analyzeMonomers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings,seaborn\n",
    "warnings.warn = warn\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from seaborn import distplot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import load,save, where,sort,array,append, hstack,mean,array_equal,argsort,std as std_deviation\n",
    "from operator import itemgetter as item\n",
    "import pandas as pd\n",
    "import numpy\n",
    "import pickle\n",
    "import os.path\n",
    "from operator import itemgetter\n",
    "from utility import *\n",
    "import seaborn as sns\n",
    "from seaborn import distplot\n",
    "from random import randint\n",
    "from sklearn.utils import shuffle\n",
    "from math import sqrt,log10,floor\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem.rdchem import ResonanceMolSupplier as resonanceMol\n",
    "from rdkit import DataStructs\n",
    "from rdkit.Chem.Fingerprints import FingerprintMols\n",
    "from scipy.stats import ttest_ind as TTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def return_true(self, other):\n",
    "    return True\n",
    "Chem.Mol.__eq__ = return_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('allFunctionals.csv','|')\n",
    "molsCsv = []\n",
    "for SMILE in data['Unnamed: 1'][1:]:\n",
    "    molsCsv += [Chem.MolFromSmiles(SMILE)]\n",
    "HOMOCsv = []\n",
    "for item in data['B3LYP'][1:]:\n",
    "    HOMOCsv += [float(item)]\n",
    "datFileID = []\n",
    "for item in data['Unnamed: 0'][1:]:\n",
    "    datFileID += [int(item)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadNumpy(name,path='.'):\n",
    "    if \".npy\" in name:\n",
    "        fullPath = path+'/'+name\n",
    "    else:\n",
    "        fullPath = path+'/'+name+'.npy'\n",
    "    return load(fullPath)\n",
    "\n",
    "def saveNumpy(obj, name, path='.'):\n",
    "    if \".npy\" not in name:\n",
    "        fullPath = path+'/'+name\n",
    "        save(fullPath, obj)\n",
    "        print name,'saved successfully in',path\n",
    "    else:\n",
    "        fullPath = path+'/'+name.split(\".npy\")[0]\n",
    "        save(fullPath, obj)\n",
    "        print name,'saved successfully in',path\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def crossVal_Diff_Arrange(Type,mols,estimator):\n",
    "    y = HOMO\n",
    "    if Type=='maccs':\n",
    "        X = maccs\n",
    "    elif Type == 'atompair':\n",
    "        X = atom\n",
    "    \n",
    "    for i in range(2):\n",
    "        X,y,mols = shuffle(X,y,mols)\n",
    "    \n",
    "    predicted, predictedRepeat = [],[]\n",
    "    for i in range(3):\n",
    "        predictedRepeat += [cross_val_predict(estimator, X, y, cv=5)]\n",
    "\n",
    "    for i in range(len(X)):\n",
    "        predicted += [(predictedRepeat[0][i]+predictedRepeat[1][i]+predictedRepeat[2][i])/3]\n",
    "\n",
    "    All = []\n",
    "    for i in range(len(X)):\n",
    "        All += [[abs(y[i]-predicted[i])]+[predicted[i]]+[y[i]]+X[i].tolist()]\n",
    "        \n",
    "    AllSorted,molsSorted = [list(x) for x in zip(*sorted(zip(All, mols), key=itemgetter(0)))]\n",
    "    diff, actualY, predictedY, actualX = [],[],[],[]\n",
    "    for i in range(len(X)):\n",
    "        diff += [AllSorted[i][0]]\n",
    "        predictedY += [AllSorted[i][1]]\n",
    "        actualY += [AllSorted[i][2]]\n",
    "        actualX += [AllSorted[i][3:]]\n",
    "        \n",
    "#     testMol = molsSorted[0]\n",
    "#     molsAdjusted = []\n",
    "#     predictedAdjusted,actualAdjusted = [],[]\n",
    "#     actualXAdjusted = []\n",
    "#     diffAdjusted = []\n",
    "    \n",
    "#     for mol in molsSorted:\n",
    "#         index = returnLocationInCsv(mol)\n",
    "#         molsAdjusted += [molsSorted[index]]\n",
    "#         diffAdjusted += [diff[index]]\n",
    "#         actualAdjusted += [actualY[index]]\n",
    "#         predictedAdjusted += [predictedY[index]]\n",
    "#         actualXAdjusted += [actualX[index]]\n",
    "        \n",
    "        \n",
    "#     return diffAdjusted, predictedAdjusted, actualAdjusted, actualXAdjusted, molsAdjusted\n",
    "    return diff, predictedY, actualY, actualX, molsSorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def trainTest_Diff(Type,mols,estimator):\n",
    "    y = HOMOTrain80\n",
    "    if Type=='maccs':\n",
    "        X = maccsTrain80\n",
    "    elif Type == 'atompair':\n",
    "        X = atomTrain80\n",
    "    \n",
    "    for i in range(2):\n",
    "        X,y,mols = shuffle(X,y,mols)\n",
    "    \n",
    "    predicted, predictedRepeat = [],[]\n",
    "    for i in range(3):\n",
    "        predictedRepeat += [cross_val_predict(estimator, X, y, cv=5)]\n",
    "\n",
    "    for i in range(len(X)):\n",
    "        predicted += [(predictedRepeat[0][i]+predictedRepeat[1][i]+predictedRepeat[2][i])/3]\n",
    "\n",
    "    All = []\n",
    "    for i in range(len(X)):\n",
    "        All += [[abs(y[i]-predicted[i])]+[predicted[i]]+[y[i]]+X[i].tolist()]\n",
    "        \n",
    "    AllSorted,molsSorted = [list(x) for x in zip(*sorted(zip(All, mols), key=item(0)))]\n",
    "    diff, actualY, predictedY, actualX = [],[],[],[]\n",
    "    for i in range(len(X)):\n",
    "        diff += [AllSorted[i][0]]\n",
    "        predictedY += [AllSorted[i][1]]\n",
    "        actualY += [AllSorted[i][2]]\n",
    "        actualX += [AllSorted[i][3:]]\n",
    "        \n",
    "        \n",
    "    return diff, predictedY, actualY, actualX, molsSorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extractMonomerHOMO():\n",
    "    data = open('HOPV_15.data').readlines()\n",
    "    lineNums = []\n",
    "    for i in range(len(data)):\n",
    "        if 'InChI' in data[i]:\n",
    "            lineNums += [i-5]\n",
    "    endLineNums = []\n",
    "    for lineNum in lineNums[1:]:\n",
    "        endLineNums += [lineNum]\n",
    "\n",
    "    endLineNums += [len(data)-1]\n",
    "\n",
    "    count = 0\n",
    "    DFT = []\n",
    "    molecule = []\n",
    "    for i in range(len(data)):\n",
    "        if i == endLineNums[count]+1 or i ==len(data)-1:\n",
    "            DFT += [molecule]\n",
    "            count += 1\n",
    "            molecule = []\n",
    "        if 'B3LYP' in data[i]:\n",
    "            molecule += [float(data[i].split('DFT,')[1].split(',')[0])]\n",
    "    \n",
    "    count =0\n",
    "    adjustedMonomer = []\n",
    "    for i in range(350):\n",
    "        if i == datFileID[count]-1:\n",
    "            adjustedMonomer += [DFT[i]]\n",
    "            count +=1\n",
    "            \n",
    "        \n",
    "    return adjustedMonomer\n",
    "\n",
    "def returnLocationInCsv(testMol):\n",
    "    testFP = FingerprintMols.FingerprintMol(testMol)\n",
    "    for i in range(len(molsCsv)):\n",
    "        mol = molsCsv[i]\n",
    "        fp = FingerprintMols.FingerprintMol(mol)\n",
    "        if DataStructs.FingerprintSimilarity(fp, testFP) == 1:\n",
    "            return i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HOMO = loadNumpy('HOMO')\n",
    "atom = loadNumpy('AtomPairCount')\n",
    "topo = loadNumpy('TopologicalCount')\n",
    "morgan = loadNumpy('MorganCount')\n",
    "maccs = loadNumpy('maccsCount')\n",
    "mols = loadNumpy('mols')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sorting by argsort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sortedIndices = argsort(HOMO)\n",
    "\n",
    "HOMOSorted = []\n",
    "atomSorted = []\n",
    "topoSorted = []\n",
    "morganSorted = []\n",
    "maccsSorted = []\n",
    "molsSorted = []\n",
    "\n",
    "for i in sortedIndices:\n",
    "    HOMOSorted += [HOMO[i]]\n",
    "    atomSorted += [atom[i]]\n",
    "    topoSorted += [topo[i]]\n",
    "    morganSorted += [morgan[i]]\n",
    "    maccsSorted += [maccs[i]]\n",
    "    molsSorted += [mols[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HOMOSorted saved successfully in .\n",
      "atomSorted saved successfully in .\n",
      "topoSorted saved successfully in .\n",
      "morganSorted saved successfully in .\n",
      "maccsSorted saved successfully in .\n",
      "molsSorted saved successfully in .\n"
     ]
    }
   ],
   "source": [
    "saveNumpy(HOMOSorted,'HOMOSorted')\n",
    "saveNumpy(atomSorted,'atomSorted')\n",
    "saveNumpy(topoSorted,'topoSorted')\n",
    "saveNumpy(morganSorted,'morganSorted')\n",
    "saveNumpy(maccsSorted,'maccsSorted')\n",
    "saveNumpy(molsSorted,'molsSorted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainIndices, testIndices = loadNumpy('trainIndices'), loadNumpy('testIndices')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HOMOTrain80 = loadNumpy('trainHOMO80')\n",
    "atomTrain80= loadNumpy('trainAtom80')\n",
    "topoTrain80= loadNumpy('trainTopo80')\n",
    "morganTrain80= loadNumpy('trainMorgan80')\n",
    "maccsTrain80= loadNumpy('trainMaccs80')\n",
    "molsTrain80= loadNumpy('trainMols80')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "atom80 = ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
    "          max_features=0.33, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
    "          min_samples_leaf=1, min_samples_split=2,\n",
    "          min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=-1,\n",
    "          oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
    "\n",
    "maccs80 = ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=25,\n",
    "          max_features=0.33, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
    "          min_samples_leaf=1, min_samples_split=2,\n",
    "          min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=-1,\n",
    "          oob_score=False, random_state=None, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best and Worst Molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "worstMols,bestMols = molsSorted[:5], molsSorted[-5:]\n",
    "worstAtom, bestAtom = atomSorted[:5], atomSorted[-5:]\n",
    "worstMaccs, bestMaccs = maccsSorted[:5], maccsSorted[-5:]\n",
    "worstMorgan, bestMorgan = morganSorted[:5], morganSorted[-5:]\n",
    "worstTopo, bestTopo = topoSorted[:5], topoSorted[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There is not much difference in the level of conjugation (number of conjugated bonds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -0.174133\n",
      "1 -0.174056\n",
      "1 -0.174\n",
      "1 -0.173929\n",
      "1 -0.173889\n",
      "1 -0.173267\n",
      "1 -0.171118\n",
      "1 -0.17075\n",
      "4 -0.167\n",
      "1 -0.164667\n"
     ]
    }
   ],
   "source": [
    "for i in range(-10,0):\n",
    "    mol = molsSorted[i]\n",
    "    print resonanceMol(mol).GetNumConjGrps(), HOMOSorted[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Worst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -0.24375\n",
      "1 -0.239\n",
      "1 -0.238\n",
      "1 -0.237211\n",
      "1 -0.234125\n",
      "1 -0.232\n",
      "2 -0.230429\n",
      "1 -0.23\n",
      "1 -0.22825\n",
      "1 -0.222\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    mol = molsSorted[i]\n",
    "    print resonanceMol(mol).GetNumConjGrps(), HOMOSorted[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjugation for the best and worst predicted (again no difference based on conjugation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff_atom, predictedHOMO_atom, actualHOMO_atom, actual_atom,mols_atom = crossVal_Diff_Arrange('atompair',mols,atom80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff_maccs, predictedHOMO_maccs, actualHOMO_maccs, actual_maccs,mols_maccs = crossVal_Diff_Arrange('maccs',mols, maccs80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NUM = 20\n",
    "worstPredictAtom, bestPredictAtom = actual_atom[-NUM:], actual_atom[:NUM]\n",
    "worstPredictMaccs, bestPredictMaccs = actual_maccs[-NUM:], actual_maccs[:NUM]\n",
    "\n",
    "worstMols_Atom, bestMols_Atom = mols_atom[-NUM:], mols_atom[:NUM]\n",
    "worstMols_Maccs, bestMols_Maccs = mols_maccs[-NUM:], mols_maccs[:NUM]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -0.184462 -0.184475733472\n",
      "2 -0.187889 -0.187921427048\n",
      "1 -0.192 -0.191939656694\n",
      "1 -0.199063 -0.198991715556\n",
      "1 -0.186 -0.186075663972\n",
      "2 -0.189071 -0.188987045375\n",
      "1 -0.2 -0.200088690889\n",
      "1 -0.188462 -0.188552785433\n",
      "1 -0.192421 -0.192520972167\n",
      "1 -0.184389 -0.184275054139\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    mol = mols_atom[i]\n",
    "    print resonanceMol(mol).GetNumConjGrps(), actualHOMO_atom[i], predictedHOMO_atom[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Worst Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -0.237211 -0.199166737778\n",
      "1 -0.234125 -0.198197342556\n",
      "2 -0.230429 -0.203220642694\n",
      "4 -0.167 -0.18965383571\n",
      "1 -0.212077 -0.1911161545\n",
      "1 -0.201 -0.220816817778\n",
      "1 -0.24375 -0.224078131667\n",
      "1 -0.239 -0.219399233333\n",
      "1 -0.222 -0.203169153889\n",
      "1 -0.22825 -0.209478674028\n"
     ]
    }
   ],
   "source": [
    "for i in range(-1,-11,-1):\n",
    "    mol = mols_atom[i]\n",
    "    print resonanceMol(mol).GetNumConjGrps(), actualHOMO_atom[i], predictedHOMO_atom[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the distribution for the ones which are predicted well or not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "monomerHOMO = extractMonomerHOMO()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Worst Predicted Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "worstOrgIndicesAtom = []\n",
    "for i in range(-1,-21,-1):\n",
    "    worstOrgIndicesAtom += [returnLocationInCsv(mols_atom[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestOrgIndicesAtom = []\n",
    "for i in range(20):\n",
    "    bestOrgIndicesAtom += [returnLocationInCsv(mols_atom[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "worstOrgIndicesMaccs = []\n",
    "for i in range(-1,-21,-1):\n",
    "    worstOrgIndicesMaccs += [returnLocationInCsv(mols_maccs[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestOrgIndicesMaccs = []\n",
    "for i in range(20):\n",
    "    bestOrgIndicesMaccs += [returnLocationInCsv(mols_maccs[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "worstOrgIndicesAtom saved successfully in .\n",
      "worstOrgIndicesMaccs saved successfully in .\n",
      "bestOrgIndicesAtom saved successfully in .\n",
      "bestOrgIndicesMaccs saved successfully in .\n"
     ]
    }
   ],
   "source": [
    "saveNumpy(worstOrgIndicesAtom,'worstOrgIndicesAtom')\n",
    "saveNumpy(worstOrgIndicesMaccs,'worstOrgIndicesMaccs')\n",
    "saveNumpy(bestOrgIndicesAtom,'bestOrgIndicesAtom')\n",
    "saveNumpy(bestOrgIndicesMaccs,'bestOrgIndicesMaccs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 -0.184461538462 0.000498518515262\n",
      "9 -0.187888888889 0.000314269680527\n",
      "18 -0.192 2.77555756156e-17\n",
      "16 -0.1990625 0.000242061459138\n",
      "16 -0.186 0.000612372435696\n",
      "14 -0.189071428571 0.000257539376819\n",
      "2 -0.2 0.001\n",
      "13 -0.188461538462 0.00230769230769\n",
      "19 -0.192421052632 0.00311728271473\n",
      "18 -0.184388888889 0.000825892708184\n"
     ]
    }
   ],
   "source": [
    "for i in bestOrgIndicesAtom:\n",
    "    print len(monomerHOMO[i]),mean(monomerHOMO[i]),std(monomerHOMO[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 -0.167 0.0\n",
      "4 -0.22825 0.000829156197589\n",
      "1 -0.232 0.0\n",
      "4 -0.201 0.001\n",
      "4 -0.24375 0.00178535710714\n",
      "13 -0.212076923077 0.0015423028966\n",
      "16 -0.222 0.0\n",
      "3 -0.178 2.77555756156e-17\n",
      "8 -0.196625 0.000484122918276\n",
      "18 -0.198888888889 0.000458122847291\n"
     ]
    }
   ],
   "source": [
    "for i in worstOrgIndicesAtom:\n",
    "    print len(monomerHOMO[i]),mean(monomerHOMO[i]),std(monomerHOMO[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAIYElEQVR4nO3dW3qjOhYGUNxfzyjz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\nACFiFCBEjAKEiFGAkH8BJwKZzumGjocAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x117733210>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molsCsv[280]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAI0UlEQVR4nO3dW5ajNhQFUMjKjDrz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\nEDEKEESMAgQRowBB/gdOmWIgO/BMuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x11865f7c0>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molsCsv[281]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Max = 0\n",
    "for HOMO in HOMOCsv:\n",
    "    if len(str(HOMO))-3>Max:\n",
    "        Max = len(str(HOMO))-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count = 0\n",
    "for HOMO in HOMOCsv:\n",
    "    for homo in avgHOMO:\n",
    "        if homo == HOMO:\n",
    "            count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-10: 241,\n",
       " -9: 222,\n",
       " -8: 20,\n",
       " -7: 143,\n",
       " -6: 189,\n",
       " -5: 213,\n",
       " -4: 89,\n",
       " -3: 218,\n",
       " -2: 53,\n",
       " -1: 122}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worstOrgIndicesAtom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n",
      "8\n",
      "18\n",
      "15\n",
      "16\n",
      "12\n",
      "10\n",
      "15\n",
      "11\n",
      "9\n",
      "15\n",
      "12\n",
      "1\n",
      "15\n",
      "18\n",
      "18\n",
      "19\n",
      "6\n",
      "13\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    print len(monomerHOMO[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avgHOMO = []\n",
    "for i in range(len(monomerHOMO)):\n",
    "    avg = mean(monomerHOMO[i])\n",
    "    avgHOMO += [float(\"{:.6f}\".format(avg))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"blue\"> Evaluate if the standard deviation of both are statistically significant </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00088167099872911848"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_deviation(monomerHOMO[bestOrgIndicesAtom[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\"> T Test on standard deviation for monomers for best and worst predicted (Atom Pair) </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestStd,worstStd = [],[]\n",
    "bestMean, worstMean = [],[]\n",
    "numBest, numWorst = [],[]\n",
    "for i in range(20):\n",
    "    bestStd += [std_deviation(monomerHOMO[bestOrgIndicesAtom[i]])]\n",
    "    bestMean += [mean(monomerHOMO[bestOrgIndicesAtom[i]])]\n",
    "    numBest += [len(monomerHOMO[bestOrgIndicesAtom[i]])]\n",
    "    \n",
    "    worstStd += [std_deviation(monomerHOMO[worstOrgIndicesAtom[i]])]\n",
    "    worstMean += [mean(monomerHOMO[worstOrgIndicesAtom[i]])]\n",
    "    numWorst += [len(monomerHOMO[worstOrgIndicesAtom[i]])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=1.4703504705411439, pvalue=0.14969914654521016)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TTest(bestStd,worstStd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.3527849947586734, pvalue=0.0018211963319228425)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TTest(bestMean,worstMean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.8601371526163999, pvalue=0.00042663720581077462)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TTest(numBest,numWorst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"green\"> T Test on standard deviation for monomers for best and worst predicted (Maccs) </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestStd,worstStd = [],[]\n",
    "bestMean, worstMean = [],[]\n",
    "numBest, numWorst = [],[]\n",
    "for i in range(20):\n",
    "    bestStd += [std_deviation(monomerHOMO[bestOrgIndicesMaccs[i]])]\n",
    "    bestMean += [mean(monomerHOMO[bestOrgIndicesMaccs[i]])]\n",
    "    numBest += [len(monomerHOMO[bestOrgIndicesMaccs[i]])]\n",
    "    \n",
    "    worstStd += [std_deviation(monomerHOMO[worstOrgIndicesMaccs[i]])]\n",
    "    worstMean += [mean(monomerHOMO[worstOrgIndicesMaccs[i]])]\n",
    "    numWorst += [len(monomerHOMO[worstOrgIndicesMaccs[i]])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.7156346853481124, pvalue=0.094373139898495884)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TTest(bestStd,worstStd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.5199980314714909, pvalue=0.0011386786215529909)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TTest(bestMean,worstMean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.4704671124630062, pvalue=0.018095206117606469)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TTest(numBest,numWorst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"purple\"> Investigating individual monomer distributions </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bc7f610>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8fHsKBmwubNpzUexyyIswqInIa3T2WlFaZYQmUIn8SePFLuem3T49FsnRwSg614aymi6x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\nIiIJY1ATERFJGIOaiIhIwv4/610uwAipZ8MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af3dcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[bestOrgIndicesAtom[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118a2fad0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Foik98z9j//bfr7ZLkj69dL4k6f8b/frNth65nA4tubhOyy6dKUl6Y3+PDnYO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\nAGAzBG8AAGzm/wdipRyAoVQTewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ab00d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[bestOrgIndicesAtom[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aaabbd0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YIcIAABghwgAAGCHCAAAYIcIAABghwgAAGLmnCF++fFl1dXVfuL2trU3hcFiRSEQnT55M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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aabf210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[bestOrgIndicesAtom[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bb6f7d0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tLSUQCOD1eikvLycnJ4e8vDx27twZ3XfatGm4XC7sdjsVFRWYpklxcTH5+fnd\nOT0REZE+c26Vtvgfu1st9dmzZ/O1r32NgoICLBYLzzzzDIMHD+Yb3/gGzz33HOPGjWPWrFlYrVbm\nz59PYWEhpmny5JNP4nA4KCgoYOnSpRQUFGC321m3bh0Aq1atYvHixYRCITweD1OmTInpxYqIiPS2\nvuz9bjEjM88PULEuC6nU1HOKYWwojj2nGPacYvjhHTxWx/c2l3GnZyz/4hnb/8vvIiIicnF9OfmM\nkrqIiEgMGUb4Zx/kdCV1ERGRWDK1SpuIiEhiiJbfldRFREQGtmj5XffURUREBrZz5ff4H1tJXURE\nJIbOTT6jlrqIiMiA1peTzyipi4iIxJBa6iIiIgki0lGuD26pK6mLiIjEkqmWuoiISGIwNPmMiIhI\nYujoJ4elDzKskrqIiEgMqfe7iIhIglD5vR8yTJOqWn9fn4aIiAwAZxtaaA+Fu72bhjrK9Tt7Dp7h\n6y++yZFTjX19KiIi0o+dbWhh2Y/e4I9vnwTO3VNXS70fOV3XDMCZjp8iIiIXU93QgmGa0bxhRldp\ni/+5KKlfQnOgvctPERGRi2lp7ZovNKNcP9QceZNaldRFROTS/K1tADR3/FTv935ISV1ERD6IaGW3\nNdJSD2/X0qv9yLnye1sfn4mIiPRnzeeX39X7vf9pjpZT1FIXEZFLO7+ya0Q7yimp9xvnl1NEREQu\npnO+ME1THeX6o/PLKSIiIhfTuYUeaAthdiy9qo5y/UTIMGgNhoBzZXgREZGL6ZwnmlvbO5Xf438u\nSuoX0RIIRf+tlrqIiFxO5zzROamrpd5PdP7W1RIIRXsyioiInK9z36vmQPu58nsf3FO3dedJbW1t\nLFu2jFOnThEMBnn00Ue5+uqr+fKXv8w111wDQEFBAV/4whfYsmULmzdvxmaz8eijj3LrrbfS2trK\nkiVLqK2txel0snbtWjIzMykrK2PNmjVYrVY8Hg8LFy6M5bV+YOe3zpsD7bhS7X1yLiIi0r/5Wy/V\nUo//uXQrqb/22mtkZGTw7LPP0tDQwJ133sljjz3Ggw8+yEMPPRTdr7q6mo0bN7Jt2zYCgQCFhYVM\nnz6dTZs2kZOTw6JFi9i+fTtFRUUsX76clStXsn79ekaPHs2CBQs4ePAgkyZNitnFflD+ViV1ERG5\nsrb2UHR1NgjPbTLger9/7nOf49/+7d+A8MT1VquVd955hx07dnDfffexbNkyfD4f+/fvZ+rUqSQn\nJ+N2u8mM/BC4AAAcTklEQVTOzubw4cOUlpYyY8YMAGbOnElJSQk+n49gMEh2djYWiwWPx8Pu3btj\nd6UfQmQeX2vHG9KiYW0iInIRzeflC39re59OE9utlrrT6QTA5/Px+OOP88QTTxAMBpkzZw6TJ0/m\nhRde4Pnnn2fixIm43e4uz/P5fPh8vuh2p9OJ1+vF5/Phcrm67HvixIkrnsvgwWnYbNbuXMYlJdnD\nYRmWmUZVjR97ip2sLPcVniWdKV6xoTj2nGLYc4rhpbV2NNIj+cJitZLsCOeQoUNdZA0J58t4xbBb\nSR2gqqqKxx57jMLCQm6//XaamppIT08H4LbbbmP16tXk5+fj9/ujz/H7/bjdblwuV3S73+8nPT29\ny7bO26+kvj62S6NmZbk5U+MFYLArmaoaP5WnmxiRkRLT4ySyrCw31dXevj6NAU9x7DnFsOcUw8s7\nWdUInMsXNXV+mpvDna3r6/1YDSPmMbzcF4Ruld9ramp46KGHWLJkCbNnzwbg4YcfZv/+/QCUlJRw\nww03kJubS2lpKYFAAK/XS3l5OTk5OeTl5bFz504Adu3axbRp03C5XNjtdioqKjBNk+LiYvLz87tz\nej0WKacMHRRO5BrWJiIiF3NBvmhtx2SAld9/+MMf0tTURFFREUVFRQA89dRTPPPMM9jtdoYOHcrq\n1atxuVzMnz+fwsJCTNPkySefxOFwUFBQwNKlSykoKMBut7Nu3ToAVq1axeLFiwmFQng8HqZMmRK7\nK/0QIkl86KDU8GPdUxcRkYuILPoVyRf+1jbstnB7uS/mfu9WUl++fDnLly+/YPvmzZsv2Hbvvfdy\n7733dtmWmprKD37wgwv2vfHGG9myZUt3TimmIh3jhmZEWuqaVU5ERC4UafQN6WiptwTasVqTgQHU\n+z3R+VvVUhcRkSuL5Ad3qp2UZGu4/G703Th1JfWLaA60YU2ykOl2dDxWUhcRkQtF8kNqig1nii08\npG2gjVNPdM2t7aQ6bKSl2KKPRUREzheZVtyZYifVYac50Lfj1JXUL6K5tZ20FBsOu5Uki0VJXURE\nLiqSH9I6GoKtgXZCSur9S3OgnTSHDYvFQlqKTeV3ERG5qGj53WEjzWHD5Fy/LC292g8E20K0tRs4\nO0rvaQ4b/vPWVG/yBwm0hS72dBERSVBt7Qb13kCXbf7WdpJtSdhtSdG84W8J5wzdU+8HIm9Gakp4\nAZe0FFuXud8DbSG+/uIb/Oz/3uuT8xMRkb7x37uP8dSPSrok9paO27UQ7iwH4IskdZXf+17kzUjr\nmLs3LcVGsN2grT08wW91Qwv+1naOVjX12TmKiEj8Ha1qoq3d4FSNL7qtOdBOWqQR2JE3IiV5ld/7\nAX+0J+O58juEJxSAcFIHqGloxewYtiAiIomvpuPzv7qhFQivUtrc2h7NE86Uc0t0Wyx9M6Ockvp5\nIuX3SDkl8g0skuxrOt7MQFsIb7NmmhMR+SgwDJOaxvDnfyS5twZDGKbZKV+cm6S1L0rvoKR+AV/z\nheV3OFdOibTUz/+3iIgkrnpvIDpULfLZH6ngpp1X2YW+6SQHSuoXiLTIz79HEuksp6QuIvLR0/Wz\nP9xi7zxGHdRS75cuLL93DFGIJPWO8gsoqYuIfFRcrEF3rhHY9XYt9E0nOVBSv8Dlyu+maVLd0EJq\nx+8i39ZERCSxVTeGE3mqIzwhmb+1LXpbNs3RtbILaqn3Gxd88+p4s5pb22j0B2lrN7hudAYW1FIX\nEfmoiDTiJmZndDxuOVd+v1hHOd1T7x+i49Q7TT4D4XsnkSR+9ZA0Bqc7qGnsmtRrG1t55/3aOJ6t\niIjE2rsV9VTV+rtsq2lowZpkIWd0JKm3XnBPPSXZGi2791FOV1I/X/SeusMKnBuv3hxojw5nG5qR\nytBBqdQ1BWgPGdHn/vyP7/Hcln3R4Q4iIjKwtAbbWfeLMl76n4Ndtlc3tDBkUArDBqcC4SQfKb9H\n8oTFYokmeIta6v2Dr6UNuy0Juy2c1KMzBHVqqWdlpJCVkYJJuHUO4UkI/nGyEYAjpxrjf+IiItJj\nR6u8tIdMjp/2EQiG1/hoDbbT1NxGVkYqWRnhpN61/H6ug1ykuqt76v2Ev6Wty32Rc+X3tk5Jvesb\nC3C2viVaui8/pSlkRUQGovKORplhmhw7Hf4sj1RpszJSyRrUOalH1grpnDPCCV5JvZ/wt7R16cFo\nt1mxWZNoDoRb6hYLDElPuSCpd26dH6lUS11EZCAq7/xZ3vHvzlVaR7KVdGdy+J56oOs99c7/Tuqj\n7Kqk3olpmvjOa6lD+H5Jc2s71Y2tZLpTsFmTOiX18De48srwN7o0h42TZ31dlmYNtIV4dUe57rWL\niPQT3uYgW/50JFphhXAOKK9siibmSNU1mtQ7WulZGSnUNrXiPW8BMDhX3e2Led9BSb2LQFsIwzC7\nTMoP4Tep0R+k3hsgKyMF4IKWevmpRuy2JD45eTghw+RYp1Xcdr9zmt++cZzX/nosPhciIiKX9Ye3\nTvD7PRX88e0T0W2R26iTx2WSme6gvLKxY36Sc+X3yM+QYXKq2k+qw9pl+JpT99T7j/OHJ0SkOWy0\ndnSYiLyp6Wl2ku1JVDe20Bps52S1j2uGu7muY7hDpOUO8PbhswD87R/VXXrLi4hI/JmmGf1cfuvw\n2eiKm5Fy+/iRgxg/YhDe5jaqG1ujE89Ek3pHi70l0H6RfNFxT1293/teJKmnnld+79yzcWjHm2qx\nWMgalEp1QwtHq7yYZvgPYdyIdODcfZkmf5DDFfVAeKrZg8fqu7z2//v9Yf7f7w/3zgWJiHzE/WrX\n+zz/q79jdFoq+8RZH2fqw4m6qraZUzXhMemRxtj4EYMY3+mzvLqhBWeKLVpaH9pRsYWu+QHO5Q+N\nU+8Hzh9zGNH5HntWpzczKyOVlkCI/eU1QPgPITM9hcFuB+WnwmWb0veqMU3Ivy4LgLcOn4k+v7yy\nkR1llewoq+T9yq495k3TpN4biO0FiogkqEZfgJDRtRJa09DC9pLjlL5bTdk/aqLb3+popUc+lyOt\n9sht1OyrXIwfOQgIt95rGlujDTqAYZ3+fX5Lva/L77Yr7/LRca78ft49dUfnpH7uzYx8W3vzYDhR\nXzsy/M1u/MhBvH34LDWNrbx1KPy7uf/fBMorm/jbezW0f87AZk3id29URF/rd28c57G7PxZ9/Ku/\nvM/23cdZdE8uN04Y2uV8DMPss9KOiPRvO8pOxfT13K4UvL7+s86FYZoXJMzqhhb+980Kxo5IZ/rH\nro5uf/PgmWgLffPr/6DRH24o7dpXic1qYdzIdP72jxp2llXiTLVz8qyPoRmpFP+9ipBhkGSx8MaB\nM7S1h78sRGLr79S5rjnQ3iXmx057w/u0tkW3z7ltYqzDcElqqXfSHOg673tE15Z66gX/bvAFGToo\nhUEuBwDXdpRt/vZeNe+eaGD8yHSGDEoh/7phNAfaOXisjsoaP3vfq2bs1emMvdrN3veqo9MSnjzr\n43dvVGACG//wbnTNXoCqWj+Li/7KK789FL0PFNESaOdsfXOMoiEi0jf8LW20Btsv2P6396rZ+qdy\nquvPjSQyDJM3DpzBMMO91SOfoy2Bdo6cbMSZYmNUlpOaxlbO1LVQ7w3gbW5jZJaLlGQbI7OcNPqD\nHDnViMm5aqw1KYkhgxzRz19X6rnGXmqKLfrFItnWNY0m28OP1fu9H/BfqqNcR1J3JFtxd3pjOyf4\nyL10IFq2+Z+S45gmfHziVQB8/PphALx16Cy/fzPcSv/CzWP4ws1jMIHfvVmBYZr81/8eJmSY3HDN\nYOq9AX5TfBQI/5Fu+OXfafAF+cv+Kv53z7lem3VNrax65S2W/fjNaCkpoqrWz39uP3TRme7ONrSo\nzC8ivaY12E6D78LPmAZvgL/+veqCNTRO1zbz678c5bXiY12e935lE39/v45AW4gdZaeildVDx+up\n9wYYnpmGBXjjwBlCIYPDFQ3hz9GxmXxs/BAA3jlaG21JXzPc3eXn/iPhdTsu1nADcKed++xPslhw\npYbzQrLd2uX8HR2zkfbV0qv9rvxuGAbf/OY3effdd0lOTubpp59mzJgxcTl2y3kr7kREknzWoNQu\n3746v+GRRA6QfZUbm9USHf8YuW8zbkQ6g90OSt+rpq3dYHhmGlNzwqX14ZlplLxzmgyXg/JTTXx8\n4jC+9MXr+cbLe/i/t0/wiUlX8d9/PUZVbTMzcq/m7+/XsnXHEUYPc3H1kDS++/O/cbZjwYEf/uYA\nXwHyJw5jf3ktP3rtAC2Bdt44eJoHZk3Ek3s1hmHyuzeP8+u/HMVqtTD31mv59NSRWCwWmvxBtu0s\n552jdXz+E9ncmjcSa8dMCsdON7FrXxVXD0nj0zeOiE6naxgm+47U4Gtt43PTx3WJX3NrG+WVTYwb\nkX7BcMG6plZstiTS05K7bDdMk0AwFF3mVkQurzXYjt12YTst2BYi0BbCfd7/sbZ2g7P1LWSmO7r8\nPwuFDI6f8WGxhD/LIgzD5P3KJs42tDBuRDrDM9OAcP+f9yub2HekliGDUph2XVa0VXu0qok3D54h\n2GYw6ZrBTJ0wFKs1iZNnffxlXxVtIYOjVV4+Nfkqxo0YxOnaZl4vPYlhmrQGQ/xhzwk+e9NoQiGT\nkndOY7clce3IQRw6Xs/OslNM/9jV7DtSQ0qylVtuHMH+8loOHa9n73s1lJ9qxGG3cu2oQdisSVyV\nmUplTTM1ja3YrBZGZjkBGJnlwppkic4tcmFSD3du7txSB3ClJdPU3BZtmUfYo491Tx2AP/7xjwSD\nQX7xi19QVlbGd77zHV544YW4HDs6O9Aler937iQHMHTQucfXdkrqdlsSY65yU17ZxLUjw53nIPzt\n7uMTh/GHt8It7M/fnB0t4Xz+E9m88rvD/M/uY6Q6rBR8ZgJ2m5UHZl3H9zaX8b3Nf6MlEOL6MYN5\n4HPXcazKy9qf7+WHv3kHZ4qdsw0t3P6pa/jYuCE8t6WMH/7mAJ96v5a//r0Ka1ISX/zUNfyp9CT/\n+dtDHDvdRMUZH0dONTLIlUx7u8HGP7zH347UMGlMJv+9+xgtgXYswM//+A927avkC58cw9uHq9n7\nXnX0On//ZgVf/OQYDBP+760TnO0Ys7/lT0eYOWUEk67J5I2Dp3nr0FmC7QZ2WxL51w3D87HhVNY2\n88aB05RXNpFksXDD2Ew+OfkqBqUl8/Z71ex9t5pGf5DxI9KZdt0wJo7JoPxUE/uO1HC4ooFhg1OZ\ncu0QpowfSqAtxDvv13HwWB3B9hDXj8lk8thMhg1O5R8nGzl8vJ6TNX6yh7mYOGYwE0YOorqxhX+c\naOTIqUZSkq3kjM4gZ3QGyfYkjpwMb2/yB7lmeDrjRw5ixNA0TlX7eb+qiZNnfWSmpzBuRDrjrk6n\nJdjOsSovx043YZgw5io3Y692405L5sRZH8dPe6lpbGH4ECdjrnIzKstJXVMrx8/4OFXtI8VhI/sq\nF9nD3Fgs4Z65J876MC0WMl3JjB7mYsigFM7UNXOy2s/pumYy3Q5GZbkYmeWkOdDOqWo/J6t9WAh/\nWI3McpLmsFFZ08ypGh8N3gDDBqcxapiTYYPTqG9qDb9WbTNpKeES5MihTgwTTtX4OFXtJ9AWYsQQ\nJyOGOslwOzhT18ypaj/VjS1kuh2MHOri6qFp+FraOFXtp7LGjzXJwoihTkZluUi2J1FZ4+dUtZ/G\n5iDDB6cxIstJVkYqtQ0tnKwJH9+Vau84Zxft7QYnq8PHD7aHGDk0vH2QM5nKWj8nz/qobmwla1AK\no4a5GDHESYMvwImzPk5W+7Hbkhg9zMXoYS7stiTeqWjgwJFqmvxBRgwNxz9rcCqna5s5ftrLyRo/\nGa5kxlzlZsxwN63BEEermjhW5SVkGIwZ7uaa4ekMciZz7HQT71c2caa+hauHpDFuRDrZw9xUN7RQ\nXtnI+5VNpCbbGD9yENeOGkSSxcJ7Jxp470QDjf4g40akc93oDK4eksb7VU0cPl7P0Sovwwancv2Y\nwVyXnUGDL8iB9+s4cKwO0zSZdE34b3mQy8H+8hr2HanlZLWPa0cOYsq1Q5k4JoP3K5t4+/BZ3j3R\nQJrDxtVDnGQPd0WT8MmzfgzTZLDbwbgR6QwdlMLRqiaOVnppCxkkWWD0VW6uHZlObWMrhysaokN4\nUx3VTJkwFKsF9pfX4m0ON1SOnGxk+JA0JowaxOHj9dFx3L6WNk6e9TF5XCZN/iBHq7zYrBZcqXYO\nHqunssbPyCwXB47WYU2ykDt+CIeO11O8/zSVNeH3xDRNbp06El9rG3sOnuUPe06QlGQhZJjMvHEE\no7KctAbbOVrlZXvJcdpDJjffkIUj2cqNE4Zy/IyXQ8fDifjGCUOxWcNJdvLYIZypO0mwzeCa4e7o\ndrstiVFZTo6f8XXp4Q5de7l3bql3fpxs69pSjzzuq25PFvP8G7N97Nvf/ja5ubn88z//MwAzZszg\nL3/5yyX3r672xuzYP/ndIXbtq+KumWO7fKutqvXzf2+d5Poxg6Ml9Iitfz5CsM1g3mcmYO30Lr51\n6CyHjteTPzGLSddknjvfhhZ+90YFaQ4bd90yLvqckGHyq53v0xxo56ZJw5iYPTj6nOL9Vbxf2YQz\nxcY/f2oMKcnhP7p/nGig5EC4I97Hxg/hxmuHYLFYOFvfwutvn6QtZJDqsHLr1JEMzUilyR/kz3tP\n0egPAjBmuJubJ11FyDDY/U74PxWE/8inThhK9lVuyo7UcOTkubL90EEpfGz8EM7Wt3D4eD0hI/zn\nk5RkYfyIdNJSbLx3orFLPwBXqp3Rw1ycrPZFPxQg/D12+JA0gu1GdGGcCIfdSrrTTk1DK+f/gQ7P\nTKO2qTXaeSUi2Z6ELSkp+uWsy+slW6OLM3SWZLF0Geoi0hsu9Xd2qb9LR7KVJAu0BLr+zppkYVjH\nl5LzX23s1W7qvAEafcEu2we5knGl2qmq8WN0elJaSvjL5OnaZho6PcduSyJndAaGYfKPkw20hzr+\nj1vg2lGDGD3MzcFjdVTVnuu/k32Vi/zrhnGmvpm971VHz3vooBQ8uVeT6rBR+m41751oCB/bYePW\nvJEMGZRCoy/In/aexNvcRpIFPj11JKOGuQA4fLyePYfCtxOnXDuEKdeGK5vtIYPfvVERLrsPSeO2\n/FHRKmrFGS87/hbuCHfPp8fj6CiPm6bJ9pLj1DUFuOXGEYwZfq4Kcey0l11llVwz3M3MG0d0id+r\nO8ppCbRz3205XTooHzhaR+m71Uz/2PAuldpQyOBn//cPhg9J47MfHw2EO8rFMldlZbkv+bt+l9S/\n/vWv89nPfpZbbrkFgE9/+tP88Y9/xGbrd0UFERGRfqXfdZRzuVz4/ecWpzcMQwldRETkA+h3ST0v\nL49du3YBUFZWRk5OTh+fkYiIyMDQ78rvkd7v7733HqZp8swzzzB+/Pi+Pi0REZF+r98ldREREeme\nfld+FxERke5RUhcREUkQCZ3UW1tbWbRoEYWFhTzyyCPU1dVdsM9PfvIT5syZw5w5c9iwYcNln1dW\nVsacOXOYN29edF+ADRs2MHv2bObNm8f+/fvjc3Fx9EHiCFBXV8esWbMIBMJTOzY0NPDII49QUFDA\no48+Sm1teBrG4uJi7rzzTgoKCigqKoo+/9vf/jazZ8/m3nvvpbS0tPcvLI7iFcNf/vKXzJkzh7vv\nvpvnn3++9y8sjuIVQ4CWlhbuuOOOaKfdRBGvGK5du5a5c+dyzz33sGXLlt6/sDiKVwy7nVfMBPaf\n//mf5g9+8APTNE3zf/7nf8zVq1d3+X1FRYV51113me3t7aZhGObcuXPNQ4cOXfJ5//Iv/2IeP37c\nNAzD/NKXvmQeOHDAfOedd8z58+ebhmGYp06dMu++++74XmQcXCmOpmmau3btMu+44w5z6tSpZmtr\nq2mapvmd73zHfOGFF0zTNM2//vWv5rJly8xQKGTecsstZkVFhWmapvkf//Ef5ltvvWUeOnTInDNn\njmkYhnn06FHzrrvuitPVxUc8Ynj8+HFz9uzZZktLixkKhczvf//7ZjAYjNMV9r54xDDiqaeeMu+4\n4w5z586dvX1ZcRWPGJaUlJhf/epXTdM0zUAgYH7mM58xGxoa4nF5cRGPGPYkryR0S720tJQZM2YA\nMHPmTEpKSrr8fvjw4bz00ktYrVYsFgvt7e04HI6LPs/n8xEMBsnOzsZiseDxeNi9ezelpaV4PB4s\nFgsjRowgFApd8pvbQHWlOAIkJSXxyiuvkJGREd125MgRZs6cCYSHKpaWllJfX096ejqjR4+Obt+7\ndy/Dhg0jJSWFYDCIz+dLuLkJ4hHD3bt3M3nyZJYuXcr9999PXl4edrv9guMMVPGIIcDLL7/M1KlT\nmTgxfstlxks8Yjh16lSeeeaZ6HNDoVBC/X+ORwx7klcSJtJbt27lv/7rv7psGzJkCG53eDo9p9OJ\n19t1mj673U5mZiamafLd736XSZMmMXbsWHw+3wXP8/l8uFyu6HOdTicnTpzA4XB0eeMi+2dmZjIQ\ndSeOANOnT79g2/XXX8+f/vQnJk2axJ/+9CdaW1vJzMyktbWV8vJyrrnmGnbt2sXEiROx2WwkJSXx\n+c9/Hq/Xy+rVq3vnAuOgr2IYCoV4++232bRpE4FAgMLCQm688UbS09MveN3+rq9iWFJSwvHjx/nW\nt74VTfIDVV/F0OFw4HA4aGtr46mnnmLu3Lk4nc7euche1lcxbG9v73ZeSZikHrkv3tnChQujs9P5\n/f6LfrgFAgGWLVuG0+lk5cqVQNdZ7SLPO3+mu8h2u91+wfbIGz4QdTeOF7NgwQLWrFnDfffdxy23\n3MLw4cOxWCx897vf5Zvf/CbJycnk5OQwePBgfv3rXzN06FBefvll/H5/NCENHz485tfY2/oqhsnJ\nydx00024XC5cLhfjxo3j2LFj5Obmxvwae1tfxfDVV1/l1KlTzJ8/n/fff58DBw6QlZXF9ddfH/Nr\n7G19FUOAxsZGHn/8cW666Sa+/OUvx/bC4qivYmixWLqdVxK6/J6Xl8fOnTsB2LVrF9OmTevye9M0\n+epXv8p1113Ht771LaxW6yWf53K5sNvtVFRUYJomxcXF5Ofnk5eXR3FxMYZhUFlZiWEYA7aVfilX\niuOlvP3228yZM4ef/exnjBkzhry8PCDcMeTll1/mpZdeoqKigk996lOkp6eTlpaG1WrF6XSSnJxM\nc3PzFY4wcMQjhnl5eezZs4dAIEBzczPl5eVkZ2f32jXFWzxiuG7dOjZv3szGjRuZMWMGS5YsGZAJ\n/VLiEcPW1lb+9V//lXvuuYfHHnus166lr8Tr/3J380rCtNQvpqCggKVLl1JQUIDdbmfdunUAvPLK\nK2RnZ2MYBnv27CEYDEZXgvv3f//3Sz5v1apVLF68mFAohMfjYcqUKQDk5+czd+5cDMNgxYoVfXOx\nvehKcfynf/qniz5v7NixLF26FIBhw4ZF77MNGzaMOXPmkJKSwu23386ECRMYN24ce/fuZd68eYRC\nIW6//XbGjRt30dcdiOIRQ4B77rmHgoKC6BfWziW8gS5eMUxk8YjhT37yE06cOMHWrVvZunUrAM88\n80z0vvFAF6+/w+7mFc0oJyIikiASuvwuIiLyUaKkLiIikiCU1EVERBKEkrqIiEiCUFIXERFJEErq\nIiIiCUJJXUREJEEoqYuIiCSI/x/UPIn4nfPh4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bb7a950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[bestOrgIndicesAtom[3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118a487d0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FofF6KLISseNIA9bursCUMXFINwfnsr6/39+vViA81q+WXGa/3B8F/S5Zr1mzBm+++SYA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\nIiIiGWAhExERycD/AyWvIfksjUqCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc816d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[worstOrgIndicesAtom[-2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bc477d0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAIxOK8JUrV9TQ0PCF29vb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\n/wP1ixgTzJvxMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc47dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[worstOrgIndicesAtom[-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bc47750>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/zXnZYygVAgwRaqQl6pCWYEBynB5qFde2CRSlQoHpOXEoP9mJVosdaQkGqUsi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\nQCYiIpKB/x+Ek7JCGQtCFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af3d810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[worstOrgIndicesAtom[-5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116e3f810>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b4fV68fDDD6OtrQ02mw1r1qxBYWHhoDWKiC6NrG0nmigLPXQee+BEAJR7IaMC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111992410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distplot(monomerHOMO[worstOrgIndices[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bestOrgIndices = []\n",
    "for i in range(10):\n",
    "    bestOrgIndices += [returnLocationInCsv(mols_atom[i])]"
   ]
  }
 ],
 "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}