tweets_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark import SparkContext, SparkConf\n",
    "from datetime import datetime as dt\n",
    "from timeit import timeit\n",
    "from pyspark.sql import Row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- created_at: long (nullable = true)\n",
      " |-- entities: struct (nullable = true)\n",
      " |    |-- hashtags: array (nullable = true)\n",
      " |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |-- text: string (nullable = true)\n",
      " |    |-- media: array (nullable = true)\n",
      " |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |-- display_url: string (nullable = true)\n",
      " |    |    |    |-- expanded_url: string (nullable = true)\n",
      " |    |    |    |-- id: long (nullable = true)\n",
      " |    |    |    |-- id_str: string (nullable = true)\n",
      " |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |-- media_url: string (nullable = true)\n",
      " |    |    |    |-- media_url_https: string (nullable = true)\n",
      " |    |    |    |-- sizes: array (nullable = true)\n",
      " |    |    |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |    |    |-- h: long (nullable = true)\n",
      " |    |    |    |    |    |-- resize: string (nullable = true)\n",
      " |    |    |    |    |    |-- w: long (nullable = true)\n",
      " |    |    |    |-- url: string (nullable = true)\n",
      " |    |-- urls: array (nullable = true)\n",
      " |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |-- display_url: string (nullable = true)\n",
      " |    |    |    |-- expanded_url: string (nullable = true)\n",
      " |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |-- url: string (nullable = true)\n",
      " |    |-- user_mentions: array (nullable = true)\n",
      " |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |-- id: long (nullable = true)\n",
      " |    |    |    |-- id_str: string (nullable = true)\n",
      " |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |-- name: string (nullable = true)\n",
      " |    |    |    |-- screen_name: string (nullable = true)\n",
      " |-- geo: struct (nullable = true)\n",
      " |    |-- coordinates: array (nullable = true)\n",
      " |    |    |-- element: double (containsNull = true)\n",
      " |    |-- type: string (nullable = true)\n",
      " |-- id: long (nullable = true)\n",
      " |-- id_str: long (nullable = true)\n",
      " |-- in_reply_to_screen_name: string (nullable = true)\n",
      " |-- in_reply_to_status_id: double (nullable = true)\n",
      " |-- in_reply_to_status_id_str: double (nullable = true)\n",
      " |-- in_reply_to_user_id: double (nullable = true)\n",
      " |-- in_reply_to_user_id_str: double (nullable = true)\n",
      " |-- retweeted_status: struct (nullable = true)\n",
      " |    |-- created_at: string (nullable = true)\n",
      " |    |-- entities: struct (nullable = true)\n",
      " |    |    |-- hashtags: array (nullable = true)\n",
      " |    |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |    |-- text: string (nullable = true)\n",
      " |    |    |-- media: array (nullable = true)\n",
      " |    |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |    |-- display_url: string (nullable = true)\n",
      " |    |    |    |    |-- expanded_url: string (nullable = true)\n",
      " |    |    |    |    |-- id: long (nullable = true)\n",
      " |    |    |    |    |-- id_str: string (nullable = true)\n",
      " |    |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |    |-- media_url: string (nullable = true)\n",
      " |    |    |    |    |-- media_url_https: string (nullable = true)\n",
      " |    |    |    |    |-- sizes: array (nullable = true)\n",
      " |    |    |    |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |    |    |    |-- h: long (nullable = true)\n",
      " |    |    |    |    |    |    |-- resize: string (nullable = true)\n",
      " |    |    |    |    |    |    |-- w: long (nullable = true)\n",
      " |    |    |    |    |-- url: string (nullable = true)\n",
      " |    |    |-- urls: array (nullable = true)\n",
      " |    |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |    |-- display_url: string (nullable = true)\n",
      " |    |    |    |    |-- expanded_url: string (nullable = true)\n",
      " |    |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |    |-- url: string (nullable = true)\n",
      " |    |    |-- user_mentions: array (nullable = true)\n",
      " |    |    |    |-- element: struct (containsNull = true)\n",
      " |    |    |    |    |-- id: long (nullable = true)\n",
      " |    |    |    |    |-- id_str: string (nullable = true)\n",
      " |    |    |    |    |-- indices: array (nullable = true)\n",
      " |    |    |    |    |    |-- element: long (containsNull = true)\n",
      " |    |    |    |    |-- name: string (nullable = true)\n",
      " |    |    |    |    |-- screen_name: string (nullable = true)\n",
      " |    |-- geo: struct (nullable = true)\n",
      " |    |    |-- coordinates: array (nullable = true)\n",
      " |    |    |    |-- element: double (containsNull = true)\n",
      " |    |    |-- type: string (nullable = true)\n",
      " |    |-- id: long (nullable = true)\n",
      " |    |-- id_str: string (nullable = true)\n",
      " |    |-- in_reply_to_screen_name: string (nullable = true)\n",
      " |    |-- in_reply_to_status_id: long (nullable = true)\n",
      " |    |-- in_reply_to_status_id_str: string (nullable = true)\n",
      " |    |-- in_reply_to_user_id: long (nullable = true)\n",
      " |    |-- in_reply_to_user_id_str: string (nullable = true)\n",
      " |    |-- source: string (nullable = true)\n",
      " |    |-- text: string (nullable = true)\n",
      " |    |-- user: struct (nullable = true)\n",
      " |    |    |-- id: long (nullable = true)\n",
      " |    |    |-- id_str: string (nullable = true)\n",
      " |    |    |-- name: string (nullable = true)\n",
      " |    |    |-- profile_image_url_https: string (nullable = true)\n",
      " |    |    |-- protected: boolean (nullable = true)\n",
      " |    |    |-- screen_name: string (nullable = true)\n",
      " |    |    |-- verified: boolean (nullable = true)\n",
      " |-- source: string (nullable = true)\n",
      " |-- text: string (nullable = true)\n",
      " |-- user: struct (nullable = true)\n",
      " |    |-- id: long (nullable = true)\n",
      " |    |-- id_str: string (nullable = true)\n",
      " |    |-- name: string (nullable = true)\n",
      " |    |-- profile_image_url_https: string (nullable = true)\n",
      " |    |-- protected: boolean (nullable = true)\n",
      " |    |-- screen_name: string (nullable = true)\n",
      " |    |-- verified: boolean (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tweetDataFrame = sqlContext.read.json('group_assignment/tweets/tweets_extended.json')\n",
    "tweetDataFrame.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "PART: Select subset of data and parse dates\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "HELPER FUNCTIONS FOR cleaning and parsing date\n",
    "\"\"\"\n",
    "def clean_up_row(row):\n",
    "    \"\"\"\n",
    "    cleans up row and return new row object\n",
    "    \"\"\"\n",
    "    return Row(       \n",
    "        id=row.id,\n",
    "        hashtags=row.hashtags,\n",
    "        hashtags_count = len(row.hashtags),\n",
    "        text=row.text,\n",
    "        created_at=convert_long_date(row.created_at),\n",
    "        )\n",
    "\n",
    "def convert_long_date(long_date):\n",
    "    return dt.fromtimestamp(long_date / 1e3)\n",
    "\n",
    "def convert_date_string(date_string):\n",
    "    \"\"\"\n",
    "    converts e.g. 2015-01-31 12:30:30 +0000\n",
    "    NOTE: unused currently, but might come handy later..\n",
    "    \"\"\"\n",
    "    if not date_string:\n",
    "        return None\n",
    "    return dt.strptime(date_string, '%Y-%m-%d %H:%M:%S +0000')\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Execute query\n",
    "\"\"\"\n",
    "tweet_data_frame = sqlContext.read.json('group_assignment/tweets/tweets_extended.json')\n",
    "tweets_subset = tweet_data_frame.selectExpr('id', 'created_at','text','entities.hashtags.text as hashtags').rdd\n",
    "parsed_tweets = tweets_subset.map(clean_up_row)\n",
    "\n",
    "\"\"\"\n",
    "PART: EXTRACTING THE MMA TWEETS\n",
    "\"\"\"\n",
    "import re\n",
    "\n",
    "def filter_mma(row):\n",
    "    \"\"\"\n",
    "    filters mma row\n",
    "    \"\"\"\n",
    "    #variable contains list of meaningful MMA expressions, after cleaning expressions such as\n",
    "    #u'TOMMARRS', u'DILEMMA', u'SUMMARY', u'MUHAMMAD_WAKAS',u'SUMMARIZING', u'SUMMARIZED' ....\n",
    "    \n",
    "    mma_accepted = ['MMA_BLOG', 'SMMMAGAZINE', 'MMA_CRM', 'MMA', 'BIG', \n",
    "                    'BIGDATA', 'SASANALYTICS', 'ANALYTICS', 'ANALYSIS', \n",
    "                    'RSTATS', 'STATFACT', 'AMSTATNEWS' 'STATISTICIANS', \n",
    "                    'STATS', 'STAT', 'STATISTICAL', 'MASTAT', 'DATASCIENCE', 'SCIENTIST'] \n",
    "    \n",
    "    #start matching\n",
    "    regex_p1 =  r'(\\w*%s\\w*)' % 'MMA'\n",
    "    regex_p2 =  r'(\\w*%s\\w*)' % 'BIG'\n",
    "    regex_p3 =  r'(\\w*%s\\w*)' % 'ANALY'\n",
    "    regex_p4 =  r'(\\w*%s\\w*)' % 'STAT'\n",
    "    regex_p5 =  r'(\\w*%s\\w*)' % 'SCIENTIST'\n",
    "    regex_p6 =  r'(\\w*%s\\w*)' % 'DATASCIENCE'\n",
    "    results = re.findall(regex_p1, row.text.upper()) + re.findall(regex_p2, row.text.upper()) + re.findall(regex_p3, row.text.upper()) + re.findall(regex_p4, row.text.upper()) + re.findall(regex_p5, row.text.upper()) + re.findall(regex_p6, row.text.upper())\n",
    "    #now see if one of the expression is an 'accepted' expression\n",
    "    results = [r for r in results if r in mma_accepted]\n",
    "    \n",
    "    if len(results) > 0:\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Execute query\n",
    "\"\"\"\n",
    "DATA_SCIENCE_tweets = parsed_tweets.filter(filter_mma)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Row(created_at=datetime.datetime(2009, 10, 27, 0, 0), hashtags=[u'M2009'], hashtags_count=1, id=5203805716L, text=u'Will Neafsey (Ford Motor Company) at SAS #M2009 is talking about using analytics in difficult economic times... great opportunities')]\n",
      "[Row(created_at=datetime.datetime(2015, 11, 15, 20, 31, 40), hashtags=[u'Deep', u'SC15', u'MLHPC2015', u'HPC', u'BigData', u'neuralnetworks'], hashtags_count=6, id=665990572323115008L, text=u'RT @HPC_Guru: Why #Deep Learning works? @ctnzr at #SC15 in #MLHPC2015 workshop \\n#HPC #BigData #neuralnetworks  https://t.co/cka7MPs9Jt via \\u2026')]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "NUMBER OF TWEETS PER DAY\n",
    "\"\"\"\n",
    "#sort data\n",
    "DATA_SCIENCE_tweets = DATA_SCIENCE_tweets.sortBy(lambda x: x.created_at) \n",
    "#print first data science related tweet \n",
    "print DATA_SCIENCE_tweets.take(1)\n",
    "\n",
    "#print last data science related tweet \n",
    "print DATA_SCIENCE_tweets.sortBy(lambda x: x.created_at, ascending=False).take(1)\n",
    "\n",
    "# collect()[-1] also works\n",
    "\n",
    "#to return to DF from RDD\n",
    "new_df = sqlContext.createDataFrame(DATA_SCIENCE_tweets)\n",
    "#select works again\n",
    "#print new_df.selectExpr('created_at as bitchin_mother_fucker').take(1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_SCIENCE_tweets.count()\n",
    "#parsed_tweets.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Number of \"data science\" -related tweets per year\n",
    "from operator import add\n",
    "year_count = DATA_SCIENCE_tweets.map(lambda x: (x.created_at.year,1)).reduceByKey(add).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NN+XnP/95qqqqkiT/9V//la5du7ZY8/rrr+fll1/OoYce2v5TAgAAVKBWBVXv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\nBQAAUEhQAQAAFBJUAAAAhQQ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tUaNGacmSJWrVqpUk6eDBg3r77bf117/+VYGBga6bZA3g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\nCAUGBlpvSpGRkaGRI0c+8Bb2AIBHZ9m9e3dldQ8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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1edddac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "x,y = zip(*year_count)\n",
    "plt.figure(figsize=(10,10), dpi=40)\n",
    "plt.xlabel(\" 2009 - 2015\")\n",
    "plt.ylabel(\"# Tweets / year\")\n",
    "plt.xticks(range(2008, 2016), range(2008, 2016), size='small')\n",
    "plt.bar(x,y, align='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# \n",
    "testx1 = DATA_SCIENCE_tweets.map(lambda x: x[0])\n",
    "#testx1.take(100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(datetime.datetime(2013, 1, 1, 0, 0), 1),\n",
       " (datetime.datetime(2013, 1, 7, 0, 0), 1),\n",
       " (datetime.datetime(2013, 1, 22, 0, 0), 1),\n",
       " (datetime.datetime(2013, 1, 25, 0, 0), 1),\n",
       " (datetime.datetime(2013, 2, 4, 0, 0), 1),\n",
       " (datetime.datetime(2013, 2, 11, 0, 0), 1),\n",
       " (datetime.datetime(2013, 2, 25, 0, 0), 1)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Truncate datetime to obtain number of \"data science\" -related tweets per day\n",
    "#dt = mma_tweets.collect()[-1][0]\n",
    "#dt = dt.replace(hour=0, minute=0, second=0, microsecond=0)\n",
    "tweets_perday = DATA_SCIENCE_tweets.map(lambda x: x[0].replace(hour=0, minute=0, second=0, microsecond=0))\n",
    "#tweets_perday.count()\n",
    "tweets_perday_2013_end = tweets_perday.filter(lambda x: (x.year > 2012))\n",
    "tweets_perday_2013 = tweets_perday_2013_end.filter(lambda x: (x.year < 2014))\n",
    "tweets_perday_2013 = tweets_perday_2013.map(lambda x: (x,1)).reduceByKey(add).sortByKey()\n",
    "#tweets_perday1.count()\n",
    "tweets_perday_2013.take(7)\n",
    "# type is pipelinedRDD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2013, 1, 1, 0, 0), 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date, timedelta\n",
    "\n",
    "def perdelta(start, end, delta):\n",
    "    curr = start\n",
    "    datelist=[]\n",
    "    while curr < end:\n",
    "        #yield curr\n",
    "        curr += delta\n",
    "        datelist.append((curr,0))\n",
    "    return datelist\n",
    "\n",
    "list2013 = perdelta(dt(2012, 12, 31), dt(2013, 12, 31), timedelta(days=1))\n",
    "RDD2013 = sc.parallelize(list2013)\n",
    "\n",
    "tweets_perAllDay_2013 = RDD2013.union(tweets_perday_2013)\n",
    "tweets_perAllDay_2013_ = tweets_perAllDay_2013.map(lambda x: (x[0],x[1])).reduceByKey(add).sortByKey()\n",
    "\n",
    "tweets_DS_2013 = tweets_perAllDay_2013_.collect()\n",
    "tweets_DS_2013[0]\n",
    "\n",
    "#test = map(list, zip(*tweets_DS_2013))\n",
    "#import numpy as np\n",
    "#tester = np.array(tweets_DS_2013).T.tolist()\n",
    "#tester[1]\n",
    "#test[1]\n",
    "#array(tweets_DS_2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K0doIi5j6eQ5tCEcjgVRy2AMpi2H5AABQiNZh+RFTP8/hJtDRSCCFycmpf3LYBymHIAwA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\nIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1fc9f2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "x,y = zip(*tweets_DS_2013)\n",
    "plt.figure(figsize=(15,10), dpi=400)\n",
    "plt.ylabel(\"# Tweets / day\")\n",
    "plt.plot(x,y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.   0.   0.   0.   0.   0.]\n",
      " [  2.   1.   0.   0.   0.   0.]\n",
      " [  3.   2.   1.   0.   0.   0.]\n",
      " [  4.   3.   2.   1.   0.   0.]\n",
      " [  5.   4.   3.   2.   1.   0.]\n",
      " [  6.   5.   4.   3.   2.   1.]\n",
      " [  7.   6.   5.   4.   3.   2.]\n",
      " [  8.   7.   6.   5.   4.   3.]\n",
      " [  9.   8.   7.   6.   5.   4.]\n",
      " [ 10.   9.   8.   7.   6.   5.]\n",
      " [ 11.  10.   9.   8.   7.   6.]\n",
      " [ 12.  11.  10.   9.   8.   7.]\n",
      " [ 13.  12.  11.  10.   9.   8.]\n",
      " [ 14.  13.  12.  11.  10.   9.]\n",
      " [ 15.  14.  13.  12.  11.  10.]\n",
      " [ 16.  15.  14.  13.  12.  11.]]\n",
      "[  2.00000000e+00  -3.00076634e-14   1.56862057e-14   3.97000954e-16\n",
      "  -1.80237769e-15   6.27969898e-16]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "def estimate_response(stimulus, response):\n",
    "    \"\"\"\n",
    "    EXPLAIN HERE\n",
    "    PUT REFERENCES\n",
    "    WHAT ARE YOUR VARIABLES? \n",
    "    \"\"\"\n",
    "    n_fir = 5 #response model order\n",
    "    time_vector_length = len(stimulus)\n",
    "    observation_matrix = np.zeros(shape=(time_vector_length, n_fir + 1))\n",
    "\n",
    "    for idx in range(0, n_fir+1):\n",
    "        updated_column  = [0]*idx + stimulus[0: time_vector_length - idx]\n",
    "        observation_matrix[:, idx] =  updated_column\n",
    "        \n",
    "    print observation_matrix\n",
    "    estimated_impulse_response = np.dot(np.linalg.pinv(observation_matrix), response)\n",
    "    return estimated_impulse_response\n",
    "\n",
    "\n",
    "#test estimate response....\n",
    "stimulus = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
    "response = [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]\n",
    "\n",
    "print estimate_response(stimulus, response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}