{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-dimensional scaling with myChEMBL, RDKit and Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### myChEMBL team, ChEMBL group, EMBL-EBI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a slightly modified version of [this](http://rdkit.blogspot.co.uk/search?updated-max=2014-02-02T20:05:00-08:00&max-results=7&start=7&by-date=false) article by Greg Landrum , which in turn is based on [this](http://nbviewer.ipython.org/gist/madgpap/8538507) notebook by George Papadatos.\n",
    "\n",
    "Scikit-learn makes it quite easy to apply multi-dimensional scaling (MDS) to either reduce the dimensionality of a dataset or to embed distance data into cartesian space. This enables one of the favorite activities of the cheminformatician: producing plots of where compounds land in an 2D space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from IPython.display import Image\n",
    "\n",
    "import pandas as pd\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit.Chem import AllChem as Chem\n",
    "from rdkit.Chem import DataStructs\n",
    "from sklearn import manifold\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 8,8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's configure our client to use local version of web services. Just delete the cell below, if you want to use the [official version](https://www.ebi.ac.uk/chembl/ws). In that case, you will need internet access."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from chembl_webresource_client.settings import Settings\n",
    "Settings.Instance().NEW_CLIENT_URL = 'http://localhost/chemblws'\n",
    "from chembl_webresource_client.new_client import new_client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grab the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to start with a larger dataset so that I can explore the impact of dataset size on the results. The analysis of George's results are below.\n",
    "\n",
    "I'll use the Dopamine D3 receptor as the target for this exercise. It's one of the targets we used in both the benchmarking and model fusion papers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'activity', 'assay', 'atc_class', 'binding_site', 'biotherapeutic', 'cell_line', 'chembl_id_lookup', 'description', 'document', 'drug_indication', 'go_slim', 'image', 'mechanism', 'metabolism', 'molecule', 'molecule_form', 'official', 'protein_class', 'similarity', 'source', 'substructure', 'target', 'target_component']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_comment</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>assay_type</th>\n",
       "      <th>bao_endpoint</th>\n",
       "      <th>bao_format</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>data_validity_comment</th>\n",
       "      <th>document_chembl_id</th>\n",
       "      <th>...</th>\n",
       "      <th>record_id</th>\n",
       "      <th>standard_flag</th>\n",
       "      <th>standard_relation</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_organism</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>uo_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>754819</td>\n",
       "      <td>CHEMBL651365</td>\n",
       "      <td>Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000192</td>\n",
       "      <td>BAO_0000219</td>\n",
       "      <td>O[C@H](CCCN1CCN(CC1)c2ncc(F)cn2)c3ccc(F)cc3</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL1126960</td>\n",
       "      <td>...</td>\n",
       "      <td>244391</td>\n",
       "      <td>True</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>Ki</td>\n",
       "      <td>nM</td>\n",
       "      <td>2000</td>\n",
       "      <td>CHEMBL234</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Dopamine D3 receptor</td>\n",
       "      <td>UO_0000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>764689</td>\n",
       "      <td>CHEMBL651365</td>\n",
       "      <td>Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000192</td>\n",
       "      <td>BAO_0000219</td>\n",
       "      <td>Fc1ccc(cc1)C(=O)CCCN2CCN(CC2)c3ccccn3</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL1126960</td>\n",
       "      <td>...</td>\n",
       "      <td>244387</td>\n",
       "      <td>True</td>\n",
       "      <td>=</td>\n",
       "      <td>Ki</td>\n",
       "      <td>nM</td>\n",
       "      <td>53</td>\n",
       "      <td>CHEMBL234</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Dopamine D3 receptor</td>\n",
       "      <td>UO_0000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>764693</td>\n",
       "      <td>CHEMBL651365</td>\n",
       "      <td>Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000192</td>\n",
       "      <td>BAO_0000219</td>\n",
       "      <td>O[C@H](CCCN1CCN(CC1)c2ccccn2)c3ccc(F)cc3</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL1126960</td>\n",
       "      <td>...</td>\n",
       "      <td>244390</td>\n",
       "      <td>True</td>\n",
       "      <td>=</td>\n",
       "      <td>Ki</td>\n",
       "      <td>nM</td>\n",
       "      <td>374</td>\n",
       "      <td>CHEMBL234</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Dopamine D3 receptor</td>\n",
       "      <td>UO_0000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>772379</td>\n",
       "      <td>CHEMBL651365</td>\n",
       "      <td>Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000192</td>\n",
       "      <td>BAO_0000219</td>\n",
       "      <td>OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL1126960</td>\n",
       "      <td>...</td>\n",
       "      <td>244384</td>\n",
       "      <td>True</td>\n",
       "      <td>=</td>\n",
       "      <td>Ki</td>\n",
       "      <td>nM</td>\n",
       "      <td>0.96</td>\n",
       "      <td>CHEMBL234</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Dopamine D3 receptor</td>\n",
       "      <td>UO_0000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>775936</td>\n",
       "      <td>CHEMBL651365</td>\n",
       "      <td>Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000192</td>\n",
       "      <td>BAO_0000219</td>\n",
       "      <td>O[C@H](CCCN1CCC(O)(CC1)c2ccc(Cl)cc2)c3ccc(F)cc3</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL1126960</td>\n",
       "      <td>...</td>\n",
       "      <td>244383</td>\n",
       "      <td>True</td>\n",
       "      <td>=</td>\n",
       "      <td>Ki</td>\n",
       "      <td>nM</td>\n",
       "      <td>1296</td>\n",
       "      <td>CHEMBL234</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Dopamine D3 receptor</td>\n",
       "      <td>UO_0000065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity_comment  activity_id assay_chembl_id                                                                                assay_description assay_type bao_endpoint   bao_format                                 canonical_smiles data_validity_comment document_chembl_id     ...     record_id  standard_flag standard_relation standard_type standard_units standard_value target_chembl_id target_organism      target_pref_name    uo_units\n",
       "0             None       754819    CHEMBL651365  Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors          B  BAO_0000192  BAO_0000219      O[C@H](CCCN1CCN(CC1)c2ncc(F)cn2)c3ccc(F)cc3                  None      CHEMBL1126960     ...        244391           True                 >            Ki             nM           2000        CHEMBL234    Homo sapiens  Dopamine D3 receptor  UO_0000065\n",
       "1             None       764689    CHEMBL651365  Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors          B  BAO_0000192  BAO_0000219            Fc1ccc(cc1)C(=O)CCCN2CCN(CC2)c3ccccn3                  None      CHEMBL1126960     ...        244387           True                 =            Ki             nM             53        CHEMBL234    Homo sapiens  Dopamine D3 receptor  UO_0000065\n",
       "2             None       764693    CHEMBL651365  Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors          B  BAO_0000192  BAO_0000219         O[C@H](CCCN1CCN(CC1)c2ccccn2)c3ccc(F)cc3                  None      CHEMBL1126960     ...        244390           True                 =            Ki             nM            374        CHEMBL234    Homo sapiens  Dopamine D3 receptor  UO_0000065\n",
       "3             None       772379    CHEMBL651365  Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors          B  BAO_0000192  BAO_0000219     OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3                  None      CHEMBL1126960     ...        244384           True                 =            Ki             nM           0.96        CHEMBL234    Homo sapiens  Dopamine D3 receptor  UO_0000065\n",
       "4             None       775936    CHEMBL651365  Displacement of [3H]-spiperone from CHO-K1 cell membranes expressing human dopamine 3 receptors          B  BAO_0000192  BAO_0000219  O[C@H](CCCN1CCC(O)(CC1)c2ccc(Cl)cc2)c3ccc(F)cc3                  None      CHEMBL1126960     ...        244383           True                 =            Ki             nM           1296        CHEMBL234    Homo sapiens  Dopamine D3 receptor  UO_0000065\n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print dir(new_client)\n",
    "activity = new_client.activity\n",
    "target='CHEMBL234'\n",
    "bio = [act for act in activity.filter(target_chembl_id=target)]\n",
    "data = pd.DataFrame(bio)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7378, 30)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4754, 30)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop_duplicates(['molecule_chembl_id'])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove assays with too many or too few compounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(492, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_chembl_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHEMBL1030601</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL1030747</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL1031083</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL1031401</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL1032031</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 target_chembl_id\n",
       "assay_chembl_id                  \n",
       "CHEMBL1030601                  12\n",
       "CHEMBL1030747                   1\n",
       "CHEMBL1031083                   4\n",
       "CHEMBL1031401                   1\n",
       "CHEMBL1032031                   1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assays = data[['assay_chembl_id','target_chembl_id']].groupby('assay_chembl_id').count()\n",
    "print assays.shape\n",
    "assays.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f6febf1d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OW5KkkbjTliRpJhzaPVXZs1ToqNAANToqNIAdU1KhYQgObUmSGuFOW5KkkbjTliRpJhza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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6fe4df50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assays.target_chembl_id.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(123, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f6e01e590>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rk3x/a+11N/2czvgc5uqMb8wd8b7YxJpHWy/sinP9d8attYMkX6iq+xcXvSrJZ9edBwBz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\nqujeXeSZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6dfb30d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "goodassays = assays.ix[(assays.target_chembl_id >= 10)&(assays.target_chembl_id <= 30)]\n",
    "print goodassays.shape\n",
    "goodassays.target_chembl_id.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2091, 30)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data.ix[data.assay_chembl_id.isin(list(goodassays.index))]\n",
    "data2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now get the SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "molecule = new_client.molecule\n",
    "cs = molecule.get(molecule_chembl_id=list(data2['molecule_chembl_id']))\n",
    "smiles = pd.Series(map(lambda x: x['molecule_structures']['canonical_smiles'], cs), index=data2.index)\n",
    "data2['SMILES'] = smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "PandasTools.AddMoleculeColumnToFrame(data2, smilesCol = 'SMILES',includeFingerprints=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by looking at assays that have between 10 and 12 compounds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(123, 1)\n",
      "(307, 32)\n"
     ]
    }
   ],
   "source": [
    "assays = data2[['assay_chembl_id','target_chembl_id']].groupby('assay_chembl_id').count()\n",
    "print assays.shape\n",
    "goodassays = assays.ix[(assays.target_chembl_id >= 10)&(assays.target_chembl_id <= 12)]\n",
    "subset = data2.ix[data.assay_chembl_id.isin(list(goodassays.index))]\n",
    "print subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols = subset[['molecule_chembl_id','SMILES', 'ROMol', 'assay_chembl_id']]\n",
    "mols.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>ROMol</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>CHEMBL28607</td>\n",
       "      <td>Oc1ccc(OCCNCc2ccccc2)cc1</td>\n",
       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "      <td>CHEMBL669161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>CHEMBL156896</td>\n",
       "      <td>Oc1ccccc1N2CCN(Cc3ccccc3)CC2</td>\n",
       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "      <td>CHEMBL669161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>CHEMBL156732</td>\n",
       "      <td>C(N1CCN(CC1)c2ccccc2)c3ccccc3</td>\n",
       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "      <td>CHEMBL669161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>CHEMBL347291</td>\n",
       "      <td>Oc1ccccc1OCCNCc2ccccc2</td>\n",
       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "      <td>CHEMBL669161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>CHEMBL2112913</td>\n",
       "      <td>CS(=O)(=O)Nc1cc(OCCNCc2cccs2)ccc1F</td>\n",
       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "      <td>CHEMBL669161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "   molecule_chembl_id                              SMILES                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      ROMol assay_chembl_id\n",
       "51        CHEMBL28607            Oc1ccc(OCCNCc2ccccc2)cc1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      <img 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alt=\"Mol\"/>    CHEMBL669161\n",
       "52       CHEMBL156896        Oc1ccccc1N2CCN(Cc3ccccc3)CC2  <img src=\"data:image/png;base64,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\" alt=\"Mol\"/>    CHEMBL669161\n",
       "53       CHEMBL156732       C(N1CCN(CC1)c2ccccc2)c3ccccc3                                                                                                                                                                                                                                                                                                                                                                  <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAYAAACtWK6eAAAUn0lEQVR4nO3deVRU5/0G8C8IDIswiFoQt8RooiQmgqkLiOA4LInSKEhPmhNjbSP2mNQutEdj9sackqRH08Za0diaNMaGRmuQGFkcHIGiqBRNjHFJXZJIVARcUJZxnt8f1BEQ3qDMvcNPn89f3GHmft9Znveu771uACBE1C53VzeAqDtjQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESIEBIVJgQIgUGBAiBQaESMHD1Q24EWfPnpWCggLJy8uT+++/X9LS0sTHx8fVzaJbmBsAuLoRHbHZbLJz507Jzc2V3Nxc2bNnj/Tv319MJpNs375d7rzzTsnOzhZfX19XN5VuUl1dnWzbtk3y8vIkMjJSUlNTxd29+6zYdKuAAJDy8nIpKCiQgoICKSkpEW9vbzGbzTJ16lSJj4+XkJAQERGpqamR+Ph4ERHJzc2VoKAgVzadOgmA7Nu3z9HplZSUiL+/v0RHR0tRUZFMmTJFVq9eLT169HB1U0WkGwSkpqZGCgoKZNOmTZKfny9VVVUSGxsrZrNZzGazjBo1qsMPq7a2VhISEsRms0leXp707t1b59ZTZ5w4cUK2bNkimzZtkm3btomIyEMPPeT4jocMGSIiIocPHxaTySSjRo2SDz/8UAwGgwtb3Uz3gDQ1NYnVanUsJSoqKqR///6SmJgoZrNZTCZTuz/0y5cvi9VqlS1btkhKSopER0eLiMi5c+ckMTFR6uvrJT8/X/r06aPn26F2XLhwQbZs2eL4jo8ePSoRERGOQERFRXW47Xj8+HExmUwyfPhwWb9+vXh7e+vc+jagoxMnTmDEiBHw8PBAdHQ0Fi9ejLKyMly5cqXd53/xxRdYunQpEhIS4OPjAx8fHyQkJKCwsLDV8y5evIjY2FiMGDECJ0+e1OGdUEt2ux27d+9GRkYGzGYzvL29ERQUhJkzZyIrKwvffvttu69rampCUVERnn32WXz22WeOx48fP46hQ4ciMTERly5d0utttEvXgMycORMpKSk4d+5cu/+vrKxEZmYmUlNT0bdvX7i7uyMqKgoZGRnYvXs3bDZbh/O+ePEiTCYThg8fjm+++Uart0BtHDlyBCNGjICbmxvCw8OxYMECWCwWNDQ0tPv8r776CqtWrcKMGTMQGBgIX19fTJkyBWVlZa2eV1lZibCwMMTExODChQt6vJV26RqQ0NBQfPDBB47ppqYm5OfnY8GCBRg9ejTc3d0xcOBApKWlISsrC2fOnLmh+dfV1SEuLg533303vvrqK2c3n9qRlJSElJSUDpcS586dQ1ZWFtLS0jBkyJAb6vS+/fZb3HfffYiOjsb58+e1egtKugXkiy++gJubG06dOgUAOH36NEJDQ+Hh4YHIyEi8/PLL2LFjh/ID64z6+nokJSXhjjvuwH//+19nNJ060NjYCH9/f2zevNnxWMvVraioKHh4eCA0NPSmO71Tp05h5MiRiIqK6nDNQ0u6BWT58uUYOXJkq8eys7NRU1Pj9FoNDQ2YNm0aBg0ahCNHjjh9/tSstLQUnp6ejlWgI0eOYNiwYXBzc0NERASeeeYZWK1WNDU1dalOdXU1HnzwQTz44IM4e/asM5reaboFJDU1Fb/4xS/0KoeGhgYkJydj4MCBOHz4sG51byevvvoqoqKiHNONjY149913O1zd6oqamhqMGTMGERERqKqqcvr8O6LLIUsAsm3bNpk0aZIe5URExMvLS7KysiQmJkaio6Nl//79utW+XRQWFrb6Tj09PWXmzJkSHBzs9FqBgYGSl5cnXl5eYjabpaqqyuk12qVHCvfu3YsePXqgtrZWj3Kt2Gw2PPHEEwgODsa+fft0r3+rqq+vh6+vL7Zu3aprXb136euyBLFYLBIeHi5Go1GPcq306NFDVq9eLbGxsfKzn/1M9/q3qp07dwoAiYyM1LWun5+f5OTkSL9+/cRkMsnJkyc1radLQAoLC8VkMulRyqGhoUHKysrEbreLh4eH/PrXv5Zdu3bJhQsXdG3Hrcpiscj48eNdcqTbz89PNm7cKH369JGnnnpK01qaB+TKlStitVp13f4QESkuLhaTySQ2m01Emr/QsWPHir+/v67tuFW13f7Qw6VLl+TYsWMiIuLv7y+LFi0Si8Xi+I61oHlAysvL5fLlyzJx4kStS7VSWFgoUVFR4uXl5ZjWeynmTI2NjWKxWOR3v/ud44Q/V6mrq5MdO3bo/nnm5eXJ6NGjxW63i4hISUmJTJgwQTw8tBvWpHlALBaLfP/739d9zIbFYnH0cA0NDVJcXKx7j9dVhw4dkmXLlklSUpIEBQXJtGnTxGq1ykMPPSSvv/66y9pVWloqBoNBxowZo2vdrVu3islkcowXafkda0XzgLii5z5//rzs2rXLUbesrEwAyPjx43Vtx406efKkrFy5Un74wx9KcHCw3HffffLRRx/JhAkTxGq1Sm1trWzdulXy8vJk8eLFsnDhQpe0s7CwUPOeuz0tA3H1O9a809NyF1lDQwN8fX1hsVi0LHOdjz/+GIGBgY7TVl566SVMnjxZ1zZ0RkNDg+NctLCwMIhIq3PRVEeNi4uLERAQgPT0dB1b3GzcuHF4/fXXda1ZWVkJNzc3HDhwAACQk5ODoKCgDs8EdxZNu4CrPbfeuwILCwtl4sSJjoFWhYWFEhcXp2sbOvL555/Lpk2bpKCgQEpLS8Vms0l0dLSkpaVJUlKSY/DQd4mKihKLxSLx8fFSV1cny5cvFzc3N41b39xz7969W5YtW6Z5rZYKCwslNDRUhg8f7piOiYnRfniulul7+eWXXdJzR0REYOnSpQCaz/D18vLCv//9b93b0dLRo0dx//33Q0TQr18/zJ49G//4xz+6fG5RSUkJjEYjXnnlHdjtTmqsQk5ODnr16qV5z93WnDlz8PjjjzumIyIi8Kc//UnzupoGJDY2FosXL3ZMr1ixAnv37tWyJKqqquDu7u6ok5+fj4CAgC6fMNdVkZGRSE5ORkVFBexO/iXv2rUfffs2YP58aB6S9PR0TJs2Tdsi7Rg6dChWr14NADhz5gzc3d1bDbLSimYBaWpqQq9evfDRRx85HktLS0Pv3r2xZ88ercpiw4YNCA4OdvwIFy1ahIcfflizep1x7tw5eHh4oLy8XLMaFRVAnz7AT34CaNm5R0RE4I9//KNjeuXKlZqPvTl27BhEBMePHwcArF+/HiEhIU7vaNqj2Qqch4eHJCQkyMKFC6WyslJERDIzM2Xu3LkyceJEzfblX103vbo+rseuwO9SVFQkgYGB8sADD2hW44EHRLZvF9m8WWTOHJH/HSpwmtOnT8vq1atl7969MnbsWBERsdvtUlBQILGxsXLixAnnFmxh27ZtMmTIEBk0aJCINH/HsbGxumxzab4X65FHHsHgwYPx5ZdfOh5//vnn4efnp8mJbvfeey/+8pe/ALjWc2u5xOqM9PR0TJ8+XZdaBw4A/foBP/oR0JWxZ/X19bBYLFi4cCHCw8Ph5uaGO+6447phsFeuXMHs2bMREhKC/fv3O+ldtDZr1iw8+eSTjumwsDBkZmZqUqstzc/mbWhowPTp068bvJSRkQFfX1/k5eU5rdbVXYEHDx4E0LxB2adPH903KNsKDw/XZYPyqv/8p3l1a9GiG3vdoUPAypXfYMqUKfDz84O3tzfi4+OxZMkSx+7VluMyru5gsNvtmDdvHoKDg/Hpp586+d0AAwcOxNq1awFc+471GuOjy+nujY2NSElJuW7wUkZGBgwGA7Kzs51SZ926dRgwYIBjOj09HcnJyU6Z983Sc4Oypb17ge8aTFlXB+TkAE89Bdx1FyAChIXV4+c/n4+cnBzU1dW1+7ra2lqMHTsW4eHhjsFLdrsdTz/9NL73ve85dVjB4cOH4ebm5hiE9f7772Pw4MFOm/930W1Eoc1mw+OPP37doviNN96AwWDAxo0bu1wjLS0NTzzxhGM6PDwcb731Vpfn2xV6blB2RAR4/vlrf7//PhAXB3h7A0YjkJwMrFgB3MgQ/traWowfPx6jRo1yjDO32+2YP38+evXqhV27djml7ZmZmQgLC3NMz5kzB7NmzXLKvDtD16uatBy81HJRvHz5chgMBqxfv75L8y8vL3f0Xq7qudtatKgWv/rVAZe2QQQICgIOHmz+OzMTeOEFoLgY6Mre7/YGL9ntdvzyl79EYGAgdu7c2eW2P/roo3j66acd03fddRfWrFnT5fl2lq4BAZpD8uMf//i6RfGKFSvg6emJ9957zyl1ukPPDQBhYc0/SFcSAZ55Bpg8uflvZ+roemSLFi2C0WhEaWlpl+afn5+PiooKANd29544caJL87wRugcEuLZR17dv31YHDletWgVPT0+8++67Nz1vm82GHTt2ID4+3uXHP06eBNzcAFdfM0IEqKkBAgOdHxCg+WwFs9mMe+65B19//bXj8eeeew49e/a87kqYN+tvf/sbhg0b5pR5dZZLAgJc26jr1atXq6vqLV++HF5eXjf0oVZWVmLNmjV49NFH0bt3b7i7u+Pee++Fn5+f7idKtvTee4CO25MduhqK3/9em4AAwKVLlxAXF4dhw4a1OnD4wgsvOGWX/meffYbJkye32t2rB5cFBLi2URcYGIgdO3Y4Hv/4449RX1/f4evOnz/f6mp9IoKwsDAsWLAA+fn5uHjxIgDg7bffhre3N/71r39p/Vba9dOfAjpuT7br66+B117r2jGRzqqvr8cPfvCD6y7a99vf/hb+/v43dJpRTU0NPvzwQzz55JMYOHAgRATjxo3D6dOntWh6h1waEODaRp1qfbW9iyMbjUakpqYiMzOz1UHIttauXQsfH58u7wC4GYMHAzpuT7Zr1Spg6FD96rW8aF/L72XNmjXKTq+9U/8HDBhw01dkdBaXB+SqZ599FgEBASgpKQHQ3INkZWVh5syZCAkJgbu7O0aPHo0XX3zxO6/p2ta6detgMBjw97//XavmX+fLL5tXZ3TcnmzXY48BaWn61mx5cFh1QO/YsWN48803YTab4efnB4PBALPZjDfffFPZ6emp2wTk6pIkICAAkyZNgo+PDwIDAzFjxgysXLnScaLazfrggw9gMBh020V44AAwd64upTpktzefdrJunf61bTYbHnvsMQwYMACHDh0CAFy+fBnZ2dmtVo1Hjx7tWDXu6MCkK3WbgFy1ZcsWvPjiiygpKenyhazb+uc//wmDwYC//vWvTp1vd/X558170f53vXDdXV3dCg0NxSOPPAKj0QgfHx8kJiZiyZIlmp275UzdLiBa27RpE7y9vbFs2TLNarQ9cu0qf/5z83EYV2psbMQ777yD9PR05Obm4vLly65t0A1y+T0KXWHz5s0yY8YMycjIkPnz5zt9/m5uIkFBIqWlIvfcI+KqTzg1VSQkROStt1xT/1bQfe63q6OHH35YNmzYIAsXLpSlS5d2eX6ffiryhz+IHDx47bG5c0XmzevyrG8aADl+/JLExLiuDbcEFy/BXCo3Nxc+Pj545ZVXbuh1p04Ba9c2H+Po1w/o0QMYNw4oKmr+v9ZHrjtj3759cHd3d9nu0VvFbR0QALBarejZsydeeumlDp/T1NQEq9WK1177BBERgLs7EBzcHJB164C2t6toe+T66FHtx4q3tWTJEkREROhb9BZ02wcEALZv3w5/f38sWLDA8djVm02mpKTAaDTC09MTU6bMwquvAnv2dP4Hb7MBw4drP1a8raSkJPzmN7/Rr+AtigH5n4KCAvj6+mL69OkYOXIkRARDhw7FvHnzsHHjxi7dRPLgQWDAACA1FWhsdGKjO2Cz2WA0GlvdO5Buzm25F6sjJSUlUlxcLEajUeLj4zt9EbfOOHZMZPJkkREjRNavFzEYnDbr65SVlcmECROkurpaevbsqV2h2wADoqMTJ0RMJpG77xbZsEFEq1trZGRkSE5OjhQXF2tT4DZyW+7mdZVBg0Ty80UOHBCZPbtWGhoanF6jqqpKsrOz/1/f6qE7YUB0duedIlaryNmzcyU5OVnq6+u7ND+bzSZFRUXy3HPPyZgxYyQ4OFh8fHxknisPwtxCuIrlIqdPnxaz2SxGo1E2b97c6TtfAZDy8nIpKCiQgoICKSkpEW9vbzGbzTJ16lSJj4+XkJAQjVt/+2BAXOjMmTMSFxcnfn5+8sknn0hAQEC7z6uurpacnBzJycmR7du3S3V1tcTExIjZbBaz2SyjRo1yXMmenIsBcbHa2lpJSEiQK1euSF5engQFBUlTU5NYrVbHUqKiokL69+8viYmJMnXqVJk0aRL3TumEAekGqqqqJD4+Xux2u4wcOVLy8/OlurpaIiMjJT4+XhISEiQ8PFz7e2HQdRiQbqKmpkbefvttOXr0qCQkJIjJZOIdebsBBoRIgctsIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFBgQIgUGhEiBASFSYECIFP4PsfnG3QMD2NgAAAAASUVORK5CYII=\" alt=\"Mol\"/>    CHEMBL669161\n",
       "54       CHEMBL347291              Oc1ccccc1OCCNCc2ccccc2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  <img 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alt=\"Mol\"/>    CHEMBL669161\n",
       "55      CHEMBL2112913  CS(=O)(=O)Nc1cc(OCCNCc2cccs2)ccc1F                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          <img src=\"data:image/png;base64,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\" alt=\"Mol\"/>    CHEMBL669161"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct fingerprints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the distance matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dist_mat = []\n",
    "for i,fp in enumerate(fps):\n",
    "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
    "dist_mat=numpy.array(dist_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now do the MDS into 2 dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breaking at iteration 398 with stress 4361.02082966\n",
      "breaking at iteration 422 with stress 4263.85201928\n",
      "breaking at iteration 450 with stress 4380.32706244\n",
      "breaking at iteration 479 with stress 4443.37044781\n",
      "Final stress: 4263.85201928\n"
     ]
    }
   ],
   "source": [
    "mds = manifold.MDS(n_components=2, dissimilarity=\"precomputed\", random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
    "results = mds.fit(dist_mat)\n",
    "coords = results.embedding_\n",
    "print 'Final stress:',mds.stress_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot the points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f6428a750>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZM9a2qenpwO+/B++92rblPvkjj7D2vn/Gv7/nnuMH3bg4fm9KSnDmJqTl9Ahz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6c030490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter([x for x,y in coords], [y for x,y in coords],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice picture. How accurate is it?\n",
    "\n",
    "The strain values above lead me to believe that there are probably some real problems here.\n",
    "\n",
    "The overall strain value isn't particularly easy to interpret, so we'll check up on things by comparing the embedded distances with the Tanimoto distances we're trying to reproduce."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "def distCompare(dmat,coords,nPicks=5000,seed=0xf00d):\n",
    "    \"\"\" picks a random set of pairs of points to compare distances \"\"\"\n",
    "    nPts=len(coords)\n",
    "    random.seed(seed)\n",
    "    res=[]\n",
    "    keep=set()\n",
    "    if nPicks>0:\n",
    "        while len(res)<nPicks:\n",
    "            idx1 = random.randint(0,nPts-1)\n",
    "            idx2 = random.randint(0,nPts-1)\n",
    "            if idx1==idx2: \n",
    "                continue\n",
    "            if idx1>idx2: \n",
    "                idx1,idx2=idx2,idx1\n",
    "            if (idx1,idx2) in keep: \n",
    "                continue\n",
    "            keep.add((idx1,idx2))\n",
    "            p1 = coords[idx1]\n",
    "            p2 = coords[idx2]\n",
    "            v = p1-p2\n",
    "            d = sqrt(v.dot(v))\n",
    "            res.append((dmat[idx1][idx2],d))\n",
    "    else:\n",
    "        for idx1 in range(nPts):\n",
    "            for idx2 in range(idx1+1,nPts):\n",
    "                p1 = coords[idx1]\n",
    "                p2 = coords[idx2]\n",
    "                v = p1-p2\n",
    "                d = sqrt(v.dot(v))\n",
    "                res.append((dmat[idx1][idx2],d))\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = distCompare(dist_mat,coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f640c74d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HcAD48EP7cVYWm5+sW+e8x7IYEAPVtm3g9wZTfDzPgk+axOx5V26Ay+LFkRmX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\n2+LAhm3VLXXKA9gNoLGL9kL0rSMI4cM4Ey1TxjkJSLD45RfdajpPHqL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NAAAK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\nHhNYT0kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6d39f950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes... that's no good. This really isn't so surprising given how much stress was left.\n",
    "\n",
    "Let's try increasing the dimensionality to see how much improvement we can get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breaking at iteration 261 with stress 122.957903637\n",
      "breaking at iteration 249 with stress 122.366087858\n",
      "breaking at iteration 271 with stress 120.722555337\n",
      "breaking at iteration 266 with stress 122.398041816\n",
      "Final stress: 120.722555337\n"
     ]
    }
   ],
   "source": [
    "mds2 = manifold.MDS(n_components=20, dissimilarity=\"precomputed\", random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
    "results = mds2.fit(dist_mat)\n",
    "coords = results.embedding_\n",
    "print 'Final stress:',mds2.stress_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f5dfb3a50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nlHzeUYEXCm4cTieioJGWJpuS3H+/DBd37pzzJV9JScDOnXLP2bOBp5+WbO6r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6422fb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = distCompare(dist_mat,coords)\n",
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so bad... of course, we can't visualize 20 dimensions.\n",
    "\n",
    "What about if we try doing dimensionality reduction on the 20D results instead of using the distance matrix?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breaking at iteration 282 with stress 4263.34187798\n",
      "breaking at iteration 347 with stress 4216.67532472\n",
      "breaking at iteration 394 with stress 4220.50386873\n",
      "breaking at iteration 371 with stress 4272.69917046\n",
      "Final stress: 4216.67532472\n"
     ]
    }
   ],
   "source": [
    "mds3 = manifold.MDS(n_components=2, random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
    "results3 = mds3.fit(coords)\n",
    "coords3 = results3.embedding_\n",
    "print 'Final stress:',mds3.stress_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strain is still really high. This isn't that surprising given that we already tried the same thing from the distance matrix.\n",
    "\n",
    "Ok, so MDS doesn't work. Now that we have actual coordinates, we can use some of the other scikit-learn manifold learning approaches for embedding the points.\n",
    "\n",
    "Instead of jumping all the way to a 2D system, where we're really unlikely to get decent results, start by dropping down to 10D.\n",
    "\n",
    "First MDS in 10D:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breaking at iteration 213 with stress 275.719972469\n",
      "breaking at iteration 221 with stress 270.524855973\n",
      "breaking at iteration 226 with stress 273.789365441\n",
      "breaking at iteration 227 with stress 277.339022756\n",
      "Final stress: 270.524855973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f5d6aef90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nlHzeUYEXCm4cTieioJGWJpuS3H+/DBd37pzzJV9JScDOnXLP2bOBp5+WbO6r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f64103190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mds3 = manifold.MDS(n_components=10, random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
    "results3 = mds3.fit(coords)\n",
    "coords3 = results3.embedding_\n",
    "print 'Final stress:',results3.stress_\n",
    "ds = distCompare(dist_mat,coords3)\n",
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try locally linear embedding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.87201313905e-16\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f5d551b10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2KYiLSJ43bBiXzKxTh+PSnnbsAC69lJnjxYrxdeIEK6olJvL16qvOgGhZwO+/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6d35e810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lle = manifold.LocallyLinearEmbedding(n_components=10,random_state=3,max_iter=1000)\n",
    "results3=lle.fit(coords)\n",
    "coords3=results3.embedding_\n",
    "print results3.reconstruction_error_\n",
    "d = distCompare(dist_mat,coords3)\n",
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ick! What about Isomap?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f5d49eb50>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l6L6tUIENHSZO9M99ExJoWIYMYYbxn3+6js0CNLSdOvG8ixc596NHjW5aVrzy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bsXzTznGwF8dPH9RwBucm8gIvtEZNXF9ycBbARQLopjiiZNAWwVkT9FJA3Ap+Bn4MqNACYBgIj8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f642560d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embed = manifold.Isomap(n_components=10,max_iter=1000)\n",
    "results3=embed.fit(coords)\n",
    "coords3=results3.embedding_\n",
    "d = distCompare(dist_mat,coords3)\n",
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also pretty grim. Does spectral embedding work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f5d4dbd50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XLWIe+KZNjHQ/fpxL/seP0+dbEElPd9det/nhB8Y4rFrFwjCrVzsBiyLnUXS6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\nEieEEEJcCpU4IYQQ4lKoxAkhhBCX8n9gtA4J61LWQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f5df4aa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embed = manifold.SpectralEmbedding(n_components=10)\n",
    "results3=embed.fit(coords)\n",
    "coords3=results3.embedding_\n",
    "d = distCompare(dist_mat,coords3)\n",
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm, there's at least some signal there. How does spectral embedding do with 2D?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f5d42c110>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tXwpSU519V1zh6K1ffbVbS75bNxp6kXuUExdCiDymZ0/+APRIx4/nhLCbbqIR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SAAAg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\ne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wlKcgpnSMunnwZ33MmTpooR4KomGAc362THEHIzZ1JIzZnD8Rjahm3baMdt0cJMc2pw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\nU/4f+NcNWiM1jscAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f5d4b5bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embed = manifold.SpectralEmbedding(n_components=2)\n",
    "results3=embed.fit(coords)\n",
    "coords3=results3.embedding_\n",
    "d = distCompare(dist_mat,coords3)\n",
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so hot..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at stress vs dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're able to do a reasonable job of embedding into 20 dimensions with MDS. 10 even looks somewhat ok. Let's look at behavior of the residual stress as a function of dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final stress at dim=140: 14.8882703507 stress per element: 0.0484959946277\n",
      "Final stress at dim=120: 15.6363159571 stress per element: 0.0509326252676\n",
      "Final stress at dim=100: 17.1525426952 stress per element: 0.0558714745771\n",
      "Final stress at dim=80: 19.4234713492 stress per element: 0.0632686363166\n",
      "Final stress at dim=60: 24.673693386 stress per element: 0.0803703367621\n",
      "Final stress at dim=40: 39.8924727179 stress per element: 0.129942907876\n",
      "Final stress at dim=20: 120.722555337 stress per element: 0.393233079273\n",
      "Final stress at dim=10: 383.132794574 stress per element: 1.24798955887\n",
      "Final stress at dim=5: 1115.25419255 stress per element: 3.63274981287\n",
      "Final stress at dim=2: 4263.85201928 stress per element: 13.8887687924\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f5d4b5f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs={}\n",
    "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
    "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
    "    results3 = mds3.fit(dist_mat)\n",
    "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
    "    vs[nComps]=results3.stress_/len(dist_mat)\n",
    "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
    "yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yeah, the stress is going up exponentially with dimension. These data really don't want to be in a 2D space.\n",
    "\n",
    "Try non-metric embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final stress at dim=140: 21.0074470128 stress per element: 0.0684281661654\n",
      "Final stress at dim=120: 24.8462805787 stress per element: 0.0809325100283\n",
      "Final stress at dim=100: 29.0718704363 stress per element: 0.0946966463724\n",
      "Final stress at dim=80: 36.5797807548 stress per element: 0.119152380309\n",
      "Final stress at dim=60: 48.1876811144 stress per element: 0.156963130666\n",
      "Final stress at dim=40: 72.5718624184 stress per element: 0.23639043133\n",
      "Final stress at dim=20: 154.832521219 stress per element: 0.504340459997\n",
      "Final stress at dim=10: 329.900416347 stress per element: 1.07459419006\n",
      "Final stress at dim=5: 749.165314679 stress per element: 2.44027789798\n",
      "Final stress at dim=2: 2116.52559942 stress per element: 6.89422019356\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f5d606710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs={}\n",
    "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
    "    mds3 = manifold.MDS(metric=False,n_components=nComps,random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
    "    results3 = mds3.fit(dist_mat)\n",
    "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
    "    vs[nComps]=results3.stress_/len(dist_mat)\n",
    "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
    "yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "helps somewhat at lower dimension, but not dramatically. Plus it's a lot slower"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A larger data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe it's possible to do better with a larger data set? This seems unlikely, but we can at least try:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(123, 1)\n",
      "(724, 32)\n"
     ]
    }
   ],
   "source": [
    "assays = data2[['assay_chembl_id','target_chembl_id']].groupby('assay_chembl_id').count()\n",
    "print assays.shape\n",
    "goodassays = assays.ix[(assays.target_chembl_id >= 10)&(assays.target_chembl_id <= 15)]\n",
    "subset = data2.ix[data.assay_chembl_id.isin(list(goodassays.index))]\n",
    "print subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(724, 4)\n",
      "(724, 724)\n"
     ]
    }
   ],
   "source": [
    "mols = subset[['molecule_chembl_id','SMILES', 'ROMol', 'assay_chembl_id']]\n",
    "print mols.shape\n",
    "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]\n",
    "dist_mat = []\n",
    "for i,fp in enumerate(fps):\n",
    "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
    "dist_mat=numpy.array(dist_mat)    \n",
    "print dist_mat.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final stress at dim=140: 78.2088255907 stress per element: 0.108023239766\n",
      "Final stress at dim=120: 83.6551686632 stress per element: 0.115545813071\n",
      "Final stress at dim=100: 93.1013936693 stress per element: 0.128593085179\n",
      "Final stress at dim=80: 111.281591781 stress per element: 0.153703856051\n",
      "Final stress at dim=60: 147.131195236 stress per element: 0.203219882923\n",
      "Final stress at dim=40: 253.492878306 stress per element: 0.350128284953\n",
      "Final stress at dim=20: 798.544457868 stress per element: 1.10296195838\n",
      "Final stress at dim=10: 2432.03911801 stress per element: 3.3591700525\n",
      "Final stress at dim=5: 6874.90956116 stress per element: 9.49573143807\n",
      "Final stress at dim=2: 25597.5135218 stress per element: 35.3556816599\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6d39fa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs={}\n",
    "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
    "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
    "    results3 = mds3.fit(dist_mat)\n",
    "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
    "    vs[nComps]=results3.stress_/len(dist_mat)\n",
    "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
    "yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nope, that definitely made things worse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## and larger still"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(123, 1)\n",
      "(1109, 32)\n"
     ]
    }
   ],
   "source": [
    "assays = data2[['assay_chembl_id','target_chembl_id']].groupby('assay_chembl_id').count()\n",
    "print assays.shape\n",
    "goodassays = assays.ix[(assays.target_chembl_id >= 10)&(assays.target_chembl_id <= 18)]\n",
    "subset = data2.ix[data.assay_chembl_id.isin(list(goodassays.index))]\n",
    "print subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1109, 4)\n",
      "(1109, 1109)\n"
     ]
    }
   ],
   "source": [
    "mols = subset[['molecule_chembl_id', 'SMILES', 'ROMol', 'assay_chembl_id']]\n",
    "print mols.shape\n",
    "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]\n",
    "dist_mat = []\n",
    "for i,fp in enumerate(fps):\n",
    "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
    "dist_mat=numpy.array(dist_mat)    \n",
    "print dist_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final stress at dim=140: 168.176814082 stress per element: 0.151647262472\n",
      "Final stress at dim=120: 182.160599986 stress per element: 0.16425662758\n",
      "Final stress at dim=100: 204.40034102 stress per element: 0.184310496862\n",
      "Final stress at dim=80: 247.402896742 stress per element: 0.223086471364\n",
      "Final stress at dim=60: 332.760745419 stress per element: 0.30005477495\n",
      "Final stress at dim=40: 597.306401455 stress per element: 0.538599099599\n",
      "Final stress at dim=20: 1941.54192483 stress per element: 1.75071408912\n",
      "Final stress at dim=10: 5902.784547 stress per element: 5.32261906853\n",
      "Final stress at dim=5: 16541.3567152 stress per element: 14.9155606089\n",
      "Final stress at dim=2: 60946.5239554 stress per element: 54.9562885081\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f5d516910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs={}\n",
    "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
    "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
    "    results3 = mds3.fit(dist_mat)\n",
    "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
    "    vs[nComps]=results3.stress_/len(dist_mat)\n",
    "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
    "yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuing to get worse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try less data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(123, 1)\n",
      "(165, 32)\n"
     ]
    }
   ],
   "source": [
    "assays = data2[['assay_chembl_id','target_chembl_id']].groupby('assay_chembl_id').count()\n",
    "print assays.shape\n",
    "goodassays = assays.ix[assays.target_chembl_id == 15]\n",
    "subset = data2.ix[data.assay_chembl_id.isin(list(goodassays.index))]\n",
    "print subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(165, 4)\n",
      "(165, 165)\n"
     ]
    }
   ],
   "source": [
    "mols = subset[['molecule_chembl_id','SMILES', 'ROMol', 'assay_chembl_id']]\n",
    "print mols.shape\n",
    "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]\n",
    "dist_mat = []\n",
    "for i,fp in enumerate(fps):\n",
    "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
    "dist_mat=numpy.array(dist_mat)    \n",
    "print dist_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final stress at dim=140: 5.02067116244 stress per element: 0.0304283100754\n",
      "Final stress at dim=120: 5.31739457656 stress per element: 0.0322266337973\n",
      "Final stress at dim=100: 5.6302199229 stress per element: 0.0341225449873\n",
      "Final stress at dim=80: 6.11308024789 stress per element: 0.0370489711993\n",
      "Final stress at dim=60: 7.31432679352 stress per element: 0.0443292532941\n",
      "Final stress at dim=40: 11.3054367059 stress per element: 0.0685177982175\n",
      "Final stress at dim=20: 30.2462507814 stress per element: 0.183310610796\n",
      "Final stress at dim=10: 98.8025008019 stress per element: 0.598803035163\n",
      "Final stress at dim=5: 300.305403639 stress per element: 1.82003274933\n",
      "Final stress at dim=2: 1230.25783895 stress per element: 7.45610811484\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f5df53210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs={}\n",
    "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
    "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
    "    results3 = mds3.fit(dist_mat)\n",
    "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
    "    vs[nComps]=results3.stress_/len(dist_mat)\n",
    "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
    "yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a lot less stress per point than before, but it's still pretty high. Look at the distances to be sure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f5d196750>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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su3ezi9eJEzy+4AIWn4lzafY8fjy/XJkmR6pz5rBNp0OvXtyiBbC06u7dLGfq\nyYQJnMoOtJQUBmlfPviAVfIaNABuvNH+2IwZ9mx6yZ6m00WiSJEiwLPPWsddu0Z2hTHJ3ooVVgAH\nmEHuuvcbYAtRx5e3M2eYcT5ihBWov/ySsyZvvMGfhg3ZJMWTChU4sg+07KbBU1I40n7+ef7pWqVt\ny5bA35N4p5G4SJhs2sQRT6NGXGuU6LVtG0eljmnlqlU5UnUtidqnD/D11+7Pf+ABZnFXrswkx/37\nmcdw6pT396xShaPeXr1C26wmMdE+fV6smH1tv2dP4JtvQnc/+YFG4iJRqE4dlqRUAI9+NWqwmEmX\nLgzUc+Z4rmn+0kueq5a9/joDf58+3Hc9darvAA6wetr114e+25zr+rdzAAciv+xtfqORuIhIkJgm\nl05mzgQuvJBT5MWKAS+/zLK2nkydypHsJ59Y52JiuIf87NnQ3LermBjOELgWdqlRw/1LxIIF9nV+\nyZ5G4iIiEWjCBAbxpUtZk/7uu3n+4YfZVvTqq91rBKSnu49ms7LCF8Ad73/99czncChcmFUBnbe9\nNWpkr9QmwaeRuIhIkNx7L/C//1nHDRu6tyIdNYpb0gBWcvvlFya6XXstMHcuzwe6jkBuxMRwDX7E\nCN7P6NHcUXHyJPDRRxyl33orULRouO80+miLmYhIBJo+HejRwzp++GFOpbtavhw4coTdy5zrqJsm\n18p37w7+vfpj+XJ2XstOZiYwZAiz7WvUAKZM4bY78UxBXEQkQn3zDeuIX3ghs9C9bRnzJi0NuO46\n1ipv2BBYudL9GsOw1x0IhmLF2HTF0ZjFl3feAQYPto7btGH9dPEsL0E8LvtLREQkt3r14o+z774D\n7rqL1c4GDGChmBUr2HO7RAlmqd90E69NTWWJ1q1b3euTOwQzgMfFAZdcAowb518AB4A9e+zHkTKT\nkB8piIuIhNDp06yR7uhC9sor7td8+y0T3AYMsM798Uf2ldSCISMDOHgQ2LCBSWv+NOvp04fLBo7P\nePPNwb3HgkzT6SIiIfTPP6y2lp327bkX/PBh4Lbb2FK2efPgT5v7MmQI8NZb/l27Zg231tWsySQ9\n8U5r4iIiUeLgQaBatez7gbuuc0+ezCn3ceOYOBaOjPXERGDZMqBePc+Pmybw55/c0x6MkrD5lfaJ\ni4hEiS+/9B3Ay5VjfXLXscwnnwBvv80OdeHacnbmDLviffaZ+2NZWRxxt2jBtfsHHwz9/RVEAQni\nhmG8bxgFNzOpAAAgAElEQVTGPsMwVvm45nXDMDYZhrHCMAw/NimIiES/48dZR/38eR677qMuWxZ4\n8knWTn//fbb39LT2PX8+XyvcMjO5R9zVokX2uvCvvaaEtlAI1Eh8IoBO3h40DKMLgFqmadYBMAjA\n+AC9r4hIxPrlFzYqqVOHI9QjR5jU1r27dU2hQlxrTkxkItvatZ5f68SJnG9PC5bERGDhQnvhGk/3\nFin3m58FJIibprkAwBEfl/QA8NG/1/4BoJhhGOUC8d4iIqGwZo17tbXsPPywNXpeuZKNTuLiOPp2\n2LWLQf3117N/vcxM/7LDg6FQIf6ZkMCp87ZtOW3+1FM837o1E/Achg/3L4FP8iZUa+KVAOxyOt7z\n7zkRkYh3771cC27UyB6osnPunP142TJgzBhOjTtbu5bB3R/Bzvtt2tTz+fR0/nnuHBPsHJ57zuql\nPnEiW+zu2MHyrBJ8EblPfITTf/3U1FSkpqaG7V5EpGDbvNle/3zSJFZea9Ik++eOGAH068f18JQU\nlmGdPt19mjk2llvJfClbln3Gg+3GG/llw1+urXRr1w7s/eRHaWlpSEtLC8hrBWyLmWEY1QDMME3T\nbWOBYRjjAfxsmubn/x6vB3CFaZr7PFyrLWYiEjG2bHEPTIsWcfrYH9u2sVxp797A0aMBv72AqluX\nTU1efz1nI/727Vla1jHlLjkTKVvMjH9/PJkO4BYAMAyjNYCjngK4iEikqVLFfR16xw7/n1+jBtCu\nHV8n0m3YwKzynI6j5s0DvvoqOPckvgVqi9lnABYCuMAwjJ2GYdxuGMYgwzAGAoBpmrMAbDMMYzOA\ndwDcHYj3FREJtpgYJnM5S0zM+et8+ikLoJQoASQnB+beIkkktEstiFSxTUQkG5MmsWHJ+fPsKDZl\nivtasLOzZ4HffwdKl2ZCnKtt21hb/KuvgH0RPidZtCin2GNigGnTWPDFVXIym7fExvJxtR3NGZVd\nFZGId+4cp6Xj48N9J7lz9Chw6hRQKZt9NSdPAldcYSWHjRkDPPKI52vPngUWL+b+8e3bI7PK2cUX\ns5Xo/Pn2PeyuZWGTk/n3A3Av/OTJ4dsOF20UxEUkoo0axUztuDjgzTc5qs2vJk2yb0MrVIij0+wC\n2jffMPkt0sTHW9XmnFWtCuzc6f15c+dyu9q2bUwMLFIkePcY7SIlsU1E8rFjx4AXXgCefRbYu9f/\n561eDTzzDNdMz51jdbIDB4J3n+Hmun4eF+ffiPTyy4HKlYNzT7507ep+z848BfCEhOyT3zZsYPBu\n2pRZ7xs25O0+xTMFcRHJVkYGcNVVwBNPcETdpg2Duj9ct1VlZlrFQfKja6+1B+P0dFZry06pUuwZ\nXqZM8O7Nk59+4n9bfxQqxAB+7hwrzTm78ELr965dueXs0CEe793LL4ASeAriIpKtHTuApUut4+3b\n/S8I0qoVg75Dz57cdpVfxcfbG5hkZACzZ/v33IoVvVdMC5b0dAZcXxo1Ajp0AB57zL0KXcWKwPLl\nXC9fswZYsoQFbVwpez04FMRFJFtlytjXNOPiuCbqj4QEro9+8QXw7bfMyM7vCU+u2dl16vj/3Dff\njJya44mJwODB/ML244+s5uZaHrZOHaDxv30p69cHmjdnJvvw4dxOB7C96rBhob33gkKJbSLil7lz\nmT2dng6MHAn07RvuOwqN9HTg4EGgfHn/u3Jt3crkvd27uSVtwwZg/Xrg+uuBp5/O/vmZmaxB/uWX\nDIbh/CexRw+2SP30UwbzpUv5+c6fZ9D++mugVi3Pzz10iBXvLrgAKF48tPcdTfKS2BaRtdNFJPJc\neSWT1AqSFSu4R3rfPm61mjuXe789OXkSePxxButrruG1AHDJJdwzDnAvdfHiwH33eX6NrCyWPJ0+\nHUhLC33wLlyY296crVvHYO1atz02FmjZ0veMTKlS/JHg0UhcRHItLY2jzauu4kg1v7nqKisYA9zv\nPWaM52tvu43byxzKluX68fHj9vXgVq2soO7qqafYFSxcnn6ajVoef9z6AnHFFeyL7s2oUVY7Uskd\nbTETkZB77jnWBL/5Znb0cs1Wzg9On/Z97Mw10O3fz8x814Su2rV57v77uVZcrBirovXp438CXDCU\nLAkMGMDp/wYNGMw7dAA6dvT9vB9/DM39iWcK4iKSK6++av3+zz/A55+H716C5fHHrQpzpUoB99zj\n/VpfW8Mca+nVqwNvvMG+22+8wUB//Di33H39dXi/CH37LVCtGnDrrZz2P3kSmDMHmDnT9/MyM+3H\nJ04wI19CQ0FcRHLFkXns7Tja/f03t1516QKMG8fA5rwX2tUdd3g+HxfHmuObNzNZrUQJbtHzxLmO\neq1anHovXDjXH8Fv7dsDl13G37dtsz/mWpXNNYmtfXv+mZnJcqtFi3JU//33wblXsdOauIjkym+/\nAb16sfratdeyVrbr9qNolZXFxiXr1vG4SBEGcV9JXPv2AQ0bWtXoYmOtUWqjRszsdjRNWbaMe+fT\n03N2XyVK8B5WrQps0luFCsw6r1gR6N+f/y0dWrViERqH997jl5Jly7ic8u67/KIxeTKf61C2bOQ3\nd4kUyk4XkZBr25b/SKenh2a0GEoHDlgBHOAU8bJlvoN4uXLAn39yK9aOHcD48dZjK1dy9J2YyDXw\niy8GFi0CZszgyHfSJP+C8pEj/Am0vXuZET94sPuywKZNHGEXKcJM/T59uHbu6vhx+/GBA1xmyY8J\nj5FEI3ERERcZGUxA27GDx4UKcSReu7Z/z9+4kSN5T3XHHa83cSLQrx+PJ04Enn+eQc+52lso9erF\nLxMTJgBDh3q+pnlzJrJ5Wjo5eBCoUsW+RW3oUGDs2ODcb36i7HQRkQCKi2NSV+/ezM6eMcP/AA6w\nuMknn3ifoUhPB26/3Qp4t9/OgjAnTgA1a+b9/nPjm2+4Hzwri9PinpqiLF3qfQucp97priVaJfA0\nEheJcrNnc+q2RAlg9Giua0r4zZvHAjm+HD3KqfYbbuD0c+vWzDUIt2rVrFkIVxddBOzZwy8olSoB\n9eoBL7/MNfDvv+cXn7NnOY2+YIH3am5iUT9xkQJqzRru0XZM2zZuzGYUEn4rVvC/jTdly7Loy2WX\nMShGg5gYz41M2re3iuLs3MmyrBdfzCx1yZ6m00UKqD//tK+7rljhXjZTwqNxY/Zej4nh9HyvXizH\n6rB/P9CsWfQEcIDZ9544d7SrWhVITVUADxVlp4tEsWbNWIzEeSSe3zLFo1V6OttzAkyU++EH4MwZ\n+zU5zTQ3jPA2Q/HWFz011X68cyf/v4yUbmz5mUbiIlGsQQMmXfXoweSo7PpCS+gMGMAqdo7pZ9cA\nnhM33MB15+TkwNxboJQrxx7jn3xinRsyhGvqFSuy250El9bERUSCoHRptuIMhEcf5TY0RyGZQKtR\nw71SW3aKF2clO8Ng4ZoLLuDyTvPm9uv27FGyZXa0Ji4iEiT79jGL3GH2bK73Fi7MZiHeOGquB8Kr\nrwYngCcns+95pUo5e55h8IvFnXdyBqh+fVZ2+/hj92u97ZWXwFAQFxHxYuBAbpUqXRp4802e69GD\na9np6cBXX3Ga25OUlMDdR7D2W586xVFy06Y5e55pcmbAsVSQmQksXgy89po9I3/gQE6tS/AoiIuI\nePDrr6wLDjBIPfigFbydLV3q+fnhKtqSU88+y6Ylffv6vs51Pb5cOc/XtWnDve6LFwPvvBOYexTv\nFMRFpEDLyGBy4LRp9qlf10S0zExe61oL/JZb7MdHj3J0ftFF3ju7FSnCKelI8cornGHwxjDYlrVh\nQ6BuXeCtt4APP2ShF1eXX85A3qJF0G5XnCixTUQKrKwsoHt3q2d2+/Zc846L4xR2x47AL7/wsbvv\nBv73Pwbpvn25Rv3ww8CNN1qvd/IkE7z27uVxkSIspeqsSBF2AmvSBLj3XtYijwaOrmxVqnCkXaUK\n/6769OG0fLlyDPRHjgCnTwODBuWsVG1BpoptIiK5sHYtt+k5W7LEyrA+f55BPCmJo8vs/PAD+4/7\nY8gQ7rteuDD7a51rAYRCkya+K/89+ijw4otc73buN+5crrVsWWD1av4pvik7XUQkFzxNaxctav0e\nHw9cdZV/ARxwb+MJAJdcwj9d3+ftt90DuLd94KHO8M6udK8j8/7UKft553rr+/fzC5EEl4K4iBRY\nVapwPTg2luVRn3+e0+G51ayZfSRepw7Qrh1/92eC8dQp3kckVt1zfAmpXx944AH+/tRT1uPVqwOl\nSlnHcXFqfhIKmk4XkQIvPZ1B1p/gaZpWQPvxRzb+aNzY6g0OcJr8uedYy/7vv91HrNHojjs4PX7s\nGPeWO3qOL1vGz3jppeyj/uCDXBN/4gng+uvDe8/RIi/T6aqdLj4tXMi+yg0aANdeG+67EQmOQoWy\nv+bgQTYxWbSIhU3uuouBzTHm2LPHCmzz5wNffhm8+w21J54Apk8H/vqLx488wi5lHTtyj3nTpszu\nv/NOBvCnn1YADxVNp4tX8+Zxu8iIEaxMNWZMuO9IJHyefJL9sTMz+eX2+eftU+TffGP9vnGj/bmG\nwfXyW27hdHm0adnS/TM5H58+zZmIgwf5+7BhLMEqwReF/ztJqHz1Ff/Bcvj88/Ddi0i47d9vP3YN\nxs7bqZxbjgIM9tdcw33jdet6fv1I2jfuatEi+z7ypCQm/DkcP+6+r37fvtDcW0Gn6XTxqnp1+7HK\nJ0pB1qAB8O231nG3bgzIf/zB6fVXX7Ue69SJ9cide4VPnGj97txStFgx7lMvWpRT1JHowAF2Kqte\nnY1S7rvPXuilfHnut58+ncf16gGXXRaWWy1wFMTFqwcfBNat497X+vVZ6EKkoFqzxn78zTfAli38\nfd8+IDHR/vi4ccBNN3neHmaa3Iv+448cnZ8+HdmZ3EeOsLDN22+zQM7SpRydO1evmzoVmDKFn+W6\n67h9T4JP2ekiIn4YMgQYP9774x98wHVh5wz3bdv45ddTk5SmTblunJXFbWjz5wf+noPp+ee59i15\np2IvIiK5dOyYf3u4n3uOwbZQIU4Vu66J33EHR6bz5lnnatTgiLtqVfu1MTFA//78YnD77dEXwAH3\nmQcJD43ERaRAOnQI6NyZU8PVq3PZyFvSmav0dCZ3OVpxOqtTx8rcHj2a260AICEBeP11JoA1aADc\ndhv3VwP2NfJI4bqm76xePa7j16zJsqs//MDCOf6WnBU71U4XEcmhoUPt09xXX201QsnO6dPeS6RW\nrGgFv3r1gA0brMdKl+Y2rORk9wIwMTGevxSEy+rVbMeamcnP8+KLPB8fz+YwxYoBkyYBAwbwCxHA\n/eQjR7ICnvhP0+kiIjl07Jj9+Phx/5+blMSA5ckNN1i/V65sf+zgQf7pGsANI7ICeO/ebKU6ZAg7\nrb3wAju0de3KAA7w72/4cCuAA1wnL1IE+Pjj8Nx3QRSQIG4YRmfDMNYbhrHRMIzHPDxe1DCM6YZh\nrDAMY7VhGLcF4n1FRHJr8GAgJYW/x8VxN4Yn58+zrWb16tzrfeAAzz/3HEuONmxov/7oUU6NHzni\n3nvcm0ibfHz6aSbqTZxo7f9OSnIvS+tpNuLMGVZuc/2SJMGR5+l0wzBiAGwEcCWAvwEsAdDXNM31\nTtcMA1DUNM1hhmGUBrABQDnTNDM8vJ6m00UkJLZv5z7vBg048vRkzBjgMaehyfXXW4WPMjL4XOfq\nZVdcASxezBGrc7Gk7MTF8fUCxVf7Ul9r8FdfzS8gixbxuE0bIC2Nr7d+PYu87NnDuhFz5gDPPgt8\n+qn76+zZw6UFyV64a6e3BLDJNM0d/97MFAA9AKx3usYE4Ng1WATAIU8BXEQklKpXdy9q5MqxF9xh\n0ybr95Ej7QG8UiX2H8+NjAyWZnWM9PPKV/vSCy9kL3VXSUkcRffubZ1buJDXNmrENf7NmxmgK1dm\npr6n/e3XXqsAHiqBmE6vBGCX0/Huf885exNAfcMw/gawEsADAXhfEZGg69XLfrxjB7B3L5uBzJpl\nf6xKFd+vlZDgO+krUAE8O54COGAVanFmGGwxOmsWM+pfeIFfVgoVYpb+5Mn261u2VInmUApVxbZO\nAJabptneMIxaAOYYhnGxaZonPV08YsSI//89NTUVqampIblJESk4Dh4ERo3iGvagQZw29qRzZ9ZF\n37yZx4cPAwMHMqi5JqN16wacOGFVd4uN5bR1oUKcfn74YU7NHzkSvM+VV65LAElJwNat/GyOz7t5\nM6fQb73VPjMB8EtANDZ5CaW0tDSkpaUF5LUCsSbeGsAI0zQ7/3v8OADTNM0Xna75DsALpmn+9u/x\nXACPmaa51MPraU1cRIKuVSuuXQMsXLJyJfd4e9K0KbB8uXVcsiSDuUO9evwi8MADXEu+7LLcZZsH\nel08EGJigKee4tKBQ7lywD//cJuZc1Z/167AjBmR3cwlEoV7i9kSALUNw6hmGEYCgL4AprtcswPA\nVQBgGEY5ABcA2BqA9xYRybHTp60ADjCj2vnY1YgRDLAA161d13vXrwc++4zT4U88kfvtYpEWwAF+\nll9/tZ9r3Jh/NmhgPz9kiAJ4qOU5iJummQngXgA/AlgDYIppmusMwxhkGMbAfy8bDaCNYRirAMwB\n8Khpmoc9v6KISHAlJdmrs8XFuW8Vc/bFF1aAPXAA6NOHo3FnS5ZwxJofp5J//tl+3KIF8O67/DJU\ntiz/7l55hSNxCS1VbBORAmnbNlZtO3KErTVdE9icuVZee/BB4L//5b7xn36yzvfuzfMNGzLpK78q\nUcK+rl+2LEvIqlJb7oR7Ol1EJOrUqMH2mfPm+Q7ggHtv7Msv54jceftZfDwT3hy10195Bfj9d66T\nX3BB4O8/mB54wPeMgusXlP37uRVNQk/9xEVEXKSnc3S+YAG3TI0bx+prGzYwS7tXLwbybdus53Tv\nDnTqxPXjLl1YWrVGDXYou+wy7p2OFhMnAg89ZK8tn5TE6XPA+tOZupqFh4K4iIiLkSO55gsA69Zx\nqnjqVNYFd1i50v6c1au5PatHD6s2+rZt3FrWsiWnmr1VcIu05ifnz/MzlCvH2ujlynFdvE0bq/67\nsyFD2HJVQk/T6SIiLtavtx/PmQO0bWtvXOJa2KV+fbY1dd0DnpXFafWEBHYx8ySSAjjAbP3x44F9\n+5jQt2cP242WK2e/7vXXudXsrbfCc5+iIC4i4qZbN/dzq1dzatzh44+B4sX5e3w8f3dtEOLszBnu\nqXbNancVqVu0Vq9m69EaNfh5b70VuPtu98AuoaXpdBERcB38q6/4e79+3HZ22232KXDnkXSTJqzm\nNmUKp58//JDPeeYZe2EUZ+fO2YvEeNKgAUu6RpoOHYBmzTjbkJzMKnQSfhqJi0iBl5EBdOwI3HQT\nfzp2BPr2ZQGXIkU48hw+nPujnc2bZz+eOZNdvY4fZ//tyy/P+b0cPZr7zxEsEyYwma9XL9ZRL12a\n0+sSftonLiIF3vLlLK3qeq5xY9Y+z8py3wO9ZQvbcm7fbp1r3pwj84ce4oj77rt5PGNG0D9CUG3b\nxqWEW2+1zsXFcdZgzBh+6ZHcC3crUhGRqFaypD1DPCbGWrs2DPcAPns2s9Cd90vHxLCtaZcuVlOQ\nRYv488MPvluDRrJq1ZiJv2uX/XxGBs/37s0vMt6S9iS4NBIXEQHw9tvAf/7D38eO5brvrl2cQr7o\nIp5ftw548kkWNtm3z7/X7dOH0+xnzwbnvkOleHEgJQXYvdv9sZUrgYsvDv095Rd5GYkriIuI/Mvx\nT8+991rbppKT2RwlOZktSbNrUlK6tOe91PnBTTdxzX7uXGbbA8CFFwK//cZys5s3Az17AjffHN77\njDaaThcRCQDH9q4pU6xzp04B333H4O0pgJcrZx+Vd+jA7ViRmGGeV7//bvVVNwyukY8dy1ryH33E\n819/zRal3buH7z4LEmWni4i4qFbN/dj1HMCAtWcPR6MOkyfnzwDeqJF9hsE0gRUrmAvw22/2axcs\nCO29FWQK4iIiLj77jNvJKlRgp7MbbuBUuquiRZn0tmdP6O8xlGrXBpYtY/lYZytWAAMGuJ93PZbg\n0XS6iIiLevW4Du7w7LPAiBH2a4oV4xYrAGjfHvj2W8+vlZhorR9HG8NgFbpixdhT/bPPgNRU+0zD\nypX8KVWKWfk9e0ZXs5dop8Q2EYlY//wDvPQSK53df7/n0bC/MjK41Ssmht3G/O19vX07S406q1aN\ngb1fP9ZEP3UKeP55Jnz98Ufu7zGSlC/PJD1HwI6JYVb+sWOsVOf4Z3rgQOCdd8J3n/mB+omLSL5z\n7hzQrh3bYb7xBtt5Zley1JvMTODqq1kTvWtXbvvyd6zg2jsbAHbsYEnWK65gW87kZOC555j45Rid\nR7MbbmBt+DVrrHNZWZw+79iRj1WowC8w27ZFZpW5gkJBXEQi0o4d9m5i//wDrFqVu9datoydyBym\nTXPvVOZN3bqs3ObJ778Dn3xiP5cfppL/+IP7452/6CQksBUpAPzyC7B3L79ozZkDPPVUeO5TFMRF\nJEJVqGDv+FW4MFCrVu5eq2hR+7FhsHCJw/nzzLzOzOS0eM+e3DrlCGJLlnCPdOXK7q/91Vf2rO3h\nw1luNZpt387a786mTQMaNuTvf/9tfyy/J/ZFMgVxEYlIKSnArFlsItK6NfDNN+49vP1Vty47ixkG\n13Zfesl6rV9/5V7vMmWAOnVYkW3aNODRR4Fx43hNXByn1T1VK5szh6+/ciWPDQN47DEgKSl39xqJ\nSpa05yPccou1pz4mhscSHkpsE5EC49QpBp3EROvcRRfZ136d9erFveBDhgDjx/t+7X79mL0NcK/4\nzTfb25hGo5gYBuvMTE6nf/01cwoANkRZvJjbyYoWZQa7awKg+EeJbSIifkhOtgdwwH3a2Fnr1lz3\nzi6AA+yz7aiPPnRo9AfwwoWZhe74HOfOsbSqw+WX80vOk0+yt3qtWsCrr4bnXgsyBXERKdAee8ya\nGq5YkSVEu3RhtvnQob73eMfHW79v2sRtZ4D/me/hFudUKaRUKX7uZs2AF1/kl5u6de3XOy8RPPAA\np9gd1dlME3jkkejt1hatNJ0uIgXe8uXMhm/blmvjzjIyuK983jz7ecNgD/I//7TOtWnDhLzvvrO2\nppUtC+zfH9z7D7SkJH6ukiVZuW7nTqBQIeD997lNb/t2lmF1FRfHLXfOX24ke2qAIiLipxkzmE19\n9dVA1ao816QJfzyJi2ORmLQ0ZqkvXMggddNN3GblHMR//93qSe4QbQEcYCCeN4/V2fbu5bn0dH7m\nmBiOwj0ZOVIBPNQUxEWkwHjiCeCFF/h7qVIMyt6SsaZMAdau5Si8TRsWnZk9m4/Vrs1Sq7t3M6Hr\n2LHoLq/qSd26/Pyu0+NZWcArr3DGwRHgHfJTRn600Jq4iOR7J07wT0ePcAA4dAiYOpUJW66ef57Z\n5qNGsSpbt25WAAfYjnPSJOCSSxjAgfwVwOPigCuv5FR6kSKer/FUtnbhwuDel7hTEBeRqHD4MHDn\nnezX/cEH/j1n506gfn1ugWrc2D3Qvv02s7ArVeKo3OGLL6zfMzOBmTPdX3vmTG5Zy4/q12eSXqNG\nDOJdulhLDw6e9sw7KrpJ6CixTUSiQteuLP7iMGsWg4svN95o7d0GOMLMyPB8bcOGVlnXXr28dyUD\nWCjmzBl7pbZoVqsWv8zs2sVktp497dvFSpXi1Pl993lvdtK8OfeNG7lKzyrYtE9cRPK9pUt9H3vi\n2pjDueKbawLWoUP8Mysr+21Su3ZlH8D97ZIWCbZsYcGb48eZee663/vQIaB4cc+jb4CB++mnFcDD\nQUFcRKLCZZdZvxsGcOml2T/nvvtYaQzgSPPddzmSfPpplnEtVcp+LcBMdE/T5zmV02IvkR4AT5/2\n/PdSpAiLwnTvHvp7Ek2ni0iUOHmS67Q7d7JVZu/e/j1v7Vq20GzeHLjgAvtju3ezB3jVqmx7CnAa\nvVevwN57flKypOeWsIMHM8dAci4v0+kK4iJSYC1dyhFws2bWufR09syeP5/HjRvzi0NcHNCqFdfh\ny5YF/vMfFoiJNoUKee6R7q/KlTm7sXWr/Xzt2qxaJzmnNXERkRy6+WZuoWreHLjjDut8oULATz+x\nu9l113EUf/gwi7Z068ZmKH368M9o5C2xz5v+/e1T/bt3uwdwwGpTKqGlIC4iBc6qVcAnn1jHEycC\n69ZZx/HxXHN3DVaObWjnzwPXX8+19Xbt7G06I53zWn18PHMEKlb0fv2OHfyS47oU4axuXfu2PAkd\nBXERyRfeeovT3b17s6yqN7NmcbrcVZyH+pWuyXOXXWbtPa9Zk8H/5ZdZvS0UnniCyXmBcv48MGgQ\n8Pff3q/57TfWTHetzuZQpAiXJTz9/UnwaU1cRKLeDz/Y94y3aQN8/DGnxitVss6fPs31bNciLUOH\nAmPH2s9lZADbtgEffghs3MjAf9dd/HnvPeu64sXdt7I5i4lhTfHdu/m+H3+c64+JQoW4Te7sWe/b\nvfKqWDHgqqs4C7F8uX/PGTaMVe4kd7QmLiIF2urV9uMlS1jApEoVew/s48fdA/jjj7OQjPM08+HD\nVjb7m29yhN+yJcusulaL8xXAr7qKTVGefJK9yZ2n8HMjPZ0lX3fvZhW6YOjUCThyhLXg/d3rvnZt\ncO5FsqeRuIhEvaVLGWA9JW0ZBnDggLUnvEsXjtwBTgU76qpfcw0wbRpHzs88w7rpzhISPNdZ92Xg\nQGDMGI5uU1Kio0yr8+eMjwfKl+fI/8AB788ZP57T8pI72mImIgXSr78Ct93GkWO3bpxuPneOzUmc\n7dvHaXSAj3/8Mde2R460Xzd/PkfLX3zhe4SdE2XL8nXr1QvM64VD//728rUVKnC/eIUK/Pu/8caw\n3Vq+EPYgbhhGZwCvgtPz75um+aKHa1IBvAIgHsAB0zTbeXktBXER8UvZsvYR4hdfMLGte3erzvpD\nD0OBcQYAACAASURBVAHjxrk/d/duexlWALj3Xk6fB1pKCovVRIP4ePeysxUrclvdt98CderwS1Ll\nyuG5v/worEHcMIwYABsBXAngbwBLAPQ1TXO90zXFACwE0NE0zT2GYZQ2TdNj5WEFcRHxx7lzHHk7\nq1KFI+ysLOCPP7iu27ix99d48EHgtdf4+8UXs2uXc+JZjRqcbj94EEhO9j0dnpTE9eRatYCXXsr9\n5wo3wwBc/wmuWxdYv97z9ZJ34U5sawlgk2maO0zTPA9gCoAeLtf0BzDVNM09AOAtgIuI+Cshgevg\nznbtYn/vmBg+5hrAT57kmnhyMnDRRfb+4qtWuW+jGjyYI/bdu9nZy5fTp1mPffx4vr83hQr5fjzc\nTNN9erxqVW6pS0lhpTqJHIEYifcB0Mk0zYH/Ht8EoKVpmvc7XeOYRm8AIAXA66ZpetxooZG4iPjr\nr7+AJk2shLbsRox33sk9z75Ur85s9UsusQezgwf5+p7qhrsqU8Z7IljRosySDxZPI+mcKFMGmDDB\nd/34adO4Vz45mevikjd5GYmHant+HICmANoDSAawyDCMRaZpbvZ08YgRI/7/99TUVKSmpobgFkUk\nWqxcCTz3HAPW//7HtXDT5PT4lVdySr1/fzZMcZaWlv1rb9/OUf6NN/J1nn2Wo+wHH+SXhp492Tfb\nF0cAL18e+Ocf+2PBDOBA3gI4ACxcyM/ty3338ZqYGBa7efDBvL1nQZOWloY0f/5n9EMgRuKtAYww\nTbPzv8ePAzCdk9sMw3gMQGHTNJ/99/g9AN+bpjnVw+tpJC4iXh0+zP3bjv7fJUpwDfzYMe5rdt7v\n/fnnLI/qcM01/rUZvesuTrXXr2819ShSBHj4YY5Sz57l1PKuXfbn5aW5SPnyrHoWrCIu/oiPBxYt\nApo25ZegKVOyf05MDPMGkpKCf3/5VbjXxJcAqG0YRjXDMBIA9AUw3eWaaQAuNQwj1jCMJACtAKyD\niEgObdpkBXCA28uOHePvrj28HQH45Enu+y5fHihXzvfrJyWxq9n+/fauXCdOcFS+dy/f0zWAA/xC\nkVsHDoQ3gAPMSm/VCpg3D5g8mZ/fucObJ1lZOe+dLoGT5+l00zQzDcO4F8CPsLaYrTMMYxAfNieY\nprneMIzZAFYByAQwwTRN1fgRkRyrU4eFWxyB3NvoNyHBKsXasyf7hgNcxy1cmKNpV47iL4MHA1u2\nANWqWe1GPW29cuU6dZ6T9elICYSZmZyxqFCBddrbtAH+/NN6PCWFe96XLuXxk0/y703CQ8VeRCTq\nONbEY2I47Tt4MEfIlSuz41Z6OveLt2zJYJ2Y6P21kpP5Wrt327eGxcRw21lCApO9evbkNHtOlCzp\nXyJcpIqJAW6/HZgzh01SMjK4hW7OHH5hKVKEWf6SN2Ev9hJICuIiklOnTzPRqmpVz2uzNWowYQ1w\nHx2npLB4SUYGcMMN7s+NiWH98xYtgOHDOS1vmu7r75507myVeM1P7rwzsN3UCrpwr4mLiIRVUhKn\neL0lV82cyb7fjRox+DzyiNXc4+RJ4NprOcK8+Wb352ZlWQ0+nn2W6+/797u/l6e937Nn80tDuBQu\nHJzXPXMmOK8rOaeRuIgUOKdPcxrdWf36bCu6cKH9fFISp+9r17afT0z0vK4eSeLiPDeF8ZenKnXF\nijHxrWnTvN2bWDSdLhIB9u5l8lTVqsDll4f7biQ7hQtnvx2sQgWuqzvWhrt149r5yy9zCv7IkdDc\nazhUqwb88gtbue7cyf33F17I2YyKFcN9d/mLgrhImO3YwX/s9+/n8QsvsE+1BN7evewKVrMm16lz\nq1Ejllr1xTXz/c47mdTlyFjPa3W0SHf55fxiGheqsmAFlNbERcJs8mQrgANWUw0JrC1bmDHety/3\nM7/9dvbPSU9nR7Off7aff+89+5R65872PdHVq7uP1N97zwrgQP4L4K7r9/Pn2xvCSORREJegOXyY\nnaTy85SjQ/Hi9uO8FP0Q7yZNYg1zgAHUU4tRZ6dPc195165A+/ZMYHPsM3/3XZZAXbaMa96PPca9\n4DVr8vcff+RxQZKS4n4up33V169nbXXXZjISJKZpRtQPb0mi3bJlplmypGkCplm6tGmuWhXuOwqu\nc+dMs1cvft5y5Uxz4cJw31H+9NJL/Dt2/DRt6vv6/v3t17v+jBnD6376yTQLF7Y/dscdpjljhmkm\nJPh+DcA0b7/dNK+/3jQ7dMj+2kj/6dTJ+r1SJdPcs8f//z5Tp5pmXByfW7Kkaa5dm/v/1gXJv3Ev\nVzFTI3EJitGjrSIXBw+ymEZ+Fh8PfP01t9788497i0wJjLvvBq66ir+XLcu2n75s9thiyfLGG6w4\ndtVV7pnmH3zA9/DVNjQuDujYkZXhpk7lenm0a96c0+iTJwPLlzOJLSuLywgnT/p+7osvWtnwhw/b\nW71KcCiIS1C4rq1Fcv/kQArWvtyC6sknuU49ZAiPExMZKI8f53RtdoltXbvajwsVsh/v2sXsa2/a\nt/e9jSwjg9Puw4ZFTtnUvHrzTS4H9e3LSnUnTwKXXsocgYoVgZ9+8v5c1217nqbnJbCUnS5BsWoV\nRzcHDrDpxNy53Icr4q8HHgBef9067t6da605kZkJjBkD/PYb0LYtcNllrAvuaJiSW5Ur25uV5HU/\ndqS57jq2dwW4nW7oUOuxevWAdV7aV61YwVmJf/5hn/c5c5h/IL5pi5lEpOPHga1bWQlLDRIkp5xL\npQIsMpLTJCtnGRlA3br8fzKv8vvWMgB4+mlg5EguhT31lHW+enVg2zbvzzt/nlPpZcoUnBm4vFIQ\nF5F8p317+7YwXyNAf+zZwxG0+G/3bpanveQSfqGKjQXefx+49dZw31n+oiAuIvnOyZMsoLN5Myun\n/fZb3oJwdiPxmBiOsF3XtvPbVHlObN/Oym3HjgFLlgBVqvDvUAJLxV5EJN9JSWHjkXPnmBmdmwC+\nezdQqRIDca1awJQpTNhyND9xePxxYMECJm21bWt/LDGRU8gAdyEMGWIvCpNf9e0LbNzI/urFijHH\nRQE88iiIi0i+sH49u5DddJPVdaxTJ/bBzsxk/e+77+bWqa++4ppt4cKsAHf8OHDoEEecn33G9XiA\no/M+fYBvv2VRmHHjgI8+Av78M3yfM1ASErw/1r078Pnn3D7XvHl090TP7zSdLiJRbf9+4NdfOUI+\ncIDnypUDNmzg6PvQIevaihW5Ng5wB0Xz5kzEcpaUxEpvruemTgWuvtpzQltCApM3nd8r0EKZTOf6\nXi+9BLRpw1mNqlVDcw8FiabTRaRA2rABaNCA5VQdARwA9u3jVLDrtsbeva3f//jDPYAD7gHcce6t\nt7wH0ZSU4AZwIPcBPDf9zF3fa+xYBvHq1YEbb8z/mfnRREFcRKLWm29atdSdlSwJfPopR+gAp8UH\nD2aFNoemTd3Xxn2pU8f79ZE63VymTPYBt3x5Jg56qxNfowa/FAF8rc8+A155JbD3KbmnIC4iUcu1\nQl7p0lzPnTMH+OYb63xWFgOas2bNgC+/5Lp5o0bW+ZIlWXksKcmqbxAby1HoDz8wwS4hAShaNPK3\nrGXX1z4+np9z3z7PsxKA5z3ho0ezt7pzRzcJD62Ji0jUOnAAuPJKYPVq1jmfNcvKHL/sMmacO7z7\nLvuBe1K6tOfpcOe14dhYtkKtVs16fPx4qyRsJHLth+4sJoZfbvKiRQtg8eK8vYZon7iIFBDnzzNw\nlytnTW1nZjJZrWxZ+8h882Zmqm/dyjXzN9/0XEEsJ0Vgvv+erXVr1ABat+a5Vq0KbiBLSgJOnQr3\nXUQ/JbaJiM1ff7FevackrWi1di17fVeqxG1hjn7VsbHMmHadWq9dG/j9d2avv/WW9xKgU6b49/5J\nSZyq79+fFcwcdd07d87d54lEbdvmLBGuU6fg3Yv4R0FcJJ955RWgYUMW52jVinug84MePaymI2vX\nBq69bWKi/bhYMX4BcE1iO33avm787LPAM88wuOcHRYsCL7zgngh30018zKF+fWDgQNZV//TT0N6j\nuNN0ukg+U6SIve/zhAnAXXeF734C4YsvgBtusJ+7/Xb2/M6rM2fYsvTnn7lV7OGHGaD85Wv/drly\nVmZ3JCpWzN7R7ZlnOMPx7rs87tQJmDmTjWc+/JCV7wYMUIvRQNN0uoj8P9dKXK49tKORa71zwwDu\nvz/3rzd8OLdWNWzIpipz57K3+D//eN9q5Y2vMYdpMmkuN3u1Q8G1JWuJEvzSt2gRkJbGAB4by3ai\n//kP28O6BvA//gBee4217SX0FMRF8pnx463A3bEja2BHo8OHrezpa66xT3vfeCPQuDGT18aPZ81z\nf82axZH2vn3MHejTh0E2Lo79sFu2tE+lx8XZn9+1q//vtX8/97EHc3IxkF8QMjKYKPjxx8Att/Cz\nOnIPPPnuO66jP/ggt7N9+WXg7kX8o+l0kXzo6FGOsqpWjdxRoDcHDvDLx4oV3M7144/ABRcAK1da\n07yPPsrP16aNtXQwdiwwdGj2r++6LSwujvvKr7mGmdYVKnBt+Lff+Pd39Cjw8svW9YHYmhVIcXEM\nvM5b4Vw7sbnytQRQpw6waZN13K0bfxYuZBJf27b8O4qNZdb/1KnWtV268EuS5ExeptNhmmZE/fCW\nRKSgeuAB02SI4U/37jw/a5ZpJiTwXNGipnnbbfbratf27/W3bzfNEiWs5918s2mmptpfa/Bg+3Oa\nNLE/Hu0/hQub5qWX+ndt0aLu5xo2NM39+03z3nvt56++OrD/LxQU/8a9XMVMTaeLSERxzaZ3HI8d\ny7akjnNr1tivK1uWe7izGyVXq8be2M8/D/Trxxrr69bZr1m0iGvmLVtyjXj5cu+vd/HFXBOuUiX7\nzxYqFSpYv3vaWtelC+/Zn1kaT7sbVq8GXnyR6+TOyw2//MIlBAkdBXERiSh3320lT8XHAw89xN+T\nk+3XNW/O6dy4OE4BHz/OkqnVq1utSL2pVQu48EK2Jf3jD66PO4JdoUKcut+3j8F+0CDfr7V6NfDI\nI56DV3w8dwvkpEZ7XpUuzaWAZ55hedRDh7i33tm8efzyYvpYuXz4YbZd9baF7vRp/p1nZFjnTp1y\nT0KU4IrL/hIRkdBp3pyBcckSdihzdCIbO5bBddcu4KKLGKROneIIeNEiFnYB+PhDDwGzZ/t+n1Wr\n7MeOEby3MqUOKSks9Tp7NnD2LAOhY4bAmWFwX7m3muTBcvAgt4M98AC/1ADMOne0YAV4T61a+V7f\n/+03Jhd6KhhUqhRw7738wlStmlVDvWJFfjmS0FFim4hEjYwMjizLlOHU+UUXcVuYq9atGdh9+fVX\nIDU1Z0lqhsH95FdcwQIoJ07k6PZDqlw5Jv6dOQO0awcMG2b/rK+9xi9L772Xs9e9+GImr61fz+WI\nQ4cYuC+9lImFtWsH9nMUBKqdLiL50rFjwNtvc6Q7cCDXqR1mzGAZVFexsZwmv+667F//+++BJ55g\nJrwvztncNWtyRuC22+yZ2ZEsJoaFXY4csc5VrszPfvfdOX+9DRsYtJ17uE+b5vm/h2RPxV5EJN/J\nyOC09bBhLM7Spg3XYDdu5Give3d7Ylbx4tzfvHy5fwEcYA101wBeujTXzJ05jyu2buUI9ssvgXvu\niY6yq1lZ7uvfiYmsIVCvnnXOn8+SkMC/a+cvBAAwf37e71NyTkFcRCLS9u3An39ax9u2AcuWcb17\nyxaeM01OG7dty17fN93EKmyeLFjAwH/99fwiAADTp7tfV6OG99cAmKz23HMMgp9+yvXnAQP8+0yO\n/uSBVriwewMYV87V2WJjgfffZ4Jbv36c7ShXzvP6d4MGHK0XKsTkwg8/5E4A5x7sAPfaS+gpsU1E\nIpJhMGg4Wl3GxVnFV5y1bg18/bX3LmUAk906d7Zea/ZsrqcvXOh+7ZIlHGl7kpTEQDdzJo/T0/ml\nwF/BWkM/e5b903/91fs1riPxMWNYcQ3gFwDnLHOAlewuuogj9QED+FnT01nlDgA6dLB/yQplBr5Y\nNBIXkYjz/vtA3boMuikpnD7/+GOuR993nz1gT5sG9O7tO0FtzRp73+vjxz0HcIezZz0HpUhu7frr\nr8xCd62d70lmphXAAX5e5yBerBhH4CNHcqTu/Lk/+ohfCBzZ6QC/BIweHZCPITmkxDaRAmTSJP7j\nXa8e8NRTkdEc5YsvGIhr12ai1dSprNvtXDp06lQGaocffmDBEmczZrBE68mTXKtu1sx6bM8eblXL\nL21Zg+2SS7h/3tMXI8PgUsKbbzL3YPVqbjOrWDH095lfKDtdRLL15Zf2qd9Bg1hHPNQOHeKa9AUX\ncG/3NddYjxUuzFGhq8mT7Y1cNm3i853Vrs2GKADXnv/6i9nsp05xhLpkCWug//xzzqqKxcYyE/uX\nX/x/jje+apYHU9Gi/Lv193P37Al8+63va+Li+OWobNm8319Bp+x0EcmW63rpggWhv4cVKxh827Th\nn198YX/cUwBv3ZqJZosWWYVYtm93v84RwAGuPY8fz2InJUvyy0vTpvwyMHw4O6C1aMGCKNnp3Rt4\n5x37NLWjqElcnO+1eFfOndhC6fhx/5cCkpKAN97gzgBnNWvajzMy3PMTJAxyW3Td+QdAZwDrAWwE\n8JiP61oAOA+gt49rAlNRXkRsPv7Y3qzi1ltDfw/XXWe/h1atfDffuPlm03zjDdOMieFx06ameeKE\naf71l2kahnVdYqJpVqliHcfHuzfuuPRS00xJsY6Tk3mdt/fu3Nk0x40zzfR03vvMmaZ55ZWm2bOn\naU6bZpr16plmbKz1fgCPmzc3zbi4wDctCfZPjx5sPnPwID/v8eOm2agRHytWzDR/+sk0W7a0ru/Y\n0TQzM0P//1B+hDw0QAlEAI8BsBlANQDxAFYAqOflurkAvlMQFwmPV181zQ4dTPO++0zz5MnQv/8N\nN9gDx/XX856aN7cCImCaRYqY/9feeYdXUW19+LdTIKEkhF6kqRRBioAUQap0UGwgWO6FKxZABEUR\nEUXFgp0iIlxArIjipYhIDwiIitJrUHoLvQVIW98fK/NNPyUnyclJ1vs8+8mZmT179p5zMmv22qvQ\nf/5DdOWKXRhPmcJtTZpEVKoUC+8FC4h27SLq2pWoRg0+1yjkfSnaiwJA1Lcv0blzRJ9+SjRzJlFy\nMtGBA0Svv879LVvWvZ1bbw2+QAaIHnjA83Hj/bFmbdNISyM6coS/ByKiy5f5ZXDWLL4nQtYQiBAP\neE1cKdUUwCtE1Dlj+4WMDo211HsaQHLGbPxHIvrBpT0KtE+CIOROduxgNe3x47xevWwZW0ED7AP+\n7becrGPAAD07VvHi5sAi06YB/fo5t9+tm+7+lRk+/5zdqqpX59jiWqa0GjW4z0ZfazcaNOCxZBUV\nKpjjnvtKgwZsG3D+PKv+L1+2+8UPGMBLDa1a8fbevcCxY2zUlpjIxxo1CnwMgmcCWRPPCj/xCgAO\nGbYPA2hsrKCUKg+gBxG1UUqZjgmCkH+oVYuN2vbt46AqxuAn8fEck7tCBaB7dz4OAOPHs9BOSWFf\n6D59zG1++SWvWcfG+i7ACxZ0TnSyZw/w8MNs5W5Mdbp7t+9jvHCBX1CcYrr7y913m13B/EF7kShX\njpO9RDg87f/6Czh4EJg6lV9URo82W6RPmAD8/rvn4DdCcMmpYC8fARhu2Pb4xjF69Oj//9y6dWu0\nbt06WzolCELOU7QoJ9EwsnAh56YG2Kq8Z0+2Jgc4Clv79pxRq3p1thYfP56jjJ08ydbuGtasXH37\nshHcypXm673yCmdFs4YO/fRTDnKSmXjiGkYDu0D53/8Cb+PYMf7rlGnt99/1++VkOX/1KgfGESGe\ntcTHxyM+Pj5L2soqdfpoIuqUsW1TpyultAyzCkBJAJcBPEZEtqCHok4XhPzH++9zBiyNIkXco5st\nWgR06eJ8LCyMZ/KXL7PP+bPP8iy0QQOz3znALwHDhpmDwBQrxts5nT40GERE2KO0OTF3LnDXXdnf\nn/xMsF3M/gBwo1KqslKqAIAHAJiEMxFdn1GqAvgewAAnAS4IQv7kjjvMgWe6dnWvq4X9dKJtW1YP\nnz6tz+zr1gVmzeKY50aOH+c1Yi0ym1IcDzw/CHCA/eo9RXeLjuYobCLAczcBC3EiSgMwCMASANsB\nzCKinUqpx5VSjzmdEug1BUEILZYtA1q2ZGG9YYP9eL16vCb+9NPA2LEcWc6Ntm3t67uFCwPPPMOR\n3U6fZhWwUa193328vqyhFK+vt20LHD3KgXD27+dIZfmFXbtYxe60Vg5wMKCRI3O2T4L/SMQ2QRCy\nlcOHeS37yhXeLlGCBWaRIv61Q8RtxcVx4o2XXwYOHOCgLW++CVSrxgZzt93Gs+zISA7ucu+9fP7l\ny8Drr/O1779f32/kwgXev2IFJ1s5dMiuhs9pSpQwr/tnB1pil5gYjoceHc2heR94gBPHCNlLsNXp\ngiAIuHQJGDKE1a9ffKHvT0jQBTjAAunoUf/avnKFZ82VKrHl91tvcf7qAwdY/a3l//7kE90qPCWF\n18U1ChcG3n6bVetOAhxgIbZ0KZ/73/9mXoAb85z7E9GtZUvup5G+fTPXBzec+nPPPWyZf+IE2w/8\n9htrQ7p2ZUv9J55gV7/oaE7nml3Z2AT/ESEuCEKW0K8fMG4crzM/8gi7iwG8Jm2Mr12tGifM8MSP\nP7J6/PPPeXvGDFa3AzyjXrxYrztvHltZA/ac2nv2mEOrXrrEQqhoURaYRjewHTvYEv7BB9nlavVq\nn4cOgIVjixY8qzUqEz1lV7Oyd6/Z0A7Iek2AtT+xsWw/UK0aq9a//NJc96WX2Gr/7Fm2Vl+wAHj1\n1aztkxAAmY0Sk10FErFNEEKS664zRwQbOVI/tns3RwUbPJho5Uqijz/mv0589525nXfeIXr/fc/R\nx+LiiDZuJDp9mujGG+3Hjx/ntocPN+/v3Zv3nztHVLq0/bzoaPdr3nknUa1a5n0tWwYeaS02Vv9c\nqhTfu+yK6hYRweFo77qLaN06jnRnreN0P3v1ys5fUv4DAURsk5m4IAhZQtOm5m2jkVj16qzq7tOH\nU4gOHAi0aeOcRW3BAvP2/PkcgEVTmSvF5xo5exbo359VvtOmmY8ppRtvWdX4R45wQJNy5ZwzfBmX\nAaykpNgDumzc6F7fV2rW1D+fOgU0b263rM8qUlM5Ec68eWzoZ02SA9it05WyB9wRgkdOBXsRBCGP\nM306C8N//mHDMSc3sc8/N2cqmzqV11uNWFOMVq8OlCrF0cXWr9fDkFoDuGjGXy1bAv/+N/DZZyxw\n3n6bjcMAbsfIlSusbqdM2NIuWmTfl5m14qpV+eUiLY3X/H/7TT9GxILcilPEuapV2bAvs6Sl2cPK\n1qrF3gIlS3IO9+LFeZmjRYvMX0fIWsQ6XRAEj2zZAixfzg/0jh0Da+u11zhamkanTnZhmJzMgnXF\nCk4ZOmUKr9v+8w8L5/37geuuYwt1YxSy8eOBp57Stw8cYGFXtixvHzrEa/HeHi/Vq/Nauj/ExLBl\ne07Svj2vw6ek8ItLuXLsFmbEGsHOSKlSHPHOExER7HdfrlzW9FlwJhDr9Bxf8/ZWIGvigpBrWLeO\nqGBBfS10/PjA2rt8mahbN16LrVuX6O+/fT+3WTPndd0GDYgWLfJ+/oIF3teIW7fW0286lbAwomrV\nuJ5xf40a3tu2ZlXzN8uatRQoQHTTTfr2iBGcXtVYp0wZ53Ovv57tBO65h9vxdJ2tWzP/fQu+gQDW\nxGUmLgiCK4MGAR9/rG/Xr581675EZjcsXyhbll2grMTEsArauJas8cMPrGI+dgz44AP3WXjdujzW\n3bs5BKwT1asDEydyHPGmTXmmn5twin0eHW1f169Th130+vXjePQtW7LWw6md224DVq1yDwjjRFoa\nu6edPMnLKtdfn7nx5CdkJi4IQrYwZox5VtalS85c9/hxop9/Jtq3T983cKD7bDEmhq2s16zR67/8\nsvfZbJkynNM8MZFzkTvVadKEZ6PVqvk/W46M5L9Vqniu58tM3p8SFkZ0ww3Ox5YtY6t3T+f36EH0\n3/8SJSX5/9098ojeTvHiRPv3B/xzyPMggJl40IW2rUMixAUh13DlCtG99xIVKkTUqFHOPJC3bGGX\nMYAoKopoyRLen5ZGNGUKu4m5Cb3YWKKzZ7l+5cq+CbwlS4jS01nwudXp3TtzwtSbqlor/qjWY2Iy\nr4qvWJFo507v9Z54Qv8+Ll/m7WbNiF56ib8HN9LTeanE2Nbkydn2U8kziBAXBCHPYPVVbtPGXmfT\nJqIiRZwFUMeOPCN3W0O3lvfe4zaN68u5tYSHB3b+Y4/xC5kvbfbty/fliSfM+z/4wPP3Z9U6LFyY\ntb+PvEggQlz8xAVByFVYo65FR9vr1KvHa/Pjx3P0NSOLFwMdOnCc9Hr1OIKa5mcdE8Ox1zUiIjgR\nSKtWQOPGvNatZTXLjQQavW3WLI6RbqR1a7b216z4NWbMYE+AX38179+0yfM1vv8euPlmtn4fNco9\nbayQNYhhmyAIuYqjRzlO+u7dLFiWLGFjLCcuXGAfZqf0oXPmcEzwy5fZt3nrVhbuAPublyvHLmif\nfqqfU7s2sH275/45GZBFRZn933MCp354w+pyphkXurUzejQXI2PGSHazrCYQwzYR4oIgBJXvvgPm\nzuX81i++yII1NZUDuhQsyNblN9xgjr+ucewYUL68fX9UFAvtG2/kaG/GeOAabdpwm99+q+8rU8Zs\nAV+sGHDunOf+V6vGgW5uv9238Wo4BWwxUrUqawouXeJxWmnSxBwYxhecrNXdqF2bffOd6j/1FGtB\nhKxBspgJghCSLFwI9OwJfP01B4IZOJD3R0Sw8Khfn92cqlUD1q2zn1+uHNCrl74dF8cBZH78kQU4\nwLNuJ1auBBo1Mu+7+25d3V6kCPerQQPPY9ixw560xBc8CXCAX14SEpwFeFiY/wK8bl3fBTjAprmT\n3QAAIABJREFUkfHcXMsmTOAgMELwkbCrgiAEjVWrzNtapjIAeO89fVZ84QIL+Z9/5u2ZMzndafHi\n7P/dpw8LqO7deQ3cSLNmnB3MStGiwODBvE4eHw80bAgMHcrq46lTOaZ4jx7mqHBWSpdmlfT8+eb9\n3tTU/mJUg8fE8IvL1Kn+tfHxxxzlzUntX7UqaxJ++IFn/lFRwLBh/ALx6KPOyxX++I4L2Yd8DYIg\nBA3rLNe4bc17rW0vX87hVzXmzuVZunVWrTF5MrB5M4ePtbb35588i77vPl4/B7jeyy/7JoATEzkc\nrTFMa1gYq8r9mfX6wi23sDHfhQv88lO0qO+x2rt04XENH85aikOHOBiLNsZ9+1jVvmULL0PUrq0n\nnOncmZcU3n6blw0ANlhzWsYQgkBmzdqzq0BczAQhX/Hhh0StWhE9+iinBNX4+2/2awaISpQg2rCB\n97/zjt0dqlkze7vp6frna9eIhg2zn2dMNTpiBNf1FFTGl9KhQ2Dn+1oKFCCqUIFowACiqVP5Hjml\nTrX6lNepw/f5m2/sde+8k2MDuJGQQHTgQMBfuWABAbiYBV1o2zokQlwQhAwuXeLgL0bhvnatXTBV\nqUK0YgVRSgrXGTqUg7cUKMABYoiIvv7as3ArV47r9e2bM0LYWtq2NQtha9AUtxIWxi87gwZxlDtf\nzrnnHqK9e52P/etfOfoVCxSYEBfrdEEQQo4pU9hC2rpe3a4d8NxzbNymoRSnKe3Xj1XvGpUrm+Of\nN2zIbmlt2vif0rN2bV4j3rzZ/7FkJQMHmmPdu1GhAnD4MPtzO7nU3XsvW+3nZp/5vIRYpwuCkK38\n9RcwbRqwbZvv5zgZQ2WGhARg6VLz+u9jj5lTmmosXw7Mnm3eR8QGaxUrmvefOMHW7eHh7Gs+eTIb\numUmJ/eOHZw+1cp77/E68wsv+N9mZlCKA7d4o3lz/rt0KVCggP34nDnAJ59kbd+E7EGEuCAIHpk3\nj6OZPfooz1aXL/dcf9s2zlxVsCDQrVtgQVCeeYazh3XowIJ2zRr92OefO5/TsKE9Q9rMmSy4unXT\no7ddvcrW12lpwKlTnL1swwbzee3bA7fe6r2fRGz4ZaViRW775ZeBIUPYbS0uDnj1VR5bVs50w8LY\nmv6XX8zthoXxdQG+L5068QsZwC8xDRs6t/f00xy1TcjlZFYPn10FsiYuCLmKjh3Na6a9enmuf9tt\n5vrvvOP/NTdv5pzX1vXa667T69x6q/OabtOmzkZbn3zC5zVq5HxeZKR9HdpTbnGntWnjtjEWe40a\nRCNHsgFaaqo+Brf47/6WsDDOl07ECWu0/YULs61AWhrRnj1EJ0/a7/WsWe7txsTY61+5woaCnkhN\nJTp82Hs9gUEAa+IyExcEwSMlSnjetnL6tOdtX3jkEY4WZuXwYT2W99tvA4UL2+usX88z0r599X3F\ni7OrFAD07u18zZQUjhRnxJ81bmM4U4DDxho/v/EG0L8/LwVo3HSTb21XqACMHcux3d2unZTEsc7n\nzNH3X77MyxBhYRwwp2RJ+7k9e7KfvRNWm4PXXmM//MKF3dfeT5zgmPXXXcf+5/4swQiZILPSP7sK\nZCYuCLmKw4eJbrmFZ2bNmjnP5oxMmGCeyW3b5t/1zpzR83A7lRdeIGrXjvOHz5tHNG6cvc7cuTz7\nnDmTaOxYzo09d64+M/zhB6JRozKf0tOX4paGtHBhfawffuhbW1Wrcv1t29yzjr31Frdt3R8VxfnD\n588nOnKE6KGHWLvy/ffm+969u/3c9u352J9/Ej35pH32f+yY/ft7+mlzvc6d/fv+8yMIYCYedKFt\n65AIcUHIlWjuW76wejXRtGlE//zj/3WefdZdmIWHs1Aybt99t71e06bcVno6u1Np+1u1IkpO5mNj\nxvgvmMPDWXhVrkxUrJh7vchIov79nY/Vrq2PtUkT5zpu7m9ERH36OJ9TqpTnvhcqpL+Madf4/Xe9\n3c6d7ef89BML8IIFndtMSLB/f9Zxt2zp/28gvxGIEBd1uiAIPuFPmM3bb2eXrqpV/b+OVf3eoAHw\n+OOcMvOZZ8yGcmlpbL1uRTPkWrWKQ4lqrFoF/PEHf961y/++paWx6pqIU226kZLiHBa1Th1zwhVr\nHyIjeSlg6FDz/ttu0z87xZAH2IDOE0lJHPFNgwh44glO2frXX2ztb/yOr7+es8nNn+8c571HDz2q\nm5GnnuLEMQAbN+aUZX6+JbPSP7sKZCYuCPmaNWv02XZYGNH06Z4NzMaMMauuS5Rgw7hffnGOYLZ1\nK19n4kT/Z+KBlMKFiS5cMI/VaPwGEPXsyfsvX2YNQkwM0e23Ex09ygZlTipvX4ubel8rU6cSxceb\nZ93TphHNmGGuV7MmG9EZDfSsHD3KdfbuzZafSJ4DAczEgy60bR0SIS4I+Z6dO1mAzJpFVL++WYgY\n18s7dGDh9tBDbM3esydHeSMyq9G1Mnq0fo30dF5HvuUW93XmrCjR0ax+X7jQPMaFC83W8BUqEB0/\n7n5Pxo61t12hAqvaS5YMvJ/Vq9vvde3afJ+GDuVrNW/O4XCFrEWEuCAIuYr0dKLly4kWL/Y8Y3Pj\nl1+I7r/feSZdogTRjh1EGzfydYYPNx9/6SVuw7j+C3g2sDp+nA3mrG5iWVWGD9dtClJTOQ689Vrd\nuzvfx4EDiW64gejGG831a9Ui+u47ezvh4URxcVzf6eXEbYxORnENGvj/3Qn+E4gQlyxmgpBHIAIm\nTeI80zExHFa0XTvv+bCzgwce0COnde4MLFjge2CThAT3lJkREcDEiWbXLGt2sq1b+W9ionm/ll/c\nyo4d7Fp2+rTuJlavXtaGUB07lotGkSJ2l7Tixe3nDR1qduUypiR97DF2xbO2k5YGnD3LJTaWXcyM\ndSpUAD76iEOrGnHKif7qq97HJgQXEeKCkEd4/32OG24kMhJYsQJo0SLn+rFnjzn06aJFnPKzcWPf\nzt+wwVmAx8QAP/2khwzVaN+er2HcBlgoHjmi769Sxd7m008D48fb9wciwJs14xcLJ6GocemSfV/T\npsDXX3Oq0N9+49SjmzaZ62jCuH17NvYbMsRzX86ft+87dIh96Z0oWVI3kOvfnyPcCbkbSYAiCDnA\nli0cMrRu3ewTqF26mIWZxsCBPHvNKQ4fBipVYs2AxtatnGzDFzZvZgGmnR8RwVqFv//msKHvvAMM\nG2Y+Z9gwYPFioGZNzuO9aRPnyzYGK2nRgkuvXjzrXrKE2/KHggU5bOrx477VV8p8H9woW9a5zUKF\n2KrciT592HLc6YXASESEPYhNeDjP2K00bMjttmxpzs9OZA9lK2QdgSRACfoauLVA1sSFPEZ8vG4Z\nrBQHIMkORoxwXut8803/2zpzhoOhDBvm2cL4wgXnNe+339Z9nbU83b6yZInn9WWl2Pf49tt5zX31\n6uw1TNNK797sa52Vbdat62ywlp3FLVytsWiBYOLjicqX59/vU0/59z0KvoMA1sSzVABnRREhLuQ1\n/v1v8wOyRYvsuc61a0TPPEPUsCFbLMfEsLX21av+tZOWxgZNWn9LlyZKTDTXSU7WY6rHxhItXWpv\n59w5fhkwsnkzG6V5YuVK3wVSdLQ9klh2ldjYrG2vfHmOZ753b/ZGjrMWt2AxxvLgg/xdlC1r3j9u\nnH+/JcE3AhHiEuxFELKZMmXM26VLZ891ChTQM3EdPcrrod9+yypgfzh6lIN/aCQmmrN7paXx+vbi\nxbx9/rw5TrlGbCyrngE2rurQgQ3GatWyq8MBzvRVqRIHB6lTx7e+XrnCKmdPhIV5DlQTHu4ek9yI\n0/oy4N62li3NyIkTrMr/4w9g506OZ37DDZyyNCKCS9Gi3vsSCLfcYjcOtHLjjSy2rYF3hg8352AX\ncgGZlf7ZVSAzcSGPcf48+zNHRPAs+eDBYPfIM1eusBuXNvuKiCDavVs//r//2WduRYu6t3fiBFHF\nivZzjPfhhx/Mx26+mUOnAu4hPwEONZqYyO5hNWoQde1K9J//2OspxcerVLG7WI0aRTR5ctbPhnv1\n4vti3Pfkk7wMULs20aRJ5vuUksKlSxfvbTdpwvfHX/X+LbfofvRE9gx1kZGsxbn+el4GefRRextf\nfZU1vzNBBwHMxIMutG0dEiEu5FHS04PdA9/57TdOdlKvHtHs2eZj331nf7C//LJ7W06xzQGiQ4f0\nOu+/bz5WpAir9Q8eZLV869bObXz0kd7GwoUclOSTT5x9oTX/8UOHiNavJ1q2jD8vWJC1/uF167It\nweXLLKyNx6zx1tessd+vHj28X2PWLK577RpRmTKe6ypF9K9/sYp8+nTz7zA1lY9FRXGwl4YNzefO\nnGkOJBMWxj7uQtYiQlwQhBzj6lWeTWoP9scec6+7YIGzYKlRw1xv505zsJF+/czH//zTvZ2rV4mG\nDDHv79zZPoOfPJk1IkWKcBa0U6eI5szxLjC9hSu1lgED9H5//70eYe666+x133jDfs+2b/ecXCUq\nirORaVhn03Xr2s8xvqSUKsWla1fOAmekfHnzea+8wpnT6tXjTGqTJ/v0ExH8RIS4IAiZ5vRpom+/\nZUtkX0lOJlq3zmyklpDAhlpGXnrJWRBphlNGtm5lofHppzxDbt6c1fp33MEqYCfDslq1iBo1su+P\njmZB17UrxycfNYojnxnrPPYY0QMPeBbITz9tj2+ulerVib74gujOO/V9SpnDqxrjt9eoYX750cqz\nz5rvw6ZNZtW+k5r/q694Fh8fz+O85x6i4sX5Wo0b22fUnsrw4bxc0q6deRklMpKznA0YoO+7+WZ7\n/HchcIIuxAF0ArALwB4Awx2O9wGwOaOsAVDHQ1vZdZ8EQbCQmMjrxNpDetSozLVjzCHdogWv/f70\nExer0KhY0S7srRit4wFep23XzryvYEFWWzsJpkKF7G1a1fpduxK9+KLz+U8+qQurzZt5dmtMgVqp\nknnWGhfHa/E//6xf7+JFc2x0TTA6Xc9oH+CkHbCmGTWum3ftyssKxuN16vjudleiBL+QWF+y1q/n\nFzNr/Q8/zNxvRHAnqEIcQBiAvQAqA4gEsAlATUudpgBiSRf46z20l203ShDcWL+eVYZVqnB2q/zC\npEnehZ8n0tP5ge8kHJTiNdgBA4jatiUaPJjor7+IkpK8t+sUx9u678UXeSbsdF2r0RgRG89pamWl\nWPuQlMQuV6VK6WrzUqWIFi1y7teCBUSffUZ077326/btS/Trr0QPP0w0aJCz7YBbMdoHHDtmXodu\n0YJ/n3XqsEre6cXD6jbmNHt3W/evVctef+pU7otVewGwsd6xY/79TgTPBFuINwWwyLD9gtNs3HC8\nGIBDHo5ny00SBDfS0uwznd9+C3avcoavvjKPu2xZ/86fOdOzcNJmohERRHPn+t5u27b2tqxryj//\nzIZd2gw9PJw1CZ6ybP3yC6+316/PM9jZs4nuu4+F4LvvmoXZxx+7t2PUXhiFodFa3E0Nby3Dh3Ob\n333HM+KqVTm96gsv8Jr5xYvmax85Yhe6Ti8zvpRy5djeoFMnfV/hwrqmxM0Hf/58379LwTvBFuL3\nAphi2H4IwHgP9YcZ6zscz5abJAhunD9vf0hp1r95ndRUfV04JoYjoBERrV3La821axN9/bX7+W+8\n4bvAaNfOtz6tW8cGYdWqmc+fNo3o8cfZaM0Y9S4tjWjXLnZl88bu3e6GataZapky7u1YVfuAsxvd\nG2/w9cLDWQU+bBh/jo5mi/6EBG5v+3a7+rt6dV6OOHvW+b5rgnzIEN5Xrpzn8VjLyJF6e5cvE732\nmq4t0dizh4P9WF/Mdu70fq8F3wkZIQ6gDYDtAOI8tJc9d0kQPNChg/6QKl0659SF164RHT6cuXSd\nWcnFi3ofrlxhIynjQ3vXLvs5KSksfIoU0evefz9Hiatf3x7e89573a//ySdE7dubDbJq1CD64AOi\nJ57Qw4Du2MFhWc+fz9w4nXzc3YonrcTmzd7XnGNi+L4+/DDPyh9/nL/vlBS7u6HVT95YihVjdbqV\nxET9dzpunFloV6jgWYjfeiv34dIlNqy7+26iL790Huvp00RTprBveoMGrDEQspZgC/GmAH42bDuq\n0wHUBZAA4AYv7dErr7zy/2XlypXZctMEwciVKywwRo8m+uefnLnmpk16WMu6de2hTY2kpXE40pUr\n+bM3Dh9mlacn1bIbhw7ZH/o//aQf37ePrZQBolateOlh7Fiib74xt5OYqAvl6tXd+/L99+7CZto0\nvd5//6sLpuuv923mrQkqjVWrfA/qor10LFnCFuiPPML3VWvX+PLiVCpUMFt2A2x978SRI56D2njS\nYly96l/s+I4d9dm91Tr/xx+931MhcFauXGmSc8EW4uEGw7YCGYZtN1nqVMoQ4E19aC+77psg5Cru\nuMP8AB02zLleejq7EBmFi6fAMRs28CwQYItqp7jmnkhLM8+iy5YlOnlSP24NRvLcc87tnDzJgta6\npmvlhRfcBU6dOvpaunVN/O233dtMSDAnLOnUiV/UnCKQuZWBA1nTYLQor1WL2z971vv5HTrY1/Z7\n9XLv89q19ljlWmnenI87vbhcueJddV6gALu3jRlj/u1YlwBefNHc9rVrnr87IWsIRIgHHDudiNIA\nDAKwJENVPouIdiqlHldKPZZRbRSA4gAmKaU2KqV+D/S6ghDqWFNMuqWc3LoV+OEHfXvOHGDbNvd2\nx48HLlzgz1ev+p9uMywMWLoUeO01jmP+66+cZ1pDyzetocXXTk4GPvuMY6C3aQOUKsVx41991fk6\nZ85w/PWqVc37jSkvt24F7r4bWLUKiIoy17Nua5w8yTnHv/lGv6c//wxMnsxjM1K2rPN1IyI4N/ua\nNUBKir5/xw6OR9+tm70tI1Wr8r3o3t2835ifOzUVGDECaNWKY5Lfeitw7BjHVR80CChWjOsVKgRs\n385jqlaNvw/rfXj9dX27Xj3zcaX4u3jlFWDkSPM4rTnemzThv1u38hgKFgQ6d+YY9UIuJbPSP7sK\nZCYu5BPmzdONrIoX51mfE3v22GdWmkGUE088Ya7bpg3RW2/Zw6f6y7VrHL3r88/1mV9UFAcdSU83\n2xVYy5Yt5rYOHmR1M8Cq4IEDeS39hReco5W99hpbo2sq7ObN2RjLiUWLnPvwyius0q9USb/nq1ax\nqjo2lrUPHTsSdeumB7GxuuABdt9vp3LHHXp/Pv+cDca0dX2NQYPM52hhYTXOnWMjv549zfU6dnTW\nxOzaxcFZUlJ4jd1ocQ6wdsaa0e78eb73nTubly6aNzef60nrIQQOgqlOz+oiQlzIT+zZw+uQ3gzp\nRo3SH6gvv8xrs+++y9HNkpPNdQ8fJqpZk+uWKWNea/UU49yNTz5hoyZtLTkqii3Wp07lvzfeaE/0\nYS3WnObG8Wjq3rvvZhW8U9hQLQraxYtEBw54tgtISLAHVSlRQs+LfukS2yOcPUv0/PPmej17mtva\nuNG8hm4M+OKpvPWW53u6b5/9ZaBLF+e6TlHlihfnZZLp0/m8p56yR1KbO9d+nie7CyNW97jnn/ft\nPCFziBAXhHzAyZNcEhP1WSxA1L27vW5qKhtLPfec+WEcFcWzaU8kJRFNmED0zjsc+MZJSN18M9d1\nigfuViZM0K/h5ppWp45ZAMfF6YFH/OGHH9iorl49nsUbY40T8Uy2f3+7kVtEBN+fPXtY0Kenc7/L\nliW64QY2rvPFMK5lSzYQdAts4xQsJiaGfdaN69Aff+y+3m11lbv/fvM1Tp/WtQ4Aa0qI+GXmgQfY\nWG/VKuf+ffyxfl5srPffjBAYIsQFIQTYs4cftN26uT88feHbb+0PdDeXK+PDWCuVKrm3nZbGFudG\noeYkQGJjWcA5hRWNjOSAIdbZeZs2+nWOH7cn2wDsAqtmTed+XrjAauBOndj9yV/69XMXwMZY7Hfd\npc/609O9B7exlmLFOMCMEW9GcTffzNe8etU31b1WKla0j/PYMX4ZmzSJ20tJ4WAy2jmFCrFWwIm1\nazk6ndtxIesQIS4IWcR777GAKleOQ2xmFcnJRJUrmx+e+/dnrq1168wP77g4dz/z1FTnNJ5u68kH\nD/omMGrX5vp9+9qPNWnCx6xr89266dfp3du5XS1ZiHH2uGYN0YoV5jHef7+53v/+5/v9W7jQP0H8\n00/8ohAd7S5UPeX1rl/ffP1Tp7xfc+VK57jznoR648a6P//+/Ry4x2rNfuSI/Txv0dfS0jgAjCc7\nDCEwRIgLQhbw11/mh1vhwr7F+fYFJ9/rQHxyx47VZ7phYUSvvuped/9+3eUM4Jm2G5cuOfsr16hh\n3n7/fa6fluYcX/v8efarNu4z+jo7uVKFhXG7//kPh8Ft3twsrDt31gW58YUIYIM4X3nrLfu1NRV5\nxYp2dXnduu5+2GFhvOTw7LPuwjUujtfk69XjTG2vv24+bpwZa2XOHOf48d5KVBQnKNHW7kuUMKvC\nU1LYhkGrX6QI2xi4kZLC912r75Q6VQgcEeKCkAU4zdCyKnKb9eFZtKg56YW/nDxpn5V99RWrqMPD\niW67zWzwtmULZxp75RXvqSSHD7ffh3/+YTX+wIFEM2aY60+daheIycksjI37jYZbxixcgJ5mNDJS\nt6Lfv9/ejzVr+Jg14Yen0LBW1q833zujCr9gQdYY+Co0K1fmF4stW5yDv1iXB5ySjaxaZfa9r1nT\n7ovvqViva9UKPPqoefz79xP961/8grRuned79eOP9u/WGDxHyBpEiAtCFnD+vFnQdu7s/RxtnfSt\nt5xDkxo5cIDVz716sQpzzZrM52bet8/+MLfObtu2zVzb58+bDeeaNuVxpqQ41589296XixfZnUsL\nJlKyJCfaIOKZ4Zw5LIhbteJEJMZza9TgeseO2QWe1oY1XadmtOUry5bxS4ZV5Q+w8ZpxOy7OOVa6\nVlq14vXmhAR+oZk9m2fcEyfqXgLWlxVj0fK4JySwStwoMH0R4sYQuU5l0CBuPy2NlwVeeskcHz05\nmW0ESpfmsRhfLq0vtkq5L8UImUeEuCBkEadO8cN3+nTv0arefts8C4qJ4QQb3pg7V1dZV61qt5z2\nBWsUt2bN7O5PRYv6367GiRPswjZxIlt6a/2NidFnwxpnz5pffh56SD92+TILNM3wbtIkXThVr873\ne8wYc781y3civsda/cGD9f2vvWY+Jzw8c1qTq1f1ELLaLNbql120KLtYTZzobiluTMhCxB4ETsJ1\n0CCioUP17e7ddcO55GR7/bZtedbsJPyNxdovTf1fujRHAty92xwCNiqKre+JeAnDeG7Xrvo4UlN1\nzYRSvIwjZD0ixAUhh1m2zPlhqq0Ve8IoNAB2N8rM2ntqKgeMmT2bQ292725ut1Ej/9u0kpZmV8+W\nKGGvd/o0Bwv5/nvPPtzWjFgffsg+5MZ9Q4eazzl50i6gV6603/sePdy1BZ44fZqNz7zNePv355c7\nJ1/xdu1YPT1iBL+wOPXv3Xf1IC2bN7Na33qvjLYHSnGdKVO8981J+BuXBYoVs79UjBnD1xw82Ly/\nTh1zn9LS+EVMrNSzDxHigpDDWNeBteJLVDQngdGoEc9+Fy/mB2Zmue8+Vl03a5a5h+7Zs6yy1mbO\nV6/a+xoe7lsSFiessbonTbJHervnHt/aMs7+tdK5s3vf1q1jl7QuXXS1PBG7u1ntC5xm3Frc9NRU\ntjlwE6AtW/Lvw/jyU7asb2rohAQeQ+PG7N5FxMF2jO1HRdn7Z7xWWBgvWVjHZM2B/vXXfB9KljTv\nb9DA7hYnZC8ixAUhh9m3zxwetEABtlD2hWXL7K5UgO43rRT7d/tKSgo/jLWIZJnlr794lg1wpDct\n9KjViOzhhzN/jQULdIHTujVrEIYMMbdvDT/qxuzZzpb0Wr+NJCaaLfRjYojOnOFj8+bZ2xgyhH3E\njfv69tXbe+ghdyFunEnXqcNC+amneCaeGRuICxdYsGoCeupUtr+47Ta2nJ8xg43jGjbk62nudtdf\nb+7P9OlELVpwnADNml9r11oKFrSHyhWyDxHighAEdu1ia+8JE+yhTz2xaZN396FSpXxr69o13Q9c\nKU6nmlmsFtHa2nZ6OguAhx5ioy9PGdR84fx5tpBOS+PZaZs2fL2ICFZJu9ki/PILxyS/4w49trf1\nZUgpPV2oEatvPcAq8PR0FvpGF7KiRfXvc8IEnr0PGWKeSf/+u/dQswBbrxtnwE2b+p47Pj2d6Ndf\nOdXr1ausWvcnTe6mTSykr7tOV51bcXJv08rEib5fSwgMEeKCEEIMG2Z/YLZvb972FFXNiNUyPCLC\nbF38xx9scdykifegHtaZ54MPZnqIRMRC78cf7YZwRsaONV9TCxRj5cQJ34Sm2/krVjgbh2k+1N9+\nS3TLLZyuc8MG5zauXWMjxB49eEnkhRd4VqzNeKOj7f701tCogG/LHOnpvDSindOvn/dzMsNHHznf\nR6UCiyoo+IcIcUEIIayCq0ULVitrM9LChX0PBPPdd/YHcHg4q1iTkszrnQUKsNuXG3/+qRs/lSnj\nnlVN48svOfNX+/ZEO3eaj126xEJRu7ZbrvQRI8x9r1GDVc9RUTyD/fVXruc0k3YqDRqY25840ewu\nZy2e7ofG+fO6tsM689cyk509y3+TkjzHky9a1Ht+dSJ+kbCeu2eP9/P8Yd48Pef5/fezj75S/AJp\nzGgmZD8ixAUhhLhyhWe94eEstDQBmJ7OYU99echrJCebk1wYhYVTsBRvccbPnOHZ+7lznutt2GA2\nrqpa1XzcqiFQigVcUhKPvUABFvLLlukvDkoRPf64+bzKlbm9s2fNfvBurl7Dh+t9cIoxbyzt2vnm\n3jdypHsb1kh5ycnOfStenH3Gly71fj0iXo+2tmEN05ucnPmIgps2mQ3fKldmi3lv37uQPQQixD2k\ntRcEITuIigLmzgVSUoBdu4CaNXm/UkDFisCxY8CUKcCqVebz3nwTqFwZaNwY2LqV90VwUhO4AAAZ\nJElEQVRGcrGSlgZUqADcfLN5/7PPAvv3u/ctLg5o1AiIjfU8hiVLgPR0fXvfPiApSd8uXNhcPzwc\nOH4ceO89YN48IDkZ2LgReP99YMsW4OuvgfXrgTp1zOcdOwa0bAl06gS89hrw2GPAE0/Y6zVrBnzw\nAd8jjQ0bnPteqRL/Xb4caNAAOHPG81jdjoeHA+3bm/dFRABFi5r3hYUBixcDO3cCd9zh+VoadeoA\nTz2lb48cyd+9xsyZfJ3ChYHnn/etTSObNwOpqfr2gQNAvXp8jV9/9b89IYhkVvpnV4HMxIV8zKZN\n5gAymnHRokXmWVn16vo5TmFCtfOcEl5Mnx5YH3ftss82W7Qg+vxz9gMvU4boiy/ssdPLliX697/N\n+6zq74MHzb7kxjXlggX1ON9Wg6yRI+39nD/fPnarL73W93LlOMjJ6dN8bnIyB0g5d441E0YXrp49\nOXDKihXO92fhQl5/V4rd0rSIbG7s30+0ZIk9WQkRj9dqqHfxoj1furbs4Cs7dzpb9gPsnijkLBB1\nupCbuXyZ1aZahCjBHWsiDS1bmDWlaFiYbsV9/LjZAjoqyuweZPUPXr06sD4OGmR/8K9da1bPRkay\ny5u13ujRZuHh5Ep38KB7mNOVK7mOMcpb4cKsCnbiq694vXfQIH75cAoRayz9+vGSQr16vF20KNHy\n5bwePWMGW4r7Qnq694h/Bw8SVaumXzsuzre83SdO2Pv988/68Rkz2Ie+bl39u754kSO/1a3LMfST\nk/nlwuh25/ZiJWQ/IsSFXMuFC/oDEeAwmnmFnTuJRo1iS+XatTk71YgR7E/sbfblhjXDVuvWvH/v\nXvvMqX9/tmC+806zzzrAUdA0Nm9m16Zq1dgaOVCsYTq1yGJWYfDXX/ZAIqtXczCbCRNYODqxdauz\ngC1XTvftJmLjv3HjvMesN3L8uLPg0krbtvaQrvXq2dtJTGTDMDdf6vR0FvjGGOVWjNbnWvHVB9/o\np96wob42vm2bWUsSF8c2GFZbAy0b2U8/mY31IiP9S+sqZA0ixIVcy4wZ5odHwYKB+xnnJCdPcljU\n2rXZpUjr+6ZNnnNIFyhg1zz4Mu6kJFaPh4Wxytxo9W2dnRof1tZkGf5k9coMLVrwdcLDid55h2ed\nxsAhjRrxvl9+4XGUKsUvKL7wxx/2+/nII75ZknsjLY2/S7fv7ZNP+MXMuO+mm8xt7N/PLxTadzB9\nOkdX69iRX6xOnTLHtR8wwLkvt99uv77TCwMRu6V99BEb66Wnc/npJ7aON/qvL1hgb/PoUfu1jO6D\np0+zJmXRIv/80IWsQ4S4kGv5+mvzwyMmJtg98o+77zb3f/Jk3m91jXIq48dz3RMnOLpWeDgHKUlM\n1NufPp1nyXfeafYfdgodOn685+vddBO7Uz33XLbdDo9cuMDq8UmT7Bb269cTvfgi+1V7e5lJSzP7\nrAfqr27EyUYA4CWIefP0Olq+8shI3Y1M4+WXzedqAl0rTt4CTjm7v/jCXq9nT3u9ffv0SHqAnpXM\niVOn9Mh/AP/e0tPtGp4vv8zsHRSyAxHiQq4lJUVPNVmgQPbPEDWuXGF3ozvvJPr008y3Y00lOWQI\n73cSqMbZsDFYhjWv9mOP8f5ffjGfU7++9/5MnsyqVGsSjrAwVhVbOXeO1c7GWOFELKgGDeJc007r\nsLt28WyyUye7AdepU6wtuO46Xme9etVzn3/7zWyI1aQJRzzzRFoaL0msXp15zU16Oq//FivGBmYb\nN3JfrUlYoqPtscLPn+frOwVmsfr5W1PAOhU3V7bXX9d/A9HRzgZq48aZ2ypUyH3M33/PseGbNeNl\nAePL1NSprBX47juvt07IYUSIC7ma9HSeiWgBMfwlNZUFoq9GRUSs1jQ++DL78mCMrqYUJyghYsOg\nBx/kB2/ZsqzunT+fBV/z5mYLcE31rJUePXj/pEnm/eHhvgssq7Fa8+b6scOHOUpbXJxZ2GsZ1pKT\nWcWt7S9RwmwZfe2aOVFJdDQLs6++YqFunWlafaWNxMdzX6xCLTKSVbgHDnC7blHSAsHqJ66pxTds\nYPVygwbsN+9PyFwiDmSjfafFi7OQ9ZT7++WXPbe3cSNb9ickOB+3GuPdeKNzveXLzf24917/xiUE\nDxHiQp4lNZUFh/ZgcltftGJN9+lJBemJtDRWET/1FK9BDh7MYTa7dnWe+Vo5dMg+a/7hBz62bZvZ\nWK1jR9/7NXeubg0eF2deL77zTmdhEhfHx//5x35MezkhYqtp6/E333QXVH36OPdx/nzPwq1vX90g\nTym2n8hKPvjAfD1flnLGjmUXv9KldfW6E+np/OKjWaB/950ecc94v60BWjLL4MHcr2rV2GbACatB\nXunSWXNtIfsRIS7kWeLj7Q9/X6JsWVXY2hrggQM8S9ZclTSuXGGh6ili1YQJ5jbvust7P6z+3YB5\nfXTNGtYajBzpX6Q2IhbcS5eyeptIX0evVctZaJYsyceTktiX2zjTNgqblBSz61NMDK9nuwljgF8c\nrDPaXr3MdawCvW1b87abUVdm2bvXHC/d24vcn3+a+1OokNlobP58/q7GjnXPW/7BB2yXcNNNnmPG\nGzl2jNX5Rsv7zGD9rXXpElh7Qs4hQlzIs1hjZivFFuPeSEpin+v27dl6moh9fbUQnwCnhiTilwIt\nN3VsrPvDd/Bgc180H25PHDpkDt5SpYp3/2F/2b6dhW5YGL9YOAU0ATjF5+rVvKyxeTNrONq04UAj\nVg4eZIH14IOsfl692j3UqVasIV2tOberVGHVevnyRE8+yRngjMdbtvRv3BcvsprfmBUsPZ1ozhy2\nHTh6lFXU777LyynelioWLrSP6dgxPrZ4sfklxFeNkDfi4/WMdmXKcICZQJg+neOhP/qoHrhGyP2I\nEBfyLKdP6y5BSukC2Y1163gN8ptveK1We+iOGmUXGlpc7meeMe+//XaemR87Zn7wL19uFmROUcKc\nWLuW18F792a/Yk+2ARs3sjDxJ4a1dc3dGikNYCt7zR+4fPnMuWv973884za6Vxmv8d575vr3328+\nfttt5uMXL7LgBthIzp/81StX6v7et97KhmhE/HKgXa98eV0I+8KFC2Zbga5d9WPWzHNu69L+YtVG\n9O+fNe0KoYUIcSHPoj3kAV4DdovMRcTGb8aoYdZi9GPW1MTnz7MFs3F/rVq6gGjb1pxkYuVKouef\n5xmPv1bTn36q92/wYPvxDz80CwlfNA5E9vSXzz+vZwKrVIlne82ames4Xd8fkpLMVtoVKtjDg1oz\nrGkud1YuXvT/XtapY277zTe5Dev37y3hi5UzZ9hXfOZM8/LAZ5/ZX4qygg4dzO0++WTWtCuEFiLE\nhTxJerp9tqf5aTthVXf7Up57jg29tHSVhQrZU1e6CR9/OHeOrc+N7a5da65jVPUDbPXsC8ac0EWL\nOs9orcE+nnnG/zGsWMHCs0YNolmzeN/atWwFbvR9N7JoEQfJmT3b/+t5wrhmD7CWhcjus+3JOM1f\nxozhWX+fProdQqD88YfuA161qrM/uZD3ESEuBJ30dPYtts7GAqVhQ/2BHBbm2c3MOJMFzA/09u2d\nhXivXnzuuXO8Fn7kiF2I16vHLkD+sHUrn1eyJAvM48ft1zZahBPZ81BPner79Vas4Jm+m5vSunW6\nJXi1arxe7A8XLvALglErsnevf21kJV99pb8UVaqk/+5WrWI1eoECrI0IBS5cYLuGzKYVFUIfEeJC\nUElN5TVfgNetP/zQfDw9ndWTr73m37onET+ce/cmuuMOnvEZGT+e9w8YwGvnffvyTLpAAfabPniQ\nhdvy5Wy5bV3X1MrQoeZ2J01ydo0yzibT01nd7RRZjciu7p01i5NraNu33WYPkrJwoW7k1LFj1hvA\nnTtHtGMHr/f7i1MyE7fY5znF7t1sne9kY5DbQ/seOsSao0GDJNSpIEJcCDJWq96wMPOswqjmjo72\nX5A78dVX5mtahe6dd9rP2bKFjdGeftqu2rZapG/apIfe1IpmkXzkiO7GdcMNzkZiRtcmQLeEX7GC\nI6i5RTm7fJlnyblNCKWkmLUilSv7Z3wn6CQlcawB7V5WqCD3Mr8TiBCPyPoM5UKokpYGnDgBFC8O\n7N/Pf0uX9u08I0RAerq+PWuW/vnKFWD+fKBOHd/7RQQoZd63YYO9jpH9+83bv/0GtGoFXLvmfI0L\nF8zb9eoBrVsDM2fq+xo25L+jRwM7dvDnv/8GRowAvv2Wt8+cAbZtA+6+G/jsM95XtCjQvTt/btPG\n+foahQpxyW1ERADLlwMff8z38PHHgdjYYPcqNNm7F/jnH337yBH+PTVrFrw+CaGLCHEBAHDsGHDH\nHfwwKViQH9SRkSyI+vTxfG6nTkC7dvyQB1jIFS6sH69SBUhM1LcrV/atT4mJwF13Ab//DjRtCsyd\nC5Qqxcduvx348EP3c3v2NG9//bVZgMfFAWfP8ueGDVlgW5k4kcexcyfQuTPQrx/vNz6AAf0FYNcu\nflFITOTzXnyRBXjlyizYS5XiF6NQJTaWxyQERsWKQLFiwLlzvF24MFC1anD7JIQwmZ3CZ1eBqNOD\nwsCBZvWvVrRQnd5ISWGjsx077Md272ZjqshINjpav965jfR07kfBgmys1K2buS9a4hCNL77gnMxG\n397y5Z0zNFmzOHXsSPTzz/ZUjr5gjCtutPR+9FHz/rJl2TJb265SxRyjXMi/rFvHgW9uv92eYEbI\nf0DU6UKgJCU5709N9e38iAigcWPnY2FhrN5OSQGOHgW6dQMOHQKiosz15sxhdS0AHDwInDxpPm6c\nzQPAQw9xSUsD5s3jmXaPHkB0tL0PQ4cCf/0FLFwI3HQT8MknmZ/9XLli3i5Xjv9GWP6bjh8H3n1X\n396/H7jvPuDGG4Gnn2aVvZA/adYMiI8Pdi+EvEBYsDsg5A4GD9bXOLX157AwYOzYwNveu5cFuMap\nU3YBDfB6vJHUVCA8nD9HRACPPurcfng4cM89QO/ezgIc4CWC2bOBy5d5PT0Q9eWgQfrn0qV11f2I\nEUCJEua6VnuBX34BZsxg9f3Ro5nvgyAIAiBCXMigfn1g+3Y2Otu2DVi7ltd4n3wy8LYbNTIbyNWr\nB5Qvb693113mej17AkWK6NvGF4Fg8sorwNKlLIw3bgQqVeL9lSrxTF978QCAm28GYmLsbZw7B2za\nlDP9FQQh76LIatYbZJRSlNv6JATO3r2sKo+OBp591j5j1Th8GFiwAChTBli5ko3LNJo0Adavz5n+\nBsKSJcC0aWzI9uqrrOE4cwZo0IAtkQFeSti+Hbj++uD2VRCE4KOUAhEp7zUdzs1tAlOEeOhy/jzw\nzTdAgQLAgw+yCjsQhg8H3nlH327TBlixwr82li1j9X3HjmyRHkx27OAxJSXx3w4dgtsfQRByByLE\n8wGXLplVy7mNpCR2A9u6lbfbtGEBGhbAgs2pU+z2tnkzULYssGgRq/19Zdgw4P33+fMNN7CvuJsG\nQBAEIVgEIsSzZE1cKdVJKbVLKbVHKTXcpc54pVSCUmqTUsqPR3H+ZscOoFo19jdu0oTVsrmRP/7Q\nBTjAqvB9+wJrs2RJtig/ehQ4cMA/AZ6WBowbp2///Tev9wuCIOQlAhbiSqkwABMBdARQG0BvpVRN\nS53OAG4gomoAHgcwOdDr5kWMUc4AthivXZvXkwEOejJmTM73yxfKlDFHVStYMGvU12Fh7MJVoIB/\n54WH84uPkWLFAu+PIAhCbiIrZuKNASQQ0QEiSgEwC8Bdljp3AfgcAIjoNwCxSqkyWXDtPMGOHUD1\n6hwhrUcP9ndesgSYMMFeV4vylNuoWZP7GxvLBl1ffBH86GRffqlbhvfty/dWEAQhL5EVwV4qADhk\n2D4MFuye6hzJ2GfxDM6fPP44kJDAn+fNY2FYsaK9XqFCXDe3MnAgl9xCly4cWvXaNXf/cUEQhFAm\nV0ZsGz169P9/bt26NVo7BbbOQ1gDn5w8CfTvz7PzPXt4X5s2wNSpbKAl+E5YmAhwQRByF/Hx8YjP\nopB9AVunK6WaAhhNRJ0ytl8Ax4Eda6gzGcBKIvo2Y3sXgFZEZJuJ50fr9HHjgCFD+HPRohxopU4d\nVp0vXMhruV27BrePgiAIQvYQVBczpVQ4gN0A2gE4BuB3AL2JaKehThcAA4moa4bQ/4iImrq0l++E\nOMBxlBMSgLZtZbYtCIKQnwi6n7hSqhOAcWBDuWlE9LZS6nHwjHxKRp2JADoBuAygLxH95dJWvhTi\ngiAIQv4k6EI8KxEhLgiCIOQngh7sRRAEQRCEnEeEuCAIgiCEKCLEBUEQBCFEESEuCIIgCCGKCHFB\nEARBCFFEiAuCIAhCiCJCXBAEQRBCFBHigiAIghCiiBAXBEEQhBBFhLggCIIghCgixAVBEAQhRBEh\nLgiCIAghighxQRAEQQhRRIgLgiAIQogiQlwQBEEQQhQR4oIgCIIQoogQFwRBEIQQRYS4IAiCIIQo\nIsQFQRAEIUQRIS4IgiAIIYoIcUEQBEEIUUSIC4IgCEKIIkJcEARBEEIUEeKCIAiCEKKIEBcEQRCE\nEEWEuCAIgiCEKCLEBUEQBCFEESEuCIIgCCGKCHFBEARBCFFEiAuCIAhCiCJCXBAEQRBCFBHigiAI\nghCiiBAXBEEQhBBFhLggCIIghCgixAVBEAQhRBEhLgiCIAghighxQRAEQQhRRIgLgiAIQogiQlwQ\nBEEQQhQR4oIgCIIQoogQFwRBEIQQJSAhrpSKU0otUUrtVkotVkrFOtS5Tim1Qim1XSm1VSk1OJBr\n5mXi4+OD3YWgIuOPD3YXgkp+Hn9+Hjsg4w+EQGfiLwBYRkQ1AKwAMMKhTiqAZ4ioNoBmAAYqpWoG\neN08SX7/Icv444PdhaCSn8efn8cOyPgDIVAhfheAmRmfZwLoYa1ARMeJaFPG50sAdgKoEOB1BUEQ\nBCHfE6gQL01EJwAW1gBKe6qslKoCoD6A3wK8riAIgiDkexQRea6g1FIAZYy7ABCAlwB8RkTFDXVP\nE1EJl3aKAIgH8DoRzfNwPc8dEgRBEIQ8BhGpzJwX4UPD7d2OKaVOKKXKENEJpVRZAIku9SIAfA/g\nC08CPON6mRqIIAiCIOQ3AlWnzwfw74zP/wLgJqCnA9hBROMCvJ4gCIIgCBl4Vad7PFmp4gBmA6gI\n4ACAnkR0TilVDsBUIuqmlGoOYDWArWA1PAF4kYh+Drj3giAIgpCPCUiIC4IgCIIQPIIasS2/BotR\nSnVSSu1SSu1RSg13qTNeKZWglNqklKqf033MTryNXynVRym1OaOsUUrVCUY/swtfvv+MercqpVKU\nUvfkZP+yEx9/+62VUhuVUtuUUitzuo/ZiQ+//Ril1PyM//utSql/B6Gb2YJSalqGHdUWD3Xy8nPP\n4/gz/dwjoqAVAGMBPJ/xeTiAtx3qlAVQP+NzEQC7AdQMZr8DHHMYgL0AKgOIBLDJOh4AnQEszPjc\nBMD6YPc7h8ffFEBsxudO+W38hnrLAfwI4J5g9zsHv/tYANsBVMjYLhnsfufw+EcAeEsbO4DTACKC\n3fcsGn8LsIvxFpfjefa55+P4M/XcC3bs9PwYLKYxgAQiOkBEKQBmge+DkbsAfA4ARPQbgFilVBnk\nDbyOn4jWE9H5jM31CO3v24ov3z8APAX26HD0+AhRfBl7HwBziOgIABDRqRzuY3biy/gJQNGMz0UB\nnCai1BzsY7ZBRGsAnPVQJS8/97yOP7PPvWAL8fwYLKYCgEOG7cOwf1nWOkcc6oQqvozfyKMAFmVr\nj3IWr+NXSpUH0IOIPgHHZcgr+PLdVwdQXCm1Uin1h1Lq4RzrXfbjy/gnAqillDoKYDOAp3Oob7mB\nvPzc8xefn3te/cQDxUuwGCuuVnYZwWK+B/B0xoxcyOMopdoA6AtWQ+UnPgIvL2nkJUHujQgADQC0\nBVAYwK9KqV+JaG9wu5VjdASwkYjaKqVuALBUKVVXnnn5B3+fe9kuxCmHg8WEAEcAVDJsX5exz1qn\nopc6oYov44dSqi6AKQA6EZEnFVyo4cv4GwGYpZRS4HXRzkqpFCKan0N9zC58GfthAKeI6CqAq0qp\n1QDqgdeSQx1fxt8XwFsAQER/K6X2AagJYEOO9DC45OXnnk9k5rkXbHV6fgwW8weAG5VSlZVSBQA8\nAL4PRuYDeAQAlFJNAZzTlh3yAF7Hr5SqBGAOgIeJ6O8g9DE78Tp+Iro+o1QFv7wOyAMCHPDttz8P\nQAulVLhSqhDYwGlnDvczu/Bl/AcA3AEAGevB1QH8k6O9zF4U3DVLefm5p+E6/sw+97J9Ju6FsQBm\nK6X6ISNYDAA4BIt5EMBWpdRGhHiwGCJKU0oNArAE/BI1jYh2KqUe58M0hYh+Ukp1UUrtBXAZ/Hae\nJ/Bl/ABGASgOYFLGbDSFiBoHr9dZh4/jN52S453MJnz87e9SSi0GsAVAGoApRLQjiN3OMnz87scA\n+MzghvQ8EZ0JUpezFKXU1wBaAyihlDoI4BUABZAPnnuA9/Ejk889CfYiCIIgCCFKsNXpgiAIgiBk\nEhHigiAIghCiiBAXBEEQhBBFhLggCIIghCgixAVBEAQhRBEhLgiCIAghighxQRAEQQhR/g/urYbh\nkZxRJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6d2ba590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mds3 = manifold.MDS(n_components=2, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
    "results3 = mds3.fit(dist_mat)\n",
    "coords3=results3.embedding_\n",
    "d = distCompare(dist_mat,coords3)\n",
    "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}