AUTHOREA
Log in Sign Up Browse Preprints

Ino de Bruijn Add reapr plot  over 9 years ago

Commit id: 043f5ffd4db6992ebefd3280e703cfe8df8026bd

deletions | additions      

       

{  "metadata": {  "name": "",  "signature": "sha256:b7f75597020059d177cecdc80cd52faca66188d63b419c82f458c4a41e5d2d10" "sha256:8ed13889677ab1c7856fba7579c45baf2105066d5d5fe173214df74dc31ba8be"  },  "nbformat": 3,  "nbformat_minor": 0, 

"#rns31_path = \"/media/milou/glob/projects/masmvali-partdeux/reassembly-filtered-reads/Sample_1ng_even/metassemble/assemblies/ray/noscaf/noscaf_31/\"\n",  "rns31_path = \"/media/milou/proj/b2010008/nobackup/projects/metassemble_validation/LIMS1884_even/REAPR/assemblies/ray/noscaf/noscaf_31/\"\n",  "# The converter changes the renamed contigs back to the original name\n",  "creapr = pd.read_table(os.path.join(rns31_path, \"reapr_out/05.summary.stats.tsv\"), \n", index_col=False,\n",  " converters = {0: {'#id':  lambda x: \"-\".join(x.split('_')[0:2])}).sort()\n", \"-\".join(x.split('_')[0:2])})\n",  "creapr = creapr.rename(columns={'#id':'contig'}).set_index('contig').sort().drop('WHOLE-ASSEMBLY')\n",  "creapr.head()"  ],  "language": "python", 

" \n",  " \n",  " \n",  " #id\n",  " length\n",  " errors\n",  " errors_length\n", 

" frag_cov_length\n",  " frag_cov_gap\n",  " frag_cov_gap_length\n",  " read_cov\n",  " \n",  " \n",  " \n",  " contig\n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " WHOLE-ASSEMBLY\n", contig-0\n",  " 151931859\n", 46549\n",  " 150179\n", 4\n",  " 39597582\n", 899\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 1480\n",  " 360162\n",  " 615\n",  " 616\n",  " 0\n",  " 0\n",  " 722\n",  " 340281\n",  " 0\n",  " 1\n",  " 504\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 286\n",  " 14024\n",  " 0\n",  " 0\n",  " 125\n",  " ...\n",  " \n",  " \n",  " contig-0\n", contig-1\n",  " 46549\n", 23387\n",  " 4\n", 2\n",  " 899\n", 744\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 1\n",  " 504\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " ...\n",  " \n",  " \n",  " contig-1\n", contig-10\n",  " 23387\n", 102117\n",  " 2\n", 5\n",  " 744\n", 945\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 1\n",  " 416\n",  " 0\n",  " 0\n",  " 1\n",  " 11\n",  " 0\n",  " 0\n",  "   0\n", " ...\n",  " \n",  " \n",  " contig-10\n", contig-1000000\n",  " 102117\n", 22324\n",  " 5\n", 2\n",  " 945\n", 670\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 1\n",  " 416\n",  " 0\n",  " 0\n",  " 1\n",  " 11\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " ...\n",  " \n",  " \n",  " contig-1000000\n", contig-10000000\n",  " 22324\n", 28569\n",  " 2\n", 1\n",  " 670\n", 309\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " 0\n",  " ...\n",  " \n",  " \n",  "\n",  "

5 rows \u00d7 26 25  columns

\n", ""  ],  "metadata": {},  "output_type": "pyout",  "prompt_number": 6, 14,  "text": [  "#id  length errors errors_length perfect_cov \\\n", "WHOLE-ASSEMBLY 151931859 150179 39597582 0 0 "contig  \n", "contig-0 46549 4 899 00  \n", "contig-1 23387 2 744 00  \n", "contig-10 102117 5 945 00  \n", "contig-1000000 22324 2 670 0 \n",  "contig-10000000 28569 1 309 0 \n",  "\n",  " perfect_cov_length repeat repeat_length clip clip_length \\\n",  "contig \n",  "contig-0 0 0 0 0 0 \n",  "contig-1 0 0 0 0 0 \n",  "contig-10 0 0 0 0 0 \n",  "contig-1000000 0 0 0 0 0 \n",  "contig-10000000 0 0 0 0 0 \n",  "\n",  " score score_length frag_dist frag_dist_length \\\n",  "contig \n",  "contig-0 0 0 1 504 \n",  "contig-1 0 0 0 0 \n",  "contig-10 0 0 1 416 \n",  "contig-1000000 0 0 0 0 \n",  "contig-10000000 0 0 0 0 \n",  "\n",  " frag_dist_gap frag_dist_gap_length frag_cov \\\n",  "contig \n",  "contig-0 0 0 0 \n",  "contig-1 0 0 0 \n",  "contig-10 0 0 1 \n",  "contig-1000000 0 0 0 \n",  "contig-10000000 0 0 0 \n",  "\n",  " frag_cov_length frag_cov_gap frag_cov_gap_length read_cov \\\n",  "contig \n",  "contig-0 0 0 0 0 \n",  "contig-1 0 0 0 0 \n",  "contig-10 11 0 0 0 \n",  "contig-1000000 0 0 0 0 \n",  "contig-10000000 0 0 0  0 \n", "\n",  " \n",  "contig \n",  "contig-0 ... \n",  "contig-1 ... \n",  "contig-10 ... \n",  "contig-1000000 ... \n",  "contig-10000000 ... \n",  "\n",  "[5 rows x 25 columns]"  ]  }  ],  "prompt_number": 14  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "rns31purdf = pd.read_csv(\"/media/milou/glob/projects/masmvali-partdeux/reassembly-filtered-reads/Sample_1ng_even/metassemble/assemblies/ray/noscaf/noscaf_31/val/contig-purity.tsv\",\n",  " sep=\"\\t\", na_values=\"N/A\").sort(\"contig\").set_index(\"contig\")\n",  "rns31purdf.head()"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "html": [  "
\n",  "\n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  "
contig_lengthmax_aln_strainmax_aln_puritycov_meanunamb_dominant_strainunamb_dom_nr_readsunamb_read_level_purityunamb_tot_nr_readsunamb_nr_reads_strainunamb_nr_reads_speciesunamb_nr_reads_genusunamb_nr_reads_familyunamb_nr_reads_orderunamb_nr_reads_classunamb_nr_reads_phylumunamb_nr_reads_superphylumunamb_nr_reads_superkingdomunamb_nr_reads_lifeamb_dominant_strainamb_dom_nr_reads
contig
contig-0 46549 Bacteroides_thetaiotaomicron_VPI-5482 1.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
contig-1 23387 Nitrosomonas_europaea_ATCC_19718 1.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
contig-10 102117 Bacteroides_thetaiotaomicron_VPI-5482 0.9999NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
contig-1000000 22324 Nostoc_sp._PCC_7120_DNA 1.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
contig-10000000 28569 Caldicellulosiruptor_bescii_DSM_6725 1.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
\n",  "

5 rows \u00d7 32 columns

\n",  "
"  ],  "metadata": {},  "output_type": "pyout",  "prompt_number": 11,  "text": [  " contig_length max_aln_strain \\\n",  "contig \n",  "contig-0 46549 Bacteroides_thetaiotaomicron_VPI-5482 \n",  "contig-1 23387 Nitrosomonas_europaea_ATCC_19718 \n",  "contig-10 102117 Bacteroides_thetaiotaomicron_VPI-5482 \n",  "contig-1000000 22324 Nostoc_sp._PCC_7120_DNA \n",  "contig-10000000 28569 Caldicellulosiruptor_bescii_DSM_6725 \n",  "\n",  " max_aln_purity cov_mean unamb_dominant_strain \\\n",  "contig \n",  "contig-0 1.0000 NaN NaN \n",  "contig-1 1.0000 NaN NaN \n",  "contig-10 0.9999 NaN NaN \n",  "contig-1000000 1.0000 NaN NaN \n",  "contig-10000000 1.0000 NaN NaN \n",  "\n",  " unamb_dom_nr_reads unamb_read_level_purity \\\n",  "contig \n",  "contig-0 NaN NaN \n",  "contig-1 NaN NaN \n",  "contig-10 NaN NaN \n",  "contig-1000000 NaN NaN \n",  "contig-10000000 NaN NaN \n",  "\n",  " unamb_tot_nr_reads unamb_nr_reads_strain \\\n",  "contig \n",  "contig-0 NaN NaN \n",  "contig-1 NaN NaN \n",  "contig-10 NaN NaN \n",  "contig-1000000 NaN NaN \n",  "contig-10000000 NaN NaN \n",  "\n",  " unamb_nr_reads_species unamb_nr_reads_genus \\\n",  "contig \n",  "contig-0 NaN NaN \n",  "contig-1 NaN NaN \n",  "contig-10 NaN NaN \n",  "contig-1000000 NaN NaN \n",  "contig-10000000 NaN NaN \n",  "\n",  " unamb_nr_reads_family unamb_nr_reads_order \\\n",  "contig \n",  "contig-0 NaN NaN \n",  "contig-1 NaN NaN \n",  "contig-10 NaN NaN \n",  "contig-1000000 NaN NaN \n",  "contig-10000000 NaN NaN \n",  "\n",  " unamb_nr_reads_class unamb_nr_reads_phylum \\\n",  "contig \n",  "contig-0 NaN NaN \n",  "contig-1 NaN NaN \n",  "contig-10 NaN NaN \n",  "contig-1000000 NaN NaN \n",  "contig-10000000 NaN NaN \n",  "\n",  " unamb_nr_reads_superphylum unamb_nr_reads_superkingdom \\\n",  "contig \n",  "contig-0 NaN NaN \n",  "contig-1 NaN NaN \n",  "contig-10 NaN NaN \n",  "contig-1000000 NaN NaN \n",  "contig-10000000 NaN NaN \n",  "\n",  " unamb_nr_reads_life amb_dominant_strain amb_dom_nr_reads \\\n",  "contig \n",  "contig-0 NaN NaN NaN \n",  "contig-1 NaN NaN NaN \n",  "contig-10 NaN NaN NaN \n",  "contig-1000000 NaN NaN NaN \n",  "contig-10000000 NaN NaN NaN \n",  "\n",  " \n",  "contig \n",  "contig-0 ... \n",  "contig-1 ... \n",  "contig-10 ... \n",  "contig-1000000 ... \n",  "contig-10000000 ... \n",  "\n",  "[5 rows x 32 columns]"  ]  }  ],  "prompt_number": 11  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "plt_df = pd.DataFrame({'reapr_errors': creapr.errors - creapr.repeat - creapr.frag_dist, \n",  " 'purity': rns31purdf['max_aln_purity'], \n",  " 'contig_length': rns31purdf['contig_length']})\n",  "pd.scatter_matrix(plt_df[(plt_df['contig_length'] >= 1000)], figsize=(12,8))"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "metadata": {},  "output_type": "pyout",  "prompt_number": 31,  "text": [  "array([[,\n",  " ,\n",  " ],\n",  " [,\n",  " ,\n",  " ],\n",  " [,\n",  " ,\n",  " ]], dtype=object)"  ]  },  {  "metadata": {},  "output_type": "display_data",  "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAIECAYAAAAqxPkrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd0XNd94PHvmz6D3jsBEuAFO0WKEiVKpJplyaYj25JL\nbCWxHMflxE5xstpNnD1rbfZkk7PxeiPt2kmcOEVxi2PJlm1JVu+NvYEkHhrR+wyAGUx/8/YPQLZE\nCQPMIwdDEL/POTwiBve++4NEiD/c+d3fBSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGE\nEEIIIYQQQgghhBBCCCGEEELkiJatByul1gN/DkwAh4G9gANIAX8LDAJfA/xAm67r31RK3Qs0AkXA\nlwH3+WOyFa8QQgghhBDZYMviswuBPwH+CPgwsAUIzv9qAz4P3K/r+heB/UqpfGCvrutfAr4NfBb4\n3HljHFmMVwghhBBCiIsuawmsruuHlVK1wM+BF4CXdV1/USm1H/gDoAronx8eAEqAsfmPB4BawHXe\nmCJgMpM4lFLFQPEFfClCCGtMsvgumhAiLfn+EyI3pnRdnzr/xawl3EqpHUCvruu3KaV+PP/yi8yV\nhziAPqCBudKSUmAIKJsf1zD/se28MYFF1rwP+OpbX6utrWVoaOgifEVCiExcddVVHDx4MNdhCLEq\nyfefEDnzglLqQ+cn3dms4b4K+C/M7VbHmavHNpkrNbmXuaT768yVmBzUdf3bSqnfB1qZ25H+AuA7\nf0yGMTQBPd/97neprq6+GF+WEGIJTp48yR/+4R8i33tCZE8qlSIej+PxeN72eja+/xZaSwjxKyMj\nI9x9990Aa3VdP/fWz2WzpOQg8JFFht193pwHzvt88PwxVlRXV1NfX3+hjxFCLNHIyAgg33tCZItp\nmjz4oyeJJF3s3dHA5g0tv/zcxf7+M02TB3/4JJGUkxuubGSjar7gZwqx2mTz0KQQQgghsiSW0PAV\nljM1HcrqOqZpEjVs+ArKCUwFs7qWEJcr6fohhBBCrDCapvH+G7cxPDLOlVdsy+paNpuN9+3bzNi4\nn53bt2R1LSEuV5JwCyGEECtQfW019bXLc0ZiTX0ta+prl2UtIS5HUlIihBBCCCFEFknCLYQQQggh\nRBZJwi2EEEIIIUQWScIthBBCCCFEFknCLYQQQgghRBZJwi2EEEIIIUQWScIthBBCCCFEFknCLYQQ\nQgghRBZJwi2EEEIIIUQWScIthBBCCCFEFknCLYQQQgghRBZJwi2EEEIIIUQWObL1YKXUeuDPgQng\nEFAJNAJFwJcBN/A1wA+06br+TaXUvYuNyVa8QgghhBBCZEPWEm6gEPgTYAh4FIjouv5BpdSNwGcB\nD3C/ruuvK6UeVUo9COzVdf2ONGO+pet68kIDm5qaIhwOX+hj3sbpdFJRUXFRnymEEEIIIVa+rCXc\nuq4fVkrVAj8HngOa5z81ANQCLqB//rUAUAKMLTKmCJi80Nj+859/k+6A70If8zbexCA/+85fX9Rn\nCiGEEEKIlS+bJSU7gF5d129TSj3Er+rFG5jb9bbN/34QKJ1/rWyRMYFF1rwP+Opisfnyi/Bq9Rl+\nRenlReMX9XlCCCGEEOLykM2SEgfwLaXUANAJDCqlvgEUA18AfMDXlVL3AA/pum4opZ5bZEwq3YK6\nrt8H3Pfmx0qpJqDn4n5ZQgghhBBCLF02S0oOAh9JMyQI3H3enAcWGyOEEEIIIcRKIm0BhRBCCCGE\nyCJJuIUQQogVyO8PoHd0Y5pmrkNZFj29/QyPjC0+UIhLUDZruIUQQgiRBYZh8KPHD+DIKycUjrJz\n+6Zch5RVHV3nePHYCIl4mE/u30VhYWGuQxIiI7LDLYQQQqxAmmaSTMRwOOy5DiXrHA47RjIORhK7\n/fL/esXlR3a4hRBCiBXGbrfzyQ/uZSYYoqa6KtfhZN3axgY+7PPicrnIy8vLdThCZEwSbiGEEGIF\nysvLW1XJZ0VFea5DEMIyKSkRQgghhBAiiyThFkIIIYQQIosk4RZCCCGEECKLJOEWQgghhBAiiyTh\nFkIIIYQQIosk4RZCCCFWoIGhEQ4eOUkqlcp1KFlnmiYn2trp7DqX61CEsETaAgohhBArjGmaPPb8\nCTxFNcQTJ7hu9xW5DimrzuqdHO4MEY+MUl5WTHFxca5DEiIjssMthBBCrECDg/0cO3oU1yq4abK4\nqJD4bADNCOPxeHIdjiWGYfDY06/y+DOvrYp3JcTbyQ63EEIIscIkEgmc3iJK8vKYjcRyHU7W1VRX\n8ak7i7Db7TidzlyHY0l7RxdjkTyMZJK+/gGaGtfkOiRh0cSkn9N6Dzu3biA/f2mXT8kOtxBCCLHC\naJpGT08PZ9rPEgrOZH290+1dPPfyIRKJRNbXWojH41mxyTZAy7omCrQpStyz1NfV5joccQGefuUk\n/dM+nnn52JLnZG2HWym1B/g8EATGgMb59VLA3wKDwNcAP9Cm6/o3lVL3zo8rAr4MuM8fk614hRBC\niJXCZrPh8hZQWFhPKJzdHW7DMHjxcA/5JbW8fvgke6/ZmdX1Llcul4u7PnBDrsMQF0GRz0nv+DBr\n1pUseU42d7iLgd/Vdf1LwHXAFiDEXALexlwyfr+u618E9iul8oG98+O/DXwW+Nx5Y6QERgghxKpn\nt9u56SpFa62Dq3ZuzupaNpuNkjyNyNQgddUVWV1LiJXg9luu5dN3Xsfe3TuWPCdrCayu648ppTSl\n1FeA7wC9uq6/qJTaD/wBUAX0zw8PACXM7YQDDAC1gOu8MUXA5EJrKqXuA756kb8UIYQQ4pLT0lTD\n8HiAqoqyrK6jaRrNjTWMTkxlfS0hVgJN03C73RnNydoOt1KqAPhH4HXgEeDNH8H9zCX6fUDD/Gul\nwBDw5ndyw/zH548JpFtT1/X7dF3X3vwFrL04X40QQghx6UilUnznxy/y7ME+nn7hQFbXMgyDg21D\nTCVLOHz8jKVnnO3o5vuPvMChY6cvcnRCrAyL7nArpT4L/AVQ/paXTV3XF+tD9DdAC/Bp4LeAaaXU\n3wCFwL3za39dKXUP8JCu64ZS6jml1DeYK0f5AuA7b4z00RFCCLHqJZNJNKcHu7sAw0hmdS273U5D\npZfAzAjN2zdZesbZrmFMTxV67xi7ctQy3DRNUqkUdvvl30ZRXHqWUlLyZ8BNwGld182lPljX9c8s\nYdjd58154LzPB88fI4QQQqx2TqeTfGecwFQ/LesWP4iXSqXQNA1N0yyt94Fbr7M0701XbGriaNs5\nVGvuunP8x0+fIzCbYu+Va9nU2pyzOMTqtJSEe1TX9basRyKEEEKIJUkkEowFotjcJXR29bN9y8YF\nxw4MjfDo8yfx2FPcfdctOBzL33+gaU0dTWvqln3dt5qJGOSV1DE6HmBTa05DEavQgt91SqlPzf+2\nVyn1CHN12Mb8a6au6w9mOzghhBBCvJOmaTTU1WC6iqmqKk07dnR0Em9hFeGgn1gslpOE+1Jwy7Ub\n6R8a5dpd23MdiliF0n3X3QSYwCwQBvae93lJuIUQQogccDqdbFpXSlfvMFs3Xp127I7tG4klTlDU\nUk9e3tJuxTvf4eOnGR2f4sY9V+Dz+Sw9I9fWNtaztrE+12GIVWrBhFvX9XsAlFLv1XX9ybd+Til1\nV5bjEkIIIcQCTNNkJGBQvXYnR07q3HT9rgXH2mw29lxt/aSiYRgcODVEYVkdh46dYd+eKy0/S4jV\nKl1Jya8zd9Pjf1dK/be3fMoJfAV4KMuxCSGEEOJdaJpGQ6WPyZlhWrYuXL99MdjtdurKPUyHhmne\nnt1LdoS4XKUrKSkE9sz/86a3vJ5kLuEWQgghRI7sv8DOIZm447brl20tIS5H6UpKvgV8Syl1i67r\nzyxjTEIIIYQQQlw2lnJU+b8qpf4MeLN5ZwqIAqeB/6nretrbH4UQQgghhFjNlpJwnwYSwD8xl3R/\nEqgHhoFvA3dmLTohhBBCCCFWuKUk3Nfqur7zLR8fV0od0nX9bqXUb2YrMCGEEEIIIS4HtiWMcSil\ntrz5wfzvbUopH+DKWmRCCCGEWFAikSAYDC5p7OzsLPF4PMsRCSEWspQd7t8HHlNKjTGXoJcAvwF8\nFbn8RgghhFh2pmnyvR8/S9jwcP32GrZuUguO7T7Xz5OvdWA349zz0VtwOp3LGKkQApaQcOu6/rxS\nah2wlbmr3c/oup5QSr2q67qZ9QiFEEII8Q5xQyOvoIRgMJx2XDA0i9tXTHw2QDKZlIRbiBxYNOFW\nSjUBXwJKme9UopQydV3/7eyGJoQQQoh3o2kav3bzFQwNj3PFti1px27b3IqmtVNUWIXX612mCIUQ\nb7WUkpIfAi/O/zKZS7plZ1sIIYTIoeqqSqqrKhcdp2ka2zZvWIaIhBALWUrC7dB1/T9l+mCl1B7g\n80AQGAUiQBNQBHyZuWvjvwb4gTZd17+plLoXaEw3JtM4hBBCCCGEyKWldCl5WSl1h1Iq044kxcDv\n6rr+JeB6YO/8778NfBb4HHC/rutfBPYrpfKXMGYpPyAIIYQQQghxyVhKAvtR5mq4UeqXp6BNXdft\n6Sbpuv6YUkpTSn0F+C6wb/5TA0Atcy0F++dfCzDX/WRskTFFwORCayql7mOue4oQQgghhBCXhKV0\nKamx8mClVAHwN8wl2y8CH57/VAMwxNzuegMwyNyBzCGgbJExaa+R13X9PuC+t8TQBPRYiV8IIYS4\nlPn9AUbGxtnYuh5N03IdTlrJZJK2sx2o5iY5uClWpaV0KXED/wloZa4n9+8Df6Xr+mId9P8GaAE+\nDfwW8JxS6hvMlZp8AfABX1dK3QM8pOu6oZRabEwq8y9RCCHEpSCRSHC6vZMN69fhdrtzHc6KZpom\nD/3iAI78SoKzp9h95dZch5TW48+8ztmhBCfbB/iNu27NdThCLLullJR8AxgHrgSSwHrmaqzTXuuu\n6/pnFnluELj7vDkPLDZGCCHEyvT4s28wlSyis+8N7nr/vsUniLRsNkjEIrgcJbkOZVGnznYxEitn\ncnAg16EIkRNLOTR5pa7rfwrEdV0PMbdbvTO7YQkhhLjcOO02YtEITttS/uoR6Wiaxl237+a6zSXs\n2L5x0fF+f4BwOP0FOdl05bZW6ks0dm5ryVkMQuTSUna4U+d1KCkHpLRDCCFERm67+RoGh4apranO\ndSgrnmmaPPyLN4hrPhLGWa7YunCf7c6uczxzsA+SYT79sZtxuTJtOnbhbr1xNxvXD1FTXbXsawtx\nKVjKNsP9wNNAtVLqfuAwc/XZQgghxJLZbDYa6uuw29M2uRJL1Ds4xsDINNPTwbTjovE4Lk8e2JwY\nhmFprQNH2vj5U68SCs1amq9pGg31dTgc0t1XrE5L6VLyoFLqMHATcwn6B3RdP5H1yIQQQgjxrpLJ\nJAUFBRiaHWzpO5Rs2ahw2DopKqq31CHEMAwOnxmmsKyOIyfOsm/PlVbDFmLVWjDhVkp9il9d4a4x\nd4ARYIdS6gpd1x/MdnBCCCGEeCeHw0E8PMXETILSPWsXHb+h1XrttN1uZ02lF39whJbtmyw/Z6Xr\n6unjwIlzrK0r4Zpdl3ZXGHHpSbfDfRO/SrjfjSTcQgghRI6UVNTRtLGBiYC1Mo9M7L/1uqyvcak7\n0zmI6alE7x3hml25jkasNAsm3Lqu37PYZKXUt3Rd/9xFjUgIIYQQaWmahptZOk69xq2/9WtZX+/I\n8TOMTkxx454rVu3FNTu2rOPAsU6aWy3dByhWuQvtzXTVRYlCCCGEEEsWDoeJ24pp3rSbzp7s9rY2\nDIM3Tg3iTxRz4EhbVte6lNXVVPHh913Hti2tuQ5FrEDSDFUIIS6SRCLBxMRErsMQq4DH46GiAFLh\nYZrXNmR1LZvNRiLsp+3Y69TXlGd1rUuZaZqMj4+TTCZzHYpYgaQ/jxBCXCQ//OkLhJIermgpuuSv\n2hYrm802d4nQwMA4DvuOrK6VSqU40z1MLOXhjH6O5nVNWV3vUvXSG0c53Rel1BPjY3fclOtwxAoj\nO9xCCHGRJAwTb34RkWgs16GIy1w8HufZw/1MpOr47kNPZnWtZDLJ8ESUuL2UE6c7LD3DNE2Ghkcs\n9wG/FEQicfLyC4kn5O4/kTlJuIUQ4iL54K1XsbXBxr5rd+Y6FHGZczgc+Ed7OXP0JcqL8xYdPzwy\nyvT0jKW1XC4X21UZJQ4/t954jaVnPPPiQR595RyPPPGKpfmXgvfsu4rNtfBrt16d61DECnShJSVP\nX5QohBDiMlBSUkxJSXGuwxCrhN3uJj+/gmA4knZce0c3Lx4bJhUPcc9HbsTtdme0jqZp7L/lGkYn\nZtio1lmKNZZI4PEVkUiOW5oPMDHpx+V0UlhYYPkZF8Jut7Pzii05WVusfIsm3Eqp55jrx/3mVVYm\nEAFOA3+ZvdCEEEII8W5M06R/1E9BZQVHjp2FTy081kil0Ox2TM2Gaaa7XmOB+YbB9376MpqrmEQ8\nzJ0feE/Gz7jtxt2cPN1Oa8vujOcCtJ3W+dbDB9CMKH/+5Y9RWFho6TlC5MpSdrjPAHHgn5hLuj8J\n1APDwD8Cd2YtOiGEEEK8K4fDgWbGsdnT/1W+qbUFr8dNQX4eHo/H4loe7N5CNC39NfILz3ewY9tm\nS3MBOrrOobmKCIdMJiYmJOEWK85SEu5rdF1/a0HicaXUIV3X71ZK/eZik5VSLcAPdV3fqZT6NmBn\nbpf8b4FB4GuAH2jTdf2bSql7gUagCPgy4D5/TAZfnxBCCHHZsdvtXLelmhNndP7oK3+86Pi1jdZb\nB9rtdq7eWsO5/glu3LPf8nMuxP7bb2Ji+ufk+ypYu3bxq+zfTSAwxcDQCFs2tVr+wUEIq5ZyaNKh\nlPpl0dL8721KKR/gSjdRKVUFfAYIzb+0df73QaAN+Dxwv67rXwT2K6Xygb26rn8J+DbwWeBz542R\nVoZCCCFWtVQqRcdIlNJ1e3j4589ldS3DMPCHbNQ27+Dk2W5Lzzijd/O9n7xg+eIcp9PJ7/zGh/n1\nO2+3nCw/9MQBjnTHePXAcUvzhbgQS0lefx94XCk1ylyCXgL8BvBV4MF0E3VdHwX+VCn1+PxL9+q6\n/oJSaj/wB0AV0D//ucD8s8fmPx4AaplL6t86pgiYXELcQgghxGXJNE38/ilcyTGChYv/Ve73B3C7\nXeTlLd7R5Hw2m43SAjsz04M0bNpoJVyefvEofTM+uvqOc/VO66UlF8KhmUQjIdxuOdgslt+i36W6\nrj+vlFrL3O60AZzRdT2hlHpV1/Uln75QShUAm4AXmCsPcQB9QANzpSWlwBBQNj+lYf5j23ljAmnW\nuI+5HwSEEEKIy5ZhGJQWF6C5nVRXlaYd29l1jmcO9kEyzKc/djMuV9o3p99B0zQ+dsfNFxIuXo+L\n1MQ0Pq/9gp5zIT7xoRuZmp6hqrIiZzGI1WvBhFsp9d91Xf+qUuqfeXuXEpRSpq7rv53BOqau60Gl\n1Aal1N8AhcC98+t/XSl1D/CQruuGUuo5pdQ3gGLgC4DvvDELdpzXdf0+4L63xNkE9GQQpxBCCHHJ\nczgc+P0BtDwfs6HZtGOj8Tguj494OGH54pm/vP+f6BmY4N7Pf4T1LZm3Biwr9hE5M0BhXbWl9S8G\nt9stybbImXQ73Ifm//nCu3wuo75Cuq6/f/6ff/Aun777vLEPnPf54PljhBBCiNXENE1OnDpLfp6P\n5nWNJJNJBoaGcHlneGQsxn/+w88uOHfLRoV/8nUqKhrxer0Zrx2NRvnJk0dw5lfwwLd+wP/9X1/J\n+BnPvnSE/oCDmamTfPY3P5zx/IthaHiU/qFRdl2xGbs98532k6c7OHCyj8bqAt5zg1x+IzKzYMKt\n6/rP5n9bq+v6/3zr55RS0n9bCCGEWCZtZ3SOdEeIhUeprCglkUjgK6ikrH4TQ22Pp53b3tFN+7DJ\n6d5u1jU1ZHzxjWEYJHCRX1jLuX5rBw5nYzHs7jIikQWrQhfV09uPx+2mprrS0vzHXziBq7CG2IFj\n7Lv2yozn9w1P4C2uY2hi2NL6YnVLV1LyV8wdarxjvrWf9pY51wB/mv3whBBCCFFUVEhsdgA7CVwu\nFw6HAyMRIRaZpqos/SHAlGmiaRpWG+E5nU4cqQix6WF2rK+39Ixb9u7icEeYteXbLc3v6DrHi8dH\nSMTCfHL/Lkt9uD1OCE5PULymxlIMe3Zu5I1j7azf0Wxpvljd0pWUPMzcIcdbmCsrefN7NQn8jyzH\nJYQQQoh5DXU13HNnCXa7HafTSTKZpLzYQyQ0wm99NH1v7I2qmTyvh/w8X8a72zDXh/v63duYmk3x\ngfddZSn+9+7bxfDgT7nthhstzXc47BiJOBhJS+UgAL/+oZuJxWL4fD5L80tKirn9Jms3ZQqRrqTk\nAHBAKfVjXdenlzEmIYQQQpznrbdExmIxZhNOfCX1HDt5etG5axrqLK9rmib9wwFS7jJ6+wYtPeP+\nf/gRbX0xuocf4Zt/+eWM569tbODO/DycDoel1oYw94OD1WRbiAu1lD7cH1ZKfY25lnxvMnVdz11v\nHyGEEGIVi0ajzPhHiceTvNiRvhnX2PgkDz3+KgU+F5/48Hsy3iE2DIP+wWEceTGOnvDzqU9mHu8v\nnn0Fe9E6us5YuzgHoLwsfftDIS5lS0m4vwrcyNy16hl1JxFCCCHExTcyMkpZwxYKy5voPtCZduxL\nrx3iQEeYRKif9998FaWlmSWumqZhd7pwOvNxe6zttbk9RZQ0bGU8NmVp/uViZmYGTdMoKCjIdShi\nmS0l4R7Qdf1U1iMRQgghxJLU19cx3nuUqbFutFQi7dhoJMrk6ACpRIhUasGrLBaUSqWYDkziSdoZ\nGLTWx3vH5mpeP/okTTWrN9EcGR3nJ8+cAEw+/v6rKCkuynVIYhktJeE+rJT6EfAkEJt/zdR1Pe21\n7kIIIYTIDpfLRV5RLSW1rfi7ZtKOLS8roaQoH5thz/iWSZir4fbkl1FUuY5oot1SvAVFVajtDRQ7\ng5bmXwzHjp/iWFsHn7jr/ZYOj16oeDyO3eXFNAzi8fiyry9yaykJdzEQAq4973VJuIUQQogcCIVC\nJKIzzE4Nk4ilv2lyQ2sz147GcNlTlg4NmqZJPBIkNDVCeaG15oJTE6MMDkaJ52W+w36x/N0PXqSg\nagPf+rcf83u/8+vLvv6ahjpuTiSx2W1y4+UqtGjCrev6PUopF9A6P/6Uruvp378SQgghRHZpNlze\nIjTNlnZYY0Mdn/lYJTabzVJLPZvNhsNuw2F3kJeX+U2VAGOBIA5vDdOhXkvzLwabGWWov52r9q7N\nWQzN6xpztrbIrUUTbqXULuBHgJ+5XtxVSqk7dV1/PdvBCSGEEOKdnE4naDaMRIyKyrKljbfIZrNR\nUOAFLcH6JmsJY0F+ATZvPcaktZsmk8kkP37sefK8bt73nuvRtMx32rdtbiVhy6euZuXWTodCswwM\nDtOqmi39OxC5k/7H4jkPAB/XdX2nrus7gDvnXxNCCCFEDthsNox4lNmZMZKJZFbX0jQNj8dHUWk1\n2K0lebu2NtF/6mlaaq0dmvzJz57k35/t4+8fOkJPT/o2iAvxuBy4nA7cbus/fOTaw794jdfPBnnp\ntSM5i8EwDJLJ7P6ZuxwtpYY7T9f1N978QNf115VSnnQThBBCCJE9hmFgaiZ2m43B4ZGsrmW326kq\nyyeKydaNLZae8Z2Hn8FW2MJTry5+Sc+76Tk3wLn2UxhGDL//OtatW5fxMz7xoRuZmp5Z0fXTNg3i\nRhKbbSnp28UXCs3y/Z++jInGR26/itLSkpzEsRItZYc7oJT60JsfKKU+DExmLyQhhBBCpBMMBnF5\ni/AWVWPTsrtjaxgG0ZhJyojRNzhu6RnJlA1vYQWmzVqsNjsUVjeTX1pruZTC7Xav6GQb4CP7r+fW\nXdVct/uKnKwfDIXAWYjDV8qkf3X3VM/UUn5E+hzwM6XUt5mr4U4B12U1KiGEEEIsqLy8HCMeJpWM\nUZSfvsVdKDTLz59+jcJ8L++7ZU/GCavNZiMSjUJeERrWSgnW1ZVwbqKXUmtnLllTX0v02RdIGbGM\nL+65nHg8HhrXNORs/ZrqKnZvmiJlpFjfkrvDpyvRUhLu24EwsAZoBv6DuZsnl9SMUynVAvxQ1/Wd\nSql7gUagCPgy4Aa+xtyBzDZd17+5lDFL/uqEEEIIC0zT5LmXDxOLJ7j1hqtwOHLzFv5CQqEQbl8J\nBeVr6Op+I+3YJ599hTc6kkRDg+zesYGyssUPWb6VaZr4/ROkQilisczbCgJ0DwYoXbePiW5rb5CP\njE9Ss+4KorMBpqZW7s5qZ3cvdruNtY25S5ov1LbNrbkOYUVaSknJ54HrdV2f1XX9BLAD+L2lPFwp\nVQV8BggppdzAXl3XvwR8G/gsc7vn9+u6/kVgv1IqfwljLq3/6wkhhLjsjI9P0DGcYDJWyPGTZ3Md\nzjskEglmp4YY7XqDcDB954/y8mJG+k4RCQzh9Wa+xRwMBgnF7Th8ZXz3oScsxRuLRvAPniYaTn9J\nz0K2blIkpntxxMepq6uz9Ixc6+sf5PkjQzz1+jlGx6yV5oiVaynJqwN465VIcebKShal6/oo8KdK\nqceBUmBs/lMDQC3gAvrnXwsAJUsYU4TUkAshxCUnEokAWErqLjVlZaWU+2LE42O0rr861+G8Q15e\nHg53Pr7CatxuPe1Yj9vDziuvJhWzdrW70+kkZSRJJWI4LfTxBijwOTG9hbhCS9nne6fWlnXUlb+K\nx+WjqGhltvXzeNwk42FMM2Xpxk+xsi0l4f4J8KxS6t+Zq+G+E/iphbXGgDffx2oAhpjbYW8ABplL\nyIeWMGbBH+WVUvcBX7UQmxBCiAvg9wf44S8OgQkfvf1KyspWdp2t3W7nrg/cmOsw6Ow6RzQeZ8tG\n9bbXE4kEdqcHu9uL3Zn+r/K6mgoCTx2mMM9p6Ychm81GNDRJIjzDpvXlGc8H8PryiTuceL3W2gI+\n8cxLRD3NTIf8dHZ2snnz5oyfcfxkOwOjk1x/9VaKCq3FcSEqK8q5+9euRtM08vPzl319kVuL/qip\n6/p/Ya6jQmMEAAAgAElEQVTvdiuwlrnyjv+a4TqmrusG8JxS6hvA7wD/D/hH4PeUUn8HPLTEMQv+\neK7r+n26rmtv/pqPVwghRJZFolHsrrld10g0mutwLgt+f4BnDvZxoD3I2fbOt33O6/USm/UzGxgi\nEY+lfc7R42cYCXk42ztDMBjMOA7DMPDmlVLetIPxcX/G8wEcdhvR2QCYhqX565rqGTz7MpODbRnX\noL/pjVP9TBulHDqWuxKhgoICSbZXqSXVQ+u6/h/MHZa0RNf198//8/wLc4LA3eeNXXSMEEKIS0td\nbQ03XjGXaNfX1eY4msuD1+tBM8IkwgmKiuoBGB4Z49DJLvJdBg6XG4fTRyplpn1O78AwM9MzRGcD\nRCIRiouLM4rDbrdjsztwuDz48qwdmuwdGKegspDATNjS/LHxSVwFNRiJMOGwtWcY0SlOHDrH3R/a\na2m+EBdCDiAKIYS4KKRN2MXl9Xq556M3z+0wz5eCHDzeSYhSuvVTgB2bzQ6kr6vee+1Ofv78v5Dv\nsVFennlJiMPhIBKawBg4Tf1aa6UYmt2By1eM3Tlsab5hJIkZGsm4YbkPt91TzLZdWxibzHyXX4gL\nZe30ghBCCCGyzuVyva3uurmxkmRomJoyLw6nm7yyurnTVWkcOnaarbvfR0ndJiYmJjKOwe/348or\no6RuI28ctXZTpNsONocDUglL8wMzQfLzvPi8PkKhkKVnNFbnY8wO09xUY2m+EBdCdriFEEKIFWLz\nhhY2b2hhbGyMP/vr7zA12E4ynr5mvq6qnL/97vdxaQZFRXdZWjeZiBEN+YlF09eLL2R2NogvESMe\nszb/ln3X8vAvvoHLCc3NzZaecdtN11iaJ8TFIDvcQgghxAozPT2N21dEfsUabPb016V/54ePYNjz\nCcZsdHd3Z7xWIpFAA+x2F6mktZsmI/EE/qEzJBLWdrgPHD5F0+brKa3fSm9vr6VnBIMhOjq7Mc30\nNe/ZFIlEiMqh4lVJEm4hhBBihTl1+iyaZiMZj6DZ0teUnBsYoXzNNnxlDYyOjma8lqZp2J0uHG4v\ndmf65H4hbk8+Neuvw5NvrYd2XXUFU+P9hKfHKCkpsfSM//a//40HfnCEH/7kF5bmX6jxiUn+9eGX\n+ZeHXmR62toFQGLlkpISIYQQYoWpqakmHp5Bs9lJRGfTjt2imnjpxEvEoyHWrv1kxmt5vV7C0xOY\nRopkLP1aC7GRIBENkkpY293duFHxnj0hbKQsJ9zhqIG3zIc/YK0G/ELNhiM4PAWkjCSRaJSiosKc\nxCFyQxJuIcSyMQyD9o4uWtY1yU1rQlyAqopy7C4PvqJq7O68tGOnZiJUt1zDbGCAmZnMd1bHxsbw\n5peSX1ZPYKzDUrwOl4dUMmF5h7yqspxPf/QmbDYbdou3Xdpifo6/fIIP/vE9luYnk0mOnDjN+rVr\nKCnJrLUiQNOaevbG4tg0jeqqSksxiJVLSkqEEMvmiefe4EBHhJ8++WquQxFiRZueniYeDRKeHgEj\nfV11KDyLf+gMQf8ASQs12Hl5eWAa2OwOTIs13NFolFh4ikTcWg338MgYf/zn3+JP/+JbxCwevDzc\nPoZZ2Mrf/esjluY/89Ih9BE7jz57xNJ8gNb166R95iolCbcQYtnYbDaMZNJyH10hxJzJyUkKSuuo\nadmN3eVOO7a8rBiHy4vD7rZ0y+HMzAyaw4XLk08qteBlz2mlEgk8+aVoprX5Dz3yOF2jBkc7/Jw5\nc8bSM0zNjt3pJZmw+kNDhKPHTjIxMW5tfdPkjcMnOXSszdJ8sbJJSYkQYtm898arOdfbz5qG7bkO\nRYgVrbi4mNnpUcbOHcE00l+X7nLYScbDJBJhS+UYBQUFRILjTAwYLHI+c0EOtw/TSGBzeizNr6wo\nxTSnsNugqMjawct8j41kxE/rxjWW5tsdHja0rsNpWLs4p6Ozh7b+OCkjQVX5EA31ciPraiIJtxBi\nWfQPDvPakQ4aa0pYt7Yx1+EIsaKlUikS0VmitkkSsfQJoN3hwldUgt3usFRSUlxcjCe/nPySOuyx\nzLucAERn/YQCQ0RDfkvzTSNJYHwYI2HtWneALZtaMGyFtLZmXn8NUFLg4onnXuHKrdYS9qqqClKH\nO9FMk7JSazGIlUsSbiHEsjjV3kfSVUlb9xC7d+U6GiEuLYePn+ZM9zjbVDXbNrcuOv7HP3+OgrIG\niirXMTM5mHZsIBAgmXCQiMcs9cEeHR0lGhwHM8XI4FDG8wEKSmtp2HIL5smnLc1/8bXDBCaGME2T\n9vZ21q7NvA76itY6uoem2b1jk6UYXj3SjqtiEyc6zlmaX1RYwGc+fiuAlNWtQlLDLYRYFts2NuFK\njrOlRU7nC3G+rr5JbL4qOnqXVh88OTFKykigaRopI30SfUbvIhmLkEzGLF0a09nZSUFZA9XNV+H2\nWWtlNzs9xtDZlwj6ByzNn/RPUbdxH+UNm5menrb0jHDSSWVdC4Mjk5bmV5cVkgqPU5xvfa9S0zRJ\ntlcp2eEWQiyLupoqPrK/KtdhCHFJ2rlpDae7hti2ZWk7t3d98DaeP/ZvhAJDGIl42rEOTUNzuLBr\nDnw+X8axlZSUEA35mRnrIRaayng+QMpIYSTjmBYPXTbUVtHV1kMiOkN19bWWnjEdmGQmPsPW+iZL\n8+/+6PtpPXKcja3rLc0Xq5vscAshhFgywzA4cuwU0zPWDo6Jd9fS3Mgd772WpjV1i47t6unj4V+8\nitPtw1NQht2Zvqf9dCiMzaaR0kwmJiYyjs3v92Mkk8TDQRxua4ce84urqFl/DQWl9Zbm77t+N/Hg\nMLbkDM3NzZaeEZyNEA7HmY1Yu3zn4JE2jnRM8fRL1tsCitVLEm4hhBBL9uzLh2kbgp89dSDXoawq\npmlimiY95/p54oUjxLUSUqkkqWQCY5GDkKFwBE2zo6HR1dWV8doFBQW4vQXkldWRMqztUM9ODTPS\neYDgZL+l+Y89+TyljVfiLVMcP37c0jOicZNI0kY4aq0XeE//MEOTcbp6RyzNF6vbspaUKKUagUeA\no8Aw4AeagCLgy4Ab+Nr86226rn9TKXUv0PjmGF3XM//xXAghxEXhdjuYHZ2hxC11qMtl0h/g4ScO\nEo+GMR15TIc1jh49jM3uxeZw4Fjk1laP00YyEcU0DNasybzDxsDAAJHQBNg0zJS1HtZ5xVXUqGsx\nkunLXxZyRu8k7HEQj4YYGEh/SHQhZzs6SJJHZ2nE0nyPx4ODKbwea7v8YnVb7hruvcwl2ibwKvA5\nXdfvUErdCHwW8AD367r+ulLqUaXUg8De88b85TLHLIQQK97sbJjnXj1GRUkBu3dttfycvbt3sGHd\nBKWlJRcxOpHO8MgYrrxKYsYUkZkxSovL8NiixJJOjFgEc5HLZExNw2azY2opS324/X4/xdWKyrU7\nmBrJfIccIBYOMdnfRixsrQZ8aHgUV0kxyWScnp5zlp6R0rwUVm1kImAtYd+0voFgOEVjrbWDo6lU\niqdfPIhN07hl31VyeHKVWe6E+wDwFDAGPAO8+Z07ANQCLuDN95sCQMn8WIDB+TELUkrdB3z1okYs\nhBCXgUPHTzNtlDCgD7FzewKn02npOZqmUVlZcZGjW31SqRSPPv0aiaTB/lt243YvfFvk5o2K4OwJ\nPI1VbFC76ekd4IH/PUHK5yOZiBJf5NBkNJ7Ck1+KkYxx+PBhPvGJT2QUq8/nY2LgFOHpYQLj1rqM\nJBMRZib7iEes1f67bHYKK9cRC0/jcFj7s+t1JJgeaWfdTdYOPaqWtagLuJb9ZNsZnnitE9NIsbah\nguZ1csX7arLcNdw7ALeu6yYQ5lcJdAMwBPTN/x6gdP61svmP65lLuhek6/p9uq5rb/4C5E+zEEIA\nG1oaITJCfYUHh2N1Nqg6eLSN514+hLHIzYzLYXhklHPjBrNmKW1nO4C5A6nvdjGNpmlce9V2dmzf\niNfr5YQ+zOhsPna7E3deKU57+gTUbhoEJweITI+xfXvmt7zquk5haT3F1YrCkuqM5wNoQGlNK5oj\nffnLgmx2UqkkpAxM09ojglHwlTaid1mrI79QhmFgdxWguXykUrn/MyiW13L/X7cD+Gul1BjwYyBP\nKfUNoBj4AuADvq6Uugd4SNd1Qyn13HljhBCCI8fPcLp7jC0t1VyxdfGLQlaCdr2LoqICqqsufq/y\nqsoK7v7wTRf9uRfi5TeOMTQ2w96rNlBTnd3+7NPT0xxu95NXWMKxE2e4cseWrK63mKOnuuju6qC8\n0MHHb/0EwWCI7//0ZdBsfOT2q9KW7KypLiLQ30F1TRU2u33R0oRYLIbbW0AyNsvZs2czjtVms5FK\nGdgdblKGtQ4fbm8RDk8+Lk+BpfmFPjczk30kEzFqa3dbesZg/zm80xEibmt9vC/Uti2bGBiZQbNp\nrLNwcY9Y2ZY14dZ1/Qjw8TRDgsDd5815IKtBCSEuCef6BukbGGH3lVve9vb61NQ0druNgoK3/0Xd\n0TeOzVdFZ9/YZZFwn2hr51BHiES4h0/deT2eVXAw62zPJL6SOk6192Y94c7Ly6PQFScWGqZxzZVZ\nXWspwjGDXVfvIc+cxOVyMTHpR3MVYXM48E9Nv2vCnUwmeeqFg7hdTgifJR7eytRoN6nFduztDmxO\nDw6Xl/b29oxj9fv9JOMREtEQRtJal5JoeIapIZ3obMDS/HNDw9RvvYpEZJpXXnudL37xdzN+hjuv\njMKKtUz256bDjt1up6KsALvdbqmWXqxsq/N9RSHEJeeZV8/gLqol8cYJbtl3FTB3WOyR59owU0l+\nff/VlBQX/XL8ri1NtHUMsnWJF4Vc6rxuF4lYGM1MYrOtjo6tW1oqGRwdZ9vGDVlfy+Fw8Mk7b8n6\nOkt12w07OX22i+1bdgJQW1PNNZtnSBkGLeua3nXOydPtdI6ZjI0OAHa8+aUUVzfjH0y/a20kDSb7\nT5JMxHEUZv7X/uTkJJqWTzwaxLFIz++FuPMLKG3YSDwWsjQ/Fk+CmULTbAyOjFp6BqYBmPjc1pLd\nkdFxDhzrYN2aCrZszLwOvKOzh7NDBkYySk3lEA31aY+licuMJNxCiEtCcb6Dcf8gVU1Nv3wtFo/j\ncHpIGQmSibf3zm1eu4bmtZm3OLtUrW9ZS2lJEV6vB9cibd4uF9dcQLeUXEkmk8TjcUs3Nr5VUWEB\n1159xdte27pJkUwmOdveSUtz0ztq7VXzWv7pP/4V0+6lqHo+4TMXv9odLUVl0w5mA8O0tz+dcazB\nYJBEFJyePOJxayUlpmGiYcNqXw4jHkez2TCBaNha0p6IBQlO9FLqXfiAajrffegpTg5Akf0Y9/+P\nzBPuosJ8jhz6BTYNPnLLyn9XTmRGEm4hxCXhzv03kEq9vW1Z05p6bp5/raKiPIfRLY+ystJch5Az\nqVSKnz/1KomEwf737L4kS2pM0+Q7Dz1DNOXihivXsFFZu/Ewnceefh1/ooD27tf54PuuB6C9o4fA\nVJBtm1uoLvMyG9MIBYYJBydJJk6Blv4dERONZCKGYSQYHR7OOKb+/n6K1n2AiqYdTA5lXpICkIjP\nMtF3gsistfppp8uNze7EZndSWWnt4GY8GsHuijE2PW5p/tDIOLFYAeNRa60Nxyf9FJWvwTQN/IEp\niouLLT1HrEyr431LIcQlT9O0d61rXNe0hsaGxa+7Fm+XTCYZHR3DtNrSYZmNjo0zEnQQsZVzur0z\n1+G8K9M0iads+ArKmJ6ezc4iGqSM5C9z6EgkwrMHe9BH4dvf+QmNrVdjMw2M2DSe/BJK6zdimulr\nuG2mjcjMBMnorKUfZE6fPk1gSKf3+JNELfbRdrl9FFatxePLtzQ/kUgQnOxndnqIiTFrJSXegnIq\n1mwlFrPWIeTO/fsodoV4z3WbLc0HmJ0aJTw9KT24VyHZ4RZCiMvQw4+9RCDmZWNdP/v25P6Q4GKq\nKitYU9pDPB5g6yZrXSjeKhwO4/V6F01sEokEZ/UuWtevW7SUx2az8f4btjIyOsHO7ZsuKL5IJMLD\nj72CqWnc9b49eL1eAPa/51p6evtZ2zjXIbd3YJj+ng5q6iM0NtTSM9pL3zkd8OFwePDkleBYpNWe\nSQpPfgmYCcLhsKV43fllFJTVW97h1jQ7bm8JNru1cqlUMklBeSOJyAwzXUesxWCzYwKaw1oN99GT\nOiP+KMdOWbv8x2G3MTA6jUYKR44OTfb2DfLasS5qK/LZd+3OnMSwWskOtxBCXIYShokvL59IbJH6\n3kuEzWbj9puv4Y7br7d8Kc+bXnnjGA8+coCfPvHyomMff+YNjp5L8Ogzbyzp2fW11ezaseWCD7YO\nDo8Ss5WQsBXTPzhX5uH3B/juT57n8MluYP5mwlfa2LjjeupK3Xzgtn2MDZ6j2+8lv3oD4wNtdB/+\nGTOLlEgYCYOQf4DwzATj49bKKeKRaUL+AcyUtS4lwakx+tueYWbC2i2PBiaz/gFmZ0bp7um29Ix4\neJrA0FkS8Zil+Q/9/FmCRhGvHumwNH9kdIKqukbKquoYm5i09Iyz7Z08+L2fEI1aq6U/0zlAyl1J\nZ7+1bjHCOtnhFkKILBodGyeVMrPe9u58d7znajp7etm2+aplXfdSMBUMU1BSRSgysuhYh8NGPBzF\nnre8b/E3r21kYHgCgPXNc512us8Nonmr8M/4GRgY4NDJLvSeEfxHzpDn8zHhn+bgiU5SWhmGq5ii\n0gYKq1tIxNLvWnt8eVS3XEPIb+2WSABfURVFFU1Mj1nb3c0rKKVu4z6GO16zNN/ucJJXWk8iFlr0\nZs2FzIb8JJMJkhaT1WgkSri/jUTQWrK697rdPPfK3+Py2dmx3dqB4f/3b0/hLW9l4l8e5o++8MmM\n5+/c2sKrh86yoVU6pCw32eEWQogsGRuf4CfPtvHT588yPDK2rGsXFOSzY9vmVdnv95brd7K2JMZt\n+xa/VfH2m6/h1l017H/PtZbXMwzjl7XyfQNDfPsHT/Pvjzybtn6+u6efofFZHA7HL8tedmzfSLVv\nltB4B9977BjPH+hg+44rGRgYpHfGx/998DH6BgaYmRiAqVEcbh8hfx+RYPq66lgkyETvMaZHrdfG\nJ6MhoqFJYrMWO4TEI4QmB0hErdW+p5JJEpEZ4pEZUu9yG+dSlNW0sG7H+7G5rB3InQ5GcLjzMGzW\n9iqPHD3BWMRH35QdvcPafwuvSyM4NUpZsbULhCoryrj9pivZsS37rTjF28kOtxDisjQ4PMqRU91s\nWFfL+ubGdx1jmiYHDp8iaRjsuXp7lg4ymfO/siuZTHLwSBsN9VXU11rr4rCSTU1No2kaRUWFeDwe\n9uy+YvFJzJWyNK6pZ3Z2lr6+fjZsaH3bn4NkMskjT7yCYaS4473XvuPQ4Yk2nVeP91FR6OCuD9xI\n38AI7sIapqZGSCQSC9aFt/cMgbeKrv4hrt89Vz6iaRqFBV66RxLovadwa2GcsVGC0wH6Bx6lfM12\npke6mR7pwOGaxeHNx+MrYmqsJ+3XmNI0UikDc5FuJukYqSTx6CwOt7Vk1Z1XTGFFE5HghLX1TWtJ\n9lsFA8Okkq8Ti1krKUGD0tpWYiFrX4PT6WSgtwc0cDqspV9/8Se/zejoGA0N9ZbmHz1xljfahijL\n1/jor11aN89e7mSHWwhxWTp0vItgqoSDJ88tOGZgcJiTvWH0kRTturW3ytOprCjnw7ds5UM3b8p6\nSclLrx+ja8LBEy+ezOo6l6KR0XG+/9hhvvfzA/gDmXfRSKVSfOWv/pkHfniMB3/wU2Du0qXBoWEG\nh0boHYvTMTBDR9c7E9vRiSkKSuuYDidpO9vJ8fZRes6+wZ4rGnC5XJimyb8/8iz/+IOnONf3q/rl\nKzatJTR6BtVYwuzsLP/0gyf5++88hk2bawM4FfATsVXy1IE+bPkN1G3Yx2j3Idx5RdRuuJ7gxDnC\ngSHi0SDJaPqSEofdSXF1C76Csoz/3bzJk1dMaW0rmsVO2pHgOIHhs4SmFy/zeTc2m5NYyE8iErQ0\nH6CwtIFadR0ub56l+clEkuB4H4motV3+4ZFRcBeSsucxNm6thtvlcllOtgEmAzPkF1cTiljr1CKs\nkx1uIcRlad2aCo6eGaSl4Z1XZL+pvKwER/IUZlKjpibziywAus/189qxbuorC7hhzztP/VcuU//w\nspJC2vsHKPFeOiUkE5N+gqHZX3bceFMsFsNut7/jYpdMHTvZzpnuUUrzNRwuD6aRxLBQbmCaJgZO\nvAVlzIYDdJ/r5d8eOUB1dRW3XbOW0eF+XPlVTAUj75i775rtvHH4FE2bN/HCK0c42jFJdGaYwnzf\n3HMNg8BsivySOvoGRujuHSbP5yGVMrAXrOFs9zgNtdX0DPo50zXAsWPHsBlhjHiK6fFe4rEYCf8A\nwcAgpbWbmBnrJL90LuGy2Z24PAU4Fkkg7XaN2amRC0pWk7EI4elRTIvvAuUX11FWv5lk7J3/DpfC\n4XBSu2Ev4Rlrhz4BwsExAiM6sdkZS/MTKRNTA9Nm7VBvKmWSSDmYO3dq7V2voyfOcLazn1tv2EW5\nhb79N+zZwcGjbTRdsfIunVrpJOEWQqwIqVSKeDy+5D7CWzetZ+um9Em01+vlno+/94Liau+eO+jW\n2T/IDZnM6+yhvXuEfK/GmtpqWpqbLiiObZsVrS2NF9zh42KJxWI89MRh7J4C9kSibNow99+it3+Q\nX7x0FoctyW/ddfMFxdveM4rpqSQYHeeWXfUEgyGeee0URT4Xt99y7ZJLhOx2O1/+9G0cPXmGD+2/\nk3/47qOMTRtEY4OYuxvxOVMUFzqJRSK8+vphrt29E03TME0Tt9vN9dfswGaz4XAcIRZLMhFI8vCz\n7Wjhp9lzzZVsay7lhdcPESnycKoniH9iiA/dtguoYGJ8jLYzZzl08A3shU0cbzuDzVUAZpLA6BB1\nrdfTf+YFNE3Dk1eC3ZXH5MApihuvJRYNERjtYmpsKO3XFwhMkT9wBjNlfVczFpvFEZ4mFrFWg+0f\nbgctxfS4tS4ls6EgPUcfxTCsHZgEcLnycftKcbis3TRJMkYqESWVsFaSEpyZZnSgA9NMEQpZa8n3\nzz96ES2/gXH/M/z+73w04/mzs2GmZiIEpoPU1lRZikFYIwm3EGJF+P5PniUYs3Pd9jq2blK5DueX\ndm1TvHb4LJs3ZfY276GTfYxOJRibDLK2IUFJceG73jTZ1z/Ea0c7aaguYs/V6Q8But0WE4ks0DSN\ngcFhzo12oc2W/zLhDgSm8eSXEZn1k0gk3pZwd3X38cKhDipLPHzg1usWXWPHpjWc7hxi66ZGqirK\n+fFjL2H6GgiEYsRisXf8cJZKpXj40ReIxE3WVOVRWVHGxtYWAJrXNdG8rgmA0uIC1hkuWms9/MfP\nXgRfPUP9nRw+Mo3dV8X/+YeH0DQHjU1NbG4uJ2r62LS2lPLSEqYmj2OaSU6c6WJkdIKOURuxqXOk\nvFUcPdNPIGzDsBXwwL8+xWzQj+ar5l9+9CzOvFIcsTGm/dN4SwqYDQYoKGtgpOsAqVSS/JJaEpEQ\npgklNS1MTgzjzS+lfM1WJvvPpP33lOfzUVqznnjE2i2PAG6Xl7ziKhw2azvcLncBxdWtxMLWdtkN\nI4bd5Ya49fMQqVQCI5UgZfEHD5fHQzIZt9wScmhskup1O0glE4yMWdupnxgfITIeocph7dDk60fP\nMmWUMHy8l80bWiw9Q1gjCfdFkkql0HX9oj+3ubl5VXYZEOJ84ZhJXlEF/oC1t4OzpaK8lDtu25Px\nvOaGUvyBLvLdScxEGI/n3ZPlto5+DHcl7b1D7Ln6QqNdPi6Xi0R0luLSOk73/OpmwO1bN2KkTlNc\n2IzP53vbnJ7+EdyFtQwtsVezamlCtTQB8NzLhyiu2cCJY4e487273vWdkHA4zHjIRtxw0HGwj7ra\nOGUlRVRWVrxt3F379zE1NcXQyDgvnxyjd3iAyuI8pmcnSc5O4Mpfz+zUOGVmET0DE7Ru28O4f4xz\n/aOs23AFofFuQoF+vHk1nD19jOqGVqanTfI1k1RkgpSnimjcxF6whrzSNSTiCYor1zLWfRQDjYKy\nBrz5ZdjdHty+Qmw2ByYmyXiU8EjH/2fvvuPjOu8733/OFGBmUAa9AwTbYe+dYhOpLluSLdlSLDtx\nrDjlZpOr3dzsptxdK/FucvcV39hO8TqONzcbx07ijRW5SZatRskSq0ixkwes6L1jMP3cPwBKlMSG\nwxkMQHzfrxdeBGbO8zw/gBzihwfP+f2Ihkbo7o1QUe3CTiTI9l8/+fIFciioNBnuabqpr+vVZOcU\nk1tUQ3aOs3bkOYWlBPJLCeQ7O2JVWFhJ/fL7CA120LD3nx3N4XJ7cbs9eBw231mzahl9xmyiOc5+\nK/PrT32aU3/4FdwBN48/9jlHc2zZuIaRZC7LZjn74XpuXQUd75ynrsJZwi7OKeFOkZHBHj7z+98h\nEEzdjVGhgU6+9aefwjSnzm6eSKbct20pLa2drFu9KtOhpMTGtcvYuHYZo6OjuFyua+5Or1w8mz2H\nLRaY06/yyKa1C3n9UAsL6t9LaA3DYM3Kq7fG3rR2KW/sO8rKVfUTXmve7GqaO05z95albL/j6p01\nc3NzWT2/iMbWDrzJXIiNkJMT+NB1Ho+HkpISvFnZLJjVRK5riIrKCtYsKKa5tZ3mrhCDkSGWVMV5\n+N77aLjQyoolyzhfVci/Pr+f+QsrmFu/hiOnG6ndvoM3DjfiTobZsnopH7lrA99/4WXOXzLYvd+i\nveMMsXCI2Eg/sZFuFpv1nDq7D2wDw+XC5XGT5c9jqLsF2/AQDQ2QjJ+CgUsMDxSTSMQZHrx+1Yy+\njla6G48SHu6d8Nf1su6mY/R3n6fPYS3v0FAPQz2Njs9g9zWf5vzhHxKPOauhDRAa7Mbl9pLrc7ZL\n/hu/+FGeffEQy+/Y6Gh8IBDgb7/8B47GXvaxe9dz4VIr61Y7ay8/f+6sa1ZtkvSa8gm3aZrVwJeA\nXmkVuDgAACAASURBVOCEZVlfy3BI1xQIlpFbWJ3pMERuS7XVldRWV2Y6jJS73NL7Wioryvj4/ZPb\nNCdVHr7/Tu7ZMXrT5+5zcgLct9NZMlNbXclnHr3xv48Na5exgWVEo1EMw7juGfJgfh6//Mn3n/G3\nbZtQKEQgEHj3jHhtzVgTkeKiQtateu9mtPt3jf3m4xceS5JMJt+9SfQ3nvr0u3MlEgk8Hg/RaBSP\nx4PL5cK2bQYHB0kmk3R2djIwMEBBQQFnzpzh7NmzHDhwgMYjl7h48NK781xPbLiJYDDIzp07sW5w\n7dXYtv3u53qjta7lD37jEX7zN3/T0dixdUMsW7aMpabJuX3OYjj15j/R3t5OfX29o/FbN61l66a1\njsamSmVF2aQ30ZLUmNzWWg6YpvlHwAuWZe01TfPHwMOWZd3UbeimadYDF77yla9QWvreDstf/M9n\naRktveY4J+zeY/RHffhyrl0RYaLCI338ykNLqay8/ZIMub3U1r6/CsXp06f54he/yAdfeyKSfnr9\niWRGV1cXTz/9NMBsy7IuXvnclN/hBiqAywfP+oAgcNUClqZpPgN84crH1q1bd/mTnxTO7t++tq9+\n9eUUzygyOSb7tSci79HrTyRjBrhK3cfpkHA3ArVAC1DEWNJ9VZZlPQM8c/lj0zTrDxw4cOHb3/42\nFRXT7/yjyHR17Ngxnn76afTaE5l8ev2JZEZ7eztPPvlkkKucIJlSCbdpmvOA71qWdWWByjPAd0zT\njAH/allWcqLzVlRUUFMzVrLLMAzyyxcSS8QID43gywkwOjBEXmkxiXiUcDhCTm4+o8NDuD0esrKz\niEai2LaN1+MZa4+LjTsrm2QkCm43LgMS8Rgej5+EHcbtySYyOoo320c8GsXrzQIXhEMjZPkCuAwY\nDYUI+PzjLXeTuG1wZ2fhdrsJj4Zwu9wYLgO3y4XL5aGoKJ/QyChDoWFcuPFmZTGrroLOnn7Kioso\nKSnm3PlGFsybjW1AdVU5La0d+LOzuNDYjNfjxu8L8MhH76Gvt58Tp88wPBJmzqxaVq5YQt/AALFo\nHJdhs+/gMXbt2Mzx0xbRSJS5c+s5euw4eflBqsrLuGPTOrZv2chLr71Jf18fP/7Jq+Tk+Kmrq+Jj\nD95H0jBIxOLk5uVy/NgpyitKuGPTeuLxOP/wT8/i83qpqqmkqqIcO5kkJ8fPkWOneODene+WW7rU\n2ExhQZD8/Dxa29oJhcLMm1vPuQuXKMjPI55IMjAwiDl/DjB2rvCMdY6iokLKSm/cTS0ej3P85BnM\nebM/VCnhWgYGh2hsamHJIhOXy8XFxmYMYHhkhIXmPHp6++js7MaGd69JJpMcO3mGgC8bn8+Hbdtk\nZWVREMzjtHWOJYvMCdchHhoapqW1nQXm3DS1Ip+YpuZWYvE4c+rr3n2svX2sm9yVrz0RmRxXe/3l\nVyxgaGQQe6jN0ZxGXgV5/gKGus44OsdtGAYFFYvobz/l+By4v9wkPhIlNnzR0XjDV0xufjGP3LuB\nb33rWxMe/+Uvf5k/+rNvkuNJ0NJ42lEMNfPX4XZ7uHR6j6Pxh985xnM/fIE//E9Pk5U18Wor0WiU\n//HNb3H3nVtZvMhZQYa29g7i8cS79y7MROFwmHMXLrHQnHfTleSmTMJtmmY58BTwwZ6pvwyYjMX6\nL7e6zuw1j1BSuxyvP4fwYA+2nWSw6xLVi7YwOtTFcE8LxXXL6bpwmJzCSry+HNrPHyQvWEVuSTUu\nt5fQQCfubD/hwW4KK+YzOtJHLDyEL6+UyFAPucU1jPS1YrizyPblEAn1EyioovvSEcrmrmWkt4Xh\nnmYKqkzseBx3to/IcC/DfW2U1a9isOsCxXXL6WtrYHSgg7I5qxloP0vA7aUyO4Avp5DB7ibCdpw5\nm5fT33qGIX8FWUV++v2L6G87S0soTk5wMU09beRUbWWw8xLZ5cv4xxfPEU/EyfLVYLuh9UwfBxtP\nk0xEyQlW0NV8mupFO/jr77xIzZJddDce4Y2je6hZuI1TZ47ha+nnUPNRvv/iHsg3ObB/H1k5NRBz\nc/Dcefad+t+sXLWKvp5Oenu6sP3VZHOJ7Gwfr771Dq8f72d4oJ/62UN4EgfZvHkzr77yPUpnraSp\n7Xv8xuc+weGjpzjUMEgycoJPPriRH7x2kqzsXKyG87SFchkdOo5huPDnlxGOnmH5kgUcPX6aQ+dD\nREcu8LlP7LhhEvuz3QfoCudy5sI+Hn/4zpv6t/Pci/vAV0Zv/yGqK0t57VArx06cYtHCRZw5+xqd\nQ3DyfCc15UX09A+z4461vPbmIaz2JEfe2U1ddTnZvgABn4e87DhJfzWNbft56N4b1xu+0rM/2YOd\nVUJn72G2bXLWPCFVunt6+fEbDXi8WbhcLurrlFyLTDWFNatY8+B/YLDr0rsNeybCMAyW3/PblNSv\n5NSr/5+jGGav/Rjmpie4dOh5R+PzKxex/p7fZHS4x9HnALB448combWSVw4+5yiGr/7ja2z8+H/m\n0vGXHI2ftWAN9Rs+TTIWYeXqrbxz6I0Jz/HUf/xrCmpWcP/jv83L//b1CY//vS/+FZ32HF790j/z\n3P/8LxMe393dww9eO4XL5eG+zczYpPuHP9tLiCIutezjgbturizslEm4LcvqAH7fNM0XPvCUMX6T\nZNw0zesWnrzaGe4PSsbjxOMRGLWJR0N4svNIJqLEwsPEw6PEYhEMDHIKygiP9IHLIBmPE4uHiYVD\nuNwekokEdjSCnYwTj4ZJxEZJxqMkoiMk4hHikRESsShu2yYWcxOLRYhFRkgmYiSioyQTUZLJKIno\nKIlEHNuAeCwCJIlFRojHwsRGh0nGo9jJBLHw2LzZ2QHsZJxoZIRELAx2kvjo2HOx8AjJRGJsTCJG\nMuEhHguTjEeJx2Pvfo6JeAQ7OVbPFcMYi2l8rlhkGDsZGy87FSM2OkgiHsWFgcvlIZmIkUzEIJnA\nm+VmJBImEQ8Ti4zgcnlJxmMYdpxkPAZ2EpdhE44M4/VEyc7Owu/LJh4OkYhHSMQiZLkSxCIh3C6D\n6OgI2RVXJMnjO7dutxuScWLRUbIKs0gMxXEDGDaxSIisrLGbVLOyvETDIVwkbmrX1+txExkdIeC7\n+R1it8smFAmR5fXjdrlIJuJAkkQijtvjBjsGybF/X1njCX92lodIuA/sJNg2yWRy/LclLgZCI5QU\nTHyH2mVAOB7D4858kxOP242djJGIJ8nyTpn/TkTkCl4vREcHSMSctVUHSCYixEYHMdzOfqvmSkJ0\npJ8kMUfj3W4viUSUZPKmaiZcPQaPF292Di7DWeMakgnCoV7spLNulwG/n3hkmEQ8Sm5+jrMYDDdZ\nvlxCcWdfB3+2l9Geftw4a/7jcrshmSCJgdvt8Ot4G3C5DGKRKO7cm/8aZP730R9gmuYLlmXdf8XH\nzwKPM/bDwT9ZlvXIBOaqBy68/PLL7ztSAn7AJquwimxfDkPtjWBHgbGkN5BfjW24GR1oIadiPuWz\nVtF26nVGB/uBKBg22B78RRUkw6NEwkOQDAFeIAa+QrANiITBCOPOqcCNTbBiPnYsRPelk+C1yXJn\nEU3EADe+HB/1NbNIJmN0tnbg9mXhxk1tXSXdfX1svWMLZ89doqmxEa/XoKikmDu3bOHQO8eIu/2U\nVc2l/eIJ7t21haRtUF5aRHtHFwX5ORw5dhK/P0AgEOCBB++ls6OTM2fOMhIaZvnSJcyaVUtjUwvB\n/DxGw2EOvH2Ye3btoKm5mUg4RnFRAYeOnKC4qJDq6grmz5/PqhXLeOfoCYYG+3n2+y9SV1PGogUm\na9euIZlMMhqOkJvjp62tg2BBkPpZdSSTSfbuO4DP56OwIJ9gMEgiaZPl9dBw7iJrVi1/N1m+eKmJ\nwsICgvl5DA8PE4nGKC4qpK29g5xAALfbRWg0TGnJe8dHurp7yAn4b+qIiG3btLW1U1ZW+m6prhuJ\nRqP09vZRXl6GYRh0dHZhGAahUIi62hoGBocYHh7G4/FQMX6Nbdu0t3dguFxjSfr4kZL8vFw6Ojqp\nqCifcNey0dFRurp7qK2pnhJHSgYHB0kkkhQWvtcQ4+DBgzz55JNc+doTkcnxwdefN28O/jw/dizM\ncPd5RzvcAOTmEixaSP+lAxOOyTAMAoESQqF+bHviSXd2wRzyC8vAY9B9dq+jHW6XO0BWQREllQtp\ndrBLffDgQbZuv597797Kc889O+HxANvvuo+sLC8/e/6Hjsb/4Ec/4bvP/ZQv/+kfUFo68SZCiUSC\nV157nTWrVlBU9OHOtjejt6+fRDzuaP3bRTwep7mllbramvd9D29ubmbXrl0wTaqU2ACmaf4F8DvA\nV4BvMpbN/vGtTl6xYCdef4C8gkqi4QGy80oZrWrHl1+EP7eYkb5Wcktn03nxHYorHiE63MOChQv5\nsz/8LMPhBC09SSoK3MyqLuZMS4QLZ0+xfM0dFGcNcP+uTSSTSQ4fOUV5eTE1VRU8+8OfsfudNsoL\n/Xxk+2Lyg8GU/9r9+ZfeorV7lE0rfyUlrVo/8ejHPvzYJx790GNrVi3nuz/czYZ7Po3f7ubB+7d+\n6JqysvfqhbpcLjZv2nDVNdeufn/L6vpZ75WZy83NJXf8/cqK8ncfz8l5/w7Blcn3jRiGQVXVxMot\nZmVlUXHF+uUf6E5XWBCksCD4oXUqK69+09JE17/M7/dTVzt1ktj8/PxMhyAi15EbzKd2yVZioUF2\nLr9vwuMLCwspNu8mp6CCppM/dxRD1cJdFFUvYLDnoqPx2b4s6pbdSTwyTJ7trHmOueWTlFQvofXE\nTx2N7x2K8Ov/8U8w65yX/9390k8cjwV46CP38dBHJv53eJnb7ebuXTd3jPJaigqddRu9nXg8Hupn\n1d34wivHpCkWxyzLemD8z98ef+j18beUSJIk25eHP6+UaGQYOxrBlZWFJzuX0aFuEsk4dmyUovLZ\nLF+2mIVVWZjzZjO3rpyT1kWGwzHC4Sh3blnLsu5ukjsXcP5iC2tWjnU2c7lcrFn1Xgeo4aiXefNM\ngu5+li9z1hnqRi6fH7Jtmzf3vYPX62H96qVpWeuDgrnZXGjvoLIm98YXi4jIpCssDGLgAgw2b765\n86ZX+t3f/V2+/dIl/PnlN2wjfy1xEiRiEZLxCdc9AMaO0pG0sW2bYL6zhM/j9eH2+sjJczb+rbct\nQq4yjljOum3KzDblEu508/vzKCyfR2iwk/DIAAYGLo+fgY5zlNevZKD9DD53mHgyztDgIGdjUeoW\nbuDQ6WYqi4PM82ST5x7AMIx3Gwp8cKfzSkvnlXOhpZd1y5an/XM7daaBhnabaGyY6oo2qh3uoE7E\nPTs2MDo6esNueSIikhmbN67mbF8hBklWrFhx4wEfcO+99/LNf/sT7GSSSMRZt4n8YDllc9dylfLE\nN8Wb7cPry8G245RWOPvelowM0d92hvoaZ6USK4rzuHCyjfL8zB/lk+lnxiXciWScSKiX/NI6wiMD\n+HKL8OUU0hcbBbebYHE1i+uL8OQUsmDhYgh3Y492MLemkC0bVtJw9gLFRbVEo9GbKsmzesUiVk/8\n/zdHqiorSB7ZSxaT9yufsXN5N1dWT0REJt/KZUvpOxkn6ncTDAZvPOADSktL8eUUkltci6/DchRD\nJDxEeLBr7J4nB8qLChgd7iEeCbF64ypHczz+0W2ca+7jtz77gKPxK5aa2L5Syif+JRSZeQm3kYxj\nGG5i0TDJZJRkIowdaiWvqAwjEcfwZjHiCrJq3kK84RbyCkupq8xn64axF3g0nuB//+wE2a4wv/zJ\ne969mSSZTNLb20txcXHGbmQrCObzucfvycjaIiIyNd1/91YOnfkegVK3o0Y4brcbT1aAQF4J2VnO\nqiO5PT4Mlxu3e+K1owE237GBt04NYCfj7NgxsVKql33hP/6ao3GXbVq3nOWLR/QbXXFkxiXcS+ZX\nca69C59h4PFm48vyEAgE8BujGJ5eCgorMOuKWF6Xhctr0jmax8nzzWxZb49XpAgTyC0gMtSBbdvv\nJtfPv7yHlj4X9aVnuffOjTcVy/mLTew/eoG6iiCb10/SNriIiMwo4XCMe+65n9GRXmKx2ISbbQWD\nQcIjvQx0nae2qvzGA64mNkp380lio/2Ohm9av5oDDW+SsBPMnTvXWQwp8MGb9UVu1oxLuKMJF768\nUmKjwxSWz6GysoIcb4Li4kKWLJzD/RtnUVJSTCAQoLOrh9f3nWRRfdG7ifX6NcvIPn6airKV7ysF\nE4nGyckrJRTuuelYTp9rIZldhtXYyub1Kf9URUREKCsrInL6BD533FF3Qq/XS35+AYFgGcuqnFUl\nys7Jp2T2Knqbjjoa/9Lut8grqSc6OsTbb7/NvHm3XpFLZDLNuKrlQ4P9uD3Z5BSU0t9xgbqCKLNL\nDUrzwGuPUFZWSntHF5FIhLLSYh77yFaqyop5fc8h4vE4hmGwctkiKsrL3jfv/XeuozZ3kHu3r2H/\noePsOXDkhnVCVyyejT/ZwwpzZnZqEhGR9KutruSpT+zglz5xz033HbiSYRjEw/30tJ6kIOgs4XYZ\nNi6X23Fb97n1NYwMtDE61El9fb2jOdra2nn51TdIJp1VSrlVtm2z58AR9h86npH1JbNm3A53eUUF\nl7pDFJQvxW8P8qmP38Wp1gTx4Q4++4m7ee3NQ1zsNggcPc8TD99JMpnk5X0WOQVVcODoNVtp7337\nJFZLjPNNPyWRXYnb46X47AXM+XOuGUt1ZTkfq3T46zkREZGb5CTRviwej+MP1lBds5TGZmfJ4tzZ\ndQzYUSrKnDVbqa+ro6qtgOhIv+Pa/1/8y3/FWzCHC03P8Su/+HFHc9yKhrMXON2aIBEPU1nWOmPb\nos9UM26H++4dm4iG+ulpPIIdH+W0dZaenm5cLgPDMEgmkxgu17s/hRuGQdDvYqS/laqyazdWGR6J\nkFdQgtvjwx0fIDnaRUX5tcsFioiITJZLjc309PQ6Guv1esl2RwgPNLFk0bU3ka4nEhpkdKiP0GCf\no/Gb1q9mfoWLFfMKqax0XvI2EY9krCV5eXkpydEu3PEBiovUPGammXE73L/1q5/mu9//GTmz1hMd\n7qahJURhXoxfePyjGIbBzq1raWg4T13dQmAs4X78kZ3Ytn3dFtz37FjDO8dOs2jzHQTznTUGEBGR\n21MikeDcuXMpn3fu3Lm43e7rXnPKOsebx7pIhAf5pUe34fP5JrSGx+Phn/7qP9HU3MraNSsdxRmx\nsymvX0HfpbCj8YsWzuepx2x82dkUFDhLVv/o3z+Bde48mzesczT+VgXz83jqiXszsrZk3oxLuA8f\nOcGs2Qs5cvwVCosr6O/Lwqw12b3nCEXBXDasXcaCBe+/GcMwjBuW+vP5fGxc5+w/IhERub2dO3eO\nz/z+dwgEy2588U0KDXTyrT/9FKZpXvc6l2Fg22Pnlp2WrS0sLMDj8TgeX1WUzamTr1Gam3A0vre3\nj/1HL+HxQF1t1YQrrQCUlpZQWlriaH2RWzXjEu7v//QtjIJFFBb38cjDDzKn1EVZUS4X+nxcauhg\n2eKQGrmIiEjKBYJl5BZWT/q6C+bPITcnQE5OgOzsidfRtm2bbz37KnEjh83Lyli+ZMGE58jOK2HZ\npm2Eu45NeCxAY0sb7pxyQiODDA4OUlx87SOeIlPRjDvDXVVaSOeldxgZ7uedtw8wp66U+XNnYYfa\nKQu6JvyrNhERkamuuqrCcYUR27Y5d6GJhvNNdHU7Owee403QeOYgRsRZp8nlSxZSXxxj1fyCW0q2\nnVZJEblVM26He8H82cy7GCYrp4iBkVGe++lBPv3Idj7z6E4ARkZCvPzzwxQE/WzbePWKJCIiItPJ\nyMgIXq/XUR1u27YJheOEEyHCkZij9UvLK9lYN4dEv7PW8C6Xi22b1jgae9n3fvQa3YMxdqyby4Lr\nVBATSYcZl3Dn52TRcqmB/p5OysqruNDn47986e9YMH8ec2dVUFtewJBdROuFDtavDGvHW0REprXz\nF5v46Z4G3HaUz35i14TPP9u2TVvXEJ68KhqbWx3FEA31s3v3d1k0y1lZwFToHY6TW1RDS3uvEm6Z\ndDPuSElzWwdzlm1mxYa7mVtbQDLUTSCvmKi7mKFoNnU1pbgjHdSVZjk66yYiIjKVDA2PkJ1TSBIv\n8Xh8wuNdLhe1FUHKgy7qq53Vjm7pibJ4/cOMJDLXGv3OdfOpyh3hjvXLMhaDzFwzbof7/rt3sPfQ\n35CIwGMPb6entw/D5SGRTNLe2c3Le6KsWlTF6hWLMh2qiIjILasoK6HtlZ+Sn5vt6EiJy+Xi33/+\nYTo6eljl8HvjLz+2nX/8t908epezZDcUCvGjl/aT7XXzkXs237AU4tXMmzuLeXNnOVpf5FbNuITb\n6/Uya9ZsuocStHQM8PEHd7z73Hee2w3+cpraelm9InMxioiIpMrFplbq5q1gaKCb0dFRcnNzJzxH\nTVUFNVUVjmPYsXUjO7ZudDz+5OlzXOyKkIhG2dzbS2mpGsvJ9DLjjpSEw2F+8vKbvH2qlc6Ojvc9\nt3n1PIqzBtm8dmGGohMREUmtNSsWU5kbYv3iUkfJNoy1dx8eHnYcw/DwCG/sPUS3w26X4UiYd45f\n5MiJBlUakWlpxu1w/+/v/4yeEQOfO8qZhrPve66+rpr6uuvXSN1/6DhHrQ7m1xWyfbOqmIiIyNTm\n8XjYtc15d0XbtvnW914mksxi2+paFn+gOdzNePG1AzS02zRc3Mfnnrh/wuNHR0cpLCwgOmoQizmr\nlPJPz/6US639fPz+9Zhz6x3NIeLUjEu4s7IDGMkw4eE+enL9DAwMEAwGb3p8S0c/gcJq2ro7bnyx\niIjINGfbNr0DI8SScQYGRhzNcf5SC819WQx7BhyNv+vOrRjGHnICJtXVzpoHvX7wPDllJj97bb8S\nbpl0My7hLi/Oo7q6gua2Xgw7i77+fv71hX143JCT7SWWhAd2riWYn3fV8VvWLebw8bMsWnr9Vroi\nIiJTQXtHJz969Qh+LzzxyE5HNxxeuNhC2BVk5XxnzXPWr1yI61Q782srHY0HCAQC+P3OS/XOqsil\nZ6SFZetVFEEm34xLuLt7+ogl3JRW1LFy1Rxa2zvx5lbQ291Bd3+I8prZnGm4wPo1y686vrSkiHt2\nrJ/kqJ1LJBL8fO9hgnm5rFyus+kiIjNNU0sH2fmVjm+aTCaTdPUOQpZBc6vD3+4aY0dbnDp24jQN\nnQbhUDd1Nb0UFU28nvf/+flH6e7po7bGWWlDkVsx4xLusvJS1iybz7kLl/C5wnT1DHDhTAvFBX5W\nLp7DyGg/K5etzXSYKXPoyEkaB3yMNLYzf24tOTmZq4EqIiKTb/WKxYTChymoq3R006RhGMypryXh\nCVJbVeIohq6eAQaGw3T1TbwOOEAwL5e3D/4Yj5HE/5CzjpN+v5/aGr+jsSK3asok3KZpVgNfAnqB\nE5ZlfW388V8D1gNh4O8sy3r7Vta5Y/0K/NlePvPQOl7de5K3G/px+Stx+91s2bDyln4Cn4pqq8s5\nah0lL8tW10wRkRnI7XazfbPzjSSXy8WqxTUMhhKY85zVsTYMF1n+fFxEHI3v7h1g3fotjAwPMjw8\njN+vxFmml6lUFvBXga9alvWbwIOmaV4+ZPYY8CvA08Azt7qI2+1m3eplVFdV4M92UxzMIc/oIWAM\n8vxPd9925YYqyst46om7ePLRux2d2xMRkZnNMAzu2rKKravn3LCS17XkBbwMdDXh8xqOxi9fuoCS\n7CHMqixKSpztsotk0lTazq0Amsbf7wOCjO12/z/A3wPngeu2yDJN8xngCze74Mfu38rIyAjJZJLf\n/e/fxR/0EIn+hE888l7Jong8zuDgoKPzYiIiItNdMpnkez/ZjztQwtDISdauWjLhOcIxm/mLloPd\n5ygGr9fL1o3LcLvdGIazpF0kk6ZSwt0I1AItQBFwuXZQNfBZxhLw6x7csizrGa7YBTdNsx64cK3r\nXS4XeXl5hEIhDDtKJDRAYbD+fdf8yw9eYyiWzap5hWxYs3Rin5GIiMhtwMAmHovg9Ti7D6ggz09r\n3yClRRNvLQ/Q2NzK82+cwUhG+czHthIIBBzNI5IpUynh/ibw56ZpfhZ4FviyaZq/A/QD3wJ8wBdv\ndZH+/gGOnjrL8kXzKCgYq78dCAT409/9FENDQ8yaVfe+62NxCOQWEBoN3+rSIiIi047L5eJTj2yj\nf2CQqkpn7d1XLVvASOgQq5cvcDR+NDRKlj+X6OiI48Y3Ipk0ZRJuy7I6gCev8tQPxt9S4uU3j9Af\ny+GU9TK/9ksff/fxoqJCiooKP3T9R+9aw6XGVlYuV1dJERGZmRKJJIlEwvH4V986wpBdyqt7TvDE\nw9snPH6BOReAbF/lhJrViUwVU+mmyUkRzPWxZ88+mvsMDh89dcPri4sKWb1yCS7Xjb9Ux0818NLu\n/UQizu7CFhERmWqSySRf+ca/8pf/+Ar73z7iaI6SwlwGe1oJ5ngdx7HAnEt9XY3j8SKZNGV2uCfL\nzq1raWjsJjuvjGQydRVJEokEPz90kbziGva+feyWSjCJiIhMFYlEgubuMLll8zh+6jzr16yY8Byb\n169g3arYbVd6V+Rmzbh/+S6Xi194aAu9ff3Mqa+78YAJzFsa9DIw0MKsxWobKyIitwePx8OWVbNo\n7+1n17ZNjufxep3vbotMdzMu4QYoCOZTEMxP6ZyGYfDoR3akdE4REZFMMwyDz3zy/htfKCLXNCMT\n7psVjUZ59vk3SSSTfOy+TSpDJCIiMkOdss6z/9glZlXks+MOZ+3lZeaacTdNAti2TWNTK8+98BbH\nTzVc87rWtnZGkrnEPMVcbGyexAhFRERuHxcbW/i3F97kxOmzmQ7FsfONHXhzK2lsH8x0KDINzbiE\n+1JjC9/855f4H//4PCNGIYdPtQBwpuECb+1/h2Qy+e61s+pqmVNmUBOMstCcl6mQRUREMurcffY0\n1gAAIABJREFU+Uv8fO9hx6UBn33+Td44McB3f/RWiiObPOtXLiDo7mP98lmZDkWmoRl3pKS5rRN/\nsIqAv4fkSCsLZlcQiUR47cB5/MEy3IePs2HNcmDs3NqdW1RtREREZq5EIsFLey0CBZVw4ChbNq6a\n8Bx+nwejfxh/jjsNEU6O0pIiHrzL+U2jMrPNuIR7w5plxPe9w6r71rLInMOPX9rDs8//HFcyxOhg\nB+VLF2Y6RBERkSnD5XLR3HSJ/jOXMD+yztEcTzy8k0PHGlg4P3XVwUSmkxmXcHs8HrbfMbZr3dfX\nR0ufTW5+JcvrYqxduRS3e/r+9C0iIpJqyWSSyppZLCisIhwLO5qjsLCAXducJesit4MZd4b7SgUF\nBcyryCLf1ceqZYuUbIuIiHyA2+1m+bxScow+Vi41Mx2OyLQ043a4r2QYBndtX5/pMERERKa0LRtW\nZjoEkWltRu9wi4iIiIikmxJuEREREZE0UsItIiIiIpJGSrhFRERERNJoRt40+dzzr9MzGGX7uvnM\nmzuxjlEH3zlBNBJj0/oVGIaRpghFRERuH6+8vo+fvH6Mtctm8cmH7850OCKTbkbucHcORPAVVHOp\npXNC49ra2jncMEhDB5w605Cm6ERERG4vu/edwl24kP1HGzMdikhGzMiEe9vauZT7h9i8bumExhUW\nFuBNDhEPdVJVWfG+52zbxrbtVIYpIiJyW9i6fgHJwbOsXVqT0Tj0fVoyZUYeKVk4fw4L58+Z8Dif\nz8dnH7/nQ4+HQiH+6ftvYAOPPbCJgmB+CqIUERG5Pdy1fRN3bd+U0Ri+96PX6B6MsX3dXEc5gMit\nmJE73JedbjjPqTNnb3me3r4Bkt4g7kApHR1dKYhMRERk6mhqaePg4eMkk8lMh+JYz1CMnMJqWtt7\nMx2KzEAzNuFuaW3njXc6+Pnxbi41Nt/SXDXVlayel8/iGi+mfmqedBcbW3jljYOMjo5mOhQRkdtO\nMpnk+d3HONmSZN/bxxzN0d8/wMuvH6CtfWL3TqXSro0LqM0Pccf6ZRmLQWauKXOkxDTNauBLQC9w\nwrKsr40//svAJiAJvGVZ1j+kYr2cnADJ6CAYLvJyc255vjUrF6cgKnHi1X2nycqr4vW9R7n3zg2Z\nDkdE5LZiGAY+t01oqIcCs87RHLv3HmfEKKZz3yl+4eGyFEd4c+bOrmPubGfxi9yqKZNwA78KfNWy\nrL2maf7YNM1vWJYVB1qBy9vG/5KqxQqC+Xz2sR0AZGdnp2payYDivCzaeptZvrI+06GIiNx2DMPg\nyUd3EY1GCQQCjuaoLA1y6Ewz86p1j5PMTFMp4a4Amsbf7wPyGdvt/i3gUcaOv/w98OqtLLL/8Am+\n95P9BANedmxczOBIlC3rl5Gbgl1uyYyH7tuKbduqiy4ikiZ/+63naOka5PNP3M2sutoJj/f5vPiy\nXPh8WWmITmTqm0pnuBuBy6/iImBg/H03MAwMcoMfEEzTfMY0TfvyG3Dhg9ecu9RB1FtB72gWz79+\ngp5okL2HTqbus5CMULItIpIe0WiUtxv6iAXm8cOfvuVojgtNPWTlVdHYNnDji0VuQ1Mp4f4m8Fum\naX4deBb4smmaXuD/Bf7X+PN/fr0JLMt6xrIs4/IbMPuD12xZt4javEGW1AVYs7iWyGAL9dWZOU8m\nIiIy1Xm9XhZUBUj2N7BzyxpHc2xYNZ+gp48NKz70bVlkRkj5kRLTNI8zliB/y7Ks9psdZ1lWB/Dk\nVZ56afwtJWqrK/kPv/Z4qqabchKJBD988S0i8QQP7lynozIiInJLDMNg+ZK59A1GKC4KOpqjoryU\nB8tLUxyZyPSRjh3ujwB+4FXTNJ83TfMT4zvVMgn6+vpoH3YT85Zx2jqf6XBERGSaSyQSnG8bJpFd\nxumzas0u4kTKE27Lsi5alvXHlmUtAv6WsWMg7aZpfsU0zeJUryfvV1xczPwKLwWePlYsW5jpcERE\nZJpzu92smFdKLr2sXGpmOhyRaSkdR0rygMeAzwDVwP9grJzfvcCLwNpUrynvMQyDXdvWZToMERG5\njWxevyLTIYhMa+k4UnIe2A48Ayy0LOtPLMs6B3wduLWWjtPYufON/OTVffT36w5tERGZXo6esPjZ\n7v1EIpFMhyIyLaUj4X7KsqzPWpb1umVZNoBpmh+3LCtpWdYjaVhvWnjz8Dl6o0HePHgi06GIiIjc\ntEQiwVtHGukczWPfoeOZDkdkWkrZkRLTNJ8AsoE/Mk2zADAAG/ACf8BYqb8Zq6okh0vtLSxbpray\nIiIyfbhcLkqDHgYGWqhfsjjT4YhMS6k8w50PbB7/884rHo8zlnDPaHdtX5/pEERERCbMMAwefXBH\npsMQmdZSlnBblvUN4Bumae6yLOvlVM0rIiIiIjKdpfJIyd9alvV54P82TfP//sDTtmVZO1O1loiI\niIjIdJHKIyVfH//zvzF2jORKdgrXERERERGZNlJ5pOTt8Xf/zLKsVamaV0RERERkOktHWcB20zS3\nmaaZnYa5RURERESmlZR3mmSsk+RrAKb5bgtY27IsdxrWEhERERGZ0lKecFuWVZrqOUVEREREpquU\nJ9ymaX6Bq9wkaVnWH6d6LRERERGRqS4dZ7iNK96ygYeB8jSsM6UdPX6G77/4Fh2dXZkORURERDIs\nkUjwwst7ePHVvSSTyUyHI5MsHUdKnrnyY9M0/xj4WarXmerePtVCdn4Vbx87ywO7dMpGRERkJjvT\ncI6OUIB4LEZjUzP1s+oyHZJMonTscH9QHlA7CetMKfPqCmG0gwVzqjMdioiIiGTYnPo6cuw+CrzD\nVFdVZjocmWTpOMN9Yfxdm7GEvhD4s1SvM5la2jrYe7iBusoi1q1afFNjtm5QKXIRERGA8xebOHTi\nImZ9GcuXLMh0OBnh8/n4xEM7Mh2GZEg6drjvA/4SOAmcB/4z8CdpWGfSHD11kYi7hBPn2jMdioiI\nyLRz7EwTUU8px892ZDoUkYxIR8L9h8BG4G+AvwC2A19OwzopFYvFOHbiNNFo9EPPLZlfiyfaycJ6\nncUWEZHp5+z5i3R2dWds/cXzqnFHO1k0uyxjMYhkUjoa36wHFlmWZQOYpvkD4EQa1kmpF17dT38s\nn4ZLe/n4A9ve91xdbRV1tVUZikxERMS5k6cb2HOqj9joWT778S34fL5Jj2H+3FnMnztr0tcVmSrS\nscPdDMy54uMyoDUN66SUx2UQjYTxuCfjPlIREZHJ4fV4iEcjkExgGEamwxGZkdKxww1wxDTNl4A4\ncCfQYprmC4y1eH/gagNM06wGvgT0Aicsy/ra+ON/wVilkxqgzbKsX0xHwPft3EhTcys11bpzWERE\nbh/z580mmJ+L3+8nOzs70+GIzEjpSLj/6wc+/qsr3v9QB8or/CrwVcuy9pqm+WPTNL9hWVbcsqzf\nNk3TC/wv4P9IdbCXuVwuZtXVpGt6ERGRjCkr0z1IIpmUjsY3rzkcWgE0jb/fB+QzttsN8DngO5Zl\nDd9adCIiIiIikytdR0qcaGSsQU4LUAQMXPHcA5ZlPXyjCUzTfAb4QlqiExERERFxYCol3N8E/tw0\nzc8CzwJfNk3zd4BcIHwzE4y3lX/m8semadYDF668xrZt3tp/hEg0zo47VuNy6SZJERGR6zly7Azt\n3f1s37QiI1VORKa7KZNwW5bVATx5laf6gMdTtU5HZxfHG8P4/QGOnTzDiqWLUjW1iIjIbSeRSLDn\nWBN5RdXsP3yCbZvWZDokkWlnxm3vFhcVkucZgXAn9bpJUkRE5LpcLhcVBVlEh1qpr1UlLxEnpswO\n92Txer186mO7Mh2GiIjItGAYBo98oCGciEzMjNvhTodDR07xved/TktbR6ZDERERue1Eo1G+/8LP\nefHVvdj29SoMi0xNSrhT4GhDOxF3CUdOns90KCIiIredo8fPMEQRF7tsuru7Mx2OyITNyIQ7HA7z\nvedf5wcv/pxEInHL8y2YVYw70sni+XUpiE5EROT20nDuEv/yg9c5fPS0o/FLFs3Dl+iiqiBJcXFx\niqMTSb8Zd4Yb4LR1joa2OJ1dvZQVHWHjutW3NN+mdcvZlKLYREREbjcnz7aQyC7j1IVOVi1fOOHx\nfr+fxz6y/ZZieGPfYdq7hti2fhHl6rwpk2xG7nAvWWTS23YWnxe6+m6qxLeIiIg4tGxBLdmJHpbO\nq8hYDKfO9xLzlnH89KWMxSAz14zc4fZ6vTz+0S1cbO5lxaL6TIcjIiJyW5tTX8uc+tqMxrB0bglt\nXV0sW7Q4o3HIzDQjE26ANSsWs2ZFpqMQERGRybB5vb7pS+bMyCMlN3LuQiO73zpILBbLdCgiIiIz\nnm3b7D1wlCPHnN10KZJpSriv4pV9Fk0DAX6+70imQxEREZnxjp08w5l2mz0nuujt7c10OCITpoT7\nKoIBFyN9LVSVF2U6FBERkRmvsryU6FAHWYTIzc3NdDgiEzZjz3Bfzycf2kkymcTl0s8jk6WppY2B\ngUGWLDIxDCPT4YiIyBRSWlLMr/zC3QAZ+x4Rj8c5dPQk5pxZFBQEMxKDTF/KKK9ByfbkCYfDPL/7\nJAcaRjh6XOfzRETkwwzDyOiGzMtvHMRqd/OjV97OWAwyfc34rHL/oRP8bPd+4vF4pkOZsdxuN4Yd\nIxIaJDcnkOlwRERkikkkEry0ez9v7nsnYzHkBnwMD/biy5rxqZM4MKOPlAwPD3PI6iE3v5iD75xg\n41qVDMoEr9fLLz12J9FolLy8vEyHIyIiU8zR46dpGwkQ6hhkwdxuSkpKJj2GOzasZMmCfn2fEkdm\n9I9pgUCAokCcRKiNuRkuyD/TZWdn6z8xERG5qtn1tdihdvKzwhQUFGQsjoKCAtxud8bWl+lrRu9w\nu1wuPvnQzkyHISIiItdREMznFx+7K9NhiDg2o3e4RURERETSTQm3iIiIiEgaKeEWEREREUkjJdwi\nIiIiImmkhFtEREREJI2mTJUS0zSrgS8BvcAJy7K+Nv74fcDDgBt43rKs5zIXpYiIiIjIxEylHe5f\nBb5qWdZvAg+apnm50OXngW7GfjhQP1URERERmVamUsJdATSNv98HBMffXwX8N+CLwH9N1+Kjo6O8\n8sZBzl9suvHFIiIiMmls2+at/e/wzrHTmQ5FxJEpc6QEaARqgRagCBgYf/wSEGHsqMl1mab5DPAF\nJ4u/se8oXeF8Lu63mKOukyIiIlPGsROnaegwCIe6qavupaioKNMhiUzIVNrh/ibwW6Zpfh14Fviy\naZpe4K+A/wV8Hfjv15vAsqxnLMsyLr8Bs2928aryIoZ7mynOz3L+GYiIiEjKVVaUERvpIIsRcnNz\nMx2OyIRNmR1uy7I6gCev8tT3xt/Saumi+SxZOA/DMNK9lIiIiExAaUkxTz1+N4C+T8u0NGUS7qlA\nL2IREZGpSd+jZTqbSkdKRERERERuO0q4r9DV3UMoFMp0GCIiIrcV27ZpbWsnHo9nOhSRjNCRknGn\nrXPsPtyKER/mc4/fjcejL42IiEgqvPz6AS50JSkOWHz8gW2ZDkdk0mmHe1wkEiXLF8A2PCSTyUyH\nIyIictuIxuL4ArnE4olMhyKSEdrGHbd86UKys85RWDiLrCyVBhQREUmVe3as5/jJM5jzNmQ6FJGM\nUMI9zjAMFi6Yl+kwREREbjsej4eVy5dkOgyRjNGREhERERGRNNIOdwrYts3zL73F8GiMe7evpiCY\nn+mQREREbhuhUIgfvbSPbK+Hj9yzGbfbnemQRCZEO9wpMDIyQmN3nHhWOSdPn890OCIiIreVk6fP\nEfWW0z7kobe3N9PhiEzYjE24bdtO2Vw5OTksqssllx5WLV+QsnlFRERuF7fyfXfFsoUE3X3MLXdT\nUlKSwqhEJseMPFLyxr7DnDzfw6L6IrZtWn3L8xmGwY471qQgMhERkdvP/kPHOXS6jXnVQe7avn7C\n471eLx+95440RCYyOWbkDndn9zB5RTV09Azf0jzhcJjX33qb5tb2FEUmIiJy+2nvGiS/uJau/lFH\n423bZv+hYxw/1ZDiyEQmx4xMuLdtWEK5f5jtGxbf0jyv7z1C02CAl948kaLIREREbj9b1y+m3D/E\ntvULHY0/fsriZHOCN4+209fXl+LoRNJvRh4pKS0pYte2olufpyjIuePNVBSoUY6IiMi1FBYWsGvb\nOsfjy0qKiR45jIckgUAghZGJTI4ZmXCnyqrlC1m2eJ7KE4mIiKRReVkJT31yJ4Zh4HLNyF/OyzSn\nf7W3wLZtXt/zDj9+6S1isVimwxEREUmLvQeP8YMX32Ro6NbufboVbrdbybZMW/qXewt6enqw2qL0\nxws5cux0psMRERFJuUQiwWGrgyG7iEPHzmQ6HJFpSQn3LSgsLKQ8N4431slCc06mwxEREUk5t9vN\n7IocjHAHC+fVZTockWlJZ7hvgdvt5mMPbMt0GCIiIml1385NmQ5BZFrTDvcUFY1G6erqznQYIiIi\nt8y2bdrbO0gkEpkORSQjtMOdYbZt88beQ9hJm22b12AYBgD//P3XGLVzWDWvnfWrl2Y4ShEREee+\n98OXOWgNMrvMxa/94iOZDkdk0mmHO8MuXGykoc3mfLcLq+H8u48nkgZ+fx6RaDSD0YmIiNy6E1YT\ndnYxDRc7Mx2KSEZMmR1u0zSrgS8BvcAJy7K+Nv74LwG/ALQBr1qW9Q+ZizL1KivKyLYbIA61Ne91\n4HrknnU0tbazbPGyDEYnIiJy6z6yaz0/fvUAG7cuyXQoIhkxZRJu4FeBr1qWtdc0zR+bpvk3lmUl\ngK1AM+AG9mY0wjTw+/18+tG7PvR4YWEBhYUFGYhIREQktdasWsKaVUq2ZeaaSgl3BdA0/n4fEGRs\nt/vvgP1AAfBNYMKHv44dO0Z7e3uKwhSRGzl9eqwu/WS99pqamm58kUgGtbW1MdB6nMhAS8rmDI/0\nceJEJYODg+97fLJffyIypqur65rPTaWEuxGoBVqAImBg/PE7gD3A0I0mME3zGeALVz62bt06nn76\n6ZQGKiI3pteeyIeNpHi+3/u9l6/6uF5/IhkzANgffNDIQCBXZZpmOfDnjCXWB4HlwO8ATwLbGIv1\na5ZlHZjAnPXAhW9/+9tUVFSkPGYRubpjx47x9NNPo9eeyOTT608kM9rb23nyyScBZluWdfHK56bM\nDrdlWR2MJdcf9Pfjb45VVFRQU1NzK1OI3JKD75wgHkuwYe2yd0s/3s4u/xpbrz2R9DllnaOzu487\n1q/E43nv27lefyJTj8oCiqRZS2sbh88OcaotwRnrXKbDEZHbQCwWY/fBizT1+9l38FimwxGRG5gy\nO9wit6uiwgLciaMkQgYVFXMzHY6I3AY8Hg/5viTDA61UL12ckRgSiQTnzqV+E2Hu3Lm43e6UzyuS\nSUq4RdLM7/fzy5+8B2BGHCcRkfQzDINPffxubNvO2P8r586d4zO//x0CwbKUzRka6ORbf/opTNNM\n2ZwiU4ESbpFJoERbRNIh0/+3BIJl5BZWZzQGkelAZ7hFRERERNJICbeIiIiISBop4RYRERERSSMl\n3CIiIiIiaaSEW0REREQkjaZslRLTNOcB37Usa7Vpmn8JRIEa4Pcsy7qQ2ehERERERG7OlNzhNk2z\nHHgKGDZNMwf4iWVZvwP8C3B3RoMTEREREZmAKbnDbVlWB/D7pmm+YFnWCPDj8R3vx4HPZTY6ERER\nEZGbNyUT7g8yTfMRYCfwWcuyRq9z3TPAFyYrLhERERGRG5mSR0quYJumORf4BlAAfMM0zYeudbFl\nWc9YlmVcfgNmT1agIiIiIiJXM6V3uC3LemD83bKMBiIiIiIi4tBU3+EWEREREZnWlHCLiIiIiKSR\nEm4RERERkTRSwi0iIiIikkZKuEVERERE0kgJt4iIiIhIGinhFhERERFJIyXcIiIiIiJppIRbRERE\nRCSNlHCLiIiIiKSREm4RERERkTRSwi0iIiIikkZKuEVERERE0kgJt4iIiIhIGinhFhERERFJIyXc\nIiIiIiJp5Ml0AFdjmuY84LuWZa02TfN3gVlAEPj3lmV1ZzY6EREREZGbN+V2uE3TLAeeAoZN08wG\ntlqW9e+A/wl8PqPBiYiIiIhM0JTb4bYsqwP4fdM0XwCKgM7xp1qAquuNNU3zGeALaQ1QRERERGQC\nplzC/QGdQPH4+zWMJd3XZFnWM8Azlz82TbMeuJCe0EREREREbmzKHSm5gm1ZVgJ41TTNvwZ+Bfjr\nDMckIiIiIjIhU3aH27KsB8b//ItMxyIiIiIi4tRU3uEWEREREZn2lHCLiIiIiKSREm4RERERkTRS\nwi0iIiIikkZKuEVERERE0kgJt4iIiIhIGqW1LKBpmlWWZbWaprkNWAb8vWVZI+lcU25sZCTEv/1k\nD4YBjz5wBz6fL9MhiYjIBNi2zXMvvMFQKM5ddyyhqrL8utf39w+w78ChlMbQ3NyU0vlEbmdpS7hN\n0/w6kBxvWvNt4KfATuDRdK0pN+dSUzNxbzHxWJSOzm5m1dVkOiQREZmAeDxOe3+M/KIqzl5svmHC\n/fxPX+XvXx3EcLlTFsNQTyvZgfyUzSdyO0vnDvd6YA3wBeDvLMv6gmmaB9O4ntykheY82rsO4fG4\nqautznQ4IiIyQV6vl/VLKunqGWD9qpU3N8aXiyuFCbc3K5CyuURud+lMuF3jbw8Dv26aZg6gV+cU\n4HK52Ll1babDEBGRW7BmxeJMhyAiNymdN03+A9AGXLIsax9wAPhGGtcTEREREZly0rnDPQpUWZYV\nH/94m2VZ3WlcT0RERERkyknnDve/uyLZRsm2iIiIiMxE6dzhbjJN8xVgP2O73QC2ZVl/nMY1RURE\nRESmlHQm3HvH/7TH/zTSuJaIiIiIyJSUtoTbsqxnTNMsAzaMr/OWZVkd6VpPRERERGQqSmfjm3uB\nvwP2Mba7/TemaT5lWdYPJzjPCuAPgSbGjqT8X6mK0bZtDMO4qceADz0uIiKSSYlEArc7dbW1RSQ9\n0nmk5E+ALZZlXQAwTXMO8G/AhBJuoAuoBpLA0VQF9+zzu+kaiLF19WwWL5gLwOt7DnHyQi9L5hSz\ndeMqAGKxGN9+9hViSYOHdq2mvKwkVSGIiIg49snP/xdGk34+efdiPvPEw5kOR0SuI51VSjyXk20A\ny7LO4+wc968D/9myrCeAbaZp+q914f/P3n3Hx3Feh97/bccW9F4JEuBDAgQ7RYpUoahmWbJlx7Fj\nx3LcY6e8yY2d994kvo6je528SXxvcuPYSRzH9k3iGid2LFmSJZm0JMoSOwgWgOAQvbfFYnud3fcP\ngDQLWABisQBxvp8PPljszjznzOzs7MHMM/MopZ5WSqUu/gDd15vW44vjyq9kaMR96blRd4CcwipG\n3YFLzwUCAaI4sLnKGBoZm0f6QgghxMLSdZ2QbqewejMtbZ2ZTkcIcRPpvkvJ7wFfZ7rQ/hjQO492\nsoDJmcd+4LrnzjRNexp4+uLfSqlarlN0P7BL0Tswxp67Nv3iububON3WyabGDZeey8/PZ1djMcFQ\nhC0bG+aRvhBCCLGwTCYT735oHafOneNz//XjmU5HCHET6Sy4PwZ8ien+10bgZ8An5tHOl4AvKKUm\ngMOapgVuNsOtqFtdQ93qmiueKy4q4KH7C66ZdsvG9QsRUgghhFgwH37qXZlOQQhxi9JZcP+Opmm/\ncruNaJrWD/zqAuQjhBBCCCHEoktnH+4nlVLpbF8IIYQQQoglL51HuN1Au1KqmStHmvxoGmMKIYQQ\nQgixpKSz4P5nfnFXktTM49R1pxZCCCGEEOIOlM6C+wOapj2SxvaFEEIIIYRY8tLZxzpLKVVz88mE\nEEIIIYS4c6XzCHcx0KOUGuPKPtxr0hjzlqRSKUKhEFarlWAwiMvlwmy+dlX4fD7MZjMOhyMDWS6O\ncDiM1WqVoYGFEGKZ6evrIxAI0NjYmOlUhBA3kc6C+7GZ3xf7by8ZBw4e4/yAn7Mnj5OwFbKuKpv/\n8vF3YbFYLk1ztrWdv/32QdCjfP7T76W0tCSDGafH+Qtd/OxYDw5TnA/+yqMYDEvqbRJCCHEdL778\nGp/5mx9gsbp4+90VfPYP/59MpySEuIG0dSnRNK0HuIfpwW4mgPtnnss4bzCCI7sIXziBK7+aUMxA\nIpG4Yprh0XHsuRUYs/KYmPRkKNP0mvT4ceYUEUmaSCaTmU5HCCHELeruG8CZX0VO8Wo6+oYynY4Q\n4ibSVnArpf4SeBx4F2ABPqKU+ut0xZuLR+/bhipN8l8/+U521cZ56u13Y7fbr5jm4X33srfRyi/d\nv4oNDesylGl67dqxkcZKI4/d2yBdSoQQYhn55Ed/lYaiKXJjZ/nq3zyd6XSEEDeRzi4lbwG2ASc0\nTfMopR4BzgCfTmPMW5Kd7eKeXVsB2LJpw6zTGAwG3vX2RxczrUVnNBrZsbUp02kIIYSYI6PRyFe/\n+P9lOg0hxC1K511K9Kv+ts3ynBBCCCGEEHe0dBbc/w58DyhQSn0KeB34bhrjCSGEEEIIseSk86LJ\nvwC+wXThXQ18TtO0PwNQSpWlK64QQgghhBBLSTr7cKNp2ovAi7O89BNgazpjCyGEEEIIsRSks0uJ\nEEIIIYQQK54U3EIIIYQQQqTRki+4lVK1SqmvK6X+Win1xwvVbjwe5+X9P6P5ZAtDwyP09w9w/MRJ\nQqEQk5MeOjq7iEajAIRCIc62niMajTLhnsTv9zM8PEIsFiMYDDI+4b6iba/Xx/j4BENDw6RSqVvO\nKZVKMTQ0TDwev+5rVw/QcyvLOdc8Lppt2cTtmZz04Pf7M52GWKHGxicIhUKZTkPM8Pv9TN7GwGr/\n/b9/lvUbti9gRkKIdElrH+4F8vtAJ7AWeHahGv2TL3yNk71Jpka7WVVVhNmWSygSpTT7FUorahgc\n9bFtXQGf+MDb+YsvfZfxaDYFpoOsqm+gu+sC1bUKhzFINA5YnOzdVsn6tWuY9Ezxbz9LboQbAAAg\nAElEQVQ5Ttv5PlbVVLBrwwj33X1r3dVfP9RM+1CcXGs7731y3xWvHTh4jO7xJAV2jV9+Yu8tL+cP\nf/JzfDEHqmKIvXtufcccj8f59o9eJ2V2sm9bFWrt6lueV8yub2CIF16/AMkYH3jHblwuV6ZTEivI\n6dbzHG6dwJDw87H3PYrRuOSPt9zR/H4/337mEAazjcfvXUt1VcWc5h8YGOCZQ2OUbPs1KhseZPDc\nz9KUqRBiISyHgrsO+BrQCrwMvHq9CZVSTwN/ciuNRuM6VnsuRrOVFEYwWrBYDST0IAajBavNSSIx\nPdx5TE9id+YS9Y9jsthIJsFis6NHA2A0Y7E5iEZjwHShajJZMZptmM0WotFrj1ZfTyyuk2V3kohd\newQqFk+Q5cghkQjfcnsACT2Fze4gFp/bfMlkkpTRjNVmJzrLEXcxd7FYHLPVhh5PkkjILenF4orH\nElhsduKJ4LzOeImFlUjoGMxWzFYbsdj89rEmSxY2Rz5Gg7yfQix1aSu4lVIva5p2vaEaf3cOTY0A\nfk3TEkqpG56L1zTtaeDpy3KoBbpnm/bp3/8Q//TP36V45zaaNigSyRQdnT08cO/dhMJhevsG2Lq5\nCZPJxKd//Zc4fPwk9+3+AG63h8furmHS46Wmeh2RSBSv14dauwaA0pJiHrsnzp6mUvRUko2Ntz4s\n/L57t3P2nEZd7a5rXnv0gZ2cbTuPqr/2tRt5xyO76Ozpo6lhbqcdbTYb73xoyxXLJm5P/ZpVGACb\nzUpeXm6m0xErzLYtjTgcHRQX1WIymTKdzoqXn5/HE/etIxqLUbd61Zznr6qqwhG+QMtLBzl3fH8a\nMhRCLKR0HuG2K6VqNE3ru/oFTdNen0M7XwD+XCnlY3ognQWRnZ3Np3/nE1c8d9e2zZce162pvfS4\npLiIJ9/6CAD5M4VSZUU5ALk52ZSWFF3RTk1VBVTNPSeTycTmpoZZXzObzdcdhv5GXC7nddu8mdKS\nomuWTdyeujVz/2IVYiEYDAYa1q3NdBriMlWV5bc1/6GDP1mgTIQQ6ZbOgrsY6FFKjQEX+zOkNE2b\n0+FSTdPagfcudHJCCCGEEEIshnQW3I/N/L7YucyQxlhCCCGEEEIsSeksuIeA3wYeBBLAC0xf/CiE\nEEIIIcSKkc6C+2tAFvBVwAR8EGgC/ksaYwohhBBCCLGkpLPg3gk0aJqWAlBKPcv0rf2EEEIIIYRY\nMdI58sEAcPkFkiVMdzMRQgghhBBixUj3wDenlFL7me7DvQ8YVEr9hOm7lTye5tizSqVSvHnkBCMj\noxw7foq+oSE2N63n2ef3s2VTA/fcvZOe3j6aT7Xyy08+TooUE+4pysuL2XvPLkpKiplwT3Li5Gny\nc3OpqamirLT4hjH9/gA9fQOo+tW0X+iiQdVhtVoBmHBP0nKqlcaGtVSUl9HZ3YvDnkVebg5aZw9N\nDWrJ3zN3yutjcGiYxvUKg2F+18aOjk3g8/lZW3/tiJbDI6OEwpF53atWiMXk9fkZGBy6rc+CuH26\nrnP2nEb96lU4nY5Mp3NbOjp7cLmcDAwOEw5HuO+enZdeyy6sw5hl5dBPf0BjY2MGsxRC3Ew6C+4/\nnfl98S4lX555bLjsuUV3+NgpvvH8ebq7uvEFgiQTWbSP9WF0NXK028ipwTPoGEnqlXzh/x4gy+HC\n5iwkx+HlzVP9/K/P/jp//80X6RyOkEol2dU0xkffcz92u/26MZ95+Qi6tZjn9v8blfVb6ew7zLse\nv59EIsFXvvkCo2EXb5wZ5u33r+dkd5h4NIjdGMXgqmJo9ChvfWj3Iq6hufvhi4cx2UvweFu49xaH\nsb9cJBLhP/e3YLXnEo3HaWpQl17z+/0880orFpuTpJ6ctSAXYqn4zxcPY7CX4J5q5v7dcxtsSiyc\n/QePMRp00NZxhF99575MpzNvbec7ONQ2yXDvBfrcCcxmGyajkT27d/Cv3/wWq3c8QXHtVh58928z\n0vZKptMVQtxA2rqUaJr2KuADkvyi0K7TNO1VTdNeS1fcm7FazSRjYfREDD0emfkdRY9HSCZi6Iko\nejSEHguT1GOk9BiJWIikHsdiNGAwGDCbIJmIkkrEMBiSGI03Xo0GA8SiYaxWM9FIGLPJOPO8AZPJ\nQDwShFSSLHsWejxKSk9gMZuIhkNYzEv76DaA0QDRWASr1TK/+Y1GDCmdeCyC1XxlGyaTCZIJ9HgU\nsyXdJ2SEuD1mk4FYLIzFJNtqJlnM0/taUzo7TS4Cq9lMIhbFbDKgx6PE4xFsWdNnR60WC8lEhEQk\nAMlEhjMVQtxMOod2/1dgN1AItAFbgB8D30hXzFuxfUsTf5zjxOP10XnhAr0DwzQ1Kn707IvcvXMj\njY0NjI6M0nKmjbc8+h50XcfvD1CQl0tj43pMJhO/97F30tXdi8vpoKioEJvNdsOY73nbfYxPuCkr\n3cHQ8AgV5WXAdDH5ux99J319A1RWVpCTk01JcRFWiwWHw87wyChVlRWLsVpuy/uevJ9Jz9Sl5Zor\nq9XKU++4h1AoTEnJld1zHA4H73/7bmLxOEWFBQuRrhBp85633ceEe3LenwWxMPbdux01NExpyY27\n+y119XW15Oa4sNu34nZ7iOsJ6tdMn+V73/veyx9+9n/Qc+QZAoHRDGcqhLiZdB6GuR9QwJeAv515\n7nNpjHfL1szssLZv/cVQ7m997C1XTPPkk09cd36Hw0HThlsfLt1qtV4aCr66qvKK15xOJw0N6y79\nfXlRefW0S1VWVtZtFxgulwuXyzXrazk52bfVthCLxWazXfqsi8wxGAzL4mDFrSguLgKYdf/Y09G2\n2OkIIeYpnSfchjRNiwHngE2aprUCctWbEEIIIYRYUdJ5hHtQKfVHwAHgC0opgLw0xhNCCCGEEGLJ\nSecR7o8B3ZqmHQV+ALwP+M00xhNCCCGEEGLJSeddSnzAEaXUE8BXgE9pmib3LRJCCCGEECtK2gpu\npdT7gGeZvmCyEHhDKfVr6YonhBBCCCHEUpTOLiV/ANwD+DRNGwG2AX8038aUUt9WSr13oZITQggh\nhBBiMaTzokld0zTfzMWSaJo2rJTS59OQUurTgJ8FGKHyzUOHefevfZpYPIaeTGAyGEgZTZDQsTod\n1JQVMjHpx2KzkuOwU1VTjnvSD8k4+QX5hEJhgpEYu7dtxGa1YsuyEQhFuNDZgc2axcN79+CenGLP\n7h1UlBbyys+PU1qcRzyRpL62ioQOZaWF9A2OsG1TAyfPtFNdUUp5WQnHTpxhcGSMLJuJk6faCYRC\nbFhXz3t+6a14fX6ee/E19t6znTVXDXE+Nu6mq3eA7ZsbmfRM0d03xI4tGzCbb+3tHRt309XTz/Yt\nGxgdG+cn+9/kHY8/QMnM7ahuxOvz03quk81N6pohlFOpFC2nz5Gbm82a2upbf5OWkakpL23nu9m6\nad0NRxsV4moezxTtF3rYumk9WVlZmU4nI0ZGx+jpH+aurU3Tg1wtcb39g5xt0yguKmTH1qbrDno2\nNDxK/9AoO7ZsuO5yRSIRvvMfz7Fz20aaGtfNOg3Aea2Db37vxwwMDzMyNkGuw0FH7yC5uVmcbmnF\nZHViCvcyNDS0IMsohEiPdBbcZ5VSvwNYlVJbgN8CWubaiFLqScADHGJ6tMrb8pFP/RW1d70Dg8FI\n2D+BxeLAZLPjGTpPVeNeJvrOYDf7KavfyVj3SU5dGKFu25MMdxwikVyFwWEgYQrx/BsaFXV3MTV8\nnoLK9YxP2imu2cSXv/lTiqvXMxbvZbTvBVylGxk82ExVZQUHjvayc+dOnv3pC9RtuIuj//JD8iub\nOHPhNNvWV/KjV9pwh4z0d7WRyiojEkpxpq8ds9XG+a4R/KYaWr+9ny985iNX7Oh/8loLJkcZgTdP\nMjDmw+wsJ/BGMw/v3XlL6+Slg6cwOMrw/vwErxxqJZVdz99+40f86R98/KbzvvxaMzFLCaMHT/BL\nb73vitdOnjrHmb4YkaDGh8qK78ii4sWDJ9GtpUwcbObJt9yT6XTEMvLSwRZ0WynjB0/w5KMrc9t5\n4dXTWLLLCB86yb57d2Q6nRtKJBK88NpZzlwYo6pKJ6GfZs/OLbNO+5PXTmPNKSdy+CR775l9uf7+\nGz9gJF5B83df5cufn73gTiQSfP6L/07PaBDdmI3VVcXoWA8F657glX/+c+7+5Y+T5Syk/Y1vL9hy\nCiHSI51dSlxABRBmenRJH9NF91y9H9gJfAj4mFLqusMNKqWeVkqlLv4A3VdPYzIkCHnHCHlHifjd\nhPzjhLyjxMM+vGNdhP0TxKJBfGM9RENT6LEYvokeokEvIe8wwalRIoFJkvEIoalBkrEAEe8YsaAX\n/0QPRmOCWHACYzJEbnYWId8ohkSAWNiLzRQjHHCTn2Mn6B2nvCifoHcCuwVyc10YiZEIT2I1xIj4\nRokGJiARoqggl8JcB1MT/biypoeXv5zDaiTgHSc3x4l95nF+3uyDyMzGbjUQmBonL8dFnsuCZ6yP\nguxbO1rrcljxecbIcV5bTOflZRMJuDEbErd8tH25cdkt+Dxj5Gbfef9MiPRyZJnxT42Td4uftTuR\n3Wog6J0gP+fW91eZYjQasZlT6DEfxALk511/QK4sy/Ry5eVef5rSojymJvqwW5I3jJntNJGKB4gE\nJgm4BwgHJvBP9JNK+Am4B/CN9xCPhm5r2YQQ6XfbR4yvRyl1HNinaZp/gdr7EBDWNO37c5inFug+\ncOAAVVVVAMTjcb785S/z3HPP0dnZybZt29i4cSNOpxNd13nrW99KW1sbOTk55OfnU1BQgMfjwWq1\n4nA4iMfjeL1e6uvrSSaTmEwmotEo4XCYVCpFRUUF0WgUl8uF3W5nYmICp9NJPB7H6XSSSCSwWq1E\no1EcDgehUAibzYbJZCIcDhOLxbBYLHi9XhKJBC6Xi/z8fJLJJG63m4KCgmtOUSaTScLhME6n84rH\nt+ryeXRdZ2JigpKSkmsK+9mkUimCwSBOp3PW6cPhMBaL5Y4tuG+2/CvV8ePHeeqpp7j8syeudHHb\nud4IqyuBruuX9oXLQSKRIBKJYDQab5jzrS7X+Pg4eXl5WCyWG8YcHBxE13W8Xi9Op5Ouri5WrVpF\nY2MjML0tXW62z993vv8jvn0oidG4cF13fOO9GM1mXPkLNypywDPIP/7hw1zsjirEcjIwMMBDDz0E\nsFrTtJ7LX0tnFZQE+pRS55k+yg2Q0jTtwfk0pmnavyxEUhaLhU996lN86lOfuu40W7bMfppwPkpK\nSq6JD1zaEV++Q7bb7Zf6AV+9ozYajRQXF88aw2g0XiqwL398qy6fx2QyUVpaesvzGgyGGxYMd3q/\n5pstvxDXI9vO9P5muRTbAGaz+Zbes1tdruvt06+OuWrVldftXCxGry60hRBLVzoL7v82y3OydxBC\nCCGEECtK2gpuTdNeTVfbQgghhBBCLBfpvGhSCCGEEEKIFU8KbiGEEEIIIdJICm4hhBBCCCHSSApu\nIYQQQggh0ujOvDnyDRgMVsrX34vRaMLvHceZXUgk6MGeXQDJJHE9jh6L4nDmEgp5sDsLiEcCmKx2\n9HgYi8VOUo9jsdlJJGKkknHMFicG4liz7KxdVcbweIBUKg56ilgiiT3LTH5ePllOBw/fu52GdfWc\nu9DFkWOnCYQiNK6tYXDcy8d/9XF0zJxta8frC/G2t9zPts2Nl3JvOdOOPxDkbHsXFSX5PLJvD//6\n/efR43FKivMZGvWwbeNaEkkjQ8ND+AMRPvaBd2I0GvniP3yTcDTBu972AN9/5gA2s5HcvByGh8dZ\nv66Od7x17zVDs9+Iruu8eewURQW5NKi6W5rH7w/wny+8itORxTsf37cshnJOl7FxN63ne9jUuIbC\ngvw5zdvV00/vwCi7dzTdkaN3Xi6VSnG0+SwWs5ltmxuueO121uHlmk+dI55IsHNb07zupR4Ohzl8\nopXVNeXU1lx7P+LjLa2kUil2bNlww/aTySRvHj1FXq6Lpoa1c85DzI970sPpti42rKulpLjw0vMn\nWtpI6DprV1dxqq2LRlVDacn0bfzi8Tj/+dx+YvEk9+/eQmfvyA23w0gkwuETZ6mpLGVNbTUAo2Pj\nnGnrorO7l+GxScZGhhkY9/HBdz9M39AkZ9vaaD3XzaQ/SNgfIGmAZDIBmDEAkbAPu9WF0WZlsHW/\n3CJQiCVuxRXclU0PUL/9HUCK3jP7Wb3lcQa1n1O76TEmB9rwT/aTW1xLTkkdvadfpqbpQbxjXejx\nOFmufIwmE5OD58ivaMBgMBDwDJJdWEMiFsZgNNE/0Ut2xXoMJDFgIpXSiQQ9hO25RI0WfnLCQ2v/\nGTStE1PuKgwOI8/87A023vde/uff/gePPPYEr77ZTWFlI9H90zvooqJCJibcHG4bp7VNI2bMp7mj\nn+6+/+D8uJ0xdxCbKYDDlc2hlpfZt+8hXn61kzVqI9/5j+exWS0cvhDFlJXPuS9+C0PBZsYGOzGm\nxtFTRkbjfvILTvPWB+++5fV49MRZeiattHb1UFdbjdVqvek8+18/wVEtiMNhoLz0zHWHRV4JXjl0\nFt1WiudwK+96/N45zfvqkfPYcit5/chpHtm7M00ZLg3ahS7aBuIk4kFKi4eprCi/9Nqrh8+SsJYy\neegMv/zE/fNqf2BwmBMXvJgtVvKyu1h3i/88Xu7g4dNMRHPoOXyOj1xVcPf09tPSFcRoNFKQ10fd\n6lXXaQVOtLTS5TYT6h5gzarKZXV/6uXs1cNniZqKGT98ll95+14ABoeGae7wYbZYOXHqZ+RVbmD8\nyDl+5e3TBfeh42d4/YwHsz2HC70vsW7Tnhtuhz8/cprRcDadh89fKrhfO3KOsx3jtPYFiEV0+rom\nWLXhQb7w1eep33A3Hf0pwoZCStfuIB4JgsEAKYiFveRXrkdr3k9FjSK/cj0Y5GS1EEvdiiu4I0Ev\n7v6zGAxGopEgE/1niQQ8jHafIOp3EwsH8Ix2Eg1OEY8EmOg7TdjvxmSxEvGPgdFENDQ9DHwqpRML\ne9HjUfR4DExGooFJMBjQYxEAjGYz8UiAWMiD2WTCXugi37GGLEsK9/B5kgawkGC8u5nqYiemhA9z\nMkRgtJ38GkV29vQgC9nZLqyEKHQZ6R3uw2U3sHnDLk4/e5hUcAqry0HUF6C80E4sNIkNH75RjQ33\n34PNauH7PzlBNDjB9vXlnDh/jlR4CltWFoFwCGMsm8qSzXNajxVlRZzuaCPbxi2PIlldXoQePkPK\nEKCibOUW2wAlBS7O9/ZTU3/zgS+uVpBtZWRygM1baxc+sSWmtLQY/UQHRgPk5+Ve8VpJgYv2nn42\n1BfNu/2C/FyMcS96HEpL6+fVRnlJPt2neqkouPZsQ1FhAYZYG8kUFBeuu3E7ZcWcbD+N3ZLCZrPN\nKxcxdyWFLlo7+llfW3Dpufy8X2wX9asr6BrqR9X84uh1ZWkhhvgUiUSANQ3l+CZuvB1WlBXSeaKL\n4rxfHJgoynNgMUYxBIdIhCLo4SnGu5spdJmIB4aIBcaJhgJMjeokYlGMRhPJZIJkPIaeiGE1xPFN\n9BGLBgl5J9OzcoQQC+aOHot6tqHdgUundRsaGvjwhz/MmjVrsFqtZGVlkZubS1lZGTA9WtjFbg9W\nqxW73Y6u61gsFpLJJKlUCoPBcGmIdwCbzUYikcBoNKLrOslk8oqC9OLjVCpFKpW69Lqu65jN5kvP\np1IpjEbjFaegL54yvPjbaDSSTCYvLdPFeS6fzmicPvJxMV+TyXRpnsvXx3xOpV9c/rnOczHmSjef\n9bcQ8y6GhRza/UbbzEKsh4XYJm+Ux1zal89HZsz2/l3+Xszn9bnEuPz74OL3C0x33bv4k0wm0XUd\ng8FANBrF7/ezf/9+fuM3fkOGdhdiicjU0O5LVrr7ul3sXnGjI78Xd7wXC/WL096o+L34/OWvXyyo\nZ3v9etNd/vh2zKcokELiF25nXayk9XijZV2I9ZDuNubS/kp6X5eS2db75c/N5/W5xLj6++DiPtpo\nNGKxWGZtr6SkhLq6Oj75yU/eNLYQIvOk45cQQgghhBBpJAW3EEIIIYQQaSQFtxBCCCGEEGkkBbcQ\nQgghhBBpJAW3EEIIIYQQabTk71KilNoDfBLwA6Oapn0+wykJIYQQQghxy5Z8wQ3kAb+laVpQKfXS\n7TZmzl1F7bpdhEMeUgkdTFaigXHySmqJRfzEomFcuaVEA24MliwghcFoJhry4swuJB4PkcKA0WQl\nHgmS5cwlEY2QQkdP6FitNpJAODSF01kwc2/VONYsF5GQF0MK7K5C/O5B7LkFxKNhzBY7BkOKaDiA\nK7cIPR7GABjNWRTmmDHZcqkuy6V3aILR0QmSKZ2sLCdVJdnkFpZiNuj0Dk4Q9Ae4+64NFBQUkdJj\neANRjCTw+sOMTXiwWIzcv2cb69ZU89Vv/ohQJMlv/trjbNm8gVffOMHwmAczCbIc2WxpXMXx1l72\nbKtnZMzHd374UxLxGI1rV7N5Yz1PvecJDAYDbx49xdm2DsY9AUoLndTXrWb7pnW8cbyVSfckpSVF\n7Lt3+6zDuB9vaWNyys++e7Zd99ZXt+Kc1sWF7kH0hE5NVQnbNzfOu63bcejYaYKhyHWXd6WbmvLy\nwoHDhMJRHrpvG6tXze3+3GfaLjAw4ua+nRtxuZw3nX50bJzmM52sq6u8NLrfUpNMJnn1jWasFhP3\n7NqyoLcFvNDZS0fPMDu3rrvukOPp5p70cPTkeepry1lbt4qhkTFOtXWzYW01NdUVV0ybSqX42cGj\nnDij8di+nWzacOOBgq4WDofZf/A4QyPj7Niy/ob7gdnW++Xryz05RWfvKDu3KE61dV2arqOrD61r\nkHg8SnFhPnt2br7iPTvRfJp//M6L3L2lnkf27ebfn32VqvICgqEwp1q7ePDerZw918mRk+2sr6um\noqyQCz1DJBMR3jihYTcbeGB3E8+/dpKQP0gqlSQai2K1ZxP0jmHLyiEWDWJ35hEJejGbbWQ5c+g8\n/gMZ2l2IJW7JF9yapr2glDIopT4DfOt226vbeD9V6+8jGvQSj4UJuAdI5BZRu/UxvOM9+Ea7qd7w\nACOdx8gtXYseDeB1DxANjFOz8S0Ep4awOQvwu/tJ6XHyytcSDU6hx2OXdoRGk5nhzqPkldRhc+UT\nDUxSVLOJ0a7jGAxGyup20nv2AHlldUwNX6By/b2EfeOM956ifN09RPxuUkkdmyMX72gXRRUb6fWO\nQXY+lUWbiQanSCYiTMSiOPK20dn6KiWr9xIfaKPLV0yve4JIOEJB5XrG+s/hyKtkKp4gt2ANxzvj\n/Lz5DeI5GzHaI3zt+6/yVMrBm61uTPYi+jtb2X53A3//7Z/SsP0t/N/vHyQUiZLI24rP3c+RcyOk\n8uppOnuOyrISzvaGOHwhSjyuc7prAEuBoue5A8St5TSfcbNnVzUnT51jx7amK96HcDjM8XOjuPJK\nOdp8lnt2bZ33e3qopZuBsSBJYxajvhE2rFtDVta1o/6lk9vt5nSXD0d2Hi2nz7F9a9PNZ1phDje3\nc7o7gG6042zpmnPBfehUL878So40t/HQ/XfdPN7JCwQp4MipriVbcJ9pO8+A10o4HKKudozystIF\na/twSzcmVxmHm9t54uHdC9bunHJobser53OopYu1das41KwRNRVxqKXjmoJ7dGycAycGiZur+OGL\nh+ZccB9pbuWkNoknbCV5dviG+4HWcxr9U1ai0RBrakaoqCjnUEsXZlc5h0+2M+EJY8mu4N+fewVH\n4VoikenpDrV0MeQOE4wZqA6Gqa8dp7S05FK7//CtF0jmb+W5n58imoCBYB4dzSNMTvkx59bz7Wfe\nxO0NYy3czDGtC8ewAauzgp7245Sufwu+iR6eeeUMjrxaCmqriIanIJXCkVdO94nnKK7dwtRoByW1\nWznx85+yYf1WStbcRSIemvubI4RYVEu+4FZKZQN/A3xb07Sf3WTap4E/udE0vrE+xh2niAam0BMJ\nUgaIBiYZ0Q4TDnqIhHwMdxwh4BkiFgqQTCXQkwmiAS8jXceIh32YTBb0ZIpELEA8GiQRC6PrcdCT\nhL3DGAxG4mEvfnc/Ie8o8WiIeDRIwDMEGEkmEwQ9gyT1ONGQhyHtTfRElEjQy2h3M3o0gAEjZpuD\nWHCSSaOOKRUjEtVJ6Dp6UsdssmDQQ3j7mjEl/Iz1HCPodeMwhSgqzsOSjBPzdGDDR9QTJRH24R3w\nUV5fxeraPF45copUysiujRVUFjvJs0Xx+fvItYYJj7WzWZUzMXqOprVluL0BDh4/QSwcpKwoG4c+\nwZraB3DYs8ixRsg2uAkkk+S5dEyxcbZuaeTomR4KHREIj7Gmdts174PNZqM420AgMEz99k3z30CA\nqhIXPp+XRMJLcU5JRobFzs3NJdcWIxoaYU3tjkWPvxysqSnh6CmNcDRAdVnFzWe4SkWRnYmpIVav\nv7UR6FZVFHKyfYD66oKbT5whtTVVnD5/hGyzgaLChc2zqjSbnuFBajeuWtB256K2qpijZ3qpLc8D\nYFVFAae1QRpWF18zbWFBPuU5KbTednbeVT/nWGtqKmg510fAO0ZJnrrhfqC2poqT5w7jNBooKioE\noLLYRd/o9PrKsnjoGx1ix6Z1tHWO4DJNT1ddmkMgGMCYCJFttlFw1ZmDHRtX8+IbJ6gustK0bhWt\nnUcoycnCmkrSP3KGpvpiznf20zfQQlG2lTyrD497AjsBRjsPE48FKHKZmPCOEvKOkdTjJFMJgu5B\n4rEgnuHzREN+JgfbKS9x4h3tIhYJMD7cO+f1JYRYXEt+WDOl1NeBeqAP0DVN+/Ac5q1llqHdhRDp\ntZBDuwsh5kaGdhciM5b10O6apn0s0zkIIYQQQggxX3JbQCGEEEIIIdJICm4hhBBCCCHSSApuIYQQ\nQggh0kgKbiGEEEIIIdJICm4hhBBCCCHSSApuIYQQQggh0mjJ3xZwoeWUN5JfVlxT1EsAAB9sSURB\nVIc9u4Cp8T7sznxiYT9Wuwuz2Uok5MXmzCPsn8Rqc2Cy2IhHw6T0BGaLFT0JyWSMLEcu8WgQPR7D\nkuUgHotisVhJ6glMVjvJRIJoOIDFZsWWlUM46MGAEWuWg4Qeh2QSDGCxOYmFfSSTMWpXNzA+3EnS\n6CIaDWG1ZhGPRbBkuUhEfTgd2UTjOhhNJCI+LFlODEYLedkOtjRWE44a6B0aIxENk5ebR262jdEJ\nL+OeIJXFTt7+lgfILyigq7ublMHKW/dtpaW1l97+IUwWCzYTtF3oZdIXprzQxYQ3RCgUoklVY7Xn\n4vWMk0gZ2bdnO2vXlNM3PEXziROcaB3AbIpTWlzBe99xL488sOfS+j54qBmPN8RD924lGArx2uFW\nhkdG2byhnr17tmEwGPjm95/njDZAbWUBWzYomhrqeOWNUxQXuNizc/OltkKhEPsPniQ3J4u9e7Yv\n+rZz4lQb/UMe9uxYT0lx4azTNJ86R9/QJHt2rKOkuIjRsXEONWvUlBewbXPDImecfslkkp++dhRd\nT/LoAzsxm+e/Szl2so3BEQ/37mxY8EFglrOltg0NDo9ytKWDuuoiNjX9YjTIZDLJS68cgVSKR/ft\nwmg08r3/fIlzHcO8+/G72dQ0nXskEuHl107gctjYd+/22xrOXuvooe3CEI1ry+noGcZkMvLI3p0Y\njdceS0qlUvzs9eMcP32e+lVVPP7wLux2+6XX3zx6ivHJAPvu2UxOtotUKsWBg8cIRWKEgwHOd4/w\n0L2bKS7Mo+3CMJsaqq8ZwTSRSPCjn7xKe+cglSX55BcUUlGczeCYn/V15TSoNZembTt/gc994V8w\nonPPzo2cbu/BbjMzNeXnVFsnsVgMDFaiET/xeAS7qwA9GiGRiGHJchIJerHYLNidRZx/43lSKfe8\n16MQIv1WXMFdWrsZZ34FFWt3M9h+kJyiVXhHu7DnlpJXVs9472lsjmxiYR82Zz7OvAqGzv+cLFcB\nzrxyAp4hknqMsrqdTPSfQY9HKKhsZEh7k7L6XUT8E6QAmz2b4Y4jlNRuI6eohs5jz5BdWIUzv4Jo\naAo9EcNsycJZUIl7oJV42I+rZjduX5iyVVsI+SZw5pXiG+vGVVBJ0DNEIhYir2gVZksWU6MXcOSW\nYc1yEY8EON3tIcuRja1kG/GpUTzxCO7REEZbOSVry5ly93Kmx0+Ox0J3j5/qNU38+4/fwFZQx7mB\nMM7cbDyj3fgjFpxF62ntPU5+5SYSeDhydpDV68oYGIhSVFZLS3eQCz0nqK7fwsGWQfJrdjLe34o5\nkcdLB1svFdyRSITWbi85BWWcPHueQDBG9wQMjqWw5QTZHgiQnZ3NG6eH0K2leC5MYLCOE4nGCFDA\ncMcQO7clLhVxJ8+cx08BQ71j3LUlhMPhWNRtp6V9GHteJc1nOnjswdkL7pPtQ9PTnO3ksX1FNJ/p\nJGQopOX84JIolhbawOAwfZNGbDYH5853sHHD+nm31XJ+CGd+FSfPdvLIXim4L7q4DZ3ShpbENnTy\nbBdhYyEt54euKLg7unoYDtgwYKCjs4fKilKOtLnJyqvnx/uPXSq4W860408VMDQ4wQ6/n5ycnPnn\n0taHbivhhQNHyK9oJB6P0dc/QO2qmmum9Xq9tPb6GQkXEu/3UXHmPLt3bgGmC+XTHRNkF1TQfLqd\nB+7ZwdjYOJ2jOvGkieZjrRTX7uDA4XbW1laQyirl5Lm+awru1vYLnO7yEkhW0tXSyd77aml5tRnV\ndDct7QNXFNz/8t0XSOZuJBiY5MWDZ7HlryYaCBEKp3CVN2HNykaPRQj6xkjFo2SXrCYRj0x/Nzly\nOfT8d9nx4BOU1u0kGpya9zoUQiyOFVdwTw13oCei6LEQU2PdhAOTRINewgE3/oluwgEPVkcOkYAX\ni9WGxdpONOwj7B/H7+4lmdTRY9M7vWjIhx6PEfSOEY8EGTz3OnoigslsI0WKaDDA8IVDTA60EQ75\niEfPE/AMk9AjpPQkpAxYx7qIhDzosRiTPYcJewYYScSJRwN4xztIRMKEfGNEQ1NYbTbCATdGo4VI\n0EPIN47ZbMVqMVFfmY9uDDEyfJJ4OIgrJwenPYXb18/4ZD+uLJ1VRWvJL7SQDBgxRwd4+L5NtHaO\nUJEPZrOXkupstK5BAsMnqcg3EZhsIxEKsqaigByzl0SOjjE6wOriQlTtOkY8U6yvctHRfxRrPIg1\nbmLXpp2X1rXNZqO2xIYvNMYGtQlfIMTwRCtxZ5TKAhMulwuAxlU5aD0DVJbmsLYqh6b1axg7dJbV\n5c4rjpg2qtX0v95CdZHliiNTi2VtTT5D42M0NNTddJr1M9Osr6/icEsXa1fdmQVkRXkphfZO9ESI\ntXV331Zba6vzGZ4YZX2TjDB3uYvbUH1N/s0nXgQN9ZUcPd1DffWV2/TqVdWcOd8PKVhduwGLxcK6\nyiw6B85z34NbfjH/ujp6Xz1BZYGF7Ozs28pl3epSznWNcs9dG+gZdGO0GaiqrJh12pycHGqLrYwN\ndlNZWE7Dul8Uv2azmdXlTjz+MRpVEwBFRYWUus4TicXZsaGanqEL3LW7kbLiAtq7x1inyq/Np341\ntac60Hp72VDrxKJ72LO1Hrd/DFVbdMW073jsHv7s736IIRln+4Y1XOgbxmlOYjKE6R4eJJmElMFI\nJOwjmYgTCU8Rj0bQE1Gs9hyq6qqYHGgn7Pcw0Hn6ttajECL9lvzQ7rdDhnYXIjNkaHchMkeGdhci\nM5b10O5CCCGEWBlSySTd3d1pabuurg6TaeH+4RBiLqTgFkIIIcSSEPaP87mvTuDI7VzQdkPeMb75\n5++XI+ciY6TgFkIIIcSS4cgtWdBuKkIsBXIfbiGEEEIIIdJICm4hhBBCCCHSSApuIYQQQggh0mjJ\n9+FWSlUC/xuYBFo1Tfv7DKckhBBCCCHELVvyBTfwCeCLmqYdVko9r5T6qqZpifk2llu5mUq1i2hg\nAoPRTCIRR49HsWW5sDpz8I/3Y7HnYLJYSISCOPJLCUwNY7W5SKYSJJMpLFY7iVgImz2HgGcEV0E5\nkYAbR04Zvsk+sguq8bl7cOVWzIwKlkMsPIXBlIXZYiWZiGEkidXuIhGPY8/KQtUW4A0bCQV8FOXn\n4A0EiSUM+P2T5BdWUJxroq9vhJjBQSrmZ52q58Pvvo9Xj3Xgstt4xyM7eXb/cfqGRmlSa3j32+7l\nVFsnHl+E1RV5vH6snRRG3rJ3Cxsb195wHSUSCX788puMjruJROK4PT6yXS62bVzDw3t3XjHtz14/\nzrgnyAN3N1JaUkxP3yBHWjqpLsu9Ylj2yw0Oj/L6sXbKi7LZu2fbnN/DVCrFiwcO4Q/FeXTvVvJy\n5z5S3YXOXk609lJfU8SOLY1znj8UCvHCgWNYrSbe9sieWYeSFmIxXfxcVRRnc//uuX+uxPUdOHiM\nianQpf3c5foHh3nzxAUqSrPZtXUDP/7pIQwGA08+uueKgbsA+vqHOHSyg4rSbO7btfWaOC1nztPe\nPcqmdZW4J6f44UtHCfimOHe+B48/TCwWRY/H0FMG9HgYa5YNe3YJ2vGXift60rkKhBC3aTkU3GVA\n/8xjD5ALuGebUCn1NPAnN2qspHodNY0P4B5qx5FTzHjfKczmLOw5JeSXr6Wv9QB5JXVYHbl4htop\nX3s3I53HsLkKsFjsRMNe7NnFhKaGKahsYEh7k/K1e/AMt1NUtYHhjiOU1e+i72yCqob7CU6NkEzE\nSJEilUzhyCmeGdo9jjO3GN9EP9kF5XQMd1G17l7Chl4G3ENYnOVYsrMxpYZxlDXR13OchL2M4som\nxrpPEqCQ7z3zOtlVW4nEYP/rzYz6TUyEHYxFnJxq7aBzKEhOYQUH3jzOZDgLU1Y27V0jNy24BwaH\ncEccDHoDjI+OEowaKLfl0jHo58Fk8lJxmUql0Po85BRVc7a9h9KSYs51DKDbStD6htizc/b227Re\nkrZS2nsH2LvnhqnMKhQK0TMeJye/hNb2Tu6Z5YvrZs51DpG0ldDePTKvgvvc+S4i5mLcU1O43W6K\ni4tvPpMQaXTpc9UzwP27M53NnSOVSqH1T5FbVE3r+d5rCu42rX96n9c9SGFuD/5kHnoiQV//IGtW\nr7pi2kv7x55B7tt1baz27lGSthLOdQ7TrnURMpUz5I9hKlDkOuOk0ImFfaRSKUYHuimsWEtp3U5C\nnpF0rgIhxAJYDgV3H1ANDAIFTBfds9I07Wng6Yt/Xxxp8vJpBrpPYbLYiQanAEgmkyTiUWxTg3hH\nO/FNDBD2uzEYTcSjISIhLyHvCFabi1RSR0+msFhsJBJRfO4+gr4JYuEA0aCbwOQQgakh4iE/AU8/\nvadeIh4JYHflEQl5MRgtTFmsGJIJTKYUsSkX8XgcU2yCbY3VjLpbsEb8VFXlM+WbIOwbxRieJDGW\noGl1Lhe6+5nofB1LKkKRrYBPvP9xXnr9NLkuO088vJsfHziCKRanJi/Kzq07ycrqxD3l5okH7+Lg\n0VYgzJaGhpuu8JrqKiovDGArM1CVX4Z70o/THqCpbu0VR3INBgOb1pYw6p5g28bp4ZC3NK7m8MkL\nrF977bDHF23ZUM/rx9qoW1t601xm43A4WFflxBdws6VpfkfyNq2v4cTZbtS6+d16auMGxeDYMUpK\nzRQVFd18BiHS7HY/V2J2BoOBzTP7ua1NTde8vmXDat48obF2XTnr1tbR1X8Yo8FA7arq606r1s2+\nf9y0rpJzncNsaahhdUU+33/hDRxlSU6d7WTc7SUaDqMn4qRIQSLGWO8Z/O4Belp+vuDLfadJ14A6\nMpiOuFVLfmh3pVQp8NeAHzimadrX5zBvLTK0uxCLToZ2FyJzlvPQ7mM9zThySxf8PtxjPc2AAUdu\nyYK1KYPpiKst66HdNU0bBZ66nTZGRuR0mxCLaXx8HJDPnhCZMNvnb8oziXd4BMMCXm8S8o5iMJpI\nRHwL16ZnED0WXNA2L7Zrc+aRiGQtWJt6LMDx48cZHR1dsDbF8rF69eprnrvRd96SP8J9O5RSecCP\ngL2ZzkWIFcjL9DUXQojFJ58/ITLjNeCdmqZNXf7kHV1ww6WiO++yp7qBa/8tSa9MxMxUXIl558Wd\nb8wU0DPPeTMhU+/pfC2nfJdTrrC88r1erimu/Y5fCsuV6RwyHV9yWBrx05nD1NXF9oqklEqthJiZ\niisx77y4txMzU+tpPpZTrrC88l1OucLyyncuuS6F5cp0DpmOLzksjfiZyEFuHiyEEEIIIUQaScEt\nhBBCCCFEGq3Egvt/rJCYmYorMe+8uLcTM1PraT6WU66wvPJdTrnC8sp3LrkuheXKdA6Zjg+Sw1KI\nD0sjByGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJhxxw/tDqCUMgO5\ngEfTtGSm8xFCCCEWg3z/icsppeyASdO0QKZzWWnu+IJbKfVbwBOABygAfqBp2tczm9WdJRM79MWO\nqZQqAn4dqAAGgb/TNM1/J8bN1LIKIRbWUvj+U0r9MXAv8C1N076plPpnTdM+vJg5XJZLgaZpkxmI\n+8fA68DnAD/wPU3TvrvIOfxvYBK4fyaHw5qm/dUixs/494pSajXwCPBtTdOCSqknNU17drHimxcr\nUAZt0DTtiYt/KKW+AqR9h5OpowoZKESv2KErpdK+Q89ETOD/ML3dDAJVwFeAp9IcM1Nx5x1TKfVB\n4DDwhZmn/kzTtGPpSPJ2KaXeCZwA/nTmqb/UNK0tgyndkFLqX4HfWg5Hpma2g37g95j+nvlrTdMO\nZDar61NKbQeeBqxAFPj8Et5udwN/CNiACPA/NU1rvs7kGfn+u0qppmlvUUp9Rin1IGBazOBKqfcC\nKaYPMH5MKfU1TdO+v5g5APnAh4BHNU1LKKX+CVjUgpvp5d+kadpjAEqpLy1y/Ex9h17uL4B/A76h\nlPow8B5ACu4FVKCUupvpnX81kJ3ugBkqCDMVNxM79EzE9Gqa9urM4wtKqXenOV4m495OzH0zPx9n\n+ijKV4CPLGx6C+Zx4EmmC61JpnP91UwmdBPlwFeVUt/RNO25TCdzE3uAeqa/0HzA14AlW3ADHwXe\nBXwG+CLT+S7WZ3yufhN4r6ZpkZnuAV8H3n+daRf9+28W2UopJ/DnTK/XmkWO/yTT+6JDTNc89kWO\nD9MHwS4AG5RSfiAnAzlUA5Uz//RMAkWLHD9T36GXG9A07YdKqdPAXzL9j9iiWQkF9/8LfAIoA/qA\nTy9CzEwdVchE3Ezs0DMRs1cp9SwwBhQCryxCzEzFvZ2Y5cAAMAXoLPIObY50YBQYZvqoZjyz6dzU\nANP7so8qpZ4BIpqmvTfDOV2Pceb3xbNslkwlcouygASwBvAyfeR4qUoxnSszv2+03Wbi++9q/wjs\n1TTtBaXU7wF/s5jBNU17Sin1aaa3yfOapv3LYsaf8Xng95n+p2ME+JMM5PBnwDjwG0xv7/9tkeNn\n6jv0clal1BOapj2vlPoZ8NXFDH7H9+HOBKXUd5k+SnKxIPwdTdPSfuokE3GVUpVcuUP/mqZpo3da\nzJm4LqZPDQ5pmqanO14m4843plLqQ8B9wDeAe4BxTdP+OS1J3ial1GNM9yncCvQCX9Y07QeZzer6\nlFJ/q2na7172d76maZ5M5nQ9SqmPAEPA7zDdheCvNE3bn9msrk8p9Ummj2j/BVAJTC1m3865UEo9\nDHyK6e/vCPA3mqYdzGxWS59S6kng/ZqmvS/TuaxUSikb09cSjC3md+hVOTg1TQvOPK7TNK1zsWJL\nwZ0GGSwIM1H8Xn4hxBDTRctiXtS3WDEzcvFRJuIuZEyllEXTtKV+5BhYXrnC8sp3OeUKyyvf5ZSr\nWLmUUs1AAAjOPJXSNO3xlZTDSuhSkglRpo88xJg+pRq6g+NevBBigOmj6ot5Ud9ixlxJ3YRuK6ZS\nysR0n0Uv8FfA7954jsxZTrnCpYuic1gG+S6nXGH5bQuXWU65ipXrV4CPaZr2Rys1Bym40yMTBWGm\n4l5+IURHBi7qW6yYmbr4aFn1kZ/l6PgP05LhAlhOucLyync55QrLL9/LXd7NSIilStO0DqXUovbf\nX2o5SMGdHpkoCDMVd7ld1Ddfmbr4KBNxbyfmbEfHv7aw6S2Y5ZQrLK98l1OusIzyzfRpcSHmazG6\n1i7lHKTgTo8Vc0cLTdP+l1LqH1jEi/oyEZOV1U3odmIuhduQ3arllCssr3yXU66wvPJdCqfmhRBz\nJAV3GmSoIMxI3BU48M1K6CZ0OzGXwm3IbtVyyhWWV77LKVdYRvlm+rS4EEIsGUqp31JKPa+U+pZS\n6gWl1Mfu1LhKqb+76u+v3KExv3yjHO6kuJlaViGEEOJOJUe402Ml3dFiWV3UdxtWTDehDMUUQggh\n7lhScKfHSrqjxXK7qG9eVlI3oUwtqxBCCHGnkoI7PVbSHS2W20V985KhfuMrqY+8EEIsKUqpB4Av\nMD0sfA/Td4dpYnr01L/UNO17Sqkcps8kVzI9GNtBTdM+ODPvZ2eaqgKOAh+fmeZFpodZD2ua9ugN\n4v8h8J6ZeC9pmvYHSqnay+aPAN8CPsz02chngS/N5FMNJIDPaJr2klLqaeDumee/DNiBDzL9HXpU\n07TfmPeKErfEmOkE7lAr6Y4W/wc4xPSQ8oeZvsDuToy5QdO0JzRN+8DMLbjuWoSYmYqbqWUVS5BS\nartS6p9mHn9CKSVDY4uVZC2wD7gAnNA0bQewF/jvSqnVwONAs6ZpewAF7FZKbZuZ927gk0ADkAX8\n9szzCnjqJsX2Y8A2pve/24AqpdRTV83/CNMjhlcCWzRN+yzTBfd+TdM2///t3X2wVVUdxvEvpBgy\nVhgFWZlaPZZohc6IaZo4OuYwMr6kU5o2apppkqlp0oSR4svYGOP0MqQ5vkzT2AuCr4whKZaJ4EuK\nNj6TSRYqaFoxjinK7Y+1iMP1cjnQPfsK9/nM3GHvfdZev30Od9+z9lq/vRfwWeAqSe+u+w2xPRq4\nAvgmsFv9WSlpmw3+hKIt6eHujIH0RItMfLPpxd2YHpEWHWb7fuDEuronyemPgeVx28slHQAMlXR8\n3b4lsFPt5d5d0umUhvU7gWFAF6Xh+wSApOsoI9AzgGW2n1pH3P2BscD9df2tlF723/Ww/wO2V9bl\nccAJALaflDS/1tNF6WXH9muS7gEWArOAH9p+en0/mFg/aXB3xkCa+GZxvcFuKeUPzZ29F+/TmM8B\nH6AMo3XaQEoTOovSKzOywZjRQWsZ3p4KzLa9fS3zHcokKlMkPUf5Mh4FfAP4FnABMAHYV9KLlIv7\nHWpjZDvgZts7N/amIprxcv13MKVX+SEASaOAf0g6DTgcmA78BhhN6XWGktKxylta1l9m3QYD02x/\nv8YbDqwARnTbv6vb+uCW+NTlVW29/5WzfYiksZQe+tmSjrY9r43jig2UlJLOWCxplqQrJd0APN5Q\n3L9KurHGndVQ3KHAj4APAVtQGsGdNoqSSrItJc948wZi9lea0BBgCXCm7amUnoqOsr3E9mTbXwb+\n9maYHSz6RPfh7fHdXu+qP1Auni+yPYbaSLB9B6U3bLLtG4FbKEPWUHJBr+no0Uf0r7nAKQCS3gM8\nSBkB3B+YbvvntdwnKI3rQcA4SaMkDaacI7eyZmN4XfGOkTRM0maUnvHDeijXvb651B5uSTsAewH3\ntJaTNELSY8Ai2+cBtwO7tHlcsYHS4O6MoZQvtHmUGx527b14n3kK+BnlhNsKeLaBmMMpM58dUB9J\nuF8DMQcBH7N9gO0jgO0aiNkfeeMAFwPPAz+VNJTy+9RRkhZIuk3SbcD5km7tdMxoxBzbT9juAq6j\nnKtdvZSfv5btq764rwKOqcufr3VGbEpaL0KnUFJKHgHuAM62/RdgGnCepHuBycBNwPZ1vyWU7+RH\nKSl6V7bU2yvbNwO/ppyHjwAP2r62h/27uq1PBPaT9DBwA2VW0qWt5Ww/D/wEWCBpIfAO4Oo2Po/4\nPySlpDNG2j5Q0iRgH8rVbhMmAP+mNAihNPw77e3An4HRkpYDb2sg5vuB90raD3iBMsTWaf2VJvR3\n2zPqH89LaOMPdR84ETgemARcavsrDcSMzus+vL0la3a6DKGM5ABg+xV6tup38G7KeXgo8KTtJi7w\nIxpj+y5qJ5Lt5ay+wGwt81vgI9231zSuJbYP6vbSYmCHNuNPpaR+tW5bY3/b19AyumT7GeDgHuqa\n0m19GuViIRqSBndnbCVpGHAR5Yp22yaC2j5a0hmUL9HH64nYaecDZ1Le67PAeQ3EnEpJXTmZMpJw\ndgMx+2symCGSxtu+RdJcSq9ER9l+SNJU4DJg607Hi8aMq3mnyygNh5mUEYwRwHLgM5SUkd68Rk3h\nst0l6RrgcpLnH9Fd957nN5C0N+X86clBuYjdtKTB3RnTgU/bvrXeudzYVaTtyyRNoKR6NBFvMXBa\nE7FaYv6xLk5uMOalki6nNECXNTjxzWn14g3bM+twZhNxl0o6lWZShKIZq4a3t6HkbP6AMiK1gDLc\nfW9L2bUNWc8BLpT0ou0ZwPWUC+6ZnT30iI1L7R2/ax1l7gbGNHNE0d/aTd6PGNAkPUCZ9OCluqmr\nPqN6k4vbQ0x6GBaNjUgd3j6nL/8f641gJwOyfXpf1RsRsSlKD3dEe46k3Hxy7gCI21/vNTpnncPb\nG2AG5RGDB/ZxvRERETFQSRo5UOL213uNiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIh405K0u6SL\n6/LBkqasa5+11HO1pC/28bGdJOlznao/IiIiAvJYwOi8nYCRALZvAm7awHo68VizPVk9Y2QTU6ZH\nRETEAJQGd/RK0iXAIZQpnacDsynTiw+nTIwy0fZCSVcD/wR2ozybdwpwA/BdYJikScDTlBk4j6sT\ncVxe670X+KjtcW0e07HA1yhT2N8PnGr7FUnPAL8EPlXrPdL24m6x/kC5CLgAmADsW/cDGC/pFMoF\nwlTbV6z/JxYRERGxpsH9fQDx5iXpCEov8M7A7sBxlB7qabY/Dnwd+JWkIXWX99neGzgY+J7tfwHf\nBmbZvrCW6ZK0GXAdcJTtXYFXaa+HeZCk0cCXgE/aHgM8B5xVXx8JzKl1zgO+WmNd2xJrBWW2xjuA\nWcBk27dTZl3dwvZYYDwwdb0/sIiIiIgepMEdvdkHuN72CtsvUXqOR9ieCWB7PvACsCOlwXx73e9R\nYOu6PKj+0LK+C7DU9qK67apuZXozDvgwMF/Sg5Re6h1bXp9d/11Uj2EXYFkbsbooDXCAx4ARbR5P\nRERERK/S4I7erGDNxukHeWNjdRCrU5NeAbC9rt7q11nzd6/dxjZ1v1/YHlN7uMcCE1e9aPvVuthV\n612fWK/XOpLPHREREX0mDe7ozTzgMEmbSdqSkh+9UtKhAJL2oKRxLOqljtd4470CfwKGS9q5rh9F\n+zct3gkcKuldkgYBP6alwd1iVcO6p1grW45t8zbjRkRERGyQNLhjrWrqyO+BB4D7gMuAvYCJkh6m\n3Ih4mO0VdZfWRvOq5fuAPSRdVLd11fJfAK6VtJByk+XLbRxSl+2HKTdkzmV1Q//itcRfW6z/1DJz\ngEmSDu/l+CMiIiIiNi6SBkm6pPaaI+kMSZdu7LEiIiIierI+ubMRfUbSOcCxlCeUPEl58si5wP49\nFF9g+6Q+jHWC7Rc3tL6IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\n2Cj8F7jCFubkCtCuAAAAAElFTkSuQmCC\n",  "text": [  ""  ]  }  ],  "prompt_number": 31  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "creapr[creapr.errors > 20]"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "html": [  "
\n",  "\n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  "
lengtherrorserrors_lengthperfect_covperfect_cov_lengthrepeatrepeat_lengthclipclip_lengthscorescore_lengthfrag_distfrag_dist_lengthfrag_dist_gapfrag_dist_gap_lengthfrag_covfrag_cov_lengthfrag_cov_gapfrag_cov_gap_lengthread_cov
contig
contig-1000031 104774 25 5227 0 0 21 3593 0 0 0 0 3 1310 0 0 0 0 0 0 0...
contig-3756000014 146264 29 5463 0 0 27 4644 0 0 0 0 1 494 0 0 0 0 0 0 0...
contig-3833000027 22626 22 4373 0 0 20 3738 0 0 0 0 0 0 0 0 0 0 0 0 0...
\n",  "

3 rows \u00d7 25 columns

\n",  "
"  ],  "metadata": {},  "output_type": "pyout",  "prompt_number": 28,  "text": [  " length errors errors_length perfect_cov \\\n",  "contig \n",  "contig-1000031 104774 25 5227 0 \n",  "contig-3756000014 146264 29 5463 0 \n",  "contig-3833000027 22626 22 4373 0 \n",  "\n",  " perfect_cov_length repeat repeat_length clip \\\n",  "contig \n",  "contig-1000031 0 21 3593 0 \n",  "contig-3756000014 0 27 4644 0 \n",  "contig-3833000027 0 20 3738 0 \n",  "\n",  " clip_length score score_length frag_dist \\\n",  "contig \n",  "contig-1000031 0 0 0 3 \n",  "contig-3756000014 0 0 0 1 \n",  "contig-3833000027 0 0 0 0 \n",  "\n",  " frag_dist_length frag_dist_gap frag_dist_gap_length \\\n",  "contig \n",  "contig-1000031 1310 0 0 \n",  "contig-3756000014 494 0 0 \n",  "contig-3833000027 0 0 0 \n",  "\n",  " frag_cov frag_cov_length frag_cov_gap \\\n",  "contig \n",  "contig-1000031 0 0 0 \n",  "contig-3756000014 0 0 0 \n",  "contig-3833000027 0 0 0 \n",  "\n",  " frag_cov_gap_length read_cov \n",  "contig \n",  "contig-1000031 0 0 ... \n",  "contig-3756000014 0 0 ... \n",  "contig-3833000027 0 0 ... \n",  "\n",  "[3 rows x 25 columns]"  ]  }  ],  "prompt_number": 28  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "rns31purdf[creapr.errors > 20]"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "html": [  "
\n",  "\n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  " \n",  "
contig_lengthmax_aln_strainmax_aln_puritycov_meanunamb_dominant_strainunamb_dom_nr_readsunamb_read_level_purityunamb_tot_nr_readsunamb_nr_reads_strainunamb_nr_reads_speciesunamb_nr_reads_genusunamb_nr_reads_familyunamb_nr_reads_orderunamb_nr_reads_classunamb_nr_reads_phylumunamb_nr_reads_superphylumunamb_nr_reads_superkingdomunamb_nr_reads_lifeamb_dominant_strainamb_dom_nr_reads
contig
contig-1000031 104774 Akkermansia_muciniphila_ATCC_BAA-835 1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
contig-3756000014 146264 Akkermansia_muciniphila_ATCC_BAA-835 1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
contig-3833000027 22626 Thermotoga_sp_RQ2 1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...
\n",  "

3 rows \u00d7 32 columns

\n",  "
"  ],  "metadata": {},  "output_type": "pyout",  "prompt_number": 23,  "text": [  " contig_length max_aln_strain \\\n",  "contig \n",  "contig-1000031 104774 Akkermansia_muciniphila_ATCC_BAA-835 \n",  "contig-3756000014 146264 Akkermansia_muciniphila_ATCC_BAA-835 \n",  "contig-3833000027 22626 Thermotoga_sp_RQ2 \n",  "\n",  " max_aln_purity cov_mean unamb_dominant_strain \\\n",  "contig \n",  "contig-1000031 1 NaN NaN \n",  "contig-3756000014 1 NaN NaN \n",  "contig-3833000027 1 NaN NaN \n",  "\n",  " unamb_dom_nr_reads unamb_read_level_purity \\\n",  "contig \n",  "contig-1000031 NaN NaN \n",  "contig-3756000014 NaN NaN \n",  "contig-3833000027 NaN NaN \n",  "\n",  " unamb_tot_nr_reads unamb_nr_reads_strain \\\n",  "contig \n",  "contig-1000031 NaN NaN \n",  "contig-3756000014 NaN NaN \n",  "contig-3833000027 NaN NaN \n",  "\n",  " unamb_nr_reads_species unamb_nr_reads_genus \\\n",  "contig \n",  "contig-1000031 NaN NaN \n",  "contig-3756000014 NaN NaN \n",  "contig-3833000027 NaN NaN \n",  "\n",  " unamb_nr_reads_family unamb_nr_reads_order \\\n",  "contig \n",  "contig-1000031 NaN NaN \n",  "contig-3756000014 NaN NaN \n",  "contig-3833000027 NaN NaN \n",  "\n",  " perfect_cov_length repeat repeat_length clip clip_length unamb_nr_reads_class unamb_nr_reads_phylum  \\\n", "WHOLE-ASSEMBLY 1480 360162 615 616 0 "contig  \n", "contig-0 0 0 0 0 0 "contig-1000031 NaN NaN  \n", "contig-1 0 0 0 0 0 "contig-3756000014 NaN NaN  \n", "contig-10 0 0 0 0 0 \n",  "contig-1000000 0 0 0 0 0 "contig-3833000027 NaN NaN  \n", "\n",  " score score_length frag_dist frag_dist_length unamb_nr_reads_superphylum unamb_nr_reads_superkingdom  \\\n", "WHOLE-ASSEMBLY 0 722 340281 0 "contig  \n", "contig-0 0 1 504 0 "contig-1000031 NaN NaN  \n", "contig-1 0 0 0 0 "contig-3756000014 NaN NaN  \n", "contig-10 0 1 416 0 \n",  "contig-1000000 0 0 0 0 "contig-3833000027 NaN NaN  \n", "\n",  " frag_dist_gap frag_dist_gap_length frag_cov unamb_nr_reads_life amb_dominant_strain amb_dom_nr_reads  \\\n", "WHOLE-ASSEMBLY 0 286 14024 "contig  \n", "contig-0 0 0 0 "contig-1000031 NaN NaN NaN  \n", "contig-1 0 0 0 "contig-3756000014 NaN NaN NaN  \n", "contig-10 0 1 11 \n",  "contig-1000000 0 0 0 "contig-3833000027 NaN NaN NaN  \n", "\n",  "frag_cov_length frag_cov_gap frag_cov_gap_length  \n", "WHOLE-ASSEMBLY 0 0 125 ... \n",  "contig-0 0 0 0 ... "contig  \n", "contig-1 0 0 0 "contig-1000031  ... \n", "contig-10 0 0 0 "contig-3756000014  ... \n", "contig-1000000 0 0 0 "contig-3833000027  ... \n", "\n",  "[5 "[3  rows x 26 32  columns]" ]  }  ],  "prompt_number": 6 23  },  {  "cell_type": "heading",