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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">HNSCC HPV- Cohort <small>  <i class=\"icon-zoom-in\"></i>  3p Deletion Exploration</small></h1>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a general exploration of copy number, chromosomal instability, and global deletion rate within our cohort. This analysis was done after the identification of 3p deletion being a major event in the cohort, so most of the analysis revolves around understanding the __specificity__ of 3p compared to global patterns of chromosomal gain and loss."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import Data and Packages  \n",
      "For full list of data and packages imported see the [Imports](../Analysis_Notebooks/Imports.ipynb) notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import NotebookImport\n",
      "from Imports import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "importing IPython notebook from Imports.ipynb\n",
        "Populating the interactive namespace from numpy and matplotlib"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "changing to source dirctory"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "<style>\n",
        "@font-face {\n",
        "    font-family: \"Computer Modern\";\n",
        "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
        "}\n",
        "div.cell{\n",
        "    width:900px;\n",
        "    margin-left:auto;\n",
        "    margin-right:auto;\n",
        "}\n",
        "h1, h2, h3, h4 {\n",
        "    font-family: Helvetica, serif;\n",
        "    color: #000000;\n",
        "}\n",
        "div.text_cell_render {\n",
        "    font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
        "    line-height: 125%;\n",
        "    font-size: 115%;\n",
        "    color: #4d4d4d;\n",
        "    width:800px;\n",
        "    margin-left:0px;\n",
        "    margin-right:auto;\n",
        "}\n",
        "\n",
        ".warning{\n",
        "    color: rgb( 240, 20, 20 )\n",
        "    }  \n",
        "\n",
        "</style>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "populating namespace with data\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rppa = cancer.load_data('RPPA')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Exploration of Chromosomal Instability"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* We know chromosomal instability has a large role in cancer, lets see if we can find any good correlates \n",
      "* For chromosomal instability I'm using the total number of amplified or deleted lesions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "#cn.features.index = cn.features.index.droplevel(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cin = (cn_by_gene.groupby(level='Cytoband').median().abs() >= 1).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_by_gene.index = map(lambda s: s.split('.')[0], cn_by_gene.index.get_level_values(0))\n",
      "cin = (cn_by_gene.groupby(level=0).median().abs() >= 1).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "cn_by_gene = cn_by_gene.ix[[i for i in cn_by_gene.index if i[2] in rna.df.index]]\n",
      "pct_del = (cn_by_gene < 0).sum() / (1.*len(cn_by_gene))\n",
      "pct_amp = (cn_by_gene > 0).sum() / (1.*len(cn_by_gene))\n",
      "pct_altered = pct_amp + pct_del\n",
      "pct_altered.name = 'CIN'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_survival_curves(pct_altered, surv, hpv.dropna())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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oklMg/vVX9bZo/bcouHIV0kuXkB8ZCfmjhDI+TQghhkcJhZmYGdACPK4FWAAs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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc382d90>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "violin_plot_pandas(hpv, pct_altered)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NX8BkMkGWC/9LKpOuuSaGt95qw3vvGTFpkhuvv14YQwuffmrAOedUYOlSC555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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x98fbd90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = mut.df.ix[(mut.df.sum(1).order() > 5)]\n",
      "tt = lambda a,b: kruskal_pandas(b,a)\n",
      "s = screen_feature(cin, tt, df.clip_upper(1.))\n",
      "s.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A2ML1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA10</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA12</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA13</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA2</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA4</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA6</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA9</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCB1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "         H   p  q\n",
        "A2ML1  NaN NaN  1\n",
        "ABCA1  NaN NaN  1\n",
        "ABCA10 NaN NaN  1\n",
        "ABCA12 NaN NaN  1\n",
        "ABCA13 NaN NaN  1\n",
        "ABCA2  NaN NaN  1\n",
        "ABCA4  NaN NaN  1\n",
        "ABCA6  NaN NaN  1\n",
        "ABCA9  NaN NaN  1\n",
        "ABCB1  NaN NaN  1"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = mut.df.ix[(mut.df.sum(1).order() > 5)]\n",
      "tt = lambda a,b: kruskal_pandas(b,a)\n",
      "s = screen_feature(pct_altered, tt, df.clip_upper(1.))\n",
      "s.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A2ML1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA10</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA12</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA13</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA2</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA4</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA6</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCA9</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ABCB1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "         H   p  q\n",
        "A2ML1  NaN NaN  1\n",
        "ABCA1  NaN NaN  1\n",
        "ABCA10 NaN NaN  1\n",
        "ABCA12 NaN NaN  1\n",
        "ABCA13 NaN NaN  1\n",
        "ABCA2  NaN NaN  1\n",
        "ABCA4  NaN NaN  1\n",
        "ABCA6  NaN NaN  1\n",
        "ABCA9  NaN NaN  1\n",
        "ABCB1  NaN NaN  1"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Screening for copy-number correlates of chromosomal instability "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* These are all fragile sites usually around large genes ( http://www.sciencedirect.com/science/article/pii/S0304383505008232, http://www.sciencedirect.com/science/article/pii/S0168952512000303 ) "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = cn.features.ix['Deletion']\n",
      "df.index = df.index.get_level_values(0)\n",
      "screen_feature(pct_altered.ix[keepers_o], rev_kruskal, df < 0).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>11q23.1</th>\n",
        "      <td> 17.16</td>\n",
        "      <td> 3.44e-05</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11q14.2</th>\n",
        "      <td> 15.45</td>\n",
        "      <td> 8.49e-05</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13q14.2</th>\n",
        "      <td> 14.55</td>\n",
        "      <td> 1.37e-04</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9p21.3</th>\n",
        "      <td>  8.23</td>\n",
        "      <td> 4.11e-03</td>\n",
        "      <td> 0.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10p15.3</th>\n",
        "      <td>  7.11</td>\n",
        "      <td> 7.67e-03</td>\n",
        "      <td> 0.07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "             H         p     q\n",
        "11q23.1  17.16  3.44e-05  0.00\n",
        "11q14.2  15.45  8.49e-05  0.00\n",
        "13q14.2  14.55  1.37e-04  0.00\n",
        "9p21.3    8.23  4.11e-03  0.05\n",
        "10p15.3   7.11  7.67e-03  0.07"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = cn.features.ix['Amplification']\n",
      "df.index = df.index.get_level_values(0)\n",
      "screen_feature(pct_altered.ix[keepers_o], tt, df > 0).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7q21.3</th>\n",
        "      <td> 6.20</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9q34.3</th>\n",
        "      <td> 5.96</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5p15.33</th>\n",
        "      <td> 5.66</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20p12.2</th>\n",
        "      <td> 5.33</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9p13.3</th>\n",
        "      <td> 4.64</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.14</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "            H     p     q\n",
        "7q21.3   6.20  0.01  0.14\n",
        "9q34.3   5.96  0.01  0.14\n",
        "5p15.33  5.66  0.02  0.14\n",
        "20p12.2  5.33  0.02  0.14\n",
        "9p13.3   4.64  0.03  0.14"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1,2, figsize=(12,4))\n",
      "violin_plot_pandas(cn.features.ix[('Deletion','5q11.2'), keepers_o].iloc[0], \n",
      "                   pct_altered, ax=axs[0])\n",
      "violin_plot_pandas(cn.features.ix[('Deletion','3p14.2'), keepers_o].iloc[0], \n",
      "                   pct_altered, ax=axs[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gDObmUhQF1OtNACMDeb2goKD5MKZWP7obUoI1mQdRMhlHs9lGsbgf+fxiX67o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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8fb99d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Honing in on 3p"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p = cn.features.ix[('Deletion','3p14.2')].iloc[0] < 0\n",
      "del_3p = del_3p.ix[keepers_o]\n",
      "del_3p.name = 'del_3p'\n",
      "old.name = 'age_over_75'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other chromosomal regions associated with 3p loss."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = screen_feature(del_3p, fisher_exact_test, cn.features.ix['Deletion'] < 0)\n",
      "s.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>odds_ratio</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3p14.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>     inf</td>\n",
        "      <td> 1.01e-50</td>\n",
        "      <td> 4.83e-49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p14.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 8976.00</td>\n",
        "      <td> 9.28e-47</td>\n",
        "      <td> 2.23e-45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p25.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>  273.00</td>\n",
        "      <td> 2.03e-34</td>\n",
        "      <td> 3.26e-33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p12.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>  225.17</td>\n",
        "      <td> 4.28e-33</td>\n",
        "      <td> 5.14e-32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18q23</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>   14.72</td>\n",
        "      <td> 3.40e-13</td>\n",
        "      <td> 3.26e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18q21.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>    8.26</td>\n",
        "      <td> 4.02e-09</td>\n",
        "      <td> 3.13e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9p23</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>    9.21</td>\n",
        "      <td> 4.83e-09</td>\n",
        "      <td> 3.13e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5q11.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>   10.85</td>\n",
        "      <td> 5.22e-09</td>\n",
        "      <td> 3.13e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4p16.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>   11.64</td>\n",
        "      <td> 1.36e-08</td>\n",
        "      <td> 7.26e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5q15</th>\n",
        "      <th>Lesion</th>\n",
        "      <td>    8.99</td>\n",
        "      <td> 4.02e-08</td>\n",
        "      <td> 1.93e-07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                odds_ratio         p         q\n",
        "3p14.2  Lesion         inf  1.01e-50  4.83e-49\n",
        "3p14.3  Lesion     8976.00  9.28e-47  2.23e-45\n",
        "3p25.3  Lesion      273.00  2.03e-34  3.26e-33\n",
        "3p12.2  Lesion      225.17  4.28e-33  5.14e-32\n",
        "18q23   Lesion       14.72  3.40e-13  3.26e-12\n",
        "18q21.2 Lesion        8.26  4.02e-09  3.13e-08\n",
        "9p23    Lesion        9.21  4.83e-09  3.13e-08\n",
        "5q11.2  Lesion       10.85  5.22e-09  3.13e-08\n",
        "4p16.3  Lesion       11.64  1.36e-08  7.26e-08\n",
        "5q15    Lesion        8.99  4.02e-08  1.93e-07"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Looking at thresholding on the 3p chromosomal arm.\n",
      "* The GISTIC2 threshold is -.1, which is nearly optimal here... best to not mess with it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "args = [run.data_path] + [cancer.name]\n",
      "f = '{}/analyses/{}/CopyNumber_Gistic2/all_lesions.conf_99.txt'.format(*args)\n",
      "lesions = pd.read_table(f, index_col=0)\n",
      "lesions = lesions.set_index('Descriptor').select(lambda s: s.startswith('TCGA'), 1)\n",
      "lesions = lesions.groupby(lambda s: s[:12],1).first()\n",
      "lesions.index = map(str.strip, lesions.index)\n",
      "\n",
      "levels = lesions.ix['3p14.2'].ix[2].ix[keepers_o]\n",
      "calls = lesions.ix['3p14.2'].ix[0].ix[keepers_o]\n",
      "calls.name = 'del_3p'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#del_3p = lesions.ix['3p14.2'].ix[0] > 0\n",
      "p53_mut = mut.features.ix['TP53']>0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1, 2, figsize=(12,4))\n",
      "levels.hist(bins=50, ax=axs[0])\n",
      "s = pd.DataFrame({c: log_rank(levels < c, surv) for c in levels.order()[2:-2]})\n",
      "s.ix['p'].plot(ax=axs[1])\n",
      "axs[0].set_xlabel('Copy Number')\n",
      "axs[1].set_xlabel('Copy Number')\n",
      "axs[1].set_ylabel('p-value')\n",
      "axs[1].set_yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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46hphA/nI+P33qFmz1vs30AhvwES8tKzRMtOvv0LRo8flddIXAgAq5syFOftI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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc70bb10>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Extended Data Figure 2: Chromosome 3p Characterization"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extended Data Figure 2a: HPV- Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = rcParams['axes.color_cycle']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = '../Extra_Data/FH_HNSC__4_16_all_data_thresholded_by_genes.txt'\n",
      "gistic = pd.read_table(f, index_col=[2, 1, 0], low_memory=False)\n",
      "gistic = FH.fix_barcode_columns(gistic, tissue_code='01')\n",
      "cn_by_gene = gistic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "coords = pd.read_csv('../Extra_Data/gene_coords.csv', index_col=0)\n",
      "\n",
      "#cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "cn_by_gene = cn_by_gene.reset_index(level=[0,1])\n",
      "cn_by_gene = cn_by_gene.join(coords).set_index(['chrom','start', 'Cytoband','Locus ID'], append=True)\n",
      "cn_by_gene = cn_by_gene.ix[:,keepers_o].dropna(axis=1)\n",
      "\n",
      "ch_3 = cn_by_gene.xs('3', level='chrom')\n",
      "p_arm = pd.Series(ch_3.index.get_level_values('Cytoband').map(lambda s: 'p' in s), ch_3.index)\n",
      "p_arm = ch_3[p_arm].sortlevel('start')\n",
      "p_arm = p_arm.ix[:-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df = rna.df.xs('01', 1, 1).ix[p_arm.index.get_level_values(0)].dropna()#\n",
      "#df = rna.features.ix['real'].ix[p_arm.index.get_level_values(0)].dropna()\n",
      "df = rna.df.xs('01', 1, 1).ix[p_arm.index.get_level_values(0)].dropna()\n",
      "#df = df.ix[screen_feature(del_3p, kruskal_pandas, df).p < .05]\n",
      "df = df.ix[:,keepers_o].dropna(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr = screen_feature(p53_mut, fisher_exact_test, p_arm < 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr2 = screen_feature(rna.df.ix['TP53'][:,'01'], pearson_pandas, df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr2 = screen_feature(rna.df.ix['TP53'][:,'01'], pearson_pandas, df)\n",
      "rr2 = rr2.ix[df.index].sort_index()\n",
      "rr2.index = p_arm.sort_index().ix[rr2.index].index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr2.ix['FHIT']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>rho</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>start</th>\n",
        "      <th>Cytoband</th>\n",
        "      <th>Locus ID</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>59710076</th>\n",
        "      <th>3p14.2</th>\n",
        "      <th>2272</th>\n",
        "      <td> 0.27</td>\n",
        "      <td> 1.16e-05</td>\n",
        "      <td> 3.27e-04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "                             rho         p         q\n",
        "start    Cytoband Locus ID                          \n",
        "59710076 3p14.2   2272      0.27  1.16e-05  3.27e-04"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,4))\n",
      "ax1 = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)\n",
      "ax2 = plt.subplot2grid((7,1), (3,0), rowspan=3, colspan=1)\n",
      "ax3 = plt.subplot2grid((7,1), (6,0), rowspan=1, colspan=1)\n",
      "\n",
      "bands = p_arm.reset_index().groupby('Cytoband')\n",
      "bands = bands.first().start.order()\n",
      "starts = p_arm.index.get_level_values('start')\n",
      "for i,(idx,v) in enumerate(bands.iteritems()):\n",
      "    try:\n",
      "        r = plt.Rectangle((v,0), bands.iloc[i+1]-v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    except:\n",
      "        r = plt.Rectangle((v,0), starts[-1] - v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    ax3.add_artist(r)\n",
      "ax3.set_xticks(bands)\n",
      "b = [{0:s,1:''}[(i%2)] for i,s in enumerate(bands.index)]\n",
      "ax3.set_xticklabels(b, rotation=270+45, ha='left')\n",
      "\n",
      "ax1.scatter(starts, (p_arm < -.1).sum(1), s=40, c=colors[2], \n",
      "            edgecolor='grey', lw=.5, alpha=.5)\n",
      "ax2.scatter(rr2.index.get_level_values('start'), rr2.rho, s=40, \n",
      "            c=colors[4], edgecolor='grey', alpha=1)\n",
      "\n",
      "#ax1.set_ybound(215,255)\n",
      "ax1.set_ylabel('# of Patients\\nwith Deletion\\n\\n', ha='center', va='center')\n",
      "#ax2.set_ybound(-.5,4.)\n",
      "ax2.set_ylabel('mRNA Survival\\nAssociation\\n(Log10 p)\\n\\n\\n', ha='center', va='center')\n",
      "ax3.set_yticks([])\n",
      "fig.tight_layout()\n",
      "for ax in [ax1, ax2, ax3]:\n",
      "    ax.set_xbound(starts[0], \n",
      "                  starts[-1])\n",
      "    ax.set_xticks(bands)\n",
      "\n",
      "for n, ss in exp2.ix[['FHIT']].iteritems():\n",
      "    ax2.annotate(n[0], (n[1], ss), xytext=(15, -5), \n",
      "                 textcoords='offset points', \n",
      "                 arrowprops=dict(arrowstyle=\"->\"))\n",
      "ax1.set_xticklabels('')\n",
      "ax2.set_xticklabels('')\n",
      "#ax2.set_yticks([2,3,4,5,6,7,8])\n",
      "fig.tight_layout()\n",
      "#fig.savefig(FIGDIR + 'supp_2a.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uwkjFUMkYkhIEZINZItkIdrdDVXmcoBbCzXvkAzlSoRSx7jjx0TiO7gASNIXa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NhPCtV48yNLcKKFEgKxc/Ea8aCztrbmuRdf48jhg+wv9EKSDWLkOjywphsleg6wiJXsoAcrzjCd\nGrwAmVgu4EMVellNtUk4GYNTOcP0YUGzQQk0mYt8zVNo0aBhiHAuY4KWXCYRSS51zgtZ+MiSMY2P\njwVxCYmYW1qTesaZ9u4+nOysmT9z/phFx7Isc+nSJYrys+gWYuiRrUgkHXsMerRrBOFNFRkkEsQ1\nnDBoEWsYzxXCeYJv0KJmp5CAUqmgS29r3OcGOpXOjP8JjPgsLCxY9dSz9PX1MTg4iK2tLYIg8MHv\n/pMntF8ZK2huNLBKu5k/Z9oRN3XaqJaKIAgsXLKcyohJXL08GUkUobqakO6b+iKARlzoFSyZNjGG\n9OorTNZeNAZSygjkaZIIjZ5KRqYWL20VrjQiA1cJpVIZwMJ/0TgNWZbJSDtN5rl0FJIOPSomTZqE\nh/d8mpqa8Le2Zklo6D9Ngvp9Sc6tAZ2yLAs1NTWe58+fj3viiSe23esYgLCwsILCwsLQG8nlAEql\nUnRzc7tr8tjJkydnbd++fXVeXl5UV1eXjY2NTdfEiRNzV65cuXP27Nkn7nbcvwKmz5wNssTnFxxR\ny3q0kooD6jVIkoQoC7i5urD20dU/WR+zpqaGPTu+QS0PcoT5LGMPCmRkIIMELOjDgXau48YBFmHC\nIAWX84iJS2DixDtrWfr7+0k7dZyigivIgMd4T0rlQGI5jxfVmDFgJDgA+UQxmWxjyi7IRHCFKrzJ\nJYpSAgmSr6JFg4wCE7TMEQ/x5+4QKisrxyRODgoOITsvgfniTVfNLqwpl72Y/xC+MGCogG3b/GfE\njioipUKaBC8GZBNS5qRgZWUFGLxf5FFSR8MC19HRQXVhFs9r3zca1cVoL7Cx6A3KwqIeOOm4t7eX\nkpISZFkmMDDQeA23w93dncef2YAoinzwu/9L9eBI1+NMRRLBgX4PRLIEQSBl6WNcnxJPybWZmCgU\nPB0aZpwC9PPz48l1z3MpK5ySq5eJGjrPIVIYwgQdajypwVzQolAIKMWRZNSKHmRZQqvVjmgrVpVf\nY7E08l1IicgEiqnCmzgxnY1FMbi7OtOLBbt5jHL8sKGLLmxwpBkfKphIHgCmNPM0W/mAN6jHjUbG\noVBpmDE3ZYSNQWVlJUf27aa3twctalToUCEZc6nqcUWLio94DQUScznGIxygHXtOMJshzNApTBnS\naoklj2CK6MSWE8ymEVcGMUVA5A3+iAUDRlG0ABQpwpg0afIdCc7AwAAlJSWIooifn98IuwlZlsnN\nzeVS5mn6+gfw8BhP4sx5Rk+aiIiIh9Z+HNq3h4bCNOZoD2FNF4WEsolnWcV2avAin0jWs4lSIYiN\nipdwkuoRZIkWnBFR8B1rAHB28yQyOpZTR/rw0H5u1OiV4Ue94M5jQUEPdX13goWFhfFlrKurC8Sh\nUSP2NnRhKQzQ3t7OuHGjK+eCIODr62tcf6qrq9m5bYBOfSreUgWNwjjS1POZs+ARJkyYwGWPULbW\nvUSMNhUFEtma6eAcwrRp03B0dGDbQTVWcg9aVCjMbFmz8smHaqP/MyDj7BmKMvaxXvs1DrTRhQ37\nLq2msXESonYQnVZLZ3urgWA/RETJT437kpw7iYAPHDiw6G6+MQDe3t5VAL29vaOavpIkKX71q1/9\n3zt55fzud79758MPP3z9hRde+Hzp0qU/3Gh3Xbp0adIzzzyz5dVXX/34F7/4xW/vd81jxVgyrG4g\nNDS08NSpUzPuNhWm0+moq6tDoVDg4eFxx2mBoaEhmhquo0TCVtlLKzYEhYQRnzQTExOTn9RWvL+/\nnx3btjBlKJUsYmjElQ95g3E0UIcHEkoERH7D/8aWTuLIZLxYw440a6bExt/xIajVatnyxad4dWfw\nlHgWAZnzpfGYyBNIIp2zJKG6zQymDQciblPeg8GY7BwJDGKGHe2cZRqHWcgatuFEK55iBS0tLWMi\nOUkz5rC5tITeAVuCdTl0C7acV89gevLsh85EOpN6HPvWczcdqkWoFr3YtVvBG2//CpVKRXBUDBdO\nJ+OtqzI+rHqxpFARgXdfF9G6tBFOvBq0TNae5mr+g5Gc7KwLnDx+GH+hEgUSJw77MW3GLOKnJt1x\nf1mWUSgULHlsNTt3ioRJuTjqr1OuiaTFNICnF4w9nUSWZQYHB1Gr1Ubdyg309PRw7OAPFJdWIMky\n3u4uSIKKWLKYxzG6sUGNFnMGOMhy8sRwerAcQYLrGI+FmckoHZaZqSk9jCZyhkpNmyGjDQiLTuBY\nTQX+UjFv8kc06OjGim08OapVp0IkgDIKCWEeR0jXT2PHtq948dU3sbW1pbm5md3ffcVi7XcEUjLs\nbBxDJgk8yddo0KFFzce8ijXdJJFuHJu1oocn+IYPeBNZVpDEaWOgpyuNeFPNh7xGi+BMpJyPJSMD\nXkMp4IIcd8ewzCtXLnNo3158hBrUaDku+xEXn2h4aQKOHz5AVV4qM7X7saeDayUT2FpZyZPrNuDq\n6oosy1y7do0r2efQDg3iGxzJpOjJ933Tbmtro6ggn1d1nxs/x86cRonIt6whkFKeYQs1eGJtY8uT\n61/g8z99iIm2hSCKmMcxzBmgDg92NT+FmVky4yOS+SjfBn+hih7BhlaFMysfXzumFztRFCkrK6O3\ntxd3d/c7kpPbYWZmhlZW0Yc5Frf8zbVo6JFMxrw+eHl58fRzL3L+7ASK6muxc3Bk+dQZeHl5AbDi\niae5cmUS+bmhyJJEcGQMUVFRKJVKwsMjCAkJpaGhAZVKhYuLyz9FG+dhIIoi5zPOsm6Y4ICBMC7X\nfc0HNY4sln/Amh7yz8ew6XIuz77w6j+c6DzU/NrMmTNTV6xYset++6WlpU176623/tjW1mZsLut0\nOrVGo9HeieS8995772ZkZEwNDAwsuXV7SkrKwVWrVu1ISEg491ORnLFkWN243k2bNj17r9H1K1cu\nc+TADzgInehlJb1KW5Y+9vioB/S+3d9hXn2IN/Q/okbPIKbsLlhLnpkFM+cu+Cluy4iCggK89SVc\nIppV7MCLGi4TzmEWEs5lfKikATdyiCaFA/gPa3b6BwY5deIYybPmjOrZX7l8Gbu+a6SIPxi3pcgH\n6cWCPCLxp5T9LGYqGUaHXAfaqMaT4Nvs/qvwRoWet/iDscR7iYnsZBUv8Qn1Si8CxjhdYmlpyc9e\nep1LOdkUlF3FzNKGx2IS/qqS/5X8PJboL3KYhbTghB0dTOEijrRSUVFBYGAgU2JiuVaYx5ZWC8K1\nmQwIlmSrE4mbOp32liZU8miXYCV6JHH09ruhqamJ08cPskH3J+yGU9e7sWbTGREPT+8R99jS0sLJ\nwz9SWnUdpQJCg4N4ct0GSoqv0tTZRsB4X5ZFRIy5UlhaWsrJQz/Q0d0HgkDIhEDmpizFzMwMnU7H\nVxs/I7gnlZ9LZ1ChJ78ukjphAaUEEMNFbLg5uluv9CLIL5AdZc+yQLsLVxqpwpv9mlXMmL1gxKIv\nSRJ2rt6kNs3GW6o0PmDrcKccPxZyiHRFMhOCgnB3d0cnK0nhoDF2wpoelrGHrTxFCodG1NracDCM\nMpOPD9X8WXqeixfOMWfeQrLOpRGjO0MQhuVHhUgCmVTjTSFhhFJAC06IKBnCdNRn2pQhPITr1Cj8\niBBHVqHMGMCZZuplN3qxpAlnVOixp3241WaFlZXVqIduR0cHh/ftYZ3uU5xpAQyt503ntbh7euPs\n7Exubg6v6T43juHHy+dQabWcPubG40//jKMH91FxOYN47XHMGeByYyx52Zms2/DKPUX+VVVVBFA+\nKt8unALOMRUXmjguzKNWE8RTq9dibW1NypJlHN69lSXyfuNkqQd1LNTtIf2UE8++9CaxU6dTXV2N\nqakp/v7+Y5ogbG5u5tutm7DRNeEgNZGGH+5efixb/RTNzc00NTVha2uLt7f3iM+SRqMhPDSUw4XL\nWKLbjho9IkqOqR7Bz9fnriTnRsjxredydnZm8fJVd9xfqVQSFRVF1F0S5JVK5T+FCPevRX9/P0g6\nI8G5AXP6sZdbcaAdVxrw1Newr18g6/y5ezq2/z0wZk3OjZaTKIrK1NTUmVFRUXn3O/bFF1/87Kmn\nnvrazMxsIC0tbdorr7zyp//8z//8Hz//+c//cLdjFArFHUUUd9MFPSzGkmEFYG9v3z44OGh6t9/f\n0NDA0X3f85Tuz4wbfnOsxIfd2/U8/8qbRsOnrq4uqqqreHOY4ACYMkiKbidfZLszfdbch/KKuBu6\nuzowFTsxx8rYrshhMvM4StRwCT+YYrypZDcrCOYqXlSjkYeozDoASMycM5J41VYUM0GbQyU+XCEc\nLRp8KSeQa5QTQAxZeFDHFtYRx3nUaKnFnQbcGU8dIVw19KqJpJgJPM1XRoIDBsPE88RzUFiM1sz1\ngfwVTE1NH9j9+V4Y0ovsYTkxXCSJNBpwZRtPYKMfQqczfDzUajVrn32Rq1evUlEcj8bUnBUTJ+Pg\n4MDRo0e5qIhnknTJKLgWUZCrmUZ82KQxX0d+ThaTxXNGggOGqZQY3WnysyOMJKe7u5uvNn7G1KEj\nLCMHnaTmbGEye2tr2fDKmw/sx1FTU8OPO79hie47/CljAFNOFc3nu7ZW1m14hatXr2I7UMks6ajx\nmGg5hzbBhdOKOXhJ1bjQjIiSDMU0dOauPLp8JRezvPg+w5nOPh3OdubMnrmA0FuqF7Iss/vbrfRU\nX8Ja6uAjXieAUnqwogZP7Ghnv2oFjWbBrJuzgOrqauzlthG5WgDONDGAOX1YYMYgEgJFBNOEM4mk\no0DCnnbs5XYa6gzfj5aGOsJuE4gCjKeGTOI4wvzh4WIRUwbpxIZxw8TiBrqww0SjpH/AYkRCvQ41\njbgylTQySWQHq9ChxoI+UjjIGfUComKSyMnJQavV4uvri4uLC5fz84iQco0EBwzC/anaY+RlBTEh\nIhofxXUjwbmBUAo5WVdPXV0d+bk5TBRzUSHiRzlBumvs7V7NhcxzTJ8x866fAVNTU3oVo13Ye7HE\nzNycvvBf4efgxNKICCNZEkURb00ziqGRy7gX1eztMIzP29nZYWc3dqfv/v5+vtv6Jcl9O4iSDeRR\nRMmOqqf5/KMmxMEevKjhguAKFi48/vRzI9p5c1OW8ONAPx9U+OKqbKNJtMfNYzyPPjqasPT09HDi\n8D6uXitDkiHQZzyzFy75lw4y/SlhZmaGJKjoxAbbW15iBjGhC1usb9kWob/IyeK4f36Sc6smBwxk\nIyEh4dzLL7/8yf2OLS8v93vnnXd+19nZabtr164VM2bMOOXn51eekpJycP78+Udu3/+dd9753cyZ\nM1NffvnlTyIjI/NvtKvy8vKiPv3005fefvvt3z/4Ld4ZY8mwAkMMBNydfP38zdcxbcric7mJZG9I\n9gYfKgmRLpOfe4mk6cmA4SFkr+xDrRv5Fm9HJ5IkUldXx4WzJ6mqqcNUoyIyOo7EadMfmvi4unmQ\nKvigkg2L/wBmNDJuVOvIh0pMGEKDluPMxZYOVuh28FmWI4nTZ4146ze3tCGfSLqxJoYLmDHAZSJp\nxw4fKhCApfzAGZI4yELMGEAcbosdYy6HWIiMgANt6FGNmsARAFNhkDr7ZJ5Yu/5vlhMzFpgqJOZx\niFAMsWu+VDKeWr4RnzKWqIHhcnS4MRi0qamJTz/8Az76IlSSBRuF54iTMxGAbM10LDxCCQ4eu5/l\nQF8PDtJoH05ruYv6vpsP0TOpJwgZukA8mYChNTZfOsiWXo+H8uPIOHWUmbr9BGDwyDRngIXiXj5r\nD6C6uprGuhp8tSOrFXpU+EjXqLBJ4puhVzCV++mXNIwbN44nVzyBUqkkLj6BuPiEu053lZeX01Zz\nlQ26T1Ei0jocDZFDNDNIpVLwZ8h9GhsefxJTU1N6enpoExzQymqGMKUaT0wZQsMganR8zKuIqJAR\nUKMlgFJjG0lGoFewxNPVQBRtHJyobfIYJSSvxhtL+ljPl5gwRAV+7OIxjjOHx4eN9sBgJ9GFJRHB\n4aTmz2W1+I2xmnGeWMzp4RxJuNCIHjUDmNGNDVt5GkuNOWmnThKorMBM7GabIlWD1aEAACAASURB\nVIyAkHDUGtNRxqJgILoD/T2YmZndta1nolLy7dZNuIlVmKAjjyhOMYO1fMUk/TmOFUbfk+QEBgZy\nCFcq8Tb6eelRclq9kClTZxheKm6Dvb09DbIr8rBi7QbqccPO6sHa8e3t7Rzau4Pa+kZkSU+WHIsd\nHXhRgxIRjb4L1+5sHr1Fa5iuS2bvDhOe2XDT2VutVvPY42vp6OigtbUVe3v7EaSlra2N08cPUV5R\niajT4kIjL8u7MGOQnIopbNlYz/Mvv/HQre//TlCpVERERvFd7jMk6k8QTDF61PzIEiZQbGwJnq6C\n3VW91FufpLa5+94n/Vtf8/12+GuM+fz8/MozMjKmTps2La2mpsZzcHDQ1NrauruiouKOQot33333\nvYkTJ+Zu37599a5du1Z0d3dbW1tbd0dFReVt2rTp2Tlz5hy/03EPg7FkWI0Fi+bOILrmMrfL5pz0\ntbR03FycHBwcaNVb0o+5caIJoAFXNGoVO7ZtYbruMAvlAnq0Vpw6V8Hu69WsenLdg98chomYA4It\ngqylEm9jxISEMMrmSkTJRHJJ4iwf8xr9mGGpGKKzs3NEUKKXXyCXLo7jNflDzIc9ISK4zLes4Sqh\nqBDp1zhTgS9h4RNpyz/Ek/ov+D/8T97h9/RjgYCEFb1s4RmKCCH8Fsv9fsxpVo7n1fUbflJ90oOi\nr68PrciIcVEAT2qxUOru6Rq8//vvmDm0h4lyLhIKCuRQsoilR+PKzJTlhIWFPRB58woI4UppDJO0\nOSPaLlc1U/AJvDmFV1GcT8ptI7wAgfrL1FaWPTDJaW5uZt5tD3sB8BbLaGpqwtregUaVF+gzaWAc\nx5lLDZ4A2EoSz77wKkNDQ5iamt4xk+329lRLSwsajYbS4gIitBkoERnAlCp86MeCAEqowpuF8n6+\naPA36kksLS0xZ5CNPEcP1nhTRQ9Ww20lgalkkkAmSvTkE0kqs4xRBxeZjE4wITHJkJIcOzWZrUWF\neMh1eFOFjMAlJlGNJ2/xB0yH2zZ+lDOLE5xkNn/g5/hQQRsO9GKJndyOX0AgOW0JfFjjSIhcSCc2\nlOGHYvhYUwZpYhzPsglH2ujElu/7luNJK4+KPwIwWzzE1qIXcZ84jyKTGBKGMkeQhquqSXj5h+Lj\n48M+lSNFQ4bMMDB8n1PVi1AoFSQP7CeGm56t6SRykEXEch6l8t7Lv1qtZsXjT7HrOxhPLdZiKyWK\nEDx8AomNi7/jMe7u7pjbjeNkyzySpROo0NOOPYc1y0mcPueev+9WaLVatm76nNj+Q6yWz6NEoogJ\n7GQV6/kSGzopJZDX+Mg48SQAU6U0slqSaG9vx/62dvedKkidnZ1s+eJTYoeOM1fOZRAz0pjGDyzj\nab4iQc6gQzeO7Kzz//CKxD8Dzp9LJz8vF1e5jwwhif0sAYUahazjGWkjIkpkBKZ6C5QFPsHPUtYR\nGRn5zxnrIMuy8NFHH73W3d1t/etf//rfa2pqPNetW7c5Ly8vasaMGae+/vrrp24fD78d77///ptr\n1qz5ds+ePctWrFixKzIyMl+v16sWLVp04G7HzJkz5/hPSWbuhrFmWN0Prl7+lF8PJkgc+YAp10QS\nNP6mZ4W5uTlRUVHszl/LIt1O7GmnHlf2ap7E2saW4KadxrRuS/pYpdvCxzUuD22SpVQqmblgMScP\n7eU7eQ1R5GJHO9lMJo6bqeDFBDOEBiV6zBkgkjxyiKZX0oya4GlpaSGCK0aCA4aFJZ5M2nGg3GYm\nE6fEIVWXkZd3CVly5o+KtzGVBmjCBXduDtVN5xTbWYMWNQGU0YojJzRLmBI95R9KcMDwt5MRDOPW\nt7gZywhICs1dWz9dXV10dLQTKRvagQokIrhCGIX8Xv5f+Pj4GCtz1dXV5GZl0N/bjad/CNGTp9xx\n2iIsLIysjAB+6FhNnP4MAhIXlYm0WQTz6C1uzgNDelpxNGqrbqCJcdxhuv6+sLWxprFvHPa3tMkA\nGpSe+NvZ0dvbS5HohxcTSWUWM0hlDdvQoyKjdxrfbFby/Ctv3TeAtbCggGOHfkQt9jEoqVCrNQQI\ndlTLnuxkFb5UYE87ZQTSiwUnmYmk6+fY4YPETU0iICCAfYINtrKWV/nI+NksIZBdrCCaHGOMQjSX\naMGJHawEBNoVTkyOn0Z2djZWVlaEhoYSGBLJjkLZWJ0xYYhASowE5wZ8qSQdLX6UUYsHIkrM6aMd\neywsLHjimQ2UlpaSnnaG1pZGBJ0eX6mY5XzP+7zFer406hps6WQV2/kTrzLIkeFKlJZE7RHO1/ii\ntPfj+5YnSNCnokbLJWUs1WZRzImJRaFQsOqJZ/jua5lsqR57qZlSIQhH1/EM1NQwmewR1x3HedJJ\nRKeyInRS3H0/B97e3rz21rsUFxczMDDASk/Pe65HBr+aZ/lxlyl/vB6DlXKIHsmcpOkziIyMvO/v\nu4ErV67gqisnXr4ZcxhCEc24kEUMyZxCRsD8FlsMMHznrJUD9PX1jSI5d8L5jDQitZkkymcBsKKX\nZezhzzxPOb74U46fvoDc6jLg/9skp6KiggunD/O87hNjq6oaL3Yo1xGbMJstaaCXlcgymAg6PD2D\n/ikcne9Kcv793//919u2bXvihkD4jTfe+MDCwqLv1KlTM/7whz/8/N13333v448/fvVeJ587d+6x\nuro6D1mWhZiYmKwFCxYcHhoaMrlTq+rvjbFkWI0Fk2Pi+MvFC9hJTUTL2YgoyVQk0Wrqz/Lw8BH7\nzl24mDQzCzZluaHXi5iZakhMnkXG6eMEySNFjEpEAqRiamtrH9oka/LkyeiGBkg7fYpiOZJBUSCN\n6VzHHW+qacCVIoLx4Dp1jMdh+IFWqQgkJDj4jg9dQaEc9dCUERgQzHlkfgrHDv5ARN9JHpEzUCCT\nRSwZyunsk5fzlPQllvQhoqACf2zo4CJTOMFsBEHAyzeChGl3L5//vWBqaorXeDcu1CSQKN20qr8s\nRGBmaXvX/rwkSSgFeVRWkkHHIRs9e86lp5GVdox43Uls5E6K6ifxRdY51j//yqgKkUqlYu2zL5J+\nZgLb80IQRT1O4zxY8cjSkQJilQkZukT8Kcdx2IekEh+KhGAWPcQYfey0ORzfU4+LtgkH2pEQyBLi\n6TcZh4+PDx/94b+YLx/iBLOJJJ9oLhkuA5FZ8nEaBvwoLCwcJcSUZZmamhqqqqoYGBigMCeDVbot\neFCHhIJs/RSOM4t8IljJTqNLdzKn2cOj9GDJCnk35TmFfHE5l7Xrn8fe2pSZncdHkO9ASvCglt2s\nYD1fGrf7U0aF9TRsHF1pq6zg4rkzqGUttso+Th13ITF5DtUVpQwM9BPBZSrwo5XRpm0NuKFFjQYt\nczhBM86kk4SEgJWVFYIgEBgYiL+/P/v2fk9JQQ4xXKQfCxRIo4SblvRhSQ+d2BnbuJb0MTQ0xLoX\nXudc+gT25oWiF0UCJ4SyLnmWcWrFzc2N13/+C65du0ZfXx8TPTyora2lqabU2C67ASV6EEAxLpLJ\nk6eM6bNgYmLyQATFwsKCNc9soLu7m76+PhwdHe9Ldm9Ha+N1PLVFo7aPp4Y0pnNFCENAplL2wZdK\n4887saVdtDSOz98PdZUlzJNuujX3YEUt43GmiVo88aecVoULVnb/vybn0vk0ErVHR2hxvKgmQCom\n+7yCWDmLBPmsoWoqR5Faa0ZXVxe2trb/wKu+B8nZvHnzur179z4aFRWVp9Pp1EePHp2Xnp6eGBER\ncfm3v/3tL6ZMmXLxbiTn97///dtvv/32728VLd8qXi4sLAy9k5HghQsXYu8nML5bXtaDYiwZVmOB\nlZUVa599npOHx3Giah6CACEBvjy9cMmoKRaFQkHyrDlMmzELrVaLiYkJgiCQf/EcnX12ON628HUq\nnfH6K6oagiCQkDiNyTFxtLS0sPvbLSzo20w7TtTjhi2dvMBn7GQVFvTTjxn5ROHpE8T8R5aNOJdW\nq2VwoJ8rUjBN2BHOFaLIQ4FMhpBIYEQMPd3duA2VMEM+aTwuSU6jXXCl020mf2p4G2uxhX7ZbNgd\nt41urFnMfkzkIfLKatn8l3rWP3/vqY+7QZZlSktLyc9KZ3CwH5/AcCbHxD7UuRYsWcHWTW3UaoPw\n1BbSoPGnWhHAEyufvOv4p62tLabmVpRoA40TOmAwTLO0tMLGxobe3l7Onk7lRf1HWNBPN1b46nZz\nSuri7KkgFjzy6KjziqJISVEBjlIjfto8WhrGs/mLBlauWWt02o4ID6Ml9yBfyutxphkdarqwQak2\nISQk5IHvf8KECXTNfIRNqWbYKnrplUywsXPkiVVP0dTUhKXcQwBlnGS2MVn9VvhqL9N0vRZuITmi\nKLLr269orS0lWJdDmRBMkpRpjDdRIBEjXyCbSSiRjAQHDERxJqfYyHP4UY6fWI6N1MLJw+PQ6/Wj\nKk5gmOzLZSKDmBh9WZoFVwSFCrHqLC/Je7Cli3rc2CMuI1zMIPXYENZyN4NYIQCPsofveYwMEojn\nPAokWnDiCPMJo4D5GITXgZTiRwVfst5IPq5fv87ObVtQDzSiltXoUGNOP3pUdGGNzS05VoOY0oP1\niGyrAuUkfAKC0Wg0JM+cfc92iUqlIvQWE9Ezxw8gywZTPs9bYijKCECtNuHJ9Rv+ppo3SZIYGBjA\nxMTkgQkOgJ2jC7Vqf9BljthehwfNSjd6zfwxGehmj7ic+RzGmyoaGcdRzTISE6ePeYLQwtKKjlZb\nPKjlJLPJIRovqunAjgr8cKKJC6rprIn9aQYa/pXR291pNIG8FVq9iJNcxQz5po1dNDm068dxMTOD\nOQtS/p6XOQp3JTmNjY3jIiIiLgPk5+dHmpiYDE2cODEXwMnJqaWlpcXpbscWFxdPgNGiZTC0we5G\nZH7zm9/87+PHj88RRVHp6elZc6d97paX9aAYS4bVrfvfi3w5OTmxeu1zSJKEIAj39UBQKBQjHrwT\n46Zz6kjNsEmWYUqihEAaBHdW/gQmWQMDA6SnHqVnQMsOYQ2ecg3zOMI4miggjA7saMSZH1mCpa0D\nq598ZsQ9GMaFP8W6I5+lcgbVeJHGNE4xA5VCwMzBg9Upizj84278dKM9cfz1BRRqZvP6z3/BmdOn\nyM3JQkk710UPXpU/NrYGfPUV7OmBi1kXSLrFKXqsOHn0ENcupZGgPY4FvRQ2TmFjznnWP//gXg12\ndna89PrbFBQU0NJYj5eDE4tumSK5EwRBYOHSlez6doBo/Tk8pCrqFD7kqOJZuXQlgiBQVlaGr1BN\nAeFkMBU1OgYxJVAsoepqwR1JzonD+/DvOcNc8aBhgw7K8WXvTiWvv/0rFAoFM+YsYGtNNQ7dldhr\n2+lV2tGusGR+yhIO/rCbktJSlAqBsPAops2cPSbiFxuXwKToKTQ1NWFqaoqjoyOyLFNfX0+/pOIs\nU7GimyacjUntN3AdN3oa6kcIjC+cz0Rfc54XdZtQIlEu+4xKsAdwooV+Rv9/mTKAlpsPr0nyJY5V\nLyDAx5Oi3gk43TKBJKGgAj8EoBUHPKingXGcU81A19PPeulbYxvLjXqW8AN7WIZClrClAzfqMWeA\nY8zHhi7yiOIsSZgxyNCwVP9G+voNuNKAnaKHxsZG3Nzc2P71ZhYObiOYYrKJ5hwJ+FHOJC6xn8Us\n43vMGWAIE35gKaYKLY3SOMzo54piEsWmsTybOLbvgSzLFBQUcDH9JF09vSBqmcJFdrGSqcbcLk/S\nScIvIORvSnAKCws5dnAvarEPraTCytaepSufNJpH3g5Jksg8l2Fo3w5q8RrvTmzSLM4qA8nTRxEp\nG2JRqvHmomYGcxcsIf3wTl4R/0gt48kgkcMsRCFAQOhUEpLGvnZMik/meH0lA1pTyvEb0fK8Sgg/\nCI+yYP5SXF1df4o/zb803L0CKG0JxUe8WTmTgQalN7Hi2VH7+4jFZNZVjNr+98ZdSY63t3dVUVFR\ncGhoaGFGRsbU+Ph4I6XOz8+PdHFxabrbsRs3bnwODKLl69evu8uyLHh4eNSVlZX5Ozo6tt6eZn4D\nBw8eTNm3b9/i5cuXf/9TkZl74X4ZVrdCFMX7jjk97MIxceJEWhpq+SjPGl9FLT1Y06FwInnWPE4c\nPYgsSgSGRuDn92BOtXDTwC+q/zhLJYOgM59INrPOMKGBOW5cpxN7FvMjR/sfo6qqakQGTm7uJSw7\nClik28nXrMWEISaTQwPjKJMDkDqa2b51I64e3rQq3UC8NOIaWhTjsLZzxMzMjPkLFpIwNZH9+34k\nsnzLiARvgCj9ec5cnfLAJKetrY3cnAu8ovvYGJQXqC/lQO8gmelnmPUQPkRqtXrY+XnsSebe3t6s\nf/4Vss9PJLvpOg4u7jwbn2jUBygUCtola9qIMOoy+rDgACmIOu2o88myTEFRCW+Kp0Zs96MCC7GT\n2tpavLy8MDMz47kXX6O4uJjrNZU4WNsy08+f77ZuImrgFBvki+hQk3FpFl9XlbH++VfHNLmnVquN\n/h5tbW3s/vYrBnvaMNV3cYVIZnOMk8zBh0rG0YQMlBBEFT6om6qpqakxTqNdyclkvu6o0TbAiRZq\n8Byh1QJDy6AJF7pvq2zkE4n/LVUjHWoUAsRPm8m28jJMGCKcAnqx4BQzcaQVFxrZrnwGa7WWbqyZ\nFDOVmqx9mIgjs4Y8qKMba9yppxkXFMgG922O0Y8F6SSiQs84GljKXn7Pu+hRjvr86mQFSqWSoqIi\nXOVaoxh4IrmU489nvEAYVxjAlPd5Cyu66RWs8fLyIi4wlNRcN7RaLX6BIaxPSr6rs/XtOHs6lYLM\no8zW/ogTzRxmAZ3YsprtXCCWAsJxpAULtUzUxLFbGDwoamtrOfLjDlZpv8SD60gI5LZG883mfl5+\n4507Vlj2fb+d7pKzPKrbjw1dFFUEs6u2joVLHiPjlDmpXYtQKSRktQWLFz9GVXkpkbpMNOjwo8IY\nhFooh3C5/cHWyKCgIBrjZnP6rMAyefeIlmcIV8nWJGPyEJXg/46ISUhkY34uFlInE+VLaNFwVjkL\nUWND/YD7qP2bBFds7O9aC/m74a4k59VXX/146dKlPzzyyCP7v/nmmyfff//9NwH27Nmz7Ne//vW/\n34kI3I7Nmzeve+edd3731VdfPe3h4VF35MiR+f/2b//2b1u3bl27cOHCQ3c6JiUl5WBQUNDoMZH/\nxhAEgXkpS4hLTDaaZJUVF3Du2B4m6c6i4P+xd97hVVVp3773aem990YaKSSBhN5CJ1RBKSKgqNgQ\ny6ufM/PqjOMUZ3yt2BUVpIhKlY7SS+gtBAghPaSH9HLa/v4IOSQkIYWEIO77urgusnP2Wmvv7PKc\ntZ7n99Oz88IgnP3CmPrQw+26ic+fO4eT+ipD9Tdfkr05RSYeJOGPGmNS8MOKEgK5SJRmP4nn+jQK\ncpIvnCZCc5jfGIk3aYxhu6HS54rozxZtHNY5uykxnUiavA/BurN43Jgiz8GZE4qBTA4M4cSJEyiV\nSgIDA/H09KIi1bxJfk8NxiiV7be1uHLlCsFiYiMnYIAoXTwbE2M6FOR0FDs7O8bENa8q3KNHDzbr\nzXm0QeKpGZVMZQPv6V+lrKzMoK1Uj16sc7q+FaWgQau9+ZKVy+WEhIQYli32/raLAPWpRsuHk7Q/\n8l2pM5cvX27XMpZOp2Pld1/Rv2I9fcTjCIh8ytP8xihAzzLmY0sxGpRoUTCTH0jTerNlvQ0mxkpc\nPXyprlE3+vv04wgreAQ7ivDnClqUHGQQlZgzmAMsYx5D2I8dRSTQk9NEMo+bj51DsmGEBgeQl5eL\nTNCRKAbzGyMwopaoG9WCW4UJhMUMITg4GFdXV8rKyjh1ZO+NAOXmxVefd+NAPnk4EsZ5JrDF8Hs/\nknmfF/ElFSM0BJBEPP0Zxl7DZ5LpQRWmmJubk5qaio066+bfBj0P8SOniGQXI9EjJ4JT5Mi8iBw+\nhsGD69Sr+w8Y0Oi8l5SUUF1dfdu8lurqao4cPsjTms8N2jyT2cTnPIUxtQxmP7UYcVA5Citnn3bp\nT7WXY4f2MEiz0+BEL0Okt3iCJG0kFy5caGIXU1BQwNWkizyv+dqgHxYtHkevkXHhjCdPPPsi169f\nR6vV4uDggCAIpKcmo6dpgK5HhtBeYzbq7HZOn4jHtqrpUoydLo+Kiopm9vrjYWVlxfwnnmbPDg/2\np6ShkMsIDQ1FuJhIMj1IIgB/khCoe+4fkg1lTv8h3T3sloOcZ5555lNvb++0vXv3DluyZMmiGTNm\nrAH497///afp06f//Ne//rXVmrC//vWvb+7Zs2d4WFjYeYDnnnvu49DQ0ISFCxd+0VKQI5fLdQkJ\nCV3qXNYWO4cdO3aMefbZZz/Jy8tzmjJlyoavv/76cSMjo6ZWs52IlZUV4eHhpKenk5xwgoXq9wxT\n6jHqY3xz9XkuXoxq18spLzsdH/X5RttqMCIFP6I4TX/iAThKDD/xEBHiafT6xpGHUmVEDUYkEMpi\nPmxUyuzPFUyoJkh3jrUpYUx+4EHWbJJjzXVk6CnU2+Du7sGGH78ngCvUyMzZscWD2NHjOSKLoJ9u\nPzbUTexpUHBYNYo+vRs/6NuCXC5HIzSVqdegRC5v+wybKIqUlZWhUCg6pcpLFEXOnT3LicN7KCuv\nxMnJEb2gwEVsrBGkQo2TqpyioqJGQY4gCPTwdOFUepSh+g7qXsoFetvbqjpnplyiv7bx8qEABNae\nIDN1RLuuoytXrmChziFarEuJq88heZAfOUE0/iRhSzEKtLiSg4BImuiFWUkCQ9hPSn4QNUI0R4TB\nTBbXAXV2BxP5hXXCdGRyBTpkmBsrcahMYIB4GCfyOUY013BFjREqalkmzMdLTEWrtKHczId5Yyey\nee0qrMQScnA15Hll4c51rLgo9GRB797Y29cFMSYmJpiYmvNR2WLMxEp6coEwzrOBqbiTRTpeKNAa\nBDNv/n00OJPLJQKJ5jij2cEy5pODC76kkIsTSQSCTFE3k3P+NNWiP2MQGpQ4i1RjQtCNpOhzhGOs\nEJoVxisrK2PjTyvJzc3BXKamQjRlyPAR9O0/sNHnioqK2LltM7a6nEbig2ZUsoCv+ZjnuGg6CL0o\nw9XLh/GjRneppcD1wgJixKZLkK7qJEquN82byszMxE9INQQ49QSLF9mfnkFmZiYuLi6NAryeob34\n6WR/+qoPG0QQdcg5poqlT0TfDo3bw8OTK0mB2Ik3c4B0yEiWBRLh1nSW4o+KnZ0d02fPM/ycmppK\nVsIhxvAjG5jKr4xEiYYSrBFlqjtylu8sbiuUMH78+K23BiPHjx9vW0o+dSXZbm5uja748PDwc4WF\nhbf1mN+3b9/QlnJgrKysSsPCws63JM7XGm2xcyguLradPXv2quXLl8/t37//kVmzZq3+xz/+8b9v\nvfXW6x3ps70knj9NlOaAIcABUKAlWr2Hi2fC2vVysrJzJE/hDdqb+dpniMCNLEZy8xt+LHsoxJ5z\nQiQPhDSuiAmN6seetER0Gjkqmi6pGFGLDD1yQY+npycvvvInMjIyKC4u5uihfRSmnGERXxryja7h\nyvc7RAYNGcFX+yFMfxYjfQUJyhg8eoS2q5KjnuDgYHbv9KcIW+xuJMfpETisHEFIZOulslAnRLfj\nl7VUVVbcMEx1ZsIDMzExuVkl0F5n3X27d3Hp6E5GqDfiQCFX0nuQxmjyccSxgWmgBiUFOqtmy15H\njJ/C8m/yKNU44KdLpEBw5LByJKPGxt02wdLYzILzQigJYigmVBPOWVzIpUThhIVFu+SgKC0txVF3\nc2biAiH4chVfUinHgpP0YT7fGap51Cg5QyQT+AUf0vDRp2FJEXvko9jOREJ0ZyjHnAOqcYSH9mHw\n8FEolUoEQWDZV5/yXpEHPuJVcnDFg0wm8QumVFEs2rBGPgfnwMHMnjKVq8nJpKZnYYGSZ/gMa0rQ\nIWcfQ/iGx7G1dzQEOGq1mmVffYpr5SmixYPokXGQwRxkME7kMIfv+YS6Wooa6ioLRTAE9WZUkIE3\nuxjFAA4zn2/Yxjh+IxZrrhPOWVIsR1JdXU1JQQ5OXGcd04hlN2ZUkkAoRxjAPL7DmhK2EodKo2XC\nLXL/oiiyetnXBF7fxmz9HuToKMKW1XuqMLOwMvhbZWVlsXr5UoI0ZygQPRuNFepmUQS5klqNDm9Z\nOqqUi3z9+WX69hvIsBFt16xpDw7ObmQU+eAhZjXanm4UQmQzOTlmZmaUCE0rl0qwRq+pYceqJZSI\nFowYPZ6o3n2AOi2ekMh+fHn6JXprDqAQ1ZxVDcLGM9QgytkatbW1nD1zhozkC5iaWxMQGsn21DEo\n1WpCuEA5FuxWxuHk7tvIr02iMdXV1VhQjieZLGIJ13BFhxxbCvlI/2p3Dw/ooHdVW5k3b96yOXPm\nrHjjjTf+7unpmZGdne32r3/968+308kBePPNN/+6f//+Ic7Ozrne3t5pubm5zikpKb7BwcEXi4uL\nbeVyuW7Dhg1T+vTpc+J27TRHW+wctmzZEhcTE3MsLi5uC8Drr7/+1mOPPfbN3QpyRJ2+Sekn1FWf\n1Jcht5VeEZF8tj+EywQQcGMq8Sp+hN5Q8m1IEJdJwa+JeWRQUBApIX2pOHuSM2IvohtobxRhSz51\ngoEmxsaYmZkhCAK2tras/WEF5rXZjOCAIcCBukTPQC6jUI3liWde4EJCAhqNhqn+/ri7u3fom6Ze\nr8fG2oYvCp8iQjyNJWUkKGMwc2lZuKwhubm5rFvzPVPUK+lBMjrkHM3qz1ef5qLTy7BS1FCuNyYi\nIoKRYye0KZ+lqqqKo/GHeVbzBeY3TCmjxRNk4s5aYTqzxZVYUUoNxmxTTMXXx7dZ4TxHR0eeePp5\njh05yKGMq1hY2/Fg/yG3ncWpra0lPz8fY+yI4DQVWLCKhwnnLAlCL56KaHueEYCzszPHBD+Dkm0l\nZoaKplASSCSEb3iMKE7VySgIA/ARUxqpB0dwmu3ieGR9nmT71UsYm5jQ3B/DLAAAIABJREFUP2Yw\nYWFhjf7mTzz7Ats2/8Lp02bIRDUPiOtQ3Vius+U6D+h+ZOVVR7Kysti8dhWm+jLGswXrGzOCcnQM\nZw9niSB6wM3crjOnT2NdfoEp9aarwCxW8yVP4EsqSnQ8ylI+4Vm2MhY5egpxwI4iQjlPMv48yrfE\n058PeAE9MjzIYAZryMOJvQzH08aRnJwcPMQ0BnGAH5nB5zyFHhm+pDCHFThSQAlWGFFLb+EM2zb+\nxAMz55Keno5MJkMQBPQVuQzV/2oYpx3FjFav58B+J0OQs2vzOsao1xHGOT7naU4RZSjl1yOwVT4Z\nuahlnuYrg+VMJWZ8c7QWN0/vdpnEtpV+g4ezPOkSdup8AklCi4LDssGUGnk2q/Tdo0cPtsgdSSSY\nnje8wNSo2MVIhop76FdzlAIcWLFDjY2tnWEZfeTYOAJDwrlwNhqdVsPwkF74+/u36dlRWVnJd199\ngkPlRYI0JykVbNiZMJSImIFcynJmR1Yuxio5Eb2jGTJsROeeoPsMDw8PftG5UIUJplQbCglOEoWX\ne+sGqneDLg1y3n333Zf/85///L/58+d/l5GR4Wlvb184bdq0tf/4xz/+93b7+fr6psycOfOHJ598\n8sv6bZ9//vlTO3bsGLN+/fqpX3311RPPP//8R4cPH273ukZb7Bxu/UxkZOTp5OTkHlVVVaampqZV\nzbXbmQSEhLPzwiBi1McMiY16ZJxSDaF3eJ92tWVubs6MOfPZ8JOSX9VlKAQdhTXKJg7NUFeFIpMr\nmzwoBEEgbsp0PP0C2bJRRYnOBn8xiQIcOMhggknkF9VMRo+ZaNj3ePwhwrTHycC50TR6PZbafKqr\nq7GxsWHQ4ObdtNtKvVlkWMUuJokJXCSYdLwowYrpk6a3ybfp6MG9DNDsNpRDK9AxUDxIssaPnlwg\nWneCCsxYf2YOe+RyRo6d0Gqb2dnZuMiLMNc0XtOfxCbeFv/M58oXMZfVUK4zIigwgEmTp7fYlpWV\nFaPGtr0U8/DB/bhUnGGquNrwouzFGT4RnmP8+ElN8n5aw9PTE53CnI2aSYzkV9zIYjtjGc5u5OiZ\nwRouEswBBlFj6o5eXckE7S+NZhY0qJAJAqPGxSEILZ8/QRAYP3ESfgGBHFz7KSp143wkJ/KoUWs5\ntGc7sZrN7GCMQdHb0AbgpihoVAyQevkcYZpjjcYkIBLJGU4RwTD2YUU5AzjMaaKYxHo8ySQbN9Yz\nBXcycSSfaI5zgRBe4l2DjL0fKahQc7TIAiuroeQJzqwRZzGM3RxkCGPZRtANRWoRgX0MoxdnGar/\njXfSY/jg/97GRV6IFgUFOmtcxevc+rp25RrXS+uupdraWq4VFDOPBATgQX5iJQ9zjl7YcJ10ZRAy\nUzvCKw4ZAhxo4Ht11L9LghwnJyemz5rHzl/M2VhWjl4U8PZ0Z+6UB5u9D+VyOTPnPMqaFQLxunws\ndEVc1bkTKiYYlJodKGCoehvHDvrg47MAqLtGPD098fDw4MqVK1w8d5KL504SFBZFQEDAbYOd/Xt2\n4VN+mPE31KURIVRzni+OyVn0wv90uxjp7wkLCwt694nm+1NPEav+BTuKSRICOaAcw8NjJnb38IAu\nDnIUCoX2L3/5yz+feuqpz7Ozs93c3Nyy7ezsilrbb82aNTO+/PLLJxtumzt37vIXX3zxfYAFCxYs\nrf9/e2mLnUN5ebmFl5dXev3PZmZmlXK5XFdaWmp1a5Dzt7/9zfD/YcOGMWzYsI4MqxF+fn44+4Xx\nzdXniVbvQYaeU6rBqFxDG2lhtBVPT08WvfQaOTk56HQ6fvj+G46rYwjlgkF4LBcnjtEXV/eWi9rC\nwsJwd3fn6OEotiUlUl1dg1qUUewwmgnDxzZ6aOZmptJXdwkZNSTS05CIDHVr3YmK3ky4ofFyp1y4\ncAHbmlSG6euW3+of6r/pxnDyaARj4ia32kZh3jUimzFn9CGVMuouDXMqmaxZzWcnXRg6Ykyr+h8m\nJiaUi+ZNlhEqsECpUvLCy/+P0tLSOmuCdpa4t8bFc6eYrN3XqF9rSumpSEOna78EsiAI2Ds4kp3h\nxge8iB4woYafmc5w9mJMFWcJpwh7rGsKKRdN+JInmcVqLClHBA7IhxMafPsXUEOKCvPJ05jfEN27\nGejk4YhKISMnJ49xpONIPml4GSqZoO5LQbbMiyENbEmMTMyooKkdRwVmFGFPGp54kMVJopnD9zjd\nWE70IJNHWMGnwjO8LfsresBdl4pAXcUXiPiTTCgX2F4Wh6OjI5WiGaGcpw+ncCaPH5jFWSKwpYgk\nAjGhmodZST5OyEU1CzRLsdfUiThm4sH3wiNNTBDT8MLBvi5/RyaTISCgQYERauwp5DmWcAV/Nsmn\nM2bCg+Rdy8L06MYmx2tJKVVVXZdM6+Pjw5OLXqayshKFQtGqXIGrqyvPv/z/uHr1KqdOnSL88veM\np3HKphO5nLjeODFYFEU2/LSa3OTT9FbXXet7koaQ4BvBAzNaLtC4lJjAI7qDjbbZcB1fWQbJyckd\nWi7/o6HX67l69SqlpaUEhfbCzsmV/Uc8qKisws3dAz8jc7788svWG7oLtDvIyc7OdluzZs2MNWvW\nzDh69Ohts7zKysos582bt2zr1q3j7ezsioqKiuzGjx+/9bvvvpt/O/sEX1/flA8//HBxfSCj0WiU\n77zzziv1Oj3Lli2b5+HhkdnS/rejLXYOt36msrLSTKfTyZsbc8Mgp7MQBIGpDz3MxYtRXDwThl6v\np3d4H0JCQjps2CkIgiEJzMPdjdKUo3zNAsyoBASqMEGPQK9WykttbGwYGzcZmgkcdDodJ44f5/zJ\nw5SVlnCYAYxhG6t5GAVaenG2zs2aWCq1cvbs2My0WXPbPatwK3nXMpskVwP46JM4kJXezB5NsXNw\nIrvQA0+x8WWVjRtB3FRetaQMlaClsrKyVSVPNzc3BFNbTqujiBLrlxFk/KqYQGREJEZGRo28wToT\nkabKy1A3cyGKt9XbbJbk5GSyruXgfaMS6jJB2FGINddZygK0yLCinGf5GGt9KTpk7GMoXwkLiRHj\nSTGKoMrUi7njmq86u5VLly5xfN82vMQ0fmES49mCCTWUYM06YTpybRVyfS05uDCIg2whDlOq8SSd\nKszYIRuPvZM7zs43p8x79enPusTThIrnDTMwJVhxit44kc8aZqJAgw65IcCpx4brWCm1eARHcikx\ngUKdHUtYZNAH2s44+hKPQqzlmy+WIBd0eN8Imt3JZhEfkUhPkumBGZXMYxkCIocYwCDxoEGlGuqC\nqjAS+EmYyRxxGcbUkIEnO1VTmTR8LFBX2u/v68nhq0MYrq8TYZOjR4sCMwtLwsPDSTI2Zv+ZaAbW\nHmp0LVxUROLVo/0Cke1BEIR2GVrK5XICAgLQ6/UcTo1HrG38xSBV1gMn18bLs1euXCE3+QyPqz8w\nJC5Hqk+zNGUxSUnRBLagMVa34No0ABKRdWlS9v1CUVERq5Z9jUltHk76axwS/LBz8eSRx59tlCP4\nwAM3BWXvSe+qhhQWFtr//PPP01evXj0rPj6+34ABAw7XV1vdjkcfffRbJyenvIKCAof6wOFPf/rT\nv+fNm7dsw4YNU1rab9WqVbNnzJix5q233nrdzs6uKC8vz6l3794n165dO23v3r3D3nzzzb+uXr16\nVnsOtJ622DlEREScWbFixZz6n0+dOhXl5+d39W4sVdUjCAI9e/bskFpta8QMimVVahomYjWeZBBI\nEoXYc5S+HD9yoEPfZERR5OdVy1BnHCNWsxNjajlJFKuZzSxWsYtRHCcaC8roxVlmiKuJzxvE6mVq\nnnzuxTt6uFjbOnBN6QOaw4225wquWNu1TachZtBwViUn4aLOxot0RGScoA85ODONnw2fK8EaDco2\nPcAFQeDB2fNZtUzLWc1gHPTXuCr4Y+/qxfBRY9t3kO0kKCSC48cGM0n7o+FxXoYFl/Entp0Ck2q1\nmnU/ruJh7beGGbkR/MYqZnOZIDzIpBxzRrPTMPMgR89w9nBB3pv8wEX0Du5JcHBwm4P0w3t2MEa9\nHj+uso1xfMRiTKmiTLDEScxjoe4/JBPANsYxi1UM4BCrmYX+hv2sJRXU5BeQm5trCHR8fHzQyU34\nRPscwVxEj4xLBDGcPVzDhetY40Y2KfQw5BgYzgEqyrRKihN+xUinogZTFvK5oTKwGBu+4kl6koim\n0pyLoh/XcKMXddVtRqiJ5AzXcK1zP8ecbNzIEHxuiN01xlHMJdN6IB9UvIZc0GNibMyYsRPp0cCi\nY8zEaSxfmk92jT/etefJVfqSJu/B7Buq3P7+/hy29eHH/NkM1u1GhZrTshiuGkXxRN/W89S6g4CA\nAPaaebBLG8dg3W8YoSaRnhxRxDLvFsuXS+dPEaXe16gyS4GWKPU+Lp6LbDHICQ4N5+jJIcTp1hnu\njSJsSdO7M6kLlvDuJ+qf833L1xNzo9pTj8CG7Jns3unE2Aktvta7jRaDnLKyMsv169dP/eGHH2bu\n2bNn+MCBAw8dPnx4wLFjx2LqZ1RaY9u2beOuX79uU196bWlpWfbee++9ZGNj07SWsAEhISEXzp8/\nH5aSkuJbWFho7+npmeHi4pID4OzsnJuWlubdjmNsRFvsHMaPH7918eLFH27ZsiWuX79+8W+99dbr\nbQnqfi9otVrMhFqixBMM5aY3UxCXWJq3AI1G024p9vT0dAozk3hK8zXyG/ojrlxjPVNZLZ+PRg8P\nij8ahLsAhuj3kFDWp5FgXEcI79WLA3sCSaQnwSQiANdw4YgyllkD2iYq6OrqysRps9n0izGiuhK1\nKEOpUKCoLeWUvjdhnEODinWqOcT07demPB+oU8N+7sVXSU5OpqysjEhX17tSrTFg8FCWXbrADxWW\nhKiPUS5Yckw5lMFDYts9c3b58mXchWuNlhzl6BnGXn7iIWbyA+/wPyjQUIEZ5jdMEwXATZ6PX0Cg\nIVm2rRSVlOJBJko0TGITo9lJBeb8KsbiQyoy6vypyrFgGfMRgQEcpDenMEKNQq/lTG0vNv5kypPP\nvWQIov0DAjC/uBwLsQwZeoazGxOq2ctLBHMB5CbIkbGdyUzW/YQcHTpk7JTHIRd16HQaLFATxWlD\ngAN1CdExHKUaE0K0x0gWvDgnhuNDKoE3ltGSCOQsESjk8IUyDDtrS3zsnLly+SqB2qRGx5+simTA\nsNH07NkTtVptSOhviKWlJU8tepnExETyc6/hZWPHhLAww/JQcnIy+YXFmOLESuERtChwdHTmsdnz\n7tm8E5lMxiMLnmLnFifevxyNiIiLvQ0z4qa2edZTAJqZxDQweNgIll25zOpKO4LUJyiT2XJCPpDR\n4+Ka9euTuEleXh615UUGKQmoq+Ibqd3Mp2eDGBM3+Z6bDWvxSe3k5JQXGBh4+fHHH//6u+++m+/k\n5JRnYmJSbWZmVtnSPrfi6up6LT4+vt/QoUP31W87duxYTH3Acjs2bdo0aenSpQuysrLcPTw8Mh97\n7LFvJk+evLE1b6vWaIudg62tbfGKFSvmLFq0aElubq7zlClTNrz++utv3Um/9xIymYwK0cSQ2FeP\nM7nYiYW8/99/Ymykws8/iICeYbi4uLQ6c5GacpWemmOGAKeeCE6Tb9mX4rIqnHVNk0PttRkUFhbe\nUZBjYmLCrLmPsX6Nit9qS1GhpUKwZMyEKYYlurpy9v3kZqViaWNH9IBheHp6NmonMDCQgIDXKCoq\nIuHcWY4ePoA3eVzFj9+EEchkcvr3HciQ4e1zI5bL5S1+q+wqTExMeOypRZw7e5akpP4Ym1kwwjcQ\nlUpFUVFRiyajzaFWqzHVN00eN6MKtaDiqBiDHhkbeIBqTPAmjYlswpha0kUPBrTRLLEhdtaWZOa5\nG5J1janBiBpycCMDL8K4gClV9OYkAVziQ+FFBojxjRSIe3GOX0uKKCwsNFgKDBo+hu+upjBAvYsg\nMYESbFireBhLSweqbabg5O7LE70i2LVlA+9n+OMmLyBHZ4+phRX2pcmUY4E7mVjRdLXdmhJKsKEG\nYyytrCkuLWezGMdWxiMioEaJj5CCWc8pTJo2A4CKigq+TEnmgK6APuJxtMg5LB9KiakfISEhKJXK\n28oEKBSKOqfnW9yeKyoq2PDTauZovjJUvFRgxrfFi8jNzb3jJeKuxMzMjKkPzWGSTodOp2vx+IPC\novjt8hCi1KcMszlaFJxQDmFYeO8W2zc1NeXxZxZz/vx5MpITMTW3Ym6fvl22dNyVaLVaEhISSL18\nHiMTM8KjYgwK5V1BdXU1FrKqJkvh5lSg0enR6XRt/gJ4t2hxNFOnTl2/bdu2cbt374719PTMaEm8\n73Z8+umnz0yePHnjtGnT1vr4+KSmpqb6rF27dtoPP/ww83b7ffzxx8+99957L73yyivv+Pr6pqSk\npPi+/PLL76alpXkvXrz4w/aO41baYucwbty4bcnJye23b/4dUB9QNLcuLSAyXLMFL00GR0/3Zd2Z\nUES5iqCAAOKmPtjiDI/KyJjrchtu0fSiEjPMzC2orKggTedNCImG3+kRyMQD16o7XwV0c3Pj2Rdf\nJTc3F51Oh4uLi2Fp5Nq1a6xc9jV9NAcZrk8iP9+Zn1OuEDtuahOJe0EQqKmp4dSRvTylXWJ4mRWL\nNnwtPk1QSHgT+w6dTkdKSgo1NTV4eXndMy8QlUpFn+hoQkJDWbt6GUmJP+EiKyRb54CHpzdTZ8xp\nk5Ghj48Pu+nRyOgS4JwQgcrYnKM1/XhS/BJ7ilCjZDexrGYWNvIqnFy9GuXFtBW/npFsyZ+ItViK\nM7moUbKX4RhTg4gRhxjIKHYBkIUHMsQmAbaAiFxfy/7du5g2YzZQN7M2//Gn2f+bD0fT0zExUhER\nM5C+/fob/q4FBQWASI1OToreGQdba4qvX0eht8CWYrzI4CI9DUtRUDdxcJGe+HGFw6pR9B82hgO7\nd2BWnoNSrEWPjGLsSCIIy6tXOHrkMDH9+mNubs78J55m9w533kuORS4T6BkcxLzR4ztkbFnP+XPn\nCBITsaWYWowwohZzKhms3s7po34EBAR0uO27hVwuv+3ypr+/P/ttPPgs/xn6iUcQgGPEUK4zaVZg\nsSFKpZKoqCiiorrO4qKrUavVLF/6GarrlwlVx1MlWPBTwhBiBo1g4JBhXdKni4sL+TpryrBoVDWb\nRCAudpb3XIADtwlyVq1aNbuiosJ83bp1D3z88cfPLVy48Au1Wq06evRoX19f3xSFQqFtad96Ro8e\nvfPMmTMRK1eufDg7O9vNz8/v6pkzZyK8vb3TbrffP/7xj/+Nj4/v1/Bz48eP3xodHX28M4KcPxpl\nZWWcPXOayrIS3Lx88fDwQIGOI/RnBLsNn8vGjTKsiOQMCrRM5BdqRGNctNfIu3KVbZtkTJrWfHwa\nGhrKF3vDiOEAlpSjohYNSvYJw6guuI4gN2K7ZhwmVONDKpWY8SujMKK2w8nUtyIIQrNGer9uWc+I\n2g1EUbfK6i2m461OZdl2JSGhYU1eJmdPxBOj2dvo27ot1+mjP8LWTa489uSzhu2ZmZn8tHo51rpC\nzClnq96dqKg+jBwbd1embfV6fSNH++bYvO4HbLN3MFu3CRl6dMjZlP4QOzabMfGBGa32YWtrS1iv\nKJafe4bB6m1YUUaiLJyzRgMxMVIwtnoj9jcsKlRoGMNO3hVextxvGNOnt95+c1haWmJNGauYjRwd\n1ZjgQyrT+JnveJQEQujLUdLxYpdyMpYmplwsC6ZngyA6HU8EUc+VpCQqKioMs5EODg5Mm/lIs/2W\nlJSw7OvPGaTeymTxNKeJ5ERBNAtZgRwdn/E0QSRynGi2EEcMRwGBw/QnF2fyld74BITRq1cv/Pz8\n2L5pLUnJqZiIFTzAWrzEDK5VubBlTxE11ZUMjR2Fra0t02fNa3Y8HSUvN5cMrRMf8AIiAl6kM45t\nWFFCVUXTWbnfIzU1NRQWXWeMuJ8M6r64jeBXros27N3pw4xHFnTzCLuW+COHsCo+yXTN94Ylul7q\n03x2QEZIeESrhREdwdjYmP4DBrLySAUj1RtwJJ8U/PhNOZEHxt17+TjQSuKxubl5Rf0MR05Ojsvq\n1atnvf/++y8uWrRoyfjx47euWrVqdmsdZGdnu73yyivvlJeXW7z//vsvbtmyJW7hwoVf3C5Iqqmp\nMb61ksnS0rKstra2fXKzEiQlJbHh59WE6M9hq83h9Pkodinc8JOlc1TXlyLs8SeJfBw5QyST2dBo\nyj+EC5wjnCman/nwkh8jq6qaLXe2tLTEzz+IrxOfQKQuZ0NAT6iYgEntZRLNh+NWk8I2xlOOBSIC\noZwHhWmX5qhotVoycgqYTWN7A0fysRFKyc7OxvuWUvaykmI8m12OKKUg9xp6vR6ZTIZarWbNyu+Y\nVFNnWQFQjQnLTz/NOVePLi1F1ev1HNy3h2Pxh9HqdKiUCgYOiSWmX/9GwU5FRQUpqem8qNtiEJiU\no2OMdiMfJQYyZoK6TbM5Y+Imcd7Th2PxPlRXV+Pl68+CwcP59OMPmziJC4h4qYoJCotoU9vN4ejo\nSIlgzfPiu5RhhQk1mFJFAqHYUkSRzImvjf4HBzs7psSORRAEVi+rIA9HvEjnGq4cZiAT2cQx5Wjy\n8/PblCx+9NB+IjSH8RQz2MBUUvDFnAouEUhfjhLCBdbzABPYxEVCWM5c1IIR1nZOhAXWFQrUX88W\nFhZMmzWXD975F7OqVxm0fNy4xgz1N3x+xJp+A4e0W0W7NUpKSki6eI4x7COMBHTIOE40y5lLD3k6\nnn5Bndpfd5Geno6HIp9I7RkiG9hw1IjG7E7Lus2e9weXzp5grKaxTIQF5QSLiVy6dIl+/dqm9N5e\nBg8bgbWdI/sOuFJWUYGzkyMPxY5rsvx/r9DmuSUXF5ecl1566b2XXnrpvYsXLwY3rD5qiVdfffW/\nGzdunHzy5MneixYtWpKXl+ckk8n0p06dilq6dGmLYfYTTzzx1aRJkzb961//+nP9ctX//u///mPB\nggVL2zre1miLf1VDQkJCLuzZs2e4o6NjfkufudfQaDRsXLuGWeqv8aDupu9Xe4SvNQux0ecTSRGZ\neJCGN9exIYQEQw5EPWVYYkI1xtRgIa+lvLy82SDn0P69FCbFs4DVOJFPAQ5sZDJmVNFPjOdw5UCq\nlL0YodlED66gxogDitFYOXl16c1RryCrQYWiQbWMCNSKymaXBGxdPDmXHk6YeL7R5xPpiRYFtbW1\nmJiYcOnSJVzFbEOAA2BCNcPVmzl0pGuDnN07t5F5ahuPqn/AjiJytU5s2FOCXq+j/8Cb4oqVlZVY\nyGtRaRsL6plShUqmpbq6uk2BiCAIhIeH1+V/NMDB2pyMQg+DiCLULUNmiy4MbkbGv624urqC3Ihf\n9JMYww5MqCIVH3YyGluKsHd2Z8GTT9/sU68HmYyT+t5cJBgzqrCklJP0plBraVhCzM3NZd+uLaRm\nZGOslBPRO4ZBQ4cbptmz05Pppc9nJQ8znD1MYDPXseE3RlCIA3Fs4QCDWCU8Qi0qnG3NGTpqAkFB\nzQcOlZWV6LXqJmKFVpRhJa+iuLi42dnHO+F4/CEi9ccNy2ky9AzgCBl4kSCL4Nn+gzq1v+5CqVRS\nIzYNEGswRim/t5Jfu47mZCK6lpaeBfcqLQY5x44di7ndjpMnT26qMnULX3311RMJCQmhCoVCu3Hj\nxsmXL18OtLCwKPf29k67XZDz9ttvv/b222+/tmDBgqVZWVnu7u7uWfPmzVv22muvvd1an22hLf5V\n9Wg0GuXSpUsXXLx4sakm+T1OamoqDkKhIcCBuhtgoH4/24TxPC1+wk/MIBcX3MgigTD6c8Tg/VSO\nBfH0YyrrKceccp1xs1OgWq2WI4cPsEC73CD170ABD/IjX/AUfTiGiMCsuY+xb6cjW7PzMVbK6NUr\nggkjx3Tpso5MJiM4wI+Dl4cxSr/NsP0SQehV1s0ayMXE9OXz+ANsJo4YjiEiI55+lGKJsUph+OZd\nWVmJzS3J1FCnqVJR2XVqA7W1tZw8eYJnNcsMdhHO5DFNvYzlB6yI6TfAsARoa2tLhWjCdawbVQPl\n4gRyIywsLO5oLAOGj2XbxiLM1d/iQi41GLNLEYe9s3uHcnHqEQQBZzd30tIqeJ+X6rzR0KJFTqFg\nT5Rf41LfzMxMTAQ1z7LE4K+mR8a3PIqxhQ329vYUFBTw/bdfMlS9hYliAhUac/bEj+en7AxmPvJY\nnbaLpTUn8/swnN304SRQl1Q5m1V8yGIG3nC7OqUczPzHnmr1GI2NjdEib1R1BnVeZWU643ZpybSV\n/Ox0onVXmmz3JQWjgEl3/De/V/D29qZUZsdVfA1VmyKwXzGSsLDfxwv4TggM682xQ0Px0K4wBDYV\nmJMo9OSJu1zocC/TYpDz0EMP/dhSJVNOTo6LRqNR6nS62yZTCIIgWltblxw6dGigl5dXuru7e1ZF\nRUWrd7VcLtf95S9/+edf/vKXf9Zvq66uNtm4cePkBx54YF1r+7dGW/yr6rG1tS2uqakxvtOqru5A\np9OhFDVNtnuSgRojVgsPM0zcw3VsOEcvROBLFuIlpCMXtaTiy2D2Y0E5a1Xz6B3Vp9mp9fLycpSi\n2hDg1GNFGaZUcUQYiL+3Gx4eHsxZ8HST/bua0XFTWJ6TQ061Lz61Z8lX+pAq82fmjDnNBlg2Njb0\nDA0n5UI1yfoeyBDxJhVBacKgobGGBFUPDw+Oy3oyml+QN/AauyQE4+HZsnL0nVJcXIyVvLqJXYQD\nhaDTUFVVZXiRKZVKBgwazJqDZcSpf8SNbDLwZLNqBkNiRzVJom4vISEhVJRPYMVuFYKuFjUqAvz8\nmD71oTtqF8DW0Z2S9PM8IX6AAh3GVHORnqxnKmHhjWfJLieep7c+vpGBrAw9fYnnrEVdpc2hPbvo\nr/6VmBvlr6ZUMV2znE+yXMjOzsbd3Z3e/Yfyw9UUgsVLjdpXocY8YlD/AAAgAElEQVSXFK7ixzWF\nL44unm0K4tRqNWbGKn7RTORBfkaBFh0ydsnj8Pby6pKAw9zanvis/pwWo7CkjChO4UQeOUrfJoJ6\nv2fkcjkPzHiYn1br8NNfwVaTSbJRFFh5MaeLdajuBfoNGMjyi+dYUWJOmDqeSsGcY8qhDBg4rNXE\n6z8SLQY5zWnR5OXlOb300kvv/fbbbyPeeeedV1prfP78+d+NHj16Z3Fxse3DDz+8Mi0tzXvRokVL\nxowZs6O9A83Ly3OaPn36z3q9/s6eyrTNv6qe8vJyC4COup53Jz4+PmzSO3EdG2waBCCnhD74BwSS\nmWXO5ipzFKKGHlxlOL+xUTkby16zyEhPRV+Yyx5iOSgMJbhnb2JHj2u2HzMzM2pEJZWYGpRkAaox\npkyw5KzRYOaP776kNHNzcxYueomLFy+Sey0LD2tbxoeF3VYTY8KU6ewxM+fkyZPI0HFFEcPAwcOI\n6XfTLs3NzQ0Hdz9+ypzHcM1WzKkgQQglXjWC+cPaV2beHiwtLSnVGlOLCqMGL/UyLNAib3JcAwcP\nw9jEnI0H7Ckur8XeyoTBw0fTq1fErU23m/z8fA7u242v/ip2uizylF5kZKgoLy9vVc6/NbJTLzFG\n3I4FN4O5niRyTD6EgoICQ1l4HQIiTR8NIgJyed1jLjMzjYHixUa/l6PHX3eRrKws3N3d8fPzw8RI\nSUmN1Q018JvkCU5cloURGhzE9DaInun1er767CNqK0tIw4d3eAVXrpErOGNr58ycaR3SM70txcXF\nJF+5jI9YQRCXKMCe73mEUM5xRdaTke00Zr3X8fb25rkXXuH8+fNUlJcz2N0df3//Ow7efw8YGRnx\n6JPPcf78eVIu9cPIxIzpvfve1rj3j0ibcnJEURSWLFmy6G9/+9vfZsyYsebSpUtB1tbWJa3t9+67\n7768ZcuWuIqKCvMHH3zwpzNnzkT07dv36AsvvPDBnQ+945SVlVm25l/VVrrCu6qzMDY2JnbUGL77\nVcsAza/YioUkKcK5rIxi/pgJqFQqNv28iqzsbK7IrDgvDGTYiNFcy0jDrOQiU8SN2FDMZTGIXYkq\nInvHNHsDqVQqwkND2ZLwIJM1qzFCjRoVvwhTcHBwYNbcx7tkWr49yOVyQkND2yxKJ5fLGTk2jmEj\nx1BTU4OpqWmTB2edovE8Du734oeTftSodfh4uTN3VNwtL+DOxczMDH//Hmy78gBx2rUo0VCDEVuU\nDxEZGdmkjFMQBPpER9MnOhpRFDt1efCXtasYXrPeYFuBBk5oo9m8zoJHFz5/R21XVlVjS9M0OQeh\nkIqKxrNYwaHhrDs9gL7qI4Yydx0yjquG0zuibuXdzNSU6+W2dTNeDbiucMK9gThe30Gx/Lovl9ma\npShveGYlEEqtiRsvPf9Sm4O3zRvXoarMYqa4FmfyuEwA2xmHp5iGRuneJcJzv23bSN/aXQzigGFb\nIEl8IzzO7FmP3LMigHeCiYkJMTG3za64b1EoFERGRhIZeW8Fr3v37mXv3r3dPQygDUHO4cOHBzzz\nzDOfymQy/fbt28fGxMQca22fegRBEG1sbK6PHj16Z0lJifX69eunuri45BgbG9fc2bDbxgcffPDC\nhx9+uPjW7eXl5RYjRoz4reG2W/2r2kpXeFd1JtEx/XByduVUfBBXyq7j5hPIk337G4KO2fOfpLy8\nnOrqauzs7CgtLWXPzq0s1nxtMEWM4AyiGvbtcmbOY08128/ouMls0aj54LI3DvJyCrTm+Pv7M/+B\nGXek99HdKBSK2wZoCoWCYbEjGRbbdTM3zTFh6kNsWqvn/ZQA7OQVFGotCOnZkxFjbu9U3pkBTmlp\nKcXFxUSIjQXQI8WT7C4YR3l5+R0tx7i5e3A5KZD+4hHDNh0yrggBRN4ieObu7k5geAxfnXuR3pr9\nKEQNZ1SDsfQIISwsDIDeA4azZ0smHurPMLmRhJ5EANcEdx5skDjcf8BACnOz+PCyA75CGiWCHWUK\nB2Y98libAxyNRkPihQSeEldhfSMXKpAk7CniGx5DVnRb0fcOIYoil69mMElsKvLpbFRRl5wtIXEX\nuPUL/z3pXVVYWGj/6quv/nfdunUP/P3vf3/jueee+7i9SzYdra5qjo7kxLzwwgsfNDdrdPDgwUH1\n+TjQvH/V/YSnpyeeng+3+HsLCwvDyyg7OxsfWWYj12eAIC6zIyevxTYUCgWTp88itryc4uJibGxs\n7hlRvPsRlUrF9FlzKSsro6SkBFtb27s+W6bT6ZALTY1AZeiRCWKH3M4bMmj4aL5PTUWlVhPCBcqx\n4DdlHM7uvk0SxgVBYEzcJAJDwkk82xudTsuQkAgCAgIMM3Dh4eEU5MTy0UkLfGRZVGBBidyembPn\nNQrEZTIZk6fPoqioiMzMTMzMzPD19W2XnlNJSQmmQo0hwKnHjiLkaLG07JrkX0Goq267Fb1kPinx\nB6XFICcgICCpqqrKdOHChV9UV1eb/N///d//iKJouEsEQRBfffXV/96u8fZWVxkbG9e0FMw07PtO\naYt/1R8VMzMzirFtsr0YG8yMWy81bhgwSXQ9lpaW3RZM2tjYYGRiTrLaH/8GZfRJBGJmboGVVbtX\nfxvh7OzMw/OfYO9OZ3Zk5WKiqiv5Hjw0ttnPC4KAj48PPj7NJ30LgsDIsXHEDBhMRkYGxsbG+Pj4\ntBi82NnZtcv+oiHm5uZUicbUYIwxNyeuqzClWjBlzNBRHWr3dgiCQHCAH/GXBxmcyQEy8aAUm3tW\nx0RCoitpMciZNGnSJqhbxikrK2v0FBVFUWjLzEp7q6suXbp0V1SqWvKvAmjoYdVwn99jdVVH8Pb2\nptbIgdPqSCJvLEOoUfGrajK9+97U16ioqCAlJQW5XE6PHj06XdBM4t5HEAQCw3rz06Ey+olH8CCT\nDLw4qRjIQ5Mf7JSZA1dXV2bPX9gJo72JpaVluw1D24uJiQlBgQFsvTyVidqfUKJFg5JNTMLD3Z2Q\nkJAu6XfkuEl8l5VFQa0HfuqzFMrdOCvvzZQHHuo0ZXEJid8Tgih27N2t1WoVrVk7vPTSS+8dPXq0\nb3111Zw5c1YsWrRoiZmZWWVz/lWrV6+eNXPmzB9aCyi0Wq1i5cqVD8+bN29ZhwbfSQiCIHb0/N3L\nFBQU8MP332BUW4gNxaTq3QkODiFuynRkMhmHD+5n/97d+Moz0aIkU+/KxCnT6NlFD26Je5Pc3FxW\nfPMZE9U/kIIfRdhhQhXJijAef3pRh2dB7hfUajWbfl5NamoKDrIS8nTW9PDzY9L0WV2ap1ZbW8u5\ns2fJybiKhY09EVF9pJJiiW5FEIROXY1pDy3O5FRUVJi/9tprb6empvrExsbuXrRo0ZKVK1c+nJWV\n5a7X62Xx8fH9tm3b1nxN8Q1ura6Kj4/vFx0dffzMmTPN1q4WFxfbRkdHHx8/fvzW2NjY3REREWfq\nq7gqKirMT58+Hbl169bxW7duHf/8889/dGeHLtESDg4OPPvCK6Snp1NRUcFId3fDQzItLY1j+3fw\njPZjLLV1BWq5OLN8gx5XN7cu8UuRuDc5ffwI0doDBJJEIEmG7bvEGs6cOs6IP4BWye1QqVRMnz2P\nkpISiouLsbOzu+MlvLZgZGREdEwM/EErjiQkGtJikLNw4cIvEhMTe8bGxu7+9ttvH126dOmC6upq\nk5EjR/5qZGRUO3jw4AMt7VuPIAjiuHHjtu3atWvUY4899s3GjRsn29jYXJ84ceIvzX3+2Wef/WTu\n3LnLv/nmm8def/31t44dOxaj0+nk9QnPffv2PTpjxow1R44c6W9qatp1krISyGSyZnMbTh89yADN\nr1hyswLfmVzCxTOcPXOKocOaz5eQuP8oLynCU9/U5cROl0tmSYsOKX84rK2tpeBfQqKbaDHI2bZt\n27ikpKQAe3v7whdffPF9T0/PjOTk5B6+vr4prTUqiqKwf//+IT/88MPMn3/+ebqlpWVZenq61+bN\nmyeMHTt2++32tbCwKK+3WdDpdPKioiI7QRBEOzu7os4Q5GurZ9X333//yN///vc38vLynGJjY3d/\n9tlnT7u4uOTcaf+/dyorSrEWm0okWWnzKSkva2YPifsVV+8ArmSEE6K50Gj7FVUEvt7+LewlISEh\ncfdoURayoqLC3N7evhDqFIFVKpW6LQEOgIeHR+bcuXOXm5mZVW7btm3c1atX/ZRKpaat+9cjl8t1\njo6O+Q4ODgWdEeDUe1YtWrRoybVr11wdHR3zFy9e/OGtnzt79myvxYsXf/jzzz9Pz8nJcXFzc8te\nuHDhF3fa//2Ah28wlxSNVxtF4JJRHzx9pRfbH4mo3n1IU4WyVzaCSkypwJxfZWPJNw743Zj3SUhI\n3N+0OJNza5JQe6qLPD09M1JTU330er1MpVKpW9/j7tBWz6odO3aMmT59+s/11g8vv/zyu5GRkadb\narcrKCgo4NCeXWRmpGFmZkpU/2H06tWr27Uuovv248sTR/lNX0qU/ihalBxSxKK18m3RiVni/sTU\n1JT5TzzDnp2efJg0GAHoGRzI/NFxUrWdhITEPUGbbB3ay+HDhwekpKT4rlixYs5DDz30o7GxcY1G\no1FmZWW5BwQEJLXeQtfQVs+qGTNmrGkY1J07dy7cycmpZSW8TiY3N5fvv/2S/upfGSRepLjChj1b\nMynIyWLUuAl3axjNYmpqymMLn2Xfbz345vJA5DIZIeGRjB0WK5Wo/gGxtrZm6kNzmNrdA5GQkJBo\nhhaDHJ1OJzcxMamu/7m2ttao4c+CIIhVVVWmLe3v6+ub8sYbb/z9jTfe+PuxY8diVqxYMWf27Nmr\nbGxsrk+dOnX9v/71rz933mG0jbZ6Vnl5eaXX/3/58uVzFy9e/OEnn3zybHNtdoV31b5dWxiq3mJw\nS7anEA/1Z3x0yoK+A4d0u5KwpaUlE6c+2K1jkJCQkJC4N7mXvKta1MlpzoX8Vry9vdPa05lWq1Xs\n3Llz9MqVKx+uF9/rClrzrFqzZs2M+m2Ojo75e/fuHXar+3hWVpb73Llzl2dmZnp88sknz44ePXrn\nre11lU7O2//8O4s0/2nk6A3wo9ECgicsNnjxSEhISEhI3Ovckzo5DQOY8vJyi9zcXOc7HaRCodCO\nHz9+6/jx47feSTutcaeeVcXFxbaDBw8+8OCDD/60ffv2sXc7r8hYJadCY94kyKnAos0GgRISEhIS\nEn90Ws3J+eSTT5598cUX3zc2Nq6Ry+WNHPeuX7/+u5LRbKtn1SeffPLsgAEDDv/3v/99tTvGGREV\nw574OB7ULENOXVHZZQIokdvj6+vbHUOSkJCQkJD43dGqrYO1tXXJ9u3bx/br1y/+Lo2pSzlx4kSf\nep2ces8qW1vb4oaeVZMnT964ZcuWuIbJx4IgiGq1upFDZVctV2m1Wn5etYy8rBQC9IkUy53JEdyY\nOWc+7u7und6fhISEhIREV9Gdy1WtBjkhISEXNm/ePMHHxyf1Lo3pd0NXeleJokh2djZZWVmYmZkR\nFBTUpX43EhISEhISXcE9HeT8+uuvIx999NFvZ8yYscbCwqLcsKMgiG+88cbfu3yE9zD3q0GnhISE\nhIREZ9GdQU6Lisf1vPnmm391dXW9VllZaZabm+tc/y8nJ8flbgzwfqWl8rquLLvrjLZv10ZXt99d\ndHRMd+tYGvZzN/psbx9dNaZb2+3ua6ct/XdkjJ15XHfzHHX336MlpOdU5+1/r92DzdFqkHP27Nle\nu3fvjv3ss8+evvXf3RhgZ3Py5MnevXr1Omtubl4RFxe3pbi42La5z3355ZdPurq6XrOysiqdNm3a\n2sLCQvvOHIcU5HRdG52NFOS03F9XfL6j7Xb3tSMFOd3XV3uQnlOdt/+9dg82R6tBzpw5c1b8XgOa\nW2mPd9Wf//znf23evHlCVlaWu1Kp1PzpT3/6d3eMWUJCQkJCQqJjtFpCfuLEiT5ffPHFwrfffvs1\nBweHgvrtgiCIiYmJPbt2eJ1LW72rdu/eHTtt2rS1UVFRpwDmzp27/I+efyQhISEhIfF7o9XE4717\n9w5r6XfDhg3b28nj6VKWLFmy6ODBg4NaUzyuT5Cqt65YuHDhF9bW1iVLlixZ1LC99piWSkhISEhI\n/FG55xSP6/m9BTK3o63eVfXBy2efffb0c88997GJiUn1jh07xtzaXnf90SQkJCQkJCRap0tcyLub\n1ryrGm4rKyuztLKyKm2unaeffvqzBQsWLP3000+fmTJlyoacnBwXhUKh7apxS0hISEhISHQerS5X\n3U/Ue1ddunQpCOq8q3r27JlYXFxs2zAn58033/xrcHDwxYceeuhHgJqaGmMzM7PKwsJCexsbm+vd\nNX4JCQkJCQmJttNqddX9REPvqpKSEuuWvKscHR3z//3vf/8pLS3Nu7q62uQ///nP/4uIiDgjBTgS\nEhISEhK/H/5QQY5CodCuXbt22pIlSxa5ubll5+XlOX300UfPAyxfvnxuvRv5E0888VVsbOzu6Ojo\n425ubtknTpzos3bt2mndO3oJCQkJCQmJ9vCHWq6SkJCQkJCQ+OPwh5rJkZCQkJCQkPjjIAU5EhIS\nEhISEvclUpAjISEhISEhcV8iBTkSEhISEhIS9yVSkCMhISEhISFxXyIFORISEhISEhL3JVKQIyEh\nISEhIXFfIgU5EhISEhISEvclUpAjISEhISEhcV8iBTkSEhISEhIS9yVSkCMhISEhISFxXyIFORIS\nEhISEhL3JVKQIyEhISEhIXFfIgU5EhISEhISEvclUpAjISEhISEhcV8iBTkSEhISEhIS9yWK7h7A\n7xlBEMTuHoOEhISEhMS9jiiKQnf0KwU5d0jp/73b7n3iPvuUJK2cf367pdH2d158GFXWVQ796c+d\nNbxW+2wPtxtfV7ffXXT0uO7WsTQc393os73no6vGdOs4uvvaact56cgYO+O+upP+O0pnjrsz6Yxz\ncC8e250eV0eO6dY+W2pj07KP+GXZkg6NqzOQlqskJCQkJCQk7kukIEdCQkJCQkLivkQKcroJM2u7\nZrc7mpjc9T7bw+3G19XtdxcdPa67dSwNx3c3+mzv+eiqMd06ju6+dtpyXjoyxs64r+6k/47SmePu\nTDrjHNyLx3anx9WRY7q1z3vxvEhBTjdhbm3f7HZHU9O73md7uN34urr97qKjx3W3jqXh+O5Gn+09\nH101plvH0d3XTlvOS0fG2Bn31Z3031E6c9ydSWecg3vx2O70uDpyTLf2eS+eFynIkZCQkJCQkLgv\nkYIcCQkJCQkJifsSKcjpJipKCpvdnl9Vddf7bA+3G19Xt99d/P/2zjwoqivf4/c2TbdBxYG4ZAyg\nYETEZVS2+AbCpoKgEiBBFCMax0ooZ+Ibl0yIVaPzh1omQExM0BqjwlNAFNDEhUVBMiQacMMIiMoq\n4BOFZsfez/vDd2vu64fY3dx7z+Hy+1T9qqCX8/t9v+ec26fv0tdcXUJpYdcnRE5T/eCrJsM6cI8d\nY3wxp0Yu5tVQ8psLl3VzCRcekKhtqLrM0WSYk0RfYJGDib7O9gEff/r8ueA5TWGw+vhuHxfm6hJK\nC7s+IXKa6gdfNRnWgXvsGOOLOTVyMa+Gkt9cuKybS7jwgERtQ9VljibDnCT6AoscAAAAAABECfzi\n8RDZW5Bv9nt/TP2G8zbNzWkKg9XHd/u4MFeXUFrY9QmR01Q/+KrJsA7cY8cYX8ypkYt5NZT85sJl\n3VzChQckahuqLnM0GeYkzRcaIbj9krnAvasAAAAA4NXguncVLHIAAAAAABAlcE4OAAAAAACiBBY5\nAAAAAACIEljkAAAAAAAgSmCRwzN8nGyF6wQuY+G7PtL1mwIOLST6J1RNJGo3BHeNuPOTgFg9GInb\nG7iEnCd6e3vH5ObmLs3Pzw8aN25c165du3aNHTu2h7Q2uYTv+kjXbwo4tJDon1A1kajdENw14s5P\nAmL1YCRvb2BPDk/U1tZO27t3b7y1tXX3kydP3nB3d7/R398/pNvE8tEml/BdH+n6TQGHFhL9E6om\nErUbgrtG3PlJQKwejOjtDUIIgqdoa2t7nfnb19e3ODMzM4rENknXPJz0k66FRP+EqolE7aTViDs/\nCSFWD0bq9gb25HDIkydP3igvL59HURTV3Nxs9+TJkzcoiqK6urrGSaVSrVQq1ZLQJpfwXR/p+k0B\nhxYS/ROqJhK1G4K7Rtz5SUCsHsD25gVwTg6HHD58eGNPT89YV1fXqr6+vtFz5sy5u3z58nOTJk1q\nvX79usf06dMfUhRF6fV6iUQi0eNqk0v4ro90/aaAQwuJ/glVE4naSasRd34SEKsHsL15gcWuXbuE\nyCN6dDqdRWJi4lY3N7dbCxcuvDZ+/Ph2qVSq7ejosJk6dWrja6+99lyr1Vo6OTnVjx07tken01lI\nJJJBf26ajzZJ1yxk+0KCQwuJ/glVE4naSasRd34SEKsHsL35N3C4iiMkEol+woQJz3755Zc/Mo/1\n9PSMra6udlEoFLb379+fcerUqaiIiIic8vLyeRYWFjr0ikvr+GiTS/iuj3T9poBDC4n+CVUTidpJ\nqxF3fhIQqwewvWGB+2QoMYRer6cRQpRarbacO3funeDg4NzVq1enWVtbd3l6epZu2rTp20OHDn2k\nVCrlycnJcS4uLvfa29tt2e8Vok3SNQ8n/SR5RUpOUmoiUTtpNZqTX6FQ2OCeS7j7YDh4gEMXyV7C\nOTkcQNM00mq1UktLS01ZWZnnpUuXFpeVlXkGBgYWyuVyVVBQUP7y5cvPURRFeXh4XLe1tVXodDoL\niqKo58+fv2ZlZdWv0+ksLCwsdHy2SbpmIdsXEhxaSPRPqJpI1E5ajebkR6xv3QghmqZp4g/bDIZY\nPcChi2Qv4XAVR0ilUq1KpZLL5XLV22+//Wtvb++Y1tbWSR0dHTZM5yqVylGVlZWz7O3tmyZMmPCs\npKTEZ8mSJQXR0dEnw8LCfujp6RnLd5ukax5O+k0BhxYS/ROqJhK1k1ajKfkdHBweyeVyVUdHh01b\nW9t4mqZRb2/vGDQMDt0Mhlg9wKGLVC9hkcMhcrlc9fTp04l79+6Nb2trGz9nzpy71tbW3RRFUQ0N\nDVMvXrwYkpSUtCUgIKCIoijq/Pnzy/z8/Io3bNhw5M0332xZuHDhNbVaLeO7TdI1Dyf9poBDC4n+\nCVUTidpJq9GY/ImJiVv9/f2vjB49uq+xsXFKYGBg4Y4dO3Zv27YtYffu3Tv0ev2w/hwRqwc4dBHp\nJe7jh2IKrVZrER8fvycsLOzshQsXQtRqteXMmTOrvL29S9zd3a8HBwfnHjhw4M/M6/v7+19jv9/P\nz+/KnTt35vLdJmmay8vL/yCkp2IaHyTmJKUmErWTVuPL8vv4+PzLw8OjLDQ09Pw333zzF/Z7tm/f\n/gVN0/olS5bknzx5cqUQcwdHHwx3D3DoItFL7B0htmhpaZl87ty5ZWq12hIhRCmVSnlOTk54SUmJ\nt0KhsNFqtRZNTU12jx8//j37fadOnXrf3t7+UU1NzTQh2iRB8+nTp9+zt7d/9PDhw7f4aD8rKyvS\nwcGhsa6uzhH3uMCpZSjjx8HBobG2ttaJFB9MrWk4jB3cNRrmV6lUsuzs7Iiff/75j9XV1TPYr33w\n4MH0PXv2xEdERGRfunRpEfM4aSf7C+VBfn7+Ety1k6aLtPFEI0TceVOiQa1Wy2QymZr5PzU1NXb3\n7t07rKys+pkTtXx9fX9SKpWjmpub7Xx8fEp27Nix+/Hjx5Otra275XK5ytLSUsN3mzg0q1QqeUND\nw1Rvb++fd+7c+Y/CwsLAzMzMlUlJSVvGjBnTO9T21Wq1rL6+3tHX1/enbdu2JVhaWmpIOAmZDQ4t\npuRsbGyc4ufnV/zpp59+UVFRMVsmk6mdnJzq5HK5igv9QtdkSp66ujonZu5IpVKtUD9ehrtGw/xs\nHj58OP3o0aMfNjQ0TI2KijoVHh5+hqJe/LAbTdOIxJNwzcEcDyiKon777be5er1e4uLiUj1q1Cil\ncBUbh7m67t69O0cqlWrNmfvm5qQo7n4wEBY5AoEQohcvXnyptrZ2Wn19vWNXV9e4I0eObOjo6LCp\nqqpyjY2NTe3v77d69uzZhH379v1t9uzZFd3d3db5+flBL7tzKx9tCqG5q6tr3J07d/6wZs2aE15e\nXqXV1dUuGzduPOzo6FhfV1fndPfu3TnMcVxz2u/s7PxdeXn5vJiYmLR58+aVz5gx4z7fWocKDi0v\ny9nd3W19+/bt+WvWrDkxadKk1qKiooDdu3fvCAoKyq+rq3MqLS314mv8CFWTMXl8fHxKFAqFrV6v\nl8yePbuipKTEx8vLq/RlG22uIanGmpqat44dO7a+rq7Oif2BNNiHmNhge8Cc+E1RFNXe3v56QkLC\nti+//HK7v7//lfr6esfbt2/PHy53L2frWrlyZea77757lqIoqqmpyf7AgQN/+eqrr/7K9dx/WU6K\noqgbN264jxo1SsnZeMa9O20kBPs3BJydne9HR0dnGL6mtLTUc926dcciIiKyi4uLffv6+qzWr19/\n1N/fv0in00mEaFNozWq12vLAgQN/dnR0rLt27drbCCFq3bp1x+zt7R/19vaOHmr7CL3Y9a9UKuUX\nLlwIyczMjNq4ceM//fz8rjC7UkkIHFqMzdnf3/9aWlra6pKSEm+EEBUbG5uyYsWKH/gYP0LVZGwe\nhBB17NixdZaWluqFCxdenTx5cktRUZE/SWNCiBqbmprsNm3a9G1MTMyJ7OzsCPZzfX19VkqlUp6b\nmxtM6vzi2oOcnJxw5nGtVmuBEKKKi4t9mfG4du3a1IiIiGy+t7Fc6zpz5sy7zOM6nU7S3d09Ni0t\nbfUvv/zyHwhxN/fZOc+ePRvGzonQi/Esk8lUXI1n7CaPlGAfn5wxY0Z1WFjYWea5wsLCgJCQkAuW\nlpbqRYsWXdq+ffsXCCGqtrbWacmSJfkvG1R8tMmn5hUrVhT+V0IAAAvYSURBVPzAPNfW1vb606dP\nJyxevLjAzc3tRlxcXDLzXHx8/J5Dhw59NBRPU1JSYr/44ovt3t7eJf7+/kWfffbZ3ujo6AyapvUf\nffTRIdzjgQQthjmXLVt27lXvKSoq8o+Ojs7g6xwMoWoazG+tVmuh0WikzP8RERHZtra27RkZGdFK\npVJOwpgQskadTifZuXPnroyMjGjmsdTU1LUJCQlbvb29SwICAgpJn19ceZCenr6KeezRo0f2zN/s\nL2UZGRnRsbGxKbhrNkXXiRMnYl71Wq7m/kDjSafTSdifSVyOZ+wmj6RgNkoqlUo2f/78W0ePHl2P\nEKK2bt2asGLFih8uXbq0qL293XbBggU3P/300323bt2aHxYWdpb5tiBUm3xpnjdv3u3U1NS1v/32\n25zDhw//CSFEdXd3j0UIUW5ubjd27dq1k3mfsRPJUP+xY8fWPX/+fJSfn9+VuXPn3mFOyuzq6rK2\nsbFRMIs9EgOHFnZODw+PsoyMjOiWlpbJeXl5Qd9///2GM2fOvKtQKGw6OzvHIYSolStXnvT19S0W\nygc+azL0OyUlJRahf3+jRAhRBw8e/NjJyan25MmTK3HsncBdI5OHPR+7urqsh+P84tKDmpqaafHx\n8Xvq6uoc+/r6rD7++OODycnJcbdu3Zq/bNmyc1FRUZmkn4w9kK76+vqpfM79gXIaLnC4Hs/YjR5p\nwXSaVqu1qK+vn9rQ0DBl9OjRvcXFxb7Ma9LT01fFxcUl6/V6mun8wfa88NEmX5rb29tts7KyIr28\nvH5ta2t7/fnz56MQQtTnn3++++9///s/2O8zdiPBbp+56qa+vn6qjY2N4vDhw39SKpVyW1vb9nXr\n1h0ztW2c40MoLeycKSkpsYGBgZffeeedn0JDQ88vWLDg5ltvvfVw2rRpNe7u7tffeOON/25paZnM\n9/gRqibDucN+7uDBgx87OjrWpaenr2LvNcE5JnDUyB5fzJej4Tq/uPAAIURduHAhxMnJqZZZ5J0+\nffo9mqb1S5cuvRgSEnJByD1+XOkqLi729fb2LuF77rNzsj+PEOJnPGM3eSQGu1MbGxsdAgICCtnP\nv//++6dCQkIuGL5+sA2HqW0KvRFi15eSkhLr5+d3hf18QEBA4SeffPI1UxtTn7F1sttnJkdnZ+c4\nGxsbhZWVVd/WrVsT2NpNbR+XV8Zo4TKnVqu18PT0LGWPlWvXrr2dn5+/5OzZs2HMxpupi0//TK3J\n3A3vQO87dOjQR05OTrUDbWxxjB1Ta+Qzhvv84iKys7Mj/P39i9iPeXp6ll6/ft2d0TwczslhBzPP\nQkNDzzOPCTn3+Zpz2I0d6aHVai3mzp17JyIiIvvixYtLV61ale7g4NBYUVExy7BT+/r6rCoqKmZd\nvXp1IbMHZChtsg9ZCbkrXqPRSKdPn/4gPDw8JyEhYevSpUsvTpw4sZW5YZuh5srKStebN28uMPXb\nUW9v72hbW9t2mqb1ycnJcYZtm+Ip7niVFi76kmlDpVLJpk2bVhMZGZk12OuE8M/UmrjwIScnJ9zK\nyqovMzMzarDFA86xY0yNfM9vY+eXufOX5NBoNNJZs2ZVREdHZ9y6dWv+pk2bvp04cWLrs2fPxg/k\nA+lh7DxjjzUu+9aUOWdqTuzmjuRgBpZarbaMiIjI/uCDD/4rKCgob6Af92ptbZ24ZcuWRBsbG4Wf\nn98VR0fHOuZ8FnPbRAhR9+/fd7579+5shBBVVlbmwffGmhnAarXaMi4uLnnz5s374+Liknt6esaw\n60foxUnSmzdv3j9u3LjO4ODgXBcXl3sDaTYMZgMTFBSUt2HDhu8fP378+6ysrEjDSWGspzjDWC1c\n9SX7HBAXF5d7QUFBeYa1CO2fKTVx4YNGo5Fevnw5UKVSyXBrH0qNXI0Jc8ekufOX9GBvYyMjI7Oi\noqIy3dzcbvz6669euGsb6phCyLh5xnXfGjOezc2J3diRHoPt+jM8TFVaWupZWFgYgBCi1q9ffzQq\nKipzoF2ig7VpeMLxwYMHP6ZpWu/p6VkqkUh0V65c8RNK80DBrq+rq8v63Llzy5iaPvzwwyOmXJo5\n0OvM9RR3DFQTX33J3tiFh4fnPHnyZBJu/wariUsfBhubpIwdY+cPl2PC2DHJrm2o85fkYG9jtVqt\nhRg0sXW9ap5x2bd8fx5gNxXi/x7DrqysdE1KSvor81xzc/ObzN/MWe4IISo5OTlusMsUDdv86quv\n/pP9PPuM9rVr16bKZDJVVlZWJC7NCQkJW1/1nqysrMg1a9YcN6Z99sA31G+up7jCUMv+/fs3892X\n7MMbNTU100jwz7AmIXyoqqqayc5D4tipqqqa+fXXX3/CtxeG7TN/V1ZWuiYmJm551XtMmb/DIQy/\nRA63Q1QvC8N5JnTfVlVVzWR/Bg41J3F3Th2JsH8SfcaMGfcLCgqWtLS0vHn79u35+/bt+1tzc7Nd\nV1fXuC1btiQdOXJkw+XLlxeVlZV5ymQytVarlaIBbk9v2GZ+fn5Qc3OzHUKIRgjRNE0jiUSiT0tL\nizl+/PgHx48f/yAyMjIbl+aioqKAnp6esRUVFbMLCwsDv/vuu03Z2dmRCoXCtqOjw4aiKCo/Pz+o\ns7Pzd2q1WjaQZjbsnwNn9A/VU1wYasnLywvmuy/Zt/6YOnVqQ15eXjBu/waqiW8fnJ2dHxijvbS0\n1Esmk6k1Go2l0HekdnZ2fpCbm7tUyPltOCYLCwsDuZy/wwHD21iI5bYWhvNM6L51dnZ+cPny5UWc\n5cS9aoT4dxjuav7xxx+Xu7u7X2fOp8nKyoqkaVofGhp6fvXq1WmGN+wzpk2NRiNlvnGkpaWtpmla\nf/z48TUkaK6qqpo5a9asivDw8JzQ0NDzHh4eZS4uLvemTJnS4O/vX2RnZ9fU2NjogNtTErwSqi9J\n9E8oH0zV/vTp0wnMa4X6Vo97fvM9fyHwBY6+5SMndiMh/n8wu4LT09NX+fj4/It5XK/X097e3iX3\n7t1zMfWyWcPLGtPT01dJJBIdzgWOYWg0GqmHh0dZcHBwLvPYjRs33AoKChYXFRX5M1cumPNDhnx4\niitw9CWJ/gnlgyna4+Pj9xQWFgaUlJR4nzhxIkaoK4pImN98zl8IvIGjb7nKid08iME72dXVtTIm\nJubETz/99M62bdu+lMlkqtbW1olDabegoGAxTdN6Y37KW6gwvITxvffeOz3Q64b6AcqXp7hC6L4k\n1T8hfDBG+/79+zfTNK23s7Nr8vf3L2KfwyMmLwxDqPkLIXzg6Fsuc2I3EGLwTlar1ZYrV648uWrV\nqnR3d/frN2/eXIDQ0HaHazQaaV5eXhBujQPVhdCLge3q6lrJXsGT7ilOz4TqS5L949uHV2lnoqGh\nYcrixYsLYmJiTjCH7oT2Bdf85nv+QuALHH3LVU7s5kG8upOZjSSz63uoCxzcuoypT6VSyXx9fYsV\nCoUNlx8SfHiK2yscOUnyTygfXqadiatXry7cuHHjP8PDw3OamprscPiCe37zPX8hRlbfcpGTRkgU\nJ4SLFvS/V0oY/i1mNBqNJfsMf651j0RPuWQk+8fWq9frJcxVRqWlpV5HjhzZ0N7e/npSUtKWKVOm\nNI40bxj4nr8APnD07VBzwiIHIBbYOALDgZKSEp/09PTVz549m5CYmLh1JC9w2IAH4gVH35qbU8pH\nMQDABbCBBIYD165dW1hdXe2Smpoa6+Dg8Ag+3F8AHogXHH1rbk7YkwMAADAEdDqdRVtb2/hJkya1\nwgIHAMgCFjkAAABmotPpLCwsLHQUBYdnAIBEYJEDAAAAAIAogXtXAQAAAAAgSmCRAwAAAACAKIFF\nDgAAAAAAogQWOQAAAAAAiBJY5AAAAAAAIEpgkQMAAAAAgCiBRQ4AAAAAAKIEFjkAAAAAAIgSWOQA\nAAAAACBKYJEDAAAAAIAogUUOAAAAAACiBBY5AAAAAACIEljkAAAAAAAgSmCRAwAAAACAKIFFDgAA\nAAAAogQWOQAAAAAAiBJY5AAAAAAAIEpgkQMAAAAAgCiBRQ4AAAAAAKIEFjkAAAAAAIiS/wGGBLwu\nKenHGwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9e51090>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,4))\n",
      "ax1 = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)\n",
      "ax2 = plt.subplot2grid((7,1), (3,0), rowspan=3, colspan=1)\n",
      "ax3 = plt.subplot2grid((7,1), (6,0), rowspan=1, colspan=1)\n",
      "\n",
      "bands = p_arm.reset_index().groupby('Cytoband')\n",
      "bands = bands.first().start.order()\n",
      "starts = p_arm.index.get_level_values('start')\n",
      "for i,(idx,v) in enumerate(bands.iteritems()):\n",
      "    try:\n",
      "        r = plt.Rectangle((v,0), bands.iloc[i+1]-v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    except:\n",
      "        r = plt.Rectangle((v,0), starts[-1] - v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    ax3.add_artist(r)\n",
      "ax3.set_xticks(bands)\n",
      "b = [{0:s,1:''}[(i%2)] for i,s in enumerate(bands.index)]\n",
      "ax3.set_xticklabels(b, rotation=270+45, ha='left')\n",
      "\n",
      "ax1.scatter(starts, (p_arm < -.1).sum(1), s=40, c=colors[2], \n",
      "            edgecolor='grey', lw=.5, alpha=.5)\n",
      "ax2.scatter(starts, rr.odds_ratio.ix[p_arm.index], s=40, \n",
      "            c=colors[4], edgecolor='grey', alpha=.7)\n",
      "\n",
      "#ax1.set_ybound(215,255)\n",
      "ax1.set_ylabel('# of Patients\\nwith Deletion\\n\\n', ha='center', va='center')\n",
      "#ax2.set_ybound(-.5,4.)\n",
      "ax2.set_ylabel('mRNA Survival\\nAssociation\\n(Log10 p)\\n\\n\\n', ha='center', va='center')\n",
      "ax3.set_yticks([])\n",
      "fig.tight_layout()\n",
      "for ax in [ax1, ax2, ax3]:\n",
      "    ax.set_xbound(starts[0], \n",
      "                  starts[-1])\n",
      "    ax.set_xticks(bands)\n",
      "\n",
      "for n, ss in exp2.ix[['FHIT']].iteritems():\n",
      "    ax2.annotate(n[0], (n[1], ss), xytext=(15, -5), \n",
      "                 textcoords='offset points', \n",
      "                 arrowprops=dict(arrowstyle=\"->\"))\n",
      "ax1.set_xticklabels('')\n",
      "ax2.set_xticklabels('')\n",
      "ax2.set_yticks([2,3,4,5,6,7,8])\n",
      "fig.tight_layout()\n",
      "#fig.savefig(FIGDIR + 'supp_2a.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DaEJFD2n4nQBFrYhAQXEUbN0CIZGeQHgKuq1X4jhcBd3SUTxB2V/C0R2wwFM9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\nJJocAACgJJocAACgJJocAACgpN+W+NqOINigPAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad05490>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extended Data Figure 2b: HPV+ Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "levels = lesions.ix['3p14.2'].ix[2]\n",
      "calls = lesions.ix['3p14.2'].ix[0]\n",
      "calls.name = 'del_3p'\n",
      "\n",
      "cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "cn_by_gene = cn_by_gene.reset_index(level=[0,1])\n",
      "cn_by_gene = cn_by_gene.join(coords).set_index(['chrom','start', 'Cytoband','Locus ID'], append=True)\n",
      "cn_by_gene = cn_by_gene.ix[:,true_index(hpv==True)].dropna(axis=1)\n",
      "\n",
      "ch_3 = cn_by_gene.xs('3', level='chrom')\n",
      "p_arm = pd.Series(ch_3.index.get_level_values('Cytoband').map(lambda s: 'p' in s), ch_3.index)\n",
      "p_arm = ch_3[p_arm].sortlevel('start')\n",
      "p_arm = p_arm.ix[:-1] #bad annotation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rna_all = cancer.load_data('mRNASeq')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df = rna_all.features.ix['real'].ix[p_arm.index.get_level_values(0)].dropna()\n",
      "df = rna_all.df.xs('01', 1, 1).ix[p_arm.index.get_level_values(0)].dropna()\n",
      "df = df.ix[:,true_index(hpv.dropna())]\n",
      "#df = df.ix[screen_feature(del_3p, kruskal_pandas, df).p < .05]\n",
      "\n",
      "test = lambda s: get_cox_ph_ms(surv, s, [age, old], interactions='just_first')\n",
      "exp = df.apply(test, axis=1)\n",
      "\n",
      "exp = exp.sort_index()\n",
      "exp.index = p_arm.sort_index().ix[exp.index].index\n",
      "\n",
      "exp2 = -exp.LR.map(log10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,4))\n",
      "ax1 = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)\n",
      "ax2 = plt.subplot2grid((7,1), (3,0), rowspan=3, colspan=1)\n",
      "ax3 = plt.subplot2grid((7,1), (6,0), rowspan=1, colspan=1)\n",
      "\n",
      "bands = p_arm.reset_index().groupby('Cytoband')\n",
      "bands = bands.first().start.order()\n",
      "starts = p_arm.index.get_level_values('start')\n",
      "for i,(idx,v) in enumerate(bands.iteritems()):\n",
      "    try:\n",
      "        r = plt.Rectangle((v,0), bands.iloc[i+1]-v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    except:\n",
      "        r = plt.Rectangle((v,0), starts[-1] - v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    ax3.add_artist(r)\n",
      "ax3.set_xticks(bands)\n",
      "b = [{0:s,1:''}[(i%2)] for i,s in enumerate(bands.index)]\n",
      "ax3.set_xticklabels(b, rotation=270+45, ha='left')\n",
      "\n",
      "ax1.scatter(starts, (p_arm < -.1).sum(1), s=40, c=colors[2], \n",
      "            edgecolor='grey', alpha=1)\n",
      "ax2.scatter(exp2.index.get_level_values('start'), exp2, s=40, \n",
      "            c=colors[4], edgecolor='grey', alpha=1)\n",
      "\n",
      "#ax1.set_ybound(215,255)\n",
      "ax1.set_ylabel('# of Patients\\nwith Deletion\\n\\n', ha='center', va='center')\n",
      "ax2.set_ybound(-.5,3)\n",
      "ax2.set_ylabel('mRNA Survival\\nAssociation\\n(Log10 p)\\n\\n\\n', ha='center', va='center')\n",
      "ax3.set_yticks([])\n",
      "fig.tight_layout()\n",
      "for ax in [ax1, ax2, ax3]:\n",
      "    ax.set_xbound(starts[0], \n",
      "                  starts[-1])\n",
      "    ax.set_xticks(bands)\n",
      "\n",
      "for n, ss in exp2.order().tail(2).iteritems():\n",
      "    ax2.annotate(n[0], (n[1], ss), xytext=(15, -5), \n",
      "                 textcoords='offset points', \n",
      "                 arrowprops=dict(arrowstyle=\"->\"))\n",
      "ax1.set_xticklabels('')\n",
      "ax2.set_xticklabels('')\n",
      "ax2.set_yticks([0,1,2,3,4,5])\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'supp_2b.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f/hCIgOyEzFYCgz1K46GxaGHQ1KM8qhQqiwoxFT1QGlGKmMoYZPfMgsDwgACE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\n+P/sHhsrSCMK/gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xee01790>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Extended Data Figure 3: TP53 Association with Chromosomal Instability"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t1 = screen_feature(pct_del.ix[keepers_o], rev_kruskal, mut.df.ix[mut.df.ix[:,keepers_o].sum(1) > 5]>0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t1.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>HRAS</th>\n",
        "      <td> 22.17</td>\n",
        "      <td> 2.49e-06</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CASP8</th>\n",
        "      <td> 21.31</td>\n",
        "      <td> 3.91e-06</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td> 21.20</td>\n",
        "      <td> 4.14e-06</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FBN1</th>\n",
        "      <td> 12.34</td>\n",
        "      <td> 4.44e-04</td>\n",
        "      <td> 0.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>COL4A4</th>\n",
        "      <td> 11.35</td>\n",
        "      <td> 7.54e-04</td>\n",
        "      <td> 0.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRDM9</th>\n",
        "      <td>  8.64</td>\n",
        "      <td> 3.28e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>OR8K5</th>\n",
        "      <td>  8.25</td>\n",
        "      <td> 4.08e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PON1</th>\n",
        "      <td>  8.10</td>\n",
        "      <td> 4.43e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AFF3</th>\n",
        "      <td>  7.93</td>\n",
        "      <td> 4.85e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDH10</th>\n",
        "      <td>  7.85</td>\n",
        "      <td> 5.08e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "            H         p     q\n",
        "HRAS    22.17  2.49e-06  0.00\n",
        "CASP8   21.31  3.91e-06  0.00\n",
        "TP53    21.20  4.14e-06  0.00\n",
        "FBN1    12.34  4.44e-04  0.13\n",
        "COL4A4  11.35  7.54e-04  0.18\n",
        "PRDM9    8.64  3.28e-03  0.42\n",
        "OR8K5    8.25  4.08e-03  0.42\n",
        "PON1     8.10  4.43e-03  0.42\n",
        "AFF3     7.93  4.85e-03  0.42\n",
        "CDH10    7.85  5.08e-03  0.42"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(t1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "1202"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t1 = screen_feature(pct_altered.ix[keepers_o], rev_kruskal, \n",
      "                    cn.features.ix['Deletion'] < 0)\n",
      "t2 = screen_feature(pct_altered.ix[keepers_o], rev_kruskal, \n",
      "                    cn.features.ix['Amplification'] > 0)\n",
      "del t1['q']\n",
      "t1['p_bonf'] = t1.p * len(t1)\n",
      "del t2['q']\n",
      "t2['p_bonf'] = t2.p * len(t2)\n",
      "pd.concat([t1.head(6), t2.head(6)], keys=['Deletion', 'Amplification'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>p_bonf</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Deletion</th>\n",
        "      <th>5q11.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 66.52</td>\n",
        "      <td> 3.47e-16</td>\n",
        "      <td> 1.66e-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1p13.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 59.27</td>\n",
        "      <td> 1.38e-14</td>\n",
        "      <td> 6.60e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p12.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 57.34</td>\n",
        "      <td> 3.66e-14</td>\n",
        "      <td> 1.76e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5q35.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 57.25</td>\n",
        "      <td> 3.83e-14</td>\n",
        "      <td> 1.84e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5q15</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 56.71</td>\n",
        "      <td> 5.06e-14</td>\n",
        "      <td> 2.43e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p14.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 54.49</td>\n",
        "      <td> 1.56e-13</td>\n",
        "      <td> 7.51e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Amplification</th>\n",
        "      <th>12p13.33</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 70.64</td>\n",
        "      <td> 4.28e-17</td>\n",
        "      <td> 1.11e-15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7q21.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 65.69</td>\n",
        "      <td> 5.29e-16</td>\n",
        "      <td> 1.37e-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17q12</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 58.10</td>\n",
        "      <td> 2.50e-14</td>\n",
        "      <td> 6.49e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2q31.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 55.93</td>\n",
        "      <td> 7.50e-14</td>\n",
        "      <td> 1.95e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2q11.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 55.41</td>\n",
        "      <td> 9.81e-14</td>\n",
        "      <td> 2.55e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12q15</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 49.86</td>\n",
        "      <td> 1.65e-12</td>\n",
        "      <td> 4.29e-11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "                                   H         p    p_bonf\n",
        "Deletion      5q11.2   Lesion  66.52  3.47e-16  1.66e-14\n",
        "              1p13.2   Lesion  59.27  1.38e-14  6.60e-13\n",
        "              3p12.2   Lesion  57.34  3.66e-14  1.76e-12\n",
        "              5q35.3   Lesion  57.25  3.83e-14  1.84e-12\n",
        "              5q15     Lesion  56.71  5.06e-14  2.43e-12\n",
        "              3p14.2   Lesion  54.49  1.56e-13  7.51e-12\n",
        "Amplification 12p13.33 Lesion  70.64  4.28e-17  1.11e-15\n",
        "              7q21.3   Lesion  65.69  5.29e-16  1.37e-14\n",
        "              17q12    Lesion  58.10  2.50e-14  6.49e-13\n",
        "              2q31.2   Lesion  55.93  7.50e-14  1.95e-12\n",
        "              2q11.2   Lesion  55.41  9.81e-14  2.55e-12\n",
        "              12q15    Lesion  49.86  1.65e-12  4.29e-11"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(t2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "26"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(t1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "48"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='segs_p53'> \n",
      "\n",
      "<b>Chromosomal segments associated with TP53 mutation in HPV- pateints.  </b>\n",
      "<p>In text as : \"While mutation of TP53 has previously been associated with chromosomal instability, we find that TP53 associates with 3p chromosomal loss far more frequently than it does with deletions in other chromosomal regions with similar fragile sites, suggesting a selective advantage of this combination of events for tumor cells.\"</p></div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t3 = screen_feature(p53_mut.ix[keepers_o], fisher_exact_test, \n",
      "                    cn.features.ix['Deletion'] < 0).head(5)\n",
      "t4 = screen_feature(p53_mut.ix[keepers_o], fisher_exact_test, \n",
      "                    cn.features.ix['Amplification'] > 0).head()\n",
      "pd.concat([t3, t4], keys=['Deletion', 'Amplification'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>odds_ratio</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Deletion</th>\n",
        "      <th>3p14.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 6.59</td>\n",
        "      <td> 3.56e-07</td>\n",
        "      <td> 8.55e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p14.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 6.59</td>\n",
        "      <td> 3.56e-07</td>\n",
        "      <td> 8.55e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p25.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 5.49</td>\n",
        "      <td> 1.81e-06</td>\n",
        "      <td> 2.89e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p12.2</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 5.07</td>\n",
        "      <td> 6.28e-06</td>\n",
        "      <td> 7.54e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10p15.3</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 4.00</td>\n",
        "      <td> 6.74e-04</td>\n",
        "      <td> 6.47e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Amplification</th>\n",
        "      <th>3q26.33</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 6.35</td>\n",
        "      <td> 9.00e-08</td>\n",
        "      <td> 2.34e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8q24.21</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 4.24</td>\n",
        "      <td> 3.98e-05</td>\n",
        "      <td> 5.18e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12p13.33</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 3.21</td>\n",
        "      <td> 2.63e-03</td>\n",
        "      <td> 2.28e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9p24.1</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 0.40</td>\n",
        "      <td> 8.88e-03</td>\n",
        "      <td> 5.77e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18p11.31</th>\n",
        "      <th>Lesion</th>\n",
        "      <td> 2.61</td>\n",
        "      <td> 1.69e-02</td>\n",
        "      <td> 8.51e-02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "                               odds_ratio         p         q\n",
        "Deletion      3p14.3   Lesion        6.59  3.56e-07  8.55e-06\n",
        "              3p14.2   Lesion        6.59  3.56e-07  8.55e-06\n",
        "              3p25.3   Lesion        5.49  1.81e-06  2.89e-05\n",
        "              3p12.2   Lesion        5.07  6.28e-06  7.54e-05\n",
        "              10p15.3  Lesion        4.00  6.74e-04  6.47e-03\n",
        "Amplification 3q26.33  Lesion        6.35  9.00e-08  2.34e-06\n",
        "              8q24.21  Lesion        4.24  3.98e-05  5.18e-04\n",
        "              12p13.33 Lesion        3.21  2.63e-03  2.28e-02\n",
        "              9p24.1   Lesion        0.40  8.88e-03  5.77e-02\n",
        "              18p11.31 Lesion        2.61  1.69e-02  8.51e-02"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Association of percentage of genes amplified or deleted TP53 mutation in HPV- pateints."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kruskal_pandas(p53_mut.ix[keepers_o], pct_del)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "H    2.12e+01\n",
        "p    4.14e-06\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(2,1, figsize=(4,6))\n",
      "p53_mut = mut.features.ix['TP53'].ix[keepers_o].map({0:'TP53_WT', 1:'TP53_mut'})\n",
      "violin_plot_pandas(p53_mut, pct_del, ax=axs[0])\n",
      "axs[0].set_ylabel('% of Genes Deleted')\n",
      "violin_plot_pandas(p53_mut, pct_amp, ax=axs[1])\n",
      "axs[1].set_ylabel('% of Genes Amplified')\n",
      "for ax in axs:\n",
      "    prettify_ax(ax)\n",
      "    ax.set_xlabel('')\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'p53_cin_supp.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jjjtq7wGpr55Kfr4QI0fG4vlVVsyaFTnB2eowDEJerCkau5Krw6VsWsBPUZk7\nd+47H3300VPdunU7LZVKb/K/eVGpn3/9S4F16xT4/vtKtG7duC7Vtm0ZfP99JUaOjIXNRmDBgsjo\nRq6Oy4WwVCuLVmeFi16YX6Kyfv36aWfPnu3apk2by8EyyBecTmfEpOqzLPCPfyjx2Wdy7N9fgeTk\npvV8tGrFYP/+Ctx/fywoisCrr5obFS8IB57yh7KQew5c+ycPJCzLQiDgVlV9v652SkrK1bqm6ggl\nDkfYTfAJlgUWLVLhyy/JgAiKl8REN777rhJ79sgwf746Yv6FHQ4aMlnoizUTBNGoruFIwBOE5pZo\n+uWpPPLII58NHTr0+2efffbfCQkJpYBnig6CINhQ5qo4ndz/FbndwLx5apw4IcF331UgNjawNjdr\n5sbevRUYMyYWs2Zp8O67Rs4neDmdbiiVoZ8VUCAQRIzw+otn/h9u1XTwS1T2798/JCEhofTLL78c\nV/O90CbAiTk9mRLLAs89p8HFi2Ls2VMJrTY4T3RMDIvduysxbpwOzzyjxfvvGzgrLAzDwO0WhWU0\nrSfrNOQfGxLcbjdEIm6FAvwSlYMHD2Z4XzMMIxQKhWEZwSaXx4CiKqBUcnN6jqtXhfjmGxkuXiyD\nUhncv0iVisXXX+vRpUs8fv9dhLvu4lbOgheKckChCM3I3ZpEs6gwjBsCAbfqC/n1v2az2eTz5s17\nu1WrVteUSqUlNzc3ffjw4fuKi4tbBMvA2lAoFLDbufuUuN2AUskGXVC8yOUs1Go3p384Docbcnl4\nRtJ6mj8REs32E4ZxQyTiVvPHL1F5/PHHPzEYDFrvqOTOnTv/2qtXr18mTZq0ub7jcnJyeqSlpZ1R\nKpWWzMzM3Xq9Xldzn82bN09KTU3NU6vVJu88ynWdTywWQySSw+n0PyuVJ/TQNA2BQBbWHjuhUMzJ\nyvNNJRzJhA3hl6js2bNn5IoVK+a0bt36CuCZfXDRokVLjx49enddxzAMIxw3btyXs2bNWlVcXNwi\nPj6+bPbs2Sur73PmzJm02bNnr/ziiy8eKikpSUxKSiqaPn36uvpsUSpjQFG8qEQCFOWAUnnL/0hI\nEYkkUSoqBCdmfqyOX6KSlJRUdO7cuS7Vt+Xl5aUmJSXVmTd+7Nix/iRJUlOnTt2gVqtNS5Ysydqx\nY8dYmqZv+Gz79u0b/tBDD32RlpZ2RqFQWOfNm/f2oUOH7q3PFpIk4XQKOJn8w/MnLMvC4SAgl4c3\n/iUSSeCkrEYZAAAgAElEQVRyRaOocM9T8UviVq1aNWv06NE7J06c+CnDMMIFCxYs+/TTTye+/vrr\nf6vrmNzc3PS0tLQz3vXk5ORCkiSpvLy81I4dO14AgAkTJmwjCOKGOpw9e7art8u6LgQCAeRyLSjK\nCLmcn6mQq9jtdpCkJuwJaCKRBA4Hh4NOjYBlWbAsEfZrWxO/RGXkyJF7srOze27duvXRKVOmfCCV\nSh27du0aVV/ha7PZrFKr1abq29RqtcloNN6YBDYlJeWq9/WmTZsmz549e+V77733XF3nXLx4MQBP\nN2V6+h144IHB/nwNnhBitzPQakOf8FaTaOwB4mKOCtCIAYWtW7e+8vLLL7/h6/5qtdpkMplueqpM\nJpNao9HcNBFsYWFh8uTJkzcVFBS03LZt24Rhw4Z9V9c5vaICAMXFV+ByuTjXruTx5qaIIZOFPuGt\nJh5Ria6mMsMwEInCf21r4rPftHPnztGvvfbaqwCgUqnMJElSJElSCoXCetddd/1W13Hp6em5Z86c\nSfOuFxQUtKQoikxNTc3zbtPr9boBAwYc6dmzZ/b58+c71ScoNVEoYmC38wFbLmKz2SGXx4TbDABe\nUYmubmUuJr4BPorK3r17RzzzzDPvd+rU6TwAuFwu0U8//XTP8uXL5yckJJR+8803D9R1bN++fU9Q\nFEWuX79+msFg0GZlZS0ZM2bM19XHEL333nvP9e/f/9jy5cvnSyQSvxSC6zkrtzMOBwuFghtV3gUC\nQdR5Km43y7nEN8BHUXnzzTcXrlu3bvpDDz30hXdbjx49cmbNmrXqn//85//93//93z/rOlYkErm2\nb9/+4KpVq2YlJSUVlZaWJrz77rsvbNq0abLXW8nOzu65bdu2CWKxmPYuvoqLSCSCSESGZS4Znrqh\naRpCoSwsZQ5qIxoT4NxulnM9P4CPMZXTp093Gzly5B7vurfXBgBGjx698+mnn/6wvuN79uyZffbs\n2a7Vt02ePHmTtwbL119/PcY/s29GLtfCbr/OmQeYB7DbnZDLE8Jtxk0IBMKwzI4YLLj6XXzyVCQS\nibO8vPxGDcScnJwe3tc2m01evTs4HJAkCYcjulzbSMfpZEGS3Orq52KR6KbAstysaueTqIwaNWrX\nsmXLFtT23jvvvDN34MCBhwNrln+IRCIIBFLOTf94u+LJXBXznuNtik/NnzfffHPhgAEDjgwdOvT7\nRx99dGvLli0LysvLm3311Vd/OX78eL8ffvhhULANbQiSVMPh0PNdyxzAbneAJMObll8bXG0uNBau\nfheffoHx8fFl2dnZPVeuXDn7008/nXj16tUUmUxmv/feew9lZ2f3bN68+fVgG9oQMhkJg4EBRzob\nbmucTje0Wu6VpWBZN2d/iI2DhZuDGX0+/62rVCrzokWLli5atGhpMA1qLFKpFC6XIOr+jSIRl4sI\nSzGm+vD8+KInngIAAgHByUGS3Bo00AQIgoBUquDLIYQZT1FyBeeE3TOfcnSJikgkBMNw73mPGlEB\nAJJUwenkg7XhxFPc2r+Z+UKBy+UCB1M6moRIJAJNc28ev6gSFalUGhFFsaMZmgYnxvrUxOl0QiyO\nLlXxiIqDc93kPolKq1atrjEMI2zVqtW1YBvUFMRiMVhWzMng1e2A2+2G2y3k5JxMdrsp5HM4hwKR\nyM25Jr9PV1mtVpvatGlzubCwMLlDhw4Xa75PEAR74cKFjoE3z39IUgW7na+xEg4cDgdIMvxlDmrC\nMAxomoJYzL1mWVMRiwWgKBunAuM+icqhQ4fu/fXXXzsPHjz4wNq1a2ewHB5EQZIKGI16hLnQ2G2J\nw8FApeJenz5FUZBKOfvINgmSlMJgMECr5cZocMBHUYmNja289957D129ejUlKSmp6NSpU92LioqS\nkpOTC7t163Y62Eb6g0wmQ2Ul37UcaliWBU0TnIynWCxVUCiiM7tXKBSCIGyw2+2cufZ+NzK7d+9+\nqqSkJLFNmzaXr1y50rp58+bXd+7cOTo5ObkwGAb6C0EQIEk17HYz58aeRDMOhwNSqYpzpQ0dDgfc\nbgoSSfQ1fbyQpAhmswEyWfNwmwKgEVN0PPjgg9tLSkoSjx071r+oqChp/Pjx/3nssce2BMvAxiCX\nq2C3cy8pKJqhKBeUSk3DO4YYT1M4unp9auIpAm/iTMDWL0/l5MmTffbv3z/Eu04QBPvSSy+9VV/h\n63Agk8mg14vCNjVqWZkAly+L8Oyz/v/I9PoO0On8P66kJHw/HLfbDYYRccb99mK32+FymaDRqMJt\nStBRKIQwGCoRH1/ndFkhwy9RSU1Nzfviiy8emjBhwjbvth07doxt3779pcCb1ngIgoBSGQubrSws\ngcPkZAYDBzrQo4f/haOKikzIzY1DZqZ/SU19+zqRmhqexD+rlYJCEce5GJbBUAalknvd28GAJElQ\nlAkUpQ17s98vUfn444+fHDZs2Hfvv//+M23atLl8+fLlNmfPnu26d+/eEcEysLEolUqUlHjmMg71\nw56c7MZ331U26thz54rwj390xJdf6gNsVXDwzOsDaLXc8gZMJhMIwg6pNHpjKTVRqWSoqroOqTQl\nrLEtv0SlW7dup/Pz89vu3LlzdFFRUdKgQYN+GD169E6lUmkJloGNRSgUQqGIhdWq5+xE7tGAzWYH\nScZwquQETdMwm0uh091e910sFkMiscJg0EOniwubHX4/CSqVysy1wGxdqNUaXL+uh0LBdy8HA5Zl\nQVFuJCRow23KDViWRWVlCZRKIed6okKBSqVAZWUFbDZ52GaFjOqr7vVWLBZbuE2JSqxWCnJ5LKe8\nFL2+AiKRg3NB41Ci1SpQVVUctmLwUS0qgMdbcTpFfKnJAMMwDOx2AhoNd7wUk8kEp1MfsOA8QQCh\n7KWlaQKBcKiFQiGUSgHKy4vCMg6u0aJiNptVOTk5PWrOPsg1BAIBNJp4mExUuE3xmUWLzOE2oUFM\nJhs0mgTONDFsNhvM5hJotYHr7RswwIlvvgmNx+NwAN99J8U99zgCcj6ZTAap1IXy8ushH8XcqCfi\niy++eKhPnz4nly9fPr979+6nPv3004mBNiyQKBQKCIVq2Gzcqz1RG1wXFYqiQBBKKJXc6FlxOByo\nqiqCVksGVOSmTbNiwwYFQvGb3LGDRJcuNNq3D1zSplIph0BggV5fEbBz+kKj7sDChQvfPHLkyIBt\n27ZNOHjwYMbcuXPf8eW4nJycHmlpaWeUSqUlMzNzt16vr7M6cqdOnc6XlZXFN8a+2tDpmsFmc/Nl\nEZqI2+2G1eqGThewW9MknE4nKioKodFIAh7bufdeJ+x2AidOBH/c0IYNckybFvjYn1qthMtVBb2+\ncSkOjcEnUXnqqac+ql4xv2XLlgX79u0b7nA4pIcPHx7oS50VhmGE48aN+3LWrFmriouLW8THx5fN\nnj17Zc39aJoWr127dsbFixc7+PdV6kckEkGtToDRyAdtm4LRaIVKlcCJ6TdomkZFRRFUKkFQ7CEI\nYOpUj7cSTC5eFCEvT4QHHgiOJ63VKuFwVKCqKjS5T4Qv7S232y3YvHnzpCNHjgyYPn36uqSkpKJ5\n8+a9ffbs2a5dunQ5t2zZsgUpKSlX6zvHkSNHBkybNm39b7/9dhcAeGuz6PV6XfV5lVUqldlut8vc\nbregpKQkMT4+vuwmgwmCbUobsaysBCKRDQoFNwcbnjt3Hl26dAq3GbVis9lB0yQnUsFpmkZ5eSGU\nSgS1lkhFhQCdOsXjwoVSxMb69txdunQZzZs387l5OG+eGioVi8WLg9fsZVkWVVUWkGR8oMok1BlS\n9slfFAgE7ieeeGLjxIkTP123bt308+fPd8rKylpSffrThsjNzU1PS0s7411PTk4uJEmSysvLS61+\nHrPZrPJ+pq/n9ofY2HiUll6FVOriVFco13G5XKAoID6+WcM7BxmvoCgULKTS4AZS4+LcyMy045NP\n5Jg92xrw89tsBLZuleP48fKAn7s6BEEgJkYJvb4UAIJaf8WnX5XBYNCuWLFijlAoZF544YV3RSKR\na/Xq1c+///77z8ydO/cdX5o/ZrNZpVarTdW3qdVqk9Fo9Hv03OLFi2+8zsjIQEZGhs/HCoVCxMQk\noqrqGnQ6JSeT4pYuVXEqWMuyLIxGClpty7ALcXVBCVUuytSpVkyfHoMXXrAGpMu3Otu3y9CnjxMp\nKcEfVU8QBHQ6FaqqysCyLGJigjPhm09PyCOPPPJZ3759TzAMI3z88cc/2bVr16gFCxYs0+v1uhUr\nVsxxOp2SN998c2F951Cr1aaa3c8mk0mt0WiM/hpdXVQaA0mSsNubwWyuhFrNvUplXBMVs9kKmSw2\nbBmaXrxBWaWSCLqHUp1+/WiIxSwOH5bg3nsDm7iyfr0CCxeG7l57PZaqqnJUVSEowuKTqBw/frzf\n119/PYYgCLb6bIQ6nU7/+uuv/62kpKTBRnZ6enru6tWrn/euFxQUtKQoikxNTc1rnOlNQ6uNQWmp\nFXa7AzIZd+p7cg273Q6GkSEuLjasdjgcDlRUFEKlEoS8HitBeLqX169XBFRUcnNFKCkRYPjwwOSm\n+IpXWAyGcuj17oCPE/Kp9+f+++//9tlnn/33Cy+88O7IkSP31Hw/MTGxpKFz9O3b9wRFUeT69eun\nGQwGbVZW1pIxY8Z8XT1IG0oIgkBsbHNYLAwnZ3njAgzDwGJhEBubGNZmokdQCqBWC8NW4HniRAr7\n90tRWhq4PJgNGxR46ilbWOYj8giLCjStR2VlYOM5Pl2hjRs3PtGvX7/jffr0Oblx48YnGvNBIpHI\ntX379gdXrVo1Kykpqai0tDTh3XfffWHTpk2Ta/NWCIIIesqRWCyGVpvIdzPXgdFog0aTGNbuY7vd\njoqKa9BoxGGd+kOjYTF2LIVNmwLTBDSbCWzfTuLJJ8P77Gm1SrhcelRUlAYs89anLmUu0dQu5drw\nKLWREyUSzp07j169hsJuLw6rHRaLDSyrQlxcQthsoCgKen0htFpp2APEAJCdLcZjj8Xg4sUy1Je4\n60uX8oYNcuzfL8Vnn1UFwVL/MRotEAg0iI2N99UrrXMnbgzcCDMxMbFwOIScqfEZ7iAtTdNwOITQ\n6cLXfewRlALOCAoA9OhBQ6dzY//+pjXBWBZ4/31FUDJoG4tGowTLGgMyVogXFXgGHep0iTCZuDGF\nZDhFxdN9bIdOlxi2wYJ/CoqMM4ICeAO2Nqxf3zSP9pdfxLBYCNx3X2gDtA2hVitBEOYmCwsvKv9D\nJpNBLm8GsznwCU6RhMViA0nGhq0eid1u56SgeHn4YQo//SRFYWHjfzobNigwdaq13iZUuPAKS2Vl\nWaOFhYNfK3xotTGgaQlnmkGhhqZpOJ0iaLXBSYpqCIfDgcpK7goKACiVLMaPp/Dxx43zVqqqCOzc\nKcOkSdwtxaFWK+F2Gxs9upkXlWp4Mg6bw2TillsaKkym8DV7aJpGZWVRUEYbB5qpU6346CMFGlP3\n69NP5Rg+3I5mzbg9Wl6jUYKm9Y0ahMj3/tSCXl8BljWEpTcoXAMKPSU3NYiNDX1wlmEYlJYWQKFw\nR0QZSIuFQEpKAizWxieYOOxFAbQoOHgHISoUiVCrb6nFxvf++INWq4PdToQtKW7p0tBOd8EwDCgK\nYWn2eApVl0EmYyJCUADg889JDBrkgMNeVOty/tefUFnx31rfO3K4DO3a0oiEsj4EQUCrVcBkKoHd\n7ntZBl5UasFTgjIBJlN4uvxCLSpmMwWNJiEsszkajQawrImzpShqY/36xhdU6tWLhkrF4scfI2No\niEAggFYrg15f4vOfLC8qdeBJXCLhcER3fMXpdIJlZWEpDWm322G1lkOj4UZZSl/IyRFDrxdgyJDG\nPRd/jiMKf6Klr4jFYshkblRWljW8M3hRqRetNh4WS3T3BJnNdmi1PmdRBgyWZaHXX4daLeFk+Ym6\nWL9ejqlTbU3qDp4wgcKhQ1IUF0fOz0+hIOFyGWGzNeyhRc63CgMymQwSiTZiCmb7C0VREIk0YYll\nmM1miETOsI7n8ReDgcCOHSQmT25as1ilYvHggxQ2bowcbwUANBo5DIaGxwjxotIAGo0ONpuLE5m2\ngcZqZaDVhr6kAcuyMJsroFRGThwFALZuJTFkiAMJCU2Psk6dasWHH8oRSQPkRSIRRCIaVmv9CaK8\nqDSAWCwGSepgtYYuWSkUafpWKwWS1IXFU7DZbBCLXWEJDDcWlvUUVJo2LTAZ1+npLthskzFu3F/r\n3W/79l2YNave+mf18vbb/0ZychckJ3fBO++subHdarVh+vR5SErqjDvv7IvPP//ap/MpFDKYTPUn\nxfGi4gMaTQwoig3Z9B7BFhXvHMhqdXhmF7RajZDJwl+N3x+OHxeDpgkMHBiYGNvu3d/DYPgCFy/W\nLeoFBcX45z9XNzrmdODAYaxd+xEOHdqJH3/8Gu+99wF+/PEnAMDCha/DZDLj999/xueff4BZsxai\noKDhkfEikQgEUf814EXFBzzTSDYLqbcSTKxWCgpFXFgyV1mWhdNpC1uxJX84fPgY7rknE889twDD\nhrWFxdIdv/9+qcnnNRiM+Nvf3sTkyY+itJTA1au3emzvvLMGHTr0w9mzt9aWz87ORc+eQ6DVtsPQ\noQ+ivLz2OX0+//xrTJ/+JNq1a4PU1LaYOnUSPv/8a1AUhc8++wqrVy+DQiFHWlonHDjwJTQa31IZ\nxOL6RY4XFR9Rq9VwOAQRXyXOm+imVvtdbzwg0DQNoTACMr/+x6lTZ5GScifk8irMnPkwHn98ZpPP\nOX/+a3jhhWlISmqG9u1dtY4jmjt3JiyWq3jllRdviueZzRb85S+TsXjxfBQX/4qBA/vV2Tw6d+4C\nunbteGM9Pb0Tzp49j7NnL6BFi+ZYvHgZmjfviLS0e3HlSgHUat9ERSKp/8+IFxUf8SbEmc2R7a1Y\nLBTU6viwlTVwu90QCCKnC1mjUUMimYVRo5z4v/97CkVFxbh8+eYprj766FNote1uLD16DEJyclc8\n9dSsW863f/8hFBQU4YknHgEAdOjgERW6zqKqN3cQfPvtfvTu3R0PPDAMcjmJBQtewP79h2odBGsy\nmaFS/SkUKpUKRqMJen0Vfv/9D8TF6XD5cg5WrPg7pkyZjby8fJ+uSUPPDi8qfqBQKMAwkTuKmaZp\nuFySmx60UOP5142cnrRWrZJulCrwpK1rodcbbtrnqacmwmD448aSk/MDCgvP4qOPVt20n8Vixdy5\nWXjvvWX/28IiJsaN9u1d9UwEf7MAFxQUYd++H28IWHx8BzidNEpKStGx491QKlOgUrXGtWtFUKtV\nMJn+nBXHbDZDo1FDoVBAJBJh0aJ5IEkS9913DwYM6Iv9+w/5dE0aivFwezgox/AUC05AVdU1xMYG\nr9ckWFN0mM12xMS0CmuymUAgCMmE54Hi2rVytGjBom9fGg6HA6WlZWjVKrlR58rPv4LLl6+he/fB\nADwTtAFAy5a/Yv36Y/jLXxrOh2rWLBZjx47EJ5+svbHt9OlzSE5ugQsXjt60b9eunXD27AUMHz7o\nf/v9irS0zkhJSQbLsnC5/uyBczicPjd/Guqw4D0VPyFJMugJccEY+2Oz2SEUqkGS4c0NEYvFYJjI\naf5UVZXjzjvfgd1O4Z//fA+9e3dHs2Y35/Z88MEnUCpTbizp6feiefNOmDz52Zv269q1E8zmKzc8\nmoULZ+PppyciN/cbnDsnxqVLtXWx36zAw4cPwsGDR3H06M9wOp3YsGEznnzy+Vq758ePH43339+I\nS5fykZeXjw8++AQPPTQKKSkt0a9fT7z22j9hsVixf/8hZGfn4r777vHpmtB0/TUfeE+lEWi1sSgt\nNUMmc4ctNuEPbrcbNhuDhITwT1kqEAggFEpB0zQnJnmvD71eAIGgDZzOI0hMXIz09E747LMNt+w3\nZcrjmDLl8Rvr/s6lLJUCkyZRuOeeu/HOO7MxceKD1d4lbvIsmzePxwcfrMTzzy/AH39cQZcuHfDZ\nZ+trPe/gwQPxzDNP4N57RwMAXnxxJgYNGgAA+Pjj1XjmmblISuqCVq2SsGnTe2jRorlP9jqd9Xsq\nfD2VRmIymUBR16HVBnYwXDCq6RsMZshkzaHRhKfHpyYmkwlOZylUKu7NDlmdmTNP48sv56G09Ae/\njvNXVADgjz+EyMiIw6VLpeByb7vb7YZe70RSUlu+nkqg8RStUYCiuD0uyG63g2XltRXZCRsKhQJ2\nOxGyZMLG8v33Umi1obGxXTsGnTq5sGcPt2vKWK0UlMr6h3aERFRycnJ6pKWlnVEqlZbMzMzder3+\nlmpA+/btG96+fftLKpXKPGnSpM0Oh4PDeu1Bp4uH1coE4cdxMCBncbvdsFhc0OkSODUSWCgUQqVq\n9r9qc9ylsFCEq1dFkMsT/Vq6du2PZs2K/D7u4EEpp4PYDMPA4RA02HsYdFFhGEY4bty4L2fNmrWq\nuLi4RXx8fNns2bNXVt9Hr9frJk6c+OnKlStnX716NaWsrCx+6dKli4JtW1MRi8VQqRJgMAS2Av/A\ngd8F5DxGoxUqVXNOjgRWq9WcLzJuMt0Bi2U/zOYSv5bTp4+hX7+9fh9ntRb71AMULoxGG7Ta5uHP\nUzl27Fh/kiSpqVOnblCr1aYlS5Zk7dixYyxN0zeidLt3787s3bv3z5mZmbt1Op0+KytrybZt2yYE\n27ZAoFarIRJpAvqvO3Bg0wtDWa0UBAI1p5o91fHMZZ0Io9HB2WaQRAKIRIBQ6P9y7BjZqOO4isVi\ng1ishULRcBws6L0/ubm56WlpaWe868nJyYUkSVJ5eXmpHTt2vFDbPt26dTt96dKl9jabTS6Xy7nt\nIwPQ6ZqhtJQCwzTdY2FZAex2GkZj087lcomQkBDfZHuCiVQqhVqdiKqqEohE0RPec7s9bZim3kOu\nwLIs3G6pz72HQRcVs9msUqvVpurb1Gq1yWg0aqrvk5KSciP3WaFQWIVCIWM0GjW1iQqX4gPB4q23\nVjW8Ew+nSUh4LdwmBBOWZdlaf4hBFxW1Wm0ymUw3+eAmk0mt0WiMde1jtVoVDMMIq+/jpa4vwsPD\nww2C7nOmp6fnnjlzJs27XlBQ0JKiKDI1NTWvrn1OnTrVvV27dn9EQtOHh4fnZoIuKn379j1BURS5\nfv36aQaDQZuVlbVkzJgxX4vF4hvjMu+///5vf/nll167d+/OrKysjF2yZEnWhAkTtgXbNh4eniDA\nsmzQl19++aVnly5dzsrlcuuIESP2VFZW6jZu3Di5ffv2ed59vv3225Ht2rW7pFAoLI899tgndrtd\nGgrb+IVf+CWwS9gNiISlXbt2l0QiES0SiWiCINze1yKRiG7Tpk2+UCh0ede1Wm3VpEmTNlksFgXL\nssjKynpdKpXaZTIZJZPJqNTU1N+95123bt0ziYmJxWq12jhu3Ljt5eXlccH+Lh999NGTI0aM2BPu\na3o738fZs2evGDt27FfedbvdLiVJ0rZgwYI3vdsuX77cmiAI92uvvfY3r00CgYARCASMd33q1Knr\nw31Na1vCbkCkLQRBuEtLS+O96xkZGT9u27btYe/6tWvXWnbt2vXMokWLlrAsi0cfffTTgwcP3lvz\nPLm5uWmxsbEVOTk53U0mk2rChAmfheIhuZ1FhSv3cceOHWMSExOLvesHDhwYFBMTo+/Ro0e2d9un\nn376aEpKypXqxy1evPjVmTNn/jvc166hJXqSAzhCy5YtC4YOHfq9N/Ccn5/ftl27dn/U3O+HH34Y\n9OCDD27v3r37KZVKZZ48efKm06dPd6vv3FeuXGndvHnz60uXLl0UExNT1bVr17MHDhwY3Ldv3xMq\nlcr8yiuv/AMADh48mNGhQ4eL3uO86z///HPvKVOmfPDdd98NGzVq1K5Af/doIpj3ccCAAUdKS0sT\nrl271goA9u/fP2T69Onrrly50rqqqioGAE6cONE3IyPjYPXjWJYl2Ajo/eRFJQBUv9GFhYXJ+/bt\nG967d++fAc/DOG3atPU6nU7fv3//Y9nZ2T0BYM6cOSvWrl07AwBsNpt869atj/br1+94Q59VXl7e\nrLy8vFlJSUliampq3tixY3esWbNm5rlz57qsWLFizoULFzrWdWzv3r1//vDDD58eNmzYd7t27RrV\n9G8eXYTqPup0On3Xrl3PHj9+vB8AHDhwYPCIESP2Dhw48PCBAwcGA8Dx48f71RQVgiA4PDLoT3hR\naSIsyxKTJ0/eRJIkRZIk1bNnz+y777776EsvvfSW3W6XJScnFz7//POri4qKkiZNmrR51KhRu0wm\nk5ogCJYgCHbNmjUzVSqV+auvvvrLI4888pkvn7d06dJFMpnM3rt375+HDx++r1u3bqdbt259pX37\n9peKioqSGjo+cN8+egj1fczIyDh4/PjxfgaDQfvbb7/ddffddx8dPHjwgf379w9xOBzSs2fPdr3v\nvvt+DMV3DzS8qDQRgiDYzZs3T6IoiqQoirx+/XrztWvXzpDJZHaZTGY/depU98zMzN0kSVIzZ85c\nExsbW3n48OGB3uNnzpy5hqIocunSpYvGjh27w+Vy1ZuQKJVKHSqVygx4fgje1wAgFAqZ2kSDF5KG\nCfV9zMjIOHjixIm+Bw8ezLj77ruPikQi1+DBgw8cOHBg8KlTp7onJiaWVM8yjyR4UQkAdf1oz58/\n32nz5s2Tqm9jGEaoUCisr7/++t8+//zzhwFAIpE4Z8yYsVav1+vMZrPPtSTrcocJgmAZhrkxPK2g\noKClr+e8nQnlfRwwYMCRs2fPdv3mm28eGDp06PcAcNddd/1mt9tln3322SOR6qUAvKgEhLp+3GKx\nmJ41a9aqQ4cO3etwOKRr166dYTabVQMGDDjSrFmz8jfeeOPlK1eutKYoily2bNmC9PT03JiYmCpf\nP7euH0HLli0Lrl69mnL+/PlORUVFSatXr37e+55QKGSsViu3S66FiVDeR51Op7/jjjt+37Jly2Ne\nUX+2jSsAACAASURBVAGAwYMHH1i/fv20mvEUIHI8Tl5U/MSfYNkdd9zx+9q1a2c8/fTTH2q1WsPG\njRuf2LVr1yiRSOSaNm3a+kGDBv3Qq1evX5KSkoqys7N7bt++/cGGzln9873t+Zr7tG3bNn/u3Lnv\n9O3b98Q999zz02OPPbbFu1+fPn1O5uXlpT700ENf+Po9opFw30cAuO+++37UarWGzp07/+rdNnjw\n4AMOh0Nam6jUdb+5RsTVqOXh4eE2vKfCMV5//fW/icViuuYikUicFy9e7BBu+3h843a+j7ynwsPD\nE1B4T4WHhyeg8KLCw8MTUHhRCSDvvffec1u3bn20Mce63W7B/PnzlyckJJTGx8eXvfjii//y5ppU\nVlbG3n///d8qlUpLenp67qlTp7p7j9u8efOk1NTUPLVabRo7duyOkpKSRJqmxSNGjNjrdrv5+9sE\nmnI/q7No0aKlI0eO3ONdz8nJ6dGjR48ckiSpu+6667dNmzZNBoCFCxe+eeLEib5N/bywE+4RjdGy\nmEwmVY8ePbLdbjfRmONXrlz5Qo8ePbIrKyt1xcXFif369Tv29ttvz2VZFhMnTtwyZcqUDUajUb12\n7drpKSkpV9xuN5Gbm5sWExOjz83NTbNYLIpnn332vVGjRu1kWRZvvPHGwvfff39auK9LpC5NvZ/e\n5dSpU91IkrSNHDnyW++2O++887f169dPpWladPLkyd4KhcJSUFCQXFhYmDRw4MBD4f7uTV34f7IA\n8eGHHz59//33f3v48OGBffr0OTl79uyVarXa1KlTp/PVS2XWxd69e0fMnTv3HZ1Op09MTCyZOnXq\nhkOHDt3rdDolO3fuHL106dJFarXaNH369HUikch17Nix/vv27Rv+0EMPfZGWlnZGoVBY582b9/ah\nQ4fuBYAnnnhi49tvvz0v+N88Omnq/QQAmqbF06dPX/fss8/+m62WuCYQCNzeyfLcbrdAJBK5ZDKZ\nPSkpqUipVFqOHDkyIFjfKxTwohIgvvjii4cGDRr0A8uyRE5OTo+WLVsWVFRUxI0YMWLviy+++K+G\njn/zzTcXZmZm7vaunz17tmtCQkLp77//fodcLrc1b978uve97t27n8rNzU2fMGHCtkWLFi2teQwA\nJCYmlqhUKrN3NC2PfzT1fgLAG2+88fKIESP2dunS5Vz17Rs2bJg6Z86cFRKJxNm/f/9jc+bMWREX\nF1cBeEqrRsqcV3USblcpGhaXyyWUSCSOqqoq7Y8//pgRGxtb4X3v4MGD97Zt2/YPlmWRkpJyxVs5\nrPpy8eLFu7z722w2cv78+ct0Ol3lb7/9dudPP/10d/WymyzLYsqUKRv+/ve/v1J928aNGydrtdqq\nLVu2TPRue+aZZ9b961//mhPu6xNpSyDu57lz5zqnpaXlOp1OcfXCWA6HQ9KqVaur77zzzosURcn2\n7NkzQqFQWPLy8tqzLIujR4/2T0tLyw33NWjKEvQpOm4HKisrYxmGEWq1WgMANGvWrNz7nlQqdXgD\npleuXGld33mOHDky4Mknn/y4devWV06cONE3NTU1z+l0Smqb4sT7WYWFhcmTJ0/eVFBQ0HLbtm0T\nhg0bdmPO1GbNmpU3VAqB51aaej9ZliX69u17YvXq1c9XL/AOAGfOnElzOp0Sr7czYsSIvRkZGQe9\nc4lHwz3jRSUAEATBCgSCJs3defz48X5jxoz5etWqVbMee+yxLd7td9xxx+82m01eUlKSmJiYWAIA\np0+f7jZnzpwVer1eN2DAgCPjx4//z969e0dIJJKbJibme38aR1Pvp8lkUp8+fbqbd6AgwzBCt9st\niI+PL9u1a9eommURBAKB2zvhXjTcs4j/AlwgNja2EgAsFouyvv2Sk5MLa0vbvnDhQsdFixYtXbZs\n2YLqggJ4/hlHjx69Mysra4nRaNSsXbt2Bk3T4r59+5547733nuvfv/+x5cuXz68pKABQVlYWn5SU\nVBTYbxv9NPV+FhcXt3A6nRJvbZb169dPGzp06PdlZWXx3bp1O02SJLV8+fL5NptNfvz48X4nT57s\nM3z48H2A5561aNGiOBTfM1jwohIABAKBu0+fPidPnz7drbaRpN71wsLCZJqmxdUXp9Mp6dix44Xs\n7Oyezz777L+rP6Def7p33333hcLCwuQWLVoUr1mzZub27dsfFAgE7uzs7J7btm2bUPOh9n7uuXPn\nuvTv3/9YaK9G5NPU+1m9PnDNYyQSifPbb7+9f+fOnaN1Op3+qaee+mjr1q2PxsfHlwGee3b33Xcf\nDcX3DBrhDupEy7Jq1arnFy5c+Ea47fAuer0+5o477vhvU/MsbtclXPdz/Pjxnx84cGBQuL9/UxZ+\nQGGAsNls8n79+h3PycnpIRKJXOG2Z9WqVbMYhhHOmTNnRbhtiUTCcT8rKiriMjMzd588ebJPKD4v\nWAgXL14cbhuigv81Pejz5893Tk9PPxNOW1wul2jx4sWvrVix4kWhUNikAPLtSjju57JlyxZMnTp1\nQ5s2ba6E4vOCBe+p8PDwBBQ+UMvDwxNQeFHh4eEJKLyo8PDwBBReVHh4eAIKLyo8PDwBhRcVHh6e\ngMKLCg8PT0DhRYWHhyeg8KLCw8MTUEIiKjk5OT3S0tLOKJVKS2Zm5m69Xq+ruU92dnbPPn36nFSp\nVOZBgwb9UFBQ0DIUtvHw8ASWoIsKwzDCcePGfTlr1qxVxcXFLeLj48tmz569svo+RqNRM2rUqF0v\nvvjiv6qqqmIGDBhw5Omnn/4w2Lbx8PAEgWAPgz58+PCAO++88zfvekFBQbJSqTQ7nU6xd9uHH374\n1JgxY3Z41ymKkuXk5HQP9xBufuEXfvF/Cbqnkpubm56WlnZjlGdycnIhSZJUXl5eqnfbyZMn+8TF\nxVX06dPnpFarNYwfP/4/3tKJPDw8kUXQa9SazWaVt/6mF7VabTIajRrvekVFRdz+/fuH7NmzZ2SX\nLl3O/fWvf/37Y489tuWHH34YVPN8BEGwr7766o31jIwMZGRkBPMr8PDw3ApR1xtBFxW1Wm2qrRq8\nRqMxetdVKpV53LhxX/br1+84ACxevHhxbGxspcViUSqVSkvNc/I1YHh4uEvQmz/p6em51Wd0Kygo\naElRFJmamprn3ZaSknKVpmmxd93hcEjFYjEtl8ttwbaPh4cnsARdVPr27XvCW1HcYDBos7KylowZ\nM+br6vOhTJw48dNvv/32/sOHDw+0WCzKxYsXL87MzNzd1GkveHh4wkAoosG//PJLzy5dupyVy+XW\nESNG7KmsrNRt3LhxcvWZ97Zv3z6udevWl0mStGVmZn5TWVmpq+1cHpN5eHjCTJ2/94grJ0kQBBtp\nNvPwRCF1Bmr5NH0eHp6AwosKDw9PQOFFhYeHJ6DwosLDwxNQgp78xnMrBFFnjMsn+EA1D5fhRSUM\nGI1GmM0Vtb5XWVmJtLReKCzMv+U9lSoWarWmlqN4eLgDLyohxu12w2Qqg04nhUBwa+uzpMQAAIiN\nld5ynF5fDoVCCaFQGBJbeXgaAx9TCTEmkxEyGQuhUAiCIG5ZvNTcLhQKQZKe43l4uAwvKiHE5XLB\naq2AUilv1PEKBQmbrRI0TTe8Mw9PmOBFJYQYDJWQy4UNBmoXLTLXup0gCMjlQlRV1R6P4eHhAryo\nhAiKouB0GiCXyxrcty5RAQC5XAaGMYGiqECax8MTMHhRCQFutxtVVdehVjcsKL6gUslQVXUdbjc/\niJuHe/CiEgIMhipIJC6IxeKGd/YBsVgMqZSBwaAPyPl4eAIJLypBhqIoUFTjg7N1oVTKYbdX8s0g\nHs7Bi0oQYRgGVVXXodHImpxFWxsaDYmqqhIwDBPwc/PwNBZeVIJIZWUZZDK3382epUtVPu0nEolA\nkkBFxXU+dZ+HM/CiEiQMhiqwrBkKBen3sb6KCoD/9SZZYDBU+f05PDzBgDPTnqakpFwlSZLyLkuW\nLMkKhW3BwGazwWYrg0ajCMnnaTRKUFQ5bDa+TjhP+OHEtKd2u10mFAoZiqJI75KVlbXk/9s77/Ao\nqu6Pf2d73+xuKiEk9N5CB4EIUgOIAi+IEqRJERQb8vqiokQpiqKgoIBSpCmIUlQUMID0AAEkICAE\n0suWbO/z+yMOvwhJdjfbZsN+nmeeZHdn5t7ZO3vm3HNP8Xff/IHVaoVaXQCplO8XO0pVEASBiAgB\n1Op8WCyWgLQZJkx1+D2g8MSJEz35fL5p6tSp6wBg0aJFb7Zs2fKqzWZjUxn1b9++3bBRo0YPhuWG\nGA6HA2Vl+RCLmWCxAhuryWQyIZGwoVQWIDo6IeDthwlDUe2dN3PmzNXU//8kmyYqvwaAzz//fJar\nBmoqe9qqVatsALh161ajgoKCei1btryqVCoVjz/++I8rVqyYKxQKDbW9sEDjdDpRWloAPp8El+sb\nJzdP4XA4EAjMKCsrQHR0/SqjoMOE8TfV3nUxMTHFsbGxRU6nk/HNN988o9PpxAqFQmm1Wjnbtm17\nisPhWN1pwJ2ypwaDQdi8efO/Dhw4MOjy5ctt8/Ly6r/66qsfVnfOhQsX3tsyMjLc6YZfIUkSpaVF\nYLMtbrnhu6ImN31X8Pk8sNlWlJYWhleEwgQFlyU6evfufWzFihVzO3XqdI567/Tp090mTpy48dq1\nay1cNbBq1arZx44d671jx46x1HvR0dElGRkZKZSmcj+ZmZmdBw8e/EtZWVnkAx2mWYkOkiShVJaA\nJMshkYi8Pt/ly1fQtm1rr8+j0xngdIoQFRUbMNtOmIeK2pfoOH/+fHLr1q2vVH6vRYsW13JzcxPc\nadmdsqdbtmx5+vLly22p13a7nRUqUx+VqgxOp28Eii8Ri4UAdFCpSoPdlTAPGS6FyoABA36bOHHi\nxosXL7YvKCiod/LkyR7jx4/fOnr06J3uNOBO2dPr1683e/HFFz8pLi6OKSsri3z77bffGTdu3HZv\nLiwQqFRlsNvVkErpJVAopFIR7HY1lMqwYAkTOFwKlY0bN05UKBTKYcOG7WvcuPHfzz777IauXbue\nWb9+/RR3GmCxWPZdu3aNWrly5Zz4+Pj84uLimE8//fSFTZs2pVHayoIFC9IbNWp0q3nz5n81bdr0\nRmJi4p133nnnbW8vzp+oVEpYrUpERNBToFBERIhht6uhUoVzsIQJDG6XPbVYLNzCwsK4hISEXCaT\nGbRgEzrYVCoEShlkMvc9X93FVzaV+9Fo9GCz5ZDLHzBThQlTG2pvUyktLY0aNWrUrsjIyLLmzZv/\ndfHixfYtW7a8mp2d3cq3fQwNVKoyvwkUCk/c9N0lIkIEm00VngqF8Tsuhcro0aN3tmnT5k+lUqlg\nMBjO5OTk83Pnzl0xceLEjYHoIJ2oEChKvwoUwD9CBagQLHZ7WLCE8S8upz98Pt+kVCoVAoHAyOfz\nTSaTiW+1WjlisVhnsVi4NR7sB4I1/VEqS2G3q/1uQ7l8+Qq6dBkAs7nAb21oNDqwWDLI5VHh5eYw\ntaX205+mTZveOHjw4GOV3ztz5kzXJk2a3PRFz+hOhR9KKex2Fe2Nsu4SESGGw6H5x7+GPj4/YeoG\nLgNE1q9fPyU1NXV/9+7dT9ntdtbYsWN3HD58uN+mTZvSAtHBYEI5tjmd5YiI8O+UJ9BIpSKUl5dD\nqQQUiuiwxhLGZ7i1+qPT6cT79u0bdvfu3QZRUVGlQ4cO/Sk2NrYoAP17gEBNfyoLlED6oQRi+lMZ\nrVYPQILIyJiwYAnjCdXeLNUKlbNnz3bp0qXL2dOnT3ejAgipoELqddeuXc/4obM1EgihEiyBAlQI\nlR9/7O5V/I+nhAVL7fDmu6oD007PhUrTpk1v3Lhxo2lSUlIOJUTu5/bt2w191EG38bdQCaZAAfzn\np+IKrVYPgpA+dFMhKhjUbjf77Jw6nQFr1sThtde0rnd2E5ksFny+51kE/YjnQuX69evNmjVrdp36\n67eueYi/hYpSWQqHI3iu98ESKgBQXq4Hg/FwCRatVguzuQgike9+sLdv30GbNo/AYMj1yfkcDgd0\nOhJxcUl0GpdqO1KtobZ9+/YXL1682L5NmzZ/5ufnx1fOp0IRHR1d4qse0oGKWB5VnTPKugtlvFWp\nGFAoooLdHb/jdDqh05VCJuODyWT68MwVPxVfnZPJZILF0kOn00EikfjknP6kWqGSmpq6v0WLFteA\nitwq939OEATpcDh8ORJBRa1WBcSxje5IpSKo1Sqo1UzIZA+kEq5T6HQ6cLlOHwsU/yAS8aFWl0Ik\nEtE++Va1vdu5c+dop9PJaNas2XWn08m4f6tLAqVCBS6tM34o3hIRIYLJVAKt1nc2AbrhdDqh15fV\nqtpBMGAymeBwnNDr9cHuikuq1VSo1Z+NGzdOPHPmTNeq9gnG6o+vMZlM0OkKIZcLaTNfTU8XB3T1\n534IgoBMJoJKVQgWiwWBwLfVFemA0WgEm+2g/VO/MkIhDxqNEmKxmDb3alU81Ks/NpsNJSV3ERHB\npk2i6ED7qdSE3W6HRmNFVFQDcDicYHfHpxQU5EAqZfhl3G/evI1Nm1rg3Xd9X9lArdZDKk2gw0qQ\n54baGzduNAWAnJycJD90KOhULB0XQiQiaCNQ6AaLxYJY7EBZWQFiYxuE1FO9JiwWCwjCChbLf9Pd\nefOUAHx/fj6fBZ1OQwehUi0us+lXpaWQJEkQBEG6k02frqjVSrBYFvB4gSn4FapwuVzYbEao1co6\nsyJkMOjA44WmSZDH40Gv18PhcNDWwFytUImJiSmuqjTH/V61oYjJZILZrIRC8XCv9LiLSCSASqWC\n0SisE/YVk0kLuTzgAfY+g8MBzGYzhEJ6PhCrFSoLFy5cWPn133//3VilUsmTkpJyoqKiQjYhh9Pp\nhFpdBKmUvuojHZFI+NBoisHjJYb0NMhisYDJDC0D7f1wuSwYjVraChWX3+y1a9datG/f/mLXrl3P\nPPXUU9saNmx4+7HHHjtYVFQU624j7tRSpvjtt98GCAQCvxUF1mrLweHYaW1HCebKT3WwWCxwuQ6U\nl2uC3RWvMJvNYLPpu3LiDhwOBxaLgbbxQy6Fyrhx47ZPmTJlfVlZWeTNmzebKJVKRe/evY89+eST\n37vTgDu1lCn0er2ocmVEX2O322EwlEEkorcKT0ehAgBCIR9GoxJ2uz3YXak1FoseHI7/HyjLlin8\ndm6CIMBkOmG1ulXPL+C4TH0gFot15eXlUgaD4aTeM5lMfLlcrjKZTC7nEMeOHes9bdq0tVThsby8\nvPotW7a8qlKp5JXLdADA888//5lEItGuWLFibnXn9mZJWa1WwelU0VqoBDP2xx0MBhMAacgm0M7N\nvYGoKIFf/Txu3ryNNm16+dUtQKvVg8+vB5EoaA6btc/8Nn78+K2rVq2aXfm9tWvXTktNTd3vTss1\n1VKuvN+RI0f6Zmdnt5o+ffoX7pzXU5xOJwwGVch4UNIVgYAHo1ENp9PpemeaYbPZwGSStHYccxc2\nmwmr1RTsblSJSz3wwoULHdetWzd14cKFCxMTE+8UFRXFFhcXxzRs2PB2y5YtrwIV2kN12fXdqaVs\nNBoFs2fPXvX9998/6U6nK9uQU1JSkJKS4vIYk8kEDsdZJ26oYEIQBLjciu+TrobC6rDb7WCx6sb4\ns9ls6HQhKlSWLVs2r6r3719urg6JRKLVarX/Cq3UarUSqVRaTr1+8803F40bN25706ZNb7jjpXvf\nwpRbGI1a8Hhsj48L8yA8HhsGgyYkhUoIL/r8CyaTCbudnkLF5VeckpKSERMTU2yz2dhWq5VDbRaL\nhZuSkpJBbdUd704t5aNHj/ZJT09fwOfzTS1btrxqsVi4AoHAmJWV1cHrK0SF96zFYggZV3N/lejw\nFRwOB1arkbarD9Vht1vBYtUNqUIQBAiCpOU01KWm8sYbb7y/YsWKuU2aNLl5f2XCgQMH/urq+Mq1\nlMeMGfNdVbWUz54924X6/86dO4ktWrS4ZjQafWZNDbW5dLADCt2Bxar4XkNFUAMVQoXLDYxQqXDT\n9y8MRkUCJ7r53LjszcqVK+dkZ2e3unTpUrsLFy50rLy504A7tZQrQ4UA1OZiqsNut4PJDK2nKt1h\nMknYbDbXO9IIp9MeMNf2QAmVkNRUWrdufUWtVsuSkpJyattI586dMy9dutSu8ntpaWmb0tLSNt2/\nb1JSUo4vtRSAkuahoaWECgwGQcsbuiYcDjsYjLpjVyOIEBUq77333v/69OlzdNCgQQfEYvE9nZwg\nCPKrr76a7N/u+Qan0xkWKj6GIEJPqDiddWv1L2Q1lZdeeunjgQMH/tqlS5ez1NTEH1MUfxNIo6LF\nAnz0kQg2m+c3cHFxYwDAu+96ZqxlsUi89JIegYyIDzVDLUnWLaEC0HMMXAqV3NzchDNnznTl8Xi+\nq2EQYJhMJgI5/c/PZ+KTT0SYM8fz1H8slhN9+lg8tgF9/rkQI0ea0apVYFzoSZKkbeh9VZAkiTom\nT0AQISpUXnnlleWvv/760vT09AWVpz+hBIPBQKC1RJnMif/9z3OhcvnybbRt67lJaefOwHoKO52+\nyxYfCCp+fIH7AS5bpvBL5rfK0HUK6nL1Z9WqVbNXrVo1WyqVlvP5fBO1+TOS2NewWCw4HK73C+M+\nDgdoHeldFYGc+vgzoLAyJEk/oeLyrjh16lT3qt4PJZsKm82Gw1HHdN8gY7dXfK+hQt2c/tDzglwK\nlfuXkgsKCupt37593I4dO8aePn26m9965kMIggCHw4fVag0pZy26YrPZwGbzaXtTP0zQUVNxyxWv\nrKwscs2aNTP69u17pGHDhrf37t07fOzYsTv83TlfwudLYLGElrMWXTGbreDx6B1KcD90NGh6C11l\nerWailarlezevfuJ7du3j/v9998f7dWr1/ETJ070PHPmTNeOHTteCGQnfQGfz4cuJM3M9MNiISGV\n0jcnzcMEHTWVGhNfN2/e/K+pU6eu27Bhw7MxMTHFfD7fJBQKDYHsoK9gsVhgMsNTIG+piKPihZQ9\nBQj86k8g3PTpOv2sdvrzxBNP7L5z507i4cOH+50+fbqb3W4PLVN/FYhEMhiN9EzBFyoYjRaIxYFZ\n2fA1gfwRBkKo0JVqhcrWrVvH5+bmJowcOfKHVatWzU5ISMi1Wq2cUBYwAoEAdjuTlmv7oYDT6YTN\nxqR1IavqCLSmEghC0k9FJBLp09LSNv36668Dz58/n/zBBx+89vHHH78UGRlZNn78+K2B6qSvIAgC\nIlHkP3lWw3iK0WiGUCinXai9u9B1ulDXcPvuiIuLK3z55Zc/On/+fPLJkyd7NGzY8LY/O+YvRCIR\nzGZ6Sng6Q5IkTCYSYrHE9c40pK76qdDRUFurR07Lli2vvvfee//zdWcCAZPJhEikgNEYsqFMQUGv\nN0IoVISUa35lSJKsc8vKdUqohDpisQQmEz3DxukISZIwmwGJROp6Z5pSoanULTf9sFChERXaSti2\n4i56vREiUWTIaikAJVQCG1Dob0LSUEthsVi4VOb8Q4cO9b9y5YpH1a7cKXv65ZdfPlevXr0CqVRa\nPmrUqF1lZWV+rVYlkUjCthU3cDqdMJuJkLWlUIRtKoHDpVDZvn37uLi4uEKNRhPxv//9770JEyZs\nfuyxxw5++umnL7jTgDtlTy9evNj+jTfeeH/fvn3D8vLy6rPZbNt///vfxbW9KHdgMBgQi6Og14dM\nsHVQMBhMEIlC15ZCEejpTyCoECr0sxO5FCrz5s1b9vPPPw+RSqXl69atm7pv375hx48f77V06dLX\n3WngxIkTPfl8vmnq1KnrJBKJdtGiRW/+8MMPI2022z2XzMOHD/cbNWrUruTk5PNisViXlpa2yd3E\n2t4gFothtTLhCOdFqBJKSwllWwqFw+Gok5qK00m/e9elE5tKpZJ37tw58+LFi+2ZTKYjOTn5vN1u\nZ2k0mgh3Gqip7GmrVq2yAWDu3LkrqM+NRqNg27ZtT/Xo0eNkbS7IEyhtxWAogkQStJq0tMVgMEEs\njg5Zv5TKOJ2el7I4doyD4mLPr72oqMKesnMnz+Nj+/e3QCZzX/ug8tTSaYxcCpU+ffocnTdv3rJb\nt241GjZs2D6DwSD8+OOPX+rQoUOWOw24U/aUys2yevXqmbNnz17F5/NNBw4cGFTdOWtT9rQ6RCIR\ndDom7QYm2FBailweWtHI1eFw2MBme6aqPP20DF262MDjeTbF0Osj0by5Bbt3e+Z5nJnJhl6vx7PP\nuj8lpzLq0+nedSlUvvnmm2fef//9N2JiYooXLVr05tGjR/tkZGSkrFu3bqo7DbhT9pRi5syZq6dM\nmbL+888/nzVy5MgfCgsL41gs1gNJV2tT9rQ6GAzGPytBJRCLQ6uMpz+psKVE0upm9Yba1PwhSWD1\nag2ioz0zht68eRuxsVEQiTzTfmfMkMJTEwkdM+q7vGPkcrnqww8/fHXevHnLsrOzW6WkpGT88MMP\nI6ni7K5wp+zpO++88/a33377HwDgcDjWGTNmrFGpVHKdTheQx2SFl23dzLlRGyrKxCLkV3wqY7fb\n6oyArAxVpZBOuPyWb9y40bRz586Zjz322MGBAwf+euvWrUZNmza9cfTo0T7uNFC57KlGo4moquxp\ndHR0yeLFi/+bk5OTZDKZ+EuXLn29Q4cOWTKZTO3NxbkLk8mEQCCDyRT2sgUAs9kMHk8a8is+lQlk\ndcJAQhBk6AmVMWPGfDd9+vQvbt261YjBYDhbt2595Ysvvpg+Y8aMNe404E7Z02nTpq3t16/f4S5d\nupyNj4/Pz8zM7Lxr165R3l6cJwiFYpjN9BqcYGE2OyAU1h0tpWLlh15TBF/BYjFht9MroyHhSuXn\n8/kmlUolp7Lom0wmvtVq5UgkEq3ZbPbcvO0l/xQz88u5CwpuIyKC5fUT7dYtJlJTFbh6tcTjYy9f\nvoK2bT3yLQQAdOwYhS1b1F7X/XE6nVCprIiPb+TVeeiExWKBWn0HMplnNo6EhBicO1caUJtKvMvH\n6gAAIABJREFUt242TJrkvqHWYrHAYuEhKirOo7Z8QLVWb5eaSnJy8vkNGzY8W/m9/fv3p7Zp0+ZP\nH3SMVggEETCbH+4kTmazBQJB6PulVMZut6MOznwAVFQ0sNnoNW13ufqzcePGiampqftXr14902az\nsbt163Y6Pz8/fvfu3U8EooOBhMfjQ6NxQPgQLwJZrU5IJHUr/6zVagGLVTelSkWhPButlpVdCpUm\nTZrcvHr1asvTp093y83NTYiMjCzr2bPniVAug1odHA4H9sBUDaUtdjvqXA5fq9UIoTAkkxW6BYtV\nkTuYy+UGuysA3BAqAHD8+PFeOTk5SU6nk5Gbm5tALf+mpaVt8m/3AguDwQCDwYbD4aiTKwWuqPB3\nYNapaydJEjabGWx23dK+KsNiEbBaraEjVF5++eWPvv7660kdO3a8wOVy/1Uctq4JFQBgsThwOGx1\n6oflLg6HA2w2PW5MX2G1WsFi1W3/Iw6HBbPZALGYHt7PLoXK2rVrp126dKldqKaP9BQWiwOn07+F\ntelKxby8bk0TzGYzQqzks8dwOBzodAbaRGK7tOwkJibeqeyoVvehZzh5ICBJkjbGPl9hNuvA49Ut\nG9H9EAQBJtMBq5UeK5cuZfi4ceO2Dxgw4LdZs2Z9HhMTUwwAJEkSBEGQ//nPf771fxcDDT2kfTCg\nayax2uJwOGC3G8Fm1/0IdA6HCaPRQAu7ikuhcvDgwcdiYmKKv//++yfv/6wuChW73QY227untVBI\nQqVi4K+/WGje3P/LSX//zURRERMikXcaVsXyZN1Z/jKZTKhjC1nVwuNxoFZrIJM9kFQx4LgUKhkZ\nGSnU/w6Hg8lkMuu0L7vdbgGL5V1Jz5gYJz74oBzDh8tx6FAZEhL89/QvKGAgNVWB9HQtGjTwbmiY\nTCZstrpjT9LrVRCJgv/kDgRMJhNMpumfuK2AO7r/C5ePZKPRKHjllVeWN2jQ4K5IJNJnZWV1GDRo\n0IGCgoJ6gehgIHE4HCBJ30SzpqWZMHOmAcOGKVBW5h87hVpNYPhwBSZNMmLKFO/TYjIYDBCEA/Y6\n4KxjtVrhdJpDruazN/B4TOh0mmB3w7VQeeaZZ77RaDQRVFRymzZt/uzSpcvZCRMmbPZ/9wKLxWLx\nOJFPTbz0kgHDh5sxcqQcOp1v7TRGI4Enn1Sgf38L5s3T++y8LBYJiyX0tRWdrhx8fh1f9rkPPp8P\nq1Ub9IeCy2/9559/HlJSUhItFot1QEXU8YIFC9IjIiKCLxJ9jMGgBZfrW/+URYt0mDmTgbFjZdi9\nWwVf2NFsNuCpp2Ro3NiOJUu0Ps29yuOxYTCUQxjCsQp2ux1mswYKRd11eKsOHo8BnU4bVNuKS00l\nPj4+//Lly20rv3fjxo2m8fHx+f7rVuCx2+2wWnU+n48SBLBqVTkkEhKTJsngbeoLpxOYNi0CLFZF\nVjJfrwBzuVzYbPqgP+28QafTgscjHspVPKGQD4NBGdQcKy5vyZUrV84ZMWLEnhdeeOFTh8PBfP31\n15cOHTr0pwULFqQHooOBQq/X+USLqAoWC9iwQQ2VioEXX/Q8ZSAFSQKvvCJBXh4T33yjgr/MBdTT\nLhSx2+0wGpUQCj3LD1tXIAgCfD4BrTZ4EwmXQmXIkCE/Z2Zmdo6LiyucMmXKei6Xa9m7d+/wSZMm\nfR2IDgYCu90Og0EJkch/6jKPB3z3nQoXLrCxaFHt3KmXLBHh+HEudu1Sge/H34xQyIfRqAxJbUWj\nUYLPZzyUWgoFNX42W3B8Vt2yZCUlJeX4u7hXMKm4Ef2vLotEJDp1suG99yXA+1Xv07mG4xf+s5kl\n/p15EgQBgYAJtboMUVGxfm3Ll5jNZlitGigU9IiBCRYEQUAorBi/6OiAJ2+qWVPZs2fPiHfeeedt\nABCLxToq+5tQKDS0aNHiWmC66F+MRiNsNk1A1OUlS0Q4dYqD4qICWMz5VW6ZZ3+t9rPSkgJ0SrbU\nWtPxBIGAB7u9HEZjaFRwJEkSanUxxOKHwy/FFXw+P2jjV61Q+eWXXwY/99xzX7Zu3foKANjtdtYf\nf/zxyLJly+bFxMQU79u3b5i7jbhTS3nz5s0TmjZtekMikWip8hy1uyT3sdvt0GiKIJH4X6B8+aUA\nmzcLsGePEhERtTOqSCQkfvxRhe++42PVKv+vzkgkfKjVhSExDSovLweTaalzuWC8QSoVQKMpDrjR\ntlqhsmTJkvlffPHF9NGjR++k3uvUqdO5OXPmrPzggw9ee+211z5wpwF3aym/+OKLn+zcuXN0YWFh\nXHx8fP706dO/qP1luYYkSSiVxeDzAZafw1i/+46HpUvF2L9fidhY77xro6Kc2L9fiU8+EWLrVv8K\nQxaLBaGQQFlZIa2DLK1WKwyGEkgkobsM7g9YLBa4XAc0GmVg263ugwsXLnQcMmTIz9RrqkQpAIwY\nMWLP5MmTv3Kngcq1lAFg0aJFb7Zs2fKqzWZjU9HPBw4cGDR69OidVHnUV155ZXnHjh0v1Pai3EGt\nVoLBMEIg8O+N+NtvXLzyihT79yvRsKFvnhgNGjiwd68KgwYpIJM5MWSI/5zV+HwebDY9VKoyKBRR\nfmuntlQ8HAohFrMfauNsdYhEAqhUahgMwoD5HlUrVDgcjrW0tDSK8kc5d+5cJ+ozo9EooEqVusKd\nWspjx47dUfl8ly5dakdFRFeFt2VPNRoNLBal30t6nj7NxqRJEfj2WxXatvXtFKJFCzt27lThySfl\n2LZNjUce8V/Yu0QiglqtgkbDQkSEzG/t1Aa1Wgk22wouN6ylVIdUKoBaXQguN8nvWjlQg1AZPnz4\n3qVLl77+6aefvnD/Zx999NHLffr0OepOA+7UUk5MTLxD/b9p06a0F1988ZPPPvvs+erO6U3ZU61W\nC6OxGHK5f8Phs7NZGDNGjnXrNOjZ0z9Le1262LBhgwbjx8uwb58S7dr5z/YRESGCWl0CBoMJiYQe\nNYEMBoNfHw4CAYn8fKbHJTpqS34+E0Kh77VOJpN5bxobE1Pf7xpdtUJlyZIl83v37n1swIABvz31\n1FPbEhIScktLS6N27979xMmTJ3scPny4nzsNuFtLOS8vr35aWtqm3NzchB07dowdOHDgr7W7pOox\nGAzQ6Qohlwv9+sXqdARGjFBg6VItBg/2bxxN//4WfPxxOUaOVODcuRLIZP6xfRAEgYgIIdTqQhAE\nEfTUhTabDRpNIWQy//kWPfecAcuXi/DNN/4vlJmVxcKVK2wMH+6ffPJ8Ph82mx5qtRJyeaRf2qCo\n1lAbHR1dkpmZ2blv375Htm7dOn7WrFmfL168+L8xMTHFmZmZnZs1a3bdnQbcqaWsUqnkvXv3Pta5\nc+fMK1eutPaXQNFo8iCXC/2e3ay0lAEWi8RTT5n82g7FqFFmSCROFBb6N68ug8GATCaEVpsPnU7n\n17Zqwul0orQ0H2Kxf5N0T59uxNGjHFy54v8pw3vvifHKK3q/OjVKJCJYLEq/j53LCoXeYrfbWY0b\nN/57wYIF6WPGjPlu7ty5K+x2O+ubb755htpn0aJFb167dq3Fli1bnnbZ4VpUKNTpdNBqCwIiUIDQ\nrlDoDk6nE2q1ASJRXFCmQiUlhWCxjAHxLVq+XIgLFzgeaSueVijMymLhyScVuHKl2K9CBaAqUBoR\nGZnobZa42lco9BZ3ailnZmZ23rFjx1g2m22jNg6H4xPLo1arhU4XOIHyMEBpLHp9AcrLy10f4EPU\nahVIUhew2J5AaCuB0FIoGAwGJBIOlMoCv/kf+V1T8TWeaCoajQYmUzEiIgIrUOq6pkJR4cWqB58f\nHZBVIYPBgPLyXMjl4oAuH3uqrXiiqQRSS6mMyWSG2cxGdHR8bX8bwdNUgoVarYLJVAyZTBTWUPwE\nQRCQyUQwmUqgUvnXwcpsNkOjyYdMJgq4P4o/tZVAaimV4fN5YLHMUKlKfX7uan9tDRo0uOtwOJgN\nGjS46/NW/YxKVQaLpTQoN+DDBkEQkMvFsFrLoFSW+sXz1mazQaUqgFTKDcoDQiQi8eKLeixe7NsV\nr6wsFs6d42DyZINPz+suYrEQDkc51GqVT89breiVSCTahg0b3s7Ly6vfsmXLq/d/ThAEmZ2d3cqn\nvfESkiShUpXBbldBJnu4I1UDjUwmRnm5GkqlEwpFtM+EObXSIxQSQc03O326Ea1aiZCdzfLZFPP9\n94OjpVRGKhVCrS6FTsf2mZtAtULlyJEjff/88882/fv3P7RmzZoZJEnS+pFf4a5dAqezHBERYYES\nDKRSEbRaLcrKSERGxngtWEiSRGlpEbhcO3i84KaGFIlIvPCCAYsXi7F5s/d+K1lZLGRmcrBxo/99\nYGqC8j9SKgvAZif6JPNhtbqkQqFQ9u3b98idO3cS+/bte0QikWh1Op1YKpWWp6SkZKSkpGR43bqP\nqCxQpNK6XziKzlQE9WlRWlrk9VRIrVaCIPR+TZ7lCTNmGHDkCAfZ2d7bVt5/X4yXXw6ulkLBYDAQ\nEcGDUpnvk8RObn07ycnJ5wsLC+MaNmx4OycnJyk2NrZoz549I+rXr5/ndQ98QFig0AuJRAStVgel\nklHrqVB5eXlA4rM8wVfaSlYWC2fPBl9LqQybzYZI5EBpaT5iYxt4Zbtyq0THqFGjdhUWFsadOHGi\nZ35+fvyYMWO+e/rpp7fUulUfolKVweHQhAUKzZBIRHA6y6FSlXl8rMlkgl5fhIgI+gUJ+kJboYMt\npSp4PB64XDvKyoq90jJdCpXTp093mz9//hLqNUEQ5KuvvvrhmTNnuta6VR+h1WphsSjDNhSaIpWK\nYLWqPHKQo1Z6IiL4tHQFqKyt1AZKS5kyJTgrPq6omGrqoNHUXotyOWpNmza9sXPnztGV3/vhhx9G\nNmnS5GatW/UBZrMZOh09n2Zh/h+ZTAS9vggmk+s4KKfTCaWyECIREZAQ/dry3HMG/PgjD0ql59O6\n9euFmDzZSDstpTISiRAmUwkMhtoJPpcjt2HDhmcHDhz465dffvlcw4YNb9++fbvhpUuX2v3yyy+D\na9WiD3A4HFCpCoPmtxDGfQiCgFTKg1pdCA4nscYAQJWqFGy2NegrPa749Vcu2re3oV581ZV/a/KH\n/vKfv+YF+T4tAudL/j8ivWJFyNMUnS6FSseOHS/cunWr0Z49e0bk5+fH9+vX7/CIESP2iEQi39Xa\n9JDycjW4XAfY7HCS41CAzWaDy7VBo1FCoYiuch+dTgebTUMrw2xVOJ0VXrBLlmhhGVR1VYOa3PRJ\nEujePQpv7tdi2DD6lpdlMpkQi1lQKgsRE5Pg0cPbLR1TLBbr6GKYBQCTSYXIyLBhNpQQiQRQKjWw\nWKQPRMfabDZotUWQy+k/lf3+ex5EIhIDB9ZOIBAE8L//6ZCeLkZqqoW22gpQUa3SajVAo1F5lIMl\nJOcOAgF959thqkcgYEKr/bcBsMLHqAgiEZP2U1lKS1mwQOeVMBg+3AySJLB/P/01bbFYCLNZ6ZZN\njILeo1gNAoFv6x2HCQx8Ph8WixZO5/+nZ9TpdCAIo89rWPsDb7UUisraSigkCZBIeFCri/41bjXh\nkVDR6XTic+fOdbo/PWSYMO7C4RAwmytSJtrtduh0oVFaw1daCkUoaStsNhscjh3l5e7VZ3ZbqOzc\nuXN0t27dTi9btmxecnLy+a1bt46vdS/DPLSwWMQ9V/DycjX4fIL20x7Ad1oKRahpKyKRwO362m4n\naWrSpMnN06dPd1MoFMq8vLz6nTt3ziwqKgp4oV2CIEiz2b+1hL3l0CEuUlMV4PE8v1tI0gmC8PxH\nZrMB2dklSEwMbDU6TzEaTQAiIBJJUFJyOyQM7k4n0LlzFBYv1mLQINdCxd0kTdRK0FtvaZGaSt+V\nIAqDwQSSFFMreNXqa9VaPCdNmvT1hAkTNvfr1+8wACQkJOQeOHBg0KhRo3YdPXq0T23yrJw7d67T\n5MmTv/r7778b9+3b98jmzZsnyOXyKpM5tG7d+srvv//+aHR0tOfp04JMSooFhYWFtRIqf/55Ffv2\ndcX8+Z4lJ2YwAO9SjgYOgmBAq9WEjMF9924ehELfaSkUlbWVoUPpvRIEAEIhH2VlGtjt8hqdE6t9\nJK5fv35Kbm5uwtSpU9edPXu2y5YtW57eu3fv8OTk5PN79uwZ8d13343xpEPulD8FAJvNxl6zZs2M\nq1evtvTk/HSCyQRkMhJ8PjzeeDwnli4Ve3xcqAgUu90BBoMJk0kDPp/+nfa1LeV+hg83w+kk8NNP\n9P8uAIDLJaDX1/zAq1bcMBgM58SJEzeOHz9+6xdffDH9ypUrrd98881FlcufeoI75U8BQC6Xq8xm\nM8/dCohhQgurFWCxHOByyZDIyucvLYUi1LQVgYAHtVpTY07iajUVjUYTsXDhwoVLliyZP2HChM0f\nfvjhq3v37h0+d+7cFXfv3m3gaWdqKn9aeT+dTie22WzsmpJCWSz0n3+GeRCr1QoWSwCbzQQuN3hZ\n3NzF31oKRShpK0wmEwxGzTlXqtVUxo0bt7179+6nHA4H85lnnvlm7969w19//fWlKpVKvmLFirlW\nq5WzZMmS+e52xp3yp+7y7rsfgc+viEfo06cH+vTp6ekpwgQBvd4CqTQaKlUBJBIaR9T9g7+1FIpQ\n01ZcxXpW+/HJkyd7/Pjjj48TBEHGxsYWUe/L5XLVu++++1ZhYWGcJx1xt/ypO8yb90bAikmF8Q1G\noxlMpgRsNhsMRmhMfT78UIS33vKvlkIxfLgZ6eliHDrExWOP0VsTZ7NrrgpZrVAZOnToT7Nmzfqc\nzWbbhgwZ8vP9n8fFxRV60pEOHTpkrVq1ajb1uqryp+6iUESjqCgHXK6d1iHytWXBguCVFPUHdrsd\nBoMTsbFRsNvtYDBCw1yWl8dEp07ep1d0B4IAkpOtyM31b+laX+DqN1etTWXjxo0Te/TocbJbt26n\nN27cONHbjnTv3v2UyWTir127dppGo4l48803Fz3++OM/VjbSuguTyYRCEQ+12uS263AoUZeEitPp\nhEZjgkIRDxaLBZIkwWDQX0sJUz2utMxqhQqHw7FOnTp13aRJk75msVhe1yRwp/zpfR2v8XHG4/Eg\nldaDWm2ok4KlLlBRc1kPsTgO/EpZiULBgzRM7Qno3KFz586Zly5dalf5vbS0tE1paWmb7t/X4XC4\n1APFYjFIMg5qdSFksnCtZDpBCRSRqN6/irgzmcywUAlxHI6avbZD/lcokUggkcRDpTL6reB0GM+o\nyMxneECgABXzcbs9LFVCGbu9ZqFSJ6ycIpEITGYCVKp8iMWOB5IAhQkcFosFOp0dMlkCBIIH00Iy\nGAwwmVzY7fQ3speWMtGhQzSiojyLp7JaI8BieZ4fpqiIiUcesXp0TDCwWms2N9B7VD2Az+cjKqoB\nysoKYLMZaVOAqjakp4tD0lhrMJhgNjMQFVVzXlOBIAJmcylEInrffrt3K5GQ4HDpl3E/d+/m4ccf\nG2HOHM8FROPG9Na2SZKE3V6zsHQ7SpkuEARB1tTniozsJXA4yiGVhp6d5fLlK+jSZQDM5oJgd8Vt\nnE4nyssNYDAkUCiia0xuDVSkjwyVCOXacPPmbbRp0yukxtBdjEYTSFIKuTyy2iWg0PrFuQGDwUBU\nVCwEgjioVMawS7+fsVqtUCoN4PNjER0d51KgAFTSH4lHKQrD0AOj0QGxuGYn+DonVCgkEgmiopKg\n1xPQ6Qxe1/UN829IkoROZ4BOB0RFJUEq9SzaQiqVw2BwhMclhDAYTODxIsBm1xy3VWeFCgBwOBzE\nxSWCyVRAqdTDaqW/ESwUsNlsUCp1YDBkiI1tUCvDOIfDAZ+vgF5v9EMPw/gah8MBk6niYeCKOi1U\ngArvP5lMDoUiETodwlqLF1DaiVbrhEKRBLk80iubVUSEDFYr+6EU9pmZWRgx4plqPy8qKsHQoeMg\nkzVG69aPYPv23bVqZ/nyz1G/flvUr98WH320+t77t27lYOjQcZDLm6Bjx0fx008HazxPebkRUmmM\nWyt2dV6oUPB4PMTGNvhHazHQ2tZCx5WfCtuJHkymArGxiT7Jfs9gMKBQxEGrtdY5r+h585TVflZa\nqsS7735Yo7v7Cy/8Fy1aNEVu7iV88cVyzJkzH3fu5HnUh0OHjmLNmq9x5Mge/P77j/jss/X4/fc/\nAADjx09H7949UFJyDcuXv4tJk+YgP7/qcD6dzgAOR+YyPSbFQyNUgIqbWCaTIzIyEUYjE+Xlelre\nzHQSKiRJorxcD50OiIxMgkwm9+mKGpfL/SfcQk87DfLo0RN45JFUPP/864iMbIauXQfgr7/cKyFe\nnVDZuXMvGjZMxqFDR2s8PiPjOF57bTZEIiEeeaQbWrRoiuzsawAqtJzOnR9DRERjDBgwCqWlVbf1\n7bc/Yvr0Z9G4cUM0bdoIU6dOwLff/oiSkjJcv/43XnvtebBYLPTr1xtt27ZEZmbWA+cwmcyw23mQ\nyep4MTFv4XK5iIlJAJcbA5XKBJPJHOwu0RKTyQSl0gAuNwZxcYl+cyoUiUQQCGKg0QStkm61nD9/\nCa1aNUNJyTVMnDgOzzwz06vzjR49HHr9HaxZs7xGIVpUlI24uBgAwJkz53Hz5m20b98GOp0eTzyR\nhoUL56Gg4E/06dMDc+ZUndbo8uVstGvX6t7rDh1a49KlK5BIRDh48Pt7Uxmj0YRbt+4gOjrqX8eb\nzWYYjQQiI+M8epA8lEIFoAqHSxEdnQSrlQ+VSucypuFhweFwQK3Ww2LhITq6IaRSqd/zn0REyMDh\nKKBW00dLAwCpVILnn58CBoOBWbMmIz+/ALdv3/nXPl9/vRUREY3vbZ069UP9+u0wadKcGs5cs1ZG\n/YiTkjqib98R6Nu3J2JiovDTTwfRtWsyhg0bCIGAj9dffwEHDx6p0i6l1eogFv9/bWqxWIzyci14\nPB6SkytC8P766yYGD/4P2rRpiR49Ot/b12KxwGAAoqLqe+z5/NAKFQo2m43o6DhIJPWhVlsf+tUI\nvd4ItdoKsTgeMTHxLpcPfYlcHnlPsNBlKtSgQfy9/wmCQEREBFSqfxfVmjRpPDSav+9t584dRl7e\nJXz99Uqv28/JuYArV/7A9et/Y9Wq9cjLK8CBA7/fE2DR0S1htdpQWFiMVq16QSRKhFichLt38yGR\niKHV/n+yRZ1OB6m0IhbL6XTivfc+Ru/ew9C/f2/s2vX1vf3MZjP0eiAqKqFW4//QCxUKoVCI2Ngk\nANKHcvm5whCrAyBFbGwShMLgVA2UyyPB5UZBpaKHYCkuLr33v8ViQXFxCRo0qO/XNnNzCzB8+NP3\nXjdqlIRhwwbizp1cREbKMXLkkH8JsSNH9qB+/XrIzj4Ovf4OdLocNGgQj3btWuPSpf/PU3/hwp9o\n374NAODVV9/G/v2/4uTJn/H22/PuCQ+TyQyDgUBUVP1aP1DCQqUSFcmfoiCXN4BOB2i1wVl+Tk8X\nu97JR5AkCa22whArlydCoYhyyyvWn8hkcgiFcVAqgz8lLS4uxWefrYfJZMIHH3yGrl2TERWl+Nc+\n69d/A5Eo8d7WoUNfREe3RlrarFq1Wa9eDK5cuYa1azfBZDIhO/sv7Ny5F/3798GgQf2QkXEcx4+f\ngdVqxbp1m/Hss7OrHLMxY0bgyy834ubNW7hx4xbWr/8Go0cPx507udi27Xvs3bsVjRs3vLe/TmeA\n2cxCdHTtNBSKsFCpAh6Ph7i4RLDZUVAqDfdq/waKQAkVs9kMpdIANjsKcXG+WSb2FVKpFDJZA6jV\n5qBqjUlJDfDHH6cRF9cGBw9mVDmlmTLlGej1d+5tWVlH4HTasGnT5zWcmXjATtW69SPYunUXmEwm\ntm37Ehs2bEe9em3wn/9MwZw5U5GaOgCxsdFYv/4TzJ79OiIjm+Prr7dh+/a1VbbQv38fPPfcRPTt\nOwIpKSMwc+Yk9OvXGxcuXIZGU47ExA7/EobffnsA0dGe21AeuDI6qJie4Cqg0NfYbDaoVCUgSX1A\nAhQDEVDodDqh1RpBknzI5TE1RhQHG4vFAqWyAHw+CYEgsELv6NETmDPnv7h48YhHx4VSQKHD4UB5\nuRE8XhRkMtfespUIbkDhuXPnOrVv3/6iSCTSp6am7lepVA/0/sCBA4OaNGlyUywW6yZMmLDZYrHQ\nIikKm81GTEz8PwGKZhiNgdBaMvx2ZpPJBJXKBD4/FrGxCbQWKAC1/N8AVisX5eX082Wpnoxgd8Al\nFosFarUZEkl9TwVKjfhdqLhT7lSlUsnHjx+/9ZNPPnnxzp07iSUlJdHp6ekL/N03T5BIJIiJSYLN\nxodarfOz01yGz89Ykd5R988ycdIDGdnoDJPJRHR0PbDZkVCp9AHN8Ff7pfQMX3bD5+h0Buj1BKKi\nEn1ulPf79OfYsWO9p02btvbatWstACAvL69+y5Ytr6pUKjmVSX/z5s0Ttm7dOv7nn38eAgB//PHH\nI5MnT/7q+vXrzR7ocICnP1Wh1Wqh05VAKGSAz/etSl4x/TkOs/k5n52zYonQAbE4JqSESVUYjUao\n1YUQCgmff/e+omL685tPx9BXVFQ3MIDNjoBcHuXNdL5aaev31Fs1lTul6jLfv0/Hjh0v3Lx5s4nR\naBQIBALaOY5IJBLw+XwolUWwWvUQifg+dQ7r3dviE02IJEno9SY4nXyvLfp0QSAQgMNJ9Nt37xtI\n9OxppF0IiNVqhV7vgERS719Ocb7G70LFnXKnOp1OnJiYeM9NUSgUGphMpqO8vFxalVCh303kewSC\nJcHuQhgvEQg+CHYX/AlZXb1zvwsVd8qd3r+PwWAQOhwOZlUlUWsq3B4mTJjg43dDbYcOHbIuXrzY\nnnpdVbnT+/c5f/58cuPGjf+m49QnTJgwNeN3oeJOudOhQ4f+dPbs2S779+9PVSqVikWLFr05duzY\nHf7uW5gwYfwASZJ+386ePdu5bdu2lwQCgWHw4ME/K5VK+caNG9OaNGlyg9rnp59+GtLHe5HWAAAM\ndElEQVS4ceObQqFQ//TTT39jNpu5gehbeAtv4c23W8h51IYJE4behGN/3KBJkyY32Wy2jc1m2xgM\nhpP6n81m2xo1anSLxWLZqdcymUydlpa2yWAwCAHgrbfeepfH45n5fL6Jz+ebmjVrdp0675dffvlc\nvXr1CqRSafmoUaN2lZWVuZ9eq5Zs2LDh2SFDhvzs73boCF3Gce7cuSueeOKJe0lnLRYLVyAQGOfP\nn39vyS8nJyeJwWA433333beoPjGZTAeTyXRQr6dNm1Z10E+wCbaqFGobQRDO4uLiaOp1SkrK7zt2\n7PgP9fru3bsJ7dq1u7hgwYJFJEniqaee2pqRkdH3/vNkZWW1VygUZefOnUvWarXisWPHbp86depa\nf/f/66+/fnbw4ME/B/t7DPYWzHH84YcfHo+LiyugXh86dKifTCZTderUKZN6b+vWrU8lJibmVD5u\n4cKFb8+cOfPzYH93rrawpuJjEhIScgcMGPAbtZp169atRo0bN/77/v0OHz7cb9SoUbuSk5PPi8Vi\nXVpa2qYLFy50rOncOTk5SbGxsUXp6ekLZDKZul27dpcOHTrUv3v37qfEYrHujTfeeB8AMjIyUlq2\nbHmVOo56febMma5TpkxZ/+uvvw4cPnz4Xl9fe13Cn+PYu3fvY8XFxTF3795tAAAHDx58bPr06V/k\n5OQkqdVqGQCcOnWqe0pKSkbl40iSJMgQcKkICxUfUHmg8/Ly6h84cGBQ165dzwAVN+O0adPWyuVy\nVc+ePU9kZmZ2BipU4DVr1swAAKPRKNi2bdtTPXr0OOmqrdLS0qjS0tKowsLCuKZNm94YOXLkD6tX\nr555+fLltitWrJibnZ3dqrpju3bteuarr76aPHDgwF/37t073Psrr1sEahzlcrmqXbt2l06ePNkD\nAA4dOtR/8ODBv/Tp0+fooUOH+gPAyZMne9wvVAiCCAkDaFioeAlJkkRaWtomaq7duXPnzF69eh1/\n9dVXPzSbzbz69evnzZ49e1V+fn78hAkTNg8fPnyvVquVEARBEgRBrl69eqZYLNbt3r37iXHjxm13\np7309PQFPB7P3LVr1zODBg060LFjxwtJSUk5TZo0uZmfnx/v6njfXX3dIdDjmJKSknHy5MkeGo0m\n4tq1ay169ep1vH///ocOHjz4mMVi4V66dKndo48++nsgrt3XhIWKlxAEQW7evHmCyWTim0wmflFR\nUeyaNWtm8Hg8M4/HM58/fz45NTV1P5/PN82cOXO1QqFQHj16tA91/MyZM1ebTCZ+enr6gpEjR/5g\nt9tr9HLmcrkWsVisAyp+CNT/AMBkMh1VCY2wIHFNoMcxJSUl49SpU90zMjJSevXqdZzFYtn79+9/\n6NChQ/3Pnz+fHBcXV1g5dCWUCAsVH1Ddj/bKlSutN2/ePKHyew6HgykUCg3vvvvuW99+++1/AIDD\n4VhnzJixRqVSyXU6nduRXtWpwwRBkA6H415+wdzc3AR3z/kwE8hx7N2797FLly6127dv37ABAwb8\nBgAtWrS4Zjabedu3bx8XqloKEBYqPqG6HzebzbbNmTNn5ZEjR/paLBbumjVrZuh0OnHv3r2PRUVF\nlS5evPi/OTk5SSaTib906dLXO3TokCWTydTutlvdjyAhISH3zp07iVeuXGmdn58fv2rVqtnUZ0wm\n00Etk4b5N4EcR7lcrmrWrNn1LVu2PE0JFQDo37//obVr1067354ChI7GGRYqHuKJsaxZs2bX16xZ\nM2Py5MlfRUREaDZu3Dhx7969w1ksln3atGlr+/Xrd7hLly5n4+Pj8zMzMzvv2rVrlCftU/P5+/dp\n1KjRrZdffvmj7t27n3rkkUf+ePrpp7dQ+3Xr1u30jRs3mo4ePXqnu9dRFwn2OALAo48++ntERISm\nTZs2f1Lv9e/f/5DFYuFWJVSqG2+6EfaoDRMmjE8Jayo0o7IHZeWNw+FYr1692jLY/QvjHg/zOIY1\nlTBhwviUsKYSJkwYnxIWKj7ks88+e37btm1P1eZYp9PJmDdv3rKYmJji6OjokpdeeuljallYqVQq\nhg4d+pNIJNJ36NAh6/z588nUcZs3b57QtGnTGxKJRDty5MgfCgsL42w2G3vw4MG/OJ3O8Ph6gTfj\nWZkFCxakVw7iPHfuXKdOnTqd4/P5phYtWlzbtGlTGgDMnz9/yalTp7p7217QCXbwUV3ZtFqtuFOn\nTplOp5OozfGffPLJC506dcpUKpXygoKCuB49epxYvnz5yyRJYvz48VumTJmyrry8XLJmzZrpiYmJ\nOU6nk8jKymovk8lUWVlZ7fV6vXDWrFmfDR8+fA9Jkli8ePH8L7/8clqwv5dQ3bwdT2o7f/58Rz6f\nbxwyZMhP1HvNmze/tnbt2qk2m411+vTprkKhUJ+bm1s/Ly8vvk+fPkeCfe3ebuEnmY/46quvJg8d\nOvSno0eP9unWrdvpF1988ROJRKJt3br1lcqpMqvjl19+Gfzyyy9/JJfLVXFxcYVTp05dd+TIkb5W\nq5WzZ8+eEenp6QskEol2+vTpX7BYLPuJEyd6HjhwYNDo0aN3tm/f/qJQKDS88sory48cOdIXACZO\nnLhx+fLlr/j/yusm3o4nANhsNvb06dO/mDVr1udkJR8TBoPhpIrlOZ1OBovFsvN4PHN8fHy+SCTS\nHzt2rLe/risQhIWKj9i5c+fofv36HSZJkjh37lynhISE3LKyssjBgwf/8tJLL33s6vglS5bMT01N\n3U+9vnTpUruYmJji69evNxMIBMbY2Ngi6rPk5OTzWVlZHcaOHbtjwYIF6fcfAwBxcXGFYrFYRwW+\nhfEMb8cTABYvXvzfwYMH/9K2bdvLld9ft27d1Llz567gcDjWnj17npg7d+6KyMjIMqAiteqOHTvG\n+uOaAkawVaW6sNntdiaHw7Go1eqI33//PUWhUJRRn2VkZPRt1KjR3yRJIjExMYfH45nu365evdqC\n2t9oNPLnzZu3VC6XK69du9b8jz/+6FU57SZJkpgyZcq69957743K723cuDEtIiJCvWXLlvHUe889\n99wXH3/88dxgfz+htvliPC9fvtymffv2WVarlV05h43FYuE0aNDgzkcfffSSyWTi/fzzz4OFQqH+\nxo0bTUiSxPHjx3u2b98+K9jfgTeb30t0PAwolUqFw+FgRkREaAAgKiqqlPqMy+VaKINpTk5OUk3n\nOXbsWO9nn312Q1JSUs6pU6e6N23a9IbVauVUVeKEaisvL69+Wlraptzc3IQdO3aMHThw4K/UflFR\nUaWuopbDPIi340mSJNG9e/dTq1atml05wTsAXLx4sb3VauVQ2s7gwYN/SUlJyaBqideFMQsLFR9A\nEATJYDC8Kkd38uTJHo8//viPK1eunPP0009vod5v1qzZdaPRKCgsLIyLi4srBIALFy50nDt37gqV\nSiXv3bv3sTFjxnz3yy+/DOZwONbK5wyv/tQOb8dTq9VKLly40JGK6XE4HEyn08mIjo4u2bt37/D7\nI5gZDIaTKrhXF8Ys5C+ADigUCiUA6PV6UU371a9fP68qD8vs7OxWCxYsSF+6dOnrlQUKUPFkHDFi\nxJ4333xzUXl5uXTNmjUzbDYbu3v37qc+++yz53v27Hli2bJl8+4XKABQUlISHR8fn+/bq637eDue\nBQUF9axWK4dKo7B27dppAwYM+K2kpCS6Y8eOF/h8vmnZsmXzjEaj4OTJkz1Onz7dbdCgQQeAijGr\nV69eQSCu01+EhYoPYDAYzm7dup2+cOFCx6qCvqjXeXl59W02G7vyZrVaOa1atcrOzMzsPGvWrM8r\n36DUk+7TTz99IS8vr369evUKVq9ePXPXrl2jGAyGMzMzs/OOHTvG3n9TU+1evny5bc+ePU8E9tsI\nfbwdz8qpPO8/hsPhWH/66aehe/bsGSGXy1WTJk36etu2bU9FR0eXABVj1qtXr+OBuE6/EWyjTl3Z\nVq5cOXv+/PmLg90PalOpVLJmzZr95a2fxcO6BWs8x4wZ8+2hQ4f6Bfv6vdnCsT8+wmg0Cnr06HHy\n3LlznVgslj3Y/Vm5cuUch8PBnDt37opg9yUUCcZ4lpWVRaampu4/ffp0t0C05y+YCxcuDHYf6gT/\nTD1sV65cadOhQ4eLweyL3W5nLVy48J0VK1a8xGQyvTIgP6wEYzyXLl36+tSpU9c1bNgwJxDt+Yuw\nphImTBifEjbUhgkTxqeEhUqYMGF8SliohAkTxqeEhUqYMGF8SliohAkTxqeEhUqYMGF8yv8BX0uU\nhLOxdUYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb5e7fd0>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Is the 3p deletion more predictive than chromosomal instability?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* It seems like the 3p14 effect is stronger than an numerical chromosomal instability\n",
      "* From text: \"While the (3p14.2) deletion event tracked with chromosomal instability as a whole, in multivariate modeling we found that this event was responsible for the majority of the prognostic impact.\""
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "violin_plot_pandas(del_3p, pct_altered)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hnq7QK0NIP9G+rrHZdpn43kd7JXSPgp+Qfor1+PM7+GMXdzOvyy8IDPX4u0Gv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\nhnYSYrdbwfNO6HS5Hf7/+Y8MO3ZIMWp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VMccccyAVz5cLqMdPCCF5hi7uEkJInqHgJ4SQ\nPEPBTwgheYaCnxBC8gwFPyH9UFdXV9XZgiNCsgEFPyGE5BkKfkL6SRAEZv78+Uu0Wq2nLxuNEJJu\nFPyE9NMPP/xwbElJSZPdbjddcsklb/R2oxFC0o2Cn5B+YlnWHd9o5Jxzzvk4vtEIIZmOgp+Qfuq4\nXaVYLOapJjzJFnSiEtJPVJWUZCsKfkIIyTMU/IT0U1cbjRCS6ag6JyGE5Bnq8RNCSJ6h4CeEkDxD\nwU8IIXmGgp8QQvIMBT8hhOQZCn5CCMkz/x9KIinaxuY4ZgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa2b49d0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Across All Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p.name = 'del_3p'\n",
      "pct_del.name = 'CIN'\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "m1 = get_cox_ph(surv, covariates=[del_3p, pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z      p\n",
        "del_3p       0.5015     1.651    0.160  3.136 0.0017\n",
        "CIN          0.0634     1.065    0.119  0.531 0.6000\n",
        "age_over_75  0.3120     1.366    0.119  2.629 0.0086\n",
        "age         -0.0375     0.963    0.136 -0.276 0.7800\n",
        "\n",
        "Likelihood ratio test=26.4  on 4 df, p=2.57e-05  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ CIN + age_over_75 + age'\n",
      "m2 = get_cox_ph(surv, covariates=[del_3p, pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z      p\n",
        "CIN          0.2833     1.328   0.0969  2.925 0.0034\n",
        "age_over_75  0.2618     1.299   0.1168  2.241 0.0250\n",
        "age         -0.0352     0.965   0.1329 -0.265 0.7900\n",
        "\n",
        "Likelihood ratio test=14.3  on 3 df, p=0.00253  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "0.00048909784102555808"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "In TP53 Mutated Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p.name = 'del_3p'\n",
      "p53_mut = mut.features.ix['TP53'].ix[keepers_o]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "m1 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==1)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z      p\n",
        "del_3p       0.4081     1.504    0.157  2.599 0.0094\n",
        "CIN         -0.0794     0.924    0.128 -0.619 0.5400\n",
        "age_over_75  0.2561     1.292    0.125  2.050 0.0400\n",
        "age         -0.0115     0.989    0.137 -0.084 0.9300\n",
        "\n",
        "Likelihood ratio test=13.8  on 4 df, p=0.00779  n= 202, number of events= 92 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ CIN + age_over_75 + age'\n",
      "m2 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==1)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z     p\n",
        "CIN          0.0836     1.087    0.110  0.760 0.450\n",
        "age_over_75  0.2179     1.243    0.123  1.778 0.075\n",
        "age         -0.0148     0.985    0.132 -0.112 0.910\n",
        "\n",
        "Likelihood ratio test=4.85  on 3 df, p=0.183  n= 202, number of events= 92 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "0.0027052080739890017"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "In TP53 Wild-Type Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "m1 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==0)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "              coef exp(coef) se(coef)      z    p\n",
        "del_3p      -0.663     0.515    0.753 -0.881 0.38\n",
        "CIN          1.435     4.201    0.559  2.569 0.01\n",
        "age_over_75  0.800     2.226    0.425  1.883 0.06\n",
        "age         -0.546     0.579    0.559 -0.977 0.33\n",
        "\n",
        "Likelihood ratio test=14.7  on 4 df, p=0.00529  n= 48, number of events= 10 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ CIN + age_over_75 + age'\n",
      "m2 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==0)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "              coef exp(coef) se(coef)      z      p\n",
        "CIN          1.054     2.870    0.323  3.268 0.0011\n",
        "age_over_75  0.896     2.451    0.418  2.143 0.0320\n",
        "age         -0.486     0.615    0.559 -0.869 0.3800\n",
        "\n",
        "Likelihood ratio test=13.9  on 3 df, p=0.00305  n= 48, number of events= 10 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "0.36082942654866224"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Figure 3c: Multivariate models with 3p and CIN"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pct_del.name = 'CIN'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(3, 1, figsize=(3.5,3.5), sharey=True, sharex=False)\n",
      "\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "pts = [keepers_o, ti(p53_mut==1), ti(p53_mut==0)]\n",
      "color_list = ['grey', '#a1d99b','#9ecae1']\n",
      "\n",
      "for i, pt in enumerate(pts):\n",
      "    ax = axs[i]\n",
      "    m1 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age], formula=fmla, \n",
      "                    print_desc=False, interactions=False);\n",
      "    ci = convert_robj(robjects.r.summary(m1)[7])\n",
      "    ci = ci.ix[['del_3p','CIN']]\n",
      "    ci.index = ['3p', 'CIN']\n",
      "    ci = ci.ix[['CIN', '3p']]\n",
      "    haz = ci['exp(coef)']\n",
      "    for j,h in enumerate(haz):\n",
      "        ax.scatter(h, j, marker='s', s=100, color=color_list[i], \n",
      "                   edgecolors=['black'], zorder=10)\n",
      "        ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "                lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=color_list[i])\n",
      "    ax.axvline(1, ls='--', color='black')\n",
      "    ax.set_xscale('log')\n",
      "    ax.set_ybound(-.5,1.5)\n",
      "    ax.set_xticks([.25,.5, .8, 1, 1.5, 2,4, 8])\n",
      "    ax.set_xticklabels([.25,.5, .8, 1, 1.5, 2, 4, 8])\n",
      "    ax.set_yticks([0,1])\n",
      "    ax.set_yticklabels(['CIN', '3p'])\n",
      "    prettify_ax(ax)\n",
      "axs[0].set_xbound(.66,2.5)\n",
      "axs[1].set_xbound(.66,2.5)\n",
      "axs[2].set_xbound(.25,20)\n",
      "axs[2].set_xticks([.5, 1, 2,4, 8])\n",
      "axs[2].set_xticklabels([.5, 1, 2, 4, 8])\n",
      "axs[2].set_xlabel('Hazard Ratio')\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'fig2c.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5CVxaWjpq7969YZcvX/7LxIkTfwcAGD169MOkpKTA4ODgBIlE8k6lOHNz8zI7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\nxjCBEaIxTGCEaOz/AyCwiWPHiiunAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcca0a10>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = pd.concat([del_3p, pct_del, old, age, surv[:,'days'], surv[:,'event']],\n",
      "                 keys=['3p','CIN','age_over_75','age',  'Days to Death/Censoring', 'Death Indicator'],\n",
      "                 axis=1).dropna()\n",
      "df1.to_csv(FIGDIR + 'fig2c_all.csv')\n",
      "df1.ix[ti(p53_mut>0)].to_csv(FIGDIR + 'fig2c_TP53_mut.csv')\n",
      "df1.ix[ti(p53_mut==0)].to_csv(FIGDIR + 'fig2c_TP53_WT.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Alternate Multivariate Model with the Kitchen Sink"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(3, 1, figsize=(3.5,4.5), sharey=True, sharex=True)\n",
      "\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75'\n",
      "fmla2 = 'Surv(days, event) ~ CIN + age_over_75'\n",
      "pts = [keepers_o, ti(p53_mut==1), ti(p53_mut==0)]\n",
      "\n",
      "color_list = ['grey', '#a1d99b','#9ecae1']\n",
      "\n",
      "for i, pt in enumerate(pts):\n",
      "    ax = axs[i]\n",
      "    m1 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age], formula=fmla, \n",
      "                    print_desc=False, interactions=False);\n",
      "    m2 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age], formula=fmla2, \n",
      "                print_desc=False, interactions=False);\n",
      "    print LR_test(m1, m2)\n",
      "    ci = convert_robj(robjects.r.summary(m1)[7])\n",
      "    #ci.index = ['3p14.2', 'CIN', 'age > 75', 'age','year','spread']\n",
      "    #ci = ci.ix[['year', 'spread', 'age','age > 75', 'CIN', '3p14.2']]\n",
      "    haz = ci['exp(coef)']\n",
      "    for j,h in enumerate(haz):\n",
      "        ax.scatter(h, j, marker='s', s=100, color=color_list[i], \n",
      "                   edgecolors=['black'], zorder=10)\n",
      "        ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "                lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=color_list[i])\n",
      "    ax.axvline(1, ls='--', color='black')\n",
      "    ax.set_xscale('log')\n",
      "    ax.set_xbound(.5,3.5)\n",
      "    ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "    ax.set_xticks([.67, 1, 1.5, 2,3])\n",
      "    ax.set_xticklabels([.67, 1, 1.5, 2, 3])\n",
      "    ax.set_yticks(range(len(ci.index)))\n",
      "    ax.set_yticklabels(ci.index)\n",
      "    prettify_ax(ax)\n",
      "axs[2].set_xlabel('Hazard Ratio')\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'fig2c_alt.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00049071280384\n",
        "0.00269718222184"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.418877206975"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/6Iv2oe7D7wP3ElG+D3zv3j1wX+4OvLauz5uXe3rBzrCd+H1gydHpYMKg6YEH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x97dfb90>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Even more significant in the presence of confounding variables such as age and year of diagnosis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "year = clinical.binary_df.ix['pre_2000']\n",
      "year.name = 'year'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(3, 1, figsize=(3.5,4.5), sharey=True, sharex=True)\n",
      "\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age + year'\n",
      "fmla2 = 'Surv(days, event) ~ CIN + age_over_75 + age + year'\n",
      "pts = [keepers_o, ti(p53_mut==1), ti(p53_mut==0)]\n",
      "\n",
      "color_list = ['grey', '#a1d99b','#9ecae1']\n",
      "\n",
      "for i, pt in enumerate(pts):\n",
      "    ax = axs[i]\n",
      "    m1 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age, \n",
      "                                      year == False], formula=fmla, \n",
      "                    print_desc=False, interactions=False);\n",
      "    m2 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age, \n",
      "                                  year == False], formula=fmla2, \n",
      "                print_desc=False, interactions=False);\n",
      "    print LR_test(m1, m2)\n",
      "    ci = convert_robj(robjects.r.summary(m1)[7])\n",
      "    #ci.index = ['3p14.2', 'CIN', 'age > 75', 'age','year','spread']\n",
      "    #ci = ci.ix[['year', 'spread', 'age','age > 75', 'CIN', '3p14.2']]\n",
      "    haz = ci['exp(coef)']\n",
      "    for j,h in enumerate(haz):\n",
      "        ax.scatter(h, j, marker='s', s=100, color=color_list[i], \n",
      "                   edgecolors=['black'], zorder=10)\n",
      "        ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "                lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=color_list[i])\n",
      "    ax.axvline(1, ls='--', color='black')\n",
      "    ax.set_xscale('log')\n",
      "    ax.set_xbound(.5,3.5)\n",
      "    ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "    ax.set_xticks([.67, 1, 1.5, 2,3])\n",
      "    ax.set_xticklabels([.67, 1, 1.5, 2, 3])\n",
      "    ax.set_yticks(range(len(ci.index)))\n",
      "    ax.set_yticklabels(ci.index)\n",
      "    prettify_ax(ax)\n",
      "axs[2].set_xlabel('Hazard Ratio')\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'fig2c_alt.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.000985887091617\n",
        "0.00265660814229"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.286580014929"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LhOGtQ/9gnOGlpaW7eusZlyxZcuncuXPLB2NHf+zduzc4PDw86NPzOByO/fTp0/Hm5uZF\nw1GvuBAfHz/oa4uLi4HBYHDMw+2mej1Bwco/ZlA9MK+c4cUBce2BsWf6wXD9+nW4fv06V3m3bdsG\nZmZmg6pHgkBLKYcLcRUwQqhA/sAIhDiCBIxAiDBIwAiECIMEjECIMEjAiH+4evUqEAgEIBAIICsr\n+8/fvaVdu3YJ2lwESNDG7iwWC3/gwIGA+Pj4NZWVlSRDQ8Myd3f3pO3bt3+fm5trs379+h+LiorM\nc3JybGfNmnX3p59++mbt2rWnsOtDQ0ND3717p/3jjz+uF2Q7hpO6ujoYN24czJ07l2O+goICqKqq\n4pgHwR8kpgcOCAg4EBcX5x0XF+dNpVKJ169f//LWrVvzNm/e/MOnC0xkZWU7AgMDI5uamtSwc9wu\nzxR1pKSkQFZWlmOSlpaY//tCj0R8EyUlJcaxsbG+Dx48mDZp0qQ/AABMTEyK4+Pj13h7e8d1dnZ+\nFJrAyMio1NLS8llgYGDkyZMn1wnG6sEhrA7vwmqXqCMRPfDt27fn6urq1mDixTA1NX2bnZ1tJy0t\n3fXpNYcOHdpy4cKFZfn5+SITT0RYHd6F1S5xQCJ64MbGRnU9Pb3qgVyjr69fFRISErZhw4bjjx8/\n/mKgboqCYKjBv//880/o7Ozkqq7nz59DWFgYV3mRI/7wIRECVlNTa2poaNDo7bOUlBS3ESNGvO/t\ns02bNh1JTExcfeLECR9Oz8A91wzb2toKzCdYWIN/I0f84UMiBDxz5szffH19Y1++fDmm515Zjx8/\n/mLDhg3HL168uLS366SkpJg//vjj+kWLFl3rz6FfGECO+JKHRAjYwsKicOXKlT+7uLikJicnrxg7\nduyLv//++38rV678OTw8PEhWVrZP7/OpU6c+dHZ2vnLq1Km12C4hwoqbm1uvDu/cOuKfOnUKTp06\n1W8+AIBx48ZxHb0BOeIPHxIhYACA+Pj4NeHh4UFfffXV1ZqaGl1TU9O3O3bsiFqzZk18Tk6Obc8h\n8qfD5aioqB1paWlO/Ld6YAhrQG1htUscQO6EQ0Sc3AkvXLgAK1as6Pc9L5PJhPXr18ORI0f4ZJnE\ng/yBhwtxEjAAQFtbn5tvfgSBQBD45JgEgfyBEdyhqKgIioqKcODAgX/+7i0h8QoHqAceIuLWA2Pg\ncLg+X0sh+A7qgREIcQQJGIEQYUTyNRKFQiGbm5sX0el0Ql958Hg8q7a2VkdLS6vPvUtbW1uV165d\ne+r69etfqqurN/r5+R3eunVrzPBYjRgIbDYbjh49Ci0tLf3m1dTUhPXrxdbLkyMiKWBeERwcvJfJ\nZEpVVlaSqqqq9O3t7bMmT56cN23atAeCtk3S6erqAj8/P5gxYwbHfCwWC37//XeJFbDIDKGPHj26\nUUNDo4FEIlX2DCW6f//+nVpaWnVEIpEaEhLC3er6/+fu3buztm3bdlBVVbV53Lhxz2fOnPlbQUHB\nBAqFQtbR0akNCwsLUVFRaTE1NX374MGDabxvlfDC7Sqr4QSPx4OdnR3HJA6xqIaCSPTAjx8//iI8\nPDwoNzfXhkQiVTo7O18B6HZEOH/+vOuTJ08m4nA4tqOjY/qkSZP+WLRo0TVuyi0oKJiArbp68+aN\nWW5urk1AQMABAID6+nrN1tZW5cbGRvWrV69+5eLiklpSUmIsLy/fPnwtFR4Gur5bHPx9RbENItED\nX7169SssaLeSktKH7du3fw8AcPbs2VXBwcF7R44cWUEikSo3btx4tGfv3B+YeKdOnfpw9OjRr8hk\nMqVnuJU9e/Z8JyUlxfz6669/0dbWfidpvTC3iIO/r6i2QSR64MbGRnUSiVSJHRsaGpYBAJSXlxu4\nubmlYEJks9m4wTy/Pnz4cGpNTY3uihUrkgMDAyO3bdt2cMSIEe9VVFT+mUFRV1dvbG5uVuVFe8SN\nofoh9waTyeT6PTSbzebaN7kvRNVnWSQEbGBgUE6hUMjYcW1trQ5Ad6DvmJiYrfPnz78J0B0utOc+\nVpzo6OiQnT179q+ZmZkLlJSUPujq6tYsW7bsQkZGhgMAwPv370fQ6XQCgUCgA3TPfJPJZEpvZQmL\nP7CgEFY/5IEgqj7LInGHly5devHKlSvO+fn5Vs3NzaoRERG7AAAWL158OTo62r+urk6roaFBY/ny\n5ee4HULLysp2dHR0yEZGRga2tbUplpWVGSYkJHja29tnAXT35sHBwXvpdDrh9OnTXng8nvXpljwY\n4hbge6AIqx/yQBBVn2WR6IFNTEyKjx079q2Tk1Pahw8flHbv3r3vt99+m/ntt98eo1AoZEtLy2c0\nGk1h+fLl57AoidzsIpmUlOTu4+NzQktLq05TU7N+1apVZ319fWPLysoM5eTkGO3t7fLq6uqNJiYm\nxenp6Y7D31LhYSDRCYfqh9wbnZ2dEBERwVVeHA435FlzUfVZRmuhe4GbhSIYaC10N7wOvN3Z2QkE\nAgGCgv4T3vkjmEwmREREQFfXf/YlHDBCHDxcMt0Jy8rKDE1NTd/29pm/v390ZGRkYG+fCYuAc3Jy\nBDYkF7QzQ1ZWFsydO7ff9rNYLMjNzR2QgAV5XwdZf58CFokh9GAxNDQs+3TPZ24Rho3cBf1DEyT3\n79+HAwcOAJVK7Tfv4sWLB1S2oO8rL+sXawEPFjKZTKHRaArDWcdAv8SB5Ocmr6B/xP2Bw+Fg27Zt\nw1I2hUIZlnIFUb9IzEKLIzk5OcOWn5u8A61fnBAnAYv1MzA/EIahNkL86SuwABIwAiHCoCE0AiHC\nIAEjECIMEjACIcIgASMQIgwSMAIhwiABIxAiDBIwAiHCIAEjECIMEjACIcIgASMQIgwSMAIhwiB3\nwiEirjtyIIQKFJ0QgRBHUA8sZLS3twOdTu83n4yMDCgpKfHBIoQwg9wJhwivh9BjLMZARUVFv9uZ\n0j7QoLKyEnR0dHhWN0JokawhtIuLS2pQUFA4dhwXF+dtZ2eXnZCQ4EkikSoVFRXb1q1bd5LJZEoB\nAPz555+fW1lZ5SsoKNCMjY1LLl26tASge3M7HR2d2oiIiF0aGhoN/LD9Q9sH2JseBLGPYzgmTV1N\naG+XiDBNCA6IpYCdnZ2vYBEWAABu3Lix0N7ePiskJCTs9u3bc8vLyw3Ky8sNjh8/vgEAwNfXN3bz\n5s0/tLa2Ku/bt2+3j4/PCeza+vp6TRqNplBTU6M7FJuysrPAx38drN+xDnz810FWdtZQihv2ugYa\n3AwhGMRSwA4ODhmFhYUWdXV1Wh0dHbK//vrr7Pv370/38/M7bGFhUaiurt64Y8eOKCyKww8//LDZ\nzc0t5cOHD0oyMjKdn4ZnCQoKCpeRkekcrD1Z2VmQ8Es8WH9jBVPXWIH1N1aQ8Ev8sIiYV3UNNdYQ\ngj+I5SQWkUik2tra5mRkZDiQSKRKMzOzN3g8nhUYGBi5e/fufVg+fX39KgCAnJwcWxcXl1QtLa26\nUaNGve5ZlqysbIecnBxjKPZcupEK83xnf3Runu9sOHr4MFTrUz46z2ByNyzuYnfC5ZLzoNmh8XFd\nqWmwxM/pP3VdjrsE9nb2AzceIdSIpYAB/h1Gk0ikShcXl9SioiLzH374YTM2PMbiIb19+9Y0IiJi\nV3FxsYmamlpTaWmpUUpKittA6uovuBmuj/kovBTvB0BSfZWJR5OV4ojYCtjR0TF9x44dURoaGg2Z\nmZkLnj9/Pi4oKCh8/vz5NzU0NBp8fX1j5eXl2zdu3HiUyWRK0el0Qn19vSY2+cVgMOS4rau/50V2\nHwHuWEzeB/9i9lUmq8+JTIQII7YC1tLSqhs7duwLGo2mYGJiUmxiYlJcWFhoYWNjk9vU1KS2YMGC\nzKNHj25UVlZu9fT0TBgzZsxLbW3td1FRUTvevHljtnr16sSoqKgdvNg2dslCF0iIjf9oGH3zaBZs\ndPED+1EfD2t3SYVyVaY0TgYWG7sCmUz+6LyeC7nXuryc1w7afoTwItbvgX18fE6YmJgUBwQEHBiu\nOrh9D5yVnQWXMy51D2VZOFjssKTXZ1KSIQm2JPiCJkmjl1L+ZcfsIHh4/+F/BDyQujgxkOiEiGFH\nsoKbdXR0yLa0tKhMnDjxSX5+vpWOjk7tcNXF64UcJEMS+BxdA+q6nOOUhzrtg7wHj3sVMELskKzg\nZvn5+VazZ8/+1c/P7/Bwinc4MCIbwYFVh/vNJycnB8rKynywCCHMiGUPzE+QNxKCD0jWUkoEQlJA\nAkYgRBgkYESvoBlo0QA9Aw8RcX0GxuFwII7tElEkaxYaMXgqKiogbG8oyMrLgucaT455Z9nOAnd3\ndz5ZhugNiRhCx8TEbCWRSJUEAoE+e/bsX6urq/UYDIact7d3nJKS0ofPPvvsr927d+/z9PRMAOhe\nJ+3u7p6kpKT0gUQiVaanpzsKug384tmzZ3DnXhas2L0McEbMPlNVZzkkJicK2lyJR+x74FevXo0+\ncuTIpkePHk1RU1NrcnNzS4mJidmqpaVV9/LlyzFVVVX6zc3NqjY2Nrlz5869DQDg5+d3GI/Hs2pq\nanRfvHgx1tHRMd3GxiZXXV29UdDt4Qda+hpgu3QGxzxPf3sGTy+/4JNFiL4QewFra2u/u3Xr1jw9\nPb3qmpoaXSUlpQ+NjY3qeXl5k/39/aOJRCKVSCRSPTw8zlRXV+ux2WxcSkqKW0VFxUhlZeXWKVOm\nPHJwcMhIT0939PLyOi3o9vRFVnYWXLqRCjipbueJJQtdhN59UBRtFjbEXsBsNhu3devWmMLCQgsz\nM7M3dDqdYGpq+rahoUFDU1OzHstHJpMpVVVV+vX19Zp0Op2A+QpjZRgZGZUKpgX9gznx93RgSIiN\nBwAQWkGIos3CiNgL+ODBg9vU1dUbS0tLjQAAQkJCwsrLyw0MDAzKy8rKDK2trX8HAKipqdHF4XBs\nVVXVZmlp6a6mpiY1AoFABwAoLy83kJeXF9oNqAayYUB/PK16Bh3MDq7y1tPfwdnXpwZUPgbaeIA3\niP0kFoPBkGtvb5fv7OyUefz48ReJiYmrGQyG3PLly89FR0f719bW6rx69Wp0XFycNwCAjIxM58KF\nC2+EhoaG0mg0hcLCQosZM2bcKykpMe6rDsxzJzQ0FHJycvjWNgx+bhjAK9DGA7xB7HvgTZs2HXF2\ndr5CJBKp06dPvx8TE7PVy8vr9MqVK3+2t7fPGj169CtVVdVmOzu7bDabjQMAOHny5Lq1a9ee0tHR\nqVVQUKDt2bPnuylTpjzqqw5BL3rg54YBvAJtPMAbJHYhR1FRkbmKikqLrq5uDQDAzp079zMYDLlD\nhw5tGUg5wrCQo7fnScyJf6DD0YyMDAiJDoKNJ9ZzzIfNQmfdHNzGfLy0WQJACzk+5fz58655eXmT\n09LSnCorK0nJyckrsGG0qIH94C/H/evEL+xCEEWbhRGJ7YHpdDph1apVZ2/cuLFQS0urztfXN9bf\n3z96oOUIQw/MSzIyMsBj7WoYM2U0yMrK9pmvoboBNBW04c7NO3y0TmKRrB05+Im4Cfj9+/eQmpoK\na9euhZ9++olj3vHjx8MXX3zBJ8skGiTg4ULcBIyBnBmECuTQj0CII0jACIQIgwSMQIgwEvsaCcGZ\nkJCQXs9TqVSor6/v9bOe4PF4MDIyAhwOLcwYTiR2EispKcn9m2+++WialcVi4UNCQsL8/f2jlZWV\nW/F4/D/LhU6ePLlu1apVZz8tR1wnsfpi3oJ58OSvJyBP4Bx5pqG2EW7dvAUzZ87kk2ViDZqF7o/q\n6mq9mTNn/nb//v3pVCqV6OPjcyI7O9uuv+skTcAzZ8+EySsnwrhpFhzzHV4TC1HBB2DOnDl8skys\nGfwsND92s2AymVIBAQEHVFRUWnR0dGpjY2N9AQC8vLxOr1+//kcs382bN+dj4T8zMzMXmJqaviUQ\nCHRnZ+crNBpNAQDAw8PjTGBgYKSlpeWzgwcPbuv/3nTj7e0dFxYWFqKjo1NbUlJibGJiUszttaIC\nP4OMDxZRsFGY4PgMzK/dLPbv37/z0aNHU0pLS42oVCpx1qxZd0eOHFnh4uKSumHDhuNYvvT0dEdX\nV9fzFAqF7O7unnT16tWvLC0tn/n5+R0OCgoKxwSbkpLiduvWrXljxox5yc1NyMrKsn/37p22m5tb\nCgBASUk/Tz8/AAAgAElEQVSJcUFBwQRjY+OS9vZ2eXd396TIyMjAnkNqUUMU/G9FwUZhg6OA+bWb\nRWJi4ur4+Pg1qqqqzaqqqs0BAQEH4uPj11y+fHkxlUolvnr1avSoUaNeX7t2bdGtW7fmnTt3brmb\nm1sK5ssbFBQUPmPGjHuYgFesWJHMrXgBun2Ed+3aFYEdd3R0yI4fP/7p/v37d7a2tiq7uLikqqmp\nNe3YsSOK2zKFDV75DDe29z+BBQDQweqAO5WZUPO6jHsbkY/wgOEoYH7tZlFZWUkyNjYuwY6NjY1L\nKisrSTIyMp2LFi26lpGR4UClUokqKiotY8eOfREbG+sbHx+/5lPnAyaTKYXD4dhEIpHK7Q0oKCiY\nUFpaauTk5JSGnduyZcsh7G9NTc36wMDAyP379+/sS8D9BfgWBgbqM/zL0avw9cavhtGi/4J8hAcO\nRwHzazcLEolUWVJSYowJv6SkxHjkyJEVAABLliy5dPTo0Y2NjY3qrq6u5wG6RbVly5ZDUVFROwAA\nurq6pJ8/fz5OSkqqD8/YvklMTFzt5OSU1nN4fPz48Q1ffvnldQMDg3KsfEVFxba+yhC0PzA3DNRn\nOC32Gt8FjHyEBw5HAffczeKvv/76LDExcbW1tfXv2G4WdnZ22VQqlRgXF+dtb2+f1XM3i5CQkDAK\nhUJ2cHDIOH/+vKuWllZdX/WsXr06cc+ePd+lpaU5vX//fsTBgwe3YcPhefPm3fLy8jpNoVDIGRkZ\nDgAAzs7OVxYtWnRt5cqVP5uZmb357rvv9uTn51vdunVrHuaUzy0ZGRkOYWFhH730zMvLm5ybm2vz\n448/rqdSqcTvv/9+u7e3d9xAyhU2BhJkHABgNXjDqlH/DQoeL5/EVX2yeFmYQ1oAc0ZxPwuNgpMP\nHI4C5sduFgDdzvSNjY3qZDKZIicnx9i9e/c+R0fHdAAAWVnZjrlz595++fLlGFNT07cAABMmTCjY\nu3dvsJOTU1pVVZX+tGnTHiQlJbkDdL/WweFwXI25mpubVYuLi01sbGxye54/cuTIJg8PjzP6+vpV\nCgoKtDVr1sT7+Pic4KZMYUUU/G9FwUZhY1DvgXm1m4U4IK7vgfvyRpo5eyaMmmMMoyaacbw+IfBn\n+CHyB/QemDfwdkeOwexmIS0t3dVbz7hkyZJL586dWz4YO/pj7969weHh4UGfnsfhcOynT5+ONzc3\nLxqOesWZ8Zbj4epP6XAH7nLMh5eSAl1dXT5ZJcGw2ewBJxqNRliyZEkqgUCgGRoaUg4cOOA/mHLE\nIXXfQvEjJCRE0CYg/qXP3x9aSjlExHUIjRAqkEM/AiGOIAEjECIMEjACIcIgh34xpaKiAjZv2Qxd\nzM5+89rOtIOtflv5YBWC16BJrCEirJNY2dnZ4LHOA77a6MAxX9Wbanj3tAEe/Pbgo/NYrCeEUIAm\nsVgsFj4qKmrHqFGjXisoKNDMzc2LIiIidnV1dUnn5OTYYu+Ec3JybPF4POvUqVMfrd8LDQ0N7emb\nLAqoaBDBat5Ejmn0pN4XZISFhfHZWsRgkJghdEBAwIH09HTH+Pj4NdbW1r+Xl5cbeHl5na6qqtJf\nunTpxZ55ZWVlOwIDAyOdnZ2vqKmpNQF097T8tFcUgl+Lgo3ijkQIuKSkxDg2Ntb3wYMH0yZNmvQH\nAICJiUlxfHz8Gm9v77jOzk6ZnvmNjIxKLS0tnwUGBkaePHlyHb/tFQXHdlGwURKQCAHfvn17rq6u\nbg0mXgxTU9O32dnZdjk5ObafXnPo0KEtY8eOfbF27dpTVlZW+XwzFnjjfF9U8RI6WNwF6m5iNPYa\nqJtT8G7kfC8cSISAGxsb1fX09KoHco2+vn5VSEhI2IYNG44/fvz4CzYHN0VeO/SLQsBu5HwvHEiE\ngNXU1JoaGho0evssJSXFbcSIEe97+2zTpk1HEhMTV584ccKH0zMwr2drhSFgt5PvIo6fI+d74UAi\nBDxz5szffH19Y1++fDmm515Zjx8//mLDhg3HL168uLS366SkpJg//vjj+kWLFl3DdgPhBwN1vu+N\n7KpsyMDf5iqvmpz6f5z3Vx3l7ESPnO+FA4kQsIWFReHKlSt/dnFxSU1OTl4xduzYF3///ff/Vq5c\n+XN4eHiQrKxsnw+LU6dOfejs7Hzl1KlTazltzMdLRMGxXRRslAQkQsAAAPHx8WvCw8ODvvrqq6s1\nNTW6pqamb3fs2BG1Zs2a+JycHNueQ+RPh8tRUVE70tLSnP5b6vBhb2c/JDHgcDgof1UOJzb3PREF\nAEBtpIKaQq9PF/0yVBsRQwetxBoiwroSi8FgQGZmJjCZ/e/zN2rUKLC0tOSDVYhBgkKrDBfCKmCE\nWIGWUiIGBloHLRqgHniIiGsP3NemdgiBgHpgBEIckZhZaIRw8/z5c6DRaP3mU1NTA1NTUz5YJBqI\n5BCaQqGQzc3Ni+h0OqGvPHg8nlVbW6vDKSJEa2ur8tq1a09dv379S3V19UY/P7/DW7dujRmILWgI\nPXSam5tBU1MTTMeZcMzX1cWE9/Xvoe5dn1+puMLbfaHFheDg4L1MJlOqsrKSVFVVpW9vb581efLk\nvGnTpj3o/2oEr2AymaBEVIJdFwM45mttboU9Dnv5ZJVoIDLPwEePHt2ooaHRQCKRKlNTU12w8/v3\n79+ppaVVRyQSqSEhIQPyQr979+6sbdu2HVRVVW0eN27c85kzZ/5WUFAwgUKhkHV0dGrDwsJCVFRU\nWkxNTd8+ePBgGu9bJbyEhIT0+Zk4BOEWhzYAiEgP/Pjx4y/Cw8ODcnNzbUgkUqWzs/MVgG5HhPPn\nz7s+efJkIg6HYzs6OqZPmjTpj0WLFl3jptyCgoIJ2KqrN2/emOXm5toEBAQcAACor6/XbG1tVW5s\nbFS/evXqVy4uLqklJSXG/UVaFBf6eo0kDn7A4tAGDJEQ8NWrV7/qGbR7+/bt39+/f3/62bNnVwUH\nB+/FQpFu3LjxaGpqqgu3AsbEO3Xq1Id5eXmTp06d+tDc3Lyovr5eEwBgz54930lJSTG//vrrX/bu\n3Rv84MGDabNnz/51uNopCvAqUHhPWptbgcniLjJsF6uLo58yN4iTL7NICLixsVGdRCJVYseGhoZl\nAN2xh93c3FIwIbLZbNxgnl8fPnw4taamRnfFihXJgYGBkdu2bTs4YsSI9yoqKi1YHnV19cbm5mbV\n3q4XhQDfvEIUfJX7Q5x8mUVCwAYGBuUUCoWMHdfW1uoAdAf6jomJ2Tp//vybAN3hQpuamtS4KbOj\no0N29uzZv2ZmZi5QUlL6oKurW7Ns2bILWAzi9+/fj6DT6QQsUDmFQiGTyWRKb2VJ0qolYfBVHiri\n5MssEgJeunTpxSlTpjxau3btKVNT07cRERG7AAAWL158OTo62v/zzz//E4/Hs1auXPmzra1tzs6d\nO/f3V6asrGxHR0eHbGRkZOCuXbsiGhoaNBISEjxXrFiRDNDdmwcHB+/du3dv8Llz55bj8XjWp1vy\nSCK88FX+lIaGBtiN527+URov3Wvg8YEgTr7MIiFgExOT4mPHjn3r5OSU9uHDB6Xdu3fv++2332Z+\n++23xygUCtnS0vIZjUZTWL58+Tl/f/9oAO52kUxKSnL38fE5oaWlVaepqVm/atWqs76+vrFlZWWG\ncnJyjPb2dnl1dfVGExOT4vT0dMfhb6nw0Ne+0OLgBywObcAQyYUcww03C0Uw0EKOodPQ0ABkYzL4\nn97MMV/bexqcCjgDzY3NfLFLiJDMhRxlZWWGpqamb3v7zN/fPzoyMjKQ3zYNhJycHLGeEMNQUlKC\nCZ9NgLT9N/45R6fRgKCg8J+8s2fP/s+5gSLo+8rL+sVawIaGhmWf7vnMLfzeyL03BP1D4xfy8vKQ\n+1vuR+eGM7SLoO8rL+sXnbl/PkImkyk0Gu2///55SE5OzrDl5ybvQOsXJygUitjUjwQsIJCABYc4\nCRhNYg0RYRhqI8SfvgILIAEjECIMGkIjECIMEjACIcIgASMQIgwSMAIhwiABIxAiDBIwAiHCIAEj\nECIMEjACIcIgASMQIgwSMAIhwoi1OyE/EFeHfoRQgYKbIRDiCBIwAiHCoCG0iEClUmG+w0Kgt/cf\nGOJ/lpZw9kwCH6xCCBqx7IFdXFxSg4KCwrHjuLg4bzs7u+yEhARPEolUqaio2LZu3bqTTCZTCgDg\nzz///NzKyipfQUGBZmxsXHLp0qUlAN2b2+no6NRGRETs0tDQaBBUewC6I/i9fvMWFm0M5pimu66D\nu3fvCtJUBB8RSwE7OztfwTZoBwC4cePGQnt7+6yQkJCw27dvzy0vLzcoLy83OH78+AYAAF9f39jN\nmzf/0Nraqrxv377dPj4+J7Br6+vrNWk0mkJNTY2uINrSE1lZWTAeO4FjMhhlwZO6JGmzelFGLB36\nqVQqUUdHp7asrMxQRUWlRV1dvXHatGkP5s6dexuL/5uTk2MbHBy89969ezPy8/OtJk6c+KS1tVX5\nzp07c5YuXXqRxWLhKRQK2cTEpJhGoynIyckxequLX7PQFAoFptrMgIhfcjnma6iugB82ukFledmQ\n6uPntrKDISs7By5dzQDASwGwmLDkKwewt7MVtFnDhWRtK0skEqm2trY5GRkZDiQSqdLMzOwNHo9n\nBQYGRu7evXsflk9fX78KoFvMLi4uqVpaWnWjRo163bMsWVnZjr7EixAMWdk5cPpiGszx2vrPudOn\nu+Oyi7GIe0UsBQzw7zCaRCJVuri4pBYVFZn/8MMPm7HhcVtbm2JZWZnh27dvTSMiInYVFxebqKmp\nNZWWlhqlpKS4DaSuhPzy4WlEDxqqq6CTxV2P2N7F4olN/GjXYLiSdBmc138cDHyO11a4fO4YErC4\n4OjomL5jx44oDQ2NhszMzAXPnz8fFxQUFD5//vybGhoaDb6+vrHy8vLtGzduPMpkMqXodDqhvr5e\nE5v8YjAYctzWlR536J+/R38+BcZMnDoMLUJgSEn3/rNl48RySocjYitgLS2turFjx76g0WgKJiYm\nxSYmJsWFhYUWNjY2uU1NTWoLFizIPHr06EZlZeVWT0/PhDFjxrzU1tZ+FxUVtePNmzdmq1evToyK\nitrBza6Tjt5b+NEkxP/D7Orq9TyOLToREnmFWE5iYfj4+JwwMTEpDggIODBcdYjrJNZwRkYYKr09\nA9+Oj4E1y5zEdQgtWZNYHR0dsi0tLSo3btxYmJ+fbyVoe0QRYRUvwL8TVZfPHQM2Dg84NkucxcsR\nsRRwfn6+1ezZs3/18/M7rKOjUytoe3iBtLQ0NNTVQtQaJ475OjsYINPHM6I4YW9nK5GC/RSxHkLz\nA34NodlsNvz999/AYPT/RktTUxOMjIyG3SYE3+hzCI0EPESQOyGCDyB3QgRCHEECRvSKME9iIf4F\nDaGHiLgOoYV9LbSEgYbQw8nly5ehoKBA0GYgJBCJEHBMTMxWEolUSSAQ6LNnz/61urpaj8FgyHl7\ne8cpKSl9+Oyzz/7avXv3Pk9PzwSA7nXS7u7uSUpKSh9IJFJlenq6Y19lW1hNg9CoGFjjvY5/DUIg\n/h+xf2H46tWr0UeOHNn06NGjKWpqak1ubm4pMTExW7W0tOpevnw5pqqqSr+5uVnVxsYmd+7cubcB\nAPz8/A7j8XhWTU2N7osXL8Y6Ojqm29jY5Kqrqzd+Wr7/sRQoef4XXD/2Hf8bh5B4xF7A2tra727d\nujVPT0+vuqamRldJSelDY2Ojel5e3mR/f/9oIpFIJRKJVA8PjzPV1dV6bDYbl5KS4lZRUTFSWVm5\ndcqUKY8cHBwy0tPTHb28vE4Luj2SjIT5AHOF2AuYzWbjtm7dGlNYWGhhZmb2hk6nE0xNTd82NDRo\naGpq1mP5yGQypaqqSr++vl6TTqcTMF9hrAwjI6NSwbRAMISEhAjahI9APsC9I/YCPnjw4DZ1dfXG\n0tJSIwCAkJCQsPLycgMDA4PysrIyQ2tr698BAGpqanRxOBxbVVW1WVpauqupqUmNQCDQAQDKy8sN\n5OXlOe4mR23vElr/2cFguNBLqNqDfIB7R+wnsRgMhlx7e7t8Z2enzOPHj79ITExczWAw5JYvX34u\nOjrav7a2VufVq1ej4+LivAEAZGRkOhcuXHgjNDQ0lEajKRQWFlrMmDHjXklJiTGnej60NEF63CF4\n+eQhfxomYSAf4N4R+x5406ZNR5ydna8QiUTq9OnT78fExGz18vI6vXLlyp/t7e2zRo8e/UpVVbXZ\nzs4um81m4wAATp48uW7t2rWndHR0ahUUFGh79uz5bsqUKY841aOkoob8gocR5APcOxK7kKOoqMhc\nRUWlRVdXtwYAYOfOnfsZDIbcoUOHBqTChPxyNjYL/dcf+cNjLEISfYB7Iln+wNxw/vx517y8vMlp\naWlOlZWVpOTk5BXYMBohfCAf4D5gs9kSmWg0GmHJkiWpBAKBZmhoSDlw4ID/YMrZsP8Ee/GG7ewJ\nEyexxYmQkBBBm4D4lz5/fxI7hOYVBCVltr29PVhPmQKBO3cI2hyegdZCCxXIH3i4QM4MCD6AnBkQ\nCHEECRiBEGGQgBEIEQYJGNErwrYWGtE7EjuJlZSU5P7NN9/81PMci8XCh4SEhPn7+0crKyu34vH4\nf5b5nDx5ct2qVavOflqOuE5iIT5m8lRrqH33rt98qkQiPPkjH6SkpHhZPZqF7o/q6mq9mTNn/nb/\n/v3pVCqV6OPjcyI7O9uuv+uQgCUDGVlZCE3KBCkZGY759iybDbS2NpCVleVl9YOfhR7O3SwwmEym\nVEBAwAEVFZUWHR2d2tjYWF8AAC8vr9Pr16//Ect38+bN+Vj4z8zMzAWmpqZvCQQC3dnZ+QqNRlMA\nAPDw8DgTGBgYaWlp+ezgwYPb+r833Xh7e8eFhYWF6Ojo1JaUlBibmJgUc3stQjLQGmkI2iPJHBMO\n16fWhgWOSymHezcLjP379+989OjRlNLSUiMqlUqcNWvW3ZEjR1a4uLikbtiw4TiWLz093dHV1fU8\nhUIhu7u7J129evUrS0vLZ35+foeDgoLCMcGmpKS43bp1a96YMWNecnMTsrKy7N+9e6ft5uaWAgBQ\nUlJiXFBQMMHY2Likvb1d3t3dPSkyMjKw55BaXEFO86IFxx64524Wzc3NqthuFteuXVuE7WZBJpMp\nHh4eZwC6Hd9TUlLcDh06tOXT3Sw41ZOYmLg6IiJil6qqajOZTKYEBAQciI+PX2Nvb59FpVKJr169\nGs1ms3HXrl1btGzZsgvnzp1b7ubmlmJtbf27srJya1BQUHhqaqoLVt6KFSuSuRUvQLeP8K5duyKw\n446ODtnx48c/zcvLm5ybm2uTlZVlf+DAgQBOZYgDmMPAZDdfmOy6Hia7+cLpi2mQlZ0jaNMQfcCx\nB2bzaTeLyspKkrGxcQl2bGxsXFJZWUmSkZHpXLRo0bWMjAwHKpVKVFFRaRk7duyL2NhY3/j4+DWf\nOh8wmUwpHA7HJhKJVG5vQEFBwYTS0lIjJyenNOzcli1b/gn4q6mpWR8YGBi5f//+nTt27IjitlxR\n5NLVjH+8fdLjDoGj9xaY47UVjpyIhgolju7QYg+30xxsNkDiH+UgLcO7Z2BPK4M+P+PYA/fczeL2\n7dtzZ82adRcAANvNAsvX224WdDqdQKfTCa9fvx7l4+NzglM9JBKpsqfDfElJifHIkSMrAACWLFly\nKTMzc8HVq1e/cnV1PQ/QLaotW7YcwupobW1Vfvjw4VQpKSkmV3ekB4mJiaudnJzSeg6Pjx8/vqG8\nvPyfu9bV1SWtqKjY1lcZWCjO0NBQyMnJGagJwgP+35nT9LjDPU7zdEYVwUM4Cphfu1msXr06cc+e\nPd81NzerlpWVGR48eHAbtoHcvHnzbv3111+fXbx4cSkmYGdn5yspKSluz549s2xvb5cPDQ0NxXpH\n9v875XNLRkaGw4wZM+71PJeXlzd5586d+6lUKrG8vNzg+++/347V3Rs9BWxrazuQ6oULVu///1jM\nAf9fRPALTq5K5eXlIydNmpRPIBBoc+fOvXX58mVnIpHYcuPGDYft27dHjRgxgmpoaEhZtWpVooeH\nRwKbzYZ3795pLVq06KqysvJ7bW3t2qNHj/pyqoPNZkNXV5fUli1bYkaMGEHV1NSsO3z48Oaen7u5\nuSV//vnnT3qeO336tKexsXGxnJxc+6xZs36tqqrSY7PZ4OHhkRAVFbW9vzrZbDY0NTWpSklJdVVU\nVJB6nm9paSE6OTn9oqio+EFTU7Nu586dkSwWC9dbGd23UDy4czebvdxnM/v04zI2ALBPPy5ju67b\nzL5zN1vQpgkcaRkZ9k8P3rBPPy7jmKRlZNgMBoPX1fPWnZBXu1mIA+L2HjgrOwcuX8uEE4e+B58t\n22HxogVoFhq63wMfzyns99n2m2mm0PbhA9/eAw/KGT44ODhs3rx5N+l0uvybN29MSSRSRWZm5nxO\n10hJSXVJS0t3fppcXV3PDcYGblJYWFh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       "text": [
        "<matplotlib.figure.Figure at 0xa5cc710>"
       ]
      }
     ],
     "prompt_number": 49
    }
   ],
   "metadata": {}
  }
 ]
}