEEKYJElydfFb\nzQAAG+pJREFUjJy598Gae/tBYgAyJkeuAYkBSAzqQ5IcIYQQQpikBk1ydDodU6dOZeHChQCUlJSw\nePFipkyZwuLFiyktLW3ItzcJ5t4Ha+7tB4kByJgcuQYkBiAxqI8GTXL27dtHmzZt9NuxsbH4+vry\n+eef4+PjQ2xsbEO+vRBCCCHMWI1JzrFjx6rc/9NPPz3wddevXyclJYXg4GD9vqSkJPr37w9AUFAQ\niYmJdamrWTL3Plhzbz9IDEDG5Mg1IDEAiUF91JjkrFq1qsr9q1evfuDrNmzYwNixY1Gp/vsWBQUF\nODk5ARVfWrW5BX337bnDhw/LtmzLtpluG1pTt0e2ZVu2G/77oNpVyHNyclAUhfDwcD7//HP9fkVR\nyMzMZP369axdu7bKQk+cOMHJkyeZMGECp0+fZu/evUybNo3Q0FDWr1+vP+/e7XvJKuQV/7HmnL2b\ne/tBYgBwMO0ycw//bpCyHsZVyOUakBiAxADqvgq5RXUH3nvvvSp/hoq7MCNHjqy20LNnz5KUlERy\ncjLl5eWUlJQQGRmJo6Mj+fn5ODk5cePGDbO/BS2EEEKIhlPtnZw75syZw8cff1zvN0hNTWXPnj1M\nmzaNzZs306JFC4YNG0ZcXBy3bt1izJgx1b5W7uQIIQBOXS0i/J8ZBinrYbyTI4SoUNc7OTWOyfkj\nCc69hg8fTnp6OlOmTCE9PZ3hw4cbrGwhhBBCiLtV2111R05ODtu2beO33367b16b6gYl361z5850\n7twZABsbG8LDw+tZVfNk7n2w5t5+kBiAzJMj14DEACQG9VFjkrNixQocHBx4+eWX9U9GCSGEEEIY\nuxqTnKysLKZNm4aDg0Nj1Efcw9yzdnNvPzycMbhaeJvcW2UGK8/GzgEwzNNVD6OH8RowNImBxKA+\nakxyAgICSEtLo2fPno1RHyGECci9VWawgcIAcwa1N1hZQgjzUePAY2dnZ6Kioli4cCHbt2+v9E80\nvPpMfmRKzL39IDEA0Go1TV2FJiXXgMQAJAb1UeOdnJs3b+rv4ly/fh2omBCwWbNmDVszIYQQQog/\noMYkZ+LEiXUutKysjIiICMrLy7GysqJ3794MHTqUkpISVq5cSU5ODi1btmTSpEk0b968XhU3F+be\nB2vu7QeJAYBaXeNXlUmTa0BiABKD+qjVI+TVadmyZZX7raysmDNnDtbW1pSXlzN9+nSefPJJ4uPj\n8fX1JTw8nLi4OGJjYx84GaAQQgghRH3VOCbnvffeq/bfg1hbWwNQWlqKTqfD0tJSViGvB3PvgzX3\n9oPEAGRMjlwDEgOQGNRHjXdy7h1gfP78eXbv3k2fPn0e+DqdTse0adO4dOkS48aNw83NrV6rkAsh\nhBBC1EeNd3Lu1b59e8aMGcM333zz4IJVKhYvXsyKFSv44YcfOH/+fKXjtR24bExLuzfF9r2xaOr6\nSPsbf/tOP7yx1Ke224bUEGNymjo+ddn+05/+ZFT1aYrtO/uMpT5NsX1vLJq6Pk19PdRGjQt0VuXU\nqVOsWLGC6OjoWp2/adMmXFxc+L//+z8iIiL0q5B//PHHLFu2rNrXyQKdQjycDLmgJlTMk/Px/vM1\nn1gLhl6g05ATH7rbWeHpYG2QsoQwRXVdoLPGP48++uijStsFBQVkZ2czatSoal9TWFiIWq3Gzs6O\noqIiUlJSCA0NJTAwkISEBIYNG8aBAwfo0aNHrStqrg4fNu+1Ssy9/SAxAOMek2PIiQ8XD/GuMsmR\na0BiABKD+qgxyQkODq60bW1tTdu2bWnVqlW1r8nPzycqKgqdToeTkxNDhw6la9eueHt7s3LlSqZM\nmaJ/hFwIIYQQoiHUmOQEBQXVuVAvLy8WLlx4335ZhbzuzD1rN/f2g8QAZJ4cuQYkBiAxqI8avzk0\nGg2xsbEcPHiQGzdu4OLiQr9+/Rg+fDgWFub9xSOEEEII41Xj01Vbtmzhl19+4aWXXmLevHkMGzaM\nX375hc2bNzdG/cxeQz2t8rAw9/aDxACMe0xOY5BrQGIAEoP6qPFWzNGjR1m8eDEODg5AxSPkPXv2\nJDw8nHHjxjV0/YQQwmyomzXj1NWi+/br3NpVuf9B5EktIWqR5IimZe59sObefpAYgPmMySko1Tzg\nUfnf61RWdU9qPazkcyAxqI8avzl69+7N/PnzGThwIB06dODcuXP8+OOP9OrVqzHqJ4QQQghRLzWO\nyRkzZgyPP/44u3bt4qOPPmL37t0EBAQwduzYxqif2TP3Plhzbz9IDEDG5Aj5HIDEoD6qvZOTlpbG\nsWPHCA0NZdSoUZUm/1u/fj3nz5/H19e32oKvXbtGVFQUBQUFODg4EBQURFBQECUlJaxcuZKcnBz9\nXDnNmzc3bKuEEKIa1Y17qa8yTZ0njRdCNJJqk5zvvvuOAQMGVHmsS5cufPfdd0ybNq36gi0sCAkJ\noV27dhQWFvLBBx/g7e1NQkICvr6+hIeHExcXR2xsLGPGjPnjLTFR5t4Ha+7tB4kBGHZMzoPHvdTd\nnEHtDVaWqJ58DiQG9VFtd1VmZiYBAQFVHvP39ycj48HTmDs5OdGuXTsAHBwc8Pb2Ji8vj6SkJPr3\n7w9UTDSYmJhYz6oLIYQQQlSv2iRHURTUanWVx9RqNTqdrtZvkp2dzaVLl/D19aWgoAAnJycAHB0d\nKSgoeOBrjWnV06bYXrVqlVHVR9rf+Nt39hlLfWq7bUjmMibHkO0sKCgwquvhj26vWrXKqOoj34cP\nx/dLtauQz5kzhyFDhtCzZ8/7jiUmJrJ3714+/vjjGt+gtLSUiIgIhg8fTo8ePQgNDWX9+vX64/du\n301WIZcF2cy9/fBwxsDQq5DPGvAoc3+8ZJCyDLmiuaHLM+bV1pvaw/g5MDSJQd1XIa/2Ts6wYcNY\nu3YtP/30k/6ujU6n46effmLt2rW8+OKLNRau0WhYsmQJ/fr106847ujoSH5+PgA3btzA0dGx1pU1\nR+Z+QZt7+0FiAOYzT46onnwOJAb1Ue03xxNPPMGoUaNYs2YNy5cvx97enqKiImxsbPSPlT+Ioiis\nXr2aNm3aMGTIEP3+wMBAEhISGDZsGAcOHNAnP0IIIYQQhvTAP48GDRpE3759uXTpEteuXcPNzY1H\nH30UGxubGgs+c+YMhw4dwsvLi6lTpwLw6quvMnz4cFauXMmUKVP0j5CL6pn77Ulzbz9IDMB8xuSI\n6snnQGJQHzXeA7axscHX1/eBc+JUpVOnTmzfvr3KY+Hh4XUqSwghhBCirmqc8Vg0LXPP2s29/SAx\nABmTI+RzABKD+pAkRwghhBAmSZIcI9dQ8448LMy9/SAxABmTI+RzABKD+pAkRwghhBAmSZIcI2fu\nfbDm3n6QGICMyRHyOQCJQX002DfHqlWrSE5OxsHBgSVLlgDICuRCCNFIDL3aurudFZ4O1gYrT4jG\n0GBJTlBQEM8++ywrV67U74uNjZUVyOvI3OdFMPf2g8QAZExOfRh6tfXFQ7ybNMmRz4HEoD4aLMnx\n8/MjNze30r6kpCQiIiKAiiQoIiJCkhwhhHgIGPLOkNwVEo2lUTu667oCOVTOXO+MLDe37btjYQz1\nkfYbx3bK2QsU6dT69d/ufJ7qu60ryuNW7iWD1c+QzGVMjiHvWBn67lferVKDLZK6eIg3535JBOp2\nPVX3++Bq4W3Ss34H6n/939n2afUIng7WTf75lu/DqrdtbW2pi2pXITeE3NxcFi5cqB+TU5cVyEFW\nIRfiQQy90rchV602dN2MdaVvQ5dnrGUZujxDr5BuyOvN1FZvNzV1XYW8Uf88urMCuZOTk6xAXkvm\n3gdr7u2HxouBIbsjyjSG/dtJxuQI+S6QGNRHoyY5sgK5EMbLkANV5wxqb5ByhGmqT0Ktc2tX7WsM\nnVQL09FgSc7y5ctJTU2lqKiId955h5EjR8oK5PVg7lm7ubcfJAZgPmNyzEX9E+rfq9xrLkm1fBfU\nXYN9c7z//vtV7pcVyMXD5mrhbXJvlRmkLHmqRAghGo/8eWTkzL0P1hjan3urzKCDGuua5BhDDJqa\njMkRQr4L6kOWdRBCCCGESZIkx8iZe9Zu7u0HiQHImBwhQL4L6kO+OYTJMeQYGjDskxvG/Ji2EEKY\nGklyDMzQv2B1RXk84dvWYOUZiqHbaWep5la59r79BQUFdZ5PqUyjMPOHc4aqmkGf3JDHtOtHxuQI\nIWNy6kOSHAMz5CBVgFl/esRgZRmSodv54NlUq35s9EFlCSGEEE2S5KSmprJhwwa0Wi0DBw7kueee\na4pqPBRcnJwM1r1R3d2S+pCuEtGYZEyOaCyG7FIGw04bYcx3cYx1qo1G/+bQ6XSsWrWK2bNn4+Li\nwocffkjXrl1p06ZNY1cFgJybZaT/XmyQsqwtVFiomhmkrDsM3b0hXSVCCFE9Q37nQv2mjWgMDTF2\n0VDDBAwZs0ZPcjIyMvDw8MDd3R2Avn37kpSU1GRJjk5RyLl52yBlOVhb4GZnZZCy7pCxCELI50AI\nMOyYnIYYcmCMGnQV8qr89NNPnDp1irfeeguAgwcPkpGRwfjx4+87Nz4+vjGrJoQQQggjZ7SrkNdV\nXRoihBBCCHG3Rp8M0MXFhWvXrum3r1+/jouLS2NXQwghhBAmrtGTnI4dO5KdnU1ubi4ajYajR48S\nGBjY2NUQQgghhIlr9DE5UPEIeUxMjP4R8j//+c+NXQUhhBBCmLgmSXKEEEIIIRqaLNAphBBCCJNk\ntE9X7dmzh82bNxMdHU2LFi0A2LdvH//+979Rq9WEhobSqVOnJq5lw9i+fTtJSUkAeHl5MW7cOOzt\n7QHzicGmTZtITk7GysoKPz8/Ro4cia2tLWAeMTh27Bg7duzgypUrzJ8/nw4dOuiPmUP77zDH2dFX\nrVpFcnIyDg4OLFmyBICSkhJWrlxJTk4OLVu2ZNKkSTRv3ryJa9pwrl27RlRUFAUFBTg4OBAUFERQ\nUJDZxKGsrIyIiAjKy8uxsrKid+/eDB061GzafzedTsf06dNxdXVl2rRpdY+BYoR+//13Ze7cucq7\n776rFBUVKYqiKJcuXVKmTJmilJeXKzk5OUpYWJii1WqbuKYNo7i4WP/zjh07lG3btimKYl4xOHXq\nlKLVahWtVqusXr1a2bx5s6Io5hODy5cvK1euXFEiIiKUc+fO6febS/sVRVG0Wq0SFham5OTkKOXl\n5cqUKVOUS5cuNXW1GlxqaqqSmZmpTJ48Wb9v06ZNSlxcnKIoivLdd9/pPw+m6saNG8r58+cVRVGU\ngoIC5fXXX1cuXbpkVnEoLS1VFEVRysrKlMmTJytZWVlm1f479uzZoyxfvlxZsGCBoih1/ywYZXfV\nxo0bGTt2bKV9iYmJ9O3bFwsLC9zd3fHw8CAjw3CzNRoTGxsbALRaLbdv38bS0hIwrxh069YNlUqF\nSqUiICCAvLw8wHxi0Lp1a1q1anXffnNpP1SeHd3CwkI/O7qp8/Pzw87OrtK+pKQk+vfvD0BQUBCJ\niYlNUbVG4+TkRLt27QBwcHDA29ubvLw8s4qDtXXFsgalpaXodDosLS3Nqv1QMcVMSkoKwcHB+n11\njYHRJTmJiYm4urrStm3bSvtv3LiBq6urftvV1VX/i88Ubd26lTfffJO0tDSef/55wPxicEd8fLx+\nmgFzjcEd5tT+vLw83Nzc9NsuLi4m29aaFBQU4OTkBICjoyMFBQVNXKPGk52dzaVLl/D19TWrOOh0\nOsLDw3njjTcYPHgwbm5uZtV+gA0bNjB27FhUqv+mKnWNQZOMyfn000/Jz8+/b//o0aOJi4tj5syZ\n+n3KAx7+atbMsIthNqYHxSAwMJDRo0fz0ksvsW3bNrZs2UJISEiV5ZhyDAB27txJ8+bN6d27d7Xl\nPKwxqE37a+Nhbb+oH3P6/y4tLWXZsmWEhITcN+7C1OOgUqlYvHgxubm5zJ8/n8cee6zScVNv/4kT\nJ3B0dKR9+/acPn26ynNqE4MmSXJmz55d5f6LFy+Sm5tLeHg4UPGX3PTp05k3bx4uLi5cv35df+7D\nPlNydTG4m7W1NQMGDCAyMhLA7GKQkJBASkpKpfNMKQa1uQbuZUrtr4nMjv5fjo6O5Ofn4+TkxI0b\nN3B0dGzqKjU4jUbDkiVL6NevHz169ADMMw7u7u50796d1NRUs2r/2bNnSUpKIjk5mfLyckpKSoiM\njKxzDIyqu8rLy4u1a9cSFRVFVFQULi4uLFy4ECcnJwIDAzly5AgajYbc3Fyys7Px9vZu6io3iKtX\nrwIVY3KOHDmCl5cXgFnF4OTJk+zevZupU6diZfXfld3NKQZVMaf2y+zo/xUYGEhCQgIABw4c0P/S\nN1WKorB69WratGnDkCFD9PvNJQ6FhYXcunULgKKiIlJSUvDy8jKb9kPFHe1Vq1YRFRXF3/72N/z9\n/Zk0aVKdY2DUkwGGhYWxYMGCSo+Qx8fH6x+d9fPza+IaNowlS5aQlZWFlZUVXbp04fnnn8fBwQEw\nnxi89957aDQa/f+9r68vr7/+OmAeMTh+/Djr16+nsLAQW1tb2rdvz4wZMwDzaP8d5jg7+vLly0lN\nTaWoqAhHR0dGjhxJr169zOrR4bS0NObMmYOXl5e+S+LVV1/lscceM4s4XLx4kaioKHQ6HU5OTvTu\n3Zvg4GCzfIQcKr4H9uzZU69HyI06yRFCCCGEqC+j6q4SQgghhDAUSXKEEEIIYZIkyRFCCCGESZIk\nRwghhBAmSZIcIUzY2rVriY2N1W8fOnSIadOmERISws2bN8nIyOCzzz4jJCTkoV0y4YMPPiA1NbWp\nqyGEMELydJUQD6mJEydSUFCAWq2mRYsWuLu7M3jwYHr16lXl+RqNhpCQEGbMmEGXLl0A+OSTT/Dz\n82PEiBGNWXWjkZiYyD/+8Q9ycnKwtLSkbdu2vP3227i7uzd11YQQBtAkMx4LIQxj+vTp+Pv7U1JS\nwunTp4mJiSE9PZ3XXnvtvnPz8/PRaDSVpoe/du3afdPF15ZOp6u0pszDJjs7m2XLljFx4kQCAwPR\n6XScOnXKoG268zekqU/BL4SxkiRHCBNgY2NDYGAgTk5OzJ49m2eeeQYPDw+ioqJwc3Pj6aefZurU\nqQCMHz8eb29vfv/9d3Jzc1m0aBFqtZro6GjKysrYsGEDKSkpNGvWjODgYEaMGIFKpSIhIYH4+Hi6\ndu1KQkIC/fv3Z/jw4WzdupVjx45RXl5Oz549CQkJwcrKitOnTxMZGcnLL79MXFwc5eXljB49mqCg\nIADKysrYtm0bP//8M7du3cLLy4tZs2ZhZWXF2bNn2bhxI5cvX+aRRx4hNDSUzp07V9n2iRMn8s47\n7+Dv78+OHTvIysqiRYsWHDt2DEdHRyZOnEiHDh3ue93ly5dxdnamT58++n1PPfWU/medTkdcXBw/\n/vgjhYWFeHp6Eh4ejqurK2fOnCEmJoarV6/i6elJaGgovr6+AERERNCtWzdSU1NJT09n0aJFaDQa\n1q1bR2ZmJo6OjowaNUq/HltaWho7d+4kPT0da2trhgwZwl/+8heDXBdCmDtJcoQwId7e3jg7O5OZ\nmYmHh4f+DoKnpydLly4lLCyMmJgY/d2KuxMEgC+++AIrKyvmzp1LcXExkZGRuLq6MmjQIAAyMjLo\n3Lkzn3/+ORYWFmzZsoWLFy/y4YcfYmlpyZo1a4iNjWX06NFAxYrBv/32G5999hmJiYlER0fTs2dP\nbG1t2bRpExcvXmTu3Lk4OjqSkZGBSqUiLy+PefPmERoaylNPPcXx48dZtGgRK1as0M/8/SA///wz\n48eP55VXXmHz5s1ER0czb968+87r2rUrRUVFrFmzhj59+uDj41Np5tS9e/dy6NAhZsyYgaenJxcv\nXsTa2pqbN2+yYMECxo0bR79+/Thy5Ajz588nMjJSP0P3Dz/8wFtvvUVAQAC3b99m8uTJDB48mMmT\nJ3PmzBmWL1+Ol5cXrVu3ZsOGDbz44otMnz6d0tJScnJy/sAVIIS428N7r1kIUSVnZ+dKC1veUdPw\nu/z8fJKTkxkzZgzu7u60a9eO4OBgjhw5oj9HrVYzYsQIbG1tsbS0ZP/+/YwcOZJHH30UDw8Pnnvu\nuUrnK4rCyJEjcXBwICgoiGbNmpGVlYVOp+PHH39kyJAhODs7o1Kp8PX1xcLCgoMHD+Lj40NQUBA2\nNjb0798fd3d3UlJSatX+Vq1aMWjQIOzs7BgwYAAXLlyo8jxra2tmzZpFcXExy5cvZ8KECXz55ZeU\nlZUBEB8fz6BBg/D09AQq1tZr0aIFycnJWFlZ0b9/f1QqFf369cPa2poTJ07oy3788cfp3r07arWa\nkydP0rx5c1588UXs7Ozo3r07/v7+HDt2DKi4Y5STk0NJSYl+CQ8hhGHInRwhTExeXh5ubm51ft21\na9fQ6XSEh4fr9+l0ukpltW3bFguLiq+NwsJCysrKWLBggf64oiiVkilnZ2f93Re1Wo29vT2lpaUU\nFRVRXl5Op06dqqzHr7/+SmhoqH6fVqslPz+/Vu1o27at/mcnJyfKy8urHT/k4+PD3//+dwB++eUX\noqKiOHz4MMHBweTl5VU5XikvL+++RKRjx47k5eUBFeNvfHx89MfudAve3R6dTqdfUT0sLIxdu3YR\nFhZG27ZtGTt2rMkuuipEY5MkRwgTkpGRQX5+fr3uBri6uqJSqVi6dCnOzs5VnqNWq/U/29vbY2Vl\nxcyZM+v8S9ne3h5LS0vS0tLo2bNnpWNubm506dJFvyBpY+nWrRtPPPEEKSkpBAcH4+rqSlpa2n1t\nc3Fx4fz585X2nTt3rlI77k6o3NzcaNmyJV988UWV7/voo48SFhaGTqdj9+7dREdHM3/+fAO2TAjz\nJd1VQjzE7tw1KS4u5sSJEyxfvpxnn31W38VSlxkinJ2d6d69O1u2bOHy5cvodDqys7OrnYNGpVIx\ncOBAtm/fTmZmJjqdjry8PE6dOlXje6lUKgYMGMCuXbtIS0tDp9Nx9uxZNBoN/fr14+zZsxw4cICb\nN29SVlbG6dOn9XdKDCUtLY34+HgKCwvRaDSkpaWRmJioH6QcHBzM/v37SUlJQavVcuHCBW7evEn3\n7t0pKyvj4MGDaLVaDh8+zO3bt3nyySerfJ8nn3yS0tJSdu/erX/CLSMjgytXrqDRaDh06BDFxcVo\ntVpUKhWFhYUGbacQ5kzu5AjxEFu4cCFqtRpbW1s8PDwYPXp0paeF6vro8sSJE/nmm2+YN28excXF\neHh48MILL1Rb3pgxY/j2229ZunQpRUVFuLi4MHjwYAICAmp8r9dee42tW7eybNkySkpKaN++PTNm\nzMDV1ZVZs2axefNmYmJiUKvV+Pj4MGHChFq1obZttrOzIykpiW3btlFaWoqDgwMDBw7Ut3fo0KFo\nNBqio6MpLCykTZs2TJkyBRcXF6ZNm0ZMTAzr1q3D09OT6dOn6wcd36t58+bMnj2bjRs3smvXLgDa\ntWvHX//6V6BigsZ169ZhYWGBn58f77//fq3qL4SomUwGKIQQQgiTJN1VQgghhDBJkuQIIYQQwiRJ\nkiOEEEIIkyRJjhBCCCFMkiQ5QgghhDBJ/w+0+ztEvlxQIwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1141e7e90>"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x11507a0d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Proportion of \"high-risk\" children"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Function for extracting unique children demographics."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "extract_variable = lambda var: np.array([_[0] for _ in lsl_dr.groupby('study_id')[var].unique()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1150b9ed0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the proportion of children that have a non-English primary language?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "non_english = extract_variable('non_english')\n",
      "non_english = np.ma.masked_array(non_english, np.isnan(non_english)).compressed()\n",
      "non_english.sum()/len(non_english)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "0.15178571428571427"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x11507af50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What proportion of children have profound hearing loss in one or both ears?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "deg_hl = extract_variable('degree_hl')\n",
      "deg_hl= np.ma.masked_array(deg_hl, np.isnan(deg_hl)).compressed()\n",
      "(deg_hl==6).sum()/len(deg_hl)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "0.51124999999999998"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x114612850>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Distribution of age at enrollment"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "age_enroll = extract_variable('age')\n",
      "figure(figsize=(10,6))\n",
      "count, vals, patches = hist(age_enroll, bins=100)\n",
      "axvline(np.median(age_enroll), color='red')\n",
      "xlabel(\"Age in months\")\n",
      "ylabel(\"Count\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.text.Text at 0x1141e7ed0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAGBCAYAAADWuZ2jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDQAAwFD/D5oJ55QMDlUIAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1150d8750>"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x115168cd0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The median age at enrollment is shown in red above."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Change in attributes with age"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Convert age to years and aggregate children 12 or older"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "age_years = np.floor(lsl_dr.age_test/12.)\n",
      "age_years[age_years>12] = 12\n",
      "lsl_dr['age_years'] = age_years"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1150c9c90>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Proportion premature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_by_age = lsl_dr[lsl_dr.premature<2].groupby('age_years')\n",
      "(1.-data_by_age['premature'].agg('mean')).plot()\n",
      "ylabel(\"Proportion premature\")\n",
      "xlabel(\"Age\")\n",
      "xlim(2,12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "(2, 12)"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEQCAYAAABFtIg2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1141e79d0>"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x115164850>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Proportion non-English"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_by_age = lsl_dr[lsl_dr.non_english.notnull()].groupby('age_years')\n",
      "data_by_age['non_english'].apply(lambda x: sum(x)/len(x)).plot()\n",
      "ylabel(\"Proportion non-English\")\n",
      "xlabel(\"Age\")\n",
      "xlim(2,12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(2, 12)"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEQCAYAAABIqvhxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115198e50>"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x1155346d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_by_age = lsl_dr.groupby('age_years')\n",
      "data_by_age['degree_hl'].apply(lambda x: sum(x==6)/len(x)).plot()\n",
      "ylabel(\"Proportion with profound hearing loss\")\n",
      "xlabel(\"Age\")\n",
      "xlim(2,12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(2, 12)"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEQCAYAAABIqvhxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIZpNiocQQohmk+IhhBCi2f4/Zmc9iOVPM+0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11552f6d0>"
       ]
      },
      {
       "javascript": [
        "IPython.notebook.save_notebook()"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x115189a50>"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}