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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook is based on the work by Randall Munroe (xkcd). The link to the original blog post is here: [http://blog.xkcd.com/2012/07/12/a-morbid-python-script/](http://blog.xkcd.com/2012/07/12/a-morbid-python-script/). The script that Randall wrote is inserted below for reference."
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "#!/usr/bin/python\n",
      "import sys\n",
      "import datetime\n",
      "\n",
      "# Calculates death probabilities based on Social Security\n",
      "# actuarial tables for a given group of people.\n",
      "\n",
      "# Run with a list of ages/genders and an optional timespan (or year in the future):\n",
      "\n",
      "# python actuary.py 63m 80m 75f 73m 10\n",
      "\n",
      "# or:\n",
      "\n",
      "# python actuary.py 63m 80m 75f 73m 2022\n",
      "\n",
      "# This will give statistics for that group, including\n",
      "# various probabilities over 10 years. Years can be\n",
      "# ommitted and it will still give some statistics.\n",
      "# If \"Years\" exceeds the current calendar year, it will be interpreted as a date.\n",
      "\n",
      "\n",
      "\n",
      "bothtables=[[0.00756, 0.00052, 0.00035, 0.00025, 0.00020, 0.00018, 0.00017, 0.00016, 0.00014, 0.00011, 0.00009, 0.00010, 0.00015, 0.00027, 0.00043, 0.00061, 0.00078, 0.00094, 0.00107, 0.00119, 0.00131, 0.00142, 0.00149, 0.00151, 0.00148, 0.00143, 0.00140, 0.00138, 0.00137, 0.00139, 0.00141, 0.00143, 0.00147, 0.00152, 0.00158, 0.00165, 0.00174, 0.00186, 0.00202, 0.00221, 0.00243, 0.00267, 0.00291, 0.00317, 0.00344, 0.00373, 0.00405, 0.00441, 0.00480, 0.00524, 0.00573, 0.00623, 0.00671, 0.00714, 0.00756, 0.00800, 0.00853, 0.00917, 0.00995, 0.01086, 0.01190, 0.01301, 0.01413, 0.01522, 0.01635, 0.01760, 0.01906, 0.02073, 0.02265, 0.02482, 0.02729, 0.03001, 0.03289, 0.03592, 0.03918, 0.04292, 0.04715, 0.05173, 0.05665, 0.06206, 0.06821, 0.07522, 0.08302, 0.09163, 0.10119, 0.11183, 0.12367, 0.13679, 0.15124, 0.16702, 0.18414, 0.20255, 0.22224, 0.24314, 0.26520, 0.28709, 0.30846, 0.32891, 0.34803, 0.36544, 0.38371, 0.40289, 0.42304, 0.44419, 0.46640, 0.48972, 0.51421, 0.53992, 0.56691, 0.59526, 0.62502, 0.65628, 0.68909, 0.72354, 0.75972, 0.79771, 0.83759, 0.87947, 0.92345, 0.96962], [0.00615, 0.00041, 0.00025, 0.00018, 0.00015, 0.00014, 0.00014, 0.00013, 0.00012, 0.00011, 0.00010, 0.00010, 0.00012, 0.00016, 0.00021, 0.00028, 0.00034, 0.00039, 0.00042, 0.00043, 0.00045, 0.00047, 0.00048, 0.00049, 0.00050, 0.00051, 0.00052, 0.00053, 0.00056, 0.00059, 0.00063, 0.00068, 0.00073, 0.00078, 0.00084, 0.00091, 0.00098, 0.00108, 0.00118, 0.00130, 0.00144, 0.00158, 0.00173, 0.00189, 0.00206, 0.00225, 0.00244, 0.00264, 0.00285, 0.00306, 0.00329, 0.00355, 0.00382, 0.00409, 0.00437, 0.00468, 0.00505, 0.00549, 0.00603, 0.00665, 0.00736, 0.00813, 0.00890, 0.00967, 0.01047, 0.01136, 0.01239, 0.01357, 0.01491, 0.01641, 0.01816, 0.02008, 0.02210, 0.02418, 0.02641, 0.02902, 0.03206, 0.03538, 0.03899, 0.04301, 0.04766, 0.05307, 0.05922, 0.06618, 0.07403, 0.08285, 0.09270, 0.10365, 0.11574, 0.12899, 0.14343, 0.15907, 0.17591, 0.19393, 0.21312, 0.23254, 0.25193, 0.27097, 0.28933, 0.30670, 0.32510, 0.34460, 0.36528, 0.38720, 0.41043, 0.43505, 0.46116, 0.48883, 0.51816, 0.54925, 0.58220, 0.61714, 0.65416, 0.69341, 0.73502, 0.77912, 0.82587, 0.87542, 0.92345, 0.96962]]\n",
      "\n",
      "def deathprob(age, years):\n",
      "    #negative ages = female\n",
      "    act=[]\n",
      "    if age<0:\n",
      "        act=bothtables[1]\n",
      "        age=-1*age\n",
      "    else:\n",
      "        act=bothtables[0]\n",
      "    while(len(act)<int(age+years+2)): # slower/bloaiter but keeps things clean\n",
      "        act.append(act[-1]**0.5)\n",
      "    liveprob=1\n",
      "    i=0\n",
      "    iage=int(age)\n",
      "    fage=age%1\n",
      "    while i<=years-1:\n",
      "        thisyear=(1-fage)*act[iage+i]+fage*act[iage+i+1]\n",
      "        liveprob*=1-thisyear\n",
      "        i+=1\n",
      "    if years%1: # Amortizes risk of dying over a partial year, which is\n",
      "                # 1-P(living last full year)^(year fraction)\n",
      "        lastyear=(1-fage)*act[iage+i]+fage*act[iage+i+1]\n",
      "        lastyearlive=1-lastyear\n",
      "        lastyearlive=lastyearlive**((years%1))\n",
      "        liveprob*=lastyearlive\n",
      "    return 1-liveprob\n",
      "\n",
      "def proballdie(ages, years):\n",
      "    probsliving=[]\n",
      "    for i in ages:\n",
      "        probsliving.append(1-deathprob(i, years))\n",
      "    prod=1\n",
      "    for i in probsliving:\n",
      "        prod*=(1-i)\n",
      "    return prod\n",
      "\n",
      "def probanydie(ages, years):\n",
      "    probsliving=[]\n",
      "    for i in ages:\n",
      "        probsliving.append(1-deathprob(i, years))\n",
      "    prod=1\n",
      "    for i in probsliving:\n",
      "        prod*=i\n",
      "    return 1-prod\n",
      "\n",
      "def calcexp(ages, prob, flag):\n",
      "    i=0\n",
      "    for interval in (10, 1, 0.1, 0.01):\n",
      "        probs=0\n",
      "        while(probs<prob):\n",
      "            i+=interval\n",
      "            if flag==0:\n",
      "                probs=proballdie(ages, i)\n",
      "            else:\n",
      "                probs=probanydie(ages, i)\n",
      "        i-=interval\n",
      "    return i\n",
      "\n",
      "ages=[]\n",
      "# print sys.argv[1:]\n",
      "for arg in sys.argv[1:]:\n",
      "    gender=1\n",
      "    years=1.0\n",
      "    if arg[-1]=='m' or arg[-1]=='M':\n",
      "        try:\n",
      "            ages.append(1*float(arg[:-1]))\n",
      "        except:\n",
      "            print \"Error parsing argument\", arg\n",
      "    elif arg[-1]=='f' or arg[-1]=='F':\n",
      "        try:\n",
      "            ages.append(-1*float(arg[:-1]))\n",
      "        except:\n",
      "            print \"Error parsing argument\", arg\n",
      "    else:\n",
      "        try:\n",
      "            years=float(arg)\n",
      "            break\n",
      "        except:\n",
      "            print \"Error parsing argument\", arg\n",
      "\n",
      "if not sys.argv[1:]:\n",
      "    print \"The format is 'actuary.py 15m 80f 23', with a list of ages and a number of years to run the projections.\"\n",
      "    raise SystemExit\n",
      "if not ages:\n",
      "    print \"No ages specified.  Format is 12m, 17f, etc.\"\n",
      "    raise SystemExit\n",
      "\n",
      "# print \"Ages:\", ages\n",
      "# print \"Years:\", years\n",
      "\n",
      "(datetime.date.today()+datetime.timedelta(days=365.242191*1)).year\n",
      "someone_years=[calcexp(ages, 0.05, 1),\n",
      "               calcexp(ages, 0.5, 1),\n",
      "               calcexp(ages, 0.95, 1)]\n",
      "someone_dates=[(datetime.date.today()+datetime.timedelta(days=365.242191*someone_years[0])).year,\n",
      "               (datetime.date.today()+datetime.timedelta(days=365.242191*someone_years[1])).year,\n",
      "               (datetime.date.today()+datetime.timedelta(days=365.242191*someone_years[2])).year]\n",
      "print \"There is a 5%  chance of someone dying within\", someone_years[0], \"years (by\", str(someone_dates[0])+\").\"\n",
      "print \"There is a 50% chance of someone dying within\", someone_years[1], \"years (by\", str(someone_dates[1])+\").\"\n",
      "print \"There is a 95% chance of someone dying within\", someone_years[2], \"years (by\", str(someone_dates[2])+\").\"\n",
      "print \"\"\n",
      "\n",
      "if len(ages)>1:\n",
      "    everyone_years=[calcexp(ages, 0.05, 0),\n",
      "                   calcexp(ages, 0.5, 0),\n",
      "                   calcexp(ages, 0.95, 0)]\n",
      "    everyone_dates=[(datetime.date.today()+datetime.timedelta(days=365.242191*everyone_years[0])).year,\n",
      "                   (datetime.date.today()+datetime.timedelta(days=365.242191*everyone_years[1])).year,\n",
      "                   (datetime.date.today()+datetime.timedelta(days=365.242191*everyone_years[2])).year]\n",
      "    print \"There is a 5%  chance of everyone dying within\", everyone_years[0], \"years (by\", str(everyone_dates[0])+\").\"\n",
      "    print \"There is a 50% chance of everyone dying within\", everyone_years[1], \"years (by\", str(everyone_dates[1])+\").\"\n",
      "    print \"There is a 95% chance of everyone dying within\", everyone_years[2], \"years (by\", str(everyone_dates[2])+\").\"\n",
      "\n",
      "\n",
      "if years:\n",
      "    yearword=\"years\"\n",
      "    if years==1:\n",
      "        yearword=\"year\"\n",
      "\n",
      "    print \"\"\n",
      "    if years>datetime.date.today().year:\n",
      "        years=years-datetime.date.today().year\n",
      "    if len(ages)>1:\n",
      "        p=100*proballdie(ages, years)\n",
      "        printable=\"\"\n",
      "        if p<0.001:\n",
      "            printable=\"<0.001\"\n",
      "        elif p>99.99:\n",
      "            printable=\">99.99\"\n",
      "        else:\n",
      "            printable=str(p)[:5]\n",
      "        print \"Probability of all dying in\", years, yearword+\":  \", printable+\"%\"\n",
      "    p=100*probanydie(ages, years)\n",
      "    printable=\"\"\n",
      "    if p<0.001:\n",
      "        printable=\"<0.001\"\n",
      "    elif p>99.99:\n",
      "        printable=\">99.99\"\n",
      "        print p\n",
      "    else:\n",
      "        printable=str(p)[:5]\n",
      "    print \"Probability of a death within\", years, yearword+\":\", printable+\"%\"\n",
      "raise SystemExit"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Loading and visualizing the input data curves"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's start by taking a look at the data that Randall used and compare it to data from SSA.GOV found on the internet [link needed!]."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# XKCD data\n",
      "data_xkcd = array([[0.00756, 0.00052, 0.00035, 0.00025, 0.00020, 0.00018, 0.00017, 0.00016, 0.00014, 0.00011, 0.00009, 0.00010, 0.00015, 0.00027, 0.00043, 0.00061, 0.00078, 0.00094, 0.00107, 0.00119, 0.00131, 0.00142, 0.00149, 0.00151, 0.00148, 0.00143, 0.00140, 0.00138, 0.00137, 0.00139, 0.00141, 0.00143, 0.00147, 0.00152, 0.00158, 0.00165, 0.00174, 0.00186, 0.00202, 0.00221, 0.00243, 0.00267, 0.00291, 0.00317, 0.00344, 0.00373, 0.00405, 0.00441, 0.00480, 0.00524, 0.00573, 0.00623, 0.00671, 0.00714, 0.00756, 0.00800, 0.00853, 0.00917, 0.00995, 0.01086, 0.01190, 0.01301, 0.01413, 0.01522, 0.01635, 0.01760, 0.01906, 0.02073, 0.02265, 0.02482, 0.02729, 0.03001, 0.03289, 0.03592, 0.03918, 0.04292, 0.04715, 0.05173, 0.05665, 0.06206, 0.06821, 0.07522, 0.08302, 0.09163, 0.10119, 0.11183, 0.12367, 0.13679, 0.15124, 0.16702, 0.18414, 0.20255, 0.22224, 0.24314, 0.26520, 0.28709, 0.30846, 0.32891, 0.34803, 0.36544, 0.38371, 0.40289, 0.42304, 0.44419, 0.46640, 0.48972, 0.51421, 0.53992, 0.56691, 0.59526, 0.62502, 0.65628, 0.68909, 0.72354, 0.75972, 0.79771, 0.83759, 0.87947, 0.92345, 0.96962], [0.00615, 0.00041, 0.00025, 0.00018, 0.00015, 0.00014, 0.00014, 0.00013, 0.00012, 0.00011, 0.00010, 0.00010, 0.00012, 0.00016, 0.00021, 0.00028, 0.00034, 0.00039, 0.00042, 0.00043, 0.00045, 0.00047, 0.00048, 0.00049, 0.00050, 0.00051, 0.00052, 0.00053, 0.00056, 0.00059, 0.00063, 0.00068, 0.00073, 0.00078, 0.00084, 0.00091, 0.00098, 0.00108, 0.00118, 0.00130, 0.00144, 0.00158, 0.00173, 0.00189, 0.00206, 0.00225, 0.00244, 0.00264, 0.00285, 0.00306, 0.00329, 0.00355, 0.00382, 0.00409, 0.00437, 0.00468, 0.00505, 0.00549, 0.00603, 0.00665, 0.00736, 0.00813, 0.00890, 0.00967, 0.01047, 0.01136, 0.01239, 0.01357, 0.01491, 0.01641, 0.01816, 0.02008, 0.02210, 0.02418, 0.02641, 0.02902, 0.03206, 0.03538, 0.03899, 0.04301, 0.04766, 0.05307, 0.05922, 0.06618, 0.07403, 0.08285, 0.09270, 0.10365, 0.11574, 0.12899, 0.14343, 0.15907, 0.17591, 0.19393, 0.21312, 0.23254, 0.25193, 0.27097, 0.28933, 0.30670, 0.32510, 0.34460, 0.36528, 0.38720, 0.41043, 0.43505, 0.46116, 0.48883, 0.51816, 0.54925, 0.58220, 0.61714, 0.65416, 0.69341, 0.73502, 0.77912, 0.82587, 0.87542, 0.92345, 0.96962]]).T\n",
      "# ssa data\n",
      "data_ssa = loadtxt(\"prob_from_SSA.GOV.csv\", delimiter=';')[:, [1, 4]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(10, 6))\n",
      "plot(data_xkcd[:, 0], label='xkcd - male data')\n",
      "plot(data_xkcd[:, 0], label='xkcd - female data')\n",
      "plot(data_ssa[:, 0], label='ssa - male data')\n",
      "plot(data_ssa[:, 1], label='ssa - female data')\n",
      "legend(loc=2)\n",
      "xlabel('Age')\n",
      "ylabel('Probability of dying within a year at that age')\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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XJz4+no0bN2Z7rf79++P17yOrDg4O1KtXz1DYQhjFk7/knkwPFNR3Y19fvsv3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\nEFWEFGZCCCGEEFXE/wOzAg15kgEu2AAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Defining the main calculation function"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The idea behind the function below is based on what Randall did. Unless what he did, I did not include the following things:\n",
      "\n",
      "- support for death probability curve extrapolation (at the expense of complexity)\n",
      "- support for non-integer arguments (well, it kinda works, but it's sketchy!)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def death_probability(sex, age, years, dataset):\n",
      "    if sex == 'm':\n",
      "        data = dataset[:, 0]\n",
      "    elif sex == 'f':\n",
      "        data = dataset[:, 1]\n",
      "    \n",
      "    survival_probability = 1.0\n",
      "    for year in arange(age + 1, age + years - (years % 1)):\n",
      "        try:\n",
      "            survival_probability *= (1 - data[year])\n",
      "        except IndexError:\n",
      "            survival_probability *= (1 - data[-1])\n",
      "    \n",
      "    if years % 1:\n",
      "        fraction = years % 1\n",
      "        try:\n",
      "            last_year_die = data[year]\n",
      "        except IndexError:\n",
      "            last_year_die = data[-1]\n",
      "        last_year_live = (1 - last_year_die) ** (fraction)\n",
      "        survival_probability *= last_year_live\n",
      "        \n",
      "    return 1 - survival_probability"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some tests below, to make sure it works."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "death_probability('m', 24, 30, data_xkcd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0.08304389875929985"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "death_probability('f', 24, 30, data_xkcd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "0.047407458185668272"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "death_probability('m', 24, 799, data_xkcd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print death_probability('m', 24.2, 15, data_xkcd)\n",
      "print death_probability('m', 24, 15, data_xkcd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0235966098158\n",
        "0.0214339789092\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Testing the interpolation scheme."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print death_probability('m', 24, 10, data_xkcd)\n",
      "print death_probability('m', 24, 10.9999999, data_xkcd)\n",
      "print death_probability('m', 24, 11, data_xkcd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0127274323426\n",
        "0.0142280864955\n",
        "0.0142873229995\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Calculating the 50 % value (aka life expectancy)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_expected_value(sex, age, target_prob, dataset):\n",
      "    assert(target_prob > 0 and target_prob < 1)\n",
      "    year = 0\n",
      "    for interval in [10, 1, 0.1, 0.01]:\n",
      "        prob = 0\n",
      "        while prob < target_prob:\n",
      "            year += interval\n",
      "            prob = death_probability(sex, age, year, dataset)\n",
      "        year -= interval\n",
      "    return year\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "calculate_expected_value('m', 26, .5, data_xkcd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "53.1"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plotting single individuals"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sex = 'm'\n",
      "age = 26\n",
      "data = data_xkcd\n",
      "\n",
      "def plot_death_prob(age, sex, data, annotation_xy=(.25, .5), plot_age_offset=True):\n",
      "    \"\"\" plots death prob for: age, sex, data.\n",
      "    Optional arguments are 'annotation_xy': position of the life expectancy label on the plot\n",
      "    and 'plot_age_offset': adds an offset to the x axis corresponding to the current age\"\"\"\n",
      "    if plot_age_offset:\n",
      "        offset = age\n",
      "    else:\n",
      "        offset = 0\n",
      "    # calculating the data\n",
      "    years = arange(200)\n",
      "    probs = array([death_probability(sex, age, year, data) for year in years])\n",
      "    upper_limit = (probs > 0.99).nonzero()[0][0]\n",
      "    # plotting\n",
      "    plot(years[:upper_limit] + offset, probs[:upper_limit], label='sex: %s, current age: %s' % (sex, age))\n",
      "    title('probability of dying')\n",
      "    xlabel('years')\n",
      "    ylabel('probability')\n",
      "    legend(loc = 2)\n",
      "    grid(True)\n",
      "    # annotation\n",
      "    ind = (probs > 0.5).nonzero()[0][0]\n",
      "    mean = (probs[ind] * years[ind] + probs[ind - 1] * years[ind - 1])/(probs[ind] + probs[ind - 1])\n",
      "    annotate('life expectancy: %.2f years' % (mean + offset), \n",
      "             (mean + offset, 0.5), \n",
      "              xytext=annotation_xy, textcoords='axes fraction', \n",
      "              arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
      "\n",
      "figure(figsize=(10, 6))\n",
      "plot_death_prob(age, sex, data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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j++6UiTXlYk25WFMu7nwxk2+/hRUr4I03PHcMX8zFF6gxExERyUGSkmDoUPjP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\nJiIiIuInVJiJiIiI+AkVZiIiIiJ+4v8Bpwk3WM+mFPQAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plotting couples"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(10, 6))\n",
      "plot_death_prob(26, 'm', data_xkcd, (.25, .5), False)\n",
      "plot_death_prob(31, 'f', data_xkcd, (.65, .3), False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Qjqujo0Nnz54VPWdAQABpaGhQYGAgyWQy8vHxIWNjY5o0aRLl5ubS0aNHSVtbmzIyMoqM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\nEEKYkDRjQgghhBAmJM2YEEIIIYQJ/R8xTD+Q+qh+qQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Calculating the \"die together\" couple parameter"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The purpose of this paragraph is to define a function that calculates the age of the other person in the couple so that the expected life duration is the same."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Defining the function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_right_age(sex, age, dataset):\n",
      "    target_life_expectancy = calculate_expected_value(sex, age, 0.5, dataset)\n",
      "    \n",
      "    if sex == 'm':\n",
      "        sex = 'f'\n",
      "    else:\n",
      "        sex = 'm'\n",
      "    \n",
      "    \n",
      "    age = 1\n",
      "    for interval in [10, 1, 0.1, 0.01]:\n",
      "        life_expectancy = calculate_expected_value(sex, age, 0.5, dataset)\n",
      "        while life_expectancy > target_life_expectancy:\n",
      "            age += interval\n",
      "            life_expectancy = calculate_expected_value(sex, age, 0.5, dataset)\n",
      "        age -= interval\n",
      "    return age"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "life_expectancy = calculate_expected_value('f', 1, 0.5, dataset)\n",
      "life_expectancy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "82.55"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sex = 'm'\n",
      "age = 26\n",
      "dataset = data_xkcd\n",
      "calculate_right_age(sex, age, dataset)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "30.01"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Parametric study"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's plot the variation of the perfect age as a function of the age."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "male_ages = arange(10, 80)\n",
      "girl_ages = [calculate_right_age('m', age, data_xkcd) for age in male_ages]\n",
      "plot(male_ages, girl_ages - male_ages)\n",
      "title('Age difference necessary for couple with similar life expectancy')\n",
      "xlabel('Age of male')\n",
      "ylabel('Difference of age of female')\n",
      "ylim((0, 6))\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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WVsL3S58DpUxNTWFtbY309HSNfeGjR49QWFhY7uu3PA8ePMCIESPg7OwMCwsLhIWFldl3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\nY68+dTbxiwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plotting parents"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bfigure(figsize=(10, 6))\n",
      "plot_death_prob(58, 'm', data_xkcd, (.25, .7), False)\n",
      "plot_death_prob(58, 'f', data_xkcd, (.65, .3), False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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toXxpvkLPV1pV5Xq/r7L8Dlf63/plLcawGOXyKot79+5RgwYNKCwsjHJzcwus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      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}