recognition_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10/15/24: recognition phase plots\n",
    "* As a reminder, 16 subjs across 29 session undergo 40-trial recognition phases, with 20 familiar & 20 novel images.  \n",
    "* Subjs rate images from 1 (familiar, sure) to 6 (novel, sure).  \n",
    "\n",
    "After wrangling all their data into 1 df, I plotted:  \n",
    "1. Distribution of RTs  .\n",
    "2. Rasters & PSTHs for example cells from each region. For the latter, I trimmed all trials to 5s.  \n",
    "3. Distribution of avg FRs across trials (non-zero FRs only).  \n",
    "4. Trial-avged FR for correct vs incorrect (note that these are imbalanced classes as printed above the plot).  \n",
    "5. Trial-avged FR for (correct+high confidence) vs others (much better balanced).  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os, pandas as pd, matplotlib.pyplot as plt, numpy as np, seaborn as sns, scipy.stats as stats, glob\n",
    "from pynwb import NWBHDF5IO\n",
    "from pathlib import Path\n",
    "from convnwb.io import load_nwbfile\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df normalized by self\n",
      "\n",
      "1457 total neurons\n",
      "\n",
      "19 neurons dropped because FR = 0 in encoding or baseline: ['P43CS_R2_76_1_990_10', 'P47CS_R1_19_4_1280_4', 'P47CS_R1_52_1_298_3', 'P47CS_R2_22_1_8375_4', 'P48CS_R1_24_1_2297_4', 'P48CS_R1_57_1_753_1', 'P48CS_R2_58_3_6420_1', 'P48CS_R2_62_2_2097_1', 'P49CS_R2_64_1_4257_1', 'P51CS_R2_48_4_5337_7', 'P53CS_R1_37_1_3731_5', 'P54CS_R1_70_2_5145_10', 'P54CS_R2_31_1_2534_2', 'P55CS_R1_49_2_5469_3', 'P55CS_R1_54_1_3978_3', 'P57CS_R2_51_1_624_3', 'P62CS_R1_27_3_8498_3', 'P62CS_R1_29_3_8468_3', 'P62CS_R1_32_6_6115_3']\n",
      "\n",
      "1390 neurons, 28 sessions, 16 subjects\n",
      "55600 = 55600 rows in df\n",
      "\n",
      "subjs who didnt undergo fMRI: ['P49', 'P55', 'P56', 'P57', 'P58']\n",
      "\n",
      "25 sessions with scene cells: ['P42CS_R1' 'P42CS_R2' 'P43CS_R1' 'P43CS_R2' 'P44CS_R1' 'P47CS_R1'\n",
      " 'P47CS_R2' 'P48CS_R1' 'P48CS_R2' 'P49CS_R1' 'P49CS_R2' 'P51CS_R1'\n",
      " 'P51CS_R2' 'P53CS_R1' 'P53CS_R2' 'P54CS_R1' 'P54CS_R2' 'P55CS_R2'\n",
      " 'P56CS_R1' 'P57CS_R1' 'P57CS_R2' 'P58CS_R1' 'P60CS_R1' 'P62CS_R1'\n",
      " 'P62CS_R2']\n",
      "\n",
      "60 scene selective cells\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subj</th>\n",
       "      <th>sess</th>\n",
       "      <th>neuron</th>\n",
       "      <th>region</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>RT</th>\n",
       "      <th>trial_spikes</th>\n",
       "      <th>trial_aligned_spikes</th>\n",
       "      <th>num_spikes</th>\n",
       "      <th>FR</th>\n",
       "      <th>trial_smooth_spikes</th>\n",
       "      <th>trial_smooth_FR</th>\n",
       "      <th>stim_phase</th>\n",
       "      <th>stimulus_file</th>\n",
       "      <th>response_correct</th>\n",
       "      <th>response_confidence</th>\n",
       "      <th>actual_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P41</td>\n",
       "      <td>P41CS_R1</td>\n",
       "      <td>P41CS_R1_21_1_679_4</td>\n",
       "      <td>amygdala</td>\n",
       "      <td>566.346460</td>\n",
       "      <td>577.078362</td>\n",
       "      <td>10.731902</td>\n",
       "      <td>[566.79785075, 568.845881, 569.40153725, 576.0...</td>\n",
       "      <td>[0.4513907499999732, 2.499420999999984, 3.0550...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.372721</td>\n",
       "      <td>[1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>0.040230</td>\n",
       "      <td>recognition</td>\n",
       "      <td>old015.png</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P41</td>\n",
       "      <td>P41CS_R1</td>\n",
       "      <td>P41CS_R1_21_1_679_4</td>\n",
       "      <td>amygdala</td>\n",
       "      <td>578.122799</td>\n",
       "      <td>582.712578</td>\n",
       "      <td>4.589779</td>\n",
       "      <td>[582.05028225]</td>\n",
       "      <td>[3.927483250000023]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.217875</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>recognition</td>\n",
       "      <td>new021.png</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P41</td>\n",
       "      <td>P41CS_R1</td>\n",
       "      <td>P41CS_R1_21_1_679_4</td>\n",
       "      <td>amygdala</td>\n",
       "      <td>583.755203</td>\n",
       "      <td>591.685106</td>\n",
       "      <td>7.929903</td>\n",
       "      <td>[589.2551855]</td>\n",
       "      <td>[5.499982499999987]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.126105</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>recognition</td>\n",
       "      <td>new030.png</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P41</td>\n",
       "      <td>P41CS_R1</td>\n",
       "      <td>P41CS_R1_21_1_679_4</td>\n",
       "      <td>amygdala</td>\n",
       "      <td>592.728012</td>\n",
       "      <td>597.998541</td>\n",
       "      <td>5.270529</td>\n",
       "      <td>[593.583746, 593.7849335]</td>\n",
       "      <td>[0.855733999999984, 1.0569214999999303]</td>\n",
       "      <td>2</td>\n",
       "      <td>0.379469</td>\n",
       "      <td>[1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>0.022989</td>\n",
       "      <td>recognition</td>\n",
       "      <td>old012.png</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P41</td>\n",
       "      <td>P41CS_R1</td>\n",
       "      <td>P41CS_R1_21_1_679_4</td>\n",
       "      <td>amygdala</td>\n",
       "      <td>599.050103</td>\n",
       "      <td>603.022414</td>\n",
       "      <td>3.972311</td>\n",
       "      <td>[599.334119, 600.3696805, 600.79902425, 602.03...</td>\n",
       "      <td>[0.2840159999999514, 1.319577499999923, 1.7489...</td>\n",
       "      <td>4</td>\n",
       "      <td>1.006971</td>\n",
       "      <td>[1, 1, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>0.040230</td>\n",
       "      <td>recognition</td>\n",
       "      <td>old021.png</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subj      sess               neuron    region  start_time   stop_time  \\\n",
       "2  P41  P41CS_R1  P41CS_R1_21_1_679_4  amygdala  566.346460  577.078362   \n",
       "3  P41  P41CS_R1  P41CS_R1_21_1_679_4  amygdala  578.122799  582.712578   \n",
       "4  P41  P41CS_R1  P41CS_R1_21_1_679_4  amygdala  583.755203  591.685106   \n",
       "5  P41  P41CS_R1  P41CS_R1_21_1_679_4  amygdala  592.728012  597.998541   \n",
       "6  P41  P41CS_R1  P41CS_R1_21_1_679_4  amygdala  599.050103  603.022414   \n",
       "\n",
       "          RT                                       trial_spikes  \\\n",
       "2  10.731902  [566.79785075, 568.845881, 569.40153725, 576.0...   \n",
       "3   4.589779                                     [582.05028225]   \n",
       "4   7.929903                                      [589.2551855]   \n",
       "5   5.270529                          [593.583746, 593.7849335]   \n",
       "6   3.972311  [599.334119, 600.3696805, 600.79902425, 602.03...   \n",
       "\n",
       "                                trial_aligned_spikes  num_spikes        FR  \\\n",
       "2  [0.4513907499999732, 2.499420999999984, 3.0550...           4  0.372721   \n",
       "3                                [3.927483250000023]           1  0.217875   \n",
       "4                                [5.499982499999987]           1  0.126105   \n",
       "5            [0.855733999999984, 1.0569214999999303]           2  0.379469   \n",
       "6  [0.2840159999999514, 1.319577499999923, 1.7489...           4  1.006971   \n",
       "\n",
       "                                 trial_smooth_spikes  trial_smooth_FR  \\\n",
       "2  [1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...         0.040230   \n",
       "3  [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, ...         0.011494   \n",
       "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, ...         0.011494   \n",
       "5  [1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...         0.022989   \n",
       "6  [1, 1, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...         0.040230   \n",
       "\n",
       "    stim_phase stimulus_file response_correct response_confidence  \\\n",
       "2  recognition    old015.png              1.0                 2.0   \n",
       "3  recognition    new021.png              1.0                 3.0   \n",
       "4  recognition    new030.png              0.0                 3.0   \n",
       "5  recognition    old012.png              0.0                 3.0   \n",
       "6  recognition    old021.png              1.0                 3.0   \n",
       "\n",
       "  actual_response  \n",
       "2             5.0  \n",
       "3             1.0  \n",
       "4             6.0  \n",
       "5             1.0  \n",
       "6             6.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# norm_choice = 'shortest_baseline'\n",
    "# norm_choice = 'session_baseline'\n",
    "norm_choice = 'self'\n",
    "# norm_choice = 'nothing'\n",
    "print(f'df normalized by {norm_choice}\\n')\n",
    "\n",
    "df = pd.read_pickle(f'./data_files/neuron_by_trial_norm_by_{norm_choice}.pkl')\n",
    "print(f'{df[\"neuron\"].nunique()} total neurons\\n')\n",
    "\n",
    "# identify neurons with zero firing rate in encoding or baseline\n",
    "zero_FR_neurons = df[(df['FR'] == 0) & (df['stim_phase'].isin(['encoding', 'baseline']))]['neuron'].unique().tolist()\n",
    "print(f'{len(zero_FR_neurons)} neurons dropped because FR = 0 in encoding or baseline: {zero_FR_neurons}\\n')\n",
    "\n",
    "# identify the 48 neurons from the 1 session with <40 trials\n",
    "neurs_with_miss_trials = df['neuron'].value_counts()[df['neuron'].value_counts() < 40].index.tolist()\n",
    "\n",
    "# now filter\n",
    "df_filtered = df[~df['neuron'].isin(zero_FR_neurons+neurs_with_miss_trials)]\n",
    "df_recognition = df_filtered[df_filtered['stim_phase'] == 'recognition']\n",
    "\n",
    "num_subjs, num_sess, num_neurons = len(np.unique(df_recognition['subj'])), len(np.unique(df_recognition['sess'])), len(np.unique(df_recognition['neuron']))\n",
    "\n",
    "print(f'{num_neurons} neurons, {num_sess} sessions, {num_subjs} subjects')\n",
    "print(f'{num_neurons*40} = {len(df_recognition)} rows in df\\n')\n",
    "\n",
    "no_fMRI_subjs = ['P49', 'P55', 'P56', 'P57', 'P58']\n",
    "print(f'subjs who didnt undergo fMRI: {no_fMRI_subjs}\\n')\n",
    "\n",
    "scene_cells = ['P42CS_R1_47_2_1347_7', 'P42CS_R2_42_3_1096_7', 'P43CS_R1_59_3_1929_1', 'P43CS_R1_76_4_1276_10', 'P43CS_R2_29_1_2303_2',\n",
    "               'P44CS_R1_15_2_835_8', 'P47CS_R1_10_2_900_8', 'P47CS_R1_18_1_1631_4', 'P47CS_R1_22_4_1379_4', 'P47CS_R1_68_1_964_9',\n",
    "               'P47CS_R1_74_2_1575_10', 'P47CS_R1_78_1_2233_10', 'P47CS_R2_19_2_3139_4', 'P47CS_R2_20_4_2437_4', 'P47CS_R2_47_1_1478_7',\n",
    "               'P48CS_R1_9_1_678_8', 'P48CS_R1_21_2_873_4', 'P48CS_R1_30_1_1720_2', 'P48CS_R1_43_2_3181_7', 'P48CS_R2_48_2_936_7',\n",
    "               'P48CS_R2_58_8_6571_1', 'P49CS_R1_7_1_2581_6', 'P49CS_R2_67_1_1165_10', 'P51CS_R1_48_1_2072_8', 'P51CS_R2_40_3_2542_5',\n",
    "               'P51CS_R2_80_1_794_9', 'P53CS_R1_10_1_2456_8', 'P53CS_R2_6_4_5480_6', 'P53CS_R2_16_2_6444_8', 'P54CS_R1_17_2_3943_4',\n",
    "               'P54CS_R1_45_1_2172_7', 'P54CS_R1_54_3_4177_3', 'P54CS_R1_55_2_6861_3', 'P54CS_R1_56_1_1540_3', 'P54CS_R1_70_3_5181_10',\n",
    "               'P54CS_R2_13_4_7736_8', 'P54CS_R2_67_1_4703_10', 'P55CS_R2_18_2_1050_4', 'P55CS_R2_45_1_1206_7', 'P55CS_R2_48_1_36_7',\n",
    "               'P56CS_R1_9_1_3423_8', 'P56CS_R1_13_1_999_8', 'P56CS_R1_19_1_1458_4', 'P57CS_R1_66_1_2442_10', 'P57CS_R2_9_1_740_8',\n",
    "               'P57CS_R2_15_2_1738_8', 'P57CS_R2_70_2_1978_10', 'P58CS_R1_34_3_3299_5', 'P58CS_R1_34_4_3301_5', 'P58CS_R1_35_4_2476_5',\n",
    "               'P58CS_R1_56_1_3134_3', 'P58CS_R1_57_3_2783_1', 'P58CS_R1_60_1_1424_1', 'P58CS_R1_62_2_3305_1', 'P58CS_R1_63_2_3170_1',\n",
    "               'P60CS_R1_39_1_1408_5', 'P60CS_R1_54_1_571_3', 'P60CS_R1_56_1_1106_3', 'P62CS_R1_30_3_6240_3', 'P62CS_R2_22_2_2532_7']\n",
    "\n",
    "sessions_with_scene_cells = np.unique([cell[:8] for cell in scene_cells])\n",
    "print(f'{len(sessions_with_scene_cells)} sessions with scene cells: {sessions_with_scene_cells}\\n')\n",
    "print(f'{len(scene_cells)} scene selective cells')\n",
    "\n",
    "df_recognition.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RT distribution & outlier sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.histplot(df_recognition['RT'])\n",
    "plt.title('RT distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "high_RT_df = df_recognition[df_recognition['RT'] > 20]\n",
    "high_RT_sess_df = high_RT_df.groupby('sess').first().reset_index()\n",
    "high_RT_sess = ['P44CS_R1', 'P48CS_R2', 'P49CS_R1', 'P54CS_R1', 'P54CS_R2', 'P56CS_R1', 'P56CS_R2']\n",
    "\n",
    "fig, axs = plt.subplots(2, 4, figsize=(15,6))\n",
    "axs, ax_count = axs.flatten(), 0\n",
    "\n",
    "for sess_id, sess_df in df_recognition.groupby('sess'):\n",
    "    \n",
    "    if sess_id in high_RT_sess:\n",
    "\n",
    "        sns.histplot(sess_df['RT'], ax=axs[ax_count])\n",
    "        axs[ax_count].set_title(f'session {sess_id}')\n",
    "        ax_count+=1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### example cell raster & PSTH by region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess_num = 0\n",
    "\n",
    "for session, session_df in df_recognition.groupby('sess'):\n",
    "\n",
    "    # if this session does not have recordings from all 5 regions, continue to next session\n",
    "    if not len(np.unique(session_df['region'])) == 5:\n",
    "        continue\n",
    "    \n",
    "    sess_num += 1\n",
    "    if sess_num < 1: # can be used to pick specific session\n",
    "        continue\n",
    "\n",
    "    for region, region_df in session_df.groupby('region'):\n",
    "\n",
    "        neuron_num = 0\n",
    "\n",
    "        fig, ax = plt.subplots(1, 2, figsize=(8, 3))\n",
    "        fig.suptitle(region)\n",
    "\n",
    "        for neuron, neuron_df in region_df.groupby('neuron'):\n",
    "            \n",
    "            neuron_num += 1\n",
    "            if neuron_num < 1: # can be used to pick specific neuron\n",
    "                continue\n",
    "                \n",
    "            # RASTER\n",
    "\n",
    "            ax[0].eventplot(neuron_df['trial_aligned_spikes'], linelengths=0.8, color='black')\n",
    "            ax[0].set_ylabel('trials')\n",
    "            ax[0].set_xlabel('time')\n",
    "\n",
    "\n",
    "            # SMOOTHED FR\n",
    "\n",
    "            num_bins = len(neuron_df['trial_smooth_spikes'].iloc[0])\n",
    "            x_time_points = np.linspace(1, np.max(neuron_df['RT']), num_bins)\n",
    "            \n",
    "            # convert df of lists to np array to be able to calculate mean\n",
    "            trial_smooth_spikes_arr = np.array(neuron_df['trial_smooth_spikes'].tolist())\n",
    "            \n",
    "            ax[1].plot(x_time_points, np.mean(trial_smooth_spikes_arr, axis=0), color='black')\n",
    "            ax[1].axvline(np.mean(neuron_df['RT']), color='red', label='mean RT')\n",
    "\n",
    "            ax[1].set_ylabel('FR')\n",
    "            ax[1].set_xlabel('time across the avg trial')\n",
    "\n",
    "            break\n",
    "        \n",
    "        plt.legend()\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### discard outliers with extreme FRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_neurons * num_trials 55600\n",
      "num_neurons * num_trials after outlier removal: 46737\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'num_neurons * num_trials {len(df_recognition)}')\n",
    "\n",
    "df_recognition = df_recognition[df_recognition['FR'] != 0]\n",
    "print(f'num_neurons * num_trials after outlier removal: {len(df_recognition)}')\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(df_recognition['trial_smooth_FR'])\n",
    "plt.title('distribution of FRs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### correct vs incorrect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "response_correct\n",
      "0.0    11640\n",
      "1.0    35097\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df_recognition.groupby(['response_correct']).size())\n",
    "\n",
    "correctness_fr = df_recognition.groupby(['region', 'neuron', 'response_correct'])['FR'].mean().reset_index()\n",
    "plt.figure(figsize=(12, 4))\n",
    "sns.violinplot(correctness_fr, x='region', y='FR', hue='response_correct', palette=['red', 'blue'])\n",
    "plt.title('for each region, for each neuron, for each type of response (correct vs incorrect), plotting mean FR across trials')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (confident + correct) vs not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conf_corr\n",
      "Confident & Correct    23537\n",
      "Other                  23200\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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27RJTpkwRMpnM7BxlT3wVOS9a8sorr4jw8PAyy48fPy4CAwNF48aNxcKFC8Xvv/8uVqxYIZ577jmRn58vhKic3+OwYcOMCWZ8fLz4/fffxfbt2yv0myzvPG7rt2KJPQl/fn6+aNu2rQgICBDTp08X27dvF2vXrhWjR48Wu3fvFkLYt99L7DmH2Cp3WTq2Dxs2TMhkMvH+++8bt0mDBg1EcHCwWcJv7z4kFaYbN24sHn30UbFhwwZj8la7dm1jJeWlS5fEM888Y0xEpFvpSg7JiRMnBADx3XffmS3PyckRcrlcjBs3zvj+vr6+om/fvmLDhg1i7969YuXKlWLo0KFlkonS7C3f2LNP5+XlGfeR//u//zN+PtMGC51OJwIDA42xV5Rp+fj9998X27dvF3PnzjWW3aQKECHKJvz27ncHDx4Ufn5+4rHHHjN+hn///Vekp6cbj2VfffWV8TGposFawm9PGWjr1q3Cw8ND9OrVS6xfv16sWbNGdOnSxVieLY9UHpcqa7Zv3y6eeOIJAUBMmzZNtG/fXvz4449iy5YtomvXrkIul5s1PNhb7s7NzRXDhw8XP/zwg9i9e7fYtm2beO+994SHh0eZcv+dlP+k39SsWbOEVqs1u5k2UFpL+J955hnh5eUlbt68We62q4qyS+n8zd6cw57fsj3nv9JUKpXYtm2bACBeffVV47576dIlm3GbPnY726z0c+/kWGXKKRJ+IW6dxNesWWNcptfrRf369UX79u3NWr8LCgpEeHi46N69u3GZtIGmTJli1/u98cYbIjAwUFy9etVs+Zw5c6y2UEgxabVaMX36dBEWFmb8EV25ckV4enqKIUOG2HxfqXXg8OHDZsvbtGkjHnnkkXLjBiBCQkJEdna22fJHHnlENGzYsEyB9u233xa+vr7G9d9//30hk8nKfL5HHnmk3IRf2vFnz55t9tzVq1cLAOLbb781LouOjha+vr5m27eoqEiEhoaKN954o9zPaYn0HZsm8WfPnhUAjK2MEqlGfOLEicZl0ra3t5a8VatW4q677irTsvXEE0+IyMhIqz0ydDqd0Gq14qGHHhJPPfWUcfny5cstFkRKAyDq1atnLAgLIURaWprw8PAQ8fHxNp8r/Y5KFwB//vlns5pbhUIhQkNDRf/+/c3W0+v1omPHjuK+++4zLqtowm9p/+zatasIDw8XBQUFxmU6nU60a9dONGzY0Pg7kgo+pb/P2bNnCwAiNTXV5ue3Rqo1NzV48GAhl8vFtWvXzJbHxsYKf39/Y4FT2qYPPvigXe8lnVjWrl1rtjwhIUEAEF9//bXF5xkMBqHVasW+ffsEAHHixAnjY02bNhVNmzYVRUVFVt9X+j5K/z5HjhwpfH19LfZIMvX4449b/J7z8/NFUFCQWY28EMXHLNMTlbSd7r77brP3SkpKEt7e3uK1114zLrvd35YQQixcuFAAED///LPZ8lmzZgmguPVcIrXg2CM6Olp4enqK8+fPmy2391zx9ttvi1q1atl8D2n/Nj0uCCHEX3/9JQCIjz/+WAhRnJxIhWdT165dE3K5XLz44ovGZVILaOnt8dhjj4mWLVsa79sT3+2eFyWtW7cWjz76aJnlffr0EbVq1bLYmi6pjN+jtC2kFjqJvb9Je8/j1n4rQtw6/ku3RYsWCQBlCuCm+/j06dMFALFz506r71mR/d7ec4ilcpcQZY/t0nl27NixZutJDTKmCb+9+5CUnLRv396sIvrIkSMCgFkl+KhRo+xK4CR33323WflQiOLWPQDi1KlTQgghfvnlFwFAHD9+3O7XldhbvrF3n5b2Q1uVnT169BBdunSpcKxC3Po+rX1/K1asMC4rnfBXZL8LCAgo09tDCCHWrFlTpnwpsZbw27P/3nvvvSIqKkqo1WrjsoKCAhEWFmZ3wg9AHD161LgsKytLeHp6Cj8/P7Pk/vjx4wKA+N///mdcZm+5uzTpGPHqq6+Ku+6667Y+uyXWWqRLH1ukhF86FmVmZooFCxYIDw8PszKzvSqr7FI6f7M357Dnt2zP+c+SjIwMAZj3uCkvbtPHrLG1zUo/906OVaacZtA+S86fP4+UlBQMHToUHh63Qg0MDMTTTz+NQ4cOQalUmj3n6aeftuu1N23ahN69e6N+/frQ6XTGm3SN7759+4zr7t69Gw8//DBCQkLg6ekJb29vTJkyBVlZWUhPTwcA7Ny5E3q9HqNGjSr3vSMiInDfffeZLevQoQOuXr1qV+x9+vRB7dq1jfdVKhV+//13PPXUU/D39zf7PI899hhUKhUOHTpk/Fzt2rVDmzZtzF7zhRdeKPd9d+/eDQBlRuR99tlnERAQgN9//91seadOndCoUSPjfV9fX7Ro0cLuz2mPPXv2WIzpvvvuQ+vWrcvEVLt2bfTp06fc17106RLOnTtnvPa39DZNTU3F+fPnjesvXLgQd999N3x9feHl5QVvb2/8/vvvOHv2rHGdrVu3wtfXF6+88kq579+7d28EBQUZ79erVw/h4eF2b7snn3zS7H6HDh0AwPj8AwcOIDs7G8OGDTP7bAaDAY8++igSEhKgUCjseq/SSu+fCoUChw8fxjPPPIPAwEDjck9PTwwdOhTXr18325b2xF8Zdu/ejYceeghRUVFmy4cPHw6lUllmwJWKHFtq1aqF/v37m23bTp06ISIiwmzwlytXruDFF19ERESE8dgiXUsr7TsXLlzA5cuX8eqrr8LX17fc97e07VQqlfFYVVFBQUF4+eWXsXTpUuM+sXv3bpw5c8bibBIvvvii2XWm0dHR6N69u/G3WtHfVmm7d+9GQEBAmcGFpGNA6d98RXTo0KHMYLH2nivuu+8+5Obm4oUXXsCvv/5qc7aE0mMKdO/eHdHR0cZtdPDgQRQVFZU5rkVFRaFPnz5lPqNMJkP//v3LfBbT34s98VXkvGhJSkpKmfFVlEol9u3bh+eeew5169a1+tzK/D2Wfsze32RFzuPWNG3aFN7e3sbbq6++CgBmy7y9vTF9+nTjc7Zu3YoWLVrg4Ycftvq6Fd3v7/QcYkraL0vvt88991yZcRMqug89/vjj8PT0NN6vjGP9yy+/jAMHDpgdR5YsWYJ7773XOEtSp06d4OPjg9dffx3Lli3DlStXKvQe9pRvKrpP2xIeHo4bN25UKMbSrH1/0vdrSVUeb20pb/9VKBQ4evQoBg4cCB8fH+N6gYGBZY6FtkRGRuKee+4x3g8NDUV4eDg6deqE+vXrG5e3bt0awK39siLlbgBYs2YNevTogcDAQGMZcdGiRWZlRHs/e3lGjx6NhIQEs1uXLl3M1lEoFMZjUZ06dfDWW2/h+eefx4wZM+x6j6oou5Q+btubc9jzW67I+bmi7C0b2rPNLLnTY5XEqRP+rKwsAMU/yNLq168Pg8GAnJwcs+WW1rXk5s2b2LhxY5mTcNu2bQHAuDMcOXIE/fr1AwB89913+Ouvv5CQkIBJkyYBKB7UCSge7RaAXYM0hIWFlVkml8uNr1We0p8xKysLOp0OX375ZZnP89hjj5l9nqysLNSrV6/Ma1paVlpWVha8vLzKFNpkMhkiIiKM35fkTj+nPcrbR0rHVJH9AygeIbf0Nh05ciSAW9t07ty5eOutt9ClSxesXbsWhw4dQkJCAh599FGzz5qRkYH69eubVV5Zc6fbrvTz5XI5gFv7q/T5nnnmmTKfb9asWRBCIDs72673Kq30Ns7JyYEQwup3BKDcfad0/JUhKyurQjFVZN/Jzc2Fj49PmW2blpZm3G8KCwvxwAMP4PDhw/j444+xd+9eJCQkYN26dQBu79gCVM22e+edd1BQUICVK1cCAObPn4+GDRtiwIABZdaNiIiwuEzanhX5bVmSlZWFiIiIMoNXhYeHw8vLq8z3VhGWvmN7zxVDhw7F4sWLcfXqVTz99NMIDw9Hly5dsHPnzjKvWd42quhxzd/fv0yBSi6XQ6VSGe/bE5+9n9WaoqKiMnHk5ORAr9eXu/9W1u/R398fwcHBZsvs/U1W9LdmycaNG80K2lOnTgWAMgXw119/3ficjIwMu7ZPRfb7yjz/Sq9der/18vIq8z4V3Yeq4ng1ZMgQyOVy48jkZ86cQUJCAl5++WXjOk2bNsWuXbsQHh6OUaNGoWnTpmjatCm++OILu97Dnu1b0X3aFl9f3zs+/1n7/mzFUZXHW1vK275SueJ2y7OS0NDQMst8fHzKLJcqFaRjakXK3evWrcNzzz2HBg0aYMWKFTh48CASEhLwyiuvmB2j7f3s5WnYsCE6d+5sdjOtQACKR+mXjkUbN25Er1698OOPP9o1nWpVlV0s5Tb25Bz2/JYrcn6uKHvKhvZuM0vu9FglceohpqWdPjU1tcxjKSkp8PDwMGtJBGD3CKZ16tRBhw4drNZmSQfkn376Cd7e3ti0aZNZQab01CPSDnn9+vUytbmVrfRnrF27trG11FrLhDTiZlhYmLHAbSotLa3c9w0LC4NOp0NGRobZD1AIgbS0NNx7770V+RiVwnQfKX1QSUlJQZ06dcyWVWT/AIAJEyZg0KBBFtdp2bIlAGDFihXo1asXFixYYPZ4QUGB2f26devizz//hMFgsCvpr0rS5/vyyy+tjuYqnTR9fX2hVqvLPG6t8G9p//Tw8LD6OzaNpzqFhYVVKKaK7DthYWHYtm2bxcelE+/u3buRkpKCvXv3mo2QnZuba7a+6bHFUZo1a4bY2Fh89dVXiI2NxW+//YZp06aZtcxJLB1L0tLSjL/Vivy2LAkLC8Phw4chhDD7TtLT06HT6e5oX7L0Hdt7rgCKWxZffvllKBQK7N+/H1OnTsUTTzyBCxcuIDo62rietW3UrFkz42cErJ/7bvczlhdfRT6rJXXq1ClTURgaGgpPT89y99/K+j1a+w7t+U1Wxnm8ffv2Zvel2WVM58EurW7dunZtn6ra78sj7Y9paWlo0KCBcblOpyuT8N3pPlQZateujQEDBmD58uX4+OOPsWTJEvj6+pbpyfjAAw/ggQcegF6vx9GjR/Hll19izJgxqFevXqVM3VrRfdqW7OzsO/6OrX1/lhJMiSP3O1tq164NmUx22+XZynh/e8vdK1asQExMDFavXm22DS2Vq6qLh4eH2TGpb9++uOeeezBt2jQMGTLE5vGvqsoupY/dFck57Pkt23t+rih7yob2bjNrKuNY5dQt/C1btkSDBg2watUqsyltFAoF1q5di27dut32tA1PPPGEccqm0jVhnTt3Np6UpKl+TAu2RUVF+OGHH8xer1+/fvD09CyT9FUHf39/9O7dG//88w86dOhg8fNIB/SePXvi9OnTOHPmjNlr/PTTT+W+z0MPPQSg+OBlau3atVAoFMbHq5PUPb90TAkJCTh79uxtx9SyZUs0b94cJ06csLg9TWtMZTKZsVVCcvLkyTLd9WJjY6FSqcrMh+oIPXr0QK1atXDmzBmrn0+q0W7cuDHS09PNTqwajQbbt2+3670CAgLQpUsXrFu3zqwW02AwYMWKFWjYsGGZbtTV4aGHHjIehE0tX74c/v7+5U5rY80TTzyBrKws6PV6i9tVSmalk0Tpfeebb74xu9+iRQs0bdoUixcvrtICQnktCKNHj8bJkycxbNgweHp6YsSIERbX+/HHH82O11evXsWBAwfQq1cvABX7bVny0EMPobCwsEyl6/Lly42PVyZ7zxWmAgICEBsbi0mTJkGj0eDff/81e1zqKSE5cOAArl69atxG3bp1g5+fX5nj2vXr143dhO+Etfhu57OaatWqFS5fvmy2zM/PDz179sSaNWts9hCoqt+j9Lns+U3aex6v7J5qsbGxuHDhgrELqyXVvd+bkvbL0vvtzz//DJ1OZ7bsTvchS26n1f/ll19GSkoKtmzZghUrVuCpp55CrVq1LK7r6emJLl264KuvvgIA/P333xWO0RJ792l7Pt+VK1fKXIpZUda+P+n7taQi+52130VV9NALCAhA586dsWHDBmg0GuPywsJCbNq0qdLex5qKlLtlMhl8fHzMEsO0tDT8+uuvVR6nveRyOb766iuoVCp8/PHHNtetrrLL7eQc9vyWyzs/m6qsfdfebVaeOzlWOXULv4eHB2bPno0hQ4bgiSeewBtvvAG1Wo1PP/0Uubm5dnU9sWb69OnYuXMnunfvjnfffRctW7aESqVCUlIStmzZgoULF6Jhw4Z4/PHHMXfuXLz44ot4/fXXkZWVhTlz5pT50ho3boyJEyfio48+QlFREV544QWEhITgzJkzyMzMxLRp0+50c9j0xRdf4P7778cDDzyAt956C40bN0ZBQQEuXbqEjRs3GgsSY8aMweLFixEbG4vp06ejXr16WLVqFc6dOwcANlue+/bti0ceeQQffPAB8vPz0aNHD5w8eRJTp07FXXfdhaFDh95W7I0bNwYAJCUlVfi5LVu2xOuvv44vv/wSHh4eiI2NRVJSEiZPnoyoqCiMHTv2tmICin+IsbGxeOSRRzB8+HA0aNAA2dnZOHv2LP7++2+sWbMGQHEB56OPPsLUqVPRs2dPnD9/HtOnT0dMTIxZYeiFF17AkiVL8Oabb+L8+fPo3bs3DAYDDh8+jNatW1dKi4K9AgMD8eWXX2LYsGHIzs7GM888g/DwcGRkZODEiRPIyMgwFnqff/55TJkyBYMHD8b7778PlUqF//3vf9Dr9Xa/X3x8PPr27YvevXvjvffeg4+PD77++mucPn0aP/74423NLSzNb71kyZIy13jZY+rUqcbrTadMmYLQ0FCsXLkSmzdvxuzZsxESElLh1wSAwYMHY+XKlXjssccwevRo3HffffD29sb169exZ88eDBgwAE899RS6d++O2rVr480338TUqVPh7e2NlStX4sSJE2Ve86uvvkL//v3RtWtXjB07Fo0aNcK1a9ewffv2MoW429W+fXusW7cOCxYswD333GOxBaBNmzbYs2cPXnrppTLXakvS09Px1FNPYcSIEcjLy8PUqVPh6+uLCRMmGNex97dlyX/+8x989dVXGDZsGJKSktC+fXv8+eefmDlzJh577DGb10HfDnvPFSNGjICfnx969OiByMhIpKWlIT4+HiEhIWV6Px09ehSvvfYann32WSQnJ2PSpElo0KCB8ZKGWrVqYfLkyZg4cSL+85//4IUXXkBWVhamTZsGX19fYzfxirAnPns/qzW9evXC9OnTy8yhPHfuXNx///3o0qULPvzwQzRr1gw3b97Eb7/9hm+++QZBQUFV9nsE7P9N2nseL++3UlFjxozB6tWrMWDAAHz44Ye47777UFRUhH379uGJJ55A7969q32/N9W6dWu89NJLmDdvHry9vfHwww/j9OnTmDNnTpnLJ+50H7JE6jUxa9YsxMbGwtPTEx06dDC7dru0fv36oWHDhhg5ciTS0tLMuvMDxePu7N69G48//jgaNWoElUqFxYsXA0ClbUt79+mmTZvCz88PK1euROvWrREYGIj69eubdf2/ePEi3nnnHbPXHz58OJYtW4bExERjOcqWdevWwcvLC3379sW///6LyZMno2PHjnjuueesPqci+1379u2xd+9ebNy4EZGRkQgKCkLLli2N4yZ8++23CAoKgq+vL2JiYmz2LLDH9OnT8fjjj+ORRx7B6NGjodfr8emnnyIwMPC2L0msCHvL3U888QTWrVuHkSNH4plnnkFycjI++ugjREZG4uLFi1Uep7169uyJxx57DEuWLMGHH35o7KFQWnWVXezNOez5LVfk/GwqKCgI0dHR+PXXX/HQQw8hNDQUderUsev3drvbrLRKO1bd0ZB/lcjaaLFCFM+z2aVLF+Hr6ysCAgLEQw89ZDZvuRCWR3AvT0ZGhnj33XdFTEyM8Pb2FqGhoeKee+4RkyZNMpsXcfHixaJly5bG+SPj4+ONI++Wngt2+fLl4t577xW+vr4iMDBQ3HXXXWYjr1obNdraSOilwcJo45LExETxyiuvGOe+rVu3rujevbtx5GfJ6dOnxcMPPyx8fX1FaGioePXVV8WyZcvKjBRpKaaioiLxwQcfiOjoaOHt7S0iIyPFW2+9VWZqCGme2tIszfFap04d41zJtlj7jvV6vZg1a5Zo0aKF8Pb2FnXq1BEvvfSS2bQ20nvbO2K35MSJE8a5e729vUVERITo06ePWLhwoXEdtVot3nvvPdGgQQPh6+sr7r77brFhwwar22/KlCmiefPmwsfHR4SFhYk+ffqIAwcOGNex9h1bmu+4NGu/I2n01tKjAO/bt088/vjjIjQ0VHh7e4sGDRqIxx9/vMzzt2zZIjp16iT8/PxEkyZNxPz5862O0m9t//zjjz9Enz59REBAgPDz8xNdu3YtM7esNIp5QkKCxc9lOsrvl19+KYDiaRrLYy2uU6dOif79+4uQkBDh4+MjOnbsWGYb2To2WaPVasWcOXNEx44djceCVq1aiTfeeENcvHjRuJ40/6+/v7+oW7eueO2118Tff/9t8bs6ePCgiI2NFSEhIUIul4umTZuajbps7fchbVNr81ZLsrOzxTPPPCNq1aolZDKZxRFm4+LiBABx6NChMo9J2+mHH34Q7777rqhbt66Qy+XigQceMBsFWWLPb8uarKws8eabb4rIyEjh5eUloqOjxYQJE8pM1VXRUfotHbOEsO9csWzZMtG7d29Rr1494ePjI+rXry+ee+45cfLkSePrSN/Fjh07xNChQ0WtWrWMo/Gb7heS77//XnTo0EH4+PiIkJAQMWDAgDIj5VubYqn079Oe+Oz9rNZcunRJyGSyMiN6CyHEmTNnxLPPPivCwsKEj4+PaNSokRg+fLjZd3anv0dr20II+3+TQpR/HrfntyKxZ1o+IYpnZhg9erRo1KiR8Pb2FuHh4eLxxx8X586dM65j735v7znE3lH6hSg+z/33v/8V4eHhwtfXV3Tt2lUcPHjQ4nnJnn3I1pzhKDUqtlqtFq+99pqoW7eucXuXdzwTQoiJEycKoHgazNIzfxw8eFA89dRTIjo6WsjlchEWFiZ69uwpfvvtt3JftyLlG3v2aSGKZ5Jo1aqV8Pb2LvP5Fy1aJLy9vUVaWprZc55++mnh5+dX7tRcpvOF9+/fXwQGBoqgoCDxwgsvlJmCzdJnsHe/O378uOjRo4fw9/cXAMxeZ968eSImJkZ4enqand+sjdJvbxlo/fr1on379sZjyieffCLeffddUbt2bZvbRPqsls4P1r5fS3HZW+7+5JNPROPGjYVcLhetW7cW3333XYXKUPaU/2z9pkzZOk6eOnVKeHh4lDuVb3WUXYSwL+ew57ds7/nPkl27dom77rpLyOVys1lJbMVt6bu1d5uVfu6dHKtMyYQw6XtJNdbrr7+OH3/8EVlZWTZrzSvbmTNn0LZtW2zatAmPP/54tb0vub7nnnsOiYmJSEhIcHQoNUbnzp0hk8ksbvO9e/eid+/eWLNmTZkRnamY1CslISHhjlqEnZk0Ev7WrVsdHQqRW3jggQfQqFGjMi2iERERGDp0KD799FObz4+Li8O0adOQkZHhsGvuq4tWq0WnTp3QoEED7Nixw9HhEDkNp+7ST1Vj+vTpqF+/Ppo0aWK83un777/H//3f/1Vrsg8UT/fTrVs3JvtUIUII7N27t8y1XVT58vPzcfr0aWzatAnHjh3D+vXrHR0SObH4+HjcddddSEhIcMhArkTuZP/+/UhISMCyZcvMlv/7779QKpX44IMPHBSZc3j11VfRt29fYzfthQsX4uzZsxUewZzI3THhr4G8vb3x6aef4vr169DpdGjevDnmzp2L0aNHV3sso0aNuqM5j6lmkslktz2vPFXM33//jd69eyMsLAxTp07FwIEDHR0SObF27dphyZIl1TJSNpG7y8rKwvLly9GkSROz5W3btkV+fr6DonIeBQUFeO+995CRkQFvb2/cfffd2LJlS5WOaUHkitiln4iIiIiIiMgNOfW0fERERERERER0e5wm4Y+Pj4dMJsOYMWOMy4QQiIuLQ/369eHn54devXrZnC+RiIiIiIiIiIo5RcKfkJCAb7/9Fh06dDBbPnv2bMydOxfz589HQkICIiIi0LdvXxQUFDgoUiIiIiIiIiLX4PBB+woLCzFkyBB89913+Pjjj43LhRCYN28eJk2ahEGDBgEAli1bhnr16mHVqlV444037Hp9g8GAlJQUBAUFQSaTVclnICIiIiIiIpIIIVBQUID69evDw8Nx7ewOT/hHjRqFxx9/HA8//LBZwp+YmIi0tDT069fPuEwul6Nnz544cOCA1YRfrVZDrVYb79+4cQNt2rSpug9AREREREREZEFycjIaNmzosPd3aML/008/4e+//0ZCQkKZx6QpferVq2e2vF69erh69arV14yPj8e0adPKLE9OTkZwcPAdRkxERERERERkW35+PqKiohAUFOTQOByW8CcnJ2P06NHYsWMHfH19ra5Xuhu+EMJm1/wJEyZg3LhxxvvShg4ODmbCT0RERERERNXG0ZeVOyzhP3bsGNLT03HPPfcYl+n1euzfvx/z58/H+fPnARS39EdGRhrXSU9PL9Pqb0oul0Mul1dd4EREREREREQuwGGjBzz00EM4deoUjh8/brx17twZQ4YMwfHjx9GkSRNERERg586dxudoNBrs27cP3bt3d1TYRERERERERC7BYS38QUFBaNeundmygIAAhIWFGZePGTMGM2fORPPmzdG8eXPMnDkT/v7+ePHFFx0RMhEREREREZHLcPgo/baMHz8eRUVFGDlyJHJyctClSxfs2LHD4QMfEBERERGRdUII6HQ66PV6R4dCVCU8PT3h5eXl8Gv0yyMTQghHB1GV8vPzERISgry8PA7aR0RERERUxTQaDVJTU6FUKh0dClGV8vf3R2RkJHx8fMo85ix5qFO38BMRERERkeswGAxITEyEp6cn6tevDx8fH6dvASWqKCEENBoNMjIykJiYiObNm8PDw2HD49nEhJ+IiIiIiCqFRqOBwWBAVFQU/P39HR0OUZXx8/ODt7c3rl69Co1GY3OqeUdyzmoIIiIiIiJyWc7a2klUmVxhP3f+CImIiIiIiIiowpjwExEREREREbkhJvxERETkMgwGA5YvX46LFy86OhQiIodQKpV4+umnERwcDJlMhtzcXEeHRE6MCT8RERG5jLNnz+KXX35BfHy8o0MhInKIZcuW4Y8//sCBAweQmpqKkJAQR4dEToyj9BMREZHLUKlUAIDMzEwHR0JE5BiXL19G69at0a5dO4e8v0ajKTPvvBACer0eXl5ML50NW/iJiIiIiIgqicFgwKxZs9CsWTPI5XI0atQIM2bMAACcOnUKffr0gZ+fH8LCwvD666+jsLDQ+Nzhw4dj4MCBmDNnDiIjIxEWFoZRo0ZBq9UCAHr16oXPPvsM+/fvh0wmQ69evcqNR61WY/z48YiKioJcLkfz5s2xaNEi4+P79u3DfffdB7lcjsjISHz44YfQ6XTGx3v16oW3334b48aNQ506ddC3b1/s3bsXMpkM27dvR+fOnSGXy/HHH39U0hakysQqGCIiIiIiokoyYcIEfPfdd/j8889x//33IzU1FefOnYNSqcSjjz6Krl27IiEhAenp6Xjttdfw9ttvY+nSpcbn79mzB5GRkdizZw8uXbqE559/Hp06dcKIESOwbt06fPjhhzh9+jTWrVtXpqXdkv/85z84ePAg/ve//6Fjx45ITEw09pK6ceMGHnvsMQwfPhzLly/HuXPnMGLECPj6+iIuLs74GsuWLcNbb72Fv/76C0IIpKWlAQDGjx+POXPmoEmTJqhVq1ZlbkaqJEz4iYiIiIiIKkFBQQG++OILzJ8/H8OGDQMANG3aFPfffz++++47FBUVYfny5QgICAAAzJ8/H/3798esWbNQr149AEDt2rUxf/58eHp6olWrVnj88cfx+++/Y8SIEQgNDYW/vz98fHwQERFRbjwXLlzAzz//jJ07d+Lhhx8GADRp0sT4+Ndff42oqCjMnz8fMpkMrVq1QkpKCj744ANMmTLFOM98s2bNMHv2bOPzpIR/+vTp6Nu3byVsOaoq7NJPRERERERUCc6ePQu1Wo2HHnrI4mMdO3Y0JvsA0KNHDxgMBpw/f964rG3btvD09DTej4yMRHp6+m3Fc/z4cXh6eqJnz55W4+3WrRtkMplZTIWFhbh+/bpxWefOnS0+39pych5M+ImIiIiIiCqBn5+f1ceEEGaJtSnT5d7e3mUeMxgMlR6PtZiEEGViMq2kMGVtOTkPJvxERERERESVoHnz5vDz88Pvv/9e5rE2bdrg+PHjUCgUxmV//fUXPDw80KJFiyqJp3379jAYDNi3b5/Fx9u0aYMDBw4Yk3wAOHDgAIKCgtCgQYMqiYmqFxN+IiIiIiKiSuDr64sPPvgA48ePx/Lly3H58mUcOnQIixYtwpAhQ+Dr64thw4bh9OnT2LNnD9555x0MHTrUeP1+ZWvcuDGGDRuGV155BRs2bEBiYiL27t2Ln3/+GQAwcuRIJCcn45133sG5c+fw66+/YurUqRg3bpzx+n1ybRy0j4iIiIiIqJJMnjwZXl5emDJlClJSUhAZGYk333wT/v7+2L59O0aPHo17770X/v7+ePrppzF37twqjWfBggWYOHEiRo4ciaysLDRq1AgTJ04EADRo0ABbtmzB+++/j44dOyI0NBSvvvoq/u///q9KY6LqIxOm/TfcUH5+PkJCQpCXl4fg4GBHh0NERER34NixY5g2bRoA4LfffnNwNERUmkqlQmJiImJiYuDr6+vocIiqlK393VnyUPbTICIiIiIiInJDTPiJiIiIiIhc0B9//IHAwECrNyJew09EREREROSCOnfujOPHjzs6DHJiTPiJiIicwOnTp/Hrr7/iv//9L697JSIiu/j5+aFZs2aODoOcGLv0ExEROYHZs2fj8OHDOHjwoKNDISIiIjfBhJ+IiMgJ5ObmAgC0Wq1jAyEiIiK34dCEf8GCBejQoQOCg4MRHByMbt26YevWrcbHhw8fDplMZnbr2rWrAyMmIiIiIiIicg0OvYa/YcOG+OSTT4zXnSxbtgwDBgzAP//8g7Zt2wIAHn30USxZssT4HB8fH4fESkRERERERORKHJrw9+/f3+z+jBkzsGDBAhw6dMiY8MvlckRERDgiPCIiIiIiIiKX5TSj9Ov1eqxZswYKhQLdunUzLt+7dy/Cw8NRq1Yt9OzZEzNmzEB4eLjV11Gr1VCr1cb7+fn5VRo3ERERERHZNmHCBGRmZlbre9apUwfx8fHV+p72EkLgjTfewC+//IKcnBz8888/GDNmDDp16oR58+ZZfV7jxo0xZswYjBkzptpiJdfm8IT/1KlT6NatG1QqFQIDA7F+/Xq0adMGABAbG4tnn30W0dHRSExMxOTJk9GnTx8cO3YMcrnc4uvFx8dj2rRp1fkRiIiIiIjIhszMTKSlZUCrrV0t7+ftnXNbz0tLS8OMGTOwefNm3LhxA+Hh4ejUqRPGjBmDhx56qNLi27ZtG5YuXYq9e/eiSZMmqFOnDtatWwdvb+9Kew979erVq9yKBsm2bdswYcIEnDt3DqGhoRgwYAC+/vpru96nurZtZVm6dCnGjBljHFTXVTk84W/ZsiWOHz+O3NxcrF27FsOGDcO+ffvQpk0bPP/888b12rVrh86dOyM6OhqbN2/GoEGDLL7ehAkTMG7cOOP9/Px8REVFVfnnICIiIiIi67Ta2jh/fmq1vFfLlhVvAExKSkKPHj1Qq1YtzJ49Gx06dIBWq8X27dsxatQonDt3rtLiu3z5MiIjI9G9e3fjstDQ0Ep7/aqgUqkwaNAgPPvss1izZg0KCwtx5MgRu55bVdtWq9VarCSxtrwmcvi0fD4+PmjWrBk6d+6M+Ph4dOzYEV988YXFdSMjIxEdHY2LFy9afT25XG4c9V+6ERERERER2TJy5EjIZDIcOXIEzzzzDFq0aIG2bdti3LhxOHTokHG9a9euYcCAAQgMDERwcDCee+453Lx50/h4XFwcOnXqhB9++AGNGzdGSEgIBg8ejIKCAgDFM5G98847uHbtGmQyGRo3bgyguKXdtKt+eno6+vfvDz8/P8TExGDlypVlYs7Ly8Prr7+O8PBwBAcHo0+fPjhx4kSFYtm3bx+++OIL46xoSUlJVreRp6cnhgwZgmbNmqFTp054/fXXHbJtFy9ejCZNmkAul0MIAZlMhoULF2LAgAEICAjAxx9/DADYuHEj7rnnHvj6+qJJkyaYNm0adDqd8fVyc3Px+uuvo169evD19UW7du2wadMm7N27Fy+//DLy8vKM2yUuLs6uz+psHJ7wlyaEMLsG31RWVhaSk5MRGRlZzVEREREREZG7ys7OxrZt2zBq1CgEBASUebxWrVoAinOVgQMHIjs7G/v27cPOnTtx+fJls57JQHEL/oYNG7Bp0yZs2rQJ+/btwyeffAIA+OKLLzB9+nQ0bNgQqampSEhIsBjT8OHDkZSUhN27d+OXX37B119/jfT0dOPjQgg8/vjjSEtLw5YtW3Ds2DHcfffdeOihh5CdnW13LN26dcOIESOQmpqK1NRUq72jfX198cgjj2D8+PFmr1/d2/bSpUv4+eefsXbtWhw/fty4fOrUqRgwYABOnTqFV155Bdu3b8dLL72Ed999F2fOnME333yDpUuXYsaMGQAAg8GA2NhYHDhwACtWrMCZM2fwySefwNPTE927d8e8efMQHBxs3C7vvfee3Z/ZmTi0S//EiRMRGxuLqKgoFBQU4KeffsLevXuxbds2FBYWIi4uDk8//TQiIyORlJSEiRMnok6dOnjqqaccGTYREREREbmRS5cuQQiBVq1a2Vxv165dOHnyJBITE42J8Q8//IC2bdsiISEB9957L4DiZHLp0qUICgoCAAwdOhS///47ZsyYgZCQEAQFBcHT09PqbGQXLlzA1q1bcejQIXTp0gUAsGjRIrRu3dq4zp49e3Dq1Cmkp6cbxzebM2cONmzYgF9++cXY+l5eLD4+PvD39y93ZrRp06bhn3/+wQsvvICePXti+/btqF+/PgDg7bffxtWrV7Fx48Yq37YajQY//PAD6tata/b8F198Ea+88orx/tChQ/Hhhx9i2LBhAIAmTZrgo48+wvjx4zF16lTs2rULR44cwdmzZ9GiRQvjOpKQkBDIZDKXnzHOoQn/zZs3MXToUKSmpiIkJAQdOnTAtm3b0LdvXxQVFeHUqVNYvnw5cnNzERkZid69e2P16tXGnZWIiIiIiOhOCSEAADKZzOZ6Z8+eRVRUlFkreJs2bVCrVi2cPXvWmJQ2btzYLGeJjIw0a50vz9mzZ+Hl5YXOnTsbl7Vq1crYGg4Ax44dQ2FhIcLCwsyeW1RUhMuXLxvv32ksAJCTk4P4+HisW7cOjz32GDw8PNCjRw/s2LEDzZs3x+nTpxEbG2vxuZW9baOjo8sk+wDMthVQvH0SEhKMLfpA8cxwKpUKSqUSx48fR8OGDY3JvrtyaMK/aNEiq4/5+flh+/bt1RgNERERERHVRM2bN4dMJsPZs2cxcOBAq+tJ14uXt7z0gHEymQwGg8HueOxJkg0GAyIjI7F3794yj5lWDNxpLABw/vx5qNVq3HXXXQCA6dOnIz8/H/fffz/mzZuHQ4cOWRxjAKj8bWvpsgBLyw0GA6ZNm2ZxsHdfX1/4+flZjcWdON01/ERERERERNUpNDQUjzzyCL766isoFIoyj0tTs7Vp0wbXrl1DcnKy8bEzZ84gLy/PrLv9nWrdujV0Oh2OHj1qXHb+/HmzKeLuvvtupKWlwcvLC82aNTO71alTx+738vHxgV6vt7lOgwYNAAD79+83Lvv888/Rv39/vPjii3jjjTeM65TmqG1799134/z582W2TbNmzeDh4YEOHTrg+vXruHDhgsXn27NdXIHDp+UjIiIiIiL35+2dc1vT5d3uewFlu33b8vXXX6N79+647777MH36dHTo0AE6nQ47d+7EggULcPbsWTz88MPo0KEDhgwZgnnz5kGn02HkyJHo2bNnmS7ld6Jly5Z49NFHMWLECHz77bfw8vLCmDFjzFqlH374YXTr1g0DBw7ErFmz0LJlS6SkpGDLli0YOHCg3fE0btwYhw8fRlJSEgIDAxEaGgoPD/N24aioKAwePBijRo2CWq1Gjx49cOXKFZw8eRIBAQH47bffMGnSJISHh1t8D0ds2ylTpuCJJ55AVFQUnn32WXh4eODkyZM4deoUPv74Y/Ts2RMPPvggnn76acydOxfNmjXDuXPnIJPJ8Oijj6Jx48YoLCzE77//jo4dO8Lf3x/+/v4VjsPR2MJPRERERERVqk6dOoiIqIuoKK9quUVE1K1QKzcAxMTE4O+//0bv3r3x3//+F+3atUPfvn3x+++/Y8GCBQCKu8Nv2LABtWvXxoMPPoiHH34YTZo0werVqyt9my1ZsgRRUVHo2bMnBg0aZJx+TyKTybBlyxY8+OCDeOWVV9CiRQsMHjwYSUlJqFevnt3v895778HT0xNt2rRB3bp1ce3aNYvrLVu2DGPHjsWMGTPQtm1bvPnmm4iNjcXVq1cREhKCJ598EkVFRRaf64ht+8gjj2DTpk3YuXMn7r33XnTt2hVz585FdHS0cZ21a9fi3nvvxQsvvIA2bdpg/Pjxxlb97t27480338Tzzz+PunXrYvbs2bcVh6PJhHSBiJvKz89HSEgI8vLyEBwc7OhwiIiILHryyScBFI903K9fPwdH47yOHTuGadOKWwh/++03B0dDRKWpVCokJiYiJiYGvr6+jg6HqErZ2t+dJQ9lCz8RERERERGRG2LCT0REREREROSGmPATERERERERuSEm/ERERERERERuiAk/ERERERERkRtiwk9ERERERETkhpjwExEREREREbkhJvxEREREREREbogJPxEREREREZEb8nJ0AERERHSLEMLRIRARVboJEyYgMzOzWt+zTp06iI+Pr5b3Wrp0KcaMGYPc3NxqeT8iezHhJyIiciIymczRIRARVbrMzExkpKWhtlZbLe+X4+19W89LTk5GXFwctm7diszMTERGRmLgwIGYMmUKwsLCAACNGzfGmDFjMGbMmEqMmKhqMOEnIiIiIqIqV1urxdTz56vlvaa1bFnh51y5cgXdunVDixYt8OOPPyImJgb//vsv3n//fWzduhWHDh1CaGhoFURrnVarhfdtVl4QAbyGn4iIiIiICKNGjYKPjw927NiBnj17olGjRoiNjcWuXbtw48YNTJo0Cb169cLVq1cxduxYyGSyMr2ytm/fjtatWyMwMBCPPvooUlNTzR5fsmQJWrduDV9fX7Rq1Qpff/218bGkpCTIZDL8/PPP6NWrF3x9fbFixYpq+ezkvpjwExERERFRjZadnY3t27dj5MiR8PPzM3ssIiICQ4YMwerVq7F27Vo0bNgQ06dPR2pqqllCr1QqMWfOHPzwww/Yv38/rl27hvfee8/4+HfffYdJkyZhxowZOHv2LGbOnInJkydj2bJlZu/3wQcf4N1338XZs2fxyCOPVO0HJ7fHLv1ERERERFSjXbx4EUIItG7d2uLjrVu3Rk5ODvR6PTw9PREUFISIiAizdbRaLRYuXIimTZsCAN5++21Mnz7d+PhHH32Ezz77DIMGDQIAxMTE4MyZM/jmm28wbNgw43pjxowxrkN0p5jwExERORGO0k9E5HykY7OtgVX9/f2NyT4AREZGIj09HQCQkZGB5ORkvPrqqxgxYoRxHZ1Oh5CQELPX6dy5c2WGTjUcE34iIiInwlH6iYiqX7NmzSCTyXDmzBkMHDiwzOPnzp1D7dq1UadOHauvUXpwPZlMZqwoMBgMAIq79Xfp0sVsPU9PT7P7AQEBt/MRiCziNfxERERERFSjhYWFoW/fvvj6669RVFRk9lhaWhpWrlyJ559/HjKZDD4+PtDr9RV6/Xr16qFBgwa4cuUKmjVrZnaLiYmpzI9CZIYt/EREREREVOVyvL1va7q8232vuhV8zvz589G9e3c88sgj+Pjjj82m5WvQoAFmzJgBAGjcuDH279+PwYMHQy6X22z1NxUXF4d3330XwcHBiI2NhVqtxtGjR5GTk4Nx48ZVMFoi+zDhJyIiIiKiKmVvUlxZ6t7GezZv3hxHjx5FXFwcnn/+eWRlZSEiIgIDBw7E1KlTERoaCgCYPn063njjDTRt2hRqtdrusVdee+01+Pv749NPP8X48eMREBCA9u3bY8yYMRX8dET2kwkHjg60YMECLFiwAElJSQCAtm3bYsqUKYiNjQVQPDjGtGnT8O233yInJwddunTBV199hbZt29r9Hvn5+QgJCUFeXh6Cg4Or4mMQERHdsSeffBJA8ajO/fr1c3A0zuvYsWOYNm0aAOC3335zcDREVJpKpUJiYiJiYmLg6+vr6HCIqpSt/d1Z8lCHXsPfsGFDfPLJJzh69CiOHj2KPn36YMCAAfj3338BALNnz8bcuXMxf/58JCQkICIiAn379kVBQYEjwyYiIiIiIiJyeg5N+Pv374/HHnsMLVq0QIsWLTBjxgwEBgbi0KFDEEJg3rx5mDRpEgYNGoR27dph2bJlUCqVWLVqlSPDJiIiIiIiInJ6TjNKv16vx08//QSFQoFu3bohMTERaWlpZt0a5XI5evbsiQMHDlh9HbVajfz8fLMbERERERERUU3j8IT/1KlTCAwMhFwux5tvvon169ejTZs2SEtLA1A8hYWpevXqGR+zJD4+HiEhIcZbVFRUlcZPRERERERE5IwcnvC3bNkSx48fx6FDh/DWW29h2LBhOHPmjPFxmUxmtr4QoswyUxMmTEBeXp7xlpycXGWxExERERFRWQ4cF5yo2rjCfu7wafl8fHzQrFkzAEDnzp2RkJCAL774Ah988AEAIC0tDZGRkcb109PTy7T6m5LL5ZDL5VUbNBERERERleHt7Q0AUCqV8PPzc3A0RFVLqVQCuLXfOyOHJ/ylCSGgVqsRExODiIgI7Ny5E3fddRcAQKPRYN++fZg1a5aDoyQiIiIiotI8PT1Rq1YtpKenAwD8/f1t9s4lckVCCCiVSqSnp6NWrVrw9PR0dEhWOTThnzhxImJjYxEVFYWCggL89NNP2Lt3L7Zt2waZTIYxY8Zg5syZaN68OZo3b46ZM2fC398fL774oiPDJiIiIiIiKyIiIgDAmPQTuatatWoZ93dn5dCE/+bNmxg6dChSU1MREhKCDh06YNu2bejbty8AYPz48SgqKsLIkSORk5ODLl26YMeOHQgKCnJk2EREREREZIVMJkNkZCTCw8Oh1WodHQ5RlfD29nbqln2JQxP+RYsW2XxcJpMhLi4OcXFx1RMQERERERFVCk9PT5dIiIjcmcNH6SciIiKimuH69ev46aefoNfrHR0KEVGN4HSD9hERERGRe5o7dy4uXbqExo0bo2vXro4Oh4jI7bGFn4iIiIiqxaVLlwAA+fn5Do6EiKhmYMJPRERERERE5IaY8BMRERERERG5ISb8RERERERERG6ICT8RERERERGRG2LCT0RERETkRIQQWLNmDZKSkhwdChG5OCb8ROT0hBAc0ZmIiGqMs2fP4ocffkB8fLyjQyEiF8eEn4ic3urVq/HSSy/h+vXrjg6FiIioyhUVFQEAUlNTHRwJEbk6JvxE5PRWrVoFADhz5oyDIyEiIiIich1M+InIZQghHB0CEREREZHLYMJPRC5DJpM5OgQiIiIiIpfBhJ+IiIiIiIjIDTHhJyIiIqJqxUu0iIiqBxN+IiI3cvDgQWRlZTk6DCIim3iJFhFR9WDCT0TkJhITExEfH4/PP//c0aEQERERkRNgwk9E5Cays7MBACdPnnRwJERERETkDJjwExERERE5EYPB4OgQiMhNMOEnIpfBQZ6IiKgmYMJPRJWFCT8RuQwO8kRERDUBK7iJqLIw4SciIiIiIiJyQ0z4iYiIiIiIiNyQQxP++Ph43HvvvQgKCkJ4eDgGDhyI8+fPm60zfPhwyGQys1vXrl0dFDERERERERGRa3Bowr9v3z6MGjUKhw4dws6dO6HT6dCvXz8oFAqz9R599FGkpqYab1u2bHFQxEREREREVYtj1hBRZfFy5Jtv27bN7P6SJUsQHh6OY8eO4cEHHzQul8vliIiIqO7wiIhcCgd5IiJyDx4evOqWiCqHUx1N8vLyAAChoaFmy/fu3Yvw8HC0aNECI0aMQHp6utXXUKvVyM/PN7sREREREbkKJvxEVFmc5mgihMC4ceNw//33o127dsblsbGxWLlyJXbv3o3PPvsMCQkJ6NOnD9RqtcXXiY+PR0hIiPEWFRVVXR+BiMih9Hq9o0MgIiIiIifi0C79pt5++22cPHkSf/75p9ny559/3vh/u3bt0LlzZ0RHR2Pz5s0YNGhQmdeZMGECxo0bZ7yfn5/PpJ+IagSDweDoEIiIiIjIiThFwv/OO+/gt99+w/79+9GwYUOb60ZGRiI6OhoXL160+LhcLodcLq+KMImInBoTfiIiIiIy5dCEXwiBd955B+vXr8fevXsRExNT7nOysrKQnJyMyMjIaoiQiMh1cNA+IiIiIjLl0Gv4R40ahRUrVmDVqlUICgpCWloa0tLSUFRUBAAoLCzEe++9h4MHDyIpKQl79+5F//79UadOHTz11FOODJ2IyOkw4SciIiIiUw5t4V+wYAEAoFevXmbLlyxZguHDh8PT0xOnTp3C8uXLkZubi8jISPTu3RurV69GUFCQAyImIiKqWqy4ISIiosri8C79tvj5+WH79u3VFA0REZHjyWQyR4dAVOVYsWUbtw8RVRanmZaPiIjuDBNFInIVPF7Zxu1DRJWFCT8RkZvw8OAhnYiIiIhuYemQiMhNMOEnIiIiIlMsHRIRuQlPT09Hh0BEREREToQJPxG5DA5iZBtb+ImIiIjIFEuHROQyOIgREREREZH9mPATERERERERuSEm/ERERERERERuiAk/ERERERERkRtiwk9ERERERETkhpjwExEREREREbkhJvxEREREREREbogJP9UYnMOdiIiIiIhqEib8VCPk5+fj5Zdfxq+//uroUIiIiIiIiKoFE36qES5evIjs7GwsWrTI0aEQEdEdYG8tIiIi+zHhJyIiIpchk8kcHQIREZHLYMJPRERERERE5IaY8BMRERERERG5ISb8VCMYDAZHh0CVgNfuEhERERHZjwk/1QhardbRIVAl4LW7trFCxD3weyQiIqLKwoSfagQm/ETkKvR6vaNDICIiqhY5OTk871UxJvxUI+h0OkeHQERkFxZ8iIioJkhLS8OwYcPwzTffODoUt8aE38VduHAB8+fPh1qtdnQoTo0t/O6BXZ1t4yUP7oEVlLbxOEBE5B5u3LgBANi2bZuDI3FvDk344+Pjce+99yIoKAjh4eEYOHAgzp8/b7aOEAJxcXGoX78+/Pz80KtXL/z7778Oitj5zJkzBzt27MDhw4cdHYpT46B97oEJLdUEPF7Zxu1DRERkP4cm/Pv27cOoUaNw6NAh7Ny5EzqdDv369YNCoTCuM3v2bMydOxfz589HQkICIiIi0LdvXxQUFDgwcueRlpYGAFCpVA6OxLmxRYiIXAW79NvGHhBE5Oz++usvjBkzBvn5+Y4OhQhejnzz0t03lixZgvDwcBw7dgwPPvgghBCYN28eJk2ahEGDBgEAli1bhnr16mHVqlV44403HBE2EZFTYsWWe+D3aBsTfiJydp999hl0Oh2OHDmChx9+2NHhUA3nVNfw5+XlAQBCQ0MBAImJiUhLS0O/fv2M68jlcvTs2RMHDhyw+BpqtRr5+flmNyIPD6fa1ek2MRGyjdvHPfB7tI09IIjI2UkVk7wEiZyB02RBQgiMGzcO999/P9q1awfgVnf1evXqma1br14942OlxcfHIyQkxHiLioqq2sDJJXh6ejo6BKoEvIbfNiZC7oEJv20chNU9cD8nIpZbqofTJPxvv/02Tp48iR9//LHMY6UL+UIIqwX/CRMmIC8vz3hLTk6uknjJtXh7ezs6BKIqxwI01QQsILoHVuASES/Rqh4OvYZf8s477+C3337D/v370bBhQ+PyiIgIAMUt/ZGRkcbl6enpZVr9JXK5HHK5vGoDdkIs6NvGhJ9qAh4HqCZgAZGIyD2wx1b1cGgLvxACb7/9NtatW4fdu3cjJibG7PGYmBhERERg586dxmUajQb79u1D9+7dqztcp8YCkG1M+N0DE1oi4vmOagJe+001AY/n1cOhLfyjRo3CqlWr8OuvvyIoKMh4XX5ISAj8/Pwgk8kwZswYzJw5E82bN0fz5s0xc+ZM+Pv748UXX3Rk6E6HPxjbvLycojML3SF2AaWagPu5bUyEqCbgfk41AVv4q4dDs6AFCxYAAHr16mW2fMmSJRg+fDgAYPz48SgqKsLIkSORk5ODLl26YMeOHQgKCqrmaJ0bE37b2DJMROQeeL6jmoDlFvfAMUds4/apHg5N+O05mMlkMsTFxSEuLq7qA3JhrAm2jS1mVBNwP3cPnEbUNhYQ3QPLLVQTsAXbNh4HqgdLFW6CBX2qCdjiYRuPA1QTsIDoHlhxQzUBeyTZxnJd9WDCT0QugwmtbWwZppqAiaJ7YCJENQH3c3IGLB26CU9PT0eHQEQOxgoRqgnYwu8eWHFDNQGPV7ax3FI9Kj3hv3HjRmW/JNmB084REVv4qSYwLUCzO6jrYsunbWzIcQ88RtnGckv1qLStnJaWhnfeeQfNmjWrrJekCuC0c0RE7oEtQraZbh9uK9fF7842tny6B36PtrFiq3pUKOHPzc3FkCFDULduXdSvXx//+9//YDAYMGXKFDRp0gSHDh3C4sWLqypWsoEHFCIi98BEyDYm/O6B351tLNe5B36PtrGFv3pUqFl44sSJ2L9/P4YNG4Zt27Zh7Nix2LZtG1QqFbZu3YqePXtWVZxUDh5QiIjcAxMh20y7yHJbuS5+d1QTMKG1jS381aNCCf/mzZuxZMkSPPzwwxg5ciSaNWuGFi1aYN68eVUUHhERUc3CwcxsYwu/e+B3RzUBL7m1jdunelSo2iklJQVt2rQBADRp0gS+vr547bXXqiQwIiKimogJv20ctM898LujmoCDatvGFv7qUaGE32AwmO24np6eCAgIqPSgqOJ44iQicg9ardbRITg1tvC7B5ZbqCZgQmsbL3moHhXqRyGEwPDhwyGXywEAKpUKb775Zpmkf926dZUXIdmF1/ATEbkHJvy2MeF3D0z4qSZgwk/OoEIJ/3/+8x+zxPKll16q9ICIiIhqMib8tjHhdw9M+ImIDZbVo0IJ/5QpU9C4cWN2vyAiIqpEOp3O+L9arXZgJM6PCb97YMJPRDwOVI8KZe7NmzdHZmam8f7zzz+PmzdvVnpQVHH8wVBNwP2c3JVGo7H4P5VlOqghBzh0XWzZo5qA5RbbWGlbPSqU8Jfeabds2QKFQlGpAdHt4YmTagLu57axYOG6TFv12cJvGxN+InIVLLfYxmN49WDffCJyGawJto0Jv+tiwm8/08sfTP8n18JEiIhYrqseFUr4ZTJZmQM0D9hEVF14YrCNNeWuiwm//Uz3cyb8RESui+WW6sFp+YjIZfDEYBsrRFwXE377mc5iwITfdbHByDb22KKagMfw6lGhhH/YsGFm9zktHxFVJya0tnH7uC4m/PYzLSByCkPXxYTfNm4fqglYbqkeFUr4lyxZUlVxEBGViycG29gDwnWZjszPhN820ySfMxq4Lia0trGF3z3we7SN5brqwUH7iMhl8MRgG7vGuS7zFn4msbaYJvnc510XE37bmCi6B1bE28ZyXfVgwk9ELoMnTtuY/Lgu01ZrvV7HQpANbOF3Dx4eLILawmOAe+B5mZwBj7ZugjXBVBNwP7eNBQvXVfpadF6bbp1pks/t5LrYwm8bz3fugQ0V5AyY8LsJnjipJmCLh21MflxX6e+OhUTrtEz43QJb+G1jwu8eWBFvG48D1cOhW3n//v3o378/6tevD5lMhg0bNpg9Pnz4cMhkMrNb165dHRMsETkcC0C2MflxXVKCbzB4AmAh0RYOcOgeWNCnmoCVt7Z5eno6OoQawaFHW4VCgY4dO2L+/PlW13n00UeRmppqvG3ZsqUaI3QdTISoJmALv21M+F2XlOAL4Q2AhURbTPdz7vOuiwk/ETHhrx4VmpavssXGxiI2NtbmOnK5HBEREdUUketil34iYvLjuqRKWyGKCz+s3LJMr9fDIARkQkDIZNznXRgTfttYrnMPbJCzzdvb29Eh1AhOf7Tdu3cvwsPD0aJFC4wYMQLp6ek211er1cjPzze7EZF7YKunbeYjvXNbuZLSCT8LiZZJ+7hvSYUIE37XxYTfNm4f98Dv0TYvL4e2PdcYTr0XxsbGYuXKldi9ezc+++wzJCQkoE+fPjav2YuPj0dISIjxFhUVVY0RExE5Dkcvd12lE3wm/JZJlz54lyT8rNhyXezKaxsTRffA/dw2tvBXD6euVnn++eeN/7dr1w6dO3dGdHQ0Nm/ejEGDBll8zoQJEzBu3Djj/fz8/BqR9LNwSDUBuznbZprwazQa+Pr6OjAaqohb3XeLj+Us7FsmJfhSws/BDV0X93Hb2KXfPTDht40Jf/Vw6oS/tMjISERHR+PixYtW15HL5ZDL5dUYFRFVFyb8tnEwM9clFe5lMu7jthgT/pJKbrbwuy4mQlQTsMs6OQOXql7NyspCcnIyIiMjHR2K02FNMNUEbM2zjQm/65JaO2Uyndl9soxnPNfHfZxqArZgkzNwaLVTYWEhLl26ZLyfmJiI48ePIzQ0FKGhoYiLi8PTTz+NyMhIJCUlYeLEiahTpw6eeuopB0ZNRI7C1jzbTLcPK0dci9QK5OGhNbtPRDUTL9V0D2yQs437efVwaIni6NGj6N27t/G+dO39sGHDsGDBApw6dQrLly9Hbm4uIiMj0bt3b6xevRpBQUGOCpmIHIgJv22mST4TftcidW+WuvSzu7NtLCISEbk+VohUD4cm/L169bJZs7N9+/ZqjIaInB2TWNtMK0Q43oFrKd3tk91ALZMqQnQlhUT2hCB3xUTIPfB7JGfAC6jcBBMhclemiSuvS7fNNOFnbwjXYprgy2QyJrJWSAm/tuT6b/aEcF3syktEVD2Y8LsJtVrt6BCIqgS7qdvPtADNwrRrMU34vby82SpkhZTga5jwuzzu41QT8FxMzoAJv5swnX+byJ1w5Hn7mfaGYJd+1+Lj42PxfzInbZuikkSf24qInBkrtsgZMOF3E2zhJ3fFhP/2sFXBtcjlcuP/Pj68ft8aT09Ps+nceOkDERGRbUz43QRb+MldMeG3n2mrPhN+12LaUm2a/FNZ3FZERET2Y8LvJnhtM7krJvxUE7BLv/3kTPipBuBlWURUWZjwuwkm/OSuTPdtJvz2Ywu/a2HCbz/zyx+4rcg9sVxHRJWFCb+b4BRc5K5Mk3wWgGzjKP2ui0ms/XxMthVb+MldsYKbiCoLE343wa5f5K7Ywm8/0ySfxwTXYjotHxN+21g5QjUBz3dEVFmY8LsJtuaRuzJN+HU69mSxxbSnD48JrsU0cTVN/qksXv5ANQFnXyKiysKE301wnk/b2BXcdbGF336mrfps4XctniXzygOcaq48TPipJmDCTzUBL0muHkz43YTpvMRUFhNF12Wa8Ov1rLixhQm/6zJN8pnw22baA4K9IchdqVQqR4dAVOWKioocHUKNwCzRTbCF3zaNRuPoEOg2sUu//dgbwnWZHsNNW/upLNMKESb85K5ME35eokXuigl/9WDC7ybYImQbE37XZdrdi12/bOOMBu6BCb9tpuc7bivXxSTWNqVSafyfrf2ui/u5bQqFwtEh1AhM+N0ECz22MflxXezSbz/ThJ8t/K6Ll2jZZnq+47nPdTERss004Tf9n8idFBYWGv/nMaHqsFThJljosY3Jj+sq3arPVn7rNCaDPHHAJ9fFS7RsM60QYeWI6+IxyjYm/FQT5OfnG/9na3/V4ZnSTTDht40t/K6r9OBzTPgtE0JAbXLpCgvT5K5MK0SY8LsuHqNsM01+mAi5LpZZbMvLyzP+b5r8U+XimdJNsNBjGw+4rqv0d8fR5y3T6XQwGAzwLNk+LEy7LnZrtI09INwDr0u3jQm/ezDtsk5l5ebmWvyfKhezRKoRmPC7rtLfHXtrWCaNdBtcsn048q3rYsJvPyb/rosJv22mST6TRtdVUFDg6BCcmmkLPxP+qsOE302w0GMbk0TXJSX8BoNXyV+28FsiJfghTPhdHhN++3FbuS5el26b0iTh57ZyLaYNFTk5OQ6MxPnlmmwfJvxVhwm/m2ASZJvpwZcFRNciVdYI4W12n8wZW/hLBqhkwu+62CPJNtPzHY/nrsV0AF0eo6zT6/VQmVyWxS79rsW01To7O9uBkTg3lUoFtUYDeck5j9fwVx0m/G6CBUTbNCaDmXHEftcifV96vRwAE35rpBagWkz4XZJp4srjuW2m24qV3a7F9LjEY5R1UoJfu6TswoTftWRkZJj8n+nASJyblODXK6ncMq0oocrFhN9NMIm1TcPRy12WtG8bDH5m98kcu/S7NtMknwm/baZJPhN+18Kp5uxTugKX28q1mCb8WVmZPE5ZIY1NUaekjM6xKqqOQxP+/fv3o3///qhfvz5kMhk2bNhg9rgQAnFxcahfvz78/PzQq1cv/Pvvv44J1skxCbLNdHAgDhTkWqTKGr3ez+w+mZMSfF+9Hj4GAxN+F2N6DGcvFttMK0RYkHYtHHnePtLxmz22XJOU8Ov1cuh0Ol7Hb4VUkVW7ZD/nMaHqODThVygU6NixI+bPn2/x8dmzZ2Pu3LmYP38+EhISEBERgb59+3LESwuYBNmm4OA3LkvqkaHTBQFghY01xoTfYICcCb/LMU34WYFrG3tDuC6OPG+f0j22WG5xLVLCr1A0AwCkp6c7MhynJe3nQTodZEKwfFeFvBz55rGxsYiNjbX4mBAC8+bNw6RJkzBo0CAAwLJly1CvXj2sWrUKb7zxRnWG6pRMCzosINrGVgXXJZ0ApISfiaxlUsWIj8EAH4MBap44XQoTfvuZ9oBgbwjXYprkKxQKCCE4y5AFUoIfwhZ+l5SZWXzdvlIZg+Dgf5GVleXgiJyTdK7zNhjgJQTPfVXIaa/hT0xMRFpaGvr162dcJpfL0bNnTxw4cMDq89RqNfLz881u7ooFRPuZ9gphDxHXIhV8tNpaAFjwsaZ0ws+actdi2kuLPbZsM63sZsLvWkwTfp1OxzF1rJDOe74GA3zZY8vlZGVlwWDwgUoVCeBWBQCZk3IXTyHgCR7Pq5LTJvxpaWkAgHr16pktr1evnvExS+Lj4xESEmK8RUVFVWmcjsSR5+1XYFLxw4TftdxK+EMAsIeGNaYJv7fBwKTRxXBgUfuxstt1SY0w0vHcnRtl7oR03vPT6+Gr17NLv4vJycmBVhsEnS4YAOeXt0Yag0UGwEMIjslShZw24ZeU7upVXvevCRMmIC8vz3hLTk6u6hAdht0a7aPX66EwOVky4XctCoUCBoMcen0AAF7LaI2U+HgJAW8hmPC7GNMkn70zbGPC77qkabdUqgiz+2ROqtj21evhp9ejkOUWl5Kfnw+9PhA6XYDxPlnHi3qqntMm/BERxSeD0q356enpZVr9TcnlcgQHB5vd3BUTfvsolUoIIYzXwjHhdy1KpRJ6va9xlH4m/JaZJvxeBgMMQnBAMxdiPj85E35bePmD65JGK1epGprdJ3NSghig18Nfp0OhQsHWTxeh1Wqh0Wig1/tBr/cHwAEqrfHwKE5DRcmNqo7TJvwxMTGIiIjAzp07jcs0Gg327duH7t27OzAy58HrGO0j1ZSHlhQMmTC6FoVCAb3eDwaDr/E+lSUdA7yEgJcQZsvI+Zkn/Lxe1xbT3hC8/MG15OTkQAgZVKr6AIDs7GwHR+ScpIQ/UKdDoF4Pg8HAc5+LkI7fer2vsdzCY7plUo9tAUDIZPD09HRsQG7MoaP0FxYW4tKlS8b7iYmJOH78OEJDQ9GoUSOMGTMGM2fORPPmzdG8eXPMnDkT/v7+ePHFFx0YtfPgXMT2kQqEwSXJD7vLuhalsggGQ23o9b4l91lhY4mU3HsIAc+ShJ8t/K7DdL/WajXQ6XTw8nLoKdppmc5AwYTftWRkZECnC4ZGEwqAg5lZI/V8CNLpEFRybM/JyUFQUJAjwyI7SMckIXwAeEAIL/ZEssLYwi+TgVlM1XJoaeLo0aPo3bu38f64ceMAAMOGDcPSpUsxfvx4FBUVYeTIkcjJyUGXLl2wY8cOHvBKmCb5LNhbJx1o/Uu2EQuIrkOv10Or1UCvlxtryllhY5l0DPAUAh5s4Xc5UsJvMHjCw6N4kC53viTtTpgeA3g8cB0GgwGZmZnQahtCq60N4NZ85WQuKysL/no9fIRArZLLtbKystCoUSMHR0blkS6vMxiKUywhvDjWiBVSC7+UzUgVAFT5HJrw9+rVC0JYv2pDJpMhLi4OcXFx1ReUCzFN+G1tx5pOSnq8S7YXK0dch1SYNxjkMBh8zJaROel44CmE8Vot9vxxHdI1nlptKOTyDBQWFjLht0AIwcsfXFROTg50Oh00mjBotSEQwgM3b950dFhOKSMjA7VKGiukhJ+VI67hVkV7cfd0ITxZ+U4Ox6oUF2ZamGfB3jqpMkRW6j45P6l3hhDeEKK4fpI15ZZJFVnS9Damy8j5SQm/RlPH7D6Z02g0MAgBecm+zUt8XIc0CHPxPu4JjSYUqanWp1muqQoLC6FUKhFWcv6T/qanpzsyLLKTdN691cLvyXOxFablcxmYy1QlJvwuzPQAwoOJdaaDgpjeJ+cn1YoL4QlAxmvhbDDt0i9dw8+Tp+uQZg9Rq+ua3SdzUkVIaEnFHxN+15GamgoA0GjCSv7WQU5ONi+zK0XaTmEl+3idknOetJycm3m5pfgyLTZUWFb6UkTmMlWHCb8LM+0ixIOJddI1QbqSRJ+jgLoO6eAvnTiF8GASa4W0rTxMuvTz5Ok6pARfo2HCb0vpWVfYE8J1SAmrVKkl9WYpPf1yTSdtp7omAw57GwxM+F0Eu/Tbz2w6YSG4naoQE34XZj5wEWvIrZESfn1Jws9BQVzHrYRf+s6Y8Ftjdg0/u/S7nIKCAgjhbRzMjAm/ZVKCL3Vz5lRlruNWC39xwi8l/kxkzaWkpAAA6pbs4zIUJ/8pN27wkkQXYHnQPiaylkg9Nr0MBngbDOztU4WY+bgw04KOUqnkicAKKcE3lCT87NLvymTcz60wTssHcFo+F5Sfnw+dzh86XYDxPpVl2qVfJgQrRlxISkoKDAY5dLrimZakFn4pwaVi0vYIN0l+wjUaKIuKkJeX56iwyE5Swi+NO1Sc8PNSREukQVd9DQbIDQaoOAhrlWHC78JMD/wGg55dG60wbfkEOGifayoZh0Hw+7NGSvhNr+HnpT6uIy8vHzpdIPT6QABs4bdG2i6BOh0CDAZuJxchhEBKSirU6jqQjucaTTgAJvylpaSkwMtkOj7gVvd+bivnZzrYMAAYDN4ce8gKaQwWX70evno9lEVFLONVESb8LkwasVWpbAyAU7ZYIyU98pLEn0kQuSOdTgcPISADW/hdjU6ng1KpgE4XyBb+ckjbxV+vh79WiwJuJ5eQl5cHlarI2J0fADSaUAgh4zX8paSmpKCOWm1WQJe69zPhd35Scm8wFCf8QnhDp9PxckQLpIZKf4MBfnp9ybSdrBypCkz4XZh03VtBQWsAwI0bNxwZjtOSugwFl7SAct5m18GaXvtptVp4lWwvL7bwuxSplVqvD4Be7w8hZEz4rZC2S6BOh0C9vmTsAx4nnN2tAfvqGJcVj1lRi0msicLCQhQUFhpH5pdILfwc78D5SdehSwm/9JeJbFnSuc+/5HgOsLK7qjDhd2GJiUnQ6YKhUDQDACQlJTk2ICdlWkD00+t5MHFpvIbfGib8rutWwu+P4oEp/dhV3QppuwTo9QjU6aDT6zk1nwsoPWCfRKOpi6ysLCZDJUqP0C+RKgDYG8L5le7SL/3lPl5WXl4eZELAv+R4DjDhrypM+F1UQUEBbt5MQ1FRQxQVNQAAXL582cFROaesrCwAQIhWixCtFpm89MFl3OoCJzP+Zbc4y7RaLbxLto30lwUM1yAlsVJ3fp0uAHl5LPRYIo1dE6jTIaCkRYgDmTk/qRVfGqhPolbXhRCCiWwJaTuVbuEP1ungw6n5XELpQfuk0fpZAV9Wbm4uAgwGeALGhD83N9ehMbkrJvwu6uLFiwAApbIRDAZ/qNXhuHDhIls/LZAKEmFaLcI0GhQUFrJFyEXcmpZPms+W0/JZo1ar4VOybXxKjgNM+F2DeQt/8d/CQrbwW5KXlwdPIeBrMLBFyIVIlxyq1fXMlqvV4WaP13RSeaVuqWO3DEAdtRopKSks5zk5S9PymS6nW3JzchBcsq8HM+GvUkz4XdSFCxcA3BqwT6lshMLCAtb+WpCSkgKZEAjVaDjwjYuRRp6XEn6DwYsnTSvUKlWZFn7OaesapIGL9PoA41+NRsMKGwvy8vIQqNNBhlstQmzhd37Xr1+HweBrnJJPIlUAJCcnOyIspyOV4epYOHbX0WigVCp5uY+TK9tQ4Wm2nIpptVoUFBYiqOQ4Lv2VeuVS5WLC76KuXLkCACgqiir528hsOd2SnJyMOhoNvIVAPZXKuIyc363Bb3xK/nJ6G2uKVCrjTBTSXw5Q6RrKtvD7mS2nW6SEH4BxkCe2CDk3rVaL5OTrKCqqj1uXZxVTqyMBAFevXnVAZM4nJSUFnkKgtoWKbTZYuIZbiX1xisWE37KcnBwAQEjJ8TykZJ+XllPl8nJ0AHR7rl27Bp0uCHp9cW25SlXfuJxuKSwsRF5eHtqVJI4RJX+vX7/uyLDITqUTfiF8oFIVOjIkp6TVaqHT6YyJvm9JwYIJv2u41cJfnPBL1/IrFAqEhYU5LC5noylp4YwuKSAGlxQQmfA7t+LWfT1UquLkPipqGeTym1Cr6yE5+T/Q6/046HCJlBs3EKZWwxPAsqgo3JTLUU+txrDkZONAfikpKWjVqpVjAyWrbrXwS22qHmbLqVh2djaAW4m+lPhLy6lyMeF3QUIIpKdnQKNpYFym0RQXCtPT0x0VllOSrgusV3KiDC/5y+sFXcOtRMjP+LeoqAgGgwEeHuygJFEoFAAAv5IChW9J4i8tJ+dWetA+KfFnC785KbEv3QWUCb9zu3TpEoBbPRLl8pvw85Mq3WUoKorC9euXUFRUBD8/PwdF6XgFBQXILyhAo5KW/JtyOa6bbI96LL+4BGmcISnhl/5y/CFzUmIvXbvvr9fDSwh26a8iLDG7oKKiIuh0Wmi1t66Fk66L4+BF5jJKRuQPLTmBBur18DYYjMvJuZVN+P0hhGAiW4q0nfxLEn7pLxNG11C2S39x4s/juTljAbGkRSiYLUIuQRpkuKgo2uLjSmUjCGGo8TMNlW6gKE3q0s8eis7tVkv+rcGGzZcTULaFX4biYzuP51WDCb8Lkro5S3N7mv7PQbrMmU7JBxQfUGpptchmDaJLkK7l0umCS/4GmS2nYlJiaLy2maOXu5T8/HwIITMm+jpdIAAORleadDyvVXI8D2CLkEs4f/48DAYfqFQRFh+XKgLOnz9fnWE5HSmRD7dSjgvS6eCv17OF38mVHmxY+istp2LG8rnJdgnRapGbk8OZKKoAE34X5OnpWfKfafcgUeoxAmCcfs/PpCuVr14PBaflcwlSTa+U8Gu1IWbLqZhUASJ1cfY1GOAtBCtGXEROTg70+kBIp2SpYotd1c1lZmYCAGqV7OcyALU0GmSyx5bTUiqVSEq6CqUyGlKLZ9l1YgAAZ8+ercbInI+U8Ftr4ZcBCFepkJKSwtZiJyYNLGwwFDfESQ1yHHDYnFQ+CTYZoDJYp4NOr2fvxCrAhN8FyeVyAICHx62Dh/S/j4+PQ2JyVtJJ0cukttBTCOhZ0+oS0tPTIYQMWm1tAIBWG2pcTrcYEyHTnixMhFxGdnY2tNpg432pgosVW+Zu3rwJAKhtUnAO1WqRk5vLwrSTOn/+PIQwGJN6S3S6YGg0YTh79lyNbtkrr0u/9JhOp+M50IlJg+Xeml1IbracihkT/lIt/KaPUeVhwu+CfHx84OXlBU/PWwcP6f+AgABHheWUpIHdzPpCyGSQccA3l5CSkgqtthaEKB5fVBqcUpqrmIpJhb9Qk5ry2iWJkNbC9E7kPAoLC6FUKo37NgBoNKzYskSajizcJLmvq1ZDCIG0tDRHhUU2nDt3DgCgUDSxuZ5CEYOCgvwaPeXcjRs3EKDXI8BG672077Nbv/MqPeuK9FdaTsWys7Phr9fD26SSj+OyVB1mPS5IJpPBz88fnp4q4zIPDyb8lki9IdQmCb7awwN+vr6OConspFQqkZmZAbX61nWf0jWgnLPZnFT421G3LmY3a4ZlUVHGRIiVI85NarWWerEAxdNP6nRBTGJLSblxA8Em008CtwYyYwLknKTr8pXKxjbXk3oA1NTr+PV6PdLS0oxT71kjPc7juvPKzs4uGYel+BIWqfcWW63N5WRnm3XnB5jwVyUm/C7Kz8+3VJf+4h+NlOBSMX//4ppVlcnYBipPT/iVLCfnJc3LrFLVNy4zGPyh1dZCYmKig6JyTsnXrqGWVovMkmmcpLmbASA5OdnB0ZEtt/Zz8wHNVKp6SE1N40CsJQoLC5F28ybql+oW26Dk/pUrVxwRFtkghMCFCxehVofDYLB9zi2+xh+4cOFCdYTmdIqTRB3qlJfwl1Rw1eSeEM7MYDDg5s10aDS3KnClylxW4N6i1WqRX1Bg7MIvke4z4a98Xo4OgO6EKPO/TCZzTChOypjwm7Twqzw9UScw0FEhkZ1utQw1QlTUMsjlN6FW14NSGY2srBPIyspCWFhYOa/i/vLz85GRmYm2RUXI8741c0d9VXEPoCtXrqBHjx6OCo/KISWqKlVDs/1cpWqIwMBLSEpKQsuWLR0cpeNJU7ZFl0r4o0ruS1O/kfNIT09HYWEBiopalLuuWl0fQnjV2Kn5pJ4+YeWMRSFNMczLfZxTRkYGNBo11Op6xmUaTRiE8MK1a9ccGJlzKT3jikS6L41LRJWHLfwuSqFQGucmBwCDobiLupKjz5vxLkmAdCYVITqZDF5erOtydtKIzUplDOTym/Dzuw65/Kaxa2hNH9FZIhWQo6wkQpcuXar2mMh+Fy5cgBAeUKkizfbzoqKGxsfpVgVgI6USy6KijJeu+BsMqKtW4+KFCxy53MlIvVeKihqUu64QXlCp6iEp6SoMBkO567sbY8JfzpgrfgYD/PV64/rkXKSKR5WqoclSTxQV1ceVK4kcU6eEVGFVu9T2kMYhyuCAw5XOqRP+uLg4yGQys1tEhOV5XGsSpVIJhaLQOJIzcOsaIf5IrDBJ+GVAjR4J2BXo9XqcPHkSanVd6HS1zB4rLGwOADh+/Hj1B+aEzpw5AwBoXKqyz89gQIRKhbNnzzIRclL5+fm4cOECFIomEMJ8hpXCwuJW/b///tsRoTmdo0ePwkMINFUocNPk0hUAaFFYiILCQrbyOxlprBXTy7JsUakaQK1W1chkVvrMoXbMNhGq0ZTMYMNyjLM5ffo0gOJBKKOilqFZs9mIiloGpTIGOp2WFbglpMsb6pTa3+UGAwJ1Ol7+UAWcOuEHgLZt2yI1NdV4O3XqlKNDcjjp+uWy1zbXxqVLl3kSMJGXlwcACDSZ9iNApzMuJ+d0/vx5KJVKFBa2KvOYStUQen0gjh37m/s6igsYHkKgiYXePc0UCqhUqhrbTdbZ/fPPPxBCoKCgbZnHdLoQFBU1xMmTp2r8dfz5+fk4f/48migU8LfQ+tuuZM7mo0ePVndoZIPUhVmtjrRrfWkci5o47ohxphU7Ev4wjQYqlYpzlTsZIQSOHj0Gvd4PRUWNzHpsSWWZY8eOOThK53D9+nUAsDhIZbhajdSUFPaGqGROn/B7eXkhIiLCeKtbt67N9dVqNfLz881u7uaff/4BUDzNjWkNokLRBDk52TXyZGmN1MJwuHZtYxfQMLUa6Tdv8vIHJ3bo0CEAQH5+2UQI8EB+fhtkZWXW+ERWoVDg3NmzaKRUwtdCItSyZBogthI7p927dwMACgraWXw8P78dtFoN/vrrr+oMy+kcOHAAQghjYl9a88JCeBsM+PPPP1kJ6ESuXbsGg0EOrbaWXetLM7LUxGudU1JS4CFEmS7OltThwH1O6fLly0hPv1lSbvE0e6ywsDkMBjn++usAj1G4VTaPVKvNLtECgEiVCnqDgTOvVDKnT/gvXryI+vXrIyYmBoMHDy53JN74+HiEhIQYb1ElO5C7EELgjz/+hMHgjcLClmY1iPn5xYXGP/74w8FROgchBE6cOAEfgwEFXl7GLqBNlUoYhDB2vSLnIoTAgQMHYDD4QaGwPNhTXl5HAKjxidA///wDvcGAtlYSoRaFhfAQAgkJCdUcGZUnNTUV//zzDwoLm5kN8GQqJ6cbhJBhy5Yt1Ryd8xBCYMuWLfAUAp1zcy2u4yME7s7NRUpKCk6cOFG9AZJFOp0OycnXUVQUieIL6con9VqsadOuCiGQcuMG6qjVpdJEy6RWUSZEzmXPnj0AgLy8u8s8JoQP8vLaIzU1pcZOPSkxGAy4eOECwtVq+BoMZS7RksYf4uUPlcupE/4uXbpg+fLl2L59O7777jukpaWhe/fuxtEdLZkwYQLy8vKMN3dr7T558iRSU1OQm3t3mWs+8/PbQ6/3w44dO6Ez6cJeU509exY3btxAh1Ld9zuW3N+xY4cjwqJynDt3Dunp6cjN7QAhLA+uWFjYCgaDH/bv/6NGDvAkOXjwIACgvZWeTH4GA1qUXNvM8T2cy6ZNmwAA2dn3W11Hq62N/Pz2uHDhAs6dO1ddoTmVs2fPIikpCR3z8oxzNFvyQMk0TjW5csSZJCcnQ6/X2X39PlC8vxsMfjVuisWsrCzkFxSgvp2X7kgzsHB6WuehVquxZ89e6HTBKChobXGdnJwuAICdO3dWZ2hO59q1ayhUKBCjUFh8PKak9+2///5bnWG5PadO+GNjY/H000+jffv2ePjhh7F582YAwLJly6w+Ry6XIzg42OzmTn755RcAQHb2A2UeE8IH2dldkZOTjb1791ZzZM5FCIGVK1cCAO4vNZ9nlEqFxgoFjhw5whHMnZBUS56b29nqOkJ4Ize3IzIy0o2D1tU0arUaCUeOIFytRqSNgmKnkgquAwcOVFdoVI7U1FRs2bIVGk1d5Od3sLluZmYfAMDSpUtrXFdQIQR++uknAMCDNir6AaBRUREaK5U4fPhwjUsYnZHpLCv2k0GhiEZycrJbXo5pjdSS2cjOywwbqFTwEIItoE5k3759KCwsQHZ2N5Tuzi9RKJpDra6LvXv31aj9uzTpEkPpksPSItRqBOt0+Ofvv2t0g05lc+qEv7SAgAC0b9++xo7Ee+rUKZw4cQIFBa1QVNTI4jqZmX0ghBd+/PFHaOwY/MVd/fXXXzh16hTa5edbHMxsQMkIoN988w1HMHciKpUK+/bth1ZbGwpFc5vr1vTa8kOHDkGlVuPu3FybHWY75ufDUwjsLalIIcdbtmwZ9HodUlOftNqLRaJUNkFeXkecOXPG2KOjpvj7779x/PhxtLFyHC/tibQ0CCGwePHiGlc54mxOnjwJAFAomlXoedL6NemSOykBamqlxbM0byHQWKnEhQsXOHCfEzAYDPj1118hhAeysqz32AI8kJX1ILRaTY3uifTnn3/CQwi0tpLwywC0y8tDbl5ejW3QqQoulfCr1WqcPXsWkZH2jfjqTvR6Pb7//nsIIUNaWn+r6+l0IcjI6IWMjAz8+uuv1Rih88jMzMTXX30FHyHwVGqqxXWaKpXonJOD8+fPY+3atdUcIVnz119/oahIiezsrijv8KRUNoFaHY4///wLhVZOHO5MGvDtXivXNUsC9Hq0zc/H5StXjPNik+McPnwYBw4cgELRpNzWfUla2pMQwhPffvtdjSnga7VaLF68GB5CGCtoy9NCoUC7/HycPHkShw8fruIIyRqFQoGEhKNQqSKg1YZW6LnSjBX79++vitCcjl6vx5HDhxGs0yG65Nple7TPz4fBYMCRI0eqMDqyx5EjR5CcnIycnHuh04XYXDc7uyv0en9s3LgJRRX4vt1FUlISLl26hNYFBQiw0dh2T0nPxJraoFMVnDrhf++997Bv3z4kJibi8OHDeOaZZ5Cfn49hw4Y5OrRqt3HjRiQmJiInpwtUKtsDEWZk9IVOF4yfflqNVCsJr7vSarX49NNPUahQ4OkbN1DXRi+HZ1JSUFurxapVq4ytEeQ4Qghs3LgRQniUJPzlkSErqwe0Wk2NOymkpaXh+PHjaFpYaHMfl3TJyQHAcSscLTs7G//735cQwhs3bgyGvYOZaTR1cfPmY8jOzsJXX31VI1qvV6xYgeTkZPTIzrZ5yUppA1JT4SUEvv7qK+SWUxlGVeOPP/6AVqtBTs69sHcfl6hUDVBU1ABHjhypEd/fX3/9hdy8PNyVm1uhAnmnvDzIhMCmTZtqxPHAWRkMBqxa9SOEkCEjo2+56wshR0ZGbxQU5BsvU65JpIbIB8q5RKuZQoEIlQp/7N9vc9w2sp9TJ/zXr1/HCy+8gJYtW2LQoEHw8fHBoUOHEB0d7ejQqtWNGzewYsVK6PVBSEsbUO76BoMfbtx4GlqtBl9++WWNugZm0aJFOHv2LDrn5KBrSZJjjb/BgOFXr8JDr8fsWbOM8+CSY5w5cwZXrlxBXl5H6HS17XpOTk5XGAw+2LhxU40aqHL79u0QQqBHqfEprGlTUIAQrRa7d++uka0KzkCv12PevHkoKMhHSspA4xRk9srIeAiFhc1w4MABt6+4+eeff7B+/XrUU6vxZAUrretpNBiQmorcvDx88cUXNer85ww0Gg1Wr/65ZJyV+27rNbKz74dOp8OaNWsqOTrnotfrsfaXX+AhBHplZlbouWFaLe7Ky8Ply5dx/PjxqgmQyrVv3z4kJSUiJ+c+aDThdj0nK6sn9PpArFnzC/JKDSrtzq5du4Y9u3ejvkqFVuX0ypQBeCgjAzq9Hj/++GP1BOjmnDrh/+mnn5CSkgKNRoMbN25g7dq1aNOmjaPDqlY6nQ5z586FRqPG9evPQa8PsOt5+fmdkJvbCadPn8aGDRuqNkgnsXXrVmzZsgUNioow+MYNu9oVYoqK8MyNG8gvKMCMjz9mMuRAq1evBgBkZva2+zkGgx+ys7shMzOjxgxUWVRUhG1btyJYp0NHOwf+8URxjbpSqcSuXbuqNkAqQwiB77//HsePH0deXnubI/Nb54Hr14dCr/fHwoXf4NSpU5UepzPIzMzEvM8/h5cQGHbtGuS30Xr5YFYW2uTn49ixY1i/fn0VREnWbNy4EVlZmcjI6FVu92ZrsrO7QqOpiy1btrr1XPNbt25FYlISOufmIkyrrfDz+2ZkQCYEvlm4EOoK9IKhyqFQKLBkyVII4YObNx+z+3kGgy/S0mJRVKTE8uXLqzBC52EwGLBw4UIYhMCTqal2JZ/35uaivkqFnTt31thZaiqTUyf8VDy408WLF5Gd3QX5+R0r8EwZUlKeh05XC8uX/2AcMdddnT59Gt9+8w2C9HqMuHoVPhUoJHbPycGDmZlITErCvHnz2D3OAc6cOYPjx4+XDEjZuELPzch4GEJ4YfXqn6G9jUKTq9m2bRsUSiUezMyEdwX21R7Z2fAxGLBh/foasZ2cyW+//YbNmzejqKgBrl8fiop2c5ZotbWRlDQCOh0wY8ZMt5t2VqFQYNq0acjJzcXA1FQ0LJl+rKJkAIZcv45aWi2WLVuGP//8s3IDJYsuX76MFStWQqcLQkbGQ2aPNW8ej1atJqFVq0nw9S1O4n19U9Cq1SQ0bx5f6pU8kZr6JPR6HebMmeOWx6uUlBQsX7YMgXo9Bpj0Yolv3hyTWrXCpFatkOLrW7yury8mtWqF+ObmA9k2UKnQOzMTKampxlmJqPosWrQIubk5uHmzn929EiXZ2d1RVNQQO3fuxD///FNFETqPX3/9FadPn0bHvDyrg/WV5gHguRs3AIMBn8+dC6Wds1iQZUz4ndi+ffvw66+/QqWKRErKMxV+vl4fgKtXh0GvF4iP/8Rtr4PJyMjAJ598Auj1eDUpCaG3UTh4KjUVLQoLcfDgQfz8889VECVZo9frsXDhNwBkuHnz8Qo/X6cLQWbmA7h5M83tW/MUCgXWrFkDf70e91fw9xyg1+OBrCxkZGZi69atVRQhlbZv3z4sXrwYOl0tJCW9AYPB945eT6lsiuTkIVAqFYiLi3ObS5G0Wi3i4+Nx9epV9MrMLHcavvIE6fV4MzERfgYD5n72WY0a9d0RCgsLMXv2p9Dp9EhOHgqDwd/scU/PQnh7F8DbuwAyWfFlFjKZAd7eBfD0LJsA5Od3RHZ2V1y6dAlLly6tjo9QbQoKCvDR9OlQqdV45sYNBJkMXlbo6YkCb28UeHvDICuuGDTIZCjw9kahZ9np3h67eRPhajU2bNiA33//vdo+Q023Z88e7Nq1C0plozKVW/bxxPXrQyCEJ+bO/RyZFbykw5UcPXoUy5YuRbBOh+ft7H0raaJUom9GBlLT0vDpp59yVq07wITfSZ07dw5ffPE/GAx+uHr1VQghv63XUSqbIjX1KeTm5uBjN+yyrtFoMHPmTOTn5+PZGzfsmrrJEk8AL1+7hlCNBqtWrcKxY8cqN1CyasOGDUhKSkRWVjcUFd3e+Bzp6Y9CpwvB6tU/4/r165UcofNYtWoVCgsL0Tc9Hf63cW3ywxkZ8DMY8NOPPyKnnDEu6M7t27cPc+d+Dp3OD4mJb0Cnq1Upr5uXdw9SUwcgIyMDEyZMREZGRqW8rqPodDp89tlnOHnyJDrm5WFgJQ02W1+txqtJSRA6HWZ8/HGNndK3qqlUKkyfPh2pqSnIyOiLwsJWlfK6KSnPQKWKxMaNG91mNh21Wo2ZM2fiRkoK+qWn4+47vIbbRwi8npQEf70eX82fXyNaix3tzJkzmD//KxgMfkhOHobiEmTFqVQNkJIyCHl5uZgxY4ZbtmCfPn0as2fNgqfBgNeTkhB4Gwn7Yzdvom3JJVr/+9//mPTfJib8TigxMRFxcdOg1eqRlPSK3QOBWJOV9SCysnrg8uXLmDFjhtt0jxNCYOHChbh8+TK6Z2ej+x0mMAF6PV67ehVeBgPmfPppjZvhwBFOnTqFH374ATpdMG7etD7dZHmKB6p8BlptcQWQu544N23ahHpq9W23fgbo9XgiNRWFCgUWLFjAy1eq0P79+zF37ufQ631x5cooqFQNKvX1MzMfQlraE8jISHfppF+r1WL27Nk4cOAAmhcWYmhyssWCSUW6OptqoVBgaHIylAoFJv/f/+HChQtV9ElqJimBPXfuHHJy7qvQtczlEcIHiYlvQqsNxbJly1x+7nKFQoGpU6fi33//xd25uXjs5s1Ked1wjQYjkpIAnQ4ff/QRp6SsQufOnUNc3DRoNHpcvTocGk3dO3q97Oz7jeXzuLg4tyq7HDt2DHFTp0KnUuHlq1fR6DYbHD0ADEtORoxCgT179mDOnDnQ2DE7EZljwu9kLl++jMmTJ0OhUCI5eSgUipaV8KoypKQ8i7y8jjh58iQ+/vhjqG7z2khnsnXrVuzatQuNlEo8XUkD+zRUqfD89etQKJWYOWOG2/WIcCbXr1/HrFmzoNfLcPXqq2UGpKzYNZ/FXUAzMvrg+vXrmDt3rttUbAFAVlYWPpszBzKDAS8lJ5tdu1/RRKhHdjZaFBbi0KFDxilyqHJt374dn332GfR6OS5fHmVzKtWK7uemMjL6IS3tCaSn38T48R+4XO8WrVaLWbNm4dChQ2hZUIA3kpKsjr9S0a7Opu7Oy8Ow5GSolEpMmTyZA0BVkoKCAkyZMqVkMMoOuH79BVR2sVKnq40rV0ZBpwvGwoULsXr1apesqMzOzsakSZNw5swZdM7JsVqxdbuaKpV4MzERHhoN4uPjsXv37kp8dQKKp1CcNOn/UFSkxrVrw1FY2LrMOhU/nheXz3Ny7sW5c+fwwQcfuvxlWkIIrF27FtOnT4coqYxqV1BwR6/pazBgZFISmhcW4q+//sKHH37ospXcjsKE34mcOHECEydOQl5eAW7ceAF5efdYXO/2CogeSE7+D/Ly2uOff/7B5MmTXXqO26NHj+K7b79FkE6HV69erdDgZeW5LzcXvTIzcfXaNXz66ac1arq36pKYmIgPP5yA/Px83LjxHJTKmDLrVPSaTwBIS+uPgoLWOHLkCGbOnOkWIxcrFMXXamdlZ+PJtDREl6qEqmgi5AHgpeRk1NJqsXjxYvzxxx/V8TFqBCEEfvnlF3z11VfQ6QJx+fLbNpN94Pb2c1MZGf2QmjoQWVmZ+OCDD12m23pRURE++ugjHDlyBK0KCio82GpF3Z2Xh+HXrhmTfnZ9vjMZGRn44IMPcPbsWeTkdEZy8nDcbtfm8mg0dXH58mhoNGFYuXIlFixY4FLdei9cuIBxY8fiypUruD8rCy9dv14lW6qFQoG3r1yBr1aLefPmYfHixS61nZyVtmTwz1mzZkGt9sDVqyOsDqJ9e8dzD1y/PgSZmT1x9WoSxo0b57KXlebk5GDmzJlYtmwZamk0GH3pkt2D9JVHbjDgzaQkdMvOxqVLlzB27FgcPHiwUl67JmDC7wSk2rApU6ZCqdTg2rWXkZPT1er6t1tAFMIb1669gpyc+3D+/HmMGTPWJVs6Tp06hU/i4+Gp1+O1pCTUtpKQ324XUAAYkJqKtvn5OHr0KL744gueNCvRrYqtfFy/Phg5Od0q8dU9cfXqa8jPb1vcnSwuzqWvVc/KysKkSZNw9epV9MzMRO9KGtinlk5nNqAZB3u6cwaDAUuWLMHy5cuh1Ybi0qXR5Sb7lSUzsw+Sk4cgP78QEydOwokTJ6rlfW9Xfn4+/u///g/Hjx9Hh7y8Kk/2JZ3y8/Hq1avQq1T4aPp0VnbdptOnT2Ps2LG4fv06MjIewvXrL0EIryp9z+KkfyyKihpi27ZtiIuLQ76d05I60q5duzDhww+Rm52Np1JS8GxKSpUWvKOLivDfS5cQoVJhw4YNmDZtmktsJ2eVmJiI//73PaxduxYaTV1cujQWBQVtq+CdPJCa+jSuXx+MvDwlpk2bhq+++spluvgLIfDHH3/g7VGjcPjwYbQsKMB7Fy+ikZXexLdbPvcWAoNv3MDz16+jKC8P8fHxmDt3LvdxOzDhd7C0tDRMmTIFy5Ytg0YTgsuXRyM/v1MVvmPxyKDFLUI5+PDDCVi5cqXLdH/et28fpk6ZAr1Gg1eTkhBjo8v9nXQBlQbxa6JQYN++fZg+fbrLHHidlV6vx4oVKzBlyhQUFhYhOXkocnK6V/r7COGNq1dfQ27uPfj333/x7rujcfz48Up/n6qWlJSE9997D1euXEGPrCw8lZp6m5O5WVZfrcabiYnw1WrxxRdfYMWKFTDcxkCAVDzo3BdffIENGzZApYrEpUtj73jslYrKze2Cq1dfgUqlQ1zcNPz111/V+v72ysjIwAfjx+PixYvolp2Nl69dq9QeWuVpX1CAkVeuwFurxZw5c7B58+Zqe29XJ4TAli1b8H//Nxl5eQrcuPE80tIGoLqKkjpdMK5cGY28vA44ceIExo37LxITE6vlvStKpVLh888/x//+9z94q9V4KzERvbOyKvUYbk1djQbjLl9Gh7w8HD9+HKPffRdnzpyphnd2H4WFhfjuu+8wduzYkkGF78fFi+OhVkdW6fvm5HTHxYvvoaioIbZv344333wLe/bscepz87Vr1zB58mR8+umnUBcU4NkbN/BWUpLZ7BOl3Un5XAagR04OPrhwAY2VSuzduxdvvvEGtmzZwsY5G5jwO4hGo8G6devw9tvv4MSJE8jPb1fyI7+9UcorRobMzD5ITBwFlSoEq1evxujRo526VUitVmPRokX47LPP4K3V4q3ExErrJmSNjxB4KzER7fPy8M8//+C9//6XAz7dpuTkZEycOBE///wz1OowXLo0Fnl5navwHT2RnPwfpKQ8jdzcQkydOhVLlixxiTEZDAYDNm3ahPf++19kZWVhYGoqnquiVqEYpRLjLl1CXY0GP//8M6ZMmcLr4ipIpVJhxowZ2LNnDxSKJrh8eTR0uhCHxFJQ0AFXroyEVuuN2bNnO90gZ9evX8cH48cbRygffONGFXUCt62pUol3L19GkFaLb775xmWvC69OarUa8+bNw8KFC6HR+OPKlXeQnd2j2uMwGOS4du0VpKU9jvT0dLz33vvYs2dPtcdhy9WrVzFu3Djs2bMHjZVKjL94ES0VimqNwddgwCvXruHJ1FTkZGVh4sSJWLNmjVMnjs5Aq9Vi48aNeP31N7Bx40YUFdVBYuJIpKQ8B4Ph9mbLqii1uj4uXx6HtLTHkZOjwOeff47x48fj1KlT1fL+9srJycHChQvx7rvv4uTJk2iXn48Pz5/HA9nZ1ZJchms0GHP5Mp5OSYG+oAALFy7E2LFjcezYMR7PLajaPlhUhl6vx969e7FixUpkZWVCrw8quV7/LqBa6n5vUSia4+LFCahXbzOE2IfJkyejU6dOGDZsGJo2bVqtsdhy6tQpzP/yS6SmpaGeWo1Xr15FRDVdmy0XAq9eu4aNERH4HcD499/HU4MG4fnnn4ev753Np10TqFQqrF69GuvXb4DBoEdu7j24ceM5GAx+1fDuMmRl9YRC0QTR0Uuxfv167N//B0aMeA3dunWDTFa9vzd7ZGRk4IsvvsDJkycRpNdjeHLyHQ92U566Gg3GXbqEHxs0wMmTJ/HO22/j9TfeQO/evZ1yGzkTpbK46+XZs2eRn98O164NhxA+Do6pGS5ffhcxMQuwcOFCKBQKPPvssw6NCSgekHbq1KnIz8/HwNRU9HHwvNMNVCqMvXQJ85s0wcqVK6FQKPDyyy9zn7fgxo0biI//BNeuXYVC0RjXrr0Mna62AyPyQEbGI1CpGqJRo+X4/PPPcfbsWbz22mvw8XHc708IgR07duC7b7+FRqtFn4wM9E9Lc0ilFlDcovdwZiaaKJVY1qgRfvjhB5w6dQpjx45F7dqO/P6cj06nw++//46fflqNrKxMGAy+uHlzILKyHqzyy1UsEcILGRmPICfnPkRG/oYLF45h0qRJ6NixI4YMGYJWrSpn6svboVQqsX79evy6YQNUajXC1WoMSklBmypuhLPEA0DPrCzcnZuLTREROCQEpk2bhnbt2uE///mPQ7eTs2HCX000Gg327NmD9evXIyUlBUJ4IyOjLzIyHoLB4O+wuAwGOVJTByEnpwsiIjbi+PHjOH78OO677z4MGjQIrVu3dlgB6PLly1i5ciWOHj0KmRB4OCMDsenp1dr9Eyg+oAxIS0O7/HysiorC2rVr8fuuXXh+8GD069cP3t7e1RqPKzAYDPjzzz+xZMlSZGVlQqMJQ0rKM1V07ZttKlUULlz4AHXr7oIQu/DJJ5/g7rvvxiuvvIJGjRpVezyWqNVqrF+/Hr+sWQONVosOeXl4/sYNm13iKlOAXo9Xr11DQq1a+KVBA8ybNw+7du3CiBEjEBNTdkBFKi70TJ06FefPn0dOzr24fv1FVNWgZRWlUjXApUtj0aTJfPzwww8QQuC5555zWDznzp1D3NSpKFIq8cKNG+jmJONqhGm1GHP5Mr6OicGGDRugVCoxcuRIeHiw86MkISEBc+Z8hqIiJTIzeyItbYBDEiBLCgra4sKF8YiOXoRt27bh8uXLmDhxIsLCwqo9FqVSia+++gp//PEHAvR6vJycjLZVXFlrryYlvQxWNmyI48ePY8zo0Rj33/+iY0fLA8/VJBqNBrt27cLatWuRkZEBg8EHmZl9kZnZp8zMQY6g09VGcvIwZGT0Qb16m3HixAmcOHECnTp1wnPPPYd27dpVWyxKpRKbNm3ChvXrUahQIESnw8C0NHTJyXH4mS9Ir8cLN26gV2YmNtWrh1OnT2P8+PHo3LkzXnjhBTQvZ9yumsA5jtpuLC8vD9u3b8fGjZuQl5cLIbyQnd0D6emPQKer5ejwjFSqBkhKehMBARdQr95WHDlyBEeOHEHLli0xcOBAdOnSBV5e1bO7nD9/HuvXr8eBAwcAAC0LCtA/Lc3q4B/VpalSiQ8uXMDvdetij8GAb775BuvXrcOgp59Gnz592OJf4sSJE1i6dCkuX74MIbyQnv4oMjIedmjLpxA+SE9/DLm596J+/V/w999/459/juOhh/pgyJAhDikgFsclcPDgQSxetAjpGRkI0enwQkoK7s7Lq+b+PsX9i+7LzUUzhQLrIyNx4vRpjB0zBo/GxuLFF19EcHBwNUfkvEyT/ezsLrhxo/KnI7tTWm0Yrlx5B02afIkVK1YAgEOS/mvXrmFaXBxUSiWGX7uGu5xscKUQnQ7vXrmChY0bY8eOHQgICMDLL7/s6LAcTgiBNWvWYOXKlTAYvJGcPMzqzEGOpNWG4fLlsahf/2dcvHgYY8eOw8SJE6q1Ze/69euYOWMGrt+4gaaFhfhPcrLVwYQdJUCvx4irV7E/LAy/CoGpU6Zg2PDhGDhwYI3s1aJWq7Ft2zasW7ceOTnZMBh8kJXVG5mZD0Gnc75znUoVhatX34S//xWEh28zNs61adMGgwcPRseOHavse1SpVNi8eTPWrV2LgsJCBOr1GJCejgeysqplsNWKiFSrMeLaNVzx98eWevVw9OhRHD16FPfddx9efPFFNGnSxNEhOgwT/ipy4cIFbN68GX/88Qd0Oh0Mhv9v776jo6q6h49/Z9LLpDdSSIDQQqQm9KpA6KCodERA4REQsfHqo4KggkrTx4rSVJqAFCnSpUMoAZQaamjpvU8y5/0Dkh+RYoAkMwn7sxZrMTO37Dsc7px9T7MhPr4jCQmtTfJmUiAjowYXLtTA1vYibm5bOX36Lz799FNcXFzp3LkTHTt2LJWuYPn5+ezbt4/Vq1dz5swZAAIyM+kWHU2NMh77dj+WStE5NpbWCQlscXdnp1J89913/PLzz4R16kTXrl1xc3MzdphGcenSJRYsWHBr/JSG5OQQYmK6otcbJ5m+m9xcdy5dGom9/Sm8vNawZcsWduzYSc+ePejduzd2dmX3RP/06dPMnz+fkydPYq4UHeLi6BgXh5WRx1i66PUMi4rijJ0dK7y9Wb9+PX9u385zzz9Pt27dsLIqm3GMpspgMDB9+nSTTvYL6PUuRZJ+Dw8P2rZtW2bnj4+PZ+KECWRkZvLClSsml+wXsMvP5z8XL/JFtWqsXLkSFxcXevbsaeywjCY/P58vv/yS7du3o9e7cOnSS2Rn+xg7rHtSyoJr1/qTne2HUr/xzjvv8tZbb9K8eclPCvtPBw8eZPq0aWRmZfFUXBzdjNiF/99ouNn9OSAzkzn+/sybN4/z588zZsyYx+a+np2dzYYNG1ix4jdSU1MwGKxvtei3JT9fZ+zw/lVmZlUuXXoFG5vLeHhs5OTJv/nggw+oWbMmffv2pWHDhiWW+Ofm5rJhwwaWL1tGSmoqtvn5dI+Lo3VCgtHrKf+mamYmoy9e5NytxL+gEbN58+b079/fZHp3liVJ+EtQTk4Ou3fvZt26dZw7dw6A7GwvEhJakZwcisFQflqAMzOrEBU1HEvLWFxdd2MwHGDhwoUsWbKUFi2a06VLlxLp7p+QkMDmzZvZuHEjCQkJaJSibmoqbePjqZaZWeatnMVll59Pz+honoyLY6+LC7vc3FixYgUrV66kSZMmdOrUiXr16j0WXUPj4+NZuHAh27ZtQylFWlotoqN7kJ3ta+zQ7kFDenoQ587VwsnpEF5e61i+fDkbN26iX7++hIWFleowjevXr/PTTz8V9mCpl5JCj+ho3HNzS+2cD6NmRgbjIyPZ4+rKH56eLFiwgLVr1zJw4EDatWv3WJTtu1m5ciUHDx4kLa2WSSf7BfR6Fy5efIXq1afx1VdfU7Vq1TKp7OTk5PDhxInE35p4slFKSqmf81HY3lrjeWa1asyZMwcPDw+aNSvJJUPLh7y8PGbMmMHu3bvJyKjC5csvkZ9vb+ywikFDQkJrsrO9CAj4kc8++4xx48bRpk2bUjvj7t27mTZtGua35ltpaOJlvIB/VhZvnjvHvMqV2blzJ8nJybz//vsVOunPy8tj8+bNLFq0mJSUZAwGG+LiOpGQ0MYkuu4/qKwsfy5ffhlr66t4eGzkzJljfPjhhwQFBTFkyJBH6uGilGL37t3MnzePuPh4rA0GOsfF0TY+HhsTT/T/KTAzkzEXL3LWzo51np7s3buX/fv20aFjRwYMGICTk5OxQywzkvCXgPj4eH7//Xc2b95Meno6SmlJTa1HQkIrMjKqU9aT8ZWk3FwPbtx4hujorjg5HcLNbRc7d+5k586dBARUoVu3rrRr1+6BEiSlFMeOHWP9+vWEh4djMBiwMRhok5hI64QEk0t87keXn09YXBxPxscT4ejITldX9u3bx759+/Dy8qJTp060b9++QnaHzs3NZdmyZfz220r0+lyysnyIju5Jenp5mSRFS3JyY1JSGuDquhODYROzZ89m9eo1vPzyS4SGhpbo2TIzM1m6dCm/r1lDXn4+VTMy6BkdTRUTXu7RDGidkEBoUtLNoSxK8cUXX7B27VpefvllateubewQy9TJkyf56aef0euduXJlMKae7BfIzXXnypUB+Pv/yJQpU5k1a2apV+5Xr17N5agoWiUkGH2CvuJyvrUCzPTq1Zn9/fc0aNDgsRqqpW79/969ezfp6dW5fPnlEp2Z/PPPP7/r+6+9NqnEznGzl+Ioqlb9hhkzZmBlZUXTpk1L7PgFCpJ9q7w8XrlwAf8SXAHmXt/TpNdeK7FzOOTlMfrCBRZUrszR48eZPHlyhU36jx07xrfffsf169cwGKyJi+tCfHybMpo8uHRlZ/sSFTUMK6sbeHqu5+TJY7z99ts0b96cESNGPHCP3KioKL766itOnz6N+a25s9rHxWFbzhL922m42YBR48IFTtnbs7pSJTZu3MjOHTvoP2AA3bt3fywaMCThfwRXr17lt99+Y/v2P8nPzyMvT0dCQhiJic2NPINtyVPKiqSkFiQlNcfW9gKurru4ePEoX331FQsXLqJXr56EhYVha3vvCQgzMzPZtm0b69et4+q1awD4Z2bSIjGRhsnJJjcW6EFYKEXj5GQaJycTZW3NHldXDhsMzJ8/n0ULF9K6TRu6du1qUqsfPIozZ87wxRdfcPXqVfR6Z6Kjnyc5OYTykgDdTikL4uOfIimpKe7um1BqF5MnT6Zdu3YMHz4cne7Ruvkppdi2bRsL5s8nOSUF19xcet24Qd3U1HLzKNDGYKBbTAwtExJY6+VF+PnzjB8/njZt2vDiiy/i4uJi7BDLxLJly1DKQFTUC+Wk1fP/pKbWJT6+NbCTvXv30q5du1I7V1JSEsuXLcMhL48e0dGldp7SUCknh6diY9mg0bBq1Sr69u1r7JDKzJ49e9ixYwcZGVW5dGmE0VeceFhZWf6cPz+GwMBZfPXV1wQFBZXoQ/dr167dfJiQl8eoCxeoXA6We70bM+CFqCi4lfTPnz+fESNGGDusEqPX6/n5559ZtWoVSmlJSGhNbGxYuei6/6BycioRFTUMW9uLeHmtZu/evfz119+MHfsqjRs3/tf9lVJs2LCBuXPmkKvX0zA5me7R0bjq9WUQfdnQAEHp6dSMjGS/iwvrvLyYM2cOhw4dYty4cRW+HiMJ/0PIzMxk3rx5bNq0CaUU2dlexMW1JyWlocnMXlt6NGRmViMzsxo3bqTg6roDg2E38+bNY9myZQwfPvyO5bzS0tL47bffWLd2Ldk5OZgrRZOkJFolJBh9Ir7SUDk7m8rXrtHrxg3CnZ3Z5erKli1b2LJlC0FBQQwcOLBMZ1YtSUopfvnlF5YvX4HBoEhIaEt0dLdyWzG8XX6+HdHRT5OU1Axf34Vs376diIijvPHG6w89m3F6ejr/+9//2LdvH5YGA91jY2kbH1/mK02UFKe8PAZevUrLhARWeHuzY8cOIo4c4fU33qBhw4bGDq9UJSQkcORIBBkZVcjMLNmJf8qi5RMgPr4dbm472bJlS6km/MuWLSM7J4eno6NLdKxnWbR8AjeHarm6smL5crp06VIhe2j9U2ZmJt999z1KWXDlysBSuae/9dZb9/ik5BOw7Gxfbtzoikazkvnz5/Pqq6+WyHGVUsyePZu8vDxejIoqlWT/Xt9TaaSpZsDgK1e4YW3NhvXr6dChQ4WY2Cw/P5+PPvqIiIgIcnI8iYoaTHa2X5mcu6zu53eTmVmFCxfG4uKyG2/vVXz00UeMGTOGDh063He/2bNns27dOuxNbIWJ0mAGtEhMpH5KCot9fDh27BivjR3L59Om4enpaezwSk1Fz05L3F9//cWMGTNJSIgnK8ubmJgupKUFU9Ytm8a8oRTIy3MkJqYHcXHtcXXdjYfHFmbNmsXu3bsZO3YsVlZWrFmzht9WrCAzKwsnvZ6O8fE0S0rCroyWGzMmG4OBNgkJtEpI4Ky9PTtdXfn75EneffddGjRowODBg8tdi//ixYtZtmzZrS7C/cnMLN34jVHOc3K8OH9+HG5u21FqHZMnf8SUKZ888LIu586dY+qUKcTGxVEjPZ2BV67gZGIzNz+sgKwsxp0/zx4XF1Z6ezNx4kSeffZZBg4cWGG7xu3evRulDCQlNTF2KA9Nr3clPb0Gf/31FwkJCaW2OsW5c+cKH+yWR1ZKEZqYyBZzc65du/ZYJPyRkZGkpqYQH/8Uen3FmHg2IaENbm47OHAgvMSOeeHCBSIiIghKSyO4giRF5krx7LVrfF21KitXruSNN94wdkiPbNGiRURERJCaWoeoqBcrRKNE8WlITGxFRkYg1ar9j2+++ZaAgIB71mE2bNjAunXr8MnKYuSlSziWUT2lrB7g3kvBcsTb3dxYBXz00Ud8/vnnFXYYlyT8DyApKYnJkz8iKyuX2NguxMZ2wFTWXTYmg8GWuLiOJCeH4uOzmEOHDjFz5kySk5O5cOECurw8esfG0jwxscxbNo19Q4Gbj4JqpadTKz2dKGtr1nl5ERERwbFjx0p9UqGStH37dpYsWUJurjvnzo0rd12aH4yW+PinyMnxwt9/NpMmTWbmzBnFXoEhKSmJCR98QHpaGl1jYugQF1eqjwSNUc61QKvERKpmZjKvcmWWL1+Ovb09zzzzTKmd05hyb80totc7lfixy7LlsyD+/FJ86JqcnIx9Xl6Jl/mybPl0uFXpTSknE7E9qqRbD2dyctyNHElJ0pKb60Z6eiR5eXklsrRwwfdUIz293AzJKo7qt1ZDSkxMNHIkJWPTpk3o9Q5cuTK4zJP9sryf309OTiWuXOlHQMAP/Pnnn/dM+JcsXoxtfj4vX75cZsm+qdAAT8bHE29pyW5g3759pdr7zZgk4X8Ay5YtIzs7i+vX+5CY2MKosZjKDeV2er0zly79hypVZnHk8GHQaGiekFDi3TrLs8rZ2fzn0iXO2Nkxz9+fGTNmYDAYysUNZu3atShlxsWLI8ss2Td2OU9Lq0N0dHc0mjXs2bOnWEt1KaX49ttvSUtP59nr12ldQSpQ9+KTnc248+eZUqMGv/zyCyEhIRVyyRsPDw8ALC3LZ6t1AQuLRDQabamOV0xLTS33vbgsb/1mpZroUoIlrSAZtra+YeRISo5Gk4uVVRzm5haoEmpsKHjwl1vBejLlaLVolSq8vvIuJycHg8EOg+Fxatm/U8EKBDk5OXf9PCcnh6TkZGpkZeFcxuP1y/IB7r8JTk1lt6srMTExRjh72ZCE/wGYmd1szddoKsYNsXQYMDfPAI2GxklJ9Ll+3ahPwU3phnK7mhkZvHr+PFNr1GDTpk3lIuEHUMqc3NyK1AL073JyvB5o+9TUVPbv30/lzExallGyb+xybpefzzPXrzO/cmW2bt3Kiy++WEZnLjteXjfLgb39KaM/8H1Y5uap2Npexd3dvURaO++lVu3aHD58mCgbm3I7odmhW8s1PcryVuVJ06ZN8fb2RqldJCa2JCen/I9ldXPbjoVFEr16PVdiS60GBwdjb2fHn25utEpIKPcPtgps9PDAoNFUmKUomzVrxvbt2/H2XsH16715HHvjmpun4Ou7COCe/64WFhZ4enhwTimuWVvjUwHn1fo3Cth1a3ibn1/ZzPNgDBXrEWUpe+6557Czs8fbeyW+vj9hbl6+W3pKmp3dGapX/wxr61gAMszM0GsqUqe3kpV8qwJib18+usY7Ozuj1ebg5rbN2KGUGY0mBze3rQDFXq/V3t4eS0tLFI/XDbag/czdvWI+EKpevTrBwcE4Oh7HweGoscN5KN7ey9Bqs+ndu3SHXRQM69hSzCEwpuaSjQ3n7O0rbG+VuzE3N+fFF19EozFQter/sLG5bOyQHoHCzW0Lnp7rcXR0onfv3iV2ZAcHB/r170+mmRnzKlcmswK09Ic7OfGnmxtenp50797d2OGUiJEjR1K5sj+urrupVm0m1tbXjB1SGVI4O++jZs1PsLKK5bnnnqNRo0Z33VKr1fLKqFEYNBq+qlqVvx5xVaLyJt3MjB/8/Tnh4ED9+vVp3ry5sUMqNeX/TlWGHBwcmDx5EtWrV8fZ+RC1an2Mt/ev2Nhc5P+qu48XjSYXR8fDBAR8TdWqX2NjE0PHjh2pVq0aJxwcmBYYyEWb8r/WaUnK1GpZ7eXFd1WqYGFuTseOHY0dUrEMHz4cNzd3KlVahbv7JqBiD9MwM0snIOA77O3P06ZNG1q2bFnM/cwIDAzkiq0t28ppwvOgEiwsWH+rBbxGjRpGjqZ0aLVaRo8ejYWFJT4+yzA3TzZ2SA/Eyekgjo7HCA4OJiwsrFTPFRwcTPXq1Tnm5MRhR8dSPVdJy9RqWerjA1Bh56O4lyZNmvDyyy9jYZFOtWpf4uh42NghPTCNJhdf34VUqrQGNzdXJk368L7LBT+Mzp0707RpU87a2zMjMJBYy/LZbdwArPH05Bc/P2zt7Xn9jTdKrCeEsdnY2DB16hTat2+PrW0UgYGf4+v7UwVP/PNxdDxCtWrT8PVdjJ2dhtGjRzNw4MD77tWgQQNGjx6N3tqaHwICWOjjQ0IFKQf3kg8ccHJiao0a/H0r2X/jjTeKrDBW0ZSLLv3ffPMNn3/+OTdu3KBOnTrMmjWLVq1aGSWWwMBAPv/8c3bs2MFPP/2MRrMbV9fd5OS4k5wcSnJyQ3JzPYwSW9nJx87uPE5Oh3ByOopWe7MLUP369XnxxRepUqUKer2en376idWrVzMzMBDfrCyaJybSKDkZm8dwPL8CLtnastfZmSPOzug1Gny8vXl7/HiqVKli7PCKxcvLi08++Zh33nkXWIuT0xGio7uTlhYEFWj6Io0mFze3P/Hw2IJWm03r1q157bXXCof0FMfo0aN5/733WAWkmZvTJSam3C7F928u29jwQ0AAqebm9OvXr8Im/ADe3t688MJgfvzxRwIDZ3Lx4ivlouuzs/NefHyWYmVlzejRo0t9JQWN5mZF89133uFnPz8slKJuORgLn6XV8m2VKlyzsaFr167UqVPH2CGVuW7duuHj48PUqZ9SufICUlMPcePGM+VgKJfCweEvKlX6DUvLRGrWrMm7776Ls7NziZ/J3Nyc//f//t+tJWqXM616dTpHR9M6IaHcdBy/am3NCm9vztvZ4ePtzfsffIC3t7exwypR9vb2vPrqq7Rp04Y5c+Zy6dIhnJ0PkZZWk4SENqSl1aYidPU3M8vAySkcN7cdWFrenKOldes2vPjii8Weq6Vjx47Url2b6dOncwA46OxMk6QkOsTG4lbGY/tLU55GwyEnJzZ6eJBgaYmFuTlDBw+mR48eFXaFoQImn/AvXbqU1157jW+++YYWLVrw/fff07lzZ06ePGm0rnZarZZ27drRunVr/vrrL7Zv387evfuwslqPp+d6cnI8SE2tQ1paHTIyqlHSN5TbJ0wzN89AozGglJa8PLtSm0zNzCwDne4kOt0JdLpTmJndHJfp4eHJk0/2om3btkV+LCwsLBg2bBhNmjRh7dq1HNi/n19tbFhVqRJ1U1J4IjWVWunpFTr5V9z8Uf3bwYGjjo7cuLXUh3elSoR16kTnzp3L3fIfXl5ezJgxnYULF7J58xYCAr4nPT2QmJiut9YmL7+Jv0aTi7PzQTw9N2BunoqDgyP9+w8hLCzsgZJ9AF9fXz77/HM+eP99tgIRjo50jYmhUXJyhelWFWdpyTpPT444OaHRaBjx8st07drV2GGVuu7du6PX61mwYAHVqs3k4sURZGWZ6kM7hYfHH3h6bsDBwYEJEyaUWaW+SpUqTPzwQz54/33mVa7MsMuXTXoZsyytlu8DArhsa0vHjh156aWXKnRrz/00aNCAmTNn8O2333Ls2DF0ujPExT1JXFwHDAarhzpmadZbrKxiqFRpBTrdaczNzenZszf9+vXDshRb3rVaLYMHD8bf35/Z33/PSq2WfS4u9L5+nZq3Zrw3RRlmZqzz9GSPiwtKo6Fly5a88sor5WZo4cOoV68eX3wxi6NHj7Jy5UqOHj2KTneGvDwHEhMbk5TUtMQa6squfm7A3v4Mzs77cXT8C40mD0tLK8LCutO9e/fCOWcehJ+fH9OnT2fv3r0sWbKEfRoN+52dCUpLo2ViIrXT0kqs/mJ/29wXGebmGDQatEphl5dX5LOSkmhhwV4XF/a5uJBmbo6FuTndO3fmmWeeKbXlaU2NRpXU1KWlpEmTJjRs2JBvv/228L3atWvTq1cvpkyZ8q/7p6am4ujoSEpKSqmupZuVlcX+/fs5cOAAR45EkJ19MyE2GGxITa1Fenpt0tJqk5dXst0bAwM/w8bmKllZvpw793YJHtmAtfU1dLpT6HQnsbW9iEZzs6h4enrRuHEozZs3JygoqFiVoqSkJLZt28bGjRuJjo4GwEwpAtPTCU5LIzg1FdcSfoo4pXp10m8lane7obwTGVmi5wPQazSctbfnb52OEw4OheP0LczNad6iBWFhYdSpU6dCVCSvXLnCTz/9xIEDBwDIyvImIaE1ycmNUOrhKoW3q159CmZm6cDdfzgjI9955HMAWFjE4+q6GxeX/ZiZZWJlZc3TT/eiV69ej9wVND09nWXLlrH299/R5+Xhk5VF55gYgkvwh/O/tWqRdo/udzq9no9Pny6hM92UaGHBNjc39ri6kq/RUL16dV588UWCg4NL9Dymbtu2bXz55f/Iz9dy7VpvkpKa8TAPvEqrnGu1Wfj4/IqT02E8Pb348MOJRmnBO3nyJBM++IDcnBw6x8bSMTb2ocp+ad7Pr1lbM7dyZeKsrGjbti1jx4594Id8FZFSiv379/Pjj3OIi4slL09HbGx7EhNbotTDd/ktqXqLhUUCHh5/4Ox8EI3GQGhoKMOGDSvzcp6ens7ChQvZsH49BqUISkujc0wM/g8xYWVplfNsrZYdrq5s8/AgS6ulsp8fL738MvXq1Xuo45Vnly5dYvPmzWzf/ifp6TcfQmZkVCE5OZSUlAaFM9s/qtKon1tZXcfJ6WZPBQuLZAD8/f3p0KED7dq1Q1dCY/ANBgN79+5lzZo1nL5Vh3DJzaV5YiKhSUk4l+DyfZ8FBnLVxgbfrCzePneuxI6bD5zU6djn4sIJnQ6l0aCzt6d9hw706NGjzBL9sspD/41JJ/y5ubnY2tqybNkynn766cL3x44dy9GjR9mxY8cd++Tk5BRZfiI1NRU/P78y/aL1ej0nT57k4MGDhIeHFya4cDMpKkj+MzOrotSjdbLw81uAlVUMOTmeXLnywiMdy8wsHXv707eS/NOYm9+8EWq1WmrXrk1oaCihoaH4+vo+dMKqlOLSpUuEh4cTHh5O5G0/Xh45OdROS6NWejqB6elYlWDRXODnR4yVFZ45Obxw5UqJHVcB0VZWnLa355ROx3l7+8KJCh10OkIbNyY0NJT69euX+DhCU3H69GnWrl3L7t17MBjyMRhsSExsQkJCC3JzS6a7c8n/cBqwtz+Nq+sudLqTaDQKR0cnOne+2fOipLuBxsXFsWjRIrZv24ZBKVxzc2mVkEDTxERsH7GXS1k82FJApJ0dO11d+cvBAaXR4O3tzeDBg2nWrFmFeID1MI4cOcLnn08jIyOdlJS6XLvW75EqiyVVzm1tL1C58k9YWCRSq1Yt3nnnnVLp2lxckZGRTJ06lbi4OGqlpTHoyhV0j9CKU1IVRAXsd3ZmuY8Peo2G5557jv79+0uy/w85OTmsXr2aFSt+Iysrk7w8R2JiOpKU1PShEv9HLecWFkm4u2/CxWUfGo2BgIAABg8eTEhIyAMfqyRdvHiROXPmcPz4cQDqpKbSJSYGv4ec+bwkynmOVstOV1e2uruTaWaGzt6ePn370qVLl1JdqaM80Ov1HDhwgC1bthARcRSlDChlRmpqHZKTQ0lLC3qkB1slVT83N0+9NYT2IDY2N+cgsLOzp3XrVnTo0IFq1aqV6m/wxYsX2bBhA39u3052Tg4apaiVnk6TpCSeSE195OGKJV0/v2FlxQFnZw46O5N2q4zXrFmTzp0706JFC6ysHr1B6kFIwl8M169fx8fHhz179hSZOfGTTz5hwYIFnDlz5o59Jk6cyIcffnjH+8b6opVS3LhxgyNHjnDkyBGOHz9euM6pwWBNamot0tKCSUsLKrO1zW+LDiurGzg4nECnO1GkFd/FxZWQkEY0aNCAevXqlVp3r8TERA4dOsShQ4c4dvQoWbd+GM2VompGBrXS0ghKS6NSTo7JdBbP0moLE/zTOl1hKz7c7MbasGFDGjduTI0aNR6rimNSUhIbN25kw4Y/SEq6uRxdeno1kpKak5JSD6UevntlSf1wWlgk4ey8HxeX/VhY3FxlIygoiC5dutCsWbNSn7DoypUr/P7772zfto2c3FwsDQZCkpJolZhYIsvhlPQPZ7ZWyyEnJ3a5uhYOSalRowbdunWjZcuWj32FESAhIYGZM2dy/Phx8vIcuXJlAOnpD7eU26OX83w8PDbi4bERMzMNffv25bnnnjOJ+1B6ejqzZs0iPDwcp7w8BkVFUf0huz6XRCKUpdWywtubcGdn7O3seOPNN+85k7W4KT09nVWrVrF69RpycrLR652JielEUlJjHmTo4sOWc3PzVNzdN+PqugeNJg8fH18GDOhP8+bNTWr87d9//82iRYv4+++/AXgiJYVOsbEPnPg/yv08R6tll4sL2zw8SDczw97enqeffpquXbtW2MaHR5GUlMTOnTvZvv1PLlw4D0B+vi3JyY1ISmpCVpYfZTlkUaPRo9P9jbPzAXS602g0BszNzQkNDaVdu3Y0atSozCdYzMzMZM+ePWzZsoVTp04BYJufT2hSEk2Tkoy6pF+WVkuEoyP7XVy4dKt8O+h0tHvySZ566ikCAgKMFpsk/MVQkPDv3bu3yBqSH3/8MT///HNhN5PbmUIL//3k5uZy8uRJDh8+zIED4URH3wBAKQ2ZmQGkpNQnJaVhiXf9/z8KG5sonJwO4+BwHEvLm4mZVqslKCiI0NBQGjVqhJ+fX5m32uXl5XH27FmOHDlCREQE586do6B4Ouv1BKWmEpSWRo0Sbv3/NwWt+Cd1Ok7odFyws8Nw67txdHSkQYMGNGjQgPr16xu1Fc1U5OXlceDAATZt2sTRo0dRSpGfb0NSUiiJiS3IyalUxhHlo9OdwMVlLzrdKTQahY2NLW3btqFTp05GmTQxPT2drVu3sm7tWqJjYgAIyMykRUICDVJSsDTybfmKtTV7XVw45OxMjlaLuZkZLVu1olu3bhV6Ur6HZTAYWL16NT/99DP5+XkkJLQkOrrnQ493fhhWVtfx8/sFG5ureHh48uabb5jcGvJKKVatWsWCBQtQ+fm0i4+n60NMaPmoD7Yi7exY6OtLoqUlNWvW5O23366wy0mWhtTUVFasWMHatevQ63PJzXUnJqYTycmNKI3Fn8zMMnB334Kb2040Gj1eXl7069eP1q1bm8TDrHs5fvw4ixYt4uTJk8DDJ/4P4o5E386Onr160b17d0n0iykqKort27ezbdv2wsaL7OxKJCS0JDk5BIOh9FaesrSMwdV1D87O4ZiZZQI3W6efeuopWrZsaTJzLVy7do0tW7awbds2kpJuNp74ZWbSPCmJkORkrMpofq5LNjbscXEhwsmJXK0WrUZDw0aN6NChAyEhISax6oQk/MXwMF36/8lUvuh7uXbtGuHh4Rw6dIgTJ05gMBhQSkNGRnWSkxuRklK/RG4ulpYxt8b9HMbSMh642SUoJKQRoaGhNGzY0GRuJAVSU1OJiIjg8OHDHD50iLT0W+NclaJWWhqNkpMJTksrlRtLwYR7R5yciHB0JPHW5D9ajYYaNWsSEhJCo0aNqFKlikm1LJiamJgYNm/ezObNW25r9a9BfHxr0tKCKc2VQc3MMnB23o+b2y4sLG6eu1atWoSFhdGiRQuTmDAxPz+fI0eOsHHjRg4dPIhBKWzy82mclESrhAQ8bvUGKgt6jYYjjo7sdnXl8q2KoYe7O2GdOtG+fXt5mFUMFy5cYObMmVy+fJncXFeuXu1PRkb1Uj5rPu7uW/H0/AONJo8OHTowbNgwk67cR0ZGMmP6dK5dv45XdjaDrlwp1SSogF6jYZ2nJ9vd3NCYmdGnTx+ee+456anykJKSkli+fDkbNmwgLy+P7OxKXL/em4yMknkoqNHk4er6J56em9Bqs3Fzc6dv3z48+eST5ebfTCnF8ePHWbx4cWHiXy8lhR7R0biX4P09H9jj4sIfXl5FEv1u3bphZ1cyY9IfN/n5+Rw7doytW7eyb98+8vLyMBisSEoKISGhDTk5Dz4x3t0ZcHD4G1fXndjbnwXA2dmF9u2fol27dvj6+pbQeUpeQR1m8+bNHAwPJ99gwMZgoEliIi1LqQ6j12iIcHRkp6srUbd+5yp5edG+QweefPJJk5uEz1TyUJNO+OHmpH2NGjXim2++KXwvKCiInj17mtSkfSUhNTWVvXv3smPHDk6cOAGAwWBFYmJT4uPbotc/aCFW2Nmdxd19Kzrdzd4Q1tbWNGvWjNatW1OvXr1y86OZn59PZGQkhw4d4uDBg1y8eBEAC6UITkmhYUoKwampj7weQqylJYecnDji5ETsrXE+dra2hISGEhISQoMGDUy+HJmi/Px8Dh06xPr164mIiAAgN9eFhIRWJCY2L9En5lZWN3B13YGLy0E0Gj1WVta0b/8UnTt3NtrKHsURFxfH5s2b2bRxI4lJSWhuTf7UNj6eGhkZpdaZMNXcnN0uLuxxdSXN3BytRkNo48Z06tSJBg0ayAOtB6TX61m6dCnLly/HYDAQH9+G6OgejzQW9F4sLWPx8/sZW9vLuLi4MmbM6HLTLT0nJ4effvqJ33//Ha1SdImJoX1cXKk9Arxmbc1Pfn7csLbG18eHca+/TvXqpf0w5vEQFxfH0qVL2bx5C0oZSE6uT3R0L/T64i0Jdjf29ifx9v4NK6tYHBwc6devLx07djSJFruHUZD4L1q0iFOnTmGmFK0SEgiLjcXuEeazUMDfOh2rK1Ui1soKWxsbnn7mGUn0S1hKSgpbtmxh/foNxMXFopSGlJQGxMZ2JCfnYSeJNODoeBQPj41YW9/s7VuvXj06d+5M48aNy039vEBSUhKbN29mw/r1JCTebGQpyV4tBb1Xtru736yraLU0bdqULl268MQTT5jsXEKmkoeafMK/dOlSBg0axHfffUezZs2YPXs2P/zwAydOnMDf3/9f9zeVL/pBxcXFsXPnTtatW098fFzhzSU6ukexfkR1uhN4eq4tnOCjbt26dOrUidDQ0DKfsKI0XLt2jV27drFz506uXr0K3Oz23yY+nmaJiQ+03J8Cztvass3dnb9vlRFrKyuaNG1Kq1ataNCgQbmtZJiiK1eusG7dOrZu3UZOTjb5+bbExnYkIaHlI43zt7CIx9NzPU5Oh9FoFF5eXnTr1o2nnnqqXFV88vPzOXDgAGvWrClsEaqUnc1TcXGElOCyfrGWlmzy8OCIkxN5Gg329vZ06tSJLl264ObmVkJneXxFRkYya9YXXLkSRXa2N1FRQ0qwRUjh5BSOj88ytNpcnnzySYYPH25yvbSK49ixY3wxaxbxCQlUT09n0JUrOJXgDNAK2OXiwipvb/I0Grp3787gwYMrxO+gqblw4QKzZ8/m5MmTKGXBjRvdSEhoy4OMfTYzy8DHZzGOjsfRarV0796dvn37lqt7+P0opdi3bx/z580jOiYG2/x8nr5+ncbJyQ/8UDfRwoLFPj6c0enQarV07tyZfv36lau6bnlT0Hjx66+/Fk46nZzciBs3niYvr/jfu63tOXx8lmFtfaNwqe/evXubdGt+cRUM61yzZk3hWP/g1FS6xsQ81Dj/XI2GnbdWmCjovdKpc2c6d+5cLoZimUoeavIJP8A333zDZ599xo0bNwgODmbmzJm0bt26WPuayhf9sPLz89mzZw+//baSCxfOYzBYEh3djYSE1tytO7S5eSqVKv2Gk9MRtFotrVq1olevXlSrVq3sgy8DSikuX77Mli1b2LRxI9k5OVgbDLSOj6djbOy/joWOtLNjjZdXYRfmoKAgunbtSuPGjaVCWMrS09PZuHEjy5evICMjHb3e6dYEUE15kK7+ZmZpeHhsvDWRUz5Vq1ajf/9+hISElPvW6XPnzvH777+za+dO8vLz8crOpltMDE+kpj50i3+SuTkbPT3Z7+yMQaPBz8+PHj160LZtWynzJSw3N5d58+axbt06lLJ4pOX7Cty+3J6trR2vvjqmyKS25VF6ejpfffUVe/fuxS4/n/5XrvBEWtqjH9fMjEW+vvzt4ICjgwPjXn+dhg0blkDE4l6UUuzcuZM5c+aSnJxEamowV64MxGD49yEmtrYXqVx5PhYWSTzxxBOMGDHCpHtlPQq9Xs/69etZvGgRmVlZ1EtJoc+1a8Vag1wBh5ycWObjQ7ZWS0hICEOHDq0QyWJ5oZTiyJEjLFq0iMjISAwGG65f7/mv93etNpNKlVbh4rIfjUZL+/ZP8dxzz+HlVVIPg02HUoq//vqLxYsXc+LECbRK0TY+ns4xMcWeh+uMnR2/+voSZ2mJvZ0dvZ5+mm7dupn0kLV/MpU8tFwk/I/CVL7oR1XwIzp79g+kpaWSlhbE5ctDi7SIWltfo2rVbzAzS6N27dqMHj0aPz8/I0ZdtgoSyN/XrCExKQmPnBz6X71K1czMO7bN0WpZ7eXFbldXNBoNzZs3p1evXtSsWdMIkT/eCmZ+XrVqNbm5OaSnV+PKlcHk5f37mHGd7m/8/BZiZpaBt7c3AwcONLkZm0tCQkICS5cuZdOmTRgMBvwzM3n2+vUHWuc5V6Nhg6cnO9zcyNNo8K9cmYGDBtG4cWOT7QpXUezfv58vvviSjIx0kpIac+1a34daktXSMo4qVb7D0jKO2rVr88Ybb+Dh4VEKEZc9pRQbN27kxx9+IFevp31sLN1iYh66R8tVa2tmV6lCsrk59evXZ9y4cTIPRRlKTk5mxowZHD16FL3ehUuXXiI72+ee27u47MTb+zfMzGDQoEE8/fTTFe4+fjdxcXHMmjWLv/76C4e8PIZdukSV+9zX9RoNi3x9OezkhK2NDSNGjqRt27ZyDzcSg8HApk2bmDdvPllZmfe9v1taxlKlyvdYWsZRtWo1Ro8eRWBgoBGiLnsRERF8/913XL9xAxe9nhcvX75v/UWv0bDM25v9Li5otVp69erF888/X64S/QKmkodKwl/OpKamMmvWLA4dOkR6eg0uXRqBUha3kv3/YW6exdChQ+nevftj8WN5Nzk5OSxatIhVq1aBwcDz16/T4tZ4Irg5ZvnLqlWJtbLC39+fsWPHPjY3XVOWlJTEjz/+yK5du8jPt+Xq1f6kpta967Y3Z2peg5vbDiwsLHnxxSF07tzZpGdsLgnXr19n4cKF7Nq164HGPV+ztmaBnx/R1tZ4uLszYOBAk5/huqKJj4/n008/5cyZM6Sl1SQqaugDzV1hY3OJKlVmY2aWXqHXi4+KimLKJ59w7fp1GiUn0//q1Qeexf+UvT1z/f3JNTNj8ODBj03yaGoMBgPLly9n4cKF5OXZc+7cWHJz73xA5eKyGx+fX3F2dmH8+LcJCgoyQrTGYzAYWLNmDQvmz8c8L48RFy8SeJeGilyNhrn+/pzU6SrcA7/yLjExkSlTpnDmzBnS06tz6dLIIvO2WFtfoWrVrzEzy+T555+nX79+FfL+fT+5ubksW7aMZcuWYXarJ1ejlJQ7tkszM+NHf38u2tkRGBjImDFjjLKaUkkxlTxUEv5ySK/XM23aNPbt20dMTCfi49tRrdosbGyiee2112jXrp2xQzQJp0+f5pOPPyYlOZmw2Fic9HoAdrq5cd3amp49ezJ48GAZn29ClFJs2bKF77+fTW5uDlev9rvVRe52+QQE/IBOdxI/v8qMH/92he32eS9///03M6ZPJz4hgWrp6QyNikJ3j66gu1xcWHlr/HLPnj0ZNGgQlpYPP1eCeHg5OTnMmDGDffv2kZXlw6VLI4u1BKtO9xf+/vMxM8tn9OjRtG/fvgyiNZ60tDQ++eQTTpw4QWB6OsMvX8a2mPOy7HN2ZqmPD+aWlrz51ls0bdq0lKMV/2bTpk189dVX6PXOnD//Gnr9//W0cHQ8TOXKP+Hk5MTUqVPw9n7YCdDKvwMHDvDp1Klo8vL4z4ULRZL+fOC7gADO6HSEhoYyfvx4uY+bmJycHGbOnMnevXtJTGzCtWv9AQ3m5qkEBk7H0jKZsWPH8uSTTxo7VKOKiIjg06lTyczKYkhUFA1vS/qztFpmVavGDWtr2rVrx6hRo8p9OTeVPFQS/nIqKyuLV14ZRUJCfOF7Xbp0YeTIkUaMyvRcunSJd995h/SMjCLv9+jRg2HDhkk3OBMVFRXFO++8S2pqOpcvv0RaWp1bnyh8fBbh4nKAkJAQxo8f/9iOO09PT+fbb79l165d+GRlMebChTuSoh2urqzw9sbZyYnXxo2jQYMGRopWFMjPz2fu3Ln8/vvvZGd7cf78uPu29NvZRVKlytdYW1vyzjv/77EZg67X65k1axa7du3CNyuLsRcu/OsSrHucnVnq64uDTsd7779PrVq1yiha8W9WrlzJvHnzSE0N4vLlEdxMhJKpWfMj7O0tmDp1CgEBAcYO0+giIiKYPGkS9tnZvHP2bOEExH94eLDe05OmTZvy1ltvSUOFicrNzeXdd9/l7NmzXL48lNTU+lSuPAdHx2MMGTKEZ555xtghmoTLly8z/u230Wdm8tq5c/hlZ6OA2f7+nHBwoHv37gwfPrxC1NFNJQ+VhL8c+/vvv1m9ejUGgwF7e3tefvnlCjOTbUmKj48vnCkUwMHBgbp161aIG0lFFhkZyTvvvEt2tuLMmffIy3PCxWUPPj5LqVmzJh999NFjm+wXUErxww8/sHbtWgIyMxlz4UJh9+eDTk787OeHi7MzUz/9tEJOClReKaWYO3cuq1evvpUAvczdJqq0tIy71TKUy8cff/RYdnX+7rvv+OOPPwhOTWX45cv3HL5yxs6Ob6tUQefoyKefffZYtxSbIqUUEyZM4OjRo1y69BJpaU/g5zcfJ6cjjB07lqeeesrYIZqMX3/9lV9++YUmiYkMuHaNq9bWTAsMxNXdnf999VW5HMf8OLlx4wb/+c8r5OZak5PjhZ3deYKCgpgyZYrUO29z5MgRJk6ciH9mJkMvX+aMTsciX18aNmzI+++/X2GGPJhKHlq+FnkURQQHBxMcHGzsMEyem5sbrVq1MnYY4gFVr16dESNe5n//+x+enuuIi+uAp+cGrKyseffddx/7ZB9Ao9EwfPhwMjIy2L59O9vc3AiLiyPV3JxlPj7Y29kxafJkSfZNjEajYciQIVy7do1Dhw7h5fU70dE9i2yj1WYTEDAbM7NMxox57bFL9gG0Wi0jRowgOjqao0eP8ruXFz2jo+/YLsbKirkBAZhZWPDf996TZN8EaTQaRowYwejRY/D03EBurgdOTkeoWbOmDEP8h969e7N3717ClaJTbCyb3N0xaDSMHjNGkv1yoFKlSjz7bG9WrFiBpeVFrK1tGTp0qCT7/9CwYUNat27Nzp07mVC7NgDm5uaMHDmywiT7pkQSfiGEyXryySdZuXIlcAAXlwMAPPNMP5lt+zYFSdHRiAg2K0WclRXRVlZka7W88sILj938BuWFmZkZb775Jm+88SZKbSM5OaTILObu7luwsoqhV69ej/WYTzMzM95++23efvtttgK109KocdsQLQPwk68vWVotb4wdK934TZiPjw+hoSHs378fT8+1APTs2VMmVPwHMzMzevbsycyZM/nF15eLdnZUqVKF+vXrGzs0UUwDBw5k4MCBxg7D5A0bNgxra2v0t+bYatiwoTRQlBK5ywohTJaZmRnjxo2jY8eOtG/fnp49e/L0008bOyyTY2try7Dhw8nVaAh3dibK1pbq1avToUMHY4cm7sPW1paRI0eg0Si8vFYXvm9unoS7+3ZcXd0YMGCAESM0Dfb29rz55ptoNBpWVqrE7SP5Dzk5ccXWlnbt2tGmTRujxSiKp6C3naPjMaysrAkJCTFyRKapRYsWuLq4cN7eHoNGQ48ePaSFWFQ4zs7OjB49mnHjxjFu3Di5h5ciaeEXQpi06tWrU716dWOHYfJat25N/fr1yc3NBcDJyUm6xZUD9erVIyQkhEOHDmFnd5aMjBp4ev6BRqNn8OBBMnTllqpVq/Lkk0+ydetWDjo50SQ5mVyNhrVeXlhaWDBo0CBjhyiKoXnz5rz00kukpaVRu3ZtrK2tjR2SSbK0tOTrb74hOTkZCwsL3NzcjB2SEKIck4RfCCEqiIo2Menjok+fPhw6dAhn53AyM6vg5BSBl5eXtHb8w8CBA9m1cyfb3N1pkpxMhKMjyRYWPNerlyRE5YSZmRndu3c3dhjlgq2trYzZF0KUCOnSL4QQQhhRjRo18PDwwNHxL9zctqHVZtOyZUsZ2/wPrq6uNAoJ4Ya1NdFWVhx1dASQoStCCCHEfUhtQgghhDAijUZzK8HPwstrHQAtW7Y0clSmqUWLFgAs9PXltE5HYGCgTPIkhBBC3Id06RdCCCGM7LnnnsPd3Z28vDzc3d2pWrWqsUMySaGhoTg5OnL51uv27dsbNR4hhBDC1GmUUsrYQZSm1NRUHB0dSUlJkfGtQgghRDmXm5tLRkYG5ubm6HQ6Y4cjhBBC3JWp5KHSwi+EEEKIcsPS0hJLS0tjhyGEEEKUCzKGXwghhBBCCCGEqIAk4RdCCCGEEEIIISogSfiFEEIIIYQQQogKSBJ+IYQQQgghhBCiApKEXwghhBBCCCGEqIAk4RdCCCGEEEIIISogSfiFEEIIIYQQQogKSBJ+IYQQQgghhBCiApKEXwghhBBCCCGEqIAk4RdCCCGEEEIIISogc2MHUNqUUgCkpqYaORIhhBBCCCGEEI+DgvyzIB81lgqf8KelpQHg5+dn5EiEEEIIIYQQQjxO0tLScHR0NNr5NcrYjxxKmcFg4Pr16+h0OjQajbHDeSykpqbi5+fHlStXcHBwMHY4QpQKKeficSDlXDwOpJyLx4GU87KnlCItLQ1vb2+0WuONpK/wLfxarRZfX19jh/FYcnBwkBuKqPCknIvHgZRz8TiQci4eB1LOy5YxW/YLyKR9QgghhBBCCCFEBSQJvxBCCCGEEEIIUQFJwi9KnJWVFRMmTMDKysrYoQhRaqSci8eBlHPxOJByLh4HUs4fXxV+0j4hhBBCCCGEEOJxJC38QgghhBBCCCFEBSQJvxBCCCGEEEIIUQFJwi+EEEIIIYQQQlRAkvALIUQpmzhxIvXr13+gfTQaDatWrSqVeITxtG3bltdee+2en8u/uxBCCCFKkiT8otj27t2LmZkZnTp1uuOz3NxcPvvsM+rVq4etrS1ubm60aNGCefPmodfrC7eLjo5mzJgxVK1aFSsrK/z8/OjevTtbt24ty0sRQgiTdOPGDTp37mzsMIR4LFy4cIF+/frh7e2NtbU1vr6+9OzZk7NnzxZuo9Fo0Gg07N+/v8i+OTk5uLq6otFo+PPPP+849ssvv4yZmRlLliwp7csQ4l8FBAQUlmVbW1uCg4P5/vvvCz+fP39+4ee3//nxxx8LtyluXV+YHnNjByDKj7lz5zJmzBh+/PFHoqKiqFy5MnDzBhAWFsaxY8eYPHkyLVq0wMHBgf379zNt2jQaNGhA/fr1uXTpEi1atMDJyYnPPvuMunXrotfr2bhxI6NGjeL06dNGvkIhhDAuLy8vY4cgRLmXm5uLpaXlv27ToUMHatWqxW+//UalSpW4evUq69evJyUlpci2fn5+zJs3j6ZNmxa+t3LlSuzt7UlMTLzj2JmZmSxdupS33nqLOXPm0Ldv35K5MCEewaRJk3jppZdIT09n/vz5jBw5EicnJ/r06QOAg4MDZ86cKbKPo6MjUPy6vjBRSohiSE9PVzqdTp0+fVr16dNHffjhh4Wfffrpp0qr1aojR47csV9ubq5KT09XSinVuXNn5ePjU/j6dklJSaUWu6iYNmzYoFq0aKEcHR2Vi4uL6tq1qzp37pxSSqmLFy8qQC1dulS1bNlSWVtbq5CQEHXmzBkVHh6uGjVqpOzs7FRYWJiKjY1VSim1Y8cOZW5urm7cuFHkPK+//rpq1apV4evZs2crX19fZWNjo3r16qWmT5+uHB0di+wzZcoU5eHhoezt7dXQoUPV+PHjVb169Qo/Dw8PV+3bt1eurq7KwcFBtW7dWh0+fLjIMQC1cuXKwtdvv/22ql69urKxsVFVqlRR7733nsrNzS2Bb1KUpTZt2qgxY8aot956Szk7OytPT081YcKEws9v/3cvKMeLFy9WzZo1U1ZWViooKEht3769cPvt27crQK1du1bVrVtXWVlZqcaNG6vjx48XOe/y5ctVUFCQsrS0VP7+/mratGlFPs/OzlZvvfWW8vX1VZaWliowMFD9+OOPSiml8vLy1NChQ1VAQICytrZWNWrUULNmzSqy/wsvvKB69uypPv74Y+Xh4aEcHR3VxIkTlV6vV2+++aZydnZWPj4+as6cOYX7FOf65s2bd8f/r5UrV6rbqy9Hjx5Vbdu2Vfb29kqn06mGDRuqgwcPFvefRJQDbdq0UaNGjVKjRo0qvOf/97//VQaDQSmllL+/v5o8ebJ64YUXlIODgxo8eLBSSqk9e/aoVq1aKWtra+Xr66vGjBlTWAeJiIhQgLp06dJ9zw2o9957Tzk4OKjMzMzC9zt06KDef/99BRQps0opNX/+fNW0aVOVnJysbGxs1MWLF0vuyxAV2nfffae8vb1Vfn5+kfe7d++uBg8erCZMmKDq1aun5syZo/z8/JSdnZ0aOXKkysvLU59++qny9PRU7u7u6qOPPiqyv7+/v5o5c2aR96pXr6769u2rlLr7vfZ2xa3rC9MkCb8oljlz5qiQkBCllFK///67CggIKPyhrVu3rurYseN9909ISFAajUZ98sknpR6reDwsX75crVixQp09e1ZFRESo7t27qyeeeELl5+cXJhK1atVSf/zxhzp58qRq2rSpatiwoWrbtq3avXu3OnLkiAoMDFQjR44sPGaNGjXUZ599Vvhar9crDw8PNXfuXKWUUrt371ZarVZ9/vnn6syZM+rrr79WLi4uRX4kly5dqiwtLdUPP/ygTp8+rf773/8qnU5XJOHfunWr+vnnn9XJkyfVyZMn1bBhw5Snp6dKTU0t3OafCf/kyZPVnj171MWLF9WaNWuUp6en+vTTT0v+ixWlqk2bNsrBwUFNnDhRnT17Vi1YsEBpNBq1adMmpdTdE35fX1+1fPlydfLkSTV8+HCl0+lUfHy8Uur/Ev7atWurTZs2qePHj6tu3bqpgICAwgdChw4dUlqtVk2aNEmdOXNGzZs3T9nY2Kh58+YVxvX8888rPz8/9dtvv6nz58+rLVu2qCVLliilblbmPvjgAxUeHq4uXLigfvnlF2Vra6uWLl1auP8LL7ygdDqdGjVqlDp9+rSaM2eOAlRYWJj6+OOP1dmzZ9XkyZOVhYWFioqKKvb1FSfhr1Onjho4cKA6deqUOnv2rPr111/V0aNHS+4fTRhdmzZtlL29vRo7dqw6ffp0YRmcPXu2UupmMuPg4KA+//xzFRkZqSIjI9Xx48eVvb29mjlzpjp79qzas2ePatCggRoyZIhSSqmrV68qrVarpk2bpvLy8u557oL/k/Xq1VM///yzUkqpqKgoZWVlpc6ePXvXhL9Vq1bqq6++Ukop1bt3b/XBBx+UwrciKqKEhARlaWmptmzZUvheYmKisrS0VBs3blQTJkxQ9vb26tlnn1UnTpxQa9asUZaWliosLEyNGTNGnT59Ws2dO1cBat++fYXHuFvC/8QTT6jevXsrpf494S9OXV+YLkn4RbE0b968sEVHr9crNzc3tXnzZqWUUjY2NurVV1+97/4HDhxQgPrtt99KPVbxeIqNjVWA+uuvvwoTiYIWSqWUWrx4sQLU1q1bC9+bMmWKqlmzZuHrTz/9VNWuXbvw9apVq5S9vX3hk+s+ffqorl27FjnvgAEDivxINmvWrMhDBKWUatKkSZGE/5/y8vKUTqdTv//+e+F7/0z4/+mzzz5TjRo1uufnwjS1adNGtWzZssh7oaGhavz48Uqpuyf8U6dOLdxWr9crX1/fwoc9BQl/QXKu1M0Ko42NTWFC3r9/f9WhQ4ci53zrrbdUUFCQUkqpM2fOKKDwnl4cr7zySmFFUambCb+/v3+RVqmaNWsW6R2Tl5en7Ozs1OLFi4t9fcVJ+HU6nZo/f36xYxflT5s2bVTt2rULGxqUUmr8+PGF92t/f3/Vq1evIvsMGjRIvfzyy0Xe27Vrl9JqtSorK0sppdRXX32lbG1tlU6nU+3atVOTJk1S58+fL7JPwf/JWbNmqXbt2imllPrwww/V008/rZKSku5I+M+ePassLCxUXFycUupmefXz87ujxVaIe+nRo4caOnRo4evvv/9eeXl5qby8PDVhwgRla2tbpIEgLCxMBQQE3HH/nTJlSuHr2xN+vV6v5s2bpwD1zTffKKVU4Ws7O7vCP56enoX7F6euL0yXTNon/tWZM2cIDw8vHINmbm5Onz59mDt3LgBKKTQazX2PoZQC+NfthCiu8+fP079/f6pWrYqDgwNVqlQBICoqqnCbunXrFv7d09MTgCeeeKLIe7GxsYWvhwwZwrlz5wonZ5o7dy7PP/88dnZ2wM3/C40bNy4Sxz9fnzp1imbNmhV575+vY2NjGTlyJDVq1MDR0RFHR0fS09OLxP5Py5cvp2XLlnh5eWFvb8/7779/3+2F6bq9XAJUqlSpSDn8p9vLj7m5OSEhIZw6deqe27i4uFCzZs3CbU6dOkWLFi2KbN+iRQsiIyPJz8/n6NGjmJmZ0aZNm3vG8N133xESEoK7uzv29vb88MMPd5S/OnXqoNX+X7XC09OzyP83MzMzXF1d77jW4lzf/bz++usMHz6c9u3bM3XqVM6fP1/sfUX50bRp0yJ1iGbNmhWWYYCQkJAi2x8+fJj58+djb29f+CcsLAyDwcDFixcBGDVqFNHR0fzyyy80a9aMZcuWUadOHTZv3nzH+QcOHMi+ffu4cOEC8+fPZ+jQoXeNc86cOYSFheHm5gZAly5dyMjIYMuWLSXyPYiKb8CAAaxYsYKcnBwAFi5cSN++fTEzMwNuTsCn0+kKt/f09CQoKOiO++8/77Xjx4/H3t4eGxsbRo0axVtvvcWIESMKP9fpdBw9erTwz969ews/K05dX5gumbRP/Ks5c+aQl5eHj49P4XtKKSwsLEhKSqJGjRr/WjmrXr06Go2GU6dO0atXr1KOWDwOunfvjp+fHz/88APe3t4YDAaCg4PJzc0t3MbCwqLw7wU/VP98z2AwFL728PCge/fuzJs3j6pVq7J+/foisy/f7Qev4GHWgxgyZAhxcXHMmjULf39/rKysaNasWZHYb7d//3769u3Lhx9+SFhYGI6OjixZsoTp06c/8LmF8d1eBuHOclgcxal4FWzzb+XWxsbmvsf59ddfGTduHNOnT6dZs2bodDo+//xzDhw4UGS7u13Xw15rQbxarfaO/2P/nA164sSJ9O/fn3Xr1rFhwwYmTJjAkiVLePrpp//1PKLiKHgwW8BgMDBixAheffXVO7YtmHQYbiY5PXr0oEePHnz00UeEhYXx0Ucf0aFDhyL7uLq60q1bN4YNG0Z2djadO3cmLS2tyDb5+fn89NNPREdHY25uXuT9OXPm0LFjx5K4VFHBde/eHYPBwLp16wgNDWXXrl3MmDGj8POHvde+9dZbDBkyBFtbWypVqnTH74JWqyUwMPCuMRWnri9Ml7Twi/vKy8vjp59+Yvr06UWe+h07dgx/f38WLlxI//792bJlCxEREXfdPyMjAxcXF8LCwvj666/JyMi4Y7vk5OQyuBpRUSQkJHDq1Cnee+89nnrqKWrXrk1SUlKJHHv48OEsWbKE77//nmrVqhVpGa1Vqxbh4eFFtj906FCR17Vr175j+aZ/vt61axevvvoqXbp0oU6dOlhZWREfH3/PmPbs2YO/vz///e9/CQkJoXr16ly+fPlhL1GUM7eXn7y8PA4fPkytWrXuuU1SUhJnz54t3CYoKIjdu3cX2X7v3r3UqFEDMzMznnjiCQwGAzt27Ljr+Xft2kXz5s155ZVXaNCgAYGBgSXain6/63N3dyctLa3I78bRo0fvOEaNGjUYN24cmzZt4plnnmHevHklFp8wDXe7r1avXr2w1fOfGjZsyIkTJwgMDLzjz71m8NdoNNSqVeuu9RSAoUOH8ueffzJ48OC7nnf9+vWkpaURERFRpM60bNkyVq1aRUJCwgNetXgc2djY8Mwzz7Bw4UIWL15MjRo1aNSo0SMf183NjcDAQLy9vR+4tb44dX1huiThF/e1du1akpKSGDZsGMHBwUX+PPvss8yZM4fXXnuNFi1a8NRTT/H1119z7NgxLly4wK+//kqTJk2IjIwE4JtvviE/P5/GjRuzYsUKIiMjOXXqFF9++eUdXZ6FuB9nZ2dcXV2ZPXs2586dY9u2bbz++uslcuyCFvSPPvqIF198schnY8aMYf369cyYMYPIyEi+//57NmzYUOSHc+zYscydO5e5c+dy9uxZJkyYwIkTJ4ocJzAwkJ9//plTp05x4MABBgwYcN9W1sDAQKKioliyZAnnz5/nyy+/ZOXKlSVyvcL0ff3116xcuZLTp08zatQokpKS7uhOPGnSJLZu3crff//NkCFDcHNzK+xN9cYbb7B161YmT57M2bNnWbBgAV999RVvvvkmcLN76AsvvMDQoUNZtWoVFy9e5M8//+TXX38Fbpa/Q4cOsXHjRs6ePcv777/PwYMHy+T6mjRpgq2tLe+++y7nzp1j0aJFzJ8/v3DfrKwsRo8ezZ9//snly5fZs2cPBw8epHbt2iUWnzANV65c4fXXX+fMmTMsXryY//3vf4wdO/ae248fP559+/YxatQojh49SmRkJGvWrGHMmDHAzQdHPXv2ZPny5Zw8eZJz584xZ84c5s6dS8+ePe96zE6dOhEXF8ekSZPu+vmcOXPo2rUr9erVK1Jf6t27N+7u7vzyyy+P/kWIx8KAAQNYt24dc+fOZeDAgcYOp9h1fWGaJOEX9zVnzhzat29fuA7n7Xr37s3Ro0c5ceIEmzdv5u233+b777+nadOmhIaG8uWXX/Lqq68SHBwMQJUqVThy5Ajt2rXjjTfeIDg4mA4dOrB161a+/fbbsr40UY5ptVqWLFnC4cOHCQ4OZty4cXz++eclduwhQ4aQn5/P4MGDi3zWokULvvvuO2bMmEG9evX4448/GDduHNbW1oXb9OnThw8++IDx48fTqFEjLl++zH/+858ix5k7dy5JSUk0aNCAQYMG8eqrr+Lh4XHPmHr27Mm4ceMYPXo09evXZ+/evbz//vslcr3C9E2dOpVPP/2UevXqsWvXLlavXl04Pvj2bcaOHUujRo24ceMGa9asKWzFbNiwIb/++itLliwhODiYDz74gEmTJjFkyJDC/b/99lueffZZXnnlFWrVqsVLL71U2GIzcuRInnnmGfr06UOTJk1ISEjglVdeKZPrc3Fx4ZdffmH9+vU88cQTLF68mIkTJxbua2ZmRkJCAoMHD6ZGjRo8//zzdO7cmQ8//LDE4hOmYfDgwWRlZdG4cWNGjRrFmDFjePnll++5fd26ddmxYweRkZG0atWKBg0a8P7771OpUiUAfH19CQgI4MMPP6RJkyY0bNiQL774gg8//JD//ve/dz2mRqPBzc3trj0EYmJiWLduHb17977rfs888wxz5sx5yKsXj5snn3wSFxcXzpw5Q//+/Y0dDlZWVsWq6wvTpFEPMwBVCCEqsJdeeomYmBjWrFlTrG1Pnz7Nrl27yiAy8Ti5dOkSVapUISIigvr16991mz///JN27dqRlJSEk5NTmcb3qIpzfUIAtG3blvr16zNr1ixjhyKEEOWOTNonhBC3pKSkcPDgQRYuXMjq1avvus20adPo0KEDdnZ2bNiwgQULFvDNN9+UcaRCCCGEEEL8O0n4hRDilp49exIeHs6IESPumKG5QHh4OJ999hlpaWlUrVqVL7/8kuHDh5dxpEIIIYQQQvw76dIvhBBCCCGEEEJUQDJpnxBCCCGEEEIIUQFJwi+EEEIIIYQQQlRAkvALIYQQQgghhBAVkCT8QgghhBBCCCFEBSQJvxBCCCGEEEIIUQFJwi+EEEKI+woICGDWrFnGDkMIIYQQD0iW5RNCCCHEfcXFxWFnZ4etra2xQxFCCCHEA5CEXwghhKigcnNzsbS0NHYYQgghhDAS6dIvhBBCVBBt27Zl9OjRvP7667i5udGhQwdOnjxJly5dsLe3x9PTk0GDBhEfH1+4T1paGgMGDMDOzo5KlSoxc+ZM2rZty2uvvVa4zT+79EdFRdGzZ0/s7e1xcHDg+eefJyYmpvDziRMnUr9+fX7++WcCAgJwdHSkb9++pKWllcXXIIQQQohbJOEXQgghKpAFCxZgbm7Onj17mDp1Km3atKF+/focOnSIP/74g5iYGJ5//vnC7V9//XX27NnDmjVr2Lx5M7t27eLIkSP3PL5Sil69epGYmMiOHTvYvHkz58+fp0+fPkW2O3/+PKtWrWLt2rWsXbuWHTt2MHXq1FK7biGEEELcydzYAQghhBCi5AQGBvLZZ58B8MEHH9CwYUM++eSTws/nzp2Ln58fZ8+epVKlSixYsIBFixbx1FNPATBv3jy8vb3vefwtW7Zw/PhxLl68iJ+fHwA///wzderU4eDBg4SGhgJgMBiYP38+Op0OgEGDBrF161Y+/vjjUrluIYQQQtxJEn4hhBCiAgkJCSn8++HDh9m+fTv29vZ3bHf+/HmysrLQ6/U0bty48H1HR0dq1qx5z+OfOnUKPz+/wmQfICgoCCcnJ06dOlWY8AcEBBQm+wCVKlUiNjb2ka5NCCGEEA9GEn4hhBCiArGzsyv8u8FgoHv37nz66ad3bFepUiUiIyMB0Gg0RT6733y+Sqk7tr/b+xYWFkU+12g0GAyG4l2EEEIIIUqEjOEXQgghKqiGDRty4sQJAgICCAwMLPLHzs6OatWqYWFhQXh4eOE+qamphQ8C7iYoKIioqCiuXLlS+N7JkydJSUmhdu3apXo9QgghhHgwkvALIYQQFdSoUaNITEykX79+hIeHc+HCBTZt2sTQoUPJz89Hp9Pxwgsv8NZbb7F9+3ZOnDjB0KFD0Wq1d23FB2jfvj1169ZlwIABHDlyhPDwcAYPHkybNm2KDCcQQgghhPFJwi+EEEJUUN7e3uzZs4f8/HzCwsIIDg5m7NixODo6otXerALMmDGDZs2a0a1bN9q3b0+LFi2oXbs21tbWdz2mRqNh1apVODs707p1a9q3b0/VqlVZunRpWV6aEEIIIYpBo+43UE8IIYQQj5WMjAx8fHyYPn06w4YNM3Y4QgghhHgEMmmfEEII8RiLiIjg9OnTNG7cmJSUFCZNmgRAz549jRyZEEIIIR6VJPxCCCHEY27atGmcOXMGS0tLGjVqxK5du3BzczN2WEIIIYR4RNKlXwghhBBCCCGEqIBk0j4hhBBCCCGEEKICkoRfCCGEEEIIIYSogCThF0IIIYQQQgghKiBJ+IUQQgghhBBCiApIEn4hhBBCCCGEEKICkoRfCCGEEEIIIYSogCThF0IIIYQQQgghKiBJ+IUQQgghhBBCiAro/wOtFITqfY+YCgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_recognition['conf_corr'] = df_recognition.apply(lambda row: 'Confident & Correct' if row['response_confidence'] == 3 and row['response_correct'] == 1 else 'Other', axis=1)\n",
    "print(df_recognition.groupby(['conf_corr']).size())\n",
    "\n",
    "confidence_correctness_fr = df_recognition.groupby(['region', 'neuron', 'conf_corr'])['FR'].mean().reset_index()\n",
    "plt.figure(figsize=(12, 4))\n",
    "sns.violinplot(confidence_correctness_fr, x='region', y='FR', hue='conf_corr', palette=['blue', 'red'])\n",
    "plt.title('for each region, for each neuron, for each type of response (correct+confident vs not), plotting mean FR across trials')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: amygdala\n",
      "Processing region: ACC\n",
      "Processing region: vmPFC\n",
      "Processing region: preSMA\n",
      "Processing region: hippocampus\n",
      "\n",
      "--- Results for Region: amygdala ---\n",
      "Confusion Matrix:\n",
      " [[   0 1007]\n",
      " [   0 3632]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      1007\n",
      "           1       0.78      1.00      0.88      3632\n",
      "\n",
      "    accuracy                           0.78      4639\n",
      "   macro avg       0.39      0.50      0.44      4639\n",
      "weighted avg       0.61      0.78      0.69      4639\n",
      "\n",
      "\n",
      "--- Results for Region: ACC ---\n",
      "Confusion Matrix:\n",
      " [[   0  695]\n",
      " [   0 1447]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       695\n",
      "           1       0.68      1.00      0.81      1447\n",
      "\n",
      "    accuracy                           0.68      2142\n",
      "   macro avg       0.34      0.50      0.40      2142\n",
      "weighted avg       0.46      0.68      0.54      2142\n",
      "\n",
      "\n",
      "--- Results for Region: vmPFC ---\n",
      "Confusion Matrix:\n",
      " [[   0  392]\n",
      " [   0 1355]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       392\n",
      "           1       0.78      1.00      0.87      1355\n",
      "\n",
      "    accuracy                           0.78      1747\n",
      "   macro avg       0.39      0.50      0.44      1747\n",
      "weighted avg       0.60      0.78      0.68      1747\n",
      "\n",
      "\n",
      "--- Results for Region: preSMA ---\n",
      "Confusion Matrix:\n",
      " [[   0  725]\n",
      " [   0 2186]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       725\n",
      "           1       0.75      1.00      0.86      2186\n",
      "\n",
      "    accuracy                           0.75      2911\n",
      "   macro avg       0.38      0.50      0.43      2911\n",
      "weighted avg       0.56      0.75      0.64      2911\n",
      "\n",
      "\n",
      "--- Results for Region: hippocampus ---\n",
      "Confusion Matrix:\n",
      " [[   0  595]\n",
      " [   0 1988]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       595\n",
      "           1       0.77      1.00      0.87      1988\n",
      "\n",
      "    accuracy                           0.77      2583\n",
      "   macro avg       0.38      0.50      0.43      2583\n",
      "weighted avg       0.59      0.77      0.67      2583\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# convert the label column for confident & correct responses\n",
    "df_recognition['label'] = df_recognition['conf_corr'].apply(lambda x: 1 if x == 'Confident & Correct' else 0)\n",
    "# df_recognition['label'] = df_recognition['response_correct'].apply(lambda x: 1 if x == 1.0 else 0)\n",
    "\n",
    "# Create an empty dictionary to store results by region\n",
    "results_by_region = {}\n",
    "regions = df_recognition['region'].unique()\n",
    "\n",
    "# Loop through each region and train an SVM\n",
    "for region in regions:\n",
    "    print(f'Processing region: {region}')\n",
    "    \n",
    "    # Filter data for the specific region\n",
    "    region_data = df_recognition[df_recognition['region'] == region]\n",
    "    \n",
    "    # Prepare features (X) and labels (y)\n",
    "    X = region_data[['FR']]  # You can add more features if available\n",
    "    y = region_data['label']\n",
    "    \n",
    "    # Train-test split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    \n",
    "    # Train the SVM\n",
    "    svm = SVC(kernel='linear')  # Change kernel if needed\n",
    "    svm.fit(X_train, y_train)\n",
    "    \n",
    "    # Evaluate\n",
    "    y_pred = svm.predict(X_test)\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    report = classification_report(y_test, y_pred)\n",
    "    \n",
    "    # Store results in the dictionary\n",
    "    results_by_region[region] = {\n",
    "        'confusion_matrix': cm,\n",
    "        'classification_report': report\n",
    "    }\n",
    "\n",
    "# Print results for each region\n",
    "for region, result in results_by_region.items():\n",
    "    print(f'\\n--- Results for Region: {region} ---')\n",
    "    print('Confusion Matrix:\\n', result['confusion_matrix'])\n",
    "    print('Classification Report:\\n', result['classification_report'])"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "bmovietools",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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