{
 "metadata": {
  "name": "",
  "signature": "sha256:543745a9dcad5da49b92da987081adc80876b0d1f86ddc6e62ada9323071cecd"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Calculating CpG ratio for transcriptome"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#test file\n",
      "!head -2 /Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/query.fa\n",
      "!echo \n",
      "!echo number of seqs =\n",
      "!fgrep -c \">\" /Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/query.fa"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ">Mmercenaria_Contig_1\r\n",
        "AAGAGTGACTGGTACCACCTGTGTACTACAATGGTTATTTGATACAACTAAATGTAAGCGGTACCACCATGTATTACAATGTGAAATTAGTATCAATAAGTGTGGCTGGTACCTTTATATATTACAGGTGCTGTTATGTTTGACAGGAATACTGATGTGAGATAGTTACTTCCATACTATGTGTAACCTACGGTCCGGCACGTTGAATGGTGGGGTG\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "number of seqs =\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8482\r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Locating GOslim Annotation file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "HardClam_contigs.fa\r\n",
        "blast_sprot.sql.tab\r\n",
        "blast_sprot.tab\r\n",
        "blast_sprot_sql.tab\r\n",
        "me_descriptions.txt\r\n",
        "me_reprot.txt\r\n",
        "me_reprotcolumns.txt\r\n",
        "me_reprotcolumns2.txt\r\n",
        "me_reprotcolumns3.txt\r\n",
        "me_reprotcolumns4.csv\r\n",
        "query.fa\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -2 me_descriptions.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Column1\tterm\tGOSlim_bin\taspect\tProteinName\r",
        "\r\n",
        "Mmercenaria_Contig_1373\trhabdomere development\tdevelopmental processes\tP\tProtein eyes shut (Protein spacemaker)\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk -F\"\\t\" '{print $1, \"\\t\", $3}' \\\n",
      "/Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/me_descriptions.txt \\\n",
      "> /Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/merc_slim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head /Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/merc_slim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Column1 \t GOSlim_bin\r\n",
        "Mmercenaria_Contig_1373 \t developmental processes\r\n",
        "Mmercenaria_Contig_1373 \t cell organization and biogenesis\r\n",
        "Mmercenaria_Contig_1373 \t cell organization and biogenesis\r\n",
        "Mmercenaria_Contig_1373 \t other biological processes\r\n",
        "Mmercenaria_Contig_6522 \t RNA metabolism\r\n",
        "Mmercenaria_Contig_6522 \t RNA metabolism\r\n",
        "Mmercenaria_Contig_6522 \t RNA metabolism\r\n",
        "Mmercenaria_Contig_6522 \t RNA metabolism\r\n",
        "Mmercenaria_Contig_369 \t protein metabolism\r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Lets use WORKING DIRECTORY"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /Volumes/web/cnidarian/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/cnidarian\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mkdir merc_cpg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mkdir: merc_cpg: File exists\r\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd merc_cpg/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/cnidarian/merc_cpg\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget http://eagle.fish.washington.edu/scaphapoda/Grace/Transcriptomes/mercenaria/query.fa "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--2014-11-01 14:44:00--  http://eagle.fish.washington.edu/scaphapoda/Grace/Transcriptomes/mercenaria/query.fa\r\n",
        "Resolving eagle.fish.washington.edu... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "128.95.149.81\r\n",
        "Connecting to eagle.fish.washington.edu|128.95.149.81|:80... connected.\r\n",
        "HTTP request sent, awaiting response... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200 OK\r\n",
        "Length: 2421307 (2.3M) [text/plain]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Saving to: `query.fa'\r\n",
        "\r\n",
        "\r",
        " 0% [                                       ] 0           --.-K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "16% [=====>                                 ] 389,235     1.67M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "25% [========>                              ] 613,675     1.36M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "39% [==============>                        ] 949,611     1.43M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "53% [===================>                   ] 1,288,443   1.48M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "67% [=========================>             ] 1,638,859   1.51M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "83% [===============================>       ] 2,010,995   1.55M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "99% [=====================================> ] 2,399,059   1.57M/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100%[======================================>] 2,421,307   1.46M/s   in 1.6s    \r\n",
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-11-01 14:44:07 (1.46 MB/s) - `query.fa' saved [2421307/2421307]\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Convert fasta to tab\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#overarching concept is place file named query in working directory and all below files are generated using a generic file name\n",
      "!perl -e '$count=0; $len=0; while(<>) {s/\\r?\\n//; s/\\t/ /g; if (s/^>//) { if ($. != 1) {print \"\\n\"} s/ |$/\\t/; $count++; $_ .= \"\\t\";} else {s/ //g; $len += length($_)} print $_;} print \"\\n\"; warn \"\\nConverted $count FASTA records in $. lines to tabular format\\nTotal sequence length: $len\\n\\n\";' \\\n",
      "query.fa > fasta2tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Converted 8482 FASTA records in 16964 lines to tabular format\r\n",
        "Total sequence length: 2201882\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -2 fasta2tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mmercenaria_Contig_1\t\tAAGAGTGACTGGTACCACCTGTGTACTACAATGGTTATTTGATACAACTAAATGTAAGCGGTACCACCATGTATTACAATGTGAAATTAGTATCAATAAGTGTGGCTGGTACCTTTATATATTACAGGTGCTGTTATGTTTGACAGGAATACTGATGTGAGATAGTTACTTCCATACTATGTGTAACCTACGGTCCGGCACGTTGAATGGTGGGGTG\r\n",
        "Mmercenaria_Contig_2\t\tACAGCTGTCTGATTACTTATACAAAGAACACGGGTTTAAAGCAGAAATGATTGATACTCTGTACAACTATGCCAAGTTTCAGTATGAATGTGGTAATTATTCTGCAGCAGCTGAATATCTCTACTTTGTTAGAATCCTGCTACCACCAAATGACAGAAATTACTTGAATGCATTATGGGGGAAGTTAGCTTCAGAGATTCTCATGCAAACGACCAGTG\r\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "#\n",
      "#  NOTE THIS REQUIRES File specific knowledge\n",
      "#\n",
      "##to make sure we don't get description name issues \n",
      "!sed 's/Mmercenaria_Contig/999999/g' <fasta2tab> fasta2tab_c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#add column with length of sequence\n",
      "!perl -e '$col = 2;' -e 'while (<>) { s/\\r?\\n//; @F = split /\\t/, $_; $len = length($F[$col]); print \"$_\\t$len\\n\" } warn \"\\nAdded column with length of column $col for $. lines.\\n\\n\";' \\\n",
      "fasta2tab_c > tab_1\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Added column with length of column 2 for 8482 lines.\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wc tab_1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    8482   25446 2353451 tab_1\r\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#this counts CGs\n",
      "!awk -F\\CG '{print NF-1}' tab_1 > CG"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk -F\\C '{print NF-1}' tab_1 > C"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk -F\\G '{print NF-1}' tab_1 > G"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!paste tab_1 \\\n",
      "CG \\\n",
      "C \\\n",
      "G \\\n",
      "> comb\n",
      "!head -1 comb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "999999_1\t\tAAGAGTGACTGGTACCACCTGTGTACTACAATGGTTATTTGATACAACTAAATGTAAGCGGTACCACCATGTATTACAATGTGAAATTAGTATCAATAAGTGTGGCTGGTACCTTTATATATTACAGGTGCTGTTATGTTTGACAGGAATACTGATGTGAGATAGTTACTTCCATACTATGTGTAACCTACGGTCCGGCACGTTGAATGGTGGGGTG\t217\t4\t34\t52\r\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Calculating CpGo/e"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://eagle.fish.washington.edu/cnidarian/skitch/BMC_Genomics___Full_text___DNA_methylation_patterns_provide_insight_into_epigenetic_regulation_in_the_Pacific_oyster__Crassostrea_gigas__1A0683A5.png\" alt=\"BMC_Genomics___Full_text___DNA_methylation_patterns_provide_insight_into_epigenetic_regulation_in_the_Pacific_oyster__Crassostrea_gigas__1A0683A5.png\"/>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk '{print $1, \"\\t\", (($4)/($5*$6))*(($3**2)/($3-1))}' comb > ID_CpG\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head ID_CpG"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "999999_1 \t 0.493223\r\n",
        "999999_2 \t 0.254657\r\n",
        "999999_3 \t 0.129997\r\n",
        "999999_4 \t 0.95738\r\n",
        "999999_5 \t 1.15181\r\n",
        "999999_6 \t 0.40192\r\n",
        "999999_7 \t 0.145004\r\n",
        "999999_8 \t 0.71746\r\n",
        "999999_9 \t 0.346284\r\n",
        "999999_10 \t 0.442564\r\n"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sed 's/999999/Mmercenaria_Contig/g' <ID_CpG> ID_CpG.tab\n",
      "!head ID_CpG.tab\n",
      "!echo line number\n",
      "!wc -l ID_CpG.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mmercenaria_Contig_1 \t 0.493223\r\n",
        "Mmercenaria_Contig_2 \t 0.254657\r\n",
        "Mmercenaria_Contig_3 \t 0.129997\r\n",
        "Mmercenaria_Contig_4 \t 0.95738\r\n",
        "Mmercenaria_Contig_5 \t 1.15181\r\n",
        "Mmercenaria_Contig_6 \t 0.40192\r\n",
        "Mmercenaria_Contig_7 \t 0.145004\r\n",
        "Mmercenaria_Contig_8 \t 0.71746\r\n",
        "Mmercenaria_Contig_9 \t 0.346284\r\n",
        "Mmercenaria_Contig_10 \t 0.442564\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "line number\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    8482 ID_CpG.tab\r\n"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Now joining with GOslim"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Using join bash"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sort ID_CpG.tab > t1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head t1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mmercenaria_Contig_1 \t 0.493223\r\n",
        "Mmercenaria_Contig_10 \t 0.442564\r\n",
        "Mmercenaria_Contig_100 \t 0.891529\r\n",
        "Mmercenaria_Contig_1000 \t 0.724268\r\n",
        "Mmercenaria_Contig_1001 \t 0.860937\r\n",
        "Mmercenaria_Contig_1002 \t 0.48348\r\n",
        "Mmercenaria_Contig_1003 \t 0.543491\r\n",
        "Mmercenaria_Contig_1004 \t 0.692323\r\n",
        "Mmercenaria_Contig_1005 \t 0.461004\r\n",
        "Mmercenaria_Contig_1006 \t 0.871567\r\n"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sort /Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/merc_slim > t2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head t2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Column1 \t GOSlim_bin\r\n",
        "Mmercenaria_Contig_1 \t DNA metabolism\r\n",
        "Mmercenaria_Contig_1 \t DNA metabolism\r\n",
        "Mmercenaria_Contig_1 \t other metabolic processes\r\n",
        "Mmercenaria_Contig_10 \t cell cycle and proliferation\r\n",
        "Mmercenaria_Contig_10 \t cell organization and biogenesis\r\n",
        "Mmercenaria_Contig_10 \t other biological processes\r\n",
        "Mmercenaria_Contig_10 \t other biological processes\r\n",
        "Mmercenaria_Contig_100 \t cell cycle and proliferation\r\n",
        "Mmercenaria_Contig_100 \t cell cycle and proliferation\r\n"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!join -t $'\\t' t1 t2  | uniq  > t3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head t3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mmercenaria_Contig_1 \t 0.493223\t DNA metabolism\r\n",
        "Mmercenaria_Contig_1 \t 0.493223\t other metabolic processes\r\n",
        "Mmercenaria_Contig_10 \t 0.442564\t cell cycle and proliferation\r\n",
        "Mmercenaria_Contig_10 \t 0.442564\t cell organization and biogenesis\r\n",
        "Mmercenaria_Contig_10 \t 0.442564\t other biological processes\r\n",
        "Mmercenaria_Contig_100 \t 0.891529\t cell cycle and proliferation\r\n",
        "Mmercenaria_Contig_100 \t 0.891529\t cell organization and biogenesis\r\n",
        "Mmercenaria_Contig_100 \t 0.891529\t other biological processes\r\n",
        "Mmercenaria_Contig_1000 \t 0.724268\t other metabolic processes\r\n",
        "Mmercenaria_Contig_1000 \t 0.724268\t protein metabolism\r\n"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wc -l t3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   12871 t3\r\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jData = pd.read_table('t3', header=None)\n",
      "jData"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 12871 entries, 0 to 12870\n",
        "Data columns (total 3 columns):\n",
        "0    12871  non-null values\n",
        "1    12871  non-null values\n",
        "2    12871  non-null values\n",
        "dtypes: float64(1), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 12871 entries, 0 to 12870\n",
        "Data columns (total 3 columns):\n",
        "0    12871  non-null values\n",
        "1    12871  non-null values\n",
        "2    12871  non-null values\n",
        "dtypes: float64(1), object(2)"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jData.groupby(2)[1].mean().plot(kind='barh', color=list('myb'))\n",
      "plt.axis([0.5, 0.7, 0, 14])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "[0.5, 0.7, 0, 14]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5+fkJTp8+Pflt8xUKhTw3N7cYAICYmBi3jRs3fvchcSKEEELvqttVOH7++eeV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\nIYRQn8MGB0IIIYT6HDY4EEIIIdTn/h/q/DGBtAcwegAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x15cab9950>"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pandas density plot\n",
      "jData[1].plot(kind='kde', linewidth=3);\n",
      "plt.axis([-0.3, 1.5, 0, 1.7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "[-0.3, 1.5, 0, 1.7]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YWNjizTff/GrUqFEbq3s/rVYLjUYDALC2toa3t3f5iouGUQ1KLRuOifH7Vq4E\nAFbWav3RqpX0f79UZQO5xGNq5SVL/DF+PADosHgxEBYmr/hMsazT6RAdHQ0A5Z+XxhKso3nHjh0h\n8fHxA1etWjURADZt2jQyKSnJd8WKFVMN50yZMiUyNTW12/79+18oKipq3qtXr6N79ux5ydnZObNS\nkNTR3Chu3gSeeAIoLmblM2doI3YiHI5j62gdOMDK3t5AcjK/7zMRnqw6mu3s7PJycnIcDOWcnBwH\nQzORgYODQ86LL77432bNmt1t06bNjeeee+5genq6l1AxyVXVb65C2baNTwg+PuadEMSqc3OmUlXe\nzjUtTYelS6WNidROsKTg4+NzPDMz0zk7O1tTXFzcdNu2bcODg4Mrbfk9aNCgmEOHDj1bWlpqUVRU\n1DwpKcnX3d09Q6iYzN369fzzMWOki4OYDycnICKCL8+dy2/QQ+RJ0HkKcXFxAdOnT19WWlpqMX78\n+DWzZs36LCoqKhwAwsPDowBg8eLF76xbt25skyZNyiZOnLhq2rRpyx8IkpqPGuzcObb/MsBu3y9f\nBtq0kTYmYh5KSoAePdjyFwBbXTUmRtqYzEV9mo9o8pqZmDWLX4Zg6FC2Vg0hYjl2jG3EY0ATJsUh\nqz4FUndCt2+XlgIbK4zpoqYj6lMQW8+eQGCgrrw8bRpw96508ZCaUVIwAwcOAHl57Hm7dmx3NULE\nNmkS0Lo1e56VVXkBPSIf1HxkBkaOZEtbAMCbbwLLlkkbDzFf330HhIez51ZWbFi0k5O0MZky6lMg\nD7hzB3j8cf5WnTbSIVIqLWV9CykprBwQAOzZw+/1TBoX9SkolJDt29u38wnB05NNICLUpyAFnU4H\nCwu2YJ4hCcTFAT/9JG1cpDJKCiau6twE+kZGpObjA7z+Ol9+8022nDuRB2o+MmGXLrGlsQHAwgLI\nzWVNSYRI7eZNwMUFyL+/BOaHHwKffiptTKaImo9IJRs28M8HDqSEQOSjdWu2ZafBF18AmZk1n0/E\nQ0lBBoRo3y4rq5wUaG5CZdSnIL6qda7VAr73100uLmbNSNQgID1KCibq0CE2Fhxg69oHBUkbDyFV\nNWkCREZW7nTevVvamAj1KZis8eOBtWvZ89dfx/19FAiRn9dfZ6upAoBGA2RkAM2aSRqSyaA+BQIA\nKCoCfvyRL1PTEZGz+fP5mc7Z2ZX7Goj4KCnIQGO3b+/aBRQWsuedO/PttoRHfQriq6nO27QBFizg\nywsXspFAk0nbAAARzElEQVRzRBqUFEwQzU0gSjNhAtC9O3t+7x7w1lvSxmPOqE/BxOTmAh06sFEc\nKhXwv/8BDg61/xwhUktKYqupGuzZAwQGShePKaA+BYJNm/hhfS+8QAmBKIevLxsgYTBtGvDvv9LF\nY64oKchAY7VvcxwQHc2XqYO5ZtSnIL661Plnn7Eh1ABw8SKweLGwMZEHCZoU4uPjB7q6up5zdnbO\nXLRo0cyazktJSelhaWlZsnPnzqFCxmPqDh8Gzp9nz1u0AIYMkTYeQozVrl3l5S4WLGBNoEQ8gvUp\nlJaWWri4uJzft29fPzs7u7wePXqkbNmyZYSbm9vZquf179//1+bNmxeNHTt2XUhIyAMbRVKfQt1o\ntXwn86RJ/NhvQpSktJQtmmfY05m2j60/WfUpJCcnP+3k5HRBo9Fkq9VqfVhY2NaYmJhBVc9bsWLF\n1NDQ0O3t2rW7LlQs5uD27cpzEyZMkC4WQhrCwgL4+mu+vHMn8N//ShePubEU6o3z8vLsHBwccgxl\ne3v73KSkJN+q58TExAw6cOBA35SUlB4qlarG2wGtVguNRgMAsLa2hre3N/z9/QHwbZVKLS9btqzB\nf8/u3UBRESs7Ourw998AII+/T47ltLQ0TJ8+XTbxmEPZcKyu548Z43//zleH8eOBixf90bSpfP4e\nOZZ1Oh2i73csGj4vjcZxnCCP7du3h0yYMGGVobxx48aRU6ZMWVHxnNDQ0B+PHTvmy3EcxowZE719\n+/aQ6t6LhWm6EhISGvwePj4cx7qaOW758obHZOoao86JcYyt8ytXOK5lS/66XrhQmLhM2f3PTqM+\nuwW7U7Czs8vLyckpHxCZk5PjYG9vn1vxnBMnTnQPCwvbCgD5+flt4+LiAtRqtT44ODhWqLjkyJDx\n6ystDTh+nD23sgJee63hMZm6htY5MZ6xdW5rC3z8MXD/hg4REax/wdm58WMjFRibRer60Ov1lo6O\njhezsrI09+7da+rl5ZWWkZHhVtP5Wq123Y4dO4ZW9xpM/E6hocaO5b9NjRghdTSENB69nuO8vPjr\n28+P40pLpY5KOVCPOwXBOpotLS1LIiMjpwwYMOAXd3f3jOHDh29zc3M7GxUVFR4VFRUu1O9Voopt\nrsa6cgXYvJkvT53a8HjMQUPqnNRPferc0pKt9mthwcq//Va5E5o0PlrmQgZ0Ol29mzPmzAE++YQ9\n79kTOHq08eIyZQ2pc1I/Danzjz7i5y80bw6kpwNOTo0Xm6mqz5BUSgoKVlgIdOwI3LjByj/8AAwb\nJm1MhAjh3j22YN7vv7Oyjw+brNm0qbRxyZ2s5ikQ4X35JZ8QNBqawUxMl5UV215WrWbl48eBDz+U\nNiZTRUlBBurT1nr9OrBkCV+eN4+1v5K6oT4F8TW0zrt1Y3stGCxeDMTHNywm8iBKCgo1dy6/kY67\nOzBypLTxECKG6dOBgAC+/OqrQGamdPGYIupTUKB9+4D+/fnyzp3UdETMx7VrgLc3cPkyK7u4sAEW\nNjbSxiVH1KdgBm7cAMaN48vBwcDgwdLFQ4jY2rcHYmKARx5h5fPngVdeAfR6aeMyFZQUZKCuba03\nbwIvvQTk3F9Rqk0bthIqbbdpPOpTEF9j1nmPHpW3nd23Dxg+nBJDY6CuSRnIzmZzDTIz2Qd8y5aA\noyPQqRN7WFsDOh0wezY7F2DnrVkDPP64hIETIqFXXgHOnWP9awCwaxcQFgZs3cqPUiLGoz4FCd28\nCUyZAmzZYvzPRkYCb7zR+DERoiQcB7z7buWReIGBLDG0aCFdXHJBk9cUJCcH6NsXuHDBuJ+zsQG+\n+w4IDRUmLkKUhuOAd95h83YMPD3ZZE5XV+nikgNKCgpRUMCWpDBsnQnoMHiwP156ic01yM9n+9Ne\nusT+e/s2a0Z6+WXg9deBtm2ljN400DIX4hOyzjmOLYUxfz5/rFkzNpdh8mTz7XerT1KgPgWRcRwb\nPWRICGo1m5lpaBclhBhPpWJrIzk7s61oi4uBu3dZE+sPP7Dk4OMjdZTKQHcKItu5EwgJ4ctbtrDO\nMUJI4zh1iu0pcuZM5ePDhwPvvcdmRpsLaj6SucJCwM0NyMtj5fBw4NtvpY2JEFP077+sOWnZMqCk\npPJrffoAb7/NhncbluQ2VTR5TeY+/ZRPCO3b8+u40Jh58VGdi0/MOn/kEeCLL9iqqlVn+ycmAoMG\nAZ07s87pggLRwlIESgoiuXYNWLGCLy9ZwuYfEEKE07kza7JNTgZGjKh8Z3DpEjBjBmBnxzqjDcty\nmztqPhLJO+/wY6m9vYHUVPMdEUGIVHJy2ByfVauAW7cefP2FF9iKw88+K3pogpBl81F8fPxAV1fX\nc87OzpmLFi2aWfX1zZs3v+bl5ZXepUuXU7179z586tSpLkLHJLZr14BvvuHLc+dSQiBECg4OwKJF\nQG4uSwxdqnza7N8P+PmxlVjT06WJUXLGbupszKOkpMSiU6dOF7KysjTFxcVqLy+vtIyMDLeK5xw5\ncqRXQUFBK47jEBcXN9DX1/dY1fdhYSrX3Ln8xuPe3hxXVlb59YSEBCnCMmtU5+KTY52XlXFcYiLH\nhYZynIUF/+8U4LgmTThu+nSOu31b6ijr7/5np1Gf24LeKSQnJz/t5OR0QaPRZKvVan1YWNjWmJiY\nQRXP6dWr19FWrVrdBgBfX9+k3NxceyFjEtu9e8DKlXz5/ffpLoEQuVCpgOeeA378EfjjD2D0aKDJ\n/U/FsjI2esnVFdi2jaUKcyBoUsjLy7NzcHDIMZTt7e1z8/Ly7Go6f82aNeMDAwP3ChmT2LZuZc1H\nAGBvDwwd+uA5NLNWfFTn4pN7nTs6spVXT51iS9AYXL7M5hINHgz89Zd08YlF0BnNKpWqzrk1ISHh\n+bVr1447fPhw7+pe12q10Gg0AABra2t4e3uXX2SGoW5yK/fp44+vvgIAVn7jDX+o1fKJj8pUpvKD\n5evXdZg9G5gwwR9vvw1cucJej431R2IiMGmSDgEBwPPPyyPeimWdTofo6GgAKP+8NJqx7U3GPI4e\nPdpzwIAB8YbyggULZi1cuHBm1fPS09O7dOrU6UJmZqZTde8DhfYpJCby7ZPNmnFcfn7158mxrdXU\nUZ2LT4l1XlDAca+/XrmvAeC4fv04LitL6uhqB7n1Kfj4+BzPzMx0zs7O1hQXFzfdtm3b8ODg4NiK\n5/z5558dhg4dunPTpk0jnZycjFwzVN7YXQIzahTbFIcQohytWrE+wYQEtiilwb59gIcHm3tUViZd\nfEIQfJ5CXFxcwPTp05eVlpZajB8/fs2sWbM+i4qKCgeA8PDwqAkTJqzetWvXkA4dOvwJAGq1Wp+c\nnPx0pSAVOE8hO5tdRIYL5swZ4KmnJA2JENIARUXAnDnA0qWVE8Gzz7INrzp3li62mtDaRzJScbJa\nv37Ar79KGw8hpHEkJbGVjjMy+GNWVsDHH7M1lSxltPa0LCevmaPCQmD1ar785psPP9/QUUTEQ3Uu\nPlOpc19ftiLB7Nl8Arh3D5g5k+2Tcvq0tPE1FCUFAaxfzzbGAdgtZWCgtPEQQhqXlRXbVz0lBeja\nlT9+4gRbmvv999mXQyWi5qNGVlYGuLjw22x+/TXwn/9IGxMhRDh6PdvEJyKC3TEY2NoCCxYAY8ZI\nt0Q39SnIwO7dQHAwe25tzRbgeuwxaWMihAjv3Dlg/HjgyJHKx7t2ZSMR/fzEj4n6FGRg2TL++cSJ\ndUsIptLWqiRU5+Iz9Tp3dQV++w3YuJEtx21w8iRbSuOVV9ioRLmjpNCIjh8HDhxgzy0sgClTpI2H\nECKuJk2AkSPZHuxz5rDNfgx+/JEljg8/BP7+W7oYa0PNR41o0CAg9v7UvLAwtv8yIcR8/fkn63Su\n+lnwxBPAZ5+xSa1NBPxqTn0KEkpPZ5vnGNBkNUKIwZEjbGj68eOVj/v4sCbn3tWu+NZw1Kcgoblz\n+echIcYlBFNva5UjqnPxmXOdP/MMm/QWHc3uEgyOH2czokeMYHcVckBJoRHodEBMDF+ePVuyUAgh\nMtWkCRue+scfrF/Byop/betWNpR9zhzgn3+kixGg5qMGKy0FevRgIwwA1sm0caO0MRFC5C87m82C\n/uGHysft7ICFC4FXX214fwM1H0ng22/5hNCsGes8IoSQ2mg0bEe3gwfZLGiDvDzWAf3MM2x1VrG/\nD1NSaIDMTODdd/nye++x3dWMZc5trVKhOhcf1Xn1/PyA5GS20qqtLX88KYntANe9O7Bhg3jDWCkp\n1NO//7Kmort3WdnTE5g1S9qYCCHKZGHBVl7NzGRDWJs25V87eZL1Rdjasg7pzZuBK1eEi4X6FOqh\nrIy1923bxspqNcv0FYekEkJIfWVlAV98wUYrGb54VuXsDHh5AV26AB07Ag4O7NG+PfDoo4BKRfMU\nRFFSwha4W7WKP7Z0KTB9unQxEUJM040b7LNm48bK+zfURq0GWrcGrl6ljmZBXb3KFrurmBDeeKP2\n/RJqQ22t4qM6Fx/VufHatGHNSWfOsAmyERFAnz6Vm5eqo9ezz6v6EDQpxMfHD3R1dT3n7OycuWjR\nopnVnTNt2rTlzs7OmV5eXuknT57sWt05Uvv7b7bKoZsbEBfHH3/tNTYbUWVUHn5QWlpaw96AGI3q\nXHxU5/WnUrFmojlz2LyoggK2l8OaNcCMGWyxvV692ECXZs0a9rsE2ziutLTUYsqUKZH79u3rZ2dn\nl9ejR4+U4ODgWDc3t7OGc/bu3Rt44cIFp8zMTOekpCTfyZMnrzx27FhPoWKqC44D7txht2qnT7MF\n7uLi2LGKZs0CPv20cdYtKSgoaPibEKNQnYuP6rzxNGvGlsjw8an+9bt3gVu3Kq/WWleCJYXk5OSn\nnZycLmg0mmwACAsL2xoTEzOoYlKIjY0NHjNmzHoA8PX1TSooKLC+evWqra2t7QM3PoGB7AP7YY+y\nstrPqek8vR64eZO14RUX1/x3dewIREUB/fs3epURQkijaNas/ncMgiWFvLw8OwcHhxxD2d7ePjcp\nKcm3tnNyc3Ptq0sKFZttpNC5M+tMHjMGaN68cd87WwmLrJsYqnPxUZ0rg2BJQaVS1Wm4UNWe8Zp/\nroEN9w30xx9s1JFQW2uuX79emDcmNaI6Fx/VufwJlhTs7OzycnJyHAzlnJwcB3t7+9yHnZObm2tv\nZ2eXV/W9jB1SRQghpH4EG33k4+NzPDMz0zk7O1tTXFzcdNu2bcODg4NjK54THBwcu2HDhtEAcOzY\nsZ7W1tYF1TUdEUIIEYdgdwqWlpYlkZGRUwYMGPBLaWmpxfjx49e4ubmdjYqKCgeA8PDwqMDAwL17\n9+4NdHJyuvDoo4/+s27durFCxUMIIaQOOI6jhwiPuLi4gS4uLuecnJwyFy5cOLO6c6ZOnbrcyckp\ns0uXLumpqaldpY5Z6Y/a6jwhIcG/ZcuWt729vU96e3uf/OSTT2ZLHbPSH2PHjl3bvn37qx4eHqdr\nOoeuc3Hr3NjrXPI/yBweJSUlFp06dbqQlZWlKS4uVnt5eaVlZGS4VTxnz549gQEBAXs5jsOxY8d8\nfX19j0kdt5IfdanzhIQE/6CgoFipYzWlx8GDB/1SU1O71vQBRde5+HVu7HVOy1yIoOKcDbVarTfM\n2ah4Tk1zNqSJWPnqUucADWJobH5+fr/Z2Njcqul1us4bX211Dhh3nVNSEEF18zHy8vLsajsnNze3\nHrszEKBuda5SqbgjR4484+XllR4YGLg3IyPDXfxIzQtd5+Iz9joXrKOZ8Bp/zgapTV3qrlu3bqk5\nOTkOzZs3L4qLiwsYPHjwT3/88UdnMeIzZ3Sdi8vY65zuFETQmHM2SN3Upc5btGhR2Lx58yIACAgI\niNPr9eqbN2+2FjtWc0LXufiMvc4pKYiA5myIry51fvXqVVvDt9bk5OSnOY5TtW7d+qY0EZsHus7F\nZ+x1Ts1HIqA5G+KrS51v3749dOXKlZMtLS1LmjdvXrR169YwqeNWuhEjRmxJTEzsk5+f39bBwSEn\nIiJirl6vVwN0nQultjo39jpXxM5rhBBCxEHNR4QQQspRUiCEEFKOkgIhhJBylBQIIYSUo6RACCGk\nHCUFQggh5f4fCscrsrlGPcQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x161d48410>"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Using SQLShare"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py \\\n",
      "-d merc_ID_CpG \\\n",
      "ID_CpG.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "processing chunk line 0 to 8482 (0.342339992523 s elapsed)\r\n",
        "pushing ID_CpG.tab...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "parsing E6A86761...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "finished merc_ID_CpG\r\n"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py \\\n",
      "-d merc_slim \\\n",
      "/Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/merc_slim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "processing chunk line 0 to 26861 (0.010213136673 s elapsed)\r\n",
        "pushing /Volumes/web/scaphapoda/Grace/Transcriptomes/mercenaria/merc_slim...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "parsing ECA84710...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "finished merc_slim\r\n"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py \\\n",
      "-s \"SELECT * FROM [sr320@washington.edu].[merc_slim]sl left join [sr320@washington.edu].[merc_ID_CpG_1.tab]cpg on sl.Column1=cpg.Column1\" \\\n",
      "-f tsv \\\n",
      "-o mercCPG_slim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wc -l mercCPG_slim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   26861 mercCPG_slim.txt\r\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!uniq mercCPG_slim.txt | wc -l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   19920\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#removing duplicate go slims\n",
      "#\n",
      "# NOte could have also uniq goslim file before joiing\n",
      "#\n",
      "!uniq mercCPG_slim.txt > cpg_slim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head mercCPG_slim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Column1\tGOSlim_bin\tColumn1\tColumn2\r",
        "\r\n",
        "Mmercenaria_Contig_1\tother metabolic processes\tMmercenaria_Contig_1\t0.493223\r",
        "\r\n",
        "Mmercenaria_Contig_1\tDNA metabolism\tMmercenaria_Contig_1\t0.493223\r",
        "\r\n",
        "Mmercenaria_Contig_1\tDNA metabolism\tMmercenaria_Contig_1\t0.493223\r",
        "\r\n",
        "Mmercenaria_Contig_10\tother biological processes\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_10\tcell cycle and proliferation\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_10\tother biological processes\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_10\tcell organization and biogenesis\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_100\tcell cycle and proliferation\tMmercenaria_Contig_100\t0.891529\r",
        "\r\n",
        "Mmercenaria_Contig_100\tother biological processes\tMmercenaria_Contig_100\t0.891529\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head cpg_slim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Column1\tGOSlim_bin\tColumn1\tColumn2\r",
        "\r\n",
        "Mmercenaria_Contig_1\tother metabolic processes\tMmercenaria_Contig_1\t0.493223\r",
        "\r\n",
        "Mmercenaria_Contig_1\tDNA metabolism\tMmercenaria_Contig_1\t0.493223\r",
        "\r\n",
        "Mmercenaria_Contig_10\tother biological processes\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_10\tcell cycle and proliferation\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_10\tother biological processes\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_10\tcell organization and biogenesis\tMmercenaria_Contig_10\t0.442564\r",
        "\r\n",
        "Mmercenaria_Contig_100\tcell cycle and proliferation\tMmercenaria_Contig_100\t0.891529\r",
        "\r\n",
        "Mmercenaria_Contig_100\tother biological processes\tMmercenaria_Contig_100\t0.891529\r",
        "\r\n",
        "Mmercenaria_Contig_100\tcell cycle and proliferation\tMmercenaria_Contig_100\t0.891529\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wc -l cpg_slim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   19920 cpg_slim.txt\r\n"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#pData = pd.read_csv('https://dl.dropbox.com/u/7710864/data/csv_hid/ss06pid.csv')\n",
      "pData = pd.read_table('cpg_slim.txt')\n",
      "pData"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 19919 entries, 0 to 19918\n",
        "Data columns (total 4 columns):\n",
        "Column1       19919  non-null values\n",
        "GOSlim_bin    19919  non-null values\n",
        "Column1.1     19919  non-null values\n",
        "Column2       19919  non-null values\n",
        "dtypes: float64(1), object(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 19919 entries, 0 to 19918\n",
        "Data columns (total 4 columns):\n",
        "Column1       19919  non-null values\n",
        "GOSlim_bin    19919  non-null values\n",
        "Column1.1     19919  non-null values\n",
        "Column2       19919  non-null values\n",
        "dtypes: float64(1), object(3)"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pData.groupby('GOSlim_bin')['Column2'].mean().plot(kind='barh', color=list('myb'))\n",
      "plt.axis([0.5, 0.7, 0, 14])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "[0.5, 0.7, 0, 14]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6+lBYUqjqMBACABzDgToYgiDIBMDnYlHHIAABPjaMlALHcHyg4uLiLvv27ZtL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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12ee87650>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pData.groupby('GOSlim_bin')['Column2'].median().plot(kind='barh', color=list('myb'))\n",
      "plt.axis([0.5, 0.75, 0, 14])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "[0.5, 0.75, 0, 14]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HuVAuHFJpYnQ6XZqens5UdRxIzsFB1RE0DX19mqpDQAiheuGQCmpXPt4NpOow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\nP31jPQR8BKx3AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12de60a50>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pData.groupby('GOSlim_bin')['Column2'].count().plot(kind='barh')\n",
      "#plt.axis([0.5, 0.75, 0, 14])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x104f10610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BIx0YDAaDwWBIBV4yi+lw4NT2GAymvUGWuTk40oHpgCASm0AFNHRU/WTWjvUr\n31pXf3l5CZCBDtXpKC0t7bZ///4FhC8UCnnEBlyqQFhYmOfLly97f6wcj8cTikQizqfWGxoayvf1\n9d0DAHDw4MF5x48fnyWPTowy4SlbgJzwlC1ADnjKFiAnPGULkBOesgXICU/ZAlSCDtXpKCkp0d+3\nb99CRdVXX1+vrqi6ABo6BwUFBX0+Vu5zl33Klp83b97BWbNmHf8Sfc0hlUo71PsIg8FgMF9Gu/2w\n2Llz5090Ol1Mp9PFu3fvXgoA4O/vH/To0aMBbDY7w8/PbyuFQkEVFRXa7u7u4SYmJndnzpx5grhe\nJBJxeDye0NzcPG3cuHGXX7161QugIcqwbNmy34YNG3Y7ODh4iWybgYGBgZ6enmE2NjYJVCpVEhkZ\n6bZixYrtDAYje/z48X8R+VOaqjsiImJKWlqa+YwZM06amZmlV1dXa27YsGEdl8tNpdPp4nnz5h2U\nbev48eOz2Gx2Bp1OF9++fXsYQEMeFldX1ygmk5llaWl5s6ldUAMDAwN37NixHADg4cOHA+3t7f9m\nsViZHA5H9Pjx4/6yZSUSCXXIkCH3Zs6ceWLo0KF33N3dw4nEcVQqVeLv7x/E4XBE4eHh7n/++ec0\nBoORTafTxf7+/kFEHZcvXx7H4XBELBYr097e/m+Ahjwq3t7eRy0sLFLMzMzSo6OjJwIA5ObmmlpY\nWKSw2ewMJpOZ9ejRowGVlZVajo6OsSwWK5NOp4vPnj3r0dLvJzg4eImpqWkuk8nMIjLbti+EyhYg\nJ0JlC5ADobIFyIlQ2QLkRKhsAXIiVLYA1UDZa8dbw9LS0jh0Oj373bt3XSsqKrRMTU1zMjIyWBKJ\nxIhGo4ll1q3zunXr9vbFixd9pFIpxdLSMjkxMdGqpqZGw9LSMvnNmzcGCCE4ffr0VG9v7yMIIeDx\neIJFixbtbardgICAQGtr64S6ujr1rKwsRteuXd9dvnx5LEIIJk2aFBkVFeXysbpFIpEZUV9xcbE+\n8XrWrFnHYmJinBBCMGrUKOHcuXMPIoQgISHBmrinxYsX79mwYcNahBBcu3bNlsViZSCEICQkhL94\n8eI9CCEIDAwM2LFjx08IIeByuSlRUVEuCCF4//5953fv3nWVvZ/8/HwqhUKRJicnWyKEwNvb+8j2\n7duXI4SASqXmb9u2bQVCCF68eNHH0NDwyZs3bwzq6urUR48eHR8VFeXy+vXrHn379n0qkUiMEEJQ\nUlKihxCOieRcAAAgAElEQVSC1atX/3rixIkZxLHBgwfnVVZWfuXr6xt88uTJ6QghqK2t7VRVVaUZ\nEREx2cfH5xChqbS0VLelZ9inT58XNTU1GkTZxr8jAEAAiMQmUAENHVU/mbVj/cq31tYPqDX5UD/I\na+1y9UpiYuJINze3yK5du1YBALi5uUXeuHHDeuLEidGNy3K53NQ+ffoUAACwWKxMiURC7datW2lu\nbq4p8c28vr5enSgDADB16tQzTbVLoVDQ+PHj/1JXV6+n0Wg5UqlUbezYsVcAAOh0ulgikVDv378/\nuKW6kcw66GvXro3etm3bynfv3n1VXFzcnUaj5Tg5OV2kUCiI+BZvbW19o6ysTLe0tLRbUlKSVWRk\npBsAgK2traCoqMigvLxcpymtFRUV2gUFBX1cXFwuAPw3PXxj+vbt+8zS0vImAMDMmTNPBAcHL1m+\nfPkO2edw+/btYba2tgIDA4MiAIAZM2acTEhIsFFXV6+3sbFJMDIyegIAoKen9xYAIC4ubkxMTIzz\n9u3bVwAAvH//vsvTp08NLS0tb27evPmX58+ff+fm5hY5cODAhwwGI3vFihXb/f39g5ycnC6OHDky\nMScnh9bcM2QwGNnTp08/5erqGuXq6hrV1D0B8AGA+uG1HgCw4L/jrcIPP1XVJ46pip7P9YljqqLn\nc3yeiun5XJ+nYno+1+epmJ7P9Xmt3h6RyZbY+VQeXygUQmhoKAAAUKlUUBTtstPxYQOo///wRghR\nmpsH0aVLl/fEa3V19XpiCMTU1DQ3OTl5RFPXaGlpVTbXNvHhraamJtXQ0KgljqupqUnr6uo6IYQo\nLdVN6KyurtZctGjR7yKRiPPtt9++WL9+fUB1dbVmS/dM3GtTx78U2esbP0fiOTT1vD9Wb2RkpNug\nQYMeyB4bMmTIveHDh9+6ePGi04QJEy4dPHhwnq2trSAjI4MdGxvruGbNmk12dnbxkyZNOt/cM4yN\njXVMSEiwiYmJcd68efMvYrGYrq6uXv/vUqEtKONhH/vYxz4p/cbbrMvj83i8f/nr168HRaCmkFpU\nDGtr6xtRUVGuVVVVXSsrK7WioqJcra2tb2hra1c0982fgEKhIGNj47zCwsIet27dGg4AUFtbq3Hn\nzp2hitDWUt06OjrlZWVlugANnQ4AAAMDg6KKigrt8PBwd6IOhBDlzJkzUwEaojp6enpvdXV1y6yt\nrW+cPHlyBkDDypwePXoUamtrV8i2jxCiIIQo2traFd99993zCxcuuAA0RBuI+RqyPH361JDQeurU\nqenW1tY3GpcZNmzY7evXr48qKioyqK+vVz99+vT3PB5POHz48FsJCQk2EomECtAw5wQAYOzYsVdk\n58NkZGSwAQDy8/P79evXL9/X13ePi4vLhezsbMbLly97a2pqVs+YMePkihUrtmdkZLCbe4YIIcrT\np08NeTyeMCgoyL+0tLRbZWWl1hf+qlQUobIFyIlQ2QLkQKhsAXIiVLYAOREqW4CcCJUtQCVol5EO\nNpudwefzQ7lcbioAgI+Pz2Emk5kFAGBlZZVEp9PFEyZMuDRhwoRLTUUCNDQ0aiMiIqYsWbIkuLS0\ntFtdXV2nZcuW/TZ06NA7H2tbtr7GdVMoFNRS3Xw+P3T+/PkHvvrqq3fJyckjfHx8DtNotJxevXq9\nsrCwSJGtR1NTs9rMzCy9rq6u09GjR70BGiaJent7H2UymVlaWlqVYWFhnkR5Qovs6+PHj8+aN2/e\nwXXr1m0gdFGpVImsZmNj47zff/99kbe391FTU9PcBQsW7G98b717934ZFBTkb2trK0AIUZycnC4S\nS5EPHTo0183NLVIqlap98803/1y5cmXs2rVrN/7444+7GAxGtlQqVevfv//j6OjoiWfPnvU4fvz4\nLA0NjdrevXu//OWXXzanpqZyV65cuY2IHB04cGB+c89w8ODB92fNmnW8tLS0G0KIsnTp0t26urpl\nH/udYTAYDKZtwLlXMM0ikUiozs7OMU2tgiErDZ0l/J7HYDDtDQq05uc5zr2CaRNwKngMBoPBKAoc\n6cB0KHAnCoPBtEdaO/cKjnRgMF+IItaaK8sEAoHSNXRU/WTWjvUr31pbPxmSvQHgSAemg/Fhea+y\nZWAwGAypwJEODAaDwWAwpKJdLpnFYFqCQpG7s47BYDAKo7XnY6gSHS7S0drp7WUTqslSUFDQx93d\nPbyla+XVYmVllfQl1zWnuf2CSGwCFdDQUfWTWTvWr3xrXn95eQl0FDpcp6O109s3tzqiT58+BbK7\nirYGSUlJVl9ynTwrOoht4zFtBU/ZAuSEp2wBcsBTtgA54SlbgJzwlC1ATnjKFqASfFKnIy8vz9jH\nx+ewg4PDVVtbW4Gtra1g9OjR11pbnLwoI709AEBWVhZzxIgRyYMHD77/xx9//ADQsNEWnU4XAzRs\nce7l5RXCYDCyzczM0oVCIa9xHc2lqS8sLOzh4OBwlUaj5fj4+BymUqkSYntx2S3Pt2zZsorBYGSz\nWKzMn3/++VcAgMOHD/twudxUFouVOWXKlIimtj2XhdghddiwYbeNjY3zYmNjHQEAQkND+RMnToy2\ns7OLd3BwuFpSUqLflNaKigpt4j6ZTGYWkYwuLi5uzIgRI5I5HI7Iw8PjLLFVub+/fxCRlt7Pz28r\nAEB4eLg7nU4Xs1iszFGjRl0HaOjorVy5chuXy01lMplZhw4dmgsA8PLly942NjYJbDY7g06nixMT\nE0d+wtsEg8FgMG3FpyzFodPp2fv27Vtw69Yti9u3b5vfvn3bPC0tjaPsJUgtmTLT2zOZzMzq6uou\nb968Mejbt+/Tly9f9srPz6cS7W7fvn35nDlz/kAIwb1794wNDQ2fVFdXdxEIBDwnJ6cYhJpPU79o\n0aK9QUFBqxBCcPny5bEUCkVaVFTUHSEE2tra5QghuHTp0vgRI0YkVVVVaSKEoLi4WB8hBEQ5hBCs\nWbNm4549exYj1JDunkhZL2t8Pj9k/PjxlxBC8ODBg4Hffffds+rq6i4hISH877777hmRqr45rX5+\nfluWLVu2k6ivpKREr7Cw8GsbG5vr796964oQgqCgoFUbNmxYW1RU1N3Y2PgeUZZIS0+n07MLCgp6\nyx47ePDg3E2bNv2CEILq6uou5ubmt/Pz86k7duz4afPmzT8jhEAqlVLKy8u1G98TACAARGITqICG\njqqfzNqxfuVbS/oBqTofNIK89kmhcQ0NjVoi5wZZUGZ6e1dX16guXbq879Kly3tbW1tBSkqKBZH7\nBaBhGGTJkiXBAA25TYyMjJ7cv39/sGw9zaWpT0pKsoqKinIFaEicpq+v/z+DgX///be9t7f3UU1N\nzWoAAKKMWCymr1mzZlNpaWm3iooK7XHjxl3+2HP08PA4CwAwcODAh/3793987969IRQKBTk4OFwl\nUtU3pzU+Pt6OSEwH0JDa/uLFi0537twZOmLEiGQAgJqams4jRoxI7tatW6mmpmb1nDlzjjg5OV10\ncnK6CNAwT8XT0zPMw8PjrJubWyRAQ6RELBbTIyIipgAAlJWV6T58+HDgsGHDbnt7ex+tra3VICIv\nH7s/DAaDwbQdn9TpcHZ2jvn9998Xubm5Rcqmgu/evbvKTrdVZnr7xqipqUkbH0OfkIK+cZmPHZet\nq6kyfD4/NDo6eiKdTheHhYV5NjWs8zEInY3v/3O0Ojg4XD116tT0xsdTU1O58fHxdhEREVP27t27\nOD4+3m7//v0LUlNTubGxsY4cDkckEok4AAB79+5d7ODgcLVxHTdu3LC+ePGiE5/PD/3pp592zpo1\n6/j/quIDAPXDaz0AYMF/x1uFH36qqk8cUxU9n+sTx1RFz+f4PBXT87k+T8X0fK7PUzE9n+vzWjj/\nwRM2+ERKeWX6QqEQQkNDAQCASqWCwviUcIiRkZGESqXmNzZFhFpay9LT09kMBiOLGF6h0WjizMxM\n5ps3bwyMjIwkRDnZIQ2EGoYKwsLCZtfU1GgMHDjwwc2bN4cjhKCmpkYjNzd3KEINwysikcisqXYD\nAgICWSxWBjG8Ymho+KTx8MrOnTuXEcMreXl5g42MjCQ1NTUaslqWLFmye+PGjWsIjWZmZiKEGoZX\ntmzZ4ocQgitXroxpanjl8uXLY0eMGJFEDGEQwytff/114evXr3vU1NRo2NvbX/Xy8jpKaG5qeMXT\n0zN0woQJsVKplPLw4cMB33333bP37993DgkJ4S9evHgPUa45rf7+/v/58ccffyPKEcMrhoaGTx4+\nfDgAIQQVFRVa9+/fH1RRUaH1zz//9EQIwdu3b7sZGBi8QQgBUQ4hBMOGDUvNzMxkHjp0yMfV1fV8\nbW1tJ+IZVlZWfvXkyRPDuro6dYQQ7N27d5Hs0A5hAGQfXsGGDVv7M0CqzgeNIK99UqRDIpEosJvT\nNigrvT2FQkEMBiPb1tZW8ObNm6/XrVu3oVevXq8kEgmVaGfhwoX7FixYsJ/BYGR36tSpLiwszFND\nQ6NWNu18c2nqAwIC1k+bNu3P48ePz7K0tLzZq1evVzo6OuVE2wANwy6ZmZksc3PztM6dO9c4OjrG\nbtq0ac3GjRvXWlhYpPTo0aPQwsIipaKiQpu4rqlnQKFQkKGh4VMul5taVlame/DgwXmdO3euaVy+\nOa1r1qzZtGjRot/pdLpYXV29PjAwMNDV1TUqNDSUP23atD/fv3/fBQBg8+bNv+jo6JS7uLhcqK6u\n1kQIUX777bdlAAB+fn5bHzx4MAghRLG3t/+byWRmMRiMbIlEQjUzM0tHCFF69uz5+vz585OEQiFv\n27ZtKzU0NGp1dHTKjx07Nvtz3zeqjxDIPQteCOTVLwTyagfA+pWNEMitXzG0uA16fHy8nZ2dXfy5\nc+cmN/WhRIyxY9qOmpqazurq6vXq6ur1N2/etFy0aNHv6enpZq3RlpeXV4izs3NMe/o9kz+1vRDI\n/Y9LCOTVLwTyagfA+pWNEJrX37pp6RWBorZBbzHSkZCQYGNnZxcfExPjjDsdqsHTp08NPTw8zkql\nUrXOnTvXHD582EfZmjBtCU/ZAuSEp2wBcsBTtgA54SlbgJzwlC1ATnjKFqAS4IRvmA4F+SMdGAym\n/YEjHf/izZs3X69fvz4gMTFxJIVCQdbW1jfWrVu3wcDAoEheARhM24Nzr2AwGNVBR0df2RLajE/a\nkfT7778/3bNnz9eRkZFuERERU3r06FHY3D4VGIyqo4gZ2MoygUCgdA0dVT+ZtWP9yreW9HeUZG8A\nnzi8QqPRcnJycmiyx+h0upjY7hqDIQsf9jBRtgwMBoMhFW06vDJmzJi4P//8cxoR3QgPD3cfM2ZM\nnLyNYzDKAKe2x6gqHSnFOaZj0mKkQ1tbu4JYtVJZWalF7KwplUrVtLS0KsvLy3XaSCcGoxDIP5FU\nCOSeBS8E8uoXQutrb70JhUKh8P93niQjWL9yUVSko8U5HRUVFdrl5eU65eXlOlKpVK2urq5TXV1d\nJ6lUqibb4cjNzTWVV0hbEhgYGLhjx47lql6nvGRlZTH/+uuv8R8rJxQKec7OzjFtoQmDwWAwHZdP\nmkj6MWTTwZOB5nKwqFqd8pKRkcG+dOnShNZsAyFEUUTvF/Op8JQtQE54yhYgBzxlC5ALMn/LBsD6\n2wsK6XSQgc2bN/9ibGycZ21tfSMvL8+YOP7o0aMB48eP/8vc3DzNxsYmIS8vz7i0tLQblUqVEGUq\nKyu1DA0Nn9bX16s3Vb5xW5mZmazhw4ffYjKZWW5ubpFv377VAwDg8XjCH3/8cRebzc6g0+ni27dv\nDwNoiJJ4enqG2djYJFCpVElkZKTbihUrtjMYjOzx48f/RSSgE4lEHB6PJzQ3N08bN27c5VevXvUi\n6vX39w+ysLBIMTY2zktMTBxZW1ursW7dug1nzpyZymazM86ePetx+/btYSNGjEg2MzNLt7KySmqc\n2bYxoaGhfBcXlwu2traCwYMH39+wYcM6gIZt8Y2NjfM8PT3D6HS6+NmzZ31Xrly5jU6nixkMRvbZ\ns2c9iDq2bNmyisFgZLNYrMzVq1f/p7lnDtAwV4hOp4tZLFbmqFGjrgM0RNEsLCxS2Gx2BpPJzHr0\n6NEAAIATJ07MJI7Pnz//gFQqVauvr1fn8/mhhI5du3b9+EVvFgwGg8G0DopYCsRisTKUvRypJUtL\nS+PQ6fTsqqoqzbKyMp2BAwc+2LFjx08IIRg9enT8gwcPBiKE4NatWxajR4+ORwiBi4tLlEAg4CGE\n4PTp01N9fHwOtVQ+MDAwgKiTTqdnJyQkWCOEYN26deuJpGc8Hk8wd+7cgwghSEhIsCYSwAUEBARa\nW1sn1NXVqWdlZTG6du367vLly2MRQjBp0qTIqKgol5qaGg1LS8vkN2/eGBCavL29jxD1rlixYhtC\nCC5dujTe3t7+KkIIQkNDPX19fYP/uyyrTIdIiHb16lX7yZMnR3xYyvWvpHeEhYSE8Hv37l1QXFys\nX1VVpUmj0cRpaWmc/Px8qpqaWn1KSgoXIQQRERGTHRwc4qRSKeWff/7pSSS5u3Tp0vgRI0YkVVVV\naSLUkPCtpWdIp9OzCwoKeiOEoLS0VBchBL6+vsEnT56cjhCC2traTlVVVZp37twxcXZ2jibuZeHC\nhb8fO3ZslkgkMnNwcIgj9L99+7Zb43sCIHvCN4EKaOio+ttCO6DWQiAQtFrdbQHWr1w+vDdBXvuk\n1Stk58aNG9Zubm6Rmpqa1ZqamtUTJ06MBmiIYCQnJ49wd3cPJ8rW1NR0BgCYOnXqmTNnzkzl8XjC\n06dPf7948eK9FRUV2s2VJygrK9MtLS3tZm1tfQMAwNPTM0y2/LRp0/4EALC2tr5BlKVQKGj8+PF/\nqaur19NotBypVKo2duzYKwANS5MlEgn1/v37g3Nzc03t7e3/BgCor69X79OnTwFRL7ElvZmZWTqR\noA81Gvp4+/at3uzZs489fPhwIIVCQbW1tRofe3ZjxoyJ09fXLyHaSExMHOnq6hplZGT0hEiml5SU\nZDV9+vRTFAoF9ezZ8/WoUaOu3759e9j169dHeXt7H9XU1KwGANDT03tbUVGhffPmTcumnqGVlVWS\np6dnmIeHx1nifiwtLW9u3rz5l+fPn3/n5uYWOXDgwIfx8fF2IpGIY25ungYAUFVV1fWbb775x9nZ\nOebx48f9lyxZEuzo6Bjb/AorPpA3tX2miun5XJ/s+lvb//eEQ1VKdY79juUrNbX9x8zCwuKWIupp\nLdu1a9fSdevWrSf8ZcuW7dyxY8dPZWVlOr179y5o6pry8nJtKpWaX1xcrG9oaPhEKpVSSktLdZsr\nT0Q6SktLdQ0NDZ8Qxx8+fDiASPXO4/EERPQEIQSGhoZPSktLdQMDAwNkU8sTKeqJerdv375cLBbT\nLC0tk5tqm8fjCUQikRlCCAoLC7+mUqn5CDVEKmRT0Ht6eobu2bNnMUIIJBKJEVGuuUhHaGiop6en\nZyjhr127dkNwcLCvRCIxIqI0xPM8evSoF+HPmjXrWHR0tPPy5cu3Hz58+AfZOlt6hgghSElJ4a5b\nt249lUrNLyoq6o4QgsePH/cLDg72HTRo0P1r167Z7tmzZ/Hq1at/ber6ysrKr86dO+fm6up6nogE\nyRoA2SMd2Nq3AcJgVJEP702Q1z55TkdWVhbzwoULLufOnZt87ty5yZGRkW7EuVu3bg1XXDdI8djY\n2CRERUW5VldXa5aXl+tcvHjRCQBAR0envF+/fvkRERFTAAAQQpSsrCwmQMNy4WHDht1esmRJsLOz\ncwyFQkG6urpljctnZ2cziHYQQhRdXd0yfX39ksTExJEAAMePH5/F4/GExPkzZ85MBQBITEwcqaen\n91ZXV7cMfcJETGNj47zCwsIexLOura3VuHPnztCWrtHV1S2TXWVUVlamS0RHQkJCvD7WJkKIcvXq\nVYeSkhL9qqqqrhcuXHCxsrJKaqzX2tr6xpkzZ6ZKpVK1wsLCHgkJCTYWFhYpDg4OV0NCQryqqqq6\nAgCUlJTot/QMHz16NIDL5aauX78+oEePHoXPnz//Lj8/vx+VSpX4+vrucXFxuSAWi+l2dnbxERER\nUwoLC3sAABQXF3d/+vSpYVFRkUFdXV0nNze3yI0bN65trey7GAwGg/kyPqnT4eXlFTJnzpwjkZGR\nbhcvXnS6ePGiU0xMjHNri1MUbDY7Y+rUqWeYTGbWhAkTLhHDAgAAJ0+enHHkyJE5LBYrk0aj5cje\n19SpU8+cOnVquuyW743LR0dHTyTOEStYwsLCPFeuXLmNyWRmZWdnM9atW7eBOK+pqVltZmaWvnDh\nwn1HjhyZQxyXXf3SeCUMhUJBGhoatREREVNWrVq1hcViZbLZ7IybN29aNnW/xPW2traCO3fuDCUm\nkvr5+W1dvXr1f8zMzNLr6+vVW2qTOMblclMnT558jslkZk2ZMiXCzMwsvXH5SZMmnWcwGNlMJjPL\nzs4uftu2bSt79uz5euzYsVcmTpwYbW5unsZmszOIJcXNPUM/P7+tDAYjm06ni62srJKISal0Ol3M\nZrMzcnNzTWfPnn3MxMTk7qZNm9aMGTMmjslkZo0ZMybu1atXvV68ePGtra2tgM1mZ8yaNet4UFCQ\n/8feG+RDqGwBciJUtgA5ECpbgFwQoXOygvW3Ez4lHGJiYnJHKpVSFBFa6cgmOwxCBms8PNMeDIDs\nwysCFdDQUfW3hXZArQXZJzJi/crlw3sT5LVPinQMGzbs9sdC+Zj2R+MIDEYV4ClbgJzwlC1ADnjK\nFiAXZN8nAutvH3xSwjehUMibOHFidK9evV516dLlPUDDB5LsfAYMhgzgThRGlcG5VzCqiqK2Qf+k\nTseAAQMe/fbbb8toNFoOkX8FAEB2Ay0MhgyQPcss2fM3kFk/mbUDYP3Khuz62zTLbM+ePV8Te1tg\nMBgMBoPBfAmfFOlYuHDhvrdv3+o5OzvHdO7cuQag4RsjsYETBkMWyB7pwGAwGGXQppGOd+/efdW5\nc+eauLi4MbLHcacDQ0YoFNXPT4fH9jEYTHvkkyIdGEx7oWEiKRne8xRo6m+T7OPCZNZPZu0AWL+y\nIbv+Nol0bNmyZdWqVau2+Pr67mlCAAoODl4irwAMBoPBYDAdgxYjHTExMc7Ozs4xoaGh/P+5kEJB\nnp6eYa0pDtP+CQwMDNTR0Slfvnz5js+57vr166M6d+5cY2lpeRMAgM/nhzo7O8dMnjz5XEvXkT3S\ngcFgMMqgTSIdzs7OMQAN/9DlbQiDaYov3TdDIBDY6ujolBOdDrz/BgaDwag+Le5I6uzsHNOc4SW0\nmC9l8+bNvxgbG+dZW1vfyMvLMwZoSPY2fvz4v8zNzdNsbGwSiOMxMTHOw4cPv2VmZpbu4OBw9fXr\n1z0lEgn14MGD83777bdlZmZm6URyvYSEBBsrK6ukAQMGPDp37txkZd5ja0H2/A1k1k9m7QBYv7Ih\nu35F0WKko6WQN/5mifkSRCIR58yZM1OzsrKYtbW1GmZmZukcDkc0b968gwcOHJg/cODAhykpKRYL\nFy7cFx8fb2dtbX2DyKz7xx9//LB161a/7du3r5g/f/4BHR2d8p9++mknce7Vq1e9kpKSrO7evWsy\nceLE6OaHWvgAQP3wWg8AWPDfLa6FH34q2//gffhHRUxAy8zM/Jff+Lyq+2TXj33sdxRfKBRCaGgo\nAABQqVRQFJ+1eqWmpqZzbm6u6bfffvuiZ8+erxWmAtNh2LVr149v377VCwwMDARo6Nh27969mIh+\nEOWI95pYLKYvX758x6tXr3rV1NR07t+//+NLly5NWL9+fYC2tnYF0TH28vIKGTNmTNy0adP+BADQ\n1dUtKysr023cPp7TgcFgMJ+PouZ0tDi8Mm/evIM5OTk0AIDS0tJuTCYza/bs2cdYLFbmqVOnpsvb\nOKbj8WFzrn+9caVSqZqent7bjIwMNmG5ubmmAAC+vr57lixZEpydnc04ePDgvKqqqq7N1U1sXAcA\nCvnjwGAwGIxiabHTcePGDWsajZYDABASEuJlbGycJxaL6enp6WZbt271axuJmPaEjY1NQlRUlGt1\ndbVmeXm5TkxMjPNXX331rl+/fvkRERFTABo6DEQywbKyMt0+ffoUAADIrqLS0dEpLy8v11HKTSgR\nIvxJVsisn8zaAbB+ZUN2/YqixU4HkVEWACAuLm6Mi4vLBQCAXr16vWptYZj2CZvNzpg6deoZJpOZ\nNWHChEtcLjeVQqGgkydPzjhy5MgcFouVSaPRcqKjoycCNCypdXd3Dzc3N0/r0aNHITGXyNnZOeb8\n+fOTZCeSys4zwnOOMBgMRvVocU4Hj8cTLl++fMe33377YvTo0dfu3r1r0rt375e1tbUadDpdfO/e\nvSFtqBWDkRs8pwODwWA+nzbZp+PgwYPzlixZEvzq1ateu3bt+rF3794vAQCuXbs22tHRMVbexjEY\n5aD60z10dPSVLQGDwWAUDs69gulQkD3LrJDk+RvIrJ/M2gGwfmVDdv1tsnoFAODSpUsTbGxsEgwM\nDIoMDAyKRo0adT02NtZR3oYxGAwGg8F0LFqMdBw+fNjn4MGD87Zu3erH4XBEAA2bO/n7+wfNmTPn\nyLx58w62mVIMRgGQPdKBwWAwykBRkY4WOx0mJiZ3ExMTRxoYGBTJHi8qKjKwsrJKwhNJMWRDkata\ndHT0oaysWFHVYTAYjMrSZsMrjTscxDG8JFG5hIaG8n19ffcANCwr3bFjx3Jl1SkSiThLly7dLW/7\njZHVEBAQsD4+Pt5OMTUjhVh5eYli5HwGZF/rT2b9ZNYOgPUrG7LrVxQtrl7R1dUty8zMZLFYrEzZ\n41lZWUwdHZ3y1pWGaYnW2JPiS+vkcDgiYvhNkchqWL9+fYCi68dgMBhM29JipGPHjh3LXVxcLgQG\nBgbGxMQ4R0dHTwwICFg/ceLEaEV8s8b8m2PHjs1mMplZLBYrc/bs2ccAAAoLC3tMmTIlgsvlpnK5\n3LtMRQYAACAASURBVNTk5OQRAJ++zbei6wwPD3en0+liFouVyePxhAAAQqGQ5+zsHEPU7eDgcJVG\no+X4+PgcplKpkuLi4u4SiYRqYmJyd+7cuYdoNFrO2LFjr1RXV2sCNMwd4nK5qSwWK3PKlCkRTW11\nzufzQ4nMsVQqVRIYGBjI4XBEDAYjm8hI21zbn/KcyAKZZ78DkFs/mbUDYP3Khuz6FUWLnY6RI0cm\npqSkWNTX16uHhobyw8LCPBFClJSUFAtra+sbbSWyI5Cbm2u6efPmXwQCgW1mZiYrODh4CQDA0qVL\ndy9btuy31NRUbkRExJQffvjhD2XWuXHjxrVxcXFjMjMzWcSuobKsX78+wN7e/u+cnBzalClTIp4+\nfWpInHv48OHAxYsX783JyaHp6em9JToRkydPPpeamsrNzMxkmZiY3D1y5MicxvVSKBRERD4oFArq\n0aNHoUgk4ixYsGD/9u3bV3ysbQwGg8EonxaHV6KiolyfP3/+3caNG9cCAHC53NT09HSzEydOzNyy\nZcsqd3f38LaR2f65du3aaA8Pj7Pdu3cvBgDQ09N7CwDw999/29+9e9eEKFdeXq5TWVmppaw6rays\nkjw9PcM8PDzOurm5RTY+n5SUZBUVFeUKADB27Ngr+vr6/z/xoV+/fvkMBiMboGFIRiKRUAEAxGIx\nfc2aNZtKS0u7VVRUaI8bN+7yx3QQbZuZmaVHRka6fazt9gLZ1/qTWT+ZtQNg/cqG7PoVRYudjq1b\nt/qdPn36e8KvqanpnJaWZl5ZWanF5/NDcadDcTSVfRWgYcgjJSXFQjaDKlG+cdnnz59/5+TkdJFC\noaD58+cfUESdjdm/f/+C1NRUbmxsrCOHwxGJRCJOU/U3da1sLh91dfV6YniFz+eHRkdHT6TT6eKw\nsDBPoVDI+5gmoi51dfX6urq6/38ff9qwEx8AqB9e6wEACwCIJoUffn6aT0wOI/6ZtLafmZnZpu1h\n/djHfsf0hUIhhIaGAgAAlUoFhYEQatY4HE6arL9w4cLfiddcLjelpWuxfZ7l5uYOHTx4cF5RUVF3\nhBAUFxfrI4Rg+vTpJ7dt27aCKJeRkcFCCEFISAh/8eLFexBCEBAQELh9+/blbVHnw4cPBxCvhw0b\nlpqVlcUQCAQ8JyenGIQQLFq0aO+WLVv8EEJw5cqVMRQKRVpUVNQ9Pz+fSqPRxMS127ZtWxEYGBiA\nEIKvv/668PXr1z1qamo07O3tr3p5eR1trIHP54ecO3fODSEEVCo1n7in27dvm/N4PEFLbcvqBwAE\ngBRkgDAYDKYj8OH/ndyfdS3O6SgpKflXAojff/99EfG6sLCwh+K6PpihQ4fe+eWXXzaPGjXqOovF\nyly+fPkOAIDg4OAlaWlp5kwmM8vU1DT30KFDcwH+d45DUxGB1qjTz89vK4PByKbT6WIrK6skBoOR\nLVs2ICBgfVxc3Bg6nS6OiIiY0qtXr1fESqfGq2MIf+PGjWstLCxSRo4cmWhiYnK3qTLN8altYzAY\nDEYFaKlHMm3atFMHDx6c2/j4/v3753///fd/KqLXg6192fv37zvX1dWpI4QgOTnZks1mp6tS20Dy\nSIdAIGjzNhUJmfWTWTtCWL+yIbt+UFCko8U5Hb/99tsyV1fXqFOnTk03MzNLBwBIT083q66u1iQm\n7GEwsjx9+tTQw8PjrFQqVevcuXPN4cOHfTpC2xgMBoP5OB/NMosQoly7dm10bm6uKYVCQaamprmj\nR4++1kb6MBiF0jAUo6jNdCnwsb8fDAaDaQ+0Se4VDKa9gXOvYDAYzOfTZrlXMJj2hiLGJRFCSulw\nEEvayAqZ9ZNZOwDWr2zIrl9R4E4HBoPBYDCYNgEPr2A6FDg7MoZM4CE8jKqA53RgMF+AYieSYjCt\nDZ6sjFEN2uWcjtDQUL6vr+8eAIDAwMBAVclkGxAQsD4+Pt7uc6+7cOGCi2yOky+tR9EIZbLCyiL7\n/BtjZWWV1PrKvgxHR8fYsrIyXWXraBuEyhYgJ0JlC5ADobIFyAXZ5xRg/e2DFvfpaGsa71j5pfXU\n19erq6ur1ytGVUP20i+57vz585OcnZ1jiF02v7SetqKlZ56UlGTVllo+h9jYWEdla8BgMBjMx2n1\nSMexY8dmM5nMLBaLlTl79uxjAA1bqE+ZMiWCy+Wmcrnc1OTk5BEAn5asSyKRUEePHn2NyWRm2dvb\n//3s2bO+AA1Jw+bPn39g+PDht1atWrXl0aNHA4YPH36LwWBkr1mzZhOxHXZFRYW2vb393xwOR8Rg\nMLKJ9OwSiYRqYmJyd+7cuYdoNFrO2LFjr8gmJDt37txkkUjEYbPZGWw2O4NOp4vV1NSkAACHDx/2\n4XK5qSwWK3PKlCkRVVVVXZOTk0fExMQ4r1y5cpuZmVn648eP+xP1AADEx8fbmZmZpTMYjOw5c+Yc\nqamp6QwAQKVSJYGBgYGEvry8POOmnoGNjU0Ch8MRcTgc0c2bNy0BGiIYPB5P6O7uHm5iYnJ35syZ\nJ4hrLl++PM7ExOQuh8MRnT9/flJzz/fZs2d9bW1tBYMHD76/YcOGdcRxbW3tCuJ3tHLlym10Ol3M\nYDCyz5496wEAIJVK1RYuXLjPxMTk7pgxY+IcHR1jiXsViUQcHo8nNDc3Txs3btzlV69e9QIA4PF4\nQn9//yALC4sUY2PjvMTExJEADZ3GlStXbuNyualMJjOL2Kb95cuXvW1sbBKI5090hKhUqqS4uLh7\nZWWllqOjYyyLxcqk0+liQlv7gqdsAXLCU7YAOeApW4BcEEm9yArW305Q1PLBpiwnJ8dUNuFYSUmJ\nHkIN26snJiZaIYTgyZMnhiYmJncQ+nfCscDAwICmEo45OTnFHDt2bBZCCI4ePerl6up6HiEEnp6e\noc7OztFSqZSCEAJHR8eLp0+fnooQggMHDszT1tYuRwhBXV2dellZmQ5CCAoLC78eOHDgA4QQ5Ofn\nUzt16lSblZXFQAiBh4fHmRMnTsxA6N/JxghbuXLlVj8/vy0IIZBNKrZmzZqNe/bsWdzUdYRfVVWl\n2bdv36cPHjwYiBCC2bNnh+3atWspQg3JzPbu3bsIIQT79u1b8MMPPxxu/AzevXvXtbq6ugtCCO7f\nvz/I3Nz8NkIIBAIBr1u3bm9fvHjRRyqVUiwtLZOTkpJGEO0Rydo8PDzOODs7RzeuNyQkhN+7d++C\n4uJi/aqqKk0ajSYWiURmCCEgnl9ERMRkBweHOKlUSvnnn396GhoaPnn58mWv8PDwKRMmTIhFCMGr\nV6++0dfXLz537pxbTU2NhqWlZfKbN28MEEJw+vTpqd7e3kcQQsDj8QQrVqzYhhCCS5cujbe3t7+K\nEIKDBw/O3bRp0y8IIaiuru5ibm5+Oz8/n7pjx46fNm/e/DNCCOrr69XKy8u1iWdWVFTUPSIiYrKP\nj88h4n5KS0t1G98jKHQbdGzYWtsAYTCqwIf3IshrrTq8cu3atdEeHh5nu3fvXgwAoKen9xYA/q+9\nMw9r6lj/+BtiEBFQoIBUpEFABMkGCFqLBBDUilZRqF4XEJdqBb11qQsqUJdq1VZBrcuVRVwLLnWp\nVkSiaAUKJIBSqQu4ArLJJghJ5veHPb+b5qJVCYSD7+d55vHMnDkz3xk15z2zvXDx4sVhimsdamtr\ndevr67u/SZlpaWmDqCPYp0yZcuDrr7/+DuDl1ICfn18CNUWQlpY2iBrFmDRp0uHFixdvBnj5Rb58\n+fJvU1NTXTU0NORPnjz58OnTp8YAABYWFoVcLjcXAMDR0TGrqKiITdVLFEZhjh49+nl2drZDUlKS\nFwBAXl4eZ+XKlWurq6t71NXV6YwYMeJ8S89R8YKCAhsLC4tCKyurOwAAAQEBcTt27Ji3YMGCbQAA\nvr6+xwEAHBwcso8fP+6r3AdNTU2awcHB23NycnhMJlN2+/Zta+qes7NzxocffvgEAIDP50sKCwst\ntLW1n1tYWBRaWlrepfqNGj1Qxtvb+4K+vn4VpSM1NdWVOgIfAODq1auf/Otf/zrEYDCIsbHxUzc3\nt8u///77wGvXrg3x9/f/CQDAxMSk1N3dPQUAoKCgwObmzZsDhg0bdhHg5SgGpU+5rVR/X7hwwTsv\nL4+TmJg4AQCgpqZG786dO1YDBw78PSgoKLq5uZk1duzYkzweL0dRO5fLzV28ePHmZcuWbfDx8Tnz\nySefXG2pjap0bd/+8a0006scp7N+6ro963+5FkBVrsr/X30HcJ2O+juWvpb0toVr+zY1OhgMBlF+\n6QK8fPGmp6e7aGpqNinnf5NyWyoTAEBbW/v5Pz178ODByeXl5R9kZ2c7MJlMmYWFRSE1jdK1a9cX\nVD4mkylraGjopvz8jRs37CMiIsJSU1NdKb2BgYGxp06dGsPhcPLi4uICRCKR8HVtUk4jhDAU0ygd\nTCZTJpVK/+fv6IcffvjK1NS0OD4+fqpMJmNqaWk1Kj+r+HxL9bXUNy3lo6aQFPO86vlXpQ8YMOAm\nNYWmzKvaun379mAvL68k5fypqamuZ86c8QkMDIxduHDh91OnTo2n7llbW98Wi8WCs2fPjlq5cuVa\nT0/P5FWrVq3531pjW5LyF8IOHucrpalbz9vG6a6/veN/H5ZXHqLHOMbbKi4UCv8Wj4iIAFWgoZJS\nXoGHh8elhIQEv8rKSgMAgKqqKn2Al1/TkZGR86l8EomED/D3l9arXmAff/zxb0eOHJkI8NKAGDp0\n6JWW8g0aNCiN+lKm8gO8/Go2NjZ+ymQyZSkpKe7379//6E3awmAwyLNnz3pOmjTpcHx8/FRDQ8MK\n6l5dXZ1Or169Spqbm1kHDhyYQr28dXV1a5V3VTAYDGJjY1NQVFTEvnv3riUAQHx8/FQ3N7fLb6KD\nakOvXr1KAF6umZHJZMzX6e7fv/+toqIi9r179/oCABw+fHhSS3kJIYykpCSvqqoq/YaGhm4///zz\nZ8q7VlxdXVOPHj36uVwu1ygrKzO6cuXKUBcXl/QhQ4ZcO3bs2HhCCKO0tNSEMrxsbGwKysrKjNLS\n0gYBADQ3N7Py8/PtXte+4cOH/7pz584vKSPkzz//7Pf8+XPtBw8emBsZGZXNnDnzPzNmzNgnFosF\nis8VFxebamlpNU6ePPng4sWLN2dnZzv8Y2fSDqG6BbQSoboFtAKhugW0CuUXDN1A/Z2DNh3psLOz\nyw8NDV3n5uZ2mclkyhwcHLKjo6ODIiMj58+bN28Hj8fLkUqlXdzc3C7v3LnzSwaDQagXtuK1IlFR\nUSHTp0+P2bRp0xJjY+OnMTEx06l7ivm3bt367ylTphxYv379iuHDh//ao0ePagCAyZMnHxw9evRp\nLpeb6+TklEntLFF+vqX4qVOnxjx48MB85syZ/6HuZ2dnO6xZs2aVi4tLupGRUZmLi0t6XV2dDgDA\nxIkTj8yaNWtvVFRUSEJCgh9VTteuXV/ExMRM9/PzS5BKpV2cnZ0z5syZs0u5zlf1wZdffrlz/Pjx\nx/bv3z9txIgR56lFni1ppurbs2fP7FGjRp3V1tZ+7urqmtrSdBaDwSDOzs4Z48ePP/bo0SOzqVOn\nxlNTK1S548aNO3H9+vXBPB4vh8FgEOrvYfz48ceSk5M97ezs8vv06fPQwcEhu0ePHtUsFqs5MTFx\nwvz58yOrq6t7SKXSLl999dUPdnZ2+S3VDwAwc+bM/xQVFbEdHByyCSEMY2PjpydOnBgnEomEmzZt\nWsJisZp1dXVr9+/fP03xuby8PM6SJUs2aWhoyDU1NZt+/PHHucp1IAiCIOqj0x4O1tDQ0K1bt24N\nAC9HOo4ePfr563ZtIK2nvr6+e/fu3esrKioMXVxc0n/77bePjY2Nn6pblyL0PxxMBPT+4hYBffWL\noP21q+5wMMW1IXQE9asXVR0O1qHO6VAlWVlZjsHBwdsJIQx9ff2q6OjoIHVr6uz4+PicefbsWc+m\npibN1atXf9PRDA4EQRBEvXTakQ4EaQn0vYLQCfS9gnQUcKQDQd4RNLQRBEHUQ5vuXkEQRLXQ3X8D\nnfXTWTsA6lc3dNevKtDoQBAEQRCkXcA1Hch7Ba7peDNwLQGCIIqoak0HGh3IewX9t8y2F6rbqokg\nCP1RldHx3k+vxMbGBoaEhEQBAISHh4dv2bJlUVvUo+hhtj1oq7Yo9tfu3bu/iI+PnwoAcOvWrf58\nPl/i6OiYRZ182hri4uICiouLTan4rFmz9ir663l/EalbQKug87w2nbUDoH51Q3f9quK9372ifAJo\nW9bTnkP7ralLKpV26dKli/Sfyv3iiy92U9cnT54c6+fnlxAaGrruTeuRy+Uayr5dKGJjYwPt7e1v\nmJqaFgMA7N27d9abtwBBEATpiHTKkY79+/dP4/F4OXw+XzJt2rT9AABlZWVGEyZMSHR2ds5wdnbO\noByQvclwUWlpqcm4ceNO8Pl8CZ/Pl1y/fn1wWFhYxLZt2xZQeUJDQ9dR/mQ2bty4lMvl5vL5fMmK\nFSvWU3mourKyshyFQqHIyckpc8SIEedLSkp6Kdd5+vTp0YMGDUpzcHDI9vLySqI84YaHh4cHBQVF\nu7u7p1haWt6NiooKoZ5Zt25dqI2NTYGrq2tqQUGBTUttCQwMjJ0zZ86ugQMH/m5jY1Nw9uzZUQAv\nX/Jjxow55enpmezl5ZVUVVWlT3lyHTx48PW8vDyOclnUaMq5c+dGbtu2bcGPP/4419PTMxkA4MCB\nA1NcXFzSBQKBeM6cObvkcrkGAICOjk7d4sWLN1P9uGbNmlXOzs4ZHA4njzJiEhMTJ2RmZjpNnjz5\noIODQ3ZjY6OWUCgUZWVlOQK89B3D5XJzORxO3rJlyzZQenR0dOpWrly5ls/nSwYPHnyd6rPOhVDd\nAloFnU9kpLN2ANSvbuiuX2W8zu89HcONGzcG9OvXr6CiosKAEAJVVVU9CSEwadKkQ1evXh1CCIH7\n9++b29ra5hNCICYmJjA4ODiKEALh4eFhmzdvXqRcpr+//9Ft27bNJ4SATCbTqK6u1isqKvrIwcEh\ni0qztLS8U1lZqf/LL7+M/Pjjj681NDRoKdYfGBgYc+zYMd+mpibW4MGDfysvLzckhMCRI0c+DwoK\n2qdcJ/UcIQT27t07c9GiRZsJIRAWFhY+ZMiQq01NTazy8nJDQ0PDcqlUyszMzHTkcDi5DQ0NWjU1\nNbpWVla3t2zZslC53MDAwJiRI0f+QgiB27dvW5mZmT1sbGzsGhMTE2hmZvaQqjc4ODjqm2++WUUI\ngUuXLrnz+XxxS/1F1aF4nZ+fbzt69OhTUqmUSQiBuXPn7ty/f/9UQggwGAx5QkLCBEpPZWWlPnU9\nderU/adPn/YhhIBQKEzJyspyoO5R8cePH39obm5+v7y83FAqlTI9PDyST548+RlV9pkzZ0YRQuDr\nr7/euHbt2lDl9gMAASAY/jEAQRAEofjrN6HV7+hON71y6dIlD39//58MDAwqAQB69uz5DADg4sWL\nwxTXBNTW1uq25PSsJVJSUtwPHDgwBQBAQ0NDrqenV6Onp1djaGhYIZFI+CUlJb0cHByy9fX1qy5e\nvDgsKCgomnI3T9UP8HKko6CgwObmzZsDhg0bdhEAQCaTMT/88MMnynU+fPiwj7+//08lJSW9mpqa\nNPv27XsP4OX0xqhRo86yWKxmQ0PDCmNj46clJSW9UlNTXX19fY9raWk1amlpNY4ZM+YUecUojr+/\n/08AAFZWVnf69u1779atW/0ZDAbx8vJKovReu3ZtyPHjx30BANzd3VMqKioMa2trdZXLIi14Bk5O\nTvbMyspydHJyygR46QeH8orLZDJl48ePP6b497Vp06Ylz58/166srDSwt7e/4ePjc0a5bCr++++/\nDxQKhSLKy+/kyZMPXrlyZehnn332s6amZtOoUaPOAgA4OjpmJSUlebX8NxoIAOy/rnvC392ti/76\ns6PGt7aT3r9if81DU19prY1v3boV+Hy+ysprz7jinHxH0IP6O5a+zqZfJBJBbGwsAACw2WxQFZ3O\n6GAwGKSlly0hhJGenu6iqanZpJz/TcptqcyZM2f+JyYmZnppaalJUFBQ9OvqV2TAgAE3qemdVxES\nEhK1ePHizT4+PmcuX77sFh4eHk7dU2wDk8mUSaXSLsr1/pMGRag+6N69e71iunIZb7NOJCAgIG79\n+vUrlNO1tLQaqXIaGxu15s2btyMrK8uxd+/ejyMiIsIaGxu1XlefchohhEGlsVisZipdQ0NDLpVK\nX/HvO/Y1yoUdPM5XSmvb+qgfI1XFFQ2Otigf4xjHuGriQqHwb/GIiAhQBRoqKaUD4eHhcSkhIcGv\nsrLSAACgqqpKHwDA29v7ArXmAgBAIpHwAVr+UlfG09MzmXKTLpPJmDU1NXoAL928nz9/fkRmZqbT\n8OHDfwUA8PLySoqJiZne0NDQTbF+gJcvTBsbm4KysjKjtLS0QQAAzc3NrPz8fDvlOmtqavSoEZDY\n2NjA12lkMBhk6NChV06ePDm2sbFRq7a2VvfMmTM+Lb20CSGMhIQEP0II4+7du5b37t3r279//1vK\n5bq6uqYePHhwMgCASCQSGhkZleno6NQpl9XStaenZ3JiYuKEsrIyIwCAyspKgwcPHpgra6EMDEND\nw4q6ujqdhIQEP+qerq5uLdXPiu10dnbOuHz5sltFRYWhTCZjHjlyZKKbm9tl5bI7L0J1C2gVyj9y\ndILO2gFQv7qhu35V0elGOuzs7PJDQ0PXubm5XWYymTIHB4fs6OjooMjIyPnz5s3bwePxcqRSaRc3\nN7fLO3fu/FJxV8mrdphs27ZtwezZs/fs27dvBpPJlO3atWuOi4tLOovFavbw8Likr69fRT03fPjw\nXyUSCd/JySmTGu5fu3btSqosFovVnJiYOGH+/PmR1dXVPaRSaZevvvrqBzs7u3zFOsPDw8P9/PwS\n9PX1qzw8PC7dv3//o9dpFAgE4s8///woj8fLMTY2furs7JzRUv8wGAxibm7+wNnZOaOmpkZv9+7d\nX2hqajYpl0stWOXxeDndu3evj4uLC1Cu/1XXtra2f6xdu3alt7f3BblcrsFisZp37tz5pbm5+QPF\nOnr27Pls1qxZe+3t7W/06tWrxMXFJZ26Ry141dbWfq44KtSrV6+SDRs2LHN3d08hhDB8fHzOjB49\n+jSlQbGdeBAYgiBIxwIPB2sFcrlcw9HRMSsxMXGCpaXlXXXreROmT58eM3r06NO+vr7H1a1FHdD/\ncDARtM9oR9scDiYSiWj7xUdn7QCoX93QXT8eDqZm8vPz7aytrW8PGzbsIl0MDgRBEARRJzjSgbxX\n4JTLm4G+VxAEUURVIx2dbk0HgvwTaGgjCIKoB5xeQRAaobjXn47QWT+dtQOgfnVDd/2qAo0OBEEQ\nBEHaBVzTgbxXtPWaDlwLgSBIZ0RVazrQ6EDeK9p+y2zbbDVFEARRJ7hlFlE5sbGxgSEhIVEA//Ug\n+65lsdnsosrKSoOioiI2h8PJa622rKwsxwULFmxrbTl0h+7zwnTWT2ftAKhf3dBdv6rA3SvI/6N8\noqeqylIFjo6OWY6OjlmqLBNBEARpX3Cko5Ozf//+aTweL4fP50umTZu2HwCgrKzMaMKECYnOzs4Z\nzs7OGdQx428ydHb69OnRgwYNSnNwcMj28vJKevr0qTEAQEVFhaG3t/cFe3v7G7NmzdqrWJZMJmPO\nnj17j729/Y3hw4f/SvlcuXv3ruXIkSPPOTk5ZQ4dOvRKQUGBDQBAQkKCH4fDyePz+RKhUCgCeOn/\nhTruvLKy0mDs2LEneTxezuDBg6/n5eVxAP57dLu7u3uKpaXl3aioqBCVdmYHgM4nGgLQWz+dtQOg\nfnVDd/0q43V+7zHQO9y4cWNAv379CioqKgwIIVBVVdWTEAKTJk06dPXq1SGEELh//765ra1tPiEE\nYmJiAoODg6MIIRAeHh62efPmRcplUmUQQmDv3r0zFy1atJkQAiEhIZFr1qxZSQiBs2fPfspgMOQV\nFRUGhYWF7C5dujTn5ORwCSHg7+9/9MCBA5MJIeDh4ZF8+/ZtK0IIpKWluXh4eCQTQoDD4eQ+efLE\nlBAC1dXVeoQQSElJEfr4+JwmhEBwcHDUN998s4oQApcuXXLn8/liQgiEhYWFDxky5GpTUxOrvLzc\n0NDQsFwqlTIV9QMAASBtGIAgCIJ0Nv76bWv1ewmnVzoxly5d8vD39//JwMCgEuClgzUAgIsXLw77\n448/bKl8tbW1uvX19d3fpMyHDx/28ff3/6mkpKRXU1OTZt++fe8BAKSmprqeOHFiHADAp59++ou+\nvn4V9YyFhUUhl8vNBXg5TVJUVMSur6/v/ttvv33s5+eXQOVramrSBAAYMmTItYCAgDh/f/+fWvIR\nc+3atSHHjx/3BQBwd3dPqaioMKytrdVlMBhk1KhRZ1ksVrOhoWGFsbHx09LSUhPKW+9/CQQA9l/X\nPeHv7uJFf/35rvG/+1ig5nFVFd+6devf3MOruvy2jtNZv+KcfEfQg/o7lr7Opl8kEkFsbCwAALDZ\nbFAZqrBcMHTMEBUVFRwaGrpWOf2DDz4oe/HihaZyemxsbMA/jXS4ubmJTp8+7UMIAZFI5CYUClMI\nIcDn88X37t2zoPIZGBhUUCMd9vb2eVT65s2bF0VERKyuqanRNTU1ffIq7enp6c6rV6+OYLPZhRUV\nFQaKIx0CgSBbsa4+ffo8qKmp0VXWbG9vn3f//n1zxXKB5iMdKSkpbVp+W0Nn/XTWTgjqVzd01w8q\nGunANR2dGA8Pj0sJCQl+lZWVBgAAVVVV+gAA3t7eFyIjI+dT+SQSCR/g72s6yCvWd9TU1OhRIwex\nsbGBVPrQoUOvHDp06F8AAOfOnRtJ1dUShBCGrq5urYWFRWFiYuIEKi03N5cL8HKth7Ozc0ZERESY\nkZFR2aNHj8wUn3d1dU09ePDgZICXaz2MjIzKdHV1a1+luTNBfZHQFTrrp7N2ANSvbuiuX1WgbUL+\nzwAAFMVJREFU0dGJsbOzyw8NDV3n5uZ2mc/nSxYtWrQFACAyMnJ+ZmamE4/HyxkwYMDNPXv2zAZ4\nueOE2nWieK1IeHh4uJ+fX4KTk1OmkZFRGZUnLCws4sqVK0Pt7e1vnDhxYtxHH310n3pGuRwqfvDg\nwcn79u2bwefzJfb29jdOnTo1BgDg66+//o7L5eZyOJy8IUOGXONyubmKesLDw8OzsrIceTxezooV\nK9bHxcUFvE4zgiAI0jHAw8GQ9wq6Hw4mUlgvQkforJ/O2gFQv7qhu348HAxBEARBEFqBIx3IewX6\nXkEQBHl7VDXSgVtmkfcONLQRBEHUA06vIAiNUNzrT0forJ/O2gFQv7qhu35VgSMdyHsHg9Hpd9Yi\nNAGn45D3DVzTgbxXtP3uFQR5G9p2txOCqArcvYIgCIIgCK1Ao6MDw2QyZQKBQMzlcnN9fX2P19XV\n6QAAFBUVsTU0NOTbt28PpvIGBwdvpw7JAgCQSqVdjIyMypYvX/5tW+lbv379ijfJp6OjU/c25QYG\nBsYeO3ZsPADArFmz9ir6iUFE6hbQSkTqFtAKROoW0CrovqYA9XcO0OjowGhraz8Xi8WC3Nxcrp6e\nXs3u3bu/oO4ZGxs/jYyMnN/c3MwC+N+toElJSV6Ojo5Z1Mu7Lfj222+Xv0m+t92mqniy6N69e2fZ\n2tr+8S76EARBkI4FGh00YfDgwdfv3r1rScWNjIzKPD09kxVHNxQ5cuTIxLlz5/7Yt2/fe9evXx/c\nUh6hUChauHDh9wMHDvzd1tb2j99//33guHHjTvTr1+/PVatWraHyHThwYIqLi0u6QCAQz5kzZ5dc\nLtdYtmzZhoaGhm4CgUA8derUeACAsWPHnnRycsq0t7e/sXfv3lmKdS1cuPB7e3v7G8OGDbtYXl7+\nAcBLny+DBg1K4/F4Ob6+vsefPXvWsyWN2dnZDnK5XCMwMDCWw+Hkcbnc3G3bti14mzZ0HoTqFtBK\nhOoW0AqE6hbQKuh8GiYA6u80qMJrHIa2CTo6OrWEEJBKpUxfX99jO3bs+JIQApTn1nv37lnY2Njc\nkslkGsHBwVGxsbEBhBBoaGjQMjMze/jixQvNffv2BYWEhES2VL5QKExZtmzZt4QQ2LZt23xTU9Mn\nJSUlJi9evNA0MzN7WFlZqZ+fn287evToU1KplEkIgblz5+7cv3//VEV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r6z/p7YjMnTv3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\noNGBIAiCIEi7gEYHgiAIgiDtwv8BTF59f/QdWx4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104f108d0>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pData['Column2'].hist(bins=400);\n",
      "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n",
      "plt.axis([0, 1.5, 0, 250])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "[0, 1.5, 0, 250]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DY/WmWRpoJycBQkYr4FZB3gWAoxj7zk9tSs8/sTHjrJ8cMI4cAJKSem8Xn7pZ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\nG1XsLaJaAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b2f8f50>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pandas density plot\n",
      "pData['Column2'].plot(kind='kde', linewidth=3);\n",
      "plt.axis([-0.3, 1.5, 0, 1.7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "[-0.3, 1.5, 0, 1.7]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7gZbONprZs/klkAcOFO1jZYeWcRZOZSXHhYTw11mHDhxXUEB1LgU0Yelswe4U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\nIVUoKRBCCKlCSYEQQkgVSgqEEEKq/D/jkoRuc5dVXAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ab0f2d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pData['Column2'].hist(by=pData['GOSlim_bin'])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 252,
       "text": [
        "array([[<matplotlib.axes.AxesSubplot object at 0x111d2d210>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x116b79b90>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x1155aea50>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x11481d1d0>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x1115c0d10>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x114d0cb50>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x115ceae50>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x116b9b7d0>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x116aed2d0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x114e778d0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x1157de6d0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x11596f250>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x115522c50>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x116c82110>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x11569f3d0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x116db4150>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VVlYauLi4JL9582bAF0ITCFDScncZMEAAAAHIqo6+NRAIBL4rfbwQGBh4/OrV\nqx7Nzc1q7e3tigAAsGbNml3Xr1+fpqCgwMzPz7fMzc21ptFoxXl5edaTJk26o6ys3MJms4nt7e1K\n5eXlRpwTEelyDAAAuPMN2bsDAMBXF8UAUTmGFV5eXvHLli37Y9myZX+kpqaOR9x2OTs7p8hCj0kb\nnJzua88nCJLgV2VlpYGBgUFlTU2Njqur6+1Dhw4tnz59+lU6nU5G4lAolPr6+noKF3lgWFjY/37L\najEF709FB+dCyubNmyWqw9CQ2Qoi2nzfysrqHWK+b2Njk3vixAn/kJCQKNx8HwcWcLNidnNzS4qK\nigohEolsTU1Nxr59+1YeOHAgCADZWzGjB33Y4nSJhq8u9mwgJ6kMHz48HV/hwSEuGBgYVAIAgI6O\nTs0PP/xwOSMjwwmZeCALKfz8IEr3SwgOcUPSX0H4QaZWzPn5+RZ2dnYvIIQERUXFtqKiIrPa2lot\n3M0NDmHAy4oZQUxMzPxLly55A9AzrJiR/a1dq4aix8HRc9Dc3Kzq7e196cCBAyvU1NSa0PfwrTJ9\nF5Le49ra2qrc2dkpp6am1tTS0qKSlJTkFhYWthnxF4wvpOCQJGQ6QOzXrx+zrKyMSqFQ/vfdrCe4\nucHRt3D8+PFAxBl7RUWFIXowKM2zmLGsHOLofUBOUvHz84tDOmqsKzz46k7vhqRXd6qrq/V++OGH\nywB07dufM2fOP25ubkmOjo7PcD+IOCQNmZ/FjFtn4ZAktm/fvl5BQYHp6+t7mlccaa3u4KuC0oek\nV3ggj5NU8BUeHOKAmZlZ4YsXL+w4r1MolPo7d+5MkoVMOL4dyHSAiFtn4ZAkYmNjAxISEqbevXt3\nInJN2lbM+KqhbCHpFR5uJ6ns2LFjbWhoaCS+woNDXOjs7JRzdHR8RqVSy65du+aBu1HCIQ3IdID4\n6NGj0WjrrAEDBrxB38f37vRtSHJ15+bNm+67du1ak5qaOl5RUbEduT59+vSrvr6+p1etWrW3vLzc\n6P37999J8izmz6uGAOArh30PvE5SAaBrb6y05cHRN3HgwIEV1tbWeU1NTWoAABAZGRnaU9woYT0l\nBUcvhLQ8cgsK4eHhYbt3717dv3//N5WVlfoQQlBRUWEgq5NUAO75XeoAInqINzMzKyASiZ0EAoFN\npVJLo6OjA83NzQsUFRXb+vXr166mptYYGBj4NxLfzc3tFolEYiooKHzavn37Wm55iqvdP/PoS059\n+fdrvvFQ6jaAAAAgAElEQVSKg6N7EJVjkgh9sT2/db5Kgl+lpaXUiRMn3rl3757LtGnTrkGI7cQx\n2IP6Srw/FR+kqcO4znylgdbWVmVkNoRYZw0ePPgVsncHAADwvTs4sODEiRMBz549c7SxscktLS01\nDgwMPO7t7X1p8+bNYe3t7Yrr16/frqOjUwsAAHl5edbV1dV6LS0tKm/fvu1//PjxBbxWgHDgEITA\nwMDjenp61YMHD36FXAsPDw+nUqll9vb2Wfb29lk3b950l6WMOHo3Vq5cuW/Xrl1riEQiG7kmzFas\n8PDw/wVxfbFRV6cAAoHwn3/D7qVXV//KfSMOFFJSUr5oQ6lCWiNRzvDhwwczW1vbF7a2ti8UFRXb\nBgwY8BpCCPLz880pFEodiURiUiiU2qKiIlPOtKCHzIpwiBdAjGcx85phR0RErI2MjAxB4k2ePPnm\n48ePR3DmJ652B0ByK4jIeeEAPzMcM7rDMW7h/v37YzMzM+3R3AsPDw/bs2fPKkFp+5JuQXPxM1/l\nvzluiptf165dm/bLL7/8H4QQJCcnOyMriJqamnR0PDKZXM8tvaQ4xk938Y/Pqd8+X0M49C3xRRSI\nm2P8gsz2ICLWWXv37l31/PlzB2Q18ejRo0twNzc4ugteM2xh3NzIZp+rPOZZOXp/I+5EmzskbcU8\nduzYB0VFRTTO61BKJx30FHC30MePj+wu0tLSRl29enV6QkLC1Pb2dsXGxkZ1Pz+/OGEcZfcWIBzC\n+dJzIFMjlbKyMmpCQsLU9evXb9+7d+8qAHA3NzjED37GTsh9btdl46Ouq1PFDVrEA1mdQnDo0KHl\nJ0+enOfo6Phsz549q3lZmOLGdr0bkp6ARERErIuIiFgHAACpqanjd+/e/XtcXJxfcHDwzp7nRqlr\nciu8sQr2STEO6UKmA0Rkb0VjY6M6cg13c4NDHOA1wxbGzQ0OHKJg6dKlRzZt2rQFAAA2bty4dfXq\n1Xuio6MXcIuLO8ru3ZD2BASZzPZMN0qirhjjk+KeCpkNEK9fvz5NV1f3o729fVZKSooztzi4m5u+\nDUnOvhFjJzabTdy/f38Qm80m+Pr6ng4ODo7y8/M7dfv27UkfPnwwr66u1reysnrLLY/Zs38GdnbW\nICRklURkxNE3gf7c9/PPP//t4eFxTZby4Oi9aG9vVxw/fnzqp0+f+jGZTAVPT88ryD1pnh+P4xuF\ntDY7coa1a9dGUKnUUhqNVqivr1+prKzcMnfu3Djczc23CyDi5lsfH58zBgYGFSQSiUmlUkuPHz8+\nv66ujjJ69OiH8vLyzIkTJ96m0+mas2bNOhcbG+s/duzY+1paWrX9+/d/ExgY+HdISEgkZ55d7b8V\nGhhYYZKd1wZrwGMzNz8jFX5x+OWNQzBE5Ri/wGkgVVFRYYD8v3fv3pWzZ88+zS1dX2ozQZz+ViAJ\nfrW0tChDCAGLxZIfPnz4kwcPHoxZs2bNzqioqGAIIYiMjAzhpsOgkBzDYiTC3Rjpa53FrV74Gal8\nq3wRBZLgGK8glUIEhZSUlPGIddaaNWt2IlamO3bsCOXVeUsaOFmlD3ETv66ujmJlZfW2vr6ezGKx\n5KdNm3YtKSnJFYsPsa72f4N5gPiZL19abop3gMg/bxyCIW6OcU5OoqOjA/38/E4OHjz45ZAhQ7I9\nPT3jEa5xhp7eZlis5L8eMIhngNhbLVol2Xm3tLQoOzo6Ps3JybGRhB9Ebu3F2Q5YdZegvPEBouiQ\n5gCR0FWe9IFeOqfT6ZoKCgqs9+/ff1dQUGDu5OT0tKmpSU1NTa0xMzPTwdTUtBidlkAgQEnJ/eXR\naBAAQACyqqNvDQQCAUAxW38eO3Zs0erVq/coKSm1TZ48+VZcXJwfmUym0+l0MgAAQAgJFAqlHvmN\nkgUCsAyoqp4Bq1cvE7iNoWuTdRdfuv6SQNfeGvDfb4C6x/mX3z3ucSCEqDIBwHnKHZzbGDZv3ix2\njokKSeoxcQALv77mPW++ilZ27+K1JHQYm80mDh06NLOgoMBi6dKlR3bu3BmMRYf9Jw8MCwv7329+\neoxbnXNe+7q9Afi67dG6jwQAYP33P2/dxckhcbQ5ui/vzae8yFSHSWskyi2IunQOJDjDAPhsRmYA\nYp4Z5efnWwwcODCvtrZWi8ViyXt5eV2Oi4ubi8WHGBB5BVGUVUJh08tzrNp85mlvXXmRFsTNse6E\nnq5buPGLdxzxriD2Vv0rSX4xGAyN4cOHP7l3756LJPwgcqtzzmvi0Gtf5sM7TneBhb+9EdLUYTI9\nQUJZWbkVAACYTKZCZ2enHJlMpl+9enW6v7//CQC63NzEx8d7yVJGTiAe4HHv7z0fz549cxw1alSa\nlpZWnby8fMeMGTP+ffz48Uh9ff2qqqoqfQAA6J0+xBCrv6/x2ZcYnet9HOIFt5NU6uvrKa6urret\nrKzeubm5JTEYDE1Zyoijb0BDQ6Ph+++/v/H8+XMHxEsDAL1Vh2HD5xNXFPB+VwaQ6QCRzWYT7ezs\nXujp6VW7uLgk29jY5ErazU13B3h4B9x7MGDAgDdpaWmjfvjhh38HDBjwevny5YeUlZVbXV1db48d\nO/aBlZXVu3HjxqVOmTIlUday4uidmD9/fgznUXqRkZGhrq6ut9+9e2c1ceLEuz3Bj2v3jzaT73ZH\nzal78ePWBKO2tlYbmWC0tbUp3b5929Xe3j7rWzmS9rMDdhbA+13pQ6Z+EIlEIvvFixd2DQ0NGpMn\nT76VnJzsgr4vCTc3onpr/3JvIg5xQNJOZm1tbbOVlJRaMzIyhlMolPrp06dfDQkJidy8efP/NuX8\nt2dIpPxxTuDgdpJKT3T23/1Td9C+6kQ77YJT9+InAQlGZWWlgb+//wk2m01ks9lEPz+/uIkTJ961\nt7fPwuoHMTU1Fdjb2wN1dXVeUXDg4AqZDhARcFs6F3SEkLgczCKdvKBNrNyPksLRHUjayWxDQ4MG\nnU6ncB6ld/v2bbeHDx+OQbjm7OycAgBYKWz+6A6u5/Hi8+kECLexch1H9yDMVxBhJrqitJ8wkxic\nH8JD0pNcTU1NhoaGRsPHjx91CQQC7Nev3yfkHlY/iO7uc8HWrSvA77//LjE5sQM/NaU3QWZWzLW1\ntdry8vIdmpqajLa2NqXJkyffCgsL23zr1q3JWlpadSEhIVGRkZGhDAZDk3P23R3rP35WWZy/sVxH\ngCvX7kPcFoAvXrywW7x48VFra+u87OxsWwcHh+f79+8PolKpZeKwYhZkifflX35xxJGe02r6awvU\n3moZ2h1IwwKwqKiI5uHhce3Vq1eDAQAAbWEKAAAUCqW+vr7+q2+owuoxUdqPl9Upt/S8dSEA3LjH\nSy9yS8Nf134tU2/lqrh1WFVVlX5VVZW+nZ3di+bmZlUHB4fn8fHxXjExMfO1tbVrg4ODd0ZFRYXQ\n6XQyt1VqAoEA5eSCwfbtFBASEiJQds46F82Kuft6jVv5onMfCJWup0MSlvI8IS1rGM6QlJQ0SVVV\ntalfv35t/fr1a58+fXo8hBDk5+ebUyiUOhKJxKRQKLVFRUWmnGkBRoskbn68wP8smzgtQTl/Y7vO\nzT8UDtEAxGyd9fTpU0d5eXlWRkbGMAghWLFixf4NGzZsFZcV8+c2h1z4IUkrZuHvfSnvt8tRcXMM\nwq8dZWNx9g+F0GNo2QW1H1a/dVjy588vXnqRW5qv43LLX9hn7YmQBL/QwdPTM/727duThPGDKCcX\nDCMjIzHJ/nV78Wozyeq1r2USng9YON8bIWmOoYPMjFQGDx6c8+DBg7Ht7e1KtbW12m/evBn4+vXr\ngUePHl0SEhISxWQyFYKDg3cdOXJkKbf0Dg4TwZo1G/mW8fkTILfNrZyWoLwsQ/lfF/T5Brd6lh2o\nVGoZlUotGzZs2FMAAJg5c+bFzMzMob3fillYcP+sgxsJSAayNCCQnhEdb0v67sXFwQtFRUW0rKws\n++HDh6dL1pgTaa+e2GbyuL6SImS2B1FfX79KX1+/CgAAVFVVmwcOHPi6vLzcCOsG78zMGaC5+QTY\ntWsrxhJls/dBVKMYHN2Hvr5+lbGxcenAgQPzLC0tCxwcHJ5bWFgU5Obm2gwaNOjV0KFDs0aOHJkm\nbAfe+4xT0AYGn4EbCXQfs2fPPpOamjq+trZW29jYuHTLli2bQkNDI7EaEPRM9KR9Yl2yfOtbd5qb\nm1W9vb0vHThwYIWamloT+h4/Y04AAGCzH4E7d5RAW1ubUAadPRPIwkxP4afkIel9rvzQI4xURJsZ\nDQMAnBCiFO6dJI6+DScnp4yYmJj5FRUVRiQSiWVsbFwSEBAQ8/Dhw7FZWVn279+//y4rK8temDxx\ngyUcCM6cOTOb2/U7d+5MkrYs4kNP0pXf3oCAEywWi+Tt7X3Jz88vDpnMYjXmBAAAInE0mDRJ8B7E\n3oWvjfD6KiRtzMkPMh8gij4z+gvU1ZWD8PDwHjYrwme8WCHpmVFZWRn1xYsXdhcvXpy5d+/eVf/+\n+++MAQMGvElNTR0fHh6+GbFg1tTUZEhMiF4G3NhKfKDRaEXq6uqNcnJynSQSiZWRkeEka5l6BiS7\nQtmXOAwhJCxYsCDa2to6LygoaD9yHdnGEBISEtWX/SDyxudtC9/y5EHikNZmR26ByWSS3Nzcbu3b\nty8IuYZlgzcAAAKQDq2shnHdxPn1AfJYNsuK/hfZOIplYzYO3gBi3nw7c+bMC5mZmfYpKSnjp02b\ndg1CCNAGKmw2m8BpsIKErnbjbqTydVuLvhlb8umxcJY7j/sixM0xfoFGoxXW1dVReN0Xto6xtI1g\nvcQ7fff42Z00X8vEi7PiqidJQdz8evDgwRgCgcC2tbV9YWdnl2VnZ5eVmJjoXldXR5k4ceKd7777\n7p2rq2sSnU7X5JYeAFGMVLAZGklSr/GWiVv6Lw1Gv07bt/SZNHWYzIxUoICZEQCib/D+0j+dpMFr\nNoxvzJYlrl+/Pk1XV/ejvb19FuThEkDQ3h0A/gBNTXUgPDxcZntApIeetO9MfEhJSQHh4eH/C9IG\nL+51D/JiMS7qWQZ0uPEBN4wZM+ZhQEBAbGVlpUFHR4d8VlaWvbu7+00AsPtBFA+k2Z995jc2INsQ\nmvhw6Et+4QZ6GCGtkShnmDp16nUAAFRUVGxDZkbnz5+fOX78+GQlJaUWZWXlFhcXl3vcZkYAcF9B\n/HrlUDoriFj/4uAPIMaZ0dq1ayOoVGopjUYr1NfXr1RWVm6ZO3dunDAuSDhXELvHL1HvSWIFkXM1\n4Nvhqzg5JiiYmZl9sLOzy3JwcHh27NixhZz3ha1jXm3LK46gNILjSncFUZAsotSTtCEJft2/f39s\nZmamPdqV0po1a3ZGRUUFQwhBZGRkSEhISCS3tACIawWxp+k1wboLG78+X+N0EdVTIU0dJrM9iKGh\noVHbtm3bOG/evJOIkUBwcPDOqVOnJqakpLggzj+F2R+GGw/gQBAREbFu6dKlR+bNm3eysLCQRiQS\nVYYNG/bUwMCg8siRI0vS0tJGZ2Vl2ampqTUzGAxNLDzrO/zqSUYIfRePHj0abWBgUFlTU6Pj6up6\ne8CAAW/Gjh37AB1H1CNDcYgH3d2vKA0L095ynGNvB+5xhAukNRLlFrg5mMXq/JPbCiIQ6wwYX0GU\nNoCYZ0aVlZX6WVlZdikpKeOnTp16w8rK6m1aWtoIU1PTIm1t7RpXV9eksLCwMG6z7652+3IFsXv8\nklV60fPuixA3x7CG8PDwsN27d69GXxO2jnm1G684gtIIjtv3VxDFzXdJ8YuzrxRmL7WgFcTe+WVE\nnCuIvcteQJo6TOZWzGhI1vln70FfssKTJdC+NsePH5/q5eUV39LSoqqoqNienp4+HH0WMz77xiFO\ntLa2Knd2dsqpqak1tbS0qCQlJbmFhYVJzz9Fr0Xf3AsrSXTXD2Lf+TKCQFgOcX5R6VmeSL55P4jc\nINiAoKe6ueEF7KT7Vpa6pUl86Z1C0HeBT1ywo7q6Wu+HH364DAAAHR0d8nPmzPnHzc0tSVC6ixcv\ngpycHAAAAIGBgcDExETCkvY04NsfsAD3g8gP/DiEZfAo2PemNHXhN+0HEQ1hSA/AQqCllS0Ty0TR\ngJCO1KNmJ7KEtIgvuq/Nz1bMPX8CInl8nrj0Hg7LavZtZmZW+OLFCzth04WG7gAFBQ6AREoHZmZm\nwN/fX0AKUVY7ODvJ3rlq9y1PWHA/iKJCtAkIJ9e+lUUcqXzH5hU491WsWbNmZ2RkZAiEEOzYsSOU\nn2UWeg8i7z0UPWsP4pd/P+974O2/qWfvhRA3gAT2VnTP1ya+B5GzfbByuKdCEhwTNXCrYwuLoRCA\nZ1BFZR6MjY39SnZ+7cYZB0ua7nNP/HsQReEjluu8+IA1LhZIgl8+Pj5nDAwMKkgkEpNKpZYeP358\nvjj8IIqn35SM7hFP+u6Wgd6byOu9wqYDRbGQRrcPOp00dZhUCuEW+JHeyMioTFlZucXMzKwAGTB+\nITT4coAofgWXLGT87vz9TDo0Ab4kHwkzuZKTkwXGERWSzBtC8ROfzWYT/Pz8TgYFBe1DX1+zZs3O\n+fPnR/fv3/+NlpZW7fjx45M503bVu7QHiPx41928+f3lVi5aOZJQ//MqQziuSppLvCBN5ZqYmOje\nv3//N5aWlu956TFOcBsgcu/Ik/m0F7ff/NoaK3eFSct5TVRu8x7AcT4rwjl+aRBwq1M0ROWnNPkF\nMXIMPUD88rkF9YGitLOw7cqPJ+Lipjhl5O8wnB8vOOMI4hivvKXJMZk5yj5z5szsiooKQyaTqVBa\nWmo8f/78GAqFUn/r1q3JioqK7bm5uTZv374dcObMmdmvX78eKF3pUqRbHE8nschyOAsAIMgRaBdS\nUlIk5gC3tzmLfvTo0ehTp07NTU5OdrG3t8+yt7fPunnzpvuaNWt2nj17djaTyVSwt7fPqqur05Y+\nx7ghpQeVi3DvM//4gztXefGwt3FJWHR2dsotW7bsj5s3b7rn5eVZd0ePcXf8n8Lxm9ORMS/Hxpzp\nhIGs0goCsn2HjjmFoMMUegM/ReHY5+cW9D6niEtMISFquaKmExb8HIbzdsbNDei+uqc67JbZAJEX\nMjIynCwtLfNpNFoRiURi+fj4nL1y5YqnrOWSLL70BI81Hi9Cfd4fgV1hdhc961SGLowZM+Yhm80m\nvnjxwi4rK8seOYUgPz//u3Hjxt3/8OGD+e3bt13nzJnzDy+OVVUVCenVH0cXPg8wpcnDnoJvU49J\nEsjpGgp83kfOvZTEr9KIUz/JWufhHOtp+HKiwn0w/uUgEh0HSYfwqiegxw0Qy8vLjYyNjUuR31Qq\ntay8vNyIM56i4jZA7HHSdxdYjzMSfsYsachiUCoqsHNsPYCQCbDNuHFghbo6BWzevBlzx/15lq0g\ndFxZdeBYOcYJIhEARcWtoLMzTbIC9jp8uULN/X3k1J/sr9KIUz/JWudh5ZicXKJ0Bfvmwc/oS3Df\nLd2jgvmjR1kxA4D9fMn29mvgzRuAaghBf7HEQf5uFjJ+d/+KnpY3EQXdFw38LY0lU6a4gZ1jl5AU\n6NQ8/vK7hzW9IN5J4p500nPnBOt//zU10THwhiV0XOzxxQusHOMuVyYAAICAgAAQEBCAxOT4uxmI\nrqdETSNseZzySkIm4dPw6jM42wKbVwXZ6TysHGMyX4HQ0FAQGoq4esVSf8LwS9R7WHiCNb2kdKeo\ncbj9/jL+Z34J5uaX96SHHjdANDIyKi8tLTVGfpeWlhpTqdQydBwIofRrCkefAc4xHJIGzjEckgbO\nMRwSh7SsYbAGFoslb25uXlBYWEj79OmTgq2t7Yu8vLyBspYLD30n4BzDg6QDzjE8SDrgHMODpEOP\nW0GUl5fv+OOPP5ZNnjz5Vmdnp9yCBQuiBw4c+FrWcuHoO8A5hkPSwDmGQ9LAOYZD4pD1CJVfEOTj\nCUIIli9fftDS0vL9kCFDsjMzM+3FlXdycrKzurp6g52dXZadnV3W1q1bN2DNe/78+cd1dXWr0U7A\nxSU3lvy7I3tJSYmxs7NzsrW1da6NjU3OgQMHfhOn/Fjy7478PYljsuCerDgpbZ7Kir/S5JigdKdO\nnZozZMiQ7MGDB78cNWrUo+zs7CHClAkhBBkZGcPk5OQ6Ll26NEOYtMnJyc52dnZZNjY2OePHj0/B\nkq6mpkZ78uTJN21tbV/Y2NjkxMTEBHSXm4LS8qsjLOVyqyMs6bjVT0/jl6w4Jiq/ehvHROVXT+KY\nxEjb3dDR0SFnYWGRX1hYSGMymSRuy+c3btyYOmXKlAQIIXjy5Mnw4cOHPxFX3snJyc4eHh5XRZH9\n/v37YzMzM+15Na6ocmPNvzuyV1ZW6mdlZdlBCEFTU5OqlZXVW3HVO9b8uyN/T+GYrLgnK05Km6fd\n4ZcknlXcHMOSLi0tbSSDwdCAsKvjRJ4DS1oknouLy73vv//++sWLF72xpqXT6ZrW1ta5paWlVAi7\nOmUs6cLCwsJDQ0N3IGkoFEodi8WS7w43BaXlVUdY0vKqI0HpuNVPT+MX1rTi5pio/MJaZk/imKj8\n6kkc67GOYrD4eLp69ep0f3//EwAAMHz48HQGg6FZXV2tJ468ARB9g+/YsWMfkMlknnbsosqNNX8A\nRJddX1+/ys7O7gUAAKiqqjYPHDjwdUVFhaG45MeSf3fkFwaS5Fh3ygRA/M8vaU6KUiYA4n9OSfNX\nWIjKsZs3b04WlG7kyJGPNTQ0GpB0ZWVlVKxlAgDAoUOHls+cOfOijo5OjTDynj592tfb2/sSYhCh\nra1diyWdgYFBZWNjozoAADQ2NqpraWnVycvLd3SHm4LS8qojLGl51ZGgdNzqh18Z3UF3dBiWtOLm\nmKj8wpq2J3FMVH5hSSstjvXYASIWH0/c4qAVQHfyJhAIMC0tbZStrW321KlTE/Ly8qy790Tdlxsr\nxCV7UVERLSsry3748OHp6Ovikp9X/pKsezQkybHulCmt5xcklzg5yQ2Sfk5J8xcLROVYbm7uIGH8\nKEZHRy+YOnVqgjBlXrlyxXPp0qVHAPjsMgVL2vfv339XX19PcXFxSXZ0dHwWFxfnhyXdwoUL/8rN\nzbUxNDSssLW1zT5w4MAK3jXHv35EaS90HWEtl1sdCQK3+hFWVmFkFFWHCeurUxwcE5VfWMvsTRwT\nlV8ASI9jPc5IBQHWyuJcgcCSDkucoUOHZpaWlhorKyu3JiYmTvHy8op/9+6dFRaZsEAUubFCHLI3\nNzerzpw58+KBAwdWqKqqNnPe7678/PKXdN0jkCTHulOmtJ6fE5LkJDdI8jklzV+s6AbH2FjLSE5O\ndjl+/Hjgo0ePRmMtMygoaH9kZGQogUCAEEICUj6WtCwWi5SZmTn07t27E1tbW5VHjhz5+Lfffjso\nKF1ERMQ6Ozu7FykpKc4FBQUWrq6ut7Ozs23V1NSaBKXtbntx1hEW8KojQeBWPyNGjHjy3XffvRdG\nZizojg4Tpg7FxTFR+TVixIgnWNL2Jo6Jyi8ApMexHruCiMXHE2ecsrIyqpGRUbk48lZTU2tSVlZu\nBQCAKVOmJLJYLFJ9fb1YjmQQVW6s6K7sLBaL5O3tfWnu3LmnvLy84jnvd1d+QflLsu7RkCTHulOm\ntJ6fn1zi5iQ3SOo5Jc1fYSAqx2xsbHIFpQMAgJcvXw5ZuHDhX1evXp2OfJLCUubz588dfHx8zpqZ\nmRVeunTJ+5dffjl89erV6VjSGhsbl7q5uSUpKSm1aWlp1Y0bN+5+Y2OjmqB0aWlpo3788ccLAABg\nYWFRYGZmVvj27dv+wtahsO3FrY6wgFcdCUrHrX6ys7NtsZYrDLqjw7CkBUC8HPvw4YOZKPzKzs62\nxVJmb+KYqPwCQIock9TmWQKBwC4oKDCHEAJ/f//YDRs2bBUmPRYfT+iNpY8fPx6BdbM5r7yXLFly\nBLGkrKqq0mOz2QQIIUhPT3cyNTUtEkb+wsJCGrLBdMqUKQknT570667cvPLnDKLInpyc7EylUkvZ\nbDbBz8/vZFBQ0D5e7ckpv4WFxXs3N7dbWOTmlX935RclsFgseR0dneqxY8emiptjvAKBQGAbGxsX\nFxYW0vz8/E7q6upWcZbZ3efn9e7x4wzncxKJxM7CwkJad+tYEE9NTU2L7t69OyE9Pd1JU1OT/vPP\nP//VnfKw8EvcbSqIY6LoMSzpiouLTSwsLPIfP348Qtgy0SEgICAGsaDEkvb169cDDAwMKtavX7+t\npaVFedCgQa+ys7MHC0q3cuXKveHh4WFI2xsZGZXV1dVR0Dzh1lcIai9+HONVR1jS8qojQelev349\nYOLEiXc6OjrkkPrJzc217i6XuNVNd/pJbmm3b9++dsyYMQ8kxTGs/OJWf1jSYuFYYWEhjUAgsDs7\nO4mS5pio/JIVxziDWDNDB3QnFRAQELNx48YtwuaRkJAwxcrK6q2FhUV+RETEWggh+PPPPxf/+eef\ni5E4v/766x8WFhb5Q4YMyX7+/PlQceX9xx9//GpjY5Nja2v7YuTIkWm8FAxnCAsLCzc1NS00MDCo\nIJFITCqVWhodHR0oLrkhhMDHx+cMv/xFkR0ZID548GAMgUBg29ravkDcjyQkJEwhEAjsrVu3ru+u\n/LzyF0fdixIkyTFe70V0dPR8Kyurt2pqao0uLi53xcU9fu+eIM6I+zmx8pREIjHNzc3zxdXOWPgl\niWeVBMcEpVuwYMHfFAqlDnnOYcOGZQhTJhI4OycsaR0dHTO0tbU/Dho06BXiSkhQupqaGu1p06Zd\nGzJkSPagQYNeLV68+M8xY8Y8QPNEWVm5xcPD4yrW9hLEMX51hOWd4FZHWNLt2rXrd2tr61x0/XQ3\n8OpHu6PDONPGxMQEWFpavpckx7Ck41V/3NL6+/vHWlpavufGsX/++ceXs8309fUrCAQC+/Dhw0sk\nyTFR+SVLjnEGiSlFzk5K2BVEboHFYslLSl5xhbCwsPC5c+fGyVoOYQMyQMTSnngQPUjivZBFGeIK\nNPdKq/oAACAASURBVBqt8O7duxNkLQcehAvi4FVMTEwAerUKyVeUxYRvIUjjXebWJj09CCsztxVE\nUUNvGJN0Jwjcg1haWmo8Y8aMf3V1dT9qa2vXLl++/BBy7/jx44HW1tZ5FAql3t3d/WZJSYmJCJ+4\nCdu2bdtAo9GK9PT0qv39/U8gZupFRUU0IpHIPn78eKCpqWnxpEmT7rDZbOLq1av36Ojo1Jibm3/4\n448/lhGJRDabzSYCAEBMTMx8a2vrPHV19UYLC4uCY8eOLULKSklJcaZSqWV79+5dpaenV21oaFgR\nGxsbgNwPCAiI3bhx41YAAPDw8LimpqbWhAQ5ObnOkydPzgMAgBUrVhwwMTEp0dDQaHB0dHz28OHD\nMQAAcPPmTfcdO3asPXfu3E9qampN9vb2WQAA4OzsnBIdHb0A6/OePHlynqmpabGOjk5NRETEOl51\nd+PGje/t7e2zNDQ0GkxMTEo2b94chtwTlFdbW5tSQEBALIVCqbexscl9+vTpMEFtdePGje8tLCwK\ndHR0aoKDg3fC/zbVxsbGBowdO/YBEi8tLW3UsGHDnmpqajKcnJwyHj9+PBK5V1hYaDZu3Lj76urq\nja6urrd//fXX//Pz84tD7j958mTEqFGj0shkMt3Ozu5FamrqeOSes7NzyqZNm7aMGTPmobq6euPk\nyZNv1dXVaWFJGxsbG2BhYVGgrq7eaG5u/uH06dO+nLJDCAkrV67cp6enV62hodEwZMiQl7m5uTbc\n6kLS74U0ysjPz7ccP358qqamJkNHR6fGx8fnLHKPSCSyP3z4YA4AAHV1dVoeHh7XNDQ0GpycnDI2\nbNiwDd3eRCKRffTo0cVWVlbvyGQyfdmyZX8g9woKCiwmTJhwT1tbu1ZHR6dm7ty5pxoaGjS4yRMe\nHh6OcAELf/39/U9QKJR6a2vrvJ07dwajrRBxiBdZWVn2Q4cOzVRXV2/08fE5297erojcu379+jQ7\nO7sXZDKZPnr06EevXr0ajNyLjIwMtbS0zFdXV2+0sbHJjY+P9wIAgNevXw9cunTpkcePH49UU1Nr\nolAo9Uia+vp6yrRp066rq6s3jhgx4gnCw28N/OocAN71HhUVFYLsw0OwYsWKAytWrDgAAAANDQ0a\nCxYsiDY0NKygUqllGzdu3Ir0n5zgp8udnZ1T1q5du2P48OHpGhoaDV5eXvF0Op0MwOf3NzY2NsDE\nxKRES0ur7s8//1zy9OnTYUOGDHlJJpPpaH0GAH+dxkvH8OIRv75REGg0WlFkZGSojY1NLoVCqQ8M\nDDz+6dOnfgB8HkPs3Lkz2MDAoHLBggXRTCZTISgoaD+yr3PlypX7mEymApLflStXPO3s7F5oaGg0\nWFpa5t+6dWuyoHbgpZv59VGfPn3q9/vvv+82NTUt1tfXr1q6dOkRhDO1tbXa06ZNu04mk+nInkWI\nxSiG3+ixo6NDbsiQIdmrVq3a09raqtTe3t7v4cOHoyGEID4+3tPS0vL9mzdv+nd2dhK3bdu2ftSo\nUY+QtFhXMaKjowMtLS3fFxYW0pqbm1VmzJhxyc/P7yR6pO/v7x/b2tqq1NbWpnjkyJEl1tbWueXl\n5YZ0Ol1z4sSJd4hEYicyG7hx48bUDx8+mEEIQWpq6jhlZeUWxCt6cnKys7y8PCssLCy8o6NDLiEh\nYYqysnIL4uSS3xK+kZFRWVlZmRGEXZ7T6+vryZ2dncQ9e/as0tfXr/z06ZMChBCEh4eHIfIjwdnZ\nOTk6OjoQ6/MuWrToaHt7e7/s7Owh/fr1a3/9+vUAbnWXkpIyPicnxwZCCF6+fDlYT0+vKj4+3pNf\nXm/evOkPIQQhISGR48aNS6XT6ZqlpaVUGxubHGNj4xJeXCAQCOwJEybcpdPpmiUlJcZWVlZv//77\n7wWQYwZXV1dH0dTUpJ86dWpOZ2cn8cyZMz5kMrm+vr6eDCEEI0aMeLxmzZqdLBZL/uHDh6PV1dUb\nkOcvKysz0tLSqk1MTHSHEILbt29P0tLSqq2trdWCEILx48enWFpavn///r1lW1uborOzczLiFJVf\n2ubmZhV1dfWGd+/efQdh194UZL8GWvabN29OdnBweNbQ0KAOIQRv3rzpX1lZqS+L90IaZfj4+JxB\nPtN8+vRJ4dGjR6O45fHTTz+dnT179um2tjbFvLy8gcbGxiVjx469j47r4eFxtaGhQb2kpMRYR0fn\n482bNydDCEF+fr7FnTt3JjKZTFJNTY32uHHjUtH7A9EriOHh4WHI6jsW/jo7OyczGAyNsrIyo8GD\nB7/kx188iB4+ffqkYGJiUrx///4VHR0dchcvXvQmkUjMjRs3bsnMzLTX1dWtzsjIGMZmswknTpyY\nR6PRCplMJglCCC5cuDATeYfOnTs3S0VFpbmqqkoPQghiY2P9OVd+/P39Y7W0tGqfPn3q2NHRITdn\nzpxTPj4+Z2RdBz2pziGEgF+9FxUVmSorK7c0NTWpQtilSwwMDCrS09OdIITAy8vr8pIlS460trYq\nffz4UcfJySn96NGjiyAUTpePHz8+xcjIqCw3N9e6paVF2dvb+yLn+7t06dLDnz59UkhKSnJVUFD4\n5OXldbmmpka7vLzcUFdXtzo1NXUchNh0Gi8dw41HWPpGXiuIpqamRYMHD35ZVlZmVF9fTx49evRD\nRIciY4jQ0NAdTCaT1NbWprhx48YtI0eOTKupqdGuqanRHjVq1COkndLT0500NDQYd+7cmQghBOXl\n5YaIDuPXDrx0M78+KigoaJ+np2c8nU7XbGpqUvXw8Li6du3aCAghCA0N3bFkyZIjHR0dch0dHXJI\nXyIo8L2ZlpY2UkdH5yO3inR3d09EBj0QQtDZ2UlUVlZuKSkpMUYaFEsnNWHChLtHjhxZgvx++/at\nFYlEYnZ2dhKRhkRvlndxcbl37NixhcjvO3fuTOTX2F5eXpeR7/PJycnOSkpKrei4urq61ciLw03O\nt2/fWunq6lajO0/OQCaT61++fDkYQu6fmNEDRCzPW15ebojcd3JySj979uxPWBpzxYoV+1euXLkX\n/RJw5nXu3LlZEEJgbm5ecOvWLTfk3rFjxxYK+sSMjn/48OGlEydOvMOpVE6ePOnHucF35MiRabGx\nsf7FxcUm8vLyrLa2NkXk3ty5c+OQAWJkZGQI5+B68uTJN0+cODEPqcft27evQ8vg7u6eKChtS0uL\nsqamJv3SpUszWltbldBx0LLfvXt3gpWV1dsnT54M5/f5QRrvhTTKmDdv3olFixYdRSY+nO1dUFBg\n3tHRIUcikZjI4BpCCDZs2LAVrZAJBAIb/X7MmjXrHK8jtC5fvuxlb2+fifxGDxDR7w4W/iYlJbki\n9/7+++8F/PiLB9FDamrqOENDw3L0tVGjRj3asGHD1qVLlx7mnFT379//DdLxcwY7O7usK1euTIeQ\n9yfmhQsXHkN+JyQkTBkwYMBrWddBT6lzpK6XLFlyhFu9379/fyyEEIwZM+YBYhiZlJTkamFhkQ9h\n1+S4X79+7WgdfPr06dkuLi73ONuEny6HsEsfIwMQCCHIy8sbqKCg8InNZhOQ97eiosIAua+lpVV7\n/vz5H5Hf3t7eF5G+GYtO46VjsHxi5tY38tLxNBqtEBmoIRxE6i85OdlZQUHhE7IgBCEEFhYW+cjC\nBIQQ3Lp1y41GoxVCCMGiRYuOrlq1ag9nGYLagZduvnfvngu3PorNZhNUVFSa0dvA0tLSRpqZmX2A\nEIJNmzZt9vT0jM/Pz7cQhod8PzGXlpYam5qaFhOJxK98chUXF5uuWLHiAJlMpiPLlgB0OX/EuJIL\nAACgsrLSwNTUtBj5bWJiUtLR0SGPPtkA/emosrLSgNOhJTq/xMTEKSNGjHiipaVVRyaT6QkJCVPR\nnyG1tLTq0M+jrKzc2tzcrMpNtoaGBg1PT88r27dvXz9q1Kg05Pru3bt/t7a2ztPU1GSQyWR6Q0OD\nRm1trba4nldfX78KLV9LS4sKt7zS09OHu7i4JOvq6n7U1NRkHD16dDH6WbnlhTxrRUWFIboeTUxM\nSgTJzhmf2wkoFRUVhpx5mZqaFpeXlxtVVlYaUCiUekVFxXbkHpVKLYP/LXUXFxebXrhw4UeEU2Qy\nmf7o0aPRVVVV+tyeR0lJqQ15Hn5plZWVW8+dO/fTn3/+ucTQ0LBi2rRp17m5PpgwYcK9ZcuW/fHr\nr7/+n56eXvXixYuPNjU1qXHGE+d7gfjamjJlSiKyneH06dO+0nj3du7cGQwhJDg5OWUMGjQoJyYm\nZj5nnJqaGp2Ojg55fu8cALx5Vl1drefj43OWSqWWaWhoNPj5+cVxcpQfsPKXm0w4xIOKigpDTpcf\niA4rLi423bNnz2r0e1dWVkatrKw0AACAkydPzrO3t89C7uXk5AwS1P56enrVyP/od/xbAr86B4B3\nvSM62dfX9/SZM2dmA9B16sacOXP+QdKxWCySgYFBJZJuyZIlf9bU1Ohwk4GbLkfrfc4+gcVikdB9\nIWdb8mpbLDoNa78IALa+kR/49XU6Ojo1CgoKTOR3RUWFIWefjsQvKyujWlhYFHDmL6gdeOlmFxeX\nZG59VE1NjU5ra6uyg4PDcyS/KVOmJCJtsWbNml2Wlpb5bm5uSRYWFgVRUVEhWOqB7wDR2Ni4tKSk\nxKSzs1OO856JiUnJsWPHFtHpdDISWlpaVEaMGPEES8EIDA0NK4qKimjI75KSEhN5efkONJHQDisN\nDAwqOX0hIf9/+vSpn7e396Xg4OCdHz9+1KXT6eSpU6cmQBGO82Kz2URfX9/TEydOvPvzzz//jVx/\n8ODB2F27dq25cOHCjwwGQ5NOp5M1NDQaIEZHs1ieFyt8fX1Pe3l5xZeVlVEZDIbmkiVL/uS1l4QT\nBgYGleg9Hlj2sHHG5+YnysjIqLy4uNgUfa24uNiUSqWWGRgYVNbX11Pa2tqUkHulpaXGSJ2ZmJiU\n+Pn5xaE51dTUpBYcHLxTkGyC0rq5uSUlJSW5VVVV6Q8YMODNwoUL/+KWz/Llyw89e/bMMS8vz/rd\nu3dWu3btWsMZR5zvBcKbxMTEKU1NTWpNTU1qvr6+p6Xx7unp6VUfO3ZsUXl5udHRo0cX/+en7Iv9\nXjo6OjXy8vIdvN45XkDadN26dRFycnKdOTk5gxoaGjTi4uL8sHKUH/jpARzihYGBQSXn5AN5x42N\njUvXr1+/Hc3F5uZm1Z9++ulccXGx6aJFi4793//936/19fUUOp1OHjRoUA5WXfktg1+dA9ClA3jV\nOwAAzJw582JKSopzeXm5UXx8vJevr+9pALraq1+/fp/q6uq0kHQNDQ0a6H2jCHjpcrTe5+wTSCQS\nS5Rj37qj07jxqDt9I/Is6P8NDQ0reJXHrU9H6sjY2Lg0Pz/fkjN/Qe3ATzdz66N0dHRqlJSU2vLy\n8qyR/BgMhiZi36Cqqtq8e/fu3wsKCiyuXr06fe/evavu3bs3QVA98K2w4cOHpxsYGFSGhoZGtra2\nKre3tyumpaWNAgCAJUuW/BkREbEOOR6roaFB48KFCz9yy4ffAG327Nln9u3bt7KoqIjW3Nysum7d\nuggfH5+z3FZOAABg1qxZ5w8cOLCioqLCkMFgaEZFRYUgDcZkMhWYTKaCtrZ2LZFIZCcmJk5JSkpy\nE1QJ3ORcv3799tbWVuX9+/cHoeM0NTWpycvLd2hra9cymUyFLVu2bEIaAYCuWU5RURGN1zML+7yc\ncqHR3NysSiaT6QoKCsyMjAyn06dP+2JVurNmzTq/Y8eOtQwGQ7OsrIx66NCh5YLS7N69+3cGg6FZ\nWlpqfPDgwd8QZYTGlClTEt+9e2d15syZ2R0dHfLnzp376c2bNwOmTZt23cTEpMTR0fFZeHh4OIvF\nIj1+/Hjk9evXpyFp586de+ratWseSUlJbp2dnXLt7e2KiJITVBf80n78+FH3ypUrni0tLSokEoml\noqLSIicn18mZx7NnzxzT09OHs1gskrKycquiomI7t3j/z951h0VxvP854JB2HBzljt5BD2kBRYwF\nC8WGRixgrBBbYqxR0KgUC6CxJtEYf4jYS4yKBaygxoYKWCAo4IGUo9ejHXD7+4Ps1/Xcvds7roH7\neZ554HbnnXn3nc/OzE55RxbvhSzyOH/+/HT4OCkdHZ16EokE8fNQWVm5a+rUqX9HRkZGtra2qufm\n5vY/fvz4HEE8Q+bJ4XC0NDU1m7W1tRtLS0tN0Drc4gDJ39LSUpPffvttGdHhkA6GDh36SEVFpXP/\n/v3LOzo6yH///ffUZ8+eDSKRSNDChQsP//HHH0vS09MHQxBEam5u1rx27doEDoej1dzcrEkikSB9\nff1qHo+nlJCQsODNmzcD4XTpdHpFSUmJaUdHBxm+Js7HfF8Els3h+4LsDkD3h523t3fa/Pnzj1pb\nW793cHB4C0B3x9PX1/fm6tWrdzc1NVF4PJ5SQUGBzf3790fw6yCoLgegu6xOnDgx+99//x3Q0tKi\nsXnz5ujp06efF+U9hMtblDoNloNl0XjUk7YRgiDSgQMHvi8tLTWpra2lbdu27WfkBj5+BAcHn966\ndevG6upq/erqav3o6OjNs2fPPgEAAKGhofEJCQkL7t69O5rH4ymVlpaavH371kFYOWDVzVhtFPwu\nrly5ci88CllaWmoC93+uXbs2IT8/3xaCIJK2tnajsrJyF1rbxg+BHUQlJSXelStXJuXn59uam5t/\nMDMzKz537twMAACYMmXKpbCwsLigoKAzVCq1wcnJ6TW8OweAT3vZgo71CQkJOTJnzpzjI0aMuG9t\nbf1eQ0OjBdlZ4ZdbuHDhYV9f35vOzs6v3N3dX0yYMOGasrJyl5KSEo9CoTTt379/+YwZM87RaLTa\n06dPB0+ePPkyUl4QSZB6njlzJujp06eeurq6dfDU3+nTp4P9/f1T/P39U+zt7d9ZWloWqqurtyKH\n4eHdY3p6ejUeHh7Pe/q8gnQ+cODA95s3b47W1tZu3LJlyyb+DpugZ42IiIiysLAosrKyYvn7+6fM\nnTv3mLAXaPLkyZfd3d1fuLm5ZU6cOPFqaGhoPL/d9PT0aq5evTpx165da/T19at/+eWXn65evToR\n3l128uTJbx8/fuylp6dXs2nTpi0zZ848Cw/Xm5qally+fHny9u3bNxgaGlaam5t/2LVr1xpko4HF\nK0GyPB5Pac+ePatMTExK9fT0ah48eDD84MGDS/nTaGxs1F60aNGfNBqt1tLSslBfX7967dq1O/nt\nIIv3QhZ5PH/+3GPIkCFPKBRK0+TJky/v379/uaWlZSF/Gr/99tuyhoYGKoPBKJ83b15icHDwaeQU\nC3/6yN8RERFRGRkZX1Gp1IZJkyZdCQwMvIClD7+ugvi4efPmaFNT0xIrKyuWr6/vzenTp59H6kRA\nciCTyR1///331KNHj87X09OrOXfu3IzAwMALAADg7u7+4vDhwwuXLVv2G41Gq7Wzs8uDvT0wmcyc\nNWvW7PLy8nrMYDDK37x5M3DYsGH/wOmOGTPmjqOjYzaDwSg3NDSsBACdr19ix1+QzQEQbHcYs2bN\nOnXnzp0x8OghjGPHjs3lcrmq8I7h6dOnn4eX8YhSl5NIJGjOnDnH58+ff9TIyIjN5XJV9+/fvxzO\nR5Rjb0Wp0/j1RONRT9pGEokEzZo16xQ8HWtnZ5e3cePGrViyGzdu3Orh4fHc2dn5lbOz8ysPD4/n\ncPxBgwY9S0hIWLBq1ao9Ojo69d7e3mnw6KSgcsCqmwW1UXFxcWG2trb5Q4YMeUKlUht8fHxuwUeX\n5uXl2fn4+NyiUChNQ4cOffTDDz/8PnLkyHtCyweCeve7l5ycPG7p0qUHkUO8BHoPZs6ceZbJZOZE\nREREyVsXAvgQFhYWV1lZaYi2ZlFeOHjw4NJz587NSE1NHSVvXQgQ+BIwatSo1Dlz5hwPCQk5Im9d\nJAkrKytWfHx86OjRo+/KWxd5Q+pnMYeEhByh0+kVTk5Or/nv7dq1a42SkhIPef5qTEzMejs7u7z+\n/fvnok0Pt7W1qV2/fn18Z2enSmlpqUlUVFTE1KlT/5b2cxCQDJ4/f+5RUFBgw+PxlJKTk8clJSUF\noJ2XKwmIyj0C6Hj79q3Dq1evnCEIIqWnpw8+cuRIyDfffHNRnjqVl5czHj58+DWPx1N6+/atw+7d\nu1fLQ6e2tjY1T0/Pp66urllMJjNn/fr1MQB0+/Lz8fG5ZW9v/87X1/dmfX29jqx1I9B7UFxcbDZq\n1KhUR0fH7IEDB76BR+IE8UhYWykLEEsC+jgkuTUfLdy/f394RkaGG/+Zgh8+fDDz8/NLsbS0ZMFn\nJWZnZzNdXFyyuFwumcViWdrY2OTzb0VvaWlRHzRoUDqFQmk0NDSsCAkJiYf9PRFB8cOVK1cmmpmZ\nfdDQ0Gh2cHDIhV0mSCOIwj0iYIdnz5552Nra5mloaDRbWVm9x3JhI8tQVFRkPnDgwNeampocExOT\nkp9++mmnvE41aG5u1oCg7lMVPD09nzx48GDY2rVrd8TFxa2DoG4XTGFhYbHythkRFDew2WxGZmam\nKwRBoKmpScve3v5tTk7OACwe4WkrpR2Q7tv6UiBOd/oYZJIJ2qHT06ZNO//y5UtnZCO9ffv29cjG\nx8/PL0Wa5/ASoe8HvNwjAhF6GpqbmzU8PDyevXnzxtHBwSEXdgjNZrMZDg4OufLWjwi9J0yePPnS\nrVu3xmLxiGgriSCLoCKPUcvLly9PNjU1LXF2dn6FvF5WVmaM3NZuampagubb7UtctPylAJLylAUW\n9/hBcKzvQtIc4/F4Sl999VVGQUGBzdKlSw86OjpmV1RU0GHXVXQ6vQLp5xQGwbG+iZ7yq7Cw0DIz\nM9PN09PzKRaPiLbyy4a020kYUl+DyI+WlhaN7du3b0CejSjoYbEILmpPOCIiok/JKLp+4shIG6Jy\nT5wyEfbs/6WMmr64PECTFZSPNPOVtlxPZaUBJSUlXlZWlmtJSYnp/fv3R/BvlBG0k/xLsfuXom9P\nweFwtAIDAy/s27dvBYVCacLLI/i+JDmG10YUii4AAAAKRVcq6UtT996Yviwh8w5iQUGBTWFhoaWL\ni8tLKysrVklJiam7u/uLiooKuomJSSnS4W1JSYkpmjNmAgTEARb3KisrDeWtG4HeDyqV2jBhwoRr\nL168cKfT6RWwywo2m20Eu9/oLdDWpgESiQS0tYk9XLJCR0cHOTAw8MKcOXOOwxv3sHikSG1lU1Md\nAAD67y+BvgSZdxCdnJxeV1RU0FkslhWLxbIyNTUtycjI+IpOp1cEBAQknTlzJojL5aqyWCyrvLw8\nu8GDB6fLWkcCfRNY3OttjTcBxUF1dbU+vLO0tbVV/datWz5ubm6ZAQEBSYmJifMAACAxMXGetHbq\nSwtEoy9bQBBECg0NjWcymTkrV67cC1/H4hHRVhKQBaS+BtHa2rqgqKjIEoIgkpmZWXF0dPTmnJwc\n5tWrVyeqqqpyKysrDRsbGyk0Gq2WyWTm0On0Ci0tLQ6JRIIiIiIiJbWGwtvbu0/JyDIvWT6TJCEK\n9ySZryzLs7fKyktfSYPNZhvNmzcvkcfjKfF4PKU5c+YcHzNmzB03N7fMGTNmnIuPjw+1tLQshJ2c\nSwq9ze5fir7i4uHDh1+fOHFitrOz8ys3N7dMALrd2ISHh8ei8YjJZObMmDHjHJPJzFFRUek8cODA\n97JebyhtG0kz/d6su0whzXl4CEJ3NXLz5k0feEt+WFhYrKhb97vVJtDX8F+5yoV7/EFaHAMAQAD8\n71mlBlnl09sgaY59+PDBzNvbO5XJZGY7Ojq+2bdv33Koew1SpImJSYmrq2umq6trZnJysj+/rCKX\nDcEf8SBpfvU0yKL8CK7IFrLkmNSnmIcPH/5AV1f3k3kKHx+fW/CZr56enk/hMwcvX748OTg4+DSZ\nTO6wtLQstLW1zU9PTx8sbR0J9E2Iwj0CBMQBmUzu2LNnz6rs7GzHJ0+eDPn9999/+PfffweQSCRo\n9erVuzMzM90yMzPd/P39U+StKwECBAiIArm4uUHiyJEjIcHBwacBwL91HwAAIiMj//e/t7e33Id0\ntbVp/1uvQ6HogsZGic5a9kmkpaWBtLQ0ueWP5B4aFI1jMGCuETwTDmlzjMFglDMYjHIAANDS0uIM\nGDDgX7jOgohTJggQINCLIdcO4rZt235WVVXl8h8mjgTWugpk460IgBd1d/9PtAt4wN/pioqS3XHM\neLinaByD8XEDAcEzYZAlx2D/dUOGDHny8OHDr3/99dcfjx07NtfDw+P5rl271ujo6NRLLXMCBAgQ\nkDBkvosZxtGjR+dfv359/MmTJ7+FrynS1n0CfRdo3CNAoCfgcDha06ZN+2vfvn0rtLS0OEuXLj3I\nYrGssrKyXI2MjNhr1qzZhSYXGRn5vyCr0XTYhQ3hxqbnSEtL+6QMxQXaufGRkZGRpqamJW5ubplu\nbm6ZycnJ4+B7inAOszAQrpL6AGSx0JH/uLPk5GR/JpOZXVVVpY+MB29SaW9vV33//r2VtbV1AY/H\nI/GnBxRwMSz430JdYrGuuABSWHyLl3v8QVplCCSwoBtPGpLIpy9CGhzjcrlkX1/fG3v27FmJdh/t\nuEdIBvUYhaILAQAgCkX3k+t46iqCP+JBXH6hbaiLjIyM2LVr12r+uKKcwyyL8sPiCsEh6UAadRhW\nkPoIorW1dYGNjU1Bdna2o5mZWfGRI0dCfvjhh9/fv39vbWpqWqKtrd0YGhr6fwB0b92H3dz0798/\nNzQ09P+Io4IIiAtRuKfIIL7EFRcQhv86NpttBP9/8eLFb5AjQ7IC4cuw9wBtQx0A6OtYe+tmTqIe\n632Q+hrExMTE+VpaWpy5c+cee/36tRMAAOTm5vbX19evXrdu3Y64uLiwuro6XQAAyMnJYVZUVNCb\nm5s1S0tLTcaOHXs7PDw8Dt51SoCAKBCFe4oMYs2h4gLNf9327ds3nD59OjgrK8uVRCJBVlZWLCdF\ntwAAIABJREFUrEOHDi2Wt649AbEJTz5AW8cqymZORQJRj/U+yMXNTVJSUsC8efMSAQBg3rx5iZcu\nXZoCQO/9MiKgmBCFewQIiAMLC4uikSNH3uvo6CB3dHSQFyxYkDBu3LjkvXv3rqTT6RWtra3qLS0t\nGv369WuXt649wcdNeMSIpKyAdx0rANibOQGQzzpXApKDpNa5igO57GKuqKig0+n0CgC6z5qsqKig\nA9C73dwQEB3ycHODxT00EBzr/ZA2x2A/iK6urlkcDkfL3d39hY+Pz62EhIQFPj4+t+CR6tjY2PDY\n2NhwqSlCoM8BeQTod99993+TJk26AoDomznl741BBZBIxKihuJCntw+5+0EkkUiQoK+f3uLmhoDo\nkCfxARDOPYJjvR/S5hiWH8SkpKSAe/fujQSge6Ta29s7re90ELsbfGKqWbpgs9lGRkZGbAA+Xcca\nEBCQNGvWrFOrV6/eXVpaaqL45zB3gu7RZ6KT2Nsglw4inU6vKC8vZzAYjHI2m20EfynJw82NIKfD\nhEPivgcs7hEg0FPAfhA9PT2fijJS3fvQ3eATa8kkh+Dg4NP37t0bWV1drW9mZlYcFRUVkZaW5o22\njlURzmFGB/Hh0Ncglw5iQEBAUmJi4rywsLC4xMTEeVOmTLkEX5f1l5GghbPEotq+ByzuESDQE3A4\nHK3AwMAL+/btW0GhUJqQ9wSNVMtmGQPRcEsLklrCcPr06WD+ayEhIUew4m/YsGH7hg0btvc4Y4mC\n+HDoc5C2H52goKDTRkZGZWQymWtqalp85MiRBTU1NbQxY8bc1tPTq9LU1OQMGDAgOzg4+FRbW1u/\njRs3blFXV28hk8lcNze3F3V1dTr8aQIJ+lUCfL6aYN9hH4NgP06fxhfsW4yAYAAJ+3cSlXuQlDjG\n/4xoHEHyCNtvHcBMg9/nHR7uCoMgnXorJM0xCEL3g+jg4JDLZrMZEASBsrIyIwcHh1x+OWnXE0gO\noPMHmx/CZIh6Dh3S4FdPgizKSDjP0O8TEA+y5BipOz/Zo7Cw0HL06NF3//333wH9+vVrnzlz5tnx\n48dfz87OduR3Q8K/dodEIkGS0rt78Wz3+ggIghC/AeheM/HxnjB5pBwEQcQUtYggkUgAksH5tVjc\ng3c3/6eLxDiGBD/fPr8OBNzj5+jHeHjiiK/r5zr1VkiaYxAEkebNm5eop6dXs2fPnlXw9XXr1u3Q\n09OrCQsLi4uNjQ2vr6/XkWY9hobP6yb0Og5Nh57Wi18qZFWH4YW0OfZfHkAwz9DvExAPsuSY3I7a\n09bWbiSTyR0tLS0anZ2dKi0tLRrGxsZliumGREUsB5+Eo1rFBBr3iCMdCYgD2A9iamrqKPhItJSU\nFP/w8PDYW7du+djb27+7e/fu6PDw8FhJ5KfIzoYVWTcCvRMEp+QLue1iptFotWvWrNllbm7+QV1d\nvdXPz++Gj4/PLbyLu2XrgkTUtRXEtn48kIebGwDQuTd27Njb/PEU380NGs/QuSeKo2NkXLxQVEfK\n0ubYkSNHQgwMDKq6urqUYWfskZGRkd99993/GRgYVGlqajavXr16t46OTr0k8lPkddGKrJuiIyQk\n5Mi1a9cmGBoaVsI8qq2tpc2cOfNsUVGRhaWlZeG5c+dmwDyKiYlZf+TIkRBlZeWu/fv3L/f19b0p\n3yeQDghOyRmymsvmD/n5+TYDBgzIqa6u1uvo6FCZMmXKxePHj8/W0dGpQ8bT1dWt5ZcFUlmDqIK6\nllDY2onP4xBrLsQFkNHaCjTunThx4ltkHGmVGRYn+PkjSAYv50Rdd4aWn+gyis13SXNMlDN0+YM4\nduIvV6zzlvnjisoFYTJ4eCppCHpWRYG4/ELj0dq1a3fExcWtgyAIxMbGhoWFhcVCkGKfxSy4nsLP\nj8/X9itunSJryKqdhCAZnMWMhefPn3sMHTr0kZ6eXo2Kikrn1KlT/378+LEXg8EoLy8vZwDQ7QdK\ndm5IYF9N+AAPfRPofUDj3qNHj4bKWy8CvQ+inKErDfR8GYt4y2ekCaxpxb68ZIc4cexTfDy5h4A8\nIbcOYv/+/XOfPHkypLW1VR2CINLt27fHMpnMnEmTJl1JTEycBwAAiueGpLsyJZFIEiMwXBkqWiXd\nl4HFPXnrRaDv4Ndff/3RxcXlZWhoaHx9fb2OvPXBBrx8RnE6XX25IygKBJ04ZmpqWgLHk8VZzEQ7\n9WVCbmsQXVxcXk6fPv0cnU4v53K5/dTU1NoWLVp0yN/fP3nw4MHPNm3atIVCoTRmZGS4y0vHz4Ec\nZRRlgODjujD+9VnIjiaxzkI2QOPewoUL/5S3XtIB9npYYpe9dLB06dKDmzdvjgYAgE2bNm1Zs2bN\nrvj4+FC0uNJY5yrOGlIC4kFW66jFPXEMAMlwTLR2iliDL0nIa60+AEB+axAhCAJz585NjI+PD4Eg\nCHR0dKjU19dTsdZdIAOQyhpEUdZ19eweev7EOgsgw7UVaNxD3pdWWSA5gX5d0D2stT2COIfNQeG6\nEGsQ8QQWi2WJXDuG9544dhLEBbQ4gteG4eOAuDLiQtrpSxM94Rc/V7B8acbExITHxMSEw/H8/PxS\nnjx54omWZk9thcfPL/66p+frZwlIpw7DCnKbYm5oaKA+ePBgOOwtXkVFpZNKpTYoppsbSULx1vx8\nacDinrz1ItA3wGazjeD/kWfoyh+CRnakUy/xryfEWl+InMIk0A341CcAPl1uFRAQkHTmzJkgLper\nymKxrKRx4hhcHtJcC0gsJVB8yG2KmcViWRkYGFQtWLAg4eXLly7u7u4v9u7duxKvmxs7u4FAXV0V\nTJkyEYwePRp12Fwxp9CI44hgyGvoHI17+/btW6GhodGCjCcLNzeKNB2oSLpICtLmmChn6Mof8BIZ\ntLpHOvUSv5sSLLcln3ZEvry6kZ9H0dHRm8PDw2NnzJhxLj4+PhR2cwOAbM5i/lge2GXRF+sLAp9C\nbiepPH/+3MPLy+vxo0ePhg4aNOjZypUr91IolKbffvttWV1dnS4cj0aj1dbW1n7yudn9MpwBysrz\nQW1tBdDW1kbNA89pEtje3gHKNcnegyD+EwrIAIBOBevQyg6y8hCPxj1tbe3G6OjozQhdIGm8G4JO\nPEFyBJk3Xhl0zmGnLzxd/Cep9JZTVxTppAtxOCbqqTp4uMB/TzA3wGdxsXTDq++n8URLX9GgSPwC\nQPx6DE9dgJ9nwjj2ebuHdToLgS/kJBVTU9MSU1PTkkGDBj0DAIBp06b9lZGR8RV+NzczgbIyWXYK\nywTwV3zTf9MtqsR0tBSAxT1560Wg9yEkJOQInU6vQE4j19bW0nx8fG7Z29u/8/X1vSmdXcyKsBFA\nNstlCJdikodinVCieDvpCXRDbh1EBoNRbmZmVvzu3Tt7AAC4ffv2WEdHx2zFdnMjK8BTQR2A/8VR\nrBe7dwKLe/LWi0Dvw4IFCxJSUlL8kddiY2PDfXx8br17985+zJgxd/jPYJYMRPPbKh3IpmEnfOJJ\nHvJd/6cIHzcEcEFWu2HQQlZWlouHh8czNTW1VgaDwa6vr6fm5+db02i0GjKZzKXRaNWFhYUW/HLg\nv51NqqoUqKGhQeBuH2E7oJBxPv0r/Xuf5i981xfeZ+qt+O+Z5MY95P2e2Bfv7jz+30geiCOD7y8E\nfX5qEFa6n8tgnWKBpr8innwhDY6h7T4tLy+nQxAE2Gw2A959yh/E4ZiodYQ4ddWnaeA5YQqdT/y6\noP3G2iUrjJeKClnWYXgClq2E2VIYZwTHEf+voPwJdEOWHJPbJhUAuv3RBQcHn37x4oV7U1MThUql\nNmzbtu3nsLCwuHXr1u2Ii4sLO3jw4FJJfYEr1qYV4itKnkDjnqTSVvzzQwVtVhAsI8ozKb4dpAO8\nG+0A6A3nfePhijh86gaezRCfo7vuVIR6XK4+6qQAfBtPpNV2EW2iwkFWPVG0UFxcbDpmzJjbd+/e\nHTVx4sQrEITv6xsA8UYQP/5GfvHKbwQR/z10ffsagAy/jNC4hww9sa+gMsLmpCxHEIWNGgkf+cZ+\nLvT0FAXS4Bj/CCKe8+QhMTkmqHzxxJE0F0TRRdhvLK7hyUdRIA1+WVhYFDo5Ob1ydXXNHDRoUDoE\nQaCmpoY2duzYW3Z2du98fHxu1tXV6aDJCn9fseuanpR/z/72jrKWF2TZTsptDSIAAKxatWrPzp07\n1yopKfHga6J8fYsP+IsXknzSUkFv01fxgcY9AgQkATqdXtGT8+TFO9bs4zGgvQPEaBFekEgkKC0t\nzTszM9MNPnNZNutcCXzpkNsU89WrVycaGhpWurm5ZaalpXmjxRF8vFAk6OpqBzExMcDPz08Bp2YI\nCIO8pmfwcA8A0af/CL9g+IBnqYekloPIg2Owg+OwsLA4cTbafXqsGRnndKq4x4DKC+JPS3+JgPjc\nmiQlJQXcu3dvJADdB0p4e3uniddJVJzpegKKB7n5QdywYcP248ePz1FRUelsa2tTa2xs1J46derf\nz549G5SWlubNYDDK2Wy20ahRo1Jzc3P7f6I0iQQBAAFVVW1QVVWC2w+iaP7jFOketo8wZKekt7/k\nsvLvhMa9wMDAC8eOHZuL0AUS9d3A47tLUfwgoumJ1ycoml3Q/CAK93mHzz+pJOsoSXMM6eCYTqdX\nREdHb548efLlGTNmnPvw4YM57OBYR0enHkUXVI4J8gko3PccEHBP8lzA+gs/F35fm+I9s7zaLyxI\now6ztrZ+T6VSG5SVlbsWL158aOHChYd1dXXrYH/BEASRaDRaLdJ/MEIfKCIi4n+/4Q/dz23a7Yuw\nG5Lil7h/0fNQtLKWFfg/cqOiomTSTgIAgEzmsYWFtLS0kfA6sLVr1+6IjY0Ng6DuMyexzmIGQJQ1\niJ/vshO01kHx7qHH+fQZe/86jf/0lxv3kEEcW6KVm6A46DKfy+GVEYdDgtPF5h32c6GnJ+h5hNlS\nkpAlx9DWjiEDXlviKyP5cEFQGvzpiKO/MBlFgzT4VVZWZgRBEKisrDRwcXHJun///nBR1rm2trai\n6ikaV8ThlyTzEOxF4UuCLOswuU0xFxcXm82dO/dYZWWlYUtLi7qWllYzAAAsXrz4j8GDBz/btGnT\nFgqF0piRkeHe89z64nQGsYZHXGBxDy/wT38iy4gMuv1aKiJE4RKeKSn+9L7MaSx47RiNRsN8aAeH\nweDnn5eDuXNny1I1Ar0IRkZGbAAAMDAwqPrmm28upqenD4bXucIzbYLWuWpoaIJHjx6CIUOGyE5p\niYM4olYekNsmFTKZ3LFnz55V2dnZjq9fv3bmcrmq//7774BDhw4tCQsLi+Nyuarr1q3befDgwaXy\n0lGxoQiOcnsnsLiHVx6/k1nk5qIOoLjlJQqX8DhH5k/vyz0pARIyFfTu3RDw4kWWrNTBAcKFiSKh\npaVFo6mpiQIAAM3NzZo3b970dXJyeg2vcwVA+IES2tqjQVNT03//E6fSEMAPuY0gMhiMcgaDUQ4A\nAFpaWpwBAwb8W1paaiLu4lvF8nEoL8CVcPdoFdIWhH0+Ao17ZWVlxgMGDPhX3roR6DsgkUjQ2LFj\nbyPXjn0eKxc8fcoBkZGRQjZCyaqDJa3Zlt49i4NVf0p7E1RFRQX9m2++uQgAAJ2dnSrffvvtSV9f\n35seHh7PZ8yYcS4+Pj4UXueKJz3x/E4qEr7M2Qi5QVZz2YICi8WyNDc3L2psbKQg11bweDwS/1oL\n6L91FQB8ugYR8K1V+BjEXU+hSPfEl0euW+C/pmgAMlxbAQeYe01NTVrI6wAASElJGQoICELVE82W\n4pQNVlxB+YnHnZ6sIRLMrU91EpyeIPvhsXFPIUuOoa0dQ97vfsad0PLlazCeXXJlJW0uYHGjp+kL\nS0NWwJunPOowQQEAAFGpY6GbN29+9hzS55d084AgxTytSdqQJcfkepIKAABwOBytwMDAC/v27VtB\noVCakPdEcXPzEb3N3YN8oAi7n+V9CgGHw9GaNm3aX/v27VuhpaXF4b/P4wWDZ88eCh3dkZ17G8Wb\npuvZs3++RlPSXJQnx9DWjg0fPvyBXJSRKWTPU6wRPmLmpK8CyTFibaLUIKueKFrgcrlkX1/fG3v2\n7FkJX3NwcMhls9kMCOr+Asd7kgoQ6yukt9yT/Agiv5wiAMjwywiNe8jQbZ9bkLv7GFQ90W0p/RHE\nnnxxix5XfP17IoNmY0lBVhxrbm7WaGxspEAQBDgcjubQoUMf3rhxwxcZp/sZ++YIoqTSF8b/z22G\n/e70lDd40pFlHYYnANB3RxCxuNDXIUuOyW2TCgRBpNDQ0Hgmk5mzcuXKvfB1URbfEhCG3na6gmyA\nxT0CBCSFiooK+vDhwx+4urpmeXp6Pp04ceJVX1/fm/LWi8DHjRr4T6khoPhQIcpUCpBbB/Hhw4df\nnzhxYnZqauooNze3TDc3t8yUlBT/8PDw2DNnzgSpqqpyo6OjN6mpqbVKJsc0ySQjlXzEkcEjh3ZE\nnzJmhxGr4hRnik6RD7CHuXfw4MGl/fr1azMxMSlNSUnxFz2lnkylocmmffKrb+84TBNfko9bitjg\nW1lZsWJjY8Pb2trU2tvb++E50lE25Z0mJ1kxcxSzHhFky49eCJoweaPI9RcSKSkp/v3798+1s7PL\ni4uLC5O3PpJFmghxRfOUIO3y7S38EQa5dRCHDRv2D4/HU8rKynLNzMx0y8zMdPP390+hUqkNTU1N\nlHfv3tk3NDToXLp06RtRXJBgI63nSUgtH3FkxJXrAp92GD8Cy31LX+sgenl5Pba2tn6fm5vbn8Ph\nUAwMDKosLCyKRE+pJ66G0GTTPvmFPHKt7yFNfEk+buF3OyQ7dHV1KS9btuy3lJQU/5ycHObp06eD\nhdVjsinvNDnJipmjmPVIty0jhMTC7lQocv0FQxyO9S6kiSGDbySR6CDig9w6iFhIT08fbGtrm29p\naVlIJpM7goKCzly+fHmyvPXq20B3bIx3VEaUERw4bndQlcvID8ExAtIGwTFJQAVERUUJvC/JJTSK\nOBItCKJwbPLkaX14NgIJuNOPPTpMAD8UroNYWlpqYmZmVgz/NjU1LSktLTXhj6es3A+QSJ2ATCbL\nVsE+CXTHxsiva0GVJ/8IjrY2DURFRX0SH5b/OEoCAdh5tKxHfvByTElpHDAwoMpUNwJ9A3g5RiKF\nAV1dgmPo6ASCRwHRltCID0UciRYEvBxrbLwLWlsbQd+djUDDpx1FeDAC/j8qKkpo51GaHwy95WNE\n4TqI2G5tPkVXFxe0t7cCDQ0NxJcR8i//tSgccSRxDy0fYXJRQtLE80zSeJbu/6Oiov5XaTY11SFe\nNNJntv/YCQSfxP+00v08X1l+3eLlGI/XCVJS/hb4vOKVm3AeiJaPtOIKlsH33mHJYHMXjVPIAH98\nYPFPUJAV8HIMgnggKmqzWLYUr3zx1hlYsrLmmqD6FF0GnWNA4PvLf11cjskSonDsPwkJ/JVEGnjz\nwFP2wv52/vc/8pjT7v/R2jJkENTmCQv8/JFk2rKE3P0g8sPExKS0uLjYDP5dXFxsZmpqWoKMAwk5\nvooAAUEgOEZA2iA4RkDaIDhGQOqQlT8dvKGjo0PF2tq6gMViWba3t6u6uLhk5eTkDJC3XkToO4Hg\nGBGkHQiOEUHageAYEaQdFG4EUUVFpfO3335b5ufnd6Orq0s5NDQ0njgjl4AkQXCMgLRBcIyAtEFw\njIDUIe8eqqCQnJzs7+DgkGtra5sXGxsbhhbnxx9/3G9ra5vn7Oz8MiMjw02YzIkTJ751dnZ+6eTk\n9Gro0KEPX7586YwnHwiCQHp6+iBlZeXOCxcuTMUjk5qa6u3q6prp6Oj4ZuTIkWnCZKqqqvT9/PxS\nXFxcshwdHd8MGzbsvqGhYcXAgQNfY+nE//wQBIEFCxYcESSHZgNhMmg2wCPDbwNhMvw2SEhImK9o\nHMMri2ZnvHny21oUffltjlcWzfZ4yhjLPuLwEK8slo3E4aQ0+dUTjonLL3lxTBx+4S0zgmOS55ek\n0k9NTfXW1tZucHV1zXR1dc3csmXLRrxp96Tse5p2T/T+8OGDmbe3dyqTycx2dHR8s2/fvuWS1B1P\n+j3RH2+QGml7Gjo7O5VtbGzyWSyWJZfLJaMNn1+7dm38uHHjrkMQBJ48eeI5ePDgJ8JkHj165FVf\nX0+FoG7i45GB9Rk1atTdCRMmXD179ux0YTJ1dXU6TCYzu7i42BSCIFBeXm4oTCYiIiIyPDw8BoK6\nK1MKhdKQnp7ugUVw/uf39PR8AkEQuH///vCMjAw3LDl+G3h6ej4RJsNvg7/++itQmAy/DaqqqvSF\nyfDbgEaj1XR0dKgoCsdgG+ORRbMzHjk0W+PNE83meGXRbJ+amuotqLyw7CMuD/HKYtlIHE5Kg1s9\n5Zi4/MKbp6Q5Ji6/Ojo6VISVGcEx6dRhkko/NTXVe9KkSUni6N+Tsu9p2j3Rm81mMzIzM10hCAJN\nTU1a9vb2byVpdzzp90R/vEHhdjHDwOPjKSkpKWDevHmJAADg6en5tLy8nGFubv5BkIyXl9djKpXa\nAMu8f//eGo8vqV9//fXHadOm/WVgYFCVn59vK0zm1KlTswIDAy/Ai4bfv39vI0zGyMiI3djYqA0A\nAI2NjdoMBqPCwMCgGstG/M9fX1+vAx/xpauri+mrgd8GJSUlpsJk+G0AAADCZPhtoK+vXy1Mht8G\nenp6NSoqKp1Y8XsCcTgG2xiPLJqd8fou47c1Xn3RbI5XFs323t7eaYLKC8s+AAjnB5p94Hvi8BGP\nHJZ9pAVxOZaSkuInDr/w5gmAZDkmLr9UVFQ6hZUZwTFs9KQOk1T6AIi/GaYnZd/TtAEQX28Gg1Hu\n6uqaBQAAWlpanAEDBvxbVlZmLCnd8aTfE/3xQmE7iHh8PPHHoVKpjXBlgCWDRHx8fKiLi8tLPPlc\nvnx58tKlSw8CAEBdXR1NmExeXp5dbW0tbdSoUakeHh7PT506FSxMZuHChYezs7MdjY2Ny1xcXF7u\n27dvhag2QlaAeBAfHx86fvz468Li8dsAj4sFfhscP358jjAZUW3QE4jDMdjGeH2QwYDtjDdPNFvj\nkcWyOR5ZcWwvCQ4CgJ+HyHxF5SMA4nGyJxCXY9nZ2QPF4ZcoeUqSY9LiF5ZOBMc+6ihuHSap9Ekk\nEvTo0aOhLi4uL8ePH389JyeHKf4TCc9fnLJHg6T0LiwstMzMzHTz9PR8irwuKd2x0pem3WEo3CYV\nGPh9PH3Wg8Yll5qaOurIkSMhYWFhsf/8889wQXFXrly5NzY2NpxEIkF4e+wdHR3kjIyMr+7cuTOm\npaVFw8nJ6fXw4cMfCJLZvn37BldX16y0tDTvgoICGx8fn1tJSUmTBMnw64PXbgB8tMHDhw+/FhaX\n3wZ47MBvAy8vr8dDhgx5QiaTO7Bk0Gzw8uVLFwqF0oT3ufBCXI6RSCRIXDvfvXt3tLD4WLbGkyeW\nzfHIYtlemFxPOAiAaDyEIQ4fAcC2j52dXZ4oOuNFDzgm9OxmGPz2w5OnpDm2fPny/cLkevJuExxD\nR0/qMEml/9VXX2UUFxebaWhotCQnJ4+bMmXKpXfv3tnjSR8Pelr2WJCE3hwOR2vatGl/7du3b4WW\nlhaH/35PdReUvrTtDoACdxBNTExKT5w4MTsoKOjMmDFj7qD5eOL3A9XQ0KDd0NCgA/9GkwEAgFev\nXjkvXLjw8Pv37621tLQ4aL6kTp48+e2xY8fm3rhxw+/FixfuQUFBZwAAoLq6Wp9MJndYWlqyYJnN\nmzdHf/XVVy+QeZiZmRXr6+tXq6urt6qrq7d6eHg8QxYemm7Hjx+f7eDg8A4AAGxsbAqsrKxY79+/\ntxZkI6TuJSUlpjweT0lJSYmXn59vgyWHtEFKSoq/sGF4AADgt0FycvI4Mpnc4ezs/ApLht8GI0aM\nuP/y5UsXDw+P51gyjx49Gvrzzz9vA+CjDd6+fesgSEZcLFu27DdTU9P/feHh4VhJSYmpiYlJKZfL\nVRXmgwyAz+2Mx3cZlq3xyMI2X7p06UEzM7Ni2OampqYlwmRh23t7e6fNmTPnuJWVFWvv3r0rbt26\n5YNlQyz7YMUXZh+8clg2CggISBIkh8VJaXUQ8ZQZmg0dHR2z79y5M1aQHADo9sPLsWnTpp0vLS01\n1dDQaBaHY0gbNjY2UvDyCwDR3m00+0yZMuVSQkLCgjFjxtwRJItmo//qR1tra+v3Gzdu3FpZWWmA\nJtcbOCYuv+B3NCYmZv379++tDx8+vFDc9JEd/HHjxiV///33B2pra2k0Gq0Wvn706NH58fHxoQ8e\nPBA4GCMsf1HrF0HA0nv16tW7zczMirds2bJJkHxHRwc5MDDwwuzZs09MmTLlkjDdc3JymGvWrNmV\nmpo6Co9+wtLHY/ceQ5oLHEUJ8+bNO7px48Yt8O+Ojg4VFRWVjpMnTwZj+XhCLgJ9/PjxkMGDBz8R\n5heqqKjI3MbGJv/x48dDSCQS7+3bt3ai+JKaP39+wrlz56YhZTQ1NZu2bt36MzLev//+23/MmDG3\nOzs7lZubmzUcHR1fm5mZfRCUz6pVq3ZHRkZGQBAEysvL6SYmJiWZmZkueBbwPn78eIinp+cTFotl\nSSKReAUFBVZYckgbIK+zWCxLYTv6YBvAO/oEyfDbYODAga+zs7OZgmTQbFBTU0OTNL8gCAKWlpYs\nIyOjMkFlgmZjmJ+icA3Ja1H5duHChakWFhaFN2/eHCtMFrb5vHnzjoaHh2+HbY4nX9j23t7eqXv2\n7FkJ215QeWHZBw+nsHjYEz6Ky8me8gsr4LE7mg3F4Vdqaqq3qalpsbgcw6svmg1fvnzphJdf/O82\nXGc5Ojri5pilpSXrzp07o8Xh2H/1o3Vf4Ji4/JJk+uXl5XQej0eCIAg8ffp0sIWFRSF/OgkJCfOH\nDRv2AC2PBw8eDAMAQF1dXUp4yl4U+wgqJyy958+fn7Bp06ZoQenyeDzSnDlzjq1cuXLNjplTAAAg\nAElEQVQPVr4kEonn7+8vlu7C0sdr954GhR1BVFFR6dTT06sJCwvbsXnz5i2wj6dDhw4tBgCAxYsX\nHxo/fvz169evj7e1tc3X1NRsTkhIWFBRUUHn9wt14MCBpcrKyrzFixcfio6O3lxXV6e7dOnSgxAE\nkaZNm/YXmi8pZD78uikrK/OQMnQ6vdLIyIiNlOnfv3+uv79/irOz8yslJSXeokWLDtvZ2eUJymfD\nhg3bFyxYkODi4vKSx+MpmZubfxg/fnxydXW1vpmZWXFUVFRER0cHWdDzA9A9rP31118/rKmpQZVD\n2gAAAMhkcoeNjU3BvXv3RiLzamtr6wfbDa2MgoODT/PLIPPht8HChQsPb9myZZMgGX4b7NixY51E\nv4j4sHz58v2CygTLxkgfZJ2dncrffffdZ7Jodk5PTx8sKt8A6J6aUFJSgoTJwjbfunXrz6qqqh0b\nN27cymQycwAAQJgsbPtnz54NysvLs9uxY8e6H3744XdB5YVlHwCE8wO2z5IlSw6SSKT/2QePLFZ5\nisNJ2D7SAJavOmEcQ5Ozs7PLE8Sv1tZWNbx59kRfNBs6Ozu/xssv/nd7/vz5RyEIIr19+9YBL8cC\nAwMv4ClvtHcQfta+wDFx+SXJ9P/6669pBw8eXKqiotKpoaHRcubMmSC86QcHB5++c+fOaAAAMDc3\nL4qOjkYtexsbm3wtLS2RdBdWToL0hoQsJ3j48OHXJ06cmO3s7PzKzc0tE4DuJRQfPnwwBwAAPz+/\nGwAAYGVlxRLH7sLS76ndcUMaXzVYIScnZ8DIkSPTdHR06hwdHd8kJSVNgiAIHDp0aBGZTOaqqqq2\na2lpNQUEBFyGoO4Rnl9++WWNs7PzSyqVWj9z5swzbW1t/eD0rly5MtHFxSVLR0enbujQoQ9fvXrl\nBN+zsLAojIuLW+fk5PRKTU2tFe3rhEQi8fbv3/+jtbV1gb6+ftXatWt3wD1y/i+ehw8fDvXw8HhG\npVLrBw0alP7o0SMv+J63t3dqfHx8CNzz37Jly0YLC4tCQ0PDirlz5yY2NDRow3ETExPnmpubF+np\n6VXD8eAv4YiIiMjZs2cfR35ZeXl5PdLR0akzMzP7cPTo0XkQBIGrV69OcHV1zdTW1m4wMzP7AH+V\nI79c0J4XtktMTEw4k8nM1tXVrV2wYMER2KapqaneJiYmJXFxcesYDAZ77ty5ie3t7aorVqzYa2xs\nXGpsbFy6cuXKPe3t7apwepcuXZrs4uKSpa2t3WBjY5OfkpLiB0EQqK+vp4aEhMQbGRmVmZiYlGzc\nuHELrFNeXp7tiBEj7lGp1Hp9ff2qmTNnnoFtt3Llyj2GhoYV2traDU5OTq/evHnjCEEQaGtr67dm\nzZpfzM3Ni+h0evmSJUsOtra2qkFQtxuJCRMmXKVQKA0qKiodKioqHYrCrwMHDiy1tbXNo1AojZs2\nbYrOz8+3GTJkyGM4Py6XSxaW3+zZs48rKSl1qaurt2hpaTXt3LnzJwiCwLRp084zGAw2lUqtHzFi\nxD3kKMX8+fMTlixZctDHx+cmhUJpHDlyZFpRUZG5qHzmfw/evHnjOHbs2Fs0Gq2GTqeXb9++fT0a\nz+bNm3d08eLFf2DlTyKReL///vv3tra2edbW1gUQBIE///xzoa2tbR6NRqsJCAi4XFZWZiQs366u\nLqWYmJhwGxubfD09veoZM2acra2t1YUgCLS2tqp9++23J/T09Kp1dHTqBg0alF5RUWEIP5e1tXUB\nhUJptLKyen/y5MlZcF7x8fEhAwYMyNHV1a318/NLQeqNxU9xg6TeRw6Ho6mmptaqpKTUpaWl1USh\nUBrZbDaDx+ORsOzDX1eMHDkybdOmTdFff/31PxQKpdHX1/dGdXW1HpresG47duxYa2BgUGlkZFR2\n8eLFKdeuXRtvZ2f3jkaj1cTExITD8QXpYWZm9oFEIvG0tLSatLS0mp48eeKZn59vM2rUqLt6enrV\n+vr6Vd9+++0J2F0N/O5i2U0Yl5AjiMiZBbge0dHRqaPRaDXDhw+/jyynnTt3/uTk5PRKS0urKSQk\nJL68vJzu7++frK2t3TB27NhbdXV1Oj3hgjRDbGxsmImJSQmFQml0cHDIxWpzhLVP06dPPzd37txE\nCoXS6Ojo+Ob58+fusCxcvhQKpZHJZGZfvHhxCnxP0AgisvwpFErj48ePhyQkJMwfOnTow1WrVu3W\n09Or3rRpU3RBQYG1IE5YWFgUYtXnaGULt/UZGRlubm5uGRQKpXHmzJlngoKCTsOcQNMbyZ+Wlhb1\n1atX77KwsCikUqn1w4cPv9/a2qqG9Ux4+xSivIvSDDLLiMvlkm1sbPJjYmLCOzo6VO7evTuKQqE0\nvn371h6C0Id1LSwsCj09PZ+w2WxGbW2t7oABA3L++OOPxXChGhoaVqSnpw/i8XikxMTEuZaWliy4\nwbWwsCh0c3PLKCkpMUFWHPwFPXr06Dt1dXU6Hz58MLO3t3/7f//3f6H8xKipqaHp6OjUnThx4tuu\nri6l06dPB+nq6tbCFRyyQY2Pjw+xtbXNY7FYlhwOR3Pq1KkX5syZcwyCIJCdnc3U0tJqevjw4VAu\nl0v+6aefdpLJZC78AkZGRkbAL2thYaEFhUJpPHPmzMzOzk7lmpoaWlZWlgsEQSAtLW0k3DC9evXK\niU6nl1+6dGkyBOHrIDo5Ob0qKSkxqa2t1f3666//gV+G1NRUbxUVlY7w8PAYLpdLbm1tVdu0aVO0\nl5fXo6qqKv2qqir9oUOHPoTL6enTp4OpVGr97du3x0AQBEpLS41zc3MdIAgCU6ZMubhkyZKDLS0t\n6pWVlQaDBw9+eujQoUUQBIGgoKDTcAPf3t6u+vDhw6EQBIGUlBQ/d3f353CHOjc314HNZjMgqLth\nnjx58qW6ujqdpqYmrUmTJiWtX79+OwRBIDw8PGbhwoWHbGxs8rdt27Y+LS1thKLwa8qUKRebmpq0\nsrOzmaqqqu2jRo26y2KxLBsaGrSZTGZ2YmLiXDz5IafUkJUuh8PR5HK55JUrV+5xdXXNhO/Nmzfv\nKIVCaXzw4MEwuFMhDp+R70FjYyOFwWCwd+/evaq9vV21qalJ6+nTp4PRnl1Q/rBtfH19b9TV1em0\ntbX1u3Pnzmh9ff2qzMxM1/b2dtUff/xx/4gRI+4Jy3fv3r0rvLy8HpWWlhpzuVzy4sWL/wgODj4F\nQRD4448/Fk+aNCmptbVVjcfjkTIyMtwaGxspHA5HU1tbu+Hdu3d2ENQ9VQN3ri9dujTZ1tY2Lzc3\n16Grq0tp69atPw8dOvShMH6KGyT5PqalpY00NTUtRqYvyD5oHURbW9u8vLw829bWVjVvb+9U2G8h\nf4B127Jly8bOzk7lw4cPf6enp1c9a9askxwORzM7O5uprq7eUlhYaCFMj8LCQgv+Ois/P9/m9u3b\nY7hcLrmqqkp/xIgR95DTbYLsJohLMPfgBh5ZL4SHh8csWbLkYGdnp3JnZ6fyP//88zUsY2lpyfLy\n8npUWVlpUFpaamxoaFjh5uaWkZWV5dLW1tZv9OjRd6Kiojb3hAvSCrm5uQ5mZmYfYK4WFRWZw8+P\nbHOEtU8RERGRampqrcnJyf48Ho+0fv367UOGDHkM53P+/PlpcB5nz56doampySkvL6fz1yP8Aa38\nExIS5quoqHT89ttvP3R1dSm1traqCeOEpaUlC6s+xyrb9vZ2VXNz86K9e/eu6OzsVP7rr78CyWQy\nF+aEsA7i999///uoUaPulpWVGXV1dSk9fvx4SHt7uyrWM+Gtg0V5F6UZZJbR/fv3hzMYDDbyWnBw\n8Cl49AtrjRjyy37dunVxS5YsOQhBEFiyZMlB/gbfwcEh9/79+8NhWWGncJBIJN6NGzd84d8HDhxY\nOmbMmNv8hXns2LE5/OsHvLy8HsEjesgGdfTo0XcOHjy4BI739u1bezKZzO3s7FSOioraPGvWrJPw\nvZaWFnVVVdV2tK+57du3r586deoFPLZdsWLF3lWrVu2GIOEdREtLSxbcUYMgCFy/fn2cjY1NPgR1\nV/qqqqrtyBFCGxub/OTkZH/4940bN3wtLS1ZEASBRYsWHVq9evUu/jzKy8vp/fr1a4NH+CAIAqdO\nnQoeNWrUXQiCwNy5cxMXLVp0qKSkxAQpd/fu3VH29vZvnzx54onUn8fjkTQ1NTnwSwlB3Q5wrays\n3kMQBDZv3hw1bNiwBwYGBpWKxi/kV6G7u/vzHTt2rIV/r1mz5he4gsOTH38HERnq6up0SCQSr7Gx\nkQI/L9wAQxAEOByOprKycmdxcbGpKHxGvgenTp0K/uqrr17g4SRW/nCZk0gkXmpqqjd8PyQkJD4s\nLCwWGZ9MJnMLCwstBOU7YMCAHKRdysrKjOD37ciRIwv4R37htHV0dOouXLgwtaWlRR15z9/fPxl+\ndgjqHqHU0NBoLioqMsfiZ0+CJN9HeA0iHvt0dXUp8dcV3t7eqdu2bdsAxz1w4MBSf3//ZDS9U1NT\nvdXV1VvgUZjGxkYKiUTipaenD0Ly/fLlywEQBIH+/fv/i1cPtHDx4sUpbm5uGXjshsUleCQYq4O4\nefPmqMmTJ1/Kz8+3QSunU6dOBcO/AwMD//r+++9/h3//+uuvy6ZMmXJREpyQdMjLy7M1NDSsgDtX\nyHvINgdP++Tj43MTvg9/BGDl6+rqmgmXv6AOIlr5JyQkzDc3Ny8S9FxonMCqz7HK9t69eyOMjY1L\nkdeQH12COohdXV1K6urqLfz1i6BnEqVPgfddlGaQmR/EsrIyY6RPIAAAsLCwKIKdP2Jt/2YwGOXw\n/+rq6q0cDkcLAACKioosdu3atUZXV7cODiUlJaZIZ5L8+aEBGcfc3PwDmjPKsrIyY3Nz8w9YuiPB\nZrONLCwsipBpdnZ2qlRUVNDZbLYRcgeYurp6q56eXg2aXsXFxWbW1tbv0e49ffrUc9SoUamGhoaV\nOjo69YcOHVpcU1OjJ+xZYQh6ZgMDgypVVVUu8tn5nweOX1JSYmpjY1PAn35RUZFFR0cH2cjIiA2X\nzZIlS/6oqqoyAACAHTt2rIMgiDR48OD0gQMHvoHXZowaNSp12bJlv/3www+/0+n0isWLFx9qamqi\nVFVVGbS0tGi4u7u/gNMbN25ccnV1tT4AAKxdu3YnlUqtb2xs1LaxsSmIi4sLA0Ax+EWn0yuQ6SN/\nq6mptTU3N2vizQ8JHo+nFB4eHmtra5tPpVIbrKysWAB077aEnxfJNU1NzWYajVZbVlZmzGazjfDy\nGQlBnOSHoPzha0j78b83mpqazXp6ejWlpaUmJSUlplj5FhYWWn7zzTcXYZsxmcwcFRWVzsrKSsM5\nc+Yc9/PzuxEUFHTGxMSkNCwsLK6zs1NFU1Oz+ezZszP/+OOPJcbGxmUTJ068+vbtWwcAusthxYoV\n++D04PezrKzMGIufeOwhCJJ6H0WxD5bDXqz3AQ16eno18Hulrq7eCsDnfEe+T6LoUVFRQQ8KCjpj\nampaQqVSG+bMmXOcv47DspsgLqHlBf233mzt2rU7bW1t8319fW8i6xEYwt5lQbaSJ2xtbfP37t27\nMjIyMpJOp1cEBwefZrPZRvzxysrKjIW1T8hn1tDQaGlra1Pj8XhKAABw7NixuW5ubplwGb9582ag\nKO0SP/jrVzycwOIvVtmWlZUZ8++KRnJHEKqrq/Xb2trU0NpAYcDTpxDlXZQWZNZBNDY2LisuLjaD\nEIs/i4qKLODCEdUvk7m5+Yeff/55W11dnS4cOByO1syZM8/yxxUEeNEn/D/aFnoTE5PSoqIiC+Q1\npO78z1lYWGiJTFNFRaWTwWCUGxkZsZGOMltbW9WxXiBzc/MPBQUFqK5qZs2adWrKlCmXSkpKTOvr\n63WWLFnyB/yS4gH/MxsbG5fBv/lthvY88HObmZkV5+fn2/Knb2ZmVtyvX7/2mpoaPbhsGhoaqK9f\nv3YCoLuS+fPPPxeVlpaaHDp0aPH3339/AHbn8+OPP/76/Plzj5ycHOa7d+/sd+7cudbAwKBKXV29\nNScnhwmnV19frwOfyqClpcUJCwvboaurW3f58uWA3bt3r7579+5oReAXnrzw5Mef18mTJ79NSkoK\nuHPnzpiGhgYqi8WyAuBjYwdBEAnpYoHD4WjV1tbSTExMSo2Njcvw8hkJc3PzD4LcLiGBlT8W1/h5\n1tzcrFlTU6NnampaYmZmVoyVr7m5+YeUlBR/pN1aWlo0jIyM2CoqKp2bN2+Ozs7Odnz06NHQq1ev\nTjx27NhcAADw9fW9efPmTd/y8nJG//79cxcuXHgYTu/PP/9chEyvublZc8iQIU8AQOcnHnsIQk/f\nRzg+Gh8F2aeneosCQXqg6b1hw4btysrKXW/evBnY0NBAPX78+Bz+Og6r7sbikjB+a2lpcX755Zef\nCgoKbJKSkgJ27969WpBLEkjKp1hIEsHBwacfPHgwvKioyIJEIkFhYWFx/HGMjY3L8LZP/CgqKrJY\ntGjRn7///vsPtbW1tLq6Ot2BAwe+wWMjrHqU/zoeTmDJo5Xt3bt3RxsbG5fxfzgg60ZNTc3mlpYW\nDfh3eXk5A/5fX1+/Wk1NrQ2tDRTWNojSp5AnZNZBHDJkyBMNDY2WHTt2rOvo6CCnpaV5X716dSLs\nZ4pOp1fgaXxgwi1cuPDwH3/8sSQ9PX0wBEGk5uZmzWvXrk0QtZf9yy+//FRfX69TXFxstn///uXI\nDgCMcePGJb97987+9OnTwZ2dnSpnz56dmZub23/ixIlX+eMGBwef3rNnz6rCwkJLDoejtWHDhu1B\nQUFnlJSUeIGBgReuXLky6fHjx15cLlc1MjIyEusFmjVr1qnbt2+PPX/+/PTOzk6VmpoaPdhpMYfD\n0dLV1a1TVVXlpqenDz516tQsURymHjhw4PvS0lKT2tpa2rZt236GywANwcHBp7du3bqxurpav7q6\nWj86Onrz7NmzTwAAQGhoaHxCQsKCu3fvjubxeEqlpaUmb9++dTAyMmL7+vreXL169e6mpiYKj8dT\nKigosLl///4IAAA4f/78dLgi0tHRqf9vhy7v+fPnHk+fPvXs6Ogga2hotKipqbUpKyt3kUgkaOHC\nhYdXrly5Fx6FLC0tNbl586YvAABcu3Ztgr6+fpWGhkbLyZMnv1VSUup6/fq1kyLwCyt9+H+8+dHp\n9ArkBwOHw9Hq169fO41Gq21ubtbcsGHDdv68rl+/Pv7hw4dfc7lc1U2bNm3x8vJ6bGJiUioKn5GY\nMGHCNTabbbRv374V7e3t/ZqamijwrmM0YOWPFjc4OPh0QkLCgpcvX7q0t7f327Bhw/YhQ4Y8MTc3\n/yAo3yVLlvyxYcOG7XBnoaqqyiApKSkAAADS0tK8X79+7dTV1aVMoVCayGRyh7KycldlZaXh5cuX\nJzc3N2uSyeQOTU3NZmVl5S44ve3bt2+ATyVoaGignj9/fjoAAGDxU5DNhEES7+OcOXOOA9DNkZqa\nGj34w0mYfbD06cnzYEGQHgYGBlVKSko8fn5ramo2a2trN5aWlprwd8QhCCL9/vvvPyDtBtfdgrgk\n6HmvXr06MT8/3xaCIJK2tnajsrJyl5KSEm6H5YqKd+/e2d+9e3d0e3t7v379+rVj8VaU9okfzc3N\nmiQSCdLX16/m8XhKCQkJC968eTMQjyxa+aNBGCf4IaxslZWVu7y8vB6rqKh07t+/f3lHRwf577//\nnvrs2bNBsJyLi8vL7Oxsx5cvX7q0tbWpRUZGRsL3lJSUeCEhIUdWr169m81mG3V1dSnDthP2THjq\nYEX4AJFZB5FMJndcuXJlUnJy8jgDA4OqZcuW/Xb8+PE59vb27wDo7mzk5OQwdXV166ZOnfo3WhrI\nEyzc3d1fHD58eOGyZct+o9FotXZ2dnnHjh2bK+qozuTJky+7u7u/cHNzy5w4ceLV0NDQeP689PT0\naq5evTpx165da/T19at/+eWXn65evToRzf1KSEjIkTlz5hwfMWLEfWtr6/caGhotv/76648AAODo\n6Jj966+//hgUFHTG2Ni4jEKhNBkaGlb269evnT9Pc3PzD9evXx+/a9euNXp6ejVubm6Zr169cgYA\ngAMHDny/efPmaG1t7cYtW7Zs4u/UCrIBiUSCZs2adQoearezs8vbuHHjVizZjRs3bvXw8Hju7Oz8\nytnZ+ZWHh8dzOP6gQYOeJSQkLFi1atUeHR2dem9v7zS4ATh27NhcLperymQyc2g0Wu306dPPw19f\nz58/9xgyZMgTCoXSNHny5Mv79+9fbmlpWdjY2Ki9aNGiP2k0Wq2lpWWhvr5+9dq1a3cCAEBcXFyY\nra1t/pAhQ55QqdQGHx+fW7Dj8by8PLvx48cns9lsxp49e1bX19frHj58eKG8+YUWF3kNT35w3PXr\n18ds3bp1o66ubt3u3btXz50795iFhUWRiYlJ6cCBA994eXk95k/722+/PRkVFRWhp6dXk5mZ6Xbi\nxInZAIjGZ6SOFAql6datWz5XrlyZZGRkxLa3t3+XlpbmjfXss2bNOoWWP5ptxowZc2fLli2bAgMD\nLxgbG5exWCwr2G2DoHxXrFixLyAgIMnX1/emtrZ2o5eX12O481heXs6YPn36eSqV2sBkMnNgB+A8\nHk9pz549q0xMTEr19PRqHjx4MPzgwYNLAQBgypQpl8LCwuKCgoLOUKnUBicnp9c3btzwA6D7/GAs\nfooLSb6P/fv3zw0ODj5tbW39nkaj1ZaXlzME2QctfSx+Yuku6DcSgvTQ0NBo+fnnn7d9/fXXD2k0\nWm16evrgiIiIqIyMjK+oVGrDpEmTrgQGBl5A4zea3QRxSdAz5ufn2/r4+NyiUChNQ4cOffTDDz/8\nPnLkyHt4nl/U05Vkifb29n7r16+PMTAwqDIyMmJXV1frx8TErAfgU71FaZ9gwL+ZTGbOmjVrdnl5\neT1mMBjlb968GThs2LB/kPGw7MNf/k+fPvVEiy+ME/zAU7ZkMrnj77//nnr06NH5enp6NefOnZsB\nu1ACAAB7e/t3mzdvjh47duxtBweHt8OHD3+AzPOXX375ycnJ6fWgQYOe6enp1axfvz4GgiCSsGfC\nUwcrAr9IEKSQnP4iAI8E5ufn2+Jd99BTWFlZseLj40NHjx59Vxb5EfgysWDBggRTU9MSYacRfOkg\n3kcCigp5tE8EFAtSH0EsLi42GzVqVKqjo2P2wIED3+zfv385AABERkZGmpqalri5uWW6ubllJicn\nj4NlYmJi1tvZ2eX1798/F55G7Cu4cuXKpJaWFo3m5mbNn3766RdnZ+dXxMsnPtra2tQ8PT2furq6\nZjGZzJz169fHAPDl8ktRoAjTI5JESEjIETqdXuHk5PSa/96uXbvWKCkp8Wpra2nwNYJjBCSFmJiY\n9Y6OjtlOTk6vZ82adaq9vb1fbW0tzcfH55a9vf07X1/fm/X19TrCUxIOon0i8AmkvU2azWYzMjMz\nXSEIAk1NTVr29vZvc3JyBkRGRkbs2rVrNX/87OxspouLSxaXyyWzWCxLGxubfEm5lFCE8N133x3W\n0dGpo1Kp9WPHjr0F+2KTVRDmLqU3hubmZg0I6j4WytPT88mDBw+Gfan8UpSA57iq3hTu378/PCMj\nw43/2K4PHz6Y+fn5pVhaWrLgIyFF4VhffB+JILnAYrEsrays3sO+VmfMmHH26NGj89auXbsjLi5u\nHQR1O8FGuvXpSZB3+0QExQpSH0FkMBjlrq6uWQB07yQaMGDAv/CuIQhllOHy5cuTg4ODT5PJ5A5L\nS8tCW1vbfEEL4XsbDh8+vBDehXvr1i0faRzgLggsFsuqr01naWhotAAAAJfLVe3q6lLW1dWtA+DL\n5JeiICEhYUF0dPRmeeshKQwfPvwBzCskVq9evXvHjh3rkNdE4VhffB8JSA7a2tqNZDK5o6WlRaOz\ns1OlpaVFw9jYuCwpKSlg3rx5iQAAMG/evMRLly5NkUR+8m6fCCgWZLZJBYBun1yZmZlusMuIX3/9\n9UcXF5eXoaGh8fAQOb8vJlNT0xIs/1UECADQ7RPQ1dU1i06nV8DLGQAg+EVAurh8+fJkU1PTEmdn\n51fI6wTHCEgKNBqtds2aNbvMzc0/GBsbl+no6NT7+PjcqqiooMM+Cel0egWWP0kCBHoCmXUQORyO\n1rRp0/7at2/fCi0tLc7SpUsPslgsq6ysLFcjIyP2mjVrdmHJou2cIkLfDOJwS0lJiZeVleVaUlJi\nev/+/RFpaWnePeEXwbG+HcThGD9aWlo0tm/fviEqKioCvoY2Yk1w7MsLkuAXjIKCApu9e/euLCws\ntCwrKzPmcDhaSG8ASB5h8Y4IfS9IkmOCIJMOYkdHBzkwMPDC7NmzT0yZMuUSAAAYGhpWwg/73Xff\n/R88BWNiYlKKdLBbUlJiiuY7TZrz7hEREb0y7d6ue09BpVIbJkyYcO358+cePeUXwbG+qbukUFBQ\nYFNYWGjp4uLy0srKilVSUmLq7u7+oqKigq4oHJOHfRUpX3nkKWk8f/7cY+jQoY/09PRqVFRUOqdO\nnfr348ePvRgMRjnsNozNZhsZGhpWYqUhC7sRMrKTkSWk3kGEIIgUGhoaz2Qyc1auXLkXvo486ufi\nxYvfwLsDAwICks6cORPE5XJVWSyWVV5ent3gwYPTpa0nP7S1aYBEIgFtbZrwyATkhurqan14+ri1\ntVX91q1bPm5ubplIj/eKyC8CvRtOTk6vKyoq6CwWy4rFYlmZmpqWZGRkfEWn0ysIjhGQFPr375/7\n5MmTIa2treoQBJFu3749lslk5kyaNOlKYmLiPAAASExMnAcPvBAgIEmoSDuDhw8ffn3ixInZzs7O\nr9zc3DIBAGD79u0bTp8+HZyVleVKIpEgKysr1qFDhxYD0O1wc8aMGefgszoPHDjwvSyHVGE0NdUB\nACDQ1NSnvHX0ORQWFlqMHDnyPo/HU4IgiDR06NBHY8aMuRMUFHTm2rVrE7hcrgf4RNwAACAASURB\nVCqVSm34559/vgagm190Or1CS0uLQyKRoIiIiEh58ItA74K1tXVBUVGRJQRBJDMzs+Lo6OjNOTk5\nzKtXr05UVVXlVlZWGjY2NlJoNFotwTECkoKamlpbbW2tLuxAuaurS9nV1TVz8eLFfwwePPjZpk2b\ntlAolMaMjAx3eeuKBm1tGmhqqgMUii5obPzMDz8BRUdPh9TlEbrVlh5SU1MhAAAEAARJOq/U1FSJ\npifL9KWt+3+2FpkPaG5usNxA4HVBIguO9ca0pZ2+onIMzc3NzZs3fWDuhIWFxSoKxygUXQgAAFEo\nup/dk7Z9sSCPfOWRp7j8whO6urqUGAwG+8OHD2Z43dyIwzFx7IYlI6gdlWQ+X5KMNDnGH+Te2RNL\naSk33hAkmNgEpIOeEr+5uVnDw8Pj2Zs3bxwdHBxyy8vL6RDU7YvTwcEhF4IgsH379vWxsbFhsIyf\nn1/K48ePh/CnJc1yF9SAE5AuesIxFotlye8HEQ5///33N99+++0JSAE4Jm7dRfCy55Bm433jxg3f\nYcOGPYAgCGDVb/xB3u0X0Y5KHrLsIEp9irm4uNhs7ty5xyorKw1JJBK0aNGiP5cvX76/traWNnPm\nzLNFRUUWlpaWhefOnZuho6NTD0C35/gjR46EKCsrd+3fv3+5r6/vTWnrSaD3gsfjKX311VcZBQUF\nNkuXLj3o6OiYjeUGoqyszBh2swSAfFyQEMsX+h6OHDkSEhwcfBoAxeCYOCB4qdg4c+ZMEMwxUdzc\nREZG/u9/b29v4O3tLWVNCUgSaWlpIC0tTS55S72DSCaTO/bs2bPK1dU1i8PhaLm7u7/w8fG5lZCQ\nsMDHx+fWunXrdsTFxYXFxsaGx8bGhufk5DDPnj07Mycnh1laWmoyduzY2+/evbNXUlLiSVtXvCDW\nVSgWYDc3DQ0NVD8/vxupqamjkPeFuQbAukdUrL0fsqhct23b9rOqqip31qxZp7DiKArHiLpLspBV\n483lclWvXLkyKS4uLoz/nrD6DckxAr0P/PVCVFSUzPKWegeRwWCUMxiMcgA+PUklKSkp4N69eyMB\n6PYE7+3tnRYbGxuOdQoB8otcmoArUEEgvrQVE7CbmxcvXrjT6fSK8vJyBoPBKEe6gRDFBYn0K1YV\nQCKRiMZaipB25Xr06NH5169fH3/nzp0x8DVZcOzTeooMAOjAxaOPdReZ4J4EIKvGOzk5eZy7u/sL\nAwODKgC6Rw3R6jcCBCQJuZyk4unp+VTQFKA8TyGAK1ACvQNYbm4CAgKS0NxAyMMFCbbLpE7Q3VgL\n/iAhoJhISUnx37lz59rLly9PVlNTa4Ovy4JjH+spCADQAUTnEcy9JkAikQCJRHzsKirq6+t1li1b\n9turV6+cmUxmztOnTz19fX1vDh8+/IG9vf27ESNG3Bs3blyyvPUk0Pcg9RFEGBwORyswMPDCvn37\nVlAolCbkPXGmAInpv94PSUzPZGZmuk6dOvViR0eHCgCA5OfnlzJmzJg7N2/e9I2IiIiKiIiIUlVV\n5cbHx4cAIB83N8SIc+8Hv5ubqKioiG3btv0Mf9Cqqqpyp0+ffi4+Pv47RXRzgz0z0t1R7AbBT0XE\n999///v/t3fmYU1c3R8/AYKCrAFk1yD7IgEFARWBqiAUt2Jd6oqW+rpVWn+taFtBfSvY1qrVtrYW\nqVtRX20VFRAXUAsqKosWZG2wLBFl3xQCmd8fOCXEJExCJhv38zz3CZncuXNm5svMnTv3nNPU1KRX\nU1NjpqGh8bK9vX1Er69CLxQKBbi/IxASQxqeMF1dXdTAwMAre/fujcSX2dvbF7FYLBMMw6CmpsYU\n98KKjY2Nio2NjcLrBQUFpd69e9eLuz0YhEfUQJ56wOV1Jcj7SthvCPEBMbyzWCyWSW5urhuGYdDa\n2qplZ2dXXFhY6BgTExO9Z8+ej3nryyIECa9eiGgMQQ7iaAzD+Ie5kWUopT7tCNOXGj7EyFdz/T+R\nHiWBuPoSVJqamnStrKz+5l2OvJiHLpLWmLAis0wqsnoF2Dea00owU4oayqgix5iYmDxzc3PLA+g/\nxxWAf25cQXNcybANf7Usan2kNfnD19f3tr6+fr8huKSkpFnLly8/CtA7j/r8+fNzAKSrsf6o8egN\nHx0c3OgS0qXsYDKZVkZGRi/Cw8MTxo0blxMREXG4vb19hKhezHiRpEMN0oV0yMjI6HcOpYlMMqnE\nxsZuiYqKips/f/6Z+Pj4VXiYGwBpZlLB5+AMdAMXVg85GcgT+BxXb2/vu5mZmZMOHDiw4dixY8s8\nPDwe7NmzZ5Oenl6TNEOQ9M0TI9ZJRK+iFQv5C6WEdwglqx+kS9nR3d2tlpOTM+7gwYPrPT0970dG\nRu6Li4uL4q4jKy9mpAvpoNRezJMnT/6Tw+HwHam8du3aNH7Lt27dumvr1q27yLVMEhDtZCLIpq2t\nTWvevHln9+/fv1FLS6ttzZo1P27btm0HAMAXX3yxc9OmTXvi4+NX8VtX0iFIBM/34h3hQZCNtMKQ\noFBKQxOy9WVhYVFlYWFR5enpeR8AYN68eWdjY2O3mJiYPJMfL2bRBkpQqCXFQWpOKggEWbDZbGpY\nWNi5JUuWnMCnKnBfMN9///1fZs6ceRFAOiFIBI8cChrh4d9x5L2Qogur6JD59C2JUEoPH5bAihXv\nQljYXInZhZAeZI/u4Nqyt7cv0tTUfPn8+fORixcvPmltbV3u6+t7m0KhYBiGQWho6GWJblgkiAyU\n8F7jiA2s9D1s94Zy6oV4WCfE4CB9DuLKlSuPGBsb144dO/YxviwmJibGwsKiyt3dPdfd3T03JSUl\nGP8tNjZ2i62tbamDg0NRWlpaINn2SRI0J0P6CJrjymKxTPG///jjj7m4/mQR5mZguD1J++h7hdPI\n9ztCtkhiHvWlSxpw/nyqNM1GKBgGBgb1I0aM6OBwOCpeXl73Pvvssy97fRV6UQwvZvHmw/Y9bLO5\n1hcnrBNCLMj2guHn/SdLD1N4w3sP9/SjDujtN3AbyGNrMIAY3lm3b9+eTKFQOAwGI8/NzS3Xzc0t\nNzk5OXjp0qXHxo4d+8jV1TV/9uzZ53GPPwzD4Msvv9xqbW1dZm9vX5SamhrEr13Jakz0T952+H1H\niI44GsMwDBYuXJhoampaQ6VSuywsLCqPHDkSXl9fT5s6deo1W1vbkunTp6c1Njbq4fWJagzgELZk\nyQdi7Qc/72MiHsqiejEj3RFHXH0JK3Q6nVlXV2fAvUwevJiF3SMF13mzPh5ZBKAvugj3MqI6HSqQ\noTFBhfRXzL6+vrcrKirovMsxETxMyc2iwv3aT9wJ3uRMDkcMzOjRo5/6+fnd5M71HRwcnOLl5XUP\nz/Xd0dGhOWzYsE58He75YrKOTzdY0Gtn6ZCYmLiI3/KpU6deP3HixBIWi2W6du3aHxISEsLb29tH\npKenB6ioqHBGjRr1j5eX1z1p20sOfa8Jkd6kB4VCwaZNm3ZNVVW1Z/Xq1T9FREQcllUuZiKZxkSp\nB9A/OQX+2llUBz9lRpa5mKXSC2UymXTeEcTRo0dXuLq65q9cuTIef/Jev379gRMnTizG661ateqX\ns2fPhvG2BzIa3RGlDYTogBhPRoLiIMpHjDryRxCR5kRDHI0JKkwmk25lZfX3q1evhmEYBvPnzz/9\n66+/LhekPd4CcjuCqNZvNEdQXcSbSFJfeKmpqTHFMAyeP39uxGAw8m7duuWrp6fXyF1HX1+/gd+6\nkj5PA2nqzXoDjwDy05Uo6w81yNCYoCLVVHs4a9as+ZHJZFrl5eW5mZqasjZt2rRHUF1h3n+ixHYS\nNSYdgnwkEd9JUBxE+YtRh1A2dHR0WqhUKrujo0Ozu7tbraOjQ9PMzKxGkPYUByIpIFF8WGlhamrK\nAgAwMjJ6MXfu3D+ys7Mn4A5SAL3zrVEuZgQZyMSLWRYepmjIWv6QtAcg0VzfRGPUEXk1w/0qRXKv\n3QYKh4PC5RCFzNczNBqtYdOmTXtGjRr1j4aGxsugoKAr06dPvyrK6z+AS/DoEQtiYmKEvv6TzVQC\nYTpDIb4AyH/919HRodnT06OqqanZ4e7unlNVVWV56tSphXguZvnwYkYoKzLpILJYLFP8qYjXw/S9\n99777eOPP/62urrafDAeppK/oIp+U0bzw6SHpHN9AxB7COk/f4YqoY4b75xWQRkyANADj3DIDENS\nXl5uvW/fvsiKigq6rq5u87vvvvu/EydOLOGuM5D2AELB1TVnQK3JJigxmls9EGSHuamtrTWeO3fu\nHy9evDBqb28fYWpqWhMYGJh28eLFULzOYL2Y0X0KIQjSXzEvWrQoceLEiVnFxcX2lpaWlUeOHFm5\nefPm3a6uro8YDEb+zZs3/fbu3fsRQP8sKsHBwSmDyaIi+ZAgorvpi57WDyEOeBzEpUuXHsdDjQh6\nBSPKKLXo8A9XI5120Ss/afPgwQOPiRMnZhkYGNSrqal1v/POO7/fuXPHBw9iDEDG6z80ejyUsLKy\nYl66dCnU0dHxyR9//DHXxsamHADg6tWrgX/++efkkpISu1u3bk1JTU0NHqgtQfC7VypqyDZFtVtu\nkdZkR0kWIDA5FYRM8B/cJ7G6graJEAyIMfmWw+FQli5deiwyMnIv9/JPPvnkq7i4uM0YhkFsbGwU\nr5NKZ2en+t9//201ZsyYcg6HQ+Ftl+i5ElUXkv1EGhMVcTQmqOTl5TGcnZ3/6ujo0OBwOJRly5Yd\nPXjw4DpB2uMtvedMuJOK4HAf5Ia5EaUuog9J6gsv8+bN+19OTo57RkaGX2ho6EUMw4DbSYXD4VB4\nnVbwIs69UtCy/suFX3uIaYc3PFxfG+JqbyjokgyNCSookwpCocnMzJx0/PjxpWpqat2HDh36j4OD\nQ1FsbOwWCoWCRUdHb4+Ojt6urq7eFR8fvxKgd5Ta2Ni4VktLq+11nRhRRqnR6xgEDoPByF+2bNmx\ncePG5dTU1JgBAJadne25f//+jV9++eVnO3bs2AYAMGHChHtNTU16enp6TaJuQ/7nTqN89GRy6dKl\n0JEjRz53d3fPzcjI8OdXR5RczPKVzhFNYSCCUoe5CQ8PPzJy5Mha7jA39fX1tGnTpl3lF2B2165d\nW2xsbErt7e2Lrly5EsivTRDwdND/aVuWI4hvBs5W9qcaSQBiPhlJMxi7YF1JUmNoBJEsxNWYsLJs\n2bKj8fHxKzEMAzabrdbU1KRLJNRN7zkTPoIomeuRpOog3Q2EpPW1ZcuWXRYWFpV0Op1pYmLC0tTU\nbF+yZMlxe3v7IhaLZYJhvWFwBhMom985FHReiWpAPJ3xu64OPIIo7L7PDV4PD9+kqJBxDRNUSJ+D\nGB4enpCamjqDe1lcXFzU9OnTr5aUlNhNnTr1elxcXBQAQGFhodPp06cXFBYWOqWmps5Yu3btDxwO\nh6+N48dPhQ0bPu23rO9pG+NaKos5O4LmjKF5YmTg6+t7W19f/43JppgIwdhF36q8zgVDGpMmzc3N\nurdv3/ZduXLlEQAANTW1bl1d3ebBhrpR1LBcuN1Ig5Jh27ZtO8zMzGp0dXWb1dXVO83MzGqOHz++\nFPditrOzK5kyZcrN4ODgFDLtkJ4eRb+u8r/vC66HUvQRRyaZVJKSkmbdvHnTD6D34unv758RFxcX\nJUomlZychVBX9x0cOPDVABbI0zA2Cg0hTQ4cOLDh2LFjyzw8PB7s2bNnk56eXpPkwtzIk664QRrj\nhuzXM0wm08rIyOhFeHh4Qn5+PmP8+PEP9+3bF0k81A3/MDfy/2qZP/yyYigzZOtr+PDhr9LT0wM0\nNTU7bty48VZYWNjZP//8c3LvQFIv0sjFTEyPknholtfr6tBEJnMQJRGjDsAbAL6TgrUIRWTNmjU/\nbtu2bQcAwBdffLFz06ZNe+Lj41fxqzuYMDcI+YbsMCTd3d1qOTk54w4ePLje09PzfmRk5D78jQiO\n8DlixMLcIOQTsvUFAKCpqdkBAODt7X3XxsamXF9fvxH3YsajNfj7+2cAwEcS37hIoPBbyoZMMqlw\nI26MOoAfobn5OeFMKgj5QxKZVAQxcuTI57i23n///V/w18iihLmZNCkEfvnlV4nahVAuLCwsqiws\nLKo8PT3vAwDMmzfvbE5OzjhyQ90ghhIcDkfFzc0tz9jYuDYgICDd2dm5QLRg7ERR+3d6AAIBIKMR\nRPypx8TE5Jn4MerWgK7ubfTkrcCQ+fQtiWDsWVkOYGKSDu+/v0JidiGUCxMTk2eWlpaVJSUldnZ2\ndiXXrl2b5uzsXODs7Fxw9OjR5Zs3b9599OjR5Xh8TgRCVFRUVDh5eXluzc3NukFBQVfS09MDuH8f\naJDF13cKuLg4g7Gx8QBezMo0Aqg83vVK7cWMYb1J7bk9TCURow7gETZqlAuGYfxihYnikUeGFzMx\nL2dF96aSNCCmd5aVlVW5iopKD4VC4VhYWFTGx8evXLBgwSktLa1WdXX1TiMjo+fFxcW2eP3AwMAr\nVCq1S11dvfPLL7/cwq/N3nP1K/bOO8vesJE8XUhSi0hj/BBXY8LKw4cP3TU0NNq1tbWb586d+zuT\nyRzt5+eXrqGh0a6pqdkeEBBwgztSA156zxV/L2bJXo8kfV3j/1t/u/uWDSXI0Bd32bFjxxdff/31\n/4nixQzgj23fvl2ozcLOL5E65P9GxDbBuuTdB0WGbI1xF6lnUklISAiPioqKu3r16nQ7O7uSGzdu\nvBUVFRUHIH4mFe6J0YoB7kiAvKkkwdGjR1c8ePDAw9nZuaCystJy5cqVR0aNGvXPF198sbOzs3PY\npk2b9hw5cmQVQK+nfG1trXF7e/uI4uJi+yNHjqwS5Cmv2CCNSYuMjIyAuXPnnvf397/5+++/v/PD\nDz+sCwkJSeno6Bixbdu2HRMmTMgWJwYiApGfn+/q6+t729nZucDJyang6NGjy93d3XNF82L2lZ7B\nCKWC9BtjYmLiopqaGrOuri71yspKy/Dw8AQajdZw7dq1aSUlJXZpaWmB3BfPrVu37iorK7MpKipy\nCAoKukK2fQjFh1+YG0FhRiQX5gaB6J0Gk5ycHPL+++//gr0OqyRuiBvuEDHKBEp/Jj4NDQ20uro6\nQyqVylZRUcGam5t1zMzManoHknoR14tZUUMp9UHEaxrNqxwMKJMKQimRjKf8eXjyhAkxMTHw1Vd7\n4OXLNmmYjpAgZM/f+eijj/Z+/fXXn7S0tOjgy0RzIOgLc9P/TYjy3ND64s9RlWZeGA7Z+goICMh4\n8uSJI/59zpw556urq80l4cWsqKGU+iASEkeZ5lVKH5l2EOl0eoWOjk6LqqpqD5VKZWdnZ09oaGig\nLViw4PTTp09H0+n0ijNnzsxX3tczyjORVp4R31N+Djg63oCYmJjXDjSKfDEdmpDpCCWJNGjcYW7I\nCJEiW3hHeJQvRqc0wtzgVFRU0HNzc929vLzuievFjKcKJY68JgQgF5RStReZdhApFAqWkZHhT6PR\n/j0DeJaVTz/99Kvdu3dvjouLi+KNK6Y8KN8FU16QjKc8AiGYrKysiUlJSbOSk5NDXr16NbylpUVn\n6dKlxwVpT7nh15FAQY8lRVtbm1ZYWNi5/fv3b9TW1m7l/m3gh5CbkJ6uAhwOR4xR6qF5DvtGvWW/\n30rvxSyo0Ol0Zl1dnQH3Mnt7+6Jnz54ZYxgGLBbLhJ93FkB/L2YYlEeeLLyYBXtaDWVgEN5Z5HjK\n93kxy0oXg99mf2/mPo9/ar/lQ4XBaExYycjI8AsNDb2ICdEeb+k9D31ezOJpSZp1JNeeskKGvrq6\nuqiBgYFX9u7dG4kvE82L+Yt/vZiJnZ/Bnl8yfpNs2xjWP/oJ73VQnrVK1jWMX5H5COK0adOuqaqq\n9qxevfqniIiIw8SHzvsCZSMUE0k9GS1atCjx5s2bfnV1dYaWlpaVO3bs2BYVFRU3f/78M59//vl/\nhw8f/srKyop548aNt7KzsyfMnDnzop6eXlN3d7eai4vL4+bmZl3lncbQf5S6/7wj+XhCVmQqKyst\nly1bdozJZNIbGhoMvvvuuw+joqLi5s6d+/uOHTu2AQBMmDDhXlNTk57yakxU0NQaomAYRnF0dHxS\nU1NjWlNTYxYZGbkPAAD3YqZQKBiGYRAaGnpZ1rYqGkMtLaQ4yDS8R2Zm5qTc3Fz3lJSU4O+//37d\n7du3+/njCx86XwO6uiOVvoOozB6A/v7+EsmkIsxT3sLCourp06ejHz165Ip7K3d2dg6LiYmJ6erq\nUl+wYMEZ5Z3CwM3QnEtENlQqlb13796PKioqrGpqasy+//77dbW1tcZeXl7Z0dHR29vb20fMmDHj\nytDQGFEGDsGkzNc9UcjMzJz0999/j7G0tKwqKyuzcXd3z01NTZ3RO5DUizRyMSsX6FpIFJl2EPFM\nF0ZGRi/mzp37R3Z29gR8/g7AUEpRhbviq//rko//3TcXAsWzExfsdfgRHHHDkCg23N58CElhYmLy\nzM3NLQ8AQEtLq83R0fFJdXW1+dDUmKj0hSDh7Qii614vkydP/pPD4ahcuXIlyMbGpiw3N9d9xowZ\nqbgXc0lJid2tW7empKamBsvaVsVBlGuhYI0OBWT2irmjo0Ozp6dHVVtbu7W9vX1EWlpaYHR09PZZ\ns2YlDb0UVdwTgXHhUriWIcRlcNMY+sLcKC/K/bpPmhO8xfMy7QtzM/Tou1Er6is+WTkQkJOLGfEm\nb2p0SHk4S2uyI2/5+++/rRgMRh6Dwchzdnb+a9euXVswDIP6+nra1KlTr9na2pZMnz49TXCKKuVy\nUiGyDWUHSJh8W1NTY4phGDx//tyIwWDk3bp1y1dPT6+Ru46+vn4D73q9x10ZnFSIfg6N1HxkaAzD\nMGhtbdUaN27cwz/++GMOhmFAXGND00nlzbpqr79TX38q5nWPLH3xOuER0Rf2r8amYP7+/lh0dDTB\n8zPY80vGb+Rvl/c8CqojbW2mp6dj0dHR/xayNMavyGwE0crKipmXl+fGuxyfOyYLmxSBIfX0IgGE\nTWMYWmFIBgKFXBIXNptNDQsLO7d06dLj+BsPpDFR4X2Lguuwb74Yuub1IZq+/CAgQA22bdumhLE2\nlR9pxtrkRaFz0FZWlpA02TSDhDYH27YaoTmJZL7ukFksJjHp6OjQbG1t1QYAwKcxjB079jE+jQEA\nQHbTGDLktG21AefbII31gWEYZdWqVfFOTk6FuIcpAIB8aEyeyBBzPbzjiEFra6tIc8G4taRsTi/k\n6itDSuuIgzjbIbJOfz8AYvSfnyjOtUver3dy2UFMTU2d4eDgUGRra1u6e/fuzYLqYVgX9F48JE0G\nCW0Otm1iE2vRzbuP2tpaY19f39tubm55Xl5e90JDQy8FBgamRUVFxZ06dWqhurp6144dO74YPnz4\nS0FtJCX9bwg9hAD0jSQKvhkjjfWRmZk56cSJE0vS09MD3N3dc3EvU1E0NjTIkEAbA2uz3xa5tKTI\nTi+LFi1KnDhxYlZxcbG9paVlZUJCQnhUVFTc1atXp1tYWFTt3LnzizNnzrwr7F4pGhlSWkccxNkO\nkXXw+ysbBN9n+WcGwnVFVgdRlg83cpeLuaenR3X9+vUHr127Ns3c3Lza09Pz/qxZs5IcHR2fyNo2\nhGIhaBqDrq5uc2trq3ZJSYkdrrEFCxac4aex7u6X0P+V11ABvxlTuS6KVABgg7r68CHqVPEmuJcp\n7/Kenh5Voho7deo4nDjxs3QMVgr6a1PZXz0nJiYu4rf8ypUrQfb29sUFBQXO6F4pDcTLKjPYaWGy\nzOoidyOI2dnZE2xsbMrodHoFlUplL1y48NSFCxdmv1nzivSNk0Pwpwt8aHz79p2EnzaU7bULUYhr\n7J70jZM7+p6S8afrrq4unrBM/T+Hmp74QVxjD7geQhCi8WY8Re7r4fbt2/locXBhS+TpmklcYwXw\n5Zdfodh/UqRPh4odqk7uOojV1dXmlpaWlfh3CwuLqurqavM3a37y+pPC55PfMlE+twv5ncy2RdtG\nn/gAem/eAAAcAOh96uiLqci/4OsSqdvb+dxOqJ64RVoQ19iPrz+JnA8ydCDuNiXRtrBtcV7/zeb7\nSVRPSGMAAL+8/hRXS9KsM5j2BntNHfhayO96iGuxr2434Iij04GumdKEuMZ+h66udq7vAx1Pce5R\n24X8RvQ8EtGBODraLoHtDvwb97Xrzc5gf52Kcr3jXV+ayN0rZorQpOO9YDyBjxEIUUAaQ5AN0hiC\nbJDGEGQjdyOI5ubm1ZWVlZb498rKSksLC4sqWdqEUC6QxhBkgzSGIBukMQTpSCvgItHCZrPVxowZ\nU85kMumdnZ3qDAYjr7Cw0FHWdqGiPAVpDBWyC9IYKmQXpDFUyC5y94pZTU2t++DBg+uDgoKu9PT0\nqK5atSoeeWUhJAnSGIJskMYQZIM0hiAdWfdQhZWUlJQZ9vb2RTY2NqVxcXGb+dXZsGHDdzY2NqWu\nrq75OTk57pJqOz093V9HR6fZzc0t183NLXfnzp2fE207PDz8yMiRI2u5UyNJym4i7Q/G9n/++cfS\n398/3cnJqcDZ2fmv/fv3fyhJ+4m0Pxj7kcaGtsbkTV9ka0wW2pOVJqWtU1noV5r6Gmi9EydOLHZ1\ndc0fO3bso4kTJ2b+8MMPawbaDoZhkJ2d7amqqtp97ty5d4jYlp6e7u/m5pbr7Oz8l5+fXwYR2168\neGEYFBSUiqfqnTx58i1RtTeQdnj3Pz8/35WIxnmPAZF1eI/BQOvw7n9CQsIKMrRFimAlUbq7u1Wt\nra3LmEwmvauri8pv+Pzy5cshwcHByRiGwd27d728vLzuSqrt9PR0/5kzZyaJY/utW7d8c3Jy3AWd\nXHHtJtr+YGxnsVgmubm5bhjWm1/Wzs6uWFLHnWj7g7EfaWxoa0ye9EW2xmSlPVlpUto6HYzGyNhX\nSeqLyHpZWVk+TU1NuhiGwaVLl0KGDRv2Slh93J6AgIAbb7/99qXTp0+/zs+wEwAAIABJREFUO9A2\nGhsb9ZycnAoqKystMKy340PEtujo6JioqKhYfB1tbe3m7OxsD1G0N5B2uPc/JSVlBpF1eI/B2bNn\nwwZah98xGGgd3v2n0Wj1bDZbTdL6kjsnFRwiMZ6SkpJmLV++/CgAgJeX172mpia92tpaY0m0DSC+\nB5ivr+9tfX19gUGPxLWbaPsA4ttuYmLyzM3NLQ8AQEtLq83R0fFJTU2NmaTsJ9L+YOwXBaQx8dsH\nkE+NyZO+AMjV2GC2CSD5Y0C2JsXZJoDk95Psa6QoiKuv1NTUoIHW8/HxuaOrq9sMAEClUrsBAAbS\n1IEDBzbMmzfvrJGR0YuysjKbgbbx22+/vRcWFnYOd64xNDSsI7JPpqamrJaWFh0AgJaWFh0TE5Na\nIyOjOkHHid8xsLOzKxGmHe799/LyuldVVWVBRG/cxwBgYI3yOwYDrcO7/wYGBvVqamrdguqLi9x2\nEInEeOJXp6qqykISbVMoFCwrK2sig8HIDwkJSS4sLHQa3B4N3m6iSMr2iooKem5urruXl1e/iNGS\nsl9Q+2Qee26QxsRHETQma30BkKuxwWxTmsdAmF2S1CQ/yN5Psq+RAyGuvgoKClyIxVDs5cSJE4vp\ndDpzoO1cuHBh9po1a34EAGhsbKQNtI3S0lLbhoYGWkBAQLqHh8eD48ePLyWyTxEREYcLCgqczczM\nahgMRv7+/fs3inqcRDkf8fHxq0JCQpIHqsd7DIiEIuJ3DAZaR9T9Fxe5c1LBIXJgAd58OiSyHpE6\n48aNy6msrLTU1NTsSElJCZ4zZ875kpISOyI2EUEcu4kiCdvb2tq05s2bd3b//v0btbS02nh/H6z9\nwton+9jjII2Jj7xrTB70BUCuxgazTWkeA27I1CQ/yNxPsq+RRBiEvjiC6vKSnp4ecOPGjbfeeuut\nG8LqRUZG7ouLi4uiUCgY0VFbNptNzcnJGXf9+vWpHR0dmj4+Pnc+/PDD7wZab9euXVvd3NzyMjIy\n/MvLy62nT59+NSkpaaawdcQ9H+np6QFHjhxZmZmZOWmgurzHgMhx4HcMvL2971KpVLagdfjtf35+\nPkNbW7uVyD4RRW5HEInEeOKtU1VVZWFubl4tiba1tbVbNTU1OwAAgoODU9hsNrWhoUEi+ZXEtZso\ng7WdzWZTw8LCzi1ZsuTEnDlzzvP+Plj7B2qfzGPPDdKY+MizxuRFXwDkamww25TmMRBkl6Q1yQ+y\n9pPsayRRxNWXs7NzAZEYio8ePXKNiIg4vG/fvo+eP39uLKz+w4cPxy9cuPCUlZUV89y5c2Hx8fGr\nHj58OE7YOpaWlpWBgYFpGhoaLw0MDOqnTJlyq6WlRXsg27Kysia+++67/wMAsLa2LreysmL+/fff\nY4geJ6LnA9//pKSkWQO9WuZ3DNauXftDUlLSLGHr8DsG+fn5DGHr8Nv/4uJi+4HsExkyJs5KohCJ\n8cQ98fTOnTveRCcCc7f9+eef79TX12/gbfvZs2fGHA6HgmEY3Lt3b8Lo0aMrRLGfyWTSiUyYFcVu\nou0PxnYOh0NZunTpscjIyL2C6gzGfiLtD/bYy5vGBLUtbD9Hjx5dcf369bcUXWN+fn4Zv/zyyypp\naUye9EW2xsjS3mAK2ZokS6eiFLKvkdLQF5H1nj59Osra2rrszp073qLGXFyxYkXCmTNn5g20zpMn\nTxymTp16rbu7W7W9vV3TxcXlcX5+/tiB1vvoo4++jYmJicbPsbm5eVVubi5DVO0J0w73/hPVG+8x\nOHfu3DsDrcPvGBQUFDgJW4ff/tfX19MkrS+JC1aSJTk5OdjOzq7Y2tq6bNeuXVswDINDhw6tPnTo\n0Gq8zrp16w5aW1uXubq65j98+HCcqG3r6+s3uLm55fC2ffDgwXXOzs5/ubq65nl7e2fxikRYWbhw\nYaKpqWkNlUrtsrCwqIyPj18pit0DeSMN1D5uO4PByPPx8RHJ9tu3b0+mUCgcBoORh4eGSE5ODpbU\ncSfS/mDsl0eNCWpb2H7S6XTmtWvXpvJrl81mqw1WY4PV8EDniMPhUHp6elT8/f3TeTuIZGpM3vRF\ntsbI0J64hWxNkqFTcQrZ10hp6Wug9VatWvULjUarx/fR1ta2eKDt4AXvHBGx7euvv/4/JyenAhcX\nl8d4yKCB1nvx4oVhaGjoRVdX13wXF5fHPj4+WaJqbyDt8O6/p6dnNhGN8x4DIuvwHoOB1uHd/5Mn\nT75HhrZIEaw8lri4uM3m5uZV2traLfb29kXXr19/KyUlZYa6unonlUrt0tLSanVzc8vFsN5Rj88+\n++y/EydOzNTQ0OgoLy8f8+TJE4dp06ZdpdFo9fb29kVnzpx5F2/78uXLIU5OTgXa2tot5ubmVd98\n880m/CS+/fbbl/T09BppNFq9r6/vLfxplrdQKBTO999/v9bGxqZ0zJgx5RiGwcWLF0MZDEaenp5e\n48SJEzMfPXo0Vtj+YFiv+3tYWNjZBQsWnNLW1m4ZN27cw/z8fFd8vcLCQkc/P78MPT29Rmdn57+S\nkpJm4r8tX77817Vr137/9ttvX9LW1m7x8vK6W15ePgb/PTIycu/IkSNrdXR0mseOHfvor7/+csYw\nDF69ejVs06ZN34waNeqpsbHxs//85z8/vnz5crisz7miliVLlhxXUVHp0dDQ6NDS0mr96quvPqFQ\nKJz4+PiVo0aNeorHCps3b97/TExMWLq6uk1Tpky5WVBQ4CTuucTXXb58+a+rV68+NH369DRtbe0W\nPz+/jKdPn47C18vMzJzo4eFxX1dXt8nT0zM7KyvLB/8N/7+ZNGnSnxoaGh2LFy8+oaqq2j18+PCX\nWlparRs2bPhO1scWFVRQQQUVYkXmBkijFBUV2VtaWv7DYrFMMKx36Bi/WcbExEQvXbr0GHd9Pz+/\njNGjR1cUFhY69vT0qDQ1NelaWFhU/vrrr8t7enpUcnNz3QwNDV88efLEAcMwMDExYf3555+TMAyD\npqYmXTwQaVRUVOx//vOfH7u7u1W7u7tV8Tr8CoVC4QQGBl5pbGzUe/Xq1bCcnBz3kSNH1mZnZ3ty\nOBzK0aNHl9HpdGZXVxdV2P5ER0fHUKnUrnPnzr3T3d2t+s0332yysrL6u7u7W7Wrq4tqbW1dFhsb\nG8Vms9Vu3LgRoK2t3VJcXGyHYb2dAwMDg7r79+97dHd3qy5evPjEwoULEzEMg9TU1KDx48c/aG5u\n1sGPKb79yMjIvbNnzz7f2Nio19raqjVz5sykLVu27JL1eVfkQqfTmXinv6KiYjSFQuEsX778146O\nDo1Xr14NwzAMEhISVrS1tY3o6uqiRkZG7sUfcAZzLpcvX/6rtrZ2y+3btyd3dnaqb9y4cd/kyZNv\nYxgG9fX1ND09vcYTJ04s7unpUUlMTFyor6/f0NDQoM/v/4bNZqv5+/unx8fHr5T18UQFFVRQQUW0\nIrdOKpJEVVW1p7Ozc1hBQYEzm82mjho16p8xY8b8DdDr2YTx8W5asWLFr46Ojk9UVFQ4qampM6ys\nrJjLly8/qqKiwnFzc8t75513fj9z5sx8AAB1dfWugoIC55aWFh1dXd1md3f3XHw5i8UyraiooKuq\nqvZMmjQpU5idW7ZsidXT02saNmxY588///zB6tWrf/L09LxPoVCwZcuWHRs2bFjnnTt3fNTU1LoF\n7Q8AgIeHx4N33nnnd1VV1Z6PP/7421evXg2/c+eOz927d73b29tHREVFxampqXUHBASkh4aGXkpM\nTFyEr/vOO+/87uHh8UBVVbVn8eLFJ/Py8twAAKhUKru1tVX7yZMnjhwOR8Xe3r7YxMTkGYZhlMOH\nD0d8++23H+vp6TVpaWm1bdmyJfbUqVMLJXcGhza4PmNiYmI0NDReDhs2rBMAYMWKFb+OGDGinUql\nsqOjo7fn5+czWltbtQF6NSzqucS3Fxoaemny5Ml/qqurd3355Zef3blzx6eqqsri8uXLb9vb2xcv\nXrz4pIqKCmfhwoWnHBwcivBJ2Lz/N3hcLt7/LwQCgUDIP0Oig2hjY1O2b9++yJiYmBhjY+PaRYsW\nJbJYLFNh63DHTHr69Onoe/fueenr6zfi5bfffnsPD3x67ty5sOTk5BA6nV7h7++fcffuXW8AgE8+\n+eRrGxubssDAwDRra+vy3bt3bxZlm3v27NnEvc2qqioLFotlam1tXS5sf7g9vigUCmZhYVFVU1Nj\nxmKxTLm3AQAwevTop3iQVwqFghkbG9fiv2loaLxsa2vTAgB46623bqxfv/7gunXrvjc2Nq5dvXr1\nT62trdovXrww6ujo0Bw/fvxD3M7g4OCUuro6Q+FnBSEq3OeOw+GoREVFxdnY2JTp6uo2W1lZMQEA\nuI+7qOcSoE8v+HojRoxop9FoDbh+Ro0a9Q+3Tdz64bURh+xQJggEAoGQPEOigwgAsGjRosTbt2/7\nPn36dDSFQsE2b968G0DwzYt7+ahRo/7x8/O72djYqI+X1tZW7e+//34dQO+I3fnz5+e8ePHCaM6c\nOefnz59/BqA3yv4333zzf+Xl5dZJSUmzvv32249v3LjxliAbebf52Weffcm9zba2Nq0FCxacFrY/\nAL1hAfC/ORyOCu7Sb2ZmVlNZWWnJPaLz9OnT0UTDL2zYsOHAgwcPPAoLC51KSkrsvv7660+MjIxe\naGhovCwsLHTC7WxqatLDo7wjxIOfLrmXnTx5cnFSUtKs69evT21ubtZlMplWAMRH6/idS3x9bv20\ntbVpNTQ00HD9PH36dDR3O7z64bUbdQ4RCARCMRkSHcSSkhK7GzduvNXZ2Tls2LBhncOHD3+lqqra\nA9CbNqmiooLOe2Pl/h4aGnqppKTE7sSJE0vYbDaVzWZT79+/71lUVOTAZrOpJ0+eXNzc3Kyrqqra\no62t3Yq3fenSpdCysjIbDMMoOjo6Laqqqj34bwMRERFx+NChQ//Jzs6egGEYpb29fcTly5ffbmtr\n0xK2PwC9sZj++OOPud3d3Wr79u2LHD58+Ctvb++7EyZMyNbU1Oz46quvPmWz2dSMjAz/S5cuhS5c\nuPAU7z7z8uDBA4979+55sdlsqqamZge+TQqFgkVERByOjIzc9+LFCyOA3mjyaWlpgaKcI0R/jI2N\na8vLy60F/d7W1qY1bNiwThqN1tDe3j5i69atu7h/F+dc4r8nJyeHZGZmTurq6lL/4osvdvr4+Nwx\nNzevDg4OTikpKbFLTExc1N3drXb69OkFRUVFDqGhoZcEbXeg/UAgEAiEfDIkOoidnZ3DtmzZEmtk\nZPTC1NSUVVdXZxgbG7sFAAAPNmlgYFDv4eHxAF+He+RDS0urLS0tLfDUqVMLzc3Nq01NTVlbtmyJ\n7erqUgcAOHHixBIrKyumrq5u888///zByZMnFwMAlJWV2UyfPv2qtrZ268SJE7PWrVv3vZ+f301+\nNvKOtIwfP/7h4cOHI9avX3+QRqM12Nralh47dmzZQPtDoVCw2bNnXzh9+vQCGo3WcPLkycW///77\nO6qqqj3q6updFy9enJmSkhJsZGT0Yv369QePHz++1M7OrgRfV9AIUEtLi84HH3zwM41Ga6DT6RWG\nhoZ1n3zyydcAALt3795sY2NT5u3tfVdXV7d5+vTpV6WRlUGZ2bJlS+x///vfz2k0WsO5c+fCeM/L\nsmXLjo0ePfqpubl5tYuLy18+Pj53uOuIey4pFAr23nvv/bZ9+/ZoAwOD+tzcXPcTJ04sAej9H7l0\n6VLonj17NhkaGtZ98803/3fp0qVQGo3WwLsNnI0bN+4/e/bsPBqN1hAZGblP8kcKgUAgEGRAwTD0\nBkiZ2L59e3RZWZkNkXyOCAQv4eHhCRYWFlU7d+78Qta2IBAIBEJ2yHQEcf/+/RvHjh372MXF5S88\n2XRDQwNt+vTpV+3s7EoCAwPTmpqa9GRpo6Ix1DxGKysrLQMCAtKdnZ0LXFxc/vruu+8+BBCuo9jY\n2C22tralDg4ORehVeH+Gmn7EYeXKlUeMjY1rx44d+1hQnQ8//PA7W1vbUgaDkZ+bm+suTfsQig/S\nGEIekFkH8a+//nL55Zdf3r9//75nfn4+49KlS6Hl5eXWcXFxUfgryqlTp16Pi4uLkpWNigi/V4vK\nDJVKZe/du/ejgoIC57t373p///336548eeIoSEeFhYVOp0+fXlBYWOiUmpo6Y+3atT9wOJwhMdWC\nCENNP+IQHh6ekJqaOkPQ78nJySFlZWU2paWltj///PMHa9as+VGa9iEUH6QxhFwgqwCM//vf/+at\nWrXqF/z7zp07P9+9e/en9vb2Rc+ePTPGMAxYLJaJvb19kayDRaKiOGX27Nnnr169Ok2Qjnbt2rUl\nLi5uM14/KCgolexUa6goXxGWJ3X16tWHTp06tQD/zq1FVFAhWpDGUJF1kdnIiYuLy1+3b9/2bWho\noHV0dGgmJyeHVFVVWdTW1hrj8duMjY1r8ViDCMRAVFRU0HNzc929vLzuCdJRTU2NGXecPwsLi6rq\n6mpzWdmMUD6qq6vNueNBWlhYVFVVVVnI0iaEcoE0hpAGarLasIODQ9HmzZt3BwYGpo0YMaLdzc0t\njzcEjKDXXegVmPKCiTkHrq2tTSssLOzc/v37N2pra7dy/zbQa1OksaGFuBobzDaQxoYO0tAXv+0g\njQ0dpKUxmc69Wrly5ZEHDx543Lx5009fX7/Rzs6uxNjYuPbZs2cmAAAsFst05MiRz/mtS9aQai/R\npG0jOjqa1CFhMtsn23ZxYbPZ1LCwsHNLly49PmfOnPMAvaOG/HRkbm5ezR0IGg8iLk2NKfp5UmTb\npYE8aAxpQDbtSwukMfltW1k0BiDjDuJnn332X2dn5wJ7e/uigwcPrg8LCzsbGBiY5uvre9vOzq5k\nypQpN4ODg1NkaSNCvsEwjLJq1ap4JyenQu44e7NmzUo6evTocgCAo0ePLsc7jrNmzUo6derUwq6u\nLnUmk2lVWlpqO2HChGxZ2S8MHR0aUCgUoFAooKNDk7U5CILMmjUrCY9ZevfuXW89Pb0m7rSHCMRg\nQRpDSAOZvWKuqKig79mzZ9OYMWOYGhoar8aNG5eTkpISwt1DplAoUu8xIxSLzMzMSSdOnFji6ur6\nyN3dPRegN4xNVFRU3Pz588/Ex8evotPpFWfOnJkPAODk5FQ4f/78M05OToVqamrdP/zww1p5fQ3T\n2toIANjrv1H0GXlh0aJFiTdv3vSrq6sztLS0rNy+fXs0m82mAgCsXr36p5CQkOTk5OQQGxubshEj\nRrQnJCSEy9pmhGKBNIaQC8gcZhVW6uvraXZ2dsUNDQ36bDZbLTQ09GJaWtp0Il7MvWaTAwBgAOkY\ngBoGAJi2tr5E209PT5doe9Jsn2zbX59XmWmSu5CpMQwjdix7tYi9LsTtQRoTzFDRGNKAbNqXJ31h\nSGMyaZvs9qWpMZlmUvn5558/2LRp0x4NDY2XQUFBV44fP75UX1+/sbGxUf+1sik0Gq0B/45DoVCw\n6Ojof7/7+/uDv7+/RGyiUCjQO2rT9ynLY6TMZGRkQEZGxr/ft2/fDpicBGqmUCiYrM97nxYBkA4l\nw+u3EkhjCFKQJ30BII0pI1LVmLR6orylrKzM2tHRsbCurs6AzWarzZkz54/jx48v0dPTa+Sup6+v\n38C7LpA+gtj/EyEdQI6evuXhvIOYI4gIwUhCYykpKTPs7e2LbGxsSrljauLlxYsXhkFBQakMBiPP\n2dn5r4SEhBX82kHnVPmQ1DUMaQwhCGneJ2U2grh3797I//73v5+PGjWqEgCgqKjI3sPD40Ftba2x\niYnJs2fPnpmamprWsFgss5KSEjvudcl8KkIjiLJDnp6+pfnkraNDg9bWRtDW1oeWlgZuGwCNIEqW\nwWqsp6dH1d7evvjatWvTzM3Nqz09Pe8nJiYucnR0fILXiYmJiens7BwWGxu7pa6uztDe3r64trbW\nWE1NrZvHFqlpDCEdJHENQxpDCEOa90mZeTG/9dZb6aamps+ysrImPnjwYDwAQFBQ0BU9Pb0mLS2t\n9pKSEjtNTc2XNBqtXlY2IhQDfnlLY2JiYiwsLKrc3d1z3d3dc1NSUoLx32Sdixn3TsY9k3FnlN5P\nhDyTnZ09wcbGpoxOp1dQqVT2woULT124cGE2dx1TU1NWS0uLDgBAS0uLjoGBQT3vjRuBEATSGEJe\nkFkHkcFg5C9btuyYh4fHAxsbm3IdHZ3WzZs3f9XY2Kjf1tamZWdnV/Ly5UuN+vp6A1nZiFAM+OUt\npVAo2Mcff/xtbm6ue25urjseLkkecjGjDqHiwi+DBW8mnoiIiMMFBQXOZmZmNQwGI3///v0bpW8p\nQlFBGkPICzILcwMA8Omnn3716aeffrVy5cojHh4eD6hUKruurs6otLTUDqDPSUWWNiLkH19f39sV\nFRV03uX8huEvXLgwe9GiRYlUKpVNp9MrbGxsyrKzsyd4e3vflYqxCIWGSEikXbt2bXVzc8vLyMjw\nLy8vt54+ffrV/Px8Bm+GHwCAmJiYf/+WpLMdQjrwOtpJAqQxBDdkaIwoMu0gAgB0dXWpX7x4cebu\n3bs38/4mLEWapEQvaP4XgnzIFv6BAwc2HDt2bJmHh8eDPXv2bNLT02uqqakx4+4MolzMCFHgzWBR\nWVlpyZ3bGwAgKytr4mefffYlAIC1tXW5lZUVs7i42N7Dw+MBb3vc1zGE4sF779m+ffug20QaQ3BD\nhsaIItMOYlNTk15ISMjlly9favj5+d1MSEgINzIyejFlypSbuJOKoaFhHb91JSX6vtd9cuEbMaQg\nU/hr1qz5cdu2bTsAAL744oudmzZt2hMfH7+KX12yHkLQw4fskfRDiIeHx4PS0lLbiooKupmZWc3p\n06cXJCYmLuKu4+DgUHTt2rVpkyZNyqytrTUuLi62HzNmzN8SMwKh1CCNIeQFmXYQN27cuL+7u1vt\n+++/X7d48eKT7e3tI/T09BpxJ5Xg4OAUeXVSIXrzR50E2cCdw/v999//ZebMmRcBRMthOtiHEN6H\nD1wLglEDCoWCtCJBJP0Qoqam1n3w4MH1QUFBV3p6elRXrVoV7+jo+OSnn35aDdCb5WLr1q27wsPD\nExgMRj6Hw1H56quvPkVTZRBEQRpDyAsyC3PT3Nysy2Aw8tra2rSZTKYVPnfC1ta2xNTU9NmzZ89M\nzMzMaqqrq83wOYn/Gi1B133usDYYhgkMc8Pb0eNdj2j7CMEMxn2/oqKCPnPmzIuPHz8eCwDAYrFM\nTU1NWQAAe/fu/ej+/fuev/3223uFhYVO77333m/Z2dkTqqurzadNm3atrKzMhncUcTAa698RHFhb\nby6nAkA31/rw7zLUeRwcQzWUEkI6yJO+AJDGlBFpakxmI4hMJtPK2Nj4eUBAQIafn9/N8ePHP9y3\nb1+kvDqpoFfR8gu/vKUZGRn+eXl5bhQKBbOysmLiT9/SyMXcl0NZXK1081m/dxnSHwKBQCCkgcxG\nEB88eODh4+NzJysra6Knp+f9yMjIfdra2q0HDx5cz51aj0ajNTQ0NNC415Vkqj2iI4jC6nEfQ3FH\nGociyppqT/yRwjfX4w6UjXQ0eCTx9J2amjojMjJyX09Pj+r777//y+bNm3fz1snIyPD/6KOP9rLZ\nbKqhoWFdRkaGPx9b0OiOkiGp0R2kMYQghkSqPRaLZUKn05n499u3b08OCQm57ODg8ITFYplgGAY1\nNTWm9vb2RbzrggTSB2lr679OZYanM1Pj+S5o+Zv1uOFdLqge4k1ASVLtvamhN7Ug2ifSkaQYrMa6\nu7tVra2ty5hMJr2rq4vKYDDyCgsLHbnrNDY26jk5ORVUVlZaYFhvWjR+baFzqXxI4hqGNIYQhjTv\nkzJ7xWxiYvKMxWKZ2tvbF2lqar58/vz5yMWLF5+0trYu9/X1vY0/+YSGhl4mY/tvvgbk91qP33JB\n9RAIQai9Hj1EKDrcWS4AAPAsF9xp0H777bf3wsLCzuGhSQRFYkAg+IE0hpAXZOrFbGBgUD9ixIiO\nnp4eVS8vr3ufffbZl59//vlO/PfXQ6myNBGBkADooUJZ4Jfl4t69e17cdUpLS23ZbDY1ICAgvbW1\nVXvjxo37ly5depxfeyiIsWJDRixXpDEEN0M2ULa6unrX1atXpxsYGPwbyubq1auBf/7552RjY+Pa\nZ8+emfj7+2cAwEeS2ubAoUYQCASCP0QcmthsNjUnJ2fc9evXp3Z0dGj6+Pjc8fb2vmtra1vKWxcF\nMVZsyIjlijSG4EaWgbJllosZoPcfYdq0adc8PDweHD58OAIAoLa21tjY2LgWAMDY2Li2trbWWJLb\n7Hu1LFl0dGjoNaKMWLly5RFjY+PasWPHPsaXNTQ00KZPn37Vzs6uJDAwMK2pqUkP/y02NnaLra1t\nqYODQ1FaWlqgoHa/+eYbuHtXeAY+/LxTKBTQ0aEJrSsZ1KS4LQQvRLJcWFpaVgYGBqZpaGi8NDAw\nqJ8yZcqt/Px8hvStRSgiSGMIuUFakx35lZqaGlMMw+D58+dGDAYj79atW756enqN3HX09fUbeNcD\nACw6Ovrfkp6eLtIET/EcBQR/CmqXdzmiP+np6f3OI4g5+fbWrVu+OTk57i4uLo/xZZ988slXu3fv\n/hTDMIiLi9u8efPmOAzDoKCgwInBYOR1dXVRmUwm3drauqynp0eFt00AwFRVZ2Bz5iwRug995xcT\n4ug02E/B+sLBna60tfUlc3KUFHE1hhc2m602ZsyYciaTSe/s7FTn50Dw5MkTh6lTp17r7u5WbW9v\n13RxcXlcUFDgxNsWuiYoH4PVF4Y0hhgASWiMaJHpK2ZTU1NWT0+PamBgYNrLly81srOzJ0g71R5C\ndkhq6NzX1/d2RUUFnXtZUlLSrJs3b/oBACxfvvyov79/RlxcXNSFCxdmL1q0KJFKpbLpdHqFjY1N\nWXZ29gTu/Mw4PT3vAYaliWCJNOca4o4vVABgv16G4iSSDZEsFw7uS69RAAATn0lEQVQODkUzZsxI\ndXV1faSiosKJiIg47OTkVChr2xGKAdIYQl6QWRzEjo4OzZ6eHtXDhw9H3Lt3b8LVq1cDT506tfDz\nzz/faWhoWJ+cnBwSHByc0tjYqHf37l2ffkYTjO3EPd+QX1xCSX1iGPH4iQjBSDKTir6+fiMeTxN7\nHXC9sbFRf8OGDQe8vb3vLl68+CRAbxq+4ODglLCwsHM8tmAAc8HevhwWLpwrcHJ33/kFkKSm+n+K\n1jbSWR/KGmsTIZ+gTCoIshkSmVRqa2uNQ0NDL/3zzz+jDA0N64yNjWsDAwPT1q1bd3D48OGddnZ2\nJWZmZjX19fUG4m6De74hGlkZulAoFEzYxG/Bv80FB4c0NFqtwMhygjcCgUAoMjJzUrGysmI6OTkV\n3rp1a8qvv/66wsbGpgwAoK6uzujWrVtTSkpK7NLT0wPq6uqMZGUjQnHBveABevMyjxw58jnAmxPA\nq6qqLMzNzauJtos7pSAnkaFLamrqDAcHhyJbW9vS3bt3bxZU7/79+55qamrdv//++zvStA+h+CCN\nIeQBmY0gXrp0KXTkyJHP3d3dc/mlCAIQPvKjeLGdeueM4a+6EeTGd5o1a1bS0aNHl2/evHn30aNH\nl8+ZM+c8vvy999777eOPP/62urravLS01HbChAnZRNtFObmHNj09Parr168/eO3atWnm5ubVnp6e\n92fNmpXEHcQYr7d58+bdM2bMSJWnV44I+QdpDCEvyKyDmJWVNTEpKWlWcnJyyKtXr4a3tLToLF26\n9Dg+8oNnWsFHfnhxcnICa2trGD9+vLRNF5NeBwbUsehDUq//Fi1alHjz5k2/uro6Q0tLy8odO3Zs\ni4qKips/f/6Z+Pj4VXQ6veLMmTPzAQCcnJwK58+ff8bJyalQTU2t+4cfflhLJO4YAgFALMsFAMCB\nAwc2zJs37+z9+/c9ZWIoQmFBGkPICzLrIG7btm3H9evXp3Z2dg7jcDgUMzOzmuPHjy/98MMPvyOS\nai88/CcYPboNCgvv/buszymF27MTh6x0Z4LaRenVpEViYuIifsuvXbs2jd/yrVu37tq6desucq1C\nKCNEslxUV1ebX7hwYfaNGzfeun//vqewBxDFexOC4EZWmVSQxoYOQzKTyvDhw1+lp6cHaGpqdty4\nceOtsLCws3/++edkbo8rYan2Ojpioadnfb9l/fMr84YbISsECdEczggEQtEhMtocGRm5Ly4uLur1\nQy5F2Os/5ACl2MgqkwrS2NBBlo52Mo2DqKmp2QEA4O3tfdfGx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zxo7OnTp6Osra3L7ty5\n402G7dxlxYoVCUS9TIm0/eTJE4epU6de6+7uVm1vb9d0cXF5XFBQ4CSp9j/66KNvY2JionFNmJub\nV9XX19OItE/2/5486ItsjZGpL7I1Rra+5EVjpIhWUiU5OTnYzs6u2NraumzXrl1bMAyDQ4cOrT50\n6NBqvM66desOWltbl7m6uuY/fPhwnKTaPnjw4DpnZ+e/GAxGno+PT5Yo4l+4cGGiqalpDZVK7bKw\nsKiMj49fKSm7ibQ/GNtv3749mUKhcBgMRh4eBiQ5OTlYUvYTaX8w9iONDW2NyZu+ZK2xVatW/UKj\n0erxY+Hp6ZktadvxIuoNnEjbX3/99f85OTkVuLi4PBYUDknc9l+8eGEYGhp60dXVNd/FxeXxyZMn\n35OH/z150hfZGiNTX2RrjCx9yZPGKBhGytQIBAKBQCAQCISCIrdzEBEIBAKBQCAQsgF1EBEIBAKB\nQCAQ/UAdRAQCgUAgEAhEP1AHEYFAIBAIBALRD9RBRCAQCAQCgUD0A3UQEQgEAoFAIBD9+H++GgPf\nrDhgxAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ed26a90>"
       ]
      }
     ],
     "prompt_number": 252
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}