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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Building a Similarity Comparison Set\n",
      "\n",
      "Goal: construct a set of molecular pairs that can be used to compare similarity methods to each other.\n",
      "\n",
      "I want to start with molecules that have some connection to each other, so I will pick pairs that have a baseline similarity: a Tanimoto similarity using count based Morgan0 fingerprints of at least 0.7. This threshold was selected empirically.\n",
      "\n",
      "**Note:** this notebook and the data it uses/generates can be found in the github repo: https://github.com/greglandrum/rdkit_blog"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I'm going to use ChEMBL as my data source, so I'll start by adding a table with Morgan0 fingerprints to my local copy of ChEMBL 16:\n",
      "\n",
      "    chembl_16=# select molregno,morganbv_fp(m,0) as mfp0 into rdk.tfps from rdk.mols;\n",
      "    SELECT 1291111\n",
      "    chembl_16=# create index fps_mfp0_idx on rdk.tfps using gist(mfp0);\n",
      "    CREATE INDEX\n",
      "    \n",
      "And now create a smaller table that only contains molecules with molwt<600:\n",
      "\n",
      "    chembl_16=# select molregno,mfp0 into table rdk.tfps_smaller from rdk.tfps join rdk.props using (molregno) where amw<600; \n",
      "    SELECT 1182986\n",
      "    chembl_16=# create index sfps_mfp0_idx on rdk.tfps_smaller using gist(mfp0);\n",
      "    CREATE INDEX\n",
      "   "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now I'll build the set of pairs using Python. This is definitely doable in SQL, but my SQL-fu isn't that strong.\n",
      "\n",
      "Start by getting a set of 35K random small molecules with MW<600:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import psycopg2\n",
      "cn = psycopg2.connect(dbname='chembl_16')\n",
      "curs = cn.cursor()\n",
      "curs.execute('select molregno,m from rdk.mols join rdk.props using (molregno) where amw<=600 order by random() limit 35000')\n",
      "qs = curs.fetchall()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now find one neighbor for 25K of those from the mfp0 table of smallish molecules:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "curs.execute('set rdkit.tanimoto_threshold=0.7')\n",
      "\n",
      "keep=[]\n",
      "for i,row in enumerate(qs):\n",
      "    curs.execute('select molregno,m from rdk.mols join (select molregno from rdk.tfps_smaller where mfp0%%morgan_fp(%s,0) and molregno!=%s limit 1) t2 using (molregno)',(row[1],row[0]))\n",
      "    d = curs.fetchone()\n",
      "    if not d: continue\n",
      "    keep.append((row[0],row[1],d[0],d[1]))\n",
      "    if len(keep)==25000: break\n",
      "    if not i%500: print 'Done: %d'%i\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Done: 0\n",
        "Done: 500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 1000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 1500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 2000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 2500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 3000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 3500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 4000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 4500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 5000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 5500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 6000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 6500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 7000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 7500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 8000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 8500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 9000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 9500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 10000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 10500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 11000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 11500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 12000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 12500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 13000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 13500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 14000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 14500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 15000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 15500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 16000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 16500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 17000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 17500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 18000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 18500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 19000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 19500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 20000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 20500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 21000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 21500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 22000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 22500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 23000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 23500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 24000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 24500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done: 25000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, write those out to a file so that we can use them elsewhere:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import gzip\n",
      "outf = gzip.open('../data/chembl16_25K.pairs.txt.gz','wb+')\n",
      "for idx1,smi1,idx2,smi2 in keep: outf.write('%d %s %d %s\\n'%(idx1,smi1,idx2,smi2))\n",
      "outf=None\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Early analysis of the data\n",
      "\n",
      "Start by loading the pairs from the file we saved and creating RDKit molecules from them"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rdkit import Chem\n",
      "from rdkit.Chem.Draw import IPythonConsole\n",
      "from rdkit.Chem import Draw\n",
      "import gzip\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rows=[]\n",
      "for row in gzip.open('../data/chembl16_25K.pairs.txt.gz').readlines():\n",
      "    row = row.split()\n",
      "    row[1] = Chem.MolFromSmiles(row[1])\n",
      "    row[3] = Chem.MolFromSmiles(row[3])\n",
      "    rows.append(row)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look at some pairs:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = []\n",
      "for x in rows[:5]:\n",
      "    t.append(x[1])\n",
      "    t.append(x[3])\n",
      "    \n",
      "Draw.MolsToGridImage(t,molsPerRow=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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qZGmJIiKQqSliMJCzM9q4ETk7k1Fee8nIyOjfv//ChQvXr18fGxsb/Z+0tDQx\nMbHevXtb/8fQ0JA4SaaEo0ePenl5/fXXX7W1tURCpaWl0Wg0ExOTPv+xtLSUkpIScKElJcjRESUk\nfH1qZobu3kXPn6NLl9C5c19fDA9HZ8+iDRtQejrKyPj2//x8Jy2tmKoqfX19PT09fX19XV3dqKio\nX3/9dcCAAY2NjcOHDxcWFo6KiqLRaIL6/kA7ISOwEELv3qE1axCLhWpr0Zw5qBOfibDdv39/1qxZ\npaWlQkJCFhYW7H/P5ubmxMweitqyZcvly5fFxMSafkfERuSdSEkJ6tkT2dt/ffr0KXr/HoWEoA8f\n0I4dX1+Mi0MzZ6KkJNS9O9LXR3p67P/X6umJy8s3bW/hwoUPHz589eqVvLz8p0+frK2tZ8+e/eef\nf3boNwXaH0mBRU3l5eXZ2dkmJiaioqKCruUn0+IZ1rNnKCwMnTnz9cWICHT6NAoORpKSbbZXV1c3\ncOBAdXX1q1evCgsLP3/+fMiQIefOnZs4cWK7fQ9AAIQFXYAgycnJWVpaQlp1Fk5O6PlzlJiIEEIM\nBtq9Gy1YwElaIYTExMQuX7784sWLHTt2IIT69+/v5+c3f/789PT0di0ZdDDK9M6Ark9aGl29iry9\nv/YtzJvHVU+olpbWhQsXRo0a1adPn1GjRi1fvvxLaqrkr7+iiAjU2a6IAa9+6ktC0PX4+fnt3r37\n9evXurq6qKoKrV6N7OzQ7NmCrguQAwILdCksFst92rT5oqJDjx9HYmKoqgpR6rY9aB0EFuhyqqpQ\n377I2hqdPi3oUgDJfupOd9A1SUujkBB05QoKCEAIoaQkNHo0cnZGgwejU6cEXRzgC5xhgS7qwgXk\n4YEyM9GQIf83en7r1hZGzwOKgMACXdeyZUhBAaWn/9/o+fPn0YULAi0L8A4uCUHXtXcvKi9HTeaK\nIm1tlJsruIIAvyCwQNclKors7FBe3rdXiJn5gLIgsECXNnbs96Pn580TdE2Ad9CHBbo6Cs7MBz8C\ngQUAoAy4JAQAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYA\ngDIgsAAAlAGBBQCgDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwI\nLAAAZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBA\nGRBYAADKgMACAFAGBBYAgDIgsAAAlAGBBQCgDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQW\nAIAyILAAAJQBgQUAoAwILAAAZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAM\nCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYAgDIgsAAAlAGBBQCgDAgsAABlQGABACgDAgsA\nQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwILAAAZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYE\nFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYAgDIgsAAAlAGBBQCg\nDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwILAAAZUBgAQAoAwIL\nAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAG\nBBYAgDIgsAAAlAGBBQCgDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUA\noAwILAAAZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMC\nCwBAGRBYAADKgMACAFAGBNZP58uXL4IuAQAeQWD9XJKSkrS0tPLy8gRdCAC8EMIYC7oG0HGGDRum\nqqp6/vx5QRcCAC9EBF0A6Dg3btx49uxZcnKyoAsBgEdwhvWzaGxsNDc3Hz9+vJ+fn6BrAYBH0If1\nszh69OiXL182bNgg6EIA4B0E1k+hvLzc19f3999/l5GREXQtAPAOAuunsGPHDjU1NU9PT0EXAgBf\noNO968vOzt63b9/ly5dpNJqgawGAL9Dp3vVNnz798+fPt2/fFnQhAPALzrC6uKdPn4aGhr59+1bQ\nhQBAAjjD6sowxgMGDDAzMzt69KigawGABHCG1ZVdunQpKSkpPDxc0IX83JKSkLc3YjJRbS2aPx95\neAi6IAqDwKKMgoKCsrIyeXl5OTk5TkYn1NbWrl27dvXq1aqqqh1QHmgZg4HGj0dhYcjcHDEYaMQI\npKGBhg0TdFlUBYFFDVu3bo2IiIiOjiae0mg0OTk5eXl5BQUF4kHz/9+7d4/JZK5atUqwlf/sbt9G\ntrbI3BwhhCQl0Zo16OhRCCyeQWBRgL+//65du+7fv29ubl5eXv7ly5em/yeUl5d/+vQpLS2N/bqE\nhISVlZWEhISgy/+55eUhbe1vT7W1UW6u4KqhPAiszu7ixYs+Pj5hYWF9+/ZFCElISKirq3NyYHJy\nsrW19cWLF6dOndrONYIfYDKRpiZ69erbK7m5SEtLcAVRHwad2K1bt8TExE6fPs3b4fv27ZOXl8/J\nySG3KsCp+/dxZSXW1cUJCRhjXF2NHRzwrVuCLovCYFgDeci+GRQdHT1kyJD169evXbuWtxYwxmPG\njKmvr79z546wMEzD6liNjWjpUnTkCHr3Dq1Zg1gsVFuL5sxBc+cKujIKg8AiCYOBLC3/72bQ1q2o\nshLJySEHByQqym17aWlpDg4Os2bN2rt3Lz91FRYWWlhYrF+//rfffuOnHcC1EyfQgwcoOFjQdaAv\njY0u796ZSEkhhDTodF8dHUFXxAcBn+F1GWFhePr0b0+vXsVTp2IvLywtjeXk8JQp+NQpXFzMYWMf\nPnzQ1dX18PBgsVhklBYmJiYWFxfHf1OAU3V1WFsbX7ki6DowxrisocEtMVHQVZADLhNI0uLNoH37\nUEkJCglBamrI1xepqyMHB+Tn9ykxsZWWvnz54uTkZGBgcOzYMSEhIf5Lc3V1HT9+/MqVXhjX898a\n4ERkaGiKkREaOVLQhXQ1EFgk0dRETXd2YN8MEhNDTk5o/36UmYni49G4cejmzVEjRujo6CxZsuTW\nrVu1tbVNm6mrq3N1daXT6ZcuXRLl/kLyRw4fPrxpE6Ow8HeyGgStqK+vX7p+/Q0nJyQuXvzgAZ46\nFdXUCLakwro6z7Q0z7S0sJISwVbCL0Gf4vGCyWQyGAxBV/H/mt8MCgrCv/+OX7zATOZ3762uro6I\niPD09NTQ0BAREbG3t/fz80tOTm5sbJwwYYKhoWExxxeP3BT4ODpalMmsJL1l8J3AwMDu3bvX1NQ0\nNDTo6+sf2rNHsPV0pUtC6gVWRkaGvb29iYmJk5PTvXv3BF1OE/HxeORI7OSEBw3Cx4/j/Hw8bBim\n07GyMp41C587h0tKvjuioaHhwYMHq1evNjY2ptPp8+fP79atW2ZmZjsVmJ+/tqami/zgdlp1dXU9\nevT4+++/cZPkEmA9n+vrIbAEg8Vi+fv7S0hIzJw5MzY21svLS0JCwtzcPDg4uL6+vuPqSEzEo0Z9\nDabg4DbeXF6OL13Cs2djFRVMo+H+/U/v2RMbG9v8jT4+PvLy8vHx8e1RMoHFaqitTcnJWZySMigt\nbURq6tD2+6yf1pEjRzQ0NGpqapoml6DUMplbs7MhsDjD1T/sthQUFDg5OSkoKISEhLBf/PTpk6+v\nr4KCgra2tr+/f3V1NZ+f0rbqaqyvj4lYqa7GAwbgu3c5OpDJxM+fN2zaZNOnj5CQkJaW1i+//BIZ\nGUlc21ZUVNjZ2V2+fLk9S8cY47KysIyMycTjxsay9v64LoWDn+fa2lotLa19+/ZhjA8fPkwkV8dW\n+X82ZWVtarcTdoFot8Di+R92S65cuaKiojJ48OAWB21XVlb6+/traWkpKyv7+vqWNLvyIlOLwxe4\nVFBQcOzYsUmTJsnKykpKSrq4uBw6dEhMTOzx48dkltqS8vKopKTe1dVvWKyG9v6sLoWzn+fMzMzZ\ns2ffvn27urpaS0tr//79HV1nEyFFRYNjYz/U1QmwBtK1W2CR8Q8bY1xZWenu7k6n0/39/dmDkvLy\n8saMGRMdHd30nXV1dcHBwaamptLS0l5eXrm5uXxU/2P79uG1a789jY3F/fvz3Fh9ff39+/dXr149\nZswYCwuLgIAAEipsS1HRkZSUQXFxKrm5Xhh/f0MAtKzFn+fsbHzr1uF9+xYuXGhvb6+oqIgQEhcX\nNzU1nTFjhoGBgQBvDb148WLS3r13SksFVUA7abdhDWRMUn/8+LG5ufnbt2+jo6OXL19ODEoKDAw0\nMTHR0NDQ19dv+mY6ne7h4fHu3btz585FR0fr6el5eHgkJSXx/Z38PzW1locv8ERUVHTIkCG7du2K\njIw0NjbumD2ZVVQWGRo+NDNLr65++eVLZAd8YlfQ/Of59Wuko4Pc3N7fvVtfXz927NiTJ0++f/++\nqqrK398/NDT0xo0bgloqIz8/f/z48SZlZSMUFQVSQDtqryS8fBnPnPntaXg4njgR79uHX73CHHSQ\n19XV+fj4iIqK+vj4sHsBCgsLR40apaio2LQb60fu3r3r5OQkIiIybdo0Mmf/tttc1s2bNzs5OZHS\nVCvq6wuZzCri8fv3LmVl4e39iV1E859nFxf8458rR0fHBQsWdERhzTAYDCsrKxcXF2az8TRdQLsF\nVvN/2OHh2Noai4hgScm6oUM3bNgQGRn5+fPn5oempKTY2tpqaGhERUWxXwwPD1dVVR0yZAhX13ox\nMTHDhg3z9PTk+/tp4rvhCyQJCQnR1NQkq7Ufqap6lpIyKC1teErKwJycJSxWY3t/YhfB5S+qJ0+e\niIiIpKSkkPLhp06dGjFihI2NzYwZM7Zu3Xrx4sXY2Ngf3WJyd3c3MTGpqKgg5aM7m/a8S9jiP+zK\nShwVVbBz54gRI2RlZYWFhc3MzBYvXnz69Ons7Gz2wIUZM2aUlZX9d0Slu7u7mJhY024szp07d05f\nX5+876q9JCQkCAkJffnyRdCFgB/g8heVk5PTjBkz+P/Yf//9l06n+/n57du379dffx02bJiWlhZC\nSEhIqEePHiNGjFiyZMmBAwdu376dlZV14MABOTk5soKyE2qfwGrk6Pd2Y2Pj27dvDx8+7OHhQXRI\naWhoSEhIHDp0iB1Mjx496tmzZ+/evROIX26cKS4udnFxISLv3r17MjIy3H8PHa2+vl5UVPTFixeC\nLgSQ4+nTpyIiIqmphfw0kpubq6qqunnz5u9er6qqiomJOX/+/JYtW6ZPn25tbU0s86+hoXHy5El+\nPrGTa5/AOnaMh4M+ffrk5OS0aNEi4inRjUWn05t2Y3GoqqoKIZSYmIgxfvfuHUKIzPs1+fm48L+f\nwszM5uPXeTZhwtELF9LIaq1FjY0VlZVPWawudau70/L1zebn8Orqaisrq8mTJxO/v9esWbNly5aL\nFy++ffu2tra2+fvz8/N79ep14MABfj60k2uHu4RFRWj7dh6OU1VVNTExqa6uJp7Onz//5MmTly9f\n9vPzExcX56opKSkpKSmpoqIihJCamhpCiHhMjn370IoVXx+vWYN27yarYRptQXS0AVmttai09Hhe\n3hIhIXq7fgogzJql3fabfmzRokX19fXHjx8n7o9jjJ8+ferj42NlZSUlJaWvr+/i4rJ69eqgoKBn\nz559/vxZQ0Nj/vz5XXsPSvIDi7llC1JTQywWD8eqqqp++vSJeLxmzZr4+HgXFxfeylBTUyOaUlJS\nEhERYTdLAlVV9F9rNdraZfWkrdkydSpSViarsZY9e5YiLDylfT+jK2IxmR8WLmSlpHB1lL4+KilB\nQkLo8OGvr5iZoS9fODr2wIEDkZGRYWFh7C3ddu3aRXRU1dXVpaWlHTlyZNy4cfX19RcuXHB3d1++\nfDlCaPbs2cnJyezdlboekjehePXq1ZzQ0Je3bsnwtCCvmpoa+1TInNgZiVeqqqpEU8LCwkpKSiSe\nYd3T1DyFELGO5C4ZmVevXl0nqWU3N5Ia+oHKysqpU48/fPiwfT+mK3qfnm507FjZ7t3y3B+ro4MO\nHkRz5yLOrxMePHjg7e0dGhraq1ev5l8VERHR1dXV1dVt+iKxTpGampqLi0tQUJC1tTX3lVIAmWdY\njY2NCxYsGD51qoyVFW8tND3D4lPT7GOfbZGiUUEh8t279mi5vd29e1deXp7YfaeravGMpqQEmZl9\ne4+ZGfr4kbtmU1NTVVVV5eXleShJTg5NnfqtpDZlZWVNnjx5w4YNY8eO5fxT2N0m8+fPP3fuHIPB\n4LbO5vLz8+fMmTNp0qT4+HieG4mOjh4xYsSmTZv4rweRG1iHDh0qKSnZtm0bzy2oqakVFxezeLqc\nbN4UO0rYZ1ukUFNTKysrq6+vJ7dlnq8dOHf9+nVnZ+cuvxsFcUbz/wsj8is1NdXQ0JDnw1esQMeO\nIaJ7trGxtXcyGIyJEycOGTKE53/hI0eOlJWVDQ0N5e1wtoKCgqFDh2ZlZQkJCfXp02fSpElv377l\nqoX09HQPD49+/fpJSkq6kXT5QNrP7ocPHzZt2vTnn3/Kysry3IiqqiqTyfz8+TP/9TSNkqZnW6S0\njDEuLi5mt4xJ2sijPf6lsWGMb9y4MWbMmHZpvX1UVVUxmUxuj+L2jIYTfAaWjAyaPRsdOIAQQomJ\nSFER2dmh9esv7ty5MyIiIj09nf1tLlq0qKGh4cSJEzyvjk2j0WbPnh0UFMRztQihrKysAQMGGBsb\nR0VFhYaGvn//XllZ2cbGZsSIES9fvmzz8LS0tClTphgaGtbW1r59+zY8PNzCwoKfethIC6y1a9ea\nmprOmTOHn0ZUVVURQqRcZH13hkXihZuqqqqwsDD7FmRdXV15eTkpLbfHvzS2mJiYkpKSkZRaZXz1\n6tUDBgx4998FOOeantEQcnLQyJFf/8vJ4boS3gKr6S+ypUvR6dOoshIZGaHTp4n+yuJbt2798ssv\nBgYGUlJSvXv3Hjp06NWrVy9evCgtLd3Y+plYq+bNm/fkyZMULm8RsGVnZw8ZMsTS0vLSpUt0Ov30\n6dMqKioBAQGpqam6urr29vYjRox48eJFi8emp6dPmTLF2Ni4pqbm+fPnISEhpqamPH8jLSBlcMS/\n//5Lo9FiYmL4b0pWVvb+/fv8t3Px4kVdXV3i8Y4dO4YNG8Z/m2xKSko3b97EGBNRRcrA4uJibGmJ\nKyqwiQmuqsKmpvi/of7k2LJly+DBg8lssf1VV1f7+PiIiIh4enpWVnK0uDPxx4gx3rkT79jx9Y+x\nuBibmn57j6kp/vCBu0pUVFSuXbvG3TEY//XXt3owxvv2YYRa+GstLy9/9erVmTNnxo8fb2ZmhjEu\nKytTVlZOTU3l9hPZhg0b5uPjw8OB+fn5xICJuro6jPEff/whKyvb9J92UlKSu7s7jUazt7f/999/\n2a9nZma6u7uLioo6Ojo+efKE58pbR05g2djYzJs3j5Sm9PX1z58/z387Dx48kJKSIh4fP36c+Dkg\ni4mJCXs8sYSExKNHj/hvs6CghX9pJLK1td21axeZLXaUp0+fmpiY9OzZ886dO22+mR0Q1dXYxAT3\n6EFCYDGZTGdnZzs7u6b/Ptu0YgXm9rdkfn4+jUZ78+YNxtjJycnb25u745s4d+6cmpoatyvx5ufn\nGxgYjBkzhkir1atXKyoqtngikpiYyI6t0NBQT09POp1uY2PTdP5veyDnklBaWpqUnnJEXn+Tqqpq\ndXU1MQyV3E53doNpaWmJiYndunX7wl/3OIOBFi1CZWVfn7KvHRBCy5cjPq9lq6urAwMDbWxsSkpK\nMjMzeegSErgBAwbExcUtWrTIxcVlypQppaWlnBwlKYkWLeJhTaP/s2/fvitXrggLC585c2bw4MFj\nxozp37//tWvX2jxw587ga9ca9+/n7uM0NDScnZ2J7qf58+efOnWqoaGB25orKioQQq6urg0NDdev\nczHkprCwcMiQIQYGBpcvXyZmmBw7diwqKsqqpZv+JiYmp06dev78uZyc3PLly6OiooKCgl6+fDl8\n+HBuC+YOKbGXmJgoLy//xx9/8N+Uq6vrhg0b+G+H+LEmNnR4/fo1jUZr5GyGIydu3bo1evRoBQUF\nERGRkSNH8nMNGxODTUywhQUuLGzh2mHSJCwlhX19MQ8L7WZkZPj4+KiqqiooKKxYsSIsLKxnz572\n9vYFBQU8VytY796969evn5qaWnBLKxS/ePEiKCiIw6YaGlpecJX4ITlx4sS6deswxnFxcU3XJioq\nKvLx8ZGQkOjfv39ERMSPGt+8ebO8vPzr19E/ekMrLl++LCcnV11dXVdXp6KiEhoaytXhRO/4y5cv\nMcZz587t37//sWPHnj59WtrWSn6FhYW9evUaNmwYMYnN19dXRkbm2bNnP3p/YmIi8e+UOBXg5+qV\nK6TNJfz333/FxMROnTrFZzu//PILeyGhsrIyntf0YbFYISEhxPznnJwchNDHjx/5rI0QERGhqqo6\ncODA7OzsDx8++Pr6ysnJ8bAXBovFOnSIKSmJFyzAP+qfYbHw2bNYWxtPmrQuMjKSk2ZramoCAgKs\nra2FhISGDx8eEhJS998iuUSnu4qKCifXVp0Tk8kMCAiQlpZ2cXFhLzT08OFDe3t7cXHxWxyvTXbk\nyJEHDx6wnxI/J7dv3x43bhzGOC8vT0lJ6UcrZ7Bja8CAAc1jy9vbW0lJied9thsaGrp160Yk8sqV\nK0eNGsX5sWVlZb169SI6Z6qrq83NzYn9peh0OkJIRUVl0KBBnp6ee/bsuXnzJnG6TRxYWFhoaGg4\ndOhQYsmazZs3y8jIPH36tJXPOnLkSJ8+fTDGjx8/FhcX77C1t8ic/Hz8+HE6nc7n1lu///478UOD\nMZ4zZ46hoeGxY8danOrJuZCQEGLn9/T0dH7aqaqqIq7V/fz8mp6vlZeX+/v7d+vWTV1d3dfXl5Ml\nYojFCMeM8QvnYAU9BoO1ffsOWVnZYcOGtbjjDiEzM9PHx0dNTU1JScnHx6fFX3osFsvPz49Op/v6\n+lJ3gbeUlJSBAwcqKSnt2rVr6tSpNBpt2rRpXP2Sf/78uZiYmJub299//33ixAltbe3a2tq6ujod\nHR1iwYz8/PzWW/j06ZOPj4+4uLi9vf3d/9Z3X716tZKSUit/R5xYvXr1oEGDMMYJCQnCwsIcLj/Z\n2Njo7Oxsa2tbU1PDZDLHjx9vbW1NnC41Njamp6dfv359z549ixYtGjJkSPfu3RFCEhISvXv3njp1\nqrm5ef/+/cvLyzHGW7ZskZaWbrPXfOXKldOnT8cYHzt2zNzcnJ/vlyskr9awceNGRUVFfu6a/fPP\nP/369SMe19bWBgcHGxoa8ry7REVFxfz586WkpNavXz9q1ChiAVLe7mYmJycbGxsbGBi8evWqxTcQ\n1RoYGMjKynp5ebVy5RUQECArK9t0zS9OfP78mfgX4ubmlpGRwX69oaEhJCRk+PDhwsLC9vb2ISEh\nzRe32LFjh5+fH3vRnuvXrysqKrq4uLS4gCIlhIaGjh8/3tnZeejQoc1XX+FEWlqao6OjlJSUoaHh\n6NGj//nnH4zxiRMnuFopNCMjY86cOSIiImPGjFm0aNGPuqi5kpqaKiQklJycjDG2s7PbsmULJ0dt\n2LBBVVWVOOvcsmWLiopKdnZra0WUl5e/fv367NmzGzZsUFJSIs7p9u7dy+GJ6ujRo4k/9tWrV0+e\nPJmTCklBcmAxmczJkycbGhryvHXN7t27iW7spm1GRET07dtXSkrKy8uL8/WO79+/r6Wl5ejoyD4k\nPT3dy8uL+K0YERHB1XKA69b5chIxRLU2NjZiYmLu7u7fZXdJScnkyZNlZGRa7IXhRE5ODrErh5eX\nV2Jioo+PT/fu3aWkpDw9PYm7Sy0KDQ2VlZV1dXUlfosS7fTt21dbW/tH+dvJrVu3zs3NDWN89OhR\nKysr3hppaGjw8/OTkpLasWOHhoYGg8FgMpk8nHi+f//ew8Nj4sSJZO175ODgQAxKOHr0qI6OTpsl\nXbx4UUxMjDgtCg0NFRUV5epCZ+PGjQYGBg0NDTdu3Lh+/Tonh+jq6hJ388eNG7dx40bOP4tP5K+H\nxWAwNs6cyRg+nKuOYuI0YdCgQUJCQmZmZrKysj4+PoWF/7f42ePHj11cXERFRd3d3Vtfz6+2tpYI\nphYXKc3MzFyyZImEhETfvn0zMzldJ+vt2zLOvx12tcLCwi4uLs+fP8cYR0ZGqqmpDRw4MCsri6um\nmrtz546JiYmOjo6uru7OnTuJ0fatIxJKS0uL6JHFGNfU1CxcuFBcXPzo0aN81tPxli1bNnfuXIzx\n33//PXDgQH6aOnfuXL9+/ebOncv+kxG4EydOEIMSKisrZWRkWu9zjI+Pl5aWJjZDTEhIkJGR2bNn\nD1cf9+XLF0VFRc5X/qurY9nZbY+NTcEY9+rV6/Tp01x9HD/aZwG/L1+wuTkeMwZzcAqTnZ1N3Mwi\nrvuIKyniXzuNRnNxcfnuxCE2NpYYn+bi4tLilXZsbKypqamFhcW7d+9a+dxPnz79+af/nj0YIXzo\n0NcXyRpnyHb37t0RI0YICQk5ODgQe2rUkbRP3IULFzQ0NLg6HWDneGBgIPvF4OBgCQkJd3d3Ae5J\nxYO5c+cuW7YMY/zHH3+MHj2an6aYTObChQs71dUxg8GQl5e/cuUKxnj27NkLFy780TuLi4t1dHSI\njnai033OnDk8fOL27dt1dHQ4/OFMTsaOjrimBjc04Jkz61NSOu4np93WdM/KwurquNV+0Kw3b4jF\n2o2MjA4cONB82Xwim0REROzt7b8bkBYfHz9r1ixRUdEzZ86wX2QymX5+fhISEpwvUvrxI9bRwaam\nX08HSQ8sQnR0dFBQUNPbUvzbsWPHkCFDiMcpKSmc3wM9deqUpKRk04SKiYnp2bOnlZVV066xTm7y\n5MnEyAMfH58pU6YIuhzyEePOMMafP3/+0SCMhoaGYcOGER3tjY2NTk5ONjY2vO01XVVVpaamxuHO\nmAI8E23PTSiio/GjRy1s8F1fj0+fxn371unqjhs37s6dO633JWVkZHh5eUlISDTveMrOzmbHXF5e\n3tChQ7/ba6dNxMDorVvx3r0Yt1tgtYcFCxawf/E6Oztz2DVLiI2N1dPTs7KyyvxvH3NixIOcnFxY\nWBj5tbaDkSNHbtu2DWO8ZMkSsmZZdCqvXr2i0WjxxF7TP9DQ0LBmzRriL3HDhg0qKir87Gi3e/du\nRwcHTnbhu3qV5w/hV3sGFsY4P7+FDb5dXLCmJvbz4yoGPn365Ovrq6CgYGZm1nzE0+nTp+Xk5Li9\n74Z/MIOvuBhLS2Nn56//SUt3xsAaMmSIn58f8VhfX7/pmSYnysvLJ02apKSkxL4l1NDQsHTp0qFD\nh/I5iKRjODg4+Pv7Y4xnz569fPlyQZfTLpYtW4YQEhMTMzExcXNz8/HxCQgIePz4cfNrkYsXL3Lb\n0d5cLYNR//ffnLzz8GFcXNxCX0oHaOfAanGD78hI/IMt1dpUUVHh7++voaGhra3t7+9fVVVVWlrq\n5uamoKDAye6qzbXTXNkO0KNHj0uXLmGMGxoaREVFiX59rhCbqhGTMIi+sAcPHkhKSvKwl5oALFqE\nb97EGNfu2lXLZVhTRX19fUpKytWrV3fu3Dl//nwHBwcVFRWEkIiIiIGBgYuLy6pVqwICAo4fPy4t\nLb2XuEbgE2e3jP/8ExcXt9CX0gHaObD27cNr1357GhuL+/fnv9Wqqqq///5bS0tLXV1dR0fH1taW\n55kB7TFXtgPU1tYKCwsTQ34yMzMRQpzcKGzRjRs37O3tq6qqMMZBQUGmTb9z0Pl8/vz5+fPnJ0+e\nXLdu3aRJk8zMzFRVVVevXk3OrxliOHTzbpxmWuxL6QAkr+n+PU1N9OrVt6e5uUhLi/9WpaSkVqxY\nsWTJkosXL6qoqAwdOlRUVJTPNom5ssuX819dR8jKymKxWHp6egih9PR0OTk54ncvD0aNGjVq1Cji\ncUZGBtEm6LQUFBTs7Ozs7OzapXUaDTEYaPx4FBaGzM0Rg4FGjEAaGmjYMOLr9fUoNRVVVyN9fYQQ\nWrEC2dkhT892qaVF7bxarpMTev4cJSYihBCDgXbvRvPmkdW2qKjorFmznJ2d+UkrZWUUF/f1sZcX\nwhjJyyNlZZSQ8O09CQlIXZ2vUkmXkZGhoqJCLO6akZHx3X4E/DRL7GhLAUlJaPRo5OyMBg9Gp04J\nupou5PZtZGuLiC1gJCXRmjXor7/Q+vVo4sS+5jVSUsjCAp058/W9TVdS7RjtfIYlLY2uXkXe3ojF\nQrW1aN485Ozcvp/4c2h6KkTiaVFGRsagQYNIaap9tXoWAPiSl4e0m2ynqK2NUlMRjYZMTH53rVXp\nJWFkhOTkUEnJ168vXYpsbVFVVQdV186BhRAyN0c3b7b7p/xkvgusFjeD4rPZTu3Wre/PAo4ehcAi\nR/NuHFtbdPEiQqjFLUI7uC+li2+g0lU1vXYjK2XKysrKysqoEVjNzwL4XKkPsHHWjdNiX0oHaP8z\nLNAOMjIypk6dihDCGGdmZpKSMhkZGaKiojo6Ovw31e60tNDr19+eknQzByDU2btxILCoh8ViZWVl\nESFVVFRUVVVFVmBpaWmJiFDhR8LJCa1ejRITkanp17OAjRsFXVMX0om7cajw0wn+X0FBQV1dHRFS\nGRkZdDpdU1OT/2bT09OpcT2IOvtZAGg/EFjUk5GRISMjo6amhhCqr68fMmQIjUYjpVnKBBbq1GcB\noP1Apzv1lJaWamlp1dXVIYQcHR1v3bpFSrMUCyzwU4LAop6RI0c2NjYOHDgwl79bYxkZGWvXrk0k\n7gdRa9Qo+FlBYFGPlJRUdHS0np6eubl5aGgot4c3NjZeuXJl5MiRvXr1evDgQWVlJUKopqamsLAQ\nzrBAJweBRUnS0tLnz5/fvXv3rFmzli9fzuF2mxkZGcuXL1dRUfH09Ozdu3dycvKLFy+IWWlZWVkI\nIbKm+ADQToQwxoKuAfDu6dOnU6ZM0dfXv3DhQrdu3Vp8T0NDw9WrVwMDA+/fvz906FBPT89x48aJ\niYmx3/DmzZtt27aVlpY+fvy4owoHgBdwhkVt9vb2cXFxoqKiNjY2T58+/e6rOTk5a9eu1dbWXrRo\nkbW1dUJCQlRUlJubG5FWDAYjKCjI1ta2X79+jY2NBw8eFMR3AAA3OmING9DOGhoafHx8REVFm24+\nyGQytbS0jIyM9u7d+91O5dHR0e7u7uLi4jo6On5+fh8623JfAPwAXBJ2HRERER4eHo6OjsHBwXJy\ncgih7OxsbW1tISEh4g3V1dVnz54NDAyMi4ubOHGip6fn0KFDhYXhLBtQBgRWl5KWljZp0qT6+vrQ\n0FBzYjEDhBBCCQkJBw4cILbbXLx48cKFCzU0NARYJwC8gd+uXUqvXr2eP39uZWXVr1+/EydONDQ0\nnDp1ysbGxsLCIjMz8+jRo3l5eZs3b4a0AhQFZ1hdEMbYz8/v999/V1dX//z58/Tp03/55RcbGxtB\n1wUAvyCwuqy4uLiCggJ7e3v5jlmpCID2B4EFAKAM6MMCAFAGBBYAgDIgsAAAlAGBBQCgDAgsAABl\nQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwILAAAZUBgAQAoAwILAEAZEFgA\nAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYAgDIg\nsAAAlAGBBQCgDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwILAAA\nZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBY\nAADKgMACAFAGBBYAgDIgsAAAlAGBBQCgDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAy\nILAAAJQBgQUAoAwILAAAZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwA\nAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYAgDIgsAAAlAGBBQCgDAgsAABlQGABACgDAgsAQBkQ\nWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwILAAAZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCA\nMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYAgDIgsAAAlAGBBQCgDAgs\nAABlQGABACgDAgsAQBkQWJTU2Ngo6BIAEAAILEpycXE5cuSIoKsAoKMJYYwFXQPgTnp6uqGhYUpK\nioGBgaBrAaBDwRkW9Zw8eXLgwIGQVuAnBIFFMUwm8+TJk/Pnzxd0IQAIAAQWxdy6dau6unry5MmC\nLgQAAYDAopigoKBp06ZJSEgIuhAABEBE0AUALnz8+PHatWvPnz8XdCEACAacYVGJ2OXLBydPtrGx\nEXQhAAgGBBZ1YKyQnu7p4SHoOgAQGBiHBQCgDDjDAgBQRhcMrIqKCkGXQKqkJDR6NHJ2RoMHo1On\nBF0NAILU1QLr5MmTdnZ2XSezGAw0fjzauRPdvo1u3kQBAejePUHXBIDAdLXAmjlzprKy8uTJk5lM\npqBrIcPt28jWFpmbI4SQpCRaswYdPSromkD7aH4qXVKCzMy+vcHMDH38KKjqOomuFliioqIhISFJ\nSUmbNm0SdC1kyMtD2trfnmpro9xcwVUD2g2cSnOmqwUWQkhdXT00NPTvv/++ePGioGvhm6Ymysv7\n9jQ3F2lpCa4a0G7gVJozXTCwEEJ2dnYBAQFz586NiYkRdC38cXJCz5+jxESEEGIw0O7daN48QdcE\n2sGPTqVzctDIkV//y8kRVHWdR5edmuPh4fHy5cuJEye+efNGWVlZ0OXwSloaXb2KvL0Ri4Vqa9G8\necjZWdA1gXagqYlevfr2lH0qra2Nbt36+mLT/qyfVdc8wyL4+/vr6OhMmzaN2wWFS0qQkBA6fPjr\nUzMz9OUL6dVxzNwc3byJbt9GDx+iuXMRQig2VnDVdJyKiooRI0Z8/Em6meFUmjPkBJaXl9eFCxdI\naYpEoqKiFy9eTEtLW7t2LbfH6uiggwdRbW171MW38+fRu3eCLqLdSUlJ/fvvvwUFBYIupEOwT6Wd\nndGoUXAq/SPkBNbgwYPnzp378uVLUlojkZqaWnh4+OHDh0+cOMHVgXJyaOrUbydZnUu3bvFbtnwR\n5FlfR6DRaAoKCiUlJYIupKM0P5VWVkYJCd/ekJCA1NUFVV0nQU5gTZo0admyZa6urp3w96GVlVVA\nQMCvv/765s2bNt/c0IBSU1F9PUIIrViBjh1D1dXtXiG3Gn791TM/f+bMmSwWS9C1tC9lZeXi4mJB\nVwE6EdL6sPz8/KysrNzc3Orq6shqkyyzZs1atGjRpEmTioqKmr5eVlb2/PnzEydObN+e5uqKjIyQ\nlBQyMkKVlQghJCODZs9GBw4IpuZWiIqJhYeHx8fHe3t7C7qW9qWsrPwTnWEBDpC5WkNZWVm/fv0G\nDBhw8uRJstokS319/bBhwzDG48ePf//+fWpqanJycnFxsYiIiK6urpPTbknJcYaGyMgIGRkhFgsN\nH47i4hCDgWxtUVUVevsWycsL+nv4fy9fvhw8ePChQ4fmcdM729DQUFlZqaio2H6FkcjV1dXU1HTb\ntm2CLqRD1S9eTNfXR6tWCbqQzojk5WVSUlLs7Oy2b9/+66+/ktgsKRISEkaOHNmtWzczMzNDQ0ND\nQ0MjIyN9fX1RUdHv3llS8jWwEEL796Ply1FZWacLLITQqVOnFi1a9PDhw759+zb/KsY4KysrMzMz\nMTExKSmJePDhwwcLC4tx48b98ccfHV8wtxYuXEij0X6qHRjz8vL0evZMjY/vaWIi6Fo6I5LHYRkZ\nGQUHB0+ZMsXExMTR0ZHr45OSkLc3YjJRbS2aPx+Ruljd9evXVVVVX79+/aM3FBai5GSUnIw8PL6m\nFULIywt5eSGEUEMDapZsAubh4fHixYsJEya8efNGXV09OzubOHNMTU1NTU1NSUn59OmTiIhIz549\njYyMrKyspk2bZmRk1NjYOHr0aF1d3blEz24nNsHAoPHDB0FX0aHOnj1rbmkJafVDuB1s3rxZSUkp\nMzOTu8Oqq7G+Po6P//p4wAB89y5ZJbFYrF69ev3zzz/E05qamrdv3166dOnPP//08PBwcnKRl8cI\nYUlJ3KcP/vKlhRZ8fMiqhUy1tbW9e/c2NTUVExNDCMnJyfXt29fDw2P79u2XL19OTEysq6trftSZ\nM2fExcVfvXrV8QVzJzQUe3sLuogOZWFhsXv3bkFX0Xm1S2CxWCw3N7fevXtXV1dzcVhYGJ4+/dvT\nq1fx1KlklfT48WMJCYkvX75gjB88eCAsLIwQ6tatm6Ojo6en519/7bl+nZWZiZnMlg+vr8daWjgk\nJI+sekg0efJkZ2fn+/fvFxYWtvI2JpOZlZWVnJxMPF25cqWWltanT586pEbAkXfv3tFotIKCAkEX\n0nm1S2BhjCsrK83MzCZNmsRisTg9Zt8+vHbtt6exsbh/f7LqmTNnzvT/0rCysvLNmzcVFRVctRAS\nEiMhIfH69WuySiJFUVERnU5//vz5d68zGIzY2NgLFy5s3rx56tSpvXv3JnYGGz16NPGGxsbGkSNH\n2tvbt3gKBgRi/fr1jo6Ogq6iU2uvwMIYZ2VlKSsr79q1q/mX6urqUhIT8eXLePt27OGB+/bFo0bh\ny5fxzJnf3hQejqdMIaWS8vJyKSmpu3xfYK5bt05dXT0vrxOdZ/3999+mpqbE4/Dw8BUrVowcOVJH\nR4c4hezRo8eIESOWLl168ODBu3fv5ufnNz22tLRUT09v6dKlgiicM4mJeNQo7OSEBw3CwcGCrqZ9\nsVgsbW3tgIAAQRfSqbXj5GcdHZ3z58+PGTOmR48empqaKSkpRE9wSkpKVlZWY2NjuZ6erJYW6tUL\nTZuGBgxApqZo9WqUmIhMTb9Op9q4kZRKLl68qK6uPnToUD7b2bZt27t379zc3B48eED0GQlcUFAQ\ne1hDfHx8QUGBjY2Nu7s7cRtUWlq6lWMVFRXDwsIGDBhgaWm5YMGCDqmXG8QSUWFhyNwcMRhoxAik\noYGGDeOtMeJ8k8FgrF69ukePHuRWyqfa2toHDx5cuHChqqpq3LhxpLdfVVW1a9euGzduIITk5eWF\nhIQkJCTExcWFhYXl5OQQQtLS0qKioqKiosQPzIIFC5SUlEgvgxTtvmvO7t27L1y48P79ewsLC1NT\nU11dXRMTE1NTU01NTTqd/v27371Da9Z8XZlgzhxE0m2s/v37jx49mpQl/SorK+3s7KytrU+1urx6\nZWVlSkpKcnIycc8uKSlp8ODBW7duVVNT478GttevXw8cOLCgoICfH6+wsLCZM2c+ePCgX79+JNZG\ngitX0KVL6Ny5r0/Dw9H584j7KatMJnPnzp1btmwZPXp0fn7+27dvZ82a5ePjY2hoSHLBzbV61/vD\nhw/Xr1+/fv16VFQUi8UaOHBgbGzssmXLSF970tvbOzQ01MfHh0ajlZWVIYQYDEZdXR2LxSovL0cI\nVVVVNTQ0NDQ0VFVVVVVVFRYWxsfHE1nW6XTAWVxpaWlDQwNvR/L/6QkJCTQajcTruJSUFHl5+f37\n97Nf+fTp07///nv48GEvL68RI0ZoaWkhhGg0mp6e3pgxY1avXh0YGDhw4EAHBwdyO4wWLVo0ceJE\n/tvx8fFRV1f/7oJR8Mjo03z79q25ubmWlha7Q+Dx48cuLi5CQkLDhw9/9uwZWcW2oMW73iwWfv0a\nb968wNVVSEiIuCS/ceMGg8HAGD969KjFHkl+xMTEiIiIPHr0iMP3s1gsGxub1atXk1gDiToisHj3\n7BlOS+Ozjd9++83Z2ZmUcthCQ0PpdPqUKVPs7e2JUeNiYmIWFhZTpkzx9fW9cOFCXFxcTU1N00Mq\nKipMTEzc3d3JqqG6ulpWVvb69ev8N8VkMkePHt2/f//a2lr+WyNN8z5NbtKZyWT6+fmJi4t7enqW\nl5ezXyQexMfHu7u7i4iI2NvbR0REkFd0Ey3e9V61CtPpeNiw14GBqamp7C++f/+euD21YsUKPT29\nyspKUkpgMpm2trazZs3CGLNYrJCQkJCQkNDQ0KioqKioqPv377958+bNmzdv377NyMjIyMgoKirC\nGD948EBcXDw7O5uUGsjVuQMrIQFbWtZVVXF7HPsfXm1trZKSUkhICP+1sFisfv36PXjwAGPc0NCg\npqbm7Oy8c+fOiIiI9+/fNzY2ttlCSkqKnJzcvn37+C8GY3zq1CktLS3mjwZicOnz58/6+vqenp6k\ntEaOykqsq4sTEjDGuLoaOzjgtWuxiwvmYPhYbm7u0KFDu3Xrxg70T58+TZgwwdfXt+nbMjMzvby8\nJCQkrKysQkJCuLijzYkWzxDfvMH/pScbg8FQVVU9cuQIxri2ttbMzGzJkiWklHDkyBEFBYWPHz9i\njOvq6nR1dXV1dXV0dBQUFBQUFGRkZL673lq8eDFx4KhRozw8PEipgVydO7DKy89PnDhlypRWfpJq\na2vfvHkTEhLi6+vr5uZmbW0tLy+vrq5OfPXy5csqKiqkXIjdv39fXFy8tLQUY3z16lV5eXniNJ4r\n4eHhdDr93r17/NczePDgDRs28N8OW3JysqysLPHPprOIj8cjR369S3j8OC4txa6umEYLXbbszZs3\nPzooICBAVlbW1dWVOF8gXpGTkxs2bFhOTk7z93/8+NHX11dOTs7MzCw4OJjH7ovmuLnrHRYWJi4u\nnpCQgDGOiYmh0+k3btzg8/M/ffqkoKDAHizdirq6us+fP3/+/Jk9cDI+Pl5ERCQ6OprPGkjXuQML\n48zMTCUlpa1bt7Jfyc3NvXTp0rZt22bMmGFtbU3c15CSkurTp8/06dP/+OOPkJCQt2/fEm8ePXr0\nihUrSKlk5syZ06ZNIx6PHTuW55MRX19fRUXF9PR0fopJS0sTFhbmei5BS0pKSohOX4zxgQMHhg4d\nStZZWzthJiTMmz1bRETE2dn54cOHTb9UXFw8adIkRUVF9jl1UVHRpEmTpKWlAwICWj+B+vjx49q1\na2VlZfv160fOn0DzM8Rbt1p5+8yZM/v06VNfX48x3rJlS/fu3Uv568OdM2cOP9/LnDlzSO9L4V9n\nDyyM8Z07d0RFRa9du0Y83bt3b/fu3YcOHbp48eL9+/ffuXMnJyenxZ/F3NxcGo327t07/mv4/Pmz\nuLj4nTt3MMYFBQUiIiIvXrzgrSkWizV58mQLC4sq7i912UgcYbh7924TExPi8aZNm/r160dKs+2N\nOC2SlZW1tLQMDg5ubGwMDw9XU1NrehoVFhamqqo6aNCgjIwMDpv98uWLhYXFnj17yKnyuzPEtj66\nR48emzdvxhg3NDT069dvxowZPH/y/fv3aTRaK+ehbcrPz5eUlIyKiuK5hfZAgcDCGO/cuVNGRiYx\nMbHNd9bV1SUkJISGhv75559DhgwxNjYmpYBDhw5pa2sTv6z8/PwsLCz4aa2ystLU1NTV1ZW3TpPG\nxkYNDY3Tp0/zUwOBxWIZGhoSk9caGxs1NTUDAwP5b7bDFBYWrlq1Slpa2tzcXFxc3M/Pj+hMLC0t\ndXNzExMTY7/Suvr6+mvXrhG/QiZPnry2ad9TB7p7966oqCjxuzAlJUVSUvLChQs8tFNfX29qavrr\nr7/yWc+aNWssLS071Rk3NQILYzx9+nRDQ8Mv/z8vubi4+PHjx0ePHvX29nZxcdHX16fRaAghdXV1\nR0dHV1dXERGRyMhI/j/d2tqa+NVHTKLeu3cvnw2mpaXJy8v/+eefPBwbGRnJWw9ac0+fPhUVFSVm\nFN68eVNaWpqs+1MdqaSkRE9Pb8uWLcTTu3fv9ujRo0+fPkSXECfq6+uFhIRiY2Mxxr/88suCBQva\nqdQ2LVu2zMjIiOhL2r9/v7y8PA8jcnbt2tWtW7fyZr373CorK1NSUjp79iyf7ZCIMoFVWVlpbm5O\n3JibP3/+gAEDiNGSdDrd2Nh44sSJa9euPXny5MuXL8vKythH7dq1S1pams+rwtjYWPZIrkePHomJ\niZWUlPD57WCMb9++3fRSl0N1dXUuLi6//PIL8dTNzY2fn6d58+a5urqym5ozZw7PTQnWiBEjtm/f\njjF++vSpiIjIypUruQ10OTk54vJn48aNEyZMIK2yhgb8/wNcWldTU2Nqaurl5YUxZrFYo0aNGjFi\nBFdn4llZWZKSkqScgGOM9+zZo6Ojw8V4l3aeTUWZwMIY3717V0tLa8CAAfPnz9+1a1d4eHhaWlqb\n93RmzJjRs2dPfiJm2bJlTk5OxOM5c+ZMnjyZ56a+8+eff8rIyPzoRIDJZGZkZERERPj5+Xl6etrb\n2ysoKCCEdHR0vP9bdCU0NFRcXJy3DrWKigopKSkiMYlJ1I8fP+b5exGs6dOn//bbbxhjFovF270t\nfX398+fPY4z9/f0dHBxIqywoCHPZgRAdHU2n02/evIkxzs/PV1RU5OrW7cSJE4cOHcpdkT9GDIbg\n9JKiPVeIIlBpI9Vbt24ZGRnduXOHq6OCgoIGDRo0ffr0mzdvEheMXKmtrT179uyhQ4cQQhUVFZcu\nXbp06RK3jfzIunXr3r59O3HixJcvX0pLS2dnZzddfi8lJaWkpERUVFRPT8/Y2HjgwIELFiwwNjb+\n+PHjlClTBg8e7OLiMmnSpFevXrm6ur5+/VpDQ4OrT7906ZKcnNzIkSMRQmfPntXT03NwcCDrW+tg\n7NXfhYSE+vTpw08L5O58cU1I6KmOjh83h/Tp02fdunULFiyIj4/X0NDYtGnTrl27jh49KiQkJC8v\njxCSlJQUExOj0WiysrIIIRkZGRERETqdLiUllZWVde3atejoaIRQfn5+WVmZubk5P/XT6fQtW7Z4\neXnNnj277ZW1b99GtraI+ERJSbRmDTp6lOfpny2iTGA1NDScOnVq37593B4oLi4eGhpqa2u7cePG\nHTt2cHv41atXhYWFXV1dEUIXL15UUlJyJm/DOCEhoaNHj/bp08fOzi4rK6u+vl5RUdHQ0NDY2Hjs\n2LHe3t7GxsY9e/Zsvoizn5/fjBkzXrx4YWJismPHjoSEBDc3t3///ZerKdlBQUGzZ88WEREhHnf+\nBUhboaysnJGRwWcLRGCpqKiQuPNFZkXFk9JSbo9av379x48fa2pq2Hc/iat1Ym+36urq+vr6xsbG\nyspKhFBBQQGTyayrq2MwGJ8/f+7evTsxTXL37t1Pnz59/fq1kJAQD5UHBgYWFBRs2bKFGC00YcIE\ndXX172ZN/9qtW4/aWiQigmRkkJkZystD2trfmtDWRrm5PHx0KygTWNeuXWOxWBMnTuTh2B49eoSF\nhQ0bNszS0nLatGlcHXv8+PEZM2YQ87SDgoI8PDyIlVvIIisrq6WlpaSkFBgYaGxsrKKiwslRK1eu\nTEhIIE7N5OTkzpw507dv30WLFnG+/UdSUtKLFy9Onz6NEHr16lVqaqoHqQtSdzD+T4vYOaWsrFxW\nVsZkMnk4H2+urq6uhUn+baHT6cRK9qtWrcrOzo6OjuZw3xDi/uD+/fu9vb03b96sp6d35swZd3d3\nbgsoKCjw9vb29/dHCJWUlHz69IlYuaCmpqa2traxsTEzMxMhJFpairKzUUMDqqpC06YhXV306tW3\nVnJzkZYWtx/dBnKvMNvP6NGj+Vy56eDBgxISElyNTGEwGD169CD6RN6+fUvWWM2m3r9/LywsnMb9\nlMmamhpbW9tx48YRd52Tk5Pl5OQOHTrE4eHe3t6DBw8mHi9atIjMbmZBuHjxYs+ePflpYfXq1VOn\nTsUY5+TkIITYo+T59Mcff4wcOZK3Yy9duiQmJvby5Uuujjp//ry8vDwx6HT79u3a2to13PT6E6ZP\nn+7o6Eh09k+bNo39uA1cjpXlATUCKzc3V1hYOCYmhs92PD09tbW1ufpZZI/i+e2334YMGcJnAc2t\nX79+0KBBvB2bm5urqqrKnh939epVOp1OzHZsXX19vaqqanBwMP5vEjW3Nys7m/v378vIyPDTws6d\nO4m+agaDgRDiZNAfJzZu3Dh+/HgeDkxOTpaRkWm6KAiHmi63UFNTo6Wl9ddff3HVAjEcjLgXdOPG\nDfZjjnAzVpYH1Aisbdu2WVlZ8d9OfX39oEGDhg0bxu18MWIS9ZkzZ/ivoSliCGgwH3d/nz59KiYm\ndunSJeLp77//rqamlpub2/pRV65cYY/kCg4O1tLS4mR0ZWcWHx+PEOJntYmgoCD2eGApKanvJv3w\nbM2aNW5ubtweVV1dbW5uTqyywIOmyy0EBQUpKChwPsuntrbWwMCAnXf6+vqdaqkZKgQWi/V00qRz\nBw+S0tjHjx81NTVXrVrV+tvKyspevHhx/PjxNWvWjB07VkdHp0ePHh8+fCClBjZShoD+888/0tLS\nxO9AYt6PlZVV69t/JCcnnzp1ing8aNCg9evX81NAZ1BYWIgQ4mdJr4iIiO7duxOPtbW1L1++TEph\ny5cvn9l0CjRn5s2bZ2Jiws8gXvZyC42NjWZmZmvWrOHwwG3btuno6BA/P1u3bu3Zsyd3W8m0MyoE\n1v37WFyclMX8CDExMZKSksebnK8WFBTcvXv3n3/++fXXX4llSRBCdDrdxMRk4sSJ69atO3bsmJWV\nlYuLC7nTFFxdXUlZ0WXhwoUGBgbEiNmysjIDAwMOF3JITk5uj465jtd0qDpvnj17RqfTicfW1tak\nrFrx+PFjPT09KSkpFxeX4OBgDv/lBwUFSUlJcXEV1pKmyy1cu3ZNXFy8xZUqvpORkSEhIXHlyhWM\ncXp6uoSExNWrV/kpo+Ut8/hAhcCaNev/FkIjQ2BgoJSU1LRp0/r160fcoFVWVnZwcFiwYMGuXbsi\nIiKaD0n9+PGjlpaWN3nb5BUWFoqIiHDbpdqi+vr6gQMHOjk5EVd26enpX9r6QSFmNU2dOnXgwIH8\nF9AZsIeq5+bmRkZGcjtPs7KyMioqiviFNHLkyKYLhPCgsrLS09OTTqdv3779+vXr7u7uMjIyqqqq\ny5Yta31B0ejoaHFx8XPnzvHz6YSmyy0MHTp09uzZbR4ycuTIsWPHEo+dnZ3HjRvHTwFlmzdf4bL7\nrE2dPrDKyrCEBOnjZauqquTk5Nzd3Y8dO/bkyRMOx8E3PzXjx65duywtLUlpCmP84cMHDQ2NFi/u\nGhoa0tLSwsPDd+3aNX/+fHt7e/asJjs7u6brXlIae6h6WFiYjIyMpaXlhQsXeOibu337ds+ePZWU\nlP7444+m07w49+jRI11dXUNDw6a/jerq6iIiItzd3aWkpDQ0NLy8vJrPK/jy5Yu+vv6iRYt4+NDm\niOUWiCVGXr58SaPRWr9tdeXKFSkpKeJELDQ0lP2YZ3FxcSIiIvysGNGcgAKruBj/tzkVxhibmmKi\ne6j5RKTDh7GeHiZ3KUiMjx07xltP8+nTpyUkJPjfM5msSdRNPXv2TExM7Pjx4y9fvjx58uTatWsn\nTpxobGxMjANSUlLidlYTtdjZ2bFXc62srPT399fQ0NDW1vb39+fwWuzz58/EGg87duy4evVqv379\npKSkvLy82ryJwVZbW+vj4yMqKurj40N0TbJYrPfv3zd9T3l5eXBwsIuLi4iIiImJia+vL/EGFos1\nbtw4a2trEheq9vHxYS+3MGHCBPYU1OYYDIauri4xgbyyslJTU3PHjh38F+Du7k7uToudKbBanIhk\na4v5OzlvUf/+/XlerpOUPZOfPHlC1iTqpv7555++fftKSUlZW1u7u7v7+fmFhIQkJCR0rsXa28e+\nfftUVVV37drF3iK3rq4uODjYwMBARUXF19e39dOle/fuaWtrW1lZNZ0qT+xYQafT3d3dU1JSWi8g\nJibGzMxMS0uLvYZUTk7O8OHDTUxMWuz6zMrK+vPPP01MTGg02ogRI+bMmSMrK0ucJJKl6XILRUVF\nxOqALWKxWBcvXiR+Try9vS0sLEj5fZaXlychIXH79m3+myJ0psBqvmj/lCmYRsNkb1yamJjIT09z\nY2PjqFGjBgwYwM/Ky3PnziVxEnVTZWVlP+dmzkwmMyIigliE1svLi70qC5PJDAkJMTExkZGR8fLy\nan6rt6qqytPTU0RExNfXt8V/0k+fPnVxcaHRaC4uLi1e4DQ2Nvr6+oqKirI3vGCxWP7+/pKSkk23\nwPiRJ0+eeHh4zJo16+zZs2QtOcnG7XILcXFxoqKiJE6DX716tYWFBVl3qwQXWNLS2Nn563/S0vjD\nhxYW7bezw7yOEm6Ft7c3n0NAS0tL9fX1WznBbh2xEzUxHR+QLioqqn///t+dFjU0NJw5c8bMzExO\nTq7pcg6vXr0yNjbu1atXm5trERvtiIqKuri4NH1zWlqanZ1d0w0vcnNzR4wYoayszOHYiPPnz3fr\n1o24S9C/f39SrsXYGAyGtrb2oEGD3NzcZs+e7enpuXjxYh8fHx8fn23btvn5+e3duzcgICAgIODi\nxYsXL17s27cvPyudNkec5ZE1hrEznWG1uGg/2f0sxCBv/v/4kpKSeN6yITAwsEePHp1qIceuh7ia\nExYWdnFxYfc5slisyMhI4jSqrq6O6G/y8vLifChcRkaGl5eXmJiYvb19eHi4v7+/hISEq6sru4uA\n2PBi/PjxnHcafPnyRVRUlDh327ZtG5mL22CMMS4vLycSaunSpZ6envPmzXNzc3Nzcxs5cuTw4cMH\nDRpkbW1tbW2tp6enq6u7dOlS0scb+vn59e7dm5SmOlNgtf9EJIxxWFiYgoICD7Ormrty5QqHU2Ga\nqq2ttbGx2bhxI/8FgDbFxMS4ubkJCQnZ29s3XZ48Li7OwsJCU1OTuInGrbS0tPnz59PpdAUFhcOH\nDxMnR4WFhaNHj5aRkeFh6oKjoyMxxYpYLZL0zk3BqqmpKS4uJqWpzhRYuN0nImGMXVxcyNr0DWO8\ncePGVqbCtLICH7GMOugY7969Y2+bevXq1R07dhAbrLY5Wq11GzZsGD58OPGY2PBi8ODBWVlZPDT1\n119/2djYYIxZLJampibpk8C6DCGMMcnrP3RihYWF2trar169srKyIqVBFos1YcKE/Pz8J0+eiIqK\nZmZmslfgIx6UlZWJiYkZGBgYGhoSC10ZGhq+f/9+7ty5UVFRgwYNIqUMwInExMQdO3ZcuXJFRkZm\n165d/C+nExAQEBgYGB0dXVdXZ2FhMWPGjA0bNhDri3ErNTXV2Ng4Ly9PQ0Pjl19+qaioOHfuHJ/l\ndUmUWQ+LFMHBwebm5mSlFULo2LFjNBqNwWCYm5vn5eU1NDSoqKgYGRkZGRlNmjTJ2NjYyMhIR0fn\nu5WVbG1tk5KSpkyZ8ubNG01NTbKKAa0zNTU9c+ZMWVkZsWIn/w2yl9ASExOLj4/np01DQ0N9ff0b\nN24sXLjQZdSoR7t3IyYTkbEgV1cj6FO8jsNisXR1dTnZCJdz1tbWf/75Z21tbVhYmIeHRzwxiIwD\nTCbTxcWlT58+pGx+AwTi4cOHEhISZLW2y8fnqpcXxhgzGFhSEpO0XEQX8xMF1oMHDyQlJfnstmgq\nJiaGRqMRKwQ8fvyY28YrKipMTEzc3d3Jqgd0sMTERIQQPxvi/p9797CZGSbuHaekYP6mxXRVZK72\n28kdP37c1dWVmOpMVoMjR44ktn4ICgqaMGECV43LyMiEhYVFRET8888/ZJUEOpKysjJCiLQF4AcO\nRNLSiFiA29AQ9ehBTrNdjKATs4OUlZVJSEjcv3+frAYZDIa8vHxoaCi78Xv37vHQTnh4OJ1O//ff\nf3kpop33gAOta2xsFBYWJnNy72+/YQx/ra3psp3unz9/Tk5OTk5OTklJSUpKio+P19bW7tevH1nt\nE+sRjx07FiEUEhLSvXv3IUOG8NDOuHHj1q9fP3ny5NevX/fs2ZOLIxkMNH48CgtD5uaIwUAjRiAN\nDXK3VAKto9FoCgoKJG6xgwYNgr/W1nWRwMrNzU1JSSESKjU1NSkpqaioSExMjBhMYG1tPWnSpJ07\ndy5evDg4OJiUTwwKCpo1axZ7N505c+bwtpkSQuj3339PTEycOHHi06dPJSUlW35TQwPKyEDJySg1\nFaWmIiEhNHZse+8BB9pE7iaGaODADtjaj9IoHFgxMTH+/v5EQlVWVqqqqhKjnMaOHbtmzRpDQ0Md\nHZ2mW3INGzbM1tZ2586dPj4+/H52Ts4uJlNi9myE0Nu3b6Ojo8PCwnhuTEhI6Pjx4/379/f09Dxz\n5gyLxcrOzk5MTExKSsrMzCQeTNHUPJKYiHR0kIkJMjVFdnYoN7e994ADbWLvZkgORcUO2NqP0qga\nWJ8+fRo8ePDMmTOXLFlCTF4lBpG3QkdH58KFC6NHjzYzMxszZgxfH3/sWJ+aGmRhgRAqu3593oQJ\n3O66/B1paemwsLC+ffvu3bv377//zs/PFxUV1dXVNTIycnBwmDdvnqWJCerVCzXdnC4srN33gANt\nUVZWLuV+n9TWaGrCX2trBN2JxiM/P78fLdf54cOHqKiogIAAHx8fFxcXXV1dOp3+4sUL4qt79+6V\nlZXlaxMnFgtPm4YDAzHGuLYWKynh0FDeW2vi9u3bxKK6KSkprSxdhDHGpaUdM/UStG7BggVkLRD6\nFfy1toqSZ1gY4+PHjy9evBghVF1d/fTpU2I2TFpaWlpaWm5uLo1G69mzp7GxsYmJiaurq7GxsTnR\nKdBsz2RePl5ICOnpoenTEUIoPBzRaGjsWFK+Lycnp7lz56alpRFbjROYTCZxYTgwN1cpJgalpKDU\nVNSrF3r5El29iry9EYuFamvRvHnI2ZmUMgDnlJWVk5KSXFxcfv3111GjRvHcj/mNtDT8tbaCknMJ\nHz9+PGLEiIKCAiUlpczMTCsrK0NDQyMjI+La0NjYWF9fv5X9waurqx0dh9jbb/3775Ec/YAlJSFv\nb8RkotpaNH8+8vBAFy+iqVMRQsjZGZmZoT17yPrWiCV6XV1dk5OTExMTibucdXV1qqqq911cTEVE\nUK9eyNgYWVggmNPTCaxbt+7Ro0d9+/Y9evSovr7+mjVrpkyZwtt0QsARQZ/i8WLOnDk8bE7ZVFYW\nU1kZb9vGwVtbXLiZWK00OxsLC2P+tmNqqqioiE6nb9261dDQcMKECT4+PsePH3/+/Pnnz5/J+ghA\nFhaLdeDAAXFx8aNHj2KMKyoq/P39u3Xrpqam1uZyzIBn1AssYrnOW3xf2D97hsXEOHhf84Wbp079\n+tjXF/fty2cZTe3du9fExITEBkE7ef/+ff/+/bt167Zy5cqdO3eyV5EvLy/fvn27qqrq6VGj8Pbt\nuK3FkQG3qDc15+LFi8rKyiNGjOCznf790b59qKQECQmhw4e/vmhmhr58+f/3/eg2M5OJgoLQvHl8\nltFUYGDg3LlzSWwQtIfAwMA+ffqoqqrGxcU5ODicOHFCW1vb19e3tLRUVlZ23bp1WVlZEyZMQIcP\nI21tlJKCkpLQ6NHI2RkNHoxOnRJ0+RQn6MTkWr9+/Yi1GUlRXIx1dLCpKSaWIDU1xd+fy7e4cDPG\n+OlTLCGBybtYe/78uaioKJ+b8YB21VBaOnXqVAkJif379zfdqDUqKmrAgAHEKvJpaWlfX2UycUQE\nvn27hS4FwCuKBdbbt29pNBrn+8S1qbgYW1rirVsxsUNgC4HVym3m7GyyysAYL1iwYMKECSQ2CPjS\nfELfq1e4e3e/WbOSk5NbPOLGjRsODg4S4uIV3t6YvSfT5cs/7FIA3KNYYK1cudLJyYmUpoqKMP4v\nsCoqsIkJrqpqKbBwRyzczKqutjU3v3LlSns0DrjW4p2W4cOxvz9ua+uQpCdPsLMzFhHB7u44MbGF\nvaD692/P0rs4Kt1/raurO3Xq1KFDh0hpbcgQtHw5cnVFCCEZGTR7Njpw4AdvNTdHN2+S8qE/IhQa\n+rK4GPM5/h6QpcUJfUePIh2dNg81trdHt26hxES0cyeytESbN6O8vG9fhpHr/KFSp3tkZKSwsPCE\nCRP4b+rJE5SRgSZN+vbK0qXo9GlUWcl/2zwZPFjo7VthUVEBfTz4fy3eaeEgrb4xNUWnTqH4eOTo\niJ4/R4mJCCHEYKDdu8m9UfOzodIZVlBQ0MyZM1sZEcpNU2j8eKSoiNgTVyUl0aJFaPly/tvmSdN/\nHkDgyJrQZ2yMEIKR62QS9DUpp3Jycti7eKempq5cuZLnjUjLy7GUFL59m9T6eANLtXVOMKGvs6LM\nGVZ+fr61tbWxsTFCSFZWNiQkRExMbMeOHTw0deECUlZGw4eTXSK3YKm2Tgsm9HVagk5MTn38+FFT\nU3PVqlXE0+fPn4uLi58/f56HpmbOzNm4kYUxZrFwSgoW2Ca7rYyhBwC0hDKd7mpqatevXz9y5MjR\no0cRQnZ2dgEBAfPmzYuOjuaqnaSkpPPne86Zk4kQevWKaWGBBDb7G5ZqA4BLlAkshJCFhcWpU6eW\nLVv2+PFjhJCHh8e8efMmTZrE1Rq1x44dGzp0qJ6eHkIoKGjxvHl7lJXbq+A2aGrCDW8AuCPoUzyu\nrVu3Tl1dPS8vD2Pc0NDg6Og4dOjQhoYGTo6tra1VUlIiLiSJSdR37txp33JbAT27nZ+PD379WtBF\ngG+odIZF2LZtm42NjZubW11dnYiISGhoaFZW1urVqzk5NjIyUkhIaOLEiQihkJAQZWXlYQLs5Gb3\n7Do7o1GjoGe3E8rOzc1MShJ0FeAb6gWWsLDwuXPnKioqFi5ciBBSUlIKCwsLDAw8fvx4m8ceP36c\nPZKL2Oqm6S4VAkCMob99Gz18iObObbZSBBCwX798OZOdLegqwDfUCyz0357J165d279/P0Kod+/e\nR48eXbJkyaumg/2aYbFY6urqxPot8fHxr1696nRruURHo8JCQRcBvlFRUSF5jwnAH0oGFkLI0NDw\nwoULa9asuX//PkJoxowZCxYsmDp16ufPn390iLCwcFBQkKWlJULo5MmTQ4cO1e5s48ulpNCsWazG\nRkHXAb4ieRcvwDeqBhZCyNnZedOmTW5ubhkZGQihvXv3bty4UV5evs0DS0tLg4OD53XCKV0GBqel\npX/z9hZ0HeArJSUlCKxOhTIj3Vu0fv36d+/eTZw48dmzZ1JSUvPnz2/+ntzcXGJPneTk5LS0tJSU\nlPz8/KlTp44fP77jC26DkpLNzp1L7eyMjY0XLVok6GoAUlFRgcDqVCi5a05T1dXVAwYM0NfXDw0N\nLSoqevfuHXu35MzMzLy8PCaTqaOjY2JiYmpqSvxfV1e3zV1XBejq1atTp069c+fO4MGDBV3Lz+7K\nlSsrVqzIyckRdCHgK8oHFkIoLS3N0dGRRqMVFhZijLW1tQ0NDU1MTIiNv4yNjZUFNjaUR5s2bTp6\n9Ojr16+1YCipQL169Wr+/Pnv3r0TdCHgq64QWAihmpqaK1euGBkZGRoaSklJCbocfrFYrAkTJuTn\n5z958kRSUlLQ5QDQWXSRwOp6ysrK+vXrN3jwYGLuJAAAUfouYdemoKCwY8eOhIQEJpMp6FoA6Cwg\nsDqvu3fvKisr02g0QRcCQGdB7WENXRiDwTh//vzJkycFXQgAnQicYXVSly9flpSUdHFxEXQhAHQi\nEFidVFBQkLu7u4gInAID8A3cJeyM3r9/b2RklJyc3KtXL0HXAkAnAmdYndHJkycHDBgAaQXAdyCw\nOh0mkxkcHNzivEgAfnIQWJ3OrVu3Kisr3dzcBF0IAJ0OBFanExQUNG3atC4wwQgA0sFNqM7l48eP\n165de/r0qaALAaAzgjOszuXs2bOmpqZ9+/YVdCEAdEYQWJ0Ixvjo0aNz5swRdCEAdFIwDqsTef78\n+ZAhQwoKCpSUlARdCwCdEZxhdSJPnjwZO3YspBUAPwJnWJ1LY2MjTMcB4EcgsAAAlAGXhAAAyoDA\nAgBQBgQWAIAyILAAAJQBgQUAoAwILAAAZUBgAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACU\nAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYAgDIgsAAAlAGBBQCgDAgsAABlQGAB\nACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwILAAAZUBgAQAoAwILAEAZEFgAAMqA\nwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADKgMACAFAGBBYAgDIgsAAA\nlAGBBQCgDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAAAJQBgQUAoAwILAAAZUBg\nAQAoAwILAEAZEFgAAMqAwAIAUAYEFgCAMiCwAACUAYEFAKAMCCwAAGVAYAEAKAMCCwBAGRBYAADK\ngMACAFAGBBYAgDIgsAAAlAGBBQCgDAgsAABlQGABACgDAgsAQBkQWAAAyoDAAgBQBgQWAIAyILAA\nAJQBgQUAoAwILAAAZUBgAQAoo/MGVmVlZUpKSk1NjaALAQB0Fp0xsN69e/frr79qaGhMnjx5wIAB\nGRkZgq4IANApdKLAqqqqCgwMtLa27tOnT0lJSVhY2KtXr2xtbXv37n3hwgVBVwcAEDwhjLGga0Dx\n8fEHDx68cOGCnJzckiVL3N3du3fvzv7qqVOnFi9ePGvWrAMHDtDpdAHWCQAQLEEGVk1NzenTpwMD\nA2NiYoYNG+bl5TV69GgajUZ8lcVilZeXKygoIITi4uImT56soKBw6dIlHR0dQRUMABAswVwSZmVl\nrV27VkdHZ/369cOHD09NTY2Kiho7diyRVkVFRTt27NDT01u5ciXx/t69e8fExGhra9va2t65c0cg\nNQMABA93rJiYmMmTJ4uKipqZmR08eLC8vJz9JRaLFRERMXz4cGFhYSsrq+Dg4JqamqbHslgsf39/\nOp3u4+PDZDI7uHIAgMB16CUhi8VydHRUVFRctWrVwIED2a+XlJQEBQUdO3assLBw1qxZnp6e1tbW\nP2rk4cOH06ZN69Onz+nTpxUVFTukcABAp9ChgZWXl9ejR4/CwsJu3boRr9y9ezcwMDAiIkJDQ8PL\ny2v27Nny8vJttlNQUDB16tS8vLyQkJB+/fq1b9EAgE6jQ/uwMjIypKSk1NXVEUL//vuvg4ODk5PT\nly9fzp8/n5qaunz5ck7SCiGkoaHx4MGD6dOnOzo67tu3r32LBgB0GiId+WEZGRk9e/YUEhJCCNXV\n1fXr1+/EiRMGBgY8NCUiIuLn52dqavrLL79UV1evX7+e7GIBAJ1Oh14Srl+/Pikp6erVqyS2eebM\nma1bt6alpZHYJgCgc+rQS8LMzEw9PT3+24mJibG3tyeilslksoduAQC6to7uwyIlsJKTk4uKiohL\nS7LaBIA39+7dazoxA7QrSgZW03YgsIBg9ejR48OHD2VlZYIu5KfQcYFVVlZWVlZGSrg0vbQk6zKz\nyystLWWxWIKuogvS0dGh0WiZmZmCLuSn0HGBlZGRISIioq2tTUpTcIbFofLy8n/++cfMzGzAgAFD\nhw79+PGjoCvqakRFRbW0tGARpI7RcYGVmZmppaUlKirKf1PskKqoqCguLobAatHdu3enTJmipqZ2\n5MgRLy+ve/fuqaiomJmZ3b59W9CldTV6enrp6emCruKn0KFnWKQkC4PB+PjxI9FUZmamsLBwz549\n+W+2yygrK9u5c6exsfHo0aMRQpGRke/evfP09NTU1AwJCdm0adO4cePWrl0Ll4ckcnObpqamK+gq\nfgodN3CUrMDKyspCCOnq6hJtamhoiImJ8d9sFxAXF3f48OHz588rKiouXrzYw8ODPQWKmDgqLCy8\nfPlyKyuradOmxcfHnzlzBiZjkmLRogWCLuFnIZgzLH5+vWdkZKirq0tKSiLowEIIIcRgMAIDA21s\nbPr06VNYWBgWFpaZmenj48NOK4TQwYMHR40aVVJSghAaNGhQXFxcfX197969X7x4IbjCAeBah/Zh\nEeFSXFxsamp648YN3trJyMggTq/QT3+L8MOHD7///ru+vv7q1avt7Ozi4+OvXbtGrM/z3TunT58u\nJCRkYmJy9+5dhJCqquqtW7dmzJgxZMgQmIzJp5ISJCSEDh/++tTMDH35Ish6uriOWcWmtrZWWFg4\nLi6OeBocHCwpKenl5VVfX89tU0uXLvXw8CAeDx8+fPv27WQWSinu7u7GxsYBAQGVlZVtvpnFYvn5\n+dHpdF9fX/ZqYuHh4fLy8jNnzqyqqmrnYrus4mKso4NNTTGxepupKS4rE3BJXVgHBRaDwRg8eLCn\np2ddXR3xSnR0dM+ePQcOHFhYWMhVU6NGjdqyZQvxuGfPnhcvXiS5VoqoqakREhKKjY3l6qjr168r\nKiqOHTv28+fPxCtpaWkWFhZGRkaJiYnkV/kTKC7GlpZ461a8dy/GEFjtrIMuCSUkJAIDA589e9av\nXz9ixEqfPn1ev34tKSnZu3fv+/fvc95UVlYWcUlYX1+fm5v7014SZmdnY4yJbz85OfnMmTOcHDV6\n9OjY2NhPnz5ZWVm9fv0aIWRgYPDy5Ut7e3s7O7tLly61b9Fd14oV6NgxVF0t6Dq6vI5Mx8rKymnT\npsnKyl6+fJl4hX2d4ufnx2KxOGmkvr6euJB8//49QqjsZ/11FhkZqaKiQjz+66+/Bg0axPmxtbW1\nnp6e4uLiAQEBxCssFmvv3r10On3Hjh3k19qlEWdYGOOdO/GOHdjUFGdn46IiAVfVVXX0mu4Y44CA\nADExMR8fn8bGRuIV4jpl/PjxX7584bwdf39/OTm5dimRCvbt22dnZ0c8Xrx48dy5c7ltgehJdHd3\nr66uJl4JDQ01NTVlMBhkFtp1MZl4165vgVVdjU1McI8eOCEBi4lhb+/njx8/FnCJXY4Ads3x9PS8\nd+/e6dOnhw8f/unTJ4TQ6NGj4+LiPnz40Ldv34SEhNYPr6qqCggIsLKy8vb2ptPpCxYsqK2t7ZDC\nOxf+5yd5eHjcvXv333//HTJkCDF3V0pK6uPHjxISEiTX2hVVVNRNnoxCQr69IimJFi1CublIQwPd\nvInKyiKHDRtmaWl55MiRqqoqwVXapQhmmy97e/u4uDgREREbGxtiKJCWltb9+/dtbW0dHBxu3brV\n4lHEXBNlZeVdu3ZNmzatoKDg2bNnr1+/trKySkxM7NjvQPAyMzPZwzuaDvXgSv/+/aOjo0eOHEks\nTg3j2jgUHx9vY2MuKXktPBwpK6O4uK+ve3khjJG8PBoyBB07ti03N3f69Om7d+/W0NBYtWqVICvu\nMgR4dtfY2Ojr6ysuLu7v789+8Z9//omJiWn6tsrKyoCAAGtraxERETc3t6ioqKZ7fNXU1MybN09a\nWvrChQsdV3onYGxsfPLkSYxxfX29iIjIy5cv+W9z5cqV06dP57+drs3f35/o02hoaODk/Uwm8/bt\n23379j1+/Hh719blCTKwCBERET8aCvT27VtPT09ZWVktLS0/P7+CgoIfNRIQEECn05sOm+jamEym\nuLg40UVC3HUtKSnhv9lx48Zt2LCB/3a6qqqqqlmzZklJSZ07d47bY93d3b28vNqjqp+K4AMLY5yW\nlmZubm5pafn+/XuMMYPBIE6phISEhg8fHhISwkkMvXnzRkdHx9bWlrjf37Xl5eUhhIghbHfu3JGX\nlyelWTMzMzgLaMXcuXO1tLRevXrFw7GbN28eM2YM6SX9bATTh/UdAwODJ0+e6Onp2dnZ/fLLL4aG\nhqtWrbK2to6Ojo6KinJzc6PT6W02Ym1t/fr1awUFBRsbmy6/nX3TDdN47sD6Dsb4J5/q1Domk3n6\n9OmAgABbW1seDoclaEjRKQILISQrKxsaGjpu3LinT5/6+PgUFBQQtwK5akRZWfnGjRtLlixxcXHZ\nvHlzF15BpemGaU173/nx8eNHBoMBgfUjNBpNV1eXmEDOAz09vezsbCaTSW5VP5vOElgIISEhIQ0N\njZ49ey5ZskRWVpa3Rmg02ubNm69cubJ///5x48Z9/vyZ3CI7iaanQiSulC8hIQH7KbRCT0+P55VF\n9fT06urqCgoKyC3pZ9OJAguR929vzJgxcXFxRUVFVlZWr1694r/BzqY9FonOyMjQ0dEhztpAi/gJ\nLFVVVRkZGVhJmU9dM7AQQj169Lh//37//v2HDh3a9UZpfRdYpFwSwiCsNvETWPwfDlAXDiyEEDE4\nq1+/fhcuXOC3raQkNHo0cnZGgwejU6cQQqikBJmZfXuDmRnqwP0d2H9QTCZz7969lpaWJLYJfkRf\nXx8CS7A6UWB9+fKltLSUlJOFpmpqapSUlPhqgsFA48ejnTvR7dvo5k0UEIDu3SOpOu40NDRcunTJ\n3t5eRkbm9u3bLBaLRqMtXLhQWVmZtwaZTGZdXR3xGAKrTXp6ekVFRZWVlTwfDoHFp04UWJmZmTQa\nTUdHh9xmSfh3ePs2srVF5uYIISQpidasQUePklIb54qKivz8/AwNDX/55Rc7O7u///776tWrjo6O\nHz584K3BgoKCLVu26OjoHDt2jHiFrEvLLqxnz57CwsI8b0EIIxv417kCi/QdJSorK4uKivgNrLw8\n1HQ7RW1tlJuLEEI5OWjkyK//5eTw9RE/wGQyL126NGLEiO7du9+7d2/nzp0fPnzYs2ePq6srb6uJ\nEedoI0aM0NbWfvPmzYkTJxYvXowQKi8vLykpgTOs1omLi2toaPB8llRXVwf3NPjUcbvmtKk9LkmI\nfcD4PXHQ1ERNbzXm5iItLYQQ0tZG7HnaTfuzyFBcXHz8+PGjR49++fJlwYIFBw8e7NWrV9M3KCkp\n3bx5c9euXaNGjdq6deuaNWta/8eQk5Nz+PDhU6dONTQ0zJ8///Dhw/r6+uyvvnnzBjZM4wRvl3UY\n4717965fvx5W0OeXoIfaf7Nw4cIFCxaQ2+bly5c1NDT4baWyEuvq4oQEjDGursYODvjWLVxcjE1N\nv73H1BR/+MDvB2GMMSYG94uJidnb24eEhNQQS4X/GLGa2IQJE1pcTayxsTEkJITYmYKY51RbW9v0\nDY8ePZo+fbqYmNjq1atJqb9rmz9//qJFi7g65PPnz6NHj1ZXV3/48GE7VfXz6ESBNXToUNKXu9y1\naxdXS3H+UHw8HjkSOznhQYMwMduO7MD6/Pmzn5+fkZGRlJSUp6fnmzdvOD82Jyenb9++hoaG7969\na/r6yZMnjY2NxcTEpk+f/ujRo6ZfIjrF9PT0JCUluf24n1lISIiJicnvv/+el5fHyftfvXrVo0eP\nAQMG5Ofnt3dtP4NOFFg6OjohISHktrl48eI5c+aQ22Z7qKurmzx5sra2tp+f36dPn3hooaamZuHC\nhd8ts3PgwIEdO3Z81yD7DM7ExCQgIOCnXWOaN42NjUeOHLG0tBQREZkwYcKtW7eaLnb0nWPHjomL\ni3O+EA1oU2cJrLq6OhqNFh0dTW6zo0Y17NpVRm6b7SE5ORkhVF5ezmc7wcHBEhISnp6ezfdPY5/B\niYqKEsuKcbiIPmhRUlKSl5eXtLR0t27dfHx8cnJymn61rq7O09NTSkrq7NmzgqqwS+osgZWWloYQ\n4mpNd07o6uLz58ltEuOPHzGXW5O1KTIyUlVVlZSmYmJidHV1HRwc2MuHxcbGenp6ysjI9OjRw8/P\nj9t91UArKioqAgICLC0taTTa8OHDIyIiWCxWVlaWtbW1sbFxcnKyoAvsajpLYN28eVNJSYn0Zj9+\nxNxv1dqWkSPx5s3kNrlv377+/fuT1dqnT5+GDBmipaW1a9eu4cOHCwkJOTg4nD179rvudkAWFosV\nFRU1ceJEERERY2NjNTU1Z2dnUpZUBN/pLOOw2mnUopoaEhUluc3q2lpmkwEBpCB3ISpVVdWoqKih\nQ4cePXq0V69eb9++ffz48YwZM8gd4wbYiJUmL1++nJOTY2Rk1L179+vXr/M7vwK0pLMEVt++fXNy\ncs6fP09KayUlSEgIHT789amZGfryhZSGEZPJVHj69BXZ2Ur6GDQajWZjYyMvL3/w4EFzYow+aH/d\nu3efOnVqWVkZjUYTZB3Np752FZ0lsGxtbfft2+fp6enh4cFgMPhvUEcHHTyISN8ALC8vr6GhgfQB\nru1xgglzAwVCT08vLy+vvr5eYBV0mqmv7aGzBBZCaNq0aW/evImJiXFwcOB5uhabnByaOvXbSRZZ\nMjIyZGRkVFVVSWyT6KZtj1H+EFgdT09Pj8lk5rTPVC2OdIKpr+2nEwUWQsjQ0PDFixcGBga2trY3\nb97ks7UVK9CxY6i6mpTSvsrMzCR9/srHjzXW1v49e/Zq+63cgDMsgVBQUFBQUBDkqgw/mvraJXSu\nwEIISUtLX7x4cceOHa6urmvXruVhXXb2yZmMDJo9Gx04QGZ57ZEC799LxcUt6tZNhcQ2MewoIThT\np05tbBRcH5amJsrL+/aUPfW1S+h0gUXw9PSMioo6derU8OHDi4qKODmEwUCBgcjGBrm6fntx6VJ0\n+jSqrERk7UfRHoGFMfr1V0TuNP4PHz7U1NRAYAnE4cOHXVxGCOzjnZzQ8+eIWGWXwUC7d6N58wRW\nDNk6aWAhhAYOHPj27VthYWEbG5uXL1+28s6cnJxt26r19NCWLWj0aHT79rcvSUqiRYtQbi4qKECk\nrJPcHoHl6Ih27SK3SdhR4idGp6OrV5G3N3J2RqNGoXnzkLOzoGsij6AHgrWhoaHBx8fnu+3s2V+6\nevXq6NGjaTSap+fD0NDWxoh++oRlZPDFi7xXUl9fHxIS0qNHj0OHDvHeSkc5ceKEiYmJoKv4GRUX\nY4Qw+2fE1BR39GRNnuaiUkVnDyzC1atX5eTk3N3dq6urMcbv3r3z9PSUl5fX1NRsfQv7pv7+G9Pp\neNeu7MbGRq4+PScnZ8OGDerq6nJycmPGjBEXFw8ICODl2/h/7fqTvWnTprFjx5LWHOBYcTHW0cGm\npphYFkgAgZWS0rGf16GoEVgY47dv3+rr6/fu3dvJyUlYWLhfv34nTpxgMBhcNfLsWaOBgdWwYcM4\nWRGBOKWyt7cnxjGHhITU1dVhjCMjI1tZf4pz7fqTPWPGjBUrVpDWHOBYcTG2tMRbt+K9ezEWRGDV\nxcR06Od1rM7bh/UdCwuLN2/e0Gg0YWHhN2/evHjxYs6cORISElw10r8/7fnzKBERETMzs6ioqB+9\nLScnZ+3atT169Fi0aJGDg0NSUhKxJAudTkcIjRkzJjY2trCwsG/fvgkJCfx8U+00WAzBAu2C1h5D\naji0+J9/bty4gTG++8cfZV1ugzvKnGER1NTUrl27xmcjLBbLz89PVFTU19e36WJGTRfn7N27d0BA\nQEVFxY8aqa2tJVYXOXfuHG9lEL+KKyqwiQmuqiL5V7GysvL169dJaw5wjPhrxRjv3Il37MCmpvjz\nZ64bKSoq4rbjgsBkMqWkpO7du0cMBCsuLuahEX5UV1dv3769lX84fKJSYFVUVCCEkpKSSGktMjJS\nQUHBxcXl8+fPxPKb+vr63C6/eerUKeIQ4mqRQ5WVOCAABwd//5NNVmCVl5cjhFK6dF9G55Sb+y2w\nqquxiQnu0QNfuoQDAjAn+cNgMM6dOzdmzBgREZGePXuGh4dzW8D79++JnLpy5Uq3bt24/gb49uLF\nCzqd3nw5NrJQKbCIUQ5trnHOuaSkJCMjI2NjY2lpaS0tra1bt/KwVlRsbKy+vr61tXVWVlabb378\nGLu5YXFx3LMnfvjw+5/ssjJcVcX1d9FiSTQaDRaT4UqL90C4Wgc7Ph7Pn/8tsDDG+/ZhhPDz59jA\noH7gwOE/WtO9rq4uJCTExcVFXFy8Z8+evr6+iYmJISEhkpKSvr6+XK2zePnyZSKntmzZ4uzszPmB\nZAkMDDQ3N2+/9qkUWGFhYZqamuS2WVFRER4efvXqVd7OwAnl5eWTJ09WUlK6detWi2+ora09e/bs\n5MkLhITwsGH40iVcX9/CT3ZZGV6zBr96xXMhXxHDL/ht5SfT4j0QzgMrISHB1jZl//6Wv1pY+GnJ\nkiV0Ot3FxYW97j6LxXr8+LGnp6eCgoKsrKynp+fjx4+JPoqioiKM8YsXL9TV1adMmcL5zSVfX18n\nJyeM8eTJkwWyq8iyZctmzpzZfu1TKbB27949ePBgQVfRMhaL5e/vT6fTfXx8mvaLZWRk+Pj4qKio\nKCgorFy5Mi2tjSvH0FAsLo6PHuWxjJycnI0bN6qoqGzfvp3HJn5WLd7d4zCwoqOjlZWVly9f2/rJ\nUGZmpru7u6ioqLu7+6ZNm8zNzYmtjE6ePEl0+pSWlh4+fNje3l5MTIxYaz8/P79Pnz52dnYfP37k\n5Lvw8/NzdXUNCwszMDA4deoUp988eQYPHuzn59d+7VMpsBYvXjxv3jxBV9Gahw8fduvWbejQofn5\n+cHBwdbW1t8NieBEUBA2MUn67bfVnHcEsEdgiIiIEOu1t7IzAmhRi/dAiouxtDR2dv76n7R0C4EV\nFRUlJSX1+++/c/hBCQkJtra2ZmZmu3fvJrbSIdZZtre3J+72+Pv7N92Sp7Kycty4cZqamjE/Hq/Q\ntPPLwMDAwMDAysrq/fv3XP8p8E1BQYG4R9lOqBRYTk5Of/75p6CraAOx45aZmZmUlNTChQt521Yj\nOjq2Z8+enOwNlZ2d7ePjo66uztUYWtBc87t7LZ5hRUS8Cg0NTU9PJ7qWwsPDxcTEuD2nGDRo0K5d\nu1gs1sOHDxcsWCAvL6+oqPjLL788efKkxR6rxsZGHx8fGRmZiIiIpq+32PmFMa6srBw7dqyGhkYH\n796Wm5uLEGrXDc2oFFh6enpN97DqtGpra5OSkvgcVlpRUTFlyhQ5ObmwsLDmX2WPwCD2PuDqDA60\nqPndvRYDKyjoupaWFkJIRkamb9++UlJSf/75Z11dHeeDSFgslry8/K1bt1gslrm5edOuq9YdPXpU\nXFzcz8+vlc4vtsbGRmLYzdWrVzn+M+BXZGRke+zM0BRlAqu+vl5EROT169eCLqRDBQQEiImJ+fj4\nsO8JsEdgKCkp+fj4pKamCrbCLqPFeyA/6sMqKyt78uTJhg0baDRaRkZGSkoKjUZr5ZKtKWJtP+J+\nNLf7FUZGRkpLSxsZGX3X+fUjO3bs0NKyO3Kk7U/JzMz09fU1MTEZM2YMzyP4tm/f7ujoyNuxHKJM\nYBED4UpLSwVdSEd7/Phx9+7dHR0dL168yNUW9qADODs7z58/H2M8Y8aM8ePHc3LItWvXeNjSLTY2\nlhgyvWjRInt7e84vu8LCqiUk8NOnLU9cra6uZnd+GRoabt26dd26daKios3XGuDE9OnTvby8eDiQ\nc5QJrDt37igoKAi6CsEgbhWpq6tPnz79R2N5gEA8evRIVFQ0Ozs7KSmJRqPFx8e3eciff/45bNgw\nbj9o9erVkyZNwhg7Oztv2bKFq2Pj43F4eAuDNnx9sbFxUtPOL8Lt27fl5OSmTZvG7S9FU1PTozzf\n4eYMZeYS/syT4zQ0NBwdHQcPHnzu3LlBgwYJupyuqb6+/v379xyuFsk2cODA/v37//XXX8bGxqNH\njz548Fybh7x7987CwoLb8uLj44ndj+Lj47k93Nwc2di0MHFVUhLt3m2cmpq2efNmExMT9utOTk5P\nnz59+fLlsGHDOP8DiY+PT0lJMTY25qo2blEpsH7m9TMzMjL0yd4M8Wfw6NGjZcuWzZo1a+fOnTdv\n3szPz2/xbWVlZZaWlj4+PsrKytx+xJo1a0pLS6urqw8ePK+ktOPz5zbez44erhA5VVRU9OHDBx7y\njk5HqNmU7DVr0JgxSFRUpPn7TU1NX79+LSIi0r9//6SkpFZaLioq2r9/f79+/Xr37q2np7d+/frP\nbf4R8IFKgdW9e/d9+/ZhjAVdiwD85HnNm9TU1AkTJnz+/FlaWjoiImLatGlaWlqKioqOjo7Lli0L\nDAx88eJFVVVVfX29q6urrKzs2bNnhYW5/hcxevTo1auPSklJaWlJ/fknUlRs7c21tbVpaWncJg47\np969eycrK8vzNihc7XKgpKR0584dBwcHOzu769evf/fV0tLSffv2OTg4dOvWLSQkZMaMGfn5+c+f\nPxcWFra0tIyNjeWtwra16wUnWRoaGoyNjTdt2qSmpjZmzJifreudxWJJSkpC7xVXPn78qKOjs3Dh\nQvYrLBYrMzMzPDx827ZtU6ZMMTY2FhERERIS0tTUVFVVzcnJ4fmzWplg+J3o6GgRERFu+4bu3r0r\nLS3NZDL37t07YMAAruv7waANTrCXNtm/fz/+8cgvtrq6ujlz5hC/IXios02dPbAKCgq2bNmioaFh\nY2NTVFRUXFzs5OSkoaHx5MkTQZfWcQoLCxFCTUc/g9ZVV1fb2NgMGTKkvr4+MTHRw8OjxXFqNTU1\n0dHRs2fPNjMz4+fjOF889sSJE8bGxhjjmzdvrl+/nsOJzXv37rWzs8MYz50795dffuGtwuaDNjgX\nHBwsJiY2fvz47t27S0pKTp8+PTIyspUxGf7+/qKiou0xR6eTBlZDQwN7aarhw4dv2rQpKiqK+FJj\nY6Ovry+dTidG0Am2zo7x+PFjCQmJn+Sb5R+LxXJzczMwMCgpKcnPz+/evftvv/3W4jvLy8s/ffpU\nWFhIo9FiY2N5/sTWF4+tqalJSEgIDw/fu3fvwIEDhw4dijF+9OiRsrLy5MmTOZnYPGfOHE9PT4yx\ntbX1wYMHea6TH48fP/7rr7/aHPnFdv36dRkZmYULF5K71EzHBRaHy3fk5uI9e/YaGBiIiYnNnDmT\nOJP666+/6HQ6MZuBeCdZ6xRTwsmTJ4lfy4ATmzdvVlBQSE5Orqqqsra2Hj9+/I/GkU+cOPHXX3/F\nGI8YMcLb25vnT2xx4rS/Px40CFtYWAsJCQkJCWlraw8fPnz69OnS0tLe3t5MJjM/P9/a2trc3LzN\nq9E+ffocOHCgoaFBXFz88ePHPNfZwd6+fdujR4/hw4eXkbc0ZYcGFifLd1y4gMeM+Wvnzp3fLZbI\nnlfMXo49JSXFzMwsdto0nJbWYd+FQGzatMnFxUXQVVDDmTNnxMTEHjx4wGQyJ02aZGlpWVlZ+aM3\nX758WUFBoba29uTJk927d+d5iaEWJ04fOID9/HB4+IO3b9827bRKS0vr1avXiBEjysrKqqqqJkyY\n0K1bt1amcLBYrF69ev37779JSUlCQkI8/4ZOTEy/fv0Zb8fyrLCw0NbW1sDAgKzlJDs0sDhZvqOV\n8Pn06dOwYcO0tLSeP39OvFJbVYVnzcJycvjt23YtXrBmzJixfPlyQVdBAc+ePRMXFz927BjGeMOG\nDZqamq3PBq+rq1NSUgoLC6uqqpKSkmJ3O3CrxYnTrSgpKRk0aJCBgUFqaiqLxfL19ZWSkrpy5Urr\nn3LhwgUdHR3eKsQYL1myxN3dnefDeVZRUTFmzBgPDw9SWuvowOJh+Y6mWt6m8NQpvHNne9cvQP36\n9SPGc4BWpKenKysrr1y5EmN86tQpKSkpTpbK8PT0nDhxIsZ4+vTpc+bM4e2jebgHV1tbO2vWLCUl\npQcPHmCMg4KCxMTEfH19m7+TPdVZXl7+jz/+4K1CjPHAgQN37drF8+H8aGxsrCJlLd2ODyzc1vId\nnNwhPnPmjHG3blUrVmD2mfbHjzgxEY8ahZ2c8KBBODiY/G9AcFRUVCIjIwVdRWe3Zs0aBweHhoaG\n+/fvi4mJhYaGcnLUo0eP6HR6SUlJZGSkjIwMsfElt3i7B0eMGBAXFz958iTG+NatW0ZGRk2H7BQU\nFPz111+WlpbCwsKOjo5BQUE8/7NnsVhycnI/WhGXQgQQWK0v38HhkJa6xETcqxe2tsaZmV8b1dfH\nxEyu6mo8YAC+e5fs70AwiB0lkpOTBV1IZzdr1ixicd6NGzdyvpwei8XS1dUNCAhoaGhQU1M7f/58\ne9bYgosXL0pISHh5eTGZTPb6yP7+/sTqj/b29gEBAfwPPMzOzkYIdYHl0gQQWJiD5Ts4wmDgOXO+\n7kAfFoanT//2patX8dSppJQtcLGxscLCwrCjRJtu3rwpJSXVShf7j2zcuHHgwIEY42XLlgnk5saz\nZ89UVVUnTpwYFhY2Y8YMSUnJ7t27r1q1ip+RFt+JiIhQVFQkqzUB6qTjsLgQEIDpdLxoEV679tuL\nsbG4f3/B1USm0NBQLS2t9PR0QRfS2TU0NKirq589e5bbA5OTk4WEhDIzM1+8eCEiIsLh6unkSk5O\ntrW11dLS8vLyao9lQktLS5WVldeuXUv1tbMpM5fwhzw90f37qKEB5eV9ezE3F2lpCa4mMmVkZOjo\n6AwYMGDevHk1NTWCLqfzEhERmTp16tmzZ7k90MjIaOTIkRkZGX379u3Wrdvjx4/bo7w2a3j16lVm\nZua+ffusra1Jb19RUTEyMvLkyZOjR48mOhmoStCJSZLCQqyrixMSMMa4uho7OGDq9y+Wlpbu2bPH\n0NDQ398/Ozvb1tbW0NCQvUkUaO7Vq1f8nCJduXJFVFSUrJ16OyFiYTUzMzNO9tDsnAQWWLt3Y5Jn\nR8bH45Ejv94lPH6c1KY72osXL2bPni0uLq6rq+vj40PcG6qpqfH09JSWlu74jmEKMTIy4m0ISNMx\nXF1YZWXl+PHjlZWVHz16JOhaeCGwwNLVxefOCerDO6mSkhI/P79evXqJi4t7enq+efPm+vXrkpKS\np0+fZr8nODhYUlLS09MTdp1o0datW/v27cvtUdnZ2Wpqaj/J6FxiqKqYmFjTnyuqEExgNTQ0DBo0\n7/VrrveFpyLO90AfPdqnW7dumzZtys3NZX/p6tWrcnJys2bNYg8Rev78uZaW1hJXV25uqf4ssrOz\nhYWFiYkghYWF5eXlbR7y5csXExOTcePGUb1DmivHjh1rvu9v5yeYwEpPT0cIlZSUCOTTOxjne6CH\nh5e3OLU9NTXV3Nzc2NiY3b1SXFxc5OGBu3fHL150xPdAKfb29sSQ8enTpwsJCfXs2XP8+PEbN24M\nCQlJTU39bsJgQ0PDyJEjW59y2FU9fvxYWVnZzc2Nt+GyAiGYwLp9+7a8vLxAPrrj8bMHOhuDwZg3\nb56MjMylS5e+vsRiYT8/bGfXboVT1aFDh/T09FgsVl1dXUxMTHBwsLe394gRI9TV1RFCEhISNjY2\n7Ekqy5cv19DQaNe9Pzuzd+/eaWpqEovzUYJgAuvQoUN9+vQRyEd3PFImUWKMWSwWsczO3T17MPs0\nvmO39qWEwsJCaWlpDw+PgICAZ8+eNV2/qaioKCoqas+ePWfOnMEYHzx4UFJSsoO3R+5sVq5c6erq\nKugqONXC+vMd4Cdcobz5ctra2ujWra+PzczabkFISGjVqlUO/frZzp6NoqLQ2bNIURERY3aSkpC3\nN2IyUW0tmj8feXi0w3dAGdu2bVNQUKDT6SdOnHj37h2DwejZs6eFhYWZmZmFhYWFhcWQIUNoNNr1\n69eXL19+/vz59hj3RCGpqam2traCroJT5ARWWlrap0+fdHV1NTQ0OHl/RkaGkZERKR9NIUuXIltb\nVFXFVyP9HBzQq1doxgxkYYEuXUL9+yMGA40fj8LCkLk5YjD+1959BzR1vX0AP4wkjLAhTAERoYIC\nihsFFRCrqNWWOgBb24qKir6/qjhqUWsrilZRa0XqrFoL1qJ1oCK4QHHhQHAQVEBWQFYCBELO+8dt\n0xRRIbnJzYXn81duSE4e2vjljnPPg/z9kbU18vUlqWqaiYmJ+e23365fv+7s7IwQEovFXC733r17\n9+/fv3///sGDBwsKCrS1tXv16lVcXBwZGfnJJ59QXTLFHjx48MUXX1BdRXuRE1hJSUnffvutUCjU\n1tbu8QY7OzsGgyH9ei6XO27cOFI+mkZ0dNDs2WjhQrkHMjFBZ86g775DI0ag+Hikp4cGDEBE5ygd\nHbR0KYqP75qBRXwPz507R6QVQkhdXb1nz549e/YMCgoinqmqqiLCy93d3cfHh7piVUJlZWVRUZEM\nfcOoooZJ6polFosLCwvz8/O5/1VTU6OpqWlrayvJLwcHh5CQkGPHjo0dO5aUj+66Tp1CW7agiRNR\nSQlav/7vJ+/dQ+HhKCOD0soocPPmzREjRuzYsYNG+wuUu3Tp0vjx42tqamTob0YJ0s5hqaur29nZ\n2dnZjRw5Uvr5iooK6fw6ceLE06dPmUzmuHHjtLS0rKysXFxcXF1dHf5hZ2enoaFBVlWdXGAg6tUL\n3b+Pbt7898lOdB9l+xUVFX300UdhYWGQVh3y4MEDV1dXuqQVInEPq/3S09N9fX1v3rz54sWLfCnP\nnz9vbGzU19cn9sKkDyq7devWaVLs1Sv0/DkaNoy8Efl85O6OTp5Erq6ovh4FBKBvvkEBAeR9gKoT\nCATDhw+3sbFJSkqi0b89VTBr1iw1NbXdu3dTXUh7UXCVkFh+gLheI/08xri4uFgSXvn5+enp6fn5\n+SUlJfPmzduxY4fyS1WEXbvQvXukBhabjZKS0OLFSCxGjY3oiy86TVodPXo0NTW1T58+vXv3dnd3\nN26rq7JYLA4NDRWJRLL1be7iHjx4EBISQnUVHUBBYOXn57c5p0FNTc3a2tra2nr48OHSzzc0NDQ2\nNiqrOoWrry/w8DBAyIDMQfv0QWfPkjmgarC0tNTW1v7jjz++/fbb6upqa2vrPn36uLm59enTh5j6\nz2QyV65cmZ6enpmZqaenR3W9NCMWi7Ozs2l0xh1RtYfVoUlY2tra2traiqtHya5dCwoJCUFoAdWF\n0ICPj4/kQt7Lly/v37//4MGD+/fvHz9+PD8/X0NDo3v37oWFhX5+fp3pG6I0eXl59fX19AosCnah\nu+CsUWld/NeXmZ2d3YQJE7755pvExMRnz57V1NT873//69+//7Nnz168eHHo0CGqC6SfW7dudevW\nzcjIiOpCOgACS6mqq6srKyu77K9PIjabbWtrm5mZaWVlFRwcDIHVfiKR6PTp09OmTfvyyy+bm5vP\nnz9PdUUdoOzAqqurKy8v77L/Yp8/f66hoWFvb091IZ3B1KlTi4qKbty4ERIS8vDhw4cPH1JdkUoT\ni8UpKSkzZswwNTX94osvOBxOenr6mjVrJkyYsGHDBqqray9lB1Z+fr66unr37t2V/LkqgsvlWltb\ns1gsqgvpDIyNjceMGXPo0CFra2tvb28ZVnPvIrKyshYuXGhnZzdx4kSE0LFjx4qLi4nF48PCwk6c\nOLF+/fpZs2Y1NzdTXen7URBYVlZWWlpaSv5cBbl37x6TyezRo4e/v//s2bM3bNiQmJh4584dgUDQ\n5uvfdoUUyCYkJOTo0aNCoTA4OPheSgpS+qRCVdbY2Pjzzz97eXl5enpmZmYuXbr0xYsXBw8e9PPz\nk57VGBAQcPXq1ZSUlHHjxlVXV1NXb7soe+Lopk2bTp06denSJWV+qOLU1tbeuHFDMo+fuDNJIBAw\nmUx7e/tW91Q6ODgsWrSopaUlPj6e6sI7iYaGBtfu3Z+8eMHoLH8CSdTc3Dx8+PCAgIDg4GAnJ6d3\nv7iiomLy5MmvX78+deqUKp+yUHZgzZ07t6mpac+ePcr8UCUrLS3Ny8trdU9lRUWFmpqasbGxoaGh\nj4+PdJDR6zKNqhHPmqW+YQNqa04p6BChUPjVV1+dPXv2zz//bDUXUnUoO7BGjx7t4+OzcuVKZX6o\nKqipqeFyuWPHjvX19WWz2cSE/sLCwubmZmNj4zeXuLCyslJTU6O6ajq4cgU5OqLqalgRTH4Y440b\nN65ZsyY+Pj44OJjqctqg7MBydHRct27d1KlTlfmhKqKpqUlHRyczM1OyYpxIJCKWuGjl9evXWlpa\nPXr06Nev38GDB6ktW9VhjF68QKNH/2dFsLVru+YCO6TYt29feHj46dOnR40aRXUtrSk1sJqbm3V0\ndDIyMmi0wiGJ8vLyevbsWVVVZWho+O5XVldXE8nV0NAQGhqqlOro7PBhdPo0OnLk780TJ9Bvv6Gj\nRymtid7u37/fu3dvFVxxQKm35hQWFopEoi57mYzL5RLnsN77SkNDw379+vXr10/xRXUKPB6ys/t3\n084OFRRQV01n4O7uTnUJbVPqtAZdXd3Y2Ng277nvCrryFH/FsrVFhYX/bnbJFcG6CKUGlrm5eURE\nhDI/UaVwuVwHBweqq+iMRo9G16+jR48QQqi+HsXEIFjGr5OipmtO18Tlcl1dXamuojPqvCuCgVYg\nsJSHy+VOmDCB6io6qU66IhhoBVZoVBKMMdyXA4CcILCUpLS0tL6+Hs5hASAPCCwl4XK5Wlpa7Ww0\nCwBoEwSWkuTn59vb20OXBADkAf9+lAQmYQEgPwgsJYHAAkB+EFhKArNGAZAfBJaSmJqauri4UF0F\nAPRGQat6AACQDexhAQBoAwILAEAbEFgAANqAwAIA0AYEFgCANiCwAAC0AYEFAKANCCwAAG1AYAEA\naAMCCwBAGxBYAADagMACANAGBBYAgDYgsAAAtAGBBQCgDQgsAABtQGABAGgDAgsAQBsQWAAA2oDA\nAgDQBgQWAIA2ILAAALQBgQUAoA0ILAAAbUBgAQBoAwILAEAbEFgAANqAwAIA0AYEFgCANiCwAAC0\nAYEFAKANCCwAAG1AYAEAaAMCCwBAGxBYAADagMACANAGBBYAgDYgsAAAtAGBBQCgDQgsAABtQGAB\nAGgDAgsAQBsQWAAA2oDAAgDQBgQWAIA2ILAAALQBgQUAoA0ILAAAbUBgAQBoAwILAEAbEFgAANqA\nwAIA0AYEFgCANiCwAAC0AYEFAKANCCwAAG1AYAEAaAMCCwBAGxBYAADagMACANAGBBYAgDYgsAAA\ntAGBBQCgDQgsAABtQGABAGgDAgsAQBsQWAAA2oDAAgDQBgQWAIA2ILAAALQBgQUAoA0ILAAAbWhS\nXQCgXrVIFPjwoYuuLkLImsmMsrenuiIA2gaBBRBCyIrF2u3kRHUVALwHHBICAGgD9rAAQggVC4Vh\nT58ihMYYG082NaW6HADaBoEFEIJDwv8Si8WVlZVmZmZUFwJag0NCAP5DLBaHhYVNnDgRY0x1LaA1\n2MPq6l4KhQYaGlRXoSpEIlFwcHB6enpqaqqamhrV5YDW1ODPSBf3S0nJV5aWVFehEkQi0fTp0zMy\nMtLS0nr27El1OaANcEjYpRUKhXf4fKqrUAnNzc3Tpk27fv06pJUqg0PCLu23sjIrJpPqKqhHpNWd\nO3euXr1qD/NmVRjsYXVdVSLRycrKUYaGVBdCsebm5qlTp969e/fSpUuQVioOAqvrOnT0aD8+f4iB\nAdWFUEkoFE6aNCkrKystLc3Ozo7qcsB7QGB1UXw+f/XChf24XPp+A1paWq5duyYSiWQeQSgUTp48\nOTc399KlS5BWtEDfryuQy/79+9lsdnBwMNWFyC43N3fEiBEmJiYTJ0786aefnj592qG3NzY2Tpo0\n6fHjx2lpaba2tgoqEpALpjV0RSKRyMnJKTw8fPHixVTXIpf6+vqMjIyUlJSUlJS7d++ampqOGDHC\nz89vzJgx786gxsbGyZMnP336NDU1FdKKRiCwuqKjR4/OnTu3oKBAT0+P6lpIU15efvny5ZSUlDNn\nzhQVFTk4OPj5+fn5+QUEBOjr60u/kti3evbsWVpaWrdu3agqGMgAAqvLwRj37dt3zJgx0dHRVNei\nKPn5+cRuV3JyckNDg7u7OxFeI0aMEIlEH330EZfLTU1NhbSiHQisLiclJWX8+PEvXrwwNzenuhaF\n4/P5aWlpFy5cuHDhwuPHj62srLp37/7q1au0tDSYwUBHEFhdBY/HKy8vLykpWb16tYODw8GDB6mu\nSNkKCwsvXLiwfPnydevWzZo1i+pygCxgpnsnUV9fX1JSUlpaWlZWVlxcXF5eXlxcXFZWJtlsbm5G\nCOnp6VlbW7u6ulJdLwW6dev2xRdfpKSk3Lt3j+pagIwgsGhMJBJt3rx57969xcXFfD4fIcRkMjkc\njpWVlbm5uYWFRb9+/TgcjrW1NYfDsbCwsLS01NbWvnv37tChQydOnDh27FiqfwMK+Pr6bty4keoq\ngIzgkJDGjhw5Mnfu3C1bttjb25ubm5ubm5u2b7HQ6OjoLVu2PHjwoCucxmrl5cuX9vb2BQUFcMad\njiCw6Apj7ObmNmnSpLVr17bzLXl5eT169FBTUxOLxX5+fvr6+klJSYqsUUU5Ojp+8803n3/+OdWF\ngA6Dme50lZyczOVy58+fjxDCGDc0NLz79UKhcOTIkdu2bUMIqaurHzx48OrVq/Hx8cqoVcX4+vpe\nvHiR6iqATDCgp5EjR86ePZt4fOrUKXt7++bm5ne/5dy5c0wm8+bNm8TmsWPHdHV1Hz9+rNhCVc/v\nv/9ubm4uFoupLoRmWlpaiouL3/s1UygILFq6ceOGhoYGl8slNr28vJYsWdKeNy5YsKBXr14CgYDY\nDA0N9fT0FAqFiipUJVVUVKirq2dnZ1NdCJ1kZ2cPHjy4d+/eTk5Ohw8fbmlpoaQMCCxamjJlyuTJ\nk4nH6enpTCazqKioPW9sbGx0c3MLDw8nNuvq6hwdHVesWKGoQlWVh4fH1q1bqa6CHvh8fkREBJPJ\njIiI4PF4cXFxFhYW9vb2cXFxIpFIycVAYNFPXl6ehoaG5Mjuo48+mjlzZvvfnp2dra2tfeLECWLz\n2rVrTCYzNTWV/EJV2OLFi8ePH091FTRw4cIFBweHXr16Xbt2TfIkn8+Pjo42Njbu1avXgQMHlLm3\nBYFFP/Pnzx8+fDjxOCcnR0NDIzc3t0Mj/Pjjj2ZmZiUlJcTmqlWrbGxsXr9+TXKhKuzs2bN6enpN\nTU1UF6K6KioqQkNDWSxWVFRUQ0PDmy+oq6uLjo42NDTs3bt3QkKCcs4JQmDRDI/H09HROXnyJLE5\na9aswMDAjg4iFovHjh0bEBBAfMmam5uHDBkyffp0kmtVYQKBgMViZWRkUF2IikpISLCwsBg4cOCD\nBw+knz9+/HhFRYX0M5WVlVFRUfr6+oMGDZJ8LRUHAotmVq9e7erqSgRNcXExi8W6fPmyDOOUlpaa\nm5tv27aN2ORyuXp6eocOHSKzVtU2fPjwtWvXUl2FyiksLAwMDGSz2XFxca2O9ZqbmwcOHKivrx8V\nFVVdXS39Ix6PFxkZqa2tPXTo0IsXLyquPAgsOuHz+SYmJnv27CE2ly9fPmTIEJlHS0pK0tLSun//\nPrG5Z88eAwOD58+fy18nLaxevdrHx4fqKlRIS0vL1q1bDQwM/P398/Pz3/ayCxcu9O/fn81mR0ZG\nVlVVSf+osLAwIiKCxWJ5eXnJ9nf0vSCw6GTnzp1WVlaNjY0Y49raWiMjo2PHjskz4KxZs1xdXYkz\nFGKxePz48aNHjyanVpVHXG3g8/lUF6ISHj9+PHz4cBMTk4SEhPe+WCwWnzx50sPDQ09PLzIysqam\nRvqnL1++DAsL09TU9PPzu337Nrl1QmDRhkgkcnBwiI6OJjY3b97cs2dPOS/Q1NbWOjg4/PDDD8Tm\n3r173dzcusiMyubmZn19/eTkZKoLoVhTU1NUVJS2tnZoaGhpaWn739jS0pKQkODk5GRqahodHV1f\nXy/90wcPHnz00UdmZmY5OTkkVguBRRuJiYkGBgbEXzOhUGhjY7Nr1y75h338+HFtbS3x2NPTs50T\nUDuHcePGdanf902vX78eOnQoh8M5cuSIbCMQsdWjRw8OhxMdHd3qeqKvr+/3339PRqV/g3sJaWPj\nxo1fffUVsTz577//LhKJSLl919nZmVjZPS0t7eHDhwsXLpR/TLoYM2bMlStXoqOj79y5IxaLqS6H\nAr///ruGhsajR4+mTZsm2wjq6upBQUG5ubnffffd9u3bnZ2dd+/eLem9ZmRkRCx8RBYILHrAGOvq\n6jo5ORGbtra2MTExLBaLxI+IiYmZPn26tbU1iWOquPnz5wcHB587d27o0KHm5uZTp07ds2dPQUEB\n1XUpT11dHYPBIFYlampqunPnjmx9HhkMRlhY2OPHj8PCwpYtW5acnEw8z2az6+rqyKyYxL01oFBx\ncXEGBgYvXrxQxOBZWVnq6uodnYDaaTQ3N9++fTs6OtrLy0tdXd3CwiIoKCguLq64uJjq0hRr586d\nAwcOJB6XlZUhhAoLC+Ucs7q6WnIadMGCBZ9//rmcA0qDPSzaCAsLGz16dEhISEtLC+mDb9my5cMP\nP/zggw9IH5kWNDU1PT09IyMjr127Vl5evm3bNiMjo/Xr19vY2PTv33/ZsmUpKSlCoZDqMsmnp6cn\nOWQjzgzIfwRnYGCgpqZGPGaz2XBI2HXt3LmTy+Vu2LCB3GELCgp+++23JUuWkDssTZmYmBC7V8+f\nP3/27FlYWFh+fn5QUJCxsbG/v/+GDRvu3LmDO8uyl9KHbNra2pqamuQewZF+SAiBhRoaGl68eJGR\nkZGUlLRz585Lly7JdhivBKampgcOHFizZs2NGzdIHHbbtm2enp4+Pj4kjtk5ODg4hIWFJSQklJSU\nJCUl9e3b9+jRo8OHDz9z5gzVpZGj1R6Qrq4uuTtEpO9hdf4mFGKxuLy8XNI85s3WMrW1tQghBoNB\ndGqor6//9NNPV69eTXXhbfP39587d25ISEhWVhYpfZurq6t37969d+9e+YfqxLS0tPz9/f39/RFC\nW7ZsWb58+bhx46guigR6enrSe0CtNkkfX36dObBKSkqWLl3622+/ESd9TExMiE4NVlZWlpaWnp6e\nRGsZS0tLDofD4XCId125csXPz8/Hx2fkyJGUlv9WGzZsuHTp0qJFi/bs2SP/aMTyRpMmTZJ/qC5i\n3LhxX3/9dWlpqYWFBdW1yIvNZotEosbGRi0tLaSAHSLYw+qATZs25eTkpKenW1lZcTicdk4C8Pb2\nXrRo0YwZMx48eGBkZKToImXAYrEOHz48YMCA0aNHT5kyRZ6hGhsbt2zZEhUVpaGhQVZ5nZ6Tk5Ot\nrW1aWprMc5dUB5vNRgjx+XwILIpVV1fHx8fv27dv0KBBHX3v999/f/ny5QULFhw6dEgRtcnP1dX1\nhx9+mDNnzpAhQ2xtbd/2MoFAUFJSImmnyuPxiCPiVatWDRgwACF05MgRNTW1mTNnKrH2zmDUqFEX\nL17sBIEluTJITMWCQ0KEEBIIBK26EBcXF3t6ek6fPt3Q0FBBH7p7926Zj3QYDMbhw4f79u175MiR\n6dOnk14bKRYuXHjhwoXQ0NDU1FRi/+jhw4e7d++WTiiBQID+213V3NycuGcVISQWizdu3Dh37lzi\nrytoP19f3xUrVlBdBQmIPSxJpihiD6uhoUEkEmlqkhM15IxSWVn54sUL6ZPZ0ue2iX82LBZLugvx\n7t2709PTDx8+TEoBrTQ1NcXGxn777bfq6jJeBnV0dIyJiQkPD/fy8rKzsyO3PFKoqant27fPzc1t\n06ZNkZGRCCGxWEzczOzl5SWdUCYmJm2OcPr06aKiIqJRGOgQPz+/0NDQvLw8R0dHqmuRC5PJJJas\nIDalp2WRgvjTKBAIDAwMSBmQnMDasWPH5s2bu3XrZmRkRJzS7t+/v6WlpWTT2Ni41Z/xwsJCd3f3\nffv2KeJ4hLiTU85b7WbPnn3y5MmIiP8lJf3xzzw41cLhcOLi4oKCgkaOHDlw4EB3d/cDBw60/+0x\nMTEzZ840NjZWXIWdlbm5uYuLy8WLF+keWOi/IaWIPSyEEJ/PV63AioyMXLlyZYf2+rp167Zt27Y5\nc+Z4eXlJbpEjhVgs3rBhw7x58+S81U5NTW3fvv2jRzdu2YL+9z+yqiPZxIkTP//88+Dg4Lt377Y5\ny+H169etpnEQB4xFRUXq6uoRERHKr7lz8PPzu3jx4uzZs6kuRF7SczvZbDZxg46cWlpaiNMUrQ45\n5UdOYMl2EiQkJOTs2bPBwcEZGRkMBoOUShBCycnJr169Cg8Pl38oc3POpk0oMBCFhKB/pj2onM2b\nN3t4eHz55ZcjRox4c6IZcUOJrq4ucYRITONwcnKytrb29vbu0aMH1eXTlb9/6M6d6mIxkvWsg6qQ\n3qvS09PjcrnyjNbS0hIbG7t///6bN29qaWnp6OhoaGiQuNdG8VXCnTt3uru7r1u3bs2aNWSNmZDg\n8X//t5esGQn+/mjePHTtGpo8mZTxyKenpxcSEvL777/n5+cTc8rc3Nykz2FZWVnp6upSXWZn4+3t\nOWkSuncP9etHdSnykb6QJ+edNJmZmbNmzaqpqdm1axexE6Ompqarq0vmhUISb6SWzdWrVxkMxqVL\nl0gZ7fp1zGBguW84/4/GRnzoEEYI79z59zOurvi/i1lTiViJdPPmzVQX0uUMHYo3bKC6CLn5+flJ\nlrG9c+fOr7/+KsMgdXV1xLLIkZGRkr7ixPOmpqYkdtOhPrAwxsuXL+/evXurlaFl8+QJfvpU/mFa\ny8vD9vbY1RUT6ym2M7BaWlpKSkoUvWr4sWPHDA0NJauGAqXJysIXLlBdhNwmTZr0zTffyDPCuXPn\nunfv7uLi0qptWnJysr29vZeXF4mLbqtEYDU3Nw8aNGjGjBlUF/JWPB52d8dr1+Iff8RYKrBqa/Hj\nx/jKFXz8+OvY2NgVK1Z8/vnnY8eO9fDwsLS0JM47ent7t2ruRq4BAwYsXbpUceODTuzq1asuLi69\nevU6fvy4DLHC4/GCgoJYLFZ0dLR0V9qioqLAwEBdXd2tW7c2NzeTWLBKBBbGOC8vT09PT4aFpXk8\nZRysEYFVW4tdXDCf//enhIVhhDBCWEsL9+1bOmTIkIkTJ86dO3f16tW7du06ceLE9evXCwoKZsyY\n0atXL+n9ZBJdunSJyWS+evVKEYODd1POdy8rK+v69etCoZDcYWtra+fNm8dgMCIiIiIjI9lstqen\n55kzZ9o/woEDBzgczuDBg7OzsyVPisXiuLg4Q0NDPz8/LpdLbs1YdQILY0z8nh1dUZPHk+VgraOI\nwMIYb9iA16//+1MeP8Y5Ofi/DSXbwOfznZycwsPDyS8L43HjxpG7oiNoP0V/9wQCwddff81gMOzs\n7Ozs7OLj48naW0lMTDQ3Nx84cODDhw+JZ2pra6Ojow0MDPr06fPevvMvX74cO3asnp5eXFyc9Cuf\nPHni7e1tYmJy4MABBfVeUqHAwhgHBQUNHz5cJBK1/y1vO1gjlySwBALs4oJtbTv2Kbdu3WIymaQ3\n8n748KGGhkaXXdeYcgr97h0/fpxYUyQrK6upqSkuLs7GxqZbt25bt24lGlPKhjhYa7OxM8a4oqIi\nKipKT09v8ODBb/u6Hj582MjIaMSIEc+ePZM8KRKJoqOjtbW1g4KCSkpKZC7vvVQrsHg8nqWlpeSa\nxXs1N+OCgjYO1hRQ2N+BhTGOjcUIdfhT1qxZY2ZmRu7/ywULlnSdvqcqqM0TBfIrKysLCgrS0tKK\njo6W3qUSCoVxcXGWlpZ2dnZxcXEd3duSPlh7R2NnjHF5eXlkZKSWlpaXl1dqamqrnx44cCA+Pl56\nB+r27dseHh42Njak/0l+k2oFFsb4/PnzTCYzMzOT2Hz9+nVOTk5aWtrhw4e3bNmyZk3cjBk4IAD3\n6YPNzbGaGt6+vY2DNdLJv3vb0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       "prompt_number": 87,
       "text": [
        "<Image.Image image mode=RGB size=400x1000 at 0xBD670D40>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Take a look at property distributions.\n",
      "\n",
      "Each plot below contains two histograms. The one in blue is for the first set of molecules, the one in green is for the neighbor molecules."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rdkit.Chem import Descriptors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mws = [(Descriptors.MolWt(x[1]),Descriptors.MolWt(x[3])) for x in rows]\n",
      "nrots = [(Descriptors.NumRotatableBonds(x[1]),Descriptors.NumRotatableBonds(x[3])) for x in rows]\n",
      "logps = [(Descriptors.MolLogP(x[1]),Descriptors.MolLogP(x[3])) for x in rows]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_=hist(([x for x,y in mws],[y for x,y in mws]),bins=20,histtype='bar')\n",
      "xlabel('AMW')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "<matplotlib.text.Text at 0x8d6e6890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2jYmhIRERiR8FgYiIxSkIREQsTkEgImJxCgKxtKqqKpKSkvjggw+Arrumk5KS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\nImJxCgIREYv7f22YX1LLrtNYAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5f9684d0>"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_=hist(([x for x,y in logps],[y for x,y in logps]),bins=20,histtype='bar')\n",
      "xlabel('mollogp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "<matplotlib.text.Text at 0x47288310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A0NjYSFFREQkJCbhcLpKTk2ltbaWrq4twOExWVhYAxcXFZh8REZlZ1mg73n///ezZs4dT\np06ZbT09PdjtdgDsdjs9PT0AnDhxglWrVpmvczqdhEIhEhIScDqdZrvD4SAUCo07vfLycvN5dnY2\n2dnZ0ZYuIvKV1NLSQktLS9T9owqEF198kcTERDIzM887cYvFYu5Kmg7nBoKIiIw1emW5oqJiUv2j\nCoS//OUvNDU1cfDgQf79739z6tQpNm3ahN1up7u7m6SkJLq6ukhMTASG1/w7OzvN/sFgEKfTicPh\nIBgMjmh3OBzRlCQiIlMU1TGE3bt309nZSUdHB3V1dXzve9+jtraW/Px8ampqAKipqaGgoACA/Px8\n6urqGBwcpKOjg0AgQFZWFklJSdhsNlpbWzEMg9raWrOPiIjMrKiPIZzr7K6hBx98kMLCQnw+Hy6X\ni/379wPg8XgoLCzE4/FgtVqprq42+1RXV7N582YGBgbIy8tjzZo101GSiIhMksU4e4pQHLNYLFwC\nZYrMasMreeN9Ty1QPqqpHPM7HW0/+XKTXXbqSmUREQEUCCIiEqFAEJGvFNuVNvO093MftittsS4t\n7k3LQWURkXgR7g+PPfYAhMvDM17LpUZbCCIiAigQREQkQoEgIiKAAkFERCIUCCJySbLZFo17NpFE\nT2cZicglKRw+yXmvcJaoaAtBREQABYKIiEQoEEREBFAgiIhIhAJBREQABYKIiEQoEEREBFAgiIhI\nhAJBREQABYKIiEQoEEREBFAgiIhIhAJBREQABYKIiEQoEEREBFAgiIhIhAJBREQABYKIiEREFQid\nnZ3cdtttpKWlcf311/PEE08A0Nvbi9frJSUlhdzcXPr6+sw+lZWVuN1uUlNTaW5uNtvb2tpIT0/H\n7Xazffv2Kc6OiIhEK6pASEhI4De/+Q1HjhzhzTff5Le//S1Hjx6lqqoKr9fLsWPHyMnJoaqqCoD2\n9nbq6+tpb2/H7/dTVlaGYQz/FmppaSk+n49AIEAgEMDv90/f3ImIyIRFFQhJSUksX74cgCuuuIJl\ny5YRCoVoamqipKQEgJKSEhoaGgBobGykqKiIhIQEXC4XycnJtLa20tXVRTgcJisrC4Di4mKzj4iI\nzKwpH0M4fvw477zzDitXrqSnpwe73Q6A3W6np6cHgBMnTuB0Os0+TqeTUCg0pt3hcBAKhaZakohM\ngc22CIvFMuZhsy2KdWlykVmn0vmzzz5jw4YNPP7448yfP3/E385+iKZLeXm5+Tw7O5vs7Oxpe28R\n+Z9w+CRgjNM+fd9nuThaWlpoaWmJun/UgfCf//yHDRs2sGnTJgoKCoDhrYLu7m6SkpLo6uoiMTER\nGF7z7+zsNPsGg0GcTicOh4NgMDii3eFwjDu9cwNBRETGGr2yXFFRMan+Ue0yMgyDbdu24fF4uO++\n+8z2/Px8ampqAKipqTGDIj8/n7q6OgYHB+no6CAQCJCVlUVSUhI2m43W1lYMw6C2ttbsIyIiMyuq\nLYQ33niDZ599lhtuuIHMzExg+LTSBx98kMLCQnw+Hy6Xi/379wPg8XgoLCzE4/FgtVqprq42dydV\nV1ezefNmBgYGyMvLY82aNdM0ayIiMhkW4+z5n3HMYrFwCZQp8pUwvLI23vftwt/DC/WjfFRTOeZ7\nzUi/UX1ni8kuO3WlsoiIAAoEEZmoOYw9FfVKW6yrkmk0pdNORWQW+S9jdsWEy8OxqEQuEm0hiIgI\noEAQEZEIBYKIiAAKBBERiVAgiIgIoEAQEZEIBYKIiAAKBBERiVAgiIgIoEAQEZEIBYKIiAAKBBER\niVAgiIgIoEAQEZEIBYKIiAAKBBERiVAgiIgIoEAQEZEIBYKIiAAKBBERAGxX2rBYLGMetittsS5t\nxlhjXYCIXBw22yLC4ZNj2ufPX8ipU70xqCg+nG9cACgf2xQuD1/UeuKJthBEvqKGF3rGmEf485Oz\nei34fOMi2kIQmX3+y5g14dm0Fiznpy0EEREBFAgiIhIRF4Hg9/tJTU3F7XbzyCOPxLqcCWlpaYl1\nCeOKx7pU08TEY03QEusCxhWPYxWPNU1WzANhaGiIn/70p/j9ftrb2/nd737H0aNHY13Wl4rX//x4\nrEs1TUw81qRAmLh4rGmyYh4Ib731FsnJybhcLhISErj77rtpbGyMdVkiccNmWzT2/PivjT1ffrad\nLSTTL+aBEAqFWLJkiflvp9NJKBSKYUVf7sCBA1RUVIz5Mn7jG98Y90v66KOPxrpkiQNftmA/9zN1\n7oJ93NMkz54pNOoR7tfZQhfLuP9/5/wfjl4mXIrhbDEMI6Yn4P7+97/H7/fz1FNPAfDss8/S2trK\n3r17zddYLJZYlScickmbzCI+5tchOBwOOjs7zX93dnbidDpHvCbGmSUiMivEfJfRTTfdRCAQ4Pjx\n4wwODlJfX09+fn6syxIRmXVivoVgtVp58sknueOOOxgaGmLbtm0sW7Ys1mWJiMw6Md9CAPj+97/P\n+++/zz//+U9+8YtfmO2/+tWvyMjIYPny5eTk5IzYtVRZWYnb7SY1NZXm5uYZq3XHjh0sW7aMjIwM\n1q9fT39/PwDHjx/nsssuIzMzk8zMTMrKymJeE8RunJ5//nnS0tL42te+xqFDh8z2WI7T+WqC2I3T\naOXl5TidTnN8/H5/zGqJx+uDXC4XN9xwA5mZmWRlZcWkhq1bt2K320lPTzfbent78Xq9pKSkkJub\nS19fX1zUNenPkxHHTp06ZT5/4oknjG3bthmGYRhHjhwxMjIyjMHBQaOjo8NYunSpMTQ0NCM1NTc3\nm9PauXOnsXPnTsMwDKOjo8O4/vrrZ6SGidYUy3E6evSo8f777xvZ2dlGW1ub2R7LcTpfTbEcp9HK\ny8uNRx99NCbTPteZM2eMpUuXGh0dHcbg4KCRkZFhtLe3x7osw+VyGZ9++mlMa3jttdeMQ4cOjfgc\n79ixw3jkkUcMwzCMqqoq8zsY67om+3mKiy2E85k/f775/LPPPuOb3/wmAI2NjRQVFZGQkIDL5SI5\nOZm33nprRmryer3MmTM8bCtXriQYDM7IdC/kfDXFcpxSU1NJSUmZkWlN1PlqiuU4jceIg5Mo4vn6\noFiPz+rVq1m4cOGItqamJkpKSgAoKSmhoaEhLuqCyY1XXAcCwC9/+UuuvfZa9u3bZ+5OOnHixIgz\nkWJ17cLTTz9NXl6e+e+Ojg4yMzPJzs7m9ddfn/F6RtcUL+M0WjyM07nibZz27t1LRkYG27Zti8mu\nB4jf64MsFgu33347N910k3mqejzo6enBbrcDYLfb6enpiXFF/zOZz1PMDyp7vV66u7vHtO/evZu1\na9eya9cudu3aRVVVFffddx/PPPPMuO8zndcqfFlNALt27WLu3Lls3LgRgGuuuYbOzk4WLlzIoUOH\nKCgo4MiRIyO2cma6pvHM9DiNFg/jNBEX89qX89W4a9cuSktL+fWvfw0MH0N74IEH8Pl8F62W84nX\na3/eeOMNrr76av71r3/h9XpJTU1l9erVsS5rhLMXpsWDyX6eYh4IL7300oRet3HjRnPNd/S1C8Fg\nEIfDMWM17du3j4MHD/LKK6+YbXPnzmXu3LkArFixgqVLlxIIBFixYkXMaor1OI0n1uM0nos9TqNN\ntMZ77713UiE2nSZyfVAsXH311QBcddVV/OAHP+Ctt96Ki0Cw2+10d3eTlJREV1cXiYmJsS4JYEQd\nE/k8xfUuo0AgYD5vbGwkMzMTgPz8fOrq6hgcHKSjo4NAIDBjZxz4/X727NlDY2Mj8+bNM9s/+eQT\nhoaGAPjwww8JBAJ8+9vfjmlNsRync527DzOW43S+muJlnAC6urrM53/4wx9GnDEyk+Lx+qDTp08T\nDg/fmuPzzz+nubk5ZuMzWn5+PjU1NQDU1NRQUFAQ44qGTfrzNM0HuqfVhg0bjOuvv97IyMgw1q9f\nb/T09Jh/27Vrl7F06VLjuuuuM/x+/4zVlJycbFx77bXG8uXLjeXLlxulpaWGYRjGgQMHjLS0NGP5\n8uXGihUrjBdffDHmNRlG7MbphRdeMJxOpzFv3jzDbrcba9asMQwjtuN0vpoMI3bjNNqmTZuM9PR0\n44YbbjDWrVtndHd3x6yWgwcPGikpKcbSpUuN3bt3x6yOsz788EMjIyPDyMjIMNLS0mJW0913321c\nffXVRkJCguF0Oo2nn37a+PTTT42cnBzD7XYbXq/XOHnyZMzr8vl8k/48xfxeRiIiEh/iepeRiIjM\nHAWCiIgACgQREYlQIIiICKBAEJmUffv28bOf/QwYvnGYfg1PvkoUCCKTcO4VqPFyNarIdFEgyKxz\n/PhxUlNT2bJlC9dddx333HMPzc3N3HrrraSkpPD222/T29tLQUEBGRkZ3HLLLbz33nsXfM+///3v\nrFq1yrwF+dl7xrz99tvm7Zp37NhhXhi0b98+1q1bx2233UZKSgoPP/zwRZ9vkS+jQJBZ6YMPPuDn\nP/85//jHP3j//fepr6/njTfe4P/+7//YvXs35eXl3HjjjRw+fJjdu3dTXFwMjL1z5NmthOLiYvbs\n2cPhw4dJT0+noqICgC1btvDUU0/xzjvvYLVaR2xVvP3227zwwgu8++67PP/887S1tc3Q3IuMT4Eg\ns9K3vvUt0tLSsFgspKWlcfvttwOQnp5OR0cHr7/+Ops2bQLgtttu49NPPzVvmzDaqVOn6O/vN++p\nU1JSwmuvvUZ/fz+fffYZK1euBIbvx3VuoOTm5rJw4ULmzZvH+vXr4+LOrzK7KRBkVvr6179uPp8z\nZ455wz2LxcLQ0BAWi+W8WwNf5nwX/1/opgCGYZi/aSESK/oEioxj9erVPPfccwC0tLRw1VVXccUV\nV4x4jWEYGIaBzWZj4cKF5hp+bW0t2dnZLFiwgPnz55s/tlNXVzei/0svvcTJkycZGBigsbGRW2+9\ndQbmTOT8Yn77a5FYGL22P/rsoYceeoitW7eSkZHB5Zdfbt7J8tx73Z/7vKamhp/85CecPn2apUuX\nmr/b4fP5+NGPfsScOXP47ne/y4IFC8zpZGVlsWHDBoLBIJs2bZq2W4CLREs3txO5iD7//HMuv/xy\nAKqqqujp6eE3v/kN+/bto62tjb1798a4QpH/0RaCyEX0xz/+kcrKSs6cOYPL5WLfvn1AfP2qlshZ\n2kIQERFAB5VFRCRCgSAiIoACQUREIhQIIiICKBBERCRCgSAiIgD8Pzt+V9xC8w+/AAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x47285490>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_=hist(([x for x,y in nrots],[y for x,y in nrots]),bins=20,histtype='bar')\n",
      "xlabel('num rotatable bonds')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "<matplotlib.text.Text at 0x56ef06d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kzjvvBEaCt6KigtLSUu68805eeukl/u3f/o3169fzuc99jtOnr76XbE1NDV//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\nxIYU/iIiNvT/AQZ5n5I2RTvVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x47285450>"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and a histogram of the similarities we used to construct the set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rdkit import DataStructs\n",
      "from rdkit.Chem import rdMolDescriptors\n",
      "sims = [DataStructs.TanimotoSimilarity(rdMolDescriptors.GetMorganFingerprint(x[1],0),rdMolDescriptors.GetMorganFingerprint(x[3],0)) for x in rows]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_=hist(sims,bins=20)\n",
      "xlabel('MFP0 sims within pairs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "<matplotlib.text.Text at 0x472ae0d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fALz99tvWDOe2227j9OnTg7azdetWnn32WQA2bdpEXl4eAL/97W/567/+a2ub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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2329e6d0>"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "compare to MFP2 similarity (more on this in a later post)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sims2 = [DataStructs.TanimotoSimilarity(rdMolDescriptors.GetMorganFingerprint(x[1],2),rdMolDescriptors.GetMorganFingerprint(x[3],2)) for x in rows]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_=scatter(sims,sims2,marker='o',edgecolors='none')\n",
      "xlabel('MFP0 sim')\n",
      "ylabel('MFP2 sim')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "<matplotlib.text.Text at 0x7d634250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wn/37xfr3iRPtq2/GDLJX79aNzCqFHYKwI37vPVLbNGum3oG8/jpfb7du4mPd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\nyviD8wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x56ef02d0>"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look at the distribution of MFP0 similarities in random molecule pairs (more on this in a later post) "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "idxs = list(range(len(rows)))\n",
      "random.shuffle(idxs)\n",
      "ms1 = [x[1] for x in rows]\n",
      "ms2 = [rows[x][3] for x in idxs]\n",
      "sims = [DataStructs.TanimotoSimilarity(rdMolDescriptors.GetMorganFingerprint(x,0),rdMolDescriptors.GetMorganFingerprint(y,0)) for x,y in zip(ms1,ms2)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_=hist(sims,bins=20)\n",
      "xlabel('MFP0 sim in random pairs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "<matplotlib.text.Text at 0x452b390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CRHh4uCgrK/N4ptraWqHVaoVarRajRo0SEyZMEE1NTR7NlJqaKsaMGSMiIyNF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\nAIiIZIoFQEQkU/8Hb7pkzqqAetsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x843a0250>"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}