{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "from collections import namedtuple\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "import pandas as pd\n",
    "import scipy.stats as sstats\n",
    "\n",
    "import datareader as reader\n",
    "import epoch_analysis, kw_dunn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trial number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_root = \"../01_data/04_formatted\"\n",
    "figdir       = Path(\"../05_figures/01_asymmetry\")\n",
    "saved        = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "alltrials = reader.load_trials(dataset_root)\n",
    "trials    = [trial for trial in alltrials if trial.has_eyedata()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_info(trials):\n",
    "    sessions = set((trial.subject, trial.session) for trial in trials)\n",
    "    subjects = set(trial.subject for trial in trials)\n",
    "    epochs   = sum(trial.states.shape[0] for trial in trials)\n",
    "    return f\"{len(trials)} trials out of {len(sessions)} sessions from {len(subjects)} subjects ({epochs} epochs)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Number of trials/epochs used for analysis\n",
      "\n",
      "- all annotated trials: 113 trials out of 21 sessions from 4 subjects (876 epochs)\n",
      "- trials with eye positions: 91 trials out of 17 sessions from 4 subjects (728 epochs)\n"
     ]
    }
   ],
   "source": [
    "print(\"## Number of trials/epochs used for analysis\\n\")\n",
    "print(f\"- all annotated trials: {get_info(alltrials)}\")\n",
    "print(f\"- trials with eye positions: {get_info(trials)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average-trace figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline subtraction\n",
    "\n",
    "Adjust so that the range of values will be normalized to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(vec, subtract_min=True):\n",
    "    m = np.nanmin(vec)\n",
    "    M = np.nanmax(vec)\n",
    "    den = vec - m if subtract_min == True else vec\n",
    "    return den / (M - m)\n",
    "\n",
    "for trial in trials:\n",
    "    for side in (\"left\", \"right\"):\n",
    "        trial.tracking[f\"{side}_whisker_normalized\"] = normalize(trial.tracking[f\"{side}_whisker_angle_deg\"], subtract_min=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>left_whisker_angle_deg</th>\n",
       "      <th>left_whisker_radius_px</th>\n",
       "      <th>left_pupil_normalized_position</th>\n",
       "      <th>left_pupil_normalized_diameter</th>\n",
       "      <th>right_whisker_angle_deg</th>\n",
       "      <th>right_whisker_radius_px</th>\n",
       "      <th>right_pupil_normalized_position</th>\n",
       "      <th>right_pupil_normalized_diameter</th>\n",
       "      <th>left_whisker_normalized</th>\n",
       "      <th>right_whisker_normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>3.513870</td>\n",
       "      <td>271.151556</td>\n",
       "      <td>0.048674</td>\n",
       "      <td>0.086702</td>\n",
       "      <td>13.644373</td>\n",
       "      <td>243.731827</td>\n",
       "      <td>0.056916</td>\n",
       "      <td>0.097801</td>\n",
       "      <td>0.026068</td>\n",
       "      <td>0.093315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005</td>\n",
       "      <td>4.533231</td>\n",
       "      <td>272.785300</td>\n",
       "      <td>0.048674</td>\n",
       "      <td>0.086702</td>\n",
       "      <td>14.096692</td>\n",
       "      <td>247.667480</td>\n",
       "      <td>0.056916</td>\n",
       "      <td>0.097801</td>\n",
       "      <td>0.033631</td>\n",
       "      <td>0.096408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010</td>\n",
       "      <td>5.875672</td>\n",
       "      <td>273.417605</td>\n",
       "      <td>0.048674</td>\n",
       "      <td>0.086702</td>\n",
       "      <td>15.003054</td>\n",
       "      <td>248.133120</td>\n",
       "      <td>0.056916</td>\n",
       "      <td>0.097801</td>\n",
       "      <td>0.043590</td>\n",
       "      <td>0.102607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015</td>\n",
       "      <td>6.574637</td>\n",
       "      <td>271.189545</td>\n",
       "      <td>0.048674</td>\n",
       "      <td>0.086702</td>\n",
       "      <td>15.494432</td>\n",
       "      <td>246.543158</td>\n",
       "      <td>0.056916</td>\n",
       "      <td>0.097801</td>\n",
       "      <td>0.048775</td>\n",
       "      <td>0.105968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020</td>\n",
       "      <td>6.493624</td>\n",
       "      <td>271.002909</td>\n",
       "      <td>0.048674</td>\n",
       "      <td>0.086702</td>\n",
       "      <td>15.364230</td>\n",
       "      <td>246.802274</td>\n",
       "      <td>0.056916</td>\n",
       "      <td>0.097801</td>\n",
       "      <td>0.048174</td>\n",
       "      <td>0.105077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time  left_whisker_angle_deg  left_whisker_radius_px  \\\n",
       "0  0.000                3.513870              271.151556   \n",
       "1  0.005                4.533231              272.785300   \n",
       "2  0.010                5.875672              273.417605   \n",
       "3  0.015                6.574637              271.189545   \n",
       "4  0.020                6.493624              271.002909   \n",
       "\n",
       "   left_pupil_normalized_position  left_pupil_normalized_diameter  \\\n",
       "0                        0.048674                        0.086702   \n",
       "1                        0.048674                        0.086702   \n",
       "2                        0.048674                        0.086702   \n",
       "3                        0.048674                        0.086702   \n",
       "4                        0.048674                        0.086702   \n",
       "\n",
       "   right_whisker_angle_deg  right_whisker_radius_px  \\\n",
       "0                13.644373               243.731827   \n",
       "1                14.096692               247.667480   \n",
       "2                15.003054               248.133120   \n",
       "3                15.494432               246.543158   \n",
       "4                15.364230               246.802274   \n",
       "\n",
       "   right_pupil_normalized_position  right_pupil_normalized_diameter  \\\n",
       "0                         0.056916                         0.097801   \n",
       "1                         0.056916                         0.097801   \n",
       "2                         0.056916                         0.097801   \n",
       "3                         0.056916                         0.097801   \n",
       "4                         0.056916                         0.097801   \n",
       "\n",
       "   left_whisker_normalized  right_whisker_normalized  \n",
       "0                 0.026068                  0.093315  \n",
       "1                 0.033631                  0.096408  \n",
       "2                 0.043590                  0.102607  \n",
       "3                 0.048775                  0.105968  \n",
       "4                 0.048174                  0.105077  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials[0].tracking.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collection of epochs: reproducing Ronny's way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coef(epochs, state, attr, turn, side, normalize=True):\n",
    "    # whisker: left - right (right turn) or right - left (left turn)\n",
    "    # eye: (left + right)/2 (right turn) or -(left + right)/2 (left turn)\n",
    "    b = 1 if (turn == \"Right\" or normalize == False) else -1\n",
    "    if attr == \"whisker\":\n",
    "        a = 1\n",
    "        c = 1 if side == \"left\" else -1\n",
    "    else:\n",
    "        a = 0.5\n",
    "        c = -1\n",
    "    return a * b * c * epochs[state][attr][turn][side]\n",
    "    \n",
    "attrs = dict(whisker=\"{side}_whisker_normalized\", # \"_subtracted\"\n",
    "             eye=\"{side}_pupil_normalized_position\")\n",
    "turns = (\"Left\", \"Right\")\n",
    "sides = (\"left\", \"right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare a set of epochs for each state (with the specified pattern)\n",
    "\n",
    "epochs = {}\n",
    "for state, pattern in epoch_analysis.PATTERNS.items():\n",
    "    epochs[state] = {}\n",
    "    for name, attr in attrs.items():\n",
    "        epochs[state][name] = {}\n",
    "        for turn in turns:\n",
    "            epochs[state][name][turn] = {}\n",
    "            diff = None\n",
    "            ndiff = None\n",
    "            for side in sides:\n",
    "                _epochs = epoch_analysis.collect_epochs_with_pattern(trials,\n",
    "                                                                     pattern.format(turn=turn),\n",
    "                                                                     property=attr.format(side=side))\n",
    "                epochs[state][name][turn][side] = epoch_analysis.get_normalized_traces(_epochs)\n",
    "                if diff is None:\n",
    "                    diff = coef(epochs, state, name, turn, side, normalize=False)\n",
    "                    ndiff = coef(epochs, state, name, turn, side, normalize=True)\n",
    "                else:\n",
    "                    diff = diff + coef(epochs, state, name, turn, side, normalize=False)\n",
    "                    ndiff = ndiff + coef(epochs, state, name, turn, side, normalize=True)\n",
    "            epochs[state][name][turn][\"diff\"] = diff\n",
    "            epochs[state][name][turn][\"ndiff\"] = ndiff\n",
    "        epochs[state][name][\"Both\"] = {\n",
    "            'diff': np.concatenate([epochs[state][name][turn][\"diff\"] for turn in turns], axis=1),\n",
    "            'ndiff': np.concatenate([epochs[state][name][turn][\"ndiff\"] for turn in turns], axis=1),\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## State-dependence statistics\n",
      "\n",
      "### Number of behavioral epochs used\n",
      "\n",
      "- AtEnd: 90 epochs\n",
      "- Backward: 90 epochs\n",
      "- Turn: 92 epochs\n",
      "- Forward: 113 epochs\n",
      "- Expect: 89 epochs\n",
      "- Lick: 87 epochs\n"
     ]
    }
   ],
   "source": [
    "# report numbers\n",
    "print(\"## State-dependence statistics\\n\")\n",
    "print(\"### Number of behavioral epochs used\\n\")\n",
    "for state in epoch_analysis.PATTERNS.keys():\n",
    "    print(f\"- {state}: {epochs[state]['whisker']['Both']['diff'].shape[1]} epochs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trace figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dfDj//PPHuwnbBG0kvW0hYKj85UvkoPlw6de/V0sCrpmhQG+rEeF98jzZLgj4bUFdBP7nYNhrCrTG4RePwyPr4by9RZMHMfX05yUVQM0wqovRGpRCjcJ7OlHHa0vYln6l8yIE+rIQd15fYrTerFBOFZKLpy8vmmbEhua4PF/5IQR6fw4aosNHBk/W8RoJoxmGdUD5AncXYEP5AcaYfmNMMvx8G+AqpV7H6/qfgVtuuWW8m7DV8AJY109F4NAPHoL2NLxpDzGJLOy9hePmwKaUfL9raD9XCn5+Grx5wZavY4xobSNp18fMFmEOcPZ80eIeWV/6XikRNsMv5Q26P4VOjy60dCKO12gwVL/6t2DiSHlCPwWZtAdp1VuhrmV8OVfUkefI82VfMix0oo1cI+tVPg8pT57Htf1D58qfrOM1EkYj0B8H9lRKzVNKRYB3ADeVH6CUmq5CFUcpdXh43v88EuhWIpfbBsPgOCMVak8Fc4sXwLNhsE/EhuN3hdZIjvmtpd8UNPTRoPBieqI4kwtKy/CRlt1zGmF2PSxZU8oFU0DWh85EgPErT6D7kgS9/ZhUWjT1LWC48RrNb3dmDNWvZH6wgAx06d4WzCQFamrWl2ejgO7s6J3SvpaxtZQUN4k6ojBoI5NxoEV7T3kSm+CH1azyQUnoJ3LCoNpSvyY7tijQjTE+8BHgDuB54G/GmGeVUh9USn0wPOw84Bml1NPA/wHvMONFcJ9AeOc73zneTRg1vEBe0GxQmTSrPHLzmHAdd/Z57+TIWcJKeds+Qy+Hh9K8vUA0ai98cuKuTBKelr+WeGXlo4G22zfvJcFJn70bVvWU9jsWZBMZgp5+gkxJGzfZHCoaQadz6GRqi/dgqPHSuTy6p3+Lv92ZMVS/8kHp/hY074wXRvhmRGP2w/0FgZwLhX1XRrTpgQJ2KGgjQqhwjfLgMEvJ8xZosCxxxKfyos13ZUTI9+fEOZ/xB6/EJtL7NVYYleXLGHObMWa+MWZ3Y8y3wn2/Nsb8Ovz8C2PMvsaYA40xRxpjHtqejZ4suPLKK8e7CaOCF4jDqj0lL5cJbegAd6+Gxgj84Sxoq5V9N1xzJREbLj4YzhzkSRFkQ4HhGxEEvhZ7aXMcCG2ita7UFLVDemStW3rxg3AZXo7DZsKnjhRhdNXTVGjkOuth8h66px/jyX7j+SilsGIRzCgSqZePV/EciRQmv5NVs95KDHwOC6pYR1om8fWhSSMbTuopLyw/b2RsDKVMmz1Zsa9rQIcms4GaemEMjZHvlCWaeTlsFQrxQJ4PR4kdvcaVfVrLKjHmiOD3gkqTz1D9+k9ANfR/HHHIIYeMdxMGIeOJo6ug7WR9qTbkhAUpHEuyIX7wVtHWntosHPFyp9h+Bw7dL2PkpdemlEzLC0o50mvc0O7tyncRW5yoM+qhrU5e4IK2P1R1I4ADp8FZe8Lq7oDOdT3hdQ0mmyeZ1SgDOpURM0nZInKgOWYoFMYrSKbxNnZgfJ8gmSkK93IE3b1bPN/OgoHPoa/De2tgc7IUeRtoMYc4loxZYQyyXlh1ypJnx1YQsUomk/Z05bnbU/J/fQI6UnLsQDhWmErCiFmlYIIBKVkIpVQTlho6f8/O+H5tb1QFehVFdKVFSPdkZbnsBZLCNmKXhGciJ45HjeRjAeGZjwRtSmaTgvAu2ErjDrTWyP+CsG6Nl2z0Ssm1C9dvjpV47HEnZEAEleaXvafIRV/stjBaY7JZjNF0Z0AHGoKAoLuvUgL4ASYI8DZ34W0e7P6psCDm8ihj0HkfZSsMChNUGu6D3uSQgn5nhzbyDEQsGYOCAB/KQR0PtWPXLj0jrlUaO41o14rSrU6E6SLW98u+6BZ4dgUtXClAib/C83WFNq7CVUIwhFD/T0NVoI8jli5dOt5NKCIIl8yOJS9qPhCWSmFJW8BL3ZW/O2Ue7Du1ct8zT1f2K9AloVsXLdUSdS0x00RsmFpbOl6pkvlmIAomGEtJPVFPy7HlL/jsRnAtzZqUI2vzTB4n4mABfRmN8QN0b0LKHoUwBoKuPggCyOaKjk4TBOhcnqCjhycef1z2eT4ohclmwbJQmAppF6QzYFnoTCnX7452nG6NYCs8h3m/pJGX+z0sJeM30MQ1FMpXajpkqGgjJrv+nNjAbSWCerQplguoccBkshjPH8SbrnEAVfkc7Ezv145CVaCPIy688MLxbkIRyXyl4HYt0dLK9xkj2nnBpn3ULHjPAYNNH+eef2HFtgGm1cL0ulKK2xpHTCsFwTHQcToSrzniiEBwLGG3RMPtgky1FEyPBizeFOeVLg2Bj20pXNeiPweBH4Bto5ySQFcW6GwOZVkijFMZoTWms5hsDp3L8Z63vQOdyYrN3LYwSRHoWBY6V/IAmt4EVixSQbfRyVTxmOGE++uth9qTKTFRNm3Bx1vuQLzwwgtJ5iQITDF4PAsCfWuVX0NYrSocy/5caEqxKjX5rULOw1F6kJmmYMIrn3R2pvdrR6Eq0McRV1999bhdu6DBpfIhIyG35eCQtf0i0E/bHX51BnxwGBPlTdcP7lcsFOCuBXWuBPtsabk9HGpdWUWUY3pd6WU2vk+NrQHF1x60+eMLUe7aEMF2bNx4BN/TqIHSxHFEmAPKdQg2dxFs7kJ394Hnowxcc+216N4EKuKibBvj5SUgybagzDFqAl3xH8DkPExGaHRBVx8DSWDaiBNyW2BCDTjryzgm82JnzniDNfVUSEfsTJeE+h//dDXZsEzgcPFVGX/L9V8HomAmKZjNylNFbDN8H6U1SskErMvue8SSFUaBHz+e79d4oSrQxxHR6DbEvI8RCpV/UnnozIApe9EeXgd/fLokDJJ5ERbPhdkaTtlt+Jc/MEP3yyrTxFtrXl/b466YWwaevxC5atIZEc6h8faeTVGuXyMRSLaCbF4ParxSqkJjt+pq0JmcaOm5PMYYXAPGL2ndRpd+W2S9lDtbtcbv7hXhHegiG8ZksuhsJUc66w9maYwWfkjnC0zJ9wEi5DckheJXKA/YnpJrGUqCz3KjW+SMx+ySRm0MfHkJ/Gn58EFbIAJ8ayeBLSII5A+gpw+6eorjo1SYwyds03i+X+OFapHoccSZZ545btdO5qEpJrRBbUrLYi8QFgvAglbYrVkqCSU9kY+z6kvRmQOhQ2bKWWdV9stSlfJzexRwBtHc+3JgpbO8f77NzWujrEy4dKQgGyh6cormqCGd1dTX2PivbkDFYzhtLUOez4qLQNDpLCoW4YxFJ0G50K8tzSom7xUFd0GgG8+HbB4a6zFaowqC3rIwyTTGdVCOvIKFNMTDsXdATCr1UTm28N9WIsid0ORU4GTHQmemNiLsM8h9jzkSam9RMs8ce9KZPLFB/BG/fQq+dgLMa6q8dvmYdWcl6dqrfWJS+e+DRj1Ew2JtPzy0VuIWTDqNVSuzvs570NePam4U85YfQM5Db+oA38cohershmlTwHGwlPQr443v+zVeqGro44hrrrlmh1/TC0Rjg1CrCxknhRe2PFDoj8vhN0tFmDdG4Ohd4COHDn1eHXKKm+Pw9+uuIR8Gm/hm622v2wJjDLURMbcYz6ctrvnv+Rm+d1AvX9qvD4AX+5zwWE3Oh87P/pjOS76/xXMrR27Q327+Z+V+q/L1Mbm8OEILQt+xMVqj+5OioftBKPQDdDJNUBaQFOiQqVFmXi9ESRZC3hN5KQaS8koU0M5MKbimYJcumM6sUGON2sJEcUKfSMaTbd/IBPiPG67hl0+IMAcRrEPfYxnnH5XlvHq2A258AW57SZ6rrV1lFGIcvngP3PoydKQNJEoOgFWvJtiQANOXwCRTGNsC3xNWUSQCjoPJ5ytWTr6B3hxcffWOf7/GG1UNfRxx1FFH7bBrFQRAIURbKdG0BgZ0PNshy+SvLYKv3wcv9cChM+Bjh498fj9kmzgWHH7kUcRDm3lHSgpXbG/oRAorFsU1AUG5hhsE7FoHda5hWbfLkW0eTixK97JVaA2O55O+5zFiRx2IFRt6ia4iLsYYjjhoeF6zirjoZBryHipkzyilwHXQ/SlUNILJe/jrNmNQWPFYUXoXUh3YSlZOzeEKKJETP0NvrpRuwQnTIazrl21PQ06XnM2wZV9IbSS8NWGA2MJDj+KFMgtQxofbV4mQ/ukbRUhf/qTktn/HvrAuLBV47Gx4YC3840XZvvY5yXtvKfjq8YNXYl4gk1JL2Qrvew/ByjLm1PregClenkROfArfWFYPCr51aJJZ9YH4LrJZcN3iPTbRKPT2o20La2qrrDw0HH7Ejnu/dhZUNfRxRDKZ3G7n9rXYyTclhY7WnRV+eT50frlWiUdcyDftBaKd7TsVZjfA+xfC1BpZBo+EQsRgTSgoTC7J1FrJiDilpjJVwPaCyeTQ2RyW58nSvPiF9HFR3/N0PfIcz/c6mLUb0D+6vKhx9v36bySuvm2L10ilRXMcNquF51doioCwYQpqtxJHacEha4JAgm9C+7VjlZyWBSHfmxVzWMQu2aML24ER2/a22t4LZpeevsrncFWPVJjqzcHaPqk69ch6ac+fV8gxh86Qv4FY0wev9MJlT1Tm1PEC+MI9Um3q1d4SjbUozMOVy7Pthr+8Wsv//stw6b2GwvruhtVRnu9WkllR6+KkeeH9jVz3Wi1kssWIowIdsj+x/d6vnRVVgT6OWLFixZieL5WHTaH21JsVjdwLRNNJeyVaV4F1UMDiV+Cjt8OPH5HUpW/YTfYftQv88BRhkIwET1fyyJ95ptSv+h3llzIGk/dwggBjlfPLNcbAiX/6KW+95Vd8b0UtyaXPA+CceRJm/70ByD7+TPE36bsfJXXrfYPohc+8+ALZR55m80VfwW/vxgQB/qbOEme9YFIpg7KsogBXrosVixSdr0E+YG2vpi9b0qqNCp2YIQtyYBwAlDTfgt9jIONntCiYY1Y8U/kcri8r1n3987AyjLOqdUW8HtAmK7aDpkv2zELhknI8ugHueKW0fe9rQov0DXz5Xvj1UnG+FzC9FubUBix+xeLOzaHXPLyXp8zM82Sny/eeruEPL8V5pDOGr+HxTtHSb1sXxRQ0E2TlEndg+YoVZLyxL0W4M6NqchlHXHzxxWN2rp6M2FaVEptqIRQbRKhHRtCSF4cvXiFr4j5lgUJbcmDmA4kILNfCx7Jfo4XxAxTgYImiVmC9RCIE191G3DZELYPSAas3ZFlQG8f9rzPwAqi58y7Sf7sdncmhHJu+y68DwJ7WSuzQUq2W/37HO+n94q9Aa7KPLifY3EX6zoepeeMx1L/jNLAque0FqGiErCdUzQK6M2D5Csvz8D1DpEYcrG5odilwtUeDbeJzh3hwLaza7WIKVpDTd4fHN4htHuDpdvlrjUtB76c2wxFheRulJDVDU2zIU/O35yRtxLwmKfi9Z7NE8d70EjyxUTT/mXWyOnzb3poFpp+r1tTyfLdNfziIx07Lc8RUj7vWu2AU92+OcL92eIeV59rVpQurWAxTZLsoFHD+hRezOSVmqxkDlJKB4zFZUNXQxxGvt4itF5TyRWe8Ek2sN1vpiBzJ5PFqbyX/+dTdtmyDNWXh9lFHzDLl2NHFeQuORqM1NhrfQH1EIhUBggefwFIws0ZzmN3Juk1psrWS09e2wJvaBkD2seV0f/M3xfP2/OAK+v7w9+L2r7/9/eJJ88tXkn9hNQDpOx6k79fXFbVvEwQkrl9M0FdSdQuJzQrI+5AyDk4qhdMuKrDO5lAKIkrjp0pRpmOFrA/3vVrKy1IYw3X3lMbrkBnwzRPh+DnCdlkQEoCm18JFB8FXjitl1Sxg/zYp4v2DU+CzA8zWD6yFP62AtA9v3VsKf787LPUZGMmf/6sz4JA2Ta3y+dD8FN8/rJ/TZ6T5+iEJ/nt+lnl1ItwbXM2PDu8HZXH9mspZ5KHNLp94spUNfaWbfMXvLhcW0AArWKBlEulKb7u5amfFhBToBXZY3+jqEuy0aG1t3fJBZSgIbpCMdu0p0fTak5U88qg9eu3usQ1ic/z1GXDVOXDBMHV1fVNauhbC7Wtd0dxqBmg6W9uv1w0/kAks0Ng6YEad3ANPg1m7AfoSWIdIx94aX08kleTJfAtrU5awPqZOw9PQd9lfi0K66eMXAJC++xFAGCzx518FwG5rIf/8K/jrN5easKlED8qteInkdYtJXHWz/NYI5bOQg8bXBYqnklB2S85PaL5RqRTRvr7i+XTeq4hELbSngKwvk3g5Ln9S/kBWa8bAva/C75aJE/Nnj8FFtwg11a2T8frOiTC/VcbzooNEs/7AwXDcbDh7gZjP9mgZ2gR05C4SDbxfG3zokBLt8ZR5ooV/7mjxzSgFi3aV75qi4mPR3b2SHC2cmGM2vH12kjlursgt//Ke7Xy9+zZaXEkO4w8QxJevrKHPUzy5sfRFQ1MrriXKTSHBGMhEkvJEqI91IfHxxoQS6CYUaJ4W9kTOH7t6keOBRYsWjfj9wJzfOV/MKTqM9HMsYTx4etsDONb2y3J0oFAeiEJeDpAJIOpAU3zo5f6W+jXW0L6PsiyMNlg6IOIIc1ABud9cK20++WhQipYH72f3jSvpi9fzradFS4/uMhWmtGCAyMK9mPLDTxM/eiH173oT+AH9f/wn+VWvcVSjZCGrecNRwjHXBmem2KecaSUuu7dKeH9BV6+0z8h9yvhSjaczVVoFqWgEXFdoecpg0hlIZ/ECw6vdvtjnUxkoy9dutJbjQ5vx9x6Cj91ReU8eWCt/OR8+cCu89yb4S+gmWL5Z6r2C3KP5hyzirPkwawg2UlutCPW9t6L+2FG7iOkG4KS58N2TK814rg0/OVUKhxvfh95+yOXB6FKUrVIYXVKt59x5K7Hr/knu/Z+lNiWT3b7PP1L8LJ1RPLexJKGPPHYRUKJqbkyKCSjvyzMcd8IgqyGiabvCSNqJJvAnlEAvRDV6gSS7H23CoJ0RvoYbbrhhxGN6sjJxgTg1+3MivDcny7jGVJI6tgadaXF4zR7iRdamVLAA5AVQlDjMI2FL/Rpz5CS3isIUTU22JSl3VVc31uEHYu+zJ9bB+6GffEZyq6cTZINQe7YsnLedid/SQnD80bizpwPgzBAplLrtfrq/cTm3bXiZqb/8ItGD9ipeesr3LsHdfTa6zETirVorub43hqYUA8GSh8k88Rzd2SECrSxLoltjMehLYPyAB7rifHmJ4t4XstAnAs8Yg+7uw3T38VqP5uO3aTq70qzqNqA1d4a+kHzZuD21MSjZnkKsLpOBn70b5m64gfP2fp1jQKVgPHIXWfUVKKtG64pVRmtcbO/GDzC2DemM/L7MgawikdIJy+wm7+t5iBPsTbzt7it4y62XM79BvptXF7Cyxy4K4dtvlufQseRdKqzss4Gs4CwlmvrafhH2iZC6WYiMTnvyDgxHakrlKyNld4ZMjxPKKZoNABP+R5ZdgQZ2AC1urNGZhuNPWDTkd76Wh6qQi2NjUhhZtgoFeJgjA0oacuE3wzmoBqI9BZ+7W+7hwGyJIPsL80Q2kPwraU+yJdZuQZvf0Rq6yXsiFCNusaJ5/sU12K2NkM1hzd0FAOcNx5JfuoKWqKb7hBMBeLjd5aBWj/jhB+AefkDRL2FbENl7t9I1UhmOP/wo7NYmlFI0ffwCjO+jIi52ayP+xk6M56Fcl6CzV1K59vSTymrMuo2Yq25AK1BX/HBIR7OKy8AZy0JZFu15yRT52CaL4xY4YjdIJMGTnDC3bmikN2t4eq0HRjjtf1phsUcLqHQKTA0oxWVPKNCaDxxi8ddn5Vr9AyoJzdx/UcV2Now0LYdvQJnKFVnWLwUz5TVgKp3v5as+k0yFeSFESOtcXhzIvg+ugwkC1BBxACaVxrvsT+gy89Z+d9zIwrem8eKalswajtk3yeOdEZqjmh+tqOXKp6W4yhHHlPrlhO9MYMRcWcgj5FolQd+Vln4Xyh+mPFCepExorSk9F/GwX3056W/cFfOvp8WENBxGigIeK0woDT0s0E6ukINCVWojEwUF09G69RuG/L43K7O/Noi9UJelHB1mxH73lCy7C9zf7AhLxbtWw6fvkpf0fw6G4+YMbl+gJQAlH1aLqQ+VpeZY6YEeDhs2DN2v7YVCcQqlFEopvFc30PWVX9D786slnL6pAT8AtWA3nPeeS81PvsRbztmdWTWay1fW8OnHG+jOyZtmW+KXyHiga2po+cqHZH9LI8kTSzHu8aMXUnO8hM2qulr8tZvYdMHnybT3EnT1ykrBGDo3J8k+s6o4AZOSJddDm11686W3WxtY2umS1TLAfXmRNO0pUI5DGkciKP0AFY/Kc2/gr6vCACWjIe/x1XvhKw+VeYTD3DLHzBaK4bePTHNAa8DuTYbvnCQC7MU1pfHyAtFo016J6lpoX2E17AXyXNihLdsLhqcG6kwGnc6ISaXMhEJHN2Zzp5iSLKtSGyeMStUG73d/RT/7kphlyuDfeDsKcLVHTHscPz3Pfk0++zfleHidoTsDj75Q6pcKV0WONXiFWb6/PSVC0VGixReY8D1Z4eX3lPkqCuSAQk7+gbVsB/Znc7Jye6jjAy0moQL6c5XbW8KEEOj5oGRPdq1SVF0h+GKiIQhn6uXPrSzapQuh88aEod6mlKEuMoIgL+ChkNP7ycWSROviW+G654Y+9qrl8n/PZmEsDIroC1kizXH5bmqtpKxtHcZmPhArV67c8kFjgKCzB10WUALgb2in+1vC2vA3dmApmDKriagLoLBOOArV0kTUhnfvLvSelK94pEMEiiUKLZ0pSOfA3VWiZ+rfezYvrX6FoWDVl0j4nf/7TfxEGjV3Ngqw+xPkNpUKZnhXXE9fRiaSHz9T+t1Nr0X5+fM1/OhZ4dd150QCdeZsVids/vfhBh7qjGLCjGDrMw4YTdZYoDW1Lrxz1yS71Gn2b/Q4c3aOPZvhf/dO8dX9eiGbQSmo8zNcMmczXz48y6x6+NEboDFZGi9NZUGJokkzjF/IByXFIh7y0msiYXDZUM9GOidCuzzHTSB8fWOATI6wvjxBmZDTBnR7F/qpZ6Eh5Bw21BH9v0uhpQkANWsaaEPu4s9junshneEN0zNoIwrOzQ+v5MKbhJpZbg4ZTksupPYt70fBNFMo8GEoZbfUobO7O0xbXCiwkQ8LwxRSN4DIqXJOvKcrJ4cCujKl1BzGiObfn6906o6ECWFyyXiQU/Kw2ZSWg4WXb6LBhC/Hue+6mFRe2AP94cBNqxXGis2W6YNZXyL6Xukp7evPC+cX4OaXJMJvVj18+NDBy+jzhosANaXw89Z4iTFTExnm+AHYETx0ozU6nZUcKaakACf+dgc6jHws/Ldbm6hxoD0vmlfhtu7VFPCHY/u49Kk6nupyOWMXMaJaFkSVmBGsuhqmX/sDAP57btuQbXF3E5NOMa7RgNptDmbNWuz+fvLtPdi7zADbQi9dgbfhZyxqOIAlx74VX8s4P9kly56X+20Wr4+wKiGDpYGvLROBtrw3yrVrajm41aM9V6ZmWorPH5BgVjTPqc05TFefCOR4Top1WJY4VuNxSWgVBCjPh7hE877jPRdLX0M/ScwR4RFxSnZlW8H0ehFkhaRfdRHRauvKUr/7Wv6Kz5rvlzRzraSwSH9CfB6OjQms4mRhtJwvH0B0w3oyX/6J3N/3nof38yuhP4lqqCPyoQvQL6/BPvoQch//GgC5S74JtXHm/+grTIlpVnbZtB1/McbAb54Ux/9uzaWsoTPrxTyypXcMQkEffs4HJXZModBK1qfYh860mGpcO4y+1lAf5v2PhvezOS5mn8I9C0Lh3hQTbdwg8sC1gYJD1xfzTn0EFlxG9NWPkxuqrRNCQy+kBXWtMPlSKMUtJdrkUDPdzoxAy4tz7VWX05GSAUuH2fZ6syJ0tvSgvdQttLQlr8Jr/dBSZjt/qUzAd6SF0fCFf8MLnSUT1bl7Dc9csK2SFlNehGK02CE89ILG5/tgNN6a9YBo6O78XXH3FG5cZO952G0tRByKJc/Kl7qWgoWtPqv6bZJeqaMql8ULDyxokL+/duj82vEjD6D12x8ncoIkvDGA2nOefNneibVqNWpqC5FPfQC151x4bQNHLF2M7Xt85al6+vOKjRmb3erlele/EsfTcNy0SmN3R9ai31Ms2SQz6/HT5fsLds+wS62WCkzJtDxcrovJSvpfZVmSliCs2qxq4hU34dqrhK9d0MyjjvhKGqOlW40qpShWSvKxRB1oiMr+lnhJQDaHViBfI8K8QHz3fEwiIb6OMMukikaLqQ8sq2TT9q/7l6SlAKyDRPOwTz5GjttzLs7pi1CN9ThvPrV0g1IZIq7FF/brozEGU56/nL3CZ3xdv0TAfnIx/PQxcQZ/4d8idLcGjiVac8wpmW5cq6Td5wNhzzgqzH6pxGySCauBZf0wCDBMl9yblQjajCcMHMKVUU+Y6thVpVq+GV+OBYap5zVBBDqEZhetMes3Y8pySWst9uaJBI28BFOnzaDGFa1HheaVrD/0krAvJ5zxtCcP5zful2g7gH2mSCj2d06U3B5QGY79wYPl999+ULjHMLzzJuWNHFU6GsyYMUSSjzGG9n1JQ+sHpBc/ROf/+wn5F1cTbOokssecYsRm9MC9UEoVhU5rmVZmPA/j+RzU4qEpacnG96GhDu0HFcJ/etvQGjpAZPfZuO97W3hi4KB9IRbFv/qfkM5gHbQvqqEO9z3n4oVBA6cnn2X6Iw/wsUfq8TQc05anOSK2gdm1mvN3qwwuWpWoHJj37JHhvXtkOCEU7EopyWlSKNThOKgwiRVBIJGUhWfLlJa2U6fNwLagQXloI/enULB7Wm3J/AfiHC9kbyyHG+ZLn1YnvpcCTzyb1/hazCu+F5DHLk6QINayuggEvQn048upj0L0riUEK14gesg+WN/4DNnAwv3DD3Df/ZZB990+4YiKbf3vh2hReb51Ihy514xioNPvlsGX75FJ5q17wZv2gK604dpnDP05EbKjgaUGr3ShpPSUK2KOFb7XdsmnVSAvOEo0/L6syHBbyb7yHPLlWVAL2wOT6Q3EhDC5QJiwqD+FcRxU3odQIEUKASRm++XZHmv4oYa+/0JxqpUXSB7qYVm+GX77pORZidmlwrvTaqUE20cOKx373ZNh2Saxjf9umew7eja82AX3vFo6bm7j4Ovkdah5vU6Bfuihw+TYHUt4IVXR98k/twqA3DMvY3Ie9sw28s+LvdueVgpyKuSVcexwlaQUxOPsauWYHtcs2RTh2CkZVCYLrc24/Sm6EgHTm+SGHLL/ASM2KQDct70JYxR2xMVfuA/6kadw3nUOzvGivVuzZ/DgBR/hwCt+yZk3/JLOnMXGttlsnD6P3RsCvntoP0lP0RoTiXjpQUlcZbjx1RhLu1yi4fgfMdXDseDEGQMCjqKRCoFZ3K8sVFcPhJpxKdOj4eADF0qitmSS+trmit9FnEp+esyF6BboeYUVXnNU024A28Y3mphrEYvbdGXkGcv5oty0RKD9qhvhyRUknjsE776lslLYYzbZGdNojIifo/BYFtgmAKahvlgDVQHB4vuxTziSqJ9j/4WHYlsS5bopJc7/U3eDmXEfHJt0Rz/3vFrHg+ts2mok0nV7yBBLlYgE5QF/hftU8FeUHz+UHBgNJoxAj6kAUilZqgWD3b5Zf8vsi50FgZal1p3/upkjDjtkiw/RjS+IsH3rArgxTFW6ZzN86bjBD2BLHE4KV/s/PKVkAz17gdjoDpwmUXxDXdNWo6c9joSbb76ZQw4ZPtXs60GhaDNeIFRFxy5WAsotewEAZ3qr2NcBe2pJQBk/QDk2DiENz3FQzQ1YHd2cMjXFn1+r5/kem31nt6IiLrS1ErT34AU2jgW33n0Xhxw+dB7hbMgIcc44sbjPffdbMScehbVgt4pjX5i2gD3bpkNqPc0Rzbv6Hmf6Wa1MCYV41C5JzLl1skQ4qs1jaZfLouk5zp6dqzimHEMJc5Cc7kar0vdBgO5PoqMxltz2D04/8TD8bJ7mIcxwwyUHgzBdgesUsx8W0BSkcX2Ltjpwojb9WZvGWIltEneFrdZ975Nk163FPLdS4hzuW4oVdVE1cWJHHEDKgtqorBzLr+8Fovl6xsI+YiFGKZxZ0/BvvB2TycKmdu6+/SaOOOwQPnGErDIK8RZ6XSc01LFoRo5nkzHaA5v2NDzXCY+th8Nnyar31T6Y21TZ90DDml7YvYUR0ReWApxVjwShOXbIwhr+Xg6FQj9Hiwkj0E1fQgr7wqDkDK4lnuE2JRrFzo58ILa8feafOui79f1i9144He5eLTbK1/rgjbvDm/cSbak3CwunbflhaKuVPxDn5lv2Gv5YY7Y9a99AnHrq4H6NFXQqg+7qhbCup3Jd/PXtAHgrZQnizJiK1VBHsLkLe0qZQM/lwYriOhZpH2w3fMnaWjnB6uPWTZob1tWy375xETzxmBybh4YYnHLc8UO2yQ9zgygGBAzVxlEDhDnAZs9l+Qc+yoLffR07mWbPlU9iP9qE39OH3tRB5IPvQm/uJPj3QzjnnY5yXQ6d4nHpQUl2qQlG5cgbChUFObQm39VPvA1OPP5EsV2GZfSCZBq7rgbj+Sh35IfCJNNQV4OybYJ0BuU6WK5LLJ/BJPK4odmgqSwHeqSvl54/3UTtWYswv72aNKB0aeJoeO+bqTlZTCkNYaKyxqg4/JXvE4k52JaYawINsz51ATkPep94QcL8V72Ktc+eHBvy0GfWl7XX98Wv0NPHnMYI3z+4l0TrND56u0TcgqxkT54Ld6+BDxwkRV8ao7BrI1y2VI5534Fi4z5yF9nfnhKHpWtLFslCsZAfnAyPv5TjxL2i1MYdujPy/k+vkyRo/3xR3uVTdxcb+W+Wwvn7yTP1bLtkrPzc0TCvWdIaTx/Wei6YAOJPYHIeN66N0xzRnNw22ODlKOGJtk2AHiXDVfJrr6zk2w8czUcOlwcm68Pn75Hv9myudG7uFsqlw2ZunzbltSx9xwIrV67k6KOPHpuTDYDJe1AWgKL7U8KcCKFcB6ulkeZPvpvc8pXYzSV7gaqJS4pdxyKXC4jGo0UnklsT4YzpKf7yWj3rE6UIR+XYJEPt+6XVqzn62GMBSIZFtaPGwwsU+cARx1iZQB9oBgwM3LUhSsJTNM+qJfp/l+LfeAfBLXfjX3Fd6biD9yN48hn0I09BJIJ77mlASVt/PQhCRpAyhojSNGf6Wf/ay+j88RhjCPqTBF19ko44m8dubRxUmakcOu+jEukwHUESXIcg4kpFIdsCy0L3p+j+7u9oePdZRPbejczDy8g+shx/U1fR0mBbUHfmCWSfeJbY4fsVz19gVtVEIOcF5F0HJ5CJJuuVMVVc8Hefh5rSQnDdbVjf+gyrV77I8SedKIFnEVfamEyH3t2olJwNNPUmx7mzfW54LQ5K+nr3GrluQTAPxBVPy/9bX4Z9p5QylQ7EZ+4G8hH+tsYiHinlvi/Ha/2SgbKAb9xf+f3X7i/RtQdmjRyInd4puikJ96zyeard4pa1Uf60Ko4fBCTTlXdGKbHJdaTEq7wzhOEOh+6MOHIfe24NL3ZJLvIXOkMvd4hyYW6xdbk0tgbGiE22LvL6naEFrFmzZmxONBT8oBhABOCt2wSUojpVfS3KsoSqeGLJPGK0xqqJYkWk7qStNDpaln414nL4DIOy7SKnHwDHJmLLi/jK2rX0ZSRApD8vzCvlOuRsl4hlioUVAP7xapRLHmsoxkkEBn72bC3XvBJjSkyzb5Pkn7GPOQRrv/nYZ56MmiUpB7xf/1mEORDcdg8mzAkzFtAh+8QoGztiQ9TltddeJdjYibIUuj+JFY9iMjkxXYxAfjZBAEajE0kR5sgKKujshUIRirxH8u934a1aS+pfIqm8l1+T36dKaT4VED/uYNp+9rkKXn/xWvk8Ta6mZnozrquIu+ILqQ3ndluBWxPFOvpQzKYOTC7P+nVrhO/eLS+TyeUwXT34yi52S0VcaO/kzKn9XHl0Nz8/DbEAGMPMUHieuSd84nDD2+b7/PgN8NXjJGNkIV3CQGFeH4EL9hrA1NBGhHmhekmIWhfO3nPYW8wbd4O2Gin/uKBF+O0jYafXZ29fBdc+DSZoKDoOPv7kFFK+YVp9wGePtYvFFSJh9ZZkTpY1I4Xhjhf6suGEA+zzxhJf+7rn4Ng5Q/9mlwYRuNsDnpbl31gJc9h+PHTj+UUzgLd2E/6a9eisvDjx4w4m//wr1JwwjEM2CFDRCCoaQW/upj4KubJOK8eheXYrh2+S0msdKck46EYjmGwSy7I4+5x30ReWhXN9D08bdGMtWSuClekrUvEA/vGaTBaPdEQ4blqem1+LsrzH4YLdM5w8I1/U3K0ZbUQ+Hd6v807HZLLkPvQlQGh6wd0P4v3xetyPvW/IXOsmlcG77CqsQw/AObEyd63RGv/amyHv4bz1NFRDHVEnDGCzbWpiNmB4/1vPQ9VEMXkfAoNyKBa91nkPewizi87lpSaqL6GSRkvUjdEGq6Y0UXZ+4Wf46yRsP+hNoNNZ8s++LNsdPRXnLDePDeyHFXHR2TyNNTZBOgKJJNNqYxSEglJCl+zZZTq+MZiN7bz9Tf+F6exBZfLoDZsh4hJEotRGFF4g90FZYLlRMffk8tSbHB/atZ8Z0+PMVBmWJms5ZPcaXBOwMJbCqmmkRafYbdcYWDYzajQvd2pe6rd5ucvwtRMtdm0E1nUy9bBpzG1S/OI+j5eSEfZqNRzf0M/RsxX5+ga60oYZTharJs7Zu2a46E5xMrx5vqHW1vzleZuz58O7wgyoq3rga/cOeYuK2Ok19PccABfvXUkWTflCUN2cNPzg4cFh7rYVhtb3Cfc0mRs5FH5HIZETzS7ji4CeufJy/niOzPQv9ZSWcZ8/psLpXSwosD1Q4BaPJbYHDz3oTeCt21yMJOv87I/o/cU1BO1dYFvEFx1Gy1c/RN3b3jj0CYxkNrRiUZRr09RWR0PcGhR+fcEBsHuzVNx5tgPJLxJIgqurb7iamAqwAg8a61Bas1HHhRpYllg7G5RerNeSNp6GB9oj7N/sc8rM/Ii+DxWPFU1K9kkioPXyFwiWPILJZPHveZjg8eWl+/LYMvSzL+H/8YaQhukRPC2RZebV9QSL7ydY8gi5S75B9ju/wnr4CWwVpu8N5fTvrxF+vbItVMEDF0jBEEKKcGEy9Tu60ZmsmC48H5TCqgsTnWiNFS+Zw/yNHUVhDuC9uIbeX1yNTmaoOeXI4n6rRShXqrbM0F6OQKPq4ihXbOd2zJFx8Spf6ngEpuwxDQwEd97PX2/4M2SyGFtJ5apEBm071PR1U//yi9Q/9wzNve3kwtQFeWzo6OLwWTBH92MTcERdAlf7mJ4+SZDm+9DVi0mkQQcc4nTxtuZNfH7PTr61Twe7mn7ISkGCA51eGtO9RBwFRvPmXdIc1ZqFTIaYAzMieejoRmcy2F3d/OL4ND9+A7xld59TGnr4/ZlQqzx0uJKZ0wCWGtn0sNNr6JaC42f6NKk8TVHDpozFrnUB922K4Nhw/bp6Lr4VfnRKZRm0gpAq1MvUQE2YeMdCnBfNQzw/wyXQKYT5biutyRgR6BGrVNJr113nAiKwr3++dOzeU+D/TpNjH98wuKDAWKEQaDPWmDt37pif0+TyqFgZJS8UoN5Lr2I3N6Bsm+g+uw/7e+U6xd8601pRtk0dQPh8FByNjVH43DHwwdvEGXbwDAfV2oTp6GbW7LmomhqUo1D1dTi2jRUaeXXZc/Fcl0XBUHHnhgh3bpBjjphSRtUYAZHPXEzw0JOomdNw3noa/o234197E/6f/148xvrpV1BNDehlpfwO5tUN+Hc/SPDwk9j7LcDaZ4/wYCU5419YRealVbhHHIx/76MEB8zFmT2dXXeRB6xiBRBxIdAEiTTUxAg2dYJSKNtGo8TG7gdFp6lE68qY+O3dZO5fihUK6Mj+exI7dD/6r/g7uaXPUX/+Gbi77UL6Lsk1P+W7n8SqjQ/L0EFrrFiMQipNFY1i2Y44xwfA3aUN+9D9CB56klnHL8B3XVxbScqCGDS98CydP76y+PzYrU00/uhLZAOwbRtP26DBdiMS2JTPQ38Sk0yjIhH02k0QdVHJJCaRkHsSi2H5AbOabEwyherrxzg2eHmUNrx3D7h1bYQ9dC/KiWE8D53NQU8/xrZQPf2oaJS6bAJUgOnLovwAxzKYTV0YbdBaY2dyfGc/wzlLhre/7vQaegELGn1m1GgOavVpiRrevGuOU6amioyXO1cP/btC4p1IGLSjkOcimZdl9caEZFnrDe3am5Ky3RXW4cwHEo67tk/CekEoahmvVGCjkMwJSibHdJi3IRFGifWFZpblm0u1FhfMnw8In/zkuUKV+nbIemuMCrXr+F1HX2Jsa/0GvqmswD5WmB/2ayxhfH/IFz7//Gqs1qYtn6DMbFBOsYtVKtdyqC2T6mMbZAyt2hqUgd32OwDVVI/VUC/BSrUlm56xLH7+XA03rI6wtN0hbsMhrZUCvDU6ujwV1u674r77LSilcM4+hchXP17KVxNqG/rlNfL/1fVY+4oR1lv2PP5DT8r+Z17E+9utmHgM92PvCzseBrHcdT/mqhvo/OyPMZ7HnvPmDWqDUgrl2CjbQnf2Fs1VynUwnic28qhbnAQkdbG0re83fyP5tztI3f4AVksjLV+8mPgxCwGI7DWPujefRGSv0jXtxrohzUlljQHbwg5zuljRCHatJBYyAwp/KKVofcsibAv2tKLFVAIxR96p/INPompi1P2XMLGCrl4ayDG1JixobknaZQgXg66LSaRAG3w/IG/Z5IyNZ7kElkMQljws3oeIC/EY2nbxlQuRCG1xzfvmZ7HjMrEb24GuHsn37jiYMNugQWHSabH7W2C6euWuRlw5PpdjasTjiJceGpagPWEE+lCIOvDFvbrZs1lz+yp4zz8lJP7pzRJcMxDldK+Cvd0KOa0Zv5Qz2Qszp/VkRMBvTpUCIdpTsDktwr0/J0I9HSbEX58Q2mFvJpwskiLI29NhSL9Vqt8J8Mh9i4uf33ugaIYFdgWAzg7OaTCwCLGnxamZ8cIUpki/CocVMuGVZ83Lh/nkC2HbY43Fixdv+aCtQCFkvQCdqTTB2SMIdJ1MoZPpIVOzgoyJpUr3roCT58nzcMliuGIZdKga7n/wnmG1yL+9VsvSLpeb18Z4sK+Ww5vT7NFQZs8xhhZ3dBr6QFjzZmPttwDiMaK/+TY4NvrlVzH9Sejtx9p/L6yF+6BvuYuIXUpchwE1dzb2wn1wP3Zh0YyXu+ZmceAaQ+bBZdx1/33DXlu5jkymllXqu+cX9xURCn8AE45PsLGT2EFhpG59LVO+83GaP3+RnDfiMuX7l9D6rY8Ne21jDMb3RXsfgmljtzZht7Wg854wWMKHPLr7bKyoy0OLb6Gxr5MpJk1rjZhkvDXrie67B/XnnUrrpR8GoP+Kv2NbpUR0jiX/fQMahYlG8WNxlNHUxWym1IQRsxGLmphN1B2cUyriiCJWSNaVD4Q26gUQoKQylePgBQoTjxEYCJQtsRG2jXJcTC6HckLZHY9LRkrHYd91zwzrHdzpTS7lMHmP4LZ7oK4W55RjULbDHi2Gt1s9fLNd7HDfuL/UpdY4nDBH+NsjoRBlNlC4OaryBrm2DFCsTJkoFPQtnMdxZHIohO3mNUTD7wMt5pYjZ0no8caZZw3f19BWR8h8KO7f3AFtU1CWRT6Axli4+ghzSGxOysPkB7IiiDliWtqUlLwQfugE7cyUUuKONc46a/h+bS2CdAbd01/hVNA9fRXHODMHh+SbkE1gNTVi1cVFyxkGtgVomRwLkXwFVlFvTnjJKzY38bY3nj3k73M+3LE+xh4NPrs1BLyYjfDG3RXT/T6Obsvzx5fjPNkdoSkizsORaIDDwf3E+8H3pfDD7ruin3+ZoFEI1tbuc7AO3h/z/MsQeLR85J309+fJ/+E6rNNPlEIw++9H6+++gf/3OyDQxI89mP6rbiJ59a2c8b7TyTy8DKe1mciCuYOubcUHRJspNWhZUxFYVDbp1b755FIfdqu0Hbq7jszBNXlPYgqGGTsrnKQt15FCGdoIx9yxqT39WE780wa6Pvk9YkfsT/SS95J5dDnBxk7iYdrjyN67EV90GJkljxfNQ/aMqWQfWkbzZ9/PjPoYmxLyPtVEFLUNsYocRwX4Gjbn5f3LeYCSdytDKUdTIWV2xA7zCamIJDFzw3Qf4TnTXknO+JZDEP6m/JGxdDCsGjYhBLrxPPxb/42++8FiXmT7qINQtTUopdij2XBCW4Z7N0XBGBa0Kl7slmCjG1+EV3pF6B7QJkySkQIzCtnvRouhji2fGCLh9305+NI9IngPagvYJeqxeNkTHHHY0BGVJp2pmPb15k6oiYn2k8+jYjHirmjZAFF8lOMwtVYeGIVUYok7FPnR+TBlgGtvmc/6evDEE0+MSaRokEhhkmnhEZdp2EHIO1e1cUwqQzxkthgvfJuUEjaMY2HVxYsv/nAoJDYrL5Y9MPS6M2OxdNXQ4/Vav/hoTp+W5JC9GrCioNMuph0abJ+LdkvwxC5N7NrsoBJJqIkNOseWoBxbeHqAve98/BtvJ79mPfYBe5GfNxfXUrT++lLcVEJ8BB6oEw8lh00mnOzj8TjWhW8unrP27EX0/vgqHrv73+zyTDcA0//8HbmeO/wEiOsMckjqbI6ur16GSaaEtgi0fOlinLYthFSOAMt1hhXm5VANtSgDJpFCuQ46naXmjONYftmvOKBhKtlHV5C+5zEy9z6BikWpPbUUIxE/5iAySx4ndVsl+bvnB1fQ8oWLmFbnYumA/j/ciH/ykUR2H+zQciyY0SDvfR+ixMXckmCuj8qknwvCKNmQaeTakluoK6TGN8VLFZICI8pF3JF9WU+Gf0tm1Z1eoCeuu4PU7/+BTmYkTPuIhQSPLiPz4a9Q88cfFo9734Ic790tzStBHbvNruWpDZqnOyxsNM90WnSkJZnVH54Wb/HeU+SGnjJP6E4PrJVUtIGGGfVyg4+bDYvmijLy9fvgxLnyNxy+db8s1S4+uLRvXb/YqR9dV8rstrAxC6kcHZs3EhiR265VUmzSHkRDBoHO5SXhUhBAZw/EouiuPvwaj6ZaRZD2UPW1+Os2486ZQbzMVlwflURJIA+D6+yYgiAbN24ck/MEPX0o1CANUfdLyrmWz12EijgloaGlBJ1Ch6HuemTbbIgC2yMarmwK4/D5Y4RrjoFfPA4vvTZ0v9b0ApbF3FofK6zIo6IRaGnC9CeoIeCEeTaoekxNFNPdO7LApNI5XzCZFQREMF+cv0oBF52PNgptwK2N4IQFn2Mu4NpEgYYhrwDRAxaAY7PumRcAWeW0f+TbqIhL28+/MGzblFLiNC2D/+pG/DDjpTOrjcb/PX9I4TdaGD/Aahyd1mGHvowgtEkrz8durCd5wkKa3vwOen94JX2//hsA8ZOOEFZOiOgB82n9+kfQmSz9l19frAObf25VcQLo/bmwgHLPrpLCJqccid3aRP75Vwh6E8SPOrA4VtHX1hDpTeDPmY4zYyotNWKnN/c+gX78WexprTjvezNOe4L6eZLErjVsTuL6xZj7nkR9+N04/UnimzZQe/qxZF9djwk09p5zUUpRl00O+xaPSqArpU4Dfoak6f6dMea7A75X4fdnAGngQmPMk6M595bgzp0l1CPAft/bME1NmEeWoRSkO/uJtTYUb6bl2uyuEqiONIcQcMgMW6Tl/HoSTg0/fgRW98JrfYbX+uVHt7w0+JpresVcsaoHVnZLaO6aPqEVvtYnvNCBmvmmJLzYLX8XLRCbbSqw+cI9laltv3AMRPNZjDa84z3CP26rFTt7JFzJRh0IAoNlO6iOTvACcpaDHY1LsWZAJdJEjEEbjcp5qJoYfkc3zrRWqQCjFM1l122Ji4DYEUW1x4KHbjwfjEJFBws+3Scauj2tBbuxFNetIq5ojiYMc7eUpJQdJVri4gMpMKQKZpdCBsy93zh0vzYkZEneMqVEs1K2DfW10NMHrc2ldLE1cXR3SIFTalhTkB+GwluhF7/gGA8MWPN2wSf8vq6WeleUhfLAptHAikeZ8t1P8oFnnocrbwUoRt0aY1BKkV+1lsSfbqb5s++v4JcPhMmU/D2N//O2rRLmhUjOin2BHvF6Q8FukGch8H1Mf5oP/Pd/E527K+78XYtpIYYy8xTMTOldZxB09dLyxYvp/tbl9P32horjgk2dJG+8i/SSx3HnzCjmDkpedwe155xI7qkXyD78dPH4uvNOJXrAfHp+/MfiffVfWUvHw8sAyJ29CPetb5BALq1JL34I05ck+OpPAEgAib/cWjxf4wfOJX7ykZz91E0J+MyQ92CLxgWllA38Ejgd2Ac4Xyk1sDTC6cCe4d/FwK+2dN7RInrIPli7zpKcGAt2Z/pe02m5+FxJNfnZr5Nb8iheILQ2k8tDbz969TqhDSmFCTSms4f6RDdfOjzPr47s5vwZPcyqDbjk8JK38IA2w2Wnwx/PhstOh5+eCq0xqQR02eNy3JGzJCT4G/cZ/vGCJMr3NdyzRvIrF/DzRwxXPZzhw7eK9OzOGPpykoN8fjQJeQ+CgGuvulyCVCS+A2MMXqBpiEDgawKjyBAhY0eZUmfTVgdGwdQaaK61sSMO2DZGS/Qkno+/uauoZZSjMOmNljHzerAtPPQgmS46tXQ2h7+hHTUgMY+3diObP/h1iTQMHW0VsEPHhW1LkZCw/udoYamhTWgxBz57FLx859D92pSE6XUKu6m+Yr9SChpqJf94+f4ZU1GzpqFqYlK9J1vp5IXQRhtq6bURaIqWqubU1bjw1jNo/OgFTK+H+pgwNLaFUuvsMo3fX3ftoP0FAZT82x3kn3+Fze/7EkFPP7mnXwxNW5B/+TURVtkcQX+y4pyjhQkDvnSYYM34flhv1BqU8Gu0sOpqIQj4/bVXC+vlqx+m7fKvUv+O04kff/Cwv2u86Fya/9/7iR4wH2dOKQX0lB9+msaLzwNARV10d19RmAP469vpu+yvFcIcIHn9Yrq+8gtxAP/wU0y/9gfUX3Am0YP2EoXvpiVsvvCLJG+6h9zylcWCLAD2jCk0fexdFefr++0NdHzy+yP2fTQa+uHAy8aYVwCUUtcC5wDlBc7OAa4y8kY+opRqUkrNMMa87rW3sixqv/MZMpk8yigcC+oWHUp28YP4azfh/Ok6zDV/J5vzKx/opgZUcyPWXrth7bcANXs6djZHzHE4dVd4Y9AJ2vD1fV0C22ZeJA35RkxWE3ddauMxvnUS3Plclmc2BZw7J8v8FsP+U5q56RmPG5+XpbVtWwTl63RjWNodCfmRYjaZGvH55IIEs+Y2YTb341s2fj5g7u57UuOWUmjm0nn8ZJrYvGZMEOBEbFrjYFlWKZl+aEqNhQqNmBQKXzqYrJT0KmfDbI1QGwtsC21R9ydBB9gN9eIniLiD2p365z3onn7JyNdQN9i5aAm9Tdk2Kh4Vs8dWwrWHXsU82wnLg/kk86U0vAVsSlIspDAQVnPToH1FQdXcKBq861Q4GfMBTI2KMz3qliaZ6U5Jc0+86SRq60sT9WirSQ1E0NXLnM409ty5BJtLpfL89Zvp+fEfi5otQPuHvwHaUH/BmejuvqLdOX7i4aV8OpYqOhi3BJPLY3yNM3cKZkMHOueBFbKWXkcpMlmd2ew5L0wH4djYjfXUveXkEX9ntzYVGVOtX/4guWdeInbkASjLwp09ndhRB6JiUXJPPo9VX4vd1kL2oWWk732CmhMPw5k9neT1d9J8yXtRdXGS1y9GJzPUv+M0rHBSrztrEZy1CJ1M0/WN3+CvWV/UwlUsSutXP0Tvr/5K/dveSOyw/XB2mSbPsTGkbrlXonNHwGgE+ixgbdn2OuCIURwzCxgTY6ptQdwWhyLI0nrqDz9N8u93kbj2dqILdiXfOgXvgaXCBGhuhHQGk0oTrF5L8K9SvKyaNgX7qIOhvhZr9kxmpTNY8+eBG8f09Is7WWu0bRGzbc5s8ThzioJMDmNqOMbezNEHQnfO4smeKOvyUeZHU6AUv13VwIf2SjK3NqDDc2mMaPryFvs1+xijMJs7Jd2rsrH9gFnTphdzj9e4EM/lSOscVi7LrJoAIvYg5k2dO/zSWlmWJKDyfHk5Qy7xSLS+7YGZM7chg5g2mFQWGurFoVleBGFjB+l/P4pfJnDs6a0VPzfGYDmOLNUbagczM0aJ+ohQUwdGzx4+E65onMkDa+H0PUr78wF0ZaWww9ZCWRaqtRmdy2Pau4pmB9cWu/7AzKGFYglQ8vO8XmTufYIpmYDoIfuQLnMM5l9YXSHM3d1n462SVzzx51sqztHz3d/LB9tixtUja5ADYc+cEmbNdFDaoBpqseuGZeWNGsqxmNE2+pXCQFgNtcSPXli5LxTKsUP3Le6rfdPx1L6plIUzul8pMUv9204b/vx1NUz55kfx124i+8QzBN39xI9ZiLvbLkz9waeKx5WbiBo/cB7GGBJ/unn48265awz12Az0tY7mGJRSFyulnlBKPfHyyy9z6aWXAqLRrVy5kqVLlxbZEZ/61Kf40Y9+BMA+B+1FomcTy5Y+yMlvPQeAD376Eq7pX8fUn32OPa76Hs47T+eet53Ixawl+uMv8clYJ7edeQiRb32a3R74I2rWdP7Z/gofu+9G/H8s5sLPfYR/fepz9PzwN8w/aDdy3/w5f/z4x/nMJz6Iiri89Zw3cN93fsiGf9/LQQv3IPeZb/PrH36Xr/3wm2BZvPPcRbR1P8ZxLOOz7zuSo1oz7LPkY9x32f+jrcbwP+ceTG7ts/iP38YbzzoRpRSX/vAb/PqKX2NWvMDhJx3Eg7ffyH333M2iRYtoUjk++smP8NfrryLo6qV13/mkUkluWXwHb36PLL0u+ND/cNMtN8iLPWMqANfceAPv/vD/APDm97yLWxbfQX8iQfMeczG5PJf/9rd84KKLMEHAokWLWLJkCWuff7EodH/0ox/xqU/JA3TIIYewdOlSVq5cWdSyL7300lGP08yZM9mwYQNXXnklixYtAsSeXjDB1NfXk0gkuPnmm4vUxne+85385U9/wgQad2Ybxve5+q/XVvTpui99m8033skeV3wbgL9sXMnnXnwQgJPfeg73PvQgG9ZvYPbe83GntfKTy365zX2KOvDtL3+KX/2f9OnAPWeyaeMG1i1fwsrfXciDa+HTH72YP/1B+rTXrHr8TILXHruZd/+X9OlD738nN/5NHGnT6+XVuPFvV/Oh978TgHf/11ksvu1mkokEu8+ox4pG+MuN1/CZL16Cr+HtF5zDkvvvY8OmTcxZKJkHf/Lry/jMpV8B4PBTT+ap5U+zctUq9jlGdKuv//D7fP2HIkz3OeYIVq5axdKnn+bwU0Ur/cylX+Env74MgDkL92PDpk3c+9CDnHPFT3j6wF347NP3cOMe9TT97/ns/eDVdC99hju71vL+F++l6eMX8ImO5dzWJrPJnPv+CMC/z1rIJzokDcH7nrmbuxObSCSTNO8xF4Df/ukqPvjpSyrHqaxPP/3Db/nsl74IwBGnvYGnVr/Eqg3rXtezt2TJEhYtWoSKuPy/73yD3/35KgCa95hLIll6n4zWXPA/H+Dqv/0VnfNwZ0zFGDPk+7Q1fRo4TkufHn6c9l10DKtNjpULZnDqjb8huv/8YcepIPc+9JmSsB8KamCgyqADlDoKuNQY88Zw+/MAxpjvlB3zG2CJMeaacPtFYNFIJpdDDz3UPPHEEyNeu4Cgu0/salswHQRauJ79uXAFq0rpTK18Di+bR9XVYvf0iqO1vRP8AO/P/5Blb4Hf3FAHZTbBAk2reKfCc1sz2sRu391b2ZDWZjlXYdnoOjhvexOqrhbvwSexn32BurecxJq9ZrD/wgPB1+IYch0JGsnlIZXFntJUPKW/qZOuL/0cZ1YbLV+6eIssCROyZOTG+GDbkjPD9wk6+7Ab64qae8EBNlZYsWIF+++//6iPD3r60QWnWt6DAVV3Or/88wptUUVd2n75JVRdDSabF6eS52FPbdkiRXE0yIe1G8uZR8bAr29bwcPe/nzpOJgfLhCWbYIfPwpfOQ722AaGXiHFru7qxct6NMcVMeVJkqttMBlt/fUNK55axv4HLSze845Lvl/MMT/lR5/BLbOJe6+sI7diJc6uM4ktLAV45FasREUiQ/LYh4Vj4wyTkGss8NQDD7Hf7ntWPEvGmKJNTbkOqiaGFY/ib+zEikcJ0lmsiDvm78RYwRjDawe8tXX3jvu7h/p+NCaXx4E9lVLzgPXAO4B3DjjmJuAjoX39CKBvLOznQ8H4QqZWtj0oSMO2pLitFS5N+3NCCerPQEZFcWJREcatLWJZmT0TBUSPkIdZd3QT3P2g8FmbG7EP2Q/97EvYxx1GEBj0T38nNSsPXICHLdXLX5Vcq0Yp7Le9CTyf4N8PYe+1O2q3Ofgvv4ra3In3538W26iUTFL3PrKG/ebvJTbHQuiwUvT/9nqyDy6j9Vsfw507i+zjz9B/xd/RiRT5F1bT8ekfMeXbHx/RVpl79mXIe8QO3x8cWzLnhfxgFXXRqQxWcwNBVy86l8edNa14/deLJUuWbJVA1+lMya48hEAuz8o35TufwJk9DeW6kksEZDKwrFFRFEeDiCMmjY2JStOLt2oJTfP356aV8NHDhAW1JtQBptYOfa5yFEoPGigqG3nJaSX86HSKqAYcGysW2fpcDtuIex99mP0PWljctqdPKQp0Z2qlwHV32wV3t10GnSO6/8h+E+P5Yg6LRYrbQ6XJHUvcv/Rx9ps9F1OoNq3A5H2UbeHMmVEhO5xdponvaUO7KEOEEcqus02BYDrvowph+2UwflBKoVuWX2ggyieUrZlctijQjTG+UuojwB2I9+0PxphnlVIfDL//NXAbQll8GaEtvm9UV99KSIBJBJPNY8LCgoWIQAIDRqNch5j2UMqiJW4DinrHo9YGEyavb0+CdsQ+7WsoWoxaWjD/dRZh4CABYObMFgGsYcYPLkGFVLLejPDFTRhjHUkkoLmRwED8zaeQ9aRJU+OQzAZ4X/wBqr0TZ1YbOpkm2NxFd6wPFWanKwxY0Jck++AyALq++H84c2bgh/zn+nechk5nxTv+/i9Te8ZxNLz3nKIDtHzQe77zOwCaPvpOogftLcK/7OEyWqOzOUzOQ4UV2f32bpyZU7fpAS5HV1fXlg8KoXN5EczDMBp0fwrd00/9O07HXTC3UphojTWlCZPKYvL5ivS1rxeOJfTRcpma7Ovi2Nlw28vw/YdKOesbo9AwCmVaA5iweHC4L+aIT0S7UeoSGnvqFOzauKSazWR3iJbY3VOZxrbm1KPx124ismDuNjmWh4IKC2jqXF5Wora11bTErUV3Tw/OjKl46zfjzp0ljtsgEIf7gGe8cJ9VfS12TUwKc6QyUqYvmxfGVVDIp6Ak9UG4khSKrQhvEwQoFHZLAyabk7zyQQDKkuRlloXVVC/JzdJZjBeAreS/MZITx1IoS5hahahcY8wWq0fBKEwu2wvbYnLB17izpxEk08JPNhB09WFFXQlCCPnXQW8/Vn0tweZuwIjQdF1Zzhsjmc60RPQYqyz8ynXwwxp+hWAOY4qTOwMp0V5YlCXny4uZzMvLWROR0+fCOqfGgMnl8Fevx91rHv2/v5HM/U+S/8wF7LLXnqSuW0x+1VriRx6Av7mb1E330PD+t9D/B8muV3fuKUT23o3I3ruDgk3v+lyxzdGD98Z7ZR1WfS1NHzlf6Hy1cdr/91vFdsaPO5imjwxcVAnXl7CyjFUTI+hLYdfXYLc2obO5bTZfbNiwYdSOUX9j55AeGJPL0/Pjq4jstweJP99Cyxc+QPTABRXH6Gwed/Y0dDqLTqRwh0gB8HqQzMkqr5CTY/VrG9D1M/l66Ds8oE1MLwdPr8zBMxwKz0ttRIR6R0rSU9SFt9lv7y4GSQXJNLo3MWarjuFgjGH96jXMmjd3TCYPY0wpkVhoTzJBgN3aJBWQGuvw1m3GntqMvZ019MJzGKSz2DWxovK3NQqLmGgCGZtprRLs19MvCbg6ujEo7IZacGyJkLUU7pwZZQVYNssqp7A0V6ri+kZr/I0dWDVxdCqDM71V6Lda461rR1kWzqypeGs3FYX72sPe/rpMLjsFTM7DqgvzLZR5wa0Zg/lidpO8Xc7MqUOeyyqrGqK7+yAeg8BH9/TjKAs0OEoVC7vK9fMYU0mlKzAOCnkdyuls5ZW+lZIldaGqTs3JR5Ja/BC/+PCn+OSMEqU/v0KinFQsSs1JR4iGVBMfFD7d9qsvo2JRkn+7vUgd070JOv/fT4bsbzk7pBzGL0U2Bn0plGuLKaahDn9TJ3ZTwyBu9Whw+eWXF51Zw13XaCP31KsMKjHG4K9vx6TS5Ja9UCr8PLsyp43OeZK/27ax62sr8nCPFaIOeFlQRuaca6+6nM98odSvdx8gmTJHC9uSPDqFVCgRu5JuWF7Q2nIdtNYUKanbEb+/9mq+8vnhI0OBohlCuY4kwgoLjSjLKhYZsWIRCBNl2U31EI0SbOrEaqjDqqtBhQLcbtg+4zUQhefQDlcCqlCheiugwgRN5cpCkTU2tRll21jRSDiR+ah4rEJGODOmjMinV5ZVNHdajWVUXNvGrq+RurSWhTuzTUyLcq5hOZ0TQqBbjXVYDbVbFfU3EsoHtrxKilVXK3m3oxEJ1/U1OudhRd0wVacXRuYYjDalBzqXFyVzCHubznmDgiTcebOI7LMb8zdLzl97anPRThxfdBi1Z56Ach2Jkh0ChTqZDe89h5pTjsJ4Hj0/+iPuHnPIPrQMgNav/S9dX/0lAEFnL97q9bjzKs9X7nQr2jYtC39jB8px0P1JVNQtZs+zGutkhdPTjz11sAcwyGSx47Fh7eeFCvH+us1YjfWyBI5VLulzT79Iz3d+V+EQtpobsJorVWAr4mBPL03mY2luKcC2ZAHjIw7SBXvvj2NJughbbZ0wB/lNsVKREjt9OSo05NCcuCPccvstGD57nWS6DMB2sFsbJUpXK+yWRnQijc7kZKzyHjqVQcWiWPGoCHDLghlTsAdQSIerTDTW2Bo/zrbALgsYU0phNzcOOmZrgqMGyg67pXQ+5TrYbpEb2zfcOSaEQN/WiLGtvo5lScUYwAkFls57BB3dqFgUp6URnUyL08px8NZtxkQcrHhUkgKlshjfA6MxKMng6DgYJecpJBoynk/zJ9/DtGlRpn/wv8UO5weiMW+lmcOZJZrD1P/7PEopsiccit3aiDt7Bm2/+QrJ6xaTvusROj/3E9ou/2pFqPyQ9yAMSioELAVdvaWgl4gLOkAn01itTZVLR98n2NCBNWcGtTWDecTG9/HXbcKe0iyV2fuTYKlBy3zvBZnkCgme6v7rVGJHHDDYHFCeznU7oRDwVYjkramTF+o9B2zj+bbGNWHbO0SYA9TWjDAzBRqrpUnyjwNWW4n/L5WKApQjKXZhcCrjgcJ8R6KubjtmoBtH7N5x/7B28gmdD31HwIq4ksIzjEy0G+qwayQdqxWP4s6ahj21Bbu5EXeXabKaaGnCndWGPa0VFZHlmt1UJ57+vIdVG8eqifNkT6k8l3LsCmFuwoowekBWu+FQEG6xhXvhzpawZbupgYb3v4VoSC9rv/hr9P3h7+RfelW87cOdq9wRZtuoiCsOn2wOk84JtbIsVD/IZNGJNCri4m9o56El90o4eDpTCudPZVDRKEF3P1YsKo7QAQ637m//luTfSzkUIvtJ3mp3gLlFMinumEk+5kgqZM/A8qUPb/N5fL11pf6UUls5AwwP4wfonCfOuSHw6FNLh29HxC0K86HaWMxR4zg7PIBtS3j44W0fr4mKCeEU3VkxGjqRCUoMDn9jJyrqYrc04m9o5/lnn2Ov/fYd8hwm7+HsMk0cwsmMsASUEtu/baEsC5P3iqHuI7ZBazo+9p0K+p/d2kTLVz6IM32wD2LY8+S9YsIrk81Jjc6menE+WxZWVPi7L770Egvm7UbBnWy1NKK7+0v1KoeAv6G9mKei9uxFxI8+SKhkQxUoTmexpzQWkzFtT6Tz0uXNaeh+7UWm7rqgmDN9tAg0ZAKYVVdK2TAa+Bs7Siwuw7ZPYtpgTWki6OguefjDFY4xhheeeXbI59B4PlZT/XZ3Xm4vvPjiiyxYsGDLB048DCt0qhr668Bolvzlwtaa0lhKCWrb3LbkbkzOE+1pQO4KFY3IiqClEWfGFJyZU0Mmj3AojQnZOwPcI0NN0MqymPLdTzLlOx+n8eLziC86jKCrl/Tdj5B/+TUAdDJN4q+3FxMvDdmXiCsVVZSktFWWhe7okdVFGQXo1n/9SxzKrotyHYL27hGTzOtsjsR1pSpHNW84GnferAphbnJ5ccaFDjerZnT5Ql4vaiLCS2+rgX8vvkW09a1MMxIYmF5bcqKPGpYltUD9YJtzmxg/QNXXYEVcrKZ6VJhj3ORLK7/b/n330D9WasIKc4BbbrllywdNMkwIG/pkgVUW3anqa/Ashd3WjOW6BJ29wne1FEabIkNHWVbRNGHX12LVxDCBFsfi9FaU4xD0JiRaMgxaMGEOl4pr19Vg1dXg7jabmpOPxHvpVVI3LSF10xIie++G1dxA9qFl2DOmUBNWdBkNhuIp5/OVdR5HYjRkn3q+mAvEmTmVKT/+7NATpVJFOpxVX7tdnKDDocBYyuVyTK0VKmMiN+IcVYGIXcpLvzVQEbfIfNCJlDiVyxSE0VQ/Kk9Da9fWQJg73GzqLB4zcLwK5x5tkq2dFbnc4CyWkx1Vk8s4Ys2aNcydO7e4bTwfnclg1dVu8UXVhVQBxqAzOYLOHqxIBHtqE95rG8EJNbFhXvq+K/5O+vYHhzx39NB9sWJRGi46d5Aw1unsyHmxjWH1ypeYN3/PLa5gck+/SPe3f1vcbvnKh4juu/swHdaibTr2kCXndgQK46WN5E13rTBegUr7eC40VbuWaOet8cqSZaOFCYIK00iwqUuKBiupSWmy+S0G5xg/qAjdL0Bnc5LALeIOOV5jmUphvDDw/ZpEqJpcdkZceeWVFdvKdbAb6kcV+GCF6WWVZWHXxsUM0toozqmWRqx4THJMDyxpH6LhgjNp+uR7mHbFN6k55chi6gGA3BPPknngSTZf+EUy9y/Fb5cYhvS9j7P5fV8i+9iKEdv2pxuu22L7M48sp/vbv8WZ1UbtmScw9af/b3hhDjJBNdYNoi/uSBTGy1JSDEMjNvG20CphjBTDaKuBWfVhtXl724Q5iLmuGMEYji8hB9x4AXZj3SBTnQmCMGYijB4exu5uxaLYM6ZCPl8cL2MkNgDAaEYVmbgzY+D79Z+AiT1iExxjUXezAGfG1OILaDc1SNi464DroLv7it8ZPxC6oOsSP1L4d40fOE9Sc3qFIgPiRNX9SXp/cQ0qFqXxorfSd9lfAciteAl/Uyf++nYaLzoXf0M7zuzp4qg1hoP2G5n/672yjt6f/QmUGlWpMuP52FOatjkl7lihfLxqI2FlKS3/C+lzB9akbRzDJlsRF2vWNPyObpQRfr5et7lUuBbEAdrWgu7oEZrpCI5UK+LCjDYOWngQBFrMOfEoOiM59XcUXXh7YSzfr4mCqkCfJBioTVnxGMSlIo5WFClrxmjwzJC270IGR+XCtN9eSv6F1VJNZelz9P7imuJx6cUPFT/rsIqNM6uNuredRvqOB/DqBjvw8s+/Qv8f/4m/sVMYMnVx2n7+hVE5N5Vjj7swHwqONdiOXr7dHN8GR+goYE9pLmnu8ZgE/oQQB3WEIAgwWV0RnDIUrIgbJgKTHPMm741Z/pYqdjyqJpdxxNKlw/N/xwrKtnFmTRP6pOPgTJtStMluyX8S2WseLZ99PzWnH0vsmIOYdsU3qD17ESBRo87MqeSefhGQMly9P7mK3IqXeOIxqbCeuuNB0nc9TO/Pr6br0svwVq/HZHNYjXU0XHDWFoW50RqdzhZDxscbWzteDdvJ/Fxu61ZRpzRZay2h55aFVVeLO3t6RUHk4fDk08tQMTHhWdEIdmP9FgPQJgJ2xPu1s6HqFB1H7EinjQ5540opgr4E+IFkW/QDVMgtH43t3nieZGisiWFSGdL3PIYzezrpOx8WnrrWvPz4U8ypqbR1Rw/Zh/ixB+PMmYEzrWWL+dxBqIpWcwN2w84R8bczOtmM5+NtaEc5Niabx911xlYzgFYtW87cPfccNoBoomJnHK8xQtUpujPi6quv3mHXsmLRomZnN9ZjtzbhzJgqUZi2g7JtdDY/bDRhAcp1i3k6rPpa6s4+kdhBe9Py2ffT+qX/oelTF3Jzpp3oIfvQ+rX/peaUI2n75Zdo+ez7iR+9EHeXaUMK80KCp0Ht3km0c9ix4zVaKNcRx6c2kuVvG+ic19x4Q0UcwWTBzjhe2xtVG/o4IhodX0pYQSN3preiLIsgkYJsjiCTRSmr5EgNgiLHfSCNrZB1rwCrJkbzWYto/tj7UEoR2WveiG0wRtIY2y0NBN194rB1nGJqgJ2pasx4j9dwsNtaJJJ0G1kpsfq6Hcrr31HYWcdre2LyjeIEwplnnjneTQBKgt2ur8XU1WAVuO1dvcKE0Ej6zryH7gzTB4TFtE2gBwmSM05+AxA6Yv0ALFt4cAOqChljUNoIfU4pyHtYjfV4GzqwYhHJP70TYWcZr4GwRmG+GglnnX32GLVk58LOOl7bE1WTyzjimmuu2fJBOxjl3Ha7oVaSidXFhSNfG8eZMwN7WqukImioC5OOeUJ51Bpl4Lo7bgPPx6qvwZkxFWeXNpzZ07EaasNSZJJZ0mRy2NNaJBpWKWFvuI7kuylLn7uzYGccr7FAtV+TB1UNfRxx1FFHjXcTRoTd1CCsibICFEopVGh2KeRlxxFCdmH76JMWYU+fUkwXXDxf6NzU/UmcWdMk3/oQS313KxKG7Ujs7OO1raj2a/KgKtDHEclkcrybsEWMpvL8QBZKKp0eJMzLj7XqakQjn2DUuIkwXtuCar8mD6oml3HEihUjh9BPVGypX6+3CPV44T91vCYqJmu/RkKVhz6O2JpiyhMJ1X5NLFT7NeFQ5aHvjLj88svHuwnbBdV+TSxU+zV5UBXo44jW1p2LljdWqPZrYqHar8mDqkAfRyxatGi8m7BdUO3XxEK1X5MHVYE+jrjhhhvGuwnbBdV+TSxU+zV5MG5OUaXU7caY08bl4lVUUUUVkxDjJtCrqKKKKqoYW1RNLlVUUUUVkwRVgV5FFVVUMUlQFehVVFFFFZME4ybQTzvtNAP8R/+tWbNm3NtQ7Ve1X9V+Tbi/YTFuAr2zs3O8Lr3TYMOGDePdhO2Car8mFqr9mjyomlzGEYsXLx7vJmwXVPs1sVDt1+RBVaCPI84666zxbsJ2QbVfEwvVfk0eVAX6OGKyZpus9mtiodqvyYOqQB9HbNy4cbybsF1Q7dfEQrVfkwfVfOjjiMmar7nar4mFar8mHKr50HdGTNZ8zdV+TSxU+zV5UBXo44j58+ePdxO2C6r9mlio9mvyoCrQxxGTdDlY7dcEQ7VfkwdVgT6OWLJkyXg3Ybug2q+JhWq/Jg+qAn0cce655453E7YLqv2aWKj2a/KgKtDHEZNVg6j2a2Kh2q/Jg6pAH0d0dXWNdxO2C6r9mlio9mvyYFQ8dKXUacDPABv4nTHmuwO+XwT8E1gd7rrRGPP1kc5Z5aFPXp5stV8TC9V+TThsOw9dKWUDvwROB/YBzldK7TPEofcbYxaGfyMK8yoEk5UnOxn6NZSeMxn6NRSq/Zo8GI3J5XDgZWPMK8aYPHAtcM72bdZ/Bvbff//xbsJ2wUTrlzHQla7cl8iBF1Tum2j9Gi2q/Zo8GI1AnwWsLdteF+4biKOUUk8rpf6llNp3TFo3yVFXVzfeTdgumGj9ygfQn6vcl/ZAD9DSJ1q/RotqvyYPRiPQh7LXDFyQPgnsaow5EPg58I8hT6TUxUqpJ5RST3R0dGxVQycjHn744fFuwnbB9u6XMQZjDDqb2/LBo0A+AEtBoGVbG8jrwQK9Ol4TC5O1XyNhNAJ9HTC7bHsXoKIUiDGm3xiTDD/fBrhKqSkDT2SMudwYc6gx5tCpU6e+jmZPDpx//vnj3YTtgrHuV5BMEyRSABjfx9/QTtDejb9uM1ubXC7ny/+MVxLg+QCUgp6MCPHutJhhCt8XUB2v8Ud+gBlsU3JofwdMrH6NFUYj0B8H9lRKzVNKRYB3ADeVH6CUmq6UUuHnw8Pz/udxhrYSt9xyy3g3YbtgrPul+xLonn6CVIagux8sC51IgeOA1ls+QYjOFHSmRVB3Z6A9tJtrA7YSs0vOh5Qny1J/wKmr4zX+SISLspwv45j2IBhGoE+kfo0VnC0dYIzxlVIfAe5AaIt/MMY8q5T6YPj9r4HzgA8ppXwgA7zDjFde3gmEXG5sTAY7G8ayXzqbkzfXstAd3ZhAY9XGoTYOXvhW2/YWz2OMCGptwNOQ9cENfxZocCzQDiTyoq1bDBYU1fHafjBGhHNtRJzRlgJ7CHUz7UF9ABuSMKte9mkjv1cDjMM7Q792NKr50McRa9asYe7cuePdjDHHaPplArFzKGvwW2uCAJ1KYzfU43f2gC/rbON5KNctHecHWE31YFnYNbERr5fxhMmigYYopPIisGfWQ3tKNHRfh1q5goglnxtjcvxo+zURsTP0y9cyPtPqZBXlKGiKy3fGyAQcc2B1L7TVQFcGptZCRwrqo5DMi4AvnwR2hn5tJ1Tzoe+MuPLKK8e7CdsFQ/XL39xF0J8sbutkGt3TD4iTU+c9gs6e0nf9qfCHJaNpuTAHwLYwmRy6vRudy4/YpowvWritRMuzQy3cD0o2WMeSv5gtGqKnRWM3Rswx/0njtaPhB3K/QezkGb/0XXcmNJWF45T1RaJlPXAt6M2Jdj5wRbUz9GtHY4smlyq2Hw455JDxbsJ2wVD9Mp6PCTQmm8dpa8HkfUw2h8l1YHwfHLuodphsHrQwWUwQoIYxqSil0OkMaE3Q2YM1a9qwbSo4Pm0gF4i2VxDa5WwWp0zFidpgQvNM2oOFB+2Y8TJaD7ly2V4Yz+fQGKHM5XXJER1oGauOVCi4fUCJAFdKhL1SkA1EI7dNaCLTyACHmKzv10ioauhVbBcYY/A7e+VzEIgwz+cxuZyYUTwP5YZS1bEh0KHQDzB5T9grBRv5CFBKoeJR8APMMA7Scu1OKRHmIJfOhQuAv6yA999U+TvHEoGRD0SDHOgk3R4wvo9OZbb/hXYC9GVF++7LltgrybyMi2uJ49PX4utwlayW3HASLpjIQFZUii0+Kv8RqAr0ccTSpUvHuwnbBUuXLiXo6EYnkuhcHt2bQLk2ViyKcl10Ois29BDKskS4uw7+RolPUI6Nv7FjUMDDQKiIi1IyKfibu8R0kyiZdrKe2M+dIayOSpUEyR2vgB/aastR0OKVgocfWzosRW7MEGjIe9v5IpXY1ucwkRvM1R8tcr7YwbO+aNy5QIR4X7a0SjKUhLYKBbilRIDblqygClBq8IQ7Wd+vkVAV6OOICy+8cLybMKYwWhN0dPPut70Dk81jxWPorj50JldhQrAi7mB7OKFgdxyU66BsGxWNYEVc0nc9zMa3fxqdGZ61oGwbjMGkMuiufozvYwz0ZiEyAgmmNws/Kos/aU8N0S8jguuct19YYdsdK5QTE7TnY/xghKPHHtvyHPoFyuA2aMUZT+5zxAZUycOnFLzaB+sT4TbixC4gEgpylc+hMxmM1uhN7UAYGDZgcpls79doUBXo44irr756vJswpjB5D53JcfUfrgBX7Bom8MUQ/TrQ99sbAAg2jhxdrCwLnc5gjCboSdCVGZ6jXMDyzfB8WcTE5iEEej5s/k3XX02qzPeaHNkPOywGMsuCnr7ShucXvbRG64pjjdYE28EcM9rnMBOudkBMJSlfVjid6ZJjOdCiZRewOSnfF1DQzEE0cicU6D94EB7fAN98AD7/b/nesYZeWZFKQ94XM1smV4wYHjjUk+39Gg2qAn0cEY1Gx7sJYwLj+2jPw+TyYFtE3EjxO+W6eK9tIrdiZel4rUnd/iDtH/02/vp28i+9SuKGO4c8d9CXKH7u/PxP8de3j9iWnAdZO0aQypLOBhVOzqHQk63c/t1T8MvH4bH1pX1+aA6IRKNFARZocdoVBNzWmB8KbB7xLQSYlDTCaI3OlmYJ3dNP0NVb2s7mCNq7R3eRrcBwz6ExpcjadKhVJ3Il56WFrHCyvkRsdqWF61/Ii6ONCPx8aAvvy4odfOB9ygUyqf788cr9KuSiS5qHsoHyxanR3ufxUF+dCHjkWgWnasabPO/X1qDKchlHnHnmmePdhDGBTmfFkWcMWBZnnHQyOpvDcmz8DR10feFnADR94t3klr1A/oXVBJs6Acgte4H+q8QbWXPCodhTmivOnbr1/ort/qtuouXzFw3ZDmOgzxf7iodCe94ge4uvK5ksa/sqvibjw6Mb5O+qMAVdzJFzn3SqjFfGgxpXTp3yIO6KwIu7svQfCSYIMIk0TG1Bd/eB62A8v3gfTSYLMZkQC/uLv02mQaki8ydIpEK/ROVrHKQzKNvGikYYDQrPoSlzHIMI33wggT4ZX2zWeS1CXBuxZad9mex8I6uhtCeacs6XYwpmk5wvgl4p+Sto3i93C498xHuWy0MyDbEw1sD3wbb44eMOm9MuB05LUpfN4jkxknlxmOb15Hm/tgZVDX0ccc0114x3E14XgkyWIJFCp8rW3MDVl/+e9g9cyuaLvkrib3cAYDU30PvTP5FZ8nhRmANkHlpW/Jx7eiVGa/Ivrikm4Mo+vIzowr1Kxyx7gewTzw7ZnpxfijLs922CvhSPb4AbV+Txsh7dGXj/zXD/a3J8fw42peCANrj4IPjRKZXnW9dfuX3zjddgKxFYqYLgCM3duZAJk/dF4HekpC0dqQGmGT8omlFMIKYhpULzSjqLVRMrqrAF1g+E5qx0FmUrdOg01X2JCudyASaRRvcmBu0fDoXnMJUvccGznmwbI6uYdEgZdC1IhhG3Ssm9Loy8a8kkF7Hk3vbnhKGijdyDggklYsnvVnXD1++Ha54Z3KbVvfBswcKWzlaaobQsETanZFZYm1DQ3UvUlmv2hcHFE/392hZUNfRxxFFHHTXeTXhd0H0p0GLHtGolrE97Pvuu7obG6ZhsntwTzxI9dF/iRxxA7y/lBWu8+DxiRy2k62u/wnv5NbAUVkMd/VffQt/vrgdtaHjfW3CmtxK0d1P7phOoe+vJWA119PzwSnp+cAVWcwNNHzmf6H57AiJI+3IiZEXYKG542eXubgOBRTTIkXRswOKKZbAhAbe+LP3YbyocO0c+v3NfuDqcLx5ZB+eVlXI56DAZL9cKNXMHgqAUeu5pEey5ADDQny/RIutCZVl7EhVjcnmM1ljxKCbvEbR3lyJhdUjhNAYVOgF0Lh/SPC3RVuMxjB/SQYMAf2MHzsw2lGVhfH9Y7v5QKDyHeQ12yOXuyEgfCuaRciuJo6QZIPdgYNi9UjKZGUrBXLmgkpUCpVw6T20a3Kav3iv/rzoH0BL9pdMZNiXhc4+08rl9S7PtawmLvZ08BD5548gKwJr479e2oKqhjyOSyeSWD9qZEfiYTB4VEcZK5tHldH35FyQT/TR97F3Fw6L7zyey3x4A2G0t1Jx8JFZNjNih+2I11tFwwVk0ffwCkQChBOm/4u90f+d3ALjzZhJZMA9nxlRqz1oEiH059/SLxWukvNIiQWuIOrAxaxcpKn9d6XDrSjnANyVhDtBWW/r8xt3h8jfBns3wwoD0culwvCrMKqGWrhBnX8aT62sjWi4MoNMFAShLOPohm0VFXEy+LK2BMehkSoSzCdMk5PLC/FFKbOldvXK/AuHqGz9A9yVF+x+Bkz8UCs9hoGVSyocTUsQuCfS4U1ppDGVWumeNcPnvf020ZNsqmbacMorhZU/IRAnQEQr0YV0PxvDimiTXrYrQk4Gn1vo8v0bMTrdvqi3SY9YlFMZxMJs7iWofwgl2wr9f24CqQB9HrFixYrybsM0wOvQ+Wapow03eeDe5pc/yQm8n0QMXUHvGcUT2nkfsqAOxWxpp+ug7afnKB4vnqP+vU5l2+aXUvul4ovvszvQ/fIOWr3yQmtOPrbiWM3t68XP8mIOoP/8M7NYm/NdKqp2vQwcaJQHhFNR1xLZPoDlsymBqypSa0udC4NHMemFolOPF50vjFQ/XtjbiKLRCtkbEDtkbYUCSE+ZZNyYU7H6Asi2R+mWJRwqTIgCuQ9CbQDk2CoNOpgn6So1RrotOplGOJJzRgZzT+IFo96jhc8oOgRUrVuCFKw0vkP64YdMCI315bD18+F9w/fOlCao9BR/5F/zlGbjiaeHy//YpuGv10NfpSMEj6+GypfL5+ucHHKA179m/rN2+z7cei3Dra1E++VgjP1sRZ2VaDO4d2dK9u78jxkNdMYxS2JkMta70ZSK/X9uKqsllHHHxxRePdxO2CSYI8Dd0gAIrLi9Y8uYl+GvWU3PGcXz0mA+johEa3ltZqTB+7MFbPHd03z2IzN8Vu6kBFHir1mLVxIvfK9fBfdNJRNZtIrfiJTFLKIWWhIwYYHmPwz5Nvgilck3VGN4yK8nqVAudZey/mUMUtpleB315EerTwu/f8Z7B42WH5peIVcreqMqEOxiCvgT5eC3dOYupBfu4MVj20K+fsixUPHQA2rYI74HJx8KAKhMEKM8HZYn2H2gxf2xFxM9FH7iYrjAXvB86Mwu+5LgjFMXfPSXbN62EphicMk8+9+dh8SuV59s0jGJcvuJ5JrSP7ze19PnrB/czp9Vl7nG1XP2M4eXNhFq4kQ/G8Hy/2K7aQ4Fe5xqSOfjty/UcM7MP0ml0bYzAcnnv+yfm+/V6UNXQxxHjUcTWGFMsFrE1CBIpgnQGv6OboDchDAzbxlu7CeN5pG69D6u+hoZ3n8WVd9025HVHC+W61L35JOrOOYnmS9476PveHET22xPdmyD78NNC9wtP351V/Py5Wi5/sZa8VoAYeKfFNT87KsHMiDcozWq5yaWAGaEQ/8zd8ERYzuXaq4YerwK9biCccEKxs1m6NybIdyVAazKeQo2Sm68cG5PNoQY0urBtUllMKhN6Jw06lwtntgH89fLEaAMqPf3mN5cXg4Re6xNhft+r8J0HxTH5icWSN+Wc+XL8c6EAXrpx6DYXBPqvl8KNL5T2v1TGuHyhE2pd+MxR8nfa7jAn7kM2x+7Nhk/vXnCcK05uS1NYe/Xmpd8F5+27d5eZudaVHQZYtS4DBn79m/+8ItFVDX0c0drausOvqdNZgu4+7PohpBjyslux6KB9ujchSpI2kPdI3ryE7EPLKnjRTZ98D3ZrEy3NldRDo7VQz6IR8HwMCmUNkT1xBBjPk4IWgSbv26iD90fV3UTvz/6M+8rJmLNPx//XElRXBua8g3Vpi3pHhPnJM/K8e49MeB7Dhw8KuGONzSMh13yggAdYUDY0L3XDnEZoah56vGIj+R+1wc7nyQYGx7Ehauj2LGa6ZvgcqIMw/JHG9zE6wKqJY3J5dEdOctsEIZsmCAi6+9GZLHaDzFLB5i7U7OnF6N2G5laitgRZ/fQxOHwmPBZOYs+HcvWwGXDu3sJ4eXCtHJsKUyr4Bg5sEypnIi/C+qVueCi0lXelYY+WSoH+8HrYf6rc+/3b5E9vFE+qae8i5uf53D69bMhH2bsp4O7NVAzU7q8sZ/aGlzn82JNZO8filnUxblkbpSWiufzFOGdnoL6pFW22TCWdTJhwAj0b8lzjo5cFOy0WLVq0w66lc3kRC9mcVOPZ1ImqjYPRqFgM3ZdE1cYwfUmsGaXygCYIZMkf2skVEPgBqX/8GxWLED1oL3JPvUDNG44idoRUWT/hiAHsAj/AnjYFf0MHVsxFGRHmppCgi1FkGAw0KI3O5LCtGlJOjKnfu4T2j3+X9D/vRtU2wF9vIRIo4u8+k05VRydwdJtXFOaFDsyr8/jwoTZnzR9eGNdG4C0L4O8vwsYkfOkeSNiLmL8aTtlt+GZ6AfzzRThzfshfD3wMELXA15pExhAYm8C2Rr08LjCIhkSY2KzwucA91yjQGt2bEFqpZRf56ybQ6FweOx7DGDjs6EXYCl6ReKeiMC/gbXuLsxjg7fuI1v7DR2T7zXuJff29B4ov4pUe+O6D8I2y8IH718ofwJv2gFtfkon2wDA5ps5mUdFo6GxA/ACWYkGjz16uSOOfHZUgZhtuXy/KxnG/+YVw3xe7zNn/eCDG9WsKZinDxqThmKMWkczLSmCoFdRkxITrZiGV6WTADTfcsMOupbv78Da0SwCQZQlVrrsP3ZOQJFjpDLqjp2I5HiRS+Gs3odMSpedv7sJ7ZV0xBL/pI++k5XMX0Xb5pTRedG7RDPD320smF+N5WPW1WPEo7i5tkqvFsXGmt6KcUJjnvYq850PCtiEI0LaNNgY/AHtKM7HjDxUmyF/+Lv00cN7Nl9HYJ6plS1QPPk8YPj+7QYokDIQX+nvfspdw1JdtFobHqgdv4KrlwugYzoL07zVw00vwlSXw12eRUP6Ii3JsdKDpy2iiTikC8/XCikVLAUFlVEWlQKcy6HQWKx5DWcJfN0GAZxT5jEe6L0PGh7/feAN3rJJ2F3D2nqXgnz1aSv6B+ii878DScXtPgW+eWHIs79YMlxw5fHuPnKk5b3aKGXVw2Mxw7NdvxvQnMUoc1wZQkUjFCq4xYojacPbsLOfMyVHjGBpcg3/NTRxw2Y/4dNNL7Nfkc0Cz3Nj+jOH2m2+gPyfpHAamaXg9icXGCtvj+hNOoHt6cFa1iYrtraEbzydIpMTkkfew4jHR0lwHFXFFm4u4krGw8DnUkoNkWoJewkhEf0M7HR/7Dp2f/ym9P7kKAGcXUbHsxkqv4vFHHgX5vAgQDVZDLUopMeU4dnHpbLU2gtZyDcepCHuv6EcQCLUv0AS1tVi+Lw48DbFzTsHMaCtaJTYtOplZG1/hpPtvAGOYXVs5USilIJMd0aYfWt4BOHU30eL3a4Mp+ywChNGxuleExDfuL9mUQaIoMYZNScOtL4POB0XSdkQZIqG9Pbuxk/S/Hy3+Lujpp/OL/0f+pVeHbddwsKJDLLRti6Crt8SecWx06PvI+or+hM/mdX109vscccwibikT5jPr4PBZcEQYKTu7ofLUB0yD6eFEOGWIxcOCVjGhvGmPsjYCJ8yB2TUBZ7Yl+N7J0EgGk0hhIhHo6xd/AUYcvCH0uo34d9xXDDTLf/dX5L5zWeUF121kj//7CZ/eP8Ul+6U4YXqe9f2aI45ZVGQZdaZKLB4QpXC85EjGk7iJrvRgod7zOlP1TBiBnsqDHxj8wGwx4dJEwYYNG7Z80DbAhPZTnc2h+5NS/Sc0bVjxSraEUqqoWSulMCgpxtzVhxWR32TuX0rHJ79f/E3Q0YPd2oTd1jLk9Tf2dINlY7kOylIVmqPVWIfV0iifIy7OzDacthaslgaUI8eVh7ybsAKCPaUJdEAQjWMrg6XCvCItzUS+/HGMgc6cxfKT3syy/Y9n/qqnOHTZvwcJdACjwORy5IKStaL85S4Ew4AIr1+/SWzEJDbw7RNl/4NrJefLS91w7bMlW3NXhpD7LlzFrr58pUMzfOMy37qMvt9cR8///RmjNem7HsF7+TW6v/Yrgs5eOj71A1K33jfk/R2IITNXhtkqi9tKnKZBezeessh4mqgFTjrDhg0b6M/BWxdIIM93TxafwfsXwndPEvMTiPApCKDPHyOaevMQAl0pcXT+164pvnqMJBu7cF+P983uglwWEOFMdz/094PrQpgnxziR4nMA4F97M/41N6GffAbz4ivFv0HwPLzr/4XJe0yrEebLuvXyfkVsWZhtSMDGhAhUT4tQHQvkfSqStm0JvVlhB6XypTw3Bf2iP7dVjNNBmDA29EQO8DN4/VJH0oSJf4ZSTiYKVq5cueWDtgF+ezdWPIpOpDE6DFJxRnejlIKgu5dgYzteKkvq5nvwVq3DntpM7KiF2K1NOLOm4syZGdpjA4wflPKSB5qX176KNaUJk0gPcudZwzhCrVgUHRo6JcjGKRa6UJEI/Z5N3LbJuxFxclmlxFhOTZTnjnsjDyXqeXR9FPv4/+K4lfdzeP/LTI8fOvhitgOpLDTF0KZUxagQ1h9zSpGOIE61M/aEp+IrmVEvHO07y7jWa/qEEXLZ6bC+n1CYA2g2ZSym1oZnsqziSsL09hMA2QeXEZx7Kpn7ngDHxng+mfufwF+3mf6rbqL2TcdXNF3SITwNWhM75iCUUnhrNxG0dxM7ZJ+KY5Vl0R/mF6+JyLZxFUFeSQCOMph0hueffRaOgNaaip/j2sLHL6Aw6UVsEeQnzpVt3d2Hqq8lsJ3KZGjJNPNcjx+fEKU5l8TkA1QmLzz5VBqDBtcVvn44iWYDiBhZyMlFZUIOHngC/dTglA/Of7+d4I77MOs2EtxyN9aus2ibdwhgeO6F0vsVKbQrpJla4ZirvIxnoZDJ1vjmvEDuUX+YIqE2Uvm9NmJai7siv2rCXD+FAC7bFsYWQFO4eC3UtXXtwdcbDSaEOOzLQjbjofwAnc6gmurJBxLYMLtxvFu37RgLHrposKZoKjFBAPk8QVaMhKomVsnFHu48eY/Mg08RO+YgMvc8Tv/vb6z4vuULHyB64ILBP9QGd1Yb3voOwGA8n//54Aex4zGCbB6zNRQDy5Jw+JqYjHM0itPSQDZv6MlCTX0dxih5EwiTQIWCeNUbzuLR18RhNrXepu2w+UxZ9gR6ya6owxeKAziETDx+gemHp6ExWpogmmKVKWELeOd7L8ZSUox4TZjUa9dGyeENogFuSoUNMwYCw+a8y/6I+laueRaSVtlIfpqgvZvGD76N/iv/SX7lYLOLTqbRqQzZx58h8aebAWjI5Kh9w1F0fvqHAEy/5vtk7n2C7GPP0PSxd2GiUUlWZUkSrdY4sgLToMIVhNE+i858L3/ugJYRfK8g5wm0CKkKRSqZxERdgpgjc1neJxK1MbkcKtC01FoYzxfzjw1e3sfpTRQn9xoL0nkpxu3YoF9ag7ZtrHmz0Ztl6VMQ5mr2DMxa4Us67z0X57jDsA/YC//2ewn+tQS9cjVtey8k7xtiR36ApzfDgdNEUdmQEDpqYZzTYXGN8nGe3VhKKlZYWBVSG2hTKlatkEjX6XWlyOBCxHDhkfcC0bgTOUmr4Fjhc4VMMIVz5nzIu7K6izki76bUyj3xjUw+8VE6dieEQA8MRLJpMvkAxwRoZCACU5olJyIuv/xyLr300td1Dp3OQiZbzFKoU2lwHEw6h10fqlzD5PXwN3fR873fA6G9vb2bYHMXyb/fPejYyP57VmybXA5QWI11KMfBch2MDrAbGrn8pz/ma1/7GqomhrU19ALLAs9HNdRCJoszcyqg6ArLktHciJ8WTZNcDsty8I2N7cCmdOk6x7TlUTOmwjLw/3gD+rmXcN9+JvrVDejVa3HPOx3jBThdXeSbW3BtRXNc7KqOVcoImPHC8mbhC3rtVZfz+S9dyiePgL89Bw+uExvy/x4Kn71bqHyehnPnpDl0qseXn6ynKze4/+X2+0BDesnjoBSxQ/Yldet95J4shVAmbrwLk8mReeBJyc4IRA/eB+N5JP74T6wy+qn38lr6fv03AHLLX4R9FmD+9SD5Iw7GbW2iPwuRsOpfwUxjtOG6v14Jx3+d5mQfprV5ENtI54UyGnEUSV/OUexLEAgNNecRrzOkPYXT04NprEMFBmNpVDZXwby0HQc/kyXiOuQDaJBiVdREoCaXY9O3fkGgwT7jRCjPFR+LEvniR1CxKKY/CWHfVWO9jO8LqwjuvJ8phxxIJljIzVdfzurga8xpFPPQ1+6Hc/eCN+3JoLTKxojQzYeRsgZx9HamZUXSEJUAKz+AvCO/9wIR8EEozDcloDYqCkZfFhpjpVw+Csk1ZKnKa1tKhHXKk/NEbfnNurDbyiot+Oqjcr0FvyT+6scZ0to+IQQ6ANksUUthFOggIO3ZxGyxRw3FVJgImDFjxus/STaHzuaLtXFNThIzWXXDq1vG80nedA/+qxsG5RcvF+ZWU30xa1/5S26CAKuuFlVXU6TJWVOaIAiw4jFmzpwp+0aZvrUA5djoRB4r1gLNDSilxL6IaCleoEjlwVIONTU2Tl6ChFYnbJ7qdpkW07xhVo5F0/PYJx+Dqomj16xHP76c3OPLpe1AcM5pWP+/vfMOl6sq9/9nrV2mn5nTk5N2QiqhmEISOqEXaYKKApdrQRTr9YKIeJXiVSwXvDZUsPATFbiKBQIKiASQhFzSCNJCgCSknpw+M2fKLuv3x9ozc3pOINyEcL7Pc55kZnZbe6/9rrd+X9ejwfZoae/AaKxBir78I1LodLcSbSxAfaN+XtUROG+mFujHTtIvvikq6X6zqoqMjUIqpOgYRKB7D/9Djxetpec3bCN8wDhkVWwAZW7m7r8O2L/qw+ciTIOWy79WDlADpO+sZBcV164jv+IlvCXLkaufw/jKZ3TMgL6aXsGD1xnHvIhDvVnU7q5+dQhs3YEzdgw1US2AIybkHIUhBapYxJMS1ZUlFpFUVSfItfmkuzIYlqEldb4IoYovwzLAiodxfW0VhE0IKYfsL+4lv+I57ZYA/L88qjXeo+bhP7kSedC08rWpRBypMzPLRGHme0/H+c6t2Bs3ccHUGdySbAQUm7oFj2zQ2/x9A5wziKEpRIUl0g9I1rzAz+542l2ScyotCTNOZdH3lR6D60OmUKm07czpeE3Y0PtlHT2nBkPRq6TQDuDwD9w6piw3B4nA212ge762PwyJdIp4RgTD0C9cf47rtwsOO2wQ/+4QUEWnL99H6XvHLbPz+V1p3Y/SkAMqC52NWzFqU8h4lNyTq8kEtLYlRI6eS+4fq8qfq7/4EcJzZ9H9m/uxp03scw1CSGRNss85pG0B1m6PqzdkKgG2BZaJUa19aZ5fMWPTRbjhcWjJJLn9NE2ubbget62LUmUprnlXhqQdaL91NZhnnYT/4isUVz6rXdrBT3G/QI8dxoqA7HQAhdeZxraryoqkKbXgKnoVk/yQ2ZVx1ccCJsAAB9XDMy2AUowNOYBFbUjRPohA9599CTGmXo9101btoRmn+WqM2mQfeuHeGHPHjXidacwgGJ34wOl4XWliZxxL65Xfofj8K1hTJyBME3dLC86WVp2x88pGnF/8Dn/BbPxZ0/oI9Cd2hLDHzeOiqTltTbkuECqnbirfRzkKw3OxpEGVLTTh444uhGWAAl8YuK6L5TqYPWlClk9nRmEEAXfX84AQMqDVCdva39zbreE8soyeh3UvwNAh03DSRcyaBMyfjTtzOmbTGIxDKzTKeVcLWR9NxmgaYBw0HScWwd+2k/edlOfZ2Qdx6kEutz9v8WSQB9+Rh0v+DDNr4ZOHwbMtMGeMZsPsLgZCVegFOlPUWrOBdu+WqILzboUK2OsVKDaDAt1SE/LS58EK1/pj2OI0tIwbSQbM20eg+0oXwYRsjHy+XGxhCm0qV70Nm5Pcd999zJs3b0TbuttbMZvqy8FNv1AMqFI9sEyc7TsRSpvQ0uirGfs9eVqvuhmjNkX9j76sg2oB4u8/lfhZixC2RfT0o3G3tmCNH4N1wHgAqi56d6+L8PB9hTm2bsCC8UbHVYKe+AKjl6+7RGglRUXDaekBpKSLCMmQT082w9YeyXkTslRJB+X1y6qZOQVj0eH4z78MLW3af5nuJlcdZPv4PkY6g5dLU9tcVQ6ElgJYltQLScyCvz94HwvnDz6ukw8IBDqVwFq17bM+3fdNVdke/GdfRB45F/uyCyl84Ruwsx0vqRew1Ocuxnlti/aFB1zxyY+/D3PiWIRtlYU5QPw9J1b+/95TyN67hPj7Ti3vq3wwFh2Ot+QpvMeXw+PLkTf/B9SkAH1vX+wysdb9ielVV2j7PsjrE75HXAaxl4gg3ZlBhRyitoVrhnFdD6/g4psGcVuQVmGMfA7fLWDZFhZFCr7E9EEKQX1c8+L4vr63Qvnkn1pLeP7BCMvEeb1CtBZ61wzMkxYBWhNO2dBz+vH09gSV0hGloMx4aQpQjQ2wYyc+EH/5Txzzwbk8utXitU5taR3SoAXt01vhs4FOUxvR3+3IwulT4IKDKGdRbUlDc1K7Y0qWWu/q05zTV5ns7/7t/ZoMpZ2PBGEjcAHuQmK/LfRap+CiBOD7dBQEOzsd3Y0GbT5257Wvq9Qea28XDIwUp5xyyoi2U74PysfPBOXrnoe7ZQfOthaU5+scb0vnk8ug2032oaVk/vg3lO/rDArAa+vEeXkTxefWYx8yjcRF7yZ2ypFlzd+eOpHosYeVhXmfa3AcZF0Koyq2S1fKSMfVG1390rXyTqUgxBAVwqsSrnoEXsqEuPEZnQM/IeohCkXEIGXy1ofeS+TbXyQ6f5b2kXd1YeR7yNy3BGkIZLobIXX2eelFNSVlN4wINLETTz5lUKpXP5evUAX0Usnqwj7tBUnWqVyTc9tdAMimgEEyIB5TgUA3kgnCs2cSP/eE8j6R4w7Dnjpx2PsXP/t4Qj+4HjVrJkaQFqoUGCf0rdot3niL5n4B7tkYZlWbxcIFxwFBsNj3cFs7CHd1kMynSRbTJKImMTeP19qJ196lXR2uj+E5GJ5L1NSCRkZCZd98XdjHsgRSgJ2IIIV2TdVG9T0trHqBzu/9ms5b7kJ5Xh+rxBxbT1W4Iigj1sDesLapK5aNYF4IgmBmQy1mq7ZMjj7iWCg6vH+WXpgXTYJPz8ryycPgwoP1d9OqtRuj1Ev2L6/AHWt1OurTW+HaJfDA+r5c7r3j/GHz/847MJIMnH1eQ+8qwOcfhohfQ71VZHW3fgFuS+WwayzdRcUIOqw4gWBAF0fs6+W+69at48gjjxzyd+W6+AUHHEeXbueLOJu26UKdkF0Jxw+CUpaKu6Od3KP/W/6+7dofgq+Iv+dEQgdNHXzn8vm9Skm+EMhEbFjNfKTjGgxFr5JB4Ksg6B1kVJTSwUoMgA1RranfutagrRgC32dSQwiUD9EwKq3TNIPGNhgCQqYkevGZ7Fz5PHR2Ix5bRfofTxMeOxZ79gyU72i2wl7pnUopYpagGDSt2LFxHe78I/t0tVNKwY427AljOG+mQb3Il526C+qKLH49xMNbbc6dpPPT/Fc3AWAcMz8YVNBuLpnoY573pgweabOKgqddEKGaZHnhEePHIhrrUDtasT72AZzb7sL9yxJIVZF6ZCP/Wh3n9fhm3Pu7sc5YhBICN5MjViXxe7R2JCIhknYRGYuiHBdDQEPYx3ElYVHEMkOk+l2LDFk0RgzSBTCFT/bh5YQXHILoyZF9Zh1+j15U8kvXsL1X1yoAoyZZfq9LVMT9Z50pwXMchC2QQm/gKbAaa/BXrMZQHq9tfI2j84cza5zPz86UuhnL5g584MSUw4lnphDApYu1X/zoCfCP1+GRDZR97ghN5WAI7dbZ0g2TUzp2si9inxfo9zyvo8vKM9hMpPxkP/ZolIWTdH7w5JS2Fi3tYsf1YVO3rmYrZy2wbwl4pWDDhg3DbuO1dZV7dcpYRKfyWQZeR1rT1o7gPS8J8+orP0T6rr/gbt5BaO6B2LOmDLufX3Qx4hFkVQx38w7M8Y0jEuaw63ENBs/XWlJ9NGBTDF5ks5cxUGo+/O5pOsukLSeIRQy+NKOL2oYaXDeitaVsLqgsFDTGdfaBZaILmkyDwqrnUS/pawxlurWZ7DPAtPM6ugjXpEihGQS3vL5h4BzyPJTywHE4u9mDdA48vdHEuM/Ceoc/b7Dx7ryX04ovY3RnMN59AiKpE7xFIqZ96KapuceDWyykpPrqj2JU9yvTHAI9xcriJWuSOvbQUIOQEvuaT+K/thl56Ez41R/wFj9CwYeJPQZ1lstT29bgNc/BmDSOwrRpxGyJEbL6pMOWCtMIrMWw9InELZSn369QP+2xtAjF/Rw7/+1b+F0Zun9WoboQYRujvprIsYeRCRqEJ/7lLIzaVNlCNA2dTlrqQOQECxa5PKFkGDseQubz+JEIhtIsC8mxNWR8Rairky1bX0cJiUpnkckE/o42lGVCW6d2XZpplIKbT0ngeNr1UhvRVmFJoJ83Axa/XOliVcKxkxhA/JV3dfOO+U0Dc9JLUAqe2aH3m1k3oOVtGZniG9P+93mBfvGhMEnkuOlpg6aurUSnjOelLgOUz/INDss3Cb7/bpNUmLKwLxUptGQrn+1gctimLrmN23uvKKm1R7+4/fPQVVBhWMp0UIWi7jEZQIRt/YKZAw1/pRT5pWtw1m8iNPfAPr9FjplLeP7B2AdNxe/OYDTU7FI4S8som+7mhDG71dLsjeTXBzVJdGjmU4pUhFsJnUHj92RIv3hZByZUCSZO0f6OgjKwDFA1KdjZhmVbGJKy+S6tELHTjyZ732PlY3rbduqenlLiFx2MXoFnlclDjd43bOpxmaJScJT3wHZdMC3IBdUlPQXNKokOIr/noV+w6PHVAPSEfBIWOiAawLr4XNyqOMyahudp92F1RN+P8Jy+z3E4pItao/UUGLOmwknHEDojcKUkqzBm66Ij+/Mf1eXznuAPZ17O5+75Ty5IaXUz/9RaEgdOIW4H7pdec6RUdaqCcSkCV8cw86Kw5kUK/3wZv1dzjvK9zRexj5xD/D0n4neliZ58BFbzuD7byF4LhWno8WWKUG35SOWSbKgCP0LbtjQhyyTv+Zh1es7ane1cdP6FWotLZ/GldicJywJDF62JoOl2Mlmpnjo/uOWlRIuzZ8ARE2BjJ/xwReXaLvmz/ve9B+qioCc3ayXkmRb4xTP6uw/PhinVkLDhvpe1tdme09uWEDF1gPZT8yu57CFDNxNZNElX6/bH7A2rLJg76D3f5wW6bcCBqx7ju7f/XqcMffqjfJ7DK5PN91n8ks8HDpG81glTqyt5xKUAQt7TnboKLjTEtSDIufpGJ8NDnnqPoX+k2/VASZ2Hfu1Xv6p/VAo/m8Pr6NIVmI5LDxa9WVJK2pIQok/Wi/J9un50VzlLJftAL6o7wByvzXcZDfdZIAZcp++Xr6VPU4ndEObwBvPrlQ4albrIe36vasEAJQ09GdLFP5u6dWEH6JehpD0ryyLvCap7EUqVEH/PSX0EeikYJ0wDlcvjZHM68KgUyimilMKUguowfPOmW/nMVdfpBtHBNaqcoy+0UNCVnq6HCM7n/vFBkqtW0BGYUjlPYEpIjOvlTqlJYX3ovfhBhWCPC1EXwrsRQHNLzaMMPdcydozQRecMml0hZ07B/u9r+d1zIdJ5C5ms4m5vG/8+bTKitZVE3By2rkPIYO6NwFgrtRAEaPz5Dai8XnX87iyFFc8RPf1ohGWS/Nh7++ynHFd3wjIM/Jx2YVVZJoYhiQqPkB3GR2DEo6iiQ1h1YAvoVgIjCPj6rR387sm/8fl/uwZlGtCV1sckKObBxCg62KbQ8971dHZYMNc/Oie4FqVoiCgaY5KvJ7Sl9oOnK9c6oOtSgM4CfHe5VkrmN2m64BJqwpon58FXtBxavUNr9otf1lk4J03W2y3ZqOXZkRO0q2dCEvA8Zm3+Z/htK9AB3L89iRX40cwf/ZxbrzTgwOlkXMFPXojw0HqDhzYASlEdhg/NFsypvDPllCAnIOkpVWl1F7Sm3rtzeal6MO9WzCEhglSlwI+2O8WPSukKtZBZYaTzFeBDc3Mz6ZY0SgisniwChRm2yBV8bDtEOgPxQeRv9x330fPQUuq++XlkMs6Oj34V0KmHIhah58EnAYifdxLOpm1Ejp8/sostEZsohax648n9zc3Nu72Pj7aqwsZA11hXAZZtDnpdop9Z6dmUgpFusCC4PnhKoGyLkDHQkpGxCGPuuJHt//IlAJzXKm+an9Yam9fRhRGLony0HW8ahC1Jc3MzVSH9LNtzYBTzeK6LGbBXCk/78EELAn/lswi0v7m9IMm6gqwreDk8gQX9rksInUkh0S/57gj0bKHXYhZUHg5lqjs+/GRrIyt9iwtm5DHfvYhxW9ah4tUYr7yG3d8sGnADJX46W44TKMch94/VRI6Z16cS1s9WcuyMhhpkPApx/QIYyQRWrxhBfyhfW6ol4SqkxPBc8MAWIKpSmCUXkGkQTUVAKWwlMeMpMA28rTuZ0DRB7y+E5otBz4+iB/UxgcCmM+tg7mgll3MJJyOIaESTxZUbiPRAoQg1KSZUCSZUwZeO8BDSIGV7fHmJQCGZVQ9rW+Arx1R4gH60QleTLttS0cTPn+ExPmUgBZw9Xc/bz/wVframMv7e/W5/tqbyW9KGmrBgrJMf0hHzthDo/Rve+v91K9aVl1Fz8HSuflcPX18Dr2R1UKwjK/j+coMPTHdY2mIzoQpOn6IYVyWw+uWFmrKSRdFV0OZqQ1SnSvUE5P0eFf+dQEeae/egHA6+qlSdlVjelKq4a5sPmE57xsUvuIQFOIZNgw3tRQPLg2JXlmJXmu5bf0fovFNh1nQiwiO7WGuYHTfdjjVlQqnKnKpLz0dGQsh4FFkVJ3baUbt1n0UkRKmF/bDc5LvA9OnTd3+nwIYfLM7x++fhsU2VzzFbm7pzxsCR4yv7x6wg3gJEklGMfBeDTXFhW6Q+cyGF1S+S+8cqco+vRDkOkRMWAuC1dQfskBbuthZEKITRWMu0KVOwDD0fOnNgptMUETg+SMNEukWEGajn3RlUSxvme88g9vsHKPqVbjvPZiIs6FcXIoQWxAItdNN5SIzAelSOQ96ztCKSz2PY9pDP7oHNIe5/PUTWFbx/cp7TxhVQF5zF5Cf/oVXPp1fjdXbr/P5ebog+12kY+NkcMhbB2byD1iu+A+jgpjG2ntiZx2E21OBs1FVW4QUHEzvnhEGPNRT0+6nvlQjZutLZcZEhC1lX3cddKKTEqEvhd6aJmDaGaWI21JC9bwnTzzqcog/hXrdDoC33cNB31JMWwveIxmyKuSJGNo9qqMMMWaiOTnBdVMEBOqCmGjyPGV47sqEBvy3Nfy9QZOPVVDlpXhprMbU6pJlNQzbfPAG+8DdojMHlh0EypFCbdyCSY0DIsp99bMRjW87gskNdfrla4RgWZ02rFCxtz2gZ2FGQSN9H+t6Qq+4+L9CVUqid7eXPoR/dQOGqG3F++lv8ww5BTmtm7us2nfYBnHxYioU1ea5bm+I3/xQgHF5rgWWvKA6qV5w72aW5TqAKLkoIukIJPKW5J6yQJhba2aOVj1BAlGMHQdZS2lLO0Q1uQWtqnq9X37BV0Yw68xUh7gT7Or4WNrmgO73hFPnj/Q/xxY9OwTPAlwYC7Qu1JRQLHtxwM63dXdo1+41bkdMPoOvg6Uj0xHS3tOBuadHBmWs+Q8EM4Rch9r5Th7QiCs7AABYE1Z/RMKAG5ojtJh566KHdznIpkWQNuC5FubMQaA02amktvTdxlCF0fKToaTeMEQsjtndpDhFr4DSPHD0XWZ0k949VdP7oTgBCs2fq4qtYGL8zoxc4dM6/2tHGg4vv56hjjglcegqjkKMhYVNQJmlH6CYNpevu6gZANNZhnHAk0eZmnsg20F2A9d2Dv3ZFT7/AxYBfpDS8YI3ts9h15HTthZ938JSFNNCNor0KXW/ve3jt6jibsgazUi6njy9wSMAbrhT8Y9njHDbvRFCKlo/fAED9966m+OJrOhU2lSDUK4hecvVl/vBw+bvC2nWwdh1eawc1V32E3GMrAnfK+3bf2gsK45RSyHAImYjhtrRBL825N4RpIlMJqpAYpsQvaO6cRx+4n7kiRi6a0BQVoRCyZSepIw+m479/R3jhoahpB1L4f78n+ZFzqG2qQbmKfLadrmwYryONYZtI28LLFVE72rGcIkUlCCmFyhWwlKKBLOR6ONTwULlqVEs7bkMNhmFw03EgfBcZjuKXaJszWURVQsu2QoEvTGpjR6yWmaEeaFY80xPnPeMdJEpPhhofevJQlUBJwc+HuXX7vEBP33FfUL2mIWJRzHNOwb3rXrxHl+E9uoxjfZhs1zDxhWqiF5/D1+aZrE8bxF5ZT/24JH/MNrGyxeS6FhtDKOKmQdYVuMrhC7N7uP9Vg7ExOH4y1BhFolEL3/XJdOd5vD3GgjqHx3eE2JyVfHpaN65QELLJRyIYuRzdSk8qPxRG9OQxQgZGNIKvwCrk8R0XMjmyhsR0Hd2UIJvj+GNPAtfHDCSs8HX6pek7+Hfdh+jswhfg2yFEoYC/7lV46VWEhOjH3kfu//0R88Cp+JObkVMnsTOrF5Kx8b58GyU4nl5UxvYS6Mr3dVs4z0PW14AU2uR9EzjrrLN2a/uAIXdQZJ0KHwZo7XywxapUti+EXlylEKhxjUNWXQJYk/sG4XJL1xA5dh5GMoGvKi3iZMhG+T5nnnp6ZV/fw5ICQ/nEQlAITPmSua26AsqEZALrkvNIAu8H7n4tTNtWOWhrtFLOuyUrhE9S6HhrZ0ELeysI0HbnIWYpCsJCep7WYm0bMUijkNezkk1Zg6lVHv9+UHaAO+bURSehmmf0SQ3c+blv9tmm/gfXlIuahGWilBpUi/e7MvQ8voLcYyuInXnsbgtzVdQxCVlXrVkc41HN32+Zw9Y/CNOkNK2rLj6Lnr8v57h/7KR47yNIKt5EQ0LPaxsoPvsyxWdfxjAMDM+j5xcOkWsvRxgGtg9Oew/JmEnGM1A+NFaZtHYV8QwTv+iidrRqPv6wqa0aKcGyoaMLB4HfnUPmctqFJQS+NHR2TSgEmRy+aUI6C4Ui1TFJjeyCouKIJoMjiq2ILk1lLYKeuIRDqEy27DoaCvu8QI+csBB+/seySQ5gnnIMxolaA1TbdyK+fzsH7GiFde0Uv/1T4l+4jDmpKgq3/BBRV8NH/+saThsn+cbaOFlX0OWWcsPgO89o/8nzaXhkm8I2QnxhVjdpz+DHL9Xg+PD7Vyua14tjbJrjHl6PT1WuQ5MWCaGZ5bq6USEb0eNAZxcyVYVq60CZJtIwtSlpWTiOT5oQzy59gjlTZ/C3zSEawz7z6hwk4N73KOLRpRjHLsC58HzCBvgP/B155Dz8f76E2LyN7Pz5WEcuLFOPSqGFia+0ACwJ9J1ZHfwNW9q9pOjrdhII5Nh6VC4Plqn50d/kM1uxYsWIK0V9pS2aoSyKriCzpcRqOJhvuDftacyqHEsIgYgEzI1SDmCmlNEwIh5BBQVb6V8vJv3rxYz59Y0Dg8dCsHLNahacoDNHIm4uuMl6KUqF9b0ucVvLQKCT6iv0akM+jg/bc5K8JzggURHApbiAlDpG19ajA8B5T//mK12e3pkpYlkWruOTD0eR6SxgaKFu2aieHvD8cmbK6nZ9cz45M4tRzEO479j++cJaZs1ZQPzcE8j86e/l76OnH43f3k1++Vqc1zaXBXr6nof7UEeEZs8kvPAQun76O5z1m+havwnrgPHEzzt54MPaBWQ8qusdLBM5pq5y++OxgVHyIRB/z4l4rR2s/et9HJqs17G3XvMmv3wt1vRJqGyuzGVUfP4VMvc+ilGTxM/kkOu3Eb34VHpkAqOjHZmoxgyZFDIFqpNhul/djuU7RKdPoMuzUJT6qwqssEVSZekIh0EKPMfD2dqKGQmhPDBRmnTMCJ5ZybISgb8/FNK00ULoXrpC6owi28IrzashMCKBLoQ4Dfge2ir+mVLqm/1+F8HvZwA9wIeUUqsGHOgNwBrfyJjbrmPrZ76JeFcljascMBk3Bjn3YLy/LEFMbEJ1pSle+92yxFKt7Ti/uocxM6bwvfpq3EkT2JKz6PEEy1pslrZYTI57fHZWlse22zy8NcS3n6/C8WFc1Kc+7NNeEJw3Kc8tL8b4wQsxTKHIeYJLp/dgCjgw5bKsJcqEmE93WuD5NgvrCtDagREJc8PqOE1Rn3+Z2kNIwtdWRenY3s3Yhx5hVf3h/E1Npb16DN+Yl6bY2gX3Lyc+YwapD70fU+nsBXmOfjnk8UfgKa3N5xztfqg8By3UM0WI+A5FX+H6NllHC/SiV/HVhi2dTSCrqzQPyyA8MW8U27YN0Q5+ELT2aMthqCBeiS/64Hot0DsCAV+i9rFkhR4ABnJ6y2gYvztT8Z0pH99zdAqoECQ/ch755c/id2covvAqAPnlzxI5emAWwbYdlfL0sFuEqFVeGEsLatTS9U3dJVKzqr4CvS6sVcUb1iTIe/CjI7qJDZKGagr9fA2h70/J3RIyAFPiOwUcJfASUYxMTyAAJCIZ1xatrxcKx4cHt4Q4tNqhxs2WUyohWPhzeXa2thC2IHzMPLL3P44I2/jpHqouOhOUYvvTz9J586/ITBxL8qPnDeABqr76owghCB92MG03/JjQu2aQuOjdux2HUb6PMOWgLjJjmOys/hBCYE2bRMekehpu/Rre9lbarvmerqWwTJzXthA79SjM8Y103fp7Up+9iM7v/Zr0b+6vnA/o7mzDzXn4L79K5+GHoDq6ka9twb7x33C+/gNwCmQPnIJ/8IHEm8dgtLZRnDgBXliPcdShxNPttP/oLuQxC6g5fi7df11K+NSjcaWFv2kLxd/ei7lwNmLRERhK4ecd3N/8D+bC2fhbW+DYhfhLVyGiYayj5lFY/Hf83z8ANQuHHvtwrbiCm2MA64CTgc3A08AHlVLP99rmDOAzaIG+EPieUmroswKHHXaYWrFixXCblOG1d1HIFukoCO1rdQp9/JXe8jU4P/411hcuQ9RWU7z6WwDIhbNRr72Oamkrb2v+6/nIA6ciGutwleDpVouDUm6Z1KmrKPjccl3McfGUHCc1Fcsa7eo2k+89P9CEnBT32Jjpqz2Mi/q05CXOIFTkRz21mNP/dgdZo4CIjdEmoYIVhy5i7tolANx97mdJzpnOR6b1IAUkrMpzKrp9aV57QymFV3BIhCU5R4Flaf4MWwt6UwLKp87S1ae7UzA0UmzdurXMuLgrbE8PHIPjwR9e1A0Urvyb/u7GE+D6x2DuWPjEvAr3NGjBXhvpm55YglIK9/Xt5UpXPE+3xis4utxfCIRl4rV1Uvjny3TdcjcA9TdfhTmuoc9xtre3MeEgTRDlbNkxIJ0zU9BxlcIDS2j/1WJEyCb802/02WZLVvLlVRUhPz7mc/2cdNlV0xte4HYxRMUtXmr4QbEI+QLuhPGYO3fqWEFjXUCSBn5bB7geL3cbfP2ZOJ+Z3s28qVEouqhcDoTUXDlFh3TrZiYf0EwoaMjs9+RR+UK5DqHlU/+J19o52OMDYOzd/zXkb7sD5XrI6qo+fD5vFM6WHWzZuIlxk5sRQuB1dCNCFqqnQPGVTUQWHtp3+41bab3q5vLnEtNoL8eAdjU5LrIqjtOVGfSZ9Ud/11rkzOPoWvsacsMmjCDZInzC4Tibt6Ne3tBnH+uoubhLV4EC68Pn49x+D3JCE88/s63xtH/+smWw841kCV0ArFdKvaqUKgJ3Aef02+Yc4FdK4ykgJYTYA9ywFVgmhJWLW3QQAQuc60Mh7+DMPRT7O9dgHDQdOaYe+4bPY7z3DLjsIuxvfQnr0guQC2cDmh+7ePW3KN7wfeTTa1jwvw8Se+wJvGWr8Na+SKK9hWuatzHZaWde+0s4f/grhU99heI3f8yhm5/lnPE5LpmS47ajujg7uZNoTzebO32qO3boC1UKu5hjS1Zg9GT7EpQAVjHPCWv+yqrJ87gyWccr4w+kPuJTG/KZu3YJncl6/n7qv9A29UCeaTf53PIqrny6qg8fiJA6p7lMwO952jQDhOPophDVNRR9vYGrtC+6pAX7RRejsQ6jLrXHhTnoPPSRYjB3/QutOnWrJMxB5+7ecgZ8PFCcBdoCV2ghag0xk4UQCMvSpnwyrjsuNdZi1Ca1CyYYv1GbInrc/HJRVvruvwzIrrrtV7cDQQHYIL7qeAhM3yVzz8NIQJ50zIBtxkZ9ZiQr+27OSp7rGNxQNqQupumj6Ho+hCzkmHoK1dVaqNSkECVLqzJyso7gf3dqjXxq3EFEtIsJ18NxXGrMAk1jo9z9p7uwZWWsMhouC3OAxL+cDUDtDZ8mNEcHjlP/fsmg1/ymoHzkCN0qu4KQkp/f9dvyZ6O6ChmNYNSlBghzAGtSE42//E9Cs/WCnfjgGfr78Y3U//Aakp/6AA0/vZbaGz6NKhQwhL4fY379TSKL5hM//2Sqv/gR7AMPIPmx85FVcYyGGuquvZzIcRWWztzix7A2vU7qvBNouPkq4vNn4T/2FMYrG3RldPDMTQlq2SqMIPPL/3/3YAiov+Jipu1YP2TzvJG4XMYBr/f6vBmthe9qm3HAyG3vESBhK1Q8prmI01nssCRkCRzXxa+tKWu6YsI4vKZxRM2Aw/jI+dhHz8dtasT9ozYX1Wuv4/z414OeZyLwxeD/pVfPf/EV/Bdf4XQhIBLGcxzOcFxOB9pcE89VJCc3ENq+HZQiq0zCuOTGjqOnqppG20UcOpPu+58gbhV49PiTyLw6gbuP+iCnH9tF4Ws/wFy3ibUfvpRz5tTwMbONz6+qJesKCh58alkV50/sYX6jxy0vRNmcNTixMUfOlzSFXc4Yn0d5LqI6iYzHcHwwTe03tqQot7wCnact+3Ne70HsTtriYNktJaKkEo6ZEHSJ6bX2lIKgpSyj4WgdzLF1fT4LKTESMTAMVKanT0enmi9+tOwjzj+1lsiRsyvjmhJw33heH82tN5xXN6NyBWqu/Fc6DzxkwO9SwJUHZ/jfnRYpW3HTP2M8tt3m0BoXVSxq7XsQlPz/+H6ZcTOSDKzFQVxmvmlwxf9GyXtQH/FJRoLesaaJl0yQDEvskEBGw8yYN6dyHtfrk08OEDn8UMK/vhFhWdRcfWmF3+ezF2E2NbDHoBiQofOGIQTTJh+wW7vIaJjqKz+Eu2UH5qQmrIljkbVJjGQCs17HD+wZzTT86D/wu7NlCy51+QXlY4Tn6orc6EkVUjR71gEk3n8qIhwie//jWNMmlrer/uxF7PjQl/U+px1Fz1+fRAC13/gcuUeews8VyqybNf9xGebYSpXxoGMYwTgHm7f99aqRbIMQ4jIhxAohxIr169eXqwmnT5/OunXrWLlyZTmYdsUVV3DTTTcBMGHWTLZu387jTy3lPR84m3B1gmtuvIbf3307NVUmMxfOgJ4u/vb3B7n0ExfRGIerv/hxHrz/f6iPKiZNr6foKv6sOvl8eCehH93AZdmXeOyU2Thf+zcOWfE/mJecx91jLb6stmB+8Gwu3P40T887gNYTFnDECw9g3/B5fh7O8A21HePw2Zz9wkM8P3cKm448mAte+DNjJqb4ydpH+YFsxTzzRM5e/Xs25bvYsPF5PvbHm+C1jfznf36Zu597HJGq4gffPQfLsols+jvnX3gO9r99hK+PyeFsvpfxMZ+D5k/j85Ne592Z37Hxx9oguvY/PsG//mAxm7IGf/2wzcNtCX537x/4jy9fzjYV5YJL/5WHH3+ETDrNzHEJRCrBb/7nDq760uewDTj/wnNY+tSTbG9rLbtEbrrpJq644goA5s2bx8qVK1m3bl1ZKF933XUjfk5NTU1s3bqVlpYWFi1aBOhy+ZLGnkgkSKfT3HfffZx11lkoBZ++9EL+8D9akxqT0NPo/nt+y/O3XMit74bC/zuLSdvvI5tJM2WsdlXc8YtbueqzlxG14L3vXsTKpUto2b51t8dkRMPMOnIBLz73PCufeYYFp2g62hvWPs7PujeSvf8xJs4+mK3bt/PY0if5zg++F4zp4/z8zt8AUD21mXQmw+KHHuTcSy7CT2f5zAuPc8+qZRgCmqbqF/AP997DJz//cQA++vGLyP5zMZOsTh79ZA2r2iy+dusdfOGrV6E8j/M/eLZ+Tju2M+fIgwH4yW0/5IavfwWAU048nGdWr+SVl9dx5Gw9pu984zq+8w39nI6cPZ2/rt1Iy/pVLLv+cGpsn+u/eT0//r5+TofNm04218kTTy/n+OOPZ/z48XzyS1/kttt/CYYxYEwAl3zu09z5B83FYo9rBOBPO17lI9/RaY7nXnIRix96kHQmQ/XUZgBuu+NXfOLKfwfgxPPO4bGlT7J1+3YmztZj+u5PbuEL130VpRQLTjmRlc+uZd36l9/U3FuyZAmLFi1CWCZ33fdnfvbrXw36nAAu/vjH+O3vf6eVnkBQ3nXfn/nIt29ACMH7rvsif1m+dMCYPnn9VzDHNQw7JkCP6ZlnePnVVznknNOQ8SjffX0t3/77YpRSzDpqIeu3bmbjB4/n7G3LSX74PXxnrMevxhvYUyZwyC3Xk7vgJF64aBEXZZ4jdMh0Lv+CntdDYSQ+9COA65RSpwafvwSglLqx1zY/BZYope4MPr8ELFJKDamh764P3S8UwfcxmxorTHJK857IkI3Xk0N1axeHQiCCxF2zsRavo5t8e4YuGcLrKWAaEiUFwvNQhoEwDBwdL+vbXqt0bwpFnQHSz2eqCkFvNFkpGValFEvXg1weUpo5zt3Sgvf9X6B2tGJ/9bMUJ0zgum9/ky9d+SUS/cj+lRdoSdkcyjRRAtqjKZ5sifDH5zQN4W3vVuTNEHc+67N0c1CEAXzrJEFjDFZvh1+sgQ8cBAur0uTSeWLCBdfDi8cJ1aVIhrXStztViSNB7xdxKGSLOkbZXehLUPTLNfDoRk1r+pVjh97fkFAd1lkf1SOPlw2A8jz8jm7dyi/wpwNk73+c7l/dS+pzFxM5cjZKKb723zdx3fXXo7I5/KIzqLuq5/EVdP3oLuq/dzWdiboh0zFLeDVtcMNqzV8wb6zihGQXazpDLBrnMi6mLYeuoqDo6AA9vo8YN2aXrrLb1yj+/rLLzEbJBxrbaK6zcFLV5W46vXP4r7vuOq699lr8tk5EPIqf6QHXGzKHf0+iXHavFNgW5pjhufZHCr9Q5NqrruarV39pwPH8gmbWNMbU4bd1gu8hU1V4Hd1ldsly3ijBOy3lbnfg6g3lOGVqYb/oYETD+Hlddbg791gpxaZDz6udsvOJ9sF+H8mRngamCSEmA1uADwAX9tvmXuDTQoi70O6YruGE+RuBKjg6Ai56mQNCllOzjGikzC3dH0Z1FdFUgiiwva2Ab9qoji78uipUVxqRyxMLS2wbWrpd7arw9YOUUsDYRuxcD053FoTUXBemRNSmtEDvyenEfykR0gDXQYVsXDuEIXQQMz6hgfyNV+sOMgIiQnHRGacRNRWeL/CVwsZFuD6iKo5IJlB2BlEsIooO9dVh3lMDzSmTCTGPUMwgBHziMMkhjbC+XTPEXfcYTK+FNYFL/9bVcIeIohyb6+Z0M6Y+gRWNkPegkNVzdmxCVz6GTU1kVcJgudIjwfnnn7/LbXocTadQsrDXtcEvn9ENBQDeN2vofX0FIVER6m8GwjAQVXFkOFRutwcQOX4+uaWr6fz+b3Be3kjikrM594STcbe1IqQoz7v+KKVAyliEsKlTMocjgRsf9dBRL4uVOxUrt1eDUjy8A344t5VY1OIba+PsyBnMrnP54IQ0Y3ch8Fwfnt4mmD/R5DMLBf4WHywTS+pipP682ueff75uLhL0pVXFIn6ugAjZ+PkicrCihjeBUq45QeEQcROvoxsjGd9jMR0Zsjn/Xy7SlAwhGz+XR0bCmvDOthBVKV1V3VSvKzsjYWQiht+dCdhMw9oXaEqkDINp4Kd7+ih1SikIjj/seF1PB6GLDlgWRnUVRiKGge7rq1y373E9b2j+JE3clx/qXLt8UkopVwjxaeBB9BB/oZR6TgjxieD3nwAPoDNc1qPTFj+8q+PuFkyJMGR5hXsjKE2Uxtow3Xkwmqo1i151SrfZ6s6gXI+mxloMp6h7T5oW2byPa5p4dhVVtkBFI6R9fdtKnDAqHIYqD1Us4hoWTmcGI5/DCtvURXURSCqiBfv2jF6QqqKwes0yZh10MNUxg2LBpTteTShi4UrdPT2UqtLkRF3p8vVrjpq+D/uoCfqv4Gk+53+2wKkHwMENcPszkC0I8p7gz+11zLEFC+NaIJYoA1oy2j9WCCoVd/RAla0FQyrcN897JFiyZAmHHDLQf1xCISBK662ZP721IsxvOA6aU0Mf31N7limznLbpevi5gu6NGo1Q+9XL6br192QfeAL7XTN4bNmTHHLooah8Ychc/RKHiYhFiMsgz76g0w0HWxz1PRCVlCUhmBQqsDFjsN5LsO5Vnx09EgzBmg6bmBHj48MzH/PYRl1xfFxzYLmNqUdJA9sYnNa1//MSto2f78BuqoeW9gppG+y2wFXFIFjfm8USEI6LTMTKwVc/3dMvqPvm8cSypRz8/gv185JSWxyRMMIyK5k0hoGIVDhjZDIBno9Rm9JeASGQtqUtue4sfq6AjEVQhaLuDhYJo3pyQYBKacHt+3p+CKEJ23wPa1yjpkzo11PAaKjB29ZayTsvOsh4BD+bL2vupZiFclxkMs6UnU/0DDXmEb0WSqkH0EK793c/6fV/BXxqJMd6I5CJOFjWmzJ5yscSWrj2hjAkBLzTJkC4skE0ot0DBQ9SQU5x3NOTsq1HByE8BJ40IWIigTGTqsluh5TpQbFAMmzj5x2skEWtUSSDRdgUZDKdNDTGoDtNtLmRXM7EUVqYlno6ykhYB2H9XfO5f+AgmFYDx06sbHvzyeB7iluecFi2xWbZFli5Taf/HTE+KIbwdZZI0YPWHFhCc9uEDJ0nrtg9gd7W1jbs7yWq1954vRvGJ+DSOcMLc6hw6uxpGKkEMhHFfX0HKmh3mLz8AvKrnie/fC3tHR1A8EyGgJ/tQURC5RzsqF3hCSq5X/qnu928sJv1spZbVsKcMYLLJ2X51NM1/KNVsGarz8GpIsfMjPDjlYIn2yJ0PKm5bKbqOB3P7YRHXtM88Qkb7noODqrT7dZAWyGlxXkw9H9eImTrdoeGoV0wXWlEwD+gLLM8Nr/Euhg0mOitVfpFnQtvpKp00RpaMKmiq62hnhxEKoF5mYhWovZ7CG1tbZiNtSjfx8/04GdyfVr4DQYhBEZtSl9TL3kjDAOzqQG/M41Rk8Rt7UA4LkYqgS+AcEDc7rhgmfgd3eXPZkMtwjAwquKDn6++GmfbTm2uCoGsTgb8MUEGW9EB00RGQkNy7JSwz1eKQjDoYV6itxoxG3pnn5eE0dhe99YNXG6lqs3I+GqtvQd+SD9fwO/OEm+qIZLJQU+Oyz7xCeyaKlRClzeHHC3Ek2GtzW/LBgyPUi8okUCJ610d2htVIZ273RtCgDQkC5otgsb3LN+q//IuTKiqCAbbCJoyB1w2BU+fY3eN4F3xoTte32v3lRboc8bAAdWD7+P6lW3rR0iO9kYgDANz4hjczdpnJUyD0CHTyS99hksuOX0Xe4PK9GhmwQCm1NfbHnBxKRhwQ2tikgU1WvAfMxFCIslxzYK/vap5/9871eGACTE2dsEDr8DzrbrBx9VHwbY0fGupPk7R04ui68Olc/tmBdmDsFiW0P959e7taiRiVCiVw5V4gyGRJQ3S0eQzSgiU4yGkFoYinkRGQviWid/RBVJijqnVjca7rT6ZVoMJuzeL0riElDpt9U3mtwspyxaFiIQh5Guum36EYUDlXmVzu3TJCMvUPDwFp+x2kqkEbkuHXlzHplD5IkZqeGEOb5Oeom8HlJpoVMrOg3+DSS/DIcyGGoxYBLuxBmt8YzlPtrRNbVTztYMOzk6oqmjOdVEtYJ2gKtINsthcn0GLl3pDCMHCyTZfOQZOOQAmBk1wfvkM3PAE/HQl3LsO1u7Q390W1PiGjIBXROlrSBe0YHKHOV/OgVt+ciuZQmW7bFEHP1t7YGu3dul8b7kmmPrpSvjQvVprf1fj4McsjbOUa76ng7j9IaQsm9UA8fedjExE++Q1DwU/m0f0i+WELf08bVP7/EvMmxAEBU0TKeDUKUEDZdPkfYEGfnJDD81j9fHOnQnfPlG7015sg+sfh1VB8eq0Gk3f+sArMKteF1qV4PqazGwo7KpuwIhHMRIxrWXWVSOTcZAGsi6FiEU0mZZtgedjja1DxmMYdSmMWEQLwVhENx5vakCGtfVipKrekhqIocYleiUu7AkYsYhe7BjcDaXdN/ERt20UpomRjOuuWmgr0EhEMeuSyHBoRMIc3iYa+v4IYZnD+plBC+66WIVpT6EFY8jU2viOLAhVYX3c1byZVqP/HE+T6ddGNNdy7w4qJTQl9AJ13zq4bG6lAKhELVAT1u6E9pwW/FmnUtnYNPUQOgrg5wKtX1H2N1gG/PVlWLld/5VwaAMcNkQpmh9UgsZDesH4v4BRq4WV19KONWEstd++gtkPLN7lfl5b56CEVNXhwFoSIPO96s2KRahODtg+YsFXjxX42wrIcArQ8Y0xcW2FPbcTXuvUf/VR+PeFcPNyTf98Zr8ygBIN61DY1TzsDyOZQFbFyzzjJYtEebojkLGHfeFvFLs7rj0NYZojbiEoQjaYRl//es3AebErjAr0vYh4fNdmphSUTfS4XWHbA/2iVtlauG8OXHa9u5OX/O6Op49R0nAtA96jC+LK3Cert2shYRu6Icfdz1eO8+1lcN5MmF4DNRH9+0H10K4tb7rz8EIbzG4MKIRDcWRwnpKL6N518OgGuHyeDtr1xn+dpAX2UAtSyZUFb43vfCjIcAivROQVCRHdRRNXr6Mbd+NWEh8Y6Jrp7e4IYmWookPBDmMPIwBFQ92A75oS8I0T4OFX4U8v6dhJzNbNFQaDtYumLCOZhwOuqx8nee9/9xW8kXHtLcj4MC/AbmBUoO9FLFu2jFNPPXXE20vRV6DFTB3oEgLqYxXiqqJX0eodXwvtbJAm2L9E/qD6yr8lLbytB774iF4ojhivy/D/8KLetzYC27Pw4XfBgia9gNyyBlZs092GFOA9sowzTjsVhLYirl2iW6sB/NdTut/ijSfA1ox2BzTsgmHVZ8REe3scImSD55G552GWfPcnnHr6aUNyjRSffRmA0JyZwx6zLuwhpUCETPJ1daQLOu15MB/3cG6Ckw/Qf8PB87VlMxx2dx6+XfB2Glep+vdNH2dXhUVvFXansGh/xUsvvcSMGTP22PFaMpWUvs68ppwtmfmgfdg5Z+hO473RltPaP+ic9oKrc9r7w5a6dVoJU1KQyrzESmcGqXCFLXF8Qi8Yjg/nTNdd00eCUjvAiak3lhP/ZuGls/hdGZwNW1j+ueuZ3nwAVRefSeSIdw3YtusXfyT32Aoaf/m1IbVV5TjIWBS/J68DhKaJr3TPyNLwrH5WVu9nuLsoeJoff7g+oXt6Hu4r2F/HxTB5CqMa+l7E4sWL9+iEM6RukNEQ6ts0uYTaCDi21pp3JdR7B9XmB8SJq7ZrTfzQBh2AAy3M6yLwhSNgW0YvGl/71mImnzGDzoLuer6gCU6fOrIx+KpSNesrHfytie4dYQ66QMjvSmNNmcBjVo6prR10/vcd+JkeYicf0WdbZ90GrKkThnU9CMPAqEkiqytBQSn0IiyFFuAlmuMyd9gbLPByfH0vhxPmsOfn4b6C/XVcw2FUoO9FFAqFPXq8moh2wRhy8L6nQuhMi3DQqq1UBg5BzrsI+JGGEB6fnq+15WjQLGPZZp1qWDrX2ASs2gZjwwUum6s1wynDp/0OgOfrayi5hiKGdv3sLQgpMeprcLe3IuYdSGLOsRRWPU/614uxDzwAa7xOzfFzBZyNW4mfe+LwxwtS2PpnPpQWUENqSmFD6pzy1h59f3fmtDU0UuQc7WqpGUGm3p6eh/sK9tdxDYdRl8texIYNG2hubv4/P2+moF0qybDuCFRKucy7gVavtN96KEra4aAUrHtlA9OnNO8yxtN76jl+pZdrzNLUAKXFaV+A8/p2Xnv1VSZPn4a7aRttX/4+ynFJfvICosfNp7DmRdpv/BnVX7qU8OzBfejKcZE1yV3yffe2UkoVtVu6K5q2UtqVEjIGj6PlXR2X6M9QORT21jx8q7G/jothXC77yOvyzsTtt9++V84bCxosV4X0iz+uSgdVx1dpv7kQemLsaq0vuQdcXwvgEu658/by/32l3TJu0GHI8StFWArt83d8rUmGAssiYulA7r4izEEXGN1xz+8AzZ2duvJDAHTdcjfOxq103f4nZDKOPWPykMdQvkKGd13tHPTdALQrRgS8NaW+mI6vNXrX1xlMrl+JY7jBvYxYI0+a2Fvz8K3G/jqu4bAPvTLvPIy07+aehhCVVLbeWTNCaGE/Jq4LnEr9SUvZL9liZdtSQVPU0hrkmHhQ+KTg4HfN67OdUHoBSdrajVAf1edvjGvhUxXS563bzebw/5cQkTBzDq7kNYdnzyT1uYsBaL3qZrxtrVRdcjYyMrR/SJhvvLilIabdZaV7Hrb0c4nawYIc0YujKQfnaxkOe2sevtXYX8c1HEZ96KPog5JWZwrtEy96urzcMDQHTk/Qxs5HC45EqOKHj1r69xJUUNBSG9XH7Z0+VxfMvIj1f5tb/kYhq2LQL7UscuRszKZ6Mvc8jLujjfD8g4fcX/n+m2oqUoqL9GbA9BREg0sKW9D0NriPo3hrMaqh70WsXLlyb1/CsBBB9kVjXAuOmgiMT8KYhE5DrI30zZapDmvXzbp/rtRuFqWF+d7KUNmTEFKy5qXnB3xvNY+j+ooPUf/tK4am1HU9hJDlsu43g973sjqyZ2gQ9vV5+Eaxv45rOIwGRfci9tegzYYNG2gc1zzioNzbBa+sWcvExrHguH3oYHcJX2E2Dd86bG9if56H++O4GA2K7pv47W93Tfb0dsRvf/vb3QrKvV1w5z33aFZBZ8gevX2gXA+VLyDrUm/thb1J7M/z8J2GUR/6XkQotBcTrN9C7K/jClfFtYBOxFCFwrANV5RSmpypqX6f4zjpj/31ee2v4xoO+/ZM289x5pln7u1LeEuwv47rrLPPRkQjGNVVu85WcVyM+tQ+L8xh/31e++u4hsO+P9v2Y9x55517+xLeEuzP4zIbanRDgmGaFiilkLHIHuXffiuxPz+vdxpGBfpexBFHHLHrjd6G2J/HVda4w6EBvnTlerpxsOshR9iQYF/A/vy83mkYFeh7EZlMZm9fwluCd8K4ZNhG+f0yxHwfig4yGn7baOfwznhe7xSMCvS9iGeffXZvX8JbgnfCuIRhIMy+r4+wLc2imNiHS14HwTvheb1TMJqHvhexdetWmpqa9vZl7HG8U8bltnaAqxuEKsdBVsV32ZV9X8Q75XntRxjNQ98XsavmvG9XvFPGJaJh/HwBVXQQhvm2FObwznle7wSM5qHvRdTW1u7tS3hL8E4Zl4yEkRPH4mdzyGh4L13Vm8c75Xm9EzAq0PciFi1atLcv4S3BO2VcQmieW+Nt5jPvj3fK83onYNTlshdxzz337O1LeEswOq63F0bHtf9grwVFhRB/VUqdtldOPopRjGIU+yH2mkAfxShGMYpR7FmMulxGMYpRjGI/wahAH8UoRjGK/QSjAn0UoxjFKPYTjAr0UYxiFKPYTzAq0EcxilGMYj/B/wdphCoOWqpgtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attr = 'whisker'\n",
    "ymin, ymax = (-0.35, 0.7)\n",
    "yticks     = (0, 0.5)\n",
    "borders = np.linspace(0, 1, 101, endpoint=True)\n",
    "x = (borders[1:] + borders[:-1]) / 2\n",
    "fig, axes = plt.subplots(2, 1, sharex=True, sharey=True, figsize=(6, 3))\n",
    "for row, turn in enumerate(('Left', 'Right')):\n",
    "    ax = axes[row]\n",
    "    tickpos = []\n",
    "    for offset, state in enumerate(epoch_analysis.STATES):\n",
    "        for side, color in ((\"right\", \"dodgerblue\"), (\"left\", \"crimson\")):\n",
    "            v = epochs[state][attr][turn][side]\n",
    "            m = np.median(v, axis=1)\n",
    "            l = np.percentile(v, 25, axis=1)\n",
    "            u = np.percentile(v, 75, axis=1)\n",
    "            ax.plot(x+offset, m, lw=1.5, c=color, alpha=.8)\n",
    "            ax.fill_between(x+offset, l, u, color=color, lw=.5, alpha=.1)\n",
    "        tickpos.append(offset+0.5)\n",
    "    ax.vlines(np.arange(1, len(epoch_analysis.STATES)), ymin, ymax, linewidth=.5, linestyles='dashed', color='k')\n",
    "    ax.hlines(0, -.2, len(epoch_analysis.STATES), color='k', linewidth=1, linestyles='dotted')\n",
    "    if row == 0:\n",
    "        ax.set_xticks(tickpos)\n",
    "        ax.set_xticklabels(epoch_analysis.STATES)\n",
    "    for side in (\"top\", \"right\", \"bottom\"):\n",
    "        ax.spines[side].set_visible(False)\n",
    "    ax.tick_params(labelsize=10, bottom=False, labelbottom=False, labeltop=(row == 0))\n",
    "plt.xlim(-.2, len(epoch_analysis.STATES))\n",
    "plt.ylim(ymin, ymax)\n",
    "plt.yticks(yticks)\n",
    "plt.subplots_adjust(hspace=0.05, bottom=.02, top=.9)\n",
    "\n",
    "if saved == True:\n",
    "    figpath = figdir / \"whisker-traces-summary.png\"\n",
    "    if not figdir.exists():\n",
    "        figdir.mkdir(parents=True)\n",
    "    fig.savefig(str(figpath), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attr = 'eye'\n",
    "ymin, ymax = (-0.2, 0.2)\n",
    "yticks     = (0, 0.1)\n",
    "borders = np.linspace(0, 1, 101, endpoint=True)\n",
    "x = (borders[1:] + borders[:-1]) / 2\n",
    "fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 3))\n",
    "for row, turn in enumerate(('Left', 'Right')):\n",
    "    ax = axes[row]\n",
    "    tickpos = []\n",
    "    for offset, state in enumerate(epoch_analysis.STATES):\n",
    "        for side, color in ((\"right\", \"dodgerblue\"), (\"left\", \"crimson\")):\n",
    "            v = epochs[state][attr][turn][side]\n",
    "            m = np.median(v, axis=1)\n",
    "            l = np.percentile(v, 25, axis=1)\n",
    "            u = np.percentile(v, 75, axis=1)\n",
    "            ax.plot(x+offset, m, lw=1.5, c=color, alpha=.8)\n",
    "            ax.fill_between(x+offset, l, u, color=color, lw=.5, alpha=.1)\n",
    "        tickpos.append(offset+0.5)\n",
    "    ax.vlines(np.arange(1, len(epoch_analysis.STATES)), ymin, ymax, linewidth=.5, linestyles='dashed', color='k')\n",
    "    ax.hlines(0, -.2, len(epoch_analysis.STATES), color='k', linewidth=1, linestyles='dotted')\n",
    "    if row == 0:\n",
    "        ax.set_xticks(tickpos)\n",
    "        ax.set_xticklabels(epoch_analysis.STATES)\n",
    "    for side in (\"top\", \"right\", \"bottom\"):\n",
    "        ax.spines[side].set_visible(False)\n",
    "    ax.tick_params(labelsize=10, bottom=False, labelbottom=False, labeltop=(row == 0))\n",
    "plt.xlim(-.2, len(epoch_analysis.STATES))\n",
    "axes[0].set_ylim(-0.03, 0.17)\n",
    "axes[0].set_yticks((0, 0.05))\n",
    "axes[1].set_ylim(-0.17, 0.03)\n",
    "axes[1].set_yticks((-0.05, 0))\n",
    "plt.subplots_adjust(hspace=0.05, bottom=.02, top=.9)\n",
    "\n",
    "if saved == True:\n",
    "    figpath = figdir / \"pupil-traces-summary.png\"\n",
    "    if not figdir.exists():\n",
    "        figdir.mkdir(parents=True)\n",
    "    fig.savefig(str(figpath), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = {}\n",
    "for attr in (\"whisker\", \"eye\"):\n",
    "    summary[attr] = {}\n",
    "    for state in epoch_analysis.STATES:\n",
    "        summary[attr][state] = {}\n",
    "        for turn in ('Left', 'Right', 'Both'):\n",
    "            avg   = np.median(epochs[state][attr][turn][\"ndiff\"], axis=0)\n",
    "            half1 = np.median(epochs[state][attr][turn][\"ndiff\"][:50,:], axis=0)\n",
    "            half2 = np.median(epochs[state][attr][turn][\"ndiff\"][50:,:], axis=0)\n",
    "            summary[attr][state][turn] = np.stack([avg, half1, half2], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BoxSummary(namedtuple('_BoxSummary', ('n', 'q1', 'q2', 'q3', 'min', 'max', 'outliers'))):\n",
    "    @classmethod\n",
    "    def from_values(cls, values):\n",
    "        values = values[~np.isnan(values)]\n",
    "        n  = values.size\n",
    "        q1 = np.percentile(values, 25)\n",
    "        q2 = np.median(values)\n",
    "        q3 = np.percentile(values, 75)\n",
    "        iqr = q3 - q1\n",
    "        lower = q1 - 1.5 * iqr\n",
    "        upper = q3 + 1.5 * iqr\n",
    "        vmin  = values[values >= lower].min()\n",
    "        vmax  = values[values <= upper].max()\n",
    "        outliers = values[np.logical_or(values < vmin, values > vmax)]\n",
    "        return cls(n, q1, q2, q3, vmin, vmax, outliers)\n",
    "\n",
    "def plot_box_around(x, boxsummary, ax=None, color='gray', mcolor='w', ecolor='k', ocolor='k',\n",
    "                    boxwidth=.5, mlinewidth=1.5, elinewidth=1, marker='o', markersize=2, boxalpha=.6):\n",
    "    v = boxsummary\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    ax.add_patch(Rectangle((x-boxwidth/2,v.q1), boxwidth, v.q3-v.q1, color=color, linewidth=0, alpha=boxalpha))\n",
    "    ax.hlines(v.q2, x-boxwidth*.45, x+boxwidth*.45, color=mcolor, linewidth=mlinewidth)\n",
    "    ax.vlines(x, v.min, v.q1, color=ecolor, linewidth=elinewidth)\n",
    "    ax.vlines(x, v.q3, v.max, color=ecolor, linewidth=elinewidth)\n",
    "    if v.outliers.size > 0:\n",
    "        ax.plot((x,)*v.outliers.size, v.outliers, marker, color=ocolor, markersize=markersize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Left- and right-turning trials plotted separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = dict(Left='dodgerblue', Right='crimson', Both='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Whisker\n",
    "\n",
    "fig = plt.figure(figsize=(6, 3.5))\n",
    "x = np.arange(len(epoch_analysis.STATES))\n",
    "for i, state in enumerate(epoch_analysis.STATES):\n",
    "    for half, baseoffset in ((1, -0.2), (2, 0.2)):\n",
    "        basepos = i + baseoffset\n",
    "        for turn, offset in (('Left', -.09), ('Right', .09)):\n",
    "            b = BoxSummary.from_values(summary[\"whisker\"][state][turn][half])\n",
    "            plot_box_around(basepos + offset, b, boxwidth=.15, mcolor='k', color=colors[turn], \n",
    "                            ocolor=colors[turn], marker='x', markersize=3)\n",
    "plt.vlines(x[1:]-.5, -1.5, 1.5, color='k', linewidth=.5, linestyles='dashed')\n",
    "plt.hlines(0, x.min()-1, x.max()+1, color='k', linewidth=1, linestyles='dotted')\n",
    "plt.xlim(x.min()-0.55, x.max()+0.55)\n",
    "plt.ylim(-1.25, 1.25)\n",
    "plt.xticks(np.arange(len(epoch_analysis.STATES)), epoch_analysis.STATES)\n",
    "plt.yticks((-1, -0.5, 0, 0.5, 1), (\"-100\", \"\", \"0\", \"\", \"100\"))\n",
    "plt.ylabel(\"Whisker asymmetry %\", fontsize=12)\n",
    "for side in (\"top\", \"right\"):\n",
    "    plt.gca().spines[side].set_visible(False)\n",
    "plt.tick_params(labelsize=12, bottom=False)\n",
    "plt.subplots_adjust(bottom=.1, top=.98, left=.15, right=.98)\n",
    "\n",
    "if saved == True:\n",
    "    figpath = figdir / \"whisker-asymmetry-boxplots-separate.png\"\n",
    "    if not figdir.exists():\n",
    "        figdir.mkdir(parents=True)\n",
    "    fig.savefig(str(figpath), dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Pupil\n",
    "\n",
    "fig = plt.figure(figsize=(6, 3.5))\n",
    "x = np.arange(len(epoch_analysis.STATES))\n",
    "for i, state in enumerate(epoch_analysis.STATES):\n",
    "    for half, baseoffset in ((1, -0.2), (2, 0.2)):\n",
    "        basepos = i + baseoffset\n",
    "        for turn, offset in (('Left', -.09), ('Right', .09)):\n",
    "            b = BoxSummary.from_values(summary[\"eye\"][state][turn][half])\n",
    "            plot_box_around(basepos + offset, b, boxwidth=.15, mcolor='k', color=colors[turn], \n",
    "                            ocolor=colors[turn], marker='x', markersize=3)\n",
    "plt.vlines(x[1:]-.5, -0.5, 0.5, color='k', linewidth=.5, linestyles='dashed')\n",
    "plt.hlines(0, x.min()-1, x.max()+1, color='k', linewidth=1, linestyles='dotted')\n",
    "plt.xlim(x.min()-0.55, x.max()+0.55)\n",
    "plt.ylim(-0.15, 0.25)\n",
    "plt.xticks(np.arange(len(epoch_analysis.STATES)), epoch_analysis.STATES)\n",
    "plt.yticks((-0.1, 0, 0.1, 0.2), (\"-10\", \"0\", \"10\", \"20\"))\n",
    "plt.ylabel(\"Pupil deviation %\", fontsize=12)\n",
    "for side in (\"top\", \"right\"):\n",
    "    plt.gca().spines[side].set_visible(False)\n",
    "plt.tick_params(labelsize=12, bottom=False)\n",
    "plt.subplots_adjust(bottom=.1, top=.98, left=.15, right=.98)\n",
    "\n",
    "if saved == True:\n",
    "    figpath = figdir / \"pupil-deviation-boxplots-separate.png\"\n",
    "    if not figdir.exists():\n",
    "        figdir.mkdir(parents=True)\n",
    "    fig.savefig(str(figpath), dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting asymmetry altogether\n",
    "\n",
    "Without distinction of left- or right- turning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Whisker\n",
    "\n",
    "fig = plt.figure(figsize=(6, 3.5))\n",
    "x = np.arange(len(epoch_analysis.STATES))\n",
    "for i, state in enumerate(epoch_analysis.STATES):\n",
    "    for half, baseoffset in ((1, -0.2), (2, 0.2)):\n",
    "        b = BoxSummary.from_values(summary[\"whisker\"][state]['Both'][half])\n",
    "        plot_box_around(i + baseoffset, b, boxwidth=.35, mcolor='k', color=colors['Both'],\n",
    "                            ocolor='k', marker='x', markersize=3)\n",
    "plt.vlines(x[1:]-.5, -1.5, 1.5, color='k', linewidth=.5, linestyles='dashed')\n",
    "plt.hlines(0, x.min()-1, x.max()+1, color='k', linewidth=1, linestyles='dotted')\n",
    "plt.xlim(x.min()-0.55, x.max()+0.55)\n",
    "plt.ylim(-1.25, 1.25)\n",
    "plt.xticks(np.arange(len(epoch_analysis.STATES)), epoch_analysis.STATES)\n",
    "plt.yticks((-1, -0.5, 0, 0.5, 1), (\"-100\", \"\", \"0\", \"\", \"100\"))\n",
    "plt.ylabel(\"Whisker asymmetry %\", fontsize=12)\n",
    "for side in (\"top\", \"right\"):\n",
    "    plt.gca().spines[side].set_visible(False)\n",
    "plt.tick_params(labelsize=12, bottom=False)\n",
    "plt.subplots_adjust(bottom=.1, top=.98, left=.15, right=.98)\n",
    "\n",
    "if saved == True:\n",
    "    figpath = figdir / \"whisker-asymmetry-boxplots-merged.png\"\n",
    "    if not figdir.exists():\n",
    "        figdir.mkdir(parents=True)\n",
    "    fig.savefig(str(figpath), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Pupil\n",
    "\n",
    "fig = plt.figure(figsize=(6, 3.5))\n",
    "x = np.arange(len(epoch_analysis.STATES))\n",
    "for i, state in enumerate(epoch_analysis.STATES):\n",
    "    for half, baseoffset in ((1, -0.2), (2, 0.2)):\n",
    "        b = BoxSummary.from_values(summary[\"eye\"][state]['Both'][half])\n",
    "        plot_box_around(i + baseoffset, b, boxwidth=.35, mcolor='k', color=colors['Both'],\n",
    "                            ocolor='k', marker='x', markersize=3)\n",
    "plt.vlines(x[1:]-.5, -0.3, 0.3, color='k', linewidth=.5, linestyles='dashed')\n",
    "plt.hlines(0, x.min()-1, x.max()+1, color='k', linewidth=1, linestyles='dotted')\n",
    "plt.xlim(x.min()-0.55, x.max()+0.55)\n",
    "plt.ylim(-0.15, 0.25)\n",
    "plt.xticks(np.arange(len(epoch_analysis.STATES)), epoch_analysis.STATES)\n",
    "plt.yticks((-0.1, 0, 0.1, 0.2), (\"-10\", \"0\", \"10\", \"20\"))\n",
    "plt.ylabel(\"Pupil deviation %\", fontsize=12)\n",
    "for side in (\"top\", \"right\"):\n",
    "    plt.gca().spines[side].set_visible(False)\n",
    "plt.tick_params(labelsize=12, bottom=False)\n",
    "plt.subplots_adjust(bottom=.1, top=.98, left=.15, right=.98)\n",
    "\n",
    "if saved == True:\n",
    "    figpath = figdir / \"pupil-asymmetry-boxplots-separate.png\"\n",
    "    if not figdir.exists():\n",
    "        figdir.mkdir(parents=True)\n",
    "    fig.savefig(str(figpath), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics\n",
    "\n",
    "- vs. baseline (test if there is any asymmetry): Kruskal–Wallis test (with Dunn's _post-hoc_ pairwise tests) is used.\n",
    "- left-turning vs right-turning: Mann-Whitney U-test is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def psign(p):\n",
    "    if p < 0.001:\n",
    "        return \"***\"\n",
    "    elif p < 0.01:\n",
    "        return \"**\"\n",
    "    elif p < 0.05:\n",
    "        return \"*\"\n",
    "    else:\n",
    "        return \", NS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Kruskal-Wallis test\n",
      "\n",
      "### Left-turning epochs, whisker\n",
      "\n",
      "p=0.00000***\n",
      "\n",
      "- AtEnd (1st half, N=54): median=0.0667, N=54\n",
      "- AtEnd (2nd half, N=54): median=0.0545, N=54\n",
      "- Backward (1st half, N=54): median=0.2073, N=54\n",
      "- Backward (2nd half, N=54): median=0.4922, N=54\n",
      "- Turn (1st half, N=52): median=0.6509, N=52\n",
      "- Turn (2nd half, N=52): median=0.0030, N=52\n",
      "- Forward (1st half, N=63): median=-0.1053, N=63\n",
      "- Forward (2nd half, N=63): median=0.1107, N=63\n",
      "- Expect (1st half, N=51): median=0.2844, N=51\n",
      "- Expect (2nd half, N=51): median=0.1260, N=51\n",
      "- Lick (1st half, N=51): median=0.1425, N=51\n",
      "- Lick (2nd half, N=51): median=0.1199, N=51\n",
      "\n",
      "#### Post-hoc pairwise tests\n",
      "\n",
      "Compared with AtEnd (1st half, N=54), with Bonferroni correction\n",
      "\n",
      "- AtEnd (2nd half, N=54): p=1.00000, NS\n",
      "- Backward (1st half, N=54): p=0.01769*\n",
      "- Backward (2nd half, N=54): p=0.00000***\n",
      "- Turn (1st half, N=52): p=0.00000***\n",
      "- Turn (2nd half, N=52): p=1.00000, NS\n",
      "- Forward (1st half, N=63): p=0.44151, NS\n",
      "- Forward (2nd half, N=63): p=0.26767, NS\n",
      "- Expect (1st half, N=51): p=0.00001***\n",
      "- Expect (2nd half, N=51): p=1.00000, NS\n",
      "- Lick (1st half, N=51): p=0.88428, NS\n",
      "- Lick (2nd half, N=51): p=1.00000, NS\n",
      "\n",
      "### Right-turning epochs, whisker\n",
      "\n",
      "p=0.00000***\n",
      "\n",
      "- AtEnd (1st half, N=36): median=-0.0412, N=36\n",
      "- AtEnd (2nd half, N=36): median=-0.0131, N=36\n",
      "- Backward (1st half, N=36): median=0.0942, N=36\n",
      "- Backward (2nd half, N=36): median=0.3368, N=36\n",
      "- Turn (1st half, N=40): median=0.3656, N=40\n",
      "- Turn (2nd half, N=40): median=-0.1651, N=40\n",
      "- Forward (1st half, N=50): median=-0.1770, N=50\n",
      "- Forward (2nd half, N=50): median=0.2326, N=50\n",
      "- Expect (1st half, N=38): median=-0.1800, N=38\n",
      "- Expect (2nd half, N=38): median=-0.1381, N=38\n",
      "- Lick (1st half, N=36): median=-0.1186, N=36\n",
      "- Lick (2nd half, N=36): median=-0.1070, N=36\n",
      "\n",
      "#### Post-hoc pairwise tests\n",
      "\n",
      "Compared with AtEnd (1st half, N=36), with Bonferroni correction\n",
      "\n",
      "- AtEnd (2nd half, N=36): p=1.00000, NS\n",
      "- Backward (1st half, N=36): p=0.73977, NS\n",
      "- Backward (2nd half, N=36): p=0.00000***\n",
      "- Turn (1st half, N=40): p=0.00000***\n",
      "- Turn (2nd half, N=40): p=0.41972, NS\n",
      "- Forward (1st half, N=50): p=1.00000, NS\n",
      "- Forward (2nd half, N=50): p=0.23079, NS\n",
      "- Expect (1st half, N=38): p=0.03486*\n",
      "- Expect (2nd half, N=38): p=1.00000, NS\n",
      "- Lick (1st half, N=36): p=1.00000, NS\n",
      "- Lick (2nd half, N=36): p=1.00000, NS\n",
      "\n",
      "### Both-turning epochs, whisker\n",
      "\n",
      "p=0.00000***\n",
      "\n",
      "- AtEnd (1st half, N=90): median=0.0378, N=90\n",
      "- AtEnd (2nd half, N=90): median=0.0399, N=90\n",
      "- Backward (1st half, N=90): median=0.1480, N=90\n",
      "- Backward (2nd half, N=90): median=0.4097, N=90\n",
      "- Turn (1st half, N=92): median=0.5124, N=92\n",
      "- Turn (2nd half, N=92): median=-0.0697, N=92\n",
      "- Forward (1st half, N=113): median=-0.1357, N=113\n",
      "- Forward (2nd half, N=113): median=0.2038, N=113\n",
      "- Expect (1st half, N=89): median=0.0804, N=89\n",
      "- Expect (2nd half, N=89): median=0.0139, N=89\n",
      "- Lick (1st half, N=87): median=0.0255, N=87\n",
      "- Lick (2nd half, N=87): median=0.0437, N=87\n",
      "\n",
      "#### Post-hoc pairwise tests\n",
      "\n",
      "Compared with AtEnd (1st half, N=90), with Bonferroni correction\n",
      "\n",
      "- AtEnd (2nd half, N=90): p=1.00000, NS\n",
      "- Backward (1st half, N=90): p=0.00755**\n",
      "- Backward (2nd half, N=90): p=0.00000***\n",
      "- Turn (1st half, N=92): p=0.00000***\n",
      "- Turn (2nd half, N=92): p=1.00000, NS\n",
      "- Forward (1st half, N=113): p=0.23766, NS\n",
      "- Forward (2nd half, N=113): p=0.01159*\n",
      "- Expect (1st half, N=89): p=1.00000, NS\n",
      "- Expect (2nd half, N=89): p=1.00000, NS\n",
      "- Lick (1st half, N=87): p=1.00000, NS\n",
      "- Lick (2nd half, N=87): p=1.00000, NS\n",
      "\n",
      "### Left-turning epochs, eye\n",
      "\n",
      "p=0.00000***\n",
      "\n",
      "- AtEnd (1st half, N=54): median=-0.0021, N=54\n",
      "- AtEnd (2nd half, N=54): median=-0.0023, N=54\n",
      "- Backward (1st half, N=54): median=0.0041, N=54\n",
      "- Backward (2nd half, N=54): median=0.0707, N=54\n",
      "- Turn (1st half, N=52): median=0.1233, N=52\n",
      "- Turn (2nd half, N=52): median=0.1156, N=52\n",
      "- Forward (1st half, N=63): median=0.0521, N=63\n",
      "- Forward (2nd half, N=63): median=0.0269, N=63\n",
      "- Expect (1st half, N=51): median=0.0258, N=51\n",
      "- Expect (2nd half, N=51): median=0.0136, N=51\n",
      "- Lick (1st half, N=51): median=0.0144, N=51\n",
      "- Lick (2nd half, N=51): median=0.0120, N=51\n",
      "\n",
      "#### Post-hoc pairwise tests\n",
      "\n",
      "Compared with AtEnd (1st half, N=54), with Bonferroni correction\n",
      "\n",
      "- AtEnd (2nd half, N=54): p=1.00000, NS\n",
      "- Backward (1st half, N=54): p=0.00402**\n",
      "- Backward (2nd half, N=54): p=0.00000***\n",
      "- Turn (1st half, N=52): p=0.00000***\n",
      "- Turn (2nd half, N=52): p=0.00000***\n",
      "- Forward (1st half, N=63): p=0.00000***\n",
      "- Forward (2nd half, N=63): p=0.00000***\n",
      "- Expect (1st half, N=51): p=0.00002***\n",
      "- Expect (2nd half, N=51): p=0.00655**\n",
      "- Lick (1st half, N=51): p=0.01102*\n",
      "- Lick (2nd half, N=51): p=0.03502*\n",
      "\n",
      "### Right-turning epochs, eye\n",
      "\n",
      "p=0.00000***\n",
      "\n",
      "- AtEnd (1st half, N=36): median=-0.0014, N=36\n",
      "- AtEnd (2nd half, N=36): median=-0.0015, N=36\n",
      "- Backward (1st half, N=36): median=0.0094, N=36\n",
      "- Backward (2nd half, N=36): median=0.0714, N=36\n",
      "- Turn (1st half, N=40): median=0.1272, N=40\n",
      "- Turn (2nd half, N=40): median=0.1163, N=40\n",
      "- Forward (1st half, N=50): median=0.0555, N=50\n",
      "- Forward (2nd half, N=50): median=0.0369, N=50\n",
      "- Expect (1st half, N=38): median=0.0155, N=38\n",
      "- Expect (2nd half, N=38): median=0.0106, N=38\n",
      "- Lick (1st half, N=36): median=0.0099, N=36\n",
      "- Lick (2nd half, N=36): median=0.0084, N=36\n",
      "\n",
      "#### Post-hoc pairwise tests\n",
      "\n",
      "Compared with AtEnd (1st half, N=36), with Bonferroni correction\n",
      "\n",
      "- AtEnd (2nd half, N=36): p=1.00000, NS\n",
      "- Backward (1st half, N=36): p=0.63806, NS\n",
      "- Backward (2nd half, N=36): p=0.00000***\n",
      "- Turn (1st half, N=40): p=0.00000***\n",
      "- Turn (2nd half, N=40): p=0.00000***\n",
      "- Forward (1st half, N=50): p=0.00000***\n",
      "- Forward (2nd half, N=50): p=0.00020***\n",
      "- Expect (1st half, N=38): p=0.66807, NS\n",
      "- Expect (2nd half, N=38): p=0.74007, NS\n",
      "- Lick (1st half, N=36): p=0.87871, NS\n",
      "- Lick (2nd half, N=36): p=1.00000, NS\n",
      "\n",
      "### Both-turning epochs, eye\n",
      "\n",
      "p=0.00000***\n",
      "\n",
      "- AtEnd (1st half, N=90): median=-0.0019, N=90\n",
      "- AtEnd (2nd half, N=90): median=-0.0021, N=90\n",
      "- Backward (1st half, N=90): median=0.0061, N=90\n",
      "- Backward (2nd half, N=90): median=0.0707, N=90\n",
      "- Turn (1st half, N=92): median=0.1250, N=92\n",
      "- Turn (2nd half, N=92): median=0.1159, N=92\n",
      "- Forward (1st half, N=113): median=0.0532, N=113\n",
      "- Forward (2nd half, N=113): median=0.0302, N=113\n",
      "- Expect (1st half, N=89): median=0.0219, N=89\n",
      "- Expect (2nd half, N=89): median=0.0131, N=89\n",
      "- Lick (1st half, N=87): median=0.0121, N=87\n",
      "- Lick (2nd half, N=87): median=0.0111, N=87\n",
      "\n",
      "#### Post-hoc pairwise tests\n",
      "\n",
      "Compared with AtEnd (1st half, N=90), with Bonferroni correction\n",
      "\n",
      "- AtEnd (2nd half, N=90): p=1.00000, NS\n",
      "- Backward (1st half, N=90): p=0.00078***\n",
      "- Backward (2nd half, N=90): p=0.00000***\n",
      "- Turn (1st half, N=92): p=0.00000***\n",
      "- Turn (2nd half, N=92): p=0.00000***\n",
      "- Forward (1st half, N=113): p=0.00000***\n",
      "- Forward (2nd half, N=113): p=0.00000***\n",
      "- Expect (1st half, N=89): p=0.00001***\n",
      "- Expect (2nd half, N=89): p=0.00149**\n",
      "- Lick (1st half, N=87): p=0.00294**\n",
      "- Lick (2nd half, N=87): p=0.01193*\n",
      "\n"
     ]
    }
   ],
   "source": [
    "attr = \"whisker\"\n",
    "\n",
    "print(f\"## Kruskal-Wallis test\\n\")\n",
    "\n",
    "for attr in (\"whisker\", \"eye\"):\n",
    "    for turn in (\"Left\", \"Right\", \"Both\"):\n",
    "        groups = []\n",
    "        group_names = []\n",
    "        group_N = []\n",
    "        for state in epoch_analysis.STATES:\n",
    "            for i, name in ((1, \"1st half\"), (2, \"2nd half\")):\n",
    "                vals = summary[attr][state][turn][i]\n",
    "                n    = vals.size\n",
    "                groups.append(vals)\n",
    "                group_names.append(f\"{state} ({name}, N={n})\")\n",
    "                group_N.append(n)\n",
    "        pairs = [(0, i) for i in range(1, len(groups))]\n",
    "\n",
    "        results = kw_dunn.kw_dunn(groups, pairs=pairs)\n",
    "\n",
    "        print(f\"### {turn}-turning epochs, {attr}\\n\")\n",
    "        print(f\"p={results.p_omnibus:.5f}{psign(results.p_omnibus)}\\n\")\n",
    "        for name, group in zip(group_names, groups):\n",
    "            print(f\"- {name}: median={np.median(group):.4f}, N={group.size}\")\n",
    "        print()\n",
    "        \n",
    "        print(\"#### Post-hoc pairwise tests\\n\")\n",
    "        print(f\"Compared with {group_names[0]}, with Bonferroni correction\\n\")\n",
    "        for (_, i), result in results.pairwise.items():\n",
    "            print(f\"- {group_names[i]}: p={result.p_corrected:.5f}{psign(result.p_corrected)}\")\n",
    "        print(flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Mann–Whitney U-test, left- vs. right-turning\n",
      "\n",
      "### Whisker\n",
      "\n",
      "- AtEnd (1st half): Left=0.0667 (N=54), Right=-0.0412 (N=36), p=0.00324**\n",
      "- AtEnd (2nd half): Left=0.0545 (N=54), Right=-0.0131 (N=36), p=0.03191*\n",
      "- Backward (1st half): Left=0.2073 (N=54), Right=0.0942 (N=36), p=0.01428*\n",
      "- Backward (2nd half): Left=0.4922 (N=54), Right=0.3368 (N=36), p=0.00432**\n",
      "- Turn (1st half): Left=0.6509 (N=52), Right=0.3656 (N=40), p=0.00001***\n",
      "- Turn (2nd half): Left=0.0030 (N=52), Right=-0.1651 (N=40), p=0.00452**\n",
      "- Forward (1st half): Left=-0.1053 (N=63), Right=-0.1770 (N=50), p=0.82388, NS\n",
      "- Forward (2nd half): Left=0.1107 (N=63), Right=0.2326 (N=50), p=0.56126, NS\n",
      "- Expect (1st half): Left=0.2844 (N=51), Right=-0.1800 (N=38), p=0.00000***\n",
      "- Expect (2nd half): Left=0.1260 (N=51), Right=-0.1381 (N=38), p=0.00002***\n",
      "- Lick (1st half): Left=0.1425 (N=51), Right=-0.1186 (N=36), p=0.00000***\n",
      "- Lick (2nd half): Left=0.1199 (N=51), Right=-0.1070 (N=36), p=0.00000***\n",
      "\n",
      "### Eye\n",
      "\n",
      "- AtEnd (1st half): Left=-0.0021 (N=54), Right=-0.0014 (N=36), p=0.30131, NS\n",
      "- AtEnd (2nd half): Left=-0.0023 (N=54), Right=-0.0015 (N=36), p=0.23400, NS\n",
      "- Backward (1st half): Left=0.0041 (N=54), Right=0.0094 (N=36), p=0.89190, NS\n",
      "- Backward (2nd half): Left=0.0707 (N=54), Right=0.0714 (N=36), p=0.72631, NS\n",
      "- Turn (1st half): Left=0.1233 (N=52), Right=0.1272 (N=40), p=0.55207, NS\n",
      "- Turn (2nd half): Left=0.1156 (N=52), Right=0.1163 (N=40), p=0.81016, NS\n",
      "- Forward (1st half): Left=0.0521 (N=63), Right=0.0555 (N=50), p=0.83289, NS\n",
      "- Forward (2nd half): Left=0.0269 (N=63), Right=0.0369 (N=50), p=0.07837, NS\n",
      "- Expect (1st half): Left=0.0258 (N=51), Right=0.0155 (N=38), p=0.07797, NS\n",
      "- Expect (2nd half): Left=0.0136 (N=51), Right=0.0106 (N=38), p=0.69972, NS\n",
      "- Lick (1st half): Left=0.0144 (N=51), Right=0.0099 (N=36), p=0.82606, NS\n",
      "- Lick (2nd half): Left=0.0120 (N=51), Right=0.0084 (N=36), p=0.89373, NS\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"## Mann–Whitney U-test, left- vs. right-turning\\n\")\n",
    "for attr in (\"whisker\", \"eye\"):\n",
    "    lab = attr[0].upper() + attr[1:]\n",
    "    print(f\"### {lab}\\n\")\n",
    "    for state in epoch_analysis.STATES:\n",
    "        for i, name in ((1, \"1st half\"), (2, \"2nd half\")):\n",
    "            v1 = summary[attr][state][\"Left\"][i]\n",
    "            v2 = summary[attr][state][\"Right\"][i]\n",
    "            p  = sstats.mannwhitneyu(v1, v2).pvalue\n",
    "            print(f\"- {state} ({name}): Left={np.median(v1):.4f} (N={v1.size}), Right={np.median(v2):.4f} (N={v2.size}), p={p:.5f}{psign(p)}\")\n",
    "    print(flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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