F31 new traces.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ec00416",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import pyautogui as pa\n",
    "from scipy.stats import binom_test\n",
    "from matplotlib.markers import TICKDOWN\n",
    "from matplotlib import rcParams\n",
    "import datetime\n",
    "from scipy.stats import norm, kstest, sem\n",
    "from numpy.polynomial import polynomial\n",
    "\n",
    "from lib.auto_GUI.auto_GUI_base import AutoGUIBase\n",
    "from lib.auto_GUI.auto_PhotoZ import AutoPhotoZ\n",
    "from lib.analysis.laminar_dist import *\n",
    "from lib.file.ROI_reader import ROIFileReader\n",
    "from lib.analysis.cell_roi import OverlapCounterROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "319d1034",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "30e8eae8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams.update({'font.size': 10})\n",
    "\n",
    "trace_sample = 'scnn1a-init/select/06-26-23/ROIs-barrels-traces.dat'\n",
    "trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                         sep='\\t',\n",
    "                         header=0,\n",
    "                         names=['Index',  'L2/3 Barrel A',  'L4 Barrel A',  'L4 Barrel B'])\n",
    "plt.clf()\n",
    "start_cut = 70\n",
    "end_cut = 160\n",
    "baseline = 10\n",
    "v1 = trace_sample.loc[start_cut:end_cut, [\"L2/3 Barrel A\"]] + baseline\n",
    "v2 = trace_sample.loc[start_cut:end_cut, [\"L4 Barrel A\"]] + baseline * 2\n",
    "v3 = trace_sample.loc[start_cut:end_cut, [\"L4 Barrel B\"]] + baseline * 3\n",
    "t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "plt.plot(t, v1, label='L2/3 Barrel A')\n",
    "plt.plot(t, v2, label='L4 Barrel A')\n",
    "plt.plot(t, v3, label='L4 Barrel B')\n",
    "plt.axvline(x=48, label=\"Stimulation Time\", color='red')\n",
    "# plt.legend(bbox_to_anchor=(0.79, .97), loc='upper center')\n",
    "plt.yticks([])\n",
    "plt.ylim(40, -10)\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.savefig(data_dir + \"scnn1a-init/select/06-26-23/ROIs-barrels-traces.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9ca2ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams.update({'font.size': 16})\n",
    "\n",
    "trace_sample = '03-05-24/barrels-traces-2.dat'\n",
    "trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                         sep='\\t',\n",
    "                         header=0,\n",
    "                         names=['Index',  'Barrel A',  'Barrel B',  'Barrel C'])\n",
    "plt.clf()\n",
    "start_cut = 10\n",
    "end_cut = 390\n",
    "baseline = 25\n",
    "v1 = trace_sample.loc[start_cut:end_cut, [\"Barrel A\"]] + baseline\n",
    "v2 = trace_sample.loc[start_cut:end_cut, [\"Barrel B\"]] + baseline * 2\n",
    "v3 = trace_sample.loc[start_cut:end_cut, [\"Barrel C\"]] + baseline * 3\n",
    "t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "plt.plot(t, 1-v1, label='Barrel A')\n",
    "plt.plot(t, 1-v2, label='Barrel B')\n",
    "plt.plot(t, 1-v3, label='Barrel C')\n",
    "plt.axvline(x=48, label=\"Stimulation Time\", color='red')\n",
    "#plt.legend(bbox_to_anchor=(0.79, .97), loc='upper center')\n",
    "plt.yticks([])\n",
    "plt.ylim(-85,0 )\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "plt.savefig(data_dir + \"03-05-24/barrels-traces.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a1837ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams.update({'font.size': 16})\n",
    "\n",
    "trace_sample = '02-07-24/barrels-traces.dat'\n",
    "trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                         sep='\\t',\n",
    "                         header=0,\n",
    "                         names=['Index',  'Barrel A',  'Barrel B',  'Barrel C', 'Barrel D',  'Barrel E'])\n",
    "plt.clf()\n",
    "start_cut = 10\n",
    "end_cut = 390\n",
    "baseline = 10\n",
    "v1 = trace_sample.loc[start_cut:end_cut, [\"Barrel A\"]] + baseline\n",
    "v2 = trace_sample.loc[start_cut:end_cut, [\"Barrel B\"]] + baseline * 2\n",
    "v3 = trace_sample.loc[start_cut:end_cut, [\"Barrel C\"]] + baseline * 3\n",
    "v4 = trace_sample.loc[start_cut:end_cut, [\"Barrel D\"]] + baseline * 4\n",
    "v5 = trace_sample.loc[start_cut:end_cut, [\"Barrel E\"]] + baseline * 5\n",
    "t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "plt.plot(t, 1-v1, label='Barrel A')\n",
    "plt.plot(t, 1-v2, label='Barrel B')\n",
    "plt.plot(t, 1-v3, label='Barrel C')\n",
    "plt.plot(t, 1-v4, label='Barrel D')\n",
    "plt.plot(t, 1-v5, label='Barrel E')\n",
    "plt.axvline(x=48, label=\"Stimulation Time\", color='red')\n",
    "#plt.legend(bbox_to_anchor=(0.79, .97), loc='upper center')\n",
    "plt.yticks([])\n",
    "# plt.ylim(0, 70)\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.savefig(data_dir + \"02-07-24/barrels-traces.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40faf0bc",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/11-27-23/selected_zda/Traces_single_stim_1.dat'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-496648bc5933>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'Traces_single_stim_1'\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;34m'Traces_single_stim_2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mtrace_sample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'11-27-23/selected_zda/'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfn\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;34m'.dat'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     trace_sample = pd.read_csv(data_dir + trace_sample,\n\u001b[0m\u001b[0;32m      5\u001b[0m                              \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'\\t'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m                              \u001b[0mheader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\jjudge3\\Anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\jjudge3\\Anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\jjudge3\\Anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\jjudge3\\Anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1048\u001b[0m             )\n\u001b[0;32m   1049\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1052\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\jjudge3\\Anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\jjudge3\\Anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m         \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1364\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\jjudge3\\Anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/11-27-23/selected_zda/Traces_single_stim_1.dat'"
     ]
    }
   ],
   "source": [
    "rcParams.update({'font.size': 22})\n",
    "for fn in ['Traces_single_stim_1',  'Traces_single_stim_2']:\n",
    "    trace_sample = '11-27-23/selected_zda/' + fn +'.dat'\n",
    "    trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                             sep='\\t',\n",
    "                             header=0,\n",
    "                             names=['Index',  'Barrel C',  'Barrel B',  'Barrel A'])\n",
    "    plt.clf()\n",
    "    start_cut = 40\n",
    "    end_cut = 180\n",
    "    baseline = 3\n",
    "    v1 = trace_sample.loc[start_cut:end_cut, [\"Barrel A\"]] + baseline * 3\n",
    "    v2 = trace_sample.loc[start_cut:end_cut, [\"Barrel B\"]] + baseline * 2\n",
    "    v3 = trace_sample.loc[start_cut:end_cut, [\"Barrel C\"]] + baseline\n",
    "    t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "    plt.plot(t, v1, label='Barrel A', linewidth=3.5)\n",
    "    plt.plot(t, v2, label='Barrel B', linewidth=3.5)\n",
    "    plt.plot(t, v3, label='Barrel C', linewidth=3.5)\n",
    "    plt.axvline(x=48, label=\"Stimulation Time\", color='red')\n",
    "    plt.legend(bbox_to_anchor=(1.59, .97), loc='upper center')\n",
    "    plt.yticks([])\n",
    "    plt.xlabel(\"Time (ms)\")\n",
    "    plt.savefig(data_dir + \"11-27-23/selected_zda/\" + fn +\".png\")\n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "365f3acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams.update({'font.size': 16})\n",
    "\n",
    "def draw_arrow(p1, p2):\n",
    "    x, y = p1\n",
    "    x2, y2 = p2\n",
    "    plt.plot([x, x2], [y, y2], 'b-', linestyle='dotted')\n",
    "\n",
    "single_stim_upper = [3.02149, 3.70894, None]\n",
    "single_stim_lower = [None, 5.9379, 4.61269]\n",
    "double_stim = [4.75271, 6.79893, 5.04772]\n",
    "recs = [single_stim_upper, single_stim_lower, double_stim]\n",
    "barrel_a = [x[0] for x in recs]\n",
    "barrel_b = [x[1] for x in recs]\n",
    "barrel_c = [x[2] for x in recs]\n",
    "\n",
    "linear_sum = 3.70894 + 5.9379\n",
    "\n",
    "draw_arrow([0, 3.02149], [1, 3.70894])\n",
    "draw_arrow([1, 5.9379], [2, 4.61269])\n",
    "draw_arrow([0, 4.75271], [1, 6.79893])\n",
    "draw_arrow([1, 6.79893], [2, 5.04772])\n",
    "\n",
    "# plot stims\n",
    "plt.scatter([0], [3.02149], color='white', marker='*', s=350, edgecolors='red', label='Stim')\n",
    "plt.scatter([1], [5.9379], color='white', marker='*', s=350, edgecolors='red')\n",
    "plt.scatter([0], [4.75271], color='white', marker='*', s=350, edgecolors='red')\n",
    "plt.scatter([1], [6.79893], color='white', marker='*', s=350, edgecolors='red')\n",
    "\n",
    "plt.scatter([0]*3, barrel_a, color='tab:blue', s=55)\n",
    "plt.scatter([1]*3, barrel_b, color='tab:orange', s=55)\n",
    "plt.scatter([2]*3, barrel_c, color='tab:green', s=55)\n",
    "plt.scatter([1]*3, [None, linear_sum, None], color='black', s=55, label=\"Linear Sum\")\n",
    "\n",
    "xtick_labels = ['Barrel A', 'Barrel B', 'Barrel C']\n",
    "plt.xticks(range(0, len(xtick_labels)), xtick_labels)\n",
    "plt.xlim([-0.5, 2.5])\n",
    "plt.ylabel(\"Response\\nAmplitude\")\n",
    "plt.yticks([])\n",
    "plt.legend()\n",
    "plt.savefig(data_dir + \"11-27-23/selected_zda/amplitudes.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf275831",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def baseline_correct(trace, skip_window, fit_type=\"Polynomial-8th\"):\n",
    "    t = np.linspace(0, len(trace) * 0.5, len(trace))\n",
    "    full_t = np.linspace(0, len(trace) * 0.5, len(trace))\n",
    "    skip_start, skip_end = skip_window\n",
    "    trace = np.array(trace)\n",
    "\n",
    "    skip_int = [i for i in range(skip_start, skip_end) if i < trace.shape[0]]\n",
    "    trace_skipped = trace\n",
    "    t_skipped = t\n",
    "    if len(skip_int) > 0:\n",
    "        trace_skipped = np.delete(trace, skip_int)\n",
    "        t_skipped = np.delete(t, skip_int)\n",
    "    if t_skipped.size < 1:\n",
    "        return trace\n",
    "\n",
    "    poly_powers = {\n",
    "        'Quadratic': 2,\n",
    "        'Cubic': 3,\n",
    "        \"Polynomial-8th\": 8\n",
    "    }\n",
    "\n",
    "    power = poly_powers[fit_type]\n",
    "    coeffs, stats = polynomial.polyfit(t_skipped, trace_skipped, power, full=True)\n",
    "    reg = np.array(polynomial.polyval(full_t, coeffs))\n",
    "\n",
    "    full_trace = (trace.reshape(-1, 1) - reg.reshape(-1, 1)).reshape(-1)\n",
    "    return full_trace\n",
    "    ################################################################################\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce688891",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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HswZZgkHi03JYuq+B2OTrKFobcEXrwFWGcV0amdceAH7eU3vNEDUiwIqi8Nofx/jvnyc4EJvGjpgU/jpykU/Wn2bY/zbw6oqjJGWUtL1WJ0XFCs/+cpCCIsuKzysTOzNWk+zZ3dXAc5pigAC/lfHPn55dwEPf7+XIhStsPJnE/d/tZfDb61l37GLVvoHqQGOCKPKP5MONFjeeAC83ZlwVVaHTtm/kx8391JnziuKrLDvyMzm8YTGfbjpt3tTI35M7B+mj7+4YGKXLejVvw2ldrllbKIqCUoqrm6R6UBRFZwN2xgzYz9ONsZ0t/8dbo5NLVGiuLdSIAM9Ze8puAb38wmK+3BrD4Lf/5vnfDnE6KbNG/nG+3X6OfefTzO1BbUK4sU/TEv06N/GnXYSvub1sf3ypri3zNp7mSm6hblvilVzu+3YPxxLsl2c/kZjB55vPcDyxfCXccwuK+GDtKUa+s4G7vt5FhtVrlwvNDPhccbjuXI+MakuAl1uFT/3EmHb4ebjyV1Ff8hSLSSFx67c6l+Bnx7Uv4Vsc5OPO9P4W08eZ5CxWHbbvlnY4Pp3R722k08t/8eB3e9l0MqnWuyPVF1KzC9RMeEZqygPCGq0ZolhBd6dbm6h2AZ6/8TQfrjul29azeWCJ6Ji8wmK+33GeUe9upO/r65i1cCev/3GU73acY/fZlCr9B8ovLNZdEE83A29e3w0hSi4WCCF0K6sJ6blsP3PZ5nkT03NZuNX2D01BkcITiw/ovpygzhjmbzzNtR9u5rU/jjHug8089+tB3S25PdYdu8iYORuZs/YkZ5KzWHvsEkcupHM04QpxqRUo0aKxAe9MDzQ/jwrxZsaAcpamtyLU14OHRrbhCj5sLO5u3j5E2Ycv6lhv7tuM63uW/BEEuGtIK9w1i3///fOEzcWVPedSuWXBdk4nZZFTUMQfhxK4/cudXD93q00PF0nVYm1SdIYJAmBwm1DdxGnxrljOXc5yylhKo1oF+O/jF3lr1XHdtlcmduK3Bwax+ZkRfHBzD6JCSiZqTs7MY8OJJBZsjuGF3w5zw/x/GPLf9Xz89ykuXql8eOHvBy9wSWPyeHB4G5rbGIeJyT30ri32zBAf/n2KPI3AvjShE4PbhJrbxxKu6H6M0nMKuGfRHt5addx8S60o8MPOWEa8s4FdZ+37MH61NYZ/fb2b2JSSt1ZXcgq4/Yud5Vusyk6B3DRz82xxuPn57HEdKlVS3MSsQVG0DPVheZElKMNDFHC1YTfdmwbw70md7R4b4e/JNI2P5/mUbN5fq4+Q23Y6mRlf7LB5F3AgLp2X6vsiai1A6wEBzjFBgFq66MmrLQU8C4sV3l97qpQjnEO1CvCQtmFM1GQpevqa9txhtO8ZDILrekSy9olhfHhLTzo38bd3GkBdfHln9UmuenMdM77YwXc7zrFkdyw/7DzP0n3x/H38okOO+oqi6MwhXm4u3F6GbbNJoJfOtWXV4cQSs6+Y5Cx+2mVxkWof4cesgVG8c2N33Sr+3A3RfLklhr3nU5nw0WbWHLVtG07PKeCB7/Zy2casbc3Ri/zf70d126zzrZ5JzuK3veVYrLJyQTunRADQNyqIazT2tMrg4erCT/cMwNBhHFmKxTd0qsd25t3W22ZYs5Ynr25HqK+7ub1g8xkOxakVENYcvcishbt0uSqaBHii9YBadTiRVTKirlpJSHdOEIYtru4UQfemAeb20v3xtS6Yp1oTsru5GHh/Wg+83AyE+Xnw4Ig2JQfgYmBS9yZM7NaYPedS2XU2lcPx6m10bEo2hVamh2IFNp9KZvMp2yHCQ9uF8cG0HuZVdGt2xKTowlxv6N2UAO+ybZtTejZl+xl1RpqZV8jqo4lcp5kZL9wao1sYevqa9rgYBI0CPPm/6zrz+E8HzON/1Uo8AdxcBP8a3Iq/jiQSk6zeKiVl5DH710N8NqO32TxyKC6dR37Yp7Ob3ty3Gc+N60hsajbZPwqzuebj9dFM6RVZasiwieLk07pf43NKBEHebrw91bZppqKE+3vy4e2DKFg8AY7+AsBADiFcrgCl364Gervz70mdeej7feqYFbjv2z0Mbx/Gj7tidZ//kLahfDqjN8cSrnDD/H/Mn9fLy48wsHWoQ9dcUn60JgiDgDC/mgvCsEYIwVPXtGfGFzsB9e7y3kV7+HRGb9pF+JVxdM1Q7TZgF4PgrSndeEpzO2ALIQR9ooK5f3hrPpnei/VPDef4f8ay7slhPDGmHU0c/CXddDKJyXO32v2ls14MvMMqjZ09xnZthIfmNlwbEJCTX6QzS3SNDGBUR8st/OQekUzqbj96rEmAJ4vvvYrZ4zrwxyODaRXqY9635uhFftipvta+86nMXLhTFxY8rU8z3pzSlQBvN7pEBhDhb/mczqdks2z/BYfe34H9u3TtRLemfHVHP1qF+do5onK49Zhmfi6UIofLFY3v2pjRms82Pi2H73ac14nv6I4RLLi9D97urvRuEcwdAy1eFUkZebz2R8kfQEnVoJ0Bh/l5lCtopzoY3CZUV0E5JjmLyZ9s5de9tSNPRI18OgaDqNAsytXFQOswXx4Z1ZbNz47k6zv7MblHE52Pny3OXc5mytxt7LNKZXjqYgZrNe5gozqEOyww/p5u5oACgK3Rlzl/WV08+uNQgs7uOL1/c937FULw7k3deW5chxKmglEdwln56BB6NlfL9Hi7u/L+zT1w1dw7v7D0EHcs3MktC7brFucGtwnlteu76F5Lve22tD9eH12my9bJixkknD5obscpoXwwYyDdmwWWelylaD0K/CyfJ3u/KTVDmgkhBP+Z3IVgO3c4U3pGMv+2XjpzxlPXtKOpJiPXkj1xbGoIoeVOIFHnguacBTgtQgjemtqNRpqJSXZ+EU8sPsDoORtZvDvWqX76dSYSzsUgGNYujPdv7sn+l69m/VPD2fzMCLbNHsm6J4fxzZ39dJ4VmXmF3Ltoj3nRrrhYYfavh3T/4/8abLvemT20i0BgmQX/sNPiL+vj7qKze5twczFw77DWrH9qOHcMiqJX80D+b1JnFtzeh0BvvZh0axrIY6PbmtuKAutPJOlyrHaNDGDubb1KzDDcXAy6WXBMchZ/lGL3zCss4tEf99MSjb04pC1D2obZPaZKcHGFHtMt7ZTTcG6bQ4c2DvBi5SNDmHlVC1pq7hb+Nbgl79zYvYTJxdvdlbemdNNte+7XQ2TmVcJlT2ITrQA39nee/VdLy1AfVjw8WDcTBjiTlMUzPx/klgXbSywe1hR1RoC1eLq50DLUh2bB3jQJ9KJ1mC9D24Wx/KFB9I2yFHy8lJHHPcYELt/uOMeec5YZ8cDWIVzVOsTW6e3Sr2Ww7h9+yZ5YjiVc0Z13Uo9IfEopQBjm58ErEzvz6wODmDkwCoOdOPn7h7fRhUJrGdYujB/vGYC/p207ZuNAT5255Kdd9vOifrQumhMJabQSlnDNyDbd7PavUnrepm/v/cbhQ1XbehfWPzWcbbNHsuXZEbw0oZPdz3Nw29ASmbLeWnWsQsOW2KY2BGHYI8zPg2/v6s+9w1rh5qL/juw6m8q1H252yl1RnRRge4T4evDlrL60DbeYFQ7EpnH1nE06dzgPVwNvXN+13GYRIYRuFnzxSh4P/7BP1+eWfs2sD6sQLgbBR7f05Md7BjCkrcWV7ea+zfh8Zp9SRd7dxcB4jXhvO33Zpl9wSlY+X2yJIVIk4SEsCXdEWLsqeQ9lEtwSWg6ztI8uhZy0cp+mSaAXTYPsuxGaeH58R92t6Lfbz7M1uoHle65GruQU6tYnmgTWHgEG9e7wuXEd2fzMSO4e0hIvjZkqJSufWQt3staOV1J1Ua8EGNQwxAW399FFbZ1Pyda5Jz0xph1RmplseZjSK1Jnn42+ZKkS0bmJP10jA2wdViGEEAxoFcKif/Vn8zMjWPvEUN6a2s2hhY0belsCGhQFmy5ppjy/rYXVQl1oDQkwQK/bLc8Lcx0qV1RR/D3deHNKV922JxbvJ9WBoBdJ2SRc0fuk1wYbsC0aBXjywvhO/P7IYF3prGIFHvlxH4fj02tsLPVOgAGiQn34+NaeusgpE52b+Jfb9qsl3M9T5+GgZcaAFlXqsqWlWbA3bcIdd50Z0DJEZxP/eW+cLsQ7S5Pn16kC3HEieFnMRtVdNXlEh3Ddj9PFK3nM/vWgzBtRBSSk1R4fYEdoHebLbw8M0q3ZZOcXcdfXu2vMJlwvBRjUIJA/HxvC/cNbM6BVMP6ernRs7M/Ht/ZyyC+2NJ6+poN5hu1qELSL8OWRkW24qU/VmB+qAoNBMFUjNOcuZ7NbY6v+Yaclz69OgD38wTeixsaJqwd0ucHSjttVsjJzFfPvSZ11yd7/OnKRH3fJPMOVxToIo1EtWYQrDS93F967qTsDNetBptwtNZH2tFoDMZxNqzBfnh3bocrP2ybcl10vjObilVwi/D2rJEy3OpjaK1IX+rxkdyx9o4LJLyzm880WkevgqsmXGtoWqmkWb5euN8KuBZb24Z9h6NPV9nK+Hq68P60HN8z/x+yi95/fjzK8fZjDuQuOJVzh621nORCXzphOETw2qq3dBcCGQqJVHoiIOiDAoNqG503vzZR5WzmdpAZB7Y9N4+O/o3l8TPXeDdZO5agDuLsaaBbsXWvFF6BFiI+uosTS/RfYcy6V1/44qsvZ2sFNK8A1aH4w0awfBGoSvR9c4pBPcGXo2TyIJzT/XNn5RfzHRoSiNbEp2Uz/fDvjPtjMj7tizfk93l1zosxj6zvaGXCor0et/t+wJsDbjS9n9dX56X+8Ppq9VrEEVU3d+YQkFULremUqv/SNpsJxU48cvAs0X7LQttQ4QujNEMkn4GL1J865d2grOjW25CBZeSiRDScu2e0fm5LNtE//YWt0yWx4n6w/3XCKt9rhgmYGXNvtv7ZoEeKjSwhVVKzw+E/7ycgtKOWoyiEFuJ5zXY9IRnWwLBpmWyURemeElfuWM2bAoJohtNiomlzVuLoYeO36Lrptryw/YrPw54W0HG79fDsXSlmceebngxyMS6vqYdYZYpIs6R5Lyy5Ym5naK5JxmqIM5y5nc+dXu8jOr56gHSnA9RwXg+CDW3rSsXHJbHNPjGnHAH+r2ZyzBDiiE4Rr0lEe/gWKq38RpFfzIJ3v9rnL2fzfiqM6r4jYlGxuXbBdl/qzVZgPyx4cxN1DLB41ecbq2g2R7PxC3Y9Tqwq6eTobIQRvXN+VCH9LEqFdZ1O56+vd1VKRWwpwA8DXw5UvZvYhXJOZany3xjw8sg1c1uRIFS4QVHEXvUrTVWOGuBIPF/bZ71uFPHNNB4I02dF+2HmeeRvVMkmH49O5fu42zl62BLK0CPHm+7sG0L1ZILPHdWRoO0vY9t7zaTrf8IaCKYOfiVZhdVOAQa3A8vntffHT2IO3nb5sjqqtSqQANxCaBHqx5L6ruLlvMx4d1ZZ3b+yu+iwnawQ4uCW42k5yUyN0uk7fPr2uRl42yMfdmHbTsu2/f57g2g82M3XeNl0ljchAL767q785zNbFIHSLeQC/7q29RSCrizNJVgIcWj1Z9GqKrk0D+Ppf/fDR1CLcdDKJuVVc2kgKcAOiRYgPb03txuNj2lmyhSVpVu9DnLAApyW4FQRFWdrRa2vspa/u3IiXJ3TSbTuacEVX4aRDIz9+uX9gibDn7k0DaK2Z8f22L77MDHT1jfo0AzbRq3kQC+/oZw5ZHtwmlPuHl8xpXhmkADdkCvP0lTBqKgeEPYSANqMt7bhdkFO9bkBa7hjU0m6U5IBWwSy+7yqbCWaE0Ae9JKTn8s9p23UD6ytnkixmlzA/D/zsJIqqa/RrGcwXM/swtnMjPp/ZBy/30qu2lJd6HYghKYPL0aBoFrpCS0+aXyO0HgW7PlefK8VwZgN0vr7GXv6Fazvi5+nKppNJ+Hm6EerrQd+oIK7vFYmHq/1/vut7RvK/v06Y3Zd/2RvHYE0SpfrOGc0MuK4uwNljYJtQBrapnmspBbghk2QVPBBWPgGOSY9h3oF5pOel83jvx+kQXAVRhy2HgMENio2+l9HralSADQbBY6Pb8djo8t0NNA7wYnCbUHOprD8PJ/KfyYUlEvCb2Hs+lc82nsFggKZB3rQK9WFsl0YlckPXBAVFxaw6nEhcajY3921uN9m9PRRF0dmAy1VFJS8Dji4H/yYQNUTNE92AaFjvVqInWV9V2FEXtPyifL44/AULDi6gwCiUx1OOs2TiEsK9bScqchgPP2g+AM5uVtvR69SouJoOj64AU3s1NQtwTkERv+2NY4aNgq/rjl3k/u/2kl+od7N7c9VxHh/dlukDWpgz3uUWFLE1Wq2BuCU6mfMp2bQO8+WqViGM6BDG4DahFU4AVVyssPzABd5bc5LzKaqXx7f/nGPpQ4MI93M8kCIpI0+X3N7hGfCVBFg4zmIG841Q/cEHPQq+lfwe1RGkADdktDNgvybgWXplaoBipZgnNzzJhrgNuu0puSk8tfEpvrjmC9wMlbT/tRllEeCMC3DpmOonXMu5pnMjArzczEmOPt8Sw639W+CiyRGx/MAFnvhpf4lis6BWwv73iqPM3XCarpEBeLgZ2HgiiSyr4JljCVc4lnCFL7fGMLJDOG9N6Up4BfIu/HvFEV1UJMCF9Fzu+WYPP94zoMwq1SZOW3tAOLIAl3UZFk3Wr0FkXoR/PobDv8JtP0NEZ7uH1xfkIlxDRivADi7A7bu0r4T4avfN2TOn8uPSLsRBjXpDVAYvdxduG2DJaXHucjarj1jybKw+ksijP+7TiW+wj7suvzSolVzWHb/EykOJJcTXmr+PX+Lq9zexqpSyU7Y4eTGjhPia2B+bxuxfHE/RWdIDogwTRF4mfDsFko7b3p9xAb4cBzGbHXr9uowU4IZKcZG6CGcizDH77frz63XtR3s9io+bZcaz6Ogi9lzcU7mxRXTRp8Q8tbpy56tBZl4VpctD/emmM0YbaSZPLj6gyzF0XY8m7Hh+FEdevYbZ4zrofE6tEUJ1d7ulXzO6RPrrLDJp2QXc/91eFmw64/A4v9+hL1M1vX9z/D0tN8RL91/g2x32S1lp0XpAuLkImgWVkVFu+1xI2G9p+zWGUS/rXRDz0lWRjt1lfXS9QpogGiqpZ6HIEmDgiP1XURTWx1oEuENwB+7qehfN/Zrz5MYnzdt/PvkzvSN6V3xsQkDbMbDvW7V9bpvqjqZN3F5LCff35PqekfxkLNi6PzaNH3fFsnBrDBkaO+kNvZvy9tRuZvPEfcNaM6VXJEt2x3EoLp0TFzNIy86nZ/MgxnZpxJiOEQRpFscOx6fz5OIDnLiYYd72+spjJGflMXtsh1Ltwjn5RfyiCRbp3MSf1yZ3YWyXRsxauMvsw/z2quOM7hheZopOrQdE82DvsvNtH1thee4VBLcvUxeAe98BP9wCsdvVfUX5sPwhuHezcwOEqhE5A26oWC/AOeABcSb9DOczLLOiEc1GAHB11NUMaDzAvH3d+XVkF5SsQVcu2l9rea4Uwam6YYYAuHuo3pf4uV8PcfKiZZbYq3kgb1zfVWcbBrXayoMj2jB/Rm/WPzWcfS9fzZez+nJTn2Y68QXoEhnA8ocHMfOqFrrtn248w9t/lp4ac8XBC2TkWn4Mbu3fHCEEQ9qG6aL6MvMKeWnp4TJNEdoZcJnmh8xLkHjQ0u5+q+W75x0Mty9VXRFNJB2HbR+Ufs46jBTghoq1/c0BH2Dt7BcsAgwwqfUk8/OcwhzWnq+kYLYaDq6ahaUTKyt3vhqkTbifLgOdllBfd+ZO710luXI9XF3496TOJYoOzN94utS0mlrzg4+7C9f1iDS377FK0bn2mGqLtkd+YTGxqfokRaVyWv8dos1IfdvNCybPA09NbcWN/4PLp0s/bx1FCnBDJUkzA/YKBp+yHc219t/GPo11fr+jmo/Cy9Vyq7r89PLKjc/dRxVhE9FrobDuFM98cUInXU0+UKtxf3xrryot1y6E4P7hrXnjen2x0ScXH+DSlZKpMw/Hp7M/Ns3cntwzUuer7OZi4O2p3dBOzl9Zfpi0bNuf/fmULF3YdZkuaNr8Hq6e0GJQyT5+ETD6/yztojz448mS/eoBUoAbKslaD4j2ZfrZJmUncTDZcus4vNlwnZ3R282bMS3GmNs7E3aSmGV/5uQQWjNE3hU4t6Vy56tBWob6sOmZEWx+ZgTf392fD2/pyV+PDWVAq5CyD64At/ZvrkureTkrn8cX76fYyt3tK2MhVu1x1nRtGsBdQ1qZ28mZ+bz2xzGbr/vPmRRdu3VpJojiYjj9t6XdYqA647VFr5nQzGLW4sx6SDhou28dRgpwQ0RR9DNgBxbgrF3PtOYHExNbT7S8BAp/nPmjwkMEoN1YffvEqsqdr4ZxMQiaBXszsHUok7o3IaqaQ3RfntCZtuEWAdwafZnvdlhczS6k5eiqdvRqHkjnJgHY4vHR7WgebEk69POeODadTCrRb8UBS0HXAC83ujUNtD/Ai4cgS3MOra3XGoMBxr2t37b3a/v96yhSgBsiVy5AvmX13JEFuK3xW83Pfd186RPRp0SfvhF9ifC2uI/9fub3yo3TLwIiNa9zYlW114qry3i5u/DRrT3x0NiX3/7zBAnGUkFfbImhoMjy+d07rHWp53prqt6s8dyvh3TmhoT0HHadtcyAx3VpVLptO9oqvWibUgQYoEkPiNR40xxcDPmVXNytZUgBbogklz8HxPEUy6Jdn4g+uLmUjHZzMbhwbSuL2SA6LZpL2fYXgxyi/TjL8/RYOP9P5c5Xz+nQyF+Xx8LkyZCWnc8POy2Lb63DfBjTMcLWKcwMbB2qM2vEp+VwLsUigH8cTND9Hk7s3qT0wWnND/6Rjvme95ppeZ53BY78VvYxdQgpwA2RS1b2vDI8IK7kXyE+03Lr2j7Yfv+BTQbq2rsSK+lI33GSvr3xv5U7XwPgriEtS3gyXD93m64e4H3DWmMwlJ1DYva4jrryPJeu5JKSpS7IrThoib4L9fUo3b6dnwXnt1varUc6lt+jy1Rw19iV93xV9jF1CCnADZHEQ5bnngEQ0NR+X+Bkit5nuLSsZz3CeuhyQexM3FmxMZoIawftx1vaZ9ZDbCXPWc+x5cmgDRduHOCpcz0rjQAvN96e2k237UxSFltOJXNA400xvmujEn7NOi4etWS4A72HS2l4+OoLtsbtVM9VT5AC3BBJ1JR8j+ha5kzkRKreZNE+yP4M2NPVk+5h3c3tnQlVIJbDntG3N7xV+XPWc7o2DeBujSeDln8NblkuP+Th7cN1ieoLi4u57Ysduj5lmh+SrO66GnW13c8WvWfp26YIyXqAFOCGRmG+PgjDgX+EEykWAfZx8yHSr/TZU79G/czP4zLjSMgsX6KYEjTpoXdJO71OzoId4JmxHXh5QifGd21Ml0h/mgR4cmPvpswcGFWBc7W3WVkboEmAJ72alxEmrjV7GdzU8lOO0qSH/nt6dGmNVMyuCaQANzSST+hvBRt1KfMQ7Qy4XVA7DKL0r03fRn117UqbIaDkLPjXuyG94RW/LA8uBsGdg1vyyfRe/P7wELY9N4r/3djdnGu4PHi4uvDRLT1KZG4DtZRTmfbkSxqzQWg7sLGIWypdplqeX4mH+N3lO76WIgW4oaG1/0KZM+DC4kKiUy1Z09oFle0z3C2sGx4uloWbKhHgJj2hncYjIvUsfDUe0mIrf26JQ7QJ96Nb00Bah/ny8oROPDGmHZ/c2stuHT0dlzR3XeEdy//inSbr2/XEG0JmQ2toaO2/BtcyXYHOpp8lv9gShupI2SF3F3d6hPdgR4JqJ9yZuBNFUSpcucHMhPfgy6OQZgwuSD0Lc6+CTpPUcja5aZCTBoHNoVl/CGldJypp1CXcXQ2E+XlwpyOiayI7BTI1UZHhFShdFdwSGvewpLE8shSufl0N2KjDSAFuaGgzUYW2B1cP+30p3wKcln6N+pkFODErkbiMOJr5NyvjqDLwbwKz/oCvJ6jiC2pAyf7v1L8S/SPVaKqOE0vuk9Qc1m6P4RWsbtJ5skWAMy6oVbOb96/MyJxO3f75kJQPRYGLmhmwI/ZfzQKcQRhoE9TGoZfSLsRBFZkhAAKbqSIc4sA4rsTD4tvVoo8S52HtAVEREwTUSzOEFOCGxJV4NbG5iYjyLcC18G+hy3hWGp1DO+v67r201/FxlkVAUzVJ98QPVdMDpZgZlGL4+c46U9aoXqKdAbt6QWBUxc4T3FJdCzBRD7whpAA3JLT2X3DIBU0bgtwhyHHbnZvBjW5hFgf+vRerUIAB3L2h90yY9Ts8fRoe2A5PRcMLiXDXOv2qeXEB/HgbxFfxGCSOoRXgsPaVs9tqZ8EZCXBuq92udQEpwA2JcnpAJOckk5JrSbbSLtixwp0meoX3Mj+Py4yrfF4Ie/iEqLe1vmFqesOmfeD6z6DTdZY+hTnw021qRQZJzaEoehe0itp/TXSZom8f+LFy53MyUoAbEhc1AuzXuMwk7NYhyI4uwJnoFdFL165SM0RZuLjClM/VnAMmrsTDTzPqVGL3Ok/mJb3ZqyIeEFoCm0OLwZb20WV1OkNa7RfgKwlqGZOTq+HEn5BRySTfDZmEA5bnDth/T6Wd0rUd8QHW0i20Gy7CUul338V95Tq+0ri6ww0L9Qt2sdthyUw5E64pLlnlbajsDBig+82W5/kZdapclTW12w3t1Br48Va1OqoJd1+4dTFE2ShlIrFP6jmL6xboFzPscCrVIsB+7n6Ee9uuc2YPbzdvOgR34MjlIwDsu1TDAgzgFQg3fw8LRllyIJ9Yqaa1HPy4Kgj+TdTwWGEAv0ZqAhhJ1VDCBa2CHhBaOl0HK5+CQmPJpQM/QtcbKn9eJ1B7BTjzEvx2n158AfIz4fub1FLWTUsmBXeI7BQ4/jucWq0K+ojn1Vub+sxpq2TY2ltzO0SnWSLg2ga2rVAgRc/wnmYBPpF6gsz8THzda1jgwtrD1M/VH3PFmJIxJxXWvFyyr8ENWlylVuPodTt4+NXsWOsbWhc0D3/VN7uyePpDh/Fw+Be1fXodZFxUE/jXMWqnCUJRYPkjkJ1se39+Jnw7RX9L7QhFhbDyaXinHSx/GI6tgAM/wGfD4WzdXk0tE201Ao8AaNrXfl+gqLiI02mWSrRtg9pW6GV7R1gqGhQrxRxIKuc1qyraj4U7VpVdfqm4AGI2wV/Pw/wh0lRRWaxLX1VVZGI3jRlCKYZDi6vmvDVM7RTgvd/ASU39r9B28K+1Rp9PI7np8ON0yL3i+Hn/fBZ2fqZPRgOQfRm+mQR76l/NKQCKCuDMRku71VB1kaoU4jPjyS2yVNVtG1gxAe4R3kPXrtGFOGua91f9h4c+o87GyiI1Rp01F+SU3Vdim2SNADtQecVhWo8EnzBL+5+5UFCyCnRtp/pNEHkZ4OYNBpey+wJkXYa/XrC0Da4w5TPVZnnLj+rMN9aYizQ9Fta8BBM/KPu8OxfArs/t7y8uhBWPgG+4vgxOdXB2qxoYIAS4uKtfzE6Tqy9vQdwufQ24NqPLPERr/wUcjoCzJtQrlBb+LTh3Rc3f4BQ7sBY3Txj5AgyfrfqRpp6FzIvqXVdhHpzbBif+sKzcx+2CZQ/C1C9kXonykpUMOZqqyQ4Uf3UYF1focydsNBbuzLigVssYcF/VvUYNUP0C/OOtELNZrbzgHaIucvS+A7rdaLv/zk/1YjFstmXByMO4ADdvoOpSBOqH3um60m2ap/+GVc/qt02Yo4re74+rETUmfrsX7t0EQVHle58mFKX0f9RDP8MvdwFWxSUHPQZj/q9ir1kW1lFgpVWjNWLtAdEmsGICDKo/sEmADyYdJKcwx+GIumrD4KJG1FlXA+k5HZIfg89HqXdZoNoam/WH/vfW+DDrNMl6N8YqFWCAAQ/AjvmW67TlPdVu7+5d+nG1iOo3QWSnAIqaqSrltBq58utdqihbk5cJOz61tAOaweDH9H28AkvOeJc9bLkI1qTHqaGopsUXgFEvq7+e3sFw41f6jPu56Wr+gPLczhTmwd5FMG8wvNlUfb0zG0uGSZ5aowq8tfgCbH2/ZNXYqkJ73tD2aj6Fsg7RLMCFe4cT4GG7fLkjaPMD5xXlsf3C9lJ61wJC28JN36h3XyY2v6deZ4njVLcAewXCVQ9b2pkXYfeXVfsa1UwNCPBl29uXPaCaJ7Ts+UoVahMDH7aduLntGOg5w9K+EqfeJlqXLC8qUMVQ6wje7WYY/ISlLQSM+6/eLSvhgJprNsmYB0FRVGFOPQcXj0B6vLqglxwNf78G73eD5Q+pgQ75meqM6ZtJMLe/OvtUFDVxyE8zVFOH+bWtPv7f7oPMJNufV0XJTLJkkIKyS4Eb0ZogKmr/NTG06VCdP/D62PWVOl+N0Go4DNPcNWUm1vmoqxonWXMXZXCDoBZV/xr97wUvTTWOLe9V/f9QNVL9Ajz0KfWL3PcuaKKJjEo7D6tfsrQL8+Cfjy1t7xC9yFpzzevgr7l9PLYCtn2k77PuVYu9GNT6ZxPfL2kicPWAG78Gz0DLtvjdMH8wfNgLXm8EbzWHD7qp5o85neC1MPi4N2z6nz7XqZbkk/DtVHi/KyyZpYbDmugyFV66rH+PWZdg6X0lf0jsUZivVpstjWNWmcAcEOD8onyzyQAq7gFhIsAjQOcNsSF2A0XFRfYPqC30uwfcNW5o2z6EujDu2oJ2BhzcqvxVMBzB0x8GPWppZ19W/9eKCuweUpuofgHue5fqZzv+XZjxK/hpivftWQjH/1Cf71ygLoqY6H9/6bYczwC44Uv9beLaV+DgEnVmveIx9R/GhLuvam5ws2N7DGph3O9j2VaUr5pNCm2YIxQbWZjcfaH5QEpk50q3qtrQZjRMnq8mJRn3NoRoBC56LRz8yfYYtRxdrv4QvBEJi2fq3X1M5GVYFilAfW8tyg5giUmPoUhjsqmM/dfEyOYWG31qXir7k/ZX+pzVjlcg9JllaV+OtnxfJWWTZMmkR1gVmx+09LsHgltb2ue2wOoXq+/1qpCadUPzCoJJVrPUxTNV31ztB+buC/3uKvt8zfvDNW9Y2kqxal9+O0oVdy0TP4DQMoSk9Qi4f6ve3c0RQtrAqFfg8cNw5yp49AD0vK1kP4Mr9L0bblqkhskCuPsYf0g0s4O/XtCbTbQUF8Haf8PiGZCVBCjqIuLc/qrvtPaHYcv7ql3MxFUP2P8B0lBiAa6CHhBaRjQboWv/ff7vSp+zRhjwgP7abH3f8TuUhkxBjnqXa6Kq7b9a3H3UaEdtgM+O+XWienLNR8K1Ha16QZgEsrhA9c3VMvgxvV2nNPrdo7oKHVpi2aa1s4JqAnE0VDG4Jdy+HA58DydWqXbagGZqlI1XkHqRc1JUO7DBFdpdA5G99WaNoBZw3SfQa5bqJpd4SK3KMOxZ9fzWNO4Ggx6Bze+q7exk1XwyYY6+X1GBakfW+kibUIph79eQaFDDa9Ni9SYd30aqp4UDaGvACQStA1qX0tsxmvg2oWNwR46lqJFRf5//m6f6PFX5MkXVjX8T6DYN9hv/meP3QMxG1UYssc/l0+gWm6tTgEFN8jN5njoxMbHiUdXLpRZfK+eEIo/7r7rYZiujfZ87YchTjp9LCDUxt4uHGtWm9XbwDlUvSruryzc+g0GdwdqaxZaHZn3hzj/Ldk0D9T0fWmKZNexeCN1vgWbGyhKKot4pWItvSBv11thEXgZc2KvaqrWmk1EvOZzjQDsDbu7fHE9XT4eOK4sRzUeYBTguM45TaafKneDHKQx61FjyyCgoG/9bq/+pawUlPCAqt47gEJ0mqf9Hm99R28WF6oTlzr8gogqSAFUDzomEc3VXHdutBa7nDLj23fI7vLt7w+RP4Mnjqkmi5TDocZtqTiiv+FYHjrwfd28Y9z/NBgW+uwHOG122ts/Vm1VcPWHKAnhoN9z8g34BsagA8jQRgo26QfdbHR6uNgl7ZT0gtIxspvfV/u6YjTputZGwdtD5ekv73FbbbpQSC8l6M5ZunaM6GfECdNbkDM67At/dqOaKqIU4LxTZ4AITP4KRL0F4Zxj6tDqTrUy2fN9wuOpBmLlcFWS/RlU33pqg/VjoMMHSzk2HbybD3IFqbgIzQv0B63aTKu4droV/rYFAG24+Lu7qAqiDn2tKbooucbojVZAdpV1QO6L8o8ztX0/9ysbYjfYPqE0MfVrf1i5uSkqSrFmA82uieivUBAaDetfb/CrLtitxajL+WujH7dxcEAaD6qb2wDYY+WKdLzFdJVz3iRp1ZaIwBy4d0fcZ8yp0nKDfFtZOLcXjE6ouSnS9Ca59Bx7aZTFjOMDxy8d17Y4hVZA+0IgQgtn9Zuu2vbztZS7n2PEVr01EdNJX2Di7Wc6CS0NrgqgJ84MWN091UU7rGRG3U/WMqmULqLU3HWVDxSsQZixVo/Gi15Tc3+8eNUDFFr5hEGacsU5dUKGXN9loTVTlDBhgUOQgprWfxk8nVFe7lNwURiweQZBnEAEeAXi6eOLv4c+09tMY02JMlb52pRn2rFqBwcRP01X/8dYj7B9jjWKMCvUIqL8TjuJiNUjJRHUvwNnCOxhu/UnNA51njJI98D34N1bvussyCx77XY0rcHFT7yyDotTF9bZXq/+jVYQU4NqIuzfc8oPqjnZoiXrho4aoOWqrORG9tgx9sGcwYV5hpfSuGE/0foIdCTs4e+UsAAoKKbkpuvpzuxJ3MW/0PAY2GVjlr19hIjqr+UNMuUNy09VAm2HPqJ4StjxctCSfUvunnVNd2/waq+I99q06lb+gTNJj9UFHzhBgUGfeN36p2oBN7pmb34UrF1Rzp8kVVEtRIfz9KmzVpjvQ3Ok8vLdKBbie/gTXA1zc4Nr/wrMxcM8GuPo/NVIFRDsD7hDcoVrcxLzdvHlzyJt4utj3rihWinl207NcyLxQ5a9fKSbM0edSVopgw5vwYQ9YMFJ1U7NFfpa6Ip9mjDAsLoD086rroM6+Xw+wLv5anUEYZdFmNFzzpn7bgR9g4Vg4vlIf2XhhHyyabCW+WkTJ5E2VRM6AJWayC7J1IchVbX7Q0iW0Cz9P+pntF7ZzKecSyTnJZOZnEpsRa/4RSMtL4/ENj/PNuG/wcPGotrGUC+9gmPm7mvtD63sOqvh+NUFN5NPWynzyx1P66hBa9iyE9tfWDo+dquCCVcrRxt2dMw4TA+5TZ7t/PGmZCcfvgR9vURcIQ1qrLmvn/yl5bEQXNUI3+7JazcO1ar+HUoAlZk6mnkTROM93DK66BThbtPBvQQt/vedGbmEut6+63SzCRy8f5dMDn/JIr0eqdSzlws3oAtiklxrurg2hL8iGH26GSR9Dj1vUbXu+Uu2PJnzCVFPGLo2dfvlDcP8/4BNSE++getEKcHArx4OqqpM+d6q5Y5bMggJN/pSMC+qfNa5eatSuKW1uXoYx8rRqkSYIiZnqXoBzBE9XT94b/h7+7ha3pSUnl5BvXRvQ2QihhnY/fhTuXK0PzCguVJMqLX8Y1v1HjciyHKjWpxv/jioKJjIvwu+P1bpV+nKjKHoB1ibgcjbtrlbNeV1v0ueQsabVcLh7nT5nuYef+mNSxUgBlpjRBmB4uXrR3N85hUqb+jXlgR4PmNtpeWn8HVtLc0cYDGpOkluXQFerIgN7v7FEZZkYPtsi1mP+A0Gahbtjy0vmMKlrpJ3XV8FwoPp2jRLWTvUQeuywGrTRcaJq0w9tp2YovHu9WvA3onONDEeaICRmjl22zIDbB7XHYJ2vuAaZ0GoC7+1+j/xideb7y8lfGBs11mnjKRNXd7j+MzV3hL1FnH736AM6PHzVcltfjrWE0P/5HDTtB426VP+YqwNr+29tE2AT/o1V7xUnI2fAEgAKigt0VTCcYX7QEuARwJgoy0LW9oTtxGXEOXFEDmAwqEEyt/0CvpoS6S4eanTWtf8rWRuxWT+1Rp2Jwlz4+Q77FV5qOxe0RVeFmmhKYhcpwBIAzqSdoUBTLboqI+AqytS2U3XtX0/96qSRlJM2o+H+bWrATLeb4a610KOUXByDHodWmmCO5JPw6TC1MktdQzsDDm2n2k4ldpECLAHgROoJXbt9cBWWEK8gfSL66LwklkUvo9A61WhtxScUrn4Npnxa9izQYFBNEdpZc2oMfD4G1r+hBg7UBYqL4YLmR6O2mh9qEVKAJQCcTLHE7huEoUqqYFQWIQRT2loyW13KucTW+K1OHFE14huuhs76R1q2FeWpSX/mdIGvJ6p+rP98ohZZzUisfR4TqTGWsF+QAuwAUoAlgD4HcJR/VK0JfJjUehKuwrJWvDR6qfMGU9006Qn3blZNGFqUIojZBLs+V6Pmvp0C77aHj3rBydXOGast6soCXC1CCrAEUIMwTNSmJOmhXqEMaWopEbUhdoMuZ0S9wydEdWkb918IKMMNMOUMfH8jrHmldhSh1AqwMECjrs4bSx1BCrCEyzmXSc5JNrdrkwADXN/Gkgy9UCnk99O/O3E0NYDBoJZbf3Q/TP9Z9S+O6KovGKtl6/uw6Hq1DpuzUBQ4pcneF9ahfiUYqiakH7CkRBHO2ibAg5sOJtgz2Dzz/S36N2Z0mlH768lVFoOLmlPClFdCUdSw54tH1XJepjp1oOYnXvaQGmXnjM8ldoc+Cbt1LgyJTeQMWKJbgIPaJ8BuBjcmtZ5kbkenRXPk8pFSjqinCKEGerQdrVZ8mfqFvhLw4Z9LRt7VFHu+1rd7zXTOOOoYUoAlOvuvn5sfjXxqXymnyW0m69o/n/zZOQOpTXS9AaZ9C0IT3PH3a2ryHy3ZKbD6JfiwJ8zpCvMGqTly4/dSJeSk6QvsRg1RM4xJykQKsERngmgb1LZW3tq3DmxNt1CLP+2y6GW61JkNltYjYJxVfboVj8LSB+HEn2oyoA96qFnbUs6oOYgvHoZTq+HrSfbzF5eHQ0v0Cdjl7NdhpAA3cAqLCzmddtrcbhtUw/W7ysEdXe4wPy9UCvlgr73E2Q2MfndD37v12/Z/Cz9MU00SeXbCmvMzYNEUSDxc8ddWFDWpvAmvIDXBjcQhpAA3cM5nnCevyFIttrbZf7WMaj6K7mGW5N5rzq3hQFIdDNetDsa9DcNmA6XcvQQ0V0OjwztZtuWmqVUg0mLL/5r5WfDznfoKGN1uVvMlSxxCCnADR2v/hdotwEIInuj9hG7be7vfQ6ltEWHOwOACI55TEwF5WyV1D2iu1p17eLcaGv2v1fqySllJsOrZ8r1e0kk1VPqIJj+HcIHesyr8FhoiUoAbONYeELXZBAHQK6IXI5uNNLf3XtrLR/s+cuKIahltRsFDu2HK53D7cnj6NDx+CAbcbymn4+EH05foZ8In/oCTfznwAgqkx8P8wXBJ44kiXGDCexDu3Cx6dQ0pwA2cU6mWBbimvk3xsefsX4t4tPejuvDkBYcW8N2x75w4olqGd7BazaHVMDUpkC28guC6T9CZLFY+XXowR36Wai9OjVHzVJjwCYOZK+TstwJIAW7gWHtA1AVaBbTiuf7P6ba9tfMtvj7yNcWmoouSsonsBX0sC5uknYMt79vuqyiw/JGSeYpbDIZ7NtZIxe76iBTgBkx2QTbxmfHmdm3IgOYoN7W/iQd7PKjb9s7ud7jzrzuJvVKBBaWGysiX9Dbjze9CwsGS/XZ8qgZ6mHDzhnH/U2e+AZEl+0scQgpwAyYmPUbXrksCDHBvt3u5pcMtum17Lu5hyvIpfHn4S12CeYkdvIPVKh4migvgt3uhINey7exWWK2p2oGAWxdD/3vUvBWSCiM/vQaMtgQRqMEOdQkhBLP7zeapPk/p0mfmFuUyZ88cpv0+jRMpJ0o5gwSAHtP1KTAvHYX1r6nPz22D76eplZ5NBEdByyFIKo8U4AaMNgDDRbjQMqBlKb1rJwZhYGbnmSyZuETnIwzqAuOsP2exO3G3k0ZXRxACJn2sLsyZ2PaRWiz026lqwIYJn1B90nhJpZAC3IDRzoCb+TXD3cXdiaOpHC0DWvL12K95of8L+LpZEtRkFmRy39r72Bi70YmjqwP4N4bx7+m3nf8HCrIt7aghEFI3FmrrClKAGzDaGXBdMz/YwsXgws0dbmbZ5GX0juht3p5XlMej6x/lt1O/lXK0hC5ToP/9tve1Ga36DltXdZZUCinADZTsgmwuZFmKPdYHATYR7h3O/NHzGdZ0mHlbkVLEy9teZt7+eTJyrjTGvQWz/oBet4NngLqt601w8/fg5uXcsdVDZEL2BsqZ9DO6dl3zgCgLT1dP5oyYw8tbX+b3M5YKGnMPzGV97HrGtxrPtS2vJcw7zImjrKVEDVb/xr8HeRmqp4SkWpAC3ECp6x4QjuBmcOP1wa8T4R3BF4e/MG8/lnKMYynHmLNnDkObDmVq26kMaToEg5A3hDpc3KT4VjPyG9dAsfaAiPKPct5gqhGDMPBY78d4sf+LJQS2SClifex6Hvr7IR75+xEKakNhS0mDQgpwA0U7A27u37xOe0A4wrQO0/h54s/c1vE2QjxDSuzfGLeRf//zb2kfltQoUoAbKDoPiID6Z36wRdugtjzb71nW3riWT0Z9wqjmo3DRlPNZfno5cw/MdeIIJQ0NKcANkKyCLBKyEszt+mj/LQ1XgytDmw7l/RHv8+HID3WmifkH5rPyzEonjk7SkJAC3AA5k1a/PSDKw9CmQ3lxwIu6ba9uf5XYDJnQR1L9SAG2Iq8oj5j0GBKzEskqyHL2cKqFE6n6/AgNbQZszY3tbuSOzpa0jFkFWczeNFsm85FUO9INzUhRcRFfHP6Czw99To6mwuvgyMHMGT4HT9f6U+dq36V95uderl51MgdEVfNwr4fZfXE3h5LV+mYHkw/y0b6PeLzX47WySrSkfiBnwEBcRhx3/HUHH+37SCe+AFvit9S7hZm9F/ean3cL64arQf4OuxnceHvI27qKIAsPL+Q/2/8jZ8KSasOp/3kFxQXsvbiXw8mHUVBwN7gT4hVC/8b9CfWyU0qlionPjGf6yumk5KbY7fP1ka+5usXVdAntUiNjqk4uZV8iLjPO3O4V3suJo6ldNPNvxgv9X+D5Lc+bty05uYTTaae5rdNtDGwysE6UbJLUHZwiwKm5qXyw9wPWnFvDlfwrNvt0DunMje1uZErbKQ7dAsZmxHIq9RSNfBrRwr+FQ/8ouYW5PL7+cZ34erl6cU+3e8guyGbBoQUAFCvFvLT1JX6a8FO1+cuuPruatefX4m5wx9/Dn1YBrbiuzXW4Gdyq9HW05geAnuE9q/T8dZ2JrSeSnJPMnD1zUFB9gvde2sveS3txNbgS5R9FU7+mtA1sy1VNrqJHeI8qv0aShkONC3BWQRZ3r767xEKQNUcuH+HIP0fYe2kvr1z1SqnCtzF2I09seIL84nzztq6hXXlv+Hs08mlk8xhFUXj1n1c5lnLMvK1NYBveH/E+LfxboCgKZ9LPsO78OkANXPji0Bfc38NOtqhK8M2Rb/jf7v+V2L4zYSdvD327zB+g81fO81v0bxQrxdyclYCbwR2fwhy8XEsmT9EKsItwKZFDVwJ3dLmDKP8oZm+eTXahJR1jYXEh0WnRRKdFsyF2AwsOLcDHzYeb2t3Ew70elkIsKTc1agMuKC7gyQ1Plim+WpafXs7dq+8mNTfV5v49F/fw5MYndeILcCj5EE9ufNKu/W7xicWsOLPC3A70COSTUZ/Qwr8FoFZbeKH/C/i7+5v7fH/8+yq3B3595Gub4guw6uwqvj7ydanH/3PhH6b9Po3PD33Ol4e/JC4jjpj0M9y75l7yi/JL9NfafzsEd8Dbzbtyb6CeMqL5CL679juGRA4p1UaeVZDFwiMLuX/t/Xbv5iQSe9SoAL++/XW2Xthqbgd7BvNi/xdZc8Madk3fxZabt/Dp6E+Z1n6azjl+76W93L367hJf8OMpx3lo3UPkaUtkaziYdJCP931cYvv5K+d5Z/c75rZBGPjv0P/SxLeJrl+Ydxj3db/P3E7LS2P7he3le9OlsPDwQt04AEK9QhGaUuFz9s7hnwv/2Dz+t1O/8cDaB8gsyCyxb9+lfby5803dtqyCLN2PnzQ/lE6boDbMHT2XzdM2879h/+PWDrcyJHIIzf2al+i7I2EHt6+8nYTMBBtnkkhsU2MCvDluM7+c+sXc9nTx5OORHzOtwzQa+TTC09WTAI8ABkYO5MUBL/LxyI91dtwTqSd4aN1DZi+FI5ePcPfqu3XiM7TpUN4Y/IbuuC8Pf8nWeIvom+y5uUWWooMP93yYq5pcZXPc41qO0/0Y/Hn2z0p8CiqKojBv/zze26OvQHBPt3v4+8a/ebbfs7rxPr3p6RLpIxceXsjL216mULHU6tIKN8DPJ3/m55OWSrYHLh3QlW3vFSEX4BzB192XsVFjea7/c8wdPZc/pvzBmhvW8Fy/53RmntPpp3lkvUzqI3GcGhHgYqWYD/Z+YG4LBG8PfZuuYV3tHjOk6RAWjVtEsKclHd6+S/u4a/VdfH7oc+766y7S8tLM+3qF9+LdYe8ysfVEXrnqFd25nt70NEcvHwXgh+M/sPeS5Ta8Z3hPnRO+NaFeofRv1N/cXnd+HbmFuXb7A+QX5bP23Fp1hrvrHd7Y8QZLo5cSeyWW3Ym7eXnbyyVc2+7pdg8P9XgIIQS3driVCa0mmPel56Vz75p7ScxKBODTA5+WEO9xUePYddsu2gbpS8a8seMNDiWpvq3a921675KK0cinEbd2vJWvx35NuFe4efvxlON8sv8TJ45MUpeokUW41WdX6259r2tzHSObjyzzuLZBbfl0zKfc+eedZBSohQEPJh3kYNJBXb+uoV35eNTH5mCJcS3HsSNhh3nGnZGfwd2r72ZMizEsi15mPs7DxYNXB76KSxllVsa1HMc/CaoZIKsgiy3xWxjdYrTNvkXFRTyy/hHdrLssHu75MPd0u8fcFkLw8lUvE5Mew5HLRwBIzEpk5qqZhHiFmIMFTNzR5Q4e6/UYBmHAwyOQJr6WookFxQU8ufFJFo1bxKa4TebtLfxb1JirX32mY0hHvrn2G25cfqP5O/rl4S8Z0nSIriySRGKLap8BFxQX8PF+ix3WzeDGA90fcPj4DsEd+GT0J3i62I5E6xXei8/GfIafu59u+7P9ntWt8F/Jv8Ivp37R3bI/3PNhogKiyhzDqBajdAsxK2PsJ2tZeGRhucT3mb7P6MTXhJerF3NHz9Xl6b2QdaGE+D7Y40Ge6P2EzkzSxLcJw5sON7cTshKYuHSizuNDzn6rjkjfSF4Y8IK5raDwwpYXSM9Ld+KoJHWBahfgZdHLOHflnLk9rf00Gvs2Ltc5eob35Jtx3zCy2Uid0A6KHMT8MfPxdfctcYyXqxfzRs+jW2g3m+cc3Xw0t3W8zaHX93f3Z3DkYHN7U9wmm3kiDiQdKLHo525wx91Q0oWubVBb3hn2DjM6zbD7usGewXw65lPCvcNt7n+89+O6RUITAnht8GtEambC2gg/d4N7qa8rKT/jW41nXNQ4czs+M57HNzwu7cGSUhHlSUDdp08fZffu3Q73zy3MZfxv47mUfQlQRXHVlFWEeJVMiO0oRcVFRKdFk1OYQ/ew7mX6yGbkZ3DvmnvNM8fmfs15rPdjjG4+ulwx/ivPrOTZzZbFsVcHvsr1ba/Xvc6NK24kPjPevO2F/i8wrf00CosLOXz5MIeTD+Pt6s3AJgPL9SN0Jv0Mr2x9hYSsBCJ9I2nq15QJrSbYXjgcPlx93LCBY5ePcdvK23Quet6u3nw86mP6Nurr8OtLHCM9L50bVtxgttUDTGo9idcGvVbhfBKKohCdFm0OAnF2Xorhxu/Xhg0bnDqOuoYQYo+iKH2st1erDfhU6ilyCiwzrxmdZlRKfEEtPd4+uL3D/f3c/Vg4diGrYlbh6+bLsGbDKuQwP7zZcLxdvc2O+T8c/4HJbSab/yE+O/iZTnxHNx/NtPbTEELg5uJGz/CeFb7tbxXQikXXLir3cR1DOvLCgBd4ZZu6KBngEcC8UfNKXfyUVJwAjwA+Hvkxt6+63fw9WX56OWfSzjCqxSia+TWjWCnGx82HPhF9yvTBPnb5GO/sfoediTsBCPMKY0DjAdzW6TY6hXSq9vdTF0jLTWP1udX8fuZ3otOiuSbqGp7t+2ydSZ5VrTNgUGcFCw8v5I+YP/h10q8lbLV1iTd2vMEPx38wt78e+zW9InqRmJXI+F/Hm2eaEd4R/DLpFwI8Amp+kJoZsImNsRs5mnKU61pfV8LXWVL1bIrbxMN/P6xz+bPG08WTYc2GMan1JIZEDtHNbFNzU3lvz3ssi15mDofW4mZw480hb3JN1DUl9iVmJTL/wHy2XthKv0b9eLbfs7pgospSm2bAa8+t5fktz5dIoNU5pDMfjPiACJ8Ih85z5PIRNsdtpqC4AA8XD8K9wxnVfFSVapW9GXC1C7CJgqIC3FzqdqhmTHoMk5ZOMrevbnE17w5/l5e2vsTS6KXm7W8OeVPnRlaj2BBgSc2z+MRiXtv+mk0BtaZjcEfu7HIn4d7hnE4/zYd7P9S5WNpCIHhxwIvc1P4mQPXO+eLQFyw6ukjn497MrxkfjPighHtiRaktAnzuyjluXHFjCfE1EeYVxrzR8+zeLRcrxaw4vYIfjv9g9jSyPv75/s/b9XYqL04X4PrCfWvuM0fzuQgXPhjxAY+sf8Q822kf1J7FExc7r8S5FOBaw5HkIyw7vYy/z//NxeyLFT7P4MjB9Inow9+xf5dwwezfqD+DIgfxzdFvSM5Jtnm8l6sXc4bPYVDkoAqPwURtEODC4kJm/jlT91n4u/ujoJCRn2HeFuoVynfXflfiri8pO4nntzzP9oSyo1pHNR/F/w38v0rfzUoBriI2xW3iwXUP2t0/f/T8KvmiVxgpwLUORVGIzYglrygPgzBwPOU4q2JWsSV+C0VKkd3jWvi34Pl+zzMwciCgLkC/vuN1lpxcUuZrugpXnctloEcgyycvJ8gzqFLvpTYI8GcHP+OjfR+Z2z3De/L51Z9zMesiD//9MKfTLQVnWwa0ZNG4RQR4BKAoCuvOr+PVf14lNa9kbhmBsHnHEukbyZzhc+gY0rHCY7YnwDIhezkZHDnYZi4AUGcjA5sMrOERSWo7Qgia+zenbVBbWge2Znyr8Xw86mNWXL+CqW2n4ir0a+GuBlfu6XYPv0z6xSy+oC5AvzTgJe7tdq/d1wr3Cuc/g/7DmhvX0K9RP/P2tLw03t39btW/uRrmUNIh5u2fZ257uXrx+qDXcXdxp5l/M7659htdjcOY9Bimr5zOpwc+5d419/L4hsd14utucOfm9jfzy6RfOHD7Abbdso0ZnWbo7mDjM+OZsWqGLoirqpAz4AqwNHopL219SbfNy9WLReMWlctDo1qQM+A6R3JOMidTToJQF9jaBbUr85b3yOUjfHX4K1afW02xUoy7wZ2ZnWdyV9e7zN4VuYW5TF0+lfMZ583HfXnNl5VyQayJGfCOhB0sPrGYpn5NuaPzHQR6BgLqgv5NK27iQtYFc99XrnqFG9rdoDs+MSuR6Sunm91f7dEmsA3/Hfpfm/bxQ0mHeHrT0zrPJoCn+jzFzM4zy/2epAmiivnz7J+cSDlBoEcgoV6h9Ino4/Cqa7UiBbhBEZ8Zz6GkQ/QI72Ez9/X2hO3cvfpuczvKP4qfJvxU4TSk1SnA2QXZvLfnPX468ZN5W5BHEE/1fYreEb15c8ebbIzbaBlLs+F8OOJDm77RJ1NPMmvVLHN4uBYX4cItHW7h0V6PluqulpabxuzNs81rPkEeQSyeuNhujvHSkALcUJACLLHi+c3P63Jf92vUj49HfWwzYX9ZVJcAJ+ckM+vPWbqo2dKI9I3kpwk/lXqnkJCZwHfHvmP1udUkZKlpQvs26svsfrNpF9TOodcpKi5i/sH5fH7wc+aNmceAxgMcOs4aKcANBSnAEitSclO4bul1Ote2fo368eHID8td484RAS4qLmLJySVsiN1Al9AuTO84vczFv9mbZ/PHmT8cGoObwY1F4xbRObSzQ/0VReF4ynGKlCI6h3SuUDRhXEYcTf2alvs4E3IRTiJpoAR7BvPRyI/wdrWYHXYm7mTk4pG8uOVF9l7cS3kmYqVxIuUEt628jdd3qMUXPj34Kdf8cg3v7n6X7IJsm8ccTznOyjOWBFehXqG8P/x97u56d4kFSoDZ/WY7LL6gLoJ2DOlIl9AuFQ7lroz4loacAdc35AxYYof9l/Zz75p7dXXuTHQL68adne9kRPMRpfqwlzYDXnd+HU9teErn/qbF5C5mXd/xvrX36TIILrh6gflWPzErkf1J+8kpyKGguIB2Qe3oEd6jjHda+5AzYImkgdMjvAefjvlUV+TAxMGkgzy24TEmL5vMsuhl5a59mJiVyEtbXrIrvqAWVHhp60u62fauxF068R3YZKDOztrIpxFjo8Zyfdvruan9TXVSfEtDCrBE0oDoEd6DVVNW8dqg13R+wiZi0mN4ceuLjFg8ggfWPsBnBz/jbPrZUs+pKAovb31Z53EwoPEAVkxewasDX8XXzZIudmXMSuYemIuiKKTnpZeoW/hYr8cq9f7qGjVell4ikTgXbzdvrmtzHde1uY4zaWf46shXrDizgsJiy+w1PS+dzfGb2Ry/mY/2fUT/Rv25pcMtNs/304mfzBVjQA3HnztqLm4ubkQFRBHhE8EDax8wR/3NPzCfM2lniM2I5VTqKfNx41qOq1S0WV1EzoAlkgZMq8BWvDroVf6c8iczOs2w65q2I3EHj214jDPpZ3RZ3vZe3KuLsHMzuPHGkDd0ibcGNhmoqxgCsPrcal2FlkCPwAY3+wU5A5ZIJECETwTP9H2Gu7vezcqYley7tI/9l/aXSCJ0OecyOYU57E7cjRCCB9c9qMu+9mCPB2362N7Y7kbS89L5aN9HJdJ0BnsG89mYzxpkqlQpwBKJxEyQZxDTO05nesfpKIrC/qT9LD6xmJUxK83CmV2QzR1/lawkPihyELM6z7J77ru63kWfiD48v+V5YjNiAQjxDOGLa76gdWDrank/tR0pwBKJxCZCCHMll8ltJvP0xqft9u3bqC9zhs8ps8J4j/Ae/DzxZ34++TPJOcnc2vHWCoX21hekAEskkjLp37g/P034iZ5v9NTl3AXoEdaDj0c6Htrs7ebN7Z1vr45h1jmkAEskEodo7NuYDsEdyCvK47nBz3Ew6SBBnkHM6jyrwsl9GjpSgCUSSbnwcPFgUutJTGo9qezOklKRbmgSiUTiJKQASyQSiZOQAiyRSCROQgqwRCKROAkpwBKJROIkpABLJBKJk5ACLJFIJE6iXBUxhBBJgGNV8+o3oUCyswch0SGvSe1EXheVFoqihFlvLJcAS1SEELttlReROA95TWon8rqUjjRBSCQSiZOQAiyRSCROQgpwxfjM2QOQlEBek9qJvC6lIG3AEolE4iTkDFgikUichBRgiUQicRJSgO0ghHhDCKEY/54qpd+tQojNQoh0IUSmEGK3EOJBIYT8bKsIIYSXEOIZIcQuIUSaECJbCBEjhFgihBhk5xh5XaoJIURTIcRHQogTQogcIUSuEOKUEGK+EKJVKcfJa2KFtAHbQAjRF/gH9QdKAE8rivKOjX6fAA8AucA6oAAYBfgBvwE3KopSVFPjro8IIVoCq4E2wCVgO5AHRAE9gFcVRXnN6hh5XaoJIURP4G8gEIgD9hh39QEigUzgGkVRtlkdJ6+JLRRFkX+aP8ADOALEo34xFOApG/2mGvclAG012yOAo8Z9jzr7/dTlP8AHiDZ+lq8Cblb7Q4B28rrU6DXZZvwMP9NeD8AN+MK474C8Jg5+ns4eQG37A942fiEmAl+VIsC7jftut7FvmOYLZ3D2e6qrf8Cbxs/x63IcI69L9V0PT+PnpwCNbOxvotnvLa9J2X8N1vZiCyFEf+BJ4HtFUVaU0q8p0BvIB5ZY71cUZSPqDLoRMKB6Rlu/EUK4A3cbm285eIy8LtVLEVBofC5s7DfZM7OAHJDXpCykABsRQngCXwMpwKNldO9pfDyiKEqOnT67rPpKykdvVBNDrKIox4QQA40Lo58KIf5PCHGVjWPkdalGFEUpQLXfAvyfEMLNtM/43GSL/0IxTm+R16RUZFVkC68D7YGbFUUpK3tTS+NjaZnhzlv1lZSPrsbHU0KIr4CZVvtfFkL8AszQ/GPL61L9PAD8iXp3Mk4Isdu4vS8QBHwAPK3pL69JKUgBBoQQA4HHgKWKovzkwCG+xsesUvpkGh/9KjG0hkyw8XEo4AK8A8wHLhu3zUVd3LkC3GnsK69LNaMoyhnj/8s3wDigqWb3bmCTcaZsQl6TUmjwJgghhBewEPUf+QFHDzM+Sh++6sP03XRFvaV9WlGU04qipCmKshyYjPr5z9T4nsrrUs0Yxfcwqlvgdaj5fsNQr0cQ8IsQ4mXtIcZHeU1s0OAFGHgDaAc8oShKgoPHZBgffUvpY9qXUUofiX20n9sC652KouxG9UE1AMOtjpHXpRoQQgQCS1FnqmMVRVmuKMplRVGSFUVZBoxFXXx7SQjR1niYvCalIAUYrgeKUWdSG7R/qF8ogPuN2z43ts8aH1uUct5mVn0l5eOs5nmMnT6m7Y2sjpHXpXoYjzrb3a4oyhnrnYqiRAM7UO9ahhs3nzU+ymtiA2kDVjGg+iPao5XxL9DY3md87CyE8LKzutvXqq+kfOzVPA8Bkmz0CTU+mmyI8rpUL82Nj+ml9EkzPpps+PKalEKDnwErihKlKIqw9YfqlgZqKLJQFKWH8ZhYVIFwB260PqcQYhjq4kQiakizpJwoihKPOpsCNWRVhxAiCOhlbO42HiOvS/VywfjYW+uCZsK4rbexGQPympRFgxfgSvCm8fFtIUQb00YhRDjqCj3AW4qiFNf4yOoPrxsfXxZC9DBtNPpszwMCUO3A2n9ceV2qj1VANupMeI4QwsO0w/j8Q1RzQirwl+Y4eU3s4exQvNr8RymhyMb9c437c4AVwK+ot2cKah4JF2e/h7r+B/zP+HnmAZuMn2u8cVscmtwC8rrUyPWYiRoNpxivw3LjZ3zBuC0XmCyviYOfp7MHUJv/yhJgY59bga2obmxZqDOyB2mAce3VeB2uR83AlWoU4lPAu0CYvC5OuR69UP2AY4yCmwucBj4HOslr4vifTEcpkUgkTkLagCUSicRJSAGWSCQSJyEFWCKRSJyEFGCJRCJxElKAJRKJxElIAZZIJBInIQVYIpFInIQU4AaEEEKpwN9XxmPPGttRzn0XlUMI0UIIkSOE+NHZY7FGCPGE8TOe4OyxSGoGGYjRgDCJqRWNgGtQI5N+trF/i6IonwshzqKmFGypKMrZ6hpjdSOE+Bk1kXgnRVFOOXs8Wow5Lk6h5lvoougrS0jqIVKAGzhCiOHAeuCcoihRpfRrDbgBp+uqMAghBgFbgEWKotzu7PHYQgjxMGpSm4cVRfnY2eORVC/SBCFxCEUtB3S8roqvkceMj5+X1snJfIua7+IRIYSt0u+SeoQUYIlD2LMBGyuFKEKI4UKIQUKIP4UQqUKIdCHEX1ZpJG8XQuwSQmQKIVKEEN8KIRpZv5amf0chxBdCiBghRK7xvGuFEJMqMP4mqHXLYoDNNvbPMtm8hRBBQogPhRDnjfbiY0KI+zR9OwshFgshLhr37xRCXGPnddsLIb4WQpwTQuQLITKMn+VvQoip1v0VRUkFfgfaAqPL+z4ldQspwJKqYiKwEbUw41+o6QmvBjYKIdoKId5BnXmmGffnA9OBtUIId+uTCSFuBvajVjzOQhWlg8AQYJkQ4tVyjm88agWYdUrpdrdA1PzCNwDbgW2oBSjnCSGeFUJcZdzeCdV0cxS1osMfQoihVu+hK7ALuB3VrrvC+N4TUO3ud9sZw1rj43Xle4uSOoez07HJP+f+odbuUoCzZfQ7a+wXZbV9g3F7MXCDZrsB+N647xBqxYNOmv3BwAnj/hlW5+yGehueAYyz2tcZOG88bkQ53ud3xmPutLN/lnG/AiwBPDX7xhm3Zxg/hyetjjXlLF5ntf1L4/bnbLyeL3CVnbF0Nx531NnfD/lXvX9yBiypKn5UFMXsRaGo1Q3+a2x2AV5WFOWoZn8KMN/YHGF1rhdQS9g8oyjKKu0ORVGOAE8Ymw+VY3w9jI/HyuiXAdyvKEqu5jVXAQdQRfOCoijvWh1jqvgw2KpUT4TxcZVVfxRFyVQUxV4JHtPn1NHoGSGpp0gBllQVf9rYFl3GfpMbWBPTBiGEAbUatYJttzhQTR0AV5VjfOHGx8tl9NutKEqyje2m91LifRh/TC6j/miEaHbtND7OF0KM0ZbwKQ1FXeg0FRoNL62vpG4jBVhSVcRZb1AUJbO0/VhERjvLCwH8AQFcshUcAlwy9g0rx/gCjI9Xyuhna5zasZa1X/te/gesA/oDq4F0IcR2IcTbRvtwaZjGGVhGP0kdRpall1QVpRZUVBwvuOhifCxCdcmqKtJQBdsf1R5tj7LG6XDhSEVRsoHRQoj+qLP6Qaiz9v7AM0KIVxRFsbeY6G98THX09SR1DynAktpGMmrhRi/gIatZdGW4hCrAIWV1rGoURdkB7AAwenzcCiwA/i2E+ElRlBPa/sY+vsZmUk2OVVKzSBOEpFahKEohFjesG6rw1HuNj52q8JzlRlGUfEVRvkJ1ZROoHh/WmMZ4VLsYKKl/SAGW1EZeBQqAD4QQN1tHhAkhDEKIUUKIseU453rjY3kW7iqFEOIBIUR7G9tbobrTAZyzcahpjOtt7JPUI6QAS2odiqLsRg1ecAN+AGKEECuFED8JIbYCF1FnycPLcdqVqKI+qgZDfO8BjgshTgshlgkhvhNCrEN1hQtCdd3baeM4UwTcshoap8RJSBuwpFaiKMqPQohdwCPAGGCYcVciqjnhD+y7qdk630UhxC/AzcBQLK5s1cmLwATURbeBqAtrF42vvQD4xfoAIUSw8ZhTWEwxknqKzIYmaTAIIQaghhnX5mxojwAfILOhNQikAEsaFEKIJahJeWpzPuAcoLNStzPPSRxA2oAlDY2nUG3B/3H2QGxwP9AUNdeEFN8GgJwBSyQSiZOQM2CJRCJxElKAJRKJxElIAZZIJBInIQVYIpFInIQUYIlEInESUoAlEonESfw/XtUMCV3UKMkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40ms\n",
      "50ms\n",
      "60ms\n",
      "70ms\n",
      "80ms\n"
     ]
    }
   ],
   "source": [
    "rcParams.update({'font.size': 22})\n",
    "for tt in range(1, 9):\n",
    "    time = str(tt * 10) + 'ms'\n",
    "    print(time)\n",
    "    trace_sample = '11-27-23/selected_zda/Traces_' + time + '.dat'\n",
    "    try:\n",
    "        trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                                 sep='\\t',\n",
    "                                 header=0,\n",
    "                                 names=['Index',  'Barrel C',  'Barrel B',  'Barrel A'])\n",
    "    except Exception as e:\n",
    "        continue\n",
    "    plt.clf()\n",
    "    start_cut = 60\n",
    "    end_cut = 180\n",
    "    baseline = 3\n",
    "    v1 = trace_sample.loc[start_cut:end_cut, [\"Barrel A\"]] \n",
    "    v2 = trace_sample.loc[start_cut:end_cut, [\"Barrel B\"]] \n",
    "    v3 = trace_sample.loc[start_cut:end_cut, [\"Barrel C\"]]\n",
    "    t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "    \n",
    "    # skip_window = [96, 126]\n",
    "    # v1 = baseline_correct(v1, skip_window)\n",
    "    # v2 = baseline_correct(v2, skip_window)\n",
    "    # v3 = baseline_correct(v3, skip_window)\n",
    "    \n",
    "    v1 += baseline * 3\n",
    "    v2 += baseline * 2\n",
    "    v3 += baseline \n",
    "    \n",
    "    plt.plot(t, v1, linewidth=3.5, label=\"Barrel A\")\n",
    "    plt.plot(t, v2, linewidth=3.5, label=\"Barrel B\")\n",
    "    plt.plot(t, v3, linewidth=3.5, label=\"Barrel C\")\n",
    "    plt.axvline(x=48, label=\"Barrel B Stim\", color='red')\n",
    "    plt.axvline(x=(48 + tt * 10), label=\"Barrel A Stim\", color='black')\n",
    "    if tt == 1:\n",
    "        plt.legend(bbox_to_anchor=(1.09, 1.57), loc='upper center')\n",
    "    plt.yticks([])\n",
    "    plt.xlabel(\"Time (ms)\")\n",
    "    plt.savefig(data_dir + \"11-27-23/selected_zda/Traces_\" + time + \".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9760c964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams.update({'font.size': 18})\n",
    "\n",
    "trace_sample = '11-30-23/selected_zda/Traces_03_03_03_red.dat'\n",
    "trace_sample = '11-30-23/selected_zda/Traces_high_amp.dat'\n",
    "n_traces = 5\n",
    "trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                         sep='\\t',\n",
    "                         header=0,\n",
    "                         names=['Index'] + ['ROI' + str(i) for i in range(1,n_traces+1)])\n",
    "plt.clf()\n",
    "start_cut = 40\n",
    "end_cut = 170\n",
    "baseline = 8.5\n",
    "t = None\n",
    "j = 0\n",
    "for i in [2,4,5,3,1]:\n",
    "    v1 = trace_sample.loc[start_cut:end_cut, ['ROI' + str(i)]] + baseline * j\n",
    "    j += 1\n",
    "    if t is None:\n",
    "        t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "    plt.plot(t, v1, linewidth=3.5)\n",
    "plt.axvline(x=48, label=\"Stimulation Time\", color='red', linewidth=3.5)\n",
    "#plt.legend(bbox_to_anchor=(0.79, .97), loc='upper center')\n",
    "plt.yticks([])\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.savefig(data_dir + \"11-27-23/selected_zda/Traces_0ms.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acffbe1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vWqGpGjAw1EUNkQt6b20bdmRjdGOEclNCC40yLin/AVR8LYeUEADFWRZpQXZkx4IStNIoP85N4RU1bc9QNdEagysDWNyiBDaLoU5ruJGLpqQVSO6EDtmTX5+gWBbY+d4OuMtRrAuzyfk8LdXj28OlFWCtNeLnsYnQk40kd5BLV+TJ7Qmy4wzxc1pgONgbmNUsHc5frZlF/vduQn304RFUTZPjclti9tYMjJ8VjaZocPrx6YWdW79PSI9SNEWD7bMtlFAIZyGivQhe6mH7dGuuouWmxCAcYPeHZFm1uIVoJ0IwC5AdZyZsJjvKcPjhIbyBRzIobsEdUgdl9Km/J5DVRQpr/XCN+TtzuAOXDrZnpPctNgVxsgyGjmA2PUuMMzCbnslwESLajRC/oPCcpmjo/bLOfr3lWHACB3Zg03+3GJkshKLhWl6T0y6vyORRCnCbww5tOCMHdmgjeZEQ5ZA3qNIK/sinkKCkQSYzpC9SZIsMzsDB7N7swnOfvEgQzIM/qE5YVALpQQolFIbXh69t8gCqVYx9MzXi0gpwepCa4lsllemC/bEPJRVkJS/wkulRCjuwEc5DAPQAdpF7spZwIgfhPMSTv3iCYl0Y2ycs0JX7wRLhLER2ktGDG9jwJz7y0xzRbvRmf7aj1KgJZm/PKIglaygdq72ujq6PvpGipaSCbCS2z7bY/+t9iFzAn/qIdiLkpzlG10bU+foWcZEux5U/uoKdDy5uUwgXIa796TU8/fdPaYqvNcUnBg4mtydQIJlTuSmRHqSYvTO7lAf424BWGqv7KyzeX6BOa6werOjzbCkdi1lo8saYIizLghu5gAZ4wDG4OoBWmiRlgrheZ+DAdm1IRxp9cLQXQRaSdsalDUQhwB0OWZx9F7Sm3w8JMI8Bmj4rL/QgMkGZE0pDlhKFLOANPXCfo8oq+EMfspY4+vkRLG5hsDuAEznmz81PcwyvDi/tff42oLWmZ/YwNRtKAKohO997/UaR+FkM2UhMbk/e+Hf2UgqwbCTSo9TIx7bPtuA2p65CFVg/WiNaRJi/d3Hd9vbpFk7owAkc0/02GXUk49tj5Cc51k/XdF0rGspjdelHZIxhcH0Ad+CSdZQzhIsQ7tBFuBN+7QKRnZAGs9yUsBwLtm+jFjUOf35Ig5SXBoxN1mD2zuyNdBxa0UNVrApUcYXkIMHzv3mOKq4gS4mmaiBqgdnbM6THKWzfhuNT12Q79mc+VOMbY8zfm2P16QplXCI7zjC+Oab3++bY3ChkQ/GHi/cXtO3hHNLD1HRm83fn39lOWSn9ykS6224BnMn01k/WSPdTSjpLyfUGmz4DJRS8oQetNDin/W5u6CJYBCg3JZqccntFKWB7NuyRDVEIoKPhNeBExB+HuyHKuISqFSzXQp3XkI2k/GChwcDgDTxwmyOchXAjF3ZgwxEOvMYDNCgvYlPCn/k0yNPa8MHBLIATOZCVpFB4APlJjsGVwe/0YflZ6A6YeJ8Ci0QlSHfd3qqboqGD8aUmotyWZuXUSXaCyd3JG52FXEoBTg9T1GmN+HmMKq2gagVv7JGAPabr0/bJFnZoX4jT00pj/WCN+ftzY7mM92Mjck8PUnCHo07JkqmFhr/w4Q09NDlNmGVJQ5I6rZEepoifxZjenX4tfaQoBbZPt7S1dluCMYbhtSHAgGQ/wezt2SuFpykaLD9eYv7u/Gvx0KIUZgV69++HPzvE8jdLuEMXtmuDcYbjD49x+HeH8IYenMiBUgr+yMfNP79Jg8mMeMOXMbk9QZ3WxIm2V1/GGYp1gWg3oqt3+/duHm8we2tmfm++zE1geZdJcF5T3CWCvVy0LwOykhcK8ODKAMOrQyw/WRoapkoqPP3Lp2hKekabtIHFLRqgMUDVynSzTujAX/iwtAVuc2SHGdmPK9qUIWvKarADyolgnKFYFVh8f4FyW8INXXhDj9QOUgEKEAV1uJZrUYHNG8hGIjvKYF21yH3XaoSbooETOmTiqNpc4bJB5EQIZyE2jzfY+9Ee6qyGLW04oQPZSJSb8vdOKywqYYpueni2QLVKKpTbkm7dQuHkoxOE8xD+xDf79vJVDn/i02fWSPO8vyl86wVY1pKkTQcpDRRKATd0Ee/HqDYVnv31M8iCPPPpaYp/+X/5lyi3Ja3hBr2Zpx+R1Gz7nIZM4SJEkzXm4asLCsLWTINtGJq4zWFt5FkaFQOgaeg3f3duHrqvMmCK92OkhynKLSkytNZYP1pD5ALu2EX8LEZ0JSLZUdYAFuANaVnj6cenWLy3+MpFOHmRmOKrGoXTj0+xur+CkgpN1sCJHKT7qSki6TFlFPgTH7EVY+cHOwjnIdEIb89e+fP9qY/itEC4COnhLQVGN0YIFyEGVyh+sfv7u6vd4MoAdVZj+2R74c/KlzmG14fm8KtT4jUndyakZmH0uvyx/4U/g/w0R3JAkrDJncnrTSZCmQM72oteuXV0TrYOls0wuk6d0eztGY5/dUx/xnGO7ZMtym2J8pRCcizPMoNiBiqs3OEY3hgimASoc2o0koME4PQZdcqGOq0RLkLIWsL2aFZRb2vqTEuJ4Y0hOe1qcstBA1BU6C3bog0caY34WQw7oEHqefecZsRLN1WDYCcAtzn8Idmdi3WBYlnAn/hYP1xjfGsM27eRn+S/dwW4U0FprU0dAYgqSg9TMtbElMushML+3+ybVLkmb1CuSwyvDTG+OX7t8/V18K0X4PQwpbzTmh5627XNw7B5skEd1xCFgBM42D7a4jf/6jd465+/ZQYSALB5sqHhREndhDciIfpgb4D0IEVxQgJ4Janj8Ee+6UBt34Y7ctEcEoeXn+R4/BePSTDvcLPee/bW7AsVgTqtUSzPpskAoBvinCEBZ03pVFVaYXh1CG9EQ7A6qTG4OqCD5On2tcXv89B1LB2KdYH147XhC0VJHKKwBLjHUSwLlJsSju+gyRrYgY0Xf/PCPHjdIELWkg7EuKKs2/2YrtdjD1pqw5nnpzkm9yZYfbIyFEu8H8NyLSTPk1dolzqr8fw/Paeur4WSCk/+8gmG14ZgjLpAi1sI5gFG10ef+RnUGRWe7mABWgXN988paNruMDvJTOdSrEkFcL7rTo9SnC855w9Dy6ahZHKQYPloieQwMYc992jRpmKKTBEeBxQgFc0kvBHdvJLnCUQpIBoBMPria0nZEd7IQ7kuyaxRKySHCYJZQAO/ooE/8ZHmKakkGEy37YQOdWWCHHGylnCHLkQuKAlNSARuAObRXjqmGRzfQRmXGN8dQ2Skgun+rvh5jOm9KS34fM1V/HcV5bY0z4goKMDICR00eQN34GL7dIv0IEW0F6EpGqzu07Oc3c9Q5zWinQgCNKyb3J688XnGt1qARUUfejeBV42C7dvY7m9x+ptT4lokFQ4tNZyhg6d/8RSDxQDDG2fDgfSIOjF/4tODaDFT2IpVAcu1KK1KKlRxBX/imw5GCYXBlQFpIltPfnc9ufrHV+EOXFRxhe2zLSa3J5/7M8XPY4iCZEHVtgK3OY5+dYTsMIOoBNyhi+GVIYJZgPSQBo9u5CLZT1CsC0zvTQHgMymAl9Gd4l23qaU2HJ5qlFnQCEmTdcuy4E29s0Qs3RoGagl/5mN5fwnLtbB4b4FgEcAbeGRxbQtWNyitsxrIgdGNkbklyFpCFAKjmyNsnmyIT3Q4nv3VM3gjkv51xVBrjWQ/ARgu0BTVlhK/3Mg1+m8lFbJjMiS8rHwB2oPm4asZt0ooJAe0BaLJGyw/Wb4iU5S1RPwsxuTOhP69kWTRPfdrXqZEot0IyWGC01+dQlXUdQohzJAXGjT8khpCCriOCy8ifW2V0sZiLSmkp/v1GuSQYxZDMAuos5W0rDOc0myiSqkr66zNUKAFnpFrds5B0y3FnboQmaDPhjNYykK1qTC+NTb8puVasDwL5arE8PrQdOH+1Ee5pRlCuAiRn+S/N/bzZD8x/1xnNcZ36OdafbpCFVewA2oAu+S5LgypWBeAplozuj7C4OoA6RHps9+kXO9bLcDJiwRN0aDO266FA9lphs2DDe3XimvSPioFVSmUqxKZl2F5fwkNjcHuALKiroZZjK4Dmq626UGK049OkRyQ5rVcl+Q28jmS53RNtywLy9USy4+Xhnv0Z1ScLcdCspdg/g4N/vLTHO7ANaqL16HbCdY5mLjDkRwnWN1foVyWKJMSTugg2o1Qx7XJi/UmHpzQwfbJFtlxhr0f7SHZTzB/d/6Zf5dWtACyWNLB0tEcUMDoFhXF5aMlaUNtjmybYXBlgOWDJRXKUlAxHnkQtUCxLiAK6sqKk8IMLvd+uAeLU4cmG4k6qanQbKlzsJiF9cM1Zm/TDSE9SA2FkB4Rv5YdZuA+x2BvAH/iY3htiHJdGqqizmp4AxpWdYaG7DiDO3QvFL9iVbxSgGVNK9vTw9RkVZzXeOcnOaKdCOuHazoUauJZvaFnDut8SbyeP/G/UEqYZVuAIuVOd+hYjIqZ1toYLhhjUA1Z4qu0MhyvqARdgdviqKU2z6aqFXa+v4PNow1YyUyoj13ZFFtZEfXguA6klLC4hfHNMbhPsw535JLypSRjRWe00UqjKSn/wQ5t4pwr+r8yKeEXPoZXhqiT2tx88tMc/thHvswxuvHZt4/PQ53WKNYFnNBBMA0uTeZZrIuz7SSgg7VOamQnGfLTnCiZuQ9u0/qm0bURuMONPj7cCWmmYzHYvg3bs9+4YupbK8BVXKFYFWfdr6QCu3m0webphpQJrQXTiRwaOlSCDAH31/AntHYlO8rMBHr1YIW9H+2hXJU4/eQUm2cbKkqSClaVV7BKC8IVsAObLJot78gDjnyZk/c+dLH6ZIXB1QFG10dwQrp+nVddvA7JfgJZSVMYi22B5a+XRodZxzXquMbzv3qO9G6K6EoEf+Sj3JQk22qvQsuPl7Bsy8jFziNf5pTX8CwmqR6jLrTaVmQ75QzrB2swmyE/zE0Klu3YYBaj4Y+gSTr3uOmgu4PDcixUW6KE4qcxssMMkzsTME6OK1lJI1Vyhy7xmFJh82SD4fUh0sMU6VEKb+xh82RDVvBSIlpEKP0S07tTyIqkT/7EB/c4qk2FG392g35t5wBr5VzRTmSmzrKRr9wMTj8+xfFHx6i2lAYma4mdD3YoHxfUaR99eEQ0T7cZWAHc5oifxxhcoUNh82SDuT83B8Bvg9Yaq4d0SxIl3XYs3wIDI262pRWUUoaHFblAkzWwPAuqpuxfy6bhmWVbxoDhT32Mro+QvqDbjOM7xPu21+WmopmBaqhJsGwL3OUY7g2BK3TFLtclqk2F4Y0h3MhFGZdkjW53zBXrwoTCa0kHhsgpuW2wM4BSip7FtpEY3iCe/rwD8IsiOUiM+xIgCVcwCxDtRfQaviWYG1cLWUqTXFcntXEibp60wUet7rrJG9JD2xzMZpjNZlg/WEOUAvf+xT0T9fmm8K28I1VSYf8n+3BD1/CT5brE5skG2+dbVJsKDAzOwKEuV0ok+wkNGfIap5+cYnxrDGYxxE9j40ZyIgeWbWFzuKEOrL2Sa03dm641WEhWUAbqfERG3J0Tkh+/Tmo4HnFj+WludIFAq7p4uMbOBzvmFO80vfkyx8F/PkC5KXH8y2OsH63pGrcu4HjOBdvv9tkWohYotyUmtyYA6Aq0870dNGmDzaMNdQ2rAjf+wQ2MblLXd/zhMQ5/cYjtky1t0C3ptbtDl76MnPSmTU6Su+3+FsVJAWYxDK4NsPx4SR1VLYAS8CZERdRxTdxw25Upoehg0xqnH59CQ2N4fWhW4WhJlINlWcR/FvT7lh8vEe1EcCKHisC2QrWuYDkWyqQED7h5HzU0inVBMieLrs+d+2v7dIvitED8PMbsnZmJZhxeG5JdOiI+9MH/5wGe/senNEh1iD4SlTBfeDuwUa5L7P/NPgCYYeveD/YQLAIoqUwwDQAc/t3hFxp+pocpVg9WsBxym0GD8nrLBtym3y9VmwehtJm2c59T/q9D/9cV366QOpFj3G7BXoBiU0AJhXpdg01o7TyzmHn2HM8BD8lK7I5dQBIF5oQONRybCv6MOtgma8xNhjFGqW0WPb9aa3P15h439mklFXXHGVESX6YAK6mwebx5JfJSSYXsJCOFx/uLb203XbEsziSELTXVHZ75krT/VVxh82CDaC9CuSnNcHZ+ZU6hSasCsID14zXcpQvLtnDnn90x3+E3gW/03dBKI3mR4PDnh/RhOxzZSUayqF8eQ5SUZSpregC8kQdv5sGChXQ/RZM0AKOh2/LjJZqsQXZK8pAqrqCZxrP/9AzZYYamaigSMG9MsZAuSX06iyYDgx3YaIoGZVySq2tD3E+4E6LaVsi3OcbDMfFtID66WBcI56ER5FdJhaNfHBH/Wwtsn1HaWHZKW2q1oghCxltRvJAoNgWYzaiTD2yyjpaCOMKCprDJfoL1ozVpmJXG4c8OcfrpKdKDlB5sRWtuvKGHYB7Asiy6ugcutvtb+jK2hpQ6q+kQEBQI4wQOvKFHSoa2gGqljXEDiiIVkxcJdn64Ay01ytW5LxMDVvdXqFOiJDpBe3ZEbrlgHsBySJJVxiWc3KGIxcMUlmPBG3rIjjKIQsC74+HwZ4dID1JsHm2QvEjgjTyoRpmBVZe1a/s2/LGPj/+fH+Ppf3hKB2DeYLAzQLgIiad7kaKMSzQFWYHz0xyWQ920N6Rpdvw0xvDakA7P0xzhIsT64RrTu1Nwj4N9hhKuCyVKX6RgYNSRtt8aLTTgwhgswEFJZad0CPoT31iKuccBQTQOOOANPLi+Cyhy2nWKBdlIw+9qTduRnYFjktGQA4JTF+dPfKJsGKlqZCmhR62Kx2r/LJuuz9IiOomB1ECiIGWFG7pEB7WNhTfyaCg5C4CbX/x7vvxk+VvXHnUFev7e/LVDrC8z3FKCZgRdQVVCwfZsjG+PaaDc2u87dNujAfpsRCmweUhhVIwzbB7TUD87yTB7e0bzkakPJ3Lw+N88NgtYbdfGtR9f+2JvyhfEN1qAm7zB+tEaJx+dwB24WD1YGW1juaHQkOQ5yXMYZybDlCm6nomaJrpKKTz9T0+xeH9BQyEJwKLr1+bRBtyhCX+VtgMoQY66KqUFiXzAUawKuAMXzGLmusUdTiHaxxWFYqekC7zxZzew8/4OvDF1AMWqQDALsH5ECoPsOEP8IkaTN9g82yB+GqPMSohMUEfTKNIW1oo6fgHoWhueNJgEyD0q2NGVCFpr1BkdKvs/2cdgb4D8OMf68ZoKUVyCc+r2bJ+26kLBHBoAUMYkVwKATFJOrSiF6QIGVwe0zDFtAN7abW3AcYjukRVlC6iKrs98jxNdklARdocubVw4zVHnNcJ5aA6v86EuFqN9ZcVpAVlL7P5oF/lxTooGzsz/dvKbk7OH0LfhhtSV1mkNb+ih3JLD7v7Rfbz46QscfXhkuNouU3d0Y4T4OUkAtdL0DGQ0CFUN8eS739+l1/5kg82TDYJ5YGIktT7ryl/XCSuhsH64NlKljkcHaykT20KTNHTYWsQBWw7J0iyXOtw6qcFsGhLLRiKYBFBawRt4aMoGiIlqcyIyxlQpbbuoNhUs16JhncWhXQ1Rkwbb8i06ZGyLlD8nKcJ5CB5wbJ9sydbsO5RPrDW4TyuOdKON2kdphXQ/NSqI4bUhddFxBYzIWCRK8Vs7VtkQp9wtKO2iLhljGN0cgTFmBuZAm6NxnGGwd3GIlR1nZvDLOFEB/tjH8PrQFOVuiMwdTkPFc8VeSzJZpAcpwp2Q8jlsRi7CilQjspHmltkFGXGXbsLxixjFSQFRC1QZhSg5kQNRCBx9eGTWQnGHY/H+4gtUvi+Ob7QAO5GD7dMtREnXxPQwRTUi40W5LrF9vkUZl3AHJJ+pvbPUfmdALp2maKhLeERfBNu3yeo5dsHQXs8kkK0yaKHhjclowGxG3KWkhCmlSBHhBi79bwHRBKIWUCVdwYN5gPhpjOPgmIZ+i4Eh3dcP12S93ZbYPN1ANcS1Jk8TaIsebsZJJM8Ygxd5wJBkQ1VSAQ0ATkMi27ZxfHRMP6OU9BoF2a+ZRXmu2ydbZMuMVpu3gxzuk/e/2tBBIaVEOA+RHJIDT9UK2SaDP/VRbSs0Zcvh+g51AYo0qNCAkAJ+RAMIJ3DOdo9piZNfncAJqIN1Ry5EKeghXdGmh/wkJ86yVUMwi7oIU9QDB42mtemP/81jaBAN40YuglmAJmuwebwx71UXHBPuhnQ173jQijrhk1+fwA5sc92PrkS49Y9vwY1cpAep6eazkwzlpsTk7gS2b2P3e7sI5oEpDsmLhIwHDgWVB/MA1ZaUAtY5bbAGAE30k6wl8qPcmFXAANuxSRIpYQaLnTmCcUYDOssyZggncswgThSCDquJB7tsh2O1JG63jaiUjaQOuF1jr21t6APucshcmgAfd+IiEAHAyO1puzbyZQ6m6XV0XLDjO5A2vVYtNcJZSI7UA0pn80ae2aixeHdByWrbEgP/YrGsEprldMHwWmmjk7Z9CvK/9V/cMrMMd+ji5KMTo6pJXiRmngPQ7WH77EwvrgVRYmmZmnyMeD82Bbcr4pNbNKeoE5JJdodzJ8vr1ArFsoA7dGnge5pDVq0XgHPURY3Tj0+R7CeotzU0yJkLSTfNzZMNsoMMGiQZtD0bxap4ZU7zdfCNFuB4P4ZqiHdL9kkBUa5LaFC2aR3XNOzJ2xDroQOR0dVIFHTad4MOrTTqhlKmuM8hagF/4JP5opGUycqpK9ag7QO2TwEl9WlN2kulIaWk0BNOoSWqVFBaQTfUoXpDD9kyQ3BAwSSiFrAdG97Uoyv8AXVDtkeGDs20mdxbHg1IurXi5bak621MP4MlLdpU23YmdVojeZ5gemcKCRLzc4tTsW8jA5WiBYud/KvOanM6AyA+84joA6boCyhKYeyq3KYV6NlxBmZTsas21GUpqSBSGkKqhtxWxbKAyKjzmb01w/StKeqkvihYt0k3LBtJHXMhkJ/QQHNwdUCvzaLBa7WtwD2O7ChDOAtRpzUOfnYAf+Ib19H41thournD4Ywc6Bca+SqngWFSwxmQmiSYBbj3z+/BDmxsn2wRLAIs7y+xeUwacmZRkPno+gizd0nuljxPqBDJdjDTroT3xh5g03uoGomuBIu8QfZsiyohJUN8EJOxJW/MsA2gqXr3+VuWRRTT2Idm9EX2xh7G18e0OqggdQgDxX0yxjC5N0FxSocad7iZaWSnGWROnz0DgzfxoDdEa3V/vztwIRU9S/O35lg9WJn/XsYlufRsC0wzyFJCWVQgudfGYDLSq1dxheWnS2yfbOHPfFRxhYOfH6BMSjLbtN2qkgrx8/hCAFbn5uyu+9zjWLy3IGquFLReybMxuj4yRVYrbbZ0ADAD19fh5Ncnr9h+89OcnKetHb7cUsJcd1t+8NEDWJwOs/hFDDd0jTJKVNThikLAGdAtuNyWRtfdqYuKZYGDv6H5jmgEDbUDG6e/PsXy/vJ3pwAPrw3hTYlyyI4ygMNMIUVDhVYJZQqWLCWyZUbSGwYMdgbYlpTmBQ1AU6eoSxKWy4AewDonbgsChv9N9kkPqgT9WQzMvMlKKNQWrZDpuo1uQpwdZnSVcjkY6Jq7Od5gdGOEelxDCnItVXGF5DCByIW51nRCfB5QwVFKkVBfKRPY3QVvy4q4qyqhYji+eVaE8lVuhmMdNWK5FqJFRNkOWQM3ciEqehCrJd0qtNDgAakMuE0DIAkJpVt3VEGdIveogFYxJWsxxqgbXFcUZi9JbXD//3sfV//eVfP5BLOAeGVGnBt3OPKGnHCiEjj6xRHpWGch5UQwul53Bpo6ryFr4oibvIE39OCPfZLGtc9FndSotySNOvrFEXVbaY0hJ6nU9X9wHXZgEw10EBsNcbku6XNJKmwfbbF4Z4FP/l+fkESv1kiPiftOT+j/u5ELJ3IwujbC8jdLhOW5AlwKnP76FOEipPlCRnMFs8VYtnytpKWa4DD5H87QAQQQ3g4xvDqkQllK5CIno8SpMKE93KNQpmJdwIkcuEOXCui6BHyACaLkLMvC+MYYSisUm4KGyxYDE9SgSCnhjulgBYBoEWGbb88C4bVGsBvQ9L/htMwzF3TlPqkAB/CHPphzdt2vs5oyWjyOKz+6YiR9HWQtjYyyg2romZI1xXSaz2ZbkvRvTIaoOiWLrxu5Fwpwp8vvimX8nHh7d0DyxCY/ox6quMLm6Qb+xMfy0yWqbWWiAIIp5Vw4hQP3nmvCsZq8oWTARQjLs9CkNDPwxkQHMUW1o0oqlKo0UaCybEPw1/kryx6+Lr7RAlwsCxK9352YUJZOWtRNjLXSYC49SOnxmbHAn/gQkk4qrbThkQASsSutUG5L07l1ciDuUuGpkxrb51tjOpA1aSh1Q/I0ralzcUMXVVYRr6fJOacaWjcuKoH1IxrUnH5yivGNMaLdiLjcg9RwoKIS4BYneqP19HddhtKky+06daWoGEpBgxvOKYSoyqlbtLgFf+SjLui6O5wNwW1Ks1rHRMMAoAFZy+U2eQPGGBrRQCbEw7khUS2dlbLbzCAyAXtqQwjqkrlNyyG529IbSQUJieKkwIufvMDpR6dwh67JIIiuRDQ8jIkrswMb9aYd7gj6fZ2l2AkdcwBZlnVBU8ks0szO3p4RpTRwzar1pmgQf0gDTmaROoYH5FI8/LtDMIskclqT1hUKGFwfID/I0ZT0Jbv/v9yHO6Ji5gzoxlDnNcpVCSUV6m1tXI9SXPT4y4YKiDt06TrrWig35Rk1INuBW3t4gdOz7AQO3NA1LkeA7NlN1oB7nJxzLmmHh1cp4N6f+LAjG03a0FBOE7efn+SABYS7IWyXDjsNjTquoRw62KUkyio7zsBdTvTb0AEaKob5KqfFn4VAvW6lV5VEdpwhWASoixrpUUrv56gxP7NWGo50ULEKP/+//xzN/6lBOLuoh1eChsxO5Bh7b7QbUf7JQYLJ7Qnlbn90iuEN4nIPfnpAA2iHGiQncpAvc5MyNtgbwBt7OP7lMbITcjDmJ6RYGN0YGblffprTd18ro1NffbyCN/ZM7kunHNk+2SI/yU1YPrc53IGL/JjmUBo0iA4WATlXfQfpKQ29bc821IRjO8gOsgsHzpvAN1qAucuhJS2E7NZrS0F0gRTSrOhmoOGFqunEtrgFmUu4IwokKcLCLCAEQEMJQXTC+S8Os86CXboQa9snxYHUbUFsO25ZSUhI2A5xi511uc5oaNJ1LuWKrIze2KMvgsexfrA29uliU5D0jdN1jtnMDGQAmKFH98WVhQRCIJgEKJZkmS63JdRTBddxUQuiZUxugc2Ndjk7pUAXKagLFjU5BsFglAldBq0G8YWe4xkHIBRoal9JMN1OnM/F8Q2uDSAeCTIACGUGlXZgg/ucog1dcm9xh/aceUMPTdLQBF5rGtrZJDPUQpvtv9qljl4rDYtZEFKYL7Ed2LC2FvIldRj5MkfynLY3wKKrvmzomSk3tAG4KegWIAU5EP0xHVq2Z5O55KCAm7ioUyqG0SKiCXglTIBOfUI5DbO3Z1DiYmejtcbq05UZqmanxMerhroix6XGQEHBsckWbIe2EfB3EjAtqFkQCXHA7sCldUGnVDT9sQ/btWHPbLLGvtiaQ1UUAhnLMLk5McXfHZFyoskb2C4pephm4B4Fs8tGYnR7RHvrjlOj/e5MQV0zED+LSXbF2iyMWoH7dAdocnpv65RuLMcfHuPOP71jvtPjW2NsHm/AXpCLL5gF4C7H9M4UohBmJtFlfqwfrxFdibB9Rjka/sTHYG+AcCc0G0aGV4ekpGlVHctPlkQrMmDkkClk84SGwPFzyr5IXtDf04U6OcKh212rmeYux/ZZK820yZKtbY2Tj05oyFgIoj8Loj+7oawsJZgip6eGNjy+F3nGQfmm8I0W4DIpzRsmazqtRUodZkcndNo71Sgzxe8E9pPdCSkW4gbBmIYpSpFgXykiyzt0RQGAcb90+RGylrAj2ziWqoKUCcxmaEryvUspwRpmuL7kMKEP0ebIj3LqPITC6tMV5SqsClqkWEgokHLA8ugaykCvpZNlddpRZpMsrUyoe4VFmynAqatnY4Z6XRuNb36aow6o+DZJYyytdUrdG1hLa7TvlxsStyhLCcnopqFle1BpbQ6gboIvFR1gzGHm9O92iHX8rg6oy/Qiij7shkj5MocoBIIpTdFLQQW36x45p+LJHHqQraItSCC5kT/yaXPJwxW8iLb9akH8W/IioWt4G0zujuhmEUwClCtyGGpJB1fXbYuGrOnZMkN2QMXSgoWKkZ233tbGgAB2dlgvP1lifGt84WopKmm6OtXQ/rUmawz9wMDo5lLTLUXU4myTNNPY+cGOMUeISsCObFiMrt7hTojNww3SIwoCL9aFsWZnx7T+qdzSOnklFPRKQ16RJqAHAPIVLfTUlUbVVGT+UNRYLN5fILwSYlfvYvuUKIJOgyxLCe1ok1OspTYaYllKwAHqbW24UccnbfDJRye48sdXML0zhZIKxx8eY/VghSqt4IQORtdHmL89p5yRAeVFrx6skJ/kRve/fbyl96oQSLKEqKJnWyw+WKDaVNj5gPI5RCWwerAyPys0NRfZSUY68y1xuOsHNBSvEjIk+XMflkW3x+hqhDqjeVFxWlBBZhbqogYX5Izt6Igmp9uJ53jgnKNaEe+vpabQI681MCmNt/4Pbxnu+k3hGy3A4+vjs+m+y6AyZa7mUPRwwwPs0KaiYLXda6NgT2062V1u7Ja2b5ukpy4vQmttXEWWTVsaZC3NQKfLm/Aij5YSNgUsbQE2TAfuDl1oqYmKEPTfZUkOt9GNEaBJHlV9UtEG3A0VwDqtzdpxC5ZxOoGf7VZzIgflpjTdHXPJitqkDcKdkGRiEsbkUJc1eM3hz3wUmwJWYtEwI7BR5ZUxQnS2147jgwaUryhJTp3pSDvqAaDXxEDdOec0QOw+i2JVoKkbY0HuTn9R0sHSNA30miiTTqDeDfzckUtDxXYyrhoFxSj3gNUMutaQtjRh4cIWsHbo/Tr99SnxnK0LKT/NqchmpOqAgpE3NWWDaluZwWx2nCFchFAhJb/xgENXrb65UUhPUwzsAbhP6gI3IgNLcpic7VfLGyzvL3GvOZcZoWkgmy9p4JMcJOZ2JoWE4zkQjaBOyaafTyhhsncZo+CbbbIlC6tvo1gWGN0cgXNOnHdFQ6OsyOD4DoKdANunW3pP21S/7jaXn+bwGpL4cZvDdmxy47kkeSu3JaqsMjbnzcMNtNaYvzc3GcTQoDlLq7Th4HCGDtmf2/dZpALSbk0ZBYVd5ac5Nk83OP3kFKPrIxP1mh6kdFigRDgNMbo5Ml1pV/C3z7Ym36NYk9EmP6Z4za4ZSQ9S3P6nt83PevLRiUlD1Ipuj03eoFgXyI4zymiwqGvvOnfNNBynzYSZeChPSzQVNWtd06JrUjLEhzF1vzk1e8wi+Wu9qSnZThHfq5pzfy4YRrdG2Hn3YojTm8A3WoCruML68Rrxfmw4UOOCgjauIFmfS3hqlxi6QxdgQH7cTl0tgCma4luuRdm/HgfX3BRf2yVlArf5mQjds+F4Dn0g5/JatSTKwbEd6o4bKqTVpkIjGti2DQ1tuKruutcJuQHid7uBjLZbGUwj4bqUIdHJdIIpde9N1UAX9CBwnzhB7tAVXUgBphlRLusCzX4DZpMmcvmbJV3FbTJvGMlN08Ygtg+reV3dRl2bOvpuCGh5lunM7ZB2lXXDGKbpNsAYIwqi7ag0awXyEibqUFTU8XHOjamgM30w0MBOKw0mmRnAqUKZNTyykoaftLiFYBZgeJN0qF1WiCwkdS7tYZgepsQVD8mRpCSFKsmCulXbt5EdZqjyygQFSUFBO9kggzcko4cd2hjfGSN+HMOf+hC1wPHPj80hBQCMW8iOMuSrHJvH1K02VUM3AqHBfPq5NDQs3SohbAuQRI+d/uaUIj9HPtnfEzKqXPnRFZM9sX1C8kwncLB9tkV6kqJJGyODsjjdgqBJ+uWOidOPFmQuyddUyLjLzcFXbksc/eKIAmckyRcHVwbkAgPJLcFaGqVWGOzRpo6E0Y2ju4EqQQVSljTAzQ4zPP/L5+CMw5sQR9upIWyfZG/x8xjj22OzT3DzZEOFvFE0b4AmxUfVmOLHfY7N0w2cv3Uwf2cOZjNsn24Bi5Qp8YsY41tjrB+vUcc1kqOENn2s6JbUPbfBLKCGwLeRH+eGmmvyhqJCawUFhSqraE5QkMFLaw3btsEchqZuwCRRk50awrJIReKNPExuTCjq4PEae9/fe2M18pt1wmmN5Hlipt+qVua6DdAb1n3YFmg4wTmH5VsQhUAJ0oLCouuxEgrelHaSeWNK/deairUbuEbILUvihKSU4D6nkPdNdaED1Eyb/NbiuKBTm5GOU5fE7SmpTFFkFqOTNWvAXCpSsqaAH800bIfeStuzDWfqRpSs1jQ0KLId+yyQZ+RB1hKj6yOoSiHf5CbD2PZt0vUWVFztwEYwCuihcCwqTpK4cq21cU4Z6VHLQ2tFxZ6BAU77pXbpi+1EDumBM0GHj6Sf1+wjY2e/tpMzNSXxjUYeqBWCcYBwJzTpZdyjvXzgIIqoe08b6sa5S9I+ntDtxB8Qd7t5vAFAh7bRbgv6+WVFh6kSCsE8oKvtpkK4CEnZcJQimAZmIIt2Rxu3SLSfHqakhhBtWLrnwBk4qNLKhKOL8nxoCw0U67hVZbRcqChIwaAtup7arm34R9u3TTawWBPlFEwCCmR3SJ7VHZB7f7xnQoW6AbEohYn7VErBHZLF3Ikc00S4YxdaaASLAM7AQX6UQzo0xyi2xF0m+9SBDvYG0FLDn/okEaxoh5wQwgzAqw1JBDuqyI1cKE0KF1lJ0rdLjXyd4/STUzgDx3TB3fNlBzbAgE//359i+tYUg90Bjn5+RO+/S1uawYFqU5nMZCckg0pHD5786gR/+3/9W/hTHxajZ1M2Ev6YdOrF6iz9TpQC6XFqTFvQwOStCYJpgOwkQ/wsNhtoVEMHTlM3KFbUOHWHDNB2vzZDNIlIwrouTXwAs8jwwm0KTRKVQPYiM1r0N4VvtAAzxkh3V9J1q7uiMzATSq2kooc0JPWCEzgmlKeLf/PGZFG2XZusyHENi1tmAh0uQoSzEP7QR3wQw58Qv5gdZmY1kTNwoKp2yGdbFM5jM2PJhGgjAn1yx8lGmklzephSopSiDr6TlFncMhZn7pBsDZpcb3VS08EwJpNJlVZnA0NNGuFgJ6DYQVXRivGcroqiouAWLaiAWpYFK7BQnJBmVCrix+zABkqSz0ktz3IsFIjqEFScHZ/sr5ZNelU7tBEuWhcdh6EdtKJul/vchON0K51kJc0DbVmWSTtzAodWDnm2WQhpuVS0O1ccNMy1tLvJdB2qqkiH7fgO/LlPRpFWNqgZ2bjtyDa3ms5GbQek2OgGpskBrT3yBh6qbUUUSE2faZM3SJ4luPcv76FJGqRHKfyJj+NfHdPBD42mOPtiqoYUNqtPVyiSwsgGoehLK0pxYamm7dP72WVTdM8+LBpaRYuItNEMmN6dwokcXPvTa5AVSbm6bQv5aWug8Ci1zh269IVnbRc8cnHt718j5U5aIX4WI19SSHx32CqLXrs39GCHJNezIxpMCi2gaw1tnUWN+nPfuCu9MSXUgYPcpbKAKkn2mR1lOPzFIa1FihwMrgzQlA2CSUArqR6tcfLLE0zenmD7eEvfB5uoomAS4Pjnx2YmwzgNLKu4IqNE5J6ZUXxOz7trIZgG2DzaUPGsKds6PSLqw42o4fKnPnSjjSKlu9EpoYzpptP+aq1NLgkAuD6ti4JNB36VVPQZM2pgHI+Gb9WW3Juzt97MCrHz+EYLcLfHylzTQW29atprczeUaXkYf05vpoamYJJagQccPigyUjlt3q0+s6OOb4/hDBwMrw6JA8o80gVLZtQOnUxLKTrdvTFdmaHbCXstAR9nKgvOUTc1mGivYUyhyivYtm26c8206QIshwqxUgocdC23A9v8rKISdE3f1vQ6tAITDPlRjuP6+Ey6JqRJaQIjygAMZGpYk+VXQwPF2YDRGdDNADl1Md0D1uVedAeeOyItJfc4ZE2HkzOg7koG0qhAuiFZOA/pkGo3iYjmLBimy7iwLNpVVsftbjQLJmaxOyBlIyHq9kBpuWpwGDqqO2wtx0J+lMMJHQhXwKoscMahGxqceZEHUZAjD4LccLKmQ6OsSqIqKlKHOEOishq0ObxCYfN8g4f/+iFmb88opDtuNclZefYMtMiXOU5/dYoypRuPKokTZJyBaXpuLb99jsFM1KUTkP1XltLQEl7kmVCbYBZgeGNoVB3X/+w6pKQi3N3wlKB8Bu5xk+rXWYsnNycYXRuZxkGDfjY7sKGWdM22YZOc8a5F2b5H+Vkuc0kHCHQbGtTGaoqGQp68sQdZUDeuLQ2mmPmu1ro2eRh7P9qDN/IwvTsFcxie/PsnpPNXwMlvTgx1ZjELV/7+FWyfbo1OnjEG0QhEOxFZ49sgKVnSbdWu6FbRZYsUx2cRA3Zgm03n3V670Y0Rqg1p8jvnYZlQLIDWGkVWkJGrfa+6mRHjJG90Axd1XptNNd37w9B+1py+D+lxSlutxe+QDrhzWgFnCgDDKSrQP9vMSJG4TS6o/JhSyUQj6MoEGOG/7dmoygra1vBHPtzIBfe54YS9oQc7IKupksrYM8GoaLkjF+E4hB1SGhRTDLqivVmdndQOKPuzTErTFWqhiSdqF1F2PDIAYwvtTm6t2ivqxKaQm7Sm6257de8Gd2Vypi0FWiVIIWgQ0+p0HY+GAN0tAoqGX10YeEdz1DZpq7ui7PgUKs1scsN1Bw8kkBc5yrw0X7puC7VlUdFkDonTg3kAkVOyl8Wp4+sUFGC09LHJ6OrOXaJcdKiNmF1WEtFehHpLSVyyoOt2t8SS++TS00KbAGzN6L/zmoPV7Ew+2F4NO7rI9tvDMBeGeilj0oVH1yPKZXAtU1DqbY2TD09IV7qISNInyORCEY1nQzgTkpTSz9fdXLQmasp2bDMEtX3aueePfaNbtn0bg2sDypud+aSx9mwMr9BaG8smyaUbubheXjd/T0e7dHZ7xhkNiAV1/HZgm5D26EqE+DA28ZSO56CqKFbUn9JOs3AeEj10RA48xukAVUKZvXNKUGHUkgaPXcPQDbjBYYwIDAzJYYJwHmJwfUDB5BaIOpMUfVltK3qu23zickumG2fgwGHUUfq+j+3jrekyZS1RliUc6ZhFuqIWqJ5U9By1mnmtNPwJ7Xi0BvQMbp+SbK/Oali+ZW6nnbZbQ5u4yY6esj0bShPlVmzoeeoKs9LUSPhT30jZuE0NRXacEY36BvGNGzG4x82am47cZoyumd0gzokchLMQ3tijgum1BbMtbKpWYH7LQYJ4RssjGkHW0nCF3ZqYOiZjgO3ZFIrSyqu6LFzm0EbZOq4BB4BPhQsC5kredZCdJraTcXX62Y4/6jziHb/qBA5ZTTlDsSyMxhnqLIpQlRR60xXcjsro5FGwAQ76UjCHmS+AbIhmcIbkmjKc+LaC4ztGfD64NiBxfhuMwl3SiHKP0/XQ5WZZpxsRRaLClvPmdKW3Xeo2LMsyV1NpSVjSMp1McpAYzo57ZP6wPMvQBGxAmRuqIas3fICH1IF3vLnlkA48mAbktW8jGbnHzVp244LUytAP5ZqSrOyI+NFuWagTOlC1QjALoAUlttVZbWiQ/DQn9Yqk9C9Vq1f05J0dtWkac3vrqBcNTVreNmSnyyuwbAsqoxuW7RIVEi5CTN+awrLJyTa6MTKrpya3J1h+soTFLRSbgnJSsgrc4oYG8saecUx278Pw+hD+iJQF7sjFo3/9CKISCHbosOzcbHVc4/TXpxjeGoJ7HMEsgDyQ0FzT4a5aM1NSYrAYgE+42d7BGDMcrhKkBugOK6BdO/VzikItVyW8sWdkYk1BMw5RUMNQbkrz3dNSI9yjEKfOdNPxzJZrmcFYsAiM+1A28kztYGtYGUnVglkAphmqqqIgJQ3UqxqWa8Gf02FYJ7X5LptdjwpncQTt3KZTPZiAnsAxt79qRTI/OyQPQJWd7ZR7E/hGC/Du93ax+4NdHP38iDqNNmij42Fh4exEc+iLrEoF5tCDbcMGHJxdG3Cm23VC6vK6DtWJqMh2ZLs/86k7a0ASmbao5+vccKlKKlPUtSY/uRPR9RUK4JpDsDOPuGVbRuoDtAXVpVDtTvZmOZaRVVk2FSMIyuKt1jSIgACYPLeqvtUId4YNWUnYrg1nQrx1g8b8/V2hqJKK+K0taTEtm7rXwdUBudSENnRHEAUYXh1SJxI6Z4egouFmMA3oalhRcL07cM+GZ9DwRp6xiHa3mjqtISUFm6ACrMIyWb7VpoI3Id2w5ZLLrcorlCvy1jsR7aQDh8mhLTa0QaHe1CRZ7JZktqt4jKyOpor059h07ZcWbXngXtsJ5hUGewPqYFoZU7klN2BTtkoDwBzclr4oLdJt7Gc3ROy4dWYxCuJps0m6kHt32FqhI9qYwBh1roMrA6POcYfuhW0K3OUY3xnj9De0lDWYBRC1IIPOpiD6YuDRISEVwjnZZ3GuAQsmAW7941s4+OkBbZ9uozlFSfkldVGjPKGtv9lRZt7LLmcYksKhLIueYZELYzThHjfFv+NMtdJoqjYSttWYh4uQbiAuHdSipCEf43RrU0qRgqSlmU5/cwqAzB5VUtFrstpFo7AgQC7LcBHSwoMuOlMqMMlQqQqc043CdmjYLXL637sgoyahm2pnBFNKmUEz4ww2pxtk50AUFQ2N3QHJFG3bNlLP7vDVWmPvB3u4+idX31B1JHyzMrRthcUHC+THOdghM9kHjJPn2hk7GO2OyE3WdrqiFCbAoxO/y5I6FCd0wJzWcCFgul8ndMhRFNhnhL5joVyXCHdDeANSHITzEG7omo7JiWgtUJNRsLYEZUt0+tau2+4eQKaZ6ca01GAegz/0z5KpIhfDm0MTTqOVNle/LkFLSgm7IYqh40A7zpbb7d+liS+0XAvSkWZ6buRiOUl5LNsCTzmi3QjjW2NKwbKZ4VfBAS+gYHPucuJ8QxvJ88TsauuiEgEaDIazEOEufam6h1jURIvYHg1rdH1m5+64UW/oQTdkCFENFSh/xzcmD6Ad9p3QsMWbeFCFMgW4yRqyMXOAK+J+HZ+MDU7o0BorTZpxCxbFOgraoeYOXOqyhDa0TvIioWFiSxPwgtQZXZAQtym3o1Pi4PxijPa2033WaFPOuo3anTa6u635Yx+iFIjm5Fyc3JlgfHtMMrlKYnBlgPGt8SsB5+WqxOgmRWoym1H6XKtVdkKHbicNuda6mwSzGHa+v0MbGzgzwyc7so0CpTimJsSOSE3DLGaKnSaCkyi1oqEuVyqs7q/MnIaBGcOKqpVRGwH071VM+SXbJ1vkRzn8mW8OI1j0GelYQzAqxhAwuSPcI2XK+ZtfU1KjpKWGJWlDCyyQNK+gwbRSiuhCpWEFlqH03MglH0FNQzdRCDPgPR9MpZW+cMP0xh7dGsEgNgKWpsbJ9toDltFgmnMaunkjD4MrA2SHGcY3x2+sRn6jBdif+li8s0Cxor1U2QHZ/7hHxcAbUVh2l0OqGkVX0ZD4su7LY4c2Tj4+oQmu1mQEAMxEdnxnjHAeUhdRkpW244QHu+TJH14dwg7PhkoyoEmqatq0/IKKG1PM8HEWa3k4zzXbDbTQJoeA2xzelAT9cAF/Rl2EE1Bnlp3S7rpwJwS3OGqP1BuVW1G6VaPoCu07hm7gnBv5jTtyqYNWlPfb5c525oRu8FTFNOUd7FDXt8k39P6PfYxvjnHjH91AtBuBexzJQUKOLalR5+Q+s0KaPs/endHGkNa4ANCQMpgExqa6ebwhM0LhmHxaN3LpZ2+5QzsgeqPKSObk+A4NHluKoCnJ6moNWw63am8h0EADMhUwBua2kYoW0GwaNLxB6ISoigrRMAK3OJARF215Fuq6Ju7doyENVPu/OXRdZqztyloJnzNwzpQjL4eBt0syu29J955oRTeUTnLXbZjubkPu0MXg6gCT2xPiD+ccuz/YfSVsvAussWwL49tjaEUdVrcO6zw4p+l/F+Kz/HgJf0K3riqpzgqloucciowPlkvdtx1Qmp84JmrAdmwITdZoZjETAcsYvd8SbYZuLY2csIt+lVqCCRo4l+uSImST0hyMnVUdCvS56cYUzyqpwEtunhvuc0NFdBvMuUt8a5VWJIVsB5bdDRhtVoio6HvuTSnQSWTCxIUyTkYT0Zxp5jvteHcj6SIIpC0RTkLjquyoBnnQKnRAt99oQZuxu/jRN4VvtABvnm6wfbpFNKf1H13nwG1yYXlDzySA2b5trquL7y8wvErpV5unG1RxhZ33d5AepKiz2lhRmWYYXhlieGUIJ6T84C7MJ1gEiK7QdbDJKemf2QzDG7QaPjvNTGfJLIbN041Z+c0DGgCRp8CCM3RM1mm+zOldk+3AyOaGx5QVLWFUjYIzdDB0hrS2htOB4wwd6pzabrdKaX3P8Cptaui6lGpb0WFS0V4xaFIXOAEZQuqcUtm6ZLZuKDe+O8bJRydUvHcoZHv+zhw3/9FNw4kOrw3hD3yTt1tntVnm6HgOiiWlj0W70YWoPqu0zGLCTspmB7bhF1VF7jfLbgsb6D3iHsfg6gCrj1fUIbcuu1rX8B2yEDc5JVExh/S3nVpF1hJ1VUOvWk1nA5rqB/QF0tDGGckdbq6+XfZHlbYSOJu6szqj4BZRU3fnz+nQEKeCut4O7Q3HYqT5teyWVmr3u1ncMpSRHVHH5A99enbfo2e3C3gfXBm8dtNDt1QUoMLX7Qh8HQZXBshPc3MgKKEuxEKG0xDRbmSMB8E8MO8zGzFzEzNdczvI7WieJiO9cXfbYZJRGJQ6Uy51IUK2pp+3zmpwzuFadOPsOHkncBDuhMa4lLxIgAYm30Qoeu+5RYdXl4XdUUxSShTbguR43DKvkbvcUEdakuROKAFsaLjLOB3Yuj7LRmENg+KKZGblWV5xMA1oz2MrzZQNSR0d18Hw2pA02YMzn4E/9SmOduQZi/qbwjdrRb4xxrU/vYaH/+YhZEF8oTf1DME+ujoytINS7Rs0cDB7Z4bbf34b3sjDi5++IG94RcLs1f0VccWVMvKmYlmYEI9uO20wCzB7a0YqCY+utHVKubLe+x6atMHhLw6NzVhrjfhZbAZI3a4ucPowOwG2P/LPAnBcGi7YPlmpO6E/LAASmL41NVPaLlRm/s7cXO/dIe3I6zgo2VAAPfc4eMZhK/vMPShpAzAYFX4ncsx6oNHtEezQxvrBGvW2Jn58SMEruz/aJacQSN1x94/uYnJ7gt/8j7+BDSqo4W4IJRWqFa1m4j5HlVRkEmmddtW2wvrhGpZnYXh9CC/zUJwWtCDyytDoOy1G3Ys7dMnssCZOmPscyOnL012li1VBX/C2y+msp7BgJF9WQClqShL3i6Z1PK1oq203XOlyPoKdANwimqGM6aCWQhqbqm407IFN3Hq75YJW+LAzfrW1tkslwcENv8nAYEVkGVdCwR2RTtcf0obh4dUhRjdH5v22bOu1W7Xj/fhCDKPt2wimwWd+jyzboljUJ5vX/u/MZqYANzmtoPenPkkt29rfGTuCRWBMEK5HW0jOK1vcgUsUQGtmgQZVic5Yo2Dyj7VFNIYWxM1brJ17VMIsJ/VGnjGyWC5x2HZAzVank0b7VhhpqqSi3zVE3WDRsinNkDEGBVJldMYKd+jC9VxoTxvOVis68CEpMH9wdUDUVuTAm5CsUTCiQrrPYHh1SPGy7UCuO7ybtMH0z6fY++Gbc8EB33QamsNx9cdXcfh3h9C3NZIDCuGwPbJIaqUxvDlEvSW3kTfxsPhgQXyXVNg+2yLajUyUoBvR6ptiVZAbptXs2T4N2LjPEU5C2uG2G5pp91v/+7fgDlwTiQnQB3b1T67i+BfHSA4SuKGLaC8iR1f7IHQdQRM3sMYW8bbtlVsKaRwynvIorMblpAXmHKN7I4ha4NqfXKNw9VpieG1oaIPlb5ZGU9hJnTqlhRaapFus3UQs6Gp0489uoNzQMlNIOjiaqjF6Sscnh1fXvS0+WJgFgtzlmL87h+1R0Prej/Zw+LNDCnmPawyvDam7aweN3KX4xy4NrE5rRFcio2hosgbRTkSRhscpDb1csuMOrg5oq8F+arSZ/sgnmVVrhlEN8eGdi08KaWSGnWaTMWYsuZrTEKXjA7tVMxazKMGOk5IimAYmewFbmC+hmQ+UdBgqm7Zyd4O48z2q1RoFeE3OsU5yxl1KhuvMEuNbRH15Aw/ju2Nc+/E1oncUFZFgRotAO+5Sa430ML3QvQKUm/15CBchvJFHW1napDGtae7gDl1KYGu3aoiCqAblKhSnhdlvxhyG4c4QzagxA9WmoOWdTkBDbQVS2wQIKOy8Hdh2srRuo7Ospfl+gMGoNLpdgv7EpwCghiRk3bIBgG5FwYTem26nHhh9rh0lAcDI4LjNwS1uFDZKko5ea9Iqy4a0/k7gILwSkp5fAEnVhu8r+jtt14YzdTC5MzH0YrcaTSmSb3azonAeolgWRjExujXCtT++9oX31n1RfKMFWJQC9/+X++QVfxGT112os8WRCX2pu+HO6NbISNZW91ekMwQ9oJsnG+iQHG3d6en4xCM7oWOWXeYnOaK9yOh1vaFnNht3GazrR7RqxgkcXP3xVbhjF4//7WM4EV1PukDoJm/guq5Z285AlloG4oo7m2+d1QgWAVlThcTo2gju0KUvGodJUxpep6vp4oMFHgQPsPzN0mzQyE9y4i4zmih30Ya6oW78yh9fQbQbUW5uJchBxOiL12mcwwVpPm3PxuzdGQa7AzghmS3m787NtZg7HDvf26Gu9tHa7I4b3RiZ0JhuOlynNbKTjIZCQ5fW9AgqaFpqTBYTs92ji950B67pvqLdCLZj06CmLeZFXBjtcWfa4AHlEXOXNkOgzSzIl7Rgs/vydWt2OlmfLKTRyDqBY9xw2UlGGx62lbmCa6GJdsgEiqwwTjbS3FokQwRlQbiBC8EFUVGAOZAZYwj3QiNVckLHrHnKj3Nzm+uGq9284rMwuj76rd3veXCXBq7RbmRMLZ30ECC3YjgPwT2OFz95YTa3VJsKVV5RN966EcFoQSUkTJpeMAnodjcEKl2B19y4MbtlodC09gcKRO+0a6W0Ik1/l7dhlCfNmfKAKQbmn2WFdAqHTnnTFA0KWZzlhLdmCW5xE4LTqZmUJM6707l7E8/MQLRNm6mZbo0k7UGupML8nTmu/vgq1vfb59gnqsT2bdMk7P3xHiCJQu3oOH/sm/fpTW52/sZ3RKcvUkrEP6FTn/sUXN7xToMrlNU6f3eOk49OjJWz3NApavs2ne4uJWdprRFOQ9N9NFmD7DgjCyy3sHh/QVmoBxTe4s98s1xx/t4c7sDF7vd3acnfaY46q7Hz/g7quMbJr09QrFsdqs+NPI0xZpYlMpDLR9nK2Ga1bOMgBy52f7Brdo0NrgyQn+S0ATdyUSwL7P3RHmzPxo1/dAOiou2s3QPYdRiWRVuEu5Dv2Tszs0UZABbvL3DyS3qtWtEePH/sQ9YS3sjD+BYFx8/enWF0Y0QbAF5KcYp2I4xvj4mnO0jM9oLF+wu6JUQwE+jxrTFUo7D8zRIMDIv3FhQ0XjZwPXISTWYTGupJcljJht4Pyybt7OTeBP7Qx9O/ekrB/JU0GRHMo6hJZ0CRi/7Yp4k6aK9guaKV9yxg5j3o5ELRzYhuTG1SmeUSZSEKGtKISlCnLYjXlJUEC+gz9IYeeECOM6ckOR0AoEu4syzwiJ+50ULH6MO5x016XxeGXue1eX/dgfu5ttXJ7Qltdv4K6LT05zG+OUaTN9j5gA5XUZGJBgXMzSQ/zo15p+NeucNNrog7dBEGIUQtUK5KbJ9szcYZJRSYy86kgUKTPb9ddeUMiNNtUrLLn5ekMU6qHNuhaM4uPEqUtBJLc22e346KC3YCMou0vHuVkeSya0qgyG3rDl0z43F8ypEolmSwcAeUdBjMifflNkf8NIY/98n+nzWY3JqQgWXiwx26ZqYysSbEd29p6Wj8PKZb5O9KAe40kk3RGH9192ErSdsQpremGN0eYef9HUS7EZ7/b8/N1Sc7yrD7g10jhp/cmZA7bODgyh9doXi7ZQ5/4pugF4C6XidwjEQHoGV+8fMY45tjUiYsQtIwtlF3V/7kCopVQVwfZ6jjmjb1tg9bvspRLAvjqnEDCsa2XMuoFhbvLnDzz28ifkJccsenMYs46aYgDaozdWBZFm79o1vEPT+NAbTcmmMZbbM/9TG4MiDJ3JCu8d0G4OHVodEsWxZdC+3QxvydOaKdCMxiuPVf3KL34TXoFmQOrgxMCHp6nMIdugh2Aqw/XVOS2zyAG7lY3V+B+xzDm0PStXZmDaXM4eqNPKOZ5gE3bijbs+nhjlzs/GAHy/tL8750ts9OP+2NyIHnhuSicjwymMiSuLzzWvI6r+EMHRNo445dpC9S5Ce50X52kkQpaCWTtCU8uz2wKopevPEPb8D5f7jApn1zOplhO9Dj0VlOiRKtE651bwJU2NzQvTAh73jg14FZDNO7UxPV+KZg+zYW7y2w/HSJa39yjRYMxDU5QS2iurpC1oUswQK8yDMKjm4/X+AFuPrHV/HJv/oE4qmAcihbo6OBjDQMDH5IFIdt2xCgQ0/Vyujau5/ZYpZZEdYFzoODhmSVMsYPZjP4E5/CjGCBhQxNSsPsbhEA9+j5ckLHOA1tzzYNHHc5RjfpRidrSbOfgWc2tOhGY3J7gmpTwX3HhRACmwcbmutIjfGtMd790bt4+L8+xLpam5r1spTwa39mb/RPew28AYXoGCuy1HDHLnbe3cHVv3eVkvTvTgEA0U6ExfsL5KekobV92wQ6AzAnT7fCZHx7DCVpO/HLa8WjnQjD60Oc/vrUTI+z48wUng5O6JhQaTdy8fBfP6QgnbZLtwPbFEYwymSwHMvoCDnnJrTZDmyUK1pz0q2v9ic+uiwGWUikJ6lZ/yMqAdd36WGbBZC1xOytGUVSbgtM7k1QnpYUqs0snN4/hQn+4Qw3//wmGBjWj6hYTm5N4M/oSz1/d/6Zxde8R7uR2cPWbXk+/NkhBlcGcIcuvScjF5tHGwyuDjB7Z2YOgBv/8AaKVUGH0Z+6WD9Y03UUZAYpVgW2z8huOrg2IGnUKem9d76/Q+HjJcm5vPGZVtmyafedN/XgDujWMMCA7NCaqAhZtkoJbhsViMUtsy9PQ5ulpO6QVh3JkvKNIQG1VLD2KDFsfG+MaO+lJYstpdNlOrsTihfVDWmSLZ/S4aKdCMOrQyy+tzA28M5JuHh/YVK5DFXA6JB1h+4bz5XtYPs2Fu8vsPp0hdv/+DacwMHRh0fwx/Rc+DMfRz8/MlxuldO+wfm7c3CHm20e/oQUIjs/2KF1PrU8O5haKScDHVCdPLSzz3epcGhzTDoNeedstEObvkft4Lw4IROON/RQbktYzMLkrYlJ0OuoIculfAke0KDWnblmU7QTOfS9HHgo4xKzt2cXbkte5JmwH9sjOWqyn8Af+fAmHpqDBqPbI7OhZHxrjGAaYPrWlMxarYU73o8xvTN9c5/XG/uTXoeWL+pWnNiejcndCXY+2MHoxggWtzB7a2ZWVAPU5XZJ9S/j5c6BMfr35IAS7rsoyE7E3v15qwcr82dsnmxMxN/L2PvBnlFBbJ9uia9cFSb2kGk6mZnFMLo2gj/xz+iHyKWNDTFtk+2KfDCjwdDqwQqIYLS4TU5Xf2fgwK/IOjm+Ocb0rSnlj8oJihWpDGbvzmhosk+TdydyMLoxwrU/uYb1ozWu716nB6wNBZrdm2F0a/S5H08wI6qkTmsj/6qTGvlJjmAWIK1S5B/lCOaBOflHN0ZGcw0Gs6NucmdC7w2nAyE9TCmEviRjxvTeFDf+4Q1s97eQjcT83TkNytpbRCcni3Yiyph43tCG5ZZTvfZn17D5eIMiLlCDVg91yzg7hUO9bRettg4uy7LgOLSnLd/kxsffTfyj6xG8gUc3kHMyNGbRlbqb2M/uzUhlEDdmzjC+N8bi3QX8iY/RjRENBbtn+GtQC28C3OGYvzdHepDi7j+/C2/sIX4Wm5D3O//kDskuG/pssuMMoxsjKKGQHWQY3yajgaoVrv/pdWyfbJHs08JLgHhYxlu3okUWcVXTFuimPlt0KUoKUfLGHsp1aXYLylKiaRri8wHDoXdRAkorFCd023TC1hYsyMwESQWV+7Q3UtaSkgwbDR5xBDsB/LkPzjnKlP5O27ONTVs2ElZFqgfucJQx/RrLPXOwAjCO08UHC6wfrqGEwuytGaLFm9uIDHzDBbgpGhqetQ83dynCcHRjBHfgYnJnQrbXc+AOx84HO2crf9qUKu5yzN6evdLVMYthdP2zi023fyo9okKhlcbm0Qbz9+avnWhe/ZOrxClnDeSI8iuqtDJcsuVZCMYBvLGH0XU6Meu0RrQXGTt09+VzI9eEr4yujxDvx3CHLqbBlLa0tpQEZvQ6gwWt3GnSxqwSqre1GVYMb5DemTGG2VszcljdHCM5IGVHt0r+i3JUjDFM703hjT1z0+AO5a+mRyncwZmdFqDCtffDPUzfmmL1yQr+2Dd71kQlUCUVrdfR1A3arm3UCvFzCkB3A7rm1mmN1f0VZSq0m6zBQIHda2H8/d0ur2ARQN6SYEdnYvw6ppAff+yj3tTGXu74NDAMFyEFCpUC/sanPXMebd6FRYeHN/TOUtq694VbZquzG7hwfAfjG2OaDygFr/YQTmn4GUyDC8XX9m0E8y82VPsmYXGSrgEAGGhe0mrS5x/MITKBw58fQgmF5cdL4uxHrtGBW5zMK9AkpyyXpPMt17QMs1MeaalNmpzJx9CtgSl0Mbk3Ib64UxVVpCqyfVqC0C0ssBwasvoTH67vIl/lJp2MMZoRcI9UF4O9gXFCdkqG7vmp0xp7P9yjzviEtPPFqqCB/yK6kDHSrfGyXAvRgAqrbGg3Ypch4kYu9n64h6ZoML03faVefV18s3nA7SrvYBqYEJW9H+3BHbi0hO+38GTBLIA/8WnQ1JLoX/XaNrw+RJ3W5JQB8cHpYUquoZdgcQt3/ss7SF4kKFZEA1TrCkoruAGJzse3xvBnPia3J9TZR3TF6gp6V7i6ax8Aw+Xmy9zoOsOdkFZ7n2Twxp6RXJWbkoZ264KWMLZSu2iXuN1wFuKt/+otU2i7cPeuO/+yCOch7v7zu3jyF0/I+TYPzJCsg+0Rv7x4bwHLtjB7e4bTj0+pUz6mw01JGnZqrUlm2N4OGKNuaftki/HtMebvzrHzvR3c/5/vUzRku2TTciyz+YJ71J1YNumOy3Vprqnc5YZ37vJAlGx1uT6lVwWzAIOrAwyvk7yO2QzpforTT05Nuh0kkDxPXtHqdgoHSMCb0TyhiitybQpajFnnNfiGNMLnMbkzeeNSpa+Lwd4AdXI2IOQ2x+yHMyQHCbKTDJN7E9pycZxT9GRGErXFuwskhwnNFDiDwxzYV2zUWY3p3SmavCE5aKuC6FLbeMAhBTnMZEka7NnbM2wfbYmT9oiS5MN2wNnK5zopncnAdq2zW907M+LySwlv6Jl1Xk7o4PQ3p2cmDU2pbJ1iROTCJOMxm2Y7w+tDuGM6bLwJ3ewGVwbwJz49h23DsX1CwVVd3Uj2kwvf6TeBb3YjhtKYvzunDaoWw83/3U3s/XCPogu/wEnCLPZaIfuXBWMMk7sTnHx0Yq4W6UFKodfRq6/DDV28/d+8jaMPjyAqgef/8Tl0RV+8YBpgcntCD0T7QYULUmVUcWXSlF6HyZ2Jcbh1WtjhlSG2z7eIn8XG1OFP2vAUY5OlAylchHAHLqb3phe63DdxKntDD3f/xV2zaVY17YZoi7qP6dt0+nfFxQkd7Hywg2ScGFcdAKNt7njt9DA1umLu0JeiO0g/+O8/wOHfHWL18YpW7DS08cJ2iKNrClLKMM0Q7UV0MxAUmtRtUAl3QzTJWQh8kzbmcLz9T27TssikhKwk1g/XNN1flybrwR245FB0zgops4gfd0MX07tTTO5MYIc2gjzA6pOV6QydgXOhKRhcGcCNXBymKY6zDFIpqPb2N/Q83JtOYVtfnftNKrJoR47zpYq8P/bJAt7SesWywPDqELs/2MXTv3oKgA7h7ZMttNbIT3P4Ix/HHx2jTsjY4898lEvKTh5eGWKwNzA272JZmEO2W0prOVRAq7ii6Ncl3R5Gt4iuqZMa3KcNy45PMZV2YGP7eItiXcAe2MaO321eXry/oPxgRdzw8NqQdNhDD4c/O2w/O0bGH/csTa7Oa0Q7kYny7JIBhztDQ2Xs/WjPSFaLNUkHm6IxC367fy9WxW9tHL8svvG19G//N29j+ckSVVrh2o+vvdEX/2VgezbGN8fGTaQ1URGL9xemkJ7H6OYIVVIhO84wfWdKHZemQuVNPPN7uj+XMfaFTkdv5MHDxUmqHVBu8Pb59uzXjDxkxxRy7Y99TN+ewglJP/1FhPtfBU7gYPYWDS/SoxRN1hglxuu+8LZv02EQ2FTc2k0RdkD82uj6CFprHPz0gIq6okFfxyFP706RHWZUoB9yJC8SjK6OKNs3KTG9N8X45hhVVqFOaszuzZC8SLB8sDRXyMHuAN67Hu0KWxeY3Jlg9/u7WLy3wPU/u05xj2taaTN7m7j053/7HE3SmA3Yi3cXr0y3J7cm+N5//z2s769h+XQzYZzRBgVN8rjzn4OyGfIRw+FyibgsX3m/kqrC/dUK78xm4F+hCL9IEhwkrYmIcyzCEIswhMO/2IaG4dWhmYV0hpDhtSFm92ZYP16DcRqWdjGU0ZUIdVyfWap3BpA57YjrDvzxTSqe3cZn7Z6txXJCx+jFO23/YHdgbObRIsLs7Rmu/dk1etYPMhz/6hg7398hWqsWqLYV7MBGtEcD9WAaYP7OHNtnWwTTwGx1GVwhvXu5pkyK+HmM2Xszc4u1bNKfu6FrMlFs38bo5uhsYNreHEc3RqTCOhdP2iGYBp9pF/+q+MadcKMbI6IA2hU9l4lwQaEbXfiIqASWny4xf3f+Cr3BGMNgb4CjXxyZLjk/zo1Zo/s1k7uTr3TtP4/h1SH8sY/JnQnS49S8lvFN0t9GVyKEMypaX/fv+iKwfRuT25Mv/OujXXIQdl25GxG/b/u2MaB0772oBN0Y2kSpvR/tIf6fY+LyW81yOA9NkllTNAhc+uJ10qKOG+7kc7Ztg/n0+3fe38H07SminbNO25/4ppBc/XtXaXPwY8oYkUJSGFN5cVfY7g924U983P4vb8OyLMQvYpNJImuJaluZoq20xvFQYXl8jKM0hWy73onvw3ccuJaF0HWR1TUerNd4ezaD1hrLokBg2wgcBydZhsBxEDoOjrMMjmVhN4qMkuI4O7MuV0LgPx8cgDOGf3TzJgau2wbpfPaz4U982tjRdnT5aY7B1QHCRYjN0w3CeYh8mWOwM0AwpWzhLmNZNhLDm0NUeYU6JsNNU5ATcud7O9j/232sH65RrkujDXYHNHDzJmSUshxSMIiKAuNv/IMbWLy3MLOVbpnr8uMlOeXmASY3J6Qo+v4C3OZmvRh36H9nnJGWvyCzhGqUGap1XHaTN8ZHMNgjSsriFiY3SUn18veXuxzDq0OK92zhjWje86aLL/AtyNAAKlRvWj/3VTG5PcFJfmKGe03eYHV/RVtZXypuHaWgCtpe4L/jU4fnn9N/vobC+CpwQrJI2gGd4AAZIH7bgPG7gi7jIj+llULBPDAdILMopyB5kZjbjzfyzHvtRA64Q6vaOwlXsAjMbUKUVLCn96bG2BDMAhx9eAStNYY3hshPc7J/Xx9h9g6paswACvT8hYsQyYsEjDPaqzZ0sX60Plu4WZ4N0izbwuTOBACMLdUbe7Sxt8XiPVJApHGBY13i0+0p9uMYLud4sF5jmeeYhyH+aG8PnFHK3Y3RCKuiQFyW8GwbtZRopMSLNEXRUGHcCUPMQ6LdailxczxGWteQqg3i0Rp/9fQpfnVyAgZgP47x4+vXobTG27MZRt5nf88GVwdmuaXWGusHa9IH5w2pBUKiv27901v49F99arJZ3NDF6OYIohSIn8dmm3W3IdsbePAnPg3hWsMF9znGN8YmT9fxHYzvjrH7vV2KeWzjUbvoyG4bcTgnHnh4bYhgHiCchVg+WJoZgMgEOTWzBvmSNiAXJwWG14e0u7EdgmZHlFkc7oa0ESawsfO9HYQ7ofEkTG5PXksXDq4MKHO6pOB3b/jN1a5vpQB/l2DZVCxOPz41SUt1WmP5yZJCZto3Oz/NUawLRLsRNk82Rq8czGig6EausUq/SQz2BkZm9zqp3HcVTuhgfOv1OanRToT04Cz9KzvKqMuqBDaPNhRU3p59wTS4QOVMbk8uFFMAuPb3r0ErTfnHjBmlSRcsP7k7eeVG0xVgAKYI12ltDgrLPjt8LZubgac/8aGkMr8XaA+VKxEexhs8q2P86vgYn65WEEphledI6xq+bcNiDD89OMDE86C0xkfHxxi4LjZliX9w7Rr2hkP87f4+NlWFoeNgXVU48n382Y0b8G0bx1mGie9jW1WQSuEkz/GzgwN8eHICISWkUvifPv4YjZTYGQxwkCRwOYdtWbg1HmMWBBj7vumMO2dp1+13w6XOwMBtClZavLtAuSqxekiUBWMUIzq+Pka5LFEKGhL7cx9aaMzfm6NY0/YbpRS8Iem6jTSxje689qfXyG5fNCZXuI5pXZfJtYhcXPtT+mzcIf17t3SzyYiT5T43S0+79L38JEewE4At6WbkDl2zqIC7HPO359j70R5llryGcnwZg703/91+HX53vuFvELZP2QjLj5fGpFFnVISd0DFB7wAVlsHOAO6YZE3fxpT7d6nwfhFYNikruhCaOiPbd7cwM1yElHDXkJa0Gz5GO9ErxRegjvvGP7yB9YO10aZ2GF4bvvZWwh3KWe5+vSgE3v8/vo+P/oePUMblS9dQCvOf3J0AoIGt2dAB6pC2osbfHR4irSo8j2MUTYNlUeDBeo1GSoxceg21pF17CsBeGGLoeRh4Hv7dkyfgloWkqqABCKUQOg6klHi82eDd+RwWY3i82UBpjcM0xcfLJX55cgKLMTzZbpHUNYRSSKoKf3zlChqlKFTGsvD3b9zA93d2wC0LdycTjH0qhKProwu6eIByu9PDlJRGtoXN443hSbtoxnA3RHpITknLsSBqgc2nG4zvjlEnFObUuQc796OSyigbOqNNndRm4Jm8SM62wrRwIzIm7f5gF8xi2P/rfROvKUraGNJtpam3NbwRuRoZJwpK3pYm8Ki7cYW7Id79795FMLl8eeDL+P36pn8JOIGD2dszLD9dXiDcX2cA2fn+zmsLQY8vjmg3upAC9vL7HM5CzN+bA6A85G7R5WeBMYbpW1NaW95Orb2h96qr7fzfsROaAqw1OeX+5P/8J1g/WMP/Hy/+XekxBfq/nP/KHdKy/2/7z9FIibiukdQ1Pl0u8TSOIZWC1BqbsoTfdqMA4Ng2XiQJdpRCKQR+niQYeR4WQYC0IW5zLwzBGcNJlmHq+9gbDJBUFU7yHM+2W/zkxQs4loXHmw2OsgyVoBzjR0LgeZIgsG3cGI0QOQ7+7aNHuDocYub7eB7HGPutE27iY/HeAtlxhnJDln9/TKFFneKoez+HV4fYPN6QvM/lGN0aIXocoSkalKclyrjEoBoYq/3srRnWj9ZwonZVVCWRHWV0w1Gg93niIxyGZIRol+l2vLTtEHU0f3cOb0jZu52+3gkdSi0c0cHWbSipEuLltaa0xY5umNydoE5qBNMAN//s5ney+AJ/wAUYIAnSzgc7NC1dvz61KpgG35jq4A8JTuAgmAWvTQfjDplsOtrgiyplOrrBG3tmP9lvu510u+06/j87yRDuhNj9/i7kS1ygZdGOwpcxvDZEXFc4yTJIAMs8x6YokDUNakU5GLUQ0AAC20YpJZTWRvcS1DVO8xxZXYO3v0YrhchxcJjncG0KvF8XBQaui0IInGYZ/mZ/H1Ip1EJgU5ZEK2iNRikUZQmbMUSui7HrInIcJFWF/9/Dh/hv33kHAA3uPLsdkg5cCuEXyoRPhfPwle+A7duIdiKjehjsDnDtT67h5NcnUDW5RKukgj/xEe1G8IYeRjdH2DzamINOiTYqwCP51/nkN9uzMbkzochKqRHtRpjcmYA7HPkyx/YpWdk7VdC9/+oeilVhOmfu0J7C5CChNUhthkq3l2/3h7uIFhF2Ptj5Qs/TZeAPugADZ1KqYTlEcpCgTijkvEsYe5PJR3/omNyZIJgGqJLKTKhtz8b0rekrWR5fFIx9ca1492s756CsJbZPt5jenV4cwDLK7ni5mPtjH8E8wJPTU6RNg6yqsC5LrIoClRBgWqMSAo1S8CwLhRBwOYdou97ItnGa51Baw7YsNC198MF8DglA5Dmk1iiaBsFohGVRQGmNdVniKMvg2TaWeQ6bc0wtC1JK5EJAKgWLcygA67LErckEoePgyWaDXxwe4kdXriCuKuzYF59ly7YQ7Zw5wJq8uRBmY/u2WbRaJ+T2XLy/ICXDxEf8nHbVTe+Sm3KwNzCzgKd/+dSEE42ujxDtRYYa6obywSwwBqQOWpFCJV9ezEwO5yH8sU//N/GxfbKFksokmDFGao30IAUYcbje0DPGoe8q+urSwvZtEwrU45sBYzTU6oaMZjvzt4iOCumKQ5e34ecNOubYCWzs/XCPLNJtCprtU0RhUlU4zjIopbAsCpxkGdK6xrY1SSgAPudwOUetNRVHUHJYKQSk1mikROA4sBjDJAiwiCJUUqKQEtAahRBYFwUmnodtVeHpdotaCAilkNU1Bo4DBWDq+xBSIpUSouV/A8dB3jQY+T5EVeE3yyWGnofdKMJO9Nn0DHdod12XHnf+c/FHF+mZ4dUhmMXMvOTef30PwYTCpOzAxvKTJa796TUkL2jX4uTuxCgPot3oMwdhSigsP12+Qk+5A1JhdAimpEzo0hDPZwV36hWL05bur3qwf1voC3CPS8OXKb5aa6y6q76UqKU0037f/uKPcaf9XH6yNP8tPUzhnZsFWdz6TB30YZoirWvUSuHZdousabCtKjRaA1ojtG24rTnCUgqwaJNKKQTAGGzGqAgrhQCAkBJHWYa9KMIiCFC2nfWqKOBwjriq8CJJkDcNhNYQSsHhHDbn0ADiuoYCUGoNblmYhyGEom0jDuc4yXM8j2Ms1mu8O399/sl5fJGCNb03RXqUmiTBalNhsDsAdzmKdWGyubvo1+7XDa4MPjMjWQmF5SfLC84zgIr94OqrRqBOKthJzrqFsJ2RZHx7/LlpgN8F9AW4x+8E9pMER2n6yn9/tF7jg50vx/F5Qw/Dq0NDRbwM+zO+uEXTIK4qJFWFpCyxLkuUTYOkrtEIAduy4Ng25kGApK7heB4qIaCkhAYoXAkUMa0BcMaM8UIBuDEcEgUhJUohsM5zLIsCR1mGbVWhEIKcb4xh7LpIqwoe56iVApMSDMT1hkEACeKXt2WJZ9stBq6Lnw8GuDebYei+Sq98GYxvjfHiJy/MLWL9cI35u3OIUmDzeHPh1y7eX3xuIZSNxPKT5QUzjGVbFBT1ORpcb+h9ozrdbxp9Ae7xnUUjJQ7TFI1SOM1z8NcUjbxpsC1LM+X/ohhcHRgu+mV8Vm3aVhWKpoFQCsd5DtG+rryuYbVOtKHjkAYYALMs1K1eN7Bt08U6lgW0fHFa13gWx9AAbg6HaKSEzzkqIfC8Hdi9SBKULQXBLQtF02BjWaikROg4KISA3+ZDHKYpDeukxDwMkTYN6jQFsyzDR3PLwsjzcH00+lK3hw6dzXz9mEwd2WmGw58f0vaTc4qiwd7gc4uv1hqr+6sLxZc7/I1vnviu4rvLTl8ydDsMeVmn+LsCrTWO0hSPNxtk9atF5ruORkp8vFziOMvwq+NjPNpsUMk26Me2L3Rwh6/pjD8PXZZ09yU/H8bzWYirCkn7Xh6nKU6yjLS47fXf5hxjz0Poughdl7rfdrVRV9VZO4BzOZkJyqZB0TQ4zTL8ZrlEoxQapZA3DQ6TBD978QJ500BpDaU1XMZQNA1WWQaHcwSOA8+2MfV9MABZ02BdVWedt1I4al/rsixx2g72NmWJh+v1l37fOkzfvjgvebmIugMXw+ufrx7KT/ILnO8fUvEF+gL8Wmit8clyiY9OTvDh8THSL1nAdMvVvQ5Fy2F+HTRS4tF6jU+Wy88srs/jGM/jGMs8x8fL5RcqUkIpbMvyM1/7t4VGSnyyXKISApUQ2JYlGiHwZLOBBvD93V1Mz3W8aV1/6c8IIL5z53s7uPJHV+B8zhdeaY20rpG0fO9pUSCpKpRSmrwGDnoPfduG13aksi3ODucY+T5Cx+k2+gBaQ7RDt7iu8Wy7RVyWOCkKxFWF+8sljvMcjZQQUsICILWG1JqeIaXgco5pECB0HLi2TTpkpbApS9Rt8ddaY10U2I9jHGUZRNtUFE2DvHlV9/5FMLwyvDC0borGRF5yh2N6b/q5NIcSrzoM5+/94RRf4PeMgugCToqmwSIMEThfjYRfFoX5QnfF4MZohN3fMkVO2mFJ1fr7AcCzbbw3n9Nm7HaIdJAkYIzh7mSCafDlxeFCKXy6WpnsgE+bBu/N5xd+1qM2DrGD1hr7cYykqnBnMnltglYtJX59cmKuqFcGA+xG0W8NePk6yJsGL5KElAC+j7HngVsWhFL4ZLmkoRWAk/xMjiTbzjCpKlwZDLAqznSrh2mKt2ezL/06GGO0keFzkFQVyvbw3BQF0qpCLgRqIcAA8LarjdoshqyuUUsJpnW3mQeR5yFwHMRlCafli6v2c6ylNLIzDeBh+/cUrdlCao2h46BRCloIog4sC2Pfx6CNvWykRNE0kFJCcI5NWcK2LHi2jUIILIsCnmVhlefmWV4VBcKv+D259uNrAGCWC2RHGS0cOJfb8dsQ78fGiQrQwO33zQX6ebjUn3Zbklj7y/J3r0PddoVd4VwVBb63s/OF4/o66Nb2+fJ/e7bdohQCt8av5h0orfFwvX6lc6yEwM+PjsBA3cbTOAZnDEPPo4l5e338opBK4dPl0hTf7r/dX63w3mIBl3OsiwLP4/i1vz+uKny6WuGDxeJCd6K1xqPWQlsKgbxpqNvUGt/b3cU7s9kXHtp0799pniN0HNyZTF4bv/hovTZFdl1QnuzE91F1igEAZfvPvm2jFAKB4yByHDyLY3xvZwcT38emfYa2ZYm8ab5yMfk8xO0QDACOkgSbujZ5DNyyYFsWQscx/7wTRRS2ozVCzgGloABEto2qVTG4lgW0/9xIibpNPdMAGThaeZljWXAYg21ZUC13zAFYnodGSow9D0JrLKIISUXOPaUUDeTCEI6UqISAEAJPtlu8zTmE1rAZw7oocGP01Vyetk9RrLZvnyWPtZnVn4cmby44I23P/q0uxstE0ypKvglcWgE+SBK8aPNNZ0GAu9OvrsFdFwWebLcmMQqgTvHJdou3ZzPUUmI/jpE1Dcaeh2vD4Wdmsnai+tfhpA1HeTlxal0Ur722y7awvTWd4rDl4eKqAgPw7mKBwHHw/kvF8LOgtManq9Vrr4y1lLi/WuHWaITHm82F/20aBNiWpXlviqbBk+0WoeNg6vtwOMdx+7qebrcXijsA/O3+Pmoh8P7OzueGiVdC4NE5zrmWEvtJ8sqhlda1KbIdukMurWtcGVAQyraqcGs8hm1ZJKVqU8KKpkHW/rquAAPU+X+d5+i3YVtV5jUfpCmqpkElJRQAmzGjdMjbfIZ5EMDjHEPHQeS6YACSukZg2zTI0xqB68LhHAz0rDTtnylavrcWAsyyEDkOhFJglgXZGi+2SkExhqyuYU8mEFrDtSxMggDbqkKlFJKmwVXOUXMOn3MorfGb5RLvzeemC66lRFbXiNyvluo3vDZEvsyNu65clyjH5W/d+Ky1vpAsB1D+9ndtkwhAdeTfPn6Mqe/j9mRivjNvCpdSgKVSF7rMTvP4VU7iuKo+c5iwLUt8fHqKon1oAeCoabAfx1hEEXbDEK5tw7Es8JYrO/+6LMaw16ZMddiU5SsF+PTcNdlqO9x1UeBFkpB8KstwnKamO9IAPl0uMfN9DD3vC/3cz7bbC3yv3b7m7rAomgY/OTjAsP0iSaVQSgmLMeRNYwqza1n4dLnEW7MZDm0bt8dj7CcJtmWJomlMrmzHJXLLwpPtFhrAven0M7+om7LEo/XabIDocJJlmAfBhd+3zPOXfzsNDbMMQinKwQXl6Xaa2nfm8wsH42me4/ZkgqHnma5vU5ZQWn8p2iSpKmRNg12tP3Mg0nHRnRLhtChQto63Tk7GbRuuZWHq+4hc1+iUx54HzRg8zuHaNqAUFlGErGmIRmg74VpK5IDhd5XWUACYUhBtByZa/XPH6wqlEFcVHm82sNtCbTFmmoHuNrMIAmyqCrVSSMsSqzyH5ziYhSFsxrAqiq9cgC3bwuDKwHC5SiqsHqwQzAKTUvcyNo83JokNgHG4fRfRNYofHh/j5nqNP791C9e/4o3hdbiUAnzS2jHP4yhN4VgW9gYD6JYH+yJfpBdJgk1ZohQCA9fFThSR40gpSqkSAvemU+R1TbpNIaC1xv3VCgPPw43hEKy93lVC4CjLMPI8DFwXizDEteEQSVUZaiOuLoazlK2UqBQCSVXh2nCIgePgb/b38Wi9xnGa4jDLwBjD1SjCNAzBcdbRdpKg35bjmjfNhSLPLcukZX28XKKREo1SeL7d4p35HLZlIalrjNsYRN+2MQ9DPFitsCoKXB8OsSoKzIMA//7JE1wZDLBuO8ndKIJn2/jZ4SFtdgB1fEprZE2De9PpK1x4KQQertefqRh5ut2aTr+z1naIXBcjz8Mvj49RtwU2qSrsDQZg5wru1cEAp23UI0CH9o3RCCPXNcO5ie8jqaovTGmdv4UNmgafFUDYfealEDjJc2Tn+GDOGBjI/ebbNlzbRui60HWNnSgCZwxxVSFq5WkPNxsMPQ+izXVwbRvLtgFx2yIL0O2BAXA4Ry4ERpYF2RZX1vK9oh38la0GuVNXSKVoRbzWOM0yjFwXpRAohEAtJT5Zr/H9nR2siwI7YYh1WeLG6Kt3oIM9WmJ5Xs1QrAqz/STajcxgLd6PL+SBWNy64HL7ruA0z1EKgf/0/Dn24xhKazxPEkRvmOL61guweindH2jXA5Ulnm63cC0L+223+E9u3cI78/ln/llpXeP+conTPIdsO4vdKELkOHi4XpuO8ZdHR+bhG7RhJQCQVhXWjoNZEBjKopu6Dz0P7y8WAICR55kvftcNddzts+0Wny6XOMkyGtCUJZ7HMY6SBM/jGKdtWMuVwQB1283sDYc4znPjcppvNvjR3t5n/pxPtxeva3cnEzN0uz0e435bWAEqFk4riTr/heom491Vf1NVuNG+pqy9NoeOY6b939/ZwePNBsdZhlIIPN1uEVcVhJSI22FeR0k83W4vFN/IdU3hAegAOc4y7LWUwXmqaBGGGLruhYFaUte41xaNDqO2aHW71uI2h2HgusagUQiBvSj63AKstcazOMbJ+UHlb/n1cSvr6oaZmRBoWprAbm8MgePAbzl917axKQqMfB+LIMCgpRqq1opcCwGrtS3zc4Uza11trmWhBKBAV2DWUg0KdPOxGG0KbqQEOAdrG5ZaSlRKoWldd0xKrKoKQaulLpsGUik8WC4xaj/reRCgkRJpXdNs4iuAWQyL9xaI92Nao9W9p0ojO8mQnWSwbFrr1GVNAGeJdt+1wdtpnuPT5RL3Vyv89fPn2GmH0aFt403rg771n7z7Qnei+tBx8PFyiY9PT5HVtUmBEkrhyWaDf3H3Lt6ZzbA3GBjeNq1rvIhj/OuHD/EiScAA2G3nsS1L4l43G0SOg4M0JS54MjFDio2UmLRf0pM8x8jzULWDCvPGWJZRP4w8z3RKAEywyd88f47/4Ve/gtQaA8dB4LqGA7XQFr2igMbZF2fouvAcB6FtIxcCWdPg6W8pwKuiuEA9jH3/QoEZeR4YYLjQdVGAWxZuj8c4TFOUQmAnDBFXFcaehwft8Etrjf+cphi6LoXH2Db22k74ey3fGzoOrg2HOM1zxG1HvZ8kcDhH0TR4f7FA0kqzOgxcF+/O52iUwq+Oj81N50WSYBoEF+gHizFM27jEyHGwbbu/wHGwPleQwzbf4Fkc41kcY1sUOC0KuJzjR3t7CBwHRdMgKUtDTXwWdPtsnP/zX/k1MPnwFHN4jrM+SlPkTWOULlZbQAeuS5SD7yNyHKxbysG3bYw8D8d5TsMzxoxSxgIVWM9xSGbWFvOqpVE6yRnTGooxqFbuJqSkWxRjcBiD1Xa9Umvo9vdwrSEZQ9U0OM5zTDzP2JdP8hybqkKz3eLGaISJ72PdNh1fFcyiYPxgGmDzeHOh0AIkOasquiV6to3IdTC+Pf7Oudi01niy2eDRZoP7yyWk1sjaw+mDnZ2vZFz5bfhWC/Ayz/HvHz9G3RbAie/j7ekUf/nkCUohcJrnWJcl0qaBxzkO0xT/t9UK1wYD/PjqVfzR1avYtNPuv9nfx0fHxxCtcD1oH+KuIBdC4Kf7+4jrGlcHA1p1zhhujsf0RbAsLIIAsn3Yuy6mG0LNwxBKazxtB3ncskznFlcV1kWB/+njj5G1v74UAm5dw+McaU17s/KmwbAtlhLAlSjCIoqQliU8x0EuBOKyROQ4qKU0fGeHjkbpwBjDzZf4J8YYwBhU+9qetwqBVVFgXRSYBgFWRYFPVyvcXy6JEwxDnBQFjlu6ZduuyMnqGhPfx7M4xrXBADdGI8q1bVO8ciEQgorpzdEIvzw6wrM4hlAKog2e+dHeHkSrT70+GuFZ270rrXGQJBconInvI23dXhPfx7Ys4dg2ho6DoyzDTjt0G7WFnwEYOA4erShQvG7X+VwdDMzndtJGPX4Wp3mS568U35cHspUQOFivEVcVLMZIgtZSTeuWKxdKEUXGGDzbhmiLat1yxePWGOHZNq4MBtjWNQ1gGTPFVreuOEtrnFYVRr4PDeCwNQBZLWVDQTYWeKspblpu3ms7Z9+2KYWt5YWtlj9ulILXHoCWZcFnjKiIpsFhmmJPazzebPDHV65gXRSvVfh8WbgDlxZrrgpkx5mhJbrC1ig6uKbXxwgjCy/n2MVVhf04hsM5dsLwjSikXkZ3WL38fQMoTe4gSSjprn1W06bBB7u7+Me3bpGp5g3iGy/AXRRfUlX4D0+e4KOTEzRKweccoeviF8fHkFISlVCW8Dgn549SSJsGeV3jOMtwWhR4uN3ixnCIXx4f48O2u/LaPNWp72PV6ndLIZCUJR5utwht2xTDv3/9Ou5Op/A4x8P1Gpu6xo3h0Axu7kwmKFtRPANQSQnHsrDfhmd3X9zncYyfHR4iP9eZNu3g5EW7FqZqGuRC4OpwaDi4G6MRGqWwLEtUUiKpKtM1HabpK1+A/SQxnRYA7LX87Hl0+bMAkLc8n9Ya66qCY9vYiSL8rw8e4FcnJ3ix3UK1v04rhVkQ4LS9keg2CPz6cAihFCopcaelOrri3qWXZXWNwzTFXz17hrSqsBNFGHge9qIIR2mKQgh8sFhgJwxxlKbGePIsjhGce/0jz8PjzcZQFMdZhmujEZ611ttFQCuG3LbjBqg7PX8NPE9nAHQT2JTlawtw5w7s0OmxPdvGeWtMp1HuDp6TLMOqzWQopUQhBBRI22vTH0TPdCuXK6XE0PPgWZZ5/a5lIWkamjW0P8e4HdhtqgpBewh77TBP2TZUy/d2VI9r2whaCdnQdY2UbOj7QFUBQsBmDAVgsimqdh6itQbvirWUWBcFfMfB8yTBO/M5orb5cM6FyH9VdLGf4TxEndao4grH2wRobDiKwx660HMXD1YrDFwX77TzjKJpcH+1IjqrtZj7to1rw+FX0sy/jO772T0Du1H0Cvd9mCRI6honrQGGt7bxcWugedP4Rguw1hq/OKLliS+SBP/58BCHWUbRelIicl3sxzH9sErBtixMfB/xuSGHbdtkrywKug6WJR6sVtiUJU2htcbt8Rh++wA/i2PTreAcL2kxBg7g8WaDe9MprgwGeLrd4sSyUCuFomkwDwKsyxLztnDEVQVuWZgGAW6Px2ikRFLX+PnhIU7y3FhjB65LV7ssw0E7FPQ4h9VOv0eui1vjMRqlcJCmEFJinecoWm3r/dUKP3pJrrVqow47OJzj6vBVa2cntrctyyg0jtvchJ3BAD/Z38cvj49x2v5ZSmucnNeaKgXe8ohxXaMSgt5P28amLLHMc8yCADdaidtOGOKT01P85ZMnOMoy2JaFbV3DtSzEo5H59Z8ul/hgscDE97FsD67DNMXt8RgWY3A4pzDzljPXWmM3iiCkxEGSIBcCQ9fF3enUFHChFLKmwa3JBC/a3xM5juG8ResYexbHr51Ur1qTTlLXaKTE+zs75ostzh0MXfbEjdHIFP6kdaYlrVNQaw1mWVCMwQJ10WGrfkDbXQ0cB9Aaz+MY66JA2BZopTX89vkomoZuYKDwnKSu4dk2lBAo2sPF5Rwe55iFIQ3+WlWEpTXmvg/NGGrOafB2rlMWrUqjaRuJwLaNCaT4/7f3plGWZdlZ2HfuPL05Xow5VlXW1FXdLXWraZBkIbBly8KIhQVoGcwsFoMwk8EgBoEsIwM2YBDLDDYSYuEBeaG2wQLJgFpIaGipW63urjGzKrMyMzLmN9733p2Pf+x9Tr6Miqwxo6Iq635r5YrMFy8j7r3v3n32+fa3v83PSJymuDYY4JFOB79w5w5C28ZqGOL8A8iGAcqI7dDGDXOOsPX6zyTOMhzMZlgNQ1xn7fkyVIH3QlW9oZ3mm2HMNablLtT92Qx5VeFSuw2DaxYHPDRVUXq2YWCWZbgzm+Hnb9/GU0v3zIPAqQZgtfofzGa4Ohjg1cEAJW+PJtyLvhvHmOU5bN5OTbIMLt88pmkiLwokXGR4/uAALx0dkfcp32Qdz4Nv2ySnyTIM53PscYBZDQIaucL85k4c4/FeD7txjNUwxCLP8fmdHViGgQVXrdtcNAn4YSirCoezGQ5nM8o2uVinsgXPsrARRXhtNELCOs6Eu45sNj/pBQEe7Xaxw1pg5RswWyzg2jYmaYov7OzgUzzd9rXxGNcGA7RcV2cjKnAdx24caxVHxkWo/TjGZqOB14ZDPHdwgHmWYVEUOptWmezRbAbB11AIQRk28+8KgW3j5cNDWuCKAj9x/ToWRYEXDg/hKn6T7RRfHY1wMJ/jSreLwLbxxd1dPL26CpOr9wm3vkaOQ1XlyURTC7Zpou15+OXdXVwfjSAAvGAYONdsatXEYLGgJocgwIy3h5IzxOUHdzeO7ymUAiTT+6WdHezP55RZCqGLkur6aUiJKRfe5iw9qwAMkgTjNNUBWAIwAc2XGyBe1jFN6nxzHKxHEX5uexuTNMVKEBCfnqawTBMDXqhXWBlTcDG64mfH4GeiyXQGeLfjcyF5o9FAw3WxPZlAOg4Cpm5sw0DOP6OS1O48SVM0XBctz8PBbAbDtlEUBQZJghcPDpByk1HI7myOaaLJnXvvFkeLxT2BTxkIqc9ssFjQZI83aIu+PZmg4boncrCKUjCFOFHffzCb6UK2ksAWVaXlgkVV4dFOB3txjGmSYMRNN23XxSTLYHLDyi/cuYMnuDD/oHCqATgrSxzMZviFO3ewPR7rbZ4BzvDmcyxYwK4E6Amn/Q7fgKp4NWPTElMI6iIyTZiCxrAEto29+Vy3bNqmiThNMU5T2r4WBa6x0ckoSXCJC1SBbaOQEgfTKZHteY6va7X0GPHusaLRgrvthtyssdlqYZ3F7N0gwPZ0CscwdIcUhMDhfI7nDw5ItM+LxjzPEVgW8qLAPMsQObQde+7gAGVVkSkLc+JrYYgnej2UVYVf2tkBADzd78O1LEzSVDdmKIVHUZKx943xGNvTKa4v7RZCx0GDZVtZWerrk3OgWQ0CjJME29MpHuGWZcnyGyXRy8oSR7MZKikxYd1tg20XhRB4dTjE9nSKx7tdRK6LEsAz/b5WkczzHK5laU4aoOyx6Ti0wHFFHgBuDIe4ORqhEwQIbZukczzYsuv72qBmnudwTBPzqoIAqVsOOIMFqC34F7a379F4d3xfTxq+X4v54XxOKhPmzqdZhnmWQfICJUCKBaXBDR0Hvm3DtyzkVQXHMHBjPIbBVIQhBDYaDVhCYJrnaLguMtYTq/qDxbUKALCFoEKeYegAY5kmQseBa1l4qt/HI+02/u316zQjjjNdMMesZGop64EH8zk2m00YnOAcsu/EIoqQchCreLF5+egIF1otrEURHu28ua/D/XCc9jGEwJVeDzvTqVZDKV5eLZimYeBKt4uD+Vw/fxXz1U8c8zQ+ms9Jp760APu2jUc6HXjsQHdrqY6yG8d6isg8z7EzncI0DNwajzFOEjx3cICYd75Nz0Oc5wi4OFlwcvUgcboZMIjADiwLnm1jhbf4t8Zj5FVFZiQcrMqqgm1ZgJQ0RdY0EXCBI81zxJzxCFDGsdloIHQcTFSFWgjc4HbaTEl38hyCJWr9RgMCtJLuTKe43O3i8W4XBoh4N4RAYdHEg8hx0PF9bEQR/v2tWxglCSZpSlX40QgxbxuTPIdjGBjzzZ2VJVzTJClQVVFxSggMOdtfDUPimfMcvm1jlmXYmU7xaK+HQZLgX129itBxYPEWfbJY4LmDA/z7mzd1oc40DHxpbw+/9vJl7PE0hkWeY8YUyp3pFDvTKXGWcYztyYQWMc4Sc5ZwTbKMuF0OBklZ4vmDA8r6WfDf831MkgSzLMNLgwHarovBbIZbzImXrBBYDwIMeRT7eqOB0XyuuWAJCiRq2zZnOukV1g0nnIX5loVJlmGwWGCUJEiLAqFt42e3t7ERRViNIggp0XRd2IaBi+02bRn5GqyFIY64/RkAXh0OdQDe4dZofV8KgR4fz850ip7v46TepoKDXpNVAkmeI+WCo2rAsAwDrmkSfZCmmKQpOtzgEGcZJqyIaXke0jzHR1ZW0HAcfGV/n0bIs6RsNQiwN5+Tjpfvc9e24bP/r+TEwqoqnXV7loW8LHG+1cId5i5LAJZpwmI6AqBtfAVgmufU9MSFvITbk4eWBVMIvAramQohsNVsasWLx8M+3wmURl+hH4awDANd39cBeCeO0XIcPTLpYqulEytl2QnQQro3m+lOSdXVeZy2WHDj0eO9Hq4v6dMTVq8sTxqZ8a7s31y/jsC2cXM8Jnme52HOdBC4wHyh3Ub3AdIPwCkHYEMImELQ0MI0pSx3aRVJy1JvIR0uLuRSwpFS82g+Z2iGEIjzXAfgwWJBvqtCYMJGJgV3HymZVeA4yKoKozSFaZpYC0OMkgSB42Cb+aBV39dZpGNZ2Gc+SgWMT25u4sdfeQUWZ7aqNXRekNXgK6MRDAAvDgYIWVupzFeKpXZRtWork5tSSoSOgztxjFujEQLbxguHh7i0JKHa54wTIJvBiFtSr49GeO7ggAYx2jam7EdbVBVeODxEymoQJckzhYAhJeamiWw+R8k8ZC4lRJ7DYAnfKE0Bnr5gGgb24hh3WOVgC0HTeScTWoBAOwLXNDFIUy2H83hLvTubwePhkM8fHOBiq4WG6+JwPsfN0UhTACln7g3HwUtHR1r0boA46u3JBGVZYnc2w9dsbsJdon1usc56uVdfSch24pjadA0D21wXAIjq2Gw0NOWgtMVr8t5OONeyYBkGjhYLsnnk7LcoS5R8z5qGAdM0YbK8a5bncCyLJnZwUc42DOzP53BNE8+urqLtediIIszVjo6z/2GaknWlEAhMEwVnzBLQqoesIl8IQwisMB+8v1hgnKa0uDKnn1UVfNdFxT9T7RJcw0CS5xAGDf0MHQcQ1Ck5t21axHkXqaSUe3FM0raqwrlm874t/PfD7rGi5xrvNlQWP1oskBcFxlKiH4bo+r5+9gQXxp8/OLhHzthiKuIkzlhhlmV4bn+f/DhKmjpywMnYz21vI+bPcn8+h2MYOFwsACmRVBUWfD1DViZtRRGO5nN0OOl4kDjVAByzguH5oyOMkwQd18WU+cgBy3kMw4Bn2zC5IKHkM1JK5KxRlUwJVJwFW4aBmINOYNs4nM20UUrF3UlgDSTKEobr4mg+R15Qb72y9wMA1zDQ830czudI+XhePjrCJzc3YXIwb7iuFv/HaaplXynTJ7vTKU1GSFPc4sBeMl/ssyzNZavAcZLgkU4Hh/M5POaub43H2Gw0sCgKHfwN3O2GGiUJZjmNqjEMAzmrHXzHQcvzsGCd4v5shjuTCRYlOWmpxWDB19Pgaj2kJPmUeph4ccmE0Fvsn719Gy3X1ZX7yLJwezQi0xfOXtRk3t04xnoQIPI8dDwPORfLlEeG0t7+qvPn8epggJSpgoqLaJ5l4epwiAkXFOM0RQHA5iytqip0gwBj7ki80u3CsSzsxDFpWBcLTLMMq2GI3dkMkW0jLwrcGI0QOQ52loLARhThk5ubuDoYaE58N47RKUsowZMQQv+sgoPaLM+RLGltwQtbxZK7SZrS7iLLMOFiqGmaus296/ukB2f1z686fx4DDp63RiMs+DO1WT9s8u/KyhJpliFgmqfhOFgLQ82FqjqFb9vwWTVU8MKo5GsG73wWeQ7PtuGpDJi31yWrLVRQtrhg3eBrN0gSvDIc4rFuF490OtqT480wz++1u+z5vl4o1b2pCrQ5q4I2Gw3cHI/hWRZWggAuZ9+Kw5VMRUSOcw9nHPGzcGdKE5PHaYrBfI4t/r8qkXnx6AgJ128A8u64nSTIucFI1ZMO53Na7EDqHdeysB3HJ0rX3g1OXYamVtDBYoH9ONbtwIssg22aND2Wt/R5WVIHD8vLXNNEXlUk6eJtSFHR+BXbMDBIEsp4DAOeaWo1Q5sNM8ZpillZQhQFbNfFUFWAqwqOENizbVwbDvFIp4Mpb8lfOjyEJQQ2Gw14loWD+RyTJMGX9/eRcmB1DAMO80tz7m5KigL7sxnmLAUqOZvJ+UE6ms3gsS1gPwxxlCRYMNdUSInt6RSXOh1M0hSPdjr0Pt+/G5hMk3YQeY4RL14N18XhbIaG62KW59idzWhcTpZphYZtmjABlIqbZl5QVhU5cRnksGUKgbwokDGneX04pC2/5yFwHEy4mAMpScQvJUI2eCnKEremU/yXly9jxLRA5Dh4ZTjEMEnItWyxwGevX8fObIbQttHhh7EbBLg1HOKIx7wDxAHOswy24yDmLFYIgZcOD3G51cLNyQTneJrDShBglCSYpilWwxA3JxNI24YABdZD3hkBpCa41OnAt21ssAoGoGCQFIUOwEpbm/Nn+9pwqDM1BcHZqc3XICsKjNMUoeNgUVUIWTJmmCYu+T7SssTNyQSDJMHFdhtXOh3kzNXHea7pikWaouU4WJQlxtxUo5QVhmFgNQxhm6YeTw8ALd/HjKV8DcfBIQd2Re0Jw4BRkkXlbhyj6ThweceZ5Tlyw8DBfK5VSeeYftibz2EZBh7tdNB0Xdzk3d4RN7ssF8RmnFg1XVcHKeV2qKAagiwh8OpohAnvjvKyhMUF+Ibr6maS10YjrIUhNhoNTQMCVFR7bTxGn7X6Y25dXw1DbQj1wsEBRkmCl4+OtHri+f19zHIyt1f0hvJyTrg3QLC6JU5TPL9Y6J3dxXYbX97bw7c8/vhbXoDeCk41AM+yTG8rj+IYh2kKzzT1ttjhAKuKDhJU1FCBOWa5kGrfVIY5FRcLxosFup6HtusiY05V+a/apokeZ2NTltvkZan1m3MAIxbGT7gYNbMszIqCspmyxEajgawsydycDWkqKSGrCoHvk5TOtrE3nWKYJJhy9pgA+hwBelgLAHuzGW135nNcabfxk6+9RqL+skQCumF76+uan1NtqR3fR8t1MUxTFEWBWZZBOg7AC5UA8VuLLEPC10twdmoJgcowtBYUUtIIc14YbCHg8HW1TbJFnOe5zmLjotAmM8rRqwBlR8q0R41g/8yLL+Jip4OO52mPi12uLOdMnThc2AsdB5daLQy5waPheciKAnFBnh5CCEz5Ad6NY5RSYjOKEOc5kjzH5+/cwWA+RwVgtFhQp57jIOKuOc+28XO3bmGUpni814PPDRGKy1wJAipcMR2jFiwA9wSWtCyxM5thmCTUbCLvTnKWvBhn/JmYBo0JMoTArCiwO5vhSq+Htu/rhXG4WGDMxkWBZeG14RB3eIxQXlUoAJicAbuWRVQRF+Yqvm5N18XFdhs+SzRLKbHFQWqUJIhZd6/UASVznyVLPS3edaqW6IQbiyAEHEEtt8IwsEgSFLzL6/E9uBIEejF8pNPRWXxaUKu/bZp4otdDy/MwXmq6EQCus+b75nhMqh2WHOZLOvND1vwrauGlw0MEjoOnVlZ0U8+MEzJbCBwlCRWUkwQe31u/uLNDmt84RlYU2ObhBIuiQGDbWAkCMsVnKmnK3s5SSiSCugtL5vltTlC+tLdH46PG4w9OAFY95rtxjN3FApILCIWUsCwLWVVB8hYuZeG4atv1mNvMpdS97Wq7JzkbsNnIZD0MIXkVPdduw+JVreG6mCQJ5qoLCdAFjbKiibU286OB6yLjwDdmKuHpfh8rYYhhkkBwVpaXJeI0xUoUoblUxZ5x95JaJCLHgW0YugijDFRGaYov7e4itG1MuAtMib4O53NsTybYjWOMuDDlKRmQELjcauHFoyP0ggApb5kCzmaOFgvKdCV1QwlA0zlgXlHlIxL0kKsbTXUGypJmjI0WCyTMz88qmmc2zjL4nIU3HAdb3Bo+SBLaoqcpXskyLJg2SQry1FA+vWoScIN5xx5nZ0eLhdYFB5aFNeb1b43H2CsKZPw+z7IwyjJ87vZt7LRaaPDIn8P5HH3+jK6NRmh7HvZGI+wdHOD6cEgdfnmOC80mnu738fLRkc74I/4Z8zy/RzNuMi11TQj6HbMZxosFMi4qKo7f5Pu24AdZfS+vKrhMPyR5jssbG3jx8BC+ZemAVbK7nVLFzLm+AQAZPwu9IEDCQdJh6sK1bUSOg24QwOUCoGFQK3QhJb64swOHay8F3+uKHxZCwGJKr1T0FGffeVXBs22UQuC5w0NdpHQsC9uTia6f7MQxGuyN8tkbN7DZaKCs7vqoAMCrgwHOt1ooqkpf4zjL0PV9bE+neG081jWKWZ4j453GiL2s1xsN+BbN1ZuwW93P376N1TDEnemUFl4p8bk7dxA5Dp7t91FWFV6aTnHEVOI2004CwPZ4jGGakv82Z77zLMOA782Ui86KFkvBSRPLSU0h4HOs2YjuZ9n0znCqAThgzwNlLG2CurBMULFG8FZeFVxcy4JQBZiCpg0o+ZYlBEpBzlMWV4bHSYLFEqcEACu+j6wsiZPlbYttmkiYrjABXdxAWaJk1UKHixwHnHWpnvlHOx00XBcJB18JevBU2+n2ZEIZKXOsDvOlLc7iHNOkrJGzj7IscXU4RMNx6MOvKsTMPUkp8YWdHa37zKsKG1GEfhShzw9EYNvIyxIRB/8xV62nWaYDieTiX+A4yDijVMFWyfKKqkLOmXEhBCRTPNM8h+AtmdIDZ7wATkHZYQMU6H3LwiPNJr7A2UElJY7mc73QKVe6pCh0gBMABQ2QrlZl3Z5lYS0McbHdxhd2dmhmGiizNwS17+7EMWlhyxJXul2EjoMS9JA2HAe7cYwbwyH5RXBWZpom9rldOasqfZ4A1SjKqnrdOCOLOdK1MMTOdIqD+RwJyySVblgIgbIssRACLS6+2twwERoGfNsmmVlZkja618MwSbA9mSByXbKK5Iyszfes0nkboC3vJMvgFgXWowhTLnSG7Gx3udUiTbXrwuHjXefR9nlFVqTHfa2LogAsCzLLEDA1Nclz3c4vpYTpODiazxGnKRqeh67nwTUMNFhidzCf41yjQV2BzEnH3MCjf09V4Ut7e8irCh/p96nDsdHAcwcH+PLeHmackStXxClrbRemSfddWSItS1Ix8c9UkrLb4zFdF5Zhdj0P25MJfNvWhk13JhMMk4Se0zwnlY1tIykK7MUxKt/HtaMjjLO7DomKpjKEoG7CqtLPuwSQCoE70yl+9OpV/O6v/uq3FgDfAk41AJuGgce7Xfz4tWuweQuVs9wsY+LfNk2kUlLRgDPIhLMK5fokWVYWcnZkCkFBhAO7BNDkzOqVwQCOZSE0TTQ8D7OiQMna00VRoABlow7TApUqqBgGfCEw4oAoQZIcpQU1DOqI6bLZyjzPsTedYlGWJNbnhaKSJJXqBAHmaYr1RgPOYoGj+RyS6ZCCz7eoKqIIJDmQ5aaJOM/RLQoqJEmJZp5jA9TBtz2dosmBfWc6RZuLYcPFAuAsXA2BlOB2VM7wE66MSylJBQEgZemYwUHN5hsW4q4HgQqcqpgGKXHA57nVakE6DjzbxhFro5X9pfqs1f8rq0ovXCEb19yZTLARReiGIZ7s9ag7brFAy3Xx2nCovRYKVrJ48zm6zDHuz+dYEyQnU+3Or41GuMMie+U8JgCshCEcw8DN8ZgmdIi7OtIZS/iOQy1sCXOtqoNNJQOmEHq2mqLJGo6DJpsleayiuNhuY8GZeuQ4uNTp4GA20xSVUjW0HAcFoB/4oiyxEUWwDYOyuOlU03bnmk3d6bcbx7h6dIQpt8+vcLt6XtFk5HmeA4ZBnP9SAhPzzkxV9U2+ZwzeaU45K7SEwFajgf3ZDI92u1hkGX7s1VfRYOe3wXyOXhjCsSwM2SfZt23MswxxnuPnt7fR4RrAT9+6hTnvFNVCJgHMubml5fsoS7JWVfI3pWjaY1MtASCrqKNUSInJYkEdiVKi6/touy5GTL8YgiSgqk4DEKX3XBzTbL/qrp67UrGgqpDibpFQGSGpz+vCA/QCBk45ALd9Hz7LTWJeaSGpEw58geZ5jqbnIQwCDOZzxHmu+RntiWoYKEE3iW+aREvwhTF5C106DvKiwKighoLUMFClKT0oXElXkwfAXI/FmVUuBO7EMS63Wmg6jv5wKg4cN8ZjrIUhHA6QHm/LAOK54zRFJslYRfm6BqaJhXG3KUNx1xACGRfSfJsGN7qs5yx5MZmwgsE1TQwWC3ypoAm3AFDYNnYnE0yyTE9p0A0ZfF0EqK214BtZyaYajoNdrhKbfD1Tfsgqfo9lmkhY+SC4CqwkXMpCsawqLIq7wzJdLoIu8/UGb8EBoCzvduBZponRYoFXpUQvDDHNc6w2Gmh5HkZsfrPHnKsEZcsFB7w4y3BtOMRgscDlTkdzk8MkwcFshu3JhAZnZpmmLhZ5TudUkI3oThyTL0OWEUVQkNXm1y1RENM0xb+7eRNxmuIFdulT56ZkkKZp6jFBynxddWI91unQdr6q0HJd3BqPaUJFmuJ8swlLCEQcMKUQ+PjqKq4NBrjKY63KqkIJsh1VumnPsqjt2jTRcF3q9uRte9N19e+TAL68t4fIcbAaBHorbnCQUjshtXgY/Lk6LK8ESH1TAToR2YoivCYl2QQwD3qx3UZSVTiMY+zMZjAA3dgzTmj6sgHgkBfURZ7jkDsZFfeqXARV2/SAHe46QuidnpoabfD9tWD6QBfsQdlxP4oQZxlmWYaW5xG9xdpvtbP2eIFXygv1iSvJqDLaUQZdquZj8vXpB4G+bg8KpxqAt8dj/OL2th7JXUkJQ1LVXDDZHzH3ajEFUQDaZFq1HAe2Tds9Xg0LSRZ9ylGqqipMk4RafAXpVVd4hbVNU2fSAO4GhaWAqHi818ZjhLyyJ6wFTKsKVZZhappYazRISmNQv/24IDvJlAuJjkHjx7OKJGqeZSHmttOCeTgV2JquiwvNJrWRSomplDrwQUrM0hR+EGCeZcQJmmRSdHMyoaYVDs5pnuvpuwLQ2Z0KCiWAqijQCQIMOLCbQqAENUiojWMlJcmULEtnb0qfrSgiFTAEX7Oj+RwZe/9apkl+uJx5uSaNTB8lCQTvPky+PouyhMgyBGw8M1kscGM0wizPcfXoiJQBWYYkz0n7XVWIXBfjokBakRZ2qGxHBwOUIM20cmZTxZONZpO2llLi1dEIlZR4rNvFuWYTA6aQplmGV5dmCQJULD1gE/3t8Zg6H3mHo5ovLOYFLVbxSIAacHjb2nVdbDWbuMPKHzUBY7BYoOV5+NS5czCYRmt5HhKuDwyZU8+53tFwHAyZJ7csS+9uVoIA1wYDbMcxUVKsLFmPIp39x2lK5keSCkrKN1gAupVa7TKUImWUJCiYsy+kxDhJcHUwwGoQ4GA2g+84KKsK14dD+LaNcZLgxcNDtHwf5xoNeKaJG9xspdq2lULJFkJrmpXSZVdRN5YFn/8oLxKXjYN24hiHPM0558XJtW3yIUkShPzMJFwwDm2bPDs4BqhEa871HfX8q4VAUYW5Uo2AdglSSlg2TRlR9/cHypB9dzbD7mx2N3sQ5F0qOft1LYuCs5SasA8ch0y7Ad3VpLbCtmlSH3mew7ZtzLgoYlkWsqJA6HlaeL4oS2wx7+dz5jBJEp2pVpxhq6AoiwIz/p7LBSG1pa4A3USy1WhgmueY5rnWbpZlCZu3Syu+j8imcS9pQdZ/kFQtT7moVFUV9uMYnmGg4XkwmH9Wv29eljC5QCUEyfIkX4OUF6+iLLFgOkMtbIIzWMcgrTM4ACsTI5VRKnmc73navcvlxabkLNUyqUtLgrqXVBu3qg4rCY8pBNYbDURqNBAvQKu+r7XJu3EMg7PSnIt7hmFQQYWDxP58jinrxuMs0+3pphCAaWKcZaRhrUhL3WK+T2XKB3GsG3vUYqsedgkqDI35vb0g0M0iyht4mcNMiwLb0ynSPMdtbl1VNQa1S1B+HgteFCKLXPdWfF9PR96JY6JSuNJugrK1Dmft61GEjUYD+3EMyd9rOI62Th0kCVaDQAdGi5OWNM9xazxGVVXYjCIabc/yy3OtFlze9SgKQt2jFXiBriq95baEQIMNjSAlQuZaSy7+FVWFPW49rwC0XReOZeGgKEitANrWT1gBJDgoDlibLaVEseSTYbFsb8r39qIogKKAm2VI2MFulKZ4qtGABPR4r6IskWQZIMhTe5FlsLnzMi9LbUQlhMCN0QieZaHFLcQpL7YJc8om39euSW3dFX/mJT9nJl9/ixOEkmm1Jnt7PEicagB+vNvF+WYTLx8dwQB0ELEMA75DgwoNIdAPAtq6800wz3M02ddACkFeudxVpLiejDMNCOoYSirSCwcs7I9cFxkHLNs0UQHYaDQQpykWXFWfZxnmIE7JEDRYUY02EoLE6ElV0bFz9rM9neoPLctzFAU3d/BK3/Q8rHBHz2oUYXrjBvbimCZUgLfjnBEOkwRb3NUTWhZy20bK2YeiEFRHoOAbo+BMWcmmKl7JS+YTfc6wBXN5QpAjm9qFGHyNL7RapHhIEuxOpyj4s1Bj0QVIDzsrCiTcqq3E/UqdUvIDsRfH1CXF209FJ+RVRQVTlTkCyEHZh3LlciwLN0Yj5Jx5qPNS22l1PEVFkx4SIQDDwCujkTYRUg+88gIxWRWw4A5IcOZ0MJ/ji7u76Pk+Prm5iVcOD/H8/j4571V3DYiUtvnV4RCzNNVbcgOUDFgAFZcVTcbXRj24XS6iHi0WeHkw0HankyzD+VZL+1BkJY2RH7DDmmmQY1lSFHAcB7Msw76UiFwXkefBMkiSqLo9FSwh8FS/j4bj4PFuFx9bW9NNLsrZa6wKyLxbsgyDmomqCn5Fkre92Yykb5zEgK/7mDPyBo+4Aoh6U9m4srycui4ut1qaqy2YJkiKAk3bhsW0lJKsqe18VVWQhoGSE4p0fx+2EGj7Pm5NJpgz3TYvS5S8UzYNg4a2NhoY5jlM00TL8zCczyEB6koFcfnq2qac4ea8i3JYIVNxVg1QUDZZw91jffWCtcMl02YPEqcagMd886rOpFlRaM4JIEPuyHUhK5qPZXKK/1i3i1vjMaas0wWArufp9tmNMERSlvAtC4eLBfKSWgYlAM+20eQsQlakPOjxyKGW56Fp2/jS/j6105omOha5nqmHNylLrNo2LnU6uDke45ALJoUku0bXtklrW1WIuZglJPHKFldiV4MAH2UXMM+2ySmrKOCr4iPoQY65wGNzYLWEQIKlaQygIFhKCck3rNpW6cYVfp9lGAgsC70wRMYdhTPm3dUDKwS1Lq8EAbq+j67vk5yrLHHEbZpSkopiIUmNYgtB/rVSUvNJQVMdVAEn4Rv4aD4n/lxQi3FeltjhDqKcg2h57NzUWJ3D+RxCCHgmtfUWFcm4FG+eFgUFZ/7dEfsrT9IUjkGi+VIt0iDOz7MsJLxAagqHM9kb4zFtbznwHDdYESAe+IhlStXS8VqCusskoB9MtcNqex6eWF3VBun7cQzbJDfAsqqwHkU0+smyENg2LrbbmHK78DhN0XQcHLGc0lbnxQvR490ubMPApVYL/TCEzwvAmHcdahjrThzjSq+HT2xuUuGs08E+qziUtlwtFLbjaEWMalfOedFWCUgBaKXInGmUBtuvqp6wTEq4bEa0M5uh63k4nM0QLDnrBa6LrUaDpGcFdfwVXPxUGbvFqpxxmuJnt7fRdBzddKLUU1JQw1DJ1+d5TtasqsKt8VgvhnNW3sRZRkU6VoVIPn9lSKQapQz+3A1eBAO+/1Q3oQDRXD9+7Rq+/dln32lIfB1ONQD7to2262qyO7AsGCb5ttq8fVX98mrIoMvbFdc0AV5xDSFgWxaNhHcc6papqOf9i7u7GHNHmWdZ5B/BGuKsqtDgoBjaNvpBgNvTKQLHwTTPSYPsOGiyqY/DGXZSFHis00FoWXiOebCsLImfW9raFXzMphCQzKNZQqDjunh5MKAJEEkCyVuvThAgThIkzEsqzknx0WoKsdISW6wzFcw5qgJYzjSGykBUVd7mgpDNhUoJumFTXqwCx8H5ZhMbzSbWg4CKIqapC0hqIVA/o2Dax+ffn0mqEisomsAAMJjPMedGELVAqcnMimZSn7PqxlPXW/B5zaWExdy5MptRfhY5Zz4VaNqJonQq3tJaTAGY/H8Ple0nXxuIu3PUfnlvD3Mu1mhbSXFXGVFIiVlRIGP5IHBX+xu4LjzD0DSOZ1nouC5WwxDPrK7iPHs2T9IUCdNbFdNDeVVhkCTIqgqXOZNquC6avo/d2QwVyCRcbd0B6vKc5zlmWYavvXABnz53Dq8Mh5izhvjxXg8l36Pq907TFFvNJr71ySfR8jy8OhppKaBlUDfdYLGAYRho2TamTBdUoNqLy/eEAGBwPUMFp5RlgWq7L4TAhSiCx1aYSs6ljIKEST7Fi6IgG0i+91XQFQB8x4HDdEQu7xp0pVxLULRVUd21nTQMQ+9CxdJrK56HjGkq5feRZBmmea53K+BdY1oUqPjfJSgxFKCgqLpbE6YpVfx4ut9/ILFR4VQDcM/30fY8VABsy4LH1WhVyQXTC+qBA+ihHCcJ+r6PSZZhlJHTlVoBN1j4vRFFeJUdj144PCT3ekGdWUqGpAL/YLHA5XYbgeOgkWVw+KK3XBcFoCmNCpTxZZL8DS53OsiqClfZy6KoKl2QUVmxmtsV8rbIMk24to3nd3bwynBI7Y2gDN4xTYwsi9zH+AFS2c6MA69vkLF3xdukMd90CQc0lRVKzoJVMdOxLIiKO+hMkzqk+GeAF7D1KIIQAuthSNMoWC/bZRN8GIbW3Yaui7brah+MaZrCkBKW6xKXJsgNTsmdVJahaJaSb+qqqmADuhgaOg51vnHGpLIvpVSphCAKx/NoXp/noeTCVCWl5uZh20g4QKrtv2WaCG1by5gWRYFckHyxUNluWWI6HsO3LC2DUsFEoZISR3GMCRfXANxV45QlpJJxGYburLrYbuORY7PoPra+js/fuaP9ggEK7vtxrFt2LcMgXtU0dYeVZRiwDQN34lgbBBkc9NX06UJKlCUNCPBM854xTNvTKZ50XfTDEJ/a2sKXL13Cj1+7Rs8GU1qP93q6yWFmGHrO3IIDtc8qBEVdFaBgDEHNHFlB+uSPra9DAHhpMKAdBycIApSATVjtkxYFaYtdl4JnWaLDC2CLOydj3jGrDljlV5FXFSTXNADajYF3YYUkHbHD6qODJCHetiKdOxRlx7snldApKgbybjHfVnRmUUBw8Vl1O3Y8DxfabWyeMBTh3eBUA/DN8Rhf2t+nsSom9cWv8E2isoNFUcDmAKFGanuOQz6mUiICVSTXeOTNpXabZF7cneVwUN+fzdDxfWTMLzkW2QMezudY4f75gI02VIFJAHD4IWx5HjURMDd2ezrFZqOBczyUMuWsN8nJulHpNQFo+ZEBatX9mddew66amFFR91c3CGhwo2nCz3OMeGWPkwQNz0OL1SAlZ7emYWDGtIEyDyolFa8sQeoF1axgcCFFOa8leU5dcUJopYMFyraudDp4nO0vVcHRtixsNRq4wYEpryptatRyHJgcPCdJgsjzIFkGVUlqc5VLRQ3Bi2BWkWdHDsri1OBJWZFBkcGLr8EPfwWeAlyW2J/NyFmMjy/kYFBKSaoISS3YgXV3lJDPf8/LUtMzFQccUwi0XBdpltGDZRiYJgkGpoknVlYwZ0mYQlVV2OainnpdzQxUD21aVYhsGy3fx1oU4Uqvhyu9nr4uoeuiKEtsclfXHtMRliAFSpKTTWs/CJBzcbfgLfPT/T617ZYl5lkGi4+/zc04Bv8bfL6qw3LA07dnWaa5+NUwxMdWV/Hl3V1MU5pzFqcpNW0EgTZLP2LfByGEnu8nDAMu/w5FhUgpISrqmoMgyaAlBHWElqW2avUsS1vMFlWl5zwqJUknitDlAKyTBdPU2b/KdJWGXFEGALQhlaovCH6Wc+bU1VinoiiowM1BFiAaSVl82qzOkBVp1APbhmNZeoemKNO1RgOXmk08s7qKo8UCKw+QBz7VAKxMltvK2FxK7a8wYx3mKE0xkRKb7L4vOZtMyxKOYWCt1UIhJZ5YWUFgWbjUbiOybVwdDPCRlRVsxzFpHmcz7MQxmaEsTT0AKKu9dnQE13FwZzLR9oXzsoTHZD4ALUnz2Ffi+nCIK70eZeBcTU45WCy3pKqMxVE35XyOKa/mSks7ThJcbLUQOQ6uD4eYCYEMd/0vPNvGehjixnhMXW2siRV8MxS4d3ikGv3usoTHNgwkZamnIeRliQ5nG75lYTOKsNVo4NFuF4PFQk+NNgS5oq2EISLHwQE7y/mWhZbn4UKrhV4Q4NrRESacDR+xSYwyxNct2EKg7boILIt8KwwDoqoAXlAii7w2iooaUgLHQZfPI2WDISHIFU/N0LOZ2w9tG+M01TPSRmzyo6gNnXlz0I2ZYoIQSJhCKjmLbroubvMi/akgwMtLXsEAqSeUi5ciXGymgCTvRpQBe9t1candxrlmEx3fp/ZsHn00ZlnhM2trWIsi7M9mxN1WFfa5WJRXlZ6TZwmBC60WvmptjSYx5zl8zpIvtFqwhMD9sBIEuD4c4lU2sLkznaLF02Ke6PdxqdPBDn+2aVVhUZZoMW8d2TZGHOyUPrcCdasmOQ1BsPn7gr+f5TmGkkymbLUY8m5NWVwCVB+ZcuebAaKsItvGpWYTlmliNQzxSzs7SCuaIJ4UBTUHcbCdMj+v6iZVVVFHn1r0LQtQiQtTZklRYC4lXTuTfE8k7xotAE3PI92vSWOdVPKgCvqq5dwzTUSOg0fabfTDEFvN5gOZELKMUw3AH1ldxae2tqifHdA+Dm3PQz8IsM1jRtRq88TKim5TzMoSHd/Hk70e9mczuplME9cGA5xrtbASBFgLQ22woYYbQkp0PA/TPKe2ZL6ZhmmKNdYWzvMcTddFvlggTlOkFXWkGaCtclmSOc5OHFO7NGskj+ZzvV2RgOaqI8dB5LqAlGSIw7rirCxhA2hyG+TVwQC+bVNhQFEI/POVVtIxTU2HqJuqAu0CDFBA8AxDj4sP2EtXViQtUhrPhufhUrsNCeBcs4nNRgOR46AfhpiORvDZAnTVJH1x2/dxvtXCS4eHGHBWVEmJm5MJRmkKx7IQCVIcZBWNWXK5EUQ1AhgA1nnI6V4cYy+OYVYVLFDWorobVSW9rCpq4+ZrafBipbIh1Tq9Hoa6CJSVpaaAlN7YMKgBZpxlVLDkz7Pgn1dUZCWqFBVq+OWd6RQ/cf06AqaPFJavO/iaOswxq2JgxLWFC80mKRt4em+XXd4G8zn2WVUQWBYe7XZxpdcDqgoHiwWSnMzRDSFwuFig7bpouS56QYCrgwEsQd65g8UCn9zawtdeuKDbvdWCP+eGnjijadwt1yW/BjbkV4W+y+02Pra2hq/s7eGI6wsTlrgpa0bfptFYqthYlCVy5uFL3FUwmXzPZlUFi6/RnDNuA3cLraopKeddjxpLVRQFGvx5PtbrUS2lqu6ZsZhVNE0kKcgTwwC0aZc0DP35QlLxzDJNJFWl6yqqi1NyQgVBBUXwLsYyTXR8X9N5a3w8wiDbUJN/dsoL7ThN0fY8zLJMT+p+UDjVAFxKia+7cAE3BgPNwahsxuaKN5ivDS3yhPjY2hpeGQ6xHkWaMviqjQ3szmZoc7Z1OJuh7Xk45AF6e/M5iqrCVhRRRgbayg5YAK86enbiGB3f16NfLNPEjdEIVVEgFSTG9riwVUmJIbt4rUcRAs4ybZN0zJILCaHrou15MEAKjBFzxBafZ2Db2rRlsFgg4uvgWxa1YXKB4lK7jevsC2swP1Vxdqe2RWrceNN1aeFhqkaA6IaVIMA0TeFWFc63WqQLbTTw1OoqbWUNA3Ge41yjgY0owp3pFKthCCklTSVwXVzpdnFzPMYeT/hQwvSW6yKeTDBly0VbkGpBZQRNdsrSHBtn5GlR3OOXq0xhVPtoXvAgSdOEC9oRlHzjq2t8odnETZ4QfSeOtYGT0rGGrIapAG0h6dk2jIo64QTYxpSDT8bcphrj0/U8JEvtyGqHo2DzPaEsOFXm6hgGnuSd2XIXmW9ZsHmhX2XdaNf38dTKCg7mc7TZHFy5wuVs+OJZFo1F5+4137bxbKuFX3P5MqlErHsf14brYo3N3V8bjbAShtqcPS0KfG57G/M8x3oU4T965BH89K1b1KpdFIg50ZEgY31VyzAkTU9Wrn8W+7nMMpq2AUENR6ZBGnNFTaiZhGqsUMIcblqRxEvVIixeWOOiwBd3d++hfhSdWJYlfFZoqNFjJi9+WVkiA/TUGPV82HwvqcK2y9RHwjtpJU9UQxzWoojm6/H5+Y4D3zRxudvFU/0+XhkM9Ij6rWYTEXuNvDIY4Jm1tXcSDk/Eqasgjnjw5DTLELCTUyWpBXEjinCdLebWgkBXc7eiiFYqg9y2Hl9ZQcwFuSaPE1E95RPutgGALuuJH+l04Fvktn99OMQ0z/Hc/j6SskSQ53i234dhGGR1Z5qoeAWtwA5XvGrbzGmqKR6ebSMsSxoLIwSato02r4gOF0lanoeIt2IBLzSqaaGsSBerOtcACtKebePV4VDzuVVVabetyHHgsAh8nudoODSqZZgkNB5F3tWiqsm8vkUm1qHj4NFej7hb10Vgk09u03WRVxUVjYTAa+wiNuLmgNUows/euoUB99nnFbUeT1g54DG//ujKCvphiC/t7emGkEJKuIahx+ZYbAgUcKtsykHQM01kkuR1oeMgrSptVeoIgW4QQBg0Jfurt7bQHQ6pMJummPNWPC0KdH0fB/M5+TlwcUVJA1UFPa/utpmqrawKPpMkoWtYLY23l3fbdAWABlNLqgkFoACt7mXXsvRYeQGii146PNQObwDwZK+H80xBqWfj5niM/TjG3myG25OJHh6rgpIQAs+urt5TIDwJgW3jyZUV7M1mOOL5aur67M1member9zaIt0z63ZvTadwDQMzlg3ahqF3ISpoqaknDdfV2l+1W4kcBwnr1hWnrzT1gq9pwc9KJSUato2AO+km3BQDABFn/5M0pYKqELoT0Bak+w74OVR0k0pOCilRcaasCnMwSPMruAhdgeoQNlN2Xd8nio1nCxZMVXU9Dw3HwWhpQXyM5X/KXrXleSd9BO8Yp5sBVxVuj8d4eTCgxgKutEauS1vMinrlq4pE3l3Pw3oUkb0g30QXWy2MkwTPrK3hl3d34XHmGDkObrC8xuLCQdvz8Djf6KagluS8LPEzt2+j4bpoG6SjfKrfhyEEjhYLEmLPZjCXVm8T0NmfJQQmJjlHdXyfGjiKAjbztk1+OAEKpkq7HNg2znNXkuKlvri7q01GyrLEkysr2tFMaU49y0KDixO9IMAiy7AaBFgJQ5KT2TQBpMcaXjV3TYIeis1GA492OhilKc43GohsGzPWyVYmKTdNIXCl38dj3S6+tLcHALqpoeE42J/P9cgbVXwsqoq8Cbh4+ling0e7XeRliYvtNl46OkKcZZpHTooCG1FEBt7sZQAhdGNDUpawigKZEEQB8fUMWP4G0LQSSwhcHw7Rsm0MbBurPIpI6ZD3ZzMddG3QFrhh23r6hG/biJhXzspS64wLLr4seKu5PAlaXU+HFzXPoSkRDhdGVQOQY1k4ShJs8NZ5ynLInThGnKY6+91sNnGBFRJq3M6rwyHON5v4xTt39O9U0z8UHbIWhm+56i6EwHoU4RsuXcJP3rihJz4fsEl7Vpa6QWjG7mVJniOW8q5kEdBty0rFIrlQKgwDrkXjlqRJM/B808RGEGCQphgnCQKL5vql/JxXuNt5qSw1I07G5nmuk6zxYkFTcbionpU0HcO3bS1L5ZNEyn4TylPG4c/TM03kQiBnuiEpyeOl5GQmcByaUh2GWIkiuLzYddm32BQCj3Q65LBXVWh73j07nbyq8Olz53C+1XpLn8dbxakG4LQo8JWDAzId52KDbVm6SBA4DtabTSTsFbrZbOoArORpbdYxrkURvmZrSzv9355MMFwsdCbZ9X18/YUL8LknPikKqrLyg5OxCF1JwwwhsBFFCG2yDVRG5BOuiJeA1t6qCc15VSFwHDyzuoqe52F/Ptetu8+srOBgsdAzsB7tdvFEr4e0KPDqcIiu5yHudDBIUzzSbqOSEo/3etiZTLR2eW82I7kUb+9aihf0fZL08fhy16JJEJASB9zCm3Jxq+t5CDlrXuEAsN5oaF0pQA0aapx8xYXRnekUW40Gzjeb6CkNLatUJMjmU5m/G0Lgay9cwDRNcWM0wmYU0cSCJKEuRMeh7j5+8DcaDRwtFpikKc63WkjLErtxjEQIFMwBq/OOHAc+WyJapgkpBPkjW+S2tcLjozJWOZRVpf0sUs6+lSGSeogNpkssPv7hYkHFwLIkCgR37UwBtswU1ALd5UXXZ5pBsBzRsyzN8+4HAUkfmX6YZpnOfn3bxkePbVk7vo/HBLXMNlyXdnOgrNg0DH0PX2q33/YMtojvzzvsjTtOaVBok++jZ9bWKDGRdzseA9vWzSqBeVfKp8baS5DKRLXzg6kkNaFCeTObNtkDmKxcEICWB/omjVpSFIkE25wyR95mVUfMXH1WVXCl1BTSWhRhnKaaOrSF0BPVlapB1VUA0iFXeY6KX/csCz2eN9dihUrELdgd38dqEGj3NeUXIwA8ubKCJ3o9mnz+gAdyAqccgPfiGOPFgrbnALZaLfyGJ57AnekUe2o0jeeh2+ng1z7yCEyDHLts08R6FOlg1uHt8QpLuaZpilcGAxiGgZAbDC7wCCGAZqg90m7jc+Mxcc0cFFR1XxlhA5Q1fvNjj2Ge5/jCzg524lh7CBuCrDDnTCGoosYmu3dd7naJe240cKHTwZf39sgsnbfGDdfF0/0+GbZnGT6+sYFf3t3V2tqj+RzPrK/jF+7cQcnV+TtxjID5w1mWoRcE2jFqxfcRZxk+0u9jNQxRVSTs3+ax8Q3bxsVOh46z1dKcay8MEacpdqZT+JyZKzc3gDoS1UQMlUkJAC2HhoaqaRiuRR1c57kaHNo2jjggRbwFz0qaOptVZI/ZcV082evBdxx8eX9fe0okRYHDkszdC0m608Cm9lFVNNyZTrWuezCf6wGZrmWRHwhvVZWBU+S6RNlw9d6zbbQ9jzJfIbRj2DBJqMOP+cnjIU4VcAJe6GzTxJV2GyNuvU744c0q0kdPsgy3JhM8sbICgHyG1cil863WiQ9uy/Ow0WjgmdVVrIYhbrM151oU6YairXeoOVUL3kajgT4X8gCinq70ejiazfDFvT2SAUrSuc44+1QU1rJXiqpHqOaYglUiLdfVVgGh4+BgqdFCdUsqq83VKMJwsUDMGanqCXD5/lPHJ5nuUjK2yjBoygl3MXq2jZ7yT2ZqxbfIC0bweagBAMq5zrdtGkfVbqMbBPfI49RQ1Kf6fe0Prbpq256Hr97YeNuL4NvBqQZgyzTxaLdLW/CyxOV2G0lZ4sZoBNc00WZnsW967DE83uvd83/VHLmsLLESBLjBvf+mYZCetdfTDlU9zp5fHgxITgLgFjub2YaBR9ttPJfnxCvirpzLNk083e3iq9bXdRX0xnCIgzgmnpmzpKQoEDoOVnwfnmmiw/KefhCg5Xn4ugsX8OLREebsr3uh1UJk21gLQxiCJrt+YWcHYAphMJ9js9nE4XxO89NsG9s8G0zN/yorMstZD0O9JS+qCpuNBraaTXismVQtsKMkwVoU4SkOAgCw2Who85CkIPP4EfvcHselTkdvxwAyrLnS62E/jvHaZEINFZaFT25sYKvZxPMHBwCAJ7pd/DseVqjd05gDvtxqwbNtdIIAT/X7eLLfxwsHB3jp8JBkWmlKE2iZN3RMExdaLWxFEVq+jyvdLq4dHdFQUkHmTQnTHEJKbcxtMqfs2zZkVWGQZWg6DtrM77ms8Zxk2d0R5/xAa7712PWwebtcSomObaPheXA4m1YTR9TQgFujETxuOgAHkwvNJnpBgD7fAyfhiMfQn2s2cand1pmyast9p1BNHQezme58U8qOrSjCJzY3UVQVXhkMyB9bCJznZ9Q1yVMh4vqNmsmn6CjV1l7x9c/KEmtRRAmOQ0N0Y9b1hq5L9yDfw6qbtKgq8myxybdF7TpbLJtUE8izqkLLttH0PEyShFQ7PE7INAw8u7pKRkOSBoqO0xQrYYhpmqIoSwwBTUF0fJ9GWhVk7erzwl8CeKzbpakyrMdWeLrfP9XgC5xyAD7fauG3fvSj+MkbN/DqcIitKMKt8ZimxDoOTMPAxzY2Xhd8Abop+mGI7ckEpiDj7aPFAqssgs7KEmthiFlRYCUI0PN9PaxSYcpOW5dYhjNmbe1uHGvp1aVWCz7fCE/1elgNAtwcjfDC0RFKpgRs3rqahoGn+n3iNjlgPNXvo8MPugp2jmniE5ubaHseboxGWA1DhGyuEjoObgyHaPk+HMPA8wcHuNBqwbEs3W5rgiiVfhDA4kDweLernbK6vo+VIMBwsSBZWZriqX4fH19bw4ALKn3W9Sr4to3Hej3cGI20TypAGcJGo4E+z/rqHpPZXOn1cH48xm4cY5U7voQgk+7bk4meARanqS5kVZLmtxnMG9qcza03GvhIv48XDw/x2evX8fPb21TgAGW1LR7ZLkHBW7WTmzzPq+IuONcwUHItIGAe0TZohHzGRZ+kJAesgmkjxdMneU4WnqDdjwDusfEEKGvr+D6ajoPAsvDx9XVYpom9OKap1Z6HXTaBNw0D4yxD2/Oo824+x9dsbmqu937zwxb5vRODu76vF8Y30vu+VZxrNvWsNOBu0tFleu7rL11C6Dj0TIAy8py10WoXd300ws3RSO8Gx4sF9lliqQZZKmlpyRTjgqWeZVVho9HQO8ZpmmKUJHpEkWNZWPM82JYFC9SO3PN9PZsuK8isyjNpysglXsyVgf4mB/2soi45lymOvKAp5nFK47qUzK7JRu2eZSEIAt05Z3Ph/A77RCv0guCBa35PwqkG4ErSbLS27+Mp29Zbz4CJ9UutFj61uXnf/78ahthnO8u25yHOMvTDUJtzb/FNpoxmer6vaQt9gqaJNRZRX+l2sT+bYXs61bOwevyACCF0ldoyaUyRwUWjBWfA55tNlFWFg9kM/TDEuWYTGxyIbi9t6Tu+j61mU3O7N0YjXGq18NzBAakIbBsv7O/TnK00xe50CoCKeFlRIHRdnGs20Q8CbLVa1KrJP7vHJjqKs7Q5ID+5soLAITvPN8LFVouMXlhRssaLyRvhQquFC8eKD6thiAnzi+uNBi5ywEyYflG2g6tBgEudDnlC8LX59LlziDkbvTYcoqwqdHly8DBJcInpkyu9HjYbDXxlf19zt6rLSY1jX5QlIotajB2TWpKV8gFC4GK7jb04xoClisqi0layK5B8UI0pB6A7AHtBgCdWVtD1fVzngq8a8FnwVlVNzo7Y1S5yHP3gKsrsJChTcIXefQL1O4UhBB7rdrEzneIO318ABeLzrRbONZtIC/LHdXkX1Q8CzAoyEbo9HqPhOHi638cOTzYvXBet8m6LsFIVTHnLX1bk29x0XW0OVVQVOp6H2+OxDtgF13ceX1khQ6OqIl0uWHNtGLBsm2g828ZqGKLJstOkKNB2XfQ5E95qNnH18BCFlOj7PgopEVkWXuTzavEi+k2PPIKG68I1TfLdYM1wNwhw9egIUkpc7nQAUCx40C3H98OpBuCkIAOOnu/jxcND2qKBbo71KMIT/f4b3ngGZ1o3RiMA0KtnWpb6xnZNEx9bW6OLyZ1IQ+4Hb3oeAosGWz7S6UCw/q/leZimKQU2QVORp1mGaZqiw9xtxFnvwWymzVoutNsIWcgNgFqTiwJf2NlBxdmfEAJXej09mtuzLDzR66HhOLg2GCDlzH2aJLjF0ylGXAVWGe9GGOLJlRVcbLfR8X0tY7N42xU6Dl4+OtLXqcWFt7cCIYQeZ/NuoM5TFVg3WL6mvDKSosBaGOJyp0OjYRYLXGi1tH/FVrOJpu9jPc9xZzKhMU6+T54bUhK3mqaIHActz8PH19cxWCwwLwrESYIBG/ALQM828yxLj1K3WLKk6gm3x2NMuTVVmfyrKQgt1o4rOKaJXhDg0U4HG1GkJUvrUUSVfNZ+q2Pt8JiqXhDANmj0kVLjnAQ1TFRB8d+ngQ1uwLkxGhG9tfT5Pd7r4fOswrjUbuOrNzbwpb097E6niFwXpmnqMWCJoiBME03bRsXZciklUJZo+D4qQdayyo1OdUvuse55lqbIQfzvxXYb61GkPafVMNh5nuNxx8EkTbEahliLIlxst7ETx9Sqz0oWi1VUM+6oVM1Pa1EEISUut9uYZhl828Ya76oaLo2YVxOkA1a3qPqHqiFdaLX083vaOPWhnG3mFWfcI64MXs5xVvVmGsdeEGCftYwAdOVVwbUsvd0DP/AAtKqhlGS6s8zlqKkPCh2ujgJEbagPfzWKcO3oiLg+pf3lai1A2cTVoyMyImesRdHrskUVcL71ySfxL69eRSXJgET5BjiWRUULIfBkt4uPbmzQVFoptRIEoNV+PYpwdSn4Anjgk1rfDlS212BN5UuHh/p7inYBaDd0MJthLYrI+Yx13CtBgJC51vUoogeYM9KiqrQaxueMaCUI8KW9PQTMbwJ0j6hp1b+4s0OmP5aFg8UCJWuk20FA213D0MbyBRcNC+CeqcgWa8kV3ZKUJdYbDW3avhaG2Go0kFUVXjo8RIeNlnT7spRa6nb8Qa6kxCuc9Ss8yDHnJ6HhunhmdZVUPjx1Qxm4/8rz58ky1nFwOJ/fHW8F6BFeV7pdXOcWZ9U633BdbS/Q9DwM0hQt18V5XmSfWV1FWVW4OhjghcNDLFhlIaTEuUYDm/xZf/tHPoJFWeLWeIwfvXpVL7j5cIhz/LOuD4fYajRgM58tKuomjNjYqe37CGxbFwaLskSj06FOVcvC0/2+dke71G7jYquF29OpznzVZ2CbJi6322QU9h7hVAMwQA/h/myGp1ZW9GiVfhiixw/TW8E5NnU/DtV0cRJs00TrHaxiqoChHK6arotrgwFlehwsBaifXEm5FCLXxae2tu67pX9iZQUtz8NPvvYa6YOPjjDillHLMPBIp4Nf/8QTSHjx2JlOdfeOKqbEWXbPIvR2st/TRsjnoTS1alK0okuUP8FuHGt96mCxwGajgcudDvnoliXRBdzUMEwSres2hMBH19YQ2Da+uLuLEcsQm3z+K0GAT2xu4vnDQ6JYZrO7tBcrQkzDwGPdLkaLBSTrg2d5fq/PBu+YlB+zctcDaIF9vNcjH2AAn9zcpCnKZYmbvM1eCUNYhoGXj46w1WjcTRAAvDYa3VOnCB3ngbe3ngQhyLcjdBytMNqJY5pczovoiGWEgU1tyWoIQj+KkJYlVqMI3SDA7nQKzzTx6miEfW7PLzmLnKYp+lGES+02Xj46Qou7Jm3D0DvOlTBEw3XxNRsbmOY5Op6Hr+zv68V6kef4mnPnaOILq1biPMczq6u0Y0wSREVBJlbcvLUZhtr2dMSLQS8IcJl3MWpo6VdtbGDB/hZKfulZFi6227jUbr+r4uc7wakGYBVEHuPtv7qZV4IAW83mm3KPCg3e8o2YwgAo8Jz2BTP4Q8rKUtthnmdOdpfF9uqYWp6Hyxyw3wjrUYRveewx/Ozt29hoNLAbx3CZpmh7HjrskaG47HGaar7ZEOIePg/Ae8ZVvRUIIahFnLfX8zzHWhhijxepSkq8zDuGwWKhxxitBAHWmYtWgzQXeY5tPte8LHGOVQVq8fkoFxxVx2LFGfQzq6to8KIZ2TZeGQ7RcBwk3EG4G8dIi4Ky5qpC23UxZmWFQuQ42Go2MecCzuFshsCy0OQML7BtraYwDQMfW1/Xvg+H8/ndUTisAfc50CVFoYMMAL3ovtku8EFD8H3d4iLx8oLQcl10PA9zVg0VZQnBEslhkuDRTgcrzIlvRhFG/Az43GWpgvjLR0cIbRtDg5zwVlnbvxlFON9q4XyrhUoITNNUSz/15GLTxMfX1vC57W1dRAxtG/tMZwqmA4c8qeWRTgdtz0MpJe5Mp+iynPVXbG3hXLOJnTjWhvXP7e/ruNNi2eLj3a52mnuvcboyNJYlJXxhr/R66AcBtcy+Tc7rQqult6RrUaTVEKeNlSDAXhzrD+2Qu8SeXFmhVtL5/G4R4y0eU+S6+IZLl7DNQyTHaaplUEfzOc1E42uWlyWJ6NnnYZnuaHveqXGH7xQdbpRQUNybOm41b01NZr7EDzRAAbofhiilxI3RCAOelWYKgZePjmCbpp58rPwHPnX+PBXkpMQG+x0/0umQYxsL8FUNwTNpNFWS05SLBjeMfM3W1j1UgWUY+MjqKhzDwAuHh7qdGKBJxcsTNBqOo2sa61GErCzx0uHhPXyrWlCWoY7zveIaT0Jg23i638ckTbE/m2HMEsWL7TYudzqY5zlZg2YZKQvyHL0gwOO9HqZZhoLVNkOedpwWNHm6wbvepkOjvZqOAz8McZ6Niza5rhNnGXzLwm4cU8uxui7tNo7mczzZ6+En4pi8U1gLH7kuLnU6NMlDSvSDQCsWtHpECFzpdnG+1cJ6FGGQJPfM/FtuutmIojMLvsApB2DHJL/Va4OB1sO+1az3OGz+We81DFZHvDIY6NdeG4/xaKeDGZvFA3TjvJ1KtmOamq++M51iZymzbXA7L0DBS/Gs7+fsV6HhOPfQEPuzGc5xNllWFcm3+GHo+D42Gg1dwAQoQAfcoHN1MIBkJU3kOJizRahCy/NwbThEk6viSsnQCwJsRBFMIdBwXTzZ7+MLd+7gleEQm1GEXb62K6wy6fn+PfelEAIXWy0yZl8s9GfTZn/dV5eO4fiOxzFNPLmyQpN82XDnJGw1Gu8p1/hGaLJyQTXSuKaJr+zvI+Cmh+3plJpSeJhoKSW6noc70ykut1pImcLxTJOMd9h24IA78fpBgMBxcJH5V9XsM2a/7iynyeK+ZVHn4VJTzyc2NqjJQpDZVD8MaeCm5+HrL1zATfaSUXhmdRUA7UxWw1B/lsd5d4Biylk/Q6fOAVsGjU1RfOAHEW3Po0m2LB1a5Dm+sr9/z3s6PDTxnUCbdnO24S4VCTcbDQwXCy0d07/P998TneLbhRDkabscpHbiGG3XxXMHB3pLqW7+Z1ZX8TIXOgForu6QNc6HsxkGiwUutloYLlFQAPREhXGSYMzb4M1GA23PQ8vzYLBccXc2w0dXV7EWhvjC7i4ix9EG9z431rhLWa4aGvvi0RHangebW7I/fe7cPdpdACeas9jcULIeRdiZTnVzi9KeKn7y/QZ1fADJDHfjWNMVszwn9VCWYbRYoM18bsEUkKqLdNij2TJNTBYL3aJ93vex1Whgs9nEznSKNps6qVmNUZKg63kIXBfZUjuwzVr7WZah43mkKW82dWF6zD4Ux7EeRZrXb7gunl1dRZxlWu2k7tPTbrR4M5x6AAZ4COcHNPgqnG82iac6tooCd2V17wZKiTFf8onNWYucleU9OmPgbJUPb4aO72M1y3SBUpm4K5iGoc3hbZYkXT060ltD5T1QVRW8Vgs2S/CWf4ZyIFvGIs/xymBAns9Zhhvjsc6299lsaT2KqEPRMLDIMrT4untLVIAA8Ets/ARQoezp1VW0PE9bRQLQOuD7weFz+yBiPYpwwINalSG70sfeHI8ROQ4+srqKn7l1C+thiCEvghtRhJj14Cr3Vw0rT/b7sA0DO0xB7cYxScNYw7/ZbGKcJJgs7RpUoH92be3Eov3ldlt7qJh8n/i2/bqdiclFwAftZvZu8Z4E4IcBNreMvsZ8ooLyh3hQ2aiqSq+GIb7MTmXH0Q/D92X2u4xzzSZ1LS1l7etRhC5v98+3WvohiRwHj3a7uMaUA0Bqg+vDISJu7TaYTlDtwxe4M+pgNtMGLwppUege/8Ml7q+UEp/Y2MCITeWVHlRNZVZYFAVujsd4rNuFIWgK8wob7iyfz/vtYX6QMA1Dm2YB0HabIV+Lw/kc55pNfGJzEzvTKbUgZxl2plPyKQ5DTPha9dnVbZqmONdswhQCu7OZ5sVT/rktpkG2Gg2k3Ir8SLuNlufd1wjHNIwzpxHeDeoA/DawEgQYJwlG3ASw1WhghVt4HzQc07xHRwvgnmLP+x2qyPTCwcE9Vo8t7rg6XrBsui4e7XTwCg+ddE0Ta1Gki1wKytqwx8VcxV3unsC59sMQeUUTcju+T/IwbjN2ua1ZqR/u2YpyV9coSdALAq3r3jvWZdl6n3C4pwXViapnsTHloKxKXW7EUEMPAttGwT4Wvm3rhpevPX8eDZZsTpkSUoXaAVuVrrOjnvLeDQwD53xf10keVtQB+G3i0W5Xt1SeNqe91Wjg2mBAhkNBQGL0M6yav12ogtQeN00oE6P7oeV5eLTTIb9fIfB0v4+0LHWDgAquq2F4T8BUJj5rHDBUa7oAL5ppShN+QQvDY90uOc8dK34exzhN8cnNTT0heFnz7bC/wMMM0zCwFkXaOc8yyMb1HA+o/OyNGzAAPNJuY8afT8PzUJQliqrCs2tr+PT58/codxY8nFMhZn8GIQT2ZzOt6xcPgNb7IKAOwO8Ab8T7PUg0XFc7ZJ11seCdwmUK4a3iOE/nczdlwZmsEELPXzvpd53U/ltJiduTiTaJV+qDzUYDDcfRNpcaQqDPZt0q+9uJ43vlS43Ge67fPQusssxsucvvaLHAgrXUIatenlld1dr1wLaxHkX4qo0NAKQNvjEa6fpJyKZSVVXpxqbrwyFcdtQDqMbxfqfZHgTqAPw+xwc18D5oWIbxjpUDBle8T0KDW+Ox1IgR2LbWJt+ZTlFKeY+2WZl7fxigTH1eXNI2z7KMxhMJoXXolzsdbLBiR0HNB2x7Hp7u93FjNKKZhaaJj6+vaxMqgAq1Tf58fQ7gHwbUAbhGjWNYbo4oquoe83qAPDk+DNmvgm2auNLr4cXDwxNVQMt+JSoAV2yopHYzjmni8V4POXsKq7l8y9p2pYi4xB4cHwbU6VWNGsegbERPQug4Oth8mKAGVC7XPULHwYVWC0+urMBgo6rlwDk6QZ+rpq4oPn+TnfnUfLi1MHzfdXeeJuoMuEaNY1CzwHbjGHvHuN93OiboYYDS/sZZhtC272leAYgmCpd8MsZpetKPAUBjm4qKhvKa7TZcw8B6FH2gJWXvBHUArlHjBBjcdLASBLgznSLOMqwEwfumffis4JjmGw6nVIMTAPIxUVNgjmM5O45sG8+urZ2pL8ZZoQ7ANWq8AdQwxxpvDW3Pu6drc5gkJwbg5bbykAepfhhRc8A1atR4YFCTsxUGi8Xr3jNZ0mUD+FBy6gp1AK5Ro8YDxbIBfV6W95hIAbhH0gfgDSmNhx11AK5Ro8YDxfGAupwFF9zirdDicU4fVtQBuEaNGg8UysdEYbhYaNnZ0TG/jtOeh/d+Rx2Aa9So8cCxnAUXVYUp0xAHS/SDzd7IH2bUAbhGjRoPHMf9OoaLBaZpes9ooNNyEvwgoZah1ahR44HDNs17ZgEOFot7pnkD+ND4abwR6gy4Ro0ap4LlLLiS8p5BpU3XfV0n3YcRdQCuUaPGqaDj+/d183urE8QfdtRLUI0aNU4FlmHgSreLw/kceVWhkhJSSrQ970PdfLGMOgDXqFHj1BA6zomtyDUINQVRo0aNGmeEOgDXqFGjxhmhDsA1atSocUaoA3CNGjVqnBHqAFyjRo0aZ4Q6ANeoUaPGGaEOwDVq1KhxRqgDcI0aNWqcEeoAXKNGjRpnhDoA16hRo8YZQSy707/pm4U4APDaO/xdKwAO3+H//aCiPucPBz5s5/xhO1/g3Z/zRSll//iLbysAvxsIIX5RSvnJ9+SXvU9Qn/OHAx+2c/6wnS9weudcUxA1atSocUaoA3CNGjVqnBHeywD899/D3/V+QX3OHw582M75w3a+wCmd83vGAdeoUaNGjXtRUxA1atSocUaoA3CNGjVqnBHqAFyjRo0aZ4R3HYCFEI8LIb5HCPFzQogDIcRUCPFFIcSfFUK8bvSpEOIJIcRnhBBDIcRMCPFTQohf826P470En8M/EUK8IIQYCyHmQogXhRB/XQixcZ/3f6DP+TiEEIEQ4roQQgohvv+E73/gz5nP7aQ/8Qnv/cCfr4IQoiuE+B+EENeEEAk/1z8hhPj6Y+/7wJ+zEOIvvsHnLIUQ+bH3P9BzfhBDOX83gD8E4P8B8E8A5AC+EcD3AvjNQohPSykXfPCPAvgZAAWAvwpgDOA7APyYEOKbpZT/+gEcz3uBcwA2APwIgNug83kWwO8D8O1CiI9LKfeBh+qcj+N7QN1Br8NDds4/hddXwI8/lA/N+QohLgL4LIAIwP8K4GUALQAfBbC19L6H5Zz/GYBrJ7z+UQB/EsA/Vy+cyjlLHhX9Tv8A+CSA1gmvfy8ACeA7l177pwBKAB9fei0CtTe/BFZlfFD/APhNfM5/6mE+ZwBfzTfhH+fz/f5j338ozpnP7QffwvseivPl4/4pALcAbHxYzvk+5/f3+PP/ltM859M8gWf5BP4u/zsEkAD4Nye898/zez911hf+XZ7zp/g8vu9hPWcAJoDPA/gXAC4dD8AP0zmrAAzAARDd5z0P0/n+B3y8f5j/bQMIHuZzvs91CEDZ7W0A5mme82kW4c7x1z3++lEALoCfPeG9P8dfv+YUj+eBQwjhCSFWhBDnhBDfBFo1AeBH+etDd84A/hiAJwF8532+/7Cd87cBmAOYCiH2hRB/WwjRWvr+w3S+/yl/vSmE+OcAFgBmQoiXhRC/bel9D9M5n4TfDKAJ4AeklCW/dirn/CA44NdBCGEC+Augber/xi9v8tftE/6Lem3rhO+9n/F7AfztpX/fAPDbpJQ/xf9+qM5ZCHEZwF8C8D1SyhtCiEsnvO1hOufPAfhhEEfYBAWo7wTwDUKIXyWljPFwne8T/PUfALgK4HeAgs4fB/CPhRC2lPIH8HCd80n4PaCM9h8uvXYq53wqARjA3wTwaQDfJaV8iV8L+Gt6wvuTY+/5oOAzAF4E8UBfBeDXA1i2nHvYzvl/BnAdwF9/g/c8NOcspfwVx176ISHElwD8dwD+CH99aM4XQIO/TgF8o5QyAwAhxI8AeBXAXxZC/CM8XOd8D4QQTwD4OhDVcH3pW6dyzg+cghBC/LegLOHvSym/b+lbc/7qnvDfvGPv+UBASnlbSvmvpZSfkVJ+Nyhj+CtCiD/Db3lozpm3oN8E4PdLKfM3eOtDc873wV8DkAH4Fv73w3S+C/76v6vgCwBSyiFI5bQOypIfpnM+jt/DX/+XY6+fyjk/0AAshPiLAP4cgB8A8PuPffsOfz0pTVevnZTef2AgpfwSgF8C8Af5pYfinIUQLijr/VEAu0KIx4QQjwG4yG9p8WttPCTnfD/w4nMHdyV4D9P53uavuyd8b4e/dvBwnbOGEMIC8NsBDEAS02Wcyjk/sAAshPhuAN8N4IcA/F7J5cElfBmUvv/KE/77p/nrLz6o4zlD+AC6/PeH5Zx9ELXyLSBuUP35LH//t/G/fy8ennM+EUIID1RgVsXlh+l8P8dfz53wPfXaPh6uc17GfwZgDcA/llIepxpO55wfkGzjL4BI6x8CYLzB+34YpKP72NJrSkf3Mj4g2kEA6/d5/Rv5/P7N0msf+HMGyZG+7YQ/f4A/93/J/378ITrn3n1e/2t4vdb7A3++fMwdABNQJhwtvb4BIAbw8sN2zsfO/1/wZ/vsfb7/wM/5XdtRCiH+EIDvB3ATpIerjr1lT0r5//F7HwOtsjmAv8Ef9neANMPfIqX8sXd1MO8RuCixAeDfgi6+B+ATAL4dxAP9ainlF/m9D8U5nwRWQVwH8HeklN+59PoH/pyFEH8DlNn8BOjejkAqiG8E8POgIpXq8PzAn6+CEOL3geSUz4FUAA5ood0A8OuklD/O73tozhkAhBCboM/58/L1xVf1ngd/zg9g1fhB0Kpxvz+fPfb+pwD83wBGoGD10wD+w7Ne/d7mOf9mAP8vqGMoARUvXgRJ0i6c8P4P/Dnf5zpcwgmdcA/DOQP4VgA/BuL1EgAzAF8E8F0AvIftfI+dy28EaVtnIEXEjwP42of8nL+L7+XveJP3PdBzrg3Za9SoUeOMUNtR1qhRo8YZoQ7ANWrUqHFGqANwjRo1apwR6gBco0aNGmeEOgDXqFGjxhmhDsA1atSocUaoA3CNGjVqnBHqAFzjXUMI8YNCiPe1oFwIsSqEmAghvuOMfv9vEEJkQogrZ/H7a7w/UQfgGq/Dm0yJPf7n0lkf71vE94KMZH7gLH65lPIzIEOXv3IWv7/G+xN1J1yN1+HY+BkA+HrQxOe/DxrauIwfAfnjmlLKBO9DCCHOgaaV/Akp5f90hsfx2wH8IwDPSCmfO6vjqPH+QR2Aa7wphBC/E5Q5/i4p5Q+e7dG8ffCQgD8NYEtKuX+GxxGBbCz/oZTyD5/VcdR4/6CmIGq8a5zEAavXhBA9/vuhEGIqhPiMEGKd3/P7hBAvCCESIcSLQohvvc/P/y1CiJ/m/z8XQvy8EOLb3sYh/iYAv3g8+AohfjUf4+8UQvxBIcRLfCxfFkJ8C7/nWSHEv2L++EgI8beEEPaxn/MRIcQPCyG2hRCpEGJXCPET6mcoSJoh91N8PDVq1AG4xqnjXwFogTyj/wGAXwfgR4QQfxLAnwRtyf80yPbw/+LBnxpCiO8F8H+AXLn+PL93DuCH2Qr1DSGEWAON0fncG7ztD4EGT/4A//wQwGeEEL8BZDn6MoA/BeAnAfxhAP/N0s/v8Xv+A9AYmz8Amh5yAOAkW8OfBbAmhHjyzY69xsOP0xrKWaOGwueklDpQCiEAGm2/BeJCJ/z6vwXwyyCu+c/wa18N4M8C+D4p5Xct/cy/JYT4DIDvE0L8kJRy+ga//2n++sobvGcTwNNSyvGxY/lnAL5NSvnP+H1/VwjxeVDA/l5+7WsBrAL4LVLKf/oGv0NBHcdHQBamNT7EqDPgGqeNv3ns36qI90Mq+AJ6nt4EwLJM67eCPFr/kRBiZfkPaEhkAyePiFmGmlI9eIP3/KAKvseO5c5S8FX4aQDrzOcCgPp/3yyEaL7JsQDAEX9dfQvvrfGQo86Aa5w2Xj327yF/vX78jfy93tK/nwIg8MaZ4tqb/H7FTYs3eM/xY1THcus+rwN0nLGU8ieFED8E4HcC+K1CiF8A8K8B/J9SyudP+P/qOOrqd406ANc4XUgpy/t8636vi2N/lwC++Q3e/2ZyrgP+2n2D97zdYwSWjlNK+TuEEH8NNLLo6wD8CQB/VgjxR6WU33/s/6njOECNDz3qAFzj/YyrAP4TADellC+8w5/xHCiIn2oHmpTyKwC+AuCvCiHaoLlx/70Q4u/Ie7Wej/HXr5zm8dT4YKDmgGu8n/GP+etfFkKYx78phHhTHlVKeQDgedwdHf5AIYToCiHueY6klCMQxRKABrYu49OgQbUvncbx1Phgoc6Aa7xvIaX8BSHEdwP4SwC+KIT4YQB3QBN6PwHa8jtv4Uf9MIA/L4TYkFLuPODD/O0A/hhPyr4Gmpj7DQD+YwD/VPLkZEA3Ynw9aNpwjRp1AK7x/oaU8ntY+vVfAfijII3uPmgL/0fe4o/5BwD+HID/AsD/+IAP8bMAvgqkb94A8cbXAfzXAI7zv/85KCv+ew/4GGp8QFG3Itf4UEAI8XcBfBOAJ6SU+Rkdw+cBvCal/I1n8ftrvP9Qc8A1Piz4CyDp2O86i1/OXXXPYqmLrkaNOgOuUaNGjTNCnQHXqFGjxhmhDsA1atSocUaoA3CNGjVqnBHqAFyjRo0aZ4Q6ANeoUaPGGaEOwDVq1KhxRqgDcI0aNWqcEf5/aqUx706GZI8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams.update({'font.size': 18})\n",
    "\n",
    "\n",
    "trace_sample = '11-30-23/selected_zda/Traces_GMM95.dat'\n",
    "n_traces = 40\n",
    "trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                         sep='\\t',\n",
    "                         header=0,\n",
    "                         names=['Index'] + ['ROI' + str(i) for i in range(1,n_traces+1)])\n",
    "plt.clf()\n",
    "start_cut = 40\n",
    "end_cut = 140\n",
    "baseline = 0\n",
    "t = None\n",
    "j = 0\n",
    "for i in range(1, n_traces+1):\n",
    "    v1 = trace_sample.loc[start_cut:end_cut, ['ROI' + str(i)]] + baseline * j\n",
    "    j += 1\n",
    "    if t is None:\n",
    "        t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "    plt.plot(t, v1, linewidth=3.5, alpha=.2, color='teal')\n",
    "#plt.plot(t, v1, label=\"hVOS GMM\", color='teal')\n",
    "plt.yticks([])\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "\n",
    "trace_sample = '11-30-23/selected_zda/Traces_manual.dat'\n",
    "trace_sample = pd.read_csv(data_dir + trace_sample,\n",
    "                         sep='\\t',\n",
    "                         header=0,\n",
    "                         names=['Index'] + ['ROI' + str(i) for i in range(1,n_traces+1)])\n",
    "\n",
    "baseline = 10\n",
    "t = None\n",
    "j = 0\n",
    "for i in range(1, n_traces+1):\n",
    "    v1 = trace_sample.loc[start_cut:end_cut, ['ROI' + str(i)]] + baseline\n",
    "    j += 1\n",
    "    if t is None:\n",
    "        t = [(i+start_cut) * 0.5 for i in range(len(v1))]\n",
    "    plt.plot(t, v1, linewidth=3.5, alpha=.2, color='purple')\n",
    "#plt.plot(t, v1, label=\"hVOS manual\", color='purple')\n",
    "plt.axvline(x=48, label=\"Stimulation Time\", color='red', linewidth=3.5)\n",
    "rcParams.update({'font.size': 14})\n",
    "#plt.legend(bbox_to_anchor=(0.26, .97), loc='upper center')\n",
    "plt.ylim([-10,30])\n",
    "plt.savefig(data_dir + \"11-27-23/selected_zda/Traces_manualGMM.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f4f443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "0\n",
      "2\n",
      "256 119 47\n",
      "0.00029375 0.00029375\n",
      "\n",
      "\n",
      "\n",
      "\t\t\t Manual \t GMM\n",
      "Num overlap expected: \t 0.87390625 \t 1.8800000000000001\n",
      "Num overlap actual: \t 0 \t\t 2\n",
      "4.760000000000001\n"
     ]
    }
   ],
   "source": [
    "# overlap\n",
    "gmm_roi = '11-30-23/converted_zda/ROIs_GMM95.dat'\n",
    "man_roi = '11-30-23/converted_zda/ROIs_manual.dat'\n",
    "red_roi = '11-30-23/converted_zda/ROIs_03_03_03_red.dat'\n",
    "\n",
    "gmm = ROIFileReader(data_dir + gmm_roi).get_roi_list()\n",
    "man = ROIFileReader(data_dir + man_roi).get_roi_list()\n",
    "red = ROIFileReader(data_dir + red_roi).get_roi_list()\n",
    "ocr1 = OverlapCounterROI(gmm, man)\n",
    "print(ocr1.get_num_rois_overlap())\n",
    "\n",
    "num_gmm, num_man = ocr1.get_num_occupied()\n",
    "\n",
    "ocr1 = OverlapCounterROI(red, man)\n",
    "red_man_overlap = ocr1.get_num_rois_overlap()\n",
    "print(red_man_overlap)\n",
    "\n",
    "ocr1 = OverlapCounterROI(red, gmm)\n",
    "red_gmm_overlap = ocr1.get_num_rois_overlap()\n",
    "print(red_gmm_overlap)\n",
    "num_red, _ = ocr1.get_num_occupied()\n",
    "\n",
    "print(num_gmm, num_man, num_red)\n",
    "\n",
    "percent_gmm_red_overlap_expected = num_gmm * num_red / (6400 ** 2)\n",
    "percent_man_red_overlap_expected = num_man * num_red / (6400 ** 2)\n",
    "\n",
    "print(percent_gmm_red_overlap_expected, percent_gmm_red_overlap_expected)\n",
    "\n",
    "print(\"\\n\\n\\n\\t\\t\\t\", \"Manual\",\"\\t\",  \"GMM\")\n",
    "print(\"Num overlap expected:\", \n",
    "      \"\\t\", percent_man_red_overlap_expected * 6400, \n",
    "      \"\\t\", percent_gmm_red_overlap_expected * 6400)\n",
    "print(\"Num overlap actual:\", \n",
    "      \"\\t\", red_man_overlap, \n",
    "      \"\\t\\t\", red_gmm_overlap)\n",
    "\n",
    "percent_man_red_overlap_expected = num_man * num_gmm / (6400 ** 2)\n",
    "print(percent_man_red_overlap_expected * 6400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "562746c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25d9b1f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db4d904c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f424df4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55ef7e95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10f5cc24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4bc7e5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84586b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# figure out if each barrel crossing is in the medial direction\n",
    "data = {}\n",
    "for stim_loc in ['L2/3', 'L4', 'L5']:\n",
    "    df2 = df[df['Stim Location'].str.contains(stim_loc)]\n",
    "    print(\"\\nBarrel pairs in\", stim_loc)\n",
    "    \n",
    "    df_medial3 = df2[df2[\"Most Medial Barrel\"] == 3]\n",
    "    df_medial1 = df2[df2[\"Most Medial Barrel\"] == 1]\n",
    "    # if most medial barrel is 3, then:\n",
    "    #    - if stim barrel is 3, crossing 2/3 is lateral.\n",
    "    #    - if stim barrel is 2, crossing 2/3 is medial, crossing 1/2 is lateral\n",
    "    #    - if stim barrel is 1, crossing 1/2 is medial\n",
    "    # if most medial barrel is 1, then:\n",
    "    #    - if stim barrel is 1, crossing 1/2 is lateral.\n",
    "    #    - if stim barrel is 2, crossing 1/2 is medial, crossing 2/3 is lateral\n",
    "    #    - if stim barrel is 3, crossing 2/3 is medial\n",
    "    \n",
    "    k_medial_cross = len(df_medial1[(df_medial1['# crossings 2/3'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 3)])\n",
    "    k_lateral_cross = len(df_medial3[(df_medial3['# crossings 2/3'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 3)])\n",
    "    k_medial_cross += len(df_medial3[(df_medial3['# crossings 2/3'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 2)])\n",
    "    k_lateral_cross += len(df_medial3[(df_medial3['# crossings 1/2'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 2)])\n",
    "    k_medial_cross += len(df_medial3[(df_medial3['# crossings 1/2'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 1)])\n",
    "    k_lateral_cross += len(df_medial1[(df_medial1['# crossings 1/2'] > 0)\n",
    "                                   & (df_medial1['Stim Barrel'] == 1)])\n",
    "    k_medial_cross += len(df_medial1[(df_medial1['# crossings 1/2'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 2)])\n",
    "    k_lateral_cross += len(df_medial1[(df_medial1['# crossings 2/3'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 2)])\n",
    "    \n",
    "    n_medial = k_medial_cross\n",
    "    n_lateral = k_lateral_cross\n",
    "    n_medial += len(df_medial1[(df_medial1['# no crossings 2/3'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 3)])\n",
    "    n_lateral += len(df_medial3[(df_medial3['# no crossings 2/3'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 3)])\n",
    "    n_medial += len(df_medial3[(df_medial3['# no crossings 2/3'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 2)])\n",
    "    n_lateral += len(df_medial3[(df_medial3['# no crossings 1/2'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 2)])\n",
    "    n_medial += len(df_medial3[(df_medial3['# no crossings 1/2'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 1)])\n",
    "    n_lateral += len(df_medial1[(df_medial1['# no crossings 1/2'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 1)])\n",
    "    n_medial += len(df_medial1[(df_medial1['# no crossings 1/2'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 2)])\n",
    "    n_lateral += len(df_medial1[(df_medial1['# no crossings 2/3'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 2)])\n",
    "    \n",
    "    print(\"Medial\")\n",
    "    print(\"\\t\", k_medial_cross, \"crossing out of\", n_medial, \"in layer\", stim_loc)\n",
    "\n",
    "    print(\"Lateral\")\n",
    "    print(\"\\t\", k_lateral_cross, \"crossing out of\", n_lateral, \"in layer\", stim_loc)\n",
    "    \n",
    "    data[stim_loc] = {\"Medial\": {}, \"Lateral\":{} }\n",
    "    data[stim_loc][\"Medial\"]['n_barrel_cross'] = k_medial_cross\n",
    "    data[stim_loc][\"Medial\"]['total'] = n_medial\n",
    "    data[stim_loc][\"Lateral\"]['n_barrel_cross'] = k_lateral_cross\n",
    "    data[stim_loc][\"Lateral\"]['total'] = n_lateral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db8a7042",
   "metadata": {},
   "outputs": [],
   "source": [
    "def significance_bar(start,end,height,displaystring,linewidth = 1.2,markersize = 8,boxpad  =0.3,fontsize = 15,color = 'k'):\n",
    "    # draw a line with downticks at the ends\n",
    "    plt.plot([start,end],[height]*2,'-',color = color,lw=linewidth,marker = TICKDOWN,markeredgewidth=linewidth,markersize = markersize)\n",
    "    # draw the text with a bounding box covering up the line\n",
    "    plt.text(0.5*(start+end),height,displaystring,ha = 'center',va='center',bbox=dict(facecolor='1.', edgecolor='none',boxstyle='Square,pad='+str(boxpad)),size = fontsize)\n",
    "\n",
    "n_L23 = {'medial' : data['L2/3'][\"Medial\"]['total'],\n",
    "         'lateral' : data['L2/3'][\"Lateral\"]['total']}\n",
    "n_L4 = {'medial' : data['L4'][\"Medial\"]['total'],\n",
    "         'lateral' : data['L4'][\"Lateral\"]['total']}\n",
    "n_L5 = {'medial' : data['L5'][\"Medial\"]['total'],\n",
    "        'lateral' : data['L5'][\"Lateral\"]['total']}\n",
    "\n",
    "plt.clf()\n",
    "i = 0\n",
    "tick_labels = []\n",
    "colors = {\"# crossing medial\": 'tab:blue', \n",
    "          \"# crossing lateral\": 'tab:red'}\n",
    "# in format # no crossing, # crossing, # no signal; l2/3, L4, L5\n",
    "\n",
    "length_of_previous_sl = i\n",
    "\n",
    "all_layers = ['L2/3', 'L4']\n",
    "totals = {'medial' : [], 'lateral': [] }\n",
    "for med in ['medial', 'lateral']:\n",
    "    if n_L23[med] > 0:\n",
    "        totals[med].append(n_L23[med])\n",
    "    if n_L4[med] > 0:\n",
    "        totals[med].append(n_L4[med])\n",
    "    if n_L5[med] > 0:\n",
    "        totals[med].append(n_L5[med])\n",
    "\n",
    "\n",
    "# in columns L23, L4, L5\n",
    "crossing_data = {\n",
    "    '# crossing medial': [data[x]['Medial']['n_barrel_cross'] for x in all_layers],\n",
    "    '# crossing lateral': [data[x]['Lateral']['n_barrel_cross'] for x in all_layers]\n",
    "}\n",
    "errbars = {}\n",
    "for k in crossing_data:\n",
    "    errbars[k] = [np.sqrt(x) for x in crossing_data[k]]\n",
    "    med = 'lateral'\n",
    "    if 'medial' in k:\n",
    "        med = 'medial'\n",
    "    for j in range(len(errbars[k])):\n",
    "        print(errbars[k][j], totals[med][j])\n",
    "        errbars[k][j] /= totals[med][j] / 100\n",
    "        crossing_data[k][j] /= totals[med][j] / 100 \n",
    "\n",
    "labels = [x for x in crossing_data.keys()]\n",
    "#tick_labels += [all_layers['medial'][j] + \"\\n(n=\"+str(totals['medial'][j]) + \")\"\n",
    "#                            for j in range(len(all_layers))]\n",
    "#tick_labels += [all_layers['lateral'][j] + \"\\n(n=\"+str(totals['lateral'][j]) + \")\"\n",
    "#                            for j in range(len(all_layers))]\n",
    "\n",
    "tick_labels = ['L2/3\\nn=' + str(n_L23['medial'])\n",
    "                   + \"   n=\" + str(n_L23['lateral']),\n",
    "               'L4\\nn=' + str(n_L4['medial'])\n",
    "                   + \"   n=\" + str(n_L4['lateral'])]\n",
    "#,\n",
    "#              'L5\\nn=' + str(n_L5['medial'])\n",
    "#                   + \"   n=\" + str(n_L5['lateral'])]\n",
    "\n",
    "print(errbars)\n",
    "print(crossing_data)\n",
    "\n",
    "# This is a binomial experiment of n independent trials.\n",
    "# We use the Binomial case of goodness of fit test to a \n",
    "# chi-squared distribution\n",
    "pvals = []\n",
    "for med in ['medial', 'lateral']:\n",
    "    for i in range(2):\n",
    "        layer = ['L2/3', 'L4', 'L5'][i]\n",
    "        n = [n_L23[med], n_L4[med], n_L5[med]][i]\n",
    "        cr = crossing_data['# crossing ' + med][i]\n",
    "        k = n * cr / 100 # number of crosses\n",
    "\n",
    "        null_hyp_p = 0.5\n",
    "\n",
    "        pval = binom_test(k, n, null_hyp_p, alternative='two-sided')\n",
    "        print(\"Binomial test pval for dir \" + med + \", layer\", \n",
    "              layer, \":\", pval)\n",
    "        pvals.append(pval)\n",
    "    \n",
    "##########################\n",
    "for nm in crossing_data:\n",
    "    x = np.arange(len(all_layers))\n",
    "    # else:\n",
    "    plt.bar(x + (i-1) * 0.2, \n",
    "        crossing_data[nm],\n",
    "        0.2,\n",
    "        label=(nm if length_of_previous_sl == 0 else None),\n",
    "        color=colors[nm])\n",
    "    plt.errorbar(x + (i-1) * 0.2, \n",
    "                crossing_data[nm],\n",
    "                yerr=errbars[nm],\n",
    "                fmt='none',\n",
    "                color='black')\n",
    "    i += 1\n",
    "\n",
    "#for i in range(3):\n",
    "    #if pvals[i] < 0.001:\n",
    "        #significance_bar(i-.5, i+0.25, 20, \"P<0.001\", fontsize=10)\n",
    "    \n",
    "plt.title(\"Frequency of Signal Pattern in Barrel Pairs by Layer\")\n",
    "plt.xlabel(\"Stimulation Layer\")\n",
    "plt.ylabel(\"% of Barrel Pair Crossings\")\n",
    "#plt.xticks(rotation='vertical')\n",
    "plt.xticks(np.arange(0.12,len(tick_labels)+0.12), tick_labels)\n",
    "plt.legend(bbox_to_anchor=(0.82, 1.0), loc='upper center')\n",
    "plt.savefig(data_dir + \"results_count-medial_oct2023.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5226216",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# latency plots\n",
    "plt.clf()\n",
    "i = 1\n",
    "xtick_labels = []\n",
    "df_datasets = []\n",
    "for stim_loc in ['L2/3', 'L4']:\n",
    "    df2 = df[df['Stim Location'].str.contains(stim_loc)]\n",
    "    df_medial3 = df2[df2[\"Most Medial Barrel\"] == 3]\n",
    "    df_medial1 = df2[df2[\"Most Medial Barrel\"] == 1]\n",
    "    medial_lat = []\n",
    "    lateral_lat = []\n",
    "    medial_lat.append(df_medial1[(df_medial1['abs(delta 2)'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 3)]['abs(delta 2)'])\n",
    "    lateral_lat.append(df_medial3[(df_medial3['abs(delta 2)'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 3)]['abs(delta 2)'])\n",
    "    medial_lat.append(df_medial3[(df_medial3['abs(delta 2)'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 2)]['abs(delta 2)'])\n",
    "    lateral_lat.append(df_medial3[(df_medial3['abs(delta 1)'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 2)]['abs(delta 1)'])\n",
    "    medial_lat.append(df_medial3[(df_medial3['abs(delta 1)'] > 0)\n",
    "                                   & (df_medial3['Stim Barrel'] == 1)]['abs(delta 1)'])\n",
    "    lateral_lat.append(df_medial1[(df_medial1['abs(delta 1)'] > 0)\n",
    "                                   & (df_medial1['Stim Barrel'] == 1)]['abs(delta 1)'])\n",
    "    medial_lat.append(df_medial1[(df_medial1['abs(delta 1)'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 2)]['abs(delta 1)'])\n",
    "    lateral_lat.append(df_medial1[(df_medial1['abs(delta 2)'] > 0) \n",
    "                                   & (df_medial1['Stim Barrel'] == 2)]['abs(delta 2)'])\n",
    "    \n",
    "    medial_lat = pd.concat(medial_lat)\n",
    "    lateral_lat = pd.concat(lateral_lat)\n",
    "    \n",
    "    #dfd1_ant = df2_ant['abs(delta 1)'].dropna()\n",
    "    #dfd2_ant = df2_ant['abs(delta 2)'].dropna()\n",
    "    \n",
    "    #df2_post = df2[df2['Slice Location'] == \"Posterior\"]\n",
    "    #dfd1_post = df2_post['abs(delta 1)'].dropna()\n",
    "    #dfd2_post = df2_post['abs(delta 2)'].dropna()\n",
    "    \n",
    "    #dfds_ant = np.concatenate([dfd1_ant, dfd2_ant])\n",
    "    #dfds_post = np.concatenate([dfd1_post, dfd2_post])\n",
    "    \n",
    "    jittered_xs_1 = np.random.normal(i, 0.04, lateral_lat.shape[0])\n",
    "    jittered_xs_2 = np.random.normal(i+1, 0.04, medial_lat.shape[0])\n",
    "    plt.scatter(jittered_xs_1, lateral_lat, alpha=0.5, color='blue')\n",
    "    plt.scatter(jittered_xs_2, medial_lat, alpha=0.5, color='green')\n",
    "\n",
    "    xtick_labels.append(stim_loc + \"\\nLateral\\n(n=\" + str(len(lateral_lat)) +\")\")\n",
    "    df_datasets.append(lateral_lat)\n",
    "    xtick_labels.append(stim_loc + \"\\nMedial\\n(n=\" + str(len(medial_lat)) +\")\")\n",
    "    df_datasets.append(medial_lat)\n",
    "    i += 2\n",
    "\n",
    "\"\"\"# calculate significance\n",
    "for sl1 in ['ant', 'post']:\n",
    "    for sl2 in ['ant', 'post']:\n",
    "        for la1 in ['L2/3', 'L4', 'L5']:\n",
    "            for la2 in ['L2/3', 'L4', 'L5']:\n",
    "                if sl1 == sl2 and la1 == la2:\n",
    "                    continue\n",
    "                elif len(dists[sl1][la1]) < 3 or len(dists[sl2][la2]) < 3:\n",
    "                    continue\n",
    "                else:\n",
    "                    p_val = kstest(dists[sl1][la1], dists[sl2][la2]).pvalue\n",
    "                    if p_val < 0.05:\n",
    "                        print('KS test for ' + sl1 + \" layer \" + la1 + \" and\", \n",
    "                              sl2 + \" layer \" + la2,\n",
    "                             \"is significant with p-value \", p_val)\n",
    "\"\"\"\n",
    "# significance: L2/3 Rostral and L4 Rostral, and \n",
    "#               L2/3 Rostral and L4 Caudal\n",
    "#significance_bar(0.75, 2.25, 3.35, \"P<0.05\", fontsize=10)\n",
    "\n",
    "plt.boxplot(df_datasets)\n",
    "plt.xticks(range(1, len(xtick_labels)+1), xtick_labels)\n",
    "plt.title(\"Latency of L4 Barrel Pair Crossings\")\n",
    "plt.xlabel(\"Stimulation Layer\")\n",
    "plt.ylabel(\"Latency (ms)\")\n",
    "plt.savefig(data_dir + cell_type_prefix + \"_results_latency_medial-bias_\" + str(datetime.date.today()) +\".png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ff4e0ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Lump layers together\n",
    "# latency plots\n",
    "plt.clf()\n",
    "i = 1\n",
    "xtick_labels = []\n",
    "df_datasets = []\n",
    "df2 = df\n",
    "df_medial3 = df2[df2[\"Most Medial Barrel\"] == 3]\n",
    "df_medial1 = df2[df2[\"Most Medial Barrel\"] == 1]\n",
    "    \n",
    "medial_lat = []\n",
    "lateral_lat = []\n",
    "medial_lat.append(df_medial1[(df_medial1['abs(delta 2)'] > 0) \n",
    "                               & (df_medial1['Stim Barrel'] == 3)]['abs(delta 2)'])\n",
    "lateral_lat.append(df_medial3[(df_medial3['abs(delta 2)'] > 0)\n",
    "                               & (df_medial3['Stim Barrel'] == 3)]['abs(delta 2)'])\n",
    "medial_lat.append(df_medial3[(df_medial3['abs(delta 2)'] > 0)\n",
    "                               & (df_medial3['Stim Barrel'] == 2)]['abs(delta 2)'])\n",
    "lateral_lat.append(df_medial3[(df_medial3['abs(delta 1)'] > 0)\n",
    "                               & (df_medial3['Stim Barrel'] == 2)]['abs(delta 1)'])\n",
    "medial_lat.append(df_medial3[(df_medial3['abs(delta 1)'] > 0)\n",
    "                               & (df_medial3['Stim Barrel'] == 1)]['abs(delta 1)'])\n",
    "lateral_lat.append(df_medial1[(df_medial1['abs(delta 1)'] > 0)\n",
    "                               & (df_medial1['Stim Barrel'] == 1)]['abs(delta 1)'])\n",
    "medial_lat.append(df_medial1[(df_medial1['abs(delta 1)'] > 0) \n",
    "                               & (df_medial1['Stim Barrel'] == 2)]['abs(delta 1)'])\n",
    "lateral_lat.append(df_medial1[(df_medial1['abs(delta 2)'] > 0) \n",
    "                               & (df_medial1['Stim Barrel'] == 2)]['abs(delta 2)'])\n",
    "\n",
    "medial_lat = pd.concat(medial_lat)\n",
    "lateral_lat = pd.concat(lateral_lat)\n",
    "\n",
    "#dfd1_ant = df2_ant['abs(delta 1)'].dropna()\n",
    "#dfd2_ant = df2_ant['abs(delta 2)'].dropna()\n",
    "\n",
    "#df2_post = df2[df2['Slice Location'] == \"Posterior\"]\n",
    "#dfd1_post = df2_post['abs(delta 1)'].dropna()\n",
    "#dfd2_post = df2_post['abs(delta 2)'].dropna()\n",
    "\n",
    "#dfds_ant = np.concatenate([dfd1_ant, dfd2_ant])\n",
    "#dfds_post = np.concatenate([dfd1_post, dfd2_post])\n",
    "\n",
    "jittered_xs_1 = np.random.normal(i, 0.04, lateral_lat.shape[0])\n",
    "jittered_xs_2 = np.random.normal(i+1, 0.04, medial_lat.shape[0])\n",
    "plt.scatter(jittered_xs_1, lateral_lat, alpha=0.5, color='blue')\n",
    "plt.scatter(jittered_xs_2, medial_lat, alpha=0.5, color='green')\n",
    "\n",
    "xtick_labels.append(\"Lateral\\n(n=\" + str(len(lateral_lat)) +\")\")\n",
    "df_datasets.append(lateral_lat)\n",
    "xtick_labels.append(\"Medial\\n(n=\" + str(len(medial_lat)) +\")\")\n",
    "df_datasets.append(medial_lat)\n",
    "\n",
    "p_val = kstest(medial_lat, lateral_lat).pvalue\n",
    "print(\"KS test p-value \", p_val)\n",
    "\n",
    "# significance: L2/3 Rostral and L4 Rostral, and \n",
    "#               L2/3 Rostral and L4 Caudal\n",
    "#significance_bar(0.75, 2.25, 3.35, \"P<0.05\", fontsize=10)\n",
    "\n",
    "plt.boxplot(df_datasets)\n",
    "plt.xticks(range(1, len(xtick_labels)+1), xtick_labels)\n",
    "plt.title(\"Latency of L4 Barrel Pair Crossings\")\n",
    "plt.xlabel(\"Stimulation Layer\")\n",
    "plt.ylabel(\"Crossing Latency Difference (ms)\")\n",
    "plt.savefig(data_dir + cell_type_prefix + \"_results_latency_medial-bias_\" + str(datetime.date.today()) +\".png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a525873",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Now do ALL latencies and see if there are any multimodal distributions\n",
    "plt.clf()\n",
    "i = 1\n",
    "xtick_labels = []\n",
    "df_datasets = []\n",
    "df = pd.read_csv(data_dir + csv_prefix)\n",
    "df = pd.DataFrame(df)\n",
    "df = df[df[\"Exclude Reason\"].isnull()]\n",
    "for stim_loc in ['L2/3', 'L4']:\n",
    "    df2 = df[df['Stim Location'].str.contains(stim_loc)]\n",
    "    \n",
    "    df_d1 = df2[df2['abs(delta 1)'] > 0]['abs(delta 1)']\n",
    "    df_d2 = df2[df2['abs(delta 2)'] > 0]['abs(delta 2)']\n",
    "    df_lat = pd.concat([\n",
    "        df_d1, df_d2\n",
    "    ])\n",
    "    jittered_xs_1 = np.random.normal(i, 0.04, df_lat.shape[0])\n",
    "    plt.scatter(jittered_xs_1, df_lat, alpha=0.5, color={\"L2/3\" : 'blue', \"L4\": \"green\"}[stim_loc])\n",
    "\n",
    "    xtick_labels.append(stim_loc + \"\\n(n=\" + str(len(df_lat)) +\")\")\n",
    "    df_datasets.append(df_lat)\n",
    "    i += 1\n",
    "    print(np.mean(df_lat), \"+/-\", np.std(df_lat))\n",
    "\n",
    "p_val = kstest(df_datasets[0], df_datasets[1]).pvalue\n",
    "print(\"KS test p-value \", p_val)\n",
    "# significance: L2/3 Rostral and L4 Rostral, and \n",
    "#               L2/3 Rostral and L4 Caudal\n",
    "#significance_bar(0.75, 2.25, 3.35, \"P<0.05\", fontsize=10)\n",
    "plt.boxplot(df_datasets)\n",
    "plt.xticks(range(1, len(xtick_labels)+1), xtick_labels)\n",
    "plt.title(\"Latency of L4 Barrel Pair Crossings\")\n",
    "plt.xlabel(\"Stimulation Layer\")\n",
    "plt.ylabel(\"Latency (ms)\")\n",
    "plt.savefig(data_dir + cell_type_prefix + \"_results_latency_medial-bias_\" + str(datetime.date.today()) +\".png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5f8e9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Actual latency of neighboring barrel activation\n",
    "# To exclude direct stim, from each row, exclude the lowest latency if it is less than 1ms\n",
    "# separate by within barrel latency and crossing latency\n",
    "# latency plots\n",
    "plt.clf()\n",
    "i = 1\n",
    "xtick_labels = []\n",
    "df_datasets = []\n",
    "df2 = df\n",
    "\n",
    "act_lat_23_home = []\n",
    "act_lat_4_home = []\n",
    "act_lat_23_neigh = []\n",
    "act_lat_4_neigh = []\n",
    "df_23 = df[df['Stim Location'].str.contains(\"2/3\")]\n",
    "df_4 = df[df['Stim Location'].str.contains(\"4\")]\n",
    "\n",
    "# which is home barrel?\n",
    "for home_barrel in [1,2,3]:\n",
    "    df_home_23 = df_23[df_23['Stim Barrel'] == home_barrel]\n",
    "    df_home_4 = df_4[df_4['Stim Barrel'] == home_barrel]\n",
    "    home_snr_label = 'Actual latency barrel ' + str(home_barrel)\n",
    "    neigh_barrels = [1,2,3]\n",
    "    neigh_barrels.remove(home_barrel)\n",
    "    neigh_snr_labels = ['Actual latency barrel ' + str(neigh_b)\n",
    "                         for neigh_b in neigh_barrels]\n",
    "    \n",
    "    act_lat_23_home.append(df_home_23[(df_home_23[home_snr_label] >= 1)][home_snr_label])\n",
    "    for nsl in neigh_snr_labels:\n",
    "        act_lat_23_neigh.append(df_home_23[(df_home_23[nsl] >= 1)][nsl])\n",
    "        \n",
    "    act_lat_4_home.append(df_home_4[(df_home_4[home_snr_label] >= 1)][home_snr_label])\n",
    "    for nsl in neigh_snr_labels:\n",
    "        act_lat_4_neigh.append(df_home_4[(df_home_4[nsl] >= 1)][nsl])\n",
    "           \n",
    "act_lat_23_home = pd.concat(act_lat_23_home)\n",
    "act_lat_4_home = pd.concat(act_lat_4_home)\n",
    "act_lat_23_neigh = pd.concat(act_lat_23_neigh)\n",
    "act_lat_4_neigh = pd.concat(act_lat_4_neigh)\n",
    "\n",
    "jittered_xs_1 = np.random.normal(i, 0.04, act_lat_4_home.shape[0])\n",
    "jittered_xs_2 = np.random.normal(i+1, 0.04, act_lat_23_home.shape[0])\n",
    "jittered_xs_3 = np.random.normal(i+2, 0.04, act_lat_4_neigh.shape[0])\n",
    "jittered_xs_4 = np.random.normal(i+3, 0.04, act_lat_23_neigh.shape[0])\n",
    "plt.scatter(jittered_xs_1, act_lat_4_home, alpha=0.5, color='blue', label=\"L2/3\")\n",
    "plt.scatter(jittered_xs_2, act_lat_23_home, alpha=0.5, color='green', label=\"L4\")\n",
    "plt.scatter(jittered_xs_3, act_lat_4_neigh, alpha=0.5, color='blue')\n",
    "plt.scatter(jittered_xs_4, act_lat_23_neigh, alpha=0.5, color='green')\n",
    "\n",
    "xtick_labels.append(\"Stimulated\\nBarrels\\n(n=\" + str(len(act_lat_4_home)) +\")\")\n",
    "df_datasets.append(act_lat_4_home)\n",
    "xtick_labels.append(\"Stimulated\\nBarrels\\n(n=\" + str(len(act_lat_23_home)) +\")\")\n",
    "df_datasets.append(act_lat_23_home)\n",
    "xtick_labels.append(\"Neighboring\\nBarrels\\n(n=\" + str(len(act_lat_4_neigh)) +\")\")\n",
    "df_datasets.append(act_lat_4_neigh)\n",
    "xtick_labels.append(\"Neighboring\\nBarrels\\n(n=\" + str(len(act_lat_23_neigh)) +\")\")\n",
    "df_datasets.append(act_lat_23_neigh)\n",
    "\n",
    "#p_val = kstest(act_lat_23, act_lat_4).pvalue\n",
    "#print(\"KS test p-value \", p_val)\n",
    "\n",
    "plt.boxplot(df_datasets)\n",
    "plt.xticks(range(1, len(xtick_labels)+1), xtick_labels)\n",
    "plt.title(\"Actual Latency of Activation\")\n",
    "plt.xlabel(\"Stimulation Layer\")\n",
    "plt.ylabel(\"Latency (ms)\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"L4 home\\t\", np.mean(act_lat_23_home), \"+/-\", sem(act_lat_23_home), \"n =\", len(act_lat_23_home))\n",
    "print(\"L2/3 home\\t\", np.mean(act_lat_4_home), \"+/-\", sem(act_lat_4_home), \"n =\", len(act_lat_4_home))\n",
    "print(\"L4 neigh\\t\", np.mean(act_lat_23_neigh), \"+/-\", sem(act_lat_23_neigh), \"n =\", len(act_lat_23_neigh))\n",
    "print(\"L2/3 neigh\\t\", np.mean(act_lat_4_neigh), \"+/-\", sem(act_lat_4_neigh), \"n =\", len(act_lat_4_neigh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1c2476e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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