Session_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update your repository with new stuff\n",
    "#### Note this is technically not good practice here, but we'll worry about that later\n",
    "\n",
    "- Open git bash to your learning python folder\n",
    "\n",
    "- git checkout -b session2\n",
    "\n",
    "- git remote add upstream https://github.com/tne-lab/learning-python\n",
    "\n",
    "- git fetch upstream\n",
    "\n",
    "- git merge upstream/master --strategy-option theirs\n",
    "\n",
    "### Open Jupyter Notebook and open Session_2.ipynb\n",
    "\n",
    "\n",
    "# Today we will look at events first\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading events...\n",
      "events loaded in!\n",
      "Loading continuous data...\n",
      "data loaded in!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import OpenEphys\n",
    "\n",
    "cwd = os.getcwd() # Gets your current working directory\n",
    "pathToEvents = cwd + '\\\\data\\\\Sample_Data\\\\all_channels.events' # All events are stored here\n",
    " \n",
    "allEvents = OpenEphys.load(pathToEvents)\n",
    "print('events loaded in!')\n",
    "\n",
    "pathToData = cwd + '\\\\data\\\\Sample_Data\\\\100_CH1.continuous' # You can change this to look at other channels!\n",
    " \n",
    "data = OpenEphys.load(pathToData)\n",
    "print('data loaded in!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "header\n",
      "channel\n",
      "timestamps\n",
      "eventType\n",
      "nodeId\n",
      "eventId\n",
      "recordingNumber\n",
      "sampleNum\n"
     ]
    }
   ],
   "source": [
    "# What is events?\n",
    "print(type(allEvents))\n",
    "\n",
    "# Lets look at what is stored in here.\n",
    "for key in allEvents.keys():\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'format': \"'Open Ephys Data Format'\", ' version': '0.4', ' header_bytes': '1024', 'description': \"'each record contains one 64-bit timestamp, one 16-bit sample position, one uint8 event type, one uint8 processor ID, one uint8 event ID, one uint8 event channel, and one uint16 recordingNumber'\", ' date_created': \"'9-Aug-2017 105450'\", 'channel': \"'Rhythm FPGA source TTL events input'\", 'channelType': \"'Continuous'\", 'sampleRate': '30000', 'blockLength': '1024', 'bufferSize': '160', 'bitVolts': '1'}\n",
      "-\n",
      "[1. 0. 1. ... 1. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "# Lots of information here\n",
    "print(allEvents['header'])\n",
    "print('-')\n",
    "print(allEvents['eventId']) # we'll look at this later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge 1\n",
    "### Build a class to store event data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes finished!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "class Events:\n",
    "    # Put code here (use Con class below as a reference!)\n",
    "    def __init__(self, data, tsStart):\n",
    "        self.eventId = data['eventId']\n",
    "        self.nodeId = data['nodeId']\n",
    "        self.eventType = data['eventType']\n",
    "        self.channel = data['channel']\n",
    "        \n",
    "        #timestamps?\n",
    "        self.ts = []\n",
    "        for ts in data['timestamps']:\n",
    "            self.ts.append(ts - tsStart)\n",
    "            \n",
    "        # Other option\n",
    "        # self.ts = data['timestamps'] - tsStart\n",
    "    \n",
    "class Con:\n",
    "    def __init__(self, data):\n",
    "        self.data = data['data'] # Volatage data\n",
    "        self.fs = float(data['header']['sampleRate']) # Sample Rate\n",
    "        self.interpTs(data['timestamps']) # Interpolate timestamps for all data points (Only has 1 time stamp for each buffer of 1024 data points)\n",
    "\n",
    "    def interpTs(self, ts):\n",
    "        tsStart = ts[0]\n",
    "        tsEnd = ts[len(ts)-1]\n",
    "        self.ts = np.linspace(tsStart, tsEnd, len(ts) * 1024) # Creates the array of timestamps\n",
    "        i = 0\n",
    "        for t in self.ts:\n",
    "            self.ts[i] = t - tsStart # Zero out the timestamps, just makes it look better when graphing/explaining\n",
    "            i = i + 1\n",
    "        self.tsStart = tsStart # Send the ts start to event data\n",
    "        \n",
    "con = Con(data)\n",
    "\n",
    "events = Events(allEvents, con.tsStart)\n",
    "print('Classes finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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35156181.0, 35193598.0, 35193698.0, 35194379.0, 35194524.0, 35226650.0, 35226750.0, 35227428.0, 35227583.0, 35259105.0, 35259205.0, 35259881.0, 35260010.0, 35290475.0, 35290575.0, 35291252.0, 35291390.0, 35324163.0, 35324263.0, 35324942.0, 35325085.0, 35354568.0, 35354668.0, 35355345.0, 35355504.0, 35388184.0, 35388284.0, 35388963.0, 35389103.0, 35423618.0, 35423718.0, 35424396.0, 35424532.0, 35462733.0, 35462833.0, 35463511.0, 35463653.0, 35497719.0, 35497819.0, 35498496.0, 35498635.0, 35527863.0, 35527963.0, 35528640.0, 35528781.0, 35562534.0, 35562634.0, 35563313.0, 35563463.0, 35597850.0, 35597950.0, 35598629.0, 35598774.0, 35628237.0, 35628337.0, 35629015.0, 35629163.0, 35663407.0, 35663507.0, 35664184.0, 35664320.0, 35696545.0, 35696645.0, 35697324.0, 35697474.0, 35732391.0, 35732491.0, 35733169.0, 35733322.0, 35771941.0, 35772041.0, 35772721.0, 35772865.0, 35806163.0, 35806263.0, 35806943.0, 35807092.0, 35840652.0, 35840752.0, 35841432.0, 35841566.0, 35873627.0, 35873727.0, 35874404.0, 35874562.0, 35903698.0, 35903798.0, 35904476.0, 35904623.0, 35940473.0, 35940573.0, 35941251.0, 35941405.0, 35971090.0, 35971190.0, 35971868.0, 35972004.0, 36001603.0, 36001703.0, 36002381.0, 36002511.0, 36032236.0, 36032336.0, 36033015.0, 36033141.0, 36065822.0, 36065922.0, 36066600.0, 36066739.0, 36097007.0, 36097107.0, 36097785.0, 36097910.0, 36129760.0, 36129860.0, 36130537.0, 36130670.0, 36163773.0, 36163873.0, 36164552.0]\n"
     ]
    }
   ],
   "source": [
    "print(events.ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And we can plot these events on top of our ephys data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(con.data[:10000], 'r')\n",
    "plt.axvline(events.ts[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check out the settings.xml file in Sample_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading continuous data...\n",
      "Phase calc loaded in\n"
     ]
    }
   ],
   "source": [
    "pathToPhaseCalc = cwd + '\\\\data\\\\Sample_Data\\\\104_CH1.continuous'\n",
    "rawPhase = OpenEphys.load(pathToPhaseCalc)\n",
    "\n",
    "conPhase = Con(rawPhase)\n",
    "print('Phase calc loaded in')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal\n",
    "def myBandpass(lowcut, highcut, fs, data):\n",
    "    nyq = 0.5 * fs\n",
    "    low = lowcut /nyq\n",
    "    high = highcut /nyq\n",
    "    band = [low, high]\n",
    "    order = 2\n",
    "    b, a = signal.butter(order, band, btype='bandpass')\n",
    "    \n",
    "    filteredData = signal.lfilter(b, a, data)\n",
    "    return filteredData\n",
    "\n",
    "filteredData = myBandpass(4, 8, con.fs, con.data[:10000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So what is phase?\n",
    "https://en.wikipedia.org/wiki/Phase_(waves)#/media/File:Oscillating_sine_wave.gif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1badd1944e0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W7jnMwHZRXlfC+VQS/H2ZzQb33WcyPrKyYM4cuOIKq1tlCZtN88jkTaRmH+eTG3rRoVm4w8+hlOL/LulMSLA/j07Z7Prhn717TRmOnj3hlltce24PkXTgKPmFpV4/3g8S/H1XaSncdptZmemBByAjw+R7+6iPft3N3K0ZPDHyDPo78Q8/KiyYpy/qxIZ9R5i4dp/TzvM3hYXmH3tpqZmv4QMrcNXGyfH+/m29e7wfJPj7pj17zIzOCRPgv/+Ft97yqRrtp1qeks3r83Yyqkdzbh/Y2unnu+zMWHrHN+Kt+bvILyxx+vkA8w5v/Xr46itISHDNOT3QsuRsOsY0oLGD7vW4Mwn+vubFF6FtW5g2zVRvfO45rx7DrcqRgmIe/n4jbaPDeOnyri4Z51VK8dSFncg+Vuyam79jx5rS208+aYryiQqdKC5j/d5cBnp5ls9JEvx9yfvvmwBw5ZWmXs+//211iyyltebJqUkcPlbM26N7EBLkulJX3VtEcGmP5oxbmkrG0ULnnWjtWtPrP/98szCLqNS6vTkUl9l8YrwfJPj7jvHj4f77TR7/t99C796uO7ebpgRO/W0/P285yMPnt6dLbEOXn//hYR0otWk+XbLHOSfYtctU6oyJMdfc0eP8bnpda2tZSjaB/qrGM7k9lQR/X/Dtt6ZO+/nnV69Ou6O48XBSVn4Rz87YSu/4Rtw12JqVmlo2DuHSHrF8s3ovWfkOnvl7/Lgpz1FYaCbtNXbgUIYbX9e6WJ6SzZktG7n0HaCVHBL8lVLjlVKZSqmkctsilVLzlVLJ9s+N7NuVUupdpVSKUmqzUqqnI9ogKvHDD2YK/4ABJggEe/+NrOp4fuY2CktsvHR5N/wtrN9y7zltKS61MW6pg3v/990H27bBN9+Y1E5xWrnHi9l64CgDfWTIBxzX8/8CGHHKtseBBVrrBGCB/WuAkUCC/WMMIPPLnWX8eJPD36cPzJxZt9mcXuTXXVnM2HSAu4e0pV2TMEvb0iY6jIu6NeerVXvJO+GgzJ/PP4cvvjCztIcPd8wxvdzKPYfR2vtLOpTnkOCvtV4C5JyyeRQwwf54AnBpue1famMVEKGU8q06wa7w3Xdmpu7QoaaIlw/W6KnIieIynpq2hTZRodxzjnsszD1mcBsKisv4fm1a3Q+WlGQmcg0ZAs8+W/fj+YhlKdmEBQfQLc47l2ysiDPH/JtqrQ8C2D+fXB4oFij/W55u3/YXSqkxSql1Sql1WVlZTmymF5o40RTuGjQIZsyQwF/Oh4tTSMs5wQuXdXWb5fm6xDakT+tIvljxO6VlttofKD/fZHKFh5v7PD668HptLE/Jpm+bSM9ZdtMBrHilFQ2w/i1tQGv9qdY6UWudGB0d7YJmeYmpU03gP+ss+PlnqO/Y+jSebP+RE3y6ZA+XdG9OPzebwXn7wNbsP3KCedsyan+Q++83GT7ffONzi+7URVpOAXsPF/hMiudJzgz+GSeHc+yfM+3b04EW5faLAw44sR2+Y+JEM8afmGjq9IRZO57tbl6ZvQOAx0aeYXFL/u68jk1pGRnCZ8tSa3eAcePMjO2nnoLzznNs47ycLyzZWBFnBv8ZwM32xzcD08ttv8me9dMXyDs5PCTq4Pvv4frroW9fmDfPvYZ63CAffP3eXGZsOsCYwW2IdXC1Tkfw91Pc0j+e9Xtz2ZxewzV/16wx4/znnQfPPOOcBlbEDa6rIyxLOUx0eDAJFt/8dzVHpXp+B6wEOiil0pVStwMvA8OUUsnAMPvXALOAPUAKMBa4xxFt8GlTp5oa7X36wKxZ7hP43SQf3GbTPD9zG03Cg/nH2e5xk7ciVybGUT/Qn29X16DgW0aGyedv1szc5HdFwTY3ua6OYLNpliVnMcgHSjifyiF3hLTW11by1LkV7KuBex1xXoGp0XP11dCrl8nqCXd8KWJP99PmA2xMO8JrV3YjNNh9b4I2qBfIxd1jmLHpAE9e2JHwelVMxisuNjd4Dx+G5ct9crnNuko6kEduQQmD2/vefUXfubXtjX780VTn7NnT/YZ63MSJ4jJemb2DLrENuKJnnNXNqdJ1Z7WioLiMaRuruA1ms8Fdd8GyZaZom0zkqpUlu0wm4cAE3/vHKcHfUyUlwTXXmB7/vHnQ0PW1aTzB2KV7OJBXyH8v7ISfhTN5q6t7XEM6xTTg29X70JWNqdts5v7OF1+Y4nzXXefSNnqTJcnZdG7ewGHLdXoSCf6e6Pff4cILTU9/+nQJ/JXIOFrIR4t3M7JLM85q416pnZVRSnHdWS3ZfvAoG9MqufH7n/+YzK6nnoKXX654H1Gl/MISNuzN9ckhH5Dg73nS082KW7m5Jp2zaVOrW+S23py3izKb5omRHa1uSo2M6tGckKBKbvx+9BG88gqMGWPWYvCTP+HaWrUnh1KbZpAPDvmABH/PcjLwHz5shnoSE61ukdtKzshn8vo0buzXipaNQ6xuTo2E1wtkVI/mzNx8kGNFpX8+MXOmKdg2cqRZm8HHslMcbcmuLEKC/OnVqpHVTbGEBH9PkZNjAn9Ghsnq6dvX6hZVnwX54K/M2UloUAD3ntPO5ed2hCt7teBESRmzttinwPz6K4weDT16mDkdrirLfToenue/NDmLvm0au02ZD1eT4O8JysrMJJ49e0wef79+Vreoeizqma79PYdftmfwjyFtiQz1zLWJe7aMoHVUKD+sT4eVK01vPybGlOyweua2F7zj2He4gN8PFzDYR4d8QIK/+ysrg9tuMzf4nnkGBg60ukVuTWvNS7O20yQ8mNsGOH8xdmdRSnFFz1hWp+aQds0t0Ly5yeVv1szqpnmFJckmxXOQj97sBQn+7q2kBG65Bb780gT+//7X6ha5vXnbMtiw7wgPDWtP/SDPfjt/WfMAlLbxQ4fB5h6P3Nx3mCW7soiNqE+bKN9d48J9pzt6GK01K3YfZk7SIdak5rD/yAmUgrhGIZzVOpJRPZrTo0VE9aeQFxeb6pyTJ5usDgn8VSots/HqnB20jQ7lql7uP6HrtHJzib3yYvp3v4EfBl3BP+NbS0/NQUrKbKzYfZiLu8f4XEmH8iT415HWmp82H+TdBcmkZB4jJMif3vGRf5QM3p11jIlr9/HFit/pGtuQf53fnrPbR5/+l6642NRr+flneO01eOQRF70azzZlfTq7s47zyY29CPDkuuxJSWbi1q5dXPl4Tx5KKmHt7zkeM1fB3W1MO8KxolIGJ/jukA9I8K+T7QeP8t9pSazbm8sZzcJ546ruXNgthnqBfx1uyC8sYfrGA3z8625u+XwtZ7eP5sXLu1ZeXfKLLyTw11BpmY0PF++me1xDzu/kwcMj27bB2WfDsWPw3XcMv3goobt+4YcN6RL8HWTJriz8FPT3sRLOp/Lg7pF1ymyaDxencMn7y0jNPs7Ll3fl538O4opecX8L/GDytm/o24qF/xrC0xd1Yu3vOZz/5q9MXFPBFP5ffoF//Qs6dIAHH3TRK/J8P285yL6cAu45p53nvpVPSzNr7gYGmn8CV1xBSFAAF3aL4efNBykoLq36GKJKS5Kz6dEigob13SBd1kIS/GvoYN4Jrvl0Ja/O2cmwTk2Z//DZXNOnJf7VqBsTFODHbQNbM/fBwfRoGcHjP27hkcmbOVFcZnb4/nu44AJo1QoWLvSeZficnA9us2k+XLSbhCZhDOvoob3+7Gw4/3w4etTM3G77Z+npy86M43hxGQu2Z57mABbwwDz/3OPFbE4/4rMlHcrzkujiGmtSc7jnm/WcKC7jrdHdubRHbK16mS0iQ/jytrN4Z0Ey7y5IZmfGUT5nK9EP3PPn8ouRkU54BS7moh74wh2Z7MzI582ru3tE8ba/KCgwRdqmTYPgYDOBr0ePv+zSp3UkTRsEM2PTAS7u3tyihpbjqe+sgOW7s9EaBvn4eD9Iz79atNZ8ufJ3rhu7igb1Apl+3wAuOzOuTsML/n6Kh4e1Z9xNiaQcyOOqLf6kXXyVGfbxhsDvIlprPlicQlyj+u4RGGuipMTM2p0+HW66CebPN+P9p/D3U1zYtTm/7swi70SJBQ31Hkt2ZdGgXgDd46QYogT/KpSW2XhyWhJPT9/K2e2jmXbfANo1cdyCKec1KOGbeW+QExbBNX3HcKBELklNrNqTw2/7jnDX2W0J9KQMH63hzjtNvZ733zfr7w4aVOnul/RoTnGZjblbD7mwkd5Fa83S5GwGtIvy7GwwB5GfwGmcKC7jH1+v59vV+7h7SFvG3pRIg6pWV6qJjRuhTx96Ja/n28GNOFpYyo2frebwsSLHncPLfbg4haiwYM/K69caHn3UBPxnnoF7ql7JtHtcQ1o1DuGnTVUs8iIqlZJ5jIN5hTLebyfBvxKHjxVx7dhVLNyRyfOXduGxEWc4djx5xgwYMMCsubp0KV0uPY9xNyeSnnuC275YS2FJmePO5aU2px9haXI2dwxqXWGWlVsqKDAT9l5/3QT9ai64rpTi4m7NWZ6STVa+dA5q41f7ql2+WsL5VBL8K5CWU8CVH69k+8GjfHRDL27s28qxJ3j7bbj0UpPOuWoVdO8OwFltGvPetWeyKT2P/0zdUvlKTgKADxftpkG9AK4/q6XVTamejAxzrV94wSy/+e67Nbp5ekmP5tg0f1b6FDWyNDmbNtGhxDXyrBLfziLB/xQ7Dh3lio9WkHO8mG/vPIvhnR1cSOvbb+Ghh+Cii2DpUoiN/cvT53duxkPntefHDfsZv/x3x57bKk74J5ackc+crYe4uX981Qudu4OcHDjvPDhwwBTp+/Zb866vBto3DadD03D3GfrxoM5JYUkZq1MP+/ys3vIk+Jez9vccrv54JX5KMfkf/ejVysFZN7/9ZiZwNW4MU6dCaMVFpe4f2o7zOzXlpVnb2ZKe59g2uJITUwI/+nU39QP9udUTKncWFMDFF8OuXSazZ/ToWs/huKRHc9btzWX/kRMObmQNeGCq57rfcykssTG4vQz5nCTB327B9gxuGLeaqLBgptzdj/ZNHZfRA5gCbQMHmj/6efNO2+vz81O8dmV3osODeWDibzKz8xRpOQVM33iAa/u0dP96/QcPmnd5K1fC11+b3n8dXNzNpLP+vNlNev8eYklyFoH+ir5SIuMPEvyBr1btZcxX6+nQLJzJ/+jn2DHB0lJ4+mm4+moz3rtmDfTsWeW3NQwJ5K3RPUg9fJznftrmuPZ4gbFL9+Cn4M7Bbt7rX7kS2rWDRYvgs8/MOH8dtWwcQpfYBsxOkpTPmliyK4vEVpGEBHnYvNZdu8zwsBP4dPAvKi3jqWlb+O+0JM5uH823d/alcViw406wezeceSY8/7ypy79woVmNqZr6tmnM3We3ZeLaNOZJfjcAWflFTFqbxuVnxhHTsJLCeO5g48Y/V99atw5uvdVhhx7ZJYbf9h3hYJ6FQz8eJPNoITsO5XteiufXX5v4cdddYLM5/PCWBX+l1Ail1E6lVIpS6nFXn3/bgaOMen85X6/ax12D2zD2pkTCgh3YK1i82JRq2L/f3NwbPx7q1avxYR4a1p6OMQ14cloSRwqKHdc+DzV+eSrFZTbuOruN1U2p3I4dpkBbeLj5h9+rl0MPP7KLSUKYI73/almSnA14SIpnfr5JAe7QAW680ZT6mDcP/Bwfqi0J/kopf+ADYCTQCbhWKdXJFedOycznkcmbuOi9pWQfK2bcTYk8cUHHahVmq7Zx42DYMIiKMsM8115b65tkgf5+vH5VN3KPF/PcTN8e/sk7UcJXK/dyQdcY2kRbvI5tRY4cMbV5hg83mTC//AItHZ+G2iY6jDOahTN7iwT/6lianEVUWBCdYhpY3ZTT27rVdBg//hhatDAjBosXQ5xzJjBaNQDWB0jRWu8BUEpNBEYBDo1uBcWlzEk6RG5BCem5BaxJzWHrgaMEBfhx+8DW3DOkHY0cecOwtBQefhjee89UaJw0CSIi6nzYzs0bcs+Qtry7MIULu8ZwrqdWrqyjr1ft5VhRKfcMaVv1zq62c6e5oZ+dbXr8ixaZ3puTjOjSjHcWJJOZX0iT8Jq/o/QVNpsp6TA4Icp9i/4dPWrSfx96COrXNx2IYcOcflqrhn1igbRyX6fbt/1BKTVGKbVOKbUuKyurVjxAUKAAACAASURBVCcpLLHx8PebeH7mNr5bs4/Q4ACeurAjKx4fypMXdnJs4C8rM7Va3nsPHnjAVOZ0QOA/6b6hCXRoGs5/pm7xvOJeDsgHP1FcxmfLUhnSIZrOzd2sKNeBAzBihHn89demB+fgoZ5TXdA1Bq1h7tYMp57ntDwgz3/bwaPkHC92z/F+reGTT8zazHfdZRJBNm50SeAH63r+Ff0L/stvktb6U+BTgMTExFr9lkXUD2TxI0OICAmkQb1A5/3nz8kxQzvz5sG//w2vvurwUwQF+PHaVd247MMV/G/mNl67qrvDz+FwDswHn7R2HznHi7n3nHYOO6ZDHD1q1mDIyjK9/d69XXLahCZhtIkOZfaWg46fgV4VD8rzP1nSYaC7jfcfPWoC/sSJcO65cP/95vco0HUTFq3q+acDLcp9HQc4PHHZz08RHxVKREiQ8wL/pk2QmGj+8D/5xCmB/6RucRGMGdyGyevTWZ6S7bTzuJviUhufLtlDn/hIese7Sblrmw2++cb84SYlmXkcLgr8YGr9XNAlhtWpOVII8DR+3ZlF5+YN3GNorKQE3noL7rgDunQxizc984wZ5hk1yqWBH6wL/muBBKVUa6VUEHANMMOittTepEnQrx8UFsKSJTBmjNNP+cC5CbRqHMKTU7f4TPG3aRv3cyCvkLvPcaOx/ieegBtugM2bYexYk9bpYiO6NKPMppm/zcKhHzeWd6KE9ftyGdLBDYZ8MjPNcM7DD5uFe6KiTELAs8/WuMyHo1gS/LXWpcB9wFxgO/C91nqrFW2pFa3hxRfNUE/nzrBhA/Tt65JT1wv058XLuvL74QLeW5jsknNaqcym+fjX3XSKacAQdxm3/fhj8w7v9ttN6QYH5vDXROfmDWgZGSITviqxPCWbMptmSIcm1jSguBg+/9xM8uzZE1avhi++MEkBGzbAOedY0y47y6a7aa1nAbOsOn+tHTli8m9nzjQzNj//vNIaPc4yoF0Ul/eM5ZNf93BJ91g6NHNwKQo3MjvpIHuyjvPh9T3dY2H2efPgvvvMDd6PP7as1wZm6Gdkl2Z8tiyVvIISGoZ4QIE7F1q8M5MG9QI4s4XjEi+q7fBhuPJKk6oJkJBgyrhXY3a/q/j0DN8aW7fOzLibM8eM3U2a5PLAf9JTF3YivF4AT/y4GZvN/bMuasNm07y/MIV2TcIY4ejqqrWxebP5h9+xo7n2tSzO5kgju8ZQatPM3y5DP+VprVm8M4tBCdGuX7Vr504zErB8uRkSLC42ZRrcKPCDBP/q2bPH3Mzr3dtcyMWL4cEHLc16iAwN4r8XdWLDviN8s2afZe1wpgU7MtlxKJ97hrS1Nke7qAjuvdf8QYeGmjTeBu4xYah7XENiI+ozJ0lq/Je37eBRMvOLONuV4/3Ll8Nrr0GfPmaEYMECc3PXxTdyq0uCf1XmzjXZPCkp5ubMb7+ZFbjcwGVnxjKgXWNenb2DjKOFVjencrXIB9da8/7CZFpE1ucSKxdm19qM7X/4oZl9OXu2U2bt1pZSihFdmrFkVzb5hS6e/+HGef6Ld5oUT5fcJyorM8tyDhxoPsfHm1GC06zJ7A4k+FemsNCsuDRypFlwZe1ak5bVxKKbRxVQSvHCpV0pLrPx7Aw3vF9eh3dGy1Ky2ZSexz1D2lm72PZzz5mUzueeM+m83d1vfsUFXZtRXGZj4Y5M15zQHe69VOHXnVl0imlAkwZOTvE8fhwuv9z0+O+4A7Zvh/XroZWL517UggT/ikyebIL8U0+ZMd6TpXndUHxUKP88N4HZSYe8KuXvvYUpxDSsx+U9Y6ve2VkmTzbv9q6/3vwuuKkzWzSiaYNgWd7R7mSK5zlnOLHXv3ChKbrWpAn89JO5B/jpp3DGGW5xL6g6JPiXp7UppnT11aYuy9SpZgZemBsWEStnzOA2dGgaztPTkzhW5PkLv6zcfZg1qTmMGdyG4AALsmkyMkwJ7muvNfM4xo1z696un59iROdmLN6ZxXEvuP515fQUzylTzIhAbi5cdx3Mn2/5PcDakOB/UkGBuZBPP20m7yxdahZZ94ALGujvx0tXdOXQ0UJen7vT6ubUidaa1+ftpFmDelzbx4Kx9eJik6I3YYIpzjd1aq1KcbvayK4xFJXaWLTTRUM/bsypKZ7vvms6h716mVz9sWPNLG8PJMEfzBjd4MEmfe/FF+HLLz3iD768ni0bccNZrfhy5e9sPeC56/4u2pnJ+r25/PPcBOoFWtDrf+ghWLbMFGibNcsU3fIAveMjiQoL8vkyz1prft3lhBTPPXtM/Z0HHjDLcv7yi1mL24P5dvDXGt54w6Rw7txppl0/8YRH9PYr8sj5HWgUEsTT07d6ZO6/zaZ5be4uWjUO4apE59QwP61PPzVZPf/6lxnn9yD+forhnZuxaGcmJ4p9o+xHRbYfzCfjqINTPF99Fdq2hfffN5V7f/wRQhy41KtFfDf4FxebFL5HHjF361NT4ZJLrG5VnTQMCeTxkWewfm8uP2xIt7o5f6pmSuDMLQfZfvAoDw9rT6ArM3y0/nPm7vnnw8svu+7cDnRB1xgKisv+qGTpdG6Y6rl4lxn2ckiKp9amM/jYY6bw2sqVpnijh9zQrYrvBX+tTdrm0KGmNMOTT5rqelFuVvK1lq7oGUevVo14efYO8gosrvtfg3dQJWU23pq/izOahXNxNxfm9W/YYDI2hg83qyd9953H/nGf1TqSRiGBzHb2hC83fme8aEemqeJZ1xTPsjIzse/ll00n8YcfzCQ/N37tNeVbwb+oCG6+2czAW7/erK37v/85ZX1Mq/j5KZ4b1ZncgmLemO85N38nrtlHavZxHjm/g+tm8x4/bjJ6ysrgzTfNWH+km5SMroUAfz/O79SMBdszfabia3k5x4tZvze37ivdbdliJmh99JEZAhw71tIaTs7iPVGvKjk5phjXV1+ZC7p9u/nD90Kdmzfkpn7xfL1qL0n73f/mb15BCW/O30W/No05t6MLJ9E99JCpuTJlinkcE+O6czvJyK7NOFZUyrJk31nv4aRFOzKxaRhWl+C/apVJ/ti61eTuv/66V/X2y/ON4J+aCv37m9obEyaYCxofb3WrnOqhYe2JDA3i6elJbn/z992FyRw5UcJTF3V0XeXOyZNNj+6xx8wQoJfo3zaKBvUCmOWDtX5+2Z5B0wbBdImtZd2lBQtMzf3ISLNI04MPOraBbsa7g392tpmM0aWLWUxh3jy46SarW+USDesH8vjIjmzYd4RJ69Kq/gaL7Mk6xoQVvzM6sYXr1uZNTTVZG336mLINXiQowI9hnZoxf1sGxaU2q5vjMoUl5kb3uR2b1q4DMW0aXHihqdu0ZInXdw7B24N/vXrmH8Dw4bBiBQwZYnWLXOqKnrGc1TqSl2ZtJyvfPZf6e3HWdoID/Hj4/PauOeGmTX/+HkycCEFBrjmvC13QtRn5haWs2O07Qz+r9hymoLis5kM+RUVmJOCKK0wn8ddfTS0vH+DdwT8szGT2/PijqbnhY5RSvHh5VwpLbDw/c5vVzfmbeVsP8cv2TO4bmuCaNVaPHze1mo4fN4G/dWvnn9MCAxOiCAsO8KkJXwu2Z1I/0J9+bWsw8So/39wH/Pe/zSzdRYu8JuuvOrw7+AvaRodxzzltmbHpgOvyv09VQT74saJSnpmxlQ5Nw7ljkIuC8EMPmdLcU6aYP3ovFRzgz7kdmzB32yFKypw49OMmef5aa37ZnsGghKjqzwrPzTXj+0uWmBXZ5s6FcO9dEa8iEvx9wN1D2tImOpSnpm1xbeGv04y9vjlvF4eOFvLi5V1dM6FryhRzg/ff//aJ4b+RXWI4UlDCqj2HHX9wN8t+2XrgKAfzCjmvUzWHfDIyzO/Ahg1mjs9dd7nda3IFCf4+IDjAn5cv70Z67gm3GP7ZnH6EL1akcv1ZLenVqpHzT5iaamqt9+ljqrb6gCEdogkN8uenTQesborTLdieiVIw9IxqpAkvXmzKuSQnw/TpZqzfR0nw9xF9Wkdy99ltmbg2jdkW1n0vLCnjoUkbaRJej0dHuOA+THExjB5thii89AZvReoF+jO8SzNmbznk9RO+5mw9RM+WjYgKC65ixzlmuM/f32T+jRzpmga6KQn+PuShYe3pHteQx3/cQlpOgSVteHn2DnZnHee1q7rRoJ6T1zbV2lRhXLsWPvvMa2/wVubSHrHkF5WyyFUrfFkgNfs42w8eZWSXZqffcc4cU6K9Y0ezxOLAga5poBuT4O9DAv39eOeaM9Fac8eEdS5f+GVpchZfrPidW/rHMyjBBWurvvWWuZn30EOmRr+P6d+2MVFhwUzbuN/qpjjNyTpGI7ueZnb27NmmMFunTl5RitlRJPj7mPioUD68vhcpWcd4cOJvlLlo9u/BvBM8OHEj7ZqE8fhIFwz3rFhhZu9efLFZX9UHBfj7cXH3GBbtyLK+yJ+TzN5yiB4tIoiNqF/xDrNmmR5/584S+E8hwd8HDUyI4tmLO/HL9kyenLoF7eSUvaIyzT++3kBhSRkf39DT+Yu0ZGWZ1Zbi4kw5Dy8sylVdl/aIpbjM5vxKnxZIyylgy/48LuhayZDPzJkm8HfpYgK/Bxftc4Y6BX+l1FVKqa1KKZtSKvGU555QSqUopXYqpYaX2z7Cvi1FKfV4Xc4vau/GfvHcP7QdE9em8b+ftzvtH4ANxRMbj7Mp7QhvXN2Ddk2cnEtdWmpu8GZlmTK8jVyQTeTGusU1pE1UqHOGfizO8/9jyKdLBUM+P/wAl10G3bpJ4K9EXXv+ScDlwJLyG5VSnYBrgM7ACOBDpZS/Usof+AAYCXQCrrXvKyzw8LD23NI/ns+WpfLm/F0O/weggZfOuZUf04t5eFh7RlR1U84RHnnEzNT8+GPo2dP553NzSilG9YhldWoOB/NOOOqgjjlOHc3acoiusQ1pEXnKqlrffWc6AL17m8Dv4x2AytQp+Gutt2utKyoaPwqYqLUu0lqnAilAH/tHitZ6j9a6GJho31dYQCnF0xd1YnRiC95bmMILDnwHoLXmnd9tjO1zOTe3Dub+oe0cctzTmj0b3nnHrLV6663OP5+HGNWjOVrDjI3ek/O//8gJNqYdYeSpQz7jx8MNN5gqvnPmQIQTFnH3Es4a848FypeSTLdvq2y7sIifn+Kly7tyS/94xi1L5Ykft9T5JrDNpnl+5nbeTrVxxZZfeKZLfeeXas7JgXvugYQEn73BW5n4qFDObBnB1N/2O/3+jqucnKvyx5BPWZnJ6rr9dlOie/ZsaFDL0s4+osrgr5T6RSmVVMHH6XrsFf2l69Nsr+i8Y5RS65RS67KyLKpJ4yP8/BTPXNzpj3sA//h6fa3TQHOPF3P7hLWMX57KrXF+vDbrHfycHfjLyuCaa2D/fnODN7iKyT4+6Iqecew4lE/S/qNWN8Uhpm88QNfYhrSOCjWVOa+5Bt5+26zU99NPEBpqdRPdXpXBX2t9nta6SwUf00/zbelAi3JfxwEHTrO9ovN+qrVO1FonRke7ICfcxyml+Nf5HXj24k4s3JHJ5R8uZ+/h4zU6xryth7jg3aUsTznM86M683SCH34V/293rMceg/nz4b33oF8/55/PA13cvTnBAX5878ZrO1TX7qxjbNmfx6gezeHoUTNTd8oUs97u55+bUu6iSs4a9pkBXKOUClZKtQYSgDXAWiBBKdVaKRWEuSk8w0ltELVwy4DWTLi1DxlHixj5zlI+W5Za5aIg6/fmcvP4NYz5aj0N6wcy5e5+3Ngv3jWrcn35JbzxBtx9tynQJSrUsH4gI7s0Y/rG/R5f7mH6b/vxU3BJXDCcc46pwT9+vOkEuMnNaE8QUJdvVkpdBrwHRAM/K6U2aq2Ha623KqW+B7YBpcC9Wusy+/fcB8wF/IHxWuutdXoFwuEGJkQx64FBPDV1C8/P3MbYJXu4KjGO/m2jiGtUH5vWpOWc4Ld9ucxKOsT2g0dpFBLIfy44g1sHtP57lU5njTMvWWIKtg0ZYm70itO6KrEF0zYeYN62DC7p3rzuB7Tg/oHWmmkbDzCgSRBNzh0EBw/C1KlwySUub4unq1Pw11pPBaZW8twLwAsVbJ8FzKrLeYXzxUbUZ/wtvVm8K4txS/fwwaIU3luY8rf9eraM4P8u6cyVveIIDT7l18mZvbD9+00ed3y8yekOdHKdIC/Qr01jYiPqM3ldWt2Cv4W96w37jrAvp4B/zn0PCgpMKueAAZa1x5PVKfgL76aU4pwOTTinQxPyCkrYmH6EjLxCAvwVTcLr0SW2AREhFlTJLC01a/Dm5Znev0zgqRY/P8VViXG8syCZ9NwC4hqFVP1Nbmb6V3MILglmePAx2LwZmlSjjLOokAR/US0NQwI5u70b3HjXGu6916Tyffihqdkiqu2KnnG8/UsyP6zfzwPnJVjdnOo7doySp/7LTFtvhpWkEb5grmT01JHU9hGe5bnn4NNP4dFHzU1eUSMtIkMY0K4xk9enYXNRUb86W7cOOnRg8c8ryAlpyKX3XiWB3wEk+AvP8cEH8OyzJpf7pZesbo3HujqxBem5J1iWkm11U6r2009w9tkQEMCkO58mOjyYIR1dUCbEB0jwF+5Pa/jPf+C+++Cii2DcOPCTX93aGtGlGY1Dg/hy5V6rm3J6H330xwIsmQuWsuiI4oqecQS4Ys1nHyA/ReHetIb//tf09K+6CiZPhgC5VVUXwQH+jO7dgoU7MkjPtWZFt0qVlsKTT0KbNqZcx8iR8OuvTEkvpsymGd27RdXHENUiwV84X13ywV94wXzceqtZg1dmbzrEdWe1BOC7NftqfxBH5/kfO2Z6+i++CC1bmus+bRo6JITJ69Lp0zrSlHMQDiHBXzhPXfPBX3rJ9Pqvu06GehwsrlEIQ89oysQ1aRSV1nDGr6Pz/LWGPXtg8GCTxfX++7B4sRnqCwhgTWoOqdnHGZ0ovX5Hkr8m4X60huefN3/8111nirVJ4He4G/u14vDxYuYkHbKuEdu3m0XV27aFXbtg2jSTylvOxLVphAcHcMHp1ukVNSZ/UcL9vP02PP20qcs+YYKM8TvJoHZRxDcO4fPlv1tT6nnuXOjbF3Jzzbu8tWvNmsvlZB8r4ufNB7m8Zyz1g3x3OU5nkOAv3Mvrr8PDD5ux3y++kMDvRH5+itsGtmZj2hHWpOa47sRamwqsF1xgynOsWQOPP27eAZziu9X7KC6zcVP/eNe1z0dI8BfuQWuT5fHvf8MVV5ibuz688LqrXNWrBY1Dg/j4192uOWFaGowZA//8p8nkWbYMWrWqcNeSMhtfrdrL4PbRtI0Oc037fIgEf2G90lITEF58EW65BSZNkgVZXKR+kD+3Dohn0c4sth908kIvkyZB+/bm5v0jj8D06RAeXunus5MOkZlfxK3S63cKCf7CWlrDP/5hAsKjj5q67NLjd6kb+8YTGuTvnN5/cbHp7T/2mFltq2dPSEoyS21WcZ2/WJ5KfOMQ96gp5YUk+Avnq+xmYmmpWYDls89McHjlFVmMwwINQwK5vm8rftp0gJTMY9X/xqpuEs+ZA82amZz9V1+F226DhQurVYxvTWoOG/Yd4eb+8fj5ye+EM0jwF85zukCen28W4Bg7Fp54Qmr1WOyuwW2oH+jPG/N2Vr1zdf5Bf/KJKcXRogV8/LHJ7Bk3rtrDeR8uTiEyNIhreres1v6i5iSVQrheeroJDElJJkiMGWN1i3xe47Bg7hzchrd/SWZT2hG6t4io+UFWroSdO2H5chPohw835ThOM65fkaT9eSzemcW/h3eQ9E4nkp6/cK2VK+Gss2D3bpgxQwK/G7ljUBsiQ4N4de6Omuf9v/MO9O9vynCMG2eGeH76qcaBH+CjxbsJDw7ghr4VZwEJx5DgL1xn3DgzhT8gAJYuNXnewm2EBQdw/9B2LE85zNytGdX7Jq3hmWfgwQfNu7kdOyAjw9zHqcXSmtsOHGVW0kFu7NeKhvVlaU5nkuAvnK+szKT23XknDBoEmzZBjx5Wt0pU4Ma+rTijWTjP/bSVguLS0++clwf3328W2Ln5ZrOQeocOdVpa8ZU5O2hQL5C7Bret9TFE9UjwF8536aXwxhtmKGDuXIioxXiycIkAfz/+d2kXDuQV8s6C5NPv3KaNWWDnnntMim4dZ2OvSMnm111Z3HtOWxqGSK/f2ST4C+fLyzOpfuPG1WooQLhWYnwkoxNbMHbJHtbvzf37DidOmM89e8LPP5sqnHUsvFdSZuP5n7fTvGE9buoXX6djieqRbB/hPH36mJINl10GvXpZ3RpRA09d1JHlu7N5aNJGZj0wiLDgcqFi9GgoKjKT8hy0vsLny1PZfvAoH9/Qi3qBkuHjCtLzF84THg7/+58Efg8UXi+QN6/uQXpuAU9PT/pr9k98vKm66qDAv/fwcd6an8x5HZsyvHNThxxTVE2CvxCiQn1aR3Lf0AR+3LDfaev9FpfauP+73wj0Vzw3qjNKZni7jAR/IUSlHjw3gfM6NuG5mdtYsTvb4cd/efYONqfn8eqV3WgeUd/hxxeVq1PwV0q9ppTaoZTarJSaqpSKKPfcE0qpFKXUTqXU8HLbR9i3pSilHq/L+YUQzuXnp3hrdA9aR4Vy11fr2Xogz2HH/nrVXsYvT+WW/vGM6CKrdLlaXXv+84EuWutuwC7gCQClVCfgGqAzMAL4UCnlr5TyBz4ARgKdgGvt+woh3FR4vUC+uLU34cEB3PTZGlIy8+t8zJ82HeDp6Umce0YTnrrw74u4COerU/DXWs/TWp+cCbIKiLM/HgVM1FoXaa1TgRSgj/0jRWu9R2tdDEy07yuEcGNxjUL45s6+KKW4+pNVbNhXQQpoNX2zei//nPgbia0iee+6Mwnwl9FnKzjyp34bMNv+OBZIK/dcun1bZdv/Rik1Rim1Tim1Lisry4HNFELURuuoUCb/ox/h9QK49tNVzNh0oEbfX1hSxmNTNvPk1CTObh/NhNv6EBIk2eZWqTL4K6V+UUolVfAxqtw+TwKlwDcnN1VwKH2a7X/fqPWnWutErXVidLQs5iCEO2gdFcoPd/enS2xD/vndbzz8/UYOHys67fdorVm0I5Phby9h0ro07j2nLeNuSpSKnRar8t+u1vq80z2vlLoZuAg4V/+ZDJwOtCi3WxxwsptQ2XYhhAeICgtm4pi+vLcwhfcXJjM36RA39G3FpWfG0qFp+B+Lr2TmF7JoRybfrN7H5vQ82kSH8u0dZ9G/XZTFr0AAqBqXbi3/zUqNAN4EztZaZ5Xb3hn4FjPG3xxYACRgev67gHOB/cBa4Dqt9dbTnScxMVGvW7eu1u0UQjhHSuYx3pq/izlbD1Fm04QFB9AoNJDjRWXkHC8GoE10KLcPbM1VvVoQFCDj+66klFqvtU6s6Lm6Dri9DwQD8+2TM1Zprf+htd6qlPoe2IYZDrpXa11mb8x9wFzAHxhfVeAXQrivdk3C+OD6nmQfK2Lh9ky2HTxK3okS6gX60SYqjAHtougYEy6Tt9xQnXr+riI9fyGEqLnT9fzlPZgQQvggCf5CCOGDJPgLIYQPkuAvhBA+SIK/EEL4IAn+QgjhgyT4CyGED5LgL4QQPsgjJnkppbKAuqwjFwU4fhki9+Zrr9nXXi/Ia/YVdXnNrbTWFVbG9IjgX1dKqXWVzXLzVr72mn3t9YK8Zl/hrNcswz5CCOGDJPgLIYQP8pXg/6nVDbCAr71mX3u9IK/ZVzjlNfvEmL8QQoi/8pWevxBCiHIk+AshhA/y6uCvlBqhlNqplEpRSj1udXvqQinVQim1SCm1XSm1VSn1gH17pFJqvlIq2f65kX27Ukq9a3/tm5VSPcsd62b7/sn2NZjdllLKXyn1m1Jqpv3r1kqp1fa2T1JKBdm3B9u/TrE/H1/uGE/Yt+9USg235pVUj1IqQik1RSm1w36t+/nANX7I/judpJT6TilVz9uus1JqvFIqUymVVG6bw66rUqqXUmqL/XveVdVZOk1r7ZUfmGUidwNtgCBgE9DJ6nbV4fXEAD3tj8MxayF3Al4FHrdvfxx4xf74AmA2Zt3kvsBq+/ZIYI/9cyP740ZWv77TvO6HMetBz7R//T1wjf3xx8Dd9sf3AB/bH18DTLI/7mS/9sFAa/vvhL/Vr+s0r3cCcIf9cRAQ4c3XGIgFUoH65a7vLd52nYHBQE8gqdw2h11XYA3Qz/49s4GRVbbJ6h+KE3/Y/YC55b5+AnjC6nY58PVNB4YBO4EY+7YYYKf98SfAteX232l//lrgk3Lb/7KfO30AccACYCgw0/6LnQ0EnHqNMetC97M/DrDvp0697uX3c7cPoIE9EKpTtnvzNY4F0uwBLcB+nYd743UG4k8J/g65rvbndpTb/pf9Kvvw5mGfk79UJ6Xbt3k8+1vdM4HVQFOt9UEA++cm9t0qe/2e9HN5G3gUsNm/bgwc0VqX2r8u3/Y/Xpf9+Tz7/p70etsAWcDn9qGucUqpULz4Gmut9wOvA/uAg5jrth7vvs4nOeq6xtofn7r9tLw5+Fc05uXxea1KqTDgB+BBrfXR0+1awTZ9mu1uRSl1EZCptV5ffnMFu+oqnvOI12sXgBka+EhrfSZwHDMcUBmPf832ce5RmKGa5kAoMLKCXb3pOlelpq+xVq/dm4N/OtCi3NdxwAGL2uIQSqlATOD/Rmv9o31zhlIqxv58DJBp317Z6/eUn8sA4BKl1O/ARMzQz9tAhFIqwL5P+bb/8brszzcEcvCc1wumrela69X2r6dg/hl46zUGOA9I1Vpnaa1LgB+B/nj3dT7JUdc13f741O2n5c3Bfy2QYM8aCMLcHJphcZtqzX73/jNgu9b6zXJPzQBO3vW/GXMv4OT2m+yZA32BPPtby7nA+UqpRvZe1/n2bW5Fa/2E1jpOax2PuXYLtdbXA4uAK+27nfp6T/4crrTvr+3br7FnibQGEjA3x9yO1voQaywbdwAAAQJJREFUkKaU6mDfdC6wDS+9xnb7gL5KqRD77/jJ1+y117kch1xX+3P5Sqm+9p/hTeWOVTmrb4I4+QbLBZismN3Ak1a3p46vZSDmrdxmYKP94wLMeOcCINn+OdK+vwI+sL/2LUBiuWPdBqTYP261+rVV47UP4c9snzaYP+oUYDIQbN9ez/51iv35NuW+/0n7z2En1ciCsPi19gDW2a/zNExWh1dfY+D/gB1AEvAVJmPHq64z8B3mnkYJpqd+uyOvK5Bo//ntBt7nlKSBij6kvIMQQvggbx72EUIIUQkJ/kII4YMk+AshhA+S4C+EED5Igr8QQvggCf5CCOGDJPgLIYQP+n81WMzyo9L3mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "\n",
    "ax[0].plot(conPhase.data[:10000], 'r')\n",
    "ax[0].set_title('Phase of Data')\n",
    "\n",
    "ax[1].plot(filteredData)\n",
    "ax[1].set_title('Filtered Data')\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(conPhase.data[:10000], 'r')\n",
    "ax.plot(filteredData)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "plt.plot(conPhase.data[:10000], 'r')\n",
    "for ts in events.ts[0:4]:\n",
    "    plt.axvline(ts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge 2\n",
    "\n",
    "Determine the average lag time between the pulse and the stim!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.025940717107262232 seconds\n"
     ]
    }
   ],
   "source": [
    "# Code here\n",
    "lagSum = 0\n",
    "numLags = 0 \n",
    "for crossIndex in range(0, len(events.ts), 4):\n",
    "    stimIndex = crossIndex + 2\n",
    "    lagSum += events.ts[stimIndex] - events.ts[crossIndex]\n",
    "    numLags += 1\n",
    "    \n",
    "avgLag = lagSum / numLags\n",
    "\n",
    "lagSec = 1 / (conPhase.fs / avgLag)\n",
    "print(lagSec, 'seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge 3\n",
    "\n",
    "Determine the average phase at cross and stim times and plot a rose plot of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rose plot code here (I used google) https://stackoverflow.com/questions/22562364/circular-histogram-for-python\n",
    "def rose_plot(ax, angles, bins=16, density=None, offset=0, lab_unit=\"degrees\",\n",
    "              start_zero=False, **param_dict):\n",
    "    \"\"\"\n",
    "    Plot polar histogram of angles on ax. ax must have been created using\n",
    "    subplot_kw=dict(projection='polar'). Angles are expected in radians.\n",
    "    \"\"\"\n",
    "    # Wrap angles to [-pi, pi)\n",
    "    angles = (angles + np.pi) % (2*np.pi) - np.pi\n",
    "\n",
    "    # Set bins symetrically around zero\n",
    "    if start_zero:\n",
    "        # To have a bin edge at zero use an even number of bins\n",
    "        if bins % 2:\n",
    "            bins += 1\n",
    "        bins = np.linspace(-np.pi, np.pi, num=bins+1)\n",
    "\n",
    "    # Bin data and record counts\n",
    "    count, bin = np.histogram(angles, bins=bins)\n",
    "\n",
    "    # Compute width of each bin\n",
    "    widths = np.diff(bin)\n",
    "\n",
    "    # By default plot density (frequency potentially misleading)\n",
    "    if density is None or density is True:\n",
    "        # Area to assign each bin\n",
    "        area = count / angles.size\n",
    "        # Calculate corresponding bin radius\n",
    "        radius = (area / np.pi)**.5\n",
    "    else:\n",
    "        radius = count\n",
    "\n",
    "    # Plot data on ax\n",
    "    ax.bar(bin[:-1], radius, zorder=1, align='edge', width=widths,\n",
    "           edgecolor='C0', fill=False, linewidth=1)\n",
    "\n",
    "    # Set the direction of the zero angle\n",
    "    ax.set_theta_offset(offset)\n",
    "\n",
    "    # Remove ylabels, they are mostly obstructive and not informative\n",
    "    ax.set_yticks([])\n",
    "\n",
    "    if lab_unit == \"radians\":\n",
    "        label = ['$0$', r'$\\pi/4$', r'$\\pi/2$', r'$3\\pi/4$',\n",
    "                  r'$\\pi$', r'$5\\pi/4$', r'$3\\pi/2$', r'$7\\pi/4$']\n",
    "        ax.set_xticklabels(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'stim delivery')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find average phases and plot them using a rose plot\n",
    "import math\n",
    "tsOffset = 1 + 1 - (conPhase.ts[1] - conPhase.ts[0])\n",
    "\n",
    "\n",
    "stimPhase = []\n",
    "crossPhase = []\n",
    "for n in range(len(events.ts)-1):\n",
    "    if events.nodeId[n] == 100 and events.eventId[n] == 1: # Stims\n",
    "        ts = int(events.ts[n] * tsOffset)\n",
    "        stimPhase.append(math.radians(conPhase.data[ts]))\n",
    "    if events.nodeId[n] == 105 and events.eventId[n] == 1: # Crosses\n",
    "        ts = int(events.ts[n] * tsOffset)\n",
    "        crossPhase.append(math.radians(conPhase.data[ts]))\n",
    "\n",
    "# Your code here\n",
    "\n",
    "# Use this to plot\n",
    "fig, ax = plt.subplots(1, 2, subplot_kw=dict(projection='polar'))\n",
    "\n",
    "rose_plot(ax[0], np.array(crossPhase), bins = 24)\n",
    "rose_plot(ax[1], np.array(stimPhase), bins = 24)\n",
    "ax[0].set_title('crossing times triggers')\n",
    "ax[1].set_title('stim delivery')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the mean and std for the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35.81679201236395 54.92380578207416\n"
     ]
    }
   ],
   "source": [
    "# Print our the mean and std for crossing and stim times\n",
    "from scipy import stats\n",
    "stimMean = stats.circmean(stimPhase)\n",
    "crossMean = stats.circmean(crossPhase)\n",
    "\n",
    "stimStd = stats.circstd(stimPhase)\n",
    "crossStd = stats.circstd(crossPhase)\n",
    "\n",
    "print(math.degrees(crossStd), math.degrees(stimStd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge 4\n",
    "\n",
    "Let's write the phases to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here\n",
    "pathToCSV = cwd + '\\\\data\\\\Sample_Data\\\\phases.csv' \n",
    "\n",
    "phasesFile = open(pathToCSV, 'w') #'r', 'a'\n",
    "\n",
    "phasesFile.write('stim, cross \\n')\n",
    "\n",
    "# Option 1\n",
    "#for n in range(len(stimPhase)):\n",
    "#    phasesFile.write(str(stimPhase[n]) + ',' + str(crossPhase[n]) + '\\n')\n",
    "    \n",
    "# Option 2\n",
    "for stim, cross in zip(stimPhase, crossPhase):\n",
    "    phasesFile.write(str(stim) + ',' + str(cross) + '\\n')\n",
    "    \n",
    "phasesFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.080069797456993,2.2700624916064247\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(str(stimPhase[0]) + ',' + str(crossPhase[0]) + '\\n')"
   ]
  }
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