demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Machine Learning for Reaction Performance Prediction in Allylic Substitution enhanced by Automatic Extraction of Substrate-aware Descriptor\n",
    "\n",
    "* Author: WANG Xi, YU Gufeng\n",
    "* Institute: Shanghai Jiao Tong University\n",
    "\n",
    "Here we will demonstrate the data extraction, data set construction and part of the training process of the model. All the data can be downloaded from our website and GitHub. We are welcome to your valuable suggestions about improving our platform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Extraction and Preprocessing\n",
    "\n",
    "The training data are extracted from papers (doi)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Required packages and functions\n",
    "\n",
    "Some packages need to be installed before running the code. We have maken the packages require by the code into a virtual environment and uploaded it to GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use dataset A for demonstration and first define the path of the dataset. Due to the complexity of the data, we have defined several functions to help obtain information and build the dataset.\n",
    "\n",
    "The data path is very important, and the parts of the code that involve the path need attention. If you want to process other data sets, you can modify **all paths** according to the actual situation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_params = {\n",
    "    'index_path': './data/data_classA/index.CSV', # Index for the molecules in the dataset (SMILES)\n",
    "    'CDFT_path': './data/data_classA/CDFT', # Directory of CDFT files\n",
    "    'gjf_path': './data/data_classA/marked_gjf' # Directory of gjf files\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function: extra_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extra_data(path):\n",
    "    \"\"\"\n",
    "    param path: csv file\n",
    "    return: SMILES of Nu, Eu, Catalyst, Base and Yield\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(path, encoding='gbk')\n",
    "    smiles_sub = df['Substrates'].tolist()\n",
    "    smiles_allyl = df['Allylic Substrates'].tolist()\n",
    "    cat = df['Catalysts'].tolist()\n",
    "    base = df['base'].tolist()\n",
    "    target = df['Yield'].tolist()\n",
    "    return smiles_sub, smiles_allyl, cat, target, base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function: getName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getName(smiles, path):\n",
    "    \"\"\"\n",
    "    :param smiles: SMILES of Nu, Eu or Catalyst\n",
    "    :param path: index file, format=CSV\n",
    "    :return: The Code of components\n",
    "    Example:\n",
    "        smiles = 'O=C(OC/C=C/C1=CC=CC=C1)OC'\n",
    "        return = E1\n",
    "    \"\"\"\n",
    "    rawdata = pd.read_csv(path)\n",
    "    par = rawdata[rawdata['SMILES'] == smiles]\n",
    "    par = par.iloc[:,0]\n",
    "    parArray = par.values\n",
    "    x = parArray\n",
    "    return x[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function: block_search\n",
    "Extract information from \"CDFT\" files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def read_file(filename):\n",
    "    file = pd.read_table(filename)\n",
    "    file = np.array(file)\n",
    "    return file.reshape([-1])\n",
    "\n",
    "\n",
    "def inline_extraction(array):\n",
    "    '''\n",
    "    :param string: a list from block extraction\n",
    "    :return: pure char and float\n",
    "    '''\n",
    "    # atom = re.findall('[A-Z]', str(array))\n",
    "    # print(array)\n",
    "    play = re.sub('[A-Z]', '', str(array))\n",
    "    play = re.sub('[a-z]', '', str(play))\n",
    "    # print(play)\n",
    "    play = re.sub('\\(', '', play)\n",
    "    play = re.sub('\\)', '', play)\n",
    "    # print(play)\n",
    "    line = play.split()\n",
    "    # print(line)\n",
    "    try:\n",
    "        temp = [float(x) for x in line]\n",
    "    except ValueError:\n",
    "        line[0] = re.sub('[a-z]', '', line[0])\n",
    "        temp = [float(x) for x in line]\n",
    "    # Add an atomic type marker at the end of the array with ASCII\n",
    "    # temp.append(float(ord(atom[0])))\n",
    "    # print(temp)\n",
    "    return np.array(temp).reshape([1, -1])\n",
    "\n",
    "\n",
    "def hartree_extration(array):\n",
    "    dig = str(array).split(':')\n",
    "    dig = re.sub('[a-z,A-Z]', '', dig[1])\n",
    "    dig = re.sub('\\^', '', dig)\n",
    "    temp = [float(x) for x in dig.split()]\n",
    "    return np.array(temp[0]).reshape([-1])\n",
    "\n",
    "\n",
    "def data_confusion(raw_data):\n",
    "    pass\n",
    "\n",
    "\n",
    "def block_search(file: list):\n",
    "    '''\n",
    "    :param file: Pandas.DataFrame\n",
    "    :return: Crucial Data Block\n",
    "    Block-1: elementary charge\n",
    "    Block-2: condensed local electrophilicity/nucleophilicity\n",
    "    Block-3: condensed local softnesses\n",
    "    Block-4: non-atomic index data\n",
    "    '''\n",
    "    Block = []\n",
    "    for i in range(4):\n",
    "        Block.append({'Start': 0, 'End': 0})\n",
    "\n",
    "    length = len(file)\n",
    "    Block[3]['End'] = length - 1\n",
    "    for line in range(length):\n",
    "        line_str = str(file[line])\n",
    "        if line_str.find(\"elementary charge\") >= 0:\n",
    "            Block[0]['Start'] = line + 2\n",
    "\n",
    "        if line_str.find(\"Condensed local electrophilicity\") >= 0:\n",
    "            Block[0]['End'] = line - 1\n",
    "            Block[1]['Start'] = line + 2\n",
    "\n",
    "        if line_str.find(\"Condensed local softnesses\") >= 0:\n",
    "            Block[1]['End'] = line - 1\n",
    "            Block[2]['Start'] = line + 2\n",
    "\n",
    "        if line_str.find(\"E(N)\") >= 0:\n",
    "            Block[2]['End'] = line - 1\n",
    "            Block[3]['Start'] = line\n",
    "\n",
    "    # check data\n",
    "    # for i in range(4):\n",
    "    #     print('Block:{}'.format(i))\n",
    "    #     for k in range(Block[i]['Start'], Block[i]['End']+1):\n",
    "    #         print(file[k])\n",
    "\n",
    "    # DES: descriptor of molecules\n",
    "    DES = hartree_extration(file[Block[3]['Start']])\n",
    "\n",
    "    # EC: elementary charge of molecules\n",
    "    EC = inline_extraction(file[Block[0]['Start']])\n",
    "\n",
    "    # EN: Condensed local electrophilicity/nucleophilicity index\n",
    "    EN = inline_extraction(file[Block[1]['Start']])\n",
    "\n",
    "    # Soft: Condensed local softnesses\n",
    "    Soft = inline_extraction(file[Block[2]['Start']])\n",
    "\n",
    "    for i in range(4):\n",
    "        for k in range(Block[i]['Start'] + 1, Block[i]['End'] + 1):\n",
    "            if i == 0:\n",
    "                EC = np.concatenate([EC, inline_extraction(file[k])], axis=0)\n",
    "                continue\n",
    "            if i == 1:\n",
    "                EN = np.concatenate([EN, inline_extraction(file[k])], axis=0)\n",
    "                continue\n",
    "            if i == 2:\n",
    "                Soft = np.concatenate([Soft, inline_extraction(file[k])], axis=0)\n",
    "                continue\n",
    "            if i == 3:\n",
    "                DES = np.concatenate([DES, hartree_extration(file[k])], axis=0)\n",
    "                continue\n",
    "    return EC, EN, Soft, DES[3:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function: getAtom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAtom(smiles, index_path, CDFT_path, gjf_path):\n",
    "    \"\"\"\n",
    "    :param gjf_path:\n",
    "    :param index_path:\n",
    "    :param CDFT_path:\n",
    "    :param smiles: Nu, Eu\n",
    "    :return: labeled atoms' index\n",
    "    We label some atoms with ISO via gauss view\n",
    "    Example:\n",
    "        smiles = 'O=C(OC/C=C/C1=CC=CC=C1)OC'\n",
    "        return = [18, 15, 13, 11]\n",
    "    \"\"\"\n",
    "    Filename = getName(smiles, path=index_path)+ '.gjf'\n",
    "    path = gjf_path + '/' + Filename\n",
    "    Atom_Coordinate = read_file(path)\n",
    "    Label = [0 for x in range(10)]\n",
    "    Label_Number = 0\n",
    "    Start_Line = 0\n",
    "\n",
    "    for count in range(len(Atom_Coordinate)):\n",
    "        line = Atom_Coordinate[count]\n",
    "        Atom_Label = line.split()\n",
    "        if Atom_Label[0] == '0' and Atom_Label[1] == '1':\n",
    "            Start_Line = count+1\n",
    "            break\n",
    "\n",
    "    for count in range(3, len(Atom_Coordinate)):\n",
    "        line = Atom_Coordinate[count]\n",
    "        Atom_Label = line.split()\n",
    "        if Atom_Label[0].find('Iso=') >= 0:\n",
    "            # print(Atom_Label[0], count-2)\n",
    "            Label_Number += 1\n",
    "            number = re.findall('\\d', Atom_Label[0])\n",
    "            Label[int(number[0])] = count-Start_Line\n",
    "\n",
    "    return Label[1:Label_Number+1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function: get_Atom_Level; get_Molecule_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "def get_Atom_Level(smiles, index_path, CDFT_path, gjf_path):\n",
    "\n",
    "    pre_data = read_file(CDFT_path + '/' + getName(smiles, index_path) + '-CDFT.txt')\n",
    "    EC, EN, Soft, Des = block_search(pre_data)\n",
    "    Label_Atom_Index = getAtom(smiles, index_path, CDFT_path, gjf_path)\n",
    "\n",
    "    # Determination of EC, EN and Soft information for Label atoms\n",
    "    Label_EC = EC[Label_Atom_Index]\n",
    "    Label_EN = EN[Label_Atom_Index]\n",
    "    Label_Soft = Soft[Label_Atom_Index]\n",
    "\n",
    "    # Determining specific information about Label atoms\n",
    "    Sepcific_EC = Label_EC[:, 4:6]\n",
    "    # print(Sepcific_EC)\n",
    "    Sepcific_EN = Label_EN[:, 1:3]\n",
    "    # print(Sepcific_EN)\n",
    "    Specific_Soft = Label_Soft[:, 1:4]\n",
    "    # print(Specific_Soft)\n",
    "\n",
    "    # Atom-Level Information aggregation\n",
    "    Atom_Level_Feature = np.concatenate((Sepcific_EC.reshape([-1]), Sepcific_EN.reshape([-1])))\n",
    "    Atom_Level_Feature = np.concatenate((Atom_Level_Feature, Specific_Soft.reshape([-1])))\n",
    "\n",
    "    # Return Ndarray\n",
    "    return Atom_Level_Feature\n",
    "\n",
    "# def get_Molecule_Level_(smiles, cdft_path = './data/data_classB/CDFT/'):\n",
    "#     pre_data = read_file(cdft_path + getName(smiles) + '-CDFT.txt')\n",
    "#     _, _, _, Des = block_search(pre_data)\n",
    "#     return Des.reshape([-1])\n",
    "\n",
    "def get_Molecule_Level(smiles, mol_data_path, index_path, CDFT_path, gjf_path):\n",
    "    rawdata = pd.read_csv(mol_data_path)\n",
    "    par = rawdata[rawdata['index'] == getName(smiles, path=index_path)]\n",
    "    par = par.drop(columns='index')\n",
    "    parArray = par.values\n",
    "    x = parArray\n",
    "    return x[0]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.93024000e-01  2.83190000e-02  5.12826000e-01  2.36264240e+02\n",
      "  1.06681451e+03  2.02649898e+03  1.37754945e+00  1.50860000e+02\n",
      "  1.47880000e+02  4.63200000e-01 -3.64130000e+00  1.05011000e+01\n",
      "  6.31300000e-01  1.14760000e+00]\n"
     ]
    }
   ],
   "source": [
    "print(get_Molecule_Level('O=C(OC/C=C/C1=CC=CC=C1)OC', mol_data_path='./data/data_classB/molecule_data_B.CSV', **path_params))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Function: TempConvert\n",
    "For solvent, we use pH values as feature. Due to the lack of solvent information in the literature and its high reproducibility, this function and solvent imformation are not used in the final const ructeddataset."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "def TempConvert(x):\n",
    "    if x == \"O=P(OO[K])=O.[K][K]\":\n",
    "        return 11.88\n",
    "    if x == \"[O-]C(=O)[O-].[Cs+].[Cs+]\":\n",
    "        return 10.33\n",
    "    if x == 'CC(N(P1OC2=C(C3=CC=CC=C3C=C2)C4=C5C=CC=CC5=CC=C4O1)C(C6=C7C(C=CC=C7)=CC=C6)C)C8=CC=CC9=C8C=CC=C9':\n",
    "        return np.array([0, 0, 1])\n",
    "    if x == 'CC(C1=C(OC)C=CC=C1)N(C(C)C2=CC=CC=C2OC)P3OC4=C(C5=C(C=CC=C6)C6=CC=C5O3)C7=CC=CC=C7C=C4':\n",
    "        return np.array([0, 1, 0])\n",
    "    if x == 'CC(C1=CC=CC=C1)N(C(C)C2=CC=CC=C2)P3OC4=C(C5=C(C=CC=C6)C6=CC=C5O3)C7=CC=CC=C7C=C4':\n",
    "        return np.array([1, 0, 0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Making Data Set"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract data from: ./data/data_classA/Reaction_A.CSV\n",
      "Utils from: {'index_path': './data/data_classA/index.CSV', 'CDFT_path': './data/data_classA/CDFT', 'gjf_path': './data/data_classA/marked_gjf'}\n",
      "Nu Feature shape:  (69, 49)\n",
      "Eu Feature shape:  (69, 42)\n",
      "Ligand Feature shape:  (69, 14)\n"
     ]
    }
   ],
   "source": [
    "data_path = './data/data_classA/Reaction_A.CSV'\n",
    "print('Extract data from: {}'.format(data_path))\n",
    "print('Utils from: {}'.format(path_params))\n",
    "a, b, c, d, e = extra_data(data_path)\n",
    "\n",
    "Nu_Atom = get_Atom_Level(a[0],**path_params)\n",
    "Nu_Molecule = get_Molecule_Level(a[0], './data/data_classA/molecule_data_A.CSV', **path_params)\n",
    "Nu_Feature = np.concatenate((Nu_Atom, Nu_Molecule)).reshape([1, -1])\n",
    "\n",
    "for i in range(1, len(a)):\n",
    "    Temp_Atom = get_Atom_Level(a[i],**path_params)\n",
    "    Temp_Molecule = get_Molecule_Level(a[i], './data/data_classA/molecule_data_A.CSV', **path_params)\n",
    "    Temp_Feature = np.concatenate((Temp_Atom, Temp_Molecule)).reshape([1, -1])\n",
    "    Nu_Feature = np.concatenate((Nu_Feature, Temp_Feature), axis=0)\n",
    "\n",
    "print('Nu Feature shape: ', Nu_Feature.shape)\n",
    "\n",
    "Eu_Atom = get_Atom_Level(b[0],**path_params)\n",
    "Eu_Molecule = get_Molecule_Level(b[0], './data/data_classA/molecule_data_A.CSV', **path_params)\n",
    "Eu_Feature = np.concatenate((Eu_Atom, Eu_Molecule)).reshape([1, -1])\n",
    "\n",
    "for i in range(1, len(b)):\n",
    "    Temp_Atom_Eu = get_Atom_Level(b[i],**path_params)\n",
    "    Temp_Molecule_Eu = get_Molecule_Level(b[i], './data/data_classA/molecule_data_A.CSV', **path_params)\n",
    "    Temp_Feature_Eu = np.concatenate((Temp_Atom_Eu, Temp_Molecule_Eu)).reshape([1, -1])\n",
    "    Eu_Feature = np.concatenate((Eu_Feature, Temp_Feature_Eu), axis=0)\n",
    "\n",
    "print('Eu Feature shape: ', Eu_Feature.shape)\n",
    "Ligand_Feature = get_Molecule_Level(c[0], './data/data_classA/molecule_data_A.CSV', **path_params).reshape([1, -1])\n",
    "for i in range(1, len(c)):\n",
    "    temp = get_Molecule_Level(c[i], './data/data_classA/molecule_data_A.CSV', **path_params).reshape([1, -1])\n",
    "    Ligand_Feature = np.concatenate((Ligand_Feature, temp), axis=0)\n",
    "\n",
    "\n",
    "print('Ligand Feature shape: ',Ligand_Feature.shape)\n",
    "\n",
    "# Solvent_Feature = np.array([TempConvert(x) for x in e])\n",
    "# Solvent_Feature = Solvent_Feature.reshape([-1, 1])\n",
    "#\n",
    "# print('Solvent Feature shape: ',Solvent_Feature.shape)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "So far, we have constructed the dataset and printed out the specific information such as the dimension of the dataset. We save it as `feature_A.csv`.\n",
    "\n",
    "Besides, we can also construct the dataset with other descriptors. We have provided these files formed by different descriptors, the following is the code."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nu Feature shape:  (69, 2048)\n",
      "Eu Feature shape:  (69, 2048)\n",
      "Ligand Feature shape:  (69, 2048)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, Descriptors\n",
    "from rdkit.Chem import MACCSkeys\n",
    "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
    "from sklearn.impute import SimpleImputer\n",
    "from e3fp.pipeline import fprints_from_sdf, mol_from_sdf, fprints_from_smiles\n",
    "\n",
    "\n",
    "fprint_params = {'bits': 1024, 'radius_multiplier': 2, 'rdkit_invariants': True} #e3fp\n",
    "confgen_params = {'max_energy_diff': 20.0, 'first': 3}\n",
    "\n",
    "def extra_data(path):\n",
    "    \"\"\"\n",
    "    param path: csv file\n",
    "    return: SMILES of Nu, Eu, Catalyst, Base and Yield\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(path, encoding='gbk')\n",
    "    smiles_sub = df['Substrates'].tolist()\n",
    "    smiles_allyl = df['Allylic Substrates'].tolist()\n",
    "    cat = df['Catalysts'].tolist()\n",
    "    base = df['base'].tolist()\n",
    "    target = df['Yield'].tolist()\n",
    "    return smiles_sub, smiles_allyl, cat, target, base\n",
    "\n",
    "\n",
    "descriptor_names = [x[0] for x in Descriptors._descList]\n",
    "descriptor_calculator = MoleculeDescriptors.MolecularDescriptorCalculator(descriptor_names)\n",
    "def calcuate_fingerprint(smi):\n",
    "    mol = Chem.MolFromSmiles(smi)\n",
    "\n",
    "    # 4 descriptors\n",
    "\n",
    "    return np.array(AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048)) # Morgan\n",
    "\n",
    "    # return np.array(list(MACCSkeys.GenMACCSKeys(mol).ToBitString())) # MACCS\n",
    "\n",
    "    # return np.nan_to_num(np.array(descriptor_calculator.CalcDescriptors(mol))) # ML.Descriptors 0\n",
    "\n",
    "    # return np.array(descriptor_calculator.CalcDescriptors(mol)) # ML.Descriptors mean\n",
    "\n",
    "    # fprint = fprints_from_smiles(smiles=smi, name='mol', fprint_params=fprint_params, confgen_params=confgen_params)\n",
    "    # return fprint[0].to_vector(sparse=False, dtype=int)\n",
    "\n",
    "\n",
    "data_path = './data/data_classA/Reaction_A.CSV'\n",
    "a, b, c, d, e = extra_data(data_path)\n",
    "\n",
    "Nu_Feature = np.array(calcuate_fingerprint(a[0])).reshape([1, -1])\n",
    "\n",
    "for i in range(1, len(a)):\n",
    "    Temp_Atom = np.array(calcuate_fingerprint(a[i])).reshape([1, -1])\n",
    "    Nu_Feature = np.concatenate((Nu_Feature, Temp_Atom), axis=0)\n",
    "\n",
    "print('Nu Feature shape: ', Nu_Feature.shape)\n",
    "\n",
    "Eu_Feature = np.array(calcuate_fingerprint(b[0])).reshape([1, -1])\n",
    "\n",
    "for i in range(1, len(b)):\n",
    "    Temp_Atom_Eu = np.array(calcuate_fingerprint(b[i])).reshape([1, -1])\n",
    "    Eu_Feature = np.concatenate((Eu_Feature, Temp_Atom_Eu), axis=0)\n",
    "\n",
    "print('Eu Feature shape: ',Eu_Feature.shape)\n",
    "\n",
    "Ligand_Feature = np.array([np.array(calcuate_fingerprint(x)) for x in c])\n",
    "Ligand_Feature = Ligand_Feature.reshape([len(c), -1])\n",
    "\n",
    "print('Ligand Feature shape: ', Ligand_Feature.shape)\n",
    "\n",
    "# Solvent_Feature = np.array([np.array(calcuate_fingerprint(x)) for x in e])\n",
    "# Solvent_Feature = Solvent_Feature.reshape([len(e), -1])\n",
    "#\n",
    "# print('Solvent Feature shape: ',Solvent_Feature.shape)\n",
    "\n",
    "feature = np.concatenate([Nu_Feature, Eu_Feature, Ligand_Feature], axis=1)\n",
    "# pd.DataFrame(np.concatenate([feature, np.array(d).reshape([-1, 1])], axis=1)).to_csv('./data/data_classA/data_set/Morgan_feature_A.csv')\n",
    "# pd.DataFrame(np.concatenate([feature, np.array(d).reshape([-1, 1])], axis=1)).to_csv('./data/data_classA/data_set/MACCS_feature_A.csv')\n",
    "# pd.DataFrame(np.concatenate([feature, np.array(d).reshape([-1, 1])], axis=1)).to_csv('./data/data_classA/data_set/ML_feature_0_A.csv')\n",
    "# pd.DataFrame(np.concatenate([feature, np.array(d).reshape([-1, 1])], axis=1)).to_csv('./data/data_classA/data_set/ML_feature_mean_A.csv')\n",
    "# pd.DataFrame(np.concatenate([feature, np.array(d).reshape([-1, 1])], axis=1)).to_csv('./data/data_classA/data_set/E3FP_feature_A.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Selection and Evaluation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data preprocessing\n",
    "\n",
    "Before starting to train the model, we get the full data and randomly split the dataset."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv(\"./data/data_classA/data_set/feature_A.csv\",sep=\",\")\n",
    "df = df.drop(columns=df.columns[0])\n",
    "df_train,df_test = train_test_split(df,test_size=0.2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Model Trainning\n",
    "\n",
    "We use autogluon for model training. Nowdays, autogluon is a well-known AutoML framework that automates model selection and learning optimal parameters. With this framework, we can quickly implement machine learning model fitting and prediction, which will greatly reduce the workload."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels\\ag-20241106_071800\\\"\n",
      "Presets specified: ['best_quality']\n",
      "Stack configuration (auto_stack=True): num_stack_levels=0, num_bag_folds=5, num_bag_sets=1\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"AutogluonModels\\ag-20241106_071800\\\"\n",
      "AutoGluon Version:  0.6.0\n",
      "Python Version:     3.8.15\n",
      "Operating System:   Windows\n",
      "Platform Machine:   AMD64\n",
      "Platform Version:   10.0.22631\n",
      "Train Data Rows:    55\n",
      "Train Data Columns: 105\n",
      "Label Column: 105\n",
      "Preprocessing data ...\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    16007.94 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.05 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 14 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('float', []) : 105 | ['0', '1', '2', '3', '4', ...]\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('float', [])     : 91 | ['0', '1', '2', '3', '4', ...]\n",
      "\t\t('int', ['bool']) : 14 | ['91', '92', '93', '94', '95', ...]\n",
      "\t0.1s = Fit runtime\n",
      "\t105 features in original data used to generate 105 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.04 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.14s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'mean_absolute_error'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Fitting 11 L1 models ...\n",
      "Fitting model: KNeighborsUnif_BAG_L1 ...\n",
      "\t-13.2982\t = Validation score   (-mean_absolute_error)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist_BAG_L1 ...\n",
      "\t-14.8027\t = Validation score   (-mean_absolute_error)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMXT_BAG_L1 ...\n",
      "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-11.589\t = Validation score   (-mean_absolute_error)\n",
      "\t3.23s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L1 ...\n",
      "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-11.5907\t = Validation score   (-mean_absolute_error)\n",
      "\t2.6s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: RandomForestMSE_BAG_L1 ...\n",
      "\t-11.4572\t = Validation score   (-mean_absolute_error)\n",
      "\t2.01s\t = Training   runtime\n",
      "\t0.17s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L1 ...\n",
      "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-10.9305\t = Validation score   (-mean_absolute_error)\n",
      "\t5.69s\t = Training   runtime\n",
      "\t0.02s\t = Validation runtime\n",
      "Fitting model: ExtraTreesMSE_BAG_L1 ...\n",
      "\t-11.752\t = Validation score   (-mean_absolute_error)\n",
      "\t0.58s\t = Training   runtime\n",
      "\t0.16s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L1 ...\n",
      "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-10.6319\t = Validation score   (-mean_absolute_error)\n",
      "\t5.57s\t = Training   runtime\n",
      "\t0.89s\t = Validation runtime\n",
      "Fitting model: XGBoost_BAG_L1 ...\n",
      "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-12.7073\t = Validation score   (-mean_absolute_error)\n",
      "\t0.37s\t = Training   runtime\n",
      "\t0.02s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch_BAG_L1 ...\n",
      "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-11.004\t = Validation score   (-mean_absolute_error)\n",
      "\t1.69s\t = Training   runtime\n",
      "\t0.12s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge_BAG_L1 ...\n",
      "\tFitting 5 child models (S1F1 - S1F5) | Fitting with ParallelLocalFoldFittingStrategy\n",
      "\t-11.0543\t = Validation score   (-mean_absolute_error)\n",
      "\t0.87s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\t-10.244\t = Validation score   (-mean_absolute_error)\n",
      "\t0.18s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 97.61s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels\\ag-20241106_071800\\\")\n"
     ]
    }
   ],
   "source": [
    "from autogluon.tabular import TabularPredictor\n",
    "\n",
    "predictor = TabularPredictor(label=df_train.columns[-1], problem_type='regression', eval_metric='mean_absolute_error').fit(df_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0      WeightedEnsemble_L2 -10.243998       1.042127  13.993238                0.001000           0.175400            2       True         12\n",
      "1   NeuralNetFastAI_BAG_L1 -10.631941       0.893072   5.566324                0.893072           5.566324            1       True          8\n",
      "2          CatBoost_BAG_L1 -10.930547       0.022029   5.690994                0.022029           5.690994            1       True          6\n",
      "3    NeuralNetTorch_BAG_L1 -11.003979       0.118021   1.686685                0.118021           1.686685            1       True         10\n",
      "4     LightGBMLarge_BAG_L1 -11.054264       0.008006   0.873835                0.008006           0.873835            1       True         11\n",
      "5   RandomForestMSE_BAG_L1 -11.457237       0.174510   2.014666                0.174510           2.014666            1       True          5\n",
      "6        LightGBMXT_BAG_L1 -11.589001       0.005511   3.228616                0.005511           3.228616            1       True          3\n",
      "7          LightGBM_BAG_L1 -11.590664       0.007006   2.604451                0.007006           2.604451            1       True          4\n",
      "8     ExtraTreesMSE_BAG_L1 -11.751958       0.160955   0.578684                0.160955           0.578684            1       True          7\n",
      "9           XGBoost_BAG_L1 -12.707284       0.019757   0.368224                0.019757           0.368224            1       True          9\n",
      "10   KNeighborsUnif_BAG_L1 -13.298182       0.013001   0.005999                0.013001           0.005999            1       True          1\n",
      "11   KNeighborsDist_BAG_L1 -14.802695       0.009497   0.006646                0.009497           0.006646            1       True          2\n"
     ]
    },
    {
     "data": {
      "text/plain": "                     model  score_val  pred_time_val   fit_time  \\\n0      WeightedEnsemble_L2 -10.243998       1.042127  13.993238   \n1   NeuralNetFastAI_BAG_L1 -10.631941       0.893072   5.566324   \n2          CatBoost_BAG_L1 -10.930547       0.022029   5.690994   \n3    NeuralNetTorch_BAG_L1 -11.003979       0.118021   1.686685   \n4     LightGBMLarge_BAG_L1 -11.054264       0.008006   0.873835   \n5   RandomForestMSE_BAG_L1 -11.457237       0.174510   2.014666   \n6        LightGBMXT_BAG_L1 -11.589001       0.005511   3.228616   \n7          LightGBM_BAG_L1 -11.590664       0.007006   2.604451   \n8     ExtraTreesMSE_BAG_L1 -11.751958       0.160955   0.578684   \n9           XGBoost_BAG_L1 -12.707284       0.019757   0.368224   \n10   KNeighborsUnif_BAG_L1 -13.298182       0.013001   0.005999   \n11   KNeighborsDist_BAG_L1 -14.802695       0.009497   0.006646   \n\n    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n0                 0.001000           0.175400            2       True   \n1                 0.893072           5.566324            1       True   \n2                 0.022029           5.690994            1       True   \n3                 0.118021           1.686685            1       True   \n4                 0.008006           0.873835            1       True   \n5                 0.174510           2.014666            1       True   \n6                 0.005511           3.228616            1       True   \n7                 0.007006           2.604451            1       True   \n8                 0.160955           0.578684            1       True   \n9                 0.019757           0.368224            1       True   \n10                0.013001           0.005999            1       True   \n11                0.009497           0.006646            1       True   \n\n    fit_order  \n0          12  \n1           8  \n2           6  \n3          10  \n4          11  \n5           5  \n6           3  \n7           4  \n8           7  \n9           9  \n10          1  \n11          2  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>model</th>\n      <th>score_val</th>\n      <th>pred_time_val</th>\n      <th>fit_time</th>\n      <th>pred_time_val_marginal</th>\n      <th>fit_time_marginal</th>\n      <th>stack_level</th>\n      <th>can_infer</th>\n      <th>fit_order</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WeightedEnsemble_L2</td>\n      <td>-10.243998</td>\n      <td>1.042127</td>\n      <td>13.993238</td>\n      <td>0.001000</td>\n      <td>0.175400</td>\n      <td>2</td>\n      <td>True</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NeuralNetFastAI_BAG_L1</td>\n      <td>-10.631941</td>\n      <td>0.893072</td>\n      <td>5.566324</td>\n      <td>0.893072</td>\n      <td>5.566324</td>\n      <td>1</td>\n      <td>True</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CatBoost_BAG_L1</td>\n      <td>-10.930547</td>\n      <td>0.022029</td>\n      <td>5.690994</td>\n      <td>0.022029</td>\n      <td>5.690994</td>\n      <td>1</td>\n      <td>True</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NeuralNetTorch_BAG_L1</td>\n      <td>-11.003979</td>\n      <td>0.118021</td>\n      <td>1.686685</td>\n      <td>0.118021</td>\n      <td>1.686685</td>\n      <td>1</td>\n      <td>True</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>LightGBMLarge_BAG_L1</td>\n      <td>-11.054264</td>\n      <td>0.008006</td>\n      <td>0.873835</td>\n      <td>0.008006</td>\n      <td>0.873835</td>\n      <td>1</td>\n      <td>True</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>RandomForestMSE_BAG_L1</td>\n      <td>-11.457237</td>\n      <td>0.174510</td>\n      <td>2.014666</td>\n      <td>0.174510</td>\n      <td>2.014666</td>\n      <td>1</td>\n      <td>True</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>LightGBMXT_BAG_L1</td>\n      <td>-11.589001</td>\n      <td>0.005511</td>\n      <td>3.228616</td>\n      <td>0.005511</td>\n      <td>3.228616</td>\n      <td>1</td>\n      <td>True</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>LightGBM_BAG_L1</td>\n      <td>-11.590664</td>\n      <td>0.007006</td>\n      <td>2.604451</td>\n      <td>0.007006</td>\n      <td>2.604451</td>\n      <td>1</td>\n      <td>True</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ExtraTreesMSE_BAG_L1</td>\n      <td>-11.751958</td>\n      <td>0.160955</td>\n      <td>0.578684</td>\n      <td>0.160955</td>\n      <td>0.578684</td>\n      <td>1</td>\n      <td>True</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>XGBoost_BAG_L1</td>\n      <td>-12.707284</td>\n      <td>0.019757</td>\n      <td>0.368224</td>\n      <td>0.019757</td>\n      <td>0.368224</td>\n      <td>1</td>\n      <td>True</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>KNeighborsUnif_BAG_L1</td>\n      <td>-13.298182</td>\n      <td>0.013001</td>\n      <td>0.005999</td>\n      <td>0.013001</td>\n      <td>0.005999</td>\n      <td>1</td>\n      <td>True</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>KNeighborsDist_BAG_L1</td>\n      <td>-14.802695</td>\n      <td>0.009497</td>\n      <td>0.006646</td>\n      <td>0.009497</td>\n      <td>0.006646</td>\n      <td>1</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.leaderboard()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The above is the model training process and the specific performance values of each model. We use the previously divided test set for model validation and visualization."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "y_true = df_test.iloc[:, -1]\n",
    "df_test_non = df_test.drop(columns=df_test.columns[-1])\n",
    "y_pred = predictor.predict(df_test_non)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: mean_absolute_error on test data: -7.135571616036551\n",
      "\tNote: Scores are always higher_is_better. This metric score can be multiplied by -1 to get the metric value.\n",
      "Evaluations on test data:\n",
      "{\n",
      "    \"mean_absolute_error\": -7.135571616036551,\n",
      "    \"root_mean_squared_error\": -9.254135908448406,\n",
      "    \"mean_squared_error\": -85.6390314120342,\n",
      "    \"r2\": -0.35748080523725845,\n",
      "    \"pearsonr\": 0.12833749219244545,\n",
      "    \"median_absolute_error\": -6.007568359375\n",
      "}\n",
      "C:\\Users\\79967\\AppData\\Local\\Temp\\ipykernel_67132\\3236984861.py:7: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored\n",
      "  plt.scatter(y_pred, y_true, alpha=0.3, cmap='viridis', norm=changecolor)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import colors\n",
    "\n",
    "perf = predictor.evaluate_predictions(y_true=y_true, y_pred=y_pred, auxiliary_metrics=True)\n",
    "fig = plt.scatter(y_true, y_pred)\n",
    "changecolor = colors.Normalize(vmin=0.4, vmax=0.8)\n",
    "plt.scatter(y_pred, y_true, alpha=0.3, cmap='viridis', norm=changecolor)\n",
    "plt.title('Test Set')\n",
    "plt.xlabel('Predict')\n",
    "plt.ylabel('True')\n",
    "plt.xlim(0, 100)\n",
    "plt.ylim(0, 100)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [
    "pd.DataFrame([y_true, y_pred]).to_csv('./pred_A.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Multi-comparison\n",
    "\n",
    "In making the dataset, in addition to our descriptors, we also used other descriptors, and here we compare the effects of these descriptors. We use 8 models to train and predict the data formed by the 4 descriptors, and finally conduct a comprehensive comparison of MAE. All models use the original parameters in scikit-learn, and the `random_state` is 42, which objectively forms a unified condition.The performance of the 4 descriptors in the 8 models is stored in the `comparison.csv` and the dimensions of the 4 descriptors are in the `Len_comparison.csv`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\software\\anaconda3\\envs\\chemDL\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.534e+00, tolerance: 1.342e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "E:\\software\\anaconda3\\envs\\chemDL\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.456e+01, tolerance: 1.261e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "E:\\software\\anaconda3\\envs\\chemDL\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.397e+01, tolerance: 1.553e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "E:\\software\\anaconda3\\envs\\chemDL\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.416e+01, tolerance: 1.149e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "import sklearn.preprocessing as preprocess\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
    "from sklearn.tree import ExtraTreeRegressor\n",
    "from sklearn.linear_model import ElasticNet, Lasso\n",
    "from sklearn.model_selection import cross_val_score, RepeatedKFold\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "path1 = './data/data_classA/data_set/feature_A.csv'\n",
    "path2 = './data/data_classA/data_set/Morgen_feature_A.csv'\n",
    "path3 = './data/data_classA/data_set/MACCS_feature_A.csv'\n",
    "path4 = './data/data_classA/data_set/ML_feature_mean_A.csv'\n",
    "path5 = './data/data_classA/data_set/E3FP_feature_A.csv'\n",
    "\n",
    "\n",
    "def eval(reg, feature, target):\n",
    "\n",
    "    return -cross_val_score(reg, feature, target, cv=5, scoring='neg_mean_absolute_error').mean()\n",
    "\n",
    "\n",
    "\n",
    "def multiC(*data):\n",
    "    # fold5 = {\n",
    "    #     'Elast': [],\n",
    "    #     'SVR': [],\n",
    "    #     'Lasso': [],\n",
    "    #     'RF': [],\n",
    "    #     'AdaBoost': [],\n",
    "    #     'ExtraTree': [],\n",
    "    #     'GBDT': [],\n",
    "    #     'XgBoost': []\n",
    "    # }\n",
    "\n",
    "    Reg1 = ElasticNet(random_state=42)\n",
    "    Reg2 = SVR()\n",
    "    Reg3 = Lasso(random_state=42)\n",
    "    Reg4 = RandomForestRegressor(random_state=42)\n",
    "    Reg5 = AdaBoostRegressor(random_state=42)\n",
    "    Reg6 = ExtraTreeRegressor(random_state=42)\n",
    "    Reg7 = GradientBoostingRegressor(random_state=42)\n",
    "    Reg8 = XGBRegressor(random_state=42)\n",
    "\n",
    "    model_list = [Reg1, Reg2, Reg3, Reg4, Reg5, Reg6, Reg7, Reg8]\n",
    "    SS = preprocess.StandardScaler()\n",
    "    nanF = SimpleImputer()\n",
    "    Eval = []\n",
    "    FeatureL = []\n",
    "\n",
    "    for data_set in data:\n",
    "        target = data_set[:, -1]\n",
    "        feature = data_set[:, 0:-1]\n",
    "        FeatureL.append(len(feature[0]))\n",
    "        feature = SS.fit_transform(feature)\n",
    "        feature = nanF.fit_transform(feature)\n",
    "\n",
    "        scores = []\n",
    "        for reg in model_list:\n",
    "            score = eval(reg, feature, target)\n",
    "            scores.append(score)\n",
    "        Eval.append(scores)\n",
    "\n",
    "    return Eval, FeatureL\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    df1 = pd.read_csv(path1, index_col=False).to_numpy()\n",
    "    df2 = pd.read_csv(path2, index_col=False).to_numpy()\n",
    "    df3 = pd.read_csv(path3, index_col=False).to_numpy()\n",
    "    df4 = pd.read_csv(path4, index_col=False).to_numpy()\n",
    "    df5 = pd.read_csv(path5, index_col=False).to_numpy()\n",
    "\n",
    "    Eval, FeatureL = multiC(df1, df2, df3, df4, df5)\n",
    "\n",
    "    pd.DataFrame(Eval).to_csv('./comparison.csv')\n",
    "    pd.DataFrame(FeatureL).to_csv('./Len_comparison.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Interpretability Analysis\n",
    "\n",
    "Interpretability is the highlight of our descriptors. To elaborate on this, we train the model on the full dataset and capture the importance of features."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20220821_163635\\\"\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"AutogluonModels/ag-20220821_163635\\\"\n",
      "AutoGluon Version:  0.5.2\n",
      "Python Version:     3.8.13\n",
      "Operating System:   Windows\n",
      "Train Data Rows:    69\n",
      "Train Data Columns: 105\n",
      "Label Column: 105\n",
      "Preprocessing data ...\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    6426.93 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.06 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 14 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('float', []) : 105 | ['0', '1', '2', '3', '4', ...]\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('float', [])     : 91 | ['0', '1', '2', '3', '4', ...]\n",
      "\t\t('int', ['bool']) : 14 | ['91', '92', '93', '94', '95', ...]\n",
      "\t0.1s = Fit runtime\n",
      "\t105 features in original data used to generate 105 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.05 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.15s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'mean_absolute_error'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 55, Val Rows: 14\n",
      "Fitting 11 L1 models ...\n",
      "Fitting model: KNeighborsUnif ...\n",
      "\t-11.1\t = Validation score   (-mean_absolute_error)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist ...\n",
      "\t-10.9451\t = Validation score   (-mean_absolute_error)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMXT ...\n",
      "\t-5.537\t = Validation score   (-mean_absolute_error)\n",
      "\t0.31s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: LightGBM ...\n",
      "\t-5.8478\t = Validation score   (-mean_absolute_error)\n",
      "\t0.45s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: RandomForestMSE ...\n",
      "\t-6.0422\t = Validation score   (-mean_absolute_error)\n",
      "\t0.66s\t = Training   runtime\n",
      "\t0.06s\t = Validation runtime\n",
      "Fitting model: CatBoost ...\n",
      "\t-5.682\t = Validation score   (-mean_absolute_error)\n",
      "\t1.11s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: ExtraTreesMSE ...\n",
      "\t-6.9041\t = Validation score   (-mean_absolute_error)\n",
      "\t0.38s\t = Training   runtime\n",
      "\t0.04s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI ...\n",
      "No improvement since epoch 5: early stopping\n",
      "\t-6.3685\t = Validation score   (-mean_absolute_error)\n",
      "\t0.69s\t = Training   runtime\n",
      "\t0.07s\t = Validation runtime\n",
      "Fitting model: XGBoost ...\n",
      "\t-7.0582\t = Validation score   (-mean_absolute_error)\n",
      "\t0.38s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch ...\n",
      "\t-5.6773\t = Validation score   (-mean_absolute_error)\n",
      "\t1.38s\t = Training   runtime\n",
      "\t0.03s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge ...\n",
      "\t-5.3947\t = Validation score   (-mean_absolute_error)\n",
      "\t0.36s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\t-4.7972\t = Validation score   (-mean_absolute_error)\n",
      "\t0.3s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 6.66s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20220821_163635\\\")\n"
     ]
    }
   ],
   "source": [
    "from autogluon.tabular import TabularPredictor\n",
    "\n",
    "predictor = TabularPredictor(label=df.columns[-1], problem_type='regression', eval_metric='mean_absolute_error').fit(df)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing feature importance via permutation shuffling for 105 features using 69 rows with 10 shuffle sets...\n",
      "\t58.14s\t= Expected runtime (5.81s per shuffle set)\n",
      "\t3.78s\t= Actual runtime (Completed 10 of 10 shuffle sets)\n"
     ]
    }
   ],
   "source": [
    "im = predictor.feature_importance(df, num_shuffle_sets=10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "pd.DataFrame(im).to_csv('./importance.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Using the existing model feature importance data, we draw a heatmap to numerically describe the model and data features. Combined with chemical knowledge, we can analyze the interpretability of the model as well as the descriptors."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "     index  importance    stddev       p_value   n  p99_high   p99_low  \\\n0       91    2.433233  0.458695  2.127917e-08  10  2.904628  1.961838   \n1        4    1.084489  0.300453  5.889387e-07  10  1.393262  0.775717   \n2       60    0.825856  0.286275  3.821018e-06  10  1.120058  0.531655   \n3       20    0.577359  0.174661  1.235669e-06  10  0.756856  0.397861   \n4       15    0.543927  0.136587  2.550010e-07  10  0.684296  0.403559   \n..     ...         ...       ...           ...  ..       ...       ...   \n100     63   -0.000125  0.000255  9.223489e-01  10  0.000137 -0.000387   \n101     85   -0.000273  0.000785  8.501263e-01  10  0.000534 -0.001081   \n102     40   -0.000668  0.000504  9.988347e-01  10 -0.000150 -0.001186   \n103     53   -0.006983  0.013776  9.282907e-01  10  0.007175 -0.021140   \n104     83   -0.008895  0.003517  9.999889e-01  10 -0.005281 -0.012509   \n\n         heat  \n0    1.000000  \n1    0.812030  \n2    0.706177  \n3    0.770081  \n4    0.859413  \n..        ...  \n100  0.004575  \n101  0.009191  \n102  0.000065  \n103  0.004212  \n104  0.000000  \n\n[105 rows x 8 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>index</th>\n      <th>importance</th>\n      <th>stddev</th>\n      <th>p_value</th>\n      <th>n</th>\n      <th>p99_high</th>\n      <th>p99_low</th>\n      <th>heat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>91</td>\n      <td>2.433233</td>\n      <td>0.458695</td>\n      <td>2.127917e-08</td>\n      <td>10</td>\n      <td>2.904628</td>\n      <td>1.961838</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>1.084489</td>\n      <td>0.300453</td>\n      <td>5.889387e-07</td>\n      <td>10</td>\n      <td>1.393262</td>\n      <td>0.775717</td>\n      <td>0.812030</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>60</td>\n      <td>0.825856</td>\n      <td>0.286275</td>\n      <td>3.821018e-06</td>\n      <td>10</td>\n      <td>1.120058</td>\n      <td>0.531655</td>\n      <td>0.706177</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20</td>\n      <td>0.577359</td>\n      <td>0.174661</td>\n      <td>1.235669e-06</td>\n      <td>10</td>\n      <td>0.756856</td>\n      <td>0.397861</td>\n      <td>0.770081</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>15</td>\n      <td>0.543927</td>\n      <td>0.136587</td>\n      <td>2.550010e-07</td>\n      <td>10</td>\n      <td>0.684296</td>\n      <td>0.403559</td>\n      <td>0.859413</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>63</td>\n      <td>-0.000125</td>\n      <td>0.000255</td>\n      <td>9.223489e-01</td>\n      <td>10</td>\n      <td>0.000137</td>\n      <td>-0.000387</td>\n      <td>0.004575</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>85</td>\n      <td>-0.000273</td>\n      <td>0.000785</td>\n      <td>8.501263e-01</td>\n      <td>10</td>\n      <td>0.000534</td>\n      <td>-0.001081</td>\n      <td>0.009191</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>40</td>\n      <td>-0.000668</td>\n      <td>0.000504</td>\n      <td>9.988347e-01</td>\n      <td>10</td>\n      <td>-0.000150</td>\n      <td>-0.001186</td>\n      <td>0.000065</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>53</td>\n      <td>-0.006983</td>\n      <td>0.013776</td>\n      <td>9.282907e-01</td>\n      <td>10</td>\n      <td>0.007175</td>\n      <td>-0.021140</td>\n      <td>0.004212</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>83</td>\n      <td>-0.008895</td>\n      <td>0.003517</td>\n      <td>9.999889e-01</td>\n      <td>10</td>\n      <td>-0.005281</td>\n      <td>-0.012509</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>105 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "df_im = pd.read_csv('./importance.csv')\n",
    "\n",
    "# Standardization\n",
    "temp = df_im['p_value'].values\n",
    "temp = -np.log(temp)\n",
    "heat = MinMaxScaler().fit_transform(temp.reshape(-1, 1))\n",
    "df_im['heat'] = heat\n",
    "df_im.rename(columns={'Unnamed: 0': 'index'}, inplace=True)\n",
    "df_im"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now we can get the importance of each feature dimension and label it. In the following, we analyze the features at the atomic-level and molecular-level respectively."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.5621715374598864}\n",
      "{1: 0.47622452272516047}\n",
      "{2: 0.8385658079989806}\n",
      "{3: 0.49331151180576005}\n",
      "{4: 0.8120297367294578}\n",
      "{5: 0.00036038732206505354}\n",
      "{6: 0.761760789411257}\n",
      "{7: 0.6708268622385133}\n",
      "{8: 0.5182970598623319}\n",
      "{9: 0.5965630805674317}\n",
      "{10: 0.47572677835119376}\n",
      "{11: 0.8036743724317287}\n",
      "{12: 0.43714352735112677}\n",
      "{13: 0.646204720946757}\n",
      "{14: 0.11816687996576283}\n",
      "{15: 0.8594132389042526}\n",
      "{16: 0.7116799882569931}\n",
      "{17: 0.01473011428850735}\n",
      "{18: 0.20743038137255698}\n",
      "{19: 0.27522099816832374}\n",
      "{20: 0.7700810513712574}\n",
      "{21: 0.5568648809539795}\n",
      "{22: 0.20510110679130025}\n",
      "{23: 0.4255868692960513}\n",
      "{24: 0.6944457198625604}\n",
      "{25: 0.2801000733532358}\n",
      "{26: 0.8026738742342305}\n",
      "{27: 0.235061126199629}\n",
      "{28: 0.039236651327727856}\n",
      "{29: 0.039236651327727856}\n",
      "{30: 0.33929619470918426}\n",
      "{31: 0.9883014560114108}\n",
      "{32: 0.33899402270491286}\n",
      "{33: 0.6532738171304128}\n",
      "{34: 0.4834590652756653}\n",
      "{35: 0.3284588919769009}\n",
      "{36: 0.2610734291093507}\n",
      "{37: 0.8724090243771041}\n",
      "{38: 0.4577986124650936}\n",
      "{39: 0.25276417391109696}\n",
      "{40: 6.537349799746777e-05}\n",
      "{41: 0.8741813601097189}\n",
      "{42: 0.7590479479899788}\n",
      "{43: 0.7050115287306111}\n",
      "{44: 0.2286807757293233}\n",
      "{45: 0.37780046837779097}\n",
      "{46: 0.7945081755716307}\n",
      "{47: 0.6385819547211051}\n",
      "{48: 0.4298554286665454}\n",
      "{49: 0.10054523298703158}\n",
      "{50: 0.5962563054246134}\n",
      "{51: 0.1293619719419166}\n",
      "{52: 0.13213456684972033}\n",
      "{53: 0.00421155001487352}\n",
      "{54: 0.39396035105618976}\n",
      "{55: 0.039236651327727856}\n",
      "{56: 0.05297630146492881}\n",
      "{57: 0.49076664417692795}\n",
      "{58: 0.039236651327727856}\n",
      "{59: 0.5344715605526924}\n",
      "{60: 0.7061766807677221}\n",
      "{61: 0.7579667627800771}\n",
      "{62: 0.19494996356368047}\n",
      "{63: 0.004575045943904347}\n",
      "{64: 0.32940436074702545}\n",
      "{65: 0.039236651327727856}\n",
      "{66: 0.31618221933722535}\n",
      "{67: 0.01162400859279539}\n",
      "{68: 0.22438795396501496}\n",
      "{69: 0.24423084540162388}\n",
      "{70: 0.039236651327727856}\n",
      "{71: 0.08498916453397434}\n",
      "{72: 0.41630062143655655}\n",
      "{73: 0.4138963343240281}\n",
      "{74: 0.6704678601160107}\n",
      "{75: 0.7083577616818845}\n",
      "{76: 0.39900283460776037}\n",
      "{77: 0.39763701840332544}\n",
      "{78: 0.09568417711979973}\n",
      "{79: 0.4610410340168694}\n",
      "{80: 0.2192733778498141}\n",
      "{81: 0.039236651327727856}\n",
      "{82: 0.04218300977742561}\n",
      "{83: 0.0}\n",
      "{84: 0.040319990215876185}\n",
      "{85: 0.00919074227723524}\n",
      "{86: 0.10214133143303089}\n",
      "{87: 0.2277005285755973}\n",
      "{88: 0.9458100986143904}\n",
      "{89: 0.127983348315507}\n",
      "{90: 0.3593377623258976}\n",
      "{91: 1.0}\n",
      "{92: 0.8187545866934243}\n",
      "{93: 0.36095253279138634}\n",
      "{94: 0.039236651327727856}\n",
      "{95: 0.039236651327727856}\n",
      "{96: 0.039236651327727856}\n",
      "{97: 0.039236651327727856}\n",
      "{98: 0.039236651327727856}\n",
      "{99: 0.039236651327727856}\n",
      "{100: 0.039236651327727856}\n",
      "{101: 0.039236651327727856}\n",
      "{102: 0.8424906658334304}\n",
      "{103: 0.6715853195631735}\n",
      "{104: 0.039236651327727856}\n"
     ]
    }
   ],
   "source": [
    "importance_dict = {}\n",
    "for i in range(105):\n",
    "    par = df_im[df_im['index'] == i]\n",
    "    p_value = par['heat'].values\n",
    "    dic = {i: p_value[0]}\n",
    "    print(dic)\n",
    "    importance_dict.update(dic)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "Nu = [x for x in range(0, 35)]\n",
    "E = [x for x in range(49, 77)]\n",
    "M_Nu = [x for x in range(35, 49)]\n",
    "M_E = [x for x in range(77, 91)]\n",
    "atom = np.concatenate([np.array(Nu, dtype=np.float64), np.array(E, dtype=np.float64)])\n",
    "molecule = np.concatenate([np.array(M_Nu, dtype=np.float64), np.array(M_E, dtype=np.float64)])\n",
    "for i in range(len(atom)):\n",
    "    atom[i] = importance_dict[atom[i]]\n",
    "    # atom[i] = -math.log(atom[i], math.e)\n",
    "for i in range(len(molecule)):\n",
    "    molecule[i] = importance_dict[molecule[i]]\n",
    "    # molecule[i] = -math.log(molecule[i], math.e)\n",
    "\n",
    "atom = atom.reshape([9, 7])\n",
    "molecule = molecule.reshape([2, -1])\n",
    "\n",
    "Atom = pd.DataFrame(atom, columns=['f-', 'f+', 'EI', 'NuI', 's-', 's+', 's0'], index=['Nu-Atom1', 'Nu-Atom2', 'Nu-Atom3', 'Nu-Atom4', 'Nu-Atom5', 'E-Atom1', 'E-Atom2', 'E-Atom3', 'E-Atom4'])\n",
    "Molecule = pd.DataFrame(molecule, columns=['HOMO', 'LUMO', 'MMDM', 'MW', 'SA', 'Volume', 'Ovality', 'PSA', 'NPSA', 'MPI', 'CP', 'Hardness', 'EI', 'NI'], index=['Nu', 'E'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "                f-        f+        EI       NuI        s-        s+        s0\nNu-Atom1  0.562172  0.476225  0.838566  0.493312  0.812030  0.000360  0.761761\nNu-Atom2  0.670827  0.518297  0.596563  0.475727  0.803674  0.437144  0.646205\nNu-Atom3  0.118167  0.859413  0.711680  0.014730  0.207430  0.275221  0.770081\nNu-Atom4  0.556865  0.205101  0.425587  0.694446  0.280100  0.802674  0.235061\nNu-Atom5  0.039237  0.039237  0.339296  0.988301  0.338994  0.653274  0.483459\nE-Atom1   0.100545  0.596256  0.129362  0.132135  0.004212  0.393960  0.039237\nE-Atom2   0.052976  0.490767  0.039237  0.534472  0.706177  0.757967  0.194950\nE-Atom3   0.004575  0.329404  0.039237  0.316182  0.011624  0.224388  0.244231\nE-Atom4   0.039237  0.084989  0.416301  0.413896  0.670468  0.708358  0.399003",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>f-</th>\n      <th>f+</th>\n      <th>EI</th>\n      <th>NuI</th>\n      <th>s-</th>\n      <th>s+</th>\n      <th>s0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Nu-Atom1</th>\n      <td>0.562172</td>\n      <td>0.476225</td>\n      <td>0.838566</td>\n      <td>0.493312</td>\n      <td>0.812030</td>\n      <td>0.000360</td>\n      <td>0.761761</td>\n    </tr>\n    <tr>\n      <th>Nu-Atom2</th>\n      <td>0.670827</td>\n      <td>0.518297</td>\n      <td>0.596563</td>\n      <td>0.475727</td>\n      <td>0.803674</td>\n      <td>0.437144</td>\n      <td>0.646205</td>\n    </tr>\n    <tr>\n      <th>Nu-Atom3</th>\n      <td>0.118167</td>\n      <td>0.859413</td>\n      <td>0.711680</td>\n      <td>0.014730</td>\n      <td>0.207430</td>\n      <td>0.275221</td>\n      <td>0.770081</td>\n    </tr>\n    <tr>\n      <th>Nu-Atom4</th>\n      <td>0.556865</td>\n      <td>0.205101</td>\n      <td>0.425587</td>\n      <td>0.694446</td>\n      <td>0.280100</td>\n      <td>0.802674</td>\n      <td>0.235061</td>\n    </tr>\n    <tr>\n      <th>Nu-Atom5</th>\n      <td>0.039237</td>\n      <td>0.039237</td>\n      <td>0.339296</td>\n      <td>0.988301</td>\n      <td>0.338994</td>\n      <td>0.653274</td>\n      <td>0.483459</td>\n    </tr>\n    <tr>\n      <th>E-Atom1</th>\n      <td>0.100545</td>\n      <td>0.596256</td>\n      <td>0.129362</td>\n      <td>0.132135</td>\n      <td>0.004212</td>\n      <td>0.393960</td>\n      <td>0.039237</td>\n    </tr>\n    <tr>\n      <th>E-Atom2</th>\n      <td>0.052976</td>\n      <td>0.490767</td>\n      <td>0.039237</td>\n      <td>0.534472</td>\n      <td>0.706177</td>\n      <td>0.757967</td>\n      <td>0.194950</td>\n    </tr>\n    <tr>\n      <th>E-Atom3</th>\n      <td>0.004575</td>\n      <td>0.329404</td>\n      <td>0.039237</td>\n      <td>0.316182</td>\n      <td>0.011624</td>\n      <td>0.224388</td>\n      <td>0.244231</td>\n    </tr>\n    <tr>\n      <th>E-Atom4</th>\n      <td>0.039237</td>\n      <td>0.084989</td>\n      <td>0.416301</td>\n      <td>0.413896</td>\n      <td>0.670468</td>\n      <td>0.708358</td>\n      <td>0.399003</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Atom"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "        HOMO      LUMO      MMDM        MW        SA    Volume   Ovality  \\\nNu  0.328459  0.261073  0.872409  0.457799  0.252764  0.000065  0.874181   \nE   0.397637  0.095684  0.461041  0.219273  0.039237  0.042183  0.000000   \n\n         PSA      NPSA       MPI        CP  Hardness        EI        NI  \nNu  0.759048  0.705012  0.228681  0.377800  0.794508  0.638582  0.429855  \nE   0.040320  0.009191  0.102141  0.227701  0.945810  0.127983  0.359338  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>HOMO</th>\n      <th>LUMO</th>\n      <th>MMDM</th>\n      <th>MW</th>\n      <th>SA</th>\n      <th>Volume</th>\n      <th>Ovality</th>\n      <th>PSA</th>\n      <th>NPSA</th>\n      <th>MPI</th>\n      <th>CP</th>\n      <th>Hardness</th>\n      <th>EI</th>\n      <th>NI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Nu</th>\n      <td>0.328459</td>\n      <td>0.261073</td>\n      <td>0.872409</td>\n      <td>0.457799</td>\n      <td>0.252764</td>\n      <td>0.000065</td>\n      <td>0.874181</td>\n      <td>0.759048</td>\n      <td>0.705012</td>\n      <td>0.228681</td>\n      <td>0.377800</td>\n      <td>0.794508</td>\n      <td>0.638582</td>\n      <td>0.429855</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>0.397637</td>\n      <td>0.095684</td>\n      <td>0.461041</td>\n      <td>0.219273</td>\n      <td>0.039237</td>\n      <td>0.042183</td>\n      <td>0.000000</td>\n      <td>0.040320</td>\n      <td>0.009191</td>\n      <td>0.102141</td>\n      <td>0.227701</td>\n      <td>0.945810</td>\n      <td>0.127983</td>\n      <td>0.359338</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Molecule"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Visualization"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def ShowGRAHeatMap(DataFrame):\n",
    "    colormap = plt.cm.hsv\n",
    "    ylabels = DataFrame.columns.values.tolist()\n",
    "    f, ax = plt.subplots(figsize=(30, 30))\n",
    "    # ax.set_title('Feature Importance via Converted p-values')\n",
    "\n",
    "\n",
    "    with sns.axes_style(\"white\"):\n",
    "        sns.heatmap(DataFrame,\n",
    "                    cmap=\"YlGnBu\",\n",
    "                    annot=True,\n",
    "                    annot_kws={\"fontsize\":42}\n",
    "                    )\n",
    "    # colorbar\n",
    "    cax = plt.gcf().axes[-1]\n",
    "    cax.tick_params(labelsize=36)  # colorbar fontsize\n",
    "    plt.tick_params(labelsize=36)\n",
    "    plt.savefig('atom_figure.png',dpi=400)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 3000x3000 with 2 Axes>",
      "image/png": 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o05e7NVZHnDnsURUVlplui4wKVfeebdW9Vzv9/OMmpaYYb0b+vvEFt8bz3syf9MXc33XaiF46bWQv9R3QUUFB9f8QVFNTo3mfrNCb//lBJSXlhu0tYpvpoy/vIymhnnjG3TFvzfhB79uZJ56sxzwxyc48cdZ5g/SID+eJs+qYJxL/N0/8YmeeWObmeeL9mT9p3tzfdeqIXjrVyXniy//NE6Um80Tz2Gb6kHmi3mKDG+bNwldeeU9vvvmZoT0mJlLTpz+oE0/sY3ffHTtSdPvtzygtzVgNYty4kXruuXvcGqsjyssrdM45t5vGNmzYQL300n2Kioqwu//773+rZ5/9r6qrjTP8nDnPaOhQx5PIbDabrrzyQa1bt82wrVevLnr11UmKj29pd/8fflimSZOmq7TU+HqcOvUuXXzxaIdjqq+dO/fqwgvvrpU41b9/ojZsMH7uuvDCkZo2zfG//dtvf6aXXnrvyPetW7fQJZeM0fnnn6527eLq1ce6ddt0//0vad8+4/UYSZo9+2mddFI/h2PzhgdW+SZJqbHa+tnX2vnNAkN7YES4TrjjJrXoYf+6Y0FaulZOf1MlB4zXHdsNO1EDb7nWnaHW28Z3P9LBP7er9aD+aj2on6I6JcjqwJJk1ZWV2vntQiV9s8CQKCRJzdq31fAnH5LVr/597l+9Xqv/87bptjYnDVbfCZcrMNx+0mRlaan+/OgL7f1luWFbUGQzjXr+8QaTuNQYPD040tcheNx/pn+id9762tAeE9NML75yp04Y0tPuvklJqbpr4sumyRTnX3CqnplmfKLfW8rLKzTuvAdMYztlWF8998JERUaF293/ww8W6oVp75uel8yc/bCGnNjLoXj++843+vjDRRo5apBGjhqsgYMSFRRU/2WiampqNPfjH/Xvlz9RSYnx809sbJS+/u5Fh5KXNm1M1jXjnzBdcmroSb311DO3KK51c7v7V1RU6u03v9Jbb3xp2BYSEqQvv31ebdrE1juexirQOsjXIcCNHlhlvG6Cxun5IcZkF0fk5ubqwgsvVGam8fNt7969NWrUKLVt21bl5eVKSUnRt99+q6ws42eiq6++Wo8++mi9xx0/frxWrVpVq+3ZZ5/VRRdd5PhB1NO8efM0adKkWm0TJ07UHXfc4Zb+qUgDn9uXlq0P5xgn+JCQQL369kR16972mPt36dZGb757l6694gXlZNd+auH7b1fr4stOVZ9+Hd0as6PCwoLVvWe7vxJnendQz97t1aZtiyPbN6zbZZpIA+Av7SKCdUNf41xQXFmta+Zv1vacY5c5T8ot0VXfbNIXF/VXy7DayVDjurXUR3/u14YDhW6N2RH9Wkbo3hMSDO0V1TW6Y9FWLdlr8tTcMRwoqdBbG9I0c9M+BZKKjgYsfV+O5r73q6E9JCRQ/37rZnVNNK/QdFjnrq312sxbdeNV/1FOdu3X8IL563ThpSepd1/fLv0ZGhak7j3aqnuvturRq5169Gqn+LYxR7ZvWr/H9Aa5u1106Ukaf/0IWZysVmW1WvWPK05RYo82uufW/xqSabIPFujbL1fpignD3REucET6vhx9YmeeeKWe88SMmbfqZpN5YuH8dRrXQOaJxP/NE919OE9ceOlJutrFeeLiK05Rtx5tdO+t/zUk0+QcLND8L1fpcuaJRistLVOzZn1laA8NDda77z6l7t2P/bk7MTFB778/VZdc8i8dPFi7Gu3XX/+sK64Yq/79fZNANGvWl6ZJNCee2FdvvPGIAgOPnVQ2fvx5Cg4O1COPGB+Gefrpt/TNNzNktTq2uvq33y41TaLp2rW95sx5us4KPmPHDlN4eKhuvvkJwzJFL700R2PGnKzwcPc/bV9TU6NHH321VhJNRESYHnroRl1++f1uH69btw765z8v1VlnnSI/B274S9LAgT00b950XX31JCUlpRi2P/PM20797dCwFB84qF0/GCsW+AUF6eQH71Jkh2Nfd2zWro2GTb5HSx97zlAtJW35KiWMOk0xXcwfivGknpeOU0CY869hv4AAdb/oXDVr10ZrXvuvbEfdcC9I3afMdZsUf8KAevVnq6nR1k+MN6QlqcOIYep33ZV1nmMEhISo/w1XKyA0RMnf1/6blecXaPsX36rP+EvrFQ+avrS0LM1593tDe0hokN6eOUmJ3Y99jt+tW3u9O+dRXXHpo8rOzqu17dtvlunSy0erX/+u7gy53ua8+71pEs2QE3tqxmv3KSDw2Lf1rrp6jIKDAvX4lHcM2559eo6++HqaQ+9tl10+WjfceJ5LnxOuvGqMevbsqFtunGZIpjl4ME/zvvhZ11x7Tr37nP7yXNMkmtOGD9D0/9xT5+8oMDBAE++8RM2bR2rq07NrbSstLde0qe9pxmv/qnc8ANCQxMTEaNasWbrmmmt08GDt60lbtmzRli1b6uxj7NixmjzZ85VMGzo+CcLn5sxcpIoKY5WFu+67sM4kmsPiWkdrytNXG9ptNptmvml84sRbHn78Cn369SP6+Y8X9Oa7d+mu+y7UGWcNrJVEA6ButwxopyB/41vWtBW760yiOSyjuFwP/ZJkaLdaLLp9kLHSjbf4WaTnTu+mgKOWnqqqsenuxdscTqI5uo+SKuoeoOF6f9bPpucAE+89t86b44e1iovSw08aL6babDa9+7Z5iV9veGjKP/TRl/fpx+VP6dWZ/9TEe8/VqDH9at0c96bwiBCnL3r9XZ/+Cbp54lmm2376cZPL/QNH+8DOPHG7g/PEJDvzxBwfzhMPTPmHPvjyPi1c/pRmzPynbj8O5omfmScatbff/tx0iaEHHri+ziSaw1q3jtXUqXcZ2m02m15//ROXY3RGSUmZZs82Ps0eERGml166r84kmsMuuWSMzj77VEP7zp2pWrjwd4distlseuMN4+/D399PL710f72XwTr11IG64YYLDe05OXmaO9cz10o++uh7rV+/vVbbPfeMV2xstFvHiYuL1XPP3aNvvpmhc845zeEkmsMiI8P1zjuPmSYV7dyZaprMhMZl57eLVFNpPJfodcVFdSbRHBbSPEYDbjapeG2zKemrH1wN0SmuJNH8XfwJA9TpjBGm2/avWlfvfvatWK3iLGPib7P2bdV3wuUOnWP0vOxCxXQ1Jiel/PybSg/l1bsfNG0z3/nG9LzkvvuvqjOJ5rC41s311NRbDO02m820Uok3lJSU6b3ZxgShiIhQPffCxDoTRA67+JIROmvsUEN7cvI+/bholcke9kVEhLrlc0L/Ad10593myXCLFqysdz/r1m7X6lVbDe0tW0Zr6nO31vt3JElXXHWmxp59kqH9l5/W6s8/d9e7HwBoaDp37qzPP/9cgwcPdmi/gIAA3XHHHXr55Zed/ozVlJBIA5/Kzy/W998YT9w6do7ThZec4lBfJw/rqaGn9DC0L//tT6X4qNrL6DED1bFznFtONIHjVWSQvy7s1srQvjO3WHO3ZjjU169ph/RrqjExZUSH5uposuyTN1zZK15dYowXwmes2atFe8zXDwaagoL8Ei34dq2hvWOnVrrgHyc61NfQU7rrxJON5dhX/LZde1OMT3F5w8gz+ymhU6smeQ5w0WUnKcJkaZatm9NUVlrhg4jQVBXkl2ihyTyR0KmVzndinhhiZ55IZZ5wuwsvO8l0CSfmicYrL69QX31lrCTbpUs7XXbZGIf6Ou20QRo2bKChfenSNdq92/vL1Xz55RLl5RmrU9588z8cTv544IHrFBBgvHkzZ44xUedYfv11renv4h//OFOJiQkO9XXrrZcpOrqZof29974xVKpxVWZmtl5++b1abX37dtMVV4x16ziSdP75p2vcuJFumUPj4lroppsuNt22ZMkfLvcP36koKlbacuPfMKJNayWMGOZQX6369lLLPsZlYrI2blFhhrGiVWPS9dwzJZPXUvbW+i+DvffnZabtva/6h0NLTkmSxWpVnwmXGdprKqu0Z9EvDvWFpik/r0jffm38N9e5cxv941LHlsMYdmo/nTLMuATjb79u0J49+52O0VnffPWb8vOLDO033HS+WsRGOdTXv+6/Sv4Bxtff+3N8kwAoSZddcYYimhkTATdv2mW6HKWZzz81X75m4l2XKDLS/pJX9jzw0HjT39OcWd853BcANCRxcXH64IMP9Morr2jAgAHH/OwUGhqqiy66SN98840mTpxIVc7/aRC/BZvNpg0bNmjWrFl6/vnn9dBDD+n222/XQw89pOeff16zZs3Sxo0bZbPZfB0q3GzJwvWmT5heNWGkUy/Sq68dZdr+w/zVDvcFoGEY2ynWtBrNrE3pcuZd4b8bzS/OX9CtpRO9ucbfatEtA9oZ2vfml+qtDWlejwfwpp8WbTQ9B7h8/GlOnQNceY35UiEL59f/KUrUj7+/nwad2MXQbrPZlH3UMpuAK9w9T1zBPOE1/v5+Gmxnnjh6OV40DgsWLDN96vvaa8c59Xo0q5IiSd9884vDfbnqm29+NrSFhgY7lfzRunWszjrLeHN+/frt2ru3/jfjvv7aGJPFYtH1149zOKawsBBddpmxSlRWVo5WrtzscH/H8vjjb6i4uPTI935+Vj355O2N4iLsBReYV+RYu5aKNI3Z/lXrTKvRdD5rlCxO/LvsfPZo0/Z9yx2r7tDQBEU2U2SC8dpEeWGRaqqMS6ccraq8Qod2pRjaw+PjFNsz0amYohLaK7qLsdpZ2vKVsrk5CRCNz8KFK03PSyZce7ZT7znXXGe+pND8b8wTxDzp229+M7SFhAbp0svN559jiWvdXGPOMlal2bhhp1L3+iYB0N/fT0OH9ja022w2ZR/Mq1cfK1caq9FERobr7HNOdiqmFrFROuOMIYb2n5asUWFhiVN9AkBDYbFYdPbZZ2vu3Llavny53nzzTT388MO666679K9//UtPP/20PvroI61cuVLPPvusOnVyfsnS999/Xzt27Kj1ddFFF7nxaIwuuugiw5h33HGH2/r36SfZxYsXa9y4cYqKitKgQYN00003adKkSXrhhRf05ptv6oUXXtCkSZN00003aeDAgYqKitJFF12kn382XlBA47Rk0XpDW2Cgv0aNqd/6u0c74cRuim0ZWa9xADQOZ3U2LoVWXlWjH3YZSwbXx4r0PGUWG59wGNsp1qn+XHFO51jFhQUZ2t9Yl6qqGpJH0bT9vNh44yYw0F8jzzQ+CVYfg4Z0UYtY49PWLDfkGa3iokzbD+Uan5wDnPWLnXlihJvnCZYb8oyWzBNNitnSRIGBARo71rGKDocNHdpXLVsalzFbsMC7N6yysnK0YYOx4sLIkUPqvXzS0ewlZCxcuLxe+1dUVOrnn4035fv3T1SHDvVb0u5o559/umn7Dz+47/f9/fe/GeKeMOF89ejh/IVYb2rdOlbx8cbPhNnZh3wQDdzFbGkia4C/4k80VsWqj9ieiQqONl53dGQJpIYqJMakApfNpoqiut+3c5OSVVNlTFhq1c94s9wRZvuXHcpT7s5dLvWLxu/HhcZlgAIDA3TmWY5VrTzsxKG91LKl8TWwyGQcT8rKytWmjcmG9tNHDFJEhHPLuZ13vvm52iIHl3dyp1ZxzU3bc3Ly69w3ZU+GDmQZq40PObGngoICnY7p1NP6G9rKyyv1y0/GCqkA0Fg1b95cI0aM0IQJE3Tbbbfp5ptv1iWXXKJBgwYpMND5ObQp80kizbp169S/f3+NGTNG3377rQoLC2Wz2er8Kiws1Ndff63Ro0dr4MCBWr+e5IjGrLy8UhvXG9eZHDCoi8LDnVtixWq16uRhxjKre/dkKTODix9AYxPoZ9HgOOMNr9UZ+SqqrPupKDM2yXR5p87RoWptktTiSRcmGpesKiiv0rfJziUJAY3FX+cAewzt/QZ2VFh4sFN9Wq1WnTSsu6E9NeWgsjLznOoT9vnbKc8eEsKHLrhHeXmlNnlgnhjKPOE19uaJYOaJRqe8vEJr1xqf/D3hhF4KD3fupo7VatVppw0ytO/Zk66MDO+dCy9fbn5d6fTTT3C6zxNP7KOQEOPniuXLN9Rr//Xrt6ukpMytMXXu3E7t27c2tP/+e/1iqktBQZGmTn2nVlvr1i10xx1XuqV/b2nRwngTNTs7z/uBwC2qKyqVk2RMuGie2FUBIc5dd7RYrWrZt5ehvSgjS6U5xusMjYnV37gsnST51ePGdOF+88oW0Z0TXAnJ7v4HNhnfk3D8KC+v0Pp1xiTYQYO7u3ReMuzUfob2lD0Zyszw3rLrK343rxQ3/HTnHjqWpCFDepmel/xhZyxvMFsGU5JCQuv+nGdvua0+fTu7FFOfvsZqmpK0fDkPXQDA8czriTQvv/yyTj75ZG3evPlIgozFYqn31+F9NmzYoJNOOkmvvPKKtw8BbrJ1y16VlxtLMA46oatL/Q4a0s20fd2anS71C8D7+sZGKNjkJszK/Xku9fvHfvMnHIbEG58s85ToYH8NjY8ytC9JyVF5NWWK0bRt+zNNFeXGJxYHnuDahQ97+69fwxOL7mZvaRazyoCAM7bbmScGeGie2MA84XbME03H5s07VV5eYWgfMqSPS/2eeKJ5dalVq7a41K8j1qwxvxnryrEFBgZowIAehvb167erqh5LpKxZ86fbY5L+SvA5WlpapjIzs13qV5Kee26WDh6s/fDSI4/corAw55IVfKXGZLmYoKAAH0QCd8jbk6KaSuN1xxY9XLvu2KKH+VJF2dsb93XH8jzjdRL/kOB6JR1VFBWbtoc0N6ly4wDTKjmSsrcludQvGrctm3eb3lM4YYjxvdcRJ5xofDhXklav9t4Sf+vWGBOEJOmEIeax1UdAoL/69TfOexvWJ9XrvMQTsg+aP/DcyqQq0NHyDhWatsfZqXJTX3GtzfdfbbKMFADg+OHVRJoXX3xR9913nyoqKmol0EiqV0UaSbWSaioqKnTffffpxRdf9OZhwE22b00zbe/ey7gmryN69Gpv2r5j+z6X+gXgfb1iw03btxx0bUmAzQfMP3T1bGE+niec1CZK/laLoX1pWuN+ig2ojx3b0k3bE3u0danf7r3M90/abv7EEpy3brUx6SChU0tFRjm3DAZwNOaJxm8980ST8eef5olmvXqZP7lbX717m++/bZuxcq2nbN1qPLbY2Gi1auXazRizYysvr9CuXebXQeqKyc/Pqh49Oro9pr/Gc+33vXLlZn3xxeJabaNGnajRo4e61K8v7NuXZWiLjTUuQYbGIS/F/PUWldDBpX6jOplfd8zfW/fru6GqKivTod0phvaYrvVLYK4oNL9GExDqXHWQI/uHme+fv3efbCaJbzg+bNtqrFopST17ufY+2au3+VKE27eluNSvI7ZtMx5bixZRpstOOcLs2MrLK7V7t/lnLk9btcqYnNKpcxtFRUfUue8hO4k0Ec1c+4wRHByowEBj8uyBA4eUfTDPpb4Bb7FYbHw1kS80HOY11Dzgq6++0oMPPngkceYwm82mmJgYnX/++TrhhBPUs2dPRUdHKywsTMXFxcrLy9PWrVu1evVqffPNN8rJyTnSx+EKNQ899JC6du2qCy64wFuHAzdITjK/WN2ps7HcsCPatY9VQIC/KitrP8G6y854ABqu7jHmiS07D5k/7VRfewtKVVFdo0C/2vmkiTHeu7FzQmvzp7FXZ9S9HjDQ2O1KyjBt79TFuNyZI9q2a6GAAD9VHrX0266d5uPBOb8s3qwDWca5asw5A30QDZoqe/NERxfniTZ25ondzBNutdTOPHEm80SjtGNHiml7167mN5Prq0OH1qaf3ZOSzMdzt+rqaiUnpxrau3Rx7bikv5ZSMpOUlKLExIRj7mv2+27TppVCQpxb1u4we8eVlJSikSOHONVneXmFpkx59cjDb5IUGhqsRx+9xan+fGnHjhTl5RlvzrVt69r7DnynINX8BnFEW9euO4a3aimrv79qqmrPXQVpjfe6Y8rPy1RTaawE2O6Ues4NNjs3fIzPDrlFdUWFirMOKrw1r8/jUdIO86S1zl1cS7hv3z7O9LxkZ5J3kuSqq2u0K9k4b3Xu0sblvjt3Nu9jZ1KaunVz/bzHEYsXrVJWpvEhwnPPO6Ve+9vszDcWD803kpSUlKoWsVGeGwAA0GB5JZGmoqJCd99995EqNNJfb3hxcXGaNm2aLr/8cgUG2l9v9bTTTtM///lPVVZWau7cuXrooYeUkZFxpDJNTU2N7rrrLo0dO/aY/aBhSd9nLB8cFByglq2iXOrXz8+q1m1ilJpyoM7xADRs7ZoZLxaXVlYrs9hYWt4RNTZpX2GZOkXVfrrJbDxP6WVS/SantEJZRx1by9BAnd05Vie3jVK3mDA1Dw6Q1WLRobJK5ZZVam9+qZbvy9PyfYeUVljmrfABl+zfZ7xoEhQc4PJyH35+VrWOj1Hq3oO1x0un0pO7pO/L0cvTvjK0R0WH6cJLTvJ+QGiyPDlPxMXHKI15wmPS9+XoFTvzxDjmiUYpLS3T0BYcHOhy1RY/Pz+1adNKKSm1bxqlpRmrgnhCZmaO4WaZJLVv79pNdknq0CHetL2uY6uurlZGxkFDuztisteH2d+3vl57ba5SUmonD9x551Vq3TrW6T595euvfzZtP+20QV6OBO5SctB4HdAvMEDB0VEu9WuxWhXSIkbFmbWvO5YcaJzXHQ/t3qsd874ztIfHxyl+SP3+/QeEmz+UVFlc4lJsx9q/+ACJNMerffsOGNr+Oi9xrYKYn59VbdrEKiWldoK92XiekGXvvKRDnMt9t2tv/lrZl+adYzssLS1Lzz4zx9AeE9NMl142ul59REaaP3RZkO/aQ5dlZRWqqDAuGSZJaalZUv3yfAAATYxXlnZ67bXXlJqaeqSCjM1m08iRI7V9+3ZNmDCh3skvAQEBGj9+vLZv367Ro0fXyj5NS0vTG2+84alDgAdkZBgvVrdoEWmoWuSM2FjjBfbMjEOm610DaLjiI4IMbQdLXEuiOeyASTJO6/AgTz0wZdAl2nihKfnQ/18kigkO0NThXfXLVUP0yCmdNbJDc7WNCFZIgJ+C/K2KCw9SzxbhGts5Vk8P76qfrxqiWWf3Vm87y2EBDUlmhnE97OYtItxyDtAitpmhLSsjj3MAN1j2y1bdeu3ryj5YUKvdarVo8uOXqlmka6Xbgb9jnmiclv2yVbfbmSceYp5otPbvN95kiY2NccvrsWVL402vjIyDXnk9mh2XJJcThCTz45Kk9PRjJ9JkZeWoqqra0O6OmFq0iJKfn/EyYHq6czfRtm/fo5kz59Vq69GjkyZMOM+p/nzp0KECffrpQtNtI0Y4V60HvleSnWNoC4pyz3XHEJNknJKc3Ea33FDqryv0+3P/VlVZ7YdyrAEBGnjLtbL6+9Wrn0A7iTSlOcbzOUeU5trfv+Sg8e+L48P+/caE0xaxUe65p2CyhFJGRraXzkvMk/FcXdbpWH3sTzf+Lj3ll5/X6tqrn9SBA7Vf11arRU88fZMio+p3PTPKzs9lZro2J2Rm2N8/fZ/3fk8AgIbFKxVpPv744yP/b7FYNHToUH3//fdOV4+JiIjQ/PnzNWLECK1YseJIgs6HH36ou+66y11hw8MO5RhL5jZvYbyo7QyzfqqqqlVYWKrISO8t3QLANc1DjO8TB0vdk0hjlpAT6GdVsyB/5ZcbnwBxp8ggfzULMr4F7/tfRZlT2kbplVE9FBNiXJv3WE5rH6PT2sfooz/368nlu1RVw3qaaJgO5RYZ2pq3qHst7PqIMemnqqpaRYVl3MB1QFlphYqKypS296D+3JyqxQs2Kmm7scy0n79VD035h4ad3tMHUaIpy2OeaPD+Pk9s3ZyqJceYJx5knmjUcnONy3S1aOH6TR1Jio019lNZWaWCgmJFRbnnNW9PTo75kqpmMTnKXh/2xqw7pihXQ5LValVMTKQOHqx9A6uumMzU1NTo0UdfrZX0Y7Va9eSTt8vPr3433huS6dM/UGGh8Un24cMHq02blj6ICO5QXmA8lwiOdM91xyCTfmzV1aosKbWbVOJrVWVlqiwpVWF6pnJ37tK+FasNVXUkyS8wUEPuvkXRnTrUu+/Q2Bam7Yd2pajNic5XdTq0K8XutvIC43VlHB9ycwsMbS1aRLml7xYtjA/nVlVWq7CgpN6JHs4yO66/YopyuW97yxLZG9NVpaXlKiosUUpKpjZvStaC71do27YUw8/5+/vpsSdv1Okj6j9PtGlr/r68edMuZ8P93/7JdrfleOj3BABo+DyeSJOdna21a9ceSXbx9/fXrFmzXF6CKTAwUDNnzlTfvn1VXf3XB/d169YpOztbLVqYn7yj4aisrFKpyc3w8Aj3LKsSHm7eT0F+CYk0QCMRYLUoLMB4EbawwviEpjMKK8yTZaK8kEgTG2r+HphVXKEzEprrP2f0UIDJ06L1dWWveHWMCtXtC7eqwM5xAr5i9xwgPMQt/R/rHIAb5EZffrpCLzwzr+4fNJHQqaUefuIy9err3TXV0fTZmyfCmCd84qtPV+hFF+aJScwTjVpFRaVKSozLh0ZEuOe1Eh5u3k9+fqHHE2ny881vwtqLyRGBgQEKDAwwLBFgb8y6Y3LPdYzw8FBDIk1dMZl5771vtWlTUq22K64Yq759u7kUny+sWLFRn3yywNButVp1551X+SAiuENNVZWqy8sN7f4h7jmXCAg176eiuNjniTR/fvKlkucvcmrf6C6dNODm8Ypo7dhSMs0Tu0gWi2Sr/TDPgU1/Slde7FQskpS18U+72yqKXFvGBY1TZUWVSkuMr223XU+wc36Tn1/k8USa/Hxj8p9kPyZH2D8vMR+zvj6du1hPPTHLqX07dW6jp565RX37dXFov8Tu7RUREarCwtpLv61auVXl5RUKCnLuvuNvv220uy0/j8Q9ADheeTyRZvXq1bLZbLJYLLJYLBoxYoQSExPd0nf37t01cuRILVr014cDm82m1atXa+zYsW7pH55TXGy8ECdJoaHGZVycERpmfnG8xM64ABoesyQaSSqpdE8iTbGdfuyN607RweaVZuLDg3R93zamSTRbs4u05WChsksrFWC1qGVYkIbGR6pVmPm8eVKbKM04s4eunb9Z1KVBQ1JSbLzoJUkhdhLMHBVq5zVRbGdcOCYgwE+DT+yqsecN0sgz+8pq9cpKsTjO+GqesDcuHBMQ4KdB/5snRjBPNHrFxaWm7aGh7nkIJizM/MaXvXHdyd4Y9mJyVFhYiOGGVV3H5Yvft6O/6/37D+jf//6gVltsbIzuuWe8S7H5Qnb2Id1//0u1lo4/7NJLx6h3b8du7qHhqCo1v/7nH+ye6472+qkqbXznEn6BgWrZr5c6nH6KWvXt5VQfQRHhioiPU2F6Rq32wvQMZW/fqRbduzrcZ/7eNB1K3m13e1Wp598n0PDYfZ+0cy/AUWF2+rF3L8Od7N23cNf9krCwYJPzEu/eKwkI8NeJQ3vpgnGn6cyzTnTqc4LVatWAgYn6den6Wu35+UVa+MMfOn/caQ73mZOTr8WLVtndXlTEfAMAxyuPX9E6cKB2icgzzjjDrf2PHj261vdZWcdebxoNQ5WdG9j+/u7J7fK3s4ZvBZUZgEbDXkWWymr3rEtsb9mjQBcqwdRXsJ0xxnVrpeCj5q8Fuw9q1EerdP7n6zR56U69vCpFz/2xR/9asl2nvL9S183frJ255k9indI2WncOrn85ZsAbKu2eA7gnic1eP5WVnAO4Q3BwoMLCg2W1Wo5+4BRwG3vzRICH54kK5gm3CPrfPGFhnmgS7L1/BgQ4tgSpPQEB5tcAjr7R4wn2j81d1yWM/dR1XPZiCgx0T0xmx+bo7/rxx98wVCmaPPlGRUQ0ruq/lZVVuuuu5wwVeiSpTZuWeuCB63wQFdylptr8XMLqpuuOFjtLmNVUeX7ucjf/kGAFhLiehND25BNM27d8+Jlqqhx7IMpWU6PN7396zJ+p4bztuOT592579xSawnmJ8di8cVx/FxwSqIiIUJc/J5x97smm7f/596eGSjX18fy09495zaiSe0oAcNzyWiLN4ac72rRp49b+D/dnsVgkSQcPHnRr//CMKjsfoPz83fNP0t5Jr71xATQ8/laLaXuVm+7I2EuksTeuOwX41T1Gjc2mJ5Yla+KibdpbYP8Jkd/2HdIFn6/T4j3ZpttvG9heHZq558kcwB3svRf7u+0cwLwfzgHco7CwVIsXbNAj93+gy857Tj/9uMnXIaEJ8vxnBfN+qpkn3KKosFRLFmzQlPs/0OXnPaefmScataoq8xsH7nvf9t1nd3s3TPzs3CB3VIBJpcu6jsvu/OemmMx+3478rufPX6qlS9fUajv11IE6++xTXY7N2x577HWtWWNcNiYgwF+vvPKA2yoTwTfsJW6XNLoPAAEAAElEQVRY3FQlzWrnNWmzk8DTkJXnFyj11xX644VX9fPkp5W9LanunUx0HDVc/sHGaw/5KWna/MGnppWf7Nn22TfK2ZF8zJ+xlyyFps3z75PmSSveOS/x9ENHxmPz9nWSwoIS/fD9Ct13z390zln3aNHClU71c9bYk9SmbayhPSszVw9PesOhB6k+/WSJvp//+zF/ptLO+TAAoOnz+NJOR5/E2LsI46zq/500H14+yl0nTfCsw4lPR7PZubHtqBo7H86sdsYF0PDYu8hilXtex/amA/fUuzm2+lw/enVtqt7fsr9e/VXU2HT7oq36ZFx/9W/VrNY2P6tFN/Vvp0d+3elMqIDbWe0kq9W46xzATj+cA5jr2aedbrp9TO1Gm01FRWUqKizT7uRM7dyx37Sq3/70XD1y3/s678IheuDRi+XnhYpeOD74ap6w9xnleNejTzvdWMc8kWxnnshIz9Wj972vcy8covuZJxoleyX3m8Lr0f6xuecTgdmx2ZvfDrN33J78fdcV02F5eYWaOvW/tdqCgwP12GO3uiU2b3r11Y/1xRc/mm6bPPkm9evnniXp4TsWO/+uHUnmOBa7/Vh8/z7Xsk9P+QcdtRSMzabKklJVFBWrIG2fCvbtl82k2m9BWrqWPztd3S4Yqx4Xn+fQuAFhoep81kjt+Op7w7aUJb+qqqxcfcZfosAw+9WrKkvLtHXuPKX89Fud47krKQqNi93Xttveu837qe97pSs8/hnIZjw2V6+T9O7TWRPvvKRWm81mU1FRqQoLipW8c5+2b99rWvkmfd9B/evuf+uii0/XlCdudOhzgp+fVbfceqGmPPy2YdvPS9bqjtte1BNP36xWrWLs9lFZUaX/vvO1Xn/1i3qNBzQGXpiqgOOOxxNpYmP/ygw9fDEgLS3Nrf0f3d/h8dCw2StJWO2mpwnsPU3q76ZSiAA8r6La/IOin5vOCP3tfFh019JRx1JZx4fg7TlFem3tXof6rLZJ9/+0Q99fOsiwLNZFia30wso9yi/nCQr4nr0nvKrd9Nqz1w/nAOYSe7RVYo+2x/yZiooq/bRooz754Dft2JZu2P7tl6tUXl6px6ZeQSIC3CLAR/OEu8qmNzX1nSd+/t88kWQyT8z/3zwxhXmi0fH4Z3c7/Xjj9eiLY6vruBpiTIdNm/Zf5eTk1Wq77bbL1a5dnDtC85oPP/xOM2Z8ZLrtllsu0ZVXnu3liOAJdivG1LjntWSWhCJJVjdVjnBFbM9ExfY8djJYZWmZ9i1fqV0Lf1Jx5oHaG202JX31vWzV1ep56TiHxu427mxlb0syrSazb/lKHdi4RR1OH6ZWA/oorFWsAsPCVFlSouID2crauEV7f16m8vyCWvuFNI9WaY5xCTa/QPcsMYjGxd57VlUT+Jzg+fslxmNz9bh69uqonr06HvNnKioqtXDBSr0/53tt25pi2D7vi19UVl6hac/f7tDnhAsvOl0rft+iH74zVpNZvmyTzjv7X7rwotM1YuQgde7cRlHRESopLtP+/dlavmyj5n3xi9JSs2rtFx0doZKSMpWX1078CQoKrHdcAICmxeNnAHFxtT9Q//DDD5o8ebLb+l+wYEGt71u1auW2vuE5gUHm//TKy9yzLmeZnX6CgvmQBTQWFXY+vAa7qYx8sEmpdUkqM/lg6W7ldXzAn7UpXXbyiI5pT36pFqfkaGzn2kmlgX5WDWkdqR9TchzvFHCzIF+dAwRxDuCswEB/nXXuIJ0xdoA+mrNUb834wfBU3KLv16tPvw66+PJTfBQlmhKffVZgnnBaYKC/xpw7SKPHDtDHc5bqbZN54sfv16s380SjY+/GQVlZhVv6Ly0tN20PDg4ybXcne695Tx5b0NFVIgzbvf/7rs/NoRUrNurLL3+q1dalSztdf/2FbonLW7755hc99dRbptsuu+ws3XvvBC9HBE+x2kmyqDapiuCM6grz16RfQOM4lwgICVbH0cPV/rSTtfXTr7R74U+Gn9n57ULFdO2suAF96t2v1c9PgyfeqN+eeEEl2cbrDxVFxdo5f6F2zl9Yr/78g4PV95ortPLl10234fhj77273E3vk2Vl5ucl3kik8PR5idmxBQV7/rgCAwN03vnDdPY5J2v2rPn6z/RPDJ8Tvp//u/r376YrrjrTob4ff/JGpezZb5qgU1pSro8+WKiPPqjnfBPgp2kv3K7bbnnesI3lHgHg+OXxmmRDhgw5stySzWbTihUrtHbtWrf0vX79ei1fvvxIpqqfn5+GDh1a7/3Ly8tVUFBQ66u83D0nJji28PAQ+ZncDC8qLnNL/yV2+mnWLNQt/QPwvKLKatPqMOF2EmAcFWannwKTZQncLe8YNwLLq2r0XfJBp/v+YkeWafuQ+Ein+wTcKSw82PQcoLjY/IKVo0rs9NOsGRc+XOXnZ9X460fowUf/Ybr9tVe+U96hYi9HhabI3jxh7/XtKHv9RDBPuMzPz6qrrx+hB+zME28wTzQ64eGh8jepsFBUVOKW/ouLS03bIyPD3dL/sTRrZj6Gu46tpMR4XaKu47K33ZO/77piKisr15Qpr9Vqs1gseuKJ2xtVJa/Fi1do0qTppkvynHvuaXr88ca3RBXsCwgJkcVkKY6qUvdcd6wqM+8n4BjLFjVEfoEB6nP1Jeo2zrwS08bZH6vGTtVve4KjInXa4w8opmsnl2ILCA/TSfdPVHBUM9PtQZHm7Wja7J2X2DufcFSxvXsKkZ5/bTdrZj5GcZF7js3svMTemJ7g52fVDTedr8eeuNF0+ysvfaxDhwpMt9kTGhqs2e9P0YhRg1yKLTg4UC++dKd69uxoWpWoeXOuqQLA8crjiTSRkZE66aSTZLPZZLFYVFNToxtuuEGFhYUu9VtUVKQbbrjhyLqVFotFQ4cOVbNm9T+JfvbZZxUZGVnr6+Xn5roUF+rPLKklP889F1Xz8orMx4wkkQZoTMySWqLcVFkqxk4/eWVeSKQ5xhJLW7OL6qxYcyzrs8w/dPaJjXC6T8DdIiKMN6sL8t1zDmDvXIJzAPc576IhOmNsf0N7WVmlvvxshfcDQpNkNk/kM080GudeNESj7cwTXzFPNDpmCSd5ea5d0znM3g0TbyTSREebXz9yx7Hl5RUeuV71d3UdV1SU+Tm7J3/fkZHH/pwwY8bHSk3NqNV28cWjNXhwL7fE5A3Llq3TPfe8oCqThIBRo07Uc8/dK6vV45dI4WUBocb39Yoi95xLVBSaX3cMDG+c5xI9Lj7PNPGlLPeQ0leucbi/oMhmOnnS3eo27mynKsc0795Vw594SDHdOqskO9f0Z4KjuLF9vDJL/rB3L8BReYfM32+9cV4SGWU+hjuOLT+vyFAFRvLOcR3ton+M0NhzTja0l5aW69O5SxzuLzQ0WNP/c48eeGi8YmIcT7Drlthe7334uEadcYL27882/ZnYllEO9wsAaBq88ilx/Pjxtb7fvHmzRo8erczMTKf6y8rK0plnnqkNGzbIYrEceZpkwgTHSrBOmjRJ+fn5tb7uffByp2KC48xOQHKyHcs6ticn23jSGxkZqmAvlCsE4D5ZxcYqYbGh7nkdtwg1JtIcKqt0KYmlvrJLKuwuXbUj17ULe/nlVcooMj5pHxPSOEpM4/gQ29J40dPsvdsZZv00iwxleUc3u2XiWNP2pUu2eDkSNFVm80Sum+YJs36YJ9zvZjvzxK/ME41Oq1bNDW3Z2Yfc0rdZP1FREV5Z2snsuCT3HJu9PuLiWvgsptLSMtMn9uPizMeUpOTkVM2e/VWttujoZrr//utcjsdbVq/eookTp6rCZEmfU07pr+nTHzStboDGLyQmytBWnu+e645lJv0EhIfJL7DxXnfscckFpu0ZazY41Z9fQIB6XHyeznj5KSVeeI4iO7aX/ldV3ozFz6rYXt015J5/atjD9yqs5V/zZXGWecXe8Pg4p+JC49eyVbShLTs7zy19m/UTGRnulXsKrVrFmLa749js9dEqznxMT7vz7ktN25f8uNqp/qxWq8ZfM1Y//Dhd9z1wlQYOSjzme7vFYlHffl00ddqt+vSLqerRM0GSlJpqXuW7Y6d4p+ICADR+XqnBev311+ull17Szp07jyS+rF69Wt27d9eUKVN0/fXXKyoqqs5+8vLy9O677+qpp55Sfn7+kXaLxaJu3brp+uuvdyiuoKAgw/rUBZWN9wNPYxMfH6Ok7ftqtR3KLVJZaYWCQ1z7O2TsN67DG9/G/sUhAA3TvsIy9WxR++mI5iEBCva3qqzKtYSXthHGp6L2FbinzHNdbJL2F5UrIdLkafty19dszyurVOvw2u9vUXbWWgZ8oXV8tHbu2F+r7VBusZvOAYxPLMa38c3FoaYsvm2Muia21s4dtZ9QT07ar+LiMoWFOf7kKfB3cV6eJ1ozT7hdfNsYdUlsrWSTeaKkuEyhzBONRps2LbVt2+5abTk5+SotLVNIiGt/x/T0AybjtXKpz/pq29Z8nH37zG+iOMLsuI415mEREWFq1ixMBQW1k+t9FVNKSrqhikvXru310UffOzx+oZ0KHjt2pOj11z8xtHfs2EZjxw5zeJy/27hxh2655UmVlhofNBg8uJdee+1hBQbyOampCmnRXPl7a193LC8oVFV5hfyDXDuXKDlovO4YGtu4rzs2T+yiwPAwQ9WenB3JLvUbGBGu7hedq+4XnauKwiLl7U1TRUGhyguLVFNZKf+QEIW1ilV0xw4KCDNW9ClI3WfSq9SsTWuX4kLjFR8fq+3b9tZqy80pUGlpuUJCXEvE3Z9urEjSpm2sS33Wl71x0vc5v/z7kT7Szfvw1rEdrW3blkrs3kE7ttf+O+7YsVfFxaUKC3Nuyd3Q0GBdc905uua6c1RcXKqtf+5RTna+cg8VqLSkXKGhwWrTNla9encyXa4pacdek16lLl3aOhUPAKDx80oijZ+fn1577TWNHTtW1dXVR5JpCgoKdP/99+vhhx/WyJEjNXjwYPXo0UNRUVEKCwtTcXGx8vLytG3bNq1Zs0Y///yzysvLj1SgOdxPQECAXnvtNcqwNjLtOxov1thsNqXuPaBu3Z0/OSkqLNWhXOMFmvYJLZ3uE4Bv7MkrMbRZLRYlRIZoe47zlVvCA/3U3OQm3J5896w7XB+7D5WYJtIUV7peEae40liyPCKQpyzRcPz1nvxnrTabzaa01Gx1TXT+SZ+iwlLlHTLODe06HPvpbzina2IbQyJNTY1NBw8UKKwjN8jhGuaJpqFbYhtDIs3heaID80Sj0bFjG0ObzWbT3r0Z6t69o9P9FhYWKzc339DesaN3nvoNDw9VixZRhqe0U1L2m+/gAHt9JCTUfWwdO7bVxo07arXt3evJmIx/32NZtWqLVq1yX2WprVt3a+vW3Yb2UaNOdCmRZuvWXbrxxsdMq/D06dNVb701xeVEMDRs4a1NksRsNhVnHlBkB+evO1aWlJou7RQe17ivO1qsVjVr10bZ25JqtVcUFqm6olJ+bkg6C4wIV8vePRzaJ3fXHkNbeHycadINjg8JHY1JVH/dU8hUYvcOTvdbWFii3FxjtakOCd5J2goPD1XzFpHKya59brR3b4adPeovda/5yhAJXjo2M917GBNpampsysrKVadOjp2bmAkLC9EJQ3o6tM+mjcbEwbCwEHXp2s7leAAAjZNXEmkkadSoUXrrrbd0ww03yGKxyPK/Uo42m03l5eVasGCBFixYcMw+/p5A8/fv33rrLY0cOdKD0cMTEu0kyyTt2OdSIs2ObWl2xuOEB2hs/sw2T5bp0TzcpUSaXi3M1wDemu2eNZXrY0t2kUYmGJ9YCwtwPSk0LMCYNFNYYUyuAXylW3fzm0g7d+x36QZ50nbzG0Tdurt+EQZGMc0jTNsL841JkICj7M0TycwTjUq0nXmigHmiUenZs7Np+7Ztu11KpDFLnpCkHj3Mx/OEHj066bff1tVqS05OVWVllQICnL9ktm3bLtN2e7/Lo2M6OpEmP79I6ekH1KaN8zfqj64q9PfxmpqdO/fq+uunGCr7SFJiYoJmznxS4eHchG/qojqYXwfMT01zKZEmf6/5dcdIO+M1JkHNzN+3K4qLFRIY5d1gJJXm5KrkgLFCSIvuXb0eCxqOHj0STNu3b9/rUiLN9m0pdsZzvk9Hde+RoOW/bazVtis53Q3nJeaVVrrb+V16g1lFGEkqyHdtyXtnlZdXmCbS9B/YjSUgAeA45tUSLtddd53mzJmjsLCwWkkxhyvL1PV1dAJORESE3nvvPV177bXePAy4Sa8+5iehWzamuNTvpo3GJxUkqaed8QA0XJsOFJq2929lfnGnvga0aubQeJ6wIct8bfZINyzBFBVs7ONQmetLRgHu0rN3e9P2PzeZX9ypry129u/Zu/Ff1G6IKirM5xU/P6pEwnU9PDRP2Nu/B/OERzBPNA19+pjfsNywYYdpe31t2LDdtL1vX+/dIO3bt5uhrbKySlu3mifC1Nf69cbfTZs2LRUTY37TqK6YJBmSaxxl9vsOCgpUYmKCS/02NHv2pOu66x7VoUPGz1udOrXVu+8+pchI8wcr0LREdUowbT+UbH7dsL5yd5onpUXbGa8xqa6sMm33VRX4/Ws2mLbHDern3UDQoPTua56UumnDTpf63Whn/959urjUryP69DEeW2VllbZtTXGpX7PfTZs2sYqJMb8+6g0Vdpa299XnhN+XbzZdCnLkyEE+iAZwjpWvJvOFhsPrf4/x48dr3bp1Gj169JEEGen/E2qO9SXpyD5nnnmm1q9fr6uvvtrbhwA3adO2heLbGqsxrFxhfjGtvlatMF5cCg4JVN/+zj8pB8A39hWWKbXAWIp7WNtol/o9xWT/kspqrbeT3OIJqzLyVVFtXMYpMSbMpX4jg/zVOty4JnROaYVL/QLuFN82RvFtYgztq/5w7cLXqhVJhrbg4AD17kcyrSfk5phX8WoWyRPecF182xi1NpknVrs4T6xmnvAq5ommoV27OLVta1wi5fffN7jU7/Llxv1DQoLUv393l/p1xNChfU3bzWKrr/37DyglJd3QfvLJ/eu1/0kn2YtpvdMxlZdXaO3arYb2QYN6KijIuORtY5WWlqlrr31EBw8eMmxr3761Zs9+Ws2bR3k/MPhEWMsWCo01Xnc8uMW1644Ht2wztPkFBiqma+Ov7lReYHJNxGLx2TJKab/9YWgLDA9TbC/vvU+g4WnbtqXatI01tK9Y4dqygyt+32xo++u8xHsJvkNO7GXabhZbfWXsz1ZKinF5qKEn9Xa6T3fIyTEu7ynJZ8muX81bamjz9/fT6DOH+CAaAEBD4ZPEpi5dumjRokXavHmzbr75ZnXs2LFeFWk6duyoW2+9VVu2bNGCBQvUqVPj/4ByvDt5mHGdyn1p2XaXZ6pLbk6h1q81luAbMjTRpfKHAHzn11TjRdAOkSHq2dy5hJOY4ACd0Nr4JOjv+w6pssbmVJ/OKKuq0Yr0PEN7zxbhCnLh6Qt71XbWZXovSQioj6HDEg1t6Wk52rHNeOOpPnJzirRxnfHp0sFDu3IO4CGbN6QY2vz9/RTX2rVkR+Awe/NEkpPzxCHmCa9jnmg6TjvN+DRuamqG05VbcnLytGaN8YbXSSf1V2Cg6xUa62vAgB5q1sz4uWLBgmVO97lw4e+m7cOHD67X/vHxLdWli7FK1uLFf6jSTrWIuixdusb0Kevhw4/9lPXo0Sdpx45v3fK1ZMl/Tce48MKRpj//+uuPOHSMWVk5uvbaR5SZaVwGJj4+VrNnP61WrYxJFWjaWvY13pQuPnBQeSnOXXcszy9Qzg7jdcfY3t1l9W/c5xJVZeXK37vP0B7aPMYnx5a9Lcl0Ga0Opw+T1Y9lVo53w07tb2hLS81yunJLTk6+1q4xJtmdeFJvBQR6799//wFdFdHMmLi2aOFKp/tctMh831NP6+90n+6wfr3xAQf/AD+1jm/h9VjSUrO09Jd1hvYzzhzi06o9AADf82mFoF69eunNN99UcnKyMjMz9d1332nOnDmaPn26nnrqKU2fPl1z5szR999/r8zMTCUnJ+u1115Tz57G5As0TmedbX4h6YtPnbto9dXny1VtUuFhjJ1xADR83yQfMG2/ole8U/1d1jNO/laLyTgHnerPFV/vNB5bkL9V53QxPllTXxcnGp8WlqQV+/Oc7hPwhDPHDjBt/+qzFU719828labnAPbGgWv+3JSqrMw8Q3tizzbyD+DCNtzjDC/NE/bGgWv+3JSqA8wTTca55w43bZ879wen+vv004Wmr8fzzjMfx1MCAvw1ZswphvYdO1K0fr3jVStsNps++WSBoT0iIqzeiTSS+e87L69QCxcudzgmSfr4Y+PfyWq1auzYU53qr6HJycnTNdc8rH37sgzbYmNjNHv202rTpqUPIoOvtT3ZvJJAyk+/OtVfyi/LZasxzl1tTzrBqf4akqwNm1VTaVxqJbqL9yt822pq9OfcLw3t1gB/dTzzdK/Hg4bnnHNPNm3/9JPFTvX3xWc/m56XnHOO+TieEhDgrzPPPNHQnrQjVRtMEk/qYrPZ9PmnPxnaIyJCderw/s6E6BabNiYrMyPH0N6zZ0efPODwyssfm/79r7nuHK/HAgBoWBrMUlstW7bU2LFjNX78eN155516+OGHdeedd2r8+PE666yz1LIlH3ibor4DOqlj5zhD+3dfr9T+dOPJ1LEUFZZq7oe/GNpjYiI0fKR5aWQADd+6zALtzC02tF/UrZXaRBiXMDqW8EA/XdunjaE9u6RCi1OMTy562sLd2couMS65dH3fNvIz5vrUqWNkiEYnGJ+yzC+v1JoMKtKgYenTP0EdOxkTv374dq0y0nMd6quosFSffviboT06JlynjjAvjQzXvPrKfNP2YcNJeIf79OmfoASTeWKBk/PEZ3bmiWHMEx7xup154hTmiUZp4MAeplVSvvzyJ9PkhWMpLCzWe+99a2hv3jxKo0YZbx552qWXjjFtf/XVjxzu67vvftWePcaqWePGjXCo0s5FF402vZH0xhufqLq62qGY1q3bZroM1/Dhg5tEhZb8/CJdd92jpr/3mJhIzZ79lDp0cO4hDDR+MV07KaJNa0N72rI/VHLQseuOlSWl2r3QeEM6qFmE4gb2czrGhqC6slLbPvvGdFvcgD5ejkZK+nah8nanGNo7jRmpkOgor8eDhqf/gG7q3Nl4fe+br35TerpjD8oVFpbow/eNSbAxzZtpxKhjV27zhH9cOtK0/Y3XvnC4rx++X6GUPcZlnc674FSvVgA82ksvmJ9jnT5ioJcjkb6bv1w/LlxlaD9r7FD16s2KGABwvGswiTQ4PlksFo2/brShvaKiSlMf/1g2W/2XWZn+wjwdyi0ytF921XAFBdX/xPDxh9/XCb0nGr6+/cq4Li8A73hno7G8cJC/Vc+c1s2hfiaf1EnNQwIN7XO2pKuiuv7zzXMjuin5n6cZvi6yUw3GnvLqGs3ebLzg2715uG4f1MGhvvws0vMjEhVgsizUe5v3q9zkyQrAlywWi6681vi0dUVFlaY9+blD5wAzXpqvvEPGhLtLrhzm0DnA04/O1cn97jd8fff16nr30RDt2pnp1v5ee+U70+VxAgP9dfZ5VAGE+xxrnnjewXniVTvzxD8cnCeeeXSuhvW73/D1fSOfJ3a7eZ54/RjzxFjmiUbJYrHohhsuMrRXVFRqypRXHXo9Tps2U7m5+Yb28ePPVVCQ8VzdnoceekWJiecZvubNc+xp9L59u2nIkN6G9mXL1uvrr3+udz+5ufmaNm2Wod3f30/XXjvOoZhatWquc889zdCenJymt976vN79lJdX6LHHXjfddtNNFzsUU0NUVFSiG26Yoh07UgzbIiPD9e67T6lLl/beDwwNhsViUZezzzC011RWacOsDx2au7Z89LkqCo3XHTueOUJ+DtyQXvfWHH09/lbDV+qv9a+4V5Dm3DKXZmw1NdrwzvsqPmBMPghqFqHWg/u7baz6SPnpN23/3JjUE9I8Wt3OP8ursaDhslgsuvaGcw3tFRWVemLKfx16bb/43AfKzTU+fHbV1Wc5dF7y8KQ31afHlYavr75cWu8+JKl3n84afEIPQ/vvyzfr22/qX8U/N7dALz73gaHd399PE649u9797Exybik8e15+8WOtW2us+hcYGKDzxxnPfTzp11/W65HJbxraw8NDdO99V3o1FgBAw0QiDXzu7POGqGuiMYN85YrtemHqZ/U68X1/1mJ9Pc/4gbNlqyhdMX6EW+IE4DtfJWVpa7bxgtWwdtF6bFjnevVxY7+2urSH8Um0zKJyzd7kvotQjnp3U7rSCsoM7RMHtdf4ei5fFWi16LUze2pAnHHd3qKKKr3rw+MDjuWscwepa6Lxdbn6j516+dmv6nUO8OHsX/Ttl8anh2JbRuqyq5rGcgWuennal7pp/Az9sXyHQxcUj1ZQUKKpj32qD2f/Yrr94stPVmyrSKf7B8yMOXeQutiZJ16p5zzx0exfNN/OPHEp84Skv+aJW8bP0Eo3zBPPPvapPrIzT1zEPNGoXXDBCHXvblziY/nyDXrqqbfq9W/nv/+dp88//9HQ3qpVc11zzQVuidMZ999/nSwWY0nIxx57TStXbq5z/6KiEt1661M6eNBYLeuqq85R27aOJdxL0p13XqWQEGMFzhkzPtL8+XXflKuoqNS9976gpKQUw7bRo4dq0KDGXR2qrKxct976lDZv3mnYFh4eqv/+9wnTf684/rQbdqKatW9raD+4ZZs2v/dJveaund8tUurS3w3twdFR6jzGvHqEJ618+Q2teH6GcpN3u9RPaW6eVr7ypvatME8I7jbubPkF1j+RQPqrmkxJtmOVA6W/quJsnzdfG2d/bNxosWjgLdcqICTE4X7RdJ13/qlK7G58CG3F75s19enZ9XptvzvzW8374hdDe8tWMbp6gu8St/51/5Wm5yVPPT5Tq1dtrXP/oqIS3XHbizp4MM+w7fIrz1SbNvVfUn7q07N11eVTtHzZRpc+J+TnF2nKw2/r3ZnGqoSSdMVVZ6pVqxiH+vzwg4Xavcvxa541NTX6+MNFuvfu6aqqNFb6m/zItWod38LhfgEATY9XFhx88sknj/x/XFycbr75Zrf1/fbbbysz8/+fnpsyZYrb+oZ3+PlZNWnK5brpmldUXVW7YsJnH/+qtNSDun/yJWrfwbi818EDefrPS19rwXfmH/jun3yJQkIdW/rFnYoKS/XJR3Vf4MrMOGTaPvMtY1nJo512eh/TRCSgKamxSVN+3amPL+hnqLgyvncbdWgWoieWJ2tvvjEhpWVooB4c2lEXdDO/eP34smSVVvmuWkt5dY0eXpqkd8/pIz/r/39ItloseuzULjoxPlIvrNyjvSbJNpI0rG20Jp/cSd1iwky3P/LrThVUVHkkdsBVfn5W3f/Ixbr1utcN5wBffPK79qVl696HxqldB+NFnoMH8vXaK99p0ffrTfu+d9I4hYQ6dtHXnYoKS/XZx8vr/LnM/ebnAO++XffT9Kee3lNdutUv4e7PTam697b/qlVclEac0Venj+qtxB5tFRRc99OzqSkHtWD+Wn31+R+mFT0kKaFTS910m/nSGIAr/Pysuu+Ri3W7yTwx73/zxD125onsA/l6/RjzxD0NYJ74vB7zRJadeWJ2PeaJYQ7OE/+67b9q+b95YriD88TC+Wv1dR3zxI3ME42an5+fnnjiNl111UOqqqp90+HDD7/T3r0ZevTRW5SQYPw3l5WVoxdemK1vv/3FtO8pU25RaGiwB6Kun759u+mqq87RBx/UXpKstLRcN9wwRRMnXqFrr71AwcHG6wu//75BTzzxhlJS9hu2tWnTUnfc4dwTzfHxLXXnnVfpuedqV7mpqanRffe9pM2bd+r22y9Xs2bhhn03b96pJ554wzTJJCIiTJMn3+RUTA3J44+/rlWrtphuO+GEXlq2bL2WLTOf/x11wQUj1KYNS843VharVf2uu0LL/o+9+w6Pqsr/OP6ZSe+FQAgBEnrvVUQERFQQRV3siGV17e7ay+qu3bWXdXVXQbE3XFGKIiCCSO+9hZIOSUjvyfz+YOFHuHdCpmVS3q/nySOcyZx7JpI7Z+79nO955hXZTqnUun/hryrKPKI+112h0NbG/8clR3O1/Yv/KuV3YyBXkvped4V8Tc4LnmaTTYe3bNfhLdsVGherNsMGKW5gX4W3i5fV9/SX+3MPJCtl+SodXLJclaXm1xpienRVh3Mcrw6R9NNi7fzme8X266W4wQPUekAf+YcZz1PHlRcVKWPDFu2ePV9FGYdNv6f31ZcppodjFYnR9Pn4WPX4327U9VOfMsxLvvjsZyUfytQjj01TQqIxlH/48FG9+vJnmvuD+Vz80b9O8+q8pHefTrry6nP1+acLarSXlJTpT398QbfdcammTpugwEDjZ5kVv2/Rs099oIMHjRUn27SJ0e13Ol6RbvOmvbr15n+odVwLjT9vmMadO0Q9enYwPf6pDuxP1w/fL9M3Xy02rfwjSR07xeuOu/7g8Ljm/bBc/3juIw0Z2kPnjh+mMecMqjWMU1xcquW/bdaM97/X1i3mQcRrp56vSRez2AIAcEy9BGn+/ve/n0jQ9uvXz61BmnfeeUebN28+8XeCNI1Tn34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P8uVrNXXqLD366C0Ox5SYmKz773tFhYXGyaagoABdO3qYLrqon1q2bKKAAH+lpaVrw4Yd+uabJVr1xwbDPlu37tGjj7ypt995zOGYKur99+Zo717nJiHttXr1Ft024WnT10uS2rVrrosu6qdevTsqKipCUVERslqlzMxsHTqYoi1b47Vxww4tX75WOU6UkUfVU1JUrJ9e+lhZR48bN1osanVeV7Ub2EvRLZooICRIhbn5OrLvoHauXKdtv682LFWUn5WrH1/8SGNef1j+wZ4pJXomH38/1W8VowZtmimiUbQiGkYrJCJMfkEB8vHzU2FevvKzcnQ04ZAObt2jnSvWKS/TuOxZaUmpFr4+S7XqRqpeqxinYvL28VF0yyZq0Ka5Iv8XU2idcPkFBsg3wF9FBQXKz8pV2oEkHd6xVzuWr1VWqsm/kaQVM75XWFSkWvbr4lRMqFmKCot1792vKynJWFXDYrHo4mG9dcWIC9S+Q3OFhQUrJztPO3bs18Kf/9SPP64wTLJmZGTrnomv6ZvvXlRoaFBlPQ1Tmzbu1tNPfWJjXBKi68dcrAEDu6tpTD35+vjo6NHjWrtmu+Z8uVSbNxuXN1q5YqPeeO1LTX7kRqdj8/X1UYeOzdWla2s1j22gmJj6qle/toKDAxUUFKC8vHxlZORoz65Ebdy4S/PnrVJyUpppX1Nf/lwNGkRp6EW97I7j808Xac5XS023BQb668qRF2jgoB6KbdFQkRFhys7JVdLhNP2xcpO+/26ZDiYeMez38Yc/Kiamvq4YcYHd8aDmKykq1tznP1GmjfFE2/5d1WlwL9Vr2ViBIUEqyM1Xyt5D2rZ8nTb/ZhxP5GXlau6zH2v8W5MVUEXGE77+fmrQOkaN2jZT7UZ1VbvR/48nfP1PjCdyM3N0JOGwDsTt0dbla5WXaUz4Ly0p1Y+vzlZ43dpq0Nr5m028fLxVv0VjNe3YUo3bxyosKkJB4SEKCA5SfnauMlKO6cCWPdr0y99KO2h+wWHR+9/IPzhQ7S/s7nQ8qFlOzHO8VM48x3kaMWLgGfMc+/43z/G7jXmOF/X9D69XgXmOnXpqyvvlzHNc+r95jganzXNs1ZdfLLQxz7FOU6fO1qOPjq+M8D3Cy8tLbdrEqGevDurZs50aN6qnyNq1FB4equzsXKWkpGnNmq2a99Nybd5sXl3n44++Va2wYN06YVQlR4+qrrioWK8/OlOpKcaxhMViUZ9BnXXBJT0U26axgkMDlZdboIRdh/TnLxu0fOFalZwxlsjOzNXUh6frpZkPeGyZEzN+/r5q2b6p2nWLVauOMYqMqqVakaEKCPJXdkaOkhJTtW39Hv027x+lpaQb9i8pLtGbj8/SY2/cprZdXVPJ3NvHW83bNFK7bi3UpnMz1YmOUK2IEAWFBionM1dHk49r+4Z4/T5/tQ7vN35PkKTpU79TcEig+g2lyh3cp7ioWP99fIbSbJwneg3srP7De6pZ65PniXwd2H1Yfy1drz8WGc8TOZm5evPR6Xp2+oNV7jwR276p2naJVYuOzRQZVUthESEKCApQdkaOUg6mavv63Vo2/x8dO5Ju2L+kuETvTJmlh167XW26uH/Fg8/f+kFZGc7d6AycK9xeOQM4B7l9aafqpqou7fTGG5/qww++MbRHRtbSG29OVu9yynfv3Jmguya+oMREYzWIK0cM1Msv3+/SWO1RUFCoyy69yzS2/v27auprkxQebns90U8/na+XXvxEJSXGNdRnznpOffp0sjsmq9WqG65/ROvXbzdsa9++hd5+51E1aBBlc/9FC//Qo4/+V3l5BYZtz79wj666aojdMVXU7t0HNGrkfWUmFDt3aa1NG43/r121dNK+fYd07TWTlGkykR8b21hPP3OnevRoX6G+CguLtHz5Ws2ePU+NGkXrxRfvdTo+V2NpJ/us+myeVs9dYmgPrBWiSyeNV+OOtkuBpiYc1k8vfqyMFOOF+rYDe2nYvc5flHbE0ve/UuLmXYrt3Ukt+nRSdIum8vbxrvD+xUVFWjP3F63+donhwp4kRcU01PWvPSQv74r3ufrbJdq0YKVie3dUbO9Oatg2Vj5+vhXe31paqk0LV+qPT+epKN947gqOCNO4d56oMslLVd25sLTTW2/O0ccfGu8IiowM09Q37lHPXu1s7rtr1wHde9frpskUV1x5vp5/6Q6XxmqPgoJCjbh8smls5/XvpJdfvUu1wkNs7v/5Z4v16kufmo5Lps18XL16V+zz8KRPPv5JX36+RIMGd9egwT3UrXtr+ftXfJmo0tJSffXlL/rv63OUm5tv2B4VFa4ff55qV/LS5k17NO7Gp03vOO3Tt4Oeff521atv+z1QWFikjz74QR++/71hW2Cgv76f94oaNrQ9zqopWNrJPstmz9efXxvHE0G1QjTy4ZvVtJPt8cSRhMOa+9zHSk82JpV1HNRLlz8wxqWxVtTCd+YoYdMute7bSa36dlT9lvaPJ/78+hf9+c0vpuOJus0aavybkyo8nvhqynvau37Hqd/rt2yiLhf3U5v+XRQYcvZzhNVq1eZf/taSD79VUUGhYXtASJD+/eETCqpl+xyKss6FpZ3eeOMzffjBXEP7iXmOSRWY53hRiSZVqk7Mc3juu+SJeY67TWM7Mc/xQAXmOabbmOd4Vn362L9824gr79OOHQmm20JCgtSuXXN17NhC69Zv18YNxvkDVy7t9NSU9zVnzuJTvzdr1lDXjr5Yl1zSX3XrRlaoj99+W63HH3tHx49nGrb5+vroxx/fVPNYzy2nURlY2sk+X32wQD/M/tXQHhYeonufu1Htu9lezuhAfJKmPjxDRw4bxxIXDO+hO5+8zqWxVsQDo1/S4QP/n4TYrlusBl3RRz0v6CD/CiwrW1xcop+/WKavP15keq6JblhbUz+fLF+/it+D++L9H2nTP/9//mjeprEGX9lHvQd2UkhYxcYSy35erZmv/6CCfONYIjg0UG/MeURh5XwfQ1ks7WSfbz5aoHmfGm8YCQ0P0V3PjFXbrrbPE4nxh/Xfx8zPE+cN66HbH7/epbFWxMM3vKSkA/8/t9G2a6wuvLyPup/fscLniYVfLdN3nyw0PU/UbVhbL85+2K7zhL3iVu/Uqw9+WKYttn1TxW81zs+ztJNjWNqpZklgaacaI4alnaoMEtSqgcTEZM2Y/oOhPSgoQNNnPFPu5JIktW4do9mfPq+oqAjDtp9+XKaNG3eY7FU5Zkz/wTSJpnfvjnrv/SfKnVySpBtvvEz/efpO023PP/eRSkuNg7yzmT9vuWkSTcuWTTRz1rPlJtFI0rDh/fXW24/Ky8v49nr9tVluW+KptLRUTz75TpkkmtDQYD3yiOOVec6mqKhYkx58zTSJ5soRA/X9D29WOIlGkvz8fDV0aF99+ukLevjhmnun3bkiPTnVdFkg3wA/XfWfieUm0UhSnZgGuvq5exQUEWbYtn3ZGiXt9MwyYv1vvEI3vz9FF9w0Qg3aNLfropck+fj6qu91l+iSB2+Sl7fxPHE04ZDiV8fZ1WfnYefr1k+e0aDbrlHTzm3sSqKRJIuXl7pceqFG/edO+Qb4G7bnHM/UlqV/2dUnaq7ExBTNmrHA0B4Y5K+Ppj1abhKNJLVq1UQzZj2pOnXCDdvm/fSHNm00v8O4MsyascA0iaZX73Z6+91J5SbRSNINYy7Wk0+Zf+6++Nwsu8cl144eoqW/v63Hn7xZfft1tCuJRjpxZ/f1N1ysDz95REFBAYbtR4+m67tvf7erzzdf/8o0ieaCC7vqvQ8ml5tEI534rL/rnqv12BM3Gbbl5RXopRdm2xUPar7jyan653vz8cR1z04sN4lGkurGNNCYF+9RsMl4Iu73NTrkoWVJB950ue74+EkNGn+lGrV1bDxxwQ2XaMRk8/HEkX2HtOufLXbHFdu9nca+ep9ufmOSug7rV6EkGunEXbqdL+qrG1+5V/4m55v87Fwt/8y5JeVQs5yY5zAm5Z6Y5/hPBec5nitnnsNzN0nNmP6jaRLNiXmOxyo4z2GeWPz8cx87NM9xUkCAn7p0ba0bb7xML798rxYseEdr1n6u2Z8+p4cm36SYmAYO922vLl1b66OPntSChe/oppuuqHASjSQNGtRLc7+dquho47ijqKhYL7443ZWhoppLOZSmn79abmj3D/TT4/+9vdwkGklqEltfU969Q+G1je/dlYvWafcWz9xwZfGyqN+QLnr100ma8s6d6n9RtwpdHJckHx9vXTl2sB569RbTMUjKoTQtmLPCobi69G2jpz+8Sy9Mv0+Dr+xToSQa6cRYYuBlvfWf9ycqMNg4lsjJytPXHy9yKCbgbI4cTtOiOcsM7f6Bfnr4jdvLTaKRpMaxDfToW3eqVqTxPPHn4nXaszXBRZHax+JlUZ/BXfXCrIf06FsT1W9od7vOE5ePGaz7XzY/Txw5lKbF3xjPra5SkF+omVPL3lhep36kRowb6rZjAgBwJhJpqoGPPpprumTO5Mk3q02bZhXqo379KL3wwj2GdqvVqvff+9rpGB2Rm5uvGTONE2ehocGa+tok+VXwQvDVV1+kSy4539C+e/cBLVls3xq6VqtV779vfD18fLw19bVJFS4Pff753XTLLSMN7WlpGZrzlXu+9H3xxQJt3FA2Ker++280nVh0lWmffKetW43LWFx8cT+98MI9Ff43NHO2yUVUfWu+/UUlJut2n3/TSEU1q9jdgaFREbr47huMG6xW/fO1ZyZQAip4XDqFzQAA9xVJREFUQelsWvbtoi6XXmi6bfefxiXiyuMfHCiLxfk7jRq0aa7zxlzmkphQc037+CfTccmkh25Q6zYVW0akXv3aevaF2w3tVqvVtFJJZcjNzdfsmcYEodDQIL386l0VvsvqqqsHatjwPob2PXsO6pclq+2KKTQ0yCXv7S5dW+me+64x3bZk0T8V7mf9uh1as3qbob1u3Qi98PIddt2Jdt0NF2n4JX0N7ct+W6etW/dWuB/UfH99s9R0PDF4/AhFN29YoT7CoiJ02X0md4Farfrjq8XG9krgqvFEm36d1eMy8yXRdpgsOWtLwzbNNPbV+3Tt0/9Wo7bNHY6nXmxjXfGgedXALb+vUbGNpWBx7vnoo29tzHPcZOc8x92Gds/Pc/xkaA8NDdLU1x6wY55jqC65pL+h/cQ8h/0J7vfce71++PENrV33pb766mU9/sStunLEQDWPbeSSsYY9YmMb6f0PHtdXX72sCy7s7vDxGzasq/c/eFzeJsmEK1euV1KScckwnJt+/PQ3FRUaxxJj7rpcTVtWLHmsTnSE/v34aEO71WrVdzN+cTpGe3Xu21avzJ6ke565UY1j6zvcT5c+bXTj3eZ3PP/209929dWyQ1M9/eFdeuS1CWrdsWLncTPNWjfSXU+ZV+9YuWidCgsYS8D15n/2q+l54ro7r1CTFhX7zlE7OkITHjM/T/w4s/LPE516t9HzMx/Snf+5UY2aO36e6NS7ra6beIXptmXz7DtP2OO7aYt0NOlYmbZx94+SXwUTgQAAcAUSaaq49PQs/fiD8U7hFi0a65prL7arr/Mv6K7+/Y1ryS5fvlZ79x50OEZH/fD9b8pIzzK033bbv+xO/nho8k3y9TVevJk5yziBVZ4VK9aZvhb/+tdQtW4dY1df/77jGkWY3Pk6e/Y8p+4gM5OcnKo3Xv+0TFunTq00+jr3lTM8cuSYPvrIWIY7NraxXp36oLztWJYGNU9+Vo62LzNeMI5sXE+dLupnV18x3dqpade2hvZ967bp2CHjnZ7VSc9RQ2XxMk4cJ272XEWOzsPPN13CKWnXftOlGnBuyUjP1rwf/zC0x8Y21L+uGWRXX/3P76zz+huXYFy5YqP27TvscIyO+umHlcrIyDa03zLhCtWJCrerrwcfukE+vsbPwU9nLXQ0PKdde91QhZrcDRq3Od50OUozc782VgWRpLvuvVq1HFiuZfIjN5q+TrOmU7UCJ+Rl5SjuN+N4ok6Teuo6zL7xRGz3dmrerY2hPX7tNqUdrN7jib5Xm48nEuwYT5x//XCnEmhO17J3RzXr2trQXpRfqH0erBKCquPEPMcyQ/uJeY6L7Orr/Au62ZjnWKe9ew85GqLDXDvPMc4l8xzSiQoubdo0k4+d1a/cYey4yzVwYE+X9NWuXXONumqw6bZff7UvgRk1U3ZmrlYuWmtob9QsWoOvNCa/l6dLnzbq3Nv4+bbhr+06vN9Y1dKdxt17pRo3r+eSvi4adZ4axhiXbks5lKbE+KQK9/OvWy52KoHmdN37t1fHnq0M7QV5hYpb67n5EtRM2Zk5WrXYeJ5oGBOtAVfYd57o1LutOvYynic2/b29zDJLleGGe0aoUTPXnCeGjDxPDZoazxNHDqXp4N6KnycqKmHnQS3+pmxVrJ4DOqtz3/IrIAMA4GoeT6SxWq3auHGjpk+frldeeUWPPPKIJk6cqEceeUSvvPKKpk+frk2bNslqtXo6VI9YtGiV6V1aN910penSQWcz3qRKiiTN+2mZ3X0568efjAlCQUEBDiV/1K8fpWHDzjO0b9ywQ/v3V/xi3I8/GmOyWCy6efwIu2MKDg7Utdcan0tKSpr++ce+ZVvO5un/fKCcnLxTv3t7e+npZ+506P9IRc2c8aNyc/MN7U8+eZtTlWhQM+z6c4Pp3ePdrxgkiwP/L7uPML9Av2PZGrv7qkqCwkNNq/PkZmarpNi4dEpl8PL2VuNOxi/9slqVcyyj8gNClbJ48T+m45KxN13i0GfOuJvN12Ke/5MxWcfd5v200tAWGOSva0YPsbuvevVr6+Jhxgm3TRt368B+45KWlcHHx1t9+nQwtFutVqUeTa9QH//8Y6xGU6tWiC651L6EhpPqRIVr6NBehvbffl2rrCz3LIWJ6mX7HxtNxxO9Rgx0aDzRe5T5xdYtvxsnzquT4PBQRTc3GU9keG480cHGRfKD26g4BWnRoj9tzHNc4eA8xwjTds/McxiXODgxz2HfjVBSefMcO7V/v+svWlVXV145wLR9/Trjkt049/z92ybTKhOXjL7QofPNZdcPMG1fuXid3X1VFV7eXuo31JiQKEk7NntmCUxJOn9Yd9P2nZsYS8C1Vv9ufp4Ydq1j54nh1w00bV+1pHqfJ/oMMT9P7HLxeaKkuETTXp6j0pL/vxE5MDhAY+4d4dLjAABQER5LpFm6dKlGjBih8PBwde/eXRMmTNCjjz6qV199VR988IFeffVVPfroo5owYYK6deum8PBwjRo1Sr//bkx0qMmWLF5laPPz89Ww4cYSvxXRp08n03WnFy0yHsedUlLStMnkbsSBg3pVePmkM11xpfkgdXEFl3cqLCzSst+NF+U7d2mtpk0dWyf8iisGmLYvWui613vhgj/0++9l77S6cezlauuiO0rN5Obm65tvlhjae/Ropz59O7vtuKg+dv+50dDm7eujlud1cai/Jh1bKTiylqF9Vw1Ybii0jsmdqVar8jKNlTEqS2idcNP2XJNqHTi3/LLYuAyQn5+vLhrW26H+evdpr7p1je+BJSbHcaeUlGPavMm4VOGAgd0VGurY8iuXX2E+Vlti5/JOrhRdr7Zpe1ra2ZPkEvYl6UjKMUN7r97t5O/veGnl8y/oYmgrKCjSst+q7yQjXGfHqo2GNm9fH7Xt38Wh/mI6tVSIyXhi+6rqP54Iq2LjicbtYk3bs49nVnIkqIrMlmA+Mc9hTBqpiD59OtqY57BvqWdn2Z7n6OnEPMcA0/aKznOcC7p0aWN6ofNo6nEPRIOq5p/fNxnafP181GeQY3NX7bu3UEQdY/Xpf34zHqc6adPJvJJMepqxwlZladPZRkzHPBcTaqY1yzYb2nz9fNRrYBeH+mvXzfw8scbkfFSdtLJ1njjm2vH9ojnLtX932aqCV906XBF1jN/jAABwt0pPpFm/fr26dOmiiy++WPPmzVNWVpasVutZf7KysvTjjz9qyJAh6tatmzZsqP6TnWdTUFCodSZ30PTo2V4hIY5d1PHy8tIFFxgz+vftO1Sp60evMpkUl6QBAxwv79u7d0cFBvob2v+0cawzbdiww7TCyoABPRyOqXlsIzVpYiyh+KdJkoEjMjOz9cILH5dpq1+/ju6+23wtYVf5denfysrKMbSPvm64W4+L6qG4sEiHthvvEGrYroX8g4xLBlWExctLzboZy3ceP3REWUer9wSpt43y6r4eXPPX28dYQl7ybEzwvIKCQm1Yb7w41L1HG6fGJf3PN05iJ+xLUnJSmkN9OuKvP80rxV04wPyOq4ro1au96bjkbxvHqgxmy0NIUmBQwFn3tbXcVsdO5hfLK6pjpxam7atWGSczcW4pLiwyrV7SpEOsU+OJ2B7G5SKPHTyizOo+njBZJk3y3Gd3cESoaXuOyZI3OLfYnudo5+Q8RzdDe9WZ53B8TqF37w5OzXOcC3x8vBVhcs5JS6Wa5rmusKBIOzYZKyW06dxcQcFnH/+a8fLyUpe+xqUiDx84qtSU6juWCK9t/rmdcdxzn9vhtY2JCJKUcYwbfOA6hQVF2rXZ+J2jdefmCnTiPNGpt/E7R9KBI0qrxueJWpHm78lMF74njxxO0/czFpdpa9amsYaMcizZGgAAZ1VqIs3rr7+ufv36KS4u7lSCjMViqfDPyX02btyovn376o033qjM8CtdXNxuFRQUGtp79TKW5bdH794dTdvXrN7iVL/2WLd2q2m7M8/Nz89XXbsav8xu2LBDxRUoaW47JvPXq6J69+5kaEtMTFZycqpT/UrSKy/P0NEzJv2feOI2BQc7dnGhoswqGPn7+2nQIMcqEqBmSd69XyUmpdobdzC/WFpRjTq2NG0/uNVYRaI6yTG5M9svKMDhi4SukHPcfNI5JDK8cgNBlbIlbq8KCozv7Z69jBNE9ujZ23yN6zVrKq8c//q1xgQhSerZy/H1t339fNS5i/G8tXHDrgqNS9wh1UaiQLRJVaAzpduYRK9no8pNRdWrb77/GpNlpHBuObzrgIpNxhNNbIwHKqqpjf0PbKne44lskztB/T04nrC1NLOPL0vAnusqf57D/Hu+O6xba/7Z5cycgu15jp0eG09URaWlxnOOvz/nm3Nd/PZE0+Va2nVzLhG8fTfzuY3tG6rvkkNm7yFJ8vXg0u2lpaWm7X5+5jcHAI7Yt8P8PNGmi3NzmG1tnCd2bIx3ql9PsjW+93Xhe3LGq9+o8LR5Jy9vL9086WqHltgCAMAVKu0TaOrUqZo0aZIKCwvLJNBIqlBFGkllkmoKCws1adIkTZ06tbKeQqXbttV8YNW+vXMDufY2LmZvM6kg4S7bthmfW1RUhKKjnbsYY/bcCgoKFR+fWIGYjM/f29vL6SWSbL7eJsezxz//xOnbb5eWaRs8uLcGD+njVL9nU1JSon/+Md5N36NnewVV4I521HxH9h40ba/boolT/Ubb2P/I3rO/v6uqwrwCJe/eb2hv0MZ9S7NVRGLcbkNbZKN6CgxzrCQ9aobt28zXvW7X3ry8b0W172D+/33H9gSn+rXH9u3G51anTrjpslP2MHtuBQVF2rv3kMmj3W/1auMFvuaxDRVuo3LE6Y7bSKQJdfK8EBDgJz+TCfojR44r9Wi6U32jeku2MX6vH9vYqX7rtzQfTyTHm49fqoPCvAId3mUcTzRy41KvZ5OeZH7TQIiNO1lx7ti21fx7cM2Y5zAe68Q8h3HZKXs4M89xLsjOzlW6SbWrqCjnxnGo/vbtNP9sb966kVP9Nm9jPhZJ2OWZMb4rpBwy/9yOsFGppjKkHDKvUGqrUg3giIRd5ueJGCfPE81snCfOXLKoOjli4zzhqvfkyoVrtHXtrjJtQ0ad5/S/BXAusVj4qSk/qDoqJYX7hx9+0MMPP3wqceYkq9WqyMhIXXHFFerZs6fatWuniIgIBQcHKycnR+np6dq2bZvWrFmjn376SWlpaaf6OFmh5pFHHlHLli115ZVXVsZTqVQ7dyaYtre0MflbUU2b1pevr4+KispmW+8ymXx1h5KSEu3ZY5zwaeHkRXZJahFr3seuXfvVunVMufuavd4NG9Y1LaNsV0wtzAfOu3YlaNCgXg71WVBQqClT3i2TCR4UFKAnnrzNof7ssW3bXmVn5xrae/Zo7/Zjo3pITTD/Uli7sXGZM3uE14uSt4+PSorLnrtS9yc51a8nxS1ZpZIi450vbS90fJk7Z+3+a6Oy09IN7W0vdLwkPWqGXTvNL9bEtnBuUqNJk3qm45Lduyrn4lBJSani9xjPW7EtGjrdd2yseR+7dyWqVSvnxz32WLpktVKSjxnaL7u8YiWSbd195s4vd7t2HVCdqHD3HQBV2tEE8+XE6jR1bjwRUT9K3j7eKjmjksPR/ebHqw42LDIfT3QY6LnP7v02KvyEO3njBKo/2/McziXJNW1qPp7w/DyHc89LklrEmo+1KjLPcS5YvXqL6TilUaNoD0SDquRAvPlcQaPmzo0l6jWqIx9fbxUXlR1L2DpedWCrmk7dBp773N6+wfwG07oNnEtOBE6XGG/+HaBRM+fOE9ENzc8TB/dW3/OErWo6US54T2Yez9aX7/5Upi0iqpauunW4030DAOAMtyfSFBYW6r777jtVhUY6MRFfr149vfTSSxo9erT8/Gyv237BBRfo3//+t4qKivTVV1/pkUceUVJS0qnKNKWlpbr33ns1fPjwcvupjhITUwxtAQF+Tldt8fb2VsOG0Uo442L3wcRkp/qtqOTkNMPkliQ1aVLf6b6bNDXv42zPraSkxHTtdJfEZKOPgyb/vhX17rtfaf8ZFxfuvud61a8f5XCfFWVWTUiS2rYre8drUVGxfv99tZb9vkZbt8br8OGjysvLV0hIkCIjaykqKkLde7TXef26qHOX1vLx8XZ77KgcGSnGu4Z8/HwVUjvcqX69vL0UVjdSxw8fOeN4zi+T5gnJew7o768WGtojG0Wr5XldPRCRlJ6cqt8/nmtoD6wVok7D+nsgIlQlBw8eMbSdGJc4N2ni7e2lhg2jlJBQdkLJ7HjukGJrXOLkxXpJatzE/ALOwcTKeW4nJSam6MXnZxnaIyPDdM21QyrUR61aIabtmRk5TsWWn1+oQpPleyQp8UCKxFLo56zjJsug+vj7KtQF44la0bV17FDZ92F6svldz1Vd0u4DWvmFcTxRu1G02vT3zHhCkrb+vta0vXl355YDRPXn3nmOuko443uyM9+77eGZeY7KeW5V3U8/LjNtP/+CbpUbCKqcI4eNn+1+/r6KjKrlVL9e3l6Kqh+ppANl5xHNjlcdFBeX6K9fNxravb291LFnq8oP6H9WLl5v2t6lj3G5O8BRR22cJyJccJ6oUy9SyYk15zzxz28bDe3e3l5q36O10/1/9tb3yj5jbmHMvSMVSPV7AICHuX1pp3fffVcHDhw4VUHGarVq0KBB2rFjh8aOHVvh5BdfX1/deOON2rFjh4YMGVLmbpPExES9//777noKHnP4sPEiS1RUpKGyjyPq1jVe9EpKSrW5/qwrmT0vSU5fiJPMn5ckHTpU/gWrlJRjpuuLOzuZJ51YGsLb2/hWO1tMtuzcsU/Tp31fpq1t22YaO/Zyh/qz157dNioS/K/MfmlpqebMWaQhgyfonrtf0nff/aqdOxOUlZWj4uISpadnae/eg/rnnzi99+5XuuGGRzR0yAR9/fVi1nivITKPGiseBEfUcsm5KzjS+EU2K/W4rJVw7nKlrb/+re+mvKPCvPwy7d5+vrr4vrHy9kBiWfzqOH392JvKOZZRpt3iZdHQidcrIJRlnc51hw8bE07rRIW75L0dZbKEUuWNS8yT8Zxd1qm8Pg4fMr6W7rLs93W6acwzOnLkeJl2Ly+Lnn5ugmqFmyfInCncxuOSnUw+SE6yvf+hg5X3OqHqyTzj/6wkhUSEueScY7a8UMbR6jee2Lz0H33xxLsqzCso0+7j56srHrzRI+MJSdq3cafpUlPh0bVVN6aBByJCVWI2noiKiqgB8xzmn1menOc4F+zde0hLl/5jaA8M9Fffvp08EBGqktRk41givLZrxhIRdYxjidSU9Eo537jaioVrdexohqG9dedmCg4N9EBEUtzaXYrfdsDQXrdBpBrHOp+gCJxU2eeJtGp6nli1aI2Om5wnWnVy/jyx6e/t+nvphjJtXfq1U88L+RwHAHie2yvSfPnll6f+brFY1KdPHy1YsMDh6jGhoaGaP3++Bg4cqL/++utUgs7nn3+ue++911VhVwnHjhkHJ3XqhLukb7My+UVFxcrMzFF4uHvXv01LMz6vEzE5f8HK1hrYto75/9vTTdtdEZOXl5ciI2vp6NGyA3NbxyxPaWmpnnzy3TIJJ15eXnr6mYny9q6cifKDB413vXl7e6levTo6fixT99//iv7+e7NdfSYlpWrKk+9q1syf9M67j6lZM+eX04Dn5GUY16cPinDNeSXYpJ/S4hIV5ORV2USPwrwCFeTk6tjBFB3esVc7V6wzVNWRJB9/P13xyK2q54Jl7s6mqKBQBTl5Sj98REm79mvXH+t0ZK9xXWgvby8NufM6xfbq6PaYUPUdO5ZpaHPZuKSOMUmuuKhEWZm5FU70cJTZ8zoRU7jTfdtalsjWMZ2Vl1eg7KxcJSQkK27zHi1a8Je2b08wPM7Hx1tPPXOrBgzsXuG+Gzaqa9oet9m8Ul1FxW02X/5FktLc9DqhesgxGU+ERBgnox1h1k9pcYnyc/IUWIXHE/k5uUpLTNHB7fu0dfk6Q1UdSfL199NVj9+i+k4uB+yokuISLf3ke9Nt3S6huh1szXM4/71bMv/+fi7Pc5wLXnxhmukNOSNHDlJAgHPLdKP6yziebWgLr+2ac0F4pLGfkuIS5WbnKyQsyCXHqAw5WXn6+iNjZTtJumiUZ0pDFheX6NP//mS6bejIfpUcDWq6zHTjeaKWyfvbEbVMkvdPnCfyFBJWNb9zmMnJytPcj83PE4NHOneeKMgr0KzXylbG9g/009j7RznVLwAAruLWRJrU1FStW7fuVLKLj4+Ppk+f7vQSTH5+fpo2bZo6deqkkpITX5jXr1+v1NRU1alTxxWhe1xhYZFyc/MN7aEumtgNDTHvJyMjy+0TTBkmk+KS7Zjs4efnKz8/X8MSAbaO+f/bjYPmEzG55stvSEiQIZHG1jHL8+nsedq8eVeZttHXDVOnTpVXajU11ZipX7t2uDIysjT2xscVH29esaYi4uMTNfrah/Tftx5Rnz5knVdHJUXFKsovNLT7B7nmLiY/G/3kZ+d6PJFm5ewftfa7pQ7tW791M110zw2KbGi+DIyjNi36Q799MMehfSMb1dNF99yg+q1iXBoTqqeiwmLl5RYY2kNCXPPeDgk1/7zNyMh2eyKNrc9jWzHZw/a4xP4xwOm+/mqpnn16ukP7No9tqGefv12dOrewa7/WbZooNDRIWVm5ZdpX/7NNBQWF8vd3bHy/cuUmm9sy0ssfv6Hmcvd4wlY/eVm5Hk+k+X3mT/prrmPjiYZtYnTZfTeodiPXjifs8cdXi3T0jKV1JCkkshaJNChnnsM137ttfX/PyMj24DxH1R1PVHffzl2qlSuNS78EBQXottuv8kBEqEqKi4pVkGccSwSFuGaZkCAb34OyM3OrVSLNzDe+V3qa8fwV06qheg3wzA013834RQfikwztEXXCSKSBS9k6TwS66Dxhq5+czNxqlUjz2X+/V8Yx43miacuG6uFk1ZhvPl5gqAo08uaLVaee8xX9AABwBbcm0qxZs0ZWq1UWi0UWi0UDBw5U69bOr5koSW3atNGgQYO0ZMkSSZLVatWaNWs0fPhwl/TvaTk5eabtQS5aFzI42PwLn63jupLN5xbsuud25gTT2Z6X7dfbNZP1Zq+3va/14cNH9d//fl6mLSoqUvfff6NTsdnr+HHj3eEhIUG6+64XTZNoIiLC1KdPJ0XXq63AAH+lpqVrx/Z92rJlT5kl2k7KyMjWnXc8p6+/maoWlVCZA6515tICJ/m66G5Av0Dz88SZSyRVBz7+forp1k4dh/ZVTLd2ng5HkuTt46PGnVqp3aBeatWvqyxebl8BEtWE+z+7bUww5bj/vZ1r4xhBQa45bwUHB5iMSyr3nOXr66PefdrryhEX6KJhveXlwHvby8tLXbu11orlZUsuZ2Rka/HCv3XFiAvs7jMtLUNLl6y2uT072/3jUlRNBbbGE4HO3ZBxkp+N93d1HE/4+vupefe26nJxX8V29+x4ImHzLv359S+m24bcOlJ+gVSHONcxz+E4R+Y5arI9exL1/POfmG67445rVK9ezbjJDo4zuwlAkgJc9FkUYGMskVfJ43xnrFi4VisXrTO0W7wsuvnBkQ59Z3DW1nV79MPsX0233XjPFTZfd8ARbj9P2OjH1nGroj8WrdGqxWsN7RYvi8beP8qp88Te7Qf0y7d/lGlrHFtfF19t/9wCAADu4tZEmiNHypaaHjp0qEv7HzJkyKlEGklKSTEuOVNdFRUVm7b7+rrmn8zH13wZoDMnZtzB7c/Nx9jP2Z5XUZH5dl8/V73e9sd0pqf/877h7r3HHrvVZVWKKqqgwJipv9dkSZjGjetp8sM3a/Bg8wt2SUlH9eabn+mnH5cZEmpyc/N17z0v65u5r7lsUhWVo6TY/P3tbeOcYy8vb/MvaCU2zitVmV+gv/yr2P9vH39f+QcHyGLxktUqOb8iNGoK93921+RxifG5VcbzOl1AoJ9CQ4Nk8bLIJIe1wi65rJ8hkUaS3vrv1xo4uIfdFQVeeelTm6+/dKISEs5NpbbGEybjfEfYWhKV8YTjMo4c0w+vzJK1tNSwrXW/zmp3QTcPRIWqxvZnrq9L+q+a8xwuem5VYDxRVWRmZuvuu14yrW7UuXMrjb/lSg9EhaqmuMi45Jdk+3uHvbxt9GPruFXNvp0HNe3Vb023XTr6QrXu2KySI5JSk4/rrSmfqrTEOJboNaCT+g3pWukxoWYrcfN5wlY/xdXkO0fCzoOa+Zr5eWLYNReqpRPniZLiEk17eY6spf8/QWGxWHTzQ1fbPL8CAOAJbk0tP5lIc/IiecOGDV3a/8n+LJYTl/qOHj3q0v49yWyNZ8n2FzV7mSWblHdcV7L1pdLWhLa9fE0mz872vGy+3i6KyWzgbM9rPX/+ci1fXjb7+/zzu2m4B8qjF1ZgsN+rV0f98ON/NXRoX5uZ6fXrR+nll+/Xyy/fJ2+T5Ij4+ER98rH5YB1VV2mJ+f9rV93J5GXjHFhSCecuV8tNz9LWX//W98+8r0/ve1GJcbs9HZIKcvK0c+V6/fzqdM248xnt+tN4wRznJvd/TnpuXFLk9skz43OrjOd1uqzMXC1c8Jcm3f+WLh12v5Ys/sehfoYN76uGjaIM7SnJx/T4o++XmxRzpq/n/KoF8/8s9zFFNpIpUPPZ+ly3lVBrL9vjCeOFm6ouJz1Lm5f+ozlPfaBP7npJ+zdX/niiMK9A3zzzkXJNlmOrFR2pS+65rtJjQtVke57DNe9t2+MJ93+e2J7ncM1zM0vwrYznVdUUF5fo/vunat++Q4ZtYWHBeu21B102PkX1ZnMs4bLzjXk/lT3Od8Tx1Ey9+vB0FZgsoxnbtrFG31751d7zcwv0yuRpyjhuXLIuqn6kbnvk6kqPCTVfsa05TBd9jti6jlMdvnOkp2bqzcemq9DkPNG8bWNdfdslTvW/4MvflXjGEm4Dr+irFu1jnOoXAABXc2tFmjO/vLr6S37J/wY7J5ePqklflr28zOsAnJ6l6wyzOwUlycvi/voDtp+bawaRpSavka1jntpu43m7Kiazf7ezxXRSenqWXnyhbMnigAA/TXnq3y6JzV5ni7p580b66OMpCqjgUj5XXDlQmVk5eu7ZjwzbPv/8Z91y6yibJbpR9VhsvZecKYFwOhvnwKqwBFFM17byO+P/vdVqVUFOnvKzcnQ04bDSDhw2vbsqNeGwvn3qbfX618Xqd/2lLoupXsumhv6sVqkwN08FOXlKPZCko/sOmt6Bn5mSpp9fma79Q/pq8B2jXXbxEtWTxe2f3TbGJRX8rHSGrWOYjSccUWo1Pjdnx1sdOsbqrnvKTiZbrVZlZ+cpKzNHe3Yf1I4d+03vVD908KgevO+/GnXVAE15+la7LvB5e3vp9jtGasrjxs/s339dp7vvnKqnn7tN0dG21zMvKizWJx//qPfeOXuyrKsuPqL6sX3OcdV3IVvjCc/XYmvWrY1hSUyrtVQF2XnKy87VkX2HdHR/kul44kjCYX3xxDvqd81FunCM68YT5SkpLtF3L07TkYTDhm2+/n4a9eh4BYbYV60KNZfn5jnc/3ni7udmNlbyxLIrnvbUlPe06o+NhnYvLy+9/Mp9atQ4uvKDQpXk7rGEre8KlfH9xRm5Ofl6+cGPdexIhmFbrYgQ3ffcWNOq2u5UXFyiNx6fpQN7kgzb/AP8dP/zYxUSxlgCrmfzeoCL5jBLbfRja+60qsjLydfUyR/r2JF0w7awiBDd9cw4p84TKQeP6oeZS8q01YoM1dW3V873F6Amq9pnF6B6cuvIOCrqxB2rJwcHiYmJLu3/zP5OHq8msLWcgK1MaXvZ6sdVZYfLY/u5ueZiXInJczvb8gy2nrc7X++KLhnx0kvTlJZW9gvuHXdeq8aN67kkNnud7f/Iiy/dW+EkmpNuuOFSLVq0SmvXbC3TnpGRrR9++E033MBAurqwteSC2cUeR9i62F4Vyn427thKjTu2KvcxhXn52r5sjdbP+13ph8tWUbOWWvXP14tUWlKi/jde4ZKYomMbKzq2cbmPKS4q0u5VG7X+p991ZK/xc3rL0r9UXFikYfePrfJf9uE+7v/sNu/HVcsrlcfWMczGE44wu9vM2efVrn0ztWtffhnlwsIiLV70jz6dtUDbtyUYtn/37TLlFxTqpVcm2vXeHjlqgP76c4sW/mysJrPqj826/JIHNXLUAA0c1F2xsQ0VHhGq3Jx8HT6cqlV/bNJ33y5T4oGyy7FGRIQqNzdfBQVlE3/8/f0qHBdqFpvjCVcl79l4f1eF8URMp1aK6VT+eKIgN19blq3Rmh+W6ZjJeGLVV4tlLSnVgHGXuzNUWUtLNe/1T7V3/Q7DNi9vL4185GbVb9HErTGgenH/PEfVG0+46rmZjZUq43lVJVNfnaVvv/3VdNuUp27XwIE9KzkiVGW2qkva+t5hL1tzHK6qaukOhQVFevWhaUrYbUx+DQzy1+Sptyqqvu2EeHcoLS3Ve898oU3/7DRs8/b20r3P3qjmbcqf0wAcZWtJSFfNBZTaqFBl67hVQWFBkV5/+BMd2G2s/BYQ5K8HXpmgOvWcO09Mf/UbwzLO1999pYJDuZEWAFD1uPX2lXr1yl7oX7hwoUv7X7RoUZnfo6Ptu/OkoKBAmZmZZX4KCozl6jzB1oUDs7KbjsjPM+/HP8D9Fyz8bD63Apf0n5dn7Mffv/zEDn9/8+QQ173eZjGd/bX+669N+uH738q0tWjRWOPHj3RJXI6w9VpJUs9eHdS5c2u7+7RYLLr11lGm21b9wdIy1Ym3n40JZJOqCI4osnGO9vFzfxKgK/gFBqjz8PN145uPquvlA0wfs+bbX7R3zZZKi8nH11dtB/TUda9OUv+xV5jeubdjxVptWriy0mJC1eP2z0kbY4DKSKSw9dzy3fjcKmW85eery6/ory+/fk73PTDa9O7YBfP/1Fdf/GJ33/955la1bRdjui0vt0BffLZYE8a/oEEXTlS3TmPVv+9tuuaqx/TfN+YYkmh8fL310qsTTcvgU5Hu3OVjazxR4KrxhHk/1WU84R8UoO6XnK9b33lEPa8cYPqYP7/5RbtXu3c8sei9b7RtxXpDu8XLosvuu0EterZ36/FR/bh/nsPGeMKj8xyueW7m8xznTsLphx/O1SeffG+67YEHb9To0RdXckSo6nxtjPFtjQHsVZBv3o9fOXNmnlRcXKI3n5it7Rv3Grb5+vlo0svjFdu28hNWpk/9Tn8u3Whot3hZdMcTo9XtvHaVHhPOHb42xv6uOk8U2uinKp8n3pkySzs3mZ8n7ntxvNOJbcvn/63t6/eUaevQs5X6DunmVL8AALiLWxNpevXqdWq5JavVqr/++kvr1q1zSd8bNmzQqlWrTt1B6+3trT59+tjVx4svvqhatWqV+XnxxQ9dEp+zQkKCTO9iyM7OdUn/OTnm/dSqFeKS/stTK8z8GNnZeS7pPzc333jMWsHl7hNm43m77vU2Prezvdb5+QV6asq7ZdosFov+8/SdHr3zrFatUJvbRowY5HC/F1zQXbVr1zK0r1u3zXXLAsHt/IMCTZcAKjR5XzqiyMbkeEA1WzLAx89XA265Sr2vGWa6/dcP5thc091dvLy91HPUUA254zrT7Stn/ai8TON65Tg32BqXmH2+OSInx/wcEXaWz29XCAszP0aOG8clto7pDt7eXrplwhV66ulbTbe/8dqXOn48064+g4ICNPPTKRo4uLtTsQUE+Gnqa/eoXbtmpncHm40LcG6wNZ4ocNF4ojDPvJ/qtgSRj5+vhk4YpfNsXEBe9N7XbhtP/Dr9B21YtMp028X/vlodqAwBE+6f5zD/7K6ceQ7zz3ZXPTfzeQ73P6+q4LPPftYbr39mum3ChFG67barKjkiVAdBwQGmy4Tm2vjeYa98G2OS4Cq4BFFpaaneffoLrV+1zbDN29tL9z03Vu27t6j0uD57Z56W/vCX6bbxD45S/4ud+64BnE2gjfNEXo6LbvbNNe8nqApWXiktLdWHz36ujX+anyfuemac2nVr6dQxMo5l6av35pVp8/Xz0bgH+BwHAFRdbk2kqVWrlvr27Sur1SqLxaLS0lLdcsstysrKcqrf7Oxs3XLLLadKe1ssFvXp00dhYWF29fPoo48qIyOjzM+jj97uVGyuFGaScJKe7txrd9Lx4+b9VMZETHiEeSJGerp9F3HM+8gyLfleXvKHJIWH24rJVa+38bmdLaZ33v5SBw4kl2m76qoh6tHDs3d22vr3k6SuXds43K+Xl5dpNZuMjGwdOGBcJxlVl7/JRaj8rByX9G0rkaO6JdKc1O/6S1W/jXF5luy0dO36w3iHd2XoMLSvWp9vnLAqLijU5kV/eCAiVBVmyR/p6a5Jrkr34LikVrj5MVzx3DLSs1VaakwG9cSFr1H/Gqjhl/YztOflFejrr8yXSShPUFCA3nzrfk1+5EZFRto3BpekVq2baPbn/9HgoT11+HCq6WOi6obb3S9qDrPP9jyXjSfM+wkIrZ7jiQvHXKpGbY3jiazUdG1f6frxxMovFuqf734z3Tbo5ivV7ZL+Lj8mag73znOYzyl4dp7D+edme56j5ifSfPfdr3r+uU9Mt11/wyV6cNLYSo4I1Umwyed6dqZrktuyMszHEiFVLJHGarXqo5e+0V+/bjRss3hZNHHK9erev/LnGedOW6z5Xywz3XbDxMs0dKTxewvgDkGm5wnXfOfIrkbniemvfK1/ftto2Gbxsuj2J25Q1/OcP098+t/vlZNVNun5irFDFd0oyum+AQBwF7cm0kjSjTfeWOb3uLg4DRkyRMnJyTb2KF9KSoouuugibdy4URaL5VSlirFj7f/y7O/vr7CwsDI/Vak0bnS0cb3J1NTjLunbrJ9a4aEKCCh/CSRXiI6ubdqemprudN+2Xp/oeubHPLXdjTHl5RWY3hlXXkx79hzQzJk/lmmLiAjTpIducjoeZ9Wz8Vr5+fmqWbOGTvXdunWMafuxY84nWaHyhET+H3v3HR5VtfVx/DfpvRISCL333kWaiGBFVEQREBS9XmzXXrH3fm1YAFGxNxRQEQVBVHrvAQIkJAES0nsy7x9ceA3nDGR6At/PffJc2Sez95rA7JyzzzprRxnaClyQKGern6DwEPnVoLnbXmeNvci0PenvdZ4N5B/OuuZC0/akv9d7OBLUJHXjow1trvg9aaufyMgwBXlgKwaz8y3JVecl5n3EO7mnuKNuvX20afuvv6x0qD8fHx+NmzBCP/7yqu66Z6y6dW9tWmngGIvFok6dW+jpZ2/SF18/fXx7qH0nbPd0TNNm9R2KC6eHcJOKRAV2Vk+yJd+kn+DwEPnX4vOJgePMf3dv+9O1v7v//uZXLf3EfMvm/lcNV5/LznHpeDj9mK9zZLukb9PzidNincO8j1Otc9R2P87/Qw8/9KZphdqRlw7Www9P9kJUqE1i4ozJ3tmZrkncM+snLCKkxm3Z8sEr32rx3BWGdovFosn3XqF+53b1eEw/fLJIX01fYHrssknn6qKxgz0cEc5k0XHGa46cLNfMEzkm69mhESE2t4P0lo9e/VZL5pnPE5PuvkJ9hjo/T2xauV0rTkjUqde4ri64ms87AKBmc/v+MJMmTdJLL72knTt3Hk98Wblypdq0aaOpU6dq0qRJioqKOmU/2dnZmjlzpp544gnl5OQcb7dYLGrVqpUmTZrkxnfhHYmJ8dq6dU+VtszMHBUVlSg42LmFoNTUg4a2BonxTvVZXQ0amI+TkmJ+E8UeZu/rZGMeEx4eqoiIUOWekHHumpjM+zhZTMnJB1R+Qhn2Fi0b6dNP5ts9fq6NJ3d3bE/W2299bmhv2jRRw0fYfoq0YcME0/aIiNDjW605KtLNlYHgGRF1Y3RoT0qVtsKcfJWVlDp9gyr3YJbJeLV7ATmxXXMFhYcaqvakbjXuSewpkfF1FNckUYeSU6u0H0pOVWlRsQKCg7wUGbypfv04bdu6t0pbVmauS85LDqQaK5IkeuipJFvjpKYccrrv1FTzPjz13k7UoEFdtW7TWNu3Vf173L59rwoKihQa6liJ6ZCQIE2YeIEmTLxABQVF2rJ5jzIP5yjrSK6KCksUEhKkxAZxat+hmel2TTu27zXpVWrRooFD8eD0EFk3Vhm7q/4eKsjJV1lxqfydTLLLMTmfiLRxE7y2aNi+uYIjQg3VdlK2uO58YvW8pfptxhzTY71GDtaAsee7bCycvhIT63p4naOuU31Wl3vXOexfU6jtfl24XHff/Yrp1o/Dh/fTU0/d7PT6A05/cfVilLzzQJW23CP5KikuVaCT5xKH0owP8tWt751keVtmvzlXP39lvg3jhNsv0ZCLens4ImnB18s0+425pscuGDNQV1xvvv014C5xCdHat7PqNYc754m4ejVrnvj87R+08Bvz6tNjbx2pgRf2cck4KXuMD9XXa1hX8z5ZZHdfh9IyTdu3r9+tObN+MbQ3b9dIHXoaq+ADAFAdbk+k8fX11ZtvvqkRI0aooqLieDJNbm6u7r77bj344IMaMmSIevToobZt2yoqKkqhoaEqKChQdna2tm7dqlWrVmnRokUqKSk5/iTKsX78/f315ptvysfH7cV1PM6suofVatXevQfUxmQrkOrKyytQVlaOob1JU8889RsWFqI6daIMT1Uln3DD1hF7kw+YtjdpcupKKU2bJmr9+h1V+9tr3p9rYrLv571yxSatXLHJ6XiO2bJlt7aYLKyfc07vkybSNG1mflMrJMT5G+u2buDlu6iMPzwj2myx2mpV9oGDimvq+E3RkoIiFeUYt1qJru+ZxXF38fH1UZ3G9ZWyaWeV9qKcfJWXlskvwDtPtMU1a2BIpLFWWpWfma2YBuYJdTi9NWlaz9BmtVq1b2+6Wrdp7HC/eXmFppXHGjcxjucOYWEhiq0TqczDVc+N9u51flvBfXvNKzA28dB7M9OmrTGRprLSqoyMLDVr5lxlOeno7/KevdrZ9ZoN65NM+2nRsqHT8aD2im1gfj6RdeCQ4p34t1pcUKRCk/OJGA/dbHcXH18fxTWup30bq36eCl10PrF+wV/6edpXpse6jjhLQ6+/1Kn+ceY489Y5XLGmYH5OYu+aQm2xdOla3X77C4aHiyRp0OAeeuHFO+Tra7sCHnBMvUbG5HWr1ar0/YfVuKXjn5/C/CLlmmwDm9Cw5mxP8uX7P+mH2eY3qK/61/kafsXZHo5IWjR3uWa+/K3psaGX9tW4Wy/2cESAlNDIeA1gtVqVkXJIjVo4fs1RmF+kvBo+T3wz/SebiSxX3HiBhl3u3nlizR+btOYP193r2LomSVvXGNcWhl1xNok0AACHuT2RRpLOOeccvfPOO7ruuutksViOPzVitVpVUlKin376ST/99NNJ+/hnAs0///zOO+9oyJAhbozee9q2a2bavnXrbqcWmMySJySpXVvz8dyhbdtmWrp0TZW2pKT9Kisrl7+/4/8st9io4NDOxs/yxJhOTKTJyclXaupBJTqxsG4zprbNHe7Tm9q3N4+7sLDY6b7NtsCSpLDwUKf7hufUtZFsdXBPqlOJNAd3p5i22xqvNgmxUY2pOL9QYTHG6g2ecLKYcGZq27aJafu2bXudSqTZtjXZxniO92mvNm2baNnSqtuf7EpKdfq8ZOtW80orbWz8LD3BrCKMJOXa2L/d3UpKSk0Tabp0a3XSbaJw+ou38fs9Y0+KU4k0GbvMzycSToPzidAo4xYWklSUV2i6VVZ1bf59tea/8Zlksr1KxyG9NPzf5tvGAWZsr3PsYZ3DBmfWOWqb5cs36pabn1FZWbnhWL9+nfXf/97r1M8SZ5YmrczPF5J3pjqVSLNnh/mDgE1tjOdpcz76TV/PMFZlkKRRE8/VJeM9vw3jsgVr9O6zX5pu1TZgRA9dd9dlHo8JkKTGLc0/t3t3HnAqkWbvTvN5wtZ4njZ39q/67gPzLdYumXCuLrqG7VoBAJAkj5VxmThxombNmqXQ0NAqSTHHKsuc6uvEBJzw8HB9+OGHuvbaaz31FjyuU8eWpu3r1213qt9167aZtnfs1Mqpfu1hNlZZWbm2bNnlVL/r1hp/NomJdRVTjRvRtt7/+vXO/ryNrw8MDFCr1p67QehKDRrEq06dKEN7bm6B6QWxPXJsbOEUHW1+Qx81U0JL83/b6TuSneo3bfse03Zb49UmFaVlpu3erLZWUWpcvJa8GxO8q0Mn80TKDet2mrZX13obr+/QsYVT/dqjY0fjeysrK9fWLclO9Wv2s0lMjFNMjPnNbk8oLTGfb3x9vfPZ/nPZRhUVlRjahwzp7oVoUJPUb2X++z11W7JT/aZuMz+fqNeqkVP91gQVZTbOJ5z4fG/7c71+ePkjWSuN5/lt+nfRBbddzfYqsIvtdY4dpu3VZXbdLUkdO5mP5w5mYx1d53BuizVn1jlqk7Vrt+mmfz2l4uJSw7EePdrpzbceUICXqnWidmrRzvx3+87N5snu1bVzk/nrm9sYz5N+/GKpPn17numxC8YM1OjJnt86acXiDXrriU9NzyX6DOmsfz1wJecS8Jrmbc0/t7ucnCeSNiXbNZ4nLfhqib6YZj5PDL9yoC67foSHIwLgKhYLX6fLF2oOj66Yjxs3TmvWrNHQoUOPJ8hI/59Qc7IvScdfM2zYMK1du1bXXHONJ8P3uAYNE0z3vP7zz3VO9fvnMuPrg4MD1aWL50rc9enTybTdLLbqOnDgkOn2UP36danW6/v27ezymEpKSrVm9RZDe/fubRUY6Nw+q95k9rMqLS3Tnj3Obc+1fXuyaXud2Cin+oVnRcbXUUR8rKF9r40kvurat974er/AANVr7fiTqzVFYY4xicziY1Ggje3OPKEwx7jVjiQFUSHqjNWgQV0lNjCWIf7rL+dK8f7150ZD29HzEs/d+OrVu71pu1ls1ZV24LCSTbZi6NO3g8N9ukJmZo5pe2RkmIcjOeq7b343tPn5+WrosF5eiAY1SVRCrKJMzieSnXyoYI/J6/0DA9TAiUoYNUXBEfPziSAHzyeSVm7WnOc/UGVFpeFYy14ddMldE5xK0sGZqUHDeDetc6w3tHl+naOjabvz6xzG7aH69TNfv6itNm1K0g2THzetdNupU0tNe+dhBQcHeiEy1GZ168eqbv0YQ/vGFc4l7pm9PjAoQK06ePchn1/n/K0PX5tjeuzcS/t5ZeuktX9u1X8f+VgVJucS3fu3082PjuVcAl4VVz9WcfWM88SmVc5dc2xaZZwnAoIC1KJDE6f6ddai7//S7P+azxNDRvbT1Tdf4uGIAACo2TxeD7VFixZasGCBNm/erNdff10LFy7U7t2nfjqnWbNmOu+88zRlyhS1a9fOA5HWDAMGdNcnn8yv0rZvX7q2bNmldu3s3xooMzNbq1ZtNrT37dvZo0/2dO3aRhERocrNrbqNwE8/LdNN/77SoT5//nmZafuAgdV7orl+/Ti1aNFQSUn7q7QvXPi3Hnn0JofKB//++yrTp6wHDOxx0tcNHdpH27Z/b/d4ZlJSMjT0nMmG9pGXDtGzz97uUJ+Dh/TSDz8Yb3ytXbNVzRwsi19ZWWla/ScuLloNGiY41Ce8p2m3dlr/49IqbTnph3Vw937VbdbQ7v4Ks/OUstlYsapR59byreWlvcuKS3TQJAktvE60V9/bga3GJ/Z9/HwVHmdcYMCZo//ZXfT5p1XLhO/fl6GtW5LVtl0Tu/vLzMzR6lXGJLnefTvIP8Bz//67dG2p8IgQ5eVW3bpswc/LdeNNlzrU54IFy03bzx7QxaH+XGXtWuOCnp+/r+rVr+PxWPbvy9Dvi9cY2s8d1surVXtQczTr0VZr5v1Rpe1I2mGl79qvhOb2n08UZOdp3ybj+USTLq1q/flEaXGJMkzOJyLiHDufSF6/Q988M0MV5RWGY027ttGl90+UL9uvwUEDBnTTJ5/8WKXt6DrHboe2K6r56xx/6iYHt0D7+ec/Tduru85RG+zYsVfXX/eY8vKMW8i2adNE773/iMLCvPeAAWq3Ln3aaME3VT9HGamZ2rM9RU1b279+lZOVp63rjevYHXq0lJ8XzyX++Hm13n/hK9NK0QPP76lJd43yeEybVu3Uyw98oPIy47lEp16tdPuTE9jKFTVCpz5t9eu3Ve8rHEzNVPKOFDVpZf88kXskT9tN5on2Xp4n/lywWh+8ZD5PnD2ipybc4b4t1oaPHqjhowe6pK+ta5P0zK1vGdpHThymUZM8X3ULAHB681rKd/v27TVt2jQlJSUpPT1d8+bN06xZs/Tqq6/qiSee0KuvvqpZs2Zp/vz5Sk9PV1JSkt58880zKolGki68cIBp+2ef/eRQf19+scD0KYALL3LNiUx1+fv76bzzzjK0b9+erLVr7a9aYbVa9cXnPxvaw8NDNfAUSSv/dOGFxp9DdnaezSSdU/ns0x8NbT4+Phoxor9D/dUUQ4b0UlhYiKH9uzmLHO5zyZLVpk/J9+5tXr0INVvrAeafuw0/OfZZ2vjLn7JWGueuNgNq/wLy7lWbTLd2qte6ieeD+Z+0HcnKO3zE0B7fvCE3zc5wF1zYz7T9i88XOtTf118uMj0vueAC83Hcxd/fT8OG9Ta079i+T+tMEk9OxWq16qsvfjO0h4eH6OyBXRwJ0SU2rE9Selqmob1du6YOJQw765WXPzX9+58w8QKPx4Kaqb2N8/g1Pzp2PrHuZ/PzCVvj1CZJKzar3OR8ItGB84mUrbv15RPvmvbXqEMLXf7Q9fLzZ3sVOM716xy/2FjnMB/HXY6ucxjPYZxb51hgaA8PD9HA0ySRZs+eVE2a+IiyTbZ5btGioWbMfMxrVfNwejhrWDfT9oXf/eVQf799v9y0UttZw7o61J8rLF+0QW89+Znp1kn9hnbRjQ+M9vjWSds37tEL985QmcmW0W27Ntddz03y6IMTwMn0HWo+TyyaY57MeiqLf/jbdJ6wNY4nrFy8Xu8+bWOLtXO66rr72GINAAAzNaJ2Yt26dTVixAiNGzdOt956qx588EHdeuutGjdunIYPH666det6O0Sv6dqtrVq0MD5t+d23vyklJcOuvvLyCvThhz8Y2mNjI3XOOcabR+42evR5pu1vvPGp3X3Nm7fEdFuhS0YOtusJtEtHnWN6I2na21+oosL4BMXJrFmzVX/+aSwvPXBgD8WblKmvTYKCAjXy0iGG9pUrNmmdA9v3WK1Wvf/+N6bHBg2u/Tc2zkT12zRVjEkloS2Llisnw3gT92RKCoq0du5iQ3tIZLia96rdiVblZWVa9vFc02PNepiXhveEpR98Z9rerId3t6SB93Xp2krNmyca2r//bqlSUw/Z1VdeXqFmf2S8YRYTG6HB53j+5tDlo42/1yTp7Te/truvH+f/peQ9xm2dLrrkbI8+GX+il174xLR90GDPL+jNm7tMv/y8wtA+fEQfte9gfzUCnJ4atG2qOo2M5xMbf12hbDvPJ4oLirTye2NFxZCocLWysR1LbVFeVqbfPzI/n2jZy77f3elJ+/X5o++orLjUcCyxdRONfuRG+dfiLWpRM3Tt1sbGOsciB9c5jP/+j65zeH6bwNGjh5m2v/HGZ3b3NW/eUpesc9RUKSkZmnjtVB0+nG041rhxPc2Y+ZhiYiI9HxhOK606NlGDpsbt5Jb8uEoH07Ls6qswv0g/frHU0B4ZHaYeZ3vnWnntX1v1+qMfm9607zmgg6ZMvVo+Pp5d/t+zPUXP3fm+SoqM5xIt2zfWvS9cp4DA2j+H4fTRsmMTJTYxzhN//LRKhxyYJ37+0jhPRESHqVt/78wT6//eqrcfN58nup/dQTc+5Pl5AgCA2oLfkDWcxWLRpOuM5TdLS8s0deqbpqX4bHnu2RnKyjJW/Bg37iIF2rEYet99r6pN64sNX99882u1+5Ckjp1aqqfJwu6yP9bqezsqm2Rl5ei5Z2ca2v38fHXttfbt6xkfH6sLTJ6OS0rar3ff+ara/ZSUlOrRR4wlBiVp8mTPl1N1h0mTLjVNOnrwgddVXGzczupkZs+ep1UrjaW4GzZM0PDhtbt6z5nKYrGox8hzDO0VZeVa+NZnds1dS2Z+q6KcfEN7lwsHys+OBeSfX/tIr4y8xfC1+de/q93H4b0Hqv29p1JZUalfXv9EOemHDcdCIsPVok/1koRcGZMkLZ01R6lbjNte+Pr7qd0QzyddomaxWCy69roLDe2lpWV6bOr7dn22X3zuY2Vl5Rrax14z3K7zkgfvn6aOba82fH33rfGG+cl06NhcPXq2NbT/uWyjfvj+D5NXmMvKytWLz31saPfz89X4a8+vdj87d+w/9TfZ4eUXP9Wa1cZk14AAf1080rNP7C9ZvFYPPTDN0B4WFqw77rrao7GgZrNYLOozyvx84sc37Duf+HX6tyo0OZ/oedEAu84nfnjlYz194a2Grw0LzbdzM3Mw2bXnE/Ne/URH0kzOJ6LC1apv9ZOOD+1N06dT31JJQZHhWELzBrrysX8pIDjQqXgB6dg6x0hD+9F1jrfsXOeYaWOd40I71zleU5vWIw1fjq1ztDe0L/tjnb6fs7ja/Zx8neNiu2KqiTIysjTx2qlKTzcmRdZPjNMHsx5X3bpsKQvnWSwWXXj1IEN7WWm53nv2S7vmm49e/1652cZzieFXnG1XYshbT36qMf3uNHwtnmdMMj+ZzWuS9MoDs0y3Turcp41ue2KcxyvK7t+drqdvf0eF+cWGY01bN9B9L09WUAjnEqhZLBaLzr9qsKG9rLRcM1/4wq554tM3vleeyTwx7HL75ol3n/pU48++w/C1dL5988TWtUl6/SEbW6z1bqMpj42n8jQAACdBIk0tcMklg9SmTVND+5/L1umJJ96p1snc9Pe/0Vdf/WJoj4+P1fgJ3luEuefuiaZlAx955C0tX77xlK/Pzy/UTTc9qUOHjNnhY8deoAYNjNnkp3LrrWMVbLJA/Prrn2ru3FPflCstLdOdd7yoHTv2Go4NHdpH3bqfHtuT1a8fZ5qotGvXft0w+XEVmCzAm/l+ziI98/T7psduvPFy9kuuxdoO6qW4JsbKFfvWb9Oi96q3YLXq24XatNBYcjksNkrdLhrkijDtMuepd/TNY28pbfsep/rJz8zW98+8p21LVpke7z36PPlVc+F/0btf6rN7X1Ly2q12XdyfqDi/UAten61V35pv0dPl/AEKi41yuH+cPi66+Gy1btPY0P7Xnxv19JMfVOvf4czpP+ibrxcb2uvGx+ia8d7b0/rOu682PS954tHpWrliyylfn59fqFv+/aIOHco2HBtz9TAlJsZVO5ann/xAY8dM1bI/1jv12c7JydfUB9/VzOnGqoSSdNXYYYqPt+9G1eyPf9buXcYn5E+lsrJSn85eoDtuf9V0Ie+Bh65Vvfp17O4Xp7cOg3uqblPj+cSetdu1YNpX1fp8/P31r1q/wJg4Gx4bqZ6XDHJFmHb58ol39dnUt5S6zbnzibzD2frqyfe0+ffVpsf7X3letavHHEk7pE8fflNFuQWGY3GN62nME1MUZLK1K+Coo+scTQztfy5bryeeeK+a6xzf6quvjOeuR9c5LnJFmA655+5rbaxzvG3HOsfTOnTIuNXq2LHnO7TOUZMcycrVdZMe0f79xupD8fGxmjXrCdWrV/1zJuBUBgzvocYt6xvaN67coZkvf1ut+eaH2Yu06AfjDeyYuEiNuPJsl8Rpj52b9+qFe2aotMS4DWP77i105zPXys/DW7empxzW07e/o7ycQsOxhs0S9MCrNyg0PNijMQHVddZ5PdSohXGe2LRyhz569ZtqzRPzPvlNv88zJtdHx0XqvCs8+/CKJO3avFev3DvddJ5o162lbn1qosfnCQAAahu3/6Z8/PHHj/93QkKCbrjhBpf1/e677yo9Pf34n6dOneqyvmsSX19fPfrYTbpm7P0qL6960+GT2fO1b2+aHnr4RjVpYjzZy8jI1IsvfKAffjBPAHl46o0KCQlyS9zV0bFTS1099nzN/nhelfaiohJdf90jmnLzGF177SUKCjImtvz55zo99tg07TV5orN+Yl3dfMtVDsVUv36cbrn1aj3/XNWnvyorK3X3XS9r48YkTZlypSIijPt0b9y4U48/Nk0bN+40HAsPD9X9D1zvUEw11b+njNGvvy7X7t0pVdpXrNioS0fepnvunaQhQ3qZlodMSzuk116brTnfLTK9GBkwoLtGXTbUbbHD/Xx8fXTOTVfqiwdeNZQPXT9/qbLTDmvw5MsVXd+4fV9+Vo6WzvpO2343TzQZPPly+ZvMC25nlfau3aq9a7cqOrGuWp3VTc17dVSdxvXlW42Lz4O792vr4pXa9MtfKi0yPqElSQ07tlTnEfYtxKVtT9a3j72l8DrRatmvi1r07aL4Zg2qlYxzJPWgti5eoQ0LlplW/pGkmAYJ6nv1BXbFhNOXr6+PHn5kkq4d97jhvOSzT37R/n0Zuv/BCWrcpJ7htQcPHtHLL36ieT8sM+37gYcmePW8pEPH5hpz9bn6dPaCKu1FRSW68fpnddOUURo34XwFBRk/W3/9uVFPPT5Te/emG47Vr19H/775Mrvj2bA+Sf+a/JwS6sVq2Hm9NfTcnmrbrqnp+CdK3pOmH75fqq+++M208o8kNWueqCm3XG53XPN/WKbnnv5QPXu11bnDemvwOd1PmoxTWFisZX9s0Iz3v9emjbtNv+eaccN10SWevwmBms/H10cjpozWR/e+ZjifWD1vqY6kHdKwGy9XTKLxfCIvM0e/zZyjzYvNzyeG/esKBXjpfGL3mm3avWabYhrUVdv+XdWqT0fVreb5RPqu/dr020qtW/CXSovMK0E27tRS3S+ofmXHTx58U/k25opGHVtqzfzqV+Y6lf5jzLf4xZnl/9c5HjjJOsdkG+scWXrxhVknWeeYXAPWOUZo9sfzq7QfXed4TFNuvlLXXnuxjXWO9XrssXdsrHPE6eZbxjgc19tvfXHK79mx3fhAkCTNnj1fEeGhJ31tj57t1bOnsRrPiW697TklJZlX3uvTp6Pm/rDklH1U1zXjLlD4KeLG6c/H10fX3325Hr3pDVWccC6x4Otlykg5rGvvuFT1GhoTuLIO5eiTN+fqjwVrTPueeMelCvJwtbb83EI9e8d7Ki40ngP4+vmqRbtGmvvJYpeMFRIerOGXV+984slbp+nIYfNziXbdmuuXb/50SUySNGriuS7rC5COzhPX3nW5nppinCcWfnN0nhh3+yglmMwTRw7n6PO3ftCfv5jPE+N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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ShowGRAHeatMap(Atom)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def ShowGRAHeatMap(DataFrame):\n",
    "    colormap = plt.cm.hsv\n",
    "    ylabels = DataFrame.columns.values.tolist()\n",
    "    f, ax = plt.subplots(figsize=(35, 2))\n",
    "    # ax.set_title('Feature Importance via Converted p-values')\n",
    "\n",
    "\n",
    "    with sns.axes_style(\"white\"):\n",
    "        sns.heatmap(DataFrame,\n",
    "                    cmap=\"YlGnBu\",\n",
    "                    annot=True,\n",
    "                    annot_kws={\"fontsize\":24}\n",
    "                    )\n",
    "    # colorbar\n",
    "    cax = plt.gcf().axes[-1]\n",
    "    cax.tick_params(labelsize=36)  # colorbar fontsize\n",
    "    plt.tick_params(labelsize=26)\n",
    "    plt.savefig('mol_figure.png',dpi=400)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 3500x200 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ShowGRAHeatMap(Molecule)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Tools\n",
    "\n",
    "In addition to the above pipeline, we also use some other tools for data cleaning and dataset construction."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### FindMCS\n",
    "\n",
    "Before performing calculations in computational chemistry, we need to determine the atoms and groups in the molecule. The key atoms in the molecule often dominate the performance of reactions, so it is very important to identify the key atoms.\n",
    "\n",
    "We perform a maximum common substructure search on the A dataset. In total, we analyzed 69 reactions and obtained the largest common substructure of nucleophiles and electrophiles."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "from rdkit.Chem import rdFMCS\n",
    "from rdkit import Chem\n",
    "\n",
    "def FindSub(mols):\n",
    "    nums = len(mols)\n",
    "    for i in range(nums):\n",
    "        mols[i] = Chem.MolFromSmiles(mols[i])\n",
    "        if mols[i] == None:\n",
    "            print(i)\n",
    "\n",
    "    res = rdFMCS.FindMCS(mols, atomCompare=rdFMCS.AtomCompare.CompareAny)\n",
    "    common = Chem.MolFromSmarts(res.smartsString)\n",
    "\n",
    "    return res, common\n",
    "\n",
    "rawData = pd.read_csv('./data/data_classA/Reaction_A.CSV')\n",
    "cat1 = rawData.iloc[:, 0]\n",
    "cat2 = rawData.iloc[:, 1]\n",
    "cat1 = cat1.tolist()\n",
    "cat2 = cat2.tolist()\n",
    "\n",
    "res1, common1 = FindSub(cat1)\n",
    "res2, common2 = FindSub(cat2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x1cca572ef40>",
      "image/png": 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\n"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x1cca5730a60>",
      "image/png": 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\n"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common2"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x1cca5730a60>",
      "image/png": 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\n"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patt = Chem.MolFromSmarts('C=CCO') # add color\n",
    "atomids = common2.GetSubstructMatch(patt)\n",
    "common2"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Script: Get Molecule data\n",
    "\n",
    "When making data set, we read `molecule_data_A.CSV` directly and obtained information at the molecular level. In fact, these data are extracted from \"CDFT\" files and \"MPI\" files. Below is the code of the script, you can save it as a `.py` file and use it in the command."
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    "#!/usr/bin/python\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import argparse\n",
    "\n",
    "def modifyFile(path):\n",
    "    # Modify spaces in a file\n",
    "    f_new = open(path + '.new', 'w')\n",
    "    with open(path) as f:\n",
    "        line = f.readline()\n",
    "        while line:\n",
    "            line = line.replace(\"   \", ' ')\n",
    "            line = line.replace(\"  \", ' ')\n",
    "            f_new.write(line)\n",
    "            line = f.readline()\n",
    "\n",
    "def getLog_inf(logfile):\n",
    "    # Extract valid information from each log file\n",
    "    rawdata = pd.read_csv(logfile, header=None, sep=' ')\n",
    "    numdata = rawdata.iloc[:, 1].values\n",
    "    numdata = numdata.tolist()\n",
    "    nameindex = rawdata.iloc[:, 0].values\n",
    "    nameindex = nameindex.tolist()\n",
    "    return numdata, nameindex\n",
    "\n",
    "def getDir(path, dataful):\n",
    "    # Read .log file by directory\n",
    "    filelist = os.listdir(path)\n",
    "    for file in filelist:\n",
    "        file_path = os.path.join(path, file)\n",
    "        if os.path.splitext(file_path)[1]=='.log':\n",
    "            # modifyFile(file_path)\n",
    "            # file_path_new = file_path + '.new'\n",
    "            # numdata, nameindex = getLog_inf(file_path_new)\n",
    "            numdata, nameindex = getLog_inf(file_path)\n",
    "            dataname = os.path.splitext(file)[0]\n",
    "            chooseColumn(dataname, numdata, dataful)\n",
    "\n",
    "    for i in range(len(nameindex)):\n",
    "        nameindex[i] = nameindex[i].replace('.txt', '')\n",
    "    dataful.loc[:, 'index'] = nameindex\n",
    "\n",
    "def chooseColumn(columnName, data, dataful):\n",
    "    columnDict = {'HOMO': 'EHOMO(a.u.)',\n",
    "                  'LUMO': 'ELUMO(a.u.)',\n",
    "                  'dipole': 'Magnitude of molecular dipole moment(a.u.)',\n",
    "                  'MW': 'Molecular Weight',\n",
    "                  'Surface': 'Surface Area (Bohr^2)',\n",
    "                  'Volume': 'Volume (Bohr^3)',\n",
    "                  'Polar-Surface': 'Polar Surface Area (Angstrom^2)',\n",
    "                  'Nonpolar-Surface': 'Non-Polar Surface Area (Angstrom^2)',\n",
    "                  'MPI': 'MPI index (eV)',\n",
    "                  'Chemical-potential': 'Chemical potential (eV)',\n",
    "                  'Hardness': 'Hardness (eV)',\n",
    "                  'Electrophilicity-index': 'Electrophilicity index (eV)',\n",
    "                  'Nucleophilicity-index': 'Nucleophilicity index (eV)'}\n",
    "    index = columnDict[columnName]\n",
    "    dataful.loc[:, index] = data\n",
    "\n",
    "\n",
    "def getArg():\n",
    "    parser = argparse.ArgumentParser(description='Script argparse setting')\n",
    "    parser.add_argument('-c', dest='CDFT', default='./CDFT', help='Path to CDFT folder, default is ./CDFT')\n",
    "    parser.add_argument('-m', dest='MPI', default='./MPI', help='Path to MPI folder, default is ./MPI')\n",
    "    parser.add_argument('-o', dest='output', default='./mol_data.csv', help='Path to output file, default is ./mol_data.csv')\n",
    "    args = parser.parse_args()\n",
    "    CDFT, MPI, output = args.CDFT, args.MPI, args.output\n",
    "\n",
    "    return CDFT, MPI, output\n",
    "\n",
    "def grepInf(path, type='CDFT'):\n",
    "    if type == 'CDFT':\n",
    "        log1 = open(path + '/Chemical-potential.log', 'w')\n",
    "        log2 = open(path + '/Electrophilicity-index.log', 'w')\n",
    "        log3 = open(path + '/Hardness.log', 'w')\n",
    "        log4 = open(path + '/Nucleophilicity-index.log', 'w')\n",
    "\n",
    "        filelist = os.listdir(path)\n",
    "        for file in filelist:\n",
    "            file_path = os.path.join(path, file)\n",
    "            if os.path.splitext(file_path)[1] == '.txt':\n",
    "                with open(file_path) as f:\n",
    "                    line = f.readline()\n",
    "                    while line:\n",
    "                        if ' Chemical potential:' in line:\n",
    "                            result1 = (line[53:].strip()).replace('\\n', '')\n",
    "                        elif ' Electrophilicity index:' in line:\n",
    "                            result2 = (line[49:].strip()).replace('\\n', '')\n",
    "                        elif ' Hardness (=fundamental gap):' in line:\n",
    "                            result3 = (line[53:].strip()).replace('\\n', '')\n",
    "                        elif ' Nucleophilicity index:' in line:\n",
    "                            result4 = (line[48:].strip()).replace('\\n', '')\n",
    "\n",
    "                        line = f.readline()\n",
    "\n",
    "                log1.write(file.replace('CDFT_', '') + ' ' + result1 + '\\n')\n",
    "                log2.write(file.replace('CDFT_', '') + ' ' + result2 + '\\n')\n",
    "                log3.write(file.replace('CDFT_', '') + ' ' + result3 + '\\n')\n",
    "                log4.write(file.replace('CDFT_', '') + ' ' + result4 + '\\n')\n",
    "\n",
    "        log1.close()\n",
    "        log2.close()\n",
    "        log3.close()\n",
    "        log4.close()\n",
    "\n",
    "\n",
    "    elif type == 'MPI':\n",
    "        log1 = open(path + '/dipole.log', 'w')\n",
    "        log2 = open(path + '/HOMO.log', 'w')\n",
    "        log3 = open(path + '/LUMO.log', 'w')\n",
    "        log4 = open(path + '/MPI.log', 'w')\n",
    "        log5 = open(path + '/MW.log', 'w')\n",
    "        log6 = open(path + '/Nonpolar-Surface.log', 'w')\n",
    "        log7 = open(path + '/Polar-Surface.log', 'w')\n",
    "        log8 = open(path + '/Surface.log', 'w')\n",
    "        log9 = open(path + '/Volume.log', 'w')\n",
    "\n",
    "        filelist = os.listdir(path)\n",
    "        for file in filelist:\n",
    "            file_path = os.path.join(path, file)\n",
    "            if os.path.splitext(file_path)[1] == '.txt':\n",
    "                with open(file_path) as f:\n",
    "                    line = f.readline()\n",
    "                    while line:\n",
    "                        if ' Magnitude of molecular dipole moment (a.u.&Debye):' in line:\n",
    "                            result1 = (line[56:67].strip()).replace('\\n', '')\n",
    "                        elif 'HOMO, energy:' in line:\n",
    "                            result2 = (line[40:56].strip()).replace('\\n', '')\n",
    "                        elif 'LUMO, energy:' in line:\n",
    "                            result3 = (line[40:56].strip()).replace('\\n', '')\n",
    "                        elif ' Molecular polarity index (MPI):' in line:\n",
    "                            result4 = (line[34:42].strip()).replace('\\n', '')\n",
    "                        elif ' Molecule weight:' in line:\n",
    "                            result5 = (line[23:36].strip()).replace('\\n', '')\n",
    "                        elif ' Nonpolar surface area (|ESP| <= 10 kcal/mol):' in line:\n",
    "                            result6 = (line[49:68].strip()).replace('\\n', '')\n",
    "                        elif ' Polar surface area (|ESP| > 10 kcal/mol):' in line:\n",
    "                            result7 = (line[49:68].strip()).replace('\\n', '')\n",
    "                        elif ' Overall surface area:' in line:\n",
    "                            result8 = (line[30:49].strip()).replace('\\n', '')\n",
    "                        elif ' Volume:' in line:\n",
    "                            result9 = (line[9:28].strip()).replace('\\n', '')\n",
    "\n",
    "                        line = f.readline()\n",
    "\n",
    "                log1.write(file + ' ' + result1 + '\\n')\n",
    "                log2.write(file + ' ' + result2 + '\\n')\n",
    "                log3.write(file + ' ' + result3 + '\\n')\n",
    "                log4.write(file + ' ' + result4 + '\\n')\n",
    "                log5.write(file + ' ' + result5 + '\\n')\n",
    "                log6.write(file + ' ' + result6 + '\\n')\n",
    "                log7.write(file + ' ' + result7 + '\\n')\n",
    "                log8.write(file + ' ' + result8 + '\\n')\n",
    "                log9.write(file + ' ' + result9 + '\\n')\n",
    "\n",
    "        log1.close()\n",
    "        log2.close()\n",
    "        log3.close()\n",
    "        log4.close()\n",
    "        log5.close()\n",
    "        log6.close()\n",
    "        log7.close()\n",
    "        log8.close()\n",
    "        log9.close()\n",
    "\n",
    "\n",
    "\n",
    "def main(CDFT, MPI, output):\n",
    "    \"\"\"\n",
    "    :param CDFT: Directory of CDFT files\n",
    "    :param MPI: Directory of CDFT files\n",
    "    :param output: Path of output file\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    dataFul = pd.DataFrame(columns=['index',\n",
    "                                    'EHOMO(a.u.)',\n",
    "                                    'ELUMO(a.u.)',\n",
    "                                    'Magnitude of molecular dipole moment(a.u.)',\n",
    "                                    'Molecular Weight',\n",
    "                                    'Surface Area (Bohr^2)',\n",
    "                                    'Volume (Bohr^3)',\n",
    "                                    'Ovality',\n",
    "                                    'Polar Surface Area (Angstrom^2)',\n",
    "                                    'Non-Polar Surface Area (Angstrom^2)',\n",
    "                                    'MPI index (eV)',\n",
    "                                    'Chemical potential (eV)',\n",
    "                                    'Hardness (eV)',\n",
    "                                    'Electrophilicity index (eV)',\n",
    "                                    'Nucleophilicity index (eV)'])\n",
    "\n",
    "    getDir(CDFT, dataFul)\n",
    "    getDir(MPI, dataFul)\n",
    "\n",
    "    Ovality = dataFul.loc[:, 'Surface Area (Bohr^2)'].values/(4*math.pi*(3*dataFul.loc[:, 'Volume (Bohr^3)'].values/4/math.pi)**(2/3))\n",
    "    dataFul.loc[:, 'Ovality'] = Ovality.tolist()\n",
    "    dataFul.to_csv(output, index=False)\n",
    "\n",
    "\n",
    "CDFT, MPI, output = getArg()\n",
    "grepInf(CDFT, 'CDFT')\n",
    "grepInf(MPI, 'MPI')\n",
    "main(CDFT, MPI, output)\n",
    "print(\"complited!\")"
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     "is_executing": true
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