scripts/burst_detection_figure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\allensdk\\brain_observatory\\comparison_utils.py:9: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib\n",
    "import seaborn\n",
    "import pickle as pkl\n",
    "import src.SessionNavigator as SessionNavigator\n",
    "import src.SessionProcessor as SessionProcessor\n",
    "import path_entry\n",
    "from sklearn.svm import LinearSVC\n",
    "from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path_entry.get_path() controls the file paths needed for this tutorial. \n",
    "# The paths returned by get_path() will need to be adjusted, as they are\n",
    "# almost certainly unique to your computer\n",
    "data_root, manifest_path, save_path = path_entry.get_path()\n",
    "bursts_path = f\"{save_path}/\"\n",
    "SAVE = False\n",
    "\n",
    "# These are the same stim and session_id in the Burst Detection script\n",
    "stim = 'drifting_gratings_contrast'\n",
    "session_id = 778240327"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a SessionNavigator, which will open and navigate the data in manifest_path\n",
    "navigator = SessionNavigator.SessionNavigator(manifest_path)\n",
    "\n",
    "# Load the session, bursts, and single spikes\n",
    "with open(f\"{save_path}/{stim}__whole_burst_trains__session_{session_id}.pkl\", 'rb') as f:\n",
    "    bursts = pkl.load(f)\n",
    "with open(f\"{save_path}/{stim}__whole_single_trains__session_{session_id}.pkl\", 'rb') as f:\n",
    "    singles = pkl.load(f)\n",
    "\n",
    "session = navigator.load_session(session_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18460\\2534475794.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# (This cell can take a minute or two to load the session)\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[0mprocessor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSessionProcessor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSessionProcessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdecoder_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconstruct_decoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"orientation\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mburst_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbursts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msingle_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msingles\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      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdecoder_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\demogorgon\\documents\\college\\marcus\\boston university phd\\ocker lab\\correlations_and_bursts\\src\\SessionProcessor.py\u001b[0m in \u001b[0;36mconstruct_decoder\u001b[1;34m(self, stimulus_type, stimulus_characteristic, name, bin_start, bin_stop, bin_width, classifier, burst_dict, single_dict, shuffle_trials)\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m         \u001b[1;31m# Get the stim info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m         \u001b[0mstim_table\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_stimulus_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstimulus_type\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    214\u001b[0m         \u001b[0mstim_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstim_table\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\allensdk\\brain_observatory\\ecephys\\ecephys_session.py\u001b[0m in \u001b[0;36mget_stimulus_table\u001b[1;34m(self, stimulus_names, include_detailed_parameters, include_unused_parameters)\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m         \u001b[0mstimulus_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoerce_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstimulus_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mf'expected stimulus_names to be a collection (list-like), but found {type(stimulus_names)}: {stimulus_names}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m         \u001b[0mpresentations\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stimulus_presentations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stimulus_presentations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'stimulus_name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstimulus_names\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[0m\u001b[0;32m    437\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0minclude_detailed_parameters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\allensdk\\core\\lazy_property\\lazy_property_mixin.py\u001b[0m in \u001b[0;36m__getattribute__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[0mcurr_attr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLazyPropertyMixin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurr_attr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlazy_class\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;32m---> 15\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mcurr_attr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurr_attr\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     16\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLazyPropertyMixin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\allensdk\\core\\lazy_property\\lazy_property.py\u001b[0m in \u001b[0;36m__get__\u001b[1;34m(self, obj, objtype)\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate\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[0m\u001b[0;32m     21\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\allensdk\\core\\lazy_property\\lazy_property.py\u001b[0m in \u001b[0;36mcalculate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcalculate\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;32m---> 30\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\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     31\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mwrapper\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrappers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\allensdk\\brain_observatory\\ecephys\\ecephys_session_api\\ecephys_nwb_session_api.py\u001b[0m in \u001b[0;36mget_stimulus_presentations\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_stimulus_presentations\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;32m---> 87\u001b[1;33m         \u001b[0mtable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mEcephysNwbSessionApi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_stimulus_presentations\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[0m\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m\"color\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\allensdk\\brain_observatory\\nwb\\nwb_api.py\u001b[0m in \u001b[0;36mget_stimulus_presentations\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     79\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcolumns_to_ignore\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m                         \u001b[0mpresentations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\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     82\u001b[0m                 \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpresentations\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'N/A'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m''\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     83\u001b[0m                 \u001b[0mpresentation_dfs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\h5py\\_hl\\dataset.py\u001b[0m in \u001b[0;36m__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    472\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Can't iterate over a scalar dataset\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    473\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\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[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\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    475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    476\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mwith_phil\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\h5py\\_hl\\dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    536\u001b[0m         \u001b[1;31m# === Scalar dataspaces =================\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 538\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\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[0m\u001b[0;32m    539\u001b[0m             \u001b[0mfspace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_space\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    540\u001b[0m             \u001b[0mselection\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfspace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\h5py\\_hl\\dataset.py\u001b[0m in \u001b[0;36mshape\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    284\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mshape\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[0;32m    285\u001b[0m         \u001b[1;34m\"\"\"Numpy-style shape tuple giving dataset dimensions\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    287\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mwith_phil\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\h5py\\_hl\\base.py\u001b[0m in \u001b[0;36mid\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    243\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mid\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[0;32m    244\u001b[0m         \u001b[1;34m\"\"\" Low-level identifier appropriate for this object \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 245\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Open a processor, then add the bursts and single spikes\n",
    "# (This cell can take a minute or two to load the session)\n",
    "processor = SessionProcessor.SessionProcessor(session)\n",
    "decoder_name = processor.construct_decoder(stim, \"orientation\", burst_dict=bursts, single_dict=singles)\n",
    "print(decoder_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the processor\n",
    "# (This cell takes around a minute)\n",
    "processor.construct_psth(decoder_name)\n",
    "processor.calculate_decoder_weights(decoder_name)\n",
    "processor.calculate_correlations(decoder_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['decoder', 'psths', 'class_psths', 'cell_correlation_matrices', 'within_class_correlations'])\n"
     ]
    }
   ],
   "source": [
    "# Organize all the data from the processor\n",
    "results = processor.results()[decoder_name]\n",
    "print(results.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unpack the results\n",
    "decoder = results[\"decoder\"]\n",
    "psths = results[\"psths\"]\n",
    "#class_psths = results[\"class_psths\"] # The psths organized by stimulus condition\n",
    "#cell_correlation_matrices = results[\"cell_correlation_matrices\"]\n",
    "#within_class_correlations = results[\"within_class_correlations\"]\n",
    "\n",
    "# Get plot-relevant data\n",
    "bins = decoder.bin_edges\n",
    "all_unit_ids = processor.all_units\n",
    "stim_presentation_ids = decoder.stim_table.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Demogorgon\\anaconda3\\envs\\correlations_and_bursts_env\\lib\\site-packages\\xarray\\core\\formatting.py:142: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif isinstance(x, (float, np.float)):\n"
     ]
    },
    {
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spike_counts'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>stimulus_presentation_id</span>: 540</li><li><span class='xr-has-index'>time_relative_to_stimulus_onset</span>: 11</li><li><span class='xr-has-index'>unit_id</span>: 784</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d8ce980d-e283-4553-9f5f-b45fdbe78975' class='xr-array-in' type='checkbox' ><label for='section-d8ce980d-e283-4553-9f5f-b45fdbe78975' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 1 0 1 0 0 1 0 2 0 0 0 ... 0 0 0 2 1 1 3 0 0 0 0 0 0 1 2 1</span></div><pre class='xr-array-data'>array([[[ 0,  0,  0, ...,  2,  0,  0],\n",
       "        [ 0,  0,  0, ...,  0,  4,  1],\n",
       "        [ 0,  0,  0, ...,  0,  3,  1],\n",
       "        ...,\n",
       "        [ 0,  0,  0, ...,  1,  1,  0],\n",
       "        [ 0,  0,  0, ...,  3,  4,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0]],\n",
       "\n",
       "       [[ 2,  0,  0, ...,  1,  2,  0],\n",
       "        [ 0,  0,  0, ...,  0,  1,  0],\n",
       "        [ 0,  0,  0, ...,  2, 13,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  0, ...,  3,  4,  0],\n",
       "        [ 1,  1,  1, ...,  0,  0,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0]],\n",
       "\n",
       "       [[ 0,  0,  0, ...,  0,  0,  0],\n",
       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
       "        [ 4,  0,  0, ...,  2,  3,  0],\n",
       "        ...,\n",
       "        [ 0,  1,  1, ...,  0,  1,  0],\n",
       "        [ 0,  0,  2, ...,  1,  1,  0],\n",
       "        [ 2,  0,  0, ...,  0,  2,  0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0,  0,  0, ...,  2,  0,  0],\n",
       "        [ 0,  0,  0, ...,  0,  1,  0],\n",
       "        [ 0,  0,  0, ...,  1,  1,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  0, ...,  2,  1,  0],\n",
       "        [ 0,  0,  2, ...,  2,  2,  0],\n",
       "        [ 0,  0,  0, ...,  1,  1,  0]],\n",
       "\n",
       "       [[ 0,  0,  0, ...,  1,  0,  0],\n",
       "        [ 0,  0,  1, ...,  1,  1,  0],\n",
       "        [ 0,  0,  1, ...,  1,  0,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  2, ...,  1,  0,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0],\n",
       "        [ 0,  0,  1, ...,  1,  1,  0]],\n",
       "\n",
       "       [[ 0,  0,  0, ...,  1,  1,  0],\n",
       "        [ 0,  0,  0, ...,  3,  1,  0],\n",
       "        [ 0,  0,  0, ...,  3,  3,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  2, ...,  2,  3,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0],\n",
       "        [ 0,  0,  2, ...,  1,  2,  1]]], dtype=uint16)</pre></div></li><li class='xr-section-item'><input id='section-c989d532-eb82-4a64-83fa-9ba3c6da5384' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c989d532-eb82-4a64-83fa-9ba3c6da5384' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>stimulus_presentation_id</span></div><div class='xr-var-dims'>(stimulus_presentation_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3798 3799 3800 ... 4335 4336 4337</div><input id='attrs-01c782d0-d6ca-4b7d-a60e-4ce8d9f5e7dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01c782d0-d6ca-4b7d-a60e-4ce8d9f5e7dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee24da55-277b-4dc9-9514-bfca0617a8af' class='xr-var-data-in' type='checkbox'><label for='data-ee24da55-277b-4dc9-9514-bfca0617a8af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([3798, 3799, 3800, ..., 4335, 4336, 4337], dtype=int64)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_relative_to_stimulus_onset</span></div><div class='xr-var-dims'>(time_relative_to_stimulus_onset)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.025 0.075 0.125 ... 0.475 0.525</div><input id='attrs-3c69ad1f-bee3-42d7-8953-87d69cae606e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c69ad1f-bee3-42d7-8953-87d69cae606e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d4c34a8-a953-430c-bbb4-0ec90c2beb27' class='xr-var-data-in' type='checkbox'><label for='data-2d4c34a8-a953-430c-bbb4-0ec90c2beb27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([0.025, 0.075, 0.125, 0.175, 0.225, 0.275, 0.325, 0.375, 0.425, 0.475,\n",
       "       0.525])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>unit_id</span></div><div class='xr-var-dims'>(unit_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>951015763 951015731 ... 951037645</div><input id='attrs-91a9c945-334c-480d-89ba-9c3ab72f9e3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91a9c945-334c-480d-89ba-9c3ab72f9e3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f7b47b8-1009-40ba-847b-69adaae84797' class='xr-var-data-in' type='checkbox'><label for='data-1f7b47b8-1009-40ba-847b-69adaae84797' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([951015763, 951015731, 951015717, ..., 951037412, 951037577, 951037645],\n",
       "      dtype=int64)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-e4a3bc58-663a-465c-816c-1d60e73e479a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4a3bc58-663a-465c-816c-1d60e73e479a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'spike_counts' (stimulus_presentation_id: 540, time_relative_to_stimulus_onset: 11, unit_id: 784)>\n",
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       "        [ 0,  0,  0, ...,  0,  4,  1],\n",
       "        [ 0,  0,  0, ...,  0,  3,  1],\n",
       "        ...,\n",
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       "        [ 0,  0,  0, ...,  3,  4,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0]],\n",
       "\n",
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       "        [ 0,  0,  0, ...,  0,  1,  0],\n",
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       "        ...,\n",
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       "        [ 1,  1,  1, ...,  0,  0,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0]],\n",
       "\n",
       "       [[ 0,  0,  0, ...,  0,  0,  0],\n",
       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
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       "        [ 0,  0,  0, ...,  0,  1,  0],\n",
       "        [ 0,  0,  0, ...,  1,  1,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  0, ...,  2,  1,  0],\n",
       "        [ 0,  0,  2, ...,  2,  2,  0],\n",
       "        [ 0,  0,  0, ...,  1,  1,  0]],\n",
       "\n",
       "       [[ 0,  0,  0, ...,  1,  0,  0],\n",
       "        [ 0,  0,  1, ...,  1,  1,  0],\n",
       "        [ 0,  0,  1, ...,  1,  0,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  2, ...,  1,  0,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0],\n",
       "        [ 0,  0,  1, ...,  1,  1,  0]],\n",
       "\n",
       "       [[ 0,  0,  0, ...,  1,  1,  0],\n",
       "        [ 0,  0,  0, ...,  3,  1,  0],\n",
       "        [ 0,  0,  0, ...,  3,  3,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  2, ...,  2,  3,  0],\n",
       "        [ 0,  0,  0, ...,  1,  0,  0],\n",
       "        [ 0,  0,  2, ...,  1,  2,  1]]], dtype=uint16)\n",
       "Coordinates:\n",
       "  * stimulus_presentation_id         (stimulus_presentation_id) int64 3798 ... 4337\n",
       "  * time_relative_to_stimulus_onset  (time_relative_to_stimulus_onset) float64 0.025 ... 0.525\n",
       "  * unit_id                          (unit_id) int64 951015763 ... 951037645"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psths[\"whole\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'single_spike_counts'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>stimulus_presentation_id</span>: 540</li><li><span class='xr-has-index'>time_relative_to_stimulus_onset</span>: 11</li><li><span class='xr-has-index'>unit_id</span>: 784</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-adf9dcf6-33ff-4e61-99d3-c458d3876c86' class='xr-array-in' type='checkbox' ><label for='section-adf9dcf6-33ff-4e61-99d3-c458d3876c86' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><pre class='xr-array-data'>array([[[0, 0, 0, ..., 2, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 1],\n",
       "        [0, 0, 0, ..., 0, 0, 1],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [1, 1, 1, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 1, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 1, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 1, 0, 0],\n",
       "        [2, 0, 0, ..., 2, 0, 0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 2, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 1, ..., 0, 0, 0],\n",
       "        [0, 0, 1, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [1, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]]])</pre></div></li><li class='xr-section-item'><input id='section-043029ee-ec02-47cd-b383-8ba27fa05981' class='xr-section-summary-in' type='checkbox'  checked><label for='section-043029ee-ec02-47cd-b383-8ba27fa05981' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>stimulus_presentation_id</span></div><div class='xr-var-dims'>(stimulus_presentation_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3798 3799 3800 ... 4335 4336 4337</div><input id='attrs-9320d9f9-dc63-4724-8c3b-a5dc7849e5f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9320d9f9-dc63-4724-8c3b-a5dc7849e5f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e53fa94d-2fb2-4b5d-be6a-aef7e279026b' class='xr-var-data-in' type='checkbox'><label for='data-e53fa94d-2fb2-4b5d-be6a-aef7e279026b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([3798, 3799, 3800, ..., 4335, 4336, 4337], dtype=int64)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_relative_to_stimulus_onset</span></div><div class='xr-var-dims'>(time_relative_to_stimulus_onset)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.025 0.075 0.125 ... 0.475 0.525</div><input id='attrs-5c41ca8c-7e9f-4677-b072-2fc40e664747' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c41ca8c-7e9f-4677-b072-2fc40e664747' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44091a2a-b350-47d2-912d-7a8db9fa7767' class='xr-var-data-in' type='checkbox'><label for='data-44091a2a-b350-47d2-912d-7a8db9fa7767' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([0.025, 0.075, 0.125, 0.175, 0.225, 0.275, 0.325, 0.375, 0.425, 0.475,\n",
       "       0.525])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>unit_id</span></div><div class='xr-var-dims'>(unit_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>951015763 951015731 ... 951037645</div><input id='attrs-cca4c880-963a-4bc4-8755-2075aaf4cccd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cca4c880-963a-4bc4-8755-2075aaf4cccd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-227e90f8-60c3-4286-b9ab-b86039bef832' class='xr-var-data-in' type='checkbox'><label for='data-227e90f8-60c3-4286-b9ab-b86039bef832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([951015763, 951015731, 951015717, ..., 951037412, 951037577, 951037645],\n",
       "      dtype=int64)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-b5d4de3b-1969-4e24-a8a6-61ec3de575b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5d4de3b-1969-4e24-a8a6-61ec3de575b2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'single_spike_counts' (stimulus_presentation_id: 540, time_relative_to_stimulus_onset: 11, unit_id: 784)>\n",
       "array([[[0, 0, 0, ..., 2, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 1],\n",
       "        [0, 0, 0, ..., 0, 0, 1],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [1, 1, 1, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 1, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 1, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 1, 0, 0],\n",
       "        [2, 0, 0, ..., 2, 0, 0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 2, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 1, ..., 0, 0, 0],\n",
       "        [0, 0, 1, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [1, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]]])\n",
       "Coordinates:\n",
       "  * stimulus_presentation_id         (stimulus_presentation_id) int64 3798 ... 4337\n",
       "  * time_relative_to_stimulus_onset  (time_relative_to_stimulus_onset) float64 0.025 ... 0.525\n",
       "  * unit_id                          (unit_id) int64 951015763 ... 951037645"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psths[\"singles\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>stimulus_presentation_id</th>\n",
       "      <th>unit_id</th>\n",
       "      <th>time_since_stimulus_presentation_onset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spike_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1838.767340</th>\n",
       "      <td>4000</td>\n",
       "      <td>951031834</td>\n",
       "      <td>0.000346</td>\n",
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       "    <tr>\n",
       "      <th>1838.767366</th>\n",
       "      <td>4000</td>\n",
       "      <td>951028439</td>\n",
       "      <td>0.000373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838.767540</th>\n",
       "      <td>4000</td>\n",
       "      <td>951029392</td>\n",
       "      <td>0.000546</td>\n",
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       "    <tr>\n",
       "      <th>1838.767541</th>\n",
       "      <td>4000</td>\n",
       "      <td>951037577</td>\n",
       "      <td>0.000548</td>\n",
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       "    <tr>\n",
       "      <th>1838.767587</th>\n",
       "      <td>4000</td>\n",
       "      <td>951018637</td>\n",
       "      <td>0.000594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2039.433408</th>\n",
       "      <td>4200</td>\n",
       "      <td>951025353</td>\n",
       "      <td>0.499145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039.433527</th>\n",
       "      <td>4200</td>\n",
       "      <td>951020244</td>\n",
       "      <td>0.499264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039.433627</th>\n",
       "      <td>4200</td>\n",
       "      <td>951019474</td>\n",
       "      <td>0.499364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039.433860</th>\n",
       "      <td>4200</td>\n",
       "      <td>951021543</td>\n",
       "      <td>0.499597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039.434474</th>\n",
       "      <td>4200</td>\n",
       "      <td>951026446</td>\n",
       "      <td>0.500211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3820 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             stimulus_presentation_id    unit_id  \\\n",
       "spike_time                                         \n",
       "1838.767340                      4000  951031834   \n",
       "1838.767366                      4000  951028439   \n",
       "1838.767540                      4000  951029392   \n",
       "1838.767541                      4000  951037577   \n",
       "1838.767587                      4000  951018637   \n",
       "...                               ...        ...   \n",
       "2039.433408                      4200  951025353   \n",
       "2039.433527                      4200  951020244   \n",
       "2039.433627                      4200  951019474   \n",
       "2039.433860                      4200  951021543   \n",
       "2039.434474                      4200  951026446   \n",
       "\n",
       "             time_since_stimulus_presentation_onset  \n",
       "spike_time                                           \n",
       "1838.767340                                0.000346  \n",
       "1838.767366                                0.000373  \n",
       "1838.767540                                0.000546  \n",
       "1838.767541                                0.000548  \n",
       "1838.767587                                0.000594  \n",
       "...                                             ...  \n",
       "2039.433408                                0.499145  \n",
       "2039.433527                                0.499264  \n",
       "2039.433627                                0.499364  \n",
       "2039.433860                                0.499597  \n",
       "2039.434474                                0.500211  \n",
       "\n",
       "[3820 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.presentationwise_spike_times(stimulus_presentation_ids=[4000, 4200], unit_ids=all_unit_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>absolute_spike_time</th>\n",
       "      <th>relative_spike_time</th>\n",
       "      <th>stimulus_presentation_id</th>\n",
       "      <th>unit_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1838.767340</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>4000</td>\n",
       "      <td>951031834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1838.767366</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>4000</td>\n",
       "      <td>951028439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1838.767587</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>4000</td>\n",
       "      <td>951018637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1838.767720</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>4000</td>\n",
       "      <td>951019758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1838.767873</td>\n",
       "      <td>0.000880</td>\n",
       "      <td>4000</td>\n",
       "      <td>951031181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2039.930831</td>\n",
       "      <td>0.996568</td>\n",
       "      <td>4200</td>\n",
       "      <td>951030028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2039.931641</td>\n",
       "      <td>0.997378</td>\n",
       "      <td>4200</td>\n",
       "      <td>951025228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2039.934298</td>\n",
       "      <td>1.000034</td>\n",
       "      <td>4200</td>\n",
       "      <td>951030570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2039.934886</td>\n",
       "      <td>1.000623</td>\n",
       "      <td>4200</td>\n",
       "      <td>951016120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2039.934964</td>\n",
       "      <td>1.000701</td>\n",
       "      <td>4200</td>\n",
       "      <td>951030628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2021 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      absolute_spike_time  relative_spike_time  stimulus_presentation_id  \\\n",
       "0             1838.767340             0.000346                      4000   \n",
       "1             1838.767366             0.000373                      4000   \n",
       "2             1838.767587             0.000594                      4000   \n",
       "3             1838.767720             0.000727                      4000   \n",
       "4             1838.767873             0.000880                      4000   \n",
       "...                   ...                  ...                       ...   \n",
       "2016          2039.930831             0.996568                      4200   \n",
       "2017          2039.931641             0.997378                      4200   \n",
       "2018          2039.934298             1.000034                      4200   \n",
       "2019          2039.934886             1.000623                      4200   \n",
       "2020          2039.934964             1.000701                      4200   \n",
       "\n",
       "        unit_id  \n",
       "0     951031834  \n",
       "1     951028439  \n",
       "2     951018637  \n",
       "3     951019758  \n",
       "4     951031181  \n",
       "...         ...  \n",
       "2016  951030028  \n",
       "2017  951025228  \n",
       "2018  951030570  \n",
       "2019  951016120  \n",
       "2020  951030628  \n",
       "\n",
       "[2021 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.presentationwise_non_burst_times(decoder_name, stimulus_presentation_ids=[4000, 4200], unit_ids=all_unit_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Highlight bursts and non-bursts in a spike train\n",
    "# Assign the plot attributes\n",
    "MS_UPPER_THRESHOLD = 1800\n",
    "MS_LOWER_THRESHOLD = 1700\n",
    "TITLE_FONT_SIZE = 20\n",
    "XY_LABEL_SIZE = 15\n",
    "X_TICK_SIZE = 10\n",
    "Y_TICK_SIZE = 10\n",
    "LEGEND_SIZE = 10\n",
    "SUPTITLE_SIZE = 25\n",
    "SUPTITLE_WEIGHT = \"bold\"\n",
    "LINEWIDTH = 3\n",
    "#                                         x   y\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "matplotlib.rcParams[\"figure.titlesize\"] = SUPTITLE_SIZE\n",
    "matplotlib.rcParams[\"figure.titleweight\"] = SUPTITLE_WEIGHT\n",
    "plt.rc('axes', titlesize=TITLE_FONT_SIZE)\n",
    "plt.rc('axes', labelsize=XY_LABEL_SIZE)\n",
    "plt.rc('xtick', labelsize=X_TICK_SIZE)\n",
    "plt.rc('ytick', labelsize=Y_TICK_SIZE)\n",
    "plt.rc('legend', fontsize=LEGEND_SIZE)\n",
    "\n",
    "\n",
    "# First, we need the whole and separated spike trains\n",
    "spike_times = session.presentationwise_spike_times(stimulus_presentation_ids=stim_presentation_ids, unit_ids=all_unit_ids)\n",
    "burst_times = processor.presentationwise_burst_times(decoder_name, stimulus_presentation_ids=stim_presentation_ids, unit_ids=all_unit_ids)\n",
    "single_times= processor.presentationwise_non_burst_times(decoder_name, stimulus_presentation_ids=stim_presentation_ids, unit_ids=all_unit_ids)\n",
    "spike_times.reset_index(inplace=True)\n",
    "burst_times.reset_index(inplace=True)\n",
    "single_times.reset_index(inplace=True)\n",
    "\n",
    "# Next, we need to select some cells that are very active, \"average\", and mostly inactive\n",
    "units_by_whole_activity = spike_times.value_counts([\"unit_id\"], normalize=True, sort=True, ascending=True).to_frame()\n",
    "units_by_burst_activity = burst_times.value_counts([\"unit_id\"], normalize=True, sort=True, ascending=True).to_frame()\n",
    "units_by_single_activity=single_times.value_counts([\"unit_id\"], normalize=True, sort=True, ascending=True).to_frame()\n",
    "units_by_whole_activity.reset_index(inplace=True)\n",
    "units_by_burst_activity.reset_index(inplace=True)\n",
    "units_by_single_activity.reset_index(inplace=True)\n",
    "\n",
    "# Some units don't have bursts or singles. We only want to show cells that have both,\n",
    "# so we drop the elements from whole_activity that aren't in burst_units or single_units\n",
    "burst_units = units_by_burst_activity[\"unit_id\"].to_numpy()\n",
    "single_units = units_by_single_activity[\"unit_id\"].to_numpy()\n",
    "burst_mask = {\"unit_id\": burst_units}\n",
    "single_mask= {\"unit_id\": single_units}\n",
    "\n",
    "burst_unit_locations = units_by_whole_activity.isin(burst_units)[\"unit_id\"]\n",
    "trimmed_units_by_whole_activity = units_by_whole_activity.loc[burst_unit_locations]\n",
    "\n",
    "single_unit_locations = units_by_whole_activity.isin(single_units)[\"unit_id\"]\n",
    "trimmed_units_by_whole_activity = trimmed_units_by_whole_activity.loc[single_unit_locations]\n",
    "\n",
    "# Store unit id's for each unit that has bursts and singles\n",
    "unit_ids = trimmed_units_by_whole_activity[\"unit_id\"].to_list()\n",
    "num_units = len(unit_ids)\n",
    "\n",
    "# Get the indices for the upper, middle, and lower \"quartiles\" of activity\n",
    "lower_quartile_idx = num_units//4\n",
    "middle_quartile_idx = lower_quartile_idx*2\n",
    "upper_quartile_idx = lower_quartile_idx*3\n",
    "\n",
    "# Select 20 cells with moderate burst activity\n",
    "example_cell_ids = units_by_burst_activity[\"unit_id\"].iloc[middle_quartile_idx-10:middle_quartile_idx+10].to_numpy(dtype=int)\n",
    "example_mask = {\"unit_id\": example_cell_ids}\n",
    "example_bursts = burst_times[burst_times.isin(example_mask)[\"unit_id\"]]\n",
    "example_singles= single_times[single_times.isin(example_mask)[\"unit_id\"]]\n",
    "example_whole = spike_times[spike_times.isin(example_mask)[\"unit_id\"]]\n",
    "\n",
    "# Generate plotable spike trains\n",
    "fig, ax = plt.subplots()\n",
    "#color_maps = {}\n",
    "height_count = 1\n",
    "height_coeff = 0.1\n",
    "for cell_id in example_cell_ids:\n",
    "    # Pull the bursts and whole train for this particular cell\n",
    "    current_bursts = example_bursts.loc[example_bursts[\"unit_id\"]==cell_id]\n",
    "    current_whole = example_whole.loc[example_whole[\"unit_id\"]==cell_id]\n",
    "    \n",
    "    # Trim the spike train\n",
    "    current_whole = current_whole.loc[current_whole[\"spike_time\"] <= MS_UPPER_THRESHOLD]\n",
    "    current_whole = current_whole.loc[current_whole[\"spike_time\"] >= MS_LOWER_THRESHOLD]\n",
    "    num_bursts = len(current_bursts)\n",
    "    num_spikes = len(current_whole)\n",
    "    \n",
    "    # Initialize the color_map for the spikes: an array that holds which color each spike\n",
    "    # in `current_whole` should be\n",
    "    color_map = np.zeros(num_spikes, dtype=str)\n",
    "    height = np.full(num_spikes, height_count*height_coeff)\n",
    "    height_count += 1\n",
    "    \n",
    "    # Initialize burst ranges\n",
    "    burst_beg = current_bursts[\"absolute_beg_time\"].iat[0]\n",
    "    burst_end = current_bursts[\"absolute_end_time\"].iat[0]\n",
    "    burst_idx = 1\n",
    "    k = 0\n",
    "    while k < num_spikes:\n",
    "        current_spike = current_whole[\"spike_time\"].iat[k]\n",
    "        if current_spike >= burst_beg and current_spike < burst_end:\n",
    "            # If the spike is inside the burst range, make it blue\n",
    "            color_map[k] = \"b\"\n",
    "        \n",
    "        elif current_spike > burst_end and burst_idx < num_bursts:\n",
    "            # If the spike occured after the end of the burst range, we need to check\n",
    "            # if there are more bursts. If not, the `else` will be triggered\n",
    "            burst_beg = current_bursts[\"absolute_beg_time\"].iat[burst_idx]\n",
    "            burst_end = current_bursts[\"absolute_end_time\"].iat[burst_idx]\n",
    "            burst_idx += 1\n",
    "            k -= 1 # We don't want to index k, because we want to check the same spike again\n",
    "        \n",
    "        else:\n",
    "            # If the spike wasn't in the burst range, then it must have been \n",
    "            # before the burst range, make it red\n",
    "            color_map[k] = \"r\"\n",
    "\n",
    "        k += 1\n",
    "    #color_maps[cell_id] = color_map\n",
    "    ax.scatter(current_whole[\"spike_time\"].to_numpy() - MS_LOWER_THRESHOLD, height, c=color_map, marker=\"|\")\n",
    "\n",
    "ax.set_xlabel(\"Time (s)\")\n",
    "ax.set_yticks([])\n",
    "ax.set_title(\"Highlighted spike train\")\n",
    "\n",
    "# The patching gives more control over the legend\n",
    "red_patch = mpatches.Patch(color='red', label='single spike')\n",
    "blue_patch = mpatches.Patch(color='blue', label='burst')\n",
    "plt.legend(handles=[red_patch, blue_patch], loc=\"upper left\")\n",
    "if SAVE: plt.savefig(f\"{save_path}/highlighted_spikes.png\", dpi=500, facecolor=\"white\", bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Save the figure and axis to tile it later\n",
    "highlighted_spike_train_fig = fig\n",
    "highlighted_spike_train_ax  = ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fBy7Z1S/JJzW8k/YB0w9QrKG+TT1NN9x9lGw90DN24GP3TpCcVNSAd7ZId/GfZJ637eLuI2QJpP9vHLsPAse4+8M6Onm4xmvZ1qPcyMfYNs1Mq8kxO8L/ks2mbcefyOL3php/IptBHBREeNKCv+Dul5nZX5G5/48mCwbdg2zYayOZAXct2YP2nF2YPj9etvZwn0EWdLxvrmc3skbyfrLh1jOBH7eJj3sVWRDzK8iG3loFxncd+ZDnM8i8aX9PNqywiWy1hq+4+7lmdnFB3/WdfCj1fWRxMH1kSUV/SmZQHdL2wxlF1NO0wrNlol5jZp8niy98Olkw9mweWvD6RrIM+ue4+02ptO4g7yQzQJ5ONjFgIdm10E92/d5J5hn7ZqtzcvdlZvYUsuDzZwN70iHbf+4V+zszO4KH8o0tJvP8rCfL0fcr4BtNucEaj/Fg/p3vITPYHkHmXb6SLBH1+WbWHJfZ8ljjJR+9eC1ZW/pmspU5nKyNPYMsnOEpu/IdBXIzmYfy6TyUz21PYD5ZLsxVZMbZT4EftMhVF+wCFu1lEARBELQm7yz9rmnzi9292xMLBwLEcGcQBEEwbTGzF5nZf+d/5zRs7zOzo3n4RAPI1lW9cFJFBtOWGO4MgiAIpjN9wD/mLzezdWTDeAvY9hlZBY7P4+GCYMIJIy0IgiAIMows1qoVK4Dj3L15RmoQTBgRkxYEQRBMW8xsAfBK4AiyJa22TnoZIpvBfi1Z3rcz3L3TCitBUDhhpAVBEARBEHQhMXEgCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgiAIgqALCSMtCIIgCIKgCwkjLQiCIAiCoAsJIy0IgmmLmV1sZj7VvyMIAk3CSAuCIAiCIOhCwkgLgiAIgiDoQsJIC4IgCIIg6ELCSAuCYEpiZrPMbNTMft+0fcDMhs3Mzez1TWX/nG//p6btFTP7oJndZmYjZnavmX3KzHrbfPeRZnaema3Nv+tWMzvZzObu4Dm8wMx+YWar8++9w8w+Y2bzduQ4QRBoEkZaEARTEnffDFwBHGZmsxuKng705f8f2fSx5+Z/L2za/kPgHcDvgP8ChoD3A99o/l4zewvwq/x7fgp8EVgLnAD8YbwGlpl9GDgPOBw4B/gycDvwXuD3ZjZnPMcJgkCXMNKCIJjKXARUgGc1bDsSqAG/psFIM7MScARwp7vf3XScRwAHu/s/ufu7gScAdwBvMLO9Go6xH5kxtRl4cr7/ie7+FDLj7jHAp7cn2syeA3wUuBR4hLsf4+7vc/cXAG/Mj/PRcf8KQRBIEkZaEARTma0esUaP2ZHA1cCPgb3N7KB8++OBBWzrRQM4wd3Xbn3j7luA08jayEMb9vtHoBf4T3e/uekY/wZsAl5vZn105p353ze7+/rGAnc/FbgWeN12jhEEgTiV1AKCIAh2gUvJhiaPBMhjwp5I5s26KN/nSOBWHhrqvIhtuarFtnvzv7s1bHtiu2O4+zoz+yOZV+/RwJ866H4qMAa80sxe2aK8F9jDzBa4+5oOxwmCQJgw0oIgmLK4+6iZXQI8z8z2JDN+ysCF7n6Tmd1PZqT9V/7XaW1grW9x+Gr+t9ywbevEgBVtJG3dPm870heQtb8f2c5+s4Aw0oJgmhLDnUEQTHUuAozMU3YkMAJsnfH5a+A5+fDjM4Eb3H3VLnzXhvzvXm3KFzXt1+k469zdtvNqjp0LgmAaEUZaEARTnca4tOcCv3f34Yay+cDbgJm0jkfbEf6Y/z2iuSCf1fl4YBi4aTvHuQzYzcwO3kU9QRAIE0ZaEARTnauB9cDRwME83BDb+v8H8r+t4tF2hB+QxZK9w8we2VT2cWAO8AN3H9nOcb6Q//2mmS1uLjSzmWb2lF3UGgTBFCdi0oIgmNK4e93MfkNmpEGDkebu95jZHWQpNmrAb3bxu5aZ2buBrwLXmNmZwIPAs8ni4W4my5e2veNcaGYnAp8EbjOzXwB3kcWg7Zcf7xLghbuiNwiCqU140oIgUGCrYbaRbWdqbi272t23Fyu2Xdz9a8ALyIYsXw68B9gT+Azw1MZUHts5zqfIZoKeQ5YY993AK4ElwCnAh3ZVaxAEUxtz99QagiAIgiAIgibCkxYEQRAEQdCFhJEWBEEQBEHQhYSRFgRBEARB0IWEkRYEQRAEQdCFyKXg2H1+2Zfu05NaRhAEQRAEwXa5+rqR1e6+R6syOSNtbNN89rj/5allFENfb2oFxTE2llpBYfjw9vKUThFiZndX4rVaagmFYL067ZfMPS+GV1WeK2e0Xf5Nzkgb3qufW999UGoZhfDFl52aWkJhfOauF6SWUBgPrNsmQfyUZPFuu5wyrGsYq5e3v9MUoe6WWkIhDI3pjGhs3tKXWkJhVIeFHvtDIufy1jPaFomc4UP0rxrjoK/en1pGIXz1O3+bWkJhzNywKbWEwnjELq3P3UXMnZ1aQWEM1OqpJRSG92oYnHPLGsYmQG1GagXFsWl/HeO5d5PGfd/WjYagkeajo1Tvvje1jGJwjQtQDhOZb7PigdQKikOlTgAraRg3KsO2asy+LLWCYEeQM9Lq82YyeOSTU8sohFqfRmMNUB7RiX+q9WsYBKVRnTpRotavcd+reDkArK5zr4iMpgNQr4iczI/PalskZ6RR0jFuSioxkcCG/TWGcABc5K6ZtTy1guKo6YQMUR5NraAYtizUuef71+kYnHWR9gtgdJZGh7kTQtWVU4fKkEavZ3SOzgU47w6doY+xmRr10rdRp04qgzrnMjpHo1nu1QlDpXe9To95TOT6Ap0OTSd0aqsRDUcal5389dQSCuOg/35ragmFUR7SuMBKYzq3/8h8Ha+N76mR7qHnrv7UEgqjZ4vO9TU6N7WC4ujVmaDeFnOxXElzSgv8KRWNdA8qAcRquEh8ik6OIbCyzkM0Au6DiUTpXlHhV9Uzrnb3Q1uV6XSlc6xUojRTY760iZwHANVqagXF0a8RAFVdvCC1hOIQ6myWxjSMNBM5DznqOvF1lDRCT7iufZGckYYZJpKp37cMppZQHEINg1U0bpvSn25NLaE4TMjrrGJw9ml0ZgB8RGMIGnTaL0DnXumAUG3l1GvUN21OraIQpNzSvToJFH14OLWEQojrK5hIlJZSklrialQn2r40Q2S0qcMkGzkjbWz3Gax47RNSyyiEa0/4r9QSCuPaUQ3DBmC/ioZXcLeSSAMXBBPMPVWdqar7VnRWGllT35JaQiHsuaR9mZyRVi/D6LzUKorhr7/0ttQSikPDrgFgeA8NF3t5VGiIUIhar8b1pTLLHqDWL1InQGWLSBwXUO9RqZd/bVsiZ6RhOsn6bnyXjiftmde/LLWEwrh/5bzUEgqh3K8zmaNe17EI5s0eSi2hEFbfNy+1hMLom6czEtDfpzOre9PGgdQSJhwRc+YhyiMw71YN6/r6UY3GGmDf2etSSyiMjUMa+Z8OmL8mtYTCeMr8O1NLKIxH9a1ILaEQrl6yf2oJhfG7Bx+RWkJh7D97bWoJhbFxvkZbvKxDmZyRVqpB30aNsbV//PJ7UksoDg27GYCaSOft9tq81BIK4+YZOg/Rkfka7VfPJp1hNdOoEgDu612UWkJhuEpoQAf0jLSNQ8z41fWpZRTCzDv2SS2hMGxEZ2ht6ID5qSUUgsoat6C1bI+JPHeUFvIuD+u0X0r566pzNTxpd3UokzPSQCe1gD2oM0TIHJ0ZRf1X3p5aQiGYUB4rRO55AAY0HjwM6cRxKeV5ZIbIUADQe9+DqSVMOIJGmuMi2e1NJZsywEadKey+RSRWsCb04FF6iG7WSCuglI9LacUUG9Ax0pgGS6jJGWled5nGob5IZ9mejQfpeNLm/kkj8La6QCdPmtVExgiF8IpOJ7M0pGOkKRk2tZkiSYZ/175IzkjDDEykcbjhjtQKCmP2dUINg0gjVxZZg1QNH9MwCEo9Oo8XlY4/AEKrJ5SVloNrg85dtBV3cI2hD9ewBQCwktLNpBH/pBK7qYZMrKBIZwa01rtUWuIKkXW66RDhoHPl5Rz0uEHOO/+a1DKCIAiCIAi2S7lDVhQ5I+2GlXtwyGdFllPScAgCUNFZa5nROakVFENNpBMKWnmsZFJwiESdgNb1pfRc0amX9jlR5Yy0eg9s2VujlavN1ohNAbBRnRa7b+FgaglBE8ObRIYIAQZFhqH7ZZ6gMKTTfllNJ/SkNKZzLu2QM9L6lm/hke+7IrWMQpCKHahrGM6ATMyjVG6xuL6CYPohMkmw06J2ckaaDfRjj3pUahmFYEK5eRCahaOSsbs+Q6cTUNosNJ4uQm2uUooXjXsewKo6nQDbIpIwuUMiBzkj7cAD13LuuT9KLSMIgmCnWFfXGE7fraRjpAXBRDKtJg7cdtNcXnzYUallFMLtb9svtYTCGHiszhJXm2+fl1pCIZSWaBgDAAt301nR4qB5q1JLKIQ/r9ZZyHv9Zh2Dc8EcjRUtAMbqGsOd8P+1LZEz0ur9PQw/WqNx6F2fWkFxzPrB3NQSCmPPBzRc7F7SiUnzym6pJRTGnT0aK43MKOuEOMwa1RkiLA/rrP5SnSFnwmyD3BmWqnX6Vm5OLaMQ9v1fnTgbH9CJfxpbMDO1hEIojek8eGpCBmepqjEJou9+jXYYyJKki2CjOrHOZaG42nbIGWleMuoDPallFEJZ6Gaq7qYzXFAa0Qgi9oqOp6PeqzLsAdUZGucyNkskoSAwMk+jTkAptxj0bhI5mQ759+WMtOqMEg8+ScOde8DrHkgtoTCGqzpxEDfcvTi1hEJYspdOnGBNJjYFHrfg/tQSCmHNqIbHGWC0puOp3Tym432qlESMtP9tXzTpRpqZzQO+BTwWcOCfgFuAM4ClwDLgVe6+Lt//A8BxQA14p7uf3/H4DiWRtXDXfWTf1BIKoyQU07FkD42+Td86nTiuWp+OkXZTn0ZMWmVQw+MMWtfX2Eydc+ndoHONtSPF0+ZLwHnu/goz6wVmAB8ELnT3k83sROBE4AQzewzwGuBgYDFwgZkd5N5+6fHyYJ0Ff9KY6VW+f3VqCcVR0emJ9tyq0QvwEY3zAK3F4n1Uo15cKM+j0gLrLpTzzUfHUkuYcCb1yjOzOcCzgGMB3H0UGDWzo4Ej8t2+B1wMnAAcDZzu7iPAXWZ2O3AYcGm77xidW+KeozSGO//0llNTSyiMr6w/MLWEwvjaL1+QWkIhzLhPJybNdWw0SiLzhVRGogBMx66RWlO1LHKv8I32uV0nu3twAPAg8F0zOwS4GngXsNDdVwC4+woz2zPffwlwWcPnl+fbHoaZHQ8cD9BvM1n6+esm7gwmkZf+4OWpJRTHBg3vJsCBg9emllAMQqtASM2+E1kOTsn7VB8aSi2hMEoDA6klFIaK17kTk30XVYAnAu9w98vN7EtkQ5vtaPUU2aY1dvdTgFMA5vYudJsnkpNrWKWboOViL80SCYgWWu9SaWhN5cHjQu2XEirXF2jd9+2YbCNtObDc3S/P359FZqStNLNFuRdtEbCqYf99Gj6/N9B56pM7jIg0DjNFjAGgvnj31BIKo7xeZKaq0ENUyWtj/RqeNAZ1vE8IxW/So5GiCsB6Rc6lW9budPcHzOxeM3uUu98CHAncmL+OAU7O//4s/8jZwA/N7PNkEwcOBK7o9B3Di/q45V814p/m3KYzHDXnHp0ez8bDNGIeleK4erboeAVVqPXqtF9KcVxKedLqKn2zr7QvSnGK7wBOy2d23gm8ESgBZ5rZccA9wCsB3P0GMzuTzIirAm/vNLMToH/VGI/66sqJ1D95VHWGCH2gL7WEwhi4X6P3Vtqo4+mo7q5hOANYVeMpamM67ZdSln5TmhEpElfbKYreXCjgFmBuaYE/pfeFqWUUQmlvjaSpALV77kstoThc4yHqQjFpSik4rKTx4Am6FNNxC1qPhivt/E2nXu3uh7Yq0zjDBhyhh88mnbXvpB6iPSJeQaU6mamz7BgljYeor9+QWkJhaAXb63jSlM6lHXJGmpmOdV3fMphaQnGUNR48IDRTdUxnCIe6hncTkDHSEJrMoXR9KflpZRwyHRC6i3JKZUpzNOJTzvnjr1JLKIwnfuJtqSUURkmk8za4MLWC4qiLODcBKiKTh2ct13mA9m3UMdJGZ4t0AoCxmSIm59e7J5ntxFMqgUiyvhe9+B9SSyiMWUtFvE9AdUCjkavozBugZ1DHIFBZ81LlPgGo60QGUBPJ8DJdkDPSRvbo4ba3brMowZSkOk9nOKpnt+HUEgqjUtF4iLqL9EKB/j4R9yZSiyfIsGmTRscfoKdP57kyZ0DkufL19kVyRlrfqhEO/NKdqWUUggslg1RKNqoSk2YD/aklFIYPiTTWgPVpuDpcKKZ2UWoBRSLUFk8H5GqrNrOPTYfvl1pGIYzM1RkuWPM3Og9RX6sRANX3oM71NbpAJ2ao3qPhSisP6lxfpTEdr7PpONJ0EvN+pH2RnJFW2jDIjJ9fk1pGIQyIeGwA5n0/tYLiUEknojILWg1XmXUrkk9QDZWRAEAm59utHcrkWumD/noL551/VWoZQRAEQRAE26XcYTxdzki77aa5vPjJL0otoxDued3S1BIKY8lvdBLzljfoxAqqUN1NJ5ltvVfDU1vr0/ByAFSGdLxPtT6N6wuU1lQ9sW2JnJFGrYZv3JRaRSHs/UUdj6D1acRxAdREgtRLQhMHSnfpzO4siSwLVRbK0l+aodMJUHro10Xa4k4o1ddDiMxhtwOXppZQGLW5Oo1caUjk4SO0ADZ9GoveA5TWaHQylZa1U1oWSonygt1SSyiGB9oXyRlpXq9TH9IYjiqv3ZhaQmFU1ok8eEDGU4tpeGxAbKkbkU4mvTqGs/VqpEUBcKEJHT6ibzzLGWlWLlOarbEsFKM6QziI5H4CdB4+KrMIAevXGU5HJTRAaBahVM7KWTNTSygOlQ7NuvZFckYaPRVYsmdqFYVQnaOT5bq8SSd2wIZEYrmGR1IrKI6KTjC0TLLRqlAnQMjrTFkm2l5qNKAd424NzOwi4E3u3tXp/KszK6w+bEFqGYWw7q9SKyiOhVfoGJz9azQ8nNVZOoaNynqXAK4ycWBIx0grD2rc8wD1fpGRAKA2INKGLWtf1NZIM7M93P3Bhk1HALPyshJwO/C37n5DERqLwurQu1nDBbrgeo3GGqDWr3MuY3M0PB1Ki0b7TJ2TKY1qtF/De4t4nAGr6pxLZUQnJq3WK+QVbEOnp81KM1sJXAtcBzgwLy8zYCnQdcETpfWDzPrJ1allFEJJKM5GanaUyIoDSjFDMkOE6KQV6FO5TwAT8W4C1JViUYXqpR2dWrYFwBOAx+d/Dfi1md0LXEFmtB0AdNUaTPVH9rLxKxprd67eoBPg+VeLVqaWUBgPbNaYmDI8pjPsMVbVMQj2nKsxe3jziE4nsyyzSCTM7NXpMD+wYU5qCcXw8vZFNt7p3mZWB44EeoGnkC0JOgxsBi5y99fsqs4imGPz/fDS36SWETQjNO1bBpF17wCt60upXkSYDh6bqYjKOqQX+FlXu/uhrco6xaRdA/yRbLjzWjLP2Wp3v97MLiAz0o4AysDzipW884wunsk9b31KahmFUNYY9QBgZIFGnA1AvUfjXEzjNABwoWdo7zoNI21sts4FVhnSucBqfTr14ipRDu8+q21Rp1P8HHAI8BLg3/JtPzKzS4AryYy2MXe/Ari0GKW7Tu+KQfb7+JWpZRSCUu/N6zoNg4rXRqpOhDCRWC4VLwdotcVKWEUjZOOuDmVtjTR3Pw04bev7fLjzdGAP4M1kMWrn5l61X7n794qRu2v4rAHGnnJIahmFYDWdh6ipJB1EaDq+UL4klUXJAUqjGsaNynkAVGdqGAMAlU06+RGrc0Rm3V7cvmhHnYVnu/t1ZlYBRoGPA4uAtwFdYaT1LBlhr4/dkVpGIfSXRYwBYE5FZ+z2MTPuTy2hEO4Z0cgnCHDxygNTSyiMxTM1loO7e6PIuopAuaSz4kC5pDESANBTElkf9uL2RTtipH0PWJv/v9Utcqm7XwN8aGd0TQS122DdizQuQpsxK7WEwnigrrPA+q2u8fBRWupmVm1FagmFsWlAwzuwoNxhrZspRn2jiDEAmMqydqCTDqkD4zbS3P2NDW/rwEeBrnMp1Gb3s/k5j04toxD+6eSfppZQGF////4+tYTCGJ2lEZ8ypLF6GgBjj9QxOOujGg+e3vuF1uvVidagrhJsD5RUUr51cHPtVHV5lrfjozspZ0Kp98LGpRqN3Oe/2SF5yhRj1piGdzNDw0ibqeN8or5KZ9kx12i+6N2oY9kozYRWotaj0RZ3QsimzrA69Ih4pns36xg2KusRAvRtiBa723CdORBUZ2jcK6NzNc4DoKzjqGVMJ4qGXo28zx2RM9JKo87sezQyKtf6dZ485REdw6Y8ojFrrSRyHgAjC3Sy2/uG1AqKoTys08mUaovv0WmL6706HYF2yBlpXjKqIostl0UWWgatm2lodw2DoDKk8xCtDug8RFXo1+kDUO/Rub4qgyqBXFAS6vy3Q85IK63fwsBPLk8tIxBGw0QLgmC8CE2BCKYY4zLSzOxZwDXuvk20l5nNAp7o7r8d75eaWRm4CrjP3V9iZvOBM4ClwDLgVe6+Lt/3A8BxQA14p7uf3/HY5TLleRopEqxfYyo+AELJbCmJ9KqrOj1qH9ZJ0GkzRCZBCF1f9Al1zYRWgvARkft+Tfui8XrSfg08FbiiRdmj8vIdGWN8F3ATsHUJ+xOBC939ZDM7MX9/gpk9BngNcDCwGLjAzA5y9/ZXWamEDYg0ciqNNYDKzSSEz5mXWkJh2JDQ9aVi3JhOiANjOonF6dPxC5qQwdmO8Rppne62WcDgeL/QzPYGXgx8AnhPvvlossXaIUuaezFwQr79dHcfAe4ys9uBw+i0Vmi5BLNnjldOd1MVugArQiPrIg2DCSWzlbpXlLzOKiglTRUynn2BxqgZK9sXtX1y5kOcRzRsepOZvbBpt34yg+v6HZDzReD9wOyGbQvdfQWAu68ws61pNpcAlzXstzzf1qz1eOB4gP7euVR308huX52lkxlaaXbUjLtF5n2PCRk2FZ3ry4ZFvDZjIh5BkFrnVgkTWgmiHZ3cG4cD78j/d+CVQPNdNwrcDLxvPF9mZi8BVrn71WZ2xHg+0mLbNt1Mdz8FOAVgbs+eXrmj6xZC2Ckq/UJxEEKeNF+7PrWEoAmlpW58RCOFkFSdDOmsPazkSZOJD+5A2yenu38G+AyAmd0FvMzdr93F73s68HdmdhSZF26Omf0AWGlmi3Iv2iJgVb7/cmCfhs/vzXaWovJajVo8RLsOE0pm6yLDnZhOA2dKw1EiKE3mUEKm/ZomjMu94e77F/Fl7v4B4AMAuSftve7+j2b2GeAY4OT878/yj5wN/NDMPk82ceBAWk9e+AtWqVDec/ci5AZFojQJQiVmSCVAHWBUZIgQQMUDJdQJYMuW1AqKo0fk+gJcJa52XfuiTjFpRwGXuPvG/P+OuPsvdkpcxsnAmWZ2HHAP2dAq7n6DmZ0J3Eg21Pr2jjM7tyLiAvXNQg3DsNBwQV3DSJMajlIyOFUePEJxXEpeQVMy0kY1QgM6Yd7GK2BmdeAp7n5F/r/Tfpanu3fHssBz+xb60xa9NrWMQvC5OouseU9XXB6FYCMaXhuraRibAC40cUDFU+u9OnGopQ3jTmDQ/URoQNdx3q2futrdD21V1uku2h9Y0fD/lGBkQS93HbNvahmFUBZyPi24WcfTURJZrmt4gdBDdEyjTgBqfRrxmz1bdJYdw2dvf58pgpc1ri+Aar/IudzavqjTxIG7W/3f7VSGYMENGoGR6w/U6fEM7qFzLtvOL56a1FQaOGB0ls65lDSaL+iOwZVCcCFHrYm0XwBjOrZzW3aoK21mfWR5yrZZr8jdbyxK1K5gdadns0YrN+dunQfP6CydVq4yrNHKDazR8W4qLYBdU0kIL5TqQSk0QMVTCzD7Hp16acd41+5cTJaH7EWtisl8C13RbSqNVBm4c21qGYUw8MeNqSUUhtK0b5XAW5V8XAAmtNQNdZFhQiEjTSrP4xal+Dqdzlk7xnvlfQt4ItkyTjeSJbHtTsaq1O9/ILWKYqiJNNYgdTOp5ORSOQ/Qmn2ncq+YUAoOHxWZcQs6nQDQOpc2jNdIezrwZnc/cyLFFEF1/gBrjj4ktYxCGFyo0xMdWqRzM828W+Ph079OZ6hgdI7OvaJSK71CK/YoDXfWKzr3SsdVxacS32hfNF4jbRUwJboSlbVD7P4/f04tI2jCR4Q8HSJDH0qeNClEUnBEqocuRcn71KPRFndivGf4YeAEM/uNu3d1oNTY7gPc/7q/Ti2jEKoa68QDMLhYJyatZ6PGw6cilCtZCZlJkSpeDqCkkRpRDlOZ+/Sp9kXjNdL+HtgXuNvMrgTWN5W7u796Z7QVTc+6EZaccWdqGcXQJ7TAer9QYLdKrOCGru5v7Ri7zU2toDg2hfUcTCBK3qfNGpMgOqXGGG9t7Q7ckf/fA+yxS4omkrrLrOdlSkvdTIPlO6YcSsNRWzTueQBEZt1KzegWuldUno8ApmRwtmG8C6w/Z6KFFEV1Xj9r/u4xqWUUQq1XZ7xAKSN8dYZGvfSvFfEIAl7SqBMl6kLPz54tQu3XgNC9onIq32tfJHQbZVTWD7PgJ12RV3eXMZEAdRBbAFsk8NZmzUwtoTiEvDYqw+lKk4WUvIKlGTrBzvUhHa9gO9paAWb2nQ6fq5LN+Pytu/+ycFW7Qr2Oi1RcXahhsF6hmLS6Rq+6/uCa1BIKQ2rYQ+W+VxoiHNPpZNbWr08tIdgBOrVsnaZIloFFwAfN7BLgKHfviqw47k494p+6DhXDOehOvBrT74Ig0KPTAutP3t6Hzexw4GzgP4B3Fydr57FKhcr83VPLKAalZVWUPGkqno6SRlJeQCe3GEBFxAMl4nEGZFaBAEDIKygzU3VZ+6JdOkN3v9zMPgacQJcYaVTKMH9eahWF4BWhhkHI4LQhDU9tfc5AagnFoRHGlSFy39ugxn0CYEKGjc/Sue9tRKde2lGEGXojsLCA4xSCj4xSu+Pu1DKKwXWePC7Uqy71a+Svq9+pE9hdnqkTDF0TWQDbhGbcKk0cUEonMh0owkjbD1hbwHEKobr7DFa/fLsjtVMC02kXqPekVlAcwwtSKyiGvnWpFRRHTWg0vXdTagXFMDo7tYLi6F+r08lUWruzPCpSL98/o23RLhlpZrYI+BBw7q4cp0h6NtfY81KNp4/36PR4bFhn6MN7NeIglK6v8gahiSkqXmelOEElVGIe0WmLO9EpBceZHT5XBvYCngTcC3ywYF07TXVGmTVPmJdaRiGUhSasVTVGCAGdZLZ9G3QeouURnZxvphPlIEO1X+OeB6G1YREabfpT+6JOZminpZ+qwN3AD4Dvu3tXLTZnIs+e0Vk6DUNlSKRSgLJIKNeWhTrXV+9mnXOpqzxEdapE5p4HrbZ4cA+hi6wNnVJwTJmloBqpbBxlwYUqEwd0biYfHk4toTCsvz+1hGJQSsEhFKQuM9yptGLKuvWpJRSGzdCZ3TlfpA37c4cynbsopzq3l1UvXJpaRiGUaiKNNTA6W+chqjJc0LdO5/pSGiJUOZcxoZGAmSu6JoHBLrNloc5jX2biwKnti3RqK6eycZQ9L7g3tYxiEOqJMqIzcYBekamqm7sqSmHXEFqPkC0i9TJHaHrnsM545yyhERrGhAK32yBkBeQ4OhmVVc4DQCS3GKAzTDh3TmoFxSGSWwzQMW6qQu1Xn1COFyF8vkgbtqp9kZyRVp/Rw+ZD90ktoxDqPSLGAFDZojINB8pjGuNRNaHryysiyeuAnk0ixo2Qx2Zsroj3HKj16AxD924Sea5c375IzkgrbRxixvnXpZZRCKayLhlILQvlIh6CyqjOELQprQ2rMnFAaL3L8qjOsJrUShAq90oHhKyAnN4eSnsvSq2iEHxA58FjYyI9HoQyC0yDBm4qUp+hERpgoxqdGQATWU8VtNri2iyRmfZXti+SM9KGF1e4+aTdUssohD0WiKwPA6zZMCu1hMLwmoaZVh8Ruv3LGkPQADPmaKSrGdwoEi8E2Aade8Xn6XgFSxWR+/617Yt0rryc/ruHOegtN6eWUQw1kQsQ2E1osXiZWbdCMUPhFexChIbVlFBaYN1FQjbu6lAm8rR5iNGFA9zzpsenllEIJZ1Z35R0Om/URHJB1jRG1QCtcxnoMNNrKlHSGVVjZG5qBcXRszm1guLoVRls+uYP2xbJGWl7zN/A21/7f6llFMLKMZ2WoaySoROYW9ZYzLvHdGKG/mamiPccuGx4aWoJhXDT0OLUEgrjoP4HUksojDGVbNzAZRsPSC2hEK75ZvuySTXSzGwf4Ptki7PXgVPc/UtmNh84A1gKLANe5e7r8s98ADgOqAHvdPfzO33Hhlv6OOeZB03YOUwmXtPpippKAljQSZw6qJNb7JzKY1NLKA6RYWgf0RkK+FO/jsEplQC2R+i50obJ9qRVgX9192vMbDZwtZn9CjgWuNDdTzazE4ETgRPM7DHAa4CDgcXABWZ2kLu3t14cXCT+qS6UEb48f15qCYXhq9ekllAMQoazKTXWIql3TCXpM+BCyZKV1u6USpLehkltDdx9BbAi/3+Tmd0ELAGOBo7Id/secDFwQr79dHcfAe4ys9uBw4BLO36RSMB9aUBkejHgQxoz1gCdnG9Cwfb11WtTSwiaEcqTphRsXxdaLB6h0aZ2JOuymdlS4AnA5cDC3IDD3VeY2Z75bkuAyxo+tjzf1nys44HjAfrLs7F5IlO/hRoGX78htYTCkPHaCF1fSjMJTWX2sBKzdVIImdAIjUwbdl/7oiStgZnNAn4MvNvdN1p7z0Srgm26/+5+CnAKwNzehS6z5qWKMQCYUCOHSnb7tetTKyiOvfdKraA4HlyXWkExzJqZWkFxCMXXyawNC3gljLTCMbMeMgPtNHf/33zzSjNblHvRFvHQcqPLgcaFOPcG7u/4BbUadRGvja8UmYuvhmkM4yhNTGGtiGGDzrI9vkJnRqTKPQ9QEorjchWHTAcme3anAd8GbnL3zzcUnQ0cA5yc//1Zw/YfmtnnySYOHAhc0fFLyiVKc0WGO5UeoipxXAAzRWZ3jmgkggRAZD1VABeZfWf9OjG1Uqi0XwAiyWxZ1r5osj1pTwdeD1xvZtfm2z5IZpydaWbHAfcArwRw9xvM7EzgRrKZoW/vOLOTzLKurXpwguRPLlKeDqGeqIynQ+n6CroOpWB7qXsl2uIphblITp6tzFqwjz/2Rf+SWkYhjM3QuQCrQp23kkjnzZRufY0J3VKUqjoXWFWoLVZa/UWFP37jPVe7+6GtyuSmEVnN6dug0espj+j0ePpvE7FsgM37aMR0qNwnALVenXulPKphcdZ6dAybPo0wZwBcyPukZDy3Q85IK20apv/Xf04toxCUkkEqMe8ynfgnFZRWtPBRDVdH1El3IlUvQrGo7ZAz0kaW9nHHJw9MLaMQait1MkN7r87Qh/doeDpm7KaxBimAu06PulbT6JzVxjTOA2Bgps5IwNCgSAohgAc1RjV413+3LZIz0vruq3Hgv21MLaMgVM4DfKbOTC8bEmmwVWZGgdbyMMMaObl8lk4nE6FRDRvWea6oZEC4q0OZnJFGrQ4bN6VWUQhKLnZWpxZQHDWRdfxKSlPxlVK8iDx4WC9kDNQ1vOcALjTrdjogZ6SNLa1w3+f3SC2jEMaqOjfT4nk6kbdrBhelllAIz9v71tQSCmPFsEhuROCG1dusfDclGRrRiX2qlHWMtHkzdMIc1m0R8da+rH2RnJHWc3eNJf8sYhD0CcUOiMxYA9hrQ4c1PKYQN8xemFpCcQgNRy1CYyRACqHF4pXypM0Z01hp5KYOZXJGGgAqud82CDXW8+elVlAYJpIRnh6h218pNEBlaE2lHUYr9MSUOv8q90oHhFrpjNrsXtY/a7/UMoImlBLzWn3P1BIKoTKs8xAd3EPHO9C/TqNe6jrRGvRs0agTgJ4tIjGPwJa9REyY77QvEjnDhyhvHGHehbenllEMFZ3qqa9dn1pCYdiAxkxVmzUztYTCmKcSbA/4iMbsThsQiRcSw4d0YtJmzBCa/NQGHSsgpzq3j9Uv1siTNmOlTqK+od33Ty2hMFQy9VcHdLxPQwt0zmU3kdU5hvbQmThQExoh7Nms4xWs9YmM0PygfZGckVbZOMLu53fKOjKFEJoqPUPEOwBAr0iLXdG5vuaJ5BYDZCZB9M3XmXFrmzTS7gBQ1ehkAjrpajogZ6RhphMQXdfp8ag8eACdnFxjOp5apQ6NDCseTK2gOHp0vIJSTIP7XsSaaULlIVoX6iUoNXIqmfprQjOjROIEARnvgM3R8aT5Rp3EvNYvdK9MA/SMtFJJqMEeTi2gOIRy81AS6QSoDNuCjuEM0CvSfimlrRAyOGXaL4AhoWdkG+SMtJHdKtz1Ko0VB3p0Om+Y0Mjt2KzUCoJmXKgPUBEJf6oLOc97ROoEYGRuagXF0auSSvQL7YvkjLS+NWPs/32NjPBKw50utE6k92rcNiYyrAZgYzrnohIr6DN0Fr1Xur68T8d6trUanozrO5RpPG0aOPDR6znn/LNTywiCIAiCINgu5Q7LQcsZabf9eSYvesRTUssoBOvT6YkqLRGjMjFFJWkqIBNsD+jEbyrFPonc8wAmNInLVZbo47a2JXJGmtcdFwkiVjkPgJJSZmgRg9NFhtUATMggcBGD08o6xkBdKEDdhCZ0qNwrnZAz0g563CDnnX9NahlBEARBEATbZVoNd974wB484ZNvSy2jGDQcNgD0Ki1F0qvhtRkTcm4Oa6x5D0Dv+tQKisGF8oyWdJxP1IWe+n0bUisoive0LRGqroyedSMsPuvO1DKKQSgOApnYAWD+vNQKikEoma336TRltTkaC5PXe4WsNKGm2KpC931FJH6zAzot21aUktkqLXkhFF/HlqHUCopBaOKACXVoKhu2pJZQDErxQkLXl0pMLQB1HYOzHXJGmo+MUr1zWWoZxaAyywvA9W+mICgElfs+7vkg2GXkjDTr66Wyz/6pZRSD0qLk62WCB2DmzNQKiqFH5/ZXGu60jSKeNCXvk9BMaCkPp8oz8v72RTot21bGxqjf/0BqFYVQmiViDIDOzQTUV6xMLaEQTMhIkwoNqGjUi1IKIRdKW1GaOzu1hMJwodQo7dBoDRqozpvB+hc/PrWMQnjwCakVFEd5WKdXPfBgagXF4DpVQlWoP1MTWfe+Z3NqBcUhs0YkUBXKkV5ScXB+pX2RnJFWHqkz53aN1XAH99B58gwtTK2gOPrXagTe9m7SiRnavFjHkza8ILWCgtC4TQAtw6as4xTEdJqwtsgZadWBEmsep2HcjDxNpys697xZqSUUhkqetE376Bg2dZ1ToSwyebgiNBJVEakTgDGNxyMg5EnrgJyRVhmuM/8mjTtqj6t0AjxLq+9JLaE4RGKGEIqzoU9kjBB0Au6V0iMIxdS6UiyqSJ60P3UoE6qtDC8bo3M11oxb92SNpJYAM1bNSS2hMHo3ahjPI/N03E9jM0QMG6AmNLSmgpQnTWilERlv7XXti6aEkWZmLwS+BJSBb7n7ye32LW0cYuBXHc54CtF/noYxAGjlTBLJY6XTBRBbYL2uEcwVddKdKNXLdKDrjTQzKwNfBf4GWA5caWZnu/uNLT9QLlOaJ+K1qQoZaSqrQAC1JRqR3aUhneFOG9UJTvHerm+Wx4WN6Fxf9Vk67Vdps4r7CZ3QgJvaF02F1uAw4HZ3vxPAzE4HjgZaG2kgEwsx+OQDUksojDUHawxBA8y7U8N4rpd1xj2GF2h4NwFKoyJeG5HnJ0BZZwU16j1CedJUbvspbqQtAe5teL8cOLxxBzM7Hjg+fzty3qqv/3mStE0s56YWUCDnsjuwOrWM4GFEnXQnUS/dR9RJd6JSL/u1K5gKRlqr/tjDuprufgpwCoCZXeXuh06GsGD8RL10H1En3UnUS/cRddKdTId6mQrOwuXAPg3v96bjSldBEARBEARTn6lgpF0JHGhm+5tZL/Aa4OzEmoIgCIIgCCaUrh/udPeqmf0/4HyyFBzfcfcbOnzklMlRFuwgUS/dR9RJdxL10n1EnXQn8vVi7iIziYIgCIIgCISYCsOdQRAEQRAE044w0oIgCIIgCLqQMNKCIAiCIAi6kDDSgiAIgiAIupAw0oIgCIIgCLqQMNKCIAiCIAi6kDDSgiCYcpjZUjNzMzs1tZYgCIKJIoy0IAiCCcbMlpnZstQ6giCYWoSRFgRBEARB0IWEkRYEQRAEQdCFhJEWBMGUxswebWY/NbO1ZrbFzC4xs+c37XNSHsN2RIvPt4xvM7NT8+0HmNk7zOw6Mxsys4vzcjOzY8zsD2b2oJkNm9m9Zna+mb063+cIM3NgP2C//Hje/H1m9kwz+z8zW25mI2b2gJldZmYfKfjnCoJgCtH1C6wHQRB0YH/gUuDPwDeARcCrgXPN7LXufkYB3/El4JnAOcAvgFq+/RPAB4C7gDOBDfn3Pxl4JXAGsAz4KPDu/DNfbDjutQBm9sL82BuBs4H7gPnAXwH/nH8+CIJpSBhpQRBMZZ4FfNbd37d1g5n9J5nh9nUzO9fdN+7idzwReIK739W0/S1kBtVj3X2wscDMdgdw92XASWZ2bP7+pBbHfzPZqMYR7v6nVscJgmB6EsOdQRBMZTYAH2vc4O5XAacB84CXFfAdn25hoG1ljIc8a40aVu/E9wwVdJwgCEQIIy0IgqnMNe6+qcX2i/O/TyjgO65os/00YClwg5l90sxeaGZzd+L4p+V/Lzezr5vZq81s750RGgSBFmGkBUEwlVnZZvsD+d+dMZraHauZfyGLNdsCnAicC6w2s5+Z2SPHe3B3/1/gJcAfgX8CTgfuNbOrzOxvdkV4EARTmzDSgiCYyixss32v/O+G/G89/9sqDnfedr7DW250r7n7l9z9kFzHy4GfAH8HnGdmfds5buOxznH35wK7AUcCXwAOBn5uZo8Z73GCINAijLQgCKYyTzSz2S22H5H//WP+d13+d58W+x66qyLcfZW7/6+7vwq4CHgE8NiGXWpAeRzH2eLuF7n7e4D/D+gFXrSr+oIgmJqEkRYEwVRmLvDhxg1mdijwOjIv2k/yzVvjyt5oZpWGffdp/vx4MLM+MzvSzKxpew9Z+gyAxhmfa4A9zGygxbGObLWdh7yEgy3KgiCYBkQKjiAIpjK/Bd5kZocDv+ehPGkl4C1b02+4++Vm9luylB1XmNlFZEbQ3wLn09rD1okB4AJgmZldDtwN9AN/Q5bf7Gx3v6lh/wvJ8qedl+sYAf7k7v8HfA5YmifJXQaMAk8Cnpsf9/Qd1BYEgQhhpAVBMJW5C3grcHL+tw+4BviYu5/ftO/RwGfyv+8AbgPeD/wSeNUOfu8W4ATgOcDTgJcCm4A7gLcB32na/z/IYt/+Fng62dDn94D/IxvWfBnZsOvzyOLn7sm3f9Hd1xEEwbTE3FvGxAZBEARBEAQJiZi0IAiCIAiCLiSMtCAIgiAIgi4kjLQgCIIgCIIuJIy0IAiCIAiCLkRudmev9ftAaVZqGcVQErKhY4JK96FUJw9PVza1UToXFer17e8zVRC6voYX96eWUAij9y5f7e57tCqTM9L6mcFhHJlaRjHUlBoGIYNTBCvpNNZeq6WWEAhj5e0uFhEkoFxenFpCIZzHF+5uVyZnpFlfL5V9l6aWUQje35NaQnEsb7cO9tTD+npTSygGlfMAqAoZadVqagWF4KNjqSUUR3idu5JbT16QWkIxvKZ9kZyR5iOj1O5qa5ROKbwu1DAEXYeSd0DJk6ZULyqUZs5ILaEwfGQktYTCOOjDm1JLKIQ7O5TJGWmg1WAHwUThVaHhdCGiXrqP2oYNqSUELajfdkdqCROOnJFmpRLlmSITB4RwpcBbkaEPq+jc/i4yRCiFUmdZ6F5Rab8A1v/941NLKIYfnNW2SOjKy/B6ndrmzallBEEQBCqMjqZWELRg7hlXpZYw4cgZaVYqUZ41O7WMYojYlK7EeoUmdKjQo1MnPjSUWkIxSM1O1wm2V4p5vOmz+6eWUAzH/qhtkZyR5vU6tU0awYRBEARBELTmwDeuSS2hEDpNdZQz0oIgCIIg0Kc0Q2TW7Zb2RZFhNAiCIAiCoAsJT1oQBFMepTibSCHUhcSKKUEiwkgLJgWrCAV2V0UyqQs9eJQMGxWDU6lOcKFJEEKY0vrWbZAz0qynh8qei1LLKIRNh+2bWkJhzLrwxtQSCsNHNGZ6ST1ETacTYD0izXKkrehKlO57211kWagOcx1FWoMGvA4jGo3D7N/dnlpCYeikT4SSSMPggyKpHtDxPgG4iNfG+vpSSygOIcNGaU3VFUctSS2hGL7avkjPSCuXYd6c1CoKobqnxnkAVG5dnlpCYfgmjWTJNmMgtYTiEDLSbEzjIep1nVUgrF/I4BQaIlz3eJ1rrB16Rlq1hq9bn1pFIZRXPphaQmEoedJkEltWdbwDSkaaD2ssgK20VJfSouQy7Rcw52Y9E6YZuTP0Wo3aOo3FcK2kczMpxUEE3YfUcKfKvSI0MUUJpXtl7zM7pYGdOvy5Q5mckWYloyTimrbe3tQSCsP6dM7Fh4ZTSygGoWEPJVwk4L40a2ZqCYVR39wh2+gUQ+m5svbZIpPrftC+SM5I87rLNHI20J9aQmHU12t4N0HH0+F1nUHoksqMSHSur+pqnSFCJe9TXWQ4HWD+BXekljDh6LRsDag0crW161JLCIIpQV2kYxZ0J17VmHGrRnXlqtQSJpwY7wiCIAiCIOhCJD1pQRAEQRBoIzMM3WEidBhpQRAEQRBMOWQmQUwnI816e6gs3ie1jGJQ6SWooZJnSCiAmB6dZaFQyS/mOhNTEMkYAEjVy5cvPi21hEI4qIPJImekUa1SX7U6tYpCkOklgI5ho4RQY62U4kUl3YMpGc4idQLgIitaABz9tfelllAQ72lbomekVSqU9tBYWxGhFAnM1FmCyHs1bhtbuzG1hOIQMghKKudSERoJEFqdQ2k5uB+89QupJRTCkz7ZvkzjadOAj45RvUdnncigC1HJpC6ykHcQBNOTDzzq2aklFET7bLZyRpqVSpRnzkotoxC8rvMQlZmFQ9YRUMCUEsAK3Ssqw9BS97xI7k1AaoRm8AWPSy2hGH7Wvkinlc7xep3a5s2pZQRB9yM0byAIgulH/9lXpJYw4cgZaYDOcFTQlagsfC/lHVBCpf1SGk5XqROQqpfy/N1SSyiGNe2LJI00lYeoEkozVWXWvosHT1ei0n55Xef6UqkTAFfqmy3aI7WCYphuRpoKSgtgMyaS+wkoiRicXtWIrQPARGZEohPL5bGealdiFZ17RWWmfSf0zzDoCqxXp2GgpuG1Kc2YkVpCYfiIiHcTQMRrU54pdH0JhQa4UIe53i/0XGnDpBtpZjYP+BbwWMCBfwJuAc4AlgLLgFe5+7p8/w8AxwE14J3ufn7H4/f2Ul6y9wSpn1zqu81MLaEwSivXpZZQHCMiHoKB/tQKCsNEZkQCMrM7ZVZOAJ17Hh1PLcCqx4o8I//QviiFJ+1LwHnu/goz6wVmAB8ELnT3k83sROBE4AQzewzwGuBgYDFwgZkd5N5hVL1axdesnfCTmAx8+f2pJRSGKy2rIoILZVFX8T6BzkNUyftkFZ1BJ6V0NWufKmI8n9K+aFKvPDObAzwLOBbA3UeBUTM7Gjgi3+17wMXACcDRwOnuPgLcZWa3A4cBl7b7jiwFh8jDRygYurZFp8FWqpcgCIKpyl/9u4Yj4+4OZZPdPTgAeBD4rpkdAlwNvAtY6O4rANx9hZntme+/BLis4fPL820Pw8yOB44H6GdGPESDIAiCQJzaAytTS5hwJttIqwBPBN7h7peb2ZfIhjbb0WoMY5uADXc/hdxhOMfmiwR0ECkSgiAIgqANKqEBdAjfnGwjbTmw3N0vz9+fRWakrTSzRbkXbRGwqmH/fRo+vzfQ0b9p5TLleSIJ7kSWHwLAdGKGKIkYz0IxQ0rxTyr3iqncJ2IoxaTd9olDUksohnf/sG3RpBpp7v6Amd1rZo9y91uAI4Eb89cxwMn5360rWZ0N/NDMPk82ceBAoPM6EJUyJpKFuLrH7NQSCqOyWmepLn9g1fZ3mgKY0uxOJYNA5VyEjAGZGbcIeZ+A5z/jj6klFMI3OpSlmLLyDuC0fGbnncAbgRJwppkdB9wDvBLA3W8wszPJjLgq8PaOMzsBHxmlevudE6l/8rg9tYDiqAk1DCqNnJT3SQmRPHxK15fSuShx17N1OprtMBfqIQDM7VvoT1v8utQyikFo2jdDw6kVFIdIg+0qy1sBNkskXxJAWcOTJpXiJehKnv3b5aklFMIHDz73anc/tFWZkBWQ4aNjVO++N7WMIAgmkw0bUisIgmCSuUglmW0H5Iy0IAjGh8qwrRoya/bGjO4g2GXCSAuCaYpUnE2kq+k+ok6CCUamo9lFKTgmHCsZpf6B1DIK4YE3PyG1hMJY+NXLt7/TFCE8HcFEUurtTS2hEOqjIkv2BF1LefcFqSUUwwPti+SMNK87dZGA6MWn3ZJaQmHUVXo8gFU0PAQmtJ6qKxkEIp2A8mydFEJKXmcf0Xg+QpbNQR05Iw2Q8RBU16xJLSFQZkRoxm3QfcT1FUwwtfXrU0uYcDSNNBWEYjrKc3R61SopEhjrEAgxxfCqzrmorDiglADWhe4VJUozZ6SWUAzr2xfJGWlmRqlPJMGdiEcQxHJy9fakllAISsMeUjkFRZLZUhIxNtEa7lR6rti8OaklFMP69kVCLVuOmcxD1GbPSi2hMHyTzrJQdaXEvCKYkNdGJlZQaFmo0oCOwYmQ13n4wIWpJRTDsvZFekZaTwVbuEdqFYUwvHR+agmF0X/jfaklFEZZZPYdfSLnAfiCuaklFEdVw7ixjTodM6voTHyiR8OJAbDvf9yaWkIx/Kp9kZ6RNlalfn+H+axTiH4h75MPDqWWEDSjNCNS6foS8QrWx8ZSSygOkToBdGIegWtOf3xqCROOnJHm9Tr1wcHUMgpB5TyCIAiCoGgWn3JtagmF8OcOZXJGmhLlWUIxaUqzo0QCb6WCoYVmQntVyAMVdB9C94rM7M4t7YvCSOtiapt1hjuDYGIRMjiD7kPIsFHpZAJUV69NLWHCCSOti7GKToBneAeCCUXpIRp0H0KGjdK9UlbxpG1qX6RppIlchOVH7JdaQmH4Aw+mllAYtQ0bUksoBJl8gkBJJV8SQpNsIti+K/FRoQ6zUL20Y9xGmpldBLzJ3e+cQD27jPX0UNlTJHfKiNDNtLdInQAVld6bUgMnZBDYXBGDU2l2p1C6GhNa73L/czQ6zDypfVFbI83M9nD3RvfHEcCsvKwE3A78rbvfUIjIoqhVqa9bn1pF0IzIotEAMmciNISjNAnCVPLwCd3zMkvBoeVJu/XdB6eWMOF08qStNLOVwLXAdWTPpnl5mQFLga5Lje11pz4kMlwQBMG0w6P9CoJxYZdcm1rChNPJSFsAPAF4fP7XgF+b2b3AFWRG2wHANROscccwkwm4lwq2F4kTDLoTE1onUuVeUfJuKnmdrayzekJ5yaLUEorh7vZFbY00d18HXJS/MLPXAc8DeoGnAK8Avm9mXwMucvfXFKd457FSSSZ3ignFQcgsGi2EK604IPTgQcm4EcEGdCbZyExMAe78p31TSyiGj7Qv6hSTdg3wR7LhzmvJPGer3f16M7sgP+wRQJnMeOseROIHlG4mmzGQWkJxqATcu9DkbqVkySWN9ktpgXWlOC6brZMkfWyezjXWjk6t9OeAQ4CXAP+Wb/uRmV0CXElmtI25+xXApROqcgfwWo3a2nWpZQTNRGLeIAiC9KxPLaA4HvEvq1JLKIS7OpR1Gu48DTht63szqwOnA3sAbyaLUTs396r9yt2/V4zcXcNKJcozNXoKLtQTlUJl6FYozkYljgsAlfg6pdmdKnWCVpjD0Is75K6YSvzszLZFOzrecba7X2dmFWAU+DiwCHgb0BVGGj0VbOEeqVUUglI+GylUcnJVhOK4Ng+mVlAcIuEaVISG01XuedAJ1wBeffK5qSUUwiU/a1+2I3fR94CtC2VtvWIvdfdrgA/tlLKJYKyKr1qdWkWgjErMkIoxAFIxaTJ50tCpEx8aTi0haMGpn3lJagkF8eu2JeM20tz9jQ1v68BHgft3XtQE4Y6PjKRWUQil2bNTSyiM6po1qSUUhkqKF+vVOA/QCuxGxSCI4fRggtn96vWpJUw4O+WPdncnM9K6DzOdnugsjVQiALZ+fWoJhaGSk8tUPIIAYXB2H0KGjVJuMaX8ddU5XZdPv3CEggZyzGQabF+7PrWEwigvmJ9aQnGoxKcIBXabUnxdTBjqPoTiuJTO5ZY3iDhkfte+SM9IA52Hj4oxQMR0BBOLIdJYA64yTKgyCxq04jdVno9A72pNE6YRuTP0Wo2ayNCalItdqGGQirVRQWloTWQ4XQml9kvp+nrkF25NLaEQbu9QJmekKa3dqWQMKDUM2SIbUx+lTkBdaHanikGgdM9LnYvSfb/PwtQSiqFDTt5xGWlm9izgGnffJm28mc0Cnujuvx2vHjMrA1cB97n7S8xsPnAGsBRYBrwqXzsUM/sAcBxQA97p7ud3PLi7zMLkSjeTUrCqSr0oGTZB96F0zyt5ar2uc99XVm9ILWHCGa8n7dfAU4ErWpQ9Ki/fkSfXu4CbgDn5+xOBC939ZDM7MX9/gpk9BngNcDCwGLjAzA5y9853v9ANpYKKYQM6no6gSxHyoAfBhFIV6gi0YbxGWidf7yxg3Om+zWxv4MXAJ4D35JuPJlusHbKkuRcDJ+TbT3f3EeAuM7sdOIwuWis0GB9KvWoVg1PK2AzDJphIlK4vJSeG0HOlHW2NtHyI84iGTW8ysxc27dZPZnBdvwPf+UXg/UBjptaF7r4CwN1XmNme+fYlwGUN+y3PtzVrPR44PhM0Q+aG8rrOzaRi2ICWwRkEE4aSMSDyTAGkzqW+cVNqCRNOJ0/a4cA78v8deCXbrvMxCtwMvG88X2ZmLwFWufvVZnbEeD7SYts23X93PwU4BWCOzddxDwjdTNsZoA6CQA2h9ivoUoRyvrWjrZHm7p8BPgNgZncBL3P3a3fx+54O/J2ZHUXmhZtjZj8AVprZotyLtoiH5josB/Zp+PzejGcpKpUeXDRywUSicp+oEfd99xH3Sldi/SIrDmxpXzSumDR3378IHe7+AeADALkn7b3u/o9m9hngGODk/O/WNeHPBn5oZp8nmzhwIK0nLzwMlenSVulPLaE4ROoEwGNWZNdRUmmsAR8dTS2hGIQMG5WMAYBUvdhu81JLKIYOS1t3ikk7CrjE3Tfm/3fE3X+xU+IyTgbONLPjgHvIhlZx9xvM7EzgRrKh1rdvd2YnOjFDKucRBBNNbbPQQzQIJhSd50pt2T2pJUw45m2WHjKzOvAUd78i/99pP8vT3b0rIsPn2Hw/3I5MLSMIgiAIgmC7XOBnXe3uh7Yq6zTcuT+wouH/IAiCIAiCYJLoNHHg7lb/B0EQBEEQBBPPDq3daWZ9ZHnKtolod/cbixIVBEEQBEEw3Rnv2p2LyfKQvahVMVm8WlfEpIFO4lSpiQNCM4pkiFQPQRBMYSoLFqSWUAyr2xeN15P2LeCJZMs43UiWxLZrkTFuhAwblbQoENdXV6JkcKrUi1KdBF1Jbf361BImnPEaaU8H3uzuZ06kmKAJoUYuVhzoQoSuLymiXoJgXFifSH7EDqk3x2ukrQKGitAy0VhPD5W9tlnec0qy5Qka5wEw8+p7U0sojPqadaklFIIN6CRLtt6e1BKKo7c3tYJC8E2bU0soDKnrq66zcuJ9r39UagnF8IXvty0ar5H2YeAEM/uNu28sRNRE4Q4iGbtnXrM8tYTiGBNKNioydOuDg6klFIaPaMShAjIPUevZoXlpXU19s8YzBZBa77IyJVxHu8Z476K/B/YF7jazK4H1TeXu7q8uUtjO4tUq1VUPppYRBMFkohLHBTrDnaNRJ8HEsvu3r0wtYcIZr5G2O3BH/n8PsMfEyNl1rFKmMk9jxkd9SKebILMQbtCVWI/OcJQPj6SWUAxCHhtUJgshNPEJuOdfHp9aQjH8x4/aFo13gfXnFCZmgvFqjeqaDquVBmkQGloLgiAI0rP3J/6QWkIh3NyhTCdoIAiCIAiCaYNKTtSdmt1pZt/ZziFXAb9191/utLAgCIIgCIKgJZ08aX/doawMLAI+aGaXAEe5u8586yAIgiAIuhql+Lp2dFpg/cnb+7CZHQ6cDfwH8O7iZAVBEARBEExvdikmzd0vN7OPASfQJUaalUqUZ85KLaMYlGZHCeHVDgEEQRpEcouBzkxomVmqIJMbEcDHdNqvseccklpCMfzyrLZFRUwcuBFYWMBxiqFcxubNTa2iGPo0Mo8D+IDOuZQ2bkktoRiqQkMFSsmSRVaCsD13Ty2hOOpCedKEjOfX/efPUksohIsPal9WhJG2H7C2gOMUgo+NUV1+X2oZQRAEQRBMID94lM7Sie3YJSPNzBYBHwLOLUbOrmNmlETWvjOR8wCtAE+V4QKZ6etieFXDKxjXV5citDrH5pc8PrWEYjhrJ4Y7zezMDocsA3sBTwLuBT64s9oKp1ymNHdOahVBE0oGp8rasFIxj2EQdB9K15fScLpQ/ObmY9anllAM7W20jp60Tks/VYG7gR8A33f37gnSqdd1AlZd52ZCxPsE6NSLkndT6FysEjnGuw2VyRwALrQOafV381NLmHA6peCYMktBNeL1OnWRJYikhgtUvE+Aq/REhRprpSEcJeNZhfrQcGoJxSF03+91ucb61jd2KJPrspnScKeKMQA63iegJOLp8BERjzNoDa0J3SsyCI0E2MDM1BIKY81BA6klFMNv2hdpPG0a8Hqd+sZY/KDrEOq9qXjSTCn3k0idADL3ilV6UksoDpE6Aahv0BnV2PPcZaklTDhyRhruMvEpSg9RqaFb0zAIlK4vr+t4Okp9GnnSVNph0Gq/rKITGlBfuFtqCcWwvH2RnJGmlIJDKcu11NAtGg8f69MJhi4pxaSJYL06njQf1ZndaSUdr+CWfUVWF7q6fZGckYaZTuPQI1Q9NZ2GwVQ8BCUdw6Y0SyfOxlUm2QjF1pnQ9SXzfARWPF2kDftJ+yIhK+AhVNzsLjJLFbSGC2RmEqoYA0BNKbBb6V4RwUo690p9k8bzEeARZ2jEn9/ZoUzPSDPDejR6CkqNtdRwgVBPVIWySogDaM1UFUGl4w9QGhCZEQmsPHR2agnFcGX7IjkjzWs1ahs2pJYRKDMilDMpCCYKFY8zSM3uVOr8Lzzt+tQSJhw5I80qFSoLOi2WMHXwqtAQjlCQugxCMWm+RSc0QCWZrVSWfpE6AaRyvt3y1QNTSyiG17cvkjPScMdHNOIHZAKIQaphkJnQIVQnUsPpIteXK2XpF/I+KXXO5l6uka6mE5PaGpjZPsD3yRZnrwOnuPuXzGw+cAawFFgGvMrd1+Wf+QBwHFneg3e6+/mdvsNrNWobN03YOUwq4WLvTkSGcZS8A1I536oaBqdSgmGl60uJRf93T2oJhfCnDmWT3WWrAv/q7teY2WzgajP7FXAscKG7n2xmJwInAieY2WOA1wAHA4uBC8zsIHfv+HRRuaE6n+UUQ8SwAR3jRspwFkKmXkTuE9C550Ho+gIYCE9aobj7CmBF/v8mM7sJWAIcDRyR7/Y94GLghHz76e4+AtxlZrcDhwGXTqbuYNdRauRUUPHYqKFSL1LGgNCohld1zoW161MrmHCSBT+Y2VLgCcDlwMLcgMPdV5jZnvluS4DLGj62PN/WfKzjgeMB+pkhYxAoNXIqdRJ0KUKeWhWDQOmej7a4O3GhuNp2JDHSzGwW8GPg3e6+0drnBWpVsE2gg7ufApwCMMfm6wRCCKHUyKmgFDNUUppJKDJhSOn6ivarO7H581JLKIZ17Ysm3Ugzsx4yA+00d//ffPNKM1uUe9EWAavy7cuBfRo+vjdw/3a/Q+WGEvIOqAzhgM71VRKZRQg6hg1oGTcq1IU8Niox2wC+poN1I8Jkz+404NvATe7++Yais4FjgJPzvz9r2P5DM/s82cSBA4ErOn5Hbw/lxYuLlp4GobQC1DWGcADoE8luX9UZ9qCiYTgDOqlRlFZOEBoiRGRFHoDd/0djWSie0r5osrvSTydL23a9mV2bb/sgmXF2ppkdB9wDvBLA3W8wszOBG8lmhr59ezM7fXSM2vLtOtumBFbRuZmk4iBEzkXKkyZSJ0ootV8qcYKg5ald9c8iyWw7YO46FQZZTNrhdmRqGUEQBEEQTCAlkTV7fznyw6vd/dBWZTpd6Rwzo9SnkTtFyTugEscFyPSqlXrUKnUC6MSiCtWJ0r2iYtgA+CEinrTL2xfJGWnuTl0oiFiFmDjQfSg9eJQMAtO4vKQ6mTKGM1Af0Vmuq3yLxooDnZAz0qxSobL77qllFIIPj6SWUBxCQcTWKxJrU9MxbGQmcwBURSYOKF1fQigZz3f+62NSSyiGD7UvkjPSvFqltnpNahnFINR7U/J0qHiglKbiB92HKQ2rqcy4FeOA/9FIwXFbhzI5Iw2Uegoq56EzRAjIGJxSa8MGXYcPDaWWUBzRYe5K/LZlqSVMOJJGmopBoGNsap2LTIMt1Fir3PNKqHicgbhXuhQZb+1g+yI5I816eijvuef2dwwmF6VkoyMiE1MqQre/ULJkH9GIRVUyBlAxBgCEJtYN/2hGagnF0CFrmFArneFjY1TvX5FaRhAEQRAEE0jleakVTDxyRloQBEEQBPrI5Hzr4DyXNNJk3OwqsU+IxaSJIHOfiKFyr0hdX0oxaULLdZX23ye1hGK4qX2RpJGm0sgpze4Mug+v6jx4gu4jrq/uROf5CPWbOyWv0EDHVRMEQRAEQSCEpCctCIIgCAJxVEKCOmSrCSMtmBxUbiaQik8JuhCle0WFuOe7kvKsmaklFMPG9kWaRppKI6fUMCidS9B9qNzzEPdKNxLXV1cik8y2A5pGmtBFGATBOIh7PphI4vrqSqprRNbp7oBQ9yAIgiAIgkAHOU+amckkuFNy5da2dFicbIpRGuhPLaEYauEd6EasVyePlQr1oeHUEgrDenQe+8PPfmxqCcVw7llti3RqK8fdqausTaZyHmLUB3UMzqALGdExCILuw6tjqSUURt8F16aWMOHIGWnWU6Gyx16pZRSC77FbaglBC+zBdaklFINQj5qqToJOGarV1AoKw4U8aUrcecJfp5ZQDP/2o7ZFQq10ho9Vqa54ILWMYnhgVWoFxRGBt0EQBEGB7Pfvl6WWUAid1k2QM9LMjFKfSMxQWWdeh5V0zsVFhqGVYh7rwx1WKJ5iRExaFyLkFVRKJ1J78qNTSyiG353ZtkjOSHN36iIxHUoLFNfrHVIqTzFKIsOESpM5yjNnpJZQGCoGp8wEG7LnigxCa3f23HpfagkTjsbTRhSlhXCVkJmYIkRt8+bUEoImapt0AtSD7qS+6sHUEiYcHb9nEARBEASBEGGkBUEQBEEQdCGaw50igZFKMWlKsztdJL7OSpZaQmFIhQaItF9K97xSW6zSfgFU9t83tYRiuKN9kZyRZn29VPbZL7WMQnClWV7LV6RWUBwiM1VtgVAevlGh+CeRnFxKhrPSjFsXmql65zGLU0sohg+3L5Iz0nxklNqye1LLKASlHo9Sr1qGjZtSKyiOuL6CYNqx9BNXpZZQCLd2KJMz0kCrBxcEE0YYNkEQTGGmw0x7PSPNDKtouKaV4iBK+++TWkJh1O/RyM3jQkOEpf6+1BKKo6LRLMdSSsFEU9p3SWoJxdDBlabRGjRg5TJlkVib2n4LU0sojuWrUysoDBMxCKxHozMDOnUC4GMaxnNp7uzUEgpDKY7LVCamADe9Z/fUEorhre2LpoSRZmYvBL4ElIFvufvJ7fb1apXqSpE1L1XOA9Bp4oKuZENqAUEQTDYHvm1NagmFcHeHsq430sysDHwV+BtgOXClmZ3t7je2/5BIT0EpZkilTtBZFqo+JmQ6C90rJZE1VaXihYTaL6V7pTxbxFu7sX3RVHjaHAbc7u53ApjZ6cDRQEsjzcxkHqKlRTrDnb5mXWoJheEixo3KfQJgM3TW7kRknciSUEytEkqxzranyHDnFDfSlgD3NrxfDhzeuIOZHQ8cn78d+eXID/88SdomlmWpBRTK7oBOYJoGOnWisSb5VnTqRYeok25kk0y9tE3uOhWMtFZp0R/W1XT3U4BTAMzsKnc/dDKEBeMn6qX7iDrpTqJeuo+ok+5kOtTLVBhoXw405m/YG7g/kZYgCIIgCIJJYSoYaVcCB5rZ/mbWC7wGODuxpiAIgiAIggml64c73b1qZv8POJ8sBcd33P2GDh85ZXKUBTtI1Ev3EXXSnUS9dB9RJ92JfL2Yi8wkCoIgCIIgUGIqDHcGQRAEQRBMO8JIC4IgCIIg6ELCSAuCIAiCIOhCwkgLgiAIgiDoQsJIC4IgCIIg6ELCSAuCIAiCIOhCwkgLgkAGM7vYzJLkFUr53UEQaBJGWhAEQRAEQRfS9SsOBEEQ7ABvAGakFhEEQVAEYaQFQSCDu9+TWkMQBEFRxHBnEARTAjP7OzO70MxWmNmImd1vZr8xs39u2GebuDAzO8LM3MxOMrPHm9k5ZrbezAbzzz+tzfctMrPvmtkqMxsys2vN7JjG4+2A9heY2S/MbHWu/Q4z+4yZzWux7+PM7Edmtizf90Ezu8bMvmhmPeP/xYIgmOqEJy0Igq7HzI4HvgE8APwfsBrYE3gc8Ebga+M4zKHA+4FLgW8B+wIvBy40s8e7+y0N37cn8AdgKfDb/P+98u/55Q5q/zDwUWAt8HNgVa77vcBRZvZUd9+Y7/s44HLAgbOBu4A5wCOBfwY+BIztyPcHQTB1CSMtCIKpwFuAUeAQd1/VWGBmu4/zGC8G3ujupzZ89i3A14F3kRlBW/kkmYH2aXc/oWH/LwJXjFe0mT2HzEC7FDjK3dc3lB0LfDcv/5d88zFAP/BSd/9Z07F2AwbH+91BEEx9YrgzCIKpQpUWXiR3Xz3Oz/++0UDL+U5+3MO2bjCzXuAfgA3AfzR915+A749fMu/M/7650UDLj3UqcC3wuhafG2re4O7r3L2+A98dBMEUJzxpQRBMBU4DPgfcYGZnAL8hM7oe3IFjXNW8wd3HzGwlsFvD5kcBA8BV7r6pxXEuAd40zu98Kplh+Uoze2WL8l5gDzNb4O5rgDPIvHo/NbOzgAvIzvOOcX5fEARChJEWBEHX4+6fN7PVZEOS7wTeDbiZ/QZ4n7tvY4C1YH2b7VWg3PB+bv53ZZv9221vxQKydvYj29lvFrDG3a8ws2cC/wa8Ang9gJndAnzU3X+0A98dBMEUJ4Y7gyCYErj79939KWSGz4uBbwPPAs7PA/2LYmP+d2Gb8nbbW7EBWOfutp3X3Vs/4O6XuvtLyLx7Twc+nn/nD83seTtxPkEQTFHCSAuCYErh7uvd/Rfu/mbgVGA+8MwCv+Jmspiwx5nZ7Bblz9iBY10G7GZmB++oCHcfcfc/uPuHeSi27egdPU4QBFOXMNKCIOh6zOyFZtYqPGOrB62wWY/uPkoWGzaXLOVFo45DyFY1GC9fyP9+08wWNxea2Uwze0rD+2ea2dzm/XjIexezO4NgGhExaUEQTAVOB4bN7BJgGWBk3rMnA1eTBdgXyYnAc4H3m9nhZHnSFgGvAn4BvBTY7kxLd7/QzE4kS+lxm5n9giz32SxgP+DZZBMRXph/5F+B55vZxcCdwGbgYOBFwDrglGJOLwiCqUAYaUEQTAVOBF4APBE4ChgG7gZOAP7L3QtN8OruK/OVCP6//PsOB24hm7iwhcxI29j2AA8/1qfM7PdkQ5bPIBuy3ADcR2Z0/bBh96+RGWOHk8WjVYDl+fbPNcauBUGgj7n79vcKgiAIADCzTwAfBF7o7uen1hMEgS5hpAVBELTAzBa7+/1N2/6abOhzFFji7sNJxAVBMC2I4c4gCILWXGVmtwN/JhviPJAs9UcJeGsYaEEQTDThSQuCIGiBmX2ELPZsKTCbLBnuZcBn3f3iVLqCIJg+hJEWBEEQBEHQhcgNd/Zavw+UZqaWURCWWkDQEpWOjdD1pdTZLImkr1SqEyVM574f3bM/tYRCGF6xfLW779GqTM5IG+iZy9P2+ofUMoqhvy+1guLYMK5sBVODAY2GgZ6e1AqKY/OW1AqCZpSMNKW2eKyaWkFh3PWVlnbNlOOWl3+kbWodOSONapX6qgdTqygE69NpGGz+vNQSimOzStL3odQCCsOrOg8e6+tNLSFowjduTi2hMEylkwnM+GWrVdu0kDPS3J366GhqGYVgdaGe6PL7t7/PFMGV6iXoOqykMRxl5XJqCYXhtVpqCYVhgyqdTFh44UBqCROOnJGmhFcLTaKeFKUGWwUVYwC0DGedc9ExbJSMNJ3rC0rTIMxBzkizSpnKvAWpZQRN1B6xzdrSU5bSiEaDXVq9PrWE4qgIdQIqIs3yJp0hQqmYtCGd9H4bvz8rtYRieH77IpHWoIGeHup7L0ytohBMKPC2NKgxBA3gfRoB9/U956WWUBg2pmE4A7iI19l314kXsmo9tYTCsFGd+M3f/fUZqSUUQqc7Xs9Ic6c0ojFMOLh0XmoJhTHjjrWpJRSGbRKJ6VDx2IBUWgGrj6SWUAxbRFKJAIjEOQNa9/00QK62fHiE6s23pZZRCAPLRVy5QF0ppkOkwZaKTVEajlK5V5SMgZqOJw2hWNQXvuz1qSUUxIfblgjdRRlWMkr9GjM+XCifjVLgrVU0hjsRmphSHxbxPqEzycaFZhFiQl5BIUpCYQ7tkDPScEDEQ2A9OtVjvSKGDVAXCby1Xp18XFLpalzDa6N0fSFiOANSoQEbDxKJe7y6fZGOFbAVMyhr9HpUhtVALDGvkPGsglSdqDxEhQwbpbZYaei2pDMY0Bahli3HDBNZ7saFbiaboTEEDToZ4X3t+tQSikPIU2siQ2tSIQ5CMY++RWelkZG5Ih2aDugZae74mIZ5Xdp3SWoJheEPrkktoTB83frUEgpBajhKiLpSLJcIKnGCgMxwOsCWF29KLaEYvt2+SM5I8xl91J74qNQyCmF4jo53oG/ujNQSCqO8TiNJZ32OjnezPqBjcFpVwwNlIkmfAbws5LERyr95w1NPSy2hEKZXnrTNQ5T+cF1qFYXQKzRcoBSfUlUKUg+6DpWry4U8NlKeNCGe/OG3pZZQEO9pW6JnpM0cwB/32NQqikGo81ZeK7TG2maR4SiVAHXA5+nkFLR1IkM4QnGoSkspMVOnXq782H+lllAI5W+2L5Mz0qzulEWWIHKhh6jSjCIG+lMrKIYtIsYmYIM6edJU1iF1kfMAqf6ylsE5DZAz0nxomNr1t6SWUQiV/fdNLaEwNj1OYz1VgNmX351aQjHEcHp3ItI5s01C3nOlFBwqnUzg6f/yltQSCuK9bUvkjDQrlSjPmplaRiG4UDB033qNGbeAjnGjlFZA6F6pD2hMGCqv0Fmvl7lzUisoDpE8ogAH/cuNqSUUwmWnty+TM9K8Xqe2WaMHZ7fclVpCYVTu0njwANRGNQxOpQSwfv/K1BIKQ6VeakLeJ5ml4EBq7c57/+0xqSVMOBqtQQPZ2p06HgIVlGZHWb9OTzToQkQ8taUZOml3lBLzUtVZE3rzYh0PejvkjDR3nYXJlRoGK+n0ql0kBYeU4SzifQKd+96VAtSV0okIJbGesUrjWd8JnZYtx0olSjNFenB1nYZBKYGiykNUJUBdDRnjuSL0eFGanS5035eHRdriDgjdRTmlEjZbI2eSCy0Po7IeISAzHGVC6136iI6n1kQ6mSojGoBUHJdMJwC466UioU0Xty+SM9J8bIzq8vtSywiCIAiCYAJ5xL+sTi2hEDpNERRybwRBEARBEOig50mbPYPqUw5NLaMYdDzsjMzTudQqwxrxKSNzdYY9+lfrDK3VBjT6zn1rdIagBxeJDKsBvRs1wjUAfv3tb6WWUAjlRe3LdJ6cOaXBEfquvC21jKCJPpE4LkAmu71G5FOO0CQblYkpSrFPc67SqBM1HvuVWGB96lGv48Ma6/iVRFZOAJ0HD4CLJOlUymOlst4lgFU17pX6JpGF4oG60iQIIfY7c0VqCYVwU4cyPSOtVMJmDqRWUQiulJtnhkadgJaHQAahBJ0qBqcN6NzzlQU656KUv+7Wt+6VWkIxtF+6U9FIM6xPI37ARzQ8ggD06KR7UBlac5Hl00DLU6uSU7AkkgoJtNpipU6m92jcK53QM9KC7qRPJ8u1SkyaCSW11DkTYKA/tYJiKGlMgAAwISNNKZntG57729QSCuGjHcr0jDRHJtkoi/ZIraAwagMa3k2A0u33ppZQDEv2TK2gMLxXpymzUY2hWxvROA9AaiTAZ4p0AqYJk96ymdk84FvAY8lMqn8CbgHOAJYCy4BXufu6fP8PAMcBNeCd7n5+p+N7fw9jBy2ZIPWTS88DG1NLKIz6bjpB6qOHPTK1hEIYuGdDagnFMSbSMUPH4PR+He+5CV1fSnx4904h91OHbvOkfQk4z91fYWa9ZJkAPghc6O4nm9mJwInACWb2GOA1wMHAYuACMzvI3dveMTZSpWfZgxN/FpPA2FIdT1rPSp2ZXj0qsVxKi5IvmJNaQnGIDEfZyFhqCUELvEcjXGO6MKmttJnNAZ4FHAvg7qPAqJkdDRyR7/Y9spWsTgCOBk539xHgLjO7HTgMuLTtl5RKMjEdPfevTy2hMEb3mZ9aQmH03ivSq67rBN3asNDQWkUjlktquFNp7U6herlwSN/gnOyu9AHAg8B3zewQ4GrgXcBCd18B4O4rzGxrsMwS4LKGzy/Ptz0MMzseOB6gvzwbtmgsTL7+mfulllAYc27fnFpCcYjk4VPx2ACYSNoKAC9rxD+5UJ3YJhHvOcg4MQDuHVuQWsKEM9lGWgV4IvAOd7/czL5ENrTZjlZPkW26/+5+CnAKwNzSAq+vWVeE1uTM+71Gjxpg+NEd1r2YYvSvFhkmFErQWZ+t8+BRMZ6Vhjt9rk5icavpeND/e/nhqSUUxO/blkz202Y5sNzdL8/fn0VmpK00s0W5F20RsKph/30aPr83cH+nL3B36iIZ4X3FA6klFEaP0LlUVWYPm04ngPs1Mo9LoXR9CSUWV6qXvpfqTEhrx6Qaae7+gJnda2aPcvdbgCOBG/PXMcDJ+d+f5R85G/ihmX2ebOLAgcAVnb7DSiXKIsspWa/O7CilPGm+SWToViTfG4gl6BTpBJjSPT+m4xVUikV91IUabfH5T2xflmLc5h3AafnMzjuBNwIl4EwzOw64B3glgLvfYGZnkhlxVeDtnWZ2kn1IJzu0SOZxQCd3Hcgk6TSl2Z1CQ7fWKxKTNqIxogFa9woVjeF0gItPPSy1hII4vW3JpF957n4tcGiLoiPb7P8J4BPj/oJKmdICkZmEc2enVlAca9enVlAcIl4bpWWhTMR7DshcX1YRMmyUPGlCIzSDOqHObRG6izJ8rEpVJP6ptEEnma2MdxOh4SgRYwDAN+nk4VOpFxcaVlOKSbOKhqcW4BGf1HhG3t6hTM5IMzNKfRozvUrz5qaWUBjVFStTSygMmetr0cLUEoqjT+fBIzPrdoOO4VwX8jp7Vccr6Afss/2dpgLXti+SM9IolyiJDH3U1+ss21MScrGrJLasr1y1/Z2mCCreJwCvi3htqiLGJmB9OmsPq7RfAJsOFFlp5Nr2RXJGmtfq1FSGCYVc7EpDHybSyEkZNiJD0FIIxaQpXV9KsYJzr9EIbeqETm3lGEIP0T6dHDBKjZyPagwXKHkHlK4vldQ7KrNUAXxwKLWEwlC5vgDWHb5XagnFcEf7IjkjzQf68YMflVpGIZRWaaycAFDdT2ex+PIWjdQCtmk4tYTC8FlCBmdqAQWhdH3ZHKGZ9kL84XPfSC2hEMo/bF8mZ6RZvU5pi8ZMQp+p40krD2p4nwBKKg8foWBo69UZulVZFsr7dB4vSouSSy2hNg3QuYu24mBVjaGPTY/dPbWEwph1h0icIEILR88TCboFvF9nCEcFlc4yaBk29X6dYejpgJ6RVq+DSPxA31od71Ntps5DtLJaYykSJWyziHcT8F6NZtlFPIIApc06BqfKiikAq2r6bbFGa9BIva6TSV1oWajK3TrpHlSCiJXWVqRHxztgm0RmdQtl6VeZLARQ2jyQWkJhnHDfi1JLKIjvtC2RM9K8v5fawfunllEI5WGdOIixR4jMwgFsTOMhWhrSefB4n8gQNFAb0DA4e9YOppZQGPUZOh0aJQ/nd/f9fmoJhdDpLOSMNBsepXzjstQyCmH5cY9JLaEw9v7WDaklFIaPaszuVEn6DGKeDpVktkLDaqWyzrko8YRPvi21hIJ4T9sSOSMNkBkm3OM6DWMAtBIoypyLUL4kmz0rtYTiEFlOyZVWHBjQGSJUeT4CjMxPrWDiMReqMIA5Nt8PtyNTywiCIAiCINguF/hZV7v7oa3KRFwCDZhhFY2YDqmM3SNCs6NUsBjC6UZUVkyRWgoullDrSuxxB6WWUAxXn9W2SM9IE8KUYjqWLEotoTDqq9emllAIJaEhHJU4QUAnlkupToQw1+gEADKJnzsxbiPNzC4C3uTud06gnl3HHa9qBBHXNmucBwCb9fPZTDXqgzqz74IgmIZc/efUCiactkaame3h7g82bDoCmJWXlYDbgb91966atmf9fZQP0HCBmkgAMYDP1Vn7zsZEAqJFVuYA8Jk6a3fakIgHSsUjCKA0u3NIJ/Hzd39/RmoJhbBo7/ZlnTxpK81sJXAtcB3Zur/z8jIDlgJd1zLW+itsfvRuqWUUwuisBaklFEbPoE58Sq1Pw8VeGtOpk55BkbQVwNgMDYNA6foqVXXOZWyWxvUFsGdZaFZ3GzoZaQuAJwCPz/8a8Gszuxe4gsxoOwC4ZoI17hDljYPMPO+61DIKYdaAznpxSphIwL1SigRUcosBMpGCSpkDlGKfhOrlyTP186SNOwWHmdWBI4Fe4CnAR4BhYDNwkbu/Zpd1FsDc/kX+tP2OSS2jEHxAJ48VYzpDaybSyCllHrcRkSFCwGdqdM5khm0BKjqzO5U456L2syKnEuVFt+14Cg4zuwb4I9lw57VknrPV7n69mV1AZqQdAZSB5xUreefxkRGqt92RWkYxiHhs5HAdr00QTBhK7ZfSPS9ULy8+RCUn6m1tSzoNd34OOAR4CfBv+bYfmdklwJVkRtuYu18BXFqM0KARlXxJekSvuusQevCoGARKedJKQqtzKN0rvqfIkgOr2he1NdLc/TTgtK3v8+HO04E9gDeTxaidm3vVfuXu3ytI7i5hPT1UFi5OLSNopkcnMa/KQ5RhoQTDQslGUUk2KpSHD6Vk3EKzbqtfEUkj9Jz2RTuazPZsd7/OzCrAKPBxYBHwNqArjDRAJ8izT6j3JmUQiDRyM2ekVlAYSvGbtknkwaOEUAohpRQcn3/EmaklFMLjOpTtiJH2PWBrqvWtfuxL3f0a4EM7I2xCKBn0d11mkJ1DxRgAGBNKzFvRCOxmVKdORLplGTURT60QXta5wkwoP+J31jwjtYSCaJ/vbdxGmru/seFtHfgocP/Oiwq2i5L3ae6c1AqKY8PG1AqKQWmIEKHh9JhJ2HVIzVRVcWIAz51zY2oJhfDFDmU7tXanZ3k7PrpTaiaakuH9Gg22CTXW/sCD299piiCz2LKSx6ZHaBlilfhNobQoSl5npc7Zi2foDN22Q6hly6k7NijigRJKNmpKiXn7RHqiSsPpQveKjHEjlGAYpfZLZWIK8JEHD04toSB2LgXH1EVl9opQ7IDUQ1Slwd4ktOi90BCOlHGjglCwvczEOuAPq/dPLWHC0TPSxqrU738gtYpiEMlsD2AVoUtNpSeqslA8aA1HiVxfLnIeAKbS8UerXtb/6AmpJUw4Qk/OnFKJkkpqAaEeT33zltQSikOkkZOJrQOZOpFCqJPpQt5NUwnXAIb2SK1g4hmXkWZmzwKucfdtxkfMbBbwRHf/7Xi/1MzKwFXAfe7+EjObTzYHdSmwDHiVu6/L9/0AcBxQA97p7ud3OrZXq1QfXD1eKUEQBEEwfRjUycO35JN/SC2hEG7qUDZeT9qvgacCV7Qoe1ReviPd8nflurbmZTgRuNDdTzazE/P3J5jZY4DXAAcDi4ELzOwgd2/bbbZSifLMWTsgpXtRckub0LIq1qsx+86VhgiVJkGIDEMr3fM+KjKZA6RmQt/8iYNSSyiGt7ZfKH68tdVp3G0WMG7T3Mz2Bl4MfAJ4T775aLLF2iFLmnsxcEK+/XR3HwHuMrPbgcPY3lqhIsOEUnFcQsMFKkO3SsMeUulERIah60NDqSUUhkwIDVqds/l/1LhX7u5Q1tYKyIc4j2jY9CYze2HTbv1kBtf1O6Dni8D7gcZ1Nha6+woAd19hZnvm25cAlzXstzzf1qz1eOB4gP7SLJmeglTMkEruJ8BUZqoqxQwJJX42kZmqUml3hAwbpQ7z2ifojDa1o5M1czjwjvx/B14JND+dRoGbgfeN58vM7CXAKne/2syOGM9HWmzb5sni7qcApwDMrezh1EUePj06Qzg+qNOrlkknItQJkBpaEzE4vaYT+yTVYVbCRJ71HWhrpLn7Z4DPAJjZXcDL3P3aXfy+pwN/Z2ZHkXnh5pjZD4CVZrYo96ItAlbl+y8H9mn4/N5Mo6WoXCWpJWh5bUR6ohpBARkqdQJCBqdKZwakvE9K7HatxqjZTg13NuLuhWSMc/cPAB8AyD1p73X3fzSzzwDHACfnf3+Wf+Rs4Idm9nmyiQMH0nrywkOUStismUXITU+fSGMNUjFDppIEVmRYTY4ZA6kVFIIpJYAdExruHNC4vgDmvWJ5agnF8F/tizrFpB0FXOLuG/P/O+Luv9gpcRknA2ea2XHAPWRDq7j7DWZ2JnAj2VDr2zvN7ASgXsdFArtROQ/EZkepeAiEri9KQn7B1WtTKygGpToRmYwGwLr1qRUUxuqfH5pawoRj3mYYyszqwFPc/Yr8f6f9CIm7e1cM2s+x+X64HZlaRhAEQRAEwXa5wM+62t1bWpydhjv3B1Y0/B8EQRAEQRBMEp0mDtzd6v9ux8plyvN2Sy0jaEYkQSfoJBlWSfUAWrPvZPJYKQ0RCuEjGrOHAR58vcjand/Y9WS2AJhZH1mesm0S4Lj7jTssbCLo68H3W5RaRSEM7a2xcgJAeVhn4kDvGo10Iu1CHaYio/N1gqFH52jMWCsPCd3z63Viakf20OmcXXVSh4j7KUT5G+3Lxrt252KyPGQvalVMFq/WHV3Z4VHs9ntTqyiEmWvnpZZQHEKejvqKlaklFIKS96lPKHFqr5DXWQUTSZAOUL5Z5/p6wslvSy2hIN7TtmS8V963gCfmR7qRLIltV+L1OrVNm1LLKAaV8wiCiWazSFqUIAjGzZ5f1lhgvRPjNdKeDrzZ3c+cSDGFYIZVNJYg8qpIbApaXhuVmLSSStJUoK6U4sU0VhoxoRQcKvc8IPN8BCjvu80qkVOTO9oXjddIWwVMjUAcd3CNWAgpw0ZlqS6QeYgqPXiC7kPpnpdqi4U6/1K5HtswXiPtw8AJZvYbd984kYKKQKlxUKGy156pJRSGyuyo+kadIcLyXJ1JNjKzboUmpkitPSw069YXLkgtoRg6hDmP10j7e2Bf4G4zuxJY31Tu7v7qndEWdEDEIwhQW/VgaglBE0qdmbpSTJrIuUh5akW854DUc6V8n8Ykrk6M10jbnYdGTXuAPSZGzq5jfX1U9hPJvSvisQHwOSLrqQLeqzHTqzSqM8vLqjoPHlfxdAjZNTYqZHAKxQqe8+sfp5ZQCOUOWcPGu8D6c4oSM9H4yAjV2zpE4QVBEARBMOV5weJDUksoiNvalmi4BJpQCfJUGo5SQmnWWhBMFErtl8ozBdAa7txn79QSiuGu9kVtjTQz+06HQ1bJZnz+1t1/udPCJgAb6Kd04IGpZRSCV3TGC0qbhlNLKIz63BmpJRSDUGC3UjA0dY2HaGlYZzhdJcRBjXPO/VFqCYWws8Odf93pmMAi4INmdglwlLt3R7RrtYat2ZBaRSHYDJ2lblwoY7cNa+TkUorjqs/QyfmmUi8m1AmwTYOpJRSGD+jcK9OBTgusP3l7Hzazw4Gzgf8A3l2crF2gVIKZIp4OoWnftu1yr1OXqkgQsdCwbWmThmEjhZJ3s6QzqmE1HeN5OmC7usiymb0dOMHd9y1G0q4xx+b74XZkahlBEARBEATb5QI/62p3P7RVWRFjUDcCCws4TiFYbw+VxfukllEMQzpxXMzSScEh4yGoCXmf6iLeTQCVgHuhFELMmZ1aQXGM6aw48O+/+VlqCYVwwdL2ZUUYafsBaws4TiH46BjVu+9NLSNowtZ0zSUS5MTsu+5EZfZwfUxn4gCrhdovodmdHz/y5aklFMRn25bskpFmZouADwHn7spxisQG+ig/8lGpZRSCjeg0cvXZOjFppUGNiQP1mSLLD4lhIkmGyyoeZ5CZcQvgQh2aaT2708zO7HRMYC/gScC9wAd3UlvxuM7sKK/o3Ewqhg1Ava8ntYRCUJmlqoZKugeLFBxBsMt0uvI6Lf1UBe4GfgB83927Zyl6RybWpr6byCxVtGYU1Xs1jGcv60zFt6rO9eUVDQ9UWWlGpIh3E6A2KzzoU4lOKTimzFJQjdT7ygwdMD+1jEKoDug0cuVhDcMZYHSehpFW0okfpjyic31VhjQmQQzupRPi0LtRx0ir9+k8V6YDcj5cqzk9mzSePpVBjR41gOucCj1bNBrs8kad2Xf1GRpD0ABe1rhZBh7U8W6Wt2g8U0AryfB0QM9IG6tSWb46tYxi6BNySytNx+8VGSYUmopfWp9aQYFURJploWTcUimEhGbdfmV9V6RnLYDptMB6tYavXZ9aRdCM0kwvlSWuRGI3ASgLDeGMahjPLjQj0jZ1x6qHRWADOsPQp3zvxaklFMSFbUtEnjYP4fU6tc06N1QQBEEQFIbQ83HxpzRGzW7sUCZnpFmpRHnmrNQyCsFrGgHEgE4WddDy2qigFGejcq+IJOVVw4WGO4eOekJqCcXws7PaFskZaeFJC4IgCAJ9+s++IrWECUfOSLPeXipL9kstoxgGB1MrKA6VYGgAlSTDJZHzAKjqeAdQ8aCrnAdAv9AkrqpOvaz6psiaqke1LxJ6cuZUq7jIOpGu9OBRGcIBTGXigNJkDqXhThGkwjWEJg4otcX8ZP/UCiYckadNA309sN/i1CoKQegRCkLZx1WozRRJJQKUhjVmRAI6xrOQ4ayy1CBAfUAnp+CVH/uv1BIKofzN9mV6RlrdsSGRNQlVPDYAQ0JDtyILFFfWbkwtoTiUJnOIXF9Sw51ClLeIXF/AlSNCnbM2TKoVYGb7AN8nW5y9Dpzi7l8ys/nAGcBSYBnwKndfl3/mA8BxQA14p7uf3/FL6g5DwxN1CpOLUC5IqZghFQ9Bj06PWikxr4zXWclIU8opqBJTC7z20jenllAQ/9a2ZLJdNVXgX939GjObDVxtZr8CjgUudPeTzexE4ETgBDN7DPAa4GBgMXCBmR3k7u3v/mqV+uo1E30ewY6i4h0AmeEoU6oTFcMZnRQJSklTXaXjD1Je590u0LnG2jGpRpq7rwBW5P9vMrObgCXA0cAR+W7fAy4GTsi3n+7uI8BdZnY7cBhwadsvKZcpzZ0zQWcwyQg1clLBqr0iHqhhoaW6hIw0U/GkCXlqbcZAagnFIeRJO+Idl6eWUAhXf7t9WbKgJzNbCjwBuBxYmBtwuPsKM9sz320JcFnDx5bn25qPdTxwPEC/zcRVHj4iy8MAWJ9OkDqjIjGPQoaNUoqX+rr1qSUUQmm2RlJxAFcaThcauv3Jrw5PLaEgzmxbkqRlM7NZwI+Bd7v7Rms/fNSqYJsni7ufApwCMHve3j74rL8qSmpSrCr0EC1rDBEC1Ho1zqUyqNNY1/pFvE9AaUSjXkZ20/HY9AwKtcVC3PaPX08toRDK72tfNulGmpn1kBlop7n7/+abV5rZotyLtghYlW9fDuzT8PG9gfs7Hb80WmfG3Rqz1up9OsMF5XU6eYZqu2skULQxncBu79ExCEobNeKf+lfqeDeVri+rhcE5lZjs2Z0GfBu4yd0/31B0NnAMcHL+92cN239oZp8nmzhwINBxHQgfGqZ2/c1FSw92EY1Q6Jw7UgsoBjcd7xOu4X2CbBp7EEwUSibai/ZXGe68rW3JZHd1ng68HrjezK7Nt32QzDg708yOA+4BXgng7jeY2Zlki8RXgbd3nNlJFnRbGpgxQfInFxMKvJWK6VCK5VJBZMYtgIvEopZVJnABrhKHitZKNmv+4fGpJRTDt3/QtmiyZ3deQvtE+ke2+cwngE/swHfINHJKAZ5SlDQMgvqQTiK+Uq/OxBSV1Cj1TZtSSwhaIXJ9Acy8X8fgbIdO0EBObf4M1r/40NQyCkElQB2gMizkfdKpFhmqfTqV0rtFo3NWF5osVO3XOZceoQlDf/jcN1JLKITyovZlckZaZd0gu511bWoZhaDSowa0hqOEhgtkUMpurxIrKOJxBjChFC9K4RrPHH1LagkF8d62JUJXXo6VMJGhD5s1M7WEwnChoTUTWVNVqhOglCxZJSO8UrhGf19qBcWhkkcUWPlKkefKWe2LNJ42DXitRm3DhtQyimFjxHQEwXRDxXh2Je+m0OxhGU8tcOB7NBwynRIGyBlp1ttDZfE+299xCuBzdTJ228bB1BKKY1DkXJRm3wktdWMbRDpnSkspqXg3QcqTds6lP08toRCmVUyaj45RW94x3+3U4T6hhiF6ot3HmrWpFQTB1EDlnkcnXAPgxc96WWoJBfHptiU6tZVjZjFc0IW4UMyQlTQMTqU6KQk9eOpjGhNTTGjigFInUynnW312f2oJE45Oy5bjCD18hHpvuEjuOsB6NYZxTOU+AbwqdH1FJ7PriE5Ad1Ie0rnv26Fz5eVYTw/lJYtTyyiGSPXQnajEpwjFpiilFWCOxtqwCMUJKl1fJSEj7d3n/DS1hEI474D2ZXJGmo/pxKRJDReIeAcAmZxcMh5nNVRiBZVGAoRQikn77Otem1pCQfx72xKd2tqKu8zQRzxCgyCYumh0ZtRQeT4CcNmfUiuYcKKrEwRBEARB0IXIedKsXKY8b7fUMgrB+oVmrggtCyUTkzY0nFpBcczWySmoMpwuFfNY15ndqdQW3/GfHRKMTSVe2X7JATkjjZ4KLNojtYpCqPfqVI+NiTx4gPpMkSViajoD6l4RMZyB0qhGYLeNCA2rCRk2VtUxOG9+xn+nllAInSK2dayAHB8eoXbDrallBEEQ7BQ6j9AgmFhesPiQ1BIK4ra2JTrdzyAIgiAIAiHkPGlWKlGeJZJnSGiqtFV0zsVHNDJ2K9WJUk4uletLKbeYTBwqOsmSAe54x0GpJRTDv0+jmDSv16ltElmgOAiCIAiCluz34dWpJRRC+8FOQSPN+vsoP1LDulYK8FQKvK339aSWUAgWK1p0JyL3ivfoeGxsWOdeMSEP5zkXtfdATSXKHSapyhlpjI7B3RorDtSFpn2X5s1NLaEwbHAotYRC8MHB1BIKQ2n1hNKASOodlVQigNd02uK6UL084n/eklpCQby3bYmckebu1JXy84hQX7c+tYTCUInpsD6RVCJksagyqMQ/CXlqlZZSsl6NkQCAA0/bklpCISzrUKZz5eVYpUJ5oUietN3npZZQGCbUe5MZhq7q1IkLPUQRGSaUuudHdAxOJc776Q9SSyiE6TXcWavhG0UmDggZafX+3tQSCqN0+72pJRSC9enUic2amVpCcaiEOYwJGTYqM24BhO77GO6cgni9Tl0k1sZuvSu1hMJwoSHoushwJ0KzoE2lYwYy4RpW0pgAATohDqAVv/noL6VWUAzLOpTJGWnW10t5n31TyyiGzRrj7QCl/fZOLaEwhvbVmAQxcPf61BKKQ8hrUxKJGVKZBQ1QElriyss6xvPHzjkttYRCOG+/9mVyRhpmeL9G42AlkaS8CK13CfStFpndKfQQdaHrS2Ud0tKQjmGjRH1A5155Uq/O0G079Iy0Wh1bvzm1iqCJ0tr1qSUUhqvMWlOJfUJrdqep1IvQZA4f1TE4S0JDtxGTNhWp13GRYULbY0FqCYUxeuBeqSUURu+dq1JLKIYeHU+aSgJYAJ8h4ukY05ndidAQoQnVyzufd35qCYXwng5lekZauYztNi+1ikLwAR1Xbu9Ny1NLKA4Vr01Jp0fts0QSwAI2KuKpHRaaESk0CULJw/mu3ZalllAI08tIq45RX6nh6bCNOg8epeECmdl3Qo21LxcxbBBacSDoToSWhTrk029LLaEg2ptpOq30XzCsonFaStO+2X1OagWFURIJvFVZ3gqQmt1Jv8b1hVL7tUUjrRMAM2ekVlAYz3vD5aklFMKfv9C+TMOaaaRkmMqMj7k6ho1MnA1gazakllAMo0LDUTOFktmqxNcpGTZCXmelNVUfP/Oe1BImnClx5ZnZC4EvAWXgW+5+crt9vVqjumbNpGmbUFTOIwgmmgdXp1YQBMEk84NHLUktYcLpeiPNzMrAV4G/AZYDV5rZ2e5+Y8v9+/soP/KgyZQ4YVhNJ3YAoRQcqHhqx3TiBJXWIWWWyHCUUp0oIRST9rcXXp9aQiFc8Oj2ZV1vpAGHAbe7+50AZnY6cDTQ0kjz4RFqN94+ifKCYGqiFPOotAQRIsvaKQ2rScXXCdXL2a98emoJBXFx25KpYKQtARpXtF4OHN64g5kdDxyfvx25oH7mnydJWzB+dgdiTKqbqEeddClRL91H1Ek3cr1MvbRdGGoqGGmtusgP89e6+ynAKQBmdpW7HzoZwoLxE/XSfUSddCdRL91H1El3Mh3qZSpk5VwO7NPwfm/g/kRagiAIgiAIJoWpYKRdCRxoZvubWS/wGuDsxJqCIAiCIAgmlK4f7nT3qpn9P+B8shQc33H3Gzp85JTJURbsIFEv3UfUSXcS9dJ9RJ10J/L1Yi40HTcIgiAIgkCFqTDcGQRBEARBMO0IIy0IgiAIgqALmbJGmpm90MxuMbPbzezEFuVmZl/Oy68zsyem0DmdGEedPNrMLjWzETN7bwqN05Fx1Mvr8nvkOjP7g5kdkkLndGIcdXJ0Xh/XmtlVZvaMFDqnG9url4b9nmxmNTN7xWTqm46M4145wsw25PfKtWb24RQ6J4opGZOWLxV1Kw1LRQH/0LhUlJkdBbwDOIos+e2X3P3wFocLCmCcdbInWdK+lwLr3P2zCaROK8ZZL08DbnL3dWb2IuCkuFcmjnHWySxgi7u7mT0OONPdOyweE+wq46mXhv1+BQyTTWQ7a7K1ThfGea8cAbzX3V+SQuNEM1U9aX9ZKsrdR4GtS0U1cjTwfc+4DJhnZosmW+g0Yrt14u6r3P1KQGjRyK5nPPXyB3dfl7+9jCwXYTBxjKdONvtDPeiZNCXwDiaE8TxXIOv8/xhYNZnipinjrRNZpqqR1mqpqCU7sU9QHPF7dyc7Wi/HAedOqKJgXHViZi8zs5uBc4B/miRt05nt1ouZLQFeBnx9EnVNZ8bbfj3VzP5kZuea2cGTI21ymKpG2naXihrnPkFxxO/dnYy7XszsOWRG2gkTqigYV524+0/yIc6XAh+faFHBuOrli8AJ7q6zSnl3M546uQbYz90PAb4C/HSiRU0mU9VIG89SUbGc1OQSv3d3Mq56yeOevgUc7e5rJknbdGWH7hV3/y3wCDPbfaKFTXPGUy+HAqeb2TLgFcDXzOylk6JuerLdOnH3je6+Of//F0CP0r0yVY208SwVdTbwhnyW51OADe6+YrKFTiNi+a7uZLv1Ymb7Av8LvN7db02gcboxnjp5pJlZ/v8TgV4gjOeJZbv14u77u/tSd18KnAX8s7v/dNKVTh/Gc6/s1XCvHEZm18jcK12/LFQr2i0VZWZvzcu/DvyCbGbn7cAg8MZUeqcD46kTM9sLuAqYA9TN7N3AY9x9Yyrd6ozzXvkwsIDMKwBQdfdDU2lWZ5x18nKyTuYYMAS8umEiQTABjLNegklknHXyCuBtZlYlu1deo3SvTMkUHEEQBEEQBOpM1eHOIAiCIAgCacJIC4IgCIIg6ELCSAuCIAiCIOhCwkgLgiAIgiDoQsJIC4IgCIIg6ELCSAuCcWBmPo7XEWa2zMym7MLxO6PfzA4zs5NabD/JzFYXJm4nNEzg973KzI5tsf1iM5u0BbfN7Nj82ps1Wd/ZDZhZb359PX4HPvMVM/vuDux/jpn9+04JDIKCmJJ50oIgAU9t+H8AuAj4D7J1FbdyI9m6fjKJFMfJYcBHgJOatn8L+L/EGiaKVwG7A6c2bf9nYGySNExnesnqexlw7fZ2NrN9gDcBT9iB7zgZONvMvuLu63dcYhDsOmGkBcE4cPfLtv7f4LW4o3F7zh8nT9X4MLMBdx+a7O919+Vky7pMG9z9xtQagpa8FbjG3W8e7wfc/XdmtgZ4PdmakEEw6cRwZxAUSPNwoZmdamZXmdmLzexGMxvMh1Hm50v//NrMtuT7PK7pWCUzO9HMbjezETO71cyOGYcGN7P3mNkXzexB4Pp8e7+ZfdrM7s2P9yczO2o7x3qqmZ1tZvfnOq81s9c1lB9L/gBrGPa9OH//l+FOM5uZf/6fW3zHVWb23w3v9zWz081sbf57nW9mj+qgsa2GfNtzzexyMxs2s5Vm9rXtDQ+a2d5mdqaZrTKzITO7w8w+npedSrYiwLMbvu+kvOxhw51bfwMzOzw/zyEzu8SyZW72NLOfmtlmM7vJzJ7bpMEty7ZO8/E66D4i/9xjm7Y36zrYzM7Lf+Mt+fe/fTu/yQwz+7KZPZD/llea2fNbfY+ZvTa/bjea2blmtnfTfh/Iy7fWyXmWrUiytXy+mX0jLxs2sz+Y2eENh9iU//1uQx0s7SD/DWTLODVqGM9v8OP8s0GQhPCkBcHEsy/wMeBDwAwyg+IUYCnwTeDTwCfJFm4+uGFJk68Ax+SfvQb4G+A7ZrbG3X++ne98H/BbMi/A1s7YWTw0LHgH2ZDd2WZ2qLtf2+Y4+wG/B74ODANPJ3sw1t39R2TDvZ8D/pWHhoS3WebL3beY2c+BVwNf27rdzA4AnkQ+TGlm84FLyIaM30q2pNuJwAVmdlAbj2BbDWb2GOA84FdkhtU+ZMNYBwAvbHPOAN8nG9Y+Hlif7//ovOzjZHU6j2x4Ezp7DGeQ1fengS3Al4H/BkaAc8l+j/cD/2Nm+7j7YIdjFcXZwM3AP+Y6HkW2XFsnvgn8HfBBsuX23gycY2bPcfdLGvY7HFhMVh8DwJfIzv8oADN7Q36ME4AbyJYkey4wMy/vAy4g+33fB6wC3kZ2DRzo7g/k+zeHHLRcmzk38PcG/rATv8EfgPeZ2W7uvm47v08QFI+7xyte8dqBFzALcODYFmXLgM82vD8VqAKPaNj26fzzb2jYdlS+7a/y948E6sAxTcf/PnDldvQ58MembUfm25/dtP23wP+009+0r5F17L4BXNSw/f9lTck2+58ErG54/zKgBixu2PYBYC3Qm7//OJmBNr9hn92ADcDbO5xzOw2nA7cB5YZtr8p/i6d2ON5m4G87lJ8FXNxi+8XAWU2/wcN+dzLDzoEPN2x7TL7tRU31+P+285sem+83K39/RP7+se10kcXSOfDXO3DN/1Xz9Uhm/P8ZOL/pezYAuzVse3f+fQP5+/8Eftzhu44DRoEDG7ZVyDoWn9nePdjieK/N953ZsG1cvwFZR8qBvxnvbxWveBX5iuHOIJh4lrn7HQ3vb8//XtRi25L875FkD8WfmFll6wu4EHi8mZW3853nNL1/HvAA8PsWx2u7mLqZ7ZYPcd1NFhA/RuZdOmg739+Kc8mMn1c2bHs18BN3H23Q+StgY4PGTcDVnXR24LD8+LWGbT8mM5yf0eFz1wKftGz25L478b2NjAK/a3g/nvqfSNYC9wJfN7NXm9me4/jMk8mM9P/ZusHd6/n75t/xSn+412lrnN7Wc7sWOMrMPmrZrNzma/l5ZPV9V8M1APAbdu4a2AsYdvctDdvG+xtsHVreq015EEwoYaQFwcSzvun9aIvtW7f15393B8pkXomxhtepZF6FRdv5zpVN73cne9CMNb1OIhsCbMepZIbUZ4Dnkz2sv9Ogc9y4+zDws/x4W4ehDiHzdjXqfHULnc/Zjs52LKLpt8gNtjXA/A6fezVwFfAF4G7LYvGO3InvB9iUGzRb2ab+G4zUHf5dd5Rcy/PJjPbvAA+Y2e/MrNPMx0XAZt92KHYlMCMfotzK+qZ9ms/tO2TDna8CLgdWmtnHG4y13YGnsO018EZ27hroJxvO/As78Bts/dyE10sQtCJi0oKgO1lL5u15OplHrZlV2/m8N71fC9wHvHS8AsysH3gx2ZDb1xu270rn7gzg/3Lv1KuBB3m4R2ktWazQx1t8dlOLbdtjBfAwL0luDCzIv6sl7n4fcGx+roeRGbNnm9m+7j5ZKVZGyFJNNNLJsIQsbpA2n/vLhAPPZjm+3Mx6gGcCnyKLL9u7yaDcygpglpnNaDLUFgKD7j7S4jMtyY//BeALlqXGeB3wCbLr8+tk9XIVWRxaM+P+ngbWAnPMrNR4buP8DeY1HCMIJp0w0oKgO7mIzJM2191/VcDxLiQL5N7s409D0Jdr+MuD0cxmkwWPNxqBo3lZf+4t68QvgXVkXpRXk8VJNQ5FXpiX3eA7ljaknYbLgZeZ2Qcbvufvydq+S9gO+cP6MjP7KFkQ+X5kXrhRJt67spwsFgz4i3H83Pa7/+Uz5J+7Jv/cPmRB8bc27+zuY8BFZvZ54IdkRkkrg+RKsjp/BVlcJGZm+fvt/o7tcPd7gZPN7I1kcXmQXQPPB+5x93adkR3xPN5CNlS7H3BXCw2dfoOl+d9tfrsgmAzCSAuCLsTdbzGzr5PN+Pw0mWehHzgYOMjd37SDh/wVcD7wKzP7FNmsujnA44F+d/9ACw0bzOxK4MNmtpHMo3ci2RBs4yy4rUbfu8zsImCju9/S5rzGzOwnwHvIhtCaU3J8nmy23UVm9hUy78pC4NnAJZ7NKG1FOw3/QZa77qdm9l9ks/w+RRbsfmmrA5nZXLLf6vtkD+c+MgP3AeCmhu872sxeSmYY3e/u97fRtrP8BHi7mf0RuJMsGWvHGZjuvjyvs4+b2SBZSMsHaTC8LEv18lkyr+adZBMzTgD+5O4tPUbufpOZ/Qj4TzObw0OzOx9Na49XW8zsG7mey8iupecAB+YaIPvd3wpcbFk6mzvJPJ+HAQ+4+xfcfdTM7gJeZWZ/JvMgXtcwbNzIFWRe6SeRG2k78Bscmmu8YUfOMQgKI/XMhXjFa6q92PHZnVc17XMsDTPy8m1L820vadhmZDPjbiDzZj1IFjz9hu3o22ZWYL69D/go2QN2lMzoOA94cQf9jyTz6m0B7iFLFXESD59haGQzVu8nM+Quzrc/bL+G/Z+Xa7wPKLUoXwx8lyzeaSTX9APg4A7n3FJDXnYkmUdtmGyY+GuNv32b3+mbZB6YQbJhwp/TMBOQLG7qJ2TGhgMn5dsvZtvZnaubjn8ErWdgPqze8uvse/l3PECWwqX5t291LT0y17ElP4ejefjszj3JUoDcmf8mDwA/AvbdznW1NX3M1nq5CnhB0z4PO/9W55tr/n1+XoPAdcBxTZ+ZS5a6416ya3U58L/A0xv2eX7+2eH8+Es7aP858O2G9+P6DchiKL87WW1LvOLV/DL35tCVIAiCINDBzF5GtkzZYh9n/FzuUV0JPM8fngcuCCaNmN0ZBEEQqPNTMs/t63fgM28DLgsDLUhJGGlBEASBNJ4NGR1PlspjvGwA3jkxioJgfMRwZxAEQRAEQRcSnrQgCIIgCIIuJIy0IAiCIAiCLiSMtCAIgiAIgi4kjLQgCIIgCIIuJIy0IAiCIAiCLuT/B7GLm1UDIrVUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate whole, burst, and single psths\n",
    "#                                         x   y\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (10, 10)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=3, ncols=1, sharex=True)\n",
    "plot_count = 0\n",
    "for spike_train_type, psth in psths.items():\n",
    "    # Get the response averaged PSTH\n",
    "    mean_psth = psth.mean(dim=\"stimulus_presentation_id\") # Mean activity across trials\n",
    "    \n",
    "    # Generate the figure\n",
    "    ax[plot_count].pcolormesh(\n",
    "        mean_psth[\"time_relative_to_stimulus_onset\"],\n",
    "        np.arange(mean_psth[\"unit_id\"].size),\n",
    "        mean_psth.T,\n",
    "        vmin=0,\n",
    "        vmax=1\n",
    "    )\n",
    "    ax[plot_count].set_title(spike_train_type)\n",
    "    ax[plot_count].set_ylabel(\"Unit #\")\n",
    "    plot_count += 1\n",
    "\n",
    "ax[plot_count-1].set_xlabel(\"Time relative to stimulus onset (s)\")\n",
    "fig.suptitle(\"Peristimulus Time Histograms\")\n",
    "plt.show()\n",
    "\n",
    "# Save the figure and axis to tile it later\n",
    "psth_fig = fig\n",
    "psth_ax = ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ISI density plots (whole train)\n",
    "#                                         x   y\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (10, 10)\n",
    "\n",
    "def generate_isi(spikes):\n",
    "    # num_spikes - 1 is the number of interspike intervals\n",
    "    num_isi = len(spikes) - 1\n",
    "    isi = np.zeros(num_isi)\n",
    "    for k in range(num_isi):\n",
    "        isi[k] = spikes[k+1] - spikes[k]\n",
    "    return isi\n",
    "\n",
    "# Select 3 cells for isi plots (lower cells), one with low activity, \n",
    "# \"average\" activity (it's actually the median based on how I'm \n",
    "# selecting it), and high activity\n",
    "example_whole_ids = [0]*3\n",
    "example_whole_ids[0] = units_by_whole_activity[\"unit_id\"].iat[lower_quartile_idx]\n",
    "example_whole_ids[1] = units_by_whole_activity[\"unit_id\"].iat[middle_quartile_idx]\n",
    "example_whole_ids[2] = units_by_whole_activity[\"unit_id\"].iat[upper_quartile_idx]\n",
    "example_whole_mask = {\"unit_id\": example_whole_ids}\n",
    "example_whole = spike_times[spike_times.isin(example_whole_mask)[\"unit_id\"]]\n",
    "example_whole.reset_index(inplace=True)\n",
    "plot_labels = [\"low activity cell\", \"median activity cell\", \"high activity cell\"]\n",
    "\n",
    "#fig, ax = plt.subplots(nrows=1, ncols=3, sharex=True, sharey=True)\n",
    "example_isis = [[]]*3\n",
    "plot_count = 0\n",
    "for cell_id in example_whole_ids:\n",
    "    example_spike_times = example_whole.loc[example_whole[\"unit_id\"]==cell_id]\n",
    "    example_spike_times = example_spike_times[\"spike_time\"].to_numpy()\n",
    "    example_isis[plot_count] = generate_isi(example_spike_times)\n",
    "    plot_count += 1\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(example_isis, histtype=\"step\", log=True, label=plot_labels, linewidth=LINEWIDTH)\n",
    "ax.set_xlabel(\"Interspike interval (s)\")\n",
    "ax.set_ylabel(\"Interval count (logarithmic)\")\n",
    "ax.set_title(\"Whole spike train interspike intervals\")\n",
    "ax.legend()\n",
    "whole_spike_isi_fig = fig\n",
    "whole_spike_isi_ax = ax\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#                                         x   y\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (25, 10)\n",
    "example_burst_ids = [0]*3\n",
    "example_burst_ids[0] = units_by_burst_activity[\"unit_id\"].iat[lower_quartile_idx]\n",
    "example_burst_ids[1] = units_by_burst_activity[\"unit_id\"].iat[middle_quartile_idx]\n",
    "example_burst_ids[2] = units_by_burst_activity[\"unit_id\"].iat[upper_quartile_idx]\n",
    "example_burst_mask = {\"unit_id\": example_burst_ids}\n",
    "example_bursts = burst_times[burst_times.isin(example_burst_mask)[\"unit_id\"]]\n",
    "example_bursts.reset_index(inplace=True)\n",
    "example_whole = spike_times[spike_times.isin(example_burst_mask)[\"unit_id\"]]\n",
    "example_whole.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "def generate_burst_isi(spike_train, burst_id_mask):\n",
    "    k = 0\n",
    "    spike_count = len(burst_id_mask)\n",
    "    intraburst_isis = []\n",
    "    interburst_isis = []\n",
    "    last_burst_loc = -1\n",
    "    current_burst_loc = -1\n",
    "    while k < spike_count:\n",
    "        is_burst = burst_id_mask[k]\n",
    "        \n",
    "        if is_burst:\n",
    "            \n",
    "            # If we have seen at least 1 burst\n",
    "            if last_burst_loc > -1: \n",
    "                current_burst_loc = k\n",
    "                current_burst = spike_train[current_burst_loc]\n",
    "                last_burst = spike_train[last_burst_loc]\n",
    "                interburst_isis = interburst_isis + [current_burst - last_burst]\n",
    "            \n",
    "            next_spike = spike_train[k]\n",
    "            \n",
    "            # Keep iterating through the spike train, count\n",
    "            # intraburst isis. This loop stops at the last\n",
    "            # burst spike, meaning when this loop is done\n",
    "            # burst_id_mask[k] is the first spike after this burst\n",
    "            while is_burst:\n",
    "                k += 1\n",
    "                current_spike = next_spike\n",
    "                next_spike = spike_train[k]\n",
    "                is_burst = burst_id_mask[k]\n",
    "                intraburst_isis = intraburst_isis + [next_spike - current_spike]\n",
    "            last_burst_loc = k-1\n",
    "        \n",
    "        k += 1\n",
    "    \n",
    "    # need to return intraburst_isis, interburst_isis\n",
    "    return np.array(intraburst_isis), np.array(interburst_isis)\n",
    "\n",
    "\n",
    "whole_isis = [[]]*3\n",
    "intraburst_isis = [[]]*3\n",
    "interburst_isis = [[]]*3\n",
    "single_isis= [[]]*3\n",
    "plot_count = 0\n",
    "for cell_id in example_burst_ids:\n",
    "    # Pull the bursts and whole train for this particular cell\n",
    "    current_bursts = example_bursts.loc[example_bursts[\"unit_id\"]==cell_id]\n",
    "    current_whole = example_whole.loc[example_whole[\"unit_id\"]==cell_id]\n",
    "    num_bursts = len(current_bursts)\n",
    "    num_spikes = len(current_whole)\n",
    "    \n",
    "    # Initialize the tag_map for the spikes: an array that indicates\n",
    "    # whether each spike in `current_whole` is a burst or single\n",
    "    tag_map = np.zeros(num_spikes, dtype=bool)\n",
    "    \n",
    "    # Initialize burst ranges\n",
    "    burst_beg = current_bursts[\"absolute_beg_time\"].iat[0]\n",
    "    burst_end = current_bursts[\"absolute_end_time\"].iat[0]\n",
    "    burst_idx = 1\n",
    "    k = 0\n",
    "    while k < num_spikes:\n",
    "        current_spike = current_whole[\"spike_time\"].iat[k]\n",
    "        if current_spike >= burst_beg and current_spike < burst_end:\n",
    "            # If the spike is inside the burst range, make it a burst\n",
    "            tag_map[k] = True\n",
    "        \n",
    "        elif current_spike > burst_end and burst_idx < num_bursts:\n",
    "            # If the spike occured after the end of the burst range, we need to check\n",
    "            # if there are more bursts. If not, the `else` will be triggered\n",
    "            burst_beg = current_bursts[\"absolute_beg_time\"].iat[burst_idx]\n",
    "            burst_end = current_bursts[\"absolute_end_time\"].iat[burst_idx]\n",
    "            burst_idx += 1\n",
    "            k -= 1 # We don't want to index k, because we want to check the same spike again\n",
    "        \n",
    "        else:\n",
    "            # If the spike wasn't in the burst range, make it a single\n",
    "            tag_map[k] = False\n",
    "\n",
    "        k += 1\n",
    "    \n",
    "    #isolated_bursts = current_whole.loc[tag_map][\"spike_time\"].to_numpy()\n",
    "    isolated_singles= current_whole.loc[np.invert(tag_map)][\"spike_time\"].to_numpy()\n",
    "    whole_isis[plot_count] = generate_isi(current_whole[\"spike_time\"].to_numpy())\n",
    "    intraburst_isis[plot_count], interburst_isis[plot_count] = generate_burst_isi(current_whole[\"spike_time\"].to_numpy(), tag_map)\n",
    "    single_isis[plot_count] = generate_isi(isolated_singles)\n",
    "    \n",
    "    plot_count += 1\n",
    "\n",
    "for k in range(3):\n",
    "    whole_isis[k] = np.log(np.array(whole_isis[k]))\n",
    "    intraburst_isis[k] = np.log(np.array(intraburst_isis[k]))\n",
    "    interburst_isis[k] = np.log(np.array(interburst_isis[k]))\n",
    "    single_isis[k] = np.log(np.array(single_isis[k]))\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=4, sharey=True)\n",
    "ax[0].hist(whole_isis, histtype=\"step\", log=True, linewidth=LINEWIDTH)\n",
    "ax[0].set_xlabel(\"Interspike interval (ln(seconds))\")\n",
    "ax[0].set_ylabel(\"Interval count (logarithmic)\")\n",
    "ax[0].set_title(\"Whole spike train interspike intervals\")\n",
    "\n",
    "ax[1].hist(intraburst_isis, histtype=\"step\", log=True, linewidth=LINEWIDTH)#, label=plot_labels)\n",
    "ax[1].set_xlabel(\"Interspike interval (ln(seconds))\")\n",
    "ax[1].set_title(\"Intraburst interspike intervals\")\n",
    "\n",
    "ax[2].hist(interburst_isis, histtype=\"step\", log=True, label=plot_labels, linewidth=LINEWIDTH)#, label=plot_labels)\n",
    "ax[2].set_xlabel(\"Interburst interval (ln(seconds))\")\n",
    "ax[2].set_title(\"Interburst intervals\")\n",
    "ax[2].legend()\n",
    "\n",
    "ax[3].hist(single_isis, histtype=\"step\", log=True, linewidth=LINEWIDTH)#, label=plot_labels)\n",
    "ax[3].set_xlabel(\"Interspike interval (ln(seconds))\")\n",
    "ax[3].set_title(\"Non-burst spike interspike intervals\")\n",
    "fig.suptitle(\"Example of interspike interval behavior\")\n",
    "\n",
    "plt.savefig(f\"{save_path}/isi_examples.png\", dpi=500, facecolor=\"white\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TITLE_FONT_SIZE = 25\n",
    "XY_LABEL_SIZE = 25\n",
    "X_TICK_SIZE = 20\n",
    "Y_TICK_SIZE = 20\n",
    "LEGEND_SIZE = 20\n",
    "SUPTITLE_SIZE = 25\n",
    "SUPTITLE_WEIGHT = \"bold\"\n",
    "LINEWIDTH = 3\n",
    "#                                         x   y\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "matplotlib.rcParams[\"figure.titlesize\"] = SUPTITLE_SIZE\n",
    "matplotlib.rcParams[\"figure.titleweight\"] = SUPTITLE_WEIGHT\n",
    "plt.rc('axes', titlesize=TITLE_FONT_SIZE)\n",
    "plt.rc('axes', labelsize=XY_LABEL_SIZE)\n",
    "plt.rc('xtick', labelsize=X_TICK_SIZE)\n",
    "plt.rc('ytick', labelsize=Y_TICK_SIZE)\n",
    "plt.rc('legend', fontsize=LEGEND_SIZE)\n",
    "all_spike_times = session.spike_times\n",
    "stim_table = decoder.stim_table\n",
    "num_cells = 1\n",
    "\n",
    "unit_id = 951035277\n",
    "\n",
    "ISI_BIN_SIZE = 0.1\n",
    "\n",
    "# The start and end times for this particular stimulus epoch\n",
    "# (we just want the spikes associated with the stimulus in \"stim\")\n",
    "start_times = list(stim_table[\"start_time\"])\n",
    "start_time = start_times[0]\n",
    "stop_times = list(stim_table[\"stop_time\"])\n",
    "stop_time = stop_times[-1]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=num_cells, ncols=1)\n",
    "\n",
    "#k = 0\n",
    "#for unit_id in example_cell_ids:\n",
    "unit_spikes = all_spike_times[unit_id]\n",
    "unit_spikes = unit_spikes[(unit_spikes >= start_time)*(unit_spikes <= stop_time)]\n",
    "unit_isi = generate_isi(unit_spikes)\n",
    "log_isi = np.log(unit_isi)\n",
    "left_bound = np.min(log_isi)\n",
    "right_bound = np.max(log_isi)\n",
    "bin_edges = np.arange(left_bound, right_bound + ISI_BIN_SIZE, ISI_BIN_SIZE)\n",
    "\n",
    "ax.hist(log_isi, bins=bin_edges)\n",
    "ax.set_xlabel(\"Log(ISI) (ln(seconds))\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "fig.tight_layout()\n",
    "seaborn.despine(fig=fig)\n",
    "plt.savefig(f\"{save_path}/log_isi_example.png\", dpi=500, facecolor=\"white\", bbox_inches=\"tight\")\n",
    "\n",
    "#ax.set_title(f\"{unit_id}\")\n",
    "\n",
    "#k += 1\n",
    "#if k >= num_cells: break"
   ]
  }
 ],
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