NEV and NSX File Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1892326f",
   "metadata": {},
   "source": [
    "Blackrock's Documentation: https://blackrockneurotech.com/research/wp-content/ifu/LB-0826-1.11-Python-Offline-Utilities-IFU-Draft.pdf\n",
    "\n",
    "Blackrock's Github: https://github.com/BlackrockNeurotech/Python-Utilities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cfd3c5",
   "metadata": {},
   "source": [
    "Here I extract the data that is necessary to populate the sessions and threshold_files tables. The sessions table requires the following fields:\n",
    "- day_key  - DONE\n",
    "- rec_time\n",
    "- sessions_key\n",
    "- paper_key\n",
    "- behavior_notes\n",
    "- behavior_quality\n",
    "- other_notes\n",
    "- task_id\n",
    "- lab_num\n",
    "- duration\n",
    "- numChannels\n",
    "- hasTriggers\n",
    "- hasChaoticLoad\n",
    "- hasBumps - check with kevin for encoding\n",
    "- numTrials\n",
    "- numReward - DONE\n",
    "- numAbort - DONE\n",
    "- numFail - DONE\n",
    "- numIncomplete - DONE\n",
    "- reward_size\n",
    "\n",
    "The threshold_files table requires the following fields:\n",
    "- sessions_key\n",
    "- array_serial\n",
    "- paper_key\n",
    "- filename\n",
    "- file_id\n",
    "- setting_file\n",
    "- is_sorted\n",
    "- sorted_by\n",
    "- num_chans\n",
    "- num_units\n",
    "- rec_system\n",
    "- connect_type\n",
    "- connect_serial\n",
    "- threshold_quality\n",
    "- threshold_notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1db6e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sqlalchemy import create_engine\n",
    "from os import path, system\n",
    "import sys\n",
    "from sys import platform\n",
    "import glob\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "import xml.etree.ElementTree as ET\n",
    "import time\n",
    "# from PyQt5.QtWidgets import QFileDialog\n",
    "\n",
    "# brpylib is the module that contains functions/classes that allow us to open and extract data from .nev and .nsx files\n",
    "from Python_Utilities import brpylib\n",
    "from Python_Utilities import brMiscFxns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d534679",
   "metadata": {},
   "source": [
    "Scrape directory and put file names into lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29bda160",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monkey data\n",
    "monkeyName = \"Greyson\"\n",
    "ccmID = \"18E2\"\n",
    "\n",
    "# directory info\n",
    "scrape_dir = '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData'\n",
    "if not path.exists(scrape_dir):\n",
    "    print(f\"{scrape_dir} does not exist! Try again!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc385ce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 55.6 ms, sys: 147 ms, total: 203 ms\n",
      "Wall time: 2min 34s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nev_list = glob.glob(f\"{scrape_dir}/*/*.nev\")\n",
    "nsx_list = glob.glob(f\"{scrape_dir}/*/*.ns*\")\n",
    "# plx_list = glob.glob(f\"{scrape_dir}/*/*.plx\")\n",
    "# ccf_list = glob.glob(f\"{scrape_dir}/*/*.ccf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7df78c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322, 260)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nev_list), len(nsx_list), #len(plx_list), len(ccf_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7c1b97",
   "metadata": {},
   "source": [
    "## Nev File exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee31979",
   "metadata": {},
   "source": [
    "### Compare time it takes to read from server vs copying locally and then loading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f47b7ab",
   "metadata": {},
   "source": [
    "#### From  server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc5b0a4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  1.3631501197814941\n",
      "total time taken to extract file data:  290.8700969219208\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.802819013595581\n",
      "total time taken to extract file data:  189.2007761001587\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  1.0039618015289307\n",
      "total time taken to extract file data:  168.155179977417\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.6872336864471436\n",
      "total time taken to extract file data:  214.1452431678772\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  70.12533617019653\n",
      "total time taken to extract file data:  292.61368894577026\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.9937379360198975\n",
      "total time taken to extract file data:  269.94694805145264\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.7900879383087158\n",
      "total time taken to extract file data:  172.42333316802979\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.7032310962677002\n",
      "total time taken to extract file data:  238.29565286636353\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  1.043764352798462\n",
      "total time taken to extract file data:  188.33867502212524\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.7693948745727539\n",
      "total time taken to extract file data:  185.7722749710083\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  1.0109050273895264\n",
      "total time taken to extract file data:  145.19987320899963\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.9762711524963379\n",
      "total time taken to extract file data:  211.34043884277344\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.6097161769866943\n",
      "total time taken to extract file data:  138.88524985313416\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.9993569850921631\n",
      "total time taken to extract file data:  90.05223083496094\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  1.0220122337341309\n",
      "total time taken to extract file data:  79.61188292503357\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  39.93728995323181\n",
      "total time taken to extract file data:  81.70591521263123\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.8865220546722412\n",
      "total time taken to extract file data:  87.05579495429993\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  1.104694128036499\n",
      "total time taken to extract file data:  88.20124912261963\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.8640599250793457\n",
      "total time taken to extract file data:  72.27575898170471\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.8364291191101074\n",
      "total time taken to extract file data:  70.66852569580078\n"
     ]
    }
   ],
   "source": [
    "# create one NEV File object\n",
    "for i in range(20):\n",
    "    start = time.time()\n",
    "    NevFileObj = brpylib.NevFile(nev_list[1])\n",
    "    end1 = time.time()\n",
    "    print(\"total time taken to open file: \", end1 - start)\n",
    "    output = NevFileObj.getdata(elec_ids='all')\n",
    "    end2 = time.time()\n",
    "    print(\"total time taken to extract file data: \", end2 - end1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cbc58954",
   "metadata": {},
   "outputs": [],
   "source": [
    "open_server_files = [1.3631501197814941,0.802819013595581,1.0039618015289307,0.6872336864471436,70.12533617019653,0.9937379360198975,0.7900879383087158,0.7032310962677002,1.043764352798462,0.7693948745727539,1.0109050273895264,0.9762711524963379,0.6097161769866943,0.9993569850921631,1.0220122337341309,39.93728995323181,0.8865220546722412,1.104694128036499,0.8640599250793457,0.8364291191101074,]\n",
    "extract_server_files = [290.8700969219208,189.2007761001587,168.155179977417,214.1452431678772,292.61368894577026,269.94694805145264,172.42333316802979,238.29565286636353,188.33867502212524,185.7722749710083,145.19987320899963,211.34043884277344,138.88524985313416,90.05223083496094,79.61188292503357,81.70591521263123,87.05579495429993,88.20124912261963,72.27575898170471,70.66852569580078,]\n",
    "\n",
    "server_times = []\n",
    "for o, e in zip(open_server_files, extract_server_files):\n",
    "    total_time = o+e\n",
    "    server_times.append(total_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e6252da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20181220/20181220_Greyson_PG_001.nev'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nev_list[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3c7e10c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total time taken to copy file:  66.49807405471802\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.02280902862548828\n",
      "total time taken to extract file data:  71.6173141002655\n",
      "total time taken to remove file:  0.16086602210998535\n",
      "total time taken to copy file:  65.82321786880493\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.21553707122802734\n",
      "total time taken to extract file data:  71.60160398483276\n",
      "total time taken to remove file:  0.1385200023651123\n",
      "total time taken to copy file:  63.388975858688354\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.20301198959350586\n",
      "total time taken to extract file data:  71.72584199905396\n",
      "total time taken to remove file:  0.14240097999572754\n",
      "total time taken to copy file:  69.53719806671143\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.8547677993774414\n",
      "total time taken to extract file data:  75.98714900016785\n",
      "total time taken to remove file:  0.13985419273376465\n",
      "total time taken to copy file:  66.88227105140686\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.36477112770080566\n",
      "total time taken to extract file data:  72.52384781837463\n",
      "total time taken to remove file:  0.14183616638183594\n",
      "total time taken to copy file:  79.47948098182678\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.35686612129211426\n",
      "total time taken to extract file data:  72.76131296157837\n",
      "total time taken to remove file:  0.14025092124938965\n",
      "total time taken to copy file:  71.94347214698792\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.22889375686645508\n",
      "total time taken to extract file data:  70.40090298652649\n",
      "total time taken to remove file:  0.13845205307006836\n",
      "total time taken to copy file:  67.96569490432739\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.19390082359313965\n",
      "total time taken to extract file data:  71.69030809402466\n",
      "total time taken to remove file:  0.15458893775939941\n",
      "total time taken to copy file:  59.21510100364685\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.524144172668457\n",
      "total time taken to extract file data:  71.41203570365906\n",
      "total time taken to remove file:  0.15239405632019043\n",
      "total time taken to copy file:  80.71410202980042\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.2035541534423828\n",
      "total time taken to extract file data:  71.75251770019531\n",
      "total time taken to remove file:  0.1474151611328125\n",
      "total time taken to copy file:  62.98839807510376\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.3157508373260498\n",
      "total time taken to extract file data:  72.01969909667969\n",
      "total time taken to remove file:  0.13489985466003418\n",
      "total time taken to copy file:  66.48040223121643\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.3054039478302002\n",
      "total time taken to extract file data:  72.41998887062073\n",
      "total time taken to remove file:  0.1480882167816162\n",
      "total time taken to copy file:  61.38100790977478\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.7855150699615479\n",
      "total time taken to extract file data:  72.70379304885864\n",
      "total time taken to remove file:  0.15896296501159668\n",
      "total time taken to copy file:  70.88359427452087\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.18939590454101562\n",
      "total time taken to extract file data:  72.14531087875366\n",
      "total time taken to remove file:  0.14416790008544922\n",
      "total time taken to copy file:  78.36129999160767\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.7764649391174316\n",
      "total time taken to extract file data:  73.55884408950806\n",
      "total time taken to remove file:  0.14118695259094238\n",
      "total time taken to copy file:  80.66551995277405\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.599384069442749\n",
      "total time taken to extract file data:  72.375981092453\n",
      "total time taken to remove file:  0.1450800895690918\n",
      "total time taken to copy file:  106.66426706314087\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.3749380111694336\n",
      "total time taken to extract file data:  71.88047480583191\n",
      "total time taken to remove file:  0.14903616905212402\n",
      "total time taken to copy file:  66.23251080513\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.2076261043548584\n",
      "total time taken to extract file data:  71.14507794380188\n",
      "total time taken to remove file:  0.14260101318359375\n",
      "total time taken to copy file:  67.59491395950317\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.3270750045776367\n",
      "total time taken to extract file data:  73.31038784980774\n",
      "total time taken to remove file:  0.14894819259643555\n",
      "total time taken to copy file:  82.71874189376831\n",
      "\n",
      "20181220_Greyson_PG_001.nev opened\n",
      "total time taken to open file:  0.6158878803253174\n",
      "total time taken to extract file data:  72.04803824424744\n",
      "total time taken to remove file:  0.15474987030029297\n"
     ]
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    start = time.time()\n",
    "    !scp '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20181220/20181220_Greyson_PG_001.nev' '/Users/aajanquail/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/20181220_Greyson_PG_001.nev'\n",
    "    end0 = time.time()\n",
    "    print(\"total time taken to copy file: \", end0 - start)\n",
    "    NevFileObj = brpylib.NevFile('/Users/aajanquail/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/20181220_Greyson_PG_001.nev')\n",
    "    end1 = time.time()\n",
    "    print(\"total time taken to open file: \", end1 - end0)\n",
    "    output = NevFileObj.getdata(elec_ids='all')\n",
    "    end2 = time.time()\n",
    "    print(\"total time taken to extract file data: \", end2 - end1)\n",
    "    !rm '/Users/aajanquail/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/20181220_Greyson_PG_001.nev'\n",
    "    end3 = time.time()\n",
    "    print(\"total time taken to remove file: \", end3 - end2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c8fc6780",
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_files = [66.49807405471802,65.82321786880493,63.388975858688354,69.53719806671143,66.88227105140686,79.47948098182678,71.94347214698792,67.96569490432739,59.21510100364685,80.71410202980042,62.98839807510376,66.48040223121643,61.38100790977478,70.88359427452087,78.36129999160767,80.66551995277405,106.66426706314087,66.23251080513,67.59491395950317,82.71874189376831,]\n",
    "open_local_files = [0.02280902862548828,0.21553707122802734,0.20301198959350586,0.8547677993774414,0.36477112770080566,0.35686612129211426,0.22889375686645508,0.19390082359313965,0.524144172668457,0.2035541534423828,0.3157508373260498,0.3054039478302002,0.7855150699615479,0.18939590454101562,0.7764649391174316,0.599384069442749,0.3749380111694336,0.2076261043548584,0.3270750045776367,0.6158878803253174,]\n",
    "extract_local_files = [71.6173141002655,71.60160398483276,71.72584199905396,75.98714900016785,72.52384781837463,72.76131296157837,70.40090298652649,71.69030809402466,71.41203570365906,71.75251770019531,72.01969909667969,72.41998887062073,72.70379304885864,72.14531087875366,73.55884408950806,72.375981092453,71.88047480583191,71.14507794380188,73.31038784980774,72.04803824424744,]\n",
    "\n",
    "local_times = []\n",
    "for c, o, e in zip(copy_files, open_local_files, extract_local_files):\n",
    "    total_time = c+o+e\n",
    "    local_times.append(total_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b757e2ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(20)\n",
    "plt.xticks(x)\n",
    "plt.xlabel('Trial Number')\n",
    "plt.ylabel('Total Time Taken (s)')\n",
    "plt.plot(x,local_times, label='Local')\n",
    "plt.plot(x,server_times, label = 'Server')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bd213b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20181205/20181205_Greyson_PG_002.nev'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nev_list[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9a9296b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20181205_Greyson_PG_002.nev opened\n",
      "total time taken to open file:  1.835198163986206\n",
      "total time taken to extract file data:  1169.410598039627\n",
      "\n",
      "20181205_Greyson_PG_002.nev opened\n",
      "total time taken to open file:  1.2071068286895752\n",
      "total time taken to extract file data:  1232.9305441379547\n",
      "\n",
      "20181205_Greyson_PG_002.nev opened\n",
      "total time taken to open file:  0.6199250221252441\n",
      "total time taken to extract file data:  1162.7887620925903\n",
      "\n",
      "20181205_Greyson_PG_002.nev opened\n",
      "total time taken to open file:  1.5169618129730225\n",
      "total time taken to extract file data:  1218.5566971302032\n",
      "\n",
      "20181205_Greyson_PG_002.nev opened\n",
      "total time taken to open file:  1.128476858139038\n"
     ]
    }
   ],
   "source": [
    "# create one NEV File object\n",
    "open_server_files = []\n",
    "extract_server_files = []\n",
    "for i in range(20):\n",
    "    start = time.time()\n",
    "    NevFileObj = brpylib.NevFile(nev_list[2])\n",
    "    end1 = time.time()\n",
    "    print(\"total time taken to open file: \", end1 - start)\n",
    "    open_server_files.append(end1 - start)\n",
    "    \n",
    "    output = NevFileObj.getdata(elec_ids='all')\n",
    "    end2 = time.time()\n",
    "    print(\"total time taken to extract file data: \", end2 - end1)\n",
    "    extract_server_files.append(end2-end1)\n",
    "\n",
    "copy_files = []\n",
    "open_local_files = []\n",
    "extract_local_files = []\n",
    "for i in range(20):\n",
    "    \n",
    "    start = time.time()\n",
    "    !scp '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20181220/20181220_Greyson_PG_001.nev' '/Users/aajanquail/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/20181220_Greyson_PG_001.nev'\n",
    "    end0 = time.time()\n",
    "    print(\"total time taken to copy file: \", end0 - start)\n",
    "    copy_files.append(end0 - start)\n",
    "    \n",
    "    NevFileObj = brpylib.NevFile('/Users/aajanquail/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/20181220_Greyson_PG_001.nev')\n",
    "    end1 = time.time()\n",
    "    print(\"total time taken to open file: \", end1 - end0)\n",
    "    open_local_files.append(end1 - end0)\n",
    "          \n",
    "    output = NevFileObj.getdata(elec_ids='all')\n",
    "    end2 = time.time()\n",
    "    print(\"total time taken to extract file data: \", end2 - end1)\n",
    "    extract_local_files.append(end2 - end1)\n",
    "          \n",
    "    !rm '/Users/aajanquail/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/20181220_Greyson_PG_001.nev'\n",
    "    end3 = time.time()\n",
    "    print(\"total time taken to remove file: \", end3 - end2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a56191f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(20)\n",
    "plt.xticks(x)\n",
    "plt.xlabel('Trial Number')\n",
    "plt.ylabel('Total Time Taken (s)')\n",
    "plt.plot(x,local_times, label='Local')\n",
    "plt.plot(x,server_times, label = 'Server')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c21cbe8",
   "metadata": {},
   "source": [
    "### Single File examination"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0eb4722",
   "metadata": {},
   "source": [
    "Getting the data from a single NEV File object takes quite some time... May be due to the fact that older files are put into cold storage and thus take longer to retrieve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f8acb56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20191007/20191007_Greyson_PG_001.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20191007/20191007_Greyson_Key_002.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20180831/20180831_Greyson_Kluver_002.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20180831/20180831_Greyson_PG_003.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20180831/20180831_Greyson_PG_001.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20190919/20190919_Greyson_FreeReaching_001.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20190919/20190919_Greyson_FreeReaching_002.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20180926/20180926_Greyson_isoWF_002.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20180926/20180926_Greyson_isoWF_001.nev',\n",
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       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20191218/20191218_Greyson_WM_003.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20191218/20191218_Greyson_WM_002.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20191011/20191011_Greyson_Horiz_WM_001.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20191016/20191016_Greyson_Horiz_WM_001.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20191016/20191016_Greyson_Horiz_WM_002.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20181127/20181127_Greyson_PG_001.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20181127/20181127_Greyson_PG_002.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20190815/20190815_Greyson_Key_001.nev',\n",
       " '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20190815/20190815_Greyson_Key_002.nev']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nev_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d23172d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "nev_folder_date_list = sorted([nev_list[i][51:59] for i in range(len(nev_list))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab82feac",
   "metadata": {},
   "outputs": [
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "nev_folder_date_list"
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cca1c463",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20180810_Greyson_cage_1001.nev opened\n",
      "\n",
      "*** File given does exist, try again ***\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m nev_file_date_list \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(nev_list)):\n\u001b[0;32m----> 3\u001b[0m     nevobj \u001b[38;5;241m=\u001b[39m \u001b[43mbrpylib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNevFile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnev_list\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     dt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(nevobj\u001b[38;5;241m.\u001b[39mbasic_header[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTimeOrigin\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdate())\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m     nev_file_date_list\u001b[38;5;241m.\u001b[39mappend(dt)\n",
      "File \u001b[0;32m~/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/Python_Utilities/brpylib.py:428\u001b[0m, in \u001b[0;36mNevFile.__init__\u001b[0;34m(self, datafile)\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextended_headers \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    427\u001b[0m \u001b[38;5;66;03m# Run openfilecheck and open the file passed or allow user to browse to one\u001b[39;00m\n\u001b[0;32m--> 428\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatafile \u001b[38;5;241m=\u001b[39m \u001b[43mopenfilecheck\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfile_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatafile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfile_ext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.nev\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfile_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mBlackrock NEV Files\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[38;5;66;03m# extract basic header information\u001b[39;00m\n\u001b[1;32m    431\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbasic_header \u001b[38;5;241m=\u001b[39m processheaders(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatafile, nev_header_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbasic\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[0;32m~/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/Python_Utilities/brMiscFxns.py:39\u001b[0m, in \u001b[0;36mopenfilecheck\u001b[0;34m(open_mode, file_name, file_ext, file_type)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m file_name:  \u001b[38;5;66;03m# no file name passed\u001b[39;00m\n\u001b[1;32m     37\u001b[0m \n\u001b[1;32m     38\u001b[0m         \u001b[38;5;66;03m# Ask user to specify a file path or browse\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m         file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEnter complete \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfile_ext\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m file path or hit enter to browse: \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     41\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m file_name:\n\u001b[1;32m     42\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapp\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlocals\u001b[39m(): app \u001b[38;5;241m=\u001b[39m QApplication([])\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/ipykernel/kernelbase.py:1161\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_allow_stdin:\n\u001b[1;32m   1158\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(\n\u001b[1;32m   1159\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1160\u001b[0m     )\n\u001b[0;32m-> 1161\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1162\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1163\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1164\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1165\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1166\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/ipykernel/kernelbase.py:1205\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1202\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m   1203\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1204\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[0;32m-> 1205\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m   1206\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1207\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "nev_file_date_list = []\n",
    "for i in range(len(nev_list)):\n",
    "    nevobj = brpylib.NevFile(nev_list[i])\n",
    "    dt = str(nevobj.basic_header['TimeOrigin'].date()).replace('-','')\n",
    "    nev_file_date_list.append(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c4f1d159",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nev_date_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adb423a",
   "metadata": {},
   "source": [
    "# Do the thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dcb241c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "day_key = []\n",
    "rec_time = []\n",
    "sessions_key = []\n",
    "paper_key = []\n",
    "behavior_notes = []\n",
    "behavior_quality = []\n",
    "other_notes = []\n",
    "task_id = []\n",
    "lab_num = []\n",
    "duration = []\n",
    "numChannels = []\n",
    "hasTriggers = []\n",
    "hasChaoticLoad = []\n",
    "hasBumps = []\n",
    "numTrials = []\n",
    "numReward = []\n",
    "numAbort = []\n",
    "numFail = []\n",
    "numIncomplete = []\n",
    "reward_size = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ba22f2c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20181220_Greyson_FreeReaching_001.nev opened\n"
     ]
    }
   ],
   "source": [
    "# create one NEV File object\n",
    "shortened_nev_list = [nev_list[0]]\n",
    "for nev_filename in shortened_nev_list:\n",
    "    # open file\n",
    "    nevobj = brpylib.NevFile(nev_filename)\n",
    "    \n",
    "    # check that date in filename is same as date in basic header, if so append to day_key\n",
    "    filname_date = nev_filename[51:59]\n",
    "    dt = str(nevobj.basic_header['TimeOrigin'].date()).replace('-','')\n",
    "    assert filname_date == dt\n",
    "    #note that appending filename_date is a placeholder - should be of type int(11)\n",
    "    day_key.append(filename_date)\n",
    "    \n",
    "    output = NevFileObj.getdata(elec_ids='all')\n",
    "    \n",
    "    words = {'reward':0x20, 'abort': 0x21, 'fail': 0x22, 'incomp':0x23}\n",
    "    words_count = {'reward':0, 'abort': 0, 'fail': 0, 'incomp':0}\n",
    "\n",
    "    for word,code in words.items():\n",
    "        n_events = np.sum([((out & 0xFF00) >> 8)==code for out in output['digital_events']['UnparsedData']])\n",
    "        words_count[word] += n_events\n",
    "    numReward.append(words_count['reward'])\n",
    "    numAbort.append(words_count['abort'])\n",
    "    numFail.append(words_count['fail'])\n",
    "    numIncomplete.append(words_count['incomp'])\n",
    "        \n",
    "    n_events = np.sum([((out & 0xFF00) >> 8)==0x30 for out in output['digital_events']['UnparsedData']])\n",
    "    numTrials.append(n_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0caf2b8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20181220_Greyson_FreeReaching_001.nev opened\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "[Errno 6] Device not configured",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:2\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "File \u001b[0;32m~/Desktop/Jupyter_Notebooks/Miller_Lab/Database_Migration/Python_Utilities/brpylib.py:477\u001b[0m, in \u001b[0;36mNevFile.getdata\u001b[0;34m(self, elec_ids, wave_read)\u001b[0m\n\u001b[1;32m    475\u001b[0m nPackets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m((lData\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbasic_header[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBytesInHeader\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbasic_header[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBytesInDataPackets\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m    476\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatafile\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbasic_header[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBytesInHeader\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 477\u001b[0m rawdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatafile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    478\u001b[0m \u001b[38;5;66;03m# rawdataArray = np.reshape(np.fromstring(rawdata,'B'),(nPackets,self.basic_header['BytesInDataPackets']))\u001b[39;00m\n\u001b[1;32m    479\u001b[0m \n\u001b[1;32m    480\u001b[0m \u001b[38;5;66;03m# Find all timestamps and PacketIDs\u001b[39;00m\n\u001b[1;32m    481\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbasic_header[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFileTypeID\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBREVENTS\u001b[39m\u001b[38;5;124m'\u001b[39m :\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 6] Device not configured"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nevobj = brpylib.NevFile(nev_list[0])\n",
    "output = nevobj.getdata(elec_ids='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a49df276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('spike_events', dict), ('digital_events', dict)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i, type(output[i])) for i in output]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "099b0dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([('TimeStamps', list),\n",
       "  ('Unit', list),\n",
       "  ('Channel', list),\n",
       "  ('Waveforms', numpy.ndarray)],\n",
       " [('TimeStamps', list), ('InsertionReason', list), ('UnparsedData', list)])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i, type(output['spike_events'][i])) for i in output['spike_events']], [(i, type(output['digital_events'][i])) for i in output['digital_events']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6c2371e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FileTypeID': 'NEURALEV',\n",
       " 'FileSpec': '2.3',\n",
       " 'AddFlags': 1,\n",
       " 'BytesInHeader': 26512,\n",
       " 'BytesInDataPackets': 104,\n",
       " 'TimeStampResolution': 30000,\n",
       " 'SampleTimeResolution': 30000,\n",
       " 'TimeOrigin': datetime.datetime(2018, 12, 20, 17, 6, 59, 781000),\n",
       " 'CreatingApplication': 'File Dialog v7.0.4',\n",
       " 'Comment': '',\n",
       " 'NumExtendedHeaders': 818}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filetypeid the same as filename?\n",
    "# TimeStampResolution/SampleTimeResolution = sampling rate\n",
    "NevFileObj.basic_header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a633ffb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20181220'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(NevFileObj.basic_header['TimeOrigin'].date()).replace('-','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "17e6663b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NevFileObj.extended_headers[0]['ElectrodeID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1b29c579",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {}\n",
    "c = 0\n",
    "for i in NevFileObj.extended_headers:\n",
    "    if 'ElectrodeID' in i.keys():\n",
    "        if i['ElectrodeID'] not in d:\n",
    "            d[i['ElectrodeID']] = 1\n",
    "        else:\n",
    "            d[i['ElectrodeID']] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f99abec6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 3,\n",
       " 2: 3,\n",
       " 3: 3,\n",
       " 4: 3,\n",
       " 5: 3,\n",
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       " 8: 3,\n",
       " 9: 3,\n",
       " 10: 3,\n",
       " 11: 3,\n",
       " 12: 3,\n",
       " 13: 3,\n",
       " 14: 3,\n",
       " 15: 3,\n",
       " 16: 3,\n",
       " 17: 3,\n",
       " 18: 3,\n",
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       " 25: 3,\n",
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       " 27: 3,\n",
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       " 29: 3,\n",
       " 30: 3,\n",
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       " 44: 3,\n",
       " 45: 3,\n",
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       " 190: 3,\n",
       " 191: 3,\n",
       " 192: 3,\n",
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       " 194: 3,\n",
       " 195: 3,\n",
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       " 202: 3,\n",
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       " 207: 3,\n",
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       " 210: 3,\n",
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       " 215: 3,\n",
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       " 218: 3,\n",
       " 219: 3,\n",
       " 220: 3,\n",
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       " 225: 3,\n",
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       " 227: 3,\n",
       " 228: 3,\n",
       " 229: 3,\n",
       " 230: 3,\n",
       " 231: 3,\n",
       " 232: 3,\n",
       " 233: 3,\n",
       " 234: 3,\n",
       " 235: 3,\n",
       " 236: 3,\n",
       " 237: 3,\n",
       " 238: 3,\n",
       " 239: 3,\n",
       " 240: 3,\n",
       " 241: 3,\n",
       " 242: 3,\n",
       " 243: 3,\n",
       " 244: 3,\n",
       " 245: 3,\n",
       " 246: 3,\n",
       " 247: 3,\n",
       " 248: 3,\n",
       " 249: 3,\n",
       " 250: 3,\n",
       " 251: 3,\n",
       " 252: 3,\n",
       " 253: 3,\n",
       " 254: 3,\n",
       " 255: 3,\n",
       " 256: 3,\n",
       " 257: 3,\n",
       " 258: 3,\n",
       " 259: 3,\n",
       " 260: 3,\n",
       " 261: 3,\n",
       " 262: 3,\n",
       " 263: 3,\n",
       " 264: 3,\n",
       " 265: 3,\n",
       " 266: 3,\n",
       " 267: 3,\n",
       " 268: 3,\n",
       " 269: 3,\n",
       " 270: 3,\n",
       " 271: 3,\n",
       " 272: 3}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9f254885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'PacketID': 'NEUEVWAV',\n",
       "  'ElectrodeID': 1,\n",
       "  'PhysicalConnector': 1,\n",
       "  'ConnectorPin': 1,\n",
       "  'DigitizationFactor': 250,\n",
       "  'EnergyThreshold': 0,\n",
       "  'HighThreshold': 0,\n",
       "  'LowThreshold': -73,\n",
       "  'NumSortedUnits': 0,\n",
       "  'BytesPerWaveform': 2,\n",
       "  'SpikeWidthSamples': 48,\n",
       "  'EmptyBytes': b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'},\n",
       " {'PacketID': 'NEUEVLBL',\n",
       "  'ElectrodeID': 1,\n",
       "  'Label': 'elec78',\n",
       "  'EmptyBytes': b'\\x00\\x00\\x00\\x00\\x00\\x00'},\n",
       " {'PacketID': 'NEUEVFLT',\n",
       "  'ElectrodeID': 1,\n",
       "  'HighFreqCorner': '250.0 Hz',\n",
       "  'HighFreqOrder': 4,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '7500.0 Hz',\n",
       "  'LowFreqOrder': 3,\n",
       "  'LowFreqType': 'butterworth',\n",
       "  'EmptyBytes': b'\\x00\\x00'}]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NevFileObj.extended_headers[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6fbd53a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'PacketID': 'NEUEVWAV',\n",
       "  'ElectrodeID': 2,\n",
       "  'PhysicalConnector': 1,\n",
       "  'ConnectorPin': 2,\n",
       "  'DigitizationFactor': 250,\n",
       "  'EnergyThreshold': 0,\n",
       "  'HighThreshold': 0,\n",
       "  'LowThreshold': -78,\n",
       "  'NumSortedUnits': 0,\n",
       "  'BytesPerWaveform': 2,\n",
       "  'SpikeWidthSamples': 48,\n",
       "  'EmptyBytes': b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'},\n",
       " {'PacketID': 'NEUEVLBL',\n",
       "  'ElectrodeID': 2,\n",
       "  'Label': 'elec88',\n",
       "  'EmptyBytes': b'\\x00\\x00\\x00\\x00\\x00\\x00'},\n",
       " {'PacketID': 'NEUEVFLT',\n",
       "  'ElectrodeID': 2,\n",
       "  'HighFreqCorner': '250.0 Hz',\n",
       "  'HighFreqOrder': 4,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '7500.0 Hz',\n",
       "  'LowFreqOrder': 3,\n",
       "  'LowFreqType': 'butterworth',\n",
       "  'EmptyBytes': b'\\x00\\x00'}]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NevFileObj.extended_headers[3:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "312a29df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "818"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(NevFileObj.extended_headers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7d9610",
   "metadata": {},
   "source": [
    "Examining Spike Events. Waveforms array (shape (426190, 96)) contains 96 copies of the EXACT SAME signal. Confirmed this by looking at individual rows of waveform and getting distinct values in each row (each row had 1 distinct value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fc40aef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(output['spike_events'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "414cc93e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426190, list)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(output['spike_events']['TimeStamps']), type(output['spike_events']['TimeStamps'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9b1fb985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426190, list)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(output['spike_events']['Unit']), type(output['spike_events']['Unit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f3c515ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426190, list)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(output['spike_events']['Channel']), type(output['spike_events']['Channel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2df1d213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n",
       "       69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,\n",
       "       86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96], dtype=uint16)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(output['spike_events']['Channel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f7d36055",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((426190, 96), numpy.ndarray)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output['spike_events']['Waveforms'].shape, type(output['spike_events']['Waveforms'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dffc4989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44,\n",
       "       -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44,\n",
       "       -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44,\n",
       "       -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44,\n",
       "       -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44,\n",
       "       -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44,\n",
       "       -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44, -44,\n",
       "       -44, -44, -44, -44, -44], dtype=int16)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output['spike_events']['Waveforms'][0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b40c478f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brMiscFxns.checkequal(output['spike_events']['Waveforms'][0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f6f1cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brMiscFxns.checkequal(output['spike_events']['Waveforms'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d39f202",
   "metadata": {},
   "source": [
    "All signals in all 96 columns are exactly the same!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "dffaa0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = []\n",
    "#iterate through waveforms in spike events and count number of times the rows are exactly the same\n",
    "for i in range(output['spike_events']['Waveforms'].shape[0]):\n",
    "    l.append(int(brMiscFxns.checkequal(output['spike_events']['Waveforms'][i,:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "2aa75890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426190, 426190)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.array(l)), np.sum(np.array(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e94c5a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12d2ea110>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(output['spike_events']['Waveforms'][:10,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b784b5a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12d359360>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(output['spike_events']['Waveforms'][:10,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73685f36",
   "metadata": {},
   "source": [
    "#### Digital Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e6433db9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TimeStamps', list), ('InsertionReason', list), ('UnparsedData', list)]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i, type(output['digital_events'][i])) for i in output['digital_events']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aad9d9bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2393347, 2393347, 2393347)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(output['digital_events']['TimeStamps']), len(output['digital_events']['InsertionReason']), len(output['digital_events']['UnparsedData'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66bad70e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(output['digital_events']['InsertionReason'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11247b4a",
   "metadata": {},
   "source": [
    "UnparsedData in digital_events contains information in bits regarding session information\n",
    "e.g. did the monkey fail the task, did he receive an award, what kind of task was it, etc...\n",
    "info on the hexabit codes that correspond to what information: https://github.com/limblab/Behavior/blob/master/src/target/words.h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6c9c09da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2393347"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(output['digital_events']['UnparsedData'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "dc50a856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[145, 157, 185, 192, 145, 157, 185, 192, 128, 157]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output['digital_events']['UnparsedData'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d4fd7668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reward: 0\n",
      "abort: 0\n",
      "fail: 28\n",
      "incomp: 0\n"
     ]
    }
   ],
   "source": [
    "# count number of rewards, aborts, incompletes and failures\n",
    "# Words are as follows:\n",
    "# Reward == 0x20\n",
    "# Abort == 0x21\n",
    "# Failure == 0x22\n",
    "# Incomplete == 0x23\n",
    "\n",
    "words = {'reward':0x20, 'abort': 0x21, 'fail': 0x22, 'incomp':0x23}\n",
    "\n",
    "for word,code in words.items():\n",
    "    n_events = np.sum([((out & 0xFF00) >> 8)==code for out in output['digital_events']['UnparsedData']])\n",
    "    print(f\"{word}: {n_events}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "528ea850",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial Start: 32\n"
     ]
    }
   ],
   "source": [
    "n_events = np.sum([((out & 0xFF00) >> 8)==0x30 for out in output['digital_events']['UnparsedData']])\n",
    "print(f\"Trial Start: {n_events}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "455e998b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tasks = {'CO':0x01, 'RW':0x02, 'FC':0x03, 'MG':0x06, 'WF':0x07}\n",
    "# for task,code in tasks.items():\n",
    "#     n_events = np.sum([((out & 0xFF00) >> 8)< for out in output['digital_events']['UnparsedData']])\n",
    "#     print(f\"{task}: {n_events}\")\n",
    "\n",
    "np.unique([((out & 0xFF00) >> 8) for out in output ['digital_events']['UnparsedData'] if ((out & 0xFF00) >> 8)<0x10])\n",
    "\n",
    "#if TASK_CO\n",
    "#define WORD_TASK_CODE 0x01\n",
    "#elif TASK_RW\n",
    "#define WORD_TASK_CODE 0x02\n",
    "#elif TASK_FC\n",
    "#define WORD_TASK_CODE 0x03\n",
    "#elif TASK_MG\n",
    "#define WORD_TASK_CODE 0x06\n",
    "#elif TASK_WF\n",
    "#define WORD_TASK_CODE 0x07\n",
    "#elif TASK_UC || TASK_UN || TASK_UN1D\n",
    "#define WORD_TASK_CODE 0x08\n",
    "#elif TASK_BD\n",
    "#define WORD_TASK_CODE 0x09\n",
    "#elif TASK_BC\n",
    "#define WORD_TASK_CODE 0x0A\n",
    "#elif TASK_VS\n",
    "#define WORD_TASK_CODE 0x0B\n",
    "#elif TASK_BF\n",
    "#define WORD_TASK_CODE 0x0C\n",
    "#elif TASK_2AFC\n",
    "#define WORD_TASK_CODE 0x0D\n",
    "#elif TASK_AT\n",
    "#define WORD_TASK_CODE 0x0E\n",
    "#elif TASK_DB_DEFINED\n",
    "#define WORD_TASK_CODE 0x0F /* this is an escape indicating that the task will be specified by the databurst */\n",
    "#endif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3e9eee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0x50,0x60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a24dc57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0xFF00 keeps the first 8 bits \n",
    "# out & 0xFF00 finds the first 8 bits of out\n",
    "# >> 8 shifts it over by 8 bits\n",
    "# out is in integer that encodes important information\n",
    "temp_list = [((out & 0xFF00) >> 8) for out in output['digital_events']['UnparsedData']]\n",
    "has_bumps = np.any(np.isin(temp_list, np.arange(0x50,0x5F)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acde0bca",
   "metadata": {},
   "source": [
    "#### Another NEV file object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66789ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "nev_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "712f650a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20181220_Greyson_PG_001.nev opened\n"
     ]
    }
   ],
   "source": [
    "NevFileObj2 = brpylib.NevFile(nev_list[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bcfdee38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 52.5 s, sys: 2.81 s, total: 55.3 s\n",
      "Wall time: 21min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "output2 = NevFileObj2.getdata(elec_ids='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fceda651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((717502, 96), numpy.ndarray)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output2['spike_events']['Waveforms'].shape, type(output2['spike_events']['Waveforms'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de2f2b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
       "       25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
       "       25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
       "       25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
       "       25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
       "       25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25], dtype=int16)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output2['spike_events']['Waveforms'][0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb059e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filetypeid the same as filename?\n",
    "# TimeStampResolution/SampleTimeResolution = sampling rate\n",
    "NevFileObj2.basic_header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afdaf357",
   "metadata": {},
   "outputs": [],
   "source": [
    "NevFileObj.basic_header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa8a3680",
   "metadata": {},
   "outputs": [],
   "source": [
    "NevFileObj.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ddd6e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "NevFileObj2.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a89a9245",
   "metadata": {},
   "outputs": [],
   "source": [
    "output2['spike_events']['Waveforms'][0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56115b2b",
   "metadata": {},
   "source": [
    "## Nsx file explortation\n",
    "\n",
    "nsx file sampling rates:\n",
    "- 1: 500\n",
    "- 2: 1000\n",
    "- 3: 2000\n",
    "- 4: 10000\n",
    "- 5: 30000\n",
    "- 6: 30000 (raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f9155ab4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20181220_Greyson_PG_001.ns3 opened\n"
     ]
    }
   ],
   "source": [
    "# create one NEV File object\n",
    "NsxFileObj = brpylib.NsxFile(nsx_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "65acd36d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.68 ms, sys: 181 ms, total: 184 ms\n",
      "Wall time: 37.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "output_nsx = NsxFileObj.getdata(elec_ids='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "aadae7a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('elec_ids', list),\n",
       " ('start_time_s', float),\n",
       " ('data_time_s', str),\n",
       " ('downsample', int),\n",
       " ('data', list),\n",
       " ('data_headers', list),\n",
       " ('ExtendedHeaderIndices', list)]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i, type(output_nsx[i])) for i in output_nsx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "765bafe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 271, 272], 14)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 14 electrodes\n",
    "output_nsx['elec_ids'], len(output_nsx['elec_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9259b3e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 'all', 1)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_nsx['start_time_s'], output_nsx['data_time_s'], output_nsx['downsample'], "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "acf66a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, numpy.ndarray)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(output_nsx['data']), type(output_nsx['data'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d0e7b3",
   "metadata": {},
   "source": [
    "14 electrodes, 1800020 data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "571aea41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1800020, 14)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_nsx['data'][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2dde4b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Timestamp': 0, 'NumDataPoints': 1800020}]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_nsx['data_headers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b624b264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_nsx['ExtendedHeaderIndices']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3f8ede",
   "metadata": {},
   "source": [
    "nsx file extended headers contain information on each of the 14 electrodes, including id, electrode label, high and low frequency type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7d9e68d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Type': 'CC',\n",
       "  'ElectrodeID': 257,\n",
       "  'ElectrodeLabel': 'EMG_FDP2',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 1,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 258,\n",
       "  'ElectrodeLabel': 'EMG_FCU1',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 2,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 259,\n",
       "  'ElectrodeLabel': 'EMG_FCR1',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 3,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 260,\n",
       "  'ElectrodeLabel': 'EMG_FDP1',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 4,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 261,\n",
       "  'ElectrodeLabel': 'EMG_FDS1',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 5,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 262,\n",
       "  'ElectrodeLabel': 'EMG_FDS2',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 6,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 263,\n",
       "  'ElectrodeLabel': 'EMG_APB',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 7,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 264,\n",
       "  'ElectrodeLabel': 'EMG_FPB',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 8,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 265,\n",
       "  'ElectrodeLabel': 'EMG_LUM',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 9,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 266,\n",
       "  'ElectrodeLabel': 'EMG_FDI',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 10,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 267,\n",
       "  'ElectrodeLabel': 'EMG_EDC3',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 11,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 268,\n",
       "  'ElectrodeLabel': 'EMG_EDC1',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 12,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '10.0 Hz',\n",
       "  'HighFreqOrder': 2,\n",
       "  'HighFreqType': 'butterworth',\n",
       "  'LowFreqCorner': '250.0 Hz',\n",
       "  'LowFreqOrder': 2,\n",
       "  'LowFreqType': 'butterworth'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 271,\n",
       "  'ElectrodeLabel': 'Force_x',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 15,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '0.0 Hz',\n",
       "  'HighFreqOrder': 0,\n",
       "  'HighFreqType': 'none',\n",
       "  'LowFreqCorner': '0.0 Hz',\n",
       "  'LowFreqOrder': 0,\n",
       "  'LowFreqType': 'none'},\n",
       " {'Type': 'CC',\n",
       "  'ElectrodeID': 272,\n",
       "  'ElectrodeLabel': 'Force_y',\n",
       "  'PhysicalConnector': 9,\n",
       "  'ConnectorPin': 16,\n",
       "  'MinDigitalValue': -32767,\n",
       "  'MaxDigitalValue': 32767,\n",
       "  'MinAnalogValue': -5000,\n",
       "  'MaxAnalogValue': 5000,\n",
       "  'Units': 'mV',\n",
       "  'HighFreqCorner': '0.0 Hz',\n",
       "  'HighFreqOrder': 0,\n",
       "  'HighFreqType': 'none',\n",
       "  'LowFreqCorner': '0.0 Hz',\n",
       "  'LowFreqOrder': 0,\n",
       "  'LowFreqType': 'none'}]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NsxFileObj.extended_headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c467b02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'elec_ids': [257,\n",
       "  258,\n",
       "  259,\n",
       "  260,\n",
       "  261,\n",
       "  262,\n",
       "  263,\n",
       "  264,\n",
       "  265,\n",
       "  266,\n",
       "  267,\n",
       "  268,\n",
       "  271,\n",
       "  272],\n",
       " 'start_time_s': 0.0,\n",
       " 'data_time_s': 'all',\n",
       " 'downsample': 1,\n",
       " 'data': [array([[   0,   -1,    3, ...,   -8, 1107, 1083],\n",
       "         [   1,  -11,    3, ...,   -3, 1110, 1086],\n",
       "         [   2,  -14,    2, ...,    0, 1112, 1089],\n",
       "         ...,\n",
       "         [   2,   -1,   -2, ...,    5,   43,   19],\n",
       "         [   2,    0,    0, ...,    4,   45,   19],\n",
       "         [   1,    0,    1, ...,    3,   47,   12]], dtype=int16)],\n",
       " 'data_headers': [{'Timestamp': 0, 'NumDataPoints': 1800020}],\n",
       " 'ExtendedHeaderIndices': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_nsx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0eeb964",
   "metadata": {},
   "source": [
    "Plot ns3 stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "9626817d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 257\n"
     ]
    },
    {
     "data": {
      "image/png": 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+levV1QfY7arDZgCDRKSxa3gf5NJMnB3Nt8puEwP2NaoUwpVMABCRCUA3YBngK6Eo8E7IgyLTD/gtsFREFrm0O4CHgDdEZBSwAbjEbZsODAWygAPANeANohSR+4D5br973cBKU8ms37af44+pY79gKyn7X63YSgwmQB/XfTemVPVLQn9/BgTZX4ExIc41AZgQu9yZ8iZz/Q4ueu4b/nHhSVzeu02ys2NiyAomlUMkswZ/48Z/GJM0a3P3A/DdjztD7vN11jZOfWAWAx/9nA53TE9U1kyM+Bc4rfBZ8URSMpmEF1A2A4fwShOqqt3imjNj/EVwc7n8hW+BwkFwpmJQG2hSKUQSTF7EtW1Q2GZiTFLYfafystJIxRZJMMlV1alxz4mpEA4czuP7nD0l7xhjvvtMsDEJpmKz/9HKIZJgslBEXgXex6vmAsBvXIipoN5dmE2NlBSGdWsR8TG3vLGYD7/fHMdcBRerHlx5R/MREVKqFT/fik17OK5RbRrWtpUVEuXhj1byzZrtAIj156rQIgkmtfGCyCC/tFh0DTZJdvPr3kQGw7oNi/iYFZsSXyopwu9n7Lx1O2hYuzqdjq0f8eEd7vyQDs3qMevPvyy27dzH59C5RQOm33RGLHJa6cWiyvHZz9YETbfAUvGUGExU9ZpEZMSYcAqruQpd8vw3AKx/KPJgCJC1dV/Ibcs37WHjjgPUq5lK47oxW3GhUvr8h9yCf886sVnU57M2k4ot3Nxcd7np3UNtP1tEhscnWyZah/Py+e9X62IySn3D9v28MGdtDHJVlKry/OfBf5kGCnejeXtBNv0e+qRYeub6HSXOaHs4L58JX64j72hh35Iz/vkp/f/1WUT5Km/GvPId05duKnnHUjpwOPRkjht3HIjpe/33q3W8PHdDTM9p4i/cOJOlwPsiMltEHhGR20Tk7yLysogsBc7Dm+XXlCPjv1hD27HTeO7zNdzz/nImz/8x6nNe8cK33D9tBTv3H466sVRVC0oGq7bs5cEPV5b6+EC3vb2EnF0/F0u/6LlvCkovoYz/Yg33frCcyfM3Fknf/fMR/jR5YZnWWbl76jIuHR/+feNl2tJNXP/KdzE9Z9bWfXT++wzeyNxY8s4x8OQnWaU+JnP9Dt6Yn5j8meBKWhyrH3Ad3lQqKcAe4H9AL1W9WVVtHvckOJqv/H3K92TvLP6L8CF3c951wJvRNxbTg/vOkR/kRr5h+35e/TZ0wFqxaQ+qyo/bD7B+235e/HIdAx/9nEUbd5F3tOj5Trp7RsiSlK9k8t6in3h57gZ2HyicsTiScQp7Dh7hgyU/BUn3rm1fkM/pvUXF9w9l78EjXP3feWza/TMvfb2euWvjM6OPqvLghytYv21/XM4fzOotewH4ZMXWoNtjXT1VlnEnFz33Dbe9vSS2GcG7dhsHE5lI2kxWA6sTkBcToe9+3MmkbzawYtMe3rzutKD7lKULbcb9s9i271CxNghfT6pgZ/z1s1+zbd9hLsloRWpK0d8m367dzm/Gz2XceZ255/3lAAx3Pcd+3HGAE5rWLbL/3oN5/HzkKPVqFv9a+jfI/u297/lq9baC15HU5N3yxmJmLi+6LOy+Q3mFbTFR3i/eX7yJz1bl8sTs+P6prN9+gOc/X8us5Vu4a1hnTmxRnxYNa8f1PRMl3PcsGT5btZWr/zuff118Mhf1bJXs7JR7kUynYsqpYDfRaLrQbtvn9fwOrC7wv+Fu2F60NLRj/+GQ59vg6tKX/RT7HmA7D4R+32BydhatBlu1eS9dx83g3YU5JR67/1Aeh/KOFrzOz1f++dFKtrrFoUqSdzSfj5dtjskvXF/pcE3ufq55aT7Dn/gy6nNGr2zfuWClwfJk9RavOjbpPRgriKQGExGZICJbReR7v7QmIjJTRFa7fxu7dBGRJ0QkS0SWiEgPv2NGuv1Xi8jIYO9VmRTe3EPfnKK5bwVWF2x3ASNYFVRpf00eygs/iUKo21K0VSmBx6/c7N0gtrqpV8KV5LqMm1Hkpj133Xae+WwNt73lfU4Pf7SSO95dGvL4Jz/JYvTLC/h0VfBqotII/H/dHiaYh3I0X2k7dhqPz1pd8HrBhtBznsWrpPCnyYuKvI5VKTFWfN8J62QWmUiW7e0XSVoZvUTx9drH4i3IlQ7Mdq8BzsVb8TEdGA086/LSBBgH9AZ6AeN8Aaiy8t0YFa+uvsi2OL5vXn7xQBDJDcB/26rNXv17zs6feSpIQ6uI90veP3AFXqNvv9IIbJ8Jl8dgVvt1J/Z9DEdcD7BQYyV8fL2dduwPvTLlewtzuOPdpWVq8AcY/uQcJn69PqJ9D7uA/sxn3uf/xOzV/PrZr8MGFAj9mQf7XkQia+veoOm7fy4fK3j6vhPVggxwNcVFUjJ5MsK0UlPVL4DAlsoRwET3fCJwgV/6JPXMBRq5lRgHAzNVdYeq7gRmUjxAVSiH8o7y17eWhKlG8b7cC3/cxUl3f8wPW4r/Ufq6cu4/dLTYtrIKVoVWGNgiW3r1J9fr6uGPVvLRsuAj6Tvc+SEjnv6SI0fz2bjjACfd/XGxG2VpBrXlHc1nVcBnFBg8Poug1HD7O0v496wfStireL58bxXqnrQ0ezd/en1RQUeG/CAlwAUbdtJ27DQWbAjesP99zh7GTV1WQt58+XG/uF1+fKW03Air7QKVFKhD56OoSH8gfLNme1y6qgfy/TdYKIlMyAZ4EekLnAakicif/TY1wOvZFS/N3QqKAJuB5u55S8C/Mj/bpYVKL0ZERuOVamjTpvyuifHR95t5PXMjB44c5cnLTim2PfCPbtXmvXRsXr/ItjcyswGY8OU6bj6nY6nz0HbsNM5Ib8rctdvD7ufd1LXg5jzsiTn8uOMAS+8eHHT/km47vj/g73P2kH7nhwXp3/24q+j7luIvfI5fY31hPormZGsEMw2/Ns/7mr08qpffcUVvwD8F6aKc5y6qmsv0iKe+pH1aPR79TXcA9geM4Zj0zXqu7teuSJpvgOBf3w5dnRYpLbhJSpHXa3L3s3LzHk48tkGpzlfWKsjAgB7pD4TL/jMXgN+fcULZ3jhCBd8RiyYRCVcyqQHUwws49f0ee4CL4p+1ggWxYlaDqqrjVTVDVTPS0tJiddq4+7+XM4uMl6gW8Nfr32U38A9y76E8hj4+hx73zSxI277vEGMj6EY5Z/U2jvj96ly8cVfxnQL+0Jb9tIe9rrutL1tvf5dd4nv5bNgeWZfXr9eED3L+NgbpQh1o38HSNwZ/lbWdXg/MLpLmu+n7e3+x18X4kRmraDt2Gouzd/NOmIb/3H3RT6E/bsr3Qdto8vO1oHQVGAQembGKIf+eU+r3Kmunj1hN2rko2PcyBgKDrgkvZMlEVT8HPheRl1Q1kcNRt4hIC1Xd5KqxfH8ROUBrv/1aubQcoH9A+mcJyGfUtuw5SNbWffTr0LRIugb8IJqxrGiX1sCvdkmj3Je73ihfrt5GWv2aDP73F0W279h/mL9N+T7YoUUEGwwX7s/sk5XFb2Yl5TVc77CymhVkfMSBw0Wr/0rTkF2WwAMEHVgJxX+hb9zxM1MW5TCie2EBu7S3s4nfbGDiNxuKdfP+es12/jNnHVD4GUR7S49Vk0JZSzi7Stmzr7RsmpfIRNJm8pKIfBL4iGOepgK+HlkjgSl+6Ve5Xl19gN2uOmwGMEhEGruG90Eurdwb/uSXXPFC4SQCew4e4YU5awtKGoFf4qnuF25gepEBbGG++F9mbSsWSMAbKzJtSdmm4PD1zgrWgH3kaOkbZl/6an2Z8hFOsF5vd75bcvAM5Q8RjjAvacCoL7AG/kKfuvgnbnI9nQ4eOcpf3lzM5t1la88IFNhY/t+v1hX7v5u6+Cfajp3GThdgS+qcUNZ7bRnb7YuJV+cv3/fGYklkIgkmfwFudY+/AYuAzFi8uYi8BnwDdBKRbBEZBTwEnCMiq4GB7jXAdGAtkAX8B7geQFV3APcB893jXpdW7gWuCDhuyjLun7aCL7O8On4BPvX7dX/jawsBb8Cfv+xdP/Pox6vIz9eCnjrBzFsXvGpoXQxGU2/a/TNb9kR/w/umhDaasgjWZpIIXcbNKPL/F+iu97z2j6+yQufvxS/X8daCbF4PM5VJST3APlm5hb++tYS2Y6cVC0r3vL+cWSuKlnx93zNfpwVfb7pQv9DLWs0VWFIr8007TtGkoDeXFU0iEskI+AUBSV+JyLxYvLmqXhZi04Ag+yowJsR5JgATYpGnWFq5eQ/pzeoHXTsjmD2uS6RvsNShvHyueWl+sf0C++e/851X/969TaOw5w9sxI6ls//f50Ve/3z4KNvKUGUVWP1U0QX7//N5bd5GHvxVt5DXvO9QHp+vin7Got+9VPjb74ctoWdMDuT71o59J3yjf7JvtfFaMM1XIztz+Rb+MrhTXN6jMolknEkTv0dTERkMNExA3sq9ddv2hxw4uGrzXob8ew6PzlxV4nlUFVVltvsVuzRnN0DQRaje+S67oHdQIP+bRrL96tmvgzfYm2JCVSN1HTeDeetjW8guzRiOnQeK7hvv8R8lFQAO5+XzwLTlxdLjNcjRF6RWbdnLlEUlz5SQaFMX/xR0DFayRFLNtQCvWmsBXpXULcCoeGaqIliwYSdn/esz/hcwVbaqknH/LCZ+sx6Apz8teYr1x2evpt3t0yN63z+/sbjUeU0Gm4KifCpNz7rr/le0UuKrrOBVkImoBdq44wAd7/qwoPOAv7gFE7/zflvCUgaJtmrzXm58bSG3vhn7yS3LqsRgoqrtVPUE92+6qg5S1fIwIVBS+Rq9vwioj8/auo9t+w4VmUn3rH99VqTHyUMfrixSz/1CkD8QUzW8tSDym3tZLMneFdfzx1boqPT1muDtSre+uZjrX41+yv0l2buYsWwza3ILqwH9S2LJrsoL9PMRr2r0p93BewgmQyTVXLVE5M8i8o6IvC0ifxKRWonIXHnmG2E+c/kW5q3bwehJmXS/9+OgVVDrtu2n+73eOA9V5bmABaHK+4R3Jn7+8mZ8S5rflTBFSkkCG/fbjp0W8Vig0grXbfzgkeAdS95ckB2200mkzn/qK/7v5QUM8Gv7e8lv1oXM9dF9jrHma4YtL/OYQWTVXJOALnhTqDzlnr8cz0xVBP71yZc8/w0fL9/CrgNHgq754S/S6ixTdcRzvYzAdo9Y+KbYgNHY/G4fE6KEkZ+vzFmd3KWTAqfjSTZfD7OS7jeJVGJvLqCrqnb2e/2piBRvBatCftiyl0dnBp+jaVi5mBLcGE+sl9QFr3fXne+VfZxOaZ1wx3QG/qJ5yTvG0NrcyHu9JVMMVuWOmUiCyXci0sdNroiI9CZG40wqoguf+YqFcexia6qeeN4PQk2mGS3/KqmDR+LfnTvRQz3KMrV/IvlKJuVpFchIgklP4GsR8bUotwFWuXXgVVW7xS135VA0gaQ8/ceb8iOeX4tEjNv5Msygy9Iqzd/I1zF4X1UtVsugqswrZ723Akk5bDOJJJhU6OncyxPfrLPGmOBC3RwDl1wGuNxvKiKfH7YUzqAdiT0H83gyYF2dd77L4ZEZJY8PS6ZwSz8kSyQN8Per6gb/h39avDNYmTw4fUWys2BMUmzde5BxU74nz83XFqrL8qYop+SZXMofbMFKQuvj1FstlgqruZKcET+RlEy6+L8QkVS8qi9TSjbFjwkm1GzClcmd737PzOVbOCM9jUZ1qnPRc98E3a/fQ9HNIVva3k3ZO4t/9iXNbB3O0uzdzF+/g9+d3q7knaPgu5VEcr1LsndRI7VaqdepKa2QJRMRuV1E9gLdRGSPiOx1r7dQOJOvKYWr+rZNdhZMOZSIBuxk880gXa0arNxcfrrZDn+yeO/LZ0Isw+wbqLx178GQbTvnPfUl934Q/86uvsk11+SWXIo6/6mvyrROTWmFW8/kQeBBEXlQVW+Pe06iJCJDgMfxVoF8QVUfKuGQhKuRGkmtoqlqdsVhLEh5c9Rvtcm74titeGnObrbuPViwaNnafwwNuYZ7sIXMwlm0cRfLftrDmFe/44z0prw8qneR7Tv9eoAt/2kPd763lIU/7uLVa3vTu90xEU/4Guj1+T+ybd9hLuvVhoa1q5NSTYrUcvgPLG1StwZzbx8Q9F7z5eptNKpTnS7HNSjzTM/hSEm9J0TkzGDpbv32ckFEUoAfgHPwlu2dD1ymqiF/ImRkZGhmZul7OJc03bcxVdUXt55Fm2PqAF5p68S/fcTCv51D47o1GPH0VyzeuIvHfnMyN7+enPnlFo8bxJY9Bzn+mDp0uuujUh8/7KQWTFtauO7P2n8MJV+VDn7LS5fk0lNb89CvI+8A2++hT4pVgzaolcqeEhZoW//QMI7mKx8sKVwbJ3B7WYnIAlXNKJYeQTB53+9lLaAXsEBVzy5zbmLMrVd/t6oOdq9vh4LSVVAWTIxJjKtPa1tkahLj6XvCMfQ8vjEp1YTt+w/xv7k/cnKrhhw4fJRzux5Lakq1kIOjo7XuwaFlLp2ECiaRrGdyXsCJWgP/LlMu4qcl4N+NIxvoHbiTiIwGRgO0adMmMTkzpoorzUzFVck3a7fz7brtRUaxL872lp9YHdBdOdYOHD5K3ZqR9L+KXFnOlg38Iqa5SBBVHQ+MB69kkuTsGFOpdGvVkInX9KJ+rdQiVT9L7x5cLkr0vdo1oVPz+ow+8wSGPj6HvVFOsNqmSR1uG9KJG15dGPExZ3ZM46nLT6FmajVqpqYAXvfko/nKz0eOUqdGKvmqpFYTjuaXrgrN34K7BpJSTRj6+Bx+Clhds0ZKtZgHEoggmIjIkxTO+FAN6A5EP+dzbOUArf1et3JpxpgEufq0tjSuWwOAObedxRn//JR5d3iLpg44sRmzV27luSt7cN3/Env7aFa/JncM/QUXnNKyIG3pPaUPcL3bNSmyrsnnt/ZHRPjHtBXFbtj+3r3+NE5p0zjkdhEhNUWon+I1mqe4jr+pKUKvtk2KLJDWuUUDLuvdhryj+dzzfvEm4eeu7MGQri0KXn99+4CC64ymnSQSkYQn/4aFPOA1Vf0qTvkpq/lAuoi0wwsilwKXJzdLxTWsXT3uq9UZkwy/P70d5518XMHr1k3qFLl5HXF1OTWrp/DFrWdx5iOfxiUfHZvXK1ia+OnLezCsW4uQ+779h778+tni410G/qI5s1YUH3HfrmldXru2D4uyd9G5RWGPqK9vL1xlfO/BI2zbd5h2TetGeykAvHFd36Dp3671Zm4+8dj6fPSnMzlwOI86NWJf2iiNSN79daCDe56lqtENUY0DVc0TkRuAGXhdgyeo6rIkZ6uYpvVqWDAxxZzTuXnQ6UIqkruGdw67fVDn5nzxQy4d0uoVBJp4VH2d3KoRH9/8y4j27Xl8k6DpxzUKvlzTKW0aUa2a0CNMKaN+rerUr1U9ovePhQbuvcIFkld+35ta1eM/LCHcoMVUEfknXhvJRLx1TTaKyD9FJHGfVoRUdbqqdlTV9qr6QLLzE8zlvY9PdhZMOdS8Qc1kZyHurujdhhX3DqF1kzph97tvRJew20tSrZQ9lL649axiaaFuzG2axKa0EUuRzM3Vr0PTkIEzlsKFq0eAJkA7Ve2pqj2A9kAj4F9xz1klZLMGm2CSXT2RCCJC7RopJe53cUbroOm/7tEqovepVsof4I3qFv9d3Pm44NOO9G1/TOlOHkfxGHQYrXAf/XDgWlUtmPtAVfcAfwCGxjtjlZGNgDfBlHVkdCSa1Y9/qeeczrFbuCrUPbJGavENj/3m5GJppS2Z1HI9qq7v374g7fyTj+P2c08s1XlM+GCiGuSntKoeJb7r+ZRrD//6pDIfe+mpNrbFFBeLNcxDSW9eL27njoeUEMEgWMC98JTipZXSBpMaqdVY/9AwbhtSNHi0Tyvfn5vv86hVveTSXqKECybLReSqwEQRuRJYGb8slW+/ObUN6x4cyvQbz+A/VxUOAn3zur78okXx4nHrJrU5I70pK+4dQo3Uanw1tvjEARf1jKwIbyqfr8eeTWpK/Eomvz/9hDIf26aE9g2fWNbeBgsGmXcN5Ld92gbdf94dA0j1CzTRFPIm/a4Xj1zkTXVSnm7SwfRo04ibB3bk0Uu6JzsrBcIFkzHAGBH5TET+n3t8DtyIV9VVZYkInY9rwDmdmzP7ll/y53M6cmrbJrz6+95c6NeXHeC3fY7n5VG9C+qLWzaqXex8/yzFXD3x7ituEuu4RrXpFLCY04nHRr64U0nOOrFZmY/t3rpRzPIRqWCTMjatV5NOx9bnuSt7cG9AA32zBrXo0KywFBFqUsdInNkxraDNpl+Hou0jNw/sWObzxoOIcNPAdNISUI0ZqZDBRFVzVLU3cC+w3j3uVdVeqmoDAp32afW4cUA6AI3r1uCuYYWTA8y7cwDXnhH6l+FfBnlf0Ej/AFo1Lh6ITNkMOyn0+IPSeG9Mv6jP4X8zBK+x+fNb+0d93rK6bUgnAG4d3InP/lI8H73aNWHqDdFfd6A7hoZvpxjStUXQZRxuPqfwRt+qcWSlqZIENnC3bRqb81ZmJbYIq+onqvqke8xORKYqgyZ1a9Csfq2gvS7q1/J679xwdnpBSeODP55e4i/Ssa5RsEGtwt4/53Y9ljev61skiJUHsQx89WvFvrdT9RhVLcXi13tewGJMiha7KZb2l/GLIzP4+OagE34X8b9RvRkeMLDv+v4dWP/QMFo3qUPbpnV59dqi09xd1KMV3Vo1KlV+IlGvZtlGHNT16w13zWltY5Sboo3y5bH3VHlj3YtiLNX1TQzXi+bbOwaw9O5BRdK6tmzIc1f25Iz0pgzu4vWOefO6vvznqgyevaIH943owvBu3gjj44/x+rtPGdOPZ6/syaltm/D7MCWg0gpWFRep7+8ZzHW/bM8nt/Tn2jOKrzb3qx4tgxwV3tK7BzP0pGPLnKffhOhuWlrhRlNHIz/Iyn4p1YT/d/HJfH5rf167tg83DUwPeXywqs8Bv2ge0Vrop6c3Lbk3TeAOxe6rsWk0icX9OppqrmLnkti0xVQVlb+De4I1rFOdx35zMv3aNw25T6hxBW2b1uXlUb3Jz1cOH80P2QjoG80a+Mf353M6smP/4SLTfZ/cqiGDuhzLzv2H+XHHAa7sczx/en0RO/wW8qlXM5V9fpPeXd67DW8tyGbdtqKruNWvmRpycrxBnZvz8fIt1KuZWlCCate0eI+YP56dzrVnnMC5jwdf+e2/15xK9s6faZ9Wl8v/821Bev+OzZi+dHPQY0pyQlrxwWZnpKfx3qKfSnWe/h3TmLakcD2LS08tXZAad15nGtauzp/fKLqeR4PaRX+R+xq0f+06Zvh+PPhc0P24kHnPOL5xqZeMLe34p+I/lJJ7p41k4F6055UkX2NFYCWTOLjwlFY0axB8SoZIVKsmYXuTPHHZKfzx7A6c1LJhkfQbB6Rz9/ldePsPp/Gvi70++G2b1mXMWR24a3hnxl+VwZkd05g8ug+/61d4wxnR/bgi5zmrUzM+/Ut/Prml6LQUR1V56vJTgubp6St6sCSgtBXs11ydGilBe7359GjdmN/2OZ7TwgTj0WdGVgq7aUA6z/+2Jye18j6n567sSUfXVXZgiLER6c1Cdwm9OKM1C/92TrH0E/zmYWpcpzAwDDupBa9d24f2Lpid0qYxvwoy+K5j8/pFqpJOKKFb6r8vPaVIVae/t/5wGkMD2oOa1K1BmyZ1WPfgUCZcXdgD0TfuKdJY0qtdE14e1Yv+nYo26kcyGLEi8i8wBjbIm+IsmFRALRrW5pZBnULW4/Y8vnGR7pKBOjavz9/PK5xL6aaB6QVVa1A4Arhd07r82a9xU4Dh3Y4r0ih7srtRV0+pVjBPkI9vmuvf+/1S9uXq+GNCNGj6ZfuTW37JlCAN3HcM/QWrHzg35PX5dG3ZkMFdjuW09k3JvGsgQ7oey1t/OI3Jo/vQsHbx+vkpY/rxZoiJ9Ua5a2hct0bBID3fZ/PJX/oX1K/7Vzc+fUUP+rY/hneu78dDvzqp4LMK7PEHcFr7pgw96VhOPLZ+yEGAHZvXK6gm/OI2bxqQsX6D634fokSy4K6BfHHbWYhIkV/YE0aeCkC+iyY1UquFHYCYIsIZ6WkhtydLvCaW8H0utw3pRKM6NeLzJpWIVXNVUi0aeiWjcPXmNVKr0bB2dZrVr8Xzv80oNvGeiHDjgHT+M2ctew/mFfyqzvf763312j7sPHCYYIad1ILcvYe4rFcbXvhyXdB93vi/vlzyfPGZWyH8L/TqKcV/Bx3XsFaRqcD989m0nlc106BWdfqc4P3KvO+Crrw4Zy3rtx8A4OQQjemBbRLPX9mTI/n5BetRQOEgsryjxe9sDWtX59JehQNWH/tNd95dWLxD5DNX9Az6/j7+Exg2qlOjSL7CdRn3/9HR3JWYbzirA6ene6W/QZ2PZcayLXx00xlBP/OS7tVlnSZo/p0DOfWBWaU+7v0bTueL1aVbv71M3GVZFVdkkhJMRORi4G68RbZ6qWqm37bbgVHAUeBGVZ3h0ocAj+PNCvyCqj7k0tsBk4FjgAXAb1U1+N2tCul9wjG8c/1pdA/T62bZPYMj+jNpe0xdlubs5pp+bYGixf+6NVNDLrRTrZoU1N83rVeTbfsOFateD6x/j6YR9vX/68uLX64raDM6s4Rf0b/tczzndWtB93tnBt0e6gZdrZpQs1rRqh1fMDmaH/lo9ksyEj9YtfNxDYr1HPx1z1YM69YiZNWqL1bEukNT4BiJSE9/UquGBVWX4FW/pdWvyeW9YjvDhO/HiDW+RyZZJZPvgV8Bz/snikhnvLVIugDHAbNExFfP8jRwDt4sxvNFZKqqLgceBh5T1cki8hxeIHo2MZdRvoWbKhuC/7oP5vnf9uTdhTkFazSErKIK44nLuvPMp2s4pm7ZBlmluZl1B/4i9CC81k3qcPf5XQqCSSR1+Y3q1GDsuSfSu110s6oWtD9EuL9vRoRk6BrQ1gaRjfgOFUySPbdSreopzL9zYMzP6/vRVNopWqqqpAQTVV0BQftujwAmq+ohYJ2IZAG93LYsVV3rjpsMjBCRFcDZFC6ENRGvxGPBJIaOa1SbMWd1KHgdaRDyd1r7pkUa1Z++vAfPfb6mxCnJfc7q1IyXrjm1SJ19nxOa0LJRnajXGL/ul+2LvD6367H0PD58IA509Wlt2bTrIP/3y/Y8GcH63RWp0TpevaXKu3iVyCqr8tZm0hKY6/c626UBbAxI741XtbVLVfOC7F+MiIwGRgO0aWOTLkYrmtXkurZsyFOX9yiWHu7vNrAX0eTRXmN5tMEk0LNXhm+7CKZOjVTuu6BrTPNR3oRsO6iksaZBbe/2GI9Bs5VR3D4lEZkFBBtpdqeqTonX+4ajquOB8QAZGRmV9E8gMbIeODcuo4L9G7UjNel3vXgnxgElGrP+/Ev2hxiPUxF1b92IYxvU4uZzQg+crIz+0L89DWtX56KesRn0WtnFLZioalkqMXMA//+5Vi6NEOnbgUYikupKJ/77mzhKLUNVV0nW/mNomUYwn9kxjTM7lp8uq4FzbVV09WtVZ+4dA0Juj6Ya7PFLu/OfOWv5PmdPQdqk3/XiqgnzynzOWKmZmsI1/YJ3tzbFlbdxJlOBS0WkpuullQ7MA+YD6SLSTkRq4DXST3XrrXwKXOSOHwkkpdRjyu7RS07m1sGdYjoVhqkYRnRvSWc3iNVX0D2zY1rMJuI0iZOsrsEXAk8CacA0EVmkqoNVdZmIvAEsB/KAMW4xLkTkBmAGXtfgCaq6zJ3ur8BkEbkfWAi8mODLqTSm3tCPzX7jNBIl2KjwaFx9WlvWb99f8o4mJmw1agPJ6831LvBuiG0PAA8ESZ8OTA+SvpbCHl8mCt1aNaJbJVin6+7zu5S8kzEmpspbNZcxpoKxkokBCybGmDLyTZp5YRmWFTCVjwUTY0yZNKvvzfNVJ8oBmFayqRwsmBhjysTX+yrI2l5lO59NqFihWTAxxkSlrLMGm8rFgokxpkx8MyDEJZRYIaXCsWBijCmTlo28NpP6IZYgMFWLfQuMMWXy9+Fd6Nu+KRlto5u+31QOFkyMMWVSu0YK5598XNTn+cvgTuw5eIRh3WwKlYrMgokxJqmaN/CWjTYVm7WZGGOMiZoFE2OMMVGzYGKMMSZqSQkmIvKIiKwUkSUi8q6INPLbdruIZInIKhEZ7Jc+xKVlichYv/R2IvKtS3/drXdijDEmgZJVMpkJdFXVbsAPwO0AItIZb+GrLsAQ4BkRSRGRFOBp4FygM3CZ2xfgYeAxVe0A7ARGJfRKjDFxM6hz82RnwUQoKcFEVT92y+wCzMVbbhdgBDBZVQ+p6jogC2+tkl5AlqquVdXDwGRghHhDcM8G3nLHTwQuSNBlGGPi7LwYdD02iVEe2kx+B3zonrcENvpty3ZpodKPAXb5BSZfelAiMlpEMkUkMzc3N0bZN8YYE7dxJiIyCzg2yKY7VXWK2+dOvOV5X4lXPvyp6nhgPEBGRobNTmeMMTESt2CiqgPDbReRq4HhwAAtnHY0B2jtt1srl0aI9O1AIxFJdaUT//2NMcYkSLJ6cw0BbgPOV9UDfpumApeKSE0RaQekA/OA+UC667lVA6+RfqoLQp8CF7njRwJTEnUdxhhjPMmaTuUpoCYw001jPVdVr1PVZSLyBrAcr/prjKoeBRCRG4AZQAowQVWXuXP9FZgsIvcDC4EXE3spxph4sbroiiMpwcR14w217QHggSDp04HpQdLX4vX2MsZUEracScVTHnpzGWOMqeAsmBhjjImaBRNjjDFRs2BijDEmahZMjDHGRM2CiTHGmKhZMDHGGBM1CybGGGOiZsHEGGNM1CyYGGOMiZoFE2OMMVGzYGKMMSZqyZqC/j4RWSIii0TkYxE5zqWLiDwhIlluew+/Y0aKyGr3GOmX3lNElrpjnnBL+RpjjEmgZJVMHlHVbqraHfgA+LtLPxdvDZN0YDTwLICINAHGAb3xZggeJyKN3THPAtf6HTckQddgjImzwnXzTHmXlGCiqnv8XtalcNmCEcAk9czFW0WxBTAYmKmqO1R1JzATGOK2NVDVuW6hrEnABQm7EGNMXFgFQ8WTrMWxEJEHgKuA3cBZLrklsNFvt2yXFi49O0h6qPccjVfioU2bNtFdgDHGmAJxK5mIyCwR+T7IYwSAqt6pqq2BV4Ab4pUPf6o6XlUzVDUjLS0tEW9pjDFVQtxKJqo6MMJdX8FbQXEckAO09tvWyqXlAP0D0j9z6a2C7G+MMSaBktWbK93v5QhgpXs+FbjK9erqA+xW1U14a78PEpHGruF9EDDDbdsjIn1cL66rgCmJuxJjjDGQvDaTh0SkE5APbACuc+nTgaFAFnAAuAZAVXeIyH3AfLffvaq6wz2/HngJqA186B7GGGMSKCnBRFV/HSJdgTEhtk0AJgRJzwS6xjSDxhhjSsVGwBtjjImaBRNjjDFRs2BijDEmahZMjDHGRM2CiTHGmKhZMDHGGBM1CybGGGOiZsHEGFPu1Ejxbk0p1Wz24IoiabMGG2NMKH8f3pm0+jUZ0uXYZGfFRMiCiTGm3GlYpzpjzz0x2dkwpWDVXMYYY6JmwcQYY0zUkhpMROQWEVERaepei4g8ISJZIrJERHr47TtSRFa7x0i/9J4istQd84TYep/GGJNwSQsmItIab12SH/2SzwXS3WM08Kzbtwne4lm9gV7AOLeuCW6fa/2OG5KI/BtjjCmUzJLJY8BtgPqljQAmqWcu0EhEWgCDgZmqukNVdwIzgSFuWwNVneumr58EXJDQqzDGGJO0lRZHADmqujhgU0tgo9/rbJcWLj07SHqo9x0tIpkikpmbmxvFFRhjjPEXt67BIjILCNZJ/E7gDrwqroRS1fHAeICMjAwtYXdjjDERilswUdWBwdJF5CSgHbDYtZW3Ar4TkV5ADtDab/dWLi0H6B+Q/plLbxVkf2OMMQkkXlNDEjMgsh7IUNVtIjIMuAFvHfjewBOq2ss1wC8AfL27vgN6urXh5wE3At/irSH/pKpOj+B9c/HWny+LpsC2Mh5bGdnnUcg+i6Ls8yhUWT6L41U1LTCxvI2An44XSLKAA8A1AC5o3AfMd/vdq6o73PPrgZeA2sCH7lGiYB9GpEQkU1Uzynp8ZWOfRyH7LIqyz6NQZf8skh5MVLWt33MFxoTYbwIwIUh6JtA1XvkzxhhTMhsBb4wxJmoWTMpmfLIzUM7Y51HIPoui7PMoVKk/i6Q3wBtjjKn4rGRijDEmahZMjDHGRM2CSSmIyBARWeVmKB6b7Pwkgoi0FpFPRWS5iCwTkZtcehMRmelmcZ7pm3gz3MzPlYWIpIjIQhH5wL1uJyLfumt+XURquPSa7nWW2942qRmPAxFpJCJvichKEVkhIn2r+HfjZvd38r2IvCYitarK98OCSYREJAV4Gm9m487AZSLSObm5Sog84BZV7Qz0Aca46x4LzFbVdGC2ew0hZn6uZG4CVvi9fhh4TFU7ADuBUS59FLDTpT/m9qtsHgc+UtUTgZPxPpcq+d0QkZZ4A6gzVLUrkAJcSlX5fqiqPSJ4AH2BGX6vbwduT3a+kvA5TAHOAVYBLVxaC2CVe/48cJnf/gX7VYYH3pQ9s4GzgQ8AwRvVnBr4PQFmAH3d81S3nyT7GmL4WTQE1gVeUxX+bvgmpG3i/r8/wJvxvEp8P6xkErlQMxdXGa4Yfgre1DXNVXWT27QZaO6eV/bP6d94Syfku9fHALtUNc+99r/egs/Cbd/t9q8s2gG5wH9dtd8LIlKXKvrdUNUc4F94azRtwvv/XkAV+X5YMDEREZF6wNvAn1R1j/829X5aVfo+5iIyHNiqqguSnZdyIhVvvrxnVfUUYD+FVVpA1fluALi2oRF4QfY4oC5VaLE+CyaRCzWjcaUnItXxAskrqvqOS97iFifD/bvVpVfmz6kfcL6bnHQyXlXX43iLuPmmJvK/3oLPwm1vCGxPZIbjLBvIVtVv3eu38IJLVfxuAAwE1qlqrqoeAd7B+85Uie+HBZPIzQfSXc+MGngNa1OTnKe4E2+dgBeBFar6qN+mqcBI93wkXluKL/0q13OnD7Dbr8qjQlPV21W1lXrzyV0KfKKqVwCfAhe53QI/C99ndJHbv9L8SlfVzcBGEenkkgYAy6mC3w3nR6CPiNRxfze+z6NqfD+S3WhTkR54Mxr/AKwB7kx2fhJ0zafjVVMsARa5x1C8ut3ZwGpgFtDE7S94vd7WAEvxerYk/Tri8Ln0Bz5wz08A5uHNdv0mUNOl13Kvs9z2E5Kd7zh8Dt2BTPf9eA9oXJW/G8A9wErge+BloGZV+X7YdCrGGGOiZtVcxhhjombBxBhjTNQsmBhjjImaBRNjjDFRs2BijDEmahZMjImSiBwjIovcY7OI5Ljn+0TkmTi9559E5Kow24eLyL3xeG9jgrGuwcbEkIjcDexT1X/F8T1Sge+AHlo451PgPuL26aeqB+KVF2N8rGRiTJyISH+/NU/uFpGJIjJHRDaIyK9E5J8islREPnJT1iAiPUXkcxFZICIzfNOSBDgb+M4XSETkRvHWm1kiIpOhYE6sz4DhCblYU+VZMDEmcdrjBYLzgf8Bn6rqScDPwDAXUJ4ELlLVnsAE4IEg5+mHNxutz1jgFFXtBlznl54JnBHzqzAmiNSSdzHGxMiHqnpERJbiLZz0kUtfCrQFOgFdgZleLRUpeFOZB2pB0cW5lgCviMh7eFOa+GzFm73WmLizYGJM4hwCUNV8ETmihQ2W+Xh/iwIsU9W+JZznZ7x5nXyGAWcC5wF3ishJrgqsltvXmLizai5jyo9VQJqI9AVv6n8R6RJkvxVAB7dPNaC1qn4K/BVvGvN6br+OeBMOGhN3FkyMKSdU9TDeVOQPi8hivBmaTwuy64d4JRHwqsL+56rOFgJPqOout+0sYFo882yMj3UNNqYCEpF3gdtUdXWI7c2BV1V1QGJzZqoqCybGVEBuQarmqvpFiO2nAkdUdVFCM2aqLAsmxhhjomZtJsYYY6JmwcQYY0zULJgYY4yJmgUTY4wxUbNgYowxJmr/H1SpwI7Rn6MRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 258\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 259\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 260\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 261\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 262\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 263\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 264\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 265\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 266\n"
     ]
    },
    {
     "data": {
      "image/png": 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H4d5fujlmL5f/TI/fOB/K4a59B8vSXpy5msIRE5i6ZFNYwEhVKDBNWbyJH/w79R5oD7y9lLlrtoWl/WVyxSezP1n+dYW0WNZu+5bH3l8edm7EivWqlWPE362796fdUyqWaL+Q8x75KCv7yjcWNBIIdUOcs9r/k7GhB8ki7T1wKO7d4Qdfbmbwwx+VndChp3E/KXYuDre88DkHY1TD7NlXHjSOuuMtxny4vKxaa922b7l8zDS+3BheAnp4yjJe8fQiSZX3+jJpodOYPm5OCdck6JP/1CcrK3R/nLpkI4UjJjBy3HwANnvaaD74Mn7jZbw7/JdmOY/4vLNoI49/uIJDpcqvX63YrpQO73EI/Z95pfPQ36YonSWS2d6PnpnF/W8vKavWBKgR4K9//Lx1Cdc58d7J/OzFuYDz2zGVg1VPJVDDvRKUJlPlEOMu7dg73+b0o1om/Hrkxe/VOSWs3/5tjLWjC92x/+vK3twQo5TzUIbGFdoapXH8hRlroqwZ7u43FlV4BiNU9fbCjDXcd1GPsLaXeFTh+55eWa/OLmFw98NpUMc5xaevLC/5jJq4mB0+xqCKZu+BQ6zfvpdOLRtWWJbo7vyeNxeFfX7us1W0b96As45pBcCvXp7HthgN1Cu27K6QFq19RlV5ZXYJF/RsS73aNcvS9+x3SliHwkoa5RnesrP8/zBsq1mqn/rpC5/7Wm/C/PU8eoXz28mkWDcYh0qVmjUqQTGrErOSRgKhKofIm7pd+w5SOGIC30my+9/HxYkfVIt2On+aoCoi1gUrVsDIlLvfWMiOvQcTrxjD1CWbwj57j/PAJBuAZ3gCw20vz6PrXZP4dPkWjotywZm4ILXe2j8bO5ezHnyfb/cf4q0F68vu9h//cEXZwIqRQj3hQqWdkDtfX8i1T80sq3J7eXZJWLdnr8jqKoheL//Rsi386pX5/HHi4rD00OnhHUvKe85c+3R5qVBVM9YM7jc4R56/80u2lb3PRilj6+793P3Gwgq9Ex+KUg1owllJI4G73T7wK7fsZs3WPXRo3gAov1OaX7Kd3fsOUq92TeaVbOOY1o3Dvh96FiMZqbSj9ronmOlUn/pkVda2vTTFhyu9rnh8etT05TEu8LHMXLU1rHvzM5+tYvRbS7j/4u5c2rsDoyIu0l7fHzONVaOHhLX9RPokys3EmA+jD0dSvGknnxR/HbW+f7fbZrNpxz7GzSlh8fod3DGka9nyX748r+y9t/S8ZVd5SWPyoo1lNynpPtw3Y4W/eVC+jQgM3/n7J2XvR02IfWxT9enyr/l0+ddsiOjw8PbCDfxy4DEZ318+saCRQKgXTfGmXZzxwHtlPX7e9dwhd/vdJAZ1O5y3F27gnGNbhX3/iseTbxD124feZNfLs9ZwbrfDOax+bcbPDa+D3+R2/xzz4Qp+G/GwYzSqGtYIH+kH/64Y3CI7BQx86EO2fbs/btfTUMn4mz37+cVLToCI1QHh5y/O48ITKvaeen56+RPSB2K0z/nl99uhaqhJCzdUKB1n4gHP/TEGqoxML960i3XbvqVt0/pp7zNfWfVUhoTmxJhXsi2saL91V8X6flM1/OqV+Yx41WmQj6znDlVvLd+829eFtdPIiWnnZ+nGnb6fVfC24cSTqCNEtGqxZCTbLfjhKcsqpKXbhRmIOq9JLL92/89NdBY0krR4/Y4KRVqvvQfCfyQ749xdmsrva7eRP/IBxlSHfsm2/85IbhylX748L+Ux1vz4JsrT2/EGB6wV5QGjTMwe+HWUzhpQsU0N/LWh7Nl/MCc9upZs2MH3/vlZpZoO2oJGkgY//BGnjo79tHC8KghT9cxYuZXXPl+bdBtIUD5M0C05Gr9jrKlqwpLDfRMXc/r975Z/J8k2kWjtPrGGxtm0cy/j5vjrMp5M78dogS5S17smcUoKowYk6543FjFj1dakuvxnm7VppCDRjY/fJ8dN1RB6VqC6+/Wr83lpVkmFJ/m9/vXhirDPyRYSonV3jXy+CMJLK/2OaUXzhnXibjeZthnvg7Lx+Aku6UqmZm7yoo30PqJZwmORLitpGJMHdu49kPU5IV6aldyDoA+8vYSHk3wWKJVnJPy0ebyQRLWd9wHIoJUNfppgvW927+fHz87ihueyPyRflQsaIjJIRJaKSLGIjEj8jdyz3k8m1658IvUZ8bJh/8FS/vH+8rCuvH4sT2Ek5Hx+GK8sHib4E0NdlnMR8KpU0BCRmsCjwGCgK3C5iHSN/y1j8l+6vZySoaplr4OHSlm2cSf7D5aGTSM89NFP4mwhurEzVsdssI5HkLA8Rb4iJ/3y48uNO/nrlC8pLVVKS5VNO/eydfd+du07GPbsVWgf3velpeFpoY4GpaXlaaF1Qq/ZX33DonU7wtKgvMvyodLYf5+qlj2zU6umhH0/GySbG880ETkF+L2qDnQ/jwRQ1fuirV9UVKSzZiVfXPt2/yGOuyuzwxYYY0yuxWt/ikdEZqtqUbRlVaqkAbQDvGMxlLhpZURkmIjMEpFZmzenNjvXzhTHJTLGmHxX1YJGQqo6RlWLVLWooKAgpW0UNK6b4VwZY0x+qGpdbtcCHTyf27tpGWVdZo2JbcUfz6NGjMbnUA+u8TefFjZ+lB/XnlaY0lhm7/2yX9RRh0N27j1A99+/E3N5pDO6tOTZ6/pw4JBWmPiqtFQ5pEqXO94CUq/+iVTyzR7q1KpBq8b1wtJ/9MxMpizexJgre8edxG3r7v2ceO9kmjaozdy7zs1InmKpaiWNmUAXEekkInWAy4DxAefJmMDd1K9z1vfRspHT/z9WwPDq0b5p0tv/3QXdUnrGIFF2GternXAdr4+WbUFEKgQMcP722glmlkxF+2YNKgQMKH++JNE+Q23TubjdrVJBQ1UPAjcDk4DFwEuqujDYXFVU3zOPgTG58LP+R2d9H6/eeCrP/+hk3+tfdlIHjmjRIKl9tG5S8cKZiJ/5zI9oEbskEuno1o2SzkO2tHCDaON68SuFQt2OczHQYpUKGgCqOlFVj1bVzqo6Kuj8RGO1WybX6tSqwXu/7JfVfbRqXI/TEkwidlO/zhxZ4FygR1/cgxGDjk1qH8e1aZx4JZxqoXbuBdLP7+3Mo/23b4Ym7ornjxd25+9XnOB7m6m6e2g37r+4O72PaBZ3vaYN6vDwZb146tqTsp6nKhc0qgKLGfnllrOPCjoLvsSr188EPxfn2wcdy7u39Sv7fCDJcUT+eGF3XvnJKWFpZ0dMN5CKosL4F10vPwMkXnFyR87v0TadLPnSuF5tvn9SR1/trEN7tYtaxZVpFjSS9MDFPXj1xlPirmMN6fnl3K6Hc2nv8HknzuiSeNper9eHn5bJLGVUgzrZq05tWr92UuvXq12TosLmYWmxniULpftpY0nmAt+lVeWpnqqMLGgk6XsndaD3Ec1jLm/dxLrr5ovj2jQBoEYNqB3RKDq0l/N40I39OvPlHwbH3U6tGkLPDk2zksdIh0e0CZx2VAtGX9Q97nfe+fn/hX2+5tTCqOulci8UbXTZnu0PS2obR7eOXmUVKhCkc4vWKkr3+nu/e3waW8x/FjQyrGGdWlY9lSdCwz7UkIpNrRef2I6xw/py+8BjEvbM+WHfIxLua16GukmeHlECuv70TlzWp2Pc77Rv1iDsb7jqlCPo1rZJhfX8NDhHihY0Xr3x1KS2EWv61bLB/NL4wYVuDLwa1q1qTyLklu+gISIN3bGfTAwjBx/LM9f1SbtRo81h2a+XzEdtM3zcQhe8mjWEH59xZNgyEaHvkS0QkYQD5oWqsurVrvhza9GwDq/eeAqHNUiuGieWU45sEZ7PFE/GaPNapHJxPubwihflWj66rL55y+m0Oaweb95yeszupqF4FG/u9VhC7T+R/3Vz7xqQ9Laqm5j/eyJSQ0SuEJEJIrIJWAKsF5FFIvInEakarYMZ9L2i8nrtN24+vcLyG87sTIfmDcJ+pp0Lsts4GfKrGHdj1Un3JKs9Io26MLxaYsTgY2nWoDYdmzegsGVDVo0ewu2DjqlQvSIirBo9hJNiNLaGqlH6dAq/oP/ryt7MvnNA3OrOeKIFqzZNncAZWpTq6AaFURrVUwk/7VLsAnp8u8P4bOQ5HN/OOdZPX3sStSL+3nSqp0K/y8HHtwlLb9ogu3NR5IN4If89oDMwEjhcVTuoaivgdGAacL+I/DAHeaw0HrikZ9n7yAtUD89nb0P48z/qm/R+UhlD8qIT2yVeKQsGdG1Nwyw2pHp1aB7/AnT1KYUZ3d85x7Xm87vOpZ7nuZub+h3F61FuGOIJXewe+8GJYemJLnbxOlwM6d6Gv19escvnqZ1bMu6mU1ly72BeG35a2UU35IFLepS971PYnLvOdwaJfua6PmHrnXe88/TxwG6ty/ObwQ4ekY3NfQrjB85+x7Ti8IiS5JDuTh5TqU7q0LwBS+4dxPdO6pB4ZRMm3tHur6oVRu5T1a3Aq8CrIpKZMnUV9datZ9C4Xi02bN/LUZ4fgfe3FXmiH9WqEcUJ5gxIdopMSL0aIl2Cc3e2e3/mx/H/749O5sbn55TNT/K93h245ZwuUScbSuWByoLGddm8M/pUoqmI9X8QekYg8uKW6CJ8YseKJRcRN2BccWKUb4R/r1e0xnfPqXVJUXu+V+RcNLu3iyw9VfxqJs+wN245nWPvLB9J2k88iryZuuuCbvys/9EpBQ1Vwm4GgKQeXKzO4pU0XhORH4hIzP5n0YJKvjoySjXTcW2a0L5ZA4oKm4cVa2Od/zN+c46vrpeplDRSvQkcf3N6XUFvOit+LWWiZxwih79o36y8NHHqUS253NOIG+9vjBZo7zy/fKqV60/vBFDWi+nPl/YM21cmnNCxadT0WF1CE3V1jRZUVt43JG7ASIZ367meISHygp3M+Rvq3VSzhtAsg1ObJnpw0TjiBY0xwPnAChF5SUQudMd7qlZ+5F5shvdLvwmnVZN6afXMOKmwGQO6to66rG6UcXL86Ng8uWEeIvXq0JQmnr74t57TJanve69Vl/fpwLMR1SR1apZfTeLdmUde9IpHDea60wrLgn1oTKNjWjdi1eghXNy7PTd5/k9TrXv3SjTUQ6S+EY3W8bYzsFtrlv/xvJTy5dW+WX1uONNp1G/l6Z4brZcThB/XbD5+5Kek/MuBzlApkc/MmNyKeaVR1ddV9XKgEKc66ipgtYg8JSLVpovBb8/vyty7BnBxEidqtItbZCMeQJMYF5nIn+8VJ3fkX1f25uWfnMpfv98r6ndiNeDdnKAk4DXmyt5l70cMPpZCn+MGPXlN+VwtyTa8HuEJWvdd1IMjC/w/WLX0D4OYc6dzKkYes1o1ayAiPHf9ydz9nW5lg+15DejamlWjh7DonoFMve3MpPIdjfcC66evf7TLZJ1aNXj3tn5M+GnFdpN0pjUN5adnh6bcPvBY/nP9yWFDa1QMGdF6T2UvatTwcc9z4QntWTV6SIVSSiqq0uRzlU3C/ypV3aOqL6rqhcC5QC+gWk1rl2yPiisj+uXPvWsAc6J05bugZ/SnVEdf1D2sjeS7vdox0B0WOdqpHjk88988DaSx+riHeH873uEafnJmZ99F/zaHld+lJ/tTTPQ0b7zt1a1Vk4Z1nQuIqlIvSnVPu6b1uTrGw2ohDerUqnAh6ndManOxgBOoI8+BaCKvwZ/fOYDZv+1PQeO6dGsb3saQ7jXuyr5HsGr0EBrWrUXNGlLheY6gr6FBtcmZ5CUMGiLSWkRuEZFPgNdwRphNq1JVRC4VkYUiUioiRRHLRopIsYgsFZGBnvRBblqxiIxIZ//Z9tOIKpqmDerQpJ7/PgPnHNeaKb8ov/Pt06m8Z4mfO6Qh3dtwRIsG/GJA/JFPf9i3Y9hFOXQnGbqY/e6CbgBR79JjSvbqk8Tq0RtnpWy3J/h46tpP9i7v05Gnr+2TeMXIbSe5fuSde7OGdWgc4zzJ9jU9VueLXMWSXI+8Y+WM1MWshBWRHwOXA8fgVE/9SlU/zdB+vwAuAv4Vsc+uOHNkdAPaAlNEJHTlexQYgDPF60wRGa+qizKUn4zye/5H/lCuOLkjRyYYdC5eLyGR8oviB786K+52Zv+2Py0a1WXr7v3l33f/DW0jFCzq1KzB4U3qcVybxry3NP4Uusn+GE8+0v8zCtHuRkPHUIlffZLcnWx6l5RsXACjze0ATlfZjAzFH/En2/BpJpZ4LXenAPcBU1W1NJM7VdXFEPVHPhQYq6r7gJUiUgyEbvmKVXWF+72x7rpZDRpnpVFFAcn/8M47vk2FaoNItWrWYNXoISzZsIMrHp/OvUP9j5NzZEFDVmzeDUCLRk7bQ6jk0qxB7bL8RjaOiwjTRp4NwEmjpsTtphoKOKEAFq9qz8+sZ9FKBjf168ye/Yec/ZSt5+9CH2+tdKtIslHFM3ZYX177fC23xxhiPJkhv+PJ1Z2397kPr1wP8hl0dVxVFjNoqOp1ofci0gOnQbyWZ/m4LOSnHc6DgyElbhrAmoj0rHaqTmcaR7/nY6KLVLum9Vm7LfrzD8ce3qSsETiRqbedycbte7nnzYoxNpRXEUFEePyqorIHFaP9sE7t3ILX566Lua/QxfsHJ3ekR7umXHRiO+717PfyPh15YcZqX/mOFLqueC+gNWsIBY3r8stzE0xClMNrUiZ31ffIFjF7WWVStIH7IPMX139dWRQ1PdcFm1SehTKOhH0EReRJoAewEAiVOBSIGzREZAoQbVLbO1T19STz6ZuIDAOGAXTsGH+gtmyL9UOY8NPTWbdtLz3bH8a6bd+yedc+5pdsr7De+7/ql5EfbeeCRnQuaERRYTOWbNjJD06ueFxCefV26Q01UjdKoptwV7cBt0+nFnwnSkP/fRd1TypoeH/c0Y6niDDzjv6+t5dN53ZrzUNTvmTg8bHncq6sRIROLRuycotTEs31RTzI6rAfn9GJsTPWJF7RAD6CBtBXVbsmXi2cqqbyS14LeJ/rb++mESc9cr9jcJ4xoaioKJDbiURVJd3aHlbWO+aJa07iB/92CleRdz+Znos4tD3vZD3xstquaX1GDj6WIT3Kx+dJ9Nvu06k5M35zTtgzAJVJNqsljmvTJK0SanWW85KG5zy4Y0hX7hiS9CWu2vITND4Tka45anQeD/xXRP6C0xDeBZiBc051EZFOOMHiMuCKHOQnLZFzG8SS6+6G3vrjRMNL33Bm5+gL4kg2YHx0+1ms2bon4Xp+7kbH33wataJ0+rd23VTl5r4rlZFqTTD8BI1ncQLHBmAfzu9PVbVH/K/FJiIXAn8DCoAJIjJXVQeq6kIReQmngfsgMFxVD7nfuRmnu29N4ElVXZjq/rOtVs0aPHxZrwozkFVu/n602Wiw7NC8AR1iPJke9kSyjzz2aN80anpdt4dR/To2hYwfuW6Yti63VYefoPEEcCWwgPI2jbSo6v+A/8VYNgoYFSV9IjAxE/vPhdDMbpVeFfr1pHNhGdK9DSs37+b6MzplLkMmLV3bNGHR+h3uJytpVBV+brs2q+p4VV2pql+FXlnPWTWUbn37X7/fi84FDWNeXKNtv7z3lL99HNcm+tSbuXBe9zaJV4qhZg3h1v5dkmrUN9k18dYzyt6nMUJKSqzLber8/II+F5H/Am/gVE8BWetyWy1lqmg+tFc7XyWcqL2QfO7jR6cfyamdW3L+3z5OKm/JePyqorJhVEK/7V8NPIa2GRhU0CQnVxfX3DdpWNRIlZ+gUR8nWHgnMU7Y5dZUDaHhuc85LvpDV5Fq1JAKE/tkWqyRfPPJg5f2TLxSgHLe5daqp6qMhEFDVa/NRUZMMBrXq820kefQIpnxpXIoX6sRLrHhvcPkvCE8T8+rXIg3R/hvRSRm9x8ROVtEzs9OtqqnoAaHO/ywehl/JiTTrEdmMPJ1wEKTungljQXAGyKyF5gDbAbq4Tw70QuYAvwx2xk0xuReri/iNvZU1ZFoEqbTgJ/gDCFSE9gB/Afoo6o/V9X4Q54akyYbIyi//dlt27GCRtXhp01jGbAsB3mptpq5I8GmOmWrX0HPVvbMdcnPURFiDaXByPY5U6tmaA6XHJc07GYkZdZpvRL4w4XH0/uIZpzcKTdPkAd1+Q0N492gTk2uO80esqtsQrMg1qwhed/GYNVTqbOgUQk0qVc74ZSk+WTRPYP8r2w/7pwZc2URb8xbR8fmDVi+eVdO953nMSqv+Jnu9TQ/acZkU77f+VYGbZvW54YzO0cMaJldQd3x271I6vyUNP5GxTnBo6WZSi6TP5Qb/u9Izj62VQa3aCqTXLUhJRpl2VQ+8eYIPwU4FSgQkV94FjXB6UllqqhMNDqOPO+4DOQksSDuCK2+O/eCnE/DJCde9VQdoBFOYGnsee0ALklnpyLyJxFZIiLzReR/ItLUs2ykiBSLyFIRGehJH+SmFYvIiHT2b6qeXFxU7G63omxfXO3iXfXEmyP8A+ADEXk6C6PaTgZGqupBEbkfGAn8WkS64kyw1A1nEqYpIhKa/PlRYADO/OAzRWR8jiaGyhtV8QcadDfhaivfH+6zVo2U+WnTeFpEKhxhVT071Z2q6juej9MoL7kMBcaq6j5gpYgUA6HO/cWqugJARMa661rQSEFVvKOuink2idk9QdXjJ2j80vO+HnAxzqx6mXId8KL7vh1OEAkpcdMA1kSknxxtYyIyDBgG0LFjxwxm05jqJ5PX9HhzmdjI6FWHnyfCZ0ckfSIiMxJ9T0SmAIdHWXSHqr7urnMHTgB63kdefVHVMcAYgKKiIjs1qji7Ew1GNi7ioae/vbq2bQJAvxz3xLPTKnUJg0bESLc1gN5AwgkVVLV/gu1eA5wPnKPlFddrgQ6e1dq7acRJNz4VFTbjuWlfcezhTYLOStJsGJH8dFybJiy6ZyAN6thzxlWFn/+p2TiBWXBKBSuB69PZqYgMAm4HzlTVPZ5F44H/ishfcBrCuwAz3H13EZFOOMHiMuCKdPJQHQ3t1Y6+R7agdZN6QWfFVBGqysjBx7Jyy+6s7SOIgNGjfXYnEstnfqqnsjFI0N+BusBkt9fENFX9iaouFJGXcBq4DwLDVfUQgIjcDEzCeUbkSVVdmIV85b2qFjCsGiEY3t5MN5zZOTPbzMhW0vfUtSfRzx0HzSTPT/VUPeAm4HSc3/BHwD9VdW+qO1XVo+IsGwWMipI+EZiY6j5N1Wa9p0ymHFXQKOddfPOJn3Lhs8BOnKFDwKkWeg64NFuZMibEGsKNqVz8BI3jVbWr5/N7ImLPRxiTx7JxH2539/nBz6w/c0Skb+iDiJwMzMpelowxxlRWfkoavYFPRWS1+7kjsFREFgCqqj2yljtT7VX14R5aNa4bdBbSkonqwSNbNmRFFntfmdzyEzSSmDHHmOyoilUbc+4ckPUpfLMlk4f7pZ+cQtEfpmRug2mK92S6SczP0fuDql7pTRCR5yLTjDHhmjesE3QWKpXKEvab2f9LWvzcBnXzfhCRWjhVVsZk3fk92gJw5tEtA85J9XJY/doAdGjeIOCcmMom3iRMI4HfAPVFZAflNwr7ccd2Mibbeh/RjFWjhwSdjWqnR/umPHF1EacdlX6wtm7T+SVmSUNV71PVxsCfVLWJqjZ2Xy1UdWQO82iMCcA5x7WmXu3MTdJZBZulTBR+2jTeEpH/i0xU1Q+zkB9jjDGVmJ+g8SvP+3o4kyLNBlKehMmYysyqU4yJzc+AhRd4P4tIB+Cv2cqQMUGx2pPsqOrP2phwqXQiLwGOS2enInKviMwXkbki8o6ItHXTRUQeEZFid/mJnu9cLSLL3NfV6ezfGBMEC8v5wM8ot3+jfITqGkAvYE6a+/2Tqt7pbv+nwF3AT4DBOHNodMGZzvUx4GR3IqjfAUVuXmaLyHhV/SbNfBhjjEmCnzYN7zhTB4EXVPWTdHaqqjs8HxtSHpSGAs+6M/lNE5GmItIG6AdMVtWtACIyGedJ9RfSyYcxxpjk+AkaLwKh+S+K05lHw0tERgFXAduBs9zkdsAaz2olblqs9GjbHQYMA+jYsWMmsmqMSYc1aeSVmG0aIlJLRB7AuUA/gzOvxhoReUBEaifasIhMEZEvoryGAqjqHaraAXgeuDkzfw6o6hhVLVLVooICm53LmMrCntPID/FKGn8CGgOdVHUngIg0AR50X7fG27Cq9veZh+dxZuT7Hc783x08y9q7aWtxqqi86e/73L4xxpgMidd76nzgx6GAAWVtETcC56WzUxHp4vk4FFjivh8PXOX2ouoLbFfV9Thzg58rIs1EpBlwrptmjKnkrHYqv8QraajbIB2ZeEhE0j0PRovIMUAp8BVOzylwShznAcXAHuBad59bReReYKa73j2hRnFjTNVgtVP5IV7QWCQiV6nqs95EEfkh5SWDlKjqxTHSFRgeY9mTwJPp7NeYyqBxPZvPwVRd8c7e4cA4EbkOZ9gQcJ6TqA9cmO2MGZOPnrr2JI5u3TjobBiTsphBQ1XX4jxYdzblc2pMVNWpOcmZMXnorGNaBZ2FnLOxvPKLn7Gn3gXezUFejDF5zLrc5oeqOYGxMcaYQFjQMMZklY1ym18saJisq1fbTjMDYp1u84L1/TNZ9+5t/Vi9dU/Q2TDGZIAFDZN1bZvWp23T+kFnwxiTAVZvYEwEq4PPLOtym18saBjjsi6h2WXHNz9Y0DDGGOObBQ1jTFZZ7VR+CTRoiMhtIqIi0tL9LCLyiIgUi8h8ETnRs+7VIrLMfV0dXK6NMamw2qn8EFjvKRHpgDMvxmpP8mCgi/s6GXgMZ/yr5jiTNBXh3LjMFpHxqvpNbnNtjDHVW5AljYeA2wkvvQ4FnlXHNKCpiLQBBgKTVXWrGygmA4NynmNjjKnmAgka7jzha1V1XsSidsAaz+cSNy1WujGmkosyl5upwrJWPSUiU4DDoyy6A/gNTtVUNvY7DBgG0LFjx2zswhiTArE+t3kha0FDVftHSxeR7kAnYJ57ErUH5ohIH2At0MGzens3bS3QLyL9/Rj7HQOMASgqKrJbHGOMyaCcV0+p6gJVbaWqhapaiFPVdKKqbgDGA1e5vaj6AttVdT0wCThXRJqJSDOcUsqkXOfdGJM8q53KL5Vt7KmJwHlAMbAHuBZAVbeKyL3ATHe9e1R1azBZNMaY6ivwoOGWNkLvFWdu8mjrPQk8maNsGWOMicKeCDfGGOObBQ1jTFY1qutUaJzauUXAOTGZEHj1lDEmvzVrWId3bzuT9s0aBJ0VkwEWNIwxWXdkQaOgs2AyxKqnjDHG+GZBwxhjjG8WNIwx1Uabw+oFnYUqz9o0jIlgTzDnp9eHn0a7ZvWDzkaVZ0HDGJfYNEF5rWeHpkFnIS9Y9ZQxxhjfLGgYY4zxzYKGMcYY3yxoGGOM8S2o6V5/LyJrRWSu+zrPs2ykiBSLyFIRGehJH+SmFYvIiCDybYwx1V2QvaceUtUHvQki0hW4DOgGtAWmiMjR7uJHgQE4kzbNFJHxqroolxk2xpjqrrJ1uR0KjFXVfcBKESkG+rjLilV1BYCIjHXXtaBhjDE5FGSbxs0iMl9EnnSncAVoB6zxrFPipsVKr0BEhonILBGZtXnz5mzk2xhjqq2sBQ0RmSIiX0R5DQUeAzoDvYD1wJ8ztV9VHaOqRapaVFBQkKnNGmOMIYvVU6ra3896IvI48Kb7cS3QwbO4vZtGnHRjjDE5ElTvqTaejxcCX7jvxwOXiUhdEekEdAFmADOBLiLSSUTq4DSWj89lnk1ijetWtiYyY0ymBfUrf0BEegEKrAJuAFDVhSLyEk4D90FguKoeAhCRm4FJQE3gSVVdGEC+TQyL7xmE2NBNxuS9QIKGql4ZZ9koYFSU9InAxGzmy6Sufp2aQWfBGJMD9kS4McYY3yxoGGOM8c2ChjHGGN8saBhjjPHNgoYxxhjfLGgYE8GmCDcmNgsaxoTYcybGJGRBwxhjjG8WNIwxxvhmQcMYY4xvFjSMMcb4ZkHDGGOMb4EFDRG5RUSWiMhCEXnAkz5SRIpFZKmIDPSkD3LTikVkRDC5NsaY6i2QUW5F5CycOb57quo+EWnlpnfFmSujG9AWmCIiR7tfexQYgDPV60wRGa+qNke4McbkUFDzadwIjFbVfQCquslNHwqMddNXikgx0MddVqyqKwBEZKy7rgUNY4zJoaCqp44GzhCR6SLygYic5Ka3A9Z41itx02KlVyAiw0RklojM2rx5cxaybowx1VfWShoiMgU4PMqiO9z9Ngf6AicBL4nIkZnYr6qOAcYAFBUV2YgQxhiTQVkLGqraP9YyEbkRGKeqCswQkVKgJbAW6OBZtb2bRpx0Y4wxORJU9dRrwFkAbkN3HWALMB64TETqikgnoAswA5gJdBGRTiJSB6exfHwQGTfGmOosqIbwJ4EnReQLYD9wtVvqWCgiL+E0cB8EhqvqIQARuRmYBNQEnlTVhcFk3Rhjqq9Agoaq7gd+GGPZKGBUlPSJwMQsZ80YY0wc9kS4McYY3yxoGGOM8c2ChjHGGN8saBjjqlXDmbqvdk37WRgTS1C9p4ypdC7o2ZalG3dyU7+jgs6KMZWWBQ1jXLVr1mDk4OOCzoYxlZqVw40xxvhmQcMYY4xvFjSMMcb4ZkHDGGOMbxY0jDHG+GZBwxhjjG8WNIwxxvhmQcMYY4xv4kxjkZ9EZDPwVRqbaIkzOZSxYxHJjkc4Ox7l8uFYHKGqBdEW5HXQSJeIzFLVoqDzURnYsQhnxyOcHY9y+X4srHrKGGOMbxY0jDHG+GZBI74xQWegErFjEc6ORzg7HuXy+lhYm4YxxhjfrKRhjDHGNwsaxhhjfLOgEYWIDBKRpSJSLCIjgs5PLohIBxF5T0QWichCEbnVTW8uIpNFZJn7bzM3XUTkEfcYzReRE4P9CzJPRGqKyOci8qb7uZOITHf/5hdFpI6bXtf9XOwuLww041kgIk1F5BURWSIii0XklOp6bojIz93fyBci8oKI1KtO54YFjQgiUhN4FBgMdAUuF5GuweYqJw4Ct6lqV6AvMNz9u0cAU1W1CzDV/QzO8enivoYBj+U+y1l3K7DY8/l+4CFVPQr4BrjeTb8e+MZNf8hdL988DLytqscCPXGOS7U7N0SkHfBToEhVjwdqApdRnc4NVbWX5wWcAkzyfB4JjAw6XwEch9eBAcBSoI2b1gZY6r7/F3C5Z/2y9fLhBbTHuRCeDbwJCM5TvrUizxNgEnCK+76Wu54E/Tdk8FgcBqyM/Juq47kBtAPWAM3d/+s3gYHV6dywkkZFoZMipMRNqzbcIvQJwHSgtaqudxdtAFq77/P9OP0VuB0odT+3ALap6kH3s/fvLTsW7vLt7vr5ohOwGXjKra77t4g0pBqeG6q6FngQWA2sx/m/nk01OjcsaJgwItIIeBX4maru8C5T53Yp7/toi8j5wCZVnR10XiqJWsCJwGOqegKwm/KqKKBanRvNgKE4gbQt0BAYFGimcsyCRkVrgQ6ez+3dtLwnIrVxAsbzqjrOTd4oIm3c5W2ATW56Ph+n04DviMgqYCxOFdXDQFMRqeWu4/17y46Fu/ww4OtcZjjLSoASVZ3ufn4FJ4hUx3OjP7BSVTer6gFgHM75Um3ODQsaFc0Euri9IergNHKNDzhPWSciAjwBLFbVv3gWjQeudt9fjdPWEUq/yu0p0xfY7qmqqNJUdaSqtlfVQpz//3dV9QfAe8Al7mqRxyJ0jC5x18+bu25V3QCsEZFj3KRzgEVUw3MDp1qqr4g0cH8zoWNRfc6NoBtVKuMLOA/4ElgO3BF0fnL0N5+OU70wH5jrvs7DqX+dCiwDpgDN3fUFp5fZcmABTm+SwP+OLByXfsCb7vsjgRlAMfAyUNdNr+d+LnaXHxl0vrNwHHoBs9zz4zWgWXU9N4C7gSXAF8BzQN3qdG7YMCLGGGN8s+opY4wxvlnQMMYY45sFDWOMMb5Z0DDGGOObBQ1jjDG+WdAwxicRaSEic93XBhFZ677fJSL/yNI+fyYiV8VZfr6I3JONfRsTjXW5NSYFIvJ7YJeqPpjFfdQC5gAnavm4RpHriLvOaaq6J1t5MSbEShrGpElE+nnm3Pi9iDwjIh+JyFcicpGIPCAiC0TkbXeoFkSkt4h8ICKzRWRSaDiOCGcDc0IBQ0R+Ks58J/NFZCyUjfn0PnB+Tv5YU+1Z0DAm8zrjXPC/A/wHeE9VuwPfAkPcwPE34BJV7Q08CYyKsp3TcEZQDRkBnKCqPYCfeNJnAWdk/K8wJopaiVcxxiTpLVU9ICILcCbpedtNXwAUAscAxwOTndolauIMsx2pDeGTQM0HnheR13CG8gjZhDPiqjFZZ0HDmMzbB6CqpSJyQMsbDktxfnMCLFTVUxJs51ucsYtChgD/B1wA3CEi3d2qq3ruusZknVVPGZN7S4ECETkFnCHpRaRblPUWA0e569QAOqjqe8CvcYbYbuSudzTO4HnGZJ0FDWNyTFX34wyTfb+IzMMZUfjUKKu+hVOyAKcK6z9uldfnwCOqus1ddhYwIZt5NibEutwaU4mJyP+A21V1WYzlrYH/quo5uc2Zqa4saBhTibkTH7VW1Q9jLD8JOKCqc3OaMVNtWdAwxhjjm7VpGGOM8c2ChjHGGN8saBhjjPHNgoYxxhjfLGgYY4zx7f8BBqluvii/Mw4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 267\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 268\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 271\n"
     ]
    },
    {
     "data": {
      "image/png": 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zjjM45Z8jaXCx8zWEp22bWTdQcuhsRKyLiD9ExEfJhr8OAO6p4Bzvj4gBEZFbBGkoMCEi+gMT0jZkE/f6p8eFZJP3ch3qw4DjyOZkDMub9d2xeDiUmXVRJSsLSftJ+qKkR4C/kA1hPbrtV7XpLGBkej4SODsv/cbIPAbsleZknAqMi4iVEbEKGAectgPnrx3fZZhZF9XWPIvPA+cCbydrhvpGRDxa4fFzsaGCbF7GtcB+EbEk7V9KNh8DqhAbiuyOhAMPPLDCYpqZWVvaGjp7PPADsiajbe08/okRsVjSG4Fxkp7J3xkRkSqSHZYqomsBBg4c6N/4ZmZVVLSyiIjP5p5LOpKso7tn3v4/lTp4fmwoSX8m63NYJmn/iFiSmpmWp+xtxYZ6X4v0B0ud28zMqqecPosRwAjgY8CH0+NDZbyuYGwosrhOuRFNg4E70/PRwPlpVNQgYE1qrroXOEVS79SxfUpKMzOzOiln8aNBEXF4O45dLDbUROA2SRcAC4FPpvxjgTOAucA64DOQzRiXdBlZQEGAS9MscjMzq5NyKot/SDq80mVM24gN9TJwcoH0AC4qcqzc3U3H5qGzZtZFlVNZ3EhWYSwlizwrsmv7kTUtWScz5P2HNLoIZmY1U05lcT1wHvA00N5RUV1WpMkVfXZzJBQz67rKqSxWRIQXGypBboIysy6snJXypki6RdK5kv419yj3BJJ6SJoi6a60/TtJ81O8qKlpIaTOGxvKzKwbKOfO4nVkfRWn5KUFUHKeRfJlYBbwhry0b0TE7S3y5ceGOo4sNtRxebGhBqbzTpY0OoX+6FD23X2XRhfBzKwmSlYWEfGZ9h5c0luBM8lWv/taiexNsaGAxyTlYkO9jxQbKh0zFxvq1vaWq1b677cHO/cQxxzUMeMcmpm1V9FmKEnfSb/qi+0/SVKpyXk/B75J647x4amp6UpJuZ/jOxwbStIkSZNWrFhRoli188637EnPncpp3TMz6zzaurN4GvirpA3Ak8AKYFeyZqIBZOtzf7/Yi1NFsjwiJkt6X96ui8kCCPYii+X0LeDSdr+DpKPEhpJ7us2sCyq1+NEJwBeAGUAP4BXgZuDYiPhqRLT1E/4E4COSFgCjgJMk3RwRS1IY8o3ADWTxoqDt2FCF0s3MrE7K6bOYA8yp9MARcTHZXQTpzuK/I+LTeUEERbaWxfT0ktHAEEmjyDq416R89wLfz1vw6JTccc3MrD7KGQ1Vbb+XtC/ZTPCpZHcu4NhQZmYdVl0qi4h4kBRWPCJOKpKn88eGMjProsoJUX5COWndVXiZJeuiRj/1IuePeKLRxbAOopw7i1/Ses3tQmndmsdAWVfzpVunNLoI1oG0tQb38cB7gH0l5U+oewPZyCgzM+sm2mqG6gXsTlah7JH3eAX4eLknKBAbqp+kx1MMqD9I6pXSd0nbc9P+vnnHuDilz5Z0asXv0szMdkhba3A/BDwk6XcRsXAHztEyNtQPgSsjYpSkXwMXkMWBugBYFRGHSDon5fs3SYcD5wBHAG8Gxks6NCK27kCZzMysAuXEpfidpPtbPso5eF5sqOvStoCTgFwQwZFkcy0giw01Mj2/HTg55T8LGBURGyNiPtnQ2txEPjMzq4NyOrj/O+/5rsDHgC1lHv/nZLGh9kjbewOrIyL3+vw4T00xoCJii6Q1Kf9bgMfyjlk0NhRwIcCBBx5YZvFqI7cgkplZV1HODO7JLZIekVRyPF0bsaFqosPEhmrUic3MaqhkZdEi8uxOwDHAnmUcOxcb6gyyO5I3AL8A9pLUM91d5Md5ysWAWiSpZzrHyzg2lJlZw5XTZzEZmJT+/gP4OllndJsi4uKIeGtE9CXroL4/Ij4FPEDzaKrBwJ3p+ei0Tdp/f5rVPRo4J42W6kcW9dYzhczM6qicZqh+VT7nt4BRki4HpgDXp/TrgZskzQVWklUwRMQMSbcBM8n6Si7qSCOh3DthZt1BOc1QuwL/DziR7Nr4MPDriNhQ7klaxIaaR4HRTOl4nyjy+uFkq+11WF7Hwsy6snJGQ90IrCUL8QHw78BNFLmwm5lZ11NOZfGOiDg8b/sBSTNrVSAzM+t4yungflLSoNyGpOPIOrzNzKybKKeyOAZ4VNKCtETqP4B3S3pa0rRiL5K0q6QnJD0laYak76X030maL2lqegxI6ZJ0VYoBNU3S0XnHGixpTnoMLnJKMzOrkXKaoU5r57E3AidFxKuSdgb+LunutO8bEXF7i/ynkw2L7U+2rOo1wHFpnscwYCBZB/tkSaMjYlU7y2VmZhUq587i8ohYmP/ITyv2osi8mjZ3To+2RpqeBdyYXvcY2eS9/YFTgXERsTJVEONofwVmZmbtUE5lcUT+RppdfUw5B0/hyacCy8ku+I+nXcNTU9OVknZJaU2xoZJcDKhi6S3PdaGkSZImrVixopzi1YxXzzOzrqZoZZHWkFgLHCnpFUlr0/YymmddtykitkbEALIQHcdKegdwMXAY8G6gD9kkvR0WEddGxMCIGLjvvvtW45Dt4ukWZtYVFa0sIuIHEbEH8OOIeENE7JEee0fExZWcJCJWk4X5OC0ilqSmpo3ADTRP0CsWA8qxoczMGqycZqi7Jb235aPUiyTtK2mv9Px1wAeBZ1I/RG5ti7OB6eklo4Hz06ioQcCaiFgC3AucIqm3pN7AKSmtQwi3OZlZN1DOaKhv5D3flexOYDLZIkZt2R8YKakHWaV0W0TclRZP2pcsmvdU4Asp/1jgDLLFjdYBnwGIiJWSLgMmpnyXRsTKMspdV25+MrOurJxAgh/O35Z0ANmiRqVeNw14V4H0gpVMijB7UZF9I4ARpc5pZma1UU4zVEuLgH+qdkHMzKzjKifq7C9pnh+xEzAAeLKGZTIzsw6mnD6L/DhQW4BbI+KRGpXHzMw6oHKaof5A1qE9Gbij3IqijdhQ/SQ9nmJA/UFSr5S+S9qem/b3zTvWxSl9tqRTK36XZma2Q9qalNdT0o/I+ihGkq1r8YKkH6VYT6XkYkMdRdZ0dVoaEvtD4MqIOARYRfMSrRcAq1L6lSkfkg4nWzXvCLIwH1enEVZmZlYnbd1Z/JhshnW/iDgmIo4GDgb2An5S6sBtxIY6CcgFERxJNtcCsthQI9Pz24GT01yMs4BREbExIuaTDa1ttdKemZnVTluVxYeAz0fE2lxCRLwC/BfZfIiSWsaGAp4DVkfElpQlP85TUwyotH8NsDeODWVm1nBtVRYRBaYnR8RW2o4eu13e/NhQZDGhaqLDxIbCs/PMrOtpq7KYKen8lomSPg08U8lJ8mJDHU8Wejw3Cis/zlNTDKi0f0/gZTp4bCjfRJhZd9BWZXERcJGkByX9ND0eAr5E1hTVpiKxoWaRVRofT9kG0xzBdnTaJu2/P93ZjAbOSaOl+pEtjvREBe+xLnw/YVYdm7duY/PWbY0uhrVQdJ5FRCwmW6nuJJrXtBgbERPKPHax2FAzgVGSLgemANen/NcDN0maC6wkGwFFRMyQdBswk2yex0WpKczMuqDDvnsPvV/fi0nf+UBT2riZy9hj154MetveDSxZ91ZObKj7gfsrPXAbsaHmUWA0U0RsAD5R5FjDgeGVlsHMOp+t24KXXt24Xdrnb8zmBi+44sxGFMloX2woMzPrZlxZmHVxIx9dwAsr1zW6GO321AurG10Ew5WFWZf26sYtDBs9g3/7zT8aXZR2+/7YWY0uglHDykLSAZIekDQzxYb6ckq/RNJiSVPT44y81xSMASXptJQ2V9LQWpXZrKvJTZVas35zg0tinV05UWfbawvw9Yh4UtIewGRJ49K+KyNiu5AhLWJAvRkYL+nQtPtXZENvFwETJY2OiJk1LLtZlyAv4WhVUrPKIq2fvSQ9XytpFgXCdORpigEFzE9DaHOjpuamUVRIGpXydtjKIjxVzzqYzvyNdH3XMdSlzyKFG38X8HhKGiJpmqQRknqntGIxoDp0bKhWAVH8xbYOJPd1dLyy1qY8v8r9IRWoeWUhaXfgDuArKRDhNWTRaweQ3Xn8tBrnaXhsKP/8sQ7IdURxH736Ua7927xGF6PTqGWfBWndizuA30fEnwAiYlne/t8Cd6XNtmJAddjYUGadQWduGnVwzo6hlqOhRBbCY1ZE/Cwvff+8bB8FpqfnxWJATQT6pxX2epF1go+uVbnNupLcaKjO3Ay1frOj+3QEtbyzOAE4D3g6rWkB8G3gXEkDyO6QFwD/CW3HgJI0BLgX6AGMiIgZNSy3WZfRieuIJlM9Ka9DqOVoqL9TuLt3bBuvKRgDKiLGtvU6Myssd0fRFSoNayzP4DbrylxLWJW4sjDrwjpzx7Z1LK4szFp4ZcNmbpv0QumMnUBTx7brDNtBjYgN1UfSOElz0t/eKV2Srkrxn6ZJOjrvWINT/jmSBhc7p1k1fOv2aXzz9mlMX7ym0UWpGt9h2I6q5Z1FLjbU4cAgsiVaDweGAhMioj8wIW0DnE42XLY/cCHZ5D0k9QGGAceRhf8Yljfr26zq7p6+FIAFL79W1eMuX7uh1aI+tdZ0Y+G6wnZQzSqLiFgSEU+m52vJ1t9+C1lcp5Ep20jg7PT8LODGyDwG7JXmZJwKjIuIlRGxChgHnFarcleq0C+2rvgfc+KClfQdOob5LzVfQF9+dSMfu+ZR1qzrmhFNb/zHwqoe79jhExh4+fiqHrOU6IpfRmuIRsSG2i8FGQRYCuyXnnfK2FBN52/xt6v5xK+z9RAmzGqagM/ZVz/C5IWrOOrS+xpVrNrqAtfZLvAWrINoRGyoJpH97KnK97nhsaG6iYUvN6+49sLK9Q0sSe11hXZ+z7OwaqlpZVEoNhSwLBfyI/1dntKLxYZqK2aU1dmhb9qj0UWom67QgtMVKjzrGOoeG4osrlNuRNNg4M689PPTqKhBwJrUXHUvcIqk3qlj+5SUZjU08tEFPLP0lVbpO9WhnW3D5q1s29b4i1zjS1AFuTuLrlDzWUPV8s4iFxvqpBZLqF4BfFDSHOADaRuycB7zgLnAb4H/BxARK4HLyAIKTgQuTWkd3rHDxzPw8nGlM3ZAw0bP4PRfPNwqvT0RQJesWc9TZcb32bB5K4d99x6uuOeZsvJPmLWMzVu3tUqPCDakAHQPzl7OQ89W3o+1tQNUWNWyI+/EFY1BbUdD/T0iFBFHRsSA9BgbES9HxMkR0T8iPpC78KdRUBdFxMER8c6ImJR3rBERcUh63FCrMlfb8rUbeenVTY0uRtkigv+7fw6LV69P263zPL9yHaOeeL6i4x7/g/s561ePlJV33absAl/OpLiH56zggpGTuGrCnFb7bn5sIYd99x6+PGoK/3HDRAaPeKLocVa9tolXNrQe0bWtA18kH3p2RcEyt5T/DiKCPz25iE1bWleubR6j434MVeHKsDyewd0Aa9Zv5uoH57bZ1FLv8fgA0xat4Sf3PctXR00tmufXDz3H0D893Sr9d4/MZ+gd03a4DOWs7LZ56zZe27iFFWuzz+iFlc2d7lOeX0XfoWO47u/zAbhz6oslz/muy8Yx8LLWQ1o76jVkxdqNDB7xBBf9/kkAnn95XdEKoKmDO+DeGUv52m1P8YsJz9arqNaFuLJogO+NnsGP7pnN3+YUbhq5asIcBl4+nlser+wX/I5auS67C3piQeWtfJf8dSajJu54iIzcgoNt/dq7YOQkjhh2b9OFUHmrFOYqifxRW+XYVKApq6Pasi0r65xlr7Jl6zbe++MHGHLLkwXz5ndw55riyqlAzVpyZdEAr27cAsCGzYUvUCtfyy7aa9bXd7LbHrtkEesP3ne3knlzTVXVsCXvQp3/S7iYv6WL3h9SU9UT85srt41FPtP26KgjiXqkUQZbtm1jS7o7nfDM8oJ58z/HW5/IPq9Fq7r2kGerjVqOhhohabmk6Xlpl0ha3KLDO7fv4hQXarakU/PST0tpcyUNbXmehmvH9STXLl/s12zuYtCzzKFHq9dt4oEiF4tK5H6h99yp9NfihCvu3+HzQTbq6pD/uZtFq7I7gaZ+gjLeeq6SyK+4xudNGqxUy2bBSpuh1qzbzPiZ7T9/uXZO/z4vvbqpqYzFvirVqO46ZpVp9VbLO4vfUTgsx5X5Hd4AKWbUOcAR6TVXS+ohqQfwK7K4UYeTrbJ3eA3L3G6qYJDQyyXuHJqbV8o73gUjJ/GZ301sdbyIqKjzLne+HenY3bYt+O3f5rFu05ay8g8bnS16eNNjC9O5s/S1G8p7fc4vxs/Z4Tux1a0+v8pe/5U/TOFzN04qede1cUvly4Q+MvelptFdPXo0fzG2pkKqyJelGp237gA2qO1oqL8B5TZ+nwWMioiNETGfbPjssekxNyLmRcQmYFTK26GV+q/Vd+/Xp4yFc1ba/PHssrUF0y+8aTL9Li6+wOCwO6c3XaQBdkoXnDnLX63o/Pnum7mU4WNnMXzMrIpet2Vrbq3o9l2Yrhz/LN8bvWOr7S5u0TxTqNL8yqgp/MuPHyj4+gdmZ81jF944icvvmln0PMtfqWzwwrPL1vKp6x5nyC1TAFj4UnN/TK6MXTXMjHUcjeizGJJCkI/Iix67Q3GhoPGxoZrLUTpPLqrpd+8sfHEr1HHbltyv8J17iA2bt/JfN0/m+ZfXMS41ibznBxP49HWPN+W/a9qLfPP2pxj5j4V89y9NrYRVGSo648VsIt/tkxcVzTNrySu8smEzTz6/qimtZ/q1vLHCYZ35/jRlxyb2v7Bq+07xQh/HX6a+WLLzfMaLrzR1tBeSu0MAeHTuS/zbb/7R5vF+MDareHNNbPl3bZE+rmJflUqHyZoVU+/K4hrgYGAAsAT4abUO3BliQz3/8joenftS2flz///vf2YZfYeOYe7ywncQNOVbzsNzXuLu6Uu5KG90zItrNvD3vPMOuWUKt01qfTHv1WPHvw6/vH8uACcd9saC+5esWc/pv3iYIy+5j3+9+tFW5/7B3c13JBHBdQ/PK2s+QTXkX8QBZi9byzf++FTZrz/qgL222/71Q88VzLcib1j0v1/3OI/PX8lrG4s3u+VXTqOeeH67CjXXDLVTkdoi1z9mxbmVrTx1rSwiYllEbI2IbWSztI9Nu7p0XKjcnIn3/vgB/j3vF34x0dQOnW3nhtA+Nq/tVr0ht0zh8zdmcxmfrnDhnr5Dx3D5mOJNJ5XauUjFs77IxStXWczNawK748nFXD5mFp/8ddu/vMvVd+gYLrxxUtH9hUan/bGNO6RWWlx1rri78Cz0fXbfpVXaq21UFlvzjjv0T0/zvb8235GuTRXpuk1bm0aJ5dstjXAz21F1rSxyAQSTjwK5NpDRwDmSdpHUj2wBpCfIwnv0l9RPUi+yTvDR9SxzNVT6yyWXPfdbcfysbKTTHye9wJ1TF/PHHVjys+/QMa3S7pya1b+lKqNKzC3S71FsQaGeqbJ4dlnz63JhPPbdo/XFtb3um7mMvz71IqOfaj3XYP3mwhXZiT+8n4sLTERsqdzoIIVGLt0zfSlfv+2pghM1WzYP5nfk53/O5xeYpZ4LWVLJAIyW/MPboLZDZ28F/gG8XdIiSRcAP5L0tKRpwPuBrwJExAzgNmAmcA9wUboD2QIMIQscOAu4LeXtVMrptF29blNTvmJ9Frvu3IMvj5rKN27f8ZnS+b7cxozt9pq55JVWw0iff3kde75u54L5d+7R+mqWu0CXO4S4XF+8dQpfunUKq17bxLRFq5vSWzZD5SxatZ5bywhxsiN9PsNGz+COJxfxtm+3HpCwrcUNT/7Q5tda3Knd8Mj2fSXV6ACvZTPNPdOXsm1b8PCcFfQdOobnS/QHff7GSfQdOoa+Q8fsUPNkyx9Nnxs5sem4VlgtR0OdGxH7R8TOEfHWiLg+Is5LcZ+OjIiP5C2CREQMT3Gh3h4Rd+elj42IQ9O+4bUqbyWWv7KhovxL28i/bVuwet0mBlw6jp+Pz2Ic5UZDPTxn+/6N/CaFie2YZd1Wp3MtfK5Fk897f/xA0eGtbVUIuVFG1Xbs98fzkf9rjlm1dsMWXi4zzMovW8SjeujZFUXvTFqKaA5LUsi2bcHWbcGHfvkwa9ZtblUJrV7fHG9sXYvmq+/9dSZ9h45hyZpsZFfuzqJYn0Yj9R06hi/cPJm3fXss512f3RW9t8hIs5xxeT9AcuFOqiF39w7ZDzdrTV1xDPXAgQNj0qTibdOlzHzxFf467UWuefA57viv93DMQdmgrfEzl7W6AN742WM5f8QTXHb2Ozhv0EGcc+0/WjXnjP/av/CmPXflHcPKi6x++P5vYOaS1uHBW1pwxZlc9/A8Lq9wmGoljuvXh8fnd4wgv38dciIf/r+/N7oYTfrs1qtptn0xC644k8fmvcw51z7WlHbMQb2ZvHBVG6+qnu+c+U8Fvx+/+8y7OfKte3HP9KWc/o430Xu3XkA2emr0Uy/ysaPf0jTs+tnLT6dXz+r/riz1K37BFWe2yvf5f+7Hbx/O7p5GfvZY/uXQ9g1myT/mvO+fsd0d3bRLTuENuxa+A+7qJE2OiIGF9rn3q4WWX+CPXfNokZyZXDvxd/8ynfMGHVSw3foDP3uoojKUU1FA6f9s1dBRKgqgQ1UUQMmKAgr/G9WrogCK/pD4jxsmNj3/9p+fZt73z2CnncSh38lu6v+7glFg7XXiIfs0jdJ714F7MeX51SVfs1PeHWitfuh2wd/PVeHYUHm2VCGY3BMd6OJqVq62mkprFSMrv2Ws2v1SO8SVRUH1jg3VR9I4SXPS394pXZKuSvGfpkk6Ou81g1P+OZIGFzpXtSxb2/6w4FP/94MAvGFX36xZ57Przj0A2K1Xj1b72vtLOyK4ffKidoU3KX7Q6h2qhofskuodG2ooMCEi+gMT0jZksZ/6p8eFZJP3kNQHGAYcRzYnY1jerO+qe8ter2v3a/d6fdbmW+6sa7OOaO8Cc0Daa9zMZfz3H5/iZ+NKr5/RngqpVhf5jhptuNHqHRvqLGBkej4SODsv/ca0Wt5jwF5pTsapwLiIWBkRq4BxFA5OaGZVUM0LZS4w40trqze6qB6XcfdZFFbvPov98obLLgX2S8+7TGwoM9teOTfb7bo++6JeVw3r4I5sKEPV/rk7Q2wos46uqr+qy1+apOyRTfUY6u86qLB6VxbLciE/0t/cTJguExuqK85bse6j0Ne3vV/pbS1inLV53vadwuqo3pXFaCA3omkwcGde+vlpVNQgYE1qrroXOEVS79SxfUpKM7MOrjnGWW0GfdSqI9o/+Aqr2TjPFBvqfcA+khaRjWq6ArgtxYlaCHwyZR8LnEG26NE64DMAEbFS0mVkAQUBLo0IT2Qwq7JaXCArWfGx3NPX4zruqqKwmlUWEXFukV0nF8gbwEVFjjMCGFHFotWUv2jWmVWz0sj98m9ZWWzasq0qC235BqC+PIPMzJoUuv62t7mn+WK+fW1x0k8fZNGq9fxz/33aPG/BY7arJCWO2aLWcSVUmMN9mFmTanZwN/VZtLizWNRirfP28kW9vhpSWUhakNa1mCppUkqrOBRIh+QvsFmmkrU0yh462/7ilMszuAtr5J3F+yNiQF443IpCgZhZ9VXzQlnszqKtvB1ChypMx9GRmqEqDQViZlVWzV/uuWOVs/BSrWJDTX1hNX2HjmHqC6srP4Ftp1GVRQD3SZos6cKUVmkoEDOrsqpO4K7Ckq6tjllhCR+cnc37vf+Z5UXzrN2w/WqDvrEorFGjoU6MiMWS3giMk/RM/s6ICEkV/ZulSudCgAMPPLB6JTXr5tp78WxuhirjzqIdZylnmG/urqatvO+6bFzF5+6OGnJnERGL09/lwJ/Jwo9XGgqk5TE7RGwo/yqxzij3vS08Gqp93+pCq0buqEqLkltTaWsFhfEoq8LqXllI2k3SHrnnZCE8plN5KBAzq5Jhd85g27aqxvZsboaq4gzuSuXuajZXsAqmR0MV1ohmqP2AP6d/xJ7ALRFxj6SJVBAKxMyqZ8zTS/jiyYfU5KJdTmyoWnVwv7IhW1PjqRfWVH4C207dK4uImAccVSD9ZSoMBdIROQiZdVa9ehRuaGh3n0UlsaHaeY5S+qQVLI86YM+yX+P/woV1pKGzZtZAvXruVDjcR/ktONu/Lhcbqv1Favv4ZVzUcxVVJRWA64rCXFmYGZAqiwJX1fYG/fvJfdna29f9fT7LXtnAuk1biuat1eJHudFQtehs724cSLDK/J20zmqXHj2KBBJsn01bmm9Jjvv+BAAWXHFmO49WP25KLqzT3FlIOk3S7BQjamjpV5hZJcZOX5JGRG2v0juLVzcWv4OoxIbNW+k7dMx2adHGViG50VAe4bTj1BlqUUk9gGeBD5LN4J4InBsRMwvlHzhwYEyaNKld52r55SxX7hdTe19vZlYNnz2hH//74cPb9VpJk/Pi9W2ns9xZHAvMjYh5EbEJGEUWM8rMzPKMeGR+TY7bWSqLusWHGv+191b8mk8Pag4v8olj3lrWa85455u22/7yyf232z7qgL2KvvbDR725zWNffvY7ANhn916t9v3604UjvJ92RFaer3wgK0dbwx0H5JVt791an6OQz53Yr1XaeYMO2m47N3Szx07NJ89/Xsj3P/pOAN739taz9ge9rQ8A3znzn8oqY7m+9sFDq3q8X5wzAIAv/MvB26X/+3Gtw9Z887S3A3DiIc0LB7330Oy9X/T+7PXH9u1T8pyfPSH79zh8/ze02tfyu1lLAw/q3fS81L91teU+g5xzj+0aYYLOefcBpTO1Q2dphvo4cFpEfC5tnwccFxFD8vLkx4Y6ZuHChQ0pq5lZZ9UVmqFKxofqKLGhzMy6os5SWUwE+kvqJ6kXcA5ZzCgzM6uDTjHPIiK2SBoC3Av0AEZExIwGF8vMrNvoFJUFQESMJQsqaGZmddZZmqHMzKyBXFmYmVlJrizMzKwkVxZmZlZSp5iUVylJK8hW22uvfYCXqlSczs6fxfb8eWzPn0ezrvBZHBQRBSeqdcnKYkdJmlRsFmN3489ie/48tufPo1lX/yzcDGVmZiW5sjAzs5JcWRR2baML0IH4s9ieP4/t+fNo1qU/C/dZmJlZSb6zMDOzklxZmJlZSa4s8kg6TdJsSXMlDW10eepB0gGSHpA0U9IMSV9O6X0kjZM0J/3tndIl6ar0GU2TVHjpvU5MUg9JUyTdlbb7SXo8vec/pDD5SNolbc9N+/s2tOA1IGkvSbdLekbSLEnHd9fvhqSvpv8j0yXdKmnX7vTdcGWRSOoB/Ao4HTgcOFdS+1Y971y2AF+PiMOBQcBF6X0PBSZERH9gQtqG7PPpnx4XAtfUv8g192VgVt72D4ErI+IQYBVwQUq/AFiV0q9M+bqaXwD3RMRhwFFkn0u3+25IegvwJWBgRLyDbKmEc+hO342I8CPr5D8euDdv+2Lg4kaXqwGfw53AB4HZwP4pbX9gdnr+G+DcvPxN+brCg2wVxgnAScBdgMhm5fZs+T0hW1/l+PS8Z8qnRr+HKn4WewLzW76n7vjdAN4CvAD0Sf/WdwGndqfvhu8smuW+DDmLUlq3kW6V3wU8DuwXEUvSrqXAful5V/+cfg58E9iWtvcGVkfElrSd/36bPou0f03K31X0A1YAN6Rmuesk7UY3/G5ExGLgJ8DzwBKyf+vJdKPvhisLA0DS7sAdwFci4pX8fZH9POryY6wlfQhYHhGTG12WDqIncDRwTUS8C3iN5iYnoFt9N3oDZ5FVoG8GdgNOa2ih6syVRbPFwAF5229NaV2epJ3JKorfR8SfUvIySfun/fsDy1N6V/6cTgA+ImkBMIqsKeoXwF6ScqtK5r/fps8i7d8TeLmeBa6xRcCiiHg8bd9OVnl0x+/GB4D5EbEiIjYDfyL7vnSb74Yri2YTgf5pdEMvss6r0Q0uU81JEnA9MCsifpa3azQwOD0fTNaXkUs/P418GQSsyWuS6NQi4uKIeGtE9CX7978/Ij4FPAB8PGVr+VnkPqOPp/xd5ld2RCwFXpD09pR0MjCTbvjdIGt+GiTp9en/TO6z6D7fjUZ3mnSkB3AG8CzwHPA/jS5Pnd7ziWTNCNOAqelxBln76gRgDjAe6JPyi2zU2HPA02SjQxr+PmrwubwPuCs9fxvwBDAX+COwS0rfNW3PTfvf1uhy1+BzGABMSt+PvwC9u+t3A/ge8AwwHbgJ2KU7fTcc7sPMzEpyM5SZmZXkysLMzEpyZWFmZiW5sjAzs5JcWZiZWUmuLMxKkLS3pKnpsVTS4vT8VUlX1+icX5F0fhv7PyTp0lqc26wQD501q4CkS4BXI+InNTxHT+BJ4OhojjvUMo9SnhMiYl2tymKW4zsLs3aS9L68NS8ukTRS0sOSFkr6V0k/kvS0pHtSSBUkHSPpIUmTJd2bC5vRwknAk7mKQtKXlK03Mk3SKGiKyfQg8KG6vFnr9lxZmFXPwWQX+o8ANwMPRMQ7gfXAmanC+CXw8Yg4BhgBDC9wnBPIIprmDAXeFRFHAl/IS58E/HPV34VZAT1LZzGzMt0dEZslPU22OM49Kf1poC/wduAdwLisFYkeZOGuW9qf7Rdfmgb8XtJfyEJu5Cwni4BqVnOuLMyqZyNARGyTtDmaOwS3kf1fEzAjIo4vcZz1ZLGFcs4E3gt8GPgfSe9MTVS7prxmNedmKLP6mQ3sK+l4yELDSzqiQL5ZwCEpz07AARHxAPAtslDXu6d8h5IFtTOrOVcWZnUSEZvIwlX/UNJTZBF+31Mg691kdxKQNVXdnJq2pgBXRcTqtO/9wJhaltksx0NnzTogSX8GvhkRc4rs3w+4JSJOrm/JrLtyZWHWAaUFh/aLiL8V2f9uYHNETK1rwazbcmVhZmYluc/CzMxKcmVhZmYlubIwM7OSXFmYmVlJrizMzKyk/w/lz+luhYbO1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel: 272\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for plot_chan in output_nsx['elec_ids']:\n",
    "\n",
    "    ch_idx  = output_nsx['elec_ids'].index(plot_chan)\n",
    "    print('Channel: '+str(plot_chan))\n",
    "    hdr_idx = output_nsx['ExtendedHeaderIndices'][ch_idx]\n",
    "    # Bug in the original code - changed np.arange(output_nsx['data'].shape[1] to np.arange(output_nsx['data'][0].shape[0]\n",
    "    t       = output_nsx['start_time_s'] + np.arange(output_nsx['data'][0].shape[0]) / 2000#output_nsx['samp_per_s']\n",
    "\n",
    "    plt.plot(t, output_nsx['data'][0][:,ch_idx])\n",
    "#     plt.axis([t[0], t[-1], np.min(output_nsx['data'][0][:, ch_idx]), np.max(output_nsx['data'][0][:, ch_idx])])\n",
    "    plt.locator_params(axis='y', nbins=20)\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.ylabel(\"Output (\" + NsxFileObj.extended_headers[hdr_idx]['Units'] + \")\")\n",
    "    plt.title(NsxFileObj.extended_headers[hdr_idx]['ElectrodeLabel'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "017fd705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20181220_Greyson_PG_001.ns3 closed\n"
     ]
    }
   ],
   "source": [
    "NsxFileObj.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "720c5775",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "x = torch.tensor(532.4521, dtype=torch.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "fc0c95e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(532.4521)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.detach().numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be36f334",
   "metadata": {},
   "source": [
    "# CCF file examination"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d83ab0f7",
   "metadata": {},
   "source": [
    "Examine one ccf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc20d5ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '/Volumes/L_MillerLab/data/Greyson_17L2/CerebusData/20181220/20181220_Greyson_FreeReaching_001.ccf'\n",
    "tree = ET.parse(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55fca971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xml.etree.ElementTree.ElementTree at 0x11c5ce4a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "92d50913",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Element 'CCF' at 0x11c4ccd60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root = tree.getroot()\n",
    "root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "405503f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(root.findall('ChanInfo'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "279c32a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Element 'FilterInfo' at 0x11c78f650>,\n",
       " <Element 'ChanInfo' at 0x11c7bd210>,\n",
       " <Element 'Sorting' at 0x123b47f60>,\n",
       " <Element 'SysInfo' at 0x123f1fab0>,\n",
       " <Element 'LNC' at 0x123f1fe20>,\n",
       " <Element 'AnalogOutput' at 0x123f1fe70>,\n",
       " <Element 'NTrodeInfo' at 0x123f38130>,\n",
       " <Element 'AdaptInfo' at 0x12473f290>,\n",
       " <Element 'Session' at 0x12473f2e0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9b4450c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'32768'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(list(list(root)[1])[0])[0].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce76fc27",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "284\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for el in list(root):\n",
    "    print(len(el.findall('ChanInfo_item')))"
   ]
  }
 ],
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