analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import nltk\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# parse sample file\n",
    "datapath =''\n",
    "filename = \n",
    "soup = BeautifulSoup(open(datapath+ 'example_ttml.xml'), 'html.parser')\n",
    "result = [(p['begin'], p['end'], p.text) for p in soup.find_all('p')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" ?>\\n<tt ttp:profile=\"http://www.w3.org/TR/profile/sdp-us\" xml:lang=\"en\" xmlns=\"http://www.w3.org/ns/ttml\" xmlns:ttm=\"http://www.w3.org/ns/ttml#metadata\" xmlns:ttp=\"http://www.w3.org/ns/ttml#parameter\" xmlns:tts=\"http://www.w3.org/ns/ttml#styling\">\\n<head>\\n<styling>\\n<style tts:displayalign=\"after\" tts:extent=\"90% 90%\" tts:origin=\"5% 5%\" tts:textalign=\"center\" xml:id=\"s1\"></style>\\n<style tts:backgroundcolor=\"black\" tts:color=\"white\" tts:fontsize=\".72c\" xml:id=\"s2\"></style>\\n<style tts:color=\"#E5E5E5\" xml:id=\"s3\"></style>\\n<style tts:color=\"#CCCCCC\" xml:id=\"s4\"></style>\\n</styling>\\n<layout>\\n<region style=\"s1\" xml:id=\"r1\"></region>\\n</layout>\\n</head>\\n<body region=\"r1\">\\n<div>\\n<p begin=\"00:00:00.620\" end=\"00:00:04.049\" style=\"s2\">though<span style=\"s3\"> many</span> have joined the stem</p>\\n<p begin=\"00:00:02.639\" end=\"00:00:05.910\" style=\"s2\">learning community and have brought many</p>\\n<p begin=\"00:00:04.049\" end=\"00:00:08.010\" style=\"s2\">improvements the whole program is still</p>\\n<p begin=\"00:00:05.910\" end=\"00:00:09.450\" style=\"s2\">very new if<span style=\"s3\"> I</span> were to<span style=\"s4\"> be</span> in the stem</p>\\n<p begin=\"00:00:08.010\" end=\"00:00:10.800\" style=\"s2\">community I think that I could allow a</p>\\n<p begin=\"00:00:09.450\" end=\"00:00:12.150\" style=\"s2\">large amount of growth in the field of</p>\\n<p begin=\"00:00:10.800\" end=\"00:00:14.759\" style=\"s2\">programming and connecting the web with</p>\\n<p begin=\"00:00:12.150\" end=\"00:00:16.049\" style=\"s2\"><span style=\"s3\">real world I have had several projects</span></p>\\n<p begin=\"00:00:14.759\" end=\"00:00:18.000\" style=\"s2\">that allow me to control and receive</p>\\n<p begin=\"00:00:16.049\" end=\"00:00:20.310\" style=\"s2\">feedback<span style=\"s3\"> from</span> our<span style=\"s4\"> website</span> to hardware</p>\\n<p begin=\"00:00:18.000\" end=\"00:00:22.199\" style=\"s2\">devices such as an<span style=\"s3\"> arduino</span> i also</p>\\n<p begin=\"00:00:20.310\" end=\"00:00:23.760\" style=\"s2\">created a homemade remote swivel camera</p>\\n<p begin=\"00:00:22.199\" end=\"00:00:26.340\" style=\"s2\">to watch stuff and things like him</p>\\n<p begin=\"00:00:23.760\" end=\"00:00:27.930\" style=\"s2\">still adjusting to my home i created a</p>\\n<p begin=\"00:00:26.340\" end=\"00:00:30.000\" style=\"s2\">python script on a<span style=\"s4\"> raspberry pi and</span></p>\\n<p begin=\"00:00:27.930\" end=\"00:00:31.949\" style=\"s2\">mounted a webcam on several allowing you</p>\\n<p begin=\"00:00:30.000\" end=\"00:00:34.739\" style=\"s2\">to remotely control the camera to get</p>\\n<p begin=\"00:00:31.949\" end=\"00:00:37.140\" style=\"s2\">better angles<span style=\"s3\"> i</span> also have plans<span style=\"s3\"> of</span> a</p>\\n<p begin=\"00:00:34.739\" end=\"00:00:38.820\" style=\"s2\">testing home automation system if I was</p>\\n<p begin=\"00:00:37.140\" end=\"00:00:41.040\" style=\"s2\">part of stem I can also try to<span style=\"s3\"> organize</span></p>\\n<p begin=\"00:00:38.820\" end=\"00:00:42.360\" style=\"s2\">information better there's a great</p>\\n<p begin=\"00:00:41.040\" end=\"00:00:44.070\" style=\"s2\">system for project management and</p>\\n<p begin=\"00:00:42.360\" end=\"00:00:45.989\" style=\"s2\">information management</p>\\n<p begin=\"00:00:44.070\" end=\"00:00:47.250\" style=\"s2\">and I<span style=\"s4\"> think</span><span style=\"s3\"> that</span> I could bring us a stem</p>\\n<p begin=\"00:00:45.989\" end=\"00:00:49.020\" style=\"s2\">to help everyone share the project's</p>\\n<p begin=\"00:00:47.250\" end=\"00:00:51.000\" style=\"s2\">steps and detailed information for</p>\\n<p begin=\"00:00:49.020\" end=\"00:00:52.770\" style=\"s2\">everyone's benefit I hope you can</p>\\n<p begin=\"00:00:51.000\" end=\"00:00:53.879\" style=\"s2\">consider my application and I hope for</p>\\n<p begin=\"00:00:52.770\" end=\"00:00:56.899\" style=\"s2\">the chance to bring<span style=\"s3\"> these</span> improvements</p>\\n<p begin=\"00:00:53.879\" end=\"00:00:56.899\" style=\"s2\">thank you</p>\\n</div>\\n</body>\\n</tt>\\n"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of word frequency (only considering nouns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#text block\n",
    "raw= ' '.join([x[2] for x in result])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tokenzie and position tagging using nltk library\n",
    "# http://www.nltk.org/book/ch05.html\n",
    "# to understand the meaning of tags: nltk.help.upenn_tagset()\n",
    "text = nltk.word_tokenize(raw)\n",
    "postags= nltk.pos_tag(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# turn the result into dataframe for the convenience of processing\n",
    "df = pd.DataFrame(postags,columns =['word','type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#filter words by type, only keeping nouns\n",
    "typepattern_prefix=['NN']\n",
    "mask = df.type.str.slice(0,2).isin(typepattern_prefix)\n",
    "filtered=df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fabddefc0f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hvrwkHQWcAgQwA/gTcDqwMvAScERE/EfSGcAmwAeAjYFvArsCHwf+BXwqIhZJ2gn4JdAX\neBEYHhHPSRoNTAP2AK6V9HhL++mRD21m1km9fsYgaRvSl/M+EbEDcDJwH7BrROwI/BH4TmGTTYF9\ngAOBq4F7I2I74E3gAEkrARcAw/KzoS8Dzi5sv3LOPfpFO/spjtEhemZWNxphxrAPcH1EvAgQES9L\n2g64TtJA0l/zTxfa/z3PCmYCfYDb8/qZQBOwJbAtcJckcpvnCttfV1h+Xxv7eUdEjABGAKwycPPo\n/Ec1M1t2vX7G0IoLgAvzTOA4YNXCewsBImIJsCgiSl/US0iFVMDsiBiU/20XEfsVtn+9yv2YmdWl\nRigMo4DPSFoXQNI6QH/SOQOAL3awv8eAAZJ2y/2tlA9XtWRZ9mNmVhO9/lBSRMyWdDYwRtJiYCpw\nBnC9pFdIhWOTDvT3lqRhwK8l9Sf9Dn8FzG6heYf343RVM6s1LT1SYvWgubk5Jk2aVOthmFkvI2ly\nRDRX07YRDiWZmVkHuDCYmVkZFwYzMyvjwmBmZmVcGMzMrEyvv1y1WpKagFsjYttajsPpqlbJ6arW\n0zxjMDOzMi4M5fpI+q2k2ZLulLSapEGSJkqaIekmSWsDSBot6bwcfveIpJ0l3SjpCUlnlTqUdKSk\nByVNk/QbSX1q9/HMzNrnwlBuc+CiiNgGeBU4FLgS+G5EbE8K0vthof1b+YaRS4G/ACeQAvaGS1pX\n0geBw4APR8QgYDFwROVOna5qZvXE5xjKPR0R0/LyZFIE91oRMSavuwK4vtD+lvxzJilY7zkASU8B\nG5Gey7AT8FBOYl0NeL5yp05XNbN64sJQbmFheTGwVpXtl1RsW0xivSIivtdlIzQz62YuDG2bB7wi\naUhEjAO+AIxpZ5uie4C/SDovIp7Pya5rRsQzrW3gED0zqzUXhvZ9EbhU0urAU8DR1W4YEQ9LOh24\nU9IKwCLSeYhWC4OZWa05XbXOOF3VzLqD01XNzKzTXBjMzKyMC4OZmZVxYTAzszK+KqnOOETPKjlE\nz3pat80YJN3fXX3XM0lrSfparcdhZtZZ3VYYImL3ru5T0vIww1kLcGEws+VWd84Y5uefe0kaI+kv\nkp6SdI6kI3Li6ExJm+Z2IyVdmsPkHpf0ybx+uKRbJI0C7lFyrqRZefvDcrs/SjqgsP+RkoZJ6pPb\nP5QTUo/r4LgGSLohb/+QpA/n9WdIuiynrD4l6aS863OATXOa6rmSBkoam1/PkjSku37nZmZdoaf+\nAt8B+CDwMunu4d9FxC6STgZOBL6R2zUBu5DC6+6VtFlePxjYPiJelnQoMCj3uR4poG4scB3wWeA2\nSSsDHwW+CnwJmBcRO0taBRgv6c4OjOt84LyIuE/SxsAdeRuArYC9gTWBxyRdApwGbJvTVJH0beCO\niDg7R26vXvnLkXQscCxAn34DOvHrNTPrOj1VGB4qJI8+CZS+mGeSvlhL/hQRS4AnckLpVnn9XRHx\ncl7eA7g2IhYD/5E0BtgZ+Dtwfv7y3x8YGxFvStoP2F7SsLx9f1K89ltVjmsosHVORwXoJ6lvXr4t\nIhYCCyU9D6zf0mcHLpO0EnBzIb31HU5XNbN60lOFoTJ5tJhKWhxD5Zdi6fXr7e0gIhZIGg18jPQM\nhD/mtwScGBF3FNtL2qvKca0A7BoRCyq2h3ensb7r9xkRYyXtCRwAjJT0y4i4sr3PY2ZWK/V2Mvcz\nkq4ANgE+ADwG7FjRZhxwXG63DrAncGp+7zrgy0AzMDyvuwP4qqRREbFI0hbAvzowpjtJh5XOBZA0\nqKW/+gteIx1aIrd/P/BsRPw2z2YGkx7+0yKnq5pZrdVbYfgH8CDQDzg+zwIq29wE7AZMJ80ovhMR\n/87v3QlcBfwlIt7K635HOncxRamzF4CDOzCmk4CLJM0g/b7GAse31jgiXpI0XtIs0uGtWcCpkhYB\n84GjOrBvM7MeVzfpqpJGArdGxJ9rPZZacrqqmXUHp6uamVmn1c2hpIgYXusxmJmZZwxmZlbBhcHM\nzMrUzaGk5Zmk44E3IuJKScOBOyNibmf6crqqVXK6qvU0F4YuEBGXFl4OJ12i2qnCYGZWa3V9KEnS\nUTn4brqkqyQ1SRqV192Ts4tKgXmXSJqYA+32ygF3j+TLYEv9zc/BdrMl3S1pl0II3oG5zXBJFxa2\nuTXfJV3a/uw8nomS1s/rz5B0So7daAauyaF5B0i6udDXvpJu6onfnZlZZ9VtYZC0DXA6sE9E7ACc\nDFwAXBER2wPXAL8ubLI26ca3bwK3AOcB2wDbSRqU26wBjIqIbUh3KJ8F7AscApxZxbDWACbm8YwF\nvlJ8M9+DMQk4Iofo/Q3YSlIpGe9o4LIWPuuxSqmykxa/Ma+KYZiZdZ+6LQzAPsD1EfEiQA7R2w34\nQ37/KlKgXslfI92tNxP4T0TMzIF8s0l3PkMKzrs9L88ExkTEorzcRPveAm7Ny5Pb2yaP5yrgSElr\n5fH/vYV2IyKiOSKa+6zev4phmJl1n950jqEYgFcZjlf6nIti6a3e77SLiCVa+hCgtykvmKsWlovb\ntxia14LLgb8CC0iF7u0qtjEzq5l6njGMIoXqrQsgaR3gfuBz+f0jSIF6XW0OMEjSCpI2Ij0foiPK\nQvTy1UlzSYfFLu+qQZqZdZe6nTFExGxJZwNjJC0GppJSTi+XdCopDO/obtj1eOBp4GHgEWBKB7cf\nCVwq6U1gt4h4k3Q+ZEBEPNLexk5XNbNaq5sQvd4sX+U0NSJ+315bh+iZWXfoSIhe3c4YegtJk0kP\nGvp2rcdiZlYNF4ZuFhE71XoMZmYdUc8nn83MrAZcGMzMrIwPJdUZh+hZJYfoWU/zjKEH5Iynz9d6\nHGZm1XBh6BlNgAuDmS0XerQw5L+cH81pqI9LukbSUEnjJT2R0053kTRB0lRJ90vaMm87XNKNkm7P\nbX9e6PeSHEI3W9KPCus/kfc3WdKvJd2a16+R01cfzPs5qLCPmyXdJWmOpK9L+lZuMzHffY2kTfM4\nJksaJ2mrvH5k3s/9ObF1WB7KOcCQnLj6zZ75bZuZdU4tZgybAb8Atsr/Pk8KwzsF+B/gUWBIROwI\n/AD4SWHbQcBhwHbAYTmyAuD7+caN7YGPSNpe0qrAb4CP50tGBxT6+T4pZXUXYG/gXElr5Pe2BT4N\n7AycTXoAz47ABOCo3GYEcGLu9xTg4kLfA/Pn+SSpIACcBoyLiEERcV7lL8TpqmZWT2px8vnpiJgJ\nIGk2cE9EhKRSwml/4ApJmwMBrFTY9p6ImJe3fRh4P/BP4LOSjiV9noHA1qSi91REPJ23vRY4Ni/v\nBxwo6ZT8elVg47x8b0S8BrwmaR4pAA9SAuv2kvoCuwPXSyqNa5XCGG/Oqa4Pl57X0J6IGEEqNqwy\ncHPfim5mNVWLwlCZfFpMRV0R+DHpy/kQSU3A6Fa2XQysKGkT0l/tO0fEK/nBPMVE1JYIODQiHitb\nKX2oivGtALyan7fQ3udTK23MzOpWPV6u2h/4V14eXkX7fqTIiXn5L/SPk4rJY8AHJDVFxBzSIaiS\nO4ATJZ2YZys7RsTUagYXEf+V9LSkz0TE9UrThu0jYnobm5UlrrbFIXpmVmv1eFXSz4GfSppKFYUr\nfyFPJZ2b+AMpHZWcavo14PacV/QaUDqA/2PSIaoZ+XDWjzs4xiOAL0maTnoQ0EHttJ8BLM6PBPXJ\nZzOra706XVVS34iYn/+qvwh4oqWTv/XE6apm1h06kq5ajzOGrvQVSdNIf9X3J12lZGZmbajHcwxd\nJs8O6nqGYGZWb3r7jMHMzDrIhcHMzMr06kNJXUnScODOiJjbnftxuqpVcrqq9TTPGKo3HNig1oMw\nM+tuDVMYcnDebfleglmSDpN0c+H9fSXdJKlPDsObJWmmpG/mMLxm4JochLeapJ0kjclBendIGpj7\nGS3pvJx99IiknXP43xOSzqrV5zczq1YjHUraH5gbEQcASOoP/EjSgIh4ATgauIwU1LdhRGyb260V\nEa9K+jpwSkRMkrQScAFwUES8IOkwUuDeMXlfb0VEs6STgb8AOwEvA09KOi8iXioOLOc8HQvQp18x\n68/MrOc1zIyBFIK3r6SfSRqSw/iuAo6UtBawG/B34ClSlMYFkvYH/ttCX1uSUljvyvdJnA68r/D+\nLYV9zo6I5yJiYe57IypExIiIaI6I5j6r9++aT2tm1kkNM2OIiMclDQY+AZwl6R7gd6T01AXA9RHx\nNvCKpB2AjwHHA59l6UygRKQv/N1a2V0xeK8ylK9hfudmtnxqmC8pSRsAL0fE1ZJeBb4cEXMlzSX9\nxT80t1uPdCjoBkmPAVfnLopBeI8BAyTtFhET8qGlLSJido9+KDOzbtAwhYH0cJ9zJS0BFgFfzeuv\nAQZExCP59YbA5ZJKh9m+l3+OBC6V9CbpsNMw4Nf5XMWKwK9I0RvLNkinq5pZjfXqEL1qSLoQmBoR\nv6/1WMAhembWPToSotdIM4Z3yXHcrwPfrvVYzMzqRUMXhvzMZjMzK2iky1XNzKwKLgxmZlamoQ8l\n1SOH6Fklh+hZT/OMoQMknSlpaCe2a5L0+e4Yk5lZV3NhqCCpT2vvRcQPIuLuTnTbBLgwmNlyoaEK\nQ/7L/VFJ1+Tk0z9LWl3SnJyhNAX4jKRBkiZKmpETV9fO24/MSau0ka66maS7c4rrFEmbAucAQ3Iy\n6zdr9gswM6tCQxWGbEvg4oj4ICkg72t5/UsRMTgi/ghcCXw3IrYnBeH9sNhBIV11WL7k9TJSuiqk\nO6kviogdgN2B54DTgHERMSg/h5qK/o7NMd2TFr8xr6s/r5lZhzTiyed/RsT4vHw1cFJevg7eieNe\nKyLG5PVXANdX9FFMVwXoAzwnaU1SZPdNABGxIPfZ5oAiYgQwAmCVgZs39q3oZlZzjVgYKr94S69f\n70AfLaar5sJgZrZca8TCsHEpFZV0Qvg+YMfSmxExT9Ir+ZkN44AvAGMq+mg1XVXSs5IOjoibJa1C\nmk0Uk1nb5BA9M6u1RjzH8BhwgqRHgLWBS1po80VSEusM0hPdziy8FxHxFild9WeSpgPTSOcTIBWS\nk/K29wPvBWYAi/MJaZ98NrO61ogzhrcj4siKdU3FFxExDdi1hW3XJT2is9Rmz8oGEfEEsE8L27a0\nzsys7jTijKFTJF0GrE469GRm1ms11IwhIuaQribqzLaVj/c0M+uVPGMwM7MyLgxmZlamoQ4l9TRJ\nc4DmiHix2m2crrqUU0XNasMzBjMzK+PCUAVJp0o6KS+fJ2lUXt4nB/LtJ2lCDs27XlLfwubfkTRT\n0oOSNqvJBzAz6wAXhuqMA4bk5Wagb77beQjp5rXTgaERMRiYBHyrsO28iNgOuBD4VUudO0TPzOqJ\nC0N1JgM7SeoHLAQmkArEEOBNYGtgvKRppLum31/Y9trCz7JspZKIGBERzRHR3Gf1/t30EczMquOT\nz1WIiEWSngaGk2IuZgB7A5sBTwN3RcThrW3eyrKZWV3yjKF644BTgLF5+XhgKjAR+HDp/IGkNSRt\nUdjusMLPCT03XDOzzvGMoXrjgO8DEyLidUkLSA/feUHScODanKYK6ZzD43l57RyotxBobVbxDqer\nmlmtuTBUKSLuAVYqvN6isDwK2LmFbZry4ne7e3xmZl3Fh5LMzKyMC4OZmZVxYTAzszIuDGZmVsaF\nwczMyviqpBZI2gs4JSI+2U67+yNi97badJTTVZdyuqpZbXjGAEjq05nturoomJnVg4YoDJJuljRZ\n0mxJx+Z18yX9QtJ0YDdJ+0t6VNIU4NOFbc+QdErh9SxJTaU+8s+9JI2W9OfcxzWSlN/7qKSpOWH1\nssJNcGZmdakhCgNwTETsRAq+O0nSusAawAMRsQMpEfW3wKeAnYD3dmIfOwLfIAXqfYAUk7EqMBI4\nLCesrgh8tXJDp6uaWT1plMJwUp4ZTAQ2AjYHFgM35Pe3Ap6OiCciIoCrO7GPByPi2YhYAkwDmoAt\nc7+leIwrgD0rN3S6qpnVk15/8jmfSB4K7BYRb0gaDawKLIiIxVV08TblBXTVVtotLCwvpgF+t2bW\nOzXCl1d/4JVcFLYCdm2hzaNAk6RNI+JJysPu5gCfBJA0GNikA/t+LPe7WUT8H/AFYExbGzhEz8xq\nrREOJd1YvcvlAAAgAElEQVQOrCjpEeAc0uGkMhGxADgWuC2ffH6+8PYNwDqSZgNfZ2lqartyv0cD\n10uaCSwBLu3sBzEz6wlKh9StXjQ3N8ekSZNqPQwz62UkTY6I5mraNsKMwczMOsCFwczMyrgwmJlZ\nGRcGMzMr0wiXqy5XHKK3lEP0zGrDM4YuJKlJ0udrPQ4zs2XRkIVBSXd89ibAhcHMlmsNUxjyX/OP\nSboSmAV8QdIESVMkXS+pb243R9JPJU3LwXaDJd0h6UlJx+c2knRuTlqdKemwvJtzgCF5229K6pPb\nPSRphqTjavPpzcyq12jnGDYHvgj8H3AjMDQiXpf0XeBbwJm53T8iYpCk80jpqB8mZSTNIt25/Glg\nELADsB7wkKSxwGkUHvCTI77nRcTOOW57vKQ7I+Lp4qByu2MB+vQb0G0f3sysGo1WGJ6JiImSPkmK\nxx6fH5uwMjCh0O6W/HMm0DciXgNek7RQ0lrAHsC1OYTvP5LGADsD/63Y337A9pKG5df9ScWprDBE\nxAhgBMAqAzf3rehmVlONVhhezz8F3BURh7fSrpSUuoTy1NQldOx3JuDEiLijQ6M0M6uhRisMJROB\ni0qpp5LWADYsPDehPeOA4yRdAaxDesbCqcCGwJqFdncAX5U0KiIWSdoC+FdEvP6uHjOnq5pZrTVk\nYYiIFyQNB64tPGrzdKpPTr0J2A2YDgTwnYj4t6SXgMX5oUAjgfNJVypNyY/6fAE4uKs+h5lZd3C6\nap1xuqqZdQenq5qZWae5MJiZWRkXBjMzK+PCYGZmZRryqqSuIqkJuDUitu2qPp2uupTTVc1qwzMG\nMzMr07CFQdLNkiZLmp2zipA0X9LZkqZLmihp/bx+0/x6pqSzJM1vob8WA/MkDZQ0NgfrzZI0pGc/\nqZlZxzRsYQCOiYidgGbgJEnrAmsAEyNiB2As8JXc9nzg/IjYDni2lf6+RA7MI+UmfUXSJqQY7jsi\nohS6N61yQ0nH5iTXSYvfmNeFH9HMrOMauTCclO9QnghsRAq3ewu4Nb8/mXTXMqS7nK/Py39opb/9\ngKMkTQMeANbNfT4EHC3pDGC7HMhXJiJGRERzRDT3Wb3/sn4uM7Nl0pAnnyXtBQwFdouINySNJsVq\nL4qlt4IvposC8yTtCRwAjJT0y4i4clnGb2bWnRp1xtAfeCUXha2AXdtpPxE4NC9/rpU2pcC8lQAk\nbSFpDUnvB/4TEb8FfgcMXvbhm5l1n4acMQC3A8dLegR4jPTF35ZvAFdL+n7etqUTAb+j5cC8vYBT\nJS0C5gNHtbUjp6uaWa05RK8KklYH3oyIkPQ54PCIOKg79uUQPTPrDh0J0WvUGUNH7QRcmGcCrwLH\n1Hg8ZmbdxoWhChExjnSpqZlZr9eoJ5/NzKwVLgxmZlbGh5LqjEP0lnKInlltNPSMQdJIScNqPQ4z\ns3rS0IXBzMzeraEKg6SjcvLpdElX5dV7Srpf0lOl2YOkvpLukTQlJ6oelNc3SXpE0m9zKuudklbL\n720m6e7c9xRJm+b1pxYSV39Ukw9uZtYBDVMYJG0DnA7sk9NTT85vDQT2AD4JnJPXLQAOiYjBwN7A\nL/I9DJCC8S6KiG1I9zSUojKuyet3AHYHnpO0X26/CzAI2CnnJlWOzemqZlY3Gunk8z7A9RHxIkBE\nvJy/62+OiCXAw6XnL5AC8X6Sv8SXABsCpfeejohSdPZkoEnSmsCGEXFT7nsBQC4M+wFTc/u+pEIx\ntjiwiBgBjABYZeDmvhXdzGqqkQpDaxYWlkuzgiOAAcBOEbFI0hxS+mpl+8XAam30LeCnEfGbLhqr\nmVm3a6TCMAq4KcdevyRpnTba9geez0Vhb+D9bXUcEa9JelbSwRFxs6RVgD6kxNUfS7omIuZL2pAU\n7f18a305RM/Maq1hCkNEzJZ0NjBG0mKWHt5pyTXAXyXNBCYBj1axiy8Av5F0JrAI+ExE3Cnpg8CE\nfNhqPnAk0GphMDOrNaer1hmnq5pZd+hIumrDXJVkZmbVcWEwM7MyLgxmZlbGhcHMzMo0zFVJtSbp\nYODxiHi4rXZOV13K6apmteEZQ885GNi61oMwM2tPQxQGSTdLmpyD747N6+ZLOjevu1vSLpJG5zC9\nA3ObVSVdnoP0puab3ZA0XNKFhf5vlbRXod+zc5jeREnrS9odOBA4V9K0UsCemVk9aojCABwTETsB\nzcBJktYF1gBG5TC814CzgH2BQ4Az83YnABER2wGHA1dIWvVdvZdbA5iYw/TGAl+JiPuBW4BTI2JQ\nRDxZ3MAhemZWTxqlMJwkaTowEdiIFGT3FnB7fn8mMCYiFuXlprx+D+BqgIh4FHgG2KKdfb0F3JqX\nJxf6alVEjIiI5oho7rN6/yo/kplZ9+j1J5/zIZ6hwG4R8Yak0aRAvEWx9LbvJeRwvIhYIqm938vb\nlBfV4iyi2O9iGuB3bGa9SyPMGPoDr+SisBWwawe2HUdKWkXSFsDGwGPAHGCQpBUkbUR63kJ7XgPW\n7MjAzcxqoRH+mr0dOF7SI6Qv9Ykd2PZi4JIcpvc2MDwiFkoaDzwNPAw8Akypoq8/Ar+VdBIwrPI8\nQ4nTVc2s1hyiV2ccomdm3cEhemZm1mkuDGZmVsaFwczMyrgwmJlZGRcGMzMr0wiXq3YrSRsAv46I\nYW20WQv4fERc3F5/TlddyumqZrXhGcMykLRiRMxtqyhkawFf64kxmZktKxeGTNIakm7LqaizJB0m\naWdJ9+d1D0paMyer3iJpFHCPpCZJs3IfwyX9Jae0PiHph7n7c4BNc7LquTX7kGZmVfChpKX2B+ZG\nxAEAkvoDU4HDIuIhSf2AN3PbwcD2EfGypKaKfnYBtgXeAB6SdBtwGrBtRAxqacc5CvxYgD79BnTp\nhzIz6yjPGJaaCewr6WeShpBykZ6LiIcAIuK/EfF2bntXRLzcSj93RcRLEfEmcCMpobVNTlc1s3ri\nwpBFxOOkmcBM0rMZPt1G89fb6qqd12Zmdc2HkrJ8ddHLEXG1pFdJJ4sHSto5H0pak6WHktqyr6R1\nctuDgWPoQLKqQ/TMrNZcGJbajvTozSXAIuCrgIALJK1G+qIfWkU/DwI3AO8Dro6ISQCSxueT1H+P\niFO74wOYmXUFF4YsIu4A7mjhrcrnN4zM/0rbzSGdbC55NiIObqH/zy/zIM3MeoDPMZiZWRnPGLpQ\nRIykMJswM1seecZgZmZlXBjMzKyMDyXVGYfoLeUQPbPaaKgZg6S1JHUqzE7SNyStXnj9t5yaambW\nqzRUYWDZUk6/AbxTGCLiExHxapeMysysjjTaoaR3Uk6Bu4Dngc8CqwA3RcQPJa0B/Il0g1of4MfA\n+sAGwL2SXoyIvSXNAZqBvsDfgfuA3YF/AQdFxJuSdgZ+DyzJ+/t4RBTveTAzqzuNNmM4DXgyp5ze\nBWxOSkMdBOwkaU+WpqzukL/Eb4+IXwNzgb0jYu8W+t0cuCgitgFeBQ7N6y8Hjsv7W9zaoCQdK2mS\npEmL35jXNZ/UzKyTGq0wFO2X/00FpgBbkb7gy1JWI6Kab+qnI2JaXp4MNOXzD2tGxIS8/g+tbex0\nVTOrJ412KKlIwE8j4jfvekMaDHwCOEvSPRFxZjt9LSwsLwZW67phmpn1rEYrDMWU0zuAH0u6JiLm\nS9qQFJ63IuUpq1+u2PbFanYUEa9Kek3ShyLiAeBz1WzndFUzq7WGKgwR8VIx5ZR0eGeCJID5wJHA\nZrw7ZRVgBHC7pLmtnGdoyZeA3+a+xgA+gWBmdU8Rfo5Md5HUNyLm5+XTgIERcXJb2zQ3N8ekSZN6\nZHxm1jgkTY6I5mraNtSMoQYOkPQ90u/5GWB4bYdjZta+qq5KknSypH5Kfi9piqT9untwy7uIuC4i\nBkXEthFxQES8UOsxmZm1p9rLVY+JiP+SLu9cG/gC6WYxMzPrZaotDMo/PwFcFRGzC+vMzKwXqfYc\nw2RJdwKbAN+TtCYp5qFXkTQcaI6Ir9dqDE5XXcrpqma1UW1h+BIpNuKpiHhD0rrA0d03rI5Rut5U\nEVHTYiWpT0Qsbu21mdnyoNpDSXdFxJRSmmhEvASc133Dap+kJkmPSboSmAX8PucNzZb0o0K7cyQ9\nLGmGpP/N60ZKujS3f1zSJwtdbyRptKQnJP2w0M+Rkh6UNE3SbyT1yevnS/qFpOnAbpLm5DiNKcBp\n+Wepj82Lr83M6lGbMwZJq5KipteTtDZLzyv0Azbs5rFVY3PgixExUdI6EfFy/sK+R9L2pKTTQ4Ct\nIiIqnp/QRArQ25SUmrpZXr8LsC3wBvCQpNuA14HDgA9HxCJJFwNHAFcCawAPRMS3AfLNci9FxOD8\neqikQTlL6WhSsF4ZSccCxwL06Tegq343Zmad0t6hpONIzyHYgBQOVyoM/wUu7MZxVeuZiJiYlz+b\nv2BXBAYCWwMPAwtIs4lbgVsL2/4pH3p6QtJTpBA9SLOjlwAk3QjsAbwN7EQqFJCykJ7P7RcDN1SM\n67rC8u+AoyV9i1Rcdqn8EBExgnRnNasM3Nx3HJpZTbVZGCLifOB8SSdGxAU9NKaOeB1A0ibAKcDO\nEfGKpJHAqhHxtqRdgI8Cw4CvA/vkbSu/gKON9QKuiIjvtTCGBS2cR3i9sHwD8ENgFDC5VHTMzOpV\nVSefI+ICSbuTDr+sWFh/ZTeNq6P6kb6M50laH/g4MFpSX2D1iPibpPHAU4VtPiPpCtKVVh8AHgN2\nJEVurwO8CRwMHEM6rPQXSedFxPP5/TUj4pn2BhYRCyTdAVxCOolvZlbXqioMkq4iHYufxtIHzgTp\nGHvNRcR0SVOBR4F/AuPzW2uSvtBXJf3V/63CZv8AHiQVlePzFzh53Q2kJ7hdHRGTACSdDtwpaQVS\nuN4JpJiLalxDOtdxZ3sNna5qZrVWVYiepEeAraOXJO7lQ023RsSfe2h/pwD9I+L/tdfWIXpm1h26\nI0RvFvBe4LlOj6pBSbqJNNvap722Zmb1oNrCsB7wsKQHKTytLCIO7JZRdbOIGN6D+zqkp/ZlZtYV\nqi0MZ3TnIMzMrH5Ue1XSmO4eiJmZ1Ydqr0p6jaXX968MrAS8HhH9umtgjcoheks5RM+sNqrKSoqI\nNSOiXy4EqwGHAhd368i6kKThki7My8dLOqoTfawl6WuF1xtI6pGrmszMelK1IXrviORm4GPdMJ5u\nFxGXdvLGvLWAdwpDRMyNiGFdNzIzs/pQ7aM9P134N0zSOaQMom4n6WZJk3Nq6rF53XxJ5+V190ga\nkNePlnR+TkCdleMwKvs7I99XgKTNJN0taXp+XOmmkvrmPqdIminpoLzpOcCmue9zc7rrrNzPqpIu\nz+2nSto7rx8u6UZJt+e01p/3xO/MzGxZVHtV0qcKy28Dc4CDWm7a5Y7JqamrkULsbiAlmk6KiG9K\n+gEpi6j0cJ3VI2KQpD2By0hJqa25BjgnIm7Kd0evALwFHBIR/5W0HjBR0i3AacC2ETEIUux3oZ8T\nSJOp7SRtRbpDeov83iBS1MZC4DFJF0TEP4uDcLqqmdWTaq9KquVDeU6SVLoXYCNS1PYSliaYXg3c\nWGh/LUBEjJXUryJq+x1KT6HbMCJuyu0X5PUrAT/JhWUJKV58/XbGuAdwQe7nUUnPAKXCcE9EzMt9\nPwy8nxTb8Q6nq5pZPan2UNL7JN0k6fn87wZJ7+vuwUnaCxgK7BYROwBTgVVbaBqtLLf0uj1HAAOA\nnfLs4D+t7LNaCwvLi6l+lmZmVhPVfkldDvwB+Ex+fWRet293DKqgP/BKfpzoVsCuef0KpBjtPwKf\nB+4rbHMY6cE7ewDzImJeDscrExGvSXpW0sERcbOkVYA+eZ/P5wfy7E36Cx/gNVIoX0vGkQrKqHwI\naWNSWuvgjn5gh+iZWa1Ve1XSgIi4PCLezv9Gkv6q7m63AyvmEL9zgNJDeV4Hdsknf/cBzixssyAn\nrV5K+zHXXyAdqpoB3E/Kg7oGaJY0EziKlNhaepzp+HxS+9yKfi4GVsjbXAcMj4iFmJkth6pNV72H\nNEO4Nq86HDg6Ij7ajWNrazzzI6JvC+tHA6eUorKXR05XNbPu0JF01WpnDMcAnwX+TUpYHQYM79To\nzMysrlV7juFM4IsR8QpAfoLZ/5IKRo9rabaQ1+/Vw0MxM+t1qp0xbF8qCgAR8TLp2nwzM+tlqi0M\nK0hau/Qizxh82aWZWS9U7Zf7L4AJkq7Prz8DnN09Q6o/PXlS2+mqSzld1aw2qr3z+UpJk1j6eMpP\nR8TD3TcsMzOrlaoPB+VC0KuLQc4/uh2YTLo5bTbpXoZim0uAnUnx43+OiB/m9XOAK0i5UisBn8nx\nGB8Bzs+bB7BnRLzW3Z/FzKyzOhy73QC2BC6OiA8C/6UQtZ19P18LvD3wEUnbF957MSIGA5cAp+R1\npwAn5HiNIcCblTuUdKykSZImLX5jXhd/HDOzjnFheLd/RsT4vHw1KSCv6LOSppBym7YBti68Vwrz\nmww05eXxwC8lnQSsFRFvV+4wIkZERHNENPdZvX8XfQwzs85xYXi3VkP4JG1CmgF8NCK2B26jPGCv\nFIPxTlheRJwDfJl06Gl8znwyM6tbLgzvtrGk3fJyZUBfP1JO0zxJ6wMfb68zSZtGxMyI+BnwEODC\nYGZ1zfcivNtjwAmSLiOdbL+E/KCiiJieA/oeJT1TYXyrvSz1jZzSuoR0MvvvbTV2uqqZ1ZoLw7u9\nHRFHVqzbq7QQEcNb2igimgrLk0rbRMSJXT1AM7Pu5ENJZmZWxjOGgoiYQ9vPiDYz6/U8YzAzszIu\nDGZmVsaFwczMyvgcQwty7lFzRLxYRdtm4KiIOEnSXsBbEXF/Z/ftdNWlnK5qVhsuDMsoX5paiuPe\nC5gPdLowmJnVWq8+lCTp1JxRhKTzJI3Ky/tIukbSfpImSJoi6XpJxUeGfkfSTEkPStosb/cZSbMk\nTZc0Nq/bS9KtOZn1eOCbkqZJGiJpgKQbJD2U/324R38BZmad0KsLAzCOlGgK0Az0lbRSXjcDOB0Y\nmhNRJwHfKmw7LyK2Ay4EfpXX/QD4WETsABxY3FG+1PVS4LyIGBQR40hx2+dFxM7AocDvWhqk01XN\nrJ709kNJk4GdJPUjBdxNIRWIIcAtpGTU8ZIAVgYmFLa9tvDzvLw8Hhgp6U8sTVJty1Bg69w/QD9J\nfSNifrFRRIwARgCsMnDzyhA/M7Me1asLQ0QskvQ0MJx03H8GsDewGfA0cFdEHN7a5pXLEXG8pA8B\nBwCTJe3UzhBWAHaNiAWd/xRmZj2rVxeGbBwpKvsYYCbwS9JMYiJwkaTNIuL/JK0BbBgRj+ftDgPO\nyT8nwDtJqQ8AD0j6OLBRxb5eIyWwltwJnAicm7cfFBHT2hqsQ/TMrNZ6+zkGSIVhIDAhIv4DLADG\nRcQLpJnEtZJmkL78i5HYa+f1JwPfzOvOzSekZ5FmINMr9vVX4JDSyWfgJKBZ0gxJD5NOTpuZ1TVF\n+JB2PWlubo5Jkya139DMrAMkTc6PJW5XI8wYzMysA1wYzMysjAuDmZmVcWEwM7MyjXC56nLFIXpL\nOUTPrDY8Y+ggSWdKGtrJbUfnNFYzs7rlGUMHRcQPaj0GM7Pu1NAzBklrSLotp6XOkvRdSTfm9w6S\n9KaklSWtKumpvH6kpGGS9pd0faGvvSTdmpfbSm01M6trDV0YgP2BuRGxQ0RsS0pHHZTfGwLMAnYG\nPgQ8ULHt3cCHcpQGpOiMP0paj7ZTW9/F6apmVk8avTDMBPaV9DNJQyJiHvCkpA8Cu5BylfYkFYlx\nxQ0j4m3gduBTklYkBev9BdiVpamt04AvAu9vaxARMSIimiOiuc/q/bv2E5qZdVBDn2OIiMclDQY+\nAZwl6R5gLPBxYBFpVjAS6AOc2kIXfwS+DrwMTIqI15QytttKbTUzq2sNXRgkbQC8HBFXS3oV+DLp\noTxXAldGxAuS1gXWJx1WqjQGuAz4CqlIQPuprW1yuqqZ1VpDFwZgO1Ji6hLSDOGrwGxSIRib28wA\n3hstpA1GxOJ8wnk46ZARuZgMJ6W2rpKbng5UVRjMzGrN6ap1xumqZtYdnK5qZmad5sJgZmZlXBjM\nzKyMC4OZmZXpFVclSTqJdEXRlIg4Yhn6GQncGhF/ljQHaI6IF5dxbPMjoupIDKerLuV0VbPa6BWF\nAfgaKYLi2VoPxMxsebfcH0qSdCnwAeDvkr4v6TJJD0qaKumg3KaPpHMlPSRphqTj8npJulDSY5Lu\nBt5T0f13JM3M/W2Wt/mUpAdy/3dLWj+v7yvp8tx+hqRDK8a5Xg7W85/BZlbXlvvCEBHHA3OBvYE1\ngFERsUt+fW6+8/hLwLyI2JkUivcVSZsAhwBbkrKNjgJ2r+h+XkRsB1xIuiMa4D5g14jYkXS383fy\n+v9Xah8R2wOjSp3k4nEb8IOIeNdxIofomVk96S2Hkkr2Aw6UdEp+vSqwcV6/vaRheX1/YHNSQN61\nEbEYmCtpVEV/1xZ+npeX3wdcJ2kgsDLwdF4/FPhcacOIeCUvrgTcA5wQEWNaGnREjABGAKwycHPf\ncWhmNdXbCoOAQyPisbKVKdjuxIi4o2L9J9rpL1pYvgD4ZUTcImkv4Ix2+ngbmAx8jJStZGZW15b7\nQ0kV7gBOzIUASTsW1n9V0kp5/Rb5ENNY4LB8DmIg6fBT0WGFnxPycn/gX3n5i4W2dwEnlF5IWjsv\nBnAMsJWk7y7j5zMz63a9bcbwY9K5gBmSViAd5vkk8DugCZiSi8YLwMHATcA+wMPAP1j65V+ytqQZ\nwEKgFKN9BnC9pFdI5xE2yevPIqWqzgIWAz8CboR3wvYOB26R9FpEXNzaB3C6qpnVmkP06oxD9Mys\nOzhEz8zMOs2FwczMyrgwmJlZGRcGMzMr09uuSlruOURvKYfomdWGZwxtkDRH0nq1HoeZWU9yYegm\nkjwbM7PlkgtDJmkNSbdJmi5plqTSXc8nSpqSU1O3ym13yUmpUyXdL2nLvH64pFty5tI9ed2phVTX\nH9Xm05mZVc+FYan9gbkRsUNEbAvcnte/GBGDgUuAUjjfo8CQnLD6A+AnhX4GA8Mi4iOS9iOF9e0C\n/7+9O4+Sq67TP/5+DAENwYDC0QhiFFDZY9JBFoMJg/z8uSCO4bC4BRcEVEAPKKP+wGFQYXKURVSM\nDANIBIYommFURLaEEJYkZGFXgTkgKJsskS2E5/fH/bZd1VR3Op2urkr38zqnT9269b33fuvmVH3y\nvffWcxkPTJS0Z/cNJ101ItpJCkOX5cB7JJ0iabLtzm/oX5THRVSxGlDlJV1S4i9OBbavWc8Vth8v\n0/uUv1uAxcDbqQpFHdszbXfY7hgxasxAvqeIiDWW4+CF7bslTQDeB5wk6cry0vPlcRVd++vfgKtt\nf1jSOOCamlX9vWZawHds/7hZ/Y6IGGgpDIWkNwCP275A0hPAZ3ppXpuwOr2XdpcD/yZplu0VkjYH\nVtp+uKcFEqIXEa2WQ0lddgRukrQEOIEqLbUn/w58R9It9FJcbf8O+BmwQNJyYDaw0cB1OSJi4CVd\ntc0kXTUimiHpqhER0W8pDBERUSeFISIi6qQwREREnVyuuoYkHQ3MtP3MatptBlwGrA8cCbweOBH4\ni+2pPS2XdNUuSVeNaI2MGNbc0cCoPrT7J2C57XfYngd8Gvhsb0UhIqIdZMTQC0kbAv8FbAGMAC4B\n3gBcLelR21MlrbA9urSfBnwAOI3qtw6vktQBXAq8C/gPSXNsH9uCtxMR0ScpDL3rDNZ7P4CkMcAh\nwFTbj/a0kO0lko4HOmx/oSw7FTjG9st+pCDpUOBQgBGv3mzg30VExBrIoaTe9RSsN6ASohcR7SQj\nhl70EqxX16xm+pWD07OIiObJiKEXJVjvGdsXADOo7rXwNPV5R3+VtK2kVwAfbkE3IyIGVEYMvdsR\nmCHpJWAlcDiwG/BbSQ+WK4yOo7os9RFgITB6rTaYdNWIaLGE6LWZhOhFRDMkRC8iIvothSEiIuqk\nMERERJ0UhoiIqJPCEBERdYbE5aqS9gPutn17k7fzNdvfrnl+ve3dB3IbSVftknTViNYYKiOG/YDt\nBmE7X6t9MtBFISKiHbRtYZD0S0mLJN1WQuaQtKLm9WmSzpW0O7Av1Q/RlkjaStJ4STdIWibpUkmb\nlGWukXSqpIWS7pA0SdIvJP1B0kmr2fbJVGmpSyTNqu2PKjMk3SppuaQDyvwpZZuzJd0paZYkDdIu\njIjol3Y+lPQp249LehVws6SfN2pk+3pJc4DLbM8GkLQM+KLtayWdCJxAdR8FgBdsd0g6CvgVMBF4\nHPiTpFNtP9Zo27aPk/QF2+MbdOOfgfHAzsCmZZm55bV3ANsDDwLzgT2A62oXTrpqRLSTth0xAEdK\nWgrcALwR2KYvC5Vo7I1tX1tmnQfsWdNkTnlcDtxm+yHbzwP3lO30Z9vvAi60vcr2X4FrgUnltZts\nP2D7JWAJMK77wklXjYh20pYjBklTgL2B3Ww/I+kaquTSgUgyfb48vlQz3fl8vV623V+121hFm+7z\niIhO7folNQb4W/lifjuwa5n/V0nbAndRJZk+Xeb/I/HU9pOS/lbunzAP+DjV/+DXdtsAKyWNtL2y\n2zLzgM9JOg94DdUI5Vjg7WuwXSAhehHReu16KOm3VP97vwM4meqQDnQlmV4PPFTT/iLgWEm3SNoK\n+CTVyehlVMf+TxyAbQPMBJZ1nnyucSmwDFgKXAV8xfZf1mCbERFtI+mqbSbpqhHRDElXjYiIfkth\niIiIOikMERFRJ4UhIiLqtOvlqsNWQvS6JEQvojUyYigk3Sdp01b3IyKi1VIYIiKizpArDJKOlXRk\nmT5V0lVleq+SbrqPpAWSFku6RNLomsW/UtJRb5K0dVnudSWhdWn5273Mf1kCa5m/oiSt3ibp95J2\nKQmr90jadxB3RUREvwy5wkAVTzG5THcAoyWNLPOWAd8A9rY9AVgIfLlm2Sdt7wicCZxW5p0BXGt7\nZ9H8x9kAABfPSURBVGACcFuZ/ynbE8s2jpT02jJ/Q+Aq29tTRXWcBLyHKsKj4S+wJR1aosAXrnrm\nybV79xERa2konnxeBEyU9GqqALvFVF/ek6mSVbcD5pfbIqwPLKhZ9sKax1PL9F7AJwBsrwI6v7mP\nlPThMt2ZwPoY8AJVrAZUCa7P214paTkNklXLemdSxW2wwdht8lP0iGipIVcYypfwvcB0qkylZcBU\nYGvgXuAK2wf1tHgP03VWk8C60l05I/9IcLX9kqQht78jYugZql9U84BjgE9R/a/9e1QjiRuAH0ja\n2vYfJW0IbG777rLcAVTBeQfQNZK4EjgcOE3SCGA0vSewrpWkq0ZEqw3FcwxQFYaxwIJy45zngHm2\nH6EaSVxYklcXUB+NvUmZfxTwpTLvKGBqORS0iOpQVG8JrBER67Skq7aZpKtGRDMkXTUiIvothSEi\nIuqkMERERJ0UhoiIqDNUL1dtSNI44DLbOzRxGzOA9wG/Bv4EPGP7fEnTgd/ZfrC35ZOu2iXpqhGt\nMawKwyA5FHhN+ZV0renArUCvhSEiotWGY2FYT9IsunKPPgFsS/UjuNHAo8B02w+VXzTfSPXL6Y2B\nT9ueV37odjIwBdgA+IHtH0uaU9axSNJ3ynpXAPdRxXLMkvQs1S+mnx2k9xsRsUaG4zmGtwE/tL0t\n8BTweeD7wLQSincO8K2a9uvZ3gU4GjihzPs0VeDeJGAS8FlJb7a9L/Cs7fG2L+5cge3ZVIF9Hy2v\n1RWFhOhFRDsZjiOG+23PL9MXAF8DdgCuKMF6I4CHatr/ojwuoisEbx9gJ0nTyvMxVCF69/anQwnR\ni4h2MhwLQ/cv3qeB22zv1kP758vjKrr2l4Av2r68Cf2LiGip4XgoaUtJnUXgYKqco80650kaKWn7\n1azjcuDwcp8HJL21BPL15mlgo7Xod0TEoBiOI4a7gM9LOge4ner8wuXAGZLGUO2T0+i6IU8jZ1Md\nVlqs6vjTI8B+q9nuucBZqzv5nHTViGi1hOi1mYToRUQzJEQvIiL6LYUhIiLqpDBERESdFIaIiKgz\nHK9KamsJ0euSEL2I1siIYRBJOlvSdq3uR0REbzJiGES2P9PqPkRErE5GDE0gaZykOyXNknSHpNmS\nRkm6RlKfriOOiGiVFIbm6Z7iekRPDZOuGhHtJIWhebqnuL6rp4a2Z9rusN0xYtSYweldREQPUhia\np3vWSLJHImKdkJPPzbOlpN1sL6BKcb0O+ODqFkqIXkS0WkYMzdOZ4noHsAnwoxb3JyKiTzJiaJ4X\nbX+s27wprehIRMSayIghIiLqZMTQBLbvo7qPdETEOicjhoiIqJPCEBERdXIoaTUkHQ3MtP3MYGwv\n6apdkq4a0RoZMaze0cCoVnciImKwpDDUkLShpP+RtFTSrZJOAN4AXC3p6tJmH0kLJC2WdImk0WX+\nfZL+XdJySTdJ2rrM37+sa6mkua17dxERfZPCUO+9wIO2d7a9A3Aa8CAw1fZUSZsC3wD2tj0BWAh8\nuWb5J23vCJxZlgU4Hvg/tncG9m200YToRUQ7SWGotxx4j6RTJE223f1beldgO2C+pCXAJ4E31bx+\nYc3jbmV6PnCupM8CIxptNCF6EdFOcvK5hu27JU0A3gecJOnKbk0EXGH7oJ5W0X3a9mGS3gm8H1gk\naaLtxwa67xERAyUjhhqS3gA8Y/sCYAYwAXga2Kg0uQHYo+b8wYaS3lqzigNqHheUNlvZvtH28cAj\nwBub/04iIvovI4Z6OwIzJL0ErAQOpzok9FtJD5bzDNOBCyVtUJb5BnB3md5E0jLgeaBzVDFD0jZU\no40rgaW9diDpqhHRYrJzm4CBIOk+oMP2o2uzno6ODi9cuHBgOhURUUhaZLtPtxbOoaSIiKiTQ0kD\nxPa4VvchImIgZMQQERF1UhgiIqJODiW1mYTodUmIXkRrDNsRg6SNJR1RpqdIuqyHdmdL2m5wexcR\n0TrDtjAAGwNHrK6R7c/Yvn0Q+hMR0RaGc2E4GdiqZB7NAEZLmi3pTkmzJAlA0jWSOsr0CknfKkmp\nN0h6XZm/VXm+XNJJklaU+WMlzZW0pCSsTm7Re42I6LPhXBiOA/5kezxwLPAOqnsvbAe8BdijwTIb\nAjeUpNS5wGfL/NOB00uy6gM17Q8GLi/b2BlY0qgjSVeNiHYynAtDdzfZfsD2S1Rf4OMatHkB6DwX\nsaimzW7AJWX6ZzXtbwYOkfRNYEfbTzfacNJVI6KdpDB0eb5mehWNr9ha6a4MkZ7a/IPtucCewJ+p\norc/MRAdjYhopuF8uWptauraugH4CHAxcGDnTElvAh6w/ZMSujcBOL+3FSVELyJabdgWBtuPSZov\n6VbgWeCva7G6o4ELJH0d+C3QeaJgCnCspJXACiAjhohoe0lXHQCSRgHP2rakA4GDbH+oP+tKumpE\nNMOapKsO2xHDAJsInFkucX0C+FSL+xMR0W8pDAPA9jyqy1EjItZ5uSopIiLqpDBERESdHEoaQJKm\nAC/Yvr48Pxe4zPbsvq4j6apdkq4a0RoZMXQjaW2K5RRg9wHqSkRESwy7wiDp/0m6S9J1ki6UdEwJ\nyjtN0kLgKEnjJF0laZmkKyVtKWmEpHtV2VjSKkl7lnXOlbQNcBjwpRKa1xmYt6ek6yXdI2laq953\nRERfDavCIGkS1S+Udwb+L1B7Te/6Ja/ou8D3gfNs7wTMAs6wvQq4iypk713AYmBy+UXzG23/ATgL\nONX2+HKlEsDY0v4DVImujfqVEL2IaBvDqjBQJab+yvZzJdDuv2teu7hmeje6wvB+SvXFDjCPKvto\nT+A7Zf4kqrC8nvzS9kvlng6va9QgIXoR0U6GW2Hozd/70GYuMBnYBfg11c1+plAVjJ7UhvOpv52L\niBgsw60wzAc+KOmVkkZTHd5p5Hq6wvA+StcX/01UJ5dfsv0cVTz356gKBgxsMF9EREsMq8tVbd8s\naQ6wjCo0bzldgXe1vgj8p6RjgUeAQ8ryz0u6nypNFaqCcVBZD1SHpmZL+lBZxxpLumpEtNqwC9GT\nNNr2ihJ8Nxc41PbiVverU0L0IqIZEqLXu5mStgNeSXXlUdsUhYiIdjDsCoPtg1vdh4iIdjbcTj5H\nRMRqpDBERESdFIaIiKgz7M4xtLukq3ZJumpEawzLEYOkEWu5fApqRAxZQ64wlGTUOyXNknSHpNmS\nRkm6T9IpkhYD+0saL+mGkqB6qaRNyvKTyrwlkmZIurXMny5pjqSrgCsljS7Jq4slLS8/aqvd/rmS\n7i792FvSfEl/kLRL6/ZORMTqDbnCULwN+KHtbYGngCPK/MdsT7B9EXA+8NWSoLocOKG0+U/gc7bH\nA6u6rXcCMM32u4HngA/bngBMBb4rqTMLaWvgu8Dby9/BVIF7xwBf697ZpKtGRDsZqoXhftvzy/QF\ndKWjXgwgaQywse1ry/zzqO6bsDGwke0FZX5nwmqnK2w/XqYFfFvSMuD3wOZ0pafea3u57ZeA24Ar\nXf3EfDkwrntnk64aEe1kqB4r757z0fm8Lwmqvald/qPAZsBE2ysl3Uf1a2qoT1R9qeb5SwzdfR4R\nQ8RQ/ZLaUtJu5X/+BwPXAe/ofNH2k5L+JmlyuaHOx4FrbT8h6WlJ77R9I10Jq42MAR4uRWEq8KaB\n6HhC9CKi1YbqoaS7gM9LugPYBPhRgzafBGaUQ0HjgRPL/E8DP5G0BNiQxumrUN3ZrUPScuATwJ0D\n2P+IiJYZcumqksYBl9neoZ/Lj7a9okwfB4y1fdTA9bB3SVeNiGZIuuraeb+kf6HaN/8LTG9tdyIi\nBteQKwy27wP6NVooy19M/f2fIyKGlaF6jiEiIvophSEiIuoMuUNJ67qE6HVJiF5Eawy7EYOkb0o6\nph/LnShp72b0KSKinWTE0Ee2j291HyIiBsOwGDFI+npJOr2OKmAPSVtJ+q2kRZLmSXq7pDGS/lfS\nK0qbDSXdL2lkSUudVuZPknS9pKWSbpK0kaQRJY315pLO+rnSdqykuSWt9VZJk1u2IyIi+mDIjxgk\nTaSKthhP9X4XA4uAmcBhtv8g6Z1Uaax7lV88vxu4GvgAcHmJvehc3/pUl7MeYPtmSa8GnqX6xfST\ntidJ2gCYL+l3wD+XdXyr3AdiVIM+HgocCjDi1Zs1bV9ERPTFkC8MwGTgUtvPAEiaQxV2tztwSVdS\nNhuUx4uBA6gKw4HAD7ut723AQ7ZvBrD9VFnvPsBOnaMKqiylbYCbgXMkjQR+aXtJ9w7anklVqNhg\n7DZD66foEbHOGQ6FoZFXAE+Uey50N4cqTvs1wETgqj6uU8AXbV/+shekPYH3A+dK+p7t8/vZ74iI\nphsOhWEu1Rfyd6je7weBHwP3Strf9iXlBjs72V5qe4Wkm4HTqTKXut+s5y5grKRJ5VDSRlSHki4H\nDpd0VTn09Fbgz8CmwAO2f1IOMU2guklQQ0lXjYhWG/KFwfZiSRcDS4GHqQ7tQHU/hR9J+gYwErio\ntIHqcNIlwJQG63tB0gHA9yW9iqoo7A2cTXUTnsWl0DwC7FfWcayklcAKqiTWiIi2NeTSVdd1SVeN\niGZYk3TVYXG5akRE9F0KQ0RE1ElhiIiIOikMERFRZ8hflTTYJF0DHGN7Ybf504EO21/obfmkq3ZJ\numpEa2TEMIBK5EVExDothaGQdKykI8v0qZKuKtN7SZol6SBJy0sQ3ik1y62Q9F1JS4Hduq3zkBLe\ndxOwx2C+n4iI/kph6DKPKlcJoAMYXfKNJgN3A6cAe1GF8U2StF9puyFwo+2dbV/XuTJJY4F/pSoI\n7wK262nDkg6VtFDSwlXPPDnAbysiYs2kMHRZBEwsaanPAwuoCsRk4AngGtuP2H4RmAXsWZZbBfy8\nwfreWbPMC1S/pm7I9kzbHbY7RowaM3DvKCKiH1IYCtsrgXuB6cD1VCOIqcDWwH29LPpcgzyliIh1\nVgpDvXnAMVTBe/OAw4BbgJuAd0vatJxgPgi4djXrurEs89pySGr/5nU7ImLg5HLVevOArwMLbP9d\n0nPAPNsPSTqO6h4NAv7H9q96W1FZ5ptUh6SeAF52H4ZGkq4aEa2WEL02kxC9iGiGhOhFRES/pTBE\nRESdFIaIiKiTwhAREXVyVVKbSYhel4ToRbRGRgyDRNJ+knqMxYiIaBcpDINA0nrAfvSSlxQR0S5S\nGPpI0jhJd5ak1TskzZY0StLxkm4uqaszJam0v0bSaZIWAl8F9gVmSFoiaauWvpmIiF6kMKyZtwE/\ntL0t8BRwBHCm7Um2dwBeBXygpv36JRzvW8Ac4Fjb423/qXalSVeNiHaSwrBm7rc9v0xfQBWnPVXS\njZKWU8Vyb1/TvsdE1VpJV42IdpKrktZM9/wQAz+kumXn/SUb6ZU1r/99sDoWETFQUhjWzJaSdrO9\nADgYuA7YHXhU0mhgGjC7h2WfBjZa3QYSohcRrZZDSWvmLuDzku4ANgF+BPwEuBW4HLi5l2UvAo6V\ndEtOPkdEO8uIYc28aPtj3eZ9o/zVsT2l2/P55HLViFgHZMQQERF1MmLoI9v3ATu0uh8REc2WEUNE\nRNRJYYiIiDo5lNQDSScCc23/fg2XGwfsbvtn/dlu0lW7JF01ojWG9YhB0oieXrN9/JoWhWIc1W8c\nIiLWSUO2MPQSenefpFMkLQb2lzRe0g2Slkm6VNImZflzJU0r0xMlXStpkaTLJY0t87eW9HtJSyUt\nLr9POBmYXMLyviRpe0k3lefLJG3Tsp0SEdEHQ7YwFI1C7wAesz3B9kXA+cBXbe8ELAdOqF2BpJHA\n94FpticC5wDfKi/PAn5ge2eqX0A/BBwHzCtheacChwGn2x4PdAAPdO9kQvQiop0M9XMM3UPvjizT\nFwNIGgNsbPvaMv884JJu63gb1WWqV5RE7RHAQ5I2Aja3fSmA7efKOrv3YQHwdUlbAL+w/YfuDWzP\nBGYCbDB2m+55TBERg2qojxgahd7BmoXbCbitjADG297R9j597kB1Enpf4Fng15L2WoNtR0QMuqFe\nGLaUtFuZ7gy9+wfbTwJ/kzS5zPo4cC317gI261yPpJGStrf9NPCApP3K/A0kjaJbWJ6ktwD32D4D\n+BWw04C+w4iIATbUDyV1ht6dA9xOFXr3xW5tPgmcVb7U7wEOqXnNtl8oJ6HPKIee1gNOA26jKiQ/\nLpe2rgT2B5YBqyQtBc4FNgA+Lmkl8Bfg2711OOmqEdFqQ70wNAq9G1f7xPYSYNcGy74WeLymzZ7d\nG5TzBY0ODXWfd3If+xsR0XJD/VBSv5QRxii6HXqKiBgOhuyIYW1C72x/amB7ExGx7siIISIi6qQw\nREREnRSGiIioM2TPMQwkSUcChwOvB06x3eNVRpKmAx22v9DgtRW2R/e2raSrdkm6akRrpDD0zRHA\n3rZflnMUETHU5FDSakg6C3gL8JuSlnpmmb+ZpJ9Lurn87dFg2TdLWiBpuaSTBrvvERH9kcKwGrYP\nAx4EpgJ/q3npdOBU25OAjwBnN1j8dOBHtnekSl5tKOmqEdFOciip//YGtqtJU321pO7nD/agKhoA\nPwVOabSipKtGRDtJYei/VwC7dsZtd2oQu50v+ohYp6Qw9N/vqAL5ZgBIGl8ylWrNBw6kuhfER/uy\n0oToRUSr5RxD/x0JdJTbdd5Odae27o6iSnddDmw+qL2LiOgn2TnS0U46Ojq8cOHCVncjIoYYSYts\nd/SlbUYMERFRJyOGNiPpaaobDLXapsCj6UP60EZ9gPbox7rahzfZ3qwvDXPyuf3c1dfhXjNJWtjq\nfqQP6UM79mM49CGHkiIiok4KQ0RE1ElhaD8zW92Boh36kT5U0ocu7dCPId+HnHyOiIg6GTFERESd\nFIaIiKiTwtAikt4r6S5Jf5R0XIPXN5B0cXn9RknjWtCH6ZIekbSk/H2mCX04R9LDkm7t4XVJOqP0\ncZmkCS3owxRJT9bsh+Ob0Ic3Srpa0u2SbpN0VIM2Td0XfexDU/eFpFdKuknS0tKHf23QZjA+G33p\nR9M/H2U7IyTdIumyBq81Z1/Yzt8g/wEjgD9R3QBofWApsF23NkcAZ5XpA4GLW9CH6cCZTd4XewIT\ngFt7eP19wG8AAbsCN7agD1OAy5q8H8YCE8r0RsDdDf49mrov+tiHpu6L8t5Gl+mRwI1UKca1bZr6\n2ViDfjT981G282XgZ432e7P2RUYMrbEL8Efb99h+AbgI+FC3Nh8CzivTs4F/UoNM7yb3oelszwUe\n76XJh4DzXbkB2FjS2EHuQ9PZfsj24jL9NHAHLw9ebOq+6GMfmqq8txXl6cjy1/0KmWZ/Nvraj6aT\ntAXwfhrfCAyatC9SGFpjc+D+mucP8PIP4D/a2H4ReBJ47SD3AeAj5bDFbElvHMDt91Vf+9lsu5XD\nCr+RtH0zN1QOB7yD6n+ptQZtX/TSB2jyviiHTpYADwNX2O5xPzTps9HXfkDzPx+nAV8BXurh9abs\nixSG6M1/A+Ns7wRcQdf/TIabxVQ5MzsD3wd+2awNqboL4M+Bo20/1aztrEUfmr4vbK+yPR7YAthF\n0g4DvY0B6kdTPx+SPgA8bHvRQK63L1IYWuPPQO3/LrYo8xq2kbQeMAZ4bDD7YPsx28+Xp2cDEwdw\n+33Vl33VVLaf6jysYPvXwEhJmw70diSNpPpCnmX7Fw2aNH1frK4Pg7UvyvqfAK4G3tvtpWZ/NvrU\nj0H4fOwB7CvpPqpDvXtJuqBbm6bsixSG1rgZ2EbSmyWtT3XSaE63NnOAT5bpacBVLmeYBqsP3Y5f\n70t1zHmwzQE+Ua7I2RV40vZDg9kBSa/vPG4raReqz82AfhGV9f8HcIft7/XQrKn7oi99aPa+kLSZ\npI3L9KuA9wB3dmvW7M9Gn/rR7M+H7X+xvYXtcVSfz6tsf6xbs6bsi6SrtoDtFyV9Abic6uqgc2zf\nJulEYKHtOVQf0J9K+iPVidEDW9CHIyXtC7xY+jB9IPsAIOlCqitdNpX0AHAC1Yk+bJ8F/Jrqapw/\nAs8Ah7SgD9OAwyW9CDwLHDjQX0RU/zv8OLC8HNcG+BqwZU0/mr0v+tKHZu+LscB5kkZQFZ3/sn3Z\nYH421qAfTf98NDIY+yKRGBERUSeHkiIiok4KQ0RE1ElhiIiIOikMERFRJ4UhIiLqpDBERESdFIaI\niKjz/wGH7flvmbPYEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fabdd713b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot word frequency\n",
    "ax=filtered['word'].value_counts().sort_values(ascending=True).plot.barh(figsize=(5,10))\n",
    "ax.set_ylabel('counts')\n",
    "ax.set_title('Word frequency', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Analysis of speech speed on the video timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2=pd.DataFrame(result, columns = ['sTimestamp','eTimestamp','words'])\n",
    "df2['sTimestamp']=pd.to_datetime(df2['sTimestamp'])\n",
    "df2['eTimestamp']=pd.to_datetime(df2['eTimestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "df2['durSeconds']= (df2['eTimestamp']-df2['sTimestamp'])/ timedelta(seconds=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2['wordcounts']=df2.apply(lambda row: len(row['words'].split(' ')),axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sTimestamp</th>\n",
       "      <th>eTimestamp</th>\n",
       "      <th>words</th>\n",
       "      <th>durSeconds</th>\n",
       "      <th>wordcounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-08-28 00:00:42.360</td>\n",
       "      <td>2017-08-28 00:00:45.989</td>\n",
       "      <td>information management</td>\n",
       "      <td>3.629</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sTimestamp              eTimestamp                   words  \\\n",
       "22 2017-08-28 00:00:42.360 2017-08-28 00:00:45.989  information management   \n",
       "\n",
       "    durSeconds  wordcounts  \n",
       "22       3.629           2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------slowest spoken line:----------------\n",
      "sTimestamp          2017-08-28 00:00:42.360000\n",
      "eTimestamp          2017-08-28 00:00:45.989000\n",
      "words                   information management\n",
      "durSeconds                               3.629\n",
      "wordcounts                                   2\n",
      "speechSpeed                           0.551116\n",
      "offsetVideoStart                         41.74\n",
      "Name: 22, dtype: object\n",
      "--------fastest spoken line:----------------\n",
      "sTimestamp                        2017-08-28 00:00:44.070000\n",
      "eTimestamp                        2017-08-28 00:00:47.250000\n",
      "words               and I think that I could bring us a stem\n",
      "durSeconds                                              3.18\n",
      "wordcounts                                                10\n",
      "speechSpeed                                          3.14465\n",
      "offsetVideoStart                                       43.45\n",
      "Name: 23, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#fastest and slowest line by speech speed\n",
    "df2['speechSpeed']=df2['wordcounts']/df2['durSeconds']\n",
    "\n",
    "vStart=min(df2['sTimestamp'])\n",
    "df2['offsetVideoStart'] = (df2['sTimestamp']-vStart)/timedelta(seconds=1)\n",
    "\n",
    "print('--------slowest spoken line:----------------')\n",
    "print(df2.sort_values(by=['speechSpeed']).iloc[0])\n",
    "print('--------fastest spoken line:----------------')\n",
    "print(df2.sort_values(by=['speechSpeed']).iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x7fabdb6c1908>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yl66UWtRCCOHq3r0hibBu7ZvyUdcxcVdWCRG1UwmFaA2bjVArpWKUUiuVUnuU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\nZUBTXz0vBd4wdj8AESLSzVXxKKWUUspz9IwJprLasK+orMnt1ucUsTw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llGqBYcOGcbJ2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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fabdddf9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fastest and slowest line by speech speed\n",
    "fig=plt.figure(figsize=(12,5))\n",
    "ax=fig.add_subplot(111)\n",
    "df2['speechSpeed']=df2['wordcounts']/df2['durSeconds']\n",
    "\n",
    "ax.plot(df2['offsetVideoStart'],df2['speechSpeed'],'--')\n",
    "ax.set_ylabel('words / second')\n",
    "ax.set_xlabel('time from the start of the video (seconds)')\n",
    "\n",
    "ax.annotate('\\\"and I think that I could bring us a stem\\\"',\n",
    "            xy=(43.45, 3.14465), xycoords='data',\n",
    "            xytext=(-30, -20), textcoords='offset points',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            horizontalalignment='right', verticalalignment='top', size=14)\n",
    "\n",
    "ax.annotate('\\\"information management\\\"',\n",
    "            xy=(41.74, 0.551116), xycoords='data',\n",
    "            xytext=(-30, 20), textcoords='offset points',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            horizontalalignment='right', verticalalignment='bottom', size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## search for sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"though many have joined the stem learning community and have brought many improvements the whole program is still very new if I were to be in the stem community I think that I could allow a large amount of growth in the field of programming and connecting the web with real world I have had several projects that allow me to control and receive feedback from our website to hardware devices such as an arduino i also created a homemade remote swivel camera to watch stuff and things like him still adjusting to my home i created a python script on a raspberry pi and mounted a webcam on several allowing you to remotely control the camera to get better angles i also have plans of a testing home automation system if I was part of stem I can also try to organize information better there's a great system for project management and information management and I think that I could bring us a stem to help everyone share the project's steps and detailed information for everyone's benefit I hope you can consider my application and I hope for the chance to bring these improvements thank you\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(df2[\"words\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(519, 583) i created a python script on a raspberry pi and mounted a webcam\n",
      "                sTimestamp                                     words\n",
      "13 2017-08-28 00:00:23.760    still adjusting to my home i created a\n",
      "14 2017-08-28 00:00:26.340       python script on a raspberry pi and\n",
      "15 2017-08-28 00:00:27.930  mounted a webcam on several allowing you\n"
     ]
    }
   ],
   "source": [
    "#examples of search for a sentence\n",
    "\n",
    "from re import finditer\n",
    "    \n",
    "#test1\n",
    "#searchWords = 'hardware devices'\n",
    "\n",
    "#test2\n",
    "searchWords = 'i created a python script on a raspberry pi and mounted a webcam'\n",
    "\n",
    "for match in finditer(searchWords, ' '.join(df2[\"words\"].str.strip())):\n",
    "    #print matches \n",
    "    print(match.span(), match.group())\n",
    "    \n",
    "    startPos = match.span()[0]\n",
    "    endPos = match.span()[1]\n",
    "    \n",
    "    #find the line indexes of the start and end position of each match\n",
    "    startLineIdx=-1\n",
    "    endLineIdx=-1\n",
    "    pos= 0\n",
    "    for index, row in df2.iterrows():\n",
    "        pos += len(row[\"words\"].strip())+1 # 1 is the space added between lines\n",
    "        if startLineIdx ==-1 and startPos<pos:\n",
    "            startLineIdx=index\n",
    "        if endLineIdx==-1 and endPos<pos:\n",
    "            endLineIdx = index\n",
    "        if startLineIdx>0 and endLineIdx>0:\n",
    "            break\n",
    "    \n",
    "    #verify \n",
    "    print(df2.loc[startLineIdx:endLineIdx,[\"sTimestamp\",\"words\"]])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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