2016-3-9FindingOptimalTimeWindowFinal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the Optimal Time Window for Detecting RR Interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "fps = 30.323427803"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function obtains a list of color component from txt file.\n"
   ]
  },
  {
   "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": [
    "This function runs through the color list, returns the peak that are local maxima for an interval of 2 times the frameThreshold. It returns the list of peaks. For exmple, if frameThreshold is 10, it returns only the maxima that are greater than the values of 10 frames before and those of 10 frames after."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": {},
   "source": [
    "This function constructs rr Interval list from the peak list generated from detectPeak function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def constructTimeListFromRRList(rrL):\n",
    "    tL = list()\n",
    "    tL.append(rrL[0])\n",
    "    for i in range(1, len(rrL)):\n",
    "        tL.append(tL[i-1]+rrL[i])\n",
    "    return tL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function graph the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(inVarList, deVarList, inVarName, deVarName, yBegin=0, yEnd=1.5):\n",
    "    plt.plot(inVarList, deVarList)\n",
    "    plt.xlabel(inVarName)\n",
    "    plt.ylabel(deVarName)\n",
    "    plt.title(inVarName+' vs '+deVarName)\n",
    "    plt.ylim(yBegin, yEnd)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function defines the criteria for being a true RR interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isTrueRRInterval(rrInterval):\n",
    "    if 0.8 <= rrInterval and rrInterval <= 1.2:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function detect the amount of errors in a list of RR interval, as judged by function isTrueRRInterval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getAmountOfError(rrL):\n",
    "    errorNum = 0\n",
    "    for rrInterval in rrL:\n",
    "        if (not isTrueRRInterval(rrInterval)):\n",
    "            errorNum += 1\n",
    "    return errorNum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function analyze the rgb txt file of a PPG video and return important information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def analyzeVideoRGB(fileName):\n",
    "    print(\"Analyzing \"+fileName)\n",
    "    rL, gL, bL = readFromFile(fileName)\n",
    "    for frameThreshold in range(6,7):\n",
    "        rRRL = constructRRIntervalList(detectPeak(rL, frameThreshold))\n",
    "        print(rRRL)\n",
    "        gRRL = constructRRIntervalList(detectPeak(gL, frameThreshold))\n",
    "        bRRL = constructRRIntervalList(detectPeak(bL, frameThreshold))\n",
    "        rError = getAmountOfError(rRRL)\n",
    "        gError = getAmountOfError(gRRL)\n",
    "        bError = getAmountOfError(bRRL)\n",
    "        print(\"For Frame Threshold of {:2d}: Red has {:2d} errors | Green has {:2d} errors | Blue has {:2d} errors\"\n",
    "              .format(frameThreshold*2, rError, gError, bError))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing /home/mong/Desktop/map/map/videos/VID_20150123_173157_Q.mp4.txt\n",
      "[0.9893340619305577, 0.9233784578018539, 0.9563562598662059, 0.9563562598662059, 0.9563562598662059, 0.8904006557375019, 1.9127125197324117, 0.9233784578018539, 0.9893340619305577, 1.0223118639949096, 0.9563562598662059, 0.9893340619305577, 0.9563562598662059, 0.8244450516087981, 0.8904006557375019, 0.8904006557375019, 0.9233784578018539, 0.8244450516087981, 0.8574228536731501, 0.9233784578018539, 0.9563562598662059, 1.0223118639949096, 0.9893340619305577, 0.9563562598662059, 1.0552896660592617, 0.9563562598662059, 0.9233784578018539, 0.9563562598662059, 0.9893340619305577]\n",
      "For Frame Threshold of 24: Red has  1 errors | Green has  1 errors | Blue has  4 errors\n"
     ]
    }
   ],
   "source": [
    "analyzeVideoRGB('/home/mong/Desktop/map/map/videos/VID_20150123_173157_Q.mp4.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The first video that has a lower avgergae rr interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEZCAYAAABSN8jfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f521faa3128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rL, gL, bL = readFromFile(\"/home/mong/Desktop/map/map/videos/VID_20150123_172926_F.mp4.txt\")\n",
    "def constructTimeList(numOfElement, deltaTime):\n",
    "    l = list()\n",
    "    l.append(0)\n",
    "    for i in range(1, numOfElement):\n",
    "        l.append(l[i-1]+deltaTime)\n",
    "    return l\n",
    "tL = constructTimeList(len(rL), 1/fps)\n",
    "pL = detectPeak(rL, 10)\n",
    "plt.plot(tL, rL)\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"r\")\n",
    "plt.title(\"time\"+' vs '+\"r\")\n",
    "plt.plot([p/30.32 for p in pL],[rL[p] for p in pL],'ro')\n",
    "plt.show()\n",
    "# plot(tL, rL, \"time\", \"R color\", yBegin=199, yEnd=204)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7255116454157423,\n",
       " 0.6925338433513905,\n",
       " 0.7255116454157423,\n",
       " 0.7255116454157423,\n",
       " 0.7584894474800943,\n",
       " 0.6595560412870385,\n",
       " 0.6595560412870385,\n",
       " 0.6595560412870385,\n",
       " 1.319112082574077,\n",
       " 0.6265782392226866,\n",
       " 0.6595560412870385,\n",
       " 0.6265782392226866,\n",
       " 0.7255116454157423,\n",
       " 0.7255116454157423,\n",
       " 0.6925338433513905,\n",
       " 0.6595560412870385,\n",
       " 0.7255116454157423,\n",
       " 0.7255116454157423,\n",
       " 0.7255116454157423,\n",
       " 0.6595560412870385,\n",
       " 0.6265782392226866,\n",
       " 0.7255116454157423,\n",
       " 0.6925338433513905,\n",
       " 0.6595560412870385,\n",
       " 0.6265782392226866,\n",
       " 0.6925338433513905,\n",
       " 0.7255116454157423,\n",
       " 0.6925338433513905,\n",
       " 0.6595560412870385,\n",
       " 0.5936004371583347,\n",
       " 0.6265782392226866,\n",
       " 0.6925338433513905,\n",
       " 0.6925338433513905,\n",
       " 0.6925338433513905,\n",
       " 0.6925338433513905,\n",
       " 0.7255116454157423,\n",
       " 0.6595560412870385,\n",
       " 0.6595560412870385,\n",
       " 0.6595560412870385]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constructRRIntervalList(pL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The second video that has higher average rr interval, which happens to fit in 0.8-1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5211388ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rL, gL, bL = readFromFile(\"/home/mong/Desktop/map/map/videos/VID_20150123_173259_Q.mp4.txt\")\n",
    "def constructTimeList(numOfElement, deltaTime):\n",
    "    l = list()\n",
    "    l.append(0)\n",
    "    for i in range(1, numOfElement):\n",
    "        l.append(l[i-1]+deltaTime)\n",
    "    return l\n",
    "tL = constructTimeList(len(rL), 1/fps)\n",
    "pL = detectPeak(rL, 10)\n",
    "plt.plot(tL, rL)\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"r\")\n",
    "plt.title(\"time\"+' vs '+\"r\")\n",
    "plt.plot([p/30.32 for p in pL],[rL[p] for p in pL],'ro')\n",
    "plt.show()\n",
    "# plot(tL, rL, \"time\", \"R color\", yBegin=199, yEnd=204)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9233784578018539,\n",
       " 0.9233784578018539,\n",
       " 0.9233784578018539,\n",
       " 0.8904006557375019,\n",
       " 0.8904006557375019,\n",
       " 0.9233784578018539,\n",
       " 0.8244450516087981,\n",
       " 0.7584894474800943,\n",
       " 0.7584894474800943,\n",
       " 0.8244450516087981,\n",
       " 0.9563562598662059,\n",
       " 0.9563562598662059,\n",
       " 0.9563562598662059,\n",
       " 1.0223118639949096,\n",
       " 1.0223118639949096,\n",
       " 0.9893340619305577,\n",
       " 0.9893340619305577,\n",
       " 1.0223118639949096,\n",
       " 1.0223118639949096,\n",
       " 0.9563562598662059,\n",
       " 0.9563562598662059,\n",
       " 0.9893340619305577,\n",
       " 0.9893340619305577,\n",
       " 0.9563562598662059,\n",
       " 0.9563562598662059,\n",
       " 0.9563562598662059,\n",
       " 0.9893340619305577,\n",
       " 0.9563562598662059,\n",
       " 0.8904006557375019,\n",
       " 0.9563562598662059]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constructRRIntervalList(pL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}