scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsl_time_stamp</th>\n",
       "      <th>Channel 1</th>\n",
       "      <th>Channel 2</th>\n",
       "      <th>Channel 3</th>\n",
       "      <th>Channel 4</th>\n",
       "      <th>Channel 5</th>\n",
       "      <th>Channel 6</th>\n",
       "      <th>Channel 7</th>\n",
       "      <th>Channel 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-49122.250000</td>\n",
       "      <td>-47196.089844</td>\n",
       "      <td>-94880.000000</td>\n",
       "      <td>3867.142334</td>\n",
       "      <td>-51044.656250</td>\n",
       "      <td>-7725.902832</td>\n",
       "      <td>-30040.408203</td>\n",
       "      <td>-45337.718750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-49146.925781</td>\n",
       "      <td>-47358.027344</td>\n",
       "      <td>-83741.726563</td>\n",
       "      <td>15081.928711</td>\n",
       "      <td>-51326.691406</td>\n",
       "      <td>-7438.839355</td>\n",
       "      <td>-22893.595703</td>\n",
       "      <td>-32746.556641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-49149.207031</td>\n",
       "      <td>-47302.570313</td>\n",
       "      <td>-82644.296875</td>\n",
       "      <td>16218.582031</td>\n",
       "      <td>-51132.097656</td>\n",
       "      <td>-7602.118652</td>\n",
       "      <td>-23056.673828</td>\n",
       "      <td>-28920.183594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-49127.480469</td>\n",
       "      <td>-47187.035156</td>\n",
       "      <td>-90344.718750</td>\n",
       "      <td>8461.766602</td>\n",
       "      <td>-50936.093750</td>\n",
       "      <td>-7780.284668</td>\n",
       "      <td>-27963.507813</td>\n",
       "      <td>-37376.722656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-49123.187500</td>\n",
       "      <td>-47177.781250</td>\n",
       "      <td>-95412.351563</td>\n",
       "      <td>3331.505127</td>\n",
       "      <td>-50995.640625</td>\n",
       "      <td>-7764.057129</td>\n",
       "      <td>-30488.136719</td>\n",
       "      <td>-45538.347656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73469</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-48207.953125</td>\n",
       "      <td>-44107.257813</td>\n",
       "      <td>-99421.718750</td>\n",
       "      <td>-7291.988281</td>\n",
       "      <td>-53507.996094</td>\n",
       "      <td>-5303.398438</td>\n",
       "      <td>-33840.429688</td>\n",
       "      <td>-39774.480469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73470</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-48197.691406</td>\n",
       "      <td>-44100.484375</td>\n",
       "      <td>-100850.468750</td>\n",
       "      <td>-8754.127930</td>\n",
       "      <td>-53508.800781</td>\n",
       "      <td>-5305.879395</td>\n",
       "      <td>-34261.804688</td>\n",
       "      <td>-42025.929688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73471</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-48184.214844</td>\n",
       "      <td>-44111.347656</td>\n",
       "      <td>-97666.015625</td>\n",
       "      <td>-5542.651367</td>\n",
       "      <td>-53564.726563</td>\n",
       "      <td>-5217.232422</td>\n",
       "      <td>-32619.353516</td>\n",
       "      <td>-38481.925781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73472</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-48192.195313</td>\n",
       "      <td>-44122.476563</td>\n",
       "      <td>-97580.851563</td>\n",
       "      <td>-5444.348633</td>\n",
       "      <td>-53536.292969</td>\n",
       "      <td>-5265.579102</td>\n",
       "      <td>-32879.058594</td>\n",
       "      <td>-37777.308594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73473</th>\n",
       "      <td>1.613434e+12</td>\n",
       "      <td>-48196.753906</td>\n",
       "      <td>-44113.761719</td>\n",
       "      <td>-99270.039063</td>\n",
       "      <td>-7156.536621</td>\n",
       "      <td>-53501.960938</td>\n",
       "      <td>-5313.054199</td>\n",
       "      <td>-33746.082031</td>\n",
       "      <td>-39569.226563</td>\n",
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       "  </tbody>\n",
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       "<p>73474 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       lsl_time_stamp     Channel 1     Channel 2      Channel 3  \\\n",
       "0        1.613434e+12 -49122.250000 -47196.089844  -94880.000000   \n",
       "1        1.613434e+12 -49146.925781 -47358.027344  -83741.726563   \n",
       "2        1.613434e+12 -49149.207031 -47302.570313  -82644.296875   \n",
       "3        1.613434e+12 -49127.480469 -47187.035156  -90344.718750   \n",
       "4        1.613434e+12 -49123.187500 -47177.781250  -95412.351563   \n",
       "...               ...           ...           ...            ...   \n",
       "73469    1.613434e+12 -48207.953125 -44107.257813  -99421.718750   \n",
       "73470    1.613434e+12 -48197.691406 -44100.484375 -100850.468750   \n",
       "73471    1.613434e+12 -48184.214844 -44111.347656  -97666.015625   \n",
       "73472    1.613434e+12 -48192.195313 -44122.476563  -97580.851563   \n",
       "73473    1.613434e+12 -48196.753906 -44113.761719  -99270.039063   \n",
       "\n",
       "          Channel 4     Channel 5    Channel 6     Channel 7     Channel 8  \n",
       "0       3867.142334 -51044.656250 -7725.902832 -30040.408203 -45337.718750  \n",
       "1      15081.928711 -51326.691406 -7438.839355 -22893.595703 -32746.556641  \n",
       "2      16218.582031 -51132.097656 -7602.118652 -23056.673828 -28920.183594  \n",
       "3       8461.766602 -50936.093750 -7780.284668 -27963.507813 -37376.722656  \n",
       "4       3331.505127 -50995.640625 -7764.057129 -30488.136719 -45538.347656  \n",
       "...             ...           ...          ...           ...           ...  \n",
       "73469  -7291.988281 -53507.996094 -5303.398438 -33840.429688 -39774.480469  \n",
       "73470  -8754.127930 -53508.800781 -5305.879395 -34261.804688 -42025.929688  \n",
       "73471  -5542.651367 -53564.726563 -5217.232422 -32619.353516 -38481.925781  \n",
       "73472  -5444.348633 -53536.292969 -5265.579102 -32879.058594 -37777.308594  \n",
       "73473  -7156.536621 -53501.960938 -5313.054199 -33746.082031 -39569.226563  \n",
       "\n",
       "[73474 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from os.path import expanduser\n",
    "\n",
    "data_dir = \"C:\\\\users\\\\oreog\\\\lab\\\\braindump\\\\data\" # os.path.dirname(os.path.realpath(__file__)) + \"\\\\data\"\n",
    "df_wordprompt_data = pd.read_csv( data_dir + \"\\\\session001-wordprompts.csv\")\n",
    "df_eeg_data = pd.read_csv( data_dir + \"\\\\session001 - Notion-255.data.csv\")\n",
    "df_keystroke_data = pd.read_csv( data_dir + \"\\\\session001 - Curia IO - Buttons.data.csv\")\n",
    "\n",
    "# get the required columns\n",
    "df_eeg_data = df_eeg_data.iloc[:, 0:9]\n",
    "df_eeg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsl_time_stamp</th>\n",
       "      <th>Button Label</th>\n",
       "      <th>State</th>\n",
       "      <th>Unix Timestamp</th>\n",
       "      <th>Recording Timestamp (Unix Epoch)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91595.377866</td>\n",
       "      <td>Mouse Left</td>\n",
       "      <td>Down</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91595.599855</td>\n",
       "      <td>Mouse Left</td>\n",
       "      <td>Up</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91597.237901</td>\n",
       "      <td>Key S</td>\n",
       "      <td>Down</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91597.362843</td>\n",
       "      <td>Key S</td>\n",
       "      <td>Up</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91597.588932</td>\n",
       "      <td>Key E</td>\n",
       "      <td>Down</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>91881.893372</td>\n",
       "      <td>Key AltL</td>\n",
       "      <td>Down</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>91882.274991</td>\n",
       "      <td>Key AltL</td>\n",
       "      <td>Up</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>91884.451929</td>\n",
       "      <td>Mouse Left</td>\n",
       "      <td>Down</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>91884.677880</td>\n",
       "      <td>Mouse Left</td>\n",
       "      <td>Up</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>91885.897892</td>\n",
       "      <td>Key CtrlL</td>\n",
       "      <td>Down</td>\n",
       "      <td>1.613434e+09</td>\n",
       "      <td>1.613434e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>737 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     lsl_time_stamp Button Label State  Unix Timestamp  \\\n",
       "0      91595.377866   Mouse Left  Down    1.613434e+09   \n",
       "1      91595.599855   Mouse Left    Up    1.613434e+09   \n",
       "2      91597.237901        Key S  Down    1.613434e+09   \n",
       "3      91597.362843        Key S    Up    1.613434e+09   \n",
       "4      91597.588932        Key E  Down    1.613434e+09   \n",
       "..              ...          ...   ...             ...   \n",
       "732    91881.893372     Key AltL  Down    1.613434e+09   \n",
       "733    91882.274991     Key AltL    Up    1.613434e+09   \n",
       "734    91884.451929   Mouse Left  Down    1.613434e+09   \n",
       "735    91884.677880   Mouse Left    Up    1.613434e+09   \n",
       "736    91885.897892    Key CtrlL  Down    1.613434e+09   \n",
       "\n",
       "     Recording Timestamp (Unix Epoch)  \n",
       "0                        1.613434e+12  \n",
       "1                        1.613434e+12  \n",
       "2                        1.613434e+12  \n",
       "3                        1.613434e+12  \n",
       "4                        1.613434e+12  \n",
       "..                                ...  \n",
       "732                      1.613434e+12  \n",
       "733                      1.613434e+12  \n",
       "734                      1.613434e+12  \n",
       "735                      1.613434e+12  \n",
       "736                      1.613434e+12  \n",
       "\n",
       "[737 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_keystroke_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Key P', 'Key P', 'Key Y', 'Key Y', 'Key T', 'Key T', 'Key H', 'Key H', 'Key O', 'Key O', 'Key N', 'Key N']\n",
      "['Key J', 'Key J', 'Key U', 'Key U', 'Key M', 'Key M', 'Key B', 'Key B', 'Key L', 'Key L', 'Key E', 'Key E']\n",
      "['Key E', 'Key E', 'Key A', 'Key A', 'Key S', 'Key S', 'Key Y', 'Key Y']\n",
      "['Key D', 'Key D', 'Key I', 'Key I', 'Key F', 'Key F', 'Key F', 'Key F', 'Key I', 'Key I', 'Key C', 'Key C', 'Key U', 'Key U', 'Key L', 'Key L', 'Key T', 'Key T']\n",
      "['Key A', 'Key A', 'Key N', 'Key N', 'Key S', 'Key S', 'Key W', 'Key W', 'Key E', 'Key E', 'Key R', 'Key R']\n",
      "['Key X', 'Key X', 'Key Y', 'Key Y', 'Key L', 'Key L', 'Key O', 'Key O', 'Key P', 'Key P', 'Key H', 'Key H', 'Key O', 'Key O', 'Key N', 'Key N', 'Key E', 'Key E']\n"
     ]
    }
   ],
   "source": [
    "\"\"\"get the timestamp dataframes for a given word prompt\n",
    "TODO: This should be changed to do for an array of word prompts\n",
    "\"\"\"\n",
    "\n",
    "# create an encorder for all the buttons\n",
    "button_label_enc = preprocessing.LabelEncoder()\n",
    "button_label_enc.fit( df_keystroke_data['Button Label'] )\n",
    "\n",
    "# Encode the button label in dataframe\n",
    "df_keystroke_data['Button_Label_Enc'] = button_label_enc.transform(df_keystroke_data['Button Label'] )\n",
    "\n",
    "# lsl_time_stamp in eeg data maps to \"Unix Timestamp * 1000\" to \"keystroke\"\n",
    "df_keystroke_data['TIMESTAMP_ms'] = df_keystroke_data['Unix Timestamp'] * 1000\n",
    "df_keystroke_data['TIMESTAMP_ms'] = df_keystroke_data['TIMESTAMP_ms'].astype('int64') \n",
    "\n",
    "df_eeg_data[\"lsl_time_stamp\"] = df_eeg_data[\"lsl_time_stamp\"].astype('int64')\n",
    "\n",
    "df_eeg_data = df_eeg_data.rename(columns={'Channel 1': 'CP5', 'Channel 2': 'F5', 'Channel 3': 'C3', 'Channel 4': 'CP3', \n",
    "                                        'Channel 5': 'CP6', 'Channel 6': 'F6', 'Channel 7': 'C4', 'Channel 8': 'CP4'})\n",
    "\n",
    "# define the search pattern - wordprompt = easy\n",
    "\"\"\" Take a given word and then get the keystroke pattern for it\"\"\"\n",
    "def get_wordkeystroke_pattern(word):\n",
    "    keystroke_list = [] # this shoudl simulate a down press and up press for a given key\n",
    "    for letter in word:\n",
    "        uppercase_letter = letter.upper()\n",
    "        keystroke_list.append(f\"Key {uppercase_letter}\")\n",
    "        keystroke_list.append(f\"Key {uppercase_letter}\")\n",
    "    \n",
    "    return keystroke_list\n",
    "\n",
    "\"\"\"For a given wordprompt, get an array eeg dataframe that\n",
    "matches with user typing the word.\n",
    "\n",
    "It parses keystroke data to get timestamp entries for valid keypress\n",
    "Then fetches period that matches with that from eeg\n",
    "\"\"\"\n",
    "def get_eeg_samples_for_wordprompt_entries(wordprompt):\n",
    "\n",
    "    pattern = get_wordkeystroke_pattern(wordprompt)\n",
    "    print(pattern)\n",
    "    pattern_enc = list(button_label_enc.transform(pattern))\n",
    "    N = len(pattern)\n",
    "\n",
    "    df_keystroke_copy = df_keystroke_data\n",
    "\n",
    "    # Find rows that match given word patterns\n",
    "    df_keystroke_copy['wordpatternmatch'] = (df_keystroke_copy['Button_Label_Enc'].rolling(window=N , min_periods=N)\n",
    "                            .apply(lambda x: (x==pattern_enc).all())\n",
    "                            .mask(lambda x: x == 0) \n",
    "                            .bfill(limit=N-1)\n",
    "                            .fillna(0)\n",
    "                            .astype(bool)\n",
    "                )\n",
    "    df_keystroke_copy['wordprompt'] = wordprompt\n",
    "\n",
    "    # this coontains cleaned data the matches a pattern\n",
    "    df_keystroke_copy = df_keystroke_copy[ df_keystroke_copy['wordpatternmatch'] == True ]\n",
    "\n",
    "    # use keystroke data to parse eeg data and get epochs for samples get wordprompt timestamp\n",
    "    # get start and end timestamp for given word press and then use that range to get the data from the eeg set\n",
    "    keystroke_entries = len(df_keystroke_copy)\n",
    "    samples_per_word_prompt = len(pattern)\n",
    "\n",
    "    wordprompt_timestamps = []\n",
    "    for i in range(int(keystroke_entries/samples_per_word_prompt)):\n",
    "        df_eeg_copy = df_eeg_data\n",
    "\n",
    "        # add a step multiplier\n",
    "        i = i*samples_per_word_prompt\n",
    "        start_timestamp = df_keystroke_copy.iloc[i]['TIMESTAMP_ms']\n",
    "        end_timestamp = df_keystroke_copy.iloc[i+(samples_per_word_prompt-1)]['TIMESTAMP_ms']\n",
    "\n",
    "        # only get data for when the word was typed\n",
    "        filter1 = df_eeg_copy[\"lsl_time_stamp\"] >= start_timestamp\n",
    "        filter2 = df_eeg_copy[\"lsl_time_stamp\"] < end_timestamp\n",
    "        df_eeg_prompt = df_eeg_copy.loc[filter1 & filter2]\n",
    "        \n",
    "        wordprompt_timestamps.append(df_eeg_prompt)\n",
    "    \n",
    "    return wordprompt_timestamps\n",
    "\n",
    "wordprompts = [\"python\", \"jumble\", \"easy\", \"difficult\", \"answer\",  \"xylophone\"]\n",
    "wordprompts_eegs = {}\n",
    "for wordprompt in wordprompts:\n",
    "    wordprompts_eegs[wordprompt] = get_eeg_samples_for_wordprompt_entries(wordprompt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsl_time_stamp</th>\n",
       "      <th>CP5</th>\n",
       "      <th>F5</th>\n",
       "      <th>C3</th>\n",
       "      <th>CP3</th>\n",
       "      <th>CP6</th>\n",
       "      <th>F6</th>\n",
       "      <th>C4</th>\n",
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       "      <td>-28784.666016</td>\n",
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       "      <td>-23435.201172</td>\n",
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       "      <td>-51198.078125</td>\n",
       "      <td>-7061.787598</td>\n",
       "      <td>-24890.835938</td>\n",
       "      <td>-31015.996094</td>\n",
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       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>1613433542922</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5070</th>\n",
       "      <td>1613433544502</td>\n",
       "      <td>-48829.218750</td>\n",
       "      <td>-47402.484375</td>\n",
       "      <td>-88274.257813</td>\n",
       "      <td>7037.581055</td>\n",
       "      <td>-51199.152344</td>\n",
       "      <td>-7014.916016</td>\n",
       "      <td>-25955.337891</td>\n",
       "      <td>-33702.496094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5071</th>\n",
       "      <td>1613433544506</td>\n",
       "      <td>-48817.347656</td>\n",
       "      <td>-47356.148438</td>\n",
       "      <td>-91653.039063</td>\n",
       "      <td>3623.933105</td>\n",
       "      <td>-51123.378906</td>\n",
       "      <td>-7090.822754</td>\n",
       "      <td>-28025.800781</td>\n",
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       "      <th>5072</th>\n",
       "      <td>1613433544510</td>\n",
       "      <td>-48817.082031</td>\n",
       "      <td>-47367.750000</td>\n",
       "      <td>-93152.187500</td>\n",
       "      <td>2095.006592</td>\n",
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       "      <td>-28620.111328</td>\n",
       "      <td>-40497.406250</td>\n",
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       "      <th>5073</th>\n",
       "      <td>1613433544514</td>\n",
       "      <td>-48830.960938</td>\n",
       "      <td>-47441.511719</td>\n",
       "      <td>-87859.117188</td>\n",
       "      <td>7434.950195</td>\n",
       "      <td>-51309.257813</td>\n",
       "      <td>-6935.254395</td>\n",
       "      <td>-25329.041016</td>\n",
       "      <td>-34518.691406</td>\n",
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       "      <td>-25493.527344</td>\n",
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       "<p>403 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      lsl_time_stamp           CP5            F5            C3           CP3  \\\n",
       "4672   1613433542906 -48842.964844 -47288.691406 -94197.382813   1279.346802   \n",
       "4673   1613433542910 -48843.433594 -47349.445313 -93820.593750   1637.891113   \n",
       "4674   1613433542914 -48850.742188 -47445.867188 -84932.757813  10602.639648   \n",
       "4675   1613433542918 -48849.066406 -47376.199219 -86304.242188   9243.764648   \n",
       "4676   1613433542922 -48824.187500 -47278.433594 -92987.234375   2505.585938   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "5070   1613433544502 -48829.218750 -47402.484375 -88274.257813   7037.581055   \n",
       "5071   1613433544506 -48817.347656 -47356.148438 -91653.039063   3623.933105   \n",
       "5072   1613433544510 -48817.082031 -47367.750000 -93152.187500   2095.006592   \n",
       "5073   1613433544514 -48830.960938 -47441.511719 -87859.117188   7434.950195   \n",
       "5074   1613433544518 -48827.207031 -47416.832031 -87480.320313   7836.007324   \n",
       "\n",
       "               CP6           F6            C4           CP4  \n",
       "4672 -51052.367188 -7223.458008 -29615.144531 -40583.437500  \n",
       "4673 -51213.703125 -7102.557129 -28784.666016 -42125.773438  \n",
       "4674 -51368.265625 -6915.406250 -23435.201172 -31351.205078  \n",
       "4675 -51198.078125 -7061.787598 -24890.835938 -31015.996094  \n",
       "4676 -51045.394531 -7201.262695 -28979.796875 -38767.714844  \n",
       "...            ...          ...           ...           ...  \n",
       "5070 -51199.152344 -7014.916016 -25955.337891 -33702.496094  \n",
       "5071 -51123.378906 -7090.822754 -28025.800781 -37620.132813  \n",
       "5072 -51174.878906 -7057.630371 -28620.111328 -40497.406250  \n",
       "5073 -51309.257813 -6935.254395 -25329.041016 -34518.691406  \n",
       "5074 -51224.296875 -7006.668457 -25493.527344 -32925.730469  \n",
       "\n",
       "[403 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordprompts_eegs[\"easy\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Extra preprocessing with mne\n",
    "\"\"\"\n",
    "from mne import create_info, concatenate_raws\n",
    "from mne.io import RawArray\n",
    "from mne.channels import make_standard_montage\n",
    "\n",
    "ch_names = ['lsl_time_stamp', 'CP5', 'F5', 'C3', 'CP3',\t'CP6', 'F6', 'C4', 'CP4']\n",
    "ch_ind = [1,2,3,4,5,6,7,8]\n",
    "ch_types = ['misc'] + ['eeg'] * len(ch_ind) # 8 notion channels\n",
    "sfreq = 250 # sampling frequency 250Hz\n",
    "\n",
    "# pre-process the wordprompy_eegs\n",
    "for wordprompt in wordprompts_eegs:\n",
    "\n",
    "    preprocessed_wordprompt_eegs = []\n",
    "    for data in wordprompts_eegs[wordprompt]:\n",
    "        # print(f\"Preprocessing for the word - {wordprompt}\")\n",
    "\n",
    "        # Transpose EEG data and convert from uV to Volts\n",
    "        data = data.values[:, ch_ind + [-1]].T\n",
    "        data[:-1] *= 1e-6\n",
    "\n",
    "        # create MNE object\n",
    "        info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=sfreq, verbose=False)\n",
    "        raw = RawArray(data=data, info=info, verbose=False) \n",
    "        montage = make_standard_montage(\"standard_1005\")\n",
    "        raw.set_montage(montage)\n",
    "        \n",
    "        # filter out frequency for noise\n",
    "        raw.filter(l_freq=2, h_freq=30, method=\"iir\")\n",
    "        # raw.notch_filter([57,58,59,60,61,62,63,64,65,66,67,68,69,70])\n",
    "        # for validation \n",
    "        # raw.plot_psd(fmin=2, fmax=120) # plotting psd allows \n",
    "\n",
    "        filtered_wordprompt_df = raw.to_data_frame()\n",
    "\n",
    "        preprocessed_wordprompt_eegs.append(filtered_wordprompt_df)\n",
    "    \n",
    "    # replace with preprocessed values\n",
    "    wordprompts_eegs[wordprompt] = preprocessed_wordprompt_eegs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"get & plot fft for single channel\n",
    "\"\"\"\n",
    "\n",
    "from numpy.fft import fft, fftfreq, ifft\n",
    "\n",
    "data_rate   = 250 # samples per second\n",
    "nfft        = 250 # number of samples to put in fourier (1sec of data)\n",
    "fft_rescale = True\n",
    "\n",
    "\n",
    "def get_fft_theo_and_freqs(y,data_rate,nfft,fft_rescale=False):\n",
    "    \n",
    "    fft_rescale_val = 1.0\n",
    "    if fft_rescale:\n",
    "        n_zeros = len(np.where(y==0)[0])\n",
    "        fft_rescale_val = len(y) / (len(y) - n_zeros)\n",
    "\n",
    "    sample_spacing = 1.0 / data_rate\n",
    "    freqs          = fftfreq(nfft, d = sample_spacing)\n",
    "    freq_mask      = freqs > 0\n",
    "    \n",
    "    # FFT and power spectra calculations\n",
    "    n_samples       = len(y)\n",
    "    fft_vals        = fft(y, n=nfft)\n",
    "        \n",
    "    fft_theo        = np.copy(fft_vals)\n",
    "    fft_theo        = fft_rescale_val * 2.0 * np.abs( fft_vals / n_samples ) # true theoretical fft\n",
    "    fft_theo[0]     = fft_theo[0] / 2.0\n",
    "    \n",
    "    return fft_theo[freq_mask], freqs[freq_mask]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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cV1en48ePD3h9ypQpOn78uFpaWlRWVqZQKNTvdfRdBh5Th2/LNMMyzbB838kFTYZBRxMAAAAAABh347YMPAiCAa8ZhnHGr5+pJ5544oyvmSiPPfbYqM4de/a3kqSnfvs7ReyTUuAofaxJCnw9/tijsqyQDr304qg/DxgK3yGcb/hO4nzDdxLnG76TON/wncT5Ziy/k6FQ6ILcLW3b9hn9dxu3oGnq1Kk6ceJE4eempibV1dUNeL25uVl1dXWqrq5WZ2enPM+TZVmF18/U3LlzFY1Gi/I7jKXHHntMV1111ejOdh7Tc5LecPUb9ewv/0eGWaby2kt0+JkndOWV8/SbnyZVXVk+6s8DBnMm30lgPPCdxPmG7yTON3wncb7hO4nzzVh/J5966iklk8kx+/yJEolEdMUVVxR+zmazwzb1jOlT5/q64oor9Nxzz+mFF16Q53n60Y9+pGuuuUYzZsxQNBotpGO7du3SNddco3A4rPnz5+u+++7r9zokO5ORYZqyQuHcMvD86Jwk+Z6rcDTG6BwAAAAAAJNcZ2enPve5z2nJkiVaunSpPvjBD+rJJ5/sd2bDhg1avXp10e45bkFTNBrV+vXr9Vd/9Ve64YYb9PKXv1zXX3+9JOnLX/6y1q1bp3e+851Kp9P60Ic+JEm68847tX37dt1www169NFH9fGPf3y8yj2v2dm0ItG4DMOQ7+eXgfcETb6jcCTGMnAAAAAAACYx3/fV0NCgiooK7dq1S7t379aKFSvU0NCglpYWSdK+ffu0c+fOot53zEfnHnjggcK/L1y4UD/4wQ8GnLnsssv0/e9/f8DrM2bM0L333jum9ZUiO5NWJBqTpFxHkxWWaeWCpsB3FInGZNsETQAAAAAATFaPPPKImpqatHLlSplmrs9owYIFWrdunXzfV2trqzZt2qRly5bp6aefLtp9x21HE4rHsTOKxOKSJN9zZFphGWao8HM4GlNn66mJLBEAAAAAgEmr/cnjav+f42Py2eXzpqp8ztQRzx08eFDz5s0rhEw9Fi1aJElauXKlVq1apaNHjxa1vnEbnUPx2JmMItGeoMmWafYfnYswOgcAAAAAwKRmmqaCIBj0vR07dmj69OlauHBh0e9LR1MJsrNpxRNlCoIgv6Op7+icq3A0yugcAAAAAAATpHzO6LqOxtLcuXO1bds2BUEgwzAKr99999265557NHv2bC1dulRtbW3q7u7W2rVrtWbNmnO+L0FTCbKzaZVX1SrwXUnqvww8PzpHRxMAAAAAAJPX/PnzVVNTo82bN2v58uWyLEt79+5VY2Oj9uzZo9raWklSY2Oj9u/fX5SQSSJoKkl2JrejyfdsScrtaLLyO5ryo3OuY8v3PJmWNZGlAgAAAACACWAYhrZs2aJ169ZpyZIlCoVCqqqq0tatWwsh01ggaCpBTja3o8n3HUnqv6PJcxTO729ynKyiVmLC6gQAAAAAABOnurpaGzduHPZMfX296uvri3ZPloGXGN/3c0+di8b6dTT13dEUicYkifE5AAAAAAAwrgiaSoyTX/KdG53LdzRZ4X5PnQtHopIkm6AJAAAAAACMI4KmEmNn0pKkcL+OpoiM05aBS3Q0AQAAAACA8UXQVGJ6upT672gKy+y7DLwnaLIJmgAAAAAAwPghaCoxPR1NkWi8f0eTYcowLAW+W+hoYnQOAAAAAACMJ4KmEtMzDheJxfrtaJIkwwrL9xyWgQMAAAAAgAlB0FRi7Gzfjqbe0bncP0P5ZeB0NAEAAAAAgPEXmugCcGYKQVMsrnR77+iclAucfM9lGTgAAAAAAFBnZ6fuuusuHThwQJZlqby8XKtXr9acOXMKZzZs2KCWlhatX7++KPeko6nE2JlceJR76lz/0TnTCivwHYUjUckw5NjZCasTAAAAAABMHN/31dDQoIqKCu3atUu7d+/WihUr1NDQoJaWFknSvn37tHPnzqLel6CpxBQ6miK9QZNhhvL/zO1oMgxD4UiU0TkAAAAAACapRx55RE1NTVq5cqVCoVxusGDBAq1bt06+76u1tVWbNm3SsmXLinpfRudKjJPNKBSOyLQs+b4twwzLMHJ5Yc+OJkkKR2Jy8qEUAAAAAAAYP08++aSeeOKJMfnsuXPn9ht9G8rBgwc1b948mWb/HqNFixZJklauXKlVq1bp6NGjRa2PjqYSY2fTisTikiTfcwpjc1JudM73XUlSJBqjowkAAAAAgEnKNE0FQTDoezt27ND06dO1cOHCot+XjqYSY2cyikT7Bk2RwnumGZbn5LqYwtEYO5oAAAAAAJgAc+bMGVXX0ViaO3eutm3bpiAIZBhG4fW7775b99xzj2bPnq2lS5eqra1N3d3dWrt2rdasWXPO9yVoKjF2Nq1I/qlyvm/LNHs7mgwrXBidi0RjPHUOAAAAAIBJav78+aqpqdHmzZu1fPlyWZalvXv3qrGxUXv27FFtba0kqbGxUfv37y9KyCQRNJUcO5tWuCdoGtDRFCosCA9HY+psPTUhNQIAAAAAgIllGIa2bNmidevWacmSJQqFQqqqqtLWrVsLIdNYIGgqMU42o1RV7gvhe3b/HU1m746mcCRKRxMAAAAAAJNYdXW1Nm7cOOyZ+vp61dfXF+2eLAMvMQN3NPVfBh70HZ2zCZoAAAAAAMD4IWgqMQN3NPWOzhlmuN/oHE+dAwAAAAAA44mgqYQEQZALmmJDdDSZIQWBpyDwFYnE5Dq2fN+fqHIBAAAAAMAkQ9BUQjzXle95Qy8Dz4dOge8qnB+vY3wOAAAAAACMF4KmEmJn05LUZ0fTwGXgudcdhaNRSWIhOAAAAAAAGDcETSWkJzQqBE2+039HUz508n1HkUgsf012nKsEAAAAAACTVWiiC8DoFTqaYjEFga/AdwfsaJIk33cL43U91wAAAAAAgMmls7NTd911lw4cOCDLslReXq7Vq1drzpw5hTMbNmxQS0uL1q9fX5R70tFUQuxM7+hcz9PlBhudCzyn8GQ6djQBAAAAADD5+L6vhoYGVVRUaNeuXdq9e7dWrFihhoYGtbS0SJL27dunnTt3FvW+BE0lxO4ZnYv1CZrMgcvAfd/p09FE0AQAAAAAwGTzyCOPqKmpSStXrlQolJuAWrBggdatWyff99Xa2qpNmzZp2bJlRb0vo3MlpKejKRyJyfdtSf07moye0TnPUTiSlMSOJgAAAAAAxtvJI4/qxOEDY/LZtTPeoJqL5o947uDBg5o3b55Ms3+P0aJFiyRJK1eu1KpVq3T06NGi1kdHUwnpGYPr19FkDdbR5DI6BwAAAADAJGaapoIgGPS9HTt2aPr06Vq4cGHR70tHUwnp3dEUk51ulzTEjiZG5wAAAAAAmDA1F80fVdfRWJo7d662bdumIAhkGEbh9bvvvlv33HOPZs+eraVLl6qtrU3d3d1au3at1qxZc873JWgqIXY2LcM0FQpHlOkcZkeT5ygcjkiGIYegCQAAAACASWf+/PmqqanR5s2btXz5clmWpb1796qxsVF79uxRbW2tJKmxsVH79+8vSsgkETSVFDuTUSQal2EYw+9o8h0ZpqlwOEpHEwAAAAAAk5BhGNqyZYvWrVunJUuWKBQKqaqqSlu3bi2ETGOBoKmE2Nl0YfdS746mgaNzvudKksLRGDuaAAAAAACYpKqrq7Vx48Zhz9TX16u+vr5o92QZeAmxs+nC7iXf6+loGjg6F/i5ECoSjTE6BwAAAAAAxg1BUwlxshlFYnFJufE4qbeLqe+/97wXjsYYnQMAAAAAAOOGoKmE9OxokvqOzvV2NMkwJRm9o3ORKB1NAAAAAABg3BA0lZD+O5oGWQZuGDKtcKGjKcKOJgAAAAAAMI4ImkqInU33js55jmSYMgyr3xnTDCvwGJ0DAAAAAADjj6CphNiZjMKRfEeTb8s0wzIMo98Z4/SOJoImAAAAAAAwTgiaSkTg+3LsTL+Opr5jcz1MMyTfz+9ooqMJAAAAAACMo9BEF4DRceysJPVZBm73XwSeZ5rhwqLwcCQm17Hl+75Mk0wRAAAAAIDJpLOzU3fddZcOHDggy7JUXl6u1atXa86cOYUzGzZsUEtLi9avX1+Ue5I+lAg7m5YkRWI9y8AdmeYgHU1WWEGf0TlJcvMhFQAAAAAAmBx831dDQ4MqKiq0a9cu7d69WytWrFBDQ4NaWlokSfv27dPOnTuLel+CphJhZ/JBU09Hk+8M2tFkmKHejqZ80MT4HAAAAAAAk8sjjzyipqYmrVy5UqFQbqBtwYIFWrdunXzfV2trqzZt2qRly5YV9b6MzpWInrCop0tp6B1NYblOd+5sfnE4C8EBAAAAABg/Dx06qf8+dHJMPvvNM2v0ppk1I547ePCg5s2bN2CVzqJFiyRJK1eu1KpVq3T06NGi1kdHU4kY0NHk2UOOzvU8da63oyk9TlUCAAAAAIDzgWmaCoJg0Pd27Nih6dOna+HChUW/Lx1NJcK2c11J4Z4dTf7QHU3BaaNzDjuaAAAAAAAYN28aZdfRWJo7d662bdumIAhkGEbh9bvvvlv33HOPZs+eraVLl6qtrU3d3d1au3at1qxZc873JWgqEYN2NA22o8kKyffd/FlG5wAAAAAAmIzmz5+vmpoabd68WcuXL5dlWdq7d68aGxu1Z88e1dbWSpIaGxu1f//+ooRMEkFTyXAKT53rCZqG7mgqjM5FWAYOAAAAAMBkZBiGtmzZonXr1mnJkiUKhUKqqqrS1q1bCyHTWCBoKhF2Jr8MPNJndM4c2NFkmuHCU+cKHU02QRMAAAAAAJNNdXW1Nm7cOOyZ+vp61dfXF+2eLAMvEXY2rVA4ItOyJPWMzg2+DDzwHQVBoHA0mr+WoAkAAAAAAIw9gqYSYWczhQ6lwPekwB98R5OZa1ILAk/hcC5oYkcTAAAAAAAYDwRNJcLOphXuswhc0pAdTbkzjgzTVDgSo6MJAAAAAACMC4KmEmFnMr2LwPPLvk1z8GXgkhT0LASPxtjRBAAAAAAAxgVBU4lwsunC6FxvR9Pgy8BzZ3oXgjM6BwAAAAAAxgNBU4mw+wVN+Y6mQUbnDCu3o8n3XUlSOBKVnc2OU5UAAAAAAGAyC010ARgdO5NRpLCjqWd0bpiOJkbnAAAAAACY1Do7O3XXXXfpwIEDsixL5eXlWr16tebMmVM4s2HDBrW0tGj9+vVFuScdTSXCttN9djSNvAw86Dc6lx6nKgEAAAAAwPnA9301NDSooqJCu3bt0u7du7VixQo1NDSopaVFkrRv3z7t3LmzqPclaCoRdiaj8IDRuYEdTYbZMzrX29HEU+cAAAAAAJhcHnnkETU1NWnlypUKhXJZwYIFC7Ru3Tr5vq/W1lZt2rRJy5YtK+p9GZ0rAZ7ryPfc3o4mb5iOpsIy8J4dTSwDBwAAAABgPD3w6Iv66f4Xx+Szr7v6Yr19/sUjnjt48KDmzZsn0+zfY7Ro0SJJ0sqVK7Vq1SodPXq0qPXR0VQC7Exu9G1UO5qs/juaItGYHJtl4AAAAAAATCamaSoIgkHf27Fjh6ZPn66FCxcW/b50NJWAntG3wlPnTtvRdPRQm5773Qm96dpXFDqaenY0hfNBU+D7MkxyRQAAAAAAxtrb54+u62gszZ07V9u2bVMQBDIMo/D63XffrXvuuUezZ8/W0qVL1dbWpu7ubq1du1Zr1qw55/sSNJWA3qDptI6mfND0m8cO6ZG9z2nhopcP2NHUE045drYwegcAAAAAAC5s8+fPV01NjTZv3qzly5fLsizt3btXjY2N2rNnj2prayVJjY2N2r9/f1FCJomgqSTY+afGRWI9y8DzHU357iU760qBZNuurMLoXH5HUz5osrMZgiYAAAAAACYJwzC0ZcsWrVu3TkuWLFEoFFJVVZW2bt1aCJnGAkFTCejZ0RTu6WjyHRmGJcO0JEnZjFv4Z1mqZxl4fnQuEpUkOTYLwQEAAAAAmEyqq6u1cePGYc/U19ervr6+aPdkaU8JKHQ09RmdM61In/d7g6ae0bmgMDoXz58haAIAAAAAAGOLoKkEOKcvA/fswn4mScpmPUlSJuPKMAwZZqiwo6lndM4haAIAAAAAAGOMoKkE9IzO9exYGrqjKb8k3AzL93KvRQiaAAAAAADAOCFoKgF2NiPDMBQK58Il37cLi8Bz77v9/mla4d6OpkjvMnAAAAAAAICxRNBUAuxsWpFoXIZhSOrpaOoNmjJpO//PfNBkhgs7msJRloEDAAAAAIDxQdBUAuxspjA2Jw0yOpcfmesZnTPMUOGpcz2jc3Q0AQAAAACAsUbQVALsTLqw1FvKLwPPj855ni/Pz3U6ZbqzknpG53LdTeFwvqOJoAkAAAAAAIyx0EQXgJE52XShM0mSfL+3o6lnL5MkZbtzYVJuGXhPd5OpcCRGRxMAAAAAAJNMZ2en7rrrLh04cECWZam8vFyrV6/WnDlzCmc2bNiglpYWrV+/vij3pKOpBERiCVXUTC387Ht2YUdTNtMbNPV0NBlWqLCjScrtaXLs7DhVCwAAAAAAJprv+2poaFBFRYV27dql3bt3a8WKFWpoaFBLS4skad++fdq5c2dR70tHUwl4x/s/KgW9P/ddBp7t29GUXwpummG5fmfh9XAkxugcAAAAAACTyCOPPKKmpiatXLlSppnrM1qwYIHWrVsn3/fV2tqqTZs2admyZXr66aeLdl+CphJgWf3/TLkdTfnRuT4dTT3LwE0rLN/rfT0SZXQOAAAAAIDx0vGbX6jj1w+MyWenrni7Uq9924jnDh48qHnz5hVCph6LFi2SJK1cuVKrVq3S0aNHi1ofo3MlJgiC/I6m/h1NITnKZjxJ+R1N/UbnYnJsgiYAAAAAACYL0zQVBMGg7+3YsUPTp0/XwoULi35fOppKTJB/mtzpy8ATZpey2dwT5gwzpMDrDZoi0Zi62lvHt1AAAAAAACap1GvfNqquo7E0d+5cbdu2TUEQyDCMwut333237rnnHs2ePVtLly5VW1uburu7tXbtWq1Zs+ac70vQVGJ6OpVOXwaeMLvVaVcW3vP93tE5njoHAAAAAMDkMn/+fNXU1Gjz5s1avny5LMvS3r171djYqD179qi2tlaS1NjYqP379xclZJIImkqO7/Us/M51NPXsZUqY3TrpBPn3GJ0DAAAAAGAyMwxDW7Zs0bp167RkyRKFQiFVVVVp69athZBpLBA0lRjfO62jqTsXIMXNLjm2Id8Pcu8FvgLfk2FaikR56hwAAAAAAJNNdXW1Nm7cOOyZ+vp61dfXF+2eLAMvMYWOpj5BkylXUSMrKbezyTBz+WFPV1OuoymrwPcnoGIAAAAAADBZEDSVmMKOJjMfNKVthQ1HYSMXQGUzbuE938vtaQpHckvCHSc73uUCAAAAAIBJhKCpxPR2NOV3NOWDpoiRC6CyGafQ7dQTSkWiMUliITgAAAAAABhTBE0lZsCOpqyT72jqCZp6O5qCPqNzktjTBAAAAADAGAqCYKJLKKqz+X0ImkpMb9CU62iyM55ChqNoIt/h1HdHk9fT0RTPnSVoAgAAAABgTMRiMZ08efKCCZuCINDJkycVi8XO6DqeOldifD8/OpfvWrJtT2HDVawsITXlO5pOH52L5YOmTHoCKgYAAAAA4MI3c+ZMHTp0SM3NzRNdStHEYjHNnDnzjK4haCoxp3c0ZW1fFYateCqV+znjDFgGHo0ncu+lu8a7XAAAAAAAJoVwOKzZs2dPdBkTjtG5EtO7DDwXJjlOoLDhKF5ZLmnwHU2xeDL3Xrp7vMsFAAAAAACTCEFTienpaOrZw2Q7UshwFUvlgqZMxpVh5Xc05YOmaD5oyqQ7x7tcAAAAAAAwiRA0lRjft2WYYRmGqcAP5HiGwiHJjoQVlq1sV6bP6FwuaAqFIzIti44mAAAAAAAwpgiaSozvOYWxOdv2JEmHEtKdR/9bIcNRpjvbZxl4bkeTYRiKxpPsaAIAAAAAAGOKoKnE5IKm/CLwbK5jqTsSqNXLKGzayqbtATuapNxCcDqaAAAAAADAWJqQoGn37t36kz/5E/3Jn/yJNmzYIEl66qmn9O53v1uLFy/WZz7zGblurhvnyJEjuvXWW3X99dfrYx/7mLq6JndXju/3Bkl2JtfR5IZ8SZJpOMqkc6N1Uu/onCRFY3Q0AQAAAACAsTXuQVM6ndYXv/hF3Xvvvdq9e7ceffRRPfTQQ/rUpz6lO+64Qz/+8Y8VBIG2b98uSfrc5z6nW265Rffff7/mzp2rLVu2jHfJ55X+HU25MM4O54Imw3RlZ12Zpy0Dl6RYPKEMHU0AAAAAAGAMjXvQ5HmefN9XOp2W67pyXVehUEiZTEZXXnmlJKm+vl7333+/HMfRgQMHtHjx4n6vT2a+Z/fuaMoHTVkrv4vJcpTNejIMU4ZhKcjvaJLEjiYAAAAAADDmQuN9w7KyMv1//9//p3e+852KxWK6+uqrFQ6HNWXKlMKZKVOm6Pjx42ppaVFZWZlCoVC/18/EE088UdT6x9Jjjz028qH2FskI6bHHHtOxlzKSpG4zK0kKTEfpbjv/OaaOHT2sY+25z2zr7FJXR/vo7gHk8X3B+YbvJM43fCdxvuE7ifMN30mcb/hOjr1xD5qefvpp/d//+3/185//XKlUSp/85Cf13//93wPOGYahIAgGff1MzJ07V9Fo9KzrHS+PPfaYrrrqqhHPHdy3R5FYtS593VX6TXBIj6mlMDrnW668rKWrrrpKv/7Fj1RZW6VLLs99pnPyBR3/wxN6/eteJ8NkBzxGNtrvJDBe+E7ifMN3EucbvpM43/CdxPmG72RxZLPZYZt6xj1xePDBB7Vw4ULV1NQoEomovr5ejzzyiE6cOFE409zcrLq6OlVXV6uzs1Oe5/V7fTLL7WjKjc5lM/mnzpm2JMm1XDmu5PuBTDPcfxl4PCkFgexsZvyLBgAAAAAAk8K4B02XXXaZHnroIXV3dysIAj3wwAO6+uqrFY1GCy1su3bt0jXXXKNwOKz58+frvvvu6/f6ZNZvGXg6NzKX7QmaQrlALrcQPHzajqZE/hr2NAEAAAAAgLEx7qNzb3nLW3Tw4EHV19crHA5r3rx5uu2223Tdddfps5/9rLq6unT55ZfrQx/6kCTpzjvv1OrVq/XP//zPmj59uu6+++7xLvm80ncZeKY7K8lXYOZG59z8CF0m7cg4raMpFk/m3+tSuaYIAAAAAACg2MY9aJKk2267Tbfddlu/1y677DJ9//vfH3B2xowZuvfee8ertPOe7zsyzXxHU3dGIcOR8murshFfIUnZrCvTCsn3+47O9XQ0dY93yQAAAAAAYJJgK3QJCQJfge/22dFkyzJy43GGYSibH53LZtzcjqa+QVMi19HE6BwAAAAAABgrBE0lpGcUridosjOOTDP3Wm2iWt2nBU2B17ujqWd0jo4mAAAAAAAwVgiaSkghaOoZncu4MsxcmDStbIq6Q07+dUeGdVpHU5yOJgAAAAAAMLYImkqI7+eeLlfoaLI9Kd/RNLVsitKh3Pu5jqZQv2XgoXBEpmUpQ9AEAAAAAADGCEFTCekdnct1NNlZX0Gho6lWvpX792wmt8fJ93tH5wzDUDSeZHQOAAAAAACMGYKmEuJ7p3U0Ob58y5VlmKpNVMs3PUlB746mPqNzUu7Jc4zOAQAAAACAsULQVEJO39FkO5JvuUqE4yqLJCVDsgwnt6PJDPcbnZOkaIyOJgAAAAAAMHYImkpI3x1NQRDIcSU35CsRSSgVLcu9ZzrKdGdlWiEFgacg8AvXx+hoAgAAAAAAY4igqYT07mgKy3V9+YEhN+QrGY4rFc09Vc7oCZrM3Hhd0GdPEzuaAAAAAADAWCJoKiG+37sM3M7kAiQn5CkZiSsVyXU0yXSVzdiFoKnv+Fw0nuCpcwAAAAAAYMwQNJWQ3h1NYWWzuaDJDnlKhBOKhiIKG5YCy1E27crILwz3/b5BU1J2pluB7w/8cAAAAAAAgHNE0FRCep86F5GdD5qyIU/JcFySVBaOybNc2VlXphnKX9M/aAqCQLadGefKAQAAAADAZEDQVEL67mjq6WjKWI4SkYQkqSyckGe5ymb9IXY05c6xpwkAAAAAAIwFgqYS4vu2ZJgyDEvZ/I4m23IKHU2pSFJOyFXW8WUOMjoXi+cWhvPkOQAAAAAAMBYImkqI7zkyzbAMwyiMzvmWp2S+oykVS8kOuXJdSbIK1/To7WgiaAIAAAAAAMVH0FRCfM8udCr1BE2e5SqR72gqj5UrG/IkSa6X+9P6/Ubnch1NGUbnAAAAAADAGCBoKiE9HU2SlE3nOpV80+3taIqneoMm15AkBT4dTQAAAAAAYHwQNJUQ33dkWhFJUqYrnXvNcnufOhcpkxvKdTA5Ti5oOv2pcxLLwAEAAAAAwNggaCohfUfnsumsTMORDBVG51KRpDwrHzTZ+aCpT0dTOBKVaVrKdHeOc+UAAAAAAGAyIGgqIb7X29GUTdsyzFyoVBidiybl54OmrB3kr+nd0WQYhqLxBB1NAAAAAABgTBA0lRDfs3t3NGWc3qCpMDqXlGflOpicbO6avjuapNz4HDuaAAAAAADAWCBoKiG5HU35p85lHMl0ZMpQNBSVJJVHywodTbbde01fdDQBAAAAAICxQtBUQnI7mvKjc1lXgekqEYrKMHL7mMr6jM5lMr5kmP2WgUt0NAEAAAAAgLFD0FRCcjua8h1NWU++5RYWgUu5peCB6UkKlM26Ms2wfN/t9xl0NAEAAAAAgLFC0FRCfN+RaeY6mmwnkGe5hUXgkmQappJmSDId2RlXphlScFpHUyyeVIaOJgAAAAAAMAYImkpIbnQu39HkBHItT8lIst+ZpBmWTEeZtCPDCg+6o8nOdCvw/XGrGwAAAAAATA4ETSUi8D0p8As7mmxHckOuEtFEv3Nloah8y1W2O5sfnRu4oykIAtl2ZtxqBwAAAAAAkwNBU4nwvdxj5EwrLN/z5fmGnJCnZPj0oCkmz3KVydgyzZB8b+COJknsaQIAAAAAAEVH0FQiejqTTDOsbDYXHmUtT8k+y8AlKRVJyg25yqZzi8ODQTqaJPHkOQAAAAAAUHQETSWit6MpIjvrSZKckKdE5LSOpkhCjuUqm3FlmGH5HkETAAAAAAAYHwRNJaInMDKt3o4mz3QHdjRFU7mOpqyXG7Pz+4/OxfKjcxlG5wAAAAAAQJERNJWIQtBkRpTN5MIj33KVPK2jKRVLybNcZW0/t6OJ0TkAAAAAADBOCJpKhO/3LgO3s71BU+K0jqbyRIV8y5XnSYYRUjDk6BwdTQAAAAAAoLgImkpE7+hcpBA0eZarZOS00bl4lTwrH0QF1oCOpnAkKtO06GgCAAAAAABFR9BUInqXgYf7j86F+4/OVZRVy88HTUFgDdjRZBiGovEEHU0AAAAAAKDoCJpKRL8dTdncv/umq8RpHU3l8YpC0OQH1oCnzkm58Tk6mgAAAAAAQLERNJWIvjuast3Z3GuD7GgqiyTlWblwyfMMBb6jIAj6nYnGE8oQNAEAAAAAgCIjaCoRvTuawsp2ZyR5khkoFor2OxeyQrLM/A4nz5IkBaeNz+U6mhidAwAAAAAAxUXQVCIKO5rMsLLprAzTVdyMyDQG/gmjlicp19EkacCeptyOJjqaAAAAAABAcRE0lQjfd2QYlgzTUjbtKLBcJUORQc/GTV+S5Lq5oCk47clzMTqaAAAAAADAGCBoKhG+58i0csFSNmMrMB0lQrFBzyZCuZ1MjmMUru0rGk8om+kesLsJAAAAAADgXBA0lQjfs2VaYUmSnfHkW96AReA9kuGQAvlynaFG55IKfF9ONjO2RQMAAAAAgEmFoKlE+H6fjibbk2e5SkYSg54tC0XlW65su/favqLx3HU8eQ4AAAAAABQTQVOJ8D1HppnvaLI9uZarZDQ56NmyUEye5crO5kbjggGjc7nr2NMEAAAAAACKiaCpRPQbnXMCOSFXyWhq0LOpSFye5ao7k3v63MCOpp6giY4mAAAAAABQPARNJaLvMnDbkRzLUzJWNujZsnBSvuUqnc4HTV7/HU2x/OgcQRMAAAAAACgmgqYS4fu50bkgCOS4km95Q+5oSsXK+gdNQ3Y0MToHAAAAAACKh6CpRORG5yJybE+SId9ylQwPHjSVx1LyLEeZ/EPlBu5oYhk4AAAAAAAoPoKmEpEbnQsrm82NweWeOhcf9GwqXinfcpXN5q89raMpHInJME06mgAAAAAAQFERNJWInmXg2UwuaPJNV4mhOpoSlfIsV07Gyp31++9oMgxD0XiCHU0AAAAAAKCoCJpKRG5HU0R2vqPJH6ajKR4vV2A58tx80HTa6JwkRWNJgiYAAAAAAFBUBE0lIAiCAaNzvuUqER48aDJjCYUMR75vSDIU+AODplg8yY4mAAAAAABQVARNJSDwXUmBTCsiO9NnR9MQo3NmJK6Q4UgyZBjWgB1NkvKjc+xoAgAAAAAAxUPQVAJ6giLTDBdG5wLTUywcHfS8GYkrYtj5H8LyPXfAmWic0TkAAAAAAFBcoZEOuK6rxx9/XM8995xM09TLX/5yXXXVVeNRG/J8Lxca9R2dC4d8mcbgOaFhhRQzerqY6GgCAAAAAADjY9ig6dvf/rb+5V/+RVOnTtWsWbPkeZ6+853vqKWlRQ0NDfrABz4gwzDGq9ZJq2eZt2lFlE3nQqdYePj/7jHLV0aSFFIw2DLweFLZTLeCIOBvCAAAAAAAimLIoOn222/X5Zdfrh07dmjq1Kn93jt58qS+853vaMWKFdqyZcuYFznZ9e1oynRnFChQPDx8M1rCCpSRFAzT0RT4vpxsRpHY4EvFAQAAAAAAzsSQacUdd9wxIGDqUVNTo9tvv13Hjh0bs8LQq++Opmx3u2S6SoQG38/Uoyxk6JQk3zfl+wN3NMXiSUlSJt1F0AQAAAAAAIpiyGXgPSHTn//5nw958bRp04pfEQbo7WiKyE7b8i1XiXBs2GtS0dyf1vWNwuhdX9F47ol17GkCAAAAAADFMuJT5zo6OtTdTRgxkXp3NIWVzeSCpmR4+C6kVDiiwPDlelIw6OhcrqOJJ88BAAAAAIBiGfGpc/F4XNdee61e/epXK5FIFF7/2te+NqaFoVe/ZeAZV57lKhlJDHtNeSQuz3LlusYQO5oImgAAAAAAQHGNGDTdfPPN41EHhuH7+dE5M6xs1pVreUpGy4a9piySlG+5cj1DvjdwRxOjcwAAAAAAoNhGDJpuuukmHTt2TL/97W/1lre8RU1NTZo+ffp41Ia8fh1NtpcbnYtVDXtNKlomz3LkOBq0o6nvMnAAAAAAAIBiGHFH0549e/T+979fn/vc53Ty5EndcMMN+tnPfjYetSGvdxl4WFnbl2e6SsbKh72mLJqSb7pyXEPBIMvAw9GYDNOkowkAAAAAABTNiEHTV7/6VW3fvl3l5eWqq6vTtm3b9JWvfGU8akNeT0eTYYZkO5JvuSpLDB80hWIJyXLkOoZ8f+DonGEYisYT7GgCAAAAAABFM2LQ5Pu+6urqCj+/5jWvkWEYY1oU+vN9W4YZlmGYueXelquyaGrYa4xIXKbZEzQN7GiSpGgsSUcTAAAAAAAomhGDpng8riNHjhTCpUcffVTRaHTMC0Mv33NkWmG5rqcgyAVNiXB82GvMaEKW4cpzLSnwFfjegDOxeJKOJgAAAAAAUDQjLgP/xCc+ob/4i79Qc3Oz3ve+9+n555/XV7/61fGoDXm5oCkiO5MLizzLVSKSGPYaMxJTyLTlOblAyvcdWabV70xudI6OJgAAAAAAUBwjBk2vf/3rtX37dv3yl7+U7/u64oorVF1dPR61Ic/3bZlmWNlsbgTON10lR9HRFJYj3yvLXeO5sk77a0fjSbWdah6TmgEAAAAAwOQzYtD01re+Vdddd53e8Y536Oqrr5ZpjjhthyLr6WjKZnMdTb7pKR6ODXuNGYkrYtjy/Nzfa7A9TdF4QtkMo3MAAAAAAKA4RkyNduzYoUsvvVRf//rXde211+qOO+7Qgw8+OB61Ic/3bJlWWHYm9/S4kOXJNIb/05nRuGKGLd/LjcsFgwZNuWXgQRAUv2gAAAAAADDpjBg0TZs2Tbfccov+9//+31q1apUefPBBNTQ0jEdt6GEYCoUTymZzQVM45I98STShmPp0NHmDdzQFvi/HzhS3XgAAAAAAMCmNODr31a9+VQ899JAOHTqkq6++Wrfffrve8pa3jEdtyLvkNTfLMAz97um0JCky4l8ttww8EdhqD4YenYvFk5KkbLpbkejwO58AAAAAAABGMmJk8R//8R/q7OzU+9//fr31rW/Va1/7WhmGMR61IS+ayC1fz2Y7JEmx8Mj//Q3TUtLy1ZLt6WhyB35uPPfkumy6S6nKmmKVCwAAAAAAJqkRg6b7779fhw8f1t69e/WNb3xDTz31lC6//HJ95StfGY/60EfPU+fikdEtZC8PGfLSubND7WiSpEy6u0gVAgAAAACAyWxUiUVFRYUqKytVVlYmz/PU3U0wMRGyXbldSonRzM5JKg+b8r3hnjrXMzrHk+cAAAAAAMC5GzGxuOWWW/S73/1OCxcu1HXXXac1a9YolUqNR204TTadlW+6KhvlPqWqaHjEZeC5zyVoAgAAAAAA527EoOkjH/mI3vKWt8iyLAVBoHA4PB51YRCZ7ow8y1UiPLqgKR6Pyc0/oM73B+5o6rsMHAAAAAAA4FyNODp3xRVX6Pbbb9eVV16p1772tfrgBz+o48ePj0dtOE13d1a+6SkZSY7qvBmJy1UuaRpsR1M4GpNhmnQ0AQAAAACAohgxaPr85z+vK664Qg899JAeeughveENb9Df//3fj0NpOF06Y8u3XCWjowyaogn5+aBpsNE5wzAUjSXoaAIAAAAAAEUxYtD0/PPP6/bbb1d5ebmqqqq0cuVKvfjii+NRG06TzTq5oCk2uh1ZRiQmX56kwZeBS7k9TRk6mgAAAAAAQBGMGDS5rqtsNlv4OZ1OyzCMMS0Kg8tmfXmWq2SsfFTnzWhCpunK8w353sAdTVLuyXPZboImAAAAAABw7kZcBn7DDTfowx/+sOrr6yVJjY2NWrx48ZgXhoEcx5cfdVWWqBjVeTMSl2Wckuebg+5oknIdTYzOAQAAAACAYhgxaFqxYoWmTZumvXv3yvd91dfX6+abbx6P2nAaxzHkJ1yVJSpHdd6MxhU2HPmeKc+zBz0TiyfVfupEEasEAAAAAACT1YhBkyS9/e1vVyqVkmVZuvrqqxmdmyCea8i3PCVHGzRF4gobtjzflO1mBj0TjSeVzTA6BwAAAAAAzt2IO5p++tOf6h3veIe++c1v6hvf+Iauu+46Pfzww+NRG/rw/UCBb+Z2NIXjo7rGiMYVNWz5ninbSQ96pmd0LgiCYpYLAAAAAAAmoRE7mjZt2qRvfetbevWrXy1JevLJJ/XZz35WO3fuHPPi0MvO5pZ5m4Yj0xwxH8ydjSQUkyPfs+Q42UHPRONJBb4vx84oEh1dgAUAAAAAADCYEROLWCxWCJkkac6cOYzOTYBsJhc0hSxv1NeY0bjiysrzzWGCpkTu81kIDgAAAAAAztGIQdMb3/hGbd26Vd3d3cpms/re976nV77ylWpra1Nra+s4lAipt6MpfCZBUySuhGz5vinPHXwZeDSelCRl0+xpAgAAAAAA52bE0blvfvOb8jxPd999d7/Xd+/eLcMw9NRTT41ZceiVLQRNo9+lZEbjSga2bM+U7zmDnonlO5oydDQBAAAAAIBzNGLQ9OSTT45HHRhBT0dTNHQGQVO+oyntWwr8oZaB09EEAAAAAACKY8jRuX/8x39UNjv4Xh9Jymaz2rRp05gUhYF6djTFwqNbBC5JRiSmSP6pc0Yw+MgdQRMAAAAAACiWITuaXv/61+vmm2/WW9/6Vl177bW6+OKL5fu+XnrpJf3Xf/2XfvGLX+iv//qvx7PWSa2noykeOYOgybQUCZvyfFOG/EHPsAwcAAAAAAAUy5BB0zXXXKOrrrpK3/72t7V+/Xr94Q9/kGVZetnLXqbFixfru9/9rsrKysaz1kktk8ntWEpErDO6Lhqz5PumzCGCpkgkJsMw6GgCAAAAAADnbNgdTclkUrfddptuu+228aoHQ+jqynUclcWiZ3RdNBaS55kyjcF3OxmmqUgsQUcTAAAAAAA4Z6OfwyqiBx54QPX19br++uv1hS98QZL00EMP6cYbb9Q73vGOfrufnnrqKb373e/W4sWL9ZnPfEau605EyROus7NLvuGrLBY/o+vCsZh835BlBAqCwbuaYomkMnQ0AQAAAACAczTuQdNLL72kO++8U1u2bNEPf/hDHTx4UHv27NGaNWu0ZcsW3XfffXriiSe0Z88eSdKnPvUp3XHHHfrxj3+sIAi0ffv28S75vNDVlZZvukpGE2d0nRGJy883M3meM+iZaDxJRxMAAAAAADhn4x40/fSnP9UNN9ygadOmKRwOa9OmTYrH47rkkks0a9YshUIh3Xjjjbr//vt1+PBhZTIZXXnllZKk+vp63X///eNd8nkhnc7Kt1wlo2e2F8uMxhUEuaQpbQ/etRSNJ9jRBAAAAAAAztmogqZjx45pz5498jxPR48ePacbvvDCC/I8Tx/5yEf0rne9S9u2bVNTU5OmTJlSOFNXV6fjx48PeH3KlCk6fvz4Od2/VGWzTi5oiqXO6DozEpfyHU0d6dZBz8ToaAIAAAAAAEUw7DJwSfrFL36hv//7v5dpmvrud7+rG264QRs3btQf//Efn9UNPc/To48+qnvvvVeJRELLly9XPD5w75BhGIVOnNNfPxNPPPHEWdU5ER577LEh3+to75ZnuTp+5ITS9tDnThdv75T83Ljdrw/+SkcTLQPOtHd2q7Ojbdj7Y3LiO4HzDd9JnG/4TuJ8w3cS5xu+kzjf8J0ceyMGTZs3b9b27dt12223qa6uTtu2bdPf/M3fnHXQVFtbq4ULF6q6ulqS9Ed/9Ee6//77ZVlW4UxTU5Pq6uo0depUnThxovB6c3Oz6urqzuh+c+fOVTR6Zk9qmwiPPfaYrrrqqiHff+C+F+Vbnbpi3htVfdGrR/25J9sOyjyR6wKrmzFFV7184D3sE8+p+fmn9PrXv/6MgzxcuEb6TgLjje8kzjd8J3G+4TuJ8w3fSZxv+E4WRzabHbapZ8TROd/3+4U7r3nNa84pjLj22mv14IMPqr29XZ7nae/evbr++uv13HPPFcbqfvSjH+maa67RjBkzFI1GC4njrl27dM0115z1vUuZ6wTyTU/JeMUZXWdGEwr5uSf1dWXbBz0TjSfl+54cO3vOdQIAAAAAgMlrxI6meDyuI0eOFMKlRx999Jw6hK644gr95V/+pW655RY5jqM3v/nN+sAHPqCXv/zl+qu/+itls1ktWrRI119/vSTpy1/+sj772c+qq6tLl19+uT70oQ+d9b1LmecaUsxRKHaGy8AjcYUcX5LUPcSOpmg8N1qXTXcpEo2dU50AAAAAAGDyGjFo+sQnPqG/+Iu/UHNzs973vvfp+eef11e/+tVzuunNN9+sm2++ud9rCxcu1A9+8IMBZy+77DJ9//vfP6f7XQh815BpujKjA/dZDceMxmW5uY6mTKZj0DPReFKSlE13K1VZc26FAgAAAACASWvEoOn1r3+9tm/frl/+8pfyfV9XXHFFYb8SxkcQBJJnyTIdGaY18gV9mJGELNeTJDnZzkHPxPp0NAEAAAAAAJytIYOmAwcO9Ps5kciFEc8++6yeffZZveENbxjbylDg2J4kQyHTO+NrjWhMIc9WEEienRn0TN+OJgAAAAAAgLM1ZND0D//wD5KkdDqtI0eO6JWvfKUsy9IzzzyjV7ziFdq9e/e4FTnZ2dnc6FvIOvOgyYwkFDZc2U5YhuxBz/QETRk6mgAAAAAAwDkYMmj64Q9/KEn6+Mc/ri996Ut6/etfL0l68skn9bWvfW18qoMkKZsPmqJWcMbXmtG4woYtx07ICg0VNDE6BwAAAAAAzp050oHnnnuuEDJJ0pw5c/TCCy+MaVHoz87mOpkiZxM0ReKKmRnZTkjhwJfv+wPORKJxReNJtZ44fs61AgAAAACAyWvEoCkWi6mxsVGe58l1XX3nO99ReXn5eNSGvHQ614kUD5/5tWY0rpiRlm2HFTcNddgDF4IbhqGaaTN18thL51oqAAAAAACYxEYMmr74xS/q3nvv1bx583TFFVdo586dWrdu3XjUhryu7rQkKR45syfOSZIRiSluZmQ7YcXMQG2ZjkHP1U6bpZPHDikYpOMJAAAAAABgNIbc0dTj0ksv1c6dO9Xa2irDMFRRUTEedaGPjnzQlIiM+OcawDBMhaIRea6lmOWrNdOuizVjwLma6TPl2Fm1t5xQRU3dOdcMAAAAAAAmnxGTiy984QuDvv7Zz3626MVgcJ35oKksdhazc8rtaQp8S5YptadbBj1TO22WJOnEsZcImgAAAAAAwFkZcXSusrKy8H/JZFK//OUvx6Mu9NHVnZEkpeKxs7rejMZl+IYkqaP75KBnqqfOkAxDJ4+ypwkAAAAAAJydETuabr/99n4/f/SjH9VHP/rRMSsIA3V3dStQoPJ44qyuNyNxhdP5z8q0DnomHImqsmaqThw7dJZVAgAAAACAyW7EjqbTJRIJNTU1jUUtGEI6nZVvuiqLJc/qejMaV8jz8p/VPuQ5njwHAAAAAADOxYgdTZ///OdlGLmxqyAI9OSTT2r27NljXhh6ZdOOfMtTWbzyrK43InGFPVuS5KS7hjxXO22Wnn3yMdnZjCLRsxvTAwAAAAAAk9eIQVNVVVW/n9/1rnfpXe9615gVhIGyWVe+5SoZP7sn/pnRhCLuKUmSn8kMea5m+kwpCHTq+GFNu/gVZ3UvAAAAAAAweY0YNFVXV+uWW27p99rWrVt12223jVlR6M/JegpMR6HY2e5oiinidCoIJMNxhzzX98lzBE0AAAAAAOBMDRk0fec731Emk9G//du/KZvNFl53HEf33nsvQdM48rxAslyZkfhZXW9GE4p5rco4UxTyPAVBUBiH7CtVWaNwNMaT5wAAAAAAwFkZMmgKhUJ65plnlMlk9MwzzxRetyxLd9xxx7gUh7yXH1a67YjMyPVndbkRiSuuLnXY0xU3PHU7aSUjA7ujDNNU7bRZOsmT5wAAAAAAwFkYMmh6z3veo/e85z362c9+pj/+4z8ez5pwmkzylMLZUzLOuqMproiRle2EFQv5ast2DBo0Sbknzz3z64eH7HoCAAAAAAAYypBB09e//nU1NDRo3759evjhhwe8/9nPfnZMC0Ov10QqZXc+JzN69kGTYUieH1bcyqgt066LUlMHPVs7fZaeeOTn6mg9qfKq2nMpGwAAAAAATDJDBk2pVErSwKfOYfwtjk1Xa/vDMkKRs7q+Z7dTEEQUtXy1ZTqGPFszbaYk6eSxQwRNAAAAAADgjAwZNL3//e+XJN1+++3jVgwG52fT+a6ksxtlM2NlkiTDiCoa9tSSbh/ybG/Q9JJmv+bKs7ofAAAAAACYnIYMmnr8v//3/7Rx40a1tbUpCILC648//viYFoZevp0+6/1MkmQlKyRJISMkywzUnmkd8mwkGld59RSd4MlzAAAAAADgDI0YNP3jP/6jVq9ercsvv5zl0BMksNMyI7Gzvr4QNMmUJHV2tw57nifPAQAAAACAszFi0FReXq53vOMd41ELhuDb6cKepbNhxpKSaSmc70jLtA89Oiflxueee+qXch1bofDZ7YUCAAAAAACTjznSgSuuuEJ79uwZj1owJFNWWeVZX20YpqxEucK+I0lyuzPDnq+ZPktBEOjk8cNnfU8AAAAAADD5jNjRtGfPHn3rW99SOBxWOBxWEAQyDIMdTeNoyp98TDJGzASHZSUqFPW65UoKsvawZ2v7LASfOnP2Od0XAAAAAABMHiMGTf/2b/82DmVgOKHymnP+DCtZoUhXh1xJlusOe7a8uk6hcEQnj7KnCQAAAAAAjN6IQdOBAwcGvBaPx5XJZPSKV7xiTIpC8VnJCkVPPaOuIKFoIGVdW9HQ4PuXTNNUzbSZOnGMJ88BAAAAAIDRGzFo2r17t371q19pwYIFsixL+/bt06xZs9Te3q6PfvSjet/73jcedeIcWckKGd2tctxyxQ1DbdkO1YWG7pSqmTZLzz7xaGFUEgAAAAAAYCQjLv4xDEPf//739c///M/avHmzdu3apalTp+oHP/iBvv3tb49HjRjBgy+d0OcffGrYM1aiQoGTketGFDeltszwT56rnTZT2XSXutpbi1gpAAAAAAC4kI0YNDU3N/cbkZs1a5aOHz+usrIyWZY1psVhdJ5r7daL7WllXW/IM1ayQpLk+RHFrEBtmY5hP7Nm+ixJuYXgAAAAAAAAozFi0FRRUaHvfe978jxPruvqe9/7niorK/Xcc8/J9/3xqBEj6LCd/D+HXvJtJSry/xZWNOSP2NFUk3/yHHuaAAAAAADAaI0YNK1du1Y7d+7UvHnzdMUVV+hHP/qRvvjFL+rHP/6xPvaxj41HjRhBT8A0XNBk5juaTEUUDXlqHSFoisWTSlXW8OQ5AAAAAAAwaiMuA7/44ov13e9+V+3t7TJNU2VlZZKkZcuWjXlxGJ3OUQRNoXzQFDJCCoUdtXQMHzRJ4slzAAAAAADgjIwYND3//PP61re+pe7ubgVBIN/39cILL+i73/3ueNSHUSh0NGWH6WhKlEuSrECyrEAdbV0jfm7NtFl68Zkn5LmOrFC4OMUCAAAAAIAL1oijc5/4xCfkOI5++ctfasaMGfr973+vV73qVeNRG0bB8wN1Obkl4O35XU2DMcNRGZG4wkHurNPZPeJn106fKd/3dKrpaHGKBQAAAAAAF7QRg6auri597nOf01ve8hZdc801+td//Vc9+eST41EbRqHT6e1iGm50Tso9eS7s2ZIkL50d8bNrpvHkOQAAAAAAMHojBk2VlZWSpEsuuUS/+93vVF5eztPmziN9x+VGDJoSFYo6nZIkc4SzklRZM1VWKMSeJgAAAAAAMCoj7mi65JJL9MUvflE33XSTPvOZz6i7u1u2bY9HbRiFvuHScDuapHxHU8cxSSGFfV+u7ylkWkOeNy1L1VNn8uQ5AAAAAAAwKiN2NP393/+95s+fr8svv1zvec979PDDD+sf/uEfxqM2jEJnfi9TbTyijmF2NEm5oEldHZKkhEy1ZztG/PzaaTMZnQMAAAAAAKMyYtAUj8e1ePFiSdItt9yif/qnf9JVV1015oVhdHo6mi5KxdQ+itE5v6NNQSDFDUPtmZGDpppps9Td2a7ujrai1AsAAAAAAC5cQ47Ove51r5NhGENe+Pjjj49JQTgzHbarwPZkdXnqtF0FQTDk381KVsgIfHl+WDFLah1F0FQ7faYk6cSxQ7o4VVHU2gEAAAAAwIVlyKBp7ty5ev7553XjjTfqxhtvVHl5+XjWhVHqtF1lD3XpJw8dVe01M5R2fSXCg+9dspK5oMj3I4pbvtoy7SN+ft8nz138yjnFKxwAAAAAAFxwhgya7r33Xh05ckS7du3Spz/9ab385S9XfX293vrWt8o0R5y4wzhpt13J9uR5gbyspw7bGTFokiKKhHy1pkceh4snU0qWV+rEUfY0AQAAAACA4Q2bGF100UVavny5fvjDH+rP//zP9Z//+Z9asmSJNm7cOF71YQSdtqvA9iVJXtod9slzViIXNFkKKxJ2dKq1c1T3qJk2SyeP8eQ5AAAAAAAwvFG3Jr3sZS/TpZdeqmg0qv/8z/8cy5pwBjpsV27WkyR5GbewHHwwPR1NlkxFIo7a2rpHdY/aaTN1qumIPG/4ZeMAAAAAAGByG3J0TpKy2ax+9rOfadeuXXryySe1ePFi3XnnnbryyivHqTyMpMN2ZacdSfmOpmGCJjNeJhmmQkGgSNhVZ0d6VPeomTZLvueqtfmYaqbNLErdAAAAAADgwjNk0LR69Wr9/Oc/1/z58/Xe975Xb3vb2xQOh8ezNozADwJ1pm05Ts/onKd22xnyvGGYshLlCvuu3IivbOfQZ/uqKTx57iWCJgAAAAAAMKQhg6Zdu3ZpypQpevHFF/WVr3xFX/nKV/q9/8Mf/nDMi8PwumxXbtYv/Bxkht/RJElWslxhL6u0JCMzulG4qtrpMi2LPU0AAAAAAGBYQwZN3/zmN8ezDpyFDtuVb+f2M9VVxXWqyx52dE6SrGSlzGxaSkoh15Mf+DKN4Vd1WaGQqqZcRNAEAAAAAACGNWTQdPXVV49nHTgLHbYrP78I/DUvq9GeXx5SS5c97DVWokI62SxVxxWXqS67W6lo2Yj3qqiZopbmY0WpGwAAAAAAXJhG/dQ5nH86bFee3RM0VUmSTrUO/yQ5M1mhoKtDkpQwTLVlOkZ1r1RlrTpaTigIgnOoGAAAAAAAXMgImkpYT0eTZRm6dFalJKmlLTPsNVaiQkrnwqi4aag10z6qe6Uqa+Q6tjLdnedUMwAAAAAAuHANGTT9+7//+3jWgbPQ09FUlYppWk1SktTVYcsfpuvISpbLcH0FgRQzA7Vm2kZ1r1RVTe6erSfPvXAAAAAAAHBBGjJo2rVr1ziWgbPRabsybF/V5VGVJyMKh025aVddjjfkNVayUoakQGFFw55OtY92dC4fNLWcKEbpAAAAAADgAsToXAnrsB0Ftq+qVEyGYaiqIiYv46oj6wx5jZWokCQZiigSdtTSOrpRuFRlbe6edDQBAAAAAIAhDPnUuZdeeknLli0b8sKvfe1rY1IQRq/DduVmPVWXxyRJU6oTOnW4VR22O+Q1VjIXNJkKKRJx1NY2/PLwHrFEUuFITB0tBE0AAAAAAGBwQwZNqVRKixcvHs9acIba0o5c21NVPmiaWp3Qwd+fVNuwHU3luX/KVDhsq7MjO6p7GYahVFWN2lsZnQMAAAAAAIMbMmiqrKzUTTfdNJ614Ay15kOi6vKoJGlmbZkCP1BTa1q6aPBrzEhMRjimcBAoEnaVbhk6lDpdqrKGjiYAAAAAADCkIXc0BcM8uQwTzw8CdXblgqaejqaL68okSUdPdg17rZUsl+W5ikQc2V1DLw4/Xaqyhh1NAAAAAABgSEMGTXfdddd41oEz1O14crO5kKg6lQuaptckJUlNp4bfu2QlK2XYtizLV2CPPlRMVdUom+6SnU2fQ+UAAAAAAOBCNWTQ9MlPfrLw7//1X/81LsVg9DpsV37WlyRV5Ufn6qoTkqRTrZlhr7USFTIyuTNxz1TGHd2epsKT5xifAwAAAAAAgxjV6NymTZvGpRiMXoftyst6MiRVluWCplgkpEjMUlv7CEFTskJBV268LmmYasu0j+qe5VX5oInxOQAAAAAAMIghgybDMAr/zr6m80+n7ci3PaXKIrKs3j9jsiyqrk572GutRLnU3SlJihuGWjMdo7pnqrJGktTewpPnAAAAAADAQEMGTX31DZ1wfsiNznmqyu9n6lFeEVVmpKApWSHDcSVJcdNQa/foOpoSZeUyrRAdTQAAAAAAYFChod5ob2/XT3/6UwVBoI6ODv3kJz/p9/473vGOMS8OQ+vIuvJsTzVT+gdNNRVxvfBsizKOq1h48D+vlayU4eYWiUcijk60tEmXjHxPwzSVqqxWBx1NAAAAAABgEEMGTRdddJG++c1vSpKmT5+ue++9t/CeYRgETROsw3YV2L5qyvsHTVOq45KkF5o69OoZVYNeayUrZLi5ReLhsKNTrZ2jvm+qspaOJgAAAAAAMKghg6a+wRLOP+0ZR17WU/VpQdP0mqQk6cXmzqGDpkSFDEmGwopEHJ1s7x71fVNVNXrht78567oBAAAAAMCFa8gdTXfccUfh30+dOjUuxWD0TnVkJUlVpwVNs6akJEmHmruGvNZKVkiSTIUUCTvqbB9+p1Nf5ZW16u5ok+s4Z1oyAAAAAAC4wA0ZND3xxBOFf//IRz4yLsVg9Fo7MpKk6vJov9dn1iQlQzp2cuigyYyXSYYpS6YiEUfpztGHRqmq3JPnOtsYnwMAAAAAAP0NGTQFQTDov+P80JZ/stzpHU2V8bCsWEjNLUOPwxmmJSuRkuUHCkccOd3+qO+bqswFTe0tBE0AAAAAAKC/IXc09WUYxljXgTMQBIG6unKjc9Wp/kFTPGQpFA+ppS0z7GeYiQoZrqdIJJCXGf3fN1VVK0nqaOXJcwAAAAAAoL8hgybf99XW1qYgCOR5XuHfe1RWVo5HfRhEt+vJzXqSpKrTRucMw1A8GVZ70/ALvq1khWSnFUlIRjYs23MUscIj3jtZXinDMNRBRxMAAAAAADjNkEHTM888owULFhTCpTe+8Y2F9wzD0FNPPTX21WFQHVlXftZXPBZSOGQNeL+sPKrWlzrUnXGUiA0eHlmJchnpFlllEUW9kNozHapNVo94b8sKKVlepY5WgiYAAAAAANDfkEHT008/PZ514Ax02q4821NlWXTQ9ysrYjok6fipbs2+qGLQM1ayQjqVlhRRQqZaM+2jCpokqbyqlqAJAAAAAAAMMOQycJy/OmxXftZTZWrwoKm6Mre36djJocfnrESFlElLkqIhXyfb20Z9/1RljTpa2NEEAAAAAAD6I2gqQR35jqaaitig70+tTkqSjp/qGvIzrGSlTDe35ykScdR8qn3U909V1aizvUW+551B1QAAAAAA4EJH0FSC2rOO/Kynusq4JOk//vs5fXTdzwr7tGpTURmWoSMnhguaKmS4viQpEnbV2to56vunKmsV+L4621vO4bcAAAAAAAAXGoKmEnSqIysFUm1FLmj67QundOREl062ZSRJ5dGwrHhIh0cMmnIdSeGIo7b29Kjvn6qskST2NAEAAAAAgH4ImkpQc1suFKpO5UbnmltzPx9q6pAkpSIhWfGQjp8abkdTeZ+OJkdd7dlR3z9VlQ+aWgiaAAAAAABAL4KmEtTaketcqirPLQNvbukJmnLjb6l8R9PJ1nRhnO50VrJChiRTIUUitjJd7qjv39vRxEJwAAAAAADQi6CpBLV12pKk6oqYPD/QybbTgqZISFbMkuv6aukYvFPJjMRlhKMyZSocc2R3+6O+fygcUaKsnNE5AAAAAADQD0FTCersygdNqZhaOzJyvVzXUs/oXHkkpFA8JEk6fnK48bkKmZ4Uizry0sYZ1ZCqrGV0DgAAAAAA9EPQVGKCIFC621E4bCoWDRXG5sqTkUJHUzRkKVYWkSQdOzXCQnDPUyTiysiG5PneqOtIVdWovYXROQAAAAAA0IugqcRkXF9uxlUymQuSeoKmK181RSfbMurOOJKkyvz+phEXgtuuImFXISeqDnvoUOp0qcoadbadVOCPfuQOAAAAAABc2AiaSkyH7cqzfZXnO5aaW3NB0utfXSdJOtyc62qqiEcUjYd07OQIHU3ZrMKWo5AbVUtX26jrSFXWyHNddXd1nO2vAgAAAAAALjAETSWmw3bkZz1VpmKSch1NyVhIr7q4SlL/heDhRHj4jqZkpZROyzR8maanplOto64jVVWbq4fxOQAAAAAAkEfQVGI6bFe+7ammPBc0NbWkNaUqoWk1SZmmUQiayqNhWTFLx4ZbBp6skOG4kqRIxNGJU+2jriNVVZOrhyfPAQAAAACAPIKmEnOiM6PACzSlMi4pNzpXWxlXOGRqek2i8OS5skhIftTUyba0HHfwPUpWokKGm1sAHgk7amntHHUd5ZX5jiaCJgAAAAAAkEfQVGKaW3PLv6dVJXI/t6Q1pSoXOs2sS/V2NEVCMmMhBYHU3DJ4V5OZLJeRD6EiEUcnW0e/bykSiysaSzA6BwAAAAAACgiaSsyJtowkqa4iru6Mo860o7p86DSzrkxHmrvkeb5S0ZCsWEiSdGyIPU1WokJmvqMpHHbU1jr0mN1gUlU1am+howkAAAAAAOQQNJWYU+25oKmqPKoT+e6mnjG6mXVlcj1fx1u6lYqEZcUtSdLxIZ48ZyV7R+eiSVvpVu+MaklV1qijlY4mAAAAAACQQ9BUYto6spKk6vJYYYyu7+iclHvyXCoSkhm1ZFnGkE+esxLlMtxAkpRMeQq6wrJde9S1pCpr1dF6UkEQnPXvAwAAAAAALhwETSWmo9OWaRpKxsNqaunpaMqNzs2oK5MkHTqeC5oMw1B5Kjrkk+cM05KVSMkMTJUlA0UzSR3pOD7qWlJVtXKyGWUzZzZyBwAAAAAALkwETSUm3e0olgjLMAw1t3TLNA1Vl0clSalERJVlUR1q6lAqktvPVJaK6vipwUfnpHxXk28omZAsL6wXm4+NupZUVY0kqYM9TQAAAAAAQARNJSUIAmUyjpKJsKTcE+hqK2KyrN4/44y6Mh1q6lTYMhUPWYolw0N2NEm5PU2m5ysRy42/vXikedT1pCrzQRN7mgAAAAAAgCYwaNqwYYNWr14tSXrqqaf07ne/W4sXL9ZnPvMZua4rSTpy5IhuvfVWXX/99frYxz6mrq6hO3Mmg6zny814SpVFJEnNLWlNyT9xrsfMfNAkSalISKF4SJ3p3NPpBmMlKmQ4rsJW7v2m422jrqe8J2hqIWgCAAAAAAATFDTt27dPO3fuLPz8qU99SnfccYd+/OMfKwgCbd++XZL0uc99Trfccovuv/9+zZ07V1u2bJmIcs8bHbYr3/ZUmYpJynU09TxxrsfMupQ6um21dWaVioZkxnMjdEM/ea5SRtZW4KcVKFDbycyo64klUwqFI2pndA4AAAAAAGgCgqbW1lZt2rRJy5YtkyQdPnxYmUxGV155pSSpvr5e999/vxzH0YEDB7R48eJ+r09mp7qyCtxA1eUxeX6gk63pwhPneszsWQief/KcHzEkaegnzyUrJDsr1+lWKBko3eaPuh7DMJSqrFFHK0ETAAAAAACYgKDp7/7u77Rq1SqVl5dLkpqamjRlypTC+1OmTNHx48fV0tKisrIyhUKhfq9PZkdbcmHRlMq4Wtoz8vxgkI6m3qCpPBKWHcoFTUPtabIS5TIdT4HvqKzKkJmOqiPbOeqaUlW1BE0AAAAAAECSFBrPm+3YsUPTp0/XwoUL1djYKCm34Pp0hmEM+fqZeuKJJ8680Any2GOPDfv+rw9lJUmZ1ibtfTi3tLvt5GE99tipwhnfDxSypMf+5/eqelWlugMpFjb0xG+f1yXlA/cvhY+fUNjNdTFFY2lFM0k9sH+PZiamjarmrOurpfnYiLWjNPF3xfmG7yTON3wncb7hO4nzDd9JnG/4To69cQ2a7rvvPjU3N2vp0qVqa2tTd3e3DMPQiRO9y6Sbm5tVV1en6upqdXZ2yvM8WZZVeP1MzZ07V9FotJi/xph47LHHdNVVVw175uGTByU1602vv1xNJ7skNWvhG16rS6aV9zs3c0+HHCOuV10yS48fPKTpU1LyrNign585lNTzz/0/SdIrXzFFTb/vUrymUlddPnwtPYKOozr+hyf12nlzFY6c//+dMXqj+U4C44nvJM43fCdxvuE7ifMN30mcb/hOFkc2mx22qWdcR+f+9V//VT/60Y+0e/durVy5Um9/+9u1bt06RaPRQqq4a9cuXXPNNQqHw5o/f77uu+++fq9PZqfac4u6p1bG1dySlqQBo3NSbnzucH5HkyRVVcZ0fMjRuQoZ+Y6maTW5z3rpSPOoa0r1PHmO8TkAAAAAACa9CXnq3Om+/OUva926dXrnO9+pdDqtD33oQ5KkO++8U9u3b9cNN9ygRx99VB//+McnttAJ1tqZlQypoiyqppZuJeNhJWLhAedm1qV0/FSX4lbuz1teHtPxU93y/YHjiFayUqbrSZLKUrnXmps6Rl1TeWWtJKmj5cQIJwEAAAAAwIVuXEfn+qqvr1d9fb0k6bLLLtP3v//9AWdmzJihe++9d7xLO291dtoKR0MyTUPNrelBu5mkXEeTH0iZLkeSlCiLyPV8tXRkVFPR/xojEpMpS5IUjbiSAnWcyo66plQVHU0AAAAAACDnvOhowuh0dTuKxXPZYHNLWnVViUHP9Tx5rq01N2oXS+S6no6c6Bpw1jAMhSJlMgJDTrZFobJA2fZAfuCPqqZEqlKmaamdjiYAAAAAACY9gqYSkk07SiYjkpTraKoavKNpxpRc0NR8okuGpGhZLmg61NQ56PlQskIhz1S6/bDKqiIKpxM61d06qppM01RZZTUdTQAAAAAAgKCplNhpV2XJiLozjrrSzpCjc7FoSFOq4jrS3KVkJCQ/YioWsfTS8cF3L5mJCoUynro7jqhmSlKRTEKH24+Nuq5UZQ1BEwAAAAAAIGgqFV22K9/xVZmKqrk1/8S5ITqaJGnmlDIdaupQKhJSh+NpZl3ZkEGTlayU2dktz01rxrSILD+sF5vPIGiqqlVHC0ETAAAAAACTHUFTiThyKrdfqbo8puaWfNBUOfiOJkmaOTWlQ02duaAp6+Z+HjJoKpfZ0p7/TFuSdPjIqVHXlqqsUVdHqzzXHfU1AAAAAADgwkPQVCJ6gqbaipiaW7olSXXVw3Q01ZUpY3uKGoY6bEez6lI60ZZRd8YZcNZKVsjqSksyFA21SJJONA8eSg0mVVkjBYE620YfTgEAAAAAgAsPQVOJOJ7vYppWlVBza1qWaagyFRvyfM+T5+T66rBdzZqa+3mwheBWokJGECgaq1bgNktGoI4We9S1lVfVSJI6WnnyHAAAAAAAkxlBU4lobssFTdOrEmpuSaumMi7LNIY8P7MuJUlyMp66HE8XTcn9fKhpYKeSlayQJMWilUp3HlG4THI7TDnewO6nwaSqaiVJ7expAgAAAABgUiNoKhGn2jOSpIuqk2puTQ/5xLkeVamoErGQujtznUllqYhClqEXjw0WNFVKkiJGQk62XdVTpEgmoeOdo+tQKiuvlgyDJ88BAAAAADDJETSViNaOrMywqVQ0pKaW7mGfOCdJhmFoZl2Z2vJPqEt7nqbXlg05OidJET8sSbpoqqtIJqnD7aN78pwVCimZqiRoAgAAAABgkiNoKhEdnbbCsZB8P9DJtozqqoZ+4lyPmXUpnTyZC5ra83uaXhrkyXNWslwyTFndue6nKVWOLD+kl5qaRl1fqqpGHS3saAIAAAAAYDIjaCoRXd22ovGQTrVn5fvBiKNzUm4heEt+iXhH1tWsupSOneyS43r9zhmmpVBlnfzWZkXi1UpE2yVJh4+N/ilyqcoatZ1skp3NnMFvBQAAAAAALiQETSUik3aVTEbU3NotSSOOzkm5oMm3c6FS7slzKfmBdKS5a8DZcPV0OaeOKZGaocDLdTKdah44ZjeU2mmz1Nl2Svd8YaXuu/erevrxh5RNd4/6egAAAAAAUPpCE10ARub7geyMq7JkRM35DqXRdTSlFLiBDEnttqMrp+aePPfi8Q5dMr2839lw9XRlXnxK8dT1am36H4XCjtpbR/fUOUl6/TXv1LSLX6Fnn3hMzz75mP5w8HGZlqWZr7hcr5h7lV7+mtcpXlY+8gcBAAAAAICSRdBUAtq7bCmQKlIRNeeXe0/ps6PJd7Ly0x0Kldf2u25aTVKmaSgsqdN2NaOuTIYhHRpkT1O4aroCJ6NYOLcYvKauSyc7w+qyu5WMjLwPyjBNzXj5ZZrx8sv01iUf0PFDz+nZJx7Vs088pp83/pt+Yfy7Ln7Va7X4A8sUicbO4b8GAAAAAAA4XxE0lYCm/LhcdSqmppZupRJhxaO9f7q2h3erbf+PdMmqf5VhWoXXwyFT02sSkhuoPesqGrZUV5XQS4M8eS5cPT33Tzf385S6rJ7/bUJHO5p0ac3LzqhewzQ17eJXaNrFr9Cb3vlenTj6op567EH95qGf6aXfPaFXzJ1/hv8FAAAAAABAKWBHUwk4cioXNNVWxNXcktaUyv4dRnbTi/IzXXJbjw+4dmZdSk7GVYedS5BmTU0N+uS5nqApaGtTKJJSdWVakUxSh9uPnVPthmFoykWX6E3Xv1emaanp8PPn9HkAAAAAAOD8RdBUAo7lO5rqKuM60ZoesAjcyQdM9onDA66dWVemdJej9mxu39KsqSkdbu6U5wf9zoUqpkhmSE7LUSXKZ6gs2iHLD+lQU3NRfodQOKzqqTMImgAAAAAAuIARNJWAnr1M06sTam7pHrAI3G3NPSXOOXFowLUz68rkZT11ZPMdTXVlclxfx0/1f/KcYVoKV02Vc+qoEqmLZAQtMk1fR4+dKtrvUTfjEjUffkFBEIx8GAAAAAAAlByCphJwsi0jI2Qoblnqyrj9Opq8dKf8TG7nkn1ysKApJd/2lPV92Z6vWfknzx06PvieJudUrqNJCpQq61LLie6i/R51M2cr092pjtaTRftMAAAAAABw/iBoKgGtHVmZEUuZ7tz4W98nzvV0M8kw5QwyOjejrky+7UuSOmxXM/NB01B7mtyWY4qX5fY1lVd0qKvNK1oH0pSLLpEkxucAAAAAALhAETSVgIzjKhQPqaM9I0n9Opqcttx+puiMV8k+eXhAKJRKRBS3cn/m9qyjsnhYVamoXmoaJGiqmq7AtWW5khWKqbqmS1Z3VC3ptqL8HrXTZ8m0LDUdeq4onwcAAAAAAM4vBE0l4LVXz9CM19bqRFs+aOqzo8ltyQVNTtd0BdlueZ0tA66fkoxKkpq6s5JyC8GHGp3LfeYxxVMXqbKiS5FMUkc6Bj7N7mxYobBqps5Q8+EXivJ5AAAAAADg/ELQVAL8iKWqiriaW9IKWYaqUrHCe07rcclKyOmszv08yELwi6sSCoJAxztzQdWsqSm9eLxjQPdTT9DUs6cpEe1UzI7rSHtxgiZJmjLjZWo6/DwLwQEAAAAAuAARNJUA1w9UHYuouSWtmoq4TNPofa/1uIxQpWTkgiZ7sKCprlxextNLbbnF3rPqypTOujqVH8XrYZXXyAhF8k+emyHT8JSK2TrU1FS036VuxsuUTXepo+VE0T4TAAAAAACcHwiaSsCtc2fpz+ZerObWbtX1WQQu5ZaBB0G5pKRkxeScHLgQfGZdmbwuR4fzwdJQC8ENw1SoapqcU0eUSM2QJFWUd+rY8dai/S5TZrxMEgvBAQAAAAC4EBE0lYApiaiq4xE1taT7LQIPfE9Oa5N8LyUZhszolEE7mi6akpTb7aolaysIAs0qBE2D72lyTh1VLDlFhhFSeXmnWk+mi/a71E6bmVsITtAEAAAAAMAFh6CpRHier1Nt6X6LwL2OU5LvSUaFJClQ1aA7mqZWJeSnXbmSWjKOqlJRJWOhwZ88Vz09t/dJUjw1XeXlHcq0BXJ9ryi/R24h+Ew1EzQBAID/n737jo6rPBM//r3T+4xGfUa9WbZly72DjcE00wkhQCAhvWzKJmFTNvtL2fRNNpsNm95IAgm9GzDGHfdeZEu2eu+jMiNNvb8/xpYtVNxksOH5nKODue973/ve8djn+DnP87xCCCGEeNeRQNNlorN3kJjKsIymsC/eO0lVHJg8dqIhB1G/j+igf9i9Wq0Gp04LQKt/EEVRyEy1jyidA9AnpEM0QqS3A4vDi9PhRz9ops0/cT2VUjJyaGuslYbgQgghhBBCCCHEu4wEmi4T7d3x8rVk16keTZETmUcoTqz5blTGPnku1Ro/qa7FHwTiJ881jFE6BydOnrN70OsiJKCjuW/iGoInn2gI3tvdPmFrCiGEEEIIIYQQ4p0ngabLRLvvRKDp9Iym7lZAAxoHlpyEcU+ey0i0oEZitPSfaAieYsfXH6QvEBo2b1igyRFvCJ5kjNHY0zJh75JyoiF4e2PthK0phBBCCCGEEEKId54Emi4T7d0BgGE9miK+VhS9E73TgiHRAhonKFrCnSMDTd4kO5FAhIae+DqZqTZg5MlzWlsCit5EuKsZsy0dFQWXLUBj28RlHyWmetFodbQ1VE/YmkIIIYQQQgghhHjnSaDpMtHePYDdYsBk1A1dC/vaQOtE7zSh0WkwuK0ohkTCHY0j7vcmW4kEwkMZTWOdPKcoCvqENMJdzWi0erT6RJyOftraeifsXbQ6PYlpGbRJRpMQQgghhBBCCPGuIoGmy0S7b2BY2RzEM5rUmAOdM95/yZBsRcVNqHNkoMmTZCMSiNAbiRKKxkhOsGDQa0dvCJ6YTrirCQCLw4vD3o+vc2BC3yfFm0N7Y400BBdCCCGEEEIIId5FJNB0mWjvDgwrm4uFBon6fcSidvROIwDGJAuxiJOIr5VYZHjvpSSXGQajALT5g2g1ChnJNurbRj95LuJrQ41GcLozMZlC6AZUBsODE/Y+Kd4cgoMBerukIbgQQgghhBBCCPFuIYGmy0S7b4AU92knzvWcOAVOiZfOQTyjCSURVJVwZ9Ow+zUahQSDHoBW/4mG4Kk2GkbLaHKngxoj0tOG1ZkBQJpOS9OEnjyXDUBbo/RpEkIIIYQQQggh3i0k0HQZ8A+ECQxGhmU0xU+cA/W0QJMxyYp64uS58Cjlc+n2+LwW/6k+TW3dAwwGI8Pm6d2e+BpdzVjs8V8nGWI097dO2DslpmbEG4JLnyYhhBBCCCGEEOJdQwJNl4G2kyfOJQw/cQ4AxYnOES+d0zmNKAY3oBDqGHnyXGaSjehg5FRD8JR4Q/CG9uENwfXudCAeaNLqzURjNtzmIE29Exdo0up0JKVl0N5YM2FrCiGEEEIIIYQQ4p0lgabLQLsv3oh7WEaTrxU0RhSdBa0lXhKnKArGZBfonIRHCTR5kq1E/BEaeuLrZaTaAEaUz2ksDhSjhXBXMwBaQypOh5/G1o4Jfa+UjFzapCG4EEIIIYQQQgjxriGBpstAe/eJQFPCaT2afG0oehd6l5mKigqeeuopYrEYhiRL/OS50QJNSTYigTBtA0FUVcWTZEOjUah7S6BJURT0CemEu+OBJrPdi9UySF1jIzE1NmHvlezNJjQ4QE/nxPV+EkIIIYQQQgghxDtHAk2XgfbuADqtBpfNOHQt7GtFVVzonUYqKyupra2ltrYWY7IVVU0g3NWMGosOW8eTbCUaiBCKqfSGIuh1GtITrTS09b/1kejdaUMZTa6kLADsoSjHO2sm7L1SvDkAtEn5nBBCCCGEEEII8a4ggabLQLtvgGSXGY1GAUBVVSK+NtSoDZ3TRFdXFwAHDx7EkGQBxQ3RMBHf8Ewht8OEEopnJA31aUq1UT/qyXMeIj0dqJEwSWm5AKTrtGxv2Dth7+VO8aLV6aRPkxBCCCGEEEII8S4hgabLQGAwQnqSdej/o/4e1HAQVXWgcxjp7u5GURQqKyuJWpWhk+dCbzl5TlEUkkzxfk6t/iAQP3muucNPJDq8JE7vTgc1RtjXisHsIBQ2km7QsaN+34T1VNLqdCSmZUpGkxBCCCGEEEII8S4hgabLwCdvn8Zn7yod+v+TJ86pOAmbVEKhENOnTycWi1FefQytNRVg1IbgXpcVYiot/nhGU0aKnWhMpbnDP2ze6SfPAeiMHtzWPgYatNT6Rq57vlK8ObQ31qLGJq73kxBCCCGEEEIIId4ZEmi6DKQlWkk5rRF4+ESgCcVBvxpvFF5QUEB6ejqHDh3CkOwGjZVQR+OItbzJViKB8FDpXFaqHWBEQ/C3BpqyC2dgMQcpaMtja/W+CXu3FG8OoeAAPV3SEFwIIYQQQgghhLjcSaDpMhTpPhloctIbjmciud1uSkpK6OzspNcSRMU9akaTJ8lK2B+hsTceoMpIsQHQ8JZAk9ZsR2O2DQWanMmTAEh3BTi0ceKCQikZOQC0NdRM2JpCCCGEEEIIIYR4Z0ig6TIU9rWh6B1ozCZ6+nrQ6XTY7XYmTZqETqejqr8RlQRCHQ0j+imlJ9mIBsJ0B8NEYjFMRh3JCWbqW0c5eS4hnXB3PNBktCRitCSRkePDUJ/CgfLKCXmXhBQPWp2OtqaaCVlPCCGEEEIIIYQQ7xwJNF2GIr5W0LnQO+ONwBMSElAUBaPRyKRJk6hqrSWqSUANBYj2+4bd60m2EglEUIH2wImG4Cl26ttGO3kufSijCcCRWESCtRdVP8hrzxwhFrvwpuBarY6ktCzJaBJCCCGEEEIIId4FJNB0GQr7WlFxoHeY6OrqIiEhYWispKSEcCRMh6KNz+0cXj7nshnRReIBopb+eKApI9VGQ1v/iMCR3p1OtLeDWDg+z5FYhBoLk1DawEAH7N5SOyHvk5KRQ3uTNAQXQgghhBBCCCEudxJousyo0TDR3k7UiB3FbqCnpwe32z007vV6SUhIoFYXD9qE3tKnSVEUUi0GgKGT57JS7YTCUdq6A8Pm6t0e4FRPKLs7HxQN03MM9DvaeWPVEfp6By/4nZK92YSDg/g6pSG4EEIIIYQQQghxOZNA02Um0tMOqKiqg0FDGFVVh2U0KYpCSUkJzbEgqqIftSG4121DDcdo9Z/IaEqJnzzX0Da8T9NbT57T6kzYXDkkxgI05RwiHImy+vmyC36nFG8uAG2N1Re8lhBCCCGEEEIIId45Emi6zIRPZBepioN+4ifHnZ7RBDB16lQURcMANkLtIwNN6clWwv0hmvvj2UiZqfFAU11L77B5pwJNTUPXHIlFhP1t5KQ6ieS1c3hfE8ePXlgmkjvFg1anp71xYkrxhBBCCCGEEEII8c6QQNNlJuKLB5pQXPRF4qVup2c0AVitVrJSM+hS9CNK5wA8STYigQjNffFAlcNqIMll5nhDz7B5GqMFrdU5oiE4wGK3l3LnblxJZl555hDhcPS830mj1ZKUnklbY815ryGEEEIIIYQQQoh3ngSaLjNhXxtodICVnoFerFYrRqNxxLxpJdPo0ZiIBXzEBv3DxrzJViL+MAPRGP2hCACFmS6O1XePWEeXkE64+1SgyeLwotNbydQqxDRRPAs1dHcG2Lzm+AW9V4o3h/ZGaQguhBBCCCGEEEJcziTQdJmJ+FpR9AlobQa6fb4R2UwnFZQUMaDES+JCnY3DxjzJNqKBeIDpZEPwoqwEWjoD9PpDw+bq3enDMpoURYM9sZBoXz3ptmSOcpBps728ue447a195/1eyd4cwqFBfB2t572GEEIIIYQQQggh3lkSaLrMhLtbQetE7zTR3d09oj/TSVqdlgRLDgB9DcOzjewWA0Y1/uvW/nhD8MJMFwDH633D5urd6UT7u4mFBoauORKLiIT6uSK1iMNt5Sy6NgeDQccrzxw67/dKyYjvdefaFzh2YAfd7c3EJLtJCCGEEEIIIYS4rOje6Q2IcxPpaUNlEjGbhoHmgTEzmgByMkqJ9qyi9dhBkuffOGwszWamV1WHMpoKMlwAHKvvZlZxytA8vdsDQLirBWNa/HS4k32aJpstRNUYR/qOsnBZHuteKae/L4jNPrKU70zcyR68ecUcO7iDiv3bANDpDSSmZpCYnkFSWiZJ6VmkZOSi0+vPeX0hhBBCCCGEEEJcfBJouoxEB/qJDfqJ6WwM6sPAyEbgp0vKSKOhzEqguQZVVVEUZWjMk2ylZzA6FGiymvV4k20cGyWjCSDc3TwUaDKYnJhsaegH2kk0J7CjYR/3FHwAKKe+uovJ09PP+d00Wi23f/yrRCNhutqa6GiuP/FTR+Wh3ZTt3AhAfskcbrjvs+e8vhBCCCGEEEIIIS4+CTRdRk6eOKfixK/ES9nGKp0DMCRZQHFjDrfR0NBAZmbm0Jg32ca+xnZa+geHrhVmuThwrH3YGvqENIBhfZoAnIlFtNW9yXxvKa9XbeEzc4zodBrqzjPQdJJWpyfZk02yJ3vomqqq+Ht9bHrpMeqPHyYWi6HRSNWnEEIIIYQQQghxqZF/rV9GwicCTShO+iIBNBoNTqdzzPnGZCtGJRWrOsih/XuHjXmSrEQCEdoCQaKxeMOmwkwXXb1BOntO9WPSGExobe4RgSZH0iRUNcpsZzLhWIQD7UfwZLmor+6aoLc9RVEUbM4E8qbMIjQ4QGdLw4Q/QwghhBBCCCGEEBdOAk2XkYivLf4LxUHvYD8ul2vczB6tRY/OmIICNFccYnDwVPaSJyl+8lxMhY6BeEPwosx4GV5FXfewdeInzzUNu2Zz5aJodDjD/TiMNnY07CUr101zYy+hYGQC3nYkT268N1RTdflFWV8IIYQQQgghhBAXRgJNl5FwdyuKzgoaI74+37j9mSCeCaRLzADAEumjqelUsMiTbCUSiPd5ajlx8lyu14lWo4zap+mtGU0arR57Qh59XceY653BnuZDpGc7UGMqjXXD758odlcidlciTTUVF2V9IYQQQgghhBBCXBgJNF1GIr5WFEMCWpsBX0/PuP2ZTjKlZ6ICdnWAzs7OoesWkx6rEv/tbz3RENyo15Kd7uDYWwJFenc6sUAvsUH/sOuOxCIG/W3MSylgMBLEZ2oDBeouQvncSZ7cSTTVVKCq6kV7hhBCCCGEEEIIIc6PBJouI2FfK6riJGRTiUajZ8xoAjCmJgAOXEpoWKAJwJNgQYmqQyfPQbxP07EG37BAjj7hxMlzo/RpAvAoMSx6M3s7DpCaZr8ofZqG9pxTxEB/L76Olov2DCGEEEIIIYQQQpwfCTRdJtRYlEhPO2rUTsAQAsY/ce4kQ5IFNG4cDI4MNCXZiAbCtPqDQ9cKMxPwD4Rp7jiVvaRPPBFo6h4eaDJZU9EbnfR3H2e2Zxo7GveRlmWnobabWDR23u86nqE+TVI+J4QQQgghhBBCXHIk0HSZiPR1QixKLGKjXxMPDJ1VRlOSBVVxY47009nRMSxTyZNsZbAvRHP/qYymoiwXABWn9WnSJaQByoiMJkVRcCQW0dd5jJsnXU0wEqRKqSAUjNLa3Hf+LzsOV1IaZpuDpmoJNAkhhBBCCCGEEJcaCTRdJiLdrQCoipP+6AAmkwmz2QxAy2ur2fv5fx21b5HGoENjSUWjRtGH+unt7R0a8yTZiPoj9IUiBMJRALJS7Rj0Wo7Vnzp5TqMzoHMkjgg0ATiSiohGBkhSVG4uXsGuge3AxevTpCgKnpwiOXlOCCGEEEIIIYS4BEmg6TIR9rXFf6E46Q32kZCQgKIoAPj27idQW0eoa/TgjuHEyXMONTCsfO70k+dONgTXajXke50jGoIbvYUEKvcSCweHXXe4CwGF3s4K3jflRtyJdqLGILWVHRf6ymPy5BTR5+ukz9d55slCCCGEEEIIIYR420ig6TIR8bWCogHs+Pp7h/VnCtTXAzDQ0DjqvcaMPFTAqfqHBZrSE61EAhGA4Q3Bs1xUNvYQPa3PkmPWdcQG+ug/vHnY2jqDFYsjg97Ocgw6A5+Ycy99tg6OHW+5aCfDeXLjTcilfE4IIYQQQgghhLi0SKDpMhH2taIYXES04A/4h/ozxcJhBpriJW1jBZpMqQmguEhUhjcENxl1uAw6UKGlf3hD8FA4Sl3rqT5LpuwS9MlZ9O56ZUQAyZFYhL+nnkh4gGmpxXiznUQHFMpqqifs/U+XmJaBwWimqUbK54QQQgghhBBCiEuJBJouExFfG4rOxaA1nmV0MqNpoLERYrFTvx6FMdkKSjLOWICOjuElbZ5EG5pwbKh0DqAo0wVAxWnlc4qi4JxzA6HWaoINR4et4UgqAjVGX9dxAG5fsgyAf25YTSw28afPaTQa0nMKaao5NuFrCyGEEEIIIYQQ4vxJoOkyEfG1ouIkYAoBp06cC9TFy+a0FguBMTKa9AlmVG0Kpqif3s62ESfPhf3hYaVz6UlWrGb9sIbgALaSK9GYbPTsfHn4dWc2Gq2R3s54hlFuZipag0JfS4zVlRsv8M1H58ktorutiUB/75knCyGEEEIIIYQQ4m0hgabLQCw0QNTfQyxiI6AJoSgKLpcLOBFo0mhwzZwxZumcolHQueINwS2hnrecPGdlsDdImz9I7EQASlEUCjNcHKv3DVtHYzBhn7Ec/9HtRHo7T1tfi91dQG9HOaoaQ9Eo5OYl4R5I47EDz9ERmPgT6Dw5RQA010ifJiGEEEIIIYQQ4lIhgabLQMTXDoAatdOnDuBwONDpdEA80GT2pGPNzSHU2UkkMDDqGsb0XAAcsbc0BE+yEQlECMdUugZCQ9cLs1zUNvcSCkeHreOYfQMAvbtfHXY9MX0moUEfXS37AMjKS4R+A4S0/Gn34xPeGDzFm4tWp5fyOSGEEEIIIYQQ4hIigabLQNjXCoCqOOkN9g+VzUH8xDlLZiaWDC8Ag01No65h8nhQMeFUh/dp8iaffvLc8Ibg0ZhKVVPPsHX0rhQshbPp3beGWORUYMqVOg2zPZ2m46+hxqJk5sb3uMK9gl1NB9jRuO8CPoGRtDodaVn5NFVLQ3AhhBBCCCGEEOJSIYGmy0BkKNDkoCfQO9QIPBoMMtjcgiUrE7M3Hmgaq0+TKcUGSjJuBujqOlXKlpZoJRYIA9DSf1pD8CwXABV1w/s0ATjnriQW6MV/ePPQNUXR4C24gdBAFx2NO/BmutBqNaQGM8lxZfCn3Y8TCI2ebXW+PDlFdDTXERqc2HWFEEIIIYQQQghxfiTQdBkId7eC1sggGiKRyFBG00BjI6gqluwsTOlpoNGMefKcIdmKqknGHu2no73t1HW9lkSrEX1UpaKrb+h6otOM22Ec0acJwJRdgj45k56dq4aVxDmSirG6smmuegONJkZ6ppOGGh+fnPtBfMFeHj3w7AR9InGe3Emoqkpz7fEJXVcIIYQQQgghhBDnRwJNl4GIrxWN0Y1fF888OpnRdPLEOUtmJhq9HlNa6pgNwbUmHRpLOhpiBDsah588l2SD/jBHO/uJxk5dL8xM4Fidb8RaiqLgnHMjodZqgg3lw657C24gHOyhvX4LWblumhp8ZNkzuLFwOa9XbqK+Z/TSvvORlpWPRqOlqUbK54QQQgghhBBCiEuBBJouA0ZvERpbCQPmeC+lkxlNgbp6FJ0OkycdALPXO2ZGE4AhORsAa7h32Mlz6clWepv9DESi1PYGhq4XZrpobO/HPxAesZat5Eo0Jis9u1YNu2535+NILKK5ei0Z2VZiUZWmOh+3Tr4WBYXtDfvO70MYhd5gJNmbTVO1nDwnhBBCCCGEEEJcCiTQdBlIWPI+VP08/Noger0em80GnDpxTnPiBDpLhpeBxibUaHTUdUzeHFQ0uN5y8pw32UZvWwAFONJxKgBVmBkPaB1v8I1YS2MwYS+9Gv+RrUR6O4eNeQquJxoOYNaVAVBX3YXL5KAwMZddjfvP+3MYjSd3Eq0N1UTCoTNPFkIIIYQQQgghxEUlgabLgKqqhHsG6VcHSUhIQFEUAAbq6zFnZg7NM2d4USMRBtvaR13HmOZEVdw4VP+wk+c8SVbUcIxkk4GyjlN9mgpPNAQfrU8TgGP2daCq9O5ZPey61ZmJK6WErqbNpHsM1FfHm4/P8U6nqruOzsDIBuPny5NTRCwaobW+asLWFEIIIYQQQgghxPmRQNNlIDoQQQ3H6A33nzpxbnCQwZZWLFmnBZpOnDw3VvmcMdkKSjIJamBYRpMnOZ4h5Va0VHb7CUbiGVF2i4H0RCvH6kcPDOkT0rAUzqF372pikeEZRZ6C64hFQxQVNVJf000spjLHOx2A3U0Hz+djGFV6TiEoipTPCSGEEEIIIYQQlwAJNF0GIj2DRInRP+g/1Z+pvgEAa3bW0LyhQNMYDcH1LhPoUjGqIXrbTs1JdVsw6DSofSGiqkpFV//QWGGma8yMJgDH3BuIBXrxl20Zdt1sS8OdPhObsQJUP+0tfXjtaaTZkie0fM5ktpKYmkFTjQSahBBCCCGEEEKId5oEmi4D4Z5B/AwCpxqBD9THT5w7vXRO77CjdzrGDDQpioLeHZ8f7agfOnlOp9VQnOOmrrIbnUbhSOfw8rn27gG6+wZHXdOcMx19UgY9O1cNO8kOwJN/LRCjIK+OuuouFEVhjreUQ20VDIRHX+98eHKLaKk7TjQaGXfegL+fsl0bicViE/ZsIYQQQgghhBBCnCKBpstAuCdI/4lA08nSuZMnzpnT04bNPdPJc6b0XABskb5hJ89NzUukprGHPKeFI6f3aTrREHysrCZFUXDOuYFQSyXBxuFZRUZLIkkZ88nKaKa5tg6AOZ7pRGIR9reUnc2rnxVPThHhUJCOprox5/T3dPPs737I2qf/THXZngl7thBCCCGEEEIIIU6RQNNlINw7iF8fBE5lNAXq6jFneFG02mFzzRleAmNkNAEYPSlEseKMDW8IXpKfiKqCS9XQ0DdAbzAMQL7XiUaBY3W+Mde0TVuKxmihZ9eqEWPpeVeDokGn7gZgUlIeNoOVXY0Hzu7lz4InpwhgzPK57vYWnv7N9+nr6UJvMFFzdGJPvhNCCCGEEEIIIUScBJouA5GeQQK6EDabDYPBAECgvn5YI/CTzBleIr29hE/LVjqdMcUKmmScqn9YQ/BJ2W50Wg2DnfHMqZNZTSajjqw0x5gNwQE0BjP20uX4j2wl0j98nsHkBP00UpKaaG+qRavRMstTwp7mQ0Rj0XP7IMZgdbhwJqbSVF0+YqytsYZnfvsDIuEQt3/8q+QUl1JTfgBVyueEEEIIIYQQQogJJ4Gmy4C9OJkBU2SobC4SGCDY1o4lc5RA09DJc02jrmVMsqIoKdjUQbraW09d12spynJRXdWFVa+lrONUoOpkQ/C39mAatsdZ10EsSt/+tSPGPHnLiUS11Fe8CsTL5/pDfso7Ks/i7c+OJ7eIpppjwwJIjVVHefb3P0anN3DHJ79BijeHnOJSBvp7aW2onrBnCyGEEEIIIYQQIk4CTZcB+9QUegb6RjQCHy2jyZJx8uS5hlHX0hi0aG0eNKgMtNQMG5ual0hlQw+FCTaOdPYNBZYKM130+kO0dQ+MuUdDogdzzjT69qxGfUumUnpmKnX1Waih4wR6GyhNm4JOo5vw8rnggJ+utniArbpsLy/8+WfYnG7u/NQ3SEiO97LKnjQNRVGkfE4IIYQQQgghhLgIJNB0GRgYGCAYDJ5qBH4y0JSdNWKuMTkZRa8ft0+TISUHALW7aViWUkl+ErGYilNV6B4M0+qP94U61RB87PI5iGc1RXo7CFTuHXZdo9UQUaYTiehprlqDWW9iWuokdjYdGDdL6lx4cycB0FRdwdE9b7Lq0YdJSsvijk98HZvTPTTPZLGRnl1IzdF9E/Lcy1EsGqX6yL4J++yFEEIIIYQQQoiTJNB0Gejq6gKGNwLXGAyYUlJGzFW0Wsye9HFPnjNmZBJDhz3SO+zkuck5bjQaBX97AGCofC473YFOq6FinIbgANaiuWitLvr2rB4xlpGbQlWNB1/bYXo665ntmU5rfzuNvS3jv/xZsickYXUksHvDy6x58g9k5BVz68cewmy1jZibM3kGHc319Pk6R1np3e/YgR28/Ndf0Fpf9U5vRQghhBBCCCHEu4wEmi4D3d3xTKKhjKYxTpw7yZzhZWCcjCZTip2o4sYZCww7ec5s1FGQ4aSqqptki4EjnfGG4HqdhoIMJ4erOsZaEgBFq8M+4xoCx/cQ9rUNGysoTqG2PoNwRMuml/9B1Qsx0mon88aWffj7gmf+EM5AURQ8uUX093SRXzKHmz70RQxG06hzc4pLAd6z5XMtdccB6GwZvbxSCCGEEEIIIYQ4XxJougx0dXWh1Wqx2+1APNA0Wn+mk8xeL4OtbcTC4VHHjcnxhuAO1U9nx/DgUUleEhV1PooSbJR39hGNxcur5k1No6LOR/s4fZoAHDOvAUWhb+/rw657Ml188f+txOqeS1pqO077IIntOdSuifKzb7/O//1oHS88vp/uzsAZP4+xzFt+K1fcfB/X3fNptDr9mPMSktNxulPe84Gmrtaxg5FCCCGEEEIIIcT5kEDTZaC7uxuXy4VGoyHS7yfU2TnqiXMnWTIyIBZjsLl51HGdw4jWkI6BKD3NtcPGSvITiURjOGIKA5EYNT1+ABZP9wCw9eDop9kNre1MxlIwi779b6BGhwe6TGY9k2ddj1ZnYMH8Libfr6Vq8hYWX5dDYrKNw/uaeOIvu4hEomOsPr6ElHRKF12DRjP+11pRFHKKS2moLCMcuvBsqstJOBSk40Qm08nG6UIIIYQQQgghxESRQNNlID09nalTpwKnNQIfL6PpxMlzYzUEVxQFfWK8kXi4rWbY2OTcRBQF+loDKMCRjnj5nCfZRk66gzcPnDk44Zh1HVF/D/7yHSPGdAYryZmL6GrZx+zkLAL2bpSCHj7w0bncef8sWpt6WfdK+RmfcaFyJs8gGonQcLzsoj/rUtLWWIMai2GxOSTQJIQQQgghhBBiwr0jgaaHH36YlStXsnLlSn7yk58AsGXLFm6++WauvfZafv7znw/NPXLkCHfeeSfXXXcd//7v/04kEnkntvyOmj9/PnPnzgXGP3HuJLMnHWDcPk1GTy4qoPS0DDt9zGbWk+txUlHdRZbDQtmJQBPA4lIPR2q66OwZv3zOnFeKzplC757XRh1PzV6KotGh6ThMssXNzsZ4CVvRlFRmL8xm64Yqqo+P3w/qQnlyijAYzVS/x8rnTpbNTZq5CH9vN8HB8y9VFEIIIYQQQggh3uptDzRt2bKFzZs38+yzz/Lcc89x+PBhXnrpJb7xjW/wq1/9ilWrVnHo0CE2bNgAwEMPPcR//Md/8Nprr6GqKk888cTbveVLSqCuHo3RiDE5ecw5WrMZQ1LSuCfPmdITCCt27JE+enp6ho2V5CdytKaLSYk2qnz9DJ4oZVs83YOqwraDo5fknaRotDhmrWCw9jChjpENp/VGG8kZC+hq2cvCtEkcaD1KMBICYMXNk0lMsvL8Y/sYCITGfc6F0Op0ZBWVUHN0P2osdtb3rX/ur+xc+8JF29fF1lpXhTMxBU/uJAC6WiWrSQghhBBCCCHExHnbA03Jycl87Wtfw2AwoNfryc/Pp6amhuzsbDIzM9HpdNx88828+uqrNDY2Mjg4yIwZMwC44447ePXVV9/uLV9SAnX1WDIzUM7Qh8hyhpPnjMlWYkoSTtVPZ2fnsLGSvCRCkRi2CERVqOjqByAz1U5mqp03D4wfaAKwl14NGh29e1aPOp6asxRF0TBFCRKOhjnYegQAg1HH7ffNpL8vyMtPHRyWbTXRcopLCfT5aG+uO6v5DZVlHNq+jn2bVxONXn6Zdaqq0lJfSVpWAe7UeM+tbimfE0IIIYQQQggxgd72QFNhYeFQ4KimpoZVq1ahKArJp2XopKSk0NraSltb27DrycnJtLa2vt1bvqQE6urG7c90ktnrJdDQOGagxpBoQatJxaoG6WoZHmyYkusGwNfiR69Rhvo0QTyr6XBVB76+8Ztoa61OrMXz6T+4nlh45FyDyUmSdx50V5JqMLOz8cDQmCfTxbLrJ1G2v5mDuy/eyWjZk6aDolB9ZN8Z56qxGG+uehyNVkdwwE9j5dGLtq+Lpc/XSaCvh7TMfOyuJHR6g/RpEkIIIYQQQggxoXTv1IOPHTvGJz/5Sb761a+i0+morq4eNq4oyqhBEkVRzuk5hw4duqB9vp1279497rg6MEC420eXRnPGuZFYlNjgILvXb0Bx2EedYzOlQT80HdnNbpNl2FiKU8f2fdWkzkxmT0MrBQPxAJ9LFyKmwuOrtjGnwDbuHnS2HOyDb3J41aOEMkpHTogmgqqy1GTjpbo9zNEUo1HisU+TQ8WdbOClJ/fT42/EYrs4X1W7O5WyPVvQJYwfvGuvLae9qY682VdRs38z29a/QvsZgm2Xmo76CgC6/EH27t2L0eqk+tgRzON8l870PRPi7SbfSXGpke+kuNTId1JcauQ7KS418p28+N6RQNPu3bv5/Oc/zze+8Q1WrlzJjh076Og41fy5ra2NlJQUUlNTh11vb28nJSXlnJ5VUlKC0WicsL1fLLt372b27Nnjzuk5XMYhoHDhAtxnmOvTGzj8ymsUuN24SqePOqepLsJgGVhCPSOePbf2AG/srOPaHA/PVjSTP3UaLpMeVVV5YecbNPj0fPIMe1DVWTRUbcDdWY731o+MOqe2rA21cSeaWBBHjptJSflDYwX5AX77s40cPxDhQ5+Zi0Z7ERLw+lrY+tpTTCrMw+ZIGHVKJBzm0TX/INmTzQ13fJDVYT8NlUeYOXMmmjOUMF5KNjWVo9MbuOKqa9FotXQd301jdfmY37uz+U4K8XaS76S41Mh3Ulxq5DspLjXynRSXGvlOToxgMDhuUs/b/q/k5uZmPvvZz/LTn/6UlStXAlBaWkp1dTW1tbVEo1FeeuklrrzySrxeL0ajcSji+Nxzz3HllVe+3Vu+ZATq4ifOWcc5ce4kS0YGcIaT59LTiWBA19c2Intsal4ig6EothOtiI529gLxjLLFpR4OHO+g1z9+s25FUXDMuo5g83GCzZWjzknLXY4CLDAb2HVa+RyAy23hxjtKqK/pZvPa0e+/UDnF8Uyr2qMHxpxzYOsa+nydLL7x/SgaDfklcxjw99FUU3FR9nSxtNRVkpKRi0arBSAh1UN/TxehwfFPERRCCCGEEEIIIc7W2x5o+uMf/0gwGORHP/oRt956K7feeivPPPMMP/rRj/jc5z7HjTfeSF5eHtdffz0AP/3pT/nhD3/IDTfcwMDAAA888MDbveVLxkB9/dCJcmeiT3ChtVjGP3kuxUZIceOIjnLyXF4iAO1Nfdj0WspO69O0aLqHWExl+6EzNwW3TVuKojeO2RTcaHaTmD6bGUY9h5r2jRifNjuDkpkeNqyuoLGu+4zPO1fuVC/2hCRqjo58NsCAv59d614ke9J0MvKnAJBVNA2tTk/loV0Tvp+LJRIO095cS1rWqYwxd0q8Ibj0aRJCCCGEEEIIMVHe9tK5b37zm3zzm98cdeyFF0YeG19cXMxTTz11sbd1WQjU1WPOzDirPlWKogw1BB+LMcUKmiQc0VY629twuVxDYwkOE95kG4equiiencKRjj5UVUVRFPK9TlLdFrYcbGbF/Oxx96E1WbFNWUL/4U0kXv0AGpN1xJy0vOV0NO0kM+yjua+NdPvw8sgb75xGXXUXzz66j0986QoMxon72iqKQk5xKUd2bSISDqHTG4aN71r3IuHgIIuuv2vomsFoInvSNKoO7+bKm+494wmAl4L2plpi0ShpmacHmrxAPNB0egBKCCGEEEIIIYQ4X5f+v5DFkLM9ce4kc4aXgYaGMce1VgMGgxctKr764yPGS/ITKavupNhtxxcM09w/CMSDM4ume9hX0Ub/QPiM+3DMuhY1HKTv4IZRx02WJKzJU5lp1PPLN3/NxprtRGLRU+NmPbfdO5OuTj9vvDzxp73lFs8gEg7RUHlk2PWezjYObnuDybOXkJiWMWwsf+oc/L0+WuqrJnw/F0NLXbz0MPW0gJLDnYxWp6NbMpqEEEIIIYQQQkwQCTRdJsI9PYR7es8p0GTJ8BLq7CISGL0Hj6IomJJzARgYpYdSSV4igcEIthMxnyOdp8rnFk9PJxJV2XG45Yz7MHoKMKbn07t39agnCQLkFt2IXlGYxSAPb/8Ln3vpP3jh6OsEQvG95+QnMmt+Fnu21dHXO3jGZ54Lb94k9AYTNUf3D7u+9bWn0Gi0zFtx+4h7ciaXotFqqTx8eZTPtdZXYk9Iwmp3Dl3TaDQkJKfT1Tp21psQQgghhBBCCHEuJNB0mTjZCNySeQ4ZTd54adS4fZo82cRQiHWOnFOSH+8F1dDQS7LFyIG2U32cirISSHKZ2XLg7LJh7LOuI9xeT/fGx0cNNpmsKXgLridXE+GrxUtJsyfz9/3P8OkXv8Ff9z5Fh7+LRVflE4vF2L6x+qyeeba0Oj2ZhVOpObpvaG8tdZUcP7iTmVdcP+ppdEaThcyCqVQe3DVm8OxS0lJXOaxs7iR3ild6NAkhhBBCCCGEmDASaLpMDAWazuLEuZPMGWcRaEpzElQcGAIdIwImSS4zaYkWDlV2sMjrpqyj7y3lc+nsKW8jMHjm8jn79GXYpl+Fb/OTdLz8a9RoZMSctNzlJKSVQusevjjtBn604uvM9kxj1bF1/MvL/8Hfjv2TnMkJ7N5ay+BZlOydi5ziUvp7uulorkdVVd585XHMNgczr7x+zHvyS2bT5+ukval2Qvcy0fp7uunv6SItK2/EmDvVQ5+vk1BwYrPEhBBCCCGEEEK8N0mg6TIRqK9Ha7VgcLvP+h5TWipoNAyM1xA82UpEScQR7R9x8hzA1LxEDld1siQjEZ1GYW1N29DY4ukewpEYu460nnEvikZL8k2fxbXkffTtf4OWJ39ELDS8pE9RFLKnvB+zPZ3qA4/iMZr4/MKP8PDK/+SGnIXsrtvDWuUlgoMRdm2Z2OBOzqTpoCjUHN1HddlemmuOMf+a2zAYzWPekzt5FopGc8mfPtdSHy+LTMsqGDGWcOLkOenTJIQQQgghhBBiIkig6TIRqKvHkpl5VifOnaTR6zGlpY0baDIkWlA0KZgI09lQM2K8JC+JvkCYHt8g8z1utjR24Q/Fs5GKs924HUbePMvyOUVRcC+9h6QbPslA1X6a/vYtIv2+YXO0OgP5Mz6Momg5vvcvRIJ96PZv4Mr1q/hKVSsZShv9jnbWvnEYX3/vWX8WZ2KxO0nNyKXq8B62vPokCcnpTJlz5bj3mK02vLnFVB7afU7lc+FQ8Lz3qaoqh3dsGNFPajytdZVodTqS0kdmw51+8pwQQgghhBBCCHGhJNB0GVBV9ZxPnDvJkuEdt3RO0WqwOHMA6Kk9MmK8JD8RgEOVHVyTk0woGmNTfQcAGo3Cwmkedh1pYzA4shRuLI5Z15J611cJdzbQ9MjXCXUOD3IYzQnklT5AMNDBkVXfonPd3zHnTqfogR/wYMcgkzztENTy3Uf/xJH2Y2f93DPJKS6lvakWX0cLi66/C41We8Z78ktm4+toOeuG2mW7NvG7b3+avZtePef9qarK1lefZN2zf2HNk38464BVS10lyZ5stDrdiDGnOxmNVk6eE0IIIYQQQggxMSTQdBkI+3xE+vrPK9BkzvAy0NSMGo2OOcfqiZdUBevLRoylui0kOU0crOokw2GhONHO2tp2IrF4Bs/i6R5C4Si7j7aNuHc81sI5pH/wu8RCgzQ98g0GG8qHxiK9nQTWP4ulpo2QVYt2xUrS7voaJm8RCQtuY1nrdlyJGuz1GXx77c958tBLxGKxc3r+aHInzwDAkzuJnBO/PpO8qbNBUc6qfK67vYWNL/wdvcHEm6se581XnjjrTChVVdn00mPs2fgKWUXTGAz0U7Zr0xnvi0YitDXWjFo2B6DRaklITqOzVQJNQgghhBBCCCEunASaLgPnc+LcSWavFzUSYbBt7ECQKT2Zbo2HxM5yIv7hfZoURaEkP4nDVZ2oqso1Ocl0D4bZ2+oDYEpeIk6b4azL54Y911OA98M/RGOy0vzot+k/sgXf1ueo/83nCRzbRdrklSR65tLpO0JX814AnPNvRmd3M9NShnbAxHztEp48/DLfXf8/dAa6z3kPp0tMy2TRDe9n+R0fPusSRavdiSe7kMrDu8edF41GeP2J36HV6bnni//JtAXL2bvxFdY8+QeiozRGP50ai7H+uUc4sGUNpYuv5eYP/ytp2QXs2/TqGe/taKknGgmTljXyxLmT3CkeyWgSQgghhBBCCDEhJNB0GRgKNGWd/YlzJw2dPDdeQ/AUG37tLLREaVn3zxHjJfmJ+PqCNLb3My3FSbLFyBvV8cCVdqh8roVgeOysqbHoE9LwfugHGFKyaXvmZ3St/Rvm3GlkfPJ/SLjiLrKm3IHNlUvN4ScJ9DagMZhIWPoB0vu34XRocDRk8pm5D1DZXcdDr32f3U0Hz3kPJymKwqwrb8CVlHZO9+WXzKazpYHu9pYx5+x843naGqq56o4PY3clcuUtH2T+itsp37uFVX/75ZhlcLFYjDee/hOHd2xg9rKVLFn5ARRFYfaVN9Ln6+T4gZ3j7q2lLt4IPDVz5IlzJ7lTvPT6Oi6od5QQQgghhBBCCAESaLosBOrr0dlt6BNc53yv2Rs/VWzchuDJVhyaTOo1SQQPriXSPzwzaGreyT5NnWgUhatzkqn0+any+QFYNC2dgWCUveXnVj53ktbqJP2D38G16A7S7v4GaXd9Db0rFQCNRkde6QPoDFaO7/0L4WAf9unLMKVmMtlwgKb6HrJjhfz42q+TbHHzk82/Zl/zyBLAiylv6mwAKg+PXj7XVF3BrvUvM3n2EgpK5gDxoNbc5bdw1e0fpq7iIM/94b8Y8PcPuy8ajfD647/j6J43mXfNbSy49s6hTKuc4lLcKR72bFw1bvlda10lVkcCdlfimHMSUj2gqnS3N5/TewshhBBCCCGEEG8lgabLQKizC2tOzjmdOHeS3m5H73QSGCfQpLPoScpOpUqXC7Eovi3PDhv3Jttw2Y0cOB5vAr44IxGzTjOU1TStIAm7RX9e5XMnafRG3Ffdh6Vg9sh3MNoomPFhIuEA5Tt/jb+3HvfyB8iJ7sFshC1rK/HYU/n28i+R5fDw862/p6Hn7Qua2F2JpGbkUXloZPlccCDA60/8DmdCMlfcfO+I8anzlnL9fZ+lo7mWZ377A/p8nUC8t9Jr//g1xw5sZ9H1dzHv6luH/f4rGg0zr7yBzpYGassPjLm3lvrKccvmIF46B9AlfZqEEEIIIYQQQlwgCTRdBnI/9iAFn/vsed9vPsPJcwCJC7IwKSk0GdLp3fMakd6OoTFFUVg0LZ2tB5vp6h3EpNOyJDOJ3S3ddA2E0Gk1LChJ5839TazeXnvWDa7PhcXhpXDWR1FjYcp3/IrOcD3WvCkUGw9SWd5OS2MPZr2Jf7vi0xg0en686Vf0BvvPvPAEyS+ZTXtjDb3dHcOub3j+b/T3drPi7k9gMJpHv3fqbG75yFfw9/l46tffp62xhlV/f5iqw3u44qZ7mbX0xlHvKypdgM2ZwJ6Nr4w6HujvpberfdyyOQBnYgoarZautrM7OU8IIYQQQgghhBiLBJouA+b0dEypKed/f4Z33NI5AFOGgxRnModIi5dRbX562PitS/OJxmK8tLkKgKuyk4mpsL6uHYD7b5zMlFw3v3xiHz98ZCe9/tB573csdnc+UxZ9meTMBbTVbaLLoyXXWY5eq7JlXbwXUbI1kYeWfIquAR8/e/N3RM7QLHui5J8oiTs9q6l831Yq9m9j7vJbzphV5M2dxB2f+DqqGuOJh79Dbfl+lt32AKWLV4x5j1anY8aS62iqLqe59viI8dYT/ZnSxzhxbmgdrQ5XUpo0BBdCCCGEEEIIccEk0PQeYPZ6ifT1Ee7tHXOOoijkzSwioDERSppO3/43CPtah8Y9STYWTkvnlS01DAQjJFuMzEx1sbGug2A0RoLdxHc/sYiP3DyVnWUtfO6n69hf0T7h76LVmciafAdFcz4FWh3BKcksmLKDowfr6O4MAFCUlMen593PkfZj/G73Y8MyrCKRc29YfjaciSkkpWcN9Wnq7e5gw3N/Iy27gDnLbjqrNZLSM3nfp79JZsFUrrnr45TMv+qM90yZuxSj2cqejatGjLXUV6LRaEn2Zp9xHXeKR0rnhBBCCCGEEEJcMAk0vQdYMjMA8FdVjzsva0Y+WjRUBrwoiobuTU8OG799WQH9A2Fe31ELwNW5KfjDUbY3dgGg0SjcvqyAn37+SiwmHd/87Rb+9OJhwhchuGN35zNl4ZdJSpuDI32AKxftYs/mTUPjS7LnceeUG1lfvZVn9q5m+8Yqfv/zTfzoG6/SWNc9zsrnL79kNi21x+nzdbLmid+jorLi/R9Ho9We9RqOhCRu/ehXKJ616KzmG4wmpi+8muqyvXS9JSOppa6SpPRMdHrDsOv9vhr2rfsWoUHf0DV3ioee7nYi4YnPRBNCCCGEEEII8d4hgab3AHvxJNBo6Dl4aNx5Op2OlMQUWsJBTLlX0n9wA6HOU8GL4mw3U3LdPL+ximg0RmGClSyHmTU1bcOyhvIzXPz8X5dyw6Icnl1/nK/8YhP1rX0T/l5anYHs6XeTritEE4vgMq+hcv/jqLEo4VCUyaEZlNZezaFHg7z2fPwkOoNBx4bVxyZ8L3CqfO7lv/6CppoKlt7yQZzu8y95PFvTF16NTm8Y1qspFo3S1lBN2ihlc51Nu4mGA/T7aoeuuVO9J06ea7no+xVCCCGEEEII8e4lgab3AJ3Fgn1SEb59+884N6sgmx7FT6B/CopOT/emx4eN376sgLauAFsONKMoCtfkptDcP0hZx/BAksmg4zN3lvIfH5lPR88AX/zv9bz8ZvVFaRSetviDmI8HqKzOwNe6i80v/4r//s6rPPfYfqxBF5HcTmpnbGXFh3NYuCyP40faaKr3Tfg+3CkeElI8dDTXUzBtLpNmnl1W0oUy2xxMnnMFFfu20t8Tzy7ramskHAqSmjW8EbiqxvC1xQOOg/2ngkoJQyfPSUNwIYQQQgghhBDnTwJN7xGuGaX0V1YR7h0/s8jr9aICrd1+zAXL8R9+k1Bb3dD4vClpeJOtPLP+GKqqMictAadRx5qatlHXmzc1jYe/chUl+Un85pkDvLGzbtR5F0JjspK37Hr6q3WUHc3DYqxj8cIq7v/0PL7w71fzpY/egd4Z48ebf03RbDcms55Nr1+crKapc5fiTvGw7PYPoSjKRXnGaGZecT2qqrJv82ogXjYHjGhC7vfVEgnFT+MbOC3Q5EpMRaPRjii/u5RVH9lHc+3F+X0UQgghhBBCCHF+JND0HuEqnQ6qSs/Bg+POS09PB8BnHCDUPxXFYKJr4z+HxjUahduWFnC8oYdDlZ3otRqWZSVzqL2X5v6BUddMcJj41scWMDUvkd8/f4gO3+jzLoRj1gqWeCvJH2wmyTkds6GOWP8rqERJMDv56hWfoT/o5xe7/sCcJVmUH26lpWns5ujna8aSa7n3X7+PyWyd8LXH40hIonD6PA7vWM9goJ+WukrMNgeOhORh87rbDqEoWuzuwmGBJq1OhzMp9bI5eS44GGD1P3/Dphcfe6e3IoQQQgghhBDiNBJoeo+wFxWitVjOWD5nMplISkqi1xYi2BbFUryCQPl2gs1VQ3OumpOJ02bgmfXHAbgyKwmdRuGNmrFPmdNoFL5w90wiUZWHn9w34SV0ilZP1m2fIMPaQXTNc9i7ovjaDlG1/2/EYhFyEzL5lwUf5nhXDZ2p1RhNuouW1fROmXXljYRDQQ5uW0tLfSVpmXnDsqpUVcXXdgh7YiE2VzbBQCexaHho3J3iofMyKZ0r37OFcChIW1Mtg4H+d3o7QgghhBBCCCFOkEDTe4Si1eKcNhXf/gNnnOv1emnv70Rj0RPqn4LGZKP7tKwmo17LTUvy2HWkldqWXhxGPQs8bjbXd7K7eewT3dKTrHxo5WR2H227KCV0psxiMj/9S5Jv+TxmXwRzbQc97WVUbPofopEg8zNmsiBjFs9XvkrJvDSOHGymrWXim5S/U5LSM8kumsa+zavxtbeQ+payuYH+ZkIDXSSklGC2pQEqg/5TJY/uFA+9XW1EwmEuZaqqcnD7OkwWG6gqDVVH3+ktCSGEEEIIIYQ4QQJN7yGu0ukEW9sYaB7/ZDGv10soFEKdbGGwMYh16vUEju9msKF8aM4NC3Mw6LU8tz7eC+iuyV5yXRZ+u7eaTfUdY6590+K8i1pCp2i02KctJeMTPydrycexd8bwB1spe+Fr+Pa8xv3TbwHgqH0Xer2WzWveZVlNy1YSHPADjDhxztd6EFBwJk/FZEsDhvdpcqd64llPHWc+eS7Q18NA//mVHqqqSm35ATa++Cih4Ll/BxqrjtLd1sSi6+9CbzRRf+zwee1DCCGEEEIIIcTEk0DTe4iztBTgjOVzHk/8BLI+exiNSUfIX4zG4qDz9T+jRiPxtWxGVszLYv2eerp6B7HodXxxXiFTkx389WAdr1aOHqw4WUIXjV2cErqTFI0W2+RFFH7gp6S6ZxGy6ag7/jz+v3yT643pbG/fTfYMO4f3NdHR9u4pvfLkFJGamY+iKKR4c4aN+doOYUvIRW+0YbIkoSjaYYGmsz15LjgY4PGHv8Off/Rl1j7zZ7rbms9qb6qqUn/8ME//5vu8+Jefc2DLGvZueu3cXhA4uG0tJouNohkL8eYV03C87JzXEEIIIYQQQghxcUig6T3E7PVgSEqiZ//4gSaHw4HNZqOptRnXLA+Baj/OhfcTbDpG17pHh+bdemU+sZjKi5vi/ZuMWg2fnZ3H3PQEni5v4umjjaMGktKTrHzoxikXrYTudIqikDHnHjIn30HYZaU/N4k5R/aRGIqwdeBJtIrKxpf2XrSA19tNURSW3/kg17z/4xiMpqHrg4EOBvpbcKWUxOdptJisyQz2tw7NSUhKQ9Foznjy3JZXniDQ56Nw2lzK927l0Z9/g5f/9r8014ydHdZYXc6zv/8xz//xp/T3dLHstgfInTKTfZtfY8B/9oG+/p5uqsr2MGXOFej0ejLzp9DT1UZv19j9wYQQQgghhBBCvH0k0PQeoigKrhnT8R04hBqNjjvP4/HQ2NiIa5YHRa9lsMODY/b19Gx/AX/5diAeMFo4zcMrW2sIDMb7+ug0Gj42I4elWUm8WtXK3w/VExsliLNyce5FLaF7q5TMhWRNeR9BQ5TQvKncnzmDVnMIs72Cw4e7Kfvlv9G1/jGCrTUXfS8XW2Kql0kzFg675ms9BDAUaAIw2dLecvKcHmdiyriBpobKIxzesYEZS65jxd2f4ENf/Slzl99CU3UFT//2Bzz1m+9TdXgPaiwGQHPtcZ77w3/x7O9+hK+jlStvvo/7v/JjSuZfxYJr7yQcCrJ346qzfrfDO9ajqiol868CILNgCgD1ktUkhBBCCCGEEJcECTS9x7hKS4n6/fQfrxx3ntfrpa+vD38ogGtGGv3lHdjn3I0xPZ/2Fx8m3B0PUNy+LB//QJjXd5zKTNIoCvdNzeTG/FQ21nfwu73VRE4EHobmnFZC98uLWEJ3uuSM+RTP/xw6ow1jqIYHE1OoLGpB0Sgc6p+Kb8uzNP7hy/i2vXDR9/J287UdxOLIwGhOIBqNUX6oBbM1jdBgN9HI4NA8d4qHrrbRS+fCoSDrnvkLTncK8665DQCLzcH8Fbfz4a/9jCtvvg9/r49Vf/8lj/7833n+jz/l6d98n87WBpas/AAPPPRjpi+6Bq1OD8QDYkWl8zmw9Q38fT1nfIdoNMLhnRvILpqOw50MxMv9rA4X9celT5MQQgghhBBCXAok0PQe4yqdBnDG0+e8Xi8ATU1NuOZ4UbQafLtaSbnjy6AotD7zM2KREJOy3UzNS+T5jZVEo6eCSYqicPskL3cVe9nd4uOXuyoJRoZnUZ0sodvzNpTQnWR1ZjJ5/hfwFq4klRD3JqjkzqjlWHcSzgcexlIwm+4N/yDsazvzYpeJ0GAP/p66oWymfTvqefzPu2hqiv/xHzitfM6d4qGns41oZOTJczvWPEdPVxtX3fFh9AbjsDG9wcj0Rddw/5d/xHX3fAq90URnSz2Lrr+LBx76CTOWXIdObxix5ryrbyMajbB73UtnfI+qw3sI9PUwbcFVQ9cURSEjfwoNlUeGsqiEEEIIIYQQQrxzJND0HqN3OrHm5p4x0JScnIxer6exsRGd1YCjJJXesjbQuEi++XOEWqroWvMIALcvzae9e4DN+0eWXF2bl8qHpmVxpKOPn+84jj8UGTa+cnEuJflvXwkdxPsTpeUuY+qirxAxJlCcXMvCeXvYtaOcpBs+AYqGztV/elv28nbwtcWzfU4Gmsr2x5t3b90czyIaHBZo8qLGYvg6Woet0dpQzb7NrzF13lIy8ieP+SyNVkvh9Pnc/S/f4iP//gtmLb1xRFDqdK6kVCbPXsKhHevp83WO+x4Ht63FkZBMVtG0YdczC6YyGOinvfntCVYKIYQQQgghhBibBJreg1wzptN3tJzowNiBHY1GQ3p6Oo2N8TKqhLnxDCffrkasRXNxLriF3t2v0n94M3OnpOFNtvHM+uOjlsAtyUzikzNzqe0N8ONtFXQNhE57jsLn3//2ltCdZLQkMm/xV1gf1mK2BnBbXqKudgvOJXcQOLYTf8XOt20vF5Ov7RBGSzImawr+/iA1xzsonJxCV6cWVR1+8pw79cTJc6f1aYpGIqx9+k9Y7E4W3fD+Cd/f3OW3ALBz7dgli50tDTRVl1Oy4Co0muF/bWUUxANfcvqcEEIIIYQQQrzzJND0HuSaUYoaidBzePx/mHs8Hjo6OgiFQuidJuyTk+k50EIkEMa97D6MGcW0r/o1ke4m7ryqgKrGHnYcbhl1rdnpCXxhbgG+wRA/2lpOU9+pINfpJXR/f/UosdjbF2wy6Y0snv5+/tgZoqk5he7mTdQN7idYmE3b2j8RCweH5oZDUTrb+wn4Q+OseGmJhAP0dVeSkFqCoigcPdiCqsLyG4spnZNJb6+Fvu5TPZlcSekoikJX66lrezauorOlgWW3fQijyTLhe7S7EimZt4wjuzePyKQ66dD2dWh1OibPXjJizOZIwJ3qlYbgQgghhBBCCHEJ0L3TGxBvP/vkYhS9Ht++A7jnzB5zntfrRVVVmpqayMnJwT0vg77DbfTsaSJxSTapt3+Jhj9+hbZnfsrS+3/Ak2utPPraUeZOSUOjUUasV5xo5ysLivjfncf58bYKPjcnn4IEGxAvoTve4OOJNRVUNfbwpXtnYbeM7OlzMczPmMnrmZvYvN1MdpOHhQt78Tsr8TtsVD79C5o6J9PaYmAgEO9bpCiQnGElJc+MM1tDzBrEHw7QF+ynL+gnyZrAXVNvQlFGfgZvt572MlBjuFLi5WZl+5tJTLaSkm5n+Q3FrHvuDfp8p7KXdHo9Dnf85LkUdxZdrY3sXPsihdPnkzt5xkXb5+yrbqJs10Z2vvE8K+7+xLCx0OAAR/dsoXD6fMxW+6j3Z+ZP4dCO9UTCYXR6/UXbpxBCCCGEEEKI8UlG03uQ1mjEMWUyPQfG79Pk8XhQFGWofM6QaMFamIhvbxPRYASdI5GUW79AqK2enjV/4t5rJ1Hd1MuWgyN7NZ2U5bDw1YWTsOl1/Pf2Y+xv9QHxErovfmAmn7pjOvsq2vjizzdQ2eCbqFcel6IoPDjr/bSmH6PLZ+fllzNYt2ke9Q3p2B2dTC3cyIzZ20mYfoym/AO0pldQ29nEoQ0dvPnXNjb8qYm1L1awcfdBdjce4qnDqzjUVv627P1MulsPoTc6sTgy8PfFy+amlMZ/X+1OE0np2ei0g1RVNAzd40710N3WhKrGWPvMn9EbjFxx870XdZ9Wu5PpC6+mfP82OluHn3pXvncr4dAg0xYsH/P+jIIpRCNhmmuPXdR9CiGEEEIIIYQYnwSa3qNcM0oJ1NYR6uoec47BYCApKYmmplOBI/f8DGLBKD374yVylrwZuJbcSd/+tczUHiMz1c5jrx0lOk75W7LFyNcWFuGxm/nVnio213cA8YDPysW5/PCzS4hFYzz0y02s2VE7QW88vgxHOiumL6CiZAPBK49xuHQb/7Af4+H+XtYFghjMfSxKb+KBSQO87+o0bv7YJK7/dA7zb/BSmJ1Bamc+mWVzKNq9nPzq+Tz52gYGB97ZErtoJERvZzmuk2Vzh+Jlc1NK04fmFE+fAsC2tTuHShbdKR58Ha00H9tPS10lV958Lxab46Lvd+aV8cbhO9Y8N3RNVVUObnuDFG8OqZl5Y97rzZuERqOl/vjhi75PIYQQQgghhBBjk0DTe5SrdDoAvv37x53n9Xppbm4mduLoeFOaHUu2C9+uRmLhKAAJV7wfU840ul77PR+eb6G+tZ+NexvGWxa7Uc9X5hcyOdHBIwfrePl481Aj8OJsN//zpWVMyXXzi8f38fCT+wideNbF9L6pK8nNSENnV1mSN4ePzb6HL3qu4IYjTczy3kTutHtxWdzY2/aS0nmAGRmZXHfNTD70ycU89N1rufvBOUyZno6jPxndfi8//dZqHvv9dvZsq6W/L3jmDUyw3s5y1FiEhBOnzR3e10RispVBcy/fXfc/tPS1YXPFm3+HB9vYv7MeiJ88F4tFqT24heyiaRTNWPi27NdstTFjyXVUHtpFW2MNAE3V5XS1NVEyTjYTgMFoJjUrTxqCCyGEEEIIIcQ7TAJN71HW3Bx0Dge+feOXz3m9XsLhMO3t7UPXEuZnEA2E6T3cBoCi0ZJ627+itTpJ3fdHpqVr+cdr5USisXHXNum0/MucPBZ43DxX0cw/yhqInQg2OW1GvvOJRdx1dSGvbavlqw9voq0rcIFvPT6z3sR/Xv0VvrP8S3xs9j1cW3AlsxbdjSs1n641f8WVUMTkBV8gY9It9HYd5/CWn9LRsANVVTEYdUwqSePmu0v58reupWvmYYIZ7XS09fPSkwf57++8ziO/Ws/2N56hbOuvaDz2Cv6eOlR1/M/oQvjaDqLVW7C5cvH3Bamt7GRKqYfHD7/EobZy/m/7I2j1djQ6E2npUda+Uk5wMDJ08pxGq2PZ7R96W3tNzVhyLUazle2vPwvAwW1rMZqtFJXOP+O9mflTaGuqZcDff1bPUmMx2hpr3taTDoUQQgghhBDi3U4CTe9RikaDa/o0fPsPjPsPbY8nHnQ42acJwJzpxJRup3tHA+qJciut1UnqXV8jFgzwIctaOjp7WLur/oz70Gk0PFiazbW5KayrbeeHW8qp6OqLr6lReODGKfz7g/No6vDzxZ+vZ/uh5gt57XOmaLQk3fAJov4eujb+E0XRkJp9BVMWfQmL3UNt2ZMc3/MHQgOnShANej23Lb6S8tQdzH/AzUc+N40VKzqYnPsquuhW2ptbaa5ax9Htv+Tgxu9TW/Y0Pe1HiEXDo+4hpsZo6mtlc+1O/rr3Kb6z7ue8cHT1uPuOxSL0tB/BlTwVRaPlyInT5tx5WnY17qcwMZfyzipePvYGZlsq6Z4I/r4gm9ceJyHZgyspjdwZV2B3JU7o53kmRpOFWUtvoLb8AMcP7qTq8B4mz1mCTn/mxvCZhVNBVWmsOnJWz9q17iWeePg7w0r1hBBCCCGEEEJcGAk0vYe5Zkwn3N1NoG7sgJDD4cButw8LNCmKQsKCDCK9QfqOnsp0MqbmkHLr59F31/LxlN388/WjhCNnLnnTKAp3Tc7go6XZ+IJh/mvbMf5vdyUt/YMALChJ5+f/upREp5nv/XkH3/vTdlo6/Rfw5ufGmJ6PY/Z19O56lWBLFQAmSxJFcz5JZvHt9PtqOLzlZ7TXbx3KUFqSNZfp9kSayp6gteL/MGiOkJxRiqf4k/iCt/P6uoXUt8zGaM2kq3kvx/f+if3rv03lvr9SW7ORN2u387d9T/OddT/nwWe/zBdXfZv/3fYnXqvcSHNfG/84+AKt/e2j7ldVY/S2HSEaGcR1omyubH8TSSk21nduxKgz8vUrPss87wz+efBFogYHsUgn02Z72Lahir7eMB/88g9JyZny9nzAbzF94TVYbA5WP/47YrEo0+aPXzZ3UkpGLnqjifqzKJ/r7Wpn1/qXMJqt7Fz7Aoe2r7vQbQshhBBCCCGEQAJN72muGaUA9Ow/c/lcU1PTsMwna54bQ5KF7u0Nw69Pmk/CsnuZFCmndGAnq7edfTPvBd5Evrd0KrcVeTja2ce3NpXx2OF6+oJhPEk2/vuLS/nwyinsP9bOZ3+yln+8dpTg29C7CSBh2b1oLXY6XvndUDBJUTSkZC1iyqIvY3VmUXfkGY7t+h0djTs5vuvX3KALkqyGiSQWM+2Kb5A77R7Sswq49Z4Z3Pi+uRw54uTFFz3YPJ+hYNZHcafPwtddSUfFixze/xivHltPMBLiiux5fGru/fzXdf/OI3f8nB+s+CpaRcM/D74wYp+R3k6a/vINGjf8Ho3WgCOxkP4TZXNZk51sadjFtflXYDNa+ficezDrTWxtqyQaDrD0mgwUBd54+ewygi5UpH/0RvR6g5HZy24iFo2QVTQNZ2LKWa2n1erw5hWfVUPwjS89hkaj4e7PfZvsSaWsf/5vVB7adU77F0IIIYQQQggxkgSa3sOMycmYPB58+87cELy/v5/e3t6ha4qikDA/g1BnAH9l17D5rkV3YJ2yhJsse9m39vVzCgYZtRpWFqTx/aVTuTIziQ117Xxjw2FWHW9BVeDO5YX8+qtXM78kncdWl/PZn6xlx+GWc3vx86A1WXFf/SGCTcfo27tm+J7Nbgpnf5ysKe/D39tA7eEniIQDZBTfxguqnX+01qE12IbdUzong499cQlWm5F//GEPu3aA1rOIh339lMf0zDYZ+c21/8YPVnyVj82+h+V5i8h2ZaDTaHGbXdw06WrerNtFVdepQN5gw1Ea//RvDLZUE7RpsRqS0Gj1HD3YjKpCrfkoWkXLTZOuAcBpcvDx2fdwtK8TAJ3Wx+KrCijb30xd1fDf04nWd2gjdb/4OIP1owe1ps5bxqQZC5l/zW3ntG5mwVR6u9rp6Wobc071kX3UHNnH3KtvxZGQxPX3fprUjDxWP/5bGqvLz+l5QgghhBBCCCGGk0DTe5xrxnR6DpcRC4/eHwhG79MEYJ+UjN5pomtb/bCsJkVRSL7pM8Tc2dyuWcu617ec874cRj33lWTx7SumUJxo59mKJr654TDbm7pIdJr4t/vn8L1PLcKg1/Kff9rOd/6wjaaOs2sCfb5sJVdiyi6hc+3fiPR2DhtTFIXkjPlMXfwQRXM/zdTFD5GatZg7Sm4e6q/0Vsmpdj72hSXMnJfJm28c57f/uw5jyMzyBZ9Bp7fQWP7CmP2zbpl0LXaDlUcPPAdA797Xafrbt1AMJtx3/yuqXoeuLR4sKtvfTEKSmTe7t3BV7kISzM6hdRZkziI/LX4CYUPbURZdlY/DaeK15w+P+exoLMqqirXsbjp4zp8hgBqL0r3xcUA98d+RdHo9K+7+BKmZeee0dmZBvNyv4fjoAaxIOMSmFx/DneKhdPEKIJ5BddOHvog9IYmX//oLOlrO3FtMCCGEEEIIIcToJND0HucqnU5scJC+8oox5yQlJWEwGEYEmhSNQsI8L8GWfgbqeoaNafRGcu/7d2IaI8l7/kB/9/llyKTbTHx2dj5fmV+IRaflD/tq+L/dVXQPhigtTOZ/v7yMj9w8lcNVHXz2J+v466oyevqD5/WsM1EUheSVn4ZYjPaXfzVqIMZgcmJPyENR4n+05mXMINeVyVOHXyYSG5nZpTdoue7OyUSmN6Lts5BzcDF9zQa8RTfi99XQ1bxn1L1YDGbunHojB1uPsvHF/6Jj1W8w50zD8+Ef0d13DFBQK47ia2qitrKTWHofMWLcUrxixFr3z7mPARUO1m4DbYyrVxbT3NBD2e5eIm/JRmvzd/LtdT/nL3uf5H+3/gnfQM+I9c6k/+AGIt0tmPNmMFBzkMH6o+e8xlgSktOxOlxjls/tXv8yvd3tXHnr/Wi1uqHrZquNWx78MnqDkRf//N/0+TpHvV8IIYQQQgghxPgk0PQe55xWAhrNuOVzGo2GvLw8ysrK6OoaHjCyT01FazXQtX1kFojOkYj+ui9gZYDKv/0AdYxT1cYTjkTZvL+Rx545xN4XjtF/3MeB1h7+Y30Zm+s70GoUbl9WwK+/ejVLSj08+cYxPvK91/nNMwcuSsNwfUIa7uX3M1C1j759b5xxvkbR8P5pN9Pq72BD9dYR4zE1xsM7HqHctJ8rPpiOy2XhH3/YweaNeowWLw0VLxONDI669lWp03GrGp7uLse+4FbS7v463Z0H6W49QKp3EUosxv4121FVOKDZweKsOaTakkesYzNaMdvSsMQGeeLQS5TM9DJnUTY1FQF+//NNNNX7ANhcu5OHXvsedb5G7pt+O6FYmMcOPn9On58ajdC9+UmM6fmk3vkQGouD7s1PnNMa41EUhYz8KdQfL0ONxYaN+Tpa2bNxFUWlC8jIKx5xryMhiZsf/BLhUJAX/vQzBvwXN0NOCCGEEEIIId6NJND0HqezWrEXFuI7Q0PwpUuXotVqee2114id9g94jU5DwhwPA3U9DDb3jbhv0uzZ7HBdj72vmpaXfzdmOdZbVTf18LvnDvKh77zGj/+6i7qWPu66uohlGUn07m6lrzPAIwfr+H9vHKKlb5BEp5kv3zebX/3bcpbO9PLatlo++cM1/PivOzl+IlAyURyzr8WUM43ONX8h3DN2L6CTZqWXUOjO4amyVYTfEmz7x4Hn2Va/h/tKb+fq0vl89AtLWLgsj7J9zaxbl0o42EdN2Ssj1hxsOk7bI1/n2o4+mo16juYXE+hrov7oCziSivFOuQVT1lTKy3sxOFX6DN3cNvm6MfeY7M4nVafnhfLXOdZVzY13TmPeMjeDgxH++L9v8pM/PMEvt/yZTIeH/7ru37l18rXcWHgV66u3cryz5qw/u74D64n42ki44m40BhOuBbcyULWfwcaxM+rOVVbhVIIDftqb64auqarKphcfRaPVsfjGu8e8Nyktk5X3f57e7nZeeuR/CIcuTnacEEIIIYQQQrxb6c48RbzbuWZMp/7Jp4n096Oz2UadY7PZWL58Oa+88gp79uxhzpw5Q2PO0jS6tjfQuaUOz+1TUDTKsHsX33YHq39Tx7UH19Kbnotz7o0j1o/GVPr8Id480MSaHbUcb+hBp9WwcFo618zLorQwGe2Jde+/cTJrdtTx0tEmmtLMfHP9IYp0Rh5cmE9mqp3P3z2T+64v5sVNVbyytYbN+5uYXpDEnVcVMnNSMoqijHj+uVAUDckrP0PD7/+Vjpd/Tdo9/2/cNRVF4e5pt/C9Df/L6kOrmFtXR2ygnzcj3Tyv7WNRSEvp2mepfe0J1EiQgnAIb6KDQ4Hp1DekoapbKVvXQklaCJvdiKLR0rfvDbS2BG543zfYvv8fPHvoeZwOC3qjg9xp96AoGjRFS2nZF6A7qZp5GTPIdHrG3KPZloqWGNlmF/+3/RF+ct2/k+wxkjkrhUcf24R6JJlZ7hu5/4rFpNgSALhz6o1srN3Bn/c8zn9e8xAaZfy4tRoN43vzKYyeQswFswBwzL4O39bn6N70BOkf+OZ5/G6MlJF/sk9TGSneHACqy/ZSW3GQJSvvwepwjXu/N6+YFXd/klcf+xWv/ePX3PDBfxlWZieEEEIIIYQQYmzyryeBa0Yp9Y8/iW//QZIWLxxz3uTJkykvL+fNN98kLy8Pt9sNgMagI2FeBp0ba6j/+z5Sri3ElHYqYJWf4eKJghs4XNvD5NV/4k8buzge8zAYijIYjDAYihI6rRdQnsfJJ2+fxtJZGdgthhH7sJj03HJlPjctyWPj4SaeOtbMMW2Yr6zaT7Fi4MMriklPsvLhm6by/muKeHVrLS9squRbv99KnsfJQ/fPJiPFfkGfmd6VQuLVH6Ljld/St/d1HLOuHXf+tNRiJjm8PHP4FQqb+qlPTOJJc5DJqpG7jKno7CY0OiOK3oii02OPhFkWCtDdo9ITUzCkdPGPnVOYbKtisvEAztwSUm75PFqLg3vV2yjf83tCg2Emz/8cOr0FgLrBLKCcDlc9n5382XH3Z7alAXBf8TK+v+dZ/r7/GQJd/bxZuZfEYhdXL57BvtUd/Ol/t7D02iIWX5WPRW/mvum38asdf2VTzQ6W5i4Y9xl9+9cR6Wkn6fpPDAXmNAYzzvm30L3+UQabjmPyFJzl78DYrA4X7lQv9ccPM2vpjYRDQTa99BiJaRlMX3j1Wa1RUDKHpTd/kA0v/I1HfvwVikoXMGnmIpLSMy84UCmEEEIIIYQQ72YSaBLYigoxJCZS99g/SZgzC63ROOo8RVFYsWIFjzzyCK+++iof+MAH0GjiWSwJc73onSba11ZR/+g+XLM8JC7ORmPQAvDAyqn832MrSQ88wXXBV9CmPEDEkoTJqMNk0GE2aDEZdUwvSCI/w3VW+9ZoFJZN87K0xMOqo028WNVKJSpfX32APJuZDy0qIDvJxh1XFXDzFXls3NvAX14q42v/t5nvfmIReV7nmR8yDvvMFfiPbqVzzSOY80rRu1LHnDtQuZdlFeX8Ns3G2gVL2dpaRqbVy1ev/jIWvXnM+1KAtrotaI8+y6z5Cju2T6IiPJXFyQUkG6zxOYFGInodG4IxppiThu49fKiNkLmfLMVHnn3svQGYTgSaUnV6ri9YxqvH1wOwKGsOH599D1aDhYWlIV555hDrXimn/FALcxblYIumM6lvFs+/vINooRMlpiESiRKNRnG7LcxdkoeiUU5kMz0dz2bKnzns2c45N9Cz/Xl8m54g7e5vjLvPs5VZMJVD29cSCYfYte4l+nyd3PGJr6PRas96jWkLl2NPSKRs50YObF3Dvs2v4U71UjxzEUUzFmJzJkzIXoUQQgghhBDi3UQCTQKNTkfB5z5D2bf/k7q/P0buRx8cc67NZuOqq64aUUKnKAr2SUlYclx0bqzBt7uJ/opOkq/Jx5bvxpts4wdfuIZwdwmNf/4qt8dewXvnD9EYxw6ynC1FUVg52cvi3BRWH29hU3U79doY39tRgVPRcFV+KvMz3Fw9N4viHDff/M0WvvGrzfy/jy1gSm7iBT03eeVnqP/dv9L+0q9Iv+9bQ6fNna7vwDraX/oVk1KyKUlKZ23jHhLMTr525WfHDTKdlJy5gI7G7XhMh/n4v36Sja9Vs3bVUfbtqOeaG2z4217H4J7EtsrdpB9dzT3Tb6W/d5D6qm58niZu7erDf2Qr9tLlYz5DpzejNzoZ6G/h3tI78IcDOAbMPLDg7qEMHovVwJ33z2Ly9DRefuogLzwebyCvJx0XsLm6EhTQaTVMnnSMiLaV159byorbrqVv31oivR0k3fipERlBGqMZ57yb6d7wD4LNVRjT84jGojxx6CVC0TBOkx2XyYHDaMdlsuM0OXAYbei1+jHfJ7NgCvvfXM2RXZvZu+kVJs1chCe36Iyf9VvlFJeSU1zKgL+f4wd2cHTvFra8+iRbXnuKzPzJTJq5iLypszBMwPdYCCGEEEIIId4NJNAkAEiYOYO0G66n6YWXcM+bGz+NbgyTJ0+moqJiRAkdgNaoI2VFAfYpKbStPk7zs2XYihJJXp6PzmZAn5BG6h1fofmx79L2/C9IvevfRg3OnA+XSc/7SzJ5f0km+2o7eXRbFe1qiOeON/Pc8WayHWbmpCfw7U8t5Ad/2M7/+91WvvHhecyalHLez9Q5k0m85sN0rPo1vbtfwznnhqExVVXxbXmW7vWPYs6dTuqd/8YDgQ7+sPuffHT2B0i0nF1GjKJoyCq+jfKdvyIW2MXdH7me40fbWPvSLroaXkdR7OTl3MXisMLLFW9wXcFSKvbHTwdMyNMyqSWRvv1rxw00QbxP00B/Kyadkc8teJDdu3ePWiY2pdRDQXEK/v4gWp0GnVbLn/f9k82NO/nZDd/EFu7j2O51qKqOBP0a1j7fTW7dWowZkzDnzRj12fGsphfo3vwEaXd9jZcr1vLskVcxaPWExjit0Ko3c/uUG7ileMWIMU9uERqNlo0vPYreYGTxDe8/w6c8PrPVxrSFy5m2cDm+jhbK926lfO9W1jz5BzTP6MjIn0zelJnkTJ6BzSGZTkIIIYQQQoj3Lgk0iSE5H74f3759HPvfh5nxi/9GZ7GMOk9RFK655ppRS+hOMnsdZD0wg+6djXRtraOvuovodCsp0zNxZU4hccWDdK7+I90b/ol72b0T/i4zshOZkZ1IWXUnf3z1CA2hELXeCLW9A5h0Gq66oZDNa6r4zz9u4ysfnMPi6WM3yj4T+4yr8R/dStfav2HJn4k+IQ01FqXz9T/Tu+sVbFOvIPnmz6Jo9eQYM/neNQ+d8zNsCbm402fRWrOeRM9s8orchJdU4u+Fzdsms37zFmZeOYdt0X08efhlBnckMmju4875y7FXZ9K9/lHCXc3o3eljPsNsS6OvfguqGjtj8M9g1GEwnvrr477Zt7KjdS9/3/ckNyh9GC1JFM35NHs2PILLuptqVzpzF90yZn8jjcmKc+5NdG96nLrqPTxx6EXmeKbz0JJPEYyG6BnspWewj55gX/y/g73saz7ME4deZHnuImxG69BakXCAgd46Jk1Pp6erk/zpt2OxX1iZ5OlcSWnMX3E78665jZba41Qe3k112V7WP/dXeO6vpGbkkTtlJrlTZuJO8UhPJyGEEEIIIcR7igSaxBCtyUThFz/Pwa9/k+o//pnCz43dQHqsErrTKVoNjjnpVEab2blzBwN7grAnHqhy2O1Md+bDm0/T6I9hn7qEhIQEHA7HhP7DfEpuIj/91GK2HWrhkZfLaB0IkjE9mTcinRgnO0mMxfjxX3fy+ffP5Jp5Wef1jJMldA2/+yLtL/0faR/4Ju0v/BL/0a0459+C++r7JyRrK6NoJb62w9SXv4DJkkSgt4686R+kYE4hq18oY8eaeqY5VrC78wDeJgdKXj+z0kuI2jx0b/gHfQfWjRvUM9nSUGMRgoFOTNbkc9qby+zkzqk30ljxEiGTgaI5n8JgcjB3+cfZ+Lcf4soIcKjidWZ78zFaXKOu4Zi3ku4dL/KbHX9Dq9fysdn3oCgKJp0Rky2ZVNvwPc31lvKV177H+vJXWeDOoL+nFr+vlkF/W3xPCeBKsJLmvbDG72NRFIX0nELScwpZfOPddLU2Un1kL1Vle9m2+mm2rX4apzuFjILJWB0JWOxOrDYnFvuJH5sDrW7s8j8hhBBCCCGEuBxJoEkM4yieRMYdt9Hw1DO4580jcf7cMeeOV0IXDoc5cOAAO3fuxO/34/V6KXLm4CtrYcAQJpqgpyJgQOlvx7nvedaUVdKjseFwOCgqKqKoqIi0tLQJCTopisLCaenMm5LKS29W8/dXjqCx6iic66F5shNTJMIvHt9LTyDIncsKz+sZOkciiSsepP2l/6P+N58n2tuB+5oP4Zp/ywXv/yS90YEnfwUNFS/RC6RkLcGdVgrA+z88h2NH2lj1zAEyKmcAcOWiEhRFQedIxJxbSt+B9SRceTeKZvSG2CdPnhvobznnQBPA0pR8KmoNlMd0lDqzAejft5bs5kM0Jd6NXr+bfRt+xuT5H8Hhzh1xv9Zk5eDUmRzvPcZH867HPUZA6iRN216+lGBH37KN2haIqUb6+l00t+Tg8zkIDCZQOnUf0aPPY3XnYXOcf4nkmSiKQmJaBolpGcy56mb6e7qpObqPqrK9HD+4i+CAf9T7jGYrjoQk5iy/mbwpsyT7SQghhBBCCHHZk0CTGCHzA++ne/ceKv/v1ziKi9A7Ry87Gq2ELhKJsH//fnbt2kUgECAzM5OVK1eSkZGBoigMTO+l+cWjxBoipFyzEMsdt9Lwp4dYHqujb+GDVDa2sWfPHnbt2oXdbqeoqIjCwkI8ngsvQdJqNdx6ZT4Lp6Xz66cPsGt1Nbn5CeQvymDPtkb+8mIZZS09fGDFJIx6LUatBqP2xH91GjRneL5t+lX4j24jULWflNu+iG3qFRe039GkZC2hs3kvWq0Bb9HKYWOFk1P47L9dxV+feYOG9jZWlN44NGYvvYq2Z/+bgZqDWMbok2Syxk+mG+xvgdRp57SvWCxCw5Fn0BgsrGprw35sPSvzr8C35RlMWVNYfMtdbF+fS6jnBSp2/obMybeTmrVg2BpdAR/PBOrJH4xQWlkBo28zPrflEK01G+jrTaa2zkVPt4uBoIXMHDe5RclcWZiEN9PJvh05hLr+zt71f2L+tV/CYDKc03udL5szgZL5V1Ey/yoAopEIA/5e/H09BPp6GOjvJdDfg7+vh8aqo7zy94fJLprGFTffhytp/BMChRBCCCGEEOJSJoEmMYJGr6fwi59j/5e/SuWvf8ukrz40ZpDHZrOxfPlyVq1axQsvvEBjYyODg4NkZ2ezYMECMjIyhs03ex1k3T+DlpfLaX31GI5pqaTe+TVa/v5Nko6uYvp93yEYiVJVVUVFRQX79u1j9+7d2Gw2CgsLmTlzJgkJF9ZsOSXBwv/76HzePNDEb589SG21j2VLciizdLFjZyM7djehMWjQ6DVoDVoUgwaNXoveqMVo1FKSm8gnFxdi0Q//46MoCql3PkTU70PnHD0jqM0fZHtTF1fnJI+4/2woGi3F8/8FRdGMWo6n02v5yN3XjrhuKZqLxmSjb/9aBpOKaWjrY1p+0rDfV63OgMHsZqC/9Zz31VK1lkF/KwUzP0LJkbU8dfhlSrt8RPu7SbntiyiKwoKr5rJzk52O+mdQjj7NQF8T2VNuQ1E0qKrK73c/RlSN8iHPfAI7XiXUXocheWQ5Y8XhejqqHycYtFJVO5dq3XEMUzr45h2fGNY3CmDWwhL2br4afWA1a559lBV3PoDeMHpG18Wk1emwOd3YnO4RY7FolANb32D7mmf5xy++yawrb2T2spXo9G9PUEwIIYQQQgghJpIEmsSorDk5ZN13D7WP/I32DRtJWbZ0zLnFxcVUVFRw/PhxcnNzWbBgAR7P2M21dVYD3veV0PlmLd3bGwi2WnEv/ySdq39Jwx+/gnvpPUyePJ8pU6YQDAapqqri2LFjHDx4kKqqKh588EG02gsLFiiKwpJSLzMKk/nLy2W8trGalAQz1yzKIRiJ0tsfoi8Qot8fItAXZmBggEBUBWDtoU527mzkkyunsnTK8Obaik4/apBJVVW2NHbxj8P1BKMxDrX38sW5BZj15/4eGs25/bGNxVSONfaz0XQde7YFadj8GgCff99UViwsGDbXbEtjoL/lnNYf6GumpXot7rSZOJMn8yFTIl965bs8WrGa+7OnYM4+dYLh3CuK2b3lHqoOPQ9sZXBQy6RZt7C1fje7mw5yf+mdFGbNo27vOro3P0Xq7V8aure9tY/VL5RhZCs5WYMkeO9l2R0zeOWYyl/2Pkm9v5F8Y/aI/c1csoI962tITijjub+/yq33XTciIDWa3sE+Xju+gcXZc/HYL16WkUarZcaSaymYPpc3Vz3OzrUvUL53K1fech85xaUX7blCCCGEEEIIcTFIoEmMyXvrzXTt2EnV7/6Ac+pUjMlJo85TFIUbb7yR/v7+s842UjQKSVfkYPLYaV1VQcc2I64lnyVw5Dlan/4vDGn5uK+6F3NuKZMnT2by5MlUV1fzzDPPcODAAWbOnDkh72izGPiXu2Zw1exMHn5yH2u21HD13Ey+fu9srOZTjZpVVWUgGKG7L8gzm6tYs62Wn/5xB49nOvnUyqlMK0gaM+vLH4rwt0N17G7xUeS2Mc+TwD8O1/O/u47zhbkFmHTnFmwKhKNoNQpG7dgNxvsHwuw92sbOIy3sKW+jpz+ERrGSrfUzNamHap+ZR187yvL5+Wg1p/ZttqXR03GUWCxyVntR1Rg1h59EqzPhzbuWgZqDmBoruDpiYLU5it9u4F/620k7rZH37EW5aLR3UHfkSTLYzF8f9nMw1Eth5mSuL1iGVqfDMed6erY+T5crlcGwwo5yIwdrDSQm9DJvTiM21UlSy056ttawKKuYx3UmVlWs5XMLHhx1n9MX38f+Df9FasJ2/vknOx/4yKIxg00xNcb66q38ff+z9If8rKveyg+u+Tdc5ok7uW40NkcC133gU0ydu5QNL/ydlx75H3KnzOSKm+7FkTD6nz0hhBBCCCGEuNQoqqqq7/QmLoZgMMihQ4coKSnBaDS+09s5o927dzN79ux3ehsjDDS3sO+LX8ZeVMjU7/w/FM2p4EYsHCbs8xHqjv8Y3AnYCwvGWW10Yd8gzS8cIdjmx5LvxGCrYeDoy0R62zFlTcW97F5MmcWoqsqTTz5JZ2cnH/3oRzEYJra0KByJ8vjrFTy59hiJThNf/MBMpheMXgLX3jfAj57bz/GydmKhGIVZLj6wYhJzJ6cOCziVdfTy5/219IUi3FqUznV5qWgUhV3N3fx+XzUFCTY+Pycf41kGm/a0dPPIgTqSLAa+tnAS+rcEm1q7Avxt1RE27W8kFlOxW/TMmpTKnCmpzCxKZs8j32PPoIteNZE9zQl84e5SrpmXM3R/V/Neqg8+Rk7qcprq2sgrKkbRm9AY4j+KwYRGH/9vpKed5orX6Og/irM9hqa2DtQYALpED4dyC3jcX0NMVbm/9A5W5F8x7LPx9/k5uuNhwsFuNmydSTBgxWY3MqkkjUlFdpQ136e8O5UDAzMJq3oKLccomt2AolNwHu9CoyrEAr0AvJTmZqtNx08KbiK9cAE6R+KIz66v6zgVu35LXX06PYMLuOej8zCahgeb6nyN/H73PyjvqKQ4KZ/rCpfym52P4rWn8u3lX8Kke3v+LolGIux/czU73ngeVVWx2J2osSixaJRoNIoaixGLRU/8xPDmTuKq2z/8ru/vdKn+PSneu+Q7KS418p0Ulxr5TopLjXwnJ8aZ4i0SaLpEXMpf+JZXV1P569/imlGKGosR6u4m7PMR6esfPlFRKP76V8c9qW4ssXCUrm319B5sJRoIozGBMaGaSOs6YgM9WApmk7D0HrpUE4899hgLFy5k0aJFE/SGw5XXdvHfj+2hqcPPLVfm8cCNUzCOUuKmqiqrK1v429pjBOr6CA9EyEl3cOdVBcwrSeelqhZer24jzWrkYzNyyXZaht2/vamLP+6roTjRzr/MyccwToZSKBrjySMNrK/rIM1qpMUfZFlWEveVxHsY9QdCPL6mgpc2V6NR4IZFuSyans6kbPdQxlIgEOCPv/8tzmAX86+5iW89XovJYuMP37weLTEGqvbTeXgNbZYOrJWtGLpGPyntpKhRR+/UDPT+EEnhJEzeYkzeIoyeQrRmGwAdgS5+s+PvHGg9wvTUyXxq3gdJspzqU7S7ZiuDR58Ggx2H68McPdjB8aNthENRNBqFWEwltzCJa2+dgjq4m8Zjq8if8SFcKfFyvEhfNwM1+6k7vpPvBiu5qjvAtV1+9MmZWHJLMaRkEwsGiA0GiA720xVpoE/Xx649U1H7dVyXdRjv7Z8mlpDKU4dX8XL5Gix6Mx8svYOluQvQKBp2Nx3kJ5t/zaz0Eh5a/Ck0mrF/nyZan6+TvRtfJTgYQKPVotFo0Gi08R9t/CcWi3F4x3pi0SiLrr+LaQuWDwsIv5tcyn9Pivcm+U6KS418J8WlRr6T4lIj38mJIYEmCTRdMFVVqfzVb/DtP4AhIQFDggu968R/E1wYEhLQOxxU/e4PBBoamfbD72HLG3l8/Vk9K6YSqOmm93Ab/uOdqJEQOuNhGNiOGglgSMmm2z9IXzBCTuFkDFYHGpMFjdGKxmTBmJKD0XPuWVVvNRiK8MhLZbz0ZjUZKTa+dO8sCjNHLwus8fn57Z4qGqp90BTA5xuMNw73WLl6Xhb3z84ds8xta2Mnf95fy5QkB5+dnTciQwmgqW+A3+2rprFvkGtzU7h9kofnKpp4raqNj0zLprXKx+Ovl+MfDLN8TiYfvH4ySS7ziHVeffVVjhw5woLIUdqnrWT/0R521Dt4cEofs3rfIBboRbHY6ZqSTFJSKV3+VKYUFaCGB4mF4j9qaODErwdoDpUTjPqZsvgrGM1jl0yqqsrrlZv42/5n0CgKD858P0tzFjAQGeTLr/4nhTodS7UDJHnnkT31LsKhKJXlbVQf6yCvKJmiqamEBjo5vOVnOJOKyZ/xoVGf85NNv6a8/Tg/Sl9MpPoQg3VlqNHw0LhiMKMxW/Hl2AlrtLyxaR5WNYDe2kWl3YA/FiAvMZO5WdOxmszo9FoMBi25hUlsaNjCH/f8k+sLlvHgrPcPZWaFQ/1otUY0Wv2oe3q79Pd0sfbpP1N37BDevGKuft9H35Xldpfy35PivUm+k+JSI99JcamR76S41Mh3cmJIoEkCTW+bUFc3+7/yVQBKf/pjDO4LOx0uOhih72g7fYfbGGxqR4ntRWvoQtUG6elvx6LXYtTEiA0GgBNfY0VD6vv+DWvRuWdVjWZveRu/eHwv3X1BPnBNEXddU4RulGBQIBzlrwdr2dXcTahzkGCjn0DHADqtwuLpXm6+IpdJ2SNPHAPYXN/BIwfrmJbs4NOzTgWbVFVlU30nj5fVY9Rp+UhpNiXJ8T5B4WiMrz21m6qDbUQGIsyalMKHb5pCrmf0PkKNjY3885//ZNqcuWzogzaNjWntB9l2xIQmGuNH85tImH4llvwZlG37H0zWZHzRaaN+J1VVpa1uMw3lL5A15X0kZ8w/q8+ypb+dX+/4K0fajzPbMw2r3sKm2h1875qHsHQdpaV6LdlT7yLJO2/E847t/j3+njqmLn4Ig2n0dzzUepTvrv8Fn557P1flLSIWDhLt60JjsqIxWVE08ay0gf5Wjmz7H1Sth5fXeImGFDSqFkUdPRhosxu59pYpHIitp6xuKyu8JXj0egb6mggHe9HqLaRkLiYlazE6g/WsPouLQVVVynZtZPPL/wQVFq+8m6lzl47ZO+xy9G74e1K8u8h3Ulxq5DspLjXynRSXGvlOTowzxVukGbiYMAZ3ApO/+XUOfv2bHPnBjyj5/nfRXkCQT2vS4ZqRjmtGOqGuAL1lhQSqugi2+ak0VFOntLMy8wqS89Iwe8xozRHan/1v2p79b9Lv/09ME5DZNHNSCg8/tJzfPnuAx1aXs+NIK1+6ZxaZqfZh8yx6LZ+cmcuUJAdVPj933O2hrzfIy29Ws2ZHHRv2NlCY6eKmJXlcMcOD/rSeTEsyk4ip8LdDdfxuXzWfnJlHKBrlrwfjDcQnJ9r56IwcnEY9g6EIB4538Pjr5VTU+TDY9Uxa5OWbt80aNRsKIBaL8cYbb2ByONmiJNGtHSTV30JFYjE5CQc42JbC/vQ7WFkUz0Iz2dIY6GsC87Th60TDdDXvobV2E4P+VuzuwhFBofGk2ZL51rJ/ZdWxtfzjwPOEYxFWFl1NYWIuqjsbf08ddUeexWL3YnF4h+7rat5DX9cxMotvHzPIBDA1ZRJZTi+rjq1jWe5CNHojGnf6iHlmWyqu7GV0V6/BOK+S/MRSpm19DWNGEY5l9xAODxAa9BMO9RPo76Gj6TD9zWuZpI8wyWYm5jtOj8lForsAsz2d/u5qmqtep6VmPUkZ80jNXjpuhtfFoigKU+cuJbNgKmuf/hPrn32EyoO7WH7ng9hdI3tWCSGEEEIIIcTFIBlNl4h3U2S1c/sOjv7wJyQuWsCkr3xpwvvFRANhOo418483nsGjS2RmKB4g0Vr1WHNNBCt/gxoO4vnQD9AnpE3Yc9/c38Tfn9vMYDjGdUtncudVheh1Z363wGCYdbvqeenNahra+rFbDEzNc1OUlUBxtpuCTBdmo451Ne08VlZPSbKD5v5BfIMhbilMp9hsZl9FO3vL2zlc3Uk4EiPRaeKD108mMdPOr/ZWc1V2MvdOzURVVfZWtNPbH2TZ7EwA9u7dy+vrN9IzbSldEZXPzc7HadDwnTcryMPPoc1NaI1OfveNFRj1WpoqV9NcuQbcdzJ7znzCwV7a6rfQUb+NSNiP2ZZOSvYVuNNnotGcX6y6obeZrXW7ubl4xVCD7XConyNb/wdFo2Xygi+g01uIhPwcfvO/MFqSmDTvMyjK+J/32qo3+c3Ov/Otq/6VqSlFoz+7p5nvbfgF1+oiZOvGz/bRaI2YrCn0B+yUl0Xx9djoSOznuPsg37r6ixQmxr97A/2ttNasp7N5DwDutBmk5SzDbB8Z6Ho7qLEYh3asZ8srT6AoGqYvugadXk80EiEajRCNRIhFI0SjUWLRCDq9gVlX3oDDPXrz+0vFu+nvSfHuIN9JcamR76S41Mh3Ulxq5Ds5MSSjSbztEufPI/uBD1L7yN+o8z5O9n33TOj6Woue1NIs5vTNYfv27Sy+fRm2gJ5AdTe9hzpxTH6AgfLf0PLP7+P50A/QWuxnXPNMotEowc6jFNjqAdizrYc399Xw2bvmUpwzekncSRaTnpVL8rhxcS77j7WzbncDR2u62HaoBQCNAllpDiZlJ1BiMbC3qgNTSMUT1fLP7fvp7gsCkJ1mZ+XiXGZOSqEkLxHDiQbl1/r8rK5uI9g9yJ5tDdQ0x09i6+od5Lp5HjZu3UZrwVwCYZXPzsljSrIDgCWZiWxpgAxXH4dbTby6tYZbr8zHbEsDVAjWUX2wmu6WfahqDGfyZFKzr8CWkH/B5VgZjnTuKrlp2DW9wUZe6f1U7Pw1NQf/Sf7MD9NQ8SKRyABFU953xiATwJKsuTy6/1lWVawdNdBU0VHFjzb9Cp1Gy8y5n8DQU4WiaNDprQSrDxHYvxHH1KUkLr4bncE6LJBWPGeQ1c+X0bWviTyzk//xP8rn0hNJQoc5ZzqZBTfhKbiOluoNdDRup6t5D1Ey6RvIJRrVEYsqRGMK0eiJnwhEogpanYkrr5tOarrjgj7T0ykaDdMWLCerqIS1T/+ZXeteHBrTaHVotTo0Wi1aXfzXA/4+yvduYdH176dk/rJ3bTNxIYQQQgghxMUngSZxUXhvv5WBxkYanngKs9dLyrIrJ/wZc+fOZf/+/Wzdu533ve99OKel0b6uCt/uJlwzP07/zodpefKHpN/7LTT6889qCwQCvPjiizQ0NDBr1ixMJhNbt24jHDvCf/2hnbkzp/HAyilYTOM3hFYUhRlFKcwoSgGg1x+ioq6b8tpuKuq62by/Cf/AqebVvRYDM4uSmTkphZmTkkl0jmzw3R8IEWv0E+sPsznSg0mn8IW7Z7KnvI0/v1RGR+MBjqdOJqwz8ZnZeUM9nv5/e/cdH8dd53/8NW27Vr1alnu345Jmp3enOSEFSAdCgPsBR34cLRA44DgOOLjjuDvqjySQkJAEEqeROL05dop7L7Ity5Zl9bZ9Z+b7+2NXq2LJdhIlVsjn+XhsZnbqd2a/3tW8853vAFw2pYo3D3SgT51MaaSVvzy/g8Unj8sGTUD0TToTHkqqF1JWcyq+4Hvf2iVUMI7qaUvYt+0Rdq//E53NG6mYcC7+vKNrmeYxPZw/+XSWbnmapkgL5aG+Mq9r3MJ/vPZbCnxhvn3WlyjyF7OdcqYV5WEZOmrsKbTGoOf1p4nnjyX/hIsGbDsv7OOqGxdw3Ox8HntoA+amefyxYT9nqD3EX22my11Ft1ZKT8qPYZ7IuLEHGF/TQIF/32HLrBSsem4FFRPOZcGpC0a0T6X8ojKu+Mw3SKeS2afWGUNuv7ujlRcf/gMvP3YPtZve4tyrbh71rZuEEEIIIYQQo5METeI9oWkak/7hsyQONlH7P7/EV15GeMb0Ed2H1+tl4cKFvPTSS+zdu5dx48ZRcuYE0h1xOtd1ULTo03Qt/y0tj/0PZVf+01G1iBns4MGDPProoyQSCS6++GJmzJgBwJQpU3jqqWWYWhN12yLcumUfn7nqBE6aefS36oWDHk6YUc4JM8oBcF3FgdYIuxu6qCwJMnFMAYY+dOhwsC3Ko6/s4rk360mkHObMKKU77KH8hHLOPL6aMxdUE420sdHnIenL4/MLJnJc2cD+jQp8FhdMKOPxWpfS/D1sOWDxxGt7uOrsSZSOXURLW5w5J1+JaR0acL2XSseeQqSzjo6D6/AGSqiceO7bWv+CyWfy6NZnWLbzZT4x/2oAVtSv4n/e+APV4UpuP+OLdHbCt1/eQLvmkm+ZXD1zDCdVFVGy+Bacng7anr4DM1REcHpfR+d2dxudKx7GWPcclwV0Xi9bzK76Kt5S1aAp8gIORXoX1Xot+XonBV0xynrG4C0eh1lchVlYhmaYuK6Nch1UdtjdcQBXrYTY/bz6+HLmLPoIhaXjAIil4rQnOqnKK0d/B/W3l+U5fNAaLizhspu/wpa3XmH5k/fz5198h0UXfpQ5J58trZuEEEIIIYQQb4sETeI9o1sW07/xNTZ8/Ta2/egnHPfTn+ArLxvRfcydO5c1a9bw6quvUlNTg6ZrVFw6jX1/3kDneoP8RdfQvfLPtD/3R4rP/9Tb2vamTZt47rnnCAaDXHPNNZSXl+fmlZSUcP3117F69WqWL3+NIqeW393XwYtTpvHZK46jMOx728ei6xrVZXlUlw1/q18skeZ3j2zkxVX70HWNM+ZX85EzJzGhKp91TZ38cvVu/rKtgY9MqaCnKkDCDBDZ3IY+rQayxXdtl8SBbvxj87lgYjkv17eSnDCB6kQXD7+4k4tPGU/NjCtpWb36fQ+ZIBNShsrPpqm5napxZ6Mbh28pNliRv4CFYxfwwp7X+NjsS3ml7g3uXPMAk4smsMBzCd/7zVpa8nQCVSHSjVHaQhZ3rN/Lst1NXDF1DLM/8n85eN/3aX70v6gMfRczXErnyqV0r30WlCLvuLMpOO0qpuSXsWb3Vp7e+irre9bjai6zy6Zx9tgzmJPSsfduIrZnPdEXV/UeGVbJGLyVk/FWTsJbOQlP+XjyK+ZhVM5h++vPkWfsYvfa/+Wg7ec1O0FtvAeAhdULuHXRzRi6MfyBv0uapjHrpDMzt9s9fBevPPYnaje+xblX30x+0cj+uxVCCCGEEEL8/ZLOwEeJv+dOyWL7G9jw9W/iLS5i1g++j6dg+CeHvRObN29m2bJlXHLJJUyfnmk1le5Osu/edaBrhKrXE1m3jOLzP0X+SZcefmNk+mN66aWXWLduHTU1NVxyySUEAoFhl29vb2fZsqdpbDxAezLA/ngVn77yeM6YXz1ShwjAjvoO/v2eVbR0xrns9Il85MxJh9xO98CW/TxX10yBAZ22y4UlXl54pYWWjjj/9n9OZUJpiAOPbCGxv5vKj8wgNLmYl+tb+NOmfRTWbmXr3hCfPxlmd75EzIayOafgnzAHb+VkNOO9z6WbmppYvuJ13tzcSEcywJyJBfzjpy5729upbavjW8/9hKnFE9nRtps8eyxt62fgODrV88qwi72cV1PKRRPK+fd7VrG9K0L5rBKSOkwuDPKR8QUEHvkBdrQL7DRKuZmA6dSrsAoODV3aY528uGcFL+xZQUu0jZAnyBnjT+a8iadRYfjoadhBw/7NHGjdw8Hug7SqFG2WQZtl0mkauNmGa/nRfM5MVzCpugmPZRP3FtMUqOSB3a9z1vhF/MNJN7yrlk1HSynF1lWvsvxv9+O6DosWX824aXMy/TuZFoZhDOjraSRv9zucv+fvSfHBJHVSjDZSJ8VoI3VSjDZSJ0fGkfIWCZpGib/3Ct+5YSNbf/BvmKEQ077+lRG9jc51Xe655x5s2+aTn/wkhpFp9ZFo7GH/AxvxlPrxeJ4htuMtyq/6Kv6J83BiXTjR/q9OnFgXqe4OdjV3sjcCNfNP5bRzF6Mfxa1DSinWrVvHK6+8QspWrG6uYv6sCfyfq+YSDnre5fEplr5Uyz1PbaU438dXrz+BGROG7oDcdl1+9No26rvjHJdq5osfuZD27gRf/59XMdMuX6spQXUl0S0dX1WYMVfNwnEV//zyJuJtTZyz83lmujswiqpIph3MniYANI8ff81M/BOOwz9+DlZpzYiGC7W761n67Gq21MdoTwZwlY6ugatgwbQSvnD1fMqKhg/7hvKPj/4rTYkG7JYqgq3Hc/bx4/CPDfHCgTbOqinhullj0TQNx3G5+8mtPPxyLRPnlGFUBelJ28wp9HLqtr9SU1pCwWlXYxWUH3GfrnLZ1LSd53Yv562G9TiuQ9gbojsZGbBc0PJTagUpcTUKIxEK25ooSdmMKRlP/pQzeHl7HsnYJqZMasA0Uti6h92JGHkF4zh9+sWE8sdivA+tzXo623hx6R+p37HxsMvphknV+CksWnw15WMnvmfl+Xv/nhQfPFInxWgjdVKMNlInxWgjdXJkSNAkQdOoEa2rY9uPf0qyuYVxn7iRqssuHbGwYvfu3SxdupRzzz2XefPm5ab3bG/l4OPbCE4tQLXfS7Jhx7Db0LwBYmmF143TGy1ZRVV4x0zFN2Yq3jFT8ZTVoB3m9qWOjg4efPBBYokUbzZWYvlCfOnj83P9ML1d7d0Jfn7fGtbtbOHUuVV84YpZxF+6A6uwksLTrj5kedd1+csjj7L7YAufu+ZqiooygdT+XW0ceGgzQU2j6KKpeDoTtL++j/GfOQEr38emVa9iv3AHoXSE5+KzCS68kpmVaebNmEJ87ybiezYSr9uA3ZF5Up4RzKfw9I8RPv7Cd3RcAD3RJMuWb+GFN/fQ0Kmj0Aj5dE6fV83p88dSXqDzw18uZV+0BE03uPaCaVx+xiQs88jB376mHv7vr5+gpCrGZ06/mPlTy1lxoJ27N9ZzYmUht8wbjz6o7r2ydj+/eGAdeSGLcy6ayqq2buK2g0fX0DQNQ9PQtcwtZnp2XNc0ZpWE+eiMMfjMgfWiK9HNy3Vv0NB9kLJgMRV5pZQHS6kIlRLyBgcsa3e1ENnyGpFNr5Bq3otCpy50Dm8dqGLc+FaqqxOgHyRgJXLrmJ4S/HnV5BWPIxAsRtMtdMNE183MePa9plsYhuew9fZwlFLs37WFWE8XjmPjOg6ObWfHbRzbJp1Ksn3tCuLRHibPOZGFF1xJQcnR91d2tD4M35Pig0XqpBhtpE6K0UbqpBhtpE6OjCPlLdJHk3jfBMePZ+5//Ds7f/G/1N35B3q2bmPyl76AeZjb0o7WhAkTqK6uZsWKFUybNg2/P9PaI29aCenOcbS9upeCE28iMGkTmmFiBPMzr0A+uj+MEzd5Yflytu3bycWzTqO8UKES+0k11xLfvZbIxpcA0CwfgckLKDzto3jKagaUwYmn8UV1lpx+EY++/DdOG9vCzliQ7//+dS44eRyfvmzWEZ9M19+qrU381/1riCcdvvjReZx/QiXND/8nsZ1vgaYTnLEIT/GYAeu88sor7N+zm4vOPTcXMiWaIqSerqXQZ/Hr1i4ST27ihzedBG/so3NtHUReILD+ebr8Jfyp5nJSrfnsfK2ecZeWYQTChGacQmjGKUAmEInXbaRn/Qu0PnMXvnGz8ZS8vVsElVI88PQm/vzcLlyl4TPh5Gkhlpw9h9mTytD7dYB+5pxC9jY0EQ0exx//toUXVu3j81cdx+xJJcNuP227/Md9q/E6BfzomisoCvtYc7CTezbWM6skzM1zxx0SMgGcMb+aseV5/Otdb7L0gU18+orZGGV+IikbV4GrVPYFiswwYTu8uq+VHe09fG7+BKrDfXU53xfmsunnH9U5MfNLKVj0EQoWfYRUcz2Rza9gbXqVgsBqVu1dxBt7q0g44zCMNPnhHgrye1+b6Wlbd8TtG6aPwvK5FFcdT7Bg/NsKeDVNY+zkWUdc7qTzLmftK8tYt/xpdm9ew6yTzuTEcy4jkDeyt8oKIYQQQgghRjdp0TRKfJiSVaUUBx55jLq7/4Svopzp3/gawfHj3vV2m5ub+dOf/sSsWbNYvHjxgP01LdtJz+Zmyi+airc0QKIpQrIpSuJgD6mWKB1OhFfYxAS9gjluX1nMsBdPWRBPfgqNg7jRvUS3vopKxfFUzscoORsnHibVFsOJpXPrRYM2y1MbsbwezPLjeey1BkoKA/zfa+Yz5zAhCUDadrj7ya088vIuxleG+fqNJ1BdGqB56X8S3fY6hWdeS+fKR/BPOI6Kq7+eW2/Dhg08++yzzJ8/n3POOQeAWH0njY9sRfeajLl6Fts7Y3z3dyuZUBXmi+EO1MFHQEXIX3g5XXMu4Udv1VHR0cKGdUmKQga333wqk8cWHFJGJ9rFvl9/EW/VZCqu/eejDi5sx+U//7SSVze0UhqIc+WZE1h85gIsa+gArrel2qWXXkqXnc9vl26guSPOuSeOpXJGMVEUs0rDzCoJE/JkcvM//m0Lf31hJ9/65EksmlPJtrYefvFWLTXhAP900mS82ZZHja1RNtS2Mq4ij0nVBbmWUt3RFD+9ZxXrdrZw8SnjueXyOYdtRbW1tYc71u8hlnb4+MxqzhhbMiIt9ZRySe7fTmTzcqI73sLubiOpPCytqmGH0rmwfCFTS+YS6+mgfvc+Olq78XhgwuQCsaqZegAAW8VJREFUJk4rIr/AQrk2rmsT695PZ9NGXDeN119MUdXxFFcdj9c/9C2Y70a0p4u3nn+UzW+9jGl6mH/6YuadvhiP993f6vdh+p4UHwxSJ8VoI3VSjDZSJ8VoI3VyZMitcxI0jVpdmzez/af/iRONMenzn6Ps7LPe9TZfffVV3nzzTa6++mrGjesLjJTj0vCXTcT3d+em6R4Db1kQT3mIp3e/Sk8iyqdu/hQezSLZFCHZFMkEUs0R0h19tyyhEmjOGjRnLRoptMBMfOMvwFc9AU9xADfl0LXhIAfrD7BC24rX9HDS3HP4zYr9tLZ3cfHxZYTLyoknbZIph0TKIZG0M8OUTVN7jKb2GJeeOoFPLZmFZUDzY/9NdPNyis77JAUnL6Fj+V/pePnPVN30Q3xjp7Nv3z7++te/UlNTwxVXXIGu6/TsaKXpb9uxCvxUXT0LKy/z7+CN9fXUPvxrFnl2oLRC8s/8DCWnngzAj59fxe644oriMPf8bSexpMu1F0zj6nOmYBgDw5auVU/R9vTvKbvyK7kWT4cTiaf5zq9epPZAnKklcb79fy6ksKDgsOu4rsudd95JXl4eH//4x0kkbR54bgcPv1SL0jXC4/IwS/xYQZOJhSFKDZMnl+3grBkVfOlj86nrjPKzN3ZS7Pfw9YVT8Ro6b205yFMr6li7oyW3H4+pM6WmkBnji5gxoYipYwtY+tIuHn6plsnV+fzTdccztnz4pwF2J9Pcsb6OLa09nFBZwI2zxxGwRu4JcUopUk17iO1cRdeOt7hDa2NHwMN1PRqnjjuR0MxTaXPLWPN6PZvWHiCdcqiszuf4RTXMnj8Gj9fEsRN0Nm2i7cAqejp2AeANjsMKzMT0T2Pc5ApMc+TK3NFykNefeYhdm1bhD4VZcMZFTJp9AuHCwweth/Nh/J4Uo5vUSTHaSJ0Uo43USTHaSJ0cGRI0SdA0qqU6Otj+0/+ke/MWyhdfQPm5Z6NZJrppopkmumWh9Y6bJrrXi3aYzrnT6TR33303ADfddNOAljJOPE33piaMkAdfeQir0I+maWzcuJFnnnmGCy+8kFmzhr5FyEnamfCpOQqApySAEXSJbX6arreeRKXiBGcsovC0j+Epq0G5DrG99exfuZLd+96iwI1Tio3mdqKh2JAay2OJE4iahfg8Bj6Pic9j4vUYBHwmFy0az8mzK1HKpeWJXxHZ8CJFZ19PwSlXAuCmEuz79RcxC8rwL/kqf/7znwkEAlx33XV4vV661jfS/OwufFV5VF0xE8Pfdx4a7v0xybq32GXMJJ+z6NItSi6fzvxpZexv7+JfVuxkvJnijHyLlbs0XlnbwPRxhXz5ugVUlYRy21GuQ8Od38CJdTH2c/+NfpgWKw0tPdz+y5dp77FZONHmnz59KT6f7yhqCLz55pu8+uqrfOITn6CkpIRY2ua2v62ndVs7PS0xAPLCXgKlflJhE0+Bj5DXZFZpmC2tPXgNnVtmjOWNdY0888Ze2rsTlOT7uGDheE45rpKG5ghb9rSzta6NXfu7cNzMV+LY8hCFeR5q93djO4qbl8zi4lOGv+3MVYqndzfxyI4DFPs9fGbeBCYUBIdc9t2KdRzk3179JbXxVm48GGFGJIanbBzhBYsxJ5/C5o1trF65l+aDPXi8JjUTi0jG08SiKWLRFKgexlQ2MaaqiVAwjuNodPUU4A1MYPyMuYydMO0d9+k02MH6XaxY9hcO7NkOQFH5GMZPn8uEGfMoHzvpqDrb7/Vh/Z4Uo5fUSTHaSJ0Uo43USTHaSJ0cGRI0SdA06inHYe+f7qPh4UeOuKxmmniKi/AWF+MpKcFbUoy3pBhPcTHekhJ8lRUcaGvjL3/5CyeccAJnnnnmYbeXSCS48847KSws5JprrnlHtzw5sR663nw8FzhZpTXYnU2odLK31EQ0LzEjTKEah6EZ6Got4FBw8hIKTrlqyJBGKUXrU7+lZ+2zFJ7xcQpP/9iA+d1rnqH1qd+yqfAE9qkw119/PX7bouOtBno2NxOYUEjlkunonr7AoH35U3S+/HsijRBt8RG86HP4W3X+tbmDaTPLueWy2fzh9TXsVH6m+1wKS0rZs6udTW824CrF+OPKKBibR9qFU6uLOdffzYE/fJP8hZdTfO5NQ56f9Tua+cGdK3Fsh0vme/jkxy/ENI++e7hYLMbvfvc7Zs+ezXnnnccfNuxlZUMb3zplOkGl8daWg7y5pYnV25pQCjweg7KqPOwCC8PUKY24bNjeggIWTCvjokXjOWFG+SEttAASKZud+zrZuqedDTub2LSrFUdplAVT4CapGTuWL127kMLw8CFZbUeE/7e2jq5kmqumV3He+LIRfUJf7ryk4/zgpV9Q39nAksKp+PbX4utoJqhZlE05icr5i+lKFrN65V6aGnvwBywCQQ/+gIXl1zB9GprHwTCaMZJ70WIH8VpdADiOie6ppnLcLIrKp+ELlaNpRx8IDaWz9SB7tq6nbts6DtTtQLkuvmCI8dPmMn76XMZOmYXH6z/suVq9ejUL5s8nlUyQTMRIJWIkE3FS8RipZJziirGUVI59V+UU4u34MP92i9FJ6qQYbaROitFG6uTIkKBJgqYPjGjdXpKtrSjbRtk2bu8wnc4ObeyeHpKtbaTaMq9kaxvKtnPb0EyT4kUns7N6DDsONHD99ddTXj78E9+ee+45NmzYwA033EBZWdm7Kn9v4JRs3IVVUo2ntAZP2Tg8JdXsa2xi6dKlFBUUcXpgDvbeJnwFa0k3r8IIFVJ09g2E5pyRu5hXStH2zB10r3qKglOupPCs6w65AHfsNNt//lnS6TTB82/DX68R29OBZurkz62g5IzxaP3ClOjeWhr/cBuuo1Nx7ffY9avfkWztofj4T9FUFuDnWw4AcMmiEla7NsrjJc/vx2vqqKRD3ZqDdDbHKK3KY/z8cvYlkty2aCrh1+6mZ+NLVN/yMzylAztIf2rFLn7z8Ea8RpqbzinnkgtOf0ehy7Jly9ixYwdnXH09v1y3l4smlXPltL6O0F9avY//uG8NZ8wfg2XqvLWlie5oCoCCkJfzT67hgpPHUVF89C2MnnrqKbZs20mnMZH1e210TTEu1E6xP8bkSRNYdPwsqqurh+xfKpqy+cPGvaxr6mJmSR4fmVr1nrRu6klG+NeX/5s9HfuGnG8pyPME8HpDxO0E8XSCpJMaclmf6eXEwhlMjOWjd7cQ9LcQDMYB0PQA4aJxBPLHEgxXEwiPxfKGhtzO0UjGY+zdsZG6revYu2MjyXg0N0/TdXRdR9eN7LiRafWk6STjMRx76PL3mjBzPieecxllY8a/4/IJcbTkt1uMNlInxWgjdVKMNlInR4YETRI0/V1Trku6uycbOrXStXETzS+8RDKRYMPJxxMIBrnhxhvx5h/65Kumpib+9Kc/Deg8+720e/duHn30UcrLyzmzaD6xTW34x8Qh/jypxp14q6ZQfMHNeKum0P783XS98Rj5J19G0bk3DRnOPPfcczSveYGF6e245rnoeceTP7+SgrmVGIGB4Ueqq5s9P/0HTG+Sksu+Rv7chaS7e9jyLz/EsGbgK59I3g3zuetvW3ltwwHmlbdRYHSQ8E8loReTdlzSaYe2rgRt3Qk0DQLVeRimRmU8zs3pP9FllfHW2JsIh7wU5nnZVtfK8g1NFHpj/ONVMznx+Lm58tiRTiKbXkb3BTHzS7HyyzDDJWjm0J2CHzx4kLv/fD+NM88gz+/jO6dOx8qGaE3tMb70Hy8yriLMjz5/Koah47iK7XWtRHuizJtVc9jOvIfS1tbGH/7wB44//njOOuss9jf38JuH1rK+tgOvYTMp3EKpL4ppGsybN48zzzzzkM9IKcXze5p5vPYgMdthRnEeF00qZ3px3oi2cFJKEU3F6E5F6ElG6E5G6OpppWXvBtobd9CTjmMbJkFfiGCwiFC4lLyiKkJ5pQQ9AfyWj7STZlXDBt5sWEd3MoJHt5jjm0dFRxl0NlOQ105hYYSAP0pv0S1fQTZ0qiYYriZYMA7DPLrbIftzHYfGvTtprK/FsW2U6+C6Lq7r4DoOqnfcdeno7GJszXg8vgBevx+vL4Dl9WEn6+lufhPb1tiytoXuzijjps3lxHOWUFEzacTOtRCDyW+3GG2kTorRRuqkGG2kTo4MCZokaPrQcZJJ2l5bwfoXX2JDQZhxe/czd9IkKi68gNDUKWiahlKKP//5z3R1dfGpT33qqPsLerd27tzJ448/zpgxYzhnwkI6X9mHpzxIwYx2Ol/7M06kA2/VFJIHdhI+4WKKL7j50JZMSZvXl73K67VrmORWcJx6E03vZuznf4kZCAx5Prb/y5fxBZoIzL6Iistvyc2zY3F2/PT3WMH5eMckqLn2PNbtaOa+ZVsJJTYTMBK0G9PAU4BpaFimQdp2qN3fRU+sr2XJKd4dfDz4On+MnM6a1ITc9KpgN1+76TQmT+6blti/jaa//gwn2jGopBpGqBAzvxSzIBM+5S04Hys/09Lsuw8/zwFvAbctmsakokxrGsdV3P7r19jd0MV/f+WsXIslJxGl6S8/IdlYS8EpV5J/8hJ06+i/Bx5//HH27NnDLbfcQqDfOV2zrZn/9+hG9jdHMHWHyYU9VHhbsfIqSQen0BOz6Y6m6Iml6ImmiCZsdFOjdHIhRkUA19AoNAxOrSjijImlFOa9t/VOKZf4ng3Edq4m2VhL6uAelJN5OqIeCOOtnIS3agq+ysl4qyaj+fPY2lrLG/vW8kbDWjriXVjKw0x3Hnkd5cQbbYK+bgoLI4wZkyYv1A1uJwCabpJfMoOiinnkl85AN4YODQdznTTRrnoSsRbCxVMP+yS8/t+TSim6W7exf8ffSESbCITHkoy1opSDwwTWvraOZDxKzZTZnHjOZVSOn/LuTqYQQ5DfbjHaSJ0Uo43USTHaSJ0cGUfKW46+oxQhPiAMr5eyc87m/HPOpvu+P1MPFK1eQ/MLLxKcMIGKCy+guayExsZGLrzwwvctZAKYMmUKF110EU8++SSvmKs4b8nptDxVS/vqMBUf/Qmx7cvoev1RQnPOGRAyKaWI7+uie1MTu7fv4g1nK1X+Es5dvBjTu4DGu79Nz5onKTzt6gH7U47Djp/+Gx5vE2bhOMovu3nAfDPgZ9o3Psuu/3mVns3N7P/rw8y96grmTS3j9de9bN68mUByL9dffxr5g1qF2Y7L/7xRy7bmbi6aswjnmUZu8K5n+uQTWL1pB6au2N2Zx++fbuBrpRUUhX10r3qStuf+CEYYx7oONB+6FcVb7GDlJdD0CHZ3K8mGnUS3rKB73XOUX/VV9gZrOOArpLCtHitSCkUhnGSSh1/exebdbXz52vm5kMnuaefg/f9KqrUB/7iZdLz8Z3rWPU/ReTcRnLbwiK2Jmpqa2LFjBwsXLhwQMgEsmF7G/0w5m6dW1HHPU1vZ1mawRw8Sak+i2I1jhgl4LUJ+D5UlQfJDXlBwsC1K89oWIsql02+xe10Tf4hvxE04mMDUmkLOO7GGeVNLKc4fvlP1t0vTdAIT5xGYOA8A5aRJNdeTPFBL4kAtycZa4rvWAZn/32CESyiunMQVlZP52Oxr2Oc1eat5G28d2MB64020Io1ATxHlkXHsXlWBipuYps2EyQ5TJkWJdOyhs3kjuuGloGwmRRXzySuegq73/dQo1yHavZ+e9lp62muJdNah3L7bX0MFEyiqWkBh+XGY1qHBKUCsu4H9O56gp70Wb6CEiXNvpKBsDulkF3s2/plIx3bOvuxkujrzWbf8OR767b9RPWkG805bzJiJ07E8o/9/PgghhBBCCPFBJS2aRglJVt8bkUiEu+66i7KSUs4oKKTp6Wfo2refdSfOJ+zz8bErriQ4rubIGxphvU+6mzx5MueddBZNj27HTTlo3n0cfOoBQpMmMesH30elNLo3N9G9uZl0d4J6s5WNbh2F+QVcd+P1eDweAA7+5SfE6zZS8/lfYgQzgZBSil2//BV2wwt48r3UfOF/MfOGbi3StmIv7Sv20fbWXVRcfBbjbrqBNWvWMGHCBO677z5CoRDXXnvtIf+W2uJJ/vmVrUwvDvGZSkXDH25jl1FF05iTuOKKK3hrewe//Ot68j2Kr0/ejFX/FmbJbJLdp1N63izMkIfIzjaiu9txEzaaqRMYX0BoSgmewgQtj/2UdEcTr064mO3lJ1C59VXGVY/h4nPO5a0v/RO74xb7L7yJr990IpqmkWo7wME//wtOrIfyq79GYOI84nUbaXv2TlLN9fjGzabkgpvxlI0b9rN5+OGHaWxs5JZbbkFPKMw8L5p+aDjVHU3xwHPb2bW/k9b2Hjp7YjiY2M7wQZauQdBvoZk6KR2UpYOrSHUkUdmn3dWU5zFvWinzp5Yxa2Ixfu97+/8D3FSc5MHdJBt3kTxQS7JxF3bHwdx8s7ACb9VkjBMu5IAJuzvq2d1Rz572fXS2JAh3lBPuqMAfyycQ1DnljADlZU30tG7GseMYpp/C8jl4A6VEOnbT07Eb18l0lO8PVZJXNJm8osl4A0V0Nm+mvXENiWgzmmaQXzqdosoF5JdkWkitfvMVivwHaG9cg2H5qZp4PiVjFw4MspTLwT0vcGDXs3h8BYyd/lH2bNvF2leeJBbpRtcNSseMZ8zEaVSNn0rV+Kl4fCMX7okPF/ntFqON1Ekx2kidFKON1MmRIbfOSdD0obdhwwaeffZZLrjgAmbPns2yhx5iy969HLduI4HuHsKzZ1F50WKKTj4JfYiOnd8updRR9cGzZs0aXnzxRaZPn855i86k/q6VKNeLa+8huqeW0MST0M1CADxj81ivdrOzYTfjx4/noosuGtDaJtW6n/2/+zLh4xdTsjhza1z9/Q/S8coD5I2Bsqu+Smj6omHLYkdS7PndW2C00vzC3ZQvPp/O+XM5YdEi6uvreeihh6ipqeGKK65ApVyant6JGfRQes5Enqlr5q/bGjjTl6DyzT8x3mmm4pM/JjhmMgB7t++g6a8/pUh10Fp2DsVdc0jMKmVDTYD55QVML8lDcxXx/d2Z0Km2DTuSAl2jcEEx9TvvoaB5G+6c82kIT2P1mrWc5Sjiy1cAMOFrX6PqtIUkGnZy8MF/A6Dy47fjrZrc95m4Dt1rnqXjlT/jJmKEF1xA4RnXYATyBpyHhoYG7r//fk477TSmaGNoe6UOf00+lZfNwPAdPvBZv349zz33HNXVNZxz/oWkHI1ILIXtKMJBD/khL0G/hZENrVyl2NjcxfN1LWxt7SYdSRNvjOF2JrFjaWxHYRoa08cXcdLMCk45roryoqFb+Iw0Jx4heXAXqcZdJA7UkqjfjEqnKF3yRUIzT80tF0lFqevYx9aWXTz31ioK9o0j2FWC12dy4mljmTXLJta1mc7mLbhOEm+glLyiSYSLJhMqmoTlObRDcaUU8Z4G2hrX0N64DjvVg2H6CBVOoqtlG5quUVZzGhUTzsG0hg+IIp117NlwH6lkF1WTLqCk+lQO7NnJgbrtNOzZTvP+PbiOg6ZplFSNo2r8VCpqJuHx+tANA90w0XUDw8wODRPDNAkVFGc6JxcC+e0Wo4/USTHaSJ0Uo43UyZEhQZMETR96SikefPBBWlpauPjii1m6dCnz58/n9OOPp+n5Fzm47GmSTc1YBQWUn38u4Zkz8JaU4C0twfAPfyGrlCLV2kZk926iu3YTyb40w2DiLTdTvOjkI5btzTff5NVXX2WsqxizYhXl53walczs0453ovsjFH70TJ566RlaW1tZtGgRCxcuHPJCt+XJ39Kz/nnGfu4XtL21kfq7f0vxTMg77mzKlnzxiGVpfHQrsX1dGOFdNDz0MABWQQG+inKaykvZoFymFpYyKzkZJ+aCgryZZRScM57bn19LzHa43Gpj+raH8JRUU3njD4htf4Pmx/8XzTBZGbqEaR3l1OebvDAhQNxxASgLeDl7XCmLqosIWiZKKZIHI3SuOcDWujbuH+fhxu7XqNrzCtbYWSxtDlJW30h9TyGn6Y34Az6mfulGmpf+B0Ywn8prv4NVVDXkMTrxHjpeeYDu1U+j+wIUnHoVvjFTMcMlGKFC/vLXh2hra+OycWcS29KOf2w+8YZuPAU+Kq+Yiab3kGraS6p5L6mmOlIte0HT8ZSMxSqppjmpsXzDDkJVE7jiqo8e9XdPdzLNmw3tPLfzIG2Og3IUbmucYlcn1p6g/mAPAJOq8zn1uCpOOa6KMaXv/Klvb5cd6aDpoZ+R3L+N/EUfoeis69B0Y8Ay7bFOfr/6z2zasZcJrXMwmvMxLZ3jF47jpNPHEgppWN48lFIk4mm6uxJ0dyZoaulgy77dtHZ2UVIcZmpNNdPG1VBcEsLy6PS019J2YA3dbduxKWb2ydfj9RceVbmddJy9Wx+m4+A6QoUTGT/7mty66VSSpn27aNidCZ6a9u3C6fcEy+F4/UHGTJzO2EkzqZ40g4LSihHt3F18sMhvtxhtpE6K0UbqpBhtpE6ODAmaJGgSQHt7O3fffTeu6+L3+wd0AK5cl86162h8ahkdq9ZAv38SZiiEt7QETzZ48paU4MRiRGp3Edm9B7u7O7OgrhOoHkNw4kRie+uJ7tlD8SmLmPjZT+MpHP6i2Ekmefw/f84uj8WUcD6X3nwzkR2tWCEvrW+8wLonn2T3rOmYPh+XXHIJ48ePH3Zbdk8H+379BfS8sTQ8v4uyeRaewkKqb/kPdO+Rbw2K1nVw4K+bKb9kKk7iADteeZUi3SDZ1ETi4EFqiytoKAszy66iaNNKvKXjMasW8Ja2hf0+i70TFnBOTQmX2Dtp/duv8U+cS3z3erxVUyi74iscXNbIG9EYT5dbqKTDp2aNw8gzeWlvK7s6o3h0jZPHFHFWTSk1+QGStsP3n99EMpHmc+0apVOb6XjlLmK2wUpjGuasj7CkJEb9Hf9BwWQdT9k4Kq65HTN05BAi1VxP67N3kqjb2DdR04grC83Kx+MU4KuqIji1hmTDAaK129HcFlDJvrpRUJ65BU8pUq37sDubQWXCMwUkzCAF46YRqJqCf9I8vJWTDglnhtLYHePe1/ewpTOCEbJAKSoNk/y4Yv/eLmr3dQIwriKPU7Kh07iKkX2S3VCUk6b1mTvpWfMM/onzKPvIlzH8A8MupRQr963hzjX3k+7SmB89nZ49OpoGY8aGiUVtursSpFPOwPVQKMNBdwa2GrP8GsWlIcrK8ikqCZJWrZxz/pH72RpcpvbG1dRvWYrrprC8YfyhCvyhSvx5FfhDFfiC5SgF7c2NOHYa13FwHBvXcXDdvvF0KsHBvbXs37WVns42AILhAqqzoVP1pBnkFRQPW45eEkz9/ZDfbjHaSJ0Uo43USTHaSJ0cGRI0SdAkst544w2WL1/OhRdeyKxZs4ZcJtXZRaKxkWRLC8mW1syrtW/ciUbRDINATQ3BSRMITZxIaPIkAuPHYWTrmWvbHHjkMervfxDD62XCpz9F6dlnHnJxaUejbP3XH9G1dRtdVyxhW2srJ554IqeffjpKKV599VVWrVpFsLuHs2fMYNo1Hz/s8SVbWtj7m+9h6gdxnACGGafqph/iq552VOdHKcXe36/GzPNQfc1xA+pk96YmDj69k1XGTg467ZxZXYPV2sqKaIK4rpjZ5WVj2MuOGfP46JvLmOjfheZ2E5x5BmVLPk/LygYe39PE6yUW40J+9q5soKU1xjduOoFFc6qo74rxUn0LbzS0k3IVEwuCFHgt1jR18vkJleS/sBflKOKtz+IxN+AaOuqUG6j0ubS/cA/puMnEr/8GK5wJmZyETffmJpxIiryZZXhLg0Meb7qtAbuzmXR3K+uWv4ge66ZSWZiBJCrRibJTaB4fVtFYUl0hXKeYolMWUHDCcYeEd246Sbq9kXTrfpp3buTAtrXka0mCThSUQvfnEZg0H/+k+QQmzsMIhA/7ecQSaR54pZYXdjXjKfOj+00sXWNqyI+v22HPng627mlDKTANDa/HxOcx8FoGPo+J12Pg8xj4vCaWqaPrGrqWfenZlwa6ruExDSpLgowtz6O6LJTpxHwY3WueofXpOzDzS6j46DfwlB7ax1lPMsIf1/6VV/a+QVW6kNm7KkjFQmhWku6QwwFfnKgnjtfvML9yLOdMP5EJY2fS3N3N6p3b2FZfT8PBNtweA08ygC+Vh5HM9Edm5SlCEx2C413MPBcXhVKZV8gboDxYSnmolIpQCWbay/bNTWxZ30hTw34qyloIh6MUFibwe3vQNDdbYg1voARfsAzT8qObXgzDh2F6MUwvuuHFMH0Ypg/LG8by5hPp6mTfri3sr93C/t1bSUQjmS3pOihF7od10E+s5fVRWjWO0qpxlI0ZR+mY8RSUVMjteB9Q8tstRhupk2K0kTopRhupkyNDgiYJmkSWUoq2tjZKSkre8TbsWBzdMo+qL6fY/v3U/u+v6dm6jYL585j0+c/hKysDIN3Vxebv/YDY3nqmfPlWSk47heeff57169dz4okn0tjYyP79+5k7dy5j1m+i/ZVXmfzF/0P5+ecdelyuy8Flz1D3x3vQNJeyeTo4CQpO+yhFZ17zto6v/Y39tL1ax7hPLWBj3VYWLFhA2/K9dLyxH39NPsUXTuIvj/yVrq4uTNPEdV3OmnQynk0JnBLFb4o9hOJRljx1N3q6h1QP+Gcu5IXjzmR7vsnp1UVcN3sciaTN9/7fSnbt7+TbN5/M8dPLAYimbVbub+fFvS00x5KcPraYm+aMI92VYO8fV+ImTTZ1bGdS+FXynMyFvadqFnsf3UzN9ddTetr5dK5rJLK9FWW7md63XYVvTJj8uRWEppagm30X9Mpx0AyDLSs38NSKZ5lnTeaUq8/GXxVGKYWbjKF7/Wiajh1L0/jIFhIHeig+YzyFJ445bMuU+vp6HnnkEQoCHi6ePxm3YQuxXWtxY92AhrdqMoHJCwhMPgFv5cRht9PYGuWHf3iTA7EE806qpt1wiaYd8jwmc8J+KveswbahMTCNeEqRTDkkUg6JlE0y5RBLpEnbDgotc0yuwlUK1wUnO55KO6RtN7fPcNCTC52qy/IYWx5iYlU+heFMK8DEvm00PfRT3HSCssu+RHBa322ibipOdOtKeta/wPq2XSwtzaPLMig1AzTbMUwFs9Ma89u7mRyJ09fGS0MP5GGGCjBCheiBAlr8XnZoKbamu6jt7sDbUkxB2xj8PUVoaCSCXURKG4kWN+F60sScFEbKQ7ijgnB7JcHsclowTXi8RmFZALvDIN6q6G5J4DFi5OVFyQ/HKClNEgrEMU0HTUujVCrXQm0ophXE8hXg8RXg8eaTtjW62rtJpxwUJuBBw4LeOqJpaEA82kNzw17aDu7DTqcAsDxeSiprKK0aR1HFGAyjr2VXXx3LDDVdI1xQQkFpBf7gwD7GxPtPfrvFaCN1Uow2UifFaCN1cmRI0CRBkziGlOty8Kll1N19LwDjb7qBopNOYPP3fkCyuYXpt32NwuMXZJZViqeffprNmzdjmiYXXHABM2bMwE2n2frDH9O5fgPTb/saxSeflNt+bH8Du375a7q3bCV/7nFM/sI/4HTuJbZzFSUXfRbNeHtPLLOjKfb89i0K5ldSH2ynsjFIdGcb4bkVlJ0zEc3Q6enp4d5778UwDK688kqKi4tpX1lP22v17JlZyF9Ics3MahaZKepfX899lNLoNzjhzReZu3szRQvmU3Ti8VgzZvPPf1zL/qYe/vmWhcydUporh6sU+7rjjMnzYeo6kd27Wfe1b6NNu5Syohr2F7dC09NMmTOfynM/yc6f34tKF2IGStAsg/DMUvLnVmDmeene1ETX+oOkOxMYfpPw7HLy51bSuvIl6v5wD2Ou/AxP79+Koys++clP4Cs8tPVTrly2S9NTO4hsbyV8XDm+MTaG30do4oQhl29oaOChhx4iHA7zsY99DL/fR7JxN/HaNUR3riJ1cDegMAomULDoSvLmDd3/ViJp8z9/WccraxtYOKeS884eT9P6l6je/jQFqU4AIlaIreUnsLN8AXHDj0onseNxnFQCy04yqzSfSxYeT1FB/qHH5SpaOuPsb+5hX1MkO8y8emLp3HJFYS+TqguYNKaACcU64bV/wt+8mcLTP0pg4lx61r9AZMsKVDqBVVxF3nHnYMxYyEN1y6nvbGDR2ONZVLOAkCeIUi5OTwfpzoPYHU2ku1pwIp04kQ6caGZoRzvBObTfpKgbYG9yIntSE+lwitFwqTAbsTWLlnQpoOEr0PBUp4iVttKiH6A52kra7bctBZ5EEH80n0CsgGC8CG8khOZkoi9NV5SVB6mo8lFW4aWoxENhkYGuxUgnu0glOnOvdKITx04MUQM0DMufCaU8IUxPENMKYnpCGKafZCJNpKuHjtY2Wg8epHn/flLJ5BDbGZovEKKwtJKC0goKSyspLKmgoLQSfygv24G5ga4bmVZW4j0hv91itJE6KUYbqZNitJE6OTIkaJKgSYwCieZmdv3qt3SuXYdmmugeDzO+/U3yZ80csJzruqxbt46ampoBLa+ceJxN3/k+sb17mfm975A3bWru9jzd42HCzZ+k7NyzR6Tvl8bHtxHb20nacjB6FCVnT6BgQdWAbScSCQzDwOrXsqt9ZT2tr9Xz8Mw8GgzF/5k/gbve3EUExU0TypjcXk/HqlV0rF6D3RNBM02KLljMb9oraOh2+f5nFzFzwqH92zjJJKu+9BU6WrtYe+6n+dyJk2l8pZZnWEtlXhkLYuPRbEU60oK3zGXCLZejewYGbEopYns76Vp/kGhtGyhItteh3ARtJaWs1mpZfN4FzJ4754jnRymVa+WV6thL145lVF54DjXXX4uR7ferv/r6epYuXUphYSEfvfqjOI0xujc1E93VDk4Uw6xFxd5EowfMMrzjzyP/+DMJjC8a2PpKKR57pZa1Tz/JpcENFNGJXljDttKFtNkpaprXMCZSj4NGbWgSG0rm0ZQ/HkwPERcUGrrrUGG6LJo4hgVjSigL+ognbTbsbKGpI0ZLR5zWzjgtnXFaexL02Daa10TTwY7ZeB3QgUg8nbsjLGw5jOEgpXo3+VaasuqxVM6cQ9mEyRSFfYSDnndcL5VSuIlIJoCKdrJj+3amTpuWaSWkaWiaTmt7mi3bImzb3o2ejlCttjChoJOJiy8nNPt0NC1zDl3lEklG6UlF6UlGiaQi9GTfR7LTWqJt7G5oRO/24YuFKUiV4ouFcWMDgxrLY+D1mni8Bh6vicdr4vO6+AMp8kIOeWEIBl28PhvTSOKkY9ipCOl0FDsVwU7Hhm0tpRteDCuIaQYwrCCGFcAwgxhmAMMMoBk+EtE4XR3tdLa20tHSTGdLI7FI97DnUdO0zJP0dBPdMPB4feQVFGdehcXkFZQQKiginB1antH/mzlayG+3GG2kTorRRuqkGG2kTo4MCZokaBKjhFKKlhdfpunZ55hwy82EJg1/u9RQ0t3dbLztdlKdnfjKyjMdji86mYmf/QyeoqN7CtfRiNV30vDgJpQBVZfNJDSp6KjXbV9ZT+2b+7hrih8bCNqKz9SUM2tedW4Z5Tj07NhJ0zPP0fziS+iBAG+WzOWN4BS+//nTmTJ24LHs/M3/o/mpZTwx6SJu+5cbyQ95idZ1sPyRF9jk1FERLGHJxZfS9Mj9tLz8Cgt+9T/4ysuGLWPbG+vYd9/zBMbMReleXmQNbqybxeUVTLzlZjTj8B12O4kEO//7l0RrO8mbej4aOk68E9fuJn/uZPLnTsJbGsTM8+YClh3rt/G3558knxCL3Gl4Az7yZpQSnl2OtzRIqiNCx/JniW1bhkq1orQClHUi/gmLCE4sxXVcEnWrSe5dhko2k9IK2GNMYrvuw9EVutIo9IQZVx6imgOY+9aikjE85RMIH38h5vRTWHewi1e27aY+CWlPpn8pj6voORgj3pHA8BpYQQtfngfdb+IaQ4dDTtIh2Z4g2RIn4ELQ0EnHonQlIDHEQ9sMQyM/5KUo30d1aYiq4iAVJUEqS4JUFgffVhB1NN+TiX1baXv2LpKNu/BWTaH4/E8ddT9lAI7rsKdjH1tadrC5eSfbWmtJxRx88TClTiVBQpjKg+lY6K6B5mRe2BpuWiMVdXHSfT+ruq6RV+iloNhPUWmAwoIgPq8H00xjGEkMPYmuJdCIo5EAFQMVRzlR0qkIdiqCY8eHLa+mGRimD93wojBwbIWrdEBHKQPlaih0XFfHdTVcF9KJNJGeHno6O+np6sRJO7j9ci+vP4hpWRiGhWFZGIaJYZoYZnbcsvAH8wjmFRAMF/QNwwX4Q+EBt/79vZPfbjHaSJ0Uo43USTHaSJ0cGRI0SdAk/o4kmpvZeNvtKMdl4uduoeSURSO+D6UUXWsb2dPTwPwzT3zb67evrOe5LQfYFjb4eH4+0y6YOuyy0bo66v5wD51r19HjzeO1shP41DdvYOKYAgA6Vq9hy7/8kLfyZ3D6t/4x15cTgJty2LRlM8+/+DzFxcVcctbZ7PjK1yletJCp/3TrkPuL1O5i4+3/jK+8jNk/+Be2bN3Jc6+9yMmhPLQnn86tq3s8Q66fbGlh6w9/QrSujnE33UDJKecSq+skUttIvL4NzQrlQhPdZ+ItDeKmHZIHIzRqHaxiJ+XFZXz0mo/i8R36vaSUS2TL63S88hB2ex3oIVxtFsrdjaFaiGtBNplj2K8Xo2keIvEg48ZP5KKpk4muP4jdlcQq9FEwrwRNbadn7TJSzfVolhf/uNnoY+fwZnseb+zejwqZ9ASLiQcLUdlbq8Iek/Kgj/KgN/vyURb0Uuz30BRNUtsRobY9wvbWHnrszJPjlOOS6k6h0i4ej4Fl6LiOIp1ySCZs3JSDk3Rwkw4q4ZCOD0yj/F6TypIgFcUBSvL9FOf7KMr3Uxz2Zcd9+LIt1I72e1Ipl8jGV2h/8V6cSDvBmadSdM4NWPl9AaRrp7C7WrA7mzMdwnc140Q70XQTzfSgWR4004MyTParBDuSXexKdtDjpkkom4RrE3fSxO0ETv/WSQrMtBdPIog3GcSTCGTGE0E8ySC6e+QnD/by+k2CQQ+BoEEoTxEK2vj8Nn6fi9fn4vG4WKaNYdhoWgrXSeLYcRw7iWMncO0Ejp3MFOqINNAMlNJRLrhKR2WDKdfVcByF64Bju6Rtl2Q8QSwSw067OLaL4yhsW+E44Avk4fH6MUwzewufiW5mh0amVZXl8RII5RPMyyeQl08glB3m5eMLhD4wnaPLb7cYbaROitFG6qQYbaROjgwJmiRoEn9n7OyT74a6TWskvZs62bGqgXhDNxWXTBtw+9dwOtetZ+fv/0BqXz1NgVLmfOEWxs+Zwpuf/780p3RaP/4FPnP1giHXraur47HHHsPn87HQ9NDz6OPM/flPCU0c2GIsfuAAG2+7Hd3rZc6P/w0jP8xdd92Fz+fjhhtu4MBjT1B35x8Iz5zBjNtvwwyFBqzftXkL23/yU1zbZtpXvpzrW6uXm0pR/8DDND+7Ak/RGArnn4Jm5oELeTNKyZtRSu2+3Tz55JOMHTuWK664AtMcuuWHUopI7Roan78Ps62OqOZlmzmWSPEUJk2ZyuTJkykpLeP/PbqZZSvrCPotygr8zA96mZOG/JSLY+m4kwoJV3QT3fUGyboNhOyOzLFoYdIlU+i0gmxuT+LmlXLCtKkcP30KZroLu/MgqfYD2O2NpNsPkO5sxgyX4K2YiLdyIp7KyUTzKtgdSbOjrYetzd1EkzbppEMsmiKVsHFtF0NBaZ6PqgIfXr9GGzoHoknSCRsVdwi7Gj5b4cYdunuSdPQkSKScQ86Hz2uQF/AQCsCsGWMYMzYfy28StR0iKTv7cvCZOmVBL+WBTEBW6gF97ZP0vPEoAP5J83EindidTTjRzoE70U2MUAG4DspOodIplJM+pCyHfFaAreukfQFS3gC214ft9eJYHhzLi21a2KaJbVqkdYO4qxN1IOo4xNIu8bRNPJ0mkbJJJNNojo5hezBtC8P24HF9+NwAluPFsC1UUscdouUYgC9gEsrz4vGaGIaOYeoYOpiWi2k6WKaNadh4PC7+gIbPp/B4FR5LYVoOpuGgVBrXTuI4yewwE1b1TlPD7XxA/dUGvHrDKuWq7LjCTjukkils20a54DgqN18p0E0PKCP3XikdV2koN7N9NB1/IJwNqcIEwwUDwqpAXj6m5UG5bu6phJnx3vcupunBHwofdajlOmkSsRaiXQfobqsjHmmmO6ox7bjzKCydiH6E1pBCvB/k70kx2kidFKON1MmRIUGTBE1CvCPvd51UjsOOx5+l7k/3EUpH0fMLSHd388y8j/LPt1+Fxxr+Iq6pqYmlS5dip9NM3bSFMRWVzPr+P+fmp9o72PCNb+EkEkz7l++ys7WFtWvX0tPTw5VXXsmECZnOvFteWc7OX/wPvsoKZn33O3hLM/1kHVz2DLt/93u85eXMuP02AtVjhi1LtK6O2v/9NZGdtRQev4Cx13wMN5kk3dVFqrOLnQcbWdXVSanjclxrB26kB+W62UYniphl0ZAfpik/D9swKEr0UILFuIoqqqZPIzhxIr6K8lzLqdfWH2BDbUumX6XOOD2tnUyPd3NKOJ+KcAmukybZ1UCPr4BAnkYe+9BSu8GuRyONQieqBTFVAh99wYoCND0frGJ0TwGaEcGNNaBS0cwCuoGntCYXPhl5xWi6AbpBR9Sm7mAPu+ob6Wo/iKUi6BqkXIPutI+oJ4wdCOENW1heHV25aKkUXjeJ107gSyfw2nG86QReO4HfSWC5KdYlang1NpU0JrrXwFPoxRv24C/wEfCbaKZOAkX/qMrQNEp8BgXRJvIjB8mzNAq8HvKDQQrCYYoKiykoLsMTLsr155Q7B8pF2elM6GSncNNJVDKGm4zhJKO4iShuIoabzA4TEdxkHDcVy7xPxXGTmfFh06F+XN0k6ffT5fPR6fHQaRl0mAYduqJdc+jAJqocNMfATHsw015M25sZ9nvpjoGmdDSl9Q1dPTduOCaGM3SrPcPS8AYMTMvAMg08lollmZhmNrgyFB7Lxet18XhdvB4H08qGWKaNoafR9TS67qBrDppmAzbKSeO6aVwnheukcZwUyrVx3fRRhVeDZcInMk9PdFxc2+17kqKTDazczFMWM8upTEut/u8VgIbl8ePxBfD6g9lXHr5gHqZpEo8cJBVvQzk96Ho69xBB5SpSKRevL/O9lEy4RCMaaTuEYZUSzC8mFC4iEArjC+bhCwTxBUL4g3mY1jvvt0yII5G/J8VoI3VSjDZSJ0eGBE0SNAnxjhyrOrlnbwsP/9vvmdu8gRUl87nx+59jQtWhT0obrKuri4ceeoiuzk4mbd7G6V/8PIXz52FHomy8/Tt0dnSQ+shlbN+/j3Q6zdixYznhhBOYOKjlU+eGjWz70b9j+H3M+PY3aXrmOQ4+9TSFx89n6j99GTM0/FPpeinH4cATT1J/759xBz9FTNdpHT+O2upKylNpFhgmmqHThMZeFK0oNKASjfG6QZGriDc0ENtbj7IzF+RGMEBo4kSCkyYSmjgRJxGnZ9sOerZvJ95wILMfTcOsmY63cgEe/KhYN5oBwfHV+KvL0TwaKl6P3bEdp2c/KT1AY9xmXyxBVA9SXjSF6UWTCeLDiaVJNEXAdtE9UbzFEQxvOyrZSKp5D26856g+27fD0U3Sph/H9ONYfnTlEureR8LMY5XvZJZHJtDSnca2M7et+TQNrwZRV2FroJk6uqmjezIv02uiWTpomYACRTatyAwtXcfSNSzLwOMx8FgGHkvH6zHxegx8XhO/1yTs91AY8lAU8hL2WQQtk6DHIGiZeA192ADBtVOoZDwTSiUTmTAqmQ2iknFU//epBG4qjkolcFMJVDqeWSedIJmKE1MOKV0j2fvSMsPeaU6/Mig0MMxM32N6Zpg2THo0g6jjIWZbJG2LVMpES1sYaS+m7ckFU3p2aGJiKBMdI9s3lY6yNXCOrjWQ7gHTo2F6tczQY2DqmZfRe+5NDUN30XQH01D4/ODza/h84PUqPB6FZbmYpouhO0Aa17Fx3RSOncJOxbHTmRZYjpPMdrjuZj9st9/7oTtiH/JzcxWJuEM6ZYAWwPQU4g9VECqsxhcoZNeO9ZQU2KQTDWh0oWmZwKuny6ajNUEi7kK/KqFB5tZBrw+P149pebJPBjTQdBNdNzNDIzOu6xaG5cE0POiWhWn5ME1PZprpwfJ4M/1nGRa6aebGc31qmWa2jy3rA3M7onh35O9JMdpInRSjjdTJkSFBkwRNQrwjx7JO1u7v5Id3vM7Hzp/GRadMOOr14vE4S5cupfHAAaZ0dnPxt7/Fin/7MbvsNO3FRei6zvTp01mwYAHl5eXDbidaV8eW7/+QVHs7AGOu/AjjbrjuiB2FD5ZsaaFn+w7McBhPQT5Wfj5mKIRmGKxevZqXXnqJ6upqOjs7iUQi5OXlMXfuXGbPnk0wODDQctNpYvX7iOzaRXTXbiK7dhOt24tKZ1ohmeEwedOm9r2mTMbw+3Prd6xdR91dfyS2t568GdOZ8KlPkDft0P6zOjo6WLVqFZs2bUK5LuWpNKWbt1KQX0T+7EVY4WrSnQo7ngZN4S0P4a1QtMcb2du4l/auVnQFRR4PBW2d+JtbMDyZfnnclMI/fiJOTQ1tsR5aujvoScVRmoalefB68/EH8wnml1BQWkphWRFFlaWEwpm+r9Y/8zDF9W+SatqJ5i0G32k0dI9lV9phVypNl+uSVgo7G76kUKQcF9vJtGwZUjYE0PTMiHLV0XVpBGiGlgm0LD0bbGkYpo5p6JimngmtLD3bMkjHYxl4LYOAxyTgzQRUIa9FyGsS8lrkZYOsvIBF0Gfh9RhDBlfKsTOtq7KBlJtKoHLDRGZeOoGbTqHSCVQ6mZ2W7JuXDbtUMrOek4yRQhExNKKGTszQM0NdI2boxAyNmK7nxh00lK6hMFCuiaYslGuBY6K5JjgmOAbYJrpjoTsmhm1hONn32b6qNNXvQwB0dHRNR1c6um2ipcwB898u3VLoVibcMjwaltfA8mh4fDo+r4Hfa+H3WQS8HvxeE8vS0EjipHuw7RSuKiGW9tDa0UN7R4SezhjJmIubyLQaUyhMnyIv7KeoMEhJUQ/hUBMeYz+a6njH5T5avS2zVO9Q9b7vG3f7Tc8krZmnNmaeIZkZalrmdkQt21eXpum5F3rmva6ZaLreF4ppmYBMNyx03cg92TA3rTcsMzKdyOt6JgjLzTOsbAfzmaHeb6j37lsbPrwVQ5O/J8VoI3VSjDZSJ0eGBE0SNAnxjhzrOqmUekcXGOl0mqV3382+zk786TRxy8JjmMw/4XjmzZtHaFDfS8NJtrSw+3d3UHLaqZSeefrbLsfReOONN1i+fDnjx49n7ty5TJw48W21OnBtm/j+/eheL76KiiOeL+U4ND3/AvX33k+6s5OS009l3I034CsvQylFfN9+2l5/g7bX36RjXz0Hqyppqq7COcoyBZSXGkoZSwl+jvy9q1k6KZ9Ls95FS7qTSCpK1EmQZGDfSAY6Ad1Hnusj3w1Q7raT765Dd1sxQmMIn3Q14QWLcKJpIjvbiOxsI9nYBaobMxTFU5hAN7tBT2EEC3CsID22h46EQWtcoy2m0R5ziSdTxJROUhmkXZ200rHdzMtxdRxXkXZcbFdhK3Vou5jei3mVuU0r12LqndLAtPTMbWyeTEsrr9fE5+1rYRXwZUKpPL9Jnt9DOOAh4DUJeAz8HgO/ZeK1DCxTxzIyIZhh6JiGNqC+KKUyfVOlEplWVelktq+qZCbAslPZ1lW9gVWiX4urzDJ90+J9/VzZKWw7he3aOJqGo4GtaaQ0jbihEdd14tkwK65rxHuHuk5a10gDaeXBdb04jhfleFGOB83xgtLQMjd5otEXR2mQCVOUDo6B7prZgMvEyIZemXETzdXR1ZEDZMdIk7YS2J4kmhHHY8Tx6gkimoeE68dM+/CkvHhtP1raA0rD50vgsfrX5UxpLcvA4zUwvQaWpWEYZF8qM9Qz46YBuqEwdIWmu2hkbkXMDB00HMiNZ1pqaTiguWhk+qPS6N+KK9u6a0BLr8xL0/qPkxtqgHaMG0LlwrNcUNZ7p3E2HB7wyQ96aVo2PNP7vdczw+w0bUCopQ/9Xs8EoOg6mmZkWqHl5mWDOMPIvc/MN3LBnJ57ZYO4XGBnZLabC9T6hX4Dwr9+5TzkOLTccaBpbFi/gbnz5g/anjZgO0K8n47135NCDCZ1cmRI0CRBkxDvyAe5Tjq2zUP/+kPaNI2ZEydxyjUfw7KsY12sIaXT6fe9bHYsTsPSRzjwyGMopShetJBIbS2JA40A5E2bStHJJ1G88GS04iK2b99OMplEpdMkDhwgVr+P+L59uMkUumHgH1ODvzuOf38zhs9Pwdx5FC6Yj7e0FJXt5FnTNIyAhR3r5sBjS2lb+Rqe4kLGf+IGSk4/LRd6KKWIt3fTuG4bzbvq6GzvJJpOETc1uvUkid7+05ViitHNlPRevOkIlE5ALx2P3XEQ1d2EHutE6/c0uBQWKc3Aq9JYHNrhuItGCrPfResQNHA1E1c3cXUr+/KgdAule7IvL8r04ehekpqXmLLoSpu0JxVdcehOOEQTDimlgWGgDB0MHWUYKN3A1XVSmoVL5qI104l19uWoTF9EjkI5Lsp+lz/fGuh65kJV1zV0IzM0dB3LyrbKMg08Hj17K2GmRZY3O56ZbuL16Hg9Rm7cY5l4ssGW19TxGDqWDpamsJSDhY3hpsFxwEmDm0Y5aZSdBscGJ4nm2BhklsV1UI6NcmxwbZTj4NqpbMftfesOHNrgpnGdNLbtkHJt0som5TqklUPadUhqEDc0YppBTDOJaxZxLBKaRRITR9MJawnK/DpVoQKqCqqoKB5PqGQsZmEFyp/P5pUvUpHnsmXferZ21rNTJThoGeiOF1/KS8D2ohwT7ExrLz3bosvIhV6ZllH0709L6Whu7/tBfW1lp73nNIWma2QzEHQNDNNF7w3FdIVuuBiGg6G76IZC0xxMw0XXFLruomluZjk9M03TFLruoKHQtL5pmfFM8KWjskFZZpj5l5m99RHVN671jmdf2aCs/zQtexwDx8n1s5XZB2ia1jet//tswy+NQe//DlpY9f3lrw0/1PpP07LncGCQ13cuMrcl94V6DAzDBoR+AwNArf/0foFZ/8BtwLLDBHADlu8N1HLve4O8ftuHXFk1bYhjyu2DYeb1nY8By/WWNzut//iw52zI+Vq/ctFvu4P323/+wM9u8LYHHuugdXJlp2+bA9bpvxyDytK3t0PqjQZr165l/vwFg8o1aN2/g39X4oPjg3yNM5pI0CRBkxDvyAe9TiYOHiS6Zy/Fi04+1kUZtZKtbez90320rVhJeMZ0ihaeRNFJJ+EtLjriuq5t0715C+1vvEXH6tWYeXmUX3A+paefOuCWveF0bd7Mnt/fRXT3HvKmTaP66iuINzbStXET3Zu34sRiAARqxpI/ZzZ506eze8MGAok4Bxsa6UiniIaCxEIBKq1uptn78GAT0fxENB8RzU/SG8YNFENeCYYWxOMYeCwPXh28dgLLiWGmo5ipHoxUBM2O46LhKg1X9XYs7eAqhYsi04bJRiONrtLoZMIQQ9kY2Jhvo++fI1FoOFi4momDiaOZONm9OOg46KTRSWs6aQxS2VcaAxsdR2m42eVcTcscF0Z2qJPEJI2ZXdfCxiStmaSUgY1ByjVIK4O0o2G7GsohF3YpJ3s/1vskE3j0BWGZUExHNzQMQ0M3e1tqadkOyzMhWW6armOaWq5Fl5WdbwAKlW2BpvqeTqcyYZ6jIK0ZpByXpOOSsl2SjpN77yowNcgPeMjzWuT7LPK9Bt50B9GuvbRH92O7cSzdxdJcPJqLR3Pw4OLVXDw4GNnQxFEursoMlVK4ZN+jcLJDG5VpEYaWqwOZLWQ/Z5V5KYzsUMdVRubpf+gopWcil+w8pTIXgkpl5mn0Bl6Dg63MOlpu3uD3/dZj0Psh5jPUdJUNJd53vcHUwEAr8y+wr38vLRuEaVomANN1B01z0XQ1IFjrfZ9ZRmXDNBdNA11zM8EdbjZrybRU07NBma6RHfa1LNN6wzj6zVOZ5XMtz3It0jLhmes6GIae28YhLdVyx02/lmxkw7u+eZn5hw61AaEFA3KM3sCl3yK55YfaVu9I3/oMWr9vvYHraoO2MXjdvviEAWWXMOODQxt+XDt0usagz1Ybav2+dbXB03MDbdC8obelDfxPv+0PXnvgtg8p74Dpg+p1b7A3YDOHPxeHlmuobR5m+qB/I4ec1wHL9JXtkHOWW3TQskPs45B1htrnsGUYfiuH09XZSUFhwVEv/072cTim5ad66qUY1pH/Xh7NJGiSoEmId0TqpHivKceh+cWX2HvPfaQ7OwHwVVWRP2d29jULT0FBbvn+dTLd3UOktpZI7S46dtZy8EAD6UgEK23jSaUx0+m+P6dMEyscRrNM7O4enHj8bZVTM70Y3jBmXnGm3G6mdY2bbYGjXBuUg3IdlMdCFRXihoO4fi+OZeAYkMbBwcZRNq7r4OLgZi9uNdV3gaejMHEwlEs2YsJUvUM3E2yRuTjVlOobzw6Nd3Wv3vAc9ExQpWX3qOnZS3A925+61ns0uUt2hZ4JtrS+6X0vPROQaHouNHH7hSeOpmOrzLittEykp3qnZW5ndLPXyP37J+q9MywT1ug4qm/7jtKzIVzftLQySCuTNDq2ygR1ql/g4SVFkRGlQI9RqEcp1KMUZIdhPU6366fJye97ufl0ugHe1h+geuaP8d5ALdMwo/97bUBLm75hdjkYMF3vnZf941/PztP7zdNzL9Vvema8N7jQ+rUQ6v8+F8BoKtNqUHNB9U3XtGzH69rAW/U0LROi5W7jUwpwMmFfdprKddquMn1L9QaBZAJARXZRLfuZZ+t7Xx9UvdP07PRcFJMpSi5cy0YuSmVDN7K34mXnZwMxskFcRu80PXP4/UIz6B2SDeL6QrXMPAZM6xvSbxvktpWb17vdfvvLba/fPofaZv/tHdWF25BUv+GhrcYGTufQadqR1meI6QyY3/+2zqG3NXi/HGFbvfW0375767cGvS3fcoGfPnD9vpZw/QLBocI6vfebRA3ICnKhoDawjLkQT5FtaNU/IMyuny1z33H3zxQGttjr20f/LKL/+enVv5uCgfvr3eCASf2zjX4bHxxwHJLBDCrbwPeDQ4/D729wFDIgwzhkZOhpfWU8ZGvDfn0fLtvQhltRGzB4m/MGxTfDlesI8weWcfgtvK0cdqjz+66XPdwJebv7O9JSigHn951nRu+IcnWOO/P/UlBS/f7ueIQdKW8xh1hn1Hn88cf59a9/TTqd5pOf/CTXX3/9sS6SEEKId0kzDMrPO5fiU06he8sWghPG4y0uPqp1rXAehQvmU7hgPmOB48h0mJ7u7ibd1U26qyv76htXaRsznIcVDmOFw/3G8zDDYQy/HycWw45EsSMR7Gh2GOkbZsqtZ/te0bNPc8sMNV3HSSQy++vsGjAcLtxyNQ3X50P1viwTlUqi4klIJVFKZVpWKZe0q9CzndVoCjTDg276skMvmuFFMy00zchsVwfXMFA6uEamfyRX11A6KEPhGqCMzDiaQunZcCDbx4+mDYyGMu1iHPoiJei9wOx/QZMLJ5SDgcJUg5bNXfpnjkfDzQ7VgNCs9zWk97DxS6ano8wOBt9m6QJxvMQ0LwksCokxTrXhwc4tk8agQ8+jzSwgqXnQXRddZaM1pfqNZ86jItOKTvU7s242CBkwXWkot28Zpegbh2zLpX7Tet+r3CeCm+vXKDtd9a3v9v9k+gU0fZf0/ffTP1AcYqh6L2W1bDu/gfvpDXAU5iHrMmCZwdOGWFb1nz7Eslq2zNkLZpUNPjP5Teb2JVTvdCAbomq5ZbTehKBvH/2u7JSWDYEGt7LpP671G9cHXhj2v8jJhBha39EdchE/cHrfOn2TlHJzDzfILNsbrgy+IFUDtzFou737Uv32oVS/suSu07R+K/Z9D/RufeAclQtThpiZKWtviKb1hYeHblQxoMjZsuUWHbTd3lJpuUIP+l7pu+bsOwUqu5ymZbetMsHd4IvmIY9Fyy2vDd6d6jt5h87r20yu/7lB8+kNHYeQCSV7/xVk/9vv2PqXQeud16/Mfcc+8PxouXPdP6jKns9+x9svbwOg919S7/b61s9ye8/DwJ7W+s5Jvzp6yDSVKbEauNzA0LPftAGHNDiIJHteB5/swev0lmWo5frNH/TvKzf/kDIMvw1Q6If8/A1Xpn7Th2w/cug0bYhpfefucIb5TR52+sB5w4dZh1v/aOa/02XfyfJHNvT57eMqD2UzeygoGfFdjyqjPmhqamri5z//OQ8//DAej4drrrmGk08+mcmTJx/rogkhhBgBZsBP0QnvvvWcbll4i4uPOqwaiuH14iksfNdlGcxJJrG7u3GSSQyvF93jQfd60S1r2KcZKsfBiSdwEgmcRDwzHo8P/ENyiD8qlevipm2UnXm56UyfRa7db5qd6etI5YZOtpWWg3IcejsQzlwg67kL7d7/8+2mbdxUEjeZwk2lcJJJ3FQKNztEqX4BnJEJ5wwLzbDQTU+mBVQ6hZPKrK/Sqcy20pl1FVqm3yrDwDVMsAyUZaFZJso0UKaZaf+iwNVA6RqOpmeehKdnW1Fp/VobaL2tK/q3XlC5VmAaLkb2va71thJTpF0PjmNh2waua4IyMTUNywDT0Oju7qHLAU3XsHwulmXjMVME9DgF7kF0XAZGaBpkW28p+i57+64wVeZCUe9/AdHvIm1AaDf44m1gcNA/3BsY8on3gpu94O5/GZwZ9j/7g+b1Bmlq4Cc14FNXg7c39D76Rbz9rmm1wTUoRw2z7b75g7avBl6+96+Bg6f3fz/wuDPbGWre4GM7tDxD7GdQkHS06w6e338bQL/zcujxDbefw297qH0xxDnt79DtHm7bR15uuH0O/Y3wdsrydiKLAdMHnOeB+xo8XR2yuYHJoBo8PTstl//ph//mU/13oA19TEOfh0PL27vfQ7ZxhCZDatD8Q3uTHFTO3n0MmDRwvjZo2iH1YrjzerhyHnGJIxznu1z/qBxhEyMTLb3zciZ1LzF99N9x9W6N+qBpxYoVLFy4kILs7ROLFy9m2bJlfPGLXzy2BRNCCCGOkuH1YpSWvq11NMPADAUxQ8H3qFSjj1IKlU7jptOZkCrbUizztK9hLlwcBze7jpvKdAjuptLZsCvb+W/2FrTMdrK9W0P2atrNdrju5sZ7rzQNvx8j4Mfw+TLlGGT16tUsWLAAJxoj2dpCsqWVZGsryZZWUq1tuOk0umWhW2YmVDT7jVsWmq6jXBflOJAdKtfNTnPBdfs1cRjw//xzb9WgY8iV31X9+tLK3qCWbfGQyaz6blHL3LZGZhtk1lNk+q7q/ZPczf43c6Hj5naeuR1OI3PbW+/Sit6pva1B+hofqH4XYv0uzfqtm9N7e5Hqd0F0SGuC3ks6NXBKNrjr3Y82YA3Vby+ZQHLARemAiKH/8QwqX26hoS+1B9P6tcTJHV+/HWsD9tOvfNnjGfoSvG+9XA3ta4JyyHEMV85D/3X1ndPhL8uGu7wfzhDnZMhjHnr5YaKTQ7Y3/PQhL/+HXGe4CGWo7Qx17MN/VkdX3qHmHXmbAz9LCZWFGL06d4+DaROPdTHeU6M+aGpubqa03x/nZWVlbNiw4RiWSAghhBDvBU3T0DwedI/n6NcxDAzDwPD53sOSHWb/mpYLBIPjxx+TMoi/D0r1hZz9AzU1xLS+kK/vPUqxdt065s2dR19zkf7NRtSg7Q3Ye7/tHjqtb3RQ+JR7O3C66rfOEbd9yKQhAq4B+eLgfQ09/YhlHrT/IbutPUIL0gHrDdft7ZD7ONKxDVOet1G2wesr1x00v997Vw09ffDn5Q4+lwOD31w3bH0bpq5uL+PHjxu6nO7g89A/qh7mOA6zjVywPeD8DjzuQadh+M9twCK94Xq/wqoh5g+7ySN9loPWH2qZQx7CMei8HGEfavD6h5zXw65+uJL138nhZx9pH0dRhiMd59FspLW1jZKSYVq/H019OOISh+cN5zP/grPf5VZGv1EfNA31Jft2nhqxadOmkSzOe2r16tXHughCDCB1Uow2UifFaCN1UowmmsfD+q1bjnUxxAdB/+spo/+11ch2gqfPmkn9iG5RiHfHmgZdx7gMLevWHeMSvPdGfdBUXl7OqlWrcu+bm5spKys76vXlqXNCvDNSJ8VoI3VSjDZSJ8VoI3VSjDZSJ8VoI3VyZPQ+dW447+FzW0bGKaecwsqVK2lvbycej/PMM89wxhlnHOtiCSGEEEIIIYQQQohBPhAtmr785S9z0003kU6nufrqqznuuOOOdbGEEEIIIYQQQgghxCCjPmgCWLJkCUuWLDnWxRBCCCGEEEIIIYQQhzHqb50TQgghhBBCCCGEEB8MEjQJIYQQQgghhBBCiBEhQZMQQgghhBBCCCGEGBESNAkhhBBCCCGEEEKIESFBkxBCCCGEEEIIIYQYERI0CSGEEEIIIYQQQogRIUGTEEIIIYQQQgghhBgREjQJIYQQQgghhBBCiBEhQZMQQgghhBBCCCGEGBESNAkhhBBCCCGEEEKIESFBkxBCCCGEEEIIIYQYERI0CSGEEEIIIYQQQogRIUGTEEIIIYQQQgghhBgREjQJIYQQQgghhBBCiBEhQZMQQgghhBBCCCGEGBESNAkhhBBCCCGEEEKIESFBkxBCCCGEEEIIIYQYERI0CSGEEEIIIYQQQogRIUGTEEIIIYQQQgghhBgREjQJIYQQQgghhBBCiBEhQZMQQgghhBBCCCGEGBESNAkhhBBCCCGEEEKIEWEe6wK8V5RSAKRSqWNckqOXTCaPdRGEGEDqpBhtpE6K0UbqpBhtpE6K0UbqpBhtpE6+e705S2/uMpimhpvzAdfT08OOHTuOdTGEEEIIIYQQQggh/u5MnTqVvLy8Q6b/3QZNrusSjUaxLAtN0451cYQQQgghhBBCCCE+8JRSpNNpgsEgun5oj0x/t0GTEEIIIYQQQgghhHh/SWfgQgghhBBCCCGEEGJESNAkhBBCCCGEEEIIIUaEBE1CCCGEEEIIIYQQYkRI0CSEEEIIIYQQQgghRoQETUIIIYQQQgghhBBiREjQJIQQQgghhBBCCCFGhARNQgghhBBCCCGEEGJESNB0jD3++ONcfPHFnH/++dx7773HujjiQ+p///d/ueSSS7jkkkv493//dwBWrFjBkiVLuOCCC/j5z39+jEsoPqx+8pOfcNtttwGwdetWrrrqKhYvXsztt9+ObdvHuHTiw+SFF17gyiuv5MILL+Rf//VfAfmeFMfWo48+mvvt/slPfgLI96Q4NiKRCJdeein79+8Hhv9ulPop3i+D6+QDDzzApZdeypIlS/jmN79JKpUCpE6+lyRoOoaampr4+c9/zn333cejjz7KAw88QG1t7bEulviQWbFiBcuXL2fp0qU88sgjbN68mSeeeIJvfetb/OpXv+LJJ59k06ZNvPzyy8e6qOJDZuXKlSxdujT3/mtf+xrf+c53ePrpp1FK8eCDDx7D0okPk3379vHd736XX/3qVzz++ONs2bKFl19+Wb4nxTETj8f54Q9/yD333MOjjz7KqlWrWLFihXxPivfd+vXrufbaa6mrqwMgkUgM+90o9VO8HwbXyT179nDHHXdw//3389hjj+G6Lvfddx8gdfK9JEHTMbRixQoWLlxIQUEBgUCAxYsXs2zZsmNdLPEhU1paym233YbH48GyLCZNmkRdXR3jxo1j7NixmKbJkiVLpG6K91VnZyc///nP+Yd/+AcAGhoaSCQSzJs3D4Arr7xS6qR43zz77LNcfPHFVFRUYFkWP//5z/H7/fI9KY4Zx3FwXZd4PI5t29i2jWma8j0p3ncPPvgg3/3udykrKwNgw4YNQ343yu+4eL8MrpMej4fvfe97hEIhNE1j6tSpHDhwQOrke8w81gX4MGtubqa0tDT3vqysjA0bNhzDEokPoylTpuTG6+rqePLJJ7nxxhsPqZtNTU3HonjiQ+qf//mf+fKXv0xjYyNw6PdlaWmp1Enxvtm7dy+WZfHpT3+alpYWzj77bKZMmSLfk+KYCYVC3HrrrVx00UX4fD5OOukkLMuS70nxvvvhD3844P1Q1zdNTU3yOy7eN4Pr5JgxYxgzZgwA7e3t3HvvvfzoRz+SOvkekxZNx5BS6pBpmqYdg5IIATt37uTmm2/mG9/4BjU1NYfMl7op3i9/+ctfqKysZNGiRblp8n0pjiXHcVi5ciU//elPefDBB9m4cWOu34f+pE6K98u2bdt46KGHePHFF1m+fDm6rvPaa68dspzUSfF+G+73Wn7HxbHW1NTEJz7xCa666ipOPvlkqZPvMWnRdAyVl5ezatWq3Pvm5uZcEz8h3k+rV6/mS1/6Et/61re45JJLePPNN2ltbc3Nl7op3k9PPvkkLS0tXH755XR1dRGLxdA0bUCdbGlpkTop3jclJSUsWrSIoqIiAM4991yWLVuGYRi5ZeR7Uryfli9fzqJFiyguLgYyt3zccccd8j0pjrny8vIh/4YcPF3qp3g/7dq1i8985jPccMMN3HzzzcChdVXq5MiSFk3H0CmnnMLKlStpb28nHo/zzDPPcMYZZxzrYokPmcbGRr7whS/ws5/9jEsuuQSAuXPnsmfPHvbu3YvjODzxxBNSN8X75q677uKJJ57g0Ucf5Utf+hLnnHMOP/rRj/B6vaxevRqARx55ROqkeN+cffbZLF++nO7ubhzH4dVXX+XCCy+U70lxzEyfPp0VK1YQi8VQSvHCCy9w0kknyfekOOaG+xtyzJgxUj/FMRGJRPj0pz/NrbfemguZAKmT7zFp0XQMlZeX8+Uvf5mbbrqJdDrN1VdfzXHHHXesiyU+ZO644w6SySQ//vGPc9OuueYafvzjH/OP//iPJJNJzjzzTC688MJjWEoh4Gc/+xnf/va3iUajzJw5k5tuuulYF0l8SMydO5dbbrmF6667jnQ6zamnnsq1117LxIkT5XtSHBOnnXYaW7Zs4corr8SyLObMmcNnP/tZzj//fPmeFMeU1+sd9m9I+R0Xx8Jf//pXWltbufPOO7nzzjsBOOecc7j11lulTr6HNDXUzYlCCCGEEEIIIYQQQrxNcuucEEIIIYQQQgghhBgREjQJIYQQQgghhBBCiBEhQZMQQgghhBBCCCGEGBESNAkhhBBCCCGEEEKIESFBkxBCCCGEEEIIIYQYERI0CSGEEOJDYdq0aSxZsoTLL78897r99tuPdbHeU/fddx8PPPAAkHmc88aNGwfMf+ONN7j00ksPu41Nmzbxne985z0roxBCCCH+vpjHugBCCCGEEO+XP/7xjxQVFR3rYrwvGhoaWLp0KQ8++OC72s7s2bO59957efHFFzn77LNHqHRCCCGE+HslLZqEEEII8aE3e/Zsbr31VhYvXszGjRvZtWsXN998M1deeSWXX345f/3rX3PL/uIXv+C8887jox/9KD/5yU+48cYbAbjtttu44447csv1f9/U1MQXvvAFrrzySpYsWcJvfvMbAPbv3895553HD37wA66++mrOP/98nnzySQBs2+ZHP/oRixcv5uKLL+b2228nlUqxePFili9fntvPt7/9bf74xz8ecky//e1vufzyy9E07ajOwTe/+c1cS6+LL76YadOmsXLlSgA+/vGP84tf/OLtnFIhhBBCfEhJiyYhhBBCfGh84hOfQNf7/j/bnXfeSXFxMel0mrPPPptf/OIX2LbN5Zdfzr//+78za9Ysenp6+PjHP87kyZNpbm7mmWee4ZFHHsHr9fL5z3/+qPb7ta99jU9+8pOcc845JJNJPvOZz1BTU8Nxxx3Hvn37OO200/jOd77D008/zY9//GMuvvhi7rvvPjZv3syjjz6Kx+Phn/7pn3jyySe59tpr+ctf/sJpp51GJBLh+eef5+tf//qA/SmleOaZZ/jsZz87YPpXv/pVfD5f7n0sFsPr9QLwox/9KLfuV77yFU466SQWLVoEwLx582hqamLfvn2MHTv27Z94IYQQQnxoSNAkhBBCiA+Nw906d8IJJwBQV1dHfX093/rWt3LzEokEW7Zsoba2lvPPP59QKARkWvoM1Zqov1gsxltvvUVXV1euVVAsFmPbtm0cd9xxWJbFmWeeCcDMmTPp7OwEYMWKFVx++eW5YOi//uu/AOju7uaXv/wl7e3tLFu2jLPOOotwODxgnx0dHfT09FBdXT1g+s9+9jPmzJmTe//GG2/wgx/8YMAyP/7xj4lGo/z0pz8dMH3s2LHs2bNHgiYhhBBCHJYETUIIIYQQQCAQAMBxHMLhMI8++mhuXmtrK3l5efzXf/0XSqncdMuycuOapg2Yl06nAXBdF6UU999/P36/H4D29na8Xi8dHR1YlpVrZdX/NjfTHPhnWmtrK67rUlZWxoUXXshjjz3G448/zne/+91DjkXXdZRSuK47oAXXkdx555289dZb/OlPf8IwjAHzHMc5ZJoQQgghxGDSR5MQQgghRD8TJkzA6/XmgqbGxkYuvfRSNm3axFlnncWyZcvo6urCdV0eeeSR3HqFhYVs2rQJyARJq1atAiAUCjFv3jzuuusuINMi6dprr+X5558/bDkWLVrEE088QSqVwnVdvve97/G3v/0NgOuvv567774bpRTHHXfcIesWFBQQDodpaGg46uN+4oknuPfee/nNb36TC916KaVoaGhgwoQJR709IYQQQnw4SYsmIYQQQoh+PB4Pv/rVr/jhD3/I73//e2zb5tZbb+X4448H4KabbuK6667D6/UyZsyY3Ho33ngjX/3qV1m8eDHV1dWcdNJJuXk/+9nP+MEPfsCSJUtIpVJceumlXHbZZezfv3/YclxzzTU0NDRw5ZVXopTipJNOynU8Pn36dPLz87nmmmuGXf+CCy7g1Vdf5brrrjuq477tttsoLy/nc5/7HK7r5spw7bXXsnHjRmpqaqiqqjqqbQkhhBDiw0tT/dt4CyGEEEKIo7Zs2TLuvfde7rnnnvd1v/X19dx4440sW7YsdzveYPv27ePWW2/loYceOuonzw3ntttu48ILL+Sss856V9sRQgghxN8/uXVOCCGEEOID5Be/+AXXXnst3/jGN4YNmSDTefdHPvIR7r///ne1v40bN6JpmoRMQgghhDgq0qJJCCGEEEIIIYQQQowIadEkhBBCCCGEEEIIIUaEBE1CCCGEEEIIIYQQYkRI0CSEEEIIIYQQQgghRoQETUIIIYQQQgghhBBiREjQJIQQQgghhBBCCCFGhARNQgghhBBCCCGEEGJE/H8vV7o2sz0A7wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize = (20,12)) \n",
    "fft_power_data = [] # power data for a single word\n",
    "test_word_sample = \"easy\"\n",
    "for word_prompt_sample in wordprompts_eegs[test_word_sample]:\n",
    "\n",
    "    # for channel in ['C4']: #['CP5', 'F5', 'C3', 'CP3', 'CP6', 'F6', 'C4']: # note that CP4 is being ignored \n",
    "    channel = \"C4\"\n",
    "    power, frequency = get_fft_theo_and_freqs( word_prompt_sample[channel], data_rate, nfft, fft_rescale)\n",
    "    fft_power_data.append(power)\n",
    "    \n",
    "    plt.plot(frequency, power, label=channel)\n",
    "    plt.xlabel(\"Frequency (Hz)\") \n",
    "    plt.ylabel(\"FFT Magnitude (power)\") \n",
    "    plt.title(f\"FFTs for {channel} when typing {test_word_sample}\") \n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize = (20,12)) \n",
    "fft_power_data = [] # power data for a single word\n",
    "test_word_sample = \"xylophone\"\n",
    "for word_prompt_sample in wordprompts_eegs[test_word_sample]:\n",
    "\n",
    "    # for channel in ['C4']: #['CP5', 'F5', 'C3', 'CP3', 'CP6', 'F6', 'C4']: # note that CP4 is being ignored \n",
    "    channel = \"C4\"\n",
    "    power, frequency = get_fft_theo_and_freqs( word_prompt_sample[channel], data_rate, nfft, fft_rescale)\n",
    "    fft_power_data.append(power)\n",
    "    \n",
    "    plt.plot(frequency, power, label=channel)\n",
    "    plt.xlabel(\"Frequency (Hz)\") \n",
    "    plt.ylabel(\"FFT Magnitude (power)\") \n",
    "    plt.title(f\"FFTs for {channel} when typing {test_word_sample}\") \n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we have data for different word prompts ready to be fed to ml model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [WiP] Pre process and train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4682d592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1148.91,1408.75,1444.268,1342.059,1203.984,1099.171,1049.046,1037.416,1029.551,993.692,918.472,815.099,705.213,610.961,554.477,553.155,603.85,675.158,724.336,722.792,664.681,561.192,437.35,327.341,256.634,225.539,217.087,217.293,217.708,205.101,168.508,119.231,86.324,84.568,98.476,102.285,82.638,37.743,-28.115\n",
      "311\n"
     ]
    }
   ],
   "source": [
    "# try to get top reply for a given fft array \n",
    "test_fft_string = convert_fft_to_string(wordprompts_eegs[\"python\"][4]['C4'], 2, 41) \n",
    "print(test_fft_string)\n",
    "print(len(test_fft_string))\n",
    "# print(gpt.get_top_reply(f'[{fft_string}]'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c209b92",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'wordprompts_eegs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-3ca8ffb41314>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mwordprompt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"easy\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"python\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m#wordprompts_eegs:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Prompt: {wordprompt} Total Samples {len(wordprompts_eegs[wordprompt])}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;31m# based on the length of total samples, we chose to send some to model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'wordprompts_eegs' is not defined"
     ]
    }
   ],
   "source": [
    "\"\"\"Trains a model based on eeg word prompt ffts\n",
    "\"\"\"\n",
    "\n",
    "from gpt import set_openai_key, GPT, Example\n",
    "\n",
    "set_openai_key('test_key')\n",
    "\n",
    "# Construct GPT object and show some examples\n",
    "gpt = GPT(engine=\"davinci\",\n",
    "          temperature=0.5,\n",
    "          max_tokens=1000)\n",
    "\n",
    "def convert_fft_to_string(fft_array, lfreq, hfreq):\n",
    "    fft_string = \"\"\n",
    "    for index in range(lfreq, hfreq):\n",
    "        fft_string += str(round(fft_array[index],3)) + \",\"\n",
    "    \n",
    "    return fft_string.rstrip(\",\")\n",
    "\n",
    "# ideally, traing vs testing split is 60:40 \n",
    "# training data will contain 4 samples for a single wordprompt\n",
    "for wordprompt in [\"easy\",\"python\"]: #wordprompts_eegs:\n",
    "\n",
    "    print(f\"Prompt: {wordprompt} Total Samples {len(wordprompts_eegs[wordprompt])}\")\n",
    "\n",
    "    # based on the length of total samples, we chose to send some to model\n",
    "    # lets start with a hardcode of 4samples\n",
    "    counter = 1\n",
    "    for sample in wordprompts_eegs[wordprompt]:\n",
    "        if counter >= 3:\n",
    "            break\n",
    "\n",
    "        # get the fft representation for a given channel\n",
    "        fft_power, fft_freq = get_fft_theo_and_freqs( sample[channel], data_rate, nfft, fft_rescale)\n",
    "        # convert fft representation to string\n",
    "        fft_string = convert_fft_to_string(fft_power, 2, 41)\n",
    "\n",
    "        # send fft prompt for channel to gpt \"wordprompt: easy; fft: [rrrbs,bsbssbsbs]\"\n",
    "        gpt.add_example(Example(f'[{fft_string}]', f'{wordprompt}'))\n",
    "        # we can replace prompt here with populating a feature matrix (fft_strings) for a given word\n",
    "\n",
    "        counter += 1\n",
    "\n",
    "# view the examples that are sent\n",
    "gpt.get_all_examples()"
   ]
  }
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