gpHSP_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# gpHSP overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:35.883106Z",
     "start_time": "2018-09-21T15:23:35.545879Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:38.773246Z",
     "start_time": "2018-09-21T15:23:35.885454Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running in Jupyter notebook \n",
      "0.9.3\n"
     ]
    }
   ],
   "source": [
    "# Common libraries\n",
    "from IPython.display import display\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import rdkit.Chem as Chem\n",
    "from collections import OrderedDict\n",
    "# Importing GPmol\n",
    "import gpmol as gpm\n",
    "from gpmol.utils.plotting import html_header, write_latex_table\n",
    "from gpmol.utils.general import delatexfy, save_dict\n",
    "print(gpm.__version__)\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "gpm.plot_settings()\n",
    "gpm.pandas_settings()\n",
    "gpm.logging.set_level('warning')\n",
    "gpm.settings['save_results'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## utility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:38.783104Z",
     "start_time": "2018-09-21T15:23:38.775266Z"
    }
   },
   "outputs": [],
   "source": [
    "import py3Dmol\n",
    "from rdkit import Chem\n",
    "\n",
    "def view_mol_3D(smiles):\n",
    "    mol = gpm.data_transforms.smi2mol(smiles)\n",
    "    mol = gpm.data_transforms.mol2molxyz(mol)\n",
    "    sdf = Chem.MolToMolBlock(mol)\n",
    "    xyzview = py3Dmol.view(width=400, height=400)\n",
    "    xyzview.addModel(sdf, 'sdf')\n",
    "    xyzview.setViewStyle(\n",
    "        {'style': 'outline', 'color': 'black', 'width': 0.05})\n",
    "    xyzview.setStyle({'stick': {'radius': .1}, 'sphere': {'scale': 0.25}})\n",
    "    xyzview.zoomTo()\n",
    "    disp = xyzview.show()\n",
    "    return disp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solvent: Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:38.840266Z",
     "start_time": "2018-09-21T15:23:38.786162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['key', 'Type', 'δd', 'δp', 'δh', 'smiles', 'ID_type', 'ID', 'Ref',\n",
      "       'organic', 'n_electrons', 'n_atoms', 'charge', 'MolWt', 'label',\n",
      "       'finished', 'job_name', 'homo', 'lumo', 'gap', 'd-moments', 'dipole',\n",
      "       'polar', 'run_time', 'n_cores', 'compute_time', 'Area', 'Hba', 'Hbd',\n",
      "       'Volume', 'sigma_mom_0', 'sigma_mom_1', 'sigma_mom_2', 'sigma_mom_3',\n",
      "       'sigma_mom_4', 'sigma_mom_5', 'sigma_norm', 'sigma_profile', 'drug'],\n",
      "      dtype='object')\n",
      "(193, 39)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>Type</th>\n",
       "      <th>δd</th>\n",
       "      <th>δp</th>\n",
       "      <th>δh</th>\n",
       "      <th>smiles</th>\n",
       "      <th>ID_type</th>\n",
       "      <th>ID</th>\n",
       "      <th>Ref</th>\n",
       "      <th>organic</th>\n",
       "      <th>...</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sigma_mom_0</th>\n",
       "      <th>sigma_mom_1</th>\n",
       "      <th>sigma_mom_2</th>\n",
       "      <th>sigma_mom_3</th>\n",
       "      <th>sigma_mom_4</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "      <th>drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1,1,1-Trichloroethane</td>\n",
       "      <td>Solvent</td>\n",
       "      <td>16.80</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>CC(Cl)(Cl)Cl</td>\n",
       "      <td>CAS</td>\n",
       "      <td>71-55-6</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0</td>\n",
       "      <td>16.60</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>4.24</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>1.54</td>\n",
       "      <td>41.43</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     key     Type    δd   δp   δh        smiles ID_type  \\\n",
       "0  1,1,1-Trichloroethane  Solvent 16.80 4.30 2.00  CC(Cl)(Cl)Cl     CAS   \n",
       "\n",
       "        ID Ref  organic  ...    Volume  sigma_mom_0  sigma_mom_1  sigma_mom_2  \\\n",
       "0  71-55-6   1     True  ...      0.13            0        16.60        -3.84   \n",
       "\n",
       "  sigma_mom_3  sigma_mom_4 sigma_mom_5  sigma_norm  \\\n",
       "0        4.24        -2.02        1.54       41.43   \n",
       "\n",
       "                                       sigma_profile   drug  \n",
       "0  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  False  \n",
       "\n",
       "[1 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gpmol.utils.general import str2array\n",
    "df = pd.read_csv('data/Solvents_exp.csv')\n",
    "df['sigma_profile']=df['sigma_profile'].apply(str2array)\n",
    "print(df.columns)\n",
    "print(df.shape)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:38.852835Z",
     "start_time": "2018-09-21T15:23:38.842919Z"
    }
   },
   "outputs": [],
   "source": [
    "from gpmol.features import RegressionTarget, MorganFP,MACCSFP, Properties, Vector\n",
    "from gpmol.data_transforms import SMILES2MOL\n",
    "targets = [RegressionTarget('δd', name='$\\delta d$',units='$MPa^{0.5}$',suffix='exp'),\n",
    "           RegressionTarget('δp', name='$\\delta p$',units='$MPa^{0.5}$',suffix='exp'),\n",
    "           RegressionTarget('δh', name='$\\delta h$',units='$MPa^{0.5}$',suffix='exp')]\n",
    "\n",
    "features = OrderedDict()\n",
    "features['FPb']=MorganFP()\n",
    "features['FPc']=MorganFP(fptype='count',transforms='Normalize')\n",
    "features['MACCS']=MACCSFP()\n",
    "features['SigmaP']=Vector('sigma_profile',name='SigmaP')\n",
    "features['ElectroS']=Properties(['dipole','polar','sigma_norm','Hba','Hbd'],name='ElectroS')\n",
    "features['Size']=Properties(['MolWt','Area','Volume'],name='Size/Shape')\n",
    "\n",
    "feats=OrderedDict()\n",
    "feats['δd']=['FPb','SigmaP','ElectroS','Size']\n",
    "feats['δh']=['FPb','SigmaP','ElectroS','Size']\n",
    "feats['δp']=['FPb','SigmaP','ElectroS','Size']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Experimental Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:57.301289Z",
     "start_time": "2018-09-21T15:23:38.855207Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>$\\delta d$</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x must be 2D at computation. \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a998454fae914b7880a4351c4c3856f4",
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       "version_minor": 0
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Adam', max=10000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>$\\delta p$</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x must be 2D at computation. \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00b0d9b6eb4c4ec7803c3bf20347f937",
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       "HBox(children=(IntProgress(value=0, description='Adam', max=10000), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>$\\delta h$</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x must be 2D at computation. \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80fdd72f26334f0793966f6e4f2d7617",
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       "version_minor": 0
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       "HBox(children=(IntProgress(value=0, description='Adam', max=10000), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "gps=OrderedDict()\n",
    "for indx,t in enumerate(targets):\n",
    "    html_header('{}'.format(t.name))\n",
    "    preproc = gpm.Preprocessor(df)\n",
    "    preproc.append(t)\n",
    "    preproc.append(SMILES2MOL(smi_col='smiles',mol_col='mol'))\n",
    "    preproc.append([ features[fname] for fname in feats[t.cols[0]]])\n",
    "    gp = preproc.make_gpmol(t.cols[0])\n",
    "    gp.minimize()\n",
    "    gps[t.cols[0]]=gp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solvents: Predict on another dataset\n",
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:57.619657Z",
     "start_time": "2018-09-21T15:23:57.303416Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Chemical', 'CAS', 'smiles', 'HSPiP_indx', 'good_smiles', 'single_frag',\n",
      "       'duplicated', 'sanity', 'label', 'organic', 'n_electrons', 'n_atoms',\n",
      "       'charge', 'MolWt', 'homo', 'lumo', 'gap', 'd-moments', 'dipole',\n",
      "       'polar', 'run_time', 'n_cores', 'compute_time', 'Area', 'Hba', 'Hbd',\n",
      "       'Volume', 'sigma_mom_0', 'sigma_mom_1', 'sigma_mom_2', 'sigma_mom_3',\n",
      "       'sigma_mom_4', 'sigma_mom_5', 'sigma_norm', 'sigma_profile'],\n",
      "      dtype='object')\n",
      "(8852, 35)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chemical</th>\n",
       "      <th>CAS</th>\n",
       "      <th>smiles</th>\n",
       "      <th>HSPiP_indx</th>\n",
       "      <th>good_smiles</th>\n",
       "      <th>single_frag</th>\n",
       "      <th>duplicated</th>\n",
       "      <th>sanity</th>\n",
       "      <th>label</th>\n",
       "      <th>organic</th>\n",
       "      <th>...</th>\n",
       "      <th>Hbd</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sigma_mom_0</th>\n",
       "      <th>sigma_mom_1</th>\n",
       "      <th>sigma_mom_2</th>\n",
       "      <th>sigma_mom_3</th>\n",
       "      <th>sigma_mom_4</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acetaldehyde</td>\n",
       "      <td>75-07-0</td>\n",
       "      <td>CC=O</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>hspip_0</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>18.40</td>\n",
       "      <td>30.60</td>\n",
       "      <td>30.10</td>\n",
       "      <td>39.10</td>\n",
       "      <td>22.20</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Chemical      CAS smiles HSPiP_indx  good_smiles  single_frag  \\\n",
       "0  Acetaldehyde  75-07-0   CC=O          0         True         True   \n",
       "\n",
       "   duplicated  sanity    label  organic  \\\n",
       "0        True    True  hspip_0     True   \n",
       "\n",
       "                         ...                          Hbd  Volume  \\\n",
       "0                        ...                         0.00    0.06   \n",
       "\n",
       "   sigma_mom_0  sigma_mom_1 sigma_mom_2  sigma_mom_3  sigma_mom_4 sigma_mom_5  \\\n",
       "0            0        33.00       18.40        30.60        30.10       39.10   \n",
       "\n",
       "   sigma_norm                                      sigma_profile  \n",
       "0       22.20  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "\n",
       "[1 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gpmol.utils.general import str2array\n",
    "ex_df = pd.read_csv('data/HSPiP.csv')\n",
    "ex_df['sigma_profile']=ex_df['sigma_profile'].apply(str2array)\n",
    "print(ex_df.columns)\n",
    "print(ex_df.shape)\n",
    "ex_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For a single molecule\n",
    "\n",
    "### Let's pick a random molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:57.737155Z",
     "start_time": "2018-09-21T15:23:57.621233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>hexanal dibutyl acetal</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chemical</th>\n",
       "      <th>CAS</th>\n",
       "      <th>smiles</th>\n",
       "      <th>HSPiP_indx</th>\n",
       "      <th>good_smiles</th>\n",
       "      <th>single_frag</th>\n",
       "      <th>duplicated</th>\n",
       "      <th>sanity</th>\n",
       "      <th>label</th>\n",
       "      <th>organic</th>\n",
       "      <th>...</th>\n",
       "      <th>Hbd</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sigma_mom_0</th>\n",
       "      <th>sigma_mom_1</th>\n",
       "      <th>sigma_mom_2</th>\n",
       "      <th>sigma_mom_3</th>\n",
       "      <th>sigma_mom_4</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7497</th>\n",
       "      <td>hexanal dibutyl acetal</td>\n",
       "      <td>93892-07-0</td>\n",
       "      <td>CCCCCC(OCCCC)OCCCC</td>\n",
       "      <td>8542</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>hspip_8542</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0</td>\n",
       "      <td>33.20</td>\n",
       "      <td>22.50</td>\n",
       "      <td>33.80</td>\n",
       "      <td>43.70</td>\n",
       "      <td>61.40</td>\n",
       "      <td>103.82</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Chemical         CAS              smiles HSPiP_indx  \\\n",
       "7497  hexanal dibutyl acetal  93892-07-0  CCCCCC(OCCCC)OCCCC       8542   \n",
       "\n",
       "      good_smiles  single_frag  duplicated  sanity       label  organic  \\\n",
       "7497         True         True        True    True  hspip_8542     True   \n",
       "\n",
       "                            ...                          Hbd  Volume  \\\n",
       "7497                        ...                         0.00    0.34   \n",
       "\n",
       "      sigma_mom_0  sigma_mom_1 sigma_mom_2  sigma_mom_3  sigma_mom_4  \\\n",
       "7497            0        33.20       22.50        33.80        43.70   \n",
       "\n",
       "     sigma_mom_5  sigma_norm  \\\n",
       "7497       61.40      103.82   \n",
       "\n",
       "                                          sigma_profile  \n",
       "7497  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "\n",
       "[1 rows x 35 columns]"
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   ],
   "source": [
    "row = ex_df.sample(n=1)\n",
    "html_header(row['Chemical'].iloc[0])\n",
    "display(row)\n",
    "view_mol_3D(row['smiles'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:23:59.991318Z",
     "start_time": "2018-09-21T15:23:57.738974Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 860x559 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 474,
       "width": 728
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "x = np.linspace(0,40,1000)\n",
    "for name, gp in gps.items():\n",
    "    preproc = gp.preprocessor\n",
    "    X_test = preproc.transformX(row)\n",
    "    Y_pred, Y_std = gp.predict(X_test)\n",
    "    plt.plot(x,norm.pdf(x,Y_pred.ravel()[0],Y_std.ravel()[0]),label=name)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T19:50:23.371443Z",
     "start_time": "2018-03-16T19:50:23.368260Z"
    }
   },
   "source": [
    "## Calculate on an entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:07.460888Z",
     "start_time": "2018-09-21T15:23:59.993165Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7a8a7c34cc74c4c8059f0c440f2904c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chemical</th>\n",
       "      <th>CAS</th>\n",
       "      <th>smiles</th>\n",
       "      <th>HSPiP_indx</th>\n",
       "      <th>good_smiles</th>\n",
       "      <th>single_frag</th>\n",
       "      <th>duplicated</th>\n",
       "      <th>sanity</th>\n",
       "      <th>label</th>\n",
       "      <th>organic</th>\n",
       "      <th>...</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "      <th>mol</th>\n",
       "      <th>δd_gp_pred</th>\n",
       "      <th>δd_gp_std</th>\n",
       "      <th>δp_gp_pred</th>\n",
       "      <th>δp_gp_std</th>\n",
       "      <th>δh_gp_pred</th>\n",
       "      <th>δh_gp_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acetaldehyde</td>\n",
       "      <td>75-07-0</td>\n",
       "      <td>CC=O</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>hspip_0</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>39.10</td>\n",
       "      <td>22.20</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381a1ab20&gt;</td>\n",
       "      <td>15.66</td>\n",
       "      <td>0.42</td>\n",
       "      <td>10.04</td>\n",
       "      <td>1.91</td>\n",
       "      <td>6.05</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acetaldoxime</td>\n",
       "      <td>107-29-9</td>\n",
       "      <td>C/C=N/O</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>hspip_1</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>324.00</td>\n",
       "      <td>21.89</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381a1a5d0&gt;</td>\n",
       "      <td>15.87</td>\n",
       "      <td>0.47</td>\n",
       "      <td>6.40</td>\n",
       "      <td>2.42</td>\n",
       "      <td>12.40</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acetamide</td>\n",
       "      <td>60-35-5</td>\n",
       "      <td>CC(N)=O</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>hspip_2</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>221.00</td>\n",
       "      <td>19.35</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381a1a350&gt;</td>\n",
       "      <td>17.65</td>\n",
       "      <td>0.52</td>\n",
       "      <td>19.53</td>\n",
       "      <td>2.26</td>\n",
       "      <td>18.60</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acetanilide</td>\n",
       "      <td>103-84-4</td>\n",
       "      <td>CC(=O)Nc1ccccc1</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>hspip_3</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>158.00</td>\n",
       "      <td>40.87</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381a1a990&gt;</td>\n",
       "      <td>19.55</td>\n",
       "      <td>0.76</td>\n",
       "      <td>9.06</td>\n",
       "      <td>1.97</td>\n",
       "      <td>8.58</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acetic Acid</td>\n",
       "      <td>64-19-7</td>\n",
       "      <td>CC(=O)O</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>hspip_4</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>411.00</td>\n",
       "      <td>19.38</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00087716...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381a1a620&gt;</td>\n",
       "      <td>15.27</td>\n",
       "      <td>0.46</td>\n",
       "      <td>5.87</td>\n",
       "      <td>2.32</td>\n",
       "      <td>14.68</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Chemical       CAS           smiles HSPiP_indx  good_smiles  \\\n",
       "0  Acetaldehyde   75-07-0             CC=O          0         True   \n",
       "1  Acetaldoxime  107-29-9          C/C=N/O          1         True   \n",
       "2     Acetamide   60-35-5          CC(N)=O          2         True   \n",
       "3   Acetanilide  103-84-4  CC(=O)Nc1ccccc1          3         True   \n",
       "4   Acetic Acid   64-19-7          CC(=O)O          4         True   \n",
       "\n",
       "   single_frag  duplicated  sanity    label  organic    ...      sigma_mom_5  \\\n",
       "0         True        True    True  hspip_0     True    ...            39.10   \n",
       "1         True        True    True  hspip_1     True    ...           324.00   \n",
       "2         True        True    True  hspip_2     True    ...           221.00   \n",
       "3         True        True    True  hspip_3     True    ...           158.00   \n",
       "4         True        True    True  hspip_4     True    ...           411.00   \n",
       "\n",
       "   sigma_norm                                      sigma_profile  \\\n",
       "0       22.20  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1       21.89  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2       19.35  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3       40.87  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4       19.38  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00087716...   \n",
       "\n",
       "                                                mol δd_gp_pred  δd_gp_std  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7f2381a1ab20>      15.66       0.42   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7f2381a1a5d0>      15.87       0.47   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7f2381a1a350>      17.65       0.52   \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x7f2381a1a990>      19.55       0.76   \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x7f2381a1a620>      15.27       0.46   \n",
       "\n",
       "   δp_gp_pred δp_gp_std  δh_gp_pred  δh_gp_std  \n",
       "0       10.04      1.91        6.05       1.41  \n",
       "1        6.40      2.42       12.40       2.26  \n",
       "2       19.53      2.26       18.60       1.73  \n",
       "3        9.06      1.97        8.58       1.77  \n",
       "4        5.87      2.32       14.68       2.34  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for name, gp in tqdm(gps.items(),total=len(gps)):\n",
    "    X_test = gp.preprocessor.transformX(ex_df)\n",
    "    Y_pred, Y_std = gp.predict(X_test)\n",
    "    ex_df['{}_gp_pred'.format(name)]=Y_pred.ravel()\n",
    "    ex_df['{}_gp_std'.format(name)]=Y_std.ravel()\n",
    "ex_df.to_csv('results/HSPiP_predictions.csv',index=False)\n",
    "ex_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polymers: Train model\n",
    "\n",
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:07.500263Z",
     "start_time": "2018-09-21T15:25:07.462867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['label', 'δd', 'δp', 'δh', 'smiles', 'test', 'poly_label', 'n_copies',\n",
      "       'organic', 'n_electrons', 'n_atoms', 'charge', 'MolWt', 'n_frags',\n",
      "       'largest', 'finished', 'job_name', 'poly_smiles', 'homo', 'lumo', 'gap',\n",
      "       'd-moments', 'dipole', 'polar', 'run_time', 'n_cores', 'compute_time',\n",
      "       'Area', 'Hba', 'Hbd', 'Volume', 'sigma_mom_0', 'sigma_mom_1',\n",
      "       'sigma_mom_2', 'sigma_mom_3', 'sigma_mom_4', 'sigma_mom_5',\n",
      "       'sigma_norm', 'sigma_profile'],\n",
      "      dtype='object')\n",
      "(31, 39)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>δd</th>\n",
       "      <th>δp</th>\n",
       "      <th>δh</th>\n",
       "      <th>smiles</th>\n",
       "      <th>test</th>\n",
       "      <th>poly_label</th>\n",
       "      <th>n_copies</th>\n",
       "      <th>organic</th>\n",
       "      <th>n_electrons</th>\n",
       "      <th>...</th>\n",
       "      <th>Hbd</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sigma_mom_0</th>\n",
       "      <th>sigma_mom_1</th>\n",
       "      <th>sigma_mom_2</th>\n",
       "      <th>sigma_mom_3</th>\n",
       "      <th>sigma_mom_4</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Polyacrylonitrile-n5</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.10</td>\n",
       "      <td>7.90</td>\n",
       "      <td>CCC#N</td>\n",
       "      <td>True</td>\n",
       "      <td>Polyacrylonitrile</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0</td>\n",
       "      <td>175.00</td>\n",
       "      <td>26.10</td>\n",
       "      <td>154.00</td>\n",
       "      <td>51.40</td>\n",
       "      <td>165.00</td>\n",
       "      <td>66.77</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  label    δd    δp   δh smiles  test         poly_label  \\\n",
       "0  Polyacrylonitrile-n5 20.00 15.10 7.90  CCC#N  True  Polyacrylonitrile   \n",
       "\n",
       "   n_copies  organic  n_electrons  \\\n",
       "0         5     True          110   \n",
       "\n",
       "                         ...                          Hbd  Volume  \\\n",
       "0                        ...                         0.22    0.34   \n",
       "\n",
       "   sigma_mom_0  sigma_mom_1  sigma_mom_2  sigma_mom_3 sigma_mom_4 sigma_mom_5  \\\n",
       "0            0       175.00        26.10       154.00       51.40      165.00   \n",
       "\n",
       "   sigma_norm                                      sigma_profile  \n",
       "0       66.77  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "\n",
       "[1 rows x 39 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gpmol.utils.general import str2array\n",
    "poly_df = pd.read_csv('data/Polymers_exp.csv')\n",
    "poly_df['sigma_profile']=poly_df['sigma_profile'].apply(str2array)\n",
    "print(poly_df.columns)\n",
    "print(poly_df.shape)\n",
    "poly_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Calculate \"Solvent\" HSP and difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:07.860329Z",
     "start_time": "2018-09-21T15:25:07.502065Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>δd</th>\n",
       "      <th>δp</th>\n",
       "      <th>δh</th>\n",
       "      <th>smiles</th>\n",
       "      <th>test</th>\n",
       "      <th>poly_label</th>\n",
       "      <th>n_copies</th>\n",
       "      <th>organic</th>\n",
       "      <th>n_electrons</th>\n",
       "      <th>...</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "      <th>mol</th>\n",
       "      <th>δd_gpsolv</th>\n",
       "      <th>δd_diff</th>\n",
       "      <th>δp_gpsolv</th>\n",
       "      <th>δp_diff</th>\n",
       "      <th>δh_gpsolv</th>\n",
       "      <th>δh_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Polyacrylonitrile-n5</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.10</td>\n",
       "      <td>7.90</td>\n",
       "      <td>CCC#N</td>\n",
       "      <td>True</td>\n",
       "      <td>Polyacrylonitrile</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>165.00</td>\n",
       "      <td>66.77</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381871030&gt;</td>\n",
       "      <td>15.86</td>\n",
       "      <td>4.14</td>\n",
       "      <td>22.19</td>\n",
       "      <td>-7.09</td>\n",
       "      <td>8.08</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Polyethylene-n5</td>\n",
       "      <td>16.90</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.80</td>\n",
       "      <td>CC</td>\n",
       "      <td>True</td>\n",
       "      <td>Polyethylene</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>91.88</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381871e90&gt;</td>\n",
       "      <td>15.49</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.14</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Polypropylene-n5</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>CCC</td>\n",
       "      <td>True</td>\n",
       "      <td>Polypropylene</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>112.13</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381871530&gt;</td>\n",
       "      <td>15.56</td>\n",
       "      <td>2.44</td>\n",
       "      <td>1.72</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>4.11</td>\n",
       "      <td>-3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Polystyrene-n5</td>\n",
       "      <td>18.70</td>\n",
       "      <td>5.90</td>\n",
       "      <td>3.50</td>\n",
       "      <td>CCc1ccccc1</td>\n",
       "      <td>True</td>\n",
       "      <td>Polystyrene</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>8.33</td>\n",
       "      <td>139.09</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381871260&gt;</td>\n",
       "      <td>17.77</td>\n",
       "      <td>0.93</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.30</td>\n",
       "      <td>6.20</td>\n",
       "      <td>-2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Polyvinylchloride-n5</td>\n",
       "      <td>18.80</td>\n",
       "      <td>9.20</td>\n",
       "      <td>6.30</td>\n",
       "      <td>CCCl</td>\n",
       "      <td>True</td>\n",
       "      <td>Polyvinylchloride</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>5.48</td>\n",
       "      <td>80.14</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2381871cb0&gt;</td>\n",
       "      <td>18.22</td>\n",
       "      <td>0.58</td>\n",
       "      <td>11.35</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  label    δd    δp   δh      smiles  test         poly_label  \\\n",
       "0  Polyacrylonitrile-n5 20.00 15.10 7.90       CCC#N  True  Polyacrylonitrile   \n",
       "1       Polyethylene-n5 16.90  0.80 2.80          CC  True       Polyethylene   \n",
       "2      Polypropylene-n5 18.00  0.00 1.00         CCC  True      Polypropylene   \n",
       "3        Polystyrene-n5 18.70  5.90 3.50  CCc1ccccc1  True        Polystyrene   \n",
       "4  Polyvinylchloride-n5 18.80  9.20 6.30        CCCl  True  Polyvinylchloride   \n",
       "\n",
       "   n_copies  organic  n_electrons   ...     sigma_mom_5  sigma_norm  \\\n",
       "0         5     True          110   ...          165.00       66.77   \n",
       "1         5     True           70   ...            0.02       91.88   \n",
       "2         5     True          100   ...            0.03      112.13   \n",
       "3         5     True          210   ...            8.33      139.09   \n",
       "4         5     True          100   ...            5.48       80.14   \n",
       "\n",
       "                                       sigma_profile  \\\n",
       "0  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                mol  δd_gpsolv  δd_diff  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7f2381871030>      15.86     4.14   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7f2381871e90>      15.49     1.41   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7f2381871530>      15.56     2.44   \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x7f2381871260>      17.77     0.93   \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x7f2381871cb0>      18.22     0.58   \n",
       "\n",
       "  δp_gpsolv δp_diff  δh_gpsolv  δh_diff  \n",
       "0     22.19   -7.09       8.08    -0.18  \n",
       "1      1.14   -0.34       2.55     0.25  \n",
       "2      1.72   -1.72       4.11    -3.11  \n",
       "3      2.60    3.30       6.20    -2.70  \n",
       "4     11.35   -2.15       2.84     3.46  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for name, gp in gps.items():\n",
    "    X_test = gp.preprocessor.transformX(poly_df)\n",
    "    Y_pred, Y_std = gp.predict(X_test)\n",
    "    poly_df['{}_gpsolv'.format(name)]=Y_pred.ravel()\n",
    "    poly_df['{}_diff'.format(name)]=poly_df[name]-Y_pred.ravel()\n",
    "\n",
    "poly_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Train polymer model on difference\n",
    "### Define features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:07.867258Z",
     "start_time": "2018-09-21T15:25:07.862221Z"
    }
   },
   "outputs": [],
   "source": [
    "from gpmol.features import RegressionTarget, MorganFP,MACCSFP, Properties, Vector\n",
    "from gpmol.data_transforms import SMILES2MOL\n",
    "\n",
    "targets = [RegressionTarget('δd_diff', name='$\\delta d_{diff}$',units='$MPa^{0.5}$',suffix='exp'),\n",
    "           RegressionTarget('δp_diff', name='$\\delta p_{diff}$',units='$MPa^{0.5}$',suffix='exp'),\n",
    "           RegressionTarget('δh_diff', name='$\\delta h_{diff}$',units='$MPa^{0.5}$',suffix='exp')]\n",
    "\n",
    "features = OrderedDict()\n",
    "\n",
    "features['FPb']=MorganFP(col='poly_mol')\n",
    "features['FPc']=MorganFP(fptype='count',col='poly_mol')\n",
    "features['MACCS']=MACCSFP(col='poly_mol')\n",
    "features['SigmaP']=Vector('sigma_profile',name='SigmaP')\n",
    "features['ElectroS']=Properties(['dipole','polar','sigma_norm','Hba','Hbd'],name='ElectroS')\n",
    "features['Size']=Properties(['MolWt','Area','Volume'],name='Size/Shape')\n",
    "\n",
    "feats=OrderedDict()\n",
    "feats['δd']=['FPb','SigmaP','ElectroS','Size']\n",
    "feats['δh']=['FPb','SigmaP','ElectroS','Size']\n",
    "feats['δp']=['FPb','SigmaP','ElectroS','Size']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train on experimental polymer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:29.602697Z",
     "start_time": "2018-09-21T15:25:07.868600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>$\\delta d_{diff}$</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x must be 2D at computation. \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb5513e9a64c47f799ae247c6fb69005",
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Adam', max=10000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>$\\delta p_{diff}$</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x must be 2D at computation. \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e22e1a67bfe4bfabe9632029244fca1",
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Adam', max=10000), HTML(value='')))"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>$\\delta h_{diff}$</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x must be 2D at computation. \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e1814fb21654cf1be05540d1450dccb",
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Adam', max=10000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "poly_gps=OrderedDict()\n",
    "for indx,t in enumerate(targets):\n",
    "    html_header('{}'.format(t.name))\n",
    "    t_name= t.cols[0].replace('_diff','')\n",
    "    preproc = gpm.Preprocessor(poly_df)\n",
    "    preproc.append(t)\n",
    "    preproc.append(SMILES2MOL(smi_col='poly_smiles',mol_col='poly_mol'))\n",
    "    preproc.append([ features[fname] for fname in feats[t_name]])\n",
    "    gp = preproc.make_gpmol(t_name)\n",
    "    gp.minimize()\n",
    "    poly_gps[t_name]=gp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polymers predict on another set\n",
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:29.654190Z",
     "start_time": "2018-09-21T15:25:29.604579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['label', 'smiles', 'organic', 'n_electrons', 'n_atoms', 'charge',\n",
      "       'MolWt', 'finished', 'job_name', 'poly_smiles', 'homo', 'lumo', 'gap',\n",
      "       'd-moments', 'dipole', 'polar', 'run_time', 'n_cores', 'compute_time',\n",
      "       'Area', 'Hba', 'Hbd', 'Volume', 'sigma_mom_0', 'sigma_mom_1',\n",
      "       'sigma_mom_2', 'sigma_mom_3', 'sigma_mom_4', 'sigma_mom_5',\n",
      "       'sigma_norm', 'sigma_profile'],\n",
      "      dtype='object')\n",
      "(53, 31)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>smiles</th>\n",
       "      <th>organic</th>\n",
       "      <th>n_electrons</th>\n",
       "      <th>n_atoms</th>\n",
       "      <th>charge</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>finished</th>\n",
       "      <th>job_name</th>\n",
       "      <th>poly_smiles</th>\n",
       "      <th>...</th>\n",
       "      <th>Hbd</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sigma_mom_0</th>\n",
       "      <th>sigma_mom_1</th>\n",
       "      <th>sigma_mom_2</th>\n",
       "      <th>sigma_mom_3</th>\n",
       "      <th>sigma_mom_4</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PNT-001</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...</td>\n",
       "      <td>True</td>\n",
       "      <td>402</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>1,107.85</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-001</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(c2cc3c(cc(c4c...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0</td>\n",
       "      <td>92.70</td>\n",
       "      <td>1.42</td>\n",
       "      <td>35.30</td>\n",
       "      <td>3.07</td>\n",
       "      <td>24.30</td>\n",
       "      <td>349.65</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     label                                             smiles  organic  \\\n",
       "0  PNT-001  CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...     True   \n",
       "\n",
       "   n_electrons  n_atoms  charge    MolWt  finished         job_name  \\\n",
       "0          402      164       0 1,107.85      True  SOLSPE2_PNT-001   \n",
       "\n",
       "                                         poly_smiles  \\\n",
       "0  CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(c2cc3c(cc(c4c...   \n",
       "\n",
       "                         ...                          Hbd  Volume  \\\n",
       "0                        ...                         0.00    1.43   \n",
       "\n",
       "   sigma_mom_0 sigma_mom_1  sigma_mom_2  sigma_mom_3  sigma_mom_4  \\\n",
       "0            0       92.70         1.42        35.30         3.07   \n",
       "\n",
       "   sigma_mom_5  sigma_norm                                      sigma_profile  \n",
       "0        24.30      349.65  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gpmol.utils.general import str2array\n",
    "ex_df = pd.read_csv('data/PNT.csv')\n",
    "ex_df['sigma_profile']=ex_df['sigma_profile'].apply(str2array)\n",
    "print(ex_df.columns)\n",
    "print(ex_df.shape)\n",
    "ex_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For a single Molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:31.310753Z",
     "start_time": "2018-09-21T15:25:29.656150Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>PNT-018</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>smiles</th>\n",
       "      <th>organic</th>\n",
       "      <th>n_electrons</th>\n",
       "      <th>n_atoms</th>\n",
       "      <th>charge</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>finished</th>\n",
       "      <th>job_name</th>\n",
       "      <th>poly_smiles</th>\n",
       "      <th>...</th>\n",
       "      <th>Hbd</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sigma_mom_0</th>\n",
       "      <th>sigma_mom_1</th>\n",
       "      <th>sigma_mom_2</th>\n",
       "      <th>sigma_mom_3</th>\n",
       "      <th>sigma_mom_4</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PNT-018</td>\n",
       "      <td>CCCC[C@H](CC)CSc1sc(-c2c3cc(-c4sc(-c5cc6c(cc(-...</td>\n",
       "      <td>True</td>\n",
       "      <td>432</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1,282.01</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-018</td>\n",
       "      <td>Fc1cc(sc1SC[C@@H](CC)CCCC)c1c2cc(c3c(cc(c4c5ns...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0</td>\n",
       "      <td>115.00</td>\n",
       "      <td>7.88</td>\n",
       "      <td>53.20</td>\n",
       "      <td>16.20</td>\n",
       "      <td>46.70</td>\n",
       "      <td>361.25</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      label                                             smiles  organic  \\\n",
       "17  PNT-018  CCCC[C@H](CC)CSc1sc(-c2c3cc(-c4sc(-c5cc6c(cc(-...     True   \n",
       "\n",
       "    n_electrons  n_atoms  charge    MolWt  finished         job_name  \\\n",
       "17          432      156       0 1,282.01      True  SOLSPE2_PNT-018   \n",
       "\n",
       "                                          poly_smiles  \\\n",
       "17  Fc1cc(sc1SC[C@@H](CC)CCCC)c1c2cc(c3c(cc(c4c5ns...   \n",
       "\n",
       "                          ...                          Hbd  Volume  \\\n",
       "17                        ...                         0.00    1.55   \n",
       "\n",
       "    sigma_mom_0 sigma_mom_1  sigma_mom_2  sigma_mom_3  sigma_mom_4  \\\n",
       "17            0      115.00         7.88        53.20        16.20   \n",
       "\n",
       "    sigma_mom_5  sigma_norm                                      sigma_profile  \n",
       "17        46.70      361.25  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
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     "output_type": "display_data"
    },
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0.5568   -2.1961 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.2435   -1.6330   -2.5000 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.0254   -0.4909   -4.3455 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.5873   -2.1608   -4.5801 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.2160    0.3818   -4.9429 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.0033   -1.2393   -4.7092 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.8830   -0.9508   -6.0858 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.2029   -1.9528   -2.1023 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.4417   -2.3068   -0.8620 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.7297   -4.3858   -1.7468 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.8021   -3.8559   -3.4421 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.3433   -3.7477   -3.2542 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.2122   -4.3007   -1.5364 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7520   -6.1572   -3.0893 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.2610   -5.8244   -4.0528 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.1273   -6.4242   -2.3403 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.8105    6.8683    0.3342 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.6576    9.0751    1.7094 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.8324    8.8304    0.4656 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  3  1  0\\n  3  4  1  0\\n  4  5  1  0\\n  5  6  1  0\\n  6  7  1  0\\n  5  8  1  0\\n  8  9  1  0\\n  9 10  1  0\\n 10 11  1  0\\n 11 12  1  0\\n 12 13  1  0\\n 13 14  2  0\\n 14 15  1  0\\n 15 16  2  0\\n 16 17  1  0\\n 17 18  1  0\\n 18 19  1  0\\n 19 20  1  0\\n 20 21  2  0\\n 21 22  1  0\\n 22 23  2  0\\n 23 24  1  0\\n 24 25  2  0\\n 25 26  1  0\\n 26 27  2  0\\n 27 28  1  0\\n 28 29  1  0\\n 29 30  1  0\\n 30 31  1  0\\n 29 32  1  0\\n 32 33  1  0\\n 33 34  1  0\\n 34 35  1  0\\n 28 36  2  0\\n 36 37  1  0\\n 25 38  1  0\\n 38 39  2  0\\n 39 40  1  0\\n 40 41  1  0\\n 41 42  2  0\\n 23 43  1  0\\n 43 44  2  0\\n 44 45  1  0\\n 45 46  1  0\\n 46 47  2  0\\n 19 48  2  0\\n 48 49  1  0\\n 49 50  1  0\\n 50 51  1  0\\n 51 52  1  0\\n 50 53  1  0\\n 53 54  1  0\\n 54 55  1  0\\n 55 56  1  0\\n 16 57  1  0\\n 57 58  1  0\\n 58 59  2  0\\n 59 60  1  0\\n 60 61  2  0\\n 61 62  1  0\\n 62 63  1  0\\n 62 64  2  0\\n 64 65  1  0\\n 65 66  1  0\\n 66 67  1  0\\n 67 68  1  0\\n 68 69  1  0\\n 67 70  1  0\\n 70 71  1  0\\n 71 72  1  0\\n 72 73  1  0\\n 64 74  1  0\\n 59 75  1  0\\n 75 76  1  0\\n 76 77  2  0\\n 77 78  1  0\\n 78 79  1  0\\n 12 80  2  0\\n 80 81  1  0\\n 81 82  1  0\\n 81 10  2  0\\n 79 13  1  0\\n 58 14  1  0\\n 74 60  1  0\\n 79 75  2  0\\n 49 17  2  0\\n 47 20  1  0\\n 42 22  1  0\\n 47 43  1  0\\n 37 26  1  0\\n 42 38  1  0\\n  1 83  1  0\\n  1 84  1  0\\n  1 85  1  0\\n  2 86  1  0\\n  2 87  1  0\\n  3 88  1  0\\n  3 89  1  0\\n  4 90  1  0\\n  4 91  1  0\\n  5 92  1  1\\n  6 93  1  0\\n  6 94  1  0\\n  7 95  1  0\\n  7 96  1  0\\n  7 97  1  0\\n  8 98  1  0\\n  8 99  1  0\\n 15100  1  0\\n 21101  1  0\\n 24102  1  0\\n 27103  1  0\\n 29104  1  1\\n 30105  1  0\\n 30106  1  0\\n 31107  1  0\\n 31108  1  0\\n 31109  1  0\\n 32110  1  0\\n 32111  1  0\\n 33112  1  0\\n 33113  1  0\\n 34114  1  0\\n 34115  1  0\\n 35116  1  0\\n 35117  1  0\\n 35118  1  0\\n 36119  1  0\\n 48120  1  0\\n 50121  1  1\\n 51122  1  0\\n 51123  1  0\\n 52124  1  0\\n 52125  1  0\\n 52126  1  0\\n 53127  1  0\\n 53128  1  0\\n 54129  1  0\\n 54130  1  0\\n 55131  1  0\\n 55132  1  0\\n 56133  1  0\\n 56134  1  0\\n 56135  1  0\\n 61136  1  0\\n 66137  1  0\\n 66138  1  0\\n 67139  1  6\\n 68140  1  0\\n 68141  1  0\\n 69142  1  0\\n 69143  1  0\\n 69144  1  0\\n 70145  1  0\\n 70146  1  0\\n 71147  1  0\\n 71148  1  0\\n 72149  1  0\\n 72150  1  0\\n 73151  1  0\\n 73152  1  0\\n 73153  1  0\\n 76154  1  0\\n 77155  1  0\\n 80156  1  0\\nM  END\\n\",\"sdf\");\n",
       "\tviewer_15375435313086493.setViewStyle({\"style\": \"outline\", \"color\": \"black\", \"width\": 0.05});\n",
       "\tviewer_15375435313086493.setStyle({\"stick\": {\"radius\": 0.1}, \"sphere\": {\"scale\": 0.25}});\n",
       "\tviewer_15375435313086493.zoomTo();\n",
       "viewer_15375435313086493.render();\n",
       "});\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row = ex_df.sample(n=1)\n",
    "html_header(row['label'].iloc[0])\n",
    "display(row)\n",
    "view_mol_3D(row['smiles'].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Calculate \"Solvent\" HSP \n",
    "## Step 2: Calculate \"Polymer\" correction\n",
    "## Step 3: Add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:34.228394Z",
     "start_time": "2018-09-21T15:25:31.312495Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 860x559 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 474,
       "width": 742
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>smiles</th>\n",
       "      <th>organic</th>\n",
       "      <th>n_electrons</th>\n",
       "      <th>n_atoms</th>\n",
       "      <th>charge</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>finished</th>\n",
       "      <th>job_name</th>\n",
       "      <th>poly_smiles</th>\n",
       "      <th>...</th>\n",
       "      <th>sigma_mom_5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>sigma_profile</th>\n",
       "      <th>mol</th>\n",
       "      <th>δd_gpsolv</th>\n",
       "      <th>δd_gpsolv_std</th>\n",
       "      <th>δp_gpsolv</th>\n",
       "      <th>δp_gpsolv_std</th>\n",
       "      <th>δh_gpsolv</th>\n",
       "      <th>δh_gpsolv_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PNT-018</td>\n",
       "      <td>CCCC[C@H](CC)CSc1sc(-c2c3cc(-c4sc(-c5cc6c(cc(-...</td>\n",
       "      <td>True</td>\n",
       "      <td>432</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1,282.01</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-018</td>\n",
       "      <td>Fc1cc(sc1SC[C@@H](CC)CCCC)c1c2cc(c3c(cc(c4c5ns...</td>\n",
       "      <td>...</td>\n",
       "      <td>46.70</td>\n",
       "      <td>361.25</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f2359d48210&gt;</td>\n",
       "      <td>17.06</td>\n",
       "      <td>1.54</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.34</td>\n",
       "      <td>8.05</td>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      label                                             smiles  organic  \\\n",
       "17  PNT-018  CCCC[C@H](CC)CSc1sc(-c2c3cc(-c4sc(-c5cc6c(cc(-...     True   \n",
       "\n",
       "    n_electrons  n_atoms  charge    MolWt  finished         job_name  \\\n",
       "17          432      156       0 1,282.01      True  SOLSPE2_PNT-018   \n",
       "\n",
       "                                          poly_smiles      ...        \\\n",
       "17  Fc1cc(sc1SC[C@@H](CC)CCCC)c1c2cc(c3c(cc(c4c5ns...      ...         \n",
       "\n",
       "    sigma_mom_5  sigma_norm  \\\n",
       "17        46.70      361.25   \n",
       "\n",
       "                                        sigma_profile  \\\n",
       "17  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                                 mol  δd_gpsolv  \\\n",
       "17  <rdkit.Chem.rdchem.Mol object at 0x7f2359d48210>      17.06   \n",
       "\n",
       "    δd_gpsolv_std  δp_gpsolv  δp_gpsolv_std  δh_gpsolv  δh_gpsolv_std  \n",
       "17           1.54       6.23           6.34       8.05           4.78  \n",
       "\n",
       "[1 rows x 38 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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6a/NYvQDpHuN/+xbL4HCpmpd5PIqNYQHAUHp6ut1F5h07duiBBx7Q3LlzlZuba9c5Fy5c0NixY5WRkeFQXo4Wvp0lMjJSn3/+ebHOTUpKUkBAgF5//XVNmzZNEydOVL9+/XTzzTfbdf7atWv1/vvvOzzvjz/+qOXLLfq/mTh16pQmTJhg942AAomJiZoxY0aRx8XHx2vcuHHKyspyOLcCu3fv1tixY4t188GMWRH9559/LvLc3Nxc0+MoztNzHgAAAACc4kSy9O1W8/jDvUu2Eez1alaXHuhp3uN+9c/5bXZ8vEtvTqCiSkmTLpdenQoO8HSXajnebrvcfPvtt5o8ebLOnz9faLx79+7atWuXzbiUX3x85513tHv3br399ttFbgI6c+bMq+1YrhcYGKhRo0apQ4cO8vDw0KFDh/TZZ5+ZtgUpb0ePHtV7771nGPP29tbQoUPVs2dPBQQEKCUlRdu2bVN0dPTVVeS5ubn64osv9Msvv2jhwoVq2rSpQ/Nf256mVq1aeuSRR9S5c2f5+/srISFB0dHR2r59u+G5kZGRGjhwoLy97f8ivHLlytXCvLu7u/r166eIiAg1aNBAp0+f1qpVqxQTE2N47ldffaXRo0erVatWhvHMzExNnDjRtPAfERGhfv36qVGjRsrMzNSOHTu0aNEiwz0Sdu7cqXfffVcTJkyw+7VZ6dWrl6ZOnWrTxmn//v06f/68/P39Tc/ds2ePLly4YDPesmVLNWrUqFTyq8wozgMAAACAE6yIk6xaEw+PKP05H+tjXpxPz5TW/CINvLP05wUqiqivpOmLpYO2tSs4UbNg6a/DpRGl2LqrLMyfP1+zZs2yGX/22Wc1ZswYZWRkaNGiRZo3b57S0217lH333Xd69tlnNXfuXNMNR48fP65Vq1YZxkJCQhQdHa2AgICrYy1bttR9992n5cuX69VXXy3V1dClYdmyZYbFZC8vL0VHRyssLKzQ+G233aZBgwZpyJAhOnLkyNXxpKQkPfnkk4qJiVG1atUczqN9+/Z69913C713zZs3V0REhCZOnKi1a9fanJOcnKzvv/++WKu3AwMD9d5776lt27ZXx0JDQ9WtWze1adNG06dPtzknLy9PK1asMC3OR0VFGRbaJWnatGkaNGhQobGwsDANGjRIjz/+uOENiAULFmjo0KGF3pPiCgoKUrt27bRz585C43l5edq0aZPuvdf8f25a2lijrQ0AAAAAOMGqH8xj7UOkto4tFrRL93ZSgzrm8RXfl/6cQEWx4Etp1AwK8xXBwRPSyOnSB6vLOxNzixcvNizMP/bYYxozZowkqVq1ahozZoxiYmLUpk0bw+vExsZq8eLFpvN89tlnhgV2Nzc3zZkzx7SQ+uCDD+qf//ynPS/FqcxW9A8fPtymMF/A39/fcEX3sWPH9OmnnzqcQ4sWLRQVFWX43rm5uWnq1Kny8fExPHfNGpM72BaqV6+u6OjoQoX5az366KO69dZbDWNr16413EQ4OztbS5YsMTzngQcesCnMF/D19dWcOXMMV/9nZWXpk08+MXsZDuvVq5fheFGtbcyK8xERZbAqoRKqECvnc3NzFRcXp7Vr12rXrl06ceKEMjMz5e3treDgYLVt21a9e/dWjx49TO88VjQJCQmmj/U46q9//atDd7l27dql1atXa8uWLTp69KjS0tLk4eGhOnXqqEWLFurRo4fuu+8+019MAAAAAEpX8gXp+3jz+JC7y2ZeV1dpYHcpcqVx/MsNUtZlycu6AwNQKU0zr4+inPwzWhp1X3lnYSs+Pt6wH3hISIief/55m/GGDRsqKipKjzzyiPbv328Tf+utt9S/f3/5+fnZxMyKwffee2+RLV3MNuUsT7///rvheHh4uOV5d9xxh+H4999/rxEjRjiUw7Rp0yxb0/j5+SkiIkIrV9p+GW7Z4vju6OPHj1eTJk0sjxk8eLB27NhhM37q1CkdO3ZMN910U6HxDRs2mLY6evLJJy3nCgoKUv/+/bVs2TKb2OrVqzV27FjL8+11zz336I033rAZt9oUNiMjw3BVf9OmTR1uYVRVlXtxfvPmzfrHP/6hhIQEm9ilS5d04MABHThwQJ9//rmaNm2q1157zam/jA4cOKBXX31VW7faNoeMjo5W586dDc87c+aMaY8pR3h4eNi1YYQknThxQlOmTFFcXJxNLDs7W8eOHdOxY8f0zTffaNasWXruuef00EMPlThHAAAAANZifpKs9gksy9Yy9/cwL86nXpJit0r3/aHs5gfKy9FT5Z0BrldRfybTpk0zXM0+fvx4uZtsBlKjRg3NmjVL/fr1s1kJnZ6erv/85z82ReZz587p0KFDhtczW5Vc0ZltUlu7dm3L82rVqmU4btbWpTjXutYdd9xhWJxPSkpSWlqafH3t3xjB3vnM/Pbbb4bFeSOhoaE2xxq56667DIvzCQkJSklJsSvnojRu3FghISH67bffCo0fOXJEp06dUlBQkM05mzdv1uXLl23GaWnzP+W6DP2jjz7SiBEjDAvzRhISEjR8+HB9/PHHZZxZ/i/SmTNnauDAgYaFeWdp2LCh6RfBtbZv367777/fsDBv5MKFC3rllVf04osvKicnp6RpAgAAALBg1dKmbROpaXDZzd29nRRou3jzqhX2/RMCqHRusq0ToZxVxJ/J9u3bDVf21qpVS3ffbf1YU2hoqHr27GkYM1ohv2/fPtNrmbVIqejMir5GG+faE3dzcytxTkasVrqb3TApiQYNGpj2zj98+LDN2O7duw2Pbd26tV3zmfWxz8vLU3y8xaN7DnK0tY3ZTQeK8/9TbsX51atX67XXXnO4MJyTk6MpU6bov//9bxlllr8z93333acFCxaU+0Ybt9xyS5HHHDp0SKNHjzbc+bgoq1at0tSpU4uTGgAAAAA7XEzPX51uZkD3sp3fzc16ji9+kq5UrP0FgVLx12HlnQGuN3l4eWdgy6y+1KFDB7sWS5qtkN6zZ49Nzev48eOGx7q7uys4uAzv0pYhs6Lwjz/+aHmeWTG3YcOGJc7JSL169UxjqampZTKn0Upys/mOHj1qeKw9q+YlqW7duvL0NO5Rd/r0abuuYQ+zorpZaxujfvMNGzY0/dzciMqlOH/q1Cn97W9/M9wAwcXFRW3bttU999yjtm3bysXFxeaYvLw8TZ48WadOle7zUCdPntTYsWM1duxYnTx5slSvXVyNGze2jOfl5en555/XxYsXTc/v1auXOnfuLC8vL8NjPv74Y3377bclTRUAAACAga835fd1N1PWxXlJGtTDPHYuVfqx9BbVARXG432lD16SQsqm1gcHhDSUFr5cMfvN79y503C8WbNmdp3fokULw/GMjAyb2lJKSorhsdWrV680eyxer0+fPobjixYtMu1Hf/HiRUVGRhrGunTpUmq5Xctq38VLly45dU6j+cw+G/a223FxcVH16tUNY6V586FVq1aGN5KMivPJyck6cOCAzTir5gsrl57z77zzjtLT023Gg4ODFRkZqZYtW14d27t3r8aNG2fTcyo9PV2RkZF67bXXSpxPdna2oqKiFBkZaZhXaRo4cKDdPeTt8eWXXxo++uLl5aXp06frvvv+98135swZvfjii4Z3L99880317NmzzB4fAgAAAG5UVi1tGteTwuyr/5RIz/aSfw3pvPGaHn29SbrrtrLPA3C2R/+Y/yclTbrMEyLlwtNdqmV/O2+nM1uxXKdOHbvOt+rlnZKSokaNGl39b6Pe25JMVzxXBv3799eiRYtsNsZNTU3VkCFDNH78eIWHh6tOnTpKTU3Vxo0b9fbbb+vIkSM21/Lz89PAgQPLJE+r9zgjI6NM5jRbJGs0n9lnw+wajhyblpZm9zXs0bt3b0VFRRUaS0xM1JEjRwotMjZaNS9JERERpZpPZef04vzvv/+uFStW2Ix7eHjogw8+sGnj0rJlS82fP1/9+/fXlStXCsU+//xzPf7443Y/4mFky5YtmjJliuGdnIouJydHc+bMMYxNmTKlUGFeyv9ieffdd9WvXz+bX4IHDx5UTEyMBgwYUFbpAgAAADecnBxp7Wbz+IDuksHDwqXOwz1/09cla43jX2+S/vVU2ecBlJeKXBxG+TLrRODt7W3X+WarlSXZLAA160F+fb2rMnF3d1dkZKSGDRumxMTEQrFz585pypQpmjJlil3X+sc//qGaNWuWRZrKysoyjdn7s3aU2Wa5RvNVr17dcIW7Vd72HutIgd8eRsV5KX/1/LXFeaN+80FBQQoLCyvVfCo7pz8zExMTY9jHffDgwab91Zs2baoHHnjAZjw7O1sxMTHFzuWXX37R0KFDTQvzrq6uGjx4sN2bLzjb1q1bdezYMZvx0NBQ0yK7l5eXnnnmGcOY0a7VAAAAAIpv64H8tjFm+juhpU2Bezubx3Ydlo6VXktaAKg0zAqXZiuZr2dWgJVsi7BmLUrKauW2szRq1EifffaZevSw6KFmwd3dXa+++qppi5zSYNW6pkaNGk6d02g+s5sS9q56z8vLM53Pz89iV/hiuO2221S7dm2b8etb2xgV53v37m3YwvxG5vTi/DfffGM43rdvX8vz/vSnPzl0PXt06tRJnTp1Moy1bt1ay5Yt02uvvWZ5F7Q8Wb2XVh/0u+++2/A1bdmypcjdtAEAAADYb80m81gtX6mLE9cB3dNJsmpp/LVFrgBQVZkVLu3t0211nL+/f6H/DggIMDwuKytL586ds2u+iqpOnTqaPn16kXsnXq99+/b6+OOP9ec//7lsEvv/zNoXSSqTzXizs7NtniSwmq9u3bqGxxotyjWSlJRkekPp5ptvtusa9nJ1ddXdd99tM75p06ar+4vu379fycnJNsfQb96WU4vziYmJ2rdvn814tWrVinyk4dZbbzV87GPv3r2mH/aiuLi4aOrUqYWuW6NGDb3yyiv67LPP1K5du2Jd11m+++47w3GzncILeHl5qX379jbj2dnZiouLK5XcAAAAAFi3tAm/TXJ3YqPR2rWk2433LZREcR7AjcmsKGq2men1Dh8+bDju5eVlU4QNCQkxvU5CQoJd81VUycnJevDBBwu1UW7atKk6duwof39/ubu7y9PTU4GBgerUqZOeeOIJLV++XJ988olT6m979uwxHK9WrZoaNiz9XaN/++0303ZFRp+DW2+91fDYvXv32jWf2etzc3MrtLdnaendu7fN2Llz567uPWC0aj4gIEAdO3Ys9VwqO6cW580+KCEhIUXuSu3m5mb6S8zeD6qRxo0b6+mnn5Yk9evXT19//bWGDh1a4XfJTktLM7x75urqavnLvkDz5s0Nx41ungAAAABw3PmL0kbjfwJJkiJud14uBaxa23y7VbpcedseA0CxGC1elKTt27fbdf7mzcZ3YW+99Vab2tJNN90kHx8fw+OvbwlipCL3pv/LX/6iEydOXP1vHx8fLVmyREuXLtXGjRu1e/du/frrr/rxxx+1ZMkSPffcc05dFGu2wLVdu3ZyL4M75evWrTMc9/LyMmyffdttxruy79u3T0lJScWer23btmXSx/+OO+4wbNNU8Dk22gy2V69ecnNzK/VcKjunVqDN7gLa+3iF2XElvbs4cuRILVu2TDNnzrR7N+7ydvDgQcPxwMBA01/01zJ7zKg472WrVq0s/+Tk5Dh8TQAAAKCy+3arlJtrHq9oxfmL6dKGXc7LBQAqgttvN/5lfPToUe3cudPy3IsXL5oWRcPDw23GXFxc1LVrV8PjP//8c8s+9zk5OXr99dct8ykvu3btslkp3bRpU9M2PqWtYLW2mV27dpne/DBqz1LS+dLT0/XRRx8Zxrp37y5PT0+b8W7duhm2WMrLy9OCBQss5zt16pTpnpz9+/e3PLe4PD091bNnT5vxjRs36vLly9qyZYtNjJY2xipEcd7egrjZcSUtzru5uZk+PlIWjhw5orfeekuDBw9Wp06d1Lp1a3Xu3Fl//vOf9e677+rUqVNFXqOk76XRxg1W17WSk5Nj+QcAAAC4Ea39xTzW4ibppiDn5VKgQ3Mp0GJfOFrbALjRdOvWTfXq1TOMzZgxw3K1+uzZs3Xx4kWb8WrVqmngwIGG55htepqYmKhp06Zd7dl9rYyMDE1UPIxkAAAgAElEQVSaNEk//vijaS7laceOHTZjv/32W4k6XTjixRdf1KZNxl9gZ8+e1XPPPWcY8/T01H333efwfFFRUYqKijKMXblyRS+99JLOnDljGDf7XFSrVk0PPvigYWzp0qX66quvDGNpaWmaMGGCsrKybGL+/v7q16+f4XlmGyFbbZx7PaPWNps3b9bmzZttNjmuWbNmkW24b1RO7HAow40AJPMNMa5nVlA2u25FtG7dOv3nP/9R7nVLaC5cuKBt27Zp27Ztev/99/XUU0/piSeeMH3cg/cSAAAAqLjy8qz7zd9TDqvmpfwNYe/pJC39xjj+3Tbn5gMA5c3Dw0OPPfaYpk+fbhPbtm2bxo8fr6lTpyowMPDqeHp6uubMmaOlS5caXvOxxx5TrVq1DGO9evVScHBwoRYwBT7++GMdOXJEjzzyiBo3bqyUlBTFx8dr6dKlOn78eDFfYdkzWvGfmZmp+++/X+3atVO9evXk4eFxNebq6qpatWqpdu3auuWWW9S1a1fDFin2unjxoh599FH17dtXffr0UXBwsNLT0/XLL79o0aJFpoXyQYMGFbuDxvTp0xUbG6sHH3zwanvpvXv3avHixaY3JUJCQixX6o8cOVIrV660yTc3N1cTJ07Ut99+q759+159fdu3b9eiRYt08uRJw+tNnDjR9H318/OTu7u7srOzC41fuHBBUVFR6ty5sy5evGj6ZIkk3XnnnfLy8ip0YyAtLU3z5s2zOfauu+4q9BnA/zi1OJ+enm44Xq1aNbvON7urY3bdiiglJaXIY7KysvTWW29p7969mj17tmGBvqTvpdHmulL+L9Ts7Owy6bcFAAAA3Cj2H5WOnTaPl0dLm6tzdzYvzm/7TUpJk2oVv0YCAJXOww8/rNWrVys+Pt4m9t1336lnz54KCwtTYGCgUlNTtWPHDtO6TGhoqJ588knTuTw9PfX888/r2WefNYz//PPP+vnnnw1j7u7u8vb2Vlpamh2vynnCwsIMx3Nzcw1X1V/Pw8ND3bt313PPPadmzZoVK4fc3Fx98cUX+uKLL+w6vk6dOpo4cWKx5ipQsErcHq6urnr11Vfl4uJiekxAQIBmzJih0aNHGz5BERMTY9q+5nq9evXSkCFDTOMeHh5q3bq1YeumghtVPj4+2rhxo2k91sfHR127drVp7WT0FAMtbcw5ta2N2aMR9t45MerJZHXdym7NmjV68803DWNmXwL2vpdWx1XV9xMAAABwlu8s9hH08pTuNK5jOEW48Z5zkvJ75H9v3WIZAKocT09PzZkzx3QVdXZ2trZu3aqvv/5aGzZsMK3JBAYG6p133jFdEFmgT58+euCBBxzOc/LkyWrbtq3D55W1Dh06qHv37sU+/8qVK1q3bp369++vhQsXOny+WfHY6vi5c+ca9ngvi/mk/A1zO3bsWORx3bt31z//+c8SLZrt2rWr3njjjSKPM2ujUyA9Pd1wY9drGbW2uZ6Pj0+JPh9VnVOL85mZmYbjJS0oX9/HqKJr2rSpevXqpYiICIWFhVn+D/fhhx/qwIEDNuNmr7mkNzqsrm3Gzc3N8g8AAABwo7EqcHdrK/lY123KVP3aUvObzOPrLW4sAEBV1aBBA3366acKDQ0t1vnNmjXTRx99pJtvvtmu46dMmWLaD/x6Hh4eevnll/Xwww8rODi4WPmVtbfeekt33nlnia6RnZ2tf/3rX6abqVrNbW97Gn9/fy1cuFC33WZxp7oIkyZNUvv27e061t3dXa+88ooee+wxu68/aNAgvf/++6pfv75Debm6uurxxx/XvHnzirxBJEn333+/unTpYnlMbGysZTw8PLzIGwkF7W9gzKm9S8x+WNf3XzdjtrmoVaG5vNSqVUvdunUrNNaiRQsNGTJEjRo1KjR++vRpvfbaa/rmG9tnS3NycrRw4ULNmDGj0LjZe2nvBqzX95S6lqPv5549eyzjrVq1YmNYAAAA3DDy8qQ4i6f4e9zqvFzM9Lw1v/WOEYrzAG5UwcHBWrZsmRYuXKioqCjDzV6v5+Pjo2HDhmns2LF2FUQLuLu7a+bMmbr77rsVGRlpuDBTyl+V/vLLL19dMV9Ri/O+vr6aP3++fvjhB61evVq//vqrkpKSlJ6ebnfdr8CMGTMUHh5uulHv9UJDQxUTE6O33npLq1atMtwc1cfHR/3799eECRPs3q/RTM2aNbV48WJFR0drwYIFOnfunM0xrq6uuvPOOzVp0iQ1b97c4Tm6d++uNWvWaOnSpVq5cqXp50PKf+979+6t0aNHq2nTpnbP4ebmpvfee0/Tpk3T559/blgrPHrU5C8L/5+fn586duyojRs3mh4TERFhd043IqcW5318fAzHjTaOMGK2Q7bZdctTixYt9MEHH9h1bN26dfX2229rxIgR+uWXX2zia9as0dSpUwutii+r99Lq2gAAAACKtv+odMr23+lXVYjifHtpnklb3p0J0rlUKaCmc3MCgIrAx8dHTz/9tIYPH65169bpp59+0t69e3Xu3DmlpKTI09NTderUUYsWLdS1a1dFRETI39+/2PPde++9uvfee5WQkKA9e/YoOTlZV65cUVBQkG677TbddFPhR53Gjh2rsWPHlvRllpnu3btbtjDJzs7W2bNntW/fPn388cf67rvvbI7JysrSypUr9dRTT9k9b0BAgF577TW9/PLL2rRpk06cOKHU1FT5+vqqSZMmat++fanWuzw8PDRq1Cg9+uij2rFjhxISEnTu3Dl5eXmpQYMG6tChQ7E3my3g5eWlkSNHauTIkTp16pR+/fVXnT17VufPn5e7u7v8/PwUEhKili1bFnvhsre3t1577TWNHz9eGzduVGJiotzd3RUYGKjWrVurSZMmRV5j0aJFkqRHH33UZr8EHx8f9ezZs1i53SgqRHHeqlBsz3FVoZjs6uqqCRMmaOjQoTax9PR07dq1q9AjM9WrVze8TknfSzc3N4fu9AIAAAAoLM6ipU01L6lTC+flYqanxdP4BSv/B5asOwEAVGo1a9bUgAEDNGDAAKfM17RpU4dWPVdW7u7uCgoKUlBQkHr06KHx48dr7dq1Nsft2rWrWNevVq2aU4vBbm5u6tChgzp06FCm8xS8Z2UlMDBQf/rTn4p9fnx8vOFGxvfdd1+VqNuWJacW52vWNF56ceHCBbvONzvO7LqVTYcOHeTj42O4scjhw4cLFedr1KhheI2UlBS75jI7zuy6AAAAAOwTZ9EWpkub/A1hy1tdf6n1LdLuw8bx9dspzgMAzJ0/f14vvfSSzfjDDz+sHj162H2dLl26GBbn7e0MgfKXk5OjKVOm2Iy7uLjo4YcfLoeMKhenFufNNsU4e/asXeebHWfvZhsVnaurq26++Wbt3bvXJnZ9/6rGjRsbXsPe9/LMmTOG41XlvQQAAADKQ2XoN1+g563WxXkAQOU1bdo0w17opeGee+5Rly5dtH79eptYwYp4e+3YYfylaW+/eZSvvLw8/eMf/zB80qFPnz5q1apVOWRVuTi1ON+sWTPD8aSkJLvOP3XqlEPXrYyqVatmOH59GxqzR51Onz6tvLw8ubi4WM5j9l7eCI9QAQAAAGXlwDEpqYL3my/Q8zYpcqVxbNdh6cwFqY6fc3MCAJSO2NhYnThxokyuffPNN+uee+5RvXr1bGp6K1as0C233KKHHnrItCWzlF+/+uCDD7Rq1SrD+B/+8IdSzRml79ChQ5o6dao2bNhgE/P19dWkSZPKIavKp0IU5612HL7W/v37Dcft2Zygsrh06ZLh+PW/0IKDgw1b4KSnp+vYsWM2G4Zcz+y9pDgPAAAAFJ/Vqnlvz4rRb75AjzDJxSV/tb+RDbulfl2dmxMAoPLo2bOnPvnkk0JjV65c0YwZMzR79mw1a9ZMgYGBql69ulxcXHTlyhWlpqbq+PHjOnbsmOl1GzRooF69epV1+nBQRkaGdu/erV27dumbb77R1q1blWfyl4jXX39dDRs2dHKGlZNTi/MtW7ZUzZo1lZqaWmg8KSlJZ86csdzF+MyZM4arvWvVqlVlHpHIyckx/eV0/QfaxcVFt99+u+EjRLt27SqyOL97927D8TvuuMO+ZAEAAADY+N5iM9iK0m++QO1aUuvG+avkjWz4leI8AMDcqFGjtGrVKmVmZtrEsrKyTGtPVjw9PfXWW2/J07MCfWFCkvTZZ5/p9ddfL/K4F198UX/84x+dkFHV4OrMyTw8PEz7TsXGxlqe+8033xiO9+zZU+7utvcYcnNztW7dOs2bN0+ff/653RulljZHNrD45ZdfDDeDdXFxUevWrW3Gze4iFvVeJiQk6NChQzbj9evXV5s2bezMFgAAAMD1frZtuXrVnWHOy8NeXdqaxzZYvBYAAG666Sa98cYb8vLyKpXr1a1bV4sXL1ZYWAX8wkSRC4G9vb01depUjRw50kkZVQ1OXTkv5W8aERMTYzO+dOlSPfjgg3Jzc7OJ5eTk6KOPPjK93vXS0tI0evRobdu27erYzJkz9d5776l9+/YlyN5+ubm5+vvf/67ff/9d7733nnx9fYs8PjIy0jAWFhamoKAgm/Hw8HC5u7srOzu70HhBX7Hg4GDD60VHRxuO9+7d2zJHAAAAAOYSz0pHLLbT6tbOebnYq0sb6d9fGMe27JeyLles1f4AAPusW7fOKfP06tVLK1eu1MyZM7V+/XrTNidWAgICNHToUI0YMaLI+hnKj1Vxvlu3bpo8eXKVaj3uLE4vzt99991q0qSJzcrtAwcOaM6cOYabBcyZM8ewL33Tpk1111132YxPmzatUGFeks6fP69x48Zp7dq1Zf4/+uXLl/X8889rzZo1kqTBgwdrypQp6tixo+Hx2dnZmjJlijZv3mwYHzZsmOF47dq1NXDgQC1fvrzQeFZWlv76179q3rx58vb2LhSLi4uzOV7Kf6phxIgRRb42AAAAAMZ+tnh6382tYvWbL9DVYuV81mVp64H8Aj4AAGaaNm2q999/XydOnND69ev166+/6tChQzp9+rRSUlJ0+fJl5eXlydvbWz4+PgoICNAtt9yiZs2aqUuXLmrfvr1cXe1v7lGjRg3Vr19fQUFBql+/vmrUqFGGry5/RXhQUJDq1aun+vXrm+6pWdU1bNhQbm5uysnJkZT/c+/WrZvuv/9+tWhRAf+SU0k4vTjv5uamiRMnasKECTaxefPm6eDBgxo8eLDq16+vxMREffrpp6ZtWiZOnGiz0v7y5cv68ssvDY8/e/asYmNjNWDAgJK/EBOXLl3S008/XWin4oMHD2ro0KG6/fbbde+99yokJEQBAQFKTU3Vzp079fHHH+vIkSOG12vXrp369OljOt/48eP1xRdfKCsrq9D4xo0bNXjwYI0aNUqhoaG6ePGiYmNjtXTp0qv/E11ryJAhbNQAAAAAlIBVS5uwppKvj/Nysdct9aV6AVLSOeP4hl8pzgMA7BMcHKxHHnmkzK6/aNEiBQQEqHr16mU2x7WeffZZ/eUvf1FAQIBT5qvoPDw89OabbyowMFC33HIL70spcXpxXpIiIiLUvXt3/fDDDzaxb7/9Vt9++22R17jzzjsNW9qcP3/eplB9raQki+dMS0FWVpbNhreSlJeXp02bNmnTpk12X8vPz08zZ860vHsYFBSkp59+Wm+88YZNbN++fXrhhReKnKdevXoaN26c3XkBAAAAsGVVnP+D7RZSFYKLS37f+RVxxvGfdknPOzclAAAMNWrUyKnzGbWYvtHde++95Z1ClePUDWGvNWvWLIWGhhbr3NDQUM2cOdMw5u/vb9PK5VoNGjQo1pz2CggIUHR0tLp27Vqi69SuXVvz589X48aNizx29OjR+tOf/lSseWrUqKHIyEj5+/sX63wAAAAAUmZWfgsYM3+owKvPu1rk9vMuqRjtgwEAAGCHcivO+/n5KTo6Wt26dXPovG7dumnx4sXy8/MzjHt6emrgwIGGscDAQN19990O5+qo6tWra/78+Zo8eXKx+tvfeeed+vzzz9WunX07Rrm4uOhf//qXRo4cKRcXF7vnuemmm7RkyRK1aVOB/6UAAAAAVALbfpMuXzGPV+TWMF0s+s4nX5B+O+68XAAAAG4k5Vacl/JXuX/wwQd688031bq19XOerVu31ptvvqkPPvjAtDBf4C9/+Yv+8Ic/FBoLCAhQZGSk0/pSubm5afjw4fr66681cuRIBQYGWh7v7e2tXr16KSoqSvPnz1f9+vUdnu/FF1/Up59+qrvuusumF/+16tWrp0mTJikmJoYNGwAAAIBSYNXSpkEd6aYK/GR8+xCpmpd5/KdfnZcLAADAjcQlL6/iPKR44sQJ/frrrzp58qQyMjJUrVo1NWjQQO3atXO4HU1eXp6+//577du3T3Xq1FHv3r1Vs2bNMsrcvnwOHjyoAwcO6PTp00pPT5eXl5f8/PzUtGlTtWrVSl5eFn8jdlBqaqq2b9+u33//XWlpaXJ3d1fdunXVokULNW/e3KEV9iXVqlUrm01o3dzctGfPHqflAAAAAJSlQX+TVtluqZUf6yF9+ppz83FU+DNS3A7j2ON9pXlFb2UFlKp9+/ZJEgvKAAB2KYvvDWfUNMtlQ1gzwcHBCg4OLpVrubi4qEePHurRo0epXK+kXFxcFBISopCQEKfMV7NmzQrz2gEAAICqLC9P+nm3ebwit7Qp0KWNeXF+yz7n5gIAAHCjKNe2NgAAAABQ2f2eJJ06Zx6vyJvBFri9lXns18NSeqbzcgEAALhRUJwHAAAAgBL4Za95zMszv6d7RdfJ4gnwnBxp+2/OywUAAOBGQXEeAAAAAEpgy37z2K3NJE8P5+VSXPVrS43qmsc3W9yAAAAAQPFQnAcAAACAErDqyd6xufPyKKlOLc1jVk8HAAAAoHgozgMAAABAMeXkSFstVs5bFbwrmtstWttsZlNYAACAUkdxHgAAAACKad9RKS3DPG7Vy72isbqRcOikdOaC83IBAAC4EVCcBwAAAIBislpRXrO6FNrIebmUVIfmkouLefwXVs8DAACUKorzAAAAAFBMRfWbd61E/+Kq4SO1amweZ1NYAACA0lWJ/qoIAAAAABWLVcG6YyVqaVPAqg0Pm8ICAACULorzAAAAAFAMWZelnQnm8UpZnLfoO795n5SX57xcAAAAqjqK8wAAAABQDPEJ0pVs8/jtFoXuisoq57Mp0rHTzssFAACgqqM4DwAAAADFYLUZbFCA1DDQebmUlja3SJ4e5vFtB5yXCwAAQFVHcR4AAAAAisGqUN2xueTi4rxcSounh9SuiXl8O8V5AACAUkNxHgAAAACKwapQXRn7zRdoH2oe20pxHgAAoNRQnAcAAAAAB2VdlnYfMY+3D3FaKqXOqjjPynkAAIDSQ3EeAAAAABy0+4j1ZrC3WRS4K7rbLG4sJJ2TEs86LxcAAICqjOI8AAAAADjIqt98oJ/UoI7zciltbZtIbm7mcTaFBQAAKB3u5Z0AAAAAAFQ2O34zj7UPrZybwRbw9pJaN5biE4zj2w9I9/3BqSkBgNPl5ORo69atio2N1fbt23X27FmdPXtWrq6u8vPzU1BQkNq3b6/OnTurW7ducnenxIbKIz09XatWrVJSUpKSkpKUmJiopKQkubu766uvvirv9G4o/OYAAAAAAAdZ9V6vzP3mC7QPMS/Os3IeQFX3ww8/6PXXX9eRI0cM4+np6Tp58qS2b9+uhQsXqn79+nrkkUc0YsQIeXp6OjdZoBjOnTunKVOm2IwHBweXQzY3NtraAAAAAIADcnKknSaFa6lqFOc7NDePbbd4agAAKruoqCiNHj3atDBvJDExUbNmzdKAAQMUHx9fdskBqHIozgMAAACAA/YfkzKyzOOVeTPYAu0tXsPRU9KZC87LBQCc5aefftKMGTOUl5dXrPNPnDghN6tNOwDgOrS1AQAAAAAHWLV1qVlduqW+83IpK2FN8/vmm9Wnth6QIm53bk4AUNZmzpxpWpgPCAhQmzZt5Ovrq9TUVO3fv1/JycmFjpk+fbpat27tjFQBVBEU5wEAAADAAZabwYZIrlXg+eTq1aQWN0l7fzeOxx+kOA+gajl8+LD27t1rGBs1apQmTZpks+lrfHy8PvzwQ/33v//VmDFj1KdPH2ekCqAKoTgPAAAAAA6w2gz21irQb77ArSEWxflDzs0FAMragQPGv9ybNWumF154QS4uLjaxdu3aafbs2Xr88cfVsmXLsk4RQBVUBdZ0AAAAAIBz5OVJOw6ax6vCZrAFwpqax+It3gMAqIxycnIMx0NCQgwL89dq3bq1XKvCY1MVxMmTJ5WVZbG5C1CF8JsDAAAAAOx0PFm6kGYer0rF+bYWxfl9R6Wsy87LBQDKWoMGDQzHDx8+7ORMblwJCQl66aWX1Lt3b/3888/lnQ7gFBTnAQAAAMBO8QnmMU8PqflNzsulrLWzKM5n5+QX6AGgqmjWrJl8fHxsxvft26cFCxaUQ0Y3hsTERC1evFiDBw9Wnz59tHLlSmVnZ2v9+vXlnRrgFPScBwAAAAA7/WpRnG91s+RRhf6FVb+2VKeWdCbFOL7zoBTWzLk5AUBZ8fX11cCBA7V06VKb2MyZM7V161a98MILatKkidNyKtik9uTJk/L09FRgYKDatGmjRo0aOS2H4srOztaePXt06NAhnTlzRhkZGcrOzlZmZqYuXLigpKQkHT58WKdOnTI8Py4urthznzt3Ttu2bdPJkyd15coVBQYGqkmTJmrTpk2xr2klKytLmzdv1vHjx5WamqqAgAAFBwerQ4cO8vT0LJU5Tp8+rT179uj48eNKS0uTt7e3AgIC1KBBA4WFhcnDw6NU5oHzVaG/OgIAAABA2bJaOW/VBqYycnHJXz2/bptx/Fc2hQVQxTz99NNav369Tpw4YRNbt26d1q9fr969e2vYsGHq1KlTseY4fvy47r77bpvxmJgYhYaGSpJiY2M1f/587dixw/Aabdq00cSJE9W9e/di5VCWjh07pnnz5umrr75SWppFH7ginDx5UgcOHLj6nkhSeHi4zc/mlVde0dChQyXlb+o7Z84cxcXF6cqVKzbXDA4O1vDhwzV8+HC79gh46aWXtHLlykJjERERevvttyVJ58+f19y5cxUTE6PU1FSb82vWrKl+/frp2Wefla+vb9Ev+jrZ2dlatWqVli1bpvj4eNPjfHx81LVrVz3xxBNq166dw/MUZcWKFXr55Zdtxt3d3bVp0ya7XtuJEycUHh5uM+7i4qK4uDgFBQWVSq6VEW1tAAAAAMBOVgXpts5bTOk0Vq1trG5UAEBlFBAQoMWLFxcqCF8rNzdXa9as0dChQ/XQQw8pNjZWeXl5pTJ3cnKy0tPT9dxzz2ncuHGmhXlJ2rVrlx5//HH985//LJW5S0NOTo7mz5+vvn37avny5SUqzBfYsmVLkcckJydLkv7973/r/vvvV2xsrGFhXsovEE+fPl3Dhg0rdn6nT5+WlL+yv2/fvlq6dKlhYV6SUlNTtWTJEvXt21cHDhxwaJ69e/eqf//+mjx5smVhXpLS09P1zTff6MEHH9SkSZN06dIlh+YqSnh4uNzdbdd3Z2dn2/UzkqQNGzYYjoeFhd3QhXmJ4jwAAAAA2CXrsrT/mHm8qq2cl6xfE8V5AFVRcHCwli9frpEjRxoWJAvs3LlT48aN05AhQ7R79+4Sz3v8+HGNGzdOX375pd3nREdH69133y3x3KXhlVde0axZs5SZmenQeQ0aNFC3bt0UHh6uQYMG6amnntLMmTO1du1aPfzww0Wef/r0ac2ePVtvvPGGaVH+elu2bNEzzzyj7Oxsh3KV8m8GxMXFady4cTpz5oxd5yQmJuqJJ54wbeFzvbi4OA0ePFgHDx50OL/Vq1dr2LBhdudmDz8/P3Xs2NEwtnHjRruu8dNPPxmO33PPPcXOq6qgOA8AAAAAdtj7u5STYx5vd4OtnD99Xjp1znm5AICzpKen211k3rFjhx544AHNnTtXubm5xZ7z//7v/0xXF1uZO3eufvvtt2LPWxoiIyP1+eefF+vcpKQkBQQE6PXXX9e0adM0ceJE9evXTzfffLNd569du1bvv/++w/P++OOPWr58ucPnnTp1ShMmTLD7RkCBxMREzZgxo8jj4uPjNW7cOGVlZTmcW4Hdu3dr7Nixxbr5YMasiP7zzz8XeW5ubq7pcRTn6TkPAAAAAHaxWike6CcFBTgvF2dpdbPk5mZ+UyI+QepdBV83qqDMFCnncnlncWNy85S8a5V3Fnb79ttvNXnyZJ0/f77QePfu3bVr1y6bcSm/+PjOO+9o9+7devvtt4u1Cei1bVYaN26s4cOHq3Xr1vLw8NCOHTs0f/58JSYmGs49e/bscltBf/ToUb333nuGMW9vbw0dOlQ9e/ZUQECAUlJStG3bNkVHR19dRZ6bm6svvvhCv/zyixYuXKimTR17DO3a961WrVp65JFH1LlzZ/n7+yshIUHR0dHavn274bmRkZEaOHCgvL297Z7vypUrVwvz7u7u6tevnyIiItSgQQOdPn1aq1atUkxMjOG5X331lUaPHq1WrVoZxjMzMzVx4kTTwn9ERIT69eunRo0aKTMzUzt27NCiRYsM90jYuXOn3n33XU2YMMHu12alV69emjp1qk0bp/379+v8+fPy9/c3PXfPnj26cOGCzXjLli0rxebGZY3iPAAAAADYwarffLum+RuoVjXeXlLzRtKeI8bx+ASpd/H2RAScY1uUFPdP6Zzj7SFQigKaST3/JrUfUd6ZWJo/f75mzZplM/7ss89qzJgxysjI0KJFizRv3jylp6fbHPfdd9/p2Wef1dy5c+3acNTIn//8Z/3tb38r1FKndevW+uMf/6jBgwfr6NGjNuesX79e586dU0CA8++WLlu2zLCY7OXlpejoaIWFhRUav+222zRo0CANGTJER44cuTqelJSkJ598Uua0bJsAACAASURBVDExMapWrZrDebRv317vvvtuofegefPmioiI0MSJE7V27Vqbc5KTk/X9998Xa/V2YGCg3nvvPbVt2/bqWGhoqLp166Y2bdpo+vTpNufk5eVpxYoVpsX5qKgow0K7JE2bNk2DBg0qNBYWFqZBgwbp8ccfN7wBsWDBAg0dOrRUPhdBQUFq166ddu7cWWg8Ly9PmzZt0r333mt6Li1trNHWBgAAAADsYLVyvk0VbGlTwGqjW/rOo0LbskBa+RiF+Yrg3EFpxaPS1g/KOxNTixcvNizMP/bYYxozZowkqVq1ahozZoxiYmLUpk0bw+vExsZq8eLFxcph8ODBevXVVw173QcEBOjvf/+74Xk5OTmKjY0t1pwlZdaKZ/jw4TaF+QL+/v6GK7qPHTumTz/91OEcWrRooaioKMMitJubm6ZOnSofHx/Dc9esWePwfNWrV1d0dHShwvy1Hn30Ud16662GsbVr1xpuIpydna0lS5YYnvPAAw/YFOYL+Pr6as6cOYar/7OysvTJJ5+YvQyH9erVy3C8qNY2ZsX5iIiIEudUFVCcBwAAAAA77LJYOW9VwK7swpqZx6yeJgDKXdzr5Z0Brrd+anlnYCg+Pt6wH3hISIief/55m/GGDRsqKipKzZs3N7zeW2+9ZdjGw4q/v7/+9re/WR7TvXt3BQcHG8Y2b97s0Hyl5ffffzccDw8PtzzvjjvuMBz//vvvHc5h2rRplq1p/Pz8TAvBW7ZscXi+8ePHq0kT6y/+wYMHG46fOnVKx47Z7i6/YcMGJScnG57z5JNPWs4VFBSk/v37G8ZWr15tea4jzFa6W20Km5GRYbiqv2nTpg63MKqqKM4DAAAAQBFOn5eSLDY/tdo4tbJra/Ha9hyRLju2Jx7gPCm27T9Qziroz2TatGmGm2eOHz/ecBW7JNWoUUOzZs2Si0FPs/T0dP3nP/9xKAcfHx+7etV37tzZcPzgwfJ5QsRs49zatWtbnlerlvE+BGZtXYpzrWuZ3QxISkoq1Le+rOeTZLiBr9kTCKGhobrpppuKnO+uu+4yHE9ISFBKSkqR59ujcePGCgkJsRk/cuTI1T0Errd582Zdvmy73wctbf6H4jwAAAAAFMFqhbirq9SqsdNScbp2FosDr2RL+ytmrQ2QahVd0IKTVcCfyfbt2w1X9taqVUt333235bmhoaHq2bOnYaw47VLsYbZi+/Dhw2UyX1HMCtVGG+faE3dzcytxTkasVrofOlT6j4E1aNDAtHe+0c9q9+7dhse2bt3arvnM+tjn5eUpPj7ermvYw9HWNmY3HSjO/w/FeQAAAAAowq8WvdVDG0rVvJyXi7MFB0oBNc3j8bS2QUXVw7pFCMpBz1fKOwMb//3vfw3HO3ToYLpq/lpmK6T37NmjnJycEuVmpH79+objGRkZhiuUy5pZUfjHH3+0PM+smNuwYcMS52SkXr16prHU1NQymTMoKMju+Yw2+pVk16p5Sapbt67pkxenT5+26xr2cLS1jVG/+YYNG5p+bm5EFOcBAAAAoAhWG59atX2pClxcrFfPx7PXJiqqjo9LAz+Uatu2YYCT1Q6R7o+SOowq70xs7Ny503C8WTOLDTeu0aJFC8PxjIwMnTx5sth5mTFbjS1Jly5dKvX5itKnTx/D8UWLFpn2o7948aIiIyMNY126dCm13K5ltiGsVHbvm9mcRvOZtZ7x9fW1ay4XFxdVr17dMFaaNx9atWpluO+BUXE+OTlZBw4csBln1XxhRd8CBAAAAIAb3C6LbgFVeTPYAu2aSet3GMdYOY8K7bZH8/9kpkg5zl9VDElunpJ30T26y4vZiuU6derYdb5V//GUlBQ1atSoWHmZ+X/s3Xd0VHX6x/HPpBNSSCMJoSYBKdIFRUAQROyCiFhQQda2K7tgQY+46qqLuFZQFllEQRaxFxB3VYrszwIo0juBUEJoCSGBBNLm9wcLa5h7b2bCZGYy836dwzm73+fe+31yB5Pw3O99vuHh5q9qmfV/r03XX3+9Zs2apS1btlQZLyws1M0336zRo0erX79+SkxMVGFhoZYtW6bJkycrOzvb4VoNGjTQ4MGDayVPq37+JSUltTKn2WdlNJ/ZWw9Wn7ezx7raU786AwYM0MyZM6uM5ebmKjs7W82bNz8zZrRqXpLp5ryBiuI8AAAAAFgoL5c2WBXn/XzlvGT9AMKq5Q/gM3y4OAzvKioqMhyPiIhw6nyz1crSqY1h3c2qAO9KIdddQkJCNGXKFN1+++3Kzc2tEsvPz9df/vIX/eUvf3HqWk899ZRiYiz6qJ2DkydPmsac/axdZfZZGc1Xv359wxXuVnk7e6y7/14YFeelU6vnf1ucN+o3n5ycrI4dO7o1n7qOtjYAAAAAYGF7jnTCYsGtVcsXf9HB4gFEbp50uMBzuQCAO5kVLp3t325VLK+Noq9ZCxabzeZ0CxR3a9KkiT7++GP16dOnRueHhITo6aefNm2R4w5WrWuio6M9OqfRfGYPJZxd9W63203na9CggVPXcFaXLl2UkJDgMH52axuj4vyAAQNks9ncmk9dR3EeAAAAACxY9ZuPjpSame8x5zfatZCCLP71uNG4rTAA+DyzwqWzfbqtjouLi6tRTlb27NljOJ6UlGTZuqW2JSYm6vnnn6+yctoZnTt31ty5c3XLLbfUTmL/Zda+SJJhD/VzVV5e7vAmgdV8DRs2NDzW7PM+2/79+00fKDVr1sypazgrKChI/fv3dxhfvny57Ha7JGnLli06dOiQwzH0m3dEcR4AAAAALFj1m++QcWrDVH9XL1xKTzWPW7X9AQBfZlYUNdvM9Gw7dxp/AwwPD6+Vou/GjRsNx1u1auX2uVxx6NAhDR06tEov+YyMDF1wwQWKi4tTSEiIwsLClJSUpG7duumee+7RRx99pPfff18dOnSo9fzM7lu9evXUuHFjt8+3bds2lZWVGcZatnTcpLpTp06Gx27atMmp+cy+vuDgYLVp08apa7hiwIABDmP5+fln9h4wWjUfHx+vCy64wO251HX0nAcAAAAAC+stNjw9v4Xn8vC2Ns1PtfgxsjHbk5kAgPt07txZq1atchg3GjPy888/G4536tRJQVavHNVASUmJfvrpJ8NY586d3TqXq8aNG6ecnP/9kIiMjNQ///lPxcfHezGr/1myZInheIcOHRQS4v7y6OLFiw3Hw8PD1a5dO4fxLl266O2333YY37x5s/bv36+UFOvX9Mzma9++fa308b/ooosUFRXl0HZn2bJlat26teFmsJdddpmCg4Pdnktdx8p5AAAAALCwyWLx5PkB0G/+tHbNzWObsj2VBQC4V/fu3Q3Hd+/erTVr1lieW1RUZFoU7devn0t5FBQUaP/+/ZbHzJ0713ST2csuu8yl+dxp/fr1DiulMzIyPFaYP71a28z69esd+qGfZtSe5VznKy4u1nvvvWcY6927t2H7oV69ehm2WLLb7Xrrrbcs5ztw4IDmz59vGLv++ustz62psLAw9e3b12F82bJlKi0t1S+//OIQo6WNMYrzAAAAAGDiZKn5anFJatvcY6l4XVuLtwQ2ZHssDQBwq169epmuSp44caJpaxJJevXVV1VUVOQwXq9ePQ0ePNilPI4fP6677767yurz31q1apUmTZpkGGvTpo1at27t0nzutHr1aoexbdu2Od2S5Vw9+uijWr58uWEsLy9PDz30kGEsLCxMV199tcvzzZw5UzNnzjSMlZWV6bHHHtPhw4cN42Z/L+rVq6ehQ4caxubMmaN//etfhrFjx47pj3/8o06ePOkQi4uL03XXXWd4ntlGyFYb557NqLXNzz//rJ9//lklJSVVxmNiYnTRRRc5fe1AQlsbAAAAADCxZY9UWWkeD6TivNXK+YNHpMMFUqLxvooA4LNCQ0M1cuRIPf/88w6xX3/9VaNHj9azzz6rpKSkM+PFxcWaNGmS5syZY3jNkSNHKjY21uVctm7dquuvv17Dhg1T7969lZiYqMOHD2vRokWaO3eu6YOCe+65x+W53MloI9ITJ07ohhtuUIcOHZSSkqLQ0NAzsaCgIMXGxiohIUEtWrRQz549FRUVVeP5i4qKNGLECF1zzTW66qqrlJaWpuLiYq1YsUKzZs0yLZQPGTJEiYmJNZrz+eef18KFCzV06NAzPeQ3bdqk2bNnmz6UaNmypeVK/bvuukufffaZQ76VlZUaM2aMFi1apGuuuebM17dq1SrNmjVL+/btM7zemDFjTO9rgwYNFBISovLy8irjBQUFmjlzpi688EIVFRWZvlkiSZdcconCw8OrPBg4duyYpk2b5nDspZdeWuXvAP6H4jwAAAAAmLDqpZ4QKyUFUDH6vKZSUJD5w4oN2VIf4/3sAMCn3XrrrVqwYIHWrl3rEFuyZIn69u2rjh07KikpSYWFhVq9erVpe5lWrVrp3nvvrXEuRUVFeuutt6ptZXJaz549ddVVV9V4Pnfo2LGj4XhlZaXhqvqzhYaGqnfv3nrooYeUmZlZoxwqKys1b948zZs3z6njExMTNWbMmBrNddrpVeLOCAoK0tNPPy2bxS7y8fHxmjhxou6++27Z7XaH+Pz5803b15ztsssu080332waDw0NVbt27QxbN51+UBUZGally5aZrrKPjIxUz549HVo7Gb3FQEsbc7S1AQAAAAATVsX5ds0li39j+5164VJ6qnl8407P5QIA7hQWFqZJkyaZrqIuLy/XypUr9e9//1s//vijaWE+KSlJb7zxhiIiIlzOITQ01OUNZJs0aaKXXnrJ5bncrWvXrurdu3eNzy8rK9PixYt1/fXXG26KWh2z4rHV8a+//rphj/famE86tWHuBRdcUO1xvXv31l//+tdz2qS2Z8+eevnll6s9zqyNzmnFxcWGG7v+llFrm7NFRkae098Pf0dxHgAAAABMWBXn2zTzWBo+w6rv/EaLjXMBwNc1atRIH374oVq1alWj8zMzM/Xee++pWbOa/XBo2LCh/va3vzldlG3Xrp3mzJnjsU1Xq/Paa6/pkksuOadrlJeX64UXXjDdTNVqbmfb08TFxentt99Wly5dapKiJOnBBx9U586dnTo2JCREf/7znzVy5Einrz9kyBC9+eabSk21eCJuICgoSL/73e80bdo0px4Q3XDDDbr44ostj1m4cKFlvF+/ftX+nT3d/gbGKM4DAAAAgIlN2eaxQOo3f5pV33lWzgOo69LS0vTBBx9o9OjRio6OduqcyMhI3Xvvvfrkk0/UtGnTc5r/2muv1aeffqpevXqZtj9JTk7WI488og8++EDJycnnNJ87RUVFafr06Xrrrbc0ePBgZWZmKioqyuW3AaRTG/Hu37/f6eNbtWql+fPna9iwYZYtWG655RZ99dVXTq1gtxITE6PZs2dr3Lhxpg9HgoKC1LdvX3366acaPny4y3P07t1bX3/9tR599NFqHxhFRUVp8ODB+vLLL/XII4843ds9ODhYU6dO1bBhw0wL7Lt377a8RoMGDaq9nwMHDnQqn0Blsxs1MQLcqG3btqqoqKgyFhwcrI0bN3opIwAAAKB6J0ul6Cuks36VPePbV6V+NV94VyfN+Va64znjWFIDaf8Xns0H/mvz5s2SpNatW3s5EwSqwsJCLV68WD/88IM2bdqk/Px8HT16VGFhYUpMTFTr1q3Vs2dPDRw4UHFxcU5fd+/evYabgqalpVXp3X348GGtWLFCBw8e1MmTJxUbG6s2bdro/PPPV3BwsFu+Rm8oLy9XXl6eNm/erLlz52rJkiWGx40ZM0b3339/lbF+/fopJyfH4dhFixapcePGkqSSkhItX75cOTk5KiwsVFRUlNLT09W5c2dFRka6lOtjjz2mzz77zGH8+eef1w033CBJqqio0OrVq5WVlaX8/HyFh4erUaNG6tq1a403mzVy4MABrVu3Tnl5eTpy5IhCQkLUoEEDtWzZUm3atFFYWNg5Xf/QoUNatmyZcnNzFRISoqSkJLVr107p6elOX2PEiBH66aefqoxFRkbqhx9+cPne10Rt/NzwRE2TDWEBAAAAwMC2veaFeUlqG4BtbaxWzh8qOPUnkDbJBeC/YmJiNGjQIA0aNMgr8ycmJnp9o9faEBISouTkZCUnJ6tPnz4aPXq0vvnmG4fj1q9fX6Pr16tXT3379j3HLJ0XHBysrl27qmvXrrU6z+l7VluSkpJ07bXX1vj8tWvXOhTmJenqq6/2SGG+LqM4DwAAAAAGNmSbx+JjpGTfaPPrUa2bSkFBUmWlcXxjttSnk0dTAgB4wZEjR/TYY485jN96663q06eP09e5+OKLDYvzpaWl55QfPKeiokJ/+ctfHMZtNptuvfVWL2RUt1CcBwAAAAADlv3mm0km7YD9WkS4lNHo1FsFRjbupDgPAL5gwoQJys/Pr5VrX3755br44ov13XffOcROr4h31urVqw3HU1JSapoePMhut+upp54yfNPhqquuUtu2bb2QVd1CcR4AAAAADGzMNo+1ae6pLHxPm+bmxXmrtw0AAJ6zcOFCw/7s7tCsWTNdfvnlSklJcdi49dNPP1WLFi100003qX79+qbXOHjwoGbMmKHPP//cMN6jRw+35gz327Fjh5599ln9+OOPDrGoqCg9+OCDXsiq7qE4DwAAAAAGrIrzVr3X/V275tK8741jVvcMAOBf+vbtq/fff7/KWFlZmSZOnKhXX31VmZmZSkpKUv369WWz2VRWVqbCwkLt3btXe/bsMb1uo0aNdNlll9V2+nBRSUmJNmzYoPXr1+vbb7/VypUrZbfbDY997rnnzmzSC2sU5wEAAADgLKVl5qvDpcBeOd+2uXmM4jwABI5Ro0bp888/14kTJxxiJ0+e1IYNG1y+ZlhYmF577TWFhYW5I0W40ccff6znnnuu2uMeffRRXXnllR7IyD8EeTsBAAAAAPA12/ZK5RXmcasCtb9r18I8dqjg1B8AgP9r2rSpXn75ZYWHh7vleg0bNtTs2bPVsWNHt1wP7tW0aVPLeEREhJ599lndddddHsrIP7ByHgAAAADOYrUCvEGUlJrgsVR8znlNpKAgqbLSOL5hp9S3s2dzAgBUtXjxYo/Mc9lll+mzzz7Tiy++qO+++860zYmV+Ph4DR8+XHfeeaeioqJqIUu4g1VxvlevXho/frzS09M9mJF/oDgPAAAAAGfZtMs81ra5ZLN5LBWfExEuZTQyb/uzMZviPABUx2azKT4+XikpKUpJSdF5553n7ZRqLCMjQ2+++aZycnL03Xffad26ddqxY4cOHjyoo0ePqrS0VHa7XREREYqMjFR8fLxatGihzMxMXXzxxercubOCgpxv7hEdHa3U1FQlJycrNTVV0dHRtfjVnVoRnpycrJSUFKWmpiozM7NW5/NVjRs3VnBwsCoqTr1amJGRoV69eumGG25Q69atvZxd3UVxHgAAAADOsmGneSyQW9qc1ra5eXHe6t4BQKBr2LChFi5cqOTkZL/rq56Wlqbbbrut1q4/a9YsxcfHq379+rU2x2+NHTtW48aNU3x8vEfm83WhoaF65ZVXlJSUpBYtWnBf3ITiPAAAAACcpbqV84GuXQvpi++NY1t2ezYXAKhLwsLC1KRJE2+nUSd5+r4lJyd7dL664IorrvB2Cn6HDWEBAAAA4DfKyqWte8zjbZp7LBWf1aaZeWwzxXkAAACnUJwHAAAAgN/YvvdUgd5Mu+YeS8VntbYozufmSUePeS4XAACAuoriPAAAAAD8htXK75j6UqNEz+Xiq1o1to6zeh4AAKB6FOcBAAAA4Deseqa3aSbZbJ7LxVdFRUpNGprHKc4DAABUj+I8AAAAAPyGVWH5vKaey8PXWd0LNoUFAACoHsV5AAAAAPgNq8LyeU08l4eva21RnN+8y3N5AAAA1FUU5wEAAADgv+x2acse8zgr5//HqjjPynkAAIDqUZwHAAAAgP86kC8dPWYetypIB5rzmpnHtudIZeWeywUAAKAuojgPAAAAAP9l1W8+OFjKSPNcLr7O6kFFeYW0Y5/ncgEAAKiLKM4DAAAAwH9ZFefTU6WwUM/l4utSE6ToSPO41b0EAAAAxXkAAAAAOGMr/eadZrPRdx4AAOBcUJwHAAAAgP/avMs8Rr95R1YPLKzuJQAAACjOAwAAAMAZVqu9W1Gcd8DKeQAAgJqjOA8AAAAAkkpOSrsOmMdZOe/ovGbmsc27Jbvdc7kAAADUNRTnAQAAAEDStr3WxWSK846s7knBMengEc/lAgAAUNdQnAcAAAAAWfdIT4yVEmI9l0tdkdFICg42j2+mtQ0AAIApivMAAAAAIOse6VYbnway8DApPdU8TnEeAADAHMV5AAAAAJB1IZnivDmre8OmsAAAAOYozgMAAACApK17zGP0mzdndW+sWgUBAAAEOorzAAAAAAJeZaX1yvlWTTyXS13DynkAAICaoTgPAAAAIODlHJaKT5jHWzfzXC51jdW92XXA+r4CAAAEMorzAAAAAAKeVfuV0BCpRYrncqlrzrN4q8Bul7bt9VwuAAAAdUmItxM4W2VlpZYuXapvvvlG69evV05Ojk6cOKGIiAilpaWpffv2GjBggPr06aOgoLr3bKG8vFz/+c9/9N1332nDhg3au3evjh07ppCQEDVo0EDp6enq1q2brrrqKjVv3typa2ZlZWnq1Kluye/xxx9XfHy8W64FAAAA1BVW/eZbNpZCfO5fTr4jIVZKaiAdKjCOb94tdcz0bE4AcK4qKiq0cuVKLVy4UKtWrVJeXp7y8vIUFBSkBg0aKDk5WZ07d9aFF16oXr16KcTFHxSlpaU6cOCADhw4oIMHD6q4uFg33nhjLX01QFUffPCB9u/ff+ZPbm6u9u/fr9WrV3s7tYDjU79i/vzzz3rqqaeUlZXlEDt+/Li2bt2qrVu36pNPPlFGRoaeeeYZXXDBBR7Lb+vWrXr66ae1cuVKh9i7776rCy+80PTciooKffTRR/r73/+uAwcOOMTLy8vP/Afx448/avLkyRo4cKAee+wxpaamWuZ1+PBhzZ8/3/Uv6CyhoaGaOHHiOV8HAAAAqGvoN39uzmtiXpzfZvHgAwB80f/93//pueeeU3Z2tmG8uLhY+/bt06pVq/T2228rNTVVt912m+68806FhYVZXvvAgQO67rrrVFBQ9ZtmWloaxXl4zJNPPuntFPBfPrP0/L333tOdd95pWJg3kpWVpTvuuENz586t5cxOfdN98cUXNXjwYMPCfHX279+v4cOH66mnnjIszBux2+3697//rUGDBmnZsmUuz1kTjRs3dvlJLwAAAOAPrDYubW2x4SlOaWVxj6zeSgAAXzNz5kzdfffdpoV5I7m5uXrppZc0aNAgrV271vLYsrIyh8I8gMDlE8X5BQsW6JlnnlFFRYVL51VUVOgvf/mLvvrqq1rKTFq0aJGuvvpqvfXWWyovL3f5/KysLN1000369ddfazR/QUGB7rvvPq1fv75G57uiRYsWtT4HAAAA4IusivPnUZyvVqvG5rHtOZ7LAwDOxQ8//KCJEyfKbrfX6PycnBwFBwe7OSsA/szry6QPHDigJ554wvAbn81m0/nnn6/U1FTl5uZq/fr1DsfZ7XaNHz9eXbt2VXJystvy2rdvn5577jktWrSoxtfIzc3ViBEjdPDgQcN4cnKy0tPTFRsbq5ycHG3YsEGVlZUOx5WUlGjcuHGaN29era5sd7bHPQAAAOBPik9Iew+Zx2lrU72WFvdoy+5TG8PabJ7LBwBq4sUXXzQtzMfHx+v8889XVFSUCgsLtWXLFh06VPWHx/PPP6927dp5IlUAfsLrxfk33nhDxcXFDuNpaWmaMmWK2rRpc2Zs06ZN+sMf/qCcnKpLL4qLizVlyhQ988wz55xPeXm5Zs6cqSlTphjm5YoTJ04oMjLSYbxNmzZ6+OGH1bNnT9l+8xvqzp07NW7cOMNXoLKysvTll19q0KBBTs8/ePBgesgDAAAA1ahuZTfF+epZ3aOCY1LeUSmxgefyAQBX7dy5U5s2bTKMjRo1Sg8++KDDgsm1a9fqnXfe0VdffaX77rtPV111lSdSBeBHvNrWZteuXfr0008dxkNDQzVjxowqhXnpVFF7+vTpCg0NdTjnk08+0e7dFu+iOuGXX37R4MGD9eKLL55zYV461Sbmww8/VI8ePc6MXXHFFfrwww/Vq1evKoX508fPmDFDjRo1MrzeF198cc45AQAAAKhq217zWEKsFB/juVzqqoxG1ivjt1rcYwDwBVu3bjUcz8zM1COPPGLYyaBDhw569dVX9emnn+pPf/pTbacIwA95tTg/f/58wz7uw4YNM+1/npGRYbh7dXl5uebPn1/jXFasWKHhw4ebfjMOCgrSsGHDXH49KTY2Vm+99ZZuvfVW9e/fX6+88orlzt0xMTG68847DWOrV682bHsDAAAAoOa2WWxY2jLNc3nUZeFhUvMU8zibwgLwdWb7ILZs2dJhceXZ2rVrp6Agn9jWsc7Lz89nw1wEFK9+5/j2228Nx6+55hrL86699lqXrueMbt26qVu3boaxdu3a6YMPPtAzzzyj+vXru3ztkJAQPfXUU5o8ebJTG4N06dLFcLy4uJhvUAAAAICbWa2ct+qljqqs7pXVAxAA8AVmXQx27tzp4UwC0+m9H/v166cFCxZ4Ox3AY7xWnM/NzdXmzZsdxuvVq6eOHTtantupUydFREQ4jG/atEm5ubk1ysdms+nZZ5+tct3o6Gj9+c9/1scff6wOHTrU6Lq/5exmrrGxsaaxsrKyc84DAAAAwP9YFucbey6Pus7qLQPa2gDwdZmZmYb7Bm7evFlvvfWWFzLyf/n5+fr44481YsQI9e/fX7Nnz1ZJSYm+++47b6cGeIzXNoTduHGj4XjLli2rfRUoODhYLVu21Lp16xximzZtUmpqao1yat68uR544AG92QnTXQAAIABJREFU9NJLuu666/Too48qMTGxRtc6F8eOHTONRUdHezATAAAAwP9RnHcPq01hWTkPwNdFRUVp8ODBmjNnjkPsxRdf1MqVK/XII48oPT3dYzkVFhbq559/Vm5uroqLi5WQkKBOnTopIyPDYznUhN1u15YtW7Rt2zYdOnRIxcXFKisr08mTJ3X06FEdPHhQ2dnZ2rvX+Afw8uXLVVJSonr16rk897Fjx/TLL78oJydHxcXFSkxMVNOmTdW5c+daaT1UUVGhlStXateuXcrPz1dcXJySk5PVrVs3w4c9NXHkyBFt3LhR2dnZOnbsmEJDQxUfH6/k5GR16tSpRvcJvsNrxfmsrCzD8WbNmjl1frNmzQyL81lZWerXr1+N87rrrrvUrVs3derUqcbXOFfbt283HG/YsKHb/sN2p7Zt21rGzfq2AQAAAN529Jh08Ih5nOK88yzb2uyVKislWjID8GUPPPCAvvvuO+Xk5DjEFi9erO+++04DBgzQ7bffbtoa2R327t2rl19+WQsXLlRpaalDvH379nriiSe8WrsykpeXp3/84x+aN2+e8vPza3ydkydPatmyZbr00kvPjN1+++1asWJFlePuuusuPfroo5JOtcV57bXX9M0336ikpMThmomJibrpppt0//33W+4Fedrrr7+uN954o8pYu3bt9Omnn0qSSkpK9Oabb+qTTz7RoUOHHM6vV6+eBgwYoHHjxikpKan6L/osdrtdCxcu1LvvvqtffvnFdA/KsLAwde/eXSNHjlSvXr1cnqc6y5cv1x133GEY++qrr5x6UHTy5El1795dJ06ccIjNnTvXtL13oPDar0ZmxXlnV6qbHWd2XWcFBwd7/ZvbDz/8YDhek7+s2dnZeu211zRs2DB169ZN7dq104UXXqhbbrlFf//733XgwIFzTVcVFRWWfwAAAABfZbVqXqI47wqrlfMnSqW9jrULAPAp8fHxmj17tlq1amUYr6ys1Ndff63hw4frpptu0sKFC2W3292aw+eff65rr71WX331lWFhXpLWrVun4cOHa+HChW6d+1x88sknuuqqqzRz5sxzKsyf9ssvv1R7zMGDByVJn332ma655hp98cUXhoV5STp8+LD+/ve/a/DgwTWuhZ0uwq9du1bXXXed3nzzTcPCvHSqeD9v3jxdffXVWrZsmUvz7NmzR7feeqseeOABrVixwrQwL0mlpaX6/vvvNWrUKN111106fPiwS3NV54ILLlBcXJxhzNmva+XKlYaF+YYNG6pz587nlJ8/8Fpx3uwvb3x8vFPnJyQkuHTduqKwsNB0Y9vLLrvMpWstXrxYV155paZOnarVq1ersLBQ5eXlKigo0K+//qpJkyZpwIABmjp1KkV0AAAABKStFu1WUhOkKN97cdVnNW0ohYWax63uNQD4irS0NH300Ue66667LPcOXLNmjf7whz/o5ptv1oYNG9wy90cffaTHHntMxcXF1R5bVlamcePGGa7y97Q33nhDjz/+uAoKClw6Lz4+Xj179tSll16qwYMH65577tGECRM0b948PfLII9Wef+jQoTP37Pjx407NuX37dt17771OH/9beXl5Wrt2re666y7t3r3bqXOOHj2qBx54QNu2bXPq+HXr1unGG2/Ur7/+6nJ+P/zwg4YNG+bWTYyDg4NNO5Q4W5w3W4Q8YMAA2Wy2GufmL7xWnDf7RuNsn6Tw8HCXrltXTJ8+3fBriI+P1+WXX+7StY4ePWr5dE069WrJa6+9prFjx1KgBwAAQMDZblHTsFoJDkfBwVKmxaaw9J0HUFcUFxcbrvQ1snr1at144416/fXXq63BWDl48KCefPJJl1biHz9+XNOnT6/xnO7w+eef6/XXX6/Rufn5+QoJCdH48eM1ceJEPfTQQxoyZIjOO+88p85ft26dnnzySZfn3bRpk6ZOneryeRUVFRo5cqSKiopcOq+oqEjjx4+v9rg9e/Zo1KhRLj/k+K29e/fqd7/7neV+lq4yq0dWt6r/tB9//NGl6wYar/WcN3tCFRpqsdTiN8z6Q9XkyZevyMrK0syZMw1j99xzj+kDCXf4+uuv9corrzj1ZBIAAADwF1arua0KzTDWsrG0Mds4trWaFkJAbSosP6Yye5m30whIobZQxYREeTsNpy1atEjjx4/XkSNVNyTp3bu31q9f7zAunWp388Ybb2jDhg2aPHmyUz3Nz1ZW9r+/n9HR0brzzjvVvXt3RUZG6tdff9XkyZMNC67ffPONnn76aZfnc4fCwkJNmDDBMBYcHKyhQ4dq4MCBSk5O1vHjx7Vu3TrNnj27ysrupUuXatWqVZo6daouuOACl+b/7eLWevXqaejQoerTp48aNmyoPXv26IMPPtDSpUsNz509e7buuOMONWzY0KU5T38GNptN/fv316BBg9S0aVMdPXpUX3/9tebOnWu4+HXNmjVauHChaVcMu92uhx9+WEePHjWM9+jRQ0OHDlVGRobKysq0adMmzZ49W1u3bnU4du/evXr22Wf1wgsvuPS1mbn44otVv359h5prQUGBNm3apHbt2pmem5+fr02bNjmMx8XF1eq+DXWJ14rzZk8gnS3Omx1n1lvK15WVlenRRx817CeWmZmp2267rUbXzcjIUIsWLRQcHKz9+/drw4YNKi8vNzz2nXfe0fXXX2/aW81McHCwZZwV+QAAAPBV2y0Kxqycd53VPaOtDbzhg0MLNHnfTGWf5OmQNzUPb6w/NRqhm5Ku9nYqlqZPn66XXnrJYXzs2LG67777VFJSolmzZmnatGmGXQ+WLFmisWPH6vXXX1dQDXfAbtu2rf7xj39U2US0ffv2atWqlUaMGOFwfF5ennJycpSW5vknyl9++aVhMdlms2ny5MkOhegOHTpoyJAhGjlyZJW2LYWFhfr973+vL7/80uViuSSlp6dr2rRpatq06ZmxVq1aqX///vrrX/+qd9991+GcEydOaMGCBRo5cqTL80VFRenVV1/VJZdcUmW8e/fuuvDCC/XHP/7R8A2Ijz/+2LQ4/+WXX2r16tWGsdGjR+uBBx6oMta+fXsNGjRIDz/8sL7++muHcz7//HONGjXK5RqfkbCwMPXp00dfffWVQ2zZsmWWxfkff/zR8F7079+/2npioPBaWxuzvl3OvgJkVvCtydNJX/Diiy9q3bp1DuOhoaF6/vnnLb+u2NhY9erVq8qf3/3ud1q4cKG++uorTZkyRZMnT9aHH36oJUuWaMCAAYbXqaio0Ntvv+1y7hs3brT8w39sAAAA8EV2u/Vq7kw2g3VZS4vifHWb7wLu9t7BeXpw53MU5n1A9sm9GrvzOc09NM/bqZiaPXu2YWF+5MiRuu+++ySdWp193333af78+Tr//PMNr7Nw4ULNnj27RjlkZGRo9uzZVQrzp/Xo0UOJiYmG5+Xl5dVovnNl1q7kyiuvNC1CR0RE6NFHH3UYP3r0qN566y2Xc0hJSdGcOXOqFOZ/a9y4cUpNTTWMGRW1qxMSEqJp06Y5FOZPu/zyy3XVVVcZxn744QfTdjPvvPOO4XiPHj0cCvOnhYWF6YUXXjD8+yJJs2bNMhyvCbNa4k8//WR5nlm/eVra/I/XivORkcY7K5ntRH22377u48x1fdnnn39u+h/M2LFj1aFDB8vzW7durRkzZlT588gjj6hJE8ffjBs2bKjJkyere/fuhtf6+uuvTe8tAAAA4E8OH5WOWrRkZeW861pZPNDYmSuV8k8NeNCkfcbFLnjPazm++ZmsXbtWEydOdBhv2bKlHn74YYfxxo0ba+bMmaa90V977TWX+4aHh4fr9ddfV1SUeQsgs9XxzvbHd7ddu3YZjpttIHpax44dDet3Zi1orIwfP17x8fGm8dDQUA0ZMsQwtnbtWqfrkKfdcsst1bbfGTZsmOF4aWmp4cLcrKws002F7733Xsu56tWrp9tvv90w9q9//ctt3SwuueQSw4XDK1eutKwjGhXvo6Oj1aNHD7fk5Q98rjjvbGHYX4rzK1eu1J///GfD2DXXXKNRo0a5fc6goCD98Y9/NIwVFxdr/fr1bp8TAAAA8DVWbVZsNindeKEdLFg90KislHbs81wuQE7pAW+ngLP46mcyYcIEwxbAo0ePNu38EB0drZdeekk2m80hVlxcrC+++MKlHBITE5WRkWF5jNlehK5sIutOZg8FEhISLM+z2WyKjo52GN+3z/UfErGxsdUec9FFFxmOV1RUaMeOHW6fr3Pnzqaf1bZt2xzGzN5AiImJMV1c+1uXXnqp4fjx48cNe9LXRFRUlC6++GKH8eLiYq1du9bwnKysLOXm5jqMX3rppXW280lt8FpxPiYmxnDc2SeLZseZXdcXZWdn6/e//73hU7ouXbqYbqrhDl27djV9kPHbjTkAAAAAf2XVZqVZshRh/O9qWGgYJ8XUN4/T2gaelBaW7O0UcBZf/ExWrVqlVatWOYzHxsaqf//+lue2atVKffv2NYzVpGVKXWNWqDbaNPe3KisrDXvV11Zb5PT0dNNYbdTAwsLC1Lix8atkRvNt3LjR8NjWrVs7dU8yMzNNi91r1qyp9nxnmbUqMmttY/bQgZY2VXmtON+sWTPDcWf7ZJkdZ3ZdX3Po0CGNGjXK8CFDRkaG3nzzTdOnbO4QFBRkeq/y8/NrbV4AAADAV1htBku/+Zqx2axb21Cchyf9qZHrGz2ido1J873PxGiTS+nUokazVfO/ZbYqe+PGjW5rKeKr2rZtazj+/fffW563atUqw1X3ZgXtc5WQkGBavC4sLKyVOc363BvNZ9YeyKyP/tmCgoLUqFEjw9jBgweduoYzzDZxXb58ueHxRv3mIyMj1bt3b7fl5A+8VpzPzMw0HN+/f79T5x84YPwqlNl1fcmxY8d09913a+9ex99M09LS9M477zj1msy5qlevnuE4PecBAAAQCKza2tBvvuasNoW1uueAu93a8Dq90uIJtQjnP2hvaxHeRK+2eEK3JF3n7VQcmK0sdra+1Lp1a8PxkpKSGrVpqUvMNj6dP3++4dsI0qm+6y+//LJhzKhtiruY1cCOHz/u9fmM3iKQZLn/gLPHuvPhQ3x8vLp27eowvnr1aoeHLWVlZVqxYoXDsb1791ZERITbcvIH1T8CrCVm3+Sc7YW0ZcsWw3GrV1V8wcmTJ3X//fdr06ZNDrGkpCTNnDlTycmeec3L7BtQ/foW76ECAAAAfmJ7jnmsJSvna8zqwcY2ivPwsGFJV2tY0tUqLD+mMjsL0bwh1BaqmBDni4yetnv3bsPxxMREp863Wlx59OhRNWnivw+HLrroIvXp08dhI9eysjLddddduv/++3XllVcqJSVFJSUlWrVqlaZMmWL4QCQ0NFTDhw+vtVzNVs6XlJTUynxm3TCM5jPblNaVjhpmxx47dszpazhjwIABDkX30tJSrVy5Uj179jwztmbNGsO648CBA92ajz/wWnG+TZs2iomJcXiCs3//fh0+fNjym+Dhw4cNV87HxsaavlLjCyoqKjR27FjDJ0cJCQmaNWuW06+suCOXPXuMfzOurdeIAAAAAF9ht1u3WKE4X3NW924LxXl4iS8Xh+FdRUVFhuPOru61WuBYXFxco5zqkhdeeEEjRozQ5s2bq4wXFxfr5ZdfNl0lf7Y//elPtVoTO3nypOF4ba3iNtss12g+s79DZjm7cqy7W2YPGDBAf/3rXx3Gly1bVqU4b9TSJiwsTH369HFrPv7Aa21tQkNDTT+QhQsXWp777bffGo737dvXsB9YZWWlFi9erGnTpumTTz4xfV2kNtntdo0fP16LFi1yiMXFxWnWrFnV7spdHbMnbUZWrFhh+EPCZrOpXbt255QHAAAA4Ov2HZaKjf/dLIni/LmwWjmfmycdr51FigBQI2bFS2drLGZFWKn2Cr++JC4uTnPmzNHgwYNls9lqdI377rtPd999t5sz+5/KykrTFfLR0dG1MqdZtwqj9jMxMTGGx7qy6t3s2AYNGjh9DWekpqbq/PPPdxhftmxZlf9vtBlsz549XWrVEyi8VpyXzHfnnTNnjummGRUVFXrvvfecvt6xY8d022236f7779crr7yixx9/XAMHDjTtfVVb/vrXv+qzzz5zGI+Pj9esWbPUsmXLGl+7srJSTzzxhEaNGuXUf7iVlZWaMmWKYaxjx44ea6sDAAAAeItV7/OQYKl5iudy8TeZadbxLP9uwQygjjErXjrbq9vquLi4uBrlVNdERUXpySefVOfOnV06r1WrVpoxY4bGjh1bS5mdkpuba7q/YlpaNT+0asisXZJRt4qGDRsaHmvW8eJsFRUVyskx7tXXrFkzp67higEDBjiMbdiw4cxbKIWFhVq3bp3DMWZ14EDn1eJ8//79DXvEb926VZMmTTI8Z9KkSYZ96TMyMnTppZc6jE+YMEG//vprlbEjR47oD3/4g9v7LpmZNGmSZs+e7TCekJCgd999V+edd16Nr11aWqoxY8boo48+0ooVKzRs2DD98ssvpseXl5frqaee0s8//2wYv/3222ucCwAAAFBXWLW0SW8kGbyQCyfFRklJFgv1rHr9A4CnmRVGd+3a5dT5O3fuNBwPDw+vtcKvrykuLtbw4cOr1N8aNWqkHj16KDExUaGhoQoNDVV8fLw6deqkO+64Q++++67mzZunXr161Xp+Rvs+nnYui2XNFBQUKDc31+n5OnXqZHjsli1bVFlZWe1827ZtM334UBvdMYyK8xUVFWfaeC9btsxh0XVISIj69evn9lz8gVd/5QwODtaYMWP0xz/+0SE2bdo0bd++XcOGDVNqaqpyc3P14Ycfmra8GTNmjIKDg6uMlZaW6ssvvzQ8Pi8vTwsXLtSgQYPO/Qux8P777+vvf/+7Yaxx48aaNm2aS9dr1qyZRo8eLenUKzIPPPBAlVdFtm/fruHDh6t79+664oor1LJlS8XHx6uwsFBr1qzR3LlzlZ2dbXjtDh06mO60DQAAAPgT+s3Xrsw06VCBcWy7xb0HAE/r3LmzYXcFZzsumC1+7NSpk4KCvLom1mMmTJigDRs2nPn/NptN06dPV2Zmphez+p/Fixcbjjdu3Nj04cy5+O6770yL6kZvF3Tp0sXw2IKCAq1atUpdu3a1nM/s60tKSjqnBcFmMjIylJ6erh07dlQZX7Zsmfr372/Yb/7CCy90e4sdf+H19SADBw5U79699X//938OsUWLFhn2aD/bJZdcYvhqxJEjRyw3T9i/f79rydbA6tWrTWNr1qwx3KHayhVXXHHmf588edLw9Sm73a7ly5dr+fLlTl+3QYMGevHFFwPmBwcAAAACG8X52pWRJv20wThGcR6AL+nevbvefvtth/Hdu3drzZo16tixo+m5RUVFpoXRQFklfOjQIX3++edVxuLi4jxWmN+yZYsuvPBC0/iBAwdMF+7279+/RvNZqays1DvvvGMYa9OmjeHbFG3atFFGRoaysrIcYtOnT7cszhcXF2vOnDmGseuvv94y13MxYMAAhwXHp/vOG/Wbp6WNOZ+oxL700ktq1apVjc5t1aqVXnzxRcNYXFyc5eYbjRo1qtGc3vTbXlHx8fF69913q+yGXBMJCQmaPn26mjdvfo7ZAQAAAHXDNos2ri0tNjSFc6wecGTR1gaAD+nVq5dSUow3Gpk4caJpuxBJevXVV8/02f6tevXqafDgwW7L0ZetX7/e4R7l5+frP//5j0fm/9vf/qavvvrKMHb8+HGNGTPGdOFuTT6jb7/9VhMnTjTcK9Nut2vixInavHmzS/PZbDbTNtNLliwxfHgkneoYMm7cOB0+fNghFhYWpltvvdXsy1BYWJjhuNlGtmczam2zbds2rVmzxqHfflBQkC677DKnrhuIvL5yXjq1avvdd9/Vww8/rO+//97p83r16qWXX37Z9LWIsLAwDR48WHPnznWIJSUl1egJmbc1bdq0yv+vX7++pk+frjlz5mjSpEku99G/5JJL9Mwzzyg1NdWdaQIAAAA+q6LCelNSVs6fuwyLNsv0nAfgS0JDQzVy5Eg9//zzDrFff/1Vo0eP1rPPPqukpKQz48XFxZo0aZLpiuWRI0cqNja21nL2JaWlpYbjd999t9q2basmTZpUKQTbbDbFxMQoPj5eTZo0Ua9evRQfH1/j+cvKyjR27FjNmzdPgwYNUvPmzVVWVqa1a9dq5syZphuzXnLJJWrTpk2N5nznnXe0bNky3XLLLWrTpo3CwsK0Y8cOzZ0790zf9bPFx8frxhtvNL3mDTfcoDlz5mjbtm0OsRdeeEHLli3TkCFD1Lx5c5WWlmrjxo2aPXu24fGSNGLECMs9DxITE7Vvn+MvQ1OmTNF1112nQ4cOqXfv3qbnt2/fXo0aNapyDbvdrpdfftnh2C5duigxMdH0WoHOJ4rz0qlV7jNmzNCCBQs0Y8aMKr2qztauXTuNGjVKV199dbXXHTdunLKzs/XTTz+dGYuPj9eUKVNUv359t+TuSUa7LAcHB+uOO+7QlVdeqbffflvz58/XoUOHTK8RERGhXr16afjw4erRo0dtpgsAAAD4nF0HpLJy83grVs6fs0yL4vzeQ1LJSaleuOfyAQArt956qxYsWKC1a9c6xJYsWaK+ffuqY8eOSkpKUmFhoVavXq3i4mLDa7Vq1Ur33ntvbafsMzp06CCbzSa73e4Q27hxozZu3Gh5flBQkC644AI9+OCDhv3YnbVkyRItWbLEqWMjIiL0xBNP1Hgu6dQms08++aTTxz/++OOWdcjw8HC9/PLLuvHGGw0feCxdulRLly51aq4OHTqc2a/STKdOnQyL8zNmzNCMGTMknbqnVl1H+vfvr9mzZ1cZM2qxTUsbaz5TnD/t6quv1tVXX62cnBytW7dO+/btU0lJierVq6dGjRqpQ4cOLrWjiYyM1DvvvKP//Oc/2rx5sxITEzVgwADFxMTUKL+z/9JVZ+LEiZo4cWKN5nJVUlKSHn30UY0bN07bt2/X1q1bdfDgQRUXFys8PFwNGjRQRkaG2rZtq/BwfhMGAABAYLLqNx8RJqWxuOucZVbz9kFWjnR+umdyAYDqhIWFadKkSRo6dKhhi5Dy8nKtXLmy2uskJSXpjTfesGyx7G9SU1M1bNgwvf/++zU6v7KyUitWrNDNN9+sO++8U48//rhL54eHh1vuN3k2m82mF154wXDxa23MJ0nDhw/XtddeW+1x5513nt544w2NGTPG9OFPdVq3bq1p06aZtq05bejQoabtgE5btGiRabsd6VRrG2fqpBTnrflccf60tLQ0y9cvXGGz2dSnTx/16dPHLdfzdTabTS1btlTLli29nQoAAADgcyz7zTeWgnxiZ666LS5aio+R8guN49spzgPwMY0aNdKHH36o++67T1u3bnX5/MzMTE2dOtWhHXEgeOKJJ1RaWqpPP/30nK4za9YshYaG6pFHHnH6nGeeeUZTp05VdnZ2tcdGRkZq4sSJGjhwYI1zHDFihLKysrRw4cJqj7XZbLr77rv14IMPOn39Pn36aPbs2Ro3bpzhBrFWBg8erD//+c9OdQq5+OKLdcMNN1h+ZgsXLrQszl9wwQWKi4vTkSNHTI9p3749rbSrwa+dAAAAAAKK1cr56lZ8w3lsCgugrklLS9MHH3yg0aNHKzo62qlzIiMjde+99+qTTz4JyMK8dKpv//PPP6/333//TB/2mJgYBQcHu3ytGTNmGLYXMpOamqpPP/1U99xzj6Kiokzzu+aaa/Tll1+eU2H+9LXeeOMNTZgwwbKzR7du3fTPf/5TDz30kGw2m0tznH/++Zo/f76ee+45tW/fXkEWqwbCw8M1cOBAffjhh5o4caJLLbyfe+453X///aZveuTm5lpuiBwcHKx+/fpZzsGq+er57Mp5AAAAAKgNVsV5+s27T0aatNyk1TCbwgLwVZGRkXrggQd0xx13aPHixfrhhx+0adMm5efn6+jRowoLC1NiYqJat26tnj17auDAgYqLi3P6+o0bN9aWLVtqlJurrZY9rXPnzpZ94ysrK5WXl6edO3fqo48+0rx58xyOsdvt+vDDD9WhQwen561fv74eeughjR49WitXrtSuXbtUUFCgiIgINWvWTF26dHHrBr02m01DhgzRkCFDtGHDhjN/P4KDg5WcnKwuXbq41JLbSHBwsIYOHaqhQ4cqPz9fa9as0eHDh8+sUo+NjT3TujoyMrLGc4wZM0YjR47UsmXLtHfvXlVWViopKUmtWrVS27Ztq73GhAkTNGHCBI0fP14ff/xxlZjNZtNVV11Vo9wCCcV5AAAAAAHFatW21UamcI3VvWTlPABfFxMTo0GDBmnQoEHeTsVvBAUFKSkpSUlJSerevbsSEhL0zjvvOBy3fv36Gl0/LCxMPXr0UI8ePc41Vae1a9dO7dq1q9U54uPjdemll9ba9WNjY8/pjYIDBw7oiy++cBjv0aOHGjfmlcTqUJwHAAAAEDDKy6Xs/eZx2tq4T4ZFcd7q7QUAgO/7/e9/r4qKiipjl19+uYYMGeL0NS6++GLD4nxpaek55wfPefbZZw3b39x2221eyKbuoTgPAAAAIGDsPiiVV5jHWTnvPlb3cs9B6cRJKSLcc/kAQKCYOnWqy5uJOqtbt24aNmyYVq5cqYKCgiqx8vJyl4rzq1evNhxPSUk5pxzhOVOmTNG3337rMN6pUydddtllXsio7qE4DwAAACBgWPU6rxcupSZ4Lhd/Z7UhrN0u7cyV2jT3WDoAEDB+/PFHrVixolauHRISomHDhikjI0MrV66sEvv+++/1yiuvaOTIkZZ9+AsKCvT+++9r2rRphvGLLrrIrTnD/Q4cOKAXXnhBCxYscIiFhIRo/PjxXsiqbqI4DwAAACBgWPU6z2gk2Wyey8XfxcdIDaKkgmPG8e05FOcBoK7q27evQ3FekqZNm6YZM2YoPT1dKSkpioqKUlBn5iQ0AAAgAElEQVRQkMrLy3Xs2DHt27dP2dnZqqysNLxudHS0brjhhtpOHy4qLS3V5s2btX79ei1ZskQ//vijysvLDY8dO3asSxv6BjqK8wAAAAAChmVxnpY2bmWznWpt88sW4zibwgJA3XXzzTdr5syZysvLc4iVl5dr69at2rp1q0vXtNlsmjBhghITE92VJtzk+++/1/3331/tcbfffrt+97vfeSAj/xHk7QQAAAAAwFOs2tpQnHc/NoUFAP8UExOjKVOmKDY21i3Xi46O1tSpU3X55Ze75Xpwr2bNmlnGg4ODNXr0aD3xxBMeysh/sHIeAAAAQMDYsc88RnHe/TIt+s5bPSgBANTc7NmzPTJP586dNW/ePL300kv697//rbKyMpevERUVpaFDh+ruu+9WQgIbv/iqJk2aKCgoyLAdUadOnfT444+rY8eOXsis7qM4DwAAACAgVFZat1LJpDjvdlYPPGhrAwB1X0pKil566SU99thjWrp0qVavXq3t27frwIEDKigoUGlpqSoqKhQREaHIyEg1aNBAzZs3V0ZGhi688EJ169ZNYWFhTs8XGRmplJQUpaamKjk5WampqbX41UmhoaFKTk5WSkqKUlJS1KlTp1qdz1eFhYUpOTlZubm5kqS0tDT16tVL119/vbp27erl7Oo2m91ut3s7Cfi3tm3bqqKiospYcHCwNm7c6KWMAAAAEIj2HpSaDTWPb39falG7/8YPOD+ul3r/wTgWFCQd/0YKC/VsTqgbNm/eLElq3bq1lzMB4AtycnIUHR2tmJgYj8yXl5enyspKJSYmysZu8ZKkpUuXKiIiQs2bN1dycrK303FQGz83PFHTZOU8AAAAgICQZdHSJjREapLkuVwChdXbCJWVUvZ+qVUTz+UDAKib0tI8+3obLXYc9enTx9sp+CU2hAUAAAAQEKx6nLdIlUJYuuR2SQ2k6EjzOJvCAgCAQEZxHgAAAEBAsOpxnt7Ic3kEEpvNevU8fecBAEAgozgPAAAAICCwGax3WG0Ku52V8wAAIIBRnAcAAAAQEKyK81YFZJyblo3NY1athgAAAPwdxXkAAAAAfs9uty4EU5yvPZYr5ynOAwCAAEZxHgAAAIDfO1QgFRWbx2lrU3us7m32fqms3HO5AAAA+BKK8wAAAAD8nlVLG5tNap7iuVwCTaZFW5uKCmnXfs/lAgAA4EsozgMAAADwe1btU5omS+Fhnssl0KTES/XrmcdpbQMjQUFBstvtstvt3k4FAODjTv+8CAqqe6XuupcxAAAAALjIauU8LW1ql81mfY+tPhsErtMFlsrKSi9nAgDwdad/VlCcBwAAAAAflLXPPJbeyHN5BCqrTWG37fVcHqg7QkJCJEknT570ciYAAF93+mfF6Z8ddQnFeQAAAAB+j5Xz3mV1j2lrAyNRUVGSpKKiIi9nAgDwdad/VkRHR3s5E9dRnAcAAADg96wKwFaruuEeVveYtjYwEhMTI+lUwYW+8wAAM3a7XYWFhZIozgMAAACAzykokvKOmscpzte+DIvWQdn7pYoKz+WCuiEsLEwREREqKyvT8ePHvZ0OAMBHHT9+XOXl5YqIiFBYWJi303EZxXkAAAAAfs2q37xkXTiGe1g9ACktk3IOey4X1B2xsbGSpJycHBUXF3s5GwCArykuLlZOzqlX8E7/zKhrKM4DAAAA8GtWLW1SE6T69TyXS6BKS5TCQs3jtLaBkbi4OCUkJKiyslJ79uzRsWPHaHEDAJDdbtexY8e0Z88eVVZWKiEhQXFxcd5Oq0bq3ha2AAAAAOCCHVb95lk17xHBwVKLVGnLbuN41j7p0i6ezQm+z2azKSkpSZKUl5enPXv2KCQkRDExMYqOjlZ4eLiCgoJks9m8nCkAoDbZ7XZVVlbq5MmTKioqUmFhocrLyyVJCQkJSkpKqrM/CyjOAwAAAPBrbAbrG6yK8zuraT2EwHW6QB8SEqKjR4/qxIkTys/PV35+fpVjAAD+y+itqYiICMXGxiouLq5O/xygOA8AAADAr1m1TMls7Lk8Ap3VWwrV7QuAwGaz2RQfH6/4+HiVlpaqqKhIRUVFKi8vV2VlpSorK72dIgCgFgUHBysoKEghISGKjo5WdHR0ndz81QjFeQAAAAB+zarwm05bG4+xekthB8V5OCksLEwJCQlKSEjwdioAAJwzNoQFAAAA4LeOl0j7DpvHM2lr4zFWD0IozgMAgEBEcR4AAACA36qu6EvPec+xKs4fKTr1BwAAIJBQnAcAAADgt6xa2sTHSHHRnssl0FXXQshqbwAAAAB/RHEeAAAAgN+y3AyWVfMeVS9capRoHmdTWAAAEGgozgMAAADwW1bFeVraeF56qnlsJ8V5AAAQYCjOAwAAAPBbO3LNY9W1WYH7pVs8EGHlPAAACDQU5wEAAAD4LasNYSnOe16GxT3fQc95AAAQYCjOAwAAAPBL5eXSrv3mcYrznmd1z1k5DwAAAg3FeQAAAAB+ae8hqbzCPG61ihu1w6o4v/eQdLLUc7kAAAB4G8V5AAAAAH7Jqt98eJiUmuC5XHCK1Sa8druUbfGmAwAAgL+hOA8AAADAL2VZ9DBvkSIF8a8hj0uMlaIjzeO0tgEAAIGEX0cBAAAA+KWdFivnW9DSxitsNut2QjspzgMAgABCcR4AAACAX9phUeil37z3WD0YYeU8AAAIJBTnAQAAAPglq+K81cakqF1WD0asWhEBAAD4G4rzAAAAAPySVXGetjbek26xKazVZwYAAOBvKM4DAAAA8DtHik79MZOe6rlcUJXVvd+xT7LbPZcLAACAN1GcBwAAAOB3rDaDlWhr400ZFivnT5RKuXmeywUAAMCbKM4DAAAA8DtWvctT4qXICM/lgqqaNpRCgs3j9J0HAACBguI8AAAAAL9jtXKeVfPeFRIiNUsxj++o5q0HAAAAf0FxHgAAAIDfsdwMln7zXmf1Gexg5TwAAAgQFOcBAAAA+B2r4rxVz3N4RobF2wtZFp8dAACAP6E4DwAAAMDvsHLet1k9ILH67AAAAPwJxXkAAAAAfqWsXNp90DxOz3nva2HxGVCcBwAAgYLiPAAAAAC/suegVFFhHqetjfdZtbU5VCAVFXsuFwAAAG+hOA8AAADAr1itvI4Ik1LiPZcLjFX39kIWm8ICAIAAQHEeAAAAgF+x2lA0vZFks3kuFxiLjpQaxpnH2RQWAAAEAorzAAAAAPzKTjaDrRPSLT4Lq88QAADAX1CcBwAAAOBXrNra0G/ed6RbfBasnAcAAIGA4jwAAAAAv7Iz1zxmtVobnmW1KSw95wEAQCCgOA8AAADAb9jt1quuW1SzESk8x+qzsHrAAgAA4C8ozgMAAADwG0eKpKPHzOPpFOd9htXK+V0HpLJyz+UCAADgDRTnAQAAAPgNq37zEhvC+hKr/v8VFdLuA57LBQAAwBsozgMAAADwG1YtbRolSvXCPZcLrKXEW38ebAoLAAD8HcV5AAAAAH5jp0VBl81gfYvNZt1mqLq3IAAAAOo6ivMAAAAA/IZVQZfNYH0PxXkAABDIKM4DAAAA8Bs7c81jVhuQwjusPpOsHM/lAQAA4A0U5wEAAAD4Das+5ayc9z1Wm8Kych4AAPg7ivMAAAAA/EJpmbTnoHncqoUKvKOFxT4AO/ZJdrvncgEAAPA0ivMAAAAA/MLuA1JlpXmcDWF9j9XK+WMl0uGjnssFAADA0yjOAwAAAPALOyz6zUdGSMnxnssFzmmWLNls5nFa2wAAAH9GcR4AAACAX7DaQDQ91boIDO8ID5MaJ5nHKc4DAAB/RnEeAAAAgF/YabFyns1gfVeGxWdj9ZkCAADUdRTnAQAAAPgFq1XWVgVgeJfVgxOrtyEAAADqOorzAAAAAPzCTovifDrFeZ9l9dmwch4AAPgzivMAAAAA6jy7XcqyKM7T1sZ3tUg1j9FzHgAA+DOK8wAAAADqvLyjUlGxeTzdogAM77JaOb/3kHSy1HO5AAAAeBLFeQAAAAB13g6L9ic2m9Q8xXO5wDVW+wHY7VL2fs/lAgAA4EkU5wEAAADUeVbtT9ISpYhwz+UC1yTEStGR5nFa2wAAAH9FcR4AAABAnWdVwGUzWN9ms7EpLAAACEwU5wEAAADUeVbFeasNR+Eb2BQWAAAEIorzAAAAAOq8nRYF3Iw0z+WBmrFaOU9xHgAA+CuK8wAAAADqvCxWztdpFOcBAEAgojgPAAAAoE47WSrtPWQep+e877MszudKdrvncgEAAPAUivMAAAAA6rRdB6yLtxTnfZ/VZ3S8RDpU4LlcAAAAPIXiPAAAAIA6zartSVQ9KamB53JBzTRLlmw28zitbQAAgD8K8ebklZWVWrp0qb755hutX79eOTk5OnHihCIiIpSWlqb27dtrwIAB6tOnj4KC6vZzhOLiYi1YsEBLly7V5s2bdfjwYZWVlSkqKkrNmjVT167/z969x0dV3/kff08m9wskIdwSIIFwx+BdtEURFGwralEr1VpRK2qNtlpbfxbv6GpdW5Rd0bWIVbzVC1pArQtiRat2waIucguEcAskISSBkHsm8/vDpQ9xzvlmksycOZO8no8Hf+z3e77nfOLM7ibv+c7ne6KmT5+ucePGRdWzAAAAgEgzBbfDss2hL9whPk4a3E/aVW49v32vdCp/vgAAgG4mYuH82rVrdc8996i4uDhgrq6uTkVFRSoqKtKSJUuUn5+vuXPn6qSTTnKsvqKiIt1777365z//GTC3ePFiTZgwIeh7vfLKK5o3b55qagK/i1lTU6Oamhp9+eWXeuaZZzRp0iTde++9ys7u3HdvnXwWAAAA4AamcJ7DYKNHfrY5nAcAAOhuIrId/aWXXtKsWbMsg3krxcXFuuKKK/Tyyy+HubKvd50/8sgjmjFjhmUw3xE+n0+33Xab7r77bsuw3Mrq1as1Y8YMffHFF659FgAAAOAm7e2cR3QYajoUlnAeAAB0Q46H82+//bbmzp0rn8/XoXU+n0/33Xef3nnnnTBVJq1atUrnnnuunn76abW2tnb5fnPnztXSpUs7vK6mpkbXXHONSkpKXPksAAAAwE1K9tnP5RPOR41hhm85mF5jAACAaOVoOF9eXq4777xTfr8/YM7j8aigoEDTpk1TQUGBPBaNIf1+v+644w6Vl9t817GT9u7dqxtuuEE33HCD9u4NzZaMVatW6c9//rPlXGJioiZMmKCzzz5beXl5ltfU1tbq1ltvtfxvFclnAQAAAG7i90vFprY2hPNRg53zAACgp3G05/zjjz+u+vr6gPGcnBwtWLBAY8aM+dfYpk2bVFhYqNLS0qOura+v14IFCzR37twu19Pa2qpnn31WCxYssKyrs3w+n/7whz9Yzk2cOFEPP/ywsrKy/jX29ttv6/bbb1dzc/NR127YsEFvv/22pk+f7opnAQAAAG6zv0aqa7Cfp61N9DC9VqWVUmOTlJjgXD0AAADh5tjO+Z07d+qNN94IGI+Li9OiRYuOCuYlacyYMVq4cKHi4uIC1ixZskS7du3qUj2fffaZZsyYoUceeSSkwbwkLV261LKf/tChQ/Xkk08eFZZL0rnnnqt7773X8l7z589XW1ubK54FAAAAuI1pR7XHI+UNcK4WdI2pBZHfL+0oc64WAAAAJzgWzi9fvtyyj/vMmTM1dOhQyzX5+fm6+OKLA8ZbW1u1fPnyTteyZs0aXX755SoqKrKcj4mJ0cyZMzVu3LhO3f/NN9+0HL/lllsUHx9vOXfhhRdqxIgRAeO7du3SunXrXPEsAAAAwG1M4fzgflJ84F4fuFRmL6lXiv38dvrOAwCAbsaxcH7lypWW4+21UTnvvPM6dL9gnHzyyTr55JMt58aNG6dXXnlFc+fOVUqK4TdDG9XV1frnP/8ZMJ6SkqLJkyfbrvN4PDr33HMt51asWBHxZwEAAABuZArnTQeMwn08nnYOhaXvPAAA6GYcCef37dunzZs3B4wnJSXp2GOPNa497rjjlJiYGDC+adMm7dvXua0THo9H999//1H3TUtL01133aXXX39d48eP79R9JWn16tXy+XwB4yeccILtTvYjTj31VMvx999/P+LPAgAAANzIFM5zGGz04VBYAADQkzgSzm/cuNFyfMSIEYqJMZfg9XotW7BIXwf0nZWXl6cbb7xRknT++efr3Xff1eWXX95uPe2xq2nUqFHtrh05cqTl83fv3q3Dhw9H9FkAAACAG5UY9uuYepjDnUyHwtLWBgAAdDexTjzE6sBSScrNzQ1qfW5urtavX2953ylTpnS6rquvvlonn3yyjjvuuE7f49u2bdtmOZ6Xl9fu2pSUFGVlZamioiJgbvv27QE7+p18lsnYsWON81a7+wEAAIBQYOd892IM50udqwMAAMAJjuyctwvns7Kyglpvd53dfYPl9XpDGsxLXwfbVvr06RPUeruf1SqId/JZJj6fz/gPAAAACIfGJqm00n7eFPTCndrbOe/3O1cLAABAuDkSzu/fv99yPDMzM6j1dmGz3X0jya6mYANzu/8mlZWBf3U4+SwAAADAbXaUmcNaDoSNPqbXrL5Rqqh2rhYAAIBwcyScr6+vtxxPSkoKan1CQkKH7hspLS0tamlpsZyzOtS2I9fV1dVF7FkAAACAG5n6zaclS316O1cLQmNIf8l0DBiHwgIAgO7EkXDeLuyNi4sLan18fHyH7hsppnpC/bM6+SwAAADAjUzh/LBsyeNxrhaERnycNLif/TyHwgIAgO7EkXC+sbHRcjzYENnuuoaGhk7XFA52P6cU+p/VyWe1x+v1Gv8BAAAA4WA8DJaWNlErn0NhAQBADxHryENirR/T1tYW1Hq7Q0Xtdn5Hit3PKYX+Z3XyWe3ZuHGjcX7s2LEcDAsAAICQM4XzHAYbvUwfrLBzHgAAdCeO7JxPTk62HG9ubg5qvV1vdbv7RoqpnlD/rE4+CwAAAHAjY1sbds5HLdMHKyX0nAcAAN1IRMN5u3A42OvcFiInJycrxub0omB/Vrtg3Sqcd+pZAAAAgNv4/e20tWHnfNQyvXbsnAcAAN2JI+F8r169LMdramqCWm93nd19Iyk1NdVy/ODBg0Gtt7vO6md18lkAAACAm1QelA4bjkqirU30Mn3roXS/1NDkXC0AAADh5Eg4n5ubazl+4MCBoNbbXWd330iyq6mysjKo9R35WZ18FgAAAOAmpl3zHo+U29+5WhBa+Tnm+R3sngcAAN2EI+H88OHDLcfLysqCWl9eXt6h+0ZSV35Wv9+v/fv3B31fJ58FAAAAuIkpnM/JkhLinasFoZWRJvW2/pKwJFrbAACA7iOi4XxRUVFQ67ds2WI5PmzYsE7XFC5d+Vl37dql+vr6gPHk5GQNHBj43U4nnwUAAAC4iWn3NC1topvHY25tw6GwAACgu3AknB8zZoxlH/OysrJ2W7BUVlZa7pzv3bu3xo4dG7IaQ2XChAmW4xs2bGh3rd01EyZMkMfjieizAAAAADcx7Z4eyl6TqMehsAAAoCdwJJyPi4vTpEmTLOfee+8949qVK1dajp955pmKjY0NGG9ra9P777+vp556SkuWLAn6cNRQKSgo0IABAwLGi4uLtX37duNau5/17LPPjvizAAAAADcx7Z5m53z0M+2cLy51rg4AAIBwciScl6Rp06ZZjr/44ovy+XyWcz6fTy+99FLQ9zt8+LB+8pOf6Oc//7nmzZunOXPm6JxzztHnn3/e+cI7YerUqZbjixcvtl1TWlqqVatWBYzHxsZqypQprngWAAAA4BbGnfOE81FvmOFQ2BJ2zgMAgG7CsXD+rLPOsuwRX1RUpPnz51uumT9/vmX/9Pz8fE2ePDlg/MEHH9S6deuOGquurlZhYaEOHz7cyco7btasWYqLiwsYf/XVV7V69eqA8aamJs2ZM0dNTU0BcxdddJEyMzNd8SwAAADADZpbpN0V9vOmXdeIDqbXcPteye93rhYAAIBwcSyc93q9uvnmmy3nnnrqKd1www1avXq1ioqKtHr1ahUWFuqpp56yvP7mm2+W1+s9aqy5uVlvvfWW5fUHDhxot31OKA0ePFgzZ84MGPf5fCosLNSDDz6otWvXavPmzVq2bJkuueQS/eMf/wi4PiEhQYWFha55FgAAAOAGu8qltjb7edraRD/Ta9jQJJVXOVcLAABAuAQ2bQ+jc845R6effro++uijgLlVq1ZZtlr5tjPOOMOypU11dbXlbvAjysrKOlZsF914441auXJlwGG2LS0teu655/Tcc88FdY/+/fu76lkAAABApJla2iQnSv0ynKsF4TGkvxQTY/8hzPZ90oA+ztYEAAAQao7tnD/i97//vUaOHNmptSNHjtQjjzxiOZeRkaHExETbtdnZzm6fycjI0IIFC5Samtqp9RdccIFmz57tumcBAAAAkbbdcBjs0IGSx+NcLQiPuFhpSD/7eQ6FBQAA3YHj4Xx6eroWL16siRMndmjdxIkT9fzzzys9Pd1yPj4+XjNmzLCc69u3r84666wO19pVBQUFeuGFFzR48OCg13g8Hv3sZz/TQw89JE8H/qpw8lkAAABAJO0wHQZLv/luI59DYQEAQDfneDgvfb3Te9GiRZo3b57GjRtnvHbcuHGaN2+eFi1aZBvMH3HbbbfptNNOO2osMzNTCxYsUEpKSpfr7owxY8borbfe0q9+9SsNGDDA9jqv16vJkyfr1Vdf1W233RbQU99tzwIAAAAixbRznn7z3YfpgxbTewAAACBaePz+yJ9zX1paqvXr12vv3r1qaGhQUlKSsrOzNX78+A63o/H7/frwww+1efNmZWVlaerUqerVq1eYKu8Yv9+vLVu2aPPmzaqoqFBra6tSU1OVm5ur448/PqR1Ovms9owdO1Y+n++oMa/Xq40bNzpWAwAAALqPk2dL64qs5x67SbrpYmfrQXg8/KI054/WcxMLpNWPO1sPAADoWZzINB09ENZOTk6OcnIM31nsAI/Ho0mTJmnSpEkhuV8oeTwejR49WqNHj+5WzwIAAACcZOw5z875bsO4c562NgAAoBuISFsbAAAAAOiM6lqp5rD9PG1tug/Ta7m3Umpocq4WAACAcCCcBwAAABA12us1nmd/9BKijOlAWIlDYQEAQPQjnAcAAAAQNUzh/IBMKTnRuVoQXhlpUnqq/TyHwgIAgGhHOA8AAAAgaph2S9PSpvsxvaYlhPMAACDKEc4DAAAAiBqmcN50gCiiE4fCAgCA7oxwHgAAAEDUMLUyYed892PqO19c6lwdAAAA4UA4DwAAACBqGHfOE853O6ad8xwICwAAoh3hPAAAAICo0Noq7Syznx9GW5tux/RtiO17Jb/fuVoAAABCjXAeAAAAQFTYs19q9dnP09am+zG9po3NUlmVc7UAAACEGuE8AAAAgKhgOgA0IV4a2Me5WuCMwf0kr9d+nr7zAAAgmhHOAwAAAIgKpsNg8wZIMfx10+3ExUq5/e3nTe8JAAAAt+PXVwAAAABRocQQxJoODkV041BYAADQXRHOAwAAAIgKpiCWfvPdV3uHwgIAAEQrwnkAAAAAUcEYzrNzvtsyhfPsnAcAANGMcB4AAABAVDDtkh7Kzvluy9TWhgNhAQBANCOcBwAAAOB6h+qkyoP287S16b7yc+znyqqk+kbnagEAAAglwnkAAAAArtde+xIOhO2+2mtZRGsbAAAQrQjnAQAAALieqaVN33QpLdm5WuCs9DQpI81+nkNhAQBAtCKcBwAAAOB6pt3R7Jrv/jgUFgAAdEeE8wAAAABcz3gYLOF8t8ehsAAAoDsinAcAAADgeqbd0RwG2/2ZDoXdzs55AAAQpQjnAQAAALiesa0N4Xy3Z9o5X0LPeQAAEKUI5wEAAAC4WltbOzvnaWvT7bXXc76tzblaAAAAQoVwHgAAAICr7a2Umlvs52lr0/2ZXuPGZqmsyrlaAAAAQoVwHgAAAICrmXqKx3qlQX2dqwWRMbjv16+1HQ6FBQAA0YhwHgAAAICrbTf0FM8bIHkNoS26h9hYKXeA/TyHwgIAgGhEOA8AAADA1UwHfubRb77H4FBYAADQ3RDOAwAAAHA1065o+s33HKbX2vTtCgAAALcinAcAAADgajtM4Tw753sM02tdQlsbAAAQhQjnAQAAALiaaVf0UHbO9xim17qYnfMAACAKEc4DAAAAcK36Rqmsyn6etjY9R77htS6vkuoanKsFAAAgFAjnAQAAALhWe+1KaGvTc7T3QQytbQAAQLQhnAcAAADgWqaWNhlpUnqac7UgsnqnSpm97OdNBwcDAAC4EeE8AAAAANcy7YampU3PYzwUlr7zAAAgyhDOAwAAAHAt42GwtLTpcYyHwpY6VwcAAEAoEM4DAAAAcC3CeXyT6VBY2toAAIBoQzgPAAAAwLV2lNnPmXZRo3syvea0tQEAANGGcB4AAACAK/n95p3zpv7j6J5M5wyUlEltbc7VAgAA0FWE8wAAAABcqbxKamiyn+dA2J7H9IFMU7O0t9K5WgAAALqKcB4AAACAK5l6iMfESEP6O1cL3GFQXynWaz9P33kAABBNCOcBAAAAuJKppc2QflJcrHO1wB1iY6W8AfbzpvcMAACA2xDOAwAAAHAl0wGftLTpuTgUFgAAdBeE8wAAAABcydSiZCiHwfZYpr7ztLUBAADRhHAeAAAAgCuZdkETzvdcpm9NbC91rg4AAICuIpwHAAAA4ErGnfO0temxTK89O+cBAEA0IZwHAAAA4DqNTdLeSvt5es73XPmG176iWjpc71wtAAAAXUE4DwAAAMB1dpZLfr/9vKnvOLq39loalbB7HgAARAnCeQAAAACus93Qbz4tWerT27la4C69U82vP61tAABAtCCcBwAAAOA6pt3Pw7Ilj8e5WuA+pm9OcCgsAACIFoTzAAAAAFzHtHO+vbYm6P44FBYAAHQHhPMAAAAAXMcUznMYLNbmwRYAACAASURBVEyHwpreOwAAAG5COA8AAADAdUxtbdg5D9POeQ6EBQAA0YJwHgAAAICr+P20tYGZqed8yT6prc25WgAAADqLcB4AAACAqxw4KB1usJ+nrQ1M74HmFqm00rlaAAAAOotwHgAAAICrmA709Hik3P7O1QJ3GtRXiou1n6fvPAAAiAaE8wAAAABcxRSs5mRJiQnO1QJ38nqlvAH284TzAAAgGhDOAwAAAHCVEkOwSksbHMGhsAAAINoRzgMAAABwFVNbGw6DxRGmQ2HZOQ8AAKIB4TwAAAAAV2HnPIJhei8QzgMAgGhAOA8AAADAVUw75/PYOY//Y2prQzgPAACiAeE8AAAAANdobpF2V9jPs3MeR+Qb3gv7a6TaeudqAQAA6AzCeQAAAACusbNcamuznzf1GUfP0t75AxwKCwAA3I5wHgAAAIBrFJfaz6UkSf0znasF7tYrRcrqbT9vei8BAAC4AeE8AAAAANcw9QofNlDyeJyrBe5nanPEznkAAOB2hPMAAAAAXMMYztNvHt9iek9wKCwAAHA7wnkAAAAArlFMOI8OIJwHAADRjHAeAAAAgGtsN/QJzyecx7eYDoWlrQ0AAHA7wnkAAAAAruD3S9sNgeqwHOdqQXRor+e8z+dcLQAAAB1FOA8AAADAFcqqpPpG+3l2zuPbTOF8S6tUWulcLQAAAB1FOA8AAADAFUw9wmNipNwBztWC6JCTJcXH2c/Tdx4AALgZ4TwAAAAAVyg29Jsf0k+Ki3WuFkQHr1fKM3xoQzgPAADcjHAeAAAAgCuYgtR8+s3DBofCAgCAaEU4DwAAAMAVig3hvKm3OHo203vD9G0MAACASCOcBwAAAOAK2w1BKofBwo4pnGfnPAAAcDPCeQAAAACuYNw5T1sb2DCF8/ScBwAAbkY4DwAAACDiauul/TX288MMfcXRs5nC+cqD0qE652oBAADoCMJ5AAAAABHX3g5nDoSFHdOBsBKtbQAAgHsRzgMAAACIOFNLm6zeUq8U52pBdElLlvqm28+b3lsAAACRRDgPAAAAIOKMh8Gyax7tMB4KSzgPAABcinAeAAAAQMQZD4M1BK+AxKGwAAAgOhHOAwAAAIg4U4BKOI/2GMN5es4DAACXIpwHAAAAEHGE8+gK06GwtLUBAABuRTgPAAAAIKJaWqWd5fbz+YTzaIfpA5wdZZLP51wtAAAAwSKcBwAAABBRu8rN4SkHwqI9pnC+pVXas9+5WgAAAIJFOA8AAAAgokyHwSbGSwP7OFcLolNOlhQfZz/PobAAAMCNCOcBAAAARFR7/eY9HudqQXSKiTH3nedQWAAA4EaxkS5Aktra2rR69WqtWLFCX331lUpLS9XY2KjExETl5OSooKBAU6dO1aRJkxQT49znCSUlJVq6dKnWrFmjHTt26NChQ4qJiVGfPn00YsQITZw4Ueeff77S09Mdq8nOr3/965Dc58c//rFOOumkkNwLAAAACAaHwSIUhg6Utuyyntte6mwtAAAAwYh4OL927Vrdc889Ki4uDpirq6tTUVGRioqKtGTJEuXn52vu3LlhD4+rq6v10EMPadmyZfL7/QHze/fu1d69e7V69WrNmzdP1113na699lp5vV7bez755JOWP2NnZGZmas6cOUeNLV++PCT3vuSSS0JyHwAAACBYxYbglH7zCJbpgxx2zgMAADeKaDj/0ksv6YEHHpDPdPrTNxQXF+uKK67QXXfdpUsvvTQsNe3YsUNXX321SkuD21rR0NCgxx57TGvXrtWCBQuUlJRked0nn3yiNWvWhKTGnJycgHA+VIYOHRqW+wIAAAB2TDvn89k5jyANM7S1KaHnPAAAcKGI9Zx/++23NXfu3KCD+SN8Pp/uu+8+vfPOOyGvqaqqSldeeWXQwfw3ffzxx7r55pstd9pHi9TUVPXt2zfSZQAAAKAH8fvNB8LS1gbBGmb4lgU75wEAgBtFJJwvLy/XnXfeaRlkezweFRQUaNq0aSooKJDH4vQnv9+vO+64Q+Xl5SGt6+6779a+fda/tQ0cOFBTpkzRd7/7XaWlpVle88EHH+j5558PaU1OysvLi3QJAAAA6GH210h1DfbzhPMIlmnn/IGD0sHDztUCAAAQjIi0tXn88cdVX18fMJ6Tk6MFCxZozJgx/xrbtGmTCgsLA3az19fXa8GCBZo7d25Ialq3bp1WrlwZMB4TE6Pbb79dl19++b96ytfW1ur+++/X0qVLA65/4okndOGFFyo1NTUkdVlJTEwM6rqcnBy9//77YasDAAAA6CpTv3mPR8ob4FwtiG5DDeG89HX7pONHOlMLAABAMBwP53fu3Kk33ngjYDwuLk6LFi0K6Hk+ZswYLVy4UBdccIFaWlqOmluyZImuueYaDRkypMt1Pfroo5bjhYWFmjVr1lFjaWlpevjhh7Vv376APvLV1dV69tlndeONNx413pkd9Q899JCeffbZgPEf/OAHHb4XAAAA4EamljaD+0kJ8c7VguiWmiz1y5Aqqq3nt+8jnAcAAO7ieFub5cuXq7W1NWB85syZtoeR5ufn6+KLLw4Yb21t1fLly7tcU2lpqeVhrRkZGZo9e7blGo/Ho9/85jeWc3/5y1+6XFNDQ4PlhxixsbG65JJLunx/AAAAwA04DBahxKGwAAAgmjgezlu1jpGk6dOnG9edd955HbpfKGqaNm2aEhISbNeNHz/esk/77t27tXnz5i7VtGzZMh06dChg/KyzzlK/fv26dG8AAADALUxtbeg3j44yHgpLOA8AAFzG0XB+3759lqF1UlKSjj32WOPa4447zrLX+qZNm2wPcQ3W3/72N8vxU089td21EyZMsBxftWpVl2p68cUXLccvu+yyLt0XAAAAcBNTYEo4j44y7Zzf3rU/GwEAAELO0XB+48aNluMjRoxQTIy5FK/XqxEjRljObdq0qUt12e1yHzVqVLtr7a7pys75zz77TFu2bAkYHzZsWFAfGAAAAADRgnAeoTTU8J5h5zwAAHAbRw+ELS4uthzPzc0Nan1ubq7Wr19ved8pU6Z0qqb9+/erpqYmYNzj8QR10KxVW5sjNXXWCy+8YDl+6aWXdvqe4TR27FjjvM/nc6gSAAAARJO6Bqmsyn4+39CiBLBi+kBnZ5nU2irFOvpXMAAAgD1XhPNZWVlBrbe7ritBuN3a3r17Ky4urt31ffr0sRzftWuXWlpagrrHN1VUVOi9994LGE9OTtaMGTM6dC9Jqqmp0bJly/TBBx9oy5YtOnjwoOLj4zV48GCdfPLJuvDCC9sN19tD+A4AAIDOaG8nMwfCoqNMbW1afdKe/VKe4RoAAAAnORrO79+/33I8MzMzqPV2QbjdfSNZU0tLi2pqatS3b98O1fPnP/9ZLS0tAePTp09XWlpah+61f/9+TZ48WfX19QG1bd68WZs3b9bzzz+v6dOn6+6771bv3r07dH8AAACgK4oN4XxmLym9Y7/+AsrOkhLipaZm6/nt+wjnAQCAezjac/7bIfERSUlJQa1PSEjo0H2dqMnqkNr27m2npaVFr776quVcZ1raNDc3B1XDW2+9pUsvvVQVFRUdfgYAAADQWaad8+yaR2fExEhDB9jP03ceAAC4iaPhfF1dneV4sK1f4uPjO3TfYNiF112tSep4XStWrLDcyX/cccd1ufVMe4qLi/WLX/yCFjUAAABwjCkoNR3sCZhwKCwAAIgWjra1aWxstBwPNgi3u66hoaHTNdmt7WpNUsd3zofjINh+/fpp9OjRSklJUXV1tdavX2/7ocHnn3+uV155RZdddlmHnuH1eo3zBP4AAACwYmprw855dJap7zzhPAAAcBNHw/nYWOvHtbW1BbXeLuQ17V7vbE3BBsqtra22cx2pa9OmTVq3bl3AeHp6ur7//e+3u37ixIlH/c/9+vXTJZdcouOPP/6o8cbGRi1cuFALFiyQ3+8PuM+iRYv04x//WDExwX+pYuPGjcb5sWPHEtADAAAgwPZS+7lhhPPoJNN7p4RwHgAAuIij4XxycrLleHOzzWk932J1UKrpvsFISUkJS02me1ux2zV/0UUX2fba/6ZFixYF9ZzExETddNNNamtr0xNPPBEwv2fPHn355ZcBoT4AAAAQSq2t0o4y+/n8HOdqQfcyzPDe2b7PuToAAADa42jPebsQ3RRwB3NdV8L5cNVkuve3HTx4UG+99VbAuMfj6VJLG5PZs2fbfniwZs2asDwTAAAAOGL3fqnV8OVKds6js0xtbaoOSTW1ztUCAABg4mg436tXL8vxmpqaoNbbXWd332CkpaVZjh88eDCo9abr7O79bUuWLLHsxz9x4kQNHjw4qHt0VHJysk488UTLuZKSkrA8EwAAADhi2x77ucR4KSfLuVrQvQw1hPMSu+cBAIB7OBrO5+bmWo4fOHAgqPV219ndNxh5eXkdeta3VVZWWo5nZWUpNTW13fVtbW16+eWXLec6ejBrRw0dOtRyvKqqKqzPBQAAALYZ+s3n50gdOAIJOEpKktQ/036eQ2EBAIBbOPor7/Dhwy3Hy8oMzSa/oby8vEP3DUZubq7i4uICxltaWoIK6O1qys/PD+r5H374oXbt2hUwnp2drTPPPDOoe3RWUlKS5XiwLX0AAACAzipuJ5wHusLU2oZDYQEAgFu4IpwvKioKav2WLVssx4cNG9bpmuLi4mx33gdTl11NwYbzdgfBzpw5UzFh3i5UV1dnOd6Rg2wBAACAzjDtnB9OOI8uMp1ZQFsbAADgFo6G82PGjLHsD19WVmbbHuaIyspKy13qvXv31tixY7tU14QJEyzHv/rqq3bXbtiwwXL81FNPbXftzp079fe//z1gPC4uTj/60Y/aXd9Vdr3lBw0aFPZnAwAAoGcz9Zxn5zy6yhTOm761AQAA4CRHw/m4uDhNmjTJcu69994zrl25cqXl+JlnnqnY2NiA8ba2Nr3//vt66qmntGTJEuPBrWeffXanaqqtrdU//vGPgPGEhASdfvrpxrWS9NJLL8nv9weMn3POOerTp0+767+tubk56GsPHz6sdevWWc4VFBR0+NkAAABAsHw+qdjQWoSd8+gqwnkAABANHD9madq0aZbjL774onw+n+Wcz+fTSy+9FPT9Dh8+rJ/85Cf6+c9/rnnz5mnOnDk655xz9Pnnn1ve45RTTlF6enrA+Jdffqkvv/zS7kfRyy+/bNmffeLEiUpOTrZdJ0kNDQ164403LOcuvfRS41orS5Ys0fe+9z0VFxcHdf3ChQtVX18fMB4fH68zzjijw88HAAAAglVaKTUbjjkinEdXDTd8GXhXhdQU/L4mAACAsHE8nD/rrLMse8QXFRVp/vz5lmvmz59v2f89Pz9fkydPDhh/8MEHA3aFV1dXq7CwUIcPHw64PjY2VldeeWXAuN/v15133qnq6uqAua+++kpPPvmkZb3XXHON5fg3LVu2TIcOHQoYHzlypE466aR213/TokWLNGfOHJWWlurSSy/Va6+9Zrkj/4jXX39df/zjHy3nZsyYobS0tA49HwAAAOgIU7/5uFhpcD/nakH3ZPqAp61NKqHvPAAAcIHAfjBh5vV6dfPNN+sXv/hFwNxTTz2lbdu2aebMmRo4cKD27dunV1991ba9zM033yyv13vUWHNzs9566y3L6w8cOKD33ntPP/zhDwPmZs2apRdeeCGg931RUZEuvvhiXXvttSooKFBTU5M+/vhjPfPMM5Y7zydPnqwTTjjB9uc/4sUXX7Qc7+iu+T/84Q9HBe0HDx7UnXfeqT/96U+aPn26jj32WPXt21fNzc3aunWrli5dqk8//dTyXmlpaSosLOzQ8wEAAICOMvWbH5YtfetXfKDD+qZLaclSbeCfbJK+/oBodK6zNQEAAHyb4+G89HVP9dNPP10fffRRwNyqVau0atWqdu9xxhlnWLa0qa6uVlNTk+26srIyy/Hk5GTNmTNHv/rVrwLm9uzZo7vvvrvdmlJSUnT77be3e93atWu1ZcsWy/Xnn39+u+uP8Pl8OnDggOVccXGx7TcRrHg8Hj3wwAPq379/0GsAAACAzjD1/OYwWISCxyONGCStC/wCtiT6zgMAAHdwvK3NEb///e81cuTITq0dOXKkHnnkEcu5jIwMJSYm2q7NzrY/Gejcc8/V9ddf36ma4uLi9OijjyovL6/da+12zZ9//vlKTU0N+pler1cPPvigrr322qDXWImNjdX999+v733ve126DwAAABAMU1ubEYZe4UBHmD7o2Wr49gYAAIBTIhbOp6ena/HixZo4cWKH1k2cOFHPP/+85QGu0tcHms6YMcNyrm/fvjrrrLOM97/lllt0++23Ky4uLuia+vTpo4ULF2rSpEntXltRUWHbpqczB8FK0q233qqnn35aubkd/17msGHD9Nxzz+lHP/pRp54NAAAAdJRp1zKHwSJUTO8l0wdEAAAATolYOC99vct90aJFmjdvnsaNG2e8dty4cZo3b54WLVpkG8wfcdttt+m00047aiwzM1MLFixQSkpKu3VdddVVWrp0qaZPn674+Hjb69LT0zV79mz99a9/DXieHb/fr6uuuipgh/2JJ56oUaNGBXUPK6effrreeust/fa3v9WIESOM18bExOj444/X7373Oy1btqzDB9ACAAAAneX3m4NR2togVIYbvoVBWxsAAOAGHr/f7490EUeUlpZq/fr12rt3rxoaGpSUlKTs7GyNHz/e2I7Git/v14cffqjNmzcrKytLU6dOVa9evTpcU319vb744guVlJTo0KFD8nq9ysjI0KhRozRu3LiAA2k7YtOmTXr33Xe1YsUKFRYWavr06Z2+17eVlZVpw4YNKi0tVV1dnbxer1JTU5Wbm6uxY8cqIyMjZM9qz9ixY+Xz+Y4a83q92rhxo2M1AAAAwB32VkqDL7KfL3qJgB6h8ff/lSbdZD0XEyPVrZDig//CNAAA6GGcyDRdFc6jeyKcBwAAwBEffilN/oX1XKz368A0NtbZmtA9lR2Qci60n9/0gjRysHP1AACA6OJEphnRtjYAAAAAepZthoM48wYQzCN0+mdKqUn28/SdBwAAkUY4DwAAAMAxpl7ftLNBKHk87RwKa/igCAAAwAmE8wAAAAAcY9qtbApSgc4wfeBDOA8AACKNcB4AAACAY0yB6PBBztWBnsH0nqKtDQAAiDTCeQAAAACO8PvNgShtbRBqxrY2hPMAACDCCOcBAAAAOKKiWjrcYD9PWxuEmmnn/I4yqaXVuVoAAAC+jXAeAAAAgCNMO5VjYqShA52rBT2D6QMfn0/aWeZcLQAAAN9GOA8AAADAEaZwPre/FB/nXC3oGQb2kZIT7edpbQMAACKJcB4AAACAI4rpNw+HeTzt9J03HFAMAAAQboTzAAAAABxhCkLpN49wMX3ww855AAAQSYTzAAAAABxh2jlPOI9wGWE4FJZwHgAARBLhPAAAAICw8/ulraa2NoYAFegK48552toAAIAIIpwHAAAAEHZVh6SDh+3n2TmPcDG9t0r2Sa2tztUCAADwTYTzAAAAAMLO1D7E45GGDXSuFvQsprY2rT5pV4VztQAAAHwT4TwAAACAsDOF84P6SokJztWCnmVgHynJ8P7aSmsbAAAQIYTzAAAAAMKOw2ARKTExUn62/bzpvQkAABBOhPMAAAAAws60O9l0YCcQCsZDYQnnAQBAhBDOAwAAAAg70+5kU09wIBRM7zHa2gAAgEghnAcAAAAQdqbdyeycR7iZ3mO0tQEAAJFCOA8AAAAgrA4c/PqfHXrOI9yGG3bOb98r+XzO1QIAAHAE4TwAAACAsGqvbYgpOAVCwdTWpqVV2l3hXC0AAABHEM4DAAAACCtTOD+4n5SU4Fwt6JlysqSEePt5+s4DAIBIIJwHAAAAEFam4HPkYOfqQM8VEyPlZ9vPm85EAAAACBfCeQAAAABhVbTbfs7UbgQIJVP7pG3snAcAABFAOA8AAAAgrEzBJ+E8nGI6eJi2NgAAIBII5wEAAACEjd8vFZnCedrawCGmD4II5wEAQCQQzgMAAAAIm30HpLoG+3l6zsMppvfa9r1SS6tztQAAAEiE8wAAAADCyLQjOdYr5Q1wrhb0bKZwvtUn7ShzrhYAAACJcB4AAABAGJkOgx06UIqLda4W9GwD+0gpSfbzpvcqAABAOBDOAwAAAAgb0855+s3DSR6PNNLUd55wHgAAOIxwHgAAAEDYmMJ5U1AKhIPpAyF2zgMAAKcRzgMAAAAIG9Nu5OGE83CYcee84YMkAACAcCCcBwAAABAWPp9UvNd+3nRAJxAO7JwHAABuQjgPAAAAICx2VUjNLfbzhPNwmuk9t2e/VNfgXC0AAACE8wAAAADCwrQTOTFeyslyrhZAkka000ppW6kzdQAAAEiE8wAAAADCxNRvfsQgKYa/RuCwjDSpb7r9PK1tAACAk/h1GAAAAEBYmA7YNPX+BsLJ1NqGQ2EBAICTCOcBAAAAhEWRKZxvp70IEC6m9x475wEAgJMI5wEAAACERXttbYBIMO6cJ5wHAAAOIpwHAAAAEHJNzdLOcvt5U0AKhJOppZLp2x4AAAChRjgPAAAAIOS275Xa2uzn2TmPSBmRYz9XdUg6cNC5WgAAQM9GOA8AAAAg5Ew7kHunSn3TnasF+Kbh7XwwRN95AADgFMJ5AAAAACG3zRDOjxwkeTzO1QJ8U1KCNKS//fxWWtsAAACHEM4DAAAACDnT7uP2di4D4WZqq0Q4DwAAnEI4DwAAACDkTAEnh8Ei0kzvQdraAAAApxDOAwAAAAg5UzjPYbCINFM4z855AADgFMJ5AAAAACF1uF7aW2k/TziPSGuvrU1bm3O1AACAnotwHgAAAEBItbfzmHAekWbaOV/faP5wCQAAIFQI5wEAAACElCmc758p9U51rhbASm5/KS7Wfr6I1jYAAMABhPMAAAAAQsrYbz7HuToAO7GxUn62/TyHwgIAACcQzgMAAAAIKVOwOcLQTgRwkum9uJVwHgAAOIBwHgAAAEBImXbOm3p9A04yvRdpawMAAJxAOA8AAAAgZPx+afMu+/nhHAYLlzAdTMzOeQAA4ATCeQAAAAAhU14lHTxsPz9miHO1ACamnfPb90ktrc7VAgAAeibCeQAAAAAhs8Ww49jrlfI5EBYuYQrnfT6pZJ9ztQAAgJ6JcB4AAABAyJha2gwbKMXHOVcLYDIgU0pNsp83HWwMAAAQCoTzAAAAAEJmiyGcH0VLG7iIx2PePW96LwMAAIRCbKQLAAAA7uPz+7S/pUr7miu0t7lC+5orVNVao1pfnQ621qrWV6dDvlo1tbWoTW1q87fJpza1+X2K88QpKSbx63/eBCXFJKq3N01ZcZnq+3//suIylRPfX/3jsuTxeCL94wIIIVOgOZpwHi4zYpC0rsh6jnAeAACEG+E8AAA92KHWw9rSUKxtjTu1tWGHtjbsVHHjTpU2l6nV7wv78xNjEpSXkKPchEHKS8zRyKShOiZ5pEYkDVVCTHzYnw8g9Ng5j2gyOtd+jnAeAACEG+E8AAA9RGNbkzbUb9WXhzfp87qN+qJuo7Y3RjZ5aGxr0uaG7drcsP2o8ViPVyMSh2pc8ggdnzpOE9KO1aikYYrx0JEPcLOGJmlHmf084TzcxvRtDtP5CQAAAKFAOA8AQDfV0taqL+s26ePaf+rjQ5/ps9r1avI3R7qsoLT6fdrUsE2bGrbp9QN/lST19qbppNQCTUg7ThN7naSClFGE9YDLbN0j+f3287S1gduYds5XHpQqa6SsdOfqAQAAPQvhPAAA3UhF8wG9V/OxVtR8pE8OrVNdW32kSwqZg75arTr4iVYd/ESSlBWboUm9J2hy71M1qfepyozrHeEKAZjagGT1lvrwv6ZwmRGDvj4Y1u5DpS27CecBAED4EM4DABDltjXs0F+rV+u/qz/S53UbIl2OYypbq7XkwLtacuBdeeTRhLRjNT1zin6QMVn947MiXR7QI3EYLKJNUoKUN0Aq2Wc9v3mX9N0CZ2sCAAA9B+E8AABRaG9zhZYdWKk3Dvy3NtRvjXQ5EeeXX/+o/UL/qP1Cd+18VKekHavpGVN0Xp+z1DcuM9LlAT2GqUf3SMJ5uNSoIfbhPIfCAgCAcCKcBwAgStT7GrSs6j29Xvmu/lH7ufwyNHYOkRjFqH98lgbG9VP/+D7q7U1Tr9g09fKmqpc3VUkxifJ6YhTjiZFXXsV4PGpua1FDW5Ma2hrV0NaoOl+9DrTWqLKlSpUt1drfWqWK5gPyyReWmv3y639qv9D/1H6h+3bP15Te39GP+07XlN7fUVwMv/oA4cTOeUSj0UOkd//Hem7zTmdrAQAAPQt/oQIA4HJf1RXpxf1L9Ubluzocph7yg+IHaERSnkYkDdWIxDwNT8rVoISB6heXqVhP6H9daPW3qrSpXDua9qikcY92NO7Rlobt+qq+SFWtNSF8jk8raj7SipqP1Cc2Qxf2OUeX9/uhhicZTgAE0Cl+/9f9ue2MIpyHS5nem+ycBwAA4UQ4DwCACzW1NesvB1ZqccUSfVG3KaT37hfXR8enjNNxqWN0XMpYjU8ZrfTYXiF9RntiPbHKTcxRbmKOJvWe8K9xv9+v8pZKbajfqvV1W/TZ4f/VZ4fXq9ZX1+VnHmit1sLyP2th+Z81qdcEXdX/Yk1JP01ej7fL9wYgle6X6hrs59k5D7cabfi8dvs+qbFJSkxwrh4AANBzEM4DAOAiB1qqtbjiTT1XsUT7W6pCcs/M2HR9t9eJ//fvJA1NGCSPxxOSe4eax+PRgPi+GhDfV2elf0eS5PP7tKm+WGtqv9Qntf/URwfXdvkbBKsP/Y9WH/of5Sbk6Ip+F+qyvuerV2xqKH4EoMcy9ZuPi/360E3AjUwfHLW1SdtKpWOGOVcPAADoOQjnAQBwgW0NO/XHspe1pPKvavQ3d/l+45NH8o8taQAAIABJREFUa1rGRE1Nn6ixySMU44kJQZWR4fV4dUzKSB2TMlJXD/iRmtta9Nnh9frbwU/1t5pPtamhuNP33tlUqvt3/6ce2/uMZvW7UD/rP1P94vuEsHqg5ygytLQZMUiK5S8PuFTfdCmzl1R1yHp+8y7CeQAAEB78igwAQARtri/W/L3PannVqi4d8OqRR9/pdYLOzZiiqRkTlR3fL4RVukt8TJy+0+sEfafXCbpjcKF2NO7R21V/0/KqVVpfv6VT96z11enxfc9rYdkruqTvubp+wGXKSxwU4sqB7s10cCb95uFmHs/Xu+c/+cp6nr7zAAAgXAjnAQCIgK/qtuixvX/SX6tXd+k+x6aM0Q/7TNX5mWdrQHzfEFUXXfISB6kw+6cqzP6pdjTu0VtV7+u1yne0rdGQFNpo8jfr+Yo39WLFUl2U9T3dkn21chNzwlA10P2Y2tqMGuxcHUBnjDKE86b3NgAAQFcQzgMA4KCihhI9vOe/9G71h52+R5/YDM3se65mZk3X8CTDKXY9UF7iIN2YfYUKB/5U6+o26JX9b2npgZUd7lHfpja9VvmO3jzw35qZNV2/zL5SOQk0zAZMTLuL2TkPtzP1nWfnPAAACBfCeQAAHFDaVK55pU/r1cp31Ka2Tt3j9F4n6yf9LtA56WcoPiYuxBV2Lx6PRyemHqMTU4/RfUNu1vKqVXq2Yom+rNvUofu0+n16cf9SvVb5jn7S9wL9Mucq9Y3LDFPVQPQ6XC/t2W8/P5rPEeFypg+QNu+S/P6v298AAACEEuE8AABhVN16UAv2Pq9nyl9TUycOek2KSdTMrHN1df9LlJ/E1tPOSPIm6pK+5+pHWT/Q53Ub9afy17S8apVa/K1B36PZ36I/Vbyu1yrf0U3Zs3TNgJlKjEkIY9VAdCnaY56nrQ3czvQBUl2DVLpfGtR9j3MBAAARQjgPAEAYNLe1aFH5K/rPvYt10Ffb4fUD4rJ0df9LdFm/85UR2zsMFfY8Ho9HJ6SO0wmp43TX4Jv0p4rX9Vz5kg69Pofb6vXQnie1uOJNzRn8c12QOVUetlICxsNgB2RKvVOdqwXojKEDpLhYqcXmc9vNuwjnAQBA6MVEugAAALqb92s+1VlfXa4Hdi/ocDA/LHGIHh16pz499g0VZv+UYD5M+sX30f8bdJ3WHPem7hp8kwbEZXVofWlzmQqL79F5G6/R2tovw1QlED2KdtvPmXp5A24RGyuNGGQ/T995AAAQDuycBwAgRHY07tG9u+ZrZc3fO7x2ZNJQ3Zx9laZnTpHX4w1DdbCS6k3R9QMv01X9L9aSynf1H3uf1e7mfUGv/7xuo3646Xr9MHOq7hnyS/WL7xPGagH32sxhsOgGRg2RNu6wnjO9xwEAADqLnfMAAHRRva9Bv9v9X5q8/rIOB/Mjk4bqj8Mf1KpjXtAFfaYSzEdIQky8Lut3vj4c/4oeyvuNBsT17dD6v1St1BnrZ+pP5a/J5/eFqUrAvUy7ijkMFtFijOG9amrdBAAA0FmE8wAAdMHK6r9r0vpL9Z/7nlOzvyXoddnx/TVv6J1675jndW7mZMV4+H/JbhAfE6cr+l2oj499TfcNuVl94zKDXlvrq9OdO+fp3A0/0+eHN4SxSsBd2trMbW1GchgsooTpWx7snAcAAOFAEgAAQCfsb6nS9dvu1JVbf6O9zeVBr0v3pumuwTfpo/GvaGbfc9kp71KJMQm6ZsBMfTz+Nd2SfbUSYxKCXru+fovO2zhbt+/4d9W0HgpjlYA77CqXGpvt5+k5j2hheq/urZQO1TlXCwAA6BkI5wEA6AC/368/71+uM//3x1petSrodQmeeN048Kf65Nglun7gZR0KexE5Kd5k/XrQbH00/lX9KOsHQa/zy6/nK97UlPU/0Yrqj8JYIRB5ph3FifHSkP7O1QJ0RXvnI3AoLAAACDXCeQAAgrS9cbcu2XyTbi15UDW+2qDXfT9jkj4Y/7J+O/gG9Y5NC2OFCJfs+H56bNhdenfcszot7YSg15W3VOqqrbepcNvdqmqpCWOFQOSYAstRQ6QY/uJAlEhLlnIMR47Q2gYAAIQavyoDANCONn+bFpb9WVPXX65Pav8Z9Lrhibl6edR8PT3idxqSkB3GCuGUgpRRem3043p6+EPKjg9+O/BfqlbqzPWXdejbFkC0MAWW9JtHtDG1tmHnPAAACDXCeQAADHY37dMlm2/Svbvmq9FvaKr8Dakxybp78C/03jEv6Izep4S5QjjN4/Ho+5lnanXBy7ph4OWKDfLcgAOt1bp+252avXWO9rdUhblKwDmbdtjP0W8e0YZDYQEAgJMI5wEAsHCkt/zZ6y/Xp7Xrgl53fubZ+mj8q7pu4KWKi4kNY4WItGRvku4YXKgV4xbr1LTjgl73TvXf6EWPbsPvlzbssJ8fk+dUJUBosHMeAAA4iXAeAIBvqWg+oCu3/ka3ljyow231Qa3Jju+v50b+Xk8Ov1/94vuEuUK4yajkYXp99BN6bNhd6hObEdSaqtYaXbX1Nt1W8jvV+xrCXCEQPvtrpKpD9vNjc52rBQgF0875rXuk1lbnagEAAN0f4TwAAN+wovojTfnqJ3qv5uOgrvfIo5/1v0R/K3hRZ6d/N8zVwa08Ho9+lPUDfVDwki7sc07Q617cv1TTvpqlLw5vDGN1QPhs3GE/FxNDz3lEnzGGD5RaWqWSMudqAQAA3Z/j37dva2vT6tWrtWLFCn311VcqLS1VY2OjEhMTlZOTo4KCAk2dOlWTJk1STIxznx2UlJRo6dKlWrNmjXbs2KFDhw4pJiZGffr00YgRIzRx4kSdf/75Sk9PD/qer7zyitauXdvl2oYPH67rr78+6Oubm5u1cuVKvf/++9qwYYPKy8vV1NSklJQUDRo0SMcdd5y+//3v65RT6IMMAEc0tjXpgV2P608Vrwe9ZnTSMP370N/qxNRjwlgZoklmXLr+M/9enZd5lm7f8e8qb6lsd01J025dsOla/SrnGt048KfyBtnDHnCDTTvt54bnSAnxztUChEJ2lpSaJB22+VLTxh3SiEGOlgQAALoxj9/v9zv1sLVr1+qee+5RcXFxu9fm5+dr7ty5Oumkk8JaU3V1tR566CEtW7ZM7f2nSEpK0nXXXadrr71WXm/7fzjffvvtevPNN7tc44UXXqiHHnooqGtXrlypBx54QGVl7W/pOP7443Xfffdp1KhRXS3RaOzYsfL5fEeNeb1ebdzILkEA7rC1YYd+vu0ubWrYFtT1MYrRjdlX6JbsqxUfExfm6hCtaloPae6u/9ArlW8HveaU1GP1eP59yknoH8bKgND5xWPSAptfdy+YKL3xb87WA4TChGulz7ZYz/3bbOn2y52tBwAARIYTmaZjW9NfeuklzZo1K6hgXpKKi4t1xRVX6OWXXw5bTTt27NBFF12kpUuXthvMS1JDQ4Mee+wxzZ49Ww0NzvWHHTp0aFDXPfroo7rxxhuDCuYl6fPPP9fMmTO1atWqrpQHAFHL7/fr5f3L9P0NVwUdzA9LHKKlY/+o/zfoOoJ5GKXH9tK8YXfqhZHzNDC+X1Br1hz+UudsmKVVNZ+EuTogNEw758fmOVYGEFKmg4xNrZwAAAA6ypFw/u2339bcuXMDPmloj8/n03333ad33nkn5DVVVVXpyiuvVGlpaYfXfvzxx7r55puDCvRDIZhw/o9//KP+67/+q8P3bmho0C9/+Ut99tlnnSkNAKLWodbDuqH4bv265CE1tDUGteZn/S/RinHP6YTUcWGuDt3J5PTTtOqYF/TDzKlBXV/delBXFN2qf9u9QC1tnDwIdzMFlaaAE3CzcXn2c6YPpAAAADoq7OF8eXm57rzzTssg2+PxqKCgQNOmTVNBQYE8Hk/ANX6/X3fccYfKy8tDWtfdd9+tffv2Wc4NHDhQU6ZM0Xe/+12lpaVZXvPBBx/o+eefD2lNdvLy8ozzGzZs0GOPPWY5FxsbqxNPPFHTpk3TyJEjLa9paWnRrbfe6ui3AQAgkjbWb9P3N1ylZVXvBXV9TvwAvTL6PzU39xYleRPDXB26o96xaVowfK4eH3av0rwpQa15Yt8LunjzDSpt4vRBuFPVIamsyn5+rOFgTcDNTB8sbdoptbU5VgoAAOjmwn4g7OOPP676+vqA8ZycHC1YsEBjxoz519imTZtUWFgYsJu9vr5eCxYs0Ny5c0NS07p167Ry5cqA8ZiYGN1+++26/PLL/9VTvra2Vvfff7+WLl0acP0TTzyhCy+8UKmpqR16/uLFizVhwoTOFW/hD3/4g+W3EsaOHav/+I//0ODBg/819umnn+qXv/ylDh48eNS1ZWVlWrx4sa677rqQ1QUAbvTq/rf1252PqLGtKajrz888Ww/n/T/1iu3Y/60HrMzIOkcnp43XL7fP1T9qv2j3+s8Or9e0r2bpsWF3aWrGRAcqBIJn2kHs8UijhjhXCxBK4wxfXG5oknaUScOynasHAAB0X2HdOb9z50698cYbAeNxcXFatGjRUcG8JI0ZM0YLFy5UXFxgD98lS5Zo165dIanr0UcftRwvLCzUrFmzjjrsNS0tTQ8//LBOOeWUgOurq6v17LPPhqSmzlqzZo0+/vjjgPH09HQ988wzRwXzknTaaadp/vz5lvd6+umnVVtbG5Y6ASDSGtuadFvJ73RLyQNBBfNJMYn6w9A5eiJ/LsE8QmpQwkC9OvpxzRl0g+I87e+TqPEd0pVbf6N/3/OUfP6OtQgEwsnU0iZvgJTMF40QpXL7S0kJ9vP0nQcAAKES1nB++fLlam0N7JU6c+ZM2z7q+fn5uvjiiwPGW1tbtXz58i7XVFpaqjVr1gSMZ2RkaPbs2ZZrPB6PfvOb31jO/eUvf+lyTV3x5ptvWo5fd911ysjIsJw77bTTdMYZZwSMHzp0iMNhAXRLu5r26ocbr9OL+wO/BWVlbPIIvTvuT/px3/MsW64BXeX1eFWY/VP9ZcxTGpIQ3PbL+Xuf1ayiX6u69WD7FwMOMO2cp988ollMjDTG0JaJcB4AAIRKWMN5q9YxkjR9+nTjuvPOO69D9wtFTdOmTVNCgv32iPHjx1v2ft+9e7c2b97c5bo6w+fz6f333w8Y9/x/9u47Pqoy++P4Z2bSgSSE0FsoKYTeRVFsqCuuHSwIKoIgUux9Xcvq2hDpWAAbCNj1hwUbYqGKlAAplNA7BNLbzO+PUVfIzJ2bMpeU7/v1mhfrfc5z5yRLQnLmmXNsNgYMGGC415+fYxGRyuT7jGVcknQLG3JSTMUPaziQzxNfp21ojH8TEwG61E7kq/Zvcmndc03F/3B8OZduHEZSdqp/ExMxwahAqX7zUtUlxnhfU3FeREREKorfivP79u3zWLQODQ2lc+fOhnu7dOlCSEjJ98Fu3rzZ6xBXs3744QeP18844wyfe731iT9dp81///13MjIySlxv1aoVDRs2NNzr7eP9+eefKSgoqJD8REROJ5fLxfR97zI09V6OF/tu2RXhqMOc2Bd4uuU9hNgN3ssuUsEiAurwWttn+U/LewiylWztd6qd+Xu5YvPtfHj4KwuyE/Fuc7r3NZ2cl6rOqDhv9HdfREREpDT8VpzftGmTx+uxsbHY7cZP63A4iI2N9bi2efPmcuXl7ZR7fHy8z73eYk7XyXlvn2MzH0uDBg2oV69eiet5eXls37693LmJiJxOucV5jNn6b57ZNQ0XLp/xHcPi+arDm1xU92wLshMpyWazcWvDgXya+Botg5v6jM9z5jNu25P8a8fLFDpLthAU8bcT2bD7kPd1o8KmSFVg9ALTph3gdFqWioiIiFRjfivOb9261eP1li3NvcfVW5y3+5px6NAhjyfNbTYbLVq08LnfU1ub8uZUHlu2bPF43ezn2NvHXNqPJzEx0fBRXKzhdSJinT35B7hq8yg+OWquTdfg+lfwSaL5vt8i/tSpVkKp2tzMPvA+g1PvUh96sZxRv3kw7tctUhW0j/G+lpMHOw9YloqIiIhUYwH+urG3Am90dLSp/d7iylMI97Y3IiKCwEDfbyP3dNIcYOfOnRQWFpq6B7hbLSxZsoQvv/yStWvXsn//fpxOJ9HR0XTo0IGLL76YSy65hIAA4/97tm3b5vG61Z9jFd9FpLJYmbmOEWkPc7jomM/YEFsQ/415gEH1jWd0iFgtPKA2r7V9lun73uW53TNxYnw885cTv3HZxuG8FfeiZiWIZYx6bjdvAHXCLEtFxC9iGkFIEOR56fi5aQfENLY2JxEREal+/HZy/tAhz+9zjYqKMrXfWyHc231PZ06FhYUeT+R7M378eEaOHMknn3xCeno6eXl5FBQUsHfvXhYvXsy9997LgAEDWL16teF9vH083vI8lbePuzyfYxGR02XuwU8ZlDzGVGE+JrgZn7efpcK8VFo2m407mwxhbvxE6gZE+IxPz9/NPzeN4IeMZRZkJ2J8cl795qU6cDggweDN1ZvUCVREREQqgN+K8zk5OR6vh4aGmtofHOx5GJ+3+1qRk6chtb7u7YmZQn56ejq33HILX375pdeY7Oxsj9eN8jQTV57PsYiI1YpcRTya/hIPpD9Hoct37+3zI87ki/azSQxra0F2IuVzTkQvvmo/h05hCT5jTxRnMTT1Pl7fPx+Xy/esBZHyMDo5n6iWNlJNJLbyvmb0NSAiIiJilt+K894Kx2ZbvwQFBZXqvmZ4KzqXNycoX17eFBYW8sADD3gd/Oqvj8cfH4uIiD9kFmdzc+p9vHnwQ1PxYxoP5c24F4gIqOPnzEQqTrPgxnycOJPron2/08OJkyd2TuKB9OcocBZakJ3UVJvTva/p5LxUF0YvNPmauyAiIiJiht+K83l5eR6vmy0ce4vLzc0tc07e9pY3JyjdafOQkBC6d+/OJZdcwnnnnUezZs28xhYUFPDEE094XKssn2OHw2H4EBHxhz35+7lq00iWHF/hMzbEHsz0Nk/xcPM7cNj0fUmqnhB7MBNaPcozLe/Dge+/w/MOfcYNKeM4Wmi+7Z6IWdm5sMNgGGZijGWpiPiVr5PzepOSiIiIlJffBsJ6G2bqdBoPNfuTtyGjRqfXy5qT2YGmRUXe2yV4yisuLo6+ffv+9d+BgYFcdNFFXHLJJYSFnTwla9myZTzwwAMcPHiwxH3WrVvHihUr6N2790nXAwICPOZu9efY28n+PyUmJmporIhUqHVZm7kl7X4OFh7xGdskqCGzY5+nY614CzIT8R+bzcYtDa+hTUgLRm15lIziTMP45Zlr+eemEbwT/zKtQ5pblKXUBMk7jYuS7dTWRqoJoxeasnJh10Fo0dCydERERKQa8ltx/tTi858KCryMuz9FYaHnt2J7u68ZtWrV8ktO3u49bNgwhg0bZureffr0Yc6cOVx11VUe81m0aFGJ4nxYWBj5+fklYk/n51hExN++PLqEMdueIM9Z8vvfqXrX6cJrbZ8hOtDc4G+RquDsiJ583n4Wt6Tex9a8nYax6fm7uXzTCObEvkDPOp0sylCqu40GgzAbRUFddQ6TaqJ1YwgOgnwvv15t3K7ivIiIiJSP39raeCvwGhW4zcSVp3Dsr5yM7l0abdu25bLLLvO4tmJFybYNlfFzLCLiLy6Xi5n75jJiyyOmCvM31b+S+fGTVZiXaql1SHM+T3yDcyN6+4w9VnSc65LH8vnR7yzITGqCJIPifHuDNiAiVY3DAQktvK+r77yIiIiUl9+K8+Hh4R6vZ2SY633qLc7bfc2oU8fzMZ7jx4+b2m8U5+3epXX22Wd7vL579+4SxfTK+DkWEfGHIlcRD6Y/z9O7puLCuMGrHTtPt7iH51s9SJDd3AwOkaooIqAOb8W9xPCG1/mMzXcVMGrLY8zcNxeXmiRLOSVt877WobV1eYhYwahN06Z0y9IQERGRaspvxfmWLT3/FHPkiO/+wEZx3u5rRkxMTKme61SHDx/2eD06OpratWuXNa2TtG7t+TeaoqIiMjNP7i1bGT/HIiIVLac4l2GpDzL30Kc+Y8PsocyJe4FhjQZakJnI6RdgC+DJlnfxYszDBNp8dyt8etdUHtnxEkUu73N0RHwxamujk/NS3Rj1nVdxXkRERMrLbz3n27Zt6/H6/v37Te0/cOBAqe5rRsuWLQkMDCxxAr2wsJAjR45Qr169MuXUpk2bMud0qtDQUK9rp+ZdGT/HIiIV6WhhBkNT7+P37I0+YxsF1uetuJfoUCvOgsykUnO5oDAHcg5D3gkozIaCbCjIcv9ZmA3FBeBygrPY/aerGJxOsNnAEVTyERACIeEQHAEhfzyCIyCg7IPqK9KNDS6nVUgzbkt7iOM+BsW+ffAj9uTvZ0bbp6nlUCs7KZ2MTNh9yPt6BxXnpZoxKs5v3O7+J8dmsywdERERqWYsL86npqaa2p+SkuLxureT5WYEBgbSsmVLtmzZ4jGvPn36lCmniizOZ2dne107dehseT7HBQUF7NjhuUliRX48IiJltTN/L4NT7mabj4GXAB3C4ngz7kUaBzWwIDM5bZzFkHUATuyG47vhxK4//twN2Yfcxficw5BzBIryrMkpMBRqNYDajaB2w5P/jGwBkTHuR3DFvMPOSJ/wbnya+BpDUu5hV8E+w9jvjv/KtZvv5J34CZrLIKWyMd14vX2MFVmIWMfo3SBZubBjP8Q0ti4fERERqV78Vpxv164d4eHhnDhx4qTr+/fv5/Dhw0RHR3vde/jwYY+nuiMiIkhMTCxXXr179/ZYnE9KSvJZnN+40fPJzTPOOKNcOf3dtm2em3hGRkaWaJ3Tq1cv7HY7TqfzpOspKSkUFhYSGOi91/LmzZspKir5lva4uDiiovRLuoicXknZqQxJvYeDhb7bdPWP7Mu0Nk/qBHB14XTC8R1wJK3k49h2cFaydiyFuZCxw/0wElbvf4X6erFQv537ER3vPpFfQWJDY/i8/RvcnHof67I3G8auz0nmyk2jmJfwCi2Cm1RYDlK9GQ2DbdUYautbsVQzbZpASBDkFXheT9qu4ryIiIiUnd+K84GBgfTr14/PP/+8xNq3337L9ddf73XvN9984/H6ueeeS0BAyZSdTidLliwhLS2N6OhoLrzwQiIiIjze48ILL2Tu3LkecxoxYoTXnDIzM1m+fHmJ68HBwV6HuIL7hHpQkPm3vC9dutTj9Q4dOpS4Vq9ePbp06cKaNWtOup6VlcXy5csN8/L2Ob7wwgtN5yoi4g8/HV/F8LSHyHLm+Iwd3vA6Hm8xFofNYUFmUuGyD8GBDSc/Dm50t56pbnKOuB97fyu5Ft7UXahv0AEad4HGXd3/7SjbQOP6gVF8kDCNO7f+m8UZPxnGbs/fxZWbRvJu/EQSw9TWTnzbaDAMVv3mpTpyONytbdZ4eXNy0ja47ExLUxIREZFqxG/FeYCLLrrIY3F+7ty5DBw4EIejZDGluLiYefPmeb3fqbKyshgxYsRJBeoXX3yRGTNm0LVr1xLxvXr1IjIykoyMjJOur1u3jnXr1tG5c2ePz/3ee++V6PkO0LdvX8LCPB8R2rVrF8OHD2fw4MEMHTrUY8zfbdmyhS+++MLjWv/+/T1ev+iii0oU5wHefvttr8X5rKwsPv74Y6/3ExE5XT498g3jtz1FoY9hlTZsPNniLm5rNMiizKTc8k7A3tWwexXsWQl7VsHxXac7q8rhxB73Y+u3/7sWEPy/Yn2THtC8DzTsAHZzL0SFOUJ5I/a/PLFzErMPvG8Ye6DwMNdsvoM5sS9wRnjJn51E/s5oGKz6zUt11b6VQXHe4GtCRERExBe7P29+wQUXeOwRn5qayqRJkzzumTRpksee6W3atOG8884rcf3ZZ58tUZw+duwYd955J1lZWSXiAwICuOWWW0pcd7lcPPbYYxw7dqzEWlJSEjNmzPCY7/Dhwz1eT01N5YYbbiA9PZ1nnnmGhx9+mKNHj3qMBdi5cyejR4/2+AJAZGQk//znPz3uGzhwIJGRkSWuL126lPnz55e47nQ6eeKJJzh8+HCJtb59+9KuXTuvOYqI+NNr+99j9NbHfRbmg2yBzGj7tArzlZnL5W5B8/tb8PEwmNQOno2EORfANw/Bpo9UmPelKN99yv63WfD5HTC9Czzzx+fw28cgZRHkeP+5AsBhc/B0y3t4osV4bBhPKzxRnMWNKXfx1bEfK/KjkGrG5YINBoVInZyX6qqjwdizJIN3k4iIiIj4YnO5XC5/PsHXX3/NuHHjPK5dcMEFXHfddTRu3Jh9+/axcOFCvv32W4+xU6ZMKXGqu6CggB49epCfn+9xz/PPP8+VV15Z4npOTg79+/f3WKBu1qwZt99+Ox07diQ/P59ffvmF2bNnexzUet555zFz5swS19esWcOoUaM4fvz4Sdfr1KnDRRddRL9+/WjSpAkhISHs27ePpUuX8v7775OX53l43eOPP87gwYM9rgHMnj2b559/vsR1m83GVVddxeWXX05UVBQ7duzg7bffZtWqVR5jP/jgA4/tc8orMTGR4uLik645HA42bdpU4c8lIlWPy+Xi2d3Tmb7vXZ+xdRy1mB37AmeGd7MgMzHN5XL3hE//EdKXuv9U8d3/bDZo1AVan+9+tDwbgut4DP3i6BLGbP03+S4vTZP/YMfO8zEPcmODy/2RsVRx+49A06u9r/8+Gzq1sS4fEat8vRIuvd/zWmAAZH7t/lNERESqFytqmn4vzoP7dPlPPxn3PDVyzjnn8Prrr5e4fuDAAc455xyv++6++25GjRrlcW3RokXcc889Zc6pVq1afPTRR8TExJRY+/HHHxk/fjy5ubllvv+fzj//fKZPn47N5v3EW0FBAQMHDiQ5ObnMz3P99dfz5JNPlnm/ERXnRcSbYlcxD6W/wLxDn/mMbRQYzTvqi1155Bxxt2FJ+wq2fA2Z+053Rt4FhkJQbQisBUF/PAJCwOYAm93dKsZmd/+3ywnOQvfJ9eKC/z0KsiFKdBe9AAAgAElEQVT/OOSfAGex7+c8HewOaNoTWv1ZrO/rbo/zh2Un1nBr2gNkFvvu6f9gs5GMbXyz4c8fUvN8uxouvtfzmsMBmV9BsPlRSyJVxp5D0OJa7+vr39Q7R0RERKojK2qalry+/9JLLzFkyBCP7Wp8iYuL48UXX/S4VrduXUJCQryeOG/SpInX+w4YMIDU1FSPJ999CQwMZOLEiR4L8wD9+vVjzpw5jBo1qkRv+9I499xzmThxos9fjIOCgpg6dSqDBw/mwIEDpX6ePn368Oijj5Y1TRGRMilwFjJ22xP839Hvfca2DWnJvPhXaBrcyILMxCNnMexZ/Ucx/ivYvdJdyD4dbHao0wQimkF4M4hoDnUaQ1h9CKsHYdFQK9r9Z3AE2Cuwi5/LBYU5kHfc/cg9Apn7IfvAyX+e2A0Z6ZBbsl2e3ziLYddy92Pps+4XIdr0h/jLIO5S+oR348N2M7gp5W4OFh4xvNXzu1/lUOFRnmxxF3abX7sgShVi1G8+vrkK81J9NYmGunXgWKbn9Q3bVJwXERGRsrGkOB8ZGcnbb7/Nfffdx88//2x6X9++fZkwYYLHnurgLkpfddVVvPfeeyXW6tevzwUXXGB4/7vvvpvIyEgmTJjgsde7J/Xq1WPChAn06dPHMK5r16589tlnPPfcc16HvHoTEhLCHXfcwe23347dZEGhefPmzJs3j3HjxrFx40bTz3XFFVfw1FNPERSk36ZExDo5xbmM2PIwS46v8Bnbo3ZH5sS+SFRghAWZyUkKctyn4jd9DKmLINe4x3mFCgiBqLZQL/Zvj7YQ1QZqNwbHaeofYLP97/R9uPdDAH/JOw4ZO+BYOmRshyNb4NBmOJzsHgLrTwXZsPkT9wOgSTfax13Gp23u5Ibjs0nP3224ffaB9zlelMnLrR8lwKZ+DWI8+FKFSanObDZ33/ml6zyvJ20DjH/1FBEREfHIkrY2f7do0SJmzZplWEBu3749t912GwMGDPB5v5ycHEaPHs2yZcv+uhYVFcXMmTPp3LmzqZy2bt3K9OnTWbx4MQUFnnuxRkZGMnDgQEaMGEFEROkKRCtWrODNN9/kp59+MnwRoHHjxgwYMIAhQ4bQqFHZTocWFRWxcOFC3nrrLdLT0z3G2Gw2evbsyciRI+nbt2+Znqc01NZGRP7ueFEmN6fex6qs9T5jL4o8m+ltnyLUHmJBZgK4T3qn/J+7IL/lKygsf4s2Qza7uwjfsCM06uT+s2FHqNu6Yk+8V0Z5J+BwirtYf2A97Pvd/bDgtP3hqKYM6RXP+sAcn7H/qNuPaW2eItiuF/JrujPvgBVefnx7chg8drO1+YhYacxEmPGJ57V/ngWfPGttPiIiIuJ/1abnvCd79uxhw4YN7N27l9zcXEJDQ2nSpAmdOnUybEfjicvlYunSpSQnJxMdHU3//v0JDw8vdU45OTmsXbuW7du3c+LECRwOB3Xr1iU+Pp727dvjcDhKfc+/y83NJSkpifT0dI4dO0ZRURFhYWE0bNiQ+Ph4WrduXa77n2rbtm1s3LiRAwcOkJ+fT61atWjWrBldunQhOjq6Qp/LiIrzIvKnQ4VHGZxyFxtz0nzGDq5/Bc/G3KcTu1bIOQIbP4CNH8L2H8BZ5L/nimoDTXtBs57uPxt3haAw/z1fVeNywfGdsG8t7F0De1a629Tklb1NnjdZDju39Yzn5/q+Dx2cE96LWbHPEeYIrfA8pGpwOqHupZDl5fW6D56Gq7yPghKp8mZ+Cne+7HmtVWPYMt/afERERMT/qnVxXmoOFedFBGBP/n6uSx7H9vxdPmPHNB7CQ83u0DBKfyrIhuTPYf089wn5YnPt3UrFEeguwMf0cw8nbdbL3RNeSsfpdJ+w37UMdv0KO5fBoYr5NzTfbmN8l7Z83tT3/y89a3fi7bgJhAfUrpDnlqpl+z5oe7339eS5ENvMunxErPbzeug31vt6xpdQR681i4iIVCvVZiCsiIjUbFty07k+ZTz7Cg76jH2k2WjubDLEgqxqoOJC2PoNrJsHyZ+4C/QVKTAUmveBludAq37QrLf7mpSP3Q4N2rkf3Ye5r2UdhO1LYPv3sO17OOL73SieBDtdTF+TRnRBIXNaGbfUW5W1nkHJY5gbP5F6gXXL9HxSdSVt874WEgStG1uXi8jp0MHHXIVN6dA70ZJUREREpBpRcV5ERPwqKTuVG1Pu4kiRcR9tGzb+G3M/QxpcZVFmNciBjbBmFqx7F7IPVey9G3aE2EvcjxZnQUBwxd5fPKvdADoOcj8Aju92tyTa9h2kfQ1Z+03fyg48nZROVEEhE+KbG8ZuyEnhms2jeS9hEo2DGpTjA5CqZqPBMNjEGChn90eRSi+yDjRvALu8nDPYsE3FeRERESk9FedFRMRv1mZtYnDKXWQUZxrGBdgcTGr9OFfWu8iizGqAvBOwYb67KL97ZcXdNyQS2vT/oyB/MYQ3rbh7S9lFNIMuQ9wPp9M9WDZ1kXu4755VPrfbgHtS91CrqJin2scYxqblpXP1+luY3/5VWoYaF/Ol+kgyKM6393GiWKS6aN/Ke3He6N0lIiIiIt6oOC8iIn6xKnM9Q1LvIbPYuHVKiC2IV2Of5cLIsyzKrBpzuWDnL/DbLEhaCIU5FXPf8GaQeBUkXg0t+oJDPz5UanY7NO3ufpz3OGTuh7Qv3YX6LV8btjMauW0/tYucPNipFS6DmQ87nce4evU1zLddRGyXce6T/FKtGZ2c79jaujxETqeOreGrFZ7XjL5GRERERLzRb9ciIlLhlp1Yw9DU+8hx5hrG1baH8VbcS5wR3tWizKqpvBOw7h1YMb3CBoUSnfC/gnyT7qDhvFVXnUbQ7Vb3ozAX0r6CjR9A8mdQkFUifPDOg9QuKmZc1zYU2e1eb7s/2MHV+V+x4NUpJDa9GHreDq3Od784INVKQSFs3uF9PTHGslRETqsOBi9EbdDJeRERESkDFedFRKRC/Xh8BcPSHiTPmW8YFxUQydz4iXSqlWBRZtXQwU2wYhqsfdtjkbXUouOh043QfqB7+KhUP4Ghf7zochUU5sGWxbDxfXehPv/EX2FX7D1CWFExI3vEke/wXmw/GhzIwN6xzF++iI4b34eotnDGGOh6C4REWPABiRWSd0Jhkff1Tm2sy0XkdDIaCnsoAw4egwaaly0iIiKlYHO5XK7TnYRUb4mJiRQXF590zeFwsGlTBZ3uFJFK45tjP3P7lkcocBUaxjUKjGZ+whRiQ2OsSaw6KS6C5E9hxVTYvqT896vTBDrd4C7KN+6qE/I1VVG++0T92ncg5XMoLgDg53rh3NornpwA42mfEQVFzFu+mS7H/2iZE1TbXaDvPQbqx/s5efG3d76GW571vFYvAg58qm8dUjPkF0CdS+CUX23+svhluKC7tTmJiIiI/1hR09TJeRERqRBfHF3C6K3/otBlcLwSaBrUiIUJU4gJaWZRZtVE3nFY/RosmwwndpfvXiER7tPxnW6EmHPAblx4lRogIBjaXeF+5B5zzyxY+w59d/7C/GWbGdI7geNB3n9sPB4UwPV92vHuimR6HMtyv5NjxVT3I/YSOGMctL1YLW+qqPVbva91bqPCvNQcwUEQ18x7m6ekbSrOi4iISOmoOC8iIuX26ZFvGLv1SYrxcpTsDzHBTVmQMIVmwY0tyqwaOJYOyybBb2+Uv3VNq/Og+23uPvKBoRWSnlRDoXWh50j34+hWuq99l/c3zuPGxNocDg7yui0zMIAbz2jHOyuS6X00838LaV+5H/Vi4ax7octQ/f2rYtZt8b7WUS1tpIZp38p7cd7ohSwRERERT3R8SUREymXhoUWM2fqEz8J8m5AWfNBuugrzZu1eBQuuh1fawrJXyl6YD28K/R6Fu7fAsO+h82AVRsW8qDZw/r9pP2IzHza8n0ZFxu+yyA5wMLh3Ar/UCy+5eCQNPhsFE2JgyTOQc9Q/OUuFMxp0qX7zUtMY/Z1XcV5ERERKS8V5EREps3cPfsLd2/+DE6dhXEJoaz5sN4PGQQ0syqyKcrkg9UuY1Q9e7QVJC8Bp/KKHRzY7tLsShiyCe3fAhf9xF1lFyspup23c9XzQ7T2aBEQbhuYGOBjaO4Ef63sZCJt9EL57DCa0gC/uggwvR1ClUth/xD3k0pvO+tYiNYxRcT5pu/HwZBEREZFTqTgvIiJl8vbBj3gw/XmfcR3C4ng/YRr1A6MsyKqKcjph44cwswe8cymkLy3bfWrVh3MegXu2w40fQ9yl6icvFapVSHM+bP8qzYIaGcblOezc2jOe7xpEeg8qyHa3bJrYBt6/CfZvqOBspSKsMzgJ7HBAu5bW5SJSGXRu632toBBSdlqXi4iIiFR9Ks6LiEipvX3wIx5Of9FnXNda7VmQMIWoQIMCXU1WXARr34WpHWD+tbB3Tdnu07wPXPsu3LcL+j8DkS0qNk+Rv2kR3ISP2s2gZXBTw7h8h53hPeJY3LCu8Q2dxbB+LkzrBO9dA/vWVmC2Ul4bDIrzCS0gJNi6XEQqg+YNILK293WjF7RERERETqXivIiIlIrZwnyv2p15L2ESkQEeek/XdEX5sOo1mBQPHw6BQ5tLfw9HEHS7Fe74DW7/1d1LPkBVMrFG0+BGfNhuOq1DjF8IKnDYGdEjli8a+SjQ/2nTRzC9K8y9AvasroBMpbyMCo3qNy81kc0GXQxOzxsNUBYRERE5lYrzIiJimtnC/Fnh3ZkbP5E6jloWZFWFFObB8inuNh6fjYRjBlMWvQmrB+f+C+7bCVfNhibdKj5PERMaBzXgg4RpxIbEGMYV2e3c0T2WL80W6AGSP4OZPeHtS2HnsvIlKuWy3qDQqOK81FSdVJwXERGRCqLivIiImGK2MH9exBm8FfcSYY5QC7KqIooKYNWr8EosLBoHJ/aU/h71YuGfM+DenXDBU1C7YcXnKVJKDYOieb/dNBJCWxvGFdntjOoex9e+WtycKu1LeP1MeLM/pP9UjkylLPILINmgf7aK81JTGf3dX6+2NiIiIlIKKs6LiIhPZgvz50ecyRuxzxFqD7EgqyqguAjWzHG3r/lsFJzYXfp7tDwbbvwUxiVDr1EQFFbxeYqUQ/3AKN5PmEb7sFjDuCK7jZE9E1jcogwzEbZ+C7POgbcugT2/lTFTKa3NO6Co2Pu60WBMkerMqK3NwWOw/4h1uYiIiEjVpuK8iIgYKk1h/vXYZwmxq+85zmJYNxemJMLHwyAjvfT3iLsURvwCw5dCu8vBrn+ypfKKCoxkQcIUOoUlGMYV2lzc3qUF3/zzEYgqw7HrLV/DzB7uwbEHN5UxWzHLqN98/UhoFGVdLiKVSWIMBDi8r69VaxsRERExSb/pi4iIVyrMl5LTCUkfwNRO8MFNcCStdPttNmh/LdyxBoYsghZn+idPET+oGxDB/ITJdK3V3jCu0FXE7baf+PbmOTDwPaifWPon2/QRTO0IH94MR8swu0FM8dVv3mazLheRyiQ4CBIM3gSk1jYiIiJilorzIiLikQrzpbTte3i1FywYCIdKeaLX7oDOQ2DMRrj+fWjS1T85ivhZREAd5sW/QtdaxgX3AlchI7Y+ynctWsCYDXDd+9CwU+mezOWEtW+720Z9PhpO7C1H5uKJUYFR/ealpjNq66ShsCIiImKWivMiIlKCCvOlsH89vP0PmHMB7C1lL2xHEPS4HcanwrVvQ4N2/slRxELhAbWZG/8KXWoZ/30ucBUyPO0hfjixAjpcC6N/hxs/gSbdSveEziJYOQMmtoGvH4TcjHJkL39yuYzb2qg4LzWdivMiIiJSEVScFxGRk6gwb1LGDvhgKEzvAmlflW6vPcBdlL8rDa54FaJa+ydHkdPEfYJ+Ep1NFOhvS3uIJRnL3XMV2l0Bo1bDTf8HzXqV7kmL8uDnF+CVtrBsEhQVlOMjkH1H4Mhx7+sqzktNZ/Q1kLILcvOty0VERESqLhXnRUTkL+8c/FiFeV9yjsJX98ErcbDuHffxUrNsduh6M4xPcRflIw0a1opUcX+2uOkYFm8Yl+8qYFjag/x4fIX7gs0G8QPg9uUw9MvSn6TPOQJf3OUeyJz0fum+RuUvRid/AxzQrqV1uYhURkYn551O2LjdulxERESk6lJxXkREAJh38DMeSn/BZ1yNLcwX5cNPL8DE1vDLBCguxalcmw063gBjN8HVb+qkvNQYkQHhvJcwiQ5hcYZx+a4ChqU+wNLjK/930WaD2EvcJ+mv/wDql7Lt09GtsGAQvH4m7PilDNnXbEb95tu1dA/EFKnJGtSFxvW8r69VaxsRERExQcV5ERHhg8Nf8kD6cz7jamRh3uWCjR/B5Haw+EHIM+jz4Eni1XDnehg0D+obnyAWqY7qBkQwP2Ey7cNiDePyXAXcmno/Px1fdfKCzQbtr3EPjr36LYiMKV0Cu5bDG31h3tVwOLV0e2uw39O8r3VUSxsRwLi1zXoV50VERMQEFedFRGq4z458y93b/oML49YPNbIwv/d3mH0ezL8GjpXy/elt+sMdv8ENH0LDDv7JT6SKqBsQwfz4KSSaKNDfknY/P59YXXLR7oCuQ91toS6bBnUaly6JzR/DlPbwxd0aGmvCWoPifFfj/xtFagyj1jZG7z4RERER+ZOK8yIiNdhXx35kzNYncOI0jKtxhfnM/fDJcJjZHdJ/LN3exl3h5sVwy+LS98oWqcaiAiNYED+ZdqEG1Swgz5nPLan3syJzreeAgCDoPRru2gIXvwChdc0n4SyCZa/AK7Gw6lVwFpfiI6g5TmRD2m7v6yrOi7gZFefXbdXICxEREfFNxXkRkRrqu4xfGbXlMYoxLk7VqMJ8YR4sfc5duPttVul+q46MgWvfdffHbtvfbymKVGVRgZEsTJhCu1Djvii5zjyGpNzL6swN3oOCwqDv/e4i/Zn3gKMUTdBzDsNno2BGd9heyhfgagBfvbJVnBdx62zwrexENmzfZ10uIiIiUjWpOC8iUgMtPb6SEWkPU+gqMozrF967ZhTmXS5I+sDdV/6bh6Egy/zesHrwj4kwPhk6Dwa7/mkVMRIVGMmChCkkhBoPRs525nBT6t2sy9psfMOwKPjHBPfXYMfrS5fM/nUw+1yYPwiOpZdubzVm1NKmVWOIrGNdLiKVWWwzCDF4XdBodoOIiIgIqDgvIlLjLD/xO7emPUC+q8Awrk+dbsyKfa76F+YPJLn7yi8YCBnp5vcFhMA5D8PdW+HMuyCgmn+eRCpQvcC6LEiYQryPAn1mcTY3poxnY46JClfdVjDoPRi5EmLOKV1CG993vzj33eNQkF26vdXQGoO5uV3jrMtDpLILCDAeCvu7ZlCLiIiIDyrOi4jUIKszNzA09T7ynPmGcT1qd+StuBcJdYRYlNlpkHfcPRhyepfS95XvdKN7KGX/ZyEkwj/5iVRz0YFRLEyYQlxoK8O4jOJMrk8eR0rONnM3btYThi2BGz+F6ATzCRXlwZKnYVI8rJtXo5tFGxUUu6mljchJuhm8YPWbivMiIiLig4rzIiI1xLqszdyUejfZzhzDuC612vFO3MvUcoRZlJnFXC5Y+467ALfsldINhGzWC0b8CgPnQmQL/+UoUkNEB0axIH4KrUOMv56OFmVwXcpYtuTuMHdjmw3aXQ5jNsBl09ztp8w6sQc+GAxzzoeDm8zvqyZy82HzTu/rXVScFzmJ0btJfk+t0a/ziYiIiAkqzouI1AAbc9K4MWU8mcXG7Rrah8UyN/4VwgNqW5SZxfatg1nnwIdDIeuA+X3hTeGad2DEMmjRx3/5idRADYLqsTBhCjHBTQ3jDhUe5brkMaTn7TZ/c0cA9B4N41PhjHFgd5jfu30JTOsMXz8A+aWYQ1HFbdgGxQavWRqdEhapiYy+Jg5lwO5D1uUiIiIiVY+K8yIi1VxKzjauTx5HRnGmYVx8aGvmx08mMiDcoswslJsBi8bBjG6w42fz+wJD4bx/u1vYdLlJw15F/KRxUAMWJkylWVAjw7j9hYcZlDyW3fn7SvcEYVEwYBLcuR7aXmR+n7MIfn4RJidA0vs14gisUb/5JtHQMMq6XESqgg6tIDDA+7rR15SIiIiIqgwiItXY1tydXJcylqNFGYZxbUJasCBhClGBkRZlZhGnE9a86W5hs3wKuJzm93Ya7C7Kn/8EBNXyV4Yi8oemwY1YmDCVRoH1DeP2FOxnUPJY9hYcLP2TNEiEoV/BTZ9DVFvz+07sgQWD4K2L4VBK6Z+3CllrMHtXLW1ESgoKhI4Gs601FFZERESMqDgvIlJN7cjbw6DkMRwqPGoYFxPclAUJU6gfWM2OQx7cBLP7wce3QnYpinhNuv3RV/5diGjuv/xEpISWIU1ZmDCVBoHGPeJ35O/huuSxHCg4XPonsdkg/jIYmwQXvwjBdczv3foNTOsI3zwKBcbzO6oqDYMVKb2uBl8bGgorIiIiRlScFxGphvbk72dQ8hj2Fxo3Om0W5D6p2jiogUWZWaAw1104m96ldC1sQuvCP2fAyJXqKy9yGrUJ/eOdPAHG7+TZlreT61PGcaTwWNmeKCAY+t4Hd6VBt2Hm9xUXwtJnYXI72PxZ2Z67kiosgg3bva8bDb4Uqcm6xXtf08l5ERERMaLivIhINbOv4CCDksewu2C/YVyjwPosSJhC02DjHs9VypbFMKWDu3BWXGhuj80GPUa4B0b2GlW6gZEi4hdxoa2YnzCZSIfxqfbU3O1clzyOo4XHy/5ktRvCVbPg9uXud86YdXwnzLsC3rvG3famGti8A/ILvK8bnQ4WqcmMhsLuO+J+iIiIiHii4ryISDVyqPAo1yWPJT3fuFBUPzCKBQlTiAlpZlFmfpa5Hxbe6O4HfWyb+X1Ne8LtK+CK16BWtP/yE5FSax8Wy3sJkwl31DaM25y7hcEpd3G8yHjotU/Ne7vfOXPZNAiJML9v00fuU/TLp4KzuHw5nGZGgyujwqFFQ+tyEalKOrUGh8Fr+xoKKyIiIt6oOC8iUk0cLczg+uSxbM3baRgXFRDJgvgptA1taVFmfuR0wsqZMDkBNrxnfl9YPbjidfdJ2WY9/ZefiJRLp1oJvBs/kVr2MMO49TnJ3JRyD1nF2eV7QrsDeo92v5Om683m9+VnwqKx8PpZsG9d+XI4jYyGwXaNdb/RSERKCgmG9jHe19XaRkRERLxRcV5EpBrIKDrB9SnjSM41PjUe6ajD/ITJxIe1tigzP9q/Ad7oC5/fAXkmW1rYbNBzlLvw1mM42PXPoEhl1712B96Jn0CoPcQwbk12EkNS7iWnOLf8T1q7AVz9Jgz/CRp2NL9v9wqY2R2+frBKDow1HAarfvMihjQUVkRERMpCVQkRkSouszibwSl3sTHH4MgjUMdRi7nxr9A+rIo3DS7IcRe+ZnSFXcvM72vSzd2y4vIZEBblv/xEpML1rtOFN+NeJMQWZBi3Mmsdt6TdT64zr2KeuGVfuGMN/GMiBBv3v/+Lsxh+fgGmtIe0ryomDws4nbB2i/f1LlX8nw4Rf+uuobAiIiJSBirOi4hUYdnFOQxJuYe12ZsN48LsobwT9zJdaidalJmfbP0OpnZ0F77M9nYOqg2XvuLuLd+0h3/zExG/6Rveg1mxzxNkCzSM++XEbwxPe4h8p8Fk09JwBMCZd8G4ZOh4g/l9Genw9j9g4Q2QdaBicvGjtN2QZfCmA52cFzHW1eBrZNdBOJRhXS4iIiJSdag4LyJSReUW53Fz6v2sylpvGBdiD+btuJfoWaeTRZn5Qe4x+Pg2ePPC0g18bXcVjNsMfca7C2wiUqWdG3kGr7Z9hgCbweRFYMnxFYzc8igFzsKKe/LwJjBoHgz9Cuq2Mr9vw3yYlACr3wCXq+LyqWArDV7jrR0KbZtal4tIVdS5jXG3PA2FFREREU9UnBcRqYLynPnclvYQyzLXGMYF24KYE/sCfcK7WZSZH2z8CCYnwprZ5vdENIcbP4UbP4KIZv7LTUQsd1Hds5ne5mkcGBfov8n4mTu3/psiV1HFJhB7MYxJgrMfdA+QNSMvAz4d4X6B8WgpXmC00Opk72vd4zWiQ8SXWqGQ0ML7ulrbiIiIiCf6MVtEpIopcBYycsuj/HhihWFcoC2A12P/yzkRvSzKrIJl7oP3roH510DWfnN77A44614YuwnaXe7f/ETktBkQdR6T2zyO3cePsl8c+4G7tj1NsctkGyyzgsLgoufc/eib9Ta/b9v37tZcv75ivjWXRYyK8z0SrMtDpCrTUFgREREpLRXnRUSqkCJXEXdufZxvM34xjHPgYGbb/3BB5JkWZVaBXC74bbb7tPymj8zva9YLRq2GS16C4Nr+y09EKoUr613EhFaPYMNmGPfxkcXcv/2/OF3Oik+iUScY8QtcNg2Cw83tKcyBL++G18+CAxsrPqcyKCwyHgbbw2DQpYj8j9FQ2N9SrMtDREREqg4V50VEqohiVzHjtj7FF8eWGMbZsTOlzRNcUrefNYlVpKPb4M3+8Mlt7jYQZgSHuwtjI36Fxl38m5+IVCqD6g/g+ZgHfcYtOLyIR3a8hMsfPd/tDug92j3fIvEa8/t2r4AZXeGHp6GogobXllHSNsgzSKFnO+tyEanKjIbC7tgPB49Zl4uIiIhUDSrOi4hUAU6Xk3u3P8unR78xjLNhY2Lrx7ii3oUWZVZBnMXwy8swtQNs+878voTLYdwmd2HMbO9nEalWBje4gqdb3OMz7p2DH/Pvna/4p0AP7oGxN3wAgz9zz70wo7gQvn8cZvaEPav9k5cJqwxa2tSLgJhG1uUiUpV1izWez2D0tSYiIiI1k4rzIiKVnMvl4qH0F3j/8Bc+Y1+IeYhro/9hQVYV6EASvNYHvroXCnPN7anVAAYtgBs/gfCm/s1PRCq9YY0G8q/mY3zGzTqwkGd3T/dfgR4g4Z/uuRd9xoPNuOXOXw6sh1d7w9cPmP8+WIEM+83Hm/8wRGq62lKjV5gAACAASURBVGHQPsb7+qrNlqUiIiIiVYSK8yIilZjL5eLfO19h7qFPfcY+0/JebmxQhYagFhfCkmdgRjfYs8r8vi5D3aflOw5SxUhE/jKq8WAeaDbSZ9z0fe8yYc8b/k0muDZc+goM/wXqm+wJ43LCzy/C1E6QvtS/+Z1itUEv7J4aBitSKkZtoFaqOC8iIiKnUHFeRKSScrlcPLNrGrMOLPQZ+3jzcdzS8FoLsqog+zfAa2fAd4+5i/RmRLSAoV/BNW9BWD3/5iciVdL4JrcwvsktPuMm7p3N5L1v+j0fWvSB0b9Dv8fAHmBuz9EtMKsffD4a8rP8mx+QkwdJ272v91BxXqRUjL5mViW7596LiIiI/EnFeRGRSuqlPa8zY/9cn3EPNhvJyMY3WJBRBSguhCX/gZndYe8ac3tsNjhjHIzdCLEX+zc/Eany7m96O6Ma3egz7vndr/Lqvvf8n1BAMFz4NIxaDU26m9+3cgZM7QjbfvBfbsDaLVBc7H1dJ+dFSqeXwdfM0ROwba91uYiIiEjlp+K8iEglNGnvm7yyd47PuLubDGOciVOilcJfp+X/Zf60fP127rYQAya520SIiPhgs9l4rPkYhjUc6DP2qV2TefPABxZkBTTuDLcvh4ueh4AQc3sy0mHO+fB/Y/x2it6o33yz+tBIb1QSKZUOrSEkyPu6hsKKiIjI36k4LyJSyczcN48Xdr/qM25045u4t+lwCzIqp7KclrcHwLmPu9tBtOjj3/xEpNqx2Ww81eJuBte/wmfsozsmMO/gZxZkBTgC4OwH4M510PJs8/tWTINpnWD7jxWeklGh0Kh3toh4FhgA3eK8r2sorIiIiPydivMiIpXInAPv8/SuKT7jbms4iEeajcZW2QeiluW0fNMecMcauOBJdzsIEZEysNlsPBfzAAOjL/UZ+0D6c7x/+AsLsvpDdBwMWwKXTYMgk+8KOrYdZp8Li8ZBQXaFpWJUKFRLG5GyMXphSyfnRURE5O9UnBcRqSTePfgJj+142WfcTfWv5MkWd1XuwnxZTss7gqD/f2HEMmjU0b/5iUiNYLfZmdDqEa6I6m8Y58LFPdue4dMj31qUGWC3Q+/Rf8zTuMT8vuVTYFpnSP+p3ClkZELabu/rGgYrUjZGL2ytSYXCIutyERERkcpNxXkRkUpg4aFFPJT+gs+4QdED+G/M/ZW7ML9/fRlOy/d0n5Y/5yF32wcRkQrisDmY1PpxLq17rmGcEydjtz7Bl0eXWJLXXyJbwJAv4Oq3ICTS3J6jW2F2P/jibijIKfNTr04xXu9u0JpDRLzrZXByPjcfNm63LhcRERGp3FScFxE5zT45sph7tz+LC5dh3JVR/Xmp1cPYbZX0W3dxIfzwNMzsUcrT8s/BiF+hYXv/5iciNVagPYBpbZ7iwsizDOOKKeaOrf/i24xfLMrsDzYbdB3qPkUfN8DcHpcLlr0C07vAjrLlazQMNq45RNYp021FarzWTSAq3Pv6SvWdFxERkT9U0gqPiEjN8H9Hv2fc1qdw4jSMu7TueUxq8zgOm8OizErpQJL7tPz3j5futPzo3+GcB3VaXkT8LsgeyKttn6FfeG/DuEJXEbenPcLS4ystyuxvwpvATZ/DVXMgJMLcniNpMOts+PJeKMwt1dMZ9b5WSxuRsrPZjL+G1HdeRERE/qTivIjIafL1saXcufVxiik2jLsw8iymtXmSAFslLGA7i+HnF2FGaXvL/3FavkGif/MTEfmbEHsws2Kfo0+dboZx+a4Cbk17gGUnTH5fq0g2G3S7BcYkQew/zO1xueDXl2FaF9j5q+ktKzZ5X9cwWJHy6WXwNWT0rhURERGpWVScFxE5Db7PWMbILY9S5DIuzJ8b0ZvX2j5LkD3QosxK4eg2mH0ufP0AFBeY26PT8iJymoU6Qngr7kV61u5kGJfnzGdo6n2sylxvUWaniGgGQxbBlbMg2KA/xt8dSYU3+rq/LxfmGYbuPAD7jnhfV3FepHx6GvSdT9oO2aV7o4uIiIhUUyrOi4hYbOnxlQxPe4hCV5Fh3Fnh3Xkj9jmC7UEWZWaSywWrXoNpnWDHz+b26LS8iFQitRxhvBP/Ml1rGX8/ynHmMiT1HtZmGRwx9yebDboPg7FJ0PYic3tcLvc7mmb2gL2/ew1bttH7LYICoZuGwYqUi9ELXE4nrEm1LhcRERGpvFScFxGx0LITa7g17QHyXcYnzXvX6cKbsS8Sag+xKDOTTuyFdwbAZyOhINvcnma9dFpeRCqdOo5avBs/kfZhsYZxmcXZDE65i6Ts01hJi2gOQ7+CK16HYJNTWg9uhFd7wZL/QHHJF4OXGxTnu8VCcCV7XVikqmkYBS0beV83aislIiIiNYeK8yIiFlmVuZ6hqfeR58w3jOteuwNvx71EmCPUosxMWj8fpnaAtC/Nxf95Wn74LzotLyKVUmRAOPPjJ5MQ2towLqM4kxtSxpOcs9WizDyw2aDHcHcv+jYXmtvjLILv/gVvnAWHUk5aMirOn9G+HHmKyF+MTs8bvXtFREREag4V50VELLA2axNDUu8hx2ncYLRzrXa8GzeR2o5aFmVmQs4RWHAdvH8D5B4zt6dJd52WF5EqISowkvkJU2gT0sIw7mhRBtenjGNL7g6LMvMisgXcvBgufxWCapvbs3slTO8CyyaD00luPvye5j1cxXmRinGGwdmEZRvdXahERESkZlNxXkTEz5KyU7gx5S4yi43bwLQPi2Vu/ETCA0wWW6yQ+gVM6QBJC83F2x1w3hNw+zKdlheRKqN+YBQLEqYQE9zUMO5Q4VGuSx5Det5uizLzwmaDnrfDmA3Q+gJze4ry4Ivx8OaFJK3ZQZHBPPI+Ks6LVIg+HbyvHTgK6futy0VEREQqJxXnRUT8aHPOFq5PGc/x4kzDuPjQ1syPn0zdgAiLMvMhPxM+vd3dXz7L5G+O9dvB7cvh/H+DI9C/+YmIVLDGQQ1YmDCVZkEGTaKB/YWHGZQ8lt35+yzKzEDdGPcp+sumQaDJVmjbf6DTVx25peEcoOSx3Wb1oVmDCs1SpMbq6mN+w69J1uUiIiIilZOK8yIifrIlN53rk8dxrOi4YVybkBYsSJhCVGCkRZn5kL4UpnWG1a+bi7fZ4Mx74I7foGkP/+YmIuJHTYMbsTBhKo0C6xvG7SnYz6DksewrOGhRZgbsdug9Gu5cB837mNoS7MxkVvwwPk68kgaBB05aU0sbkYoTHAQ94r2vL1NxXkREpMZTcV5ExA+25e1iUPJYDhcZ92iPCW7KgoQp1A+MsigzA4V58OW9MPtcOLbd3J7IGLj1B/jHBPOnNkVEKrGWIU1ZmDDV5/flHfl7GJQ8loMFRyzKzId6sTD8J+j/X9PvXro8+jPWd+/A1dEf/nVNLW1EKpbR15SK8yIiIqLivIhIBduZv5dByWM4UHjYMK55UGMWJkylcVAl6B+w5zeY0R1+fdn8dLLuw2HMemjVz7+5iYhYrE1oCxbETyEqwPgdTdvydnJdyliOFJoclu1vdgec8xCMXAUNO5naUj/oMO8nXsvb8TcRGXBMJ+dFKphR3/n12yAzx7pcREREpPJRcV5EpALtyd/PoM1jfLY6aBzUgIXtptI02Li3sd8VF8IPT8FrZ8ChTeb21G4IN/0fXPk6BNfxb34iIqdJfFhr5idMJtJh/H0uNXc7N6SM99nCzFKNO8OolXDOw2Az9+P+4IZzWd+9I93si/2cnEjNYnRy3umElZuty0VEREQqHxXnRUQqyJ78AwxMHsOuAuMhgQ0Do1mYMJUWwU0sysyLg5vh9TPh+3+Ds8jcnvYDYUwSxA/wb24iIpVA+7BY5sVPoo6jlmHcxpw0BqfczYmiLIsyMyEgGPo/C8N/dre8MaFp8B6C5l0Mn4+Ggmw/JyhSMzSMgtYGP/KptY2IiEjNpuK8iEgF2FtwkEHJd7Ijf49hXHRAXRYkTKF1SHOLMvPA6YRfX4EZ3WDPanN7QuvCwHlw3QKoFe3f/EREKpHOtdvxbtxEwuzGczXWZW/mptS7ySyuZEXtFn1g9O/Qe4z5PStnuAeD7/jFf3mJ1CBGp+d/VXFeRESkRlNxXkSknPYVHGTg5tGk+yjM1w2IYH7CZGJDY6xJzJNj6fDmBfDl3VCUZ25P24thzAbodAPYbH5NT0SkMupRpyPvxE0gxB5sGPdbVhI3pdxNVmUr0AfVgsumkH/jN+zKb2Zuz9GtMOscWPwQFOX7Nz+Ras6o7/yKTe5zEyIiIlIzqTgvIlIO7sL8GJ+F+QhHHebHT6JdWFuLMjuFywVr5sC0TrB9ibk9QbXg8pkw9EsIb+rX9EREKrszwrsyJ/YFgm1BhnGrszYwuDIW6IGVBRfSefUG3j4w1NwGlxN+eh5m9oR9a/2bnEg1dqZBcT4jC5J3WpeLiIiIVC4qzouIlNH+gkMM3DyG7fm7DOPqOGoxL/4VOtSKtyizU2Tuh7lXwMfDID/T3J4WZ8Gd66DnSJ2WFxH5wzkRvXg99r8E2gIM41ZnbeCmlHsqXYH+1yQ4XhzJrSlvcfXGjzlYUN/cxgMb4NVe8OOzUGxyRomI/KVDK6ht0BlLrW1ERERqLhXnRUTK4EDBYQYm+y7M17aHMTf+FbrUTrQos1MkfQBTO0DK5+biHUFw8Qtw248Q1ca/uYmIVEEXRJ7JzLb/IcDmMIxblbWeISn3VqoC/dJ1//vfnx65kk6/JfHx4avMbS4uhG8fhVlnw5E0/yQoUk05HNDb4EdBDYUVERGpuVScFxEpJXdh/k625Rm/B/nPwnz32gbvZfaX3GPw/k2wYCDkHDG3p1EXGLUa+t4PduOik4hITXZJ3X5MbfMkDoy/V67MWseQlHvJLs6xKDPviotLns49VNiAazd9yC3Jb5FvDzd3o13L3cNil09Vo2yRUjBqbbNso3V5iIiISOWi4ryISCkcLDjCoOQxbPVRmK9lD+Pd+In0qNPRosz+ZstimNoR1s81F2+zQ7/HYOQKaHQa8hURqYL+GXUBU9s8Ya5An3r6C/Trt8IJj4f4bbxzcChbr06CNheau1lhLiwaC29fDMeN30EmIm5GQ2FTdsKhDOtyERERkcpDxXkREZMOFR5lUPIYtuTtMIyrZQ9jbvzL9KzTyaLM/lCQDZ+PhrcuhhPGA2r/Ui8ORvwKFz4NAcZDDkVE5GSX17uQKSYK9Csy1zL0NBfof17vfS2iNsR3aA5Dv4bLpkKgQXPsv9v6rfvF4LXvuAePi4hXvdsZj/Ex+hoVERGR6kvFeRERE/4szKflpRvGhdlDeTf+ZXrW6WxNYn/a+au7zcDKGeb3nDEWRv8OzXv7Ly8RkWruij8K9HYfP1Yv/6NAn1Oca1FmJ1tqUPg7q6O7JzZ2O/S+0z0QvPkZ5m6cdxw+HArvXQPZhyokV5HqKLIOdGztff3vMyFERESk5lBxXkTEh8OFR7kueSypudsN4/4szPeysjBflA+LH4I3zoajW83tiWgOt3wLAyZDUJh/8xMRqQGuqHchU00W6Iek3mN5gd7lMj6Ve/apb/SqFwu3/QQXPguOQHNPsvljmNIBNn9a5jxFqrsSX2t/85OK8yIiIjWSivMiIgaOFB7juuSxpORuM4wLtYfwTtwEetfpYlFmwL51MLMn/PQ8uEwO5et6M4zZAG0u8G9uIiI1zBX1+psu0Ft9gj5tNxw85n3dY8HQEQD9HoaRq6ChyXkk2Qdh3pXw0a3uE/UicpJzDH5MXLsFMjKty0VEREQqBxXnRUS8OFR4lIHJY0g2WZg/I7yrNYkVF8GP/4VXe8KBDeb21KoPN3wMV78JIRF+TU9EpKa6ol5/prT5t88C/bLM3xmaep9lBXqjdhmhwdA93mBz484wahWc/aB7gLgZv78JUzvBth9Kk6ZItWd0ct7lgl+SrMtFREREKgcV50VEPDhQcJhrN4/2eWI+xB7MW3Ev0Se8mzWJHUmDWWfDt49AcaG5Pe2ugjFJkHilf3MTERGurHcRk1s/bqJAv8ayAr1RS5szEiHIV+eagGC46Dm4bSlEtTH3pMd3wpzzYdF4KDh9g3BFKpOGUZDQwvv60rXW5SIiIiKVg4rzIiKn2FtwkGs2j2ZL3g7DuD8L82eFd/d/Uk4nrJgG07rAruXm9gSHw9VvwQ0fQu0G/s1PRET+clX0xUwyWaC/KfUesoqz/ZqPUS/rvgYneUtoeRaMXgu97jC/Z/lkmNENdq8sxROJVF9nG4wm+snghTQRERGpngJO55M7nU5+/PFHFi9eTFJSEnv27CEvL4+QkBCaNm1Kx44d6d+/P/369cNut+51hO3bt/Ppp5+ycuVK0tPTOXHiBHa7nXr16hEbG0vfvn25/PLLiYyMLNP9Dx8+zNdff82vv/7Kli1bOHjwIPn5+YSGhtKoUSPi4+M5++yzueSSS/h/9u47PKpq6+P4dzLpEAi995oAAooURaoUFZAmqAjSpRe7gr1dVJr0JggqCqiAIqKioCgCgiCQBKSEGmoogfSZef+YV6/cTDmTZIaU3+d5eO7l7L3PWUFC5qyzztohISGGzvnpp5+yY8eOTMXzb9WrV2fYsGFZPo9IbnUq5QwPxIziWMopl/OCTYEsqfEOzQs18n5QV07AFwPh8PfG11RtC90X2zd/FRERn+tevAM2bIw78hpWnO8Lsi1hNw/FjOOjWlMp5F8w2+M4cQ5izzgfd5UodCioIHSeDbXvt/9sSjjtfs2FA7DgDmjxPLR6wfgmsyJ5UIv6sOBLx2O/H4BriVAw1LcxiYiIyM1jstlstptx4R07dvDSSy9x+PBht3OrVavGq6++SqNG3k2CXbp0ibfeeou1a9fi7o8lJCSExx57jKFDh2I2mw2d//z580ybNo01a9aQlua+HUXhwoUZNmwY/fr1w9/f9XOUZ599li+++MJQHK50796dt956K8vn+bfIyEgsFssNx8xmM1FRUdl6HZGsOpESxwPRIzmRGudyXrApkMU136FF4cbeDchmgz0fwrrRxjfWCwiB9m9D4xHgw4eaIiLi2GcXvnGboAeoXyCCj2tNI9y/ULZe/+PvoO/rjsf8zRC/DgoYqwXJKOkSfDUa/vzI+Jqyt0L3pVCqTiYvKpK7nTwHlR5wPv7Nu9Dudt/FIyIiIs75Iqd5UzI3H3/8MY8++qihxDzA4cOH6devH8uXL/daTLGxsfTo0YM1a9a4TcwDJCUlMW3aNIYMGUJSkvteod999x333nsvq1atMpSYB7hy5QqTJk1iwIABXL161dCarKpSpYpPriOS08Qmn6R79HD3iXm/IJbUfNf7ifnr5+GTnvBZP+OJ+QpN7e0Gmo5SYl5EJIfoUbwj06q+gAmTy3l7rkfTK2YU8WmXs/X6rvrN31YrC4l5gJAi8MCH0HslhBYztub0Lph7G/wyGawW9/NF8pjyJaFqWefjrjZwFhERkbzH59mbdevW8eqrr2Z46uCOxWLhlVde4euvv872mOLj4+nfvz+nTrluY+HIL7/8wrhx41wm9D/44ANGjx6d6QT79u3bGTZsGOnp6Zla7wkl5yU/Opx0nB7RwzmdetblvFC/EJbVnMJdhb1czhS9BmbUhajPjc03B8Ddb8Cgn6F4Te/GJiIiHvs7Qe+uB/3+xL/oFTOKC2nx2XZtVz2sPeo370rdnvaNx2t1MjY/PQW+eRLebw3xrjdeF8mL7nLxvedqjwgRERHJe3yanD979iwTJ050mMg2mUzUq1eP9u3bU69ePUymjNVFNpuNCRMmcPas6wSap1588UXi4hxXy5YpU4Y2bdpw5513EhYW5nDOpk2bWLZsmcOxlStX8uabbzr9mmvXrk3btm1p27YtZcqUcRrjzp07WbBggYGvJmsqV67s9WuI5CR/JcXSM2YEZ9IuuJxXwC+Uj2pN5Y5Ct3ovmOQr8PkA+LgrXD9nbE3JuvDYdmj5PJhv6jYiIiLiQs/i9zCj2kuYcd0OMTrpMD2jR3I21fXPJSPOXYKoWOfjrhKEHgsrDX3WQtdFEGiwd/6xn2HWLfD7AnsrN5F8okUD52PboiEpxXexiIiIyM3l00zOzJkzSUxMzHC8XLlyzJo1i4iIiH+ORUdHM3LkyAzV7ImJicyaNYtXX301W2LatWsX3333XYbjfn5+PPvsszzyyCP/9JRPSEjgtddeY82aNRnmz549m+7du1Ow4I03IzabjYCAgBta2ZhMJnr37s3gwYOpUKHCDXO//PJLXnrpJYd/TosWLaJv374ZruHK0qVLadKkieH5IvlJTOJheseM5kL6JZfzwswF+LDmVBqF1fNeMEd+hM/7w5Xjxuab/KD5U9DmFfAP8l5cIiKSbboWa4+/yZ+Rh18k3eb8LdK/kmPpET2CFREzKRtYMtPX+/EP1+N3ZvePNZMJbhsIVVvbHzbHbna/JvU6rBkK0auh60IIc16sIpJXtHCxEXNqGmyLglYNfRePiIiI3Dw+q5w/duwYn3+esUVDQEAAixYtuiExDxAREcGCBQsICAjIsOazzz7j+HGDCSw3pk6d6vD4yJEjefTRR2/Y7DUsLIxJkybRuHHGXtOXLl1iyZIlGY736tWLRYsWER4eDti/3ilTpvDKK6/ckJgHe9K+S5cuvP322w5jSkhI4IcffjD6pYmIC/sT/+KBmFFuE/OFzWEsrzXde4n51ERYNxYWtzGemC9aDQb9BO3/o8S8iEgu06loG+ZXf5MAk+samaMpJ+gZPYKTKa73QnHlx13OxxpUh6LZu/fsfxWpAgN+gHumGP85dfBre0u3vSu8FJRIzlGlDJQv4XxcrW1ERETyD58l57/88kuHPdN79+7ttM95tWrV6NmzZ4bj6enpfPnll1mO6dSpU2zfvj3D8SJFijBkyBCHa0wmE0899ZTDsdWrVzs83qRJE1auXEmNGjV45513uPfee13G1a5dO2rVquVwbNcuF3dZImLIn9dj6BU9ivh015vuhZvD+LT2DBoWrOOdQE5uhzm3wm/vGV/TeLh909dKd3onJhER8boORVqwqMYkgkyBLucdSzlFj+gRHEv2fF8kgB9cfGxs7cUubYB9Y/I7xsPwXVD2NmNrkuJhRW9Y8RAkZl/ffZGcxmSCu1xUz29y89aLiIiI5B0+S847ah0D0KmT642jOnfu7NH5siOm9u3bExTkvMrnlltucdib/cSJE8TExDhcU7FiRVavXs0999xjKLbbbnN8E3P69GlD60XEsR0Je+gVM4rLFtcbNBfxL8yKiFnUK+D4QVmWpKfCxhdhwR1w4YCxNWFlod830Hk2BBlvbSUiIjlT2/A7WFLzHYLdJOhPpp6hR8wIjiSf8Oj8x87AYRc5/TYG8+VZVjIShm6F1i+Dn+t++//Y+wnMrAsH13s1NJGbyVVrm1/3Q2Ky72IRERGRm8cnyfm4uDiHSeuQkBDq13fxqQRo0KABwcHBGY5HR0c73cTVqB9//NHh8aZNm7pd66yP+8aNG52u8fc33uK/UCHH7xk7evtARIz56cp2HjowjgTLdZfzivsXYVXtWdQJrZH9QZzdD/ObwqbXwOq83/ANbnkYRu+DGh2yPx4REblpWhRuzNJaUwjxy/hZ99/iUs/RI3o4MYmHDZ/bVdW8vzmbN4N1xxwAbV6Cob9BiQj38wES4mDZvbB2GKRc8258IjeBq7dXUtPgl72+i0VERERuHp8k56Oiohwer1GjBn5+rkMwm83UqOE4QRYdHZ2luJxVuTtrKWNkjrNzeur6dcfJw7CwsGw5v0h+8+2ln3n04JMkWV2XIZUMKMaqiNnUDq2WvQFY0uGn/9jb2MQZfFc5tBj0XgkPfAQhRbI3HhERyRHuLHQbH9WaSgG/UJfzzqVdpEf0CPZcM/b511VyvnEEhLm+nHeUawTDd9rb3ZhMxtbsmAez6sOxLd6NTcTHqpeDiqWcj2/c6btYRERE5ObxSXL+8GHHVT6VKlUytN7ZPGfnNeL8+fNcvpyx37TJZKJixYpu1ztqa5PVmP7t0KFDHl33ZoqMjHT5y2IxWB0s4iWrL37L4L+eI9WW5nJe6YDirKw9ixohlbM3gHPRsPBO+O45sKQaW1OrE4zaB3Uz7rshIiJ5S5OwBnxcaxph5gIu5122XKVXzCh+u+r6Ia/N5noz2Dbe7jfvSkCIfaPYAT9AuLF7AS4dgUUtYMPTkKZeH5I3mEzQ1kV7KSXnRURE8oebmpwvXry4ofXO5mUlEe5sbeHChQkICHC7vlixYg6PHz9+nLQ01wlAdxITE51u/HrrrZ7dTdlsNjZt2sQzzzxDhw4dqF+/PvXq1aN169aMHj2ar776KsutciwWi8tfIjfTx+fWMurwy1hw/XexbGApVkXMpnqIwUSBEVYLbHkX5jS0b/5qRGBB6LoI+qyFsNLZF4uIiORojcLq8WmtGRQ2u35L8po1kT4HxvHj5a1O58Qcg7iLzs/h9c1gjajSCkb+CbcNMjbfZoMt78Dc2+GUspaSN7hKzv/xF1y84rtYRERE5ObwSXL+/PnzDo8XLVrU0HpniXBn572ZMaWlpTmsyPfEV199RUpKSobjBQsWdNrr3pmxY8fy2GOPsXr1amJjY0lOTiY1NZXTp0/z7bff8sQTT3Dffffx+++/ZylmkZxowZlPeCr2LWzYXM6rHFSOLyLmUCW4QvZd/MJBWHgXbHgK0jN+PzsOpCWM2gu3DTT+ur+IiOQZ9QtGsKL2DIr4F3Y5L9mWyoC/nmZdvOP9k1y1tAkOhGZ1shJlNgouBF0X2h9IF3TR3+Pfzu2D+U3g+xfsG6yL5GKu3mKx2eBHg50QRUREJPfySXI+MTHR4fGQkBBD64OCgjw6ry9icrRJrbtzG5GamsrcuXMdjnXt2tXldR0x8qAgNjaW/v37s379eo/OLZJT2Ww2pp9ewsvHp7udWyukKp9HzKV8UJnsubjVAr9OtffHPeG8bQtbCwAAIABJREFUqvEG/kFwz1T7K/5FKmdPHCIikivVLVCLVbVnUTLAcSHI39Js6Qw7NJEV59dlGHOVnL+zHgQFZjXKbFa7s72VWx2DrdysFtj8OsxtBKddfLEiOVypolCvqvNxtbYRERHJ+3ySnHe2uamR9jEAgYGO7yCcndcIZwn0rMYEWYtr7ty5nDp1KsPxoKAghg4dmunzupOWlsbTTz/tdPNekdzCZrPx1sk5vH1yntu5t4TWZlXtWZQKNNZiy62Lh+D9VrD+cUg32BO3XCMY/gfcMQ7cbJAtIiL5Q+3QanwWMYdyga7bm1mxMv7o6yw+u/KfYxYLbHJRbeuqjcZNVaA49F4BPT+C4HBja87uhXmNVUUvuZr6zouIiORvPskEJSc7TlIZTYQ7m5eUlJTpmJytzWpMkPnK+b179zJ//nyHY8OHD6dUKYOv+/5LcHAwt912Gx07dqR169aUL1/e6dzU1FRefvllj69hNptd/hLxFavNysRjk5kVt8zt3MYF6/Np7RkUDTCYAHB5YSv8NgNm3QLHthhbYw6Au9+AIVuhZETWYxARkTylanAFvoiYS9Xgim7nTjw2hRmnPwBg9yG4fM353Ju6Gaw7JhPUfxhG74Pq7Y2tURW95HJtGzkfO3wKYuN8F4uIiIj4nr9PLuLv+DJWq9XQemebirqqXs9sTEY3MHW1iWpm4rpy5Qrjxo1zuJlsnTp1GDx4sMv1NWvWpHnz5v/8PiAggPbt29OxY0dCQ0NvmLt161aefvppzp07l+E8e/bsYdu2bR71tndXbR8ZGamNYcXrUq1pPH70db64+K3buS0KNWZRjf8QajbWxsql+CPwxUCI3Wx8TZmG0H0JlL4l69cXEZE8q1xQKT6PmMNDMWOJTjrkcu5/Ts7lmuU65p3DAcf7lhQuCLfW9EKg2a1QOej3DeyYC988CWkGCl/+rqJv8Ty0nAj+Oa13j4hjLW4BfzOkO7ld2rgLBt3n25hERETEd3ySnP/f5PDfUlONvX7qKGHt6rxGFChQwCsxuTq3MxaLhccff5yTJ09mGAsLC2Pq1KluK/oHDhzIwIEDDV2vWbNmLF68mG7dujn8etetW+fxxrMiN1OiJYmhh57nxyu/uZ3bIbwFc6q/RpBfFm/arVb4fZ59w9dUg62s/Pyh1QvQ4jl75byIiIgbJQKKsipiFo8ceJw/ru93OXdm3DKK2K6B6QmwZXx7sWUDyDUvNZpM0Hg4VGsHXwww9maa1QKbXoPoNfaH4GUbej1MkawqGApNI2HLXsfjG39Xcl5ERCQv80lbG2dJdFcJbiPzspKc91ZMrs7tzJtvvsmWLRlvOPz8/Jg8eTKVKlXy6HxGVK9enU6dOjkc27ZtW7ZfT8RbLqVf4cEDYwwl5rsVa8+86m9kPTF/+Rh80B6+HGE8MV/qFhi2A1q/qMS8iIh4JNy/EJ/Uns4dYe4bxl+q8QXhfV4Ac8YCjDa5MVddrDoM3Az3ToMAg2+8nf3TXkW/8SX1opdcwVVrmx922WtCREREJG/ySXK+UKFCDo9fvnzZ0Hpn85yd14iwsDCHx69cuWJovat5zs7tyOLFi/nwww8djj3//PO0bNnS8Lk8dddddzk8fvLkScMPKURuprjUc/SIHsHOa/vczu1T4n6mV32RAL8svDBktcKOeTCjLhzZaGyNn9leLT9sB5RpkPlri4hIvlbQXIBltSZzd/idbucG1/+RIoMexxR04wPkdrd7Kzov8/ODZmNh5B6o1Nz9fABrOmx61Z6kj9vt3fhEssjVprDnL8PeI76LRURERHzLJ8l5Z5XfFy9eNLTe2bysVJRXrlzZo2v9rwsXLjg8Xrx4cQoWLGjoHOvWrWPSpEkOx4YOHUrfvn0NnSezqlat6vB4eno6CQkJXr22SFYdTjpO16jHOJDk/m5lSKkHmVT5GcymLLzLH38YlrSFtcMg1cVOe/9Wsg4M3QZtX1XvWxERybJgvyAWVv8PXYre7XZuUI2dFB02Er8C8QBULg213O8tm7MVqwEDN8E9U41X0Z/ZA3NvVxW95GiNI6Cgi7/S3273XSwiIiLiWz5JzlevXt3h8TNnzhhaf/bsWY/Oa0SlSpUc9nFPS0szlKB3FlO1atUMXf/XX3/lmWeewWazZRjr1asXTzzxhKHzZEVIiPNPgKqcl5zsz+sxdIsexslU9/+GPFVuKC9VHIPJ5HhzPLesFvh1KsysB0c3GVtj8rP3lR++E8q5b0EgIiJiVICfPzOrvcxDJTq7n1v+AEVHPoa5yGk6NLa3cc/1/Mxwxzh7FX1F928RAKqilxwvwB9auHjBcr26joqIiORZNzU5f/DgQUPrDxw44PC4s8pvIwICApxW3huJy1lMRpLzf/75JyNHjnSYAL/vvvt45ZVX3J4jO1y/7rxXtqeb2or4yi9Xd/JA9Egupl9yOc+EibcqP8W4cgMyn5g/FwULm8P6xyEtydia4rVh6FZo9yb4B2XuuiIiIi6YTWbeqfwcQ0s/5Hauf4mTFB35GHWbHvJBZD5UrAYM2gz3TAH/YGNr/q6i/34ipCV7Nz4RD3Vs7Hxsy164YvDFTREREcldfJKcj4iIcNgf/syZM07bw/ztwoULDqvUCxcuTGRkZJbiatKkicPj+/a571+9f/9+h8ebNm3qct3hw4cZOnQoiYmJGcbat2/P22+/jZ+fT/6zcOSI43Yg4eHhhlvziPjS1/GbeOTAeK5ZM37//FuAyZ/Z1V6lX8numbuQJQ02vwmzG8IJ9xvNAvZyxOZPwYg/oLyLuysREZFsYDKZeLHCaCZUGOl2rrnwBd4LGs62hDxWNe5nhjvGw6g/Paui3/yG/Wf8sV+8G5+IB+5xcRtpscB3v/suFhEREfEdn2SBAwICnG5s+v3337tc+9133zk83qpVK/z9M27saLVa+eGHH5g3bx6fffaZy41b777bcb9OdzElJCTw228ZE3ZBQUFON1kFOH36NIMGDeLSpYwVv23atGHKlCkOvyajUlM966P5008/OTxet27dTMcg4i0fnVvDY4cmkGpz3XIp1C+EpTUn06WY+368DsXthrmN4fsJYDH4PVWsBgzeAh3ehgCD1XsiIiJZZDKZGFHmESZXeR5srj/WJ1iv8XDMWL699LOPovOhzFTRX4iBRXfBujGQopJkufmqlnW9L8Q3am0jIiKSJ/mmRBt7VbgjH330ERaLxeGYxWLh448/Nny+a9eu0adPH4YPH86UKVN4/vnn6dChA3/88YfDczRu3Jjw8PAMx/fs2cOePXucfSksX77cYUua5s2bExoa6nDNhQsXGDBgAHFxcRnG2rRpw/Tp0x32wDfqxIkTdO7cmaVLlxqaf+jQIb7++muHY+3atct0HCLZzWaz8e7JBTwd+x+sWF3OLeJfmBW1Z9CicCYq19NT7K+5z70dzhisLDT5wR2Pw4jdUPEOz68pIiKSDe4v1JlrH/4HW5rrzceTbakM/us5Pj3/lY8i86G/q+hH7jH+M9lmg99mwIw68NcG78YnYsA9jl/sBmD9b/a/siIiIpK3+Cw537ZtW4c94g8ePMj06dMdrpk+fbrD/u/VqlWjdevWGY6/+eab7Nq164Zjly5dYuTIkVy7lrEixt/fn/79+2c4brPZmDhxosMK93379jFnzhyH8Q4ePNjh8StXrjBw4EBiY2MzjN1999289957BAa6vply5eDBgzz00EPExsbyxhtv8NxzzxEfH+90/vHjxxkxYoTDBwzh4eF07ux+gzERX0izpvPE0TeYevp9t3PLBJbki4i5NCxYx/MLndgGs2+1v+ZuTTe2pkQkDPkV7pkMgY4fyomIiPjCT3vg2p93Eb9gOtYk160JLVh4/OgbTD+9BFtezPQVrwmDfoKOk41X0V85Dks7wuf9IdH5Z2gRb3PV2uZMPOz+y3exiIiIiG+YbD78VL5hwwbGjBnjcKxt27b07t2bMmXKEBcXx4oVK5y2l5kxY0aGyvnU1FQaNWpESkqKwzWTJk2ia9euGY4nJibSrl07h73vy5cvz9ChQ6lXrx4pKSn88ssvvP/++w43Um3dujVz5851eO3evXuze3fGSlyz2Uz79u09bmXTo0cPmjVrBsCuXbsYNmxYhvY9YWFhtG/fnpYtW1K2bFmCg4OJi4vjp59+YuXKlSQnO94E68UXX6RPnz4exeNOZGRkhrcjzGYzUVFR2XodyVuuWa7z2KEJbLri/h3e6sGV+LjWdMoFlfLsIqmJsPEF2DoNbK6r8v/h5w93PQutJmrDVxERyRHGz4D3Vtn/v3+ZQxQZPA5zoYtu1z1cogtvVX4Kf1Pm2yrmaBcOwhcD4PivxtcUKAmdZkGdHvb9ZER8KCUVSnSB60mOx18bDM/39W1MIiIi+Zkvcpo+Tc6Dvbr8558z3+uyRYsWLFiwIMPxs2fP0qJFC6frxo8fz7BhwxyOrVu3jscffzzTMRUoUIDPP/+cypUrOxyvVatWps/tyPz58//p4b9582bGjh1LUpKTT3AeaNOmDbNnz8aUzTciSs6Lp86lXqTfwSfYm3jA7dwGBSJYVnMKRQMytqhy6fD3sOYxuOR4Y2SHyjSEbu9DmQaeXUtERMSLIvvCgeP//b25yGmKDBmLf4mTbte2KdyMudVfp4A5j74FZrXA9tnw3XOQmrHAxqmIbtB5FoSV8V5sIg50mwBrtzgeu6Mu/DzLt/GIiIjkZ77Iafqsrc3f3n33XWrWrJmptTVr1uSdd95xOFakSBGCg52/ulq2bFmnY/fdd5/TxL07AQEBTJ061Wli3hsqVvzvTkEtW7Zk8eLFDnvne6JVq1ZMnTo12xPzIp46lHSMLlFDDCXm7yp0O5/WnuFZYv76BfjsUVjSznhi3hwId78Jj21TYl5ERHKUo3E3JuYBLJfKEj9rHmkn3ReI/HBlKz2iR3Au1X2lfa7kZ4amo2HUPqjmwb5K0V/AexGw8301+hafctV3/rcouHjF+biIiIjkPj5PzoeHh7N06VKaN2/u0brmzZuzbNkyp0nowMBAunXr5nCsRIkStG3b1uX5x48fz7PPPuvRpqzFihVjwYIF/1Sx+4LZbKZ8+fI3HGvYsCFr167l3nvv9fh8wcHBjB8/njlz5rh8uCHiCzsS9nB/1FBOpGbcOPl/dS/WgaU1J1PQXMDYyW022P2h/UZ7t7GNkwGo0My+4WvL58Cc+U2bRUREvOGrXxwft14vypUFs2gSepvbc+xNPEDnqMH8lRSbvcHlJEUqw6MboNtiCDb4UD/5CqweBB+0h/jDXg1P5G+u+s5brfDtDt/FIiIiIt7n8+Q82KvcFy1axJQpU6hTx/XmjXXq1GHKlCksWrTIbXX4008//U8v9r8VLVqUWbNmUaCA+wTegAEDWLNmDZ06dXK5QWt4eDhDhgxh/fr1Ga7nbWXKlHH4AKFUqVJMnTqVpUuX0qZNG7cPGcqUKcPgwYPZsGEDw4YNw8/vpvxVEPnH1/Gb6B0zhsuWq27njirTl+lVXyTQz2CyPP6IfaO3z/pCYsb9JRwKCIV7psLgn6FkhLE1IiIiPrbWSXIeoGm1AiyPnELnoq6LVABOpp6ha9RQtiVk3CcpzzCZ4Nb+MCYaInsYX3f4e5hRFza/BZY0r4UnAlChJNSt4nz8G/fbMYmIiEgu4vOe846cOnWKvXv3cvr0aZKSkggJCaFs2bLccsstLtvROGKz2fjpp5+IiYmhePHitGvXjkKFCnkcU2JiIrt37+bo0aNcvXoVs9lMkSJFqFWrFnXq1MFsNnt8Tl9KSkpi3759xMbGcunSJdLT0wkNDaVUqVLUqlWLqlWr+iwW9ZwXV2w2G/PPfMJrJ2Zgw/U/RyZMvF7pcfqX6mns5JZ0+2avP7wIaR7sy1ClNXRdCEV9930iIiLiqfirULor/M/HrH9MGgZPPgRWm5U3Tsxi7pmP3Z4z0BTA9Kov0qXY3dkcbQ60/zP4aiRcO2t8Tcm6cP98qOjbAh3JX56dC+8sdzxWvDCc/gJy+O2oiIhInpAnN4SV/EfJeXEmzZrOxGOT+fD8ardzg02BzKr+Kh2LGGwjdWonrBkCcX8YDygoDDq8C42G2KvrREREcrAPv4VH33A+Hv0h1Kzw398vPruSF45NdfswHGBChZEML90n7+9HlBgPG56EXYuNrzGZ4PZh0O4tCC7svdgk39q8G9qMdT7+43vQor7v4hEREcmv8uSGsCIiAFfTr/HowScNJeaL+Bfm09ozjSXmU6/D+idgXmPPEvO1u8DoKLh9qBLzIiKSK6zd4nysdsUbE/MAA0o9wILqbxJsct6+8W9vnJjFk0ffJNWax9u4hBaFbu/b+9GHVzK2xmaD7XPs+9jsW6UNYyXb3VEXChd0Pr76Z9/FIiIiIt6l5LyI+NzxlNPcHz2UzVfdN82sGFSWNRHzaRRWz/2JD66HGXXg1ylgsxoLJqwMPPgZPLwaCpd3P19ERCQHSEmFDdudj3dp7vj4PUVb8WntmRTxd1/x/cmFr3j4wDgupV/JZJS5SPX2MGofNB1j/CF9Qhx8+gB82BkuH/NufJKvBPjDfS46J63+Wc+ERERE8gol50XEp35P2Eun/YM5mHTU7dxbQmuzNnIB1UIqup549RR88gAsu9ezm+Pbh9mr5et0V7W8iIjkKj/sgmsutlPpcqfzsUZh9VgbuYDKQeXcXmdrwi467x/C4aTjmYgylwkqCPdNh8FboESk8XUH19mLA36ZYt/vRiQb3O/kARvAsTOw55DvYhERERHvUXJeRHxmzcXv6RUziovpl9zObVP4DlZFzKJEQFHnkyzp8OtUmF4b9q8yHkiJCBj8M3SZAyHhxteJiIjkEGtctLQpVRSauMktVw2uwJrI+TQs4D4JfTTlBF2iBvPL1Z0eRplLVbwDRvwBbV8H/yBja1KvwzdPwPwm9n1vRLKoY2MIctGBSq1tRERE8gYl50XE62w2G9NOLWbE4RdIsaW6nT+oVC8W15xEAXOo80nHt8LcRrD+cUi9ZiwQcyC0ecV+w13JRTmSiIhIDma1wpe/OB/vfAf4GfiUXzygKCtrz6JDeAu3cy9bEnj4wFiWn1/rQaS5mH8gtJoAo/ZC1TbG153eZd/3Zt0YSM4H7YDEawqGQrtGzseVnBcREckblJwXEa9KsiYz5sirvHNqvtu5fvjxeqXHebXSePxN/o4nJcbDmqGw4A44s8d4IJWaw4jd0PpF41VwIiIiOdCOGDgT73zcWb95R0LMwSyo8SaPlX7I7dx0m4Unj77F68dnYrFZjF8kNytWA/p/D90/gNBixtbYrPDbDJheC3Z/qObgkmld73I+tvcIHD7lu1hERETEO5ScFxGviUs9R8/oEXx+8Ru3cwv4hbKk5jsMKPWA4wk2G+xaYr/R/X2B8SCCC0OXeTBwM5SMML5OREQkh1rroqVNgRBoe6tn5zObzLxYcQxvV34Wf5PZ7fw5Zz5iyF/Pc81y3bML5VYmEzTsB2NioOGjxtddOwuf9YX3W8PZ/d6LT/KsTm7egnHV3kpERERyByXnRcQrdl7bx737B7L7erTbuWUDS7E6ch5tw+9wPOHsfljUEr4YAIkXjAdRpyeMiYbbhxp7v19ERCQXcJWQ69AYgjP5glifkvfzYc2pFDaHuZ274fJPdIkaSmzyycxdLDcqUBy6L4EBG+0V9UbFbobZDWDD05BisBWfCFAiHO66xfm4WtuIiIjkfspWiUi2+/T8OnpGj+Bc2kW3cxsUiGBd5CIiQ6tnHEy9Dhuesd/QHvPg7qNIFej7NTy4EsLKeBC5iIhIzrb/KEQfcz7e5c6snf+uwrezNnI+lYPKuZ17IOkI9+0fyE9XtmftorlN1TYw8k9oORHMAcbWWNNhyzvwXgTsW6VWN2KYq9Y2v+6Dsy5aXImIiEjOp+S8iGSbdFs6Lx2bxuNHXyfVluZ2/r1FWrOq9mxKBv5PD1ebzX7j+l4kbHnbfkNrhDnAfqM8ej/UvCcTX4GIiEjOtvJH52NmM9zXLOvXqB5SmS8jF9E0rIHbuZctCfQ5MJ65cR9jy08J54BguPs1+342FT14InL1JHz6ACztCBf/8l58kme4euBms6m1jYiISG6n5LyIZItL6Vfoe+AJFp791ND8kWX6Mq/664SYg28cOLsfltxtv3G9ctx4AFVa26vY7n4NAkI8iFxERCR3sNlg1Sbn421uhaKFsudaRQMKs7zWe/Qqfp/buVasvHZiBmOOvEqSNTl7AsgtSkbCoJ/g/gXGN4wFOPQtzKgL378AaUnei09yvcploKGLLkqu/k0QERGRnE/JeRHJsoNJR+m0fxA/XXX/WnuwKZAZVV/m+Qoj8DP965+g5Cvw9XiYXR+O/GD84gVLQc8P7f1fS9TORPQiIiK5g7uWNj1bZe/1Av0CmFJlAs+XH2Fo/ucXv6F71HBOpZzN3kByOj8/aDQYxh6ARkOMr7OkwubX7W8KRq9RqxtxylVrmx//gDPuO0mKiIhIDqXkvIhkybr4H+m8fzCxKafczi0dUJzPIubQvXiH/x60WmHXEphWE7ZOA6vF2IVNJmgyEsbEQP0+9t+LiIjkYe5a2nRzkcDLLJPJxMiyfVlc420K+oW6nf9nYgz37h/A9oQ92R9MThdaDO6fD0N/gzINja+7HAsfd4UPOsC5aK+FJ7lXj5bOx6xW1/82iIiISM6m5LyIZEq6LZ3Xj89k6KHnuWZNdDv/1gJ1+brOYhoUjPzvwVO/w8I74YsBcP2c8YuXvQ2GboNOMyEkPBPRi4iI5C7uWtq0vQ2KFfbe9dsXuYsv6yykclB5t3MvpF+iV8wolpz9LH/1of9bhSYwbIf9c0qwB/9RDn8Hs+rB1+Mg6bL34pNcJ6Iy1K/ufPyTjT4LRURERLKZkvMi4rHzafE8GDOWOWc+MjS/d/H7WBUxi1KBxe0Hrp+H1UNgXmM48ZvxCwcVgvtmwGPboPztmYhcREQkd9p3BGJcbMXyQCvvx1AzpArr6iyiVeEmbuem2dKZcOxdxhx5hURLPuyp7me2v+E39gDU72t8ndUCW6fDtBrw+wLjbxRKnte7jfOx36LgaJzvYhEREZHso+S8iHjk94S9dNz3KFsTdrmda8bMKxXHMbnKBIL8AsGSBr/NtLew2bnQs96q9R+BsTHQdJT9hldERCQfWe6iMtbf7LondXYK9y/E0pqTGV66j6H5n1/cQKeowRxKctEsPy8rWAp6LoVBm6FkHePrEi/AmqEw93Y49ov34pNcw1VyHmCFB1s2iYiISM6h5LyIGGKz2Vh8diU9Y0ZwJu2C2/nh5jA+rDWFwaV7YzKZ4OB6mHkLrBsNyR68ql26AQzeAj2XQViZLHwFIiIiuZPVCh9/53y87W1QtJDv4jGbzEysOIqZVV8m2BTodv6BpCPct38g6+LzcWPsyi1gxB/Q4R0ILGB8XdwfsLA5rOwDV056Lz7J8SqXgWYunu+otY2IiEjupOS8iLiVaElizJFXmHhsCmm2dLfza4ZU4as6i2hRuDGc3Q8fdIRl98KFGOMXDSkKnefA8N+h0p1ZiF5ERCR327IXTrjYmqV3W9/F8m/dindgdeR8ygaWcjv3mjWRoYee55Xj75Fmdf9ZIk8yB0DzJ+2b2dft5dnaPz+G6bVg0xuQlg/bBAkAD7r4Xv/zMETF+iwUERERySZKzouIS0eST9A5ajCfX9xgaP79RdvxVeRCqliC4csRMLs+HDK2FgCTCW4fBuMOQuNhamEjIiL5nquq+ZAg6OajljaO1CtQi/V13qdpWAND8+efWU6vmFGcST3v5chysMLlofenMHATlK5vfF1aImycCNNrw56P7a9USL7SsxX4ubiDV/W8iIhI7qPkvIg4tfrit3Tc15+YpCNu5/qbzLxacTyzKj1Pga2z7RuZbZ/j2UZmFe+EYTuhyxwILZaFyEVERPKG1DRYtcn5eOc7oZAHXVK8oXhAUT6pNYMhpR40NH/7tT102Pcov151v39NnlalJQzfaX9TMKSo8XVXjsOqPjC/KRzb4r34JMcpXQxaN3Q+/slGz7Z0EhERkZtPyXkRySDJksxTR99i5OGXuG5NdDu/VEBxVtaayaD4AEwz68KGpyD5ivELFiwNPZbB4J+hrIs7DhERkXxm/Ta4lOB8/OG7fReLKwF+/rxcaSxzq79OAb9Qt/MvpF+id8xopp5ahMXmwYP8vMbPbH9TcNxf0MTDTe9P7YCFd8HyHnDxkPdilBzFVRurw6dge7TvYhEREZGsU3JeRG7wV1IsnaIG8fH5tYbmNw1rwDfFn6DxqvGwvDvEHzZ+MXMA3PkkjD0ADR6xt7QRERGRf3z0rfOxooWgQ2PfxWJE56Jt+brO+9QMqeJ2rhUr755aSO+YMfm7zQ1AaFHoNANG7IaqbTxbG/U5zIiEr8dDYrx34pMco3sLCPB3Pv7Bet/FIiIiIlmn5LyI/GPF+XXcs3+AoTY2AEPD7+WTfVcoOb81HN3k2cUiusLo/dDxHQgu5HmwIiIiedyFy7D2F+fjvVpDYIDv4jGqekglvopcSLdi7Q3N35qwi3b7+rHx8q9ejiwXKFUX+n8PD30O4ZWNr7OkwdZpMK06/DoV0lO9FqLcXEXCoGMT5+Of/ADJKb6LR0RERLJGyXkR4bolkbGHX2X80ddJsia7nV/AL4S5CTV56ePXCfhzuWcXK10fBvwAD38BxWpkMmIREZG8b/n3kJbufPzhdr6LxVMFzKHMqPoyb1R6kgCTizLf/xeffpl+B5/g1ePvkWpN80GEOZjJBJHdYEwUtH0NAty3CfpH0iVY/7i9kn7/Z2pAnkf16+h87Mo1WOPioZ6IiIjkLErOi+Rzf16PoeP+Aay6aOwd2FrWMNb9vJfOm5aCxYOqrIKloOtC+8ZnVVtnMloREZH8Y7GLH83Vy8EddX0XS2aYTCaC6Fn1AAAgAElEQVT6l+rB5xFzKRtYytCaeWeW0y36MY4ln/JydLlAQAi0mgjjDkLD/p61/4s/DJ/0hPnNPH+7UXK8Ts2gWGHn40vU2kZERCTXUHJeJJ+y2CzMOr2MzlGDOZJ83NCah+Kuse6bjdSIv2D8Qv5B0OI5+0Zntw3ybKMzERGRfOqPg7DHxR6f/e/JPVu13FqwDhvqLKFVYRe9OP5l9/VoOux/lM8ufINNld9QqBx0XwzDdnrej/7kNni/NSy9B+J2eyc+8bnAANebQX//O5zK59s4iIiI5BZKzovkQ6dSztI7ZgxvnpxNus3idn6oxcaMXX/x7u/7CLFYjV+obm8YEwPt3oSgsCxELCIikr8s/tr5mJ8f9O3gu1iyQ9GAcJbVnMLz5Udgxv2D+gTLdcYceYWRh1/icvpVH0SYC5RtaO9H32ctFK/l2dq/voHZDWHFw/aqesn1HnXR2sZqhWUbfBeLiIiIZJ6S8yL5zJfxG2m3ry9bE3YZmh955TrfbN5D91MXjV+k3O0weAv0/gSKVM5coCIiIvlUcgos3+h8vF0jKF/Sd/FkFz+THyPL9uXziDmUDyxtaM2a+O+4e19ffrm608vR5RImE9TuDKP2QqeZEFrcs/V7l8P02vDVKLh21jsxik80rAn1qzsfX7TOnqQXERGRnE3JeZF84prlOuOPvM6wQxO5YkkwtKZf7Bm+3LKPatfdbxILQJEq8MByGPobVLozC9GKiIjkX6s2Q7yLYvH+9/guFm9oFFaPDXU/4N4irQzNj0s9R++Y0bx+fCYpVg/2u8nLzAHQZCSMPwTNnwZzoPG11nTYNgumVoPvX4BkvZmQW7mqnj9yGr7XMy0REZEcT8l5kXxgW8Ju2u97lBUX1hmaH5aWzpzfD/LW3liCrQZ6vYYWh3un21vY3PKg/X17ERERyZT5a52PFS0E9zf3XSzeEu5fiPnV3+TNSk8RZHKfWLZhY86Zj+gUNZgDiUd8EGEuEVwYOkyCsTH2doKeSL0Om1+HqVXhlymQluSdGMVrHm5n7z/vjKt/S0RERCRnUAZNJA9LsibzyvH36BE9gmMppwytuT3+Kt9t3kuXuHj3kwNCoOUEGH8Ymo0Bfw+qtkRERCSDvYfhl73Oxx++G4LyyI9bk8nEo6W682WdhVQLrmhoTVTiX9yzfwDz4pZjMbBvTr5RpIq9neBj26BKa8/WJl6Eb56wV9L/NhPSU7wTo2S7EuHQvYXz8bW/wOkLvotHREREPKfkvEge9ce1/XTc15/5Z5Zjw331u9lq46mYE6z6NYoKSW5uykx+0GgIjDsEd78OwYWyKWoREZH8bZ6bStfH7vdNHL5UJ7QG39RZwiMluhqan2JL5dUT9uKDI8knvBxdLlO+MQzYCP2+gTINPVubEAfrRsPU6rBjHqSrhVBuMLSL8zGLxfXm0iIiInLzmWw2m4GeFSKZFxkZicVyY2WT2WwmKirqJkWUt6VYU5l66n1mxS3DirFdoCpfT2bGrkPcevma+8m174d2b0HJiCxGKiIiIv92LRHK94CERMfjLerDj+/5NiZf+/bSzzx59C0upl8yND/YL4jnyg9nYKkH8DOp7ugGVivsWwEbJ0L8Yc/Xh1eGVi9Ag772HveSI9lsULcfxBx3PF6xFBxaDmazb+MSERHJC3yR09QnWJE8ZN/1g9y3fxAz4j4wnJjvffwcGzb/6T4xX6EZDP4Z+qxWYl5ERMQLPvreeWIe8mbV/P9qX+QuNtb7kLaF7zA0P9mawkvHp/FAzEhik096Obpcxs/PvhfQ6CjoNAsKlvJs/eVYWD0I3ouA3cvAqjZCOZHJ5Lp6/vhZe3sbERERyZlUOS9ep8p570uxpvLe6SXMjFtKusH+q4VT05n05xE6u+stX+oWe+uaWp3sn/5FREQk29lscEt/iIp1PF4iHI6tzDv95t2x2WwsPfcFr554j2SrsR7oIX7BTKwwkn4lu6uK3pHU6/DrNNjyNqRc9Xx98VrQ6kWo2wvM/tkfn2Ra/FX7WzcpTjoRtWoAG6f7NiYREZG8QJXzIuLWjoQ/6bDvUaadXmw4Md/y3GW+3/yn68R88VrQ61MY8QfU7qzEvIiIiBd9/7vzxDzAwPvyT2Ie/rtZ7IY6S6gXWsvQmiRrMhOOTeaBmJEcSjrm5QhzocAC0GoCPH4E7nwC/IM8W3/hAKzqY6+k37UYLGneiVM8VrQQ9G7jfHzTbvgzE52NRERExPuUnBfJpa5ZrjMxdjLdoofxV3KsoTUF0i1M2nOEj7bFUDbZSWlNeGXothhG7YN6veyvRIuIiIhXTV/lfMzPD4Z29l0sOUn1kMqsjVzA+LIDMWOsafZvCbtpv68f004tJtWqBHIGocWg47sw7hA0Hu55P/n4Q/DFQJhWA7bPgbRk78QpHhndw/X4jM98E4eIiIh4Rm1txOvU1ib7/XB5K8/ETuJ06lnDa5pduMqU3YepmOTk1fCwstBqItw6CPzzUWmeiIjITXbgOET2dT7erQWses138eRUe68fYPyR14hOMl4CXDukKm9XeY7bCtb1YmS53OVjsPkNezW8Nd3z9WFloflT0GgoBIZmf3xiWMtRsGWv47HgQHtrrOLhvo1JREQkN1NbGxG5wcW0S4w+/DJ9Dz5uODEfbLHw6r5YVmyNcpyYDy0OHSfD+P+vnlJiXkRExKfcVbSO7embOHK6egVqsa7O+4wp299wFX1M0hHujxrKxNjJXLNc93KEuVR4Jbh/Pow9AA37g6f9+hNOw/rxMKUy/DQJUhK8EaUYMMpF9XxyKsxd47tYRERExBhVzovXqXI+66w2Kx+fX8tbJ2Zz2WL8hufW+ASm7T5MtesOXjcOLQ53PglNRkBQWDZGKyIiIkZduAxVekOik84gt9aE7fO19cv/2n0tivFHX+dg0lHDa8oEluTNSk/SvshdXowsD7hwEH58FfZ+bN+p2FMhRaDJKPuvgiWzPz5xKj0dqj0IJ887Hi9eGI6ugNBg38YlIiKSW6lyXkTYd/0AXaKG8kzsJMOJ+WCLhRf3x7L6l/0ZE/MFSkKHd+CJWGjxjBLzIiIiN9HMz50n5gHG9FRi3pEGBSNZX2cxo8r0xc/gLU1c6jkG/PU0/Q8+xfGU016OMBcrXhMe+NC+/1Dd3p6vT7oEm16DyZXgy5EQr51IfcXfH0Z0cz5+4QosWe+7eERERMQ9Vc6L16lyPnOupl/jnVPzWXL2M6xYDa+76/xlJv15lEqJ/9PCpmApaP403D5M/UBFRERygIREqNILLjl59l6qKBz9FILUcc6l3deiePLoW0QnHTK8JtgUyKiyjzK8TB+C/YK8GF0ecHY//PQm7P0EbMY/k/7D5Ad1eto/h5a7LfvjkxvEX4XKveB6kuPxyqXhwEf2RL6IiIi4psp5kXzIZrOx+uK3tNz7IO+fXWk4MV84NZ0puw+z/LeYGxPzYWXg3mkw/gjc+bgS8yIiIjnEwi+dJ+YBxvRQYt6Iv6vonys/nGCTsT+wZFsq755aQNu9ffjh8lYvR5jLlaoDD3wEY2Pg1gHg52FW12aFfStgbiNY3BYOfZu5djliSNFCMLiT8/HYM7Bqs+/iEREREddUOS9ep8p546ISD/HSsWn8mrDTo3WdTl/ktX2xlExJ++/BsLLQ4lm4bTAEhGRzpCIiIpIVKalQ/SE4fcHxeFgoxK6AcHWf88iR5BM8c3SSx5+l7inSklcqjqNcUGkvRZaHXIqFLW/DzkVgSc3cOUrXh+ZPQZ0HwF9PoLLbiXNQ/UFItzgev6Ua7FwIfirVExERcUmV8yL5RHzaZZ6NfZsO+/p5dDNZOimVRTsOMG/nX/9NzBerAV0XwuNHoOloJeZFRERyoCXfOE/MAwzvqsR8ZlQNrsCK2jOYUmUC4Wbjf4DrL22mxd4HmXxyIUkWF5sACBSpDJ1n2z9rNhuXuc+aZ/bAqkdgShXY/CZcd/HNIB6rUBIeutv5+J+HYc0W38UjIiIizqlyXrxOlfPOpVnT+eDsSqacmMcVUtwv+H9mq42BR8/wxMGThP1dElOmIbR4DiK7g5/ZSxGLiIhIVqWkQq0+9upWR4IC4fAnUKaYb+PKa86nxfPSsWmsif/Oo3VlAkvyfPkRdC3WDj+TapncunYWfpkCO+ZAios+Ta74B0ODvtB0rL2NjmTZviNQf4Dz8frV4fcFqp4XERFxxRc5TSXnxeuUnHds07mNvHR0EodMnt3E3BafwFt7j1LnaqL9QJVW9qR8tXZgMmV/oCIiIpKt5nwBo6Y5Hx/aBeY84bt48rqfrmxn4rHJHE4+7tG6hgXq8EqlcdxWsK6XIstjkq/AjnmwdRokxGX+PNXawR3joHpHZY6z6P7n4KtfnY+veg26tfBdPCIiIrmNkvOSJyg5f6MDJzbwxtHJbAz2LCkfnprGxKjj9D5x3t6Pqvb99p7yFZp6JU4RERHJfskpULMPnDrveNxshpgPoWpZ38aV16VYU5l/ZjnTTi8m2Wr8bUWA7sU68Gz54ZQLKuWl6PKY9BTY8yFseQcuHMj8eYrVhGZjoUE/CCqYffHlIzsPQOOhzsfVe15ERMQ1JeclT1ByHrCkExf1Ie+eXsiK8HSsHla4P3TsHM/HHKeoxQS3PAx3PqVXfkVERHKhmZ/B2Pecjw+6D+Y/7bt48ptTKWd46fg01l/a7NG6YL8ghpR6kBFlHqGQvxLFhlitcOBL+HkSnNia+fMEFYKGj8Ltw6FkRPbFl0+4q55f/hL0auO7eERERHITJeclT8jXyflr57i6cw6zzq9gYbkCJJs96wV/y+VrvLovltuTA6HxcGgyEsLKeClYERER8aaERKj5MJy75Hjc3wwHPoLK+lHvdT9c3soLxyYTm3LKo3Xh5kKMKdufR0t1J9gvyEvR5UHHtsDPb9uT9VlRpRU0HgERXcEckC2h5XXuquerlYP9SyHA33cxiYiI5BZKzkuekC+T86nXSf32KZZeWMu06qW5FOjZzUOJ5FSeiznBA4nh+N0xHho8CoGhXgpWREREfOHl9+G1D5yPD+kMc5/0XTz5XbI1hflnljPz9DKuWxM9WlsusDRPlR9C92IdMJs8K77I185FwdbpsHsppCdn/jxhZeC2IdBoCBQun33x5VHuqudnjoPh3XwXj4iISG6h5LzkCfkuOW+1cOiDZvSvlMrRgiEeLQ2wWhly5AxjUioT1vQJqNVJTSBFRETygLiL9qr5RCf5yAB/OPgxVFRbc587m3qBt0/O49ML67Dh2a1RREg1nqswgjaFm2HysG1hvpZ4EXbMh20zIeF05s/jZ4ZaXaDJCKjSRp+bnfjjoP05hjOlisLBj6CgaoFERERu4Iucpj69iGSzq3s/pGsNq8eJ+Q5nLvHj6YpMaPkZYQM2Q0QX3WCIiIjkEa8sdp6YB3isixLzN0upwOJMrjqB9XUW0ySsgUdro5MO0+/gE3SNfoyfr+xAdU8GhRaDls/BE7HwwMdQ7vbMncdqgegvYEk7mFYDNr8JVz1rVZQfNKwJvV30lT8bD1NW+C4eERER+S9VzovX5bfK+SU/DWRCcLTh+RHXUnnR3IwWt05QP3kREZE8aO9heweO//k49I+wUPhrOZQI921ckpHNZuPrS5t47fgMTqTGeby+aVgDnig3hDsK3eqF6PIwmw1O/AZbp0HUZ/ake2aZ/KDmvXDbYPv/qjc9AIdPQWRfSHfyRxsSBFHL9JBQRETk31Q5L5ILXQgytptSmVSY4t+ODXdtoUXLmUrMi4iI5EE2G4x9z3liHuDph5WYzylMJhP3FW3NpluWM6HCSAqbwzxa/1vCbh6IGckD0aPYlrDbS1HmQSYTVGwGvT+F8UfgrmcgtHjmzmWzwoGv4OOu8G4F+PZZuPhX9sabC1UrZ39Dx5mkFHhmru/iERERETtVzovX5bfK+dVxqxl5YpLT8UJWP0aFd2ZgzXGE+AX7MDIRERHxtU9/gIdfcT5etjgc+AhC9ZEgR7qUfoVZp5fx/tmVpNhSPV7fvFAjnig3mMZh9b0QXR6Xlgz7V8H22XBia9bPV7kF3DoIIrtDUMGsny8XOncJajwE15Kcz/nxPWihv64iIiKANoSVPCK/JectNgv37enD3tRjNxwPtPnRv3gXRlccRtGAwjcpOhEREfGV60n2NhInzzufM/9pGHSf72KSzDmVcpbJpxaw8sJ6rFg9Xt8krAGjy/SjVeGm2jg2M+J2w/Y5sOdDSEvM2rkCC0BEd2jYD6q0tm8qm4/850OYsMD5eP3qsH0e+Bt7GVhERCRPU3Je8oT8lpwHuG5J5K0Tc9h86RfSsdK6yJ0MK9OHikFlb3ZoIiIi4iPPzoV3ljsfv7Um/DYXzPkrN5irHUg8wn9OzuXbyz9nan3d0JqMKtuPe4u0wmzSf3iPJV+B3Uth22y4EJP184WVhfqPQIO+UKpu1s+XCySnQL3+cOS08znvjIDHe/ssJBERkRxLyXnJE/Jjcl5ERETytz8OQpNhrnvNb5kFzfJHPjDP2Z6wh7dPzmNrwh+ZWl81uCKjyvSlW7EOBPppw1KP2WwQu9leTR+9GiyetxzKoExDaNAPbnkICubtXVHXbIHuE5yPhwbD3iVQWVtiiYhIPqfkvOQJSs6LiIhIfpKeDneMgJ0HnM/p2wGWPO+7mMQ7frm6k3dPLmD7tT2ZWl86oASDSvfi4RJdCPcvlM3R5ROJF+3tbn5fCOf2Zf18fmaoejfUexAiukJI3tut2WaDjk/C9787n9OxCXw1yb5Xr4iISH6l5LzkCUrOi4iISH4y5VN4arbz8bBQiPkQShfzXUziPTabjZ+v7uDdUwvYeS1zyeFQvxB6F7+PQaV7USW4QjZHmE/YbHByO+xaBH8uh9RrWT+nORBq3AP1ekOtznlqI9mYY9BwEKSmOZ/zwQR4pL3vYhIREclplJyXPEHJeREREckvomOh0RBIdtFlY8Y4GNHNZyGJj9hsNjZf2ca7pxbwx/XMfc41YaJDkbsYWvohGhesr81jMyvlGuxbYU/UH/81e84ZEGJP0NftDTXvsf8+l3tlMby6xPl4eEH4cwmUK+GriERERHIWJeclT1ByXkRERPKDtHS40007m2Z14KeZ4Ofnu7jEt2w2G5uu/MbMuKX8lrA70+e5JbQ2/Uv1pEuxtoT4BWdjhPnMuSj44wN765sEF7ugeiIoDGrfD3V6QvX2uTZRn5IKDQbCwRPO53RoDOveVnsbERHJn5SclzxByXkRERHJD15+H177wPl4gD/sXAh1qvguJrm5diTsYcbppWy8kvnq7XBzIR4s0Ym+JbtRObh8NkaXz1gtcPRH+GMpRH0GaYnZc96AUKh5L0R2t/9vcOHsOa+PbPoD2o5zPWfW4zDsft/EIyIikpMoOS95gpLzIiIiktf9shdaj4X/+chzgxcehZcH+i4myTn2J/7FrNPL+DJ+I1asmT5P68JNebRkD9qEN8NsMmdjhPlMyjWI+hz2LIMjG+396rODOcC+mWxkd4i4Hwrkjn4wwyfD/LXOx4MDYds8qFvVdzGJyP+1d9/xUVX5/8ff0zJppNJC6EgNzYYFFCOCBdHVXVb9qayuIK5tLd9dWZEvFlZ3F8uygmLBr+KCigoqunRcsMCiS5FQNfQqEALpmczc3x8TSpg7yWQymSST13Mf85jJuad84nJzZz5z7jkA6gOS84gIJOcBAEAkO5wrnTNS2nvIf52zO0vfvipFOcIXF+qfbcW7NXX/DH10eJ5KjEo2JqhC66iWurnZMP266VClO1uEMMJG6Ngead0Mae106VAIP59YrFK7S6Tuv5C6DZNSOoWu7xDLK5TO/q20fb//Ot3beRP0cQ1zBR8AAIJCch4RgeQ8AACIVB6PdO1j0oJV/us4o6TvXmc5G5xy2JWjd3+eo7cPfqzDZUeD7sciiwYm9tPNzYZpSNIlclqjQhhlI2MY0sEsaf37UtYHUk52aPtv2s2bpO96rdTmYslmD23/NfTVOu/dP5VlB+64Wpo2JnwxAQBQ10jOIyKQnAcAAJHqz9Ol/51WeZ3n75Uevik88aBhKfaU6JMjC/X6gfe1pWhbjfpKtifqxtQrdXOzYeoRe1aIImykDEPa919von79B9LxPaHtPyZZ6ny1N1Hf+Srvz/XAH16RXvyg8jqvPCqNvi488QAAUNdIziMikJwHAACR6NOvpRvHVl7n8nOkBS9IVmt4YkLDZBiGvjr+nV47MFP/PvafGvfXO7abft3sGg1LGaSmjpQQRNiIeTzS7hXlM+pnSQU/h7Z/q01q29+brD/rSqllnzr7g1FSKg24T1q91X8dh11a/JI0oHf44gIAoK6QnEdEIDkPAAAiTdY2qf+9Un6R/zotUqTVb0otU8MXFxq+7KJdevfnOZp1+Asdc+fVqC+bbLo08XzdkHqlrky+RPG2uBBF2Uh53NKOZd7NZDfOkfL2hX6MuOZSp8HSWUO8jyYtQz9GJX7aI503yrsOvT8tUrzrz7dpHr64AACoCyTnERFIzgMAgEhy4Ig3Mb/jgP86Vqu08AUp85zwxYXIUuQu1ic5C/X2wY+VVVjJVOYARVudujLpUv0idbAuS7xQUVZ2J64Rj0fau8qbpN/4cejXqD+hRW+p85XeRH3bAZIjunbGOc37S6Rbn668Ts8O0vLJUmJ8rYcDAECdITmPiEByHgAARIq8QinzQWnNj5XXe+q30hO/CU9MiGyGYWhNwUa9c/Bjzc1ZohKjtMZ9JtkSNDQlU9emXK6Lmpwjh7V+bU7a4JzYTHbjbO/j4A+1M44jRmo/UOp4udQhU0o727ssTi144O/SK3MqrzPoXOnzv0pRfM8DAIhQJOcREUjOAwCASFDqkoaNkRZ/X3m96wdIHz3DOvMIvaNlx/TJkYV6/9DnIZlNL3kT9UOSL9HVyQN1aWI/RVudIem3UcvJljZ9Im35XNr5lXc5nNrgTJDaXyp1uMybrG/ZJ2TJeleZNOQRafm6yuvdcoX0zuOSrXa+IwAAoE6RnEdEIDkPAAAaOleZdPOT0idfVV6vZwfpm1ek+NiwhIVGLKtgi94/9LnmHFmg3BquTX9CnDVWVyT119UpA3V54kWKs/EPucaKcqWfFkib50o/zpOKcmpvrOik8mR9pjdh36J3jb4l/PmodP4oac+hyuuNvFaa+j+SxRL0UAAA1Esk5xERSM4DAICGrKxMuv3P0qyllddLTZRWTpU6tgpPXIAkFXtKtODocr13aK6+Pv69DIXm4120JUoDEs/XFUn9NSipv1pFsftnjbnLpD0rvTPqN8+VDtXy56GYZKnNxVLb/lK7/lL6+d6lcaph9Vbpsgelgko2v5akB34pvfQACXoAQGQhOY+IQHIeAAA0VKUu6TfPVp2Yj46SFr0kXdwzPHEBZvaU7NecIws158hCbSnaFtK+e8R21qDEizUo6WKdE58hm4V1TGosZ5v043zvzPptS6XS/Nodz+aQ0s7xJutPJOzjW1TZ7F8rpV88LrmrWJ3nnuullx9iSS8AQOQgOY+IQHIeAAA0RIXF0q/HS/NWVl7PavWuMX/9gPDEBVTFMAxtKsrWnCML9MmRRdpXejCk/SfbE5WZeJGuSLpYAxLOU6ojOaT9N0pul7R7hfTTQm+yft9/vRvN1raUTqeS9a37Sc17SjbfDYJf/0z63QtVd3f7ldKbf5Ts7DEMAIgAJOcREUjOAwCAhubIMemXT0hf/VB13VcelUZfV/sxAcHwGB6tylunOUcW6vOcpcp1Hw/5GBmxnXVpQj9dkni++jXpoxhrdMjHaHQKDkvZi6XshdKPC6S8feEZ1xHjnV3fup93GZzW/aTkjpLFomfelp78v6q7uOoC6b3xUkJcrUcLAECtIjmPiEByHgAANCQ/7pGGPeZ9rsoL90kP/br2YwJCodTj0rJj/9G/jv5bC48uD9lGsqdzWqJ0XpNe3mR9wvnqGdeFJXBqyjCknzdI25ZI2/8t7VgmFR0N3/gxKVLrfjJana/pWf005vPz9bOr8uVwenWUPvuL1LbqVXMAAKi3SM4jIpCcBwAADcWCVdJtz0g5AUwufvZu6bFbaz8moDa4PGVakbda/zr6b80/ukyHXDm1Mk4TW5zOj++jC5v01YUJZ6t3bDc5rKx5UiMet3TgB2nHv6VtX0o7l0vFx8Iawr6SNK0r6Ks1+Wdrbf7ZWpvfV9uKO8rQqQXnW6RInzwr9ese1tAAAAgZkvOICCTnAQBAfed2S0+/Lf353cCWef7zKGnMbbUeFhAWbsOt/+Zn6YucLzXv6DLtLT1Qa2PFWKN1bnxPXdikry5ocrbOju/BMjg15XFL+9dK27/0zqzfuVwqCf1dEVU5XtZE6wr6lCfrvQn7H10ZevaeKN13o2SxhD0kAABqhOQ8IgLJeQAAUJ8dzPHOll+6OrD6kx+SfndD7cYE1BXDMLSh8Ectzv1GS3K/1ZqCDTJUex8Z7RabMmK76Jy4DJ0dn6Fz4jPU3tlaFjK5wXOXSQfWSbu+OfU4vrdOQnF57Npa1EVHY3vqnAEZim3TU2rR07sRrZXljgAA9RvJeUQEkvMAAKA+Mgxp1pfSg3+XDgewIoTdJk0bI902pPZjA+qLI66jWnpshZbkfqtlx/6j4+78Wh8z2Z6os+O8ifpz43uqb1wPJdjja33ciGUY0rFd0s5vpN3fep8P/iAZnrqLyR4tNesuNe8pNc/wJuxb9JQS2zLFHgBQb5CcR0QgOQ8AAOqbA0ek+16SPvkqsPpJ8dJHz0iZ59RuXEB95vKU6fv8H7Qk91stP75KGwp/DNvYHZxt1Cuuq3rGdjn5nOJICtv4Eaf4uLTnP95Z9btXSntXhXeTWX+i4qTULlLTrhUfqV0kJ1/QAADCi+Q8IgLJeQAAUF+4yqTXPpWe/D/paIBLMndsJc39i9StXe3GBjQ0h105+mzqrlAAACAASURBVPr49/rq2Hdafvw77Ss9GNbxW0W1UM/YLicT9hmxXdQqqjlL4gTDMKScbGnvd9KeVd5k/b7VUllxXUd2SkK6edI+sa1kY5NhAEDokZxHRCA5DwAA6pphSP9aKf3xFWnzrsDbDesv/d+fpOQmtRcbEAkMw9C24t366vgqfXX8O317fHVYlsA5UxNbnLrGdDz1iPU+N7Unk7SvLrdL+nlDebL+O+9M+0MbvRvQ1idWm5TYTkrp6F3LPrmj95HSyVsWnVjXEQIAGqiISc57PB4tW7ZMCxcuVFZWlvbu3avi4mJFR0crPT1dvXr10uDBgzVw4EBZrdbaDuek7du369NPP9WqVau0Y8cOHT9+XFarVampqercubMGDBig6667TklJNb9dMlLHCgTJeQAAUFcMQ/pyjfTnd6R/rw28nc0mPTtKevRmlj8GguE23NpctE0rj6/Ryry1+k/eWh0pq7tlU1LsSeoa00FdYzqqc0wHdYxuo07RbZUW1VxWS/g+gzZ4riLpYJa0f410YG358zpveX0Vk3IqaZ9SnrhPauedcZ/YRoqKresIAQD1VEQk57/77juNHz9e2dnZVdbt1KmTnn76aZ133nm1GZKOHj2q5557Tp999pmq+vVjYmI0evRo3X333bLZqr+bfKSOVR0k5wEAQLh5PNKCVdKz70rfZlWvbXozacY46ZI+tRMb0BgZhqHs4p0nE/Ur8tZof+nPdR2Woq1OdXC2UcfoNuoY3fbkc6eYtkq2M+M6IB63dHjrqWT9/jUq3bVGUa4jdR1ZYGKbSklty5P1bX1fx7WQwjiJEABQfzT45PzMmTM1YcIEn1+iMjabTePGjdMtt9xSKzHt2LFDv/3tb7V3795qtevfv7+mTJmimJiYRj9WdZGcBwAA4ZKbJ01fIL36ibR1d/Xb3zVUmnivlMi+g0Ct21f6s9bkb9Dq/CytKdiodQWbVOwpqeuwTkq2J6q9M11tnK3UxpmmNs40tXW2UhtnK6VHtZDTGlXXIdZfhqGDuw/olalZytuRpYzYDcqI8z43sYd/uaMasTmkhDbeNe+btJISWklN0ryvTzwSWklO1j8DgEjToJPzX3zxhR599NEqZ3CbsVgsevHFF3XNNdeENKacnBzdeOON2r9/f1DtL7vsMk2dOjWgtQojdaxgkJwHAAC1ye32Ll0zc5H04b+lwiD2L2zXUnrtf6TB54c8PAABcnnKtLkoW6vLE/arCzZoW3E1NokII4ssahnVTG2iTiXt050tlOZorrQo76OJLa7Rr3NvGNLHy6TfT5IO5EgWedTWuUs947LUI3aDesZlKSMuS91jNynaWn++mAlKVJxvwr5JmhSfJsW3kOKaS/HNpZhUNrAFgAaiwSbnDx48qKuuukqFhYW+A1os6tmzp9LS0rR//35lZWWZJvBjY2M1f/58tWjRImRx3X///Vq0aJHpsbS0NHXv3l0lJSX64YcflJeXZ1pv7NixGjFiRKMdKxgk5wEAQKiVlUlfr5c+/VqatdSb9AmGwy49+Ctp3G+kJiw7DNQ7uWXHlVW4VVkFW5VVuEVZBVuVXbxLHnnqOrQqxVljlRbVTGlRzdWy/DktqtnJBH6LqKZKsSfKZqndZUbrg6N50mOvSm/9y5uwP5NNZeoQvV1dY7eoS8yWCs8tow6GP+DaZLF418GPa37qEd/c/OfYVCk6ybvpLQAg7Bpscn7cuHGaNWuWT3l6erqmTJmi7t27nyzbtGmT7rvvPtPlWG666SY9/fTTIYlp9erVpkvlWK1WjRkzRrfddtvJtdfz8vL0zDPP6NNPP/Wpn5ycrMWLFys+3v+9zpE6VrBIzgMAgFDYdVD69xpp4XfSvJVSbg1XRvjFJdJf75HOah2a+ACER6G7SBsLf/Im7csT9luKtqnUcNV1aNVmlVUp9kQ1c6SqmSNFTR0pauZIVjNHSsUye4pSHUkNPpG/Zqv02FRpyX8Db5NgO6ausVvUNWaLOsdsVdfYLeoR730dpSBulWpoLBZvgj4mVYpN8T7HpJx67e/ZmcBa+QBQQw0yOb9z505dc801Kisrq1DucDg0d+5cdejQwadNdna2rr/+erlcFd9M2e12zZs3T23btq1xXLfffrtWrVrlU/7AAw/o/vvv9yk3DEMjRoyoVptIHytYJOcBAEB1ud3Slt3Sqk3S8rXSsrXSjgOh6fvintIzI6XLzg5NfwDqnstTph0le7SlaJu2FG7T5qJt2lKUre3FexrELPtAWGRRsj1RyfaE8udEPz9XLKtva+MbhnfD7jFTpfXbgu/HIo9aRh1Qx+htujh9m/qnZ6tH4jalW7PlLNgmS36EzbivLovVm6CPTpKiE70PZ+Kp1yfKTy9zJlas74j1fjkAAI1Ug0zOT548WS+//LJP+W233aZx48b5bffkk0/qvffe8yl/8MEHdd9999Uopr179+ryyy/3KU9OTtayZcvkdDpN2/3www8aPny4T3mbNm20ePHiRjVWTZCcBwAAlck5Lm3ZJW3aKa39UVrzo7T2p+DWjq9M5tnS2BHepDy5BqBxKPaUKLt4l7YUbtOWom36sWiHtpfs1o7iPQ1ypn0wYqzRSrTFq0n5I8EepwRbEzWxxSnBFq8m9vjTjnvLEuxNlGCLU7wtTrHWGEVZHSGPy+ORPvtG+usM7xexoZQQJ114VoEyO2zTuc23qUtstpprm5x52VLuLunYTqm0ILSDRiKrTYpqIkXFex/O+FOvo5qc8fNpD2eTM34uf3bESfZoZvQDaDDCkdMM+S4k/tY+v/baayttN2zYMNPk/KJFi2qcnPcX05AhQ/wmsCWpd+/eat++vXbs2FGhfPfu3dq8ebO6devWaMYCAAAIVnGJtOeQtOtnaecBaffP3iVqftojbd4lHcqtvbHtNu/yNQ/+Surfq/bGAVA/RVudyojtrIzYzhXK3YZbe0sPKrtop7YV79a24l0nn/eWHpShkK/+WmeKPMUq8hTrgOtw0H04LHbFWWMUa4stf45RrDVGcbYYxVljFWeLUYw1WnHlx+NsMYq1xirWFq0Ya7SirU5FW5yKtkZ5X1udclqdGnSxU9dc7NTXa216/n2LFvje4B2U4wXSwnVxWriul6RTf/ybJ0udW0tntTLUq1OuuiXtUhvnLrWw7lKye5fs+bukY7ulY7uk43slIzLuugiaxy0V53ofoWR3emfl22MkR4z39YnnM8vsZxw/s8zulGzOU8+2KO/rM8vtTtbuB1AvhTQ5v3//fm3evNmnPCYmRn369Km0bd++fRUdHa3i4opTpDZt2qT9+/crLS0t6Li+/PJL0/ILL7ywyrYXXHCBTxJbkpYsWWKaxI7UsQAAAFxlUl6hN+lx/IznI8e8SfZDudLPudLh014fORb+WNNSpVHDvI9WTcM/PoD6zWaxqa2zldo6WylTF1U4VuQp1s7ivdpRske7SvZpd8n+Cs9FnkawzvkZXEaZct15ynXn1Ur/VlkVfZNTXW9yylUcpYI8p8pKnDLKoiSXU4bL+9pwOSW3Q0aZQ4bbfvK13HYZbkf5z6e9dtulMof35zKHct0OrSqwa9Vmh4wsh+SOk+HuLbnPkeGxKTnOprRkm5om2NQszlDHuJ/VPmaf2tj3qrntgFIt+9WkbJ+iS/Ypqni/7AV7ZXExA7/aykq8Dx0N77hWW8UE/pnJ+wplUafq2hyS1V7+7Dj1fGbZiXqn1zmzzF/bk882yWIL8JnbAIFIENLkvL8p/Z07d5a1ituWbDabOnfurPXr1/sc27RpU42S82ZfGEhS165dq2zrr46/PiN1LATnYI53dmCwarLoVE3m+tRo3LpqG3xTft+G0Db4pvy+YWpb47HratwG+v9xmdu7JnuZW3J7Kj57PL7Hz6zjdksut1RSKhWf8SgplYpKKpYVlniT8kUlNQg8DOJjvLPkbx0iXX62ZA/5PaIAGoMYa7S6xXZSt9hOPscMw1BOWa52lezTrpL92l2yT7tK9mlf6UHtL/1Z+0sP6VgtJbAjmUceFXqKJBVJUZIlVQr9QjqBOVz+2CzpqzOOGW6b5LHJ8NgkT1vJ00HyWGQ3vMkVhzyKMjyKkltRKlOU4ZLTOPHsktUwZDMkqwxZDclqlD/r1LPF0MnXNqP8Z1Wsazl53FtuqXD8xGuj/PUZdU6rayl/R2KRZDHKnyVZyuufOKaTx4wKdXWi7LS2OrPfk/V92+q0sU4f27ett2OLoYrHzmhvxmL6pssjqfzf24kfSyVLaaDtzcfzG4Ofd35mffvvw4QhGRabZLHKsFglWWVYbbLIWl5Wfqz8Z1ls3jJZJatNhsUqi6wV+jBkk8VilWGxeOvJUv4lgNX7fFq5IYsslvLXFot3DJ/yU30YFfo7rf4ZbSuUl497erlP29P7lMrrn/5f7vR/IRYZljOPqUJ74+TPOq1vVejDt71U4ayp0P7E/13m4/kcq9D+tH4tZ7aXaf2KMfr7+RSjqrp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\n",
      "text/plain": [
       "<Figure size 860x559 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 474,
       "width": 755
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "x = np.linspace(-10,40,1000)\n",
    "for name, gp in gps.items():\n",
    "    X_test = gp.preprocessor.transformX(row)\n",
    "    Y_pred, Y_std = gp.predict(X_test)\n",
    "    row['{}_gpsolv'.format(name)] = Y_pred.ravel()\n",
    "    row['{}_gpsolv_std'.format(name)] = Y_std.ravel()\n",
    "    plt.plot(x,norm.pdf(x,Y_pred.ravel()[0],Y_std.ravel()[0]),label='{}_gpsolv'.format(name))\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "display(row)\n",
    "for name, gp in poly_gps.items():\n",
    "    X_test = gp.preprocessor.transformX(row)\n",
    "    Y_pred, Y_std = gp.predict(X_test)\n",
    "    row['{}_gppoly_pred'.format(name)]=Y_pred.ravel()\n",
    "    row['{}_gppoly_std'.format(name)]=Y_std.ravel()\n",
    "    row['{}_gp_pred'.format(name)]=row['{}_gpsolv'.format(name)]+Y_pred.ravel()\n",
    "    row['{}_gp_std'.format(name)]=row['{}_gpsolv_std'.format(name)]+Y_std.ravel()\n",
    "    mu = row['{}_gp_pred'.format(name)].values[0]\n",
    "    sigma = row['{}_gp_std'.format(name)].values[0]\n",
    "    plt.plot(x,norm.pdf(x,mu,sigma),label='{}_gppoly'.format(name))\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polymers: Predict on all data\n",
    "\n",
    "### Steps:\n",
    "1. Calculate \"Solvent\" HSP \n",
    "2. Calculate \"Polymer\" correction\n",
    "3. Add together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-21T15:25:35.571624Z",
     "start_time": "2018-09-21T15:25:34.229939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>smiles</th>\n",
       "      <th>organic</th>\n",
       "      <th>n_electrons</th>\n",
       "      <th>n_atoms</th>\n",
       "      <th>charge</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>finished</th>\n",
       "      <th>job_name</th>\n",
       "      <th>poly_smiles</th>\n",
       "      <th>...</th>\n",
       "      <th>δd_gp_pred</th>\n",
       "      <th>δd_gp_std</th>\n",
       "      <th>δp_gppoly_pred</th>\n",
       "      <th>δp_gppoly_std</th>\n",
       "      <th>δp_gp_pred</th>\n",
       "      <th>δp_gp_std</th>\n",
       "      <th>δh_gppoly_pred</th>\n",
       "      <th>δh_gppoly_std</th>\n",
       "      <th>δh_gp_pred</th>\n",
       "      <th>δh_gp_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PNT-001</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...</td>\n",
       "      <td>True</td>\n",
       "      <td>402</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>1,107.85</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-001</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(c2cc3c(cc(c4c...</td>\n",
       "      <td>...</td>\n",
       "      <td>19.93</td>\n",
       "      <td>2.52</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>1.77</td>\n",
       "      <td>4.45</td>\n",
       "      <td>7.98</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>2.60</td>\n",
       "      <td>5.64</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PNT-002</td>\n",
       "      <td>CCCCCCCCCCc1cc(-c2c3cc(-c4sc(-c5ccc(-c6cc(CCCC...</td>\n",
       "      <td>True</td>\n",
       "      <td>508</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>1,382.37</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-002</td>\n",
       "      <td>c1c(cc(s1)c1ccc(c2c1nsn2)[C]1C=C([C](S1)c1cc2c...</td>\n",
       "      <td>...</td>\n",
       "      <td>18.58</td>\n",
       "      <td>2.76</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>1.84</td>\n",
       "      <td>3.74</td>\n",
       "      <td>8.19</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>2.70</td>\n",
       "      <td>6.72</td>\n",
       "      <td>7.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PNT-003</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...</td>\n",
       "      <td>True</td>\n",
       "      <td>412</td>\n",
       "      <td>167</td>\n",
       "      <td>-1</td>\n",
       "      <td>1,132.88</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-003</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)C/C(=C/[C@@H](S)c1cc...</td>\n",
       "      <td>...</td>\n",
       "      <td>20.23</td>\n",
       "      <td>2.21</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>1.70</td>\n",
       "      <td>4.05</td>\n",
       "      <td>8.03</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.24</td>\n",
       "      <td>7.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PNT-004</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...</td>\n",
       "      <td>True</td>\n",
       "      <td>388</td>\n",
       "      <td>160</td>\n",
       "      <td>-1</td>\n",
       "      <td>1,050.76</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-004</td>\n",
       "      <td>CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(c2cc3c(cc(c4c...</td>\n",
       "      <td>...</td>\n",
       "      <td>19.28</td>\n",
       "      <td>2.10</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>1.58</td>\n",
       "      <td>4.12</td>\n",
       "      <td>7.77</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.43</td>\n",
       "      <td>6.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PNT-005</td>\n",
       "      <td>CCCCCCCCCCCC[C@H](CCCCCCCCCC)Cc1csc(-c2cc3c(cc...</td>\n",
       "      <td>True</td>\n",
       "      <td>436</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1,163.98</td>\n",
       "      <td>True</td>\n",
       "      <td>SOLSPE2_PNT-005</td>\n",
       "      <td>CCCCCCCCCCCC[C@H](CCCCCCCCCC)Cc1csc(c2cc3c(cc(...</td>\n",
       "      <td>...</td>\n",
       "      <td>19.64</td>\n",
       "      <td>2.11</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.77</td>\n",
       "      <td>7.84</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.38</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     label                                             smiles  organic  \\\n",
       "0  PNT-001  CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...     True   \n",
       "1  PNT-002  CCCCCCCCCCc1cc(-c2c3cc(-c4sc(-c5ccc(-c6cc(CCCC...     True   \n",
       "2  PNT-003  CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...     True   \n",
       "3  PNT-004  CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(-c2cc3c(cc(-c...     True   \n",
       "4  PNT-005  CCCCCCCCCCCC[C@H](CCCCCCCCCC)Cc1csc(-c2cc3c(cc...     True   \n",
       "\n",
       "   n_electrons  n_atoms  charge    MolWt  finished         job_name  \\\n",
       "0          402      164       0 1,107.85      True  SOLSPE2_PNT-001   \n",
       "1          508      213       0 1,382.37      True  SOLSPE2_PNT-002   \n",
       "2          412      167      -1 1,132.88      True  SOLSPE2_PNT-003   \n",
       "3          388      160      -1 1,050.76      True  SOLSPE2_PNT-004   \n",
       "4          436      185       0 1,163.98      True  SOLSPE2_PNT-005   \n",
       "\n",
       "                                         poly_smiles    ...      δd_gp_pred  \\\n",
       "0  CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(c2cc3c(cc(c4c...    ...           19.93   \n",
       "1  c1c(cc(s1)c1ccc(c2c1nsn2)[C]1C=C([C](S1)c1cc2c...    ...           18.58   \n",
       "2  CCCCCCCCCC[C@@H](CCCCCCCC)C/C(=C/[C@@H](S)c1cc...    ...           20.23   \n",
       "3  CCCCCCCCCC[C@@H](CCCCCCCC)Cc1csc(c2cc3c(cc(c4c...    ...           19.28   \n",
       "4  CCCCCCCCCCCC[C@H](CCCCCCCCCC)Cc1csc(c2cc3c(cc(...    ...           19.64   \n",
       "\n",
       "   δd_gp_std  δp_gppoly_pred δp_gppoly_std  δp_gp_pred  δp_gp_std  \\\n",
       "0       2.52           -0.43          1.77        4.45       7.98   \n",
       "1       2.76           -1.61          1.84        3.74       8.19   \n",
       "2       2.21           -0.31          1.70        4.05       8.03   \n",
       "3       2.10           -0.24          1.58        4.12       7.77   \n",
       "4       2.11           -0.10          1.57        3.77       7.84   \n",
       "\n",
       "   δh_gppoly_pred  δh_gppoly_std  δh_gp_pred  δh_gp_std  \n",
       "0           -3.08           2.60        5.64       7.35  \n",
       "1           -2.07           2.70        6.72       7.48  \n",
       "2           -5.05           2.37        2.24       7.17  \n",
       "3           -4.47           2.28        3.43       6.97  \n",
       "4           -4.47           2.28        2.38       7.04  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "x = np.linspace(-10,40,1000)\n",
    "for name, gp in gps.items():\n",
    "    X_test = gp.preprocessor.transformX(ex_df)\n",
    "    Y_pred, Y_std = gp.predict(X_test)\n",
    "    ex_df['{}_gpsolv'.format(name)] = Y_pred.ravel()\n",
    "    ex_df['{}_gpsolv_std'.format(name)] = Y_std.ravel()\n",
    "\n",
    "\n",
    "for name, gp in poly_gps.items():\n",
    "    X_test = gp.preprocessor.transformX(ex_df)\n",
    "    Y_pred, Y_std = gp.predict(X_test)\n",
    "    ex_df['{}_gppoly_pred'.format(name)]=Y_pred.ravel()\n",
    "    ex_df['{}_gppoly_std'.format(name)]=Y_std.ravel()\n",
    "    ex_df['{}_gp_pred'.format(name)]=ex_df['{}_gpsolv'.format(name)]+Y_pred.ravel()\n",
    "    ex_df['{}_gp_std'.format(name)]=ex_df['{}_gpsolv_std'.format(name)]+Y_std.ravel()\n",
    "\n",
    "ex_df.to_excel('results/PNT_predictions.xlsx',index=False)\n",
    "ex_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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