Vis and Reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLAN\n",
    "start from the beginning\n",
    "- take whole dataset\n",
    "- get SMILES\n",
    "- split into organic and inorganic\n",
    "- split into simple hydrocarbons/not\n",
    "- use RDKit to get descriptors\n",
    "- plot histograms \n",
    "- Compare RDKit descriptors to pubchem ones\n",
    "- SEND THIS DATA TO JEZ\n",
    "- start doing multiple linear regressions\n",
    "    - See how branching affects constant - volume?\n",
    "- look into covariance matrices \n",
    "    - ensure there isn't too much correlation between descriptors\n",
    "- Start fitting models from there - use MLR to pick descriptors?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation and regression for the Henry's Law dataset + descriptors\n",
    "## Initialisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T17:07:43.374014Z",
     "start_time": "2019-12-10T17:07:19.780998Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd ; pd.set_option('display.max_columns', None, 'display.max_rows', None)\n",
    "pd.set_option('expand_frame_repr', True)\n",
    "import numpy as np\n",
    "import pubchempy as pcp\n",
    "from rdkit import Chem\n",
    "import cirpy #for changing between identifiers\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "#size = (120, 120)  # Smaller figures than the default.\n",
    "#for molecular drawings\n",
    "\n",
    "from ipywidgets import IntProgress #these 3 for progress bars\n",
    "from ipywidgets import widgets\n",
    "from IPython.display import display\n",
    "import time\n",
    "#Run this part outside of the loop - create progress bar\n",
    "\n",
    "#max_count = len(cal_df.index.values)\n",
    "#f = IntProgress(min=0, max=max_count, description='CIDs') # instantiate the bar\n",
    "#display(f) # display the bar\n",
    "#count = 0\n",
    "    \n",
    "    \n",
    "#Place this part inside the loop\n",
    "\n",
    "    #f.value += 1 # signal to increment the progress bar\n",
    "    #time.sleep(.1)\n",
    "    #count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bring in chunks of dataframe from `exploring_and_splitting.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Include carbon_eight? (y/n)n\n",
      "Include carbon_five? (y/n)n\n",
      "Include carbon_four? (y/n)n\n",
      "Include carbon_nine? (y/n)n\n",
      "Include carbon_one? (y/n)n\n",
      "Include carbon_seven? (y/n)n\n",
      "Include carbon_six? (y/n)n\n",
      "Include carbon_ten? (y/n)n\n",
      "Include carbon_three? (y/n)n\n",
      "Include carbon_two? (y/n)n\n",
      "Include carbons? (y/n)y\n",
      "Include cyclic_five? (y/n)n\n",
      "Include cyclic_four? (y/n)n\n",
      "Include cyclic_multi? (y/n)n\n",
      "Include cyclic_one? (y/n)n\n",
      "Include cyclic_six? (y/n)n\n",
      "Include cyclic_three? (y/n)n\n",
      "Include cyclic_two? (y/n)n\n",
      "Include cyclic_zero? (y/n)n\n",
      "Include data? (y/n)n\n",
      "Include hydrocarbons_df? (y/n)y\n",
      "Include ketone_five? (y/n)n\n",
      "Include ketone_four? (y/n)n\n",
      "Include ketone_one? (y/n)n\n",
      "Include ketone_three? (y/n)n\n",
      "Include ketone_two? (y/n)n\n",
      "Include ketone_zero? (y/n)n\n",
      "Include linear_hc_df? (y/n)y\n",
      "Include noble_gases? (y/n)y\n",
      "Include unsaturated_one? (y/n)n\n",
      "Include unsaturated_poly? (y/n)n\n",
      "Include unsaturated_three? (y/n)n\n",
      "Include unsaturated_two? (y/n)n\n",
      "Include unsaturated_zero? (y/n)n\n"
     ]
    }
   ],
   "source": [
    "dataframes_list = list(pd.read_csv('file_list.csv').iloc[:, 1])\n",
    "df_list = []\n",
    "\n",
    "for i in range(0, len(dataframes_list)):\n",
    "    df_file = dataframes_list[i]\n",
    "    df_name = df_file.split(sep='.')[0].split(sep='/')[1]\n",
    "    string = 'Include %s? (y/n)'% (df_name)\n",
    "    choice = input(string)\n",
    "    if choice == 'y':\n",
    "        exec(\"%s = pd.read_csv(%r)\" %(df_name, df_file))\n",
    "        df_list.append(df_name)\n",
    "    if choice == 'n':\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Oxygens</th>\n",
       "      <th>Chlorines</th>\n",
       "      <th>Carbons</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>xlogp</th>\n",
       "      <th>AtomStereoCount</th>\n",
       "      <th>Constant</th>\n",
       "      <th>BondStereoCount</th>\n",
       "      <th>CID</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Complexity</th>\n",
       "      <th>CovalentUnitCount</th>\n",
       "      <th>DefinedAtomStereoCount</th>\n",
       "      <th>DefinedBondStereoCount</th>\n",
       "      <th>FeatureAcceptorCount3D</th>\n",
       "      <th>FeatureAnionCount3D</th>\n",
       "      <th>FeatureCationCount3D</th>\n",
       "      <th>FeatureCount3D</th>\n",
       "      <th>FeatureDonorCount3D</th>\n",
       "      <th>FeatureHydrophobeCount3D</th>\n",
       "      <th>FeatureRingCount3D</th>\n",
       "      <th>HBondAcceptorCount</th>\n",
       "      <th>HBondDonorCount</th>\n",
       "      <th>HeavyAtomCount</th>\n",
       "      <th>IsotopeAtomCount</th>\n",
       "      <th>MonoisotopicMass</th>\n",
       "      <th>RotatableBondCount</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>UndefinedAtomStereoCount</th>\n",
       "      <th>UndefinedBondStereoCount</th>\n",
       "      <th>Volume3D</th>\n",
       "      <th>XStericQuadrupole3D</th>\n",
       "      <th>YStericQuadrupole3D</th>\n",
       "      <th>ZStericQuadrupole3D</th>\n",
       "      <th>SMARTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>methane</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.043</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.03130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>[#6](-[H])(-[H])(-[H])-[H]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethane</th>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>CC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.070</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.04695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>[#6](-[#6](-[H])(-[H])-[H])(-[H])(-[H])-[H]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>propane</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>CCC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.100</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6334.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.06260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.4</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.62</td>\n",
       "      <td>[#6](-[#6](-[#6](-[H])(-[H])-[H])(-[H])-[H])(-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>butane</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>CCCC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58.120</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7843.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.07825</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.3</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.62</td>\n",
       "      <td>[#6](-[#6](-[#6](-[#6](-[H])(-[H])-[H])(-[H])-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-methylpropane</th>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>CC(C)C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58.120</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.07825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.4</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.66</td>\n",
       "      <td>[#6](-[#6](-[#6](-[H])(-[H])-[H])(-[#6](-[H])(...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Unnamed: 0  Unnamed: 0.1  SMILES  Oxygens  Chlorines  \\\n",
       "Species                                                                 \n",
       "methane                  73            73       C      0.0        0.0   \n",
       "ethane                   74            74      CC      0.0        0.0   \n",
       "propane                  75            75     CCC      0.0        0.0   \n",
       "butane                   76            76    CCCC      0.0        0.0   \n",
       "2-methylpropane          77            77  CC(C)C      0.0        0.0   \n",
       "\n",
       "                 Carbons   MolWt  xlogp  AtomStereoCount  Constant  \\\n",
       "Species                                                              \n",
       "methane              1.0  16.043    0.6              0.0  0.000014   \n",
       "ethane               2.0  30.070    1.3              0.0  0.000019   \n",
       "propane              3.0  44.100    1.8              0.0  0.000015   \n",
       "butane               4.0  58.120    2.9              0.0  0.000012   \n",
       "2-methylpropane      4.0  58.120    2.1              0.0  0.000009   \n",
       "\n",
       "                 BondStereoCount     CID  Charge  Complexity  \\\n",
       "Species                                                        \n",
       "methane                      0.0   297.0     0.0         0.0   \n",
       "ethane                       0.0  6324.0     0.0         0.0   \n",
       "propane                      0.0  6334.0     0.0         0.0   \n",
       "butane                       0.0  7843.0     0.0         2.0   \n",
       "2-methylpropane              0.0  6360.0     0.0         4.0   \n",
       "\n",
       "                 CovalentUnitCount  DefinedAtomStereoCount  \\\n",
       "Species                                                      \n",
       "methane                        1.0                     0.0   \n",
       "ethane                         1.0                     0.0   \n",
       "propane                        1.0                     0.0   \n",
       "butane                         1.0                     0.0   \n",
       "2-methylpropane                1.0                     0.0   \n",
       "\n",
       "                 DefinedBondStereoCount  FeatureAcceptorCount3D  \\\n",
       "Species                                                           \n",
       "methane                             0.0                     0.0   \n",
       "ethane                              0.0                     0.0   \n",
       "propane                             0.0                     0.0   \n",
       "butane                              0.0                     0.0   \n",
       "2-methylpropane                     0.0                     0.0   \n",
       "\n",
       "                 FeatureAnionCount3D  FeatureCationCount3D  FeatureCount3D  \\\n",
       "Species                                                                      \n",
       "methane                          0.0                   0.0             0.0   \n",
       "ethane                           0.0                   0.0             2.0   \n",
       "propane                          0.0                   0.0             1.0   \n",
       "butane                           0.0                   0.0             2.0   \n",
       "2-methylpropane                  0.0                   0.0             1.0   \n",
       "\n",
       "                 FeatureDonorCount3D  FeatureHydrophobeCount3D  \\\n",
       "Species                                                          \n",
       "methane                          0.0                       0.0   \n",
       "ethane                           0.0                       2.0   \n",
       "propane                          0.0                       1.0   \n",
       "butane                           0.0                       2.0   \n",
       "2-methylpropane                  0.0                       1.0   \n",
       "\n",
       "                 FeatureRingCount3D  HBondAcceptorCount  HBondDonorCount  \\\n",
       "Species                                                                    \n",
       "methane                         0.0                 0.0              0.0   \n",
       "ethane                          0.0                 0.0              0.0   \n",
       "propane                         0.0                 0.0              0.0   \n",
       "butane                          0.0                 0.0              0.0   \n",
       "2-methylpropane                 0.0                 0.0              0.0   \n",
       "\n",
       "                 HeavyAtomCount  IsotopeAtomCount  MonoisotopicMass  \\\n",
       "Species                                                               \n",
       "methane                     1.0               0.0          16.03130   \n",
       "ethane                      2.0               0.0          30.04695   \n",
       "propane                     3.0               0.0          44.06260   \n",
       "butane                      4.0               0.0          58.07825   \n",
       "2-methylpropane             4.0               0.0          58.07825   \n",
       "\n",
       "                 RotatableBondCount  TPSA  UndefinedAtomStereoCount  \\\n",
       "Species                                                               \n",
       "methane                         0.0   0.0                       0.0   \n",
       "ethane                          0.0   0.0                       0.0   \n",
       "propane                         0.0   0.0                       0.0   \n",
       "butane                          1.0   0.0                       0.0   \n",
       "2-methylpropane                 0.0   0.0                       0.0   \n",
       "\n",
       "                 UndefinedBondStereoCount  Volume3D  XStericQuadrupole3D  \\\n",
       "Species                                                                    \n",
       "methane                               0.0      20.6                 0.62   \n",
       "ethane                                0.0      33.4                 1.19   \n",
       "propane                               0.0      46.4                 1.67   \n",
       "butane                                0.0      59.3                 2.64   \n",
       "2-methylpropane                       0.0      59.4                 1.40   \n",
       "\n",
       "                 YStericQuadrupole3D  ZStericQuadrupole3D  \\\n",
       "Species                                                     \n",
       "methane                         0.62                 0.62   \n",
       "ethane                          0.62                 0.62   \n",
       "propane                         0.78                 0.62   \n",
       "butane                          0.76                 0.62   \n",
       "2-methylpropane                 1.40                 0.66   \n",
       "\n",
       "                                                            SMARTS  \n",
       "Species                                                             \n",
       "methane                                 [#6](-[H])(-[H])(-[H])-[H]  \n",
       "ethane                 [#6](-[#6](-[H])(-[H])-[H])(-[H])(-[H])-[H]  \n",
       "propane          [#6](-[#6](-[#6](-[H])(-[H])-[H])(-[H])-[H])(-...  \n",
       "butane           [#6](-[#6](-[#6](-[#6](-[H])(-[H])-[H])(-[H])-...  \n",
       "2-methylpropane  [#6](-[#6](-[#6](-[H])(-[H])-[H])(-[#6](-[H])(...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carbons = pd.read_csv('dataframes/carbons.csv')\n",
    "carbons.set_index('Species', inplace=True)\n",
    "carbons.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "esters, aldehydes, ketones, ethers, unsat. HCs, acetals, amino groups, rings, fused rings, crown ethers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Previewing Chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear hydrocarbon plot\n",
    "Molecular weight is divided by 10 to fit all properties on the same plot nicely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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sg3sDg7oqPx5fAIL1YFoTPaWC8lk+ov4oQW9woqciIiIiIiIiIiIlRMF9GVFwP4bgfsiI9Tlzb6Lr2nvHVJ+T1ZHsHqrR2U7PsBqdi0OzWNe0muuqlzCQLO/7x+exqA36wDAgWAu+qb8qPeAJEPVHCXgCEz0VEREREREREREpAQruy4iC+7EH91kj1ucs+RV6r1g/pvqcrJSTYlvbC2xo3sJbfYdz41WeSlbVLmdN3Q1U+6LnOMPUFg54qfQNfV99lRCsAWNqblw7XMAToDpQjd/yT/RURERERERERERkAim4LyMK7i88uAdO1uc8932sRA8Ag9FZdC7/2Jjrc3KndF329R1iQ/MWdrS9kKvRMTG5OrqQm+tXckllpkannOQqc7Lv27Sgsg7KZEV60Bsk6o/im8Ib9YqIiIiIiIiIyNkpuC8jCu7HGdwPyXd9TlZnsoeNR7fxyPHt9KZP1ujMrGjk5vpVXFd9NT7TO+75TxY+y6K28rTgOhCBighQHg8yKr2VRANRvGX0cxcREREREREREQX3ZUXBfX6C+6x81+cAJFI2rQMxnu9+iU1tOzgYO1mjU2kFubFuOavrbqDWV52X91DqTqnMyfL4IFgHVnmE2YZhUOmtJOKPKMAXERERERERESkTCu7LiIL7/Ab3wFB9zmaqn/vX0+pzPkqicdGYT5dI2XTFB3P/PjhwmE1tO9jd/SK2m5m/gcHV0YWsrVvJZaF5U7xGx6Au5MdrnvYeDQMqouAPT8y0JoACfBERERERERGR8qHgvowouC9AcD8kU5/zI6r2PTSsPudGupb9Onbl6OtzTg/us3pSvWxrf5qt7bvoSffmxmcGGllbv5Lraq7Gb07NPvQRK3OyvBUQrM104JeRkC9ExBfBWyafOhARERERERERKTcK7suIgvvCBfdZvo4D1Oz65gXX52zZP0hTJE3IN/Iq+rST5vnul9nUvoMDA2/nxiutIKtqr2NN/YopWaMzYmVOlmlCRS34gsWdVAlQgC8iIiIiIiIiMjUpuC8jCu4LH9wDF1yf8+8vDPIvTw+ycBp8cq2J5/R6mNMcih1hc9sOnu3aQ3pYjc6SyJXcXL9qitXonKUyZzhfCILVYJjFm1aJqPRWEvVHFeCLiIiIiIiIiEwRCu7LiIL7IgX3Q8Zan/PYGym+tCkJwC2XGPz6tcaogvfeVD/bOnaxpe2pU2p0ZgSmc3P9SpbXXDMlanTOWZmTZXkyG9d6/MWZVIlRgC8iIiIiIiIiMjUouC8jCu6LG9xn+ToOUPP0Nwm0nr8+51u7EvzHnjQAH15q8M75o189nnZtXuh+mU1t29k/rEYnaFVkanTqVlDnr8nDO5o4VQEvobNV5gxXEYVApPATKlHZTWx91uR/YCMiIiIiIiIiUo4U3JcRBfcTE9wD4DpU7t9Mze5h9TmRWXRe/xESjYtzL4sNprnvsQTPHAHDgP9vtcmSGWOvu3k7dpRNbdvPqNFZFLmCd9SvYn7o4klaozOKypwsjw8q60e1t8BUFfQGifqjCvBFRERERERERCYZBfdlRMH9BAb3Q8xkP9E9/07V3gdz9TkDc26k89pMfU4iZXOsL8kXHnM42AUVXrj/NpOmyIWF7H2pfrZ1PM2W9qfoTvXkxmcEprG2fiXXVy/FP8lCXZ9lUVPpY1TfEcOAYE2m/76MKcAXEREREREREZlcFNyfg2EYXuC3gSXA1cAVgBf4Ldd1/+95jv0w8ImhY2zgBeBrrutuHMP11wCbz/GSv3Zd91OjPZ+C+4kP7rMy9TnfItD6OgCOJ0D3kl+h9ZI76Rp06Yy5fPoRh+44NIQy4X04cOEr5NOuzZ7uV9jUtoO3Bg7mxoNWBStrrmVN/Qrq/Wf27peqUVfmZPkqMwF+GW5cO1yFp4JoIIrfKs89AEREREREREREJgsF9+dgGEYU6Br65wlgEJjFeYJ7wzC+BvwxcBT4KeADfgWoAX7Xdd1/HuX115AJ7rcCW0Z4yXbXdR8fzblAwX0pBfcAuO5Qfc73cvU5yXATby26l96Gq9jf4fKFxx0GbVjQAJ9ea+Kxxl9vczjWzKa2HTzT9QJpN9Onb2BwVfhybq5fyeVVl06CGh2DupAPrzmGIN60MhvXegOFm9YkoQBfRERERERERKS0Kbg/B8MwfMA7gD2u6x4zDOM+4HOcI7g3DGMFsAPYD1zrum7X0Pgc4DmgEljguu6hUVx/DZng/vOu6943vnej4L7kgvshmfqc/6Bq7wO5+pz2pht4+6oP8mR7Df+wPTO2Zp7BR5YbeQvV+9IDbG9/mi3tO+kaVqPTGGhgbd1Krq9ZSqCEg12vaVIb8o+uMme4QDizee3Yj5xyAp4AUX+UgEcPM0RERERERERESkkhgvsp00Xhuu6g67oPua57bAyHfWzo7y9lQ/uhcx0Cvg74gXvzN0uZ7Bx/iM7lv0XL+r8jVr8AgLrmp1jy2B/y3sTP+ZWFmQcOWw64PLA3fw/FqjyV3DH9Zv7yyj/jo3M+yKWheQAcS7Ty70f/m0+++kV+fPTntCbb83bNfEo5Dv3J9NgPTPRC33GwU/mf1CSTSCc4PnCc4wPHSaQTEz0dEREREREREREpoCmz4v50o1xxfxRoAmacHvgbhnEDsJNMxc2No7jeGjIr7n8E7ALCwHFgm+u6b451/lpxX5or7odLDKax9j3O7Fd+hC+ZWQUfC83g7zz38u3jV2IAf7La5JqmwqwWPxJrYXP7Dp7ufJ7UsBqdheEFuRods6R64i+gMid3qJFZee8P539ak1TAEyDij1DhqZjoqYiIiIiIiIiIlDVV5YzB+YJ7wzAqgX6g33XdqhG+Xge0Aa2u604bxfXWcPbNaX82NI+us3z9DAruJ0Fwn7Lpig9iDQ4w6/X/ZPr+hzHI3E9brOv51MCv0u2p5b5bTWZXF67qpT89wPaOZ9jStpPOVHdufJq/nrX1K1lRs5SAVRr1KhdcmZM7QQUEazMd+AIowBcRERERERERmWgK7sdgFMH9DKAZaHZdd+YIX/eS2eB20HXd85aHG4ZxJbAOeAA4BASAZcBfAleT6dK/yXWHitFHPsdHgI8AzJw1c+lTrz5F2rmAepHJbpIF91nB7kPMffF7hDv2AhDDzz+k3sv/+u7gc+/0Ea0obE+77dq81PMaT7Tt4I3+/bnxgOlnRe21rK1bwbRAfUHnMBohv5cqv+fCT2CaUFELvmD+JjUFKMAXEREREREREZkYUz64NwzjEDB7DIf8yHXdXzvLue4jP8F90nXdC16ubBhGGNgDzAXudl33f0dz3LJly9zdu3eTtJP0D/YTS8Ww3dIPs/Nikgb3ALgudUe2Mfvlf8vV57zlzOBfgh/mrtsX47OKs8nq0fgxNrftYFfn86Tck/3wC8MLWFu3kivDl01gjc44KnOG84egohpKqg5o4vktPxF/hKBXDzZERERERERERIqhEMH9OJa9FsR+YCy7LraM41o9Q39HzvL1yGmvuyCu6/YahvHvwKeBm4BRBfdZfsuPv8JPTaCGeDpOLB0jlorhnH3hvkwkw6D9opvoalzGrNd+wrT9D3OJ2cJXEl9m1yPL8az5EKlgXcGnMbOikQ9e9Iu8d8ad7Oh4hs3tO+kY7OKV3r280ruXBn8da+tWsqJ2GRVFr9Fx6YmlxleZA5Dsh3QyU53jOe+HYspG0k7SGmtVgC8iIiIiIiIiMomVVHDvuu47initAcMwmoEmwzAaT9+cFrh06O838nC5tqG/Ky/0BIZhEPQGCXqDuAGXWDpG/2A/CTtBKX1qQjJsb5BDi++hdc5awju/w9z4Pq5PPE3ykT0cv+IXOHbpXbhm4W+/Sk+Q26at4ZaGm3ip5zU2te1gb/9btCbb+XHz//I/xx5mRc0y1tavYHqgoeDzyUo5Dv3J9PgqcwDsFPSfgEAk80dysgG+z/IR9UcV4IuIiIiIiIiITCIlFdxPgE3AB4F3At877Wt3DHvNeF0/9PeBPJwLwzCo9FZS6a3EdmwG0gPEUjES6bF8WEGKIRaZzcDt9/Holm28t+tH1NPD7Ff/nYa3t3Bwyb30NCwqyjxMw2RJdCFLogtpjh/P1Oh0PUfSSbK5fQeb23dwZdVlrK1fxcLw/KLU6PQn0wS85vgrc1wX4t2QjkOwDorwQGQyGbQHFeCLiIiIiIiIiEwyJdVxn0/n67gfes0KMpvG7geudV23a2h8DvAcmRXyC1zXPTTsmDqgDmh3Xbd92PhK4KnTN581DOPXgB8AKWD+8HOdS7bjfixSTopYKkZ/qp+UnTr/AaVqMnfcn0XKdvn7J/pZ1/1TPmQ9isfI/Jq0N13P21d9kMEi1OecbiAdY0fHs2xp30n7YGduvMFfx5q6FayoXUbQKuxGp17THH9lznCGAcEa8IXydcYpx2f5iPgjVHov+ANAIiIiIiIiIiIyzJTfnHa8DMP4FLBg6J9LgMXATuDNobHtp4f4hmH8DfBHwFHgp4AP+GWgFvhd13X/+bTX30fmgcDnXde9b9j4IcAcut5RIABcC1wHpMk8QPjX0b6XCwnuhxu0B+lP9TOQGsCeBCH4KaZgcA/Qm3D57CMONbHD/KX/e1zDPgBsy8/RBcWrzzmd4zq81Ps6m9t28Hrfm7lxv+njhpqlrK1fSWNgWsGuH/J7x1+ZczpfZSbA18a1Z+W1vET9UQX4IiIiIiIiIiLjpOD+PAzD2AKsPsdLvu+67j0jHPdh4HeAKwAHeB74quu6G0d47X2MHNx/EriFzIODOsAAmoEngb93XffFsbyX8Qb3w8XTcQZSA8RTcWy39APxqRrcAxzpdvncYw7xlMtHwjv4E+NH+JKZ/Y/joRkcXHwvPdOKU58zkpb4CTa37+Cpzt0MOic/tXF51aW8o34VC8MLClCjY1AX8o2/Mud0ppWpzvEWe/PdycVreYn4IoT0KQURERERERERkQui4L6M5DO4z3JdNxfix9Kx0t3UdgoH9wB7Wly+stXBdWHNzDh/GfkpjQcewRhqWepoWs6hqz40IfU5WbF0nB2dz7K5bccpNTp1vhrW1q9kZc21BD35q9HJe2XOcIEwVEShMGefMrIBfqW3EsPQ90pEREREREREZLQU3JeRQgT3wzmuk+vDL7lNbad4cA/w0F6HHzyfuffeu9Dgw7OPMHfPdwl37AWy9Tnv5dil6yakPifLcR1e6d3LprYdvNb3Rm7cb/q4vmYpa+tWMqMiPzU6BanMyfL4IFgLlq8w559CPKaHiD9CyBtSgC8iIiIiIiIiMgoK7stIoYP74dJOOrMKPxUjaSeLcs1zKoPg3nVdvvOsyxNvZe6/31lhsHK2Qd2R7cx++YclVZ+TdSzRyua2TI1O0jn5vi+vupS19StZFL58nDU6BarMyZ3eyKy894cLc/4pRgG+iIiIiIiIiMjoKLgvI8UM7odL2SkGUgP0p/pJO+miXx8oi+AeIO24fHmzw2snwGvCX9xickmdgZWKMeu1nzC9xOpzsmJ2nKc6drOpbQdtgx258TpfDWvqVrCy9loqPcELOndBK3NyF6mAyjptXDtKlmkR9UcV4IuIiIiIiIiInIWC+zIyUcH9cEk7Sf9gP7FUrLib2pZJcA/Qn3T57KMOx/sgEoAv3W5SW5kJR4M9b5dkfU6W4zq82ruPTW3beXVYjY7P9HJ99VJurl/JjIrpYz5vVcBLyFfg92eaQxvX5q+nf6qzTIuIP0KVt0oBvoiIiIiIiIjIMAruy0gpBPfDxVIxYukYsVQMZ2gVeMGUUXAPcKzX5bOPOAykYHYU7rvVJOAdCkZdd4T6nMah+pzF4752vhxPtLKlfSc7OnaTdE7WLS0IXcLa+pUsjlwxhhqdAlfmDKeNa8dMAb6IiIiIiIiIyKkU3JeRUgvus1zXJZaO0T/YT8JOUJDfnzIL7gFeOZ6pzXFcWDYT/vBGE3NYKGqlYsx8/T9p3P/wyfqcGcs5tKg06nOy4naCpzozNTqtyfbceK2vmjV1K1hVe92oanR8lkVtZZE2kvX4MqvvLW9xrjdFWKZF2Bcm7AsrwBcRERERERGRsqbgvoyUanA/nO3YxNIxBlIDJNKJ/J24DIN7gMfedPjus5n78V1XGLx/yZkrzoM9h5m75zun1ee8h2OXrMMtoeDZcR1e63uDTW07eKV3b27ca3i5vuYa1tavZGZF4znPUZTKnCzDgGA1+KqKc70pxDIswv4wVb6qcW5OLCIiIiIiIiIyOSm4LyOTIbgfLuWkiKVi9Kf6Sdmp8Z2sTIN7gO/vdnj4jcw9+bHrDVbPGyEIzdXn/Bu+ZDdQmvU5WScSbWxu38nOjmdJDKvRuSx0MTcP1ehYhjXCkQZ1IT9es4irub1BqKzVxrUXQAG+iIiIiIiIiJQrBfdlZLIF98MN2oMMpAboT/VjX0gAX8bBve24fGWrw0vHwDLhMzebLGgYObieLPU5WQk7wVOdz7GpbQcnkm258RpvlDX1mRqdkKfylGOKWpmTZVpDG9cGinvdKUIBvoiIiIiIiIiUGwX3ZWQyB/fDJdIJ+lP9xFNxbHeUYXwZB/cAsUGXv3jUobkXqvxw/+0m00JnX3Ue7DnM3Be/S7j9daB063OyHNfh9b43czU6Lpn/BnkND8trrmFt3UpmBWfkXh8OeKksVmXOcNq4dlwsw6LKV0XYH1aALyIiIiIiIiJTmoL7MjJVgvss13WJp+MMpAaIpWPn3tS2zIN7gBN9Lp951KE/CU0R+MJtJkHvOQLks9bn3EPPtCUFmWM+tCbb2dy2kx0dz5JwTu6TcGnlXG6uX8WS6JVYhqf4lTlZHh8Ea8Eq8qr/KcQ0TMK+zAp8yxypEklEREREREREZHJTcF9GplpwP5zjOrk+/BE3tVVwD8DrrS5f2uRgO7CkEf5ktYl1nvA6U5/zUxr3PzSsPuc6Di36cEnW52Ql7CS7Op9jc/sOjiVac+PV3ghr6lZwc8MNzInWTMzkDCOz8t4fnpjrTxGmYWZW4PvCCvBFREREREREZEpRcF9GpnJwP1zaSTOQGmAgNcCgPRSCK7jP2bzf4V+eztyjd8w3+NDS0VWOVPQcZu6L3yPS/hoAtuWjef57abm0NOtzslzXzdXovNz7eq5Gx2N4WFV3DXfPWsvFVbMmZnLeQKb7XqHzuCjAFxEREREREZGpRsF9GSmX4H64QXuQ/lQ/A237sO3CBuL5UIzgHuBHLzhsfD1zn/7mdQbvuGSUfeGuS93RHZn6nEQXMDnqc7Lakh1sad/J9o5niNsnP5lxRfhi1jWt5oa6xXiKHfwaBgRrwBcq7nWnINMwCflCRHwRBfgiIiIiIiIiMqkpuC8j5Rjc53QeJJ6KMZCKEbMTOEOVL6WmWMG947j8zTaH55vBMuBTa00WTh9937uVijFz789ofOvBSVWfk5W0B9nV9Ryb2nZwLHEiN17ri3LHjFW8s3ElEV9VcSflq8wE+Np0ddwMw8itwPeYE7AJsYiIiIiIiIjIOCm4LyPlHtxnq3Jc1yVmJ+hPx4iP1Ic/gYoV3APEUy6fe8zhSDdU+uD+20waw2PbrHWy1udkua7Lvv79bOvYyXNdr+AMq9G5qWEp65tWc0nVRcWbkGllqnO8geJdcwozDIOQN0TEH1GALyIiIiIiIiKTioL7MlLWwX2yH2IdYKdOGbZdm/50nFg6TrIEqnSKGdwDtA24fOYRh94ENFbBF243CfnGFt7jutQe3cmcl394Sn3OoUX30D299OtzAAwMHG8/j7Rs59HjO+lPx3Jfuzw8j3VNN7Gi7uri1egEwpnNaxnjz0JGpABfRERERERERCYbBfdlpKyD+6xEL8S7zgjwAVJOiv50nP50DHuCNrItdnAP8EabyxefcEg5sHAafHKticcce2BspWLMfP2nNO5/aFh9zrVD9Tn1+Z523vk8FrVBHwl7kK2tz7KxeSuHBlpyX6/xRbhjxipub1xJtS9c+AlZXqisA8tX+GuVCcMwqPRWEvVHFeCLiIhqbUV9AAAgAElEQVSIiIiISElTcF9GFNwPk+iFeCfY6RG/HLeTE9KHPxHBPcD2gw5ffypz395yicGvX2tgGBe22nvk+pz30HLp+pKvzwkHvFT6MoGu67q80vMmG5q38nT7S6fU6NzYcA3rZqzmsvDswk7IMCAQyfyRvMkG+BF/BK9Z2r+TIiIiIiIiIlKeFNyXEQX3p3FdSPZCrDPXf3/mS4rbhz9RwT3Aj190+J9XM/fuPUsNbp8/jk1SJ2l9joFBXZUfz2kPLVoTnTzYso1Hj+2gb1iNzvyqOaxrWs3K+qvxFnIFt9cPwfpMB77kjQJ8ERERERERESlVCu7LiIL7s3BdSPRkKnTOUZGT7cMfSMUYdM6s2smHiQzuHdflH7Y7PHMks9D7k6tNFs8YX8e6lYoxc+/PaHzrwUlTn5OtzBlJ0h5ka+tuNjZv5eBAc2682hfmjsZVvHPGqsLV6BgGBGvAFyrM+ctYLsD3RfCW+KdCRERERERERKQ8KLgvIwruz8N1IdE9FOCfux6nUH34ExncAyTSLl94zOFgF1R44f7bTJoi498gtaL3CHP3fHfS1OdEKrwEvWdfQe+6Lq/27Gdj81aean8Rh8zvi8ewWFV/DeuaVjM/PKcwk/NVZgJ8YxyfiJCzCvlCCvBFREREREREZMIpuC8jCu5HyXUz4X2i+7wBPkDCTtKfjhNPx7HH2Yc/0cE9QGfM5dOPOHTHoSEEX7zdpMo//vB+xPqcyukcWnxvydXnGBjUV/mxRtHz35bo4qGWbTx8bAd96YHc+KVVs1nftJpV9Vfnv4bFtCBYB95Afs8rOdlNbBXgi4iIiIiIiMhEUHBfRhTcj5HjnFyBP4rfadd1idsJBtJxYnaCC7kPSiG4B9jf4fL5xx1SNixogE+vNfFYeQjvATMVZ9benzH9rQcx3cynFTobr+XQog+RrGzIyzXywe+xqDlLZc5IkvYg29qeY0PzVg70H82NR71VvHPGKt7ZuJJafzS/kwyEoSIK5OdnI2fKduD7rNH/LoiIiIiIiIiIjJeC+zKi4P4COTbEuzMh/ih/tx3XIZbObGqbsJOjvlSpBPcAuw5nOu8B1swz+MhyA2MUK9BHq6L3KHNf/C6RtleBbH3O3UP1OaURkp6vMmckruvyWu8BNjZvZWfbnlyNjmWYrKy/mvUzVjM/PDd/30uPD4K1UCLfs6kq6A0S9UcV4IuIiIiIiIhIUSi4LyMK7sfJsYcqdHpGHeADpB2bATtOLB0naZ87lC+l4B7gv152+M+XM+/1V682WHd5nnvVz1qfcw/d06/O77UuwFgqc0bSnuzioZbtPHxsB72p/tz4JVUXsX7Gam5suCY/NTqGkVl57y/QxriSowBfRERERERERIpBwX0ZUXCfJ3Y6E+Ane8cU4ENmU9uBdJz+dJy0kz7j66UW3Luuyz/vdNn5tosB/PFNJktn5r+WZeT6nGUcWvThCa/PGWtlzkgGnRTbWjM1Ovv7j+TGI94Q72xcxR0zVuWnRscbyHTfm9b4zyXnpA58ERERERERESkkBfdlRMF9ntlpiHdCsm/MAT5kOtH70zFiwza1LbXgHmAw7XL/Ew5vdUDAA/fdajK7ujCd6qfX5ziml6Pz30PLZRNbn3MhlTkjcV2Xvb0H2di8lR3tL+R+7pZhsqJuCeub1rBgvDU6hpGpzvFVjnu+cn4K8EVERERERESkEBTclxEF9wVipyA2FOBfgMymtkli6TgdiX46Y6PvxC+W7rjLZx5x6IhBXRDuv90kWlGgDVFdl9rmp5jz0g9y9TmJymkcXHzvhNXnmIZBXejCK3NG0pHsHqrR2U7PsBqdi0OzWNe0mpsaluIbT42OrxKCNWDkud5IRhTyhYj4I/mpPhIRERERERGRsqfgvowouC+w9ODQCvz+87/2LAaSKY729WT68J1BXErnXnq7y+Vzjzkk03BpHXzmHSY+q0DhPZn6nJl7f0ZjidTnBLwW1RX5X/WfclJsa3uBDc1beKvvcG487A1xe+MK7pxxI3X+6gs7uWllqnO8gTzNVs7FMAwqvZUK8EVERERERERk3BTclxEF90WSTmZW4A8OjPnQ2GCajoHMinvbdYjbceJ2gqRTGvU5u4+6/O2TDi6wco7BJ24wxlfrMgqZ+pzvEWl7BZjY+pxohY8Kb2H6413XZV/fITY0b2FH28kaHROTG+oXs75pDVeE513Y9zsQzmxeS2F/VpKRDfCj/igec/wVSyIiIiIiIiJSfhTclxEF90WWSmRW4A/GRn3I8OB+uLSTJm4niNlxUu6Zm9oW089fc/iPPZl7/JcXG9x9ZRGqWM5Wn7PoHrobryn89YeYhkF9KIBZ4Py7M9nDw8e281DLdrpTJyuY5oVmsr5pNTfWL8U/1ocWHl+m+34C9wooN4ZhEPJmKnQU4IuIiIiIiIjIWCi4LyMK7idIKg6xjkyQfx5nC+5POZ2TImbHidkJ7KEKmWJyXZdvPe2y9UDmPv+DVSbLLyrOSu5SqM8pVGXOSFJOih1te9jQvIU3+t7OjVd5KodqdG6iPjCGGh3DgEAk80eKxjAMqnxVRHwRLLMwn9gQERERERERkalFwX0ZUXA/wQZjmQA/ffZgfjTB/XAJO0ncThC34zhF7MNP2S5f2uSwrw18Ftx3q8ncmuLVsIxUn9M8/26aL3tXUepzClmZczb7eg+xsXkr29ueJz300MLE5Pq6RaxvWsOVkYtHX6Pj9We677UKvKhMw8wE+P4IpjYNFhEREREREZFzUHBfRhTcl4jBgaEA/8ze+rEG91mu65JwksTsOAk7WZRNbXsTLp99xKF1AGoq4P7bTWqCRexQd11qm3cx+6Uf4E90AsWrzylWZc5IugZ7ebhlOw8d207XYG9ufE5lE+uaVrO6YRmB0Ty8MAwI1oAvVMDZykhMwyTij1Dlq1KALyIiIiIiIiIjUnBfRhTcl5hkfybAt1O5oQsN7odzXCe3Cj9R4E1tj3S7fO4xh3gK5tXAX9xi4vcUN80204lMfc6bDxS1PqfCaxEtUmXOSFJOmh1tL7CxeSv7+g7lxqs8QW4bqtFpCNSc/0TeIFTWggLkorMMi7A/TNgXLvgmzyIiIiIiIiIyuSi4LyMK7ktUogdineDYeQnuh7Ndm5idIJaOk3JT5z/gAuxpcfnKVgfXheUXGfzeSgNzAkLIit6jzHnxe0SLWJ9TXeEjUOTKnJG80fs2G1u2sq31uWE1OgbL6xaxvmk1CyOXnjsYNk2oqAVfsEgzluEs0yLqjxLyhhTgi4iIiIiIiAig4L6sKLgvYa4L8S5iPe109McLcomUkyZmx4nb8Vy4my8P7XX4wfOZ+/69Cw1+adEErd52XWqadzGnSPU5E1mZM5KuwV4eObaDh1q20TmsRmd2ZSPrZqxmzbTrzl2j4wtBsFqr7yeIx/RkAnzVF4mIiIiIiIiUPQX3ZUTBfekbiCdpazuOmezBKGBPfdIeJG7HiTsJbNcZ9/lc1+U7z7o88VZmzr+7wmDFnIkLfzP1Of9F45sbC16fM9GVOSNJOWmeat/DxuYneb33QG485Aly6/QbuKvpJqYFakc+2PJkVt97A0WarZzOa3mJ+qNUeisneioiIiIiIiIiMkEU3JcRBfelbyCZ5kRvAuwUVqILM9VX0Ou5rktyaFPb+Dg3tU07Ln+12eHVE+A1M333l9RN7FL0YtXnlEplzkje6jvMhuatPNn6HGk3DWRqdK6tvYr1TatZFL1s5HqWQBgqokCJfJygDPksH1F/lKBXFUYiIiIiIiIi5UbBfRlRcF/6csF9lp3EindipmMFv7bjOiTsBLFxbGrbn3T57KMOx/sgEoAv3W5SWznBwW8R6nMsw6Qu5C+ZypyRdA/2DdXobKdjsDs3flGwkXVNN7F22nUELP+pB1leCNaC57RxKSoF+CIiIiIiIiLlR8F9GVFwX/rOCO6HGKk4ZqIT0z7za4WQ3dQ2bscZdMa2qe2xXpfPPuIwkILZUbjvVpOAd+IT7RHrc6Yv5dDie/JSn1OKlTkjSTs2T7W/yMbmrbzWuz83XumpyNTozLiJ6RV1Jw8wDAhEMivwtfp+QvksHxF/RBU6IiIiIiIiImVAwX0ZUXBf+s4W3GcZg/1YiU6MMYbp45Hd1DZmx7FHuantK8ddvrzZwXFh2Uz4wxtNzJHqWCZAoK+ZuXu+e1p9zrtpuezdOOOsz6kO+gh4SrMyZyT7+46wsWUrW0/sJjVUo2NgcG3tlaybsZol1QtO1uh4fBCsy6zClwmlDnwRERERERGRqU/BfRlRcF/6zhfcA+C6mIO9mIkujFEG6fmSsDN9+Ak7gXOePvzH33T4zrOZ17zrCoP3L5m4zWrPkK3PefmH+OMdQLY+58N0Ny694NNOhsqckfQM9vHo8Z082LKN9uTJGp1ZwencNeMmbp6+nArLn1l9XxEFf3gCZytZXstLxBch5AtN9FREREREREREJM8U3JcRBfelb1TBfZbrYCa7MRM9GDiFndjpl3Zd4k6CWDpOwkme9XXf3+3w8BuZ/x587HqD1fNKKLznbPU51wzV50y7oHMGvR4iFZNzVbrt2uxqf4kNzVt5teet3HjQCnDL9Bu4q+kmZlTUg9efWX1veiZwtpKVDfArvZUjbzQsIiIiIiIiIpOOgvsyouC+9A2mHY71xLGdMdxDjo2Z6MIc7MU4zyr4Qsj24cfScVLuqRU+tuPy1a0OLx4Dy4TP3GyyoKH0gsVAXzNzX/we0daXgaH6nMveTcv8C6vPmWyVOSM52H+UDc1b2dq6O7fPgYHB0porWN+0hiU1CzAra8FXNcEznZz+/stR/uGvo+x89QiNTfn55IzH9BDxRwh5QwrwRURERERERCY5BfdlRMH95JBM2xzvSYwtvAewU1iJLsxUX2EmNgopJzXUh5/I9eHHBl3+4lGH5l6o8sMXbzdpCJVgqOi61LQ8zZyXfnCyPifYwKHF99A1xvqcyVqZM5LeVD+PHnuKB1uepC3ZlRtvqpjGuqabuHnmaoLhmWBO7gcVhfL3X45y5eIkt94ZP2M838F9lgJ8ERERERERkclPwX0ZUXA/eVxweA9gJ7HinZjpWP4nNgbD+/CP9Tl85lGH/iTMjMDnbzMJekszUMxXfc5krswZie3aPN3+Mhubt/Jyz5u58QorwC2N17Pu4nczo/qSCZxhaZobncP7PtjHX/9TxynjhQzusyzTIuwLE/aFFeCLiIiIiIiITDKFCO5Lq8RaZBLyeyymRwJYF7Jk2/JjhxpJh5pwPBX5n9woBSw/Nb4o0wMNXF4T5VM3+bBMONoD/7TdwbmQhxJF4HgCHF74AV685Wt0NywCoOb48yx57I+Z+dp/YtqDozpPLJUmmS7u3gOFZBkWK+qX8JdLfp9/XPpn3DZ9BT7TS9xOsOHoFj669Q+5b9ufs/v4szju1Hnfk5nt2HQlujjad5SeZI9+LiIiIiIiIiJlTsG9SB6MK7wHXE8AOzSDdOUMHCuQ59mNnmmYBD1BVl/UwJ+siACw5xj82wulGdxnJapm8PrKP2ff8j8iWVGL6aSYtfenLH7sj6k+9tyoztGbSFGizyfGZW6oid+d/wH+9fovcu+8u2nw1wDwXMfLfH7XF/j44x/j5/t/Tiw1sZ/6KLS2VpNP/2Et118+k8vqZ7Pm6ia+8bcRHAeOvu1hbnQOAD/5YRVzo3OYG53Dn3y87pRzxAZM/vwPalkyZxZXNl3Exz9UT1fnqf83+sxOP7/76/Wsumom8xtms+ySWfzBR+o41nxqPdFPfxRibnQOTz0Z4Cufj3LtZbNYMP0i3v/uOl7a28fRvqN0JbqwncwK//374QMfgPp68Pth4UL49rcL9/0SERERERERkYmlqpwSpaqcyWlctTnDGIP9WIkuDGd0K8YL5RvPdPMfL/cD8DvXB7jpYifXh1+qxlOfE/R5iASmTmXOSGzX4ZmOTI3OS91v5MYrPBW8Y9Y7uGveXcysmjmBM8y/zg6Td69tJJk0eP+H+5nWmGb3rgD//eMQH7i3j09/sZNHNgb5o4/Wc/2qOO/7YOZ3fvbcNNdcl8xV5Vy1JEldg82aW+O8fcDD9/8lzJ13D/CP32nPXetzf1rD/je9LF+ZoK7B5uBbXv7f96uorbN5aEcLgYrMfxt++qMQf/qJOq5aksQfcLnz7gG6Oiy+/c9hrrhqkJ89ehzIPEw7/naYO9ZEqKkxuPdeqK6Ghx6CDRvgy1+GT32q+N9TERERERERETmpEFU5nnyeTKTcZVfejze8d30h0r4QxmDfUICfyuMsR++jyyIc6Umz/XCC//N0gkuq67hyGsTsBHE7gUvpPfjL1ue0zl7D3Be/R7T1JWqOP0+09WWaL3s3LfPfjWP5Rjw2Npgm4LHwe6buh5Esw+SGusXcULeYtwda2Ni8lU0nniGejrPx4EY2HtzINQ3XsH7eeq6Zdg2mMfm/F1+7v5qBfpOHd7bQMD3zMOcD9/bT2JTm//xdhN/4RA/v+eUB/uij9Vw0N817fnlgxPPMnpfmn77blvu36xr84NtVfPFvOwhHMvfCpz7fRUXw1Pvi1jtjvO+ORh7eEOTu9516bp/f5f89cBxraEF+tNrh/j+v4Y3XvVx2eQrHdfiTP/ATrUnzxFO9zKiJ4DE9fPzjmRX4998PH/84RCL5+m6JiIiIiIiISCmY/ImMSIkZb23OcK6vinTVLNIV9bhG8Z+zWabBZ1fXcHGNF9uFzz7RQWu/RY0vSmOggRpvhIA5cgg+0S60PmeqVuaMZHblDD5x2fv5/vVf4tfnvYeGQC0Az7c+z+d3fZ6PDdXoDKRGDrInA9eFB/8nyJpb43i8Lp0dZu7PjTfHcV2DXdtGV0/1a7/Re8q/l69MYNsGLUdP3pvDQ/v+PoPODpOLL0sRjti8sufMe+X99/TlQvvsOQEOH8qcs6fbZPvmAHfcPUB77wAvv32MfUc6OHYixR13QCwGu3aN+tshIiIiIiIiIpOEVtyLFIDfY9EYqeB4T4K0M85NJg0D1x8m7avCHOzFTHRjuOn8THQUgj6Tv7qllo/8vJWuhMOnHuvgW+sbqPJn+vCDniC2axNLx4nZcVJFnNt5GQadTcvpnrY4V58TiLWy4Kmv0DX9Gg4u+jDJ0PRTDkk7Dn3J1JSvzBku5A3ynlnv4F0z17K74xU2HtvGns7XOTZwjG+//G1++NoPufmim1k3bx2zqmZN9HTHpKPdpKfb4r9/HOK/fxwa8TXtbdaI46drmnXq73Ykmrm3u7tOPgM/ccziy5+rZtPDQfp6T3023ttz5rPys52zZ+icB/d7cF2Dr38tyte/Fh1xXq2to5q+iIiIiIiIiEwiCu5FCsTnMXO1OeMO7wEMA8cfwfGFMZM9mMlujCL1zU+v8vCXt9Ty+w+1caQnzWc3dfC12+vwDH2qwDIsqrwhqrwhUk6KmJ0J8W03D+87D0aqz6k+/jyR1pdpvuxdtMy/+5T6nNhgmgqvhc8qrw8lWYbJ8rpFLK9bxOHYcTae2MWm5m0k7AQPHnyQBw8+yJL6Jay/eD1Lpy3FMkYXeE8k18n8jq577wC//KG+EV9z0ZzRPWyyzvZ23cw1HAc++J5ptJ2w+Mjv9XDp/BTBkINhwO/9ej2Oc+ancM52TnfonNn53/PRXt5xx5kbCBsYXL3Ih+2EsczS/3mIiIiIiIiIyOgouBcpoLyH95AJ8ANRHH82wO8pSoC/cJqfT66q4f6tnTzXkuTvn+rmj1dEMYxTw0iv6SViegl7qkg6SWJ2nLidHLEPf29LL5v2tdEdGyQa9HHz/HoWzAgX7D1k63NqWp5hzkvfxx/vYNben1F/+EkOLbqHrsalMPR+euIp6kJ+xl94NILm5+CVn8JAO1TWwcJfhKalhbjSBbsoOJ3fnns3H5q7nsfb9/DAwYc4HjvOnrY97Gnbw/TgdO6adxe3XHQLId/IK9knyrMnnuUn+35CW6yNOv80KkL/RmoQVq1JFPS6e1/18uZeH1/7Rhu/8IGT9UKJuEFP94U9BLpobmZ/C8tyzzH/OM39fUT8EcK+8Bn3pIiIiIiIiIhMPuW1nFRkAvg8Jo3RAB4zz7ebYeIEqkmHL8IOVOMW4Xa+7ZIgH1pcBcD/7h3gZ6+dvfvcMAwCVoAaXzWNgQaqT+vD39vSy8+eb+ZwZ4yeeJrDnTF+9nwze1t6z3rOvBiqz9lz699ydP57cAyLQKyNBbu+yoKdf42//zgwVJmTKMCmwM3PwdPfhPa3INaR+fvpb2bGS1AIi7vrl/HN1X/DZ6//LEvqlwBwPHac77zyHe595F6+8eI3ONx7eIJnmvHsiWf5+gtf543ON+hIdPBmz14i1z7KYw9V8OLzZ3bM9/cZJJOZ/x2sdEassxmt7C1++sr6b/9zeMTV9qNRW+ewcnWcn/xbFUcPn7mivqM9c1HHdehKdHG0/yh9gyN/skBEREREREREJg+tuBcpAq+VCe+P9yRI2XmujzFMnEANji9ycgU+hauo+Y2lYd7uSbP1UJx/erqbWREPy2eee3NP0zCp9ASpHNaHv2Xf2/Ql07hDC/FdF/qSaTbtayvoqvssxxPgyJW/QttFN52szznxApHHX6HlsnfRfNm7GQAC+a7MeeWnEO+G7KckXDvz71d+WnKr7nNcFyvWxXWhuVy34vMc6W/mgQMP8MThJ0jYCR46+BAPHXyIJfVLWDdvHcumL5uwGp2f7PsJXYkunKF7wMGh5j1fpe/1pbzvjkbe98E+Lr9ykP5+kzdf9/LQzyt5eEcLM2enWbh4kO2bK/jO18M0TLeZOTvF1csGR33tS+anmHtJii99ppqWoxZ1DQ5P7/Dz3NMBqmsu/FMxX/ibDn7p9kbuXNXEL3+oj3mXpujutHjtZR+PPhBk34m3c6+1HZuOeAe9g71U+6sJeoMXfF0RERERERERmTgK7kWKxGudrM3Je3gPYFo4FTU4/sjJDvwR6mnGfRnD4NM3VXOsL80bHSk+t6mDb76rgTnR0W3mmu3D7+8NYKRcMFO45iDg4LrQHRt9UJoPp9bn/AB/vJ2Ze39G3eEnObTow/Q0XUddVSB/lTkD7SdD+yzXzoyXulQMehPMCtbxscUf44NXfJAnDj/BxgMbOTZwLFejMy04jbvm3sWts28teo1OW6wtF9pnmeEOFt//ERY8/0MeezDIj79fRTjiMOfiFL/zp93UT8v8PD7/1Q4++8e1fO2LURJxk194fz9XLxv9z8Xjge/8+AT3/1kN3/lGBIDrViT49w3H+dV3TT/P0Wc375I0P9/Swj9+JcrG/6qko80iWmNz6fwUn/5i54jHpOwUrbFWAp4AUX+UgOfcD9dEREREREREpLQYrpv/YE/Gb9myZe7u3bsnehpSAGnb4VihwvvhHBsz2T20Aj//93nbgM1v/fwEHTGHGVUW33pXA9HA6FdZ37/xdfa39eNkV9wbaQwryaw6Hx9fOzfv8x0NM52gad//MOPNDZhOZsPSrmlXc2LZbxCovyg/F3nkzzL1OMPDe8OCukvg9i/n5xrF4A9BRXXmEx+uw/MnnmfDgQ083/r8yZdYftbOWsu6eeuYHZ5dlGn96ZN/yhudb5wS3puYXFZzGV+96atFmUMpCnqDVPur8Vqje8AmIiIiIiIiIqNnGMZzrusuy+s5FdyXJgX3U1vRwnsAJ42Z6MYc7M17gL+3bZDfeaCNpO2yeLqPv3tnPV5rdGvT9xzu5ns7DtGTSOG4YBoQCXi5Z8VsFsysIH6OTW0LLdDXwtwX/5Vo64sAOKaHrivfS//iX8L1+Md38mzHfbYux7CgIgrLP1a6VTlnY3kgWAvDVnMf7TvKAwczNTrxdDw3vqhuEesvXs+1068taI1OtuM+W5djYlIdqOYTV3+Ca6ddW7DrThYhX4ioP4rH1AfuRERERERERPJFwX0ZUXA/9RU1vAdw0liJLozBvrwG+JsPxviLTZm6jrsuC/LJVdUYxujD+w0vHaOjP0ltyM/6RY0suSh6csquQ8JOELPjJJziVujgutS0PMucl76PP56pS0mFGui87jeJz1oOo3yPI2p+LtNpP9AOlXWw8BcnX2g/XCACFREYVigUS8VyNTotAy258YZgQ65Gp8pXVZDpPHviWX6y7ye0xdqoD9bzvvnvU2g/jGEYhLwhIv6IAnwRERERERGRPFBwX0YU3JeHoof3AHYKK9md1wD/X1/o5TvP9wLwiesi/MpV+Q9kbdchbseJ2XEGnVTez382ZjpJ077/PqU+JzZzGZ3X/Sbp8IyizaPkeXwQrIPTqlgc1+GF1hfYcGADz514Ljfus3ysnZmp0ZkTmVPkyQpkAvxKbyURfwSvqQodERERERERkQul4L6MKLgvH7bj0tIdL254D5kAP9GFmeob96lc1+X+rZ08tj+OAXz51lpWXlQx/jmeRdpJE7cTxOwEKbc4If7p9Tmu6aHnql+g56pfHH99zlRhGJnaH394xC+39LfwwIEHeOzwY6fU6FxVdxXr5q1j+fTlWGbhanRkZArwRURERERERMZHwX0ZUXBfXmzH5VhPnMF0kcN7AHtwKMDvH9dpkmmX33+wjVfbBqnwGnxjXT2X1PjyNMmzSzlpYnacuB0nPXzD10JwXeqP72bOS9/HM9AGQLqygY7leajPmUq8FZnu+7OE8LFUjM1HNrPhwAaa+5tz4/UV9dw5905um3MbYd/I4b8UjgJ8ERERERERkQuj4L6MKLgvPxMa3gPYyaEAf+CCT9ERs/nIz1tpHbCZVmnxL+9uoKaieCuoB53B3Ep8u4AhfpVlM3Pf/xB55b8wsvU5TUvpXP5bqs/JMk2oqAVf8KwvcVyHPW172LA/U6OT3YjYZ/pYPWs16+etZ25kbrFmLENyAb4vgtdSgC8iIiIiIiJyPgruy4iC+/I04eE9jDvAf6tjkMpHTc4AACAASURBVN/e2EY87bKwwcff31GP31P8legJO0ncTpBwEthuvr+fBnUhHxX9x6nd9S0qWl4AhupzFr6XnkW/pPqcLF8IgtVgmOd8WUt/Cw8cfIDH336cWDqWG7+y9krWz1vP9Y3Xq0ZnAoR8IQX4IiIiIiIiIueh4L6MKLgvX47jcqw3QTJV4NqX8zDSCcxEF+awEHW0tr8d588f78AFbrs4yGdWV2NMUI2M67oknSQxO07CTuLkaUNer2lSG/JjuC7Bw7uoeeb/nlKf03ndbxK7SPU5AFieTHWOJ3Del8bTcTYf2czGAxs50nckN173/7N33/FV1fcfx1/n7nsz7s0iQAIkYYmgiKioCLhYGpz86qparaNqbdW22traaqvVuuqurbO2aN0jQRBcgAtlKaACEmZIyB5333Pu+f1xMiGBJCS5N+TzfDzyCPfkjO+9IVHe53vfX2e6UaMzbAZuu7snRyvakGBNwGP3SIAvhBBCCCGEEEK0QYL7fkSC+/4tXsJ7aAzwqzC1WEy0I+Z9U8+TX9UCcOXEZC45Ivad5bquE4gGjZn4WqipmqWrEu1WkuwWABQ1hPubV/eozzmSqklXSX1OI4cbnG5g/zczdF3n6/KvKSgq4KvSr5q+V1aTlWnZ08jPy2e4Z3gPD1jsqbED32bu+fUrhBBCCCGEEEKIvkKC+35EgnsRjeqU1gUJxkF4D6CogYYZ+B0L8HVd5+5l1SzYZMzY/8vJqZyY237feW/T9ChBLUhACxCMhrt4FqMyx2pqroGx1O0idfm/cBWvAqQ+Zy8WG7jSoRMzt0t9pczfMp/FWxfjU5srnA5NO7SpRsdisvTEaEU7XFYXHrtHAnwhhBBCCCGEEAIJ7vsVCe4FxF94D6BEAsYMfC24333Dms5NC8r5encYu1nh8fwMRqfHX9Cn6VrDorYBwtFIp45tqsxpuVHXcW1f3lCfUwaAmpDRUJ9zrNTnKAo4PWDv3LswgmqQj3Z8REFRQasanTRHGrNzZzMrZ5bU6PQyl9WF2+7GbpabUkIIIYQQQggh+i8J7vsRCe5Fo3gM7wGUiN+Ygb+fAL86oHF1QRkl9RrpLhNPnZFJekL8LjKqRtWmED+iqx06JslhJdG294xvqc/ZD6vDmH3fyUVndV3nm4pvKCwq5MuSL4liLD5sNVmZkjWFOcPnMMIzoidGLNrhtDjxODwS4AshhBBCCCGE6JckuO9HJLgXLcVreA9GgG8OVKFEQ+3us6U6wjUFZfgiOoekW3n09AwcFlO7+8eLSDSCXwvg14Jo+r5ee4X0RDtWU9sz6Y36nKdwFa8EpD6nFZMJnKlgS+jS4bt9u3l3y7ss2rYIb8TbtH1M6hjm5M3huMHHSY1OL5IAXwghhBBCCCFEfyTBfT8iwb3YUzwtWNsWJezDHKxuN8D/YkeAWxZXEtXhpFwnt5+UiqkPVcYEtRB+LUBQCxJtY1Fbm9lMWsI+aoB0HeeO5aQtl/qcNtkSwZUCStdu6ATVIEt2LqGwqJCtdVubtqc6Ujkt9zRm5szEY/d002DF/jgsDjx2Dw6LI9ZDEUIIIYQQQgghepwE9/2IBPeiLfEe3gMoYW9DgL/3gq+vrqvnkeW1APxkQhI/PbLv9ZHruk4gGiSgBQlqIfQWIX57lTktSX3OPpjMRnWOtethr67rrKtYR0FRActLljfV6FhMFqZmTSU/L5+RKSO7a8RiPyTAF0IIIYQQQgjRH0hw349IcC/a0xfCewAlXN8Q4Dcv9qrrOvd/WsM7G3wA/OnEVE4d7orVEA+YpkcJNvThh6Jh9leZ01Lb9TlnU3vY/6EfQHB9UHAkG4vXcmDvQijzlxk1OlsXUR+pb9o+OmU0c4bP4fjBx2M1WQ9wsKIjHBYHbrsbp8UZ66EIIYQQQgghhBDdToL7fVAUxQpcCxwBTAAOBazAlbquP93OMdnApS2OycNIikbquv5DF8dxKXBdw/U1YDVwv67rhZ05jwT3Yl+0qE5JbYCwGo31UPZN1zGF6zAFa1AaFnpVozq/WljBqpIQNjM8cloGYwf0/T7sxkVtI0oYt7ODdS+6jmv7clK/3LM+56f4hx7Xv+tzLDZwpYF5H/VDHRTSQizZsYSCooJWNTop9hRm585mVs4sUhwpB3wdsX8S4AshhBBCCCGEOBhJcL8PiqJ4gOqGh7uBMDCEfQf3ZwFvAjqwBUgFPHQxuFcU5X7gV8BO4DXABpzfcN7rdV1/rKPnkuBe7I8W1dlVEyCixXl4D0aAH6rBFKpF0TXqQlGufqeMnXUqqU4T/zpjAJmJB88Cok4bYIngVwNE9f1/fxQ1hHvta7jXvt5UnxMYPIHKSVehurN6eLRxTFGMmff25G45na7rrK9cT0FRAV+UfNH0vbEoFk7IOoE5w+cwKmVUt1xL7JvdbMdtd+Oy9t133AghhBBCCCGEEI0kuN8HRVFswCnAGl3XSxRFuR34E/ufcZ8LfK3rep2iKB8D0+hCcK8oyvHAp8Bm4Ghd16sbtucAK4EE4BBd17d25HwS3IuO6FPhPYAexRQ0AvwdtSGufqcMb1hnRKqVx/MzcFm7tjBp/FHITHZgNSn4tSBe1U9ADe73KKnPaYfVYXTfm8zddspyfzkLti5g4daF1Ieba3RGpYxiTt4cJmdNlhqdXiABvhBCCCGEEEKIg4EE953QkeC+jWM+puvB/QvAxcDluq4/t8fX/gzcBvxZ1/U/deR8EtyLjlK1KCW1wb4T3gNENUzBalZvLePXC8vRdJgyzMGdp6RhOkjqYWwWM5lJzWG7GtXwqX68aoBIi97/veg6zh3LSVvesj4nnapjrujf9TmKYlTn2BK69bQhLcSyncsoKCqgqLaoabvH7mF2zmxm5c4i1ZHardcUe7OZbXjsHgnwhRBCCCGEEEL0SRLcd0IMgvudQBYwWNf1kj2+dhzwGfCJrutTOnI+Ce5FZ/TJ8B5AizB/VREPLC0F4KLDk/jZ0e4YD6r7eJw2khx7z9oOaWHqVf8+q3Sa63PeaFrgV+pzMIJ7Vyoo3fvuDF3X+bbqWwo2F/B5yeetanQmZ00mPy+f0SmjUfrrjZNeIgG+EEIIIYQQQoi+SIL7TujN4F5RlATAC3h1XU9q4+vpQDlQput6ZkfOKcG96Kw+G94Dj32wgTfWGOH976emMGtk986qjh2FgW4HVlPbIbOu6/ut0rHU7SL1y6dx7TR+H+gmC7Vjz6L28B/13/ock9mozumh518RqGDBFqNGpy5c17R9hGcEc/LmMCVrClaz1Oj0JJvZhtvuJsF6sPwuEEIIIYQQQghxMJPgvhN6ObgfDBQDxbquZ7fxdSvGYrlhXdft+zjPVcBVAEOHDp24bdu2jg5BCKDvhvdaVOfWN9fy1dZqrCZ4aHYGhw9s90elT9mzMqc9+6zSaajPSf3yaazeFvU5R1+Bf1g/rs+xJ4ErBeiZ5x/WwizduZTCokI2125u2u6xe5iZM5PZObNJc6b1yLWFwWq24rF7JMAXQgghhBBCCBHXDvrgXlGUrcCwThwyT9f1H7dzrtuJv+A+pOt6h6aIyox70VV9Nbz3hlSuf3E126r8uB0W/nnmILIS4+f304HwuGwk2Ts+Q7u9Kh2jPud13Gtfl/qcRmar0X1v6bkbPbqu833V9xQUFfDZrs/QdM24tGJm8mCjRueQ1EOkRqcHWc1W3DY3ibbEWA9FCCGEEEIIIYTYS38I7j/A6InvqHd0Xb+5nXPdjlTliH6qr4b3xTUBrpu3irqgSk6ai8fOHUFytA5FV2M9tAOioJC5j8qc9jRW6dRHfAS1UNN2S10JqV8+JfU5LTnc4HTTU7PvG1UGKptqdGrDtU3bh7uHM2e4UaNjM9t6dAz9WWOAn2BNkBslQgghhBBCCCHixkEf3HcnWZxW9Hd9Nbz/emcNv3n1G9SozqTcVO48cyzWSC2mUC1Kw0znvqijlTnt2atKR9dx7viS1C+fkvqcRhabMfu+F4LziBZhWfEyCooK+KGm+T8XbpubmTkzOS33NKnR6UEWkwW33U2iNVECfCGEEEIIIYQQMdcTwX3npn+Kffmw4fOsNr42e499hOhxFrOJwR4nVnPf+jEfn+3hxumjAFi+pYp/Lisi6khBTRqCZk9B7+EZ1T0lrGrUhyL737EdFpMZty2JLNcABjkzSLQlEhp2HLvOepya8eejm6xYfBUM+PgeMhffjqW2uBtH30eoYagvhWDt/vc9QFazlZOHnsyD0x7kvqn3MTV7KmbFTG24llc2vsLliy7nb1/9jfWV6zlYb5DHkhpVqQxUUuwtpj5cL6+xEEIIIYQQQoiDjsy4b33Mx+xnxr2iKIMAN1Ci63pti+3HA58Cm4GjdV2vbtieA6wEEoBDdF3f2pGxyIx70V20qE5JbYCw2rdm3v9zyWZeXrETgJumjyL/8EHGF6Iq5mA1Srgehb72+0thYBcqc9rTskpHrd4q9TktWe3gSgeTpdcuWRmoZOHWhSzcupCaUE3T9jx3HnPy5jA1e6rU6PQQs8mMx+6RGfhCCCGEEEIIIWJCqnL2Q1GU3wKHNDw8AhiPUVGzqWHbJ3uG+IqiPN/i4SwgE3gDqG/Y9rSu65/ssf+lwGW6rrc8FkVRHgBuAnYCrwE24DwgDbhe1/XHOvpcJLgX3akvhvdaVOePb6/n86JKzCaFe889jAlDU1rsEMYcrMYU8cZukF1woJU57Wms0lGLPiL5i39IfQ4Yz9fpAXtyr142okX4ZNcnFGwuYFPNpqbtSbYkZuXMYnbObDJcGb06pv7CbDLjtrtJsiZJgC+EEEIIIYQQotdIcL8fLWbMt+ffuq7/ZI9j9vcCtAro9xXcN3z9UuDnwKFAFFgF3KfreuF+n0ALEtyL7qZFdUrrgoQifacn3h9W+cX/1lBU7iPJYeHxCyeQneJqtY+iBjEFqzCpgRiNsvM8ThtJDmuPnT8UqkNb+RzOr19C0Yx6nsDgI6icdBWqO7vHrhu3rI6G2ffmXr/0hqoNFBYV8knxJ6gNiyybFBPHDTqO/Lx8xqaNlYC5B0iAL4QQQgghhBCiN0lw349IcC96QjSqU9LHwvvSuiDXzVtFtT9CdoqTxy+c0GborUQCRoCvBWMwys5SyEx2YOvh9Qf0mh1oy+7Fsu0z47HJQu3YM6k9/Lz+V5+jKOBKBVtiTC5fHaxm4daFvLvl3VY1OrnJueTn5TNtyDTsZntMxnYwM5vMJNuSSbYlS4AvhBBCCCGEEKLHSHDfj0hwL3pKXwzv1++q5aZXviai6Uwc6uHucw7D0k7orUT8mANVKNFQL4+yc3qqMqdNW5aiL7sPpW4XAKornapjfop/2PH9rz7H6oKENFBis2hzJBrh0+JPKSwqZEP1hqbtSdYkZuTM4LTc0xjgGhCTsR3MzIqZZHsySbYkTPv43vt8kJDQiwMTQgghhBBCCHFQ6IngPjbJhRAiZkwmhUHJDhzW3q8N6aqxg938ZuZoAFZur+Hxjza3u69udaEmZ6O6MtFN8bsQaFjVqAtGeudiuVNRLnwVjrkazDYs/goGfPw3Mhf/CWvNzt4ZQ7yI+KGuGML+mFzearJy4pATuX/a/Tww7QFOzD4Ri2KhPlLP65te58pFV/LX5X9lbfla5MZ61zx0t4dcTw4bvrXyq5+lM37YEA7NzuLii8x8XVRCbaiWqB7lxBNhxAhYtw6mT4ekJLjooubzFBTAsceCywUeD5x5Jnz3Xetr3X67ce9r3Tq49FJISYHkZLjgAigra73vsmVw/vmQkwN2OwwYAD/+Mezc40fw+eeNc370EfzudzBwIDidxhi3bNn7+W7eDBdeCBkZxnnHjYOnnjrw11EIIYQQQgghRGzJjPs4JTPuRU+LNnTeB/vQzPtnPtnCvOXbAbj+5BGcPSFrv8co4XrMwWqUaC+F5J2ikJlsx2buxZsotTth2f2wdRlg1Of4xp1D5WHnoludvTeOeGBLBFdKzGbfN2qs0Vm4dSFVwaqm7TnJOUaNTvY0HJZ+Vm10AB6628PDf/MwZlyYlFSNGfl+tm628t9nkhh5SIS3PtyF027mgvxBbPzejNmscMYZMHGiEZBffDG89JIR4h92mBHI19XBo49CNApffWUE/mAE93fcAePHQ1oanH02bNoETzwBY8fCl1+CreH+4fXXw/ffw7RpkJkJGzfCv/5lBPjffGNcG4zg/rLLjPE4HPCjH0FFBdx/PxxxBHz2WfNz3bgRjjsOUlONY1JSYMEC46bD3XfDb3/bm6+8EEIIIYQQQvRfPTHj3tKdJxNC9B0mk8LAZAe764MEwn0jvL9scg47qvws3VTB4x/9QHaKk6NzUvd5jG5LQrUmNgf4DQuExgedKl+YzGQHCr1UWePOhvyHYMtSWHY/Sl0xid+8QsLmj/Efdy01Q44hElevUQ8Ke0ENGAvXxrDzP8WRwgWHXMDcUXP5bNdnFBYV8n3V92yt28pjax7j+fXPM2OYUaOTmZAZs3H2NZ4UjRfe3E3jfbFRh4a59ZfpvDYvkQsv86JGVcrLLdx5T4Df3WxvqtCJROCmm2D4cPj0U0hsWBbhnHNgwgS49VZ45ZXW10pNhUWLaLrWuHFw1VXw3HNw9dXGtr/9zZi939KZZ8KUKfDGG61n+4Mxe37JkuZzpqbCjTfC+vXGTQEwbgakp8OqVc0VP9dcY8zA/8tfjD+73Qf4QgohhBBCCCGEiAmpyhGiH2sM7522vlGbY1IUbpl9CCMHJBLV4c+F37Kt0rf/AxUF3Z6MmjwUzZmObtp7cdtYiWhRagMxCMpzp8KFrzTU59hRfGUkvH87We/fwaCgl0Rrwj67wA8aUQ28u8FfBXo0pkOxmqxMy57GfVPv48FpD3LykJOxmCx4I17e+OENrlp8FXctv4uvy7+WGp0OuPSqelq+mWXuhV6S3RofLGxOz00mnXMuKWNn/U5qgjVoUY2VK6G01Ai9E1usZXz44TBrljGjPbrHX5Xrr6fVtX7yE6Nep7CweVvL0L6+3phFf8ghxn4rV+49/quvbn3OadOMz0VFxufqali8GP7v/yAQMM7X+DF7Nvj98MUXHXuthBBCCCGEEELEn36Qyggh9kVRjPDeZesbb8BxWs3cedY40hJs+EIav39rHbWBDtbgKApRuxs1aQiqMyNuAvz6YJiQGoN3PVgccMxVRoCfM9XYtmM59lcuIX3lCwyxJJNuT8Fhtvf+2HpbqB7qS0ANxnokAIxMGcmNE2/kuZnP8eMxPybVkUqUKF+UfMEfPv0DP//w5yzYsoBgnIw3HuWNbP17wWqFIcNUinc0/67LyNRwunSiepSaUA3F3mLWbfQCRqi+p0MPBa8Xystbbx89uvVjqxVyc2Hr1uZtu3YZnfYej9GDn5FhfNTUGB97Gjas9eOUFONzVUOb0qZNoOtw113N52r8uOQSY589e/aFEEIIIYQQQvQdfSOpE0L0KEUxutZL6/Q+UZuTkWTnzrPGccPLa9hVE+T2d9Zz79zDsZo7eC+ycQa+PbmhQqcGJRru2UHvR5UvzEB3L1bmtOTOhvy/N9XnUFcMq/6NsnEhiSfcSOLwU1H1KD7Vj1cNEInL9QK6gaZC/W5wuMHphlh8L/bgsXs4b/R5nDvyXD7f9TkFRQV8V/Ud2+u388TXT/Dvb//N9GHTOT33dAYmDIz1cOOK0sa3b883KjgcrTdE9SgBNQAk4g3Xo0VdmE3mvY7f89z7u1Y0aiwuW1oKv/mNcQMgMdE47vzz957BD61n27d13sZjfvELmDOn7X0bK3WEEEIIIYQQQvQ9EtwLIYDmmfeldX2j8370wCRumXUIfy78lq931vLw+5v41YxRKG0laPug25JQbUkoYV/DIrahdvdds72Ggm9KqPSGSEu0M+fwQRwx1HOgTwUANRql2h8h1WXrlvN1Se5UGDIJVv0bVj5vVMgs/C1kH4Nl2s24U3Jx25IIaiG8agC/GiAa43qZNhWvhHWvga8CEtJh3FzImtjx44O1Dd33aWCO4fejBYvJwpTsKUzJnsIPNT9QWFTI0p1L8UV8vPXDW7z9w9scM/AY8vPyGZ8xvtM/B33ZV7u/4pUNr1DuLyfDlQG+2wEPmzdaGTG6+SZTJAI7t1s46tj2f8YBsoca1VXfrI9w7MnFJNoScdvcmE1mvv/eCNzT01sf8/33MGZM8+NIxJhtf8IJxuO1a+Hbb42FZy+9tHm/QMCovOmK4cONz2YznHpq184hhBBCCCGEECJ+SVWOEKJJY3jfVzrvTxydwWXH5wDw7rpSXl25s8vn0m0JqMnZqAmD0E17V8Os2V7Dc59uZXO5lyp/hM3lXp77dCtrtrfRcdFFvlCEYCTGN00sdqM+56JXIbehVHvnl/DS+fDZIxD24zDbSbd7GOIaGH9VOsUrYfmTUPED+CuNz8ufNLZ3hhqG+lIjxI8zIzwjuOHIG3hu5nNcPOZi0p3p6OgsL13ObZ/dxnUfXse7W95tmDl+cPtq91c8vvpxNlZtpDJYycaqjXxV8hUA//5XElqLH6fXXkykrtbMyTP3/bocNiFERqbKvOeS8Hp16kJ17PTu5NOVtSxcqHPaaWDa4/+eHn2UVtd6/nmj/ub0043HjfvvObP+gQfanm3fERkZcMop8MwzsG3b3l/fs85HCCGEEEIIIUTfIjPuhRCt9LWZ9z8+dijbq/x88H0Z/1xSxJAUF8cNT+vy+XSrC9XqQgl7G2bgGxU6Bd+UUBuMEG2sqdChNhih4JuSbpt1D82VOaZYz5hOzoLTH4Sty2DpfU31OWxYAFNuguGnoigKiVYXiVYXalSLjyqdda9BoAb0hr+7umY8Xvda52bdg9FJEqiBiB8SMsAUX//JdNvd/Gj0jzh35Ll8UfIFBUUFrK9cz476Hfzj63/wwvoXOHXYqZyedzqDEgbFerg94pUNr1AdrCaKkX5HiTbdsKipNnPJ2ZnMyPezrcjKf55OYvShYeZeVL/Pc1os8Ie7qrnhynTmzhzEuRd48dYr/PtfiSQkRvn1bXWo0SQsLf4+VFXBjBlw9tnwww/w+OMwbhxcdpnx9TFjYNQo+NWvYPt2yMyEJUvg008hreu/rnjiCZg8GcaPhyuuMLr2KythzRp46y0IyhIIQgghhBBCCNFnyYx7IcReFEUhM8mBwxr/M+8VReE3M0czZlASOnDn/O8oKvce8Hl1WyJqUraxiK1iodIbagrtG0V1qPTuu3ajPUNHjefmW7P32q7pUar9se3bbyVnClz4KhxzNZjt4Csz6nPevhaqtzTtZjGZcduSyHINYKAznURrAiYlBv+J8VU0h/aNdM3Y3lVqGOp2Qdh3YGPrIWaTmclZk7lnyj08fOLDTB86HZvJhk/18fbmt7l68dX8+fM/s6psFfqeJe99XLm/vCm0b6RjPMe//6uczEEaD97l4ZX/JjJzjp//vLkbewfeIHLGXB//nFeGza5z3188PPOEm4mTQrz2XikZQ2op9hZTGahsqor6738hKwtuu82YAX/OObB4MU3XsligsBCOOw7+/ne45Rbw+eCjj4zqna4aNQpWroS5c+F//4PrroOHHzZm2z/wQNfPK4QQQgghhBAi9pSD7R/xB4ujjjpKX7FiRayHIfq5aFSntC4Y+/qWDqjyhbl23irK6kNkJtt54qIjSemuvnhd59YXl1G6uxSlRUhoUmB4RiK35Y9p87DPlyfw+fJErry8nKTE1uHi0FHjOX9uJff+te16n7QEOy5bfM3wpq4Ylj0AW5YYj00WOOIiOOoKsLn22l3XdXxqAK/qJ6h17QZHp733O6Mep2V4r5ghfQTMvPvAz29PBFcq8bBw7b7UhetYtHUR87fMpyLQfNMiOzGb/Lx8ThpyEi7r3t+zvuY3S3/DxqqNrcL7sjevpezta/hs/Q4GZfXs766H7vHw8D0etmxTyRkaZz+vQgghhBBCCCF6jaIoK3VdP6o7zykz7oUQ7TKZjNocex+YeZ+aYOOus8bhsJrYXRfij2+vJ6x208KpisIZx47F58yijgR0jNDe7bAy5/D2K0g+X57IQ48NxOvt/OtX7Q+jxduN1cb6nPyHjD9HVaM+Z965sGmRUS3TQmOVzkBnOtmugaTYkrGarD07xnFzwekxwnowPjs9xvbuEPJCXQlocfSuiDYk25KZO2ouT09/mt8d8zvGpY0DYKd3J09+8ySXvXcZT33zFLu8u2I80gPzo9E/IsWRgqnhf2dMmHBanL03gIa/8qW+UioCFahRtfeuLYQQQgghhBDioCbBvRBin0wmhUF9JLwfPiCR3582BgVYv6uOBxZv7LZqkGOHp3HjjEMYNCiboCubQQMGcNnk3G7tt28pquvxVZnTUlv1Oe/9zqjPqdrS5iEtq3QGOTNIsiZg7okqnayJMOlnxgx7V5rxedLPOt9vvy9axFi4NlTXfefsIWaTmeMHH8/dU+7mkZMeYcawGdhMNvyqn3eK3uHq96/mjs/vYOXulU21L33J0ZlHc92E6xiVOoo0RxqjUkdx9KCje30cuq7jDXsp9hZLgC+EEEIIIYQQoltIVU6ckqocEW+0qE5JbaD7ZrH3oP99uZ1/LTMC5CtOyOXCSUNbff3he208cr+Nhct8PPtPG4vmW4hG4bQzVP50d4hIGP76JzuL3rUQDMDMfJU77wvhaDGR9913zDzzhJXvvzVhMescc5SP3/66hENGG6tB3nTLEF57M3WvsX364bcMyY40VeXMnlnLvQ8O4ofNdgYOjPCrX5Zy1pyapv0TrDYefjyTBR+4+GGLlUBQYfTwCDdcXcNPLmi9yGbOkcMYkRvh7j9UcsPv01m11k56qsaNP6vlpmtqWu2r6/Dk88k8+W83G36wkuDSmT7Nz71/qmRodidCx73qc8xwxI/brc9pPQadgBbCp/rxa8G+2b9udUFCGsSiz7+L6sJ1LN62ZN4HBAAAIABJREFUmPlF8ykPlDdtz0rM4vTc0zll6Cl9ukbnobs9PPw3T+9U5bRzLUVRSLAm4LF7Wi1iK4QQQgghhBDi4NQTVTkS3McpCe5FPOor4b2u69z73gbeW78bgNvPOJSpIzOavt4Y3I89TCNriM4J01RWLDfzzhtWLr86zKYNJpwuOGGaysovzbz9upVrfhnm1783ZsA/9biVe+6wM+M0leOnaPjrVeb920ZtjYnCNzaSlxtm5WoX/3w6g4WLPdzxh2LcbiMMnzW9DpcrytBR4xkzOkB5hYWLzq8kLVXlf6+m8f1GB4sLNzByhNEJX11lYXr+aM47y8voEREiEXhrQSJLPnPyrwfLuPLi5lnfOUcOw2bVqas3cfGP6skbFuHlt5JY8pmThS/vYubJ/qZ9f35LOk/+281F59Zz3NFByirMPPq0B7tNZ81H20lP6+T3eOsnsOw+qG3o7E8YACfcCCOmg7L/PvioHsWnBvCpgd7rw+8uJjMkpIPFEeuRdIoW1fiy9EsKiwr5puKbpu1Oi5NThp5Cfl4+WYlZMRxh3ycBvhBCCCGEEEL0DxLc9yMS3It4pUV1dtUEiGjxHd6H1Si/ee1r1hbX4bCYeOj8IxiVmQQ0B/dnnhvhwX80h8TnzHLyzWoTZ85VeeDx1tuLdygsX+9nV7HCSUe7uPLnEX59a3OVTUWZwowTnJw4tZ5H798KwIOPZPLQYwNZvvRbBg2MtBrf0FHjMZt1Fr69kdGjjFn65RUWjps2hssuqeD3t5QAoGlgUaxkp7VeaHf63MFs3W5h05fbm7blHDmMbTusLPjfLmadYoT0oRAMnZDD1GODvPpsKQCff+Xg+NOy9wr+139vY8LJQ/jNddXc9fuqzr/oaghWvwArnoPG8D37aJh6C6Tmdvg0kaiKT/XjU4NEopH9HxAvnB5wuGM9ii7ZWruVwi2FfLTjI8It+vuPHHAkc4bP4cgBR2LqQ+8qiDfGeg+JuO1uCfCFEEIIIYQQ4iAki9MKIWLObFIY5HZgNcf3rw+bxcQdZ4xlYLKDoBrlD2+to8Lbeib3BZe2DoUnHKWh6wrnX7z39opyEwE/vFdoQVUV5pytUlVJ04fJrDPhqCiffZGE6spE78AirJOO9jaF9gAZ6SrD80Js39Ec0pvNoJsieMPGbPuqahMVlSZOPiHAD1ts1Na1/j7kDI00hfYAdjscOzFI0bbmsPDltxJxOqPMmeGjotLU9JGZoTJ6RJgPP+liTYrFDkdfCRe9CrnTjG07v4L/nQefPgxhX4dOYzVZ8NiSe74Pv7sFasBbCtGerWfpCTnuHH5+xM95fubzXDb2Mga4BgCwqmwVd3x+B9e8fw3vbH4HX6Rj30PRmq7r1IfrKfYWUxmolA58IYQQQgghhBD7JdO+hBCdZjGbGOR2UFIbjOuZ9x6XjbvOHsf1L62mwhvmtrfW8/fzxjd9fXBW63ccJSfve3ttrcKWzUaAfNq0tsNtk0lHtyWiWhPQrft+R1PW4L1nk7vdGjW1rX81v/pGCk89l8HGTQ6i0da1M7V1JtzJzd+DnCF7nzPFHeWbb5sXF9642UogYGLQuLZnweflHOAs9+QsOP3B1vU5q1+AjQvghJs6XJ8DYDfbsJttpNrcBLQgXjVAIJ778CMhqN8FzrT9dvzHoyRbEueMPIczR5zJitIVFBQV8HX51+zy7eKptU/x3+/+y8lDTub0vNMZkjQk1sPtcxoDfG/EKzPwhRBCCCGEEELsk/xrUQjRJX0lvM9NT+C2/DH8/s11bNhdz70LN5CMEd6bzG0f0952XYdow1N9el4Am63t/QBQFHSzHQDN7kFXqlD01jOxzaa2w+eWmfQ78z386rdDOfnEOn7200pGDTFhteq8+34Cf3/S0zSepnPuY+yNolHwuDVefaa0zX2djm4KxXNOMKpyGutzfOXw3u9g/Rsw9WZIzevwqRRFwWVx4rI40XQNvxrEq/oJtah1iRvRqPFc1SRwpQAdu0kRT8yKmUmDJjFp0CS2122ncEshH27/kIAaYP6W+czfMp8JAyYwJ28OEzMnSo1OJ0mAL4QQQgghhBBif+RfiULEgQ+/280TH2+muCZAlsfJtScO5+QxmbEZzMZFsOx+Y5a0Oxum/BpGzWhzV4vZxEC3g5KaIOqeCXIcmZSbxpyjzLz9lcbHG8uJfvsVcEKXzjUs13iegwbrHDJ2389ZUYwAPGp3oyYnYgrVYArWotDx16pgvoch2SGe++cWFAU8ThtJDisfLnN2afwAI3IjLPrYxdETQq1m6/eIxvqc0afDJw9A0ccN9Tnnw/gLja/ZEjp1SrNiJsmaQJI1oakP36sG4q9+JFRvdP270sG8/+qkeDU0eSjXjr+WS8Zcwvvb32d+0XxK/aWsLlvN6rLVDEoYxOm5p3PqsFNJsHbue9nfSYAvhBBCCCGEEKI9MkVOiBj78Lvd3PrmOlbvqKakNsjqHdXc+uY6Pvxud+8PZuMiKPglFK+EumLjc8Evje3tsJpNDPI4sJji99fJmorPWavdh83zFQB1tUZ/97dVKzt9rln5KhaLzkP32vaa7Q5QWdH8Z2dDU0pdrQKKiagjFTV5KJo9pcPXMzXMym+8Vk0gQkk5PPticqfH3uj8s73ousIf70lt8+sVlT3wvUweDKc9AHMeMW4IRTVY/R+Ydy5sfK/1WwI6obEPP9uVyUBnOonWhPia/a2Gob4EwvWxHskBS7QlctaIs3hy+pPcduxtHJFxBAAlvhKeXvc0P1n4E574+gl21O+I8Uj7HunAF0IIIYQQQgixJ5nWJUSMPfHxZsq9QRrbZrQolHuDPPHx5t6fdb/sfvCVQWNoFFWNx8vub3fWPRjh/UC3g9La+Jx5/3rRC1QFK7ENfBMtnAqMBOCV9e9zyrgjO3WuIcN0bvljmLv+aOfsmU5m5at4UnR2FZtY+qGZUYdEue9RYxHcceON1+KBv9qYPUfFYoVTZoArwQjMo2YbOgoK7YfW00+pY8EiD5ddlcuM6bVUVlp46ZV0Bg9U2V3etV/hJxwb5BdX1vDIUx7WfmfjtFP9JCZE2bLdytsLEjj/bC+331zVpXPv17DJcMErRmi/8lmjUmbRrUZ9zrRbOlWfsyeH2Y7DbEePtz58XQdfldF/70qFeLqx0AVmxcwxA4/hmIHHsKN+B4VFRo1OUAuyYMsCFmxZwBEZRxg1OgMnYlba6W8Se5EZ+EIIIYQQQgghGsm/BoWIseKaAHtWxGtRY3uvq93ZHNo3iqrG9v2wWRpqc2oDaNH4Wjh0t78ETddQFHBm/xdMUwHYuPFoyuqCDEh2dOp8l/8sQu7wKE//w8qTD9tQNRiQqXPUJI0LL21e2PW4EzSuvTHMay9a+GixnWhUYckKH66EhtfH4kBNHoo5WI0Srm8zwJ97djU1NWb+PS+dO+7MYvDgCFdeXsaAVIXrfjW4y6/Jw3+tYOL4EE885+b2e40bCUOyVE6eEuBHZ/bw7HCLHY6+Akaf1lyfU7zigOpzWtqzD9+nBvHFQx9+2NdcnWOxx3Ys3WRI0hCuGX8Nlxx6Ce9ve5/5W+ZT4ithTfka1pSvYaBrIKfnnc6pQ08l0ZYY6+H2GRLgCyGEEEIIIYRQYj4TUbTpqKOO0lesWBHrYYheMPcfn7F6R3Wr8N5sgglDUnjtmuN7dzDPzDDqcVqG9yYLZE2En7Zfl9NSSNUorQ3GVXh/25fXsL5yLVrD4rDRUDq+rddB1MnwjAQeOX8CTluMZwVrYczBakwRbwcPUBiQZMduOQhmM2/7FJbeB7UNFSsJGTD5Rhg5A5TuW9g1Eo3gVQP4Yt2HryjgcBsfB5moHmXl7pUUFhWyqmxV03a72c7JQ04mPy+foclDYzjCvklRFAnwhRBCCCGEECKOKYqyUtf1o7r1nBLcxycJ7vuPxo77xrocswkyEh389exxvV+V09hx31iXY7JAwgCY8/A+q3L2FG/h/ZqKz3l83T1UBirRdA2zYsYZmcDuH+YS1WHyiDTuOGMspm4MibtMCzUE+L797moxGe9yUIiDcR8oNdRcn6MaVUNkHQVTb4a04d1+uaAWwqsG8KsBonqM6p2sDmP2vekguPnShh31O3i36F0+2PEBAbX5HUTjM8aTn5fP0QOPlhqdTpIAXwghhBBCCCHikwT3/YgE9/3Lh9/t5omPN1NcEyDL4+TaE4f3fmjfaOMio9O+dqexiOiUX3cqtG8UUjVKaoJE4+R3zJqKz3m96AV2+0vIdA3i3LxL2LZzGA9/sAmAC44ZwpVTut6v3t0UNYgpWI1J9e9zv0S7lRSXrZdG1QvqShrqcz4yHpvMcPgFcMxVB1Sf0x5d1/FrQbyqn6AW6v0+fJPJCO+tzt69bi/yR/y8v/195hfNZ5dvV9P2Aa4BnJ57OtOHTSfJlhTDEfY9EuALIYQQQgghRHyR4L4fkeBeHAyCEWPmfbyE92155INNvLXGCBN/O2s0M8YOjPGIWlPUAKZAFSYt2O4+A5IcB0dlTkvbPoOl9+5Rn3MDjJzZrfU5LWm6ZlTpRPyEo5H9H9CdHMng9MDB8O6JdkT1KKvKVlFYVMjK3SubttvMNk7KPon8vHxy3DmxG2AfJAG+EEIIIYQQQsQHCe77EQnuxcEi3sN7Lapz65tr+WprNVazwv1zx3NYdvx1jyuRAKZg2wG+xWQiM9kRH1U/3UkLG/U5K55pUZ8zEabe0iP1OS019uF7VT9aVOvRazWx2IwbFP0ggC32FjO/aD7vb3+/VY3OYemHkZ+Xz6SBkzAfpBVCPUECfCGEEEIIIYSILQnu+xEJ7sXBJN7De29I5foXV7Otyo/baeUfFx3JQLcj1sNqkxL2YQ5Wo0RDrbYn2KykJhxElTkt9XJ9zp4CWghfxI9fC/Z8H76igCsVbIk9e5044Y/4+XDHhxQWFVLsLW7anuHM4LTc05iRM4NkW3IMR9i3SIAvhBBCCCGEELEhwX0/IsG9ONgEwhqldcHe7xDvoOKaANfNW0VdUCUnzcWjF0wgwR6/wZcS9jYE+OGmbemJdpzW+B3zAduzPseVDifc2KP1OS3puo5PDeDTAgTU9quLuoUtwQjwFVPPXidORPUoa8rWUFBUwMrdK9Exfk/YTDamDZnGnLw55LpzYzzKvkNRFJJsSSTbkiXAF0IIIYQQQoheIMF9PyLBvTgYxXt4//XOGn7z6jeoUZ1JuancedY4zKb4rp9RwvUNAX4Es2Ii0+3AfLBV5rSkhWH1f2HF0831OYMnwrSer89pSY1q+LQe7sM3mcGVdlAvXNuWXd5dzN8yn/e3vY+/xeLMY9PGMidvDscOOlZqdDpIAnwhhBBCCCGE6B0S3PcjEtyLg5U/rLK7LhS34f2CtSXct2gjAHMnZnHtiSNiPKIO0HVM4TpMwRoSrJCWYI/1iHpeXQl88iAUfWg8Vsww/vyG+pzerZkJaxG8qh+fFuiZPnx7ErhSOJgXrm1LQA3w4XajRmend2fT9nRnulGjM2wGbnv8rUcRjyTAF0IIIYQQQoieJcF9PyLBvTiYxXt4/+SSzbyywggKb5o+ivzDB8V4RB2k65hCtQywBnBZ+knIu+0zWHof1G43HrvSYfINMGpWr9Tn7KnH+vDNVmP2vaUf3JTZg67rrClfQ2FRIV+VftVUo2M1WZmWPY38vHyGe3rv3RZ9mQT4QgghhBBCCNEzJLjvRyS4Fwe7eA7vtajObW+v44uiKswmhXvPPYwJQ1NiPawOMys62Y4Q5lAtxOHr2+3arc+5GdJi844JXdfxa0F8aoCA1k31UIoCDjc4kulvs+8blfpKmV80n8XbFuNTfU3bD007tKlGRwLp/WsM8N02t9QOCSGEEEIIIUQ3kOC+H5HgXvQHvpBKWX18hvf+sMovXlpDUYWPJIeFxy+cQHaKK9bD6rAEu4XMBAsEqiFU1z8C/PoS+OTvsPkD43EM63Na0nQNv2qE+EEtdOAntNiMdxaYrQd+rj4qoAb4eMfHFBQVsKN+R9P2NEcas3NnMytnltTodIAE+EIIIYQQQgjRPSS470ckuBf9RTyH96V1Qa6bt4pqf4TsFCePXziBJEffCUszkuzGeLUI+Csh5I31kHrHts9g2f1Qs814HOP6nJbUqIZP9eNTAwe2qK2igNMD9uTuG1wfpOs631R8Q8HmAr4s/bJVjc6UrCnMGT6HEZ4+sE5FjEmAL4QQQgghhBAHRoL7fkSCe9GfxHN4v35XLTe98jURTWfiUA93n3MYFrMp1sPqEJOikJ3ibB6vGjIC/LA/tgPrDXFYn7OnsBbBpwXwqv6uL2prdRg3JiRspdRXyrtb3mXRtkX4Is01OmNSx5Cfl8/xg4+XGp39kABfCCGEEEIIIbpGgvt+RIJ70d/Ec3j//ne7+eu73wNwxvjB/PKUESgxnrndUU6bmUFuZ+uNkYAR4EeCsRlUb2qrPufw82FSbOtz9hTQQvjUAH410PlFbRUFXKlx9XxiKagG+XjnxxQWFbKtblvT9lRHKqflnsbMnJl47J4YjjD+9ZUA//bb4Y47YMcOyM6O9WiEEEIIIYQQ/ZkE9/2IBPeiP4rnBWuf+WQL85ZvB+D6k0dw9oSsGI+o49IS7bidbVT8hH1GgK+Ge39QvW3757D0vhb1OWkN9TmzY16f05Ku6wS0IN6uLGprdUFCGih94x0hPU3XddZWrKWwqJDlJcuJYtwQsZgsTM2aSn5ePiNTRsZ4lPEt3gP8ngjuH3oIUlPhkku653xCCCGEEEKI/kGC+35EgnvRX8VreB/Vdf5c8C1LN1VgUuDucw7j6JzUWA+rQ5SGyhxrexU/oXrwVxld+AezNutzjoRpt8RNfU5LXVrU1mQCZxrY+s5Cyr2hzF/Gu1ve5b2t7+GNNK/1MDplNHOGz+H4wcdjNfWd9St6m0kxkWRLItmWHFcBfk8E9zk5MGIEvP9+95xPCCGEEEII0T9IcN+PSHAv+rN4De8DEY0b/reGTWVeEmxmHrtwAsPSEmI9rA5xWM0M9jj3vVOwDgJVoKm9M6hY6SP1OS1Foip+NYBXDRDpyKK2tkRwpcjs+z0E1SBLdi6hsKiQrXVbm7an2FOYnTubWTmzSHGkxG6AcS7eAnwJ7oUQQgghhBDxoieCe/kXvRAi7rhsFjKT7XHXI++0mrnzrHGkJdjwhTVufXMdtYG+MUs9GNGo8e+nEseRDJ5hkHCQL3aaNAhm3wtnPGY8X12Dr+fBf8+BDe9CnN0wArCaLLhtSWS5BjDYOYBka+K+g9OwF+p29Y91DDrBYXEwM2cmj5z0CH894a8cP/h4TJioDlXz4vcvcvl7l/PAigfYWL0x1kONS1E9Sm2olmJvMTXBms6vx9BDamrg0kshJQWSk+GCC6CsrPnrJ55ofOzp9ttbN2UpCmzbBh98YPxZUZqPq6qCm2+G8eONayQkwPHHQ2Hh3udVFLjiCliwACZMAIfDuBnw4ot77xuJwF13wSGHgN0OAwfCVVcZ1xNCCCGEEEL0bzLjPk7JjHsh4nfB2g2l9fzy5TWE1Sjjs93cO/fw9mto4oiiKAz2OLBbOhDK6zoEqo2POHv9u1VTfc4zoDaE3IOPhKk3Q3r8958bVTp+/Pvqw7cnGbPvia8bYfGizF/Ggi0LeG/re9RH6pu2j04ZTX5ePpOzJkuNTjtMiolkWzLJ9mRMMXh3R+OM+/HjIS0Nzj4bNm2CJ56AsWPhyy/BZmsO3z/+uO3jG390/vtfuPFGyMyE3/7W2JaZCdOnw4oVMHcu/N//wfDh4PXCvHnw9dewcCHMmNF8XkWBww+H0lL42c8gIwOefhrWroV162DMGGM/XYczz4TFi+GnPzWOKSqCRx+FkSPhiy+M0F8IIYQQQggR/6Qqpx+R4F4IgzekUlYXf7OGP95Qzp8LvwXgtHED+dWMUXH3DoG22CwmsjzOjo81qhnhfbD24A7w26zPOQ+OucoIvuNcVI/iV4N4VX/bffhmq7Egr8Xe+4PrI0JaiKU7l1KwuYAtdVuatnvsHmbnzGZW7ixSHX1jXYveZlbMJNuTSbIl9WqA3xi8n3SSEX6bG+5JPvWUMWv9ySfh6qs7HtxD+1U5oRBYLM3XAAiHjRn1gwcb12+kKMZ+a9bAuHHGtt27YehQ+MUv4L77jG0vvQQXXgiLFhk3BxotWgQzZ8K//gVXXtmll0YIIYQQQgjRy6QqRwjR7yTaLWQkxV/YeOLoDC47PgeAd9eV8trKnbEdUAeF1Sg1/k7U+5jMRnWOZyjY47P/vVs01ec83qI+50WYd27c1ue0ZFJMJFpdDHSmk+0aSIotufUscS0C9aUQqAHi+7nEit1sZ/qw6Tx80sPcc8I9TB48GZNioiZUw0sbXuKn7/2U+1fcz4aqDbEeatzRdI3qYDXF9cXUhmp7/V1S11/fOlD/yU/A42m7xqar7Pbma4RCUFkJdXUwdSqsXLn3/lOnNof2YMzcP+QQY0Z9o5dfhrw8I/yvqGj+OPJIcLvhww+7b/xCCCGEEEKIvscS6wEIIcT+JDmMALK8vo2ZxDH042OHsq3Kz4ffl/HkkiKyU1wcNzwt1sPar5pABJfd3LHKnEZmKyQNBGcIfBUQCfTcAGNp6LFwwf9gzTz46mnwV8Li22D9GzD1lj5Rn2MxmXHbknDbkghpYbyqH78aQNOjxjsn1IAx+95si/VQ45KiKIxNH8vY9LGU+8tZsHUBC7cupD5cz5KdS1iycwkjPSPJz8tnStYUrGap0WnUGODXhepItieTbEvulXcijR7d+rHVCrm5sHVr911D1+HBB+Gf/zTqeFpq6ykOG7b3tpSU1t31GzcaQX5GRtvXbNnTL4QQQgghhOh/JLgXQvQJ8RjeK4rCb2aMoqQ2wHcl9dw5/zseveAI8jLie2a6ruuU14c6V5nTyGIHdxaEfUaore5nwdu+yGyDiZfBqNnwyYNGfc6u1fDyRX2qPgfAbrZhN9tItbkJaEG8aoCAFkSvLwWH2/gQ7cpwZXDJoZdw/ujzjRqdogKKaovYVLOJv6/6O8+tf46ZOTOZnTObNGf837TrLU0BfrgOt91NkjWpRwP8tk7dctK/orT9phlN6/g17r3X6L2/+GL4058gPd2Ygf/cc20vOmtu575oy3FEo8Ys/EcfbXvflJSOj08IIYQQQghx8JHgXgjRZ8RjeG+3mvnLmeO4dt4qyupD/P6tdTxx0ZGkuOJ7NnNYjVLtj5Ca0MVx2hKMj2AdBKpAU7t3gO34vKiCF5dvZ3ddiMxkOxdOGspxeek9c7GkgUZ9zvYvYOm9ULPNqM/Z9B5MvsEI9vvAugZg3GRyWZy4LE40XcOnBvGGfITVALjSwdSJ/x3Y+qmxmG99qfEaHfVTyJncc4OPAzazjVOHncopQ0/hu6rvKCgq4LNdn1ETquHlDS/z2sbXmDx4Mvl5+RySekifWO+iN2hRjapAFbWhWjx2D4nWxAN+bZbuXMpTa5+ixFtC2aZrgXP4/vvmBV8BIhFjtv0JJxiPU1JaV9Q0amtGfnvD+9//jK78F15ovf3ZZzv/HBqNGAHLl8PJJ4NJyiuFEEIIIYQQe5B/Jggh+pQkhzXuOu9TE2zcedY4HFYTu+tC/PHt9YTVaKyHtV+1gQjBSCemnLbFkWx0wiekG334PejzogoeXLyJb0vqKPeG+LakjgcXb+Lzoooeva5Rn/MyHHc9WBzN9TlvXgkVm/Z/fJwxK2aSrQkMdg1gsMVNcqAOc9jXsYO3fgof3gm714F3t/H5wzuN7f2AoigcmnYotxx9C8/OeJbzRp2H2+ZG0zWWFi/l5mU3c9OSm/hg+weEtYPw3ShdpEU1KgOVFHuLqQ/Xd7kDf+nOpfz58z+ztnwtu/27KfMbXTJ/ub+m1ez555+Hmho4/XTj8YgR8N13xgKxjYqL4a239r5GQoJx7J5MJmOGfEubNsGbb3bpqQBw/vlGp/1DD+39NU1rXasjhBBCCCGE6H8kuBdC9DlJDisDkh1xNat1xIBEbp09BgVYv6uOBxZv7PUFGjtL13UqvKEDH6eigNMDKTngSu2xqaMvLt9OlS9EtGG4UR2qfCFeXL69R67XitkKE38CF70OI041tjXW5yy7H0L1PT+GHmAzW0m1JZONjQGqistk2/fP1YpnIFAJ0YaUNKoZj1c80zsDjiNpzjR+fOiPeXbms9x45I2M8IwA4IeaH3ho1UNc/t7l/Ofb/1AZqIzxSOOHGlWbAnxv2Nvp459a+xQVgQo03fj7F9WNJH1raS0zZsBjj8ENN8C11xoLw152mXHcFVeAqtK0z513wjHH7N2NDzBxIqxeDXfdZcyyb1wg9swzYelSuOgieOop+MMfYNIko+qmqy66CM46C371KzjnHHj4YWN8N95odPS/807Xzy2EEEIIIYTo+6QqRwjRJyXaLZgU2F3XDcFzNzlhZDpXTMnlqWVbWPztboalurhw0tBYD2ufwmqUuoCK29UNC2wqihHcOzwQrIFAddvF0l20u645tG8U1Y3tvSZpIMz62x71OS/BpkVw/C9h9Gl9pj6nJUVRcOngCgfQnKl4zSZ8Ed/es8brS5tD+0ZRzdjeT9nMNk4eejInDTmJDdUbKCgq4NPiT6kN1/LKxld4fdPrHDf4OObkzWFM6pi4uuEYK2pUpSJQQW3YqNBJsCZ06LgSb0lTaN/SqGv/QtbXT3PbbUZA3xiC2xvenDVqFLz0khG233STMQP/wQeNWfgrV7Y+1513GjPz77kHvF6YNs2osvnd7yAUgv/8B15/3TjnY48ZC8yuWdO110FR4LXXjI7755+HBQvAZjMWtj3vPOO6QgghhBBCiP5LiZfAS7R21FFH6StWrIj1MISIe8GIRmltkGic/C7TdZ1739vAe+uNTobbzziUqSMzYjyqfVMUhewUJ1ZzN8+Uj2pGeB+s7ZYA//oU+2b/AAAgAElEQVSXVvFtSV2r8N6kwKGDknn0giMP+PydpkVgzTz46ilQg8a2QUfAtN9C+sjeH093siVA4gDCuoY34sUX9hmB6WuXG/U4LcN7kxkyx8HcAyj7PshUBipZuHUhC7cupCbU3LuS585jTt4cpmZPxWaO73UwepPNbMNj9+Cyuva538ULLmZt+dpW4b1ZMXNYxmH8Z/Z/enqYQgghhBBCCNEuRVFW6rp+VHeeU6pyhBB9msNqZqDbgdkUH7NYFUXhxlNHcVhWMgD3vPs9G3fHd42KrutUenugj9tkNrrvPcPA4T7gmegXThpKaoKdxm+1SYHUBHvs3tXQsj5n+CnGtpI1Rn3O0vv6bH0OAGEf1GzDpoZIdaSSnZTNANcAXJOuQXGmNa9nYDKDM81YoFY0SXOmcdGYi3h2xrPcNPEmRnqMGzlFtUU8vPphLnvvMl749gXK/eUxHml8CGthyvxllHhL8Ef87e535WFXku5Mx6wYf//Mipl0ZzpXHnZlbw1VCCGEEEIIIXqNzLiPUzLjXojOCanGzHttzy6VGKnxh7l23mpK64KkJ9p44qIjSU+Mr0V19zQg2UGivQcb1LQI+KsOKND+vKiCF5dvZ3ddiMxkI7Q/Li+9Gwd5AHYshyV/M+pzAFxpcPwvYPTpfbI+p4k9ERIymsJ6bcNCfJ/cj69+F6GkgUZonzM5xoOMfxuqNlBYVMgnxZ+g6ioAJsXEcYOMGp1D0w6VGp0GdrMdj8OD0+Lc62tL/5+9Ow+Pqrz7P/4+sy/JTBJIQiBsAUE2WUTEFTdAhdQV97ZY+3TBR23tY7V20Vqt2tZW22pt1Z+2irZoK5a4b4ioCAqisgphh4Tsyezb+f1xZyeQAMmcmeT7uq5cYQ5nzrmzDeFz7vO5dy/jsS8eY59vHwUZBfzPhP/h9MLTDRilEEIIIYQQQrToiRn3EtynKAnuhTh8qVabs63Szw3PrSEQiTM6P5M/XD4Rh9Vs9LAOymzSKMx29fzdC7EIBKrUrO7eJh6Ftc+q+pxoUG0rmAQzboX+o4wd29EwmSEjT1XotBKNR/FFffiiPuLtu+9Fh2pCNby2/TVe3fYqNeGa5u3DPcMpHqFqdOzm1L7IlywOiwOv3dthgC+EEEIIIYQQqUSC+z5EgnshjkyqhfcrSqv42eIvSehwxqhcfjZ3DKYUnlWb6bCSm5mk0DAWbgzwD16NkbZ85bD8D7DlTfVYM8GEy+DE74E909ixHQ17ZuPs+wOb9oKxIP6on0A0QEJPGDC49BJNRPlgzweUlJawqWZT8/ZMWyazh87mvOHnkefKM3CEqcNhcZBlz8JhcRg9FCGEEEIIIYTokAT3fYgE90IcuWAkTll9iFR5fXvh0908snQrAN84aSjzTx5m7IA6UeB14rQl8c6AaFAF+NFQ8s6ZLLs+hmW/gZrt6rEzB065Kb3rcw4y+76Jruv4o358UR+hWC/8mvaAzTWbKSkt4f3d77fU6GDixIITKR5RzPh+46VGBxXgZzuy5Y4EIYQQQgghRMqR4L4PkeBeiKOTSuG9ruv8/s2vePmLfQD8bM4Yzjo2dWfSWs0mBmU5MSV7wd+IXwX4sR5YKNdI8SisfQ5W/a131ec4PODq3+Hs+yaxRKw5xI/Go0kcXHqqCdXw+vbXeXX7q1SHqpu3D/MMY27RXGYUzpBZ54DT4iTLkSUBvhBCCCGEECJlSHDfh0hwL8TRS6XwPhZP8ON/f85nu+qwmjUevHwSYwo8Rg/roDxOq3GL6YYb1CK2vS3o9ZXDBw/CV2+ox72hPsdkhox8sLk63TUSj+CL+vBH/MR16cM/lGgiykd7P2JJ6RI2Vm9s3p5hzWDW0FmcP/x88t35Bo4wNTgtTrId2djMNqOHIoQQQgghhOjjJLjvQyS4F6J7pFJ4Xx+Mcv2za9hTGyTHbeORqyaT50nd2bMDs5zGLqYbqlMBfm9b9HTXSlh2f++qz+nC7Psmuq639OHHAinxs5nKttRuYcnWJSzbs4xYoqVGZ1rBNIqLipnQf0Kfr9FxWV1k2bMkwBdCCCGEEEIYRoL7PkSCeyG6TyotWLuzOsD1z67GH44zMjeDh66YlNw++cNgNZsozHYaGwrqOoRqIVgDiV604GlvrM8xW8Cd16XZ900SegJ/1I8/6pc+/E7Uhmt5ffvrvLLtlTY1OkM9Q5kzfA5nDj6zz9foSIAvhBBCCCGEMIoE932IBPdCdK9UCu8/3VHDrf/+nIQOp4zsxy+/Ng5Tis6Y9Tqt9DOqMqe1RKIlwE+Br2G36bA+Zx6c+P30rc9xeMHd/7DvHogmogSiAenD70QsEeOjfR+xZOsSNlRvaN7utrqZOXQmc4bPYYB7gIEjNJ7T4sRr9/b5CxlCCCGEEEKI5JHgvg+R4F6I7pdK4f1Ln+3lobe/AuDKaYP5n9OKDB7RwRlemdNaIq7C+1Bd7wrwe1t9jtmquu+tRxachuNhfBEfgWhA+vAPYUvtFkpKS1i2exnRhLrYoaExbcA0ikcUc1z/4/p0jY7D4sBr9+K0OI0eihBCCCGEEKKXk+C+D5HgXoiekUrh/R/f/orFn+0F4LZzRzNrXGrOkrVZTBRmd73+JCniUQhUQdhn9Ei6T4f1ORNhxm3pW5/jzAZXzhFffNB1nUAsQEOkQap0DqEuXMfr21/n1e2vUhmsbN4+OHMwc4vmcubgM/t0eG032/HavbisKfY6JoQQQgghhOg1JLjvQyS4F6LnhKJxyutDxBPGvv7FEzq3v/gFq7bXYDVr/O7SiUwo9Bo6poPp57bjdVmNHsaBoiEIVKr3vUVvq8+x2NTse8vRVS5FE1F8ER++qI94b1uwuJvEE3FW7FvBktIlrKta17zdbXFzztBzmFM0hwJ3gYEjNJbVbMVr85JhyzB6KEIIIYQQQoheRoL7PkSCeyF6VjimZt4bHd77wjFueHYNO6oDeJ1W/nL1FAZ4U6+XWdM0CrOdWM0mo4fSsYgf/JVq1npvsWslLPsN1GxTj505cPKNcOwcFeanE01rmX3fDZq68IOxIPJ7TMdKa0spKS3hvd3vEUlEAFWjMzV/KnNHzGVy7uQ+W6NjMVnw2r1kWDP67OdACCGEEEII0b0kuO9DJLgXoudFYgnK6kLEEglDx7GnNsj1C1dTH4oxvL+bP14xCbfdYuiYOuKyWVLyokIboToIVKsu/N6go/qcARNhxq2QO9rYsR0Jq0PNvjd3z90b8UQcX9QnC9oeQl24jjd2vMEr215pU6NTmFHYXKPTVytkzCYzHpsHj80jAb4QQgghhBDiqEhw34dIcC9EckTjKryPxo0N79furuWW5z8nltCZXpTDry4Yj9mUekFSnsdBRgpeVGhD19UCtsGa3rOArW9/Y33O6+qxZoLx82B6GtbnaBq4+oEzq1sPG4qF8EV9+KN+mYXfgaYanZLSEr6s+rJ5u8vi4pwhqkZnYMZAA0doHLNmxmP3kGnLxJRud7MIIYQQQgghUoIE932IBPdCJE8snmBfCoT3r3yxj9+9sRmAeccX8v0zRhg6no6YTRqF2a6UvKhwgERczb4P1/eeAH/3Knjv/lb1OdmN9Tlz068+x+ZSs+9N5m49bEJP4I/68UV8hOPhbj12b7GtbhslpSUs3bW0uUYHUDU6RXOZnDe5TwbYEuALIYQQQgghjpQE932IBPdCJFc8obOvLkgkZmx4/5elW3n+090A3DxzFHOPS72FJDMcFvIyU7wyp7V4FAJVEPYZPZLu0Vyf8xhEA2rbgOMa63OONXZsh8tkAnce2HtmsdBoPEpDtAF/xE9c7yX1Sd2oPlLPmzve5OXSl6kIVjRvH5QxiDnD53D2kLP7ZI2OBPhCCCGEEEKIwyXBfR8iwb0QyZdI6OyrDxGOGhfwxRM6P3/pS1aUVmM2afzmkglMHpJt2HgOpsDrxGnr3pnSPS4aUgF+U1d8uutN9TkOD7j6qyC/B+i6TjAWpCHSQDDWS77+3SieiLOybCUlpSV8Xvl583anxcnZQ85mbtFcBmUMMnCExpAAXwghhBBCCNFVEtz3IRLcC2GMREKnrD5EyMDwPhCJceNzn1Fa6SfTYeHhqyZTmJ1as16tZhODspyY0qEyp72IXwX4sUjn+6aD3atg2W+gulQ9Ttf6HLNVVedYe/Zujlgipqp0ZEHbDm2v207JthLe3fUukXjLz8iUvCkUFxUzJX9KnwuxJcAXQgghhBBCdEaC+z5EgnshjJMK4X1ZfYjrF66mJhBlcLaTP181mUyH1bDxdMTjtNI/w270MI5cqF4F+IleUKESj8Ln/4SVf0vv+hxNUxceXDlJOV3TgraBaICEbmxNVqppiDSoGp1tL7M/sL95e4G7gLlFczl7yNm4rW4DR5h8EuALIYQQQgghDkaC+z5EgnshjKXrKrwPRowLdb/cU8ePnl9LNK5z/JAs7r14AhZzaoVFA7OcOKxpVpnTmq5DqBaCNZDoBcFth/U5l8KJ31d1NOnC6lCz783JuVjVtKCtP+onFAsl5ZzpIq7HWVW2ipLSEtZWrG3e7rQ4OWvwWcwpmsPgzMEGjjD5JMAXQgghhBBCtCfBfR8iwb0QxkuF8P7N9eXc++pGAC6YOJCbzjnGsLF0xGo2UZjtRNPSsDKntURChfehWhXmp7uO6nNOuhHGpFF9jqaBOzfpFxyi8Si+qA9f1Ee8N9yN0Y121O/g5dKXeWfXO4Tj4ebtk/MmM7doLlPzp/apINusmcm0ZZJpy8RsSuMLmEIIIYQQQoijJsF9JzRNswILgEnAZGAsYAX+R9f1xw/ynELgm62eUwRowDG6rm85zPPPB548xC7f13X90a4cS4J7IVJDKixY+8TybSz8eCcAN541kgsnp9YikVkuGzlum9HD6B6JOASqIVyf/gF+b6nPsbnU7HsDgtFgLIgv4iMQC9Cbfl86Wr6Ij7d2vsXLpS9TFihr3l7gLmDO8DmcM/ScPlWjo2kabqsbr82LNUl3iQghhBBCCCFSiwT3ndA0LQuoaXxYDkSAwRw6uL8QeBHQgW1ADpDF0QX3LwGfdbBLia7rXUrjJbgXInXEEzp7a4NE48ZUqSR0nV8uWc/7X1Vi0uDeiydwwrDkdIB3haZpDMpyYrP0opm28WhjgN9g9EiOnq8CPvhDetfnmEzgzgN7hiGnb6rS8UV8bWaa93VxPc6nZZ+ypHQJn1W0/NrjMDs4c8iZFBcV97kaHZfVhdfuxW5O4/U/hBBCCCGEEIdNgvtOaJpmA84GPtN1fZ+maXcCd9D5jPvhwFpd1+s1TVsKzODogvtrdV1/6kg/DpDgXohUE40n2FcbImZQD3owGucH//yMr/b7cNvN/PnKyQztlzozWh1WMwOznEYPo/vFwmoB20jA6JEcvd2fwLL729Xn3ABjitOnPseeqepzTMaNV6p0OrarYRclpSW8s/MdQvGWdQIm5U6iuKiY4wccj1nrO3UyDosDj82Dy+oyeihCCCGEEEKIJJDg/jB1Jbjv4DlLkeBeCNGBSCzBvrog8YQxr5sVDWEWLFxNlT/CwCwHD181Ba8zdWoZcjPtZDpSZzzdKhpUAX40zRcujUfh83/Byr+21OfkT1D1OXljjB1bV5nMqjrHZnwgGogG8Ef9UqXTij/q5+2db1NSWsI+/77m7fmufOYWzeWcIeeQYTPmzgkjWM1WPDYPGdaM9F8LRAghhBBCCHFQEtwfJgOD+4eA7YAD2AO8q+v67sM5lgT3QqSmUDTOvrqQYSHdxrJ6fvCvtURiCSYWevnNpcdhNafGbGmzSaMw24XZ1IvDqbBPBfjxqNEjOTq+CvjwQdj8WuMGDSZcCicuSJ/6HIcXXP0MnX3fpKlKxx/1E4ql+cWdbpLQE6wuX82S0iWs3r+6ebvdbOfMwWcyt2guQz1DDRxhcpk1Mx67CvBlIVshhBBCCCF6HwnuD5OBwX17ceBx4Ae6rnfpf/QS3AuRugKRGOX1YcPC+6WbKrirZD0A548fwI9mjUqZmZyZDiu5mX2g2zlUrwL8dK9K2fMpvHc/VG9Vjx1ZcPKN6VOfY7ao2ffW1KlpaqrS8Uf9xBIxo4eTEnY37Obl0pd5e9fbBGPB5u3H9T+O4hHFnDDghD5To2PSTGohW7sXi8li9HCEEEIIIYQQ3USC+8NkQHA/A5gAvAHsBrzAqcC9wAjgOV3XrzrE878DfAdgyJAhx+/YseNwTi+ESCJfOMb+euNm1j790Q6e/HA7AN+fUcS8qamzAOTALCcOax8I4XQdQrUQrAGD1j7oFvEofLEIPv4rRP1qW7rV5zizwZUDKXIBq0kwFlRVOtEACT2Nv0e6SSAa4K2db/Fy6cvs9e9t3p7nymPO8DnMHDqTTFumgSNMHk3TmgN8q6mXVowJIYQQQgjRh/SJ4F7TtO3A4dw7vVDX9WsOcqw7SWJwf4hjDgbWAtnAJF3X13b2HJlxL0TqqwtGqfKFDTm3ruvc/fIG3t1UgQbcfeF4ThrRz5CxtGc1myjMdqbMXQA9LpFoCfBT7N/Uw+KvgA8egs2vNm5Is/oci13NvrfYjB7JARJ6gkA0gC/qkyod1Odjzf41LCldwqflnzZvt5ltnFmoanSGeYcZN8Aky7Bl4LV5sZolwBdCCCGEECJd9ZXg/m1g0GE85b+6rv/4IMe6kxQI7huP+wxwNXCjrut/6mx/Ce6FSA81/gg1gYgh5w5H49z8/Fo27GvAaTXzpysnUZSbGos+Zrls5LhTL0DtUYk4BKohXJ/eAX461+domuq9d2YZPZKDiiVi+KN+fFEf0XRfK6Eb7PHt4eXSl3lr51ttanQm9J/A3KK5nDjgxD7TCd80A99m7mOvnUIIIYQQQvQCfSK4704pFtz/AfgB8BNd1+/rbH8J7oVIHxUNYRpCxgRw1f4ICxauZn9DmHyPnUeunkK2KzVCnz5TmdNePNoY4DcYPZIjl+71OTYXuPNUB34KC8fDNEQapEoHVaPzzq53KCktYY9vT/P2XGcu5w8/n1nDZuGxpcGdH93AaXGS5cjCbu4D64UIIYQQQgjRS0hwf5hSLLj/EDgJuFzX9UWd7S/BvRDpZX99CF/YmIUot+z3ceM/1xCKJhg30MMD8yZisxg/M9pqNjEoy4nJ1Ecqc9qLhdUCtpGA0SM5ch3V54y/BKYvAIfX0KF1ymRS4b09Ne5CORRd1wnEAvgivjazzvuihJ7gs/2fNdfo6KjfU20mGzMGz6C4qJjh3uEGjzI5HBYHWfYsHBaH0UMRQgghhBBCdEKC+8PUU8G9pmkFqIVn9+m6Xtdq+2m6rr/fbl8NuA34NVAJjNB1vb6zcUhwL0R60XWdsvoQwUjckPN/sKWSX7y0Dh04Z0wePznv2JTomM9wWMjL7OOhUzSoAvxoGnebd1ifcwOM+Vrq1+fYM8Gdq4L8NCBVOi32+vby8raXeWvHWwRiLRfAxvUbR3FRMdMLpveJGh2HxYHX7sVpcRo9FCGEEEIIIcRBSHDfBZqm3QYc2/hwEjAR+BD4qnHb8vYhvqZpT7V6eC6QD/wHaOo5eFzX9eXt9v8mcK2u60+12q4Dm4FVwB5UuH8KMB4IABfpuv5GVz4OCe6FSD+JhM6++hDhqDHh/XMrd/LY+9sA+Papw7nqxCGGjKO9fI8Dtz21K0uSIuJXAX7MmDURjlqH9TnjYcZtqV+fY7aohWut6RV8SpWOEowFeWenqtHZ7dvdvL2/s7+q0Rk6C689xe8AOYRTJxQyrCjKMy+VH3I/u9mO1+7FZXV1ekxNgzvugDvv7KZBCiGEEEIIIQ6pJ4L73piknIuaMd/ayY1vTdrPvv9mB8e5uNWflwLLO9invd8B04CzgBwgAewEHgZ+r+t6aReOIYRIUyaTxgCPg721QaLx5IdsV5wwmJ3VAV5fV87jy7dRmOPk9GNykz6O9ip9YewWExZzesx47jE2t3oL1UOwGuLGVCsdMbMVJl0Nx8yCD/8Im16B8i9h0ddh/MUw/frUrc+Jx6Bujxqfu79KNdOA3WzH7rSjO/p2lY7T4mRO0RzOH34+n1V8RklpCavKVrFzXT73PTaQX2x0Ea8swOPVmTgpwU231XLc5NS6QLb+cxtvvOxi3jUNDBp8ZBd3w/Ew+wP7ZQa+EEIIIYQQfUSvm3HfW8iMeyHSVyyeYG9tiFgi+eF9JJbg/55fy5d763FYTDx4xSRG5WcmfRztuWwWBnj7eGVOa7oOoToV4BvwfdIt9qyG9+5rVZ/jhZNugLEXpHZ9TprOvm8iVTrKPv8+vnWVhy1rCvBMfQPHkI3EAx58y64isD+Pvz1XxjmzUye8f2FhBrdc359Fr+7jhJPCbf6uqzPu2ztUB77MuBdCCCGEECK5emLGfQr/z1oIIdKTxWxigNeB2YBFWW0WE3ddMI4BHgehWIKfLf6SSl+48yf2sEAkRl2w74aMB9A0cGZB1jBw5aTNDPA2Bk2ByxfCqT8Cq1tdiHj3bnhhPpSvN3p0B9c0+95fqS6gpBmLyYLX7mVQxiAGuAeQYcvAlMoXSnpIgbuAu2/P4ZON5dz9QA0Ti1eSe/6TDP7ZpZgyavjhrT7+telf1IXrOj9YmgrFQpT5yyjzlxGO9+zrvN/fo4cXQgghhBBCdKDv/U9PCCGSwGYxke9xGLJAbJbLxj0XjcdlM1Ppi/DzxesIGdS731q1P0Iklqazy3uKyaSC++xhKshPtwDfbIVJV8E1/4bR56tt5evg+W/A0l+rMD9VBWuhdmdaLxrssDjo7+xPYWYh/Zz9Opx53Zsdf2IYr8vJecPP4+GzHubuU+7mlJHHknHsKvy7h/LMhme49vVr+cOnf2BL7ZYDnv/CwgyGZw3jw/cc3HdHNieMGsz4wiEs+GYu9bUmYjH47V1ZnHhsIccOGMK3r8ijqvLAX50/eM/BVcX5jC8cwtiBQ7j8/AGs/NDe/PcP3pvFLdf3B+Cy8woYnjWM4VnDWPF+26/X2tU2Lp09gGMHDOHkcYU8/mdP89/F4zB9TCHXzss74PyhWIjx4zROPDly0AD/qafUy8vbb8MPfwj5+eB2w/nnw5Z2n5r588FigV274OKLISsLTj211cf7AZx9NmRmQkaG+vNHHx35+b74Ar71LRg5EpxOyMmBCy6A9e2u/y1dqo759NPwwAMwdCg4HDB9OqxefeDHXF4O3/seDBoENps6/r33pu9NTkIIIYQQou/pjR33QgiREhxWM/keO+X1YZJdSza8v5ufzRnDzxZ/yabyBn7z2iZ+NncMJgODYV3X2d8QYlCW05ALGinNZFbd644sVZ8Tqjd6RIfHnQszfwXjLlb1OVVb4Mt/w5a3Urs+Jx6Fut3qoomrX/pdOGlk0kxk2jLJtGUSjUfxRX34oj7iCeMv2CWLpmlMzJ3IxNyJfEIOpVlh3FY3/qifd3a9wzu73mFMzhiKi4o5aeBJWEwtvwL/+ufZZHoS/O//1bL1KyvPPJ6JngBvdoKd2ywsuLmO0q+sPP14JnfdlsNDj1c2P7fkRRc3XZfLiaeEuPn2WnQdXng2g2suGMDTi8s48ZQws4v97C8389xTmdxwSy3DR6q7j0aMbqny2bPbwrevyOeiy31ceJmfkv+4uednORwzJsKMs0OYzXDR5X4e+5OHiv0mcvNa0ufP19jYssnGPQ9Wss/na1y89sCAH+BHP1Kh/E9+AhUV8NBDcMYZ8PnnKjBvouswezYcdxzcd19L2L1sGcycCQMHwu23q21//SuceaYK6U855fDP98YbKqS/5hoVsu/erY552mmwbh0MGND2mA89BOEw3Hgj6uLKb+Gii9QFAatV7VNZqQL9UAi+8x013g8+UGPesQMeffSQ305CCCGEEEKkBAnuhRCiB7lsFnIzYX998mf1Ti/qx/dmjOCRpVtZurmCIR+5mH/ysKSPo7VILEFNIEqO22boOFKW2QIZeY0Bfg2EG4we0eEZOFnV53yxCD5+tKU+Z/2LMOMnkDfG6BF2LFgLEX9j9316z1q3mq1km7PJsmcRjAXxRdWCtn1lTaOVH9r5fFUm31pQz//Nfoqlu5eyZOsSdjbsZEP1BjZUbyDHkcP5w88nHLsSAJdbZ+F/yzE1XluqrjTzymIXJ54SarO9qtLMK4vd/PrBKtwZOgG/xs9v7kfxJX4efKwlzL/6Ww2ce/JAfntXNi+8XsaY8VGmnBDmuacyOe2s4AEd9wDbt1p56oVyZpyjFh+ed00Dp4wfzL/+kcmMs9W/H5de5ePRB7389/kMrru+5eLef57LwGbXmXthAIBAVL33R/1E4zasZmvzvqEQrFmjZraDCtFnzVLh9733townkYBzzoE//rHtOG++GVwuWLFCzaIHNUP/2GPVzPqVK9vu35Xzff/7KuBv7etfh/Hj4Ykn4Kc/bft31dUq0G865ujRKrh/4w2YM0dt++lPoaFBzeYvKFDbvvtdKCxUFyJuvhlGjTrgyyCEEEIIIURKScHpb0II0btk2C30z7R3vmMPuGTKIOZMUKnFPz7awTsb9xsyjtZqA5GUqO5JaRYbZOZD1hCwZxg9msNjssDEq+Dq/8DoxhStfB0s+npq1+c0zb5P0+779jRNw2V1kefKozCjkGxHdpsAtzfaX2bmpm/nMrAwzo0/rsVhcXDusHP581l/5p5T7mF6wXRMmKgOVfPMhmd4/MvHATjz0i3N4TzAlBPC6LrGvGt8B2yPxTT27lbzXpa/66S2xsyFl/mprjI1vwUCGqecEWLNKjvBQNfu4igcEm0O7QHsdpg8Ncyu7S1zbEaMijJpapgX/+Vu3haLQcl/3JxzXgBPVtsOmGg8yh7fHiqDlcQTMUCF185W6zLPnAljx0JJyYFjWrCg7eOyMvj0UxWqN4X2oILxa66BVatUPU1rXTmfy9XyZ78fqqpUPc+oUep87c2f3/aYM2ao96Wl6r2uw/PPq1oeq668SX0AACAASURBVFXNvm96mz1b/f277x54XCGEEEIIIVKNzLgXQogk8Dis6Amo8id3oVhN07jp7JHsqQ3w2a467n9tIwVeB2MKPJ0/uQdVNIQZlOXEZMACvmnFYoPMAeCMqAqdsM/oEXWduz/MvAvGXgjL7k+f+pxeNPu+idlkxmv34rV7CcfD+CI+/FE/Cb33lH3X12lcOy8Pv8/Eolf34fGqiy+RCNTVWChgMt8ZNpmL8ir4qO5l3trzCjWN/S/PVzxA6TIfc4vmcvLAk/F41faBhbE252jaXlervm9Lt6pfo6+dl8/B1FSbcLo6v1BZOCR2wDZvVoKN69pebJl3dQM//WF/Nm+wMmpMlPfeclJVaeaSKw/+2uCL+KgJAfRnxMgY7X/9Hz0a3nzzwOcVFbV9vH27en/ssQfuO3Zsyz6tQ/3Row/ct/356upUhc3zz6s6ndb69z/w+UOHtn2cna3eV1er9xUVUFOjuvCffvrA5wPsN/4athBCCCGEEJ2S4F4IIZLE67IS13VqA5HOd+5GFrOJO4vHcf2za9hTG+TnL63jkasmk+cxLpSMxhNU+sPkZfaOYLTHNQf4YQhUq2A5XQyacvD6nNNvg/yxRo/wQL2k+74jdrMdu9NOjiOHQCyAL6KqdNJZMKBx3RX5lG6x8o//lHPsuGjz363+2MGVxa1L0gfz3JKBXD17Hvdu2cc/AExxNlZvZGP1RnIcOeTuuwWYi9nc8fmabsjQE+r74v4/VTJw8IHBO0C//l27OHLwc7X93pt7sZ+7fpLDf/6ZwW2/rOE//8ygX26c08/u2tewOlxFZVBdyLGarI3n6Hg8tsNoNGs6RvsflY5+dNqf74or1MKzN98MU6aoRW9NJvjBDzpeSLazr0vTcy6/HL797Y73bX9RQgghhBBCiFQkwb0QQiRRjtuGruvUBaMH3WfF1iqeXbmT8voQ+R4HV00bwvQR/Y7qvB6nlXsuGs/1z66m2h/hZ4vX8dAVk3DaDpKAJIEvFMNli5Fhl3+KusxiB08BxMIQqIJIIPlj2L4cPnkCGsrUxYSp18GwUw/9nKb6nJGz4MM/wqaXVX3O899QC9pOX6BC8lTTPPs+D6zOzvdPI5qm4ba6cVvdxBIx/FE/vqiPaPzgr02pYFX5KhZtWkRFoIJcVy4XD7+cR38wh89W2fnrwv0H9MePmRDh6cVlB2xzWBxM6D8BgO9O+C5bc/7Ox/s+pjpUzdZ9K4C5/HPjP/EeO45R2R2XoQ8tUp+r7H5xTj3j0OuYaFr31C95vDqz5gR46Xk33/9hHW+/5uTqaxuwdPFldOtmK76Z9fijftxWN167l82brQwb1vlzm/bZuPHAv2va1n42/MaNcN55bbdt3txyrNpaeO01uPNOuOOOtvvV1HQ8474zubng8ai7Lc455/CfL4QQQgghRKpIwXvUhRCid+uXYSfT0XHX9IqtVfz+zc2s31dPhS/C+n31/P7NzazYWnXU5x2S4+LO4nGYNNhS4ePXr24gYXCXd5UvTCzee+o6ksZiB89A9WZJ4voJ25fDu/eo0N2/X71/9x61vSua6nMufhz6jQR0WPdvWHgxrPsPpGJ1SzwKdXt6Tfd9RywmC167l0EZgyjIKCDDloEpBWuMVpWv4uE1D7O5ejNVoSo2VW7hxutyef9dB7/7SyVnzT5w1rk3K8GpZ4TavHnbdcEXZRVx+4m389isx7jkmEtwmNWdQGsq1vCj937E/733fyzdtZS43nZW/elnBfF44/z5d1mEO2hBq6ps+Rw63ep7p77u6D+vl1zlo2yvhZ/d3I9wyMTFV3T9Dpzn/p5BKKih6zq+iI9//bea9evh3PM7r/MZMACmTlX1M62rZsrK1LZp09rW5AD87W8QbPVlefNNWL++ZRHZptnz7WfWP/MM7N3b5Q+rDbMZ5s2D//5X9e6319BAh18vIYQQQgghUo1McxRCCAPkZtrR0fGF2gZBz67cSXUgQqLpln8dqgMRnl2586hn3QMcPzSbG846hofe/ooPtlTxxPJt/M9pxnUGxBM6Fb4wBd7eNZs5aWwu9RZuUDPw4x3XdXSbT55Q59EbQz49rh5/8kTns+5bGzi5g/qce2DdYphxK+SP65nxH41gLUR8jd33vff7talKR3foBGIBGiINhGKHnk2eLIs2LaImVEMClfLu/efNVK2cQd6ktSQSA9ss2gowe24Al7vrF1vyXHnMHzcf+wQ7twMF7gLqWcOmmk1s+nQT4S/3ArdTH6kH7GRk6vz6oSpuui6Xc08exEWX+cgriFO2x8LHH6gLas+VqNVax0+MoGk6f/mDl9pqEzY7nHR6kP65h3+x6rQzQxQMilHyHzejx0YYN7Hr9Ws2G1x23gAuutxHZaWZpx71kDcgxtXf30dl0InX7gUOvojxAw+oBWanT1cLz+o6/PWvEArB739/4P52O5x2mlrQdv9+eOghtZjtLbeov8/MhDPPhN/8Rh1jxAj45BN44YWjq7O5915Vv3PaaXDddXDccSqwX7dOHfuLL+jSXQZCCCGEEEIYSYJ7IYQwSF6mA/QQvnBL2FpeH2oO7ZskdLW9u1wwaSA7qvws/mwvz63cxdAcF7PGDej8iT0kGIlTF4jidR08LBKdsGeCLQNCtRCs6bgYujs0lLWE9k30uNp+uDqqz9m/Dp7/Joy7CKZfn3r1OfGYmn3fC7vv2+uoSqch0kAs0cMXhw6hIlDRHNoDBHeoVVL3fzaRm7974P7vr92Ny334423qfr9p8k24R53OktIlfLTvIwJRVU1138r7WON2U1xUzJwLRzOgoIxHfu/l//3FQzBoIjcvznFTwlz+9ZYFY4cMi3HH/dU8/mcPt97Qn3hc47klZfTPPfzXdpMJLrrcxyO/z+LiQyxK25Gf3lPN26+6ePiBLPx+jWknh7nzN1VkZcebFy0OxfMBO3Dg9/fpp8Pbb8MvfgG/+pXaNm0aLFwIJ5984PkeeACWLIF77gGfTz3/T3+Cfq2uQz/7rOq3f+IJFd5Pm6Zm5v/oR4f1obWRmwsffwx33w0vvQSPPQZZWXDMMfDzn6u7B4QQQgghhEh1mt5Lb/tOd1OnTtU/+eQTo4chhOhhuq6zvyGMvzG8v/G5NazfV98mvDdpMLbAwx+vnNxt540ndG5/8QtWba/Batb43aUTmVDo7bbjHy5N0xiU5cRmSb16jrSTSKjwPlTb/dUuL1yr6nFah/eaWc2Qv/TJozv23jXw3n1QtUU9tnvhpP+FsReAybi1GA7KbG2cfd+3FlgOxUL4oj4C0QCJJFcb3bLsFjZXb24T3pswMSpnFL89/bc9eu6KQAWvbn+V17a/RkOkoXn7MVnHMLdoLqcNOg2rOXkXH//w6ywefsDLh+t2kzeg85qbFxZmcMv1/Vn06r4D1gHoSNOFmyx7FhbT4c/zeeopuPZaeP99OPUwbsYRQgghhBAiXWma9qmu61O785iSkAghhIE0TSMv047LpoKRq6YNIcdlw9Q40dGkQY7LxlXThnTrec0mjZ/PHcvQHBfRuM4v/ruOsjrj6jDUBYwQcjG5G5hM4O4HWUPVTPzuNPW6xpnmjUG6ZlaPp1539Mduqs857f/A5oZwHSy9B16Yry4WpJp4FOp2g7+q13bfd8RhcdDf2Z/CzEL6O/vjsCTvwsVloy8j25GNqfHXVxMmsh3ZXDb6sh4/d64rl2+M/QZPzn6SmybfRJFX9bh8VfsVf1j9B771xrd4ZsMzVAWPfj2SzsRi8PzCDE47K9il0P5INHXg7/HtoTJYaeidFkIIIYQQQvRVMuM+RcmMeyH6Fl3XKa8PE4jEWLG1imdX7qS8PkS+x8FV04Z0S799R/bUBrl+4WrqQzGG93fzxysm4bYb16KW5bKR47YZdv5eKRZRPfSRri9geUjbl6tO+4YyyBygQvvD6bfvCn9lS30OAFrq1ucAWGxq9n0yFwpOIdFEFH/Ejy/q6/GAd1X5KhZtWkRFoIJcVy6Xjb6ME/JP6NFzdkTXddZXr6ektIQP937YfPeBWTNzysBTmFs0l2NzjkXrxjqlygoTHyx18vZrTpb8O4NnXirjlBldu+B6uDPu2zvcGfgy414IIYQQQvQ1PTHj/rCCe0397+McYCZwOjAE6A8Egf3AZ8A7wH91Xd/TnQPtayS4F6LvaV+bkyxrd9VyywufE0voTC/K4VcXjMdsMq67e2CWE4c1BatR0l0s3BjgB4weSdftXQPv3Q9VX6nHqVyfo2ngzAZXjtEjMVQwFsQf9eOP+vvMHTSVwUpe3fYqr29/nbpIXfP2kVkjKS4q7rYanRXvO7iyeAA5/eJ8+3/r+f4P6zp/UqOjDe6baJpGhjUDr917yABfgnshhBBCCNHXGBbca5rmAm4EvosK65sSnRBQDTiBrFbbY8AS4AFd1z/szgH3FRLcC9E3GRXev/LFPn73xmYALj1+EAvOGJnU87dmNZsozHZ260xV0Uo0qAL8qHHVSIclEYMvXoCPH2m5ayBvLMy4TXXrpxqLXd2JkMS+81SU0BPNAX4olibfa0cpEo+wfM9ylpQuYUvtlubtXpuX2cNmc/7w8+nn7Jm7p5KtqwG+EEIIIYQQfYUhwb2madcCdwMFwEbgn8AHwCpd1+tb7acBo4HpwGzgAsAOvADcouv6zu4ceG8nwb0QfZeu61Q0hPElObz/y9KtPP/pbgB+NHMUc44rSOr5W/M4rfTP6Ju1I0kTCagAP3bks2+TKlCl6nM2ljRuSOH6HE1T3f+pNi6DRBNRfBEfvqiPeKJnOtlTia7rbKrZxJLSJXyw5wPijYs5mzUzJw08ieKiYsbkjOkVFyclwBdCCCGEEEIxKrhPAIuBe3VdX9XlA2uaB/gmcBvwV13X7zqagfY1EtwLIfY3hPCFkhfexxM6P3/pS1aUVmM2afzmkglMHpKdtPO3N8DraF60V/SgsE+F4vGo0SPpmn2fqfqcSnWHSErX51idkJHX52fft9bXqnSqglW8tv01Xtv+GrXh2ubtRd4iiouKOb3wdGzm9F/XQwJ8IYQQQgjR1xkV3E/RdX31EZ9A0xzAMF3XNx7pMfoiCe6FEABVvjB1weQFqoFIjBuf+4zSSj+ZDgsPXzWZwmxX0s7fmsVkYlC209C+/T4lVKcC/ETC6JF07qD1ObdC/nhjx9aezL7vUF+r0onGoyzfu5wlW5fwVe1Xzds9Ng+zh83mvGHnkevKNXCE3UMCfCGEEEII0VcZvjitSB4J7oUQTWr8EWoCkaSdr6w+xPULV1MTiDI428mfr5pMpsOYGcNuu4V8j8OQc/dJiQQEayBUC+nw+0FH9TljL4STrlcLxaYSqwMy8mX2fQei8Si+aN+p0tlUvYmS0hKW71lOTFd3VZk0EycVqBqdsf3Gpn2NjqZpuK1uvDZvtyzMK4QQQgghRKozcnFaO3Bs48NNuq73/qlRBpPgXgjRWl0gSpU/eV3kX+6p40fPryUa1zl+SBb3XjwBi9mUtPO31j/TjsegCwd9VjymQvFwg9Ej6ZoO63OuVyF+KtXnyOz7TgVjQXwRH4FYoNdX6dSEanht+2u8uu1VasI1zduHe4Yzt2guMwbPwG5O/7U+nBYnXrsXh0UuwgohhBBCiN7LqKqcmcBCoB+gAUHgOeB2Xdf3d+dgRAsJ7oUQ7TWEolQ0JC+8f3N9Ofe+qlrOLpg4kJvOOSZp525N0zQGZTmxWYy5cNCnxcLgr4Ro0OiRdK65PucvEPGpbblj4IzbUq8+R2bfd6qpSscX8RGOp8kCykcomojywZ4PKCktYVPNpubtmdZMZg2bxfnDzyfPlWfgCLuH3WzHa/fishpTv3Y0li5VbzffDB6P0aMRQgghhBCpyKjgfhPwOXArEAGmATcBI4ATdF3f150DEooE90KIjvjDMfY3hJM2E/WJ5dtY+PFOAG48ayQXTh6UlPO2Z7OYGJTlTPv6iLQV8asAPx0WsO2wPucCtYBtKtXnyOz7LutLVTqbazZTUlrC+7vfb6nRwcSJBSdSXFTM+P7j0/510Gq24rF5yLBmpM3Hcued8Mtfwq5dUFho9GiEEEIIIUQqMiq4DwITdF3f0m7704BL1/VLunNAQpHgXghxMMFInPL6EIkkhPcJXeeXS9bz/leVmDS49+IJnDAsp8fP2xGv00q/jPSvjUhroToIVEM6hKcH1Od4GutzLkqt+hybS82+T6UxpbBANIA/6u/1VTo1oRpe3/46r25/lepQdfP2YZ5hqkancEbaV8+YTWY8Ng+ZtkxMWmrfUSXBvRBCCCGE6ExPBPdd+S35C6CjX1HvBmZ352CEEEJ0zmkzM8DrwJSEmYomTeO2845lZF4GCR3uKlnPjip/j5+3I3XBKKFoGgTGvZnDC1lDwZWjZoynsoJJcNnTcPqPwZYB4XpYei88/w0o+8Lo0bWIBKB2p3ovOuWyush15TI4czD9nP16RQd8R7Id2Vxx7BU8Putxbpl6C2NyxgCwvX47f/7sz1z7+rU8+eWTlPvLDR7pkYsn4tSEatjdsJvqUDWxROyQ+995p3rZ+fJL+OY3ITtb1dZceSXsb1feuXo1fO1rah+nE6ZOhcWL2+7z1FPqeO++Cz/5CQwYoPadORO2bWvZb/58FdoDDB6snqNpsH17y3FmzoSCArDZoKhIHS/cruFp/nywWKC8HC67TI09Oxu+/W0IdtBG9vbbcNZZkJkJbjfMmAHvv3/IT5EQQgghhOhlujLj/gfA9cB5rWfda5p2KvCcruuDe3aIfZPMuBdCdCYUjVNWl5yZ9xUNYRYsXE2VP8LALAcPXzUFrzP5/dxWs6rMMZlSPDTuCxLxlgVsU33mc6C6sT5nScu2sRfASTekVn2OM0vV56T6RZEU01eqdLbUbmHJ1iUs27OsOeQ2YWJawTSKi4qZ0H9C2lTPdETTNNxWN16bF2sH6z80zXqfOBH69YOLLoKvvoJHHoFx42DlShWcv/8+zJoFY8eqUN/hgEWL1PZnn1XbQAXu114Lxx+v9rnsMqishN/9DiZNgg8/VPt99BH89rfw4ovwxz+qsB3U+d1uOOEEGD1aPScjQz1v4UK44gr1vsn8+fD003DccWpsp54Kq1bBk0/CbbfBvfe27LtokRrnjBnqAoSuq/Fu2ABvvQWnn94DXwAhhBBCCHFUjKrKSTT+MQYsAdagZupfCdyh6/qi7hyQUCS4F0J0RTASp6w+lJTKiI1l9fzgX2uJxBJMLPTym0uPw2pOfr1BpsNKbmbvnGWblmIRFeBHjLkT47DsW9tYn9O4AKjdA9MXwLiLU6eqxmKHzAGycO0R6gtVOrXhWl7f/jqvbHulTY3OUM9Q5gyfw5mDz0z7Gh2X1YXX7m1zR0VTcH/mmfDmm2Bu/JF97DH4znfg0UfV+7FjITdXzaRv2kfX4bTTYMcO2LlTXRtrCu5PPhmWLWvZ98EH4Yc/VDP7x41re+6OqnICAXC1W2/37rvhF79Q52raf/58+Pvf4dZb4b77Wva98EL44AOoqFCP/X4YMgTOPbdt8B8MwoQJkJ+v9hdCCCGEEKnFqKqcXGAWcDsQBK4Afg6MBO7QNO2fmqbdrmna3O4cmBBCiM45bWbyPfakzLI8doCH284dDcDa3XU89NZXhgRjDaEogcihKxVEElls4CkA7yAVOqeygomN9Tm3ttTnvHdfatXnxMKqOifcYPRI0lJTlU5hRiE5zpxeWaWTZc/i8tGX88SsJ/jx1B831+jsqN/BI2sfYf7r8/l/X/4/yvxlBo/0yAWiAfb59lHmLyMYa9sjc8MNLSE7qEA8KwtKSmDtWti4Ea6+Gmpq1Az6ykqoqoLzz4fdu2Hz5rbn+u532x5vxgz1vrS0a2NtCu0TCaitVec7/XR1sWD16gP3X7Cg7eMZM9RzGhp/5N98E6qr4ZprWsZfWakC/XPOgRUr1MUCIYQQQgjR+1k620HX9SrgrcY3ADRNswMTgEmNb+cDPwayemaYQgghDsZls5CXCfsbwj0epJ8xOo+d1QGe+nAHr3xZxtB+LuZNTX5jWkVDmMJsM2apzEkdVidkDYawT83Aj0eNHlHHTGY47jIYeQ589CfY8F+o2AgvzIcxF8DJKVCfo+vQUK567925YErthTtTUdPCpx6bh2g8SkO0AX/U36uqdCwmC6cVnsZphaexpXYLJaUlLNu9DH/Uz4tbXmTxlsVMGzCN4hHFHNf/uLSs0QnFQoRiIexmO5F4P8DG6NFt97FaYfhw1TnfFMp/73vqrSP799PmGEOHtv37piqc6mq6ZMUKuP12VZHTvte+trbtY5PpwBn7rc+XmdnyMZx//sHPWVV14Cx/IYQQQgjR+3Qa3Guadg5Qr+v6yqZtuq6HgU8a34QQQhjMbbeQC+yvD/X4ub4+fSg7qgK8u6mCR98rpTDbxUkj+vX4eVuLJ3QqfWHyPeldB9Er2TPUW6gegtUQT9G7I1w5cPYdMPYiNeu+chNseAlK34Hp16dGfU64AWIhyMgHq3yvHymr2UqOOYccR06vrdIZmTWSH0z5AdeOu7a5RqcqVMXHZR/zcdnHDMkcwtyiuWlboxOOhwlEA4CNQNSPrrvaXIho+lImGgs+f/1r1T3fkfHj2z42H+THvCvfHtu2qQVkR4yABx5QFwEcDtizR90J0DSeJpp28Otw7T+GJ55QlTkdyc3tfGxCCCGEECL9dRrcA78HfgesbL1R07QJQLmu6/t7YmBCCCEOT4bdQiLTTmVDuPOdj4Kmafx49mj21YXYWNbA3S9v4E9XTqIoN6NHz9uePxzDF46RYe/KP2Ui6RwesGdCqE4F+O0TrFRRcJyqz1n3b1jxiArL37sP1i+GGbfBgAnGji8ehfo96i4AZ7YsXHuUXFYXLquLeCKOP+bHH/ETjvfsa2Yyee1eLht9GRcfczEr9q1gSekS1letZ2fDTh5Z+wh/X/93Zg6ZyZyiOQxwDzB6uEfk0y/85BdV47F5yLRlEo+Z2L5dLfY6cqTax+1WtTLd5WA/dv/9r+qeLylpO3P/jTeO/FxNH0P//t37MQghhBBCiPTTlXuvjwGWd7B9GvB09w5HCCHE0fA4rPRz93yns91q5lcXjCM3w04wGueni7+kJhDp8fO2V+ULE4unaCAMvLOhnEv/8iEn3fs2l/7lQ97ZUG70kJJL08CZBdnD1Qz3VA2dTWaYcBlc86Kqy4GW+py374JgTfLGsn05vHAtPHmeer99uZqKG6iGul2qA18ctaYqnYKMAgZmDMRj92DWUmSB4m5gMVk4ddCp3H/a/Tx4xoPMHDITq8mKP+pn8dbFfOfN7/CrFb9izf41KX3nwaryVdyy7BbmvzafN3aoNPzvf8skEo1TE6phT8MeHnksQG0tzJkDU6bAMceo2e/ta2qgZQHYw+V2q/cdVd9A2+uSiYQ6/5GaPVt19t9994HVO3DkH4MQQgghhEg/XZmmWItaoLb9Ek3Lgd92+4iEEEIcFa/LSkLXezxI75dh556LxnPjP9dQXh/mFy+t44F5E7FZktfHHU/oVPkjKVmZ886Gcm5/8UsqfCHiCdjfEOL2F7/k18BZY/KNHl5yaZoK7h1ZKgQP1XathyLZnNlw9i9g7IWw7D6oSHJ9zvbl8O49ao0APa7ev3sPnPlTGHYqxCJQt1tm33czm9lGjjmHbHs2gViAhkgDoVjP144ly4isEdw45Ua+Oe6bvLHjDV7Z9gqVwUpWlq1kZdlKBmcOZs7wOZw15CycFqfRw222qnwVD695mJpQDQkSVAWrANhbEeYbF+Uza26AHaVWnn7cyeixEb52RQNxPDz5pJVZs2DsWPjWt2DYMCgrg48/hvXrYevWwx/L8cer97ffDvPmqV794mI491yw29VFg+9+V4X2ixZ1HLh3VWYm/O1vcOWVMGGCWqR24EC1sO5776l93n33yI8vhBBCCCHSR1fSlcXArR1sNze+CSGESDHZbhtep7XHzzMyL4PbzxuDBqzbW8/v3tiU9Nmb/nCMhlDqLYT6yNKtzaE9QDwBFb4Qjyw9gtSotzCZwN0PsoeBw5u6wXPBcTDvaZhxq6r7aarPef4bUPZFz533kydaQntoCe8/eaJln+bZ97tVkC+6jaZpuK1uBrgHMChzEF67F7PR6xx0I6/dy7xR83h85uPcdsJtjOs3DoBdDbt49PNHmf/afB774jH2+vYaPFJl0aZFzaE9gI76t2XU9XeRXxDn9/dkseiZDGYXB3j6xXIiWgN7GvYwanIFH3wU5fTTVQC+YAH89a/qR+eee45sLGeeCT/9KXzyiequv/JKNfP9mGNUXY7LBT/5Cdx/vwr5//GPo/vY582DZctg1Ch48EH43/+Fp55S9Tm3dvS/MiGEEEII0StpnQUsmqblAGuBz4Bf6Lq+RtO0DOBRYKiu66f1/DD7nqlTp+qffCJr/wohjk5FQzgpofZzK3fy2PvbAPj2qcO56sSDrKjXQ0yaRmG2E4s5ebP9O3PSvW+zr+7AWbsFXgcf/eRsA0aUguJRFUyHfUaP5OCCNfDhn9TM+yZjLoCTb1Cz3rvTk+eBv4Olg9x5cO2rB25vupOhu8ch2ghE1Sz8YCxo9FC63ba6bZSUlrB011IiCXUhSEPj+PzjKS4qZlLeJEyaMa+r81+bT1Woqvlx+Yvfp+KlBUx/eB7PXd35Tb8OiwOv3ZtSdxEIIYQQQojeS9O0T3Vdn9qdx+z0N3Fd16uB6Y37fqppWhioA84FftydgxFCCNG9cjPtSVm89YoTBjNrrKp/eXz5NpZ9ldwS3oSuU+lLrdnHg7KctL+OYDap7aKR2QqZA8BbCNYU/bw01edc8iTkjlbbNrwEz1wEXyyCRLz7zpU5ANr3rGtmtb0jug7+KqjdJbPve5DL6iLfnU9hZiHZjmwspt6zIPZw73BumHwDT577JPPHzSfXmYuOzifl0UDYfQAAIABJREFUn3DHR3ew4O0FlJSWEIgGkj62XFcupg7+q5LjzOnS80OxEOX+cvb69uKL+FK6y18IIYQQQoiOdGkKja7re3RdnwMMBeYBc4FRuq5/1JODE0IIcfRyM+24bD0bNGmaxs0zRzF+oAeA+17ZyObyhh49Z3uBSGpV5iw4YwS5GY7m8N5sgtwMBwvOGGHswFKR1QHeQeAZCBab0aPpWIf1Ofer+px9n3fPOaZeB65+LeG9ZlaPp1536OfFwmrh2mAHq3GKbmMxWfDavRRmFpLvzsdtdaOlat3TYfLYPFxyzCU8NvMxbp92OxP6TwBgj28Pf/38r8x/fT5/+/xvSa3RuWz0ZWQ7spvDew31uS4eUXxYx4nEI1QGK9nj20NduI6EnroLmgshhBBCCNFap1U5whhSlSOE6E66rlNWHyIY6cbZwR2oDURYsHANZfUh+mfYeOTqKfTPsPfoOVszmzQGZaVOZc47G8p5ZOlW9tQGGZTlZMEZI/rewrRHItygKnTiMaNH0rEO63O+BifdoKprjsb25arTvqFMzbSfep1amLarrE7IyAdz75kVnsoSegJf1Ic/4iccP4oVSVPQ9rrtlGwr4d1d7xKJt9zRcXz+8cwtmsuUvCk9XqOzqnwVizYtoiJQwf7FC9jwz8v4cN0uCgYd+b9lZs1Mhi0Dj83Tq9YwEEIIIYQQxuqJqpwuBfeapl0HfAJ8qet6z6Y+ApDgXgjR/RIJFd6Hoj37Mr6t0s8Nz60hEIkzOj+TP1w+EYc1eeFIht1CnseRtPOJHqLrEKqDYDUkUnSGbNkXatHaio3qsT0TTlwA4y8BIwNBTQN3Ljg8xo2hD4rGozREG/BH/MR70a/LDZEG3tzxJi9ve5n9gZY1GAa6BzKnaA7nDDkHl9Vl4AiPTNNixF67F6up5xdzF0IIIYQQvZuRwX0C0IEwaqHaT1q9rddl2n63k+BeCNETEgmdffUhwj0c3n+8rYqfvvglCR3OGJXLz+aOwZTESol8jwN3Err9RRIkEmqGe6hWhfmpJhGHdf+BFY9AuF5tyx0Np9+m6nWMZHNDRp6xFxH6IF3XCcaC+KI+grFgr+lWj+txVpWtoqS0hLUVa5u3Oy1Ozh58NnOK5lCYWWjgCI+cy+rCa/diNyfvDjEhhBBCCNG7GBncPwpMA8YDTUlI0xMDwGe0CvN1Xd/UnYPsiyS4F0L0lGSF9y98uptHlm4F4BvThzL/lGE9er7WLCYThdlOTKbe0T8tULU5gSpVo5OKgjXw0Z9gfav6nGOL4eQbj74+52iYTODOA3uGcWPow+KJuKrSifrb1M2kux31OygpVTU6rSuCJudNpriomOPzj+/xGp2e4LA48Ng8PXoHwbBhMHIkvPVW9x1T0+COO+DOO9Xjp56Ca6+F99+HUw+jaUsIIYQQQhy5ngjuu7o47fd0XZ8CeIHTgVuBrYAGuIFTgJuAfwDrNU2r0zTtne4cqBBCiO5hMmkUeBzYe7i+5pIpg5h7XAEA/1ixg7c37O/kGd0nlkhQ5e89IZlAdbZn5kPWYLClYC2HMxvO+gVc+hTkjlHbNi6BhRfD54vUzHwjJBKqL7+hPHUrh3oxs8mM1+5lYMZABmYMJNOWiVlL/zsghnqGcv2k63lq9lNcN/468l1q7Y41+9dw14q7+N5b32PxlsX4o/5uO+fa1Tbu/HEO5548kHGDhjB9TOH/Z+++w6Mq0z6Of89MpqVMGj0BYkCpSscCAmLBArbXgl3sZcV17bu6tkXXdXXVXevaVtfuumiiCKyKqAiIgEpRhNCFkJ5My7Tn/ePJkEICCcxkJnB/rmuuyZyZc85zBpNx7nOf38Pl53bhh2XRm9DaF/Sxw7ODra6tuPyuvb5aYvlyXUTftClqQ0sIr7wCTz4Z71EIIYQQQhw42tQKo5TyKqW+Uko9AixCd91nAccBtwFvAWvQxfzxUR6rEEKIKGmP4r1hGEyf2JehPTMA+Mvsn1i9rTpm+2uqxheIeZ6/iIMkGzh76FtSAsZadDsUzv4XTLgTbE59hcD8h+Gdi2Db93teP1Zqa6ByI/g98RvDAc5qtpLtyCY3LZcuyV1ItiRjtGOEWCykWlM5ve/pPHf8c9x9+N0M7TwUgG3ubby44kUu/eRSnv7+aTbXbN7nfT37eDqF/01h1FE+7nqwnEuvqWbtzxbOOLY7n89x7PP2GwqEApR6S9ni2kJVbRVh1baTXsuXw333SeFeCCGEEELsm30OAFZKVQKf1d0AMAwjBRiyr9sWQggROyaTQTennV8rvQRCsenETTKbuHfKQK5/YxlbK73cNXMFz1wwvN0mjy2pqSU309Hhi2OiGdZkfautAXdp/Dram2Myw+CzoM+x8M0/YNVMKP0Z/nMZDDgVjrwhPvE54RBU/wqODEjO1vkaot0ZhkGyJZlkSzKhcAh30I3b724UOdPRmA0zo7uPZnT30Wyu2UxhUSGfbfoMX8jHrPWzmLV+FkM7D9UxOt1G7NVVB1dcX80TL5RgbdBgf+5FLo4/PIdH/5TBMSd4o3hEWigcosJXQVVtFanWVJxWJ0kmmT9FCCGEEEK0j5iETyql3EqpBbHYthBCiOgxmwy6p9uxmGOXRex0WJhxxmBSbGYqPAH+MHMFXn/7FFkDoTAVnkC77EvEiS0NMvN0ITzRCtGOTJh4d+P4nNUfxj8+x1sJlZsg2HELxfsLs8mM0+qke2p3clJzyLBldPjCcM+0nlw75FpenvQyVwy+gm7J3QBYXrKcBxY9wNVzr2bm2pm4/K42bXfE4bWNivYAmVlhjhjr45ef9xyX897rqRyUkceCL+z8+Z5MRh3Sk8G5vbjuks5UV5oIBuGR+zM4vH8u/bv14oqpXSgr1Z+NYRWmuraara6tvP9xFcccEyYtDVJSYPx4nSUfce+9Ol8e4Oij9Z8lw4B58xqP59tvYcwYcDigZ0947LH650IhyMmBk09u/lgGDmxddn1tLdx0E3Ttqsd68smwdm3j11x6qc7db+qVV/S4N2zQj/Py4IsvYN26+mOKrOf364z90aMhK0sf09ChehtN5eXBccft/vgjlIJnnoEhQ8Buh+xsmDp1/7uSQQghhBCiJR1v1ighhBBRlWQ20dVpxxzDiVx7ZSVz75RBmAxYV+LmwVmrCe9ldnBbVXkD1AYTqBtbRJ9h6MJ9Rm9dyE80iRifEwpA1RbwlMdn/2IXFrOFDHsGuWm5dEvpRqo1tUNO8BqRak3ltL6n8dzxz/HHI/7IsC7DACj2FOsYndmX8vTyp9lUvW9V2OLtZjKzWv83/sG7M/n+Oyu/uaWSM89z8cmHydx+QzZ3/S6bZd/auO53VZx7kYvPZju4/47GV8YUvO/g7ClOakO13HZ3DffeH6SyEo49FubP168580y46ir98913w2uv6duAAfXb2bQJpkyBo47SBes+feDmm2H2bP282QwXXQRz5kBxcePxL1kCq1fDxRfv+VhvvlmfVLjzTvjtb/UYJ0yA8r34tX/8cejfX58EiBzT44/r56qr4dln4Ygj4IEH4KGHICNDn8D45z933daejj/ihhv0behQva8bb4RPP9XrlZa2/RiEEEIIIToaY28nXTIM4zXgfKVUx59hKwGNHDlSLVmyJN7DEEIcQGqDIbZV+mJaUP9g+a888ekvAJw3uidXHp0fs301ZE0ykZMhkTkHjIAP3CWJ2VHuraiPz4noPwWOmh6f+BzQcwWkdoWk6E3yKaJDKYUn6MHld+EL+fZ6stREsblmMx8VfcRnmz/DG6yPthnSeQiT8yczqtuoNsXoLF5gY+op3bjsumrumlGx29e+93oqt17fiVFH+njro+2Y6s6J/GZaZz6emczhY3y8/mFxo+WzC5JZvmETKakKj9tgzOBcxh/n5fF/NqgaBxwce0QXunU1+PprveiVV3TR+ssvd+2Mz8uDjRth1iw48US9rLYWevWCcePg3Xf1sp9+0sX+xx7TXfMR06fD88/D9u26OA763OU99+hu/4b779cPli3TXe0Ac+fCCSfAHXfo4jrojvt58+o76yMi21i/vr6zfsIE2LJl1679UAiCQbA1mXbk+OP1dn/5pe3H/803ukD//PNw5ZX1669cCcOGwa23wowZCCGEEEIkDMMwvlNKjYzmNlvVxmMYxtGGYSRHc8dCCCESiy3JTLd0e0yL26cN7cHpQ3sA8ObizcxZuT1m+2rIHwxT5ZXInAOGxQ4ZPSG1i86bTyTNxef8VAD/PgN+eBvCwfYfU7AWqjbrCB2RUAzDIMWSQteUruSm5pLlyMJq7rgnWHqm9eSaIdfw8qSXufLQK+me0h2A70u+Z8aiGVw19yre/+X9VsXo7Nhu5sYrOtMjN8T021r/3+7US2p2FucBho+qRSmDsy907bI8GDT4dYuOLvrqcweVFWZOP8dNeZmp/lZdyxHjXCxcqCiv9rVqDHl59UVr0AXvI46AoqL6Zf37w+GH6872iGAQ3noLTj21vmi/O1dfXV+0B11IHzgQCgtbNcxWM5vri/aBgO7oLy2FiRN1kb+qqvHrW3P8b7+txz5lit5W5Na1qz4h8dlnCCGEEELs91obovkFEDQMYyWwEFgMdInZqIQQQsSF3WKmq9NGcXVtzDo7rz+mL1sqvCzZWMGjc9fQI8PB4Jz0mOyroQpPgGRrEtakjhs9IdrI7tTROd4KfUukbuVIfM6qmboDv7Ya5v9FPx5/O3Qf2r7jUUpP8hvwQEoXMHfsnPX9USQP32l14g/5cQVcuANuQok0MXMrpVhSOLXPqUzOn8zS4qUUFBWwdMdSdnh28PLKl3n9p9c5pucxTMmfQm9n713Wr64ymHZ2F9wuE+/M2oYzXf9u+/1QVdH4ZF16ZqhRNn6P3MYnx5zp4d0ur6rUnxlF6/TvxLSzu7Z4XD9tLiE/z4I/lAW0fIKluUz5zEz44YfGy6ZNg2uu0V3mgwbBJ59ASQlcckmLm26kX7/ml82d27r12+Jf/4JHH9VjDTeZ776qCtIbfMy35vjXrAGvF7p3b35/+e1zwZ4QQgghRFy19lvZp8AwYEjd7arIE4ZhfAUsA5bW3VYqpeLQLiaEECIakq1JdE6DHa3sHGwrs8ngj5MH8ps3l7Gp3MMfP1jJ0xcMp1u6PSb7i1BKUeqqpUeGY88vFvuPSP69zQmeUqht24SYMWUyw+D/gz4T4ZundNG+dA3853LoP7kuPie7fcfk90BwE6R0Tsz5AgQAVrOVLHMWWfYsPAEP7oAbT9DT4aJ0TIaJkd1GMrLbSLa6tlJYVMinmz7FG/Qye8NsZm+YzWGdDmNy/mRGdx+N2TDj9RhcPrUrRWstvPp+Mf0H1V9NtXSRnfOmdGu0jzcLtnPE0fWfZ+YWLsJpaXnkLVVhfTXaw38vpUfP5r/qZHcK4wv6qK6tBjrhC/qAXT/b9rSviKlTdTb9q6/Cww/r+y5dYNKk5tdvqrkL6Jruo6WL7EJtOB/09ts6cueUU+B3v9Nd8RYLfPwx/O1vuxbyW3P84bC+qiASndOUQz7KhRBCCHEAaFXhXil1PIBhGPnAyAa3YcBRdbfI/2r56zrzv1NKXR31EQshhIi5VFsSoVQbZa7WZYQvXFfGG4s3UVzto6vTzvmje3FEn5YLjqn2JGacPpjr31hKpTfAH2au4MmpQ0mxxbbL1xcIUenxk5HccaMmxF4yJ0FaN7AnYP69IxMm3gWDToc5d+nYmp8K4eePYeAZMP42MLVjB3w4DDXF+iRHIsYNiUaSLckkW5IJqzDugBt3wF1XMO5YclJzuPqwq7lowEV8tvkzCosK2erayg+lP/BD6Q8kmZIY23UiS/98L8u/tfHc6zsYdWTj3+MBh/p5beb2XZZFQ+98fYIgMzvE2Am7f38NQ38tqvBVsM0F6bZ0ki1tTx1NT4fTT4fXX9e59AUFugM/qZV/Dn76CU46qfGyNWsad7xnZkJlM0lDTTPvoeUi/1tvwUEH6fE1fM2+xNn07asn5x01qnG3vhBCCCHEgaRNeQFKqSKl1DtKqduUUhOVUplAf+BC4AngayAADAeuiPpohRBCtJt0h4XMVhS4F64r47G5a1i1rZoSl59V26p5bO4aFq4r2+16OZkO7jt1EGaTwfpSN3/6aDWhcOy7RSs8AXyBjhctIaIkkfPvvZV6Yl3qKl8qDCv/A/8+E35d1v7j8buhciPU1rT/vkWbmQwTadY0uqV0IycthwxbBkntecInSpItyUzOn8xlgy8jzVJ/1UcgGObVP0xiwbxkjr/9dQ46YtUu66ZnhBk7wdfolp4R3uV1e2PcRC/O9BD/+GsGtc2c9ysrrf9a5UjRn2XVVSZqQ7Xs8OzgV9evuAPuNu/30kth61a49lrw+eDii1u/7vPP67iZiLlzYdUq3Rkf0bevjrJZ1uBPjMulo2+aSklpvsgfmRugYWd9WRm89FLrx9rU1Km6A/+Pf2z++dLS5pcLIYQQQuxP9vn/5pVSa4A1wBsAhp7VcAAwYl+3LYQQIr4yU6yEldrtxK5vLN5EucdPpOYeVlDu8fPG4k277boHGNIzg5uOO5i/zlnDovXlPD+/iGsn9InmIexCKcWO6lpyMh2YTbGbiFckuETMv1/yInjLqb+IsU71Vnj/ivjE50S67/1uHZ+TaCc7RLMsJgsZ9gwy7Bl4g96dnfgdKUrn3TXvNip0b3/rFqqXnEDKoAX8WLKSi2esJDc1l2FdhtEnow8nTfGRnBK740tNUzz4RBk3Xt6ZE4/K4YxzXHTpHmL71iQWfa1nZn2zsBiAwUP8GIbimb+lU1luwmqDI8d58XcuIaRshMKglLlVk8Effzzk5uo4mkMPhWHDWj9mmw2OPhouugh27IAnntCZ8bfeWv+a887T3fxnnAE33qgnl33pJR13s3lz4+2NGAEffQS33KJ/Tk3Vk8eedhq8/z5MnqyvENixQ5806NEDiotbP96Gxo6F6dPhySfhxx/h5JP1/tavhw8+0IX9e+/du20LIYQQQnQUUW/DUfobwaq6mxBCiA4uO9WGAqpbKN4XV/to2igfVnp5a5x8aHc2lnl497stvPvdFnplJXPKYS3MRhclwXCYkpramOfqiwSXaPn3NdtBNXM1iCkJwkEdn1M0Dw6/Fg49q33jc2pd9RPX2lLbb79inzmSHDiSHGTaMnEH3bj8Lvyh6MTHxFKJp4Qw9S3c3o39AXCvPAr3yqMA2AIsrHu+tNtzTD3yCNJtsctVOeV0D926b+fpx9J56RknXq+Jzl1CHDa8lnMvqv/70SsvyD0Pl/PCP5zcfkMnQiGDNwu206mzD6UgEA6wxbUNp9VJmjUNk9HyRdAmky68P/RQ27rtQU8WW1AAM2boLvpx4+Dvf4fsBuf+MjNh5kydTX/77ZCTo39OS9OT4zb0u9/p+J0XX9Tb7t1bF+4vvlh32D/1lC7+9+qli/vp6btuoy2eeEKfIHj66foifc+eMHEinHPO3m9XCCGEEKKjMPbUeWMYhkMp5d3ti/a0kyhs40AzcuRItWTJkngPQwghdiqpqaXGt2vxfvqby1i1rbpR8d5kwMDuTp48r3WtgaGw4u4PVrCwqByzyeCRsw5jaM+MaA29RdkpNtKTLTHfj+ggAj5dwA/EKR/8vWlQvLJx8d4wQ5eBMOBU+OYfUFull2cfDONvhx5taL+NFluqdN93cLWhWmr8NXgCHsIqOlEy0Xbr/FtZU76mUfHehIlDsg7hL0f/heUlyyksKuTb7d+i6q5SsZqsjM8dz+T8yeRn5Mdr6G0SiThKs6a1GG10zz26+L55s+6YF0IIIYQQiccwjO+UUiOjuc3WZNyvNwzjRsMwbG3duGEYQwzD+AC4pe1DE0IIkUg6p9lIte9aVDh/dC+ykq1EUmdMBmQlWzl/dK9Wb9tsMrjrlAHkd0ohFFbc++FKtlbE/nxvuccvefeinsUO6bl6EltzHLLBR16uY3CMuoK4YdaPR10Bg8+EC9+HQWcCBpT9ouNz/ncPeHY/n0TU1bqgclP8r1AQe81mttHJ0YnctFyyHdnYzG3+3/yYO6ffOWTaMzHVfV0xYSLTnsk5/c7BMAyGdRnG3UfczXPHP8fpfU4nJSkFf9jP3E1zuXHejdzx5R18tfUrQuHE/hsfVmGqaqvY6tpKqbeUQLjxCfJgUEfXnHCCFO2FEEIIIQ40rem4fxW4AKgC3gbeARa21EFvGEY+MAm4GBgNbAYuVEp9FcVx7/ek414Ikah2VPtw1QYbLVu4row3Fm+iuNpHV6ed80f32mO+fXO2V/u4/vWlVHgC9Mx08NT5w5s9WRBNFrOJnAwHJsm7Fw0pBb4qnTkfbseO5A1f6az7mu36BMLIyyFvbOPXFK+EL/4MO+pSCa2p8YnPAbCm6O77eJzoEFHlD/mp8dfgDrgTpgv/2+JveefndyjxlNA5uTPn9DuHUV1HNftab9DLvM3zKCgqYHNNfTh7J0cnTso7iUl5k2IaoxNNKZYUaqvSmf+5lYICeOst+N//4Nhj4z0yIYQQQgjRklh03O+xcF+345HAg0DkfxdDwGpgG1AB2IFsoB/QCTCAYuAJ4G9KqdpoDvpAIIV7IUSiUkqxvdqH1x+bLsYVW6u4+d3vCYQUI3pn8uczD435JLKp9iS6pEnevWhGOKyL976qxJjANiIcglUfJEZ8jmHoKwMcsY+3ErGnlMIdcOMKuPAF4xQbtQ+UUvxQ+gMF6wpYvH3xzhgdi8nCuNxxTM6fTN+MvnEe5Z4t/NLOeVO60amT4uabDe64I94jEkIIIYQQuxO3wn2DARwMXI4u4A8FmoablgDzgf8A/1FKNT+TodgjKdwLIRJZOKzYVu2jNkYxM3NXFfPQrJ8AOG1oD2489uCY7KehLk47qTbpGhYtCAV0JE2ixcN4K2HhU7Dyv1BXoKTfKTDmRl1Mb09JNkjtou/FfiEQCuAKuHAFXAkfOdOc7e7tfLz+Y+ZsnIM74N65fEDWAKbkT+HIHke2mCufSGxmG+m2dJItyfEeihBCCCGEaEHcC/dNBpMM5KA77b3ADqXUtiiO7YAmhXshRKILhRW/VnoJhGITqfDiV+t5fdEmAKZP7Mvpw3Jisp8Is8kgJ8NBkrk107+IA1bApzvw/Z54j6SxXeJzUuric85u3/gcwwB7BiRn6Z/FfsMT8OAOuPEEPezt94d48QV9zNsyj4J1BWyq2bRzeZY9i5MPOplJeZPIsCX+FSMWswWn1UmqJRVDfr+EEEIIIRJKQhXuRWxJ4V4I0REEQ2F+rfQRjEEGeFgp7itYxZe/lGIy4KEzD2VUXlbU99OQw2qme7ojpvsQ+4lELOAnUnyOOQlSuoBVOoT3N2EV1lE6fhe1oY6VhqmU4sfSHykoKmDxtsWE0Z9dSaYkxuWMY0qfKR0iRsdsMuO0OkmzpmEy5GSzEEIIIUQikML9AUQK90KIjsIfDLOtyksoHP3PE28gxG/fWs4vO1yk2Mz847xh9M5Oifp+GspOsZGebInpPsR+JFgLnnLwu/f82vbirYSFT8PK92kUn3PUdEjp1L5jsaXpyWtNUlzcH3XkKJ1idzGzNsxi9obZuAL1EVj9s/ozJX8KR/U4KuFjdMyGmVRrKk6rE7OpaYKpEEIIIYRoTwlTuDcMIwWdcW8H1imlNkRzUEIK90KIjqU2GGJ7lS8mxfuSmlque30pZW4/PTLsPHX+cNIdsSusG4ZBjww7tiQpgog2SMQCfvGquviclfqxNQUOvwYOPad943NMZp19b43tSTcRXx01SscX9PHFli8oLCpkQ/WGncuz7FmcmHciJ+adSKY9M34DbAXDMEi1pOK0ObGY5MSzEEIIIUQ8JETh3jCM0cAHQJcGi6uAZcB3wFLgO6XUL9Ea5IFICvdCiI6mNhhiW6WPcAwKNj9vr+HGt5fjD4YZkpvOX846DEsMs+itSSZyMhySISzaLlgL3orEmcRWhevic/4Ovkh8Tl8Yf0f7x+fY0nTHv3QG79fCKqy78P0u/CF/vIfTakopVpStoLCokIW/LmwUozO2x1im9JnCIZmHxHmUu2cYBimWFJxWJ1azNd7DEUIIIYQ4oCRK4f5r4EjgfWA1kA8MBw4GTOy8JpsapVTiz/KUoKRwL4ToiHwB3Xkfi+L9vJ93cH/hagBOHtyNm084JKaF9YxkK1kpUvgQeylYC56yxMnAbzY+52Q46sb2jc8xmXV0ji21/fYp4sYf8lPjr8EdcBNWsZnIPBZ2eHYwa72O0akJ1Oxc3i+zH5PzJzMmZ0zCd7Y7khyk29KxJ9njPRQhhBBCiANCohTua4B5SqkpTZanAsOAEcBIYLhSamC0BnqgkcK9EKKjimXx/tVvNvDKgo0AXDs+n7NH9oz6PhrqkeHAbpHuYLEP/B5dwA8myCSexatg/p+hOM7xObbUuux7+f06ECil9IS2ARe+oC/ew2m12lAtX2zWMTrrq9fvXJ5py9QxOgedSJY9tpOm7yt7kh2n1UmyRSaKFkIIIYSIpUQp3G8HXlFK3RHNgYjGpHAvhOjIYlW8V0rxp49W8/nPJRjAn04fzJF9sqO6j4YsZhO5mRKZI6KgtkYX8EPBeI8kceJzTCZI6SLd9weYQChATaAGt99NSHWMCW2VUqwqW0VBUQHfbPtm59UDSUYSY3LGMCV/Cv2y+sV5lLtnNVtxWp2kWFLkM00IIYQQIgYSpXD/BpCslDo9mgMRjUnhXgjR0cWqeF8bCHHTO9/z0/YaHBYzfz9vKPmdY1f4S3dYyE61xWz74gCilC6Ue8shnACxIb4qWPgUrIhzfI503x+QlFJ4g15q/DV4g954D6fVSjwlzNowi082fEKNvz5G5+CMg5mcP5mjc47GYk7cGJ0kUxJOm5M0S5oU8IUQQgghoihRCveDgG+BI5VS30dzMKKeFO6FEPuDWBXvy1y1XPcKjxFFAAAgAElEQVT6MkpctXR12nj6guFkJscuj757ugOHVYqKIkrCYT2Bra9SF/Pjrbn4nNHXwGHtGJ8j3fcHtGA4iMvvoiZQQyjcMbrw/SE/87fMp6CogKKqop3LM2wZTMqbxEl5J5HtiN0VYfvKbJh1Ad+ahsmI3WTvQgghhBAHikQp3M8A+gFjgUuVUp9Ec0BCk8K9EGJ/Eavi/dodLqa/uQxfMMygHk4ePXsI1qTYFB+STDoyx2SS7kQRRaGg7r73Vcd7JC3H54y7HXKGt984pPv+gOcJeHAFXHiDXtr6PSUelFKsLl9NQVEBC35dsDNGx2yYGdNjDJPzJ9M/q3/CdrebDBOp1lScVidJ7TnPhRBCCCHEfiZRCvdh9PXURt39JmAO8B2wBPhBKZUAAa4dmxTuhRD7k1gV77/6pZR7PlyJAo4b0IU7T4pdcSTVnkSXNHtMti0OcMFacJdCIAHiQlqMz5muC+rtQbrvBRAKh3AFXLgCLgKhQLyH0yql3lJmrZ/F7A2zqfJX7VzeN6Pvzhgdqzl2V4ftC8MwSLGkkG5NT4ion3vvhfvug82bITc33qMRQgghhNizRCncHwcMA4bX3fcFTOz8docfWAF8q5S6LnpDPbBI4V4Isb/xBUJsq/JFvYPyzcWb+OeX6wG4fGweFxzeO6rbb6iL006qTToSRYz43bqAnwhFyqbxOZYUOLyd43NsaTprX7rvD3i+oA9XwIU74O4QXfj+kJ8vt35JYVEhayvX7lyebk1nUt4kTj7o5ISO0UmxpOC0ObGZ4ze/ixTuhRBCCNHRJEThfpcNGEYKMJT6Qv5wYACQpJSSb1p7SQr3Qoj9kdcfYnt1dIv3Sike/uRn5qwqBuDeUwcy7uDYdAabTQY5GQ6SzJIHLGLIVwWesvhPYKvCsPpDWPBkfXxOVh8Yf0f7xeeYzLrTX7rvBRBWYd2F73fhD/njPZw9Ukrxc8XPFKwr4OtfvyakdH6/yTBxVI+jmJw/mYFZAxM2RseeZCfdlo4jydHu+5bCvRBCCCE6mrgU7g3DmAosVUqtafVGDcMKDFZKLd3H8R2wpHAvhNhfefxBiqtro1q89wfD3PLu96z4tRp7konHpw7lkK5pUdt+Qw6rme7p7V/EEAeYRJrA1lcFC5+GFf9h5wWWh5wEY25sv/gc6b4XTdSGaqnx1+AJeHbmyieyMm8ZszboGJ3K2sqdy/PT85mSP4VxueMSNkbHaraSbksnxZLSbvuUwr0QQgghOppYFO5b0zL4BrDaMIwqwzC+MAzjMcMwLjQMY6DRQnuIUsovRXshhBDNSbYm0SXNFtUOQ2uSiftPG0Q3px1fMMxdM1dQ6qqN2vYb8vpDVHkTIMpE7N9MJkjJhozeumgdT/Z0mHAnnPMadB2sl62ZBf/+P1j+evtE+9TWQOUmfS8EYDPb6OToRG5aLp0cnbAnJfYcJNmObC4ccCEvnfASNw2/ib4ZfQEoqiriiWVPMG32NF5d9SolnpI4j3RX/pCfEk8JW2q2UOOvade4IpcLrr4asrIgNRXOOgvKyhq/ZulSOPVUyMwEhwNGjoSZM+ufVwpOOAHS02Hjxsbrnn46pKTAL7/ox+XlcNttMGQIOJ36uaOOgsLCXcdmGHDFFTBrFgwbBnY79O0Lb7yx62sDAZgxA/r3B5sNunWDq67S+xNCCCGEaElrOu5/R30ETj8a59l7ge/RE9MurbutVKruOlCx16TjXgixv3PVBimpiW7n/fpSNze8uQyPP0S/rmn87dwh2C3R79A1GQY5mQ4sEpkj2kuwVsfn+D3xHcfO+Jy/66sBoC4+53bIGdE+Y7Am68lrzTLfhGgsEA7gCXg6xIS2kRidwqJCvtr6VaMYnSO7H8mU/CkMzE7MGB2zYcZpc5JmTcNkxOZzMNJxP2IEdO0KJ58Ma9fC3/8OZ58Nb76pX/fll7ooP3AgnHeeLp6/845e/sYbehnA1q1w6KG6IP/ZZ7ro/tJLcPnl8NRTcF3dzGxLluiTA2efDX366BMHr78O338Pn3yi9xVhGHDYYbB9O1xzDXTuDC+8AD/+CCtWwIAB+nVKwWmnwdy5en+HHQZFRfpYDj4YFi7U4xZCCCFExxb3jHvDMJLRefYj0IX8nXn21Bfza4Efge/ac3JawzAswHV14xsGDAQswJVKqRdaWGcMcBpwDJAHOIFfgU+BPyul1ja33h7GcQlwfd3+Q8Ay4K9KqWb6NFomhXshxIEgFsX7hUVl3DVzBWEFEw7pzN2TB8Sk8CGROSIuAl5dwA/44juOeMfnGAYkZ4EjM/b7Eh1SR4rSKfeV88n6T5i1YVajGJ2DnAcxpY+O0YnnRLEtMRkmUq2pOK1OkqI8aXWkcH/uufDWW/XLf/tb+Mc/dNe906kL9p07w+efg7nuPL1ScPTRurt+0yb95wLg7bdh6lT461/hzDN1Ef+oo3RBPqK2FpKS6rcF4PfrjvoePXTxPcIw9OuWL4fBdRcjFRdDr14wfTo88gjk5ekrBVauhDlz4Pjj69efMwcmTYLnn4crr2z9e2MYcM89+j0SQgghROKIV1TOTkopj1JqgVLq70qpaUqpIUAacATwG+AlYDW6eH51NAfaCinA48ClQDdgeyvW+Q9wM+ADXgf+ji7cXw4sNwzjyLYMwDCMvwKvAN2BfwL/Bg4FCgzD+E1btiWEEAeCVFsSnaMcm3NEfjZXj+8DwLw1Jfzrm417WGPveP0hqn2J3dEp9kMWB6TnQlo3SIpjHna843OUAncZVG7WVyMI0UQkSqdnWk86J3fGkeRIyO51gCx7FucPOJ+XJr3EzSNupl9mPwDWV6/nyWVPMm32NF5Z+Qo7PDviPNLGwipMdW01W11bKfWWRvUqh61b9f3ixbrwnZMDkyfr+1BIF+S//x5++gkuuAAqKqC0VN/KynSH/pYtsKbBLG3nnqs78P/wB124T0rSXfcN2Wz1RfvaWr2t6moYNw6++27XcY4bB8GgLqJv2qSvDujfX3fURxQXQ36+Lv5HxlhaCsOH6/iezz6L2tsWF8uX1x+/EEIIIaKrTR33rd6o7n4/tD1z7usmxD0WWK6U2mYYxr3APey+4/524DWl1K9Nlv8emAGsUEod2sr9HwV8DawDRimlKuqW56GjhFKA/kqpDa3ZnnTcCyEOJNHuvFdK8djcX/jox20A3HXKACb27xKVbTdkMgxyMx0kSWSOiBdfte7AD8cxpVCFYXUBLHgyPvE5hqFPJCRn17fWCtGMUDiEK+DCHXDjD/njPZzdWlOxhoJ1BXy19SuCKgiACROHdz+cKX2mMDh7cEKeiEi2JJNuS9/nKwQGDoTVq+Gii3T3fEUFPPccbNig5+6eN08XxM89d/fbmT9frx9RWalz6MvK4N//1kX/hpSCxx7T+4rk3kcYht53w8eXXgrjx8O0aTqeZ+xYmDBBP/f557rjvqQEPLtJOZs4ET79dI9vSaP9JlLH/SuvND5+IYQQ4kAVi477mISDKqUC6Lz7dqOU8gOz2rjOwy089TBwFzDYMIxspVRZC69r6Jq6+xmRon3dPjYYhvEUcDcwDX0yQQghRAOpNv1xtKM6OvEfhmEw/di+bK30sHxzFX+Z/TPd0+0M6O6MyvYjwkpR6vLTLV3CaUWc2J168lpfJXgrGleV2othgoGnQf4EWPQM/PgelK+D/14Fh5wIY34b2/gcpcBbCX6Xzr63JsduX6JDM5vMpNvSSbel4w/5dRHf796ZL59IDsk8hJtH3sxlgy/jkw2f8MmGTyj3lfPNtm/4Zts35DnzmJw/mfG54xNqYl5PwIMn4MGeZMdpdZJs2bvfxyOP1IX7Bx+E3Fy97PLLdSZ8RYX+tY/8uXvwQRg1qvntRCJsIhYtqp/c9scfd339X/4Cd9yhTxjccw906qQ78F9+uflJZ80tTKMT6UPYskX/3L+/zrRvTuZeJH7Nm9f2dYQQQgjR8UiLYPMUEKz7ubX/Jz+x7v6TZp6b1eQ1QgghmojE5kSLxWzi3imDyMlw4A+GufuDlVE7MdCQxx+kZj+JzPlsdTFnPbOAIx/6lLOeWcBnq4vjPSTRGoahs94z8nTue7y6cO3pMP6OJvE5n7RffE4oCNW/Qs32lq9AWDMHXjwBHhuo79fMie2YRMKymq1k2bPITculk6NTQhW/G8q0Z3Je//N44YQXuGXkLfTP6g/AhuoN/GP5P5g2exovr3iZYndi/b32BX3s8Oxgq2srLr9rj1fUzd8yn4tmXcRx7x7HRbMuojyos3LGjIGUFF3cPvlkHUUT0bevvk9JgeOOa3zbskXnyS9bBrffDt266cid00/XxfzLL4eHH9aFeYcDTj1Vd8a/9ZbumH/1Vd2Nn5SkTwy8847e1/jxurM8YulS3W0OurPfMOCLL3Rnf4TZDNu26RMBU6bo1//wQ/1Yhw7VEUAnn9z8ezNw4O472V95Re/300/hppv0e5SSUj+hb1Pr1sH55+u5AWw2/X7885+NXzNvnt7ma6/Bo49C7956At0jjtDHHHHvvbsev2HUn1j48EP93ubm6n3l5sK11zZ+fyLbMQwdfXT11ZCVpf+9zjqr/kRLQ0uX6u1mZup/v5EjYebMlt8jIYQQoqOSwn3zzkZn9y9USlXu6cWGYaQAOYBLKbWtmZdELrQ8JHpDFEKI/U+a3UKnKBbvnQ4LM84YTIrNTLnbz10zV+L1R7+zsszlJxhK7MkP9+Sz1cX8/r8rWLa5gm1VPpZtruD3/10hxfuOxGTShfvMPF1Ej1cBv8sAOOtlmPhHsGdAwA1fPQZvXwBbmwmJjrZaF1Ru1BPoNrRmDhTcqMdQvVXfF9woxfsDnGEYpFpT6ZbSjZzUHJw2J2ajhTbqOLKYLIzPHc8j4x7hsfGPMbHnRJJMSbgCLt5f+z5Xzb2KGYtm8EPJD1Gd8H1fBUIBSr2lbHFtoaq2qtmJgudvmc/939zPjyU/Uuwp5seSH/mmaBWgs+gffxweeEAXpH/6qX694cN1B/6jj+5aCI645Radk3/XXdC9O/h8uogfCumCr2HAJZdAYSHceKP+Mxrp5H/nHTjhBPB667dXWQnHHqsjeEBH4Ywfr3++5BJd6O7fH5IbXGhgGFBVBVarjuHp0wduvhlmz9bjqKrSHf5z5uj4n4aWLNFXHlx88Z7f65tv1icV7rxTT+I7f74+CVFeXv+aNWtg9Gj49ltd5H/8cZ2/f9VV8Oc/77rNJ57QJwamT9eTBa9dC2ecAYG687Bnnrnr8b/2GgwYoJe99JI+cfGb3+hJhU87TV+9MHly88dw4YX6pMsDD+gJe2fO1Os29OWX+oTO1q16voJHHtHv9xlnwJtv7vl9EkIIITqSmETldGSGYRyEnqQ2iJ64tjXS6+6rWng+sjxjD/u+CrgKoFevXq3ctRBC7F+cdgtKQZkrOhNO9spK5t4pg7j9Pz+wtsTFg7NWc9+pgzBFsai5P0TmPD1vHSUuH5HzD6EwlLh8PD1vHRMHdN39yiKxmMyQ2hkcGeAph9qa9h/D7uJzDp4EY27SY4yVcBhcJfrYU7roiXy//Cu4d0C47qLKcFA//vKvcMgJsRuL6DAsZgtZ5iwybZm4A25cARe+YPSv1NpXB2cezE0jbmLa4GnM3jCbj9d/TLmvnIXbFrJw20J6O3tzykGncEzPYxLmSoJQOESFr4Kq2ipSrak4rU6STPqr6D9//Cel3tKdkUUhFYJO64Djufnm+qicIUOgoED/7PfrIvvLL+vi+sCBcNllupC+fTu8955+XWqq7kR/6y1ddB4yRD8ePx7+9z89uSzA2WfDu+/CbbfpDvtzz4WPPtIF6J9/1l3py5fDwoVw6KG6OA66MzwrS3fZjx6tC88vNJldze/X0T/z5ukO8SlTdMf4DTfoEwn336+z8h9+WMfx3HRT/bqvvqo71c85Z8/vsc+nrzBwOPTjCRP0e/PII/DQQ3rZDTfoqwyWLtVd+aA74M8/XxfLr71WT5gbUV4OK1fWb7NfP10gnzMHTjkFDjtMX/nQ8PgbeuONxicxQL8XF10EX3+tC/AN9e2r/60ilNIF/2ef1eNSSp9kGDVKzyEQiSq6/nrd8X/bbTB1qkx3IoQQYv+RUB33hmFsMAxDteH27yjvvws61qYzcKNSakE0t4+O4Gn5SaWeV0qNVEqN7Nw5hl9mhRAiwaU7LGSnRq/zfkTvTG6YeDAAX68t46Wv1kdt2xEef5DqDhyZs7XSS9OLBkJhvVx0UGYLpHWFjJ7xy31vFJ9zqF72y2x4/UxY9lrs43MCPqjaDO4yqNxcX7SPCAehaktsxyA6nKZd+GnWNExGQn1tAiDDlsG5/c7lxRNe5LaRtzEgS7c5b6zeyNPfP82lsy/lxRUvst29Pc4jrRdWYaprq9nq2kqpt5RAKMA217Zd5hlo2pm/bRucd159AdlfN7fwmDG6o37cOHj+ebjuOj2xbCReZcwY3bF94YX1RV6l6rvsHQ79+oULIRjUBfLf/14X991uKC2FLl108Rp0t/2gQfr1oAvgL76of77++uajcux23Wmfl6c7yG+5RW9782Z9gmD8eL3d9HTdrR4RDOoittXacoc6wFdf6ftjjtFjj0Tl/O1vuhBeWKifr6iAuXN1PM/FF+sCvs2mT07Y7XoC3chxLV+u74cNg6efro/K+dOf9PKiIn1/7727Hn/DqJz//a9xVE6PHjCrLkT2uwYXYEVef8opjaNyFi3SVyVs2qSf//57fdXF0Ufr12Zk6H/DYcP0+7tli76qQAghhNhfJFrH/TqgLW0tv0Zrx3VF+8+Afuii/dNtWD3SUZ/ewvN76sgXQgjRRLrDAkSv8/60oT3YWOZm5vJfeWPxZnplJXPCoG5R2XZEuctPssVMkjnxCjx7kpPhYEeNr1Hx3mzSy0UHl2QDZw8IeMFdCsHo/E61SZcBcNZLsLoQFjyhJ9P9+nFY/SGMux1yR8Zu30rpiXtTO4OrGBoWCE1JkJ4bu32LDs9itpDtyCbLnoU74KbGX0NtKA6/Q7uRZEri6NyjOTr3aNZWrqWwqJD5W+bjDriZuXYmH6z9gNHdRjOlzxQO63QYRgK0IyulcPlduPwush3ZlHhKCFP/AdTjzOeZdPU3ZGW9RlGRLlyXl+tf51Gj9GO/Xy/LztaRL6ALvlYrXHGFLij/5z86P/7JJ3XB9+m6b3jvvacL1Q89pDvqn3pKL3e5YMYMXUS//XYdXdMwvuabb+p/3rRJF8OnT9fF+ksu0UVx0Pn6EYahs/UvukjH0rz9tn59erruhgf93COP6I75lSv1yYFPPtG5+9C6qJzZs/Xx33mnXu+JJ3TRO3LC4pdf9Pv3dJNvuT/9VB9BtGNH4+eWLtWF8OnT9YmEyHgj4zrzTF10b3r8kaicJ5/Ux1NWpuN1tm2rn+S3uXijRx6Bnj119//atXp9qI/7iRTlH3yw8Xrff69vkWPo12+Pb5cQQgjRISRU4V4pdWw89msYRnfgU6A/cH0bi/YopdyGYWwFcgzD6N5Mzv3Bdfdy/l8IIdog2sX764/py5YKL0s2VvDo3DX0yHAwOKelc65tF1aKElct3dM7XrH7ugl9+P1/V+yMyzGboHOqnesm9In30ES0WBy6+95XBZ6y+iDn9mKYYOCp9fE5K96D8iKYeXVdfM5vIbVL7PY/4jL4fIY+dhXSRfuULnD0LbHbp9hvRLrwU62p+EN+XAEXbr97l07xeOub0ZffDv8t0wbVx+iU+cpYtH0Ri7YvomdaT6bkT0moGJ2zDjmLp5Y9RYWvgjBhzIaZTo5OXHnoldx5Z33xFnRneqTDe8EC3WXe0Oef64iYiORk3T1vqjufvnixznf/9VedHx9ZvmiRXu5268e+ulayww+v7zIHqK3VUS0ZGXpy1549dx8VAzoj//334cQT9ePLLtPr1zRIMYtE5SQl6Xichx/W9w6H/lPdmqicQEDnvzeNyrHo/5Xa+Sc/I6N+2xEPPqhPSowe3XibbjesWrVrVE5k0tvdReVUV+tJeB0OfYLk4IP1v8ecObpAv2HDrseQl6cntI3YvFmffIm8V6FQ/euefbbxVRQ33aSL9oMG7fm9EkIIITqKjtcSGGWGYeQCX6CL9te0tWjfwGd19yc289xJTV4jhBCildId0Zuw1mwy+OPkgfTKSiYQUvzxg5Vsr9r1Qq+F68qY/uYyzn3uG6a/uYyF68pavQ+vP0SVt+NF5kwc0JUHzxjMsJ6ZdE+3M6xnJg+eMVjy7fdH9nTI6A12Z5z274Txt+v4nG6H6WW/zIbX/y+28Tl5Y2Hg6ZBkB0z6fvjFkm8v2sxqtpJlzyI3LZcuyV1ItiQnRBd7Q+m2dM7pdw4vnPACt4+6nUHZupq5uWazjtH55FJe/DExYnRGdR3F8b2Px55kx8DAZrZxct7JjO48lqVLdXH91lt1RzfornjQefVz5za+DRnSeNuTJ9cX50EXmQFOPrn55ZHu+sg+Jk2CoUPrb4cfrjPqf/qp9ec+7fb6oj3oyJjOnXUHe0T//nrbaWnw+uu6kz9SwD71VF1s35MRIxoX448/Xu8rcpzZ2fr+kEN0znzD45o2TZ+saFpMP/roxtuMTERbUbHn8Xz+ue7Mf/VV+N3v9ImEoUP1ewq6+76pplcWRP49I1cCRN7z0aP18UbGP2yYzukvKam/GkAIIYTYHyRUx317MwyjF/A5kAdcrpR6uRXrdEdH32xTSjWMvnkWuAj4g2EYM5VSFXWvzwOuB2qBPW5fCCHErpx23S5WWrPvnfep9iRmnDGY619fSqU3wB9mruDJqUNJsemPxIXrynhs7hrKPX7CCsrcfh6bu4bfcQhH9Mlu1T4q3H6SrWYsHSwyZ+KArlKoP1CYzLq73eYEd0l84nM694f/e1HH53zzpI6z2Rmfcxvkjoru/jZ8BatmQtAHhHV00JKXoftQ6H/SHlcXoinDMEi2JJNsSSYUDuEKuHAFXARiPXdDGySZkhibM5axOWMpqiyisKiQeVvm4Q66mbluJh+s+4CRXUcypc8UhnYeGpcTEN8Wf8vcjXPxBX0oFL6gjw/WFvLO3efw7YIcXvlXmIsu1K3Vv/+9zrP/+WdwOvWksQ1FJlyN6NrkIy0yUWpLyyOd3Wlp+v7++/WtOWVlej2brfG6TdmbubDBaq3vHo+YNg2uuUYXxa+9Vnf3Q/0Jiz357jvd3R8ptM+dq7dhterHkWL74sX6xEFzmkbldOrU+HFmpr73Npj+pqXjj5ww+P3vdWa+x9P4eV8zAbm5TZLLIv8O1dX6PtJh/847+tbSMUhUjhBCiP3FflW4NwzjDnTnPMDQuvtphmGMrfv5K6XUCw1W+QJdtP8O6G0Yxr3NbPYVpdSGBo8fAi4BpgGvRBYqpRYYhvEY8DvgB8Mw3gOswLlAFnBDk+0IIYRog2gW73MyHNx36iBufe8H1pe6mfHxah44bTBmk8EbizftLNoDhBWUe/y8sXhTqwv3YaXYUVMr+fAi8VnsOj7HWwne8gSKz7mmLj7nJp1NHw1LXqyPyQF97ymFLx6Cg44GW2p09iMOSGaTmXRbOum2dGpDtbj8LtwB9y6TrMZTfkY+04dP55JBlzBn4xw+Xv8xpd5Svi3+lm+LvyU3NZfJ+ZOZ2GsijqT2+/x65+d3dsbkAIQJs+qVaZTOyWXsMV7KvG6eecmGI8lBamoS5eV60tbBg/cclRMp9DbV0vKISLyMYegJVY89Vnesl5bWZ6lHit+9e+v7f/8bunXTheyJE/WktpFttMbUqfDb3+q4nLff1hn4Nlt9h/qeWCy6Q/6ii3Tx+okn9PpJdd/4I3/e7XZ9/CedpAvlNTU6+mbBgvqM+ghTK/oPWjr+Qw/Vx75okb7iICdHZ+ZXVMC6dTrepqk9/bs0PNa0NL3drl31Nlevho0bdz2ZI4QQQnRk+1XhHh1TM77JsqPqbhENC/d5dfcj6m7NmQdsaM3OlVI3G4bxA/Ab4CogDCwFHlFKFbZmG0IIIVoWzeL9kJ4Z3HTcwTwyZw0Li8p5fn4R107oQ3G1DxNgNhuEwwqTyUCFFcXVbZk7HWoDISo9fjKSrfs8ViFizpEBtjQ9eW1tC22jsRSJzxl4GnzxMGz/QcfnbPgSRl8Fh00Fs2Xf9lGzHTDAbIVwSF91EA5D9Tb9nD8VUjrr5ULsA5vZhs1hI8uehSfoocZfgy/Yts+QWEq3pXP2IWdzZt8zWbhtIQVFBawsW8kW1xae/eFZXl31Ksf1Oo5T8k+hR2qPmI+nxFOCCRNmk5lQOITZZMa7Uc9u+tXnDr76fNeTCFu2wAUX6K7yhppG5eytSHTOQw/p3PiCAt0x3q2bnhz38svrO+kj3fulpXp5KKRPIHRp45Qd6el6EtuPPtKP3W7dhZ/Uym/skybpovmMGTrqZ9w4XSCPjLNP3ZQ1F1yg7z/5RBf4s7Nh4ED429+ge/e2jRlaPv6qKl2cHzRIZ+BbrfpkwfXXw1FH7X6bLYn8u9x8M6xfr//9Kyv1GIYM0Sc+IlcFCCGEEPuD/apwr5Sa0MbXt/laUKXUpcClu3n+X8C/2rpdIYQQrRPN4v1Jh3ZnQ5mHd7/bwrvfbaFXVjIOq5kyF4RDuhUsFFKYAIe17cW8Ck8Au8WM3SKFQNEBmMyQ1lUX0d2l8Y3P+akQFkQ5Pseaoo8rVBcsHQoBJr0coNYFAY+esFa670UUGIZBiiWFFEsKwXAQd8BNjb+GYDi455XbgdlkZkzOGMbkjGF91XoKigr4YvMXeIIePiz6kIKiAkZ0HcGU/CkM7TIUkxGb+DdHkoMKKgjXtYSHw2F6X38rvXrYeea4Zxq99tbrsnnvjTRGjCvFkmLn2GNTmo33GTtWT2J72GF7t3zSJJ0r/5//6MK9rclUOw1z1CPxPP/8p87Ubyg3t77Y3HR/wWb+M7j0Un01gWHo55tmvtFxjOEAACAASURBVEc0vKogYtYs3XX++OP68dy5enLa22/Xjzt31lcOvPuunjT2hRcar9/wmIbWXbs+Zsye99/S8RcU6PvHH298ZcSVV+66jQkTdHG/6YmDyDgi98OH60lu33hDX/nQNPtf8u2FEELsb/arwr0QQogDQzSL91eNy2dzhYeFReU8/ukvZKdYaRpsoB+3PfdXKUVJTS25mY6Em7hQiBZZHDo+p7ZGF7rDoT2vE02GCQacCgdNgEXPwop3m8Tn/Fbn8++V5n+76x+G67rv0yClk3Tfi6hJMiXtjNLxBDy4Ai68QS+qubyQODgo/SCmD5vOpYMuZc4GHaNT4i1hSfESlhQvISc1R8fo9JxIsiU56vsPN/ld3PzKXWzzdeGxRRn06BmkstzE/2Yl890iOydOcTNqjItSr4uK2gqcVidp1rSonlhIS4Pnn4fzztORLxdeqCNztmzRBWbQXeWgi8mGobvzy8p2jcppi2OO0XExoZDe77BhrV+3uaic7t31xL4RTz+ti/FDhsAVV+gs+LIynUE/c2bzufN70tLxjxmjM/IvvhhuuEHPB1BYuGuOfluYTPDyy/qExMCBcNllkJcH27frSJ5Vq/RVBkIIIcT+Qgr3QgghOiSn3YIKQ5l734r3ZpPBXacM4IY3l7O+1E1JCycDvP6965AMhMKUuf10SrXt+cVCJBJbGlhTdde7t6L5QOJYsjth/G0N4nO+r4/PGXUlDDmvbfE5fnfrl9fW1HXfd5buexF1kQltE7EL32l1ctYhZ3FG3zNYtH0RhUWF/Fj6I1tdW3nuh+d2xuhMzp8ctRgdb9C7y7KMw2fhWnAOb7+WSkWZGZtdcXB/P/f/tYzzp9XHeYXCISp8FVTXVpNqTcVpdWKO0gm3s8/WuewPPqi7xptG5UTk58OTT8Kjj+59VM6OHfC//+ku9ciktS1127fk+uthw4bGUTl//7uOwok45BA9ie399+vO/oZROY8+2rb9RbR0/BMm6KsAbr4ZHnigPirn1Vd3nRy4LcaM0RPsPvCAPrnSMCpnxoy9364QQgiRiIxE6fIQjY0cOVItWbIk3sMQQoiEV+H2U+Hx7/N2tlf5uO71pVR6A7s8ZzJgYHcnT57Xhta3Jrql20m2yvly0UGFQ+Aph9rq9i/gA6hw4/gcgKz8tsXnvDcNilfWT04LYJih6yA46+WW17NJ9r2IvUTswo9YX7Wej4o+4vMtn+MP1X/ejug6gsn5kxneZfg+dbvfOv9W1pSvadR1b8LEIVmH8Mi4R9q0LcMwSLWk4rQ5sZj2cV6MdjRvnu6279RJx/V88QVs3ty6zPkrrtAxP089BdddF/OhCiGEEKIFhmF8p5QaGc1txiaoUAghhGgnmSlW0h37/uW8W7qd+08bhNnUONLGZEBWspXzR/fap+2X1vgJhxOrGCNEq5nMkNoZ0nuCNfoxGXsUic+58L9w6Ln6cSQ+Z/bvwdWK7IWRl0Nyti7Wg75PztbLd6fWBZUbwVe978chRAuSLcl0Se5CTmoOmfbMqHWNR8NB6Qfxm2G/4ZVJrzBt0DS6OHQr+XfF33HfN/dx7f+u5cN1H+IJePZq++f0O4dMeyamuq+mJkxk2jM5p985bd6WUooafw2/un6l1Fva6ERDIpswQZ8T3bYN1qzRUTCtnSh27Fh93zS3XwghhBAdn3TcJyjpuBdCiLYpqamlxrdrt3xbzV1VzEOzfgLAbjHRt3Mq54/uxRF9svew5p6l2S10TpPIHLEf8HvAUwrBOBXFSn6uj88BsCS3Lj5nw1ew5EWdY5/WTRft88a2fr8Wh87Xb0tEjxB7KdKFv7cF8VgJqRCLty2msKiQH0p/2LnckeTg2J7Hckr+KeSm5bZpm98Wf8s7P79DiaeEzsmdOaffOYzqug+TUTfgSHKQbkvHnmSPyvZioWFUzltv6Z+PPbZ1677yCkybpifRHduGP2eiXizew3vvhfvui89FakIIIeIjFh33cs2+EEKI/ULnNBtKKVy1+5YTfPzArmwq9/D6ok34AmEm9u8SlaI9QI0vQKotCYc1cTophdgr1mSw9tJd6J6y9p/AtnM/+L8X4KePYMETOj5nwROw+kOdi587uvn18sa2rVDfVMALlZsgOQscmXu/HSFaoWEWvsvvoiZQQ6i9f9eaYTbMHNnjSI7scSQbqzdSWFTI55s/xxv0Uri+kML1hQzvMpzJ+ZMZ0XVEq2J0RnUdFbVCfVPeoBdv0Is9yY7T6ozJ5Lr7atUquOACHZXz0EOtL9rvD1av1kXuJUv0JLNWq87i/81v9KTAhrHHTbTa449DVlbb5w/YXxzoxy+EEB2RdNwnKOm4F0KItlNKUVxdi2cvJ5KNCCvFvR+u4qu1pZgMeOjMQxmVlxWVMVrMJnIyHJhMUfwmKkQ8KRW/CWxBTyS76Bn48V2dhQ9w8Akw5ibdHR8rSTa9/SS5ika0D6UUnqCHGn8NvqAv3sNpxOV3MXfjXD5a/xHFnuKdy7undOeUg07huN7HkWJJieMI61nMFtKt6aRYUjCiWRUWe2XOHD2x7RFHQG4u1NbqZQUFcOut8Je/RG9feXnQt6++oqGhA6XjvqXjF0IIER2x6LiXwn2CksK9EELsHaUU26p8+AL71pXoDYS48a3lrN3hIsVm5h/nDaN3dnSKDk6HhU6pUuwT+5l4T2Bb8jPMfxi2ReJzHDDqqj3H5+wLwwB7hu7AlwKgaEeBUIBqfzXugJuwCu95hXYSUiGWbF9CYVEhy0uW71xuN9uZ2Gsik/Mn0zOtZxxHWM9sMuO0Okmzpu3T5LoiNqZMgblzoaoKbFH6XyYp3EvhXgghYkkmpxVCCCH2wDAMujntWJP27SPOYTEz4/TBZKVYcdeG+MPMFVR59z1DH6DaG8Drj3/cgRBR1WgC2zh01nbuB2e+CMfdB44sHWuz4Al4aypsXhybfUauNqjcpPcnRDuxmC1kO7LJTcsly5GF1WyN95AAHaNzePfDeWDMAzw18SlOOugk7GY7vpCPj9d/zHWfXsfdX9/N4m2LCan4fg6GwiEqfBVsqdlCua+cYHjfrtYT0dW7t+6+97biT2tNDdx2Gxx0kI7a6dVLd+s3XNcwYONG+PRT/bNh6EmBGwoE4M47oVs3cDjg+ONh/fr65599Vq+3uJmPlLff1s/triielwfHHQfz58Po0XofeXnw2GO7vlYpeOYZGDIE7HbIzoapU2HTpsavmzBBF+PXroVJkyAlBbp0gTvugHCDc3q7O/7ycv3+DRkCTqfexlFHQWHhruMyDLjiCpg1C4YN02Pr2xfeeGPX1wYCMGMG9O+vT7506wZXXaX3J4QQonWk4z5BSce9EP/P3n2HR1WmfRz/Tp8kk0YSQi8BQUSUaqVZwALY1gJWWMW+9rbuvquylrWtbWUVRVHsaw82BESwoIIgqChI6CUQSJmZZDL1/eNmmCQEyEwyM4Hcn+vKFeZk5pznnCQkuc/9/B6lGicQDLGprApfoHGdiL9tqeCGN3/C6w9yeIdMHjr7MCymxt/31sgcdcDzVYG7BPzViT92tRO+ewaWvZXY+Bx7BqTmglF7Y1TiefwenF4nlf5KmtPfeC6vi1nrZvFR0Udsqdyya3ub1DaMKhjFiZ1OxGF1JHGEwmAwkGZJI8Oa0WxuhLQklZXy5nTCF1/AX/4CvXvXXySvyeOBIUNg5UopCnfvDkuXwrPPSpH844+l2PzKK3DjjZCfL0VtkH+PGBHpuB8wQArR554LJSXwyCPQty988408v6wM2raVwvVTT9Uex+jRsGSJFNaNxvo77rt0AYsFtm2D8eOhWzd4912YOxf+9S+4/fbIc6+9Vm4UXHABHH20LGD81FNSAF+yRNZDACm+L18uNwFOOUWK7599Bu+/L6+/4gp53t7Of+FCOPtsOOccGZPLBa++Cj/9BJ9+CiNHRsZlMMBhh8l6BFdeCXl58PzzsGwZ/Pwz9OolzwuF4PTTZdbEpZfKa4qK5BwOOggWLJBrrZRSBxKNymlBtHCvlFKN5w8E2VzuaXTxfu7vW5k0YzkApx7ahptH9miSXFyHzUzrDP2rRR3gql2ygG2gaWasRKVkBXz5r8TG5xhNkJYHtuQXIlXLFAgGcPlcVHgrmsVitmHBUJBFxYsoLCpk8dbFu7bbTDaO7ygxOp0yOiVxhBF2s51MWyYp5pRkD6XFuOUWyboPO+EEmDpVOu/35oEHpED+ww/Qp09k+5QpUrSeOVOK07DvqJxjjpFueJNJtj/+uBS7f/5ZbiKAdL3Png2bNkkRHqSo3r493HyzFOBhz4X7tWvhhRfkeACBABx3nBTPN22CrCz49lsZy5QpMHFi5PW//CJd7rfeKp3sIIX7L7+U7vwrr4w8t29fGd8PP9Q+fn3nX10NZnPkvAG8XjlWu3ZSfA8zGOR5S5bAoYfKtuJimeVw3XXw8MOy7fXX4fzza19/kMcnnbT7uSml1IFAo3KUUkqpKJhNRtpm2hvdIT+8Z2vGHyN/OX788xbeXrShKYaHq9qP05OEYqZSiWRzQHZnidExmvb9/KaU2yPx8TnBADi3QMVmCGj0hko8k9FEpi2TjukdaZ3autkUn40GI4PaDGLSMZOYfMJkRnUdRYo5hepANZ+s+YRr5lzD37/+Ows2L0h6jI7H76HYXcwm1yZcXlezmsFwoLriCikQv/YaXHKJbHO59v26N9+UjvS2baVLPvx24ony8TlzohtDzeL1sGHyvqgosm38eNn/J59Etr3+Ovj9cPHF+z5GTg5cdFHksckkswuqquSGQPicUlIk57/mOeXnQ8+eu5+TxSKzAGoaNqz2uPfGZoucd3U1bN8OFRUwdCgsWrT784cOjRTtQcZ18MG1j/fmm1BQIMX/mufQvz9kZkb3eVFKqZbMnOwBKKWUUvFkNhlpl5XS6Nici47qzNrtlXzx+zae+bKIDtmpHN0tp9Hj2+7yYreYmiR+R6lmzZ4JtgzwlEkufDBBC2oaDHDwaOg6LBKfU7oGPrgKuo+AwTeCI7/pj+t1g68S0nLl3JVKglRLKqmWVHxBHy6vC5fP1Sy68Dumd+TKw6/kokMuYva62cwomsFm92Z+2vYTP237idaprRnVdRQjO49MaoyON+ClpKqE0upSXcg2zg46SN4Axo2DO++UAvHvv0vuet1c9FatJM9+xQopeufl1b/frVsbPoa63f3Z2fK+5rFHjpTu+unT4bTTZNv06TBwIBxyyL6PUVAg3e019ewp79eskffhc2rbds/7qKl9+933mZ3d8Cz5UEhy9p99ViKHaqpvgml9syDqHm/FCinkN8XnRSmlWjIt3CullDrgmYwG2mWlsLm8Cq8/tmKhwWDgtpN6srncw29bnNz70XL+c34/uuY2bhHOYCjEVmc17TLtTRK/o1SzZjBASjbYMqV47ymrnSMQT7Z0GHorHHJ6JD7nj89h7VcwaCIcfn7Tx+eEQuDaJpn7aa3BrLnZKjksRgvZ9myybFlU+auo8Fbg8XuSPSzSLGmc1u00RheM5sfiHyksKuTHrT+ytXIrL/7yIq/99hrDOw5nTMEYOmfsIzMljsIL2ZZXl+OwOsiwZmA26p/S8TR2rMTgvPeeFPSPO672x7/4QmJigkHpLv/73+vfT7t2DT+maQ+Twmr+mDIapWP+scck837zZulKf/LJhh2jvl/16v4YDAYlMud//6t/Hyl1JtHsadwN9dBDknt/0UVw112Sn28ywYsv1r/obEOuUzAoXfh11wIIC98UUUoptXf624ZSSqkWwWQ00DazcZ33NouJf57em6tfXcw2VzV3vreMyRf0Jzu1ccW4al+A0kofrdJi38+c5cVMnruKjWVVtM9K4erh3Ti+Vxy6iJVqCkYjpOVIJ3rVDilsJ6qAH47P+f0j+PoJOf43T8LyD2Ho7dDxiKY/ps8D5evlpkVKdv2VmwPFipkw/xEo3wCZHWDILdBj5L5fpxLCYDDs3oXvdSU9msZoMDKwzUAGthnIRtdGZhTNYPa62VT5q/hszWd8tuYzDss9jNEFozmi7RGYDAmO3dopGApSUV2B0+vUhWwbYd6GeTy37Dk2uzbT1tGWiX0mMrTD0FrPqaqS96WlsuBqzZx1kG0gme0VFZFonL1pqv96x4+XLPu33oLVqyWqZty4hr121SqJ1anZIb9ihbzv0kXed+8uWfCDBkmsTFPZ0/m/8YbcBHn55drbX3gh9mN17w7ffQfHH6/rtSulVGPof6FKKaVaDJPRQH6GHZMx9r/cchw27jvzUOxmI8UV1fzjg19i7uKvqazSS5U3tsLJnOXF3PnezyxeX8rmcg+L15dy53s/M2d5caPHpVRcmczgaA1ZnaQjPlEF7XB8zoXvwmFjwWCMxOd8ege44vC9EwpB5Q4p4PuS3+kcFytmQuH1sHERVGyU94XXy3bV7IS78DukdyAvNQ+7uXkslt7e0Z4rDruCaSdN44rDrqC9oz0AS0uWcv/393P5zMt5d+W7OL3OpI0xFArh8rrY5NrEFvcWKn2VSRvL/mbehnlM+nYSy7Yto7iymB9XbWDSt5OYt2Ferec984y8HzRIurNPPLH2W7hje+xYWLwY3n1392N5POCs8WWSliZd8o3VsyccdRS89BK8+iqccop0qTfE9u0SrRMWCEhXut0uRW6QcwqF4B//qH8fJSWxjXtP52807p5et3KlzHaI1dixMs7HH9/9Y4FAw2N8lFKqpdOOe6WUUi2K1WykdbqdLRWemBeb697awZ2n9uKuD3/hl00VPPr5Cu44uWejo262Oj20y0qJOu9+8txVbHN5CE8kCARhm8vD5LmrtOte7R9MFkjPh0AridBJVAd+rficB2HzkvjH5/i90o2ekgWpOQdW9/38R8C9FYI7F+UN+uXx/Ee0674ZMxgMpFnSSLOk4Qv4cPqcuL3upHfhp1pSGV0wmlO7nsqSrUsoLCpkYfFCtlZJjM6rv73K8A7DGV0wmq6ZXZM2To/fg8fvwWqykmHNIM2SptF3e/HcsucoqSrZ9fW1/sW/s9adybX913Pd8VLY/vBD+OYbOOus3SNy6rrlFpgxA845By68EI44Anw+6WJ/6y14+23pJgcYMABeeQXuuw+6dYPWrSPF8mhNmCCL2UL9xek96d4dbrwRli6VMbz7LsyfD/ffH7kZMXgwXHedxO8sWwanngoOh3T3f/CBFMXvvjv6Me/p/E8/XSJyLrhArtXatTB5skTdLFkS/XFA9vXOO3DzzfDVVxJnZDLJjIN33oFJk2TmglJKqb3Twr1SSqkWJ8VqolWale2u6pj3MfigXC4b0pXn5q/m81+L6dwqlfOP7NSocQWCseXdbyyrom76TyAo25Xar5gs0oGfkp3YAn5uDzjrefj9Y/jmCajcXiM+5zboeGTTH7OqDLwuyb63pjb9/pOhfEOkaB8W9Mt2tV+wmCy0MrUi25aNy+fC6XXiDXiTOiajwUj//P70z+/PJtcmPlr9EbPWzqLSX8nMtTOZuXYmh+YcyphuYziyzZGYjMmJ0am7kK3D4kjaWJqzza7NtW4KZR35CaXzz+D3T4dw9dvSed67Nzz9dKQwvjd2O8yZIzntb7whbw4HdO0K114Lhx0Wee6990JxscTcuFxSTI61cH/eeXDDDZI3P3p0w1/XuTNMnSo3HP77X8jPh4cflsc1PfGEFNonT44U6Tt2lPGee25sY97T+f/1r1BdLTMB3nkHevSA//xHbn7EWrg3GOSmyVNPwbRp8Mknsphw585y7WK97kop1dIYYu02VPE1cODA0MKFC5M9DKWUOqCVuKqpqPLF/PpQKMSDn/7OzF8lVuOe03oz5KAGzpXei3S7hbx0W4Off/Z/v2Hx+tJaxXuTEfp1zObtq45p9HiUSpqAL7EFfJBjff8sLH0TQju/qbqfCINvAkecZrDYMyA1d/8PAp46UuJxahbvjWZoPwAu1bic/ZXH78HpdVLpr4x5plpTq/RV8sX6L5hRNIMNrsiNobyUPE7teioju4wkw5qRxBHKDYc0SxoZtgwsxiaeubMfu+iTi1i2bVmt4r3JYKJPXh+mnzJ9L69sXlwuKbpfcokU1xuiSxfpuJ81K65DU0oplSQGg2FRKBQa2JT73M//OlBKKaVil5NmJcUaezecwWDgphE9OLSdFAce+Hg5K4sbn7nr9Pio8DT8hsLVw7uR57ATTtgxGSHPYefq4d0aPRalkircgR/OwE8EW7osqHrea9C2r2z7Yxa8+idYNE1uJjQ1TwWUrZWbBvuzIbfIDALjzkm9RrM8HnLL3l+nmjW72U5eah7tHe3JsmU1iy7yVEsqowpGMfmEyUw6ZhKD8gdhwMC2qm289OtLTPh0Ak8ufpLV5auTNsZgKIjT62SjcyPbKrdRHYh9lt+BZGKfieSm5O5aYNhkMJGbksvEPhOTPLLovPIKVFZq3ItSSqn40o77Zko77pVSKjGCwRCbKzxU+2LP8i2r9HL1q4vZUuEhz2Fj8gX9yHE0vGO+PgaDgXZZdmzmhhVI5iwvZvLcVWwsq6J9VgpXD++m+fbqwOOvBve2xC3uGgrVjs8ByOoMw26PT3wOgMUOaXlgbtz/IUmzYqZk2pdvgMwOUrTXfPsDSigUotJfidPrxONvPgstb3Zv5qMiidFx+927tvfO6c2YgjEc1faopN90sJvtZNoySTGnJHUcyTZvwzyeW/Ycm12baetoy8Q+ExnaYWiyh9Ugc+bAb79JJvzBB0s+fUNpx71SSh3Y4tFxr4X7ZkoL90oplTiBYIhNZVX46gbFR2F1iZu/vL6YSm+Anm3Sefzcw7FZGlcgsJiMdMhO0UXulKqr2imF9IB/389tquN9/ywsfQvC8Q7dT4Rjb4T0NvE5pj1DFq9tBt3NSu2JN+ClwluB2+duNjE6Vf6qXTE6653rd23PTcmVGJ3OI8m0ZSZxhGAz2ci0ZZJqOUDWt2hBhg+XhXMHDYKXXpJCfENp4V4ppQ5sWrhvQbRwr5RSieUPBNlU5sEfjL14v6BoO39//2eCIRjeI4//G92r0UX3jBQLuY3s3lfqgBQKSf59VWni8u9LVsKX/4LNO1frM9th0GXQ90KJ9WlqRiOktIKUrKbft1JNKBAM4PK5cPlc+OIRJxWDUCjET9t+YkbRDL7f8j0h5P8Ji9HCsA7DGF0wmm5ZyY2Us5gsuxay1Zv0Siml1P5NC/ctiBbulVIq8bz+IJvLqwgEY//Z+L9FG/jv3FUAXHx0Z8Yf06XR42qbmdKoLH6lDmgBv3TfJyofPhSCFZ/A14/Xjs8Zeht0Oio+xzRbZfFaq3bnqubP4/fg8rmo9FUSDMV+M7wpbXFv4aPVH/H52s9x+yIxOr1a9WJMtzEc3fZozOG1GZLAZDSRYc0g3ZqO0aDL0CmllFL7Iy3ctyBauFdKqeTw+AJsKfcQjPHnYygU4t+fr+SjZZsB+PuoXhx/cOtGjclslMgco1G78ZTaI59H8u/9CVoAstoJ30+BpW9G4nO6nQCDb4pffI41DdJy49Pdr1QTC4aCVPoqcflczSYL3+P38MX6LygsKqwVo5Njz+GUrqdwUpeTyLIlb4aLyWAi3ZpOujU96Xn8SimllIqOFu5bEC3cK6VU8jS2eO8LBLn9naUsWV+O1WzksXMPp1fbjEaNyWEz0zrD3qh9KNUieCqkEz4Y+4LTUSlZCfMehE2L5XG843MMBkjJljeN1lD7CV/Qh9vrxulzEkjU9+ZehEIhlpYslRidzd8TRGYGmI1mhrYfyphuY+ieFUV4eRMzGAw4LA4ybZlJnQmglFJKqYbTwn0LooV7pZRKrsYW7yuqfFzz2mI2llXRKs3K5PP7Nbrw3jrDjsOmf8ArtU/BoGTfe8oSk3+/x/icW6HT0fE5ptEk3fe29PjsX6k4qfJX4fK6qPRXNosFbYvdxXy8+mNmrp2Jy+fatf3gVgczpmAMx7Q7JmnFc4PBQKo5lQxbBjaTrnejlFJKNWdauG9BtHCvlFLJ19ji/bodlVzz2o+4qwN0z3PwxNi+jcqqNxkNtM9KwWzS/FulGiTgA3cJeN37fm5T8Lrguymw9I068Tk3Qnrb+BzTYoe0PDBrUU/tX4KhIG6fG5fXRXUgQRFXe+Hxe/hyw5cUFhWytmLtru2t7K04ucvJnNzlZLLt2Ukbn91sJ8OaQapF17pQSimlmiMt3LcgWrhXSqnmweMLsLncE3NX4KK1pdz+zlKCITi2ew73nNYbYyPiLVKtZtpkamSOUlHxVkJlCfi9iTleyUqY9xBs+lEem+0w8FLodyGYrPE5pj0DUnOkE1+p/Yw34KXCW4Hb5056F34oFOLnkp8pLCrku83f1YrRGdxuMGO6jaFHdo+kjc9sNO/KwdeFbJVSSqnmQwv3LYgW7pVSqvmo9PoprqiOuZjwwZJNPDF7JQDnH9GRy4YUNGo8eek20u26OKVSUfOU78y/D8b/WMmIzzEYpHhvz9T8e7VfCgQDOL1OnF4ngVDys/C3Vm6VGJ01M3H6nLu298zuyeiC0Rzb/lgsxuT8PDYajDisDtKt6Ukbg1JKKaUitHDfgmjhXimlmhdXtZ+tFZ6YX//k7JW8v2QTAHec3JORvdvEvC+jwUCHbI3MUSomwSBU7ZAifiJ+D05GfI7JIvE5Vo3UUPunUCiE2+emwluBN5CgmTJ74fF7mLdhHoVFhaypWLNre7Ytm1O6npL0GJ1USyoZ1gzsZp2Rp5RSSiWLFu5bEC3cK6VU81Ne5WO7K7Yc3kAwxF/fXcbCtaVYTAYePedwDm2fGfNYNDJHqUZKdP799j/gywcTG59jc0BqLph0UWu1//L4PVR4K6j0VSZ7KIRCIX7Z/guFRYUs2LyAYGhnjI7BzOD2gxldMJqerXombXw2k40MWwap5lQMOutGKaWUSigt3LcgWrhXSqnmqazSAhUFYgAAIABJREFUyw53bN1/Lo+fa19fzLodlWSlWJh8Qf9GFd9z021kaGSOUo3jq5ICvj8Bi2OGQrDiU/j6sUh8TmYnic/pfEx8jmkwQGorsGdpfI7ar/mCPpxeJy6va1fBPJm2Vm7lk9Wf8Nnaz3B6IzE6B2UdxJhuYxjcbjAWU3J+RpuMJjKsGZqDr5RSSiWQFu5bEC3cK6VU87XdVU15lS+m124sq+KaV3+kwuOna24aT47tS5ottm5Yo8FAu6wUrGb9o1ypRqt2SjE94I//sbwu+H4K/JTA+ByzVeJzLCnx2b9SCRIMBXH5XFRUV+APJuD7dR+qA9XM2zCPGUUzKCov2rU9y5bFyV1O5pSup9DK3iopY9McfKWUUipx4lG417/0lVJKqSjlOGw47LEV29tnpXDPab0xGw2sLnFz38fLCQRju4keDIXY6vTEvGiuUqoGW7osHpuWA8Y4/4psdcDgm2Dsa9BugGxbNRtePRsWvgDxyPT2e6F8IziLIZj8RT+VipXRYCTDmkGH9A60Tm1Nijm5N6NsJhsjOo/g8eGP868h/+LYdsdiNBgpqy7jjd/f4M+f/ZmHFz7Mbzt+S/jP62AoSEV1BRudG9lWuY3qQAJmFinVzE2bJhPQvvqq6fZ5990tc1KbwQCXXdZ0+1uzRvY5bVrT7VOp/Z0W7pVSSqkY5DlspFhNMb328I5Z3HDiQQAsKNrBlHlF+3jFnnn9QbbFmLuvlKrDYICUbCngpyQgWianO5z5LIy4F1JzwO+BBU/Da+fB2m/ic8xqJ5Sugaqy+OxfqQRKtaSSn5ZPh/QOZNoyMRli+7ncFAwGA71zenPHEXcwdeRUzulxDhnWDAKhAPM2zOPWebdy05c3MWfdHHyB2GbtNYbb52azazNb3FuaxXoBqmVavhzOOw+6dYO0NMjOhiOPhOnTm369+Mcfh5dfbtp9NoWvv4YzzoDOnSElBfLyYPhwmDEj2SOr39y5cmOioiLZI0mOln7+Kvm0cK+UUkrFwGAwkJ9ux2aJrUhwap+2nDOgAwD/W7SBj5ZujnksLo+fCk/iiwBKHbCMJkjLhcyOYE2L77EMBuh5Clz4LvS9AAwmKF8HhX+Bj28FZ+z/N+xRKCS5/mXrwOdp+v0rlWBmo5lsezYd0juQl5qH3ZzcxdtzU3K5+JCLefGkF7m+3/V0y+wGwB9lf/DYj48x4bMJvPLrK2yv2p7wsXn8HrZWbmWjayMV3gqdtacSav16KCuDCy+Uwvo//wn5+XDxxXD77U17rOZauF+xQn4MT5wITz0Ff/sbBIMwZgxMnpzs0e1u7ly4556WW7hu6eevki+2ef5KKaWUwmg00CbDzqayKnyB6BfKu3xoAetLK1lQtIPHZ6+kXZadfp2yYxrLdpcXm9mIzZy8bkOlDjhmK2S0BW8lVJZI3Ey8hONzep0GXz4EmxZB0RxY9zUMvAz6XQgma9Me0++F8g1gz5SO/3hHBCkVZwaDgTRLGmmWNHwBHxXeCtw+d9IWs7WarJzY+URO6HQCv+34jcKiQr7Z9A3l3nLeXPEmb698m2PaHcPogtH0atULQwKzNnwBHzuqdlDuKd+Vg282anlAxdfIkfJW07XXStH6ySelkG+zJWdsiTJhgrzV9Je/wIAB8PDDcPXVyRmXUqp50t/OlVJKqUYwGQ20ybRjMkb/x7bJaODvo3rRNTeNQDDE3YW/sqE0tunroVCIrRXVMeflK6X2wpoKWZ3A0Vq68eMpHJ8z8j5IzQV/dfzjczzlULYWql3x2b9SSWAxWchJyaFjekdyUnKwmZJXDTQYDPTK6cVtg25j6sipnNfjPDKtmQRCAeZvnM/t82/nxrk3MmvtLLzxWONiLwKhAOXV5Wx0baSkqiQpMT7hfPANGxJ+aNVMdO4M1dVQVbXv5zqdcNtt0LUrWK3QqRPcemvt1xoMsHYtzJ4t/zYYJI6mJp8P/vpXaNNGImtGjIDVqyMff+YZed333+8+hjfflI/NmhXT6e7GZIIOHWQ2QkOEv2d+/VU693NyJHboiivA6wWXS/6dlwcOB1xySf3X9n//g6OOgtRUyMiAUaNg2bLIx8ePl25zgI4dI9dyzZra+/nkE+jXD+x26N4dXnst8rGyMrm+9d2QqK6WcV944b7P9eef5Tyys2Ws48bB1q27P//HH+G00+R5KSkwcCC8/37t54TXOfjii71/Dezr/KdNk9e0bStfiwUFsr/qOimm48eD2QzFxXDuuTL+7GxZH6C+z8vs2XD88ZCeLpFSw4bB/Pl7vkbqwKaFe6WUUqqRLCYj+Rn2mDrlUq1m7jvjULJSLDg9fv723s84Y4y98QWCbHNq3r1ScWPPkPz71Fbxzb83GKDHyXDhO9D3wjrxObdARRzic4IBcG6Bik2QhMKdUvFiMBhIt6bT1tGWto62OKyOhHa215WTksOFh1zIiye9yI39b6R7VncAVpWv4onFTzDhswm8/OvLlFSVJHRcoVAIl9fFRtdGit3FVPkbUEE9AGh+dXJUVkJJiRRJX3gBXnwRBg2CrKy9v87jkYLmlClwzjnwn/9Ikfbxx+GssyI5+dOnQ24u9O4t/54+XSJparr1VimG3nmn/Pvrr+GCCyIfHztWCtHTp+8+junToX17GUusnE65BitXwiOPwKefShE4GhdeCNu3w733wqmnynW54w74059g2zaYNEny9F9+WWYz1PTII1JEbtdOOv3/9jf45Rc49liJ8wEp/p95pvz7yScj1zIvL7KfH36QwvRpp8k+HQ646CJZzwDkc3r66XKzw1vnvmRhoRT2L764Yee6YYOcx4QJ8PbbMnuj5j7nz5fxb9wo5/Pww3JT4swz4fXXd9/nvr4G9nX+Tz8tUU833ywfHzwYHnoI/vzn3Y8VCsHJJ4PFAg8+KPudOlU+RzW99VZkVso//ymf27IyOOEEmDdv39dJHXgMmmnXPA0cODC0cOHCZA9DKaVUFNzVfoorYsuL/nljOTf/7yd8gRADOmXxwFl9MJtiu7/eKs1KVmoTR2oopWoL+KFyuyz2Gm/bV8G8B2HjInlstsHAS6HfRU0fnwNy4yC1FdgTsECvUkkQCAZw+Vw4vU78QX9SxxIKhfi99HcKiwr5euPXBEIBAIwGI0e3PZoxBWM4JOeQpNxssJqsZFgzSLOkxfX4d98tXa3r10vXcSIl89gt2S23wKOPRh6fcIIUMTt33vvrHnhAPl8//AB9+kS2T5kiRdaZMyPF7y5dpPu7blf8tGlS+D3mGCmEmnZOpHv8cbjxRuns7t1bto0dK93PmzZJwRWky7t9eynW/utfsi38dRRNee3ss+Gdd+TfJpMU2KdMgVat9v3a8PEuuABeeSWy/cgj5dpceGHtfP8jj5QZCFu2yOP166U7/Lbb4L77Is8rLoZeveCUU+DVV2sfq77vEYNBxr5kCRx6aGQfnTrBdddJ4RzkpsQpp8C770YK4SDF/kWLZN9Go3Syd+0qN3LGj699/OOOg88/j3y+nnsOLr9cZkZccYVc+0MOkaL6F19EnhcKwZAhcv7r1smYo/ka2Nv5V1bKjYGa7r0X/vEPOVb4+ePHw0svyToO4a8ZkM/511/LTRYAt1uu3cknR64/SFd+nz5yk+Drr1HNmMFgWBQKhQY25T61414ppZRqImk2MzmO2KbiH9o+k1tG9gRg0boynp67KuZxlFb68PgCMb9eKdUAJjOk50NmB7CkxPdYOd3gjGdh5P014nMmxy8+JxQC93YoX6+L16oDksloItOWSYf0DrRObU2KOc7fw3thMBg4uNXB3DrwVl446QXG9RxHli2LYCjI15u+5o6v7uCGuTfw+drPqQ4kdladN+ClpKqEDa4NlFeXJ22tAHXgueIKKcK+9prEn4DEu+zLm2/C0UdLNElJSeTtxBPl43PmRDeGcMEWJI4EoKgosm38eNn/J59Etr3+Ovj9DesS35u77pJr8NJL0pHu90shOBpXXFH78dFHRxa+rbu9uDiy/3fekeONG1f7OppM8txoruPQoZGiPUhx+eCDa1/HkSPlZkfN2QslJVLQv+CChi2x85e/1P58jR8v3fwzZsjjn36C336T/ZWWRs5p+3aZjbBhQ2QmQVhDvgb2Jly0DwalK76kRK5HKCSRPXXVjQsaNkxe49zZA/L557Bjh9x4qfl5cbvla3zBgui/RtT+Twv3SimlVBPKTLGQmWKJ6bUjDsnngiM7AfDBkk28v3hjTPvRvHulEshih8z2soitKbbv/QYxGKDHSXDhuxKfY0xAfE548VrXNvmrVKkDUKollfy0fNqntyfDloHJkLxF3lvZW3F+r/N5YeQL3DzgZnpk9wCgqLyIJxc/yYTPJvDSLy+xtbKeYOc4CgQDlHpK2eDcwA7PDnzB+MRphXO5W7WSuI2zz5aiW03R5FfPni3ds/n5khN96qnwxx+R52l+dfIcdJAUIseNk+t8xBFS8CwpkeiTLVtqv4XjUFaskHijvLzab926ycfryzzfk7rd/dnZ8n7Hjsi2+grO06fL190hh+x53y7X7udQV58+cg0uvlgK6amp8nUR/vqqqtp9H4E6fTmdOtV+HI4a2tP20lJ5Hy5g9+mz+7X8+OPGXUeQa1nzOhqNEp/z0UeR7W+8IesMNPQGSM+etR9bLNKdH/5+DZ/TlVfufk7hmKS659WQr4G9WbBAPmepqfLavLxI8b/uegVG4+4d+3WPFz6HU0/d/RyefVZ+Fav7f6I68Omy8UoppVQTy3HY8AdDuKujn34/4dgurN1eyVd/lPCfL/6gfXYKg7o0YM5sHf5gkK1OD20zk9dFqFSLYk2TN085VO6QzPh4HWfwjdDrtEh8TtEXsO6b+MXneMrB65LFea1pTbtvpZoJi9FCK3srsm3Zu2J0Er1Q7K6xmCwM7zic4R2Hs6J0BYWrCvlq41c4vU7eXvk27658l6PaHcWYgjH0zumdsBidYChIRXUFFdUVpFnSyLBlNOmivxdeKEX2f/5TCuxPPSXFuXA29fz5Ukg95BApxNntkgd95pnSuT1uXO393XyzFNT/+leJonjiCVmgdOlSuTlwxRWSbf/ee5JPHS6i1cyv7tkTTjpJbiR8843kV69bVzvGAiL51YccIvnVP/wg0S95eRLvEvbWWzLOYcPkPEMhKVyfcIJEugwd2mSXc78ydqxcp/fek6L+ccfV/vgXX8jnLhiUa/f3v9e/n3btGn5M0x7u0dWMuwkXnB97TAqxmzdLtMuTT+593488ErkpVN9+6zN2rHytz5snN4zefFPiXGpavVoigPZ1Dvs6t/C9+BkzwNbIb+GGXEeQG1z/+pec11VXSZRP//61u/X3pr7/5moeI3xO998v6yXUp+6xGjr2+qxeLUX7bt0k9qlzZ/k/aeNGOde6/Q4Gw55nFtT9vEyduvvNl7Ca6wuolkEL90oppVQctE63sSkYojrKyBqjwcBfTz2Y699Ywh9bXUya8StPj+tPp5zUfb+4jipvgFK3l+y0xObdz1lezOS5q9hYVkX7rBSuHt6N43vlJ3QMSiWNPRNsGVBVKm/xWk8qHJ+zciZ89W+oLJH4nOWFMPQ26HxM0x4vGJCufpsD0vKk418l3oqZMP8RmQmR2QGG3AI9RiZ7VAeU8GK26dZ0PH4PTq+TSn8lyVobrkd2D24eeDN/PvTPfLrmUz5Z/Qml1aV8s+kbvtn0DV0yujC6YDTDOgzDbrYnbFxunxu3z43dbCfDmkGqJfrfU+rq3l26cMNCIVl89JlnpIv98sulIFczv/qaayS/+rbbpPBZs7jn8cDixdKZD1L4HTlScrcfeEAiQQ47TIrFZ565ezfsl1/Wzq++8kro0UPyqx98sPbzg0Ep8Ifzq6+6Srpon38+Urh3u2X72LG1C/9XXimdz3/964GZXz1vwzyeW/Ycm12baetoy8Q+ExnaofYdivDMhNJSOPxwiQyp6fDD5X337nKzJRyNszdNdT8rXHB+6y0p1losu98kquvii2Wh0mjUvAYgX091r0ObNtHtc0+6y5rYdOwo3wN701TXsWdPOOoombFw/PFyc+vxxxv++t9+k/z9MJ9Puu3D1zl8TmlpDfv6aKg9nf+HH8rnbMaM2p37M2fGfqzwOeTmNu05qP2bRuUopZRScWAwGGiTYccSwwKzKRYT951xKK3SrLirA9z5/jLKq2Kbll5a6aXSm7iF9+YsL+bO935m8fpSNpd7WLy+lDvf+5k5y4sTNgalki68uGt2Fynkx6sbtt74nPU743NuhopNTX/MaheUrZUufJVYK2ZC4fUyy6Jio7wvvF62q7iwm+3kpebR3tGeLFsWpiTesMq2ZzPu4HFMPWkqtwy8hZ7ZkhuxpmIN/1nyH/782Z+Z9su0hMfoePwetlZuZYNTcvADjZhtVF/+cyAgHe6x5len1Jh4OGKEdMSHM7H3RfOrG2/ehnlM+nYSy7Yto7iymB9XbWDSt5OYt2Ferec984y8HzRIZj6ceGLtt/BsiLFj5WbMu+/ufiyPJ3KtQQq4deNKYhEuOL/0ktxwOeUUKazuTUHB7ucQVl8MTTAoC9MaDBLDAxLRVHcf9ia6N/enP8lslLvuqj8JL7xYKsh1hKa5luPHw7ffys0vsxnOP7/hr33qqdpRQdOmyZhGjZLH/fvLbI1HH61/rDXPKRp7Ov9w93zN6xcM1l50OVonnSSxRvfeu3skF8R+Dmr/ph33SimlVJyYjAbyM+xsLq+KOm8+L93GvWf05oY3f2JTmYe7P/yFh84+LKYbAduc1bTLMsb02mhNnruKbS4PgZ2/xAaCsM3lYfLcVdp1r1oeowkceVK8r9wOXnd8jlNvfM5cWPctDPizxOeYmy7OgmBQcu+rXdJ9b07srJ4Wa/4j4N4KwZ03Y4N+eTz/Ee26jzOz0UyWPYtMWyZV/ioqvBV4/MlZuNlitDCswzCGdRjGytKVzCiawbyN83D6nLyz8h3eW/keR7Y9kjEFYzg099CExej4g35KPaWUVZdJjI41A2uUsV17y5su3nn//8or5a0+W7fWzsGum4kd3la3i3lPFiyAO++UiJy6RbRo86vT02vnV+/J9u21u/z3d88te46SqhICIam4rn/x76x1Z3Jt//Vcd7yc74cfyjU+66zdI3LquuUWufFyzjlyA+SII6TzesUK6Yh/+22ZWQEwYAC88grcd5/EmbRuLZ3esZgwIbIQbDRd4vU5+WQpyh91lET7bNkiM01+/hluukmK/vHWtavEPt10k1zDP/0JcnLkJtmnn0qkzLRp8twBA+T9nXfKdbdYYMyYSEE7GmPHyroTb70l+4gm9mXHDpkxc+aZEqX19NMyznCckNEIL74YidP6858lVmjLFvjuO/j1V1i1Kvox7+n8Tz5ZYoZGjZKvjWBQzqu+gntDpafLDZxx42QWzoUXytfIhg0yAwhkxpFqWbRwr5RSSsWR1WzcWbz3RD3N/uA2Gdxxck8mzVjOTxvKeWLWSm4e2SPqP8IDwRDFFR7aZ6XE/Q/4jWVVu4r2kePLdqVaLLNVFq/1VYF7myz6Gg+74nM+g68ek/ic7/4Lv82AobdC52Ob9ni+KunwT8mWtwQVCFus8g2Ron1Y0C/bVUIYDAZSLamkWlLxBXw4fU7cXveuomSiHZR9EDcOuJEJh07gszWf8fHqj9nh2cG3m7/l283f0jmjM6MLRjO8w/CExeiEQiFcXhcurwu72U66NZ1Uc2qDfv/YW950LPnV+8rE3pto86uDQSkYzpq152NGm19tMEhH9N13y+Np06RIOX9+9DEsybLZtbnW90fWkZ9QOv8Mfv90CFe/Lde0d28pwoYL43tjt8OcOVJ0fuMNeXM4pBB97bW1Y1/uvVdu+PzrX7JY7LBhsRfuzzsPbrhBZnCMHh3bPsIuu0wKvE89JbNHHA7o27f+dRri6cYbJfrp0UclzsnvlyLx4MG1b44dd5ysKfHCC7K4bDAo3x+xFO4zM+GMMyTLv6GL0oa98op83v/v/2SsZ50l61bUzOg/9lj4/ntZP2LKFLnBlp8vUUv33Rf9eGHP53/QQXLT6c47JeYqI0MW1A5HX8XqnHNkQeT775ebRJWVEpE0aBBcemns+1X7L0OysvrU3g0cODC0cOHCZA9DKaVUE3FX+ymuiK077+Vv1zDtm7UAXDWsgHMGdoxpPw6bmdYZ8f3D/ez/fsPi9aW1ivcmI/TrmM3bVzVt5rZm6av9lqdCOvDjtYAtSHf/D8/BT69FjlMwHAbfDBlRrN7XUGYrpLWG1fM0gz1epo6U2RQ1i/dGM7QfAJdqXE6yhEIh3D43Tq+T6kAjWi2bgD/o59tN31JYVMjyHct3bXdYHIzoPIJRXUeRn5b4n5Mmo4l0SzoOqwOz0Vwr8zx1xwCW/fsfrFmRRkqKFOF69JAibIcOUmj94gspbg4aJIW6667b+/HCBe5//1uKkzX17i2ducuWyeN77pHC+Pr1tTvmn3hCCrVr1kRmAixZIkXOV16Rzt7x42X7+PESoxJeYLbuOMILir79thTlPvgATjtt39ftQCjcX/TJRSzbtqxW8d5kMNEnrw/TT5mexJFFx+WSAvAll8Dkyckezf7tkkugsFAW+m3Iwrh33y3fp3W/R5VqjgwGw6JQKDSwKfepGfdKKaVUAqTZzOSmxxZVcdFRnTmup7RgPfNlEQuKtse0H1e1n/LK2LLyG+rq4d3Ic9gJp/KYjJDnsHP18G5NehzN0lf7NXuG5N+ntopfl7o1DY69Aca+Ae13/v1QNBdePRt+eB78TVxg9Hvhpzfgg2thw0LNYI+HIbfIzRHjzknTRrM8HnJLcsfVwhkMBhxWB20dbWnnaIfD6khYPE1dZqOZIR2G8NDQh3hs+GOc0OkELEYLLp+L9/54j8s/v5x7F9zLT9t+Suhiu4FggLLqMja6NlK4qpB7vrlnV+b5L39UUFwua2bcfbd0yubnSzfus89G9hFLfvWUKZEFP0Eicn79NZKJDdHlV//4oxTtY9US86sn9plIbkouJoNMpzAZTOSm5DKxz8Qkjyw6r7winc/hmzUqNqWl8L//SWROQ4r2SimNylFKKaUSJsNuIRAIUVoZXUyGwWDgtpN6srncw29bnPxzxnL+c34/uuZGP0d1R6UXm8WI3RKfBfaO75XP/RD3TnjN0lf7vfACtrYMqNohXfjx0KoAzngG/vgcvvq3RPV891/4rRCG3ta08TkLp0o8T7izUjPYm1aPkTDmCZ3R0IxZTVZyU3JpZW+Fy+fC6XXiC8T3hvmedM/qzg39b2BC70iMznbPdr7b8h3fbfmOTumdGF0wmuM6HpfQGJ2Xf32ZkqoSgsgP8NRDvyLj6A+pev9Kzj9fOmqvvVYypGsuQBpLfrXNBkOGwEUXSf79E09Itvitt0aeE01+9X/+07jzb4n51UM7DOUfR/9j1wyLto62TOwzkaEdhiZ7aA0yZ44sinzXXTLL4Ygjkj2i/dPq1fD11/Dyy7ImwfXXJ3tESu0/tONeKaWUSqDsNCvpdkvUr7NZTPzz9N7kOWxU+QLc+d6yqG8AgPzRvM1ZHfViudE4vlc+b191DN/+9QTevuqYuBTSNUtfHTBMZnC0hqyOYEmJzzEMBjhoJFzwDvS7WBbNLd8AhdfBRzdBxaamOY5zS6RoH6YZ7E2rx0iJxbnpV3mvRftmyWgwkmHNoL2jPW3S2pBmSUtaF36mLZNze57L8yOf5/ZBt9M7pzcA65zrmPzTZMZ/Np6py6ayxb0lIePZVrltV9E+LBgK7va8zp2lwFdTOL966FApgF99tcThzJghxfDbb5cs6Kuukuffcw8cc4zkT99/v3Tf9+pVu4s+nF/99dfS5T9unOTMX3aZZGKnpsrr/+//oKgo8roJE+S/1rlza4/xhx9knCkpsvBnTYGAxO8MGiRxQI8/Ljcppk2D3Fy58dCQCByfT8bUpo0cZ8QIKYzWtWoVnH++nI/NJmsAPPdc7edcdpncrKib0nv99bLmwPz58tjrleL1EUdAq1Zy3L59IwuY1tSlC5x4YuRanHTQUOZePp1T189i+inTaxXtQyH4738lg9xulwVSx46VRVKbg0mT5HPWo4fcOFKx+fJLuYH266/yNVjfwtFKqfppxn0zpRn3Sil1YCuu8OCu9u/7iXX8sdXFda8vxuMP0rtdBo+eczhWc/T34dNsZvLjnHcfT4nM0lcqoaqd4C6Jb/79jiKY9xBs+EEem2wwcIIU9c2NmLv+9gQo/qV28d5ggraHw8Q5unitatH8QT8urwunz0kgnt/fDVBUVsSMohnM3TAXX1Cq4wYMDGoziDEFYzg87/C43Wi4dd6trNixolbx3oiRbml9uP+Ix8GbzjfzbPzlL5JH//33e99fOPe9Xz9Z+PJPf4L33pNO6WHDJF5n1SpZyPL332UR1LFjZSHQsLfekoL9sGGSPR8KyX6XL5fM+qFDYelSee2UKVLE79FDXjtihET7dOkCVitUVEiBsqAA3nxTCpaffioxOQB33AGPPCKL3ObX6GtYuFAK+s8+C5dfLtv2lHE/YIAUuc89F0pKZH99+8I330T2t2IFHH20FNknTIDsbPjkE8kWf+ABGQdIdvvhh0vxfvFiKcjPni3ndcstshgoyHF695ZFWnv2lJsH778v5zdlCkyskXzT0GsBcuPimWfgggtkvFu3yqKtNpusKZCbu/fPv1JKNSfxyLjXwn0zpYV7pZQ6sIVCITaXe/D4ov/j/auVJdz14S+EgBGH5HPHyT1j+gM7x2EjMyX67v/mIJxxH47LCWfp33/moRqVo/Z/wSBUlYKnTCpI8RAK1Y7PgZ3RK7dClxhXPVzzFXxxnyy8GwpI0T41B477G3Q7DtLywJradOeg1H4oFApR6a+koroi6YvZlleXM3PtTD5e/TElVSW7tndM78iorqOvZ8oVAAAgAElEQVQ4vtPxpJibdibQD8U/8PTipyn1lBIkiBEj2fZs0mc+y4wXIm24w4/z8+ILJrp02fvvN+Fi9pAh0v1uNEa2GQxSjJ89O5JZf955EsFTWioL3rrd0KmTROO8+mpkv1VVEmeTny/d+DWPVd/isF26wNq1Uhw/+WTZVl0t+x46VHK9QWJXevXafeHc666TAviWLZKDD3su3B9zDMybJx3xIJ37N94IP/8sxXWQ4viaNZLLn1YjWfH882Vx3E2b5EYHSMF/6FCZqfDPf8p5Z2dLx3w4hzwQAL9/91zyESPkOCtXRn8tvv1WzqVu4f+XX+RGzK23yqwHpZTaX+jitEoppdQBwmAwkJ9hx2KK/kfx4INyuWxIVwA+/7WY179fH9MYdri9VPuT2/UXq+N75XP/mYfSr2M2bTPt9OuYrUV7deAwGiEtBzKTEJ8z4/rY43O6DJYifX5vWTQ1v7c87jIYAj7Zp3MLBKKfbaTUgcJgMJBmSWsWi9lm2jI5p8c5PD/iee4YdMeuGJ31zvU8s/QZJnw6geeWPccmVxPFaQGD8gdxTb9r6NGqBzn2HHq06sE1/a7hpmvSmP7+Fp54fht/GufCF/Txx9YtlHnKGjRDYeLESHE+LBSSLPya248+WgrQ4SiWzz+HHTskb76kJPLmdkvcy4IFsihpQ3TpEilUgxS5jzqqdsTOwQfDkUfC9OmRbX4/vPGGdPuHi/Z7c8UVkaI9yM0JiByntFTO65xz5AZEzfM65RQ5nwULIq8/5hi47TaZUXDSSdL1Pn167SK9yRR57PPJNSspgeOPhz/+gJ3rC0d1Ld58Uzr8x4ypPcb8fOnqnzNn39dCKaUOdLo4rVJKKZUkJqOBNpl2Npd58Ad3z3fdm7GDOrJuRyWf/VLM81+tpmOrVIYcFN184lAoxNaKajpkpyStaNAYx/fK10K9OrCZrZDZPr7xOdY0OPZ66HUazHtQ4nNWfwnrFsQWn9Nl8N479qtd4HVDSra87Yf/9yjVVMKL2WbbsnctZusPJv7Glslo4tj2x3Js+2NZXb5aYnTWz8Xtd/Phqg8pXFXIgPwBjOk2hr55fTEaGtf/Nyh/EIPyB9XemO+nazc599POdvPwpCzOObk1sxduJD2jHK/TQbo1HavJCkgEjNUaeXmnTvUfq+72cGG8tFTer1gh7089dc/j3b5dsu73pUuX3bdlZ0vMTk0TJsCVV0pnee/eEh+zbRtccsm+jwGS/1/3GCDFdJDu91BIutX31LG+dWvtx/fcIxFD338P994r8Tl1vfQSPPqojLvur63l5ZEOfmjYtVixQm4stG1b/xgLCurfrpRSLYkW7pVSSqkkspiM5Gfa2FzmIRhFJIbBYODGE3uwsbSKnzdV8MDHy2kzti8H5adHdXxfIMg2ZzWt9+O8e6UOeLZ0sKTFNz6nVVc4/b+143O+ewZ+m9G4+Jz6hEJQuQOqKyA1F2yOptu3Uvshk9FEpi2TTFsmlb5KKrwVePyepIyla2ZX/tLvL1zS+xI+X/s5HxV9xLaqbSwsXsjC4oW0d7RndMFoju94PKmW+EVfjT7LzeR/Z/HZjFS6FvgZNyan1sfnzAlx3HGRG381O9DHj5f3EybU3l5T+L/RcAF66tQ9F//z8ho25n0dK2zsWFnw9OWX4cEH5X3r1rWz3xtznPA5XXeddLPXJxypE/bbb5EFbpct2/35b74p13XUKFl0Nz9fcvE//hgee2z3Qn5DrkUwKDdSwtE5daXEacKZUkrtT7Rwr5RSSiWZzWwiP8POlgoP0aw9YzUbmXR6b65+dTFbKjxc89piMuxm2mWlcP4RnTiqW86+dwK4qv1Y3F6y06z7frJSKjnC8Tm2dCmq+6qa/hjh+JzOx8IPz8NPr0bic7oOgyE3Q0b7ve9jzVewcKpE4qS3gYGX7rnoH/DL86pTpYBv1v+DlEq1pJJqScUX8FHhrcDtcxMMRTcrrylkWDP400F/4oxuZ/D9lu+ZUTSDpSVL2ejayLNLn+XlX1/mxE4nMrpgNO0c7aLa9w/FP/DW72+xrXIbeal5nNvz3N068D0eKcqXlxnp1cfL9Pe31Pp4bjc/pZ40AsF0GlPW6N595/5yJRpnb5pqglBmJpxxhmTq33GHLBh75ZVgbqLqTLdu8t5k2vc5AXi9sohsbq50/d9/v4xv7NjIc954A7p2lbHWvA6NibPp3h1mzpRFeWt26yullIrQjHullFKqGUixmsh1RF+0ykq1cu7ADhgAfzDEjkofv2yq4N+fr2DBqu0N3k9ppRenxxf18ZVSCRaOz0nPl1z6eAjH54x9EzrsLKat/hJePQe+nwL+PSyoGV6ctvgXcG+V91/cJ9v3xlsJ5et3xgElvkCpVHNkMVnIScmhY3pHclNysZuTMzPOZDRxdLujuW/wfTx13FOc1OUkrCYrVf4qCosKuWLWFdz97d0sLF7YoBsM4cVpV+xYwXbPdn5dU8LTi5/mh+Ifaj3vtRdkBuHh/bxkZgUZPNxT6y090095dTmlnjIAPP7omh/CTjpJur7vvVcWUK1r27bIv8OLvJaVRX2Y3YwfDxs3yoKwHg9cfHHj9xmWlwcnnCCzCNau3f3jNc8J4P/+TyJsXnhBFqcdMgSuuUYWsA0LrxNQ87/o7dvlNbEaO1Y68P/xj/o/XlJS/3allGpJtONeKaWUaibS7RYCwRA73N6oXjfnt9pBpSFgu9vLa9+va3DXPUCJy4vFZMRuiVMxUCnVdHbF5+wAT3kC4nMek2L8989KfM7QW6HLkNrPXzgVKrdDaGcWfyggjxdO3XfUTigEVWWS55+aA/aMpj8fpfZDBoMBh9WBw+rAF/Dh9Dlxe90EQolfXL5LZheu7XstlxwSidHZWrWVRcWLWFS8iHZp7RhVMIoTO524xxidt35/i1JPKUGkArx+2t9Z687krr5ruPhYB2U7jMz6JJVF39k5eYybo4c2LDKo1FPKBpcfh0WuFVga9Lr0dJgyBcaNgz59ZJHadu1gwwb48kt5zhdfyPv+/aXb/IEHpGhts8nirK1bN+hQtYwYAR06SARNnz7Qr1/0+9ibyZPh2GMlq/6yy2Sx1+3bYckSeP99uVkA8NVX8MgjcPXVMHKkbHvpJTjsMLj0UvjkE9l2+unw7rswerR042/dKtetXTsoLo5tjIMHS5zPk09KPM+pp4LDIZE9H3wghf277270pVBKqf2aFu6VUkqpZiQr1YovEIqq+724wkPdkl1o5/ZohEIhiis8tMtKwWLSSXlKNXtGI6Tlgi0jAfE5g2Hh87DkFajYCDNugC5DYegtkfgc55ZI0T4sFJDtDRUMgGur3IxIywWLhhwrFWYxWWhlakW2LZsqfxVOr5Mqfxy+7/ch3ZrOWQedxendT+eHLT9QuKqQpSVL2eTexHPLnuOV5a9wQscTGFUwig7pHWq9dlvltl1Fe4CsIz+hdP4ZrJo5nH+8m4PNHuKgg71MemQ7509wRjWuQDBAeXX5zrdsIHNnF/7eM27OOQfat5eImMcfh8pKaNNGIlwuvTTyvIICKTI/+qhsDwSkqB9L4d5olHiaBx5o2m77sB49YNEimDRJYm62boWcHDjkEBk/gNMpx+7eHR56KPLarl0lt37iRHjmGYnxufhiKfw//TRcf72sB3DLLRJxM2FC7ON84gkYMEBuNISL9B07yg2Rc8+Nfb9KKXWgMMQynUzF38CBA0MLFy5M9jCUUkolyZZyD5Vef4Oee93ri/l1cwXBOj/SO2an8NKfj4j62BaTkfZZKRiNTRTmqpRKjGrnzriZOHbi7lgN8x6CDd/LY5MNBoyH/hfD+1dJPE7N4r3BBPm94ewXYzuezSH59ybtN1KqPv6gH5fXhdPnJBDP7/19WFuxlhlFM/hi/RdUByKZM/1b92dMwRj65/fHaDBy67xbWbFjRa3ivREjPVr14OGhDzf5uExG064ufIuxYV34iXLXXXDffbB+PbRtm+zRKKWUaiyDwbAoFAoNbNJ9auG+edLCvVJKtWyhUIhN5R6qffv+I3zBqu38+/MV7Kj01irem40Gnhjbl15to4+cSLOZyc9ITp6uUqoRgsH4xueA7HfVLJj/b4nPAem673kq/PpBJC7HYJLYm+P+tu+onL0xGCAlW96aanVIpQ5Alb5KXD4XVf6qmPLem4LL62LWulnMKJpBcWUkQ6VtWltGF4wm257N1GVTd8XlGDGSbc/mmn7X7LZAbVNLMaeQbk3fY4xPIvn90tnepw98/HGyR6OUUqopaOG+BdHCvVJKqUAwxKayKnyBfS/2tmDVdl77fh3FFR5yHDa2OavZ7vbSKs3K5PP70TqGInx2qpXstOgXzFVKNQP+aomc2dNCsk3BW7kzPudVCO6cIZR/KAT8cvMgvQ0MvLRxRfuajCbNv1eqAQLBAC6fC5fPhS+QnIXnA6EAC7csZEbRDJZsW7Jru91k59DcQ9nu2U5FdQV5qXmc2/PcuBftazIZTaRb0nFYHZiNiZ3Ns3UrzJoFhYUSYTNrliwkq5RSav+nhfsWRAv3SimlALz+IJvLqwjUzcHZh3U7KrnmtR9xVwfonufgiXF9SYlh0dn8DDtpNo2oUGq/5SmX+Jx4/s5fuhrmPQzrv5PHNeNzzHGYuWO2QVoeWHRWkFL74vF7cPlcuH3upHXhr6tYx4zVM5izbk6tGJ2+eX0Z020MA/IHYDJE/ztKU0i1pOKwOBLWhT93Lhx3HOTmws03wx13JOSwSimlEkAL9y2IFu6VUkqFeXwBNpd7ov6De9HaUm5/ZynBEBzbPYd7TuuNMcqYCaPBQNssOzZzcv6gVko1gYAfKkug2hW/Y4RCsGo2fPVvcO2Mx8hoD0Nuga5D43NMzb9XqsGCoaB04XtdeAPepIwhHKPzUdFHbKmMLFrdJrUNowpGcWKnE3FYHUkZm9loJt2ajsPiwGTU33mUUkpFTwv3LYgW7pVSStXkrvZTXOGJ+nUfLNnEE7NXAnD+ER25bEhB1PuwmIy0y0rBpIvVKrV/87rBvU0K+XE7Rj3xOV2GwtBbpJDf1AwGsGdJ/r3R2PT7V+oA5A14cXqduH1ugqF9x/E1tUAowKLiRRSuKqwVo2Mz2Ti+4/GMLhhNp4xOCR8XgMFgINWcisPqIMWckpQxKKWU2j9p4b4F0cK9UkqpusqrfGx3RZ9X/eTslby/ZBMAd5zck5G920S9D5vFRNsMO0Yt3iu1fwuFoHIHeMoOrPgco3Fn/n1m0+9bqQNUKBTC7XPj8rnw+KNvDmgK653r+ajoI2avm40nEBnD4XmHM6ZgDAPbDExajI7FZMFhcWgXvlJKqQbRwn0LooV7pZRS9dnq9ODyRNctGwiG+Ou7y1i4thSLycCj5xzOoe2jL26lWE20ybBjiDJuRynVDPm94N4KvjgW65IRn2O2SnyONTF51UodKHwBH06fE7fXTSAUSPjx3T43s9fNZkbRDDa7N+/anp+az6iuoxjReUTSYnQMBgNpljQcFgf2eNx4VEopdUDQwn0LooV7pZRS9QkGQ2wsq8IXiG5qu8vj59rXF7NuRyVZKRYmX9CfNpnR//HpsJlpnaF/tCp1wPBUSP59MI5xGXuKzxlyM2R2iM8xralSwDdb47N/pQ5QoVCISn8lTq8zKV34wVCQH4t/pLCokB+3/rhru81kY3jH4YwpGEPnjM4JH1eYxWTZlYVvNGg8l1JKqQgt3LcgWrhXSim1J15/kE1lVQSj/Bm+sayKa179kQqPn665aTw5ti9ptugXdUy3W8hLt0X9OqVUMxUMQuV28JTH9zi7xedYof94GHBJfOJzDAawZUiEjubfKxW1ZHfhb3Bu4KPVEqNT5a/atf2w3MMYXTCaI9oekbQYHe3C339NmwYTJsD8+TB4cNPs8+674Z57aifQdekC3bvDrFlNcwylVPMXj8K9/garlFJK7WesZiO5MRTO22elcM9pvTEZDawucXPfx8sJBKO/ge/0+Ch1e6N+nVKqmTIawZEHWR3BEscCVHZXOO1pOPlBcORDwAs/TIHXzoXV85r+eKGQ3IwoWyszC5RSUbGYLLSyt6JDegdyU3KxmRJ7075DegeuOOwKpp00jcv7XE67tHYALC1Zyv3f38/lMy/n3ZXv4vQ6EzoukJkJLq+LLe4tbHJtosJbkZSFfvc3y5fDeedBt26QlgbZ2XDkkTB9etMvu/L4/7N33uFNlW0cvk/StOketLSlzLIRBAEFRIYoIAiuTxEUtQ4+RXGifooLcaGg4kZUQMCFA7UoQwUEN0NUECir0JYCLd1t0mac74+HkKSDDpqy3vu6cqU5OeddOV2/53l/zwyYN69+21QoFIqGRgn3CoVCoVCchIQE+BEWaKr1dV2bRXDvhW0B+G1XDrNW76pT/7klZRRabXW6VqFQnKD4BYh1TUhj8FUhRk2DNhfCNZ9Jtr3BDwoy4Jt7YfE9kJ9e/306HVB0EPIzxNtfoVDUCk3TCPEPIT4kniYhTQjxb1ibmCBTECNbj+StC99icp/J9IjtAcBBy0HmbJ5D0rIkXv/zdVLzUxtsTJ6UOcrIseSQVphGtiWbUkfpcRnHyUBaGuTlwdixIqw/9RTExsL118P//le/fSnhXqFQnArUfn+8QqFQKBSKE4JGwf6U2Z1YbbXbvj68Szx7DpXw6fp0Pl2fTvOoIC4+M77W/WcXleFnMBDof3y2qSsUCh9hDgP/ELDkSMa6L6w1/YPg3Duh40hY/YLY56SukefuN0CPpPq3z7FZID8NzBEQGKnscxSKOuBv9Cc6MBqn2UmxrZjCskLKHA0TEDNoBnrE9qBHbA/2Fe3jm13f8N3e77DYLSzbs4xle5bRJboLIxJH0CuuF0ZfBSCrwJWFX1RWhL/RnxD/EOWFX44hQ+ThyYQJMHIkvPqqCPkByo1RoVAojqB+gygUCoVCcZKiaRqxYWZMxtr/Ov9v/0R6J0YBMOOH7fy5N7fWbei6zoECK2V2tTVcoTjlMBggOFoy8E2BvusnsuVh+5wXPOxz3vGtfY4lV+xzSovqv32F4jTBoBkI9Q+lSUgT4kPiCfEPQdO0Buu/SUgTxp05jrlD53LbmbeREJIAwD/Z//DcH88x7rtxfJbyGQVlx8cmy5WFn16YrrLwa0CLFlBaChZL9ecWFsKDD0KrVuDvD82bwwMPeF+rabBnD/zwg3ytaTBwoHc7Nhs8/DDExUFgIAweDLt3u9+fOVOu++OPimP45BN5ryb+9WvXQt++0kezZvDSSxXP0XV46y3o2hXMZmjUCEaPhr173eekpIi90OWXe1+7Zw+Eh0tAxBVnX7NGrm/ZUgIhjRvLLof0cpva5s6VeaxcefS1cLFzJ1xzDcTESLudO8M771S/BgqFou6o4rQnKKo4rUKhUChqSpndSWa+pdZ+9SVldu76aCO7sosJM/vxxjXdSYisvUBnMhpoEhGI0dBw/7ArFIoGprRQCtg67L7rw2aBde/Bn/PBebifFudB//shvJlv+vQPguAYMNbeekyhUHjj1J0U2STjvKGy8D373pi1keSdyaw/sB4d+ZvI3+DPgGYDGJk4klbhrRp0TOVRWfhuSkrkUVgoovGdd8IZZ1QukntitUK/frB9O/z3v1L89e+/4e234cIL4dtvRYhesADuvVdseB56SK6NjRVB+pZb4L33oH17iI6GUaMgOxumT4du3eCXX+T8vDyIj5fzX3vNexwjRsDGjSKsGwwSFPjxx4rFaf39oaAArrsOEhNF8P/xR1i6FIYOdZ87YYIECq69Fvr0gYMHpc+AAOknOlrOe/NNuOMOmD1bCuw6nTBokKzBP/9AgsSvuPNO2LoVBgyQeaekwKxZIuD//beI8+Au1NujhwQMqloLkDb69IGoKLkmMhJuv13ee+459zofC6mpEpCZMweSko69PYWiofFFcdpTSrjXNM0E3A50A84COgEmYJyu6+9WcU1f4FLgfKAlEAbsA34Apuq6vqMW/ScBc45yynhd12fWpC0l3CsUCoWiNlhtDjLzrdT29/r+fCu3f7CBPIuNZpGBvHFNd0LMtXfSCzAZaRJubtBsO4VC0cC4stUtub6xz3GRmwqrp0Hab/La6O87+xwQlScwUh7qZ5hCUS+UOkopLCukxFbS4EVb9xXt45vd3/D9nu8psZccOX5GozMYmTiS3vG9G9xGxxNN0wg2BRPqH9rgBX+PN1u2wOTJsGwZ5Oe7j3fqBN98I2L30XjuOXjyScli79LFfXzWLLj1Vli+XMR5EIE5Ph42b/ZuwyXcd+4sorjx8K0wY4aI/Zs2SRABJGv9hx9g3z4wHY7vHjwoAvnEiTB1qhyrSrjfsweWLIGLLpJjpaWS1R4RIaL83r0QFgYHDohN0KOPuq/fvBnOOkt2EzzzjPv4sGHw888iwH/2mbz/4YcwZoz7nJISCArynvdPP0nQY8ECCRCAW7g/91xYvbrytcjKglWrJIs/PR02bJDMf5BfmYmJsH+/rFF4eNWfXU04EYX7Vavkcd998lkpFEfDF8L9qRbmDQZmAElAHLC/Btd8DkwErMAHwGuIcH8zsFHTtD51GMdXwJOVPJQSr1AoFAqfYDYZiQ7xr/V1ceFmplx6BiajRlquhScX/1vrzH2AUpuDg4VqG7hCcUqjaRAUBREtICDEd/1EtoRLXodh0xrOPqckR9nnKBT1SIAxgOjAaJqGNiUqMAp/Y+3/RqkrTUKaMK7LOOZeNJfxXcfTLFR27Gw+tJmpa6dyy3e38GnKp+SX5lfTkm9weeFnFmWyr2gfBWUFDR7cOF54Fqe991644QZo2hT+/RemTKn++k8+kazv+HjJDHc9LrxQ3l+xwn1uQQFkZlbd1qWXuoVqkOx0gF273MeSkqT9JUvcxz76COx2KahbHS1bukV7ELFe10XkHjFCfP1bHd4I8uyzMn7XnGJjZVeA55xAsu39/cUy59FH4eqrvUV78BbtCwulvQ4dJGCwfn3Fcd56a9VrsWqVBEtWrICrrhJLItcYQex/Skrgt9+qX4+TEdf8C46P65ZCccoVpy0BhgMbdV3P1DRtMvBENde8DMzXdX2f50FN0yYBzwCzgC6VXXgUvtR1fW4tr1EoFAqF4pgINZuwO3RyS2q3Pb1zQjgTh7Rn6pKtrN+Ty+srd3D3BW1r3X9xqZ1DRaU0Cjm9sscUitMOox+ExoHZAsVZYPeBJYamQetB0LyP2z6nIAO+udd39jkOOxTuB2ug2Of4NZzQqFCcqhg0A2H+YYT5hzV4Fn6gXyDDWw1nWMth/JX1F8m7klm7fy3Zlmzm/TuPj7Z+xICmAxiROILWEa19Pp7KcHnh51pzCTYFE2IKweyLnUUnCJUVpwVo21ayrJ96SvzTc3K834+KErE6JUWE45iYyts/eLDmY4mN9X4dGSnPnn0PGSLZ9fPnwyWXyLH586FnT9klUB2V7SDo3VtsbKZPl9dffCHPFgtccEHF8xMTvV/Hx8PLL0vgIDZW7HPKs2+f1AFYvNh7ZwNI4KQ8LVp4v65sLUAy/z2z/0F2GkDt1l6hUNScUyrjXtf1Ml3Xl+i6fpS4aoVrni8v2h/mecACdNY0rVG9DVKhUCgUCh8SGexPqLn2Xs1DOsVyzTkign21cR9fbcyoU//5Fhv5FludrlUoFCcZpkCIaA4hMWLy66s++kyAMZ9As95ybM9Pkn3/+0ywW+u/T5sF8tOgOFsMhBUKRb1wvLLwNU2jW+NuPNb7MWYNnsVlrS8j2C8Ym9PG93u/555V9/Dg6gdZk7EGu9OHdTyOgisLf3/xfjKKMk6rLHwQj3UQ65dffhFx2vPh8lp3OqXQ66hRYjnj5yci/lVXiUg9caKcp2ngcEBubtXFaXXduyDr2LHu4yCe80ajiOnJySJ4b9kiGeudO9esOK2xEkemuDjv106nZMJ36CDZ69995/2YN69iGy+8IM85OTBunBSzjYyUzHmrVcb82WdSgNdslh0JixfLeZ6/1tauleehQ8UG5uKLxSvfxTvvSLZ5eebPl7GB7Ch45hl4/nnpq00bse5xkZcn6+vyw/ektFTG7Vr7ypg8WdZ60ybZoREZKWMdM6byYMGGDRJkiYyUfnv2hC+/9D6npkV5k5Lc82/WzH0vpaa62xk8WO5Rf38Jsjz8sMzLk6QkuVcPHJB7NyxMxnfLLZUXZP7hB6ldEBoqtkQDBohVkeL05FTLuK9PdMD1W9tRy2u7aZp2D2AGMoCVuq6nV3ONQqFQKBT1QkxoAA6nTklZ7f75vOm8VuzNsfDTjmxeW7GDhIhAeraMqnX/h4pK8TNoBAcc258ZK7Yc4M1VO8nIs5AQEcjtA1szqGNs9RcqFIqGxRwO/qFgyQFrvm/87132ObtWwprpUHRA7HO2fiPZ9y37168/va6DJU+K8gY1AvMJYmybslzmn58O4U2h3/3QrpL0VYXiBMYzC99qt5Jfmo/FXol65QPiguO4ucvNXNPxGlalrSJ5VzJphWlsydnClpwtNDI3YlirYQxtOZSIgIgGGVN5bA6bVxb+ye6Fvzp9Ne/88w6ZRZnEh8Qzrss4ekb19ypO+9VXcm5pKXTt6haFXXTtKs+JifDnnyLili9OW1goxWlBhOUbbhAxdubhKoPlM+zffFOKvk6aJLYv06Z5vz96tNj5OBwyroULRdQ1mSAjQzLxBw069vVp00Y8/0NCoHlzt+1PVXzyiczfxfffw9NPi1XNrFliW7N1qwRDbr5Z/PA/+ECKzebmuq+bPt2drX/77bI+b70lgRGX0D1woBS0XbRIXl90kfjjX3652+d+3z545RW47TYJorz7rhTjPess6NhRghKXXirjnjFDBG4XroBITWyHxo6VwMNTT0lx4jfflEDKH3+421yzRnZJdOoEjzwigYSFC2W85esAgNQHMJvd98D06TI/V6Do1lvFIvBBlBMAACAASURBVGfRIrE1cu1EcO34eOMNsTMaOlQ+v19+kaDK3r2y5p7ouqxfp04S5Fi7VmotxMRI7QYXCxfKOAcMkLnqugQILrhAPuv+/atfK8WphRLuq+YqIBT4Tdf1SjYTHZW7y712aJr2LnCPrus+SAtSKBQKhcKbxqEBZBbolNpqHns2aBoPD+/A3R9tZEdWEU8u/pc3xnSneaOg6i8ux8HCUuINGmZT3Yq/rdhygEmLNpFVZMXhhIOFViYt2sSzoMR7heJExGCA4GgICIOSbCgrqf6a2lKZfU7hPvjmPmjRF/o/UP/2OU4HFB2UgERwDJiOo4VFynJIvhuKD4LTLsGL5Lth5CtKvFectJj9zJj9zNgcNvLL8im2FaP7svj1YQL9AhnWahgXtbyIv7P/JnlnMn/s/4ND1kMs2LKAj7d9TP+E/oxsPZI2EW18Pp7KcGXhF5UVEWAMINQ/lGBTMNpJVER7dfpqpvw6hWxLNg7dQeYBB1OKphD9/Rw+meX+eR0fL370Z58t4mhV4nVCgoi1L70korqLM88UkfXrr0UkHjsWbrpJfjVVlc1tMonNi2dW/FNPiSgPbsF52TI45xx4/33x6B80SDKiJ06sn81mo0eLAJyeLvY25cnOlgADiEg+frwI/Hv3SrHZlStlzB98ADt2uAXjO+6QwrPjx4vQPXOmO9s+LU0yw0eMkEz8K66A886TzPCOHUWkBgkq+PmJcN23rwjTM2e6RXuQIrorVrgF5auukvHNnu0OhiQliXD/zTcioruYNw+aNKk+WAFil7R8ufvz6txZgjdz5shnr+vy+uyzZU1c591xh6zTgw/KWnt++wQEeN8DUVFyX23eLAWK+/SRe2vRIhl306beY/rxR+96ArfdBu3aweOPizjveb7TKQK/q6Dx+PGyY+Ldd93CfXGxHB892lv4v+02Kcb88MMSiFGcXpxSVjn1haZprZAitXakcG1N2Q3cCbRHCuU2AUYBqcCtwOxq+v2vpmnrNE1bl5WVVYeRKxQKhUIhGAwacWFmTMba/aoPNBl55vLORAX7U1zqYNKX/9TJ+kbXdQ4UWCmz122b95urdh4R7QEcTsgqsvLmqp11ak+hUDQQfv4Q1gTC4sUL3xccsc9Z6GGf8/Nh+5y3xOqmvrGXSpZ74QHxwj8erJnuFu1BnosPynGF4iTHZDSJjU5IU8IDwjFqdQv81xZN0+ga05VHez/KrMGzuLzN5QSbgrE77axIW8G9q+7lgdUPsDp99XGz0QEodZSSbckmvTCdHGsONsfJYUv4zj/vHBHtAdLmPMpvj03lz31buPdeEShjYkS0v/BCOP/8o7d38KDYh0ycKEVZn39ess1XH65bvnCh+1x/f8nCf+YZ+PjjikVeyxen7dVLnj2lGFdx2p49RbROSxOhuabFaWtCZKQEEQA+/1yyvmfOhP/9T+xzXn9d3tN1EeLtdrjsMjk2ebIIvdOmyfj69JHzmjaVNXrySWkrLw+KikSYdvVjt7vnnJcn8zQapQ1Xxrknzz4ra9q1K9x/v9jogGTBe9YtiI2VcXsW+fWsFeAiOxuWLpUM95oEQO680/vzSkqS4MrixfL6r79kp8G118rOAlcB3UOHYPhwCYykpHi3ebSivDXBJdo7ne417N9fPoMNGyqeX94uaMAAuaawUF5/952I+WPHehdfLi6W74/ffpMdFYrTixMu417TtFSgRXXnefCBrutHccSqdf+NgSVADHCHruuV/MiqHF3XfwR+9DhUAnyqadpvwF/AGE3Tntd1/a8qrp+FFMOlZ8+evk8zUCgUCsUpjdGgERtmJjPfgsNZ818rMaEBPH3ZGdzzyV/sy7My+evNvHDlmbUOAjicOvvzrTSJMONXy2sz8ixHRHt3e3JcoVCcBPgHgykILLny8Il9Tgu3fc5PL0ph2bXvwtZvod9EaDWgfu1zQKxzyoogMFIeDZn5mp/uFu1dOO1yXKE4RTAajESaI4kIiKDQVkhBaUGDCeZxwXHc1PkmrulwDavSV7F412L2FOxha85WtuZsJcocxbCWYqMTaY5skDGVx6E7KCgtoKC0ALOfmVD/UIL8gk7YLPzMoswjoj1ARK8l5K65jNSfu/P6MrEpOeMMsVVZt05EyrCwqovTbt/u9gRfuNBbqAfvH8mRkZJRPXWqiNYDBnhb25S3zgkPl+eiIvcxl+CckSG2O4GBIhBXV5y2qMhtsbN/vxwr720PEggYNkzeu+8+CTBMnizvJSSIiH7++dLGnDmScf7OOyJCg2S2T5smFirXXy/+6SAC+bRpUsRW1919BwbKs0vAfuwxeR450ntclQnpiYni7z9liozT5S9vMMCLL3qfGxnp/RkaDGKf89JLcjwqStqw2WoeAGnf3vu1yQStWrn95l1zuu02eVTGwYPe7dS0KG9V/Pab2Oz88ktFX/vyRYANhooZ+579hYa65zB8eNV9HjrkneWvOPU54YR7YCdQGzuZygrL1onDov0KJGP+bl3XK6nPXXt0XU/TNO1b4FqgPyLiKxQKhULhc/z9DIfFe2uttp53iAvjoYvaM2XxFv5Kz+eV77czcUi7Wv9jaHc6ycy30iQiEKOh5tcmRARysNDqJd4bDXJcoVCcJGgaBEUdts85JKK3L/o4Yp8z222f8+1Esc/p9wBE1LN9jq5DyWE//4b0vw9vKvY4niKmwU+OKxSnGJqmHfHBL7YVU1BaQKmjtPoL6wGzn5mLWl7E0BZD2ZS9ieRdyfye+Ts51hw+2PoBn6R8Qr+EfoxIHEG7yHYNMqbKsNqtWO1WjAYjoaZQQvxD8DOcWBJPfEi8V8Z9eK9lRPX+ni4xXZg/zJ1+/fffksm9aBG0bVsx837lSvFbdzpFgH/00cr7a9LE/bXRKAGB8gVkzztPvMXPOsv7uEtUPe889zGX4Pzyy/L1kCEiOLusZFwMHCi2KS6mTxdxPT1dbIDAHb+eO1ees7KkPYtFvNk7dIB77nG3MXeuZNi//7772O7d0LKlW9w3GkXE3bZNXruOt2kjtjQuJk+W7PvffpN5uoTtxYsluFEZLvsaV5sggYJ333W/1jQpaHvHHRWvL/9vR1KSBFE++UR2CcybB927i+VNTajsXxDPPlw2QM8+K3Y5lVG+r8qKB5dvtyp275ZAUOvWErho0UICURkZMtfyte01reqdBa7+XNe8956sdWW4/PUVpw8n1k91QNf1C45Hv5qmxQM/AB2QTPt6Ee09cG24Cj7qWQqFQqFQ1DNmk5GY0AAOFtSuzMrA9o3Zc6iE93/dw7eb9tOiURDNIoP48I+9HCiwEhtm5ppzmtO7daOjtmNzODlQYCU+3Fxj4f/2ga29PO6NBogJMXP7wNa1moNCoTgBMPpBaKwUsS3OEtuZ+sYUCH3ugA4jYM002Pur2Oek/QHdr4ceN8o59ckR//u8w/73Pg4s9rsfFt0qGf+6EzQD+IfIcYXiFCbYFEywKRir3UpBWQEltobxitA0jS4xXegS04WDJQf5dve3LE9dTqGtkJVpK1mZtpL2ke0ZkTiCvgl9MRlMDTKu8jicDvJK88grzSPIFESIKYQg04mRkjuuyzgeWfMIxfZinLoTg2Yg2C+YcV3GeZ3nyqLPzT16cdo2baRYaE080etrE4JLcAbJ+jeZKhY5Lc/113sHAMqTny9+5xkZYuHToUPFc4YOrbgOlWXt14U2h8s2NGsmHu5Ho77WsX176N1bdgMMGiTFWWfMqPn1W7eK/74Lm02y7V3r7JpTcHDN7o+aUtX8v/5a7tvFi70z95cvr3tfrjlER9fvHBQnNyeccH880DStKZJp3wa47bBlTX1z2D2MGrplKRQKhUJRf4QE+GEP9ienuKxW113fpwV7c0pYuS2LmT/uIjTAj6IyO04dDhWX8dJ3KdxHu2rFe6vNwYGCUmLDAmok3g/qGMuziNd9Rp6FhIhAbh/YWhWmVShOZkxmyX635ErGuq/sc0a+5m2fs+492PatCNy+sM+xl0F+htgDBUeD0cfinaN2P8cVilMFz0K2BWUFFNtEDG4IGgc1JumMJEa3H83q9NUk70omtSCVbbnb2LZ+G7M3zT5S7PZ42egAlNhKKLGVnHBZ+DanePKX5ocTHF3xM5s5U56rK047erTYu3zxhRRU9cRqFTE3NFReBwdXtCupLStWiGDs5yc2MytXirWNq1hsVSQmyqMySkrECmXLFliyRGx3KiM+3p2tX9/85z9S6PSJJ8TvvnwmeFaWO7PbVYg2L6+i1UttSUqSbP/HH5c1veaaml/72mtwySXuLPm5c2VMF18sr7t3l90aL74ogZOICO/rPedUG6qav2vNPDPrnc6KtkG1YehQGffTT8vX5XdD1HUOipOb4/8T/DijaVpzYCXQErhZ1/U5NbgmHggHMnVdz/c43k/X9TXlztWAh4A+QDawtP5Gr1AoFApFzYkI8sfm0Cm01rygmaZpPDi0PZn5VrbuL6Sg1G3R4NQhp6SMD//YW61wD1BSZierCBqHmmvU96COsUqoVyhORQIjJVO86KBvCsl62uesnwMb5kFhpm/tcwDKisFWIjsLAiPBUM/FNddMh9IC72OlBXK83ZDKr1EoTkFMRhONAhsRaY6ksKyQwrLCBvPBN/uZGdJyCINbDGbzoc0k70rmt8zfyC3N5cOtH7Jw20LOSziPEYkjaB/VvvoGfYRnFn6gX6B44R+HLPx3/nmHQpvbJi1j7uOkFYczoXsadw0Sv+6vvxaP8CuuqL447f33S4bzVVdJAc9zzhGxPiVF/O4/+0xsawB69IAFC6Q4bevW0Lixt8d9TZgyRcbWvLkULC0srF2WeGVce620OWaMWOksWOD9/th6q+BYNa1awQsviK/+OeeIkN+oEezdKwVjO3d2W/r06CHPkybJuptM4okfXAcvidGj4d575bMaObJ2InROjlgLXX457NgBb7wh47zxRnnfYJA6AEOGSP2Bm24SW6H9++H33+Hff2HnztqPuar5X3SRCOsXXyxFbp1OmVd5r/vaEBoKs2bJvdGli9wLTZrIfeKyYlq5su7tK05OTjnhXtO0hxC7G4Buh59v1DTNtVHpJ13XPVy5+BER7dcDLTRNm1xJs3N1XU/1eP0ccANwIzDX4/hqTdNSgLVABiLu9wU6I4Vqr9V1vdxf2wqFQqFQNBzRIf7YnU4sZY7qTz5MgMnIU5eewdWzfqN8jVunDgdqYcFTZLVj1EppFFKFoaZCoTg9MJogPAGsBVCSXdEMtj4wBULv28U+Z/ULDWOfo+tgyRP/+/ouYKuK0yoUXhg0A+EB4YQHhFNiK6GgrACrvXa2gHVF0zQ6R3emc3RnskqyWJK6hKWpSyksK2RV+ipWpa+ibURbRrYeyXlNzsPk6504R8Fit2CxWzAajISYQgjxD2kwW5+qitNuW9qP2z9zF6d94w0RP6vDbJYs+BdeEK/5jz+GkBARoidM8LZ9efppOHCg6uK0NWHVKnnOz5fs98BAGDGidm2U588/5fmjj+RRnoYQ7kEE9HbtJEP8uefAbheR+LzzvIu7nn8+PPIIzJ4tvvlOp/i710W4Dw+Hyy6Tede0KK2LBQvkc3/sMRnrFVfAK694Z6X37Qt//AFPPSUCeF6eFCHu2lUCOHWhqvm3bStBp0mTZPdCWBhceaWsXZcudesLJDiQkCBe/TNmyA6NuDjZjXLzzXVvV3HyotWmUN3JgKZpq4ABRznlfV3XkzzOr8kCnK/r+iqPa+ZyWLjXdX2ux/FpwDlAWyAKcAJ7ge+Bl3Rdr7FNTs+ePfV169bV9HSFQqFQKGqMw6mzL8+CzVE7oWzcvHXszCr2OmbQoFN8GK+OOauKqyqnUXAA4UHH759YhUJxAuF0iPd9aZHv+tB1b/scgNB439nnuDAY66+A7XtDIGN9xeK0CT3g5mMw1VUoTiHKHGVHbHQaWusodZSyJn0NybuS2ZXv/tc/IiCCi1pexLBWw4gyRzXomKrClYUf6BdY4/pDdeG6JdfxT9Y/XuK9UTNWKE57olNUJALwDTfAm/VdDfE044YbIDkZMjOrLozriauwblrasVv1KBS+RtO09bquV2GAVcc2TzXh/lRBCfcKhUKh8CWldgf78qy1+qf2t52HeG7JVgo97HIaBZmYOKR9jaxyyhMTGkCoWYn3CoXiMKVFIuA7a74jqNbYLG77nMOeyz61z3Hh5w9B0eB/DFYVKcsh+W4oPijivcEPghvDyFeUVY5CUQ6H00GRrYiCsgIcvvyZUgm6rvNvzr8k70zm18xfj/jwGzUjfRP6MjJxJO0j2/tUMK8pRs1IiL/vsvBXp69myq9TyLZk49AdGDUj0YHRPN7ncfo37V/v/fmKmTNh/HixXDnnnOM9mpOX3FzJJk9KqnkARAn3ipMJJdyfRijhXqFQKBS+pqjUzsFa2NyAiPev/LCdA4Vi4Di0Uyz/G9ahmqsqR9M04sLMBPrXsw+0QqE4eXE6oeSQWM34kry9bvscAIPJd/Y5npgCpYCtXx3twlKWi6d9fjqEN5UdA0q0VyiOSrGtmMKywgaz0fEk25LNt7u/ZVnqMgrK3K65bSLaMDJxJP0S+h1XGx1PzH5m8cL3C6rXoMLq9NW88887ZBZlEh8Sz7gu404a0d5VnPaJJ6BDB1izpvprFBXZvRt+/hnmzROP9k2boH0NS0Ao4V5xMqGE+9MIJdwrFAqFoiHIKS4jr6SsVtfous7zS7ex/N8DADx5yRn0axtdp/4NmkZ8hJkAPyXeKxQKD+ylUrzWfgxV3qqjSvucidBqoO/scwACQsVCx3jKlRxTKE5YjqeNTpmjjNXpq1m8azE7890VMsP9w4/Y6DQKrP3uRV9g1IwE+wcTago9YYIKx4uBA6WQ7Nlnw/vvQ5s2x3tEJydz50oR2YQEqT2QlFTza5VwrziZUML9aYQS7hUKhULRUOzPt1JSZq/+RA/K7E7u//QvNu0rwOxn4JXR3WgbG1qn/v0MBppEmPEzGup0vUKhOIXxZfFaF5XZ5zQ/F/o/ABHNfdevpoE5QgrYGtTPP4WiobA77RSUFVBUVnTExqah0HWdrTlbSd6VzC/7fjni/W7UjJzb5FxGJI6gY1THE8JGB3yXha9QKBSnIkq4P41Qwr1CoVAoGoq6FqvNKynj9g/+ZH+BlZiQAN689iwahdTN/sFkNJAQEYjBoP4pVCgU5XA6wZIj9jm+/N8lb6/Y0Oz5WV43lH2OwSDivTnCt1n+CoXCC6fupLCs8Lj44AMcshxiye4lLE1dSn6Z2x4sMTyRkYkj6d+0P/5G/wYfV2WoLHyFQqGoHiXcn0Yo4V6hUCgUDUlditUC7M4u5s6P/qSkzEH7uFBmjOpKgKlutjeB/kbiwswqo0uhUFSOvRSKsyVD3lfoOuz+UQT8wkw5FhoH502ExPN9K6wb/cQ+J6Buu5cUCkXd0HX9SCFbm8PW4P3bHDbWZKwheVcyO/J2HDke5h/G0JZDGd5qONGBdbMk9AUqC1+hUCgqxxfCvdqTqVAoFAqFggA/I9Ehtc/qahUdzKMXd8Sgwbb9hbywbFudfWMtZQ4OFpY2uO+sQqE4SfALgPAECI0Fg4/qYmgaJA6Eaz6Fs2+RrPvC/bDkAUi+U7LyfYXDDoUHIC/Nt8EJhULhhaZphPqHkhCSQOOgxpj9zA3av8loYlDzQbw04CWm9Z9G/6b9MWpGCsoK+DTlU25efjNT/5jK5uzNJ8TfSFa7laySLNIL08mx5hyXYIdCUVPmzpVf7T/9VH9tTp58em6Q0zS45Zb6ay81VdqcO9d9zLW26en118/JjhLuFQqFQqFQABBqNhEWWPvtz70TG3HrgNYArNyWxbxf99R5DMWldrIKfViMUqFQnPwEhEJECwiM8F0fpkDoNV4E/BZ95djeX+HDUfDrG74V1u2lkJ8BBZlgr13xcIVCcWwEmYKIC44jPiSeIFNQg/ataRodojrwQM8HeG/Ie4xuP5qIgAicupOf9/3MQz89xN0r7+a7Pd9R6jj+fys5dAcFpQVkFGWQWZRJYVlhg9cMUJzYbNkCV18NrVtDcDBERkKvXjB/fv07382YAfPm1W+b9cHPP8Nll0GLFhAYCDExUvR48eLjPbLKWbVKxPOCguM9EoULJdwrFAqFQqE4QqNg/zpZ3VzZPYHhXeIAeP/XPazcerDOYyhS4r1CoagOgwGCoyG8KfjSbzmiGYx4BYa/CKHxUrx2/Wz48ErYucK3nvtlxZLhX3RQsvEVCkWDEWAMoHFQYxJCEgjxD2lwS5hGgY24tuO1zB4ym/t63Ee7yHYA7C7Yzat/vsqNy27k/c3vc7Ck7n9v1SeljlIOWQ6RVphGVkkWFrvaNaSAtDTIy4OxY0VYf+opiI2F66+H//2vfvs6UYX7lBT5U2HcOHjtNXjkESndM3IkvPnm8R5dRVatgiefVML9iYTf8R6AQqFQKBSKEwdN04gNDSAjz4LDWXNBStM07r6gLfvyLGxMy+f5ZduICzfTMT6sTuMotNowaNS52K1CoThNMJkhojmU5IA1zzdCuss+p1kv2DAXNsxz2+c07wP9H5Qx+AprAZQWSvHawEgJWigUigbBZDQRHRhNZEAkRbYiimxFDWoNYzKaOL/Z+Zzf7Hy25Wxj8a7F/JTxE4VlhXy2/TO+2P4FveJ7MbL1SDo36nzcPed1XafYVkyxrRg/gx8hphBC/EPwMyjp6XRkyBB5eDJhgojWr74qQn7AKf6n/o03ysOTO++EHj1g2jS4/fbjMy7FyYP6q0+hUCgUCoUXfkYDMaG1/yvaZDTwxMgzaBJhpszu5LGvNh9T5ny+xUZusbKJUCgU1aBpENxIsu/9al+ro8a47HPGLGx4+xxdB0su5KWCxUcBCoVCUSVGg5HwgHASQhKIDY4l2BTc4CJ5+6j2TOw5kdlDZ3NNh2uIDIjEiZNfM39l0k+TuGvlXSxLXYbVbm3QcVWF3WknrzSP9MJ09hfvp9hWfEJ49CuOPy1aQGkpWGrwa7OwEB58EFq1An9/aN4cHnjA+1pNgz174Icf5GtNEzsaT2w2ePhhiIsTy5rBg2H3bvf7M2fKdX/8UXEMn3wi733/fZ2mWwGjEZo2ld0INcHl+/7vv5K536iR2A7deiuUlUFRkXwdEwMhIXDDDZWv7aefQu/eEBQEYWFw8cXwzz/u95OSJNseoFkz91qmpnq3s2QJnHUWmM3Qpg18+KH7vbw8Wd/KAhKlpTLusWOrn7NrTlFRMqcrr4RDhyqet2EDXHKJtBsYCD17wpdfut/XdQkehYfLPeLJZZeJhdP27fI6J0futa5dZX2Cg+Hccyu3NXL5/ZdfC7g1qpJzTZrGI5rGVk2jVNPYr2nM0jQqnFsZSrhXKBQKhUJRgSB/PyKCai+AhQeaePayLgQHGMkpLuORLzdhsTnqPI7ckjIKrKromUKhqAF+ARDeDIKifFs1zmWfc/FLDW+f43RCcTbk7ZEsfIVC0eAE+gUSExRD05CmRJojGzybPNIcyZgOY3hv6HtM7DGR9pHtAUgtSOX1ja9z47Ibmbt57gljowPugrZphWkcshw6ITz6FQ1HSQlkZ4tQPns2zJkDZ58NEdWUqrFaYdAgmDULrroKXn9dRNoZM+CKK9y/bufPh+hoOOMM+Xr+fLGk8eSBB2DNGpg0Sb7++We49lr3+6NHi/g6f37FccyfDwkJMpa6Ulgoa7B9O0yfDkuXSvCgNowdK+L100/D8OGyLg89BP/5D2RlwZQpIkbPmye7GTyZPh1GjYImTSTT/5FHYPNm6NtX7HxAhPLLL5evX33VvZYxMe521q4Vgf+SS6TNkBC47jqpZwDymV56qQQ7ysrlXyUni7B//fU1m2t6usxj3DgR4ydM8D5nzRoZf0aGzGfaNAlKXH45fPSRnKNpcr8ZjTJu1z0zezZ89ZVc07atHNu1CxYuFKH/hRckiGGxyFyXL684xsrWAt5opWl0dJ2jaWjA58CjwPfAncBc4FpghaZRbTV0TUU8T0x69uypr1u37ngPQ6FQKBSnMbquk5lvxVoH4X1dag4PffEPTh3OaxPN5Es6YTgGIS02zExwgNpmrVAoaoi9DIoPgs3Hmac2i9s+x3H4P9TmfaDfAxDZwrd9gwQrghqBf8MW0VQoFN5Y7BaKyooosZccl6zylNwUFu9azJqMNdidUhPDgIFe8b0YkTiCLtFdjruNTnn8jf6E+ocSbArGoKmc0lOZ+++HF190v77gAnjvPcm8PxrPPSfi6dq10KWL+/isWSIyL1/uFr9btpSM5/JZ8bfcIn117gwbN4qACyL+33svbNokgj+IeP/DD7BvH5gOl885eFBE+4kTYepUOTZwIPz4Y+3i9FdeCZ9/Ll8bjSKwz5olGeXVMXmyrMO118KCBe7jvXrJ2owd6+3v36uXZJfv3y+v09IgMVGyyZ95xn3egQPQsSMMGwYffODdV1qa7ArwxJWB//ffsp6uNpo3h7vuEhEcJCgxbBh88YU7EAAicK9fL20bDPDTT9CvH9x8M7z7rpzjWtuRI+Hrr93X3nOPBG4OHZLseV2HTp0kqLBypftz1XVpc88e2LvXncfxySfy+U6fLkGfrl0lm37pUncfpaXg5+duCyT4cNZZEvD47jvvtTAa5Z7yXIu4uDId/F/UdR44fN4Y4ENgiK7zncf1Q4BlwH91nXc4Cuqno0KhUCgUikrRNI3GoQEYDbX/R69nyyjuHNQGgJ92ZDPn59RjGsvBwtI6BRAUCsVpip+/WOcER/s2+74q+5yPrva9fQ6AvRQK9snDrqzFFIrjhSsLPyEk4bhk4beLbMd9Pe5j9pDZXNvhWqLMUUdsdB75+RHuXHknS1OXnjA2OgBljjIOWQ6RXphOtiW7Tln4J2pBUoWwZQtcfbVkMZvNkg0dHQ2ZmZKBXh2ffAJ9yFjdnwAAIABJREFU+kB8vGSrux4XXijvr1jhPregQNqtiksv9RZkBwyQ51273MeSkqT9JUvcxz76COz2mmWJH40nnhDh97//BYdDRO2Sktq1ceut3q/79HEXvi1//MABd/uffy5zGDPGex2NRjnXcx1dtjhVfT5xcW6hGqTYcIcO3us4ZIgEOzx3L2Rni0h+7bU1K9VTfr0HDJB127tXXv/1F2zdKu3l5rrndOiQ7EZIT3fvJAC5D8eMkcz8K64QgX72bO8+AgLc90hpqbRVUAD9+0vAoTz9+1dcC9htBRI9Trsa2AX8qWlEux7ABiAfqHYfh0pdUygUCoVCUSUuv/v9+bX/R+/SbgmkHirhq437+OD3vTSLCmJIp9g6jUPXdQ4UWIkPD8TfT+UdKBSKGhIYAQGhYi/jS2sZl33O7h9hzYtQuE/sc7Z9A/3uh8TzfRtAKCuBsr1gDpMMfIOx+msUCkW942fwIzwgnPCAcEpsJRSWFWKx+ziA50GkOZLRHUbzn3b/4dd9v7J412K25GxhT8Ee3tj4BnM3z2VIiyEMbzWcuOC4BhvX0XDqTorKiigqK6p1Fv6MGZJlfayiqsI3pKWJNcqNN0r2dmmpZMknJ4tVTlqaeInn5HhfFxUlfvYpKWJV4mnV4slBDzeogoKjjyW23L8gkZHy7Nm3p+B8ySVybP588U3v1KnqtouK5OFJXLlvry5dZIfBddeJd3pJiVjv/POPCMYWC+Tne18TE+MdbGje3Pt9l9VQVcdzcyVY4hKwPXcteOIppLuE+/LzcSF2MN5ERnqvo8Eg83zpJTkeFQUffyx1Bmr6vVo+27/85+Wa0223yaMyDh6E9u3dr998U+6/jRtl50KTJt7n67qM+e233b73Lir7E67yHSP5DvDyrm+HCPlZlY+SxlUcP4IS7hUKhUKhUBwVl999XkntszknnN+GjFwL6/bk8uLybTQJN9M5IbxO43A4dfbnW4mPMGMyKvFeoVDUEIMRQmPBHA7FWZKl7gs0DRIHQvPesH4ubHgfig7AkgegWW/o/6Dv7XOsBRKgCIyUxwlmjaFQnE4EmYIIMgVhc9jIL8tv0OKsJoOJ/k37079pf3bk7SB5ZzKrM1ZTbCtm0Y5FfLnjS86JO4eRrUdyZvSZJ4yNjisLP9eaS5ApiFD/UAKMAcd7WIo6MmSIPDyZMEGyp1evlkz8Tp3g/PO9z1m5UixTnE4599FHK2+/vPB6NIxVxLM9vyVdgvPLL0vAITNTMq1fffXobU+f7i7qWlm7Lh57TETsCy6QTP7t22UdBg+W3QU33uh9/u7dYgFU3Ryqm5vTKc+LF0uQ4Fio6kdF+fkmJYm10CefwPjxsjOme3fvDPWjUdM5PfusBIEqo3xfv//uLnDrWZTXxQsvSM2A666THRLR0TKOOXO8C/BWN0bAc5UMwFbE274ycqtsxaMBhUKhUCgUiqMSFexPkH/t4/1Gg8bjIzrRPCoIm0Pn8a821yl734Xd6WR/vhW7w1nnNurCii0HuPKtX+jz3A9c+dYvrNhyoEH7VygU9YDJLJnxITE126ddV/zM0Os2uMbDPiftN/hoFPz6uu/tc3QdSnIgNxWs+dWervAgZTm8NwRe6iTPKZVUo1MoaonJaCI6MPpIMVtjA++IaRPRhnt73MucoXMY23EsjcyN0NH5ff/vPPrzo9yx4g6W7F7SoDY6KVtMjBvTmDObN6djfHMuvzCeH5YGHnnfqTvJLS7i8adKaN/RhtmsExOjM3iwFKQEERD37BFPcpf39sCB8l5Ojvh5d+0qGd3BweJnvXhxxbFomvigL1kiXtZms2TxVybU2WziEd6hgwigcXFifVI+Y/x0ZnX6aq5bch0Xfnoh1y25jtXpqyuc0/hwjnFWlnxG333n/ejaVd5v00ayxpcvFzuY4cPhpptg2TIpSurKgtc0sVHJza14L7iw2+Hhh+UzCwwUX3hPZs6U67p3l50BCxeK2GwyyT2haRX9811cf33FOZTn77/hjTfglVfEpsVF7mHZdujQim24svZXrZLnlBRZh0aNJAM9OVmOFxeLjU5MjGTEf/mld99txL2ULVskCHLJJWIX8/LLshPBZT2UlCT+8gDnnONeS1cWvovy3ysHPP4tysuT9X3lFejdW3YsbNsmXvzXXCPjLr/2leGaU1SUzOmJJyqfU06OBFauukp88R96SHYLXHih9KXrEjwKC5PPqVs3WcNp08TqJjjYnV2/YAE0ayY2POPHyxo9/rjY7lTGmjUV1wIalf+HeQcQDfQGXge+ARYAo4ANuk4lJjzeqOK0JyiqOK1CoVAoTjQcTp19eRZsdRDNM3It3PHhBgqsdlpFB/Pq6G7HVGzWZDTQJCKwTv77tWXFlgNMWrSJrCIrDicYDRATYubZyzszqGPdrH8UCsVxxumEkkO+F7Z1HVJXw+rpYp8DEBIL590HrS9omIx4P//DBWyDfd/XyUzKcki+W4oaO+1g8IPgxjDyFWg3pPrrFYoaous6xbZiCsoKKHM0fG0Ku9POr5lio/PvoX+PHA82BTO4+WAuTrzYpzY6u3b4cdmgJvj56Vw/rpCQUCeffRhCyhYTr83J4uLLSnA64ZbRjVm5PIgLh5Vw7gALDruBjWuDOLuHkSce82PBAikuGhsrYh3I14MHw7p1Ugz0qqugdWsR8j74QAS5pUu9s8A1Dc48Uwp53nabiJ/vvisZuZs2SfFOkB/nl14qgurNN8s1u3bBa69B27bw228i3p3OrE5fzZRfp5BtycahO3AWRhMfa+TBbpPpHnkehYWSTX/rrSKkr1hRMdvekyeegClTxO7ljjtEGP37b7EyGTQIPv1UBNkFC0R0NpngncNlPl33gqs4bfv2kkE9apR4oU+bBlarZFMnJYngHB8v569bJ/H9tDQRZR0OsVfZu1eO16Y47cGDck/17y/9L1oEN9zg9n/fsUMKxx4NV3+dO8u9NniwFHZ1BZf69xcxf/Bg+Plnd6FZV4HZ3btl7ZxOKYp74YXyPfHWWyJ8L18uIvuvv0ogatMm+Z5yFe29/HIRuDVNhHQ/P+/vlb/+EjuhtWvl/NGj5ftkyhTZYTFqlHj6v/22fO8sWybfg0crTtuli4jow4fLGr36qozfczdGixaQkSHfi2PHyvffwoUiqM+c6a4JkJEha2yzyVhbt4ZWreSzeekl+TkCEpBLTYU773T/3Jg9WwIe4P15V70WOqCt1XXOOXzeWGA+YAfeBv5GrHPuBLYDw3Sdo1RnUFY5CoVCoVAoaojRoNE4LIB9edZab/VOiAxk8iVn8MBnf7M7u5hnvt3CU5d2rrPwbnM42V9gJT7MjMHH4v2bq3YeEe0BHE7IKrLy5qqdSrhXKE5WDAbJvDeHiX2OzUeZppoGrQZAs17e9jlL/yfH+j8IkS1907cLexkUZIJ/kAj4fsp2olLWTHeL9iDPxQfluBLuFfWIpmmE+IcQ4h+CxW6hoLSgQX3w/Qx+9EvoR7+EfuzM28niXYv5Mf1Him3FfLnzS77a+RVnx53NiMQRdIvpVu82OtOmRFJSrPHtT5m062gDYPQNhQw/rwlPPRzF0BElfPVpMCuXB3H3//K45+E8j6vz0XXYV+TPpaNCefTREOLitAoZvF26wM6d3lYWd90lIuy0aRXtWzZvFmHWZa1x1VXiGz57tpwP4tGdnCwi5+DBYmHSpo0IsUOHihBbvkhobdE0EasnT5bXc+eKfcqaNXDeecfWdkPwzj/vHBHtAdLmPMqe4nCu1cLJ3up97kUXHV20BxFFNU383w8cEKG2TRvJtl++HGbNgvvvF9H2ttskS3vPHhFdy9uYmEwiCHsef+opEXVBfOEvvVQ+5ylT4Pbb5fiTT4qYPXFi3TbrXXSRCP///iv9PfMMfP21iMD33Ve9aO9J27YigINkhP/4o4w/JgY++8x9fNUq97zAO8M/LU3qATRqJCL+669LJvyuXVKotnt3Ee7XrZMdDSaT9xhycyV44vm9Eh8vgS8XSUlikRMeLtn3CxdKNvyXX4q9kSvD/2i0bCnr5Dnuzz93e+9rmnyWmib3Rl6eXDN4sIz/jjvkc9M0WaeywzHS5cslk97VzrZt7j5GjZLPaN8+aNfObZdkNkuQpzyVrUVcnA5onpFPx+FnP6AJItanA0uBK4BngXImSd4oqxyFQqFQKBQ1JsDPSHSIf52u7dYsgnsvbAvAb7tymLV61zGNpdTmYH9B7YMItSUjz0L5TQYOpxxXKBQnOX4BEN4UQho3oH3OYfUl7Xf46Gr45TXf2+eAFLDNS4Oig+B0VH/+6UZ+ulu0d+G0y3GFwkcE+gUSGxxLQkgCIf4hDe413zqiNXd3v5s5Q+dwfafriQ6MRkfnj/1/8Pgvj3PHijv4Ztc39RZYcDhg9Q+BDBxsOSLa//FLAF2atSBtj4kDmX5s+sufb78MJiTUyfh7K+6K0jS3F75Dd2BzllXYuRAQ4BZoS0vF17qgQDKT11diTNG/v7cfdmysZN9u2CAi+t69IkQmJor4n50tGb82mwid4eGSPX6qsXGje/41IbMo84hoDxDRawmayUr+vkYYjSKARkdL9vvzz1ff3uefS0b2/fdLJvm994qQ7vIp97QwiYyUDOipU2HMGBHfPbn0Um/Rvlcvec7yKBmalCSfbUSECM5RUZKRbrfXvQDytddKxra/v3jcT58u96fBAC++WPu2PHEVcB0zpvLjlsPftp9/Lvfr66/LZ/DMM3D33fDVV2LH41nYt1Uref77b1mPMWO81yguruL3SlCQt7DtKvL7xRcSHAB5XrpU5lCTP7fKr7fLPslVkPivvyRI8+ijsltg5kwJWrz1llgxORxiLZSeLiJ+v36yE+CRR0S4DwiQYN7bb8u4QD6fSZMkUDZhgox/6lT3zoPyVLYWYHUCgR6nXQ3sAh4F2gJTEbG+A1CKdyHbSlHCvUKhUCgUiloRajYRFmiq/sRKGN4lnqt6yF+Tn65P55u/j7ozsFqsNgcHCkp9Kt4nRARSvhau0SDHFQrFKYI5DCJayLMvCW8m1isXvwyhTUQY3jAXPvgP7PihZvvujxVrgfjfl+Q0TH8nC+FNxR7HE4OfHFcofIynD35EQARGrWF98MMDwrmq3VW8O/hdHjr7Ic5oJEpVWmEaM/+eSdLSJN755x32Fe07pn5yso2UFBto3U5Ee7sdHr+/EUHB7gyJ9D1+7NntR8tEGwHmo/+M0nVwOB3sK9pHZlEmhWWFOHUnui6iaLt2brE4JkbEvby8iu20qKRueGSkZC0/+aQI1ykpkpUcEyOPtDQpLBoTA/n5bkHxVGLjRvf8a0J8SLzXvRveaxmtH5jA1R/fjd0uQnJWlgin558vInlZmWRrez5c2dEpKbLG06bJDgqbTT6/TZvk/cJCd99GowRVCgvlvnD5wrt2Kgwe7D1Wlxh71lnuYy7BeeFCCRBdfbUUke3Z0+2nDxX984uKKs7BRWqqCLpZWTL+3FzJwi8fo7NYKrbhcHj317On9zUXXXT040FB8pySIs8TJkgApLhYhPbUVLnHcyspj7p+vfSv694FcocOrXhuz57eAraryO8330jAIDJSggM2m7cg7woweO4mcc21fMHZfv3kuXVr7zlNmSKZ/IcOyc+TzEx3Fv3Bg9JHbq7cR2+9JX75GzeKxdUrr8j8XOvl5yfBGrNZ7sEtW2Qnx/r1lTsbVrYWsKkE2ORxoB1ijfM00BkwA6FAJyAACKmsFU+UVY5CoVAoFIpa0yjYnzK7E6ut9lmb/+2fSFpuCb/tymHGD9tJiAykW7OIOo+lpMxOViE0DvONsejtA1tX6nF/+8DWPulPoVAcJwxGybwPCBOLFLsPvadb9Ydm58D690W4LzoASx9sOPscVwFba77Y5/g6YHEy0O/+yj3u+91/vEemOI0wGoxEmCMIDwin0FZIQWkB9vI7QXzcf9+EvvRN6Mvu/N0k70rmx7QfKbGX8PXOr0nemUyP2B6MTBxJt8bdMGjHlgv6/tthZB80cvV1RcyZefjnkAa6rqFptQssljpKKbWUkmvN5b1XGzHlsWCuu06sZ6KjRdidM6fyorPlbVUqw+mULPzXXpPXY8eK7ccLL8jryMhaDfeUZFyXcV4e90bNSHRgNOO6eHsIjR4Nzz0nNkNt21a0zPH0MR8wQLKqK6NJk5qPrarP2DN+7RKcp08XEXjAABF7X3316G1Pny4BjvLtbt8u1z/3nHdgx2XTkpoqInFcnOzouLGcYcru3d6ieVVzqG5uzsNxscWLJdP8WKjJOoJk60+dKlnrt9wi33fdu3sL/MfSj2tOzz5bUeR3Ub6v339379b455+K57/wgnj718PPDU+Z3wBsRTztK6OSsIk3SrhXKBQKhUIBSBHWN1ftJCPPQkJEILcPbF2lh7umaTQOFb97u9NZ6TlVYTRoPDK8I3d9vJHd2cVM/nozb1zTnYTIumewF5XaMRSVEh3i/ddobeZUFYM6xvIsHHM7CoXiJMFkhojmYMn1bVa6nxl63QodhsOaFyF1jds+p9tYOPsWMJX7uZj6E6x7Dwr3Q2gc9LwZWh6D8bHTIdY51nwIjq7Y3+lEuyHQ/Xr47Q2xFTIFymvlb684DmiaRph/GGH+YZTYSsgvzafUUdqgY2gV3oq7zrqLpDOSWJ66nG93f0uWJYt1B9ax7sA6EkISGJE4gkHNBhFkCqqynbUH1rJw20KySrKIDoglIOgDdqaYOLjfyCvPRzDp6Rz2Z7ilqabN7bRMtPHHL2ZKrVqFrPvPPgjhgTui+eCr/eTnafzxi5nOTZvT/wILU185RFCIkzdeDcDkr/Ppp5Cd42DubCONG2vMnu1u54cfxDIE4P33RWR9+ml3Zm9qqlhxgPsYiLg7aJAIrlFRYpNzzz1iqxMdLXYu990n5zoc4pXftSt8+23FtenUSdr46aejfxY2Gzz8sAiI+fmSoTxrltvWxMXOnWL38d13kuHctq1Yonh6769aJUL5++9LJv3bb0vme48ekoHco4ecN3myW4z2nL9LVP/6aynGuWGDZJPHxMDIkf0ZOuZqvkifTYm9hEC/QPy/f5YBo85hyxZ4+WUpKOuycElPlyLC333nbn/7dhF8L79csp7/+EOEbpflSlXUh8vUihViaWO3i5f+xo3i8V7eiqY8119feQ2CjAy5Bx58UB7ladVKggOrVkn2tuc6gAj69UGbNvLcrJkUcj0a9bGOu3dLodrwcLlfR46UAMaMGcfetgvXnIKDa+aZn5MDN90E3bqJ0D9tmoyrb1/3OR9/LPf2vHne13r+3KgDO4BewApdp3b/NB9GWeUoFAqFQqFgxZYDTFq0iT/TcsnMt/JnWi6TFm1ixZYDVV7jZzTQOCygTn6swQF+PHNZZyICTRRY7Tzy5SaKrMeWUVZgsZFb7M6QrcucqmJQx1g+G38uvz58AZ+NP1eJ9grF6UBgpNjn+Af7tp/wZjBihtjnhCWUs8/53h04SP0JVj4DBzZLVviBzfI6tRrFpybYSyE/QwICDtuxt3cykrIcNsyTegO6Q543zJPjCsVxJMgURHxIPHHBcQT6NXxwLcw/jCvbXck7g9/h4XMepkt0FwAyijJ4+++3SVqWxKy/Z1Vqo7P2wFre+PMNUnJSOGQ9xPb8rYR2+YlV35t5+O5GtEy0MWps0RFrlOjGdjp3LWP4ZcUUFRp46+XwCm26ZPxnH4vE4dCIbmznijFFLP06iP/d2YhH72uE1WKgSYKdUdcVsvRbI+PuKOH3f3JYtEiuXrjQu0Btjx5iwXLBBWKpASLCx8fL1489JpYZIP7XLvbuFfGvd28patm6tRQxXbZM3jcaJXt3+XIpoOnJunVixVET7/QHHpB+J02Sr3/+uaLfeUoKnHOOCKb33isiaWKiFOicOrVimy+9JALlXXdJu9u3S0Bi+3Z5/4or5FrX/OfPl0fHjnJs9myZ34QJ4p1+6aUwe46TZ/7bB4vdglN3UpBjZmuOVKUdO1aE+qeechdkXbNGdipceKE8AgIk6JGVJV7kw4eLyH/55WJZ44nV6m2VExxcuQ1SbZgyRbLjQ0IgLAw++ACGDZN74WgkJrrn4HqAZHwvWlTxcf75EgBatEiCRSD3Wvk2zPW0mfg//xEbmCeecGeqe+LpYR98+E+eY1nLH3+U+971L+LcudL/NdfUvc3ydO8ugakXX6x8rJ5zAvG/P3RI7uEZM+R79YYbxDbIhcFQcX22b5fP6Rj4GIgG7in/hqZh1LTqPe5Vxr1CoVAoFAreXLXziBUMSPHVrCIrb67aeVSR2mwyEhXsz6Gi2meBxYWbmXLpGUz89C/25pTw5OJ/mXpFF4yGuqd65JaUYTBohAea6jwnhUKhOILRD8LioawYirPA4UPLikrtc/7nts9Z9x6UHBJRGeS55JAcP5ase09Ki2Su5ggIiqqf1LuThTXT3TY5IM/FB+W4yrpXVEPLlpIB+v339demponQNnmyvP54gZkbbzSzcpWdzufkUWwr9mmNn/IYDUbObXIu5zY5l935u/lm1zesTF+JxW4heVcyybvcNjpnNT4Lg2Zg4baF5FpzcR5ONHXiJOqyl8jd2JsVywIZNbaQOW+FsXBBKAB3P5SHnx9cfnUxX38WwivPR/DvP/6cO8CC06Gx/o+AI8JaULDORSNL+OrTYBrHOujWs5Rli4Po1dfKuAn5zJgaSX5eKZ27lbH4iyBWfWcmsW0Z//4TwG3jnYy6Gj760ICmicD62mvQpYtktv/8s1u8zcwUkb9vX/EdnzxZLDXKyiQz+7LLJIt8yhQR7Js3l0x0l/91UpIUYv3wQxHUXcybJ0L1qFHyuvyP27lz3dYpAQEihrqsOaKipK3Nm91e7XfeKQLzhg1u8XX8eBFLn3pKvg73iIOkpYnY36iRvL7ySpn/44+LSH7mmdCnj2T2DxlSMaP8ww/dHuouNga9zS/Tx1OQ0oXgthtJm/Mo1j0dj8yvf3+57t9/RThdvVoyssPDJUb93/9KNvTKle6gwMCBYnFyyy1uH/OUFAm+fPaZ2xO9Rw9YsEB2UbRuLQVKBw062h1dEZcv/qxZcOut8vWxZIlHR1e+U+DLL2U9qttFUF+0aiX37H33SXDnP/+Rz33vXinM2rmz3G/g3nExaRJcdZXsOBg50n1P1YSkJHnk50tAYuFCaSMmpv7mZDDIDpQhQ2Tnyk03yc/h/fvlfvn3X9mBAnLPLVwoWfYu+5x58+SenjhRal+ABJ+eeEKCYgMHyo6bN98Ui6yNG+s81A+A/wAvahrnAT8CDqD14eOPA3OPOtc6d61QKBQKheKUISPPckTgduFwyvHqCA80EWKuWy5A54RwJg5pD8D6Pbm8sXJHndrx5FBRKUWl9mOak0KhUHjhHwzhzX3vBe+yz7lmIbQ87E3gss85tMMt2rvQHZIlX5/outgE5aZKIdvThfx0t2jvwmmX4wqfsXatZPx26SJCaUICjBgh2cgnGhs3imhb00KdvsLP6Ed0YDQJIQmEB4Qfs898XWgV3ooJZ01g7tC53HjGjTQObAzA+gPrmfzrZMZ/P57knckcKD5wRLQHKNl1BlnfjcbpMKJp8On/2Tvv8KaqN45/knSkbboXo0DZsgUBQaaIIEtFQRBBUUAERBTEgeDArYgiigsUmSoOFJT1UxAQkCHIlL0K3SsdSdMm+f3xEtJ00NItns/z5LnNyb1n3JukN9/znu+71JfXZgReXlh0S2+5R9Nq4dNlsTwxLZkTx9x5fUYQH77jT5pRS70Gsipo6ANpPDkjmc43m/n4PX/27tJjt2sYPDyd8VNSGT8lhV3b9Rze74HNpmH85FRu7Sv1Jydp6X1XPIfOShi8yWQnI0OinHfsgMxMsWdJSHCOWaMRofjdd8W6JidHyk6elCSmPXqIwN6hgySxdXDddXDjjRLp6yAnR2w5br8dAoqR5mnsWFc/7W7dZOtoJzlZbFYGD5YI9YQE56NPHxnPjh2udQ4f7hTtQSLpe/cWSx+7Xd7vK1cW3ieHaG+3iy1PQgIQ+QcA5jOSyTXgxjWgk+/VfftEVLfZ4MMPZRWA1er8PP39N/zzj4imyclSX3q6CK49esgYJk+WSYidO0XUz2378sorkoD2jTfE2mbmzKLPa2EMGQJeXjJB0r9/yeupSjzxhHjc+/nJqoLHH5f3YPPmztUkIKsBnntOvoNHjpRzmTd6vbj4+zsnJ4qzsuRq6dRJ3gtdu8pky/jxYv1ktzttsKKiYMIEsXtyWFiBfE6feUb2X7tWyp59ViYstmyRibCffnKuJikpdjt2YBDwBJKk9g3gVeAW4Gvgt6Lq0FTkDK2i+LRt29a+uyrerSgUCoXimmTQR9vYez7ZRejWaaF1rUC+HXdTkcfb7XYupprJKkGyWoD5W06xbOd5ACbd0oA7rq9ZonocaDQaJn+9j/0XUko8JoVCoSgQS4ZEw19lfo8ScXqzRHwbLxT8ukYH4c1g0Bfl1wd3PXiHyPZaZkEvuLDHVbzXukHNG2CUssspLwYNEpFk0CDxHk5OFiHlzBlYtUqsMqoKjgjoLVvyRx9XRMR9Ye3b7DbSLGkYLUastpLdh5UWq93KzuidrD61mv0J+y+Xa9G6CPfnPniH9AOdsOd48vQLRux2WP6FL+fOuAEath06T/WaViwWSE12zfzoH2jFw8Ppcb98dTQdOjtXfBZVvmJtNG07ZPHxHD/efOHK7hTnzokHviPiPe85j4wU65sOHSQ6PDcjR0p0/OnTzrJPPhFx9OBBiZBfvVoikFevhn79ZB9HxL3jmueOuN+0ySnWg3w+6taVfR54QMTLG2+84pBYtEjsSxwe9x9+KEJnbiZPlomJhAT5/BU2fhCR/emn5T2fmen6WtjADwm7Q8KY41dOIHblI5w5A3XqOPdx9MMxtm++EcH8Smze7Oq3X14qZECaAAAgAElEQVSkp0N4uJzbefPKv71rmQcekPdSdHTpE+P+G9BoNHvsdnvbsqxTWeUoFAqFQqFgfPf6TPvh4GVrGZ0WQg16xnevX6zjNRoN4SVMVgvwUOe6nEsysfVEAnN/O0HNAC/aRhZp+Vcodrude9pGcDHFREJGVonGpFAoFAXi4SPe9+lxIuKXJw77nL8WiSWOS0S4FryDJUFteZJtlqhzT19pT3eN/oTs8iSsmuS0y9G6gU+YlCvKjcmTxcbAw8NZNmqUWB9Mn161hPuqilajxd/THz8PPzKyMzBajFislqIPLEN0Gh0da3SkY42OnDWeZfWp1Ww8vzFfQt2gbt+ScbATvYedpt8d4tff5WYTg3pXx2zSEHNRvl/OnnLn3gGumTmXr4qhQxezs01XXb/IckfMqt0mCvmbcxOoUSu//Zmb1g0PP3esNgNQSGXFbMvB0KES4bxokdjmLFokVi4OO52iKKodx633Y4/JhEBBOCx1HBTkhFbcuF6jUcR2vV4i2xs2lAj8fTEHmDqiBRq7dFin0eHt5n1VY3jtNbHLKQiHzUl5s2SJTEaMHFkx7V2rJCeLjdTIkf8N0b68UFY5CoVCoVAo6NEknNcGNqd1rUCq++tpXSuQ1wY2vyoveDedlnD/kiWr1Wo0PNv3OhqEGbDZ4aXVh/lp30UeW76XIZ9s57Hle9lxMvGq6ryxXjBP9GxIq4iAEo9JoVAoCkSrE+97Q1j5+8C76aH9w3DfdxBQO9cLNvCtDjXalG/7DrLSIOUsZCYVX935N9GoFwyYIxH2fjVlO2CO8rcvZ266yVW0B7Hv6N5dPIqLYuFC+Qj+9ptE/1arBr6+YhmSkiKWJNOmQY0aYn1x++0F2z78+qvYcfj6ipdzt26uSUhffNEZfdyli7Sp0Tg9sR3s2iX2DV5eUKuWJAB1YLWKFVBhkxFNm+aPai6I7GyxdKhWTdq59VaJ7tZoNBg8DNQw1CDMO4yYcz5MGh3CDfVr0TisDr071mD5lwaXup6eGEzDkDrs3+t6EV56Ooj6QXXYuU3UNosF3n0tgDt6VOf6yFpcV602fTvX4NulrvUB3NepE9tfmMmTYV+TPnsdh8bs4p/JG0hYez8e1c5iNfuwZnFDurSKoEurCPp3rYnZJNLUXbdW55Z2NfEPzGHxyhiXR5MWlsvR8wBLFvjSrlEtmkfUZvwDoZgy5bt4+UIDN14XwXXVajN6aBjp6a7f0XXqidXO5x/58sjwMB4eFsbctwLw8LDTubuZDl3Tef11O6NHOZVmxzXPe72NxuJdb4dlyNKlImauWiXvTYc/e1F8/LHr9T5/3vX1+pdiUtatE2uTfv1kouD0aWei0+rVnVHuIJMHtWpJnZ07w5494h3v5wfvv+98vxc0/jffhLg4EbenTZPI/e++g+7XSeLiMO8wwr3Dsa17ntPfikfK00+L9YzBICtsUlNdx9CggWyXLpXP74ABYmWSnu4cQ2Bg8c5XSfntN4mwnzFDzkn79uXb3rXK6dMy+TFkiHxfTZpU2T36d3ONhksoFAqFQqG4Wno0CS+1qO3ppiPU15M4o7nonfPg5a7j1TubM27pXyRlWHj/t+PY7WAHEjMszN5wjMk0okP94CLrctC+XjA3NQileoAed52KV1AoFGWM3g/cvcU6J7uc82ckn5Hod7TgsJ6I+RsWDYDuz0D9nuU/iWC3i3BvTpXo+/L2/K9oGvVSQn0V4eJFV//tonjySRFHp08XC4958ySCNyhIPMinTYOjR8UeZNIkifJ38M03InZ26yb+2Xa7TAjccovYgHTtCnfdJVYPn34qol6jRnJskybOes6dE7FxxAjxD//6a0l82KyZRFbrdPLarFlisxKe65Zr9244ckQsVYpi6lSJdJ42TSxNZs0SX/Bt25z7RJ32pl93bwKDbIx9NANvPwubNngxbVIIKUk6xj0hqunzryexY4ueJx4O5efNF9F72fnjdz1ffurLmIlG2t8kEfPpaVqWfuFL/4EZ3H1vOjnZGtb/7M3UCSFk58C9D6S79PFClBsTR0TSsf8J3Np+RfQfXYj5airuYWepPVFUPA+dB7btozizqzFubnYsFi239s1gxxY9D91TjZ+3XCQouPBVnCeOuvPokymcPO7Okvm+nD8r8taxIx6Mn5zKqePuLJ7vS2KC6/1fZoYGsBN1zp3HnkpBp4NvlxkYfkc1Fq+MoUHjbHoPyCAuVsfyhZI0d/SjqbRprSWyoRsgKwVycsSX/bHHXK93z57STt7rPXKkeIqPGwdmszyK6/u9f7/r9c4rhCYng5sbHD8uq1jq1YM1ayTZa2KirGLJmxh05055L+t04uPdrZv0adAgSV4aEyPvd5A6W7d2vt9//VW2d94p4va+fbBgAfzwg5Tf1fAuXhx8Fy8eggOX2ouOls/XiROSCDgpybU/GRnyL+z4cfkcBwbKZ3PgQPm89uxZtslNC2LmTPkctWsniU8VJeP332Xip2ZN+OwzaNy4snv070YJ9wqFQqFQKMoUg6cb2d4eJGde/TLtUF9PXrmzGROW7sWWK6DTZoekTAvLdp67KuEeIMdmIybVTHV/PW5KvFcoFGWNzg38a0pC1/KMRt+9ADITgTxCljkF1j4DEe2h21MQWLd82s+NzSpWQeYUEfA9fMq/TcV/hi1b4I8/JJlicTEYREzUXvo3Hx8vFg3durmWx8VJ+aefyjEZGSKkDh0qkb4OHnlEEuY++6z0pWVL6NhRjuvVq+DI+OPHRSy97TZ5/tBDULs2zJ/vtEQZOVKilZctcx3fokViJXHPPUWP1dNThDGH9UhQkNR16JDTDmXiRAgJgb/+0uLj40u2NZtHxqUy6n47c9/2576HjPj52/Ex2Jn9SQJD+lbj9ecDmfJcClPHh9CoSTaTn0u+3KZ/gI0/Dp53sbt4aLyR4XeG8+kc/3zC/ZmT7iz8NpbVHtPxTjpGnY5LODplA6nbbqf2o1PQoCHjREtO/XkdwX0+J3GNhHe/8FYCGWlu9O9agwXz/Jg6I6XQ8/DCm0mXJxaSEnT8/INYsjz/hrM8MUHHmh+9Lx+TmaHh1eeCaNvBzN5depZ/6cvAe9IZ9mAa77wSwOihYTRvZWH56ljatMti+UJfNBo7e3d5cl3TNFatg87dk7DZq5GTo+P66+Htt12v9/Hjzj7mvt6TJkFEhAj8wcESSV6c6w0idDs87h3XOzcTJ0rdaWkilI4eLZNI586JCP7iiyLK56ZpU+nXmDHy/n//fRH/Z86UiSnH+12jkcS2LVuKn3+PHpKkt3VrSerZsKEI+o0awYEDFMqiRdJHkH+Tc+c6X7PbnZ+5Eyfgo4/kfI4YAWfPyt81asgkXHmSd0WFomSMHKlshsoS9etVoVAoFApFmRPo44GPZ8niA66r5ofBM78Rps0OsSWI5AfIttqIMZqx2q5BeweFQlE18AoE/1rll8Q1LQbsBSSe1F2yuIjaCcuHwrb3wZKZf7/yIMcCxmhIvQA5WUXvr1AUQXS0RL/Xrg3PP1/848aMcYrzIKKj3S6CX97ynBwRNAE2bJDI3+HDJZrZ8cjIkAjfHTvyJ94sjMhIp2gPIrB36ACnTjnLrrtOkoguXuwsy8mRSOzbb4eAgKLbGTvW1S/cIeg62klOlnENHgwmk4wnNdkdMkIY2F+PKVPLvt3O76kbbsxi7KRUFs/35f67w0mM1zH7k3gXkV6nc3pUZ2dDSrKWpEQtN3U1c+aUO8ZU19U+EbWz6dbTRHxmPDZsaN2z8a6/H0u8KLcBngFEnJyCxt1M6rYBaPQi/D+55lV2ZqyiToMstm++8ndp7gVGbdplAZoCy202Z8HWjV6kJOuYMMXIZ8tjiaidw4J5frwyLYicHA3paVoeHGd0aefBR4zERut4emIIj40K5ehhHTa7HZ3OjiEgi9SsVKw26+XrnZbmPDb39dZqRYgGEdGLe72h4OvtwHG977tPVoj07y+C/IQJksTWapWI+7xMnSr9mTNHVnoEBso1btjQdb9Jk2QFzKhR8tk8fFgE+DVrJLL/5Zfls1qvXvHG4hhD7pRYf/8tK2XGjxe7nxtvlD5NmCCWOmaz/K1Q/BdREfcKhUKhUCjKhVCDJ5YcG9nWq09WWyfYh0MXjeSW2bUaCPcruSBmyRHxvrqfHq22nO0kFArFfxM3D/CPECuZzERXZaK0+FaTOnOL9xodhF4HNzwIW2aB8QL89SUcXQNdJleMfQ6ITVDK+UsJbINA517+bSquOVJTxQ88PV2i7v39pdxiyW+rERTk6o1fu7br6w5BtLDy5EvB5MeOyfZKSXATEyXxZlFERuYvCwwUm5PcPPigRBc7IuTXrpUVAg88UHQbAHXq5G8DnOfo+HGZtHj1VXm4IgqwPS2UAE8jaZY0rHYrjz+bwrrV3vy9x5Mp05Np2iI7X7vfLfPhsw/8Of6Pu4sYDpBm1OLn7/xuiqgtSV9DvUNJNidjw4bO24j5fEO0aAn3CScmujH2bD05qc57ux1TP2XHpb99w5PYeyKaCN8I/AOtLtd7+epo2nV0Thb6+duuWL5ibTRtO2Tx8Ryx93pwcH5rSItFxtS8leuK0dtuz2TG68n59m/X0czilbEkmyElKwW9To+vfxB+fm6cPu08P7mvt7u7CPgZGQVf7xdekOj43Gza5LrCw3G9Fy6UOnbuLPx6O3zkO3TI35ZWK1Y5M2bI88mT4d135X2U26bq7rulPDf//COC/e7dzomtH3+U7Usv5Z90O3PGGW2fewybNomI/8038vyRR/L30zGGwhLWKhTXOkq4VygUCoVCUS5otRpCfT2JTjVjv0rriGHta/PO+qMkZjp/OBo83BjWvvYVjiqarGwrsWlmqvnpS5REV6FQKIqF3h88DJCRIEldy4K2o2Djq07xXqMTm5q2oyCyM9S6UUT7PQshI67i7XNAxmpJl/F7BUoSX4WiGGRmSqTw0aOwfr1YZjjYts2ZUNPBxo2uiT11hbzVCit33JY45tYWLMgv8jsorq92UW05GDpUEocuWiQ2KosWQViY006ntO04xvTYY2KXUhDNmmkJ0Afg7+lPWnYafx7J5PxZmXA7etgj3/6rv/fmyfGh9OidyegJRkLCrLi529m03osF8/yx5xHyHX28p/E9fLj3Q5LNl4Rvu4ZAfSB31L6XCXs9ATvTX0uicdNs4jPj+TP6T/7YnU7U8sdJiw3irrYdAZjx5W88cHudfPVf7blx9PPNuQnUqJVT4L7BIcWbcM3dlt1ux5RjwpyTRY5NQ4IpBYO7Ab2b/vL1XrhQVlaEhck1qtjrXXQ7xb1VNxpFbNfrxVanYUOZ2LJaZcVJQfPVxR3Da68VLtA3b168/ikU1xpKuFcoFAqF4hrgtyOxzNt0kgspJmoGeDG+e/1SJ5otC/TuOoJ8PEhMvzoLhQ71g+nXsjordkdhypG7eVOOlXD/0ltQmCxW4tKyCPP1VOK9QlHWHFsvkd+pURJ53uXJ/26yT60OfMMlCj0jDqwFi0TFJrIzNL0T9i2VCHc3vTyPvBSK6eYJ7R+G6/rBlnfg9O9O+5zr74O2o8GjGGHDpcVuB1OKrDrwCgR9gKtXieI/z+aozXx24DOi06OpbqjOyMYP8/b4LuzYAStX5vePb9VKrEDylpUFDRrINiTEmVS0MMrqlsHfX5J6Ll0KzzwDq1ZJpLFbGakz9evLVqcrzpg06PFj6iO+hITYufvedObO8qVXv0wG3J1xeb9V3/lQq04287+KczkPRdnZtAtvx611buWnkz9dbu/mGr35bEof0ozyvTD4vnT8/O2ALwPpycXbTSzt8gM7oneQZJZlBN+mH2bbBi9CY6cCxVS8C6BOPQkICQy20rn7le0XNZqS2Sva7ZBuSSfdko67zh2T0Zfrrzfw4YdaTCaJup8woXKu95U4dgz8/GQ1CxT+ft+4UXJFOKLlcx9fUhyfQx+f0o1BobgWUXdQCoVCoVD8y/ntSCzTfjjI3vPJRKea2Xs+mWk/HOS3I7GV3TUA/L3cMVyl3/2Ok4n8ciCGrFw2O9lWO0+u+LtESW/zkpGVQ/xVTiYoFIoiOLYeVk2CC3vEsuXCHnl+bH1l96xy8fCGgDoiYpdG+TuzFQ6vhBwzYJPt4ZVSnhu/mtBvNvR/T/625Ugk/tK74fj68kuemxe7XZL1ppwRIb+i2lVUaTZHbWbm9pkciD9AbGYs+2MPce8wOxs22PnyS+jXL/8xgYEi5uV+OKw2Skvv3mKf88orkFXAbUF8vPNvn0s5mFMKz5dabEaOhAsXJDGu2Qz331/6Oh2EhsItt8gqgrNn87+ee0wgVin792v44nMt773pS6fOVl54MpjYaGeYtGPuLXc0dXKSlm+W+F6xL7tid7Hh7AbMOeZLx9v54Mmb2LJRz8SpKYCGd19zvZg1Qr2YOqw1nzwwjrdH9abrzRZ0PkZiM2PZcmELAN8f/56zxgIGVwRde5jw87fywayAAq93YoJTIvPyke8sY2rJZbNsaza7/85kxw4R7Zu3sJKdXbnX20FuL/4jR8Rbvk8f57+pwt7vBb0XwJmktyS0aSOR+++8U/Dnq7AxKBT/BVTEvUKhUCgU/3LmbTpJfLoZh8ZttUF8upl5m05Wiah7gBCDJ1lX4Xe/bOc5kjIt5M0lm5yZzQs/HmLW4FZ4uJUu/iDdnIOGLEJ9PYveWaFQFM2WWRJZbrsUWW7LkedbZv13o+4daDTgE+yMvs8uQaLt3QtcPe7tVnm+e4Ez6j43kV3EKie3fc66Z+HQD9D1KQiqIPscm00sg8wp4BUEer+KaVdRJfnswGckmBKwXnofX1j+BIl/dqV66wPYbC1YssR1/4EDnQJieeDrC59+Kkk3W7SQJLU1akBUFPz+u+yzcaNs27SRj/Lrr4vvvacn9Ogh1idXy623iuf3119Lu61bl92YAObNg06dZGXC6NHQuLH0ed8+WdVgvvQVtHUrzJolSUF7XfqaXrxIR8uWMOPxGiz6Lh5zjpmefTNZu8qHUUPC6NUvk4R4HV996Ut4tRwS4gq3xPrm6DeXPe4BbJn+JOzsRtj1fxNZvwZdbjax8BM/tm7S06S5hdZts4iNcWPDL94MuCuDx59tS9tqbYlKi+Ln0z/z9Tax8/kz5k8O/vYxLUNa0r9ef2zcUqzzYvC189qcRCaNCuW2m2oy8J50wqpbibngxp9/yP3g8tUS+NK8lQWNxs5H7/qTkqTFwxM6djUREnp1uUs6dDFzMukMnVtEcPCAG9c1sxDZJINsmwF3bdnkAynu9XbQqpX41qeny4TV3Lng5SXWNw4Ke7936iQrVO6/HyZOFJuc1aslCr+kaLXwxRfyHmzaVJJKR0ZCTAz8+ackxD15suT1KxT/ZpRwr1AoFArFv5wLKSby6uFWm5RXFbRaDeF+ei6kmIrldx9rNOcT7R0cvGhk9oZjPH1b41Jb3aSZs9FqINigxHuFotSkRjlFewe2HClXCJeT1xohM+HqktemxbgmpgV5nhZzhfYKsc/5aghcP7zi7HNArILS45wCvqehYtpVVCmi06Mvi/YAprPXSfneFowYkX//06fLV7gHGDwYatYUf+333hO//WrVxGt71CjnfvXqwfvvS1TwqFHi6b1xY8mEe60WRowQUbQso68dNGoEe/aIEPvVVyKqBgeLKPrOO7JPWpq03aABvPWW89i6dUXUHTNGx8rF1XhwTBYj7k8lJSmJxfN9eemZYGpE5DDm0VR8/exMnVD4PVR8Zvxl0R7AniNCddy+Vkwe69zvxFEPThz1YMMv3tSslcNNXU30G+i06onwjWBsy7EYmrszHQjRh2AC9ifsZ3/CfqyHzgNPkZmdiSMBb2H0uzOTatVjmDfbn88/8sNk0hIaZqVlmyyGjEi/vF/tyBxeeDOJ+R/48fTEEKxWDctXxRASaibmoo70tOLfg2q1MHBIOvNmBzBwaDqpWUZSs1LRu+kxuBvwcfcBNGza5Dxm66XFVPv357ePyktxrnduJk+W6Pw5cyAhAW64Qd77jRo59yns/d69O6xZA1OmSIJaDw+J1F+0CMJLES/UqZMk2n35ZZlMS0mR+lq1KijJskLx30FztcniFBVD27Zt7bt3767sbigUCoXiX8Cgj7ax93yyi3iv00LrWoF8O+6myutYAaSZs4lPK9qi5rHlezkcbXQR77UaCDV4Envp+NGd6zLsxtIlq3UQ6O1BoE/+ZGwKheIqWNBL7HFyi/daN6h5A4z6j9vlFITNKlYy5tTi7f/tgxB7yFW81+ggvBkM+qJ4dZzZApvfFisjAJ8w6PwENLi17Ay8i4ubpyTXraiJA0WVYMSaERyIP+Ai3us0OlqEtmBxn8WV2LOK54UXRJA8fx6qV6/s3hRNtjWbVEsqGdkZxQrCAJi6eSrHko5dFu/NF+sSv3ICOedaYTOG4e5up26DHO4fY2TgkIxifw3Z7Db2xu1l9anV7I516iYeOg9ujriZ/vX6E+kfebVDdOHwfg/W/+zN4OFp1KzlOmnauUUEkfWyWfJj8W0p330tgA/f8WfboSjCqrnWp9VoqeNfm+kzrLw8UyYeFi6EBx+ELVuKFu6Ly6ZNkuh58WJZXXIlHNH6Dz1UeOJmhULhikaj2WO329uWZZ3K416hUCgUin8547vXJ9SgR3fpv7pOC6EGPeO716/cjhWAr94dX33Ry4KHta9NkLcH2ks/4LQaCPL24LEeDejVVMJ55m89zZbjCWXSr+RMC6mZ2WVSl0Lxn6XLkyIEay8t6tW6yfMuT1Zuv6oqWh0YQiUC360Yq37ajhKhW3MpmlSjk+dtR135uNxEdoFhK+DGR0Dn6bTP+XEcJJ0u2ThKSk4WGC9C6oWSWQcp/pWMaTGGEK8QdJfexzqNjhCvEMa0GFPJPatYcnLg88/FGuTfINoDuOvcCfEKoYahBr4evsVa9XhP43sI1AeivSQ9WZNqoDUH0ufuRGa8nsTk51IICbUy5ZFQ3nih+IkLtBotN4TfwAsdX+CTnp9we73b8XLzwmK1sO7sOiZunMi0rdPYdnEbVpu16AoL4PABD+a8GcDFqNIbVeTkwIqlBrr0MOUT7UEmIgDSLGlcTL+I0WK8XFZZ7NsHL70E585VajcUiv88yipHoVAoFIp/OT2ahPMa4nV/IcVEzQAvxnevX2X87fMSYvAgK8eKJafwHyQd6gczmUYs23mOWKOZcD89w9rXpkP9YG6IDOJiiomDF428/ssRqg29nobhV06OVhwSM7LQaMGvGBMLCoWiABr1ggFzxNM+NUoE6S5PKn/7onDXQ0AtibzPTCzcPieyM9z8nHjap8WAbzUR7Qvyt78Sbp7Qbgw07pvLPmeX2Oe0ug/ajQaPcvYmyU22Sd4vngax0HFTq5+uZbpGdOX5js/z2YHPiE6PprqhOmNajKFrRNfK7lqFEBcH//sfrFolPvoLF1Z2j64ed607wV7BBHgGYLQYSbeku6ygyE278HZMaD2Bb45+Q3xmPNd1TeSeMWdpF94OEFuaBx5OY9SQMBZ+4sfk55LxvEr3whqGGoxpOYb7mtzHxvMbWXVqFRfSL3Ag4QAHEg4Q6hVK37p96RXZCz+Pis2xkRCv5Y9NXvy61ovoC268Pa/ogBOL1UKSKYkkswUIISsnC1CWjgrFfxVllVNFUVY5CoVCobiWybbauJBswlbC+5CUTAvjl+4lxmgm1ODJvPtal5lPfZifHoOnim1QKBSVgO1SwlmzseLaPLMVtrztzEXgEwqdJ1eOfQ5I8lqvINCp72HFtYfDqiQkRDzCn3mmsntUeux2O+nZ6RgtRrKtJVu9+PzUIBZ/5sffZ87hF1B4YMe3Sw1MnRDC0h9j2PybF98tN2DK1ND1FhNvzEnE22Bj9qv+fLVUjzFFh3fTbdR86Hnc/JLx0HrQrVY3BtQbwMV9Tfhwlj/793pis0GL6y1MmZ5M+5vEjvG91wOY82ZAvvaXr4qhQxfzZaucqS8k8/KzQRz824OgYBsPjTMy+lH5/rZaoV3DWiQn6QgKtjL6USPjnnBao916Yw0CAm2sWCt5SuoGRDLp6RQefzbFZazLforhj03erFhqwJiqoXNnDZ9+KrkIcnPyJMyYARs2gNEIDRvCpEkwJtdilvfegyeegLFjJTnyJ584Pe7nzJEtwIsvSrR9Xhwe9z/9BPPnw19/QXw8hIbCgAGSsyEg12lz1HPkiORNWLECLBa47TZpOzjYtf6//pJjtmyRhLrNmsH06XDnnYW+JRSKKkV5WOWouyGFQqFQKBQVjrtOS6ivJ7HGktkjBHh78OrA5jy6bC/x6VlM//EQ793TCk/3KyckKw7xaVloAB8l3isUiopGqwNDGHj6QUa82MmUN5GdIaId7F0Eu7+Qdtc9C4e+h65PQVC98u9DbsxGyEoDvT94Bco5USiuEbp3h2stdlKj0eDr4Yuvhy+mHBPGLCOmHNMVjzFlajCZNGSkadm+Rc+3Sw20bJN1RdE+N6/NCMTXz8ajT6Zw8rg7S+b7YreBf6CNc6fdmPRkJqeOu7N4fjd0P83FZ+RYMnIy2HB2A9+u0BL1cTeatI3j8Wfd0KDl22UGht9RjcUrY7ixUxa9B2QQF6tj+UJfJk5NoW4DmZCo39hyuQ8XotwYPTScgUPSufOeDFZ/78Or04No2MRCt1vM6HQw5P50Ppvrx9rtFwgNc45t/15Jxvvqe0VH4L/+fCCeejuPPJFMcqKO+R/4c+8wG9u3aS/bFR07Bh07QlCQCPOBgZJA9uGHITHROUF0/fWy3bFDkjA/9pgI5B98AD16wO7dIvjfdRdER0uS2BkznElrmzSR7eefg04Hjz4q4vu+fbBgARw44Eyqm5vhwyXR7Msvw4kTMHcuuLvD8uXOfbZsEeuopk3huedAr4dvvoGBA2HZMrj33mK9NRSKaw71i1ShUFJXk2EAACAASURBVCgUCkWl4OPphr+XO6mmkkVn1Q3xYUb/JkxfeZCjMWm8te4o0/s1KZbn6pWw2+3EpWURrgFvD3WrpFAoKgGHfY4pWRLYlrfSd9k+px9sfQdObbpknzMUWg2T1yrSPsduB1OK2Ad5BYI+ALQqPZtCUdXxcvPCy82LbGs2RouRjOyMAr3aZ78WwPwP/C8/79TNxBtzi5+3yNvHztKfYi9/LSQl6PhlpTc3djK7lCcm6Fi3qiXb5y9kZ/JvrDy8gSNfTsev/Rq0jzzDH14h9K3bl4X39WbIzU14e2Yg366LoUnzbNq0y2L5Ql+69DDRrmP+SdQzJ91Z+G0s3XrKJMXg4Wl0al6Lrxf50u0WCUwZNCydj9/z56cVBkZNcK6k+n65AQ9PO/3vzCxyrB6edr76OQbdpTnMgEAbL08L4ted0bRp5Ymvuy8TJ7oTEiIR6z6XvqrHjYNhw0QsHzcO/J2nm/PnRex3RLwPGgQtWsDzz4uY3rKlTAR8+qmI6XmT4y5bBt558op37AgjRsAff0CnTq6vNWgAX33lfG63y2TBxx9Lv+x2mWRo106i+h1jnTABunSBp56CoUMrZxGYQlHZqLsfhUKhUCgUlUaQjwf6UkTJd6gXzNhukoR349F4Fm0/Wyb9stvtxBqzMFlKltBMoVAoygSvQPCvVbzktWWBXw3o+w4MeF9yFNissHcxLL0bjq2r+FBhu10mLlLOiJB/rYUqKxTXKO468cGP8I0gUB+Im9Y1EGLYg2ksXhnDnPnx3H2veN1nZhRfnhr6QJrLXF6bdlnY7RoGD0/PV56ToyE5xpe+dftyt/1jrBkB3NjvONa0QGLicvj8z58Y8/Pj+DXfxd5dnpgyi6cOR9TOvizaA3h6Quu2WZw/4xxr/UbZXN82ix++dk585uTA6u996Nkns1grDO4dmXZZyAa4sZNMCpw5rcWYZeRwVDQbNti5/a4sMjJtJCRw+dGnj0TW79jhWufw4a42NU2aQO/e8MsvxfuadYj2drvY8iQkOMX6PXvy7z9+vOvzbt3ESsiR+Pbvv+Gff+C++yA52dn/xETo21fyQRw7VnS/CmLhQhH8C1oJUFJefDH/JEJkJPTsWXZtKBQOVBiZQqFQKBSKSkOj0RDm68nFFDM5hSVkLIJBbWpyNjGDXw7E8OX2s9QO8ubm68JK3TcR781U89eXanJBoVAoSoWbh0TfZyZJBH5FiNd1OsG934hov+dzsc9ZP03sc7o9XfH2OTYbZCRIBL53sCSyVSgUVR6tRou/pz/+nv5kZmditBgx55ipWz+HuvVzALh9UAZvzwxgSN9q/Lr7AgZfG6nJrvdd/oFWPHLlra4RkePyup+/7YrlqSmi5p8+6Q7A+hmPA48X2Oen1r7JkBtvwmq/9Ypji6idk6/MP8DGP4fcXcoG35fGc0+EcOyIO42aZPP7/7xITNBdnrAoipq1XNvxvyT2pyY7xuSG3a5h1huezHqj4Dri4lyfN26cf5/GjUW4T0rK7z2fl3/+gaeflkTLmXkWDaSkOP+Oj5ftiBEixHt4iO3OLbdIeVKSbB2i/COPyKOwMRTUbwfvvSdWQffff+W+X6v818d/LaOEe4VCoVAoFJWKm05LmJ8n0alm7CUQpDQaDZNuacjFFBP7zqfy5rqjVPPX06S6X6n7ZrPbiUlV4r1CoagCeAeBhwHSYyvG+97NE9qNhsZ9nfY5F3ZXnn0OgDUb0mLArBcB392rYttXKBQlxtvdG293byxWy2UbHcd9X/+7Mpg3O4B1q72pWy+HewdUcznWkRTWga6QW7LCyh23l3abhEm/OTeBGpcEcYvVwt9x+9gR/SdxpjhO5fzFm7u2kH0wCniGjOwMCpLOCm/LNRS7/10ZzHw2iO+/MvDMS8l8/5WB4FArXW+5ch6A4rbjGNPIsUZu6eNU0d20bnjp9OjdvGjZwrWSgixninsLbjRKxLxeDzNniie+t7dE0N92m8yz5t4XxDO/aVPIyoL16yWJbe42Hce89prY5RRE8+ZX7td774klz39VuP6vj/9aRgn3CoVCoVAoKh29u44gbw8SM5xi1I6TiSzbeY5Yo5lwPz3D2temQ/2CQ4DcdVpeGNCMR5ft5UKKiRk/HmLesNaE+elL3TeHeF89QI+nmxLvFQpFJXJqE2yZBSnnwCcM2j4kyWXLE4d9ztk/YPNbkBolkfjH1kKnJ6Bhr4o3Hs42Q+oFmTjwDpZVCQqFokqyOWoznx34jOj0aKobqjOmxRi6RnQlwDOA1KxU0rPTMZvlOyQ1RUuTFhYWr4xxqaNJC0tBVV81depJXqXAYCuduzsnAnrQEru9BfsT9rP6FOyM3kl6ThoAr/35Gnt9fBlQfwANAhpcdZt+/nZ69cvkxxU+jHsilV/XenHfg2m4lZEaV7uujEmns7uMSUhHo9Ggc/MiM9sAiMfNP//kr+fYMfDzk6htKPxrfeNGiX7ftEkE/NzH56W+uFkyZQpERMjfjz4KN90E27eD5dJlbXDptPr4KLsZhSIvyuNeoVAoFApFlcDf2/1yMtgdJxOZveEYh6ONxKdbOBxtZPaGY+w4mVj48V7uvHpnc3w8dSRlWJi+8hCm7LLxqHeI95acktn5KBQKRak5th5WTYILeyAtGmIPwsZX4UwZGvdeCYd9zo3jJBrfYZ+z8hFIOlUxfciLJQNSz0N6vPjxKxSKKsXmqM3M3D6TA/EHiM2M5a+TUczcPpPNUZtx07oR7BVMTUNNVnwpgRmtWlvwD7DRubvZ5eFfDC/44tC1hwk/fysfzAogK8/CJY1GQ4SmNc/d+Byf3vopHeu0BCArw4vfzv/GE5ue4KnNT7E5ajNwdStE7x6WTsxFN6ZPDibLrOWuoRllMh6A4BAbnbqZ+GaJL1Hn8geYJMRryMzOJC4zjrhM8cxZssROYq5b6iNHYN068cR3CPaOJLe5rW/AmSc8r8Pl228Xv8/h4bJ1CPdt2kjk/jvv5G8vLU3E/rp1xWqndm2YOhVMuRYsaDRw9iz8+qv8rdFA9+6u9WRnw7PPQrVq4OUFt94Kp087X//4Yzlu5878/f36a3ntf/8remy7donfv5cX1KoFs2fn38duh48+glatZOVCcLAk33V4/jvo3l0mNQ4dgptvlmtSvTo895zkSijO+JOSJLlvq1YyMePjIxMnq1fn75dGA6NHw5o10Lq19K1BA0lGnJfsbHj1VbjuOsnvUK2aJBh22B8pyg4Vca9QKBQKhaLKEGLw4EKKlWU7z5GUacHmWEJrh6RMC8t2nis06h6gdrA3L/RvyjPfH+BEfDqv/XKEl25vhrYMokGtNmfkvbtOxT4oFIoKZsssyIgD26Vf63YrmJLgr0VQt0vFeN9XRfscu12877OMksxXH4BLdkqFQlFpfHbgMxJMCVjtMrF2/ovpnM3w59E253mshyQf/eknN7Ztc2PgQDt9btVjtFiw2csnUMLga+e1OYlMGhXKbTfVZOA96YRVtxJzwY0//5Ak4MtXxxLuE87EATX57nk7bhtnoLMuJzH7Igea7uRI0tskmG7FKzOLlKwUAjwDimy3y81mqtfMYfX3PjRuaqFZq7JZQeBg5juJDO5dnb6dazLk/jTqNcwmJUnH4QMerP/Zm6OxZwGwXVLbw2tk076DlodG2bBlu/PBBxq8vMT6xkGbNiLkvv66XCdPT+jRQ0TpkBCxZJk4UWxyVq/O76OfG5NJPO7T0iRif+1aKTdcSlei1cIXX0CvXmKp89BDkuz1/HmYM0eOmzJFROT9+8UW5uBB8eTXaGDxYnjiCZkQeOYZqdMxOeBg6lQRoqdNk77MmiXJcLdtk9eHDpU6Fi+G9u1dj128GGrWlPFfiXPnYMAA8fQfPlwE/ylToFkzSf7rYOJEmSi47z4YN07O3dy5Iqjv2yfn10Famkwy9OkDgwfDhg1iKZSUJOK/o3+Fjf/UKfjmGzm2fn1IT4elS+H22+U69OrlOoZdu2DVKsk1MGoUzJ8v42ndWpIYg/zbvftu6cuoUdCypbQzd65MfOzYIedaUTYo4V6hUCgUCkWVwU2nJdjgSazRfFm0d2CzQ6wx7xLg/LSNDGJijwbM+fUEf5xI5POtpxndpWwSKebYbCLe++txU+K9QqGoSFKjnKK9A1sOpMeAf62K876HPPY5b0vUe2Xb59jtksDXnOIU8Cu6DwqFwoXo9OjLoj1AwI1rSN5yJ0fXdmH8tyLuNWsGH34IY8dq0OkC8PP0I82ShjHL6HJsWdHvzkyqVY9h3mx/Pv/ID5NJS2iYlZZtshgywpkwtnZkDi+8mcT8D4I5OO9xrFYN/d6axXm/xdjsNqIzYnhw3Ti61uxK/3r9aRjYsNA2tVoYOCSdebMDuKuYSWmvhnoNcvhp00XefyuA1d/7kBivIyDISsPG2Tz3Sv4Q6FETUrlw3o0P5vqSnAitWmfz7rvQqJEzsW69evD++xIFP2qUeNhv3CiR3GvWiCD98ssSBd+nDyxalF8sdzBrFnz6qfN5mzbw11+u+3TqJKLvyy/LvikpErWeni6TB0895dy3ZUsYO1Yi4G+9VUTy6dMl6nv48IL74OkJv//uzBkQFCRi96FD8h4MCIA77oCvvpIoefdLpyIuTlYjTJlS9Jzw8eNybm67TZ4/9JCsEJg/3yncb98u7/dPP4UxY5zH3n23iOPvviuR7A7i4uD55+Gll+T5+PEi+H/yCUyaJBHvVxp/ixZw8qRrroTHHpO23n47v3B/6JBMHjhyCgweLGP4/HPnqoqvvhJxf/16Of8OevSQcS5e7Do2RenQlCQJnKL8adu2rX337t2V3Q2FQqFQKCqF2+du5eDFVBfxXquBptX9eP/e1sWq4/1fj7Ny30UAnulzHb2aFvJrogS467TUCPBCp1WikEKhqCAW9BKbnNzivdYNat4Ao9bLc1OyiNcV+RsvJwv2LYHdC5wTBzXbQtenILh+xfUjLzo38AoCfekTlSsUipIxYs0IDsQfcBHgdRodLUJbsLjP4isea7fbMVqMGC1GrFXICisuM45fTv/C+jPrSctOu1zeOLAxA+oP4KYaN+Gudc933LuvBfDhO/5sOxRFWLXKGc+OLXruHVCN2Z/EM3BIfrseT50nBg8DPu4+aDVlF6By/LhYucTHiwgeFSWR9M2aXfm466+HwEBYscK13GiU6PFnnnEmuo2MlIj8vHY2o0fDggViL/PKK87yvXtlAuGnnyRKHiQCvU8f+PFHiUgH6efjj4ug3bSplHXvLpMAuf/VurmJ2G/Kk3P4jjtkvHv2yPPHHxfR/tQp8uU5uPlmWYWwfbtrO4mJztwDIFHx7dvLhMiUKVcef26ysmQixG6HGTNkRUBuaxuNRvrw22+ux7VqJXV/9508v/NOOHAA/vwzfxsNGsg5XL688H5cy2g0mj12u71tWdapQsUUCoVCoVBUOR67pQFBPh44dHGtBoK8PRjWvnax65hwcwPa1gkE4J31Rzl4IbXM+pdttRFjNGPLuyxAoVAoyosuT0pCWu2lX/paN3ne5UnnPl6BEFAb3L0qrl9untB2FAz7DurdLGUXdsPX98LWd8WHvjKw5kB6nCTyraw+KBT/cca0GEOIVwg6jYT76jQ6QrxCGNOi6HBcjUaDv6c/EYYIgr2CcdNWDcOIMO8wRjYbyRe3fcHE6ydS168uAEeTjzJr9yxGrRvF8n+Wk2xOvnxMTg6sWGqgSw9TpYn2xSHLmkWiKZHzaedJMCVgzil6pWtxaNhQks5GRMCXX4oXe+fOYlljsUBMjOvD4X1/7JgkwQ0NdX04kt7mtucxGiE6uvA+5F0NECg/EVyE6169xBJnca45pcWLoW1bp2h/JQqyhwkMdG3j2DER96tXzz+ugwfzWw6FhrqK9gCNG8v2zBlnWWHjt9tl1USjRtK/kBCp8+OP8+cTAKhTp3hjOHUqf/9DQyE19cq2SYqrp2p88ykUCoVCoVDkomfTarxstfHhxpPEGs2E++kZ1r72Ff3t86LTani+f1MeXb6Xc0mZPP/jIebd14Zq/mVjupiVbSXGaKaanx6tirxXKBTlTaNeMGCOeN2nRoF/hIj2jfKsc9e5g39NMKVAZmLFRd/7VYe+s+DsNtj8ltjn7FsCx9dBp8ehYe/Ksa7JsYAxGtz14B1csZMaCsV/nK4RXXm+4/N8duAzotOjqW6ozpgWY+ga0bXYdWg0Gnw9fDG4G8jIziDVkkq2Nbsce108PHWe9Irsxa11buVQ4iFWn1rN9ujtJGcls+yfZXxz9BvaeN9G6IV7ObylHtEX3Hh7XkJld7tY2O120i3ppFvScde5Y3CXKPzSTJ7k5MCECZIcNSNDROMffhBR/+abXfd1WPLYbNCtm9jAFESNGs6/jcYrt6/Ln7cXcP0XqdWKn/u770r/oqMlUv7994scHlD4v7jcbdhsYsuTdxWBA688/6KK+2+zsPG/9ZasTBgxAl54QYR7nU5yChSUdLY458lmE4ueuXML3tcxKaIoG5Rwr1AoFAqFokrSp0UN2tcNJtVU8h9nBr0br97ZnAnL/iLFlM1zKw8y997r8fYom1sgc7aV2DQR7zf+E8e8TSe5kGKiZoAX47vXp0eTsrPnUSgUChr1yi/UF4ZXAHgYJKGtJbN8+5WbOjfBsG9g7xLYPR8y4mH9c3Doe+j6dOXZ52SbIfWCJM/1DgY3j/z7HFtf9MSIQqG4KrpGdL0qob4wNBoNBg8DBg8D6Zb0KiPgazQamoc0p3lIc+Iz41lzZg1rz6wlzZLGb7tjOPNmSzz8Uhk0aQ/tu/gB+W10qjLZ1mySrcmkZKXg5eaFwd2At7t3oftvjtpc4ETN3LkSTT96tNjPACQniw3Lhg2udbRqJdsGDUSQ7tmznAZXACNHwhtvSELX06fF/ubee8uu/gYNxBu+XTvw9y96/7g4iXbPHXV/9KhsIyOLPv6rr2QSZNEi1/LPPy9uj/PToIHY5PTooXLBVwTqFCsUCoVCoaiyBPl44OFWutuVmoFevHh7M3RaDacTMnjl5yNYy9DixmSx8v2eKJ79/gB7zycTnWpm7/lkpv1wkN+OxJZZOwqFQnHV6NwkkawhrGKj3XUe0PahPPY5eyrfPgek7dTzYqOT2zf72HpYNUn6abwg21WTpFyhUFQpDB4GahpqEuodioeugEm4SiLUO5T7m97PF72/YFLrSbTskETzhS1o9H5n/mk9kofWPcTSI0tJMudPGFsRdOhi5nTKmQL97YvCbreTmZ1JXGYc59POk2xOzjdxsjlqMzO3z+RA/AFiM2P562QUM7fP5Ic9O3jxRXjtNYk2d9CunURn9+zp+nBEbA8dKl70d94JdetKItzatWHqVBH90y6lGNBoJHlucrL8rdGIWJ2bnBx49llJ4OrllT+J68cfy3GpqdChg1j6LF0qfu2//iqvXck/3sGuXZJo18sLatUSb/zcDB0q0et33ikTFHo9BAdL+blzYh/kYN8+2T73nKxK8PERi50HH5Tyvn2LHr/NBidOSFt+flJHmzbw7bcF93/LFkmw27q19K1BA4jN83Nm6FDpZ//+Ennv6Snn9eGHJY9BUuW8va9ZVHLaKopKTqtQKBQKhWDJsXEhxURp71l+3h/NOxuOATD4hgjGdS+7qM/Hlu/lcLTRJZmuTgutawXy7bibyqwdhUKhKDHWbEiPlcjziia3fQ6Adwh0fqLy7HMcaDSSF0AfAF/cVnTyX4VCUSXJzM4kJSsFi9VS2V1xwW63czjpMKtOrmJ79HZsdhsgXv+danZiQL0BNA5sjKYyvwdLSe6Etg+sfcAlGfHZ99/DluGPmzmcAGoxdizMmyfJWm+7TQTiK5GSIr74GRmSyLZFCzl22zaxdFm3TgTtJUskkj8rCwYNgrAw8Wp/6ilnctrGjcUm5p57RHR++20wm8UyZuRIaat6ddm/RQsYO1b68N13Ep2+b58I61pt4clpPTxEHB8xAurVk+Svv/8u/cktfrdqBfv3i9h9003i6f/rr5CdDU88IfY2IJMcRqP8q6pfXwT3zZvFwqdJEzh8WPa70vjHjpVkuE2ayPExMZI3wHpp3jr3GDQaiex3c4NHHhHP+vnz4e+/xed/1y7Zz2aTXAAxMTLOtm1FrN+0Ser48EMYU3Qai2sSlZxWoVAoFArFfw4PNy3BhtJHU/VrWZ3BN0QAsGJPFL8cuEIGq6sk1mgmbxC/1QYXUkxl1oZCoVCUCp272L/4BFe8WO6wz+kwQZLZZiaIfc4PYyHxZMX2JTd2O2QmQcpZSWKbW7QHeZ4aVTl9UygUxcbb3ZsahhqEeYdVqQh8jUZDs+BmPNP+GRb0WsDgRoPx8/DDareyOWozUzdPZfLvk/nt3G9VwvanJOROaBuVFnVZtAcIuHENNosHqWcjiIqC1193CsUff1x03R99JJHyjzwi2++/F7E6IkJEbkdk9/DhziSua9fK5MAvv7jW5e4uIvpjj8HMmRK1D3DhwqW+BsAdd4i1zF13ScR8UBC0by8TBMOHF20LYzLBwoUyKTBunByn10N6unOf7dtFtH/wQRHX160TT/9q1UQQT011rdNul0j2mjXhp59kn6ZN4Z9/5FHU+N95Rzzu09Mlyj4uTiYiQkMLHkNyskwivPQSPPqo9E+jEZHewddfy/NHHgFfX+nXzp35r4uibFDCvUKhUCgUiiqPn94dH8/S+9I/3LUeHeqJSeS7/zvO3+dTSl0nQLifnrz5aXVaqBmgkiAqFIoqhlegCPgFebyXJy72OT2k7OIe+Ope2DobLOlXPr48sVnBJxQ0ebLyad3kXCkUin8FuQV8T51nZXfHhRCvEBcbnXr+9QA4kXKCd/96l4fWP8SSw0tINCVWck9Lht1uJ8QrBG0umdGv7f+wW7ype/Mf5ORI9Pjo0fJaYUlQc/P119CxI7z8MmzdKiL70aMidAPkNqnQ6cTeJS1NxO5Nm6S8c2fZTp3q2ubAgbJt2dJZNnKkROM7rHGGDJGI+5wcZ32Q34YHRLSOjJSVBA48PaFXL7GTyT0mLy+J9O/aFc6ckcfOnSLI79+fv+5XX5UxZ2aKYL5woYzx55+LHr/BIBMm587JpMDGjWKxc/fdBSeR7d4dmjd3Pg8PlxUI7du7jqFePbku//sfnD8PJ09KRL6/v9PiR1E2KOFeoVAoFArFv4IQgydupcyApNNqeK5vE+qG+GC12Xnhp0NlEhU/rH1tgrw9Lov3Wg2EGvSML0M7HoVCoSgz3DzBv5aI+BWNX3Xo+zYMmAv+tcFuhX1LYcndcHSN67r9iqTtKEla6xDvNTrwCZMEtQqF4l+Ft7s31Q3Vq6SA76HzoGednrzX/T3e7PImXWp2QavRkpKVwtfHvmbU+lG8testjiQeKbVNZEVzT+N7CNQHXhbvY7+dRFZ0JOlHb8DHx05gIHz2mezrGJrFImJ07oflkuPRsWMiQIeGuj7qX7q9josrft/q1HF97hCtHdHh+/ZJNHy1amJVk5kpQv7ixWKBYzAU3UZByWIDA10j0I8dk8h8kMj83OM6eDD/mLRa18S0ILY/IIJ/YSxcCO+/L+f5nXegUSOJ/g8JkbY+/ljsgfKS9zwVNoZTp/Jfl9BQmRy4muuiKJrSh64pFAqFQqFQVAA6rYYwP0+iU82l+iHj4+nGq3c2Z/zSv0gxZfPcDwf54N7WGPQlvy3qUD+YyTRi2c5zxBrNhPvpGdOlLj2ahJe4ToVCoShXNBqxzfHwFu97a07Rx5QldW6CYV/D3iWwe77Y52yYDod+gG5PQXCDiu1PZGe4+TnYvQDSYsC3GnR8FOrfXLH9UCgUZYa3uzfe7t6YckykZqVizqmEHB+FoNFoaBrclKbBTUk0JbLm9BrWnllLqiWVLRe2sOXCFur716d/vf50jehapSyACqNdeDsmtJ7AN0e/ITohkyO/DSM4PIt+A6yEhZuxW91ZsdQb0PLss/DGG5I49eY8X7MbN0rkt80G3brB9OkFt1ejRvH7VliEv+Mnxb59YqHTrp1EjnfoIDYwe/YUHJlekjZAxhQQIKL5iBFw//2u+3rlWaxbUme7hQslH4DJJFY5I0bACy+IcK/TScT/smUlH8N118HcuQXvW9zzpSgeSrhXKBQKhULxr0HvriPY4EFCWlap6qnmr2fmHc2YsuJvziVlMnP1YV6/qwW6vH43pSAjy0pyhoVAn6r/Q0uhUPyHcfeSyPfMBDAbK7Zth31O4z6w9V04+esl+5xh0HIo3PgweBQjzLG8yDaJ973eX1YnaIvh7aBQKKocXm5eeLl5VUkBHyDYK5jhTYdzT+N72HphK6tOreJEyglOpp5kzt45fHHoC3pH9qZv3b6EeIVUdneLRU66L9YsTxKiPVn4Sf7Xly6FLVvs7NunYcMG19datZJtgwZir9OzZ9HtlVXqlr/+km2/frBokXjj+/iUTd0gY1p/Kd95vXpFj81qlWj33FH3R4/KNneEf2Hj/+ormQRZtMi1/PPPr6bXrjRoAH/+CT16FO37ryg96hQrFAqFQqH4V+Gnd8fPy73U9TSv6c+UXrLWdPfZZD7ceKLEde04mcjsDcc4HG0kPt3C4WgjszccY82BaFIyLaXuq0KhUJQrWi0YwsTGpjLEad/q0OctuP0DCKgj9jl/L4Uld8HRXyrGPufMVtj4KsQegow42W58FU5vAVMKJJ+RRLb/MusKhULhxMvNi2o+1ajmUw29m76yu5MPD50HPWr3YHa32bzd9W26RnRFp9FhtBhZcWwFo9aP4o2db3Ao8VCVtNHZFbuLD/d+yLGkY6R5nqTOxMdpNmU6T374B58sieOTJXH0vyvj8v7PvhKP2T2a9l2N9LjFRs+eImQ7IrYbNYK9eyVR6tNPi42Nry8MHiyWOsnJMG2aRN6fPSue9/HxBfftscfkWB8fieLfudP5ynpNCwAAIABJREFU2osvSrJYEKEcYMYMEbf79HFGoe/aBZ06wSuvyPPZs13b2LJF/OMLomlT8dsfOrTwfyPnz8OgQWI5o9eLtz7AnDmu+733nmwXLABvbzlfcXFO65zISEnEe/KkrCTYtMkp8lssknT2q6/kuZcXXH+9ROjnJTJSrkdamlwHLy+oVUt87BMSnP0AGdNHH0nOAE9PCA6WsZ47V/BYFcVHUxU/7Apo27atfXfuTBsKhUKhUChciE41YbJYS13P/C2nWLbzPACTbmnAHdfXvOo6Hlu+l8PRRmy5bqu0Gmha3Y/3721NsI8n/t6ln2xQKBSKcsdmhYwEyEqrnPatlkv2OQvAERVbow10e7p87XO+fVDEenuu/ysaHYQ3g0FfOMu0OvHC1/uVX18UCkWFkGXNIsWcgimn9PmOyotEUyJrz6xl7Zm1pGQ5Tcnr+ddjQL0BdInoUmV8/KdunsqxpGPYsF0u06Klnk9zZrR5nYw0LTOfDeJ/a7y5rnkWa7ZGX95Po9Hg7eaNj7sP3u7egPjhP/ywvB4eLslco6MlUa2Hh4jKJhPcdRd88IFEordsCc8+C2FhEhE+frwIyq1biy2N3S4i9eHDIox/8QW0aQMffgiffiqCfVSUlIMkp508WdozGsVy5vBh+OUXeX3tWujd2ymMR0VJEt3wS26ZI0dKhH10NHzyiYxn0iTxn4+MhAkTxD//8GHpJ0h7NWtKX4xG8PMTQf+GGyQZ7A8/yH7du8vKAA8PEfdPn5aEsUlJsGKFiPSdOsH338u5Gz4cjhwRwT04WMT3d9+FlStF6AcYNQrmz5e/IyOl7jNnZDzTpkli2t9/h5tugm3bJMlvt25ynrZuFXF/0CDJQzB3roj4+/aJRc9/AY1Gs8dut7ct0zqVcF81UcK9QqFQKBRXxmqzczHFRLbVVvTOV8Bmt/PiT4fZeiIBrQbeuKsFbSODij4wF0M+2U58ev7I+lCDB1+P7QigxHuFQvHvIitdIs9tpfuOLTFp0U77HBARvTztc77oI+PNi08YPLgmf7mbB3iHSI4AhULxr8ZitZCSlUJmdmZld6VQsq3ZbL24lVUnV3E85fjlcl8PX3rXERudUO/QSuwhjFw7kkRzYr7ylBXTifp5iEvZd+sv0qZ9watSdVodBncD3y/zZfQoN2rXFkH49GkRue12ibbv1Ak2b5ZFY+fPQ/v2EokPIib//LMIzhkZEg3fubO8ZjKJR/u5cyLQjxwpYv6DD8p+LVpA9erSZnS0RP6fPQtr/s/efYdHUa59HP/OtmxLTwiQQu8gojSlWwApCoooYEGx9/Lq8dgVsYEeLKAeUVGPYBcFbCj2AmIHFJBekpCe7Cbb5/3jySakQBISSAj357r2Wnd2duaZAcPmnmd+90cwerSaoX/fferiwNChqkjetq36zI8/qpn4N95Yek6mq8J5uAlvTIxarmmqOB9uEBsRoQru4eJ++BhWrVLH26+fivGx2dR6F1ygxhyOyNm5Ey69FL77DlwuNSs+IUEV6u+9VzXZ3btXHcvNN6tzed995bP/Tz1VXRSoXLjfvl2dj7g4NXPf64W0NBgyRJ3PhQvVPnw+dTxTpsC116p11q1TF0xuuQVmzarlX6Ij3KEo3EtUjhBCCCGOSEaDRlKUFUM9Qy0Nmsa/x3SlYwsnIR3uW7aeHTl1+8VNjaPydtXysBy3l4ISf73GKoQQh02EU8XWRDRSxvzhjs+JbKkuDuxLM6rl1Qn4oHAPFOyGQP36rgghGpfFaKGFvQWtna1xmBsw0LwBmY1mRqSO4PHhjzNn6ByGpwzHpJko8hXx9qa3uWTFJTy0+iHWZq9ttBidRHsihkplRgMGeoz/lleXZPDEgizOmuJi0LASnJH7H2MwFKTAW0CeR1W177i3hPV/hfB6IScH7r5brXf55eUZ66mpKk4HVMH4yy9hxQpVtP/wQ1Woz85WD7dbReAYDDB5ctX9G42qIH7OOWrGOagi9ujR6r/vvVf9EzRwIGzZopZt2wY//AADBqgiediCBSr25vTTy4v2YZdcoorsLpeKx+nZs7xoD2oMMTGqKH7ddVBcDFOnqvceeqhirn1qqpr9X1SkxnbssWq52ayK5jt2gMcDf/wBF16ojsHnUxcBsrPV3QkAjz1WcYxt26rPfPmleh0RoY5761a44QY1m/7KK9UFhTVr1J+B3a62mZQEXbrAypVVz7GoPSncCyGEEOKIZTEZGqT5q81sZNaEnsQ5LLi9Qe5YsrZORfap/dOIs1vKivcGDeLsFqb2T6uwXo7LK5n3Qogjh6G0cB2Z1Hgd6NJOgCmvwwnXgMkKxTmw4i547zLI3lTz52ur7wwVgRMu3mulkTh9Zxz4c/4SyN8JRZkQDDTceIQQh53FaCHRnkiyM7nJFvABusR14ea+N/PiqBeZ2nUqsRGxhPQQ3+/5nn9/+2+u++I6Ptn2yWFvwju5y2RirbFlxXsDBmKtscwYMZTBwz2cPsnNnGey6X28l3PGtCQ3x4DPB1mZxgoPX6WvylEt8tlZtJOs4iyK/cVER6uif1rFr9llhfG8PPW8caN6HjNGFcb3fTz3nLqhLKfqDQL873+qSD59evmyfRvB3nuvKprHxqrC974uukjlwa9bp15//LHK3b/wwgOfu23boFu3qsvDFw62blXP//yjZr+3bn3g7R3Iyy+rSCGrVUXmJCaqGByAgoKK61ZugHvJJVWPe+NGdRdDq1ZVz/PatWqmf3W2bVPb3DdfP3xud+06+ONrbkyNPQAhhBBCiPqItplxewN4/PXLu0+MjGDmGT248c3f2Z1fwn1L1/HIWcdgNtZcrBrYIZ6b6Myi1TvILPSQFGVlav80BnaIr7JurtuHrtMgFxyEEOKwiIgEsx1ce8Hnrnn9hma0wPEXQefR5fE5e36BN6bBMedA/8vUGOuj7WAYcYfK1i/KUBcs+s5Qy2vDWwQ+F1hjwBbbeBc6hBD1ZjaaSbQnEhOMocBXgNvvbpLNYGOtsUzpOoVJnSfx3e7vWLZlGRvyNrCtcBtP//Y0L697mZFtRzKm3Rha2Fsc8vH0S+rH1X2u5s0Nb5JVnEVEzrEUvXEz/3dbS7L2GjGbddp1DHDKaW7y84x8ssxOu/YBpoyveGfT4qUZDBxSftHBaARd13H73bj9bvI8kUA8QXyA+j49d255sTz8R7Vv0tt//qNmtFeWWCld6L334JVXVAxM//7ly42VbsgKq/zX4txz1Uz0V16BRx6BOXNUgfzmm1VT3cRENWO9rsKz63W94kz7unrjDXVBYuxYlaWflKRm5X/4oTpH33+vmvLedJNavzbHHQqpiyZvvVX9ujbbwY9XSOFeCCGEEM1AgjOC3fkl9f6lqlurKP41qgszl//FbzsLePLzf7jp1E5otfiGPLBDfLWF+urkFfsI6TrxzqbRTEwIIWpkMEJUK/AUQnF242Tfh+NzdvwIXz8K+dvh90Ww6RMYdAN0Pq1+FY22g2tfqK+OrkNJHngLVPHeGlO/8QghGpXZaCbBlkBMRAwF3gJcfleTLOCbDWaGpw5neOpwNuZtZNmWZXyz6xuK/EW8s+kd3tv0HgNaDWB8+/H0TOhZq++1B6tfUj/6JfUD4OuVVhZ4o5kw2U3L5AA+r8Y3K2089oDqJVWQb6BbLx+vLsmosI1uvQ58d2qo9M8gpySHXUVBnGYnc+dG43BUPK6O+/Qz79u3POO+OuFT8uSTqmAfbk5bV9HRMGECvPYa3HabyuCPjIRx41Rcz549qhEuqPz8sLZt4e+/q24vfPdBeOZ7p07wySeqAW5ycs3HU9nrr0O7drB0acV1wnE2P/6oGt1eckltjlbp2FE14O3XTx2/aFhSuBdCCCHEEc9iMhBrN5Prrn8MzYiuLdieW8wrP2xn+Z/ptIm3M+n4lBo/9+PmnFrNuA8rKPGjoy46CCHEEcMapWbfu/eCr5EaOaYNVPE5v70GPy0oj89Z9y4M/RckdDq47W779uBn3O8rFAJ3DngKVNxOfe8GEEI0KpPBRLwtnpiIGAp9hRT5igjpjdS4uwadYztz0/E3cVGPi/hk2yd8tO0jcj25/JD+Az+k/0DbqLaMaz+OYSnDsJqsNW+wjn7K/Klsxn2krwPXPjO6rJAPcOFlRZzYPYX0PSa69/QRHRNi8PCDj/QJhALke/MJhJz4gzpgJhgKAkZGjVKzvUtKqBK/AyrCJjzj3lGajPTee6rIXh/Tp6sC+ZVXquu5K1fC8ceXvz91qirAf/11+bLx49Xs/BUrVKNYUM1ihw9X8TWnnKKWnX02PP003Hmnmhm/b/F939n4Dkd549t9hW8GC4XKZ9Pn5KhtHaxzz1UXI+6+WxX9K8vOVo1yxcGR+/eEEEII0SxE28xYTA3z1eaCE9owvLP6Jv/sV5v5cUs1AZj7+HFzDo+v2Mj69EKyXD7Wpxfy+IqN/Lj5wJ8rLPGzt+jw5o8KIUS9GU0Q1RqciY03ozwcnzPtHehwslq251cVn/PNYyq6pi62fQtfzILMdeqiROY69Xrbtwc/xmBAZd/n72iciCEhRIMyGozEWmNJiUwh1hqLsXJD6yYk1hrLuV3PZcHIBdzS9xa6xnUFKIvRueiTi3hp7UtkujMbbJ8/Zf7EvF/nsTF3IzmeHL6eO41LJ3Ti/24v5vVXnDw7N4pJo1qSvkfNIT72+NpNuPnfC5EMOSaZzoltGNQzhWXv2iu83y6mLbt3mti0wQzA8GFGThziQ4twMX26mp1/wQUwZIiaEW42q5z48ePLtxGe7X777SoD/vXXy7PZ3W74/HNVSK+NU0+FlBQVS9OrV8WiPZTfCZCVVb7sX/9Sn5kwQc3Uf+op1TD2u+/UuOPjYf161fg2IkLlwicnw6OPqoayXbqoprBOp8rTP+YYVZD/v/+DxYvVDPu33oLff1d5+RERamzXXKMa2YYz88OF99RU2L5dHfe2bRXHv2uXunPAalXHsmOHap775JNqrGYzDBumjqlrV3WhAcDrVfn4551X8zl0uVTz4bg4dUyTJlXfj+CXX1Tj39hYdZGmb19YsqT8fV2HkSPVn/v27RU/O2GCusCxqbRdTm4u3Hor9O4NUVHqvRNPhGXLqu43nPf/0UfQp0/5uYDL46pZ16xp3KFp/K1peDWNDE3jv5pGlXWrI4V7IYQQQjQLmqaRGBnRILf/GjSNf43uQpeWkYR0eGD5X2zN3n/RZdHqHeQW+wiFMzV1yC32sWj1jhr35fIEyCz0NMlbr4UQ4oCs0RCTBuaGn7VZa5EtVXzO6fMgpg3oQRWf89pZsOHDqgHE+7PmBTVzXy/tl6IH1es1L9R/jAEfFKZDwW7wy8VaIY50Bs1AdEQ0KZEpxNniMBqabgHfbDAzNGUos4fO5j/D/8NJqSdhMphw+V28+8+7XLbiMh748QF+z/q93t9F39zwJnmePEKouxGiB3xI0Ohm2eKW3HVTPE/NjiErU2XdH9PHS1TMge9a8PvV82cf2RkzoZh7Z+dw8mnFfPOFCk0PD/fx57KIiw+S1Eo1CL/mlnyuvDmX7JJsOvRSXVSLinR++EEV4R0OKCxUj7BrrlEF523bYMYMmDJFFcpBfSYiQhXSa8NggPPPV/99wQVV3w+P277P9YeEBFWknzABFixQBff8fDUT/sQT1TrnnaeK148/rgrU6enqQsNtt6nl06erz7/yiiqST54ML7ygZvhfcIF6fcwxcOaZqpC9di0884x677rr1D5GjVLPTz6pLhZ0716xD8BPP6lx2u3qDgGnUx3rFVeoCx5+vzq+r7+G999X52zyZPXZpUvVMVV3Tio77zx1gWDmTLj0UlWMv+aaiut88w0MGqRig+64A2bPVuOaOFFdrABVYH/pJXV3wfTp5ef+xRfV+GbPVnc/AGzZAm++qQr9jz4K992n7tY4/XQVBVTZTz+pbZ5+evm5gHntNI2yNsOahga8A9wJfAZcCywEpgErNY0av0BJVI4QQgghmo0Ik5EYm5m84vpH5kSYjTxwRg+ufO0Xsl0+7nhvLfOm9SHWXrWpbGahp6xoHxbS1fLacHsDZOqQFNUwFx6EEOKwMZohOkVluxfn1r5Q3tDSBsKUN0rjc56ve3xOUUZ50T5MD6rlDcVfAgW7wOJQETomaVIuxJFM0zSiLFFEmiNx+90U+ArwB/2NPaz96hjTkRuPv5GLeqoYnQ+3fkiuJ5dVGatYlbGKtMg0xrUfx4jUEQcVo5NVnFVWtAeIHvAJ0QM+If+tO9m1/BwCAY0dbgODhpXw8FPZNW4vL9eIJULnnU/T6dqj/Lx27eHjjhsS8HrVd+aJ57h57IFY2rb38+NfuypsI3wxolM3L+98nE201YnD4mDek2ZuvFE1tO3RQzVXPfNMNcM8J0cV8UHNuvf7VXPZyj3HFy7c/9jNZlUsnjat6nuvvaaeb7ml4vK0tPL39nXvveq5e3f43//Uf191FQwYoIrH552nivVhmzap7WSU/vO1cye0b6+K/LNmla+XmQnduqmLFbNmqSL0vfeqDP2JE+Haa6uOZd06+O238ka/Z5+txv3ii6oIfsEF8PHHcNpp8NBDajthr7yiZvaHY38OpGNHdddDmK6rmfvPPqsuOug6XHaZytX/4ovy2J+rr1az/m+9VUX4aJq6M+GZZ9Trxx9Xf8433KAuUlx1Vfk+evWCzZsrNuS97jo1o372bFXQr+lctGwZ1MF4MRD+0z0XGA+M1HVWhD+raawEPgHOB54/0LmQGfdCCCGEaFZi7GYizA0z8yneGcGsCT2xmgxkFHq45/11+AJVZwclRVkxVKq3GzS1vLaKfQHSCzyEKl8BEEKII4EtFqJTwdSIfTuMZjh+uorP6VhaGSiLz5lz4PicyJZQOfZCM6rlDc3nVvE5RZkqTkcIcUTTNA2nxUmyM5kW9hZEGJt2/6KYiBjO6XIOL4x8gVv73Ur3+O4A7Cjawfzf5zP9k+m88OcLZLjrduEy0Z6IoVKZ0YCBHuO/5dUlGTyxIIuzprgAKHbXXI5c9q6D4/p5aNEySG6OoewRzsT/4evaf8+eMr0IXVN5+LuLdtOjv7pw8M/m8u/106erPPaPPir/3OLFEAjUbpZ4WCCgCtkjR0KrVhXfW7dOFYv79lWz1Ovi8ssrvj7hBFXAvvTSqsszM6G4tA3NO++oMU2Zoo4v/DAa1brhxrS1MXRoeaEaIClJxeFs2VK+bORIVSx/9dXyZdnZqqA/bVrVCyDV2begDip6JxhUsTygIn/+/lttLy+v/JhycmDMGDVbf+PG8s+fc446/jvuUIV7k6lqrn9ERHnR3utV2yosVMf888+1Oxew1QO032e1c4AtwK+aRkL4AfwCFAA13schM+6FEEII0axomkaiM4Ld+SUNEj/TKSmSf4/pxj0frGPtHpVd/6/RXSrMjJ/aP43HV2wsi8sxaBBntzC1f1qd9uXxB0kv9NAqyoqh8pUAIYRo6kyW8tn3JXmNN/s+siWMfgR2roKvHoH87fD7Ytj0KZx4PXQZUzWbv+8MlWkfjsvRjGpWfN8Zh26c3iLwuVTkkC0WmnDchhCiduxmO3aznZJACQXeAjyBphuPZTKYGJI8hCHJQ/gn/x+WbVnG17u+xu13s2TzEt7f/D79WvZjfPvx9E7sXeNdoZO7TGber/PK4nIMGIi1xjLjhKH0S1Ln4fRJbmbfH8M5Y1ry+ZrdOCNDFORV/NkXHRvEYoGtm014Siwc36H679M5WbX/mZmcWvEiqc2pxvPPnlyyinWcFicjR9rKCs6nn67We/VVVWTv3n3/23a51CM7W0XEfPqpKhxXnpG/c6eaiR4To6JfwrP6QUWyFBRUXD8xseLs77RKpyEm5sDL8/JUdEy4gN2rV/Xjr00hPaxNm6rLYmNVPvy+2zv/fDW7PTdX5dS//rq6c6G2F0Aq7yc2Vj2H9xM+piuu2P8FkL17VfZ/2Pz56s/mt9/UnQvhXP8wXVdjfu658tz7sOr+6ld3LqAgCBWy6zujCvlZ1a0NtNjP8jJSuBdCCCFEs2MxGYizW8hxextke0M6JXDJ4HYs+HYrn67PJC3OztQB5d+SB3aI5yY6s2j1DjILPSRFWZnaP42BHeLrvC+vFO+FEEcyTQN7HJjt4MqExoyNSB2g4nN+X1Qen/PZ3So+Z9htFeNz2g6GEXeoTPuiDFX87ztDLT+UdB1K8sFToAr41hjV/FcIcUSzmWzYTDa8QS8F3gKK/cWNPaQD6hjTkRuOu4GLeqgYnY+2fUR2STarM1azOmM1qZGpjG03lpPSTsJmslW7jX5J/bi6z9W8ueFNsoqzSLQnMrnLZPol9auw3rgz3cx/PIZPltlp1z7AlPEV72xavDSDgUM8hEIaAwZ5uOaW/Gr3l9QyWO3y6hj3U+PXQ+D2u3H73RgNRs6eksAzT1nJz9dIT1czrZ988sDbnjNH5aHv66GH4OSTy19nZamZ6CUlKps9Obni+m+8ARddVHHZ1q3Qtm3Nx7DfYwv33iq9qWDZMjWrvD5q2lfY9Onw8MPquK68UsXkHHdcxRnq9dlP+JgefFDF5VSn8r5WrSpvcPvnn1XXf/RR1S/g/PPhnntU3wGjUWXkL1pU+zEC+/4CZwD+RmXbVydvv1spJd8IhBBCCNEsRdvNrFifwcLvt9W7mA4wpX8q23OLWbE+kwXfbiU1zs6QTgll7w/sEH/Q265MivdCiCOe2aoa1xbnqMJ0YzGa4bgLofNo+PZx+OczSP9NxeccMxn6Xw4RkY03vrB9C/gRUWoGvhTwhTjiRRgjaGFvgT/op8BXgNvvbpA7Qg+V6IhoJneZzFmdzuLH9B9ZumUp63LWsbNoJ8/+8Syvrn+Vk9uczNh2Y2ntbF3zBqvh8ajvtgX5Brr18vHqkoqRPN16qV5Vbdv7cRVpZdE4B6Jp9T+nwVCQcefkMHdOCs+/kk/GLhtms4UpUw78XfyCC2BwpWu8++a4FxSoPPXdu1UsTdeuVbcxahSsWFFxWcsGSmrr2FE9p6aq5rQH0lCttrp0gYED1R0LJ52ksvjnzm2YbUP5MTkctcvMz82Fiy+GY49Vhf7Zs2H8eNXcNuz112H48Ir9AqBqpE4d/QMMAFbqOlXzVmuh2WTca5pm1jTtek3TXtI07TdN03yapumapl1ygM8M1TTtVU3T1mqalqNpmkfTtK2apn2gadrJ+/tcDeO4UNO01ZqmuTRNK9A07UtN08Yd/JEJIYQQ4mCs/CuT2Z9sYH16IVkuH+vTVczNj5tzDmp7mqZx86md6dE6CoCHPvyLTZkHyEuup3DxXjLvhRBHLE0DRwJEtW78GBhnkorPOWM+xLRRcTi/L4bXzoK/l8PWb1RUTuY6cO9Vz1/Mgm3fHt5x6roq3udvB1eWZOAL0UyYjWYSbAkkO5OJiojCoDXtcpzRYGRQ8iAeHvIwTwx/glPTTsVisOAOuPlg8wdc8dkV3PfDffyy9xdCuqpH/pT5E/N+ncfG3I3keHJYvy2beb/O46fMnypse9GL6mJp7z4+omNCDB7uqfCIjlHbG3emm3V/RPDxB/Yq4/N6NFxF5VVmu0OnsKD+57RDpwB9+nl4Y5GV1xcbGXZqCTiyKQmU7Pcz7dur4vG+j7DiYpW5/tdf8MEHKnanOq1aVd2Gte79gat11lkq0/2ee8pnqu8ra58QF4dDPec3wPX26dPhhx/g7rvV/qdOrf82w447Djp1gsceq36sWZWCaa68Us22f/VVdQGhQwe48EJwu8vXMRiqnp9Nm+C99+o11NeBBOCGym9oGkZNqxCrU63mdAnfAYSv32QCGUBqDZ85qfSxClgJuIE04HRgvKZpD+i6fldtB6Bp2hzgZmAXqiuwBdVBeKmmadfquv507Q9HCCGEEPUx/8vNZLu9hOveIR1yi30sWr3joGfGW0wG7j+jB1e/9isZhR7uXLKO+dP6EO88NE3IZOa9EKJZsNhVsdyddeAGsYfD/uJzzHbweyA8IU4PqvfWvHDo43KqEy7gewtlBr4QzYjJYCLOGke0JZoiXxFFviKCeu0jXxpD+5j2XHfcdUzvOZ1Pt33K8q3LyS7JZk3mGtZkriHZmcz49uP5fOfnZfn2ADsX3sl2dzT3HLuNCwY5yc818NlHdn5eZWX0eDcnDD3wTPrLri1k5Sd2rp6eyITJbnof7yXg19i62cTy9xzMfzmLgUPUNnr29rHkTQdPz4mmTbsA8QlBThx2cP0FJk1zcccN6q7aux7MxeUrxuVzYTKYcJqdBPVIoHYXo6dNg++/V41Rd+1S2er7Ou+8gxpinbRrp2JgbroJ+vdXhfz4eNXo9eOPVaRMOI//+OPV8+23w9lnqxz+8ePLC/p1ce65cOON8OabahuJiQ12SBgMKsJm5EjVf+Dii1WsUEaGisRZvx42b1brLlqkxjB7dnl8ziuvqLskbr4Znn1WLTvjDHVxY9o0NfN++3aVi9+1q8rFP0ivAWcBj2kag4GvgCDQoXT53cDCA22gOf3LXwyMAX7TdT1d07R7gXtq+MzDuq7fW3mhpmnJqA6/t2uaNl/X9fSadq5p2omoov1moJ+u63mly2cDPwNzNE1bpuv6ttofkhBCCCEO1u78EoKVZk2EdMgsrF+TsFi7hVkTe3LNol/Jcnm56/11/GdybyLMh2Y2qdcfJKPQQ6toa41NwYQQoskyGCAyCSwONaO9uml/h0uF+Jz/wD8roLr8aT2o8u4bU4UCfmRpAd9c8+eEEE2a0WAkxhpDdEQ0hb5CCn2FBENNu4AfZYliUudJTOw4kVUZq1i6eSlrc9ay27WbZ/94Fg0NnfI7RWMGfETeNxPY/Olw7n43ngirTqeuPu6fk8PUi2q+iBth1Vn0QQbPPRHN0ncdLHvXgd0RIrVNgAsuLaJrT1/ZujffmUd2lpFn50bjdhkYMMjDicMO7ufiGonFAAAgAElEQVT3uIluZv47DqtV56TR5f82BEIB8r35FHkBYnD5XDjMjgN+P//1V/W8eLF6VHY4CvegCuidO6sZ6g89BIGAas46eHDF5q4jRsAdd6h4mOXL1T/VW7ceXOE+OhomTFDHXdumtHUxaBCsXg0zZ8J//6tm3iclQe/eMGuWWmfXLrj6ahgyRF24CBs4UOXZz5qlxjh6NPz73+D1qln577yjztfTT6tGuAdbuNd1dE1jEirjfjpwGuADtgNvoCaRH5DWlLO16mOfwv2luq4vOIjPvwdMAAbruv5dLdZ/BTgfuFjX9ZcqvXc/cBdwv67rNV1MAKBv3776mjVr6jpsIYQQQpSa9Mz3/Lozr0Lx3qBB91ZRPDmlT723/+OWHO5cspaQDiO6JHLn2G6HtLBusxhpGSXFeyFEMxAKqsa1vibSrHHnKlh2IwQrNzQ3QMueMOmlaj/WKDRNCvhCNEO6ruP2uynwFeBvzKbedbS1YCvLtizjy51f4gv5qryvodElrguzh85uhNEdHLdLo1/nVM6a4mLmY7kHXNegGbCb7URaIokwHpo7cI9kF14IS5dCenr9G+MeCTRN+1nX9f0EIh2cph2q1Ug0TWuBah7gBTbU8mMnlT5/XM17H1VaRwghhBCH2FXDO5DotGIs/bZj0CDObmFq/7QG2f7A9vFcPqwDAF9syOKVH7Y3yHb3p8QXZG+Rt0k3NBNCiFoxGFXuvTOx4Trh1UfqABj1IJgrTSk0GNWs/KZE18FTCPk7wLUXjqACnxBi/zRNw2lxkuxMpoW9xRFTBG4X3Y5r+1zLS6Nf4uS0kzFUKjNqmkbbqLYUV3dXUxO15E0nJcUGzprqqnHdkB7C5XOR7kpnt2s3Bd6CJn/nxOGSlwdvvaUic46Gov2hIjPu1bp9gXGo6KAUVMZ9FHCtruvP1GJfDsAFuHRdj6zm/QQgC9ir63rSAbZzGXAZQFpa2vHbtx/aAoAQQgjR3K38K5P5X25md34JyTE2zh+YRq+UmAbbvq7rPLZiIx/+qW7FvWtsN0Z0bdFg26+OM8JEi6gG6lYlhBCNLRhQ0TlNYfb9tm9h1TOQu7V89r1mgF6TYcAVaqZ7U6NpYHGCPU5m4AvRzJQESij0Fh6wMWpTsypjFS+tfYnM4kwCofLm2jaTjZNTT2Zs+7GkRKY04gj37/uvrGzeZGbuQzG07+znrY8OLmpH0zSsRitOixO7yX7U3S27dSt8953Kkf/iC1i7Frp0aexRHR6HYsa9FO7VulcA+xboi4CrdV1/tZb7ag3sBnbrul7lJ5CmaWZUhpFP1/VaXWeSqBwhhBCi4em6zp4CD15/w82E8QdD3Pr2H/y+qwCLycDcc3rTtWVUg22/Ok6riRaRUrwXQjQj3iLVvLYxs+/3tXMVfP0o5G1Tr21xMOh66DK2adwlUJkU8IVotnxBHwXeAtx+d2MPpU62FWxj2dZlfLHzC3zB8hid41ocx/j24zku6TgMWtMJAjl3bEt+WR3BMcd5mfNMNm3bB2r+UA2OxiidhQvhoosgORkeeACmT2/sER0+zb5wr2naNqBNHT7ymq7r1bZyOJiMe03TrEA74ArgOuA5XdevOPCn6lS49+q6XqvfsqVwL4QQQhwavkCIPfklhBrwO1BBiZ+rF/3CnnwP8Q4L86cdR2Lkof1yHmUzk+A8On4BEEIcJUJBVbz31hxPcFgE/fD7IvjpefCXznht1RuG3QYJnRt3bPtTloEfB0ZTY49GCNGA/CF/WQG/KdXyalLkK2LF9hUs37qcvcV7y5a3crRiXPtxnJx2Mo7KUWXNkNloxml24jA7MBnk53NzdDQU7j8HkuvwkQ90Xb91P9u6l/o1p30WuBw4W9f1t2tYt0GicvYlhXshhBDi0HF7A2QWehp0mztyirl68S+4vUE6tnDyxLnHYjMbG3QflUXbzMRL8V4I0dz43Cq/vankBLsy4bu5sOlT9VozQK+zYcCVTTM+B1QB3xoDthiV1S+EaDaCoSCFvkKKfEWE9CZyl1ItBPUgP2X8xLIty/g96/ey5TaTjZNST2Js+7GkRqY24ggPH6vJitPsxG62N6m7DkT9NPvCfUNqgML9GcASYJ6u69fUYv1dqIsOrXVdT6/03gnA98C3uq4Pqc3+pXAvhBBCHFq5bh/5xb6aV6yDNdtyue3dPwnpMKRTAveM747hEEcqxNotxDosh3QfQghx2IVCUJytGrE2FTtXl8bnbFWvbXFw4nXQdawq5jdFmga2WPVoihE/QoiDFtJDFPmKKPQVHnENUbcXbmf5luWs3LkSb7inCNCnRR/GtR9H36S+R0VBOxyl4zA7sJlsjT0cUU+HonDf/P8vOHjhmf+1DbVaWfo8upr3Tqu0jhBCCCEaWZzDgs3SsLMQ+7aN45oRHQH4ZlM2L323rUG3X5284oa/ACGEEI3OYABnC4hq3XQiX1L7w7mL4cTrwWyDklz4/F549xLI3tjYo6uerkNxrsrq9xSo10KIZsGgGYiOiCbFmUK8LR7zEdTfok1UG6469ioWjlrIjJ4zaGlvCcCve39l5o8zuXzF5Sz5ZwkuXxOJTjtEQnoIl89FpjuTnUU7yfXkVugHIMRRPeNe07RhwDe6XvHeIk3TOgBfA62Bkbqur9jnvVZANJCu63rBPstPBL4DNgP9dF3PK13eFvgZcABddV3fVpvxy4x7IYQQ4tALhnT25JfgDzbsbcZPfL6J93/bA8Btp3VlZPdaJeXVS7wjgmj7kfMLmxBC1FoopOJqfE2oMeORGJ8DqnGtPa5pj1EIcdCK/cUUeAsqzGI/EgT1ID9n/szSzUv5Leu3suVWo5URaSMY124caVFpjTjCwyuch2832zEb5Pv9kUKicmqgadptQNfSl8cCvVERNZtKl327bxFf07R8IB9YBewETEAH1Kx5E/CUruvXVdrHQuBC4CJd1xdWeu8x4CZgF/A2YAHOAeKBa3Vdf7q2xyKFeyGEEOLw8AaC7Mn3NGiTr2BI57Z3/+Tn7XmYjRqPnd2bnsnRDbb9/UmIjCDKKl/uhRDNlKcA3NlNa9b4rtXw1REWnwNgigBHgrpzQAjR7HgCHgq8BZQEShp7KHW2s2gny7YsY+WOlXiC5T2peif2Znz78fRt2RejdvT07rCarDjMDuwmO0bpWdKkSeG+BpqmfQkMO8AqL+u6Pn2f9a8HRgI9gUTACGSiCvkLdF3/pJp9LGQ/hfvS9y8ErgG6AyHgF2C2ruvL6nIsUrgXQgghDp9Cj5/sooadmeTyBLh60S/szCshxmZm/nnH0TLK2qD7qE5iZASRUrwXQjRXAS8UZUDQ39gjKRf0w++L4af/gr+0SNayNwz7FyR2adyx1cTiUAX8IyhiQwhRe76gj0JfIW6/u0EnqRwObr+bz3d8zrIty0h3l7eSTLInMbbdWE5tcypOi7MRR3h4aZqGzWQrK+Jr0rekyZHC/VFECvdCCCHE4bW30IPLW9vWNrWzO6+Eqxf9QqEnQLsEB09NORa75dBnNbeIsuKMaCKZ0EII0dB0HdxZTatxLVQfn9PzbBjYxONzNA2s0aqBrczmFKJZCoQCFPoKcflchPSGjYg81EJ6iJ8zf2bZlmX8sveXsuURxghGpI5gXPtxtIlq04gjPPykqW3TJIX7o4gU7oUQQojDKxTS2X0I8u5/25nPLW//QTCkM7B9HDPP6InRcGhnyGiaRlJUxGG5SCCEEI3GWwSuvU0rOgeqic+JLY3PGde043MMBjVWa4wq5gshmp2QHqLIV0Shr5BgKNjYw6mzXUW7WL5lOZ/v/LxCDNAxCccwrv04+rfqf1TF6AAYDUYcZgeR5sgjqkFxcySF+6OIFO6FEEKIw+9Q5N0DLP8jncdWbATg7ONTuHJ4hwbdfnU0TaNllBWb5ej65UUIcZQJ+lV0TqCJNWKsNj7nmNL4nK4H/mxjM5rAngARR08EhRBHG13XcfldFPoK8Tel6LFaKvYX89mOz1i+ZTl73HvKlrewtyiL0Ym0NOE7nQ4Ri9GC0+LEYXJIHn4jkML9UUQK90IIIUTjKCj2k+Nu+ALQM19u5q2fdwHwfyM7M6ZXqwbfR2WaptEq2orVLF/chRDNmK5DcS6U5DX2SKpy7S2Nzyltn6YZoOckGHAlWKMad2w1MVtVAd986PuzCCEaT7G/mAJvAd5gE7sAWgshPcSve39l6Zal/Jz5c9lyi9HCiBQVo9M2um3jDbCRaJqG1Wgl0hKJzWSTPPzDRAr3RxEp3AshhBCNJ7PQg7uB8+6DIZ273l/Lj1tyMRo05kw6ht6pMQ26j+oYNI2WUrwXQhwNAl6VMx/wNfZIqtr1E3z1SMX4nBOug25NPD4H1Mx7e7w0sBWimfMEPBR4CypE0BxJdrt2s3zLcj7b8VmFY+iV0Itx7ccxoOWAo3IWulEzYjPbiLREEmGMaOzhNGuHonDfxL8hCCGEEEIcfgnOiAbPoTcaNO4Y0412CQ6CIZ17PljH7rxD/4tRSNcpLDnyboEWQog6M0VATBo44pteRntKPzh3MQy6Acx2dXfAyvvgnRmQ9Xdjj+7AvC7I3wHuHAgdWU0thRC1ZzVZSXIkkexMxmlx1nuW9tyHYmgX05bNG83cfkM8x7ZNpUdyGldekEhebsVy5Gcf2Zh4Siu6tUrjmLQ0Lp3Sgn82mOu0vWRnMpcdcxkLRy3klMB9ZP/3KTbc/DGvT1jIBQNGMej0LSz45mMKfeWNzd9+zUm7mLb88LWVR++LoV/nVLq2TOO8CUns3Fa1V9T2rSauvySB4zuk0qVFG0ad0JrFLzftWLGgHsTlc5HuSme3azcF3gICoYadoCQOHZlx30TJjHshhBCicRV5/GQVNfwtwxkFHq567RfyS/ykxdl5ekofnNZD20TWEWEiKUqiDoQQR5GgX8XU+JvgzNEjOT7HYABbHFijm97FESFEgwqEAhT6CnH5XIT0ul+0m/tQDE88EkOvY70ktAgy/NQStm8x8fJ/oxgzwc2TL2QD8MHbDm64NIEu3f2cNcWFq8jAy/+NJBSC979Ip237QJ22B3DPLXFs3mQirfdWtuur2bhRI/erszBF5dJ91hRGdBjI+Pbj+XlZL265OoFex3qJsOqMmeAmL8fI809H0b2Xj3c+zSjb5pZ/TJx1aitiYkNMmuYiOibElytsfP6xnVvvyePKGwvqecYPL6vJitPsxGF2SJROAzkUM+4P7W+JQgghhBBHqEirGZc3QIkv2KDbbRlt5f4zenDzW7+zI7eY+5et56EzezX4DH8hhDiqGc0QnQyeQijOblozxZ0tYNSD0GMifP0o5G6BP9+Ef1Y0/ficUAjc2eApAEcCWByNPSIhxCFiMpiIs8YRExFDka+IQl8hwVDdvxe3aR/gqRezyl7rusYrz0fywOM52Ow6D9wRS5t2Ad7+JB2HU00uHjXezbihrZl9fyzzFmbVentR0erzt92Xh82uAw5gBHtce3j2/cW8evVlZK8eygrTclZsX4F941XAlZgtIV5fnomxNEknJjbEzNvj2PiXmc7d1J2r994aT2x8iGVf7cHuUPs5b0YR11+SwFOzo5l2cWHZ/o8EnoAHT8BDricXu9mO0+zEapKJPk1NE/02IIQQQgjR+BKcEYdkBkrP5GhuHtkFgDXb85j3xT8Nvg8hhBCoGewxbVROe1OT0g/OWVQ1Pufti2HvX409ugML+qEwHQp2q94CQohmy6AZiI6IJsWZQrwtHnMd+12cN6OwwusBgzwEgxp7dplY+1sEWZkmpl1cVFa0B+jW08+wU0r46jNbleuuB9pemCraK64iDas3hRtGjyYqOkgn97mkOFMAlYsPUNx3Lu9ufosCb0HZNgF2lMblFOQb+PYLK2MmuPF4NHJzDGWPYaeUUFJs4Nc1R2Z+fEgP4fK5yHBnsKtoF/mefInSaUJkxr0QQgghxH6YjQbi7BZy3A1flBjZPYkdOW4Wrd7Jkt/20CbezhnHJjf4foQQ4qhnMEJkS4hwq5iag5gxesgYzdDnfOg0Gr6fCxs/hsw/4a0LoOdZMOCqph2f4y+B/J3SwFaIo4CmaURaIom0RFLsL6bAW4A3WPN35OTUikXg6BhVic/PM5CVqcqSHTpX7cfUsYufLz61k5NtILFFefX+QNsLy0w38tA9saz82E5RYcU5ywl04JGT5/Nb1m/85488dgPeqI28sn4Ni/9ezLCUYfQ1TwJaU1C6za2bTei6xrw5McybE1PtceZkHfmNbwOhAPnefPK9+RKl00RI4V4IIYQQ4gCi7WZcvgBef8MXei4e3I4duSV8+082T638h+QYG33bxjX4foQQQqBiXWLSwJ2lGq42Jc5EGDkLuk+Erx8pjc95CzatgBOvg27jm258Dqjz6XOr7HtbrLpYIoRotuxmO3aznZJACQXeAjwBz37XNe7vx4F+4GJwuCVn5ZpxTdsLheD8iUlkZRq57LoCOnXxY3eG0DS47uJEQiENTdPo06IPp3dw8g0wJPVENpj+wh1w89mOz/gwaz3wCRtyNzK4V3/iE9TFgemXF3LyacXV7r5z16oXH/anXUxbrv9XPjf8O7/WnzncJEqnaZDCvRBCCCGarZV/ZTL/y83szi8hOcbGVcM7cFK3pDpvJ8FpYU++B11v2NxKg6bx7zFduX7xb/yT5eK+ZeuZN+U40uLtDbofIYQQpcKz782FqoC/78/1bd/CmhegKEOt03cGtB18eMeX0lfF5/zxBqx+Djz5sPJ+WPceDPsXtOh2eMdTF7oOJfkq/94Wqx4yS1OIZs1msmEz2fAGveR78vl699e8ueFNsoqz2P7LHcCpnDchiYw9JiKjQvQ4xsdJo8oL3ylpavb85o1mRoys2Ex8yyYzDmeIuPi69Sj5e52ZTX9bmDM/i7OmusuWe0o0CvKrvwDaU5+A8/cJtB7xMd+VLGZzlpqw8+HWj8gu6YBZ9wFpGI06g4fv/yJFU/H2a06KijQuuqKo3tsKR+m4fC5MBhORlkgcZgcmg5SUD4cmfMleCCGEEOLgrfwrk9vfW8uvO/NIL/Dw6848bn9vLSv/yqzztiJMRuLslkMwSrCZjTwwoQdxDgtub5A7lqyloKT2M3aEEEIchHD2vdmmXm/7Fr6YBZnrwL1XPX8xSy0/3Ixm6HMeTHsXOo9WyzL/hDfPh68eVg13mzJdh+JcyNvW9McqhGgQEcYINuRt4NnfnmVj7kZyPDmkr20PQEqPPdz5YC7Tryjknw1m7vq/+LLP9erjJTEpwGsvRVLsLr/Qt2G9ma8+szH81BIMdaxchtcPhSpeOHz+6agqy8K2bTEzf3YinbVTmHfSPG7uezMAGhDSg2T59uDsvopXX7bw3dpdVT6fk920yqtvL3Ly8nMNH7MWCAXI8+Sxq2gXme5M3H53g09sEhU1rb9ZQgghhBANZP6Xm8lyeQiWTtIJhiDL5WH+l5sPanvRdjOOiEMzs6RFlJWZZ/TAYjKwO7+E+5auwx+s2+wiIYQQdWQ0QXQyOOJhzYtQnAN6aSyaHlSv17zQeOMLx+dMeA7i2gO6is/530RY/z7oTfzfiVBQ9RTI36FidIQQzdrzfz5PjieHEOpnk63j7wA4T3+IKRe6uOL6QpasTCcyqvxnl8kEd87KY/sWE5NGteKFeVE88XA0U8a1xOEM8X935dV5HB27+GnX0c+sO2N54uFoXnsxkutmJPD6y5HExtUcfalpGt3jugMwvcdF2M12jJqRVhfMJGgo4YJTj2HU9D+5f2468x53cu3FiZzYI7XO4zzSlQRKyCrOYlfRLnJKcvAFfY09pGZJCvdCCCGEaJZ255dQufYdDKnlByvRGYHZeGi+PnVrFcWto7oA8NvOAp78/B+ZwSKEEIeDLbY0NqdSQUcPqticxhaOzxl0I5jt5fE5b18Ee/9q7NHVLOCDwnQo2KWa2QohmqV0VzrBfX6OmuPUz888Tx6G0h4dsXEhuvesWOA9fZKb517biyVCZ/bMGF6YH83xA7xcdm0hI45L4fuvrDx8TyzPP61mkN9xYzyF+QYCAZh9fwxXXpgIwOyZMeRkGzCZ4IU3Mjmuv5cX5kcz685Yvv3CRl6ugfw8A199ZmP19xEVxjD/cdVwdvJprWgX05YhvVMAiI6IxmmOpHt8d85MuQhnixxCXgcb3x/DS/f158knNf7akcX1d+1iYLcULjq7RbXn5tQBrTl7dMsDnr89u4zccFkCx3dIpUtSG04b1Jq3X3NWWU/XYdFLTsYPa0W3Vmn0bpPK2aNb8ulydQfZ4F4prPrOyvatZtrFtKVdTFsG91LH4/PBfx6M4YyTWnFs21S6tkxjzODq9zO4VwrnnZHE779YmDSqJV1bpnFijxQWPF1xJn9QD1LoLeLRJ4ro3suP1aoTH69z7rmwY8cBD1nUggQSCSGEEKJZSo6xsbfIU6F4bzSo5QfLYNBIjIwgvaDh8+4BTuragh25xbzyw3aW/5lOm3g7k45PafD9CCGEqCQmFVyZFYv3WmkeflMQjs/pNAq+nwsbP4bMtSo+p+dZMPAq1Ri2KfN7oGC3iieyx4NZmhwK0Zy0crYiuyS7rHifNPEZWp/5Xzok9iLZmUyRr4giXxGBoEZSqwADh5RnxZ86poRTx1S8sBcuJj94VyyRUSFuvSePzZvM/G9BJP+6Np7o2BA7tpq44bZ8tmwy8+qCSO6/LY4nFmTTrkOAF9/cy7L37Fw/I5Fjj/dyymkl6LqKkTnvjJa8uiSDSdNc9Ojt5dUFUSxeGMm1t+TTrqOKrBw8ooTEFiHmPhxDxm4LL9w0gcmTizAmrOP9dyNI/6MLrWbcjq3X96wwmEge9ihfv3UyWXsNJLYo/wXkj18t/LPBwqy52fs9d7k5BiaNakVeroELLysiqVWAD5c4uOXqBHJzDVx2bXns2J03xbPopUgGDi7hxtvzMZt1/vglgm9W2hg5toS7H8rl0ftjKMg3cvvMXAAcDvV7i6vIwGsvRTJuopuzprgI+DU+XW7nlqsT8AdgyoUVG7fv3mXiknOTmHiOiwmT3Sx718GsO+Po1M3HsJPL//zuuTWORS9GcsbZbqZdXEhOtpFX/hvFCSdq/P6bRkJC7f8eiYqkcC+EEEKIZumq4R24/b21ZXE5RgMkOq1cNbxDvbZrNRuJc1jIcXkbaKQVXXhCG3bmFvPFhiye/WozKbE2BraPr/mDQgghDt6Q/4Ol16t8+1BAFe3t8apBbVMSjs/pcSZ89Qjkboa1b8M/n8GJ10G38aA18Rvr/SVq9r3FAfY4MEXU/BkhRJN3aa9Luf+H+8uK90bNSIItgUt7XYrRYCTGGsPvq6P4+UeNS6521bzBUnaHzmsfZJZl1+dmG/lwiZ0BgzwVludkG/lwiYMH5+bgcOoUuzXuuime8We5mft8edF82sVFjD6xNbPvj+XtTzLo1tPPcf28LF4YyZCTSuh3QtXv+Ns2m1n4dibDTikBorjhSjihhx/HL9cRedyfFPmL8B7zFKHXT+WSh1dy440wKHkQZoOZdxc7sUTojJtQXGW7Yc/8J5r03SYWvpNRVhA/b0YR545tyeOzYjh7movYuBCrvotg0UuRTJpaxKPzcvbp/11U1mt95LhiXnwmioBfY+I5FWPKomNCfLd2JxH7/Ni9+KpCzpuQxH+fiK5SuK943HD2eUUM6pnKG69Elo3zl9URvPp8FA8+kV3h86PHuxk3tDX3PVLIzJkhHBYHZoN5v+dAVK+J/4suhBBCCHFwTuqWxIMTe9InNZZW0Vb6pMby4MSenNQtqd7bjraZcR6ivHtN07h1VBe6tIwkpMMDy/9ia7ZkAwshxCHVeSSMfwKSj4eoZPU88gFoO7ixR1a95OPhnNdg8E1gdhx58Tmgcu/zd6o4oqA0ZRfiSDc0ZSh3n3A3vRJ7kWRPoldiL+4+4W6GpgwFID0dpk01kJam8ehMJ7HWWIwGY43bPffCogoNao/r50XXNc4+z1VleSCgsWeX+o7+7Rc28vOMTJjsJjfHUPYoLtYYNNzDrz9FUFJcfbPaylLS/GXFa4CICDi+nx9DbkdeHPUi1xx7DV266tja/87Gz/vz2M+PMeOTGfxv7et88I6NU04rJipm/31JVn5ip0v3irPYzWZVVPd6DHz3lbpD6cMlDgD+7678fYr2SuXX1TEaKSva+/2Qn6fOyYlDPWzbYqawoOJGqjvuPn297NxW/nvQsncdWG0hThldUuE8J7QI0r6Tn+++spDvzWd30W4y3Bm4fC6JA60DmXEvhBBCiGbrpG5JDVKor068MwKPP0Qg1PDNASPMRh44owdXvvYL2S4fd7y3lnnT+hBrtzT4voQQQpTqPFI99uV1lc7Cb4KNYI1mOHYadBoJ3z0BGz868uJzQJ1jnxsiIsEWp5oGCyGOSENThpYV6vdVUABjxoDLBd98AzExGhCNlSh2ZLoo9BUSKv05Gx0bxLLPV97WKYEK24qKDh1weUG+quZv2ax+llx09v5/F8jLNWCz19ywNiUtUGVZdEyIv9eZsZqsjGo7ipFtRjLnoiLm39Ud3+5O5CVv4vm3d5KXY8bQ9wU25rWnc2znare/a4eJU06rOiO/Uxd1UXPXdnUs27eaiIkNktSq5jHvzzuLHDz/dDSb/jYTClUs1BcVGoiKLt/2gY47bOtmE54SA/27VN+gN61t+YVZT8CDJ+Ah15OL3Wwn0hJJhFHuujoQ+RdRCCGEEOIgGA0aCZEWMgo8Na98EOKdEcya0JPrX/+NjEIP97y/jjln98ZikhsmhRDisIlwqkx2115VXG6KHInq7oAeE6uJz7kWup3e9ONzdB08heAtgogoFaFTi5m4Qoimr7gYxo2DDRvg00+hV6/y9374QWPEiEggsmzZ4qUZFfLvjfv5UbC/5eHJ3HppUfqRp7JpnVq1AA0Qn1C7i7L738d0AvcAACAASURBVFd54VvTNC6/IJoXZoUYsPtpOgx/nqeeGYwxKoetrZ7l5q+CdI7tzPj244Hptdpv2cR0rXx/tZlZvz/L3rXzf1clctKoYi65upCEFkFMZp0vP7XxwvzosnMWVpvjDoU0oqKDzHs5q9p1rdaqs+tDegiXz4XL58JsNOM0O3GYHZgMUqauTM6IEEIIIcRBsltMRFrNFHn8/Lg5h0Wrd5BZ6CEpysrU/mkM7FC/bPpOSZH8e0w37vlgHWv3FPL4io38a3QXtPp8YxdCCFG9jZ/CN3NU/np0isq97zxSFZCjWqnCsjtrn0pKExOOz/nzLVj1bGl8zkxY9x4Muw1adGvsEdZM18FTAN5CsMaALUYK+EIcQb7e9TXP//k86a50WjlbMb3LZcy+agg//ghLlsDgSuljvXvDihXlr3Vdp8sxDgyG6gvtddGmvZrpHRsfZPDwA0+00bSG+bkeFa0zcmwxK5a04OZbL+Kh31MYPmkdjrg2bCnYwsa8jTz282PAdP7M/pNcj4M4axwpaQE2b6ya/755k1oWnvnetr2frz+3kbHHSMvW+591v7/jWfqOg9Q2fha8vrfCBYAfvj74ZuFt2vv5ZqWVY/p4iYqu+3n0B/3kBfPI9+ZjNVqJtERiM9nk951STfyyuxBCCCFE0xbvsPDT1lweX7GR9emFZLl8rE9XRfYfN+fUe/tDOiVwyeB2AHy6PpPFq3fWe5tCCCEq2fipak67+2co3K2el16vlodZoyCmjZqB31QZzXDsVDjvHegyRi0Lx+d8+aAqih8JdB1K8iBvGxTnNs2oIiFEBV/v+pr7f7ifP7P+JLM4kz8y1zFlqs6KFTovvwxjx1b9TGwsnHJK+ePUUzXSkiJJiUzBaXHWazxDTyohKjrI03Ni8FbtN0tOdnlJ1OZQBefCgvqXSc+a6iJjj4k7b4rH6zFw46XxzB0+l4eHPMyg1oMwlN4B9Wf2n8z4ZAZz1syhz/BdbFhv4ZsvygvogQC89EwUEdZQ2YWHMRPUnV+PPRBT5Rryvq/tDr3aYwn3BNj3R2peroE3/xdZZd3aGn+mG13X+M+DsdW+n5tTu3Oq6zolgRL2Fu9lV9Eucj25+IK+gx5XcyEz7oUQQggh6sFg0Hj9p53kFvsIlX5hDumQW+xj0eod9Z51DzClfyrbc4tZsT6TBd9uJTXOzpBOCfXerhBCiFLfzCnNsi+d5RkKqNffzKmYe280QXSyKioX5zbd2feORDh1JnSfCF89XBqf846KzznhWuh+RtOPzwF1fotz1d0Dtlg1C19mYQrRJD3/5/Nkl2QT1NVM8N2LbyRn1VBa9fmTUKgX//tfxfUnTgSHY//bs5pUETvGGoPFmFfnIq4zUufBJ3K4fkYio09MZuJkFy1aBcnYbWLVdypXffGyTAB69vahaTrP/Cea/FwDlgg4YWgJCYl1v2g4ZISHVskBlr3roEt3Hz16+wCNHvE96BHfg+ySbPoBEcYIAnqAr3Z9ReCY37HFv8clU+K58LIiWifrfLjEwZofrfx7Zi4xsWocAwZ5OeeCIt54JZJdO0ycPLoEs0Vn7W8WrHadmXNyy45n5Sd2Zt0ZS69jfdgdIU45rYRTxhTz8VIHM85pwcixxWRnGXn95UiSWgbI3ntwdzf1O8HL9MsLWfhcFBvWmxk+sgSHQ2fndhMrPrQz/kw3N/w7v07bDOpBCr2FFHoLsRgtOC1OHCZHrZoZNzdSuBdCCCGEqKeMQk9Z0T4spENmYcPk32uaxs2ndmZPfgnr9hTy0Id/0fLcY+mUdPCzY4QQQuyjYFd50T4sFFDLq2OLBbMdijIg6K9+naYg+bhK8TkF8MUDsP49GHobJHVv7BHWTigE7hwoCRfwo6WAL0QTk+5KLyvaA5Rs76qW/9qL88+vuv7WrQcu3IfZTDZaO20U+4vJ99atADx2QjEtW2Uw//FoXnwmipISA4ktghxznJdzzneVrZfWNsA9j+Sy4Oko/nVtAsGgxuKlGSQk1v27vMEAE89xMf/xGM6c4qryfoJNTb6Z0HECvfvoLN2ylC1sIfX2KWS+dQMvvTgI3eukXUcfjz6dzdnnVdzGQ0/k0L2Xj8ULI5k9MxabLUSnbn4uv678jqoZVxeweZOZN191suBpI8mpAU45bRdnTXGTn2vk1QWR3HdbPK1TAlx6TQGRUTq3XH3wTWLveSSXnsd6+d8LUTzxcAwArZIDnDi0hLET69cfxhf0kVuSS56Wh91kx2F2YDfb67XNI4mmN9UZAke5vn376mvWrGnsYQghhBCiFiY98z2/7MirULw3aNC9VRRPTunTYPvJ+3/27jw8qvJ84/j3zGSSTDJZyAYhYdcqKCiKC4iAiKAIdatLVSyo1IpaN6w7WtfaqnUp2J9oa0VbrYpacANBAdcWFYsWRUCQIEsCyWSbzHp+f7xZCARIQpKZJPfnunJN5sw573nO6EWS+7zzvJUBpj3/OVtL/WR7Eph1wWAyPfv+JTs5IY6uqc3vXSki0uE9Pda0x9k5vHfEmb7xlyzY83G2bfreV5W2fo37q6IQPnoMvn2zeoMFh54Jx15hgvD2xBkH7gzTvkhEYsKktyaxsnBlvfDeaTkZmD2QOafMabHz1AT4sdxG5Y/3pTPzoTQ++rqAnG577kUPpkXMqh2rmL9uPh/++CER28yud1pOjut+HBP6TuDgjIPV830nTocTj8uDx+XB5dx9bYBosSzrM9u2h7TkmO3gs3EiIiIisW3aqH7kpCTiqP592mFBRlI85x/ds0XP0yUpnntPPxS3y0lhuZ/bX/8af3DvfwyIiEgjHD8dknNMWA/mMTnHbN8bywJPDqR0jf0Z4DXtc858CjIPAGzTPue5M8wCtnY76iMfDkH5NijeAP6yaFcjIsDUgVPJcmfhtEw7E6flJMudxdSBU1v0PEmuJLp7upOTlEOCs/mzxFtLKAQvPe/h+NG+fYb2YD5ZOyBzAL856jf8ZexfOPcn55IWn0bYDrN001J+s+w3XLfkOhb9sCimb1a0pXAkjNfvZVP5JrZUbKEsUFZ7w6Oj0Yz7GKUZ9yIiIu3L4lVbmfX+Wn7YUUlOSgLnH92zRfrbN+Tjtdu57bWvsIETDsrmtlP773UWjmbci4g0wuoFpqe9twDS8k1ov3N/+30Jh6B8CwRbpk1aq4qEYOU/TfucQHUbg66HtK/2OTuLi4ekTIhvRN8NEWk1SwuWMnvlbDaXbybXk8vUgVMZkT+iVc9ZGazE6/fiDzewAm0bKip08OH7bha97WbeKx6ee30Lx41s3s+DQDjAB5s+YN66eawpWVO7PS0+jXG9xzG+z3gy3a3zd0Z7ZVkWya5kkl3JuOOis4h8a8y4V3AfoxTci4iItE/hiE1BcSXhXZvet7CXlm/kiSXrAPjF0F78YljvPe6r4F5EpA1V7jBf7UFFUXX7nDeqN1hwyJlw7DRwp0e1tGZxJZoA3xWd0EZEoscX8uH1e6kKRefm6SfLEvn5xG5kZIa59MpSLr/Wu++D9sG2bb4t/pZ56+bx4aYPa9sQOS0nQ7sPZWLfifTP2PsEns4ozhGHx+UhOT4Zl6PtWukouO9EFNyLiIi0X6VVQYrKWnfWj23bPLRgNW9+tQWAGRP6M+qgnAb3VXAvItLGgj6zcG2knbQz+/ELWPI72F49szMxDYZeCQNOB6sddtiNTzIBflzstdEQkdYV7QC/tWz3beft9W/z9vq36y3S2zetLxP7TmRE/gjinfFRrDA2JcYl1s7Ed7TyzzMF952IgnsREZH27ccSH1Wt3H8+GI7wm5f/y5cFXuLjHDxy7mEc3G33hfoU3IuIREEkbPqw17SiiXUdrX0OQILHBPgxtHihiLSNqlAVJf6SDhfgB8NBPvjxA+atncd3Jd/Vbk+NT2Vc73Gc0vsUspOyo1hhbLIsi6S4pNpWOq3xKQUF952IgnsREZH2LRCKsKnER2v/ruX1Bbni75/zY0kVmcnxzLrgCLJT6s8wVHAvIhJFVaVQUQjt5W/vjtY+x7IgIRWSMsDhjHY1ItLGOmqAD/Dtjm+Zv24+H2z6gJAdAsBhORiaa9roDMgcoDY6DXBaTpJcSXjiPS26wLGC+05Ewb2IiEj7V1IZYEdFoNXP88P2Sq74x+dU+MMckOPh0fMOx+2qCycU3IuIRFk4BOVbTQud9mLX9jkJaTCsHbfPsSxwd4HEdHC0w/pFZL/4w35KqkrwhdrRv8ONVFxVzNvr3+at79+i2F9cu71Pah8m9J3AyB4jWzSg7khcTpfph+9KJs4Rt19jKbjvRBTci4iItH+2bbOpxEcgFGn1cy1fv4Ob5q4kYsPxB2Zxx8QBOKpn2Ci4FxGJEb4SqNzefmbfR0Kw8iX49Im69jk5h8DIG00bnfbI4QB3hunjr5moIp1ORw7wg5EgH276kPnr5vNt8be121NcKYztPZbxfcaTk9Twmlhi+uF7XB6SXEnN6oev4L4TUXAvIiLSMVQFw/xY0jZ/GLz2xSYeW2xmRl5wTE8uGd4HUHAvIhJTQgEz+z7UuouYt6gG2+ecAcde0T7b5wA440z/+4SUaFciIlEQCAco8ZdQGayMdimtYnXxauavm8+ygmV1bXRwcEzuMUzsO5FDsw5VG509aG4/fAX3nYiCexERkY5je7kfry/YJud6dNF3vL7iRwBuPuVgThrQVcG9iEissW3wFZuv9vQ3+Y9fwJIHYHv1gogJaTD0ShhwWvvtHx8XbwL8+ORoVyIiURAIB/D6vVQE28lC4k1UXFXMO+vf4a31b7Gjakft9t6pvU0bnfyRJMbp74Q9cVpO3C43Hpdnn++TgvtORMG9iIhIx2HbNgXFPoLh1m+ZE47Y3DR3JZ9tKMbltHjo7MM4pm+mgnsRkVgUrDKz78Ntc3O3RURCsPJl+HTWTu1zBsDIm9pv+xwAl9sE+C79vBTpjILhICX+kg4b4AcjQT7+8WPmr5vPqh2rard7XB7G9jJtdLomd41ihbHP6XDW9sOPd8bv9rqC+05Ewb2IiEjH4guE2extm5Y55VUhrvj752ws9pHudvHMxUdxeI8ubXJuERFpIts2rWiqvNGupGkqt8OHj9ZvnzPgdBh6hVkEtr1K8JgA3+mKdiUiEgXBcBBvwEt5oDzapbSa74q/Y/66+SzdtJRQpK6NztG5RzOh7wQGZQ1SG519qFnUNsmVhMthfl4ouO9EFNyLiIh0PIVlfsqq2mZW5aZiH1f8/XNKq0IckO3htSuPw5MQ1ybnFhGRZghUQPk2iISjXUnTbF5h2ucUrTbPE9JMeD/g9PbbPseyICEVkjLa7zWIyH4JRoK1LXQ6anZa4i/hnfXv8Ob3b9Zro9MzpScT+k7ghB4nqI1OIyTGJZIan0pyfLKC+85Cwb2IiEjHE4mYljmhSOu3zAFYsbGEG17+L+GIzYkH5/DkRUNwOjR7RkQkZkXCJrwPtLNWDbXtc56AmlmqOQNg5I3Q9dDo1rY/LMt8eiApI9qViEiUhCIhvH4v5cHyDhvghyIhPt78MfPWzqvXRifZlcxJvU7i1D6n0i25WxQrjH3uODfdPN0U3HcWCu5FREQ6pspAiC3eqjY73xv/3cxDC1dzVO8uPD35KFIT9dF/EZGYV+U17XPa29/rldvho8fgm/nVGzpA+xyHAzL6RrsKEYmycCRMaaCUskAZEbttJuFEw9qStcxfN58lBUsIRswnhS0sju52NBP7TVQbnT1QcN/JKLgXERHpuNqyZQ7AR2u3c/aQfBLi9HF/EZF2IxyEsi0Q8ke7kqbrSO1zFNyLyE7CkTBlgTLKAmWE7XbW2qwJvH4vCzYs4M3v36TIV1S7vUdKj9o2Ou44dxQrjC0K7jsZBfciIiIdV1u3zElOiKNrqvpTioi0O7YNvmKo3LHvfWNNJARfvQKfzGrf7XMU3ItIAyJ2hLJAGaX+0g4d4IcjYT7Z/Anz1s3j6+1f125PjktmTK8xnNr3VHKTc6NYYWxQcN/JKLgXERHp2NqyZY6CexGRdi5YBeVbIByKdiVN197b5yi4F5G9sG2bsmAZXr+XcHtbXLyJ1pWsY/7381mycQmBSAAwbXSGdB3ChH4TGJw9uNO20VFw38kouBcREen42qpljoJ7EZEOIBKBikLwl0W7kuZpr+1zFNyLSCPYtk15sByv30so0g5vsjZBaaCUBetNG51CX2Ht9nxPPhP6TmB0z9Gdro2OgvtORsG9iIhIxxeJ2GwsriQcad3fxxTci4h0IP4yKN/W/hauhYbb52T3h1E3xW77HAX3ItJEFcEKvH4vgXAg2qW0qnAkzKdbPmX+uvmsLFpZuz0pLokxPU0bne6e7lGssO0ouO9kFNyLiIh0DuX+ENtKW7dljoJ7EZEOJhyC8q0Q9EW7kuap3A4fPQ7fzKveYMGA02DolbHXPkfBvYg0U2WwEq/fiz/cDhcZb6Lvvd/zxro3eK/gvdobFhYWR3Y9kol9J3J4zuE4LEeUq2w9Cu47GQX3IiIinccWbxWVgdb7SK2CexGRDqpyh1m8tr3+Xb/5y+r2Od+a5wmp1e1zzoid9jkK7kVkP/lCPrx+L1WhtlnfKprKAmUs3LCQN9a9wTbfttrteZ48Tu1zKif2PJEkV1IUK2wdCu47GQX3IiIinUcwHKGg2Edr/V6m4F5EpAMLVpnZ9+HWXzOlVeypfc7IG6HbwOjWBgruRaTF+MN+vH4vlcHKaJfS6sJ2mH9v/jfz183nv0X/rd3ujnNzYs8TmdB3AnmevChW2LIU3HcyCu5FREQ6l5LKADsqWqcPpoJ7EZEOzrbNwrVVpdGupPkqd8DHj8Oqf9VtG3B69NvnKLgXkRYWDAfxBrxUBCtabeJOLNlQuoH56+azeOPien3/j8g5gon9JnJEzhHtvo2OgvtORsG9iIhI52LbNptKfARCkRYfW8G9iEgn4S+Him0QafmfJW0m1trnKLgXkVYSjATx+jtPgF8eKGfhhoXM/34+2yrr2uh0T+7OqX1NG51kV3IUK2w+BfedjIJ7ERGRzqcqGObHkpZfaFDBvYhIJxIOmfA+0I5bMUTC1e1zZu7UPudgGHlT27fPUXAvIq0sFAlRGiilPFBOxG7HN14bKWyHWb5lOfPWzePLwi9rt7vj3IzuMZpT+55Kj5QeUayw6RTcdzIK7kVERDqn7eV+vL6W7VOs4F5EpBPyFZv2M+35b/4G2+ecBkOvarv2OQruRaSNhCNhSgOllAXKOkWAD6aNzhvr3mDxxsX4w/7a7YNzBjOh7wSGdB3SLtroKLjvZBTci4iIdE62bVNQ7CMYbrlf1hXci4h0UiG/Wbg21DprqLSZhtrnHDsNDjmz9dvnKLgXkTYWjoQpC5RRGijtNAF+eaCcd394lzfWvcGWyi2123OTcxnfZzxjeo7BE++JYoV711rBfezfshARERHpRCzLIjslAcuyol2KiIi0d3EJkNYDEtOiXcn+yT0MzpkDI26EhBTwl8KS38FLF8GWldGuTkRi1DPPgGXBBx+03Jh33mnGbE1Oh5P0xHTyU/LpktgFpxWF9T120Se9Nzdeldli4xVsiKNPem9eft6E8Z54D+tfmsy75yxkWq/7ODz7cAA2V2zm6a+eZso7U5j15Sx+KP2hxWpoDxTci4iIiMSYRJeTdLcr2mWIiEhHYFngyYbU3Ogs7tpSHE4YdA5cMBf6/9RsK/wGXp4Mi+4yrYFEJGYtW2b+ObIsKCho2bEfeQSefbZlx2wJixfDpZfCwQdDUhL06gXnngurVzfueIflIC0hjbyUvDYJ8D9Zlsgj96dTVhrdCUSH5xzO3cfdzczRMxnfZzyJzkSqwlW89f1bXLH4Cm7/8HY+3fwpYTsc1TrbgoJ7ERERkRiUnuQiwdWOAxYREYkt8cmQ3tM8tmdJGXDiHXDWXyHrILNt1evw3Bmw8p9mYVsRiSmhEFxxBSS30j8/sRrc/+Y3sGgRTJgAjz0Gl1wC778PgwfDF180fpyaAL92Bn4r3YT95INEHn0gnfKy2IiLe6b25PLDLuev4/7KpYdeSm5yLgArCldwz6f3cNnCy3j1u1cpr1nEvAOKjf8SIiIiIlKPZVlke9QyR0REWpDDaWbee7Jbv9dDa8sdZNrnjKxpn1Nm+uCrfY5IzHn8cdiyxcw+70wefhjWroUHHzTXPmMGfPghRCJw991NH8+yLBPge/LJcGe0WoAfazzxHk474DT+PObPzDh2BoNzBgOwtXIrf/n6L0x+ZzIzV8xkQ+mGKFfa8hTci4iIiMSo+DgHGUnx0S5DREQ6msQ00/s+rp3/jHE4YeA5cOGr0P80s03tc0RiyubNpi/8ffdBenrTji0rM7PW+/SB+Hjo2RNuuAF8vrp9LAs2bDAz22ta8YwaVX+cYBBuvhm6dQO3G046Cb7/vu71P//ZHPfvf+9ew4svmtfefbdptQOMGGHWt97ZAQfAoEHwv/81boyanvr/+x9MnQqZmZCRYXHDVankxOeTEM7ktmuzOLJfDw7J68n1v8qiyrf7jdk3XkvijDG59M/tycAePbn4nBy++bquNef0y7N49AHzH2jYIT3ok96bPum9KdgQV2+c9xe6OfX4XA7q2otRg/N4/aW6j1GUljg4uFtPbr8+Y7fz+/1wWK8eXPPLrH1ec2WFg1uuyeTw3uaaLr8om+IdDhyWg6O6HcVdw+5i1omzODI4lYJHZ/LFZYt56KS7OXlEFhf9YS6fbP6EsB3GtmHSGV0Z1LMnBT/Uv8nxy/NzGNC9J9+vNddXUuzg/hldOOW47gzs0ZMB3Xty1thuLHrbvVt9Nf3+d34vjh2UCVy224VbFi7L4lbL4hvLwm9ZbLEsnrQsdn+TGqDgXkRERCSGpSW5cMd3jtk0IiLShuLiTXjv7hLtSvafuwucOMO0z8lW+xyRWDJ9Ohx4IFx8cdOOq6qC0aPhySfh7LPhT3+Cn/7UtMU580ywbbPfnDmQlQWHHGK+nzMHbr21/lg33GB67N9yi/n+ww/hggvqXj/vPEhMNMfuas4cyMsztbQE2zafPshs4jqvF14I27fDPffA+PHmfbn5ZovJP0+hsiSZ2+8IMm5CFXNf8PD4H+ovSP7k46lcOTmHrt1C3HxXMVdcX8LqVfGcfXIu69aY4Pr8KWWMm1ABwB0PbOfh/yvk4f8rJCOr7t/P/36ewPRpWYw5xcctd+8gKdnmusuyWPOtuQGQmh7hpPE+5s9NJhCoX/+it5Mo9To587x9t7W59pdZbPnRyXW3lnDeRWUsfCOJO26on3NvXnkAL1x1Jd3DRzFx6pf8ZNJTOOKrWHbvddzw6Of8cuEveXXNXO54ZD0Oh80N07Jq/5/55xwPC99M4ua7iunTLwTAD+vjeOPVZI4f7eOm3xZzzc0lVPkspv48h6WLE3ercdf3IjnZBmb2sSz61+xjWVjAK8BtwLvAVcAzwAXAYsti94F3EbevHUREREQkurI9CWwq8RGO2NEuRUREOhLLguRMcLmhfGv7D7hzB8HZc+DrV+CTWXXtc/73Goy4ybwuIm1myRL4xz/ggw92n3m+L3/8I6xcCf/5DwwcWLd90CC47DIzA/6kk0ygfdttZjb9hRc2PFZCgqnFWT0XJiMDrr0Wvv7aBP7p6XDaafDCC6a9jat6Ivq2bfDOO3D99U2vf0+efx5++MHc0GiKAQPguefM95dfDmvWmJsYF14Izz5rAYlccxUctS7MS8+lcMOMEgB+LHDyh992Ydr1Jdxwe0nteGedX85JR+fx2APpPDK7iCOO9nPwIUHemQ/jJlSSm7f7z4PvvnHxxrIfOWhAEIDxp1cw/NAe/PM5D7fcbT7h9LPzy5k/N5n33kli3MTK2mPn/sND19wQw0dV7fNae/UN8fhfCmuf27bFs7NTuOfh7aSm2dg23HJNJoMGB/jH/K04nT2I2Hl8tvVzrjgzhS3/vI60Y07ir1//lXjn8wy/6hreuHsST/0plZMnVnL3LRmMONHHpEvLas9x0IAAS1YU1P4/AjD5slImjOjOk4+lMWJ0/bp3fS/OOivCwH5pNjgvBm6o3u08YCIw1rZZWHOsZbEYeAeYBMze23uhGfciIiIiMS7O6SDTkxDtMkREpKOKT4L0XpDgiXYl+2/n9jkDatrnfAuvTIFFv4XKHdGtT6STqFmQ9oILYNiwph//4oswdCjk5kJRUd3XmDHm9cWLGz/WZZdRL5AdOdI8rltXt23yZDP+W2/VbfvHP8x1XHRR0+tvyNdfw7RpMGQI/OpXTTv2ssvqPx861Mzenzq1/vbjhjkp3OYkmWzinfG8/a9kQiGLn55VwY7tjtovpxOOONrPR0v3Oem71tHDqmqDaoDsnAh9DwyycX3dvPDjR/vo1j3Eqy/WtdDZsd3B0kVuTju7olE3QC68pLTe82OOqyIctvixwJxn1cp41q6O5/RzyvGWmOsp2RFHP9fR/OKsbEI7ujHMNRl3nJtAOMCGfr8n7dg3+d1dHiadl4TTafP7PxXVO0dCQt3/I34/FO9wUF7m4OhhVXy1Yve2cru9F10jwPdVQN+ddjsXWAd8YVlk1XwBnwNeYJ+f49CMexEREZF2wJMQR2ViHOVVoWiXIiIiHZHDASndwFUKFYV1fSjaK3cXGD0DBpwBS+434f2qf8G69+CYaXDoWSbkF5FW8eijpvf8ggV73icQgB273EvLyDD97FevNr3ss7MbPnbbtsbX0qtX/eddqjuE7XzusWNNS5w5c0xLHjDfDxliZrvvSXm5+dpZt26777dxI5xyipnd/9prdbP6wVyn11t//+zs+jcbevas/3rNegF72h4oTyYvL5ktG0y4fPKwvAbrdzga/299Xo/d/w5JS49QUlyXxjsccMa55Tw9M42SYgfpXSLMeyWZYNBqVJuchs6Tlh4BqD3Puuq+9Ldem8Wt1zY8xpiMgqzsFQAAIABJREFU87nmqFNYvHEx89fNJzzpHlZ/NZQN32TQ/8p7+aA8kZMCJ5EanwqYH3lP/SmVvz+Twvq1rnpjWdbu71FD7wV4w1Cvd/1PMEF+YQM7A+TsYXstBfciIiIi7URWcgL+YIRgOBLtUkREpKNKTDWtc8q2QMgf7Wr2X7eB1e1z5sInM037nKUPwCq1zxFpLV4v/Pa3pq99IADr15vtJdWdWgoKzOOaNXDCCfWPfe89s7hsJGJmxt92W8Pn6N698fU493CPbuf7kw4HTJpkWvSUlJhFdT/7DB57bO9jP/igudY9jQtQWGhuDPh8ptd+3i4Z+osvwpQp9bd9/z307r3va9jXtTkwIfQrr1URdFQQjAQbPqAR9nSv07brL4Z71vnlPPHHdObPTebCS8qY+4KHQw/z15uhvjd7uiaqz2NHzOMNM4oZdETDP6cOGhAkyZXEhL4TGN9nPH995UfuKTd3bArXdeOZrx/h76v+zsgeI5nYdyLv/PUwHrgzgzPOLefqG0vokhnG6YSXn/fw+ku7fxptL/d9d34zHMA3mN72DdnnCuoK7kVERKTDWrxqK7PeX8umEh956W6mjerH6P5do11WszkcFtkpCfxY4ot2KSIiHc/qBbDsQfAWQFo+HD8dfjI22lVFh9MF6T1MW5mO0FrG4YSBZ8MBY+Djx+F/r9e1z+n/Uxh6FSRl7HscEdmrpQVLmb1yNuvWRSgre57HHms4+B461MyC/+ILWLiw/muHHWYeDzgASkvrWuPsjWXte5/GmDwZfvc7+Oc/TXDucsHPf773Yy66CIYP3/PrXi+MGwebNpn2PgcfvPs+48bt/j40NGu/OQ44oPqxTyKDBiXiC/nw+r1UhXbvNd/QzPLm6HdgiMFHVfHqi8kMHeHjv58nMOP+7S0yNkCvvuYGgDsp0qie+aUlccy+/VgGDPRzwMBi5r0whewhnxDo+wkLNyxk4YaFFDz7L/ofFeAPT1Tg3CmVf+m5/WohtwY4Blhs2zRr5pWCexEREemQFq/ayi2vfkVheRXhCGwrq+KWV7/iPmjX4X2iy0ma24XX1/zZMiIisovVC2De1VCxDSIhs1DrvKth4qOdN7wHE2a7kqB8C4Q7QKs2tc8RaTVLC5Zy18d3UeQrIuhy0fvX15Ian8I5B53LIVmHAGbx1xdfhKeegh49TMuaPQXz550Ht98Oc+fCmWfWf62qCoJBSEkxz5OT62bz74+DDoJjj4W//a2utU1W1t6P6dvXfDWkshLGj4dVq0zv/CFDGt4vN9d8tYazzoKbb4Y77oBXXgF3nBt3nLs2wN+0JUBmlsmU3ckmuC/1OhpcnLYpfnZ+Obdem8Uf7+tCXJzNT8+u2O9rqXHoYQF69wvy1J/SOOu8ClLT62fi24sctdcEcNt1mRTvcPK3V7bSs3eI/34apvzZmVzz/F9ZuPk1CsoLqAr72FC6nUsX3sD4PuMZ22ssOwoyeWd+0v6U+gJwKnAN8PDOL1gWTiDNttnr3XEF9yIiItIhzXp/bW1oDxCOQGF5FbPeX9uug3uAjOR4KgNhtcwREWkpyx6sC+3BPFZsM9s7c3AP4EqEtJ6m772/LNrVtIx67XNmgb/UtM/532sw8kbIPSzaFYq0O7NXzqbIV0TYDuNICOM54l0sy8k32d9z6ylzAFixwuw7bhzk5+99vOnTYf58OPtsuPBCOPpoE9avXm1mxL/8smmpA3DkkfDcc3DvvdCvH+TkwOh9LvvZsClT6haCfeSR5o1R44IL4KOPzKz9ggJT484uvHD/xm+MPn3g97+H664z7+FZZ0FmJvzwg5u333bTf0CYB58owhfyMfCwAAB/uKsL40+vwOWCE0+uJCm56TPxJ5xZwd23ZPDGq2aRWl+FA7Ja5m8XhwP+MLOIi87syknHdufsC8vJ7xmicKuTFcsTWPOtiyUrNgHw+kvJvPFqMjffvaO2Vc/Dfy7i7JO78dlT5zPr4bGsKFzB3SNW88Wc0/nij9NZffB/eGjHfyh573x69K1gzf9Smlvq88BZwEOWxXBgCRAG+lVvnwE8s7cBFNyLiIhIh7SpxMeuuXY4Yra3d5ZlWuZs9lZht/fFA0VEYoG3oC60rxEJme1SvXBtV4hPMgF+pAPcOG6ofU7Rt/DKxXDwRBj2a7XPEWmCzeWbCdv1Z2mH7TCbyzc3a7zERNNa5ve/NzP1X3gBPB4TRF95JQzaaXmKe+6BrVtNm5vyctMbv7nB/bnnwjXXgNsNEyY0b4waX3xhHv/xD/O1q7YI7gGuvRZ+8hN46CG4/34IhcwaAcOHw69+5aRrclf8YT8nnujliuklvPSch/cWuIlELJZ9WUBSctM/cZWaZjP21Er+9fJ+tZrZoyHH+nlt0WYe/0M6LzyTQqnXQVZOmP6HBph+u/n4xeZNTmZMz+CooVVcekVp7bGDj/Lzq2u9zHwwnbGnVjJyzGBefBjuSS/gtRePY/NnY4jvuoHs83+Ld0sv+N80Ptj0AcfmHkuco/FRum1jWxY/w/S4nwycAgSADcCLwOJ9jWHpj73YNGTIEHv58uXRLkNERKTd+tkTH/HFxuJ64b3TAYN7dOHly4dFr7AWtKMiQEllYJ/7JSfE0TU1sQ0qEhFpp54eC5s+qx/eO+Ig70i4ZEH06opF4epWQsH2fyO8ni0rYcnvoPAb8zzeA8dOg0FnQ9ZPolubSDsw6a1JrCxcWS+8d1pOBmYPZE71jPv2oLwcunaFX/wCZs2KdjVtLxAO4PV7qQjuf2ub63+VxVuvJ+HzOVj2ZQH5vdpHyzVfyMf7G99n3rp5bCzbWLs9MzGT8X3GM673ONIS0uod445z083T7TPbtvfQEKl5HC05mIiIiEismDaqH9meRJzVv+04HZDtSWTaqH7RLawFdUlyER+nX+dERPbb8dMhOceE9WAek3PMdqnPGQdpeWY2ekutCBkLug2Es5+FUTdDQioEymHp7+GFC+CHT6JdnUjMmzpwKlnuLJyWWSfCaTnJcmcxdeDUKFfWNM89Z3rTT54c7UqiI94ZT3ZSNnmePDzxzZ8t7y1x8ObrSRw+xN+C1bUNd5ybU/qcwszRM7nnuHs4ptsxWFhsr9rOnFVzmPLOFB75/BHWlKxp9Vo04z5Gaca9iIjI/lu8aiuz3l/LphIfeelupo3q1+772+/KHwrzY8neW+Zoxr2ISCOsXmB62nsLIC3fhPadvb/9voT8ULYFwh1swXRfMXz8J9PzvsaUt6BXx/jEnkhrWVqwlNkrZ7O5fDO5nlymDpzKiPwR0S6rURYvhm++MYu4HnwwLFsW7YpiQzASrJ2Bv68M+Yv/JHD79Rl8+794nE6bYNDiuluLefDujHoz7j9cksjMB9P47xcJRCIw8PAA199WzNHD6of8hdscPPb7dBa/nUTRNicZWWGOHlbFzXcV0617mEAAZj6YzvvvutmwLo6qKou+B4S4+PJSfnZBeb2xhg/Mp3ffIDfcUczdN2fw1ZfxZGRGuPjyUi69srTevrYNz/8lhef/ksK6NS6SkiIMOb6YARf9jU/9L9X7NEL/jP5M7DuR0T1Hk5+a3+Iz7hXcxygF9yIiItJYJZUBdlTsuWWOgnsREWk1tg2V28FXEu1KWt6WlWbR2oRUmPK26fUvIh3SqFFmIdmjjoK//Q0OOCDaFcWWYCRIqb+U8mB5gwH+d9+4OP3EXJxOKCt14EkJk5AAOd3CrPoqvja4n/9qEldfks0xx1Ux5hQftg0v/93D2tUu5ry2hWOOM+F94TYHZ5zYnW1bnJx7URn9Dw2wvcjJewvc/OaOYo4d7mfHdgdjj81jwhkV9D0wSChoseCNJD79MJH7Hi3i57+oC++HD8zHFW9TXubgjHPL6dk7xPy5yXz6YSLPvLKFkSdW1e4744YM/v6XFE47u4IjjvazvdDB355MJT7BZu573/OlbxHz1s7jh7Ifao/JTMxkyXlLFNx3FgruRUREpCl+LPFRFQw3+JqCexERaXVBn+l9H24fPYwbz4bEdPBkR7sQEZGoC0VClAZKKQuU1QvwL5+UzbtvJfHOJ5voe4D5ObC9yMEJR+RTVmp63GdkhTnu0HxGjvHxyOyi2mOrfBYnD+tOVk6Yl9/ZAsBvrsjkpedT+Pu/tjB0RFW9GmzbdGoLh81CuwkJ9Wu88PSubPohjvc+31S7bfjAfDZtjOOZl7cycoxZo8Xvh+MO7cHRw6qY9bdCAD7/dwJnjc3dLfhfvcrFhBHdmfprLzfcXoJt26wsWsm8dfP49+Z/EyHCV5O/Uo97EREREdlddkoCjo7Ua1hERNoXlxvSekJi2r73bU8cToX2IiLV4hxxZCRmkO/JJy0hDYflIByGJYvcjDrJVxvaA2RmRTjtnLrw+4P33JQUOzn9nAp2bHfUflVWWhw3qoov/pOAr9IiEoG35yUz/ATfbqE91C2v4nTWhfbBIJQUm/GGjahi/ToXpd76fxvl9wzWhvZgjh08xM/G9XG12+bPTSbRHWHMyb56NWblhOl7YJCPlyZW12AxKHsQtx5zK0+e9CTn/uTc/X5vGxK3711EREREJNa5nA4yPfEUlrW/BaBERKSDcDhMyB2fbGbfRxr+JJiIiLRvToeTLoldSI1P5bsfyvBVOuh74O7rnfQ9oG7burUmhp5y9p7XHCve4cAVb1NW6uCgAXtuBVrjlb8nM/tPaXz3jYtIpH5QX1bqIDWt7udQfs/dPxGWlh7hm69dtc+/XxtHlc/B0Qf1aPB8PXvvfo1dk7syddBUbuf2fdbbVAruRURERDqIlEQXvkCYcn9Ha1MgIiLtSnwSpPeCyiKoKt33/iIi0i45HU7SEtIBcLsScVpOwvZON2136tBuVwfrDzxeRPceDf+9kpkVodRrGsTs68PE8+cmMX1aNqPHVXLpFaVk5YSJc9m8v8DN07PSas9XW6uz4XFsu26/SMQiNS3MzOrWObtKTGzblvMK7kVEREQ6kExPAlXBCKFIJNqliIhIZ+ZwgCenevb9Ns2+FxHpoHJyICkJvv8ugbyUPMoCZZT6SwnbYdatrZvN3quvma3eJTPM8FG7t8CpkRkfJiU1wjdfx+/1vPNeSaZHryBPvbCtXshf086mOXr1DbJscSKDBvtJTYv+urDqcS8iIiISIxav2srPnviIofcv4mdPfMTiVVubPIbTYZGdkrDvHUVERNpCfDKk94SC5fDyFPjrKeZx/QfRrkxERJppacFSJr01iTEvjWHygkkcOaKIN9+ENd85SEtIIz8ln3BZBv962VN7zIjRPlLTwvzpwXT8DXT33F5kYmqHA06eWMEH77kbDOFr1sR1VKfaO89XKt7h4J/PpTT7uiaeWYFtW/zxvi4Nvr5je9tG6R1qxr1lWS5gGnA4MBgYALiAqbZtP7WHY0YAU6v3zwWSgc3ASuBR27YXNeH8k4G/7mWXy23b/nNjxxMREZHOY/Gqrdzy6lcUllcRjsC2sipuefUr7gNG999zH8iGuOOdpLldeH2792AUERFpc2sWwaLfmr73dhgqt8N798IJt0Lv4dGuTkREmmBpwVLu+vguinxFhO0wRb4iEsfcRNyS/2PkSCdXXgkul8WTT6bSp5fNlyWmpY4nJcR9j27n6kuyOXlYHmecU05Obpgtm+L49EMz8egf883EpRtmlPDB+25+cVZXzr2ojP6HBigpdvLeQjfX31rMscP9jBlfydvzkrnk3BzGnlpJUaGTF/6WQtduIYq27aEvzj4cNdTP5MtKeeb/Uvn2fy5GjfWRnGyzcUMcC99MYuKZFVxzc0mLvZf70qGCe0zo/kj191uBLUDDqwnUGV399SmwGKgAegI/BSZalnWPbdtNXV3gdWBFA9uXN3EcERER6SRmvb+2NrQHCEegsLyKWe+vbXJwD5CRHI8vGCYQUsscERGJsmUPQsU2E9pDXXi//GkF9yIi7czslbNrQ3uAsB2mKns5I+++F+/LM7j7btM+Z9o06NrV4uKLoVtyN7LcFZx+lpduuVuY9XAaf3kiFZ/PQXZOmEFH+Dl3UnntObK7hnlt0Wb+eH86C95I4sVnU8jKCXP0cVX06Wf645/18wpKdjiZ81QKv70pk+75IaZe6SUl1eaGK5r/CeQ7HtjBoYf7ee7pVB79nenfn5sXYtgIH6eeUbEf71zTWbYd/X49LcWyrHjgRGCFbdubLcu6E7iDvc+4T7Rte7fGSpZl5QGfA1lAvm3bmxtx/smYGfdTbNt+prnXATBkyBB7+XLl/CIiIp3F0PsXsdm7e6/H3LREPr75xGaN6Q+F+bGkiqR4J11Tm9/rUUREZL88PABKN+2+PTkHprzV9vU0hcMBGX2jXYWISMwY89IYtlbu3tKza1JX3j373X0eXxGsoMRfQjDccT4d7I5z083T7TPbtoe05Lgdqse9bdsB27bfakzIvtMxDa6GYNv2JuAjzHukn9IiIiLSqvLS3Th3+c3M6TDbmyshzklG0t4XdRIREWl1afng2OUD/4440/ve0bx2BiIiEh25nlycVv1/u52Wk1xPbqOOT3Ylk+fJIycph3in/lbZmw4V3Lcky7JygGMAP/BtEw8/3LKsayzLusmyrEmWZeW3fIUiIiLSkUwb1Y9sT2JteO90QLYnkWmj+u3XuGlJLtzxCkVERCSKjp9uZtfXhPeOOPN85I0mvI9Pjm59IiLSaFMHTiXLnVUb3jstJ1nuLKYOnNqkcZJcSXT3dCcnKYcEZ/Nb23RkHa3HfbNZljUEmIB5T/IxPe5Tgats2y5q4nBX7/I8bFnWU8A1e5rhX13DL4FfAvTs2bOJpxQREZH2bHT/rtyH6XW/qcRHXrqbaaP6Nau//a5SE137X6CIiEhz/WQsTHzU9Lr3FpgZ+MdPN9sBUnOhygsVRdCB2vmKiHREI/JHMGPoDGavnM3m8s3kenKZOnAqI/JHNGu8JFcSSa4kfCEfXr+XqtAeo9NOp0P1uN9VY3rc77Tvr4AndtpUBlxh2/acJpxvJDAQWAAUAGnAcOB+oB/wD9u2z2/MWOpxLyIiIiIiIp1KKADlWyHkj3YlddTjXkSkTVWFqvD6vfhCvmiX0midpse9ZVnrLcuym/D1XEuc17btP9u2bQFuYABmkdlnLcv6cxPGWGLb9p9s215t23albdubbdt+CTgBKAZ+blnWYS1Rr4iIiIiIiEiHEhdvZuO7u0S7EhERiZLEuES6Jncl15NLkisp2uVEVSy2ylkLNOUzET+25MmrW9msAq62LCsBuMyyrHdt2355P8bcaFnWm8AFwAjgy5apVkRERERERKQDsSxIzoT4JCjbApFwtCsSEZEoSHAmkJOUQzAcxBvwUhGsoCN3jmlIzAX3tm2fGO0advIWcBkwCmh2cF+tsPpRq+6IiIiIiIiI7I3LDem9oGIb+MujXY2IiESJy+kiy51FekI6pYFSygPlROxItMtqEzHXKifG5FU/hlpgrGOqH9e1wFgiIiIiIiIiHZvDASndwJNjZuKLiEinFeeIIyMxgzxPHukJ6TgtZ7RLanWdPri3LGukZVm7vQ+WZfUDbq1++sYur+ValnWwZVlpu2w/voFxLMuybgaGAkXA2y1WvIiIiIiIiEhHl5gK6T0hLiHalYiISJQ5HU7SE9PJT8knw51BnCPmGsq0mA53ZZZl3QQcXP308OrHKZZlDa/+/gPbtp/a6ZDXgRLLsj4FNmLek37AydXfP27b9sJdTnM/8AtgCvDMTtuXWpa1GvgPsAlIA44DDgUqgQts2y7d74sUERERERER6UycLkjvAZU7zJeIiHRqlmWRGp9KiiuFimAFpYFSAuFAtMtqUR0uuMcE7iN32Tas+qvGzsH9HcBY4FhgIuAEtgKvAU/Ztv1OE879IHA0MBrIACLAD8BM4GHbttUmR0RERERERKS5kjJM//vyrRBuia62IiLSnlmWhSfegyfeQ2WwktJAKVWhqmiX1SKszrYab3sxZMgQe/ny5dEuQ0RERERERCT2RCJts3CtwwEZfVv3HCIi0qKqQlV4/V58IV+bnM8d56abp9tntm0PaclxO+KMexERERERERHpyGoWrnWVQkUhaFKiiIhUS4xLJDEukUA4QIm/hMpgZbRLahYF9yIiIiIiIiLSPiWmmtY5ZVsg5I92NSIiEkPinfHkJOUQDAcp8ZdQEayIdklN4oh2ASIiIiIiIiIizeZ0QVo+uLtEuxIREYlBLqeL7KRs8lLy8MR7sCwr2iU1ioJ7EREREREREWnfLAuSMyEtDxzOaFcjIiIxyOVwkeXOIs+TR0p8SswH+AruRURERERERKRjcLkhvSckeKJdiYiIxKg4RxyZ7kzyPHmkJqTisGIzIo/NqkREREREREREmsPhNAvXerLNTHwREZEGxDniyEjMIM+TR1pCWswF+LFVjYiIiIiIiIhIS0hMg7QeEJcQ7UpERCSGOR1OuiR2IT8ln/SEdJxWbLRcU3AvIiIiIiIiIh1TXLwWrhURkUZxWA7SE9PJS8mjS2KXqAf4cVE9u4iIiIiIiIhIa6pZuNblhvKtEAlHuyIREYlhDstBWkIaKfEplAXKKPWXErbb/meHZtyLiIiIiIiISMcXn2QWro1PjnYlIiLSDtQE+Pkp+WYGvqNtZ+AruBcRERERERGRzsHhhNRcLVwrIiKNZlmWCfA9bRvgq1WOiIiIiIiIiHQuiWkQV906J+SPdjUiItIO1AT4qfGplAXL8Pq9hFux/ZqCexERERERERHpfGoWrq3cAb7iaFcjIiLthGVZpMankuJKoTxYTiAcaJXzKLgXERERERERkc5JC9eKiEgzWZZFSnxKq42vHvciIiIiIiIi0rnVLFyb4Il2JSIiIoCCexERERERERERs3BtSjctXCsiIjFBwb2IiIiIiIiISI3ENEjrAXEJ0a5EREQ6MfW4FxERERERERHZWc3CtVUl0a5EREQ6Kc24FxERERERERHZlWWBu0u0qxARkU5Kwb2IiIiIiIiIiIiISAxRcC8iIiIiIiIiIiIiEkMU3IuIiIiIiIiIiIiIxBAF9yIiIiIiIiIiIiIiMUTBvYiIiIiIiIiIiIhIDFFwLyIiIiIiIiIiIiISQxTci4iIiIiIiIiIiIjEEAX3IiIiIiIiIiIiIiIxRMG9iIiIiIiIiIiIiEgMUXAvIiIiIiIiIiIiIhJDFNyLiIiIiIiIiIiIiMQQBfciIiIiIiIiIiIiIjFEwb2IiIiIiIiIiIiISAxRcC8iIiIiIiIiIiIiEkMU3IuIiIiIiIiIiIiIxBAF9yIiIiIiIiIiIiIiMUTBvYiIiIiIiIiIiMgueveGMWNadkzLgjvvbNkxpWNScC8iIiIiIiIiIiKtZtkyE1hbFhQURLua+lasMEH6Dz9Eu5KW8cwz8Nhj0a5CWoKCexEREREREREREWkVoRBccQUkJ0e7koatWAG//a2Ce4k9Cu5FRERERERERESkVTz+OGzZApdeGu1KRNoXBfciIiIiIiIiIiLS4jZvNm1o7rsP0tMbf9wzz5i2OosXw403QrdukJICZ58NJSVmFv8tt0D37uB2w09/CoWFu4+zaBGMHm2OTU6GkSNN254ad94JU6aY748/vq6dz/vv1x/nP/+B444z5+rRAx5+uO61cBjy8mD8+IavZcAAGD5879e7cSNceCFkZ0NiIhx2mHkPdmXb8H//B0ceCUlJ0KWLGfv1183rvXvDkiWwdm3dtfTubV4LBOCOO+DooyEjw1zL4Yc3fJ6a3v57u+6da3riCVNzYiJkZsJ553WcTzBEk4J7ERERERERERERaXHTp8OBB8LFFzf/+H//G267DX7xC3jlFbjkErj8cvjkExPeX3opzJ8PV19d/9h//hPGjjXf33033HOPCf1PPBGWLjXbzzwTfvlL8/3tt8OcOearf/+6cX74ASZOhGHDTHDdrx9cfz2884553emESZNgwQLYurV+DcuXw6pVcNFFe77GoiIz9ty55n36/e/NjYYpU+DBB+vve/nl8KtfQWoq3HWXafHTp09dLY88AgcfDF271l3LI4+Y10pL4c9/hmOPNe/H/febmylTpsDs2bvXta/rrnHVVebr8MPNua6+2twwGTbMXJs0n2XbdrRrkAYMGTLEXr58ebTLEBERERERERERabIlS+CEE+CDD0yIe+edJmjeuBHy8/d+7DPPmED5+OPN7HdH9dTjc8+Fl14yM+cXLaq/fe5cKC4GjwcqKqBnTzj5ZHj++bpxfT4YONAE2x9+WP9cy5btPjO+d2/YsAHeesuMBeD3m7FHjDC1AHzzjQn7H34Yrr227vhf/xqefNK0Cqr5xIFlmZnvd95pnk+fDg89BG+/DePGmW3BoLnGL74wi/lmZpqbDSNHwuTJ8Je/mHFq2Hbd81GjzDFr1tS/lnDYfFIhIaH+9pNOgvXr4bvvmn7dH39s/ts++SRMnVp3/Ndfw+DBcMMNcO+9dAqWZX1m2/aQlhxTM+5FRERERERERESkxdQsSHvBBSbYba6pU+vCeYChQ01IffHFu28PherasyxcCDt2mPYzRUV1XxUVpgXMJ59AZWXjaujduy68BhN8H3ssrFtXt+3gg+GYY8wM9xqhELzwgmnjs7c2QfPnw6GH1oX2AC6XuQFQVWVuUEBdWH7vvfVDe9j9eUOczrrQPhg0709RkWkltGYNeL1Nv+4XXzRtdCZOrP8+d+0KBx1kWh1J88VFuwARERERERERERHpOB591MzYXrBgz/sEAiY83llGBsTH1z3v2bP+6zUB+J62Fxebx9WrzeOe+s4DbN9u+sTvS02P+J116QL//W/9bVOmmDY2X38NhxxiZtAXFpoWP3uzfr0J93c1YIB5/P5787hmjXl/unffd8178re/mdn9X38NkUj917xeSEure96Y61692nyKITe34fP17dv8WkXBvYiIiIiIiIh4mMLBAAAZlUlEQVSIiLQQr9e0xLn4YhPOr19vtpeUmMeCAvO4Zo1ppbOz994zrV5qOJ0Nn2NP22s6gteE0k8/vXvIXyM7e29X0fhz1TjvPLjmGnj2WXjgAfOYk1N/Jn1T1IxfM5t+53Y4zfHii6bNzqmnwnXXmVnxLhe8+Sb88Y+7B/mNue5IxNw0qfk0wK7c7ubXKwruRURERFrE4lVbmfX+WjaV+MhLdzNtVD9G9+8a7bJERKSxVi+AZQ+CtwDS8uH46fCTsdGuSkSk3VhasJTZK2ezbl2EsrLneewxeOyx3fcbOhR69TL92xcurP/aYYe1TC0HHGAes7JMa5y92Z8wfGdpaXD66aan/k03wbx5ZgZ+3D7S1969TY/8XdVsq5n5fuCBZmHYTZsgL2/P4+3pel54wSxkO29e/X32p53NAQeYT1UcdVT92frSMtTjXkRERGQ/LV61lVte/YovNhaz2VvFFxuLueXVr1i8amu0SxMRkcZYvQDmXQ2bPoPSTeZx3tVmu4iI7NPSgqXc9fFdrCxcSYlrNb1/fS2Dps/gntlf8+qr8OqrZgFZgKeeMouZduliQvWdv7p0aZl6xo0zM8HvuccsqrqrwsK675OTzWPNJwL2x+TJJli//HLTn/6ii/Z9zMSJsHJl/ZsYoRA88ggkJtbdeDj7bPN42227z/bf+XlycsPXUrMmwM4z67dvNwvdNtd555lzz5jR8OtFRc0fWzTjXkRERGS/zXp/LYXlVYSrfwkOR6CwvIpZ76/VrHsRkfZg2YNQsQ0iIfM8EjLPlz2oWfciIo0we+VsinxFhO0wjoQwniPexbKcfJP9PbeeYlZsXbHC7DtuHOTnt249KSnm5sDPfw4DB5pFart3N216liwx+7z3nnk84ggzA/3++02QnZBgFmzNyWn6eU86yVzbiy+a8w4evO9jbrzRzIY//XS46iozm/6ll+DDD+EPfzB97QFGjIBLLzU3PtavN4F/fDx89pnp1T9zptnvyCPhjTdg+nTzvcdj9j3tNJg7FyZMMOfats28R927w9ZmzjcaPhx+/WvzyYqVK82aAh6P6cv/+usm2L/zzuaNLQruRURERPbbphJfbWhfIxwx20VEpB3wFtSF9jUiIbNdRET2aXP5ZsJ2uN62sB1mc/nmKFVkZqjn5cF995nZ65WV0K2baetyySV1+/Xta4Lnhx4y28NhE+o3J7h3OGDSJHMToDGz7cG08/nwQ7j5ZhPKl5XBQQeZmfBTptTf98kn4fDDzeMtt5jA/pBD4De/qdvnuutMm52nnzbX1KuXCe4vusjcmJg5E66+2vT+nz7dtLjZ9TxN8eij5gbBrFl1IX2PHubmxznnNH9cAcve9bMVEhOGDBliL1++PNpliIiISCP87ImP+GJjcb3w3umAwT268PLlw6JXmIiINM7TY017nJ3De0cc5B0Jl6hdjojIvkx6axIrC1fWC++dlpOB2QOZUz3jvrO44w64917YuBFyc6NdjbQVy7I+s217SEuOqR73IiIiIvtp2qh+ZHsScVb/ZuV0QLYnkWmj+kW3MBERaZzjp0NyjgnrwTwm55jtIiKyT1MHTiXLnYXTcgImtM9yZzF14NQoV9a2QiEzU37sWIX2sv/UKkdERERkP43u35X7ML3uN5X4yEt3M21UP/W3FxFpL34yFiY+anraewsgLd+E9upvLyLSKCPyRzBj6Axmr5zN5vLN5HpymTpwKiPyR0S7tDaxbRu8+y7Mm2f66D/zTLQrko5ArXJilFrliIiIiIiIiIiIxL7334cTTjD96q+/Hm66KdoVSVtrjVY5mnEvIiIiIiIiIiIi0kyjRoHmRktLU497EREREREREREREZEYouBeRERERERERERERCSGKLgXEREREREREREREYkhCu5FRERERERERERERGKIgnsRERERERERERERkRii4F5EREREREREREREJIYouBcRERERERERERERiSEK7kVEREREREREREREYoiCexERERERERERERGRGKLgXkREREREREREREQkhii4FxERERERERERERGJIQruRURERERERERERERiiIJ7EREREREREREREZEYouBeRERERERERERERCSGKLgXEREREREREREREYkhCu5FRERERERERERERGKIgnsRERERERERERERkRii4F5EREREREREREREJIYouBcRERERERERERERiSEK7kVEREREREREREREYoiCexERERERERERERGRGKLgXkREREREREREREQkhnSo4N6yLJdlWVdblvVXy7JWWNb/t3fn0ZJV1R3Hvz9BQVHAaCtJNCjKFOcBlBbtblACiCMYcUBUBqcEcHY50UaNJitRAkYZTWMg0ioKDoAD0LaKoqg4RBAQWwMCgmiDMgns/HFvQVlWvX7vdXcNr7+ftWqdfvecune/x1lF3V2n9sktSSrJ/jM4xwZJftw+77JZxLBekkOS/DDJjUmuTXJakvkzPZckSZIkSZIkad2z/qgDWMM2Ag5r/30VcCXwwBme45+BzWdz8SQBTgL2An4KfAj4C+D5wPIke1bVqbM5tyRJkiRJkiRp3TCnVtwDNwC7A39VVZsBH53Jk5MsBF4LvHGW19+bJml/DvDoqnpjVe0HLAJuA45Jcq9ZnluSJEmSJEmStA6YU4n7qrqlqk6vqitm+twkGwNLgDOr6shZhvCqtn17Vd3UFdd3gKXAPJrEviRJkiRJkiRJfc2pxP1qOhy4N7DfbJ6cZANgPs2q/6/1GXJ62+40q+gkSZIkSZIkSesEE/dAkucA+wKvq6pfzvI0DwXWAy6tqlv79F/ctltNEceBSc5Lct7VV189yzAkSZIkSZIkSZNsnU/cJ7k/cBRwelUdtxqn2qRtVw7o7xzfdNAJquroqnp8VT1+3rx5qxGKJEmSJEmSJGlSjV3iPsmKJDWDxwmrecljgLsCB6yB8KeStq21fB1JkiRJkiRJ0gRbf9QB9PEz4KZVjrrTr2Z7oSQvAZ4B7FtVl8/2PK3OivpNBvRv3DNOkiRJkiRJkqQ/M3aJ+6raeYiXe2zbHp/k+D79f52ks0L+3lX1uynOdQlwG7BFkvX71Lnfsm0vmn24kiRJkiRJkqS5buwS90P2TeCeA/r2A24APt7+fPNUJ6qqm5OcAzy5fZzdM2S3tj1rdqFKkiRJkiRJktYF63TivqqWAkv79SXZD/htVe3fp+9vgHsAv6yqG7q6PkKTtH9Pkp2r6qZ2/HbA84GrgZPX7G8hSZIkSZIkSZpLUjW39kpN8hZgm/bHRwOPAs4BLm6Pfb2qjp3GeQq4vKoe0KdvGbAAWFRVy7qOB/gEsBdwIfA54D40SfsNgT2r6tRp/h5XA7+Yzth12H2Ba0YdhLQanMOaZM5fTTLnryadc1iTzPmrSecc1iRz/q49m1fVvDV5wrm44n5XmqR6t/nto2OVifvZqKpK8gKaDwpeDvwjzUa7y4H3VNU5MzjXGv0PPRclOa+qHj/qOKTZcg5rkjl/Ncmcv5p0zmFNMuevJp1zWJPM+TtZ5lzivqoWrqHzZDbXaDel/WD7kCRJkiRJkiRpRu4y6gAkSZIkSZIkSdKdTNxrkh096gCk1eQc1iRz/mqSOX816ZzDmmTOX00657AmmfN3gsy5zWklSZIkSZIkSZpkrriXJEmSJEmSJGmMmLiXJEmSJEmSJGmMmLiXJEmSJEmSJGmMmLjXREnygCQfTfKrJDcnWZHksCT3HnVs0lSS3CfJ/kk+k+SSJDcmWZnk60n2S+LrsSZOkn2SVPvYf9TxSNOR5MlJTk5yRfte4ookX0qy+6hjk6aS5OntXL2sfR9xaZJPJtlh1LFJAEn2SnJEkq8lua59f3DCKp4zP8lpSa5NckOSHyY5JMl6w4pbgpnN3yRbJnlzkrOS/F+SW5JcleTUJIuGHbsEs3sN7nn+cV33dg9dm7Fq+tYfdQDSdCV5CHAOcD/gVOBCYHvgYGDXJE+qqt+MMERpKs8DPgJcAZwN/BK4P/Bc4FhgtyTPK3cM14RI8kDgCOD3wD1HHI40LUneDrwbuAb4PM1r8n2BxwALgdNGFpw0hST/ArwJ+A1wCs0cfijwLGDPJC+pqmnfnEtryduBR9G8N7gM2GaqwUmeBZwM3AQsBa4FngF8EHgSzftnaVhmMn/fDTwf+AnNe4drga2BZwLPTHJwVR2+dsOV/syMXoO7JXkG8HK8txs7MUekSZHki8AuwEFVdUTX8Q8ArwWOqqpXjio+aSpJdgI2Ar5QVbd3Hd8M+DbwQGCvqjp5RCFK05YkwJeBBwOfBt4AHFBVx440MGkKSZ4HfAL4CvDcqrq+p/+uVfXHkQQnTaF9r3A5cDXwyKr6dVffIuAs4OdVtcWIQpSAO+bjZcAlwAKaxSonVtWL+4zduB23CfCkqjqvPb4hzZzeAXhBVZ00pPC1jpvh/H0p8IOq+n7P8QU075ELeFBVXbG245Y6ZjKHe543D/gRsAzYrH3ullV1yVoNWNNiaQZNhCRb0CTtVwD/2dN9KPAHYJ8kGw05NGlaquqsqvpcd9K+PX4lcGT748KhBybNzkHATsDLaF5/pbHWliP7F+AG4IW9SXsAk/YaY5vT3Led2520B6iqs4HrgXmjCEzqVlVnV9XF0/wG6V408/akTtK+PcdNNKtGAV61FsKU+prJ/K2qJb1J+/b4V2mSn3cD5q/5KKXBZvga3O3otn3Nmo5Jq8/EvSbFTm37pT6Jz+uBbwD3AJ447MCkNaCTLLp1pFFI05BkW+D9wH9U1fJRxyNN03yab4icBvy2rRX+5iQHWx9cE+Bi4BZg+yT37e5I8hTgXjTfJJEmSef+7ow+fctpPmidn2SD4YUkrRHe22litN8eeTbwSktPjydr3GtSbN22Fw3ov5hmRf5WwJlDiUhaA5KsD7yk/bHfjYs0Ntr5+t80ezS8dcThSDOxXdteBXwPeER3Z5LlNOXKrh52YNKqVNW1Sd4MfAD4SZJTaGrdP4SmnvKXgVeMMERpNgbe31XVrUl+DjwM2AK4YJiBSbOVZHNgZ5oPnlzgorHWztf/AE6oqlNGHY/6M3GvSbFJ264c0N85vukQYpHWpPcDDwdOq6ovjjoYaRXeSbOJ545VdeOog5Fm4H5t+0rg58BTgXNpSpD8O/B3wCexZJnGVFUdlmQF8FHggK6uS4AlvSV0pAng/Z3mlPbbIScCGwBvqqrfjjgkaaC2jOTxNJvRHjTicDQFS+VorkjbutuyJkaSg4DXAxcC+4w4HGlKSbanWWX/71X1zVHHI83Qem0bmpX1Z1bV76vqf4Hn0GzktcCyORpXSd4EfApYQrPSfiPgccClwIlJ/nV00Ulrhfd3mhhJ1qP5VuqTgKXAv402ImmVXkuzCe0Bfsg03kzca1J0VlxsMqB/455x0lhL8hqar6X9BFhUVdeOOCRpoK4SORcB7xhxONJsdG5ILq2qH3R3tN8e6XzjafuhRiVNQ5KFNJsrf7aqXldVl1bVDVX1PZoPni4HXp9ki1HGKc2Q93eaE9qk/QnA84BPAC+exeag0tAk2RJ4L/BfVXXaqOPR1Ezca1L8tG23GtC/ZdsOqoEvjY0khwAfAn5Mk7S/csQhSatyT5rX322Bm5JU5wEc2o45pj122MiilAbrvI/43YD+TmL/7kOIRZqpPdr27N6OqroB+DbNfd1jhhmUtJoG3t+1CwYeTLO556XDDEqaiXaufhzYG/gf4IVV5aa0GncPoynp9LLu+7r23m5BO+bi9tizRxemwBr3mhydG5Vdktylqm7vdCS5F81X0m4EvjWK4KTpajeXez9wPvC0qrpmxCFJ03EzcNyAvsfSJIu+TnMTbhkdjaPlNAmgLZPcrapu6el/eNuuGGpU0vRs0LbzBvR3jvfOa2mcnQW8CNiVJvHZ7SnAPYDlVXXzsAOTpiPJ3WhW2D8L+Bjwsu48hTTGVjD43u7pwGY0ez9dh++NR87EvSZCVf0syZeAXYDXAEd0db+Lps7nUVX1h1HEJ01HkncA/wR8F9jF8jiaFG0pkf379SVZTJO4P76qjh1mXNJ0VdU1SZbSJIneCby905fkaTSb064EzhhNhNKUvgb8A3BgkqOq6vJOR5LdaBaw3AScM6L4pNn4FE0JqL2THFFV5wEk2RB4TzvmI6MKTppKuxHtp4HdaRKgB5q016SoqvMZfG+3jCZx/9aqumSYcak/E/eaJK+muSE5PMnOwAXAE4BFNCVy3jbC2KQpJdmXJml/G80N+EFJeoetqKolQw5NktYVr6N53/C2JE+hKS+yOU2N8NtoNucaVEpHGqVPAV8BngpckOQzwJU05cv2oNnE8y1V9ZvRhShBW1KhU1Zhs7bdIcmS9t/XVNUbAKrquiQH0MzvZUlOAq4Fngls3R5fOqzYpZnMX+BImqT9NTT7jLyzz73dsqpattYClnrMcA5rQpi418RoV90/nib5uSvN/yivAA4H3uXqZY25B7ftesAhA8Z8FVgylGgkaR1TVb9O8gSa1fbPAZ4IXA98AXhfVVluT2Opqm5PsjvNt073ppm/96BJcp4GHF5VXxphiFLHo4F9e45t0T4AfgHckTSqqlOSLKBZgLUnsCFwCc0HrYe7waeGbCbzt3Nvd1+ab/INsmxNBSdNw4xegzUZ4v8LJUmSJEmSJEkaH3cZdQCSJEmSJEmSJOlOJu4lSZIkSZIkSRojJu4lSZIkSZIkSRojJu4lSZIkSZIkSRojJu4lSZIkSZIkSRojJu4lSZIkSZIkSRojJu4lSZIkSZIkSRojJu4lSZIkSZIkSRojJu4lSZKkOSBJ9TxuS3JNkrOSvGjU8Q1L+7svG3UckiRJ0upYf9QBSJIkSVqj3tW2dwW2Bp4NLEryuKp63ejCkiRJkjRdqapRxyBJkiRpNSUpgKpKz/GdgS+3P25RVSuGHNpQtX+Hr1bVwlHHIkmSJM2WpXIkSZKkOayqzgQuBAJsB5DkQW1JmSVJtkqyNMmvk9yeZGHnuUm2TPKxJJcnuSXJr9qft+y9TpLF7TkXJtk3yfeT3Nie96NJNusXX5K/SPK+JBe041cmOTPJLn3GvrS9xkuT7JpkWTu+c6yzKmlBT9mgxUm2af991qC/VZIfJfnjoFglSZKkYbFUjiRJkjT3dVbh937d9iHAucBFwInA3YHrAJJsB3wFuBfwWeAnwDbAi4BnJdm5qs7rc63XArsAS4EzgB2BlwELkzyhqq6+I6hkc2AZ8CDga+34jYA9gDOSvKKqjulzjb2AXYHTgSPb559PUyboUOAXwJKu8cuq6sIkZ9OUDdqqqi76kz9QMh94OHByVV3Z55qSJEnS0Ji4lyRJkuawJE+lqXVfwHd6uncE3ldVb+15ToCPARsDL66qE7v6ng+cBJyQ5G+r6vaec+4GPKGqvt/1nA8ChwDvB/brGns8sDnwgqo6qWv8pjQJ/cOTfLaqruq5xu7A7lV1Rs/x85McCqyoqsV9/hwfBhYBBwJv6Ok7sG2P6vM8SZIkaagslSNJkiTNIW1ZmMVJ3pvkUzSr2AMcVlW/6Bl+FXduZtttPs3q+m92J+0Bqmop8HWaDwN27PPc/+5O2rcWAyuBFybZoI3zUcACmhXuJ3UPrqrf0ayc3xDYs881Tu2TtJ+OU4BfAS/txNHGsinw98DPaL5lIEmSJI2UK+4lSZKkueXQti3gdzQlaI6rqhP6jP1BVd3c5/hj23ZQPfizaJL2jwGW9/R9tXdwVa1Mcj5Non5bmrI2O7TdmyRZ3Oca89p22z593x4Q15Sq6tYkxwLvpPlA4H/arn1oygQdXVW95YQkSZKkoTNxL0mSJM0hVZVVj7rDoFrum7TtFQP6O8c37dPXW9am91qdc9+nbZ/WPga55xTnmo2jgbcCr+DOxP2BwC3Af63GeSVJkqQ1xsS9JEmStO4atLp8ZdtuNqD/L3vGdbv/gOd0zrWypz24qg4fGGF/s14VX1WXJ/kc8Jwk2wL3ptmUdmn3xrmSJEnSKFnjXpIkSVKvTo36hQP6O8e/16dvQe+BJJsAjwZuAi5oD3+rbZ88qwgHux1YbxVjPty2B+KmtJIkSRpDJu4lSZIk9foG8FNgxyR7dXe0Pz8FuIhmk9pe+yR5TM+xxTQlcj7eqalfVefR1N9/bpKX9wsiySOS3G+Gsf8GeOAqxpxJE/++NJvSXlRVZ8/wOpIkSdJaY6kcSZIkSX+iqirJvsCXgaVJTgUuBLYGng1cD7ykqm7v8/TTgW8k+QRNLfwd28cK4C09Y19Is9HtcUkOAs6l2VD3AcAjaUrY7AD8egbhnwns3ZbD+S5wK7C8qu7YRLf9/Y4EPtAecrW9JEmSxoor7iVJkiT9mao6F9iOZgPXHYA3AvOBjwPbtf39fBB4NU1pnEOAbYAlwPyq+pMEfFVdBjwOeBtwG/Ai4KD2Or+k2UD2RzMM/eA2xu2BdwDvBnbqM24JTVmdm4HjZ3gNSZIkaa1K1az3dZIkSZIkAJIsBg4FFlXVstFGs2pJFgJnAydU1T4jDkeSJEn6E664lyRJkrQuelPbfmikUUiSJEl9WONekiRJ0johySOAPWjK8+wGfH6Kkj+SJEnSyJi4lyRJkrSueBzwz8B1wCdpavFLkiRJY8ca95IkSZIkSZIkjRFr3EuSJEmSJEmSNEZM3EuSJEmSJEmSNEZM3EuSJEmSJEmSNEZM3EuSJEmSJEmSNEZM3EuSJEmSJEmSNEb+H2g9oXHsmw4lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "variable_list = ['xlogp', 'Carbons', 'MolWt']\n",
    "label_style = dict(size=17, color='blue')\n",
    "ax_style = dict(fontsize=20, color='black')\n",
    "title_style = dict(size=25, color='black')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(25, 15))\n",
    "y = np.log(np.array(linear_hc_df.Constant))\n",
    "x1 = np.array(np.array(linear_hc_df.xlogp))\n",
    "plot = sns.scatterplot(x1, y, label = 'xlogp')\n",
    "line = sns.regplot(x1, y)\n",
    "\n",
    "\n",
    "x2 = np.array(np.array(linear_hc_df.Carbons))\n",
    "plot = sns.scatterplot(x2, y, label = '# Carbons')\n",
    "line = sns.regplot(x2, y)\n",
    "\n",
    "\n",
    "x3 = np.array(np.array(linear_hc_df.MolWt/10))\n",
    "plot = sns.scatterplot(x3, y, label = 'MolWt / 10')\n",
    "line = sns.regplot(x3, y)\n",
    "for line in range(0, len(linear_hc_df.index)):\n",
    "     plot.text(linear_hc_df.MolWt.iloc[line]/10, np.log(np.array(linear_hc_df.Constant.iloc[line])),\n",
    "               linear_hc_df.Species.iloc[line], **label_style)\n",
    "\n",
    "# x and y swapped as per Jeremy's advice\n",
    "\n",
    "ax.legend(prop={'size':20})\n",
    "ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "ax.set_ylabel('$ln(H^{cp})$', **ax_style)\n",
    "ax.set_xlabel('Property', **ax_style)\n",
    "plt.title('$ln(H^{cp})$ against various properties', **title_style)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versatile plot\n",
    "This chunk will work for some pre-defined chunks of the dataframe, plotting the above graph for *one* variable against $ln(H^{cp})$, then performing linear regression to pull out slope and intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ethane'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carbons.index[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a857a1b9fcb946809707b5c64be70e3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='DataFrame', index=5, options=('Linear hydrocarbons', 'Noble gases'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_list = list(carbons) #update when more descriptors are added\n",
    "filter_by = ['Chlorines', 'Carbons'] # for colouring by catagorical variables\n",
    "number_of = list(range(0, 13)) ; number_of.append('all')\n",
    "label_style = dict(size=17, color='blue')\n",
    "ax_style = dict(fontsize=20, color='black')\n",
    "title_style = dict(size=25, color='black')\n",
    "\n",
    "@widgets.interact(DataFrame = ['Linear hydrocarbons', 'Noble gases', \n",
    "                               'Carboxylic acids', 'Carboxylic acids (minus outliers)',\n",
    "                              'Benzene Derivatives', 'Linear Chloroalkanes', 'All'],\n",
    "                  Variable = var_list, AtomFilter = filter_by, Filter = False,\n",
    "                  NumberOf = number_of, Labels = True)\n",
    "\n",
    "def plot_henry_graph(DataFrame = 'Linear Chloroalkanes', Variable = 'MolWt',\n",
    "                     AtomFilter = 'Chlorines', NumberOf = 1, Filter = False,\n",
    "                     Labels = True):\n",
    "    if DataFrame == 'Linear hydrocarbons':\n",
    "        if Filter == False:\n",
    "            df = linear_hc_df\n",
    "        if Filter == True:\n",
    "            df = linear_hc_df.loc[linear_hc_df[AtomFilter] == NumberOf]\n",
    "    if DataFrame == 'Noble gases':\n",
    "        if Filter == False:\n",
    "            df = noble_gases\n",
    "        if Filter == True:\n",
    "            df = noble_gases.loc[noble_gases[AtomFilter] == NumberOf]\n",
    "    if DataFrame == 'Carboxylic acids':\n",
    "        if Filter == False:\n",
    "            df = carbox_acids\n",
    "        if Filter == True:\n",
    "            df = carbox_acids.loc[carbox_acids[AtomFilter] == NumberOf]\n",
    "    if DataFrame == 'Carboxylic acids (minus outliers)':\n",
    "        if Filter == False:\n",
    "            df = carbox_minus_outliers\n",
    "        if Filter == True:\n",
    "            df = carbox_minus_outliers.loc[carbox_minus_outliers[AtomFilter] == NumberOf]\n",
    "    if DataFrame == 'Benzene Derivatives':\n",
    "        if Filter == False:\n",
    "            df = benzene_derivatives\n",
    "        if Filter == True:\n",
    "            df = benzene_derivatives.loc[benzene_derivatives[AtomFilter] == NumberOf]\n",
    "    if DataFrame == 'Linear Chloroalkanes':\n",
    "        if Filter == False:\n",
    "            df = linear_chloro_alkanes\n",
    "        if Filter == True:\n",
    "            df = linear_chloro_alkanes.loc[linear_chloro_alkanes[AtomFilter] == NumberOf]\n",
    "    if DataFrame == 'All':\n",
    "        if Filter == False:\n",
    "            df = data\n",
    "        if Filter == True:\n",
    "            df = data.loc[data[AtomFilter] == NumberOf]\n",
    "        \n",
    "    label_style = dict(size=17, color='blue')\n",
    "    ax_style = dict(fontsize=20, color='black')\n",
    "    title_style = dict(size=25, color='black')\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(20, 10))\n",
    "    y = np.log(np.array(df.Constant))\n",
    "    x = np.array(np.array(df[Variable]))\n",
    "    \n",
    "    try:\n",
    "        line = sns.regplot(x, y, data=df, marker='x')\n",
    "    except Exception as e:\n",
    "        print(e, 'This data isn\\'t available yet / can\\'t be plotted')\n",
    "    try:\n",
    "        plot = sns.scatterplot(x, y, data=df, s=100)\n",
    "    except Exception as e:\n",
    "        print(e, 'This data isn\\'t available yet / can\\'t be plotted')\n",
    "    if Labels == True:\n",
    "        for line in range(0, len(df.index)):\n",
    "            plot.text(df[Variable].iloc[line], np.log(np.array(df.Constant.iloc[line])),\n",
    "                      df.Species.iloc[line], **label_style)\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "    ax.set_ylabel('$ln(H^{cp})$', **ax_style)\n",
    "    ax.set_xlabel('Property', **ax_style)\n",
    "    plt.title('$ln(H^{cp})$ against %s for %s' % (Variable, DataFrame), **title_style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['carbons', 'hydrocarbons_df', 'linear_hc_df', 'noble_gases']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bf3c40481df45d48b4666b3c969e6f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='DataFrame', options=('carbons', 'hydrocarbons_df', 'linear_hc_df',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_list = list(carbons) #update when more descriptors are added\n",
    "filter_by = ['O', 'N', 'P', 'S', 'F', 'Cl', 'Br', 'I', 'C'] # for colouring by catagorical variables\n",
    "number_of = list(range(0, 13))\n",
    "label_style = dict(size=17, color='blue')\n",
    "ax_style = dict(fontsize=20, color='black')\n",
    "title_style = dict(size=25, color='black')\n",
    "\n",
    "@widgets.interact(DataFrame = df_list,\n",
    "                  Variable = var_list, AtomFilter = filter_by, Filter = False,\n",
    "                  NumberOf = number_of, Labels = True)\n",
    "\n",
    "def plot_henry_graph(DataFrame = 'carbons', Variable = 'MolWt',\n",
    "                     AtomFilter = 'Chlorines', NumberOf = 1, Filter = False,\n",
    "                     Labels = True):\n",
    "    df = eval(DataFrame)\n",
    "    \n",
    "    if Filter == True:\n",
    "        df = df.loc[df[AtomFilter] == NumberOf]\n",
    "    label_style = dict(size=17, color='blue')\n",
    "    ax_style = dict(fontsize=20, color='black')\n",
    "    title_style = dict(size=25, color='black')\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(20, 10))\n",
    "    y = np.log(np.array(df.Constant))\n",
    "    x = np.array(np.array(df[Variable]))\n",
    "    \n",
    "    try:\n",
    "        line = sns.regplot(x, y, data=df, marker='x')\n",
    "    except Exception as e:\n",
    "        print(e, 'This data isn\\'t available yet / can\\'t be plotted')\n",
    "    try:\n",
    "        plot = sns.scatterplot(x, y, data=df, s=100)\n",
    "    except Exception as e:\n",
    "        print(e, 'This data isn\\'t available yet / can\\'t be plotted')\n",
    "    if Labels == True:\n",
    "        for line in range(0, len(df.index)):\n",
    "            plot.text(df[Variable].iloc[line], np.log(np.array(df.Constant.iloc[line])),\n",
    "                      df.Species.iloc[line], **label_style)\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "    ax.set_ylabel('$ln(H^{cp})$', **ax_style)\n",
    "    ax.set_xlabel('Property', **ax_style)\n",
    "    plt.title('$ln(H^{cp})$ against %s for %s' % (Variable, DataFrame), **title_style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations\n",
    "Higher $H^{cp}$ = higher solubility\n",
    "\n",
    "This shows a few trends:\n",
    "- Increased lienar hydrocarbon molecular weight decreases $H^{cp}$\n",
    "- Increased noble gas atomic weight increases $H^{cp}$\n",
    "- Increased carboxylic acid molecular weight decreases $H^{cp}$\n",
    "- Decreased affinity for water (higher $log(P)$) decreases solubility (both hydrocarbons and carboxylic acids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Moving forward\n",
    "These trends are to be expected. We now need to look at other possible factors affecting solubility. I'll look into :\n",
    "- Branched hydrocarbons\n",
    "- Functional groups\n",
    "- Volume of molecules\n",
    "\n",
    "\n",
    "- Look into electronic structure? - polarity/dipole moments\n",
    "\n",
    "- try logp$^2$\n",
    "\n",
    "- multilinear regression\n",
    "\n",
    "- look into log(p)$^2$? benz ders look a bit quadratic\n",
    "\n",
    "- swap axes for plots\n",
    "\n",
    "- try rdkit descriptors first.\n",
    "\n",
    "- why are the odd ones odd - do any descriptors \n",
    "\n",
    "- SMILES STRING into rdkit to get volume?\n",
    "\n",
    "\n",
    "- For the set of linear chloroalkanes, seperate by number of chlorines attached\n",
    "- For the benzene derivatives, split by functional group type / number of\n",
    "\n",
    "To improve the notebook and make my life easier later on:\n",
    "- Define a function which plots the graph above for any given slice of the dataframe\n",
    "- Introduce a direct link to the supplement so the set of constants can be updated when the dataset changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions and Ideas\n",
    "\n",
    "Each species has several constants - for now I've used the first constant in each case. How should I approach this?\n",
    "> This is fine - the paper's author gave the Henry's Law constant he thought was best as the first in each case. Fine to use the first in each set\n",
    "\n",
    "As Henry's Law deals specifically with gases (?) - is it reasonable to provide Henry's law constants for species that are not gases at standard temperature?\n",
    "> vapor presssures - see how they did the measurements (look at miscibility) (could raise errors at low temperatures)\n",
    "\n",
    "Are there any other laws dealing with concentration of gases in liquids? Can/should we relate these to Henry's law?\n",
    "> \n",
    "\n",
    "Are there any other predictors Jeremy has in mind?\n",
    "> look at the descriptors available in rdkit\n",
    "\n",
    "Could the 'stability' of a species in gaseous state play a role in it's solubility? Perhaps a species which ~repels other molecules of its kind~ would find more stability in gaseous form? \n",
    "XlogP site says: *Also it is a good measure of the solubility in water and organic compounds, although also other factors (e.g. the stability of the crystalline material) play a role.*\n",
    "> \n",
    "\n",
    "Henry's Law doesn't account for chemical equilibria in the aqueous phase - is there a way to account for this for all molecules or would this have to be done case by case? Would this already be accounted for in the given constants?\n",
    "> dont worry about these right now - they should lie out of where expected\n",
    "> for acid base reaction - look at pkas? see if theres a region where relationship falls apart\n",
    "\n",
    "This relationship is temperature dependant - is there a way to factor this into predictions? Would I need Henry's Law constants for the species at varying temperatures?\n",
    "> dont worry about at this stage\n",
    "\n",
    "Some species have datapoints that are clearly outliers (see octadecanoic acid) - is there a reason these constants vary so wildly in some cases?\n",
    ">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\[N+\\]([O-])=O "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'contains_carbon' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-23d27c21c6f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcontains_carbon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'Species'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'SMILES'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'contains_carbon' is not defined"
     ]
    }
   ],
   "source": [
    "contains_carbon.loc[:, ['Species', 'SMILES']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0-15, 530, 531, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contains_carbon.loc[contains_carbon.Species == 'propene']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "henry_df['Species'] = name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_list = []\n",
    "for name in henry_df.Species:\n",
    "    if name[0] == ' ':\n",
    "        string = name\n",
    "        minus_space = string.split(sep = ' ')[1:]\n",
    "        name = str('')\n",
    "        for chunk in minus_space:\n",
    "            name += chunk\n",
    "    name_list.append(name)\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(name[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "henry_df.iloc[351:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Same plot for linear hydrocarbons but using Kh rather than H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T17:24:08.266961Z",
     "start_time": "2019-12-10T17:24:06.585472Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
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       "      <th>SpMin3_Bh(m)</th>\n",
       "      <th>SpMin4_Bh(m)</th>\n",
       "      <th>SpMin5_Bh(m)</th>\n",
       "      <th>SpMin6_Bh(m)</th>\n",
       "      <th>SpMin7_Bh(m)</th>\n",
       "      <th>SpMin8_Bh(m)</th>\n",
       "      <th>SpMin1_Bh(v)</th>\n",
       "      <th>SpMin2_Bh(v)</th>\n",
       "      <th>SpMin3_Bh(v)</th>\n",
       "      <th>SpMin4_Bh(v)</th>\n",
       "      <th>SpMin5_Bh(v)</th>\n",
       "      <th>SpMin6_Bh(v)</th>\n",
       "      <th>SpMin7_Bh(v)</th>\n",
       "      <th>SpMin8_Bh(v)</th>\n",
       "      <th>SpMin1_Bh(e)</th>\n",
       "      <th>SpMin2_Bh(e)</th>\n",
       "      <th>SpMin3_Bh(e)</th>\n",
       "      <th>SpMin4_Bh(e)</th>\n",
       "      <th>SpMin5_Bh(e)</th>\n",
       "      <th>SpMin6_Bh(e)</th>\n",
       "      <th>SpMin7_Bh(e)</th>\n",
       "      <th>SpMin8_Bh(e)</th>\n",
       "      <th>SpMin1_Bh(p)</th>\n",
       "      <th>SpMin2_Bh(p)</th>\n",
       "      <th>SpMin3_Bh(p)</th>\n",
       "      <th>SpMin4_Bh(p)</th>\n",
       "      <th>SpMin5_Bh(p)</th>\n",
       "      <th>SpMin6_Bh(p)</th>\n",
       "      <th>SpMin7_Bh(p)</th>\n",
       "      <th>SpMin8_Bh(p)</th>\n",
       "      <th>SpMin1_Bh(i)</th>\n",
       "      <th>SpMin2_Bh(i)</th>\n",
       "      <th>SpMin3_Bh(i)</th>\n",
       "      <th>SpMin4_Bh(i)</th>\n",
       "      <th>SpMin5_Bh(i)</th>\n",
       "      <th>SpMin6_Bh(i)</th>\n",
       "      <th>SpMin7_Bh(i)</th>\n",
       "      <th>SpMin8_Bh(i)</th>\n",
       "      <th>P_VSA_m_1</th>\n",
       "      <th>P_VSA_m_2</th>\n",
       "      <th>P_VSA_m_3</th>\n",
       "      <th>P_VSA_m_4</th>\n",
       "      <th>P_VSA_m_5</th>\n",
       "      <th>P_VSA_v_1</th>\n",
       "      <th>P_VSA_v_2</th>\n",
       "      <th>P_VSA_v_3</th>\n",
       "      <th>P_VSA_v_4</th>\n",
       "      <th>P_VSA_e_1</th>\n",
       "      <th>P_VSA_e_2</th>\n",
       "      <th>P_VSA_e_3</th>\n",
       "      <th>P_VSA_e_4</th>\n",
       "      <th>P_VSA_e_5</th>\n",
       "      <th>P_VSA_e_6</th>\n",
       "      <th>P_VSA_p_1</th>\n",
       "      <th>P_VSA_p_2</th>\n",
       "      <th>P_VSA_p_3</th>\n",
       "      <th>P_VSA_p_4</th>\n",
       "      <th>P_VSA_i_1</th>\n",
       "      <th>P_VSA_i_2</th>\n",
       "      <th>P_VSA_i_3</th>\n",
       "      <th>P_VSA_i_4</th>\n",
       "      <th>P_VSA_s_5</th>\n",
       "      <th>P_VSA_s_6</th>\n",
       "      <th>nCp</th>\n",
       "      <th>nCs</th>\n",
       "      <th>nCt</th>\n",
       "      <th>nCq</th>\n",
       "      <th>nCrs</th>\n",
       "      <th>nCrt</th>\n",
       "      <th>nCrq</th>\n",
       "      <th>nCar</th>\n",
       "      <th>nCbH</th>\n",
       "      <th>nCb-</th>\n",
       "      <th>nCconj</th>\n",
       "      <th>nR=Cp</th>\n",
       "      <th>nR=Cs</th>\n",
       "      <th>nR=Ct</th>\n",
       "      <th>nR#CH/X</th>\n",
       "      <th>nR#C-</th>\n",
       "      <th>nRNCS</th>\n",
       "      <th>nRCOOH</th>\n",
       "      <th>nArCOOH</th>\n",
       "      <th>nRCOOR</th>\n",
       "      <th>nArCOOR</th>\n",
       "      <th>nRCONH2</th>\n",
       "      <th>nArCONH2</th>\n",
       "      <th>nRCONHR</th>\n",
       "      <th>nArCONHR</th>\n",
       "      <th>nRCONR2</th>\n",
       "      <th>nArCONR2</th>\n",
       "      <th>nROCON</th>\n",
       "      <th>nArOCON</th>\n",
       "      <th>nRCOX</th>\n",
       "      <th>nRCOSR</th>\n",
       "      <th>nArCOSR</th>\n",
       "      <th>nRCHO</th>\n",
       "      <th>nArCHO</th>\n",
       "      <th>nRCO</th>\n",
       "      <th>nArCO</th>\n",
       "      <th>nCONN</th>\n",
       "      <th>nC=O(O)2</th>\n",
       "      <th>nN=C-N&lt;</th>\n",
       "      <th>nC(=N)N2</th>\n",
       "      <th>nRC=N</th>\n",
       "      <th>nArC=N</th>\n",
       "      <th>nRCNO</th>\n",
       "      <th>nArCNO</th>\n",
       "      <th>nRNH2</th>\n",
       "      <th>nArNH2</th>\n",
       "      <th>nRNHR</th>\n",
       "      <th>nArNHR</th>\n",
       "      <th>nRNR2</th>\n",
       "      <th>nArNR2</th>\n",
       "      <th>nN-N</th>\n",
       "      <th>nN=N</th>\n",
       "      <th>nRCN</th>\n",
       "      <th>nArCN</th>\n",
       "      <th>nN+</th>\n",
       "      <th>nRNHO</th>\n",
       "      <th>nArNHO</th>\n",
       "      <th>nRNNOx</th>\n",
       "      <th>nArNNOx</th>\n",
       "      <th>nArNO</th>\n",
       "      <th>nRNO2</th>\n",
       "      <th>nArNO2</th>\n",
       "      <th>nN(CO)2</th>\n",
       "      <th>nC=N-N&lt;</th>\n",
       "      <th>nROH</th>\n",
       "      <th>nArOH</th>\n",
       "      <th>nOHp</th>\n",
       "      <th>nOHs</th>\n",
       "      <th>nOHt</th>\n",
       "      <th>nROR</th>\n",
       "      <th>nArOR</th>\n",
       "      <th>nO(C=O)2</th>\n",
       "      <th>nSH</th>\n",
       "      <th>nRSR</th>\n",
       "      <th>nRSSR</th>\n",
       "      <th>nSO</th>\n",
       "      <th>nS(=O)2</th>\n",
       "      <th>nSO2</th>\n",
       "      <th>nSO3</th>\n",
       "      <th>nSO4</th>\n",
       "      <th>nSO2N</th>\n",
       "      <th>nCH2RX</th>\n",
       "      <th>nCHR2X</th>\n",
       "      <th>nCR3X</th>\n",
       "      <th>nR=CHX</th>\n",
       "      <th>nR=CRX</th>\n",
       "      <th>nR#CX</th>\n",
       "      <th>nCHRX2</th>\n",
       "      <th>nCR2X2</th>\n",
       "      <th>nR=CX2</th>\n",
       "      <th>nCRX3</th>\n",
       "      <th>nArX</th>\n",
       "      <th>nCXr</th>\n",
       "      <th>nCXr=</th>\n",
       "      <th>nCconjX</th>\n",
       "      <th>nAziridines</th>\n",
       "      <th>nOxiranes</th>\n",
       "      <th>nAzetidines</th>\n",
       "      <th>nOxetanes</th>\n",
       "      <th>nPyrrolidines</th>\n",
       "      <th>nOxolanes</th>\n",
       "      <th>nPyrroles</th>\n",
       "      <th>nPyrazoles</th>\n",
       "      <th>nImidazoles</th>\n",
       "      <th>nFuranes</th>\n",
       "      <th>nThiophenes</th>\n",
       "      <th>nIsoxazoles</th>\n",
       "      <th>nThiazoles</th>\n",
       "      <th>nTriazoles</th>\n",
       "      <th>nPyridines</th>\n",
       "      <th>nPyrimidines</th>\n",
       "      <th>nPyrazines</th>\n",
       "      <th>n135-Triazines</th>\n",
       "      <th>nHDon</th>\n",
       "      <th>nHAcc</th>\n",
       "      <th>C-001</th>\n",
       "      <th>C-002</th>\n",
       "      <th>C-003</th>\n",
       "      <th>C-004</th>\n",
       "      <th>C-005</th>\n",
       "      <th>C-006</th>\n",
       "      <th>C-007</th>\n",
       "      <th>C-008</th>\n",
       "      <th>C-009</th>\n",
       "      <th>C-010</th>\n",
       "      <th>C-011</th>\n",
       "      <th>C-012</th>\n",
       "      <th>C-013</th>\n",
       "      <th>C-014</th>\n",
       "      <th>C-015</th>\n",
       "      <th>C-016</th>\n",
       "      <th>C-017</th>\n",
       "      <th>C-018</th>\n",
       "      <th>C-019</th>\n",
       "      <th>C-020</th>\n",
       "      <th>C-021</th>\n",
       "      <th>C-022</th>\n",
       "      <th>C-023</th>\n",
       "      <th>C-024</th>\n",
       "      <th>C-025</th>\n",
       "      <th>C-026</th>\n",
       "      <th>C-027</th>\n",
       "      <th>C-028</th>\n",
       "      <th>C-029</th>\n",
       "      <th>C-030</th>\n",
       "      <th>C-031</th>\n",
       "      <th>C-032</th>\n",
       "      <th>C-033</th>\n",
       "      <th>C-034</th>\n",
       "      <th>C-035</th>\n",
       "      <th>C-036</th>\n",
       "      <th>C-037</th>\n",
       "      <th>C-038</th>\n",
       "      <th>C-039</th>\n",
       "      <th>C-040</th>\n",
       "      <th>C-041</th>\n",
       "      <th>C-042</th>\n",
       "      <th>C-043</th>\n",
       "      <th>C-044</th>\n",
       "      <th>H-046</th>\n",
       "      <th>H-047</th>\n",
       "      <th>H-048</th>\n",
       "      <th>H-049</th>\n",
       "      <th>H-050</th>\n",
       "      <th>H-051</th>\n",
       "      <th>H-052</th>\n",
       "      <th>H-053</th>\n",
       "      <th>H-054</th>\n",
       "      <th>H-055</th>\n",
       "      <th>O-056</th>\n",
       "      <th>O-057</th>\n",
       "      <th>O-058</th>\n",
       "      <th>O-059</th>\n",
       "      <th>O-060</th>\n",
       "      <th>O-061</th>\n",
       "      <th>O-062</th>\n",
       "      <th>O-063</th>\n",
       "      <th>N-066</th>\n",
       "      <th>N-067</th>\n",
       "      <th>N-068</th>\n",
       "      <th>N-069</th>\n",
       "      <th>N-070</th>\n",
       "      <th>N-071</th>\n",
       "      <th>N-072</th>\n",
       "      <th>N-073</th>\n",
       "      <th>N-074</th>\n",
       "      <th>N-075</th>\n",
       "      <th>N-076</th>\n",
       "      <th>N-077</th>\n",
       "      <th>N-078</th>\n",
       "      <th>N-079</th>\n",
       "      <th>F-081</th>\n",
       "      <th>F-082</th>\n",
       "      <th>F-083</th>\n",
       "      <th>F-084</th>\n",
       "      <th>F-085</th>\n",
       "      <th>Cl-086</th>\n",
       "      <th>Cl-087</th>\n",
       "      <th>Cl-088</th>\n",
       "      <th>Cl-089</th>\n",
       "      <th>Cl-090</th>\n",
       "      <th>Br-091</th>\n",
       "      <th>Br-092</th>\n",
       "      <th>Br-093</th>\n",
       "      <th>Br-094</th>\n",
       "      <th>Br-095</th>\n",
       "      <th>I-096</th>\n",
       "      <th>I-097</th>\n",
       "      <th>I-098</th>\n",
       "      <th>I-099</th>\n",
       "      <th>I-100</th>\n",
       "      <th>S-106</th>\n",
       "      <th>S-107</th>\n",
       "      <th>S-108</th>\n",
       "      <th>S-109</th>\n",
       "      <th>S-110</th>\n",
       "      <th>SsCH3</th>\n",
       "      <th>SdCH2</th>\n",
       "      <th>SssCH2</th>\n",
       "      <th>StCH</th>\n",
       "      <th>SdsCH</th>\n",
       "      <th>SaaCH</th>\n",
       "      <th>SsssCH</th>\n",
       "      <th>SddC</th>\n",
       "      <th>StsC</th>\n",
       "      <th>SdssC</th>\n",
       "      <th>SaasC</th>\n",
       "      <th>SaaaC</th>\n",
       "      <th>SssssC</th>\n",
       "      <th>SsNH2</th>\n",
       "      <th>SssNH</th>\n",
       "      <th>SdNH</th>\n",
       "      <th>SsssN</th>\n",
       "      <th>SdsN</th>\n",
       "      <th>SaaN</th>\n",
       "      <th>StN</th>\n",
       "      <th>SddsN</th>\n",
       "      <th>SaasN</th>\n",
       "      <th>SaaNH</th>\n",
       "      <th>SsOH</th>\n",
       "      <th>SdO</th>\n",
       "      <th>SssO</th>\n",
       "      <th>SaaO</th>\n",
       "      <th>SsSH</th>\n",
       "      <th>SdS</th>\n",
       "      <th>SssS</th>\n",
       "      <th>SaaS</th>\n",
       "      <th>SdssS</th>\n",
       "      <th>SddssS</th>\n",
       "      <th>SsF</th>\n",
       "      <th>SsCl</th>\n",
       "      <th>SsBr</th>\n",
       "      <th>SsI</th>\n",
       "      <th>NsCH3</th>\n",
       "      <th>NdCH2</th>\n",
       "      <th>NssCH2</th>\n",
       "      <th>NtCH</th>\n",
       "      <th>NdsCH</th>\n",
       "      <th>NaaCH</th>\n",
       "      <th>NsssCH</th>\n",
       "      <th>NddC</th>\n",
       "      <th>NtsC</th>\n",
       "      <th>NdssC</th>\n",
       "      <th>NaasC</th>\n",
       "      <th>NaaaC</th>\n",
       "      <th>NssssC</th>\n",
       "      <th>NsNH2</th>\n",
       "      <th>NssNH</th>\n",
       "      <th>NdNH</th>\n",
       "      <th>NsssN</th>\n",
       "      <th>NdsN</th>\n",
       "      <th>NaaN</th>\n",
       "      <th>NtN</th>\n",
       "      <th>NddsN</th>\n",
       "      <th>NaasN</th>\n",
       "      <th>NaaNH</th>\n",
       "      <th>NsOH</th>\n",
       "      <th>NdO</th>\n",
       "      <th>NssO</th>\n",
       "      <th>NaaO</th>\n",
       "      <th>NsSH</th>\n",
       "      <th>NdS</th>\n",
       "      <th>NssS</th>\n",
       "      <th>NaaS</th>\n",
       "      <th>NdssS</th>\n",
       "      <th>NddssS</th>\n",
       "      <th>NsF</th>\n",
       "      <th>NsCl</th>\n",
       "      <th>NsBr</th>\n",
       "      <th>NsI</th>\n",
       "      <th>CATS2D_00_DD</th>\n",
       "      <th>CATS2D_01_DD</th>\n",
       "      <th>CATS2D_02_DD</th>\n",
       "      <th>CATS2D_03_DD</th>\n",
       "      <th>CATS2D_04_DD</th>\n",
       "      <th>CATS2D_05_DD</th>\n",
       "      <th>CATS2D_06_DD</th>\n",
       "      <th>CATS2D_07_DD</th>\n",
       "      <th>CATS2D_08_DD</th>\n",
       "      <th>CATS2D_09_DD</th>\n",
       "      <th>CATS2D_01_DA</th>\n",
       "      <th>CATS2D_02_DA</th>\n",
       "      <th>CATS2D_03_DA</th>\n",
       "      <th>CATS2D_04_DA</th>\n",
       "      <th>CATS2D_05_DA</th>\n",
       "      <th>CATS2D_06_DA</th>\n",
       "      <th>CATS2D_07_DA</th>\n",
       "      <th>CATS2D_08_DA</th>\n",
       "      <th>CATS2D_09_DA</th>\n",
       "      <th>CATS2D_00_DP</th>\n",
       "      <th>CATS2D_01_DP</th>\n",
       "      <th>CATS2D_02_DP</th>\n",
       "      <th>CATS2D_03_DP</th>\n",
       "      <th>CATS2D_04_DP</th>\n",
       "      <th>CATS2D_05_DP</th>\n",
       "      <th>CATS2D_06_DP</th>\n",
       "      <th>CATS2D_07_DP</th>\n",
       "      <th>CATS2D_09_DP</th>\n",
       "      <th>CATS2D_01_DN</th>\n",
       "      <th>CATS2D_02_DN</th>\n",
       "      <th>CATS2D_03_DN</th>\n",
       "      <th>CATS2D_04_DN</th>\n",
       "      <th>CATS2D_05_DN</th>\n",
       "      <th>CATS2D_06_DN</th>\n",
       "      <th>CATS2D_07_DN</th>\n",
       "      <th>CATS2D_08_DN</th>\n",
       "      <th>CATS2D_09_DN</th>\n",
       "      <th>CATS2D_02_DL</th>\n",
       "      <th>CATS2D_03_DL</th>\n",
       "      <th>CATS2D_04_DL</th>\n",
       "      <th>CATS2D_05_DL</th>\n",
       "      <th>CATS2D_06_DL</th>\n",
       "      <th>CATS2D_07_DL</th>\n",
       "      <th>CATS2D_08_DL</th>\n",
       "      <th>CATS2D_09_DL</th>\n",
       "      <th>CATS2D_01_AA</th>\n",
       "      <th>CATS2D_02_AA</th>\n",
       "      <th>CATS2D_03_AA</th>\n",
       "      <th>CATS2D_04_AA</th>\n",
       "      <th>CATS2D_05_AA</th>\n",
       "      <th>CATS2D_06_AA</th>\n",
       "      <th>CATS2D_07_AA</th>\n",
       "      <th>CATS2D_08_AA</th>\n",
       "      <th>CATS2D_09_AA</th>\n",
       "      <th>CATS2D_01_AP</th>\n",
       "      <th>CATS2D_02_AP</th>\n",
       "      <th>CATS2D_03_AP</th>\n",
       "      <th>CATS2D_04_AP</th>\n",
       "      <th>CATS2D_05_AP</th>\n",
       "      <th>CATS2D_06_AP</th>\n",
       "      <th>CATS2D_07_AP</th>\n",
       "      <th>CATS2D_01_AN</th>\n",
       "      <th>CATS2D_02_AN</th>\n",
       "      <th>CATS2D_03_AN</th>\n",
       "      <th>CATS2D_04_AN</th>\n",
       "      <th>CATS2D_05_AN</th>\n",
       "      <th>CATS2D_06_AN</th>\n",
       "      <th>CATS2D_07_AN</th>\n",
       "      <th>CATS2D_08_AN</th>\n",
       "      <th>CATS2D_09_AN</th>\n",
       "      <th>CATS2D_01_AL</th>\n",
       "      <th>CATS2D_02_AL</th>\n",
       "      <th>CATS2D_03_AL</th>\n",
       "      <th>CATS2D_04_AL</th>\n",
       "      <th>CATS2D_05_AL</th>\n",
       "      <th>CATS2D_06_AL</th>\n",
       "      <th>CATS2D_07_AL</th>\n",
       "      <th>CATS2D_08_AL</th>\n",
       "      <th>CATS2D_09_AL</th>\n",
       "      <th>CATS2D_00_PP</th>\n",
       "      <th>CATS2D_02_PP</th>\n",
       "      <th>CATS2D_03_PP</th>\n",
       "      <th>CATS2D_04_PP</th>\n",
       "      <th>CATS2D_05_PP</th>\n",
       "      <th>CATS2D_06_PP</th>\n",
       "      <th>CATS2D_07_PP</th>\n",
       "      <th>CATS2D_09_PP</th>\n",
       "      <th>CATS2D_01_PN</th>\n",
       "      <th>CATS2D_02_PN</th>\n",
       "      <th>CATS2D_04_PN</th>\n",
       "      <th>CATS2D_05_PN</th>\n",
       "      <th>CATS2D_06_PN</th>\n",
       "      <th>CATS2D_02_PL</th>\n",
       "      <th>CATS2D_03_PL</th>\n",
       "      <th>CATS2D_04_PL</th>\n",
       "      <th>CATS2D_05_PL</th>\n",
       "      <th>CATS2D_06_PL</th>\n",
       "      <th>CATS2D_07_PL</th>\n",
       "      <th>CATS2D_08_PL</th>\n",
       "      <th>CATS2D_09_PL</th>\n",
       "      <th>CATS2D_01_NN</th>\n",
       "      <th>CATS2D_02_NN</th>\n",
       "      <th>CATS2D_03_NN</th>\n",
       "      <th>CATS2D_04_NN</th>\n",
       "      <th>CATS2D_05_NN</th>\n",
       "      <th>CATS2D_06_NN</th>\n",
       "      <th>CATS2D_07_NN</th>\n",
       "      <th>CATS2D_08_NN</th>\n",
       "      <th>CATS2D_09_NN</th>\n",
       "      <th>CATS2D_01_NL</th>\n",
       "      <th>CATS2D_02_NL</th>\n",
       "      <th>CATS2D_03_NL</th>\n",
       "      <th>CATS2D_04_NL</th>\n",
       "      <th>CATS2D_05_NL</th>\n",
       "      <th>CATS2D_06_NL</th>\n",
       "      <th>CATS2D_07_NL</th>\n",
       "      <th>CATS2D_08_NL</th>\n",
       "      <th>CATS2D_09_NL</th>\n",
       "      <th>CATS2D_01_LL</th>\n",
       "      <th>CATS2D_02_LL</th>\n",
       "      <th>CATS2D_03_LL</th>\n",
       "      <th>CATS2D_04_LL</th>\n",
       "      <th>CATS2D_05_LL</th>\n",
       "      <th>CATS2D_06_LL</th>\n",
       "      <th>CATS2D_07_LL</th>\n",
       "      <th>CATS2D_08_LL</th>\n",
       "      <th>CATS2D_09_LL</th>\n",
       "      <th>T(N..N)</th>\n",
       "      <th>T(N..O)</th>\n",
       "      <th>T(N..S)</th>\n",
       "      <th>T(N..F)</th>\n",
       "      <th>T(N..Cl)</th>\n",
       "      <th>T(N..Br)</th>\n",
       "      <th>T(N..I)</th>\n",
       "      <th>T(O..O)</th>\n",
       "      <th>T(O..S)</th>\n",
       "      <th>T(O..F)</th>\n",
       "      <th>T(O..Cl)</th>\n",
       "      <th>T(O..Br)</th>\n",
       "      <th>T(O..I)</th>\n",
       "      <th>T(S..S)</th>\n",
       "      <th>T(S..F)</th>\n",
       "      <th>T(S..Cl)</th>\n",
       "      <th>T(F..F)</th>\n",
       "      <th>T(F..Cl)</th>\n",
       "      <th>T(F..Br)</th>\n",
       "      <th>T(Cl..Cl)</th>\n",
       "      <th>T(Cl..Br)</th>\n",
       "      <th>T(Cl..I)</th>\n",
       "      <th>T(Br..Br)</th>\n",
       "      <th>T(I..I)</th>\n",
       "      <th>B01[C-C]</th>\n",
       "      <th>B01[C-N]</th>\n",
       "      <th>B01[C-O]</th>\n",
       "      <th>B01[C-S]</th>\n",
       "      <th>B01[C-F]</th>\n",
       "      <th>B01[C-Cl]</th>\n",
       "      <th>B01[C-Br]</th>\n",
       "      <th>B01[C-I]</th>\n",
       "      <th>B01[N-N]</th>\n",
       "      <th>B01[N-O]</th>\n",
       "      <th>B01[N-S]</th>\n",
       "      <th>B01[N-Cl]</th>\n",
       "      <th>B01[O-O]</th>\n",
       "      <th>B01[O-S]</th>\n",
       "      <th>B01[S-S]</th>\n",
       "      <th>B02[C-C]</th>\n",
       "      <th>B02[C-N]</th>\n",
       "      <th>B02[C-O]</th>\n",
       "      <th>B02[C-S]</th>\n",
       "      <th>B02[C-F]</th>\n",
       "      <th>B02[C-Cl]</th>\n",
       "      <th>B02[C-Br]</th>\n",
       "      <th>B02[C-I]</th>\n",
       "      <th>B02[N-N]</th>\n",
       "      <th>B02[N-O]</th>\n",
       "      <th>B02[N-S]</th>\n",
       "      <th>B02[N-F]</th>\n",
       "      <th>B02[N-Cl]</th>\n",
       "      <th>B02[N-Br]</th>\n",
       "      <th>B02[O-O]</th>\n",
       "      <th>B02[O-S]</th>\n",
       "      <th>B02[O-F]</th>\n",
       "      <th>B02[O-Cl]</th>\n",
       "      <th>B02[S-S]</th>\n",
       "      <th>B02[S-F]</th>\n",
       "      <th>B02[S-Cl]</th>\n",
       "      <th>B02[F-F]</th>\n",
       "      <th>B02[F-Cl]</th>\n",
       "      <th>B02[F-Br]</th>\n",
       "      <th>B02[Cl-Cl]</th>\n",
       "      <th>B02[Cl-Br]</th>\n",
       "      <th>B02[Cl-I]</th>\n",
       "      <th>B02[Br-Br]</th>\n",
       "      <th>B02[I-I]</th>\n",
       "      <th>B03[C-C]</th>\n",
       "      <th>B03[C-N]</th>\n",
       "      <th>B03[C-O]</th>\n",
       "      <th>B03[C-S]</th>\n",
       "      <th>B03[C-F]</th>\n",
       "      <th>B03[C-Cl]</th>\n",
       "      <th>B03[C-Br]</th>\n",
       "      <th>B03[C-I]</th>\n",
       "      <th>B03[N-N]</th>\n",
       "      <th>B03[N-O]</th>\n",
       "      <th>B03[N-S]</th>\n",
       "      <th>B03[N-F]</th>\n",
       "      <th>B03[N-Cl]</th>\n",
       "      <th>B03[N-Br]</th>\n",
       "      <th>B03[O-O]</th>\n",
       "      <th>B03[O-S]</th>\n",
       "      <th>B03[O-F]</th>\n",
       "      <th>B03[O-Cl]</th>\n",
       "      <th>B03[O-Br]</th>\n",
       "      <th>B03[O-I]</th>\n",
       "      <th>B03[S-S]</th>\n",
       "      <th>B03[S-F]</th>\n",
       "      <th>B03[S-Cl]</th>\n",
       "      <th>B03[F-F]</th>\n",
       "      <th>B03[F-Cl]</th>\n",
       "      <th>B03[F-Br]</th>\n",
       "      <th>B03[Cl-Cl]</th>\n",
       "      <th>B03[Cl-Br]</th>\n",
       "      <th>B03[Br-Br]</th>\n",
       "      <th>B04[C-C]</th>\n",
       "      <th>B04[C-N]</th>\n",
       "      <th>B04[C-O]</th>\n",
       "      <th>B04[C-S]</th>\n",
       "      <th>B04[C-F]</th>\n",
       "      <th>B04[C-Cl]</th>\n",
       "      <th>B04[C-Br]</th>\n",
       "      <th>B04[C-I]</th>\n",
       "      <th>B04[N-N]</th>\n",
       "      <th>B04[N-O]</th>\n",
       "      <th>B04[N-S]</th>\n",
       "      <th>B04[N-F]</th>\n",
       "      <th>B04[N-Cl]</th>\n",
       "      <th>B04[N-Br]</th>\n",
       "      <th>B04[N-I]</th>\n",
       "      <th>B04[O-O]</th>\n",
       "      <th>B04[O-S]</th>\n",
       "      <th>B04[O-F]</th>\n",
       "      <th>B04[O-Cl]</th>\n",
       "      <th>B04[O-Br]</th>\n",
       "      <th>B04[O-I]</th>\n",
       "      <th>B04[S-S]</th>\n",
       "      <th>B04[S-F]</th>\n",
       "      <th>B04[S-Cl]</th>\n",
       "      <th>B04[F-F]</th>\n",
       "      <th>B04[F-Cl]</th>\n",
       "      <th>B04[F-Br]</th>\n",
       "      <th>B04[Cl-Cl]</th>\n",
       "      <th>B04[Cl-Br]</th>\n",
       "      <th>B04[Br-Br]</th>\n",
       "      <th>B04[I-I]</th>\n",
       "      <th>B05[C-C]</th>\n",
       "      <th>B05[C-N]</th>\n",
       "      <th>B05[C-O]</th>\n",
       "      <th>B05[C-S]</th>\n",
       "      <th>B05[C-F]</th>\n",
       "      <th>B05[C-Cl]</th>\n",
       "      <th>B05[C-Br]</th>\n",
       "      <th>B05[C-I]</th>\n",
       "      <th>B05[N-N]</th>\n",
       "      <th>B05[N-O]</th>\n",
       "      <th>B05[N-S]</th>\n",
       "      <th>B05[N-F]</th>\n",
       "      <th>B05[N-Cl]</th>\n",
       "      <th>B05[N-Br]</th>\n",
       "      <th>B05[N-I]</th>\n",
       "      <th>B05[O-O]</th>\n",
       "      <th>B05[O-S]</th>\n",
       "      <th>B05[O-F]</th>\n",
       "      <th>B05[O-Cl]</th>\n",
       "      <th>B05[O-Br]</th>\n",
       "      <th>B05[S-S]</th>\n",
       "      <th>B05[S-F]</th>\n",
       "      <th>B05[S-Cl]</th>\n",
       "      <th>B05[F-F]</th>\n",
       "      <th>B05[F-Cl]</th>\n",
       "      <th>B05[F-Br]</th>\n",
       "      <th>B05[Cl-Cl]</th>\n",
       "      <th>B05[Cl-Br]</th>\n",
       "      <th>B05[Br-Br]</th>\n",
       "      <th>B06[C-C]</th>\n",
       "      <th>B06[C-N]</th>\n",
       "      <th>B06[C-O]</th>\n",
       "      <th>B06[C-S]</th>\n",
       "      <th>B06[C-F]</th>\n",
       "      <th>B06[C-Cl]</th>\n",
       "      <th>B06[C-Br]</th>\n",
       "      <th>B06[C-I]</th>\n",
       "      <th>B06[N-N]</th>\n",
       "      <th>B06[N-O]</th>\n",
       "      <th>B06[N-S]</th>\n",
       "      <th>B06[N-F]</th>\n",
       "      <th>B06[N-Cl]</th>\n",
       "      <th>B06[N-Br]</th>\n",
       "      <th>B06[N-I]</th>\n",
       "      <th>B06[O-O]</th>\n",
       "      <th>B06[O-S]</th>\n",
       "      <th>B06[O-F]</th>\n",
       "      <th>B06[O-Cl]</th>\n",
       "      <th>B06[O-Br]</th>\n",
       "      <th>B06[O-I]</th>\n",
       "      <th>B06[S-S]</th>\n",
       "      <th>B06[S-F]</th>\n",
       "      <th>B06[S-Cl]</th>\n",
       "      <th>B06[F-F]</th>\n",
       "      <th>B06[F-Cl]</th>\n",
       "      <th>B06[Cl-Cl]</th>\n",
       "      <th>B06[Cl-Br]</th>\n",
       "      <th>B06[Br-Br]</th>\n",
       "      <th>B07[C-C]</th>\n",
       "      <th>B07[C-N]</th>\n",
       "      <th>B07[C-O]</th>\n",
       "      <th>B07[C-S]</th>\n",
       "      <th>B07[C-F]</th>\n",
       "      <th>B07[C-Cl]</th>\n",
       "      <th>B07[C-Br]</th>\n",
       "      <th>B07[C-I]</th>\n",
       "      <th>B07[N-N]</th>\n",
       "      <th>B07[N-O]</th>\n",
       "      <th>B07[N-S]</th>\n",
       "      <th>B07[N-F]</th>\n",
       "      <th>B07[N-Cl]</th>\n",
       "      <th>B07[N-Br]</th>\n",
       "      <th>B07[O-O]</th>\n",
       "      <th>B07[O-S]</th>\n",
       "      <th>B07[O-F]</th>\n",
       "      <th>B07[O-Cl]</th>\n",
       "      <th>B07[O-Br]</th>\n",
       "      <th>B07[S-S]</th>\n",
       "      <th>B07[S-F]</th>\n",
       "      <th>B07[S-Cl]</th>\n",
       "      <th>B07[F-F]</th>\n",
       "      <th>B07[F-Cl]</th>\n",
       "      <th>B07[Cl-Cl]</th>\n",
       "      <th>B07[Cl-Br]</th>\n",
       "      <th>B07[Br-Br]</th>\n",
       "      <th>B08[C-C]</th>\n",
       "      <th>B08[C-N]</th>\n",
       "      <th>B08[C-O]</th>\n",
       "      <th>B08[C-S]</th>\n",
       "      <th>B08[C-F]</th>\n",
       "      <th>B08[C-Cl]</th>\n",
       "      <th>B08[C-Br]</th>\n",
       "      <th>B08[C-I]</th>\n",
       "      <th>B08[N-N]</th>\n",
       "      <th>B08[N-O]</th>\n",
       "      <th>B08[N-S]</th>\n",
       "      <th>B08[N-F]</th>\n",
       "      <th>B08[N-Cl]</th>\n",
       "      <th>B08[N-Br]</th>\n",
       "      <th>B08[O-O]</th>\n",
       "      <th>B08[O-S]</th>\n",
       "      <th>B08[O-F]</th>\n",
       "      <th>B08[O-Cl]</th>\n",
       "      <th>B08[O-Br]</th>\n",
       "      <th>B08[S-Cl]</th>\n",
       "      <th>B08[F-F]</th>\n",
       "      <th>B08[F-Cl]</th>\n",
       "      <th>B08[Cl-Cl]</th>\n",
       "      <th>B08[Cl-Br]</th>\n",
       "      <th>B08[Br-Br]</th>\n",
       "      <th>B09[C-C]</th>\n",
       "      <th>B09[C-N]</th>\n",
       "      <th>B09[C-O]</th>\n",
       "      <th>B09[C-S]</th>\n",
       "      <th>B09[C-F]</th>\n",
       "      <th>B09[C-Cl]</th>\n",
       "      <th>B09[C-Br]</th>\n",
       "      <th>B09[C-I]</th>\n",
       "      <th>B09[N-N]</th>\n",
       "      <th>B09[N-O]</th>\n",
       "      <th>B09[N-S]</th>\n",
       "      <th>B09[N-F]</th>\n",
       "      <th>B09[N-Cl]</th>\n",
       "      <th>B09[N-Br]</th>\n",
       "      <th>B09[O-O]</th>\n",
       "      <th>B09[O-S]</th>\n",
       "      <th>B09[O-F]</th>\n",
       "      <th>B09[O-Cl]</th>\n",
       "      <th>B09[O-Br]</th>\n",
       "      <th>B09[F-F]</th>\n",
       "      <th>B09[F-Cl]</th>\n",
       "      <th>B09[Cl-Cl]</th>\n",
       "      <th>B09[Br-Br]</th>\n",
       "      <th>B10[C-C]</th>\n",
       "      <th>B10[C-N]</th>\n",
       "      <th>B10[C-O]</th>\n",
       "      <th>B10[C-S]</th>\n",
       "      <th>B10[C-F]</th>\n",
       "      <th>B10[C-Cl]</th>\n",
       "      <th>B10[C-Br]</th>\n",
       "      <th>B10[C-I]</th>\n",
       "      <th>B10[N-N]</th>\n",
       "      <th>B10[N-O]</th>\n",
       "      <th>B10[N-S]</th>\n",
       "      <th>B10[N-F]</th>\n",
       "      <th>B10[N-Cl]</th>\n",
       "      <th>B10[O-O]</th>\n",
       "      <th>B10[O-S]</th>\n",
       "      <th>B10[O-F]</th>\n",
       "      <th>B10[O-Cl]</th>\n",
       "      <th>B10[O-Br]</th>\n",
       "      <th>B10[S-F]</th>\n",
       "      <th>B10[F-F]</th>\n",
       "      <th>B10[F-Cl]</th>\n",
       "      <th>B10[Cl-Cl]</th>\n",
       "      <th>B10[Br-Br]</th>\n",
       "      <th>F01[C-C]</th>\n",
       "      <th>F01[C-N]</th>\n",
       "      <th>F01[C-O]</th>\n",
       "      <th>F01[C-S]</th>\n",
       "      <th>F01[C-F]</th>\n",
       "      <th>F01[C-Cl]</th>\n",
       "      <th>F01[C-Br]</th>\n",
       "      <th>F01[C-I]</th>\n",
       "      <th>F01[N-N]</th>\n",
       "      <th>F01[N-O]</th>\n",
       "      <th>F01[N-S]</th>\n",
       "      <th>F01[N-Cl]</th>\n",
       "      <th>F01[O-O]</th>\n",
       "      <th>F01[O-S]</th>\n",
       "      <th>F01[S-S]</th>\n",
       "      <th>F02[C-C]</th>\n",
       "      <th>F02[C-N]</th>\n",
       "      <th>F02[C-O]</th>\n",
       "      <th>F02[C-S]</th>\n",
       "      <th>F02[C-F]</th>\n",
       "      <th>F02[C-Cl]</th>\n",
       "      <th>F02[C-Br]</th>\n",
       "      <th>F02[C-I]</th>\n",
       "      <th>F02[N-N]</th>\n",
       "      <th>F02[N-O]</th>\n",
       "      <th>F02[N-S]</th>\n",
       "      <th>F02[N-F]</th>\n",
       "      <th>F02[N-Cl]</th>\n",
       "      <th>F02[N-Br]</th>\n",
       "      <th>F02[O-O]</th>\n",
       "      <th>F02[O-S]</th>\n",
       "      <th>F02[O-F]</th>\n",
       "      <th>F02[O-Cl]</th>\n",
       "      <th>F02[S-S]</th>\n",
       "      <th>F02[S-F]</th>\n",
       "      <th>F02[S-Cl]</th>\n",
       "      <th>F02[F-F]</th>\n",
       "      <th>F02[F-Cl]</th>\n",
       "      <th>F02[F-Br]</th>\n",
       "      <th>F02[Cl-Cl]</th>\n",
       "      <th>F02[Cl-Br]</th>\n",
       "      <th>F02[Cl-I]</th>\n",
       "      <th>F02[Br-Br]</th>\n",
       "      <th>F02[I-I]</th>\n",
       "      <th>F03[C-C]</th>\n",
       "      <th>F03[C-N]</th>\n",
       "      <th>F03[C-O]</th>\n",
       "      <th>F03[C-S]</th>\n",
       "      <th>F03[C-F]</th>\n",
       "      <th>F03[C-Cl]</th>\n",
       "      <th>F03[C-Br]</th>\n",
       "      <th>F03[C-I]</th>\n",
       "      <th>F03[N-N]</th>\n",
       "      <th>F03[N-O]</th>\n",
       "      <th>F03[N-S]</th>\n",
       "      <th>F03[N-F]</th>\n",
       "      <th>F03[N-Cl]</th>\n",
       "      <th>F03[N-Br]</th>\n",
       "      <th>F03[O-O]</th>\n",
       "      <th>F03[O-S]</th>\n",
       "      <th>F03[O-F]</th>\n",
       "      <th>F03[O-Cl]</th>\n",
       "      <th>F03[O-Br]</th>\n",
       "      <th>F03[O-I]</th>\n",
       "      <th>F03[S-S]</th>\n",
       "      <th>F03[S-F]</th>\n",
       "      <th>F03[S-Cl]</th>\n",
       "      <th>F03[F-F]</th>\n",
       "      <th>F03[F-Cl]</th>\n",
       "      <th>F03[F-Br]</th>\n",
       "      <th>F03[Cl-Cl]</th>\n",
       "      <th>F03[Cl-Br]</th>\n",
       "      <th>F03[Br-Br]</th>\n",
       "      <th>F04[C-C]</th>\n",
       "      <th>F04[C-N]</th>\n",
       "      <th>F04[C-O]</th>\n",
       "      <th>F04[C-S]</th>\n",
       "      <th>F04[C-F]</th>\n",
       "      <th>F04[C-Cl]</th>\n",
       "      <th>F04[C-Br]</th>\n",
       "      <th>F04[C-I]</th>\n",
       "      <th>F04[N-N]</th>\n",
       "      <th>F04[N-O]</th>\n",
       "      <th>F04[N-S]</th>\n",
       "      <th>F04[N-F]</th>\n",
       "      <th>F04[N-Cl]</th>\n",
       "      <th>F04[N-Br]</th>\n",
       "      <th>F04[N-I]</th>\n",
       "      <th>F04[O-O]</th>\n",
       "      <th>F04[O-S]</th>\n",
       "      <th>F04[O-F]</th>\n",
       "      <th>F04[O-Cl]</th>\n",
       "      <th>F04[O-Br]</th>\n",
       "      <th>F04[O-I]</th>\n",
       "      <th>F04[S-S]</th>\n",
       "      <th>F04[S-F]</th>\n",
       "      <th>F04[S-Cl]</th>\n",
       "      <th>F04[F-F]</th>\n",
       "      <th>F04[F-Cl]</th>\n",
       "      <th>F04[F-Br]</th>\n",
       "      <th>F04[Cl-Cl]</th>\n",
       "      <th>F04[Cl-Br]</th>\n",
       "      <th>F04[Br-Br]</th>\n",
       "      <th>F04[I-I]</th>\n",
       "      <th>F05[C-C]</th>\n",
       "      <th>F05[C-N]</th>\n",
       "      <th>F05[C-O]</th>\n",
       "      <th>F05[C-S]</th>\n",
       "      <th>F05[C-F]</th>\n",
       "      <th>F05[C-Cl]</th>\n",
       "      <th>F05[C-Br]</th>\n",
       "      <th>F05[C-I]</th>\n",
       "      <th>F05[N-N]</th>\n",
       "      <th>F05[N-O]</th>\n",
       "      <th>F05[N-S]</th>\n",
       "      <th>F05[N-F]</th>\n",
       "      <th>F05[N-Cl]</th>\n",
       "      <th>F05[N-Br]</th>\n",
       "      <th>F05[N-I]</th>\n",
       "      <th>F05[O-O]</th>\n",
       "      <th>F05[O-S]</th>\n",
       "      <th>F05[O-F]</th>\n",
       "      <th>F05[O-Cl]</th>\n",
       "      <th>F05[O-Br]</th>\n",
       "      <th>F05[S-S]</th>\n",
       "      <th>F05[S-F]</th>\n",
       "      <th>F05[S-Cl]</th>\n",
       "      <th>F05[F-F]</th>\n",
       "      <th>F05[F-Cl]</th>\n",
       "      <th>F05[F-Br]</th>\n",
       "      <th>F05[Cl-Cl]</th>\n",
       "      <th>F05[Cl-Br]</th>\n",
       "      <th>F05[Br-Br]</th>\n",
       "      <th>F06[C-C]</th>\n",
       "      <th>F06[C-N]</th>\n",
       "      <th>F06[C-O]</th>\n",
       "      <th>F06[C-S]</th>\n",
       "      <th>F06[C-F]</th>\n",
       "      <th>F06[C-Cl]</th>\n",
       "      <th>F06[C-Br]</th>\n",
       "      <th>F06[C-I]</th>\n",
       "      <th>F06[N-N]</th>\n",
       "      <th>F06[N-O]</th>\n",
       "      <th>F06[N-S]</th>\n",
       "      <th>F06[N-F]</th>\n",
       "      <th>F06[N-Cl]</th>\n",
       "      <th>F06[N-Br]</th>\n",
       "      <th>F06[N-I]</th>\n",
       "      <th>F06[O-O]</th>\n",
       "      <th>F06[O-S]</th>\n",
       "      <th>F06[O-F]</th>\n",
       "      <th>F06[O-Cl]</th>\n",
       "      <th>F06[O-Br]</th>\n",
       "      <th>F06[O-I]</th>\n",
       "      <th>F06[S-S]</th>\n",
       "      <th>F06[S-F]</th>\n",
       "      <th>F06[S-Cl]</th>\n",
       "      <th>F06[F-F]</th>\n",
       "      <th>F06[F-Cl]</th>\n",
       "      <th>F06[Cl-Cl]</th>\n",
       "      <th>F06[Cl-Br]</th>\n",
       "      <th>F06[Br-Br]</th>\n",
       "      <th>F07[C-C]</th>\n",
       "      <th>F07[C-N]</th>\n",
       "      <th>F07[C-O]</th>\n",
       "      <th>F07[C-S]</th>\n",
       "      <th>F07[C-F]</th>\n",
       "      <th>F07[C-Cl]</th>\n",
       "      <th>F07[C-Br]</th>\n",
       "      <th>F07[C-I]</th>\n",
       "      <th>F07[N-N]</th>\n",
       "      <th>F07[N-O]</th>\n",
       "      <th>F07[N-S]</th>\n",
       "      <th>F07[N-F]</th>\n",
       "      <th>F07[N-Cl]</th>\n",
       "      <th>F07[N-Br]</th>\n",
       "      <th>F07[O-O]</th>\n",
       "      <th>F07[O-S]</th>\n",
       "      <th>F07[O-F]</th>\n",
       "      <th>F07[O-Cl]</th>\n",
       "      <th>F07[O-Br]</th>\n",
       "      <th>F07[S-S]</th>\n",
       "      <th>F07[S-F]</th>\n",
       "      <th>F07[S-Cl]</th>\n",
       "      <th>F07[F-F]</th>\n",
       "      <th>F07[F-Cl]</th>\n",
       "      <th>F07[Cl-Cl]</th>\n",
       "      <th>F07[Cl-Br]</th>\n",
       "      <th>F07[Br-Br]</th>\n",
       "      <th>F08[C-C]</th>\n",
       "      <th>F08[C-N]</th>\n",
       "      <th>F08[C-O]</th>\n",
       "      <th>F08[C-S]</th>\n",
       "      <th>F08[C-F]</th>\n",
       "      <th>F08[C-Cl]</th>\n",
       "      <th>F08[C-Br]</th>\n",
       "      <th>F08[C-I]</th>\n",
       "      <th>F08[N-N]</th>\n",
       "      <th>F08[N-O]</th>\n",
       "      <th>F08[N-S]</th>\n",
       "      <th>F08[N-F]</th>\n",
       "      <th>F08[N-Cl]</th>\n",
       "      <th>F08[N-Br]</th>\n",
       "      <th>F08[O-O]</th>\n",
       "      <th>F08[O-S]</th>\n",
       "      <th>F08[O-F]</th>\n",
       "      <th>F08[O-Cl]</th>\n",
       "      <th>F08[O-Br]</th>\n",
       "      <th>F08[S-Cl]</th>\n",
       "      <th>F08[F-F]</th>\n",
       "      <th>F08[F-Cl]</th>\n",
       "      <th>F08[Cl-Cl]</th>\n",
       "      <th>F08[Cl-Br]</th>\n",
       "      <th>F08[Br-Br]</th>\n",
       "      <th>F09[C-C]</th>\n",
       "      <th>F09[C-N]</th>\n",
       "      <th>F09[C-O]</th>\n",
       "      <th>F09[C-S]</th>\n",
       "      <th>F09[C-F]</th>\n",
       "      <th>F09[C-Cl]</th>\n",
       "      <th>F09[C-Br]</th>\n",
       "      <th>F09[C-I]</th>\n",
       "      <th>F09[N-N]</th>\n",
       "      <th>F09[N-O]</th>\n",
       "      <th>F09[N-S]</th>\n",
       "      <th>F09[N-F]</th>\n",
       "      <th>F09[N-Cl]</th>\n",
       "      <th>F09[N-Br]</th>\n",
       "      <th>F09[O-O]</th>\n",
       "      <th>F09[O-S]</th>\n",
       "      <th>F09[O-F]</th>\n",
       "      <th>F09[O-Cl]</th>\n",
       "      <th>F09[O-Br]</th>\n",
       "      <th>F09[F-F]</th>\n",
       "      <th>F09[F-Cl]</th>\n",
       "      <th>F09[Cl-Cl]</th>\n",
       "      <th>F09[Br-Br]</th>\n",
       "      <th>F10[C-C]</th>\n",
       "      <th>F10[C-N]</th>\n",
       "      <th>F10[C-O]</th>\n",
       "      <th>F10[C-S]</th>\n",
       "      <th>F10[C-F]</th>\n",
       "      <th>F10[C-Cl]</th>\n",
       "      <th>F10[C-Br]</th>\n",
       "      <th>F10[C-I]</th>\n",
       "      <th>F10[N-N]</th>\n",
       "      <th>F10[N-O]</th>\n",
       "      <th>F10[N-S]</th>\n",
       "      <th>F10[N-F]</th>\n",
       "      <th>F10[N-Cl]</th>\n",
       "      <th>F10[O-O]</th>\n",
       "      <th>F10[O-S]</th>\n",
       "      <th>F10[O-F]</th>\n",
       "      <th>F10[O-Cl]</th>\n",
       "      <th>F10[O-Br]</th>\n",
       "      <th>F10[S-F]</th>\n",
       "      <th>F10[F-F]</th>\n",
       "      <th>F10[F-Cl]</th>\n",
       "      <th>F10[Cl-Cl]</th>\n",
       "      <th>F10[Br-Br]</th>\n",
       "      <th>Uc</th>\n",
       "      <th>Ui</th>\n",
       "      <th>Hy</th>\n",
       "      <th>TPSA(NO)</th>\n",
       "      <th>TPSA(Tot)</th>\n",
       "      <th>MLOGP</th>\n",
       "      <th>MLOGP2</th>\n",
       "      <th>SAtot</th>\n",
       "      <th>SAacc</th>\n",
       "      <th>SAdon</th>\n",
       "      <th>Vx</th>\n",
       "      <th>VvdwMG</th>\n",
       "      <th>VvdwZAZ</th>\n",
       "      <th>PDI</th>\n",
       "      <th>BLTF96</th>\n",
       "      <th>BLTD48</th>\n",
       "      <th>BLTA96</th>\n",
       "      <th>cRo5</th>\n",
       "      <th>DLS_01</th>\n",
       "      <th>DLS_02</th>\n",
       "      <th>DLS_03</th>\n",
       "      <th>DLS_04</th>\n",
       "      <th>DLS_05</th>\n",
       "      <th>DLS_06</th>\n",
       "      <th>DLS_07</th>\n",
       "      <th>DLS_cons</th>\n",
       "      <th>LLS_01</th>\n",
       "      <th>LLS_02</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMILES</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>methane</td>\n",
       "      <td>11.123407</td>\n",
       "      <td>11.176453</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.05</td>\n",
       "      <td>3.210</td>\n",
       "      <td>2.054</td>\n",
       "      <td>4.767</td>\n",
       "      <td>2.523</td>\n",
       "      <td>5.830</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.505</td>\n",
       "      <td>1.166</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.610</td>\n",
       "      <td>3.610</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.610</td>\n",
       "      <td>3.610</td>\n",
       "      <td>1.288</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.600</td>\n",
       "      <td>2.064</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.562</td>\n",
       "      <td>1.844</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.763</td>\n",
       "      <td>2.277</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.790</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.864</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.173</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.913</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.308</td>\n",
       "      <td>1.207</td>\n",
       "      <td>1.207</td>\n",
       "      <td>1.207</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.703</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.597</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.228</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>1.530</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>47.084</td>\n",
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       "      <td>47.084</td>\n",
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       "      <td>15.116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.084</td>\n",
       "      <td>15.116</td>\n",
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       "      <td>47.084</td>\n",
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       "      <td>15.116</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.115</td>\n",
       "      <td>1.243</td>\n",
       "      <td>62.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.445</td>\n",
       "      <td>20.363</td>\n",
       "      <td>25.86</td>\n",
       "      <td>0.666</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC</th>\n",
       "      <td>ethane</td>\n",
       "      <td>10.798677</td>\n",
       "      <td>10.871072</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.08</td>\n",
       "      <td>3.760</td>\n",
       "      <td>3.580</td>\n",
       "      <td>7.651</td>\n",
       "      <td>4.284</td>\n",
       "      <td>9.246</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.536</td>\n",
       "      <td>1.156</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.134</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.414</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.402</td>\n",
       "      <td>-0.805</td>\n",
       "      <td>0.500</td>\n",
       "      <td>7.500</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.012</td>\n",
       "      <td>0.693</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.091</td>\n",
       "      <td>1.386</td>\n",
       "      <td>1.386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.386</td>\n",
       "      <td>1.386</td>\n",
       "      <td>24.000</td>\n",
       "      <td>6.490</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.490</td>\n",
       "      <td>6.490</td>\n",
       "      <td>6.490</td>\n",
       "      <td>2.490</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.189</td>\n",
       "      <td>2.189</td>\n",
       "      <td>2.189</td>\n",
       "      <td>2.689</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.275</td>\n",
       "      <td>1.098</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.035</td>\n",
       "      <td>2.483</td>\n",
       "      <td>2.195</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.455</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.757</td>\n",
       "      <td>3.085</td>\n",
       "      <td>88.277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.850</td>\n",
       "      <td>29.955</td>\n",
       "      <td>43.16</td>\n",
       "      <td>0.735</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCC</th>\n",
       "      <td>propane</td>\n",
       "      <td>11.161176</td>\n",
       "      <td>11.107460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.11</td>\n",
       "      <td>4.010</td>\n",
       "      <td>5.107</td>\n",
       "      <td>10.534</td>\n",
       "      <td>6.046</td>\n",
       "      <td>12.661</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.550</td>\n",
       "      <td>1.151</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.728</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.833</td>\n",
       "      <td>2.231</td>\n",
       "      <td>1.155</td>\n",
       "      <td>0.342</td>\n",
       "      <td>-0.243</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.286</td>\n",
       "      <td>7.333</td>\n",
       "      <td>1.109</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.747</td>\n",
       "      <td>1.099</td>\n",
       "      <td>1.946</td>\n",
       "      <td>2.197</td>\n",
       "      <td>2.565</td>\n",
       "      <td>2.833</td>\n",
       "      <td>3.219</td>\n",
       "      <td>3.497</td>\n",
       "      <td>3.892</td>\n",
       "      <td>4.174</td>\n",
       "      <td>4.575</td>\n",
       "      <td>1.609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.174</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.743</td>\n",
       "      <td>1.946</td>\n",
       "      <td>1.946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.777</td>\n",
       "      <td>1.606</td>\n",
       "      <td>38.054</td>\n",
       "      <td>9.299</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.918</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0.918</td>\n",
       "      <td>1.561</td>\n",
       "      <td>2.755</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.918</td>\n",
       "      <td>1.500</td>\n",
       "      <td>2.755</td>\n",
       "      <td>1.363</td>\n",
       "      <td>0.662</td>\n",
       "      <td>4.174</td>\n",
       "      <td>2.042</td>\n",
       "      <td>1.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.845</td>\n",
       "      <td>1.096</td>\n",
       "      <td>1.686</td>\n",
       "      <td>1.371</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.299</td>\n",
       "      <td>12.054</td>\n",
       "      <td>18.544</td>\n",
       "      <td>15.085</td>\n",
       "      <td>5.247</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.614</td>\n",
       "      <td>2.364</td>\n",
       "      <td>1.774</td>\n",
       "      <td>2.088</td>\n",
       "      <td>2.982</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.301</td>\n",
       "      <td>1.061</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.631</td>\n",
       "      <td>1.633</td>\n",
       "      <td>1.228</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.355</td>\n",
       "      <td>2.869</td>\n",
       "      <td>2.850</td>\n",
       "      <td>2.195</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.799</td>\n",
       "      <td>1.920</td>\n",
       "      <td>1.614</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.539</td>\n",
       "      <td>3.190</td>\n",
       "      <td>3.248</td>\n",
       "      <td>2.648</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.219</td>\n",
       "      <td>2.544</td>\n",
       "      <td>1.747</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.435</td>\n",
       "      <td>1.646</td>\n",
       "      <td>1.130</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.014</td>\n",
       "      <td>1.163</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.114</td>\n",
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       "      <td>0.090</td>\n",
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       "      <td>-0.267</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.083</td>\n",
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       "      <td>-0.267</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.083</td>\n",
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       "      <td>-0.267</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.083</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.276</td>\n",
       "      <td>5.180</td>\n",
       "      <td>114.354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.256</td>\n",
       "      <td>39.547</td>\n",
       "      <td>60.46</td>\n",
       "      <td>0.772</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCCC</th>\n",
       "      <td>butane</td>\n",
       "      <td>11.334422</td>\n",
       "      <td>11.330604</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.14</td>\n",
       "      <td>4.153</td>\n",
       "      <td>6.634</td>\n",
       "      <td>13.418</td>\n",
       "      <td>7.807</td>\n",
       "      <td>16.076</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.558</td>\n",
       "      <td>1.148</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.378</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.722</td>\n",
       "      <td>1.802</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>3.047</td>\n",
       "      <td>1.821</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.144</td>\n",
       "      <td>-0.454</td>\n",
       "      <td>0.165</td>\n",
       "      <td>7.250</td>\n",
       "      <td>1.488</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.495</td>\n",
       "      <td>1.386</td>\n",
       "      <td>2.398</td>\n",
       "      <td>2.833</td>\n",
       "      <td>3.296</td>\n",
       "      <td>3.761</td>\n",
       "      <td>4.234</td>\n",
       "      <td>4.710</td>\n",
       "      <td>5.187</td>\n",
       "      <td>5.666</td>\n",
       "      <td>6.146</td>\n",
       "      <td>1.946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.509</td>\n",
       "      <td>1.386</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.386</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.100</td>\n",
       "      <td>2.398</td>\n",
       "      <td>2.398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.063</td>\n",
       "      <td>1.753</td>\n",
       "      <td>53.303</td>\n",
       "      <td>12.084</td>\n",
       "      <td>0.863</td>\n",
       "      <td>1.459</td>\n",
       "      <td>2.446</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.971</td>\n",
       "      <td>8.755</td>\n",
       "      <td>24.464</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.918</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.750</td>\n",
       "      <td>0.906</td>\n",
       "      <td>6.056</td>\n",
       "      <td>1.089</td>\n",
       "      <td>1.328</td>\n",
       "      <td>3.132</td>\n",
       "      <td>0.863</td>\n",
       "      <td>1.149</td>\n",
       "      <td>1.842</td>\n",
       "      <td>1.842</td>\n",
       "      <td>1.441</td>\n",
       "      <td>0.524</td>\n",
       "      <td>12.084</td>\n",
       "      <td>16.084</td>\n",
       "      <td>25.793</td>\n",
       "      <td>25.793</td>\n",
       "      <td>20.178</td>\n",
       "      <td>7.334</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.138</td>\n",
       "      <td>2.944</td>\n",
       "      <td>2.659</td>\n",
       "      <td>1.965</td>\n",
       "      <td>1.965</td>\n",
       "      <td>2.366</td>\n",
       "      <td>3.283</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.142</td>\n",
       "      <td>1.577</td>\n",
       "      <td>1.443</td>\n",
       "      <td>1.083</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.892</td>\n",
       "      <td>1.980</td>\n",
       "      <td>1.748</td>\n",
       "      <td>1.228</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.597</td>\n",
       "      <td>3.148</td>\n",
       "      <td>3.243</td>\n",
       "      <td>2.850</td>\n",
       "      <td>2.195</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.055</td>\n",
       "      <td>2.250</td>\n",
       "      <td>2.095</td>\n",
       "      <td>1.614</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.777</td>\n",
       "      <td>3.452</td>\n",
       "      <td>3.622</td>\n",
       "      <td>3.248</td>\n",
       "      <td>2.648</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.996</td>\n",
       "      <td>3.801</td>\n",
       "      <td>3.236</td>\n",
       "      <td>1.849</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.938</td>\n",
       "      <td>2.458</td>\n",
       "      <td>2.093</td>\n",
       "      <td>1.196</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1.738</td>\n",
       "      <td>1.479</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.067</td>\n",
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       "      <td>1.750</td>\n",
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       "      <td>0.843</td>\n",
       "      <td>0.758</td>\n",
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       "      <td>1.750</td>\n",
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       "      <td>0.843</td>\n",
       "      <td>0.758</td>\n",
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       "      <td>1.750</td>\n",
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       "      <td>0.843</td>\n",
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       "      <td>1.750</td>\n",
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       "      <td>0.843</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.729</td>\n",
       "      <td>7.447</td>\n",
       "      <td>140.430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.661</td>\n",
       "      <td>49.140</td>\n",
       "      <td>77.76</td>\n",
       "      <td>0.795</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCCCC</th>\n",
       "      <td>pentane</td>\n",
       "      <td>11.716003</td>\n",
       "      <td>11.736069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.17</td>\n",
       "      <td>4.245</td>\n",
       "      <td>8.161</td>\n",
       "      <td>16.302</td>\n",
       "      <td>9.568</td>\n",
       "      <td>19.491</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.563</td>\n",
       "      <td>1.147</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.6</td>\n",
       "      <td>29.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.847</td>\n",
       "      <td>3.310</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.700</td>\n",
       "      <td>3.864</td>\n",
       "      <td>2.488</td>\n",
       "      <td>0.241</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.095</td>\n",
       "      <td>7.200</td>\n",
       "      <td>1.866</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.308</td>\n",
       "      <td>1.609</td>\n",
       "      <td>2.708</td>\n",
       "      <td>3.219</td>\n",
       "      <td>3.761</td>\n",
       "      <td>4.290</td>\n",
       "      <td>4.844</td>\n",
       "      <td>5.380</td>\n",
       "      <td>5.938</td>\n",
       "      <td>6.475</td>\n",
       "      <td>7.034</td>\n",
       "      <td>2.197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.192</td>\n",
       "      <td>1.609</td>\n",
       "      <td>1.386</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.609</td>\n",
       "      <td>1.386</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.888</td>\n",
       "      <td>2.773</td>\n",
       "      <td>2.773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.287</td>\n",
       "      <td>1.889</td>\n",
       "      <td>69.487</td>\n",
       "      <td>14.858</td>\n",
       "      <td>0.874</td>\n",
       "      <td>1.846</td>\n",
       "      <td>3.146</td>\n",
       "      <td>1.522</td>\n",
       "      <td>2.291</td>\n",
       "      <td>18.464</td>\n",
       "      <td>62.929</td>\n",
       "      <td>0.971</td>\n",
       "      <td>2.250</td>\n",
       "      <td>7.610</td>\n",
       "      <td>2.002</td>\n",
       "      <td>1.049</td>\n",
       "      <td>8.593</td>\n",
       "      <td>0.853</td>\n",
       "      <td>1.175</td>\n",
       "      <td>2.340</td>\n",
       "      <td>0.874</td>\n",
       "      <td>1.160</td>\n",
       "      <td>2.027</td>\n",
       "      <td>2.352</td>\n",
       "      <td>2.111</td>\n",
       "      <td>1.385</td>\n",
       "      <td>14.858</td>\n",
       "      <td>19.712</td>\n",
       "      <td>34.467</td>\n",
       "      <td>39.977</td>\n",
       "      <td>35.890</td>\n",
       "      <td>23.540</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.339</td>\n",
       "      <td>3.213</td>\n",
       "      <td>2.928</td>\n",
       "      <td>2.060</td>\n",
       "      <td>1.736</td>\n",
       "      <td>1.976</td>\n",
       "      <td>2.703</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.346</td>\n",
       "      <td>1.793</td>\n",
       "      <td>1.719</td>\n",
       "      <td>1.463</td>\n",
       "      <td>1.083</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.099</td>\n",
       "      <td>2.237</td>\n",
       "      <td>2.089</td>\n",
       "      <td>1.748</td>\n",
       "      <td>1.228</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.791</td>\n",
       "      <td>3.365</td>\n",
       "      <td>3.524</td>\n",
       "      <td>3.243</td>\n",
       "      <td>2.850</td>\n",
       "      <td>2.195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.258</td>\n",
       "      <td>2.498</td>\n",
       "      <td>2.418</td>\n",
       "      <td>2.095</td>\n",
       "      <td>1.614</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.970</td>\n",
       "      <td>3.660</td>\n",
       "      <td>3.893</td>\n",
       "      <td>3.622</td>\n",
       "      <td>3.248</td>\n",
       "      <td>2.648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.762</td>\n",
       "      <td>5.042</td>\n",
       "      <td>4.726</td>\n",
       "      <td>3.321</td>\n",
       "      <td>1.916</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.433</td>\n",
       "      <td>3.261</td>\n",
       "      <td>3.056</td>\n",
       "      <td>2.148</td>\n",
       "      <td>1.239</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.720</td>\n",
       "      <td>2.305</td>\n",
       "      <td>2.161</td>\n",
       "      <td>1.518</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.360</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.360</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.360</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.360</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.360</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.222</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.138</td>\n",
       "      <td>9.850</td>\n",
       "      <td>166.507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.066</td>\n",
       "      <td>58.732</td>\n",
       "      <td>95.06</td>\n",
       "      <td>0.811</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-4.30</td>\n",
       "      <td>-4.37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Species    Kh_mean   Kh_first  Varying_1  Varying_2  Varying_3  \\\n",
       "SMILES                                                                   \n",
       "C       methane  11.123407  11.176453        1.0        1.0        0.0   \n",
       "CC       ethane  10.798677  10.871072        1.0        1.0        0.0   \n",
       "CCC     propane  11.161176  11.107460        0.0        0.0        0.0   \n",
       "CCCC     butane  11.334422  11.330604        1.0        0.0        0.0   \n",
       "CCCCC   pentane  11.716003  11.736069        0.0        0.0        0.0   \n",
       "\n",
       "        Varying_4  Varying_5  Varying_6  Varying_7  Varying_8  Varying_9  \\\n",
       "SMILES                                                                     \n",
       "C             0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "CC            0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "CCC           0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "CCCC          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "CCCCC         0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "\n",
       "           MW    AMW     Sv      Se     Sp      Si     Mv     Me     Mp  \\\n",
       "SMILES                                                                    \n",
       "C       16.05  3.210  2.054   4.767  2.523   5.830  0.411  0.953  0.505   \n",
       "CC      30.08  3.760  3.580   7.651  4.284   9.246  0.448  0.956  0.536   \n",
       "CCC     44.11  4.010  5.107  10.534  6.046  12.661  0.464  0.958  0.550   \n",
       "CCCC    58.14  4.153  6.634  13.418  7.807  16.076  0.474  0.958  0.558   \n",
       "CCCCC   72.17  4.245  8.161  16.302  9.568  19.491  0.480  0.959  0.563   \n",
       "\n",
       "           Mi   nAT  nSK   nBT  nBO  nBM  SCBO  RBN    RBF  nDB  nTB  nAB  \\\n",
       "SMILES                                                                      \n",
       "C       1.166   5.0  1.0   4.0  0.0  0.0   0.0  0.0  0.000  0.0  0.0  0.0   \n",
       "CC      1.156   8.0  2.0   7.0  1.0  0.0   1.0  0.0  0.000  0.0  0.0  0.0   \n",
       "CCC     1.151  11.0  3.0  10.0  2.0  0.0   2.0  0.0  0.000  0.0  0.0  0.0   \n",
       "CCCC    1.148  14.0  4.0  13.0  3.0  0.0   3.0  1.0  0.077  0.0  0.0  0.0   \n",
       "CCCCC   1.147  17.0  5.0  16.0  4.0  0.0   4.0  2.0  0.125  0.0  0.0  0.0   \n",
       "\n",
       "          nH   nC   nN   nO   nS   nF  nCL  nBR   nI  nHM  nHet   nX    H%  \\\n",
       "SMILES                                                                       \n",
       "C        4.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  80.0   \n",
       "CC       6.0  2.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  75.0   \n",
       "CCC      8.0  3.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  72.7   \n",
       "CCCC    10.0  4.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  71.4   \n",
       "CCCCC   12.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  70.6   \n",
       "\n",
       "          C%   N%   O%   X%  nCsp3  nCsp2  nCsp  nCIC  nCIR  TRS  Rperim  \\\n",
       "SMILES                                                                     \n",
       "C       20.0  0.0  0.0  0.0    1.0    0.0   0.0   0.0   0.0  0.0     0.0   \n",
       "CC      25.0  0.0  0.0  0.0    2.0    0.0   0.0   0.0   0.0  0.0     0.0   \n",
       "CCC     27.3  0.0  0.0  0.0    3.0    0.0   0.0   0.0   0.0  0.0     0.0   \n",
       "CCCC    28.6  0.0  0.0  0.0    4.0    0.0   0.0   0.0   0.0  0.0     0.0   \n",
       "CCCCC   29.4  0.0  0.0  0.0    5.0    0.0   0.0   0.0   0.0  0.0     0.0   \n",
       "\n",
       "        Rbrid  MCD  RFD  RCI  NRS  NNRS  nR03  nR04  nR05  nR06  nR07  nR08  \\\n",
       "SMILES                                                                        \n",
       "C         0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "CC        0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "CCC       0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "CCCC      0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "CCCCC     0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "        nR09  nR10  nR11  nR12  nBnz  ARR  D/Dtr03  D/Dtr04  D/Dtr05  D/Dtr06  \\\n",
       "SMILES                                                                          \n",
       "C        0.0   0.0   0.0   0.0   0.0  0.0      0.0      0.0      0.0      0.0   \n",
       "CC       0.0   0.0   0.0   0.0   0.0  0.0      0.0      0.0      0.0      0.0   \n",
       "CCC      0.0   0.0   0.0   0.0   0.0  0.0      0.0      0.0      0.0      0.0   \n",
       "CCCC     0.0   0.0   0.0   0.0   0.0  0.0      0.0      0.0      0.0      0.0   \n",
       "CCCCC    0.0   0.0   0.0   0.0   0.0  0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "        D/Dtr07  D/Dtr08  D/Dtr09  D/Dtr10  D/Dtr11  D/Dtr12   Wap  S1K  S2K  \\\n",
       "SMILES                                                                         \n",
       "C           0.0      0.0      0.0      0.0      0.0      0.0   0.0  1.0  0.0   \n",
       "CC          0.0      0.0      0.0      0.0      0.0      0.0   1.0  2.0  1.0   \n",
       "CCC         0.0      0.0      0.0      0.0      0.0      0.0   4.0  3.0  2.0   \n",
       "CCCC        0.0      0.0      0.0      0.0      0.0      0.0  10.0  4.0  3.0   \n",
       "CCCCC       0.0      0.0      0.0      0.0      0.0      0.0  20.0  5.0  4.0   \n",
       "\n",
       "          S3K  PHI    PW2    PW3    PW4  PW5  MAXDN  MAXDP   DELS    TIE  \\\n",
       "SMILES                                                                     \n",
       "C       0.000  0.0  0.000  0.000  0.000  0.0  0.000  0.000  0.000  0.000   \n",
       "CC      1.450  1.0  0.000  0.000  0.000  0.0  0.000  0.000  0.000  0.134   \n",
       "CCC     2.000  2.0  0.333  0.000  0.000  0.0  0.250  0.125  0.500  0.728   \n",
       "CCCC    3.378  3.0  0.417  0.167  0.000  0.0  0.181  0.181  0.722  1.802   \n",
       "CCCCC   4.000  4.0  0.433  0.200  0.067  0.0  0.156  0.212  0.847  3.310   \n",
       "\n",
       "        Psi_i_s  Psi_i_A  Psi_i_0  Psi_i_1  Psi_i_t  Psi_i_0d  Psi_i_1d  \\\n",
       "SMILES                                                                    \n",
       "C           0.0    0.000    0.000    0.000    0.000     0.000     0.000   \n",
       "CC          4.0    2.000    1.414    0.500    0.405     0.402    -0.805   \n",
       "CCC         5.5    1.833    2.231    1.155    0.342    -0.243     0.646   \n",
       "CCCC        7.0    1.750    3.047    1.821    0.288     0.144    -0.454   \n",
       "CCCCC       8.5    1.700    3.864    2.488    0.241    -0.084     0.294   \n",
       "\n",
       "        Psi_i_1s  Psi_e_A  Psi_e_0  Psi_e_t  Psi_e_0d  MWC01  MWC02  MWC03  \\\n",
       "SMILES                                                                       \n",
       "C          0.000    0.000    0.000    0.000     0.000  0.000  0.000  0.000   \n",
       "CC         0.500    7.500    0.730    0.125     1.012  0.693  1.099  1.099   \n",
       "CCC        0.286    7.333    1.109    0.049    -0.747  1.099  1.946  2.197   \n",
       "CCCC       0.165    7.250    1.488    0.019     0.495  1.386  2.398  2.833   \n",
       "CCCCC      0.095    7.200    1.866    0.007    -0.308  1.609  2.708  3.219   \n",
       "\n",
       "        MWC04  MWC05  MWC06  MWC07  MWC08  MWC09  MWC10  SRW02  SRW03  SRW04  \\\n",
       "SMILES                                                                         \n",
       "C       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000    0.0  0.000   \n",
       "CC      1.099  1.099  1.099  1.099  1.099  1.099  1.099  1.099    0.0  1.099   \n",
       "CCC     2.565  2.833  3.219  3.497  3.892  4.174  4.575  1.609    0.0  2.197   \n",
       "CCCC    3.296  3.761  4.234  4.710  5.187  5.666  6.146  1.946    0.0  2.708   \n",
       "CCCCC   3.761  4.290  4.844  5.380  5.938  6.475  7.034  2.197    0.0  3.045   \n",
       "\n",
       "        SRW05  SRW06  SRW07  SRW08  SRW09  SRW10  MPC01  MPC02  MPC03  MPC04  \\\n",
       "SMILES                                                                         \n",
       "C         0.0  0.000    0.0  0.000    0.0  0.000  0.000  0.000  0.000  0.000   \n",
       "CC        0.0  1.099    0.0  1.099    0.0  1.099  0.693  0.000  0.000  0.000   \n",
       "CCC       0.0  2.833    0.0  3.497    0.0  4.174  1.099  0.693  0.000  0.000   \n",
       "CCCC      0.0  3.611    0.0  4.554    0.0  5.509  1.386  1.099  0.693  0.000   \n",
       "CCCCC     0.0  4.043    0.0  5.106    0.0  6.192  1.609  1.386  1.099  0.693   \n",
       "\n",
       "        MPC05  MPC06  MPC07  MPC08  MPC09  MPC10  piPC01  piPC02  piPC03  \\\n",
       "SMILES                                                                     \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0   0.000   0.000   0.000   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0   0.693   0.000   0.000   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0   1.099   0.693   0.000   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0   1.386   1.099   0.693   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0   1.609   1.386   1.099   \n",
       "\n",
       "        piPC04  piPC05  piPC06  piPC07  piPC08  piPC09  piPC10    TWC    TPC  \\\n",
       "SMILES                                                                         \n",
       "C        0.000     0.0     0.0     0.0     0.0     0.0     0.0  0.693  0.000   \n",
       "CC       0.000     0.0     0.0     0.0     0.0     0.0     0.0  3.091  1.386   \n",
       "CCC      0.000     0.0     0.0     0.0     0.0     0.0     0.0  5.743  1.946   \n",
       "CCCC     0.000     0.0     0.0     0.0     0.0     0.0     0.0  7.100  2.398   \n",
       "CCCCC    0.693     0.0     0.0     0.0     0.0     0.0     0.0  7.888  2.773   \n",
       "\n",
       "         piID  PCD    CID    BID    ISIZ     IAC    AAC    IDE    IDM   IDDE  \\\n",
       "SMILES                                                                         \n",
       "C       0.000  0.0  0.000  0.000  11.610   3.610  0.722  0.000  0.000  0.000   \n",
       "CC      1.386  0.0  1.386  1.386  24.000   6.490  0.811  0.000  0.000  0.000   \n",
       "CCC     1.946  0.0  1.777  1.606  38.054   9.299  0.845  0.918  1.500  0.918   \n",
       "CCCC    2.398  0.0  2.063  1.753  53.303  12.084  0.863  1.459  2.446  1.000   \n",
       "CCCCC   2.773  0.0  2.287  1.889  69.487  14.858  0.874  1.846  3.146  1.522   \n",
       "\n",
       "         IDDM    IDET    IDMT   IVDE   IVDM    S0K  HVcpx  HDcpx  Uindex  \\\n",
       "SMILES                                                                     \n",
       "C       0.000   0.000   0.000  0.000  0.000  0.000  0.000  0.000   0.000   \n",
       "CC      1.000   0.000   0.000  0.000  1.000  0.000  1.000  0.000   0.000   \n",
       "CCC     1.561   2.755   6.000  0.918  1.500  2.755  1.363  0.662   4.174   \n",
       "CCCC    1.971   8.755  24.464  1.000  1.918  4.000  1.750  0.906   6.056   \n",
       "CCCCC   2.291  18.464  62.929  0.971  2.250  7.610  2.002  1.049   8.593   \n",
       "\n",
       "        Vindex  Xindex  Yindex    IC0    IC1    IC2    IC3    IC4    IC5  \\\n",
       "SMILES                                                                     \n",
       "C        0.000   0.000   0.000  0.722  0.722  0.258  0.000  0.000  0.000   \n",
       "CC       0.000   0.000   0.000  0.811  0.811  0.811  0.311  0.000  0.000   \n",
       "CCC      2.042   1.704   0.000  0.845  1.096  1.686  1.371  0.477  0.000   \n",
       "CCCC     1.089   1.328   3.132  0.863  1.149  1.842  1.842  1.441  0.524   \n",
       "CCCCC    0.853   1.175   2.340  0.874  1.160  2.027  2.352  2.111  1.385   \n",
       "\n",
       "          TIC0    TIC1    TIC2    TIC3    TIC4    TIC5   SIC0   SIC1   SIC2  \\\n",
       "SMILES                                                                        \n",
       "C        3.610   3.610   1.288   0.000   0.000   0.000  0.311  0.311  0.111   \n",
       "CC       6.490   6.490   6.490   2.490   0.000   0.000  0.270  0.270  0.270   \n",
       "CCC      9.299  12.054  18.544  15.085   5.247   0.000  0.244  0.317  0.487   \n",
       "CCCC    12.084  16.084  25.793  25.793  20.178   7.334  0.227  0.302  0.484   \n",
       "CCCCC   14.858  19.712  34.467  39.977  35.890  23.540  0.214  0.284  0.496   \n",
       "\n",
       "         SIC3   SIC4   SIC5   CIC0   CIC1   CIC2   CIC3   CIC4   CIC5   BIC0  \\\n",
       "SMILES                                                                         \n",
       "C       0.000  0.000  0.000  1.600  1.600  2.064  0.000  0.000  0.000  0.361   \n",
       "CC      0.104  0.000  0.000  2.189  2.189  2.189  2.689  0.000  0.000  0.289   \n",
       "CCC     0.396  0.138  0.000  2.614  2.364  1.774  2.088  2.982  0.000  0.254   \n",
       "CCCC    0.484  0.379  0.138  2.944  2.659  1.965  1.965  2.366  3.283  0.233   \n",
       "CCCCC   0.575  0.516  0.339  3.213  2.928  2.060  1.736  1.976  2.703  0.218   \n",
       "\n",
       "         BIC1   BIC2   BIC3   BIC4   BIC5  ATS1m  ATS2m  ATS3m  ATS4m  ATS5m  \\\n",
       "SMILES                                                                         \n",
       "C       0.361  0.129  0.000  0.000  0.000  0.290  0.042  0.000  0.000  0.000   \n",
       "CC      0.289  0.289  0.111  0.000  0.000  0.918  0.436  0.062  0.000  0.000   \n",
       "CCC     0.330  0.507  0.413  0.144  0.000  1.301  1.061  0.463  0.062  0.000   \n",
       "CCCC    0.310  0.498  0.498  0.389  0.142  1.577  1.443  1.083  0.463  0.062   \n",
       "CCCCC   0.290  0.507  0.588  0.528  0.346  1.793  1.719  1.463  1.083  0.463   \n",
       "\n",
       "        ATS6m  ATS7m  ATS8m  ATS1v  ATS2v  ATS3v  ATS4v  ATS5v  ATS6v  ATS7v  \\\n",
       "SMILES                                                                         \n",
       "C       0.000    0.0    0.0  0.720  0.348  0.000  0.000  0.000  0.000    0.0   \n",
       "CC      0.000    0.0    0.0  1.275  1.098  0.485  0.000  0.000  0.000    0.0   \n",
       "CCC     0.000    0.0    0.0  1.631  1.633  1.228  0.485  0.000  0.000    0.0   \n",
       "CCCC    0.000    0.0    0.0  1.892  1.980  1.748  1.228  0.485  0.000    0.0   \n",
       "CCCCC   0.062    0.0    0.0  2.099  2.237  2.089  1.748  1.228  0.485    0.0   \n",
       "\n",
       "        ATS8v  ATS1e  ATS2e  ATS3e  ATS4e  ATS5e  ATS6e  ATS7e  ATS8e  ATS1p  \\\n",
       "SMILES                                                                         \n",
       "C         0.0  1.562  1.844  0.000  0.000  0.000  0.000    0.0    0.0  0.925   \n",
       "CC        0.0  2.035  2.483  2.195  0.000  0.000  0.000    0.0    0.0  1.455   \n",
       "CCC       0.0  2.355  2.869  2.850  2.195  0.000  0.000    0.0    0.0  1.799   \n",
       "CCCC      0.0  2.597  3.148  3.243  2.850  2.195  0.000    0.0    0.0  2.055   \n",
       "CCCCC     0.0  2.791  3.365  3.524  3.243  2.850  2.195    0.0    0.0  2.258   \n",
       "\n",
       "        ATS2p  ATS3p  ATS4p  ATS5p  ATS6p  ATS7p  ATS8p  ATS1i  ATS2i  ATS3i  \\\n",
       "SMILES                                                                         \n",
       "C       0.626  0.000  0.000  0.000  0.000    0.0    0.0  1.763  2.277  0.000   \n",
       "CC      1.424  0.835  0.000  0.000  0.000    0.0    0.0  2.224  2.833  2.648   \n",
       "CCC     1.920  1.614  0.835  0.000  0.000    0.0    0.0  2.539  3.190  3.248   \n",
       "CCCC    2.250  2.095  1.614  0.835  0.000    0.0    0.0  2.777  3.452  3.622   \n",
       "CCCCC   2.498  2.418  2.095  1.614  0.835    0.0    0.0  2.970  3.660  3.893   \n",
       "\n",
       "        ATS4i  ATS5i  ATS6i  ATS7i  ATS8i  ATSC1m  ATSC2m  ATSC3m  ATSC4m  \\\n",
       "SMILES                                                                      \n",
       "C       0.000  0.000  0.000    0.0    0.0   0.537   0.201   0.000   0.000   \n",
       "CC      0.000  0.000  0.000    0.0    0.0   1.416   1.258   0.472   0.000   \n",
       "CCC     2.648  0.000  0.000    0.0    0.0   2.219   2.544   1.747   0.562   \n",
       "CCCC    3.248  2.648  0.000    0.0    0.0   2.996   3.801   3.236   1.849   \n",
       "CCCCC   3.622  3.248  2.648    0.0    0.0   3.762   5.042   4.726   3.321   \n",
       "\n",
       "        ATSC5m  ATSC6m  ATSC7m  ATSC8m  ATSC1v  ATSC2v  ATSC3v  ATSC4v  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0   0.347   0.130   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0   0.916   0.814   0.305   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0   1.435   1.646   1.130   0.363   \n",
       "CCCC     0.616   0.000     0.0     0.0   1.938   2.458   2.093   1.196   \n",
       "CCCCC    1.916   0.653     0.0     0.0   2.433   3.261   3.056   2.148   \n",
       "\n",
       "        ATSC5v  ATSC6v  ATSC7v  ATSC8v  ATSC1e  ATSC2e  ATSC3e  ATSC4e  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0   0.002   0.001   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0   0.006   0.005   0.002   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0   0.009   0.010   0.007   0.002   \n",
       "CCCC     0.399   0.000     0.0     0.0   0.012   0.015   0.013   0.007   \n",
       "CCCCC    1.239   0.422     0.0     0.0   0.015   0.020   0.019   0.013   \n",
       "\n",
       "        ATSC5e  ATSC6e  ATSC7e  ATSC8e  ATSC1p  ATSC2p  ATSC3p  ATSC4p  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0   0.245   0.092   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0   0.647   0.575   0.216   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0   1.014   1.163   0.799   0.257   \n",
       "CCCC     0.002   0.000     0.0     0.0   1.370   1.738   1.479   0.845   \n",
       "CCCCC    0.008   0.003     0.0     0.0   1.720   2.305   2.161   1.518   \n",
       "\n",
       "        ATSC5p  ATSC6p  ATSC7p  ATSC8p  ATSC1i  ATSC2i  ATSC3i  ATSC4i  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0   0.028   0.010   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0   0.073   0.065   0.024   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0   0.114   0.131   0.090   0.029   \n",
       "CCCC     0.282   0.000     0.0     0.0   0.154   0.195   0.166   0.095   \n",
       "CCCCC    0.876   0.299     0.0     0.0   0.193   0.259   0.243   0.171   \n",
       "\n",
       "        ATSC5i  ATSC6i  ATSC7i  ATSC8i  MATS1m  MATS2m  MATS3m  MATS4m  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0  -1.000   0.250   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0  -0.429  -0.333   0.333   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0  -0.267  -0.262  -0.083   0.375   \n",
       "CCCC     0.032   0.000     0.0     0.0  -0.192  -0.242  -0.041  -0.067   \n",
       "CCCCC    0.098   0.034     0.0     0.0  -0.150  -0.235  -0.024  -0.035   \n",
       "\n",
       "        MATS5m  MATS6m  MATS7m  MATS8m  MATS1v  MATS2v  MATS3v  MATS4v  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0  -1.000   0.250   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0  -0.429  -0.333   0.333   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0  -0.267  -0.262  -0.083   0.375   \n",
       "CCCC     0.400   0.000     0.0     0.0  -0.192  -0.242  -0.041  -0.067   \n",
       "CCCCC   -0.056   0.417     0.0     0.0  -0.150  -0.235  -0.024  -0.035   \n",
       "\n",
       "        MATS5v  MATS6v  MATS7v  MATS8v  MATS1e  MATS2e  MATS3e  MATS4e  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0  -1.000   0.250   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0  -0.429  -0.333   0.333   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0  -0.267  -0.262  -0.083   0.375   \n",
       "CCCC     0.400   0.000     0.0     0.0  -0.192  -0.242  -0.041  -0.067   \n",
       "CCCCC   -0.056   0.417     0.0     0.0  -0.150  -0.235  -0.024  -0.035   \n",
       "\n",
       "        MATS5e  MATS6e  MATS7e  MATS8e  MATS1p  MATS2p  MATS3p  MATS4p  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0  -1.000   0.250   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0  -0.429  -0.333   0.333   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0  -0.267  -0.262  -0.083   0.375   \n",
       "CCCC     0.400   0.000     0.0     0.0  -0.192  -0.242  -0.041  -0.067   \n",
       "CCCCC   -0.056   0.417     0.0     0.0  -0.150  -0.235  -0.024  -0.035   \n",
       "\n",
       "        MATS5p  MATS6p  MATS7p  MATS8p  MATS1i  MATS2i  MATS3i  MATS4i  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0  -1.000   0.250   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0  -0.429  -0.333   0.333   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0  -0.267  -0.262  -0.083   0.375   \n",
       "CCCC     0.400   0.000     0.0     0.0  -0.192  -0.242  -0.041  -0.067   \n",
       "CCCCC   -0.056   0.417     0.0     0.0  -0.150  -0.235  -0.024  -0.035   \n",
       "\n",
       "        MATS5i  MATS6i  MATS7i  MATS8i  GATS1m  GATS2m  GATS3m  GATS4m  \\\n",
       "SMILES                                                                   \n",
       "C        0.000   0.000     0.0     0.0   2.500   0.000   0.000   0.000   \n",
       "CC       0.000   0.000     0.0     0.0   2.000   1.167   0.000   0.000   \n",
       "CCC      0.000   0.000     0.0     0.0   1.833   1.273   0.764   0.000   \n",
       "CCCC     0.400   0.000     0.0     0.0   1.750   1.327   0.843   0.758   \n",
       "CCCCC   -0.056   0.417     0.0     0.0   1.700   1.360   0.881   0.840   \n",
       "\n",
       "        GATS5m  GATS6m  GATS7m  GATS8m  GATS1v  GATS2v  GATS3v  GATS4v  \\\n",
       "SMILES                                                                   \n",
       "C        0.000     0.0     0.0     0.0   2.500   0.000   0.000   0.000   \n",
       "CC       0.000     0.0     0.0     0.0   2.000   1.167   0.000   0.000   \n",
       "CCC      0.000     0.0     0.0     0.0   1.833   1.273   0.764   0.000   \n",
       "CCCC     0.000     0.0     0.0     0.0   1.750   1.327   0.843   0.758   \n",
       "CCCCC    0.756     0.0     0.0     0.0   1.700   1.360   0.881   0.840   \n",
       "\n",
       "        GATS5v  GATS6v  GATS7v  GATS8v  GATS1e  GATS2e  GATS3e  GATS4e  \\\n",
       "SMILES                                                                   \n",
       "C        0.000     0.0     0.0     0.0   2.500   0.000   0.000   0.000   \n",
       "CC       0.000     0.0     0.0     0.0   2.000   1.167   0.000   0.000   \n",
       "CCC      0.000     0.0     0.0     0.0   1.833   1.273   0.764   0.000   \n",
       "CCCC     0.000     0.0     0.0     0.0   1.750   1.327   0.843   0.758   \n",
       "CCCCC    0.756     0.0     0.0     0.0   1.700   1.360   0.881   0.840   \n",
       "\n",
       "        GATS5e  GATS6e  GATS7e  GATS8e  GATS1p  GATS2p  GATS3p  GATS4p  \\\n",
       "SMILES                                                                   \n",
       "C        0.000     0.0     0.0     0.0   2.500   0.000   0.000   0.000   \n",
       "CC       0.000     0.0     0.0     0.0   2.000   1.167   0.000   0.000   \n",
       "CCC      0.000     0.0     0.0     0.0   1.833   1.273   0.764   0.000   \n",
       "CCCC     0.000     0.0     0.0     0.0   1.750   1.327   0.843   0.758   \n",
       "CCCCC    0.756     0.0     0.0     0.0   1.700   1.360   0.881   0.840   \n",
       "\n",
       "        GATS5p  GATS6p  GATS7p  GATS8p  GATS1i  GATS2i  GATS3i  GATS4i  \\\n",
       "SMILES                                                                   \n",
       "C        0.000     0.0     0.0     0.0   2.500   0.000   0.000   0.000   \n",
       "CC       0.000     0.0     0.0     0.0   2.000   1.167   0.000   0.000   \n",
       "CCC      0.000     0.0     0.0     0.0   1.833   1.273   0.764   0.000   \n",
       "CCCC     0.000     0.0     0.0     0.0   1.750   1.327   0.843   0.758   \n",
       "CCCCC    0.756     0.0     0.0     0.0   1.700   1.360   0.881   0.840   \n",
       "\n",
       "        GATS5i  GATS6i  GATS7i  GATS8i  GGI1   GGI2   GGI3  GGI4  GGI5  GGI6  \\\n",
       "SMILES                                                                         \n",
       "C        0.000     0.0     0.0     0.0   0.0  0.000  0.000   0.0   0.0   0.0   \n",
       "CC       0.000     0.0     0.0     0.0   0.0  0.000  0.000   0.0   0.0   0.0   \n",
       "CCC      0.000     0.0     0.0     0.0   0.5  0.000  0.000   0.0   0.0   0.0   \n",
       "CCCC     0.000     0.0     0.0     0.0   0.5  0.222  0.000   0.0   0.0   0.0   \n",
       "CCCCC    0.756     0.0     0.0     0.0   0.5  0.222  0.125   0.0   0.0   0.0   \n",
       "\n",
       "        GGI7  GGI8  GGI9  GGI10   JGI1   JGI2   JGI3  JGI4  JGI5  JGI6  JGI7  \\\n",
       "SMILES                                                                         \n",
       "C        0.0   0.0   0.0    0.0  0.000  0.000  0.000   0.0   0.0   0.0   0.0   \n",
       "CC       0.0   0.0   0.0    0.0  0.000  0.000  0.000   0.0   0.0   0.0   0.0   \n",
       "CCC      0.0   0.0   0.0    0.0  0.250  0.000  0.000   0.0   0.0   0.0   0.0   \n",
       "CCCC     0.0   0.0   0.0    0.0  0.167  0.111  0.000   0.0   0.0   0.0   0.0   \n",
       "CCCCC    0.0   0.0   0.0    0.0  0.125  0.074  0.063   0.0   0.0   0.0   0.0   \n",
       "\n",
       "        JGI8  JGI9  JGI10    JGT  SpMax1_Bh(m)  SpMax2_Bh(m)  SpMax3_Bh(m)  \\\n",
       "SMILES                                                                       \n",
       "C        0.0   0.0    0.0  0.000         2.790         0.083         0.083   \n",
       "CC       0.0   0.0    0.0  0.000         3.178         1.946         0.083   \n",
       "CCC      0.0   0.0    0.0  0.250         3.357         2.499         1.566   \n",
       "CCCC     0.0   0.0    0.0  0.278         3.456         2.830         2.032   \n",
       "CCCCC    0.0   0.0    0.0  0.262         3.516         3.040         2.390   \n",
       "\n",
       "        SpMax4_Bh(m)  SpMax5_Bh(m)  SpMax6_Bh(m)  SpMax7_Bh(m)  SpMax8_Bh(m)  \\\n",
       "SMILES                                                                         \n",
       "C              0.083         0.000         0.000         0.000         0.000   \n",
       "CC             0.083         0.083         0.083         0.000         0.000   \n",
       "CCC            0.083         0.083         0.083         0.083         0.083   \n",
       "CCCC           1.403         0.083         0.083         0.083         0.083   \n",
       "CCCCC          1.749         1.325         0.083         0.083         0.083   \n",
       "\n",
       "        SpMax1_Bh(v)  SpMax2_Bh(v)  SpMax3_Bh(v)  SpMax4_Bh(v)  SpMax5_Bh(v)  \\\n",
       "SMILES                                                                         \n",
       "C              2.864         0.262         0.262         0.262         0.000   \n",
       "CC             3.229         2.039         0.262         0.262         0.262   \n",
       "CCC            3.399         2.570         1.669         0.262         0.262   \n",
       "CCCC           3.493         2.887         2.115         1.513         0.262   \n",
       "CCCCC          3.551         3.089         2.459         1.841         1.439   \n",
       "\n",
       "        SpMax6_Bh(v)  SpMax7_Bh(v)  SpMax8_Bh(v)  SpMax1_Bh(e)  SpMax2_Bh(e)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000         0.000         0.000         3.173         0.941   \n",
       "CC             0.262         0.000         0.000         3.452         2.431   \n",
       "CCC            0.262         0.262         0.262         3.585         2.874   \n",
       "CCCC           0.262         0.262         0.262         3.661         3.138   \n",
       "CCCCC          0.262         0.262         0.262         3.709         3.306   \n",
       "\n",
       "        SpMax3_Bh(e)  SpMax4_Bh(e)  SpMax5_Bh(e)  SpMax6_Bh(e)  SpMax7_Bh(e)  \\\n",
       "SMILES                                                                         \n",
       "C              0.941         0.941         0.000         0.000         0.000   \n",
       "CC             0.941         0.941         0.941         0.941         0.000   \n",
       "CCC            2.100         0.941         0.941         0.941         0.941   \n",
       "CCCC           2.470         1.966         0.941         0.941         0.941   \n",
       "CCCCC          2.758         2.233         1.905         0.941         0.941   \n",
       "\n",
       "        SpMax8_Bh(e)  SpMax1_Bh(p)  SpMax2_Bh(p)  SpMax3_Bh(p)  SpMax4_Bh(p)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000         2.913         0.380         0.380         0.380   \n",
       "CC             0.000         3.264         2.103         0.380         0.380   \n",
       "CCC            0.941         3.428         2.618         1.739         0.380   \n",
       "CCCC           0.941         3.519         2.926         2.172         1.587   \n",
       "CCCCC          0.941         3.576         3.123         2.506         1.905   \n",
       "\n",
       "        SpMax5_Bh(p)  SpMax6_Bh(p)  SpMax7_Bh(p)  SpMax8_Bh(p)  SpMax1_Bh(i)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000          0.00          0.00          0.00         3.308   \n",
       "CC             0.380          0.38          0.00          0.00         3.554   \n",
       "CCC            0.380          0.38          0.38          0.38         3.672   \n",
       "CCCC           0.380          0.38          0.38          0.38         3.740   \n",
       "CCCCC          1.515          0.38          0.38          0.38         3.783   \n",
       "\n",
       "        SpMax2_Bh(i)  SpMax3_Bh(i)  SpMax4_Bh(i)  SpMax5_Bh(i)  SpMax6_Bh(i)  \\\n",
       "SMILES                                                                         \n",
       "C              1.207         1.207         1.207         0.000         0.000   \n",
       "CC             2.600         1.207         1.207         1.207         1.207   \n",
       "CCC            3.009         2.283         1.207         1.207         1.207   \n",
       "CCCC           3.252         2.626         2.159         1.207         1.207   \n",
       "CCCCC          3.407         2.893         2.403         2.103         1.207   \n",
       "\n",
       "        SpMax7_Bh(i)  SpMax8_Bh(i)  SpMin1_Bh(m)  SpMin2_Bh(m)  SpMin3_Bh(m)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000         0.000         1.703         0.000         0.000   \n",
       "CC             0.000         0.000         1.862         1.088         0.000   \n",
       "CCC            1.207         1.207         1.938         1.417         0.809   \n",
       "CCCC           1.207         1.207         1.983         1.605         1.085   \n",
       "CCCCC          1.207         1.207         2.010         1.725         1.298   \n",
       "\n",
       "        SpMin4_Bh(m)  SpMin5_Bh(m)  SpMin6_Bh(m)  SpMin7_Bh(m)  SpMin8_Bh(m)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000         0.000           0.0           0.0           0.0   \n",
       "CC             0.000         0.000           0.0           0.0           0.0   \n",
       "CCC            0.000         0.000           0.0           0.0           0.0   \n",
       "CCCC           0.705         0.000           0.0           0.0           0.0   \n",
       "CCCCC          0.900         0.659           0.0           0.0           0.0   \n",
       "\n",
       "        SpMin1_Bh(v)  SpMin2_Bh(v)  SpMin3_Bh(v)  SpMin4_Bh(v)  SpMin5_Bh(v)  \\\n",
       "SMILES                                                                         \n",
       "C              1.597         0.000         0.000         0.000         0.000   \n",
       "CC             1.777         0.960         0.000         0.000         0.000   \n",
       "CCC            1.863         1.308         0.671         0.000         0.000   \n",
       "CCCC           1.912         1.510         0.964         0.563         0.000   \n",
       "CCCCC          1.944         1.638         1.190         0.769         0.514   \n",
       "\n",
       "        SpMin6_Bh(v)  SpMin7_Bh(v)  SpMin8_Bh(v)  SpMin1_Bh(e)  SpMin2_Bh(e)  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0         1.228         0.000   \n",
       "CC               0.0           0.0           0.0         1.490         0.505   \n",
       "CCC              0.0           0.0           0.0         1.615         0.934   \n",
       "CCCC             0.0           0.0           0.0         1.685         1.188   \n",
       "CCCCC            0.0           0.0           0.0         1.729         1.351   \n",
       "\n",
       "        SpMin3_Bh(e)  SpMin4_Bh(e)  SpMin5_Bh(e)  SpMin6_Bh(e)  SpMin7_Bh(e)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000         0.000           0.0           0.0           0.0   \n",
       "CC             0.000         0.000           0.0           0.0           0.0   \n",
       "CCC            0.179         0.000           0.0           0.0           0.0   \n",
       "CCCC           0.539         0.050           0.0           0.0           0.0   \n",
       "CCCCC          0.817         0.308           0.0           0.0           0.0   \n",
       "\n",
       "        SpMin8_Bh(e)  SpMin1_Bh(p)  SpMin2_Bh(p)  SpMin3_Bh(p)  SpMin4_Bh(p)  \\\n",
       "SMILES                                                                         \n",
       "C                0.0         1.530         0.000         0.000         0.000   \n",
       "CC               0.0         1.723         0.878         0.000         0.000   \n",
       "CCC              0.0         1.816         1.239         0.583         0.000   \n",
       "CCCC             0.0         1.869         1.450         0.886         0.471   \n",
       "CCCCC            0.0         1.902         1.584         1.121         0.686   \n",
       "\n",
       "        SpMin5_Bh(p)  SpMin6_Bh(p)  SpMin7_Bh(p)  SpMin8_Bh(p)  SpMin1_Bh(i)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000           0.0           0.0           0.0         1.097   \n",
       "CC             0.000           0.0           0.0           0.0         1.394   \n",
       "CCC            0.000           0.0           0.0           0.0         1.533   \n",
       "CCCC           0.000           0.0           0.0           0.0         1.611   \n",
       "CCCCC          0.421           0.0           0.0           0.0         1.659   \n",
       "\n",
       "        SpMin2_Bh(i)  SpMin3_Bh(i)  SpMin4_Bh(i)  SpMin5_Bh(i)  SpMin6_Bh(i)  \\\n",
       "SMILES                                                                         \n",
       "C              0.000         0.000         0.000           0.0           0.0   \n",
       "CC             0.341         0.000         0.000           0.0           0.0   \n",
       "CCC            0.804         0.000         0.000           0.0           0.0   \n",
       "CCCC           1.079         0.388         0.000           0.0           0.0   \n",
       "CCCCC          1.256         0.688         0.143           0.0           0.0   \n",
       "\n",
       "        SpMin7_Bh(i)  SpMin8_Bh(i)  P_VSA_m_1  P_VSA_m_2  P_VSA_m_3  \\\n",
       "SMILES                                                                \n",
       "C                0.0           0.0     47.084     15.116        0.0   \n",
       "CC               0.0           0.0     70.627     17.650        0.0   \n",
       "CCC              0.0           0.0     94.169     20.185        0.0   \n",
       "CCCC             0.0           0.0    117.711     22.719        0.0   \n",
       "CCCCC            0.0           0.0    141.253     25.254        0.0   \n",
       "\n",
       "        P_VSA_m_4  P_VSA_m_5  P_VSA_v_1  P_VSA_v_2  P_VSA_v_3  P_VSA_v_4  \\\n",
       "SMILES                                                                     \n",
       "C             0.0        0.0     47.084        0.0     15.116        0.0   \n",
       "CC            0.0        0.0     70.627        0.0     17.650        0.0   \n",
       "CCC           0.0        0.0     94.169        0.0     20.185        0.0   \n",
       "CCCC          0.0        0.0    117.711        0.0     22.719        0.0   \n",
       "CCCCC         0.0        0.0    141.253        0.0     25.254        0.0   \n",
       "\n",
       "        P_VSA_e_1  P_VSA_e_2  P_VSA_e_3  P_VSA_e_4  P_VSA_e_5  P_VSA_e_6  \\\n",
       "SMILES                                                                     \n",
       "C          47.084     15.116        0.0        0.0        0.0        0.0   \n",
       "CC         70.627     17.650        0.0        0.0        0.0        0.0   \n",
       "CCC        94.169     20.185        0.0        0.0        0.0        0.0   \n",
       "CCCC      117.711     22.719        0.0        0.0        0.0        0.0   \n",
       "CCCCC     141.253     25.254        0.0        0.0        0.0        0.0   \n",
       "\n",
       "        P_VSA_p_1  P_VSA_p_2  P_VSA_p_3  P_VSA_p_4  P_VSA_i_1  P_VSA_i_2  \\\n",
       "SMILES                                                                     \n",
       "C          47.084        0.0     15.116        0.0        0.0     15.116   \n",
       "CC         70.627        0.0     17.650        0.0        0.0     17.650   \n",
       "CCC        94.169        0.0     20.185        0.0        0.0     20.185   \n",
       "CCCC      117.711        0.0     22.719        0.0        0.0     22.719   \n",
       "CCCCC     141.253        0.0     25.254        0.0        0.0     25.254   \n",
       "\n",
       "        P_VSA_i_3  P_VSA_i_4  P_VSA_s_5  P_VSA_s_6  nCp  nCs  nCt  nCq  nCrs  \\\n",
       "SMILES                                                                         \n",
       "C          47.084        0.0        0.0        0.0  1.0  0.0  0.0  0.0   0.0   \n",
       "CC         70.627        0.0        0.0        0.0  2.0  0.0  0.0  0.0   0.0   \n",
       "CCC        94.169        0.0        0.0        0.0  2.0  1.0  0.0  0.0   0.0   \n",
       "CCCC      117.711        0.0        0.0        0.0  2.0  2.0  0.0  0.0   0.0   \n",
       "CCCCC     141.253        0.0        0.0        0.0  2.0  3.0  0.0  0.0   0.0   \n",
       "\n",
       "        nCrt  nCrq  nCar  nCbH  nCb-  nCconj  nR=Cp  nR=Cs  nR=Ct  nR#CH/X  \\\n",
       "SMILES                                                                       \n",
       "C        0.0   0.0   0.0   0.0   0.0     0.0    0.0    0.0    0.0      0.0   \n",
       "CC       0.0   0.0   0.0   0.0   0.0     0.0    0.0    0.0    0.0      0.0   \n",
       "CCC      0.0   0.0   0.0   0.0   0.0     0.0    0.0    0.0    0.0      0.0   \n",
       "CCCC     0.0   0.0   0.0   0.0   0.0     0.0    0.0    0.0    0.0      0.0   \n",
       "CCCCC    0.0   0.0   0.0   0.0   0.0     0.0    0.0    0.0    0.0      0.0   \n",
       "\n",
       "        nR#C-  nRNCS  nRCOOH  nArCOOH  nRCOOR  nArCOOR  nRCONH2  nArCONH2  \\\n",
       "SMILES                                                                      \n",
       "C         0.0    0.0     0.0      0.0     0.0      0.0      0.0       0.0   \n",
       "CC        0.0    0.0     0.0      0.0     0.0      0.0      0.0       0.0   \n",
       "CCC       0.0    0.0     0.0      0.0     0.0      0.0      0.0       0.0   \n",
       "CCCC      0.0    0.0     0.0      0.0     0.0      0.0      0.0       0.0   \n",
       "CCCCC     0.0    0.0     0.0      0.0     0.0      0.0      0.0       0.0   \n",
       "\n",
       "        nRCONHR  nArCONHR  nRCONR2  nArCONR2  nROCON  nArOCON  nRCOX  nRCOSR  \\\n",
       "SMILES                                                                         \n",
       "C           0.0       0.0      0.0       0.0     0.0      0.0    0.0     0.0   \n",
       "CC          0.0       0.0      0.0       0.0     0.0      0.0    0.0     0.0   \n",
       "CCC         0.0       0.0      0.0       0.0     0.0      0.0    0.0     0.0   \n",
       "CCCC        0.0       0.0      0.0       0.0     0.0      0.0    0.0     0.0   \n",
       "CCCCC       0.0       0.0      0.0       0.0     0.0      0.0    0.0     0.0   \n",
       "\n",
       "        nArCOSR  nRCHO  nArCHO  nRCO  nArCO  nCONN  nC=O(O)2  nN=C-N<  \\\n",
       "SMILES                                                                  \n",
       "C           0.0    0.0     0.0   0.0    0.0    0.0       0.0      0.0   \n",
       "CC          0.0    0.0     0.0   0.0    0.0    0.0       0.0      0.0   \n",
       "CCC         0.0    0.0     0.0   0.0    0.0    0.0       0.0      0.0   \n",
       "CCCC        0.0    0.0     0.0   0.0    0.0    0.0       0.0      0.0   \n",
       "CCCCC       0.0    0.0     0.0   0.0    0.0    0.0       0.0      0.0   \n",
       "\n",
       "        nC(=N)N2  nRC=N  nArC=N  nRCNO  nArCNO  nRNH2  nArNH2  nRNHR  nArNHR  \\\n",
       "SMILES                                                                         \n",
       "C            0.0    0.0     0.0    0.0     0.0    0.0     0.0    0.0     0.0   \n",
       "CC           0.0    0.0     0.0    0.0     0.0    0.0     0.0    0.0     0.0   \n",
       "CCC          0.0    0.0     0.0    0.0     0.0    0.0     0.0    0.0     0.0   \n",
       "CCCC         0.0    0.0     0.0    0.0     0.0    0.0     0.0    0.0     0.0   \n",
       "CCCCC        0.0    0.0     0.0    0.0     0.0    0.0     0.0    0.0     0.0   \n",
       "\n",
       "        nRNR2  nArNR2  nN-N  nN=N  nRCN  nArCN  nN+  nRNHO  nArNHO  nRNNOx  \\\n",
       "SMILES                                                                       \n",
       "C         0.0     0.0   0.0   0.0   0.0    0.0  0.0    0.0     0.0     0.0   \n",
       "CC        0.0     0.0   0.0   0.0   0.0    0.0  0.0    0.0     0.0     0.0   \n",
       "CCC       0.0     0.0   0.0   0.0   0.0    0.0  0.0    0.0     0.0     0.0   \n",
       "CCCC      0.0     0.0   0.0   0.0   0.0    0.0  0.0    0.0     0.0     0.0   \n",
       "CCCCC     0.0     0.0   0.0   0.0   0.0    0.0  0.0    0.0     0.0     0.0   \n",
       "\n",
       "        nArNNOx  nArNO  nRNO2  nArNO2  nN(CO)2  nC=N-N<  nROH  nArOH  nOHp  \\\n",
       "SMILES                                                                       \n",
       "C           0.0    0.0    0.0     0.0      0.0      0.0   0.0    0.0   0.0   \n",
       "CC          0.0    0.0    0.0     0.0      0.0      0.0   0.0    0.0   0.0   \n",
       "CCC         0.0    0.0    0.0     0.0      0.0      0.0   0.0    0.0   0.0   \n",
       "CCCC        0.0    0.0    0.0     0.0      0.0      0.0   0.0    0.0   0.0   \n",
       "CCCCC       0.0    0.0    0.0     0.0      0.0      0.0   0.0    0.0   0.0   \n",
       "\n",
       "        nOHs  nOHt  nROR  nArOR  nO(C=O)2  nSH  nRSR  nRSSR  nSO  nS(=O)2  \\\n",
       "SMILES                                                                      \n",
       "C        0.0   0.0   0.0    0.0       0.0  0.0   0.0    0.0  0.0      0.0   \n",
       "CC       0.0   0.0   0.0    0.0       0.0  0.0   0.0    0.0  0.0      0.0   \n",
       "CCC      0.0   0.0   0.0    0.0       0.0  0.0   0.0    0.0  0.0      0.0   \n",
       "CCCC     0.0   0.0   0.0    0.0       0.0  0.0   0.0    0.0  0.0      0.0   \n",
       "CCCCC    0.0   0.0   0.0    0.0       0.0  0.0   0.0    0.0  0.0      0.0   \n",
       "\n",
       "        nSO2  nSO3  nSO4  nSO2N  nCH2RX  nCHR2X  nCR3X  nR=CHX  nR=CRX  nR#CX  \\\n",
       "SMILES                                                                          \n",
       "C        0.0   0.0   0.0    0.0     0.0     0.0    0.0     0.0     0.0    0.0   \n",
       "CC       0.0   0.0   0.0    0.0     0.0     0.0    0.0     0.0     0.0    0.0   \n",
       "CCC      0.0   0.0   0.0    0.0     0.0     0.0    0.0     0.0     0.0    0.0   \n",
       "CCCC     0.0   0.0   0.0    0.0     0.0     0.0    0.0     0.0     0.0    0.0   \n",
       "CCCCC    0.0   0.0   0.0    0.0     0.0     0.0    0.0     0.0     0.0    0.0   \n",
       "\n",
       "        nCHRX2  nCR2X2  nR=CX2  nCRX3  nArX  nCXr  nCXr=  nCconjX  \\\n",
       "SMILES                                                              \n",
       "C          0.0     0.0     0.0    0.0   0.0   0.0    0.0      0.0   \n",
       "CC         0.0     0.0     0.0    0.0   0.0   0.0    0.0      0.0   \n",
       "CCC        0.0     0.0     0.0    0.0   0.0   0.0    0.0      0.0   \n",
       "CCCC       0.0     0.0     0.0    0.0   0.0   0.0    0.0      0.0   \n",
       "CCCCC      0.0     0.0     0.0    0.0   0.0   0.0    0.0      0.0   \n",
       "\n",
       "        nAziridines  nOxiranes  nAzetidines  nOxetanes  nPyrrolidines  \\\n",
       "SMILES                                                                  \n",
       "C               0.0        0.0          0.0        0.0            0.0   \n",
       "CC              0.0        0.0          0.0        0.0            0.0   \n",
       "CCC             0.0        0.0          0.0        0.0            0.0   \n",
       "CCCC            0.0        0.0          0.0        0.0            0.0   \n",
       "CCCCC           0.0        0.0          0.0        0.0            0.0   \n",
       "\n",
       "        nOxolanes  nPyrroles  nPyrazoles  nImidazoles  nFuranes  nThiophenes  \\\n",
       "SMILES                                                                         \n",
       "C             0.0        0.0         0.0          0.0       0.0          0.0   \n",
       "CC            0.0        0.0         0.0          0.0       0.0          0.0   \n",
       "CCC           0.0        0.0         0.0          0.0       0.0          0.0   \n",
       "CCCC          0.0        0.0         0.0          0.0       0.0          0.0   \n",
       "CCCCC         0.0        0.0         0.0          0.0       0.0          0.0   \n",
       "\n",
       "        nIsoxazoles  nThiazoles  nTriazoles  nPyridines  nPyrimidines  \\\n",
       "SMILES                                                                  \n",
       "C               0.0         0.0         0.0         0.0           0.0   \n",
       "CC              0.0         0.0         0.0         0.0           0.0   \n",
       "CCC             0.0         0.0         0.0         0.0           0.0   \n",
       "CCCC            0.0         0.0         0.0         0.0           0.0   \n",
       "CCCCC           0.0         0.0         0.0         0.0           0.0   \n",
       "\n",
       "        nPyrazines  n135-Triazines  nHDon  nHAcc  C-001  C-002  C-003  C-004  \\\n",
       "SMILES                                                                         \n",
       "C              0.0             0.0    0.0    0.0    1.0    0.0    0.0    0.0   \n",
       "CC             0.0             0.0    0.0    0.0    2.0    0.0    0.0    0.0   \n",
       "CCC            0.0             0.0    0.0    0.0    2.0    1.0    0.0    0.0   \n",
       "CCCC           0.0             0.0    0.0    0.0    2.0    2.0    0.0    0.0   \n",
       "CCCCC          0.0             0.0    0.0    0.0    2.0    3.0    0.0    0.0   \n",
       "\n",
       "        C-005  C-006  C-007  C-008  C-009  C-010  C-011  C-012  C-013  C-014  \\\n",
       "SMILES                                                                         \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "        C-015  C-016  C-017  C-018  C-019  C-020  C-021  C-022  C-023  C-024  \\\n",
       "SMILES                                                                         \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "        C-025  C-026  C-027  C-028  C-029  C-030  C-031  C-032  C-033  C-034  \\\n",
       "SMILES                                                                         \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "        C-035  C-036  C-037  C-038  C-039  C-040  C-041  C-042  C-043  C-044  \\\n",
       "SMILES                                                                         \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "        H-046  H-047  H-048  H-049  H-050  H-051  H-052  H-053  H-054  H-055  \\\n",
       "SMILES                                                                         \n",
       "C         4.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CC        6.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCC       8.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCC     10.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCCC    12.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "        O-056  O-057  O-058  O-059  O-060  O-061  O-062  O-063  N-066  N-067  \\\n",
       "SMILES                                                                         \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "        N-068  N-069  N-070  N-071  N-072  N-073  N-074  N-075  N-076  N-077  \\\n",
       "SMILES                                                                         \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "        N-078  N-079  F-081  F-082  F-083  F-084  F-085  Cl-086  Cl-087  \\\n",
       "SMILES                                                                    \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0     0.0   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0     0.0   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0     0.0   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0     0.0   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0     0.0   \n",
       "\n",
       "        Cl-088  Cl-089  Cl-090  Br-091  Br-092  Br-093  Br-094  Br-095  I-096  \\\n",
       "SMILES                                                                          \n",
       "C          0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0    0.0   \n",
       "CC         0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0    0.0   \n",
       "CCC        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0    0.0   \n",
       "CCCC       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0    0.0   \n",
       "CCCCC      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0    0.0   \n",
       "\n",
       "        I-097  I-098  I-099  I-100  S-106  S-107  S-108  S-109  S-110  SsCH3  \\\n",
       "SMILES                                                                         \n",
       "C         0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  0.000   \n",
       "CC        0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  4.000   \n",
       "CCC       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  4.250   \n",
       "CCCC      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  4.361   \n",
       "CCCCC     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  4.424   \n",
       "\n",
       "        SdCH2  SssCH2  StCH  SdsCH  SaaCH  SsssCH  SddC  StsC  SdssC  SaasC  \\\n",
       "SMILES                                                                        \n",
       "C         0.0   0.000   0.0    0.0    0.0     0.0   0.0   0.0    0.0    0.0   \n",
       "CC        0.0   0.000   0.0    0.0    0.0     0.0   0.0   0.0    0.0    0.0   \n",
       "CCC       0.0   1.250   0.0    0.0    0.0     0.0   0.0   0.0    0.0    0.0   \n",
       "CCCC      0.0   2.639   0.0    0.0    0.0     0.0   0.0   0.0    0.0    0.0   \n",
       "CCCCC     0.0   4.076   0.0    0.0    0.0     0.0   0.0   0.0    0.0    0.0   \n",
       "\n",
       "        SaaaC  SssssC  SsNH2  SssNH  SdNH  SsssN  SdsN  SaaN  StN  SddsN  \\\n",
       "SMILES                                                                     \n",
       "C         0.0     0.0    0.0    0.0   0.0    0.0   0.0   0.0  0.0    0.0   \n",
       "CC        0.0     0.0    0.0    0.0   0.0    0.0   0.0   0.0  0.0    0.0   \n",
       "CCC       0.0     0.0    0.0    0.0   0.0    0.0   0.0   0.0  0.0    0.0   \n",
       "CCCC      0.0     0.0    0.0    0.0   0.0    0.0   0.0   0.0  0.0    0.0   \n",
       "CCCCC     0.0     0.0    0.0    0.0   0.0    0.0   0.0   0.0  0.0    0.0   \n",
       "\n",
       "        SaasN  SaaNH  SsOH  SdO  SssO  SaaO  SsSH  SdS  SssS  SaaS  SdssS  \\\n",
       "SMILES                                                                      \n",
       "C         0.0    0.0   0.0  0.0   0.0   0.0   0.0  0.0   0.0   0.0    0.0   \n",
       "CC        0.0    0.0   0.0  0.0   0.0   0.0   0.0  0.0   0.0   0.0    0.0   \n",
       "CCC       0.0    0.0   0.0  0.0   0.0   0.0   0.0  0.0   0.0   0.0    0.0   \n",
       "CCCC      0.0    0.0   0.0  0.0   0.0   0.0   0.0  0.0   0.0   0.0    0.0   \n",
       "CCCCC     0.0    0.0   0.0  0.0   0.0   0.0   0.0  0.0   0.0   0.0    0.0   \n",
       "\n",
       "        SddssS  SsF  SsCl  SsBr  SsI  NsCH3  NdCH2  NssCH2  NtCH  NdsCH  \\\n",
       "SMILES                                                                    \n",
       "C          0.0  0.0   0.0   0.0  0.0    0.0    0.0     0.0   0.0    0.0   \n",
       "CC         0.0  0.0   0.0   0.0  0.0    2.0    0.0     0.0   0.0    0.0   \n",
       "CCC        0.0  0.0   0.0   0.0  0.0    2.0    0.0     1.0   0.0    0.0   \n",
       "CCCC       0.0  0.0   0.0   0.0  0.0    2.0    0.0     2.0   0.0    0.0   \n",
       "CCCCC      0.0  0.0   0.0   0.0  0.0    2.0    0.0     3.0   0.0    0.0   \n",
       "\n",
       "        NaaCH  NsssCH  NddC  NtsC  NdssC  NaasC  NaaaC  NssssC  NsNH2  NssNH  \\\n",
       "SMILES                                                                         \n",
       "C         0.0     0.0   0.0   0.0    0.0    0.0    0.0     0.0    0.0    0.0   \n",
       "CC        0.0     0.0   0.0   0.0    0.0    0.0    0.0     0.0    0.0    0.0   \n",
       "CCC       0.0     0.0   0.0   0.0    0.0    0.0    0.0     0.0    0.0    0.0   \n",
       "CCCC      0.0     0.0   0.0   0.0    0.0    0.0    0.0     0.0    0.0    0.0   \n",
       "CCCCC     0.0     0.0   0.0   0.0    0.0    0.0    0.0     0.0    0.0    0.0   \n",
       "\n",
       "        NdNH  NsssN  NdsN  NaaN  NtN  NddsN  NaasN  NaaNH  NsOH  NdO  NssO  \\\n",
       "SMILES                                                                       \n",
       "C        0.0    0.0   0.0   0.0  0.0    0.0    0.0    0.0   0.0  0.0   0.0   \n",
       "CC       0.0    0.0   0.0   0.0  0.0    0.0    0.0    0.0   0.0  0.0   0.0   \n",
       "CCC      0.0    0.0   0.0   0.0  0.0    0.0    0.0    0.0   0.0  0.0   0.0   \n",
       "CCCC     0.0    0.0   0.0   0.0  0.0    0.0    0.0    0.0   0.0  0.0   0.0   \n",
       "CCCCC    0.0    0.0   0.0   0.0  0.0    0.0    0.0    0.0   0.0  0.0   0.0   \n",
       "\n",
       "        NaaO  NsSH  NdS  NssS  NaaS  NdssS  NddssS  NsF  NsCl  NsBr  NsI  \\\n",
       "SMILES                                                                     \n",
       "C        0.0   0.0  0.0   0.0   0.0    0.0     0.0  0.0   0.0   0.0  0.0   \n",
       "CC       0.0   0.0  0.0   0.0   0.0    0.0     0.0  0.0   0.0   0.0  0.0   \n",
       "CCC      0.0   0.0  0.0   0.0   0.0    0.0     0.0  0.0   0.0   0.0  0.0   \n",
       "CCCC     0.0   0.0  0.0   0.0   0.0    0.0     0.0  0.0   0.0   0.0  0.0   \n",
       "CCCCC    0.0   0.0  0.0   0.0   0.0    0.0     0.0  0.0   0.0   0.0  0.0   \n",
       "\n",
       "        CATS2D_00_DD  CATS2D_01_DD  CATS2D_02_DD  CATS2D_03_DD  CATS2D_04_DD  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_05_DD  CATS2D_06_DD  CATS2D_07_DD  CATS2D_08_DD  CATS2D_09_DD  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_01_DA  CATS2D_02_DA  CATS2D_03_DA  CATS2D_04_DA  CATS2D_05_DA  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_06_DA  CATS2D_07_DA  CATS2D_08_DA  CATS2D_09_DA  CATS2D_00_DP  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_01_DP  CATS2D_02_DP  CATS2D_03_DP  CATS2D_04_DP  CATS2D_05_DP  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_06_DP  CATS2D_07_DP  CATS2D_09_DP  CATS2D_01_DN  CATS2D_02_DN  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_03_DN  CATS2D_04_DN  CATS2D_05_DN  CATS2D_06_DN  CATS2D_07_DN  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_08_DN  CATS2D_09_DN  CATS2D_02_DL  CATS2D_03_DL  CATS2D_04_DL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_05_DL  CATS2D_06_DL  CATS2D_07_DL  CATS2D_08_DL  CATS2D_09_DL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_01_AA  CATS2D_02_AA  CATS2D_03_AA  CATS2D_04_AA  CATS2D_05_AA  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_06_AA  CATS2D_07_AA  CATS2D_08_AA  CATS2D_09_AA  CATS2D_01_AP  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_02_AP  CATS2D_03_AP  CATS2D_04_AP  CATS2D_05_AP  CATS2D_06_AP  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_07_AP  CATS2D_01_AN  CATS2D_02_AN  CATS2D_03_AN  CATS2D_04_AN  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_05_AN  CATS2D_06_AN  CATS2D_07_AN  CATS2D_08_AN  CATS2D_09_AN  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_01_AL  CATS2D_02_AL  CATS2D_03_AL  CATS2D_04_AL  CATS2D_05_AL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_06_AL  CATS2D_07_AL  CATS2D_08_AL  CATS2D_09_AL  CATS2D_00_PP  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_02_PP  CATS2D_03_PP  CATS2D_04_PP  CATS2D_05_PP  CATS2D_06_PP  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_07_PP  CATS2D_09_PP  CATS2D_01_PN  CATS2D_02_PN  CATS2D_04_PN  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_05_PN  CATS2D_06_PN  CATS2D_02_PL  CATS2D_03_PL  CATS2D_04_PL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_05_PL  CATS2D_06_PL  CATS2D_07_PL  CATS2D_08_PL  CATS2D_09_PL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_01_NN  CATS2D_02_NN  CATS2D_03_NN  CATS2D_04_NN  CATS2D_05_NN  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_06_NN  CATS2D_07_NN  CATS2D_08_NN  CATS2D_09_NN  CATS2D_01_NL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_02_NL  CATS2D_03_NL  CATS2D_04_NL  CATS2D_05_NL  CATS2D_06_NL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             0.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_07_NL  CATS2D_08_NL  CATS2D_09_NL  CATS2D_01_LL  CATS2D_02_LL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           1.0           0.0   \n",
       "CCC              0.0           0.0           0.0           2.0           1.0   \n",
       "CCCC             0.0           0.0           0.0           3.0           2.0   \n",
       "CCCCC            0.0           0.0           0.0           4.0           3.0   \n",
       "\n",
       "        CATS2D_03_LL  CATS2D_04_LL  CATS2D_05_LL  CATS2D_06_LL  CATS2D_07_LL  \\\n",
       "SMILES                                                                         \n",
       "C                0.0           0.0           0.0           0.0           0.0   \n",
       "CC               0.0           0.0           0.0           0.0           0.0   \n",
       "CCC              0.0           0.0           0.0           0.0           0.0   \n",
       "CCCC             1.0           0.0           0.0           0.0           0.0   \n",
       "CCCCC            2.0           1.0           0.0           0.0           0.0   \n",
       "\n",
       "        CATS2D_08_LL  CATS2D_09_LL  T(N..N)  T(N..O)  T(N..S)  T(N..F)  \\\n",
       "SMILES                                                                   \n",
       "C                0.0           0.0      0.0      0.0      0.0      0.0   \n",
       "CC               0.0           0.0      0.0      0.0      0.0      0.0   \n",
       "CCC              0.0           0.0      0.0      0.0      0.0      0.0   \n",
       "CCCC             0.0           0.0      0.0      0.0      0.0      0.0   \n",
       "CCCCC            0.0           0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "        T(N..Cl)  T(N..Br)  T(N..I)  T(O..O)  T(O..S)  T(O..F)  T(O..Cl)  \\\n",
       "SMILES                                                                     \n",
       "C            0.0       0.0      0.0      0.0      0.0      0.0       0.0   \n",
       "CC           0.0       0.0      0.0      0.0      0.0      0.0       0.0   \n",
       "CCC          0.0       0.0      0.0      0.0      0.0      0.0       0.0   \n",
       "CCCC         0.0       0.0      0.0      0.0      0.0      0.0       0.0   \n",
       "CCCCC        0.0       0.0      0.0      0.0      0.0      0.0       0.0   \n",
       "\n",
       "        T(O..Br)  T(O..I)  T(S..S)  T(S..F)  T(S..Cl)  T(F..F)  T(F..Cl)  \\\n",
       "SMILES                                                                     \n",
       "C            0.0      0.0      0.0      0.0       0.0      0.0       0.0   \n",
       "CC           0.0      0.0      0.0      0.0       0.0      0.0       0.0   \n",
       "CCC          0.0      0.0      0.0      0.0       0.0      0.0       0.0   \n",
       "CCCC         0.0      0.0      0.0      0.0       0.0      0.0       0.0   \n",
       "CCCCC        0.0      0.0      0.0      0.0       0.0      0.0       0.0   \n",
       "\n",
       "        T(F..Br)  T(Cl..Cl)  T(Cl..Br)  T(Cl..I)  T(Br..Br)  T(I..I)  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0        0.0       0.0        0.0      0.0   \n",
       "CC           0.0        0.0        0.0       0.0        0.0      0.0   \n",
       "CCC          0.0        0.0        0.0       0.0        0.0      0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0        0.0      0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0        0.0      0.0   \n",
       "\n",
       "        B01[C-C]  B01[C-N]  B01[C-O]  B01[C-S]  B01[C-F]  B01[C-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B01[C-Br]  B01[C-I]  B01[N-N]  B01[N-O]  B01[N-S]  B01[N-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B01[O-O]  B01[O-S]  B01[S-S]  B02[C-C]  B02[C-N]  B02[C-O]  B02[C-S]  \\\n",
       "SMILES                                                                         \n",
       "C            0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC          0.0       0.0       0.0       1.0       0.0       0.0       0.0   \n",
       "CCCC         0.0       0.0       0.0       1.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0       0.0       0.0       1.0       0.0       0.0       0.0   \n",
       "\n",
       "        B02[C-F]  B02[C-Cl]  B02[C-Br]  B02[C-I]  B02[N-N]  B02[N-O]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CC           0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCC          0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B02[N-S]  B02[N-F]  B02[N-Cl]  B02[N-Br]  B02[O-O]  B02[O-S]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CC           0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCC          0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCC         0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCCC        0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "        B02[O-F]  B02[O-Cl]  B02[S-S]  B02[S-F]  B02[S-Cl]  B02[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CC           0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCC          0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC         0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC        0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        B02[F-Cl]  B02[F-Br]  B02[Cl-Cl]  B02[Cl-Br]  B02[Cl-I]  B02[Br-Br]  \\\n",
       "SMILES                                                                        \n",
       "C             0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CC            0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CCC           0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "\n",
       "        B02[I-I]  B03[C-C]  B03[C-N]  B03[C-O]  B03[C-S]  B03[C-F]  B03[C-Cl]  \\\n",
       "SMILES                                                                          \n",
       "C            0.0       0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B03[C-Br]  B03[C-I]  B03[N-N]  B03[N-O]  B03[N-S]  B03[N-F]  \\\n",
       "SMILES                                                                \n",
       "C             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B03[N-Cl]  B03[N-Br]  B03[O-O]  B03[O-S]  B03[O-F]  B03[O-Cl]  \\\n",
       "SMILES                                                                  \n",
       "C             0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B03[O-Br]  B03[O-I]  B03[S-S]  B03[S-F]  B03[S-Cl]  B03[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        B03[F-Cl]  B03[F-Br]  B03[Cl-Cl]  B03[Cl-Br]  B03[Br-Br]  B04[C-C]  \\\n",
       "SMILES                                                                       \n",
       "C             0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CC            0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCC           0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0         0.0       1.0   \n",
       "\n",
       "        B04[C-N]  B04[C-O]  B04[C-S]  B04[C-F]  B04[C-Cl]  B04[C-Br]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "        B04[C-I]  B04[N-N]  B04[N-O]  B04[N-S]  B04[N-F]  B04[N-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B04[N-Br]  B04[N-I]  B04[O-O]  B04[O-S]  B04[O-F]  B04[O-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B04[O-Br]  B04[O-I]  B04[S-S]  B04[S-F]  B04[S-Cl]  B04[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        B04[F-Cl]  B04[F-Br]  B04[Cl-Cl]  B04[Cl-Br]  B04[Br-Br]  B04[I-I]  \\\n",
       "SMILES                                                                       \n",
       "C             0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CC            0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCC           0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "\n",
       "        B05[C-C]  B05[C-N]  B05[C-O]  B05[C-S]  B05[C-F]  B05[C-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B05[C-Br]  B05[C-I]  B05[N-N]  B05[N-O]  B05[N-S]  B05[N-F]  \\\n",
       "SMILES                                                                \n",
       "C             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B05[N-Cl]  B05[N-Br]  B05[N-I]  B05[O-O]  B05[O-S]  B05[O-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B05[O-Cl]  B05[O-Br]  B05[S-S]  B05[S-F]  B05[S-Cl]  B05[F-F]  \\\n",
       "SMILES                                                                  \n",
       "C             0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        B05[F-Cl]  B05[F-Br]  B05[Cl-Cl]  B05[Cl-Br]  B05[Br-Br]  B06[C-C]  \\\n",
       "SMILES                                                                       \n",
       "C             0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CC            0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCC           0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "\n",
       "        B06[C-N]  B06[C-O]  B06[C-S]  B06[C-F]  B06[C-Cl]  B06[C-Br]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "        B06[C-I]  B06[N-N]  B06[N-O]  B06[N-S]  B06[N-F]  B06[N-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B06[N-Br]  B06[N-I]  B06[O-O]  B06[O-S]  B06[O-F]  B06[O-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B06[O-Br]  B06[O-I]  B06[S-S]  B06[S-F]  B06[S-Cl]  B06[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        B06[F-Cl]  B06[Cl-Cl]  B06[Cl-Br]  B06[Br-Br]  B07[C-C]  B07[C-N]  \\\n",
       "SMILES                                                                      \n",
       "C             0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CC            0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CCC           0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CCCC          0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CCCCC         0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "\n",
       "        B07[C-O]  B07[C-S]  B07[C-F]  B07[C-Cl]  B07[C-Br]  B07[C-I]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CC           0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCC          0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCC         0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCCC        0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "\n",
       "        B07[N-N]  B07[N-O]  B07[N-S]  B07[N-F]  B07[N-Cl]  B07[N-Br]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "        B07[O-O]  B07[O-S]  B07[O-F]  B07[O-Cl]  B07[O-Br]  B07[S-S]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CC           0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCC          0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCC         0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCCC        0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "\n",
       "        B07[S-F]  B07[S-Cl]  B07[F-F]  B07[F-Cl]  B07[Cl-Cl]  B07[Cl-Br]  \\\n",
       "SMILES                                                                     \n",
       "C            0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CC           0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CCC          0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CCCC         0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CCCCC        0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "\n",
       "        B07[Br-Br]  B08[C-C]  B08[C-N]  B08[C-O]  B08[C-S]  B08[C-F]  \\\n",
       "SMILES                                                                 \n",
       "C              0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B08[C-Cl]  B08[C-Br]  B08[C-I]  B08[N-N]  B08[N-O]  B08[N-S]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B08[N-F]  B08[N-Cl]  B08[N-Br]  B08[O-O]  B08[O-S]  B08[O-F]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CC           0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCC          0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B08[O-Cl]  B08[O-Br]  B08[S-Cl]  B08[F-F]  B08[F-Cl]  B08[Cl-Cl]  \\\n",
       "SMILES                                                                     \n",
       "C             0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CC            0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCC           0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCC          0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCCC         0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "\n",
       "        B08[Cl-Br]  B08[Br-Br]  B09[C-C]  B09[C-N]  B09[C-O]  B09[C-S]  \\\n",
       "SMILES                                                                   \n",
       "C              0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CC             0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CCC            0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC           0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC          0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B09[C-F]  B09[C-Cl]  B09[C-Br]  B09[C-I]  B09[N-N]  B09[N-O]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CC           0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCC          0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B09[N-S]  B09[N-F]  B09[N-Cl]  B09[N-Br]  B09[O-O]  B09[O-S]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CC           0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCC          0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCC         0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCCC        0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "        B09[O-F]  B09[O-Cl]  B09[O-Br]  B09[F-F]  B09[F-Cl]  B09[Cl-Cl]  \\\n",
       "SMILES                                                                    \n",
       "C            0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CC           0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCC          0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "\n",
       "        B09[Br-Br]  B10[C-C]  B10[C-N]  B10[C-O]  B10[C-S]  B10[C-F]  \\\n",
       "SMILES                                                                 \n",
       "C              0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B10[C-Cl]  B10[C-Br]  B10[C-I]  B10[N-N]  B10[N-O]  B10[N-S]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        B10[N-F]  B10[N-Cl]  B10[O-O]  B10[O-S]  B10[O-F]  B10[O-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        B10[O-Br]  B10[S-F]  B10[F-F]  B10[F-Cl]  B10[Cl-Cl]  B10[Br-Br]  \\\n",
       "SMILES                                                                     \n",
       "C             0.0       0.0       0.0        0.0         0.0         0.0   \n",
       "CC            0.0       0.0       0.0        0.0         0.0         0.0   \n",
       "CCC           0.0       0.0       0.0        0.0         0.0         0.0   \n",
       "CCCC          0.0       0.0       0.0        0.0         0.0         0.0   \n",
       "CCCCC         0.0       0.0       0.0        0.0         0.0         0.0   \n",
       "\n",
       "        F01[C-C]  F01[C-N]  F01[C-O]  F01[C-S]  F01[C-F]  F01[C-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          2.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         3.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        4.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F01[C-Br]  F01[C-I]  F01[N-N]  F01[N-O]  F01[N-S]  F01[N-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F01[O-O]  F01[O-S]  F01[S-S]  F02[C-C]  F02[C-N]  F02[C-O]  F02[C-S]  \\\n",
       "SMILES                                                                         \n",
       "C            0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC          0.0       0.0       0.0       1.0       0.0       0.0       0.0   \n",
       "CCCC         0.0       0.0       0.0       2.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0       0.0       0.0       3.0       0.0       0.0       0.0   \n",
       "\n",
       "        F02[C-F]  F02[C-Cl]  F02[C-Br]  F02[C-I]  F02[N-N]  F02[N-O]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CC           0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCC          0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F02[N-S]  F02[N-F]  F02[N-Cl]  F02[N-Br]  F02[O-O]  F02[O-S]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CC           0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCC          0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCC         0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCCC        0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "        F02[O-F]  F02[O-Cl]  F02[S-S]  F02[S-F]  F02[S-Cl]  F02[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CC           0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCC          0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC         0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC        0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        F02[F-Cl]  F02[F-Br]  F02[Cl-Cl]  F02[Cl-Br]  F02[Cl-I]  F02[Br-Br]  \\\n",
       "SMILES                                                                        \n",
       "C             0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CC            0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CCC           0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0        0.0         0.0   \n",
       "\n",
       "        F02[I-I]  F03[C-C]  F03[C-N]  F03[C-O]  F03[C-S]  F03[C-F]  F03[C-Cl]  \\\n",
       "SMILES                                                                          \n",
       "C            0.0       0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       1.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       2.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F03[C-Br]  F03[C-I]  F03[N-N]  F03[N-O]  F03[N-S]  F03[N-F]  \\\n",
       "SMILES                                                                \n",
       "C             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F03[N-Cl]  F03[N-Br]  F03[O-O]  F03[O-S]  F03[O-F]  F03[O-Cl]  \\\n",
       "SMILES                                                                  \n",
       "C             0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F03[O-Br]  F03[O-I]  F03[S-S]  F03[S-F]  F03[S-Cl]  F03[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        F03[F-Cl]  F03[F-Br]  F03[Cl-Cl]  F03[Cl-Br]  F03[Br-Br]  F04[C-C]  \\\n",
       "SMILES                                                                       \n",
       "C             0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CC            0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCC           0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0         0.0       1.0   \n",
       "\n",
       "        F04[C-N]  F04[C-O]  F04[C-S]  F04[C-F]  F04[C-Cl]  F04[C-Br]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "        F04[C-I]  F04[N-N]  F04[N-O]  F04[N-S]  F04[N-F]  F04[N-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F04[N-Br]  F04[N-I]  F04[O-O]  F04[O-S]  F04[O-F]  F04[O-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F04[O-Br]  F04[O-I]  F04[S-S]  F04[S-F]  F04[S-Cl]  F04[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        F04[F-Cl]  F04[F-Br]  F04[Cl-Cl]  F04[Cl-Br]  F04[Br-Br]  F04[I-I]  \\\n",
       "SMILES                                                                       \n",
       "C             0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CC            0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCC           0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "\n",
       "        F05[C-C]  F05[C-N]  F05[C-O]  F05[C-S]  F05[C-F]  F05[C-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F05[C-Br]  F05[C-I]  F05[N-N]  F05[N-O]  F05[N-S]  F05[N-F]  \\\n",
       "SMILES                                                                \n",
       "C             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F05[N-Cl]  F05[N-Br]  F05[N-I]  F05[O-O]  F05[O-S]  F05[O-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F05[O-Cl]  F05[O-Br]  F05[S-S]  F05[S-F]  F05[S-Cl]  F05[F-F]  \\\n",
       "SMILES                                                                  \n",
       "C             0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        F05[F-Cl]  F05[F-Br]  F05[Cl-Cl]  F05[Cl-Br]  F05[Br-Br]  F06[C-C]  \\\n",
       "SMILES                                                                       \n",
       "C             0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CC            0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCC           0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCC          0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "CCCCC         0.0        0.0         0.0         0.0         0.0       0.0   \n",
       "\n",
       "        F06[C-N]  F06[C-O]  F06[C-S]  F06[C-F]  F06[C-Cl]  F06[C-Br]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "        F06[C-I]  F06[N-N]  F06[N-O]  F06[N-S]  F06[N-F]  F06[N-Cl]  \\\n",
       "SMILES                                                                \n",
       "C            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F06[N-Br]  F06[N-I]  F06[O-O]  F06[O-S]  F06[O-F]  F06[O-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CC            0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCC           0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F06[O-Br]  F06[O-I]  F06[S-S]  F06[S-F]  F06[S-Cl]  F06[F-F]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CC            0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCC           0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCC          0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "CCCCC         0.0       0.0       0.0       0.0        0.0       0.0   \n",
       "\n",
       "        F06[F-Cl]  F06[Cl-Cl]  F06[Cl-Br]  F06[Br-Br]  F07[C-C]  F07[C-N]  \\\n",
       "SMILES                                                                      \n",
       "C             0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CC            0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CCC           0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CCCC          0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "CCCCC         0.0         0.0         0.0         0.0       0.0       0.0   \n",
       "\n",
       "        F07[C-O]  F07[C-S]  F07[C-F]  F07[C-Cl]  F07[C-Br]  F07[C-I]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CC           0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCC          0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCC         0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCCC        0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "\n",
       "        F07[N-N]  F07[N-O]  F07[N-S]  F07[N-F]  F07[N-Cl]  F07[N-Br]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CC           0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCC          0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCC         0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "CCCCC        0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "        F07[O-O]  F07[O-S]  F07[O-F]  F07[O-Cl]  F07[O-Br]  F07[S-S]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CC           0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCC          0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCC         0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "CCCCC        0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "\n",
       "        F07[S-F]  F07[S-Cl]  F07[F-F]  F07[F-Cl]  F07[Cl-Cl]  F07[Cl-Br]  \\\n",
       "SMILES                                                                     \n",
       "C            0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CC           0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CCC          0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CCCC         0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "CCCCC        0.0        0.0       0.0        0.0         0.0         0.0   \n",
       "\n",
       "        F07[Br-Br]  F08[C-C]  F08[C-N]  F08[C-O]  F08[C-S]  F08[C-F]  \\\n",
       "SMILES                                                                 \n",
       "C              0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F08[C-Cl]  F08[C-Br]  F08[C-I]  F08[N-N]  F08[N-O]  F08[N-S]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F08[N-F]  F08[N-Cl]  F08[N-Br]  F08[O-O]  F08[O-S]  F08[O-F]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CC           0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCC          0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F08[O-Cl]  F08[O-Br]  F08[S-Cl]  F08[F-F]  F08[F-Cl]  F08[Cl-Cl]  \\\n",
       "SMILES                                                                     \n",
       "C             0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CC            0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCC           0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCC          0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCCC         0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "\n",
       "        F08[Cl-Br]  F08[Br-Br]  F09[C-C]  F09[C-N]  F09[C-O]  F09[C-S]  \\\n",
       "SMILES                                                                   \n",
       "C              0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CC             0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CCC            0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC           0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC          0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F09[C-F]  F09[C-Cl]  F09[C-Br]  F09[C-I]  F09[N-N]  F09[N-O]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CC           0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCC          0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F09[N-S]  F09[N-F]  F09[N-Cl]  F09[N-Br]  F09[O-O]  F09[O-S]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CC           0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCC          0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCC         0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "CCCCC        0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "        F09[O-F]  F09[O-Cl]  F09[O-Br]  F09[F-F]  F09[F-Cl]  F09[Cl-Cl]  \\\n",
       "SMILES                                                                    \n",
       "C            0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CC           0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCC          0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCC         0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "CCCCC        0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "\n",
       "        F09[Br-Br]  F10[C-C]  F10[C-N]  F10[C-O]  F10[C-S]  F10[C-F]  \\\n",
       "SMILES                                                                 \n",
       "C              0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CC             0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCC            0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC           0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC          0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F10[C-Cl]  F10[C-Br]  F10[C-I]  F10[N-N]  F10[N-O]  F10[N-S]  \\\n",
       "SMILES                                                                 \n",
       "C             0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CC            0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCC           0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCC          0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "CCCCC         0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "        F10[N-F]  F10[N-Cl]  F10[O-O]  F10[O-S]  F10[O-F]  F10[O-Cl]  \\\n",
       "SMILES                                                                 \n",
       "C            0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CC           0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCC          0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCC         0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "CCCCC        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "        F10[O-Br]  F10[S-F]  F10[F-F]  F10[F-Cl]  F10[Cl-Cl]  F10[Br-Br]   Uc  \\\n",
       "SMILES                                                                          \n",
       "C             0.0       0.0       0.0        0.0         0.0         0.0  0.0   \n",
       "CC            0.0       0.0       0.0        0.0         0.0         0.0  0.0   \n",
       "CCC           0.0       0.0       0.0        0.0         0.0         0.0  0.0   \n",
       "CCCC          0.0       0.0       0.0        0.0         0.0         0.0  0.0   \n",
       "CCCCC         0.0       0.0       0.0        0.0         0.0         0.0  0.0   \n",
       "\n",
       "         Ui     Hy  TPSA(NO)  TPSA(Tot)  MLOGP  MLOGP2    SAtot  SAacc  SAdon  \\\n",
       "SMILES                                                                          \n",
       "C       0.0  0.000       0.0        0.0  1.115   1.243   62.200    0.0    0.0   \n",
       "CC      0.0 -0.631       0.0        0.0  1.757   3.085   88.277    0.0    0.0   \n",
       "CCC     0.0 -0.792       0.0        0.0  2.276   5.180  114.354    0.0    0.0   \n",
       "CCCC    0.0 -0.861       0.0        0.0  2.729   7.447  140.430    0.0    0.0   \n",
       "CCCCC   0.0 -0.898       0.0        0.0  3.138   9.850  166.507    0.0    0.0   \n",
       "\n",
       "             Vx  VvdwMG  VvdwZAZ    PDI  BLTF96  BLTD48  BLTA96  cRo5  DLS_01  \\\n",
       "SMILES                                                                          \n",
       "C        41.445  20.363    25.86  0.666   -2.34   -2.38   -2.35   1.0     1.0   \n",
       "CC       64.850  29.955    43.16  0.735   -2.88   -2.99   -2.99   1.0     1.0   \n",
       "CCC      88.256  39.547    60.46  0.772   -3.32   -3.48   -3.51   1.0     1.0   \n",
       "CCCC    111.661  49.140    77.76  0.795   -3.71   -3.91   -3.96   1.0     1.0   \n",
       "CCCCC   135.066  58.732    95.06  0.811   -4.06   -4.30   -4.37   1.0     1.0   \n",
       "\n",
       "        DLS_02  DLS_03  DLS_04  DLS_05  DLS_06  DLS_07  DLS_cons  LLS_01  \\\n",
       "SMILES                                                                     \n",
       "C         0.50    0.83     0.2     0.0     1.0     1.0      0.65    1.00   \n",
       "CC        0.50    0.83     0.4     0.5     1.0     1.0      0.75    1.00   \n",
       "CCC       0.50    0.83     0.4     0.5     1.0     1.0      0.75    1.00   \n",
       "CCCC      0.67    0.83     0.4     0.5     1.0     1.0      0.77    1.00   \n",
       "CCCCC     0.67    0.83     0.4     0.5     1.0     1.0      0.77    0.83   \n",
       "\n",
       "        LLS_02  \n",
       "SMILES          \n",
       "C         0.88  \n",
       "CC        0.88  \n",
       "CCC       0.88  \n",
       "CCCC      0.88  \n",
       "CCCCC     0.88  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_hc_df = pd.read_csv('filtered_organics_desc_kh.csv')\n",
    "linear_hc_df.rename({'Unnamed: 0':'SMILES', '0':'Species'}, axis=1, inplace=True)\n",
    "linear_hc_df.set_index('SMILES', inplace=True)\n",
    "linear_smiles_list = []\n",
    "for i in range(1, 40):\n",
    "    smiles_linear = i * 'C'\n",
    "    linear_smiles_list.append(smiles_linear)\n",
    "    \n",
    "\n",
    "linear_hc_df = linear_hc_df.loc[linear_smiles_list]\n",
    "linear_hc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T18:25:34.953089Z",
     "start_time": "2019-12-10T18:25:34.044642Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in log\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_style = dict(size=17, color='blue')\n",
    "ax_style = dict(fontsize=20, color='black')\n",
    "title_style = dict(size=25, color='black')\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 5), sharey=True)\n",
    "\n",
    "y = np.log(np.array(linear_hc_df.Kh_mean))\n",
    "\n",
    "x1 = np.array(np.array(linear_hc_df.MLOGP))\n",
    "ax1.plot(x1, y, 'bo')\n",
    "ax1.set_ylabel('$log(K_H)$')\n",
    "ax1.set_title('MLOGP')\n",
    "#ax1 = plt.scatter(x1, y, label = 'mlogp')\n",
    "#line = sns.regplot(x1, y)\n",
    "\n",
    "\n",
    "x2 = np.array(np.array(linear_hc_df.Sv))\n",
    "ax2.plot(x2, y, 'bo')\n",
    "ax2.set_ylabel('$log(K_H)$')\n",
    "ax2.set_title('Sum of atomic van der Waals volumes')\n",
    "#ax2 = plt.scatter(x2, y, label = '# Carbons')\n",
    "#line = sns.regplot(x2, y)\n",
    "\n",
    "\n",
    "x3 = np.array(np.array(linear_hc_df.MW))\n",
    "ax3.plot(x3, y, 'bo')\n",
    "ax3.set_ylabel('$log(K_H)$')\n",
    "ax3.set_title('Molecular Weight')\n",
    "#ax3 = plt.scatter(x3, y, label = 'MolWt / 10')\n",
    "#line = sns.regplot(x3, y)\n",
    "\n",
    "x4 = np.array(np.array(linear_hc_df['Mp']))\n",
    "ax4.plot(x4, y, 'bo')\n",
    "ax4.set_ylabel('$log(K_H)$')\n",
    "ax4.set_title('Mean atomic polarizability')\n",
    "#ax4 = plt.scatter(x4, y, label = 'MolWt / 10')\n",
    "#line = sns.regplot(x4, y)\n",
    "\n",
    "\n",
    "# x and y swapped as per Jeremy's advice\n",
    "\n",
    "#ax.legend(prop={'size':20})\n",
    "#ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "#ax.set_ylabel('$ln(K_{H})$', **ax_style)\n",
    "#ax.set_xlabel('Property', **ax_style)\n",
    "#plt.title('$ln(K_{H})$ against various properties', **title_style)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "\n",
    "to_image = plt.gcf()\n",
    "to_image.savefig('linear_plots_linear_hydrocarbons.png')\n",
    "plt.show()\n"
   ]
  }
 ],
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