UCSD Mentor Program 2016.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UCSD Mentor Program 2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student: Mong H. Ng\n",
    "## Mentor: Dr. Giorgio Quer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter notebook is a diary-like notebook that recorded the research I did under Dr. Giorgio Quer. We mainly focused on PPG and ECG signal data extraction and alignment between ECG-derived RRIT signal and PPG-derived RRIT signal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table of Content\n",
    "<ol>\n",
    "<li><a href=\"#Abstract\">Abstract</a></li>\n",
    "<li><a href=\"#Introduction\">Introduction</a></li>\n",
    "<li>\n",
    "    <a href=\"#The-Senario\">The Senario</a>\n",
    "    <ol>\n",
    "        <li><a href=\"#What-we-need-to-do\">What we need to do</a></li>\n",
    "    </ol>\n",
    "</li>\n",
    "<li><a href=\"#Hrm-Files\">Hrm Files</a></li>\n",
    "<li><a href=\"#Video-Processing\">Video Processing</a></li>\n",
    "<li><a href=\"#R-Peaks-Detection\">R-Peaks Detection</a></li>\n",
    "<li><a href=\"#Looking-at-Data-in-a-Statistical-Standpoint\">Looking at Data in a Statistical Standpoint</a></li>\n",
    "<li><a href=\"#The-Alignment\">The Alignment</a></li>\n",
    "<li><a href=\"#Result-of-the-Alignment\">Result of the Alignment</a></li>\n",
    "<li><a href=\"#Visual-Confirmation\">Visual Confirmation</a></li>\n",
    "<li><a href=\"#Conclusion\">Conclusion</a></li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The focus of our research is on the computation of RR interval time series. In the first part of our work, we extract the RR intervals from the videos of a fingertip. We explore means of extracting RR signals by converting each frame of the video into a representative number (average color intensity) that can be quantitatively analyzed. This is the principle at the basis of the PPG (Photoplethysmography) signal analysis. In the second part of our work, we align the RR signals that we obtained previously with another longer RR signal (from the same subject and recorded at the same time) obtained from an ECG (Electrocardiography) sensor. We then align each PPG-derived RR with the ECG-derived RR using linear interpolation and minimizing the absolute value of their distance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R-peak and RR Intervals: In a electrocardiography graph, we call the peaks R-peaks and the distance between consecutive R-peaks RR interval.\n",
    "\n",
    "ECG sensors: Sensors that can provide RR signal by measuring the electric signal generated from the expansion and contraction of heart chamber. They are the most accurate heart rate sensors.\n",
    "\n",
    "PPG sensors: Light-based sensors that provide RR signal by detecting the color change caused by blood flow in a body part. A variety of these devices exist, and even a smartphone camera can serve as a PPG sensor. For example, if a person places his finger on the flashlight of a smartphone, the tip of the finger would exhibit changes in redness as blood flow comes and goes. The camera would then record this change in a video."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Senario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two subjects each carry an ECG sensor while simultaneously pressing his or her finger against a smart phone camera (acting as the PPG sensor). For ten minutes, the ECG sensors record the RR interval, while the phones record 10 short 30-second videos of the person’s fingertip at 30 frames per second. Our goal is to align the 20 PPG signals correctly with one of the two long ECG signals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What we need to do"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ECG-derived RRIT comes in the form of .hrm file, so it requires minimal processing. However, the PPG comes from video; therefore, we need to convert those frames in videos to representative numbers (we use red color component averaged in all the pixels in each frame), then to rr interval. After that, we align each PPG-derived RRIT with each ECG-derived RRIT. The pairing of each PPG with minimal alignment distance would signify its time delay in the ECG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "import re\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "fps = 30.323427803\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hrm Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we learn how to process a hrm file obtained from ECG sensor. We get the file from http://bliss.calit2.net. We read the hrm file through python, store it in a list, and graphed it using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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BA4BLAFR1JnA3MBOYCJyt+t9guDHAjcAcYK6qTiqnDo2NHm7sOI5TiFqlzd9SVY8TkeOt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\nua2PvG+txtK5c9zoBbNHIVN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d69c13198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#obtain the hrm file from online\n",
    "url = 'http://bliss.calit2.net/analysis/device_data/a2abe3083aceab9eeb5617ffeaf7b141.hrm'\n",
    "response = urllib.request.urlopen(url)\n",
    "data = response.read()\n",
    "htmlContent = data.decode('utf-8')\n",
    "sectionList = re.split(\"\\\\[.*\\\\]\", htmlContent)\n",
    "rrSection = sectionList[-1]\n",
    "#convert string of rr into list of rr\n",
    "rrList = rrSection.split('\\r\\n')\n",
    "#remove empty string\n",
    "rrList = list(filter(None, rrList))\n",
    "#get rid of the last three sentinel values '3999'\n",
    "rrList = rrList[0:-3]\n",
    "#convert the strings to numbers\n",
    "rrList = list(map(int, rrList))\n",
    "#construct time array from the rr intervals\n",
    "tList = list()\n",
    "tList.append(0)\n",
    "for i in range(len(rrList)-1):\n",
    "    tList.append(tList[-1] + (rrList[i])/1000)\n",
    "\n",
    "#plotting the graph\n",
    "plt.plot(tList, rrList)\n",
    "plt.xlabel('Time in Second')\n",
    "plt.ylabel('RR Interval in Milisecond')\n",
    "plt.title('Timve vs RR Interval')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Video Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we learn how to obtain the same list of RRIT list from PPG signal from videos recorded by fingertips on smartphone. We used ffmpeg instead of the more popular opencv because, while opencv provides wide range of features, we really only need to convert the video into rgb list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#set up ffmpeg\n",
    "FFMPEG_BIN = \"ffmpeg\"\n",
    "video = 'your_video_name.mp4'\n",
    "width = 1920\n",
    "height = 1080\n",
    "command = [ FFMPEG_BIN,\n",
    "            '-i', video,\n",
    "            '-f', 'image2pipe',\n",
    "            '-pix_fmt', 'rgb24',\n",
    "            '-vcodec', 'rawvideo', '-']\n",
    "pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=10**8)\n",
    "\n",
    "#return red value from each frame\n",
    "def captureFrame():\n",
    "    # read 1920*1080*3 bytes (= 1 frame)\n",
    "    raw_image = pipe.stdout.read(width*height*3)\n",
    "    # transform the byte read into a numpy array\n",
    "    image =  numpy.fromstring(raw_image, dtype='uint8')\n",
    "    frameTotalByteSize = width*height*3\n",
    "    if len(image) < frameTotalByteSize:\n",
    "        return False\n",
    "    image = image.reshape((width,height,3))\n",
    "    # throw away the data in the pipe's buffer.\n",
    "    pipe.stdout.flush()\n",
    "    totalRed = 0\n",
    "    for col in range(0, width):\n",
    "        for row in range(0, height):\n",
    "            totalRed += image[col][row][0]\n",
    "    return totalRed/1.0/width/height\n",
    "\n",
    "rValueList = list()\n",
    "while True:\n",
    "    r = captureFrame()\n",
    "    if not r:\n",
    "        break\n",
    "    else:\n",
    "        rValueList.append(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we are able to obtain a list of average Red color component from each frame of a PPG signal video. We usually save the result because this process is time-consuming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def writeToFile(fileName):\n",
    "    file = open('fileName', 'w')\n",
    "    numpy.savetxt(file, rValueList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def readFromFile(fileName):\n",
    "    l =numpy.loadtxt(fileName)\n",
    "    return l[0], l[1], l[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then focus on getting a RRIT list from the Red color componenet list. Each peak in the red color component list correspond to a real time R-peak, so RRIT(RR interval) would simply be the distance between each peak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#obtain rr intervals from red value\n",
    "def constructRRListFromRedValueList(redValueList, fps):\n",
    "    timePerFrame = 1.0/fps\n",
    "    rrList = list()\n",
    "    lastMaxIndex = False\n",
    "    for i in range(1, len(redValueList)-1):\n",
    "        previousSlope = redValueList[i] - redValueList[i-1]\n",
    "        afterSlope = redValueList[i+1] - redValueList[i]\n",
    "        # if at that point it is concave down = local max\n",
    "        if previousSlope*afterSlope < 0 and previousSlope > 0:\n",
    "            if lastMaxIndex:\n",
    "                rrInterval = (i-lastMaxIndex)*timePerFrame\n",
    "                rrList.append(rrInterval)\n",
    "            lastMaxIndex = i\n",
    "    return rrList\n",
    "rrList = constructRRListFromRedValueList(rValueList, 30.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wanted to make sense of these RRITS data, so we graph them against time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def constructTimeListFromRRList(rrList, beginT, endT):\n",
    "    #construct time array from the rr intervals\n",
    "    tList = list()\n",
    "    tList.append(float(rrList[0]))\n",
    "    for i in range(1, len(rrList)):\n",
    "        tList.append(tList[len(tList)-1] + float(rrList[i]))\n",
    "    if tList[len(tList)-1]<endT:\n",
    "        endT=tList[len(tList)-1]\n",
    "\n",
    "    #limit the time interval\n",
    "    beginIndex = 0\n",
    "    endIndex = 0\n",
    "    i = 0\n",
    "    while tList[i]<beginT:\n",
    "        i+=1\n",
    "    beginIndex = i\n",
    "    while tList[i]<endT:\n",
    "        i+=1\n",
    "    endIndex = i\n",
    "    tList = tList[beginIndex: endIndex]\n",
    "    rrList = rrList[beginIndex: endIndex]\n",
    "    return tList, rrList\n",
    "\n",
    "def plot(tList, rrList):\n",
    "    #plotting the graph\n",
    "    plt.plot(tList, rrList)\n",
    "    plt.xlabel('Time in Second')\n",
    "    plt.ylabel('RR Interval in Milisecond')\n",
    "    plt.title('Timve vs RR Interval')\n",
    "    plt.show()\n",
    "tList, rrList = constructTimeListFromRRList(rrList, 0, 50)\n",
    "total =0\n",
    "for r in rrList:\n",
    "    total+=r\n",
    "plot(tList, rrList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before this point, we have been using an arbitrary threshold for finding the RRIT from the Red color list. Next we run through all the logical possible options, and find out the optimal frame window that yields the greatest amount of realistic RRIT and least number of incorrectly identified RRIT."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R-Peaks Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the function detectPeak that returns a list of RRIT list depending on the parameter of frameThreshold. This way, we can reuse the code for different threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def detectPeak(colorL, frameThreshold):\n",
    "    peakL = list()\n",
    "    colorlistLen = len(colorL)\n",
    "    for i in range(1, colorlistLen-1):\n",
    "        biggerThanPrevious = colorL[i] > colorL[i-1]\n",
    "        biggerThanAfter = colorL[i] > colorL[i+1]\n",
    "        #if it is a local maximum, then check if it is a RR peak by comparing its maxima interval to frameThreshold\n",
    "        if biggerThanPrevious and biggerThanAfter:\n",
    "            for k in range(2, frameThreshold+1):\n",
    "                if i-k >= 0:\n",
    "                    if not colorL[i] > colorL[i-k]:\n",
    "                        #this is not a rr peak\n",
    "                        break\n",
    "                if i+k < colorlistLen:\n",
    "                    if not colorL[i] > colorL[i+k]:\n",
    "                        #this is not a rr peak\n",
    "                        break\n",
    "            else:\n",
    "                #this IS a rr peak, store the array index\n",
    "                peakL.append(i)\n",
    "    return peakL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We also improve upon some of my previous code to create a more readable notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def constructRRIntervalList(peakL):\n",
    "    return [ (peakL[i]-peakL[i-1])/fps for i in range(1, len(peakL))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a natural logical RRIT to range from .6 to 1.2 and some other helper function.\n",
    "\n",
    "$$\\epsilon^{(i)} = \\frac{\\text{# of wrong RR}}{\\text{# of RR}}$$\n",
    "\n",
    "This equation is checked by the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isTrueRRInterval(rrInterval):\n",
    "    if 0.54 <= rrInterval and rrInterval <= 1.2:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getPercentError(rrL):\n",
    "    errorNum = 0\n",
    "    for rrInterval in rrL:\n",
    "        if (not isTrueRRInterval(rrInterval)):\n",
    "            errorNum += 1\n",
    "    return 100.0*errorNum/len(rrL) if len(rrL) else 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All these helper functions culminate in the analyzeVideoAndFindPercentErrorList function, which encapsulates everything and return information about the PPG video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def analyzeVideoAndFindPercentErrorList(fileName):\n",
    "    rErrorL = []\n",
    "    gErrorL = []\n",
    "    bErrorL = []\n",
    "    rL, gL, bL = readFromFile(fileName)\n",
    "    for frameThreshold in range(1, 26):\n",
    "        rRRL = constructRRIntervalList(detectPeak(rL, frameThreshold))\n",
    "        gRRL = constructRRIntervalList(detectPeak(gL, frameThreshold))\n",
    "        bRRL = constructRRIntervalList(detectPeak(bL, frameThreshold))\n",
    "        rErrorL.append(getPercentError(rRRL))\n",
    "        gErrorL.append(getPercentError(gRRL))\n",
    "        bErrorL.append(getPercentError(bRRL))\n",
    "    return rErrorL, gErrorL, bErrorL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given this function, I could finally begin finding the optimal threshold window. First, this is the list of video that I used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "videoList = [\"/home/mong/Desktop/map/map/videos/VID_20150123_172926_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173259_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173329_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173358_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173457_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173556_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173629_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173700_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173757_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173028_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173727_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173527_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173230_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173157_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173127_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173057_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_172956_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_172827_F.mp4.txt\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterward, we process all these video, and find the general best threshold for each color. Red's lowest threshold tend to have the lowest percent error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at Data in a Statistical Standpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this, I began looking at the PPG signals at a statistical standpoint. I categorize PPG videos in beams of 50% errors, 25%, 10%..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/mong/Desktop/map/map/videos/VID_20150123_173629_F.mp4.txt|red|14     21.21212121212121\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173757_Q.mp4.txt|red|10     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173556_Q.mp4.txt|red|12     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173028_F.mp4.txt|red|8     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173527_F.mp4.txt|red|24     33.333333333333336\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173457_Q.mp4.txt|red|10     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173329_F.mp4.txt|red|10     53.57142857142857\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_172926_F.mp4.txt|red|14     2.5641025641025643\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173057_Q.mp4.txt|red|12     3.225806451612903\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173259_Q.mp4.txt|red|12     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173727_F.mp4.txt|red|18     14.285714285714286\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173358_Q.mp4.txt|red|14     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_172956_Q.mp4.txt|red|10     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173700_Q.mp4.txt|red|12     0.0\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173230_F.mp4.txt|red|14     4.761904761904762\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173127_F.mp4.txt|red|8     2.3255813953488373\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_172827_F.mp4.txt|red|10     2.3255813953488373\n",
      "/home/mong/Desktop/map/map/videos/VID_20150123_173157_Q.mp4.txt|red|12     0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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AL6JzCXnE8cDr6dzTODJzugKYU1WvG9XXJlV1R7P6MuD6UdsPBL5UVXc356rm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d674e5a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rAvgErrorL = [0 for i in range(0, 25)]\n",
    "gAvgErrorL = [0 for i in range(0, 25)]\n",
    "bAvgErrorL = [0 for i in range(0, 25)]\n",
    "vidPPGL = {}\n",
    "for video in videoList:\n",
    "    rErrorL, gErrorL, bErrorL = analyzeVideoAndFindPercentErrorList(video)\n",
    "    vidPPGL[video+'|red|'+str(rErrorL.index(min(rErrorL))*2+2)] = min(rErrorL)\n",
    "def isInRange(begin, end, value):\n",
    "    return begin <= value and value < end\n",
    "def numOfVidInBeam(begin, end):\n",
    "    num = 0\n",
    "    for vidName in vidPPGL:\n",
    "        if isInRange(begin, end, vidPPGL[vidName]):\n",
    "            num += 1\n",
    "    return num\n",
    "probabilityArray = [numOfVidInBeam(0,3),\n",
    "                    numOfVidInBeam(3,5),\n",
    "                    numOfVidInBeam(5,10),\n",
    "                    numOfVidInBeam(10,15),\n",
    "                    numOfVidInBeam(15,25),\n",
    "                    numOfVidInBeam(25,101)]\n",
    "for v in vidPPGL:\n",
    "    print(v, '   ',vidPPGL[v])\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "beams = ('0%-3%', '3%-5%', '5%-10%', '10-15%', '15-25%','25%-100%')\n",
    "y_pos = numpy.arange(len(beams))\n",
    "\n",
    "plt.barh(y_pos, probabilityArray,align='center', alpha=0.4)\n",
    "plt.yticks(y_pos, beams)\n",
    "plt.xlabel('Number of Video')\n",
    "plt.ylabel('Percent Error')\n",
    "plt.xlim(0, len(videoList))\n",
    "plt.title('Percent Errors in 4 Beams')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that while a few videos are noisy, most are suitable for RRITS calculation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Alignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After all thes steps, I finally have both the ECG and the PPG signal in the form of a list of RRIT. I then began aligning the signals, that is, finding which part of the either ECG signal correspond to each PPG signal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before we do this, however..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We realize that we need to increase code reusability for enhancing performance and readability. Therefore, we began putting previous codes and rewriting them for better readability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we create a class called RRIT (RR interval). It contains one rr interval value and its corresponding time in a signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class RRIT:\n",
    "    def __init__(self, time, value):\n",
    "        self.value = value\n",
    "        self.time = time\n",
    "    def __str__(self):\n",
    "        return '{0:<6.2f} {1:<4}'.format(self.time, self.value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define a class called RRSignal extending list that holds many RRITS. That is, it is a special list of RRIT. The object of this class is supposed to record all the information known about the signal.\n",
    "\n",
    "For alignment, there are 3 main functions: interpolate and align. \n",
    "\n",
    "\"Interpolate\" takes in a time, and returns the mapped RR value on this signal.\n",
    "\n",
    "\"Align\" takes in two signal, one from ecg and one from ppg, and interpolates each RRIT from the ppg onto the ecg. It returns a total alignment distance.\n",
    "\n",
    "\"AlignMultiple\" builds upon \"align\" and align a ppg's RRIT signal with multiple ecg, and return the one with minimal alignment distance, that is, the ecg signal the ppg mostly likely to be originated from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def withinRange(begin, endExclude, value):\n",
    "    return begin <= value and value < endExclude\n",
    "\n",
    "class RRSignal(list):\n",
    "    def __init__(self, rrValueList, name, color=None, threshold=None, percentError=None):\n",
    "        list.__init__(self)\n",
    "        self.constructRRITList(rrValueList)\n",
    "        self.name = name\n",
    "        self.color = color\n",
    "        self.threshold = threshold\n",
    "        self.percentError = percentError\n",
    "    def getRRITList(self):\n",
    "        return [RRIT.value for RRIT in self]\n",
    "    def getTimeList(self):\n",
    "        return [RRIT.time for RRIT in self]\n",
    "    def duration(self, beginIndex=-1, endIndex=-1):\n",
    "        beginT = self[beginIndex].time if withinRange(0, len(self), beginIndex) else self[0].time\n",
    "        endT = self[endIndex-1].time if withinRange(0, len(self), endIndex-1) else self[-1].time\n",
    "        return endT - beginT\n",
    "    #this function is only used internally\n",
    "    #create a corresponding RRIT list from the rr value list\n",
    "    #rr value in ms; time in second\n",
    "    def constructRRITList(self, rrValueList):\n",
    "        firstRRValue = round(rrValueList[0]*1000)\n",
    "        self.append(RRIT(0, firstRRValue))\n",
    "        pastTime = 0\n",
    "        for i in range(1, len(rrValueList)):\n",
    "            curTime = pastTime + rrValueList[i-1]\n",
    "            curRRValue = round(rrValueList[i]*1000)\n",
    "            curRRIT = RRIT(curTime, curRRValue)\n",
    "            self.append(curRRIT)\n",
    "            pastTime = curTime\n",
    "    def interpolate(self, targetTime, indexOffset=0):\n",
    "        targetTime = self[indexOffset].time + targetTime\n",
    "        #targetTime is not in range of array\n",
    "        if targetTime < self[0].time or self[-1].time < targetTime:\n",
    "            return -1\n",
    "        #if targetTime is in range, locate its position on this set\n",
    "        leftIndex = -1\n",
    "        for i in range(len(self)):\n",
    "            if targetTime < self[i].time:\n",
    "                leftIndex = i-1\n",
    "                break\n",
    "        leftRRIT = self[leftIndex]\n",
    "        rightRRIT = self[leftIndex+1]\n",
    "        slope = (rightRRIT.value - leftRRIT.value) / (rightRRIT.time - leftRRIT.time)\n",
    "        deltaTime = targetTime - leftRRIT.time\n",
    "        deltaValue = deltaTime * slope\n",
    "        return leftRRIT.value + deltaValue\n",
    "    def align(ppg, ecg):\n",
    "        minAlignmentDistance = 100000000\n",
    "        bestFitTime = None\n",
    "        for i in range(len(ecg)):\n",
    "            if ppg.duration() > ecg.duration(beginIndex=i):\n",
    "                break\n",
    "            alignmentDistance = 0\n",
    "            for ppgRRIT in ppg:\n",
    "                interpolatedValue = ecg.interpolate(ppgRRIT.time, indexOffset=i)\n",
    "                alignmentDistance += abs(interpolatedValue - ppgRRIT.value)\n",
    "            if alignmentDistance < minAlignmentDistance:\n",
    "                minAlignmentDistance = alignmentDistance\n",
    "                bestFitTime = ecg[i].time\n",
    "        return bestFitTime, minAlignmentDistance\n",
    "    def alignMultiple(ppg, *ecgSignalList):\n",
    "        minAlignmentDistance = 1000000\n",
    "        bestFitTime = None\n",
    "        bestFitEcg = None\n",
    "        for ecg in ecgSignalList:\n",
    "            alignmentTime, alignmentDistance = RRSignal.align(ppg, ecg)\n",
    "            if alignmentDistance < minAlignmentDistance:\n",
    "                minAlignmentDistance = alignmentDistance\n",
    "                bestFitTime = alignmentTime\n",
    "                bestFitEcg = ecg\n",
    "        return bestFitEcg, bestFitTime, minAlignmentDistance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following functions (mostly previous functions rewritten) are used in translating the rgb values of txt files to a list of RRIT. It culminates in the function \"analyzeVideoAndObtainBestRedRRInterval.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def readFromFile(fileName):\n",
    "    l =numpy.loadtxt(fileName)\n",
    "    return l[0], l[1], l[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def detectPeak(colorL, frameThreshold):\n",
    "    peakL = list()\n",
    "    colorlistLen = len(colorL)\n",
    "    for i in range(1, colorlistLen-1):\n",
    "        biggerThanPrevious = colorL[i] > colorL[i-1]\n",
    "        biggerThanAfter = colorL[i] > colorL[i+1]\n",
    "        #if it is a local maximum, then check if it is a RR peak by comparing its maxima interval to frameThreshold\n",
    "        if biggerThanPrevious and biggerThanAfter:\n",
    "            for k in range(2, frameThreshold+1):\n",
    "                if i-k >= 0:\n",
    "                    if not colorL[i] > colorL[i-k]:\n",
    "                        #this is not a rr peak\n",
    "                        break\n",
    "                if i+k < colorlistLen:\n",
    "                    if not colorL[i] > colorL[i+k]:\n",
    "                        #this is not a rr peak\n",
    "                        break\n",
    "            else:\n",
    "                #this IS a rr peak, store the array index\n",
    "                peakL.append(i)\n",
    "    return peakL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def constructRRIntervalList(peakL):\n",
    "    return [ (peakL[i]-peakL[i-1])/fps for i in range(1, len(peakL))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isTrueRRInterval(rrInterval):\n",
    "    if 0.54 <= rrInterval and rrInterval <= 1.2:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getPercentError(rrL):\n",
    "    errorNum = 0\n",
    "    for rrInterval in rrL:\n",
    "        if not isTrueRRInterval(rrInterval):\n",
    "            errorNum += 1\n",
    "    return 100.0*errorNum/len(rrL) if len(rrL) else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def analyzeVideoAndObtainBestRedRRInterval(fileName):\n",
    "    rErrorL = []\n",
    "    rL, gL, bL = readFromFile(fileName)\n",
    "    bestRRL = None\n",
    "    bestPercentErrorSoFar = 101\n",
    "    bestFrameThreshold = None\n",
    "    lowestPercentError = None\n",
    "    for frameThreshold in range(1, 26):\n",
    "        rRRL = constructRRIntervalList(detectPeak(rL, frameThreshold))\n",
    "        percentError = getPercentError(rRRL)\n",
    "        if percentError < bestPercentErrorSoFar:\n",
    "            bestPercentErrorSoFar = percentError\n",
    "            bestRRL = rRRL\n",
    "            bestFrameThreshold = frameThreshold*2\n",
    "            lowestPercentError = percentError\n",
    "    return bestRRL, lowestPercentError, bestFrameThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convertStrLToSecFloatL(array):\n",
    "    for i in range(len(array)):\n",
    "        array[i] = float(array[i])/1000.0\n",
    "    return array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Begin the alignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these tools, we can finally begin the alignment. First, we process the 2 ECG signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#first hrm file\n",
    "f = open('/home/mong/Desktop/map/map/videos/15012301_GIO.hrm', 'r')\n",
    "inputSectionList = f.read().split('\\n\\n')\n",
    "rrSection = inputSectionList[-1]\n",
    "rrData = rrSection.split('\\n')\n",
    "#get rid of header\n",
    "ecg1 = rrData[1:-1]\n",
    "ecg1 = convertStrLToSecFloatL (ecg1)\n",
    "ecg1 = RRSignal(ecg1, '15012301_GIO.hrm ecg1')\n",
    "\n",
    "#second hrm file\n",
    "f = open('/home/mong/Desktop/map/map/videos/15012301_GLO.hrm', 'r')\n",
    "inputSectionList = f.read().split('\\n\\n')\n",
    "rrSection = inputSectionList[-2]\n",
    "rrData = rrSection.split('\\n')\n",
    "#get rid of header\n",
    "ecg2 = rrData[1:]\n",
    "ecg2 = convertStrLToSecFloatL (ecg2)\n",
    "ecg2 = RRSignal(ecg2, '15012301_GLO.hrm ecg2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "videoList = [\"/home/mong/Desktop/map/map/videos/VID_20150123_172926_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173259_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173329_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173358_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/new_VID_20150123_173427_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173457_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173556_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173629_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173700_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173757_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173028_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/new_VID_20150123_172857_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173727_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173527_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173230_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173157_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173127_F.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_173057_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_172956_Q.mp4.txt\",\n",
    "                \"/home/mong/Desktop/map/map/videos/VID_20150123_172827_F.mp4.txt\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we align each ppg with each two ecg. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aligning VID_20150123_172926_F.mp4.txt\n",
      "Aligning VID_20150123_173259_Q.mp4.txt\n",
      "Aligning VID_20150123_173329_F.mp4.txt\n",
      "Aligning VID_20150123_173358_Q.mp4.txt\n",
      "Aligning new_VID_20150123_173427_F.mp4.txt\n",
      "Aligning VID_20150123_173457_Q.mp4.txt\n",
      "Aligning VID_20150123_173556_Q.mp4.txt\n",
      "Aligning VID_20150123_173629_F.mp4.txt\n",
      "Aligning VID_20150123_173700_Q.mp4.txt\n",
      "Aligning VID_20150123_173757_Q.mp4.txt\n",
      "Aligning VID_20150123_173028_F.mp4.txt\n",
      "Aligning new_VID_20150123_172857_Q.mp4.txt\n",
      "Aligning VID_20150123_173727_F.mp4.txt\n",
      "Aligning VID_20150123_173527_F.mp4.txt\n",
      "Aligning VID_20150123_173230_F.mp4.txt\n",
      "Aligning VID_20150123_173157_Q.mp4.txt\n",
      "Aligning VID_20150123_173127_F.mp4.txt\n",
      "Aligning VID_20150123_173057_Q.mp4.txt\n",
      "Aligning VID_20150123_172956_Q.mp4.txt\n",
      "Aligning VID_20150123_172827_F.mp4.txt\n"
     ]
    }
   ],
   "source": [
    "ecg1Result = []\n",
    "ecg2Result = []\n",
    "for vidName in videoList:\n",
    "    print(\"Aligning\",os.path.basename(vidName))\n",
    "    ppgRRSignal, percentError, threshold = analyzeVideoAndObtainBestRedRRInterval(vidName)\n",
    "    ppgRRSignal = RRSignal(ppgRRSignal, os.path.basename(vidName), color='red', percentError=percentError, threshold=threshold)\n",
    "    bestFitEcg, bestFitTime, distance = RRSignal.alignMultiple(ppgRRSignal, ecg1, ecg2)\n",
    "    if bestFitEcg.name == '15012301_GIO.hrm ecg1':\n",
    "        ecg1Result.append((bestFitTime, distance, ppgRRSignal))\n",
    "    else:\n",
    "        ecg2Result.append((bestFitTime, distance, ppgRRSignal))\n",
    "ecg1Result = sorted(ecg1Result, key=lambda ppg: ppg[0])\n",
    "ecg2Result = sorted(ecg2Result, key=lambda ppg: ppg[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result of the Alignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15012301_GIO.hrm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At t= 26.8 to  55.2 is new_VID_20150123_172857_Q.mp4.txt with %error of  0.00 with distance of 953.6\n",
      "At t= 84.7 to 112.0 is     VID_20150123_172956_Q.mp4.txt with %error of  0.00 with distance of 843.4\n",
      "At t=145.3 to 173.4 is     VID_20150123_173057_Q.mp4.txt with %error of  3.23 with distance of 1173.6\n",
      "At t=205.4 to 232.8 is     VID_20150123_173157_Q.mp4.txt with %error of  0.00 with distance of 556.2\n",
      "At t=267.3 to 294.5 is     VID_20150123_173259_Q.mp4.txt with %error of  0.00 with distance of 372.0\n",
      "At t=278.9 to 306.0 is     VID_20150123_173527_F.mp4.txt with %error of 33.33 with distance of 10283.3\n",
      "At t=326.3 to 353.1 is     VID_20150123_173358_Q.mp4.txt with %error of  0.00 with distance of 563.8\n",
      "At t=349.4 to 377.3 is     VID_20150123_173329_F.mp4.txt with %error of 53.57 with distance of 13860.6\n",
      "At t=384.9 to 412.9 is     VID_20150123_173457_Q.mp4.txt with %error of  0.00 with distance of 564.6\n",
      "At t=444.3 to 472.7 is     VID_20150123_173556_Q.mp4.txt with %error of  0.00 with distance of 333.2\n",
      "At t=507.6 to 534.4 is     VID_20150123_173700_Q.mp4.txt with %error of  0.00 with distance of 514.1\n",
      "At t=565.2 to 592.8 is     VID_20150123_173757_Q.mp4.txt with %error of  0.00 with distance of 622.6\n"
     ]
    }
   ],
   "source": [
    "for alignment in ecg1Result:\n",
    "    s = 'At t={0:>5.1f} to {1:>5.1f} is {2:>33} with %error of {3:>5.2f} with distance of {4:>4.1f}'.format(\n",
    "        alignment[0],\n",
    "        alignment[0]+alignment[2].duration(),\n",
    "        alignment[2].name,\n",
    "        alignment[2].percentError,\n",
    "        alignment[1])\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15012301_GLO.hrm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At t= 36.6 to  65.1 is     VID_20150123_172827_F.mp4.txt with %error of  2.33 with distance of 1553.4\n",
      "At t= 96.2 to 122.8 is     VID_20150123_172926_F.mp4.txt with %error of  2.56 with distance of 1304.5\n",
      "At t=157.0 to 184.7 is     VID_20150123_173028_F.mp4.txt with %error of  0.00 with distance of 794.4\n",
      "At t=161.9 to 191.2 is new_VID_20150123_173427_F.mp4.txt with %error of 59.38 with distance of 17719.0\n",
      "At t=216.3 to 246.1 is     VID_20150123_173127_F.mp4.txt with %error of  2.33 with distance of 1440.6\n",
      "At t=278.9 to 307.2 is     VID_20150123_173230_F.mp4.txt with %error of  4.76 with distance of 1237.2\n",
      "At t=448.4 to 472.8 is     VID_20150123_173727_F.mp4.txt with %error of 14.29 with distance of 6158.1\n",
      "At t=519.4 to 546.8 is     VID_20150123_173629_F.mp4.txt with %error of 21.21 with distance of 7407.8\n"
     ]
    }
   ],
   "source": [
    "for alignment in ecg2Result:\n",
    "    s = 'At t={0:>5.1f} to {1:>5.1f} is {2:>33} with %error of {3:>5.2f} with distance of {4:>4.1f}'.format(\n",
    "        alignment[0],\n",
    "        alignment[0]+alignment[2].duration(),\n",
    "        alignment[2].name,\n",
    "        alignment[2].percentError,\n",
    "        alignment[1])\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual Confirmation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create graphical confirmation of the alignment. For each PPG's RRITS, we graph its supposedly corresponding RRITS on either ECG signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def constructTimeListFromRRInterval(rrList):\n",
    "    #construct time array from the rr intervals\n",
    "    tList = []\n",
    "    tList.append(float(rrList[0]))\n",
    "    for i in range(1, len(rrList)):\n",
    "        tList.append(tList[-1] + float(rrList[i]))\n",
    "    return tList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15012301_GIO.hrm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_VID_20150123_172857_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  953.6457622890721\n"
     ]
    },
    {
     "data": {
      "image/png": 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dl0w4/fSynMYSS5RB/h/5iMFNktSd5trylpmTACLii8CmmTmrdv9/gT814Nqj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6719d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ik0NZZkOSJEl9G8gsztuBj2bm/dX1TYBfZebWw1DfUrMFTZIktYuhzOJcvjuc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\nJNWMAU2SJKlm/j+zWMkI3nbiEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6725f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_172956_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  843.4060107566347\n"
     ]
    },
    {
     "data": {
      "image/png": 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ieJMkSaoQw5skSVKFGN4kSZIqxPAmSZJUIYY3SZKkCjG8SZIkVYjh\nTZIkqUIMb5IkSRVieJMkSaoQw5skSVKFGN4kSZIqxPAmSZJUIYY3SZKkCjG8SZIkVUhLwltErBQR\nf4qIeyNickQcXDu+VESMi4gHIuKqiFiyy3OOiogHI+L+iPhkK+qWJElqtcjM5l80YgVghcycGBGL\nA3cAOwL7AP/IzBMj4ghgqcw8MiLWA34ObAqsBFwNrJndFB8R3R2WJElqOxFBZkZ/ntOSlrfMfDoz\nJ9ZuTwfup4SyHYGxtYeNBXaq3d4BuCgzZ2bmo8CDwGZNLVqSJKkNtHzMW0SsBmwE3Awsn5lToQQ8\nYLnaw1YEHu/ytCdrxyRJkoaVloa3Wpfpr4BDai1wc/d32v8pSZLUxQKtunBELEAJbudn5m9rh6dG\nxPKZObU2Lu6Z2vEngZW7PH2l2rFujRkz5s3bI0eOZOTIkXWsXJIkaWA6Ojro6OgY1DlaMmEBICLO\nA57LzMO6HDsBeD4zT+hhwsLmlO7S8ThhQZIkVdxAJiy0arbpVsB1wGRK12gCo4BbgYsprWyPAbtm\n5j9rzzkK2A94g9LNOq6HcxveJElSJVQmvDWS4U2SJFVFZZYKkSRJ0sAY3iRJkirE8CZJklQhhjdJ\nkqQKMbxJkiRViOFNkiSpQgxvkiRJFWJ4kyRJqhDDmyRJUoUY3iRJkirE8CZJklQhhjdJkqQKMbxJ\nkiRViOFNkiSpQgxvkiRJFWJ4kyRJqhDDmyRJUoUY3iRJkirE8CZJklQhhjdJkqQKMbxJkiRViOFN\nkiSpQgxvkiRJFWJ4kyRJqhDDmyRJUoUY3iRJkirE8CZJklQhhjdJkqQKMbxJkiRViOFNkiSpQgxv\nkiRJFWJ4kyRJqhDDmyRJUoUY3iRJkirE8CZJklQhhjdJkqQKMbxJkiRViOFNkiSpQgxvkiRJFWJ4\nkyRJqhDDmyRJUoUY3iRJkirE8CZJklQhhjdJkqQKMbxJkiRViOFNkiSpQgxvkiRJFVKp8BYR20bE\nlIj4S0Qc0ep6JEmSmq0y4S0i5gN+DHwKWB/YIyLWaW1VAujo6Gh1CcOOr3nz+Zo3n6958/maV0Nl\nwhuwGfBgZj6WmW8AFwE7trgm4f/sreBr3ny+5s3na958vubVUKXwtiLweJf7T9SOSZIkDRtVCm+S\nJEnDXmRmq2vok4jYAhiTmdvW7h8JZGaeMNfjqvEDSZIkAZkZ/Xl8lcLb/MADwMeBp4BbgT0y8/6W\nFiZJktREC7S6gL7KzFkR8TVgHKW79xyDmyRJGm4q0/ImSZKkITRhwQV8my8iHo2IuyPiroi4tdX1\nDEURcU5ETI2ISV2OLRUR4yLigYi4KiKWbGWNQ00Pr/noiHgiIu6sfW3byhqHmohYKSL+FBH3RsTk\niDi4dtz3eoN085p/vXbc93qDRMTbIuKW2t/MyRExuna83+/zIdHyVlvA9y+U8XB/B24Dds/MKS0t\nbIiLiIeBTTLzhVbXMlRFxNbAdOC8zNywduwE4B+ZeWLtg8pSmXlkK+scSnp4zUcD0zLzlJYWN0RF\nxArACpk5MSIWB+6grOO5D77XG6KX13w3fK83TEQsmpkzauP4bwAOBj5PP9/nQ6XlzQV8WyMYOu+h\ntpSZ1wNzh+MdgbG122OBnZpa1BDXw2sO5f2uBsjMpzNzYu32dOB+YCV8rzdMD69559qpvtcbJDNn\n1G6+jTLvIBnA+3yo/OF1Ad/WSGB8RNwWEQe0uphhZLnMnArlFzCwXIvrGS6+FhETI+Jsu+8aJyJW\nAzYCbgaW973eeF1e81tqh3yvN0hEzBcRdwFPA+Mz8zYG8D4fKuFNrbFVZm4MbA8cVOtuUvNVf+xD\n+zsNeHdmbkT5pWuXUgPUuu9+BRxSaw2a+73te73OunnNfa83UGbOzsz3U1qWN4uI9RnA+3yohLcn\ngVW63F+pdkwNlJlP1f59FriM0n2txpsaEcvDm+NWnmlxPUNeZj6bcwYInwVs2sp6hqKIWIASIs7P\nzN/WDvteb6DuXnPf682RmS8BHcC2DOB9PlTC223AeyJi1YhYCNgduLzFNQ1pEbFo7RMbEbEY8Eng\nntZWNWQFbx2DcjnwpdrtLwK/nfsJGrS3vOa1X6iddsb3eiP8FLgvM/+nyzHf6431L6+57/XGiYhl\nOruhI2IRYBvKWMN+v8+HxGxTKEuFAP/DnAV8j29xSUNaRKxOaW1LyqDLn/ua119E/AIYCSwNTAVG\nA78BLgFWBh4Dds3Mf7aqxqGmh9f8o5QxQbOBR4GvdI5R0eBFxFbAdcBkyu+UBEZRdtK5GN/rddfL\na74nvtcbIiLeS5mQMF/t65eZeUxEvIN+vs+HTHiTJEkaDoZKt6kkSdKwYHiTJEmqEMObJElShRje\nJEmSKsTwJkmSVCGGN0mSpAoxvElqGxHxjoi4KyLujIinIuKJ2u27IuL6Blxvk4j4UT+fs29ETIqI\nu2v/frbedfWhhlUjYnKzryupPbjOm6S2FBHfA6ZnZtvsrRgRKwLXAhtl5vSIWBRYNjMfa3IdqwK/\ny8wNm3ldSe3BljdJ7SrecidiWu3fj0RER0T8JiL+GhHHRcSeEXFLrTVs9drjlomIX9WO3xIRH/yX\nC5Rz/a52e3REnBMR19TO+/VualoOeAmYAZCZMzqDW0S8OyL+GBG3RcS1EbFW7fhyEXFpREystSBu\nUTt+WERMrrXeHVI7tmpE3BcRZ0bEPRFxZUS8rfa9TTrPARxUjxdYUjUZ3iRVRddugg2BLwPrAf8O\nrJmZmwPnAJ2h63+AU2rHdwHO7sN516bsN7g5MDoi5p/rsXdTNo1+JCJ+GhGf6fK9M4GvZeamwOHA\nT2rH/xfoyMyNgI2BeyNiY8oehpsCWwIHRMT7ao9/D3BqZm4AvAh8vnb8p8BBmfn+Hn4OScPEAq0u\nQJIG4LbMfAYgIh4CxtWOT6bsSwrwCWDdiOhswVs8IhbNzBm9nPcPmTkT+EdETAWWB/7e+c3MnA1s\nGxEfAD4OnFILYicDHwQu6XK9BWv/fowSMMkyTmVaRGwNXJaZr9Z+hkuBDwG/Ax7JzM7xbHcAq9U2\ns14yM2+oHT8f2LYvL5SkocfwJqmKXutye3aX+7OZ83stgM0z841BnLfb35GZeTtwe0RcTWkR+yHw\nQmZu3N3D+3H9uWuYBSxcux3dPFbSMGS3qaSq6G94GQcc8uaT53RLDryAiHdGRNduy/cDj2XmNEpX\n6i5dHts5mWAC8NXasfkiYgTwZ2CniFg4IhYDPlc7Bt38nJn5IvBCl3F7ew32Z5FUXYY3SVXRUwtW\nT8cPAT5Qm8RwD/CVOlxvQeAHtUkFdwL/xpyAuBewX21SwT3ADrXjhwIfjYhJwO3Aupl5F/Az4Dbg\nJuDMzLx7Hj/PvsBptetKGsZcKkSSJKlCbHmTJEmqEMObJElShRjeJEmSKsTwJkmSVCGGN0mSpAox\nvEmSJFWI4U2SJKlCDG+SJEkV8v/duw/qA+TcWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d693b0be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p88WAge8kIEmSpJ71JaBdXN0kSZI0CPq6inNxYN3q4b2ZObOprRoA56BJkqR2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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6716dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173057_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 3.225806451612903\n",
      "Alignment Distance:  1173.631978322339\n"
     ]
    },
    {
     "data": {
      "image/png": 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JkjpNPbNNV+oNbgCZeS8VmX1qeJMkSZ2mnpa3myLidOD/1W5/ALipeSU1juFN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\nJkmSVCH/H5KiUZxKH7tBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6714bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p7u4e1GMj85UbsyJiDKVV7KWQl5lXDeoVy/NtB5wGbAvMBE4HrgPWBp7KzGMj\n4ivASpl5WDUR4WxKcBwD/B7YMPsoPiL6OixJkjTiRASZ2dDkyldsaYuIY4G9gb9TlueA0m056NCW\nmddGxM+Bm4DZ1deTgeWB8yLiQOAByoxRMnNSRJwHTKruf7DJTJIkjSav2NIWEXcBm2fmzOEpaWhs\naZMkSXUxkJa2RmaP3gcsObSSJEmSNBSNzB6dDtwcEX+gjD8Dhr4jgiRJkhrXSGi7uLpIkiSpTRqd\nPboUsFF1867MnN3SqobAMW2SJKkumj17tIuypdT9lMV114qIA4ay5IckSZIGppHZozcA+2XmXdXt\njYCfZebWw1DfgNnSJkmS6qLZs0eXnBfYADLzbpxNKkmSNKwamYhwfUScCpxV3d4fuL51JUmSJGlh\njXSPvgo4BNipOvQn4KSRutiu3aOSJKkuBtI92khoWw54MTN7qtuLA6/KzOlDrrQFDG2SJKkumj2m\n7Q/AMr1uLwNcPpjCJEmSNDiNhLalM/P5eTeq68u2riRJkiQtrJHQ9kJEbDXvRkRsDcxoXUmSJEla\nWCOzRz8PnB8Rj1AW110d2LulVUmSJGkBjW5jtSTwxuqm21hJkiQ1QVMnIkTEssBXgEMz83Zg3YjY\nbYg1SpIkaQAaGdN2OjALeGt1ewrwjZZVJEmSpJdpJLRtkJnHAbMBqvXZGmrGkyRJUnM0EtpmRcQy\nQAJExAbAiNwNQZIkqVM1Mnv0COBSYK2IOBt4G/DxVhYlSZKkBTU6e3RlYAdKt+hfM/OJVhc2WM4e\nlSRJddHs2aNvo+w9egnwGuDwiFhniDVKkiRpABoZ0/ZDYHpEvAX4D+Be4KctrUqSJEkLaCS0zan6\nG8cCJ2bmicDyrS1LkiRJvTUyEeG5iBgPfBR4R0QsBizZ2rIkSZLUWyMtbXtTlvg4KDMfA9YEvtXS\nqiRJkrSAhmaP1omzRyVJUl0MZPboIrtHI+LqzNwpIp6jWlh33reAzMwVhlinJEmSGmRLmyRJUps0\nq6Xttf09MDOfGmhhkiRJGpz+Zo/eQOkW7Sv9JbB+SyqSJEnSy9g9KkmS1CbN6h7dODPvjIit+vp+\nZt442AIlSZI0MItsaYuIUzLzkxFxZR/fzszcubWlDY4tbZIkqS4G0tJm96gkSVKbNKt79MP9PTAz\nLxhoYZIkSRqc/rpH5wI3VxdYcBZpZuaBLa5tUGxpkyRJddGUljbgw8A+wObARcDPMvOeJtQnSZKk\nAXrFMW0WDTwBAAAS20lEQVQRsRwwlrJx/MrAVzPzj8NQ26DY0iZJkupiIC1tizVwnxeBZ4BngVcD\nSw+hNkmSJA1Cf2PadqZ0j24HXA5MzMzrh7G2QbGlTZIk1UVTlvyoJiLcClxN2bZqgTtm5ueGWGdL\nGNokSVJdNGsiwieaVI8kSZKGyMV1JUmS2qTZExEkSZLUZoY2SZKkGjC0SZIk1UB/ExEAiIhVgE8C\n6/a+/1C3sYqIFYFTgTcDc4EDgbuBc4F1gPuBvTLzmer+46v7zAEOzczLhvL6kiRJddLIjgh/Af4E\n3AD0zDuemb8Y0gtH/AT4Y2aeHhFLAMsBhwNPZuZxEfEVYKXMPCwiNgXOBrYF1qSsG7dhXzMOnIgg\nSZLqoinrtPV6spszc4umVDb/OVcAbsrMDRY6fifwzsycGhGrA92ZuXFEHEbZpP7Y6n6/BSZk5t/6\neG5DmyRJqoVmzx79dUS8f4g1LWw94ImIOD0iboyIkyNiWWC1zJwKkJmPAatW9x8DPNTr8VOqY5Ik\nSaPCK45pAw4FDo+ImcBsICitXisM8XW3Ag7JzOsj4rvAYSy060IftxsyYcKEl653dXXR1dU1uCol\nSZKaqLu7m+7u7kE9ti2L60bEasA1mbl+dXsnSmjbAOjq1T16ZWZu0kf36KXAEXaPSpKkOmtK92hE\nbFx93aqvy1AKrLpAH4qIjapD7wb+DlwMfLw6dgBwUXX9YmCfiFgqItYD3gBcO5QaJEmS6qS/DeNP\nzsx/j4gr+/h2ZubOQ3rhiLdQlvxYEriPstfp4sB5wFrAA5QlP56u7j8eOIjSRbvIJT9saZMkSXXR\n1NmjdWNokyRJdeHeo5IkSR3G0CZJklQDhjZJkqQaWOQ6ba80QzQzb2x+OZIkSepLf7NH+5o1Os+Q\nZ4+2ihMRJElSXTh7tMPekyRJ6kwDCW2NbGNFRLwZ2BRYet6xzPzp4MqTJEnSQL1iaIuII4AuSmj7\nDfA+4GrA0CZJkjRMGpk9ugdlm6nHMvMTwFuAFVtalSRJkhbQSGibkZlzgTkRsQLwOGWbKUmSJA2T\nRsa0XR8RrwFOAW4AngeuaWlVkiRJWsCAZo9GxLrACpl5a6sKGipnj0qSpLpo6t6jEXFxROwXEctl\n5v0jObBJkiR1qkbGtB0P7ARMioifR8QeEbH0Kz1IkiRJzdNw92hELA7sDHwS2DUzV2hlYYNl96gk\nSaqLViyuuwzwQWBvYCvgjMGXJ0mSpIF6xZa2iDgP2A64FDgX+GO1BMiIZEubJEmqi6a1tEXEYsDN\nwL6Z2dOM4iRJkjRw/U5EqFrU9jSwSZIktVcjs0f/EBEfiYiGmu4kSZLUfI2MaXsOWA7oAWYAAaSz\nRyVJkoamqbNHM3P5oZckSZKkoWhkR4SIiI9GxH9Vt9eKiO1aX5okSZLmaWRM20nAW4H9qtvPAye2\nrCJJkiS9TCOL626fmVtFxE0AmTktIpZqcV2SJEnqpZGWttnVFlYJEBGrACN2cV1JkqRO1Eho+x7w\nS2DViDgKuBo4uqVVSZIkaQENbRgfERsD76Ys9/GHzLyj1YUNlkt+SJKkuhjIkh+NrNN2ZmZ+7JWO\njRSGNkmSVBcDCW2NdI++aaEnXxzYejCFSZIkaXAWGdoiYny1G8LmEfFsRDxX3X4cuGjYKpQkSVJD\n3aNHZ+b4YapnyOwelSRJddHUMW3VE44B1qHXum6ZedWgK2whQ5skSaqLpu49GhHHAPsAkyibxkNZ\ns21EhjZJkqRO1Ej36F3A5pk5c3hKGhpb2iRJUl00e/bofcCSQytJkiRJQ9HI3qPTgZsj4g/AS61t\nmfm5llUlSZKkBTQS2i6uLpIkSWqThmaP1olj2iRJUl00ZfZoRNxGmSXap8zcfBC1SZIkaRD66x7d\nbdiqkCRJUr/sHpUkSWqTZi/5IUmSpDYztEmSJNXAoEJbRLyt2YVIkiRp0fqbPbo4sBcwBrg0M2+P\niN2Aw4FlgC2Hp0RJkiT119J2GvBvwMrA9yLiLODbwHGZ2ZTAFhGLRcSNEXFxdXuliLgsIu6KiN9F\nxIq97js+IiZHxB0RsUszXl+SJKkuFjl7NCJup2wUPzcilgYeAzbIzCeb9uIRXwC2BlbIzN0j4ljg\nycw8LiK+AqyUmYdFxKbA2cC2wJrA5cCGfU0TdfaoJEmqi2bNHp2VmXMBMvNF4L4mB7Y1gfcDp/Y6\nPBY4o7p+BjCuur47MDEz52Tm/cBkYLtm1SJJkjTS9be47sYRcWt1PYANqtsBZBN2RPgu8CVgxV7H\nVsvMqZQXeCwiVq2OjwGu6XW/KdUxSZKkUaG/0LZJq140Ij4ATM3MmyOiq5+72s8pSZJEP6EtMx/o\n63hELAbsC/T5/Qa9Ddg9It5PmYm6fEScCTwWEatl5tSIWB14vLr/FGCtXo9fszrWpwkTJrx0vaur\ni66uriGUKkmS1Bzd3d10d3cP6rH9TURYATiE0g15MfB74DPAF4FbMnPsoF7x5a/zTuCL1USE4ygT\nEY5dxESE7at6fo8TESRJUs0NZCJCf92jZwLTKGPJ/o2yPlsA4zLz5iFX2bdjgPMi4kBKS95eAJk5\nKSLOAyYBs4GDTWaSJGk06a+l7bbM3Ky6vjjwKLB2NZN0xLKlTZIk1UWzlvyYPe9KZvYAD4/0wCZJ\nktSp+mtp6wFemHeTMmFgOvOX/FhhWCocIFvaJElSXTRlTFtmLt68kiRJkjQU/XWPSpIkaYQwtEmS\nJNWAoU2SJKkGDG2SJEk1YGiTJEmqAUObJElSDRjaJEmSasDQJkmSVAOGNkmSpBowtEmSJNWAoU2S\nJKkGDG2SJEk1YGiTJEmqAUObJElSDRjaJEmSasDQJkmSVAOGNkmSpBowtEmSJNWAoU2SJKkGDG2S\nJEk1YGiTJEmqAUObJElSDRjaJEmSasDQJkmSVAOGNkmSpBowtEmSJNWAoU2SJKkGDG2SJEk1YGiT\nJEmqAUObJElSDRjaJEmSasDQJkmSVAOGNkmSpBowtEmSJNWAoU2SJKkGDG2SJEk1YGiTJEmqAUOb\nJElSDRjaJEmSasDQJkmSVAOGNkmSpBowtEmSJNWAoU2SJKkGDG2SJEk10JbQFhFrRsQVEfH3iLgt\nIj5XHV8pIi6LiLsi4ncRsWKvx4yPiMkRcUdE7NKOuiVJktolMnP4XzRidWD1zLw5Il4N3ACMBT4B\nPJmZx0XEV4CVMvOwiNgUOBvYFlgTuBzYMPsoPiL6OixJkjTiRASZGY3cty0tbZn5WGbeXF1/HriD\nEsbGAmdUdzsDGFdd3x2YmJlzMvN+YDKw3bAWLUmS1EZtH9MWEesCWwB/BVbLzKlQgh2wanW3McBD\nvR42pTomSZI0KrQ1tFVdoz8HDq1a3Bbu17SfU5IkCViiXS8cEUtQAtuZmXlRdXhqRKyWmVOrcW+P\nV8enAGv1evia1bE+TZgw4aXrXV1ddHV1NbFySZKkwenu7qa7u3tQj23LRASAiPgp8ERm/kevY8cC\nT2XmsYuYiLA9pVv09zgRQZIk1dxAJiK0a/bo24CrgNsoXaAJHA5cC5xHaVV7ANgrM5+uHjMeOAiY\nTelOvWwRz21okyRJtTDiQ1s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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d67049780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173157_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  556.1584386526453\n"
     ]
    },
    {
     "data": {
      "image/png": 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VNK8JC2OAu4HFgOHApNrXhsAifbloRLwdOBAYnpnrU0LkZ4Ejgb9k5juBG4CR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\nRSNWBFbMzHERsRRwN7ATsBc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d67156fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cuiJJkiQJGmv6fD1wMLBVteta4OS6ToBr06ckSeoUi2r6bCSoLQu8lJnzqvuL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\nPJIkSa3UMUEtIhYD/gf4MPA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66f7e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173259_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  372.0277972281509\n"
     ]
    },
    {
     "data": {
      "image/png": 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3Lhe9NzPXGFBl/37NEcA9mbniLMcfAj6VmVMiYimgMzNXjohDgMzMcdV5VwBj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6701eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uBrPMxjLAjMycUz0eBSyRmdObUukgGdAkSVK7GMwYtIuB7uuzLwVc1KjCJEmS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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66ec50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173527_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 33.333333333333336\n",
      "Alignment Distance:  10283.252014782805\n"
     ]
    },
    {
     "data": {
      "image/png": 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0KQyjaIT/V0Wh8CYiPUorvDV6zxuETb7vuSe8wcyaBQcdFEpFeUt73FtHRygZ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\nCb5gP4HgOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e29fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JUpsMpAbtNX09YGY+2YyCSZIkaXF9jeK8ldK02dPC6Als2JISSZIkjXI2cUqS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e18a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173358_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  563.8135614303249\n"
     ]
    },
    {
     "data": {
      "image/png": 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TFiRJkjRCGN4kSZJqxPAmSZJUI/1NWAAgItYDPghs2vPxQ708VkSsBfwAeDHQ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\n/w8fle1NkTNt1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e9e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0jPVqFGc7cRSnJElqFz2N4uyxiTMirs/MbSPiBapJajvvAjIzV2hCOSVJkoY9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66f59240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173329_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 53.57142857142857\n",
      "Alignment Distance:  13860.570403447828\n"
     ]
    },
    {
     "data": {
      "image/png": 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bGzWzaYQq2wHAPEIF7vXsOceu6Zy7tHbsCLutT5vW7TXlR5U3SWjOWzpJePMu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\niMKbiIiISET+L8wvOJjJerB6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e35080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8E9gYGJOZJ2fmycCOlJA2VAsDS0bEIsDiwFRgT+DM6utnAntVz/cAzsvM2Zk5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\n7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e4ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173457_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  564.5634020773489\n"
     ]
    },
    {
     "data": {
      "image/png": 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0OP2ZbXo9sHtm3lndXxv4bWZuNgT1GzBb3iRJUl20arbp4t3BDSAz78LZp5Ik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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d674f37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MK67InDWruXW48srMTTbJ3GuvzEcfbe626mLhwvK6P/vZzLXXztxoo8wvfznzxhtf/D58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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d671b90b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173556_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  333.19363884106895\n"
     ]
    },
    {
     "data": {
      "image/png": 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8YGlJ9bHUUmUSwwMPwGabwdZbl0lQs2a98rY//nHpeu2644ekajC8DVBEmWRw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66f3b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vlhFmI0eWCTQPPxyWWGLhn2trKzVNv/sd3HxzmRz3m9+E9dZreMgLddNNpa/d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66fdb320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173700_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  514.0767044829404\n"
     ]
    },
    {
     "data": {
      "image/png": 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VsJeZlw37ohHrAkcD6wDTgWOBa4G3A09m5uSIOABYPDMPrCYsnEQJkMsCFwLv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\n73vjrOAmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66f4b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mSZJUMQY0SZKkijGgSZIkVYwBTZIkqWIMaJIkSRVjQJMkSaoYA5okSVLFGNAk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\nJK0QbUJpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d67166a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173757_Q.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  622.6045157875539\n"
     ]
    },
    {
     "data": {
      "image/png": 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HjtRFe+02lSRJdTGcbtPBhLdlgacy85nq/uLACzJz7rBr2kKGN0mSVBetGvN2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\ncXNE7Nvp+kiSJLVbbcJbRCw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6704bbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9PQNYDrg4IvYDnqCMMCUzH4yIi4EHq+2/bkKTJEkLowXWvEXEI8AmmTlzcIo0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\nkyRJqhHDmyRJUo38f/UIYxjARjohAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d670833c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecg1TimeList = ecg1.getTimeList()\n",
    "ecg1RRITList = ecg1.getRRITList()\n",
    "ecg2TimeList = ecg2.getTimeList()\n",
    "ecg2RRITList = ecg2.getRRITList()\n",
    "\n",
    "#first for ecg1, which represents 15012301_GIO.hrm\n",
    "for alignment in ecg1Result:\n",
    "    time = alignment[0]\n",
    "    distance = alignment[1]\n",
    "    ppgRRITSignal = alignment[2]\n",
    "    print(os.path.basename(ppgRRITSignal.name))\n",
    "    print('Percent Error for the RRITS in this video:', ppgRRITSignal.percentError)\n",
    "    print('Alignment Distance: ', distance)\n",
    "    plt.plot(ppgRRITSignal.getTimeList(), ppgRRITSignal.getRRITList())\n",
    "    plt.xlabel('Time in Second')\n",
    "    plt.ylabel('RR Interval in Milisecond')\n",
    "    plt.title('Timv vs RR Interval')\n",
    "    plt.xlim(0,30)\n",
    "    plt.ylim(0, 1200)\n",
    "    plt.title(\"PPG\")\n",
    "    plt.show()\n",
    "    plt.plot(ecg1TimeList, ecg1RRITList)\n",
    "    plt.xlabel('Time in Second')\n",
    "    plt.ylabel('RR Interval in Milisecond')\n",
    "    plt.title('Time vs RR Interval')\n",
    "    plt.xlim(time, time+30)\n",
    "    plt.ylim(0, 1200)\n",
    "    plt.title(\"ECG\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15012301_GLO.hrm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_172827_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 2.3255813953488373\n",
      "Alignment Distance:  1553.3987154150086\n"
     ]
    },
    {
     "data": {
      "image/png": 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Oly9332uv5CaTnzUr1KY+80x8xxw8OPye7u7vvRdatAr9jLztNvfjjoutaH7f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\nAClCeAMAAEgRwhsAAECKEN4AAABS5P8Bow0OHr+2dsUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6700c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5dbnluv/czDJNSHsYu/POMvJyww1LmB81qpS5L8PlvHllDdcf/rA02R9xxMAa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\noEmSJFXM/wfAB21OZvuO1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d67265cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_172926_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 2.5641025641025643\n",
      "Alignment Distance:  1304.5355055541277\n"
     ]
    },
    {
     "data": {
      "image/png": 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ccyUgXHttdecCeOYZ+PSn4Yc/7GxwgzLM4bLLyvIWEyeW5S26yZe/XL70jMXg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\nklQjhjdJkqQaMbxJkiTViOFNkiSpRv4/ouK94ZjINyYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d67513b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qrQRjx7bufWcouOYa2G+/8sHhjjvKaMt3vxv22ANe+cp2l27oGkgN2nuX9ISZ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6725fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173028_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 0.0\n",
      "Alignment Distance:  794.4187166365408\n"
     ]
    },
    {
     "data": {
      "image/png": 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BWVX6oIPgoYdKq9Dqqw++jvvuKwFozz1L1+D995c++Z59C4fjHp1/+EMZu3T8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\nkiTVyP8DtLmlm49sGccAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d671b50f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4e82ZU5rDf/KT0gVxyCFl5fS6ruAsSaqnrq4y7m7SpDLTefXVy44l//u/I2vX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66ff7e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_VID_20150123_173427_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 59.375\n",
      "Alignment Distance:  17718.95734043981\n"
     ]
    },
    {
     "data": {
      "image/png": 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evGR8dVq0UGTbVOEtHfPmwZYtvb2Rjm2fN6hn5W0S8FF3P8vdzwI+RjjXaW2s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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d671aa2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e0c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173127_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 2.3255813953488373\n",
      "Alignment Distance:  1440.565370449147\n"
     ]
    },
    {
     "data": {
      "image/png": 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T177WmPFcGprZs0tF+VbV/5s/v4zpW2ONzIkT6zvWbbeVN6eBllLpz4IFmd/9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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66f62c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R778/fOlL5ZvlosycWa4Reeyx5Zp348eXGrO6yoQLLig1h2PHljA61D5QunPf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\nGQOaJElSzRjQJEmSasaAJkm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e52eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173230_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 4.761904761904762\n",
      "Alignment Distance:  1237.2418962301397\n"
     ]
    },
    {
     "data": {
      "image/png": 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nzEMOaf556tU5uXMrBjP8+MeZn/zk4Owjdddd5Trccce8v99ZJh05cuBl0t5c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\nSZJUIf8fPaWMIFAJpVcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66ed18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1iy4q03Qdc8zwGan9lVfKYLlXXVWGZnj44RK29tqrhLbNN3/9h/f995ehE375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\nJEmSasaAJkmSVDMGNEmSpJo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66ff0588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173727_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 14.285714285714286\n",
      "Alignment Distance:  6158.145437806068\n"
     ]
    },
    {
     "data": {
      "image/png": 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hRgxvmXnncMcjYhKwDzDs15u0I7BbRLyVUrm6RkR8C5gfEetl5oKI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d672656a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xwANeOtDS2uqObEssAXvvXTpqnntu6Ty+wQblFOgTTzRnG93dZTTXrbeWZTR1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66e7bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VID_20150123_173629_F.mp4.txt\n",
      "Percent Error for the RRITS in this video: 21.21212121212121\n",
      "Alignment Distance:  7407.786013130281\n"
     ]
    },
    {
     "data": {
      "image/png": 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1fDdQ5q0VcXIzY4BRVT9vXznWMnd/GrgSeIIQtK1y99uAse6+tHKbJUCU55gA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\nRKREFLyJiIiIlIiCNxEREZES+f8Bc2Q1sc9FZTQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d67244dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B8cDOwN7Ac5l5XEQcDiyTmUdExHrAecAWwCjgBmDN7KXwEdHbZkmS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\nE+Lk53UaBJ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d6726d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for alignment in ecg2Result:\n",
    "    time = alignment[0]\n",
    "    distance = alignment[1]\n",
    "    ppgRRITSignal = alignment[2]\n",
    "    print(os.path.basename(ppgRRITSignal.name))\n",
    "    print('Percent Error for the RRITS in this video:', ppgRRITSignal.percentError)\n",
    "    print('Alignment Distance: ', distance)\n",
    "    plt.plot(ppgRRITSignal.getTimeList(), ppgRRITSignal.getRRITList())\n",
    "    plt.xlabel('Time in Second')\n",
    "    plt.ylabel('RR Interval in Milisecond')\n",
    "    plt.title('Timve vs RR Interval')\n",
    "    plt.xlim(0,30)\n",
    "    plt.ylim(0, 1200)\n",
    "    plt.title(\"PPG\")\n",
    "    plt.show()\n",
    "    plt.plot(ecg2TimeList, ecg2RRITList)\n",
    "    plt.xlabel('Time in Second')\n",
    "    plt.ylabel('RR Interval in Milisecond')\n",
    "    plt.title('Timve vs RR Interval')\n",
    "    plt.xlim(time, time+30)\n",
    "    plt.ylim(0, 1200)\n",
    "    plt.title(\"ECG\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, we see a positive correlation between the PPG signal's RRIT identification percent error and its success in aligning to the ECG RRIT signal. In addition, we have now been able to find out the section of ECG that is equivalent to each PPG."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating My Own PPG signals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "After this, I also begin recording my own PPG video. I pleace my right index finger on the flashlight of my smartphone while recording through the camera. Here we have two ppg signal I recorded on my smartphone. I convert these two to RRSignal and graph them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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58Y/hnntK996C5tVXy+996qklxB50UDlX4jLL9H6fb74J++xTWgnGjKnHm0+j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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d670a5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vidName = \"/home/mong/Desktop/map/map/videos/ccc.3gp.txt\"\n",
    "ppgRRSignal, percentError, threshold = analyzeVideoAndObtainBestRedRRInterval(vidName)\n",
    "ppgRRSignal = RRSignal(ppgRRSignal, os.path.basename(vidName), color='red', percentError=percentError, threshold=threshold)\n",
    "plt.plot(ppgRRSignal.getTimeList(), ppgRRSignal.getRRITList())\n",
    "plt.xlabel('Time in Second')\n",
    "plt.ylabel('RR Interval in Milisecond')\n",
    "plt.title('Timve vs RR Interval')\n",
    "plt.ylim(0, 1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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33b0mxrzddhusv378ZznzTNhtt8LfNRSiWicrtGabbWJ20bLLVq4N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\nEZH69L3vwYQJ0LWrwlu+Wg1vZjaGmFX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d66fbc438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vidName = \"/home/mong/Desktop/map/map/videos/ddd.3gp.txt\"\n",
    "ppgRRSignal, percentError, threshold = analyzeVideoAndObtainBestRedRRInterval(vidName)\n",
    "ppgRRSignal = RRSignal(ppgRRSignal, os.path.basename(vidName), color='red', percentError=percentError, threshold=threshold)\n",
    "plt.plot(ppgRRSignal.getTimeList(), ppgRRSignal.getRRITList())\n",
    "plt.xlabel('Time in Second')\n",
    "plt.ylabel('RR Interval in Milisecond')\n",
    "plt.title('Timve vs RR Interval')\n",
    "plt.ylim(0, 1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was an extrememly rewarding experience. Not only did I learn a lot about how the heart and the physiological signals it generate every moment, I learned how to manipulate and analyze these data. I had a glimpse in what real world research is like, and make me more determined on this long learning road. I learned that readability is important, and that sometimes, spending more time to writing classes and function saves more time in the long run, despite the longer time to write them in short time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to thank Dr. Giorgio Quer again for mentoring me. I also want to thank University of California, San Diego, and the San Diego Supercomputer Center for providing such a wonderful opportunity."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}