stt_eda_hits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _Explore Hits_\n",
    "\n",
    "- Perform _EDA_ on _hits_ Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os, glob, yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import trackml.dataset\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append('src')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Dataset_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mu- data (old)\n",
    "# input_dir = './data_sets/pandaml/data_3.0_7.0_GeV/'\n",
    "\n",
    "# mu+mu- data (current)\n",
    "# input_dir = './data_sets/pandaml/data_0.1_1.5_GeV/'\n",
    "\n",
    "# pbarp data (coming)\n",
    "# input_dir = os.environ['HOME']+'/current/2_deepana/pandaml/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dir = \"./data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find All Input Data Files (hits.csv, cells.csv, particles.csv, truth.csv)\n",
    "all_files = os.listdir(input_dir)\n",
    "\n",
    "# Extract File Prefixes (use e.g. xxx-hits.csv)\n",
    "suffix = '-hits.csv'\n",
    "file_prefixes = sorted(os.path.join(input_dir, f.replace(suffix, ''))\n",
    "                       for f in all_files if f.endswith(suffix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data/event0000000000',\n",
       " './data/event0000000001',\n",
       " './data/event0000000002',\n",
       " './data/event0000000003',\n",
       " './data/event0000000004',\n",
       " './data/event0000000005',\n",
       " './data/event0000000006',\n",
       " './data/event0000000007',\n",
       " './data/event0000000008',\n",
       " './data/event0000000009']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_prefixes[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_id = 0\n",
    "event_prefix = file_prefixes[event_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load an event\n",
    "hits, tubes, particles, truth = trackml.dataset.load_event(event_prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hits\n",
    "\n",
    "The hits file contains the following values for each hit/entry:\n",
    "\n",
    "* __hit_id__: numerical identifier of the hit inside the event.\n",
    "* __x, y, z__: measured x, y, z position (in millimeter) of the hit in global coordinates.\n",
    "* __volume_id__: numerical identifier of the detector group.\n",
    "* __layer_id__: numerical identifier of the detector layer inside the group.\n",
    "* __module_id__: numerical identifier of the detector module inside the layer.\n",
    "\n",
    "\n",
    "Here we have the $x, y, z$ global coordinates (in cm) of where the particles hit the detector surface. The volume/layer/module id could in principle be deduced from x, y, z. They are given here to simplify detector-specific data handling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hit_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>volume_id</th>\n",
       "      <th>layer_id</th>\n",
       "      <th>module_id</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-6.72099</td>\n",
       "      <td>2.01500</td>\n",
       "      <td>10.42000</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.36853</td>\n",
       "      <td>-0.98500</td>\n",
       "      <td>1.98000</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>329</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-5.03063</td>\n",
       "      <td>-3.96500</td>\n",
       "      <td>15.22000</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.88378</td>\n",
       "      <td>1.93577</td>\n",
       "      <td>-3.57089</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.43258</td>\n",
       "      <td>-1.19140</td>\n",
       "      <td>4.42000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hit_id        x        y         z  volume_id  layer_id  module_id\n",
       "0       1 -6.72099  2.01500  10.42000          2        12        352\n",
       "1       2 -1.36853 -0.98500   1.98000          2         4        329\n",
       "2       3 -5.03063 -3.96500  15.22000          2        13        359\n",
       "3       4  4.88378  1.93577  -3.57089          2         6         15\n",
       "4       5 -1.43258 -1.19140   4.42000          2         5        333"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assign New Quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (1) add a new column\n",
    "# hits['r'] = hits.apply(lambda row: np.sqrt(row.x**2 + row.y**2), axis=1)\n",
    "# hits = hits.assign(r=hits.apply(lambda row: np.sqrt(row.x**2 + row.y**2), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hit_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>volume_id</th>\n",
       "      <th>layer_id</th>\n",
       "      <th>module_id</th>\n",
       "      <th>r</th>\n",
       "      <th>phi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-6.72099</td>\n",
       "      <td>2.01500</td>\n",
       "      <td>10.42000</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>352</td>\n",
       "      <td>7.016547</td>\n",
       "      <td>2.850313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.36853</td>\n",
       "      <td>-0.98500</td>\n",
       "      <td>1.98000</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>329</td>\n",
       "      <td>1.686149</td>\n",
       "      <td>-2.517734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-5.03063</td>\n",
       "      <td>-3.96500</td>\n",
       "      <td>15.22000</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>359</td>\n",
       "      <td>6.405346</td>\n",
       "      <td>-2.474106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.88378</td>\n",
       "      <td>1.93577</td>\n",
       "      <td>-3.57089</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>5.253428</td>\n",
       "      <td>0.377371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.43258</td>\n",
       "      <td>-1.19140</td>\n",
       "      <td>4.42000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>1.863255</td>\n",
       "      <td>-2.447851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hit_id        x        y         z  volume_id  layer_id  module_id  \\\n",
       "0       1 -6.72099  2.01500  10.42000          2        12        352   \n",
       "1       2 -1.36853 -0.98500   1.98000          2         4        329   \n",
       "2       3 -5.03063 -3.96500  15.22000          2        13        359   \n",
       "3       4  4.88378  1.93577  -3.57089          2         6         15   \n",
       "4       5 -1.43258 -1.19140   4.42000          2         5        333   \n",
       "\n",
       "          r       phi  \n",
       "0  7.016547  2.850313  \n",
       "1  1.686149 -2.517734  \n",
       "2  6.405346 -2.474106  \n",
       "3  5.253428  0.377371  \n",
       "4  1.863255 -2.447851  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) add new columns\n",
    "r = np.sqrt(hits.x**2 + hits.y**2)\n",
    "phi = np.arctan2(hits.y, hits.x)\n",
    "hits.assign(r=r, phi=phi).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort a Quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hit_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>volume_id</th>\n",
       "      <th>layer_id</th>\n",
       "      <th>module_id</th>\n",
       "      <th>r</th>\n",
       "      <th>phi</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>-1.254720</td>\n",
       "      <td>0.431039</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>329</td>\n",
       "      <td>1.326694</td>\n",
       "      <td>2.810690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.368530</td>\n",
       "      <td>-0.985000</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>329</td>\n",
       "      <td>1.686149</td>\n",
       "      <td>-2.517734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.432580</td>\n",
       "      <td>-1.191400</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>1.863255</td>\n",
       "      <td>-2.447851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.987059</td>\n",
       "      <td>1.680400</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>328</td>\n",
       "      <td>1.948853</td>\n",
       "      <td>2.101896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1.047470</td>\n",
       "      <td>-1.852940</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>271</td>\n",
       "      <td>2.128516</td>\n",
       "      <td>-1.056281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hit_id         x         y     z  volume_id  layer_id  module_id  \\\n",
       "16      17 -1.254720  0.431039  1.98          2         4        329   \n",
       "1        2 -1.368530 -0.985000  1.98          2         4        329   \n",
       "4        5 -1.432580 -1.191400  4.42          2         5        333   \n",
       "5        6 -0.987059  1.680400  2.42          2         4        328   \n",
       "22      23  1.047470 -1.852940  1.98          2         2        271   \n",
       "\n",
       "           r       phi  \n",
       "16  1.326694  2.810690  \n",
       "1   1.686149 -2.517734  \n",
       "4   1.863255 -2.447851  \n",
       "5   1.948853  2.101896  \n",
       "22  2.128516 -1.056281  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort \"r\" column\n",
    "hits.assign(r=r, phi=phi).sort_values(by=['r']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hit_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>volume_id</th>\n",
       "      <th>layer_id</th>\n",
       "      <th>module_id</th>\n",
       "      <th>r</th>\n",
       "      <th>phi</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>-1.254720</td>\n",
       "      <td>0.431039</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>329</td>\n",
       "      <td>1.326694</td>\n",
       "      <td>2.810690</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.368530</td>\n",
       "      <td>-0.985000</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>329</td>\n",
       "      <td>1.686149</td>\n",
       "      <td>-2.517734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.432580</td>\n",
       "      <td>-1.191400</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>1.863255</td>\n",
       "      <td>-2.447851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.987059</td>\n",
       "      <td>1.680400</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>328</td>\n",
       "      <td>1.948853</td>\n",
       "      <td>2.101896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>1.047470</td>\n",
       "      <td>-1.852940</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>271</td>\n",
       "      <td>2.128516</td>\n",
       "      <td>-1.056281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hit_id         x         y     z  volume_id  layer_id  module_id         r  \\\n",
       "0      17 -1.254720  0.431039  1.98          2         4        329  1.326694   \n",
       "1       2 -1.368530 -0.985000  1.98          2         4        329  1.686149   \n",
       "2       5 -1.432580 -1.191400  4.42          2         5        333  1.863255   \n",
       "3       6 -0.987059  1.680400  2.42          2         4        328  1.948853   \n",
       "4      23  1.047470 -1.852940  1.98          2         2        271  2.128516   \n",
       "\n",
       "        phi  \n",
       "0  2.810690  \n",
       "1 -2.517734  \n",
       "2 -2.447851  \n",
       "3  2.101896  \n",
       "4 -1.056281  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort \"r\" column and re-index\n",
    "hits.assign(r=r, phi=phi).sort_values(by=['r']).reset_index(drop=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creat an Event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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",
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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>hit_id</th>\n",
       "      <th>tx</th>\n",
       "      <th>ty</th>\n",
       "      <th>tz</th>\n",
       "      <th>tpx</th>\n",
       "      <th>tpy</th>\n",
       "      <th>tpz</th>\n",
       "      <th>weight</th>\n",
       "      <th>particle_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-6.71486</td>\n",
       "      <td>2.016780</td>\n",
       "      <td>10.41000</td>\n",
       "      <td>-0.171281</td>\n",
       "      <td>0.035818</td>\n",
       "      <td>0.258613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.36106</td>\n",
       "      <td>-0.979766</td>\n",
       "      <td>1.97000</td>\n",
       "      <td>-0.489971</td>\n",
       "      <td>-0.356742</td>\n",
       "      <td>0.712462</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-5.02722</td>\n",
       "      <td>-3.964340</td>\n",
       "      <td>15.21000</td>\n",
       "      <td>-0.262345</td>\n",
       "      <td>-0.186827</td>\n",
       "      <td>0.765993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.87547</td>\n",
       "      <td>1.929970</td>\n",
       "      <td>-3.56461</td>\n",
       "      <td>0.568398</td>\n",
       "      <td>0.232614</td>\n",
       "      <td>-0.416260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.42838</td>\n",
       "      <td>-1.188260</td>\n",
       "      <td>4.41000</td>\n",
       "      <td>-0.251019</td>\n",
       "      <td>-0.204107</td>\n",
       "      <td>0.768383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hit_id       tx        ty        tz       tpx       tpy       tpz  weight  \\\n",
       "0       1 -6.71486  2.016780  10.41000 -0.171281  0.035818  0.258613     1.0   \n",
       "1       2 -1.36106 -0.979766   1.97000 -0.489971 -0.356742  0.712462     1.0   \n",
       "2       3 -5.02722 -3.964340  15.21000 -0.262345 -0.186827  0.765993     1.0   \n",
       "3       4  4.87547  1.929970  -3.56461  0.568398  0.232614 -0.416260     1.0   \n",
       "4       5 -1.42838 -1.188260   4.41000 -0.251019 -0.204107  0.768383     1.0   \n",
       "\n",
       "   particle_id  \n",
       "0            3  \n",
       "1            2  \n",
       "2           10  \n",
       "3            4  \n",
       "4           10  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>particle_id</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>px</th>\n",
       "      <th>py</th>\n",
       "      <th>pz</th>\n",
       "      <th>q</th>\n",
       "      <th>nhits</th>\n",
       "      <th>pdgcode</th>\n",
       "      <th>start_time</th>\n",
       "      <th>primary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.417332</td>\n",
       "      <td>0.938952</td>\n",
       "      <td>-0.457557</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.493657</td>\n",
       "      <td>-0.352344</td>\n",
       "      <td>0.712380</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.166948</td>\n",
       "      <td>0.059120</td>\n",
       "      <td>0.264588</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573950</td>\n",
       "      <td>0.219286</td>\n",
       "      <td>-0.417693</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.296571</td>\n",
       "      <td>0.510891</td>\n",
       "      <td>0.733194</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   particle_id   vx   vy   vz        px        py        pz  q  nhits  \\\n",
       "0            1  0.0  0.0  0.0  0.417332  0.938952 -0.457557  1     27   \n",
       "1            2  0.0  0.0  0.0 -0.493657 -0.352344  0.712380  1     31   \n",
       "2            3  0.0  0.0  0.0 -0.166948  0.059120  0.264588  1     36   \n",
       "3            4  0.0  0.0  0.0  0.573950  0.219286 -0.417693  1     28   \n",
       "4            5  0.0  0.0  0.0 -0.296571  0.510891  0.733194  1     30   \n",
       "\n",
       "   pdgcode  start_time  primary  \n",
       "0       13           0        1  \n",
       "1       13           0        1  \n",
       "2       13           0        1  \n",
       "3       13           0        1  \n",
       "4       13           0        1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "particles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Noise or Not\n",
    "\n",
    "What does **left** and **inner** means here?\n",
    "\n",
    "```\n",
    "# whether noise is true or false\n",
    "if noise:\n",
    "    truth = truth.merge(particles[[\"particle_id\", \"vx\", \"vy\", \"vz\"]], on=\"particle_id\", how=\"left\")\n",
    "else:\n",
    "    truth = truth.merge(particles[[\"particle_id\", \"vx\", \"vy\", \"vz\"]], on=\"particle_id\", how=\"inner\")\n",
    "    \n",
    "# this is strange to build pt from tpx & tpy\n",
    "truth = truth.assign(pt=np.sqrt(truth.tpx**2 + truth.tpy**2))\n",
    "```\n",
    "\n",
    "Investigate as one should expect that why the pt is constructed from `tpx` and `tpy` in both cases. What is the impact of merging from `left` or `inner`.\n",
    "\n",
    "\n",
    "This is from Exa.TrkX-HSF Processing Stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if noise==false, then else clause will be executed.\n",
    "# first merge truth & particles on particle_id\n",
    "event = truth.merge(particles[[\"particle_id\", \"vx\", \"vy\", \"vz\"]], on=\"particle_id\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign pt (from tpx & tpy ???) and add to truth~event\n",
    "event = event.assign(pt=np.sqrt(truth.tpx**2 + truth.tpy**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.hist(event.pt, bins=10)\n",
    "plt.xlabel('p_t [GeV]')\n",
    "plt.ylabel('counts')\n",
    "# plt.xlim((0.1, 1.5))\n",
    "# plt.ylim((5, 40))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>hit_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>volume_id</th>\n",
       "      <th>layer_id</th>\n",
       "      <th>module_id</th>\n",
       "      <th>r</th>\n",
       "      <th>phi</th>\n",
       "      <th>tx</th>\n",
       "      <th>...</th>\n",
       "      <th>tz</th>\n",
       "      <th>tpx</th>\n",
       "      <th>tpy</th>\n",
       "      <th>tpz</th>\n",
       "      <th>weight</th>\n",
       "      <th>particle_id</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>pt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-6.72099</td>\n",
       "      <td>2.01500</td>\n",
       "      <td>10.42000</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>352</td>\n",
       "      <td>7.016547</td>\n",
       "      <td>2.850313</td>\n",
       "      <td>-6.71486</td>\n",
       "      <td>...</td>\n",
       "      <td>10.41000</td>\n",
       "      <td>-0.171281</td>\n",
       "      <td>0.035818</td>\n",
       "      <td>0.258613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.174986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.36853</td>\n",
       "      <td>-0.98500</td>\n",
       "      <td>1.98000</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>329</td>\n",
       "      <td>1.686149</td>\n",
       "      <td>-2.517734</td>\n",
       "      <td>-1.36106</td>\n",
       "      <td>...</td>\n",
       "      <td>1.97000</td>\n",
       "      <td>-0.489971</td>\n",
       "      <td>-0.356742</td>\n",
       "      <td>0.712462</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.390586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-5.03063</td>\n",
       "      <td>-3.96500</td>\n",
       "      <td>15.22000</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>359</td>\n",
       "      <td>6.405346</td>\n",
       "      <td>-2.474106</td>\n",
       "      <td>-5.02722</td>\n",
       "      <td>...</td>\n",
       "      <td>15.21000</td>\n",
       "      <td>-0.262345</td>\n",
       "      <td>-0.186827</td>\n",
       "      <td>0.765993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.321714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.88378</td>\n",
       "      <td>1.93577</td>\n",
       "      <td>-3.57089</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>5.253428</td>\n",
       "      <td>0.377371</td>\n",
       "      <td>4.87547</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.56461</td>\n",
       "      <td>0.568398</td>\n",
       "      <td>0.232614</td>\n",
       "      <td>-0.416260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.226291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.43258</td>\n",
       "      <td>-1.19140</td>\n",
       "      <td>4.42000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>1.863255</td>\n",
       "      <td>-2.447851</td>\n",
       "      <td>-1.42838</td>\n",
       "      <td>...</td>\n",
       "      <td>4.41000</td>\n",
       "      <td>-0.251019</td>\n",
       "      <td>-0.204107</td>\n",
       "      <td>0.768383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.321370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hit_id        x        y         z  volume_id  layer_id  module_id  \\\n",
       "0       1 -6.72099  2.01500  10.42000          2        12        352   \n",
       "1       2 -1.36853 -0.98500   1.98000          2         4        329   \n",
       "2       3 -5.03063 -3.96500  15.22000          2        13        359   \n",
       "3       4  4.88378  1.93577  -3.57089          2         6         15   \n",
       "4       5 -1.43258 -1.19140   4.42000          2         5        333   \n",
       "\n",
       "          r       phi       tx  ...        tz       tpx       tpy       tpz  \\\n",
       "0  7.016547  2.850313 -6.71486  ...  10.41000 -0.171281  0.035818  0.258613   \n",
       "1  1.686149 -2.517734 -1.36106  ...   1.97000 -0.489971 -0.356742  0.712462   \n",
       "2  6.405346 -2.474106 -5.02722  ...  15.21000 -0.262345 -0.186827  0.765993   \n",
       "3  5.253428  0.377371  4.87547  ...  -3.56461  0.568398  0.232614 -0.416260   \n",
       "4  1.863255 -2.447851 -1.42838  ...   4.41000 -0.251019 -0.204107  0.768383   \n",
       "\n",
       "   weight  particle_id   vx   vy   vz        pt  \n",
       "0     1.0            3  0.0  0.0  0.0  0.174986  \n",
       "1     1.0            2  0.0  0.0  0.0  1.390586  \n",
       "2     1.0           10  0.0  0.0  0.0  0.321714  \n",
       "3     1.0            4  0.0  0.0  0.0  0.226291  \n",
       "4     1.0           10  0.0  0.0  0.0  0.321370  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge hits with event~truth, first find r & phi \n",
    "event = hits.assign(r=r, phi=phi).merge(event, on=\"hit_id\")\n",
    "event.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign event_id to this event\n",
    "event = event.assign(event_id=int(event_prefix[-10:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hit_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>volume_id</th>\n",
       "      <th>layer_id</th>\n",
       "      <th>module_id</th>\n",
       "      <th>r</th>\n",
       "      <th>phi</th>\n",
       "      <th>tx</th>\n",
       "      <th>...</th>\n",
       "      <th>tpx</th>\n",
       "      <th>tpy</th>\n",
       "      <th>tpz</th>\n",
       "      <th>weight</th>\n",
       "      <th>particle_id</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>pt</th>\n",
       "      <th>event_id</th>\n",
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       "      <td>...</td>\n",
       "      <td>-0.171281</td>\n",
       "      <td>0.035818</td>\n",
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       "      <td>-0.98500</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1.686149</td>\n",
       "      <td>-2.517734</td>\n",
       "      <td>-1.36106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.489971</td>\n",
       "      <td>-0.356742</td>\n",
       "      <td>0.712462</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-3.96500</td>\n",
       "      <td>15.22000</td>\n",
       "      <td>2</td>\n",
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       "      <td>359</td>\n",
       "      <td>6.405346</td>\n",
       "      <td>-2.474106</td>\n",
       "      <td>-5.02722</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.262345</td>\n",
       "      <td>-0.186827</td>\n",
       "      <td>0.765993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.321714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.88378</td>\n",
       "      <td>1.93577</td>\n",
       "      <td>-3.57089</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>5.253428</td>\n",
       "      <td>0.377371</td>\n",
       "      <td>4.87547</td>\n",
       "      <td>...</td>\n",
       "      <td>0.568398</td>\n",
       "      <td>0.232614</td>\n",
       "      <td>-0.416260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.226291</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.43258</td>\n",
       "      <td>-1.19140</td>\n",
       "      <td>4.42000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>333</td>\n",
       "      <td>1.863255</td>\n",
       "      <td>-2.447851</td>\n",
       "      <td>-1.42838</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.251019</td>\n",
       "      <td>-0.204107</td>\n",
       "      <td>0.768383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.321370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hit_id        x        y         z  volume_id  layer_id  module_id  \\\n",
       "0       1 -6.72099  2.01500  10.42000          2        12        352   \n",
       "1       2 -1.36853 -0.98500   1.98000          2         4        329   \n",
       "2       3 -5.03063 -3.96500  15.22000          2        13        359   \n",
       "3       4  4.88378  1.93577  -3.57089          2         6         15   \n",
       "4       5 -1.43258 -1.19140   4.42000          2         5        333   \n",
       "\n",
       "          r       phi       tx  ...       tpx       tpy       tpz  weight  \\\n",
       "0  7.016547  2.850313 -6.71486  ... -0.171281  0.035818  0.258613     1.0   \n",
       "1  1.686149 -2.517734 -1.36106  ... -0.489971 -0.356742  0.712462     1.0   \n",
       "2  6.405346 -2.474106 -5.02722  ... -0.262345 -0.186827  0.765993     1.0   \n",
       "3  5.253428  0.377371  4.87547  ...  0.568398  0.232614 -0.416260     1.0   \n",
       "4  1.863255 -2.447851 -1.42838  ... -0.251019 -0.204107  0.768383     1.0   \n",
       "\n",
       "   particle_id   vx   vy   vz        pt  event_id  \n",
       "0            3  0.0  0.0  0.0  0.174986         0  \n",
       "1            2  0.0  0.0  0.0  1.390586         0  \n",
       "2           10  0.0  0.0  0.0  0.321714         0  \n",
       "3            4  0.0  0.0  0.0  0.226291         0  \n",
       "4           10  0.0  0.0  0.0  0.321370         0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The $x, y, z$ Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (+) Vertical Intersection ($x, y$) in Detection Layers\n",
    "\n",
    "As shown in the figure below, the hits are semi evenly distributed on the detector surface $x, y$.  The white circle in the center of the plot is where the beam pipe lies.  Thanks [agerom] for [the clarification][clar].\n",
    "\n",
    "The colors represent different detector volumes.  Thanks to [Joshua Bonatt's notebook][josh].\n",
    "\n",
    "[josh]: https://www.kaggle.com/jbonatt/trackml-eda-etc\n",
    "[clar]: https://www.kaggle.com/wesamelshamy/trackml-problem-explanation-and-data-exploration/comments#323803\n",
    "[agerom]: https://www.kaggle.com/artemiosgeromitsos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting with Seaborn & Matplotlib:\n",
    "g = sns.jointplot(data=hits, x='x', y='y', space=1, height=8, color=\"r\")\n",
    "g.ax_joint.cla()\n",
    "plt.sca(g.ax_joint)\n",
    "\n",
    "volumes = hits.volume_id.unique()\n",
    "for volume in volumes:\n",
    "    v = hits[hits.volume_id == volume]\n",
    "    plt.scatter(v.x, v.y, s=5, label='volume {}'.format(volume))\n",
    "\n",
    "plt.xlabel('x (cm)')\n",
    "plt.ylabel('x (cm)')\n",
    "plt.xlim((-40,40))\n",
    "plt.ylim((-40,40))\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.grid(True)\n",
    "plt.legend(fontsize=10, loc='best')\n",
    "plt.savefig(\"xy_barrel.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (+) Horizonal Intersection ($y, z$) in Detection Layers\n",
    "You can think of the chart below as a horizontal intersection in the detection surface, where every dot is a hit.  Notice the relationship between the different activity levels in this chart and the one above for $x, y$.\n",
    "\n",
    "Again, the colors represent different volumes in the detector surface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(data=hits, x='z', y='y', space=1, height=8, color=\"r\")\n",
    "g.ax_joint.cla()\n",
    "plt.sca(g.ax_joint)\n",
    "\n",
    "volumes = hits.volume_id.unique()\n",
    "for volume in volumes:\n",
    "    v = hits[hits.volume_id == volume]\n",
    "    plt.scatter(v.z, v.y, s=5, label='volume {}'.format(volume))\n",
    "    \n",
    "plt.xlabel('Z (mm)')\n",
    "plt.ylabel('Y (mm)')\n",
    "plt.xlim((-40,40))\n",
    "plt.ylim((-40,40))\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.grid(True)\n",
    "plt.legend(fontsize=10, loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (+) 3D Intersection ($x, y, z$) in Detection Layers\n",
    "\n",
    "And here is how the hits in this event look like in 3D.  Again, a sample from one event.  This combines the previous two charts in 3D.\n",
    "\n",
    "Notice how the particles penetrate the detector surface along $z$ coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Op1NrnHK5XIaKVJ7nNZGabyEGQDPyF0URPM+TSCUIoimgOxhBNAH5o0ILpX7zRWn+qFAzCwszRkqLwXEcHA4HHA4Henp6wBhDIpHQ7Kfi8ThcLpdWDuB0OusWSVU/a5Ikab/PrUklkUoQRKMgUUoQDYYxhmw2C1mWi1o9qcJtv1GhhPFUK5w5joPL5YLL5UJvby8YY4jH4wiFQpienkYikYDb7dbS/Xa73XCRqlJIpOam+0mkEgRRL0iUEkQDqcR7lOd5SJKEa9euFRwVambMECnVU5hxHAe32w23243+/n4wxhCLxRAKhTAxMYF0Or1HpBpFIZGa32RHIpUgiHpAopQgGkB+M1M5ndrJZBIrKysYGhoqOCqUMC8cx8Hj8cDj8WBgYACKomgi9datW8hkMvB6vVpNarlWX9WuJV+kZrPZPSJVnThFIpUgCL0gUUoQdabaUaFra2vo6+s7kILUDJHSesLzPLxeL7xeLw4dOgRFURCNRhEKhXDjxg1ks1n4fD6tJtVqtRq2FrXmVKWQSM21nyKRShBEtZAoJYg6okZHqxkVeuTIEa3uj7i74HkePp8PPp8Pg4ODUBQFkUhEs6CSZRk+nw9+v99wcV9IpGYyGaTTaW2tqkgVRbGszzlBEARAopQg6kI16fr8UaHLy8sHNppIkdLK4HleqzcFAFmWNZGaTCbx8ssv74qkGmkVVkykqp/13HQ/iVSCIPaDRClBGEwx79H9/n5iYgKxWGzXqFCe5/c1zzc7JFJqQxAEBAIBtLS0IBgM4r777tNE6uzsLDiOg9/v1/4Z2SCXK1LVB41MJoNMJgMAe0SqUZOvCIIwHyRKCcIgyvEezafYqNCDHk08yMdWL9SyEFEU0draitbWVgBANptFJBJBMBjE9PT0rkir1+s1TKSqn99CIvW1117DmTNnSKQSBKFBopQgDKBc79FcVlZWMD09jTNnzmjz1XM5yKL0IB9bM2CxWNDW1oa2tjYAOyI1FAphY2MDk5OTEARhl0g1ShjmitR0Og2e57V0f24kNb9xiiCIuwMSpQShM5V4jwJ7R4VaLJaCf0fCrfEclPffYrGgo6MDHR0dAHYil6FQCKurq5iYmIDFYtFEqsfjMVSk5ttPMcaQTqcLNk6RSCWIgw2JUoLQiWqamSoZFapGlYxEFdL7kUqlMD8/r3lm6pX2NYPgNkPda6nztx9WqxWdnZ3o7OwEAKTTaYRCISwvLyMajcJms2lNUx6Pp67TpvJFqjoSVRAErbufIIiDAYlSgtABxhiCwSCi0Si6urrK8h6tdFQox3ENbXRS3QB6enq02sR6pX2J+mKz2dDV1aV54iaTSc1+KhqNwuFwaOfd5XLVVaQqioJUKqX9TBWpaiSVRCpBmBcSpQRRI2ozUzKZRCQSQXd3d9G/z2azGB0drXhUqNHRRHX7+Td1RVFw+/ZtzQ1A/VtgJ+0bDoextraGiYkJWK3WXWnfcgWCGSKldzMOhwMOhwM9PT1gjGkidXZ2FvF4HE6nUzvvTqeTRCpBEFVBopQgqiQ/XS8IQklhpU7kOXr0aMWTmeolSnNJJpMYHh5Ge3s7zp8/DwBaQwqwk/bNrU1MpVJaRC0Wi+2KqBkpVogdqk3fVwLHcXA6nXA6nejt7QVjDIlEAqFQCNPT00gkEnC73dpIVIfDQSKVIIiyIFFKEFVQyHu0mGhUR4Vubm7i/PnzcDgcFe+z3qJ0fX0dt2/fxunTpzWT9lL7t9vt6O7uRnd3966I2szMDBKJBFwulyZSc98DipSaF47j4HK54HK50NfXB8YY4vE4QqEQJicnkUql4Ha7tZrUaj77laylkEhNJpO7Ov9JpBJEc0KilCAqIN97NPcmuJ+5fe6o0EuXLlVdd2l0o5MqDFXz/ng8jkuXLlU9V71QRC0WiyEUCmFiYgLpdBoejwctLS2QJMnQqUNE/eA4Dm63G263G/39/WCMIRqNIhwO7zrv2WwW6XRaGw5h1Fpymw4LiVR1HCqJVIJoPHQXIIgyyU/X59+8ConS/FGhtWB0oxPHcUgkErh16xY6Ojr2mPfn/l212/d4PPB4PBgYGICiKIhGo5oVkSzLiMfjWkRtP2ssYn+aMdrMcRy8Xi+8Xu+u8761tYUbN24gm83C6/VqEfRqH4LKXUu+SFUfMnNFqhpJ5XmeRCpB1BESpQRRBuV4j+amoPcbFVoLRqe4M5kMRkZGcObMGS1dbyQ8z8Pn88Hn88FqtUKSJLjdboRCIczPz4MxBr/fj0AgAJ/PZ+hozINEs4so9bzbbDbcf//9UBQF29vbCIVCWFpagizL8Pl8Wk2qkQ8n+4lUSZK077pqPWW1WkmkEoTBkCgliCLkR1KKpd5V0VhsVGgtGCVKFUXB+Pg40uk0Ll26BI/Ho/s+SqGKg0AggEAgAGBnqEA4HMbW1hampqbIfuqAwvM8/H4//H4/Dh8+DFmWNZG6sLAARVE0ger3+w0t8yhUkxoMBrGxsYFjx45pI1zVfyRSCUJfSJQSxD6o4w9zm5mKwfM8EokErl27htOnTxccFVoLRohSVUB3dXXB5/M1VV2nKIq7RmPuZz8VCATgdrvrIg6aMT2eS7OvrxxyHz6AHcu1cDiMcDiMubk5LYLe0tJi+Gc2t15cddeQJGlXTTmJVILQj+a5AxFEE1HNqFA12vjwww8bknLUu9FpdXUVU1NTOHPmDPx+P0KhUFnbN8J2qBzBvZ/91MLCQl3sp8wiNsywzko+x4IgoLW1VavJViPowWAQMzMz4Dhul0jVu8yDMaZF5QtFUrPZ7B6Rqk6cIpFKEJVBopQgcqhlVGhPTw8ymYxhNXB6NTopioJbt24hnU7j8uXL2nobactUzY17P/upXK/MQvZTROOp5cEmP4KezWYRDoexubmplXnkitRayzyKrVWtOc3923yRmms/RSKVIIpDopQg7lBpuj5/VKjNZsP6+rph69NDNOam60+dOrXrGBvtFVrLvsu1nwoEAoZ3eDeSepjnNxsWiwXt7e1ob28H8HqZx/r6OiYnJyGK4q4pY5WK1Ere00IiNZPJIJ1Oa9cUVaSKoljWdYYg7iZIlBIEsKdOrNSNQh0VarVatVGhsiwbbtlUi3BbXV3F9PQ0zpw5A5/Pp/v2a0HvfRezn1peXoYkSfD5fGQ/1SCMFM/5ZR7pdFo779FotOJRuLWstVyRqqb7SaQSdzskSom7mlLeo4XYb1So0aKu2ppSWZZx69YtZDIZXLp0aV8BVs6xq39jtohcrv3U4OAgZFlGJBIh+6kGUc/Pj81mQ1dXl/ZdzR+Fa7fbtXR/oYY5RVF0c3rIFanqdzmTyWije3me31OTShB3EyRKibuWQqNCi1FqVGgjZtOXIh6PY3h4GL29vejv7y96jOXWrBpxnPWO0gqCcGDtp8z0sNAI8muRVZE6Pz+PWCwGp9OpRdBdLpdhAjp37ClAIpUgABKlxF1I/qjQci705YwKbTZLopWVFczMzODs2bPwer0Grsr8FLKfUidNqfZTiqLAarUiEAg0rfAziyVUs0TaOY6Dw+GAw+FAT0+P5jMcDocxOzuLeDwOnufhcDjQ2toKh8Nh2LrLFan5jVMEcZAgUUrcVajdsbIsl52u13NUaC2UezOUZRk3b96EJEm4fPly2T6OeltOVUKjm6zysVqt6OzsRGdnJ4Cdh5Lx8XFsbm5iZWXFcPupg06ziNJ8OI6Dy+WCy+XSGuZmZmaQSCQwNTVVV1eHQiKVMYZ0Oo10Og2ARCpx8CBRStw1VOo9asSoUKOJxWIYGRkpK11fiGYShs2E3W6H2+3W6k6b1X6qWcVePmZZJ8dxsFqtsNvtWiQ139Uh99zb7XZD15LvkVpIpAKAw+HQuvsJwkyQKCUOPGoz0+TkJFwulxb9KoZRo0KNZHl5GbOzs1Wn6w9S971RqGKqlP2U1+vVhMpBtZ+qFTN8p4Cdh1M1WlnI1UE992ozodfr1RqnjHyQLSRSk8kkbt++jbNnzwLYibLmRlLN8p4Tdy8kSokDTa73KICyGnnUWkwjRoUagZqul2W5onR9PmYRho2imIF6IfupYDBI9lP7YKbPWe5Ep3x4nofX64XX68WhQ4d2WY/duHED2Wx217k38gElN92vjkRVFAWpVEr7GxKpRLNDopQ4sKhWT2p0i+f5oqI0txazmHVSM6Gm6/v7+9Hb21vTTYYipfqQaz8FYI/9FADDxmKa5T00S/oeqGyt+dZjiqJo535xcRGyLBv6gJK71kKRVBKpRLNDopQ4cOw3KrSYKFVHhQ4MDNQs7urF0tIS5ubmcO7cOXg8npq3d5CEYTNRb/spM3x2D6oozYfnee3cAnsfUBhju0RqtVkOlWKeqvuJ1GQyuSvKSiKVaCQkSokDRTHv0UKiNH9UqNvtrveSK0aSJNy4cQMAakrX50OR0vpQjv2UOg61kJn7QcBMolS9luhBoQcUVaTOzs6C4zj4/X7tX6VR9GKlBvmo10f17wuJVHUcKolUol6QKCUOBOV4j+abwxcaFarXWoy6eMuyjCtXrmBgYAB9fX26bvtuEobNRCH7qVAohIWFBUSjUc3MvRz7KTp/+lOJ0KsUURTR2tqqWc1ls1lEIhEEg0HMzMyA53mt1MPr9Za8RtUioAuJVPWamitSVSN/EqmEEZAoJUxPud6juZHS/UaF1ooq7PS+WDPGsLS0hGQyiYceesiQiG65ovQgTHRqZvInDiUSCc1+KplMwuVyFbWfMoNQMFOktJ5rtVgsu6Lo2WwWoVAIGxsbmJycLFnqofdI1EIiVZIk7W9UkSqKInieN805JZoXEqWEqanEe5TneciyjKmpqX1HhdaKEeJKTderxt5GlRg0On1P7CXXzL2vr6+k/ZRZIFFaHhaLBR0dHejo6ACwt9TDYrFo597j8ehaapBPoZrUXJHKcdyudD+JVKIaSJQSpmS/ZqZiyLKM5eVldHd37zsqtFb0FnZqA9ahQ4fQ29uLn//857ptO59GRyspUlqaUvZT6gPaxsYGWlpadKs31hsSpdWRX+qRTqcRCoWwvLyMaDQKjuNgsVgQjUYNr0cuJFIlSdJKqEikEtXQnFcsgihCrvdoJaNCb9++DZ/PhxMnThi2tlK2U+XCGMPi4iIWFxf3NGAZdZOkSGlpmk0459tPbW9vY2pqCtvb21p3txpJ09t+qlbMcs71TInrjc1mQ1dXl1aCtLCwgHA4jIWFBcRiMdjtdu38u1yuuovUbDa7R6SqNakkUolCkCglTIVaeF/NqNChoSEEg0FD16eHKJUkCWNjYxAEYU8DllE1q7nbbhTNJvjyMcMNlOd52Gw2HD16FIDx9lPV0uznOpdmipSWQhRF+P1+9Pf3axOeQqEQ5ubmEIvFKmqaqxWO43ZduwqJVEEQNKFKIpUASJQSJqGadH3+qNBIJKJLFLMYtQq77e1tjI6OYnBwED09PXt+r4peM5QemGXfB5lS9lM2m00TKfW0nzKT0DPrWguNw81tmkskEnC73Vp3v8PhqLtInZmZ0UoS1NIDNd1fbhaMOFiQKCWanmLeo/uxsrKC6elpnDlzRhsVyvO84cKnWnGV65d67733wuVy6br9StZBmJdSAkpP+ykj19lMmGmtpczz85vm4vE4QqEQJicnkUql4Ha7NSN/vZtAC62HMaal89WyrHQ6DWDnem2xWLTfk0i9OyBRSjQt+d6j5VyUckeFXr58edcYv3yfUiOoJn2fzWYxNjYGi8VS0i/VSFFaztozmQxu3LihmYDrNSqRIqWNoZj9lBpJa2lpQSAQgN1u122/ZhJ6Zlqroihl1w1zHAe32w23262l+6PRKMLhsObs4PF4tIcUm81myHpVEZ0bSVWvBZlMBplMBsDO9Sm/JpU4eJAoJZqS/HR9OTeFUqNC9WpCKkal4ioSiWBsbAyHDx9Gd3e37tuvlGLvTzgcxtjYGAYHByEIwq5Ric3aTEOUTzH7qfHx8T32U1artdFLrgvN3OiUTy1G/xzHwev1wuv17nJ2UD2ds9ms7ud/v/c2d+wpQCL1boJEKdF0VOI9CpQ/KrQeorTcfTDGMD8/j+Xl5aLp+kLbN0qU7vc+M8YwNzeH1dVV3H///bBYLFAUZdeoxFAohM3NTUxNTUEURS265vF4ynqgoEipPugZ1StmP7W0tARZlrW57ZXaT5kp+mimteopoHOdHQYHB6EoCra3txEKhXadf7UmtZqMSbmRXRKpdw8kSommIX+sXTkXlUpGhTZLTam6ZpvNVvF4UyNLEAqtPX+tPM9r5RQqoiiivb0d7e3tAF73TlxcXNxVpxgIBAxvpiCMI1ekHD58GLIsa3PbK42Ym0nomWmtRprnqyNP/X6/dv5VkbqwsADGmPaQ4vf7y3pIqVZEFxKpak1qrkhVG6dIpJoHEqVEU1CN92ilo0KboaY0EolgdHS06vGmRkYU87etOgHklxaUOje53om5dYpTU1NIJBLweDwIBAK76tQoUmo+1Lri3Ih5ufZTZhJ6ZltrvcRX7vkFdur5w+EwwuEw5ubmwBjToqg+n6+gSJVlWZf1FvJIZYwhnU5rjVOCIGhRVLW7n2g+SJQSDUdRFASDQczPz+P06dNlpeunp6crHhXayJrS/BS40+nUdft6oG4717i/UGlBJRfzQnWKagr4xo0bkCQJPp8PTqfT8HNzN9BIAVWJ/ZSZHkCMjD7qTSPrXwVBQGtrK1pbWwG8/pASDAYxMzMDjuN2iVRBEAy1t8sXqYqiIJVK7VpvbiTVLOf4oEOilGgY+c1Matq+GOl0GsPDw/D5fBWPCm1UTWk2m8XIyAgcDoeWAq8Wo0WpLMsYGRkBx3H7lhbUsv/cZorBwUEtBby+vo5wOIxXXnllVwq42VJuZhJTjSbffiqZTGrThsLhsCYK6mHkXgv1jD7WSjOtNf8hJZvNIhwOa7XngiAgnU4jGo3CarUaum4SqeaBRCnREPLT9epTczE2NjYwMTGBkydPahe6SqhHijh/H2rH+rFjx7Sbcy0YWRebSqWwtLSE48ePo6+vz5B95KOmgB0OB7LZLIaGhhAKhbC+vo7bt2/DarVq9aj1NHcvBN2kasPhcMDhcKC7uxurq6uIRqMAsMt+Si3r0NN+6m6imaO6FotlV+15JpPBq6++is3NTczOzmoNki0tLfB4PCRS71JIlBJ1R5KkPd6jxaKYiqLg9u3biEajuHjxYtV+efW4qKjHwRjD7Ows1tfXa0rX52OUsF5eXsbc3Bza29vrJkhzyTXS7ujoQEdHB4DXzd3n5+cRi8Xgcrm0Gxc1Te3FTPWPNpsNfX19prCfMst7aib7KqvVClEUcfLkSfA8rzVILi8va9HTXJFq9LSpQiI1mUzuaqoikWo8JEqJulHMe3Q/UZo7KvTChQtNfyFQyxBeffVVuFyuiksMytm+niUIucMGTp48iXA4XNbr9BY/+22rkLl7MBjUJtDkNk01WrgQ5ZP/YFWu/ZQ6sKES+6m7iWaOlBYi9zqS2yAJvP5Auri4iFgsBrvdrtWkGp01yQ2WqOtUFEWL8Pf395NINQj6ZhN1odSo0EKitNCo0GYnlUppDVtqtE9P9IyUJhIJXL9+Hb29vejv70cwGGxozWSpfec2TfX39xcULupNi4RL81PsJl7Mfmpubg4A9jTNEM1VU1ou5T6Q5mdNVKs5v98Pl8tVF5GqZsHU+1VuJFUURe0fidTqoas2YSj5o0L3u2DmitJio0KbFcYYZmZmsLGxgYGBAUMEKaCfKF1dXcXU1BTOnj0Ln8+n67aroZoLeCHhEg6HEQqFMDs7q/kqBgKBPZZEBxWzpO8rXed+9lO5TTO5AxvuhnNdCDOl7yuB4zitJrmnpweMMSSTSe27Ho/H61LaI8uyJjjzI6myLEOSJO1vVSN/URTB87wpvpfNAIlSwjAYY8hms5BluaT3qPq7UqNCm5FMJoORkRG43W4cPnzY0H3Vmr5XFAXj4+NIJpN7BH+jvUJr3Xe+JU0mk0E4HN5jSRQIBAyPrBDFqVU872c/tbKygvHx8V32U41ukKsnZkvfVwvHcXA6nXA6nejt7QVjDPF4HOFwWPNDdrvdu0SqHhQbi5pfk5orUjmO2xVJJZG6PyRKCUOoZlRoJpPB6Oho0VGhzUYwGMTNmzdx/PhxdHR0YHFxEbIsG7a/Wrrvk8kkhoeH0dHRgaGhoT3npNGRUr33bbVadzVNFYqsqPWoet20iMawn/1Ufqq32e2nasWM6Xs94DgObrcbbrd7T+Pc7du3kUqldonUat0dZFkuqyyokEjNb/AlkVoYEqWEruQ3M1UyKlSW5YrHbjaKXAP/CxcuaBe5QmM49aRa8abaaZ0+fVqbwKLXts1CfvovHo8jGAxiYmJC6/ZWG2nM2jRlpvS9keIp134qd6rY9PQ0ksmkLgKlGTmo6ftKKdQ4p4rUW7duIZPJwOv1anXJ5Tq61DIWlURqeZAoJXSj1lGhyWSyLoK01ht3Op3GyMgIvF7vnu56o4VdpdtXFAWTk5PY3t7GpUuXioqtct8TIy6W9RbEuZEV9aalzvFeXFyEoii7mqbM8KBkJuopngtNFWtm+6laMYuYqef3ned5bWjHoUOHtCZJ9f4jSZL2GSj2UKrWlNZKIZGazWb3iFR1LOrdJFJJlBK6oEZHK0nXq5FG1cdzenra8HWq4qfaL7iarj9x4oRmAp2Lkeb2lW5fnX7V0tJSlp2W3nZTZkJtivL7/Th8+PCuEYnT09PajUG1hqFoVG00MqJbKIqmPpAUcnEgjKGR36PcJsnBwUEoiqK5O6glWD6fT/sMqLX3Ro5FzRW7hUSq2jQlimLZQR8zQqKUqIlq0vW1jAqtFbXLv9J9MsYwNTWFYDC4K12fj9HCrtyI4tbWFm7duoWhoSGt8aeZabbSgUKNNBMTEwiFQtjY2IDdbteappqpRtEs6ftmIv+BJN9+Kh6PY2pqiuyndKaZHu54ntci5QB2fQbm5+fBGIPf70c8HtccIIykkEjNZDJIp9Paei0WixZJPUgilUQpUTWlvEcLUWpUqNE31WKTo/ZDFdF+vx8XL14seiGthygttn01Ar21tVVUPO+37WYShs2E1WqFx+OB3W5HR0eH1jQ1MzODeDxOIzIrpJnFc679FGMMV69ehdfrJfspnWkmUZpPIQuySCSCra0tTE5OQhCEuvrk7idSM5kMgJ37Wn6636yQKCUqplzv0VzKGRVaa2q9HCoVpWrEcT8RXWj7jaopzbWmKiWeK9220ZhJEBeyo4nFYggGg1oThZr6a2lpMYXPbr1pZlGai9qQlT+zneynakdRFNNEnUVRRGtrK1ZWVnDkyBFYLBZNpE5PT2vR9paWFni93rqKVPW6qYrUP//zP8djjz2Gxx9/3NA1GAWJUqIiKvEeVSl3VGi1qfVKKFeUMsYwOTmJcDhcUcSxmkhsJewn3sLhMMbGxjRrKj23TRQnt0ZRbaJQU38LCwtgjGmixeioilnOn1lEaSHfz0L2U4UmDR10+6laaeZI6X6ojU4Wi2VXeU82m9VKeyYnJyGK4i6RauRxqp8v9bqyublp6gdhEqVE2VTqPQpUNirUaEEHlCe8UqkURkZGtHR9JTeVenTf575HjDHMzc1hdXVVaxirZdsUKa2d/Pq0/OlDoijuSv/qLVrMIILMIkrLsa7KtxrLtZ9KJBLweDwH0n6qVmRZNqUoLbRmi8WyyxNZjaargzusVqsmUo0u+VA9mM0KiVKiJNU0M1UzKlQQBEON54HSwndzcxPj4+NVNwgZLa54ntemhKj+rjabDZcvX675QneQhGEzkd80lU6ntS7faDRKkbUmplLx3Ej7KbN9d80YKS235CA/mq5+55eXlxGNRg0t+VBr3M0KiVKiKIwxbG5uIpPJoLW1tawvT7WjQusRKd1vH6qfZyQS2bfmtZbt64UaKd3e3sbo6CgOHz6M7u5u3bbdyEipGdDj/bHZbOjq6kJXV5c2w1u1nsqNrAUCgYo/h2YRJmaKlNayzkrtp8qZFmTUWuuNWUVpNe9x7nceeH3i2MLCAmKxmObm0dLSUvMI5FgsBo/HU/XrGw2JUmJf1GamWCyGZDJZstGHMYaFhQUsLS1VNSq0UaI0lUpheHgYgUCg4nR9PvXw+gyHw1hbW8O9996ra5qGIqXFMWpogNo0pUbWotEogsGgZuqd2zRV7ojDZscsAkrvWfKl7KcAVN3VbZb3VMWMohTQ5/uVP3FMrUuem5uruS5ZfbA1KyRKiT3kp+tFUSwptNRUstVqrXpUaCNqSlWLqlOnTuniP2dk970kSZifn4csy3jggQd0b5gpV5Sa6cZnNjiO0ybPDA4O7hEtHMfB7/cjEAjA5/OZ8qYOmEdAGT0ONd96KJvN7qk/Vs93qVpEs4k8s63XKAq5eeTXJbvdbu1hxeFwFP3uJBIJOByOOh6BvpAoJXZRyHu0lFjMHRWqpieqoZ6R0nIsqqrBqGhjLBbTpjOJomhIBzdFSpuP/UTL+vo6bt++DavVqqX63W43nT+dqbd4tlgsVdtPmU3kmW29QH0eyAvVJcfjcYRCIUxOTiKVSsHtdmslH/kClDFmGqutQpAoJQDs9R7N7a4XBKGgWGSMYWZmBhsbGzV3fgP1E6WpVAovv/wyWltbyxq/Wen29T6G5eVlzM7O4ty5c0in09ja2tJ1+yokSpuffNGSSqU066loNApBEGC1WrVoSbNGI80UKW3kOiuxnzLbVB8z+ZQ2Eo7j4Ha74Xa70d/fr5X4hMNhTExMIJVK4a/+6q9w+fJlvP3tb695f7Is4+LFi+jt7cU3v/lNBINBPPXUU5idncXg4CCef/55zVnECEiUEnvS9fkXNp7n93TFp9NpjIyMwOv16jYqtB6iNJlMajWvRoyL01PY5TsYiKKoWXIZAYlS82G329Hd3a3Vpi0sLOyKqOQ2TenZ6V0rjRZ75dJs6yxmPxWPx6EoClZWVkxhP2U2Uap3fXG15Jb4DAwMQJIkJJNJvPjii/jUpz6FhYUFfPrTn8Zb3vIWPProoxX7Vv+3//bfcOrUKWxvbwMAnn32WTz++ON45pln8Oyzz+LZZ5/FZz/7WSMODQCJ0ruecrxH88ViqVGh1WKkKFXT9ZFIBEePHjVsfrFex5BIJHD9+nX09vaiv79fOy9GCkcSpeaG4zjY7Xb4fD4MDg5CURREo1GEQiGMjo7q2uldK80m9vajmdeZn+aNRqOYnp5GNpvVJosZZT+lB4qimMrkvVnLDURRxKOPPopHH30UiqLgkUcewUc+8hH86Ec/wv/8n/8T29vbePDBB/HYY4/hve99b9HP8+LiIr71rW/hP/yH/4D/+l//KwDga1/7Gl566SUAwNNPP41HH32URCmhP7np+lLeo2r63qg6TBWjRGkymcTw8DDa29vR19dn+HSNWoXd6uoqpqamcPbsWfh8Pt23TxxcckUUz/Pw+XyaSJVlGeFwGKFQCLOzs+A4TouiGj11ptg6m5lmFSL7YbVaMTAwYLj9lB6YzTxfnebUzKTTaTidTjzyyCN45JFH8B//439EMpnEP//zP+PatWslv3N/+Id/iP/8n/8zotGo9rO1tTXNdrC7uxvr6+uGHgOJ0rsQxhgymcyuZqZi8DyPTCaDK1eulBwVWgtGiFK1IeT06dNoaWnB/Py8oSUCtRyDoigYHx9HMpncd+CAkd395Z7TTCaDaDQKr9drCmFB7CAIAlpbW7WhEOpoxLW1NUxMTNAM9wKYRTwDewV0Mfsp9aGkWvspI9bb7JhhvYWmOTkcDjz22GN47LHHir72m9/8Jjo6OnDhwgUtMtoISJTeZVQzKnRjYwORSASXLl0qOSq0FvQUpYqiYGJiAvF4HJcuXdJSV4XqY/Wk2huYGs3t6OjA0NDQvttpdKQ0HA5jbGwMDocD8XgcTqdT6w43sw1JuTR7lLoSEZU/GjG/icblciEQCGg2NI1aZyMxyzqB0qJJT/upeqy32TBDpFT93lbDz372M3z961/Ht7/9baRSKWxvb+NjH/sYOjs7sbKygu7ubqysrFRco1opJErvEmoZFZrNZuHxeAwVpIB+gjGRSGBkZAQdHR04efLkrptK7pjOZkGt0VWjucWohzl/IRhjmJ+fx8rKCu677z4IggCO45BIJBAMBjExMaGNUFSFjJnqxcrBLOKkWvKbaFQbmtxzq1d9olnEnlnWCVS+1lrsp/TAbKLUDI1ZtYwY/bM/+zP82Z/9GQDgpZdewl/8xV/gb//2b/FHf/RHeO655/DMM8/gueeew5NPPqnnkvdAovQuoNJ0PbB7VGhPTw9+8YtfGL5Onuc1S6pqWVtbw+TkJM6cOVNQRDdK1BVCHW26vb29K5pbjEZESiVJwtjYGERRxOXLlwHs3MByGy36+/u1GrZgMIiFhQUwxrSaRTMbvd+N5NvQ5NcnKoqyqz6x0pu1WcRes3Rcl0OtIq8c+6ncyHmt74vZRKkZamALpe9r5ZlnnsGHP/xhfO5zn8PAwABeeOEFXbefD4nSA44kSQW9R/ej1lGhtWB0PWat+9CTdDqtmeFXUqNbb1Eai8W0h5Pe3l4A+9+oc2vYgJ3PXigU2mX0rqYPa53vTBTGqM9Gfn2iJEmIRCIIBoOYnp6GIAjaA0g9Ur/1wuiJTnqit8jbz35qamoKyWRSM3Cv1n7KjKL0IEdKc1G7+QGgtbUVP/jBD2reZrmQKD2glPIeLYQeo0JroVrBmEgkMDw8jK6urqL1mLXsQ0+2trZw69YtDA0NaU0n5VJPUbq6uorp6WmcO3euqlnKoijuMXoPBoPafGe3261FXprdU9FM1EPsi6K4q2lKTf0uLy8jGo3CbrdrgqXQA4hZIqVmWSdgbFS30JShWCyGYDBYtf2UGdLhuZhBRNdSU9oskCg9gBQaFVoKvUaF1kI1grGYfdJ++2hUswpjDNPT09ja2sKFCxeqEmL1ENVq1DmVSuHSpUu61Yba7Xb09PRokZfcm1o2m0UqlcLm5mZT2NUQlVEo9RsMBjE7O6tFb9RIqt1uN43YM8s6gfpGdTmOg8fjgcfjwaFDh6qynzKDyMvlboqUNhK68h8g8keFlvOF13tUaC1UIrhkWcb4+DjS6XTRdH0+jaopzWQyGBkZgdvtxsWLF6u+GBsdKVUUBVevXi3pAlAr+Tc1WZZx5coVRCIRza5GjaLW20PTzDSLO4DD4UBvby96e3u1B5BQKKRF1bLZLLa2ttDR0dHUDXFmEqWNFHn72U+pDyaF7KfMUKOZixlEtJqBMjMkSg8IjDFks1nIslx2dNSIUaG1UK4ojcfjGBkZQXd3N06dOlXRTaMR6Xs1Cn38+PGa7TSMFKVbW1tIJBK4cOFCybICvW/WgiBAFEUcPXoUwI6ID4fDWF1dxcTEBOx2uyZSnU6naYRCI2i29yb3AUQ1dX/11VcRj8cxPDwMxphmRdQIv8ximKnRqZnqX8uxn0omk4hGo6ZpgpRluemmYuWTSCS02n+zQqL0AFCt92ilo0KNjhqoT8/FWFlZwczMDM6cOVNWuj6feopSxhjm5uawurqqWxTaiPIDNVq+ubkJp9NZVp2r0Tdqq9WqeWgyxrRO4OnpaSSTyaad6X4QSEgJfH/h+5iLzqHN3oZ3DLwDAbt+Y3l5nocoihgcHITVaoUkSXsES27TVCNFYTMJvVIoitK0ZS+F7KdefvllrK6u4vbt26YY3GCG9H0ikaCaUqJxVOM9Wu2oUDVCZ+TFolhqXZZlrfawljrHetWUZjIZjI2NwWaz4fLly7rd2PSOlGazWYyMjMDpdOLixYtlWX/V+4bBcRycTiecTqeWDo5GowgGg7tmugcCgarsiYjXYYzhH6b/ATPbM2ixtWAhvoC/nfhb/M7p34Fd1K8ZLfdaIooi2tratIfjdDqt1SZub2/D4XA0LEpO6XtjsFqtsFgsOHXqFADj7af0wAzvbywWq6optZkgUWpSGGNIpVIYGxvD2bNny/rS5prKVzoqVBAEw7+U+wlG1Zaot7cX/f39NV2g6lFTqtZlHjlyRJsZXPZrEwnEvvUtZGZmILa3w/2ud0G8E10A9F3/9vY2RkdHceTIkYY1t1UDx3Hwer3wer27Zrqr9kRGRtqapWZzP2oVUQkpgdntWXQ6OsFxHGyCDevJdWykNtDv7q/LOm02G7q6utDV1aVFydVzm0gkdkXJy32oNmKdzYaZ1pqP0fZTemCWSCnVlBJ1R42OqhGjci5EKysrmJ6e3tdUvhTqtCUj00Oq8M1leXkZs7OzOHv2LLxeb837MDJ9zxjD4uIikskkHnjggYrXyxjD9pe/jMzMDITWVmSXlhD+vz+PwO/9HnjnzphHvSKlS0tLmJ+fr7sXrRHkz3RXI22Li4uIRqO6jUI16w0/I2fwk5WfYHp7Gm32Nryl9y3w2/x7/o4xhtntWawmViEpErpd3eDAQWYyLHxjmpFyo+SqFVE0GtXqtCVJgs/n0wSL3tcnM9WUmiGSVw6F7KfUc642yqnn3O/31618xwyNWUaY59cbEqUmIj9dX85TmzoqVJKkirrU86lHLWbumFF13bIs4/Lly7rdbIw6DkmScOPGDfA8D6/XW5X4YcnkjiDt6to5v62tkFZWIa2vwTo4CKB2YZT7vl66dKlpa9BqIT/SdjeNQi3Et+a+hZHgCPxWP8bD41iKL+G3Tv3WnnT8j5d/jB8v/xgCJ2AkOIL52DwG3AO4r/0+dDo6dV1TtVG93Ci56toQiUS01C8ArZTD6/XWHNkyW02pWdZaCfnnPNd+anFxsSz7KT0wg69qPB6n9D1RH6rxHlVHhfb396Ovr68mQVNOE1KtqIJRTdf39fXVvO5C+9A7BRuLxTA8PIxDhw6ht7cXV69erWofnCgCHA9IEmCx7GyDKeB0Ek7qkIGenp6iZRBmTgPmc7eNQs0XUWk5jbHgGLod3TtRR9GJtcQaVhOrGPQO7vq7n638DJ2OTvS6etHr6sVifBGP9z+OB7se1P3zoNdnbL8u742NDUxOTkIURe0BpJpSDjN9F8y01looZD8VDocRCoX2tZ/SA7NESs2e+SJR2uRU6z2qjgqtdhpPPvWKlKZSKQwPD+u27nz0rilVywty11vte8VZrXC/41cQ++a3AJ4DkxU4zt8Psaen5nWqbgulyjfq0dDWSO62UagCJ4DjOEQyEWSUDCycBQpTIPC7b9SSIoGBgTGGeDYOt9WNDtaBQc8gBE7/6JBRn7H8Lu90Oo1gMLinlKPcBhozfRfMFCnVMzCQX75TyH5KLe+oZQQuRUrrA4nSJqYa71GjRoUaLUolSTIkXZ+PXseRXxaRu95aorHOBx+E2NUFaX0dgtcL68mTNd0UGWOYnJxEJBLBpUuXStZfNfoGXG8RsN8o1NxJRLmROLMh8iIG3AP42szXwHM8FKbgdMtpdDl2N7Y5RScCtgBeWnkJIidCZjJOtZxCu6N9ny2bA5vNhu7ubnR3d+9qoJmcnEQqlSppLUai1BiMXGuhB5PcEbjV2k+ZodEpmUyafmQzidImpRrvUSNHhRqZvlfT3wMDA9je3ja0zlGPRqFEIoHr16/v6wZQazTWevgwrIcP17RGYMeWanh4GD6fr2y3BXXtet0w5FAIyR/+EHIoDMuxo3A8/PBOmUKTst8o1Bs3biCRSMDpdMJqtTbtKNR8ESUpEhZiC7i39V4k5SRsvA3ggK30Frqcr18jJCYhlo2h29GNtJKGlbeCMYa0nIZNMKbDvRHWYrkNNIqiaA00Y2NjkCRpT22imUSpmdZaTwGdW2MO7LWfcrlcmkgtFj03i+g3wxqL0XxX1bucSr1HOY6DLMuYm5szdFSoUZHSxcVFzM/Pa+lvtVnBKGq9aK+urmJqagpnz57d17zf6FGg5RAOhzE2NoYTJ05oEYN6oySTiD73HJR4ApzDgdSPXgKLRuF697sL/n2zlQ7kj0Kdn59HOp1GOBzeNQpVtZ5qxptBSk4hLafR4+zRUvbryXWk5NSuv0tkE8iyLM62ntV+tp5cx3ZmG15r7a4XzQjP8/D5fPD5fJq1WP5oTEVR4HA4TFFvbBbRBDR2rbXYTzXLtakQzXTtrAUSpU0EYwyZTKaiZiaO4/Dqq6/C5/MZOipUb1GqdqsDMDRdrxeKomB8fBzJZLKki0EjRpmqqPXEy8vLVT2g6NkIJi8tQdnehnAnQsGcDqRffQ3OJ55o6mjpfvA8D5fLhZ47Nb6ZTGZXWrDZRqFuJDfw5ckvYy46h4nwBO5rvQ+iIMIqWNFuf/1BJZaN4bsL38V4aBwL0QWcCZzZuf6Ag89a+dQ0s1KoaWp0dFQ7x2q9cbNOHSJRWjmV2E816ppeCQdBmJrvznBAUZuZKh0VGo1Gcfbs2YpN2itFz/S96gqgdqs3O8lkEsPDw+jo6MDQ0FDJc9OoSKkkSRgbG4MgCLh06VLV9U+6rZ3nAYW9fqFUlJ2f7fP+NUOEuRKsVis6OzvR2dm5x+S9UaNQ1fdaZjK+PPllpKQULndcxujWKF7beg1v7HojPnj0g3BZdrwMFabghckXsBJfwZnAGVzfuo5frv0Sp1pO4QNHPwCP1dxNE7VgsVhgt9vR19cHj8ej1Rvnp31r9b/VCzMJkmYRpfkUs59KpVK4evVqXeynqsFM185iNM87epdS66hQ1fbCaPSI/qnm8ouLi6YxbVe71k+fPo2WlpayXtOISGk8Hsfw8LBm/1UtlQhDxhjk5WUosTiE9jYIec1A4sAAxIF+ZOfmwFmtQDoNx6/8CrgmbxYoxn7vTb7Ju1qvmDsKVU0J1mMUaiKbQDgd1upGz3ecx3pyHU8efhLdrtcfYGPZGJbjy+hwdIDjOLy5581Yii3h14//Oo74jhi6RjOQK/Ty643j8ThCodAu/1v1HNfrISSXZhV6hTDLWnPdOjY3N3H+/Pm62E9VQzKZNKR0r96QKG0g1XiP5o8KHRkZqYsAqlVoqVE8nudLugI0wxO/oiiYnJzE9vZ2WV3rudQ74re6uorp6Wldpl7tt3YmSYj/4AdIXrkKWERY3/IWsM0tpH72My366f7Qh2AdOvn6tkQRno9+FOlr16CEwxAOHYL15Mk92y6172ahks9kbr3i4cOHIUlS3UahAoBDdEDkRUxGJpGSU7DzdjgsDrgtux8E1UlNG8kNpJWdpiaLYDmwdaSVst9EJ47j4Ha74Xa7Nf9b9SFkaWlJM3QPBAJ1eQhR12oGoQeYw14pF/W6VC/7qWo4CNOcABKlDSHfe7RcQVpoVGjuFCQjEQQB6XS6qteqM9YHBwe1erz9aIZml3Q6jeHhYbS0tJTdtZ5LvSKliqJgYmICiUQCly5d0mU60X7CMPHTnyLx43+C0NUJJsmIffFL4BQF4vFj4HgeSjKJ2Ff/AS1/9Ee7IqGczQb7Aw+Uve+DiiiKaGtrQ1tbG4C9o1D1SgVrN09OQIu1Ba9svAILZ0GWZXFv4N49YtMhOuCz+fCjpR9pllEX2i8gYDOnBZbelDvRKf8hRDV0DwaDmJmZAc/zux5CjBArjb5uVoIZjOhz2U/wG2U/VQ1qSYnZIVFaZ/LT9eV8SIuNCq3HpCWgOqGVa+Jfbrpe3U+jLlhbW1u4desWhoaGtKfhSqlHxE8dMtDW1oaTNXqZ5rLf2tM3boIPtICzWMCJFoDjoMTj4O6cJ97hgByOgGWzNaXn6x0pzUxOIjN2A5zdBvvlyxDKLNGolfxRqIVSwdWOQuU4DgkpgY3UBt7c/Wak5BQsvAWxbAwbyY096ftQKoTLHZeRltOw8BZIioRwJoyAnYRptUIvP6KmNsWtrKxgfHwcNptNO796Dmkwiyg1U1QXKH+9etlPVQNFSomKMWJUaL2icpXuR5IkjI6OQhTFikz863U8+Tcbxhimp6extbWFCxcu1GRAbPQxyLKMV155pSbhvB/7iVLe44G8tQXcuejxggDZbtsRpk4n2OYWhO5ucLbqPS3rnb5Pj44i/sLfAXYbIMnIjIzA99u/Db4ONdq5FEoF6zUKVWYyFChgjIGBAXmXnO3MNlJKCgFbAK32nc/SWnINWSWr5yGaFr2ij7lNcQC0prjcIQ3qOTa7+Xk5mE2UVmucv5/9lDq8oZj9VKUchBGjAInSupCbri+3mancUaH1ipRWsh81XX/48OGKXQHqNc4092aTyWQwMjICt9uNixcv1nyxNEpcMcYwOzuLVCqFN77xjYYUte+3dvfb34bQ5z4HaXkZTGEQBw/B8+hbEP/GN6CsrkHs7YHrgx80TaQGAFI/+Sk4nw+8e0doy0vLyExMwH7pUkPXlT8KVa1bK2cUqvq5dopOtNpb8cPFH0LgBchMxn2t96HD0aH97VRkCs9PPY+l2BImw5M4HTgNG29Dq72VoqR32K+mtFYcDgd6e3vR29urDWkIhUIYHx9HOp3WbIiqiZSbgbtFlOZSif2U3++vuFmOIqVEWVTjPVrJqNBmSt8zxjA/P4/l5WXce++9VX1B6iFKc6cWqVOwjh8/jo6OjtIvLgMjjkH9TNjtdng8Hl27e7Nzc0gND4MTRfAuV0FRKnZ2IvC7v4vs3DwgCkBfH3i7HZYTnwEkCZyB9ayGocgAn/d9bEIvwvy6NTUlWGwUakbJIJgOYqhlCEkpCbtghwwZ0UwUfpsfaTmNr0x/BW7RjQc6H8B4eByTkUk8MfAE3jX4Lq0B6m6n3JrSWsgd0jAwMFAwUq42TTW6w1svzCZKjVhvMfupxcVFKIqyS6SWsp+iSClRkmpGhaqTeModFSoIQlOk7ysR0rXsRw/U5rDFxUWsrq7qPgVLb3GllnCokecrV67otv301BQin38OsIjgFAXWyDbkvj6gQGRe8Pkg3HNOe9AC7tSwmTSSY3vDg0h87WtQMlmwbAacwwHLiRMF/1ZaWwMbHgasVrC2tprKFGolPyWYOwo1Ho/D4/EgJsegyAoOeQ5pr1tPriMhJeC3+RHPxpFVslpD09nAWawn1/FY32PUeZ9DI5qH8iPlqnPD1tZWwzu89cJsorQec+9zz3tus1y+/VQgEIDX692znlgsRqKUKEw13qOMMczMzFQ8KrSe3ff77ScSiWBsbKyqdH0+9aopHRkZgdPpxOXLl3W/OOp5DEtLS5ibm9vVKKan6E3+9GfgnA4Id26A2AoiMzoKHKm/R2W9I6W28/eDs1qQGR0F53DA8dBDBRudMlNTiH3xi0A0BjAF21PT8D79iYYKU5X8UahTU1OIyBF8efLLeC30Gka4EZz1n4XNZoPVYkWLbef4VPP8a5vXkJEzsApWdDo7SZDm0Qwd7fs5N+RPEpNluSnWWw6KojSV8XwpGiGi97Of2tjYwOTkJERRxObmJkRRxBvf+EYkEomqRGkqlcKb3vQmpNNpSJKED37wg/iTP/kTBINBPPXUU5idncXg4CCef/75sr26a8E8nwqTwBhDMBhEKBQq2JhUiHQ6jZGREXg8nopHhQqCoFlLGUkhocUYw9zcHFZXV3HffffpEm00OvK7vb2NSCSC48eP49ChQ6VfUAV6iCtFUXY5LuRewPUUvUyRd01Y4ngeTCr9kGOGG18pOI6D7dw52M6dK/p3ye99H5zTCTicABjkpSVkxsdhu+ee+iy0AhROwYuhFyG6RbzZ+2a8uvEqRqOjOJE9gYfsD+H2jds7kRafFxbOgmAqCJ7nsZ3dRr+7H1a+/qbvzUwzirx85wa1nCOTyeDKlSu7JonZmuDBqRD1iDzqSTOst5D91I9+9CM899xz+Nf/+l/D6/Xi1KlTePjhh3H27NmydYTNZsMPf/hDuN1uZLNZPPzww3jiiSfw93//93j88cfxzDPP4Nlnn8Wzzz6Lz372s0YeIgASpboiSRKy2SwymQy2t7fLuphtbm5ifHwcJ0+e1J6EK6FRNaVqut5ms+kabeQ4zpDjyZ0m5ff7q3qvy4Xn+ZoeFJLJJK5fv47u7m4MDAzs+RzpGVF0PPAAIv/rfwGMAbIMcIB4+lTJ12WzWYiiqOsNu1nN81kyuTORKpMBGACOA7tTvtBsxKU4olJUS9s/0vMIVhOr+M1Tv4kuZ5fW9T08OYzltWWccZ4Bb+XhdXoRlsIIp8Nocxj33TAjzSZKc1EniTkcDiwvL+PixYta88yNGzeQzWZ3jcVslqYpSt/Xjs1mwzve8Q684x3vAAD88R//MSRJwrPPPovR0VGcOnUKjz32GB577DEcO3Zs38+x6v4B7FzX1Ybsr33ta3jppZcAAE8//TQeffRREqVmIT9db7FYSgqr3FGhFy9erPqJthHp+0rrXivBiPS9JEm4ceOGNk1qbGzM0GhsLeKqnLGmeoo3++nTwMc/jvQrrwCiCPnhh8EVafhijGFiYgLr6+taoX6+n6aSSgHZLDiXS/MxNTPWc+eQ/Mk/AQ4HkE4DggjLwECjl1UQK7cT6ZyPziMlp+AQHBAFES6LSxMwdocda9Y1SFEJkkWCU3Fic2MTISmEhbkFcO1c3aYQEfqQ27eQ2zwjy7LWNDU/Pw8ATTEW02yi1Czrfd/73oe3v/3tYIzhxo0b+OEPf4h/9+/+HT71qU/h3e9+976vk2UZFy5cwOTkJH73d38XDzzwANbW1rRyvO7ubqyvr9flGEiU1kgh79FS0Ut1VGh7e3tVE4NyqWekVJZlzMzMYG1tTffmoNz96Bkti8ViGB4exqFDh9Db26vtw0hRWu2ggampKYRCoZJjTfWOKNqHhmAfGgIALN+8ue+2M5kMrl+/Dr/fj8uXL4MxtrtLWFHQOjMDx/VhWCwWWA4dgvvDHwJfpgtDs0ZKHW95FOA5JP/5nwGnC54PfwiCTk4NemPjbXAIDrwaehUiJ0JiEh7ofAAey07jGmMM35v/Hn65/ksoTMFIZATtjnZ0ejvxKx2/gj5Xn+GjUJvxHJud/USTIAhaUxRQeCym+lBpxLjbStfbrMiy3PQ1sLk1pRzH4cyZMzhz5gx+//d/v+RrBUHAtWvXEA6H8b73vQ+jo6NGL3dfmvtdbmLyR4XmfsGKCcVCo0JroV6NQZIkIR6PI5VKGdIcpKKnyF5eXsbs7Owen1ej37NKxVUmk8Hw8DC8Xi8uXrxY8sagt3DP33YhIpEIRkdHceLECbS1tSGTyezpEk7cuoXQy68g4XEjnc3Ccv06thlD4Nee0n16ST3hRBHOxx9H+NQpKIoCS19fo5e0LzE5hoScwBu73oi0koaVtyIhJxBKhxCwBxDJRPDyxsvodnaj19WLjeQGVhOreM/h9+D+tvvBcdyumrVgMKj7KFRCf8r1Uy1Ul5h7jp1O565zbNR31oyiVE8bPiPQw6fU7/fj0UcfxT/+4z+is7MTKysr6O7uxsrKim6WiaUgUVoFjDFks1nIslzQ6qmQsCo2KrQW6hEpVeuTLBYLTp0qXW9YC3oIxvz3Ov8J1+iIXCWiURV7lfikGr3+/Pd/cXERCwsLWnR8v31zwSDsLhfcnZ0AAzL+GNLLS9r0klKjM5s1UmomZEUGBw4W3gIFCqy8FUk5uTPNCdiZ1MQBClOQklNosbVAwY51VP51zGazobu7G93d3QVHoVZr8N6MzUNmp1o/1fxznD9xSG2aamlp0bVpymyiVFGUpi9nUe3gKmVjYwMWiwV+vx/JZBIvvvgi/v2///d4z3veg+eeew7PPPMMnnvuOTz55JMGrHovJEorpBzv0XyhWGpUaC0Y2a2uThBaX1/H+fPn8dprrxmyn1xqFaXxeBzDw8Po7e1Ff39/wfe6HpHScgYNLCwsYHl5ueJSiHK2Xy2575eiKLhx4wYURSnLe1bw+8HJMpiigON5iMkU7EePoe+eewoagquG716v11Q3qGbGI3pgF+z4p5V/gsiLkJSd9L3f5gcAtNhaYOEseGn5JS29f6n9EnzW4qNVC41CjUQiCIVCWFhYAIBdBu/Fzmc1onQuOocra1cAAJc6L2HQM1jR6w86eoi8/IlDiqLs8sCVJAk+nw+BQKAsM3ej11tPZFlu+vVWa56/srKCp59+GrIsQ1EUfPjDH8a73vUuPPjgg/jwhz+Mz33ucxgYGMALL7xgwKr3QqK0TCrxHlUvuOWOCq0Foxqd1NGbLperYpuqWqjleFZXVzE1NYWzZ8/CV2R+eaNFqSzLGBsbA8/zuHTpUsVP4EZGFNVtJ5NJDA8Po6urq6ADQCEsJ0/Cev5+ZK5fBzgevN8H5xM7naGFRmeGQiGsrq5iYmICdrsd6XQaiUQCTqfz9e+QLEOanwdLpyF0db3up0oUJK2kkZSTGPQMIiWlYBNtSEgJpOU0nKITaTmNjJJBh6MDaTkNm2BDWklDZjLECm4HPM8XrFVUR6HabDYtDVxoFGolzEfn8TfjfwObsBOpGw+P4+MnP75rMMDdjhEij+d5rWlqcHAQsiwjEokgGAxqZu7qZ6DUg0ih9TZ75DEXM6y3WlF6zz33FAw4tba24gc/+IEeS6sIEqVlUM2oUMYYrl27VvOEo1IYkb43YvRmuVRjp6QoCsbHx5FMJssqjahHo9N+N141kqtGzavBaFG6vb2N27dvaw4A0RdfxPYXvwSWycD1pkfg+9SnAOyNeHGCANeTT8L+4INAVoLQvv/kI4vFgo6ODnR0dGgi+MaNG1hcXMTMzAy8Xi9afD7Yf/QS5IlxgOcB0QLvJz4OsYlqOlkqBSUWA+d2g7fbG70cJKQEBE7ACf/rk6nWkmuIZ+Nwik5Es1EInIB7Wl/3WF1PriOejWuirxoqGYUqCEJFkdLXNl+Dhbdoxv/BVBCvbrxKojSHepRECIKwa5yt+mCpPohYrVatPMftdhddjxkij7k0oyVUPul0uml9aSuBRGkJ1OhopaNC4/G4IZZJ+eiZvs+dKnX+/PmGNDNUKhjViF5HRweGhobKOj9GNgoV2/7a2poWyfV6q5+cY5QoZYwhHA4jlUrh4sWLsNvtSF59GaG//G/gHA5AFBH96tcAQYTr4x+DkskgdeUK5OVlCB0dsD/4IDibDWJnZ8XH43Q6tbShy+XamQH96qvY/vnPIbcG4LDZYJMkxL7+dfg//Wndj70aMtPTiD//PFg2C04U4frgB2E9frxh69lKbeHHwR/jVuwW1jJrONt6FpIiwS7Y4bV6EUlH8OLCi7gZvIlV+yqG/EOQFAkW3qJNeNKLYqNQVS/Ezc3NkmngeDaO5fgyNpIbcIku2EU7GBgErrkFQr1pRDo898ES2JkMFAqFMD8/j1gsVrQxzmzpe7Os1wxrLAWJ0n2odVSo0+k0XJAC+gkUtQO81FQpo5/IKxGl5Xh6FsLImsxC21c9aWOxGC5dulRzk5sRolqSJIyOjkKSJBw5cgT2O1G/+D//HIzjINwpPVGYgsRPfwrnxz6K+Fe/iuzoGDi3C+kbNyAtLMD9kY+AqzKioH6W1VS/IxBAvL0NXEcnkqkkEpkMwpOTmB8e1iI2tXYIs3Qa2clJMEmC2NcH4c5Iv33//s77zlIpxJ9/AbDZIAQCUBIJxF/4O4h/+K/AG2CVVoq0nMYXJ76IsBTGWd9Z3IzdxC/Xfon7Wu/DU8efAs/x+OLtLyKSieCE/wRGg6PYzmzjdMtpfOjoh2qKkpYifxRqMpnE6OgowuEwZmdntVIA1XpKvfZEM1F8/tbnsRxfxnx0HkvxJZzwnYBNtOFix0XD1mtGqm100hO73V6wMe727dta05QaSTVbs1uzR0rN9n4Wg0RpAQp5j5Yif1ToL37xC9N8UILBIG7evIkTJ05o6bdCqILRyC9nOaJUURRMTk5ie3u7pKdntfuohdwHhXQ6jevXr6O1tRXnz5/X5fNQSFQrsRik9fWdSGVPT0X7Ub1cBwcHkU6nd/2OdzrB5e4rmwXX6gDb3kbmxk0IvTv74nw+ZKdnIG9uVhwp3Q+hqwsAB2QzO6bv29uwvPFhCMeOIRgMlt3Vvx8slcL2c1+AvLwMcAAsVniffhpiX2/Bv9/VBBaLgWUzEAI7D0O80wl5OwplO9oQURpKhxDNRuETfXBYHXi051Esx5fx9NDTaHO0YTWximA6iE7Hzrlps7dhObGMp08+jU6XPuerXARBgNVqxbFjxwDsPBAXmuV+M3sT4UwYR31H0ensxGRkEgIv4DeGfgPdrm7D12kmJ4hyLaHqRaHGuGg0imAwiKWlJcTjcUxOTmqNcc3uAdrsohQ4OMK0uT8JdaaY92gxCo0KVWs9m/nLxhjD9PQ0tra2cOHCBS06th/NIEpTqRRGRkbQ0tJS9eABo6dgqcegiv2hoSG0lojAVUJ+dDy7vIzI558DS6fAZAX2C+fhefLJXdOUlEQS8R/+ANnFJVh6e+B6/HHwTueekoLZ2dld2/a++91I/OCHkFdXduo6eQH+j398n5XVdlHMPy6xqwuuD38Iia9/A0ooDMvQKbje+QR4hwNOp1PrEM7t6gegRd1KdfWnb9yAtLwE8c5QBTkcRuL734P3N36j5Fp5txucaIGSSIB3OqEkk4AogPdU3migB1beCoUpUNiOOFGYAgtvgUPcSZtaeAsYY1CYAp7jwXM8bIINbmv915sv9qxWKzo7O9HZ2anVF29tbeHG/A2sxdcguAS4nC4cdh9Gh6ujLoJUXadZbvLNnl7meR4+nw8+nw+HDx/GlStXEAgEEAqFMDMzs6txrhndOJr9/W22h5JaaF7FVGdKeY8Wotio0HqZ2leLGtlVDdvL+cKpQtvI+cnF3retrS3cunWrZpHH8zwkSar69eUQi8Vw+/btssR+peSLt+g/fBWM24ksMkVB6uVXYD97DtbjO5EoJsuIfPGLyM7Ngvf6kLx6FdLKKjYeeQTRRHxXScEeYdjZia6//D8R/e73wFIpON/4EOxnziCdTsN65jSyo2OAywmWSMB69Ch4HcU3ANhOnYJ1aAhQlIJlAeV09e+X6mfJJMC/vk3OZgOLx8taF2e3w/WhDyH+/POQt6OAKMD9/g+UPb1Kb1psLXiw60F8Z+I7iCfjsCgWvLX3rVqtaMAWwPn287i6fhUCJ0BmMh7pfkT3WtJyKCb2OI6DzWHDq9KrWLIuYTW5ingmjkPcIYSSIXQnujEpTGqz3I18QCZRahwcx6G1tVW7jmcyGYTDYe17W8y9oRE0Q3lEMRKJRM3G+c0CiVKU5z2aT6lRoaIoQpKkppwCoYq7Uun6fOohtAvto9KIbimMrCmVJAm3bt2CLMt46KGHDLmQ5deUylub4O90xHI8D/AclHjs9d+Hw8jOz0Ho7t45drsNq9euQbzvXpy/dGl3B32BGmWxsxMtn9gdHeU4Dq73vhfpnh7IS0sQOjthf8Mbqq4n3W/f6s9R5nYLdfWrqf5kMqn5LLa0tMByaBBgDEo8Ds5igbK1Bdtb3lL2eq3HjkL8w3+1k7L3ehqStlfhOA6P9T4GMSRCcAsYaBtAv7t/1+/fMfAOHPUeRTAdRLujHUe9Rxuy1lJi72bwJq5tXsMx7zH4rX7cDN7EFreFj9z7EVxovYDtyPauUahGjck0kyg101oLYbVadzVNFXJvUCOpNE1sL/F43JCx343grhal1TQzAa/7YRYbFVrvSGk5FyV1vnowGKxK3DVClKp+qW63u+yIbqX70At1SEJfX5+hT9b5otpy9Bgy4+MQujqBTAYAByHnYYPjeYABYAzpdAbra2tocTnRe/TYns9MJY1znCjC8cY36nFIhiCvr4NdvQq/JKHzvvsgnD27J9UfeORhOF99FZasBMcjb4LjkUcq2gfvdDZUjObCcRy6bd3oaOmA3+Pf83ue43Gy5WT9F5ZHqWvVZmoTIieC53h0ODrg6fTAIljwYNeDAIC2tjatTMrIUahmEnpmi5SWIt+9IR6PIxgMatPEqq0jP6hU61HajNy1orQa79FKRoXWY/xn/r6K1a+m02kMDw/D7/dXLe7qcUy5gtEov1QjROny8jJmZ2dxzz33wGazYXV1Vdft55IvHL3veTcizyeRnZsDJ4jwvP99sPS+3qzD+/2w338/tv7px4hnJbS7XHA/8AD4wF7HAqOdCYqhp9WVvLGB7c/9NcAUgOeRuXYd7o99FP5jx/ak+oO9vdje3t5J9a+uGj73m9ifG8EbeHHxRYyHxjHgGcAx7zFsZ7Zxb9u9Bf/eqFGogLmEnpnWWul3PLdpamBgoOB0OL/fX5eSjmYlFouRKDUzajNTJen6aDSK0dFR9PX1lTUqtN6itJiQUNP1uY1Y1VCvSKksy5idncXq6mrFIzjL3Yde4kdRFNy6dQuZTAaXL1/WyjaM7NzNF2+8xwP/pz4FlkqBs1jA5T2cMMawcPwYFCmLQ04XLD3dsN97b8HP8EGZP58eHgGTshDv2LIp4QhS//zPsN7p+AaKp/pr6epvNM0upveLQC7Hl/GV6a+gy9EFSZEwtT2FRDaBh7oewmN9j5XcbqlRqIwxLYpazgQiM0VKzbTWWgV0fh25JEkIh8NaSYdq8q+WdJhFrNdCPB6nmlIzUq336OLiIhYXF3H27NmyR4U2IlKaD2MMk5OTCIfDexqxqqEeolR9CnY6nbh8+bIhFxS9ooGqcX9nZydOnTql3RTqaTmV+zOuQLoylUrh+vXr6OzsxKEPfajkjauRolTffefdpDnslDAU2bfT6Sza1W+xWGC325s6KmWGB4piohQA7KIdx3zH0O3sRlpJ46MnPwqeq/z9rmUUarF1NiOKojS100suen9/RFHcVdKRyWQQDAZ3WYyp5zl3hHEl6232zwGJUhMiy7KWois3OprNZjE2NgaLxVLxqNB6itJCFkepVArDd0zGL168qMuXyuhj2t7exsjICCwWC06fPm3YfvQQjaoNWCHjfqOFXbnrVyPklQwXKPdz0uwRVevZs0j94heQNzd3OuyTSdiffKDs1xfq6p+ensb29jZefvnlol39KiydhrS2Bk4UIXR17bLoupspJPZC6RBe2XgF05FpMIWh29UNSZHQZm/bI0glRcLV9auY2Z5BMBVEwB7AIc8hPND5AER+/1taJaNQbTabqUSpmdZq9EOd1WpFV1cXurq6tAxIKBTC9PQ0EokEPB6P9rBSTl9FMz+EqlBNqYnI9R597bXX8NBDD5U9KnRsbKzqUaGNTN+rgklvf0yjIoCMMSwsLGBpaQn33HMPRkdHdd9HLrUch9osFgqF9o0+G31zKBXpZYxhdnYW6+vrFTe0HZSaUrGzE95PfQqpX/4SyGZhvf/+Xan7SrFYLPD5fHA4HOjv7y+Z6pfDYUT/5m+ghEIAY7CcPAn3Bz+4p7TCKBhjSF99Gamf/AQAYH/4jbBdvtyUwiWWjeG5W88hLsVhFawYDg5jI72Bfnc/nhh4YtffMsbwrblv4bXN17AcW0YoHYLP6kOfpw+riVW8/8j7yz7GYqNQJUmCy+VCJpOBJElNH4U0g3BSqedaczMgvb29u86zWnalOnL4/f6CZTpmMM4nUWoS8tP15URIc0eF1lLP2IhIae6kIz3S9YX2o7dgkSQJY2NjEARBi0YbHYGrtqZUdQLweDy6RZ+roZh4U8eFWq3WouNi7wbE7m643/te3bdbTqq/9epV2Dc2Ye/tAQBkbtxAenQU9vvu0309uagRs8zoKBLf/Ab4OynNxLe+Bc7hgO2eewzdfznkR/Xmo/OIZqPodnaj3d6OzdQm4tk4/uWZfwmv1bvrtUk5iZGtEfgsPkzL02iztyEhJeAVvbgZuoloNrrnNeWQPwpVlmWsrKwgGo3i2rVr+45CbRZIlJZH/nnOrTuen5/X6o5bWlrg8/m0gE+zv7ckSk2AHqNCa/kgCoKwZ2SjUQiCgGQyidu3b6O1tbXqSUfl7EdPUaqOtzx06BB6ewuPdjSCaqKBkUgEo6OjujsBVMN+ojQej2vvZ09PT1XbbuTQh2YvCdiPQqn+jR+9hAQYQsvLEEURjkwWwtoabHVKs2bHxwGXC5waJXe7kR0fb0pRmgvP8fBZfbAJtoLiciW+gmAqiKwlq31WGKf/Z0YQBHi9XsRiMQwNDe07CrVZnBrMIJxUmmmt+XXHkiQhFAphc3MTU1NTEEURLpcLiqI0dYlEPB5Hp07jnRvNgROluen6SrxHC40KrYV6RkpTqRRWV1dx7tw5BO6YqBuBnuM5VQulc+fOld08pheVCK/cRjcjnACqoZB4yx8Xaka4eBzSjZtI2GywDp2E2N9f+kVNiMVige/Madh+eQVCVxey6TTSkQiWMllMXL1qaFe/+rngXC4gk339F+nMzs+agPyb+2HvYQRsAawmVmHlrUjKSbxz4J17XjeyNYKvznwVcSmOudgcwICElECLvQXb0jbuDdwLj0W/a0nuOguNQs0t3/B4PNo5bcTAlGYWTPkYPaq6FkRR3FV3nE6nsby8jGAwiKtXr8LhcOzywW2W95wipU1KOd6j+V/eYqNCa6EeolRdeyQSwZEjRwwVpMCOmMtms6X/sAj5Xq+NqNUqV5TKsoyxsTFwHFdxo5uR5JYfMMZ2fX5rvSE2KlqpRKPgv/IVKKk0Um4XUj/7Gdwf/Qisx4/ru59YDOlXXoUSj8Ny/FhF21cSCaDMh1znW94CJRxGduI2BA5ofde70PvmN4MxtifVr97k9Jr5zXEc7A8+iOzNW5CWdjraBZ8X9oceqnnbepB/DXaIDjw99DReXn8Z0WwUx3zHMOQf2vUahSn49vy3EbAF0OnoxHJ8GQuxBZz0n8SAZwCHPIdwqeOSriJhP6FXqHwjGo0iGAxiaWkJiqLA7/dr1lP1uG40U/SxFLIsm2atNpsNfr8fkiTh2LFjSCQSCIVCmJqaQjKZ1JrjWlpadC+XqwQSpU1IOaNC803mS40KrQWjRalqR9Te3o6BgYG6PLHVmr5X08u9vb3o7+9v2FNmOTWl6lr7+/vR19dXp5WVh1p+kMlkMDw8DJ/Ph/Pnz+vyfjZKlGZu3ABiMaC3F4LLBWU7iuSPf6yrKFUSCWz/9f8NJRgErBakfvkLuN77Xtjvv7/42iYnEf7P/xnKyirgcCD1R38E+6WLRV/D2e1w/9qvgSWT4AQB3J0bFsdxe1L9oVBIm/m9X1qYKQrSw8OQFxbBtwZgv3BB22YhBL8f3t/5bWSnpwEGWI4eAd/ENy23xY1Hex/d9/dJKYnt9DacvBO8wKPP3QeLYMH7j74fx3zVN7AVo1wrIJ7n4fP54PP5cPjwYc03MzcFrJ5Tt9ttyHXPTKLUTGsFXm904jgOLpcLLpdLm9qnPoyozXG5wxrqGXAhS6gmohLvUdXYXBTFskaF1oKRolT12VOtfhYXF+tSB1hL+l59v8+ePQufz6fzyiqjVE3p2toaJicnm2KtheA4DqlUClevXtW9xrVRopTJMpBr/SMKQI1R+XyyU1NQglsQ7tTbKskkUi+9VFSUKpkMQv+vPwWLx4FAAFwijvCf/zna/sf/B2LOKNdCcBwHrkS5R7kG/vaXX0H2F78A57CDpVLITkzA87GP7e3mlySwRALM7QbvdjdFDWk+laaaN5Ib+NLtL2EtuYbb4ds46juKVlsrrLwVnQ7j6uiqHRWc75upjkJdWFjQRqGq0TW95ribLX1vJlG633o5joPX64XX68Xg4CBkWdaapubm5sBx3K6mKSOPmSKlTUKlo0IFQUAmk8Hk5GRZo0JrwQhRqigKJiYmEI/HcenSJS1Vq0davRyqaYJRFAXj4+NIJpOGvt+VsN9xqOUQsVhs1/vbbGxtbWFzcxMPPPCA7k/HjRKl1mPHwAkCWDgChTEo4Qgc736XvjtRGHIPjRMEsEzx742ysQElHIbQ0Q5kMoDTBRaNQpqZKSlKK2Xfrv7VVUT+8TuQ/S1w8DxsgQAwOwd5dRViThQ/PTIC2xe+gKTdAdbfD/eHPwTBgAfuWqlUQH1j9htISSlcbL+IifAEZqIzaHe049eP/zo8VuPq0fUSeoVGoebOca9lFKqKmYSemdYKlG8JpU6SUsvo8oc1WK1WrVRH74h5PB6ve2+GUZhWlEqSpAmxcrvrFUXRupPLGRVaC3qLUjVd39HRgZMnT+5ae72aqipN3+eueWhoqCJjdiMvXIWEVzqd1oYN6JUK1xtFUXDz5k0kEgl0dnYakq5plCgVOjrA3v8+cK++BsFqgePNb4bt/Hld92EZPATe5YSyuQnYbFAi23C+9fGir+F9PnCCACWVBngOkCWAMfB1EHtqV7/Xbkc40Aq0tyOVSSMRjyO7tYWVmzfhBxAIBGCNRhH/+38AczrBt7dD3thA/B++Cu9vfNLwdVZKuWJP/RyuxlfRam+FwAs4HTiNgD2At/W/Dd2u7qZYZyUUmuOeOwoVwK561HKvgWaYOqRiRlFazXrzhzWkUinNeioWi8HlcmkPI7U2TVGktIEU8h4t5zWLi4sIh8M4efJkXeyH9BSKajp5v8k89bLxqSR9v7GxgYmJiYqmCeXux2hRmksoFMKNGzd0c15Q0fOmljsutKenBysrK7psN5+Gjhnt6gL/3ifhrWJYxX4wWQZLp8HZ7eB9Png/9SkkX/oxlFgMjkcege1i8dpQ3u2G62MfRewLf7OTGlcUON75TlhPnNBtjaXgbDZY77kHmWvX4PB5YZdkCKeGIF66hFAshsnJScjj4/BHIpB8XiiKAktbK6T5OTBFMd0kKZnJeGnpJfxi7RdYji9jI7EBkRdxrvUcWmwtYGAI2I1t6gTqkxKvdRRq7lrNIvTMJkoVRdEla2a323dFzBOJhG4ODolEgkRpI6jGezR3VGhPT0/dUrJ6iNLc1HexdHK9IqXliN9cA/9qU+D1EtmMMczNzWFtbQ3nz5/Xrb4LeF3c6XFTCwaDuHnzJk6dOoVAIIBIJGLY+1OuKDVDVCYzNYX4330FSiIB3uOG5yMfgdjTA/cHP1DRdjzvfS9sp09j6/p1yF4v/L/yKwatuDAcx8H17neBbw1Amp2FMNQGx5vfvCOY/X709fUh4/Ui+NprSGYlBLe2wCdTsLtd4Le3devq14tS34sra1fw05WfIpaNIZQOQeAFcODw6uarOOE7gfcMvge9LuMDC40QT5WOQm3kWqtFUZSmn5CVixETnXKbpvr7+/c4OMiyDL/fj5aWFvj9/pLvVyaTaWj3v56Y5pOh1o9W8kSYPyp0amqqbt6htUacEokEhoeH0dXVVTL13Szp+1QqhZGREbS0tNTkZlAPUcoYw/Xr1w2bfKRHtDdXNOeOCzUymtlIUarncSnRKGL/vy+DgUGan4cSjSL96mto+Q9/DNuZMxVvz3riBHifD0ildFlfpXAWC5xvfjPw5jcX/L3l0CF4Hn0UiW9/B26vF4LfD+lXf7VkV38jKCVKJ8IT8Fl9WIwtwmPxIC2n0enohFWw4u39b8cDXQ80xTrrQalRqOqITDPZLDWzT2kh6iH48x0cZFlGOBzWHkjUUh49reOaFdOI0nLHhAL7jwqtp6F9LRezSp0BmiF9v7W1hVu3bmFoaAitra0178fI44lGo4jH4zh8+HDVk49KUe0oU5Vi40KbQZTGYjEwxppimEAh5K0gmCRBmpsFFBlCawByOILY8y9A/Ne9TdkAVAscx8H5trch5XCgra0N7v5+8G43OoGiXf1GGPiXotjnazY6i8nIJNaT6zsPFIoEhSmwCTaIvFiXtH3uOhstSnMpNAo1EokgGAwiFovh2rVr2oNHM45CVTGTgAaMiZSWQhAEtLa2avfSTCaDcDisPWTabDa8+OKLeNOb3oTzNdTeLyws4BOf+ARWV1fB8zx+53d+B//qX/0rBINBPPXUU5idncXg4CCef/75isvwqsU0ohQoT6wUGxWqWkI1K4qi4NatW0in0xV1qjcyUsoYw/T0NLa2tnZF82rBSFGqTpJSow9GUc0oUxXVI3VgYKBg/XOjReni4iLm5+chiiIymYz2BN/S0lLTxVvP4+LdLiCbAUumwHs9YFkJvM0Kjud3uukPmCgF7nzmWlogHjoEPudhYd+ufgMN/Iuxn9ibi87hb8f/FjbehrgURyKbALDjYQoAR31HcdJ/0tC1lbPOZiG32zsUCuHs2bMIh8NYXl7G9vY2HA5H00THczFTqQHQGFGaj9Vq1azjgJ17xI9//GM8++yzuH37NhRFwV//9V/jrW99Kw4dOlT2dkVRxH/5L/8F58+fRzQaxYULF/C2t70Nn//85/H444/jmWeewbPPPotnn30Wn/3sZ406vN1rqste6kSpUaGqJVQzkpuuP3XqVEUXEL1n0u9HfqQ0k8lgZGQEbrcbFy9e1O1CY4QoVQV/JpPB5cuXceXKFV23n0+1AkttcDh37ty+40JrjcIWo9i61e5/SZJw4cIF7WdqtGZmZqYuRuHlILS1wfH2X0Hmv/93yKEwOKsVlpMnAUUBr+MYVqYokJeXwVJpCF2dTW1Qr6KmAis18NeTQtt8deNVWHkrAvYAAo4AFqIL6HZ149GeR+GyuHDIcwgiX79blpmah4Ad66lmHoWqYjZR2ozrdblc+IM/+AP8wR/8AdLpNN785jcjEong05/+NJaWlvCGN7wBjz/+OB577LGimUu18QoAPB4PTp06haWlJXzta1/DSy+9BAB4+umn8eijj5IorYRyR4XWM32vUs7Tdq3G8nrOpC9GrmBRO9b1Nm8H9BelqjVVZ2dnxYK/Wio9BsYYJicnEYlESjaI1RKFLcV+ojS3+19NHUqStMebTzUKn5ub07zz1N+XuhHqHQF2vukRCB4PYi+8AAg8ODA43/WrEHQax8tkGfGvfhWZkRGAF8DZ7fB84uMQdXQPqAflGvjrleovdE2MZ+NYji9jM7UJl8UFm2CDz+bDEe8R3NPWmAEAZqt9zKUZR6GqNKPIK0YzREqLkUql0Nrais985jP4zGc+g0wmgytXruDFF1/E9773PfzVX/1VWduZnZ3Fa6+9hgceeABra2uaWO3u7sb6+rqRh7AL04vSSkaFCoJQ1/R9qQ5sWZZ3Re+qveDXS2yrxzM7O4vV1VXdO9ZV9BSlavS8kDWVkem5SgRW7rjQchrEjE7f56M+gJRTL5xvFB6NRrG1tYXR0VEoiqKliffzYNT7uGz33wfLyRNQQiHwbvdOs1IN5K4vOzmF9PVhCL094DgOciiExDe/Be9v/Waty65pfbnnUEmlkPzH7yJz+zZ4nxeuX/1ViEUs8eqR6s9fYzQTxedvfR5L8SXMRGewFF/Ccd9xWAUrLrRfqHj7emG2SGkxmmEUqorZRGmzrzcej++q7bdarXj44Yfx8MMPl72NWCyGD3zgA/jLv/zLfTN09cJUojT/i1JpQ5AoinWNlKpisdAHWq0b7OnpqXl2vZHp3Fyy2SySySQSiQQuX75s2BdVD1Gq1roGg8GC0XP1PTPq4lvuMWxvb2NkZKSiiLPRojR33QsLC1haWqrqASR3DJ96IwyFQlhbW8Pt27d3pYmdTqchx8UyGaSvvgxpeQlCdzccDz5YdGZ8qePZte1YDBzPaz/n3W7Iwa2KtqkkElCCIXBulyF1rvFvfBOZsTEI7W1QIhFEv/AF+D796bLFuRGp/vxz/Ormq4hkIjjmO4ZOZycmw5MQeRG/MfQbhhvkF6PZa0prYb9RqLnG7nqPQlVpdpGXT7NHSms1zs9ms/jABz6Aj370o3j/+98PAOjs7MTKygq6u7uxsrKieza0GKYSpSqyLGu1bc3YEJS/v/z1raysYGZmBmfOnGnK2eqF2N7exujoKCwWC06fPm3ovmoVpdlsFsPDw3C73bhw4cK+c4vrPTUqn6WlJczPz+O+++6raDpTPR5CFEXBjRs3oCgKLl26pMtFWRRFzYNRTRNvbW3h9u3bSKfTsFgssNvtkCRJFx9Dxhhif//3yN66Bc7lQmZ8HPLCAtwf+Qg4HY5H6NyZu84yGcBigbK5CevZs2W/Pjs/j9gXvwiWlQBFgfOJJ2C/fKnmdakwWUb2xg0IXZ074tnng7y6Bml1FdYqrzt6pfpzxV48G4eF3/lbj8WDo76j6HR2NlSQAgdblOZTzijUQCAAv99fcwmH2URps683FotVLUoZY/jN3/xNnDp1Cp/5zGe0n7/nPe/Bc889h2eeeQbPPfccnnzySb2WWxLTidJoNIrR0VH09fVVPCq0UaJUJV9Mm8FAmDGmRcvuvfdeXL9+3fB91iJKI5EIRkdHcezYMXTeEQ1676Mcim0/t+nq0qVLFX8OjKwpVeuTr169qtWPGtXooqaJVfPo6elpRKNRXLt2DTzPIxAIoLW1tep0ohKJIDs+Dr67e8dOzutFdnoGSjAIQYe59WJfL5xPvgeJb38bkCRYjh2D84knynotUxTEnn8BsFohBAJg2SwS3/kOLEcOQ6hhqtiuhxWeB2e3A5kMYLeDMbYz4UmnJpdiqf75+XlwHFcw1Z+fFj/uO46X119GUkqC53hEs1E82vKoLmusBTON7tST/UahqucVgDaFqpJRqCpmrNVt5s9Bfvq+En72s5/hb/7mb3Du3Dncd999AIA//dM/xTPPPIMPf/jD+NznPoeBgQG88MILOq64OM2vinJQ035nz56Fx+Op+PWNFKWxWAwjIyPo7e1Ff39/U3/IVSRJwtjYGARBwOXLl+t2IalWMC4uLmJhYaGsyKPRorSchqFqm66MTN9HIhFEIhGcP3++Zr/ZSuB5Hm63G1arFQMDA3vSieokm9bW1go7hwu8vzp+9+z33w/bPfcAklRRWQBLp8HicQjdO01RnMUCcByU7e2aRCnw+g2U4zg43/WriP/d3wEMAFNgHToFcWCgpu3vR26q/8iRI/um+tPp9K6b6DHfMbx78N34ycpPkFWyeFvf23Bf232GrLESDlJNaS0UGoUaCoV2jUJVo+PFRqGqNHvk0WzUkr5/+OGH972X/OAHP6hlWVVjKlHq9/trEkf19ilVRanqjXn27NmGFxGXSywWw/DwMA4dOrTHK9PotFalglGWZdy4cQOMsbI/H42IlOaPC60Wo0TpwsICFhcX4Xa7SwpSox+q8tOJsVhsV8OU3+9Ha2tr0UgN7/PBcvo0sqOjgNMBJJOwHD8BXqfuexVOEIAKr0mc3Q7e74cSDoP3+8FSKYDjwOtsUG07cwZCSwvklVVwLicsx47pUrpQDvul+jc3NyFJEiKRiCZm7m+/H/e331+XdZWLWdL39egnyCX3vALQzqs6CtXj8WgR8kJOOHdrBNoo1Pf8oGAqUarWm1VLvRqCVDiOw9TUFCwWS13S9XpdRFURfe7cuT0fdtUT1cioaSWCMZFI4Pr16xWXcxiZAle3r37W9hsXqse29SC3fvT8+fO4du2abtuuhP2OK3eSzeDgoNYwlRupaW1t3dNsw3Ec3O97L1K9PZCXlyF0dcN++RK4JojScBwH9689hdiXvgR5bQ0cL8D1/vdDqFGUFroGiD09EA0cFFEOual+RVFgsVjgcDhKpvobiVlEaaMjjw6HA729vejt7dUcN3JHoRaa4W6G99UsxOPxinoSmh1TiVIzfZBjsRhWV1fR0dGB06dPG752VcjVIhbVmldZlvcV0WrNodGiNJvNlvw7VZRU4+9q9AOKej6KjQutFj0/S2o5QVdXl1Y/Vu/IS6XkNkwBOw8muc02alNGS0sLRFGE46GHGrziwoidnfD93u9BicXAOxxVuwKYDTUtXk6qX3VmaNQ6zXDPaaZ15jpuDA4OFpzhnk6nEYlEmnoUqkqzXwuBHVFqlgxsOZhKlJqFpaUlzM3Nobu7G16vty4XDLVUoFqxqFpUlap5NTrtDewcSyqV2vf3iqJgcnIS0Wi0pNH8ftSjpjSZTOLq1av7jgttNKr/aG45gZH1qqWodt/5zTZqU8bc3JzWMKXOA2+Wm7cKJ4oHcuRpMQqJqHob+JdDM6WZlUQCiX/8LqSZafCtbXD96ju1Zr1GR0qLUWiG+8svv4zl5WVEo1E4HA4tQt5Mo1BVmvm9VYnH4015f6kWEqU6IkkSbty4AQC4fPkylpeX69ZYVcuo0UomStVjpGkxwZhOpzE8PIyWlhacP3++6ouY0aJUjd6dP3++6Z5iVUeF5eXlPf6jjRSlepDflJHJZBAMBrG4uIhoNAqXy6Wl+veb/GYGskoWGTkDp7jj75qUkkjLachMbrobez6lInvVdvUbsc5mECSMMcS/8vfITk+Bb22FtLqC7eee2/GbvVMO0QzrLAer1QqLxYJTp0419ShUFTO8t7X6lDYbphKlel1sjUh3RKNRjIyMaFExjuMgCEJZaWg9qGbUqKIoGB8fRzKZLNvvtR4jTfer91QjeydPntRMn/XeR63kjgs9cuRI0wnSUv6jjRQ0Rghiq9WKrq4udHV1aQ1TufVuuROmyskyNINgv7Z5Dd+Z/w5kRUa3qxvHfcfx05WfgoFBCkvoS/ehw/q62TXLZJB48UVkbtwA73LD+c4nYDl0qIFHUBmNSvU3S1qcpdPITk+D7+raua8EApBX1yCvrYE/fLipIrqV0MyjUFWa3TgfIFFqetQ0t15NR4wxzQQ9vzGoVBpaTyq1u1LnwXd0dGBoaKjsi1q90ve5+8htFNJrtKkRNaWZTAYjIyPwer3o7+9vuifs/PrRYudciUaRXVwEBAHWwUHdvC0bSW7D1KFDhyDLMkKhEDY3NzE5OalZ2+ROmMp/faNZSazgG7PfQKutFVbBitntWfxs+We41HkJFt6CieAEvjH/DfzmmddHnSa+/32kr1wB394OJR5D9G/+Fr5/+S9qtp6qllrFXqlUv8/n06LltaT6m0WUcqII8DwgSYDFsnPdyvGbbZaIbjkUu+Y20yhUFTOI0kQiQY1OZka1hdJDlKrpeo7jCjYG1dMXtZJ9bWxsYGJiouA8+HL2U8/0vRGNQvn70IP8caHz8/NNEVVTKVQ/uh98JILg//gfUOIJgDGIfX3wf/Jp8DW6BpSi3qUDgiDsGrWoipvp6Wkkk8mG1DGWYiu1BQ4crMKOILELdsSkGERu59rj4T1YTizvElSZkRHwnZ3gRBGc1QoptgJpadm0ojSXQtG2SCSCUCi0K9Xf2tpacWNNM4lS5zt+BYlvfgvgOUBWYL33HgjdOxOvzJBiVqlEQDdyFKqKGd7bWiY6NSOmEqV6XCD0Eopqur6Qj6fe+yqHckSW2iC0vb1dU4OQ0cekHovqlTo4OIgenS1t9BSlhcaF1uN9Kodi9aP7YX/1VTBFgdizc9OTFhaQunYNzje8wejlNpRca5tCdYw2mw2CIDRMrARTQVxdu4qpyBRkJsNv9WNqewrxbBzz0Xn0e/oRV+IYtA/uWh/ndIKl0+BEcUf0MwbO1rjIt5HvX25NcW6qf3l5Gdvb23A4HGWn+pspAmm/dAlCRwfk9XXwHg8sx49r1mb1XidjDEowCCZJEFpbdyK5ZSLLctVrreco1Nz1NnuklHxKTU6tQpExhsXFRSwuLuKee+4p+oTSTJHSVCqFkZERtLS04MKFC03bIKTuQ3UDKOSVqtc+aj2OYuNCm6FhSB0qAKCi+fVcPAEukBNBt1igbEeNWOLu/TbBe6aSX8eYyWQwNzeHUCiEK1euaFGaQCBQs+9sOUQzUTw3/hxSUgo+qw/XN69DVmS4rW4c8R3BzfBNhDIheDkv3n3o3bte63ziCcS++CXIkW0wpsBy+DAsx44ZvuZmoJZUf7PValoOHSpYC1zPdTJZRuIb30T6+jWA4yF0dsDz0Y+CLzNSp1fk0ehRqCq1iOh6Qen7BlPrjasWoVjp2M16i9L9RNbW1hZu3bqFoaGhmkdHGp2+V2egJ5NJPPLII4YNHKj1c1RqXGi9BzXkk0qlcO3aNfT09JQcayutrSH6ta9D2tiA5chhSB3tkNc3duamSxKQzcB6+PCu1xh1E2wWUZqP1WrVzL8HBwe1KM3Nmzc1g3A1SmNEZGUuNod4No4uZxfaHe1wik5MRCbwcNfDsAgWHPUeRTgdxjuc70Cns3P32o8fh/df/AtIS4vg7Q5YThyvKLqlN42KNO+X6s/v6ldT/c2Svi9FPVPMmRs3kHr1VQg93eB4HvLaGhLf/z7c73tfWa83aq16j0LNXa8ZIqWUvjcx1Y4a3d7exujoaEWp5Hqn7/P3xRjD9PQ0tra2dJkktN9+9EIVeqqnpJETsGqJlKpipJjIN3piVDEqGWeqJJIIf/45MCkL3uNB5tY4BFmC48EHkXrlFXCiAM+TT8Jy7GjB10sLC0j94hdgigLbhYuw7vN35WAGAQDsjdKoBuFbW1uYmpqC1WrVoqiV3ACLwYOHAgWhdAiSIoHjOIi8iEg2Aiktwcpb4bV6YVUKp+XFrk6IXZ0Ff1dvmkXs7Sdk1FR/JpPBysoK2traGmbgXw71fD/lzU1wFotWOsB5PJBXV8t+fb0EdK2jUFXMkL6XJKkp7LP04q4TpZUKRbUmb2lpCffee29FYfJGpu/VTnC3242LFy82bYOQihrNPXXqFNxuN65fv677PnKpRlxXMi60HmUO+aif1ZWVlbIfQqT1dSiJOMSuLgCA0NkBcXgY7sfeAs+7fhXA/mJRWlpC7POfBywWgOOQvXkT7o99vGph2kzp+0rINwhPpVLY2trCzMwMEomE1jAVCASqrnXrc/dhPbGOtcQaBE6AxCRYeAteXn8ZPLcTlf/k0CeBoI4HZhDNIkrzyU/1X716FQD2TArTs2ZRD+oZKRU6OsCyWTBFATgObHsb4v33l/36RjUOVTMKtZHrvZsxnSitZ/o+m81ibGxMm11f6RNTvUVpJpMB8HqntdoJrvd+9PRezY3mXrx4ETabDZIk1aVutZLjUEs3RFEsywWgHgIr9+aeWz968eLFsj+rvM0KKAqYouxEPyQJ4HhwVmtJ4ZC5fh3geQh3orEygPTLV2uKlh4E7Hb7roapaDSKra0tLC4uAkBV3eAriRW02Frgs/o04/y15BrOBs4io2Rg422Yic6gi3UZeWi60YyiNBeO48BxHPr6+vbULM7NzdXU1a839RRO1lOnYH/DA0hfuQrwHMT+fjgef7zs1zeDyCs2CnVmZgaCIGhRVEmSmuoBJB/1HtPs36dKMJ0orZVy0/eRSARjY2M4fPgwuu9Yb1RKPaNlPM9DkiTMzs5idXVVNz/PQvvR65iy2SxGRkbgcrl2RXPr1UxVrmhUm676+/vR19en+/arQRW9HMdVVD+aj9DVBfuFC0hduQoIAsAUJC9fAlfOhZgXgNxjZAwcX32qy6yR0mLkei8Ce1PETqcTgUAAra2tRSPbKSkFm2BDh6MDClOwGl/FcmIZva6dQR2SIiGa3WlGM/IGxRjDzdBNjIfH4RJdeKDzAfhsxafAFdqGWVDfy1KpfvU8trS01D3VX0+hx/E8nE88AcfDD4PJMnivF1wFwZpmEKX5FBqFGgwGsby8jK2tLdjtdsiy3LSjUA8ad50ozY0oFoIxhvn5eSwvL1ecrs+n3h/e5eVltLe34/Lly4Z98fUSjGqN7tGjR9HZubvWrR7ipNyaT7VQvpwRrNVsv1rU90itH63Gc1bdjuc974Ht9GkosRjEtjZM3onolcJ2/33IvPYq5PX1HXPvrATbA5crXoNZ0ONzmZ8iTiQSWulKJpOB3+9Ha2vrnoapLmcXgukgboVuISklEc1GIXIirqxdwdnWs4hkI7jQdgHYqvUoi/PKxiv41ty34BSdyCpZ3AzfxG+d+i24LOVfJ5s1fV+I/dZZ6Dzmd/XXK9Vf7/eT4zhwVU6qM0PjUO4UuKmpKa3mtBlHoWazWUN7LxqB6Y6m1i9fsZR6NpvF6OgobDZbVen6RrG9vY2JiQm4XC6cOnXK0H3pUZKwuLiIhYWFfUV/PS6wpcR17rjQajxd6yGs5+fnsb6+XnMTG8fzsJ048foPyhSlQmcnXE++F/FvfgPIZOF84h01ja88iJHSYnAcB5fLBZfLtathSjXwF0URra2tCAQCyLAMOHBgYIhlY7AJNvS4epCUkrgVuoV3D74bb+1/K0a2Rgxd809XfopWeyvsws7nbTm+jOntaZxrPVf2NswkSssh9zz29/fXPdXfjNHH/TDTWoGd9bpcLrS0tDTlKNR4PN7UTXjVYDpRWiuCIBRM30ciES1y19Vljrqs3Cas48ePIxg0vsuhlkipLMu4efMmFEVpuOgvdhzZbBbDw8PweDxVe7oaWYIgyzKSySSi0aiuU64qXsf6OuJf/zqgyIAoIPGd70Bob4clzz6KKI9CDVNqx/D14HVwGQ6H7YfBMQ4uqwuSIuGhrocQy8bwzsF3AjBW8G1nthFKh2DjbbA6rOA5vqp9HTRRmk+5qX41HVwrZno/zeD7mUu+iG62UagHzQ4KuAtFqSiKuyJ9akf16uoq7r//ftM8deR7piYSCWxsbBi+32rFViKRwPXr19Hb21tx3aMR7FfzWaysQI/t10oymcT169chiiJOnjzZuAt8ehvZn30PSG1D6D0McIAcCiH1z7+oWpTebZHSUtjtdvT09MAesOMX07/AxtIGvCkvYskYtuJbsIgW3Ny6iXvb7zV8Lcvrk/ibW19AWNrEUmYdbbY29Hv64bF4cNhLDyHF2C/Vr9ckIjNFH820VqC0JVT+KNRUKqWNuM0dhWrUkA0SpQeA3PSz2mjjcDgMrcPU+0lWHb+ZO+K0Xk1V1aTvq63LNJJCNZ/Ly8uYnZ2tuZZ4v+3XSm796PT0dOMEXGgGll/8D4iT8xDWQuBtaShtQ+A4fscqhtCN7cw2Pn/r80jJKbhtbizFl5DgEmACg1NwYiG6gKPyUQxLw2htbTXkGpCZnsbXvv+fIPEpHJNEBPp7MGeNodPZiQ8c/QDclspuimaK7OlNOal+VcSUm+o3k9Az01qBytdrt9sLjkJVa8b1rjWOx+MHapoTYEJRqldNaTgcxtjYmOHpenV/ehUjLy0tYW5ubs/4zXrZT1UifhljuH37Nra3t6uqyzSS3OPIHRd6+fJlXc6VnlG/Qv6oRkcViwkH4bUvgAGwH25HYiqK9PAwZPs6OIcHzjveptVAkdK9TEWmEJfi6HZ2o8PRAafgxGx0Fg90PgCe4yFwAmQm4+jRowgGg0gmk3j55Zd3TZiq5fPMZBmxv3sBwQ4ZdtEDTuHRshBE9kw3TrWcQout8ua6u1mU5lMo1a92fpeb6jfT+6koiqkac2oxzy82CjW31jgQCMDr9VYl1tVo7EHCPJ8OneB5HrFYDLdu3apLul4vUarWY8qyXFA4GT3+U6VcUZrJZHD9+nW0tLRUXZdpJOpxqFOkOjo6Co4LrXX7tSLLMsbGxsDz/K76USMFXK7d1B6YAoTnwIfnYc1KEJFANqGAsyvgnQ5kxsZg1fF9JACFvT7JKSknwcBgE2xwiA4kpAQsvEWLvql2cOrNb2ZmpqY6t2RyG191jmPGHkWIX8Og1IJNdxjx7TiUuW/BKlhxqeNSRcdDDx77Y7FY0NnZic7OzqKp/paWFu0eYKboo5nWCug70Wm/WuPV1VVMTExUNQqV0vcmJ5PJYHR0FJIk4eGHH67Ll0OPCKbqk1msHtPI8Z+5lHM8ahT6xIkTaG9vr3pfRkYAeJ5HOp3GK6+8UnRcaLXoIRrV+lH1vOdiZLlG0bVzPLhkGHIyiljCgWwCcHZLUI4dgezrR/bGTbBotCrLGIqU7mXQM4iV+Ao2U5uIZ+PIKBl4RA9+uvJTHPcdh1Ww4gNHP7DrNTzPayIUANLptDbfPRaLaZY2gUCgZPbih5s/w4w/jTNRL6bcAkYsq3BzIs4H7kGruwvfnf8u+lx96HZV5uVMDy2lKTfVn0wmTfN+mk2UGrne/UahqpPgyhmFSqK0Caj2y5c75SidTtdvLFuNonR1dRVTU1Ml6zHrVVNabD+qx+vKykrN5v3qfozo0GeMYXl5GdFoFG984xsNKUCv9Xyo3pX7+Y/WI1JaCEVREGEuCLwLdi6NGFOQElyIBMPgJBfs6TRkRUG13y4SpbtZTiyjzd4GnuOxKC/Cb/XDb/PDbXGDcQyfGPoEBj2Du16Tf4202Wy76txUS5vR0VEoiqLd+Hw+357r4mx0Fp3H7wNu3sbJMMOKX8RQ2zl0+XcekjhwCKVDFYlSM6Wbm4n9Uv1ra2u4desW3G63rl39RmA2UcoYq9t6yx2F2tLSot0XSZSaEMYYZmZmsLGxoQmlycnJuu2/WlGqKArGx8eRTCZx+fLlkkXR9brI7ye2JEnC6OioNpK11i+yUaJUlmWMjo5qo+aMEKRA9aKxUP2ontsvh/22nc1mcf36dRxtHUKrrwWStQW20G1kFjbgd7QgFYkgcXgQr42Pw2a3ax6b5ZbImEGo1Fs0x7Ix2EU7OoVORLNROAQH0koa57znkFWyewRpKfJHLEqShFAopDUj2u12LYU4lZrCTHQG0UwUJ+89gc3YOhLhTcywdbRlonCIDihQ4LVWFhUnUaoPaqo/GAyit7cXgiCUTPU3GjOY5zcDxUah/tVf/RW+853v4OGHH4YoilV5k3/qU5/CN7/5TXR0dGB0dBTATiPtU089hdnZWQwODuL555+vaiBLrTTHJ9UgMpmMNsayUX6O1YjSZDKJ4eFhdHR0YGhoqKku4IWsjgq5AdSK+r7pOQ1FtaXq7+9HZ2cnXnvtNd22nU81kVK1flQQhJKf13qLUvUcHzt2DC1nfx945a8hbN6G72IrYve9GVnJC2dvL2wXL+KoKCKZTGJra2vXhJvW1tZdT/mFaOZIaSO+h72uXqwn17GWWMNGcgMKFPgsPlxZu4L3H31/zdsXRRHt7e1ob28HY0xLIX575Nv4SfgncFgciGai+GH8R7ByVhzyHEIsG8Mv13+J477jePvA29Hrquw7bwZR2syfw3xUoad3V78RmM2ntFnI9TD+3/63/w0f//jH8Z3vfAdf+cpX8JWvfAXf/e538ba3vQ1vfetbcfTo0ZLfr09+8pP4vd/7PXziE5/Qfvbss8/i8ccfxzPPPINnn30Wzz77LD772c8afWh7MJ0oLfdiptrnHD9+XKvZaASVitKNjQ1MTExUPTay3qysrGBmZmaPG0Ct6F2OkG9LJctyXWbTl0ux+tFCGOWDCuxd+9raGqampnDPPfdoqSL5wT9AJrENTrTBxgvIr3hyOBzo6+vTpqBEIhFsbW1pjTdqFDW3oL/ZhUojkBQJAgTwHA+e4zVBZxftyMj7j0uuBo7j4HQ64XQ6Ed4O44j9CERFhD/ux8+CP0Or2IqjlqOweW1YzaziVw/9Kt7Q9YaK92MGwWcG4axSaK16dPUbgdnS981KX18ffvu3fxtLS0t45plncPz4cXz/+9/Hv/k3/wZzc3O4fPky/u2//bc4kTupL4c3velNmJ2d3fWzr33ta3jppZcAAE8//TQeffRREqV6wBjD9PQ0Njc3i9Y11uuiU64oVRQFk5OTTWmfVAi1vCCVSuHSpUu6z3fWy+eTMYapqSmEw+Fd72u9ZtOXQ6n60Vq3XynqtvPfu13nmOMAi2Pnf/eDMXCReYipEALONrQcOwZgp/Fma2sLs7OziMfj8Hq9aG1thcPhMIVgqScriRW0Olrhs/kgciIc4s717FzrOSwnlg3ZZzgdxlZyC/FsHL3uXth4G4SoAMEugBM4xKNxxBIxBC1BrLCVoo0Y+9Hsgk9RlKZfo0o5Qq+arv5GrZUoH7WmdGhoCENDQ/j93/99ZLNZXL16teIg0draGrq7d2rDu7u7sb6+bsSSS3KgRGk6ncbIyAg8Hk/R9Kfe3qHFKEeUplIpDA/vmF/Xap9UD7GtKAquXr1qaHmBHhZXxcaFGt3pXc57Um796H7bN7L7XpIk3Lx5E06ns+rPpDD1fYi3vw0oMjhFQvbcr0EefDNsNht6enrQ09OjzZJWRWo6ncbs7KyWZszdL79+A8LCzwBehDT4KFjLwZ4ktJncxOjWKG6Hb2u2UBklg05HJ2LZGAbcA7rvcy2xhi+MfwHBdBDT29OYiEwgy7IQeREb6Q0klAQOew7j/o778UDPA4iGohgbG4Msy1rDlN/vLyo8zBCFrGeDS61UKvSMMPA3aq2NxAwPJolEYk+jk8ViwUMPPdSgFdWO6UTpfh8SNV1fjg2RKIqQJKkpRKkaJdPDlkhN6Rr5Rdra2kIikcDFixc1yxkjqDV9H41GMTIysu+40EZfbCqpHy2EkaJalmVcv34dhw8fRk9PT3UbSYYgTv4juEwcfGQOYAqsP/kzJFtPAJ7XO7VzZ0n39PTg1q1bsNlsWFhYQCwWg9vtRmtrK9rlNdiu/zVgc+9sa30UmTf8IZivdKmDGQmlQ/j8rc8jmo1iK7WFpJyEyInYSm+h3d4Op+jErwz8iu77/dHSj8AYwwn/CXQ5u/Dj5R+j3d6O+9rvQ0bOYGZ7Bufbz+OJQ0/AJtjQ6mvVGqbC4TA2NjYwOTmpeS6qEfDc75tZRGmzr1Gl1rXWM9VvNlHa7GtVr5F60NnZiZWVFXR3d2NlZaVhZY+mE6X5qCnGYDBYdrSpXtOP1H1lMntrv9Qyg62trYqjZMX2ZVQhuepisLm5qV2kjKQWUaqOC82tgWwmkskkrl27hr6+vrLqRwthlCjd2tpCJBLBuXPnCor5cuGyCXCpCPjIHJjNu+NvGluD5ebfI3v5dwEAMpPx85Wf4/rijxEKTUBmDCHFCluiFT6rD2c7zuLhwMOIhqLYvPYV2JJJWLweOBxu2OUQhOVXIB1QUToeGkdSToLneLTYWxBAACIn4oj3CGyiDb9z5ndgEypLmRcjLafBGMNWagscxyEhJSBwAmy8DW6LGzbeBrtgR4ezA4e9h/fsO38GeH6jm1qiYYY6ecBcolRv8WRkqt8MQk9FT+N8o4jH47r1crznPe/Bc889h2eeeQbPPfccnnzySV22WymmFKXqDTmdTmN4eBg+nw8XL14s+8Neb1Gavy/VFcDtdle07lKoBvp613dms1mMjIzA6XTi4sWL+OUvf2n4xaUaUarWuabTad3GheqNGhk/c+YM/H5/1dvRW5TmlhK0tLSUFvNMARff2FmHsxXgdn8WmCOwU28qZQEbB2TiYHYfuNjrdUo/X/05fjj9dQgbN3GDxcCYAkWRIXLd8LTdi+tb12ETbHjH4Dtg2eoHtjJIcFbEYjHEIuuIcYuAbxmtra0V1zQ2O1rzF3b+18pbYRWscFvdcFlcuglSSZHw7blv49rmNcxF55CQEthMbmqNVApTkJbT4DgOPc4eWHkr+t2lHwQKNbqp6eF4PI65uTm0trbuKdFoFu5mUZqL3ql+M4lSM6y1UPq+HH79138dL730EjY3N9HX14c/+ZM/wTPPPIMPf/jD+NznPoeBgQG88MILBqy4NM131y4T9eZ+8uRJ7em8XBopSnNN/PUOjxtxXNvb2xgdHcWRI0fQ1dUF4HXB2EyiVK3LbW9vbzobLWDnJjc7O4v19XVcvHixZhGlZ/e9oigYGxsDx3G4dOkSRkZGim9bSkF4+XPg127svL7zHLL3fgwQcprzLA5k7/kYbP/0n4DMNpjNB+buBvO/Xgc5sjmCjnQMMywDl5xFhDEwKHAmQwilgzjqO4aJ8ATeMfAOSIcfg3XjJty8ArdNATq7Id77BDbTkmYuXW5Noxk44T+Bnyz/BAkpAQaGcCaMo96j2M5s65q2/8XaL/Da5mtISSlk5Azi2TgsvAUxKQYRInw2H3pcPdhIbWDAM4Cnjj2FgL2yLEl+eviXv/wlHA4HFhcXEY1G4XK5NDeGZnm4MEM9YS719KmuNdVvlvfVLJFSl8tV8eu+9KUvFfz5D37wg1qXVDOmFKW3b99GKBSqOu2t1pTWA1UoqpEodTa1ETYcetsoLS4uYmFhAffee++uD349pkdVsg9V6BsxLlQPVMN+URR188vVq9EplUrh+vXr6O7u1kbYlorC8lM/ALdxE4qnF+AAfvU6hJbDkA+/ZdffyYNvQibx/4Aw/xOAE8Ccbcieet1b0y7aEWUybJk4ZJEHxzEoChCV0+AS6xhnMjwWD16YfAEiJ6Lr2JvwQDIJq+iA3P8GON1dGAAwMDAAWZYRCoWwubmJyclJzQRerWk0Gy22Fnxy6JO4sn4FR31HwRhDwB7AkH8IR3xHdNvPXHQOHosHG8kN2EU7QukQnBYnIAFuixsOwQGf1YcORwce7n4YPa4qa4xz4HkeXV1d6OrqAmMMsVhs1+Sa3AlTjRIFZmp0aiTN0tVvBGYQpYqimO59LYUpjyYQCJRlELsf9Y6UZrNZXLt2DXa7XZdpR8X2pcdxybKMmzdvQlEUXLp0ac+Hvh7vXzmiVB1raqTQr5Vcw/6+vj7dtqtH+j4cDmNsbAynTp3aVSNcctvheXDJCISNcYADmKMNfGQBez4RHA/p9PshH34zIGXAXG27oqmP9z2O/xW6DQtjyDAGCxjiHJARgFByDUpyDR2ODtwK38KAewABewCzvmN46th7wOeVCwiCsKumMf/m6Pf70draCr/fX9ONpp6WVW2ONrzz0DsN2/7t8G1MhCcwEZ5AWk4jlokhwzJIySlYeAsURYHMy7AJNihMQatd/wc+juPg8Xjg8Xhw6NChPQ8XVqt112SwekXZzJS+bxbKSfWn02lEIpGGGviXS7On7w+qfZ4pRWlbW1tNUaJ6itJkMonNzU2cPXtWS38bhR7HlUgkMDw8jJ6eHi1ylk8zREpr7V6vB5Ik4bXXXqu5frQQtYpSNQpeSMyX2jYnZ8Bt3gRz7rhc8FsTkAf2sSDhODBn4fKaQ55D+K37/jWW0sC7V1/DvMWKF5UYfIILE1YbHKILG+kN9Lv7EUwHcdJ/ElORKWylttDuKO6woZrAqzWN4XAYW1tbmJ6ehsVi0aKolQidgyRSpiJT+NLkl5CUkgimg8jIGUiKtGPSz/NgYBB5EW6LG2DAW3rfgsMe4y248h8u1AlT09PTSCaT8Hg8WsOU3rXzuZAorZ38VH8mk8HLL7/ccAP/cjFDpBQ4WNclwKSitFbqkb5njGFhYQGLi4vweDyGC1KgdrGoTpMqJaIaLUr1jD4acfNR60fT6TTe9KY3GVInV21NaW4zWKEoOFBalDKLA8zVCS4b3/lvdzfAVXfx7nB2oOPh/wjx1tcgLP4YvvgyWrrugyM2CwtnAcOOiX9WySKWiSGejWMuOodwOgwLb0Gfuw8iX/wyxvO8dvMDdkoWcoVObmf4QUuF7cerG6/CITiwJW+h19WL1fgqrFYrnKITHY4OeKweXO68jEe6HoFV3Pl5I3A4HOjt7UVvby8URcH29jaCwSAWFhYAQDuvXq9X1+8xiVL9sVqtsFgsOHXqlClS/c0eKW0m1O8Lx3FeAPcC8AKwAZAAzAKYB7DNGCspHBp/5qug1ovFfjZNeiFJkhbFu3DhAq5du2bYvnKpNlLKGMPk5CQikUhZ06Tqlb7PZrN7fq4KZ3VcaK37UOdG64VaP2qxWOByuQybzFVNTWkmk8H169cRCASKNoOVjMI6W8Faj0G23umcToZ3uu2rxeKAdO7X0Hn4LUj//C8gW1wQORHhTBgu0YXp7WlYBAu+u/hd+K1+/MW1v0CLrQXdzm4c9h7GU8eeglUo/3222+27zPu3t7extbWFubk5TcC2trbC7XYfWGEi8iIUpkDgBGSVLBSmIKtkwXM83BY3LIIF7fZ2+O3+Ri9Vg+d5+P1+7YE5t8nm1q1b/3/2/jw6kvs+74U/1dX7CqAb+zbA7PtgZjAkRZGmKUrUYktWYkuybDmK7RvfJHbyxnl9o3t5c06cvDo8cZL3PdG1E8eJYlmSbcpLtFrWQlnWanETB+tgG+w70N0Ael+q6v0DrGKjpxvopbrRPTOPD48pAqj6dXUtTz3f5/t8cTgcGkktN2LvISHRH5n3lOMM8C8Uta6UJpPJmpn8KAgC3/3udwH+KXAB6ADCgAD0A1HgTwRB+CNFUYKHbasuSWm5qCSpCofDDA8P09vbq73dV1pVVFHKFKRkMqnFahU6uec4lFI1jzYYDOo2hlXv+fHZCu6PfvQj3UmvimJJqTpM4NSpU0emPhxFSuX+pxHXbiOE/QgCyNZG0plNToqMEFoFKbmvopoKK815rV5+WjzD4vYr2KQEcUcP46kgbpObneQOgiiQkBJ4RA+RVASbaGNub46xwBgDzQMF7SMb2UQnmUwSCARYXFwkHA5r5eJK5/JWG4+0PsKd4B0EBNYiaygoJNNJ0kqaDnsH7Y52LnsvH/cyD0V2k00kEtGGqKTTaRoaGrQ0hmKvwYdKqf447JjmKvUHg8FjLfXXOikNh8PY7cdTwciF2dlZgA3gPyiKcqAULQiCHfgnwL8QBOF5RVFi+bbzwJLSSpTvV1ZWWFhY4PLly1qgrd7E5zCoOaWFQm10KWQKVvZ+qklK1XGhaq6rXg8LPUd1bm9vMzk5ecD6UI359IVgfX2d2dnZgocJHLltWyPpt/4fSGujIAjI3tNgeSPAWZYwDv8x4vKPQFFQnK2kHvmN/SzTIyDuzHFj7bs809EHZpFQaJ3/5PLQ4rvE99a+h91oZym0hEHY9zyq4y/3UnsFHYdCYDabD3SGqyNQl5eXSafTiKLI7u6u7uXiaqPD0cEvn/9l/mDsDzjtOY3L7MIgGAilQlz0XuRnT/4sDlPxUTPHBUEQcDqdOJ1OLY1hZ2dHs2moPuKmpiYcDseR3129kNJ6anYpRn02m83H3tVf653t6tz7WoCiKHz0ox/lox/96P9U/9sbRBRABhKKovzHQrZVu0f8EJR7szAajboqpWq3uiRJxxrarnb6HwXV77q6usrAwEDRb1vV7L4/alyoHvsoB6p/dGtr65780UqS90JIqWrL2NvbY3BwsODGkCO3LaUwTH8Nw9x3QTSRPvvTSN1vAUHAsDGEaeqvEGLB/SYn/ySYnSQf/3+/uf3o9n6YvqMZTG+ee4aNESTBCNZ9W4ZN8uKILRNKhmgwN+CP+7EYLQQSAVJyCgUFr9VLl0O/VIPs4+B2u3G73fT19bG5ucn6+vqBcnGt5WsWA5/Vh9fqxWQw4bP6EASBrdgWXc6uuiKkuSCKIl6vV4uIU33Ec3NzRKNR3G63RlJzXRf1RErrYZ1AyVWj4yr117pSWkukVBAE7XgJgtABPAk0AxKQAEyCIPy5oij+o7ZVl6S0XOhJqiKRCMPDw3R2dubtVq8WRFEkkUgc+juq31XNzCzloquWUrq3t8f29nbFxoWW+znS6TSjo6OYzeack7kqqZIfte10Os3w8DAOh4Pr168XdV4e2X1/95sY5v4WydEOShrj6J+h2L3IvnMY/DMIoZX9znyDCMkw4ty34S3/EgQBceYbmKa+AnIaxeImeeufavPrBZMVg/LmdWmS03yo4Sp/Iuzndu6l9jAbzKyn13GYHCiKsq++UB2/mdFoxG63c+rUKa1c7Pf7D+Rrer1ePB5PzfsRo+kon5v5HHf37rIYWqTN3sYJ1wkkReJsw9njXp7uyPYRZyrgiqIcaJhSr616IHv1sk5AtxHY1Sr117qvuNTg/Eoh4zz8HcAL3GFfJTUCPuCLhWznISktA2pZ9OLFi2U33eiBo8r34XCYkZERenp66OzsLGs/lSSlsiyztLREJBLh8ccfr5jyXM7nKCQBQE97QK5t5yOO6tp6e3vp6Cg+7PzI8PyNUbB590mnIIJoRgjcBd85FJMDFGV/xKiiIMgystkKUhIhsolp5AUMoZX9DUkpzFKSxLP/CQQBqetRUj/+EsLO4v7YUtFE27mf5dddbYSSIWySkT985RNEre24GlsxWG1sx7eZDc1ywtVLcmSU9MoKorcJy/XrCBWMDMosF/f29pJOpwkGg2xubjI9PY3VatVU1FqMu/ne6vdYDa9yvuE8DeYGpnemidvj/Pzpn6fLWRnluVZgMBjweDzaPTuVShEMBllbW2NychK73Y7JZKppQqKi1olTJiq11kqV+utBKa1RUnoKeJuiKJFStvOQlJYANVYnFosVXBatxhvtYZ9LJdCXLl3C7XZXbD/lIpFIMDQ0hNPppKmpqaJWiFI9n6p/9KgEgEp7SnMR3kLXdtS2D42EsjVBeBOM+2RLkJJg2T+n5I7rKO5uiG4jGAwoJhty+4194hrewBC8i2L17IfopxOIK69AMrT/97ZG5ns+iK/TBHIaufk8irMVE9CAndBnP40tcZdtWwTr7Arma9dIC2kcRgfRF18k/v0fIFgtkEiQmp7G+fM/j6DzQyXfcTEajTQ3N9Pc3IyiKMRiMfx+v/ZgzByBWgsPuvXovtosCAIdjg4sooXL3suc9Jw87qVVHSaTiZaWFlpaWjRSs7CwQDAY5OWXX9YaphobG2viu8vEQ1J6EHqW+mudlIbD4Zop38MBUvpJ4B8JgvBDIMh+531UUZRAIdupS1Kqh6e01EanWCzG8PAwLS0tBc9Yr0T00GH7yYQsy0xNTRGNRovyFR61n0K8q8Uic1yo2Wxmbm5O931kolilVFEU5ubm2N7eLmh+fTU9peoY283NzYLWVsy2syGf+2nEwCzC3jKgIDeeQOq4ub8OVzuJx/4FptHPgZJGsXlJ3fjVfeXUaAZF2ldBYb+Eb7IjJCMolv3GobTJidQ7eM8+k6OjpFdXeaL7Cp+zDbORjMDcbbouPcZl51kSf/f/ILa3IYjifq7p3btI6+sYy6gI5Douhf6eGt7f3d2ds+nmOKYUZaLL2cVieBGH0YGCQlyK0+5or/o6ag0qqWlqasLpdNLZ2cnu7i5+v5+5uTmMRqMWGVZIw1SlUU/l++Mg0OWU+mud8NeSpzQLUeD/BP4BkAIMgEsQhKuHdd2rqEtSWi5K9fqpGZkXLlzQTvJCoCqLlSal2QpmPB5neHgYn8/H2bNndbt5FeJdLQbquNC1tTVtwlAkEjn2qVGZOMo/mgvV6r7PnG5V6NqO2vahcLaSfuJfkd6aRhCNyI0nwfgmCZa7HyHRdnmfbFobQNx/EZKbTiN7zyDsLiIIBhSDCdl7el85PQJKOIJgFGmWHfxSdIDl1BZi2szVcx/FlJBIoMAbn3t//QJUKYrtKGQ33WRPKTqO0PC3tr+VjegGs3uzAFxvvs4V75Wq7LseoJI9URQPDF5IJBL4/X7m5+eJRCK4XC7t58eRGVnrxCkTtbDWYkr9ta6U1lr5PgP/CXiM/dB80xv/mAshpPCAktJiIcvygS7mYm8+1RprmrkfNa/v3Llz2sNQLxQbPXUYVEJlMBgONF5Vs8P/KKgezWK9uJVUStUXq3g8zu3bt7VGOz1QkBfW6kZpu0Jeym2yo5iyUh1MNpKP/xamH/8PSCfAZCM18MsHOvDzwdh3Av7228ixGC6jkTMbAra3PYlFtKDYFEwXLpIaHUHweFAiEcTmZsQC0hrSyyukFuYRbDYsFy4glBm6XgiypxRllhdVElRpJc4iWvjQ6Q8RSoa0wPx6UdyqAVmWcx4Pi8WiNUxlRoaNjIygKIrW7KY2TFVjncdN9ApFra31qFJ/NBpleXmZ5ubmYwvwPwyRSET3RJpyIQiCCHyH/a57O/sTnZJAQYQU6pSUVvPmqaqNXq+34HD5bFSLlKpkcW5ujs3NTW7cuFH2ZJN8+9GDbB3WLFSNDv9ClMxyJkhVWimNxWK89tprRSv3uiC8ibj82n5OadvVvPPtsyE3nyfx9L9DiO/sq6gFEFJFlpHW11FMZtLjdxBbW7E98QS2xx8H3mg6et97iXmbSC8sIJ4+g+0nn0I44uUxMTFB+IXP7VsLJInEK6/i/ug/QKhivFN2eTGRSBAIBDQl7qjoorL2LRjwWI6/QbMWoSjKkSQkOzJMbZhaX19namqqKs1uD8v3+iH7WnzllVdwOBzHGuB/GGq0fG9kv1z/h8BXgTj7Jfww8CeFbuAh8sDv9zMxMVG22lgtUqooCru7u9jtdgYHByt2AyhlclQ2jiJ7xzE1KhPF+keL3X652NraIhgM8thjj+l+gzySTO+tYvzB/xc5+cbL790XST72z1GcbYXtIJeKegjiP/wh0a9/A0NjA8KJXgTAcvMmQkapWzCbsT/9dM6/VxIJpK0tMBoRW1oQ3rguYt/4BgaPG8MbJTBpZZXUzAzmixcLXpvesFgstLe3097ejqIo2qz35eVlAE1FdblcdUNG6hGlkL1cDVPZpWGv10tDQ4NuNo1aJ3qZqPVyeC60tbVp1+JxBPgfhholpQDfZ79k7wXMgA0I8ZCUHo18Nx5FUZidncXv9+uiNlaDlIZCIYaHhzGZTJw/f76i+yqnfF/ouNDjJKWqf9RisZTl0ayEUirLMhMTE0SjUXw+X0Xe2I+MhJr7W5BlFM++ui2E1hEXvk/64s/qvhaAxCuvIDb79kvrTifS6iqp2TnEG0erw9LODqHPfBY5GABlv8zvfP/PIBiNKIkkgi3j2hZAqcCkt1IhCIIWXaQqcSpBDYVCWkKF1+utmRnYRyHXebWb2OXbK99mPjRPIBHAa/FyyXuJn+j4CYyG43lElatA5ioNq81uc3NzB2waTmfp1ol6IqX1tFYV6vdyVKlfVVn1DvA/DNFotBY9pRLwp4qibJa6gbokpXooBCpRzH7DSSaTjIyM4HK5dGkaydxXpbCyssLi4iJXr15leHi4YvtRUSphTKVSjIyM4HA4uHHjxqHHtpKlbxW5Pkep/tFCt18OkskkQ0ND+Hw+urq6WFhY0G3bmTjy2KfjIGZcNwYjgqRP41uu/QpGE3I6hXrVK7JyQCU9DNFvfhN5dxfxjbGhyZERkmfPYLlyBcu1a8S+910MXi9KIgEmM0adfLmVQPas93A4jN/vZ3R0FFmWaWxsJJ1O1/TDP5vsxdNxPjP1GbZj20zvTpOW07Q52thJ7iDJEu/oecexrDOfp7RUGAyGexqmAoEAi4uLWrSPWuov5gXjYfn+eFCtAP/DUKNK6Rn2w/N/ShAEk6IoKUEQbgG/oijKrwmCIChHPNjrkpTqATUWKpOUqpFEp0+fpqWlRbd9VYqUSpLExMQE6XSawcHBqpUQSinfq+NC+/v7aWs7usxbjRttNvkqxz9ayPbLwd7eHiMjI5w5c4bm5mbC4XBVOvtzQem8Rejuj5hYCpOWZDoscbyXByp2M7E+/TSRP/8zpEgE0mlEnw/T6VMF/a28sYng2r9xC4KAYDIiB4IA2H7yKTAZSY2OIbR4sD/zdsQ3SEOtQxAEXC4XLpeLEydOaOH9q6urvPrqq9hsNk2Jq4SvvFRkn1fr0XV2E7uYRTOiIOK2uAkmgpxxn2HYP3xspLQQT2k5yLZphEIhAoHAgReMpqamI6eD1RPRq/VZ8uWgUgH+h6GWSGnGy1ET+6V7FEVRcyNloPeNfxcgf38s1DEpLfeBn0kU1YzH9fV1LZJIT1SClEajUYaHh2lvb6enp6eqb8vFlu/X1taYm5ur2LjQUqEqmap/1O/3l5SucNT2y4U6+ODatWtauabS06IO2/a6/TR/kX4H59I/wGQy8ZfGt2Gfc5GeXWQnmuJsq5N3nG/GYtTnYWm5eAGD46OkpmcQbFYs165hsBfmSTWe6CXx2msoVitIEkoqjdi+n8cpGI3Yn3oKnnpKl3UeJ9Tw/vn5eW7evKk9FCcmJkilUjQ0NGgjUI/b15d5rxINIgqKNipWQUFQBFJKCouxeg1n2aimApnZMJX5gpE5HUxV3exZ5329kdJ6WWs5KKbU73a7Sz7Pai08/w3EgYQgCNeAZfabnK6xH6JfEOqWlJYLlSimUilGR0exWq3cunWrIheN3qRUVfQuXrxIQ0ODbtstFIWSLXXyVTwe59atWzX3lqwOAbh9+zZWq/VIS0GxKPfFSVEUpqenCYVC9xy/amWg5sLs5i4/DvvY9H0ISZLZDccYe3mBgU4nTW4HfzcbJJ6SeOJUE/5wCrfNSFeDtayHvOnECUwnThT9d7a3vQ05uENqdhYJhZHHWxlLfJ2Nlz69H+FjaeSJjie41XKroPVV2lKiB7Ifimp4//b2NjMzM1gslgPh/dVENtlrt7dzwnWCmd0ZREFkK75Fu6OdUCrEz538uaqu7bB1VhOZ08EA7QVjZmaGeDx+QHV7WL6vDPS8zg8r9U9MTJRc6o9Go7hcLt3WWQ4yzsER4BvAJ4C/AS4DDuBfvPHzIw9sbbGEKkIURfb29hgdHS24pFzOvvQIm1cUhZmZGXZ2dnRV9IpFIeV7dVyoz+crePJVtZFKpVhYWODMmTMlzYg/CqUOaYD9Zqvh4WGcTifXr1+/5/gdFynd2dlhbnoSp8uFx7PfoBERYlijIdwWgb2gHySJL7++w0uz21jNZoLRFGajgTa3hRs9Hn7yjA/RUJ3zwWCz4fzFX0CJRPhh4FW+t/Ed0tE0M3szGA1GPGYPX1v8Gi6TiwtNFw7dVi2ew4UgV3i/3+8/QHK8Xm9VxmhmkyijwcgHTn2AYf8wW7EtUnKKZmszve5eOhz6X5OlrvM4oU4H6+rqukd1S6fT2Gw2Ghsbaz6RoZ5IaSXXqlepPxqNVv2l8igoipIAflcQhG8Cj7OfWfo9RVHSb/z8/iWl5TyU1RPB7/dz/fr1inew6aGUJpNJhoeH8Xg83Lx581hvPkeV7zPHheod3K8Xtra2WFxcpKWlpSKEFEovsUciEYaGhg59WToOUrq6usrCwgI/9cR1YkPb3FnbxQAkJJmeJjtutwOPx0MonmJoapseV4Lwzh5zOyAYRJpsjXxr0o/RIPDUmcJyTfX6PILTydjCJI2WRmb3ZnEYHaTkFOFUGKfJyczuzJGk9H6BzWajq6vrAMnJHKOpqqiVCO/PdV6ZRTM3W27qup9yUUukNBPZqtvCwgKRSISlpSWtnKuqbuWMGq4EqjFqWy9IklQVAl1OqV9RlJo5nq+//jqjo6P80i/9Uo+iKIuKokwCkwDCPszs881Uhtc0J+qWlJaKdDrN2NgYyWSSkydPViVSoVxSuru7y+joaEENWCoRquQFle9mrSgKS0tLrK6uVsSbqwfUuK9AIMCpU6eIxQoeNFE0SiGlqjXj8uXLuN3uvL9Xjgp7FLJJqaIoTE1NEY1GtYa6D940881RA9GUxNUuNz+8G2RyM4LRIBBLSfT6nHS0uZjbjmKNRZCkfZ+coMh8ZyzK1WbxyCYOvWEz2ggnw5gNZhJSgmg6ynp0HZn9TuuF0AK9rt6jN3QfIVd4vzpGUy0Pyk4ZxaoQlaOIgshOcgeLwUK7o51+d3/R5K0WyV426kXVE0WRhoYGbcKUmsgwNjaGJEkaoWloaDj2z1MtoqcHjotAH1Xq/6//9b9y9epV3vWud+m2z6997Wv883/+z5EkiV/91V/lYx/7WNHbiEaj/OAHPwD4qCAIC8AqsMN+k1M78H72pzt9jCP8pQ8UKQ2HwwwPD9Pb24vH46maP6xUUppJ8gYGBgqS6tV9VfvizzcutFzoqVik02lGRkaw2WzcuHEDv99PJBLRZdu5UExDmKIozM/Ps729XZA1o9KNTuq1odoIXC4X165dQxAEokmJ//H9RVaC+8ducjPCP3yki4EeD+F4mnaPlS8Nb7AdTmESBVIyOK0Wejsb2YslcRllNjY2mJ6exmazaaVli8VSUcLydOfTfGbyMwBsxjb3h00kdzEajLTaWvn05Kf58OkPc9JzsmJrqHVkjtGUZZnvL3yfv5r5K9YiawSlICbRBAbocnXhtXp5e9fbeUv7Wwrefq0qkNmol3VmdrTnSmTI9hJnNkxV+/PVC9GH2gn6zy71m0wmvva1r/HP/tk/Y2lpid/4jd/gHe94B0899dShIkY+SJLEP/2n/5RvfvObdHV1MTg4yHvf+14uXCi8aqQoCo8//jiPP/44/+2//bdx4H3ARcAD7AGvAp9UFOWHhWyvbklpsRfUysoKCwsLXL58GZfLxfLyMukqBWWXEqGUTqcZHx8vmuTpMW2pWKjZnl1dXbrNX4c3G6r0uDmoJfETJ05o5fpKB/QbDIaCzjFJkhgdHcVkMhXcbFWN8r36vZ44cYL2NzrWAX68GGR1N0ZngxVFAX8kyben/fzirTdHxX7kkU7+/MdrzAckPDYjzU4zm6EERtHA3x88QXejDUVRiEQiBAIBxsfHSafTJBIJgsFgRVTULmcXv3rhV/mTqT8hJadISAn8cT9mg5m0ksYu2nlp46UHmpRmIi7H+X7g+7Q2trImrdFEE6uRVZppZi24RqOrka/e/SpXG6/isBZWcaoXslcv6zyM6BmNRnw+Hz7fvlUmFosRCAS4e/cu8Xj8wAjbajSh1hMprcW1CoLA9evXuX79Oh/72Md44okn+Nmf/Vm+8Y1v8Pzzz2OxWHjb297Gu971Lm7eLMwO8/LLL3Pq1Cn6+/sB+NCHPsQXv/jFokipIAhavKaiKH8B/EUpn09F3ZLSQiFJEnfu3EGSpAMdzKIokkwmq7KGQsmJikgkwvDwcM6Z8IXsqxojTVVUMglAL1KaryReyRI4FKZmxmIxjdAX811XmpRGo1Fef/31nJmt4YSEWXzzhm0zGdiLHzy/vQ4z//sTvciKQiItM7kRIZWW6PM58DnN2n6cTidOp5Oenh7S6TQvv/wyGxsbTE1NYbfbD6ioeqDZ1kyHo4OUnCKYCBJOhZEUiXAqjEkwIRgE9pJ7uM3Fqw73ExRFIZaKkZbTCKKAIAiIgohBMGC320nJKdwuN1vhLUZGRzBi1AiO2+3O+0CvF7J3P67TZrPR2dlJZ2cnsiyzt7eH3+9ncXERQRAqPsK2FolePtSKUpoPiUQCu93OU089xVNvRNptb2/zrW99i+985zsFk9KVlZUDQlJXVxcvvfRSUWtRFCVTrTdysMNeURSlKOXnvialKrnr7Oyku7v7YD5elebRw/4ba6GKnJpJeenSpZLk+Gp9LnVcaCAQqFgSQLlKZqZ/NNcaK1kCV7d/GHFUG8IuXLigeYgKRSUJ9dbWFoFAgMceeyxn8PrpFid/M7lFLCUhANNbUc40O3h1YYdr3R6MGZ31BkHAZhK51uVG2LqDceL7YLKT7n0rSsNB/6bRaMRkMnHu3LmcKqpKespVUa/6rnIneAeraMUf95OQEmxGNxEEgYtNF/lvY/+NXzz7i7Tb24/e2H2ChJTgm0vfZDwwzmpkFaPBSDARJJQKYTKYEBGJK3GMBiOhVAi32U1EiTDQOcAjpx7RsjXX1taYnJzE4XBoJKfWGm4Kgd4TnSqFUomewWCgoaFBExKSyeSBEbaV+P4eklL9EIlE7rHz+Xw+PvjBDxa1nZwT9ErwiL/44otcvnwZtcu+kMlN+VC3pPSoA7e+vs7du3fzTucRRbFq5ftC1EtZlg80k5hMppL3VenyfSqVIhaLkUqldM/2zEQ5nyXbP5prjdUo3+fb/tLSEisrK9y4caPkiTt6k1JZlpmYmCASidDa2pp3XSebHXzoZhdfHlphcjOCIkMoKfGXt9e5ux3lA9fb77k+jWN/gflH/xkEAcVox7D8I5Jv/S0UT0/OfeRSUYPBoC4q6pmGM/zcqZ/j83c/j8fsQRREdpO72EQbRoMRAYGvL36dj5776D1rul/x1YWvMuIfYS2yxk5yR1OLLaIFEZFwOozX6uWE+wSSLNFmb+Nsw1me7noaQRAwmUy0tLTQ0tKS84VCbbgxm811cRwrPdFJL+hF9MxmM21tbbS9MY43Eong9/u170+P4Qv1REprfa16Bed3dXWxtLSk/e/l5eWS0mh++MMf8ud//uf8wR/8wSOKorxUKiGFOial+aAGtsdiMW7dupWX3BmNxqoqpYftKx6PMzw8jM/n4+zZs2XdtCutlKrjQs1mM2fOnKnohVsqaczlH9Vz+4Uil1Iqy7JmJymnIUzvB3symdRyZVtaWtja2jr096/3NNDlNvH/+etpJEVmN5ai2WFieGWPt531aSV6AGF3CdPtP0Ix2cHiQkiEMQRnEZf+jnQeUpqNzEDxfCqq1+s9tHScifON51lsWcRgMJCQEtzdvYvZYCaSimB2mNmKbSEpEqJQu2qJXpAVmfHgOM22ZmZ2Z3CZXATiASyiBQWFi00XSUgJPnruo3Q5j7aYZL9QSJJEMBhke3ubQCBAOp1meXkZr9dbkwkdcH+W7wtF5vfX29t74PubmZnBbDYfGL5Q6P5rnehloh6UUj1I6eDgINPT08zNzdHZ2ckLL7zAn/zJnxS9nd/4jd/g4x//OMA/FgTBB7wGJNif6CQpilJwzM19RUpjsRjDw8O0tLQcGdhezfL9YWXiQCDAnTt3dMv0rOTnyhwXOj4+XvGbTCmkUR3Nd1Skkrr9SnpKs9evDhRoaWmht7e3Zh56airFqVOnaGlpIRgsbCLcdiTJ5GYEswihhEQkKeG0GHltcYdnL+wrZom0jD28CYIAhv2bvGJ2IES2oMRjn09FXV9fZ3JysmAVtdvZzY82foRdtBNNRVlPrWMUjMyF5uh19vLfx/87Hzz1QRotb1oranmiU7Fr24xushxZxh/zsxnZJJqKIiAgK/ula0mREDL+z2EqLT5PFEWt4SYcDjM7OwughYWrKlxDQ0PNEIF6IaXVIHqZ3x+82TA1OztLNBrF7XZrwxeOqvDVwzGF2ifQucr3pcBoNPK7v/u7PPvss0iSxC//8i9z8eLForfT2NjIf/yP/5H/9J/+0zeBPwX+HFh848c2QRD+g6Io/oLWVPTeawTZJ7fazFKoP6/apDQbagTQ5uZmWSXcbFRC/cs1LlS1JFSya7OYz6J6XIPBYMEe10p7SjNJr5o1e/bsWe3mXgvY3NxkZmaGK1euHHjzPorgBCJJXnhthVhKIhSXkRQQFACFb0/58dhM/N1cEH8kRZ8Q4peNTThiKyhKBCGdAJMVqefeKKFSSF8uFVUtPapZjblU1PON53mq8ym+tvA1UnIKi2ghkU5gNVlxmpzsJHb40tyX+Afn/kHRa6p13N29ywszL7Ad22YxvIjJYGI5soxoEAmnwjRZmoin4/isPmLpGO/ofscBcl4OjEaj1tgnSZIW3j87O4vJZNJU7+OILVLxkJTmR66GqUAgoJWBM6+3ejiGuVDpZ1u50EspBXj3u9/Nu9/97rK2EY/H+cIXvgBwHvg88DqQBMzsR0MV3FVeu0e9QMiyzMzMDHt7e0U13FSTlGYjlUoxOjqK1WplcHBQ15uK3p8r37jQanhXC92HmqXpcDiK8rhWo3wvy7KmMF+7dq0qwxoKgaIozM3N4ff7uXnz5oHr5qgGrVA8zX/9zhxz2zFsJgNb8TQmo4DLZqTNbcUoCvzxKyu0uS10eCxsxTr5kvI4P+v+Eca4H8XiIvGW/H7ScpBdejxKRf2Jjp/AbDBjFa2YDCbu7NzBLtrZSe5wpuEMy5HluiEoxeDrS1/HYXRwN3lXI56nPacJpUI83vY45xrOoQgKDqMDt9lNs61Zl/1mH0tRFLUGNth/uKkqXCwWO6DCVZMk1Euj03Gfm9kNU6lUikAgcGCuu1rqrydIklTTDXp6eUr1wm//9m8zMjICsK4oyv9dzrbqmpSqXkyv18uNGzeKujiNRmPVGp0yoXoy+/r6DmQ/6gU9c0p3dnYYGxvLqe5Vg9QXQhrV0nMpx7MaxHpnZ4dEInEgjuy4oQ46MBqNOUn8UaR0djvCXjzNmRYne7E0dotIIq1gM4l0N1qIJGXSkkKjfb+U57aZeC35BI/ceCftdhnF0QKm6ngJC1FRRdN+ydgiWhAQSMgJXGYXe8k9mm3NdUFOoDiCEkvHsBvt+6V69v/GIlqwiBYuNl2s2MjVo9ZotVoPhPersUXqyEVVRXU6nRX9Xh60Rie9YDKZDoS9q95vtYFyenpamzBVK1aNXKi145oNPZVSPeB0Ovnc5z6Hw+H4XSHjwiyl4ak2npIlIBKJ8Nprr5Xsxay0nzAXVldXmZ+fv6dUqif0yCktZFxoLSilaum51PisSp4DqVSKyclJFEVhYGCgZohNPB5naGiI9vZ2enryd74fdlzUT+KyGhnodjOxHmZpJ06Hx0JahpM+G2u7SRJpGYvRQEqSURCwNbah2EpLldAD+VTU+HYcR8jBiryCFSu70i7dzm6MBiM/c+Jnjm29lcSlpku8tPESzdZmliJLGAUjaTmNy+wqqJmpVBRDnPPFFi0uLhIOh3G5XJoKV2paiR7rPE7UMnnO9n6//PLLNDU1aUq40WjUvj+Hw1FTx7vWG53C4XDNVN0AnnvuOaC8KCgVdUtKHQ4HN2/erGmJXYUsy8TjcTY3NyuumJU7FKDQcaHVIKX51FjVP7qzs3NP6bkYVMpTqqq3HR0d7O7u1szNVvW1HvUidxQpPdnswOs0s7YbxyQK2MwGLnW4sJtFLne4+HvX2rizHuEvX18DARQZfupyC54CCWm1CEGminru7DlmtmbYCm4R3YuiyArd9m7MSTOytbZVk1LwdNfTCAiMBkYxG800mhvpdnbzdNfTFR8cUOp3mx1bFAqF8Pv9LC8vA/p6GeuFlNabzUC1zsCbVo35+XkikciBCVN6v2QUi1pXSqPRKJ2dnce9jHtQLiGFOialgiDUBSFVJ/YYjUYuXLhQ8RJuOeX7YsaFHlf5PtM/ev369bJuHJVQSlX19vLly4iiWHAne6WxtrbG/Pw8AwMDR3ZtHkVKHRYj//sTJ/ju1BbTmyHW9xL0NtoQDQLDKyH6fXYeOdFIV4OVYDSFx2akxVXYtXpcD1jRIHK29SxnW88C3ONFdTgc2Gy2Y/OhF4JiiJTJYOIdPe/gHT3vqPCqDkKv600QBNxuN263m76+PlKpFMFgUPMyOhwOTYUr9TlRD2Sv1smTinQggHlujqTDgenUKQSz+YBVQ1EUrWFqeXnfx13IhLBKodaV0mg0WlNKqZ6oW1JaD8hMBJidna3KTPpSy/fb29tMTk4WPC60GkpptpJZjn+00sicHqWqt7FY7NgjhBRFYXp6mnA4zODgYEEvRYWMMHXbTDx7oZl4Ms12JIXdsn8D99iMjK6GeOREIz6n+UBeaTFrPm5CkMuLurKyQiAQ4NVXX83b0f8Qh6NS322u8P7M8He9poHVGuqBlKZXVtj71Kewb2wQGhvH2NWJ+5d+CSHjZUEQBDweDx6P58BLhjohzG63a99hNbJta/241lqjk56oW1Kq142tEjfJzHgi1WJQrW7/YveTTaYKVRWq7SnNVCBdLldF91ss0uk0o6OjWCyWA41D1ThGR61reHgYp9NZlK+1EFKqwmERSUkZWawpGael9NvKcZPRXFC9ceqL0MmTJ+9RUctV5R4UVOOFI593WM0wtlqt2vdVq+H9haIWXuCOQvSb30QxGFB8zRjbWkkvLZMYH8c6MJD3b7JfMqLRKIFAgMnJSZLJJA0NDTQ1Ne03Kr6haCqpFLEf/ID03Byiz4ftqacwlPisqAel9CEprUEU8/DMBZXA6VlSTyaTDA8P43a7uXnzpnbDqBYpLYYIpVIpRkZGio5Sgup8HtUfOzMzU7Z/tFKIxWLcvn2b7u5uuroONoiUe36WA9WK0dvbW/TYuELXLckKJtFAIJJiI5TE5zDjtIj85JnSh0Ac5zErBIqi5O3oHxsbQ5blY1NR64GgHMcas7+vWCyG3+/XwvvVEai13hGeDzX/nYfDYLYgpN94XogiSqzgAT8IgoDD4cDhcNDd3X0g23Zubg6j0bjfLPWDHyJMTmDweEgvLZNaXMLzq79yQJEtFLVOSmut+15P1DUpLRdqLJRepFRtJDl9+jQtLS0HflZrSqkaTdXf309bW1vR+6mGCqgoCsvLy7S0tBQd+VUNqNO48lkeqnWMso+Luq5Lly7h8XiK3mYhxFBRFL48ssHfzQbwOkxshVM02oz86uM9NDlq68Whksinyqllx4cqam1BEATsdjt2u10jODs7O1pHuMlkwuv1IstyXZD8eoDpwgXiL34LwWxGjkYRFBlTnuSPQpCdbZtIJPCvrbH78sskXC4ssRhWpwPL5gbptXVMJ3qL3ketl+8jkUjNVQz1wgNNSvUiimqE0srKSt5GkmqS0qOIUOa40FLftipNuMLhMHfv3sXj8XD27NmK7acUqN/32traodO4Kq36qdvPfHCq52E5U8IKWXconubVhR06G6wogNMish1JkZLK+7y1rpQehVpVUWsFtUb0RFE80BGujtBMJBK88soreDwerUxcKznD9Qbbk08S29vD8Hc/QvB4sH/gAxi79Isds1gstHd0EGxsxNDcTFJKE4tEiQaDLI2P4ZbSRfuJazlqC/ZJ6cNGpxqEXuX7cpAZoXTr1q28kn8tKKWyLDM1NUUsFis7mkoURRKJRMl/fxg2Nja4e/cufX19xOPxiuyjVMiyzPj4OIqicPPmzUNLPJUm7pnpAbIsMzExQSqVOjTKqxAUcl3JCihANJFmeDVMIi0RTcn83WyAix0u/nY6gCQrPHKigWtd9TtusBzkUlEDgUDFVNRaI3y5UOtrVEdorq6ucuPGDXZ3dwkEAiwsLGgKndfrrblczVqGYDRi/MmfJHX+PA0VEhgEsxnrW99K7Dt/i9FmwxVPYLxxgxNPP81OKMTGxobmJ1a/w3r2Ez9USu9TlEsUI5EIw8PDOf2Eeu+rUOTrvk8kEtr0q7Nnz5Z9Q60E4VIUhZmZGXZ3d7l58yahUIhoNKrrPspBIpHg9u3btLa20tvbe+QxrEZDh6IoJJNJhoaG8Hq9nD9/vuz9FkJKPTYjZ1odfOH2OgAGQaDZYeZvpvx8Z2a/pL8ZSvK3U37eft7HLwx2YjUdTZTrXSk9DEajMWeH+IOkotbTd2swGGhsbKSxsRHYv/5rNVez1lGNcrjtJ59CbPaRXlrG0NSE9foAgsVCs9Wa10/s8Xjwer00NDTUlRIei8XqmlQfhvr5FiqAckaNqmpeodOEjlMpPWxcaKnQm5SmUimGh4dxuVyaf7Ra3euFqDeFBs9XE4IgEAqFuHPnDqdOnbrHx1zOdo8iD4Ig8LazPl54ZZWUJOF1Wrja5WJ4NYTLYmR1N8HG3r6S/vU7W4DARx/tQjQ8VJeg+ipqLaFeFUaLxUJ7ezvt7e335GoCmgLncrnq9jNWCtUgpYLBgOXKFSxXruT+eZafWJZlzU88Nzd3QAmvh5en+/XF9YEmpaUQRVmWD+Q+FvqGLIoiqVSqlGUWhcybYSHjQkuFniRbzR/NbrqqZuzUYeXu1dVVFhYWCgqeryZSqRRjY2NcvXpV11JOIQ/UcCLNZ19ewWkVkWQDiZTE8EqIaErCZBAIRlO4rEbiKYkGm4k5f4StcII29+E+1/tZKT0MD4qKWuvl+0KRK1dTJaihUAin06kRnFpLDDkO1GLjkMFguKdhSh1jG41GGRsbq8nv8H6/P9Y1KS335lYssUokElqZ9Pr160XtXxTFqvojJUlifHwcoGyPYS7oRRhVxTlX/mi1SGm+i1xRFCYnJ4nFYgUHz1cDiqIwPz+vrUtvb1Eh41eXgzHCCYnBngaGV/bY2EuwFIjR4DSxFIiDouBzmbGZRHq9NiJJGah/MlKNB0KhKmr2w7IeCF+9rLFYmEwmWltbaW1tRVEUwuEwfr+f0dHRir1U1BM5OerFvxagKuGtra1Eo1F6enoOfIcNDQ14vd6aGcBQ69dRqaiNp+wxoZjyfTAYZHx8vOQSeLXK97B/A3jllVfo6Oigu7u7IidvuYRR9Y/u7e3lVZyPY2qUilQqxdDQEA0NDVy7dq1mbgCyLGuNdY2NjcfmZTMa9hVNm2l/7v1SMIbJaEBQ9o9TXFLYjSZpb3cRSkicbnbQXMB0p1pWSo/rHMinomYTnnrwmNULKS1njYIg4HK5cLlcnDhx4sDI2qmpKWw2m6bAlZqQocc6q4laVErzQSXQub5DdQCDxWI5MIChmt9Drd4f9cIDTUrVcPbDoCgKCwsLbGxslBWzU+r4z2Kxvb1NNBrl1q1bBY0LLRXlkOxM/+hhinO1p0apUO0EJ0+epLW1taL7LwZqo1V7ezs9PT3cvn27IjeoQohhr9dOn8/OzEaYeFoilpLxOU2EExIuq4ghCada7MSSMk+f8fLEKa/mJ01JMnfWw8SSEt1NNjo8pT+YHzQcpqLu7u6STqdZW1uruZKjinp4oOpN9rJjwtTpRGpahqrANTQ0FEXc6onoSZJUV2vNVnUzv0NA+w5nZmaIx+NVjQ67n5ucoM5JaaXL9+l0mpGRESwWC4ODg2VdVEajsaKkNHNcqN1uryghhdIJYz7/qJ77KAbZ+6jVcaZ7e3uMjIwcaLQqpMxeCgq5rkyigX/wSDc/XgywGU4xsRFFfOPPkmkZo8FAj9dOOq3w1BkfxgxC+umXlpnZimIQQEDgw7c6uNDm0vZdD8SlVpCposbjccbGxkgmk5qKqipybnftxHLVyjryoZIKZK7pRDs7O2xvbzMzM3NAgTvKv15vSmmt2J+OQiHTnNSGqa6uLmRZPhAdlulVrUTT2/2cUQp1TkrLhSiKecv36sSjvr4+beZ1OagkwVLJs81m48aNG7z00ksVv2GV8nkO84/qtY9ioe5DJfXBYLDmxpmur68zOzt7T6PVcRM4s9HAzZ59dUcA/tv3F0mkZWRZodFhYjkQ5+3n3ySkAHe3otzditLVYEEQBKJJia+MbGqkFOpDTatFCIKA0Wikt7f3gIq6urrKxMREXi9qNVEPRKqaa8wV3u/3+w8ocF6v98CMdxX1pJTez2vNjg5LJpM5m970StIIh8P37YhReMBJaT71cnV1lfn5+bImHmWjUp5SVXnMJM+qVaCSb6bFfJ5C/KO5UC1PaTqd5vbt29hsNq5fv14zN8/M45Zr2MFhTVrVxrsvttDvc/B735ln3h/FajKQkGTMooHdWAqL0YDVJJKUZAwGQXvoW4wG/JE3UylqnbDUOjKP31Fe1ONQUWt9Ug4c7xptNhtdXV0HFLjMGe+qiupwOO5ronecKHfuvdlspq2tjba2Nq3pLRAIMD4+TjqdprGxkaampqLtGioeKqU1DL3L9+pUnGQyWfbEo6P2pQdUBS1beVT3VUlSWihhVP2jbre76MSCapBSWZYZHR2lv7+fjo6OiuyjFOVFVb/tdnve43bcSqksy9rDRhAEbCYDDTYj7760n5caS0r88aur/GA2CMB7LrVwtdON0SCwG0thNYlshZPc6m04sN1aIdr1hsOOWz4varVV1Hr4bmVZromXo1zh/X6/Xwvvt9vtJJNJUqlUzYf31xMp1XOtmU1v6nWXbddQVVS73V7QefeQlN7HyCSKsViM4eFhWltbdZmKc9i+yoU6LjQajeZUHkVRPJYGoWyoFohC/KO5UOkHg9/vx+/3c+bMmYoR0lzz6Y9CLBbj9u3b9PT00NnZeei2qzFcIBcURUFRFGRZJpVKvbEWBTI+59RmhGA0hSLvE5H/37fmeO6dp/iVt3Tz5ZFN9uIpHutr5Nnzzdrf1AIZeBCQraJmRxhVUkWt9e+4Vi0GFouFjo4OOjo6kGWZjY0NFhcXGRoaqriPsVzUEyktVyk9DEajEZ/PpyX4xGIxAoEAd+/eJRaLHWiYyveiEYlEHpbv71eoRHF7e5vJyUkuXLigvZVWal/lQh0X2tTUxMDAQM6bTzU6/Y+66akqrp4WCL2gKAqLi4usr6/T2tpa0UD8YtVMNXrs4sWLRzarHZdSqigK6XQag8GAxWJBkiQURaHVaaTFYWI5GMNhFlkMxkABh0VENAhsh5P82Y/X+A9/7zz/5MnenNs+bvW3nlEqmcoVYVQpFbVWCV8m6mGNBoMBh8OBx+Ph3LlzOX2Maqm/Fvzx9URKq7lWm81GZ2cnnZ2dyLKsTQlbXFxEEATtRSPz5fAhKa1h6FG+D4fDzM3NcfPmzYqO89PjJlfouNBqZqJmQ1EUpqenCYVCRflHqwU151MQBAYHB5menq6o2ljIxCgVy8vLLC8vFxw9Vm0CpyqjanlTPafVG7jRaORXHu/hb6e2SG7NctnyIuFUlPnUVSZN5zCJBlKSQjwlYzfXdpD2g4xKqqj1QPjqYY1w0Puay8eY6ztzuVzHQg7rITxfRSWV0sNgMBhoaGigoaGB/v5+kskkwWCQlZUVPvOZz/Ctb32Lp59+GqPRWHb5/s///M/5N//m33Dnzh1efvllbt68qf3s+eef55Of/CSiKPKJT3yCZ599ttyPVhTqmpSWg2QyycjICIqicOPGjZp+i1MUheXlZVZWVgoadVmtmfHZKMc/Wg3E43GGhoZob2/XhgpUulmoEOIoyzKTk5MkEomipm9Vk5TmI6QA8ZREUpJxWoy47Rbe1w/Gtc+z02rgu5Fd+iN/RdSUZt19hQ63CZNByatG1LpSWstrqwSyVdRUKkUwGCxZRa0HwlcrntKjkG+dh31ne3t7OBwOjaRWUojJRL3nlB4HzGazNiXs3LlzPPLII3z1q1/l61//Oru7u2xubvLss8/y5JNPFp2ffunSJf7X//pf/Nqv/dqB/z4+Ps4LL7zA2NgYq6urPPPMM0xNTVX1eDyQpHR3d5fR0VFOnz5NIpGo6YullHGhx6GUqv7RWgucV6GqzJk5n1B5An/U9tXJUY2NjZw7d67oRrBqkKTDCOkPZwN8dXQDBej0WPmFW100rg8jKNDga2dA9DC7ss7T4iQ/8j3Ghwc74I3yv/pSoP7/WketE5VqED6TyXRsXtRqoR4SAqDwMnP2dxaJRHTtBtdzrbWAWsxUNRgMDA4OMjg4SENDA62trXR1dfHXf/3X/F//1/+Fz+fj2Wef5ed//ucL6t84f/58zv/+xS9+kQ996ENYLBb6+vo4deoUL7/8Mo899pjeHykvauvIVxiq4ri8vKwpjjMzM8e9rLyIxWIMDQ0VPS60Go1Omahl/yjAysoKi4uLXL9+/Z5JGNUgpfmIYyQSYWhoqGQiXw1VUVEUzTOaTUgXAlG+PLxBi8uMSTSwtpfgC0Nr/MNmk7au7kY7bWYPcWsrb33LKYaX9/j6hB+7ycBTp5todZmRZVkjqeo+H6L2UYqKWg9KaT2sEUpbZ2YKQ09PD5IkEQwGtW5wq9WqvVjoOTWonkhprSil+RCJRGhqauJd73oX73rXuwBYXFzkG9/4Bru7uyU1FatYWVnh0Ucf1f53V1cXKysrZa+5GNQ1KS3mgsxUHG/dunVsJ12hNxK1+aqQhpdsVGukqaIoTE5OEg6HK+ofLfUhkV0Wz/X2W+kO9nzbV7/fK1eulDw5qtJrVxuagJwPlO1wEgQFk7j/M6/dxLw/hnzlOuLcdyA4jyG0hiUVRrz4s3x3bosvjQVpsJlYT8vMBVb59af6aLSZkGWZWCxGKBRCURRSqVRuFVVRIB0DgwnE2vIrP+goREWNx+M1+eKaiXohpXoQPVEUD3SDq+Mzp6amSCQSB0aglvPMrCdSWutrzRWe39PTw6/+6q8e+G/PPPMM6+vr9/z9xz/+cd73vvfl3HYuQaDa10Jdk1IoTC2KRCIMDw9rocTZB7laN6FC8kMVRWFubo7t7e2Sm6+qUb5PpVLEYjEEQaiof1RVGovdfjKZZGhoiKampkPL4tX2lCqKwsLCApubmwwODpbVGVtJpVRRFJLJJKIo5j12bqsJRQZZUTAIArvxNK0uC9i9pB75dUzf/jeAguw7h2FjhNSkH1/z38dmNuICVnbizGyGeaSviWg0ytjYGGfOnMHtdiPLsnYOS5K0T07TUSzDn8HgnwHRSPrc+5F63lKRz/8Q5SGfirq1tcXOzg6bm5uaIlcL3eGZeJBIaTYyx2dKkqSF98/OzmIymbTvrNBMzUqutVKodaU0Go0W9GL34osvFr3trq4ulpaWtP+9vLxcsbjEfKh7UnoUNjY2mJmZ4dKlS3g8nnt+Xo2g+UL3lTku9ObNmyVfxJUu36v+UavVSl9fX8XHmRZrklfXd+rUKVpaWgrafqWQaQ+QZVlT68v5flVUgpSq/tGWlhZeeeUVHA6HpqRkk4dTzXYeP9nED2eDiAZwWIz8vYH9qWKCnAKjFVkljYpC5/oEr6fDYG7Q9iUaBK10ePnyZa2rVBRFTCaTRk4VRcE4+uewPU3a2Y5BTmMc/RyyuwOl4YSux6BeUctkSlVRQ6EQHo8Hi8VSs17UeiFQlV6nKIpaJBHsN4oGAgFmZ2eJxWK43W5tBGohz8/j/l4LRa2T0kpGQr33ve/lwx/+ML/5m7/J6uoq09PT3Lp1qyL7yof7lpTKssz09LRWWs73Nm40Gkmn01UlpbmQa1xoqTAYDKRSqaN/sQRk+kcnJydrIqQ/ExsbG9y9e7dgf2sljxW8SRwTiQRDQ0O0trbS09Ojyw1ab5U3s6Gpr6+Pvr4+IpEIW1tbDA8PI8syXq8Xn8+nkYefutzKI32NxFMyzS4zNtMbN3NBAJT9cvsb/97hsbAbl5ClBClJoclhxpneY35+i+vXr+e8Rg0Gw5uRU7t3UVxtGAQRRTAgoyDvLCM5uw783kPULtQmosO8qOqs8ONSUWuZ3Gei2uu0Wq0Hwvv39vbw+/0sLCxo4f1erxen01kXxy8fav2lJBKJlGz5UvH5z3+e3/iN32Bra4v3vOc9XLt2ja9//etcvHiRD3zgA1y4cAGj0cjv/d7vVZ2g1z0pzaUWZQbMH1Varmaner595RsXqvd+yoGiKExNTRGJRDT/aDW8q4WS0sw58cX4W6vR6BQKhRgdHT0yX7ZY6OkpzdfQpDZF9PX1kUql8Pv9LC0tEQqFcLvd+Hw+vF4vRpeFUDxNMJXEYzNhcLYj+85j2BwDkx0hFcF97if4SMtZJjajWI0CDclt0rE4169fL+wh4GzFEN4CuxcUGQQBwdYA7Ksb6rlYTx39DxpyEala6+ivF1J6nF3imZmasG+X8vv9LC4uEg6HcblcWnh/rWVVH4V6UErLzSl9//vfz/vf//6cP3vuued47rnnytp+Oah7UpoNdSJOoQTgOEmpquZmEj09oDdZTCaTDA8P09DQcGCKVDXyUAvZRyFz4g/bfiU9pbFYjO3tbW7cuKH7vGK9yvdHNTSpMJlMBwK69/b22NraYm5+nlc3ZKZCRmxWK/0tLn7xkS4cN34ZZfEHCOF1FE8vctcjnDSI9DbZGBkZwePxFGX/kC59EOPL/wVhbwVFkZC7H0Nsv4wo7J8jKrFWz5d0Oq15Yh8EgloPZOqoNR7V0V8NFbUejiPU1jrNZjPt7e20t7ejKAqhUAi/38/y8jKwLxTt7u7WhD3jKDwISmkt474hpWoDycbGRs7on3w4LlKa2YiTb1xoOfvRiywe5s+sRvTUUaQ0Go0yNDREb29vSYbsSnWwK4rC3bt3CYfDnD17VndCCvqsXSVx2XFPhezb4/Hg8XhI2FuZX5qn1SURi4Z4dSJAKhTgQ7d6aOx58oDqEIvFGB4epre3t+joEsXdSeqJ/xMhvAqiFcXT/YY14E0yre5L9aKqdgT1ulN/XssPnfsdxZxnx6Gi1lN4fi2ex4Ig4Ha7cbvdWoXllVdeuScqrKmpqWrh/cWg1pXSRCJRk8dNL9Q9KRUEgXQ6zejoKGazmcHBwaIuVNVTWg2opFQNcj9z5gzNzc0V20+5WFtbY25uLq8/87jL92qsUr4mtnK3XyokSdIawdra2ip2gzMYDCWfu4cF4heL9b04VrMRj9uJx+3B1ZgmlErg9/uZmZnBYrHg8/mwWCzcvXuXCxculPx9YXGiWM4c+WuZHlP1c2Y2TZUS3F/r06bqAeUcv0JUVD3mvd9v4fnHDaPRiMlk4vz581p4v9/vZ3x8HEmStPB+j8dTE5+nHo5rra+vHNQ9KQ2FQty+fZsTJ06UpJRVWynd3NwkFAoVNC60nP2UQ7Qy/aO3bt3K61s6rvJ9ZqxSqbFZh22/HKgDD9T4sZmZmYoRmVJJkkpI1VSDclUhn9NMUlK0aKhQQuJKZyNnz3YC+2r23NwcGxsb2Gw2Njc3kWW5Mg8hWcIw/10Mq6+BxYl05j0YPN0HCGomIVePg0pO6/lmX0vl3HzQc42VUlHr4ThCfa4zM7y/t7eXdDpNMBhkc3OT6elprFar9mKhZ3h/sajV4/ogvBTXPSnd3d0tq0GoWqRUkiQ2NjYQBKHi4f3lKJj5/KO5cBzle0mSGBsbQxRFXWKV9PSUqn7mCxcu0NjYCFQ24L6UbWc2NOlBSAEudbgZ7I3w+tIuggCtLgvvvNii7W99fZ1kMsmTTz4JQCAQYG1tTSvlNTc36+YRNMy+iHHiyyg2L0S3Mf7o/yH11t8Cx35F4rAy/8NmqcqjUkQqn4q6srJStIpaL2SvHhQ9OHydRqOR5uZmmpubURSFWCyG3+/XwvszR6DWckm92qiH87NU1D0pVUN+S0U1yveqema32/F4PBW/uEol2nt7e4yMjHD69Okj8z2h+uX7eDzO7du36ejooKenR5ft60Ua840yraSaXKxSetjI0HIgGgR+dqCdp854SUsKPuf+2FE1l9VoNHL16lXtwZStbm1vb3P79m3u7khspG00N7p5+6UOWlzW4tey+EMUZxsYrYALdhcx+GeQHbltMtll/uxmqUwV9SHKR7UIXzkq6sPyvb4odJ2CIGjh/d3d3ZrVTc1GNZvNB0ag3s/ELB/qxe9cDuqelJb7BYmiSDKZ1Gk190L1PV64cIFYLEYikajYvlSUomCq/tGrV68WHMxbzfK9qkKeP39eC3PWc/ulQh21GovFco4yraQPsZhtZzY0VeJBJggCzc43bRSq4t7S0pL3BSJT3drAw4+XVjApaaa31vm7iWU+MtBEX0cLTU1NBUffKKIFIZ14g5QCKCAW9rf5VFSVqCYSiXvK/bWEelH4qo1cKmogEMirotbLcbzfSGk2RFHE6/Xi9XqBfXEnEAgwMzNDPB7H4/HQ1NRUcHj//YBoNFox21+t4MH4Jg9Bpcr3ucaFplIpIpGI7vvKRjEKpizLTE1NEYvFDvWP5kK1yvdbW1uEw+GiUhWK2X6pnyGVSjE8PIzH4+HatWs5H2SVjJwqhJTq2dBUKCKRiJbYUGgu6/dmArS4bNjMIh2+BpaDUaJGN3t7e8zNzWE0GrXJUoclGUhnfwrTa/8DkiEUOYXibENuvljS58hUUUOhEDMzM5w8efJA5JT6O/VADmoBtUD4TCYTra2ttLa2HlBRR0ZG9qeMiSINDQ01sdbDUOvrU6EXebbZbHR2dtLZ2Yksy+zu7hIIBFhYWNCmT3m9XhwOR8nHpdY9m3pklNY6HpJSUdS9fK/mZlqt1gO+x2ooi+p+Crm4MmOpzp49W/SFXOnyvSzLbG5uoihKxXy4pZLGSCTC0NAQ/f39h0YbVdJTetTaj4OQBgIBpqamuHTpUlGj8ATgwFESBFwuJ6d6uzl16hTxeJzt7W2mp6eJx+M0Njbi8/lobGw88MBT2q6Qeuz/hbB1B8wO5I6bYC5PWQgGg0xMTHDp0iXNu/7Qi1oaao1I5VJRJycnteYbvTr6K4F6Ukr1vncbDAYaGxs1734ikSAQCDA/P08kEjkwArWY/O9aP6aVHDFaK6h7UlruDc5oNOpKrA4bF1rNTv+jUKx/NBcqOaJTJcxmsxmfz1cxH24ppNHv9zMxMcHly5dxu91Hbr9S3/lha6+Uf/QwLC8vs7a2xsDAQNGJCE+d8fLnP14jlhJJpWU8VhNnWt+8+VqtVi3RQLVzbG1taR27qopqtVpRmvpRmvp1+Uxra2ssLS0xMDCA1fqmx7UQL2q1CWqtEb5cqHUlymQy4XA4aG1txefzHVBRARobG6s6Xeow1MP3DW9eC5WExWI5EN6/t7dHIBBgaWkJQFNRXS7Xoces1jNKw+HwQ6X0foeeRPGocaG1QkpXV1eZn5/n2rVrZZ3glSrfq4T5zJkzJBKJih6zYtRrRVFYXFxkfX294Ciq4yjfV5uQKoqiKZjXr18v6aZ+o6cBu1lkbC2MwyzyWH8jbmtuhcNgMGheM0VRiEajbG9vMzY2Rjqdxuv14vP58Hg8ZZXx5ubm2N3d5fr164faWjK9qJmk9GFwf27UOpHKvG6K8aJWG7Wu6qmo9jozB3uo4f2BQIDl5WVCodChU8Fq/Zg+LN8/ANCDKBY6LvS4SWk5/tFcqET5XiX2KmFeXV2tmBoLhZNStZNcUZSiBjRUkpTm2nYmCarGzVUdFOB0Orl8+TKClEQILoLBiOLqAENhBFUQBC60u7nQfrjynOvvHA4HDodDyz30+/2srKxw584dnE6npqIWWsaTZZk7d+5gMBgOpAYUgkyrjrqtbHJ6HCpqraAe1L18Hc75vKijo6MoilKwGqfnOuvh/DnudR72vcmyfED9rnWl9GH5vg6gR/d9OcSqmHGhx0lKy/WP5oKeHllFUZiZmWFvb+8Asa+0D7cQ0phMJrl9+zYtLS309vYWPY6zGpFQx+EfjcfjDA8P09XVtT+4Ir6D8aX/ghDZBBTklktIAx8FsXBPV7kwGo0HHkChUEiLnAI0gup0OnMeo1QqxcjICE1NTUV/17mQq8yfTVD1DO6vdcJXD6S0kEiofCpqphpXaRX1YXRV8cj+3tTw/vX1daampjCZTMiyTDweP2DXqRU8JKUPAMrJKd3d3WV0dLTgcaHVJqXqAyCzHK7nWFO9yvfpdJrh4WGcTifXr18/8NCqNCk9qoM9FAoxPDxc8rGrdCRU9oSiahHSvb09xsbGOHfunNZsIE5+BSG6De4uUBTE9WGUlVeQe95S8fXkQuYM7v7+fpLJJNvb28zNzRGJRPB4PDQ3N9PU1IQoisRiMYaHhzlx4gStra26ryezzK8+/DIjp9R/F0WxJBW11v2aUD+ktNg1HtbRD4V7GotBvWRW1hIpzUZ2eP/GxoZmz0ilUjQ0NOD1emloaKiJz/CwfP8AoNTy6tLSEsvLy0WNC632SFNZltnY2NDFP5oLepTv1S72XI1hUJ3YqXzY2Njg7t27RWW3ZqMa4fnVJqSbm5vMzs5y9erVA+e+EFoHi1tdHBgtCJGtiq+nUJjNZjo6Oujo6NAiZba3t5mdnUUQBGKxGGfPnq0IIc2FYoL7a+GBqAceBOJcLRW1Hgg+1DYpzYQgCJhMJhoaGjh58qQW3r+9vc3MzAwWi0X73o4rK/ShUloHqPZFKUkSd+7cQZblomOKKukvzIYgCExMTJBMJnXxj+ZCuYRra2uLqampQ7vYqzE1KhuKojA7O0swGDzUI1wIKq2URiIR9vb28paj9YSiKCwsLBAIBLhx48Y9x0VpOoUw9zdgcoAiQTqO0tBb0TWVisxIGbWLv6uri7W1Nebn52lsbKS5ublqCslRwf2FqKj1QlJqfY16K5CVVFFr/VhC/ZBSOLjWXOH9fr//QHi/GjtVLR9qJBKp2kvzcaHuSWk1oY4L7ejooLu7u2ZvCMlkklAohNvtzhvqrgdKVTEVRWF+fp7t7W0GBwcPVQuqle2qQm3csVgsXL9+veybaaXWrygKJpOJrq4u5ubmiEajB7I79b5JyrLMxMQEANeuXct5XKQz74ToNobN0f3/fepZ5Lar5e9cURA2RjBsjaGY3cgnnnhTkT3wa8WTsqWlJTY2Nrh586Z2HkqSpOVUTk5OYrfbNS9qsVFXpeIoFbVeg/vrgThX0qtZC17UakOW5bqZuHRYo5PNZjsQS7e7u4vf79eGe6jfWznh/UfhoVJaJ6ikGqVCzaa8cOGC5qGrRag+V6fTWXHiXIqKKUkSo6OjmEwmbty4ceTNv5qkNB6Pc/v2be3Gowf0Pjczy/UGg+HAhJOdnR22traYmZm5J7uzHKiTq3w+Hz09PfnPKaMV6cavICUj+133Jn2mbxmWfog4/KdgsoOURFz7Mam3/CaY9+0ou8ldvjD7BRZCC3gtXt5/8v10ODoO3aYaY5VIJBgYGDjwIBJFUTt2iqIQiUTY3t5mdHQUSZK0yKlqZVXmU1Gzg/tVNbWWUS+ktFprzFZRQ6FQxb2o1YYkSVV7mSsXhaq6ucL7/X5/2eH9R+Ghp/QBQr4bUa5xobWK1dVVFhYWuHbtGrOzsxUvexerlKpKc2dnJ93d3QX9TbVI6c7ODmNjY7q/dOidUJDPP2owGGhqaqKpqQlAI1JjY2NlEaloNMrw8DD9/f2FDVkQBLDo+yYvTv01OFrfJLm7Sxi2J5E7rqMoCn82/WdsxjZptbUSToX57MRn+SdX/glOU+51qC9GDoeDS5cuHXo8BEHA6XTidDrvUbb29vZwuVz4fD68Xq+uD5/DkK2iqv/s7OxoAy1qNXKq1kkzHB9xzmzMy5evqZaTq3Wu6YF6Kt9LklSSqmuxWA741dWXi8XFxQP35nJfLh4qpQ8I1Aak7JMx37jQcqH3TU+WZSYnJ4nH4wwODmI0GqvSVFWMUhoMBhkfHy+a9FWDlCaTSe7cucP169ex2fRR91TopZQW29CUmd2pPtyWlpY0W4dKpA67Aecar3ksUGQQMq895Y1/IJqOsh5Zp82xP+rVbXazEd3AH/fnJKVqNFp7e3tJani2srW3t8f29rb28FEV1kqW8DKhks/Z2VmSySTnzp0D3iSr6XQaQRAQRbFmiEGtK361oubmU1GHh4eBfXVOfTGqhfXmQ72R0nKFJ4PBoIX3q6kfmS8XLpdLI6nFWjQektI6QbkPfjUWKvMBrY4LPXHixH4Go05QSZZenj/1Iev1ejl37px2c6oGmSv0uC8tLbGyssKNGzeKLiVX8nMoisLU1BTpdJq3vvWtNdkMBuVPaMp+uKld5/Pz81okis/nO9BRurq6qqVLHHden9T3FMaJL6FYG/abp8wuVqwuQsEpdpO7+ON+AFrtrSgoyIqMVbx3zZFIRButqzYvlIPMyTEnT57USnh3794lGo3S0NCAz+fTIqcqAVmWGR0dxWaz3aP6Zob2Z5b5j1NFrRXCdxhqMf8zl4r6yiuv1IWKWk+ktBJrNZvNtLW10dbWpr1cBAIBLbw/06Jx1L4fktIHBNmq4lHjQvXYlx4PqcNyUquhlB71cFEn40iSxODgYEmfuVKkNJ1OMzQ0hNvtxmazVYw0lPvCpCiKlqOrV7h6Q0MDDQ0NnDp1ing8zvb2NpOTkyQSCRobG0kmk0iSxI0bN2piuol88hnSJgeGjWEUi5tvOZ18Z+ZPWY+usxndpNHcyGJ4kRZbC232Nh5re4wW20GrQTVU3+wSnhonc/fuXSwWi6ai6qXGq17flpaWnHYYtcxvNBqrEtxfKGqdlNZD/qfJZMJkMnHhwoWcKmoteVHriZRWeqJT5suFagcKBoOsrq6yt7eHw+HQvrtciu1DUvqAQCVwqnIWDofLjgI6al/lYmVlhcXFxbw5qceZ7wn7paWhoSGam5s5ceJEyTfGSpDSaDTK7du3tWxUv9+vq3qdiVLXX638UavVqjV2qaq7ei2MjY1pROpYO4EFA3Lv48i9j7MZ2+S7I79Pg7mB6Z1pGiwNpOQUj7U9xlp0jZ879XNcaLpw4Hitra2xtLRUVdU32+MbjUbZ3t7mzp07JJNJmpqa8Pl8JUdOxeNxLd+3EK9vpYP77yfUi5qr4igvqlouPi4VtZ5IabXXajKZaGlpoaWlRWuqDAQCjI+Pk06naWxsZHd3l4sXL2K1WolGo2W/VP/Wb/0WX/7ylzGbzZw8eZI//MM/pKGhAYDnn3+eT37yk4iiyCc+8QmeffZZHT5lcbgvSKkeo0bj8TiTk5M0NjbeM1VIT5RLSlX/aCKR0PyjuXAc+Z4q1AlSZ8+exefzlbUtvUmpmqJw6dIlPB7PgX1UgpSWopQeRyB+IpFgeHiYjo4OOjs7tTzF7e1thoaGgKNHdFYD0VQUg2AAAQQETAYTCSmB3WinydJEi71FW5saPbazs8P169ePNZbGbrfT09NDT08PkiQRCATY2NhgcnISh8NRFPkPh8OMjIxw/vx57WFSLB7E4P5CUQ+k9DDydJQXtdoqaj2R0korpYchs6lSvU8Eg0H+4A/+gG9+85u0t7eTSqVYXV2lv7+/5P28/e1v5/nnn8doNPKv/tW/4vnnn+ff//t/z/j4OC+88AJjY2Osrq7yzDPPMDU1VfXjcV+Q0nKhBuJfuHBB1zGcuVAOKVWJQ7Z/VO/9lIO1tTXm5uZ0myCl501zcXGRtbW1e1IUKjnUoFhSfRyENBQKMTY2xpkzZzRlLzNPsa+vj2QyqWXyRSKRqvglVfjjfn60/iM2o5usRdYYD4zTbGvGIloIxAO4zW6CiSBeq5dGy34TnZqrKggCV69eramHoiiKB0YbquR/eHgYWZbxer00NzfnJA2BQICpqSmuXLmiWzRMoZFTtdrRrzfqgZQWusZaUFEr9cJfCdQSgVaj6X7nd34HgLGxMT760Y/yz/7ZP2N1dZUnnniCd77znfzET/xEUROm3vGOd2j//uijj/IXf/EXAHzxi1/kQx/6EBaLhb6+Pk6dOsXLL7/MY489pu8HOwIPPCldXl7G7/fT19dXcUIKpZNF1T9aqPooiiKJRKKUJZYE1foQiUQqNkGqVGR6W2/evHnPDVKdIV8JFKOUltvQVAq2tra4e/culy9fPpTkmM1m2tvbaW9vz+uXbG5u1r08vpPY4ZPjnySaijIRnEBSJLqd3SyHl3GanLQ72mm1t3LSc5J39r4To2G/aXF4eJimpiZ6e3trmmBkk/9UKqVFyWQnJaxvrjM2P8a1S9cqOuawkOD++7nMXw+e0lLJ03GoqJIk1c15cpxK6VG4cOECVquVL3/5yyQSCb7//e/zta99jX/9r/81ly5d4lOf+lTR2/yf//N/8sEPfhDYtwQ++uij2s+6urpYWVnRa/kFo3aYQ5UhyzLj4+PIskxPT0/VSFQpZfWj/KP59lMtT2kymWRkZAS3283AwEBN3dBVn6TP58vrba3ksSp023o3NBWyv6WlJba2trh+/XpRntF8fsnx8XFSqZSWierxeMo+F6Z2poilY5hFM0aDEYfBQTgd5umup9mMbfJ/3/y/Dxwv1Wt54sSJuhzHZzKZDnTqqkkJtydu8+3Qt1HsCj8Y/QGPdTzGO3vfWfFr7TAVNbNpSv15vZCPo1BL97Bc0EPRy6Wi+v1+3VXUWlIfj0Itk1IVgiBgtVp55plneOaZZ4B9y1wmnnnmGdbX1+/5249//OO8733v0/7daDTyC7/wC0DuDOHjuA7uC1Ja7IGLxWIMDw/T1tZGT08PKysrGiGoNIxGY8GkVC1BJpPJQ/2juVDN8v0rr7zCyZMnaWtrq8r+CkUoFNIigA5TwStJSgtRStWHfLXUUdWXLMsyAwMDZT8wMv2S6XSaQCDAysoKd+7cKStcXlEUwskw8XQcs9mM8sb/AaSUFDaj7cDxCoVCjI6OluW1rCWokVMbGxvcEe5g99lxKA7CkTBfGfsKhqCBG903qjp7O19wf6bCfz+U+WudlFbCYpD9QqSXilpPpLSW13qY4ux2Hxy7/OKLLx66rT/6oz/iK1/5Ct/61re077Krq4ulpSXtd5aXl3WNwywU9wUpLQa5xoWKokgymazK/gslQGr3us/n4/z580XfgKrR6LS1tUUkEuHGjRuaalYr2NzcZGZmhitXrhwZoVFJT+lh39tx+EdTqRQjIyM0NjaWlYqQD0aj8UA3aa5w+ebmZux2+6H7Tstpvjj7RV7ffp27e3exiTZMgolAIkCfq49APMD7+9+vbWN7e5uZmRmuXr1a0dJ2NSFJEmNjY/ufxw1NhibMBjMOp4NkOEnCup+LOjMzg8Vi0fJmq5kwkF3mzyaqD2qzVKVRafJUiIqqznov5GWz1km+ilrMqFWh14jRr33ta/z7f//v+c53vnPgXvne976XD3/4w/zmb/4mq6urTE9Pc+vWrbL3VyweGFKqduJubW3d0+hSTVVRFMUjVdli/aP59lPJ0Pm5uTn8fj8NDQ01RQIy13bz5s2CytKV9JTmQ2ZmpKosVRpqhaBape1c4fLb29tMT08Tj8dpbGzE5/PR2Nh4z4Pg9a3XGfGP0OPswWf1MR4Yp8vZxYBvgCZrE92ubnpcPcD+cIaNjY2ibQi1jFQqxdDQEG1tbXR1ddEy1cK3l79NOBVmJ76DaBBJykka+hp44swTxGKxAxYKNXLK4/FU5SFbaLOUet7X6oO/XnAc0UW5VNTl5WWgtnJR71foRUp//dd/nUQiwdvf/nZgv9np93//97l48SIf+MAHuHDhAkajkd/7vd87FivDfUFKj7oI0uk0o6OjWCyWnONCCyGKekEURVKpVN6fLy8va5mK5ZC9ShFtSZIYGRnBYrFw48YNrWO40iikXKXONDeZTNy4caPgm3Y1/bdwsKGpWoR0Z2dHS5hQo7CqDYvFQmdnJ52dnciyTDAYZGtri6mpKex2uxaLZLFY2IhtYDVaEQQBh8nBucZz+Kw+frr/p7XtKYqiEdyBgYGa94IVCvXlob+/X7OdWAwWIukIe8k9UnIKs2jGa/HyN8t/Q4+zhxPuE/dYKNbW1piYmMDpdGoWimqR9lwq6srKCmazWSOpD1XU0nGcip7eKupDFAa9gvNnZmby/uy5557jueeeK3sf5eC+IKWHoZBxocX4PMuFmomajXL8o/n2ozfRisVi3L59m+7ubm1ueDVsAqov8zDypja4dHR05JxucxiqSUqPo8N+fX1da5Q77pGhcihE+MtfJjU3h+j1cep970U8e5ZIJMKdlTt86+632E3vsiFssJxa5qL3Ih6Lh1AqxBXfFW07amnbZrNx+fLl+0adUX2x2S8Pi+FFHmt9jJc2XsJis5CQE8TSMWRk1qPrnHCf0H4320IRDofZ2trS8mbVyKlq5c0KgsDCwgK7u7tcu3YNQRAOBPergsD93NGvN2opIeAoFTWRSLC3t/dQRS0T4XBYtxi4WsZ9TUo3Nja0uJvDpiBUu3yfva/M6Uel+EdzQW+yGAgEuHPnDhcvXjzQRFINQqfuI9/DSrU7nD9/viRva7VIaWZDU7U67GdnZ9nb2zv28Hh1PaE/+zNSy8uIXh/Szg57n/40Db/+62ykN/ja3tfYE/eYCc1gwYIgCXxn4Tv02Hu47LvMW1rfAryZqNDe3q69HN0P8Pv9TE9P58wg9Zg9+ON+3GY3S6ElYukYm9FNLEYLCSmB0WDkZsvNe7aZGTnV39+v5c3Oz88TDofxeDxa3mwlzg81Ki6dTnPlyhXtvD8qckpVUB8S1NyoVQtELhX1lVdeYWlpiXA4/FBFLQMPSWkdIZvEqWW9UChU0LjQ4ySlevhHC9lPqVCjg9bW1rhx48Y9Sls1xpkeRhpXV1dZWFgoy+5Q7nz6QlDtDntJkhgfH8dsNmvq1HFDicVILS4itrUjCAJiQwPpjQ2kzU1ejLyIw+hgO7aNx+IhJac413yOpJzktP00j1gfYeT1EQwGA7FYjJMnT9LZ2XncH0kXKIrC2soKK2treX2x7+p9F5+e+DTxdJxoOoqsyMiKjA0bXouXr85/lQ5HBx2Ow7tls/Nm9/b22NraYm5uDqPRqFko9Hj4ybLM2NgYVquVCxcu5DwHHwb3l4Z6CPiHfRXVZDJx8eLFmvei1pL6nAvRaPS+n3sP9wkpzUQymWR4eJiGhoaCx4UeFynVyz+abz/lksXMLNdcofNQnfJ9LlKqvniEw+Gy7Q6VVErVxo5gMKhLbmchUK+B1tbWoq0MlYRgMoFohFQKzGYUWQZJQrBYie/FMYlvvjwKCEiKhNVopcXTwtneswSDQcbHx2lvb2djY4OlpaUDmaj1SFjiU1OsffrTJHd2OT14E+P585CDlLY72vnHl/8xz7/6PP3ufkYCIzhNTuLpOAk5QVpOsxndPJKUZsJgMNDQ0KBVPuLxeMGNaEdBHWDg8/no6ekpak1HjT99SFBrVynNRibRy1ZRk8kkgUCgZlTUWj+mkUik7Ln39YD7ipSqquOZM2eKms5kNBqr2uiUTqe1Ltlbt25VbOZ6OUQrkUhw+/ZtWltbD52KU83yvQr1ged0OnUJ66/UZ1AfqKdPn2ZlZYWJiQncbjfNzc0VK5eGw2FGR0c5ffo0Xq9X9+2XA8FkwvlT7yH8hS+AYECRJWyDtxDb27iqXOXby9/GZ/OxFlkDAWRFRkDgWvM1zRebqdarM+SzG3p8Pl9dlAbTW1us/NffR7bbab5wgfT8PKEvfBHPL/5Czt93m910u7qJp+N4zB42IhtEpAj+uB+zaCYlpzAajFzyXippPVarla6uLrq6urS522ojms1m047tUb5k1V7R1dVFe3t7SWuBBze4vxDUOoFScdg6zWZzTXX013pwfiQSqamkm0rhviClgiCUpTpWMqcyG5Ik4ff76e/v180/mgvlbFcl9+fOnTuS2FSjfJ+pLkejUYaGhujt7dUt2LcSpDSzoUlV89QJPWq51GQyadmSNput7H2qnsRLly7VbJnHev06Ymsr0tYWBqcTU38/giDwRMcTGAQDw9vDPNr2KB6zhxZ7CzeabxDbiLGzs3OPLzZ7hnwoFGJ7e5vXX39dy0RVS9G1VpaTJImJ7/8AuyDg6+5CQEBobiE1M4Miywh5HuTvOfEe/njyj0lICULp0H4ZHxmr0UqjpZHPz36eNnsbPlt5ViB17rZ63qpTu8bGxkin03mndsViMYaGhiryUnRYcD88WCpqvZTvCyXPtaCi1gMpzQ7Jvx9xX5BSSZK0meu1fFLt7OwwOjqKzWajr6/vuJeTE8V6NKtZvlebrS5duqRrrJHentJ8DU2CIBwol8ZiMba2trhz5442nrO5uRm32130A2dpaYn19fW6yOo0dXZiyvKDioLIkx1P8mTHk9p/UxMpAK5evXrowy3zodbf308isR8sf/fuXaLR6IFSdDXvEVIwiByNIjY1YXjjxUNVEttaW7BaLW+QUBElGsXgcuUlpAC9rl7+0cV/xPOvPU+/p5/R7VHcZjdxKU5K2Y+a245vl01KMyEIAg6HA4fDQW9vL+l0Gr/ff2Bql9frxWq1aoNJKh07li+4P5OglhI5VS1xolzcD0rpYShERfX5fLomSNT6MY1EIveNj/4w3BekVBRFzp07V9M3FFXJvXbtGqOjo8e9nHugKAqTk5PEYrGiPJrVKt+vr6+zs7OTs9lKj+3r1RRWzIQmm812IFtSvenu7e0VXOZXu5uTySTXr1+v6ZeyYqBaNJqamg61j+SDxWKho6ODjo4OzdOrTn2yWq0Fl6LLQfR73yP6rb8BAQxWG+6P/CKpxkaGhobod7sR//qvSa6ukZycxNR/EoPbhfsXcpfuM+Gz+ehy7qurTrOT7fg2MSnGVHCKdkc7TlNlVXKj0Uhrayutra0aYVBfipxOJ4FAAIPBULXIqcwyv8lkOjD2tNhmqftNgTxu6LHOfCrq4uKiripqPSilD7vvH6JsyLLMnTt3SKfT3Lp1q+pB7YVAnR7T0NBQdKf2UcMAyoVKKEwmU95mq3JhMBjK/gzljgzNftDv7u6yvb19aJk/nU4zMjKC2+3mzJkzdfEwLQRq5mxvby9tbW1lb89gMOD1erVyciQS0UrRkiRppehSFOp8SK+uEv3mi4gtLQhGI9LuLtuf/WPmnngr50+fRv7UH6FIEtZHHkFaW0OOx/H8yq9gKtCS8p7e9/C5mc+RklPsJfewGW1E01EiqQhuU/VKfIIgEI/HCYfDPP744xgMBu28jUQieDwe7eWqWg/8Qpql8qmo9UJK62WdlSDPR6mo6rVe7EtRrRN9vcLzax0PSWkG9L7QC20WqiSO+kzqcIGTJ0+WNHqykiRbLXOaTCZOnDhRsYdauZ9B70D8zDL/qVOntBGSmWV+t9vN3NwcPT09ZTWT1BrU8Pjz58/jcbmIv/460sYmYmsLlitXEHQ4BzJL0alUSvOuhUIh3G63Nv2onEY0aXcXDAaEN7YRNxrZm5nhyv/+a1jTaYKRMMbWfcJt7OggvbGOQOHnzZnGM/zy+V/mP/z4P3Dacxqr0YpFtLCX3GM9to7bUh1iury8rNlGVJUqU6FWX65mZ2cxmUyaQl2tho18zVKZKqqiKFpwf63HAqmodQKlQpblir6M6Kmi1rpS+jASqs5Qri9QbabRqyN6Z2eHsbGxgpqFKgWVbOW70DY3N5mZmTlyuEAh+9AbKlk+deoUe3t7FbVmlNPoVo0JTTabje7ubrq7u0mn0ywtLTE2NobJZNJKpeWSqFqAWl6/cuUKdrud8Be+SPy11xAsFpREgtT8PM6f+Rldj7HJZMqpUM/Pz2M0GjWFulgSJXq9oMgoySThRILwyjItFy/gcLuRY7F9D2kisf/ZkkkABGdxpbl2RzutjlZcJhcW0bK//uQuZkPlPcWKojA3N0coFMo74tVgMNDY2EhjYyOA9nI1OTlJIpHQfIENDQ1VI1hHqajJZFIjp7VM+mp9fSrU5rNqoRwVtdaPaTgcfkhKHySosVB6PNhV/+j169d16aouFWpnfPYDQ530EwwGuXnzZlmNMZXIeN3a2mJ6elojy+FwuKKWh1LjsxRFIZ1OV7Xb1+/3s7m5yaOPPorVatXCz+fn5zUlqrm5+VjPu1KQqbiZzWakYJDE7dcxdnQgGAwoskzi9m3sTz2F+AbJ0RvZCrWa21kKiTK2tOB43/tY/5M/JZVM0NLfT8OHPgSAwWbD8TPvI/y/Pg8ooIDjPe9GLLKz1iAYeE/ve/j87OeB/fPxfNN5ul2VzaZV/eeyLHP58uWCz/3Mlys1zmtjY4PJyUnsdrumolosloquX0W2ippOp5mYmKCxsbHmg/sf5PJ9oShWRa11pfRh+f4Bgx7kKts/etwnuNrAk1m2UH2INpuNh+6a+wAA0uJJREFU69evl33D0FMpVRSF+fl5tre3D5DlSvtwi91+uf7RUqAem2AweKBU6vF48Hg895T5k8mk1s1frdD+UqAoCjMzM8RisYOKmyQBAqjrFvb/XalSnjDcm9uZSaIcDodGonK91MmyzJzVivCLv8Cpnh6MbrdWygewXr6MqasLObiDwePeV1ZLwBXfFbxWL2uRNRxmB6c9pxGFyt13ZFlmdHQUu93OyZMnSz6vsuO8VJ/vyMgIsixXxOd7GCRJOhD2nxk5VYvB/bWu6qmopXVmq6h7e3sHVFSj0YjL5apZwv+QlNYZyj2JyiWlanNGIf7RapWHsj+TmvHZ09OjW7SEXoRRkiTGxsYQRZEbN24cODa1REqPg5CqLzsGg4Fr167lPW+yy/yBQOBAbE9zc3NNlfnV79xms3H58uUDx9LQ2Iipp4fU4iIGtxs5FMLY1Y3Y1HQsa80mUeFwmO3tbYaGhgA0gup0OpFlmZGRETweDycOySIWGxt1UX07nZ10OisfFZNOpxkaGqK5ubmoKU1HQRAEnE4nTqeTEydOkEql8Pv9ms/X5XJpPt9KZFSmUilu375NZ2enln2cXeaHN6PesoP7j4N01RLZOwy1uk5BELQXeti3bUxPT2sDI457ulQuPPSUPmAoh5Sq4w8L9Y+q+6r0xZpJtvx+PxMTE7pnfOqhMKsNYe3t7TkfdpXOQi3UU3ochDSZTDIyMkJzczPd3d0F79NoNNLS0kJLS4umCqhlftUreZxlfnUUaltbG11dXff8XBBFXB/+eaJ/8zekV1Ywnz2D/emndWl0KheCIOByuXC5XFpZ0O/3Mzc3RzgcJpVK0dbWRk9PT00qLqUgmUxy+/Ztenp6dElEOAwmk+keRWt7e5vFxUXdhyKo956+vj5aWlpy/o56n84O7ldL/MdR5lcUpSbJXjZqlZRmw2w2Y7fbaWlpwefz3aOiltrRrydSqVTNZ1DrgYek9A2UOmp0aWmJlZWVovyjKpGr9BuYOtJ0YWGB9fV1bt68qbtfq1wVs5DpUXpENh2GQjyl1WhoykYkEmFkZISTJ08WNTY3G5mqgOqVVEP7j6PMr36uU6dO4fPth7wrskx6YQE5FsPY0oLo82Gw2XC+5z0VX0+5MJvNtLe34/F4tCirZDLJK6+8gsVi0Xy+lcxErSSi0SjDw8PHMro289w9efIkiUSC7e1tbShCQ0MDPp+vpMgpdfrUmTNnaCpCgVdVVKPRqGtwfzF4mBKgP9S15lJRK5GL+hC5cd+Q0mqX72VZZnx8HFmWGRwcLOqGWInmoFwwGAzcvXsXq9XK4OBgRW4O5ZDStbU15ubmjpweddzle7WhSf3daiAQCDA1NcXFixdLTkbIB6vVeqDhJHs6TyXL/Ds7O9pULvVzKbJM+ItfIvHjH+/HKBkMOH/+57GcOa37/iuF3d1dxsfHuXjx4oFRgIWO56xVqBFd2Z/ruGCxWOjs7KSzsxNZltnZ2dFIqvoCUMjY3nA4zMjISNnTpwoJ7s+MnNLr/lEvZE+W5ZqxCx2FfI1Oh3lRBUGgqamp4ipqNcWQ40Z9nC1VQDFEsRj/aC5UI0A/Ho+zsbFBc3MzFy5cqNjJXArBVptb9vb2uHXr1pE3reMipcdRrgdYWVlhdXWVgYGBincii6J4T5l/e3ubhYUFzUepV67k+vq6NsI2UzVMLy2ReP3HiO3tCAYDcjRK+PP/C/P/8X/UxU14a2uLu3fvcu3atXvIkN1uPzC1K9vnq3olxWSS6Le/TXpjE1NvD/YnnkCoUhd6PqgvRlevXq1armgxMBgMNDU1aSqn+gKQWQFQXwAyCZz6AnH58mXdPXrlBPcXg1ptxsmGJElVS1MoF4V03xeioqrjjB+qqKXhISl9A4WW74v1j5azr1KhZqSqPphK3ryKJYxq97/dbuf69esFra0apDTbU3pcHfYzMzNEo9FjGRmaXSpVy/xqJFK+h/xRUBSFhYUFAoEAN27cuOclRInFwCBqM98Fmw15d2e/A7/GVZaVlRXW1ta0KKvDkMvnu729zeLcHM6vfQ17JILV6yU9P4e0tYXrQx86NuKxsbGhvUDUC6nIfAFQ0xLW1taYmJjQ0hJEUWRubi7nC4TeKCS4X/29YlXUelJK62GdUNpa86moqv9ZLxU1lUode5pPtVDbd/wioEf5PvlGiHU+lOIfzYVKkqyVlRUWFxcZGBhgc3Oz4jaBYj5LLBbj9u3b9Pb2al2ueu+jFGR7So+DkEqSxOjoKA6HgytXrtSECpKrzK8+5AvtiJZlmYmJCYC8yQFiayuIBuRwGMFuR9raxHTq1IEIpVqDmvUbDofzhscfhswXgF6bjYAkE29tZScSJS0r2F56meRb3kJTd3fVH+pLS0tsbm4yMDBQt2pPrrSE+fl5tra2cDgcrK6u0tzcjMvlqtq1dpSKmk6nCy7zP2x00h/l5pRmXtP9/f33qKhutxuv11uSivqgzL2H+4iUlovDytDl+EeL3VepkGVZU7QGBwcxGo1VsQkU2rmuKswXL16koaGh6H1Uq3x/HA1N8Xic4eFhurq6iiLr1US+Mv/i4iKiKGrNPJllXlUVb2ho4MSJE4dGI7l/4RcJf/7zSJubmE6dxPUzP1OlT1Y81IguURT1eYEQBAyiAZfThdvlRpLSxBaX2A4EmF5dLTlYXo5Gibz4LdJLSxjb23C8/e0YDvEnZxLta9eu3TfKjCAIhEIhEokETzzxBIBmUVHJgh6jZYtBPhU1O3Iqn4r6sNFJf+gdnq+nivqgZJTCQ1KqQe1Uz4bqH9Uz4kVvUqrOiG9qauLcuXPaGkVRrGjXeqFYXl5meXmZGzdulNSBXC1SehwNTWojyblz57RxjLWOXGX+7MlHHo+H+fl5ent7C4oQMvf30fQvfxNFlrUyfi1CJdqNjY0l+clzQWxpwXzmDImJCQwWK3I8RsMTT9A1MJAzWF4lqIepfIosE3rhc6QWFjB4PCRGR0mvb9Dwv/0qQg6VRlEUTdGuFaVeL6jWkUxFu729nfb29pyjZdXja7fbj01FzfxHfUHOJKj1QvbqZZ1Q2bWWq6I+VEofQBiNxnuIoqrunT9/vqjIkKOgJykNhUJavE52zp4oisTjcV32UwpU9TaZTJalMFeLlKojQ6v1IFIbZGq1kaRQZE8+WllZYXx8HJPJxNbWFkDBwee1TEgTiQRDQ0N0d3fT3t6u23YFUcT1gQ9g+vGPkba2MHZ0Yrl2df9nOYLlM1U+j8fzZrNUxvUl7+6SWlxAbGvbJzJ2O+n1dSS/H2PWS4JqHXE6nfT39983hFRRFO7evUssFuPq1as5CUe+0bLT09PEYjEaGxu1xpVqKce5yvyZJDWdTle8AqYXHpLS3Minoi4tLWkd/Y2NjbhcLgwGgy5K6b/+1/+aL37xixgMBlpaWvjUpz6lVeaef/55PvnJTyKKIp/4xCd49tln9fiYJeG+IaV6RkIpisLS0hKrq6sVmV+vFynd2Njg7t27XLlyJecJW43yfT6kUimGhoZobGw8oN6Wgkp+DvWGbzKZeO2117QytB7B3Iftc3Fxke3tbW7cuFG3vr1cCAaDrK6ucuvWLex2O6FQiK2trQNlfjX4vJ6gZqsWm2lZKASTCdsjjxz5eyaTSVP5ZFnWVL65uTlMJpOWlmAxGkFR9v8RBBRZBlmGLGKVSqUYHh6mpaWF7u5u3T/XcUFRFCYnJ1EUhUuXLhV8LWe+YMmyTDAYZHt7m5mZGaxWq3b+VitzNleZf3l5WctIVe+LtTL+NBv1REqhfB5R6j6zO/r9fj8//OEP+djHPsbFixe5ePFi2cH5v/Vbv8W/+3f/DoBPfOIT/Nt/+2/5/d//fcbHx3nhhRcYGxtjdXWVZ555hqmpqWOz79w3pLRcqERR9Y8qiqKLfzTfvo5qqjoMqgKwu7vL4OBgXlJTrTzUbITDYYaHhzl58iStra1lb69SpDRTgRgYGNBUKFVdaWxspLm5mYaGBl3zBScmJlAUhYGBgbq6YR+F5eXlezrR3W43brf7QJl/ampKK/P7fL68x1dJp4l+//skx8YxuJw4nnk7xg79FMpCkStbtRZgMBhobGzUbB+xWOzNSKREAt+JE9ju3MHscEIqiXXgOuIbwwrgTeW3t7dXl+u0ViDLsja+9uTJkyUTDYPBoCWYAJqNQs2cbWpqorm5GbfbXbXreHl5Gb/fz8DAAIIgHEtwfzGQZfm+8SZXC+owjvb2dt7+9rfzve99j89+9rP88Ic/5KmnnuLZZ5/lXe96F1evXi3q3M7MGY5EItrffvGLX+RDH/oQFouFvr4+Tp06xcsvv8xjjz2m+2crBA9J6RtQieIrr7xS8RGB5ZDFYiKVRFGsulK6tbXF1NQUV65c0e0BXglSmquhyWw209HRQUdHB5IkEQwG2djYYHJyEqfTqYXKl6psplIpRkZGaGpq0s2PWG2k5TRpOY1FtGjrV6OsYrHYoVFW2WX+QCDA+vq6dnxVFUo9vtG/+Tax734XQ1MT6d1ddv/wD2n4J/9Yl3nxhWJzc7NqEULlwmazHUhL2O7rI+jzEVtaJt7q4k5vEHnsk5xrOsdV91XGRscqpvweFyRJYnh4WLvG9ITD4cDhcNDb26tlzq6urnLnzh3t/PV6vRUbBTk3N8fe3t49VoR8wf2qP17v4P5iUI1x2vczjEYjP/mTP4nf7+fy5cv82q/9Gl//+tf5nd/5HUZHRxkcHORf/It/waVLlwra3nPPPcenP/1pPB4P3/72t4H9xJ5HH31U+52uri5WVlYq8nkKwX1DSst9wIdCIfb29rh582bFb9KlktJoNKopG4V0aVd6ZrwKQRCQJEmLkhkcHNT1xqw3KS2koSmz1Kwoyj1l6GJnx6ujGvv7+/PO2K513N66zV8t/BWSLNHr7uXvn/z72Aw2TZW6fPlywddhdmSPenxff/11bb659Uc/wtLaut+YY7eTXlslvbhUNVK6tLTExsYG169frzuLhSiKtLa10frTP004GeZ3b/8uO3s7KNsKry+8zqviq/z8wM/XTXNdIVAtQ+3t7XR2dlZ0X9mZs6FQiO3tbYaGhgC00b16TPlRX/oSiQSXL1/Oe8+qVnB/Mai38n2tQvWUtrS08JGPfISPfOQjSJLEyy+/fMC698wzz7C+vn7P33/84x/nfe97Hx//+Mf5+Mc/zvPPP8/v/u7v8tu//ds503OOUzC5b0gpoJUzikGmf9Rut1dFNSiFlKrdzZcuXSp4LF61yveCIDA6Ooooity8eVP3m5Ce5Fq9SRfT0CQIwj1l6K2tLSYmJkgmk1oZL9/oyGAwyMTERM2MaiwFq5FVvjj3RXxWHyaDicXQIl+a+RJnImdobW0ty4+YfXwTiQRbW1vsRiOkQyGsDR7sdgdGWQFT5W9Zqj0mGo2WlEFaa1iOLJMixcnWk0SjUba2t1i3rLO2tsbs7OyxNPPojWQyqWUgV9uKkHn+qp3Vfr+f+fn5A81oTU1NRUdOZXpjL168WPA967DIqXKD+4tBvZDSYnlDtRGJRO4Z1iOK4j0l9hdffLGg7X34wx/mPe95D7/9279NV1cXS0tL2s+Wl5ePNZrwviKlxUKSJMbHxwEYHBzkpZdeqsp+iyGL6jSczc1Nbt68WVROYTXK94lEgnA4TEtLC319fRV5w9LjpqZnIH5mqHz26Ei3262V+UVRZG1tjaWlpXtGa9YbNmObCAiYxX0F3CN6eHXuVd4++Haam5t13ZfFYqGrq4v4L/4ioRc+RyIcJrK+QcztYi0ao3lt7UCZPxdi6Rjze/PsJHYIxAOIBpELTRfocfUcum/VU24ymYpSfmsZBsFAUk4yuTnJZmgTxaIgpAXCLWEeaX2EvZ29Y23mKRexWIyhoSFOnz5d8pQ9PZHpCczVjJYZOXUYFEVhfHwcs9nMqVOnym4WPUpFrRRBrYdrqNbJcyQSKduOMj09zenTpwH40pe+xLlz5wB473vfy4c//GF+8zd/k9XVVaanp7l161bZay4VDywpjcfj3L59m46ODrq7u6t64RRKSiVJYmxsrGQFstLl+729PUZGRnA6nXR0dNTszSezGUC98eqF7DLe7u4uW1tbzM7OahNarly5UjcP+FwIp8IshZbYiG7gMrswSkYWNhboa+3TnZBmwnrpEuL/5iI1N4dgd2C+dImolL6nzK+mJagIJUN8auJTrEfWmd6dxoCBc43neHnjZX7hzC9wsuFkzv2l02mGh4fxer26+xGPE232NhaDi2zGNkkICZSowmnPab659E1kZJ7seDJnM48kSdpoWbfbXZPXdyQSYXh4mAsXLhRcQaomcjWj+f1+LdM3U6XOvL/LsqzFdOn9sl9ocL/681oma3pB7+B8vaFHTunHPvYxJicnMRgM9Pb28vu///sAXLx4kQ984ANcuHABo9HI7/3e7x3rsXggSWkgEODOnTu6548WikJIaSZp7uk5XN0pZz+lYn19ndnZWa5du8b09PSxdPkXgsyGJr0JaTbUzEOXy0UsFkMURZxOJxMTE0iSpBEoPXxm1UI4FeZ/jv9P/HE/CSnBd5e+S4vQQl9rHz93/ucqvn9Tby+mDILoAlwuF/39/SQSCS1TMh6Pa2kJr4VfIxgPYhAMmA1mBEEgko7Qam/l++vfz0lK1U70np6egsL+6wWKovDSxEu4ceNscrIUXsJldJGQEvisPl7bfI0nO57Ufj+zmSeVShEIBFhaWiIUCh3L5KPDsLe3x9jYGJcvX66baTc2m+1As18wGNSaQ9XJXY2NjUxNTWkDGiqNfMH9mR39tRo5pRfqQSkt9xz/y7/8y7w/e+6553juuefK2r5eOP47i444ylOq5kOura3lnS6kdmNXEkeRRb1C+yvVtZ4dR3WceaiH4ThGhiYSCYaHh2lvb6erqwtAe8CrJbxIJKIRKFUhURIJMBhyTts5Try+9TrBRJBORyeOlIPFxCJnO87yDy/+Q5ym4yUCFouFzs5OOjs7D6QljCyPEJJDJIwJ0lIaq8lKQkpgwEBavndqW6UzSI8LavxYKp3C5/UhCuL+y4WcIJgM8v3V72M32fnh2g95tO1RDMLBh7LJZKK1tZXW1tack4/UTNTjGPwQCASYmpqqi1SEfMhupoxGo2xubvLSSy9hNBpxOp3s7Ozk9apXAkcF99da5JReqHWlNBqN1s2LV7m4r0jpYcj2j+Y6AVWyWGkV4DBSury8zNLSki6h/XrfyNLpNKOjo1it1gNxVMcRPXUUMhuaqnXzDIfDjI6O5vS2ZYee7+zssLW1xfT4OJ6XX8axvoHVZsP1jrdjffzxmlFS41Ico2Bke3sbgP72fhwWR1UIqaIopOfnSW9uYnC7MZ89m3Pik6IojO+M8/r26yxHllk3rTO5M4kgC8SkGKIgcs59jm1xm7d1v+3A3+7s7DAxMcGlS5fuq5u+OqXJ5XLx5KknmRibIJgIEkwEiUtxZFkmLsW5ZLvE1xe/jtFg5FZrfh9ZvslHmaNlD8uc1ROqPWZgYKAoj30tQ42k8/v9nDt3jubmZvx+v+ZVd7lcmkpdrSSIWmmWqgZqXSkNh8N1N3CkVDwQpLRQ/6jRaCSdTleclObav6pqJJNJbt26VXNvbbFYjNu3b9PT03NP3Eq1oqcKgZ4NTcVAbRS5fPnykTcPg8FAU1MTTU1NhGfuEtr2k2hqZDscJvjHf4JFkmi+efNYb0KyIvPd1e/yt0t/y8jGCL2OXk42nySYDB4o91YS8R/9iMhX/xoMBpAkLAMDOP/e++/5Toe2h/j87OeJpWLc3buLrMgYRSNJkjRZmhAQcBgd3BJvEZ2OMumfxOfzkU6nmZ+f59q1a3Xt+c2GGo3U1tamqfUfPf9RPjP5GXxWH7IiE01FcVlcxOQY/ZZ+RrZHDiWl2ciVOatm+jocDk0B1Duzc21tjeXl5bqM6ToManrAiRMntMi4TJV6b2+/GW1xcVEL9ff5fFW1AtVi5JReqHWlNBKJ1NTgjkriviKluS5O1T964cKFIzP5jmsCUjKZZGhoCK/Xy/nz52tGJVOh2gnyHcNqle+PslYcFyFdXFxkc3PzwCSjQpGavYulpQWbzUZDYxMJ0UgywyeZGTdVzRv9q5uv8uLCiwghgfON51lMLOJNenlX77u43nK94vtXkkki3/gGYksLgsmEoigkhoawPfbYPVOdXt54mUZLI4F4ALfZzXZ8G6vBis/qo9nWTIejg2ZbMz9z7me0Mv/s7CyhUIimpiaCwWBFQ8+riXg8ztDQEH19fQfycJusTby1/a3sJHaQZInJnUnCqTCRVISN6AbN3tIb1rIzZ8Ph8IHMTpWglkug1NG8hw1oqEckEglu377NyZMn8WVM3FKROYby5MmTJJPJA1aghoYGLXKqWscln4qqEtVMslrrKiTUPimNRqMPSWm9Q/WPrq+v5/WPZuM4SGkoFGJ4eJgzZ85UtJO5VCwvL2vKRD47QTXK96pfON9D7Tj8o7IsMzU1RTqd5vr16yXdeEWvj+TsLAabDUVREGWJ5v5+uq9d0xSotbU1JiYmcLlcWtxUpdX84fVh4jtxelp6sNlsNMYbuey9zKNtjx79xzpASaX2Z7W/8TkFQQCDASV1cDxvPB0nnAoTTASJpWP75el0nDhxjKIRs8FMKBXisu8ysP8wDQaDmgUlGo0eIFCqT9LhcNTcy+FRUL2xZ8+ezfnyeMpzCrvRjj/uJxAPkFbSOI1OpnameFfvu3RZgyAIuFwuXC4XfX19WmZnOQRKURRmZ2eJRCJcu3at5glOMVDjrIrxM2dOnlOtQOp4ZLPZrDVUVtNrm0tFnZ+fx+FwaNOl1N+pxe+v1omzHt339YL7kpSq/lFBEBgcHCz4ZDMajVUlpZkd7JU84Upp3lJDm+PxODdv3jyUBFWjfK+qsTlnpB8DIVXHvXo8Hs6ePVvyPh3vfJb0pz5FemMdJBnzufNY3hgZl61AqSW8hYUFjEaj7g+fpJQklAqx7d9mfm0eXGCxWrSfVbOxSbDbMZ3oI7WwgNjUhBwKYXA6ETPUv+3YNp+Z/Axze3OMBkYREYmkIxgEAzbRxnZ8mx5nD2dcZ3ii/QltJrrFYuHSpUs5CZT6cI9Go5pPMjuupxaxu7vL+Pg4ly5dyquoeCwefvnCL/M/xv4HHY4OmixNuM1uTKKJ+dA8T6K/LSM7szOTQFksFu0czicaqPchWZbvm9xYFXrEWWVagWCf5G5vbzMxMVF1r2/mmtSXEHX8Za17UWtdKa1Gr0ut4L76lIIgaG+epeSPiqKojZ+sJBRFIR6Ps7KyonWwVwpHKYy5kEqlGB4exuPxcPXq1SP/thrl+3z7yIwuqdbNLRaLMTw8TG9vb9nxQWJTEw3/+B8jra2D0YixswMhx80xu4SnPnzu3LlDKpXSxhqWmie5El7hhekXWAosMbs3S3dDN1vhLfwJPz2uHlpsLdxouVHWZy0GgiDg+sDPEfnrvyY1v4CxsxPnT70HQwYB/6v5vyIp7SunrdZWtuJbNFmaMBlN9Dp6cZgcXPZd5gOnP4Cclnn99ddpbm7OG7GWrUBlxvVU0idZLvx+P9PT0wV1onutXm613iKcCiMrMv64n93kLtuxbR5tfZQzjWcqts5sAqWq1GNjY6TTac0nqXabq4MMLBZLWS9+tYhwOMzIyMihLxGlwGazaYM9sr2+drtdq7RUqkFMVbVjsRiXLl3S7snHFdxfKGpZKa31aVN6474ipcFgUHvzLGWmczXK92pAt8Fg4MqVKxU366ufqdALLhKJMDQ0RH9/f8GEqxrHLZuUHpd/VFWk9AzrNthsGPr7ivqbzIdPOp3G7/dreZIej4fm5uaCS6RpOc3npj/H7s4u/qgfj91DKB3iVustliPLPNr6KD/Z9ZNYjdVtBjI4HLh+9mfz/nwrvoXL7CIpJbGb7FhSFswGM06jE4fZgcvkwml2kkqktJeIQkdQqs0kXq8XRVGIRCJsbW0d8Emqof3HSZbUiWHF+Jm7nF1M70wTTUcJpUIYMNBib+FPp/+Uj5z9CP2e/gqveh92u52enh56enq0c1jtNnc6nUQiEZqbmzl5Mvewg3qFmq965cqVilbIsist6mCEkZERZFnWfTCCGheYSCTyjkTN9KJmktJcwf3VJIm1rkRW8xl33Kjdb6EEmM3mgv2juVBpcqUSvr6+Pu2NsdJQP1Mh5FeNeLl8+XJRM9oNBgOpVKqcZRa0D/UmdlyEdH19nYWFhZrLRjQajQc6ddW4qbt372K1WjWfZD51JJKMsLy5TIutBaPNiMlgIpaOAdBkaaLF3lJ1QnoY0nKav1v/O5bCSyzuLZKSU+wkdhAFkagQJSbH6HH1ICkS5+zneP311zl37lxJL6qw/0BwOp3adJ3sMn925my1kNn4U8wDdTG0yCnPKeb25jSrQ1pOYxEtjPhHqkZKM5F5DqdSKX784x9jsVjw+/3s7OxoKnW9++qCwSCTk5NVv4dknsMnTpwglUodeJFV/epNTU0lCSWKojA9PU06nebChQsF3ZNzqaiZJf5qlvklSarpeLEHSS29r0ip0+ksixypkVCVgFoGVAnf+vo66XS64qXAQkrriqKwsLDA5uYmN2/eLPriFEWRRCJRzjKPhPo5joOQKorC3Nwcu7u73Lhxo+bfqDPHGqoKn6qOqARV7YSOxqN8+eUvEyaM3WSnSWhiJbqCUTCisH8jbHe0H7bLquNbS9/ih+s/JCWliKQiJOUkBsGAQTDQat9XQtvsbbzN9zb8s37dJ/4cVuZXS6SVLPMrisLMzAzxeLykxp+UnMJustPmaGMlvEJUijK3O8dqZJXt2DbnG89XtIx/GDKjkVRVW81EzZzcVS9e30xk2iyOO4LMZDLR1tZGW1ub5lff2tpiYWFBG99baMOf6vsFykqPUZugjEbjgXt95mSpSkVO1XL5PplM1pxlqJKo3afrMUAURZLJ5NG/WATULsStrS0GBwe1k6tagfNHqb+qb0tRFG7evFnShVmtRif17bnaHfbj4+MYjUauXr1aszeufFDHRp44ceKeTmiH08FXVr7Crm0Xn8vHRHACr82L0+jEZXKhoPD3T/59Ohwdx/0xNCiKwqtbr9Jub2cpvESvq5el8BINlgaMBiOnPKcwi2baDG2EV8IVD1ivdplflmXu3LmD0WjUmrWKxSXvJV7eeBmDYGArtkVKTqEoClbJytmGs7ww/QK/cuFX6HR2Hr0xHaHmSZ86depANFJ2Jmqul4BK+iT1wNbWFnNzcyXFxlUamX51QBvfm1kJUF8Csu1AiqIwMTGBwWDgzJkzut2TM8v8JpMpZ+SUoiiIoqiLilrLjU4PUnA+PCSlB6B3+V6dqmIyme4hfNUKnD9MKVVViZaWFnp7e8t6w61Go1M0GsVut1eNGCaTSYaHh2ltbaW7u7sq+6wkMjuhA4EA3xv+HpvCJq6oC4vFws2Gm+woO/zL6/8Sr9WLQO35mELJENFUlGQ6SSKdIK2kMYkm9pJ7pOQUaSmNS3BxynWK648UV9YuF/nK/GqcUbllfkmStMSHEydOlPzddDm7+Mi5j/BfRv4LnY5OdpO7GA378VkpOYXVaGVmd6aqpFSNszp37hwNDQ15fy97NGe2T1L9mcvlqplzd319naWlJQYGBuoi8D9zfG/m9LmZmRktMcHn82G1WhkfH8dsNnPq1KmKHu9KB/fLslyzpFSPuff1hPuKlJZ7UejZfa+mAHR2duYkNNXKRM23HzUf9ezZszkDm4vdR6VIqVrCaW1tZX5+ntnZWU190sugnwvqQzJbtbkfsLGxwfz8PBcvXuT23G3a7e0kk0kikQjRUJSJkQl6Wntobm4+lrnm+TC7O8vnpj/HenSdu7t3sYpvzLQXDCiygsVkIZFMIBgErpy7cuw2i1xl/u3t7ZLK/OqUpvb29nsmqpWCPncfN1tushBaYDG0uN+NL8soKEiKVFUPcSgUYnR0tOhO9Fw+STUyLRwO4/F4tNGcx0U4VlZWWF9fZ2Bg4NjPx1KQLzHhzp077O3tYbfbaW9vLyl2sJw1weHB/cWqqMU0A1cbD1JGKdxnpLRc6JVTetQEJDheUrqxscHdu3d1y0etlOqb6R9taGhgYGCg7E7zQqB6v+63eeiqdzgQCGDvs/MXi3/BVHCK+b15LjRdIG6J81jrYwz2DB6Ya+71emmMRLCurCBYbVhv3kDUKXmgUMiKzF/e/UvsRjuyItNubycqRTnfdJ7F0CJdzi6cCSdWsxWDzcB8eJ5z3nNVXeNhyFXmL3TqkTqlqb+/X9cBG4Mtg0wEJ2iyNLEZ20SS969hr9XLpaZLuu3nMKiNP1evXi37BchkMh3IRN3d3dUmH5lMJu0loFoNRouLi/j9fq5du1azKlyxsNvtdHV1sbOzQ1dXl9YfUenxsofhKBW1kOD+Wi7fPySlDzD0IIpLS0usrKwcmQJQTVKa2bU+OztLMBjUNR+1EuX7fA1NmV266oNH7TS32WxlN5ksLy+ztrZWcS9itSHLshZC3nGmg/9+57/TYG7g0bZHGfYPsxZZ45297+TJziexigc9fFt/93fsfO7PSKJgMhiw/eAHtPzGr2MusZu9FCSkBDEphsfsQVIk3GY3xrSRJksTa+E1tne26fB20NTQxFpkrapB/8UiW+HL9vqqHr6mpiYtE/f8+fOHlrVLQb+nn18690u8tvkapxtO47P6aHO0cbbxLHZj5RXyra0tbXiI3o0/BoPhQMNfZq5vMpk8kIlaCYVsbm6Ovb29uvShHwZZlg9YSABaWlruGS+rKMqxWCnyqahHBffXcqPTw/L9A4xyiKIsy0xMTJBOpxkcHDzyratapDSzQWhkZASLxVLySMx80Lt8X+iEpswHT3aTiSAIWkZfIQqMGmkSj8fvu9namdOn+vr6GA2MoqBoJdobLTcIxAO8o+cd9/ytKIpYhoYw9/YiOB0kEgmic/OMfvkrcOmidowr2U0cS8f49vK3WQ4tsxZew2vxshJdAQVub90mlohhMpkYCY/QL/bT5mhjoHmgYuvRG7mmHm1tbWlK9YkTJyqm7vW5++hzF5eRqwfW1tZYXl5mYGCgKqpadqi83+/Xxvc6nU6NQJX7oq5mdcbjcS5fvlyzRKcUyLLM8PAwjY2N9Pb2HvhZvvGyqpXC7XZrVopq2hiyVdTMf9Tni/qMrNV7fjgcfkhK6xXlvo2VWr5XG4aam5sLbkCoplIaj8d55ZVXNPVLb+hZvlcURfP1FnNDz24ySSQSBx7s6sQjdVpMJtLpNKOjozidzvtulGE8Hmd4eJju7m7a29uJS3HWo+sEYgEazY1YjVaiqSge8yHleEkCgwEBAavFitHtov3cWTh/nq2tLcbGxpAkSTvGeiojsiLzFzN/wdzeHP2efoa3h9ljD5/Vx05sByWp8ETHE7jsLmZ2ZxhoHuBdve+qqVzVYqB6+GRZJhAIcPXqVUKhECMjIyiKoh3jXGX+esHS0hJbW1vH5rMURZGWlhZN4QuFQmxvb/P6668XHYeUCTUaSVGUvOHx9QpJkhgeHsbn8xXU9Jn5oqUoimalmJ+f10Yk+3w+7HZ7VVXU7DK/LMvE43ESicQBL2otvUw8LN/XOdSxmqWglEanvb09RkZGim4Yqka2J6CNM7127VrJ4eFHQY/yvd75oxaL5UAJOnNaTKYPVR2pqpK2+wlqA4kaHB9Lx/ijiT9iPbJOKBXiu2vf5Yz7DE6zk587/XN5t2N95BHCX/oypNMoySSCxYr51ClEm02byKOGcS8uLhIKhWhoaNBK0OUoEKFkiPnQPG32NgRB4KnOp1iJrPDBzg/y2ZHP4u324rbtD3rwWr01F/RfClQV8caNG5jNZrxe74FGHrXM39DQoHXz16rKkwk17zcUCpWUr1oJCIKA2+3G7XbT399PIpHA7/cXFIeUCUVRGB8fx2Qycfr06fuOkA4NDdHS0lKSqCEIAg0NDTQ0NHDq1Kl7cmfV87ihoaFq57F67kmSxPj4OGfPnsVsNh9LcP9ReFi+f4BhMBiKIrRra2vMzc2V1DBUDaVU7fzs6OioGCGF8sv3lQ7Ez1ZG1PLo1NQUiUSC7u5uvF6vrvvMhOT3E3nxReRAENOZ09iffBKhwtEwarNW5ijD21u32Yhu0OnspMPRwUJ4gXZ7Ox85+xE8lvxKqfXWLQSTmcTICAarFdtPPIn4RjeuiswwbrUErWYd2mw2LTGh2FLtbnKXveQebpMbu2nfhhGLxfjCnS+wY9lhZnuGy02XcZvdyIpMr6v3iC3WNhYWFvD7/TktJNmNPJlRPYVM7jpOKIrC1NQU6XS6psvaFoslZ2KCeowz45BUyLLM6OgoDoeD/v7++46Q3r59m/b2djo69MkrzsydzTzG09PTeY9xJZBMJnn99dc5deqUdv8/juD+oxCJRAoej3w/4CEpLQGq/zAcDnPr1q2SSlCVJKXqAyAajXL69GnC4XBF9qOinPJ9tSc0qROPUqkUgUCAK1euEAqFNB9qZti5HpCjUXb/8FPI8TgGu53o3/4tSjiM833v02X7ubCyssLq6uo9Qd2hVAiTwYS0u0tqZgZXOoLRp+A+f7jnVhAErNcHsF4vzKeZGSOjKArRaLSkQPlXN1/lq/NfZS+5x3d2v0O/qx8lqZBKpog3xLnsvMzUzhQjgREGfAN84NQH6HLqb0+pBtR7SjKZLEhFzI7qyZ7cpR7jWijzq4H/JpOp4BGUtYDsxAQ1DinTrtLU1MT8/DyNjY1a48/9gnQ6ze3bt+ns7KxYFSnzGANaKsXY2BjpdJqmpqa8tqtykIuQZq4JqhfcfxQelu/rHOWU7wuBWu51u90MDAyUfKFUipSm02mGhoZwu91cu3YNv99f8WD7Uo9BoQ1NeiIzFunGjRuYTCZ8Pp/mQ80sKx3mQy0U6eUV5HAIY9v+TV2wWon/+HUc73kPgs5+OrXJIhKJHFDaFEXhx1s/5tXNVxndHKJ3JYVPcbJrSXL1ToCI8Zs43/1uXdeiQhCEe6ZKZU6LUR86DQ0NB27uu8ld/nrhr/HZfLTZ21gOL7MUWOLd3nczI87gsXgwGoxcaLpAo6WRd/a+k/NN5yvyGSoNdWqYyWQq2YuYeYxzlfn1sFKUArVb2+12lxX4f9zIPI97e3u1YzwyMgLseyg3Njaq3shTKaRSKW7fvk1PT09VVbrMY5xOpwkEAprtSm1I83q9ZTXHHUZIc6HSwf1H4WH5/iHyIhwOMzw8TH9/P21tbWVtqxKkNBKJaHmG6vqq1VBVLEptaCoHqmIjCEJONSpzkkm2D9XtdtPS0lL8g90ogqy8GS4tSSCKUIHZzWNjY1gsFq5cuXLg4T+0PcSX5r5Eo6WRThqZsk4hCRaeSPQxaO8gMTRUMVKajexA+UAgwMbGBpOTkzidTm1kZCQVQUDAZNhXKkwRE82WZi6dvsTI1AiRVIRuVzcCArIiYzNWJ3tSb6gNJHoqbfnK/Hfv3tXKo83NzRUv86svyC0tLffFRLRsrKyscPr0adra2u5p5FGtFLU0fKJQqIS0t7eXlpaWY1uH0WjM2ZBWSLZvPqhNyYUS0myUGjlVDh6S0ofIOZ1C9SBeuXKlqKkj+aA3WfT7/UxMTHDp0iVthrG6n0orpcVCvYirpY7Cmwq3z+ejp6fnyP1m+1Az81BV/14hHklTdzem3l6S8/P7PtJkcl8l1ZGUqp8t38N/2D+Mx+zBYXLQb+/BtrjJgLmbJ1J9yMkIVClMPBuZnc5qzuHm5ibjc+O8Fn2NsfgYG/YNGlONGKwGzCYzL0y/QFyKc3f3LiuRFXpdvZxpPMMpz6lj+QzlIJlMalPf9PLrZeOoMn8lEhPgzc/W3d1d9gt8rUElNr29vZqKmKuRR03+aGpqwufz3VMNqEWon62vr0/XQQ3lIrshTa24qNUAdXpXU1NTXqVa/WwnT57UrYegEBW1XIIajUYfktIHGSpZVE9stWPU7/czODioW6aenqR0cXGRtbU1bt68eY/6UalpS6Wg2v5RFdFoVFO4S3nzz+wePX369IE8VEAjqLl8P4LJhPsjv0j89hDy3i6mnl5Mp/UjUOpnO3nyZM6HSFyKk0gnCCVDOE1OxKYmZKcDw+oO6cgaoOD68Id1W0+pUHMOTTYTXwl/hbghThddjAfGcYgOLpguIAsyLa4WbE4bnY5O5kJzPNX5FG/teCtGQ33dyuLxuPaArObDP1eZP3Mspx7T0So1gaoWkEgktO8tX9pKZiOPJEkHqgHHNfWoEKhl7cM+W60gu+KSPb0rM3IK3iSk/f39FWtqPUxFzWyaUn9eKEl96Cmtc+iRVZpOp7XMUjVw/saNG7oHzpdLFtVytCzLDA4O5lxfrZTvM7sZ1TfHaiAYDGoKsh4KN3CPR3Jra+tQH6pgNmO7NajLvjOxu7vL+Pg4Fy9exO123/PzrdgWn538LFuxLaZ2pliLrtFmb6Pp/FUeu/AWHEkDpp4ejBVS6UrBanSVveQejab9ZrS3974df8rPP+z9h/zn0f/MVngLq9WKw+GgydxEm6Ot7ghpOBxmZGSkIlOaikGuMr/q97VYLFoJupguaPUl6ezZs/9/9s47Oo7qfP/PrnovK60kS1a3rS65YRsMONjGsY0tGRsXEnBoCSQE8iOBQJzkC0nApsQBAokTQgsBJ6i4yQZMM71b1epWbzvbJK2krTPz+8O5k9V6Ja22zi7zOSfnBKvs3dHs3Oe+5Xld6vjhCbRaLerq6rBw4UIu8jwbfn5+3KHVcuoRYF8K2hUQsW1vWtuTTDe9i0SqIyMjMTIyguzsbLeK7emM+807+m2Joo6Pjztt7/IGvOtp7gaIiNNqtaitrcX8+fNdYjjvqFi01bDfnel7a2UP5N9JQ5M7Beng4CA3NcZV9iKBgYFT6lDNC/MjIyO5GklnN5jIZDJ0d3ejpKRk2mk/J7pOwEAbkBGZgfjgeHSMdqAkvgRXpVyFyMCLRSwf8Bf5Q6vTwjhhRGJiIkR+IgSxQUhOTMZlk5ehUdWIEFEI5Bo5JnWTUJxXoD+xH3FxcfDXaMBoNPCLjYWfB8XeTIyMjKC5uRmFhYW8SslZS/NbdprPluYnvrjOPADyBSK2c3Nzp5RHzQVrU4/40JBGovZzEdt8xnx6l1arxdmzZxEaGorz589jaGiIOwi40zptOuN+c6E6XbPU5OSkz32eZkIQpRb4+flBpVKht7cX+fn5LotkzNUT1RyNRoP6+nosXLhw1vSYu9L3xPXAcsPyVId9R0cHJicnsXTpUrc94C2jIqQOtbOzE0FBQZBKpQ4/DFmWRW9vL+dlOdNYREpLcZOawgPDkRCagIzIDN4KUgAQjYkQrguHNkqLEdMI9Ho91s1fB3+xPzZnbEagXyBaR1qRnpCOzembES2KhkKhQNt//oPAL79EUHAIQkJDEf296xGck+PptzMFci+48pDkLCw7zc1HRlpL8xOxbe6L6yuQyLazxbZlCtoZkeq5QqK/OTk5Ho3auwKDwTBlsA0ZRa1QKNDY2Aiaprl6X2dbTs3EXJulJicnvbJhzl58TpQ6emNptVqMjo5i2bJlvNw4KIpCR0cHiouLbYq0uHOcKcMwU0555g1N7irwp2kajY2NCA0NvagL3Z1MV4dKxkXGxcVBKpXOaQMnYwxpmrbJyzIlPAU9mh5IQ6QwMkawYBEbzM9ICMuy6O7uxsjICP7flf8P50bOYUQ/gpSwFCyKWQQACPYLxpaMLdiCLVN+NjksDKGdncCiRdAZjBhTq6D8y18hvu1WxCclITY21uMNJoODgxgYGJj1IMFHLAcjmDf9BQUFISQkBCqVCkuWLOHlM9MRxsbGcO7cOZeLbctItbknqslkgkQicbp4IoLUkegvXzGvISUpe/NR1KSm2jyzFRERwVlOufMzOlOzlMlkgkwmc9ta+IDPiVJ7IfWZBoMBCxYs4N3DlWVZdHZ2Qq1WY/ny5TZ/aNx5+iMNYp5qaNLr9aivr8e8efOQnJzslte0FWtenR0dHdBqtVO8Oqe7VqS+OTIyEhkZGTZd0y0ZW/Cftv9geHIYIojw3dTvYn44/6x5WJZFS0sLWJZFcXExxGIxlkmX2fzzzPg4IBbDPzAI4YFBCA8Ph3FoEOKoKCiUSnR0dCAkJISLPLm7waS7uxtqtdrqlCZvw7J+r6enB729vQgODkZ9fT0nniIjI73Wk5QwMjKClpaWGUtkXEVoaCg3wtdkMk2xp4uIiOAi1faKp8nJSdTV1U1bj+7NWBOk1ggICEBCQgISEhLAsizGxsagUCjQ29vLmfrbMuTDmZhHUU0mE3784x9j8+bNPuF9ayvfnnc6A3q9nvPTCwoKcqn5vj2Q6F9gYCCWLFni8aiPNUik1FOCVKPR4Ny5c15RF2WetiN1qENDQ2hpabFah0ruz/nz589pskpUYBRuyb8F48ZxBImDeDkT3h6xbYlfbCxEfv5gJiYgDgsDrVLBLzYWMcnJkPj5cWk7a44JoaGhLrtHzac0EbHtS/T390OhUGDVqlXw9/fn0vx9fX3QaDScTY8raqpdDRnTW1JS4vEAhb+/v1Xx1NPTAz8/P65G0tZI7sTEBOrr632y9pd4rM4mSC0RiUSIiopCVFQUsrKyoNfroVQquSEf7q73pWkad955J7KysvCHP/zB5a/HJ0SzCDB+qTMbYBgGRqPR5u8fHR1FY2MjV3fS3d2NgIAAt0TaPv30U6xatWrGTZEUoScnJ9ttQP3pp5/i0ksvtXeZNlFXV4esrCwEBwe7tX4UAJdKLCws9Op6NvM6VKVSiaCgIERGRkImkyEnJ4f3YnuuEC9LZ0S2DZ2d0LxeDlanhV9MLCKu3wP/aeqtiWOCQqGAVqtFTEyM1alSjkCmNAUFBSE7O9vro4aWdHV1YXR0FIWFhVY3aZZluRpJci8T035Pi7zZkMvl6OrqQklJCe9smywhnqgKhQI6nQ4xMTGIi4tDTEyM1XuZ1MfyrdHOGRiNRtTU1MxZkM6Geb2vSqVCYGAgl3VxRQSdYRj87Gc/Q0xMDB599FFvO8w6/KD7VovSwcFB9PT0oLi4mCsk7u/vB03TSEtLc+UyAQCfffYZVqxYMe1NRwRzbm6uQ4LEHaK0oaEBycnJXHeuuxqa+vr6IJfLUVhYyPsNZK4MDAxwdXvEaN7d6SRXQbqZZ7NpYcbHofv6GzDjGgRkZyNw0aJp3ztL02D1eoiCg20eTkAi1QqFAiMjI1OmStmbGiVTmmJjY93yHHEnpIlQr9cjLy/P5g2T1EjK5XKuRjI+Pp53af7h4WH09fWhpKTE62p/aZqGWq2GQqGAWq1GaGgody8HBQVx7gi+2IxGBKk7TP+J5ZRcLofBYHDqcASGYXDvvfciICAATz75pLcJUkAQpRdjiyhlWRZtbW2YnJxEYWHhlHqNoaEhaLVaZGZmunqp+Oqrr6Z9+FkTzPbialFK0zSGh4fR1dWF0NBQrsvclQ91hmHQ2toKhmGQm5vrjR/eGSGNMUVFRQgKCuLqUOVy+ZQ61KioKK9776R5ZLZ6Nkarxeg//gFaroAoMBCsToewrVtd4vkKgBtlSCLV5m4KtkZESPQ3JSVlTqUW3gDLsmhuboafnx8WLlxot5gkDSZyuRwajcal1mlzYWBgAMPDwyguLvb6Gj7zTnOFQgGj0QiDwYCcnBxIpVJeHQQcxZ2C1BLLQ63lQWAuMAyDffv2Qa/X4y9/+YvXPdf/iyBKLWFZFgaDYdqvG41G1NXVITo6GllZWRd9OCmKwujoKBYsWODqpeKbb75Bfn7+lHQWEcwTExMoKipyysNxtoisvVjWjwIX6pUoioJCobBrU7cFo9GIhoYGbl64Lz1gWZbF+fPnMTExgYKCAqubNHkQyuVyjI6Oco0PEomE95spafAqLi6e9Z7QNzVB8+9/w3/ehdQ+q9eD0ekg+eV97lgqdDod5HI55HI5jEbjrNE90s3sbpNud8AwDBobGxEeHm537a81rJWseCLNT2zWioqKvK7+dTbIkI3k5GSMjo5ytl7eWu9rjicFqSXmtetKpZIb42tL4x/DMHjooYegVCrx3HPPefPfxOEHA793MCczPj7OjWQkM4stIV1v7sDSrslkMqG+vh7h4eFYvHix0x78YrH4IrsmR5muoYlYbmRmZnKbenNzM4xGI2eD5Mj0Eq1Wi/r6eqSnp0/7N/RWSB1iQEDAjHZWln6oY2NjkMvl6O7u5uqd4uPj3WoObQsDAwMYHBzEkiVLbCu1YBhMecaJxYAbp5MFBwdzJtykA9q8icfcq5PU6uXl5fmcvQ5N06irq0NcXBxSU1Od+rstrdOsWSG5Os3f1dWFsbExn2xGs+YgYD6Ws7Oz0+U1kq6CT4IUmGo5lZGRcVHjX2RkJNcsZZ5FZFkW+/fvx/DwMF566SVvFqRO4VsTKSX+noWFhTN2HI6OjqKvrw8FBQWuXCaAC3WYaWlpiIyM5Cw60tPTnZ72+/rrr1FYWOg0kWKPIT6Zsy2XyzExMTHFBsnWjYAYdPvixm80GlFfX4/4+HiHNv7JyUkuuscwDCdQPVmHyrIst/FP1xhjDUajwchfD4HR6yEODgY9OorQq76DsKuucvGKZ8YyuicWi6HT6VBYWOhzozVJN7MnyhHIQcAyzR8bG+uUjADJSuh0ujnVx3oLarUara2tszoIWNZImnui8vWa8E2QzgZ5ZpDxp/v378fatWtRWlqKd955By0tLfjXv/7F+0yXDQjpe2vo9Xru/5v7exYVFc0aoRkfH8f58+dRXFzs6mWiqamJmztN5rO7QmzV1NRg0aJFTpkK4YwJTQzDcOnnkZERREREQCqVzphKGhoaQl9fH4qKinjfvTtXSPQ3IyMDUqnUab/XvA51cnLSroOAo5B7W6TRICMsDOLgYARkZEBkozCllUpMnjkDRqNBYE4Ogi+5xOYmJncgl8vR3t4OqVSKkZERMAzDpZ89Pc/cUcg89MzMTI9v/OYHAZVKhYCAAIeie6RMimEY5OTkePXfyRrK//rzlpSUzCkYYTKZuBrJ0dFRhIeHc5ZTfGn88jZBao2uri4cPXoUR44cQWdnJ/bs2YMtW7ZgzZo13r6/CaLUGkSUmkwmNDY2Ijg4GAsXLrRpI9ZqtWhubsaSJUtcvUxuOo9Go0FxcbHLbkZi1+SoBQjLsjCZTE7trifpZ4qioFQqERwczEX3AgMDuUMFibL5wElyCqTey9Um1qQz1/wg4Oo6VNKFHj05iYj33gNMNMAwCMzLRcTOnTYLU74yMDCAoaEhFBcXcxu2ZUaA2E1NZ9HDV4g7Al99f7VaLWfrZUu9rzmkYcvf3x8LFizwOUFKRpUuXrzYIUcSy8Y/4gBCPFE9cd18QZACF67t3/72N5w5cwavvfYavvjiC5w8eRJnzpxBcnIyNm/ejG3btnljiZogSq1hMBgwMTGBuro6pKamzskDkaSrli93TYcvgWEYfPHFFxem1yxb5tI6ksbGRsyfP9/uKKw7DfFJobhcLgdwQdiEh4cjPz/f5zYPiqLQ1dWFoqIit9ZymdehKpVKBAQEcI4JzjoYkS705ORkhFRWgtXpIY6IuBBpHxxA5Pe/j8BFi5zyWu6GjESdyacTuPAZJwcBtVqNsLAwLrrHl6iTNUh9rLdM+yFpfoVCgbGxsRnT/AzD4Ny5cwgNDUVmZqbPPVNc6bGq1+u5bv7JyckpnqjuqIP0JUH6wgsv4NSpUzhy5MhFz9yOjg6cOnUKy5Ytc7mVowsQRKk1hoaG0NzcbFc6nIjFVatWuWh1/9uwRSIRkpKSXG7U39zcjMTERLvq3Tw1oclgMKCmpgahoaEwmUxcrZNUKuW8UL2Znp4eKBQKFBUVeVygmHtI0jTtcPqZRNkWLFgAiUQC5cOPQBwVBdF/BYJpaBDh27YhuKTEye/E9ZC0r8lkmtWKjNFqoW9uAfQ6+KelQxcRzm3qIpFoiu8sXyCRe2/1sjSv3SMHLnIQCAoKuhC5j45Genq6p5fqdCiKQnd3NxYvXuzyZwo5cBFPVJLhItfZ2fiKIAWAf/7zn6isrMTx48e9qrHMRoTue0tYloVMJsPSpUvtivqIxWKXjhklDgDZ2dnQ6XRgGMZlr0Ugc+nniqcE6fj4OBobGzlRA/wvGtLb2wuNRuO1aVGWZdHa2gqTyYTFixfzYu3mc7ZJ+rmrq8uuhjQiasxHGAbmLIK+vh5+CYlgdTpAJIa/F3p4kihbSEjIrD6djFaL0RdfgmloECKxHyASIfKG7yMjKwsZGRlc1Km9vR06nW7KdfbUgct8tKa3bpbm3fzZ2dlcE09TUxNGR0e5r5GaeF9BJpOht7fXLYIUADcbXiKRgGVZrsGyoaFhTlZItuBLgvTw4cP4z3/+g+rqaq/9jLkan4yUGo1Gh8Seq8zmiQNAUVERwsPDMTAwAKPR6PJTe0dHByIiIuZUn+KMhiZ7IBtjQUHBtDWwlmnR8PBwrlGKzzWnzpjz7k6sNaTNVIcql8vR2dl5UTkCo9Vi/EQ1DE1NEIeFIqy0FEELF7rzrTgMsWuz1RZJV1sLTWUlApJTAFxwEhCHhyH6jjsu+l4++M7KZDL09PR4xWjNuWIymVBbW4uEhAQEBQVBLpdzaX7i1cnn58ZsDA0NYWBgACUlJbx4H8QKSaFQOOyaQMrp0tLSnNoE6gkqKyvx3HPP4eTJkzM6AHk5QqTUGyA1aAqFAsuWLeMe+n5+ftDpdC5/fT8/vzmJdNLQBMCtkby+vj7IZLJZfSwtT+mkGL+7u5urj+SbT6der+cm/cybN8/Ty7EJ88YGcp1JipCkRYnJeX9/P4aHh7FkyZKLIjXikBBE7rzOa6NTBoMBtbW1SE1NRWJiok0/wxoMU10CAgLATPNZn8l31vI6uwLSsOWuKJs7IX+7tLQ07lAulUqtXmdSTuFNEazBwUEMDQ3xRpACQEBAABITE5GYmDilnKKrqwv+/v5cmn82NxhfEqTHjx/HoUOHfF2QOgUhUmoFZ0ZKaZrGuXPn4O/vj5ycnCkij0T6Fro4atTT0wM/Pz+kpKTM+H2eSteTOj2DwYC8vDyHiua1Wi0oioJcLgfLspxhvyfr40g5Al87me2BdD+TLnN/f3+uMcYbhed0kClN5qUktmCSyTBy6G8QhYRAFBgIWqGwy2PVssucHBKcdZ27u7s5uzxfM+0mllZZWVmzTtgy9+o0Go1Txvjy9X7u7+8HRVEoLi72mr8duc4KhQJ6vX7aufG+JEjfeOMNPP744zh16pTPPP9nQGh0sobJZLKrhpLw6aefYtWqVQ4/jHQ6Herq6pCUlGQ15adSqSCTyZCbm+vQ68xGf38/aJpGWlratN/jKUFqMpm4lLazu2GJTydFUdDpdJxtjDs3GpVKhba2thnLEbwVhmHQ3NwMkUiEmJiYKTZIUqnUrX6orkCj0aCxsdHuYQ3G7m5MvHUarFaLwKJChF5xBdfsZQ+WZvKWU6XmAjGO12q1yM/P9+q/kzV0Oh1qa2vtOggSr06S5ufjGN++vj6uUdJbBKkllnPjiTtFZGQkmpqafEKQvvPOO/jDH/6AU6dO+dzo4WkQRKk1HBWlX3zxBZYuXerQA2h0dBSNjY3IycmZNsLirulRg4OD0Ov1yMjIsPp1TwlSnU6H+vr6OaVF7YWmaW5DHxsbc2hDt5XBwUEMDAygqKiIV6UEzoDUWMbGxiItLY27Z0i9L0VRGBkZQXh4OJeu48uGbgtkGk5hYSEvu9DJqEhiJj+X7meWZS8MNBCJsGjRIt5GAu2FuD/k5OQgOjraod9laZ9G0s+eTPP39vZCpVKhqKjIZw4TLMtifHyca9gKDg5GUlIS4uLivHYIxQcffIBf//rXOHXqlDf6jdqLIEqt4ago/eabb5Cfn293DdfQ0BC6u7tRXFw8Y92Mu6ZHyWQyaDQaZGdnX/Q1TzU0kS7t3NxchzeOucKyLEZGRkBRFFQqldP9I4nhv0ajmdNYTW+B1MfOnz9/xtGTlubb5hs6n6eWEP9YVw60cDbE31ehUHBlK9bGyzIMg8bGRoSGhiIrK8srN/uZIB6r5u4PzkSn03HXWa/Xuz37Yu6P6yuClGCeso+OjubS/BMTE4iOjubmxnvD8/STTz7BL3/5S1RXV3tND4GTEESpNRwVpbW1tViwYMGcIyQsy6KjowMajQZFRUWzRobcNT2KePYtsjAr91RDk0wmQ3d3t9tN463BsiwmJiZAURQUCgX8/Py4Ril7BAnDMGhqakJAQMCstkHeyMTEBBoaGuxKi5rXodI0zfnO8ikSQhq2zKc0eRvTjZeNiIhAY2MjF932NcbGxnDu3DkUFha6pVSGZF/ISE5Xp/k7OzsxPj6OgoICnxaklil7hmEwMjIChUIBlUqFoKAgLojAx0PjF198gXvuuQfHjx/H/PnzPb0cdyOIUmvQNM2JLXtoaGhAWlranKaZkNrIsLAwm0fXERN9V0+Psla7StO0RxqaSGNFYWEhLzd9EgmhKGrORvJGoxH19fWIj4+3yTbI2xgZGeGGUjgaheJsY2rrwHz1JUIDgxCx+jLErVzpkUgIy7Lo6uqCRqNBQUGBV0RjbIGUUwwPD0MmkyEsLAypqam8nyo1V0ZGRtDS0oKioqJZu7pdAUnzkwAAcVSwpcvclt/d2dkJrVaLvLy8b5UgtYb5sA+TycR5ovKhKe2bb77BnXfeiWPHjvnkgAYbEESpNRwVpXOdgDQ5OYm6ujqkpaXNKVRP0zS++uorrFy50t6l2oR57aqn6kdJBNHf3x8LFy70iger+RxzMlaPNPBYXjetVov6+npkZGR4fXG+NVyR0jb29WH0+ReAoCDojQboFEqoV61EUEEBF3Fyh3AiAw0YhkFubq7HNzZnQ8ot0tLSEBoayqWf/fz8uEOXJ4ScsyDNhCUlJbyJnOl0Ou7ZQdL8pMt8LvcXyb4RZxJfuzcd7bK3HDFLotWxsbFuP3TV1dXhRz/6EaqqqqyWyn1LEHxKXYGfn5/NolalUqG5uRn5+flzro0Ui8VumehEfEo9OTK0oaHB6yKIAQEBSEpKQlJSEmckPzQ0hJaWFs4QWiKRYHx8HE1NTXZ3afOdvr4+UBRl1YPUEfSNjYBYDP+YGPgDCA4KRpzBAHF6OiiKQm9v7xQPT1eUevh6jSWxtDIvt4iIiEBmZiYnnFpbWz1SH+kMyKz32byN3U1wcDBSUlKQkpLCdZmTZ0dERARn2j/T54llWbS3t8NkMgmCdBr8/f2RkJCAhISEKdFqYoNILNRc3ax47tw5/OhHP8Lrr7/udEFK0zSWLVuG5ORkVFdXQ6VSYdeuXeju7kZ6ejpef/11u0aI8xWfFKWOfnj9/Pxsqknt6+vDwMCA3SNN3fWQEYvFXJ2tuxuaSA1iVlaWV4+IszSSJ53PbW1tMBqNyMjI8Hh9rLMhURqtVuuSkaiigADA/FBG0xAFBCIiIgIRERHIysriyimam5u5VB2pj3T0HjaZTKirq4NUKvXJ2i/S9DPdYclSOCmVSgwMDKC5udmhKTzuwt2jNe3FcjgCaf4jhy5r0WoSvQfgk9F7MmUrNTXVaZklkUiEqKgoREVFcc8O81G+MTExiIuLc/po6paWFtxyyy04fPgwcnJynPZ7CU899RRyc3MxNjYGADhw4ADWrl2L+++/HwcOHMCBAwfw6KOPOv11PYVPpu8ZhoHRaLT758mEj+Tk5Gl/f2trKwwGg8P1Z64aaWqOXq/HF198gezsbMTFxbmtXo6k1fLz831yikVvby/kcjmysrKgVquhUCggEom4DcibU6Kk3CIwMNDmGum5QiuVGPn7c2B1OuC/m0TUTTchINW6QCR1qHK5HOPj44iJiUF8fLxdm4x5StsX7VqIu4WtTT/Gzi4YOs9DHBqGwJJiaP7bLKVUKhEYGMgJJ76kx8kko+LiYt6KZluwTPMTM/nh4WH4+/u77LPnSUwmE2pqapCamuq2zx5N09wzWq1WIzQ0lKv5dSTC3t7ejhtuuAGvvPKKS1x0+vv7sXfvXuzbtw8HDx5EdXU1Fi1ahDNnziApKQlDQ0NYs2YNd4DhAUJNqTUcFaUzmc0bjUbU1dUhNjbWKfPLXS1KSUPT2NgYZ4EUEhICqVTq0maHgYEBDA4O+qRHJ5lAZTQaL2o80Ov1XKOU0WjkOsydEdlzF8SDVCKRuLxLm1YqoaurAxgGQXn58J83vcWUOaSBh0xFm4utF/Gx9KUJW+aQw2BxcbFN0XtdXR00FRUQBQQCRiP8EhMRdcvNEP9XgFpzTXBWtNoe+vr6IJfLvWqSkS2QaHV7ezv37HBnbbU78IQgtYQ4rhCrOoZhuCzYXO7p7u5u7N69Gy+++CKWLl3qkrXu2LEDDzzwADQaDZ544glUV1cjOjoaIyMj3PfExMRArVa75PXtQKgptYYz0vcGg+Gifx8fH0d9fT2ys7N538xiWT8aHR2N6OjoKRZINTU1TveONE/5LlmyxKc2DeDCxtHQ0ICIiAirlk9BQUFcSpQU4ff09Dgc2XMXZKCBIxFElqZhGhwETCb4JSZCPIMw8pNI5jx6E7hQTiGRSCCRSDjjbXJPz1SHSqY0kZGovgZFUeju7sbixYttPgxOvv02/GIl3N/JNDgIY2cngvLyAAAhISFITU1FamoqF60m93R0dDSX5nfHPU18OktKSnj7GbIXkUgEiqKQmJiIjIwMjI+PX5Tmd0d9pKvggyAFLlzn8PBwhIeHIyMjg2toJfd0VFQUV/M73f7V19eHPXv24LnnnnOZIK2uroZUKsXSpUtx5swZl7wGH/FJUeoo1mpK5XI52tvbUVhY6PRUNKnzdObvYxgGNE1DLBZP+d3mH8jMzEwuCtLY2AiWZbnN3J4HH03TaGxsRFhYGAoLC70mMmgrer0e9fX1SE5OtsllwbwI3zyy19bWhvDwcEilUl6NLhwfH0djYyMWLVpkd+E8azRC83o5DK2tgFgMcWQkom76AfxcWIgvEommrUMl8+Lj4+NhNBrR3t4+61ALb2VwcBCDg4NzrrFkjUaIg8wOpCIAJus19QEBAUhMTERiYiLnH0mejc5KiVpd43/Houp0Op80jicNd+S5DACRkZGIjIycUh/Z1tY248x4vsIXQWoNy4bW0dFRKBQKdHZ2IjAwEKGhodz0M+DC52z37t149tlnsWLFCpet65NPPsHx48dx6tQp6HQ6jI2N4fvf/z4SEhIwNDTEpe/5HiCbKz6ZvmdZ1mqk01aUSiUoikJubi7nrSmXy1FSUuL0h60zRpqa48iEJoPBwKXpdDod4uLibE49kwhbSkqKT06wIILNGSlf0uxAURRXs0cOA54qdVCr1WhpaXHYeFxXW4vxykr4zUuGSCSCSS5H4IJsRO7e7cTV2g6JVvf29mJsbAwJCQlITEx0W2TPXfT29kKpVNo1C33inXcweeYM/GJiufre6Dtuh98c3ETMU6IKhQIAOIFqOVVqrpByGZqmfbLph2EYNDQ0ICoqyiZvS9LNL5fLMTo6ivDwcC6Kysc0P58F6WxotVp89dVXePDBB6FWq3HJJZfg7NmzeOqpp7Bu3Tq3rePMmTNc+v7ee++FRCLhGp1UKhUee+wxt61lFoSa0unQ6/V2/yzx9czLy8O5c+cgFouRm5vrkk3s66+/RmFhoVPEiDNHhpLNXC6XQ6PRTPHotLwOZJJKTk6OT1lTEEiNXkFBgUsmxUxOTnKHAUej1fZAJmzZ6kFKnhnW7q/JM2cw+cGH8P/v5sNotRAFBCDmzp84d9FzgFhaFRYWcilRV4yX9QTEWH1iYsLuST8sTUP76acwNDVBFBGBsLVrub+fvZhPldJqtVzpylwjeyzLorm5GX5+fj45IY1hGNTX1yM2NtYuuzzLUb7EJcSdz4+Z8GZBasn58+fx4x//GOHh4RgYGEBRURE2b96M7373uy7f98xFqVKpxM6dO9Hb24vU1FSUl5fzqTZeEKXT4YgoHR8f5xpZEhMTkZqa6rKHYU1NDRYtWuRwOtGVE5pI6pmiKIyMjCAiIoJLPSuVSnR2dnpskoqrGRwcRH9/P4qLi90SxbSMVpNGqcjISJfcg8RBoKioaFZhxrAMPhr8CJ8MfQIAuDTpUlwx7wqIRf8TGYa2doz+85/wT0wE/PxADw0ieOVKhG/a5PS1zwYRbGQ0o3kEkdShmhvJu9IP1RUQ2yCWZZGTk3MhMj08jIlTp0Cr1AhYkI2wq6+esabXHZDOZ7lcjpGREYSHh9vUwMMwDM6dO4fQ0FBkZmb6nCClaRr19fWIi4tzmiUZabRUKBScDZI9hwFnQGyf5s+f7/WCVKlUYvv27XjwwQexadMmsCyLmpoaVFdX46233kJAQAAee+wxXHLJJZ5eqqcRROl0GAwGzPLepoWiKNTX12Px4sWQSCROXtlUyBQge+tU3W2ITwyKZTIZhoeHwTAMsrKykJiY6LXRJmsQQaPRaFBYWOiRhi3SjUtRFDQaDaKioiCVSp2SeibG3Hq9Hvn5+Tb9vhp5DY52HkViaCIAYHhyGKUZpVgiXTLl92o//hiT770HMCwCc3MQXlYGcXAwlDol6uR1MLEmFEgKMC/MdWUeLMuipaUFADjBNhOkDlUul0/pfHbVYcBRiGVXUFAQsrOzIRKJwGg0UD/7F4BhIA4PB62QIzAvH5G7d3l6uRyWkb3pDgNzTWl7GzRNcx65KSkpLnsNlUoFhUKBkZERt2YGfEmQjoyM4Nprr8UDDzyA0tJSq98zPDyMgIAAl+sFL0AQpdNhrygdHh7G+fPn4efn5/Lxn8CFSRDJyclzngYFuF+QEhiGQUtLC1iWRWpqKpem8/Pzg1Qq5ZWfoT2Yj0RdtGgRL0SJeVOJo6lnEoEKDg7mBI0tVHRUoGusC9FB0QCAEf0I0iPScd2C6y76XtZoBEvTnK2QUqfEP879A0bGCBFEYMFib+5ezA93vmk9abgjTSNz/ftZlq64u8N8NkiELSYmZopgM7S1Y+y11y5EqfHfcp7hYUh+ve/CoAIeYu0wEBsbi66uLp8dakCGNiQlJbmt/t4yM+DKND8RpCkpKUj8773orYyNjWH79u342c9+huuuu/g5J3ARgiWUsyBWRmNjY1i2bBnOnj3rlte1dXqUJc6sH50LRqNxiocl6XzOyMiATqcDRVE4d+4caJpGfHw8pFIpL2qbbIW8P76NRBWLxYiNjUVsbOxFFkhzsfUi72+uG/6oYRSjhlHIJmUIDwiHv9gfOlqHqCDrY1VFAQFThNBZ+VkYGSMSQi9ETVQ6FT4d+hS7Fjg3ikfeX0JCgt0RKEvXBMsOc1d7/M4EETSJiYkXDfcQBQaA/e84YZFIBBiNQIA/wGNbtuDgYMyfPx/z58+HyWSCXC5HQ0MDRCIR5608kzWPt0EEW3JyMpKSbPPkdQbmDhWZmZnQ6/UXTTtyRprflwTp+Pg4du7ciZ/85CeCIHUjPitKRSKRzZFSk8mEhoYGhIaGYsmSJXP6WUexR5R6SpAS0/HMzEyrNhTBwcGcnyFpdCAPPVfXRjoDrVbLlVPw2WbD0gKJ2HqRwwBxTbDsetbpdKirq5vz+5NNyvBy88vQGDUYGB/A0MQQsqKyIA2VYlXiKpt+h9aonfKZ8hP5wcjYP+DCGq6Y0mR5GCAd5sQP1dqISFdhMBi4WeHW3p///PkIys2BvqkJIrEfwDIILyuDiAfRXVtgWRb9/f3IycmBVCrlRvl2dnYiKCjI4w4VjkJmvfOh6ScoKAjJyclITk7man5lMhlaW1u5LIxEIpmT24wvCdKJiQns3r0bt9xyC66//npPL+dbhc+m741GIxjzudrToNVquQeFeeTBHeM/AaCzsxMhISE2n5qJ/ygAt6YSiWWQPabjlrWRfDSRJ2MZp5sT7i0QI2iKoqDVahEbG4v4+Hj4+/ujsbERubm5cy4Vea3tNfRqeiEJlsDEmNA+2o7VSauxMW0jQvxnbqIxMSac7D6JT4c/RcdoB5JCk5AWkYYJ4wR2LdiF3NhcB97t//DElCbzEZEGg8GldaharRZ1dXVYsGDBjHVrrMkEQ2srmPFx+CclIYBH0f6Z0Ov1qK2tRWZmJuLj4y/6OnGoUCgU3MErPj4e4eHhvD3kmmM0GlFTU4P09HReH3jN0/xKpRIikWjKwWu6a+1LglSr1WL37t3YuXMnbrvtNk8vx9sQ0veOoFar0dTUhPz8fLtqOp2BrZFST9WPAsDQ0BD6+vqwePFiu2pFSa2pVCq9yETevJPfUyk6uVyO8+fPo6SkxGs6r6fD3AiaNDp0d3dDpVIhLi4ORqMRNE3P6VprDBpOfPqL/SEJkiA2OHZWQQoAnw9/jrPys8iMzERsUCyaVE2gGdqpgpRYkhUUFDh9sMVMBAcHXzS9q6+vj2tKI3Wojt7XExMTaGhoQE5OzqzPKZG/P4Ly8x16PXej0+lQW1s744EiNDQUaWlpSEtL4w5eXV1dmJiY4OUh1xwS4c7IyLAquPnEdGl+MqXPWprflwSpXq/H97//fZSVleHWW2/19HK+lfisKJ1NtPX396O/vx9Lly71aFOOLaLUU4KUTFEZHx/H0qVLnSIaLcdDkrqxzs5OBAcHc41S7qrXI5ZIS5cu9Sn3AOB/95bRaMRll13GNZWcP38eISEhs07fMTJGnBk4g47RDgxODKJYUowQ/xCYWBPSI9JtWkO3phuRgZEQi8SIDY5FXmwecmJynCZIyaxwT09psqxDHR0dBUVRNl/r6SCC29GhBnyFRLhtEdwAoG9qhu6rrxDm74+41ZfBr6AAarWam3bkyqlS9mAwGFBTU4OsrCzExcV5ejlzxjzNzzAMVCoVKIri0vyxsbEYHBxEamqq1wtSg8GAvXv34uqrr8aPf/xjr4jA+yI+m743mUxWxR7DMGhtbYVer5/R6ufTTz/FqlWrXH5jDg0NQavVcqPlLPGUIKVpmuvQXrBggVted2JiAhRFcd2hpFHKFYcGMiXGaDQiLy+PlxEWR+np6eGm/JhPDLOcvkOutaUtzxs9b+Dz4c8hCZagY6QDMq0MxZJiXJNxDYriimZ9/aGJIbze/jrqFHXwE/lBLBIjxD8E27O34/J5lzv8/oaHh9Hb2+uSSWvOwvJai0Qi7lrPJqLVajVaW1t91gN4fHwcDQ0NNpcE6ZuaMXb4MMTh4QDDgNXrEXXrLQj4b0ObtWvtSSN5UpKwYMECPpmbOwWWZTE6OoqGhgaIxWJuKp0zJnh5AqPRiJtvvhmXXHIJ7rvvPq9bP48Q0vdzwWg0oq6uDjExMbN6F5Iok6vnks8UKfVUQxOZ8Z6UlOQyDz1rhIWFISMjg+vkt6V5xx7MLYN8cUqMueAuKSm5SHCLRCKEh4cjPDx8yrU2nxUvlUpRJ69DYmgi/MX+KIwrRNxEHLZkbkGhpHDWNXSPdeOV1leg0qrQM94DMcSIDopGkCkIMYGOTz/p7e2FQqHAkiVLXP4ZdQTLa03MzcnBeLoGQNLgs3jxYq9t7JkJeyLAuq+/hjg8HH7/rfk2URQMDY2cKLV2rc2bLUl9dVRUlMsPoaQkYdGiRT455Y6maXR0dGDBggVITEzkGlvPnz/Ppfnj4uJ4W1Jhjslkwo9+9COUlJQIgpQH8Pdp7iCWN9bExATq6uqQlZVlU+ejv78/TCaTx0Qpy7IwmUwA3NvQpNFocO7cuVkbKlyNuVWM0Wjk0s5arZZrKImKiprzA4QI7uTkZLd5BLoTEuEODQ21WXBb2vIoFAqcO38O/bJ+qIPUSI5ORnBwMBgw8Bfb9nl4v//9CzWnIiAlLAWTpkmkR6QjNjgWbaNtKIgrsOv9kZKSyclJq4Kb7wQFBXF1qKQB0LIO1WAwYHBwEEuWLPG5khLgghl5S0vLnEsuRP5+gHnzKssC/tOXFFl2mKtUKgwNDaGlpQURERFch7mzn/GkKc3WkgRvw9zWiqTsAwMDMW/ePMybN++ivoGwsDDExcXxpqTCHJqm8ZOf/ATZ2dn49a9/LQhSHuCzotQchULBpcFsbYSw1z90rli+jicbmkhBe2FhIa+8RQMCArgHHtnI+/v70dzcjOjoaEilUptO5KRhxNOC21WQTEBiYqJDHp2TIZN4z/QegqOC0apuhUwvQxjCkBKWgghdhE2NUnpGDxFEMNAG6Gk9Av0CEewfDJZlEeRnX+SPDG0Qi8UoLCz0+g3EvAGQZVmMjIygs7MTo6OjiI2NhVwu5+VG7ggqlQptbW0oKSmZc1lOyGWXwdDWDhNFAQwDUVAgghcvtulnzSdHkVp2uVyO7u5uBAQEcKlnRxsdiSDNzc31aheP6bDFZ9Wyb4CUVNTV1QHAlJIKT36GGYbBz372MyQlJeF3v/ud1z9PfAWfrSklDR49PT2gKGrOs8sbGhqQlpY2Z/ujuaLRaNDV1YWioiKPCtLe3l5QFIWioiKv2QSJsTlFUVCr1QgPD+eMzS1FE9kMCwoKfLJhxFkeqzRL42DNQQT6BSLUPxQjuhH0jvdiR9YO5IXlYVQ5CoVCgaCgIK4pzdr98l7/e/h7499hYkxQ6VXwF/ujJK4EccFxuDnvZsSFzK3pg6bpKWMnfW0DYVkWXV1d0Gg0KCgo4Lxn+VAb6SxISUJJScmUZzFL02DUasDfH+JZsh/GgQEY6huAAH8El5TAzwnNQ1qtlrP2cmTELGnaysvLc/m+4QlomkZtbS3mzZtnt/E/SfPL5XJMTk56zDmBYRj84he/QFBQEP70pz855bV1Oh2uuOIK6PV6mEwm7NixAw899BBUKhV27dqF7u5upKen4/XXX/fJko7/IowZnQ4y+QSAXY0szc3NSEhIcHmB+uTkJFpbW1FSUuKxkaFtbW0wmUxe3fBDZmqTRilz0URSpHM9mHgLGo3Gbg9SSyZNk3ii5gkkhf5v05FNyvC9Rd9DRmTG/75vchIURUEulwMAF4UioqmqowofDH4AHa27UIrCmLAmeQ22ZG5BTNDcHsjOiADzGVIDTD6Dlp99UhtJURRXh2pv+YqnkMlkXFOaeUkCMzGBsddeg6l/AACL4OXLEbZpk8cM/y1HzNpq7TUxMYH6+nq325K5C2cIUkvM0/xqtdptzgkMw+BXv/oVjEYjnn32WafteSQqHB4eDqPRiNWrV+Opp55CVVUVYmNjcf/99+PAgQNQq9V49NFHnfKaPERodJoOEjkjozDnirvT955oaCKTrKKjo3kz491eRCIRIiMjERkZiezsbK6T/4svvgBN00hLS7NpmIK3QSyRioqKnBJFC/ELQVxwHFQ6FWKDYzFpmuTsnMwJDQ1Feno60tPTYTAYuPoxvV6PuLg4DI4OIj0iHeGBF6LScq0c88LnzVmQ2juFyltgGAbNzc0ICAiwKkiBi2sjlUolBgYG0Nzc7FQ/VFcxODiIwcFBLF68+KL6zYnTb8M0MAC/xESAZaH7/Av4p6cjuMC+mmNHsWbtRerZg4ODOdFkfrglLgK+atvlCkEKeCbNzzAMHnroIUxMTODvf/+7U4MwpNEOuHCQNhqNEIlEOHbsGM6cOQMA2Lt3L9asWePLotRhfFaUSiQSh1Io7hKlYrEY4+PjGBgYgFQqdVvqnKR709LSvN5fzhohISGYnJxEfHw80tPTue5yk8nk1E5+T2I+1MBZEWCRSIRdC3bhP+3/wfDEMAL9ArEzeyeiAqevjwsMDOREk9FkxAfnP0CrohXD2mEsCl+E2IhY6FgdksOSp/0d1iA1wL7cwUxKEjIyMmb/AVxch2ruh0pE03QlFZ6gr68PcrkcixcvtiqaTQMDEEdFX/gcikRAQABoivLASi9GLBYjJiaGu/eIaGpoaADLsoiLi0NoaCjOnz+P4uJiry6tmA5XCVJLLJ0TSJq/s7PTaQMSWJbF/v37MTw8jJdeesklhziaprF06VJ0dHTgJz/5CVasWAGZTMZdu6SkJFA8ub/5is+m7xmGgdFo/2xtUgBvPnrUmZjXj5L6Mblc7nJ/TuBC92tzc7PXj9ScDqPRiIaGBkgkEqSmpk4RntbGcEqlUq9KhbIsi56eHqjVahQWFrrEIYJhGWhNWgT7B8NPZPvD+9OhT/FW71sI8w9D+0g7lFol5gfOR65/LlZJViEhIQFxcXGzrpmMffXVdCgpL0pISHBaSQIRTdOVVLib7u5ujI6OorCwcFohoamqgr6hAf4JiWAZBqbBQUTs2ongwtltxzyJwWBAf38/enp6EBgYyJVUeIMFkq24S5DOBknzKxQKqFQqu9L8LMviiSeeQEtLC1555RWXu+qMjIxg27Zt+POf/4zVq1djZGSE+1pMTAzUarVLX9+DCDWl08GyLAwGg90/39/fz6V9nc1MDU3EM5KiKNA0zQlUZ20sw8PD6OnpQVFRkdeP1LQGiQCnp6fPav1FbGIoisLY2BiioqIglUoRGxvL242FZVm0traCpmnk5ubybp1P1j4JP7Efgv0udNoPTAxgc9pmLE9YjvHxcVAUBaVSCX9/f040WR6+zKc0+eI9ajAYUFdXh/nz57ssS0FKKuRyOXQ6nVvrUFmWRWdnJyYnJ5Gfnz/jPcqMj1+oKR0cAlgGwUuWImzLNR6rKbWV0dFRNDc3c3Xq5rWRYWFhnGjyVksvvghSS0ianzRLAeDspsLDw63e2yzL4umnn8bXX3+Nf//73277mzz00EMICwvDc889hzNnziApKQlDQ0NYs2YNWltb3bIGDyCI0ulwVJTONmnJXubSYU9SGDKZDAaDgUs7T/fhm+11u7q6uMgFnw3H7YUYctsTASad/HK5HCqViuvkd4WPob2Ym/5nZmbyLrKro3V4subJC2nP/9aODk4MYlvmtosmQJlnB8jhKz4+HmNjYxgYGEBxcTFvUtDOhNTIunPsJKlDlcvlGBsbQ2RkJOfR6ewUJmnaIocmW+5R1mQCrVZD5O8PcXQ07+5rS8x9Vi0PTSzLYnx83Oq0NG+ZysVXQWoNg8HA3dskzT82NoaCggKEhoaCZVkcOnQIH3zwASoqKlz6TJHL5QgICEB0dDS0Wi2uvvpq/PKXv8QHH3wAiUTCNTqpVCo89thjLluHhxFE6XQ4KkopisLo6CgWLFjg1DXZ29BETM0pisLExAQ3CcaWyAfDMGhqaoK/vz8WLlzIu+iaMyDNCM4YyWjeya9UKhEYGDij/ZE7MBgM3JQtV5WUOIJsUoZX217FgGYAXZouzA+bD0mIBNFB0bgl7xaEBUwf6SfDEXp6eqDVajFv3jwkJiZ6VUmFLZAaWU+aqpM6VLlcDqVS6dQ6VJZl0dzcDD8/P5+clAb8b/SrrT6rZIKXXC73CucEbxKklpDAwh//+EdUV1dj3rx5SE9PR39/P06ePOmycjhCfX099u7dC5qmwTAMdu7cid/+9rdQKpXYuXMnent7kZqaivLycp8bO2uGIEpnQq/X2/2zSqUSFEUhNzfXKWtx5oQmy7TzTAbyRMwkJCRg/vz5Dr0uX+nr6+M8Vl2RmjG3PyKzy6VSqdtSy8SQOysrC/Hx8W55zbnyl4a/YMI4geigaMgn5ejR9OC7ad/F+tT1CA+YuSOZZVl0dHRAp9MhJyeH854lJRV87y63BWLbxbcaWWfVoZKDb3BwMLKysngpuBxFqVSio6PjIp9VW7EWsY6Li+NNNoYI0qSkJK+fdscwDA4ePIijR48iPDwcNE1j48aN2LJlC4qKinzy/uQJgiidCUdE6ejoKPr6+lDgBGsScnJyhd2TpYF8REQEl3bW6XRoaGhAdna221KF7oSkCg0Gw6y1a86CRD4oipoyJ96ekgpbcKQkwV3QLI2Hv3oYiaEX6iNFIhFkkzJsz96O3JiZD3XEEolE8c2voXl3uUqlQkhICCeavKlWj0TXnBHFdyWWdahkVnz0LCl1hmGmDDbwRchc98WLFzslW2I+VUqpVHJTpazVWLsDXxKkAHD48GG8+uqrOHHiBMLCwqBSqfDGG2+guroaTU1NWLNmDZ588klBnDofQZTOhMFgwCzvb1rGx8c5qw97cfeEJvKgoygKMpkMRqMR6enpSElJ8apN3BZIfWVYWJjHIjOkk59MJ3F2J7+5BymfxQwAPFv/LOoUddCatPAT+yE+JB6/WPwLJIRO32xG0zTq6+sRHR0965Qmcx9DuVw+ZWwkn5uhiJgpLi72iNiwF5KNkcvlGB0dnbYOlaZp1NXVIT4+3mczMXK5HF1dXSgpKXFZ+Y5ljTVJ80dERLj82eZrgrSiogL/+Mc/cPLkSatZCaPRiMbGRiy2cUStwJwQROlMOCJKtVotmpubsWTJErt+nghSmqYhFovdKpr6+/sxODiI7OxsrnknICCA8zf09gYS0r08b9483tRXOruTf3Bw0Ksafl5qfglv9bwFP7EfTIwJSWFJ+PXyX0MSLLH6/WRKk701ssSlQi6Xw2QycTXWropY28Pw8DA3Scwb/obTYVmHGhQUxJVUNDc3+4yYsQZFUejp6bloEpUrMRqNXJp/fHwc0dHRnN2Us0tYfE2QHj9+HH/+859x8uRJj9Vtf8sRROlMOCJKjUYjampqcMkll8z5Zx1paHIEks7W6/XIz8+f8gCzrIskjTt8jjJZgzSLLFiwABKJdcHjaViW5UoqVCoVwsLCIJVKbfLnZFl2ir+jN9RRsiyLh79+GJJgCQyMAQHiACi0CmzP3o782PyLvp90oGdmZjqlRpZs4qQJ0Dzt7KmmPlLnXFxczIt6QWcyMTEBmUyG7u5uBAUFYd68eU6fvAMALE2DnZyEKCQEIg9cQ3KocKcgtcTcFUStVnMlLM4YxelrgvSNN97A448/jlOnTvlyIxHfEcaMzoRIJLJblNo70clTgtRkMqGxsRERERFWO1/Nx0Lq9XpQFIWmpiaXeKG6CrVajZaWFt6P8xOJRNwkGGIRQyIuJGIdHx9/UbMEwzBobW0Fy7IoKiryGpcEkUiEiIAI6Gk9wgLCLmQJwCDY7+J0NRnJmJub67RIRkBAABITE5GYmAiGYaBSqSCTydDa2jqlxtodAp8cKsbGxlBSUuIVh4q54u/vD4VCgcLCQkRFRXElCmQYhS11qLNhGh7G2OHDYEbHIAoKQsSunQh0sj3fTAwNDWFgYMDqaFR3IhaLERsbi9jYWLAsi8nJSaeM4vQ1Qfr222/j0UcfFQSpD+DTkVKj0ejQvPNPP/0Ul156qc3f78qGppnQ6XSor6/H/Pnz52zjQex4KIqCTqfjGnfcUcs0F8hIzaKiIq+qzbOEbCpyuRwsy3IHgqCgIDQ0NCAyMhIZGRm8uva20DnaicPth0EzNBgwyIvJw/bs7VOmQZEpTe46VJjXWDvb/mi612tvb4fRaOTlYANnoNPpUFtbi4ULF160+VvWodp7IGBNJqifehqs0Qi/6GgwExNgdVrE3H03xG64bwYHBzE0NMT7QwXxsZbL5dBqtdwoztkyBKQOODEx0ScE6ZkzZ/Db3/4WJ0+enHVgioDLEdL3M+EuUeruhiZzSHe2MyJPNE1zXqjj4+Nc446jUQ9H8GXTf9LJL5PJMDo6itjYWGRlZfGqLnIuKHVKDE8OI9gvGOmR6VMEqUKhQEdHh0enNJk3SolEIu4A5owmMpZlp3gBe+PfbzYmJydRX19vk8+q5YGA1KFayxBYQo+OQv2nJ+FvNu3KJJMh6uabEODiZqr+/n6u7ILPgtQSmqa5qVIjIyMIDw/nGtPMSw98TZB+/PHHuP/++zlfUgGPI4jSmTCZTHal4Am2iFJPClKKotDV1YXCwkKnd2eTNCgZIuCJEZzELkgsFmPRokU+GXkiG31mZiYYhplSF+npA4Gz4GPTlrmpucFg4LqdIyMj53y9iSVSRESEV0a5bYGUXeTn5yMyMnLOP2+ZIZgp7cwajVA99jhEoaEQh4SANRpBK5WIufsu+MXEOOstXURfXx8UCgWKioq8SpBaQoZ/kMY04lQRGxuLtrY2JCQk8KZB1BE+//xz3HPPPThx4oTPOj94IYIonQlXi1JPNjT19PRApVKhsLDQ5UX4lo07ZARnXFycyx7eRqMRDQ0NkEgkSE1N9cmNnqSzLTd6ywNBZGQkdyDwts2yu7sbarWa1xu9yWTiup01Gs2Mwyis/Wx9fb1PWyIR439nlV3YknbWt7ZC85/XAZYFWBZhmzYh5JLlDr/2dPT09HD3qa8dfnU6HSiKQmdnJ/z8/JCYmMjrqVK28M033+DOO+/EsWPHfNYb10sRROlMOEOUrlq1yuoH11OClEQPRSIRcnJy3P4ANR/BqVAoEBISwglUZ4ljrVaL+vp6pKen+2yNkK3pbHMDeaVSibCwMK77ls/es6S+0mAwIC8vz2s2esthFCQNas05wWg0ora2FikpKV43ktFWyJx3V3nlWqtDJWln0cQEGLUa4shI+LmwecXc7cJb7tO5QFL2CQkJSEhI4K43mSplzX+Wz9TV1eFHP/oRqqqqkJ2d7enlCExFEKUzQdM0N9rTHr744gssXbr0os3IUw1NRqMR9fX1iIuL4030kHSWKxQK+Pv7T9tZbivOrJHlKwMDAxgaGkJRUdGc0tnmnfxKpdIp19sVkJGTgYGBWLBgAS/uU3swT4MqFIopzgksyzrV1oqPqFQqtLW12Tzn3VGsTTkiB15XvX5nZycmJibcNhHO3ZgLUsuU/XT+s6683o5y7tw53HLLLSgvL8eiRYs8vRyBixFE6Uw4Kkq/+eYb5Ofncx9QT9aPEn9OPs8/12q1nBcqy7KcWb+tjS1yuRznz5/3iglG9sCyLDo7OzE+Po6CggKHIxOW15t08nvy2pEpTTExMT6XViPXWyaTYXx8HImJiUhLS+O9lZo9yOVydHZ22j3n3RmQOlSFQgGaprnGNGf4oZLPolarRX5+vtcenGZiJkFqDWvXOz4+njeNly0tLfjBD36Aw4cPIz//Yv9jAV4giNKZcFSU1tbWYsGCBQgLC/OoIFWpVGhtbUVBQYHVsWl8xHxGvMlkmnVD6evrg0wmQ3FxMa/T0vbCMAxaWlq4pi1n3z9kbjlFUVzjjrutvcikreTkZJ/thCX1lQsXLuTuca1WyzVKeXOdHkEmk6G3t5dXjWlkpC9FUdxIX3sHJLAsi46ODq60xNv/XtaYqyC1xHyE8sTEBGJiYhAXF+fWRldz2tvbccMNN+CVV15xaPS3OX19fbjxxhsxPDwMsViMH/7wh7j77ruhUqmwa9cudHd3Iz09Ha+//jpiXNhg52MIonQmGIaB0Wi0++cbGhqQlpaGiIgIj9SPAhc6l/v7+1FcXMyrFO1cMN9QyAYulUq55p729nZuCpUvptBMJhMaGhpsmvHurNcjE46ItZerJxxptVrU1dUhOzsbcXFxLnkNTzMyMoLm5mYUFRVNiY7SNM01SpERs6Tb2Vvq9AiDg4MYHBxESUkJb+3XSCMgsT8yr0O1ZWJaW1sbGIZBTk6OIEhtwHyqlEqlQmhoqNOmStlCd3c3du/ejRdffBFLly512u8dGhrC0NAQlixZAo1Gg6VLl+Lo0aN46aWXEBsbi/vvvx8HDhyAWq3Go48+6rTX9XEEUToTjorS5uZmJCQkIDIy0iMd9h0dHZicnHRKqpcvkA2coihoNBrQNI2oqCifFaQGgwG1tbV2DTZwBu7o5HfFlCa+oVQq0d7ePmt9pblTBRkL6exGQFfR19cHuVzuVR6d1upQiR+q5d+JZVm0trYCgEuyFXzA2YLUEpZlOb9fhULB+f0Sey9n09fXh507d+Lvf/87VqxY4fTfb05paSnuvPNO3HnnnThz5gySkpIwNDSENWvWcPeNwKwIonQmHBWlra2tCAwMRHJyslsf0jRNo7GxEaGhocjOzvbJhycRa1FRUaBpeopgkkgkPiFQSR3wggULIJFIPL2cKZ38KpXKKYKJRA/5PvrVEWQyGXp6elBSUjLnxrSJiQmuEZD4RUqlUt41knR3d2NkZMTrLZG0Wi3nh2peFxkWFoaWlhb4+/t7dfPdTBBBKpVKkZKS4pbXJGVDcrkcOp2Oy8pERUU5fB8NDg5ix44deOaZZ7B69Wonrdg63d3duOKKK9DY2IjU1FSMjIxwX4uJiYFarXbp6/sQgiidCZZlYTAY7Po5hmGg0WjQ09MDjUaDmJgYzrvQlQ80vV6P+vp6zJs3zycMjq1hTawRwSSTyaZ4odqSkuMjxIOUr3XAloLJ399/2gjTdJBmmOLiYt6JLGfR39/P1To7eh/qdDqu7pcvjSSk4WdyctLnshXmdZFKpRIhISFYsGCBTf6z3oYnBKm1NUxn7zXXz87w8DB27NiBgwcPYs2aNa5Z8H8ZHx/HlVdeiX379uHaa69FdHS0IErtRxClM2GPKLXW0MQwDNRqNTcO0lXTjUgTxaJFiy6aK+0rqNVqtLS0zCjWzL1QiVUJseLhewoU+J9YKyoq8thIzbliHmFiGGZKY5o1SO2hrzamAf+LHhYWFjo9U2LZSOKJCV7ES9ZkMiE3N9cno4fEniw4OBjR0dEXjeG05j/rbRDHi/j4eI8JUkss7dTMD72zPRMpisL27dtx4MABrF+/3qXrNBqNuOaaa7Bhwwbcc889AC6Udgjpe7sRROlMzFWU2tJhT2rGZDIZ1Go1IiIiuIieIxsXsUMqLCz0SYsZ4EJheV9fH4qKiuYUWSMRPblcDj8/P85qio+NX/39/RgeHvZqsUYm7lAUBZ1Ox0X0SGOaearXW2oP5wKp59br9W4x/rdW9+tqQ3OWZTk3iIULF/qsIG1sbOTGvxKs+c/ONUvAF/goSK2h1Wq5Q5jRaJx2rK9SqcS1116Lhx56CJs2bXLpmliWxd69exEbG4snn3yS+/d7770XEomEa3RSqVR47LHHXLoWH0IQpTMxF1Fqz4QmUmQvk8m4aTukRs/W0zfLsujt7eVmLnurkJkJlmWnRJ0ciUyQiB5FUbzx5gQuvMfz589zaVBfEWs0TXObiUajAQAEBQWhqKjI6yNM1mBZFs3NzS6z7rLl9c0NzYODg7lnirM6nc2jh1lZWT4rSBsaGhAVFTWrX65lHSpxB+GLP+d0eIsgtcR8rO8zzzyDsbExbNmyBatXr8bevXvxwAMPoLS01OXr+Pjjj3H55ZdPmeT1yCOPYMWKFdi5cyd6e3uRmpqK8vJyn81cugBBlM6GXq+f9XtYluX8TO2NipBpOzKZDAqFgttMZko5MwyD1tZWMAyD3Nxcn6tzAv43FpVs8s58j5benCTl7O7NhGzyAQEBPh91AoCAgADOiscZWQK+QN5jWFgYMjMzefF3NK/7FYlEXETP3kMYEWuRkZFTooe+BMMwqK+vR2xsLFJTU+f0s9b8OePj43lXh+qtgtQSk8mE9957D1VVVXjnnXcwb9483H777bjmmmuQmJjo6eUJzB1BlM6GwWDAdO/RlYb4ZBykXC7nxuVJpVIu2mE0GtHQ0MBNvuHDBuhsTCYTtzmkpaW59D2aTKYp5trmXqiuft36+npIJBKkpaW57HU8ibX3SLIEpO43JCSEE0zeGO0njSJkhC8fIWb9crncrgEJRMjExcVh/vz5blix+3HmeyS9BHK5HGq1mqtDlUgkHr3HfUWQEsbHx7Fjxw7ccccdWLZsGU6cOIHq6mro9Xps3LgRW7duRWFhoU/ukT6IIEpnYzpR6s4JTZOTk5xAFYvFiI6OBkVRyMzMREJCgste15PodDrU1dUhPT3d7e/R0guVOCc42zxer9ejrq4OqampPnuqJ1OaUlJSZvRZHR8f5wQTqfv1lho9o9GI2tpar5pEZTkgYbaInslkQl1dHZKSkrzmPc4VV3p0mtehKpVKrnEnLi7Orc2MDMNwhydfOFhMTExg165d2Lt3L/bu3TvlayqVCm+88QZOnDiBQ4cO+awHso8hiNLZsCZKPTkylKIoNDc3IygoaErTjrd0advC2NgYzp07xwszdRLtoCgKIyMjTjOPJ7ZWCxcu9Nl6IzKlaa4+qzqdjjuE0TTN1f3ysYFPr9ejtrYWGRkZkEqlnl6OXViL6JnbqRHRPX/+fJ89PLlbdFurQ42Pj3fpWF9fE6RarRa7d+/Gzp07cdttt3l6OQLOQRCls2E0GsEwDPff9jQ0OQvL7nODwQCKorj58HzevG2FuAgUFRV5vPnIEkvz+NDQ0Dk3pgHfDsN4MqUpLy8PUVFRdv8eo9HI1f3qdDq3lVXYAhHdvnSwsLRT8/f3h1arRWZmps9GSE0mExfp9sTUNKPRyDXu2BK1tgdfE6R6vR7XX389tmzZgjvuuMPjzwIBpyGI0tkwF6XOaGiyB2JQPTY2Nm33Odm8ZTIZ9Ho9J1D53gFqTl9fH2QyGYqKitwyE9kRSGMaaSIJDAzkUs4zrZ2iKHR1dfm0YbxarUZra6vT7cmslVV4qonEWaKbz+h0Opw9exbR0dGYnJzk3CpcNRLSE5AocGpqKi9KoSyj1mFhYVya3946VF8TpAaDATfccAPWrl2Lu+++22v2NwGbEETpbBBRStO0R9L1NE2jqakJgYGBNndme6ppx16ICbdOp/NaOyQyz5nU/VobB9nX1weKonzWugtwn+i23LzJ9Je4uDiX3z9k2pYvR7pJFDgnJ4crobH0n+X7c2U2jEYjampqkJ6ezsvSC/ODr1Kp5MbM2mIgT/A1QWo0GnHTTTdh5cqVuPfee73yvhOYEUGUzobBYIDJZPKIICVNIklJSXZ3SVpGlzwx+WW29Z07dw4hISHIzs7mxZocxXwcJJlupNVqQdO0z41iNGdgYABDQ0NuN/637OR3hTcnQalUor29HcXFxT5Vx23OxMQE6uvrkZ+fzw08sIQ8V+RyOcbGxlw2pc5VGAwGrhY4Pj7e08uxCfJcIQbyZCjFdHWoviZITSYTbrvtNhQUFODXv/61T+wVAhchiNLZ+Otf/4rLL78c6enpbn3Yjo+Po7Gxcc5NIjNBJr/IZDKMjY0hOjoaUqnUYx56zhDdfIc0whiNRgQEBHjMC9WVkOEGo6OjLhmpOVcsJ3hZi1rbA0VR6O7uRnFxMS+ngTkDMqp4LlFghmEwMjICuVwOlUrllJSzKzEYDKipqUF2drbTnq3uxrIO1fJZ7muClKZp3HHHHcjIyMDvfvc7n3l2ClyEIEpngmEY/O1vf0NVVRVGR0exadMmlJWVYcGCBS79UCgUCnR0dKCgoMBl6UGykchksild5RKJxC0ClXSfZ2dnIy4uzuWv5wlIR298fDxSU1OnlFVMTExw6c+oqCivfciyLMsNcMjJyeFdlMw8ak3TNHcoCAsLm9M1HxwcxODgoFePf52N0dFRNDc3O9RkSFLOZAQn3+y9yCFxwYIFPtOcRp7lFEVBrVYjNDQUWq0WiYmJs06j8gYYhsHdd98NiUSCAwcO8O4ZI+BUBFFqKwqFAkePHkVVVRUoisJ3v/tdlJWVITc316mCwhPNPqSrXCaTQaVScZYwrqrPU6vVaGlpQUFBASIiIpz++/mATqdDfX090tLSrDZQ0DTNzSvnQ9TaHsgEo9DQUK8YN0mm7VAUBa1Wy9nwzHYo6OnpgUqlQlFRkcejwK5CpVKhra3N6WUJltZH9h4KnIFOp0NtbS0WLVqEmJgYt762u6BpGjU1NfDz84PRaLSrDpVPMAyDX/ziFwgODsbBgwe95tkoYDeCKLUHtVqNEydOoLKyEr29vVi/fj22bds2ZQbuXGFZFm1tbTAYDMjLy/PY5mdt0g6JdDhjVvnw8DB6e3s5WytfhJRe2Lr5WUY6vGH8JpnSxOcJRjNhWRNp7VDAsizOnz8PrVbr07XACoUC58+fR0lJiUvLEiwPBbGxsYiPj3dLfbu1xi1fg4xHlUgkXMresg6VHMS8oTmNYRj86le/gslkwjPPPOOznz+BKQii1FHGxsZw8uRJVFZWoq2tDevWrUNZWRmWLFli84fIZDKhoaEBUVFRyMjI4M3DgmVZTExMQCaTcbZHCQkJdo2CJHWHIyMj09pa+QLEDsne0ovpDgV8qs8jTSK+MonK8lBAxkEqlUqIxWLk5OTw5jPpbGQyGXp6elBSUuJWGzaSKZDL5RgdHUVkZCQ3gtPZB7HJyUnU19cjNzfXZ+27iCCNjY2d9pBIpnjJ5XJoNBpER0cjPj6el81pDMPgwQcfhFqtxnPPPce79Qm4DEGUOpPJyUmcOnUKlZWVaGxsxJo1a1BaWooVK1ZM+6DVarVcmpfvG7x5A4m/vz8XQZ0tusIwDFpaWgCAl3WHzoJs8M6KAlseCgICArgJXp7ycbV3SpO3QEpZmpqaYDQauVrr2fxnvZGhoSEMDAx4vE6WXHMygjMkJIRrlHL0mtviJODt2CJIrf0MX5vTWJbFww8/jL6+Prz00ku8zRYJuARBlLoKnU6H06dPo7y8HDU1NVi9ejXKyspw6aWXclHCDz74AE899RReeOEFr0spabVabpqUSCTixJKlGCNp3tjYWKSlpflsxKmnpwdKpRJFRUUuiwJPTk5yhwJyzd1ZK0Y6s315g6dpesr9au4/64lr7ir6+/tBURSKi4t5t+mTw69CoXDomhNB6su16/YIUkusNaeR2l933+csy+Lxxx9Ha2srXnnlFac+S2+++WZUV1dDKpWisbERwIVa6l27dqG7uxvp6el4/fXXfbbe2EsQRKk7MBgMePfdd1FRUYHPP/8cK1euRFxcHI4dO4bDhw9j0aJFnl6iQ+j1ek6g0jTNCVSxWIz6+nqfSfNagxj/k1pgd0WBLa+5q0fMkkYYZ09p4hNGo5GzKEtOTr7o6+Say+VymEwmjzbtOEJPTw/UajUv7LtmQ6fTcXWotnhzEsjELV8ecOAMQWoNy2vurjpUlmXx9NNP4+uvv8a///1vp0dsP/zwQ4SHh+PGG2/kROl9992H2NhY3H///Thw4ADUajUeffRRp76uwJwQRKm7MRgMuOOOO/D+++8jMjISRUVFKCsrw3e+8x2f8D40GAyQy+UYHBzE2NgYkpKSkJqa6pMbA+k+97Txv+V8eCKWZtu4beXb4M9J6mSnc0uwxFrTDt/tvci44snJSa9s3CKWauYz4skgEPP3QiL6RUVFPnuAcpUgtcSyDpUMSYiJiXHqgYZlWRw6dAgffPABKioqXFYq093djWuuuYYTpYsWLcKZM2eQlJSEoaEhrFmzBq2trS55bQGbEESpOzEYDPjhD3+IiIgI/OlPf4JIJMLHH3+MiooKvP/++ygsLERZWRnWrVvn1elB4rOal5fHpeK0Wq3TxZInMRqNqK+vh1Qq5ZU5NU3TnFgaHx93eIJXf38/Z1Hm6VozV+FonaylvRcfpxuRiL7RaEReXp7Xf/7ImFmKojAyMsKNmQ0MDERra6tDXqt8x12C1NrrmtehhoaGOqX2l2VZPP/883jzzTdRVVXlUlcWS1EaHR2NkZER7usxMTFQq9Uue32BWRFEqbtQKpXYuXMnysrK8NOf/vSirzMMg88//xwVFRV45513sHDhQmzbtg3r16/3qihjf38/hoeHL/JZJWJJJpN5vXG8TqdDXV0dMjIyeDkzm0AmeFEUhdHR0TmJJZZl0dXVBY1Gg4KCAt6nee2F1B3m5eU5pTPbcuMmnfxxcXEec5xgWRYtLS0QiURYtGiR133eZoM4VpBnT1RUFBITE21qwvQ2PCVILSFNmKTeWiwWc36ocz0MvPzyyzhy5AiOHTvm8mCMIEp5jyBK3cXjjz+O/Px8bNq0adbvZRgGZ8+eRXl5Od566y2kp6dj69at2LRpE28bTFiWRUdHB+fpOJOIIZElmUwGjUbDpeFiYmJ4v2GS1GBubq5XNaexLMvZHs02IMF8SpOzh0PwibGxMZw7d85ldYcsy0Kj0XD2XoGBgW7v5GcYBk1NTQgODvaKAQf2MjIygpaWFhQXF4NlWa5RimVZTix5eyqfYRg0NDQgJiaGd97Aer2eE6gGg8HmOtTXXnsNr732Gk6cOOGWv4+Qvuc9gijlO+RBVF5ejjfeeAOJiYnYunUrrrnmGt50CdI0jXPnztlVW0nScDKZbM7RPHfjK80+M3mh+vn5obGxEWFhYcjMzPRZEeOqCUYzYemeQJrTXPX65NkRGRmJjIwMl7wGHyB/y5KSkotSv6TGnaIo6PV6LkPjDebx5pC/ZXR0NNLS0jy9nBmxVodKJqeZH8YqKirw/PPP4+TJk27LBlqK0nvvvRcSiYRrdFKpVHjsscfcshYBqwii1JtgWRbNzc2oqKhAdXU1oqOjUVpaimuuuQbx8fEeWZPBYEB9fT0SExORkpLi0O9iWZarE+PbZCMyicoXm33Gx8c5sUSadhYtWuRz75NAURS6urpcPsFoJkhkybyrXCqVIjw83CliiVhbSSQS3kXVnIlSqURHR4dNf0syxYuiKN6bx5vjTYLUEoZhMDo6CoqicOONN0IikWDTpk2IiorCK6+8gpMnT7ptoMGePXtw5swZKBQKJCQk4KGHHkJZWRl27tyJ3t5epKamory8HLGxsW5Zj4BVBFHqrZB0eUVFBY4fP46QkBBs3boVW7duRUJCgluiABMTE2hoaEB2djbi4uKc+rtJNE8mk0GpVCIsLAwJCQmQSCRurc1jWXbK7HNfnUSl1+s5OySGYSCXy8GyLGfv5c2Nd+YMDg5iYGAAJSUlvGncIl3lFEVhYmLC4eY0k8mEuro6JCYmWrW28hXIeNTFixfPuRzCmnk8OQDz5b4AvFuQWsIwDGpra3Ho0CGcPn2a65soLS1Fdna2p5cnwA8EUeoLkBGelZWVOHr0KMRiMbZs2YKysjLMmzfPJQJ1ZGQEzc3NbjGmJubOZLJRcHAwV5vnyg2EZVm0tbXBZDIhNzeX19EURyBjGBcuXDglSmAezfNmX05Cb28vFAoFLw3jCZbjN0nqUyKR2HT/GY1G1NbWYv78+T7rDQwAcrmci3Y7Wp9Lni+knMXf35+rQ3VlJ/hs+JIgJbz99tt4+OGHcerUKZhMJlRXV+PYsWMYHBzE1VdfjbKyMqxYscLTyxTwHIIo9TVYlsXAwAAqKytx5MgRGAwGbNmyBaWlpU6bqERS2c4apzlXzNPNrhq9SdM0V1vpyw0ipNlntilNlr6c3lSbR/w5JyYmUFBQ4DWHC8vmNBLNm66Tn3itZmRkeKycxx1QFIWenh6XRbu1Wi3XtEMGU5BGKXfd674oSM+cOYPf/va3OHny5EVewBMTE3j77bdx7tw57Nu3z0MrFOABgij1ZViWhUwmQ1VVFaqqqjA2NobNmzejrKzMLrN3EpFVq9W8SWWbN4+IxWIuguqIWCaTfZxRJ8tnSIPIXD0dLWvz+Oye4CtOAubRPIVCwR3GiO0RsSnLzs62y2vVWxgeHkZfX5/byi/IYArzemtXW9n5oiD96KOP8MADD+DkyZNISkry9HIE+IsgSr9NyOVyHD16FFVVVZDL5di4cSNKS0tt2qwZhkFLSwsAICcnh5fRJp1Ox43etLceUqvVor6+HpmZmT4dbZLJZOjp6XG4ccvSPSEyMpKrzfP0PULskIKCgjw6ccsVTE5OTonm6fV6LFy40KdT9kNDQ1w9sCcOxJZDEsi9Hhsb67RyECJIo6KikJ6e7pTf6Wk+//xz3HPPPaiurvbpQ76AUxBE6bcVtVqN48ePo7KyEn19fbj66quxbds2q+lNtVqNr7/+GtnZ2UhPT/eKzd1gMHAC1WQy2TQbnniQOstIna/09fWBoigUFxc7dXOfzgvV3c1pwAUBQaJNvrK5W2NiYgK1tbVISEiARqOBwWBweic/HxgcHMTQ0BBKSkp4UQ/MsizXVa5SqRASEsKl+e2N4PqiIP3666/x05/+FMePH/eZqK+ASxFEqcCFusLq6mpUVlaio6MD69atQ1lZGRYvXozu7m7s3LkTP//5z7Fnzx5PL9UuSApOJpNBr9dzAtV801YqlWhvb/fp8YTurK00N453Z3Ma8L/u84SEBJ+OzJBDlLn5v7M7+flAf38/d4jigyC1xHK6kZ+fHydQbc3SMAyDxsZGREZG+owgra2txe23346qqiqhu17AVgRRKjCV8fFxvPHGG6ioqMDZs2eh1+vxs5/9DLfddhsvN4S5QjZtmUzGNeyIxWIolUqndPLyFeJxKxaLPTJqcmJigqv99fPz40ornO0TSpp90tLSLmqm8CVGR0fR3Nw84yAHyzGzrkg3u5q+vj4oFAoUFRV5zZp1Oh0nUE0mE9cUOF3k2hcFaWNjI2699VaUl5dj0aJFnl6OgPcgiFIB67z11lu4//77cdttt+Gzzz5DTU0NLr/8cpSWluLSSy/lRZOTo5hMJjQ3N0OtViMgIMAnokrWIE4CERERyMjI8Ph7I93NpPaXRK4djVDrdDrU1ta6xDeXT6jVarS2ts5pGpVlujk0NBTx8fGIi4vjlS+nOT09PVxTpafrk+3FaDRyTYEkch0fH4/o6GiIxWKfFKTNzc246aabcPjwYeTn53t6OQLehSBKBS7mueeew7/+9S9UVFRwzT56vR7vvvsuKioq8OWXX2LlypUoKyvD5ZdfzttNbSZYlkVLSwtYlkVOTg6AC93oMpkMY2NjiI6O5jrKvXVDBC5sivX19bxNZZuPgXSkHpIMcsjJyUF0dLTrFuxhiGG8I9OorPlymnfy84Hu7m6Mjo6isLDQqz9/5pDItVwux8jICMLDw6HVahEXF4fMzExPL88ptLe344YbbsC//vUvFBUVeXo5At6HIEoF/gfLsti3bx86Ojrw8ssvTxuFMRqN+OCDD1BRUYGPPvoIy5YtQ1lZGdasWcObTW0mZosckmkvMpkMIyMjvOoonwtkSlN6ejqkUqmnlzMrxAtVLpdjcnLSZi9U4rXqjkEOnoSiKHR3dzu9zESr1XKlFc6MXNsLqXvOz8/3qs/bXKBpGrW1tWBZFiaTCUFBQdzBwFtLiLq6urBnzx689NJLWLJkiaeXI+CdCKLUlTzxxBO49957IZfLuXTi/v378fzzz8PPzw9PP/00NmzY4OFV/g+WZXH8+HFs2bLF5s3AZDLh448/RkVFBc6cOYOioiKUlZVh7dq1vBxNaTAYUFdXh3nz5tk0gpGkPWUy2ZSO8ri4OF7XuJHI4aJFixATE+Pp5cyZ6bxQSdqTQFLZvtygBvzPDqm4uNilmQnzyLVer+ci1xERES4v+2BZFufPn4dOp0N+fr7Hy0xchbWUvXmjlEgk4q67t9zTvb292LVrF5577jlccsklnl6OgPciiFJX0dfXh1tvvRUtLS345ptvEBcXh6amJuzZswdffvklBgcHsW7dOrS1tfFa3MwFmqbx+eefo6KiAu+++y4WLVqEsrIyXH311TNaMbkLrVbLGYzbU3PIsizGxsa4tGdISAgX3eBTja2vRQ6JFypFUVMi1wzDoLu7G8XFxR4dB+lqPNV9bjKZuIPB+Pj4tAcDZ8CyLDo6OmA0Gr16yMFsEEFKsjTW0Ov1nIOCwWCARCJBfHw8b6enDQwM4LrrrsMzzzyD1atXe3o5At6NIEpdxY4dO/Cb3/wGpaWl+PrrrxEXF4f9+/cDAB544AEAwIYNG/Dggw9i1apVnlyqS2AYBt988w3Ky8tx+vRpZGRkYOvWrdi4ceOM4yxdBRFqzvIgJTYwMpkMCoUCgYGBSEhIcIvl0UwQa6u5NMF4EyRy3dXVBbVajdjYWCQmJk47etPbIc0+hYWFHj28WjsYxMfHQyKROLwulmXR1tYGhmGQk5PDS+HlDGwRpJaQg4FcLodGo+Fdrfvw8DB27NiBgwcPYs2aNZ5ejoD34/CH3/d2ASdw/PhxJCcno7i4eMq/DwwMYOXKldx/p6SkYGBgwN3LcwtisRjLly/H8uXLceDAAdTX16OiogKbNm3CvHnzsHXrVmzevNktqWWFQoGOjg4UFxc7LR0mEokQHh6O8PBwZGVlcZZHNTU1HmscGR4eRm9vL5YsWeK1dWmzIRKJoNFowLIsLr/8cm6KV09PDwIDA72+Lo/Asiy6urowPj7Oi+5zsVgMiUQCiUTCHQzkcjk6Ozu5jIE9nfxkDKxIJBIEqRX8/f2RkJCAhIQErtadoii0tbUhPDycc1DwxIGMoihcd911ePTRR10qSN98803cfffdoGkat956K+6//36XvZaA9/OtFaXr1q3D8PDwRf/+8MMP45FHHsHp06cv+pq1qLKvPoTNEYvFKCkpQUlJCX7/+9+jqakJFRUV2LZtG2JiYlBaWoprrrnGJTY+g4ODGBgYcLlQCwsLQ0ZGBjIyMrjGkfr6eohEIs6T05Up5t7eXigUCixZssQnI4bA/4SaRqNBSUkJxGIxAgICEBERwR0M5HI56urqIBaLuYYdb0vtsyyL9vZ2GI1GFBYW8u4ZIRKJEB0djejoaGRnZ085kBEP2vj4+FmvO/HO9ff3x4IFC3j3Pp2FvYLUErFYjNjYWMTGxnLDKeRyOXp6ehAQEODWg7BSqcR1112H3//+91i/fr3LXoemafzkJz/B22+/jZSUFCxfvhxbt25FXl6ey15TwLsR0vcWNDQ0YO3atVxErr+/H/PmzcOXX36JF198EcC3I31vC2TzraiowIkTJxASEoLS0lJs2bIFCQkJDm1SRMCMjY15NPWp1+u5cac0TXMC1VkRW9IcotVqfbpbmaR4TSYTcnNzZ32fxMCcoigwDMM1jvChtnkmiFWZSCTyyJADRyEetHK5fMbrzrIsmpqaEBQUhKysLK97n7bCMAzOnTuH8PBwhwTpbFh6/7ryfler1bj22muxb98+bN261em/35zPPvsMDz74IN566y0AuKgEzh5YlvXZ+80HEGpKXU16ejpXU3ru3Dlcf/31XKPT2rVr0d7e7jONTo5ARGRlZSWOHj0Kf39/bNmyBWVlZUhKSprTQ4RhGG5j51NK0NKT03zcqT0wDIPm5mb4+fl5pYCxFfI+AwIC7IqoGQwGbooXn2fDMwyDpqYmBAcH+4RQI9edoijodDrExcUhPj4e4eHhaGpqQmhoKDIzM73+fU6HuwSpJeS6y+VybmpdfHw8oqKiHL7Wo6Oj2L59O37+859j+/btTlrx9FRUVODNN9/EP/7xDwDAK6+8gi+++ALPPPOM3b9zcnJySlBAEKm8QqgpdSf5+fnYuXMn8vLy4O/vj2effVYQpP9FJBIhMzMT9957L37xi1+gv78flZWVuOWWW2AymbBlyxaUlpYiNTV1xgcITdOor69HdHQ00tPTefWwCQwMRHJyMpKTkzlPThLlnKv1Dk3TaGhoQFRUFO/epzMhnrLEPsee9xkYGIh58+Zh3rx53JjZrq4uTExMcF6oztiwHYFhGDQ0NCAyMtKtAsaVmF93mqahUCi4MpOwsDAkJyf7rCDwlCAFLr7uKpUKAwMDaG5uRlRUFOLj4+0aNavRaLBr1y789Kc/dYsgBZxb8sayLFd2cMMNN0AikaC0tBQikchn78NvI0KkVMClsCyL4eFhVFVVoaqqCuPj49i8eTNKS0uRnZ095UEyODiIf//739i9ezfmzZvnwVXPDbJhy2Qym4SS0WhEXV0dkpKSbPJa9VZMJhPq6uoglUoxf/58p/9+smFTFOXRKV7kICWRSJCamuq213U35sI7MjKS6+SPiIjghlP4wiHdk4J0JliWxcjICORyOVQq1Zwa1CYmJrBr1y784Ac/wI033uimFbsmff/FF1+gq6sL5eXlCA8Px8svvwxAiJjyBCF9L+BdyOVyHD16FJWVlVAqldi4cSNKS0tB0zS+//3v4//+7/+wbds2Ty/TbohQkslkU0zjY2JiIBKJoNPpUFdXh4yMDK+Y0mQvZMjB/PnzkZiY6PLXM+9sVqvVbhNKRHgnJib69AGDYRjU19cjNjZ2ivC29P4NDg7mGna8dXxxY2MjwsLCeD06lFjaURQFhUIBPz+/aRsDtVotdu3ahd27d+PWW2916zpNJhMWLlyId999F8nJyVi+fDlee+015OfnO/y7DQYDNmzYAIlEgoqKCgCCMOUBgigV8F5UKhWOHz+O559/Hi0tLdixYwd+8IMf+EzDD/GGlMlkGB0dRVhYGMbGxpCbmwuJROLp5bkMIrwzMzMRHx/v9tefbkiCPZZHM2E0GlFbW+s24e0pSCQ4Li5u1oj3+Pg41yg1l05+PuAtgtQapDFQLpfj+eefR2hoKHbs2IHCwkJ8//vfx9atW3HHHXd4RLCdOnUKP/vZz0DTNG6++Wbs27fP7t81MDCAmJiYKTWla9euRVxcHP7zn/84Y7kCjiGIUgHv5uTJk3jwwQfxwgsvoL6+HpWVlejs7MS6detQVlbGWQd5OyMjI2hsbERUVBTGx8d9LuVJmJycRH19PW/Go1oOSSDWO1Kp1CGLMYPBgNraWqSnp/t0xJumadTV1SEhIWHOkWDiQSuXy0HTNOLj47lGKb7hzYLUErlcjqqqKlRXV6O5uRkLFy7Egw8+iNWrV3u13dy//vUvHDp0CDqdDn/605+wbNkyhISEgGVZXH/99SgtLcXu3bs9vcxvO4IoFfBe/vGPf+DVV19FZWUlYmNjuX8fHx/HqVOnUFFRgZaWFnznO99BaWkpli9f7pUCztz8nzxEx8bGIJPJoFQqERYWhoSEBEgkEq/eNDQaDRobG3k9HnVycpITSsSDNj4+fk7Ts0gkODs726cj3qQ0ISkpyeEab6PRyDlX6HQ6ru6aD6M3fUmQEoxGI2666SYsW7YMixcvxrFjx/Dpp59i6dKlKCsrw/r1651ma+dKSDpeo9Hg+uuvx9NPP4233noLVVVVuOuuu7Bu3ToEBwejoqICAwMDuPvuuz295G87gij9tnLvvffixIkTCAwMRFZWFl588UVER0cDuFBM/vzzz8PPzw9PP/00NmzY4NnFWuHxxx/HF198gVdeeWVGQaDVavHWW2+hsrISNTU1uOKKK1BaWopVq1Z5hYAbGhpCf38/iouLrUbmWJbF+Pg4F8nz1pq8kZERtLS0oLCwkPdeogRLD1pSkzfT+rVaLerq6ngTCXYVJpMJtbW1SE5ORlJSklN/N03TUCqVoCjK46M3WZbFuXPnOHsrX8BkMuG2225DYWEh9u3bx4l+hmHw1Vdf4ejRozh9+jQqKip41cg1HadPn0Zvby8+/PBD/POf/wQAvPTSS3jttddw6623YufOnejs7MQf//hH/Pa3v0VCQoKHV/ytRhCl31ZOnz6Nq666Cv7+/vjlL38JAHj00UfR1NSEPXv2cF6q69atQ1tbG+8ijOfPn0d6evqc1qXX6/HOO++goqICX331FVatWoWysjKsXr2alwKup6cHSqUSRUVFNgvo8fFxLpLnrFSzqyHWWMXFxV5RO2gNy0ieNYuviYkJ1NfXIy8vD1FRUR5esesgtbKpqaku3+CtNaiR0ZuufmYRQRoSEoKsrCyXvpa7oGkad9xxBzIyMvC73/1uxig0n5uCyNpqa2txww03YP369Xjvvfewfv16PP744wCAF198Ea+99hoOHz6MuLg4qNVqnz4oegmCKBUAjhw5goqKCrz66qsXWW746tQpo9GIM2fOoKKiAp988gmWLVuGsrIyrFmzxuMCjmVZdHR0QKfTOdS0ZZ5qFovFvGwaGR4eRm9vL0pKSjx+3Z0FsfiiKArj4+OIjY1FeHg4enp6UFRUxMuaSGdhNBpRU1PjkVpZUtYil8unZA3i4uKcfm/5oiBlGAZ33XUX4uPjsX//fq+vxa+pqcGhQ4dw1VVXYdeuXfjmm2/wzDPPIC4ujhOmSqUSEomE1wL7W4bDfwTvvmsFAAAvvPACNm7cCOBCd6J5h2xKSgoGBgY8tTSXERAQgPXr1+Nvf/sbamtr8YMf/ABvv/02Vq9ejdtuuw0nT56ETqdz+7rIVB+GYVBQUODQxhAaGor09HQsX74c+fn5XO3bV199hZ6eHmi1WieufO709/djYGAAS5Ys8RlBCgB+fn5ISEhAYWEhVqxYgeDgYLS3t4NlWc48nmEYTy/T6RgMBtTU1HjMrkwkEiEqKgrZ2dlYuXIlFixYAL1ej7q6OnzzzTfo7e11yj3vq4L05z//OSIjI71akJIgGcMwGB0dxccff4yamhoYjUYsXrwYd999N/r6+rjaURIZFQSp78D/orxvMevWrcPw8PBF//7www+jtLSU+//+/v743ve+B8C5EzS8BX9/f6xZswZr1qwBTdP47LPPUFlZid///vfIycnhCvtdXetIrHNiYmKQlpbm1OseHByM1NRUpKamQq/XQy6Xo7m5GSaTyaZaSGfT1dWF0dFRlJSU8K40xJmMjo5iaGiIE6ck1dze3u5TDgp6vR61tbW8at4KCwtDRkYGMjIyOMujpqYm0DQ9ZTb8XD5nvipIH3jgAfj5+eHgwYNeK0gZhoFYLMbZs2dRXl6O/fv34+DBg9i/fz/Ky8uxc+dOlJSU4IEHHuDKtbz1vQpMjyBKecw777wz49dffvllVFdX49133+UezCkpKejr6+O+p7+/36umIzmKn58fVq9ejdWrV4NhGHz99deoqKjAo48+iqysLGzduhUbN250enc4qcNLTk52+fUOCgpCSkoKUlJSuFrItrY26PV6TqC6ai48KU3Q6/UoKiry6U2B1MouXrwYQUFBAC5EZmJiYqZ4oXZ2drrMC9UdEEG6YMGCKS4YfCI4OBjz58/H/PnzLxrxa+tseF8VpA8++CC0Wi3+/ve/e+3nkQjSuro67N69mytD27BhA2iaxsGDBzE5OYm9e/eiuLjYw6sVcCVCTamX8uabb+Kee+7BBx98MMWg/Ny5c7j++uu5Rqe1a9eivb3d6yM5jsIwDOrq6lBRUYE33ngDycnJ2Lp1KzZv3sy5FtiLp83iCWQuvEwm4zZrZ9rusCyL5uZm+Pn5YeHChT4dgacoCt3d3TbXypIGNYVCAX9/f67+l4hZvqLT6VBbW+u1bgKkk18ul884atYXBSnLsnj44YfR39+PF1980Suf8QaDgft80TSNP/zhD5BIJLjzzjthMpng5+cHkUiEd955B7/5zW/w73//G2lpaR5etcAMCI1O31ays7Oh1+u5VNvKlStx6NAhABdS+i+88AL8/f3x5JNPcvWmAhcgG1RFRQVOnjyJ2NhYlJWV4Zprrplz6nJ8fBwNDQ3Izc11WNw6E7JZy2QyrllHKpUiOjraLjHJMAwaGxu5eeC+LEiHhoYwMDCA4uJiu6KeWq2Wa1BjWZZzUJiLF6o7IPZWOTk5vLp37cWykz88PBxSqRSxsbFobW31OUH6+OOPo62tDf/85z+9wh7PGg8//DAmJibwyCOPAACeeOIJtLS04M9//jP3eamrq0N6ejpYlvWJ+9THEUSpgIAjsCyLtrY2VFRUoLq6GmFhYdi6dSu2bNkCqVQ6o/gaGRlBc3MzCgsLed2RzTAMVCoVZDLZjNGk6SBTfeLi4qbMPfdF+vv7QVEUiouLnRJ5IvW/FEXBZDLZXQvpbMjkrdzcXJ+0t2JZFhqNBhRFob+/HwEBAUhPT0d8fLzXN+WxLIunnnoKZ8+exeHDh72uXMSc9vZ2VFRU4Oc//zkCAwPx3nvv4ejRo9iyZQuuvPJKKJVK7Ny5E4888gguv/xyTy9XYHYEUSog4CxYlkVnZycqKytx7NgxBAQEYMuWLSgtLUVSUtIUEfH666/DaDRi+/btvLJomg0STZLJZBgZGUFkZCTXrGNNoJJa2ZSUFKebqPONnp4eqFQqFBUVuSQVSmohKYpySXmFrRC/1fz8fERGRrrtdd0Ny7JoampCcHAwEhMTudnwIpGIq73mW/R6NliWxV//+ld89NFHKC8v93qBTVEU9u7di61bt+KOO+4AAC4CTOrkf/CDH+D222/38EoFbEQQpQICroBlWfT396OyshJHjhyByWTCli1bUFZWhuPHj+PVV1/FkSNHkJiY6Oml2g3LshgdHYVMJoNKpeLSncS4nDTAeLpW1tWwLIuuri6Mj487bONlK5ZTjWJiYrjotSsFKik34fMoWGdABGlQUBCysrKmXFMyyUsul3PR6/j4eJc1BzoLlmXx/PPP480338SRI0d4X688G8RbtK6uDqWlpfj1r3+NW2+9FcCFEprh4WEEBASgoKDAwysVmAOCKBUQcDUsy2J4eBhVVVV46qmnwLIs9uzZgx07dly04Xkr5t3kSqUSgYGBmJiYQE5Ojs8L0o6ODhgMBuTl5Xnkb8kwDNRqNWQyGUZHRxEVFcXVQjpTIBNByvdyE0eZSZBaYhm9JrXXs3Xye4KXX34ZR44cwbFjx7wuwksYHh5GYmIiJ0hJ1/0HH3yAH//4x7jllltwzz33eHqZAvYjiFIBAXfAMAzuu+8+yOVyPPLIIzh58iQqKyuhUqmwadMmlJaWYtGiRbzbyOxBo9Ggrq4OsbGx0Gg0CAwMREJCAuLj4726fs0SlmXR2toKALz527EsyzXrmEevJRKJQ80sGo0GjY2NKCoqcqufrbuZiyC1hKZpqFQqUBSFsbExlx0O7OHVV1/F4cOHUV1djdDQUJe8Rnl5OR588EE0Nzfjyy+/xLJly7iv7d+/H88//zz8/Pzw9NNPY8OGDXP+/X/+859x8uRJvPnmm1a/3tDQgD179mD37t2QSqX4wQ9+4PXlCd9CBFEqIOBqjEYjbrnlFiQlJeHAgQNTNjqVSoVjx46hsrISg4OD2LBhA7Zt24a8vDyPb2T2QJq3zMXLxMQEl+70JrujmWAYBs3NzQgMDER2djYvBKkl5s065mM353o4GBsbQ1NTE4qKilwmaPiAI4LUEjJRiBwOwsLCuNIWd3e6l5eX44UXXsDJkyddGuFubm6GWCzGj370IzzxxBOcKG1qasKePXs4m8F169ahra1tznXX7e3teOSRR/Diiy9e9DUSMZXL5fjkk0/Q0tKC9PR07N692ynvTcBtCKJUgP+8+eabuPvuu0HTNG699Vbcf//9nl6SzbAsi7KyMlx55ZWzppVGR0dx4sQJVFVVobOzE+vXr0dZWRmKi4u9QqAqlUq0t7ejuLh42vQgsTuiKAoikYizO/K2Zq/GxkZEREQgIyPD08uxGfPDgZ+fH3ftZzocjI6Oorm5eca/qS/gTEFq7XdrNBrI5XIoFAoEBgZyjVKujuQdO3YMzz77LE6ePOk2l4Q1a9ZMEaXEyP6BBx4AcMHQ/sEHH8SqVats+n0dHR1IT0+H0WjEJZdcghdffJH73QzDQCQSQSQSgabpKULXZDJ5rdXVtxiHP3jCX1zApdA0jZ/85Cd4++23kZKSguXLl2Pr1q3Iy8vz9NJsQiQS4Y9//COys7Nn/d6oqCh8//vfx/e//31oNBqcOnUKTz31FFpaWnDVVVehtLQUy5cv56VAlclk6OnpmXWOfUhICNLS0pCWlsY1jJw7dw40TXMiic/RODIKViKReJ29lfnYTa1WC7lcjoaGBrAsy4kk82s/MjKClpYWlJSUeNWhYa64UpACF54BkZGRiIyMRFZWFiYnJ0FRFOrq6lzayX/q1Ck8/fTTbhWk1hgYGMDKlSu5/05JScHAwIBNP/vGG2/gzjvvxOLFi7loc2NjI6RSKVJTU6c8Cy0jr4Ig/XYi/NUFXMqXX36J7OxsZGZmAgB2796NY8eOeY0oBWCTILUkIiICu3btwq5du6DVavHWW2/h+eefx1133YUrrrgCpaWlWLVqFS+msAwMDGBoaAiLFy+eU1o4KCiIG/1oMBggl8vR2toKg8EwZdwpXzCZTKirq0NiYiKSk5M9vRyHCAkJQWpqKlJTUy+69nFxcQgODkZvby8WL14sCFInExoaivT0dKSnp3M+tM3NzTAajZwPraOd/G+//TYee+wxnDp1yqmjX9etW4fh4eGL/v3hhx9GaWmp1Z+xlk219b1t3LgRH330EUQiEU6dOoXTp0/j//2//4fCwkKkpqYiLi4OGzZswNq1a4X6UQEAgigVcDEDAwOYP38+998pKSn44osvPLgi9xMSEoKysjKUlZVBr9fjnXfewWuvvYZ77rkHq1atwrZt23DZZZd5pImou7sbarUaixcvdkggBwYGIjk5GcnJyRfNJicbdUREhMdqN33Zb9Xy2nd3d6OtrQ1BQUHo6+vziBeqO/CEILUkKCgIKSkpSElJ4cb8dnV1YWJiwu4pau+//z5+//vf49SpU4iLi3Pqet955505/0xKSgr6+vq4/+7v78e8efNs/nnyvbfccgsSExOh1WpRXl6Ot99+G5988gmam5uFqYMCHIIoFXApjpyyfZGgoCBs3rwZmzdvhtFoxPvvv4+Kigrcd999WL58OVe/6uqoAcuynGh0ds1rQEAAkpKSkJSUBJqmoVAo0N3djYmJCc4w3p2WOwaDAbW1tUhPT4dUKnXLa3qK0dFRqFQqXHbZZfDz84NSqURfX98UL9To6GhelpDMBZZluUY1vtiy+fv7IzExEYmJiWAYBkqlEkNDQ2hpaUFUVBTi4+OnHVJB+Oijj/Cb3/wGJ0+e5M29unXrVlx//fW45557MDg4iPb2dlxyySV2/a61a9fiySefxOTkJK655hpcc801Tl6tgLcjiFIBl+LoKduXCQgIwNVXX42rr74aJpOJm9Kyb98+lJSUoKysDFdddZXT068sy6KlpQUikQgFBQUu3dD9/PyQkJCAhIQEznKnv78fzc3NbjGM1+l0qKurQ3Z2NiQSiUtegy/I5XJ0dXVh8eLF3KGG1Pmae6G2trZyk7xiY2N5UUIyF4ggDQgI4K1zglgsRnx8POLj46fYfHV0dHC1ldHR0VM+25999hl++ctforq62iPR/CNHjuCnP/0p5HI5Nm/ejJKSErz11lvIz8/Hzp07kZeXB39/fzz77LN23TOkw16v1+OTTz4RBKmAVYTuewGXYjKZsHDhQrz77rtITk7G8uXL8dprryE/P9/TS+MtNE3j008/RWVlJd59913k5eWhrKwM69evd7iJiHSeh4WFITMz02MbujsM47VaLerq6rBo0SLExMQ45XfyFZlMht7eXpSUlMxaBkImeZFBCZ60O5or3iBIZ4JlWYyPj4OiKNxxxx0wmUzYvHkz8vPz8dBDD+H48eNIS0vz9DJdyu9+9zvMmzePm94k4FMIllAC/OfUqVP42c9+BpqmcfPNN2Pfvn2eXpLXwDAMvvrqK1RUVOD06dPIzs5GaWkpNmzYMOcxkaTzPDY2llcbH8uyUKvVoCgKarUaERERnGG8vVE8Mt89Ly/Po53L7mB4eBh9fX02CVJLzEUSsTsiXqh8azzxdkFqjebmZvzjH//A0aNHMX/+fOzcuRPbtm1DVlaWp5fmMlQqlVObtwR4hSBKBQS+LTAMg7q6OpSXl+PNN99ESkoKtm7dik2bNiE6OnrGnzUajairq8O8efN4XT5Bxp3KZDIuipeQkDCniUZkepGvz3cHLswIHxgYQElJiVOinBMTE5DL5ZDL5VwKmg8+tL4oSAGgsbERt956K8rLyxETE4MTJ07gyJEjoCgKGzduRFlZGUpKSnzm/Qr4PIIoFRD4NsKyLBobG1FRUYFTp05BIpGgrKwMmzdvvqh2sq+vDydPnsS1117Lm+YJWyBRPJlMxk00SkhIQFxc3LQRQWIWX1hY6NPjNAFgcHAQQ0NDKCkpcUldqE6ng1wuB0VRYBiGq5F093X1VUHa3NyMm266Cf/+978vssjTaDR48803cfz4cRw6dMjn72UBn0EQpQIC33bIDPeKigpUV1cjIiICW7duxZYtW6BWq7Fr1y784Q9/8PrGAvM0Mxl3aj5VR61Wo7W11eenFwEXGgYpikJxcbFbGpWIFypFUZwXqjP8OGfDVwVpW1sbbrzxRvzrX/9CUVGRp5cjIOAsBFEqICDwP1iWRWdnJyoqKnD48GHIZDLccMMN+NGPfoTExESf2dTJVB2SZg4LC+P8Vj2danY1fX19UCgUKCoq8kjnPPHjpCjKpTZfvipIu7q6sGfPHrz00ktYsmSJp5cjIOBMBFEqICBwMV9//TVuu+02PPHEE6ivr8eRI0fAMAy2bNmCsrIypKSk+Mwm39/fj66uLk6MkgiqL0ZLe3p6MDIygsLCQl54jRKbL4qiMDY2hujoaM7my5H1EdsyPz8/LFiwwGfu1d7eXuzatQvPPfec3V6fAgI8RhClAgICU3n//ffxi1/8AlVVVVyXPcuyGBoaQlVVFaqqqqDVarF582aUlpZ61BrKUYaGhtDf3891npOxjxRFwWQycY06vlCT19XVBY1Gg4KCAl4IUksYhuH8OB1xUfBVQTowMIDrrrsOzz77LC677DJPL0dAwBUIolRAwBH6+vpw4403Ynh4GGKxGD/84Q9x9913Q6VSYdeuXeju7kZ6ejpef/11r/C6PH78OB555BEcO3YMCQkJ034fRVE4cuQIqqqqoFaruU7fhQsXeo0I6O/vh0wmQ3FxsdXOc6PRCLlcDplM5tY6SFfQ2dmJiYkJ5Ofn81KQWkJcFIgXakhICGc1NZNLgK8K0uHhYezYsQMHDx7EmjVrPL0cAQFXIYhSAQFHGBoawtDQEJYsWQKNRoOlS5fi6NGjeOmllxAbG4v7778fBw4cgFqtxqOPPurp5c7Kxx9/jIKCglktosxRKpU4duwYqqqqMDQ0hA0bNmDbtm3Izc3lrQDq7e2FUqm0ua6S1EHKZDJotVquDpLvM+HJOFidTof8/Hxer3U6WJbFxMQE56IQGBjIRbDNvVB9VZBSFIXt27fj0Ucfxbp16zy9HAEBVyKIUgEBZ1JaWoo777wTd955J86cOYOkpCQMDQ1hzZo1aG1t9fTyXM7IyAhOnDiBqqoqdHd3Y926ddi2bRuKiop4I1AdTWPTNA2lUgmZTIbx8XHExsZyYx/5JIRYlkVHRweMRiNyc3N5tTZHMG9SE4lEXAS1u7vb5wSpQqHA9u3b8bvf/Q4bN2709HIEBFyNIEoFBJxFd3c3rrjiCjQ2NiI1NRUjIyPc12JiYqBWqz23OA+g0Whw8uRJVFZWorW1FWvXrkVpaSmWLVvmEYFKRJrBYEBeXp5ThAvDMFAqlU5v1HEUlmXR1tYGhmGQk5PjMyLNEuKF2tXVBQBISUlBQkKCT9QAq9VqXHvttdi3bx+2bt3q6eUICLgDQZQKCDiD8fFxXHnlldi3bx+uvfZaREdHf+tFqTlarRZvvvkmKioqUF9fjyuvvBKlpaVYuXKlW2yJiBcrACxatMglIo006shkMoyMjCAyMpJr1HGnQCXvVSQSeVWNrz2QlL1YLEZGRgZnNaXT6bga4IiICK+7BqOjo9i+fTt+/vOfY/v27Z5ejoCAuxBEqYCAoxiNRlxzzTXYsGED7rnnHgAXhM+3MX1vC3q9Hm+//TbKy8vxzTff4NJLL8W2bdtw2WWXOWXUpSUsy6KpqQmBgYFu86tkWRajo6OQyWRQqVQIDw+HVCpFXFycS0U48eb09/f3qTS2NcwFqaX4NplMXASbzyUW1tBoNNixYwd+8pOfYPfu3Z5ejoCAOxFEqYCAI7Asi7179yI2NhZPPvkk9+/33nsvJBIJ1+ikUqnw2GOPeW6hPMVgMOD9999HRUUFPvvsM1xyySUoKyvDFVdcMaWJxV4YhkFjYyPCw8ORkZHhEUFibye5Pa/T1NSEoKAgZGVl8V58OcJcosEMw3BeqKOjo4iKioJUKkVsbCxv6pwJExMT2LlzJ2666SbceOONnl6OgIC7EUSpgIAjfPzxx7j88sunmJE/8sgjWLFiBXbu3Ine3l6kpqaivLwcsbGxHl4tvzGZTPjwww9RUVGBDz/8EIsXL0ZZWRm+853v2DVliaZpNDQ0ICYmhvNb9TQsy04ZdxoYGIiEhATEx8cjICDA7t/LMAzOnTuH0NBQZGVlOXHF/MOR8gSWZaFWqyGXy6FSqez2QnUFWq0Wu3btwp49e3DLLbe47HXuvfdenDhxAoGBgcjKysKLL77IuW3s378fzz//PPz8/PD0009jw4YNLluHgIAVBFEqICDAP2iaxieffILKykq89957yM/PR1lZGdatW4fQ0NBZf95kMqGurg4JCQlISUlxw4rtY2Jigusk9/f35yKoQUFBNv8OEg2OiIhARkaGC1freZxZLztdBDsuLs6hA4I96HQ6XH/99SgtLcXtt9/u0ij36dOncdVVV8Hf3x+//OUvAQCPPvoompqasGfPHnz55ZcYHBzEunXr0NbW5nGxLvCtQhClAgIC/IZhGHz11VcoLy/H22+/jezsbJSVlWHDhg0IDw+/6PsVCgVaWlqQlZWFpKQkD6zYPrRaLSiKAkVRnNWRVCqdMUrMMAwaGhoQFRWF9PR09y3WA7i6gcs8gm3vAcEeDAYDbrjhBqxbtw533XWXW8sujhw5goqKCrz66qvYv38/AOCBBx4AAGzYsAEPPvggVq1a5bb1CHzrcfjmd35XgoCAgIAZYrEYK1aswIoVK8AwDGpra1FeXo6DBw8iNTUVW7duxaZNmxAVFYWhoSGUlpbi//7v/7xKkAJASEgI0tLSkJaWBr1eD4qicO7cOdA0zQlU8ygxwzCoq6uDRCJBamqqB1fuetzhKBAeHo7w8HBkZmZyB4SGhgYA4Mz6Q0JCnPqaRqMRN998M6644gq3C1IAeOGFF7Br1y4AF8aYrly5kvtaSkoKBgYG3LoeAQFHEUSpgICA2xCLxViyZAmWLFmCRx55BI2NjSgvL8eWLVsQGRmJ3t5e3Hfffdi8ebOnl+oQQUFBmD9/PubPnw+DwQC5XI6WlhYYjUbEx8cjLi4OHR0dkEqlvC5PcAaesLiyPCDI5XI0NzfDZDJNGTfrCCaTCbfddhuWLFmCX/ziF059X+vWrcPw8PBF//7www+jtLSU+//+/v743ve+B+DCdbbEl5vlBHwTIX0vICDgcbq7u7Fp0yZcdtllqK+vR1RUFLZu3YotW7YgLi7OZzZXo9EIiqLQ0dEBsViMpKQkr/XitAW+ea4ajUbOC9WRcbM0TeOOO+5AZmYmHnroIbe/r5dffhmHDh3Cu+++y0XfhfS9AA8QakoFBAS8m7a2NuzatQt///vfsXz5cm7ee2VlJY4dO4agoCBs3boVpaWlSEhI8LiwcQTSwDVv3jxIpVIoFArIZDJMTExwAikqKsqr3yPBHQMPHIGMm6UoChqNBrGxsYiPj0dMTMyMa6VpGnfddRekUin279/vdluqN998E/fccw8++OADxMfHc/9+7tw5XH/99Vyj09q1a9He3i40Ogm4E0GUCggIeC8NDQ244YYb8Morr6CwsPCir7Msi97eXlRWVuLo0aNgWRZbtmxBWVkZkpOTeSd0ZsJkMqG2thYpKSlITEyc8jWapqFSqSCTyaDRaBATE8ONO/Wm90ggY1JZluWlILXEVi9UhmFwzz33ICwsDH/84x894pOanZ0NvV4PiUQCAFi5ciUOHToE4EJK/4UXXoC/vz+efPJJbNy40e3rE/hWI4hSAYFvGzRNY9myZUhOTkZ1dTVUKhV27dqF7u5upKen4/XXX0dMTIynl2kT9957L2677TYsXLhw1u9lWRZDQ0OorKxEVVUVdDodrrnmGpSWlnrMWN9WjEYjamtrkZqaioSEhBm/l2EYqNVqyGQy3pvFW8PbBKklLMtiZGQEFEXh1VdfRUNDAxepP3DgAFiWxZ///Gev+FsICLgZQZQKCHzbOHjwIL7++muMjY2huroa9913H2JjY7npU2q1Go8++qinl+lSWJYFRVE4cuQIqqqqMDIygk2bNqG0tJQXtYvmGI1G1NTUID09HVKpdE4/S8ziKYqCWq3mlVm8NbxdkFrCMAw+++wzzs6MZVk8+OCD2Lp1qzBMQ0DgYgRRKiDwbaK/vx979+7Fvn37cPDgQVRXV2PRokU4c+YMkpKSMDQ0hDVr1nC1fN8WlEoljh49iqqqKshkMmzYsAHbtm1Dbm6uR4WRwWBAbW0tMjIyptT/2QMxi5fJZFAqlQgLC0NCQgIkEolTx506sj5fEqQElmXxhz/8AYODg7jvvvtw/PhxnDhxAuHh4di2bRvKysq8zr5MQMBFCKJUQODbxI4dO/DAAw9Ao9HgiSeeQHV1NaKjozEyMsJ9T0xMDNRqtecW6WFGRkZw/PhxVFVVoaenB+vXr8e2bdumjJJ1B3q9HrW1tcjOzubq/5wFGXcqk8mgUCgQHByMhIQEj0wzIuvxVUH62GOPob29Hf/85z+niP/e3l4cPXoUR48exZ49e3Dbbbd5cKUCArxAEKUCAu5maGgIkZGRCAsLc+vrVldX49SpU/jLX/6CM2fOCKLUBjQaDU6ePInKykq0tbVh7dq1KC0txdKlS10qUHU6HWpra7Fw4UK3pHktpxklJCQgPj4egYGBLn9tXxakTz31FM6ePYvDhw/PKPZZlvWZ9y0g4ACCKBUQcCcsy6KyshK//e1vkZOTg61bt2Lbtm2Iiopy+Ws/8MADeOWVV+Dv7w+dToexsTFce+21+Oqrr7716XtbmJycxBtvvIHKyko0NjbiyiuvRGlpKVasWOHU+kwiSBctWuSRhrPJyUlQFAW5XA6xWMyN25xp3Km9EEHKMAxycnJ8RpixLIu//vWv+Oijj1BeXu4WcS8g4AMIolRAwBMYjUa88847qKioQHV1NVavXo2//vWvc25ksRfzSOm9994LiUTCNTqpVCo89thjblmHt6LT6fD222+jvLwcZ8+exWWXXYZt27bh0ksvdag+U6vVoq6uDjk5OYiOjnbegu1Ep9OBoihQFAWWZblxp84Yt+nLgvT555/Hm2++iSNHjiAoKMjTSxIQ8BYcfgh4vjpeQMDLMJlMCAgIwMaNG0HTNHp7e1FQUOD0ukFbuf/++7Fz5048//zzSE1NRXl5uUfW4U0EBwdjy5Yt2LJlCwwGA9577z1UVFTgF7/4BVasWIGysjJcccUVc6rPnJycRH19PXJzc90SObeF4OBgpKamIjU19aJxm2QevD1lKCzLor293ecEKXBhWlJ1dTU3uEFAQMB9CJFSAQE70Ov1uO+++3D69Gm89tprWLx4sdXve+qpp3DppZdi+fLlbl6hgD2YTCZ88MEHqKiowEcffYQlS5agrKwM3/nOd2YUKBMTE2hoaEBeXh4iIyPduGL7MBqNkMvlkMlkMBgMU+bBzyYwiSCladrnBOmrr76Kw4cPo7q6mhvfKSAgYDNC+l5AwN289957ePnll6HT6fD0008jISEBDMNc1Dhz6NAh1NXVYd++fUhJSQHDMAAgmG57CTRN45NPPkFFRQXef/99FBQUoKysDOvWrZuS/j579ixefPFFPPLII4iIiPDgiu3DZDJx405nmwfvy4K0vLwcL7zwAk6ePInw8HBPL0dAwBsRRKmAgDt58cUXcejQIdx444244YYbEBkZOaXzlojTt99+G08++ST+9re/ISUl5aLfI3TrehcMw+CLL75ARUUF3nnnHSxYsABlZWWIi4vDnXfeiRdffBFLly719DIdhsyDl8lkGB8fR2xsLKRSKVcf66uC9OjRo/jLX/6CkydP8qb0QkDACxFEqYCAO2AYBr/97W/xyCOP4OzZsygpKeG+Zk1g7tixA0uXLsUDDzwAAGhra+Psm3bt2jWljo9lWbAsK0RQvQSGYVBTU4NnnnkGx44dwxVXXIGtW7di48aNPiVoGIaBUqkERVEYGxsDcKFGtaioiJfTpOzl5MmTOHjwIE6dOuU143kFBHiKIEoFBNwBy7J488038Ze//AVyuRxpaWkoLS3Ftddee5HVjlKpxJVXXonTp09j3rx5eO+997B//36sXLkSbW1tGB8fx1//+lcAgEQiuajRhGEYiEQin4pE+Rpff/01brvtNrz++uvQarWoqKjAqVOnIJVKUVpaimuuucZnBA5J2U9MTCA4OBgjIyOIjIzkxp1682Hq9OnTeOSRR3Dq1CnExcV5ejkCAt6OIEoFBNyNVqtFdXU1Xn31VSQlJeHXv/41kpOTudR9dXU1fv/73+OLL76ASqXCvn37UFRUhDvuuAMAQFEUpFIpduzYgcDAQCQlJSE9PR233377lG5v8vvGxsYuKhMQ8Byff/457rjjDhw5cgTp6encv7Msi5aWFs4mLCoqihOojo4Y9RQsy6KjowNGo5Eb2cqyLEZHRyGTyaBSqRAeHg6pVIq4uDiviqC+//77+L//+z/uMCEgIOAwgigVEHAX1tLs5s1LNE3Dz88P/+///T9ERUXhwQcfRH19PQ4dOoRbbrkFS5cuhV6vR1BQEGQyGa677jpcddVVuPzyy/HLX/4SBw4cwGeffYbg4GD86Ec/4rq477rrLixfvhw33HCDR963I4yMjODWW29FY2MjRCIRXnjhBSxatAi7du1Cd3c30tPT8frrr3tNVPGrr77C7bffjqNHj2L+/PnTfh8Rc5WVlTh+/DiCg4OxdetWbN26FQkJCV5xuLAmSK19z9jYGCiKglKpREhICGfW74jfq6v56KOP8Ktf/QrV1dXC3HoBAefh8IPNe/MuAgJuRiQScYKUZVkukkn+jUSJFixYgAULFgAAsrKy0N3dDb1eDwCcrdAbb7yBzMxM3HXXXVi7di2uvPJK/PKXv8SVV16Jb775BgcOHOBet6CggNcb/Ezcfffd+O53v4uWlhbU1dUhNzcXBw4cwNq1a9He3o61a9dOea98JzMzEydOnJhRkAIX7pUFCxbg/vvvxyeffIIXXngBNE1j79692LhxI5599lkMDAxglqCAx7BFkAIX3mdUVBQWLFiAFStWIDMzE5OTk/jmm29QU1ODwcFBGI1GN69+Zj777DPcf//9OHbsmCBIBQR4hiBKBQTswFygWrJ48WJ89tlnAICwsDBs2LAB+/btwzPPPIO2tjYAF1KHK1euRGxsLGQyGSYmJvDDH/4QV1xxBTZu3Ij6+nru90mlUnzyySeuf1NOZmxsDB9++CFuueUWAEBgYCCio6Nx7Ngx7N27FwCwd+9eHD161IOrnBsSiQTz5s2b08+IRCKkp6fj5z//OT788EMcPnwYQUFB+OEPf4j169fjySefRFdXF28Eqq2C1BKRSISIiAhkZWVhxYoVWLhwIfR6PWpqanD27Fn09/dzhzNP8fXXX+Oee+7B0aNHrbpiOIvf/OY3KCoqQklJCa6++moMDg5yX9u/fz+ys7OxaNEivPXWWy5bg4CANyKk7wUEnExvby86Ojpw1VVXcf92+vRpvPnmmygoKMD27duxevVqvPLKKygpKcFXX32F3/3ud3j22WeRmpqKm2++GUuXLsWPf/xjiEQi3H777YiMjMRjjz1m1Q+Vr9TW1uKHP/wh8vLyUFdXh6VLl+Kpp55CcnIyRkZGuO+LiYmBWq323EI9BMuyoCgKVVVVqKqqwtjYGDZt2oTS0lIsWLDAIyl+ewXpbGi1Wm7cqUgk4sadWjYJupLa2lqu9CIzM9Olr0XqwAHg6aefRlNTEw4dOoSmpibs2bMHX375JQYHB7Fu3Tq0tbV5VS2ugMAMCOl7AQG+kZqayglScui7+uqrcfDgQdx8881gGAZ33303srOzYTKZ8MknnyA8PBypqakYHR1Fd3c3VqxYwQmCDz74AGVlZQDgFbWIBJPJhLNnz+KOO+5ATU0NwsLCvCpV72pEIhESEhJwxx134O2338bJkyeRkpKCX/3qV7jyyiuxf/9+NDU1uS2C6ipBCgAhISFIS0vD8uXLUVhYCJFIhHPnzuHLL79Ed3c3JicnnfZa1mhsbMTtt9+OiooKlwtSAFOmek1MTHDX8tixY9i9ezeCgoKQkZGB7OxsfPnlly5fj4CAt+CdhWoCAl4C2YyIsBCJRIiJicGtt94K4IJZeVJSEjZt2gTggol3ZGQk19V99uxZaLVaXHrppVN+nzeQkpKClJQUrFixAsAF79YDBw4gISEBQ0NDSEpKwtDQkND5/F/i4uJwyy234JZbbsHIyAiOHz+O3//+9+jr68P69euxbds2FBQUuCRS7kpBaklQUBDmz5+P+fPnw2AwQC6Xo6WlBUajEfHx8dy4U2fR3NyMW2+9Ff/+97+xcOFCp/3e2di3bx/++c9/IioqCu+//z4AYGBgACtXruS+JyUlBQMDA25bk4AA3xEipQICbsDcd9Q88uXn54ddu3ZxnfWhoaHYtGkT4uLiQNM0/vWvf2Hnzp0A/tfp7y0kJiZi/vz5aG1tBQC8++67yMvLw9atW/Hyyy8DAF5++WWUlpZ6cpm8JDo6GjfeeCOOHTuGM2fOoKSkBE888QRWr16N3/zmN/j666+ddj+wLIvz58+7RZBaEhgYiOTkZCxZsgRLlixBSEgIOjo68Pnnn6OjowNjY2MORYrb2tpw00034V//+hfy8vKcuHJg3bp1KCgouOh/x44dAwA8/PDD6Ovrw/e+9z0888wzAGD1vXjTQVNAwNUINaUCAh5mOv/R3//+99DpdLjtttum+GF6E7W1tbj11lthMBiQmZmJF198EQzDYOfOnejt7UVqairKy8sRGxvr6aV6BZOTkzh16hQqKirQ1NSENWvWoLS0FJdccolddYlEkOr1euTl5fFGIJlMJm7c6cTEBCQSCaRSKaKiomxeY1dXF/bs2YOXXnoJS5YscfGKp6enpwebN29GY2Mj9u/fDwDcpLcNGzbgwQcfxKpVqzy2PgEBJyL4lAoI+BKkkamzsxMHDx7Eww8/7FOjKwWch06nw+nTp1FRUYGamhpcdtllKCsrw6WXXmqThRhfBaklNE1DpVJBJpNBo9EgJiYGUqkUMTEx0665t7cXu3btwj/+8Q8sX77czSsG2tvbOVu4P//5z/jggw9QUVGBc+fO4frrr+canYg1mtDoJOAjCKJUQMAXMZlM3AYsTHISmA2DwYB3330XFRUV+OKLL7By5UqUlZXh8ssvnzIljMAwDJqamiASiXgtSC1hGAZqtRoymQyjo6PcgS0zM5Pr5B8YGMB1112HZ599FpdddplH1rl9+3a0trZCLBYjLS0Nhw4dQnJyMoALaf0XXngB/v7+ePLJJ7Fx40aPrFFAwAUIolRAQEBA4H8YjUZ8+OGHKC8vx8cff4ylS5eirKwMa9asQVBQEBiGwT333AN/f388/vjjXiNILWFZFmq1Gs899xxeeeUV5ObmYv369XjxxRfx9NNP48orr/T0EgUEvm0IolRAQEBAwDo0TePjjz9GRUUFzpw5g4KCAhiNRuj1erz22ms+kzamaRrV1dV47LHHMDY2hmXLlmH79u3YtGmTUzv5BQQEZkQQpQICAgICs0PTNG699VbU1NSAZVksXPj/27uXkKj3Po7jnyGjxEXaRc2mstspSjGjwoV0NclKpzQmjWjUWoSBERFYLqygsiiiRa2MiAosnXTsagi1KcguFNUYSSjpZIU43W9qPotzjs/x6TzdLH+Ovl8wi/nP6HzdyJv//P6//x9avHix4uPjFRAQYHq8TmlsbFRKSoq2bdum+fPn686dO3I6nTp37pyGDx+ulJQUJSYmKjAw0PSoQE9GlAIAvq6trU15eXmqr69XQUGBpD/3wC0qKtLFixcVHh6upKQkJSQkdNj43Rd4vV4lJycrNzdXSUlJX7xeVVWlU6dOKSIigu3HgN+LKAUA/H9tbW3asmWL6urqVFBQ8MXm+58/f9bdu3dVXFysc+fOKTQ0VDabTQsXLlRQUJChqb/Py5cvlZKSog0bNiglJcX0OEBvR5QCwK+yb98+FRQUyGKxKDIyUocPH9a7d++0bNky1dbWKjw8XCdPnuz2sfZPJSUlOn369L8G6f9qa2tTVVWViouLdfbsWQUGBspms2nRokUaPHhwF038fV6/fq2lS5dq7dq1Sk1NNT0OAKIUAH4Nj8ej2NhYud1u+fv7y263a8GCBXK73Ro4cKBycnKUn58vr9erXbt2mR73u7W2tkrSD1/U9PetR4uLi3X69Gn1799fNptNiYmJCgkJMXrV/tu3b2W325WRkaGVK1camwNAB53+p8BtRgHgLy0tLXr//r1aWlr07t07hYWFyeVyyeFwSJIcDodKS0vNDvmD+vTp81NX2VssFo0bN06bNm3SlStXdOjQITU3N8vhcGjBggU6ePCgPB5Pp24D+jPev3+v1NRUrVixgiAFehjOlALAX/bv36/c3Fz5+/srPj5ex48fV2BgoF68eNH+nqCgIHm9XnNDGtbW1iaPxyOn06mSkhI1Nzdr0aJFstlsGjly5G89g/rhwwctX75cNptNa9as8dk9VoEeijOlAPAreL1euVwu1dTU6MmTJ3r79q2OHTtmeqxux2KxyGq1at26dbp06ZKcTqcGDBig7OxszZkzR3v27FF1dfUvP4P66dMnrVy5UgkJCQQp0EMRpQAgqaKiQqNGjdKQIUPUt29fJScn6+rVqwoJCVFDQ4MkqaGhQcHBwYYn7T4sFotCQ0OVlZWliooKnT17VmFhYcrJydGsWbOUn5+vqqqqTgdqc3OzMjIyNHPmTGVnZxOkQA/F1/cAIOnatWvKzMzU9evX5e/vr/T0dE2dOlWPHz/WoEGD2i90ampq0u7du02P2+15vV6VlZXJ6XSqvr5e8fHxWrJkiSZNmvTNXQD+qaWlRatWrdLkyZO1efNmghTovrj6HgB+lby8PJ04cUJ+fn6Kjo5WQUGB3rx5I7vdrsePH2vEiBEqKirSwIEDTY/qU169eqUzZ87I6XTq0aNHiouLk81mU3R09FcDtbW1VWvWrNGYMWO0detWghTo3ohSAIDvePPmjc6fPy+n0ym3263Zs2fLZrNp2rRpHXYJaG1tVXZ2tkJCQrRjx44fOrsKwAiiFADgmz58+KDy8nIVFxfr9u3bio2N1eLFixUTE6ONGzcqICBAe/fuJUgB38DV9wAA3/T3hvxHjx7VjRs3lJiYqBMnTmjChAnyer1dEqR79uyRxWJRY2Nj+7GdO3dq7NixGj9+vMrLy3/r5wP4L86UAgC6lZcvX6p///7q16/fb/2curo6rV69Wg8ePNDNmzc1ePBgud1upaWlqbKyUk+ePFFcXJwePnz4UzcgAHoZzpQCAHqWAQMG/PYglaT169dr9+7dHS6gcrlcSk1NVb9+/TRq1CiNHTtWlZWVv30WAEQpAKAXKisr07BhwxQVFdXhuMfj0fDhw9ufW61WeTyerh4P6JWIUgDwMZmZmQoODlZERET7saamJs2bN0/jxo3TvHnzOtwKtbeukYyLi1NERMQXD5fLpe3bt2vbtm1f/My/LWljKyqgaxClAOBj0tPTdeHChQ7H8vPzNXfuXFVXV2vu3LnKz8+XJLndbhUWFur+/fu6cOGCsrKy1NraamLsLldRUaF79+598Rg9erRqamoUFRWl8PBw1dfXa8qUKXr69KmsVqvq6uraf0d9fb3CwsIM/hVA70GUAoCPmTFjxhcb+LtcLjkcDkmSw+FQaWlp+3HWSHYUGRmp58+fq7a2VrW1tbJarbp165ZCQ0OVlJSkwsJCffz4UTU1Naqurtb06dNNjwz0Cn6mBwAAdN6zZ880dOhQSdLQoUP1/PlzSX+ukYyJiWl/H2skv27SpEmy2+2aOHGi/Pz8dODAAa68B7oIUQoAPRhrJL+ttra2w/Pc3Fzl5uaaGQboxfj6HgB6gJCQEDU0NEiSGhoaFBwcLEmskQTgM4hSAOgBkpKSdOTIEUnSkSNHZLPZ2o+zRhKAL+DrewDwMWlpabp8+bIaGxtltVq1detW5eTkyG6369ChQxoxYoSKiooksUYSgO/gNqMAAADoLG4zCgAAAN9HlAIAAMA4ohQAAADGEaUAAAAwjigFAACAcUQpAAAAjCNKAQAAYBxRCgAAAOOIUgAAABhHlAIAAMA4ohQAAADGEaUAAAAwjigFAACAcUQpAAAAjCNKAQAAYBxRCgAAAOOIUgAAABhHlAIAAMA4ohQAAADGEaUAAAAwjigFAACAcUQpAAAAjCNKAQAAYBxRCgAAAOOIUgAAABhHlAIAAMA4ohQAAADGEaUAAAAwjigFAACAcX7feN3SJVMAAACgV+NMKQAAAIwjSgEAAGAcUQoAAADjiFIAAAAYR5QCAADAOKIUAAAAxv0HATpHeKXi91YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "for volume in volumes:\n",
    "    v = hits[hits.volume_id == volume]\n",
    "    ax.scatter(v.z, v.x, v.y, label='volume {}'.format(volume), alpha=0.5)\n",
    "\n",
    "ax.set_title('Hit Locations')\n",
    "ax.set_xlabel('Z (cm)')\n",
    "ax.set_ylabel('X (cm)')\n",
    "ax.set_zlabel('Y (cm)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}